aaahabhia,georgesterpu/avsr-tf1,avsr/io_utils.py,16d8a7ecc65331890529baddbac70dd19aa2b1e2,STILL_EXISTS,TODO: this function needs a generalisation to lists of data records
aaahabiee,georgesterpu/avsr-tf1,avsr/io_utils.py,d8ab3a1951fdc3d57fc84cb942b1c8d14b45fe3e,STILL_EXISTS,TODO (cba; two stacked lists of inputs)
aaahacbhc,georgesterpu/avsr-tf1,avsr/dataset_writer.py,3dcd22df287cbba886204778e06713dc7e9fd4cd,STILL_EXISTS,area better when decimating
aaahacbic,georgesterpu/avsr-tf1,avsr/decoder_bimodal.py,4313747ffcf31ce63b9b550c044c7ec010725ce2,STILL_EXISTS,TODO potentially broken; please re-check
aaahaccfg,georgesterpu/avsr-tf1,avsr/visualise/beam_search.py,4daed8a10fe61db0bb22ee72f8c94e3c2bf6a732,STILL_EXISTS,r''' || TODO || ==== || *. properly normalise the scores || *. toggle scores on \/ off || *. URL to dataset dir - play example || *. colour legend || *. display likelihood of the ground truth sequence || *. highlight best translation - stronger line width and different colour || *. update to latest d3.js version || '''
aaahacdaj,georgesterpu/avsr-tf1,avsr/dataset_writer.py,1b0e7a50cec9b40cc65aec335383a00bdc1be1fd,STILL_EXISTS,potentially unused variables; kept for speedup
aaahacddj,georgesterpu/avsr-tf1,avsr/io_utils.py,bd723e80c123fdec632a4eb01cfea6f0a2fda708,2d24600231e469a2a3bb93a2d1a0bbdd27afb41f,todo support AUs for single modality
aaahghgcd,gunthercox/ChatterBot,engram.py,c6826f0f598eb974b662562b9966a7ec261b9b8b,STILL_EXISTS,If the difference ration is too low; seek a better response
aaahghgfh,gunthercox/ChatterBot,engram.py,08d96a3a8a7637995fd943317051dba0ffaee27c,05e638ced198498c57cf8a3edda267bc5e4d49ba,This is needed to import settings from the parent directory
aaahghghc,gunthercox/ChatterBot,fuzzywuzzy/fuzz.py,08d96a3a8a7637995fd943317051dba0ffaee27c,STILL_EXISTS,todo: skip duplicate indexes for a little more speed
aaahghgjg,gunthercox/ChatterBot,fuzzywuzzy/fuzz.py,08d96a3a8a7637995fd943317051dba0ffaee27c,STILL_EXISTS,TODO: numerics
aaahghhgi,gunthercox/ChatterBot,__init__.py,54f8411d332e37cb09ca60d4a88395d0ab79e5d7,bd8c7045748420df3ca985e9a4a5585db02d871f,TODO: use csv writer here.
aaahgiaad,gunthercox/ChatterBot,chatterbot/conversation.py,81b327ea5d4519380a21b2a0c5aaf3a0039139d5,ebf7a26f6c19d9166284d08b9bfddb975ce2d97c,TODO: return the average sentiment
aaahgiabi,gunthercox/ChatterBot,chatterbot/__init__.py,3649cd1fd4431df0281f79824084685418896fa3,71721738190eef8ffa2d068d1be348fac63189e1,Why 'if line?' This needs a comment or a fix.
aaahgiacf,gunthercox/ChatterBot,chatterbot/algorithms/engram.py,f35b4ca11439449a716bddaef2de3eaf082ca925,940a6f7909dd17a879049071ed8672d1ad1c5158,Seek a better response if the difference ratio is too low or the choice list is empty
aaahgiacg,gunthercox/ChatterBot,chatterbot/algorithms/engram.py,f35b4ca11439449a716bddaef2de3eaf082ca925,940a6f7909dd17a879049071ed8672d1ad1c5158,TODO Search the web or use other algorithms
aaahgibea,gunthercox/ChatterBot,chatterbot/__init__.py,1c936fcd0526d21f60afb5d150937cb0c6572471,STILL_EXISTS,TODO
aaahgibeg,gunthercox/ChatterBot,chatterbot/__init__.py,1c936fcd0526d21f60afb5d150937cb0c6572471,STILL_EXISTS,TODO; change user_name and input_text into a single dict
aaahgibjc,gunthercox/ChatterBot,chatterbot/matching.py,cad01b94d806017853ac4a646d55836b67a2879b,STILL_EXISTS,TODO
aaahgicaf,gunthercox/ChatterBot,chatterbot/algorithms/engram.py,6a67113fc545e7a77c8b74fe2e040b363adc79a9,20eae143ed6459275bc951752ee0447d24618d2f,TODO: Move this into a database adaptor class
aaahgicah,gunthercox/ChatterBot,chatterbot/algorithms/engram.py,6a67113fc545e7a77c8b74fe2e040b363adc79a9,20eae143ed6459275bc951752ee0447d24618d2f,TODO:
aaahgicca,gunthercox/ChatterBot,chatterbot/__init__.py,006ffec802e20398e3bcab8bfd6f2a2e46dd0ada,STILL_EXISTS,TODO:
aaahgicee,gunthercox/ChatterBot,chatterbot/adapters/logic/engram.py,ffebc964e4ff68103d5613188f5359b9f098ece8,STILL_EXISTS,TODO? If the two statements occure equaly in frequency; should we keep one at random
aaahgicfc,gunthercox/ChatterBot,chatterbot/chatterbot.py,ffebc964e4ff68103d5613188f5359b9f098ece8,86e3a62c8e45646c69bbac8b5fb0c226c146b6de,TODO: Change database to storage
aaahgichi,gunthercox/ChatterBot,chatterbot/adapters/logic/closest_match.py,8afea1f79ad0e8e0aca20fed13d14663baa05865,4b3acb1e12abe4b906826cbaab7bc8ccdacc283d,TODO: The call to the hidden _keys method should not be made here
aaahgicij,gunthercox/ChatterBot,chatterbot/controllers/storage.py,8afea1f79ad0e8e0aca20fed13d14663baa05865,b31300ed065662216d5127c1254e0885a4cf0045,TODO: Don't make this lookup here
aaahgicjg,gunthercox/ChatterBot,chatterbot/controllers/storage.py,8afea1f79ad0e8e0aca20fed13d14663baa05865,STILL_EXISTS,TODO? If the two statements occure equaly in frequency; should we keep one at random
aaahgicji,gunthercox/ChatterBot,chatterbot/controllers/storage.py,a13a34110daedf4498ef9c45da75c2680433c88a,STILL_EXISTS,TODO: Call to _keys is bad
aaahgicjj,gunthercox/ChatterBot,chatterbot/controllers/storage.py,a13a34110daedf4498ef9c45da75c2680433c88a,a234dffb9d74af4d139ad03e8b6cb5b9f062cc4f,TODO: if list empty
aaahgidad,gunthercox/ChatterBot,tests/test_utils.py,a13a34110daedf4498ef9c45da75c2680433c88a,6c396a093c1d1cb3c8491dc2bd1aa74053bd370e,TODO
aaahgidaj,gunthercox/ChatterBot,tests/io_adapter_tests/test_terminal_adapter.py,fc4d312adb42a422a1394cf0a2f65699cdf64a7f,3742f14c02647ab68d8c2671cb01c533107b7790,TODO: How can you test this?
aaahgidbg,gunthercox/ChatterBot,chatterbot/chatterbot.py,b1b2b49bcda299cc92eeb10a1e355bb83f83ef73,0e54313a0aa2394b5c4baaadd500ff3b3d864a3d,TODO: Create a custom exception class
aaahgidbi,gunthercox/ChatterBot,chatterbot/controllers/storage.py,0e54313a0aa2394b5c4baaadd500ff3b3d864a3d,5567d1040a7af0885fb60760955d1c7f142bd4fa,TODO: Rename to read_only
aaahgidch,gunthercox/ChatterBot,chatterbot/adapters/storage/jsondatabase.py,0648b673b101f3db9f36f297ff7ddae8eaab8fbc,5ca1b2598ce9a96c048da0391baf480667f68bca,TODO
aaahgiddc,gunthercox/ChatterBot,chatterbot/conversation/statement.py,0648b673b101f3db9f36f297ff7ddae8eaab8fbc,5c4db23be1fd09348d2f26167462c6ade7076500,TODO: Make this a list of statement objects instead of a list of strings
aaahgided,gunthercox/ChatterBot,chatterbot/chatterbot.py,5ca1b2598ce9a96c048da0391baf480667f68bca,STILL_EXISTS,TODO? If the two statements occure equaly in frequency; should we keep one at random
aaahgidha,gunthercox/ChatterBot,tests/test_chatbot_output.py,bd164320fe0afd13d6e43b2bdb2cee387545fa73,3742f14c02647ab68d8c2671cb01c533107b7790,TODO
aaahgieab,gunthercox/ChatterBot,chatterbot/adapters/logic/closest_meaning.py,780635fc38eef1af70cecbcea3325aff9ae87710,484a83e4f62dff955de8218b1eea3938a43806e9,Download data if needed
aaahgiece,gunthercox/ChatterBot,chatterbot/training.py,c6b40dd99e2de6558ccdc8ad1fb048b9941f6a58,c83bcae3a0bb703c1e748e1b396fecca386106f6,TODO: If I just choose .english; it should recurse through all sub-modules of that corpus.
aaahgieci,gunthercox/ChatterBot,tests/corpus_tests/test_corpus.py,c83bcae3a0bb703c1e748e1b396fecca386106f6,8a667e6fab16b3faca38448d45fcaeda884f98a8,TODO
aaahgiedf,gunthercox/ChatterBot,tests/test_chatbot_output.py,35ec7fa46e20c10777a29ddf908937d6d5262edd,STILL_EXISTS,TODO: self.assertEqual(statement_object.get_occurrence_count(); 2)
aaahgieef,gunthercox/ChatterBot,chatterbot/chatterbot.py,caeac2409f1a0f043b2c105ef62de1650f5e4780,71dddc3062fbe466379736bacfd7e11e6baba87f,TODO: Why is checking if the input is equal to the closest match not the same here?
aaahgiehc,gunthercox/ChatterBot,chatterbot/chatterbot.py,dfd3bba9420e7f4763c2cd687885564bf7917960,STILL_EXISTS,TODO Make sure that recent_statements & database are updated as needed
aaahgifce,gunthercox/ChatterBot,chatterbot/adapters/logic/logic.py,71dddc3062fbe466379736bacfd7e11e6baba87f,308d63816137ede398cd7902290ae0e28cab20e2,TODO
aaahgifif,gunthercox/ChatterBot,chatterbot/adapters/logic/multi_adapter.py,b68c2be913752028a36e6d3072ddfc45e4a27ec2,080f3407f3828d3e82506da50e8fcf6486fe6e95,TODO; will this actually set the context on the instance?
aaahgifje,gunthercox/ChatterBot,chatterbot/adapters/storage/twitter_storage.py,b31f5c28bc995928cc1de27323a216828128ed9a,STILL_EXISTS,TODO: Handle non-ascii characters properly
aaahgigia,gunthercox/ChatterBot,chatterbot/adapters/logic/closest_meaning.py,717721018eff9897d2488b48aa932fcaa3694615,1abff669ea174cbaac7c9664a1cfc0eec61e310e,Download data if needed
aaahgigic,gunthercox/ChatterBot,chatterbot/adapters/logic/weather.py,126dee09532a5fd61234f0fa49354a2efefa0104,STILL_EXISTS,@TODO: Find some way to suppress the warnings generated by this.
aaahgigid,gunthercox/ChatterBot,chatterbot/adapters/logic/weather.py,7f15f7a9c6fa2a00bd24037a1f8dcbe8a8fc28e9,STILL_EXISTS,@TODO: Add more options for getting weather. This could include
aaahgihag,gunthercox/ChatterBot,chatterbot/adapters/storage/twitter_storage.py,08d04ec99fb3aac7089825f1653bf73c50db4d1f,STILL_EXISTS,TODO: What if a word is not found?
aaahgihbd,gunthercox/ChatterBot,chatterbot/adapters/input/input_format_adapter.py,26d585fe050778106e99d0af391252d9ca00e088,STILL_EXISTS,TODO subclass this error
aaahgihbe,gunthercox/ChatterBot,chatterbot/adapters/input/input_format_adapter.py,26d585fe050778106e99d0af391252d9ca00e088,STILL_EXISTS,TODO: Should this just be a parameter for the Terminal adapter?
aaahgihbh,gunthercox/ChatterBot,chatterbot/adapters/output/output_format_adapter.py,26d585fe050778106e99d0af391252d9ca00e088,e7cafa213fd01f79ca584108506d911a1f8fc6d1,TODO subclass this error
aaahgihbi,gunthercox/ChatterBot,chatterbot/adapters/output/output_format_adapter.py,26d585fe050778106e99d0af391252d9ca00e088,e7cafa213fd01f79ca584108506d911a1f8fc6d1,TODO: Should this just be a parameter for the Terminal adapter?
aaahgihdh,gunthercox/ChatterBot,chatterbot/chatterbot.py,373629ba611df317d4662cddd9fe5a5d041b3b71,9af559b30dc48b3712c81cb601778db8746948da,TODO: test adapter validation
aaahgiiaa,gunthercox/ChatterBot,docs/conf.py,c71257f7457c100a5a66f871f6ccf13dce764ed3,8171475674d95c8494e9761554f6e04bcfcf8792,If true; `todo` and `todoList` produce output; else they produce nothing.
aaahgijdf,gunthercox/ChatterBot,docs/conf.py,c71257f7457c100a5a66f871f6ccf13dce764ed3,8171475674d95c8494e9761554f6e04bcfcf8792,Fix unsupported image types using the Pillow.
aaahgjgad,gunthercox/ChatterBot,tests/storage_adapter_tests/test_storage_adapter.py,2e85db764b3c2c7c8ebaf7ff71f28180fa829ef9,4fc4b9dae8325c7328cbe0d95772c8282b59fed7,TODO Add the above statements to the database
aaahgjhdg,gunthercox/ChatterBot,chatterbot/adapters/storage/sqlalchemy.py,6b1a616e9d631c5b28941220f9d1c60ad4871602,2ea136b5c93448df555cb60bf95668de3cf21631,TODO Extra data
aaahgjjaf,gunthercox/ChatterBot,chatterbot/filters/language_filter.py,66aa473da8f0f695724c58c6c4a1fe1916d801f9,STILL_EXISTS,TODO
aaahgjjag,gunthercox/ChatterBot,chatterbot/filters/repetitive_response_filter.py,66aa473da8f0f695724c58c6c4a1fe1916d801f9,STILL_EXISTS,TODO
aaahgjjbb,gunthercox/ChatterBot,chatterbot/filters.py,aa170aadfe1b9c24ac39db16785d5a5399ae0649,41426f93bee31e73b1a3d0d192dc6095c82f0703,TODO: Unzip\/decrypt? data file on first use?
aaahhadfh,gunthercox/ChatterBot,chatterbot/trainers.py,50d75a7fbd48b8740bd9f51a83569ae973c572d1,STILL_EXISTS,TODO: Handle non-ascii characters properly
aaahhbeih,gunthercox/ChatterBot,chatterbot/storage/sqlalchemy.py,8e27a2de8c2f7d07b98bafbb7e58fc147f7f5c80,6b8a7d260964d3b6dd3c4eb503f05db4a58ca02b,FIXME Optimize... query = _response_query.filter(StatementTable.in_response_to.text('%' + _filter + '%'))
aaahhbfge,gunthercox/ChatterBot,chatterbot/storage/__init__.py,70caa0becd2b5a890e4ec2bfaaf6a84125abd010,1c01393b6c7bef184fd7a3fefd9fecbc0146b820,FIXME Better way manage import
aaahhbhhh,gunthercox/ChatterBot,chatterbot/storage/sql_storage.py,68797153454c6ee46dc33f193823cd90a1d7831e,STILL_EXISTS,Get or create the response records as needed
aaahhbhif,gunthercox/ChatterBot,chatterbot/storage/mongodb.py,b8ffab19c432c3af9fcb7e7b61f34006a4a40b78,STILL_EXISTS,TODO: Check if ordering is needed
aaahhbhig,gunthercox/ChatterBot,chatterbot/filters.py,583e71b15bef0b83cf58ca57be298c778cf4d029,STILL_EXISTS,TODO: Add a larger quantity of response history
aaahhbjge,gunthercox/ChatterBot,chatterbot/stemming.py,ebaceeda0777385e2230f49601c7aa75b360d9a3,STILL_EXISTS,Chop off the ends of the word
aaahhbjgg,gunthercox/ChatterBot,chatterbot/stemming.py,17fde372d687c4ff8d09dc6429b1192e07a5121b,9315e308470ad4e6d8035bc1efc927ac13efb4bc,Download the stopwords corpus if needed
aaahhcaej,gunthercox/ChatterBot,chatterbot/search.py,030ce1dcd63468f2ea2843d817346ea5d3892bf8,3ddbbe97ab3291fe1a2d30a9fff77327dcf4a305,TODO: Use input_statement.search_text if already generated
aaahhcafa,gunthercox/ChatterBot,chatterbot/search.py,030ce1dcd63468f2ea2843d817346ea5d3892bf8,3ddbbe97ab3291fe1a2d30a9fff77327dcf4a305,Maybe do this ahead of time (when the chatbot gets the input)
aaahhcdeh,ageitgey/face_recognition,setup.py,e15120e6ba08cde1216607d2eb27e0eb0f0ea37c,3a2b6f4acf30fe39b359e8e470ca53e670936dbc,TODO: put package test requirements here
aaahhcdff,ageitgey/face_recognition,travis_pypi_setup.py,e15120e6ba08cde1216607d2eb27e0eb0f0ea37c,STILL_EXISTS,workaround for https:\/\/github.com\/travis-ci\/travis-api\/issues\/196
aaahhcdhe,ageitgey/face_recognition,examples/facerec_from_webcam_faster.py,64cb085f9e02f39233e3452305b58c3c768daf60,STILL_EXISTS,other example; but it includes some basic performance tweaks to make things run a lot faster:
aaahhceff,ageitgey/face_recognition,examples/benchmark.py,93d04bd353f0786609d3d219928d4ff6a89932b7,STILL_EXISTS,This is a very simple benchmark to give you an idea of how fast each step of face recognition will run on your system.
aaahhcegi,ageitgey/face_recognition,examples/facerec_from_video_file.py,f37e636e22306d4efcc35fb1ef33d47183cfc5e7,STILL_EXISTS,Quit when the input video file ends
aaahhcejc,ageitgey/face_recognition,examples/find_faces_in_batches.py,f37e636e22306d4efcc35fb1ef33d47183cfc5e7,STILL_EXISTS,Bail out when the video file ends
aaahhcgeh,ageitgey/face_recognition,examples/facerec_from_webcam_much_faster.py,86d9ab671f469408fffa239c41e7c6e4d6c3dbea,STILL_EXISTS,other example; but it includes some basic performance tweaks to make things run a lot faster:
aaahhched,ageitgey/face_recognition,examples/facerec_from_webcam_multiprocessing.py,df84e2cd8a225e0f3094bd8cb13172aa9b918f29,STILL_EXISTS,Fix Bug on MacOS
aadfbdjgh,microsoft/graspologic,graphstats/embed/svd.py,ed6b67d1d58bbf6b30407f4a96ac09698a992b49,ab59598c4d64e67094f364acbc645fd5a6e7a3b4,TODO: comment out below to add dynamic dimensionality selection.
aadfbeaci,microsoft/graspologic,graphstats/embed/ase.py,deda18c6c2708ca900bbf2d72394d06c580ce7da,a847ac8be51590fb5c9d102b81444a06a4fe489b,TODO other parameters here?
aadfbeebh,microsoft/graspologic,graphstats/embed/omni.py,127ec5157ebee81b90a065ba8af1ccb08f19a888,STILL_EXISTS,TODO: Convert networkx.Graph to np.arrays if Graphs are given.
aadfbeeci,microsoft/graspologic,graphstats/embed/omni.py,03fd7263f3c6133f2b1537b4c4646fd0a6170e6c,STILL_EXISTS,Super fast and efficient
aadfbeeej,microsoft/graspologic,graphstats/embed/mds.py,ebdcb0186a9614f784afa9bcfaf85d3767a914c5,STILL_EXISTS,TODO: use dimselect here
aadfbeejh,microsoft/graspologic,graphstats/inference/semipar.py,4df34e14c8ae1199cb7574225a36c78e1b81b458,STILL_EXISTS,TODO: Note that we can do semipar on vertex basis or
aadfbefaa,microsoft/graspologic,graspy/embed/ase.py,f553bd38de79ebec7a9c2fc60d5b2d45d4762409,0c9e902caacbe6a3641e86706b970f1f65cdc93d,TODO other parameters here?
aadfbehai,microsoft/graspologic,graspy/embed/svd.py,0702914a04fb308cc2c530a5e40bdc6d92776877,STILL_EXISTS,Check to see if svd is needed
aadfbehbb,microsoft/graspologic,graspy/embed/svd.py,0702914a04fb308cc2c530a5e40bdc6d92776877,STILL_EXISTS,TODO this method does not properly catch error if k=min(X.shape);
aadfbehdj,microsoft/graspologic,graspy/plot/plot.py,142783d62c69c4c8e94fc0f6b87a0320cae76a9b,STILL_EXISTS,hacky; but np.log(arr; where=arr>0) is really buggy
aadfbeiac,microsoft/graspologic,docs/conf.py,939979d8305739691f401de75d34168665f9dab6,STILL_EXISTS,Below is needed to prevent errors
aadfbfbif,microsoft/graspologic,graspy/inference/semipar.py,dbe07709be9192b774ecbd88f4d9fef2f13dd0e7,STILL_EXISTS,TODO asymmetric case
aadfbfbih,microsoft/graspologic,graspy/inference/semipar.py,dbe07709be9192b774ecbd88f4d9fef2f13dd0e7,STILL_EXISTS,Continuity correction - note that the +0.5 causes p > 1 sometimes # TODO
aadfbfccc,microsoft/graspologic,tests/test_semipar.py,dbe07709be9192b774ecbd88f4d9fef2f13dd0e7,STILL_EXISTS,TODO : remove when we implement
aadfbfcea,microsoft/graspologic,graspy/plot/plot.py,2aecdd6407dd6bf78485bb6b029b69c87931bbf4,STILL_EXISTS,TODO would it be cool if pairplot reduced to single plot
aadfbfcif,microsoft/graspologic,graspy/inference/nonpar.py,3eac78c844cb3ed424b537bf38e30523b141e9f2,STILL_EXISTS,TODO asymmetric case
aadfbfdfc,microsoft/graspologic,graspy/plot/plot.py,df833a0197287b12e17f1166bfd0ed75d5006bc1,STILL_EXISTS,TODO would it be cool if pairplot reduced to single plot
aadfbfgbb,microsoft/graspologic,graspy/models/base.py,aa7ad671075a1025b558970d39fcd0841ac4f8eb,STILL_EXISTS,TODO: use nonzero inds here will be faster
aadfbfgcd,microsoft/graspologic,tests/test_models.py,aa7ad671075a1025b558970d39fcd0841ac4f8eb,STILL_EXISTS,hack just for testing likelihood
aadfbfgcj,microsoft/graspologic,graspy/embed/base.py,adda83bc7b5932117f8ffe34b0428c19afc90fbb,STILL_EXISTS,This check is needed because np.stack will always duplicate array in memory.
aadfbfgih,microsoft/graspologic,graspy/models/sbm.py,29567bd36a7f759043f15432504b48311569d075,STILL_EXISTS,TODO use directed LSE
aadfbgafa,microsoft/graspologic,graspy/embed/base.py,5d0aa9a77de6691cc3995eadf0f632601e2a4ff8,STILL_EXISTS,This check is needed because np.stack will always duplicate array in memory.
aadfbgdgg,microsoft/graspologic,graspy/version/version.py,c8a569cb1637ad09e694a83a18ad23c92f98b105,STILL_EXISTS,TODO: #454 Update in https:\/\/github.com\/microsoft\/graspologic\/issues\/454
aadfbgdgj,microsoft/graspologic,setup.py,c8a569cb1637ad09e694a83a18ad23c92f98b105,24a3d00ce425678a66ac41a13dc735f1e2455b43,TODO: #454 Change path in https:\/\/github.com\/microsoft\/graspologic\/issues\/454
aadfbgdhd,microsoft/graspologic,graspy/embed/base.py,7877d2f659fc3ed8fd4d5f86ef4e75a9842bad5a,STILL_EXISTS,This check is needed because np.stack will always duplicate array in memory.
aadfbggce,microsoft/graspologic,tests/test_match.py,660055a438b6eda21ea807b8f12ed0a63a662005,STILL_EXISTS,TODO: def test_sim(self):
aadfbghje,microsoft/graspologic,graspologic/utils/utils.py,80506c962ef424f8b2976b6072b99d0698e218ba,STILL_EXISTS,brute force.  if anyone has a better way; please PR
aadfbgjfc,microsoft/graspologic,graspologic/layouts/nooverlap/_quad_node.py,fd8a27721e7c50b1ca2f023583541faeef9cb124,STILL_EXISTS,print ('original_total cells %d; (%d; %d) needed: %d' %(total_cells; x_cells; y_cells; cells_needed))
aadfbgjfd,microsoft/graspologic,graspologic/layouts/nooverlap/_quad_node.py,fd8a27721e7c50b1ca2f023583541faeef9cb124,STILL_EXISTS,print ('new_cells %d; (%d; %d) needed: %d' %(total_cells; x_cells; y_cells; cells_needed))
aadfbhadb,microsoft/graspologic,graspologic/layouts/render.py,fd8a27721e7c50b1ca2f023583541faeef9cb124,STILL_EXISTS,TODO; test at different dpi
aadfbhahf,microsoft/graspologic,graspologic/simulations/simulations.py,238756d43195b45f5ae6837884499b3ad1fd0168,STILL_EXISTS,Naming convention follows paper listed in references.
aadfbhcac,microsoft/graspologic,graspologic/plot/plot_matrix.py,1c9b301aa5fd2275e596ddc130ffd6ce39db0a6a,STILL_EXISTS,create new columns in the dataframe that correspond to the sorting order
aaecijfah,src-d/ml,ast2vec/repo2nbow.py,7dcf94e820f60458dab4b5ecbe0d04f64de888f3,0d005cf461d027c9aa13b3d7fd56045798b5ff31,FIXME(vmarkovtsev): remove this hardcode when https:\/\/github.com\/bblfsh\/server\/issues\/28 is resolved
aaecijfai,src-d/ml,ast2vec/repo2nbow.py,7dcf94e820f60458dab4b5ecbe0d04f64de888f3,3abbc2323b17528b77d7fd4daa0ded3e1aa35432,TODO: multithreading in chunks
aaecijfaj,src-d/ml,ast2vec/repo2nbow.py,7dcf94e820f60458dab4b5ecbe0d04f64de888f3,3abbc2323b17528b77d7fd4daa0ded3e1aa35432,TODO: make request to bblfsh
aaecijfbc,src-d/ml,ast2vec/repo2nbow.py,095239c52500955d989857a49352c82083422ed7,0d005cf461d027c9aa13b3d7fd56045798b5ff31,FIXME(vmarkovtsev): remove \"+1\"
aaecijfbd,src-d/ml,ast2vec/repo2nbow.py,095239c52500955d989857a49352c82083422ed7,0d005cf461d027c9aa13b3d7fd56045798b5ff31,FIXME(vmarkovtsev): add --json when we implement https:\/\/github.com\/src-d\/enry\/issues\/39
aaecijfbe,src-d/ml,ast2vec/repo2base.py,0d005cf461d027c9aa13b3d7fd56045798b5ff31,1e8f5e099ea3c943037ff9646559644bc2ebcadc,FIXME(vmarkovtsev): remove \"+1\"
aaecijfbf,src-d/ml,ast2vec/repo2base.py,0d005cf461d027c9aa13b3d7fd56045798b5ff31,STILL_EXISTS,FIXME(vmarkovtsev): remove this hardcode when https:\/\/github.com\/bblfsh\/server\/issues\/28 is resolved
aaecijfbg,src-d/ml,ast2vec/repo2base.py,0d005cf461d027c9aa13b3d7fd56045798b5ff31,5678028a86a73e31d303a143233873fde40b06d0,FIXME(vmarkovtsev): add --json when we implement https:\/\/github.com\/src-d\/enry\/issues\/39
aaecijfch,src-d/ml,ast2vec/repo2base.py,81d43a401bd92731056e5354ca4509efa7472104,5678028a86a73e31d303a143233873fde40b06d0,FIXME(vmarkovtsev): change to check_output() when we fix https:\/\/github.com\/src-d\/enry\/issues\/40
aaecijfdd,src-d/ml,ast2vec/enry.py,9773e1a9918ab454333543446783aba973fdbf4e,5678028a86a73e31d303a143233873fde40b06d0,FIXME(vmarkovtsev): change to gopkg.in when we fix https:\/\/github.com\/src-d\/enry\/issues\/37
aaecijgib,src-d/ml,doc/conf.py,c99d1fa473d09a584a31807e63244a7e35f918f8,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaecijhga,src-d/ml,ast2vec/bblfsh_roles.py,1e8f5e099ea3c943037ff9646559644bc2ebcadc,83a07e019f933a525f11b24075d55e43d05c9cb7,FIXME(vmarkovtsev): remove \"+1\"
aaecjabjd,src-d/ml,ast2vec/repo2/cooccbase.py,4ac6880c34292b924b323f0d25463f6aba0bd28d,34e84bff18e061956170973bc5d995d0db9e0ea0,we do not care about how many times each tokens appears
aaecjacai,src-d/ml,ast2vec/engine.py,179c03db84a7a12c3eb043903cf929ad3863c9d8,STILL_EXISTS,TODO(vmarkovtsev): figure out why is this option needed
aaecjaccg,src-d/ml,sourced/ml/uast_ids_to_bag.py,6ff864362bd8e862b4a96ec58c450ab710bbc640,da872f919fce6e9e5365fb12eb65d1b14f480854,FIXME(vmarkovtsev): remove this workaround when https:\/\/github.com\/bblfsh\/client-python\/issues\/60 is fixed # nopep8
aaecjaceh,src-d/ml,sourced/ml/transformers/coocc.py,7dc52dc9b603d503f40b48857b850c5dd7a6ef61,0df2892d14f1fb00d3ad573455ceab17ada29b8c,TODO(zurk): implement pruning
aaecjacej,src-d/ml,sourced/ml/transformers/token_mapper.py,7dc52dc9b603d503f40b48857b850c5dd7a6ef61,cd31be19a3c561a222dbe54fff5308140d42656c,TODO (zurk): reimplement uast_ids_to_bag.py using new engine and use it instead
aaecjadge,src-d/ml,sourced/ml/cmd_entries/preprocess_id2vec.py,5658be42083799a289950cf62c3607c592865fcd,4f29c32d2c225543f78002f2d055308b8d58925e,so we take all words with the cutoff frequency; sort them and take the needed amount
aaecjaece,src-d/ml,sourced/ml/algorithms/uast_ids_to_bag.py,3c40ff48366f8cfe20dc1202212d0588afae0acb,309917edf6b2e53d33b1a36ca6f2c2726d6ca478,FIXME(zurk): change to simple function. Vadim Markovtsev comments:
aaecjaech,src-d/ml,sourced/ml/algorithms/uast_struct_to_bag.py,3c40ff48366f8cfe20dc1202212d0588afae0acb,309917edf6b2e53d33b1a36ca6f2c2726d6ca478,FIXME(zurk): change to simple function. Vadim Markovtsev comments:
aaecjaeea,src-d/ml,sourced/ml/algorithms/uast_ids_to_bag.py,87d64bdc33f0f05d63e50a7db82aced0414db53e,STILL_EXISTS,FIXME(zurk): change to simple function. Vadim Markovtsev comments:
aaecjaeed,src-d/ml,sourced/ml/algorithms/uast_struct_to_bag.py,87d64bdc33f0f05d63e50a7db82aced0414db53e,STILL_EXISTS,FIXME(zurk): change to simple function. Vadim Markovtsev comments:
aaecjaefb,src-d/ml,sourced/ml/algorithms/uast_to_role_id_pairs.py,8433bdf0149ef571bf0f34e2eddfbe053bdeb5eb,0aadd5bb8aa68dd72a58b24b18cb4ccacb53b81e,FIXME(zurk): Rewrite without recursion
aaecjafgi,src-d/ml,sourced/ml/tests/test_id_splitter_features.py,bedb1c39f7af5cc0930e0b9a22924e308e321caa,STILL_EXISTS,read last two columns as identifiers
aaecjafgj,src-d/ml,sourced/ml/tests/test_id_splitter_features.py,bedb1c39f7af5cc0930e0b9a22924e308e321caa,STILL_EXISTS,read wrong columns
aaecjafhb,src-d/ml,sourced/ml/algorithms/id_splitter/features.py,65102b85d3fab912683a182b6420c9b7c8aeb06c,6adef5dfe1e4ebc3d993120a6fdbe8b66588ec17,In the CSV file; columns 0;1;2 contain statistics about the identifier.
aaecjafhd,src-d/ml,sourced/ml/algorithms/id_splitter/features.py,65102b85d3fab912683a182b6420c9b7c8aeb06c,STILL_EXISTS,TODO: Update dataset loading as soon as https:\/\/github.com\/src-d\/backlog\/issues\/1212 done.
aaecjahch,src-d/ml,sourced/ml/cmd_entries/id_splitter.py,3ea485c66c39ee7954256b0d705fed53486c4d56,STILL_EXISTS,TODO: list available optimizers from keras and add their arguments
aaecjahig,src-d/ml,sourced/ml/cmd/train_id_split.py,4bdc930b3eddb9ab71e9c98996be03f53b014979,STILL_EXISTS,TODO: Use modelforge to save the model
aaecjaidc,src-d/ml,sourced/ml/tests/test_dump.py,b214746bf02f985efd698c6355e75be968d27994,f613baaf045d57cbac39784a95df0932b044c84e,TODO: when dulwich is fixed; update
aaecjajea,chakki-works/seqeval,seqeval/metrics/sequence_labeling.py,662a2d465faa710c3836c138161666947c8a9351,STILL_EXISTS,\"\"\"Metrics to assess performance on sequence labeling task given prediction || Functions named as ``*_score`` return a scalar value to maximize: the higher || the better || \"\"\"
aaecjajge,chakki-works/seqeval,seqeval/scheme.py,448a1e4edc4e874aca41afa01f454f04f0b92971,STILL_EXISTS,Todo: IOE1 hasn't yet been able to handle some cases. See unit testing.
aaecjbafj,openml/openml-python,openml/apiconnector.py,0cdb7c1a05920940f71d316521b5f21256d3252c,7e051db80726145e0f6f2c2377c2f7d12f7e67a1,TODO add debug information
aaecjbage,openml/openml-python,openml/apiconnector.py,0cdb7c1a05920940f71d316521b5f21256d3252c,aae311fff0615151910da8e2def9fa6ff1b8732a,TODO: catch possible exceptions
aaecjbahj,openml/openml-python,openml/apiconnector.py,0cdb7c1a05920940f71d316521b5f21256d3252c,7e051db80726145e0f6f2c2377c2f7d12f7e67a1,TODO add proper error handling here!
aaecjbaic,openml/openml-python,openml/apiconnector.py,0cdb7c1a05920940f71d316521b5f21256d3252c,7e051db80726145e0f6f2c2377c2f7d12f7e67a1,TODO replace with a more efficient while loop!
aaecjbaid,openml/openml-python,openml/apiconnector.py,0cdb7c1a05920940f71d316521b5f21256d3252c,7e051db80726145e0f6f2c2377c2f7d12f7e67a1,TODO implement a cache for this that invalidates itself after some
aaecjbajb,openml/openml-python,openml/apiconnector.py,0cdb7c1a05920940f71d316521b5f21256d3252c,7e051db80726145e0f6f2c2377c2f7d12f7e67a1,TODO: it is inefficient to load the dataset in memory prior to
aaecjbaji,openml/openml-python,openml/apiconnector.py,0cdb7c1a05920940f71d316521b5f21256d3252c,7e051db80726145e0f6f2c2377c2f7d12f7e67a1,TODO useful debug
aaecjbbbd,openml/openml-python,openml/apiconnector.py,0cdb7c1a05920940f71d316521b5f21256d3252c,aae311fff0615151910da8e2def9fa6ff1b8732a,TODO: do input validation!
aaecjbbbe,openml/openml-python,openml/apiconnector.py,0cdb7c1a05920940f71d316521b5f21256d3252c,aae311fff0615151910da8e2def9fa6ff1b8732a,TODO logger.debug(url)
aaecjbbbf,openml/openml-python,openml/apiconnector.py,0cdb7c1a05920940f71d316521b5f21256d3252c,5639fba33aa30d2da1f270bb74d8bd8c6827d1ea,TODO maybe switch on the unicode flag!
aaecjbbbg,openml/openml-python,openml/apiconnector.py,0cdb7c1a05920940f71d316521b5f21256d3252c,ba37ed7d4436166584d5c5e20861e89726d3d0f8,TODO ask JAN why this happens
aaecjbbcb,openml/openml-python,openml/entities/dataset.py,0cdb7c1a05920940f71d316521b5f21256d3252c,STILL_EXISTS,TODO: add a partial read method which only returns the attribute
aaecjbbcj,openml/openml-python,openml/entities/task.py,0cdb7c1a05920940f71d316521b5f21256d3252c,13f16b38a37fbcaea9fedeff9337ee3d18bc777b,TODO: this can become its own class if necessary
aaecjbbda,openml/openml-python,openml/entities/task.py,0cdb7c1a05920940f71d316521b5f21256d3252c,13f16b38a37fbcaea9fedeff9337ee3d18bc777b,TODO: ideally this has the indices for the different splits...but
aaecjbcjj,openml/openml-python,tests/entities/test_dataset.py,0cdb7c1a05920940f71d316521b5f21256d3252c,STILL_EXISTS,TODO this is not yet supported!
aaecjbdad,openml/openml-python,tests/entities/test_dataset.py,0cdb7c1a05920940f71d316521b5f21256d3252c,STILL_EXISTS,TODO test multiple ignore attributes!
aaecjbdah,openml/openml-python,tests/test_apiconnector.py,0cdb7c1a05920940f71d316521b5f21256d3252c,aae311fff0615151910da8e2def9fa6ff1b8732a,TODO return error messages
aaecjbiic,openml/openml-python,openml/apiconnector.py,37172aef94eaf43b8469c035c1c995bea37b982b,7e051db80726145e0f6f2c2377c2f7d12f7e67a1,TODO add debug information
aaecjbiie,openml/openml-python,openml/apiconnector.py,a98ad8afec08c9d6172cc1d705467985f3e9dd36,STILL_EXISTS,TODO logger.debug; create CACHEEXCEPTION
aaecjbiii,openml/openml-python,openml/apiconnector.py,8aea41c3d65acd2a6152862a132169d41f511487,STILL_EXISTS,TODO logger.debug; create CACHEEXCEPTION
aaecjbijb,openml/openml-python,tests/entities/test_dataset.py,8bf9625ecdf74d1491b6a598102dba2aba247b6c,STILL_EXISTS,TODO this is not yet supported!
aaecjbijf,openml/openml-python,tests/entities/test_dataset.py,8bf9625ecdf74d1491b6a598102dba2aba247b6c,STILL_EXISTS,TODO test multiple ignore attributes!
aaecjbjcc,openml/openml-python,openml/apiconnector.py,2bfe552a4c0258ffa63511f62e56652a029462fb,0e61650c65ff6f04c59bb3510ffb0fb3fca5fa18,TODO: catch possible exceptions
aaecjbjcd,openml/openml-python,openml/apiconnector.py,2bfe552a4c0258ffa63511f62e56652a029462fb,0e61650c65ff6f04c59bb3510ffb0fb3fca5fa18,TODO: do input validation!
aaecjbjce,openml/openml-python,openml/apiconnector.py,2bfe552a4c0258ffa63511f62e56652a029462fb,0e61650c65ff6f04c59bb3510ffb0fb3fca5fa18,TODO logger.debug(url)
aaecjbjci,openml/openml-python,tests/test_apiconnector.py,2bfe552a4c0258ffa63511f62e56652a029462fb,0e61650c65ff6f04c59bb3510ffb0fb3fca5fa18,TODO return error messages
aaecjbjef,openml/openml-python,openml/apiconnector.py,6b95a17479d2850332a70aabcd8e4ee10f1e6ad1,675031a8a50040f110acfca41e045cc64d91b7d8,TODO add debug information!
aaecjbjid,openml/openml-python,openml/apiconnector.py,4ce6d82e6d0322d74a0f0be201d28f0d2e62ff9a,05bd462c0e09abf40f3d9540571cb4992c3da38f,Perhaps returns the -1\/-2 business with proper raising of exceptions?
aaecjbjie,openml/openml-python,openml/apiconnector.py,4ce6d82e6d0322d74a0f0be201d28f0d2e62ff9a,ba37ed7d4436166584d5c5e20861e89726d3d0f8,TODO logger.debug
aaecjbjif,openml/openml-python,openml/apiconnector.py,011b25b6cea32488ba7e9317ecf19a0dda9480ae,8a2f7ae2162f6ffc48bfcd53ed146297c91ae423,TODO look into either adding the class labels to task xml; or other
aaecjbjih,openml/openml-python,openml/apiconnector.py,011b25b6cea32488ba7e9317ecf19a0dda9480ae,7e051db80726145e0f6f2c2377c2f7d12f7e67a1,TODO improve performance; currently reads the whole file
aaecjcabd,openml/openml-python,openml/autorun.py,242ed25b111dcba4aa9187d7d5ed60c1a4b95066,STILL_EXISTS,While serializing the model with joblib is often more efficient than pickle[1];
aaecjcabh,openml/openml-python,openml/autorun.py,242ed25b111dcba4aa9187d7d5ed60c1a4b95066,STILL_EXISTS,TODO (?) Return an OpenML run instead.
aaecjcbcf,openml/openml-python,doc/conf.py,98ec3e6b11217f31b475e4ede896a3d346184ac6,STILL_EXISTS,Fix navigation bar to top of page?
aaecjcbde,openml/openml-python,openml/autorun.py,c96b29069fd178b004e01f303f0ece48486a7f97,STILL_EXISTS,it to work properly.
aaecjcbgf,openml/openml-python,openml/entities/run.py,f043740ee4d053e3a5c18631e1cd0545292714ff,STILL_EXISTS,it to work properly.
aaecjcbgi,openml/openml-python,openml/entities/run.py,f043740ee4d053e3a5c18631e1cd0545292714ff,STILL_EXISTS,While serializing the model with joblib is often more efficient than pickle[1];
aaecjcbhc,openml/openml-python,openml/entities/run.py,f043740ee4d053e3a5c18631e1cd0545292714ff,STILL_EXISTS,TODO (?) Return an OpenML run instead.
aaecjcbhf,openml/openml-python,openml/entities/run.py,e2ee3370b7648efca9f2f85c440e8918453b65f6,STILL_EXISTS,fixme str(classifier) might contain (...)
aaecjccif,openml/openml-python,openml/datasets/dataset.py,7e051db80726145e0f6f2c2377c2f7d12f7e67a1,STILL_EXISTS,TODO improve performance; currently reads the whole file
aaecjccjd,openml/openml-python,openml/datasets/functions.py,7e051db80726145e0f6f2c2377c2f7d12f7e67a1,33c3a8101efe1cb66599831c03a6b960248a8718,TODO create NOTCACHEDEXCEPTION
aaecjccje,openml/openml-python,openml/datasets/functions.py,7e051db80726145e0f6f2c2377c2f7d12f7e67a1,25136676c51cb6ad64d104ae77fa91067cdf60a5,TODO add proper error handling here!
aaecjccjh,openml/openml-python,openml/datasets/functions.py,7e051db80726145e0f6f2c2377c2f7d12f7e67a1,33c3a8101efe1cb66599831c03a6b960248a8718,TODO replace with a more efficient while loop!
aaecjccji,openml/openml-python,openml/datasets/functions.py,7e051db80726145e0f6f2c2377c2f7d12f7e67a1,STILL_EXISTS,TODO implement a cache for this that invalidates itself after some
aaecjcdag,openml/openml-python,openml/datasets/functions.py,7e051db80726145e0f6f2c2377c2f7d12f7e67a1,STILL_EXISTS,TODO: it is inefficient to load the dataset in memory prior to
aaecjcdbd,openml/openml-python,openml/datasets/functions.py,7e051db80726145e0f6f2c2377c2f7d12f7e67a1,33c3a8101efe1cb66599831c03a6b960248a8718,TODO useful debug
aaecjcdbe,openml/openml-python,openml/datasets/functions.py,7e051db80726145e0f6f2c2377c2f7d12f7e67a1,922554c5da52e88aa4a1564bc8f087c134b77fcc,TODO add debug information!
aaecjcdbf,openml/openml-python,openml/datasets/functions.py,7e051db80726145e0f6f2c2377c2f7d12f7e67a1,33c3a8101efe1cb66599831c03a6b960248a8718,TODO add debug information
aaecjcdci,openml/openml-python,tests/test_datasets.py,7e051db80726145e0f6f2c2377c2f7d12f7e67a1,3523874a7842b167851cd953c2d92a36c1bf64d3,FIXME REFACTOR with test apiconnector
aaecjcejg,openml/openml-python,openml/apiconnector.py,dda48461e6757d994162cb123073cbe358a91e9c,05bd462c0e09abf40f3d9540571cb4992c3da38f,fixme raise appropriate error
aaecjcfdi,openml/openml-python,openml/tasks/functions.py,8a2f7ae2162f6ffc48bfcd53ed146297c91ae423,36ec6861e32caa20c5843d735363e8329576eb4d,TODO look into either adding the class labels to task xml; or other
aaecjcffa,openml/openml-python,openml/tasks/task.py,8a2f7ae2162f6ffc48bfcd53ed146297c91ae423,d761ddf39b44376fca541322faf5d925ac6e5c18,TODO add debug information!
aaecjcgcg,openml/openml-python,openml/flows/flow.py,05bd462c0e09abf40f3d9540571cb4992c3da38f,c40edf1fcdb767b84ad6c86d4b398ec84d151e80,fixme raise appropriate error
aaecjcgfh,openml/openml-python,openml/config.py,027a7766685d396520bedf5697fe302b59988c92,STILL_EXISTS,TODO add debug information
aaecjcgii,openml/openml-python,openml/datasets/functions.py,33c3a8101efe1cb66599831c03a6b960248a8718,STILL_EXISTS,TODO not used yet; figure out what to do with them...
aaecjchaa,openml/openml-python,openml/flows/flow.py,33c3a8101efe1cb66599831c03a6b960248a8718,STILL_EXISTS,TODO add scikit-learn here!
aaecjchab,openml/openml-python,openml/flows/flow.py,33c3a8101efe1cb66599831c03a6b960248a8718,4dbc93c2b17f81bf90543a7b4ebc8d1c14a7f73f,TODO add numpy and scipy version!
aaecjchac,openml/openml-python,openml/flows/flow.py,33c3a8101efe1cb66599831c03a6b960248a8718,c40edf1fcdb767b84ad6c86d4b398ec84d151e80,TODO check with latest version of code if this raises an exception
aaecjchad,openml/openml-python,openml/runs/run.py,33c3a8101efe1cb66599831c03a6b960248a8718,7eb3905f79bcfba1dd39bf76ab10a4812a5fac9f,TODO move this into its onwn module. While it somehow belongs here; it
aaecjchae,openml/openml-python,openml/runs/run.py,33c3a8101efe1cb66599831c03a6b960248a8718,7eb3905f79bcfba1dd39bf76ab10a4812a5fac9f,adds quite a lot of functionality which is better suited in other places!
aaecjchaf,openml/openml-python,openml/runs/run.py,33c3a8101efe1cb66599831c03a6b960248a8718,7eb3905f79bcfba1dd39bf76ab10a4812a5fac9f,TODO why doesn't this accept a flow as input?
aaecjchag,openml/openml-python,openml/runs/run.py,33c3a8101efe1cb66599831c03a6b960248a8718,7eb3905f79bcfba1dd39bf76ab10a4812a5fac9f,TODO use different iterator to only provide a single iterator (less
aaecjchcc,openml/openml-python,openml/datasets/functions.py,587ffec03e8828db7dcc1fe17ce00e953a4796d3,26ac44ea75c0ea88036da74e76206447597dabd4,TODO create NOTCACHEDEXCEPTION
aaecjchcd,openml/openml-python,openml/datasets/functions.py,587ffec03e8828db7dcc1fe17ce00e953a4796d3,26ac44ea75c0ea88036da74e76206447597dabd4,TODO replace with a more efficient while loop!
aaecjchcg,openml/openml-python,openml/datasets/functions.py,587ffec03e8828db7dcc1fe17ce00e953a4796d3,STILL_EXISTS,TODO: it is inefficient to load the dataset in memory prior to
aaecjchdb,openml/openml-python,openml/datasets/functions.py,587ffec03e8828db7dcc1fe17ce00e953a4796d3,26ac44ea75c0ea88036da74e76206447597dabd4,TODO useful debug
aaecjchdc,openml/openml-python,openml/datasets/functions.py,587ffec03e8828db7dcc1fe17ce00e953a4796d3,26ac44ea75c0ea88036da74e76206447597dabd4,TODO add debug information
aaecjchdd,openml/openml-python,openml/datasets/functions.py,587ffec03e8828db7dcc1fe17ce00e953a4796d3,STILL_EXISTS,TODO not used yet; figure out what to do with them...
aaecjchdg,openml/openml-python,openml/datasets/functions.py,26ac44ea75c0ea88036da74e76206447597dabd4,STILL_EXISTS,TODO not used yet; figure out what to do with them...
aaecjchfi,openml/openml-python,openml/datasets/functions.py,9da3d7bd2a8930c45d364802dcaedc4e0f67260d,785213f9364f27a2303ed767deeebc671830ea9b,TODO create NOTCACHEDEXCEPTION
aaecjchfj,openml/openml-python,openml/datasets/functions.py,9da3d7bd2a8930c45d364802dcaedc4e0f67260d,785213f9364f27a2303ed767deeebc671830ea9b,TODO replace with a more efficient while loop!
aaecjchgc,openml/openml-python,openml/datasets/functions.py,9da3d7bd2a8930c45d364802dcaedc4e0f67260d,STILL_EXISTS,TODO: it is inefficient to load the dataset in memory prior to
aaecjchgh,openml/openml-python,openml/datasets/functions.py,9da3d7bd2a8930c45d364802dcaedc4e0f67260d,785213f9364f27a2303ed767deeebc671830ea9b,TODO useful debug
aaecjchgi,openml/openml-python,openml/datasets/functions.py,9da3d7bd2a8930c45d364802dcaedc4e0f67260d,785213f9364f27a2303ed767deeebc671830ea9b,TODO add debug information
aaecjchgj,openml/openml-python,openml/datasets/functions.py,9da3d7bd2a8930c45d364802dcaedc4e0f67260d,STILL_EXISTS,TODO not used yet; figure out what to do with them...
aaecjchha,openml/openml-python,openml/datasets/functions.py,785213f9364f27a2303ed767deeebc671830ea9b,STILL_EXISTS,TODO not used yet; figure out what to do with them...
aaecjchhg,openml/openml-python,openml/datasets/functions.py,785213f9364f27a2303ed767deeebc671830ea9b,STILL_EXISTS,TODO: it is inefficient to load the dataset in memory prior to
aaecjchid,openml/openml-python,openml/runs/run.py,785213f9364f27a2303ed767deeebc671830ea9b,c40edf1fcdb767b84ad6c86d4b398ec84d151e80,TODO move this into its onwn module. While it somehow belongs here; it
aaecjchie,openml/openml-python,openml/runs/run.py,785213f9364f27a2303ed767deeebc671830ea9b,c40edf1fcdb767b84ad6c86d4b398ec84d151e80,adds quite a lot of functionality which is better suited in other places!
aaecjchif,openml/openml-python,openml/runs/run.py,785213f9364f27a2303ed767deeebc671830ea9b,c40edf1fcdb767b84ad6c86d4b398ec84d151e80,TODO why doesn't this accept a flow as input?
aaecjchig,openml/openml-python,openml/runs/run.py,785213f9364f27a2303ed767deeebc671830ea9b,c40edf1fcdb767b84ad6c86d4b398ec84d151e80,TODO use different iterator to only provide a single iterator (less
aaecjcibb,openml/openml-python,openml/datasets/functions.py,f220098e5513d5e8c7eb59a4c2ba29082690ae31,b6fa2533bb77b655ea4bf430f4e2a192e1fffbf0,TODO logger.debug
aaecjcigc,openml/openml-python,openml/flows/functions.py,c40edf1fcdb767b84ad6c86d4b398ec84d151e80,5f78c7365830c14714f5ef597504536c2d724a70,TODO check with latest version of code if this raises an exception
aaecjcigd,openml/openml-python,openml/flows/functions.py,c40edf1fcdb767b84ad6c86d4b398ec84d151e80,5f78c7365830c14714f5ef597504536c2d724a70,fixme raise appropriate error
aaecjcigg,openml/openml-python,openml/runs/functions.py,c40edf1fcdb767b84ad6c86d4b398ec84d151e80,STILL_EXISTS,TODO move this into its onwn module. While it somehow belongs here; it
aaecjcigh,openml/openml-python,openml/runs/functions.py,c40edf1fcdb767b84ad6c86d4b398ec84d151e80,8fadddc94b9f9fd67ea762669ba2f8ff19357873,adds quite a lot of functionality which is better suited in other places!
aaecjcigi,openml/openml-python,openml/runs/functions.py,c40edf1fcdb767b84ad6c86d4b398ec84d151e80,STILL_EXISTS,TODO why doesn't this accept a flow as input?
aaecjcihb,openml/openml-python,openml/runs/functions.py,c40edf1fcdb767b84ad6c86d4b398ec84d151e80,STILL_EXISTS,TODO use different iterator to only provide a single iterator (less
aaecjdbfd,openml/openml-python,openml/flows/flow.py,4882cda9f29535eda784d4437076ff4e244a3a73,52d01402e7316e3ff09bcc578716ef75f82d5ade,TODO update these - the sklearn transformation class should be able
aaecjdbfh,openml/openml-python,openml/flows/sklearn.py,4882cda9f29535eda784d4437076ff4e244a3a73,STILL_EXISTS,I think this exactly we want here as there shouldn't be any built-in or
aaecjdbfj,openml/openml-python,openml/flows/sklearn.py,4882cda9f29535eda784d4437076ff4e244a3a73,STILL_EXISTS,TODO maybe remove those functions and put the check to the long
aaecjdbhj,openml/openml-python,openml/flows/sklearn.py,4882cda9f29535eda784d4437076ff4e244a3a73,STILL_EXISTS,TODO remove potential test sentinel during testing!
aaecjdbid,openml/openml-python,openml/flows/sklearn.py,4882cda9f29535eda784d4437076ff4e244a3a73,STILL_EXISTS,XXX this is copied from sklearn.model_selection._split
aaecjdcbf,openml/openml-python,openml/flows/flow.py,5f78c7365830c14714f5ef597504536c2d724a70,60372ea81eaa09c0f550e9dc4c377acdd36ebf20,TODO check with latest version of code if this raises an exception
aaecjdcbg,openml/openml-python,openml/flows/flow.py,5f78c7365830c14714f5ef597504536c2d724a70,60372ea81eaa09c0f550e9dc4c377acdd36ebf20,fixme raise appropriate error
aaecjdccf,openml/openml-python,tests/flows/test_flow.py,5f78c7365830c14714f5ef597504536c2d724a70,STILL_EXISTS,TODO maybe get this via get_flow(); which would have to be refactored to allow getting only the xml dictionary
aaecjdcfc,openml/openml-python,openml/flows/flow.py,d09fe4f232a3d4e2b6e34bee5ee325910ad87961,STILL_EXISTS,TODO @Jan can you find better descriptions for binary_url; binary_md5;
aaecjdcfg,openml/openml-python,openml/flows/sklearn_converter.py,d09fe4f232a3d4e2b6e34bee5ee325910ad87961,STILL_EXISTS,JSON strings are used to encoder parameter values. By passing around
aaecjdcfj,openml/openml-python,openml/flows/sklearn_converter.py,d09fe4f232a3d4e2b6e34bee5ee325910ad87961,413aaae1d0316d362abeb48df055884de9311608,TODO check if this code is actually called
aaecjddbh,openml/openml-python,openml/runs/run.py,3e9a80fce1aeaacfa5816109b523fb103a42c895,STILL_EXISTS,TODO: don't we have flow object in data structure? Use this one
aaecjddih,openml/openml-python,openml/flows/functions.py,8855e6efa04dfa5db6c9aa2d83a7cfd5a4fd983b,815c259c0f95c81a5acb665967c3f7613652d8c3,TODO check with latest version of code if this raises an exception
aaecjddii,openml/openml-python,openml/flows/functions.py,8855e6efa04dfa5db6c9aa2d83a7cfd5a4fd983b,815c259c0f95c81a5acb665967c3f7613652d8c3,fixme raise appropriate error
aaecjdecf,openml/openml-python,openml/flows/sklearn_converter.py,c23c00be7aae7b485ff4b9131115482c65210944,STILL_EXISTS,TODO fill in dependencies!
aaecjdgba,openml/openml-python,openml/flows/functions.py,e42ca51485c5771dee5028b928b29d6b483859ae,17ebae315fbd016efd3ee51a3dfacf326c409f00,TODO add caching here!
aaecjdgbb,openml/openml-python,openml/flows/functions.py,e42ca51485c5771dee5028b928b29d6b483859ae,ba193edc4f1b4b70ab125976150d6392bf6b1fa1,TODO add proper error handling here!
aaecjdgee,openml/openml-python,tests/test_runs/test_run_functions.py,96fedbea37a876aa28533157c58b479f554e41fd,9ea66243d5125acb5ff62850749c0e5d2438562e,TODO: download and check whether it really contains the error message
aaecjdiig,openml/openml-python,tests/test_runs/test_run_functions.py,34efa1b77868380f0e9e0fda155663ccc91f37cd,b11d5a55b3a249802415682648a48c6004e4bca9,TODO: download and check whether it really contains the error message
aaecjdiii,openml/openml-python,openml/datasets/dataset.py,b8bb34b5b27f5779b6b16775ef50ec4348fd2781,STILL_EXISTS,todo add nominal values
aaecjdiji,openml/openml-python,openml/utils/preprocessing.py,09f6ff4b088286f04bca78ac1e1d60afeb48d7d9,STILL_EXISTS,impute completelly empty columns with constant
aaecjdijj,openml/openml-python,openml/utils/preprocessing.py,09f6ff4b088286f04bca78ac1e1d60afeb48d7d9,STILL_EXISTS,Delete the invalid rows\/columns
aaecjdjac,openml/openml-python,openml/runs/functions.py,e5b23ed5e9a12d9571e8a0f82a3cee1598281a77,0f8b7f0966a1ebb4e7c848268e904402818891ef,TODO: WHY IS THIS 2D???
aaecjdjad,openml/openml-python,openml/runs/functions.py,e5b23ed5e9a12d9571e8a0f82a3cee1598281a77,STILL_EXISTS,JvR: why is class labels a parameter? could be removed and taken from task object; right?
aaecjdjae,openml/openml-python,tests/test_runs/test_run_functions.py,e5b23ed5e9a12d9571e8a0f82a3cee1598281a77,9ec141c28bf0a6babe8fee8dcc9e998c3985e7cb,TODO use different iterator to only provide a single iterator (less
aaecjdjag,openml/openml-python,openml/datasets/dataset.py,d0e063804a347ce236522a8fe0bd559485570973,07eab1af265bed398828434bd1c4e82eaf0e2393,TODO: check
aaecjdjbc,openml/openml-python,openml/runs/functions.py,18b8f9797501e92aac7d1f200f62b587c695bb0a,bd5fdb116f0042227d2bedf28155badc58b3e919,TODO: would be nice if flow._ensure_flow_exists already handled this
aaecjdjbj,openml/openml-python,tests/test_flows/test_flow.py,71ec3fc53277fd117f714baf251914f084b9ff7f,b4e052d4933cd12b475d5960a8766959d1e20902,TODO: Test if parameters are set correctly!
aaecjdjcb,openml/openml-python,openml/flows/sklearn_converter.py,929fec14bec17bc47fb441efc1649ac031ab5e66,STILL_EXISTS,TODO: assert that only on first recursion lvl `parent_model` can be None
aaecjdjge,openml/openml-python,openml/runs/functions.py,cab211bfbee83543aca1e22cd1b705b78d47a6f6,9ec141c28bf0a6babe8fee8dcc9e998c3985e7cb,TODO: would be nice if flow._ensure_flow_exists already handled this
aaecjdjhf,openml/openml-python,openml/runs/functions.py,83c0c102adb6ac8b462e4f4ee5fc46ef387bc505,STILL_EXISTS,TODO: also tag the flow if it already exists
aaecjdjhg,openml/openml-python,tests/test_flows/test_flow.py,f9bf4f2a123e37f74fb2d0dbd084eed369e3c29b,STILL_EXISTS,TODO: no sklearn flows.
aaecjdjhh,openml/openml-python,tests/test_flows/test_flow.py,f9bf4f2a123e37f74fb2d0dbd084eed369e3c29b,STILL_EXISTS,TODO: Test if parameters are set correctly!
aaecjdjhi,openml/openml-python,tests/test_runs/test_run_functions.py,f9bf4f2a123e37f74fb2d0dbd084eed369e3c29b,0f8b7f0966a1ebb4e7c848268e904402818891ef,check number columns
aaecjeabh,openml/openml-python,openml/runs/functions.py,4dbc93c2b17f81bf90543a7b4ebc8d1c14a7f73f,STILL_EXISTS,also; if the flow is not present on the server; the check is not needed.
aaecjeace,openml/openml-python,openml/runs/functions.py,ff2fa4b459146ab73e5372c30adfea0ec997b68b,00be4019b38f693d51d7f971a8820d6e3cdd3636,TODO (neccessary? is this a post condition of this function)
aaecjebbi,openml/openml-python,openml/runs/functions.py,3ea5027d4c242ba7d930058c1a712a385044aa15,00be4019b38f693d51d7f971a8820d6e3cdd3636,TODO (neccessary? is this a post condition of this function)
aaecjecbh,openml/openml-python,openml/setups/functions.py,8ceb2905ef6237b4fcca991ed6955c3669426610,STILL_EXISTS,TODO: parse value. If serialized object (e.g.; steps; estimator); skip it (?)
aaecjecie,openml/openml-python,tests/test_runs/test_run_functions.py,08e2ae8bdc42977b0d8b441d7d891d2ea7f7200f,b8ced46da474daaa3eec9550f2677dd3c12ef42a,TODO: think of a different check
aaecjedei,openml/openml-python,tests/test_runs/test_run_functions.py,cb55127ea807b9b9fe4e0f323d35d5f0b7408d2d,STILL_EXISTS,TODO: assert holdout task
aaecjedej,openml/openml-python,tests/test_runs/test_run_functions.py,cb55127ea807b9b9fe4e0f323d35d5f0b7408d2d,STILL_EXISTS,TODO: implement testcase
aaecjedgb,openml/openml-python,tests/test_runs/test_run_functions.py,0ddfea918a7d6fd3f1498ee1bb3e94c0806f6707,STILL_EXISTS,engine is behind. TODO: mock this? We have the arff already on the server
aaecjeecf,openml/openml-python,tests/test_runs/test_run_functions.py,8325c72829f4375599129614fbc301e124134a01,STILL_EXISTS,however; sometimes it is good to wait (a bit) for this; to properly test
aaecjeeff,openml/openml-python,tests/test_runs/test_run_functions.py,eab17202176607580ccd2151fe51ded095555e49,a0b65cd3f4280a3b19c6290b8d82dee892079fef,todo: check if runtime is present
aaecjeehe,openml/openml-python,openml/runs/functions.py,8fadddc94b9f9fd67ea762669ba2f8ff19357873,00be4019b38f693d51d7f971a8820d6e3cdd3636,TODO (neccessary? is this a post condition of this function)
aaecjefad,openml/openml-python,openml/flows/functions.py,c46f5b70bdde9bf0d897e3b05f4188566a65324d,STILL_EXISTS,TODO as they are actually now saved during publish; it might be good to
aaecjefei,openml/openml-python,openml/flows/sklearn_converter.py,67f8e19ec14eed5f58893be6bee44e50519ea2c4,STILL_EXISTS,TODO: add more tags based on the scikit-learn
aaecjeggd,openml/openml-python,tests/test_runs/test_run_functions.py,61c113cffb9c1fcec608bceb4d8d173620f50b7c,a0b65cd3f4280a3b19c6290b8d82dee892079fef,todo: check if runtime is present
aaecjehde,openml/openml-python,tests/test_runs/test_run_functions.py,bef46b78abccb3ff4ae15091887547b960b07181,STILL_EXISTS,todo: check if runtime is present
aaecjehei,openml/openml-python,tests/test_setups/test_setup_functions.py,c0c6643f26bc12fd55abeab901edb17fd8552c1b,a7d9ed41988745ea7cb424075e5c95243021534e,TODO: please remove for better test
aaecjehfa,openml/openml-python,tests/test_setups/test_setup_functions.py,c0c6643f26bc12fd55abeab901edb17fd8552c1b,a7d9ed41988745ea7cb424075e5c95243021534e,TODO please change
aaecjehfb,openml/openml-python,tests/test_setups/test_setup_functions.py,c0c6643f26bc12fd55abeab901edb17fd8552c1b,STILL_EXISTS,TODO: please adjust 0
aaecjehga,openml/openml-python,openml/runs/run.py,1c285a803b58dca963e4c51930251ac334d94d19,STILL_EXISTS,TODO: this one might be zero
aaecjehgb,openml/openml-python,openml/runs/run.py,1c285a803b58dca963e4c51930251ac334d94d19,STILL_EXISTS,TODO: these could be cached
aaecjehgc,openml/openml-python,openml/runs/run.py,1c285a803b58dca963e4c51930251ac334d94d19,STILL_EXISTS,TODO: can be sped up bt preprocessing index; but OK for now.
aaecjehgj,openml/openml-python,tests/test_runs/test_run_functions.py,1c285a803b58dca963e4c51930251ac334d94d19,STILL_EXISTS,also check if we can obtain some other scores: # TODO: how to do AUC?
aaecjehhd,openml/openml-python,tests/test_setups/test_setup_functions.py,6dce2740866e2e984554e2548120ec02728621aa,STILL_EXISTS,TODO: remove after pull on live for better testing
aaecjehhf,openml/openml-python,openml/runs/run.py,59433d8952c10d9207afeaced09ed2c23ca1875c,STILL_EXISTS,TODO: make this a stream reader
aaecjeiaf,openml/openml-python,openml/runs/functions.py,b54427299e4fd4121875deea02d2dc230df8556d,811f9cee3bdbca535eb898282edab9ab6ee71594,TODO: if someone needs it; he can use the parameter
aaecjeiai,openml/openml-python,openml/runs/functions.py,b54427299e4fd4121875deea02d2dc230df8556d,811f9cee3bdbca535eb898282edab9ab6ee71594,and if we are going to duplicate this functionality
aaecjeidd,openml/openml-python,tests/test_runs/test_run_functions.py,9568cf0543267c27383efef2c3e2d4ef6312d6da,STILL_EXISTS,task 1 (test server) is important; as it is a task with an unused class
aaecjeidf,openml/openml-python,tests/test_runs/test_run_functions.py,9568cf0543267c27383efef2c3e2d4ef6312d6da,STILL_EXISTS,TODO: programmatically check wether these are indeed features (predict; correct)
aaecjeifd,openml/openml-python,tests/test_runs/test_run_functions.py,c778f77eb13484810fa66a414097ff317b807f03,STILL_EXISTS,todo: check if runtime is present
aaecjeigg,openml/openml-python,tests/test_runs/test_run_functions.py,98aede1337aff856696d0ad5215d69af8cb71603,STILL_EXISTS,task 1 (test server) is important; as it is a task with an unused class
aaecjeigi,openml/openml-python,tests/test_runs/test_run_functions.py,98aede1337aff856696d0ad5215d69af8cb71603,STILL_EXISTS,TODO: programmatically check wether these are indeed features (predict; correct)
aaecjeihi,openml/openml-python,openml/tasks/functions.py,df89fe6862ad8bbb2d4dc1746e6d7fde1d6833f7,922554c5da52e88aa4a1564bc8f087c134b77fcc,TODO look into either adding the class labels to task xml; or other
aaecjeija,openml/openml-python,tests/test_datasets/test_dataset.py,af1de066dc4f08a36ae1f8a2e6cf69b132fa8e60,STILL_EXISTS,TODO: re-add row_id and ignore attributes
aaecjejcg,openml/openml-python,openml/tasks/functions.py,ca029b85fb507ce81ee283fb706f07d197b464d6,735026c8e123a91f1985f2691d5782a045416454,TODO extract this to a function
aaecjejch,openml/openml-python,openml/tasks/functions.py,ca029b85fb507ce81ee283fb706f07d197b464d6,735026c8e123a91f1985f2691d5782a045416454,TODO look into either adding the class labels to task xml; or other
aaecjejdc,openml/openml-python,openml/datasets/functions.py,03c995521f7b6b01214d591782597b04c3cb6d1d,d5434d42812173fe41493d109127740c754fdb79,TODO add debug information!
aaecjejde,openml/openml-python,openml/datasets/functions.py,aa758f9ab6e0608ede4c4199d88fd533c6eb2ad1,90fab5387d5591256792a7208395e767205f5e42,TODO not used yet; figure out what to do with this...
aaecjejdh,openml/openml-python,tests/test_datasets/test_dataset.py,90fab5387d5591256792a7208395e767205f5e42,6b22bb658b016cd0ad8d6aaed2d70cd5eb5b5db6,longley; really small dataset
aaecjejee,openml/openml-python,tests/test_datasets/test_dataset.py,d05790bdf77e0f105c299dabff4f7b398ae055e2,c6f85b6d20accd6bca795b1014d7987340b3dcac,longley; really small dataset
aaecjejfi,openml/openml-python,tests/test_datasets/test_dataset.py,1fff169fd6044593be3b3d2b2c7f69d850072eb8,STILL_EXISTS,longley; really small dataset
aaecjejgg,openml/openml-python,openml/runs/functions.py,7ec40452fb71ce9c08de36902f607391415a16ce,de999ad0f65c13a7a0f9e449bddfaddd14341000,TODO: if possible; give a warning if model is already fitted (acceptable in case of custom experimentation;
aaecjejib,openml/openml-python,tests/test_runs/test_run_functions.py,c878872710df5abc01efaeb4bf211644d7c34f41,0f8b7f0966a1ebb4e7c848268e904402818891ef,check number columns
aaecjejii,openml/openml-python,openml/runs/functions.py,de999ad0f65c13a7a0f9e449bddfaddd14341000,STILL_EXISTS,JvR: why is class labels a parameter? could be removed and taken from task object; right?
aaecjfabj,openml/openml-python,openml/runs/functions.py,fcfa7d9493ec65073bc7ce860d8ea0c9f4addbc2,STILL_EXISTS,TODO: if possible; give a warning if model is already fitted (acceptable in case of custom experimentation;
aaecjfaij,openml/openml-python,tests/test_utils/test_utils.py,fdd6c2579704becbcecfc83d882f17f24090fdeb,529f4674264b2a32053bca6674f404bda0233790,TODO implement these tests
aaecjfbcf,openml/openml-python,tests/test_utils/test_utils.py,529f4674264b2a32053bca6674f404bda0233790,STILL_EXISTS,TODO apparently list_setups function does not support kwargs
aaecjfbci,openml/openml-python,tests/test_utils/test_utils.py,529f4674264b2a32053bca6674f404bda0233790,STILL_EXISTS,TODO apparently list_evaluations function does not support kwargs
aaecjfbdi,openml/openml-python,tests/test_tasks/test_split.py,805059d92f0d08c82edfccffb2d8b0aa9543a2c9,71af3dd2072381b0b7e14722b885f0728983e9c6,TODO Needs to be adapted regarding the python version
aaecjfbea,openml/openml-python,openml/datasets/dataset.py,5b1eb290a2de2a04b76ae40aff1610a7283f02bf,STILL_EXISTS,TODO add function to check if the name is casual_string128
aaecjfbgj,openml/openml-python,openml/runs/trace.py,906992a725d448fedf8d448e675e23cb4c76b6e1,811f9cee3bdbca535eb898282edab9ab6ee71594,TODO probably we want to integrate the trace object with the run object; rather than the current
aaecjfbje,openml/openml-python,openml/runs/functions.py,531038d759c613a70f4ebba50c7d84ac543816b6,STILL_EXISTS,TODO: JvR: the following lines of code can be replaced by
aaecjfcaj,openml/openml-python,doc/conf.py,c08dd0f506b04716e7923b8993b8a1abe36f7713,STILL_EXISTS,TODO: fix back\/forward references for the examples.
aaecjfeaa,openml/openml-python,examples/tasks_tutorial.py,c08dd0f506b04716e7923b8993b8a1abe36f7713,STILL_EXISTS,to have better visualization and easier access:
aaecjfefh,openml/openml-python,tests/test_runs/test_run_functions.py,cd7d74bd15d642bbd1ee6a0e0dedac49c24e5cf7,STILL_EXISTS,TODO add test about initializing a model from a run given a parameter distribution - also
aaecjffcg,openml/openml-python,openml/runs/trace.py,811f9cee3bdbca535eb898282edab9ab6ee71594,STILL_EXISTS,TODO allow to pass a trace description when running a flow
aaecjffdh,openml/openml-python,tests/test_runs/test_run_functions.py,811f9cee3bdbca535eb898282edab9ab6ee71594,STILL_EXISTS,TODO make sure that these attributes are instantiated when
aaecjfgcb,openml/openml-python,tests/test_utils/test_utils.py,8646ef2d44676c2f58bc212f9641e9b7299b1739,STILL_EXISTS,TODO: JvR: Why is this not a staticmethod?
aaecjfhii,openml/openml-python,openml/runs/functions.py,237594076d262397fb4f00ad1bfebc50bff2cd2e,STILL_EXISTS,TODO: currently hard-coded sklearn assumption.
aaecjfiac,openml/openml-python,openml/setups/functions.py,237594076d262397fb4f00ad1bfebc50bff2cd2e,0f8b7f0966a1ebb4e7c848268e904402818891ef,TODO: currently hard-coded sklearn assumption
aaecjfidh,openml/openml-python,tests/test_runs/test_run_functions.py,237594076d262397fb4f00ad1bfebc50bff2cd2e,STILL_EXISTS,TODO: assert holdout task
aaecjfiec,openml/openml-python,tests/test_runs/test_run_functions.py,237594076d262397fb4f00ad1bfebc50bff2cd2e,STILL_EXISTS,TODO: mock this? We have the arff already on the server
aaecjgehd,openml/openml-python,openml/runs/functions.py,3ed08f04e02f35ae977df97cd02e73495a947b2d,0235c512b1d258335327d56e8a6d3dec0906cc7b,is not needed.
aaecjgeii,openml/openml-python,openml/runs/run.py,3ed08f04e02f35ae977df97cd02e73495a947b2d,STILL_EXISTS,TODO: these could be cached
aaecjgfdd,openml/openml-python,openml/study/functions.py,a2a4adeb68b5f772acd0a720c6a41247101ba6a0,STILL_EXISTS,tags is legacy. remove once no longer needed.
aaecjgfha,openml/openml-python,tests/test_datasets/test_dataset_functions.py,45fe2a151e37e7224790389a930695c9e2b0fe90,STILL_EXISTS,Replaced a bare except. Not sure why either of these would be acceptable.
aaecjgfhg,openml/openml-python,tests/test_tasks/test_split.py,45fe2a151e37e7224790389a930695c9e2b0fe90,STILL_EXISTS,Replaced bare except. Not sure why these exceptions are acceptable.
aaecjggjb,openml/openml-python,examples/datasets_tutorial.py,94102f3ac7424e60a7c95ca606b1e517db1a3d36,STILL_EXISTS,Instead of manually creating the dataframe; you can already request a
aaecjghfc,openml/openml-python,openml/extensions/extension_interface.py,0f8b7f0966a1ebb4e7c848268e904402818891ef,STILL_EXISTS,TODO a trace belongs to a run and therefore a flow -> simplify this part of the interface!
aaecjghgd,openml/openml-python,openml/extensions/sklearn/extension.py,0f8b7f0966a1ebb4e7c848268e904402818891ef,STILL_EXISTS,JSON strings are used to encoder parameter values. By passing around
aaecjghhf,openml/openml-python,openml/extensions/sklearn/extension.py,0f8b7f0966a1ebb4e7c848268e904402818891ef,STILL_EXISTS,TODO: assert that only on first recursion lvl `parent_model` can be None
aaecjghic,openml/openml-python,openml/extensions/sklearn/extension.py,0f8b7f0966a1ebb4e7c848268e904402818891ef,STILL_EXISTS,I think this is exactly what we want here as there shouldn't be any
aaecjghih,openml/openml-python,openml/extensions/sklearn/extension.py,0f8b7f0966a1ebb4e7c848268e904402818891ef,STILL_EXISTS,it to work properly.
aaecjghii,openml/openml-python,openml/extensions/sklearn/extension.py,0f8b7f0966a1ebb4e7c848268e904402818891ef,STILL_EXISTS,fixme str(model) might contain (...)
aaecjghji,openml/openml-python,openml/extensions/sklearn/extension.py,0f8b7f0966a1ebb4e7c848268e904402818891ef,STILL_EXISTS,TODO: add more tags based on the scikit-learn
aaecjgiac,openml/openml-python,openml/extensions/sklearn/extension.py,0f8b7f0966a1ebb4e7c848268e904402818891ef,STILL_EXISTS,TODO fill in dependencies!
aaecjgiha,openml/openml-python,openml/extensions/sklearn/extension.py,0f8b7f0966a1ebb4e7c848268e904402818891ef,STILL_EXISTS,XXX this is copied from sklearn.model_selection._split
aaecjgiji,openml/openml-python,openml/extensions/sklearn/extension.py,0f8b7f0966a1ebb4e7c848268e904402818891ef,4923e5b5707f202c57cd5ce0e55944f66928b5d0,TODO: if possible; give a warning if model is already fitted (acceptable
aaecjgjbi,openml/openml-python,openml/extensions/sklearn/extension.py,0f8b7f0966a1ebb4e7c848268e904402818891ef,38e02ef76865f1305e8735d519aba8914fc11f09,TODO: WHY IS THIS 2D???
aaecjgjej,openml/openml-python,openml/extensions/sklearn/extension.py,0f8b7f0966a1ebb4e7c848268e904402818891ef,STILL_EXISTS,OpenML convention - this also guards against name collisions
aaecjhabj,openml/openml-python,tests/test_extensions/test_sklearn_extension/test_sklearn_extension.py,0f8b7f0966a1ebb4e7c848268e904402818891ef,STILL_EXISTS,TODO add some mocking here to actually test the innards of this function; too!
aaecjhacd,openml/openml-python,tests/test_extensions/test_sklearn_extension/test_sklearn_extension.py,0f8b7f0966a1ebb4e7c848268e904402818891ef,STILL_EXISTS,check number columns
aaecjhbef,openml/openml-python,tests/test_extensions/test_sklearn_extension/test_sklearn_extension.py,deda557a1d4caa4084df4a211b794faabcc6362b,STILL_EXISTS,task 1 (test server) is important: it is a task with an unused class
aaecjhcfd,openml/openml-python,tests/test_tasks/test_task.py,813daebea2d0c4932641f013ef79ba0ca72a9f46,STILL_EXISTS,TODO consider implementing on the diff task types.
aaecjhchi,openml/openml-python,examples/fetch_evaluations_tutorial.py,eec86a976a96df8643331e2e745002f627ed3889,STILL_EXISTS,\"\"\" || ==================== || Fetching Evaluations || ==================== ||  || Evalutions contain a concise summary of the results of all runs made. Each evaluation || provides information on the dataset used; the flow applied; the setup used; the metric || evaluated; and the result obtained on the metric; for each such run made. These collection || of results can be used for efficient benchmarking of an algorithm and also allow transparent || reuse of results from previous experiments on similar parameters. ||  || In this example; we shall do the following: ||  || * Retrieve evaluations based on different metrics || * Fetch evaluations pertaining to a specific task || * Sort the obtained results in descending order of the metric || * Plot a cumulative distribution function for the evaluations || * Compare the top 10 performing flows based on the evaluation performance || \"\"\"
aaecjhdaf,openml/openml-python,examples/fetch_evaluations_tutorial.py,eec86a976a96df8643331e2e745002f627ed3889,STILL_EXISTS,We shall now analyse how the performance of various flows have been on this task;
aaecjigde,openml/openml-python,examples/fetch_evaluations_tutorial.py,1c9f64d8201b42e05fd17caa2d5daca0c4d321dd,STILL_EXISTS,with hyperparameters. parameters_in_separate_columns returns parameters in
aaecjigdf,openml/openml-python,examples/fetch_evaluations_tutorial.py,1c9f64d8201b42e05fd17caa2d5daca0c4d321dd,STILL_EXISTS,separate columns
aaecjigdh,openml/openml-python,tests/test_evaluations/test_evaluation_functions.py,1c9f64d8201b42e05fd17caa2d5daca0c4d321dd,STILL_EXISTS,check if parameters in separate columns works
aaecjihhb,openml/openml-python,examples/40_paper/2015_neurips_feurer_example.py,8eac076e957eda2652bbb583bc6baeb98a1fb526,8cc302dcb73accc3384c222e74d319b262ad748f,\"\"\" || Feurer et al. (2015) || ==================== ||  || A tutorial on how to get the datasets used in the paper introducing *Auto-sklearn* by Feurer et al.. ||  || Auto-sklearn website: https:\/\/automl.github.io\/auto-sklearn\/master\/ ||  || Publication || ~~~~~~~~~~~ ||  || | Efficient and Robust Automated Machine Learning || | Matthias Feurer; Aaron Klein; Katharina Eggensperger; Jost Springenberg; Manuel Blum and Frank Hutter || | In *Advances in Neural Information Processing Systems 28*; 2015 || | Available at http:\/\/papers.nips.cc\/paper\/5872-efficient-and-robust-automated-machine-learning.pdf ||  || This is currently a placeholder. || \"\"\"
aaecjiiei,openml/openml-python,examples/40_paper/2018_ida_strang_example.py,5a2830cc494dbffe93fb324aa7eb98b8bf3f0b33,STILL_EXISTS,gives us a table with columns data_id; flow1_value; flow2_value
aaecjiifc,openml/openml-python,examples/40_paper/2018_ida_strang_example.py,5a2830cc494dbffe93fb324aa7eb98b8bf3f0b33,STILL_EXISTS,now we have columns data_id; flow1_value; flow2_value; meta_feature_1;
aaecjiigb,openml/openml-python,examples/30_extended/task_manual_iteration_tutorial.py,4020c1ee836ec31cead92e29fb1188aa1173a588,STILL_EXISTS,\"\"\" || Tasks: retrieving splits || ======================== ||  || Tasks define a target and a train\/test split. Normally; they are the input to the function || ``openml.runs.run_model_on_task`` which automatically runs the model on all splits of the task. || However; sometimes it is necessary to manually split a dataset to perform experiments outside of || the functions provided by OpenML. One such example is in the benchmark library || `HPOlib2 <https:\/\/github.com\/automl\/hpolib2>`_ which extensively uses data from OpenML; || but not OpenML's functionality to conduct runs. || \"\"\"
aaecjijac,openml/openml-python,examples/30_extended/tasks_tutorial.py,04a6b65c78f3b68299b4d6b155e710dcbac6289e,STILL_EXISTS,As conversion to a pandas dataframe is a common task; we have added this functionality to the
aaecjjbcb,openml/openml-python,examples/30_extended/suites_tutorial.py,4853d7cfa279e5fb348cc96471c4cf61fdaf8b23,STILL_EXISTS,And we can use the task listing functionality to learn more about them:
aaecjjbdj,openml/openml-python,examples/40_paper/2018_neurips_perrone_example.py,2796b9a2133136816ce3447bac280b21b2f0b2e1,STILL_EXISTS,\"\"\" || Perrone et al. (2018) || ===================== ||  || A tutorial on how to build a surrogate model based on OpenML data as done for *Scalable || Hyperparameter Transfer Learning* by Perrone et al.. ||  || Publication || ~~~~~~~~~~~ ||  || | Scalable Hyperparameter Transfer Learning || | Valerio Perrone and Rodolphe Jenatton and Matthias Seeger and Cedric Archambeau || | In *Advances in Neural Information Processing Systems 31*; 2018 || | Available at http:\/\/papers.nips.cc\/paper\/7917-scalable-hyperparameter-transfer-learning.pdf ||  || This example demonstrates how OpenML runs can be used to construct a surrogate model. ||  || In the following section; we shall do the following: ||  || * Retrieve tasks and flows as used in the experiments by Perrone et al. || * Build a tabular data by fetching the evaluations uploaded to OpenML || * Impute missing values and handle categorical data before building a Random Forest model that ||   maps hyperparameter values to the area under curve score || \"\"\"
aaecjjbic,openml/openml-python,examples/30_extended/plot_svm_hyperparameters_tutorial.py,c40e474fff97829f12b90b35cc784ca5a3d80af2,STILL_EXISTS,We can see all the hyperparameter names in the columns of the dataframe:
aaecjjcac,openml/openml-python,openml/base.py,43596e0a9af52b0916461c8021bd07354c514c05,STILL_EXISTS,We take advantage of the class naming convention (OpenMLX);
aaecjjcec,openml/openml-python,examples/40_paper/2018_neurips_perrone_example.py,cfba39d56043ed89e3e4c774de434565842c9457,STILL_EXISTS,Separating data into categorical and non-categorical (numeric for this example) columns
aaecjjchh,openml/openml-python,tests/test_datasets/test_dataset_functions.py,1c025dbb3447cecc25b7e2561650960f0cc49a15,STILL_EXISTS,Test column names are automatically converted to str if needed (#819)
aaecjjcjg,openml/openml-python,examples/30_extended/configure_logging.py,34d54d92ebe6a69d851f82d1aeac4a5bff6a184c,STILL_EXISTS,These file logs are automatically deleted if needed; and use at most 2MB of space.
aaedaaebi,openml/openml-python,examples/30_extended/create_upload_tutorial.py,df864c2da2ad217a453a39295fcd659c861f6070,STILL_EXISTS,and 'humidity' are kept as numeric columns. Then; we can
aaedaafie,openml/openml-python,openml/extensions/sklearn/extension.py,3d85fa7a46b54064627e0cbc0a5f403fdbdc0ac1,STILL_EXISTS,TODO fill in dependencies!
aaedaagca,openml/openml-python,examples/30_extended/run_setup_tutorial.py,bf3cd2ebaac10bd05809a1ce90e346248c4c61b1,ab793a65efe42da18264252eceec4085f3e68b9f,Helper functions to return required columns for ColumnTransformer
aaedaagcj,openml/openml-python,openml/extensions/sklearn/extension.py,bf3cd2ebaac10bd05809a1ce90e346248c4c61b1,STILL_EXISTS,adding missing columns with 0 probability
aaedaagdc,openml/openml-python,tests/test_extensions/test_sklearn_extension/test_sklearn_extension.py,bf3cd2ebaac10bd05809a1ce90e346248c4c61b1,STILL_EXISTS,Helper functions to return required columns for ColumnTransformer
aaedaagdg,openml/openml-python,tests/test_extensions/test_sklearn_extension/test_sklearn_extension.py,bf3cd2ebaac10bd05809a1ce90e346248c4c61b1,STILL_EXISTS,TODO add some mocking here to actually test the innards of this function; too!
aaedaagej,openml/openml-python,tests/test_runs/test_run_functions.py,bf3cd2ebaac10bd05809a1ce90e346248c4c61b1,STILL_EXISTS,task_id=2 on test server has 38 columns with 6 numeric columns
aaedaagib,openml/openml-python,openml/extensions/sklearn/extension.py,4923e5b5707f202c57cd5ce0e55944f66928b5d0,STILL_EXISTS,We re-order the columns to move possibly added missing columns into place.
aaedaaici,openml/openml-python,examples/30_extended/flows_and_runs_tutorial.py,ab793a65efe42da18264252eceec4085f3e68b9f,STILL_EXISTS,Extracting the indices of the categorical columns
aaedaajda,naver/claf,claf/config/factory/optimizer.py,d9685792cfa23ae5bfbe82f3df8e3e65e12c8175,STILL_EXISTS,hack to remove pooler; which is not used
aaedaajgf,naver/claf,claf/data/reader/bert/seq_cls.py,d9685792cfa23ae5bfbe82f3df8e3e65e12c8175,927035b732927d95e937e4484461d26002e97daf,TODO: fix hard-code
aaedaajha,naver/claf,claf/data/reader/bert/squad.py,d9685792cfa23ae5bfbe82f3df8e3e65e12c8175,927035b732927d95e937e4484461d26002e97daf,TODO: fix hard-code
aaedaajij,naver/claf,claf/data/utils.py,d9685792cfa23ae5bfbe82f3df8e3e65e12c8175,STILL_EXISTS,TODO: hard-code
aaedabagi,naver/claf,claf/machine/open_qa.py,d9685792cfa23ae5bfbe82f3df8e3e65e12c8175,STILL_EXISTS,TODO: fix read whole wiki
aaedabcdf,naver/claf,claf/model/semantic_parsing/mixin.py,d9685792cfa23ae5bfbe82f3df8e3e65e12c8175,STILL_EXISTS,for one-example (TODO: fix hard-code)
aaedabcdg,naver/claf,claf/model/semantic_parsing/sqlnet.py,d9685792cfa23ae5bfbe82f3df8e3e65e12c8175,STILL_EXISTS,NOTE: need to fix
aaedabceg,naver/claf,claf/model/semantic_parsing/sqlnet.py,d9685792cfa23ae5bfbe82f3df8e3e65e12c8175,STILL_EXISTS,2. Columns of conditions
aaedabebf,naver/claf,claf/modules/encoder/lstm_cell_with_projection.py,d9685792cfa23ae5bfbe82f3df8e3e65e12c8175,STILL_EXISTS,which way around is best?
aaedabece,naver/claf,claf/modules/encoder/lstm_cell_with_projection.py,d9685792cfa23ae5bfbe82f3df8e3e65e12c8175,STILL_EXISTS,Masks for the unused states of shape (1; new_batch_size; 1)
aaedabedj,naver/claf,claf/modules/encoder/lstm_cell_with_projection.py,d9685792cfa23ae5bfbe82f3df8e3e65e12c8175,STILL_EXISTS,that there are some unused elements (zero-length) for the RNN computation.
aaedabefh,naver/claf,claf/tokens/elmo.py,d9685792cfa23ae5bfbe82f3df8e3e65e12c8175,STILL_EXISTS,reshape the input if needed
aaedabfdf,naver/claf,claf/tokens/elmo.py,d9685792cfa23ae5bfbe82f3df8e3e65e12c8175,STILL_EXISTS,handle the different gate order convention
aaedabfeb,naver/claf,claf/tokens/embedding/bert_embedding.py,d9685792cfa23ae5bfbe82f3df8e3e65e12c8175,STILL_EXISTS,TODO: add text_unit option
aaedabiaj,naver/claf,tests/integration/test_reading_comprehension.py,d9685792cfa23ae5bfbe82f3df8e3e65e12c8175,STILL_EXISTS,TODO: subword ---> word
aaedaebbe,naver/claf,claf/data/reader/bert/regression.py,3e4a95ecaebd499be9cd888a16c633d1412185d1,927035b732927d95e937e4484461d26002e97daf,TODO: fix hard-code
aaedaebhi,naver/claf,claf/learn/experiment.py,dd573c51f3f8d2885e78d16ce1879bb22ea3c0cf,STILL_EXISTS,TODO: distributed training and 16-bits training (FP16)
aaedaebig,naver/claf,claf/data/dataset/bert/multi_task.py,6f8f01b3155cf2af9b42822adf17982960958d64,STILL_EXISTS,TODO: Dataset to DataLoader
aaedaecae,naver/claf,claf/data/reader/bert/multi_task.py,c8d2b0f755ce209d79f52061f7c027c30a18cce3,82acc30d4203cd368505f6d34dcd4a0a30e10451,TODO: tasks (category - issubclasses; label_count - num_classes or labl)
aaedaecah,naver/claf,claf/model/multi_task/mixin.py,d94ff35c43e75ecc5f2e0d8b728bc014358ca130,STILL_EXISTS,TODO: split predictions by task_index -> each task make_metrics then add task_index as prefix
aaedaecba,naver/claf,claf/model/sequence_classification/mixin.py,d94ff35c43e75ecc5f2e0d8b728bc014358ca130,STILL_EXISTS,TODO: Need to Fix
aaedaecce,naver/claf,claf/model/regression/mixin.py,aa88cf273e729b1b08942affe3aec6a55c3194d6,STILL_EXISTS,TODO: Need to Fix
aaedaecgd,naver/claf,claf/model/multi_task/bert.py,82acc30d4203cd368505f6d34dcd4a0a30e10451,STILL_EXISTS,TODO: add ReadingComprehension and TokenClassification forward
aaedaecgf,naver/claf,claf/model/reading_comprehension/roberta.py,7b755984f100d68c969ad3dfc32140bb81b8a0f2,60ad285f57d77b94ff481dab3623557681872741,TODO: append ForQuestionAnswering part
aaedaedaa,naver/claf,claf/model/reading_comprehension/mixin.py,24af45305fb2a63bd338b6414400015daf258d3c,STILL_EXISTS,TODO: start and end logits (TypeError: Object of type 'Tensor' is not JSON serializable)
aaedaedag,naver/claf,claf/model/reading_comprehension/mixin.py,24af45305fb2a63bd338b6414400015daf258d3c,STILL_EXISTS,# TODO: Need to Fix
aaedaeehh,scikit-multiflow/scikit-multiflow,skmultiflow/core/pipeline/Pipeline.py,6150d1774641103142c9d9f6cea79a9cc9c09c1c,STILL_EXISTS,\"should implement fit and transform.\")
aaedaefcc,scikit-multiflow/scikit-multiflow,skmultiflow/evaluation/EvaluatePrequential.py,e7e81bbe83b26f6ad78014d3325a68a8e2f39a4c,d2edcf9d03de65f28aca4777cfa83e9625baebb1,# TODO
aaedaefcd,scikit-multiflow/scikit-multiflow,skmultiflow/evaluation/EvaluatePrequential.py,e7e81bbe83b26f6ad78014d3325a68a8e2f39a4c,STILL_EXISTS,# fix the problem you created; the visualizer has to be dumb; he just receives statistics
aaedaeggd,scikit-multiflow/scikit-multiflow,skmultiflow/evaluation/measure_collection.py,fd74fb557cfd42a08a9953758d82bc04c36db1db,STILL_EXISTS,Verify if its needed to decrease the majority_classifier count
aaedaegje,scikit-multiflow/scikit-multiflow,skmultiflow/core/pipeline.py,0808b192293f20e60affcf8bcbfd2485215a0c18,8400bca8c0b4af01970ca07432c571ad9237b3da,raise TypeError(\"Last step of pipeline should implement partial_fit.\")
aaedaejjb,scikit-multiflow/scikit-multiflow,skmultiflow/evaluation/measure_collection.py,3546ba97854e6707fd370b53147596f8b2dfcba9,STILL_EXISTS,Verify if its needed to decrease the count of any label
aaedafafh,scikit-multiflow/scikit-multiflow,doc/conf.py,4643c73d6bfbf53ddca24b1bbc09583149d9b309,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaedafbfd,scikit-multiflow/scikit-multiflow,skmultiflow/classification/core/split_criteria/info_gain_split_criterion.py,b9f4643836e9e18e4e7379bd3450630cb09b35ef,STILL_EXISTS,TODO: How small can d be before log2 overflows?
aaedafbfj,scikit-multiflow/scikit-multiflow,skmultiflow/classification/trees/hoeffding_tree.py,b9f4643836e9e18e4e7379bd3450630cb09b35ef,490eee6297ca60a4fb734033e4760282f63a7191,TODO define
aaedafbgb,scikit-multiflow/scikit-multiflow,skmultiflow/classification/trees/hoeffding_tree.py,b9f4643836e9e18e4e7379bd3450630cb09b35ef,98dba2dd3e4c1031922a26e285d8be87a647bc96,TODO check
aaedafbha,scikit-multiflow/scikit-multiflow,skmultiflow/classification/trees/hoeffding_tree.py,b9f4643836e9e18e4e7379bd3450630cb09b35ef,6c0b55a12832bcb3992ef1cc14bd2eea534331dc,Allowed error in split decision; closer to 0 takes longer to decide.
aaedagbbc,scikit-multiflow/scikit-multiflow,skmultiflow/classification/trees/hoeffding_adaptive_tree.py,1b29c74f81bd018cdbe0e7332b8130e1d7c0ba35,STILL_EXISTS,TODO (check the casting to object)
aaedagbfg,scikit-multiflow/scikit-multiflow,skmultiflow/classification/trees/hoeffding_adaptive_tree.py,1b29c74f81bd018cdbe0e7332b8130e1d7c0ba35,27362279c66da88a838df998461465e94993729e,Example TODO Create a demo\/test from this
aaedagedf,scikit-multiflow/scikit-multiflow,skmultiflow/classification/meta/adaptive_random_forests.py,988129bfba353dfc802641df2976716ca1ccd7a7,cbbbe64e1563b4297582bab4bc6992533ddaf36b,TODO use skmultiflow evaluator
aaedagedg,scikit-multiflow/scikit-multiflow,skmultiflow/classification/meta/adaptive_random_forests.py,988129bfba353dfc802641df2976716ca1ccd7a7,cbbbe64e1563b4297582bab4bc6992533ddaf36b,TODO add code to support the selection of evaluation metric
aaedageeb,scikit-multiflow/scikit-multiflow,skmultiflow/classification/trees/arf_hoeffding_tree.py,988129bfba353dfc802641df2976716ca1ccd7a7,893c9a41d298006383dabe6e48a35eeec266987b,TODO Add HT parameters to ARF Hoeffding Tree constructor signature
aaedageee,scikit-multiflow/scikit-multiflow,skmultiflow/classification/trees/arf_hoeffding_tree.py,988129bfba353dfc802641df2976716ca1ccd7a7,893c9a41d298006383dabe6e48a35eeec266987b,TODO Pass all HT parameters once they are available at the ARFHT class level
aaedageef,scikit-multiflow/scikit-multiflow,skmultiflow/classification/trees/arf_hoeffding_tree.py,fa4115f03bc5d8bdff401caf178e4a0ca8c7d9f4,2f40363912b1474840e961a274422a174e530a85,TODO check attr-1
aaedageei,scikit-multiflow/scikit-multiflow,skmultiflow/classification/meta/adaptive_random_forests.py,eb3d55ff9f6a55fb97dd1cd06e441f4fbd351e7f,cbbbe64e1563b4297582bab4bc6992533ddaf36b,TODO Verify approach
aaedagefc,scikit-multiflow/scikit-multiflow,skmultiflow/classification/meta/adaptive_random_forests.py,eb3d55ff9f6a55fb97dd1cd06e441f4fbd351e7f,STILL_EXISTS,TODO check vote normalization
aaedagegg,scikit-multiflow/scikit-multiflow,skmultiflow/classification/meta/adaptive_random_forests.py,899feb8f1035dc6f4dfa2b52cf478d4db9b3856a,STILL_EXISTS,TODO Pass all HT parameters once they are available at the ARFHT class level
aaedagfgi,scikit-multiflow/scikit-multiflow,skmultiflow/demos/_test_file_stream_multiple_cfier.py,b86f892603b15c17aa12e71090eed976d6838b26,STILL_EXISTS,Demo 2 -- csv output should look nice
aaedagfja,scikit-multiflow/scikit-multiflow,skmultiflow/classification/meta/batch_incremental.py,73afbe4a65a544baebec506655d4093c873606a6,STILL_EXISTS,(TODO: not very python-esque ot the moment)
aaedaghad,scikit-multiflow/scikit-multiflow,src/skmultiflow/evaluation/evaluate_holdout.py,f56388a32146e1eb704d26eea782ce320773acae,ba2f6f12c0777aad31f5874e9f89d2f5aea1ec70,logging.info('Pre-training on 1 sample.')   # TODO Confirm if needed
aaedaghbc,scikit-multiflow/scikit-multiflow,src/skmultiflow/evaluation/evaluate_holdout.py,f56388a32146e1eb704d26eea782ce320773acae,STILL_EXISTS,TODO Confirm place
aaedaghga,scikit-multiflow/scikit-multiflow,src/skmultiflow/data/dataset_stream.py,4d1db4906551f7e380b8706266bad266cb44d30f,STILL_EXISTS,Take only n_targets columns to the right of target_idx; use the rest as features
aaedahadi,scikit-multiflow/scikit-multiflow,src/skmultiflow/evaluation/base_evaluator.py,e6021756974b3d140d053799223d9f61dcaa84e8,STILL_EXISTS,TODO consider new plot types
aaedahaea,scikit-multiflow/scikit-multiflow,src/skmultiflow/evaluation/base_evaluator.py,e6021756974b3d140d053799223d9f61dcaa84e8,ba2f6f12c0777aad31f5874e9f89d2f5aea1ec70,TODO extend the original MULTI_OUTPUT problem evaluation for
aaedahaec,scikit-multiflow/scikit-multiflow,src/skmultiflow/evaluation/base_evaluator.py,e6021756974b3d140d053799223d9f61dcaa84e8,99ab85ce23fc9ad7c7b04e2ca047f641120539e0,TODO Implement ARMAE
aaedahaef,scikit-multiflow/scikit-multiflow,src/skmultiflow/trees/intra_cluster_variance_reduction_split_criterion.py,6ea0008eeb0e076ec69b6f8eeff9d00fcac5adcb,STILL_EXISTS,TODO Also consider passing different weights for the targets
aaedahaeh,scikit-multiflow/scikit-multiflow,src/skmultiflow/trees/hoeffding_nominal_class_attribute_observer.py,30d91e1001864fc5621ba08d65ac1800fb7e87ae,2086050514fb5fed25f92575d4055a1aba793135,TODO Also consider nominal attributes
aaedahafa,scikit-multiflow/scikit-multiflow,src/skmultiflow/evaluation/base_evaluator.py,e8d2ba45ff841707c0de9db10280f868fd0d8fb9,ba2f6f12c0777aad31f5874e9f89d2f5aea1ec70,TODO extend the original MULTI_OUTPUT problem evaluation for
aaedahafc,scikit-multiflow/scikit-multiflow,src/skmultiflow/evaluation/base_evaluator.py,e8d2ba45ff841707c0de9db10280f868fd0d8fb9,99ab85ce23fc9ad7c7b04e2ca047f641120539e0,TODO Implement ARMAE
aaedahafh,scikit-multiflow/scikit-multiflow,src/skmultiflow/trees/multi_target_regression_hoeffding_tree.py,e8d2ba45ff841707c0de9db10280f868fd0d8fb9,STILL_EXISTS,TODO: Verify perceptron update
aaedahagj,scikit-multiflow/scikit-multiflow,src/skmultiflow/trees/multi_target_regression_hoeffding_tree.py,e8d2ba45ff841707c0de9db10280f868fd0d8fb9,STILL_EXISTS,TODO Verify
aaedahahf,scikit-multiflow/scikit-multiflow,src/skmultiflow/trees/multi_target_regression_hoeffding_tree.py,e8d2ba45ff841707c0de9db10280f868fd0d8fb9,1dfc8af04096eaf1731b2e617fddd1bda8711cf9,################## TODO Verify #########################
aaedahcag,scikit-multiflow/scikit-multiflow,src/skmultiflow/trees/hoeffding_nominal_class_attribute_observer.py,a4a452254b9f1b3ed5b3419aaa00f6180e50d894,STILL_EXISTS,TODO Also consider nominal attributes
aaedahdbb,scikit-multiflow/scikit-multiflow,src/skmultiflow/evaluation/base_evaluator.py,6ebb1804cb132d991aed9002d172c3e16665404f,91f1bd5ef455c3839ffb972abdf43bf15c3db12c,TODO extend the original MULTI_OUTPUT problem evaluation for
aaedahfge,scikit-multiflow/scikit-multiflow,src/skmultiflow/meta/classifier_chains.py,295cb5a19dd434c2dfac9aaffb8a767fa89a7481,STILL_EXISTS,TODO: much of this can be shared with Regressor Chains; probably should use a base class to inherit here.
aaedahfhe,scikit-multiflow/scikit-multiflow,src/skmultiflow/meta/adaptive_random_forests.py,901d7c65d6331a40a365a32316ea0013cb2dff4e,116aea1ab6caea74f990a7cadff70391f425fbea,TODO: Replace with version which works for unspecified classes
aaedahfia,scikit-multiflow/scikit-multiflow,src/skmultiflow/evaluation/base_evaluator.py,259a336c8f0d8c3de29c76c2e3c7a34d8a05c361,bcc9dc9678867077d5a4dbf70aa018e2ba44e5e4,TODO let the user choose the feature indices of interest
aaedahgaa,scikit-multiflow/scikit-multiflow,src/skmultiflow/visualization/evaluation_visualizer.py,259a336c8f0d8c3de29c76c2e3c7a34d8a05c361,STILL_EXISTS,TODO consider a fading\/update strategy instead
aaedahgbg,scikit-multiflow/scikit-multiflow,src/skmultiflow/visualization/evaluation_visualizer.py,6b59ec4f9570c82442c252b2d77d7c7114b55678,STILL_EXISTS,TODO confirm buffer update inside the loop
aaedahgcj,scikit-multiflow/scikit-multiflow,src/skmultiflow/evaluation/base_evaluator.py,3e9cdf545d08e5c2f9f2b8767ba032b19b5d5afd,STILL_EXISTS,TODO let the user choose the feature indices of interest
aaedahgdi,scikit-multiflow/scikit-multiflow,src/skmultiflow/visualization/evaluation_visualizer.py,3e9cdf545d08e5c2f9f2b8767ba032b19b5d5afd,STILL_EXISTS,TODO confirm buffer update inside the loop
aaedahgeb,scikit-multiflow/scikit-multiflow,src/skmultiflow/visualization/evaluation_visualizer.py,78d78912465c00be2793a04f21ba7706576bdc97,STILL_EXISTS,TODO confirm buffer update inside the loop
aaedahgie,scikit-multiflow/scikit-multiflow,src/skmultiflow/trees/multi_target_regression_hoeffding_tree.py,3bbfc26e86eeb660ae7b5c2850c399d66c936add,5cd2acad278d36dee9a54870d5b9bc82c3ef4ae0,TODO Check with new functionalities
aaedahgif,scikit-multiflow/scikit-multiflow,src/skmultiflow/trees/multi_target_regression_hoeffding_tree.py,3bbfc26e86eeb660ae7b5c2850c399d66c936add,STILL_EXISTS,TODO reactivation procedure???
aaedahgjd,scikit-multiflow/scikit-multiflow,src/skmultiflow/trees/hoeffding_anytime_tree.py,e37fead880016c8b48f8b9c10da082ee95c4fbae,STILL_EXISTS,Todo : Add memory management
aaedahhbd,scikit-multiflow/scikit-multiflow,src/skmultiflow/trees/hoeffding_anytime_tree.py,e37fead880016c8b48f8b9c10da082ee95c4fbae,STILL_EXISTS,Move in depth
aaedahhbe,scikit-multiflow/scikit-multiflow,src/skmultiflow/trees/hoeffding_anytime_tree.py,e37fead880016c8b48f8b9c10da082ee95c4fbae,STILL_EXISTS,Todo : raise error for nominal attribute
aaedaicjf,scikit-multiflow/scikit-multiflow,src/skmultiflow/meta/additive_expert_ensemble.py,edf12bfa0092e58dc23cb2d7739168e9cdbc0bfa,STILL_EXISTS,# TODO Pruning to max_estimators
aaedaicjh,scikit-multiflow/scikit-multiflow,src/skmultiflow/meta/additive_expert_ensemble.py,edf12bfa0092e58dc23cb2d7739168e9cdbc0bfa,b532302b6110a058fde413ae78063b2ad08d2a80,# TODO Improve efficieny
aaedaiegc,scikit-multiflow/scikit-multiflow,src/skmultiflow/meta/additive_expert_ensemble.py,becc945240a424bc08f74476c46f6a50b69928a5,STILL_EXISTS,# 4.1 Pruning to self.max_estimators if needed
aaedaiegd,scikit-multiflow/scikit-multiflow,src/skmultiflow/meta/additive_expert_ensemble.py,becc945240a424bc08f74476c46f6a50b69928a5,b532302b6110a058fde413ae78063b2ad08d2a80,# TODO Improve efficieny
aaedaieih,scikit-multiflow/scikit-multiflow,docs/conf.py,bbddf0ca76ae600aba7a0156d4230b57fd24a50c,STILL_EXISTS,this is needed for some reason...
aaedaigcf,scikit-multiflow/scikit-multiflow,src/skmultiflow/core/base.py,8a545145d6a50e56d31ba0b6a697e84d07ffef3c,STILL_EXISTS,XXX: not handling dictionaries
aaedaigei,scikit-multiflow/scikit-multiflow,src/skmultiflow/core/base.py,8a545145d6a50e56d31ba0b6a697e84d07ffef3c,STILL_EXISTS,apprx number of chars to keep on both ends
aaedaiggi,scikit-multiflow/scikit-multiflow,src/skmultiflow/core/base.py,8a545145d6a50e56d31ba0b6a697e84d07ffef3c,STILL_EXISTS,XXX: Remove the check in 0.23
aaedaigji,scikit-multiflow/scikit-multiflow,src/skmultiflow/utils/_pprint.py,8a545145d6a50e56d31ba0b6a697e84d07ffef3c,STILL_EXISTS,NO REPRESENTATIONS OR WARRANTIES; EXPRESS OR IMPLIED. BY WAY OF EXAMPLE; BUT
aaedaihcb,scikit-multiflow/scikit-multiflow,src/skmultiflow/utils/_pprint.py,8a545145d6a50e56d31ba0b6a697e84d07ffef3c,STILL_EXISTS,needed for _dispatch[tuple.__repr__] not to be overridden
aaedaihdg,scikit-multiflow/scikit-multiflow,src/skmultiflow/core/base.py,a49eff04b956f5dbfebffd99482cd361e9362152,2f3cffca59e6d6efd14444f6fc98713cd5dfa6a7,non-optimized default implementation; override if a better
aaedaijgi,scikit-multiflow/scikit-multiflow,src/skmultiflow/core/base.py,a8530947d48a1e1486017b53944bf130768a2480,STILL_EXISTS,non-optimized default implementation; override if a better
aaedajbif,scikit-multiflow/scikit-multiflow,tests/neural_networks/test_perceptron.py,173f2688750e194413e99bf4a13ed7f9a0adb712,STILL_EXISTS,This is a workaround until a fix is made available in sklearn
aaedajjdb,scikit-multiflow/scikit-multiflow,src/skmultiflow/trees/nodes/node.py,f32719662bba6e98dd684af85e800c64d828bd6d,STILL_EXISTS,TODO
aaedbaahj,scikit-multiflow/scikit-multiflow,tests/trees/test_hoeffding_adaptive_tree.py,910fa62605de49dea3e4599bb233c3d9c6f4527b,STILL_EXISTS,Removes the last two columns (regression targets)
aaedbaaia,scikit-multiflow/scikit-multiflow,tests/trees/test_hoeffding_tree.py,910fa62605de49dea3e4599bb233c3d9c6f4527b,STILL_EXISTS,Removes the last two columns (regression targets)
aaedbadec,scikit-multiflow/scikit-multiflow,src/skmultiflow/meta/adaptive_random_forest_regressor.py,56798201b77d1546bec413b57e3561c5f13f5122,STILL_EXISTS,TODO: check whether this is enough
aaedbaded,scikit-multiflow/scikit-multiflow,src/skmultiflow/meta/adaptive_random_forest_regressor.py,56798201b77d1546bec413b57e3561c5f13f5122,STILL_EXISTS,TODO: check the posssibility of using HATR as base learner
aaedbadee,scikit-multiflow/scikit-multiflow,src/skmultiflow/meta/adaptive_random_forest_regressor.py,56798201b77d1546bec413b57e3561c5f13f5122,STILL_EXISTS,TODO: check necessity of this parameter
aaedbadef,scikit-multiflow/scikit-multiflow,src/skmultiflow/meta/adaptive_random_forest_regressor.py,56798201b77d1546bec413b57e3561c5f13f5122,STILL_EXISTS,TODO: verify the posssibility of renaming to 'swap' of something similar
aaedbadeg,scikit-multiflow/scikit-multiflow,src/skmultiflow/meta/adaptive_random_forest_regressor.py,56798201b77d1546bec413b57e3561c5f13f5122,STILL_EXISTS,TODO: add here option to monitor errors
aaedbadeh,scikit-multiflow/scikit-multiflow,src/skmultiflow/meta/adaptive_random_forest_regressor.py,56798201b77d1546bec413b57e3561c5f13f5122,STILL_EXISTS,TODO: posssibility of using HATR as base leaner
aaedbadei,scikit-multiflow/scikit-multiflow,src/skmultiflow/meta/adaptive_random_forest_regressor.py,56798201b77d1546bec413b57e3561c5f13f5122,STILL_EXISTS,TODO: options to monitor error
aaedbadhi,scikit-multiflow/scikit-multiflow,src/skmultiflow/meta/adaptive_random_forest_regressor.py,4f1afcc6a96382fcf1ae922a32cf7000acebef2e,STILL_EXISTS,TODO: check the posssibility of using HATR as base learner
aaedbadhj,scikit-multiflow/scikit-multiflow,src/skmultiflow/meta/adaptive_random_forest_regressor.py,4f1afcc6a96382fcf1ae922a32cf7000acebef2e,STILL_EXISTS,TODO: check necessity of this parameter
aaedbadia,scikit-multiflow/scikit-multiflow,src/skmultiflow/meta/adaptive_random_forest_regressor.py,4f1afcc6a96382fcf1ae922a32cf7000acebef2e,STILL_EXISTS,TODO: posssibility of using HATR as base leaner
aaedbadib,scikit-multiflow/scikit-multiflow,src/skmultiflow/meta/adaptive_random_forest_regressor.py,4f1afcc6a96382fcf1ae922a32cf7000acebef2e,STILL_EXISTS,TODO: options to monitor error
aaedbaeae,scikit-multiflow/scikit-multiflow,tests/meta/test_adaptive_random_forest_regressor.py,46dc0cecc2dfdfebaf3867320937c22dbd056e1a,e8f02e56f356dfecd5d7bb271f2fa54deb71ee00,TODO: add assert statements
aaedbbccf,scikit-multiflow/scikit-multiflow,src/skmultiflow/trees/__init__.py,612c6cd623c2417217e361844dd14c5d2561c7b1,STILL_EXISTS,TODO remove in v0.7.0
aaedbbegg,onnx/onnx-caffe2,onnx_caffe2/backend.py,3cc1e2323b6c12c63793dc10b82801741d38f08d,cef2b1b8681a57b6d07d6c1b3cdbef8ba881b309,Caffe2 (apparently) doesn't provide any native method of generating
aaedbbegh,onnx/onnx-caffe2,onnx_caffe2/backend.py,3cc1e2323b6c12c63793dc10b82801741d38f08d,cef2b1b8681a57b6d07d6c1b3cdbef8ba881b309,a fresh; unused workspace; so we have to fake it by generating
aaedbbejf,onnx/onnx-caffe2,onnx_caffe2/backend.py,3cc1e2323b6c12c63793dc10b82801741d38f08d,594689919328a8db36f3a98205f62b4cc1dc16e2,TODO: Using this for FLOAT16 seems questionable
aaedbbejg,onnx/onnx-caffe2,onnx_caffe2/backend.py,3cc1e2323b6c12c63793dc10b82801741d38f08d,STILL_EXISTS,TODO: replace this with a version test
aaedbbfag,onnx/onnx-caffe2,onnx_caffe2/backend.py,3cc1e2323b6c12c63793dc10b82801741d38f08d,STILL_EXISTS,TODO: This method needs a refactor for clarity
aaedbbfcd,onnx/onnx-caffe2,onnx_caffe2/backend.py,3cc1e2323b6c12c63793dc10b82801741d38f08d,93115578ff2def527a815aedd5726db1ff0ef537,TODO: Make this robust against an adversarial model namer
aaedbbfgd,onnx/onnx-caffe2,tests/caffe2_ref_test.py,6a9327f1fa4d72a663a73e06ef93a810c7ba634c,STILL_EXISTS,TODO: In some cases we should generate multiple random inputs
aaedbbfgf,onnx/onnx-caffe2,tests/caffe2_ref_test.py,6a9327f1fa4d72a663a73e06ef93a810c7ba634c,STILL_EXISTS,TODO: These Numpy specs will be generally useful to backend implementations;
aaedbbfhc,onnx/onnx-caffe2,tests/caffe2_ref_test.py,6a9327f1fa4d72a663a73e06ef93a810c7ba634c,STILL_EXISTS,TODO: Some of these tests will be done most conveniently by running a Caffe2
aaedbbfhf,onnx/onnx-caffe2,tests/caffe2_ref_test.py,6a9327f1fa4d72a663a73e06ef93a810c7ba634c,STILL_EXISTS,TODO: Add all the other operators
aaedbbgga,onnx/onnx-caffe2,onnx_caffe2/backend.py,5059375a81a8ca8c6c656741c7d46fb29abdd0aa,STILL_EXISTS,TODO: Is this really coherent?  If multiple operators map to the
aaedbbggb,onnx/onnx-caffe2,onnx_caffe2/backend.py,5059375a81a8ca8c6c656741c7d46fb29abdd0aa,56e3a1127dcc615074e067358718ecc6d469332f,same name; no way to implement divergent attribute renaming in this case.
aaedbbggc,onnx/onnx-caffe2,onnx_caffe2/backend.py,5059375a81a8ca8c6c656741c7d46fb29abdd0aa,56e3a1127dcc615074e067358718ecc6d469332f,TODO: Using this for FLOAT16 seems questionable
aaedbbggg,onnx/onnx-caffe2,onnx_caffe2/backend.py,5059375a81a8ca8c6c656741c7d46fb29abdd0aa,56e3a1127dcc615074e067358718ecc6d469332f,TODO: Postel was wrong
aaedbbggj,onnx/onnx-caffe2,onnx_caffe2/backend.py,5059375a81a8ca8c6c656741c7d46fb29abdd0aa,56e3a1127dcc615074e067358718ecc6d469332f,TODO: Hmm; this is going to mutate the original op_def.arg; isn't
aaedbbghb,onnx/onnx-caffe2,onnx_caffe2/backend.py,5059375a81a8ca8c6c656741c7d46fb29abdd0aa,STILL_EXISTS,TODO: replace this with a version test
aaedbbghj,onnx/onnx-caffe2,onnx_caffe2/backend.py,5059375a81a8ca8c6c656741c7d46fb29abdd0aa,STILL_EXISTS,TODO: turn me into a helper
aaedbbgjd,onnx/onnx-caffe2,onnx_caffe2/backend.py,56e3a1127dcc615074e067358718ecc6d469332f,STILL_EXISTS,TODO: Is this really coherent?  If multiple operators map to the
aaedbbhae,onnx/onnx-caffe2,onnx_caffe2/backend.py,56e3a1127dcc615074e067358718ecc6d469332f,STILL_EXISTS,TODO: replace this with a version test
aaedbbhbc,onnx/onnx-caffe2,onnx_caffe2/backend.py,56e3a1127dcc615074e067358718ecc6d469332f,STILL_EXISTS,TODO: turn me into a helper
aaedbbhdb,onnx/onnx-caffe2,onnx_caffe2/workspace.py,cef2b1b8681a57b6d07d6c1b3cdbef8ba881b309,STILL_EXISTS,Caffe2 (apparently) doesn't provide any native method of generating
aaedbbhdc,onnx/onnx-caffe2,onnx_caffe2/workspace.py,cef2b1b8681a57b6d07d6c1b3cdbef8ba881b309,STILL_EXISTS,a fresh; unused workspace; so we have to fake it by generating
aaedbbhfg,onnx/onnx-caffe2,onnx_caffe2/frontend.py,a5ad9ebbf82488d69b79a870ab2d4712d3f6607d,STILL_EXISTS,TODO: This is cheating. Invent some magic to infer the types and
aaedbbiaa,onnx/onnx-caffe2,onnx_caffe2/backend.py,41cabb10f02d2a80cdee443aab1ee6344fbb05fd,STILL_EXISTS,TODO: Caffe2 Concat has an extra output. It should be only
aaedbbiad,onnx/onnx-caffe2,onnx_caffe2/frontend.py,41cabb10f02d2a80cdee443aab1ee6344fbb05fd,0d414582e6aec05562e83e4758e52518334e067b,TODO: refactor frontend to allow special handling for individual ops
aaedbbibf,onnx/onnx-caffe2,onnx_caffe2/backend.py,31c7d0b94826a83c6b074e0f2c09bb09a4ea9522,6b5ffebdf39f5d70f27506a1e915e5ad3a8bb84f,TODO: we cheat and rely on the fact that ONNX weight layout matches
aaedbbibi,onnx/onnx-caffe2,onnx_caffe2/backend.py,31c7d0b94826a83c6b074e0f2c09bb09a4ea9522,6b5ffebdf39f5d70f27506a1e915e5ad3a8bb84f,TODO: fix Caffe2 to accept initial_h and initial_c as optional inputs
aaedbbicd,onnx/onnx-caffe2,onnx_caffe2/backend.py,a133b6878f71f88348306b37de52b8b5490b4914,STILL_EXISTS,TODO: we cheat and rely on the fact that ONNX weight layout matches
aaedbbicg,onnx/onnx-caffe2,onnx_caffe2/backend.py,a133b6878f71f88348306b37de52b8b5490b4914,STILL_EXISTS,TODO: fix Caffe2 to accept initial_h and initial_c as optional inputs
aaedbbidj,onnx/onnx-caffe2,onnx_caffe2/backend.py,3625468b3e07cb44db7eb7c8e5521cc6a36e9aa9,STILL_EXISTS,Slice only accepts ends as int
aaedbbjbc,onnx/onnx-caffe2,onnx_caffe2/backend.py,ba3351fb6f195e5c4ef4d9055fe1530defd028ab,STILL_EXISTS,TODO implement support for return_params in gru_cell.GRU.
aaedbcbef,scikit-learn-contrib/polylearn,doc/sphinxext/gen_rst.py,faff0fdd0d23d26c9df9d32dccffcb1b9853f902,STILL_EXISTS,better than nested.
aaedbcbja,scikit-learn-contrib/polylearn,doc/sphinxext/gen_rst.py,faff0fdd0d23d26c9df9d32dccffcb1b9853f902,STILL_EXISTS,auto examples gallery to the _build folder. This works fine as is; but it would be cleaner to
aaedbccah,scikit-learn-contrib/polylearn,doc/sphinxext/gen_rst.py,faff0fdd0d23d26c9df9d32dccffcb1b9853f902,STILL_EXISTS,Sphinx hack: sphinx copies generated images to the build directory
aaedbccbg,scikit-learn-contrib/polylearn,doc/sphinxext/gen_rst.py,faff0fdd0d23d26c9df9d32dccffcb1b9853f902,STILL_EXISTS,The following is a hack that prevents this behavior by clearing the
aaedbccbj,scikit-learn-contrib/polylearn,doc/sphinxext/gen_rst.py,faff0fdd0d23d26c9df9d32dccffcb1b9853f902,STILL_EXISTS,it should probably not cause a crash).  Tested successfully
aaedbcccb,scikit-learn-contrib/polylearn,doc/sphinxext/gen_rst.py,faff0fdd0d23d26c9df9d32dccffcb1b9853f902,STILL_EXISTS,HACK: Stop nosetests running setup() above
aaedbcceb,scikit-learn-contrib/polylearn,doc/sphinxext/numpy_ext/numpydoc.py,faff0fdd0d23d26c9df9d32dccffcb1b9853f902,STILL_EXISTS,Only needed to check Python version
aaedbcchf,scikit-learn-contrib/polylearn,polylearn/tests/test_common.py,faff0fdd0d23d26c9df9d32dccffcb1b9853f902,a461066eab9f87bee7642f74999bad8c9f8f17e6,TODO: can't actually pass the test.
aaedbcdah,scikit-learn-contrib/polylearn,polylearn/tests/test_kernels.py,faff0fdd0d23d26c9df9d32dccffcb1b9853f902,STILL_EXISTS,TODO maybe move to a util module or something
aaedbcdeg,scikit-learn-contrib/polylearn,examples/plot_xor.py,cba20fd17ee77d4f75231593382ec35e67d3c01f,STILL_EXISTS,\"\"\" || =============================================== || Factorization machine decision boundary for XOR || =============================================== ||  || Plots the decision function learned by a factorization machine for a noisy || non-linearly separable XOR problem ||  || This problem is a perfect example of feature interactions. As such; || factorization machines can model it very robustly with a very small number of || parameters.  (In this case; (1 + n_features) * n_components = 3 * 1 params.) ||  || Example based on: || http:\/\/scikit-learn.org\/stable\/auto_examples\/svm\/plot_svm_nonlinear.html || \"\"\"
aaedbcdfd,scikit-learn-contrib/polylearn,polylearn/tests/test_common.py,471e52809f4bbe7942b20b4c2e666f2d8b2bc803,STILL_EXISTS,temporary workaround until fit_linear is implemented
aaedbcdii,inferno-pytorch/inferno,inferno/trainers/basic.py,821b3ae247c010a6cf749c1af8a28d3b20de5a68,STILL_EXISTS,TODO validate of_loader
aaedbceeg,inferno-pytorch/inferno,inferno/trainers/basic.py,821b3ae247c010a6cf749c1af8a28d3b20de5a68,STILL_EXISTS,TODO some sanity checks on config_dict (e.g. whether the model is actually a model; etc)
aaedbcegg,inferno-pytorch/inferno,inferno/trainers/basic.py,40bd47e4f08ec079480114d5edb12ae9343bf963,STILL_EXISTS,TODO: Dummy logger when not logging
aaedbcfag,inferno-pytorch/inferno,inferno/extensions/initializers/base.py,3c1c3bbdfe7d3605df24923f6836967d87142eca,STILL_EXISTS,TODO Support LSTMs and GRUs
aaedbcfbb,inferno-pytorch/inferno,inferno/extensions/layers/reshape.py,fc9d3549ccfa339d2878ede06d98688ae5b7f185,STILL_EXISTS,Move channel axis to z
aaedbcfgh,inferno-pytorch/inferno,tests/io/core/zip.py,a8c37f9da64cd2f4277d6323c9e4563d522c5dd4,STILL_EXISTS,TODO
aaedbcfjd,inferno-pytorch/inferno,inferno/trainers/basic.py,a2a7dfa98b3140dbf8d9da9a62aa52f873f5483f,STILL_EXISTS,TODO Set up logging as a callback
aaedbcgbi,inferno-pytorch/inferno,inferno/trainers/callbacks/logging/tensorboard.py,cabc97cdda35d56a8bc3b759bcf0c5052ee80edf,STILL_EXISTS,TODO Continue
aaedbcgeg,inferno-pytorch/inferno,inferno/trainers/callbacks/logging/tensorboard.py,73c76193b8738c53de04ea6a112de544c7a54117,STILL_EXISTS,FIXME Debug this
aaedbcggc,inferno-pytorch/inferno,inferno/trainers/callbacks/logging/tensorboard.py,4a308fc7e15ef773db901250e35f59078207502c,STILL_EXISTS,Apparently; some SwigPyObject objects cannot be pickled - so we need to build the
aaedbcgge,inferno-pytorch/inferno,inferno/trainers/callbacks/logging/tensorboard.py,4a308fc7e15ef773db901250e35f59078207502c,STILL_EXISTS,FIXME Debug this
aaedbcgih,inferno-pytorch/inferno,inferno/extensions/metrics/categorical.py,ab394498ddce4f142021e3990ccef7f572ac2cec,STILL_EXISTS,TODO
aaedbcgjb,inferno-pytorch/inferno,inferno/extensions/layers/graph.py,6265f10bf31b2b50ae34b69f597149cc37bf70d5,STILL_EXISTS,TODO Check whether only input nodes are sources and only output nodes are sinks
aaedbchgj,inferno-pytorch/inferno,inferno/trainers/callbacks/base.py,97266eeceabfe848d75127d6f8254168bf1510c8,STILL_EXISTS,FIXME This makes bind_trainer in register_callback reduntant;
aaedbcjbj,inferno-pytorch/inferno,inferno/extensions/criteria/set_similarity_measures.py,16657668bf10dbf0a93536b7ce799803b15f8cf1,STILL_EXISTS,TODO This should be compatible with Pytorch 0.2; but check
aaedbcjec,inferno-pytorch/inferno,inferno/io/box/camvid.py,e638ea7c7ac89c8fc41ecec06cc770b55aeb023b,STILL_EXISTS,For when we implement download:
aaedbcjef,inferno-pytorch/inferno,inferno/io/box/camvid.py,e638ea7c7ac89c8fc41ecec06cc770b55aeb023b,STILL_EXISTS,TODO: please download the dataset from
aaedbcjeh,inferno-pytorch/inferno,inferno/io/box/camvid.py,e638ea7c7ac89c8fc41ecec06cc770b55aeb023b,STILL_EXISTS,TODO
aaedbcjff,inferno-pytorch/inferno,inferno/io/transform/image.py,02f878802eb0821717de28855ddf7c754c87d41d,STILL_EXISTS,There's a channel axis - we move it to front
aaedbcjji,inferno-pytorch/inferno,inferno/io/box/cityscapes.py,b549b52118ac032ead2c127f59e1acaa05f00583,STILL_EXISTS,TODO: please download the dataset from
aaedbddgb,inferno-pytorch/inferno,setup.py,294ae8801abbc7bf8ab5106f66d2c4ff721bb710,STILL_EXISTS,TODO: put package requirements here
aaedbddgd,inferno-pytorch/inferno,setup.py,294ae8801abbc7bf8ab5106f66d2c4ff721bb710,ca8c105c9c1b44645edec279d1f450c983f48d44,TODO: put package test requirements here
aaedbddhd,inferno-pytorch/inferno,travis_pypi_setup.py,294ae8801abbc7bf8ab5106f66d2c4ff721bb710,STILL_EXISTS,workaround for https:\/\/github.com\/travis-ci\/travis-api\/issues\/196
aaedbdjeh,inferno-pytorch/inferno,inferno/trainers/callbacks/scheduling.py,044c0f205c8aa0fa6bb553ac9d0db7b95372b589,dbb3b71780130aded4a2acd072acd4a77659671a,TODO
aaedbefcf,inferno-pytorch/inferno,inferno/utils/io_utils.py,cfda114048455be2f439842886e4623316dfb5a0,STILL_EXISTS,TODO we could also do **h5_kwargs instead
aaedbefdh,inferno-pytorch/inferno,inferno/extensions/metrics/base.py,dfd359868d1c1d0d7fdfc8bd96886644289b3e3b,STILL_EXISTS,Move to CPU
aaedbefjc,inferno-pytorch/inferno,inferno/io/volumetric/volumetric_utils.py,da3b0906dd8af3828642629e460f32117efd04ea,d228959b20da9dcc45e475baf3d3155e62cda823,TODO downsampling ?! should this really be done here?
aaedbegei,inferno-pytorch/inferno,inferno/extensions/metrics/cremi_score.py,d495947020c208d626744b6c63b2a49165b16d26,557002083e470fca12b133ba146b5b77af29e602,TODO build metrics object
aaedbeghh,inferno-pytorch/inferno,inferno/io/segmentation/bsd500.py,4df22a2326675cf07eec34ea5233f2bcd1da70b6,STILL_EXISTS,TODO: shuffeling should actually be done; when a new batch is loaded
aaedbegjj,inferno-pytorch/inferno,inferno/io/box/bsd500.py,c9831d4c0f9cbd459646ab3e9598306687021e24,STILL_EXISTS,TODO rotations; transpose; flips
aaedbehab,inferno-pytorch/inferno,inferno/io/box/bsd500.py,c9831d4c0f9cbd459646ab3e9598306687021e24,STILL_EXISTS,FIXME this can be done by the dataloader ?!
aaedbehae,inferno-pytorch/inferno,inferno/io/box/bsd500.py,c9831d4c0f9cbd459646ab3e9598306687021e24,STILL_EXISTS,TODO apply all trafos
aaedbehaf,inferno-pytorch/inferno,inferno/io/box/bsd500.py,c9831d4c0f9cbd459646ab3e9598306687021e24,STILL_EXISTS,TODO: shuffeling should actually be done; when a new batch is loaded
aaedbehag,inferno-pytorch/inferno,inferno/io/box/bsd500.py,c9831d4c0f9cbd459646ab3e9598306687021e24,STILL_EXISTS,TODO also apply image and joint transform
aaedbehai,inferno-pytorch/inferno,inferno/io/box/bsd500.py,b75a6ac9d5b74e0bf720ee91fe92b08a2f21c0af,STILL_EXISTS,TODO figure out what exactly size does
aaedbehaj,inferno-pytorch/inferno,inferno/io/box/bsd500.py,b75a6ac9d5b74e0bf720ee91fe92b08a2f21c0af,6081b52fb441cfd40e61e1970bd6904603ae95e9,TODO gamma correction ?
aaedbehjj,inferno-pytorch/inferno,inferno/extensions/criteria/set_similarity_measures.py,aa41fb57571629213a81c5a5b954103888a19625,STILL_EXISTS,TODO move weight here as optional argument ?!
aaedbeiaa,inferno-pytorch/inferno,inferno/extensions/criteria/set_similarity_measures.py,aa41fb57571629213a81c5a5b954103888a19625,STILL_EXISTS,TODO we could also make the channel-wise ?!
aaedbeiab,inferno-pytorch/inferno,tests/extensions/criteria/set_similarity_measures.py,aa41fb57571629213a81c5a5b954103888a19625,STILL_EXISTS,TODO better test
aaedbejab,inferno-pytorch/inferno,inferno/io/box/bsd500.py,6081b52fb441cfd40e61e1970bd6904603ae95e9,STILL_EXISTS,TODO figure out what exactly size does
aaedbejfc,inferno-pytorch/inferno,inferno/io/box/bsd500.py,7ab028d2156122a80e7e638e56eafedb9e0d2425,1e54ede4ab89c7837a84c00c87495faf1cea8f95,TODO gamma correction ?
aaedbejhb,inferno-pytorch/inferno,inferno/io/box/bsd500.py,1e54ede4ab89c7837a84c00c87495faf1cea8f95,STILL_EXISTS,TODO figure out what exactly size does
aaedbfaba,inferno-pytorch/inferno,inferno/extensions/metrics/cremi_score.py,561d96fc336ec40d97177fa00cdc47b5af545dbc,STILL_EXISTS,TODO build metrics object
aaedbfaid,inferno-pytorch/inferno,tests/extensions/criteria/set_similarity_measures.py,6289c244428a855f257daeb2001bc4e72ec92875,STILL_EXISTS,TODO better test
aaedbfbeb,inferno-pytorch/inferno,examples/regularized_mnist.py,2a868fbc3361de600996c0f85d1d49c19db1fa76,STILL_EXISTS,\"\"\" || Regularized MNist Example || ================================ ||  || TODO ||  || \"\"\"
aaedbfcab,inferno-pytorch/inferno,inferno/io/box/__init__.py,fa4bcc62574073377886c9c439dd138a3c9637df,c70aee0cd7e70ca38b25edb7fd4260f88fa1e6be,FIXME Camvid requires some deprecated torch function
aaedbfcec,inferno-pytorch/inferno,inferno/trainers/callbacks/logging/tensorboard.py,ea992b6a87700802932fd2d077cf38fa21b1292b,STILL_EXISTS,Using persistent=True in a property getter is probably not a very good idea...
aaedbfcgb,inferno-pytorch/inferno,inferno/extensions/layers/prefab.py,e6c06cb400574a600ce63073883ca605526f0e57,STILL_EXISTS,TODO PreActBottleneckResidualBlock
aaedbfcgc,inferno-pytorch/inferno,inferno/trainers/basic.py,325d8412d641af7e52f97e3422e9ef516e4ef726,STILL_EXISTS,FIXME The volatile=True is required for compatibility with older 0.3 code.
aaedbfcgd,inferno-pytorch/inferno,inferno/trainers/basic.py,325d8412d641af7e52f97e3422e9ef516e4ef726,STILL_EXISTS,FIXME Remove when support is deprecated.
aaedbfchj,inferno-pytorch/inferno,inferno/io/volumetric/lazy_volume_loader.py,e07e062532e4111a64e3019c179d541014222ecd,STILL_EXISTS,TODO is this correct ???
aaedbffie,inferno-pytorch/inferno,inferno/trainers/basic.py,3f95d090346dec9b658cacd3cf7528215e18bd88,a6ce6f93a6746b79f32cfb81ce5b6839c5602439,retain_graph option is needed for some custom
aaedbfhie,inferno-pytorch/inferno,inferno/io/transform/base.py,bfa1867d3d630066260f23d6a8bd9cbf2a9c98fe,STILL_EXISTS,FIXME This loops one time too many
aaedbfhih,inferno-pytorch/inferno,inferno/trainers/basic.py,47d7be45a38bc577fc1e466498c1bc7b35c07d40,82e1b1bc7fbf2a5c8bdb9bea5cff6adee992d903,retain_graph option is needed for some custom
aaedbfhij,inferno-pytorch/inferno,inferno/trainers/basic.py,9ce802caec7afd851779c30b02d32e75a09667ab,82e1b1bc7fbf2a5c8bdb9bea5cff6adee992d903,These are fetched from globals; they're not unused
aaedbfjca,inferno-pytorch/inferno,examples/train_side_loss_unet.py,3f0d1289dfb96abf6f27896fae42b1891f5959e8,STILL_EXISTS,TODO show all side outs
aaedbfjcf,inferno-pytorch/inferno,examples/train_side_loss_unet.py,31371deaea42ffe74c886504fe7fb52e8e248fa1,STILL_EXISTS,Imports needed for this example
aaedbfjfd,inferno-pytorch/inferno,examples/train_side_loss_unet.py,31371deaea42ffe74c886504fe7fb52e8e248fa1,STILL_EXISTS,TODO show all side outs
aaedbggga,inferno-pytorch/inferno,examples/plot_unet_tutorial.py,5d553972d8e4bf9cde573a5a9044f02a4252fb9f,STILL_EXISTS,We start with some unspectacular multi purpose imports needed for this example
aaedbgggi,inferno-pytorch/inferno,examples/plot_unet_tutorial.py,5d553972d8e4bf9cde573a5a9044f02a4252fb9f,STILL_EXISTS,convert labels from long to float as needed by
aaedbgghe,inferno-pytorch/inferno,examples/plot_unet_tutorial.py,5d553972d8e4bf9cde573a5a9044f02a4252fb9f,STILL_EXISTS,<-- number of channels needed for the prediction
aaedbgici,inferno-pytorch/inferno,examples/plot_unet_tutorial.py,d795f811eb13a20e18aa30c435cef0ad99e830ea,STILL_EXISTS,Here we show how to implement such a customized UNet.
aaedbhich,inferno-pytorch/inferno,inferno/trainers/callbacks/logging/tensorboard.py,5acb0453fb5cd1c61b056292ccc98b0b65fccd0d,STILL_EXISTS,Using persistent=True in a property getter is probably not a very good idea...
aaedbidhh,inferno-pytorch/inferno,inferno/trainers/callbacks/logging/tensorboard.py,57570908dceb73559ee0f1e8d2bf98966a729eb9,STILL_EXISTS,FIXME this can throw ugly warnings
aaedbidif,inferno-pytorch/inferno,inferno/io/volumetric/volume.py,3649afd7b5f303ff2f005172248b8bfec2024263,STILL_EXISTS,TODO implemnent downsampling and padding for multi-channel volume
aaedbidjd,inferno-pytorch/inferno,inferno/extensions/layers/convolutional_blocks.py,cd3b611d2ca1ba2fe63ad0002d67e93eb4f7dcf3,2c6d464fdc7d5821bf808bfcc10cae8a60424080,TODO rename ResidualBlockBase
aaedbidjg,inferno-pytorch/inferno,inferno/extensions/layers/convolutional_blocks.py,cd3b611d2ca1ba2fe63ad0002d67e93eb4f7dcf3,2c6d464fdc7d5821bf808bfcc10cae8a60424080,TODO rename ResidualBlock
aaedbieaa,inferno-pytorch/inferno,inferno/extensions/layers/prefab.py,cd3b611d2ca1ba2fe63ad0002d67e93eb4f7dcf3,STILL_EXISTS,TODO merge with convolutional blocks
aaedbieee,inferno-pytorch/inferno,inferno/extensions/model/unet.py,cd3b611d2ca1ba2fe63ad0002d67e93eb4f7dcf3,2c6d464fdc7d5821bf808bfcc10cae8a60424080,TODO rename ResidualBlockUNet
aaedbiefb,inferno-pytorch/inferno,inferno/extensions/model/unet.py,cd3b611d2ca1ba2fe63ad0002d67e93eb4f7dcf3,2c6d464fdc7d5821bf808bfcc10cae8a60424080,TODO rename to ResidualBlock
aaedbiefi,inferno-pytorch/inferno,inferno/extensions/model/unet.py,3c939d3da482791ad46b726f54dce6981e8004c4,STILL_EXISTS,TODO implement 2d from 3d input (see neurofire)
aaedbiegb,inferno-pytorch/inferno,inferno/extensions/model/unet.py,3c939d3da482791ad46b726f54dce6981e8004c4,2c6d464fdc7d5821bf808bfcc10cae8a60424080,TODO test
aaedbiegd,inferno-pytorch/inferno,inferno/extensions/layers/convolutional_blocks.py,2c6d464fdc7d5821bf808bfcc10cae8a60424080,STILL_EXISTS,TODO PreActBottleneckResidualBlock
aaedbiehj,inferno-pytorch/inferno,inferno/extensions/model/res_unet.py,2c6d464fdc7d5821bf808bfcc10cae8a60424080,STILL_EXISTS,TODO not sure how to handle out-channels properly.
aaedbieii,inferno-pytorch/inferno,inferno/extensions/model/unet.py,2c6d464fdc7d5821bf808bfcc10cae8a60424080,STILL_EXISTS,TODO implement function to load a pretrained unet
aaedbjfea,inferno-pytorch/inferno,inferno/trainers/basic.py,3a3c393ddfc1d955c6a77666842db9a3a84b1f6e,STILL_EXISTS,These are fetched from globals; they're not unused
aaedbjfed,inferno-pytorch/inferno,inferno/trainers/basic.py,3a3c393ddfc1d955c6a77666842db9a3a84b1f6e,STILL_EXISTS,retain_graph option is needed for some custom
aaedbjjji,inferno-pytorch/inferno,inferno/extensions/layers/device.py,80b6deaf7d264da8adc54681cb349c91d60b3bb6,624f3169f10dd3fec2e6c504cb4478f9854bd665,FIXME Removed async support for compatibility with python 3.7.
aaedbjjjj,inferno-pytorch/inferno,inferno/trainers/callbacks/logging/tensorboard.py,1f9831f9e2049fe8610326d89923beacd8b833a8,STILL_EXISTS,FIXME in tensorboardX > 1.12 this will lead to some error.
aaedcaaah,inferno-pytorch/inferno,inferno/extensions/layers/device.py,45220b4750fbde307e2aadc377cca4ab039c194b,617df5f5209c85d6c8bc240a545709c939b44dd7,FIXME Removed async support for compatibility with python 3.7.
aaedcachg,inferno-pytorch/inferno,examples/plot_cheap_unet.py,a9a2b2f25a452ce12c65c7339fbe6f4661fb31c3,STILL_EXISTS,We start with some unspectacular multi purpose imports needed for this example
aaedcacif,inferno-pytorch/inferno,examples/plot_cheap_unet.py,a9a2b2f25a452ce12c65c7339fbe6f4661fb31c3,STILL_EXISTS,convert labels from long to float as needed by
aaedcacjc,inferno-pytorch/inferno,examples/plot_cheap_unet.py,a9a2b2f25a452ce12c65c7339fbe6f4661fb31c3,STILL_EXISTS,<-- number of channels needed for the prediction
aaedcadcb,inferno-pytorch/inferno,examples/plot_cheap_unet.py,a9a2b2f25a452ce12c65c7339fbe6f4661fb31c3,STILL_EXISTS,Here we show how to implement such a customized UNet.
aaedcaeff,inferno-pytorch/inferno,inferno/utils/io_utils.py,d8287f5d9c636bdf9c95d3c71a1a02fa4eb301b6,STILL_EXISTS,TODO we could also do **h5_kwargs instead
aaedcagae,inferno-pytorch/inferno,inferno/trainers/callbacks/logging/tensorboard.py,ea532572c4e2fbb36a0fc3bc21a3a6585ce664b3,STILL_EXISTS,FIXME this can throw ugly warnings
aaedcagaj,inferno-pytorch/inferno,inferno/trainers/callbacks/logging/tensorboard.py,ea532572c4e2fbb36a0fc3bc21a3a6585ce664b3,STILL_EXISTS,FIXME in tensorboardX > 1.12 this will lead to some error.
aaedcagba,inferno-pytorch/inferno,inferno/io/volumetric/lazy_volume_loader.py,4fdf06a9268df0d9d74b2da2fe6ae8db6afd93f4,STILL_EXISTS,TODO support h5py as well
aaedcagbh,inferno-pytorch/inferno,inferno/trainers/basic.py,e8f204de36f37fb138ef9c2d32371fc115b14941,STILL_EXISTS,TODO Make unwrap a method for folks to overload
aaedcagcf,inferno-pytorch/inferno,inferno/trainers/basic.py,3bc705917dcf60749395ba1760205f7d4274bf58,STILL_EXISTS,FIXME The volatile=True is required for compatibility with older 0.3 code.
aaedcagcg,inferno-pytorch/inferno,inferno/trainers/basic.py,3bc705917dcf60749395ba1760205f7d4274bf58,STILL_EXISTS,FIXME Remove when support is deprecated.
aaedcagdj,inferno-pytorch/inferno,inferno/trainers/basic.py,8c9473262b8ec0984424eece38fc583007bf5f4f,STILL_EXISTS,TODO Make unwrap a method for folks to overload
aaedcahbb,inferno-pytorch/inferno,inferno/io/transform/volume.py,bcd5f60772d77841412d81d6fda88b9695b78b9f,STILL_EXISTS,TODO this is obsolete
aaedcaibc,microsoft/NimbusML,src/python/docs/sphinx/ci_script/conf.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,But regular is better without it.
aaedcaide,microsoft/NimbusML,src/python/docs/sphinx/ci_script/conf.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaedcaiji,microsoft/NimbusML,src/python/docs/sphinx/ci_script/update_all_toc_yml.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,4 fix label reference
aaedcajdg,microsoft/NimbusML,src/python/docs/sphinx/conf.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,But regular is better without it.
aaedcajfi,microsoft/NimbusML,src/python/docs/sphinx/conf.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaedcbaaj,microsoft/NimbusML,src/python/nimbusml/__init__.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,so here is a workaround to achieve that.
aaedcbbih,microsoft/NimbusML,src/python/nimbusml/examples/AveragedPerceptronBinaryClassifier.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,ae4f4de3110f00bc2f8be179cab1a01569624744,TODO: Replace with CV
aaedcbcef,microsoft/NimbusML,src/python/nimbusml/examples/CharTokenizer.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,TODO: Bug 140529 : CharTokenizer example is incomplete
aaedcbcgh,microsoft/NimbusML,src/python/nimbusml/examples/ColumnConcatenator.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,the final output; the vector column will convert into multiple columns for
aaedcbdec,microsoft/NimbusML,src/python/nimbusml/examples/FactorizationMachineBinaryClassifier.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,ae4f4de3110f00bc2f8be179cab1a01569624744,TODO: Replace with CV
aaedcbdge,microsoft/NimbusML,src/python/nimbusml/examples/FastForestBinaryClassifier.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,ae4f4de3110f00bc2f8be179cab1a01569624744,TODO: Replace with CV
aaedcbdig,microsoft/NimbusML,src/python/nimbusml/examples/FastForestRegressor.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,79ac758c626ee5526a230034004a4befe79125e8,TODO: Replace with CV
aaedcbeai,microsoft/NimbusML,src/python/nimbusml/examples/FastLinearBinaryClassifier.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,ae4f4de3110f00bc2f8be179cab1a01569624744,TODO: Replace with CV
aaedcbeda,microsoft/NimbusML,src/python/nimbusml/examples/FastLinearClassifier.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,ae4f4de3110f00bc2f8be179cab1a01569624744,TODO: Replace with CV
aaedcbefd,microsoft/NimbusML,src/python/nimbusml/examples/FastLinearRegressor.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,ae4f4de3110f00bc2f8be179cab1a01569624744,TODO: Replace with CV
aaedcbehf,microsoft/NimbusML,src/python/nimbusml/examples/FastTreesBinaryClassifier.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,ae4f4de3110f00bc2f8be179cab1a01569624744,TODO: Replace with CV
aaedcbejh,microsoft/NimbusML,src/python/nimbusml/examples/FastTreesRegressor.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,ae4f4de3110f00bc2f8be179cab1a01569624744,TODO: Replace with CV
aaedcbfbj,microsoft/NimbusML,src/python/nimbusml/examples/FastTreesTweedieRegressor.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,ae4f4de3110f00bc2f8be179cab1a01569624744,TODO: Replace with CV
aaedcbfgb,microsoft/NimbusML,src/python/nimbusml/examples/GamBinaryClassifier.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,ae4f4de3110f00bc2f8be179cab1a01569624744,TODO: Replace with CV
aaedcbfid,microsoft/NimbusML,src/python/nimbusml/examples/GamRegressor.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,ae4f4de3110f00bc2f8be179cab1a01569624744,TODO: Replace with CV
aaedcbfjh,microsoft/NimbusML,src/python/nimbusml/examples/GlobalContrastRowScaler.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,merge a few columns together
aaedcbgfj,microsoft/NimbusML,src/python/nimbusml/examples/KMeansPlusPlus.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,ae4f4de3110f00bc2f8be179cab1a01569624744,TODO: Replace with CV
aaedcbgib,microsoft/NimbusML,src/python/nimbusml/examples/LightGbmBinaryClassifier.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,ae4f4de3110f00bc2f8be179cab1a01569624744,TODO: Replace with CV
aaedcbhad,microsoft/NimbusML,src/python/nimbusml/examples/LightGbmClassifier.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,ae4f4de3110f00bc2f8be179cab1a01569624744,TODO: Replace with CV
aaedcbhca,microsoft/NimbusML,src/python/nimbusml/examples/LightGbmRanker.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,ae4f4de3110f00bc2f8be179cab1a01569624744,TODO: Replace with CV
aaedcbhec,microsoft/NimbusML,src/python/nimbusml/examples/LightGbmRegressor.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,ae4f4de3110f00bc2f8be179cab1a01569624744,TODO: Replace with CV
aaedcbiad,microsoft/NimbusML,src/python/nimbusml/examples/LogisticRegressionBinaryClassifier.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,ae4f4de3110f00bc2f8be179cab1a01569624744,TODO: Replace with CV
aaedcbicf,microsoft/NimbusML,src/python/nimbusml/examples/LogisticRegressionClassifier.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,ae4f4de3110f00bc2f8be179cab1a01569624744,TODO: Replace with CV
aaedcbiie,microsoft/NimbusML,src/python/nimbusml/examples/MutualInformationSelector.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,TODO: bug 244702
aaedcbiig,microsoft/NimbusML,src/python/nimbusml/examples/MutualInformationSelector.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,features from. We concatenate the target columns
aaedcbiij,microsoft/NimbusML,src/python/nimbusml/examples/MutualInformationSelector.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,are selected. We will support multiple columns in the future to
aaedcbija,microsoft/NimbusML,src/python/nimbusml/examples/MutualInformationSelector.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,concatenate columns automatically.
aaedcbjai,microsoft/NimbusML,src/python/nimbusml/examples/NGramFeaturizer.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,79ac758c626ee5526a230034004a4befe79125e8,TODO: Bug 149583
aaedcbjaj,microsoft/NimbusML,src/python/nimbusml/examples/NGramFeaturizer.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,79ac758c626ee5526a230034004a4befe79125e8,TODO: Bug 149700
aaedcbjch,microsoft/NimbusML,src/python/nimbusml/examples/NGramFeaturizer2.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,79ac758c626ee5526a230034004a4befe79125e8,TODO: Bug 149583
aaedcbjci,microsoft/NimbusML,src/python/nimbusml/examples/NGramFeaturizer2.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,79ac758c626ee5526a230034004a4befe79125e8,TODO: Bug 149700
aaedcbjeh,microsoft/NimbusML,src/python/nimbusml/examples/NGramFeaturizer3.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,79ac758c626ee5526a230034004a4befe79125e8,TODO: Bug 149583
aaedcbjei,microsoft/NimbusML,src/python/nimbusml/examples/NGramFeaturizer3.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,79ac758c626ee5526a230034004a4befe79125e8,TODO: Bug 149700
aaedcbjgi,microsoft/NimbusML,src/python/nimbusml/examples/NaiveBayesClassifier.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,ae4f4de3110f00bc2f8be179cab1a01569624744,TODO: Replace with CV
aaedccaag,microsoft/NimbusML,src/python/nimbusml/examples/OneHotVectorizer.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,columns
aaedccacg,microsoft/NimbusML,src/python/nimbusml/examples/OneVsRestClassifier.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,ae4f4de3110f00bc2f8be179cab1a01569624744,TODO: Replace with CV
aaedccaei,microsoft/NimbusML,src/python/nimbusml/examples/OnlineGradientDescentRegressor.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,ae4f4de3110f00bc2f8be179cab1a01569624744,TODO: Replace with CV
aaedccaha,microsoft/NimbusML,src/python/nimbusml/examples/OrdinaryLeastSquaresRegressor.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,ae4f4de3110f00bc2f8be179cab1a01569624744,TODO: Replace with CV
aaedccajc,microsoft/NimbusML,src/python/nimbusml/examples/PcaAnomalyDetector.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,ae4f4de3110f00bc2f8be179cab1a01569624744,TODO: Replace with CV
aaedccbdj,microsoft/NimbusML,src/python/nimbusml/examples/PoissonRegressionRegressor.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,ae4f4de3110f00bc2f8be179cab1a01569624744,TODO: Replace with CV
aaedccbjg,microsoft/NimbusML,src/python/nimbusml/examples/SgdBinaryClassifier.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,ae4f4de3110f00bc2f8be179cab1a01569624744,TODO: Replace with CV
aaedcccdi,microsoft/NimbusML,src/python/nimbusml/examples/SymSgdBinaryClassifier.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,ae4f4de3110f00bc2f8be179cab1a01569624744,TODO: Replace with CV
aaedccdbh,microsoft/NimbusML,src/python/nimbusml/examples/WordEmbedding.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,79ac758c626ee5526a230034004a4befe79125e8,TODO: Bug 146863
aaedccdbi,microsoft/NimbusML,src/python/nimbusml/examples/WordEmbedding.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,79ac758c626ee5526a230034004a4befe79125e8,TODO: Bug 149666
aaedccdhg,microsoft/NimbusML,src/python/nimbusml/examples/examples_from_dataframe/Binner_df.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,generate two new Columns - Petal_Normed and Sepal_Normed
aaedccdjf,microsoft/NimbusML,src/python/nimbusml/examples/examples_from_dataframe/ColumnConcatenator_df.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,TODO: fix as_matrix() requirement
aaedcceaa,microsoft/NimbusML,src/python/nimbusml/examples/examples_from_dataframe/ColumnDuplicator_df.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,view the three columns
aaedccfcd,microsoft/NimbusML,src/python/nimbusml/examples/examples_from_dataframe/GlobalContrastRowScaler_df.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,generate two new Columns - Petal_Normed and Sepal_Normed
aaedccfdc,microsoft/NimbusML,src/python/nimbusml/examples/examples_from_dataframe/Handler_df.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,Creates 2 new columns;
aaedccfja,microsoft/NimbusML,src/python/nimbusml/examples/examples_from_dataframe/LogMeanVarianceScaler_df.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,generate two new Columns - Petal_Normed and Sepal_Normed
aaedccgbb,microsoft/NimbusML,src/python/nimbusml/examples/examples_from_dataframe/MeanVarianceScaler_df.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,generate two new Columns - Petal_Normed and Sepal_Normed
aaedccgbe,microsoft/NimbusML,src/python/nimbusml/examples/examples_from_dataframe/MinMaxScaler_df.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,generate two new Columns - Petal_Normed and Sepal_Normed
aaedccgeg,microsoft/NimbusML,src/python/nimbusml/examples/examples_from_dataframe/OneHotVectorizer_df.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,input columns concatenated into vector type
aaedccgfe,microsoft/NimbusML,src/python/nimbusml/examples/examples_from_dataframe/OneHotVectorizer_df.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,input columns concatenated; output_kind = \"Bag\"
aaedccgjc,microsoft/NimbusML,src/python/nimbusml/examples/examples_from_dataframe/PcaTransformer_df.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,target and features columns
aaedcchbf,microsoft/NimbusML,src/python/nimbusml/examples/examples_from_dataframe/SgdBinaryClassifier_infert_df.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,target and features columns
aaedcchdd,microsoft/NimbusML,src/python/nimbusml/examples/examples_from_dataframe/SymSgdBinaryClassifier_infert_df.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,target and features columns
aaedccifd,microsoft/NimbusML,src/python/nimbusml/internal/core/base_pipeline_item.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,It assumes all columns are used as input.
aaedccife,microsoft/NimbusML,src/python/nimbusml/internal/core/base_pipeline_item.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,Default options for output columns. Depends on the model.
aaedccifi,microsoft/NimbusML,src/python/nimbusml/internal/core/base_pipeline_item.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,Handles parameters columns.
aaedccihc,microsoft/NimbusML,src/python/nimbusml/internal/core/base_pipeline_item.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,Needed for learner. % is also used to define feature roles.
aaedccihe,microsoft/NimbusML,src/python/nimbusml/internal/core/base_pipeline_item.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,raise RuntimeError(\"Too many feature columns.
aaedcciid,microsoft/NimbusML,src/python/nimbusml/internal/core/base_pipeline_item.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,No columns specified. The user plans to fit the pipeline as
aaedccjaj,microsoft/NimbusML,src/python/nimbusml/internal/core/decomposition/pcatransformer.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,We concatenate the columns.
aaedcdhej,microsoft/NimbusML,src/python/nimbusml/internal/utils/data_roles.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,Maybe every learner and transform could declare the roles it requires to
aaedcdhfj,microsoft/NimbusML,src/python/nimbusml/internal/utils/data_schema.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,not efficient
aaedcdhhd,microsoft/NimbusML,src/python/nimbusml/internal/utils/data_schema.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,merging columns
aaedcdhid,microsoft/NimbusML,src/python/nimbusml/internal/utils/data_stream.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,Maybe every learner and transform could declare the roles it requires to
aaedcdhjc,microsoft/NimbusML,src/python/nimbusml/internal/utils/data_stream.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,0dcfbe85b90766e18df9f09c9f69a953dfb1d4d6,REVIEW: would be good to figure out a way to know the schema of the
aaedcdiad,microsoft/NimbusML,src/python/nimbusml/internal/utils/dataframes.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,Workaround; empty dataframe needs to be sent as an array to
aaedcdiah,microsoft/NimbusML,src/python/nimbusml/internal/utils/dataframes.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,Workaround; empty dataframe needs to be sent as an array
aaedcdifh,microsoft/NimbusML,src/python/nimbusml/internal/utils/entrypoints.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,todo: load & return model blob
aaedcdjgh,microsoft/NimbusML,src/python/nimbusml/model_selection/cv.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,Predictions include features columns as well. Only keep
aaedcdjgj,microsoft/NimbusML,src/python/nimbusml/model_selection/cv.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,columns
aaedcdjhd,microsoft/NimbusML,src/python/nimbusml/model_selection/cv.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,Remove non-metric columns from predictions
aaedcdjif,microsoft/NimbusML,src/python/nimbusml/model_selection/cv.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,unused.
aaedceaci,microsoft/NimbusML,src/python/nimbusml/pipeline.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,happens when ML.NET is needed to know the output column names
aaedceacj,microsoft/NimbusML,src/python/nimbusml/pipeline.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,It usually produces only only columns (single or vector).
aaedceaej,microsoft/NimbusML,src/python/nimbusml/pipeline.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,d08b702ade3e4d8bf487fe583f9632c40a7a774b,todo: ideally all the nodes have the same name for params
aaedceagb,microsoft/NimbusML,src/python/nimbusml/pipeline.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,graph_nodes contain graph sections; which is needed for CV.
aaedceagi,microsoft/NimbusML,src/python/nimbusml/pipeline.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,REVIEW: ideally we should remove output completely from the
aaedceagj,microsoft/NimbusML,src/python/nimbusml/pipeline.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,graph if its not needed
aaedceahg,microsoft/NimbusML,src/python/nimbusml/pipeline.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,Multiple columns to transform.
aaedceaii,microsoft/NimbusML,src/python/nimbusml/pipeline.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,REVIEW: we should have the possibility to keep the model in
aaedceajj,microsoft/NimbusML,src/python/nimbusml/pipeline.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,same set of columns as it takes in
aaedcebaf,microsoft/NimbusML,src/python/nimbusml/pipeline.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,multiple columns. However; in that case; The python
aaedcebah,microsoft/NimbusML,src/python/nimbusml/pipeline.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,concatenation of all provided columns.
aaedcebbe,microsoft/NimbusML,src/python/nimbusml/pipeline.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,Other usage could be implemented the same way but the
aaedcebbj,microsoft/NimbusML,src/python/nimbusml/pipeline.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,todo sweep
aaedcebci,microsoft/NimbusML,src/python/nimbusml/pipeline.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,more efficient as it will not pass the unnecessary columns
aaedcebdc,microsoft/NimbusML,src/python/nimbusml/pipeline.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,a36a6c04dfb7b983bae5011d411542d57b46aad7,[todo]: this is a bug; predict_proba should not change
aaedcebea,microsoft/NimbusML,src/python/nimbusml/pipeline.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,more efficient as it will not
aaedcebeb,microsoft/NimbusML,src/python/nimbusml/pipeline.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,pass the unnecessary columns from ML.NET to Python.
aaedceeeb,microsoft/NimbusML,src/python/nimbusml/tests/feature_extraction/text/test_wordembedding.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,TODO: Bug 146763
aaedceeec,microsoft/NimbusML,src/python/nimbusml/tests/feature_extraction/text/test_wordembedding.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,TODO: Bug 149666
aaedceeed,microsoft/NimbusML,src/python/nimbusml/tests/feature_extraction/text/test_wordembedding.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,TODO: Bug 149700
aaedcefdb,microsoft/NimbusML,src/python/nimbusml/tests/metrics/test_metrics.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,TODO: JRP comment for now. Debug fluctuations on build server
aaedceffg,microsoft/NimbusML,src/python/nimbusml/tests/model_selection/test_cv.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,The following tests are only needed once. We bundle them with regressor
aaedcegbh,microsoft/NimbusML,src/python/nimbusml/tests/multiclass/test_onevsrestclassifier.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,TODO: why symsgd does not sum to 1.0
aaedcehcd,microsoft/NimbusML,src/python/nimbusml/tests/preprocessing/missing_values/test_filter.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,columns ordering changed between 0.22 and 0.23
aaedcehci,microsoft/NimbusML,src/python/nimbusml/tests/preprocessing/normalization/test_globalcontrastrowscaler.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,generate two new Columns - Petal_Normed and Sepal_Normed
aaedcehgd,microsoft/NimbusML,src/python/nimbusml/tests/preprocessing/text/test_ngramfeaturizer.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,columns ordering changed between 0.22 and 0.23
aaedceigd,microsoft/NimbusML,src/python/nimbusml/tests/test_syntax.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,The best would be to handle regular expression inside nimbusml.
aaedceigh,microsoft/NimbusML,src/python/nimbusml/tests/test_syntax.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,REVIEW: the pipeline drops all columns but one -->
aaedceiha,microsoft/NimbusML,src/python/nimbusml/tests/test_syntax.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,columns should play that role
aaedceihb,microsoft/NimbusML,src/python/nimbusml/tests/test_syntax.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,(maybe because the label column is here too even though
aaedcejej,microsoft/NimbusML,src/python/nimbusml/tests/test_utils.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,require changing the param
aaedcejhg,microsoft/NimbusML,src/python/nimbusml/utils/utils.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,select columns from DataFrame insize a pipeline
aaedcfaag,microsoft/NimbusML,src/python/setup.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,You can just specify the packages manually here if your project is
aaedcfaci,microsoft/NimbusML,src/python/setup.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,besides those needed to install it; you can use this option to
aaedcfagc,microsoft/NimbusML,src/python/tests/test_docs_example.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,REVIEW: fix ssl issue on test centos7 & ubuntu14
aaedcfahe,microsoft/NimbusML,src/python/tests/test_docs_example.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,TODO: Investigate.
aaedcfaid,microsoft/NimbusML,src/python/tests/test_docs_notebooks.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,REVIEW: Work is needed to fix the jupyter kernel config file to run
aaedcfaje,microsoft/NimbusML,src/python/tests/test_docs_notebooks.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,REVIEW: remove skip list once resource download over ssl is fixed on
aaedcfbai,microsoft/NimbusML,src/python/tests/test_estimator_checks.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,fix pending in PR; bug cant handle csr matrix
aaedcfbga,microsoft/NimbusML,src/python/tools/doc_builder.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,TODO: write help
aaedcfbgf,microsoft/NimbusML,src/python/tools/doc_builder.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,template = 'List of {} columns.' if is_list else 'Name of {}
aaedcfbjh,microsoft/NimbusML,src/python/tools/entrypoint_compiler.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,columns for entrypoint
aaedcfcdd,microsoft/NimbusML,src/python/tools/entrypoint_compiler.py,793739b8ab6b002bfccdbda2bedf6f915ce6125b,STILL_EXISTS,Add parameter columns (part of the syntax) and roles.
aaedcfegj,microsoft/NimbusML,src/python/tools/code_fixer.py,9e57f196cd1d646d99fc9c6c20ab704d4d385265,210b220f74d13ccb6586e034e83a5939ef395cef,if fix[0] in code:
aaedcfeid,microsoft/NimbusML,src/python/tests/test_docs_example.py,210b220f74d13ccb6586e034e83a5939ef395cef,STILL_EXISTS,Bug todo: CustomStopWordsRemover fails on ML.NET side
aaedcfeie,microsoft/NimbusML,src/python/tests/test_docs_example.py,210b220f74d13ccb6586e034e83a5939ef395cef,STILL_EXISTS,System.Drawings.Common.dll 4.0.0 is needed
aaedcffba,microsoft/NimbusML,src/python/tests/test_docs_example.py,a5803318a1bd2cc73398059511584530f7f3347d,STILL_EXISTS,System.Drawings.Common.dll 4.0.0 is needed
aaedcfibj,microsoft/NimbusML,src/python/nimbusml/examples/CharTokenizer.py,c5153c285c56aa31ab31aeb45358bd20b23a8272,STILL_EXISTS,[5 rows x 152 columns]
aaedcfijf,microsoft/NimbusML,src/python/nimbusml/examples/examples_from_dataframe/SsaForecaster_df.py,3993365bb94b50061fe9fcb94dc9696d656a7cc2,STILL_EXISTS,The fc.x columns are the forecasts
aaedcfjfg,microsoft/NimbusML,src/python/nimbusml/examples/LinearSvmBinaryClassifier.py,08d8abf004e4132798417790fa160f16f74337b7,ae4f4de3110f00bc2f8be179cab1a01569624744,TODO: Replace with CV
aaedcggjb,microsoft/NimbusML,src/python/nimbusml/tests/pipeline/test_pipeline_split_models.py,3f3725a75ed47af149d3ae151991f50533b665dd,STILL_EXISTS,but featurized data has only columns 'c0.a' and 'c0.b'
aaedcgjdi,microsoft/NimbusML,src/python/nimbusml/tests/pipeline/test_csr_input.py,d67dd626b5a45f5bf313c111a9267eaf46785d41,STILL_EXISTS,Note: the relative order of all columns is still the same as in raw data.
aaedcgjdj,microsoft/NimbusML,src/python/nimbusml/tests/pipeline/test_csr_input.py,d67dd626b5a45f5bf313c111a9267eaf46785d41,STILL_EXISTS,print(featurization_pipeline.get_output_columns())
aaedcgjea,microsoft/NimbusML,src/python/nimbusml/tests/pipeline/test_csr_input.py,d67dd626b5a45f5bf313c111a9267eaf46785d41,STILL_EXISTS,need to remove extra columns before getting csr_matrix featurized data as it wont have column name information.
aaedcgjeb,microsoft/NimbusML,src/python/nimbusml/tests/pipeline/test_csr_input.py,d67dd626b5a45f5bf313c111a9267eaf46785d41,STILL_EXISTS,Note: the relative order of all columns is still the same.
aaedcgjec,microsoft/NimbusML,src/python/nimbusml/tests/pipeline/test_csr_input.py,d67dd626b5a45f5bf313c111a9267eaf46785d41,STILL_EXISTS,print(csr_featurization_pipeline.get_output_columns())
aaedcgjee,microsoft/NimbusML,src/python/nimbusml/tests/pipeline/test_csr_input.py,d67dd626b5a45f5bf313c111a9267eaf46785d41,STILL_EXISTS,Note: order & number of feature columns for learner (parameter 'feature') should be the same as in csr_matrix above
aaedcgjei,microsoft/NimbusML,src/python/nimbusml/tests/pipeline/test_csr_input.py,d67dd626b5a45f5bf313c111a9267eaf46785d41,STILL_EXISTS,see the order of Feature.* columns that get passed to learner algo
aaedcgjej,microsoft/NimbusML,src/python/nimbusml/tests/pipeline/test_csr_input.py,d67dd626b5a45f5bf313c111a9267eaf46785d41,STILL_EXISTS,print(predictor_pipeline.get_output_columns())
aaedchafa,microsoft/NimbusML,src/python/tests_extended/test_docs_example.py,8120d0ee5c51b3540e96fb0d3ed5365b61458801,b57cc2597c80fb9b981647c51c5141e5b874f3dc,Bug todo: CustomStopWordsRemover fails on ML.NET side
aaedchahi,microsoft/NimbusML,src/python/tools/temp_docs_updater.py,be0ab53b6d0fb98f53b74f317213287df90fd074,STILL_EXISTS,TODO: the fixes in this method shouldn't be necessary.
aaedchbbc,microsoft/NimbusML,src/python/nimbusml/base_predictor.py,d08b702ade3e4d8bf487fe583f9632c40a7a774b,STILL_EXISTS,todo: ideally all the nodes have the same name for params
aaedchbgh,microsoft/NimbusML,src/python/nimbusml/model_selection/cv.py,d08b702ade3e4d8bf487fe583f9632c40a7a774b,STILL_EXISTS,TODO: refactor this.
aaedchcha,microsoft/NimbusML,src/python/nimbusml/examples/examples_from_dataframe/DateTimeSplitter_df.py,0fc3f1073038bc1b6e4cfdb2343fcc0045d8ea37,STILL_EXISTS,view the three columns
aaedchcie,microsoft/NimbusML,src/python/nimbusml/examples/examples_from_dataframe/OnnxRunner_df.py,0fc3f1073038bc1b6e4cfdb2343fcc0045d8ea37,STILL_EXISTS,Note; the extra columns and column name differences
aaedchehg,microsoft/NimbusML,src/python/tests_extended/data_frame_tool.py,0fc3f1073038bc1b6e4cfdb2343fcc0045d8ea37,STILL_EXISTS,in ONNX models trained in ML.NET input coming from \"categorical columns\" is 1 based indices; whereas Categorical columns save indices that are 0 based; and that need to be retrieved from .array.codes
aaedcheic,microsoft/NimbusML,src/python/tests_extended/data_frame_tool.py,0fc3f1073038bc1b6e4cfdb2343fcc0045d8ea37,STILL_EXISTS,TODO: remove this. These extra columns
aaedchfbi,microsoft/NimbusML,src/python/tests_extended/test_export_to_onnx.py,0fc3f1073038bc1b6e4cfdb2343fcc0045d8ea37,STILL_EXISTS,GlobalContrastRowScaler currently requires a vector input to work
aaedchfcd,microsoft/NimbusML,src/python/tests_extended/test_export_to_onnx.py,0fc3f1073038bc1b6e4cfdb2343fcc0045d8ea37,STILL_EXISTS,If col_pairs is an int then slice the columns
aaedchfcf,microsoft/NimbusML,src/python/tests_extended/test_export_to_onnx.py,0fc3f1073038bc1b6e4cfdb2343fcc0045d8ea37,STILL_EXISTS,ONNX does not export categorical columns so convert categorical
aaedchfcg,microsoft/NimbusML,src/python/tests_extended/test_export_to_onnx.py,0fc3f1073038bc1b6e4cfdb2343fcc0045d8ea37,STILL_EXISTS,columns received from ML.Net back to the original values before
aaedchfig,googleapis/python-dialogflow,dialogflow_v2beta1/gapic/agents_client.py,ba0ad603ae83ee44c4f523d3c2b0f6d5e011201d,STILL_EXISTS,The scopes needed to make gRPC calls to all of the methods defined in
aaedchgca,googleapis/python-dialogflow,dialogflow_v2beta1/gapic/contexts_client.py,ba0ad603ae83ee44c4f523d3c2b0f6d5e011201d,STILL_EXISTS,The scopes needed to make gRPC calls to all of the methods defined in
aaedchgei,googleapis/python-dialogflow,dialogflow_v2beta1/gapic/entity_types_client.py,ba0ad603ae83ee44c4f523d3c2b0f6d5e011201d,STILL_EXISTS,The scopes needed to make gRPC calls to all of the methods defined in
aaedchgje,googleapis/python-dialogflow,dialogflow_v2beta1/gapic/intents_client.py,ba0ad603ae83ee44c4f523d3c2b0f6d5e011201d,STILL_EXISTS,The scopes needed to make gRPC calls to all of the methods defined in
aaedchhcg,googleapis/python-dialogflow,dialogflow_v2beta1/gapic/session_entity_types_client.py,ba0ad603ae83ee44c4f523d3c2b0f6d5e011201d,STILL_EXISTS,The scopes needed to make gRPC calls to all of the methods defined in
aaedchhfe,googleapis/python-dialogflow,dialogflow_v2beta1/gapic/sessions_client.py,ba0ad603ae83ee44c4f523d3c2b0f6d5e011201d,STILL_EXISTS,The scopes needed to make gRPC calls to all of the methods defined in
aaedchjhd,googleapis/python-dialogflow,docs/conf.py,ba0ad603ae83ee44c4f523d3c2b0f6d5e011201d,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaedcigie,googleapis/python-dialogflow,dialogflow_v2/gapic/agents_client.py,b53ed76655cf89b436f35b76e68ed92d7be6e107,STILL_EXISTS,The scopes needed to make gRPC calls to all of the methods defined in
aaedcihcb,googleapis/python-dialogflow,dialogflow_v2/gapic/contexts_client.py,b53ed76655cf89b436f35b76e68ed92d7be6e107,STILL_EXISTS,The scopes needed to make gRPC calls to all of the methods defined in
aaedcihfc,googleapis/python-dialogflow,dialogflow_v2/gapic/entity_types_client.py,b53ed76655cf89b436f35b76e68ed92d7be6e107,STILL_EXISTS,The scopes needed to make gRPC calls to all of the methods defined in
aaedciiab,googleapis/python-dialogflow,dialogflow_v2/gapic/intents_client.py,b53ed76655cf89b436f35b76e68ed92d7be6e107,STILL_EXISTS,The scopes needed to make gRPC calls to all of the methods defined in
aaedciidg,googleapis/python-dialogflow,dialogflow_v2/gapic/session_entity_types_client.py,b53ed76655cf89b436f35b76e68ed92d7be6e107,STILL_EXISTS,The scopes needed to make gRPC calls to all of the methods defined in
aaedciigh,googleapis/python-dialogflow,dialogflow_v2/gapic/sessions_client.py,b53ed76655cf89b436f35b76e68ed92d7be6e107,STILL_EXISTS,The scopes needed to make gRPC calls to all of the methods defined in
aaedcjdai,googleapis/python-dialogflow,dialogflow_v2beta1/gapic/contexts_client.py,ae4ab91cf0575b40d0eb20c329e37e5919f8676e,STILL_EXISTS,The scopes needed to make gRPC calls to all of the methods defined in
aaedcjeee,googleapis/python-dialogflow,dialogflow_v2/gapic/transports/agents_grpc_transport.py,a6af8b3e500226a9df2400afc29e2ad5ac4b832f,STILL_EXISTS,The scopes needed to make gRPC calls to all of the methods defined
aaedcjegh,googleapis/python-dialogflow,dialogflow_v2/gapic/transports/contexts_grpc_transport.py,a6af8b3e500226a9df2400afc29e2ad5ac4b832f,STILL_EXISTS,The scopes needed to make gRPC calls to all of the methods defined
aaedcjeih,googleapis/python-dialogflow,dialogflow_v2/gapic/transports/entity_types_grpc_transport.py,a6af8b3e500226a9df2400afc29e2ad5ac4b832f,STILL_EXISTS,The scopes needed to make gRPC calls to all of the methods defined
aaedcjfba,googleapis/python-dialogflow,dialogflow_v2/gapic/transports/intents_grpc_transport.py,a6af8b3e500226a9df2400afc29e2ad5ac4b832f,STILL_EXISTS,The scopes needed to make gRPC calls to all of the methods defined
aaedcjfdd,googleapis/python-dialogflow,dialogflow_v2/gapic/transports/session_entity_types_grpc_transport.py,a6af8b3e500226a9df2400afc29e2ad5ac4b832f,STILL_EXISTS,The scopes needed to make gRPC calls to all of the methods defined
aaedcjffd,googleapis/python-dialogflow,dialogflow_v2/gapic/transports/sessions_grpc_transport.py,a6af8b3e500226a9df2400afc29e2ad5ac4b832f,STILL_EXISTS,The scopes needed to make gRPC calls to all of the methods defined
aaedcjhch,googleapis/python-dialogflow,dialogflow_v2beta1/gapic/transports/agents_grpc_transport.py,a6af8b3e500226a9df2400afc29e2ad5ac4b832f,STILL_EXISTS,The scopes needed to make gRPC calls to all of the methods defined
aaedcjhfa,googleapis/python-dialogflow,dialogflow_v2beta1/gapic/transports/contexts_grpc_transport.py,a6af8b3e500226a9df2400afc29e2ad5ac4b832f,STILL_EXISTS,The scopes needed to make gRPC calls to all of the methods defined
aaedcjhha,googleapis/python-dialogflow,dialogflow_v2beta1/gapic/transports/documents_grpc_transport.py,a6af8b3e500226a9df2400afc29e2ad5ac4b832f,STILL_EXISTS,The scopes needed to make gRPC calls to all of the methods defined
aaedcjhjd,googleapis/python-dialogflow,dialogflow_v2beta1/gapic/transports/entity_types_grpc_transport.py,a6af8b3e500226a9df2400afc29e2ad5ac4b832f,STILL_EXISTS,The scopes needed to make gRPC calls to all of the methods defined
aaedcjibg,googleapis/python-dialogflow,dialogflow_v2beta1/gapic/transports/intents_grpc_transport.py,a6af8b3e500226a9df2400afc29e2ad5ac4b832f,STILL_EXISTS,The scopes needed to make gRPC calls to all of the methods defined
aaedcjidj,googleapis/python-dialogflow,dialogflow_v2beta1/gapic/transports/knowledge_bases_grpc_transport.py,a6af8b3e500226a9df2400afc29e2ad5ac4b832f,STILL_EXISTS,The scopes needed to make gRPC calls to all of the methods defined
aaedcjifj,googleapis/python-dialogflow,dialogflow_v2beta1/gapic/transports/session_entity_types_grpc_transport.py,a6af8b3e500226a9df2400afc29e2ad5ac4b832f,STILL_EXISTS,The scopes needed to make gRPC calls to all of the methods defined
aaedcjihj,googleapis/python-dialogflow,dialogflow_v2beta1/gapic/transports/sessions_grpc_transport.py,a6af8b3e500226a9df2400afc29e2ad5ac4b832f,STILL_EXISTS,The scopes needed to make gRPC calls to all of the methods defined
aaeddafji,googleapis/python-dialogflow,dialogflow_v2/gapic/transports/environments_grpc_transport.py,7bf592684b4d5df0cd1f66dd414efe2350d0461e,STILL_EXISTS,The scopes needed to make gRPC calls to all of the methods defined
aaeddagii,googleapis/python-dialogflow,dialogflow_v2beta1/gapic/transports/environments_grpc_transport.py,7bf592684b4d5df0cd1f66dd414efe2350d0461e,STILL_EXISTS,The scopes needed to make gRPC calls to all of the methods defined
aaeddahee,googleapis/python-dialogflow,synth.py,7bf592684b4d5df0cd1f66dd414efe2350d0461e,57c90a5b72668e599047b358f634f939d70a051f,TODO: remove during microgenerator transition
aaeddahef,googleapis/python-dialogflow,synth.py,7bf592684b4d5df0cd1f66dd414efe2350d0461e,57c90a5b72668e599047b358f634f939d70a051f,fix unit test
aaeddbhhf,googleapis/python-dialogflow,google/cloud/dialogflow_v2/services/agents/client.py,57c90a5b72668e599047b358f634f939d70a051f,STILL_EXISTS,Create SSL credentials for mutual TLS if needed.
aaeddccej,googleapis/python-dialogflow,google/cloud/dialogflow_v2/services/contexts/client.py,57c90a5b72668e599047b358f634f939d70a051f,STILL_EXISTS,Create SSL credentials for mutual TLS if needed.
aaeddchab,googleapis/python-dialogflow,google/cloud/dialogflow_v2/services/entity_types/client.py,57c90a5b72668e599047b358f634f939d70a051f,STILL_EXISTS,Create SSL credentials for mutual TLS if needed.
aaedddbeg,googleapis/python-dialogflow,google/cloud/dialogflow_v2/services/environments/client.py,57c90a5b72668e599047b358f634f939d70a051f,STILL_EXISTS,Create SSL credentials for mutual TLS if needed.
aaedddeei,googleapis/python-dialogflow,google/cloud/dialogflow_v2/services/intents/client.py,57c90a5b72668e599047b358f634f939d70a051f,STILL_EXISTS,Create SSL credentials for mutual TLS if needed.
aaedddihe,googleapis/python-dialogflow,google/cloud/dialogflow_v2/services/session_entity_types/client.py,57c90a5b72668e599047b358f634f939d70a051f,STILL_EXISTS,Create SSL credentials for mutual TLS if needed.
aaeddecab,googleapis/python-dialogflow,google/cloud/dialogflow_v2/services/sessions/client.py,57c90a5b72668e599047b358f634f939d70a051f,STILL_EXISTS,Create SSL credentials for mutual TLS if needed.
aaeddehdc,googleapis/python-dialogflow,google/cloud/dialogflow_v2beta1/services/agents/client.py,57c90a5b72668e599047b358f634f939d70a051f,STILL_EXISTS,Create SSL credentials for mutual TLS if needed.
aaeddfcag,googleapis/python-dialogflow,google/cloud/dialogflow_v2beta1/services/contexts/client.py,57c90a5b72668e599047b358f634f939d70a051f,STILL_EXISTS,Create SSL credentials for mutual TLS if needed.
aaeddfgbi,googleapis/python-dialogflow,google/cloud/dialogflow_v2beta1/services/documents/client.py,57c90a5b72668e599047b358f634f939d70a051f,STILL_EXISTS,Create SSL credentials for mutual TLS if needed.
aaeddgaif,googleapis/python-dialogflow,google/cloud/dialogflow_v2beta1/services/entity_types/client.py,57c90a5b72668e599047b358f634f939d70a051f,STILL_EXISTS,Create SSL credentials for mutual TLS if needed.
aaeddgfde,googleapis/python-dialogflow,google/cloud/dialogflow_v2beta1/services/environments/client.py,57c90a5b72668e599047b358f634f939d70a051f,STILL_EXISTS,Create SSL credentials for mutual TLS if needed.
aaeddgiea,googleapis/python-dialogflow,google/cloud/dialogflow_v2beta1/services/intents/client.py,57c90a5b72668e599047b358f634f939d70a051f,STILL_EXISTS,Create SSL credentials for mutual TLS if needed.
aaeddhcgg,googleapis/python-dialogflow,google/cloud/dialogflow_v2beta1/services/knowledge_bases/client.py,57c90a5b72668e599047b358f634f939d70a051f,STILL_EXISTS,Create SSL credentials for mutual TLS if needed.
aaeddhgde,googleapis/python-dialogflow,google/cloud/dialogflow_v2beta1/services/session_entity_types/client.py,57c90a5b72668e599047b358f634f939d70a051f,STILL_EXISTS,Create SSL credentials for mutual TLS if needed.
aaeddhjgb,googleapis/python-dialogflow,google/cloud/dialogflow_v2beta1/services/sessions/client.py,57c90a5b72668e599047b358f634f939d70a051f,STILL_EXISTS,Create SSL credentials for mutual TLS if needed.
aaededacd,googleapis/python-dialogflow,google/cloud/dialogflow_v2/services/answer_records/client.py,f383bb98356e7e4c29838d8888cd0a3cf80fbd6a,STILL_EXISTS,Create SSL credentials for mutual TLS if needed.
aaededdcg,googleapis/python-dialogflow,google/cloud/dialogflow_v2/services/conversation_profiles/client.py,f383bb98356e7e4c29838d8888cd0a3cf80fbd6a,STILL_EXISTS,Create SSL credentials for mutual TLS if needed.
aaededhdc,googleapis/python-dialogflow,google/cloud/dialogflow_v2/services/conversations/client.py,f383bb98356e7e4c29838d8888cd0a3cf80fbd6a,STILL_EXISTS,Create SSL credentials for mutual TLS if needed.
aaedeebjg,googleapis/python-dialogflow,google/cloud/dialogflow_v2/services/documents/client.py,f383bb98356e7e4c29838d8888cd0a3cf80fbd6a,STILL_EXISTS,Create SSL credentials for mutual TLS if needed.
aaedeegaj,googleapis/python-dialogflow,google/cloud/dialogflow_v2/services/knowledge_bases/client.py,f383bb98356e7e4c29838d8888cd0a3cf80fbd6a,STILL_EXISTS,Create SSL credentials for mutual TLS if needed.
aaedefaaf,googleapis/python-dialogflow,google/cloud/dialogflow_v2/services/participants/client.py,f383bb98356e7e4c29838d8888cd0a3cf80fbd6a,STILL_EXISTS,Create SSL credentials for mutual TLS if needed.
aaedeffhb,googleapis/python-dialogflow,google/cloud/dialogflow_v2beta1/services/answer_records/client.py,f383bb98356e7e4c29838d8888cd0a3cf80fbd6a,STILL_EXISTS,Create SSL credentials for mutual TLS if needed.
aaedefijd,googleapis/python-dialogflow,google/cloud/dialogflow_v2beta1/services/conversation_profiles/client.py,f383bb98356e7e4c29838d8888cd0a3cf80fbd6a,STILL_EXISTS,Create SSL credentials for mutual TLS if needed.
aaedegdba,googleapis/python-dialogflow,google/cloud/dialogflow_v2beta1/services/conversations/client.py,f383bb98356e7e4c29838d8888cd0a3cf80fbd6a,STILL_EXISTS,Create SSL credentials for mutual TLS if needed.
aaedegiha,googleapis/python-dialogflow,google/cloud/dialogflow_v2beta1/services/participants/client.py,f383bb98356e7e4c29838d8888cd0a3cf80fbd6a,STILL_EXISTS,Create SSL credentials for mutual TLS if needed.
aaedfbddf,GRAAL-Research/poutyne,docs/conf.py,6a1f7d23f5e1c8e65ab342558ad466e68a436a04,STILL_EXISTS,-- Options for todo extension ----------------------------------------------
aaedfbddg,GRAAL-Research/poutyne,docs/conf.py,6a1f7d23f5e1c8e65ab342558ad466e68a436a04,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaedfbdga,GRAAL-Research/poutyne,pytoune/framework/train.py,2a65dc66c428d8df03c41e8617aead5007dd1e8b,STILL_EXISTS,Restarting optimization if needed.
aaedfbdgc,GRAAL-Research/poutyne,pytoune/framework/train_and_test.py,79d7b73779d45b4a5884d944b5a9464c8744a3e7,STILL_EXISTS,Restarting optimization if needed.
aaedfbdhb,GRAAL-Research/poutyne,pytoune/framework/experiment.py,d53861ee4cb2552599958a26ed831e1ce3e6af40,STILL_EXISTS,Restarting optimization if needed.
aaedfbecb,GRAAL-Research/poutyne,pytoune/framework/callbacks/policies.py,0d0ac8bf44ac23b208b449ab0a6196221c1a07a6,STILL_EXISTS,\"\"\" || The ``policies`` module is an alternative way to configure your training process. || It gives you fine grained control over the process. ||  || The training is divided into phases with the ``Phase`` class. || A ``Phase`` contains parameter spaces || (e.g. learning rate; or momentum; or both) || for the optimizer. || You chain ``Phase`` instances by passing them to the ``OptimizerPolicy`` || ``OptimizerPolicy`` is a ``Callback`` that uses the phasese; || steps through them; and sets the parameters of the optimizer. ||  || \"\"\"
aaedfbeic,GRAAL-Research/poutyne,poutyne/framework/callbacks/lr_scheduler.py,d304546a340a269590c49a0d9d3337583ff2dc3c,STILL_EXISTS,\"\"\" || Poutyne's callbacks for learning rate schedulers are just wrappers around `PyTorch's learning || rate schedulers <http:\/\/pytorch.org\/docs\/master\/optim.html#how-to-adjust-learning-rate>`_ || and thus have the same arguments except for the optimizer that has to be || omitted. || \"\"\"
aaedfcaee,tensorflow/addons,tensorflow_addons/examples/demo.py,2b004243f5a3f57e24f075a8139594916a994fb4,STILL_EXISTS,TODO: Build this out
aaedfcaja,tensorflow/addons,tensorflow_addons/layers/python/layers/poincare_normalize_test.py,2b004243f5a3f57e24f075a8139594916a994fb4,STILL_EXISTS,TODO: Is this the prefered way to run tests in TF2?
aaedfcbgj,tensorflow/addons,tensorflow_addons/opt/python/opt/lazy_adam_optimizer.py,2b004243f5a3f57e24f075a8139594916a994fb4,STILL_EXISTS,FIXME: Which way to import?
aaedfccii,tensorflow/addons,tensorflow_addons/text/python/ops/skip_gram_ops_test.py,2b004243f5a3f57e24f075a8139594916a994fb4,STILL_EXISTS,This is needed since tests set a graph-level seed by default. We want to
aaedfcdfj,tensorflow/addons,tensorflow_addons/text/python/ops/skip_gram_ops_test.py,2b004243f5a3f57e24f075a8139594916a994fb4,STILL_EXISTS,vocab_freq_file only has two columns.
aaedfcdia,tensorflow/addons,tensorflow_addons/text/python/skip_gram_ops_test.py,f7f2381cbc2586b99988fb3a7caaeb9cfdf21393,STILL_EXISTS,FIXME: Why is this not failing?
aaedfcjfh,tensorflow/addons,tensorflow_addons/optimizers/python/lazy_adam_optimizer_test.py,76843b298efee5a5c617abd76d1dc781850757e6,STILL_EXISTS,TODO: remove v1 tests (keep pace with adam_test.py in keras).
aaedfdaji,tensorflow/addons,tensorflow_addons/layers/python/maxout_test.py,f18b2aa9e89a6f7513b1285360dd5ffe04253bb6,f8349a9cf3f6c608933c1f232e9ea8b138c54fee,TODO: more simple way to deserialize the layers in addons.
aaedfddec,tensorflow/addons,tensorflow_addons/losses/python/metric_learning.py,bfa919ed514aef264d0259b96c741d42b2530d49,STILL_EXISTS,TODO: add unit test later.
aaedfddjc,tensorflow/addons,tensorflow_addons/layers/python/wrappers.py,4aedd363160841cdf0d9f913cc985bb0a3db27c6,STILL_EXISTS,TODO: Check if this needs control deps in TF2 graph mode
aaedfdedc,tensorflow/addons,tensorflow_addons/image/python/transform_test.py,86d0bcea3ad6d8645c14285af279ed58a3bde9ef,STILL_EXISTS,TODO: switch to TF2 later.
aaedfdfag,tensorflow/addons,tensorflow_addons/__init__.py,bfaa4ed79faa44460fc344bd7cf6d51041509af1,STILL_EXISTS,\"\"\"Useful extra functionality for TensorFlow maintained by SIG-addons\"\"\"
aaedfdiac,tensorflow/addons,tensorflow_addons/utils/python/keras_utils.py,b6a10910edeed2d7f4df350fdf20f87f0bd3c618,STILL_EXISTS,TODO: find public API alternative to these
aaedfdibj,tensorflow/addons,tensorflow_addons/utils/python/test_utils.py,b6a10910edeed2d7f4df350fdf20f87f0bd3c618,STILL_EXISTS,TODO: find public API alternative to these
aaedfdjaj,tensorflow/addons,tensorflow_addons/custom_ops/image/python/distort_image_ops.py,a4882be8b76c8514bff7de784681701423970b0b,STILL_EXISTS,Remember original dtype to so we can convert back if needed
aaedfdjji,tensorflow/addons,tensorflow_addons/seq2seq/attention_wrapper.py,167310d65d21a8b41dda210d6a27606bafd637c2,STILL_EXISTS,convention which takes all the tensor inputs via __call__().
aaedfeabf,tensorflow/addons,tensorflow_addons/seq2seq/attention_wrapper.py,167310d65d21a8b41dda210d6a27606bafd637c2,STILL_EXISTS,TODO(omalleyt12): Remove this hack once the mask the has proper
aaedfebjd,tensorflow/addons,tensorflow_addons/seq2seq/beam_search_decoder.py,167310d65d21a8b41dda210d6a27606bafd637c2,a17cd52982880a7dc1062a5979f60fe35240f2c4,This is a bad hack due to the implementation detail of eager\/graph TA.
aaedfeead,tensorflow/addons,tensorflow_addons/seq2seq/sampler.py,167310d65d21a8b41dda210d6a27606bafd637c2,STILL_EXISTS,unused by next_inputs_fn
aaedfeeah,tensorflow/addons,tensorflow_addons/seq2seq/sampler.py,167310d65d21a8b41dda210d6a27606bafd637c2,STILL_EXISTS,unused by sample
aaedfeeai,tensorflow/addons,tensorflow_addons/seq2seq/sampler.py,167310d65d21a8b41dda210d6a27606bafd637c2,STILL_EXISTS,unused by next_inputs
aaedfefdc,tensorflow/addons,tensorflow_addons/image/dense_image_warp_test.py,d921dfac15e40a2eb9c74f65df9af698b6f0a31d,91374856ec7d3ffc4f3f515fe4bca37a2fa5cbb8,TODO: switch to TF2 later.
aaedfefdd,tensorflow/addons,tensorflow_addons/image/dense_image_warp_test.py,d921dfac15e40a2eb9c74f65df9af698b6f0a31d,8bab32264837494638da0f9a2b7652b282aac274,TODO: run in both graph and eager modes
aaedfegfi,tensorflow/addons,tensorflow_addons/image/dense_image_warp_test.py,83195c98032409aeb6ae9a27b4047597a5b04a2b,8bab32264837494638da0f9a2b7652b282aac274,TODO: run in both graph and eager modes
aaedfeihb,tensorflow/addons,tensorflow_addons/seq2seq/attention_wrapper.py,ef4b4105e4cec0c9562b0ee59e0ec4a7c1add1d1,STILL_EXISTS,TODO: Find public API alternatives to these
aaedfeihg,tensorflow/addons,tensorflow_addons/seq2seq/attention_wrapper_test.py,ef4b4105e4cec0c9562b0ee59e0ec4a7c1add1d1,28ec920a7c24acb08de67ee087d7a92e09e872e8,TODO: Find public API alternatives to these
aaedfeihh,tensorflow/addons,tensorflow_addons/seq2seq/basic_decoder.py,ef4b4105e4cec0c9562b0ee59e0ec4a7c1add1d1,b086968285088816c02a38f28852178bfbe4bb98,TODO: Find public API alternatives to this
aaedfeihi,tensorflow/addons,tensorflow_addons/seq2seq/beam_search_decoder.py,ef4b4105e4cec0c9562b0ee59e0ec4a7c1add1d1,b086968285088816c02a38f28852178bfbe4bb98,TODO: Find public API alternatives to these
aaedfeihj,tensorflow/addons,tensorflow_addons/seq2seq/decoder.py,ef4b4105e4cec0c9562b0ee59e0ec4a7c1add1d1,STILL_EXISTS,TODO: Find public API alternatives to these
aaedfeiia,tensorflow/addons,tensorflow_addons/activations/sparsemax.py,6c7f5597b9912fdc33f289587d769255f47d8676,31394e461288359c3b5e6506a5a6c284d71a4888,TODO: Adjust dimension order for TF2 broadcasting
aaedfeiid,tensorflow/addons,tensorflow_addons/seq2seq/sampler.py,6c7f5597b9912fdc33f289587d769255f47d8676,be2a68d96f9d2006c52368f5d8083f50b6071826,TODO: Adjust dimension order for TF2 broadcasting
aaedffbfd,tensorflow/addons,tensorflow_addons/metrics/utils.py,575bc9ee5c7b1dfb2041d487b602e800b9c923c2,STILL_EXISTS,TODO: Add checks for ragged tensors and dimensions:
aaedffcia,tensorflow/addons,tensorflow_addons/seq2seq/beam_search_ops_test.py,a7afaa703eb7e62526612867e9b0468dda51344b,5d289cdfbb0a70a59d5763552c387edff4e2d786,TODO: Fix #348 issue
aaedffeei,tensorflow/addons,tensorflow_addons/image/interpolate_spline_test.py,6ec28e5f051c10e37ba1b6d83045f0d0d93b5cd5,64dfc0c2562af7bc8e8acf5a6e5f0caf41a15bff,TODO: locate placeholder
aaedffejc,tensorflow/addons,tensorflow_addons/image/sparse_image_warp_test.py,6ec28e5f051c10e37ba1b6d83045f0d0d93b5cd5,64dfc0c2562af7bc8e8acf5a6e5f0caf41a15bff,TODO: port TF1 test files?
aaedfffad,tensorflow/addons,tensorflow_addons/image/sparse_image_warp_test.py,6ec28e5f051c10e37ba1b6d83045f0d0d93b5cd5,64dfc0c2562af7bc8e8acf5a6e5f0caf41a15bff,TODO: fix TF1 ref.
aaedfffgj,tensorflow/addons,tensorflow_addons/text/crf.py,47e687725eb4d65e45498e67a9026177fda684dd,STILL_EXISTS,TODO: Wrap functions in @tf.function once
aaedffggi,tensorflow/addons,tensorflow_addons/layers/wrappers.py,cada1c99a265e5cb0a2d17853dde9a2b8e35c0f7,STILL_EXISTS,TODO: Get data init to work with tf_function compile #428
aaedffghb,tensorflow/addons,tensorflow_addons/layers/wrappers_test.py,28acaf444b4e405efc7f228fbb0036860dfad528,cc89403ae1de9438dd7ac2dd3205b1512a1a2660,TODO: Fix the bug thats causing layer test to run a
aaedffiab,tensorflow/addons,tensorflow_addons/losses/npairs.py,68e8bb9603c79e87b1859e4edc8dae1663aa9d0b,STILL_EXISTS,Enable efficient multiplication because y_true contains lots of zeros
aaedffidi,tensorflow/addons,setup.py,463f5f8d7de9b1d9516f92f0629e144d05194b49,84671d0de142c271b4aafdea4241e89a1b29f842,TODO: remove if-else condition when tf supports package consolidation.
aaedffidj,tensorflow/addons,setup.py,463f5f8d7de9b1d9516f92f0629e144d05194b49,86707616678697417b4c5cc4da19e60487106f79,TODO: remove if-else condition when tf-nightly supports package consolidation.
aaedfgdbj,tensorflow/addons,tensorflow_addons/activations/rrelu.py,8a49f91483d67e85c8e8d4f67cd6aea8c45cd2b8,8ae8116f42cd740bf35c3af56dab8a34d7e8329c,TODO: get rid of v1 API
aaedfgdde,tensorflow/addons,tensorflow_addons/activations/rrelu_test.py,8a49f91483d67e85c8e8d4f67cd6aea8c45cd2b8,8ae8116f42cd740bf35c3af56dab8a34d7e8329c,TODO: investigate the difference between CPU and GPU
aaedfgedf,tensorflow/addons,tensorflow_addons/losses/triplet_test.py,2b070a15fb516e6b3c1cca3898ae611024d7481d,b7a66a7f3ac20f087198c2ca839726b01d1d5e5d,TODO: https:\/\/github.com\/PyCQA\/pylint\/issues\/3139
aaedfgegi,tensorflow/addons,tensorflow_addons/optimizers/stochastic_weight_averaging.py,8868537159be8d1a326f32fbc70d8242647b6cc4,STILL_EXISTS,\"\"\"An implementation of the Stochastic Weight Averaging optimizer. ||  || The Stochastic Weight Averaging mechanism was proposed by Pavel Izmailov || et. al in the paper [Averaging Weights Leads to Wider Optima and Better || Generalization](https:\/\/arxiv.org\/abs\/1803.05407). The optimizer || implements averaging of multiple points along the trajectory of SGD. || This averaging has shown to improve model performance on validation\/test || sets whilst possibly causing a small increase in loss on the training || set. || \"\"\"
aaedfgfcj,tensorflow/addons,tensorflow_addons/metrics/matthews_correlation_coefficient.py,6dde75371fe705eb866c687f3c7d53b734083ca2,STILL_EXISTS,TODO: sample_weights
aaedfgfgi,tensorflow/addons,tensorflow_addons/text/__init__.py,fa4771dcd01fa639f592cfd781dbc8fab43e8c87,117f836abb44b0a8d893f6333ef9a0dae6445969,TODO: https:\/\/github.com\/tensorflow\/addons\/issues\/663
aaedfghce,tensorflow/addons,tensorflow_addons/optimizers/yogi.py,604a70de563f8797984c9c3f002aff70bef6c90b,STILL_EXISTS,\"\"\"Yogi: Extension of yogi adaptive nonconvex optimizer in Keras. ||  || Implementation of Additive Averaging. || m_t+1 = beta1*m_t + (1-beta1)*g_t || v_t+1 = v_t + sign(g_t-v_t)(g_t^2) || Experiments show better performance across NLP and Vision tasks. || Paper: || https:\/\/papers.nips.cc\/paper\/8186-adaptive-methods-for-nonconvex-optimization.pdf || \"\"\"
aaedfgija,tensorflow/addons,tensorflow_addons/activations/rrelu_test.py,4997d706f7fab8643856152de4f9bd64c9a23009,baa65a8a0f1c16f422028d98de7d4b8b8a3e2d31,TODO: Benchmark fails for windows builds #839
aaedfgjbi,tensorflow/addons,tensorflow_addons/optimizers/novograd.py,10ccec38321cd8d93bd5cf13bea0479ac1d8fbc1,5f746971d0d9491716f2f13206299a2c45941b0c,TODO: Find public API alternatives to these
aaedfgjha,tensorflow/addons,tensorflow_addons/utils/test_utils.py,e3e94efe2caf3ef5ef79120ae2263efa01dbcff5,STILL_EXISTS,TODO: Add support for other distribution strategies
aaedfhbdh,tensorflow/addons,tools/ci_build/verify/check_typing_info.py,354ecdf120d4d5892150d7082851862e72620feb,caec35513fa393e54e2d6dce0a587b6194455dcc,TODO: add types and remove all elements from
aaedfhcfi,tensorflow/addons,tools/format.py,4008a97148ce70c75a9c64cb3f912f4716a79cca,STILL_EXISTS,todo: find a way to check if files changed
aaedfhcii,tensorflow/addons,tensorflow_addons/activations/gelu_test.py,697e53a5a786abcd68a70fa211d3fca5e26131f8,STILL_EXISTS,TODO: lower atol to 1e-6
aaedfhcjb,tensorflow/addons,tensorflow_addons/register.py,1458f7f6cb44b1e5fe6b033c11b49ee6bbbda3ab,STILL_EXISTS,TODO: once layer_test is replaced by a public API
aaedfhehf,tensorflow/addons,tensorflow_addons/image/translate_ops_test.py,061b888280ca0e3efa5fc84c146d448354851fb7,STILL_EXISTS,TODO: Parameterize on dtypes
aaedfhgie,tensorflow/addons,tools/testing/source_code_test.py,2ed801d43ca6ba32cccefcd76fa35a34b1b6ce9f,STILL_EXISTS,TODO: remove all elements of the list and remove the blacklist
aaedfhhbi,tensorflow/addons,tensorflow_addons/optimizers/conditional_gradient_test.py,9acb49d9ad50c692b91076e2506d29dc9130e8dd,STILL_EXISTS,TODO:
aaedfhheh,tensorflow/addons,tensorflow_addons/optimizers/conditional_gradient_test.py,5915873b93136b2e1d6f86a21ed9f32bede06153,STILL_EXISTS,TODO:
aaedfhidc,tensorflow/addons,tensorflow_addons/optimizers/tests/lazy_adam_test.py,82ed39808bd286c1f9f5115c61d3f27a7f1fc186,STILL_EXISTS,TODO: remove the with tf.device when the execution on cpu is enforced
aaedfhide,tensorflow/addons,tensorflow_addons/optimizers/tests/lazy_adam_test.py,82ed39808bd286c1f9f5115c61d3f27a7f1fc186,STILL_EXISTS,TODO: remove v1 tests (keep pace with adam_test.py in keras).
aaedfhidf,tensorflow/addons,tensorflow_addons/optimizers/tests/lazy_adam_test.py,a587e0cba215fb6d79f066c6649908a3ee9f4a93,STILL_EXISTS,todo: remove the with tf.device once the placement on cpu is enforced.
aaedfhjbc,tensorflow/addons,tools/testing/source_code_test.py,0da23c184bceeebc748979043b9f81c7a622eaa8,STILL_EXISTS,TODO: remove all elements of the list and remove the blacklist
aaedfhjbf,tensorflow/addons,tools/testing/source_code_test.py,2ef9c20507e7d99b995b92c78c745345e8d9ef8b,STILL_EXISTS,TODO: remove all elements of the list and remove the blacklist
aaedfiehb,tensorflow/addons,tensorflow_addons/utils/test_utils.py,4b50b94bc96a763882098184d4fd5d61d2f0b2c8,STILL_EXISTS,workaround for DocTestItem
aaedfighj,tensorflow/addons,tensorflow_addons/utils/types.py,2969e63b1a448ed198379d24888cdb223e45c919,STILL_EXISTS,TODO: Remove once https:\/\/github.com\/tensorflow\/tensorflow\/issues\/44613 is resolved
aaedfjbbh,menpo/menpo,ibugMM/mesh/face.py,1dd7e5dd26bc2d3e915a0ce19359a956f06a8bb6,STILL_EXISTS,TODO deal with texture coordinates here
aaedfjced,menpo/menpo,ibugMM/importer/model.py,4a07f621aca399a8bca44208554937bce584d23f,STILL_EXISTS,TODO: make this more intelligent in locating the texture
aaedfjhge,menpo/menpo,pybug/alignment/__init__.py,9d818ed51784222389b09683fc54b8efcd41ee24,STILL_EXISTS,plot how the landmarks move from src to target
aaedfjhjb,menpo/menpo,pybug/io/model.py,fac2aad6d239900f1e5b21399a9a1c80559032b6,STILL_EXISTS,TODO: make this more intelligent in locating the texture
aaedfjidc,menpo/menpo,pybug/spatialdata/mesh/__init__.py,6fe91ba259c3fd9bdca6062c13731555fe76cf0e,STILL_EXISTS,TODO transfer metapoints and landmarks
aaedfjiff,menpo/menpo,pybug/spatialdata/mesh/__init__.py,4188d9618a05bd1c2bb2dbb6982c7f531f909959,STILL_EXISTS,TODO make this more solid - don't want to directly touch the all_landmarks
aaedfjiie,menpo/menpo,pybug/spatialdata/mesh/__init__.py,4d42151632301bf51c6b0bc71d596599582dd055,0bc360f02cb7320898bd42ff0737b060d28cdfac,TODO delauny triangulate if no trilist added
aaedfjiih,menpo/menpo,pybug/transform/__init__.py,4d42151632301bf51c6b0bc71d596599582dd055,STILL_EXISTS,TODO - be able to calculate rotations based on angles around axes
aaedfjjad,menpo/menpo,pybug/spatialdata/collection.py,0bc360f02cb7320898bd42ff0737b060d28cdfac,STILL_EXISTS,TODO pybug collections - do we need them?
aaedgabbg,menpo/menpo,pybug/spatialdata/mesh/__init__.py,3839326fc1eb187242816ff76ff5c0f660ede636,STILL_EXISTS,TODO delauney triangulate if no trilist added
aaedgabci,menpo/menpo,pybug/alignment/__init__.py,f274b4bb4c2e4dd0d413fd4ce43f48dd76b84037,STILL_EXISTS,plot how the landmarks move from src to target
aaedgabde,menpo/menpo,pybug/transformation/__init__.py,f274b4bb4c2e4dd0d413fd4ce43f48dd76b84037,c09a736124a5e19631a9fe192faba0eb6a101eab,TODO - be able to calculate rotations based on angles around axes
aaedgabej,menpo/menpo,pybug/spatialdata/mesh/__init__.py,96d9ce108cb5f6df0f9341f6ee5a986cfd2d5af5,STILL_EXISTS,TODO this is broken due to Landmark Manager changes. Fix after new
aaedgabff,menpo/menpo,pybug/shape/pointcloud.py,c4aaa955d48af49f55207acbddfa0c37a7f8181e,f92e7ca20eddfb7bf29f2874a9214391bcfb8143,TODO caching update is currently not done
aaedgabfg,menpo/menpo,pybug/shape/mesh/__init__.py,57198d41c2cba0f2a651897dfe14722a5a599b59,STILL_EXISTS,TODO this should probably be made part of Graph with some adjustments.
aaedgabij,menpo/menpo,pybug/image/landmarked.py,f0a4e0f858a2d755d6081b6dde60ecce30c164e6,STILL_EXISTS,TODO implementation of landmarks
aaedgabja,menpo/menpo,pybug/shape/graph.py,f0a4e0f858a2d755d6081b6dde60ecce30c164e6,STILL_EXISTS,TODO Graph implementation
aaedgabjb,menpo/menpo,pybug/shape/graph.py,f0a4e0f858a2d755d6081b6dde60ecce30c164e6,STILL_EXISTS,TODO Graph Shape abcmethods
aaedgacbg,menpo/menpo,pybug/shape/mesh/base.py,f0a4e0f858a2d755d6081b6dde60ecce30c164e6,b5eff649fa52f3f40cc7d79f35c55d6849eba644,TODO Delaunay triangulate if no trilist added
aaedgacbh,menpo/menpo,pybug/shape/mesh/base.py,f0a4e0f858a2d755d6081b6dde60ecce30c164e6,STILL_EXISTS,TODO add inheritance from Graph once implemented
aaedgacbi,menpo/menpo,pybug/shape/mesh/base.py,f0a4e0f858a2d755d6081b6dde60ecce30c164e6,STILL_EXISTS,TODO this is broken due to Landmark Manager changes. Fix after new
aaedgacdc,menpo/menpo,pybug/shape/mesh/base.py,f0a4e0f858a2d755d6081b6dde60ecce30c164e6,STILL_EXISTS,TODO transfer metapoints and points.
aaedgaceb,menpo/menpo,pybug/shape/tree.py,f0a4e0f858a2d755d6081b6dde60ecce30c164e6,STILL_EXISTS,TODO Implement Tree
aaedgacec,menpo/menpo,pybug/shape/tree.py,f0a4e0f858a2d755d6081b6dde60ecce30c164e6,STILL_EXISTS,TODO Tree Shape abcmethods
aaedgacgc,menpo/menpo,pybug/transformation/affine.py,c09a736124a5e19631a9fe192faba0eb6a101eab,STILL_EXISTS,TODO - be able to calculate rotations based on angles around axes
aaedgacif,menpo/menpo,pybug/transform/affine.py,6ba9d225644b6c3f9c08e6f90f3157de6eb1ad49,STILL_EXISTS,TODO build rotations about axis; euler angles etc
aaedgacjj,menpo/menpo,pybug/align/base.py,469d1b36b559d5526025956efe77f1f5e0bfe36b,beb7a4f62c64f53e2cd49bf63ec889fd6940bdec,TODO this should be separated out into visualize.
aaedgadaj,menpo/menpo,pybug/align/base.py,469d1b36b559d5526025956efe77f1f5e0bfe36b,STILL_EXISTS,plot how the landmarks move from src to target
aaedgadei,menpo/menpo,pybug/transform/affine.py,ec5508db26b1fa26bf1bc4baaf3861dac07767ab,STILL_EXISTS,TODO confirm that multiple eigenvalues of 1 means the rotation
aaedgafij,menpo/menpo,pybug/transform/affine.py,b09fa29b65415811009a2874f79f75e43cae115a,STILL_EXISTS,TODO Check am I a valid Affine transform
aaedgafja,menpo/menpo,pybug/transform/affine.py,b09fa29b65415811009a2874f79f75e43cae115a,STILL_EXISTS,TODO check that I am a similarity transform
aaedgafjc,menpo/menpo,pybug/transform/affine.py,b09fa29b65415811009a2874f79f75e43cae115a,STILL_EXISTS,TODO check that I am a valid rotation
aaedgafjd,menpo/menpo,pybug/transform/affine.py,b09fa29b65415811009a2874f79f75e43cae115a,STILL_EXISTS,scale_factor better be a numpy array then
aaedgahaj,menpo/menpo,tosort/base.py,6b85957b6a160182cc48b178df2960cc4368b0dc,STILL_EXISTS,TODO: make this more intelligent in locating the texture
aaedgahfd,menpo/menpo,pybug/io/base.py,ee8f06a3a7335223907e4bb0e45a27cde32a240e,70d907560b5c5c2aa75e760d7844d7e504115d05,TODO reimpliment smart import
aaedgahfe,menpo/menpo,pybug/io/base.py,ee8f06a3a7335223907e4bb0e45a27cde32a240e,70d907560b5c5c2aa75e760d7844d7e504115d05,TODO add ability to grab all files in folder
aaedgahff,menpo/menpo,pybug/io/metadata.py,ee8f06a3a7335223907e4bb0e45a27cde32a240e,STILL_EXISTS,TODO json landmark format
aaedgahfg,menpo/menpo,pybug/io/metadata.py,ee8f06a3a7335223907e4bb0e45a27cde32a240e,STILL_EXISTS,TODO Import landmarks (subclass of Importer)
aaedgaici,menpo/menpo,pybug/io/mesh/base.py,39e9d0304578168f42dc79388275b0ed7b23ac4f,b4150aceef063843e90e35200d51170249118bc9,Build expando object (dynamic object hack)
aaedgaidj,menpo/menpo,pybug/io/mesh/base.py,c49272b99dbe3cdf02591a0f170c1a6799fd56f6,b4150aceef063843e90e35200d51170249118bc9,Build expando object (dynamic object hack)
aaedgaief,menpo/menpo,pybug/io/mesh/base.py,025bf1a3d9e21af79e4f5a8662bbe30815873e66,b4150aceef063843e90e35200d51170249118bc9,Fortran ordering). First three columns are 3D coordinates
aaedgaieh,menpo/menpo,pybug/io/mesh/base.py,025bf1a3d9e21af79e4f5a8662bbe30815873e66,b4150aceef063843e90e35200d51170249118bc9,Build expando object (dynamic object hack)
aaedgaifc,menpo/menpo,pybug/io/mesh/base.py,025bf1a3d9e21af79e4f5a8662bbe30815873e66,b4150aceef063843e90e35200d51170249118bc9,Apparently the texture coordinates are upside down?
aaedgaifh,menpo/menpo,pybug/io/mesh/base.py,34286934e04dd6b909b77077b30e4d7b9f96f443,b4150aceef063843e90e35200d51170249118bc9,Currently these are unused; but they are in the format
aaedgaigb,menpo/menpo,pybug/io/mesh/base.py,69dd924e1d7cf8af672272f4ad1126c3b34dc20e,b4150aceef063843e90e35200d51170249118bc9,Currently these are unused; but they are in the format
aaedgaigh,menpo/menpo,pybug/io/mesh/base.py,69dd924e1d7cf8af672272f4ad1126c3b34dc20e,b4150aceef063843e90e35200d51170249118bc9,Build expando object (dynamic object hack)
aaedgaihd,menpo/menpo,pybug/io/mesh/base.py,69dd924e1d7cf8af672272f4ad1126c3b34dc20e,40a21abf7c3ebb6a906080560914af4ff0eea472,TODO: Although texture exist for this dataset; there is no texture
aaedgajbh,menpo/menpo,pybug/align/lucaskanade/similaritymeasure.py,35f213a95c470e44a866d18cae50b5ce18797228,STILL_EXISTS,TODO: do we need to copy the image?
aaedgajcc,menpo/menpo,pybug/align/lucaskanade/similaritymeasure.py,35f213a95c470e44a866d18cae50b5ce18797228,STILL_EXISTS,TODO: Should be other step described in paper
aaedgajga,menpo/menpo,pybug/convolution/__init__.py,35f213a95c470e44a866d18cae50b5ce18797228,STILL_EXISTS,TODO: merge the 2D and 3D versions if possible
aaedgajgi,menpo/menpo,pybug/convolution/__init__.py,35f213a95c470e44a866d18cae50b5ce18797228,STILL_EXISTS,TODO: Is adding the mean REALLY a good idea?
aaedgajhe,menpo/menpo,pybug/convolution/__init__.py,35f213a95c470e44a866d18cae50b5ce18797228,STILL_EXISTS,TODO: Do we need to flip S as in the 2D version?
aaedgajif,menpo/menpo,pybug/convolution/__init__.py,35f213a95c470e44a866d18cae50b5ce18797228,STILL_EXISTS,TODO: Why is this done??
aaedgbgjj,menpo/menpo,pybug/transform/base.py,a865b620b0eabd9641d2bfda8bd1beb16f38ea92,035b076865e650425a5bfc57ea201a61e69644ac,TODO this actually doesn't register as an abstract method
aaedgbhaa,menpo/menpo,pybug/image/base.py,56eee6a31534060e04b78678723d64441b8a45be,054a91e5e175c0f62aced2f43733cd357a00517a,TODO fill out the  from_flattened method for images
aaedgbhai,menpo/menpo,pybug/model/linear.py,054a91e5e175c0f62aced2f43733cd357a00517a,ab87c143f2cfedd713dbdd54417daadcfd93aef0,TODO Implement project_out on PCAModel
aaedgbhie,menpo/menpo,pybug/align/nonrigid/tps.py,dd5eb0c8b8d556c4b8e82c02d8cab4cc578377b2,STILL_EXISTS,TODO: This may end up being a method in class later on ...
aaedgbhji,menpo/menpo,pybug/transform/tps.py,dd5eb0c8b8d556c4b8e82c02d8cab4cc578377b2,39ce5fc9904bb9122ce8875cf65aa7d8861f626e,the names on my transfer; that would; perhaps; facilitate the
aaedgbidg,menpo/menpo,pybug/transform/statisticallydriven.py,a0a2850065cc6cdc8165c9603254c1c9524f7e7f,c9f90c0f6f72b8c77aa591f5a1664badb4f95632,TODO we need an example of a transform_constructor.
aaedgbidh,menpo/menpo,pybug/transform/statisticallydriven.py,a0a2850065cc6cdc8165c9603254c1c9524f7e7f,STILL_EXISTS,TODO the model needs to be able to generate it's jacobian.
aaedgbidi,menpo/menpo,pybug/transform/statisticallydriven.py,a0a2850065cc6cdc8165c9603254c1c9524f7e7f,STILL_EXISTS,TODO these need to be chained together property
aaedgbihj,menpo/menpo,pybug/align/lucaskanade/base.py,f85b343a0b47f9869ec9954593fdf806807e64e5,57fbfd837b175b95c30a02bcf9c2f4c86e5cfb7c,TODO remove this dependence on greyscale only (pixels[...; 0])
aaedgbiia,menpo/menpo,pybug/transform/statisticallydriven.py,f85b343a0b47f9869ec9954593fdf806807e64e5,STILL_EXISTS,TODO check if the jacobian of the warp is components transposed.
aaedgbiif,menpo/menpo,pybug/warp/base.py,f85b343a0b47f9869ec9954593fdf806807e64e5,d0520b557c68aa4090f08c757e58ad01043e414b,TODO why is this transposed?
aaedgbiig,menpo/menpo,pybug/align/lucaskanade/residual.py,e559cccae78d2fcce81d4d86c2914b562bf58f0d,b4909b9ea3f839f9234ff95bbbbe47d26ee6a260,TODO think this is a bug fix; should be tested
aaedgbjbc,menpo/menpo,pybug/warp/warp_test.py,d0520b557c68aa4090f08c757e58ad01043e414b,795df8e89dcaa01df03226b506742bc1853f10bb,TODO: What can we do about this?? The interpolation is totally different!
aaedgbjbd,menpo/menpo,pybug/warp/warp_test.py,d0520b557c68aa4090f08c757e58ad01043e414b,795df8e89dcaa01df03226b506742bc1853f10bb,TODO: Visually they look identical but numerically they are different.
aaedgbjeb,menpo/menpo,pybug/transform/statisticallydriven.py,57fbfd837b175b95c30a02bcf9c2f4c86e5cfb7c,STILL_EXISTS,TODO check if the jacobian of the warp is components transposed.
aaedgcaah,menpo/menpo,pybug/align/lucaskanade/residual.py,ad32aa2a5aff08f0f2cb3763d276d2e9a7d16d3e,STILL_EXISTS,Fix the derivatives - yx = xy
aaedgcafc,menpo/menpo,pybug/transform/piecewiseaffine.py,c534c37c8f377d9e1651777ddda27d7a0f376ced,STILL_EXISTS,todo this could be cached if tri_containment is being tested at
aaedgcafi,menpo/menpo,pybug/image/base.py,0ad3b4093b7e7446c629320985512eda25ebe637,STILL_EXISTS,TODO: can we do this mathematically and consistently ourselves?
aaedgcbaf,menpo/menpo,pybug/image/base.py,df493ef8fd5c99c7150d00e9b830fdd10bb093ba,STILL_EXISTS,TODO this kwarg could be False for higher perf True for debug
aaedgcbag,menpo/menpo,pybug/image/base.py,df493ef8fd5c99c7150d00e9b830fdd10bb093ba,STILL_EXISTS,TODO something is fishy about this method; kwarg seems to be making diff
aaedgcbah,menpo/menpo,pybug/image/base.py,df493ef8fd5c99c7150d00e9b830fdd10bb093ba,STILL_EXISTS,TODO make a unit test for gradient of masked images (inc_masked_pixels)
aaedgcbfe,menpo/menpo,pybug/io/landmark.py,99b814f87180c8c5ae111c78c5459b559de5452e,STILL_EXISTS,TODO: Scale properly!
aaedgcbhe,menpo/menpo,pybug/io/landmark.py,fa4de7db4be65a99f92c88134c8c55fb21719a51,STILL_EXISTS,TODO: Use connectivity and create a graph type instead of PointCloud
aaedgcbhi,menpo/menpo,pybug/landmark/labels.py,dbf506fad777cc8737501a94876df2559602fe90,2ffba0f0356271b4e3b7982bc57249571bc74fc4,TODO: This should probably be some sort of graph that maintains the
aaedgcbjd,menpo/menpo,pybug/visualize/viewmatplotlib.py,3592dc5e8e70a314df3ca397e537974a6e157cef,STILL_EXISTS,TODO: Should we enforce viewing landmarks with Matplotlib? How
aaedgcbjj,menpo/menpo,pybug/shape/mesh/base.py,e3a99198d5d5b3c628b77fed80b4b95927976ab7,eb1059edf4f2a5252b19a6f448aa128df4a48b44,TODO: This function is totally broken at the moment
aaedgccad,menpo/menpo,pybug/shape/mesh/base.py,e3a99198d5d5b3c628b77fed80b4b95927976ab7,eb1059edf4f2a5252b19a6f448aa128df4a48b44,handles landmark and metapoint translation (or will do; still TODO!)
aaedgccec,menpo/menpo,pybug/landmark/labels.py,5fc72af32b022c7926b35388d09c64e4d5631155,2ffba0f0356271b4e3b7982bc57249571bc74fc4,TODO: might have to rethink what's the v=bes way of implementing this
aaedgccei,menpo/menpo,pybug/landmark/labels.py,5fc72af32b022c7926b35388d09c64e4d5631155,STILL_EXISTS,TODO: ibug_68_all? imports points; contour and trimesh?
aaedgccej,menpo/menpo,pybug/model/linear.py,5fc72af32b022c7926b35388d09c64e4d5631155,e289232c0bab165cd53db709551973cd1a0b7408,TODO: remove 2d real data assumption
aaedgccga,menpo/menpo,pybug/model/linear.py,5fc72af32b022c7926b35388d09c64e4d5631155,e289232c0bab165cd53db709551973cd1a0b7408,TODO Implement project_out on SimilarityModel
aaedgccgb,menpo/menpo,pybug/transform/statisticallydriven.py,5fc72af32b022c7926b35388d09c64e4d5631155,e246d0db6cca510a7b036522e5d20698a4579489,TODO; need to
aaedgccgc,menpo/menpo,pybug/transform/statisticallydriven.py,5fc72af32b022c7926b35388d09c64e4d5631155,STILL_EXISTS,TODO: Rethink this transform so it knows how to deal with complex shapes
aaedgcchh,menpo/menpo,pybug/transform/statisticallydriven.py,5fc72af32b022c7926b35388d09c64e4d5631155,e246d0db6cca510a7b036522e5d20698a4579489,TODO: this may need rethinking.... we might want a from_source() method
aaedgcejd,menpo/menpo,pybug/transform/affine.py,35d54ec91fbdcb3e66bfe2a7682c75043689d521,09bd45110417e99bbb79a6d6fdd8a017765373f9,TODO: Implement
aaedgcfaf,menpo/menpo,pybug/transform/affine.py,1a38a9790ed1647a7af2eb24bd142caeafa5bcae,STILL_EXISTS,TODO: implement 3D Jacobian
aaedgcfda,menpo/menpo,pybug/model/base.py,0bf02b7da9ac9889122e9644c698e6ffa41d4af1,67279c6029933afef696ece947157042f47fb06e,TODO: this class seems a bit bare? Needs to be documented better.
aaedgcfdb,menpo/menpo,pybug/model/linear.py,0bf02b7da9ac9889122e9644c698e6ffa41d4af1,e289232c0bab165cd53db709551973cd1a0b7408,TODO: better document what a linear model does.
aaedgcfdc,menpo/menpo,pybug/model/linear.py,0bf02b7da9ac9889122e9644c698e6ffa41d4af1,e289232c0bab165cd53db709551973cd1a0b7408,TODO: give a description of what it means to be a PCA model
aaedgcfdd,menpo/menpo,pybug/shape/pointcloud.py,c2db8286e3f1369e5a6aa5417c08cd00ead2ed06,6fe13eac971d8726f8c5f9755ec57995124ebd33,TODO: sort of pointfields?
aaedgcfhd,menpo/menpo,pybug/transform/tps.py,c058c0fb23414aa4795ff97e3d4449aef49f9eb2,1c5728c654b6a153eca2ed7b1a9365790c2d7454,TODO: this is needed for composition
aaedgcfia,menpo/menpo,pybug/align/lucaskanade/base.py,e246d0db6cca510a7b036522e5d20698a4579489,ebab87bdac3cd2a5d5cd68fbb7577d1fe5852540,TODO: rename kwarg \"forward\" to \"forward_additive\"
aaedgcgig,menpo/menpo,pybug/align/lucaskanade/base.py,4e04db3b0b1d3f55dc605f6cdd162888c46c40e8,eee183d94847899e822cf269de6a348e25282a5d,TODO: define a consistent multi-resolution logic
aaedgcgja,menpo/menpo,pybug/align/lucaskanade/base.py,4e04db3b0b1d3f55dc605f6cdd162888c46c40e8,eee183d94847899e822cf269de6a348e25282a5d,TODO: add \"forward_compositional\" kwarg with options
aaedgchag,menpo/menpo,pybug/transform/affine.py,a59c9c5fb86bea70db89ac3b952cfc810b3bc7a8,STILL_EXISTS,TODO: Implement 3D rotation vectorisation
aaedgchcj,menpo/menpo,pybug/transform/statisticallydriven.py,efff78fac4daca047dfe543ddd36929c4a86c597,9ff23a269e48c509aab89d0da1a3441c3fe0683c,TODO: Could be implemented as optimization option in LK???
aaedgchdd,menpo/menpo,pybug/transform/statisticallydriven.py,efff78fac4daca047dfe543ddd36929c4a86c597,STILL_EXISTS,TODO: The call to transform.apply will not work properly for PWA
aaedgchdh,menpo/menpo,pybug/transform/statisticallydriven.py,efff78fac4daca047dfe543ddd36929c4a86c597,9ff23a269e48c509aab89d0da1a3441c3fe0683c,- For PWA it should implement Bakers algorithmic approach to
aaedgchif,menpo/menpo,pybug/transform/tps.py,61b8585c3ea32f449ddff55889e4c6c496d3c84f,STILL_EXISTS,TODO: this is needed for composition
aaedgcicg,menpo/menpo,pybug/transform/statisticallydriven.py,f35f18471a339dd7733cafb726bd63de6583f160,ab87c143f2cfedd713dbdd54417daadcfd93aef0,TODO: Can we do this without splitting across the two dimensions?
aaedgcigb,menpo/menpo,pybug/transform/affine.py,03e9e3a7cf6dd6c46906ca4dd453cc040cb8c05e,STILL_EXISTS,TODO: Implement 3D rotation vectorisation
aaedgciij,menpo/menpo,pybug/model/linear.py,1e4d7655b883d6d25c4432d4cc9a6b390ea459bf,ab87c143f2cfedd713dbdd54417daadcfd93aef0,TODO: better document what a linear model does.
aaedgcija,menpo/menpo,pybug/model/linear.py,1e4d7655b883d6d25c4432d4cc9a6b390ea459bf,STILL_EXISTS,TODO: remove 2d real data assumption
aaedgcjab,menpo/menpo,pybug/model/linear.py,1e4d7655b883d6d25c4432d4cc9a6b390ea459bf,ab87c143f2cfedd713dbdd54417daadcfd93aef0,TODO Implement project_out on SimilarityModel
aaedgcjac,menpo/menpo,pybug/model/linear.py,1e4d7655b883d6d25c4432d4cc9a6b390ea459bf,ab87c143f2cfedd713dbdd54417daadcfd93aef0,TODO: give a description of what it means to be a PCA model
aaedgcjdc,menpo/menpo,pybug/align/lucaskanade/base.py,0f6717f4c3751978b13a74810a4cf427832c8d87,STILL_EXISTS,TODO: define a consistent multi-resolution logic
aaedgcjdg,menpo/menpo,pybug/align/lucaskanade/base.py,0f6717f4c3751978b13a74810a4cf427832c8d87,86f34934e3101da59f8bd2f1da6c49abc9d97ae6,TODO: add \"forward_compositional\" kwarg with options
aaedgcjfh,menpo/menpo,pybug/transform/statisticallydriven.py,783d76369715c5a3f864262383ba2ddfac4f2a33,9ff23a269e48c509aab89d0da1a3441c3fe0683c,TODO: Could be implemented as optimization option in LK???
aaedgcjgb,menpo/menpo,pybug/transform/statisticallydriven.py,783d76369715c5a3f864262383ba2ddfac4f2a33,STILL_EXISTS,TODO: The call to transform.apply will not work properly for PWA
aaedgcjgf,menpo/menpo,pybug/transform/statisticallydriven.py,783d76369715c5a3f864262383ba2ddfac4f2a33,9ff23a269e48c509aab89d0da1a3441c3fe0683c,- For PWA it should implement Bakers algorithmic approach to
aaedgcjjf,menpo/menpo,pybug/transform/statisticallydriven.py,3d8387037fb385ff3a95c3d672ce1bddeebfe2f1,ab87c143f2cfedd713dbdd54417daadcfd93aef0,TODO: Can we do this without splitting across the two dimensions?
aaedgcjjg,menpo/menpo,pybug/transform/statisticallydriven.py,0ee74513f02705d0ebd9879a8f335a2dfe68e38d,ab87c143f2cfedd713dbdd54417daadcfd93aef0,TODO: document me
aaedgdabc,menpo/menpo,pybug/align/lucaskanade/base.py,ab87c143f2cfedd713dbdd54417daadcfd93aef0,86f34934e3101da59f8bd2f1da6c49abc9d97ae6,TODO: add \"forward_compositional\" kwarg with options
aaedgdbcd,menpo/menpo,pybug/model/linear.py,ab87c143f2cfedd713dbdd54417daadcfd93aef0,STILL_EXISTS,TODO:
aaedgdbeh,menpo/menpo,pybug/align/lucaskanade/base.py,f22725a9463e844bb4ec09eda7229139605fce42,ebab87bdac3cd2a5d5cd68fbb7577d1fe5852540,TODO: rename kwarg \"forward\" to \"forward_additive\"
aaedgdcad,menpo/menpo,pybug/align/lucaskanade/base.py,f22725a9463e844bb4ec09eda7229139605fce42,86f34934e3101da59f8bd2f1da6c49abc9d97ae6,TODO: add \"forward_compositional\" kwarg with options
aaedgdcjh,menpo/menpo,pybug/model/linear.py,86f34934e3101da59f8bd2f1da6c49abc9d97ae6,67279c6029933afef696ece947157042f47fb06e,TODO: better document what a linear model does.
aaedgdcji,menpo/menpo,pybug/model/linear.py,86f34934e3101da59f8bd2f1da6c49abc9d97ae6,0677723c80e529a75bf1268fdfb1ede21da99c29,TODO: give a description of what it means to be a PCA model
aaedgdcjj,menpo/menpo,pybug/transform/affine.py,86f34934e3101da59f8bd2f1da6c49abc9d97ae6,STILL_EXISTS,TODO: Implement 3D rotation vectorisation
aaedgddac,menpo/menpo,pybug/transform/statisticallydriven.py,86f34934e3101da59f8bd2f1da6c49abc9d97ae6,9ff23a269e48c509aab89d0da1a3441c3fe0683c,TODO: document me
aaedgddad,menpo/menpo,pybug/transform/statisticallydriven.py,86f34934e3101da59f8bd2f1da6c49abc9d97ae6,STILL_EXISTS,TODO: Can we do this without splitting across the two dimensions?
aaedgddfe,menpo/menpo,pybug/align/lucaskanade/base.py,10e990668acb5821432e82ba46484f1b5a9602b6,39ce5fc9904bb9122ce8875cf65aa7d8861f626e,TODO: add \"forward_compositional\" kwarg with options
aaedgdehe,menpo/menpo,pybug/visualize/viewmayavi.py,eb1059edf4f2a5252b19a6f448aa128df4a48b44,STILL_EXISTS,TODO: This is due to a bug in mayavi that won't allow rendering text to an empty figure
aaedgdggb,menpo/menpo,pybug/align/lucaskanade/image/base.py,976b0cccb40f14ce1b566d0664eea2a0c98a3e09,STILL_EXISTS,TODO: rename kwarg \"forward\" to \"forward_additive\"
aaedgdghc,menpo/menpo,pybug/align/lucaskanade/image/base.py,976b0cccb40f14ce1b566d0664eea2a0c98a3e09,STILL_EXISTS,TODO: add \"forward_compositional\" kwarg with options
aaedgdjdd,menpo/menpo,pybug/align/lucaskanade/base.py,a91f9357c5e6613b4e3ce22a6c04017a7a61506b,df7bb2bb77b3bf0f9de315e8dc8d2010e2b709fe,TODO: rename kwarg \"forward\" to \"forward_additive\"
aaedgdjee,menpo/menpo,pybug/align/lucaskanade/base.py,a91f9357c5e6613b4e3ce22a6c04017a7a61506b,df7bb2bb77b3bf0f9de315e8dc8d2010e2b709fe,TODO: add \"forward_compositional\" kwarg with options
aaedgeaaf,menpo/menpo,pybug/model/linear.py,a91f9357c5e6613b4e3ce22a6c04017a7a61506b,c61ceca129537217f6f25e6cba856d77b9694f51,TODO Implement project_out on SimilarityModel
aaedgeaag,menpo/menpo,pybug/model/linear.py,a91f9357c5e6613b4e3ce22a6c04017a7a61506b,STILL_EXISTS,TODO Implement project_out on PCAModel
aaedgeaah,menpo/menpo,pybug/model/linear.py,a91f9357c5e6613b4e3ce22a6c04017a7a61506b,STILL_EXISTS,TODO: document me
aaedgeabf,menpo/menpo,pybug/transform/tps.py,a91f9357c5e6613b4e3ce22a6c04017a7a61506b,c61ceca129537217f6f25e6cba856d77b9694f51,the names on my transfer; that would; perhaps; facilitate the
aaedgefaj,menpo/menpo,pybug/model/linear.py,c61ceca129537217f6f25e6cba856d77b9694f51,STILL_EXISTS,TODO: document me
aaedgefcb,menpo/menpo,pybug/model/linear.py,c61ceca129537217f6f25e6cba856d77b9694f51,STILL_EXISTS,TODO Implement project_out on PCAModel
aaedgfiee,menpo/menpo,pybug/transform/piecewiseaffine.py,2d90f9d3a4497234232a497909c15126dbd290f3,STILL_EXISTS,todo this could be cached if tri_containment is being tested at
aaedgfjaj,menpo/menpo,pybug/shape/mesh/base.py,2f02b2685fc567f689052346f878ee04de7dc448,d24d204271f80733c1d56bc24f42fa953d5b216a,TODO Delaunay triangulate if no trilist added
aaedgfjeh,menpo/menpo,pybug/io/depth_image.py,b4150aceef063843e90e35200d51170249118bc9,STILL_EXISTS,Currently these are unused; but they are in the format
aaedgfjfb,menpo/menpo,pybug/io/depth_image.py,b4150aceef063843e90e35200d51170249118bc9,STILL_EXISTS,Fortran ordering). First three columns are 3D coordinates
aaedgfjgb,menpo/menpo,pybug/io/depth_image.py,b4150aceef063843e90e35200d51170249118bc9,STILL_EXISTS,Apparently the texture coordinates are upside down?
aaedgfjie,menpo/menpo,pybug/io/mesh/base.py,1b4ce97dc213a3fc9311eb999347750db7f88b3f,a0ce23a76691b09af936cbbc6f7e15061a6e46f4,Fortran ordering). First three columns are 3D coordinates
aaedgfjig,menpo/menpo,pybug/io/mesh/base.py,1b4ce97dc213a3fc9311eb999347750db7f88b3f,a0ce23a76691b09af936cbbc6f7e15061a6e46f4,Build expando object (dynamic object hack)
aaedgfjjb,menpo/menpo,pybug/io/mesh/base.py,1b4ce97dc213a3fc9311eb999347750db7f88b3f,a0ce23a76691b09af936cbbc6f7e15061a6e46f4,Apparently the texture coordinates are upside down?
aaedgfjji,menpo/menpo,pybug/io/mesh/base.py,1b4ce97dc213a3fc9311eb999347750db7f88b3f,a0ce23a76691b09af936cbbc6f7e15061a6e46f4,Currently these are unused; but they are in the format
aaedggace,menpo/menpo,pybug/image/base.py,a0ce23a76691b09af936cbbc6f7e15061a6e46f4,STILL_EXISTS,TODO: This doesn't work at the moment; texture comes out
aaedggafg,menpo/menpo,pybug/io/depth_image.py,40a21abf7c3ebb6a906080560914af4ff0eea472,STILL_EXISTS,Currently these are unused; but they are in the format
aaedggaie,menpo/menpo,pybug/transform/piecewiseaffine.py,f8c7bbdc6d6c8b349f65e89bf11ae26cef93ad8c,dd336e81e6301fa70d46e8f949a83c4e9efa38eb,todo - implement alpha beta for the C fast pwa
aaedggaif,menpo/menpo,pybug/transform/piecewiseaffine.py,f8c7bbdc6d6c8b349f65e89bf11ae26cef93ad8c,dd336e81e6301fa70d46e8f949a83c4e9efa38eb,this is not needed for the apply method
aaedggajg,menpo/menpo,pybug/transform/piecewiseaffine.py,160ffc64cb186c5c045fdd4a2c1b345f3d5620b9,STILL_EXISTS,reshape is needed to make it broadcastable with the other indexing
aaedggcie,menpo/menpo,pybug/image/base.py,001d3eec53c9b53a3ccedbc2c30db07f65b95cdf,93b60ee2a1d852ad98fd06624461dd3357c39869,TODO consider unifying this somewhere
aaedggdbi,menpo/menpo,pybug/io/mesh/base.py,2f4a1f05582d2cd085408270f529cb4054d923e8,16405f7835dbf282b53afe990697c07843ca186f,Fortran ordering). First three columns are 3D coordinates
aaedggdcf,menpo/menpo,pybug/io/mesh/base.py,2f4a1f05582d2cd085408270f529cb4054d923e8,16405f7835dbf282b53afe990697c07843ca186f,Apparently the texture coordinates are upside down?
aaedggdee,menpo/menpo,pybug/io/mesh/base.py,2f4a1f05582d2cd085408270f529cb4054d923e8,16405f7835dbf282b53afe990697c07843ca186f,TODO: Although texture exist for this dataset; there is no texture
aaedggdfe,menpo/menpo,pybug/image/base.py,27438d858a02f1189bc1106627eeddc3cd7974a9,STILL_EXISTS,TODO insert the luminosity algorithm in here.
aaedggdff,menpo/menpo,pybug/image/base.py,27438d858a02f1189bc1106627eeddc3cd7974a9,b89b22161e6f8c596223309b21ac70f3e311faea,TODO is this is a safe copy of the landmark dict?
aaedggecb,menpo/menpo,pybug/image/base.py,20e045f16e624f398addc5aa80da705ae633de57,0cbb0df21fc49cde1938cb9da02de90b7f10f00c,TODO consider unifying this somewhere
aaedggejd,menpo/menpo,pybug/image/spatial.py,b89b22161e6f8c596223309b21ac70f3e311faea,STILL_EXISTS,move origin to top left
aaedggfab,menpo/menpo,pybug/image/standard.py,b89b22161e6f8c596223309b21ac70f3e311faea,STILL_EXISTS,TODO is this is a safe copy of the landmark dict?
aaedggfah,menpo/menpo,pybug/image/base.py,722e2f185a5914544b52368c343babed4baf2d89,35233f10ef45be0684a27af72ae1f00ae81ba5ce,TODO choose landmarks by def
aaedggfaj,menpo/menpo,pybug/image/masked.py,722e2f185a5914544b52368c343babed4baf2d89,35233f10ef45be0684a27af72ae1f00ae81ba5ce,TODO Landmarks should be copied over here
aaedggfie,menpo/menpo,pybug/align/lucaskanade/residual.py,2b863bd32bebe86bb3805a7a5bd265869c8541f2,ae040937b87984c22e565743eef19aead685d23e,ToDo: Note that; fft_sdi is rectangular; i.e. is not define in
aaedgggbe,menpo/menpo,pybug/align/lucaskanade/residual.py,2b863bd32bebe86bb3805a7a5bd265869c8541f2,ae040937b87984c22e565743eef19aead685d23e,ToDo: is this supposed to be per channel normalization?
aaedgghdf,menpo/menpo,pybug/align/lucaskanade/residual.py,ae040937b87984c22e565743eef19aead685d23e,STILL_EXISTS,Fix the derivatives - yx = xy
aaedggijb,menpo/menpo,pybug/align/lucaskanade/residual.py,2cce2706450713dffce2182dcf131df7c7935ea1,STILL_EXISTS,ToDo: Note that; fft_sdi is rectangular; i.e. is not define in
aaedggjcb,menpo/menpo,pybug/align/lucaskanade/residual.py,2cce2706450713dffce2182dcf131df7c7935ea1,STILL_EXISTS,ToDo: is this supposed to be per channel normalization?
aaedghcdh,menpo/menpo,pybug/transform/tps.py,beb7a4f62c64f53e2cd49bf63ec889fd6940bdec,0397051a0d677681d33b4bce7638b570e9bad940,TODO this is hardcoded and should be set based on kernel
aaedghcgc,menpo/menpo,pybug/landmark/labels.py,2ffba0f0356271b4e3b7982bc57249571bc74fc4,2ffba0f0356271b4e3b7982bc57249571bc74fc4,TODO: This should probably be some sort of graph that maintains the
aaedghfhi,menpo/menpo,pybug/lucaskanade/base.py,1da190d97fb2530b7c75e0ce208a2d728e2f9a04,6d52b25857390f17c6b50a6d6783a7518af5d402,TODO: define a consistent multi-resolution logic
aaedghfjd,menpo/menpo,pybug/lucaskanade/image/base.py,1da190d97fb2530b7c75e0ce208a2d728e2f9a04,STILL_EXISTS,TODO: rename kwarg \"forward\" to \"forward_additive\"
aaedghgae,menpo/menpo,pybug/lucaskanade/image/base.py,1da190d97fb2530b7c75e0ce208a2d728e2f9a04,STILL_EXISTS,TODO: add \"forward_compositional\" kwarg with options
aaedghgbh,menpo/menpo,pybug/lucaskanade/image/base.py,1da190d97fb2530b7c75e0ce208a2d728e2f9a04,STILL_EXISTS,TODO: Pre-compute the inverse
aaedghgfh,menpo/menpo,pybug/lucaskanade/residual.py,1da190d97fb2530b7c75e0ce208a2d728e2f9a04,STILL_EXISTS,ToDo: Note that; fft_sdi is rectangular; i.e. is not define in
aaedghgja,menpo/menpo,pybug/lucaskanade/residual.py,1da190d97fb2530b7c75e0ce208a2d728e2f9a04,STILL_EXISTS,TODO: do we need to copy the image?
aaedghgjb,menpo/menpo,pybug/lucaskanade/residual.py,1da190d97fb2530b7c75e0ce208a2d728e2f9a04,STILL_EXISTS,ToDo: is this supposed to be per channel normalization?
aaedghhba,menpo/menpo,pybug/lucaskanade/residual.py,1da190d97fb2530b7c75e0ce208a2d728e2f9a04,STILL_EXISTS,TODO: Should be other step described in paper
aaedgiabi,menpo/menpo,pybug/lucaskanade/image/base.py,d8f0282b3acdf9650c2cd086534475ab28070ba0,STILL_EXISTS,TODO: Pre-compute the inverse
aaedgieca,menpo/menpo,pybug/transform/piecewiseaffine.py,e2c72f59ac14172049eb12fef89cd29dceae7e8a,STILL_EXISTS,TODO View is broken for PWA (TriangleContainmentError)
aaedgieff,menpo/menpo,pybug/transform/affine.py,88e16ab9b135f0c056deab3318613572372b1863,82e89dd4b0e283a0f7a203358ecb922bda036ec9,TODO remove matlab here
aaedgiegf,menpo/menpo,pybug/transform/affine.py,88e16ab9b135f0c056deab3318613572372b1863,STILL_EXISTS,TODO scale per dim should be used.
aaedgiehc,menpo/menpo,pybug/transform/affine.py,6963b3cee2f6239963384670aa8e30fe286c82b9,STILL_EXISTS,already have a matrix set! The update better be the same size
aaedgiehd,menpo/menpo,pybug/transform/affine.py,6963b3cee2f6239963384670aa8e30fe286c82b9,STILL_EXISTS,TODO add a check here that the matrix is valid
aaedgiejj,menpo/menpo,pybug/transform/affine.py,4510b1db4945ea1ecc098bec762cfc8d65a5c7ad,STILL_EXISTS,TODO scale per dim should be used.
aaedgiffa,menpo/menpo,pybug/transform/tps.py,b39f5b7b44fcc7f84e89a36b27aecdddc51ccf0f,STILL_EXISTS,TODO: this is needed for composition
aaedgiffd,menpo/menpo,pybug/transform/statisticallydriven.py,0d199dcc5a34f1cb81b33ee10454218b7c52d93e,STILL_EXISTS,TODO check this is correct
aaedgiffi,menpo/menpo,pybug/transform/tps.py,0d199dcc5a34f1cb81b33ee10454218b7c52d93e,STILL_EXISTS,TODO: this is needed for composition
aaedgigeh,menpo/menpo,pybug/transform/statisticallydriven.py,0c463e6430f80818b56b47e2d9ce6085c05e6a22,9ff23a269e48c509aab89d0da1a3441c3fe0683c,TODO: document me
aaedgigfc,menpo/menpo,pybug/transform/statisticallydriven.py,0c463e6430f80818b56b47e2d9ce6085c05e6a22,9ff23a269e48c509aab89d0da1a3441c3fe0683c,TODO: Could be implemented as optimization option in LK???
aaedgigfi,menpo/menpo,pybug/transform/statisticallydriven.py,0c463e6430f80818b56b47e2d9ce6085c05e6a22,STILL_EXISTS,TODO: The call to transform.apply will not work properly for PWA
aaedgiggc,menpo/menpo,pybug/transform/statisticallydriven.py,0c463e6430f80818b56b47e2d9ce6085c05e6a22,9ff23a269e48c509aab89d0da1a3441c3fe0683c,- For PWA it should implement Bakers algorithmic approach to
aaedgighd,menpo/menpo,pybug/transform/statisticallydriven.py,0c463e6430f80818b56b47e2d9ce6085c05e6a22,STILL_EXISTS,TODO: Can we do this without splitting across the two dimensions?
aaedgighi,menpo/menpo,pybug/transform/statisticallydriven.py,0c463e6430f80818b56b47e2d9ce6085c05e6a22,STILL_EXISTS,TODO: Rethink this transform so it knows how to deal with complex shapes
aaedgigjg,menpo/menpo,pybug/transform/statisticallydriven.py,9ff23a269e48c509aab89d0da1a3441c3fe0683c,STILL_EXISTS,TODO investigate the impact of this; could be problematic
aaedgihca,menpo/menpo,pybug/image/masked.py,b079ed411f14804fec0aaf9b836d28a7f1b42df0,STILL_EXISTS,TODO maybe we should be stricter about the trilist here; feels flakey
aaedgjbjd,menpo/menpo,pybug/image/masked.py,f7d5f757ae065f1d058872b99d831b12ff53ed4a,bba261aafd1f72028a7ea6d49899f35331cb4cc3,ToDo: Per channel normalization
aaedgjbje,menpo/menpo,pybug/model/linear.py,4b171b01d565a52675cb026443612f89621d90ab,0677723c80e529a75bf1268fdfb1ede21da99c29,TODO: give a description of what it means to be a Similarity Model
aaedgjbjf,menpo/menpo,pybug/model/linear.py,4b171b01d565a52675cb026443612f89621d90ab,0677723c80e529a75bf1268fdfb1ede21da99c29,TODO: note this is 2D only
aaedgjbjg,menpo/menpo,pybug/transform/modeldriven.py,4b171b01d565a52675cb026443612f89621d90ab,STILL_EXISTS,TODO global_transform shouldn't be optional; need to refac as
aaedgjcca,menpo/menpo,pybug/model/instancebacked.py,0677723c80e529a75bf1268fdfb1ede21da99c29,bf91e82068b24394d67b13f5ab3cd52000f7f510,TODO think about if non-InstanceLM's should have a Jacobian
aaedgjccf,menpo/menpo,pybug/model/linear.py,0677723c80e529a75bf1268fdfb1ede21da99c29,91155da90de603b41f1f831e8f2042bbac7a6ce5,TODO check this is right
aaedgjccg,menpo/menpo,pybug/model/linear.py,0677723c80e529a75bf1268fdfb1ede21da99c29,91155da90de603b41f1f831e8f2042bbac7a6ce5,TODO ask Joan
aaedgjcch,menpo/menpo,pybug/model/linear.py,0677723c80e529a75bf1268fdfb1ede21da99c29,91155da90de603b41f1f831e8f2042bbac7a6ce5,TODO document and check
aaedhadih,menpo/menpo,pybug/model/linear.py,91155da90de603b41f1f831e8f2042bbac7a6ce5,200c07883e9e4e034e3c2ce8c492010602ef19bc,TODO: better document what a linear model does.
aaedhadii,menpo/menpo,pybug/model/linear.py,91155da90de603b41f1f831e8f2042bbac7a6ce5,200c07883e9e4e034e3c2ce8c492010602ef19bc,TODO: give a description of what it means to be a PCA model
aaedhafej,menpo/menpo,pybug/transform/modeldriven.py,91155da90de603b41f1f831e8f2042bbac7a6ce5,STILL_EXISTS,TODO global_transform shouldn't be optional; need to refac as
aaedhagcb,menpo/menpo,pybug/model/linear.py,200c07883e9e4e034e3c2ce8c492010602ef19bc,STILL_EXISTS,TODO check this is right
aaedhagcc,menpo/menpo,pybug/model/linear.py,200c07883e9e4e034e3c2ce8c492010602ef19bc,0614a6d9b8ca9d8068b5d64c6638003ec36ba6da,TODO ask Joan
aaedhagcd,menpo/menpo,pybug/model/linear.py,200c07883e9e4e034e3c2ce8c492010602ef19bc,STILL_EXISTS,TODO document and check
aaedhagef,menpo/menpo,pybug/image/masked.py,2c1ce1700180d1311e28e2919493d14c5357e26b,8be0521eb271e79d87b3569f2d7b485dcb2b7698,TODO an optimisation could be added here for the case where mask
aaedhaghj,menpo/menpo,pybug/transform/tps.py,a691609d4a535e3a1c2f30c942670fe2e59f4624,STILL_EXISTS,Fix log(0)
aaedhagib,menpo/menpo,pybug/transform/test/tps_test.py,d26fcecb4e1d4495971956f3621a9a682ef0842b,STILL_EXISTS,TODO: test the kernel
aaedhahfd,menpo/menpo,pybug/transform/modeldriven.py,04014837a8c41960c958890843017cb29370cc04,117e371227f91eac5d7375ca9ccc7332f1a93009,TODO: Could be implemented as optimization option in LK???
aaedhahig,menpo/menpo,pybug/image/masked.py,2bc88d90e94f375dd25af494af5b71cf83b6a46c,STILL_EXISTS,TODO an optimisation could be added here for the case where mask
aaedhdcia,menpo/menpo,pybug/activeappearancemodel/builder.py,c50ccb7df253257fba92a983a7aa913d283a8aa9,STILL_EXISTS,TODO: Should this be a method in MaskedNDImage?
aaedhdcid,menpo/menpo,pybug/activeappearancemodel/builder.py,c50ccb7df253257fba92a983a7aa913d283a8aa9,STILL_EXISTS,TODO: Bug!!!
aaedhdcii,menpo/menpo,pybug/activeappearancemodel/builder.py,c50ccb7df253257fba92a983a7aa913d283a8aa9,STILL_EXISTS,TODO: Should this be a method on SimilarityTransform?
aaedhddfa,menpo/menpo,pybug/image/base.py,88a3efcef639a88e9b91f65b0da93d080dc252dd,df8750e87a98cf50344f9b9724969066b5412cdf,TODO: This if should disappear once the image package is refactored
aaedhddfc,menpo/menpo,pybug/image/boolean.py,88a3efcef639a88e9b91f65b0da93d080dc252dd,df8750e87a98cf50344f9b9724969066b5412cdf,TODO: Revise this; there might be better options
aaedhdeaa,menpo/menpo,pybug/activeappearancemodel/functions.py,3f5518a07c91255c3a4a6a69dbfc69e4be3af2bf,STILL_EXISTS,TODO: revise kwarg trilist in method constrain_mask_to_landmarks;
aaedhdeab,menpo/menpo,pybug/activeappearancemodel/functions.py,3f5518a07c91255c3a4a6a69dbfc69e4be3af2bf,STILL_EXISTS,perhaps the trilist should be directly obtained from the group landmarks
aaedhdeae,menpo/menpo,pybug/activeappearancemodel/functions.py,3f5518a07c91255c3a4a6a69dbfc69e4be3af2bf,STILL_EXISTS,TODO: These should disappear with the new image refactoring
aaedhdeca,menpo/menpo,pybug/image/base.py,fd187a1430f9fc98affeaba04c21b32a8281f781,STILL_EXISTS,TODO: This if should disappear once the image package is refactored
aaedhdece,menpo/menpo,pybug/activeappearancemodel/base.py,2a704a829ab68fa863de684319017e1c77496263,STILL_EXISTS,ToDo: Do we need a blank (identity) method for Transforms?
aaedhdecf,menpo/menpo,pybug/activeappearancemodel/functions.py,2a704a829ab68fa863de684319017e1c77496263,STILL_EXISTS,TODO: Should this be a method on SimilarityTransform? AlignableTransforms?
aaedhdecg,menpo/menpo,pybug/image/base.py,b627e35693f97e4a6b2da0da23de7061629f4ec4,1f1d4bb29c648dc05f05597d0d2d077f3d288d05,TODO: This should disappear once the image package is refactored
aaedhdede,menpo/menpo,setup.py,365aef3bde23008035290e9e4fecdf4b87526e71,9f5ea4ed9ce77d3d26fe09e86acbb5284231f66d,TODO why does it compile without these on OS X?!
aaedhdegc,menpo/menpo,setup.py,82208d8c1148dd20df3146b57dd2e8b89222dbd1,0725aa03f595f6d857fa9d59464a419a2845e7f0,TODO why does it compile without these on OS X?!
aaedhdejd,menpo/menpo,setup.py,857c90e1f11320a16e3282dda936a44cb6e03740,e4c2b825440150c4ba91e51bb7836189e04983d0,TODO why does it compile without these on OS X?!
aaedheaig,menpo/menpo,pybug/image/feature.py,af893885e0e7c0d9466e62943f09c74a198731b6,STILL_EXISTS,fix mask
aaedhecfd,menpo/menpo,pybug/image/feature.py,108c3b69180e0fe3ee6c213230c6959698d3152f,STILL_EXISTS,fix mask
aaedhecff,menpo/menpo,pybug/image/feature.py,108c3b69180e0fe3ee6c213230c6959698d3152f,STILL_EXISTS,fix landmarks
aaedhegfi,menpo/menpo,setup.py,2dc01e7b00fa6b0fe5943565e9d1987f320082e3,033183f333f8a2844a2066542e8165f585cf7ce3,TODO why does it compile without these on OS X?!
aaedhehhg,menpo/menpo,setup.py,25133a7c20b4513871047b535fce9cd06621bc61,649a622b03ef6621f2d18377dd4f562ef90b8017,TODO why does it compile without these on OS X?!
aaedhehia,menpo/menpo,pybug/io/mesh/base.py,9dfe1ebdfc7dbbe36b241d67efd337a986436d73,STILL_EXISTS,TODO: Disconnect with AssimpImporter
aaedhejge,menpo/menpo,pybug/rasterize/opengl.py,cce603a72572fb3711d70c29f5930c8a9230f1a2,STILL_EXISTS,TODO: This should use a different shader!
aaedhejjf,menpo/menpo,pybug/aam/fitter.py,52a6c3f0733c26b5d9a51ee3c0a8849de75ab18b,STILL_EXISTS,ToDo: Do we need a blank (identity) method for Transforms?
aaedhfadh,menpo/menpo,pybug/io/base.py,320244a49f90a266dd7121e5f1ef51e1bd872dc0,STILL_EXISTS,TODO handle landmark_resolver
aaedhfaeg,menpo/menpo,pybug/io/landmark.py,320244a49f90a266dd7121e5f1ef51e1bd872dc0,63daec8352471e4d014695dc3e9b79dc8d601e7b,fix that here
aaedhfafh,menpo/menpo,pybug/io/base.py,924da2e709d9406fa41657e2944ddbd3af6caf6a,STILL_EXISTS,TODO handle landmark_resolver
aaedhfbad,menpo/menpo,setup.py,9e87dd738608a01b14be095ca1fbca5b44a8fb53,c718454a3ec48560b6f38724a67be4f75a0539b2,Need to decide if this is really needed
aaedhfbbf,menpo/menpo,pybug/io/landmark.py,c862b4da361432ed22d20e248e4e72337c3ebdb7,STILL_EXISTS,fix that here
aaedhfdja,menpo/menpo,menpo/transform/affine.py,bdf63a8b763dd5ea94869610d9707f320dd4dfee,STILL_EXISTS,TODO this shouldn't be possible with composes_with
aaedhffcc,menpo/menpo,menpo/io/mesh/base.py,6a16e4f68d7c12d3d5ef218b6574b277a717922a,ca702fc814a1ad50b27c44c6544ba364d3aa7e31,TODO: Disconnect with AssimpImporter
aaedhfgdj,menpo/menpo,menpo/transform/affine.py,bd4f846a910f57c4170dad76a07e2a1d485c0f18,STILL_EXISTS,TODO calling super setter correctly
aaedhfgeb,menpo/menpo,menpo/transform/affine.py,bd4f846a910f57c4170dad76a07e2a1d485c0f18,e8bedc630e19354601074a58829aa0415992de75,TODO is this correct?
aaedhfgfc,menpo/menpo,menpo/transform/homogeneous/affine.py,96ea5bfc92a5f1adfa02539e13697f272cb3daa6,d52af44406e90cc69bab7d11e79e8ee322b2855a,TODO maybe this should change
aaedhfgff,menpo/menpo,menpo/transform/homogeneous/affine.py,96ea5bfc92a5f1adfa02539e13697f272cb3daa6,STILL_EXISTS,already have a matrix set! The update better be the same size
aaedhfgfg,menpo/menpo,menpo/transform/homogeneous/affine.py,96ea5bfc92a5f1adfa02539e13697f272cb3daa6,d52af44406e90cc69bab7d11e79e8ee322b2855a,TODO add a check here that the matrix is actually valid
aaedhfgha,menpo/menpo,menpo/transform/homogeneous/affine.py,96ea5bfc92a5f1adfa02539e13697f272cb3daa6,d52af44406e90cc69bab7d11e79e8ee322b2855a,TODO calling super setter correctly
aaedhfgjf,menpo/menpo,menpo/transform/homogeneous/rotation.py,96ea5bfc92a5f1adfa02539e13697f272cb3daa6,STILL_EXISTS,TODO build rotations about axis; euler angles etc
aaedhfgji,menpo/menpo,menpo/transform/homogeneous/rotation.py,96ea5bfc92a5f1adfa02539e13697f272cb3daa6,STILL_EXISTS,TODO check that I am a valid rotation
aaedhfhae,menpo/menpo,menpo/transform/homogeneous/rotation.py,96ea5bfc92a5f1adfa02539e13697f272cb3daa6,STILL_EXISTS,TODO confirm that multiple eigenvalues of 1 means the rotation
aaedhfhbe,menpo/menpo,menpo/transform/homogeneous/scale.py,96ea5bfc92a5f1adfa02539e13697f272cb3daa6,STILL_EXISTS,scale_factor better be a numpy array then
aaedhfhbg,menpo/menpo,menpo/transform/homogeneous/similarity.py,96ea5bfc92a5f1adfa02539e13697f272cb3daa6,09564c666875177872f7318facb12e2190a1bbf5,TODO check that I am a similarity transform
aaedhfhbh,menpo/menpo,menpo/transform/homogeneous/similarity.py,96ea5bfc92a5f1adfa02539e13697f272cb3daa6,ea29f6e9bb4afe3de83aa83524e2a2b7e825296f,TODO: implement 3D Jacobian
aaedhfhdj,menpo/menpo,menpo/transform/homogeneous/base.py,d52af44406e90cc69bab7d11e79e8ee322b2855a,STILL_EXISTS,TODO add logic here
aaedhfhei,menpo/menpo,menpo/transform/homogeneous/rotation.py,8ade175cfb0c5e50f716ac33dcc91f489708d030,STILL_EXISTS,The update better be the same size
aaedhfhej,menpo/menpo,menpo/transform/homogeneous/rotation.py,8ade175cfb0c5e50f716ac33dcc91f489708d030,STILL_EXISTS,TODO slightly dodgey here accessing _h_matrix
aaedhfhia,menpo/menpo,menpo/transform/pdm.py,c33f8d8d6768b59af689626c586fc89b28eefe3c,STILL_EXISTS,TODO: Rethink this transform so it knows how to deal with complex shapes
aaedhfhib,menpo/menpo,menpo/transform/pdm.py,c33f8d8d6768b59af689626c586fc89b28eefe3c,STILL_EXISTS,TODO: this is just done here to be able to use the Alignable
aaedhfhic,menpo/menpo,menpo/transform/pdm.py,c33f8d8d6768b59af689626c586fc89b28eefe3c,STILL_EXISTS,interface; need to rethink this whole transform probably.
aaedhfiah,menpo/menpo,menpo/transform/pdm.py,c33f8d8d6768b59af689626c586fc89b28eefe3c,STILL_EXISTS,TODO investigate the impact of this; could be problematic
aaedhficb,menpo/menpo,menpo/fit/base.py,7f83053d0cae9b49fa6c2172187fb9c44df2ee70,41995f80d491c959d14aa5aa37066f10529f545e,TODO: document me
aaedhficc,menpo/menpo,menpo/fit/fittingresult.py,7f83053d0cae9b49fa6c2172187fb9c44df2ee70,b3551de68afe70d247c379ca304aaf219332e7fa,TODO: document me
aaedhfidb,menpo/menpo,menpo/fit/gradientdescent/base.py,7f83053d0cae9b49fa6c2172187fb9c44df2ee70,STILL_EXISTS,TODO: deal with regularization\/prior inside the transforms; this is also
aaedhgagh,menpo/menpo,menpo/fit/lucaskanade/image/base.py,7f83053d0cae9b49fa6c2172187fb9c44df2ee70,STILL_EXISTS,TODO: rename kwarg \"forward\" to \"forward_additive\"
aaedhgahi,menpo/menpo,menpo/fit/lucaskanade/image/base.py,7f83053d0cae9b49fa6c2172187fb9c44df2ee70,STILL_EXISTS,TODO: add \"forward_compositional\" kwarg with options
aaedhgajb,menpo/menpo,menpo/fit/lucaskanade/image/base.py,7f83053d0cae9b49fa6c2172187fb9c44df2ee70,STILL_EXISTS,TODO: Pre-compute the inverse
aaedhgbdf,menpo/menpo,menpo/fit/lucaskanade/residual.py,7f83053d0cae9b49fa6c2172187fb9c44df2ee70,STILL_EXISTS,ToDo: Note that; fft_sdi is rectangular; i.e. is not define in
aaedhgbgi,menpo/menpo,menpo/fit/lucaskanade/residual.py,7f83053d0cae9b49fa6c2172187fb9c44df2ee70,STILL_EXISTS,TODO: do we need to copy the image?
aaedhgbgj,menpo/menpo,menpo/fit/lucaskanade/residual.py,7f83053d0cae9b49fa6c2172187fb9c44df2ee70,STILL_EXISTS,ToDo: is this supposed to be per channel normalization?
aaedhgbii,menpo/menpo,menpo/fit/lucaskanade/residual.py,7f83053d0cae9b49fa6c2172187fb9c44df2ee70,STILL_EXISTS,TODO: Should be other step described in paper
aaedhgdag,menpo/menpo,menpo/fitmultilevel/aam/base.py,86b28ca75f40b1f9acffaafa156885cfaabf8535,8838fb5042810fdbcf306f6cf7b179b0be3af805,TODO: Can this be moved up?
aaedhgdah,menpo/menpo,menpo/fitmultilevel/aam/base.py,86b28ca75f40b1f9acffaafa156885cfaabf8535,b0d3104f1a274d85e3229da1ac88e2c795265be9,ToDo: Do we need a blank (identity) method for Transforms?
aaedhgdbj,menpo/menpo,menpo/fitmultilevel/aam/builder.py,86b28ca75f40b1f9acffaafa156885cfaabf8535,STILL_EXISTS,TODO: Document me
aaedhgdca,menpo/menpo,menpo/fitmultilevel/aam/builder.py,86b28ca75f40b1f9acffaafa156885cfaabf8535,STILL_EXISTS,TODO: revise kwarg trilist in method constrain_mask_to_landmarks;
aaedhgdcb,menpo/menpo,menpo/fitmultilevel/aam/builder.py,86b28ca75f40b1f9acffaafa156885cfaabf8535,STILL_EXISTS,perhaps the trilist should be directly obtained from the group landmarks
aaedhgdce,menpo/menpo,menpo/fitmultilevel/builder.py,86b28ca75f40b1f9acffaafa156885cfaabf8535,e9c6ab85b7bbe770e88476b44946201f9756b95c,TODO: Document me
aaedhgdcj,menpo/menpo,menpo/fitmultilevel/clm/base.py,86b28ca75f40b1f9acffaafa156885cfaabf8535,8838fb5042810fdbcf306f6cf7b179b0be3af805,TODO: Can this be moved up?
aaedhgddc,menpo/menpo,menpo/fitmultilevel/clm/base.py,86b28ca75f40b1f9acffaafa156885cfaabf8535,5842066240660584ef0d4f8cdaac2e042d74e751,ToDo: Do we need a blank (identity) method for Transforms?
aaedhgdea,menpo/menpo,menpo/fitmultilevel/clm/builder.py,86b28ca75f40b1f9acffaafa156885cfaabf8535,b4b2c1ae2b7e3443967afc3faf842c8105a1f498,TODO: Document me
aaedhgdei,menpo/menpo,menpo/fitmultilevel/fittingresult.py,86b28ca75f40b1f9acffaafa156885cfaabf8535,STILL_EXISTS,TODO : this should overwrite __str__
aaedhgdfg,menpo/menpo,menpo/fitmultilevel/functions.py,86b28ca75f40b1f9acffaafa156885cfaabf8535,STILL_EXISTS,TODO: Should this be a method on SimilarityTransform? AlignableTransforms?
aaedhgdgd,menpo/menpo,menpo/fitmultilevel/sdm/base.py,86b28ca75f40b1f9acffaafa156885cfaabf8535,fbadd74717403d6d0d89222c790d2e3ec1823a62,TODO: Can this be moved up?
aaedhgdgg,menpo/menpo,menpo/fitmultilevel/sdm/trainer.py,86b28ca75f40b1f9acffaafa156885cfaabf8535,STILL_EXISTS,TODO: document me
aaedhgdjc,menpo/menpo,menpo/transform/modeldriven.py,8d1a5d6fecb8c47e14b79a57b72091b91abcf4e3,e8bedc630e19354601074a58829aa0415992de75,TODO investigate the impact of this; could be problematic
aaedhgeah,menpo/menpo,menpo/transform/test/affine/align_test.py,8d1a5d6fecb8c47e14b79a57b72091b91abcf4e3,STILL_EXISTS,TODO check composition works correctly on all alignment methods
aaedhgebh,menpo/menpo,menpo/transform/test/affine/align_test.py,8d1a5d6fecb8c47e14b79a57b72091b91abcf4e3,STILL_EXISTS,TODO check from_vector; from_vector_inplace works correctly
aaedhgfaa,menpo/menpo,menpo/transform/affine.py,e8bedc630e19354601074a58829aa0415992de75,STILL_EXISTS,TODO scale per dim should be used.
aaedhgfaf,menpo/menpo,menpo/transform/affine.py,e8bedc630e19354601074a58829aa0415992de75,STILL_EXISTS,TODO calling super setter correctly
aaedhgfed,menpo/menpo,menpo/transform/modeldriven.py,e8bedc630e19354601074a58829aa0415992de75,67db71a21e03c39a49cf8f0841a789f82e5c1e5f,TODO this seems to be the same; revisit
aaedhgfhb,menpo/menpo,setup.py,e8bedc630e19354601074a58829aa0415992de75,180854617eb01318c909cc11e19d9e9cdfae9a3f,TODO why does it compile without these on OS X?!
aaedhgijc,menpo/menpo,menpo/transform/pdm.py,b285f677229e93588fb12974932517eb0bdd2c69,b6963972af309f5e3373fed8c8a1cc36625ba15b,TODO: document me
aaedhgijd,menpo/menpo,menpo/transform/pdm.py,b285f677229e93588fb12974932517eb0bdd2c69,b6963972af309f5e3373fed8c8a1cc36625ba15b,TODO update _global_transform first
aaedhgjhj,menpo/menpo,menpo/transform/modeldriven.py,b0d3104f1a274d85e3229da1ac88e2c795265be9,80282f631d5e1daa2c37f65bfd309eec7bab48f9,TODO: Rethink this transform so it knows how to deal with complex shapes
aaedhgjih,menpo/menpo,menpo/fitmultilevel/clm/base.py,99a86c0a8e2dbc997f07ae8782cc2c1c5796c523,STILL_EXISTS,TODO: Residuals (SSD) is not used at the moment
aaedhhfig,menpo/menpo,docs/sphinxext/apigen.py,ba0f0553c024ca29b101c432dbf219d2f910cd44,STILL_EXISTS,XXX maybe check for extensions as well?
aaedhhiaj,menpo/menpo,versioneer.py,9ad6869e03b74d3b3358bc1d52a6a397dc23dff3,STILL_EXISTS,robust in cases where setup.py was invoked in some weird way (e.g. pip)
aaedhidha,menpo/menpo,menpo/math/multivariate.py,b91ce4bafd618eb977555a04c0e4eb684d5de474,STILL_EXISTS,TODO: the code to set cond\/rcond is identical to that in
aaedhidhc,menpo/menpo,menpo/math/multivariate.py,b91ce4bafd618eb977555a04c0e4eb684d5de474,STILL_EXISTS,into scipy.linalg it should probably be shared between all of
aaedhigbb,menpo/menpo,menpo/model/pca.py,8367146d983a1f7f4ffd2641bf21c507d20b3270,STILL_EXISTS,n_components needed to capture desired variance
aaedhigbf,menpo/menpo,menpo/image/interpolation.py,8c73ce4921c3cb0d28c99ecc69b754f60728b778,STILL_EXISTS,Note that map_coordinates uses the opposite (dims; points) convention
aaedhihfa,menpo/menpo,menpo/fitmultilevel/builder.py,0781ab74c04a504057f64ecd982d1e79f973ba8d,457f0c142cc983a40cb8e2f5b1e13e046418c354,fix the reference_shape's diagonal length if asked
aaedhihfi,menpo/menpo,menpo/transform/homogeneous/rotation.py,ae43be310f36fc1ebcfabaa0aba5d5ea731a6b2c,a9d70c473d733ef809ad2ed258239c23c5cb6ea1,TODO vectorizable rotations
aaedhihfj,menpo/menpo,menpo/transform/homogeneous/scale.py,ae43be310f36fc1ebcfabaa0aba5d5ea731a6b2c,a9d70c473d733ef809ad2ed258239c23c5cb6ea1,TODO d_dp on NonUniformScale
aaedhihga,menpo/menpo,menpo/transform/homogeneous/scale.py,ae43be310f36fc1ebcfabaa0aba5d5ea731a6b2c,a9d70c473d733ef809ad2ed258239c23c5cb6ea1,TODO d_dp on UniformScale
aaedhihgb,menpo/menpo,menpo/transform/homogeneous/translation.py,ae43be310f36fc1ebcfabaa0aba5d5ea731a6b2c,a9d70c473d733ef809ad2ed258239c23c5cb6ea1,TODO implement d_dp for Translation
aaedhiici,menpo/menpo,menpo/transform/modeldriven.py,0d9ca24196760d9a11af80b57aea678d9c176762,STILL_EXISTS,TODO confirm with @ja310 this is correct
aaedhiidd,menpo/menpo,menpo/transform/modeldriven.py,0d9ca24196760d9a11af80b57aea678d9c176762,892e9e30ef6cff2540473f6114670f2116b22092,TODO check with @ja310 this is correct
aaedhijge,menpo/menpo,menpo/fitmultilevel/sdm/trainer.py,457f0c142cc983a40cb8e2f5b1e13e046418c354,42d845d1dc3f09b28e0d60e0b6610649ada776f1,TODO: repeated code from Builder. Should builder and Trainer have a
aaedhjadc,menpo/menpo,menpo/fitmultilevel/builder.py,a5eaa0464d9bda6dfeb5d8afc6c995a528a75d93,03cb1a4c1b2def705829c4d6d1cb9969ddf06474,fix the reference_shape's diagonal length if asked
aaedhjajd,menpo/menpo,menpo/model/modelinstance.py,42d845d1dc3f09b28e0d60e0b6610649ada776f1,STILL_EXISTS,TODO this seems suspiciously different in shape
aaedhjaje,menpo/menpo,menpo/transform/modeldriven.py,42d845d1dc3f09b28e0d60e0b6610649ada776f1,892e9e30ef6cff2540473f6114670f2116b22092,TODO @jalabort which is the correct d_dp to use here?
aaedhjajf,menpo/menpo,menpo/transform/modeldriven.py,42d845d1dc3f09b28e0d60e0b6610649ada776f1,STILL_EXISTS,TODO confirm with @ja310 this is correct
aaedhjajg,menpo/menpo,menpo/model/modelinstance.py,9e23e745ba7259d104975470d928e1e3a322880c,STILL_EXISTS,TODO this seems suspiciously different in shape
aaedhjbeg,menpo/menpo,menpo/fitmultilevel/aam/base.py,c151cb3fe9187ff852769ff7d4df1c7774fa05fa,c77e99a3b7319e1fdb7232e4b0bcea0b9539505d,TODO: Add residual as parameter; when residuals are properly defined
aaedhjcca,menpo/menpo,menpo/benchmark/base.py,7fda92433aa6f4fbbe35cb719b58eec0725c4adc,STILL_EXISTS,convert it to grayscale if needed
aaedhjccf,menpo/menpo,menpo/transform/homogeneous/rotation.py,a573a0ba3d76a1a21d2c911a0d7cdb4ecb96d26a,16286abeb65f6ea2c6cc75eb80e803f234e91ebf,TODO vectorizable rotations
aaedhjccg,menpo/menpo,menpo/transform/homogeneous/scale.py,a573a0ba3d76a1a21d2c911a0d7cdb4ecb96d26a,16286abeb65f6ea2c6cc75eb80e803f234e91ebf,TODO d_dp on NonUniformScale
aaedhjcch,menpo/menpo,menpo/transform/homogeneous/scale.py,a573a0ba3d76a1a21d2c911a0d7cdb4ecb96d26a,16286abeb65f6ea2c6cc75eb80e803f234e91ebf,TODO d_dp on UniformScale
aaedhjcff,menpo/menpo,menpo/transform/homogeneous/translation.py,a573a0ba3d76a1a21d2c911a0d7cdb4ecb96d26a,16286abeb65f6ea2c6cc75eb80e803f234e91ebf,TODO implement d_dp for Translation
aaedhjcfi,menpo/menpo,menpo/fitmultilevel/sdm/trainer.py,8ba23be95e8368051bc587349226fa7a5353d688,STILL_EXISTS,TODO: repeated code from Builder. Should builder and Trainer have a
aaedhjcga,menpo/menpo,menpo/transform/modeldriven.py,8ba23be95e8368051bc587349226fa7a5353d688,STILL_EXISTS,TODO @jalabort which is the correct d_dp to use here?
aaedhjcgf,menpo/menpo,menpo/transform/modeldriven.py,8ba23be95e8368051bc587349226fa7a5353d688,0b60a24b1b23b75a52ff51acba95c71f8f8b23d1,TODO check with @ja310 this is correct
aaedhjdbj,menpo/menpo,menpo/transform/modeldriven.py,0b60a24b1b23b75a52ff51acba95c71f8f8b23d1,STILL_EXISTS,TODO: Can we do this without splitting across the two dimensions?
aaedhjdci,menpo/menpo,menpo/transform/modeldriven.py,0b60a24b1b23b75a52ff51acba95c71f8f8b23d1,STILL_EXISTS,TODO @jalabort which is the correct d_dp to use here?
aaedhjehh,menpo/menpo,menpo/fitmultilevel/builder.py,0e45faee3d56a72a13fad29eee78a30c1a89124e,03cb1a4c1b2def705829c4d6d1cb9969ddf06474,fix the reference_shape's diagonal length if asked
aaedhjgff,menpo/menpo,menpo/transform/modeldriven.py,16286abeb65f6ea2c6cc75eb80e803f234e91ebf,STILL_EXISTS,TODO: Can we do this without splitting across the two dimensions?
aaedhjhef,menpo/menpo,menpo/landmark/base.py,30a916e5c54486ec02e478868a2eb0b40962ddf4,af0b6c8e9b0d950941ab62fe8c911350238182d7,TODO: replace with copy function
aaedhjheg,menpo/menpo,menpo/landmark/base.py,30a916e5c54486ec02e478868a2eb0b40962ddf4,af0b6c8e9b0d950941ab62fe8c911350238182d7,TODO: Replace with copy function
aaedhjhei,menpo/menpo,menpo/landmark/base.py,91a3f18dda22c03c245801cd801583c33aadca76,STILL_EXISTS,TODO: Use the copy function
aaedhjhji,menpo/menpo,menpo/landmark/base.py,4eecf656a8e2f1997f32f7b0451cd1d9e090a731,STILL_EXISTS,TODO replace with copy function
aaedhjibd,menpo/menpo,menpo/transform/homogeneous/rotation.py,4eecf656a8e2f1997f32f7b0451cd1d9e090a731,STILL_EXISTS,TODO vectorizable rotations
aaedhjibe,menpo/menpo,menpo/transform/homogeneous/scale.py,4eecf656a8e2f1997f32f7b0451cd1d9e090a731,ea29f6e9bb4afe3de83aa83524e2a2b7e825296f,TODO d_dp on NonUniformScale
aaedhjibf,menpo/menpo,menpo/transform/homogeneous/scale.py,4eecf656a8e2f1997f32f7b0451cd1d9e090a731,ea29f6e9bb4afe3de83aa83524e2a2b7e825296f,TODO d_dp on UniformScale
aaedhjibg,menpo/menpo,menpo/transform/homogeneous/translation.py,4eecf656a8e2f1997f32f7b0451cd1d9e090a731,ea29f6e9bb4afe3de83aa83524e2a2b7e825296f,TODO implement d_dp for Translation
aaedhjiji,menpo/menpo,menpo/landmark/base.py,baaacffebe6d5e154ca95777b78a17e8a70af05f,STILL_EXISTS,TODO replace with copy function
aaedhjjac,menpo/menpo,menpo/landmark/base.py,565df2fffe007b26892dc66521f8a40e3c15c4cc,STILL_EXISTS,TODO replace with copy function
aaedhjjif,menpo/menpo,menpo/landmark/base.py,2c5c5491bda72cc6e30c78a58afe54aa19d1e0e5,STILL_EXISTS,TODO replace with copy function
aaediabai,menpo/menpo,menpo/fit/regression/trainer.py,0ed70f33610470bde661619afdd098c26bcacccc,41995f80d491c959d14aa5aa37066f10529f545e,TODO: document me
aaediabfg,menpo/menpo,menpo/fitmultilevel/sdm/trainer.py,97f961fa0e7a4caeedc93bb9525c1401d7f0d1a0,c18da2ffdbbeca5a29883e27265e1b867e78b3f2,TODO: Finish me
aaediabje,menpo/menpo,menpo/landmark/base.py,863673c311e0a7d2eda897a556323e417f8e5100,STILL_EXISTS,TODO replace with copy function
aaediacad,menpo/menpo,menpo/fit/base.py,f3cae7ce16287f0f7542c9275bc3414ccc7aecce,40865be71a6c9602f222f1f26ca3cc62aec9d480,TODO: document me
aaediacai,menpo/menpo,menpo/fitmultilevel/aam/base.py,f3cae7ce16287f0f7542c9275bc3414ccc7aecce,40865be71a6c9602f222f1f26ca3cc62aec9d480,TODO: Can this be moved up?
aaediaceb,menpo/menpo,menpo/fitmultilevel/clm/base.py,f3cae7ce16287f0f7542c9275bc3414ccc7aecce,STILL_EXISTS,TODO: Can this be moved up?
aaediacec,menpo/menpo,menpo/fitmultilevel/clm/base.py,f3cae7ce16287f0f7542c9275bc3414ccc7aecce,STILL_EXISTS,TODO: document me
aaediacgg,menpo/menpo,menpo/fitmultilevel/sdm/base.py,f3cae7ce16287f0f7542c9275bc3414ccc7aecce,STILL_EXISTS,TODO: Can this be moved up?
aaediacgj,menpo/menpo,menpo/fitmultilevel/sdm/trainer.py,f3cae7ce16287f0f7542c9275bc3414ccc7aecce,STILL_EXISTS,TODO: document me
aaediache,menpo/menpo,menpo/fitmultilevel/sdm/trainer.py,f3cae7ce16287f0f7542c9275bc3414ccc7aecce,STILL_EXISTS,TODO: Finish me
aaediafba,menpo/menpo,menpo/fitmultilevel/aam/test/aam_builder_test.py,48b5ab17b51de0cd8515e3cadee27594e0914777,5e9439128af30268d27f77eb1f64e7734f3b01d6,TODO: Why is this broken?
aaediagae,menpo/menpo,menpo/fitmultilevel/clm/base.py,40865be71a6c9602f222f1f26ca3cc62aec9d480,STILL_EXISTS,TODO: Add residual as parameter; when residuals are properly defined
aaediaghh,menpo/menpo,menpo/fitmultilevel/sdm/trainer.py,40865be71a6c9602f222f1f26ca3cc62aec9d480,STILL_EXISTS,TODO: Document me
aaediaghi,menpo/menpo,menpo/fitmultilevel/sdm/trainer.py,40865be71a6c9602f222f1f26ca3cc62aec9d480,STILL_EXISTS,TODO: Finish me
aaediajfb,menpo/menpo,menpo/fitmultilevel/aam/test/aam_builder_test.py,015064c81e26f72cfe50804927454b66ffb0679d,ac34f662b1016ad8fd373507da65d68f78b166c1,TODO: Why is this broken?
aaediajhj,menpo/menpo,menpo/fitmultilevel/sdm/trainer.py,015064c81e26f72cfe50804927454b66ffb0679d,9331e086069dea46109988a43d316b6fed442239,TODO: this should use check_feature_type
aaedibaac,menpo/menpo,menpo/fit/regression/trainer.py,dba432cca8a2cae1a0ab4aed7624d477b2d3f9ba,84acb3d44fb7df7785c899f96575f4d281b12073,TODO: Is this correct or should I return np.hstack((features; 1)) as
aaedibabe,menpo/menpo,menpo/transform/homogeneous/base.py,09564c666875177872f7318facb12e2190a1bbf5,462fe8908bfc8e338ba0499f4c8d9df34d9b07fa,avoid the deepcopy with an efficient copy
aaedibaef,menpo/menpo,menpo/fit/regression/regressionfunctions.py,84acb3d44fb7df7785c899f96575f4d281b12073,09823a19f648a41c3892dd08c3fe51a2601ac089,TODO: document me
aaedibafi,menpo/menpo,menpo/transform/homogeneous/similarity.py,84acb3d44fb7df7785c899f96575f4d281b12073,b8348f5f5b43f0d359a39d3dc7cfbda1789284b7,TODO check that I am a similarity transform
aaedibahh,menpo/menpo,menpo/transform/homogeneous/base.py,b8348f5f5b43f0d359a39d3dc7cfbda1789284b7,c7302170f9eacdde7651aa827c0ff1b1e6f863e8,avoid the deepcopy with an efficient copy
aaedibbba,menpo/menpo,menpo/image/base.py,95e85a6725ac2384d943c731c991c8fe634ee745,STILL_EXISTS,TODO do this elegantly
aaedibbbc,menpo/menpo,menpo/image/base.py,95e85a6725ac2384d943c731c991c8fe634ee745,STILL_EXISTS,TODO flip the transform order here
aaedibbci,menpo/menpo,menpo/image/base.py,71411e43b8255d640078b15b830a3a9225cb6855,STILL_EXISTS,TODO flip the transform order here
aaedibbdd,menpo/menpo,menpo/fit/gradientdescent/base.py,8e3af766539650bd53bc0d3a3fb04bce97e508b7,STILL_EXISTS,TODO: a similar approach could be implemented in LK
aaedibdge,menpo/menpo,menpo/fit/regression/regressionfunctions.py,c7302170f9eacdde7651aa827c0ff1b1e6f863e8,dfbbbf1608acf7ab1b53556cbd9dd2443316a8f5,TODO: document me
aaedibedd,menpo/menpo,menpo/transform/homogeneous/similarity.py,c7302170f9eacdde7651aa827c0ff1b1e6f863e8,6b8acfff27a87e72a7586da94972457b2d5568b4,TODO check that I am a similarity transform
aaedibeea,menpo/menpo,menpo/fit/gradientdescent/base.py,dfbbbf1608acf7ab1b53556cbd9dd2443316a8f5,STILL_EXISTS,TODO: a similar approach could be implemented in LK
aaedibejj,menpo/menpo,menpo/transform/homogeneous/base.py,6b8acfff27a87e72a7586da94972457b2d5568b4,STILL_EXISTS,avoid the deepcopy with an efficient copy
aaedibfch,menpo/menpo,menpo/fit/regression/trainer.py,78671e6614e4d095fb8eaddcd940e2d44ee2166c,a6db61cefe6ea8ee0fb09dec054517590b0638ec,TODO: in the future this should be extract_local_patches_fast
aaedibfeb,menpo/menpo,menpo/fit/regression/trainer.py,2e743d45f3e9e6247120511a3820b27438fadc70,98a77d7182b8730b1b98c0bfe94d42cbf1d8bb4f,TODO should the template be a mask or a shape? warp_to_shape here
aaedibijd,menpo/menpo,menpo/rasterize/opengl.py,ec6140544a634301af3ccb91ad957e5e38e5b08a,STILL_EXISTS,TODO This should actually use the colour provided.
aaedicbac,menpo/menpo,menpo/fit/regression/trainer.py,6209c9c57bae1c08df981aeebc358f76577a6a4a,cde79fdc37e20f9d55c5bf8ddad58b2736f4e033,TODO should the template be a mask or a shape? warp_to_shape here
aaedicbia,menpo/menpo,menpo/base.py,1ad019a7a4e2feda1e1934e44433359385754f7a,STILL_EXISTS,exactly what types have been skipped in copying and why.
aaedicfaj,menpo/menpo,menpo/io/output/base.py,9c668424b115a36ea62b5071aeb50f400ab44ddd,STILL_EXISTS,Apparently in Python 2.x there is no reliable way to detect something
aaedicfgb,menpo/menpo,menpo/landmark/labels.py,cb11ea2e4c75636246002472d7eab2eb0f7a06cf,f00c0f54df123b61b100d04769be8349a91e8186,TODO: Not sure this makes a lot of sense...
aaedicfje,menpo/menpo,menpo/fitmultilevel/fittingresult.py,645042e8b51019576117a0e2d2b80ad0aec3c19e,63483090b8e0fe74464178ff8c84894db768b04b,TODO: this should print more information than just this...
aaedicgej,menpo/menpo,menpo/fit/fittingresult.py,f00c0f54df123b61b100d04769be8349a91e8186,e325c03378820d04f898df85dc89addf34165c73,TODO: document me
aaedidiad,menpo/menpo,menpo/fit/regression/trainer.py,548a05b6224a64ee41cc1a6372f7deb252fce78c,996eda4f3059fb980d713c9efb01f831fc0d3e46,TODO should the template be a mask or a shape? warp_to_shape here
aaedidife,menpo/menpo,menpo/landmark/labels.py,ac5d89abbab25ca89e3c2b21bb3584e61f1877f1,STILL_EXISTS,TODO: Not sure this makes a lot of sense...
aaediechh,menpo/menpo,menpo/fitmultilevel/aam/fitter.py,c77e99a3b7319e1fdb7232e4b0bcea0b9539505d,3f81968edca81b97a9495b96fbaad42d13360347,TODO: Add residual as parameter; when residuals are properly defined
aaediecja,menpo/menpo,menpo/fitmultilevel/clm/base.py,496ce4665da6a7863af4a986efd2f51e6a38cf12,b5916a9166db3da1a81d3aad0b8f76c40ab5d362,TODO: this bit of logic should to be transferred down to PCAModel
aaediedbc,menpo/menpo,menpo/fitmultilevel/clm/fitter.py,496ce4665da6a7863af4a986efd2f51e6a38cf12,3f81968edca81b97a9495b96fbaad42d13360347,TODO: Add residual as parameter; when residuals are properly defined
aaediedgb,menpo/menpo,menpo/fit/regression/trainer.py,b5916a9166db3da1a81d3aad0b8f76c40ab5d362,3a028bbdc20920bf1bca472f914f5e6ae0afd2c1,TODO should the template be a mask or a shape? warp_to_shape here
aaediedgc,menpo/menpo,menpo/fitmultilevel/aam/base.py,b5916a9166db3da1a81d3aad0b8f76c40ab5d362,STILL_EXISTS,TODO: Add residual as parameter; when residuals are properly defined
aaediefbe,menpo/menpo,menpo/fitmultilevel/clm/base.py,4f8c4f94dbf4bfbeb408f62d8abdefeac0a6345c,STILL_EXISTS,TODO: this bit of logic should to be transferred down to PCAModel
aaediefcd,menpo/menpo,menpo/fitmultilevel/aam/base.py,6a2aed969190927b6a4f7c0f4c1f7b6d572ed551,STILL_EXISTS,TODO: this bit of logic should to be transferred down to PCAModel
aaediefig,menpo/menpo,menpo/fit/regression/trainer.py,859c1ad4d1772188554ec62839a62811bee7a929,b59c7e57c38d7e09fef80f03d19bc21a91917674,TODO should the template be a mask or a shape? warp_to_shape here
aaediegbc,menpo/menpo,menpo/visualize/widgets/helpers.py,e7ea2443cca3e3f6518ca31e12bd79f6dff483e7,STILL_EXISTS,Define sum functionality
aaediegec,menpo/menpo,menpo/visualize/widgets/helpers.py,0c23e3b3034fd00f3ff6e08dd8e9d01ff551a142,efdb11dd1483bb6f931779acc639495501066c7e,fix figure scale sliders width
aaediehig,menpo/menpo,menpo/image/masked.py,a27af56efdb8d8c493facbb32ee1650287a4954e,STILL_EXISTS,TODO an optimisation could be added here for the case where mask
aaedieibf,menpo/menpo,menpo/visualize/widgets/base.py,521fa0558bdf2c2e137f23d3daf16df5b3a8d901,STILL_EXISTS,fix n_parameters
aaediejba,menpo/menpo,menpo/image/interpolation.py,8553e0e6b93cf2342897ee1350bb9b76fc720e40,STILL_EXISTS,Note that map_coordinates uses the opposite (dims; points) convention
aaediejcf,menpo/menpo,menpo/image/masked.py,4900d2d886d381d7517c0597ac97c668f5edc536,STILL_EXISTS,TODO maybe we should be stricter about the trilist here; feels flakey
aaediejcj,menpo/menpo,menpo/image/masked.py,4900d2d886d381d7517c0597ac97c668f5edc536,STILL_EXISTS,TODO an optimisation could be added here for the case where mask
aaediejed,menpo/menpo,menpo/image/masked.py,6008825b5a37d049ba6bc3948370ce3aea9d7d29,STILL_EXISTS,TODO an optimisation could be added here for the case where mask
aaediejfa,menpo/menpo,menpo/image/masked.py,6008825b5a37d049ba6bc3948370ce3aea9d7d29,STILL_EXISTS,TODO maybe we should be stricter about the trilist here; feels flakey
aaedifadc,menpo/menpo,menpo/image/masked.py,ef05fd8ca75232cc4299070d7ec7b95586bcbe6d,STILL_EXISTS,TODO an optimisation could be added here for the case where mask
aaedifafc,menpo/menpo,menpo/fit/regression/trainer.py,449510258a8bb2169f1cca9561a52cc1fd0b1a6b,STILL_EXISTS,TODO should the template be a mask or a shape? warp_to_shape here
aaedigbjf,menpo/menpo,menpo/fitmultilevel/atm/fitter.py,0678bbfa769f9b517cb600de93bc70ab8ae7c0fa,STILL_EXISTS,TODO: Add residual as parameter; when residuals are properly defined
aaedigeea,menpo/menpo,menpo/external/PADS/MinimumSpanningTree.py,84c94a2d11aef6352f7a48309e6c226e70fbee44,STILL_EXISTS,implement once UnionFind exists; and second; because the only slow
aaedihcbg,menpo/menpo,menpo/visualize/widgets/base.py,3cf7539e551cfed51292a71315caec93d29fcfff,9aa863d600b36678982c67e6160e65ea3e53d64d,Formatting is a bit ugly but this is MUCH easier to read.
aaediibij,menpo/menpo,menpo/visualize/widgets/base.py,3e2f744adf5a395694c660caf9ea1b487027d3e4,9aa863d600b36678982c67e6160e65ea3e53d64d,Formatting is a bit ugly but this is MUCH easier to read.
aaedjaajh,menpo/menpo,menpo/visualize/widgets/lowlevelhelpers.py,efdb11dd1483bb6f931779acc639495501066c7e,STILL_EXISTS,fix figure scale sliders width
aaedjabbg,menpo/menpo,menpo/feature/features.py,7fe2657fb524fb63871eea524a191c97d347810c,b962802c91fe60a8497d9ffb215efe0463081daf,TODO: This returns slightly different results than gradient
aaedjabbh,menpo/menpo,menpo/feature/features.py,7fe2657fb524fb63871eea524a191c97d347810c,1f28ad9c57db82c971f68ecec159f00578fcdf7a,TODO: Nontas might want to make this nicer ...
aaedjabcd,menpo/menpo,menpo/image/base.py,7fe2657fb524fb63871eea524a191c97d347810c,7b985edc96bbb2e9b9373b9d8c2a1ea9a1e8057e,TODO: Needs to be updated for channels at front!
aaedjabce,menpo/menpo,menpo/image/base.py,7fe2657fb524fb63871eea524a191c97d347810c,7b985edc96bbb2e9b9373b9d8c2a1ea9a1e8057e,TODO: revise this method!
aaedjabcf,menpo/menpo,menpo/image/boolean.py,7fe2657fb524fb63871eea524a191c97d347810c,7b985edc96bbb2e9b9373b9d8c2a1ea9a1e8057e,TODO: Revise
aaedjabcg,menpo/menpo,menpo/image/masked.py,7fe2657fb524fb63871eea524a191c97d347810c,7b985edc96bbb2e9b9373b9d8c2a1ea9a1e8057e,TODO: Revise
aaedjabdb,menpo/menpo,menpo/visualize/widgets/lowlevelhelpers.py,b9ed8d7b6e30cd2b2a2bbcf0e547ef2ee2806d19,STILL_EXISTS,fix figure scale slider width
aaedjabhi,menpo/menpo,menpo/visualize/widgets/lowlevelhelpers.py,fea62beeb2dca7772ff46d040a182b8727c0a9c6,STILL_EXISTS,align n_columns with spacing and set width
aaedjabjc,menpo/menpo,menpo/visualize/widgets/lowlevelhelpers.py,fea62beeb2dca7772ff46d040a182b8727c0a9c6,STILL_EXISTS,update n_columns text box
aaedjacaa,menpo/menpo,menpo/visualize/widgets/lowlevelhelpers.py,435756207fc2750e6d34202be56459b5895f3065,STILL_EXISTS,set width of n_columns and markerspace
aaedjaece,menpo/menpo,menpo/visualize/image.py,70a30e1bd1adec48d52b85c87e29df3da734d4f2,657d28a65eb483546baa7288986bf1110ad3430b,TODO: This is a temporal fix
aaedjaeda,menpo/menpo,menpo/visualize/image.py,1f28ad9c57db82c971f68ecec159f00578fcdf7a,657d28a65eb483546baa7288986bf1110ad3430b,TODO: Needs fixing ...
aaedjaedb,menpo/menpo,menpo/io/input/base.py,6a1ca7d2eca2bdcf4fca908ea9a6028fda9da6c1,STILL_EXISTS,that's fine! Probably a dict\/list from PickleImporter.
aaedjagbf,menpo/menpo,menpo/visualize/widgets/tools.py,f90e3b513d58dede055a02ad30343c11c13cf8e3,e7ea10a73a7813d3141875eb70a016396365eb01,fix alpha slider width
aaedjcbec,menpo/menpo,menpo/shape/mesh/base.py,4b96d25862d0f5f73441694843ee1113162b977d,d7f64272fe62b64bbf680678647ce73c3395616b,TODO: add inheritance from Graph once implemented
aaedjcbed,menpo/menpo,menpo/visualize/viewmatplotlib.py,4b96d25862d0f5f73441694843ee1113162b977d,STILL_EXISTS,TODO: All marker and line options could be defined as lists...
aaedjigjd,menpo/menpo,menpo/visualize/image.py,05f15bb53ad26aba797f39f440c0c1aadfa1b34a,STILL_EXISTS,TODO: Needs fixing ...
aaedjigje,menpo/menpo,menpo/visualize/image.py,05f15bb53ad26aba797f39f440c0c1aadfa1b34a,STILL_EXISTS,TODO: This is a temporal fix
aaedjihbh,menpo/menpo,menpo/io/test/io_export_test.py,3d175b315a705ee64f62349e384fa93cd6108369,STILL_EXISTS,This is a bit ugly; but we parse the write calls to check that json
aaedjihcj,menpo/menpo,menpo/shape/graph.py,8227561dcd5478397b5108e44264d3ec8302d1ff,f784a1267ab3282f97b0b8718abd66408e3de6a0,get rows\/columns of edges
aaedjihea,menpo/menpo,menpo/shape/graph.py,fa705d4d1ad28527611f668d5a89ec3eaa9b8563,f784a1267ab3282f97b0b8718abd66408e3de6a0,get rows\/columns of edges
aaedjihhc,menpo/menpo,menpo/shape/graph.py,45528b1a198d30cdeeabafa70ced5777bfd1ec2a,STILL_EXISTS,Remove rows and columns from adjacency matrix
aaedjihhf,menpo/menpo,menpo/shape/graph.py,45528b1a198d30cdeeabafa70ced5777bfd1ec2a,f784a1267ab3282f97b0b8718abd66408e3de6a0,of rows (columns) that have at least one non-zero element.
aaedjjjhi,menpo/menpo,menpo/shape/graph.py,9cd82d232327ff29b4e84f26a882413dccf2bc55,STILL_EXISTS,Remove rows and columns from adjacency matrix
aaedjjjia,menpo/menpo,menpo/shape/graph.py,9cd82d232327ff29b4e84f26a882413dccf2bc55,4e43ac1a10173d2731865b400487d7945dd5bdf2,of rows (columns) that have at least one non-zero element.
aaeeaaacc,menpo/menpo,menpo/shape/graph.py,4e43ac1a10173d2731865b400487d7945dd5bdf2,STILL_EXISTS,Remove rows and columns from adjacency matrix
aaeeaaafh,menpo/menpo,menpo/shape/graph.py,9aca95762c7f62d8273e11c3613306a643e1006c,STILL_EXISTS,Remove rows and columns from adjacency matrix
aaeeaaafj,menpo/menpo,menpo/shape/graph.py,9aca95762c7f62d8273e11c3613306a643e1006c,6df3c12a3cbae785450d8a3e4b75f49ecf849b22,of rows (columns) that have at least one non-zero element.
aaeeaabaa,menpo/menpo,menpo/visualize/widgets/tools.py,ff9da6eb250aed70f642732781ecd805dbdc016d,14ab8a319c6603b34fed1824fcce5de8b1fce860,TODO: How to change the width of a *Text widget?
aaeeaabbc,menpo/menpo,menpo/visualize/widgets/tools.py,619640a91e6c95acb53aadde9f36f410b8efce89,14ab8a319c6603b34fed1824fcce5de8b1fce860,TODO: How to change the width of a *Text widget?
aaeeaaeej,menpo/menpo,menpo/visualize/widgets/tools.py,6df3c12a3cbae785450d8a3e4b75f49ecf849b22,20e1f4fedf7a2a3f197896d30d72be554a6437c7,fix alpha slider width
aaeeaafah,menpo/menpo,menpo/visualize/widgets/tools.py,6df3c12a3cbae785450d8a3e4b75f49ecf849b22,20e1f4fedf7a2a3f197896d30d72be554a6437c7,fix figure scale slider width
aaeeaafce,menpo/menpo,menpo/visualize/widgets/tools.py,6df3c12a3cbae785450d8a3e4b75f49ecf849b22,20e1f4fedf7a2a3f197896d30d72be554a6437c7,fix figure scale sliders width
aaeeaafeg,menpo/menpo,menpo/visualize/widgets/tools.py,6df3c12a3cbae785450d8a3e4b75f49ecf849b22,20e1f4fedf7a2a3f197896d30d72be554a6437c7,set width of n_columns and markerspace
aaeeaafeh,menpo/menpo,menpo/visualize/widgets/tools.py,6df3c12a3cbae785450d8a3e4b75f49ecf849b22,20e1f4fedf7a2a3f197896d30d72be554a6437c7,align n_columns with spacing
aaeeaaghb,menpo/menpo,menpo/visualize/widgets/options.py,20e1f4fedf7a2a3f197896d30d72be554a6437c7,1c0eb70303548c75de26eae333996a07b57cbb35,To fix the alignment within this widget; please refer to
aaeeabfdc,menpo/menpo,menpo/visualize/widgets/options.py,20e1f4fedf7a2a3f197896d30d72be554a6437c7,0026839331412ed6920c49cc2fb5ee0e370e6d6a,'legend_n_columns':1;
aaeeadech,menpo/menpo,menpo/shape/graph.py,758283e74831b5b4300a24de32d0aa96ed10d102,STILL_EXISTS,Remove rows and columns from adjacency matrix
aaeeadecj,menpo/menpo,menpo/shape/graph.py,758283e74831b5b4300a24de32d0aa96ed10d102,STILL_EXISTS,of rows (columns) that have at least one non-zero element.
aaeeafiad,menpo/menpo,menpo/visualize/widgets/base.py,0cd773e42a7bf08ca16cdea60ea7211796194b25,a5c4dcec5c93424fd8117897dc010ddbd1fc3d79,tmp3['numbers_vertical_align']; tmp4['legend_n_columns'];
aaeeahgjf,menpo/menpo,menpo/visualize/widgets/options.py,13f758c256b24728febe84855bd8e27972c22ccf,976a598e5d4fa1570e88608c0afa57b8694cd193,Define sum functionality
aaeeahjii,menpo/menpo,menpo/visualize/widgets/tools.py,13f758c256b24728febe84855bd8e27972c22ccf,cec132b4dcfebb90892a55e49d11cb01e8897cab,fix alpha slider width
aaeeaiaeh,menpo/menpo,menpo/visualize/widgets/tools.py,13f758c256b24728febe84855bd8e27972c22ccf,cec132b4dcfebb90892a55e49d11cb01e8897cab,fix figure scale slider width
aaeeaiage,menpo/menpo,menpo/visualize/widgets/tools.py,13f758c256b24728febe84855bd8e27972c22ccf,cec132b4dcfebb90892a55e49d11cb01e8897cab,fix figure scale sliders width
aaeeaiaig,menpo/menpo,menpo/visualize/widgets/tools.py,13f758c256b24728febe84855bd8e27972c22ccf,cec132b4dcfebb90892a55e49d11cb01e8897cab,set width of n_columns and markerspace
aaeeaiaih,menpo/menpo,menpo/visualize/widgets/tools.py,13f758c256b24728febe84855bd8e27972c22ccf,cec132b4dcfebb90892a55e49d11cb01e8897cab,align n_columns with spacing
aaeeaifjc,menpo/menpo,menpo/visualize/widgets/options.py,031675cbb025d8840ed81a23fff7a8546455a051,976a598e5d4fa1570e88608c0afa57b8694cd193,Define sum functionality
aaeeajgej,menpo/menpo,menpo/visualize/widgets/base.py,de6381dd0489568c453f39deb4de2d798366cc86,870475e788a4a00eb68cf44b7f35c4a84f234a2a,tmp3['numbers_vertical_align']; tmp4['legend_n_columns'];
aaeebbdaf,menpo/menpo,menpo/io/input/image.py,055dc3fa756bbea2a4122be50ad46aec934c2c1f,STILL_EXISTS,Currently these are unused; but they are in the format
aaeebbfii,menpo/menpo,menpo/image/base.py,874ddb8c080209b23e9b5332b9579907ea1574d1,62474a2157bd4743ea58ae786dcabcd7b244eb90,only needed for the deprecation period of the inplace crop methods.
aaeebbhec,menpo/menpo,menpo/visualize/widgets/options.py,7782a2d4633b73d1984d1563a41b8960a184e074,976a598e5d4fa1570e88608c0afa57b8694cd193,Define sum functionality
aaeebcagc,menpo/menpo,menpo/visualize/widgets/tools.py,7782a2d4633b73d1984d1563a41b8960a184e074,75b05865e280d2254c62e15d92fde3acd12729ea,fix alpha slider width
aaeebcbca,menpo/menpo,menpo/visualize/widgets/tools.py,7782a2d4633b73d1984d1563a41b8960a184e074,75b05865e280d2254c62e15d92fde3acd12729ea,fix figure scale slider width
aaeebcbdg,menpo/menpo,menpo/visualize/widgets/tools.py,7782a2d4633b73d1984d1563a41b8960a184e074,75b05865e280d2254c62e15d92fde3acd12729ea,fix figure scale sliders width
aaeebcbfi,menpo/menpo,menpo/visualize/widgets/tools.py,7782a2d4633b73d1984d1563a41b8960a184e074,75b05865e280d2254c62e15d92fde3acd12729ea,set width of n_columns and markerspace
aaeebcbfj,menpo/menpo,menpo/visualize/widgets/tools.py,7782a2d4633b73d1984d1563a41b8960a184e074,75b05865e280d2254c62e15d92fde3acd12729ea,align n_columns with spacing
aaeebcjif,menpo/menpo,menpo/image/base.py,27408c80e730a5cba1b900f04de7202873625eae,492ae451d944f0f5a10b1df3a0a4b6527216f927,only needed for the deprecation period of the inplace crop methods.
aaeebdahd,menpo/menpo,menpo/_version.py,42c9b22a000766ebaf13e62927a13fa1b2a5a139,4bd994898cb7b2c8fc7c85b6876a6895e9ab8480,maybe improved later
aaeebdahj,menpo/menpo,menpo/_version.py,42c9b22a000766ebaf13e62927a13fa1b2a5a139,4bd994898cb7b2c8fc7c85b6876a6895e9ab8480,unparseable. Maybe git-describe is misbehaving?
aaeebdbea,menpo/menpo,versioneer.py,42c9b22a000766ebaf13e62927a13fa1b2a5a139,STILL_EXISTS,maybe improved later
aaeebdbeg,menpo/menpo,versioneer.py,42c9b22a000766ebaf13e62927a13fa1b2a5a139,STILL_EXISTS,unparseable. Maybe git-describe is misbehaving?
aaeebdcib,menpo/menpo,menpo/image/interpolation.py,923aafaab73587fc2aecd0b8aa0edc346c6ffcff,ab9870f4410a841500bc9250f0af3af1fb07bd0e,TODO: Can Cython support bool types? If so; skip this
aaeebddbb,menpo/menpo,menpo/visualize/widgets/tools.py,02c00a070ce93d8d6d2e63bdd6501fdb39e47395,6c8e09b3f0c75cba4bdb9f56abb5c2a7604e42d2,if cmd starts or ends with ';'; raise an error
aaeebdecc,menpo/menpo,menpo/visualize/widgets/tools.py,b8e00d8d8c21662a5253785f948e52e89aa2a659,c062f65f92965732ea9bb021e41ea63f881ce375,if cmd starts or ends with ';'; raise an error
aaeebdfeb,menpo/menpo,menpo/visualize/widgets/tools.py,afa6361a12401a4f0ff5ab7c76424cf28bff527f,STILL_EXISTS,if cmd starts or ends with ';'; raise an error
aaeebdgbd,menpo/menpo,menpo/landmark/labels/human/face.py,da2c3936f252dfab691912c2d3030ff5697a8662,STILL_EXISTS,TODO: Not sure this makes a lot of sense...
aaeebedcj,menpo/menpo,menpo/math/decomposition.py,d9a9f5d2e804e62b29aab816658aea1975e4cc60,ad05dcdd72f7c190bfc6e09720cabb12fd6b8e5c,TODO: document me!
aaeebfdgg,menpo/menpo,menpo/model/linear.py,2c20cd58ad763dc68ef4e509b703f61d8ff792a6,STILL_EXISTS,TODO: Deprecate in 0.7.0
aaeebfeih,menpo/menpo,menpo/landmark/base.py,654f9c6460b6cb982b3e2486b9250406d6556c29,13e77e16fc51870a037964aa37f5d249c96d6888,TODO: Deprecate this - this handles importing old-style LandmarkGroup
aaeebfeii,menpo/menpo,menpo/landmark/base.py,654f9c6460b6cb982b3e2486b9250406d6556c29,13e77e16fc51870a037964aa37f5d249c96d6888,This is a crazy hack that means when old style LandmarkGroup
aaeebffba,menpo/menpo,menpo/shape/group.py,654f9c6460b6cb982b3e2486b9250406d6556c29,STILL_EXISTS,TODO: Deprecate this - this handles importing old-style LandmarkGroup
aaeebfhab,menpo/menpo,menpo/image/masked.py,62406818467d3f8cb02cc27442a46524b0a3b910,STILL_EXISTS,TODO: Replace _from_vector_inplace with this.
aaeebfhdg,menpo/menpo,menpo/io/input/video.py,5b50f223aa18b0b922fa12d8b32f177b2fe98182,a1a11dc02fa612a799e2c66e0ef8d647ebf81a4a,TODO: Remove when imageio fixes the ffmpeg importer duration\/start
aaeebfhgi,menpo/menpo,menpo/io/input/landmark.py,4d8116ead8f071e9d486a2ca6e83701593d7ee36,STILL_EXISTS,TODO: Use connectivity and create a graph type instead of PointCloud
aaeebfhie,menpo/menpo,menpo/io/input/base.py,8df15df5a7170ecd8687538053d4df57be40eae3,STILL_EXISTS,TODO: Remove once deprecated
aaeebfhih,menpo/menpo,menpo/io/test/io_import_test.py,8df15df5a7170ecd8687538053d4df57be40eae3,STILL_EXISTS,TODO: remove once the normalise argument is removed.
aaeebfifj,menpo/menpo,menpo/image/test/image_test.py,c19bd2fcd3d6a1c74cef87633ac0125abcbfecfb,STILL_EXISTS,TODO: Remove when Pillow 3.3.0 release on all platforms
aaeebfigc,menpo/menpo,setup.py,6be1aecf2955fef2ee9c58cd592c1d8afd87abe0,STILL_EXISTS,Perform a small amount of gymnastics to improve the compilation output on
aaeebfjjb,menpo/menpo,menpo/landmark/base.py,b1637f3ca063c328e49a6f22ae4fe4f90addbdc1,STILL_EXISTS,TODO: Deprecate this - this handles importing old-style LandmarkGroup
aaeebfjjc,menpo/menpo,menpo/landmark/base.py,b1637f3ca063c328e49a6f22ae4fe4f90addbdc1,STILL_EXISTS,This is a crazy hack that means when old style LandmarkGroup
aaeebgbei,menpo/menpo,menpo/image/rasterize.py,a86a4a790725d728538d9817c23be6692139fdcc,STILL_EXISTS,TODO: Since upgrading to Matplotlib 2.0 it seems that the rendering acts
aaeebgbhd,menpo/menpo,menpo/io/input/landmark.py,f79f83c71b643432252f229b1d5b3a3bf8d36a03,STILL_EXISTS,TODO: create the metadata label!
aaeebgdbh,menpo/menpo,menpo/_version.py,4bd994898cb7b2c8fc7c85b6876a6895e9ab8480,STILL_EXISTS,TODO: change this logic when there is a git pretty-format
aaeebgfec,feedly/transfer-nlp,transfer_nlp/models/nmt.py,6997da88295f9b653c9d9e324ad76ca18e980deb,STILL_EXISTS,TODO: change List to Tuple
aaeebgfej,feedly/transfer-nlp,transfer_nlp/models/perceptrons.py,6997da88295f9b653c9d9e324ad76ca18e980deb,STILL_EXISTS,TODO: experiment with more layers
aaeebgffa,feedly/transfer-nlp,transfer_nlp/models/perceptrons.py,6997da88295f9b653c9d9e324ad76ca18e980deb,STILL_EXISTS,TODO: experiment with other activation functions
aaeebggcb,feedly/transfer-nlp,transfer_nlp/runners/utils.py,6997da88295f9b653c9d9e324ad76ca18e980deb,STILL_EXISTS,Save model if performance improved
aaeebggic,feedly/transfer-nlp,transfer_nlp/runners/runnersABC.py,5770f658a72c31c646854a10d69fed99c81e3c31,STILL_EXISTS,Register useful parameters and objects useful for model instantiation #TODO: do proper testing on this part
aaeebggid,feedly/transfer-nlp,transfer_nlp/runners/runnersABC.py,5770f658a72c31c646854a10d69fed99c81e3c31,STILL_EXISTS,TODO: see if this fails
aaeebgiad,feedly/transfer-nlp,transfer_nlp/runners/runner.py,459944bedcc8c5e18237123570bf8b377a1cd81d,STILL_EXISTS,TODO: include this into the NMT training part
aaeebgjjc,feedly/transfer-nlp,transfer_nlp/plugins/generators.py,63faad106136efcd375d5a41b8eee875d09c565c,STILL_EXISTS,\"\"\" || This file shows an example of batch generators implementations. || To implement your own batch generators; you should use the decorator @register_batch_generator which allows the framework to || reuse your custom batch generators || \"\"\"
aaeebhbia,feedly/transfer-nlp,transfer_nlp/plugins/registry.py,cc2b1cbfac085ef3507d716151ae14ea06dd195a,STILL_EXISTS,TODO: prevent user to override existing plugins
aaeebhbib,feedly/transfer-nlp,transfer_nlp/plugins/registry.py,cc2b1cbfac085ef3507d716151ae14ea06dd195a,STILL_EXISTS,TODO: add message errors when key is missing
aaeebhbid,feedly/transfer-nlp,transfer_nlp/runners/single_task.py,cc2b1cbfac085ef3507d716151ae14ea06dd195a,f5c28a8d1d295f0674897b05d24fb0f63ac32c57,TODO: see if we can improve the online avertage (check exponential average)
aaeebhbig,feedly/transfer-nlp,transfer_nlp/runners/single_task.py,cc2b1cbfac085ef3507d716151ae14ea06dd195a,f5c28a8d1d295f0674897b05d24fb0f63ac32c57,TODO: see other averaging
aaeebhced,feedly/transfer-nlp,transfer_nlp/plugins/registry.py,12a1e219909678fad760504b610921cb32f601da,STILL_EXISTS,TODO: add message errors when key is missing
aaeebhcfh,feedly/transfer-nlp,transfer_nlp/runners/runnersABC.py,f5c28a8d1d295f0674897b05d24fb0f63ac32c57,456951cf6dc6c381760b4808c2da2ce1d042e922,sample_probability = (20 + self.epoch_index) \/ self.config_args['num_epochs']  # TODO: include this into the NMT training part
aaeebhchd,feedly/transfer-nlp,transfer_nlp/runners/runnersABC.py,f5c28a8d1d295f0674897b05d24fb0f63ac32c57,30e40868c491dfa634e46d17f96234f7ed705d9b,#  unfreezing the fc2 layer for extra tuning if needed
aaeebhcjc,feedly/transfer-nlp,transfer_nlp/runners/single_task.py,f5c28a8d1d295f0674897b05d24fb0f63ac32c57,456951cf6dc6c381760b4808c2da2ce1d042e922,TODO: see if we can improve the online average (check exponential average)
aaeebhdab,feedly/transfer-nlp,transfer_nlp/plugins/generators.py,30e40868c491dfa634e46d17f96234f7ed705d9b,STILL_EXISTS,TODO: create a simpler abstraction for generators that fit well into ignite
aaeebhdce,feedly/transfer-nlp,transfer_nlp/runners/single_task.py,30e40868c491dfa634e46d17f96234f7ed705d9b,456951cf6dc6c381760b4808c2da2ce1d042e922,TODO: See if we want to optimize loss or loss + penalty
aaeebhdgf,feedly/transfer-nlp,transfer_nlp/plugins/config.py,04627751d3c61d5257f76531ebb0b17756ba48e6,STILL_EXISTS,TODO: prettify?
aaeebhdhb,feedly/transfer-nlp,transfer_nlp/predictors/predictor.py,8c483e1a28f37259b68a2f1a946ffaeb2e4fe0f3,501e38c3bce0f90396cdb1bef49a124f76e53137,Register useful parameters and objects useful for model instantiation #TODO: do proper testing on this part
aaeebhdhc,feedly/transfer-nlp,transfer_nlp/predictors/predictor.py,8c483e1a28f37259b68a2f1a946ffaeb2e4fe0f3,501e38c3bce0f90396cdb1bef49a124f76e53137,TODO: see if this fails
aaeebheab,feedly/transfer-nlp,transfer_nlp/models/perceptrons2.py,aafaa8686bb56bf82a8d04aa7f3ea5d6b9e61218,STILL_EXISTS,TODO: experiment with more layers
aaeebheac,feedly/transfer-nlp,transfer_nlp/models/perceptrons2.py,aafaa8686bb56bf82a8d04aa7f3ea5d6b9e61218,STILL_EXISTS,TODO: experiment with other activation functions
aaeebhfca,feedly/transfer-nlp,transfer_nlp/experiments/surnames.py,501e38c3bce0f90396cdb1bef49a124f76e53137,STILL_EXISTS,TODO: experiment with more layers
aaeebhfcb,feedly/transfer-nlp,transfer_nlp/experiments/surnames.py,501e38c3bce0f90396cdb1bef49a124f76e53137,STILL_EXISTS,TODO: experiment with other activation functions
aaeebhgbd,feedly/transfer-nlp,transfer_nlp/loaders/loaders.py,232811f4793fba3af960b2c40de337f0eaa3ec8b,a9d94b80eed0a8847ac8554ab1d994537149fb83,TODO: move these to independant experiment python files
aaeebhgia,feedly/transfer-nlp,transfer_nlp/loaders/vectorizers.py,232811f4793fba3af960b2c40de337f0eaa3ec8b,a9d94b80eed0a8847ac8554ab1d994537149fb83,TODO: move this part to a NMT experiment python file
aaeebhhjd,feedly/transfer-nlp,experiments/bert/data.py,e7e45ca17bcbcba4dd959f34ec8fd96daf19bfbf,STILL_EXISTS,text = \"[CLS] A new release of BERT (Devlin; 2018) includes a model simultaneously pretrained on 104 languages with impressive performance for zero-shot cross-lingual transfer on a natural language inference task. [SEP]\"
aaeebiece,feedly/transfer-nlp,transfer_nlp/runner/experiment_runner.py,312b82a11120c3d4aabcc15ddda968871c1b7b74,STILL_EXISTS,TODO configurable?
aaeebifib,feedly/transfer-nlp,experiments/transformers/model.py,ce0bc2697f1c68b8083980bb31e8d21e94444d99,STILL_EXISTS,\"\"\" || This file contains models presented in the Transfer Learning for NLP Tutorial at NAACL 2019 || Models are adapted from https:\/\/colab.research.google.com\/drive\/1iDHCYIrWswIKp-n-pOg69xLoZO09MEgf#scrollTo=_FfRT6GTjHhC&forceEdit=true&offline=true&sandboxMode=true ||  || This is a WIP document and work is needed so that we don't have to replicate so many transformer classes || Ideally we'd like to have flexible transformer classes from which we can easily add || task-dependent heads and add adapter tools; e.g. freezing the backbone and add || residual connexion between layers.  || \"\"\"
aaeebigch,feedly/transfer-nlp,transfer_nlp/transfer_learning/transformers/model.py,61e7e61ce5aaf7ce9f1adb0b56f1de7f1e2c9b03,STILL_EXISTS,\"\"\" || This file contains models presented in the Transfer Learning for NLP Tutorial at NAACL 2019 || Models are adapted from https:\/\/colab.research.google.com\/drive\/1iDHCYIrWswIKp-n-pOg69xLoZO09MEgf#scrollTo=_FfRT6GTjHhC&forceEdit=true&offline=true&sandboxMode=true ||  || This is a WIP document and work is needed so that we don't have to replicate so many transformer classes || Ideally we'd like to have flexible transformer classes from which we can easily add || task-dependent heads and add adapter tools; e.g. freezing the backbone and add || residual connexion between layers.  || \"\"\"
aaeebihcc,feedly/transfer-nlp,transfer_nlp/plugins/trainers.py,2362ad636fde9f5832a5a74aa824cc01e9404e1e,STILL_EXISTS,Tensorboard is used within PyTorch but is not a dependency; so it should be installed manually by users
aaeebjede,chainer/chainer-chemistry,chainerchem/models/weavenet.py,c426e08351a5bf2e8989aaf8d79b76fceb177e05,STILL_EXISTS,last layer; only `atom_x` is needed.
aaeebjedf,chainer/chainer-chemistry,chainerchem/models/weavenet.py,c426e08351a5bf2e8989aaf8d79b76fceb177e05,STILL_EXISTS,not last layer; both `atom_x` and `pair_x` are needed
aaeebjeie,chainer/chainer-chemistry,docs/source/conf.py,c426e08351a5bf2e8989aaf8d79b76fceb177e05,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaeebjfdb,chainer/chainer-chemistry,examples/qm9/train_qm9.py,c426e08351a5bf2e8989aaf8d79b76fceb177e05,b3dd81d287d96de9f8cdf63cb023ea65907b921d,TODO: Not test yet; check behavior
aaeebjfdi,chainer/chainer-chemistry,examples/qm9/train_qm9.py,c426e08351a5bf2e8989aaf8d79b76fceb177e05,8f56eaba5a25913d182d8f8aa0fede82f78abf6f,TODO: Review default parameter
aaeebjfeb,chainer/chainer-chemistry,examples/qm9/train_qm9_features.py,c426e08351a5bf2e8989aaf8d79b76fceb177e05,STILL_EXISTS,TODO: Not test yet; check behavior
aaeebjffh,chainer/chainer-chemistry,examples/tox21/train_tox21.py,c426e08351a5bf2e8989aaf8d79b76fceb177e05,c2d9e5ce1978fdc95f2585bc52412552014eb43f,TODO: Not test yet; check behavior
aaeebjfgi,chainer/chainer-chemistry,tests/dataset_tests/preprocessors_tests/test_atomic_number_preprocessor.py,c426e08351a5bf2e8989aaf8d79b76fceb177e05,STILL_EXISTS,TODO (Oono)
aaeebjfhh,chainer/chainer-chemistry,tests/dataset_tests/preprocessors_tests/test_nfp_preprocessor.py,c426e08351a5bf2e8989aaf8d79b76fceb177e05,STILL_EXISTS,TODO (Oono)
aaeebjfig,chainer/chainer-chemistry,tests/datasets_tests/test_qm9.py,c426e08351a5bf2e8989aaf8d79b76fceb177e05,d88ac6ab9ee5c967a073e5a8623cb1227e5672c5,TODO
aaeebjhgc,chainer/chainer-chemistry,examples/qm9/train_qm9.py,1ed6735f1468f2a148b87a0949e7d6a1935fc598,8f56eaba5a25913d182d8f8aa0fede82f78abf6f,TODO: Review default parameter
aaeebjjbc,chainer/chainer-chemistry,chainerchem/dataset/preprocessors/weavenet_preprocessor.py,a117565c7795bb032dd8fe459213f7ebf22d6da9,STILL_EXISTS,TODO (Oono)
aaeebjjbf,chainer/chainer-chemistry,chainer_chemistry/dataset/preprocessors/weavenet_preprocessor.py,59354006f22e3a9c36f8470f59c54bd6b1d56078,STILL_EXISTS,TODO (Oono)
aaeecaafg,chainer/chainer-chemistry,examples/tox21/train_tox21.py,c1a80aa6afa91c065e92ba849d5bd8cd0ae8e30b,b903f8ec84d21253a1eebff9b29519a9e934f254,note that this is dirty hack implementation.
aaeecahbc,chainer/chainer-chemistry,tests/models_tests/prediction_tests/test_classifier.py,b5d726dd2bdcce66fb8195d07f697ec934d06a12,STILL_EXISTS,As a workaround; we wrap the function and invoke it in __call__ method.
aaeecahcd,chainer/chainer-chemistry,chainer_chemistry/models/prediction/classifier.py,f8d96aac34bce714eb4b318f5ef61bf90daa2e4d,0ed52a6e2ced8b5d62812a5f5b8c609002a13667,TODO: review
aaeecaifc,chainer/chainer-chemistry,tests/models_tests/prediction_tests/test_regressor.py,92c84f562c1b41dcf76913983bd1305b4396cfe4,382ebb0ee53ddb9ffbf10d233520a64fd13d0695,As a workaround; we wrap the function and invoke it in __call__ method.
aaeecbcda,chainer/chainer-chemistry,examples/qm9/train_qm9.py,7784bb5d8dce8fa868ef9445759d4a015c7c0575,b3dd81d287d96de9f8cdf63cb023ea65907b921d,TODO: Not test yet; check behavior
aaeecbhch,chainer/chainer-chemistry,chainer_chemistry/functions/mean_squared_error.py,72a6ab4ca7ea8978dbe8f89083b2b83300e09add,STILL_EXISTS,TODO(mottodora): implement task weight calculation
aaeecbhdf,chainer/chainer-chemistry,chainer_chemistry/functions/mean_absolute_error.py,c977c0b4542a50a71fa2913bda2a99ddc128a1e9,STILL_EXISTS,TODO(mottodora): implement task weight calculation
aaeecbhhh,chainer/chainer-chemistry,chainer_chemistry/dataset/splitters/stratified_splitter.py,11243eb4cdccfb66bece3147ba224fd643a5f29d,1d6b05d07d1b794689dfe88175c7ff420d07cc29,TODO: feature?
aaeecbhhi,chainer/chainer-chemistry,chainer_chemistry/dataset/splitters/stratified_splitter.py,11243eb4cdccfb66bece3147ba224fd643a5f29d,1d6b05d07d1b794689dfe88175c7ff420d07cc29,TODO: add remainder
aaeeccafb,chainer/chainer-chemistry,chainer_chemistry/training/extensions/batch_evaluator.py,2eb0d451ff95576b538d88257aa1cc857ea5daef,STILL_EXISTS,TODO(mottodora): use better name or infer
aaeeccejh,chainer/chainer-chemistry,tests/models_tests/test_gat.py,731a95a219a0376c086686e96b6f607637ce449f,STILL_EXISTS,TODO(nakago): check why tolerance is high
aaeecceji,chainer/chainer-chemistry,tests/models_tests/test_gat.py,731a95a219a0376c086686e96b6f607637ce449f,STILL_EXISTS,# TODO(nakago): check why tolerance is high
aaeeccfce,chainer/chainer-chemistry,chainer_chemistry/utils/extend.py,f0cb4675b971311b36df7a9cdd30863357126dda,STILL_EXISTS,TODO: update ref
aaeecchie,chainer/chainer-chemistry,examples/custom_dataset/predict_custom_dataset.py,856017638afa89a545a534d3c979c38a57681dfe,STILL_EXISTS,when using the GPU. This hack will be removed as soon as the cause of
aaeecchif,chainer/chainer-chemistry,examples/custom_dataset/predict_custom_dataset.py,856017638afa89a545a534d3c979c38a57681dfe,STILL_EXISTS,the issue is found and properly fixed.
aaeecchjb,chainer/chainer-chemistry,chainer_chemistry/models/gat.py,16e5900dfa7ca844f97f902077dde220f65dfce8,babe2e9ace7bf78a46ff2011d95d97a08461cf2a,TODO(mottodora): find better way to ignore non connected
aaeecdabh,chainer/chainer-chemistry,examples/qm9/predict_qm9.py,3787c0cc4c60ff4660b27eee985fd47517b84e91,7c85f442a09f040a329f80a49c7812c94c563169,when using the GPU. This hack will be removed as soon as the cause of
aaeecdabi,chainer/chainer-chemistry,examples/qm9/predict_qm9.py,3787c0cc4c60ff4660b27eee985fd47517b84e91,7c85f442a09f040a329f80a49c7812c94c563169,the issue is found and properly fixed.
aaeecdedi,chainer/chainer-chemistry,examples/molnet/predict_molnet.py,687119359b75e221ceca9df6252725e6a5f1fba4,dafad16f16be81a9443bd8fe9d3492662184aaed,when using the GPU. This hack will be removed as soon as the cause of
aaeecdedj,chainer/chainer-chemistry,examples/molnet/predict_molnet.py,687119359b75e221ceca9df6252725e6a5f1fba4,dafad16f16be81a9443bd8fe9d3492662184aaed,the issue is found and properly fixed.
aaeecdfci,chainer/chainer-chemistry,chainer_chemistry/models/prediction/regressor.py,7c85f442a09f040a329f80a49c7812c94c563169,STILL_EXISTS,same values become arrays instead. This seems to be a bug inside the
aaeecdggc,chainer/chainer-chemistry,examples/own_dataset/predict_own_dataset.py,3702edd30a1c61606039e489e281cba9e6a59217,e23229a57d86a718ce3ac8cb3147d09c20b2ca70,when using the GPU. This hack will be removed as soon as the cause of
aaeecdggd,chainer/chainer-chemistry,examples/own_dataset/predict_own_dataset.py,3702edd30a1c61606039e489e281cba9e6a59217,e23229a57d86a718ce3ac8cb3147d09c20b2ca70,the issue is found and properly fixed.
aaeecdggf,chainer/chainer-chemistry,examples/qm9/predict_qm9.py,3702edd30a1c61606039e489e281cba9e6a59217,e23229a57d86a718ce3ac8cb3147d09c20b2ca70,when using the GPU. This hack will be removed as soon as the cause of
aaeecdggg,chainer/chainer-chemistry,examples/qm9/predict_qm9.py,3702edd30a1c61606039e489e281cba9e6a59217,e23229a57d86a718ce3ac8cb3147d09c20b2ca70,the issue is found and properly fixed.
aaeecdiie,chainer/chainer-chemistry,chainer_chemistry/link_hooks/variable_monitor_link_hook.py,03ab3945a686e89e57a05dd5c4761786bee87ada,STILL_EXISTS,This LinkHook maybe instantiated multiple times.
aaeecdjca,chainer/chainer-chemistry,chainer_chemistry/saliency/calculator/gradient_calculator.py,03ab3945a686e89e57a05dd5c4761786bee87ada,6f868707240cc2aa84afaf0d80170f3e2cc1d420,I think option 1 \"take sum\" is better; since gradient is calculated
aaeecdjda,chainer/chainer-chemistry,chainer_chemistry/saliency/calculator/occlusion_calculator.py,03ab3945a686e89e57a05dd5c4761786bee87ada,6f868707240cc2aa84afaf0d80170f3e2cc1d420,TODO: xp and value assign dynamically
aaeecdjdj,chainer/chainer-chemistry,chainer_chemistry/saliency/calculator/occlusion_calculator.py,03ab3945a686e89e57a05dd5c4761786bee87ada,STILL_EXISTS,TODO: expand_dim dynamically
aaeecdjea,chainer/chainer-chemistry,chainer_chemistry/saliency/calculator/occlusion_calculator.py,03ab3945a686e89e57a05dd5c4761786bee87ada,STILL_EXISTS,TODO: test
aaeecffjj,chainer/chainer-chemistry,chainer_chemistry/links/update/gat_update.py,97525b38c534f1cd6b33d7e0ab8752155bf70279,STILL_EXISTS,TODO(mottodora): find better way to ignore non connected
aaeecfhgg,chainer/chainer-chemistry,tests/links_tests/readout_tests/test_general_readout.py,7a49af0e98a783fb747073613da78a689b65b95b,STILL_EXISTS,TODO (nakago): check why tolerance is so high.
aaeecfije,chainer/chainer-chemistry,chainer_chemistry/links/scaler/base.py,1551e337c06e5a6ceb09d61995f104e5d4719749,921d0348ee1608cd7472cfba332f99d60ae20c31,x maybe array or Variable
aaeecgigc,chainer/chainer-chemistry,chainer_chemistry/models/weavenet_gwm.py,dcf025cbee30761ee060b5be591501c579fd0d20,STILL_EXISTS,last layer; only `atom_x` is needed.
aaeecgigd,chainer/chainer-chemistry,chainer_chemistry/models/weavenet_gwm.py,dcf025cbee30761ee060b5be591501c579fd0d20,STILL_EXISTS,not last layer; both `atom_x` and `pair_x` are needed
aaeecgjhf,chainer/chainer-chemistry,examples/molnet/predict_molnet.py,9f60b38db87675d7b8a5fed40c3f8b3613382e91,fc80986d48413740742ddb420e9ecfbe69861bbd,ToDo: considre go\/no-go with the following modification
aaeecgjib,chainer/chainer-chemistry,examples/molnet/train_molnet.py,9f60b38db87675d7b8a5fed40c3f8b3613382e91,STILL_EXISTS,ToDo: consider go\/no-go of the following block
aaeechibd,chainer/chainer-chemistry,chainer_chemistry/models/gwm.py,80dad51ce270c73e886055d8c1493fa8917455f2,STILL_EXISTS,TODO: fail backward test
aaeecibfa,chainer/chainer-chemistry,tests/models_tests/test_gwm.py,ec53738a3187d098cbf9d4b83248b2938098e268,STILL_EXISTS,TODO: rtol is too high! GWM is too large to calculate
aaeecidcc,chainer/chainer-chemistry,tests/links_tests/scaler_tests/test_standard_scaler.py,f690a76e24e853e1a4177ed0336863b34be7f499,STILL_EXISTS,TODO(nakago): fix Chainer serializer.
aaeecidde,chainer/chainer-chemistry,tests/links_tests/scaler_tests/test_max_abs_scaler.py,cd9b7fc5978fd931e4d124e4679d900be808406e,STILL_EXISTS,TODO(nakago): fix Chainer serializer.
aaeecidfd,chainer/chainer-chemistry,tests/links_tests/scaler_tests/test_min_max_scaler.py,a627dad47d94705386e6ec807b372759699c03cb,STILL_EXISTS,TODO(nakago): fix Chainer serializer.
aaeeciehh,chainer/chainer-chemistry,tests/dataset_tests/preprocessors_tests/test_weavenet_preprocessor.py,4bd35912e53d875330d2792911f6eedee62389d2,STILL_EXISTS,TODO (nakago): test feature extraction behavior...
aaeecifei,chainer/chainer-chemistry,chainer_chemistry/models/graph_conv_model.py,7cd6f858014433c54cd4696515d9e07d56448ae8,7502ed784d8d00a0edcfc64b7bc1e2ad34872fd9,TODO: check axis
aaeecifej,chainer/chainer-chemistry,chainer_chemistry/models/ggnn.py,708aed97837c189226462facb9ee41898c905cdb,9f1f3888f7e66d7b2c9bbf354aeedb2c5f3ff2b1,TODO: activation
aaeeciffe,chainer/chainer-chemistry,chainer_chemistry/models/gin.py,708aed97837c189226462facb9ee41898c905cdb,9f1f3888f7e66d7b2c9bbf354aeedb2c5f3ff2b1,TODO: dropout_ratio; activation
aaeecifga,chainer/chainer-chemistry,chainer_chemistry/models/mpnn.py,708aed97837c189226462facb9ee41898c905cdb,9f1f3888f7e66d7b2c9bbf354aeedb2c5f3ff2b1,TODO: nn
aaeecifhb,chainer/chainer-chemistry,chainer_chemistry/models/graph_conv_model.py,45cda00ba3065fae22c58b0f53001a62681e4f6b,STILL_EXISTS,TODO: For RelGCN. Support other
aaeecifhc,chainer/chainer-chemistry,chainer_chemistry/models/graph_conv_model.py,45cda00ba3065fae22c58b0f53001a62681e4f6b,STILL_EXISTS,TODO: For GIN. Support other
aaeecifhf,chainer/chainer-chemistry,chainer_chemistry/models/graph_conv_model.py,158db4f1695e662db7f44e59dfcfbe45bf1d929c,STILL_EXISTS,TODO: same channel
aaeecifhg,chainer/chainer-chemistry,chainer_chemistry/models/graph_conv_model.py,158db4f1695e662db7f44e59dfcfbe45bf1d929c,4d4c3a765df3935b6cce64e8284214ff9afacabd,TODO: check
aaeecifhh,chainer/chainer-chemistry,chainer_chemistry/models/graph_conv_model.py,158db4f1695e662db7f44e59dfcfbe45bf1d929c,STILL_EXISTS,TODO: GraphLinear or GraphMLP can be used.
aaeecifhi,chainer/chainer-chemistry,chainer_chemistry/models/graph_conv_model.py,158db4f1695e662db7f44e59dfcfbe45bf1d929c,d3c05c4116587976f794dcef6dc4bcb4e92bbca4,TODO: RelGCN use GraphLinear here.
aaeecifhj,chainer/chainer-chemistry,chainer_chemistry/models/nfp.py,158db4f1695e662db7f44e59dfcfbe45bf1d929c,7502ed784d8d00a0edcfc64b7bc1e2ad34872fd9,TODO: use sum_hidden option
aaeecifia,chainer/chainer-chemistry,chainer_chemistry/models/relgcn.py,158db4f1695e662db7f44e59dfcfbe45bf1d929c,STILL_EXISTS,TODO: use GGNNReadout option
aaeecifid,chainer/chainer-chemistry,chainer_chemistry/models/schnet.py,c38b08e1edfaf30dbb011381a6bbd2ebb27ac00f,d3c05c4116587976f794dcef6dc4bcb4e92bbca4,TODO: use readout_hidden_dim
aaeecifie,chainer/chainer-chemistry,chainer_chemistry/models/schnet.py,c38b08e1edfaf30dbb011381a6bbd2ebb27ac00f,STILL_EXISTS,TODO: use num_rbf; radius_resolution; gamma
aaeecifih,chainer/chainer-chemistry,chainer_chemistry/models/graph_conv_model.py,8b58961c7bde1c13ebf8eb307fb1fb8ce310f97d,4d4c3a765df3935b6cce64e8284214ff9afacabd,TODO: check
aaeecifii,chainer/chainer-chemistry,chainer_chemistry/models/graph_conv_model.py,8b58961c7bde1c13ebf8eb307fb1fb8ce310f97d,STILL_EXISTS,TODO: mix use
aaeecifij,chainer/chainer-chemistry,chainer_chemistry/models/graph_conv_model.py,8b58961c7bde1c13ebf8eb307fb1fb8ce310f97d,STILL_EXISTS,TODO: the place of activation is various
aaeecifja,chainer/chainer-chemistry,chainer_chemistry/models/relgat.py,3f890a7928ad522a482870c91e0275a432c940bf,STILL_EXISTS,TODO: activation; softmax_mode; concat_heads
aaeecifjb,chainer/chainer-chemistry,chainer_chemistry/models/relgat.py,3f890a7928ad522a482870c91e0275a432c940bf,STILL_EXISTS,TODO: consider in_channels when concat_heads is True
aaeecifjf,chainer/chainer-chemistry,chainer_chemistry/models/graph_conv_model.py,c2f8bbae18f8abe35e094b4d2884b9131c92da6b,4d4c3a765df3935b6cce64e8284214ff9afacabd,TODO: check
aaeecifjg,chainer/chainer-chemistry,chainer_chemistry/models/rsgcn.py,c2f8bbae18f8abe35e094b4d2884b9131c92da6b,9f1f3888f7e66d7b2c9bbf354aeedb2c5f3ff2b1,TODO: check
aaeecigba,chainer/chainer-chemistry,chainer_chemistry/models/graph_conv_model.py,bd5a731c9d3a72e39948ad1b73f102113bc5187f,4d4c3a765df3935b6cce64e8284214ff9afacabd,TODO: need cool implementation
aaeecigbe,chainer/chainer-chemistry,chainer_chemistry/models/graph_conv_model.py,9f1f3888f7e66d7b2c9bbf354aeedb2c5f3ff2b1,c6843fca71d910cdbf2ef39084afa39772c6bb2c,TODO: This position is right?
aaeecigbf,chainer/chainer-chemistry,chainer_chemistry/models/graph_conv_model.py,9f1f3888f7e66d7b2c9bbf354aeedb2c5f3ff2b1,c6843fca71d910cdbf2ef39084afa39772c6bb2c,TODO: Now this activation is same as relgcn one.
aaeecigbg,chainer/chainer-chemistry,chainer_chemistry/models/graph_conv_model.py,9f1f3888f7e66d7b2c9bbf354aeedb2c5f3ff2b1,c6843fca71d910cdbf2ef39084afa39772c6bb2c,TODO: RSGCN activations are only applied self.n_layers - 1 times.
aaeecigbh,chainer/chainer-chemistry,chainer_chemistry/models/graph_conv_model.py,9f1f3888f7e66d7b2c9bbf354aeedb2c5f3ff2b1,7502ed784d8d00a0edcfc64b7bc1e2ad34872fd9,TODO: SchNet's concat axis is 2.
aaeecigcf,chainer/chainer-chemistry,chainer_chemistry/models/graph_conv_model.py,3dda09418b9aa1dd06f6f2a28fe4d3d0d9edb6eb,STILL_EXISTS,TODO: cool implementation
aaeecigdh,chainer/chainer-chemistry,chainer_chemistry/models/graph_conv_model.py,80e2d65264ea9baf2fb273b57b57f881c16a6910,STILL_EXISTS,TODO: raise warning
aaeeciggb,chainer/chainer-chemistry,chainer_chemistry/models/gwm.py,c67ed1ed05ac7eb79bf242796d2a4163d18988ce,STILL_EXISTS,TODO: more efficient computation. Maybe we can calculate self.G(g)
aaeecigge,chainer/chainer-chemistry,chainer_chemistry/models/gwm.py,c67ed1ed05ac7eb79bf242796d2a4163d18988ce,STILL_EXISTS,TODO (nakago): Consider to delete `V_super` maybe not necessary.
aaeeciggf,chainer/chainer-chemistry,chainer_chemistry/models/gwm.py,c67ed1ed05ac7eb79bf242796d2a4163d18988ce,STILL_EXISTS,TODO (nakago): Consider to move `V_super` to calculate `h_j`??
aaeecihdj,chainer/chainer-chemistry,tests/models_tests/gwm_tests/test_gwm_graph_conv_model.py,e853f7ffcdfdf7714f38349cf0218905b2b6a3c6,STILL_EXISTS,TODO (nakago): SchNetUpdate need `in_channels` kwargs; not supported.
aaeecihea,chainer/chainer-chemistry,tests/models_tests/gwm_tests/test_gwm_graph_conv_model.py,e853f7ffcdfdf7714f38349cf0218905b2b6a3c6,STILL_EXISTS,TODO (nakago): Support MPNNUpdate.
aaeecihee,chainer/chainer-chemistry,chainer_chemistry/models/prediction/graph_conv_predictor.py,acd145841d2d79726e97f46b2104434db6604b43,STILL_EXISTS,TODO (nakago): support super_node & is_real_node args.
aaeecihef,chainer/chainer-chemistry,chainer_chemistry/models/prediction/set_up_predictor.py,acd145841d2d79726e97f46b2104434db6604b43,STILL_EXISTS,TODO (nakago): Add nfp_gwm; ggnn_gwm; rsgcn_gwm; gin_gwm
aaeecihei,chainer/chainer-chemistry,examples/molnet/train_molnet.py,acd145841d2d79726e97f46b2104434db6604b43,a0a99b418fc30e0a3e0ca61d0a825b373818950d,TODO (nakago): support 'nfp_gwm'; 'ggnn_gwm'; 'rsgcn_gwm'; 'gin_gwm'
aaeecijbf,chainer/chainer-chemistry,chainer_chemistry/training/extensions/auto_print_report.py,24b335c82990f6ba17684c73ef014355c8ebffa8,STILL_EXISTS,TODO: sort other entries if necessary
aaeecijbg,chainer/chainer-chemistry,chainer_chemistry/training/extensions/auto_print_report.py,24b335c82990f6ba17684c73ef014355c8ebffa8,STILL_EXISTS,move iteration to head
aaeecijbh,chainer/chainer-chemistry,chainer_chemistry/training/extensions/auto_print_report.py,24b335c82990f6ba17684c73ef014355c8ebffa8,STILL_EXISTS,move epoch to head
aaeecijbi,chainer/chainer-chemistry,chainer_chemistry/training/extensions/auto_print_report.py,24b335c82990f6ba17684c73ef014355c8ebffa8,STILL_EXISTS,move elapsed_time to tail
aaeecijch,chainer/chainer-chemistry,examples/molnet/train_molnet.py,b3ebc614b592547ecd4e392cbf7f3547b8783d80,a516366ea8866c12469a104cc86b1b712224121e,TODO (nakago): support 'nfp_gwm'; 'ggnn_gwm'; 'rsgcn_gwm'; 'gin_gwm'
aaeecijgh,chainer/chainer-chemistry,examples/molnet/train_molnet.py,b3ebc614b592547ecd4e392cbf7f3547b8783d80,220ad21ab23095579ef56b15fb8795bc936708a9,TODO: Use standard scaler for regression task
aaeecjfdh,chainer/chainer-chemistry,chainer_chemistry/utils/train_utils.py,631ac9256134ba5c78f4855eafcde2b45f211fde,STILL_EXISTS,TODO: consider to include snapshot as default extension.
aaeecjfhd,chainer/chainer-chemistry,chainer_chemistry/models/ggnn.py,bd0a049349dfaa54e68ba28094eb3596089f5ceb,dff09252729218259ae477fb1ee93367493cb365,TODO: support this
aaeecjfib,chainer/chainer-chemistry,chainer_chemistry/models/prediction/regressor2.py,b3ae8ef23421cac86f0a6da574e5f8538b5d86ad,STILL_EXISTS,same values become arrays instead. This seems to be a bug inside the
aaeecjfjj,chainer/chainer-chemistry,chainer_chemistry/links/readout/megnet_readout.py,55b1e11df9af5e22ce74db953ad1a3bf3dc08d22,STILL_EXISTS,TODO: check the thesis
aaeecjgjc,chainer/chainer-chemistry,chainer_chemistry/datasets/mp.py,6dd054f2b5eb53b4812d299a38bf3c1b5550fa4a,STILL_EXISTS,TODO: data_dir\u306F\u4ECA\u5F8C\u306FURL\u3092\u6307\u3059\u3088\u3046\u306B\u306A\u308B
aaeecjgjd,chainer/chainer-chemistry,chainer_chemistry/datasets/mp.py,6dd054f2b5eb53b4812d299a38bf3c1b5550fa4a,507256cce045e2070d3ea47fb5fe943712b4cf7f,TODO: why??
aaeecjgje,chainer/chainer-chemistry,chainer_chemistry/datasets/mp.py,6dd054f2b5eb53b4812d299a38bf3c1b5550fa4a,507256cce045e2070d3ea47fb5fe943712b4cf7f,TODO: why more than 1 ??
aaeecjgjf,chainer/chainer-chemistry,chainer_chemistry/datasets/mp.py,6dd054f2b5eb53b4812d299a38bf3c1b5550fa4a,24c83ac75577d2f8516c391bd92a586e828b5a1d,TODO: is_stable\u306F\u5916\u3067\u53D7\u3051\u53D6\u308B
aaeecjgjg,chainer/chainer-chemistry,chainer_chemistry/datasets/mp.py,6dd054f2b5eb53b4812d299a38bf3c1b5550fa4a,STILL_EXISTS,TODO: data_dir\u306FURL\u3092\u6307\u3059\u3088\u3046\u306B\u3059\u308B
aaeecjhah,chainer/chainer-chemistry,examples/mp/predict_mp.py,6dd054f2b5eb53b4812d299a38bf3c1b5550fa4a,STILL_EXISTS,TODO : \u4ECA\u5F8C\u4E0D\u8981\u306B\u306A\u308B
aaeecjhch,chainer/chainer-chemistry,examples/mp/train_mp.py,6dd054f2b5eb53b4812d299a38bf3c1b5550fa4a,STILL_EXISTS,TODO : \u4ECA\u5F8C\u4E0D\u8981\u306B\u306A\u308B
aaeedaaec,chainer/chainer-chemistry,chainer_chemistry/dataset/preprocessors/cgcnn_preprocessor.py,1ed09907c8895686c6c01f3a6fa1b33305ef4f1f,STILL_EXISTS,TODO: \u3053\u3053\u3082\u9069\u5F53\u306APATH\u306B\u7F6E\u304D\u63DB\u3048\u308B
aaeedabab,chainer/chainer-chemistry,chainer_chemistry/utils/train_utils.py,b2f322a794f9bba2a18479e93365fb8926a77d6d,STILL_EXISTS,TODO: consider to include snapshot as default extension.
aaeedebbj,chainer/chainer-chemistry,examples/qm9/train_qm9.py,53ed4f1171e12bc27a335a8f4675bda423789f79,STILL_EXISTS,TODO: support caching of other dataset type...
aaeedehja,chainer/chainer-chemistry,chainer_chemistry/dataset/preprocessors/neighbor_node_expansion.py,2e83d8955e81e626f000c31d7620225d76576f50,STILL_EXISTS,Purpose:     Implement Neighbor Node Expansion
aaeedeidb,chainer/chainer-chemistry,chainer_chemistry/dataset/preprocessors/neighbor_node_expansion.py,2e83d8955e81e626f000c31d7620225d76576f50,STILL_EXISTS,ToDo: try another indexing: e.g. oirignal node label + extneions
aaeedejai,chainer/chainer-chemistry,chainer_chemistry/dataset/preprocessors/test_neighbor_node_expansion.py,2e83d8955e81e626f000c31d7620225d76576f50,STILL_EXISTS,ToDo: wrie a more formal test...
aaeedface,chainer/chainer-chemistry,chainer_chemistry/dataset/preprocessors/wle_io.py,b90e4514134ab53277eb1dd5d4f3e7ac0077e9c9,STILL_EXISTS,ToDo: try another indexing: e.g. orignal node label + extneions
aaeedfahj,chainer/chainer-chemistry,chainer_chemistry/models/cwle/cwle_graph_conv_model.py,b90e4514134ab53277eb1dd5d4f3e7ac0077e9c9,STILL_EXISTS,TODO: GraphLinear or GraphMLP can be used.
aaeedfbaf,chainer/chainer-chemistry,chainer_chemistry/models/gwle/gwle_graph_conv_model.py,b90e4514134ab53277eb1dd5d4f3e7ac0077e9c9,STILL_EXISTS,TODO: GraphLinear or GraphMLP can be used.
aaeedffga,chainer/chainer-chemistry,examples/artificial/predict_graph.py,b90e4514134ab53277eb1dd5d4f3e7ac0077e9c9,STILL_EXISTS,ToDo: consider go\/no-go with following modification
aaeedfgij,chainer/chainer-chemistry,examples/artificial/train_graph.py,b90e4514134ab53277eb1dd5d4f3e7ac0077e9c9,STILL_EXISTS,ToDO: scaler should be placed here
aaeedfgja,chainer/chainer-chemistry,examples/artificial/train_graph.py,b90e4514134ab53277eb1dd5d4f3e7ac0077e9c9,STILL_EXISTS,ToDo: fit the scaler
aaeedfgjb,chainer/chainer-chemistry,examples/artificial/train_graph.py,b90e4514134ab53277eb1dd5d4f3e7ac0077e9c9,STILL_EXISTS,ToDo: transform dataset_parts[0-2]
aaeedfhae,chainer/chainer-chemistry,examples/artificial/train_graph.py,b90e4514134ab53277eb1dd5d4f3e7ac0077e9c9,STILL_EXISTS,ToDo: We need to incoporeat scaler into download_entire_dataset; instead of predictors.
aaeedfhaf,chainer/chainer-chemistry,examples/artificial/train_graph.py,b90e4514134ab53277eb1dd5d4f3e7ac0077e9c9,STILL_EXISTS,ToDo: scaler must be incorporated into download_entire_datasets. not here
aaeedfhah,chainer/chainer-chemistry,examples/artificial/train_graph.py,b90e4514134ab53277eb1dd5d4f3e7ac0077e9c9,STILL_EXISTS,ToDo: set label_scaler always None
aaeedfhcd,chainer/chainer-chemistry,examples/molnet/DumpEmbeddings.py,b90e4514134ab53277eb1dd5d4f3e7ac0077e9c9,STILL_EXISTS,ToDo: consider go\/no-go with following modification
aaeedfhfh,chainer/chainer-chemistry,examples/molnet/computePI_WLE.py,b90e4514134ab53277eb1dd5d4f3e7ac0077e9c9,STILL_EXISTS,ToDo: consider go\/no-go with following modification
aaeedfief,chainer/chainer-chemistry,examples/molnet/optuna_train_molnet.py,b90e4514134ab53277eb1dd5d4f3e7ac0077e9c9,STILL_EXISTS,ToDO: scaler should be placed here
aaeedfieg,chainer/chainer-chemistry,examples/molnet/optuna_train_molnet.py,b90e4514134ab53277eb1dd5d4f3e7ac0077e9c9,STILL_EXISTS,ToDo: fit the scaler
aaeedfieh,chainer/chainer-chemistry,examples/molnet/optuna_train_molnet.py,b90e4514134ab53277eb1dd5d4f3e7ac0077e9c9,STILL_EXISTS,ToDo: transform dataset_parts[0-2]
aaeedfigh,chainer/chainer-chemistry,examples/molnet/optuna_train_molnet.py,b90e4514134ab53277eb1dd5d4f3e7ac0077e9c9,STILL_EXISTS,ToDo: We need to incoporeat scaler into download_entire_dataset; instead of predictors.
aaeedfigi,chainer/chainer-chemistry,examples/molnet/optuna_train_molnet.py,b90e4514134ab53277eb1dd5d4f3e7ac0077e9c9,STILL_EXISTS,ToDo: scaler must be incorporated into download_entire_datasets. not here
aaeedfiha,chainer/chainer-chemistry,examples/molnet/optuna_train_molnet.py,b90e4514134ab53277eb1dd5d4f3e7ac0077e9c9,STILL_EXISTS,ToDo: set label_scaler always None
aaeedfjbi,chainer/chainer-chemistry,examples/molnet/train_molnet.py,b90e4514134ab53277eb1dd5d4f3e7ac0077e9c9,STILL_EXISTS,ToDo: fit the scaler
aaeedfjbj,chainer/chainer-chemistry,examples/molnet/train_molnet.py,b90e4514134ab53277eb1dd5d4f3e7ac0077e9c9,STILL_EXISTS,ToDo: transform dataset_parts[0-2]
aaeedfjca,chainer/chainer-chemistry,examples/molnet/train_molnet.py,b90e4514134ab53277eb1dd5d4f3e7ac0077e9c9,STILL_EXISTS,ToDo: We need to incoporeat scaler into download_entire_dataset; instead of predictors.
aaeedfjcb,chainer/chainer-chemistry,examples/molnet/train_molnet.py,b90e4514134ab53277eb1dd5d4f3e7ac0077e9c9,STILL_EXISTS,ToDo: scaler must be incorporated into download_entire_datasets. not here
aaeedfjcc,chainer/chainer-chemistry,examples/molnet/train_molnet.py,b90e4514134ab53277eb1dd5d4f3e7ac0077e9c9,STILL_EXISTS,ToDo: set label_scaler always None
aaeedfjhb,chainer/chainer-chemistry,examples/molnet/visualize_WLE.py,b90e4514134ab53277eb1dd5d4f3e7ac0077e9c9,STILL_EXISTS,ToDo: consider go\/no-go with following modification
aaeeeedcg,chainer/chainer-chemistry,examples/molnet/predict_molnet.py,611417a5ab16402833f7cb80fa7b6c3c88013740,STILL_EXISTS,ToDo: consider go\/no-go with following modification
aaeeeedig,chainer/chainer-chemistry,examples/molnet/train_molnet.py,611417a5ab16402833f7cb80fa7b6c3c88013740,STILL_EXISTS,TODO: consider go\/no-go of the following block
aaeeefchg,online-ml/river,skmultiflow/core/pipeline/Pipeline.py,6150d1774641103142c9d9f6cea79a9cc9c09c1c,STILL_EXISTS,\"should implement fit and transform.\")
aaeeefdcb,online-ml/river,skmultiflow/evaluation/EvaluatePrequential.py,e7e81bbe83b26f6ad78014d3325a68a8e2f39a4c,d2edcf9d03de65f28aca4777cfa83e9625baebb1,# TODO
aaeeefdcc,online-ml/river,skmultiflow/evaluation/EvaluatePrequential.py,e7e81bbe83b26f6ad78014d3325a68a8e2f39a4c,STILL_EXISTS,# fix the problem you created; the visualizer has to be dumb; he just receives statistics
aaeeefegc,online-ml/river,skmultiflow/evaluation/measure_collection.py,fd74fb557cfd42a08a9953758d82bc04c36db1db,STILL_EXISTS,Verify if its needed to decrease the majority_classifier count
aaeeefejd,online-ml/river,skmultiflow/core/pipeline.py,0808b192293f20e60affcf8bcbfd2485215a0c18,8400bca8c0b4af01970ca07432c571ad9237b3da,raise TypeError(\"Last step of pipeline should implement partial_fit.\")
aaeeefhja,online-ml/river,skmultiflow/evaluation/measure_collection.py,3546ba97854e6707fd370b53147596f8b2dfcba9,STILL_EXISTS,Verify if its needed to decrease the count of any label
aaeeefifg,online-ml/river,doc/conf.py,4643c73d6bfbf53ddca24b1bbc09583149d9b309,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaeeefjfc,online-ml/river,skmultiflow/classification/core/split_criteria/info_gain_split_criterion.py,b9f4643836e9e18e4e7379bd3450630cb09b35ef,STILL_EXISTS,TODO: How small can d be before log2 overflows?
aaeeefjfi,online-ml/river,skmultiflow/classification/trees/hoeffding_tree.py,b9f4643836e9e18e4e7379bd3450630cb09b35ef,490eee6297ca60a4fb734033e4760282f63a7191,TODO define
aaeeefjga,online-ml/river,skmultiflow/classification/trees/hoeffding_tree.py,b9f4643836e9e18e4e7379bd3450630cb09b35ef,98dba2dd3e4c1031922a26e285d8be87a647bc96,TODO check
aaeeefjgj,online-ml/river,skmultiflow/classification/trees/hoeffding_tree.py,b9f4643836e9e18e4e7379bd3450630cb09b35ef,6c0b55a12832bcb3992ef1cc14bd2eea534331dc,Allowed error in split decision; closer to 0 takes longer to decide.
aaeeegjba,online-ml/river,skmultiflow/classification/trees/hoeffding_adaptive_tree.py,1b29c74f81bd018cdbe0e7332b8130e1d7c0ba35,STILL_EXISTS,TODO (check the casting to object)
aaeeegjfe,online-ml/river,skmultiflow/classification/trees/hoeffding_adaptive_tree.py,1b29c74f81bd018cdbe0e7332b8130e1d7c0ba35,27362279c66da88a838df998461465e94993729e,Example TODO Create a demo\/test from this
aaeeehcdd,online-ml/river,skmultiflow/classification/meta/adaptive_random_forests.py,988129bfba353dfc802641df2976716ca1ccd7a7,cbbbe64e1563b4297582bab4bc6992533ddaf36b,TODO use skmultiflow evaluator
aaeeehcde,online-ml/river,skmultiflow/classification/meta/adaptive_random_forests.py,988129bfba353dfc802641df2976716ca1ccd7a7,cbbbe64e1563b4297582bab4bc6992533ddaf36b,TODO add code to support the selection of evaluation metric
aaeeehcdj,online-ml/river,skmultiflow/classification/trees/arf_hoeffding_tree.py,988129bfba353dfc802641df2976716ca1ccd7a7,893c9a41d298006383dabe6e48a35eeec266987b,TODO Add HT parameters to ARF Hoeffding Tree constructor signature
aaeeehcec,online-ml/river,skmultiflow/classification/trees/arf_hoeffding_tree.py,988129bfba353dfc802641df2976716ca1ccd7a7,893c9a41d298006383dabe6e48a35eeec266987b,TODO Pass all HT parameters once they are available at the ARFHT class level
aaeeehced,online-ml/river,skmultiflow/classification/trees/arf_hoeffding_tree.py,fa4115f03bc5d8bdff401caf178e4a0ca8c7d9f4,2f40363912b1474840e961a274422a174e530a85,TODO check attr-1
aaeeehceg,online-ml/river,skmultiflow/classification/meta/adaptive_random_forests.py,eb3d55ff9f6a55fb97dd1cd06e441f4fbd351e7f,cbbbe64e1563b4297582bab4bc6992533ddaf36b,TODO Verify approach
aaeeehcfa,online-ml/river,skmultiflow/classification/meta/adaptive_random_forests.py,eb3d55ff9f6a55fb97dd1cd06e441f4fbd351e7f,STILL_EXISTS,TODO check vote normalization
aaeeehcge,online-ml/river,skmultiflow/classification/meta/adaptive_random_forests.py,899feb8f1035dc6f4dfa2b52cf478d4db9b3856a,STILL_EXISTS,TODO Pass all HT parameters once they are available at the ARFHT class level
aaeeehdgg,online-ml/river,skmultiflow/demos/_test_file_stream_multiple_cfier.py,b86f892603b15c17aa12e71090eed976d6838b26,STILL_EXISTS,Demo 2 -- csv output should look nice
aaeeehdii,online-ml/river,skmultiflow/classification/meta/batch_incremental.py,73afbe4a65a544baebec506655d4093c873606a6,STILL_EXISTS,(TODO: not very python-esque ot the moment)
aaeeehfab,online-ml/river,src/skmultiflow/evaluation/evaluate_holdout.py,f56388a32146e1eb704d26eea782ce320773acae,ba2f6f12c0777aad31f5874e9f89d2f5aea1ec70,logging.info('Pre-training on 1 sample.')   # TODO Confirm if needed
aaeeehfba,online-ml/river,src/skmultiflow/evaluation/evaluate_holdout.py,f56388a32146e1eb704d26eea782ce320773acae,STILL_EXISTS,TODO Confirm place
aaeeehffi,online-ml/river,src/skmultiflow/data/dataset_stream.py,4d1db4906551f7e380b8706266bad266cb44d30f,STILL_EXISTS,Take only n_targets columns to the right of target_idx; use the rest as features
aaeeehidg,online-ml/river,src/skmultiflow/evaluation/base_evaluator.py,e6021756974b3d140d053799223d9f61dcaa84e8,e8d2ba45ff841707c0de9db10280f868fd0d8fb9,TODO consider new plot types
aaeeehidi,online-ml/river,src/skmultiflow/evaluation/base_evaluator.py,e6021756974b3d140d053799223d9f61dcaa84e8,ba2f6f12c0777aad31f5874e9f89d2f5aea1ec70,TODO extend the original MULTI_OUTPUT problem evaluation for
aaeeehiea,online-ml/river,src/skmultiflow/evaluation/base_evaluator.py,e6021756974b3d140d053799223d9f61dcaa84e8,99ab85ce23fc9ad7c7b04e2ca047f641120539e0,TODO Implement ARMAE
aaeeehied,online-ml/river,src/skmultiflow/trees/intra_cluster_variance_reduction_split_criterion.py,6ea0008eeb0e076ec69b6f8eeff9d00fcac5adcb,STILL_EXISTS,TODO Also consider passing different weights for the targets
aaeeehief,online-ml/river,src/skmultiflow/trees/hoeffding_nominal_class_attribute_observer.py,30d91e1001864fc5621ba08d65ac1800fb7e87ae,2086050514fb5fed25f92575d4055a1aba793135,TODO Also consider nominal attributes
aaeeehifc,online-ml/river,src/skmultiflow/trees/multi_target_regression_hoeffding_tree.py,e8d2ba45ff841707c0de9db10280f868fd0d8fb9,STILL_EXISTS,TODO: Verify perceptron update
aaeeehige,online-ml/river,src/skmultiflow/trees/multi_target_regression_hoeffding_tree.py,e8d2ba45ff841707c0de9db10280f868fd0d8fb9,STILL_EXISTS,TODO Verify
aaeeehiha,online-ml/river,src/skmultiflow/trees/multi_target_regression_hoeffding_tree.py,e8d2ba45ff841707c0de9db10280f868fd0d8fb9,1dfc8af04096eaf1731b2e617fddd1bda8711cf9,################## TODO Verify #########################
aaeeeiaac,online-ml/river,src/skmultiflow/trees/hoeffding_nominal_class_attribute_observer.py,a4a452254b9f1b3ed5b3419aaa00f6180e50d894,STILL_EXISTS,TODO Also consider nominal attributes
aaeeeibah,online-ml/river,src/skmultiflow/evaluation/base_evaluator.py,6ebb1804cb132d991aed9002d172c3e16665404f,91f1bd5ef455c3839ffb972abdf43bf15c3db12c,TODO extend the original MULTI_OUTPUT problem evaluation for
aaeeeidga,online-ml/river,src/skmultiflow/meta/classifier_chains.py,295cb5a19dd434c2dfac9aaffb8a767fa89a7481,STILL_EXISTS,TODO: much of this can be shared with Regressor Chains; probably should use a base class to inherit here.
aaeeeidha,online-ml/river,src/skmultiflow/meta/adaptive_random_forests.py,901d7c65d6331a40a365a32316ea0013cb2dff4e,116aea1ab6caea74f990a7cadff70391f425fbea,TODO: Replace with version which works for unspecified classes
aaeeeidhg,online-ml/river,src/skmultiflow/evaluation/base_evaluator.py,259a336c8f0d8c3de29c76c2e3c7a34d8a05c361,bcc9dc9678867077d5a4dbf70aa018e2ba44e5e4,TODO let the user choose the feature indices of interest
aaeeeidjg,online-ml/river,src/skmultiflow/visualization/evaluation_visualizer.py,259a336c8f0d8c3de29c76c2e3c7a34d8a05c361,STILL_EXISTS,TODO consider a fading\/update strategy instead
aaeeeiebc,online-ml/river,src/skmultiflow/visualization/evaluation_visualizer.py,6b59ec4f9570c82442c252b2d77d7c7114b55678,STILL_EXISTS,TODO confirm buffer update inside the loop
aaeeeiecf,online-ml/river,src/skmultiflow/evaluation/base_evaluator.py,3e9cdf545d08e5c2f9f2b8767ba032b19b5d5afd,STILL_EXISTS,TODO let the user choose the feature indices of interest
aaeeeiede,online-ml/river,src/skmultiflow/visualization/evaluation_visualizer.py,3e9cdf545d08e5c2f9f2b8767ba032b19b5d5afd,STILL_EXISTS,TODO confirm buffer update inside the loop
aaeeeiedh,online-ml/river,src/skmultiflow/visualization/evaluation_visualizer.py,78d78912465c00be2793a04f21ba7706576bdc97,STILL_EXISTS,TODO confirm buffer update inside the loop
aaeeeieia,online-ml/river,src/skmultiflow/trees/multi_target_regression_hoeffding_tree.py,3bbfc26e86eeb660ae7b5c2850c399d66c936add,5cd2acad278d36dee9a54870d5b9bc82c3ef4ae0,TODO Check with new functionalities
aaeeeieib,online-ml/river,src/skmultiflow/trees/multi_target_regression_hoeffding_tree.py,3bbfc26e86eeb660ae7b5c2850c399d66c936add,STILL_EXISTS,TODO reactivation procedure???
aaeeeieij,online-ml/river,src/skmultiflow/trees/hoeffding_anytime_tree.py,e37fead880016c8b48f8b9c10da082ee95c4fbae,STILL_EXISTS,Todo : Add memory management
aaeeeifaj,online-ml/river,src/skmultiflow/trees/hoeffding_anytime_tree.py,e37fead880016c8b48f8b9c10da082ee95c4fbae,STILL_EXISTS,Move in depth
aaeeeifba,online-ml/river,src/skmultiflow/trees/hoeffding_anytime_tree.py,e37fead880016c8b48f8b9c10da082ee95c4fbae,STILL_EXISTS,Todo : raise error for nominal attribute
aaeeeiffe,online-ml/river,creme/__init__.py,2e3cb318ca5359d077657c51acbf8728d5c23b85,STILL_EXISTS,\"\"\" || ## Introduction ||  || creme is a library for in**creme**ntal learning. Incremental learning is a machine learning || regime where the observations are made available one by one. It is also known as online learning; || iterative learning; or sequential learning. This is in contrast to batch learning where all the || data is processed at once. Incremental learning is desirable when the data is too big to fit in || memory; or simply when it isn't available all at once. creme's API is heavily inspired from that || of [scikit-learn](https:\/\/scikit-learn.org\/stable\/); enough so that users who are familiar with || scikit-learn should feel right at home. ||  || Most machine learning algorithms (be it supervised or unsupervised) assume a batch regime. However || some of these algorithms have online variants. For example || [stochastic gradient descent](https:\/\/www.wikiwand.com\/en\/Stochastic_gradient_descent) is the || online version of [gradient descent](https:\/\/www.wikiwand.com\/en\/Gradient_descent) whilst || [incremental k-means clustering](http:\/\/www.cs.princeton.edu\/courses\/archive\/fall08\/cos436\/Duda\/C\/sk_means.htm) || is the online adaptation of [k-means clustering](https:\/\/www.wikiwand.com\/en\/K-means_clustering). || In general online algorithms perform slightly worse than their batch counterparts; although the gap || is usually very small. However; online learning algorithms only consume a tiny amount of RAM; which || thus makes them scalable and ideal candidates for commodity hardware and embedded systems. ||  || The objective of creme is to provide a nice interface for putting an incremental learning || pipeline in place; a bit like what scikit-learn does for batch learning. Of course there are other || open-source solutions available; but they are somewhat specialized towards certain tasks and can || require a steep learning curve. Moreover some of these solutions aren't \"truly online\" as they || mostly assume the data is contained in a file. With creme it is possible to learn from a stream || in it's largest sense; be it a database query or a Kafka instance. ||  || ## API ||  || Just like scikit-learn; each of creme's estimators provide a similar API. Every estimator has a || `fit_one(x; y)` method which will fit the estimator with a given set of features `x` and a || target `y`. In addition; estimators have a `predict_one(x)` or a `transform_one(x)` method; || depending on their type. Classifiers also implement a `predict_proba_one(x)` method. Each call to || `fit_one` will also return the predicted value for the current `x`; which makes it possible to || monitor the progress of the estimator online. Although creme's purpose is incremental learning; || each of it's estimators also implements the scikit-learn's `fit\/predict\/transform` API; which makes || it possible to reuse scikit-learn's toolbox. This is mostly intended to help comparing batch || algorithms to their online versions. ||  || Rows in creme are represented by `dict`s that map feature names to values. The main advantage of || using a `dict` over a `numpy` array is that features can be accessed by name rather by position. || Moreover `dict`s can store values of different types; whereas all the values in a `numpy` array || have to be of a single type. What's more `dict`s are sparse by default; indeed a feature with a || `None` value can simply be omitted. Finally Python has some nice tools for working with `dict`s; || such as `collections.defaultdict`. ||  || The creme library is organized in modules; following the fashion in which scikit-learn is organized. || There is thus a `preprocessing` module as well as a `feature_extraction` module; amongst others. || Furthermore an effort is made to keep the naming of creme's classes and parameters consistent || with scikit-learn. ||  || For more information please check out the [documentation](https:\/\/creme.github.io). ||  || ## Quick example ||  || In the following snippet we'll be fitting an online logistic regression. The weights of the model || will be optimized with the [AdaGrad](http:\/\/akyrillidis.github.io\/notes\/AdaGrad) algorithm. We'll || scale the data so that each variable has a mean of 0 and a standard deviation of 1. The standard || scaling and the logistic regression are combined using a pipeline. We'll be using the || `stream.iter_sklearn_dataset` function for streaming over the || [Wisconsin breast cancer dataset](http:\/\/archive.ics.uci.edu\/ml\/datasets\/breast+cancer+wisconsin+%28diagnostic%29). || We'll measure the ROC AUC using [progressive validation](http:\/\/citeseerx.ist.psu.edu\/viewdoc\/download?doi=10.1.1.153.3925&rep=rep1&type=pdf). ||  ||     >>> from creme import linear_model ||     >>> from creme import model_selection ||     >>> from creme import optim ||     >>> from creme import pipeline ||     >>> from creme import preprocessing ||     >>> from creme import stream ||     >>> from sklearn import datasets ||     >>> from sklearn import metrics ||  ||     >>> X_y = stream.iter_sklearn_dataset( ||     ...     load_dataset=datasets.load_breast_cancer; ||     ...     shuffle=True; ||     ...     random_state=42 ||     ... ) ||     >>> optimizer = optim.AdaGrad() ||     >>> model = pipeline.Pipeline([ ||     ...     ('scale'; preprocessing.StandardScaler()); ||     ...     ('learn'; linear_model.LogisticRegression(optimizer)) ||     ... ]) ||     >>> metric = metrics.roc_auc_score ||  ||     >>> model_selection.online_score(X_y; model; metric) ||     0.992977... ||  || \"\"\"
aaeeeiffh,online-ml/river,creme/cluster/k_means.py,2e3cb318ca5359d077657c51acbf8728d5c23b85,STILL_EXISTS,Move the cluster's center
aaeeeifhf,online-ml/river,creme/optim/nesterov.py,2e3cb318ca5359d077657c51acbf8728d5c23b85,1d41378e4f4e0ca43814fbc39cf6c028102b7c1c,Move the weights to the future position
aaeeeifie,online-ml/river,creme/tree/mondrian.py,2e3cb318ca5359d077657c51acbf8728d5c23b85,da37e424f00b91d1956fa2d4588a193bda941a73,\"\"\" ||  || - [Decision Trees and Forests: A Probabilistic Perspective](http:\/\/www.gatsby.ucl.ac.uk\/~balaji\/balaji-phd-thesis.pdf) || - [Mondrian Forests for Large-Scale Regression when Uncertainty Matters](http:\/\/www.gatsby.ucl.ac.uk\/~balaji\/mfregression_aistats16.pdf) || - [Mondrian Forests: Efficient Online Random Forests](https:\/\/papers.nips.cc\/paper\/5234-mondrian-forests-efficient-online-random-forests.pdf) || - [Intuition behind Mondrian Trees](https:\/\/scikit-garden.github.io\/examples\/MondrianTreeRegressor\/) || - [Mondrian Forest](https:\/\/ldocao.wordpress.com\/2016\/08\/26\/mondrian-forest\/) || \"\"\"
aaeeejaih,online-ml/river,creme/imputer/numerical_imputer.py,d91b653da212dd740c9b01f05219bbb5b1d80bc9,3b0d2b86a65bfde128cdd2703248a5b1802eb162,TODO: Fix me when first value is 0; it return weird_result
aaeeejaii,online-ml/river,creme/imputer/numerical_imputer.py,d91b653da212dd740c9b01f05219bbb5b1d80bc9,3b0d2b86a65bfde128cdd2703248a5b1802eb162,TODO: Check if value is missing; find nice way to do it.
aaeeejcfg,online-ml/river,docs/conf.py,bf3353a776b2f073447ec71725a61b5fb241f5a5,156f49bd2ba60ca87a435b1d74a6c40443772336,If true; `todo` and `todoList` produce output; else they produce nothing.
aaeefaaii,online-ml/river,src/skmultiflow/meta/additive_expert_ensemble.py,edf12bfa0092e58dc23cb2d7739168e9cdbc0bfa,STILL_EXISTS,# TODO Pruning to max_estimators
aaeefaaja,online-ml/river,src/skmultiflow/meta/additive_expert_ensemble.py,edf12bfa0092e58dc23cb2d7739168e9cdbc0bfa,b532302b6110a058fde413ae78063b2ad08d2a80,# TODO Improve efficieny
aaeefabii,online-ml/river,docs/conf.py,5376a1d99a37114ec93f7e8567ea395372d1ee62,90a4b7218884b68b3d1a2bf2e3c2b31c3035e8e0,If true; `todo` and `todoList` produce output; else they produce nothing.
aaeefadif,online-ml/river,src/skmultiflow/meta/additive_expert_ensemble.py,becc945240a424bc08f74476c46f6a50b69928a5,STILL_EXISTS,# 4.1 Pruning to self.max_estimators if needed
aaeefadig,online-ml/river,src/skmultiflow/meta/additive_expert_ensemble.py,becc945240a424bc08f74476c46f6a50b69928a5,b532302b6110a058fde413ae78063b2ad08d2a80,# TODO Improve efficieny
aaeefaefc,online-ml/river,docs/conf.py,bbddf0ca76ae600aba7a0156d4230b57fd24a50c,STILL_EXISTS,this is needed for some reason...
aaeefaegb,online-ml/river,docs/conf.py,bbddf0ca76ae600aba7a0156d4230b57fd24a50c,61a5da74ad7c2be5e9f5503bfecb6ba0e3302a64,If true; `todo` and `todoList` produce output; else they produce nothing.
aaeefaghb,online-ml/river,docs/conf.py,caef462863cfe68298bfcf1eca67f46fdc633b1e,STILL_EXISTS,this is needed for some reason...
aaeefagia,online-ml/river,docs/conf.py,caef462863cfe68298bfcf1eca67f46fdc633b1e,48c3add8a88ba8add5c76f47de48b8c76bc9fdae,If true; `todo` and `todoList` produce output; else they produce nothing.
aaeefaijf,online-ml/river,src/skmultiflow/core/base.py,8a545145d6a50e56d31ba0b6a697e84d07ffef3c,STILL_EXISTS,XXX: not handling dictionaries
aaeefajbi,online-ml/river,src/skmultiflow/core/base.py,8a545145d6a50e56d31ba0b6a697e84d07ffef3c,STILL_EXISTS,apprx number of chars to keep on both ends
aaeefajdi,online-ml/river,src/skmultiflow/core/base.py,8a545145d6a50e56d31ba0b6a697e84d07ffef3c,STILL_EXISTS,XXX: Remove the check in 0.23
aaeefajgi,online-ml/river,src/skmultiflow/utils/_pprint.py,8a545145d6a50e56d31ba0b6a697e84d07ffef3c,STILL_EXISTS,NO REPRESENTATIONS OR WARRANTIES; EXPRESS OR IMPLIED. BY WAY OF EXAMPLE; BUT
aaeefajjb,online-ml/river,src/skmultiflow/utils/_pprint.py,8a545145d6a50e56d31ba0b6a697e84d07ffef3c,STILL_EXISTS,needed for _dispatch[tuple.__repr__] not to be overridden
aaeefbaag,online-ml/river,src/skmultiflow/core/base.py,a49eff04b956f5dbfebffd99482cd361e9362152,2f3cffca59e6d6efd14444f6fc98713cd5dfa6a7,non-optimized default implementation; override if a better
aaeefbaib,online-ml/river,creme/utils/histogram.py,2ae938038d3c99a9fb222ba667aff657b6b88419,bff1082958f1f9652bebb025eb2fe808f214953b,TODO: this loop should be Cythonized
aaeefbchb,online-ml/river,docs/conf.py,db28f78b00535e36b9c703037ab4c532eee4034f,STILL_EXISTS,this is needed for some reason...
aaeefbcib,online-ml/river,docs/conf.py,db28f78b00535e36b9c703037ab4c532eee4034f,b00a58b8e2163a826f6e9757b164528f4f4f0059,If true; `todo` and `todoList` produce output; else they produce nothing.
aaeefbdde,online-ml/river,src/skmultiflow/core/base.py,a8530947d48a1e1486017b53944bf130768a2480,STILL_EXISTS,non-optimized default implementation; override if a better
aaeefbffb,online-ml/river,tests/neural_networks/test_perceptron.py,173f2688750e194413e99bf4a13ed7f9a0adb712,STILL_EXISTS,This is a workaround until a fix is made available in sklearn
aaeefcaii,online-ml/river,creme/base.py,017546f3b7a17d66b74c0fc0fd2b3b023159dc94,97bf570575eb5c5d6b398ece53da408bbe5dac30,FIXME : return is of type dict or Transformer ?
aaeefcaij,online-ml/river,creme/base.py,017546f3b7a17d66b74c0fc0fd2b3b023159dc94,97bf570575eb5c5d6b398ece53da408bbe5dac30,FIXME: wrong return hint (int vs Clusterer)
aaeefcbid,online-ml/river,creme/tree/splitting.py,24444db4c9f941e6b746c830e05ab57fde27f2d9,873aed0fd929ace64fccc1fd627eaf67e13df9d4,3. Check if the split is better
aaeefcbje,online-ml/river,creme/tree/leaf.py,873aed0fd929ace64fccc1fd627eaf67e13df9d4,STILL_EXISTS,Check if the gain brought by the candidate split is better than the current best
aaeefcbjj,online-ml/river,creme/tree/tree.py,873aed0fd929ace64fccc1fd627eaf67e13df9d4,b00a58b8e2163a826f6e9757b164528f4f4f0059,TODO: Test Naive Bayes prediction using MOA paper (from page 79 onwards of https:\/\/www.cs.waikato.ac.nz\/~abifet\/MOA\/StreamMining.pdf)
aaeefccaa,online-ml/river,creme/tree/tree.py,873aed0fd929ace64fccc1fd627eaf67e13df9d4,b00a58b8e2163a826f6e9757b164528f4f4f0059,TODO: initialize new leafs with class counts after split
aaeefcigi,online-ml/river,creme/datasets.py,f01bc774f147053e0b03e34119389e9e7bbedc17,STILL_EXISTS,Uncompress if needed
aaeefdcjd,online-ml/river,src/skmultiflow/trees/nodes/node.py,f32719662bba6e98dd684af85e800c64d828bd6d,STILL_EXISTS,TODO
aaeefddjf,online-ml/river,creme/linear_model/test_glm.py,3881ffc26b55cb0d458297b1cd6ded2eaa110d15,STILL_EXISTS,TODO: check momentum optimizers
aaeefdead,online-ml/river,creme/linear_model/test_glm.py,3881ffc26b55cb0d458297b1cd6ded2eaa110d15,STILL_EXISTS,TODO: reactivate this check
aaeefdeag,online-ml/river,creme/linear_model/test_glm.py,3881ffc26b55cb0d458297b1cd6ded2eaa110d15,STILL_EXISTS,TODO: decrease the tolerance
aaeefdjba,online-ml/river,creme/datasets/base.py,0334f5868c18b5b9702c07cad741411d0f4710e7,STILL_EXISTS,Download if needed
aaeefdjbb,online-ml/river,creme/datasets/base.py,0334f5868c18b5b9702c07cad741411d0f4710e7,STILL_EXISTS,Uncompress if needed
aaeefeaej,online-ml/river,tests/trees/test_hoeffding_adaptive_tree.py,910fa62605de49dea3e4599bb233c3d9c6f4527b,STILL_EXISTS,Removes the last two columns (regression targets)
aaeefeafa,online-ml/river,tests/trees/test_hoeffding_tree.py,910fa62605de49dea3e4599bb233c3d9c6f4527b,STILL_EXISTS,Removes the last two columns (regression targets)
aaeefebie,online-ml/river,creme/naive_bayes/test_multinomial.py,03c0cef84c76106935561f7bc8feaff3cb86271d,STILL_EXISTS,Assert model parameters needed to calculate the likelihoods
aaeefeghb,online-ml/river,tests/utils/test_utils.py,5875ede5ebfc7e2d46c8d8714e87c11a84ad5d39,STILL_EXISTS,No asert is needed since they are based on the 'byte' size.
aaeeffebf,online-ml/river,docs/scripts/index_api.py,7177329f683fbe44c80b4674015d1e70c94d19a5,869ef6fcd2d3663a19e92aaf8b2ea2d0239bc0bc,TODO: link to classes in type annotations (maybe using https:\/\/facelessuser.github.io\/pymdown-extensions\/extensions\/pathconverter\/)
aaeeffebg,online-ml/river,docs/scripts/index_api.py,7177329f683fbe44c80b4674015d1e70c94d19a5,STILL_EXISTS,TODO: display children and parents inheritance (maybe using inspect.getclasstree and inspect.mro;
aaeeffebi,online-ml/river,docs/scripts/index_api.py,7177329f683fbe44c80b4674015d1e70c94d19a5,STILL_EXISTS,TODO: type hinting for Cython classes
aaeeffegg,online-ml/river,src/skmultiflow/meta/adaptive_random_forest_regressor.py,56798201b77d1546bec413b57e3561c5f13f5122,STILL_EXISTS,TODO: check whether this is enough
aaeeffegh,online-ml/river,src/skmultiflow/meta/adaptive_random_forest_regressor.py,56798201b77d1546bec413b57e3561c5f13f5122,STILL_EXISTS,TODO: check the posssibility of using HATR as base learner
aaeeffegi,online-ml/river,src/skmultiflow/meta/adaptive_random_forest_regressor.py,56798201b77d1546bec413b57e3561c5f13f5122,STILL_EXISTS,TODO: check necessity of this parameter
aaeeffegj,online-ml/river,src/skmultiflow/meta/adaptive_random_forest_regressor.py,56798201b77d1546bec413b57e3561c5f13f5122,STILL_EXISTS,TODO: verify the posssibility of renaming to 'swap' of something similar
aaeeffeha,online-ml/river,src/skmultiflow/meta/adaptive_random_forest_regressor.py,56798201b77d1546bec413b57e3561c5f13f5122,STILL_EXISTS,TODO: add here option to monitor errors
aaeeffehb,online-ml/river,src/skmultiflow/meta/adaptive_random_forest_regressor.py,56798201b77d1546bec413b57e3561c5f13f5122,STILL_EXISTS,TODO: posssibility of using HATR as base leaner
aaeeffehc,online-ml/river,src/skmultiflow/meta/adaptive_random_forest_regressor.py,56798201b77d1546bec413b57e3561c5f13f5122,STILL_EXISTS,TODO: options to monitor error
aaeefffac,online-ml/river,src/skmultiflow/meta/adaptive_random_forest_regressor.py,4f1afcc6a96382fcf1ae922a32cf7000acebef2e,STILL_EXISTS,TODO: check the posssibility of using HATR as base learner
aaeefffad,online-ml/river,src/skmultiflow/meta/adaptive_random_forest_regressor.py,4f1afcc6a96382fcf1ae922a32cf7000acebef2e,STILL_EXISTS,TODO: check necessity of this parameter
aaeefffae,online-ml/river,src/skmultiflow/meta/adaptive_random_forest_regressor.py,4f1afcc6a96382fcf1ae922a32cf7000acebef2e,STILL_EXISTS,TODO: posssibility of using HATR as base leaner
aaeefffaf,online-ml/river,src/skmultiflow/meta/adaptive_random_forest_regressor.py,4f1afcc6a96382fcf1ae922a32cf7000acebef2e,STILL_EXISTS,TODO: options to monitor error
aaeefffde,online-ml/river,tests/meta/test_adaptive_random_forest_regressor.py,46dc0cecc2dfdfebaf3867320937c22dbd056e1a,e8f02e56f356dfecd5d7bb271f2fa54deb71ee00,TODO: add assert statements
aaeefgdcd,online-ml/river,creme/linear_model/test_glm.py,413ca20a5b3cfbab1d4d87d09570d6316c5ecb25,STILL_EXISTS,Pick half of the columns at random
aaeefgddf,online-ml/river,creme/preprocessing/test_scale.py,413ca20a5b3cfbab1d4d87d09570d6316c5ecb25,STILL_EXISTS,Pick half of the columns at random
aaeefgdig,online-ml/river,docs/scripts/index_api.py,f8f92de0b408e21f2395ef3885268f4ef0915bc7,STILL_EXISTS,TODO: remove >>> and ... in code blocks; put output in a separate fenced block
aaeefhaag,online-ml/river,creme/compat/creme_to_sklearn.py,5e6e666692c162749ea50e0f518164ee40288429,STILL_EXISTS,TODO: change to a ValueError when fixed
aaeefhaei,online-ml/river,creme/expert/__init__.py,4d2a671bb5a73400b7be8ef2713e85401b585c6c,STILL_EXISTS,\"\"\"Expert learning. ||  || This module regroups a variety of methods that may be used for performing model selection. An || expert learner is provided with a list of models; which are also called experts; and is tasked with || performing at least as well as the best expert. Indeed; initially the best model is not known. The || performance of each model becomes more apparent as time goes by. Different strategies are possible; || each one offering a different tradeoff in terms of accuracy and computational performance. ||  || Expert learning can be used for tuning the hyperparameters of a model. This may be done by creating || a copy of the model for each set of hyperparameters; and treating each copy as a separate model. || The `utils.expand_param_grid` function can be used for this purpose. ||  || Note that this differs from the `ensemble` module in that methods from the latter are designed to || improve the performance of a single model. Both modules may thus be used in conjunction with one || another. ||  || \"\"\"
aaeefhafe,online-ml/river,creme/utils/estimator_checks.py,4d2a671bb5a73400b7be8ef2713e85401b585c6c,STILL_EXISTS,Some classifiers do not implement predict_proba_one
aaeefhafh,online-ml/river,creme/utils/inspect.py,4d2a671bb5a73400b7be8ef2713e85401b585c6c,STILL_EXISTS,TODO: maybe all of this could be done by monkeypatching isintance for pipelines?
aaeefhcgc,online-ml/river,scikit-multiflow/src/skmultiflow/_demos/_test_file_stream_multiple_cfier.py,9aadf6260dd867c26f2b5d2b4f09fb9dacf5cfa2,STILL_EXISTS,Demo 2 -- csv output should look nice
aaeefhecd,online-ml/river,scikit-multiflow/src/skmultiflow/core/base.py,9aadf6260dd867c26f2b5d2b4f09fb9dacf5cfa2,STILL_EXISTS,XXX: not handling dictionaries
aaeefheeg,online-ml/river,scikit-multiflow/src/skmultiflow/core/base.py,9aadf6260dd867c26f2b5d2b4f09fb9dacf5cfa2,STILL_EXISTS,apprx number of chars to keep on both ends
aaeefhegi,online-ml/river,scikit-multiflow/src/skmultiflow/core/base.py,9aadf6260dd867c26f2b5d2b4f09fb9dacf5cfa2,STILL_EXISTS,XXX: Remove the check in 0.23
aaeefheid,online-ml/river,scikit-multiflow/src/skmultiflow/data/data_stream.py,9aadf6260dd867c26f2b5d2b4f09fb9dacf5cfa2,STILL_EXISTS,Take only n_targets columns to the right of target_idx; use the rest as features
aaeefheih,online-ml/river,scikit-multiflow/src/skmultiflow/data/file_stream.py,9aadf6260dd867c26f2b5d2b4f09fb9dacf5cfa2,STILL_EXISTS,Take only n_targets columns to the right of target_idx; use the rest as features
aaeefhfhd,online-ml/river,scikit-multiflow/src/skmultiflow/evaluation/base_evaluator.py,9aadf6260dd867c26f2b5d2b4f09fb9dacf5cfa2,STILL_EXISTS,TODO let the user choose the feature indices of interest
aaeefhfih,online-ml/river,scikit-multiflow/src/skmultiflow/evaluation/evaluate_holdout.py,9aadf6260dd867c26f2b5d2b4f09fb9dacf5cfa2,STILL_EXISTS,TODO Confirm place
aaeefhhaa,online-ml/river,scikit-multiflow/src/skmultiflow/meta/adaptive_random_forest_regressor.py,9aadf6260dd867c26f2b5d2b4f09fb9dacf5cfa2,STILL_EXISTS,TODO: check whether this is enough
aaeefhhhf,online-ml/river,scikit-multiflow/src/skmultiflow/meta/additive_expert_ensemble.py,9aadf6260dd867c26f2b5d2b4f09fb9dacf5cfa2,STILL_EXISTS,Pruning to self.n_estimators if needed
aaeefhhic,online-ml/river,scikit-multiflow/src/skmultiflow/meta/batch_incremental.py,9aadf6260dd867c26f2b5d2b4f09fb9dacf5cfa2,STILL_EXISTS,TODO not very pythonic at the moment
aaeefhhjb,online-ml/river,scikit-multiflow/src/skmultiflow/meta/classifier_chains.py,9aadf6260dd867c26f2b5d2b4f09fb9dacf5cfa2,STILL_EXISTS,TODO: much of this can be shared with Regressor Chains; probably should use a base class to inherit here.
aaeefhjgh,online-ml/river,scikit-multiflow/src/skmultiflow/metrics/measure_collection.py,9aadf6260dd867c26f2b5d2b4f09fb9dacf5cfa2,STILL_EXISTS,Verify if it's needed to decrease the count of any label
aaeefhjgj,online-ml/river,scikit-multiflow/src/skmultiflow/metrics/measure_collection.py,9aadf6260dd867c26f2b5d2b4f09fb9dacf5cfa2,STILL_EXISTS,Verify if it's needed to decrease the majority_classifier count
aaeefhjha,online-ml/river,scikit-multiflow/src/skmultiflow/metrics/measure_collection.py,9aadf6260dd867c26f2b5d2b4f09fb9dacf5cfa2,STILL_EXISTS,Verify if it's needed to decrease the correct_no_change
aaeefiaha,online-ml/river,scikit-multiflow/src/skmultiflow/trees/extremely_fast_decision_tree.py,9aadf6260dd867c26f2b5d2b4f09fb9dacf5cfa2,STILL_EXISTS,Move in depth
aaeefiahb,online-ml/river,scikit-multiflow/src/skmultiflow/trees/extremely_fast_decision_tree.py,9aadf6260dd867c26f2b5d2b4f09fb9dacf5cfa2,5363eab4956c0c23df220d3b208056928804ccd4,Todo : raise error for nominal attribute
aaeefibga,online-ml/river,scikit-multiflow/src/skmultiflow/trees/isoup_tree.py,9aadf6260dd867c26f2b5d2b4f09fb9dacf5cfa2,5363eab4956c0c23df220d3b208056928804ccd4,TODO Verify
aaeefibhf,online-ml/river,scikit-multiflow/src/skmultiflow/trees/isoup_tree.py,9aadf6260dd867c26f2b5d2b4f09fb9dacf5cfa2,5363eab4956c0c23df220d3b208056928804ccd4,TODO reactivation procedure???
aaeeficjg,online-ml/river,scikit-multiflow/src/skmultiflow/trees/nodes/node.py,9aadf6260dd867c26f2b5d2b4f09fb9dacf5cfa2,STILL_EXISTS,TODO
aaeefiddb,online-ml/river,scikit-multiflow/src/skmultiflow/trees/split_criterion/info_gain_split_criterion.py,9aadf6260dd867c26f2b5d2b4f09fb9dacf5cfa2,STILL_EXISTS,TODO: How small can d be before log2 overflows?
aaeefidde,online-ml/river,scikit-multiflow/src/skmultiflow/trees/split_criterion/intra_cluster_variance_reduction_split_criterion.py,9aadf6260dd867c26f2b5d2b4f09fb9dacf5cfa2,STILL_EXISTS,TODO Also consider passing different weights for the targets
aaeefidja,online-ml/river,scikit-multiflow/src/skmultiflow/utils/_pprint.py,9aadf6260dd867c26f2b5d2b4f09fb9dacf5cfa2,STILL_EXISTS,NO REPRESENTATIONS OR WARRANTIES; EXPRESS OR IMPLIED. BY WAY OF EXAMPLE; BUT
aaeefiebd,online-ml/river,scikit-multiflow/src/skmultiflow/utils/_pprint.py,9aadf6260dd867c26f2b5d2b4f09fb9dacf5cfa2,STILL_EXISTS,needed for _dispatch[tuple.__repr__] not to be overridden
aaeefiehd,online-ml/river,scikit-multiflow/src/skmultiflow/visualization/evaluation_visualizer.py,9aadf6260dd867c26f2b5d2b4f09fb9dacf5cfa2,STILL_EXISTS,TODO consider a fading\/update strategy instead
aaeefiehg,online-ml/river,scikit-multiflow/src/skmultiflow/visualization/evaluation_visualizer.py,9aadf6260dd867c26f2b5d2b4f09fb9dacf5cfa2,STILL_EXISTS,TODO confirm buffer update inside the loop
aaeefiheb,online-ml/river,scikit-multiflow/tests/neural_networks/test_perceptron.py,9aadf6260dd867c26f2b5d2b4f09fb9dacf5cfa2,STILL_EXISTS,in sklearn 0.21.0. This is a workaround until a fix is made available in sklearn
aaeefihgg,online-ml/river,scikit-multiflow/tests/trees/test_hoeffding_adaptive_tree.py,9aadf6260dd867c26f2b5d2b4f09fb9dacf5cfa2,STILL_EXISTS,Removes the last two columns (regression targets)
aaeefihia,online-ml/river,scikit-multiflow/tests/trees/test_hoeffding_tree.py,9aadf6260dd867c26f2b5d2b4f09fb9dacf5cfa2,STILL_EXISTS,Removes the last two columns (regression targets)
aaeefiica,online-ml/river,scikit-multiflow/tests/utils/test_utils.py,9aadf6260dd867c26f2b5d2b4f09fb9dacf5cfa2,STILL_EXISTS,No asert is needed since they are based on the 'byte' size.
aaeefijfb,online-ml/river,creme/metrics/exact_match.py,ba1298ba4f706356233f21cce3f28548ae0e179e,2f895953c8943bc49374b2b6eca6bc3b8d42396c,TODO confirm usage and enforce if required
aaeefijfe,online-ml/river,creme/metrics/jaccard.py,ba1298ba4f706356233f21cce3f28548ae0e179e,2f895953c8943bc49374b2b6eca6bc3b8d42396c,TODO confirm usage and enforce if required
aaeefjaae,online-ml/river,src/skmultiflow/trees/__init__.py,e12d781e139ce7cf84d2ca109b22f5b5a93f6438,STILL_EXISTS,TODO remove in v0.7.0
aaeefjbgi,online-ml/river,scikit-multiflow/src/skmultiflow/trees/__init__.py,5363eab4956c0c23df220d3b208056928804ccd4,STILL_EXISTS,TODO remove in v0.7.0
aaeefjgdc,online-ml/river,docs/scripts/linkify.py,9a5acef94a615b1a56ff823595d4f04b3c34a27a,STILL_EXISTS,TODO: one expression for between fences ``
aaeefjgdd,online-ml/river,docs/scripts/linkify.py,9a5acef94a615b1a56ff823595d4f04b3c34a27a,STILL_EXISTS,TODO: one expression for in a type annotation
aaeefjjib,online-ml/river,docs/scripts/index.py,3675ccefae43b8c973e9574da503d6e8792b1157,STILL_EXISTS,TODO: this is necessary for Cython classes; but it's not correct
aaeegaaac,online-ml/river,creme/tree/base_tree.py,06e2efedbbb923d294be79df0fc2cc78409f4243,STILL_EXISTS,TODO review later
aaeegaaae,online-ml/river,creme/tree/base_tree.py,06e2efedbbb923d294be79df0fc2cc78409f4243,STILL_EXISTS,TODO review
aaeegaabi,online-ml/river,creme/tree/__init__.py,2222f2b54b2dafb2639980392989cec127defb01,STILL_EXISTS,TODO remove
aaeegaade,online-ml/river,creme/tree/_nodes/hatc_nodes.py,f73024418cb37aeca0a9abdf21eedd861b7983d6,STILL_EXISTS,Update stats as traverse the tree to improve predictions (in case split nodes are used
aaeegaaed,online-ml/river,creme/tree/_nodes/hatc_nodes.py,919c22af104c606b2fd76a148a3ede832b1d7220,22643b07f6e7cf34e42fa3dcee513056f4c1c583,TODO: check whether or not children can be None; after get_child. Perhaps checks such as
aaeegaaef,online-ml/river,creme/tree/_nodes/hatc_nodes.py,919c22af104c606b2fd76a148a3ede832b1d7220,STILL_EXISTS,Update stats as traverse the tree to improve predictions (in case split nodes are used
aaeegaaif,online-ml/river,creme/tree/_nodes/hatr_nodes.py,e5aabf0d0ddf835521941d30d36d2756c0a0519d,STILL_EXISTS,Update stats as traverse the tree to improve predictions (in case split nodes are used
aaeegabab,online-ml/river,creme/tree/hoeffding_adaptive_tree_regressor.py,e5aabf0d0ddf835521941d30d36d2756c0a0519d,STILL_EXISTS,TODO: change to appropriate 'clone' method
aaeegabfb,online-ml/river,creme/tree/hoeffding_adaptive_tree_regressor.py,13345f8edb93d91e8c00b50cc8a96b0d60eb0d39,STILL_EXISTS,TODO: change to appropriate 'clone' method
aaeegabhf,online-ml/river,creme/tree/arf_hoeffding_tree_regressor.py,6e7b2f6928b6fd27f87941d38b14e0bd34629a3a,STILL_EXISTS,TODO: change to appropriate 'clone' method
aaeegacbd,online-ml/river,creme/datasets/synth/random_rbf.py,50099aaf18f6beaecbb1087ee25aead2edb4e34c,STILL_EXISTS,Move centroids
aaeegaceb,online-ml/river,creme/tree/__init__.py,d98cff240d0dc49268b58afa2a76ce4c304099a8,STILL_EXISTS,TODO: remove before merging
aaeegaced,online-ml/river,creme/tree/_attribute_observer/numeric_attribute_class_observer_binary_tree.py,d98cff240d0dc49268b58afa2a76ce4c304099a8,STILL_EXISTS,move the subtrees
aaeegacee,online-ml/river,creme/tree/_attribute_observer/numeric_attribute_class_observer_binary_tree.py,d98cff240d0dc49268b58afa2a76ce4c304099a8,STILL_EXISTS,move the exact value from the parent
aaeegaceg,online-ml/river,creme/tree/_base_tree.py,d98cff240d0dc49268b58afa2a76ce4c304099a8,STILL_EXISTS,# TODO review -> compat with rule-based algorithms
aaeegaefa,online-ml/river,river/cluster/k_means.py,736a6def97efd3f6a606119cbab5453c0be30558,STILL_EXISTS,Move the cluster's center
aaeegaehd,online-ml/river,river/compat/river_to_sklearn.py,736a6def97efd3f6a606119cbab5453c0be30558,STILL_EXISTS,TODO: change to a ValueError when fixed
aaeegafid,online-ml/river,river/datasets/synth/random_rbf.py,736a6def97efd3f6a606119cbab5453c0be30558,STILL_EXISTS,Move centroids
aaeegagag,online-ml/river,river/datasets/test_datasets.py,736a6def97efd3f6a606119cbab5453c0be30558,STILL_EXISTS,TODO: test the following synth datasets also
aaeegahaa,online-ml/river,river/linear_model/test_glm.py,736a6def97efd3f6a606119cbab5453c0be30558,STILL_EXISTS,TODO: check momentum optimizers
aaeegahai,online-ml/river,river/linear_model/test_glm.py,736a6def97efd3f6a606119cbab5453c0be30558,STILL_EXISTS,TODO: reactivate this check
aaeegahbb,online-ml/river,river/linear_model/test_glm.py,736a6def97efd3f6a606119cbab5453c0be30558,STILL_EXISTS,TODO: decrease the tolerance
aaeegahbe,online-ml/river,river/linear_model/test_glm.py,736a6def97efd3f6a606119cbab5453c0be30558,STILL_EXISTS,Pick half of the columns at random
aaeegahgh,online-ml/river,river/naive_bayes/test_multinomial.py,736a6def97efd3f6a606119cbab5453c0be30558,STILL_EXISTS,Assert model parameters needed to calculate the likelihoods
aaeegaief,online-ml/river,river/preprocessing/test_scale.py,736a6def97efd3f6a606119cbab5453c0be30558,STILL_EXISTS,Pick half of the columns at random
aaeegajdf,online-ml/river,river/tree/decision/leaf.py,736a6def97efd3f6a606119cbab5453c0be30558,STILL_EXISTS,Check if the gain brought by the candidate split is better than the current best
aaeegajhf,online-ml/river,river/utils/estimator_checks.py,736a6def97efd3f6a606119cbab5453c0be30558,STILL_EXISTS,Some classifiers do not implement predict_proba_one
aaeegajii,online-ml/river,river/utils/inspect.py,736a6def97efd3f6a606119cbab5453c0be30558,STILL_EXISTS,TODO: maybe all of this could be done by monkeypatching isintance for pipelines?
aaeegegee,online-ml/river,river/tree/_attribute_observer/numeric_attribute_class_observer_binary_tree.py,4011f5a95ac5ff1f094e8cd56b1f5300273455c5,STILL_EXISTS,move the subtrees
aaeegegef,online-ml/river,river/tree/_attribute_observer/numeric_attribute_class_observer_binary_tree.py,4011f5a95ac5ff1f094e8cd56b1f5300273455c5,STILL_EXISTS,move the exact value from the parent
aaeegeggj,online-ml/river,river/tree/_base_tree.py,4011f5a95ac5ff1f094e8cd56b1f5300273455c5,672bb2716e6c77404dd72d4ac90ceaf9ce66f8ff,# TODO review later
aaeegehcg,online-ml/river,river/tree/_nodes/efdtc_nodes.py,4011f5a95ac5ff1f094e8cd56b1f5300273455c5,STILL_EXISTS,TODO verify the possibility of using dictionaries to go from O(m) to O(1)
aaeegeheb,online-ml/river,river/tree/_nodes/hatc_nodes.py,4011f5a95ac5ff1f094e8cd56b1f5300273455c5,STILL_EXISTS,Update stats as traverse the tree to improve predictions (in case split nodes are used
aaeegehhc,online-ml/river,river/tree/_nodes/hatr_nodes.py,4011f5a95ac5ff1f094e8cd56b1f5300273455c5,STILL_EXISTS,Update stats as traverse the tree to improve predictions (in case split nodes are used
aaeegeiag,online-ml/river,river/tree/arf_hoeffding_tree_regressor.py,4011f5a95ac5ff1f094e8cd56b1f5300273455c5,672bb2716e6c77404dd72d4ac90ceaf9ce66f8ff,TODO: change to appropriate 'clone' method
aaeegeiic,online-ml/river,river/tree/extremely_fast_decision_tree.py,4011f5a95ac5ff1f094e8cd56b1f5300273455c5,STILL_EXISTS,Move in depth
aaeegejfb,online-ml/river,river/tree/hoeffding_tree_regressor.py,4011f5a95ac5ff1f094e8cd56b1f5300273455c5,672bb2716e6c77404dd72d4ac90ceaf9ce66f8ff,TODO: change to appropriate 'clone' method
aaeegfbhj,online-ml/river,river/tree/hoeffding_tree_regressor.py,23f5014a5c0d072d0f75342c6d08ba2df8081fef,672bb2716e6c77404dd72d4ac90ceaf9ce66f8ff,TODO: change to appropriate 'clone' method
aaeeggcgg,online-ml/river,river/ensemble/streaming_random_patches.py,69ad941923233d40278caf5e5a8846b54f093bf1,362184b61cbe4c8ffe0eb8b15138e099ade9b63e,TODO replace with clone
aaeeggche,online-ml/river,river/ensemble/streaming_random_patches.py,69ad941923233d40278caf5e5a8846b54f093bf1,362184b61cbe4c8ffe0eb8b15138e099ade9b63e,TODO Replace with clone
aaeeggcif,online-ml/river,river/ensemble/streaming_random_patches.py,69ad941923233d40278caf5e5a8846b54f093bf1,STILL_EXISTS,TODO Find a way to verify if the model natively supports sample_weight
aaeeggcjj,online-ml/river,river/ensemble/streaming_random_patches.py,69ad941923233d40278caf5e5a8846b54f093bf1,STILL_EXISTS,TODO Hard-reset the warning method
aaeeggdai,online-ml/river,river/tree/_nodes/base.py,56572130ac9843d620272ce3d982eafb62a31ec4,554e4c542fd085fb0fee1d3f0e0f0a57f71be71a,TODO: fix that
aaeeggdej,online-ml/river,river/ensemble/streaming_random_patches.py,d0dc434f8247752e226d357231df628681ad33cf,362184b61cbe4c8ffe0eb8b15138e099ade9b63e,TODO Replace with clone
aaeegghfj,online-ml/river,river/ensemble/adaptive_random_forest.py,b724922e49b539b81872b4df74c8b2cf3452885a,STILL_EXISTS,TODO Replace deepcopy with clone
aaeeggjgc,online-ml/river,river/tree/_nodes/efdtc_nodes.py,dca8dbe686e4754c9f351a0bb48d5490bdf53760,ec1dcc11d45213357e6d43ed4f6099de52de2326,TODO update that
aaeeggjgf,online-ml/river,river/tree/_nodes/htc_nodes.py,dca8dbe686e4754c9f351a0bb48d5490bdf53760,ec1dcc11d45213357e6d43ed4f6099de52de2326,TODO update that
aaeehbhgf,online-ml/river,river/expert/bandit.py,da7a823081fd604031a72fb9e7ef14233c1883c8,13154a60b67d3e906e04237a8afe248d86c7a5db,TODO :
aaeehbhhb,online-ml/river,river/expert/bandit.py,13154a60b67d3e906e04237a8afe248d86c7a5db,0ba1cc1bf0e45259a2fdb66e01df9cb39bbed91f,TODO:
aaeehcbfd,online-ml/river,river/neural_net/mlp.py,b99cbdb6c8316a762e5a60d6721936e313429a0e,STILL_EXISTS,As a convention; we sort the columns of the input DataFrame. This allows the user to
aaeehcddf,online-ml/river,river/tree/__init__.py,b829a57fae98c52022ba2ee52416e7cdb4b45e62,STILL_EXISTS,\"\"\" ||  || This module implements incremental Decision Tree (iDT) algorithms for handling classification || and regression tasks. ||  || Each family of iDT will be presented in a dedicated section. ||  || At any moment; iDT might face situations where an input feature previously used to make || a split decision is missing in an incoming sample. In this case; the river's trees follow the || conventions: ||  || - *Learning:* choose the subtree branch most traversed so far to pass the instance on.<\/br> ||     * In case of nominal features; a new branch is created to accommodate the new ||     category.<\/br> || - *Predicting:* Use the last \"reachable\" decision node to provide responses. ||  || **1. Hoeffding Trees** ||  || This family of iDT algorithms use the Hoeffding Bound to determine whether or not the || incrementally computed best split candidates would be equivalent to the ones obtained in a || batch-processing fashion. ||  || All the available Hoeffding Tree (HT) implementation share some common functionalities: ||  || * Set the maximum tree depth allowed (`max_depth`). ||  || * Handle *Active* and *Inactive* nodes: Active learning nodes update their own || internal state to improve predictions and monitor input features to perform split || attempts. Inactive learning nodes do not update their internal state and only keep the || predictors; they are used to save memory in the tree (`max_size`). ||  || *  Enable\/disable memory management. ||  || * Define strategies to sort leaves according to how likely they are going to be split. || This enables deactivating non-promising leaves to save memory. ||  || * Disabling \u2018poor\u2019 attributes to save memory and speed up tree construction. || A poor attribute is an input feature whose split merit is much smaller than the current || best candidate. Once a feature is disabled; the tree stops saving statistics necessary || to split such a feature. ||  || * Define properties to access leaf prediction strategies; split criteria; and other || relevant characteristics. ||  || All HTs have the following parameters; in addition to their own; that can be selected || using `**kwargs`. The following default values are used; unless otherwise explicitly stated || in the tree documentation. ||  || | Parameter | Description | Default | || | :- | :- | -: | || |`max_depth` | The maximum depth a tree can reach. If `None`; the tree will grow indefinitely. | `None` | || | `binary_split` | If True; only allow binary splits. | `False` | || | `max_size` | The maximum size the tree can reach; in Megabytes (MB). | `100` | || | `memory_estimate_period` | Interval (number of processed instances) between memory consumption checks. | `1_000_000` | || | `stop_mem_management` | If True; stop growing as soon as memory limit is hit. | `False` | || | `remove_poor_attrs` | If True; disable poorly descriptive attributes to reduce memory usage. | `False` | || | `merit_preprune` | If True; enable merit-based tree pre-pruning. | `True` | ||  || \"\"\"
aaeehcjdg,THUNLP-MT/THUMT,thumt/utils/search.py,c78d6dc5ee88a9f356c10402c4e8949733a2a013,STILL_EXISTS,needed for the gather
aaeehdaca,THUNLP-MT/THUMT,thumt/launcher/ensemble_translator.py,48aafdfe4ece5e5f9aea5b766bad40a9a49188a5,STILL_EXISTS,TODO: more general cases
aaeehdacc,THUNLP-MT/THUMT,thumt/launcher/ensemble_translator.py,48aafdfe4ece5e5f9aea5b766bad40a9a49188a5,4fc3c7b361f7f7383250dc84775d013eb1b72dc9,TODO: replace rnnsearch with model_cls.name
aaeehddce,THUNLP-MT/THUMT,thumt/utils/sampling.py,5b4b5308c19ba71c7daa3bbf0fb81fcc23d1eaae,STILL_EXISTS,Suppress <eos> if needed
aaeehdjia,explosion/spacy-models,tests/lang/en/test_parser.py,9609fbe6a1c71cfec21e559cbb8f54cc29d35ee5,STILL_EXISTS,vs lex norm changes I made? This should probably be investigated.
aaeehecbe,explosion/spacy-models,tests/lang/pl/test_tagger.py,124d903546447a8f5399f5f820a73dfc536b2ba9,11a5e6bee72026208de32f363146802155af7105,TODO: switch back to nkjp when model config is updated
aaeehefbh,openeventdata/mordecai,resources/utilities.py,a69dc70bf2bc5a8388369f612e8d3340ce5f0ed4,05fd1d0ff0495b18332e2923d2bee88362b4da67,locations.columns = ['country'; 'score']
aaeehegah,openeventdata/mordecai,mordecai/geoparse.py,aa57764ddf2212caddffb703a16d699a5f96d09b,dd23902a49be1b6c32edc1014b28000d7a573ef1,maybe have 'city'? Works differently in different countries
aaeehegee,openeventdata/mordecai,mordecai/geoparse.py,aa57764ddf2212caddffb703a16d699a5f96d09b,836b3e64cd9c809dfa2440fe5a1b49af77a380af,maybe skip later if it's slow...
aaeeheghc,openeventdata/mordecai,mordecai/utilities.py,aa57764ddf2212caddffb703a16d699a5f96d09b,STILL_EXISTS,maybe remove these? Sometimes they mean something; though \"Northwest Provice\"
aaeehehcc,openeventdata/mordecai,mordecai/geoparse.py,963200b8ecdf80eeacb546991c645111319cb1af,STILL_EXISTS,no idea why this comes up
aaeeheieh,openeventdata/mordecai,mordecai/geoparse.py,834340875d6baae452eaf527443025c356a621d2,STILL_EXISTS,maybe also get min edit distance to alternative names...
aaeehejci,openeventdata/mordecai,docs/source/conf.py,5e5b3877e37cb2cc08850b04a61549a578982485,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaeehffbf,openeventdata/mordecai,mordecai/geoparse.py,d0a0b8445fc4847637924bcc3697e4d6bb77b60b,STILL_EXISTS,TODO: incorporate positions; especially now that we don't split by
aaeehffdh,openeventdata/mordecai,mordecai/geoparse.py,fa5d364e3307365974ca33557b8ddb8981010b85,STILL_EXISTS,TODO: incorporate positions; especially now that we don't split by
aaeehffec,openeventdata/mordecai,mordecai/geoparse.py,fa5d364e3307365974ca33557b8ddb8981010b85,STILL_EXISTS,TODO check if most_common feature really isn't that useful
aaeehfgia,logpai/loglizer,DecisionTree/decisionTree_BGL.py,84eba11876f6660c2b70b7c499003e65cc13a0a9,STILL_EXISTS,select the corresponding columns in the raw data; no need to change
aaeehfgib,logpai/loglizer,DecisionTree/decisionTree_BGL.py,84eba11876f6660c2b70b7c499003e65cc13a0a9,STILL_EXISTS,the index of time in the selected columns; start from 0
aaeehfgjc,logpai/loglizer,DecisionTree/decisionTree_BGL.py,84eba11876f6660c2b70b7c499003e65cc13a0a9,STILL_EXISTS,fix imbalance problem
aaeehfhca,logpai/loglizer,DecisionTree/slidingWindow_DecisionTree_BGL.py,84eba11876f6660c2b70b7c499003e65cc13a0a9,STILL_EXISTS,select the corresponding columns in the raw data
aaeehfhcb,logpai/loglizer,DecisionTree/slidingWindow_DecisionTree_BGL.py,84eba11876f6660c2b70b7c499003e65cc13a0a9,STILL_EXISTS,the index of time in the selected columns; start from 0
aaeehfhdc,logpai/loglizer,DecisionTree/slidingWindow_DecisionTree_BGL.py,84eba11876f6660c2b70b7c499003e65cc13a0a9,STILL_EXISTS,fix imbalance problem
aaeehfheh,logpai/loglizer,LogClustering/LogClustering_Online_BGL_slidingWindow.py,84eba11876f6660c2b70b7c499003e65cc13a0a9,STILL_EXISTS,select the corresponding columns in the raw data
aaeehfhei,logpai/loglizer,LogClustering/LogClustering_Online_BGL_slidingWindow.py,84eba11876f6660c2b70b7c499003e65cc13a0a9,STILL_EXISTS,the index of time in the selected columns; start from 0
aaeehfjci,logpai/loglizer,LogisticRegression/logisticRegression_BGL.py,84eba11876f6660c2b70b7c499003e65cc13a0a9,STILL_EXISTS,select the corresponding columns in the raw data; no need to change
aaeehfjcj,logpai/loglizer,LogisticRegression/logisticRegression_BGL.py,84eba11876f6660c2b70b7c499003e65cc13a0a9,STILL_EXISTS,the index of time in the selected columns; start from 0
aaeehfjga,logpai/loglizer,LogisticRegression/slidingWindow_LogisticRegress_BGL.py,84eba11876f6660c2b70b7c499003e65cc13a0a9,STILL_EXISTS,select the corresponding columns in the raw data
aaeehfjgb,logpai/loglizer,LogisticRegression/slidingWindow_LogisticRegress_BGL.py,84eba11876f6660c2b70b7c499003e65cc13a0a9,STILL_EXISTS,the index of time in the selected columns; start from 0
aaeehfjhc,logpai/loglizer,LogisticRegression/slidingWindow_LogisticRegress_BGL.py,84eba11876f6660c2b70b7c499003e65cc13a0a9,STILL_EXISTS,fix imbalance problem
aaeehgaad,logpai/loglizer,MiningInvariants/miningInvari_BGL.py,84eba11876f6660c2b70b7c499003e65cc13a0a9,STILL_EXISTS,select the corresponding columns in the raw data
aaeehgaae,logpai/loglizer,MiningInvariants/miningInvari_BGL.py,84eba11876f6660c2b70b7c499003e65cc13a0a9,STILL_EXISTS,the index of time in the selected columns; start from 0
aaeehgacj,logpai/loglizer,MiningInvariants/miningInvari_BGL.py,84eba11876f6660c2b70b7c499003e65cc13a0a9,STILL_EXISTS,save the mined Invariants(value) and its corrsponding columns(key)
aaeehgahd,logpai/loglizer,MiningInvariants/miningInvari_SOSP.py,84eba11876f6660c2b70b7c499003e65cc13a0a9,STILL_EXISTS,save the mined Invariants(value) and its corrsponding columns(key)
aaeehgbac,logpai/loglizer,MiningInvariants/miningInvari_slidingWindow.py,84eba11876f6660c2b70b7c499003e65cc13a0a9,STILL_EXISTS,select the corresponding columns in the raw data
aaeehgbad,logpai/loglizer,MiningInvariants/miningInvari_slidingWindow.py,84eba11876f6660c2b70b7c499003e65cc13a0a9,STILL_EXISTS,the index of time in the selected columns; start from 0
aaeehgbda,logpai/loglizer,MiningInvariants/miningInvari_slidingWindow.py,84eba11876f6660c2b70b7c499003e65cc13a0a9,STILL_EXISTS,save the mined Invariants(value) and its corrsponding columns(key)
aaeehgbfa,logpai/loglizer,PCA/PCA_BGL_slidingWindow.py,84eba11876f6660c2b70b7c499003e65cc13a0a9,STILL_EXISTS,select the corresponding columns in the raw data
aaeehgbfb,logpai/loglizer,PCA/PCA_BGL_slidingWindow.py,84eba11876f6660c2b70b7c499003e65cc13a0a9,STILL_EXISTS,the index of time in the selected columns; start from 0
aaeehgcbd,logpai/loglizer,PCA/PCA_BGL_timewindow.py,84eba11876f6660c2b70b7c499003e65cc13a0a9,STILL_EXISTS,select the corresponding columns in the raw data
aaeehgcbe,logpai/loglizer,PCA/PCA_BGL_timewindow.py,84eba11876f6660c2b70b7c499003e65cc13a0a9,STILL_EXISTS,the index of time in the selected columns; start from 0
aaeehgchh,logpai/loglizer,SVM/SVM_BGL.py,84eba11876f6660c2b70b7c499003e65cc13a0a9,STILL_EXISTS,select the corresponding columns in the raw data
aaeehgchi,logpai/loglizer,SVM/SVM_BGL.py,84eba11876f6660c2b70b7c499003e65cc13a0a9,STILL_EXISTS,the index of time in the selected columns; start from 0
aaeehgcii,logpai/loglizer,SVM/SVM_BGL.py,84eba11876f6660c2b70b7c499003e65cc13a0a9,STILL_EXISTS,fix imbalance problem
aaeehgdcc,logpai/loglizer,SVM/slidingWindow_SVM.py,84eba11876f6660c2b70b7c499003e65cc13a0a9,STILL_EXISTS,select the corresponding columns in the raw data
aaeehgdcd,logpai/loglizer,SVM/slidingWindow_SVM.py,84eba11876f6660c2b70b7c499003e65cc13a0a9,STILL_EXISTS,the index of time in the selected columns; start from 0
aaeehgdde,logpai/loglizer,SVM/slidingWindow_SVM.py,84eba11876f6660c2b70b7c499003e65cc13a0a9,STILL_EXISTS,fix imbalance problem
aaeehggec,logpai/loglizer,PCA_bgl.py,b03d96aac2c941a15169e1d243e0bea7954ec264,STILL_EXISTS,select the corresponding columns (label and time) in the raw log file
aaeehggid,logpai/loglizer,mining_invariants.py,b03d96aac2c941a15169e1d243e0bea7954ec264,STILL_EXISTS,save the mined Invariants(value) and its corresponding columns(key)
aaeehggif,logpai/loglizer,mining_invariants.py,b03d96aac2c941a15169e1d243e0bea7954ec264,STILL_EXISTS,invariant of only one column (all zero columns)
aaeehggig,logpai/loglizer,mining_invariants.py,b03d96aac2c941a15169e1d243e0bea7954ec264,STILL_EXISTS,check invariant of more columns
aaeehggjf,logpai/loglizer,mining_invariants_bgl.py,b03d96aac2c941a15169e1d243e0bea7954ec264,STILL_EXISTS,select the corresponding columns (label and time) in the raw log file
aaeehghbh,logpai/loglizer,supervised_bgl.py,b03d96aac2c941a15169e1d243e0bea7954ec264,STILL_EXISTS,select the corresponding columns (label and time) in the raw log file
aaeehghee,logpai/loglizer,demo_bgl/log_clustering_bgl.py,98ef6d8fa8211ae8da3dbecf8439ab59badaad35,STILL_EXISTS,select the corresponding columns (label and time) in the raw log file
aaeehgidc,logpai/loglizer,loglizer/models/InvariantsMiner.py,d990f23b72c2409084a799bdf49109a996a02256,STILL_EXISTS,save the mined Invariants(value) and its corresponding columns(key)
aaeehgide,logpai/loglizer,loglizer/models/InvariantsMiner.py,d990f23b72c2409084a799bdf49109a996a02256,STILL_EXISTS,invariant of only one column (all zero columns)
aaeehgidf,logpai/loglizer,loglizer/models/InvariantsMiner.py,d990f23b72c2409084a799bdf49109a996a02256,STILL_EXISTS,check invariant of more columns
aaeehgidi,logpai/loglizer,loglizer/models/InvariantsMiner.py,d990f23b72c2409084a799bdf49109a996a02256,STILL_EXISTS,print('A valid invariant is found: ';scaled_theta; selected_columns)
aaeehgjcc,logpai/loglizer,demo/InvariantsMiner_demo_without_labels.py,0dda132d5ea46d33207d6d4c15fab1d72f70ba18,STILL_EXISTS,''' This is a demo file for the Invariants Mining model. ||     API usage: ||         dataloader.load_HDFS(): load HDFS dataset ||         feature_extractor.fit_transform(): fit and transform features ||         feature_extractor.transform(): feature transform after fitting ||         model.fit(): fit the model ||         model.predict(): predict anomalies on given data ||         model.evaluate(): evaluate model accuracy with labeled data || '''
aaeehgjdg,logpai/loglizer,demo/PCA_demo_without_labels.py,0dda132d5ea46d33207d6d4c15fab1d72f70ba18,STILL_EXISTS,''' This is a demo file for the PCA model. ||     API usage: ||         dataloader.load_HDFS(): load HDFS dataset ||         feature_extractor.fit_transform(): fit and transform features ||         feature_extractor.transform(): feature transform after fitting ||         model.fit(): fit the model ||         model.predict(): predict anomalies on given data ||         model.evaluate(): evaluate model accuracy with labeled data || '''
aaeehgjhe,onnx/onnx-tensorflow,onnxtf/backend.py,3555744ad2873f3c11874ece2ed31f9acb18ac42,STILL_EXISTS,TODO: allow more flexible placement
aaeehgjhf,onnx/onnx-tensorflow,onnxtf/backend.py,2283242d066eab7bc955a8091aa9b2211fe1cb00,STILL_EXISTS,TODO: is constant the best way for feeding inputs?
aaeehgjia,onnx/onnx-tensorflow,onnxtf/backend.py,2283242d066eab7bc955a8091aa9b2211fe1cb00,STILL_EXISTS,TODO: Per op attribute name mapping has the final say.
aaeehgjic,onnx/onnx-tensorflow,onnxtf/backend.py,75dcd4f587b51d50f6966bc6b84826c84d359d11,STILL_EXISTS,TODO: better naming
aaeehgjie,onnx/onnx-tensorflow,onnx_tf/backend.py,20cc6c4a5f647b7d7dffa8f50c6a4449c41ef665,486d98928289c88b2c696962a2d53a4ea2e307f6,TODO: allow more flexible placement
aaeehgjii,onnx/onnx-tensorflow,onnx_tf/backend.py,20cc6c4a5f647b7d7dffa8f50c6a4449c41ef665,STILL_EXISTS,TODO: is constant the best way for feeding inputs?
aaeehgjje,onnx/onnx-tensorflow,onnx_tf/backend.py,20cc6c4a5f647b7d7dffa8f50c6a4449c41ef665,486d98928289c88b2c696962a2d53a4ea2e307f6,TODO: Per op attribute name mapping has the final say.
aaeehgjjh,onnx/onnx-tensorflow,onnx_tf/backend.py,20cc6c4a5f647b7d7dffa8f50c6a4449c41ef665,fd80e62974074cd26ec035ea39e0ba973e74312a,TODO: better naming
aaeehgjjj,onnx/onnx-tensorflow,onnx_tf/backend.py,307c56af49d933568272e7309c764bbf21dfbafa,c72e29f1d799bde43efe0df9485ba62519e9b09c,TODO: uncomment this in the future
aaeehhaia,onnx/onnx-tensorflow,test/test_node.py,a39084b995d9d6d88300632c54895488985e5cfd,STILL_EXISTS,TODO: pass axis attribute which is supported in newer
aaeehhaic,onnx/onnx-tensorflow,test/test_node.py,a39084b995d9d6d88300632c54895488985e5cfd,STILL_EXISTS,TODO: pass axis=3 and uncomment the line below
aaeehhbed,onnx/onnx-tensorflow,onnx_tf/backend.py,67db4a103406ebc472c1db2979de646bfdee4238,c72e29f1d799bde43efe0df9485ba62519e9b09c,TODO: uncomment this in the future
aaeehhbfc,onnx/onnx-tensorflow,onnx_tf/backend.py,49e00384a125239accd126c471ca595505e25f78,STILL_EXISTS,TODO: map ONNX padding to TF padding. For now default to \"SAME\".
aaeehhbff,onnx/onnx-tensorflow,onnx_tf/backend.py,49e00384a125239accd126c471ca595505e25f78,STILL_EXISTS,TODO: do pooling using other TF operations.
aaeehhbgj,onnx/onnx-tensorflow,onnx_tf/backend.py,1805db2612a82d93d1dc357944843fba562de47a,c72e29f1d799bde43efe0df9485ba62519e9b09c,TODO: uncomment this in the future
aaeehhbia,onnx/onnx-tensorflow,onnx_tf/backend.py,ecdaca16eca5c9de877b48e417845f42bc29a9fb,c72e29f1d799bde43efe0df9485ba62519e9b09c,TODO: uncomment this in the future
aaeehhbjc,onnx/onnx-tensorflow,onnx_tf/backend_rep.py,ecdaca16eca5c9de877b48e417845f42bc29a9fb,054095d922edda5134e520522bc82a1b95cc5bd4,TODO: handle name scope if necessary
aaeehhcai,onnx/onnx-tensorflow,onnx_tf/backend.py,3015cdabfa015e607280ca9058f83ef0e0a4d0de,2db9d34ce36625c51b98f4d6fec37a1a07d555c8,TODO: uncomment this in the future
aaeehhcce,onnx/onnx-tensorflow,onnx_tf/backend.py,43063583a884a78951c9152aa13ced9a33d829db,2db9d34ce36625c51b98f4d6fec37a1a07d555c8,TODO: better support optimized rnn
aaeehhccf,onnx/onnx-tensorflow,onnx_tf/backend.py,43063583a884a78951c9152aa13ced9a33d829db,2db9d34ce36625c51b98f4d6fec37a1a07d555c8,TODO: handle data types
aaeehhccg,onnx/onnx-tensorflow,test/test_node.py,43063583a884a78951c9152aa13ced9a33d829db,2db9d34ce36625c51b98f4d6fec37a1a07d555c8,TODO: better testing for RNN. For now; we are just making sure
aaeehhcfc,onnx/onnx-tensorflow,onnx_tf/backend.py,c14b7eb2e582079894f8d7f22423cad0d1793041,17075f44c9071600beccfc62c92b22d1cd957bfd,TODO: uncomment this in the future
aaeehhcgi,onnx/onnx-tensorflow,onnx_tf/backend.py,c14b7eb2e582079894f8d7f22423cad0d1793041,STILL_EXISTS,TODO: better support optimized rnn
aaeehhcgj,onnx/onnx-tensorflow,onnx_tf/backend.py,c14b7eb2e582079894f8d7f22423cad0d1793041,eb502f2c1304ade6c7e73f13066d09a7e2c38a80,TODO: handle data types
aaeehhchi,onnx/onnx-tensorflow,test/test_node.py,c14b7eb2e582079894f8d7f22423cad0d1793041,STILL_EXISTS,TODO: better testing for RNN. For now; we are just making sure
aaeehhcia,onnx/onnx-tensorflow,test/test_node.py,b4fccd0c21a6e0ed19f7d82f143fdf8e66cb943c,10902930dcd12210c6946897e7f6203e085fbad0,TODO: uncomment this in the future
aaeehhcid,onnx/onnx-tensorflow,test/test_node.py,53e1c91f28186548987d74830fc62dd6749e5077,a02875d10f6501b097a149181f87f1ea8e1a8928,TODO: uncomment power schema is wrong in onnx
aaeehhcie,onnx/onnx-tensorflow,test/test_node.py,53e1c91f28186548987d74830fc62dd6749e5077,STILL_EXISTS,TODO: API update or fix onnx version
aaeehhcii,onnx/onnx-tensorflow,onnx_tf/backend.py,6d8fd50a05261bd3978bbde4ed3831ef38a9b13d,eb502f2c1304ade6c7e73f13066d09a7e2c38a80,apparently this is what's needed for squeezenet to work
aaeehhcjc,onnx/onnx-tensorflow,onnx_tf/backend.py,a5990b39af4ede3122d2c5e34ecc6004e865d88e,2a4e2b1b131d9a1b48d8eba5479b585bf328d3ba,TODO: better broadcast
aaeehhcjd,onnx/onnx-tensorflow,onnx_tf/backend.py,a5990b39af4ede3122d2c5e34ecc6004e865d88e,eb502f2c1304ade6c7e73f13066d09a7e2c38a80,TODO: need to conform to the documentation here
aaeehhdbe,onnx/onnx-tensorflow,test/test_node.py,0a2e8beac36bf6c797bf45cdfa27cdd6d2924796,STILL_EXISTS,TODO: fix this test
aaeehhdce,onnx/onnx-tensorflow,test/test_node.py,a8ec204de582d580313374a394dc5de3b589fe80,a02875d10f6501b097a149181f87f1ea8e1a8928,TODO: test obsolete
aaeehhdde,onnx/onnx-tensorflow,onnx_tf/common.py,836f979891aea7ea5e2c98f4b50651e4dda32c6b,STILL_EXISTS,TODO: uncomment this in the future
aaeehhdfc,onnx/onnx-tensorflow,onnx_tf/frontend.py,3d479ef21f4d008341c75803f14cd674893182ae,871d867e13935e813b37f218950787e4e1d2d6e2,TODO: currently `dtype` is translated to `to`.
aaeehhdga,onnx/onnx-tensorflow,onnx_tf/frontend.py,4948889d5b477b00aef11aef632646d12978b5d4,871d867e13935e813b37f218950787e4e1d2d6e2,needed\/allowed in ONNX.
aaeehhdgb,onnx/onnx-tensorflow,onnx_tf/frontend.py,458a73666df26ac920ce5404c5931e93752bcd60,1c0519709b374a9233351de2e1045d078b1f20bc,TODO: default to BOOL; cf.
aaeehhdhc,onnx/onnx-tensorflow,onnx_tf/common.py,0d413fc24ead48c0bd885cd7c4a328772c216386,STILL_EXISTS,move this to op specific translator
aaeehhdih,onnx/onnx-tensorflow,onnx_tf/common.py,cb7a68428c6a59ca9f13c5cc08157b0001ec902a,STILL_EXISTS,TODO:
aaeehhebe,onnx/onnx-tensorflow,onnx_tf/backend.py,42cf810d07249d2506a7d3b5ce4baa8870d2b0c9,d0a252d56e6b9d936d2831a539a406f47467a4cd,TODO : Arrange indices to the right data format
aaeehhffa,onnx/onnx-tensorflow,onnx_tf/common.py,17075f44c9071600beccfc62c92b22d1cd957bfd,STILL_EXISTS,TODO: uncomment this in the future
aaeehhffc,onnx/onnx-tensorflow,onnx_tf/backend.py,154a1bab3e9c726345859594d42ea4fa98ee155f,eb502f2c1304ade6c7e73f13066d09a7e2c38a80,TODO: handle data types
aaeehhfje,onnx/onnx-tensorflow,onnx_tf/backend_v1.py,eb502f2c1304ade6c7e73f13066d09a7e2c38a80,STILL_EXISTS,TODO: need to conform to the documentation here
aaeehhfjf,onnx/onnx-tensorflow,onnx_tf/backend_v1.py,eb502f2c1304ade6c7e73f13066d09a7e2c38a80,STILL_EXISTS,apparently this is what's needed for squeezenet to work
aaeehhgaa,onnx/onnx-tensorflow,onnx_tf/backend_v1.py,eb502f2c1304ade6c7e73f13066d09a7e2c38a80,STILL_EXISTS,TODO: LRN in tf accepts radius
aaeehhgad,onnx/onnx-tensorflow,onnx_tf/backend_v1.py,eb502f2c1304ade6c7e73f13066d09a7e2c38a80,STILL_EXISTS,TODO: handle data types
aaeehhhai,onnx/onnx-tensorflow,onnx_tf/third_party/get_info.py,b1fdc122318b99fc53c55b6a2d31dea1aed19686,STILL_EXISTS,workaround expected behavior from unittests
aaeehhhdc,onnx/onnx-tensorflow,onnx_tf/frontend.py,abbabcd096ce252ed1bacfc6a4438510a90158eb,871d867e13935e813b37f218950787e4e1d2d6e2,TODO per domain frontend_tf_opset_version?
aaeehhhea,onnx/onnx-tensorflow,onnx_tf/frontend.py,2eaf80038f768274767215b447420fe8c710a83a,871d867e13935e813b37f218950787e4e1d2d6e2,needed\/allowed in ONNX.
aaeehhhfh,onnx/onnx-tensorflow,onnx_tf/frontend.py,553798503445da59a6b4ad9346c2a88b6cf35ce9,871d867e13935e813b37f218950787e4e1d2d6e2,TODO: currently no onnx release support value_info; thus ensuring
aaeehhhhg,onnx/onnx-tensorflow,onnx_tf/backends/backend_v1.py,02f5001f261f7a7f4e7b20e1a0e9e31fa0b38640,STILL_EXISTS,a copy from the original dimension size is needed.
aaeehhhhj,onnx/onnx-tensorflow,onnx_tf/backends/backend_v5.py,02f5001f261f7a7f4e7b20e1a0e9e31fa0b38640,STILL_EXISTS,a copy from the original dimension size is needed.
aaeehhihb,onnx/onnx-tensorflow,onnx_tf/common/__init__.py,486d98928289c88b2c696962a2d53a4ea2e307f6,STILL_EXISTS,TODO: allow more flexible placement
aaeehhijc,onnx/onnx-tensorflow,onnx_tf/handlers/backend/batch_normalization.py,486d98928289c88b2c696962a2d53a4ea2e307f6,STILL_EXISTS,TODO: need to conform to the documentation here
aaeehhjbe,onnx/onnx-tensorflow,onnx_tf/handlers/backend/lrn.py,486d98928289c88b2c696962a2d53a4ea2e307f6,STILL_EXISTS,TODO: LRN in tf accepts radius
aaeehhjee,onnx/onnx-tensorflow,onnx_tf/handlers/backend/reshape.py,486d98928289c88b2c696962a2d53a4ea2e307f6,STILL_EXISTS,a copy from the original dimension size is needed.
aaeehhjjb,onnx/onnx-tensorflow,onnx_tf/handlers/backend/dynamic_slice.py,39b5ce7bfeb6e5aa0476c54555e64a3956596d50,STILL_EXISTS,take care of starts; ends that are larger than the dim size.
aaeehhjjc,onnx/onnx-tensorflow,onnx_tf/handlers/backend/dynamic_slice.py,39b5ce7bfeb6e5aa0476c54555e64a3956596d50,STILL_EXISTS,take care of starts; ends that are negative
aaeehiace,onnx/onnx-tensorflow,onnx_tf/handlers/frontend/strided_slice.py,4fe611422ad3236973c498c5bff51fdd55657a4e,STILL_EXISTS,TODO: assert strides must be 1.
aaeehiaej,onnx/onnx-tensorflow,onnx_tf/pb_wrapper.py,93954860a66eab98f488da724da488a85e149166,STILL_EXISTS,TODO: Move this into ONNX main library
aaeehiaif,onnx/onnx-tensorflow,onnx_tf/optimizer.py,b5fef1b6471ad6c5bac214e9799c38d41658ee73,STILL_EXISTS,TODO: allow selective enablement of optimization passes
aaeehiaja,onnx/onnx-tensorflow,onnx_tf/pb_wrapper.py,b5fef1b6471ad6c5bac214e9799c38d41658ee73,STILL_EXISTS,Either way; data_type_cast_map is empty when initialized.
aaeehiajb,onnx/onnx-tensorflow,onnx_tf/common/data_type.py,f847bee1d954988ccbe4e1b2256400115c373637,STILL_EXISTS,TODO (tjingrant) unify _onnx_dtype into any_dtype_to_onnx_dtype
aaeehibeh,onnx/onnx-tensorflow,onnx_tf/handlers/backend/slice.py,a93a05f1945f948b33b5ac65b9667a10a8705cf2,STILL_EXISTS,take care of starts; ends that are larger than the dim size.
aaeehibei,onnx/onnx-tensorflow,onnx_tf/handlers/backend/slice.py,a93a05f1945f948b33b5ac65b9667a10a8705cf2,STILL_EXISTS,take care of starts; ends that are negative
aaeehidje,onnx/onnx-tensorflow,test/backend/test_node.py,9d9a209f1cfed0d8247159f86efc3eb0de7a075f,86a5ee7dc69bd34fe63a695eeed7f99f96b1aef8,TODO: pass axis attribute which is supported in newer
aaeehidjg,onnx/onnx-tensorflow,test/backend/test_node.py,9d9a209f1cfed0d8247159f86efc3eb0de7a075f,86a5ee7dc69bd34fe63a695eeed7f99f96b1aef8,TODO: pass axis=3 and uncomment the line below
aaeehidjj,onnx/onnx-tensorflow,onnx_tf/handlers/backend/scatter_elements.py,6bf1221a0348282a7544e617a04e4ffc8cad438f,STILL_EXISTS,Move on to convert ONNX indices to tensorflow indices in 2 steps:
aaeehieda,onnx/onnx-tensorflow,onnx_tf/handlers/backend/dilated_maxpooling.py,75c4f0ec9aa4ebe1d657567b91c6353a0cf29648,13a3af27651612847e902ea622b00045805b08a2,implement it using: a % b = a - (a \/\/ b) * b
aaeehiegg,onnx/onnx-tensorflow,onnx_tf/handlers/backend/dilated_maxpooling.py,611be2ba4a19a1eb3ff21993af1d5ae5870639b9,13a3af27651612847e902ea622b00045805b08a2,implement it using: a % b = a - (a \/\/ b) * b
aaeehifgi,onnx/onnx-tensorflow,onnx_tf/handlers/backend/dilated_maxpooling.py,47462402c76719b29b043dd8b4f4e785254730ab,STILL_EXISTS,implement it using: a % b = a - (a \/\/ b) * b
aaeehihdg,onnx/onnx-tensorflow,test/backend/test_node.py,b8bba3090b5ac83cf10bf628962d4afbabc4742f,d944ab7d79bb353a15448c9826a452841c9e1fb2,TODO: pass axis attribute which is supported in newer
aaeehihdi,onnx/onnx-tensorflow,test/backend/test_node.py,b8bba3090b5ac83cf10bf628962d4afbabc4742f,d944ab7d79bb353a15448c9826a452841c9e1fb2,TODO: pass axis=3 and uncomment the line below
aaeehjgij,onnx/onnx-tensorflow,test/backend/test_node.py,4ca2541496d2113d7a02c2b3f4180c7d2b451afe,04cd23d79845e8fef361353dd39e949214a6fc11,TODO: fix this test
aaeehjjic,onnx/onnx-tensorflow,test/backend/test_node.py,3d246d625d0db308aaa0922c8812d49964eab753,3776542a28c00cec48817d88256a2a088a85ab64,TODO: fix this test
aaeeiabab,onnx/onnx-tensorflow,test/backend/test_node.py,9edb176ecb0b6f061c2f7c196ac1f9563664baff,dcfeb1277c4814d13152dc4a9e734a724eb13038,TODO: fix this test
aaeeiaefc,onnx/onnx-tensorflow,onnx_tf/handlers/backend/split_to_sequence.py,da32863a0a64f2426dfe7954d8925972ac295857,STILL_EXISTS,Then append m if needed [n; n; n...; m] where m=size(mod n)
aaeeiaghb,onnx/onnx-tensorflow,onnx_tf/handlers/backend/conv_mixin.py,054095d922edda5134e520522bc82a1b95cc5bd4,STILL_EXISTS,this is a workaround for tensorflow AutoGraph not detecting
aaeeibahc,onnx/onnx-tensorflow,onnx_tf/handlers/backend/dequantize_linear.py,b8a88ded3dc16f438b9d6360a482d25ba8d620f7,STILL_EXISTS,Reshape process is needed for per-axis dequantization
aaeeibahe,onnx/onnx-tensorflow,onnx_tf/handlers/backend/quantize_linear.py,b8a88ded3dc16f438b9d6360a482d25ba8d620f7,STILL_EXISTS,Reshape process is needed for per-axis quantization
aaeeibbbb,onnx/onnx-tensorflow,onnx_tf/handlers/backend/conv_mixin.py,db092105ceebe076610a1b27c1fc1553978c17cd,STILL_EXISTS,this is a workaround for tensorflow AutoGraph not detecting
aaeeibbbi,onnx/onnx-tensorflow,onnx_tf/handlers/backend/div.py,ad85cc8b018fe68d826485064fc568215d5bacec,STILL_EXISTS,case the output back to the original data type when needed.
aaeeibcac,onnx/onnx-tensorflow,onnx_tf/handlers/backend/batch_normalization.py,fc8ac52bde43ec34f55cdaf9be53e76fd2e40772,STILL_EXISTS,TODO: need to conform to the documentation here
aaeeibcbe,CPJKU/madmom,cp/lib/features/onset.py,0baafd8e9533c3d40a171f26d66cfa5064f412a7,STILL_EXISTS,TODO: reimplement this function to make it faster
aaeeibcfi,CPJKU/madmom,cp/audio/filterbank.py,426aced9d6acc50e909b59c75a68e4d40655b44c,STILL_EXISTS,# FIXME: this is NOT faster!
aaeeibdbh,CPJKU/madmom,cp/audio/filterbank.py,426aced9d6acc50e909b59c75a68e4d40655b44c,STILL_EXISTS,FIXME: this code is taken from Reini; make it nice and behave the same
aaeeibdga,CPJKU/madmom,cp/audio/spectral_odf.py,426aced9d6acc50e909b59c75a68e4d40655b44c,STILL_EXISTS,TODO: under some circumstances it might be helpful to init with the spec
aaeeibdie,CPJKU/madmom,cp/audio/spectral_odf.py,426aced9d6acc50e909b59c75a68e4d40655b44c,STILL_EXISTS,note: the original MKL uses sum instead of mean; but the range of mean is much more suitable
aaeeibeci,CPJKU/madmom,cp/audio/spectrogram.py,426aced9d6acc50e909b59c75a68e4d40655b44c,STILL_EXISTS,only shift and perform complex DFT if needed
aaeeibecj,CPJKU/madmom,cp/audio/spectrogram.py,426aced9d6acc50e909b59c75a68e4d40655b44c,STILL_EXISTS,circular shift the signal (needed for correct phase)
aaeeibedf,CPJKU/madmom,cp/audio/spectrogram.py,426aced9d6acc50e909b59c75a68e4d40655b44c,651eb857a793a7d7dda07c4319eb382dd2b32ddf,FIXME: does not perform the seeking the proper way
aaeeibehb,CPJKU/madmom,cp/audio/spectrogram.py,426aced9d6acc50e909b59c75a68e4d40655b44c,STILL_EXISTS,TODO: should the filter stuff be included here?
aaeeibeid,CPJKU/madmom,cp/audio/wav.py,426aced9d6acc50e909b59c75a68e4d40655b44c,STILL_EXISTS,TODO: overwrite filename or just read in the new file?
aaeeibfdj,CPJKU/madmom,cp/audio/filterbank.py,4fdccd16022dd8e75e9f46a9513a5cde5ae29719,70adaf31d5fbbb185d0f6dd37acd6b269f5617de,TODO: modify this class; so that a spectrogram object can be used
aaeeibfea,CPJKU/madmom,cp/audio/filterbank.py,4fdccd16022dd8e75e9f46a9513a5cde5ae29719,STILL_EXISTS,directly for init. It has all the needed information (# of ffts & fs).
aaeeibfhj,CPJKU/madmom,cp/evaluation/onsets.py,07db89e26db6fecd4c8f5f4ca20b69f966b87c79,b4b0df783a640181b918e33978403b6cb494c132,needed only for mean and std.dev in Counter.print_errors()
aaeeibgcb,CPJKU/madmom,cp/evaluation/onsets.py,07db89e26db6fecd4c8f5f4ca20b69f966b87c79,STILL_EXISTS,TODO: find a better way to determine the corresponding detection\/target files from a given list\/path of files
aaeeibgdi,CPJKU/madmom,cp/audio/filterbank.py,e6079a7acdc782cf4881b2f1a06ca937af8be668,STILL_EXISTS,too much weight if simply summed up (as in the spectral flux)
aaeeibgee,CPJKU/madmom,cp/audio/onset_detection.py,e6079a7acdc782cf4881b2f1a06ca937af8be668,STILL_EXISTS,TODO: under some circumstances it might be helpful to init with the spec
aaeeibgge,CPJKU/madmom,cp/audio/onset_detection.py,e6079a7acdc782cf4881b2f1a06ca937af8be668,STILL_EXISTS,note: the original MKL uses sum instead of mean; but the range of mean is much more suitable
aaeeibgjb,CPJKU/madmom,cp/audio/onset_detection.py,e6079a7acdc782cf4881b2f1a06ca937af8be668,STILL_EXISTS,TODO: get rid of it here?
aaeeibhfi,CPJKU/madmom,cp/audio/spectrogram.py,e6079a7acdc782cf4881b2f1a06ca937af8be668,STILL_EXISTS,functionality is also implemented by the iterable Wav class
aaeeibhhd,CPJKU/madmom,cp/audio/spectrogram.py,e6079a7acdc782cf4881b2f1a06ca937af8be668,STILL_EXISTS,only shift and perform complex DFT if needed
aaeeibhhe,CPJKU/madmom,cp/audio/spectrogram.py,e6079a7acdc782cf4881b2f1a06ca937af8be668,STILL_EXISTS,circular shift the signal (needed for correct phase)
aaeeibhih,CPJKU/madmom,cp/audio/wav.py,e6079a7acdc782cf4881b2f1a06ca937af8be668,651eb857a793a7d7dda07c4319eb382dd2b32ddf,# TODO: use this code if normal indexing behavior is needed
aaeeibhjg,CPJKU/madmom,cp/audio/wav.py,e6079a7acdc782cf4881b2f1a06ca937af8be668,651eb857a793a7d7dda07c4319eb382dd2b32ddf,more padding than a whole frame size needed
aaeeibiac,CPJKU/madmom,cp/audio/wav.py,e6079a7acdc782cf4881b2f1a06ca937af8be668,STILL_EXISTS,FIXME: what is the length? samples; frames?
aaeeibiaf,CPJKU/madmom,cp/audio/spectrogram.py,661c0d5de551366f616082eeed616f751c7b3140,0766df777e85f37e0a337bd42bff462f01c80999,TODO: remove this function?
aaeeibibe,CPJKU/madmom,cp/audio/wav.py,661c0d5de551366f616082eeed616f751c7b3140,STILL_EXISTS,TODO: setting the samples attribute here has some speed improvements
aaeeibicd,CPJKU/madmom,cp/audio/filterbank.py,4443089bf2f71aa6dedd529f60fa0dff702bfc2f,STILL_EXISTS,TODO: check the formulas
aaeeibidd,CPJKU/madmom,cp/audio/filterbank.py,4443089bf2f71aa6dedd529f60fa0dff702bfc2f,STILL_EXISTS,# FIXME: why this == 0 check?
aaeeibifb,CPJKU/madmom,cp/audio/filterbank.py,4443089bf2f71aa6dedd529f60fa0dff702bfc2f,c471697104f2e3c20cfb94dc7d8c7aa592fe97af,TODO: move this to bark_frequencies & bark_double_frequencies?
aaeeibifc,CPJKU/madmom,cp/audio/filterbank.py,4443089bf2f71aa6dedd529f60fa0dff702bfc2f,STILL_EXISTS,TODO: this is very similar to the Cent-Scale. Unify it?
aaeeibigc,CPJKU/madmom,cp/audio/filterbank.py,133af468629527e4f3031aa3dd3837972e2aaf63,STILL_EXISTS,given too much weight if simply summed up (as in the spectral flux)
aaeeibjdh,CPJKU/madmom,cp/evaluation/beats.py,8cea3ee9cdec0d0e86f4b9aade9ac05d3b755afd,STILL_EXISTS,TODO: include a third tempo as well? This might be needed for fast Waltz
aaeeibjgg,CPJKU/madmom,cp/evaluation/beats.py,8cea3ee9cdec0d0e86f4b9aade9ac05d3b755afd,STILL_EXISTS,TODO: if condition 2) is included; uncomment the 4 lines above
aaeeicaci,CPJKU/madmom,cp/evaluation/beats.py,8cea3ee9cdec0d0e86f4b9aade9ac05d3b755afd,STILL_EXISTS,TODO: find a better way to determine the corresponding detection\/target files from a given list\/path of files
aaeeicadb,CPJKU/madmom,cp/evaluation/beats.py,8cea3ee9cdec0d0e86f4b9aade9ac05d3b755afd,STILL_EXISTS,must be the same number FIXME: find better solution which checks the names
aaeeicbaj,CPJKU/madmom,cp/evaluation/simple.py,b4b0df783a640181b918e33978403b6cb494c132,309a857a560f40d4c837fc04192b8190f3a4ca09,TODO: rename _ to __ again?
aaeeicbba,CPJKU/madmom,cp/evaluation/simple.py,b4b0df783a640181b918e33978403b6cb494c132,c31137994e0bd0e767fab581fa384248c71c623e,TODO: move this to a helper function? might be useful in other cases
aaeeicbdj,CPJKU/madmom,cp/evaluation/simple.py,b4b0df783a640181b918e33978403b6cb494c132,c31137994e0bd0e767fab581fa384248c71c623e,# FIXME: invalidate (i.e. reset them to None) if the detections or
aaeeicche,CPJKU/madmom,cp/audio/onset_detection.py,1849847408b412a0c2eedd99e0280d57a025522e,STILL_EXISTS,FIXME: fps must be encoded in the file
aaeeicchf,CPJKU/madmom,cp/audio/spectrogram.py,1849847408b412a0c2eedd99e0280d57a025522e,651eb857a793a7d7dda07c4319eb382dd2b32ddf,TODO: if kept; make sure the framing is performed correctly or split
aaeeicchh,CPJKU/madmom,cp/audio/spectrogram.py,1849847408b412a0c2eedd99e0280d57a025522e,STILL_EXISTS,TODO: split into 2 functions and put the framing stuff into audio.py
aaeeiccic,CPJKU/madmom,cp/audio/spectrogram.py,1849847408b412a0c2eedd99e0280d57a025522e,STILL_EXISTS,normalize the window if needed
aaeeiccie,CPJKU/madmom,cp/audio/spectrogram.py,1849847408b412a0c2eedd99e0280d57a025522e,STILL_EXISTS,TODO: use yield instead of the index counting stuff
aaeeiccja,CPJKU/madmom,cp/audio/wav.py,1849847408b412a0c2eedd99e0280d57a025522e,651eb857a793a7d7dda07c4319eb382dd2b32ddf,more padding than a whole frame size needed
aaeeiccjh,CPJKU/madmom,cp/audio/wav.py,1849847408b412a0c2eedd99e0280d57a025522e,651eb857a793a7d7dda07c4319eb382dd2b32ddf,TODO: make this nicer!
aaeeicdcd,CPJKU/madmom,cp/audio/audio.py,651eb857a793a7d7dda07c4319eb382dd2b32ddf,STILL_EXISTS,FIXME: does not perform the seeking the right way (only int working properly)
aaeeicdee,CPJKU/madmom,cp/audio/audio.py,651eb857a793a7d7dda07c4319eb382dd2b32ddf,STILL_EXISTS,TODO: use this code if normal indexing behavior is needed
aaeeicdeh,CPJKU/madmom,cp/audio/audio.py,651eb857a793a7d7dda07c4319eb382dd2b32ddf,STILL_EXISTS,TODO: add a complete frame_size; to cover the whole audio signal?
aaeeicdei,CPJKU/madmom,cp/audio/audio.py,651eb857a793a7d7dda07c4319eb382dd2b32ddf,STILL_EXISTS,modifications to signal_frame() needed
aaeeicdfi,CPJKU/madmom,cp/audio/spectrogram.py,651eb857a793a7d7dda07c4319eb382dd2b32ddf,STILL_EXISTS,TODO: make an intelligent class which handles a lot of different file types
aaeeicdhe,CPJKU/madmom,cp/audio/spectrogram.py,651eb857a793a7d7dda07c4319eb382dd2b32ddf,STILL_EXISTS,TODO: split into 2 functions and put the framing stuff into audio.py
aaeeicecc,CPJKU/madmom,cp/evaluation/beats.py,c31137994e0bd0e767fab581fa384248c71c623e,c70fd07a39d7ee64f943beab289eb85814b7b02e,TODO: put the following part into re-usable functions in helpers.py
aaeeiceda,CPJKU/madmom,cp/evaluation/beats.py,c31137994e0bd0e767fab581fa384248c71c623e,STILL_EXISTS,TODO: include a third tempo as well? This might be needed for fast Waltz
aaeeiceec,CPJKU/madmom,cp/evaluation/helpers.py,c31137994e0bd0e767fab581fa384248c71c623e,STILL_EXISTS,FIXME: raise an error instead?
aaeeicfaf,CPJKU/madmom,cp/evaluation/helpers.py,c31137994e0bd0e767fab581fa384248c71c623e,STILL_EXISTS,thus; the needed interval is from the closest target towards the next one
aaeeichgh,CPJKU/madmom,cp/evaluation/beats.py,c70fd07a39d7ee64f943beab289eb85814b7b02e,STILL_EXISTS,FIXME: what if only 1 target and detection are given; same with none?
aaeeichih,CPJKU/madmom,cp/evaluation/beats.py,c70fd07a39d7ee64f943beab289eb85814b7b02e,STILL_EXISTS,FIXME: can we speed up this? I.e. is it safe to always evaluate the longer
aaeeicibb,CPJKU/madmom,cp/evaluation/helpers.py,c70fd07a39d7ee64f943beab289eb85814b7b02e,ec9aed24104b9311116e0ba19806689a0ea1f411,take the needed values (columns) an evaluate accordingly.
aaeeicjid,CPJKU/madmom,cp/evaluation/simple.py,c70fd07a39d7ee64f943beab289eb85814b7b02e,STILL_EXISTS,TODO: those numbers are some kind of meaningless; since we just
aaeeicjie,CPJKU/madmom,cp/evaluation/simple.py,c70fd07a39d7ee64f943beab289eb85814b7b02e,STILL_EXISTS,sum them up. Maybe use also the mean on these?
aaeeicjjc,CPJKU/madmom,cp/audio/audio.py,d0762d5a281ad7846e16acdc714a7b3361ada8e8,STILL_EXISTS,Note: type conversion needed because of integer overflows
aaeeidabi,CPJKU/madmom,cp/audio/ffmpeg.py,dd9f4f7657dc0c332f2b72f692b47b8397c16563,STILL_EXISTS,TODO: make this nicer!
aaeeidafg,CPJKU/madmom,cp/evaluation/simple.py,73495f79e9428bc0931536a81ab9c5eea19ad935,STILL_EXISTS,TODO: implement for multi-dimensional arrays
aaeeidagf,CPJKU/madmom,cp/evaluation/simple.py,73495f79e9428bc0931536a81ab9c5eea19ad935,STILL_EXISTS,FIXME: what is the error in case of no TPs
aaeeidahe,CPJKU/madmom,cp/audio/spectrogram.py,844bfcbf16d3fa00b75928a322cfb28e776e0d12,STILL_EXISTS,FIXME: if these change; recalculation is needed!
aaeeidahf,CPJKU/madmom,cp/audio/spectrogram.py,844bfcbf16d3fa00b75928a322cfb28e776e0d12,STILL_EXISTS,determine the number of time frames needed
aaeeidaia,CPJKU/madmom,cp/audio/spectrogram.py,844bfcbf16d3fa00b75928a322cfb28e776e0d12,STILL_EXISTS,determine the number of frequency bins needed for the magnitude spectrogram
aaeeidaii,CPJKU/madmom,cp/audio/spectrogram.py,844bfcbf16d3fa00b75928a322cfb28e776e0d12,STILL_EXISTS,maybe similar to the solution proposed in:
aaeeidajg,CPJKU/madmom,cp/audio/spectrogram.py,844bfcbf16d3fa00b75928a322cfb28e776e0d12,STILL_EXISTS,take the logarithm if needed
aaeeidbad,CPJKU/madmom,cp/audio/spectrogram.py,844bfcbf16d3fa00b75928a322cfb28e776e0d12,STILL_EXISTS,filter if needed
aaeeidbbf,CPJKU/madmom,cp/audio/spectrogram.py,844bfcbf16d3fa00b75928a322cfb28e776e0d12,STILL_EXISTS,FIXME: this uses unneeded memory; if only STFT and LGD are of
aaeeidbbi,CPJKU/madmom,cp/audio/spectrogram.py,844bfcbf16d3fa00b75928a322cfb28e776e0d12,STILL_EXISTS,FIXME: remove duplicate code
aaeeidbca,CPJKU/madmom,cp/audio/spectrogram.py,844bfcbf16d3fa00b75928a322cfb28e776e0d12,STILL_EXISTS,TODO: remove this?
aaeeidbda,CPJKU/madmom,cp/audio/filterbank.py,c471697104f2e3c20cfb94dc7d8c7aa592fe97af,STILL_EXISTS,(needed; because range might be bigger because of the use of floor\/ceil)
aaeeidbdc,CPJKU/madmom,cp/audio/filterbank.py,c471697104f2e3c20cfb94dc7d8c7aa592fe97af,STILL_EXISTS,FIXME: check the formulas; they seem to generate weird results
aaeeidbii,CPJKU/madmom,cp/evaluation/simple.py,8f3f2337a94c9bdaa6586dcdfdbb1e81f7c6ea4f,STILL_EXISTS,FIXME: why is this hack still needed? If not; we get a
aaeeidbja,CPJKU/madmom,cp/evaluation/simple.py,8f3f2337a94c9bdaa6586dcdfdbb1e81f7c6ea4f,STILL_EXISTS,FIXME: is returning an empty list?
aaeeidcba,CPJKU/madmom,cp/evaluation/simple.py,8f3f2337a94c9bdaa6586dcdfdbb1e81f7c6ea4f,309a857a560f40d4c837fc04192b8190f3a4ca09,TODO: is there a nice one-liner to achieve the same?
aaeeidcdc,CPJKU/madmom,cp/evaluation/helpers.py,dd84356bf51cd579d2a35fbaffe453fb2f8b0e05,ec9aed24104b9311116e0ba19806689a0ea1f411,TODO: numpyfy
aaeeidcgi,CPJKU/madmom,cp/evaluation/beats.py,c4d6ca4b41faef9bf58ad787dd775c4f473a2c04,STILL_EXISTS,Note: this is needed; otherwise a historgram with all bins = 0 would
aaeeidchh,CPJKU/madmom,cp/evaluation/onsets.py,c4d6ca4b41faef9bf58ad787dd775c4f473a2c04,STILL_EXISTS,must be the same number FIXME: find better solution which checks the names
aaeeiddii,CPJKU/madmom,cp/audio/filterbank.py,4f794563cbf5cb2d17d677ef61afa8582bf2d3c6,STILL_EXISTS,FIXME: check if the result is the one expected
aaeeideih,CPJKU/madmom,cp/audio/onset_detection.py,affd0153bd94530c0dc0406a04f3b9916d378278,STILL_EXISTS,TODO: make this use an integer or a window function
aaeeideja,CPJKU/madmom,cp/audio/onset_detection.py,affd0153bd94530c0dc0406a04f3b9916d378278,STILL_EXISTS,TODO: make the averaging function exchangable (mean\/median\/etc.)
aaeeidejh,CPJKU/madmom,cp/audio/onset_detection.py,affd0153bd94530c0dc0406a04f3b9916d378278,STILL_EXISTS,TODO: implement the paper in pure python
aaeeidfaa,CPJKU/madmom,cp/audio/onset_detection.py,affd0153bd94530c0dc0406a04f3b9916d378278,STILL_EXISTS,TODO: is it better to init the detections as np.empty(0)?
aaeeidfaf,CPJKU/madmom,cp/audio/onset_detection.py,affd0153bd94530c0dc0406a04f3b9916d378278,STILL_EXISTS,TODO: use at least 1 frame if any of these values are > 0?
aaeeidfag,CPJKU/madmom,cp/audio/onset_detection.py,affd0153bd94530c0dc0406a04f3b9916d378278,STILL_EXISTS,TODO: do not smooth in online mode
aaeeidfca,CPJKU/madmom,cp/audio/onset_detection.py,affd0153bd94530c0dc0406a04f3b9916d378278,STILL_EXISTS,TODO: also add an option for evaluation and load the targets accordingly
aaeeidfef,CPJKU/madmom,cp/evaluation/helpers.py,309a857a560f40d4c837fc04192b8190f3a4ca09,ec9aed24104b9311116e0ba19806689a0ea1f411,TODO: is there a nice one-liner to achieve the same?
aaeeidghb,CPJKU/madmom,cp/utils/helpers.py,ec9aed24104b9311116e0ba19806689a0ea1f411,STILL_EXISTS,TODO: is there a nice one-liner to achieve the same?
aaeeidgib,CPJKU/madmom,cp/utils/helpers.py,ec9aed24104b9311116e0ba19806689a0ea1f411,STILL_EXISTS,take the needed values (columns) an evaluate accordingly.
aaeeidhab,CPJKU/madmom,cp/audio/audio.py,14845f5ad52f7ea1aea460daeb3390dba5956910,8e4ac1fc557e69853cac0558af64eefd537a2e42,TODO: make index -1 work so that the diff of a spectrogram for the
aaeeidhaf,CPJKU/madmom,cp/audio/onset_detection.py,14845f5ad52f7ea1aea460daeb3390dba5956910,STILL_EXISTS,FIXME: do use s.spec and s.diff directly instead of passing the number of
aaeeidhbg,CPJKU/madmom,cp/audio/spectrogram.py,14845f5ad52f7ea1aea460daeb3390dba5956910,STILL_EXISTS,TODO: does this attribute belong to this class?
aaeeidhcg,CPJKU/madmom,cp/audio/spectrogram.py,14845f5ad52f7ea1aea460daeb3390dba5956910,STILL_EXISTS,TODO: make the filling of the first diff_frames frames work properly
aaeeidhei,CPJKU/madmom,cp/mirex/OnsetDetector/__main__.py,14845f5ad52f7ea1aea460daeb3390dba5956910,STILL_EXISTS,delete objects not needed any more
aaeeidhhg,CPJKU/madmom,cp/evaluation/simple.py,e9fc45c7dd30a936867f4d678d355c9a54093114,STILL_EXISTS,\"\"\" || This file contains basic evaluation functionality used by cp.evaluation modules. ||  || @author: Sebastian B\u00F6ck <sebastian.boeck@jku.at> ||  || \"\"\"
aaeeidiag,CPJKU/madmom,cp/evaluation/scorefollowing.py,0d1e16973ed7e1aceb7b39c1f0d35b1db9447911,abdd170f4770890727b15a2184830adbe9ab5c49,TODO: Find out why this is set to this value; and find a more sensible
aaeeidicb,CPJKU/madmom,cp/evaluation/scorefollowing.py,0d1e16973ed7e1aceb7b39c1f0d35b1db9447911,STILL_EXISTS,TODO: Check out why the following computation is so complicated and
aaeeidice,CPJKU/madmom,cp/evaluation/scorefollowing.py,0d1e16973ed7e1aceb7b39c1f0d35b1db9447911,abdd170f4770890727b15a2184830adbe9ab5c49,TODO: unclear; for what this option was used...
aaeeididh,CPJKU/madmom,bin/LogFiltSpecFlux.py,e0e8a5d7bb971c027d9373c8afd1ceae543c9875,STILL_EXISTS,\"\"\" || LogFiltSpecFlux onset detection algorithm. ||  || @author: Sebastian B\u00F6ck <sebastian.boeck@jku.at> ||  || \"\"\"
aaeeidifj,CPJKU/madmom,bin/SuperFlux.py,e0e8a5d7bb971c027d9373c8afd1ceae543c9875,STILL_EXISTS,\"\"\" || SuperFlux onset detection algorithm. ||  || @author: Sebastian B\u00F6ck <sebastian.boeck@jku.at> ||  || \"\"\"
aaeeieeaf,CPJKU/madmom,cp/features/beats.py,763a0a784d916e863de806ce1481d77fe82a856b,STILL_EXISTS,\"\"\" || This file contains all beat tracking related functionality. ||  || @author: Sebastian B\u00F6ck <sebastian.boeck@jku.at> ||  || \"\"\"
aaeeieeai,CPJKU/madmom,cp/features/beats.py,763a0a784d916e863de806ce1481d77fe82a856b,STILL_EXISTS,TODO: implement some simple algorithms
aaeeieeig,CPJKU/madmom,cp/features/beats.py,763a0a784d916e863de806ce1481d77fe82a856b,STILL_EXISTS,threshold function if needed
aaeeieeii,CPJKU/madmom,cp/features/beats.py,763a0a784d916e863de806ce1481d77fe82a856b,STILL_EXISTS,TODO: make this processing parallel or numpyfy if possible
aaeeieeja,CPJKU/madmom,cp/features/beats.py,763a0a784d916e863de806ce1481d77fe82a856b,STILL_EXISTS,TODO: make this faster!
aaeeieejc,CPJKU/madmom,cp/features/beats.py,763a0a784d916e863de806ce1481d77fe82a856b,STILL_EXISTS,TODO: threads?
aaeeiefba,CPJKU/madmom,cp/features/beats.py,763a0a784d916e863de806ce1481d77fe82a856b,STILL_EXISTS,TODO: is it better to init the detections as np.empty(0)?
aaeeiefbg,CPJKU/madmom,cp/features/beats.py,763a0a784d916e863de806ce1481d77fe82a856b,fe838d1d4ee12c5d9cf57b678adc97fac0da9495,TODO: decide whether we should go the common way and accept a file
aaeeiefdc,CPJKU/madmom,cp/features/beats.py,763a0a784d916e863de806ce1481d77fe82a856b,STILL_EXISTS,TODO: make this _much_ faster!
aaeeiefgj,CPJKU/madmom,cp/features/beats.py,763a0a784d916e863de806ce1481d77fe82a856b,STILL_EXISTS,FIXME: fps must be encoded in the file
aaeeiefhb,CPJKU/madmom,cp/features/beats.py,763a0a784d916e863de806ce1481d77fe82a856b,STILL_EXISTS,create filterbank if needed
aaeeiefjc,CPJKU/madmom,cp/features/onsets.py,763a0a784d916e863de806ce1481d77fe82a856b,STILL_EXISTS,TODO: include code
aaeeiefjh,CPJKU/madmom,cp/features/onsets.py,763a0a784d916e863de806ce1481d77fe82a856b,STILL_EXISTS,TODO: common stuff should be moved into an Event class
aaeeiehac,CPJKU/madmom,cp/features/onsets.py,6ca6543bc5be40bc4679543b40c4c42238337541,STILL_EXISTS,TODO: also add an option for evaluation and load the targets accordingly
aaeeieheh,CPJKU/madmom,cp/features/beats.py,c900f7d07032ad002439e420b45213513e6dc872,STILL_EXISTS,threshold function if needed
aaeeiehej,CPJKU/madmom,cp/features/beats.py,c900f7d07032ad002439e420b45213513e6dc872,STILL_EXISTS,TODO: make this processing parallel or numpyfy if possible
aaeeiehfg,CPJKU/madmom,cp/features/beats.py,c900f7d07032ad002439e420b45213513e6dc872,STILL_EXISTS,TODO: unify with dominant interval
aaeeiehgj,CPJKU/madmom,cp/features/beats.py,c900f7d07032ad002439e420b45213513e6dc872,STILL_EXISTS,TODO: make an extra Tempo class!
aaeeiehhe,CPJKU/madmom,cp/audio/audio.py,fe838d1d4ee12c5d9cf57b678adc97fac0da9495,STILL_EXISTS,FIXME: should 1 be added? the index 0 is the first sample; thus if the
aaeeiehhh,CPJKU/madmom,cp/audio/mfcc.py,fe838d1d4ee12c5d9cf57b678adc97fac0da9495,STILL_EXISTS,\"\"\" || This file contains all MFCC related functionality. ||  || @author: Sebastian B\u00F6ck <sebastian.boeck@jku.at> ||  || \"\"\"
aaeeiehia,CPJKU/madmom,cp/audio/mfcc.py,fe838d1d4ee12c5d9cf57b678adc97fac0da9495,STILL_EXISTS,TODO: move this to cp.audio.spectrogram? It's closely related
aaeeiehja,CPJKU/madmom,cp/audio/mfcc.py,fe838d1d4ee12c5d9cf57b678adc97fac0da9495,STILL_EXISTS,TODO: set other defaults than those in cp.audio.filterbank for MFCCs?
aaeeiehji,CPJKU/madmom,cp/audio/mfcc.py,fe838d1d4ee12c5d9cf57b678adc97fac0da9495,STILL_EXISTS,FIXME: include all bins or skip the first?
aaeeieiaj,CPJKU/madmom,madmom/utils/rnnlib/rnnlib.py,9f8ca10ade46714f91c2f288559677da1215a48b,STILL_EXISTS,\"\"\" || This file contains all functionality needed for interaction with RNNLIB. ||  || @author: Sebastian B\u00F6ck <sebastian.boeck@jku.at> ||  || \"\"\"
aaeeieida,CPJKU/madmom,madmom/utils/rnnlib/rnnlib.py,9f8ca10ade46714f91c2f288559677da1215a48b,STILL_EXISTS,FIXME: if we inherit from scipy.io.netcdf.NetCDFFile and omit the self.nc
aaeeieidc,CPJKU/madmom,madmom/utils/rnnlib/rnnlib.py,9f8ca10ade46714f91c2f288559677da1215a48b,STILL_EXISTS,work! why?
aaeeieidi,CPJKU/madmom,madmom/utils/rnnlib/rnnlib.py,9f8ca10ade46714f91c2f288559677da1215a48b,STILL_EXISTS,dimensions needed for indicated tasks
aaeeieifb,CPJKU/madmom,madmom/utils/rnnlib/rnnlib.py,9f8ca10ade46714f91c2f288559677da1215a48b,STILL_EXISTS,variables needed for indicated tasks
aaeeieiia,CPJKU/madmom,madmom/utils/rnnlib/rnnlib.py,9f8ca10ade46714f91c2f288559677da1215a48b,STILL_EXISTS,FIXME: expand this also to patterns (regression)
aaeeieiie,CPJKU/madmom,madmom/utils/rnnlib/rnnlib.py,9f8ca10ade46714f91c2f288559677da1215a48b,STILL_EXISTS,TODO: return a tuple (fd + filename)?
aaeeieijc,CPJKU/madmom,madmom/utils/rnnlib/rnnlib.py,9f8ca10ade46714f91c2f288559677da1215a48b,STILL_EXISTS,TODO: which exception should be raised?
aaeeieijf,CPJKU/madmom,madmom/utils/rnnlib/rnnlib.py,9f8ca10ade46714f91c2f288559677da1215a48b,STILL_EXISTS,TODO: make regression task work as well
aaeeifcbd,CPJKU/madmom,madmom/evaluation/notes.py,159866464e4a9fb023cfa185f9421e98062d0074,STILL_EXISTS,\"\"\" || This file contains note evaluation functionality. ||  || @author: Sebastian B\u00F6ck <sebastian.boeck@jku.at> ||  || \"\"\"
aaeeifcde,CPJKU/madmom,madmom/evaluation/notes.py,159866464e4a9fb023cfa185f9421e98062d0074,STILL_EXISTS,TODO: find a better way to determine the corresponding detection\/target
aaeeifcdi,CPJKU/madmom,madmom/evaluation/notes.py,159866464e4a9fb023cfa185f9421e98062d0074,STILL_EXISTS,must be the same number FIXME: find better solution which checks the names
aaeeifceb,CPJKU/madmom,madmom/evaluation/notes.py,159866464e4a9fb023cfa185f9421e98062d0074,41d61a1523572f8f390c3c24e30f8a5490de440b,shift the detections if needed
aaeeifcgb,CPJKU/madmom,madmom/evaluation/beats.py,b7db9d62b7070997833cdbf380a0b245cfd0ccdf,ca5d61545014dad8f8d6a02d9f5ae54830a0db9f,thus; the needed interval is from the closest target towards the next one
aaeeifcij,CPJKU/madmom,madmom/evaluation/notes.py,b7db9d62b7070997833cdbf380a0b245cfd0ccdf,STILL_EXISTS,TODO: extend to also report the measures without octave errors
aaeeifcjb,CPJKU/madmom,madmom/evaluation/tempo.py,b7db9d62b7070997833cdbf380a0b245cfd0ccdf,STILL_EXISTS,\"\"\" || This file contains tempo evaluation functionality. ||  || @author: Sebastian B\u00F6ck <sebastian.boeck@jku.at> ||  || \"\"\"
aaeeifcje,CPJKU/madmom,madmom/evaluation/tempo.py,b7db9d62b7070997833cdbf380a0b245cfd0ccdf,STILL_EXISTS,implement the McKinney Paper with merging multiple annotations
aaeeifeag,CPJKU/madmom,madmom/utils/helpers.py,1a7bb8f48c787cfd39ce6a0526730240fae6b4d3,STILL_EXISTS,no matchin needed
aaeeifeaj,CPJKU/madmom,madmom/utils/helpers.py,1a7bb8f48c787cfd39ce6a0526730240fae6b4d3,STILL_EXISTS,TODO: remove duplicate code with files()
aaeeifebg,CPJKU/madmom,madmom/utils/helpers.py,1a7bb8f48c787cfd39ce6a0526730240fae6b4d3,6c89335dd361032938583b37dff79d2e49cd6a99,FIXME: if we use \"with filename:\" here; the file handle gets closed
aaeeifeca,CPJKU/madmom,cp/audio/audio.py,b84fde4681159e907b64a19d2ddf8c866371e0f2,8e4ac1fc557e69853cac0558af64eefd537a2e42,FIXME: do we need this check here? should be enough if numpy raises
aaeeifecg,CPJKU/madmom,cp/audio/audio.py,b84fde4681159e907b64a19d2ddf8c866371e0f2,8e4ac1fc557e69853cac0558af64eefd537a2e42,FIXME: omit the checks here to be able to do more fancy stuff?
aaeeifecj,CPJKU/madmom,cp/audio/audio.py,b84fde4681159e907b64a19d2ddf8c866371e0f2,8e4ac1fc557e69853cac0558af64eefd537a2e42,FIXME: this is the only save version of how to handle
aaeeifedc,CPJKU/madmom,cp/audio/audio.py,b84fde4681159e907b64a19d2ddf8c866371e0f2,8e4ac1fc557e69853cac0558af64eefd537a2e42,hop_size; maybe always use the origin here?
aaeeifeeb,CPJKU/madmom,madmom/audio/audio.py,df915ca0af196bbec4095ebe4aeb80ce3aa7d55f,STILL_EXISTS,FIXME: taking the mean and keeping the original dtype makes the
aaeeifeeh,CPJKU/madmom,madmom/audio/audio.py,df915ca0af196bbec4095ebe4aeb80ce3aa7d55f,STILL_EXISTS,when downsampling by an integer factor; a simple view is more efficient
aaeeifeej,CPJKU/madmom,madmom/audio/audio.py,df915ca0af196bbec4095ebe4aeb80ce3aa7d55f,STILL_EXISTS,TODO: maybe use sox to implement this
aaeeifega,CPJKU/madmom,madmom/audio/audio.py,df915ca0af196bbec4095ebe4aeb80ce3aa7d55f,STILL_EXISTS,FIXME: use sox instead to convert from different input signals
aaeeifegb,CPJKU/madmom,madmom/audio/audio.py,df915ca0af196bbec4095ebe4aeb80ce3aa7d55f,STILL_EXISTS,TODO: cache this value and invalidate when signal changes
aaeeifegc,CPJKU/madmom,madmom/audio/audio.py,df915ca0af196bbec4095ebe4aeb80ce3aa7d55f,STILL_EXISTS,FIXME: remove this method; since we can limit the range of interest in
aaeeifege,CPJKU/madmom,madmom/audio/audio.py,df915ca0af196bbec4095ebe4aeb80ce3aa7d55f,STILL_EXISTS,FIXME: remove this methods; since we can adjust the range of interest in
aaeeifegi,CPJKU/madmom,madmom/audio/audio.py,d85d1babae9733aa7b6dcd1fa603973ecbf7ce7f,STILL_EXISTS,maybe it works; haven't checked
aaeeifegj,CPJKU/madmom,madmom/audio/audio.py,d85d1babae9733aa7b6dcd1fa603973ecbf7ce7f,STILL_EXISTS,use the given sample rate to resample the signal on the fly if needed
aaeeifeja,CPJKU/madmom,madmom/evaluation/onsets.py,484be70dfc6505999aa78f6dc1dbcee8aa5c5efd,ca5d61545014dad8f8d6a02d9f5ae54830a0db9f,FIXME: is there a nice numpy like way to achieve the same behavior as above
aaeeifejf,CPJKU/madmom,madmom/audio/ffmpeg.py,fbcbd5ead00458a15fe11ec7d361aada0b9bdba8,STILL_EXISTS,FIXME: remove this class completely or make it fit into the new inheritance scheme
aaeeiffce,CPJKU/madmom,madmom/audio/io.py,96c96f125d496b15b69717b02178805d6ec0696d,STILL_EXISTS,\"\"\" || This file contains basic audio input\/output functionality. ||  || @author: Sebastian B\u00F6ck <sebastian.boeck@jku.at> ||  || \"\"\"
aaeeiffdd,CPJKU/madmom,madmom/audio/io.py,96c96f125d496b15b69717b02178805d6ec0696d,STILL_EXISTS,FIXME: use sox instead to convert from different input signals
aaeeiffde,CPJKU/madmom,madmom/audio/io.py,96c96f125d496b15b69717b02178805d6ec0696d,STILL_EXISTS,use the given sample rate to resample the signal on the fly if needed
aaeeiffdf,CPJKU/madmom,madmom/audio/io.py,96c96f125d496b15b69717b02178805d6ec0696d,STILL_EXISTS,TODO: add sox audio file handling
aaeeiffhc,CPJKU/madmom,madmom/utils/helpers.py,6c89335dd361032938583b37dff79d2e49cd6a99,STILL_EXISTS,close file if needed
aaeeifhdi,CPJKU/madmom,madmom/audio/signal.py,aeaf14f9be368a09e716b34a5a9ed6956fd603ce,STILL_EXISTS,FIXME: remove this method; since we can limit the range of interest in
aaeeifice,CPJKU/madmom,madmom/audio/filterbank.py,5c2087c94cf16ae005891894b2d396f8fa482de7,bd58e056474f02de462d8692a59ff582149f9294,FIXME: Does this have any meaningful effect except when
aaeeififh,CPJKU/madmom,bin/BeatDetector.py,d684dbace033638eba432509ca480f3cabaf30a9,cf3fa6b5b88244459841cda4e5689f966f5ed0c0,TODO: this information should be included\/extracted in\/from the NN files
aaeeifigb,CPJKU/madmom,bin/BeatTracker.py,d684dbace033638eba432509ca480f3cabaf30a9,cf3fa6b5b88244459841cda4e5689f966f5ed0c0,TODO: this information should be included\/extracted in\/from the NN files
aaeeifigf,CPJKU/madmom,bin/OnsetDetector.py,d684dbace033638eba432509ca480f3cabaf30a9,cf3fa6b5b88244459841cda4e5689f966f5ed0c0,TODO: this information should be included\/extracted in\/from the NN files
aaeeifigj,CPJKU/madmom,bin/TempoDetector.py,d684dbace033638eba432509ca480f3cabaf30a9,3861f861d56828009c884cc70166baff51ec7a0c,TODO: this information should be included\/extracted in\/from the NN files
aaeeifihc,CPJKU/madmom,madmom/ml/io.py,d684dbace033638eba432509ca480f3cabaf30a9,STILL_EXISTS,\"\"\" || This file contains functionality needed for the conversion of from the universal || .h5 format to the .npz format understood by madmom.ml.rnn. ||  || The .h5 files must conform to this format: ||  || The `model` group contains just attributes. ||   - `type`:    the type of the model ||   - `comment`: free text for comments (optional) ||  || The `layer` group contains a subgroup for each layer. ||  || The subgroups are numbered consecutively; starting at index zero. ||  || Each layer subgroup contains the following attributes: ||   - `type`: the type of the layer || Each layer subgroup can contain the following datasets: ||   - `bias`:              bias of the layer ||   - `weights`:           weights of the layer ||   - `recurrent_weights`: recurrent weights (optional for recurrent layers) ||   - `peephole_weights`:  peephole weights (optional for LSTM layers) ||  || Each of the previous layer subgroups can contain the same named datasets with a || 'reverse_' prefix to indicate that they belong to the reverse\/backward layer of || bidirectional layers. ||  || @author: Sebastian B\u00F6ck <sebastian.boeck@jku.at> ||  || \"\"\"
aaeeifiib,CPJKU/madmom,madmom/ml/rnn.py,d684dbace033638eba432509ca480f3cabaf30a9,STILL_EXISTS,\"\"\" || This file contains recurrent neural network (RNN) related functionality. ||  || It's main purpose is to serve as a substitute for testing neural networks || which were trained by other ML packages or programs without requiring these || packages or programs as dependencies. ||  || The only allowed dependencies are Python + numpy + scipy. ||  || The structure reflects just the needed functionality for testing networks. This || module is not meant to be a general purpose RNN with lots of functionality. || Just use one of the many NN\/ML packages out there if you need training or any || other stuff. ||  || @author: Sebastian B\u00F6ck <sebastian.boeck@jku.at> ||  || \"\"\"
aaeeifjci,CPJKU/madmom,madmom/ml/rnn.py,d684dbace033638eba432509ca480f3cabaf30a9,STILL_EXISTS,pop the params needed for the reverse layer
aaeeifjdb,CPJKU/madmom,madmom/ml/rnn.py,d684dbace033638eba432509ca480f3cabaf30a9,STILL_EXISTS,pop the params needed for the normal layer
aaeeifjgh,CPJKU/madmom,madmom/ml/rnnlib.py,7867704f75b08dad1341393fb19dbea0f8c71d2c,STILL_EXISTS,FIXME: I know that works only with layer nums 0..9; too come
aaeeigabe,CPJKU/madmom,madmom/audio/spectrogram.py,bfcb23605145a7a9f16aa1b263c481c1a6abef93,dd81026a534dcb49b6c74fd4217cadc733012853,TODO: does this attribute belong to this class?
aaeeigafj,CPJKU/madmom,madmom/audio/signal.py,7f6b3ae83ff0ce877728d852eb410aaea1d2044b,STILL_EXISTS,FIXME: should we save the mode; or is it enough to use it for the
aaeeigagb,CPJKU/madmom,madmom/audio/signal.py,7f6b3ae83ff0ce877728d852eb410aaea1d2044b,STILL_EXISTS,FIXME: return a new object with just that slice
aaeeigaib,CPJKU/madmom,madmom/audio/signal.py,c6e351a8435c7a5aa18503d1d27c77679ae951c8,STILL_EXISTS,FIXME: should we set this to integers or allow floats as well?
aaeeigaie,CPJKU/madmom,madmom/audio/signal.py,c6e351a8435c7a5aa18503d1d27c77679ae951c8,STILL_EXISTS,FIXME: do we need to save and access this property?
aaeeigajg,CPJKU/madmom,madmom/audio/spectrogram.py,cb06cd2c82e71875ceb944280bc1aeb9f383f49c,dd81026a534dcb49b6c74fd4217cadc733012853,TODO: come up with a better description
aaeeigbab,CPJKU/madmom,madmom/audio/spectrogram.py,cb06cd2c82e71875ceb944280bc1aeb9f383f49c,STILL_EXISTS,FIXME: return a whitened spectrogram instead of altering the spectrogram?
aaeeigbeg,CPJKU/madmom,madmom/audio/filterbank.py,bd58e056474f02de462d8692a59ff582149f9294,STILL_EXISTS,FIXME: skip the DC bin 0?
aaeeigbff,CPJKU/madmom,madmom/audio/mfcc.py,bf90d8f01a85ec76463fc2313ce20e91237ea4b9,5ad8efbebbc161796c82e442b31938c9f84d9491,TODO: move this to cp.audio.spectrogram? It's closely related
aaeeigbfh,CPJKU/madmom,madmom/audio/signal.py,bf90d8f01a85ec76463fc2313ce20e91237ea4b9,STILL_EXISTS,TODO: cache this value and invalidate when signal changes
aaeeigcaa,CPJKU/madmom,madmom/audio/spectrogram.py,bf90d8f01a85ec76463fc2313ce20e91237ea4b9,dd81026a534dcb49b6c74fd4217cadc733012853,TODO: does this attribute belong to this class?
aaeeigcbd,CPJKU/madmom,madmom/audio/spectrogram.py,bf90d8f01a85ec76463fc2313ce20e91237ea4b9,STILL_EXISTS,determine the number of time frames needed
aaeeigcbj,CPJKU/madmom,madmom/audio/spectrogram.py,bf90d8f01a85ec76463fc2313ce20e91237ea4b9,STILL_EXISTS,determine the number of frequency bins needed for the magnitude spectrogram
aaeeigccd,CPJKU/madmom,madmom/audio/spectrogram.py,bf90d8f01a85ec76463fc2313ce20e91237ea4b9,5ad8efbebbc161796c82e442b31938c9f84d9491,TODO: use yield instead of the index counting stuff and cache the results
aaeeigcce,CPJKU/madmom,madmom/audio/spectrogram.py,bf90d8f01a85ec76463fc2313ce20e91237ea4b9,5ad8efbebbc161796c82e442b31938c9f84d9491,maybe similar to the solution proposed in:
aaeeigchb,CPJKU/madmom,madmom/audio/signal.py,60a0ac32723039ed1b53c13baae9cce1f8251ffa,STILL_EXISTS,TODO: cache this value and invalidate when signal changes
aaeeigdbc,CPJKU/madmom,madmom/audio/spectrogram.py,5ad8efbebbc161796c82e442b31938c9f84d9491,dd81026a534dcb49b6c74fd4217cadc733012853,TODO: come up with a better description
aaeeigddi,CPJKU/madmom,madmom/audio/spectrogram.py,5ad8efbebbc161796c82e442b31938c9f84d9491,STILL_EXISTS,determine the number of time frames needed
aaeeigdee,CPJKU/madmom,madmom/audio/spectrogram.py,5ad8efbebbc161796c82e442b31938c9f84d9491,STILL_EXISTS,determine the number of frequency bins needed for the magnitude spectrogram
aaeeigdge,CPJKU/madmom,madmom/evaluation/beats.py,ca5d61545014dad8f8d6a02d9f5ae54830a0db9f,STILL_EXISTS,TODO: find a better way to determine the corresponding detection\/target
aaeeigdgi,CPJKU/madmom,madmom/evaluation/beats.py,ca5d61545014dad8f8d6a02d9f5ae54830a0db9f,STILL_EXISTS,must be the same number FIXME: find better solution which checks the names
aaeeigead,CPJKU/madmom,madmom/evaluation/onsets.py,ca5d61545014dad8f8d6a02d9f5ae54830a0db9f,STILL_EXISTS,TODO: find a better way to determine the corresponding detection\/target
aaeeigeah,CPJKU/madmom,madmom/evaluation/onsets.py,ca5d61545014dad8f8d6a02d9f5ae54830a0db9f,STILL_EXISTS,must be the same number FIXME: find better solution which checks the names
aaeeigfai,CPJKU/madmom,madmom/evaluation/beats.py,8901738f04c3ad54b3c5d95c3803816eb6f76d12,STILL_EXISTS,TODO: find a better way to determine the corresponding detection\/target
aaeeigfba,CPJKU/madmom,madmom/evaluation/beats.py,8901738f04c3ad54b3c5d95c3803816eb6f76d12,STILL_EXISTS,must be the same number FIXME: find better solution which checks the names
aaeeigffc,CPJKU/madmom,madmom/evaluation/onsets.py,8901738f04c3ad54b3c5d95c3803816eb6f76d12,5f056fef0b8d3c25c93a7c4fbdf9abcc3ad5d50b,FIXME: is there a nice numpy like way to achieve the same behavior as above
aaeeigfhh,CPJKU/madmom,madmom/evaluation/onsets.py,8901738f04c3ad54b3c5d95c3803816eb6f76d12,STILL_EXISTS,TODO: find a better way to determine the corresponding detection\/target
aaeeigfhj,CPJKU/madmom,madmom/evaluation/onsets.py,8901738f04c3ad54b3c5d95c3803816eb6f76d12,STILL_EXISTS,must be the same number FIXME: find better solution which checks the names
aaeeigfjc,CPJKU/madmom,madmom/evaluation/simple.py,8901738f04c3ad54b3c5d95c3803816eb6f76d12,a37bac5bc8f03bf934b30a38b203482c5fb955cb,FIXME: why is this hack still needed? If not; we get a
aaeeiggaa,CPJKU/madmom,madmom/evaluation/beats.py,5f056fef0b8d3c25c93a7c4fbdf9abcc3ad5d50b,STILL_EXISTS,TODO: find a better way to determine the corresponding detection\/target
aaeeiggae,CPJKU/madmom,madmom/evaluation/beats.py,5f056fef0b8d3c25c93a7c4fbdf9abcc3ad5d50b,STILL_EXISTS,must be the same number FIXME: find better solution which checks the names
aaeeiggdi,CPJKU/madmom,madmom/evaluation/onsets.py,5f056fef0b8d3c25c93a7c4fbdf9abcc3ad5d50b,STILL_EXISTS,TODO: find a better way to determine the corresponding detection\/target
aaeeiggec,CPJKU/madmom,madmom/evaluation/onsets.py,5f056fef0b8d3c25c93a7c4fbdf9abcc3ad5d50b,STILL_EXISTS,must be the same number FIXME: find better solution which checks the names
aaeeigibi,CPJKU/madmom,madmom/audio/filterbank.py,013eaf70ac4899595118950eebfae2d803b2fdcf,STILL_EXISTS,**kwargs needed to be able to pass other (ignorable) parameters
aaeeigicb,CPJKU/madmom,madmom/audio/mfcc.py,013eaf70ac4899595118950eebfae2d803b2fdcf,STILL_EXISTS,TODO: move this to cp.audio.spectrogram? It's closely related
aaeeigici,CPJKU/madmom,madmom/audio/spectrogram.py,013eaf70ac4899595118950eebfae2d803b2fdcf,dd81026a534dcb49b6c74fd4217cadc733012853,TODO: does this attribute belong to this class?
aaeeigieb,CPJKU/madmom,madmom/audio/spectrogram.py,013eaf70ac4899595118950eebfae2d803b2fdcf,STILL_EXISTS,determine the number of time frames needed
aaeeigieh,CPJKU/madmom,madmom/audio/spectrogram.py,013eaf70ac4899595118950eebfae2d803b2fdcf,STILL_EXISTS,determine the number of frequency bins needed for the magnitude spectrogram
aaeeigifb,CPJKU/madmom,madmom/audio/spectrogram.py,013eaf70ac4899595118950eebfae2d803b2fdcf,daf678feaeef9f08195cb09c350801d3711debbf,TODO: use yield instead of the index counting stuff and cache the results
aaeeigifc,CPJKU/madmom,madmom/audio/spectrogram.py,013eaf70ac4899595118950eebfae2d803b2fdcf,daf678feaeef9f08195cb09c350801d3711debbf,maybe similar to the solution proposed in:
aaeeigihb,CPJKU/madmom,madmom/audio/filterbank.py,56dd41509f30a38483f65b47d1e2d1a45483ac11,dd875c34510043a894952740db5cc13f1f94ecfa,TODO: use functions from above instead of the logic here!
aaeeigjaj,CPJKU/madmom,madmom/evaluation/beats.py,a37bac5bc8f03bf934b30a38b203482c5fb955cb,STILL_EXISTS,TODO: find a better way to determine the corresponding detection\/target
aaeeigjbc,CPJKU/madmom,madmom/evaluation/beats.py,a37bac5bc8f03bf934b30a38b203482c5fb955cb,STILL_EXISTS,must be the same number FIXME: find better solution which checks the names
aaeeigjff,CPJKU/madmom,madmom/evaluation/onsets.py,a37bac5bc8f03bf934b30a38b203482c5fb955cb,STILL_EXISTS,FIXME: is there a nice numpy like way to achieve the same behavior as above
aaeeigjia,CPJKU/madmom,madmom/evaluation/onsets.py,a37bac5bc8f03bf934b30a38b203482c5fb955cb,STILL_EXISTS,TODO: find a better way to determine the corresponding detection\/target
aaeeigjid,CPJKU/madmom,madmom/evaluation/onsets.py,a37bac5bc8f03bf934b30a38b203482c5fb955cb,STILL_EXISTS,must be the same number FIXME: find better solution which checks the names
aaeeihbgi,CPJKU/madmom,madmom/ml/rnnlib.py,651a2a11dc9312bba4f4aef3d0a115f3eac37530,STILL_EXISTS,FIXME: I know that works only with layer nums 0..9; too come
aaeeihdhd,CPJKU/madmom,madmom/utils/helpers.py,1fe757a266ddb8c2eee8f34ecda6927bd47486df,STILL_EXISTS,no matchin needed
aaeeihdhg,CPJKU/madmom,madmom/utils/helpers.py,1fe757a266ddb8c2eee8f34ecda6927bd47486df,daf678feaeef9f08195cb09c350801d3711debbf,TODO: remove duplicate code with files(); add pattern parameter to files()
aaeeihdid,CPJKU/madmom,madmom/utils/helpers.py,1fe757a266ddb8c2eee8f34ecda6927bd47486df,daf678feaeef9f08195cb09c350801d3711debbf,FIXME: if we use \"with filename:\" here; the file handle gets closed
aaeeihdje,CPJKU/madmom,madmom/audio/spectrogram.py,daf678feaeef9f08195cb09c350801d3711debbf,dd81026a534dcb49b6c74fd4217cadc733012853,TODO: does this attribute belong to this class?
aaeeiheae,CPJKU/madmom,madmom/audio/spectrogram.py,daf678feaeef9f08195cb09c350801d3711debbf,dd81026a534dcb49b6c74fd4217cadc733012853,TODO: come up with a better description
aaeeihecg,CPJKU/madmom,madmom/utils/helpers.py,daf678feaeef9f08195cb09c350801d3711debbf,STILL_EXISTS,close file if needed
aaeeiheee,CPJKU/madmom,madmom/features/onsets.py,947a9319dcb3248297715859b3c364a716c0d3a2,cd1dbaabd6d42e59ae58c1703ee364c822bf5068,TODO: recode with the new ml.rnn module.
aaeeihghf,CPJKU/madmom,bin/PianoTranscriptor.py,921dd3713200f72851a99833883c3a4808b2cd33,330a68c044e2d20a8d3f93cfaa5a40a71431bbae,TODO: this information should be included\/extracted in\/from the NN files
aaeeihhaa,CPJKU/madmom,madmom/features/notes.py,921dd3713200f72851a99833883c3a4808b2cd33,STILL_EXISTS,\"\"\" || This file contains all note transcription related functionality. ||  || @author: Sebastian B\u00F6ck <sebastian.boeck@jku.at> ||  || \"\"\"
aaeeihhbd,CPJKU/madmom,madmom/features/notes.py,921dd3713200f72851a99833883c3a4808b2cd33,STILL_EXISTS,TODO: make the averaging function exchangable (mean\/median\/etc.)
aaeeihhce,CPJKU/madmom,madmom/features/notes.py,921dd3713200f72851a99833883c3a4808b2cd33,STILL_EXISTS,TODO: is it better to init the detections as np.empty(0)?
aaeeihhdd,CPJKU/madmom,madmom/features/notes.py,921dd3713200f72851a99833883c3a4808b2cd33,STILL_EXISTS,TODO: use at least 1 frame if any of these values are > 0?
aaeeihhdh,CPJKU/madmom,madmom/features/notes.py,921dd3713200f72851a99833883c3a4808b2cd33,STILL_EXISTS,# FIXME: make this work for notes instead of simple 1-D onset arrays
aaeeihhfj,CPJKU/madmom,madmom/utils/params.py,44caf228ef7ecc64f7ce30877f1a377e4675cde9,08c8b23f2ac258b94b8d89b0cb5125104a86e4b9,return the argument group so it can be modified if needed
aaeeihicc,CPJKU/madmom,madmom/audio/cepstrogram.py,6278f998d5f492b8d7720fdf665d8e207cb2a901,STILL_EXISTS,\"\"\" || This file contains all Cepstrogram related functionality. ||  || @author: Sebastian B\u00F6ck <sebastian.boeck@jku.at> ||  || \"\"\"
aaeeihicj,CPJKU/madmom,madmom/audio/cepstrogram.py,6278f998d5f492b8d7720fdf665d8e207cb2a901,STILL_EXISTS,TODO: set other defaults than those in cp.audio.filterbank for MFCCs?
aaeeihiei,CPJKU/madmom,madmom/audio/cepstrogram.py,6278f998d5f492b8d7720fdf665d8e207cb2a901,6839e23c5b7405db83ece3f22b7c9ceb5f5f23c8,FIXME: include all bins or skip the first?
aaeeihifb,CPJKU/madmom,madmom/audio/spectrogram.py,aa1bcb146381d86bd4daf18aa4cbca53966661bc,dd81026a534dcb49b6c74fd4217cadc733012853,TODO: move this to an extra module?
aaeeihigd,CPJKU/madmom,madmom/audio/spectrogram.py,f46de9822f2d524e60287a067bd20bc44a6281a5,dd81026a534dcb49b6c74fd4217cadc733012853,STFT were accessed previously; better call compute_stft() with
aaeeihigh,CPJKU/madmom,madmom/audio/spectrogram.py,f46de9822f2d524e60287a067bd20bc44a6281a5,dd81026a534dcb49b6c74fd4217cadc733012853,phase were accessed previously; better call compute_stft() with
aaeeihihd,CPJKU/madmom,bin/PianoTranscriptor.py,5051fc7fdfd037204953a9880e910271a0d5820d,330a68c044e2d20a8d3f93cfaa5a40a71431bbae,init a pool of workers (if needed)
aaeeihjdc,CPJKU/madmom,madmom/utils/params.py,5051fc7fdfd037204953a9880e910271a0d5820d,5f73b262103e0e818aea04e2f7732623fb96a87a,return the argument group so it can be modified if needed
aaeeihjfc,CPJKU/madmom,madmom/features/notes.py,e887d2c6df4b0d82b881e19c56350f77b8b3865b,f00ec7b17c282456c3dbcd069738db9420b617b2,TODO: this code is very similar to features.onsets.peak_picking();
aaeeihjgj,CPJKU/madmom,madmom/utils/midi.py,37a7dfacf0ef6d1adc7a929c8e52a725873e0628,STILL_EXISTS,TODO: can this method be removed?
aaeeiibcd,CPJKU/madmom,madmom/utils/midi.py,a2be7c6ecbec2a1931a12bd2df630506af9194f0,3371c122eb94ac30cb0acb1a3aae8f693e9a4fce,TODO: numpy-fy this!
aaeeiibdh,CPJKU/madmom,madmom/utils/midi.py,a2be7c6ecbec2a1931a12bd2df630506af9194f0,3371c122eb94ac30cb0acb1a3aae8f693e9a4fce,(bit mask 0x00FF) defines how many clock ticks or track delta
aaeeiibfg,CPJKU/madmom,madmom/evaluation/notes.py,08930fc9026075d6b83804266bf954384c2e143d,STILL_EXISTS,until then only use the first two columns (onsets + pitch)
aaeeiicii,CPJKU/madmom,madmom/utils/midi.py,480eb495668bf556f135e6053ac7f0b606bc0b09,STILL_EXISTS,needed for correct SysEx event handling
aaeeiiddd,CPJKU/madmom,madmom/utils/midi.py,480eb495668bf556f135e6053ac7f0b606bc0b09,STILL_EXISTS,TODO: numpy-fy this!
aaeeiidfe,CPJKU/madmom,madmom/utils/midi.py,480eb495668bf556f135e6053ac7f0b606bc0b09,STILL_EXISTS,(bit mask 0x00FF) defines how many clock ticks or track delta
aaeeiidjd,CPJKU/madmom,madmom/evaluation/onsets.py,96aaf161d5c542eb293ad7acf417d42dfba91612,414bf51e7b143a19aa152e2a6b8233c320a44f5d,FIXME: what is the error in case of no TPs
aaeeiieje,CPJKU/madmom,madmom/evaluation/simple.py,414bf51e7b143a19aa152e2a6b8233c320a44f5d,06a157273089c745dcb95efff259be9e45cd311d,FIXME: is returning an empty list ok?
aaeeiiejh,CPJKU/madmom,madmom/evaluation/simple.py,414bf51e7b143a19aa152e2a6b8233c320a44f5d,06a157273089c745dcb95efff259be9e45cd311d,FIXME: what is the error in case of no TPs
aaeeiiggh,CPJKU/madmom,madmom/utils/params.py,414bf51e7b143a19aa152e2a6b8233c320a44f5d,3861f861d56828009c884cc70166baff51ec7a0c,return the argument group so it can be modified if needed
aaeeiijed,CPJKU/madmom,madmom/evaluation/notes.py,06a157273089c745dcb95efff259be9e45cd311d,STILL_EXISTS,FIXME: what is the error in case of no TPs
aaeeiijef,CPJKU/madmom,madmom/evaluation/onsets.py,06a157273089c745dcb95efff259be9e45cd311d,STILL_EXISTS,FIXME: what is the error in case of no TPs
aaeeijchg,CPJKU/madmom,madmom/evaluation/notes.py,c3e8c0067bec12085abcbe6e32c033658cdaf4a6,41d61a1523572f8f390c3c24e30f8a5490de440b,shift the detections if needed
aaeeijchh,CPJKU/madmom,madmom/evaluation/onsets.py,c3e8c0067bec12085abcbe6e32c033658cdaf4a6,41d61a1523572f8f390c3c24e30f8a5490de440b,shift the detections if needed
aaeeijdhc,CPJKU/madmom,madmom/audio/spectrogram.py,de025bea2b93223159352d6cd4db34269772ed3f,dd81026a534dcb49b6c74fd4217cadc733012853,is block wise processing needed?
aaeeijdhd,CPJKU/madmom,madmom/audio/spectrogram.py,de025bea2b93223159352d6cd4db34269772ed3f,dd81026a534dcb49b6c74fd4217cadc733012853,no filtering needed; thus no block wise processing needed
aaeeijdhi,CPJKU/madmom,madmom/audio/filterbank.py,efbc182bf294a11c70eaea00574f6950c66d672b,STILL_EXISTS,TODO: rename function to assemble_filterbank()?
aaeeijdhj,CPJKU/madmom,madmom/audio/filterbank.py,efbc182bf294a11c70eaea00574f6950c66d672b,STILL_EXISTS,TODO: allow inference of num_bands from the highest filter id?
aaeeijdib,CPJKU/madmom,madmom/audio/filterbank.py,efbc182bf294a11c70eaea00574f6950c66d672b,0e0ddafb378446e75abb24142546a76ea2b92a13,FIXME: fix handling of negative start values and stop positions
aaeeijdig,CPJKU/madmom,madmom/audio/filterbank.py,efbc182bf294a11c70eaea00574f6950c66d672b,STILL_EXISTS,TODO: if needed allow other handling (like adding values)
aaeeijdjj,CPJKU/madmom,madmom/audio/filterbank.py,efbc182bf294a11c70eaea00574f6950c66d672b,0e0ddafb378446e75abb24142546a76ea2b92a13,TODO: please document filter_starts!
aaeeijeah,CPJKU/madmom,madmom/audio/filterbank.py,efbc182bf294a11c70eaea00574f6950c66d672b,STILL_EXISTS,TODO: it's a bit unclear to me what happens here; nicer would be
aaeeijebh,CPJKU/madmom,madmom/audio/filterbank.py,0e0ddafb378446e75abb24142546a76ea2b92a13,STILL_EXISTS,TODO: allow using a list of weights\/widths instead of a function
aaeeijecc,CPJKU/madmom,madmom/audio/filterbank.py,0e0ddafb378446e75abb24142546a76ea2b92a13,STILL_EXISTS,TODO: rename function to assemble_filterbank()?
aaeeijecd,CPJKU/madmom,madmom/audio/filterbank.py,0e0ddafb378446e75abb24142546a76ea2b92a13,STILL_EXISTS,TODO: allow inference of num_bands from the highest filter id?
aaeeijedf,CPJKU/madmom,madmom/audio/filterbank.py,0e0ddafb378446e75abb24142546a76ea2b92a13,STILL_EXISTS,TODO: it's a bit unclear to me what happens here; nicer would be
aaeeijeeb,CPJKU/madmom,madmom/utils/helpers.py,e9e5bd963a07e741fd1821ef3fe2acf3986e502d,7f2bfcafd1fc0dceabeace348afbee67d07ea5de,combined; this check is needed since we're altering the content of
aaeeijejb,CPJKU/madmom,madmom/audio/cepstrogram.py,46d208873b951d548a0f9997ca0a8cd624ca61c3,4c00f102298bd0f466ca3f3d4ff5297598b35e45,TODO: set other defaults than those in .filterbank for MFCCs?
aaeeijejg,CPJKU/madmom,madmom/utils/midi.py,fd3b7ca74d6711fcf3684b60d3bbfbc519362229,STILL_EXISTS,TODO: can this method be removed?
aaeeijfcb,CPJKU/madmom,madmom/utils/midi.py,3827479ea4eac7ca6d66a65149e36b380aed82fb,c9efaa3bf5dff6baec9c46df360b3c2daf901896,raise ValueError('please remove the TODO as it seems to be needed')
aaeeijfdj,CPJKU/madmom,madmom/utils/__init__.py,3872d6ed585443181da92beda5c73d0aa2ee2df0,e699222f0c25a98d372c8c2eda601acafb521b46,close the file if needed
aaeeijfgb,CPJKU/madmom,madmom/audio/signal.py,d7337c55336f4794409738776c125f7693043189,1539bd9ec2b940df531d2fb82d79ea65e4acaf26,TODO: remove warning?
aaeeijfgf,CPJKU/madmom,madmom/evaluation/notes.py,d7337c55336f4794409738776c125f7693043189,41d61a1523572f8f390c3c24e30f8a5490de440b,shift the detections if needed
aaeeijfgg,CPJKU/madmom,madmom/evaluation/onsets.py,d7337c55336f4794409738776c125f7693043189,41d61a1523572f8f390c3c24e30f8a5490de440b,shift the detections if needed
aaeeijfhe,CPJKU/madmom,madmom/utils/__init__.py,d7337c55336f4794409738776c125f7693043189,STILL_EXISTS,no matching needed
aaeeijgai,CPJKU/madmom,madmom/evaluation/__init__.py,8581e6ccbb6d6cd80481d556ea33377279327de8,STILL_EXISTS,FIXME: raise an error instead?
aaeeijghg,CPJKU/madmom,madmom/utils/helpers.py,70598ed15a6e036fff1e82b7865d0e437ac32050,STILL_EXISTS,open file if needed
aaeeijhae,CPJKU/madmom,madmom/audio/spectrogram.py,fb8f0559bb59530e05317180c6e22205ac2610f5,c9861cd8d686079a6b2c68fd48f70f2b290d249e,TODO: make FramedSignal a proper iterable so we can use enumerate
aaeeijhbd,CPJKU/madmom,madmom/audio/spectrogram.py,bba0f1843fc2f98878cc958e9f16edc3ec17aa39,e4cb6d601407098281106c82b6ca33bd8ad3421b,filter with the given filterbank if needed
aaeeijhdf,CPJKU/madmom,madmom/evaluation/__init__.py,ae7e374c8077ef467079974da98b2dea3a22208d,STILL_EXISTS,subclasses are required to redefine as needed
aaeeijibe,CPJKU/madmom,madmom/test/test_utils.py,ae7e374c8077ef467079974da98b2dea3a22208d,STILL_EXISTS,TODO: write a test for speed
aaeeijibh,CPJKU/madmom,madmom/evaluation/beats.py,818244c9985c26a454a525aa73d31b2b402150ad,STILL_EXISTS,TODO: find a better solution for this!
aaeeijjcc,CPJKU/madmom,madmom/evaluation/onsets.py,665b789d6c71c1aad9096da511903b673d9501b7,4b58f2888b834e04f133fda88dd7f2670e774e33,FIXME: use < instead? some beat stuff uses < as well...
aaeeijjdi,CPJKU/madmom,madmom/evaluation/onsets.py,12c459699df97fc8983c3292a6f0c61e781357ac,STILL_EXISTS,save the detections and targets (needed for error calculation)
aaeejaadh,CPJKU/madmom,madmom/evaluation/__init__.py,4a5c3af125335da6d020429b8648eca008fa726c,STILL_EXISTS,because different classes implement the latter differently
aaeejacid,CPJKU/madmom,madmom/evaluation/beats.py,6d8d2034586a555566c9818913b0630f0cdd3be8,STILL_EXISTS,FIXME: what if only 1 target and detection are given; same with none?
aaeejacig,CPJKU/madmom,madmom/evaluation/beats.py,6d8d2034586a555566c9818913b0630f0cdd3be8,STILL_EXISTS,at least 2 detections and targets are needed to calculate the intervals
aaeejadeh,CPJKU/madmom,madmom/evaluation/onsets.py,6d8d2034586a555566c9818913b0630f0cdd3be8,029343b45d2fc3ac966fa03131f3f0b90c31c5ac,FIXME: is there a numpy like way to achieve the same behavior as above
aaeejadgf,CPJKU/madmom,madmom/evaluation/onsets.py,6d8d2034586a555566c9818913b0630f0cdd3be8,029343b45d2fc3ac966fa03131f3f0b90c31c5ac,FIXME: what is the error in case of no TPs?
aaeejaeci,CPJKU/madmom,madmom/evaluation/beats.py,a9d60e165d08a55696d31fffd763027d14961e10,STILL_EXISTS,FIXME: what if only 1 target and detection are given; same with none?
aaeejaedb,CPJKU/madmom,madmom/evaluation/beats.py,a9d60e165d08a55696d31fffd763027d14961e10,STILL_EXISTS,at least 2 detections and targets are needed to calculate the intervals
aaeejafgb,CPJKU/madmom,madmom/evaluation/beats.py,5010193e9a1d04c4cbd061922c9931d9cce46ca9,STILL_EXISTS,FIXME: what if only 1 target and detection are given; same with none?
aaeejafgf,CPJKU/madmom,madmom/evaluation/beats.py,5010193e9a1d04c4cbd061922c9931d9cce46ca9,STILL_EXISTS,at least 2 detections and targets are needed to calculate the intervals
aaeejageg,CPJKU/madmom,madmom/evaluation/onsets.py,5010193e9a1d04c4cbd061922c9931d9cce46ca9,9fd701d0f7e223958a5da4066062d6f63a239e96,FIXME: is there a numpy like way to achieve the same behavior as above
aaeejagge,CPJKU/madmom,madmom/evaluation/onsets.py,5010193e9a1d04c4cbd061922c9931d9cce46ca9,9fd701d0f7e223958a5da4066062d6f63a239e96,FIXME: what is the error in case of no TPs?
aaeejaghd,CPJKU/madmom,madmom/features/__init__.py,ad8163a36ac166631b635e56943d6ecc7a26d9f8,STILL_EXISTS,TODO: is it better to init the detections as np.zeros(0)?
aaeejahfj,CPJKU/madmom,madmom/utils/__init__.py,ad8163a36ac166631b635e56943d6ecc7a26d9f8,e699222f0c25a98d372c8c2eda601acafb521b46,TODO: include automatic (un-)zipping here?
aaeejaich,CPJKU/madmom,madmom/evaluation/beats.py,9fd701d0f7e223958a5da4066062d6f63a239e96,STILL_EXISTS,FIXME: what if only 1 target and detection are given; same with none?
aaeejaida,CPJKU/madmom,madmom/evaluation/beats.py,9fd701d0f7e223958a5da4066062d6f63a239e96,STILL_EXISTS,at least 2 detections and targets are needed to calculate the intervals
aaeejbdfb,CPJKU/madmom,madmom/features/notes.py,59fdb74dc063896c182d62c8e2eba9b1b75943d5,c69d597debc90c032737f18aa29974c21f2bb6a0,FIXME: fps must be encoded in the file
aaeejbdfe,CPJKU/madmom,madmom/features/notes.py,59fdb74dc063896c182d62c8e2eba9b1b75943d5,c69d597debc90c032737f18aa29974c21f2bb6a0,create filterbank if needed
aaeejbdgb,CPJKU/madmom,madmom/utils/midi.py,1fab9a1baff5a224fc8975a20a158396d6806fd9,STILL_EXISTS,TODO: copying needed?
aaeejbdgh,CPJKU/madmom,madmom/utils/midi.py,1fab9a1baff5a224fc8975a20a158396d6806fd9,STILL_EXISTS,FIXME: what we do here s basically writing a MIDI format 0 file;
aaeejbdgj,CPJKU/madmom,madmom/utils/midi.py,1fab9a1baff5a224fc8975a20a158396d6806fd9,STILL_EXISTS,tempo and time signature stuff is just a hack!
aaeejbdib,CPJKU/madmom,madmom/ml/rnnlib.py,677571edfbb3ad1d4f63c047e10db4acbae723e4,STILL_EXISTS,FIXME: function only works if called in the directory of the NN file
aaeejbfad,CPJKU/madmom,madmom/features/notes.py,e9c00633797ece0064af840003290cc7f689f822,STILL_EXISTS,FIXME: fps must be encoded in the file
aaeejbfag,CPJKU/madmom,madmom/features/notes.py,e9c00633797ece0064af840003290cc7f689f822,STILL_EXISTS,create filterbank if needed
aaeejbfbe,CPJKU/madmom,madmom/evaluation/__init__.py,ee1ad85383ceb722c825a4d645f5ad4744e51391,STILL_EXISTS,TODO: extend the errors list instead of appending; might lead to
aaeejbfcb,CPJKU/madmom,madmom/evaluation/__init__.py,ee1ad85383ceb722c825a4d645f5ad4744e51391,STILL_EXISTS,TODO: add individual class output
aaeejbfci,CPJKU/madmom,madmom/evaluation/notes.py,ee1ad85383ceb722c825a4d645f5ad4744e51391,41d61a1523572f8f390c3c24e30f8a5490de440b,FIXME: do this for all notes individually
aaeejbfcj,CPJKU/madmom,madmom/evaluation/onsets.py,ee1ad85383ceb722c825a4d645f5ad4744e51391,STILL_EXISTS,convert numpy array to lists if needed
aaeejbfdi,CPJKU/madmom,madmom/ml/rnnlib.py,4babeebaf5a637151363265fea3f01d45f980a51,STILL_EXISTS,FIXME: function only works if called in the directory of the NN file
aaeejbffb,CPJKU/madmom,madmom/utils/midi.py,95f78b2e29559ebe4cb727fd964a985b8b1653f5,STILL_EXISTS,TODO: right now we only write format 0 files
aaeejbfgd,CPJKU/madmom,bin/tools/giantsteps_format_converter.py,894c3764606b2c58f84115270e01ec09a28e44f6,STILL_EXISTS,add the extension if needed
aaeejbfha,CPJKU/madmom,bin/tools/giantsteps_format_converter.py,9cd7d7a3fa543cd81673a8b8cbd1b5baefa1167b,STILL_EXISTS,TODO: code a proper definition file parser
aaeejbfje,CPJKU/madmom,bin/tools/renumber_beats.py,7ecf1c5c683d3d75e5afcb3b35f62198f7ff64e5,STILL_EXISTS,add the extension if needed
aaeejbgbc,CPJKU/madmom,bin/OnsetDetectorLL.py,49fc3c2d4cdf25d3e2aed1a046374cfb7a8ad452,cf3fa6b5b88244459841cda4e5689f966f5ed0c0,TODO: this information should be included\/extracted in\/from the NN files
aaeejbgda,CPJKU/madmom,bin/OnsetDetectorLL.py,49fc3c2d4cdf25d3e2aed1a046374cfb7a8ad452,cf3fa6b5b88244459841cda4e5689f966f5ed0c0,init a pool of workers (if needed)
aaeejbgdc,CPJKU/madmom,bin/OnsetDetectorLL.py,49fc3c2d4cdf25d3e2aed1a046374cfb7a8ad452,cf3fa6b5b88244459841cda4e5689f966f5ed0c0,average activations if needed
aaeejbged,CPJKU/madmom,madmom/utils/__init__.py,4de8de70a1cc27e6c14f363131d8c7166221cbfb,STILL_EXISTS,shift all events if needed
aaeejbgef,CPJKU/madmom,madmom/ml/rnnlib.py,7ca0fea24141421023f01745f58488cd12f801e1,STILL_EXISTS,shift the notes if needed
aaeejbibi,CPJKU/madmom,madmom/evaluation/tempo.py,a18c58526165be4cbcb0f855c516c6cde5ab81cb,STILL_EXISTS,TODO: decide whether we want multiple annotations per file or
aaeejbihj,CPJKU/madmom,bin/tools/correct_annotations.py,0fb08ce6615a622d0ae580477d2323c183c516a5,STILL_EXISTS,TODO: extend to alter all timestamp columns
aaeejbjbi,CPJKU/madmom,madmom/audio/filterbank.py,42b545f673a5618488f6bc4ba3602d7a1cd6416e,STILL_EXISTS,TODO: maybe I misunderstood eq. (3.36); but it does not do anything
aaeejbjfe,CPJKU/madmom,bin/tools/calculate_tempo.py,39f64098764702390995f084f213642f561e8e1a,STILL_EXISTS,TODO: normalise strengths with IBI!?
aaeejbjgb,CPJKU/madmom,madmom/audio/filterbanks.py,cb4b1a30de66515b16abdc9ed10e1cdfb4f4eace,STILL_EXISTS,\"\"\" || This file is deprecated. All filter related functionality should go into || madmom.audio.filters ||  || @author: Sebastian B\u00F6ck <sebastian.boeck@jku.at> ||  || \"\"\"
aaeejbjgf,CPJKU/madmom,madmom/features/chroma.py,f8978ccc44fc3e2514e55559da66e03cc68a30ee,STILL_EXISTS,\"\"\" || This file contains chroma related functionality. ||  || @author: Sebastian B\u00F6ck <sebastian.boeck@jku.at> ||  || \"\"\"
aaeejbjhi,CPJKU/madmom,madmom/features/chroma.py,f8978ccc44fc3e2514e55559da66e03cc68a30ee,STILL_EXISTS,create a filterbank if needed
aaeejcadc,CPJKU/madmom,madmom/evaluation/tempo.py,f1cd837c63eb33857cab5af7272d3f587e50b3f5,STILL_EXISTS,TODO: what to do if more information is in the file?
aaeejcahi,CPJKU/madmom,madmom/test/test_tempo_evaluation.py,c63d7f1655b2a3279a921b9015263ccdb0c4a689,STILL_EXISTS,TODO: what should happen if we supply a dictionary?
aaeejcbcj,CPJKU/madmom,madmom/test/test_beat_evaluation.py,42ee72dd124136d8353f25126a0805131e0fcf62,05c73b8f0c2b41b8c66e4c7ff19131de9398acf7,FIXME: solve this discrepancy in global\/normal information gain!
aaeejccfg,CPJKU/madmom,madmom/features/tempo.py,14462f03e9588bac30770d9f14d000a6ac7a9abc,STILL_EXISTS,\"\"\" || This file contains tempo related functionality. ||  || @author: Sebastian B\u00F6ck <sebastian.boeck@jku.at> ||  || \"\"\"
aaeejccgi,CPJKU/madmom,madmom/features/tempo.py,14462f03e9588bac30770d9f14d000a6ac7a9abc,d95cb8063455bab8f01651401bf6149964d7b69a,threshold function if needed
aaeejcchb,CPJKU/madmom,madmom/features/tempo.py,14462f03e9588bac30770d9f14d000a6ac7a9abc,STILL_EXISTS,TODO: make this processing parallel or numpyfy if possible
aaeejcdca,CPJKU/madmom,madmom/features/tempo.py,14462f03e9588bac30770d9f14d000a6ac7a9abc,9e193fc501f9be13e9f9ea55dc0c33003b546cd4,# TODO: check the influence\/benefit of CQT compared to our spectrogram
aaeejcdfj,CPJKU/madmom,madmom/features/tempo.py,14462f03e9588bac30770d9f14d000a6ac7a9abc,9e193fc501f9be13e9f9ea55dc0c33003b546cd4,# TODO: use a smaller frame_size for this! (20ms; 5ms hop)
aaeejcdge,CPJKU/madmom,madmom/features/tempo.py,14462f03e9588bac30770d9f14d000a6ac7a9abc,9e193fc501f9be13e9f9ea55dc0c33003b546cd4,# TODO: use a larger frame_size for this! (80ms; 5ms hop)
aaeejcedh,CPJKU/madmom,madmom/features/tempo.py,14462f03e9588bac30770d9f14d000a6ac7a9abc,9e193fc501f9be13e9f9ea55dc0c33003b546cd4,# TODO: also add an option for evaluation and load the targets accordingly
aaeejcegb,CPJKU/madmom,madmom/features/tempo.py,14462f03e9588bac30770d9f14d000a6ac7a9abc,9e193fc501f9be13e9f9ea55dc0c33003b546cd4,# FIXME: fps must be encoded in the file
aaeejcegj,CPJKU/madmom,madmom/features/tempo.py,14462f03e9588bac30770d9f14d000a6ac7a9abc,9e193fc501f9be13e9f9ea55dc0c33003b546cd4,# create filterbank if needed
aaeejchcg,CPJKU/madmom,madmom/utils/params.py,d95cb8063455bab8f01651401bf6149964d7b69a,3861f861d56828009c884cc70166baff51ec7a0c,return the argument group so it can be modified if needed
aaeejchch,CPJKU/madmom,madmom/features/tempo.py,acf02f17e49f0dc66a9ebe2267c5c2af758220c1,STILL_EXISTS,group the corresponding tempi if needed
aaeejddia,CPJKU/madmom,bin/tools/beat_variants.py,4749ac0d0e8c44192e30e322d88e3b67e74969d9,STILL_EXISTS,TODO: this is super hack-ish; do it properly
aaeejddid,CPJKU/madmom,madmom/evaluation/beats.py,4749ac0d0e8c44192e30e322d88e3b67e74969d9,be9c8780d2de9f961337f4160f31c809c29e9988,TODO: is this the right thing to do?
aaeejdeci,CPJKU/madmom,madmom/evaluation/beats.py,3c45aebe01dc70bfd7a6036df1e0de736c09f6d2,STILL_EXISTS,TODO: is this the right thing to do?
aaeejdecj,CPJKU/madmom,madmom/audio/filters.py,fbe6adc3f9ba2eb6463f73001e79f85820f84ddc,bfba471f9b68936160423160433208f3e32f8c29,TODO: how can we handle bot the cython and pure python comb filter?
aaeejdeje,CPJKU/madmom,madmom/evaluation/tempo.py,93e15251e1c8a3dde06b54da8f467a6deeae21de,b9854258afb530bd6fa6edc1df807679214a1c4a,TODO: this is kind of hack-ish; find a better solution
aaeejdejf,CPJKU/madmom,madmom/evaluation/tempo.py,93e15251e1c8a3dde06b54da8f467a6deeae21de,STILL_EXISTS,TODO: should this logic go into the TempoEvaluation class?
aaeejdejg,CPJKU/madmom,madmom/features/beats.py,b449a99d56cbf753e4faf7cd6d1c7e0b16fcf584,20831660617b5953a2b7cdb88612c425adac37fa,TODO: this information should be included\/extracted in\/from the NN files
aaeejdfag,CPJKU/madmom,madmom/features/beats.py,b449a99d56cbf753e4faf7cd6d1c7e0b16fcf584,344a19faf5a4bed1846bf7326478bd6f6479c5bc,init a pool of workers (if needed)
aaeejdfai,CPJKU/madmom,madmom/features/beats.py,b449a99d56cbf753e4faf7cd6d1c7e0b16fcf584,344a19faf5a4bed1846bf7326478bd6f6479c5bc,average activations if needed
aaeejdfcb,CPJKU/madmom,madmom/features/beats.py,3de720673bb275defb8525db88530ec3582a125c,344a19faf5a4bed1846bf7326478bd6f6479c5bc,TODO: make this _much_ faster!
aaeejdfdg,CPJKU/madmom,madmom/features/__init__.py,344a19faf5a4bed1846bf7326478bd6f6479c5bc,STILL_EXISTS,init a pool of workers (if needed)
aaeejdfdi,CPJKU/madmom,madmom/features/__init__.py,344a19faf5a4bed1846bf7326478bd6f6479c5bc,cf3fa6b5b88244459841cda4e5689f966f5ed0c0,average activations if needed
aaeejdgci,CPJKU/madmom,madmom/features/__init__.py,cf3fa6b5b88244459841cda4e5689f966f5ed0c0,STILL_EXISTS,TODO: this information should be included\/extracted in\/from the NN files
aaeejdgda,CPJKU/madmom,madmom/features/__init__.py,cf3fa6b5b88244459841cda4e5689f966f5ed0c0,e3b7b8e191158eabab3540af1cdb6d2b4d4233eb,return the argument group so it can be modified if needed
aaeejdgdf,CPJKU/madmom,madmom/features/__init__.py,cf3fa6b5b88244459841cda4e5689f966f5ed0c0,STILL_EXISTS,init a pool of workers (if needed)
aaeejdgef,CPJKU/madmom,madmom/features/beats.py,cf3fa6b5b88244459841cda4e5689f966f5ed0c0,STILL_EXISTS,TODO: replace this once tempo classes are finished
aaeejdgfg,CPJKU/madmom,madmom/features/onsets.py,cf3fa6b5b88244459841cda4e5689f966f5ed0c0,6affec6ff17f6ea1cdd4a2188b4ea47eb33017b7,return the argument group so it can be modified if needed
aaeejdhaa,CPJKU/madmom,madmom/features/onsets.py,cf3fa6b5b88244459841cda4e5689f966f5ed0c0,3861f861d56828009c884cc70166baff51ec7a0c,Notes: If no moving average is needed (e.g. the activations are
aaeejdhcj,CPJKU/madmom,madmom/ml/rnn.py,cf3fa6b5b88244459841cda4e5689f966f5ed0c0,9abd4b73fc9635486f3374400470c7744b4b16c1,init a pool of workers (if needed)
aaeejdhdb,CPJKU/madmom,madmom/ml/rnn.py,cf3fa6b5b88244459841cda4e5689f966f5ed0c0,STILL_EXISTS,average activations if needed
aaeejdhjg,CPJKU/madmom,madmom/features/notes.py,274e8ce6623be136614e204e01ceefcbf234dd7e,330a68c044e2d20a8d3f93cfaa5a40a71431bbae,Notes: If no moving average is needed (e.g. the activations are
aaeejdijj,CPJKU/madmom,madmom/features/tempo.py,274e8ce6623be136614e204e01ceefcbf234dd7e,3861f861d56828009c884cc70166baff51ec7a0c,:param alpha:       scaling factor for the comb filter
aaeejdjce,CPJKU/madmom,bin/OnsetDetectorLL.py,3861f861d56828009c884cc70166baff51ec7a0c,STILL_EXISTS,TODO: this information should be included\/extracted in\/from the NN files
aaeejdjcf,CPJKU/madmom,bin/OnsetDetectorLL.py,3861f861d56828009c884cc70166baff51ec7a0c,STILL_EXISTS,# TODO: these do not seem to be used in the original implementation
aaeejdjed,CPJKU/madmom,madmom/features/onsets.py,3861f861d56828009c884cc70166baff51ec7a0c,6affec6ff17f6ea1cdd4a2188b4ea47eb33017b7,return the argument group so it can be modified if needed
aaeejeabg,CPJKU/madmom,madmom/features/tempo.py,3861f861d56828009c884cc70166baff51ec7a0c,STILL_EXISTS,return the argument group so it can be modified if needed
aaeejebeb,CPJKU/madmom,madmom/features/notes.py,330a68c044e2d20a8d3f93cfaa5a40a71431bbae,STILL_EXISTS,TODO: this information should be included\/extracted in\/from the NN files
aaeejebec,CPJKU/madmom,madmom/features/notes.py,330a68c044e2d20a8d3f93cfaa5a40a71431bbae,f6a25f3a8a077de58397b3332f3ee2e3eb0c2e4a,TODO: These do not seem to be used anywhere
aaeejebee,CPJKU/madmom,madmom/features/notes.py,330a68c044e2d20a8d3f93cfaa5a40a71431bbae,STILL_EXISTS,TODO: use at least 1 frame if any of these values are > 0?
aaeejecdb,CPJKU/madmom,bin/OnsetDetectorLL.py,18cd7e3bd8c93029e50628611e325865ee2a3292,STILL_EXISTS,TODO: this information should be included\/extracted in\/from the NN files
aaeejecdc,CPJKU/madmom,bin/OnsetDetectorLL.py,18cd7e3bd8c93029e50628611e325865ee2a3292,STILL_EXISTS,# TODO: these do not seem to be used in the original implementation
aaeejecdf,CPJKU/madmom,madmom/audio/filters.py,18cd7e3bd8c93029e50628611e325865ee2a3292,3160db98cca537272a70f4242784455de6c6d79e,TODO: split this among the individual classes
aaeejecdi,CPJKU/madmom,madmom/audio/filters.py,18cd7e3bd8c93029e50628611e325865ee2a3292,3160db98cca537272a70f4242784455de6c6d79e,return the argument group so it can be modified if needed
aaeejeced,CPJKU/madmom,madmom/audio/spectrogram.py,18cd7e3bd8c93029e50628611e325865ee2a3292,STILL_EXISTS,add log related options to the existing parser if needed
aaeejecff,CPJKU/madmom,madmom/features/__init__.py,18cd7e3bd8c93029e50628611e325865ee2a3292,e3b7b8e191158eabab3540af1cdb6d2b4d4233eb,return the argument group so it can be modified if needed
aaeejechg,CPJKU/madmom,madmom/features/__init__.py,18cd7e3bd8c93029e50628611e325865ee2a3292,c9efaa3bf5dff6baec9c46df360b3c2daf901896,TODO: refactor the write_events() function into this module?
aaeejecib,CPJKU/madmom,madmom/features/__init__.py,18cd7e3bd8c93029e50628611e325865ee2a3292,STILL_EXISTS,TODO: move this to the ml.rnn module?
aaeejecjc,CPJKU/madmom,madmom/features/onsets.py,18cd7e3bd8c93029e50628611e325865ee2a3292,6affec6ff17f6ea1cdd4a2188b4ea47eb33017b7,TODO: use at least 1 frame if any of these values are > 0?
aaeejecje,CPJKU/madmom,madmom/features/onsets.py,18cd7e3bd8c93029e50628611e325865ee2a3292,STILL_EXISTS,TODO: check if access to FramedSignal.origin is possible\/applicable
aaeejedcb,CPJKU/madmom,madmom/features/onsets.py,18cd7e3bd8c93029e50628611e325865ee2a3292,STILL_EXISTS,FIXME: do use s.spec and s.diff directly instead of passing the number of
aaeejedeg,CPJKU/madmom,madmom/ml/rnnlib.py,18cd7e3bd8c93029e50628611e325865ee2a3292,42638c2c7c9b1695790efbc36221b6db49e1f4dc,TODO: inherit from features.Activations
aaeejedfa,CPJKU/madmom,madmom/utils/midi.py,18cd7e3bd8c93029e50628611e325865ee2a3292,STILL_EXISTS,return the argument group so it can be modified if needed
aaeejedff,CPJKU/madmom,madmom/features/beats.py,d59bdbb4c7b45457b7bf1ed2beb92c6d1449a025,521734a5c734ed277300c2535dc3b800cffc4e64,TODO: where should the NN_FILES get defined?
aaeejedfh,CPJKU/madmom,madmom/features/beats.py,d59bdbb4c7b45457b7bf1ed2beb92c6d1449a025,20831660617b5953a2b7cdb88612c425adac37fa,TODO: this information should be included\/extracted in\/from the NN files
aaeejedfi,CPJKU/madmom,madmom/features/beats.py,d59bdbb4c7b45457b7bf1ed2beb92c6d1449a025,60ed6dc3183a584806be86dea282453366a72ac0,TODO: remove this hack
aaeejedgd,CPJKU/madmom,madmom/features/beats.py,d59bdbb4c7b45457b7bf1ed2beb92c6d1449a025,STILL_EXISTS,TODO: refactor this stuff to use the TempoEstimation functionality
aaeejedhh,CPJKU/madmom,madmom/features/onsets.py,20831660617b5953a2b7cdb88612c425adac37fa,65971295ced9dcf4d4ec230bf7ba20ab8be830e5,TODO: the signal processing parameters should be included in and
aaeejedib,CPJKU/madmom,madmom/features/onsets.py,5426c640c85461fbbaf9bb97d6cbb590a7573d2c,5ac5fe29faa05f31dff018b6cc7996675e8a5f46,TODO: add 'OnsetDetector'; 'OnsetDetectorLL' and 'MML13' to the methods
aaeejedjd,CPJKU/madmom,madmom/features/onsets.py,e0772691e2dcd969affa4d910fd27774bb7adce5,90a305697b46925ff70688cccc4dee0e09b4ecb8,FIXME: remove this hack
aaeejeece,CPJKU/madmom,madmom/features/notes.py,60ed6dc3183a584806be86dea282453366a72ac0,253d144f0b08855dac0a670595d287dbcf0ad554,:param harmonic_frames: perform harmonic median filtering over N frames
aaeejefbh,CPJKU/madmom,madmom/features/notes.py,60ed6dc3183a584806be86dea282453366a72ac0,65971295ced9dcf4d4ec230bf7ba20ab8be830e5,Notes: If no moving average is needed (e.g. the activations are
aaeejefdh,CPJKU/madmom,madmom/features/notes.py,60ed6dc3183a584806be86dea282453366a72ac0,65971295ced9dcf4d4ec230bf7ba20ab8be830e5,TODO: the signal processing parameters should be included in and
aaeejegej,CPJKU/madmom,madmom/features/__init__.py,65971295ced9dcf4d4ec230bf7ba20ab8be830e5,e3b7b8e191158eabab3540af1cdb6d2b4d4233eb,TODO: the signal processing parameters should be included in and
aaeejehbj,CPJKU/madmom,madmom/ml/rnnlib.py,918269db25314a51950a9f7a1d5c87d8361f9137,860ddcb4542bbda3767bcd2c51867417ffb48a9c,TODO: inherit from features.Activations
aaeejeheh,CPJKU/madmom,madmom/features/beats.py,e8f81ebfce6c2405a3fa9c9a0f15aac58e524867,STILL_EXISTS,TODO: refactor this to use new feature.tempo functionality
aaeejeicc,CPJKU/madmom,madmom/features/beats.py,705ce3a29c62edc10be274e80c62bce090b10630,521734a5c734ed277300c2535dc3b800cffc4e64,TODO: where should the NN_FILES get defined?
aaeejeidi,CPJKU/madmom,madmom/features/beats.py,705ce3a29c62edc10be274e80c62bce090b10630,854a1fda3bc812bdf4df065ab09b0c856900f18a,TODO: use at least 1 frame if any of these values are > 0?
aaeejeidj,CPJKU/madmom,madmom/features/beats.py,705ce3a29c62edc10be274e80c62bce090b10630,STILL_EXISTS,TODO: add DBN stuff here!
aaeejeieh,CPJKU/madmom,madmom/features/beats.py,705ce3a29c62edc10be274e80c62bce090b10630,6fe73c8c73c5114530173944337d38e47bfc59e0,TODO: add DBN related arguments here!
aaeejeiei,CPJKU/madmom,madmom/features/beats.py,705ce3a29c62edc10be274e80c62bce090b10630,f8b0b536b6a089cd6deb2432515701c097804a11,return the argument group so it can be modified if needed
aaeejeiid,CPJKU/madmom,madmom/features/beats.py,854a1fda3bc812bdf4df065ab09b0c856900f18a,STILL_EXISTS,TODO: a more sophisticated solution (e.g. interpolation) should give
aaeejeiie,CPJKU/madmom,madmom/features/beats.py,854a1fda3bc812bdf4df065ab09b0c856900f18a,STILL_EXISTS,better \/ more accurate results
aaeejejbg,CPJKU/madmom,madmom/features/beats.py,29102b82f3efd30df0ea77835ed83c3276ed7f9f,STILL_EXISTS,better \/ more accurate results
aaeejejec,CPJKU/madmom,madmom/features/beats.py,688deaaf61e45deaa0922aa9f4dc3f1bac5b0966,cf513993cf1338edc515d9a2c9bc734cab72655f,init a pool of workers (if needed)
aaeejejef,CPJKU/madmom,madmom/features/beats.py,688deaaf61e45deaa0922aa9f4dc3f1bac5b0966,STILL_EXISTS,concurrent threads if needed
aaeejejeg,CPJKU/madmom,madmom/features/beats.py,7314fb5185ca29a413454b752e32df8e79ca2976,3429f727e6f7ac9dc3586eeaa3380cd720467596,FIXME: \/2 assumes hyper-threading; fix this properly
aaeejejeh,CPJKU/madmom,madmom/features/beats.py,537349ba79da380eabcc63827e3500403bbda375,ceb9180c6b1edd69270fb7b3a73d12ff573f6fee,TODO: refactor the whole CRF Viterbi stuff as a .pyx class including the
aaeejfach,CPJKU/madmom,madmom/features/beats.py,6067da06dfc94ca53e1e064e591095c7dcbe58b8,3acc071b545727b41c88d6bdb78b5a89077646cc,FIXME: \/2 assumes hyper-threading; fix this properly
aaeejfbbg,CPJKU/madmom,madmom/audio/filters.py,7d2f5e10bec8b240a1d25344f77b253d9c710431,f6cbffc7fcc9254003a1a4f80cc1c85c21ecf695,bins can be given too much weight if simply summed up (as in
aaeejfbca,CPJKU/madmom,madmom/audio/filters.py,7d2f5e10bec8b240a1d25344f77b253d9c710431,STILL_EXISTS,are given too much weight if simply summed up (as in the spectral flux)
aaeejfbch,CPJKU/madmom,madmom/audio/filters.py,7d2f5e10bec8b240a1d25344f77b253d9c710431,STILL_EXISTS,# TODO: if someone needs this code; please adapt the harmonic_filterbank stuff
aaeejfbej,CPJKU/madmom,madmom/audio/filters.py,7d2f5e10bec8b240a1d25344f77b253d9c710431,da0e216fbe80810ffbce8118e3ce22526a23b86a,# truncate the filter if it ends after the last frequency bin
aaeejfbff,CPJKU/madmom,madmom/audio/filters.py,7d2f5e10bec8b240a1d25344f77b253d9c710431,da0e216fbe80810ffbce8118e3ce22526a23b86a,# TODO: if needed; allow other handling (like adding values)
aaeejfcae,CPJKU/madmom,madmom/audio/filters.py,7d2f5e10bec8b240a1d25344f77b253d9c710431,da0e216fbe80810ffbce8118e3ce22526a23b86a,# are given too much weight if simply summed up (as in the spectral flux)
aaeejfcjd,CPJKU/madmom,madmom/audio/filters.py,7d2f5e10bec8b240a1d25344f77b253d9c710431,da0e216fbe80810ffbce8118e3ce22526a23b86a,TODO: inharmonicity_coeff should depend on the fundamental
aaeejfdad,CPJKU/madmom,madmom/audio/filters.py,7d2f5e10bec8b240a1d25344f77b253d9c710431,da0e216fbe80810ffbce8118e3ce22526a23b86a,filter_ends = filter_centers + filter_widths[:; np.newaxis]
aaeejfdai,CPJKU/madmom,madmom/audio/filters.py,7d2f5e10bec8b240a1d25344f77b253d9c710431,da0e216fbe80810ffbce8118e3ce22526a23b86a,filter_ends = np.round(filter_ends \/ factor).astype(int)
aaeejfdaj,CPJKU/madmom,madmom/audio/filters.py,7d2f5e10bec8b240a1d25344f77b253d9c710431,da0e216fbe80810ffbce8118e3ce22526a23b86a,filter_ends = np.maximum(filter_ends; filter_centers + 1)
aaeejfdbe,CPJKU/madmom,madmom/audio/filters.py,7d2f5e10bec8b240a1d25344f77b253d9c710431,da0e216fbe80810ffbce8118e3ce22526a23b86a,end = filter_ends[harm_id; band_id]
aaeejfdcg,CPJKU/madmom,madmom/audio/filters.py,7d2f5e10bec8b240a1d25344f77b253d9c710431,STILL_EXISTS,TODO: if needed; allow other handling (like adding values)
aaeejfdhf,CPJKU/madmom,madmom/audio/filters.py,8712986ecb0782ddceec0fc53cfae76837af4d5a,STILL_EXISTS,truncate the filter if it ends after the last band bin
aaeejfdhg,CPJKU/madmom,madmom/audio/filters.py,8712986ecb0782ddceec0fc53cfae76837af4d5a,STILL_EXISTS,TODO: I'm not completely sure if this is the right way to do this;
aaeejfdia,CPJKU/madmom,madmom/audio/filters.py,cd78cd597b8e5e7e6c92cbb6c0a8de517b9bfb3b,STILL_EXISTS,TODO: I'm not completely sure if this is the right way to do this;
aaeejfgdj,CPJKU/madmom,madmom/ml/rnn.py,c1cda6ddf43ecaff28205dce04e0a090bbd964d4,STILL_EXISTS,FIXME: although everything seems to be ok; np.dot doesn't accept the
aaeejfgej,CPJKU/madmom,bin/SuperFluxNN.py,de96cd59fb5a1b68b8eeba94ad9cbd1b4e7afe97,STILL_EXISTS,\"\"\" || SuperFlux onset detection algorithm. ||  || @author: Sebastian B\u00F6ck <sebastian.boeck@jku.at> ||  || \"\"\"
aaeejfhfb,CPJKU/madmom,bin/ComplexFlux.py,2ba82d088feba825bec5c7e39b8a98035fc5b10a,STILL_EXISTS,\"\"\" || ComplexFlux onset detection algorithm. ||  || @author: Sebastian B\u00F6ck <sebastian.boeck@jku.at> ||  || \"\"\"
aaeejfhhg,CPJKU/madmom,madmom/features/onsets.py,2ba82d088feba825bec5c7e39b8a98035fc5b10a,STILL_EXISTS,TODO: use HPSS instead of simple temporal filtering
aaeejgaji,CPJKU/madmom,madmom/features/beats.py,ec2ca64c32ff3a76c88805f1072be0645b4d6384,STILL_EXISTS,TODO: refactor this to use TempoEstimation functionality
aaeejgajj,CPJKU/madmom,madmom/features/beats.py,ec2ca64c32ff3a76c88805f1072be0645b4d6384,STILL_EXISTS,TODO: refactor interval stuff to use TempoEstimation functionality
aaeejgbaa,CPJKU/madmom,madmom/features/beats.py,ec2ca64c32ff3a76c88805f1072be0645b4d6384,STILL_EXISTS,TODO: unify look_aside with CRFBeatDetection's interval_sigma
aaeejgbab,CPJKU/madmom,madmom/features/beats.py,ec2ca64c32ff3a76c88805f1072be0645b4d6384,STILL_EXISTS,TODO: refactor this to use the new TempoEstimation functionality
aaeejgbdb,CPJKU/madmom,madmom/evaluation/notes.py,30f66aa9052c122bbc462772c714c034f488f44b,41d61a1523572f8f390c3c24e30f8a5490de440b,shift the detections if needed
aaeejgcdf,CPJKU/madmom,madmom/evaluation/onsets.py,02285f61a9b4b1a1d050ddf5b81c6e7ac0b39085,41d61a1523572f8f390c3c24e30f8a5490de440b,shift the detections if needed
aaeejgcfe,CPJKU/madmom,madmom/__init__.py,dd81026a534dcb49b6c74fd4217cadc733012853,181d670f5a97e5b4cf624df5fb2fa94540b3dbff,\"\"\" || This package is used internally by the Department of Computational Perception; || Johannes Kepler University; Linz; Austria (http:\/\/www.cp.jku.at) and the || Austrian Research Institute for Artificial Intelligence (OFAI); Vienna; Austria || (http:\/\/www.ofai.at). ||  || All features should be implemented as classes which inherit from Processor || (or provide a XYProcessor(Processor) variant). This way; multiple Processor || objects can be chained to achieve the wanted functionality. ||  || Please see the README for further details of this module. ||  || @author: Sebastian B\u00F6ck <sebastian.boeck@jku.at> ||  || \"\"\"
aaeejgcgb,CPJKU/madmom,madmom/__init__.py,dd81026a534dcb49b6c74fd4217cadc733012853,STILL_EXISTS,init a pool of workers (if needed)
aaeejgcge,CPJKU/madmom,madmom/__init__.py,dd81026a534dcb49b6c74fd4217cadc733012853,181d670f5a97e5b4cf624df5fb2fa94540b3dbff,return the argument group so it can be modified if needed
aaeejgcgh,CPJKU/madmom,madmom/audio/filters.py,dd81026a534dcb49b6c74fd4217cadc733012853,STILL_EXISTS,TODO: including float & out of range bins
aaeejgcha,CPJKU/madmom,madmom/audio/filters.py,dd81026a534dcb49b6c74fd4217cadc733012853,3160db98cca537272a70f4242784455de6c6d79e,FIXME: how to handle this option?
aaeejgchd,CPJKU/madmom,madmom/audio/filters.py,dd81026a534dcb49b6c74fd4217cadc733012853,STILL_EXISTS,TODO: add comments!
aaeejgcif,CPJKU/madmom,madmom/audio/signal.py,dd81026a534dcb49b6c74fd4217cadc733012853,a964b80fbcecdc1e2494cb584ffe241aae998a0e,TODO: use sox to convert from different input signals and use the
aaeejgcig,CPJKU/madmom,madmom/audio/signal.py,dd81026a534dcb49b6c74fd4217cadc733012853,a964b80fbcecdc1e2494cb584ffe241aae998a0e,given sample rate to re-sample the signal on the fly if needed
aaeejgcjf,CPJKU/madmom,madmom/audio/signal.py,dd81026a534dcb49b6c74fd4217cadc733012853,STILL_EXISTS,process it if needed
aaeejgdaa,CPJKU/madmom,madmom/audio/signal.py,dd81026a534dcb49b6c74fd4217cadc733012853,1539bd9ec2b940df531d2fb82d79ea65e4acaf26,TODO: add comments!
aaeejgdah,CPJKU/madmom,madmom/audio/signal.py,dd81026a534dcb49b6c74fd4217cadc733012853,3e277f8672bbbba99c6c862037daba4a9dba0706,TODO: include end_of_signal handling!?
aaeejgdcd,CPJKU/madmom,madmom/audio/spectrogram.py,dd81026a534dcb49b6c74fd4217cadc733012853,STILL_EXISTS,filter the magnitude spectrogram if needed
aaeejgdce,CPJKU/madmom,madmom/audio/spectrogram.py,dd81026a534dcb49b6c74fd4217cadc733012853,STILL_EXISTS,take the logarithm of the magnitude spectrogram if needed (inplace)
aaeejgdcf,CPJKU/madmom,madmom/audio/spectrogram.py,dd81026a534dcb49b6c74fd4217cadc733012853,dd81026a534dcb49b6c74fd4217cadc733012853,STFT were accessed previously; better call compute_stft() with
aaeejgdch,CPJKU/madmom,madmom/audio/spectrogram.py,dd81026a534dcb49b6c74fd4217cadc733012853,STILL_EXISTS,compute STFT if needed
aaeejgdci,CPJKU/madmom,madmom/audio/spectrogram.py,dd81026a534dcb49b6c74fd4217cadc733012853,STILL_EXISTS,compute spec if needed
aaeejgdcj,CPJKU/madmom,madmom/audio/spectrogram.py,dd81026a534dcb49b6c74fd4217cadc733012853,STILL_EXISTS,compute phase if needed
aaeejgdda,CPJKU/madmom,madmom/audio/spectrogram.py,dd81026a534dcb49b6c74fd4217cadc733012853,STILL_EXISTS,TODO: this also stores the STFT; which might not be needed
aaeejgddb,CPJKU/madmom,madmom/audio/spectrogram.py,dd81026a534dcb49b6c74fd4217cadc733012853,STILL_EXISTS,compute the local group delay if needed
aaeejgddc,CPJKU/madmom,madmom/audio/spectrogram.py,dd81026a534dcb49b6c74fd4217cadc733012853,STILL_EXISTS,TODO: this also stores the phase; which might not be needed
aaeejgdde,CPJKU/madmom,madmom/audio/spectrogram.py,dd81026a534dcb49b6c74fd4217cadc733012853,STILL_EXISTS,TODO: add option to automatically calculate `fref`
aaeejgdge,CPJKU/madmom,madmom/audio/spectrogram.py,dd81026a534dcb49b6c74fd4217cadc733012853,STILL_EXISTS,TODO: add norm_window & fft_size
aaeejgegf,CPJKU/madmom,madmom/audio/filters.py,650c59bd53ad3b09d292dbcb2d5ed81b5d0f334a,STILL_EXISTS,TODO: make these filterbanks accept a list of frequencies (the bin's freqs)
aaeejgegh,CPJKU/madmom,madmom/audio/signal.py,650c59bd53ad3b09d292dbcb2d5ed81b5d0f334a,8b867af683fffd7436e1423fc114551cff3d8661,TODO: is this needed?
aaeejgejc,CPJKU/madmom,madmom/audio/spectrogram.py,650c59bd53ad3b09d292dbcb2d5ed81b5d0f334a,b1fba67e480970d6e67b5b21a6082ff94c737747,circular shift the signal (needed for correct phase)
aaeejgeje,CPJKU/madmom,madmom/audio/spectrogram.py,650c59bd53ad3b09d292dbcb2d5ed81b5d0f334a,STILL_EXISTS,is block wise processing needed?
aaeejgejf,CPJKU/madmom,madmom/audio/spectrogram.py,650c59bd53ad3b09d292dbcb2d5ed81b5d0f334a,STILL_EXISTS,no filtering needed; thus no block wise processing needed
aaeejgejh,CPJKU/madmom,madmom/audio/spectrogram.py,650c59bd53ad3b09d292dbcb2d5ed81b5d0f334a,STILL_EXISTS,apply a maximum filter if needed
aaeejgfdj,CPJKU/madmom,madmom/audio/spectrogram.py,650c59bd53ad3b09d292dbcb2d5ed81b5d0f334a,STILL_EXISTS,TODO: move this to filterbank and make it accept a list of frequencies
aaeejgfef,CPJKU/madmom,madmom/audio/spectrogram.py,650c59bd53ad3b09d292dbcb2d5ed81b5d0f334a,STILL_EXISTS,TODO: filter spec \/ diff individually or spec first and calc diff
aaeejgfha,CPJKU/madmom,madmom/audio/spectrogram.py,650c59bd53ad3b09d292dbcb2d5ed81b5d0f334a,STILL_EXISTS,TODO: add norm_window & fft_size
aaeejgfhg,CPJKU/madmom,madmom/features/onsets.py,650c59bd53ad3b09d292dbcb2d5ed81b5d0f334a,6affec6ff17f6ea1cdd4a2188b4ea47eb33017b7,return the argument group so it can be modified if needed
aaeejghfg,CPJKU/madmom,madmom/audio/spectrogram.py,e4e380322a9cdd23ddc9e094703a0f1f69fe9f57,STILL_EXISTS,FIXME: this does not work with more than 1 threads!
aaeejghgb,CPJKU/madmom,madmom/ml/rnn.py,e4e380322a9cdd23ddc9e094703a0f1f69fe9f57,STILL_EXISTS,ravel them if needed
aaeejghgd,CPJKU/madmom,madmom/ml/rnn.py,e4e380322a9cdd23ddc9e094703a0f1f69fe9f57,STILL_EXISTS,return the argument group so it can be modified if needed
aaeejgjfg,CPJKU/madmom,madmom/__init__.py,5ac5fe29faa05f31dff018b6cc7996675e8a5f46,181d670f5a97e5b4cf624df5fb2fa94540b3dbff,wrap the processor in a list if needed
aaeejhbdc,CPJKU/madmom,madmom/features/beats.py,e3b7b8e191158eabab3540af1cdb6d2b4d4233eb,STILL_EXISTS,TODO: should we inherit from RNNBeatProcessor?
aaeejhbfb,CPJKU/madmom,madmom/features/beats.py,e3b7b8e191158eabab3540af1cdb6d2b4d4233eb,f8b0b536b6a089cd6deb2432515701c097804a11,return the argument group so it can be modified if needed
aaeejhbhb,CPJKU/madmom,madmom/features/onsets.py,e3b7b8e191158eabab3540af1cdb6d2b4d4233eb,STILL_EXISTS,TODO: this information should be stored in the nn_files
aaeejhbib,CPJKU/madmom,madmom/features/onsets.py,e3b7b8e191158eabab3540af1cdb6d2b4d4233eb,6affec6ff17f6ea1cdd4a2188b4ea47eb33017b7,TODO: make the averaging function exchangeable (mean\/median\/etc.)
aaeejhbjd,CPJKU/madmom,madmom/features/onsets.py,e3b7b8e191158eabab3540af1cdb6d2b4d4233eb,6affec6ff17f6ea1cdd4a2188b4ea47eb33017b7,TODO: use at least 1 frame if any of these values are > 0?
aaeejhcad,CPJKU/madmom,madmom/features/onsets.py,e3b7b8e191158eabab3540af1cdb6d2b4d4233eb,6affec6ff17f6ea1cdd4a2188b4ea47eb33017b7,return the argument group so it can be modified if needed
aaeejhcea,CPJKU/madmom,madmom/ml/rnn.py,e3b7b8e191158eabab3540af1cdb6d2b4d4233eb,9abd4b73fc9635486f3374400470c7744b4b16c1,init a pool of workers (if needed)
aaeejhceg,CPJKU/madmom,madmom/__init__.py,6affec6ff17f6ea1cdd4a2188b4ea47eb33017b7,181d670f5a97e5b4cf624df5fb2fa94540b3dbff,no processing needed; just return the data
aaeejhcfa,CPJKU/madmom,madmom/features/beats.py,6affec6ff17f6ea1cdd4a2188b4ea47eb33017b7,b7d7659ea8f3dbbf86d7995dffe67b7f63fcd45f,TODO: implement comb filter stuff and remove this...
aaeejhcfc,CPJKU/madmom,madmom/features/beats.py,6affec6ff17f6ea1cdd4a2188b4ea47eb33017b7,STILL_EXISTS,FIXME: remove this hack of setting fps here
aaeejhdbb,CPJKU/madmom,madmom/features/onsets.py,6affec6ff17f6ea1cdd4a2188b4ea47eb33017b7,STILL_EXISTS,FIXME: remove this hack of setting fps here
aaeejhdbd,CPJKU/madmom,madmom/features/onsets.py,6affec6ff17f6ea1cdd4a2188b4ea47eb33017b7,4fae108646cc9b98eef79bd1473e099e4bcfed4c,swap in\/out processors if needed
aaeejhdej,CPJKU/madmom,madmom/features/peak_picking.py,6affec6ff17f6ea1cdd4a2188b4ea47eb33017b7,STILL_EXISTS,\"\"\" || This file contains peak-picking functionality. ||  || @author: Sebastian B\u00F6ck <sebastian.boeck@jku.at> ||  || \"\"\"
aaeejhdff,CPJKU/madmom,madmom/features/peak_picking.py,6affec6ff17f6ea1cdd4a2188b4ea47eb33017b7,STILL_EXISTS,TODO: make the averaging function exchangeable (mean\/median\/etc.)
aaeejhdgh,CPJKU/madmom,madmom/features/peak_picking.py,6affec6ff17f6ea1cdd4a2188b4ea47eb33017b7,STILL_EXISTS,TODO: use at least 1 frame if any of these values are > 0?
aaeejhdgj,CPJKU/madmom,madmom/features/peak_picking.py,6affec6ff17f6ea1cdd4a2188b4ea47eb33017b7,STILL_EXISTS,TODO: make this multi-dim!
aaeejhdhi,CPJKU/madmom,madmom/features/peak_picking.py,6affec6ff17f6ea1cdd4a2188b4ea47eb33017b7,STILL_EXISTS,return the argument group so it can be modified if needed
aaeejhegd,CPJKU/madmom,madmom/__init__.py,9abd4b73fc9635486f3374400470c7744b4b16c1,STILL_EXISTS,close the file if needed and use its name
aaeejheha,CPJKU/madmom,madmom/__init__.py,9abd4b73fc9635486f3374400470c7744b4b16c1,181d670f5a97e5b4cf624df5fb2fa94540b3dbff,TODO: check the input and output processors!?
aaeejhehf,CPJKU/madmom,madmom/__init__.py,9abd4b73fc9635486f3374400470c7744b4b16c1,181d670f5a97e5b4cf624df5fb2fa94540b3dbff,wrap the input processor in a SequentialProcessor is needed
aaeejhehg,CPJKU/madmom,madmom/__init__.py,9abd4b73fc9635486f3374400470c7744b4b16c1,STILL_EXISTS,wrap the output processor in an IOProcessor is needed
aaeejhehi,CPJKU/madmom,madmom/__init__.py,9abd4b73fc9635486f3374400470c7744b4b16c1,181d670f5a97e5b4cf624df5fb2fa94540b3dbff,TODO: unify this with _process!?
aaeejhfad,CPJKU/madmom,madmom/features/beats.py,9abd4b73fc9635486f3374400470c7744b4b16c1,b9f0f90ef3d8e1994bbc6bd5c2324d79e0708322,# TODO: add multi_model stuff!
aaeejhfcb,CPJKU/madmom,madmom/features/beats.py,9abd4b73fc9635486f3374400470c7744b4b16c1,b9f0f90ef3d8e1994bbc6bd5c2324d79e0708322,# swap in\/out processors if needed
aaeejhfci,CPJKU/madmom,madmom/features/beats.py,9abd4b73fc9635486f3374400470c7744b4b16c1,STILL_EXISTS,TODO: split the classes similar to madmom.features.onsets?
aaeejhfdb,CPJKU/madmom,madmom/features/beats.py,9abd4b73fc9635486f3374400470c7744b4b16c1,4fae108646cc9b98eef79bd1473e099e4bcfed4c,TODO: remove this fps hack!
aaeejhfeb,CPJKU/madmom,madmom/features/beats.py,9abd4b73fc9635486f3374400470c7744b4b16c1,STILL_EXISTS,TODO: should we inherit from RNNBeatProcessor?
aaeejhfee,CPJKU/madmom,madmom/features/beats.py,9abd4b73fc9635486f3374400470c7744b4b16c1,STILL_EXISTS,TODO: this information should be stored in the nn_files
aaeejhfff,CPJKU/madmom,madmom/features/onsets.py,9abd4b73fc9635486f3374400470c7744b4b16c1,STILL_EXISTS,TODO: make the averaging function exchangeable (mean\/median\/etc.)
aaeejhfgh,CPJKU/madmom,madmom/features/onsets.py,9abd4b73fc9635486f3374400470c7744b4b16c1,9df0d380e1039ceeaf57249b209f5f360a423cb2,TODO: use at least 1 frame if any of these values are > 0?
aaeejhfgj,CPJKU/madmom,madmom/features/onsets.py,9abd4b73fc9635486f3374400470c7744b4b16c1,STILL_EXISTS,TODO: make this multi-dim!
aaeejhfhi,CPJKU/madmom,madmom/features/onsets.py,9abd4b73fc9635486f3374400470c7744b4b16c1,STILL_EXISTS,return the argument group so it can be modified if needed
aaeejhfig,CPJKU/madmom,madmom/features/onsets.py,9abd4b73fc9635486f3374400470c7744b4b16c1,STILL_EXISTS,TODO: should we warn about miscornfigurations here or build its own class
aaeejhfjb,CPJKU/madmom,madmom/features/onsets.py,9abd4b73fc9635486f3374400470c7744b4b16c1,8add40148b9fd4b5838c0087390675f2314707ca,TODO: split the classes similar to madmom.features.beats?
aaeejhgbc,CPJKU/madmom,madmom/features/tempo.py,9abd4b73fc9635486f3374400470c7744b4b16c1,STILL_EXISTS,return the argument group so it can be modified if needed
aaeejhgbe,CPJKU/madmom,madmom/features/tempo.py,9abd4b73fc9635486f3374400470c7744b4b16c1,STILL_EXISTS,TODO: this is super hackish; split RNNBeatTracking in RNN & writing
aaeejhgbg,CPJKU/madmom,madmom/features/tempo.py,9abd4b73fc9635486f3374400470c7744b4b16c1,4fae108646cc9b98eef79bd1473e099e4bcfed4c,swap in\/out processors if needed
aaeejhgbj,CPJKU/madmom,madmom/ml/rnn.py,9abd4b73fc9635486f3374400470c7744b4b16c1,STILL_EXISTS,return the argument group so it can be modified if needed
aaeejhggc,CPJKU/madmom,bin/tools/bar_remover.py,b9f0f90ef3d8e1994bbc6bd5c2324d79e0708322,STILL_EXISTS,add an extension if needed
aaeejhhae,CPJKU/madmom,madmom/features/notes.py,b9f0f90ef3d8e1994bbc6bd5c2324d79e0708322,f6a25f3a8a077de58397b3332f3ee2e3eb0c2e4a,TODO: These do not seem to be used anywhere
aaeejhhdi,CPJKU/madmom,madmom/utils/midi.py,b9f0f90ef3d8e1994bbc6bd5c2324d79e0708322,STILL_EXISTS,FIXME: what we do here s basically writing a MIDI format 0 file;
aaeejhhea,CPJKU/madmom,madmom/utils/midi.py,b9f0f90ef3d8e1994bbc6bd5c2324d79e0708322,STILL_EXISTS,tempo and time signature stuff is just a hack!
aaeejhhjh,CPJKU/madmom,madmom/ml/rnn.py,4c00f102298bd0f466ca3f3d4ff5297598b35e45,STILL_EXISTS,ravel the predictions if needed
aaeejhibd,CPJKU/madmom,madmom/ml/rnn.py,f4b4840c2aa57e4042bd946af7562049132fd528,fdb00c09ecd61799dbf9ac79f1cc750e742946e7,init a pool of workers (if needed)
aaeejhicb,CPJKU/madmom,madmom/audio/hpss.py,32f19e911124200d35592b8c1b4ad1f22d7edd3d,STILL_EXISTS,\"\"\" || This file contains all harmonic\/percussive source separation functionality. ||  || @author: Sebastian B\u00F6ck <sebastian.boeck@jku.at> ||  || \"\"\"
aaeejhidi,CPJKU/madmom,madmom/audio/hpss.py,32f19e911124200d35592b8c1b4ad1f22d7edd3d,1692f9e26deb0d0af22924da36f909f0830d9953,TODO: split this among the individual classes
aaeejhiea,CPJKU/madmom,madmom/audio/hpss.py,32f19e911124200d35592b8c1b4ad1f22d7edd3d,STILL_EXISTS,return the argument group so it can be modified if needed
aaeejhifg,CPJKU/madmom,madmom/ml/rnnlib.py,437ba0413caa6729236b9f195291b33821a72f4b,STILL_EXISTS,TODO: unify this with RnnlibConfig.test()
aaeejhjfb,CPJKU/madmom,madmom/test/test_audio_filters.py,27ca8712bf976dbbded4274087ef246d972e7276,STILL_EXISTS,TODO: why is this error not raised? it does not really matter; though
aaeejhjgj,CPJKU/madmom,madmom/test/test_audio_filters.py,27ca8712bf976dbbded4274087ef246d972e7276,STILL_EXISTS,TODO: why can't we test the inherited constants? it does not matter
aaeejiaae,CPJKU/madmom,madmom/audio/signal.py,8b867af683fffd7436e1423fc114551cff3d8661,STILL_EXISTS,have to use the workaround of np.repeat(signal[:1] * 0; frame_size)
aaeejiaeb,CPJKU/madmom,madmom/audio/spectrogram.py,7e35f4ddab34177cdd5bc417d48e2ae6ee75e6cc,3e277f8672bbbba99c6c862037daba4a9dba0706,TODO: make this function accept just a signal?
aaeejiaec,CPJKU/madmom,madmom/audio/spectrogram.py,7e35f4ddab34177cdd5bc417d48e2ae6ee75e6cc,STILL_EXISTS,compute phase if needed
aaeejiaed,CPJKU/madmom,madmom/audio/spectrogram.py,7e35f4ddab34177cdd5bc417d48e2ae6ee75e6cc,STILL_EXISTS,TODO: this also stores the STFT; which might not be needed
aaeejiaef,CPJKU/madmom,madmom/audio/spectrogram.py,7e35f4ddab34177cdd5bc417d48e2ae6ee75e6cc,STILL_EXISTS,compute the local group delay if needed
aaeejiaeg,CPJKU/madmom,madmom/audio/spectrogram.py,7e35f4ddab34177cdd5bc417d48e2ae6ee75e6cc,STILL_EXISTS,TODO: this also stores the phase; which might not be needed
aaeejiahi,CPJKU/madmom,bin/PickleProcessor.py,fdb00c09ecd61799dbf9ac79f1cc750e742946e7,051a7db8578b7029c78960bf868b23ecc78fc493,append the suffix if needed
aaeejibag,CPJKU/madmom,bin/BeatDetector.py,ccd7694eb20dc5c9663c9f5972f6d2417e55dd29,STILL_EXISTS,pickle the processor if needed
aaeejibah,CPJKU/madmom,bin/BeatTracker.py,ccd7694eb20dc5c9663c9f5972f6d2417e55dd29,STILL_EXISTS,pickle the processor if needed
aaeejibai,CPJKU/madmom,bin/CRFBeatDetector.py,ccd7694eb20dc5c9663c9f5972f6d2417e55dd29,STILL_EXISTS,pickle the processor if needed
aaeejibaj,CPJKU/madmom,bin/ComplexFlux.py,ccd7694eb20dc5c9663c9f5972f6d2417e55dd29,STILL_EXISTS,pickle the processor if needed
aaeejibba,CPJKU/madmom,bin/DBNBeatTracker.py,ccd7694eb20dc5c9663c9f5972f6d2417e55dd29,STILL_EXISTS,pickle the processor if needed
aaeejibbb,CPJKU/madmom,bin/LogFiltSpecFlux.py,ccd7694eb20dc5c9663c9f5972f6d2417e55dd29,STILL_EXISTS,pickle the processor if needed
aaeejibbc,CPJKU/madmom,bin/MMBeatTracker.py,ccd7694eb20dc5c9663c9f5972f6d2417e55dd29,STILL_EXISTS,pickle the processor if needed
aaeejibbd,CPJKU/madmom,bin/OnsetDetector.py,ccd7694eb20dc5c9663c9f5972f6d2417e55dd29,STILL_EXISTS,pickle the processor if needed
aaeejibbe,CPJKU/madmom,bin/OnsetDetectorLL.py,ccd7694eb20dc5c9663c9f5972f6d2417e55dd29,STILL_EXISTS,pickle the processor if needed
aaeejibbf,CPJKU/madmom,bin/PianoTranscriptor.py,ccd7694eb20dc5c9663c9f5972f6d2417e55dd29,STILL_EXISTS,pickle the processor if needed
aaeejibce,CPJKU/madmom,bin/SuperFlux.py,ccd7694eb20dc5c9663c9f5972f6d2417e55dd29,STILL_EXISTS,pickle the processor if needed
aaeejibcf,CPJKU/madmom,bin/SuperFluxNN.py,ccd7694eb20dc5c9663c9f5972f6d2417e55dd29,STILL_EXISTS,pickle the processor if needed
aaeejibcg,CPJKU/madmom,bin/TempoDetector.py,ccd7694eb20dc5c9663c9f5972f6d2417e55dd29,STILL_EXISTS,pickle the processor if needed
aaeejibch,CPJKU/madmom,madmom/__init__.py,ccd7694eb20dc5c9663c9f5972f6d2417e55dd29,181d670f5a97e5b4cf624df5fb2fa94540b3dbff,close the open file if needed and use its name
aaeejibdh,CPJKU/madmom,madmom/features/beats.py,ccd7694eb20dc5c9663c9f5972f6d2417e55dd29,cf513993cf1338edc515d9a2c9bc734cab72655f,init a pool of workers (if needed)
aaeejibjd,CPJKU/madmom,madmom/utils/__init__.py,051a7db8578b7029c78960bf868b23ecc78fc493,4fae108646cc9b98eef79bd1473e099e4bcfed4c,append the suffix if needed
aaeejigfd,CPJKU/madmom,madmom/audio/signal.py,4cc2359df98985cf40478b087a825c0fc24141d0,STILL_EXISTS,down-mix if needed
aaeejigfh,CPJKU/madmom,madmom/audio/signal.py,4cc2359df98985cf40478b087a825c0fc24141d0,d19253a51b0b57768716c2c7472d532ec5948b7c,get the needed information from the file
aaeejihac,CPJKU/madmom,madmom/features/beats.py,f3c08d12f064f1c9a47ea9dd9cf848e1299c0aa8,STILL_EXISTS,correct the beat positions if needed
aaeejihde,CPJKU/madmom,madmom/features/beats.py,f3c08d12f064f1c9a47ea9dd9cf848e1299c0aa8,b2cce71a2fb1383d48b35728a3f48c1f79e8bf21,TODO: make parsing nicer!
aaeejihdh,CPJKU/madmom,madmom/features/beats.py,f3c08d12f064f1c9a47ea9dd9cf848e1299c0aa8,f8b0b536b6a089cd6deb2432515701c097804a11,return the argument group so it can be modified if needed
aaeejihdi,CPJKU/madmom,madmom/features/beats.py,f3c08d12f064f1c9a47ea9dd9cf848e1299c0aa8,STILL_EXISTS,TODO: split the classes similar to madmom.features.beats?
aaeejihea,CPJKU/madmom,madmom/features/beats.py,f3c08d12f064f1c9a47ea9dd9cf848e1299c0aa8,4fae108646cc9b98eef79bd1473e099e4bcfed4c,swap in\/out processors if needed
aaeejihee,CPJKU/madmom,madmom/audio/spectrogram.py,bcdb1c2aa54bc83858535bc36c05c77e028b009b,STILL_EXISTS,TODO: should this class always act on just the differences? filtering
aaeejihfb,CPJKU/madmom,madmom/features/beats.py,bcdb1c2aa54bc83858535bc36c05c77e028b009b,f8b0b536b6a089cd6deb2432515701c097804a11,return the argument group so it can be modified if needed
aaeejihid,CPJKU/madmom,madmom/ml/gmm.py,19d0812135fb887766106d75316db9122d0016ac,STILL_EXISTS,copy the needed information to self
aaeejiiee,CPJKU/madmom,madmom/features/beats.py,51cb3fb62e9d454fd9a122412266c8e18be2eabf,STILL_EXISTS,expand num_tempo_states and alpha to lists if needed
aaeejiiej,CPJKU/madmom,madmom/features/beats.py,51cb3fb62e9d454fd9a122412266c8e18be2eabf,4fae108646cc9b98eef79bd1473e099e4bcfed4c,TODO: remove this fps hack!
aaeejiihe,CPJKU/madmom,madmom/evaluation/beats.py,fdd077947ae61b3622097db275de343f83d79d71,STILL_EXISTS,TODO: is this the right thing to do?
aaeejijib,CPJKU/madmom,madmom/evaluation/beats.py,1969c10fdce88e4606e2bc9341f119b158861140,STILL_EXISTS,TODO: remove this; see TODO below
aaeejijjd,CPJKU/madmom,madmom/evaluation/onsets.py,1969c10fdce88e4606e2bc9341f119b158861140,STILL_EXISTS,TODO: find a better name; this is misleading since it does not evaluate the
aaeejjade,CPJKU/madmom,bin/tools/DataProcessor.py,ce5a5c546f485883046c2ec8ef563b77aa28a8a1,STILL_EXISTS,TODO: add default settings for onsets\/beats\/etc.?
aaeejjaea,CPJKU/madmom,madmom/audio/spectrogram.py,ce5a5c546f485883046c2ec8ef563b77aa28a8a1,dc1bf3a2409aa1421dba0480e8362fbec51f5976,and the differences only if needed
aaeejjaed,CPJKU/madmom,madmom/ml/rnnlib.py,ce5a5c546f485883046c2ec8ef563b77aa28a8a1,150d497f6b3d0cda796aef257b3a4aebd7483db8,stack diffs only if needed
aaeejjaej,CPJKU/madmom,madmom/audio/signal.py,3ed12ad9b0f9bbcd1f5b774ce1bc0b2bc6e679cc,STILL_EXISTS,TODO: add weighted mixing
aaeejjbbe,CPJKU/madmom,madmom/audio/signal.py,59739b75033369be65eff49a040243391b49ae48,1058ec49b54f54cbdef77a7d96be4f518ed280af,needed for correct pickling
aaeejjbbj,CPJKU/madmom,madmom/audio/signal.py,59739b75033369be65eff49a040243391b49ae48,da1c051a55596df2f7fb8dec17233a60c88e230b,needed for correct un-pickling
aaeejjbce,CPJKU/madmom,madmom/audio/signal.py,59739b75033369be65eff49a040243391b49ae48,STILL_EXISTS,TODO: add weighted mixing
aaeejjbdj,CPJKU/madmom,madmom/audio/signal.py,da1c051a55596df2f7fb8dec17233a60c88e230b,STILL_EXISTS,TODO: add weighted mixing
aaeejjbei,CPJKU/madmom,madmom/audio/signal.py,da1c051a55596df2f7fb8dec17233a60c88e230b,1058ec49b54f54cbdef77a7d96be4f518ed280af,needed for correct pickling
aaeejjbfg,CPJKU/madmom,madmom/audio/signal.py,4b53df2f3307d30f4da4e5e14bf59562bef66f85,1058ec49b54f54cbdef77a7d96be4f518ed280af,needed for correct pickling
aaeejjbgb,CPJKU/madmom,madmom/audio/signal.py,4b53df2f3307d30f4da4e5e14bf59562bef66f85,1058ec49b54f54cbdef77a7d96be4f518ed280af,needed for correct un-pickling
aaeejjcba,CPJKU/madmom,madmom/__init__.py,e699222f0c25a98d372c8c2eda601acafb521b46,181d670f5a97e5b4cf624df5fb2fa94540b3dbff,TODO: include automatic (un-)zipping here?
aaeejjcbc,CPJKU/madmom,madmom/__init__.py,e699222f0c25a98d372c8c2eda601acafb521b46,181d670f5a97e5b4cf624df5fb2fa94540b3dbff,close the file if needed
aaeejjcje,CPJKU/madmom,madmom/utils/__init__.py,51f5b6d40256beefe03c464128a25b64ff286c49,893fb4fb676eb85b8e87067af21e2d8b0a34dd37,TODO: include automatic (un-)zipping here?
aaeejjcjg,CPJKU/madmom,madmom/utils/__init__.py,51f5b6d40256beefe03c464128a25b64ff286c49,893fb4fb676eb85b8e87067af21e2d8b0a34dd37,close the file if needed
aaeejjdff,CPJKU/madmom,madmom/audio/spectrogram.py,e4fd383c930411622802f31db5770cf956aab7ef,e040a83f6c6cdd82e94d353f263aeaab45295df3,and the differences only if needed
aaeejjdfi,CPJKU/madmom,madmom/ml/rnnlib.py,e4fd383c930411622802f31db5770cf956aab7ef,7e0c6d5306ad6a1fc83e71e52e58a65f7d85dd1e,stack diffs only if needed
aaeejjdgg,CPJKU/madmom,bin/BeatDetector.py,4fae108646cc9b98eef79bd1473e099e4bcfed4c,e2295d97d3cea3360f9914f6463333fa2134f88a,TODO: remove this hack!
aaeejjdhf,CPJKU/madmom,bin/BeatTracker.py,4fae108646cc9b98eef79bd1473e099e4bcfed4c,e2295d97d3cea3360f9914f6463333fa2134f88a,TODO: remove this hack!
aaeejjdie,CPJKU/madmom,bin/CRFBeatDetector.py,4fae108646cc9b98eef79bd1473e099e4bcfed4c,e2295d97d3cea3360f9914f6463333fa2134f88a,TODO: remove this hack!
aaeejjeaa,CPJKU/madmom,bin/DBNBeatTracker.py,4fae108646cc9b98eef79bd1473e099e4bcfed4c,e2295d97d3cea3360f9914f6463333fa2134f88a,TODO: remove this hack!
aaeejjecf,CPJKU/madmom,bin/MMBeatTracker.py,4fae108646cc9b98eef79bd1473e099e4bcfed4c,e2295d97d3cea3360f9914f6463333fa2134f88a,TODO: replace this hack nn_ref_files=True hack with a proper solution
aaeejjecg,CPJKU/madmom,bin/MMBeatTracker.py,4fae108646cc9b98eef79bd1473e099e4bcfed4c,e2295d97d3cea3360f9914f6463333fa2134f88a,TODO: remove this hack!
aaeejjedg,CPJKU/madmom,bin/OnsetDetector.py,4fae108646cc9b98eef79bd1473e099e4bcfed4c,STILL_EXISTS,TODO: remove this hack!
aaeejjeef,CPJKU/madmom,bin/OnsetDetectorLL.py,4fae108646cc9b98eef79bd1473e099e4bcfed4c,STILL_EXISTS,TODO: remove this hack!
aaeejjefe,CPJKU/madmom,bin/PianoTranscriptor.py,4fae108646cc9b98eef79bd1473e099e4bcfed4c,STILL_EXISTS,TODO: remove this hack!
aaeejjehh,CPJKU/madmom,bin/TempoDetector.py,4fae108646cc9b98eef79bd1473e099e4bcfed4c,e2295d97d3cea3360f9914f6463333fa2134f88a,TODO: remove this hack!
aaeejjfac,CPJKU/madmom,madmom/__init__.py,4fae108646cc9b98eef79bd1473e099e4bcfed4c,181d670f5a97e5b4cf624df5fb2fa94540b3dbff,append the suffix if needed
aaeejjfbc,CPJKU/madmom,madmom/features/beats.py,4fae108646cc9b98eef79bd1473e099e4bcfed4c,7c547b70e51b9b150dd965b4fb74408e0f4661a8,FIXME: this is kind of hackish; but being able to simply set
aaeejjfjf,CPJKU/madmom,madmom/features/onsets.py,f6a25f3a8a077de58397b3332f3ee2e3eb0c2e4a,STILL_EXISTS,TODO: make the averaging function exchangeable (mean\/median\/etc.)
aaeejjgah,CPJKU/madmom,madmom/features/onsets.py,f6a25f3a8a077de58397b3332f3ee2e3eb0c2e4a,9df0d380e1039ceeaf57249b209f5f360a423cb2,TODO: use at least 1 frame if any of these values are > 0?
aaeejjgcj,CPJKU/madmom,madmom/features/onsets.py,f6a25f3a8a077de58397b3332f3ee2e3eb0c2e4a,STILL_EXISTS,return the argument group so it can be modified if needed
aaeejjhbb,CPJKU/madmom,madmom/features/beats.py,e2295d97d3cea3360f9914f6463333fa2134f88a,5ec649f246bb23a0325d0a6cebd79a6abcdad8d9,TODO: threads?
aaeejjhbd,CPJKU/madmom,madmom/features/beats.py,e2295d97d3cea3360f9914f6463333fa2134f88a,bb0027537f9ea221e94b923c9d9ead62a9fa51e4,TODO: implement comb filter stuff and remove this...
aaeejjhbg,CPJKU/madmom,madmom/features/beats.py,e2295d97d3cea3360f9914f6463333fa2134f88a,STILL_EXISTS,TODO: refactor interval stuff to use TempoEstimation
aaeejjhbh,CPJKU/madmom,madmom/features/beats.py,e2295d97d3cea3360f9914f6463333fa2134f88a,bb0027537f9ea221e94b923c9d9ead62a9fa51e4,TODO: use the tempi returned by the TempoEstimation instead
aaeejjhce,CPJKU/madmom,madmom/features/beats.py,e2295d97d3cea3360f9914f6463333fa2134f88a,5ec649f246bb23a0325d0a6cebd79a6abcdad8d9,swap in\/out processors if needed
aaeejjhcj,CPJKU/madmom,madmom/features/beats.py,e2295d97d3cea3360f9914f6463333fa2134f88a,5ec649f246bb23a0325d0a6cebd79a6abcdad8d9,TODO: remove this fps hack!
aaeejjheb,CPJKU/madmom,madmom/features/tempo.py,e2295d97d3cea3360f9914f6463333fa2134f88a,5ec649f246bb23a0325d0a6cebd79a6abcdad8d9,swap in\/out processors if needed
aaeejjhfa,CPJKU/madmom,madmom/features/beats.py,bb0027537f9ea221e94b923c9d9ead62a9fa51e4,STILL_EXISTS,TODO: refactor interval stuff to use TempoEstimation
aaeejjhff,CPJKU/madmom,bin/BeatDetector.py,5ec649f246bb23a0325d0a6cebd79a6abcdad8d9,STILL_EXISTS,TODO: remove this hack!
aaeejjhge,CPJKU/madmom,bin/BeatTracker.py,5ec649f246bb23a0325d0a6cebd79a6abcdad8d9,STILL_EXISTS,TODO: remove this hack!
aaeejjhhd,CPJKU/madmom,bin/CRFBeatDetector.py,5ec649f246bb23a0325d0a6cebd79a6abcdad8d9,STILL_EXISTS,TODO: remove this hack!
aaeejjhic,CPJKU/madmom,bin/DBNBeatTracker.py,5ec649f246bb23a0325d0a6cebd79a6abcdad8d9,STILL_EXISTS,TODO: remove this hack!
aaeejjiaa,CPJKU/madmom,bin/MMBeatTracker.py,5ec649f246bb23a0325d0a6cebd79a6abcdad8d9,STILL_EXISTS,TODO: replace this hack nn_ref_files=True hack with a proper solution
aaeejjiab,CPJKU/madmom,bin/MMBeatTracker.py,5ec649f246bb23a0325d0a6cebd79a6abcdad8d9,STILL_EXISTS,TODO: remove this hack!
aaeejjibc,CPJKU/madmom,bin/TempoDetector.py,5ec649f246bb23a0325d0a6cebd79a6abcdad8d9,STILL_EXISTS,TODO: remove this hack!
aaeejjice,CPJKU/madmom,madmom/features/beats.py,5ec649f246bb23a0325d0a6cebd79a6abcdad8d9,7c547b70e51b9b150dd965b4fb74408e0f4661a8,FIXME: this is kind of hackish; but being able to simply set
aaeejjjci,CPJKU/madmom,madmom/processors.py,181d670f5a97e5b4cf624df5fb2fa94540b3dbff,STILL_EXISTS,\"\"\" || This file contains all processor related functionality. ||  || @author: Sebastian B\u00F6ck <sebastian.boeck@jku.at> ||  || All features should be implemented as classes which inherit from Processor || (or provide a XYZProcessor(Processor) variant). This way; multiple Processor || objects can be chained\/combined to achieve the wanted functionality. ||  || \"\"\"
aaeejjjdi,CPJKU/madmom,madmom/processors.py,181d670f5a97e5b4cf624df5fb2fa94540b3dbff,STILL_EXISTS,wrap the processor in a list if needed
aaeejjjfc,CPJKU/madmom,madmom/processors.py,181d670f5a97e5b4cf624df5fb2fa94540b3dbff,STILL_EXISTS,return the argument group so it can be modified if needed
aaeejjjfd,CPJKU/madmom,madmom/processors.py,181d670f5a97e5b4cf624df5fb2fa94540b3dbff,STILL_EXISTS,TODO: check the input and output processors!?
aaeejjjfi,CPJKU/madmom,madmom/processors.py,181d670f5a97e5b4cf624df5fb2fa94540b3dbff,STILL_EXISTS,wrap the input processor in a SequentialProcessor is needed
aaeejjjfj,CPJKU/madmom,madmom/processors.py,181d670f5a97e5b4cf624df5fb2fa94540b3dbff,STILL_EXISTS,wrap the output processor in an IOProcessor if needed
aaeejjjgc,CPJKU/madmom,madmom/processors.py,181d670f5a97e5b4cf624df5fb2fa94540b3dbff,e9b06b18f947f1aeec678e7ece01c65b797cc874,TODO: unify this with _process!?
aaeejjjge,CPJKU/madmom,madmom/processors.py,181d670f5a97e5b4cf624df5fb2fa94540b3dbff,e9b06b18f947f1aeec678e7ece01c65b797cc874,no processing needed; just return the data
aaeejjjia,CPJKU/madmom,madmom/processors.py,181d670f5a97e5b4cf624df5fb2fa94540b3dbff,STILL_EXISTS,append the suffix if needed
aaeejjjjd,CPJKU/madmom,madmom/utils/__init__.py,181d670f5a97e5b4cf624df5fb2fa94540b3dbff,4ed094a2f0061af772ed1c179323f6106a5b0c8b,TODO: include automatic (un-)zipping here?
aaeejjjjf,CPJKU/madmom,madmom/utils/__init__.py,181d670f5a97e5b4cf624df5fb2fa94540b3dbff,4ed094a2f0061af772ed1c179323f6106a5b0c8b,close the file if needed
aaefaabdj,CPJKU/madmom,madmom/audio/spectrogram.py,d288d565fa9004ff099714540e31ab9971b45ba5,b1fba67e480970d6e67b5b21a6082ff94c737747,instantiate a FramedSignal if needed
aaefaabge,CPJKU/madmom,madmom/audio/spectrogram.py,d288d565fa9004ff099714540e31ab9971b45ba5,b1fba67e480970d6e67b5b21a6082ff94c737747,instantiate a ShortTimeFourierTransform object if needed
aaefaabgf,CPJKU/madmom,madmom/audio/spectrogram.py,d288d565fa9004ff099714540e31ab9971b45ba5,b1fba67e480970d6e67b5b21a6082ff94c737747,TODO: just recalculate with circular_shift set?
aaefaabje,CPJKU/madmom,madmom/audio/spectrogram.py,d288d565fa9004ff099714540e31ab9971b45ba5,STILL_EXISTS,instantiate a Filterbank if needed
aaefaabjg,CPJKU/madmom,madmom/audio/spectrogram.py,d288d565fa9004ff099714540e31ab9971b45ba5,14d8cd92953e575078aec5f75176045d5582963f,TODO: reactivate this or move this whole block\/batch processing to
aaefaacch,CPJKU/madmom,madmom/audio/spectrogram.py,d288d565fa9004ff099714540e31ab9971b45ba5,STILL_EXISTS,TODO: add literal values
aaefaacie,CPJKU/madmom,madmom/audio/spectrogram.py,d288d565fa9004ff099714540e31ab9971b45ba5,STILL_EXISTS,instantiate a FilteredSpectrogram if needed
aaefaafad,CPJKU/madmom,madmom/audio/signal.py,8a0ed29387d7a396787b9c61d86ceac7974ec357,1539bd9ec2b940df531d2fb82d79ea65e4acaf26,TODO: implement batch processing and set to a sensible default
aaefaafbb,CPJKU/madmom,madmom/audio/spectrogram.py,8a0ed29387d7a396787b9c61d86ceac7974ec357,b1fba67e480970d6e67b5b21a6082ff94c737747,set default values here; also needed for views
aaefaafbc,CPJKU/madmom,madmom/audio/spectrogram.py,8a0ed29387d7a396787b9c61d86ceac7974ec357,b1fba67e480970d6e67b5b21a6082ff94c737747,needed for correct pickling
aaefaafbh,CPJKU/madmom,madmom/audio/spectrogram.py,8a0ed29387d7a396787b9c61d86ceac7974ec357,b1fba67e480970d6e67b5b21a6082ff94c737747,needed for correct un-pickling
aaefaafcc,CPJKU/madmom,madmom/audio/spectrogram.py,8a0ed29387d7a396787b9c61d86ceac7974ec357,STILL_EXISTS,TODO: this is a bit hacky to define another processor here
aaefaaffb,CPJKU/madmom,bin/ComplexFlux.py,417098d0f2b1af93749b4041da295fbb3d396cf5,STILL_EXISTS,add a STFT processor so that we can set the circular shift needed for
aaefaafgc,CPJKU/madmom,bin/SpectralOnsetDetection.py,de4f6168aa44029af9146b046aaf45ca62df3f53,STILL_EXISTS,add circular shift for correct phase and remove filterbank if needed
aaefaafgh,CPJKU/madmom,bin/SpectralOnsetDetection.py,de4f6168aa44029af9146b046aaf45ca62df3f53,STILL_EXISTS,append additional processors as needed
aaefaafij,CPJKU/madmom,madmom/audio/spectrogram.py,0ecb88374f4be115be9a433142b0ea06e3f4b979,fd8ac9a9b3c393e90a0cfffe37a19e0b16364ad4,TODO: use `axis` and `np.concatenate` instead?
aaefaahgj,CPJKU/madmom,madmom/test/test_audio_spectrogram.py,d6730264dad0df6e3e0ad1b085554994851d3dca,b1fba67e480970d6e67b5b21a6082ff94c737747,TODO: write a test which catches the warning about the circular_shift
aaefaahhg,CPJKU/madmom,madmom/test/test_audio_spectrogram.py,d6730264dad0df6e3e0ad1b085554994851d3dca,STILL_EXISTS,TODO: should we return a LogarithmicFilteredSpectrogram?
aaefaajab,CPJKU/madmom,madmom/features/__init__.py,2a81bb0e1f2bb8debed456cc63262da6872dfbba,1058ec49b54f54cbdef77a7d96be4f518ed280af,needed for correct pickling
aaefaajag,CPJKU/madmom,madmom/features/__init__.py,2a81bb0e1f2bb8debed456cc63262da6872dfbba,1058ec49b54f54cbdef77a7d96be4f518ed280af,needed for correct un-pickling
aaefaajde,CPJKU/madmom,madmom/audio/filters.py,f6cbffc7fcc9254003a1a4f80cc1c85c21ecf695,f6cbffc7fcc9254003a1a4f80cc1c85c21ecf695,bins can be given too much weight if simply summed up (as in
aaefabagg,CPJKU/madmom,madmom/audio/stft.py,b1fba67e480970d6e67b5b21a6082ff94c737747,STILL_EXISTS,\"\"\" || This file contains STFT related functionality. ||  || @author: Sebastian B\u00F6ck <sebastian.boeck@jku.at> ||  || \"\"\"
aaefabaih,CPJKU/madmom,madmom/audio/stft.py,b1fba67e480970d6e67b5b21a6082ff94c737747,STILL_EXISTS,instantiate a FramedSignal if needed
aaefabbbc,CPJKU/madmom,madmom/audio/stft.py,b1fba67e480970d6e67b5b21a6082ff94c737747,1058ec49b54f54cbdef77a7d96be4f518ed280af,needed for correct pickling
aaefabbbh,CPJKU/madmom,madmom/audio/stft.py,b1fba67e480970d6e67b5b21a6082ff94c737747,1058ec49b54f54cbdef77a7d96be4f518ed280af,needed for correct un-pickling
aaefabbcg,CPJKU/madmom,madmom/audio/stft.py,b1fba67e480970d6e67b5b21a6082ff94c737747,STILL_EXISTS,instantiate a ShortTimeFourierTransform object if needed
aaefabbch,CPJKU/madmom,madmom/audio/stft.py,b1fba67e480970d6e67b5b21a6082ff94c737747,STILL_EXISTS,TODO: just recalculate with circular_shift set?
aaefabbhe,CPJKU/madmom,madmom/test/test_audio_stft.py,b1fba67e480970d6e67b5b21a6082ff94c737747,STILL_EXISTS,TODO: write a test which catches the warning about the circular_shift
aaefabbjg,CPJKU/madmom,madmom/audio/spectrogram.py,b8fa6f1b3f5daa0fb262d4a5728a1f17988d3d95,3e277f8672bbbba99c6c862037daba4a9dba0706,instantiate a FramedSignal if needed
aaefabccb,CPJKU/madmom,madmom/audio/spectrogram.py,b8fa6f1b3f5daa0fb262d4a5728a1f17988d3d95,3e277f8672bbbba99c6c862037daba4a9dba0706,needed for correct pickling
aaefabccg,CPJKU/madmom,madmom/audio/spectrogram.py,b8fa6f1b3f5daa0fb262d4a5728a1f17988d3d95,3e277f8672bbbba99c6c862037daba4a9dba0706,needed for correct un-pickling
aaefabcde,CPJKU/madmom,madmom/audio/spectrogram.py,b8fa6f1b3f5daa0fb262d4a5728a1f17988d3d95,3e277f8672bbbba99c6c862037daba4a9dba0706,instantiate a ShortTimeFourierTransform object if needed
aaefabcdf,CPJKU/madmom,madmom/audio/spectrogram.py,b8fa6f1b3f5daa0fb262d4a5728a1f17988d3d95,3e277f8672bbbba99c6c862037daba4a9dba0706,TODO: just recalculate with circular_shift set?
aaefabcgd,CPJKU/madmom,madmom/test/test_audio_spectrogram.py,b8fa6f1b3f5daa0fb262d4a5728a1f17988d3d95,29197bb4ab3bd446cc24354cda0cdf32c83cf296,TODO: write a test which catches the warning about the circular_shift
aaefabcjh,CPJKU/madmom,madmom/audio/stft.py,afa68523a0149e6819af9892a98ba64a9dbecc19,STILL_EXISTS,size of the FFT circular shift (needed for correct phase)
aaefabdac,CPJKU/madmom,madmom/audio/stft.py,afa68523a0149e6819af9892a98ba64a9dbecc19,STILL_EXISTS,no scaling needed; use the window as is (can also be None)
aaefabeab,CPJKU/madmom,madmom/audio/stft.py,3e277f8672bbbba99c6c862037daba4a9dba0706,STILL_EXISTS,TODO: add multi-channel support
aaefabedf,CPJKU/madmom,madmom/evaluation/alignment.py,7e02ba5229c06116a3f4db727fedd0c9c391c3ed,STILL_EXISTS,\"\"\" || This file contains global alignment evaluation functionality. ||  || @author: Filip Korzeniowski <filip.korzeniowski@jku.at> ||  || \"\"\"
aaefabehe,CPJKU/madmom,madmom/audio/signal.py,dbc794811c8e597d3e252cb5642fa5014787bb7f,29197bb4ab3bd446cc24354cda0cdf32c83cf296,TODO: include end_of_signal handling!?
aaefabibg,CPJKU/madmom,bin/MMBeatTracker.py,7c547b70e51b9b150dd965b4fb74408e0f4661a8,e9b06b18f947f1aeec678e7ece01c65b797cc874,FIXME: this is kind of hackish; but being able to simply set
aaefabicg,CPJKU/madmom,bin/OnsetDetector.py,7c547b70e51b9b150dd965b4fb74408e0f4661a8,STILL_EXISTS,TODO: make sure newer models are trained with mul=1
aaefabidf,CPJKU/madmom,bin/OnsetDetectorLL.py,7c547b70e51b9b150dd965b4fb74408e0f4661a8,fec156c30936936d60d6dc4ed748576db9d43528,TODO: make sure newer models are trained with mul=1
aaefabidg,CPJKU/madmom,bin/OnsetDetectorLL.py,7c547b70e51b9b150dd965b4fb74408e0f4661a8,fec156c30936936d60d6dc4ed748576db9d43528,TODO: make sure newer models are trained with diff_ratio=0.5
aaefabjah,CPJKU/madmom,bin/OnsetDetector.py,e9b06b18f947f1aeec678e7ece01c65b797cc874,STILL_EXISTS,TODO: make sure newer models are trained with diff_ratio=0.5
aaefacaji,CPJKU/madmom,madmom/utils/__init__.py,1539bd9ec2b940df531d2fb82d79ea65e4acaf26,STILL_EXISTS,TODO: add comments!
aaefacbac,CPJKU/madmom,madmom/utils/__init__.py,1539bd9ec2b940df531d2fb82d79ea65e4acaf26,STILL_EXISTS,TODO: remove warning?
aaefacbea,CPJKU/madmom,madmom/evaluation/__init__.py,b957361498816347989b61ca15b067e3a21f8876,73bd5b3814625e8c1212036e19c4ef0f586d75de,TODO: use an ordered dict?
aaefacbgb,CPJKU/madmom,madmom/audio/cepstrogram.py,eb91b7008212480307811c75168ee09ad24c6c1c,STILL_EXISTS,instantiate a Filterbank if needed
aaefacbhb,CPJKU/madmom,madmom/audio/cepstrogram.py,eb91b7008212480307811c75168ee09ad24c6c1c,STILL_EXISTS,needed for correct pickling
aaefacbhg,CPJKU/madmom,madmom/audio/cepstrogram.py,eb91b7008212480307811c75168ee09ad24c6c1c,dd13853625ba0be23f0cbc52b1c5a2465e989310,needed for correct un-pickling
aaefaccbi,CPJKU/madmom,madmom/audio/cepstrogram.py,dd13853625ba0be23f0cbc52b1c5a2465e989310,STILL_EXISTS,TODO: what are the frequencies of the bins?
aaefaccbj,CPJKU/madmom,madmom/audio/cepstrogram.py,dd13853625ba0be23f0cbc52b1c5a2465e989310,STILL_EXISTS,needed for correct pickling
aaefaccjf,CPJKU/madmom,madmom/ml/rnnlib.py,ad45e7192be67b2cbf506557e49c7da9bb2be02b,STILL_EXISTS,FIXME: evil hack
aaefacdfj,CPJKU/madmom,madmom/ml/rnnlib.py,5ae97330783caa46da7b4d7c43a56722f3ff556a,STILL_EXISTS,fix the weird counting in gather layers
aaefacdji,CPJKU/madmom,madmom/audio/hpss.py,07f919f38885bee647b493f1de79e2a2c74c5475,STILL_EXISTS,TODO: keep this as Processors or should it be done as np.ndarray classes?
aaefacecf,CPJKU/madmom,madmom/features/beats.py,1779e7be70461791fd954883bcfd3e1547c88918,cf513993cf1338edc515d9a2c9bc734cab72655f,FIXME: we might miss the first or last beat!
aaefacejh,CPJKU/madmom,madmom/evaluation/__init__.py,41d61a1523572f8f390c3c24e30f8a5490de440b,STILL_EXISTS,TODO: unify this with SimpleEvaluation but
aaefacfab,CPJKU/madmom,madmom/evaluation/__init__.py,41d61a1523572f8f390c3c24e30f8a5490de440b,STILL_EXISTS,TODO: use e.metrics dict?
aaefacfaf,CPJKU/madmom,madmom/evaluation/__init__.py,41d61a1523572f8f390c3c24e30f8a5490de440b,STILL_EXISTS,TODO: use e.metrics dict
aaefacfag,CPJKU/madmom,madmom/evaluation/__init__.py,41d61a1523572f8f390c3c24e30f8a5490de440b,STILL_EXISTS,TODO: add a generic totable() function which accepts columns separator;
aaefacgaf,CPJKU/madmom,madmom/evaluation/tempo.py,41d61a1523572f8f390c3c24e30f8a5490de440b,STILL_EXISTS,TODO: truncate the length of the detections to the length of the
aaefacgbc,CPJKU/madmom,madmom/evaluation/tempo.py,41d61a1523572f8f390c3c24e30f8a5490de440b,STILL_EXISTS,TODO: allow a different max_len here?
aaefacgbg,CPJKU/madmom,madmom/evaluation/tempo.py,41d61a1523572f8f390c3c24e30f8a5490de440b,STILL_EXISTS,TODO: add option to evaluate any other than the default number of tempi?
aaefachdf,CPJKU/madmom,madmom/evaluation/notes.py,7354c7a5fc3234ea3357e0b12089c55e393fd189,STILL_EXISTS,shift the detections if needed
aaefachgh,CPJKU/madmom,madmom/evaluation/onsets.py,7354c7a5fc3234ea3357e0b12089c55e393fd189,STILL_EXISTS,combine the annotations if needed
aaefachgi,CPJKU/madmom,madmom/evaluation/onsets.py,7354c7a5fc3234ea3357e0b12089c55e393fd189,STILL_EXISTS,shift the detections if needed
aaefachhd,CPJKU/madmom,madmom/evaluation/tempo.py,7354c7a5fc3234ea3357e0b12089c55e393fd189,STILL_EXISTS,TODO; also return the errors?
aaefachjb,CPJKU/madmom,tests/test_evaluation_beats.py,7354c7a5fc3234ea3357e0b12089c55e393fd189,74863bccf58ceb98a63ea098554a706d45e2ad37,TODO: why does dict work?
aaefacida,CPJKU/madmom,tests/test_evaluation_onsets.py,7354c7a5fc3234ea3357e0b12089c55e393fd189,df56219b1c1522d3ee9035fe93087b49733d4344,TODO: why does dict work?
aaefadbcb,CPJKU/madmom,madmom/features/beats.py,208b62e69e02da5401551b2db13fd421cb8442e6,cf513993cf1338edc515d9a2c9bc734cab72655f,TODO: this should not be lists (lists are mutable!)
aaefadbjb,CPJKU/madmom,madmom/audio/ffmpeg.py,d19253a51b0b57768716c2c7472d532ec5948b7c,STILL_EXISTS,get the needed information from the file
aaefadchd,CPJKU/madmom,madmom/utils/__init__.py,5d5baadb2a3cdd85b8f087d592c9106186172dde,STILL_EXISTS,convert to numpy array if needed
aaefadebe,CPJKU/madmom,madmom/features/__init__.py,1692f9e26deb0d0af22924da36f909f0830d9953,STILL_EXISTS,TODO: should we return the data or the Activations instance?
aaefadeec,CPJKU/madmom,madmom/audio/filters.py,f7e5a8bdc6e3878cbd0a79e61ea6c436dcb5a919,STILL_EXISTS,TODO: property should return multiple corner frequencies
aaefadeed,CPJKU/madmom,madmom/audio/filters.py,f7e5a8bdc6e3878cbd0a79e61ea6c436dcb5a919,STILL_EXISTS,TODO: property should return multiple center frequencies
aaefadeeh,CPJKU/madmom,madmom/features/notes.py,f809dc40a8ce74de8040906e2604f43ae8aee75c,caa12bc2609791f031c58af8da07e335bc532cfe,expand the notes if needed
aaefadfcg,CPJKU/madmom,docs/conf.py,12b262ec2e2c1b36bcc3c212821bee116460590f,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaefadgfb,CPJKU/madmom,madmom/utils/__init__.py,2efca95872f4228584db867cc3fc1c22405064de,e70ebe774ae5947d62bbc49e07609483a88eacb3,needed to preserve docstring of the decorated function
aaefadgfj,CPJKU/madmom,madmom/utils/__init__.py,6b491d60d7c5aa23bb26d1345256e38b46e21394,STILL_EXISTS,needed to preserve docstring of the decorated function
aaefaeaed,CPJKU/madmom,madmom/utils/__init__.py,841358160f711ec3cd0a0f74b05f6c8d584a5c07,STILL_EXISTS,remove the rightmost path separator (needed for recursion depth count)
aaefaebia,CPJKU/madmom,madmom/features/beats.py,d4e69605b83c1f7153b6aa77b515e9ef2a62450d,cf513993cf1338edc515d9a2c9bc734cab72655f,TODO: make this parametric
aaefaefej,CPJKU/madmom,madmom/audio/cepstrogram.py,1058ec49b54f54cbdef77a7d96be4f518ed280af,STILL_EXISTS,TODO: what are the frequencies of the bins?
aaefaefgc,CPJKU/madmom,madmom/audio/chroma.py,1058ec49b54f54cbdef77a7d96be4f518ed280af,STILL_EXISTS,set default values here; also needed for views
aaefaeheg,CPJKU/madmom,madmom/audio/signal.py,5c2c33a304b0baa6ad53f0df74fdc17821d273ea,STILL_EXISTS,process it if needed
aaefaeifb,CPJKU/madmom,madmom/audio/filters.py,54dbca8392a207437120df1e12e998272addd778,STILL_EXISTS,TODO: add a list with filterbank options?
aaefaeifd,CPJKU/madmom,madmom/audio/filters.py,54dbca8392a207437120df1e12e998272addd778,STILL_EXISTS,TODO: add a second argument with num_bands_per_octave and rename the
aaefaeijc,CPJKU/madmom,madmom/ml/nn/__init__.py,25412888c6b12ff1ec3ab195a7ebaac78fdcda79,STILL_EXISTS,average predictions if needed
aaefaeijj,CPJKU/madmom,madmom/ml/nn/__init__.py,25412888c6b12ff1ec3ab195a7ebaac78fdcda79,STILL_EXISTS,ravel the predictions if needed
aaefaejac,CPJKU/madmom,madmom/ml/nn/__init__.py,25412888c6b12ff1ec3ab195a7ebaac78fdcda79,STILL_EXISTS,return the argument group so it can be modified if needed
aaefaejah,CPJKU/madmom,madmom/ml/nn/__init__.py,25412888c6b12ff1ec3ab195a7ebaac78fdcda79,65fbfeb8aff65547bb6241075dd8876db0a44a32,pop the parameters needed for the reverse (backward) layer
aaefaejaj,CPJKU/madmom,madmom/ml/nn/__init__.py,25412888c6b12ff1ec3ab195a7ebaac78fdcda79,65fbfeb8aff65547bb6241075dd8876db0a44a32,pop the parameters needed for the normal (forward) layer
aaefaejfc,CPJKU/madmom,madmom/ml/nn/layers.py,25412888c6b12ff1ec3ab195a7ebaac78fdcda79,ea4a95d8e75027c4e28d2711197db94bfba73072,FIXME: although everything seems to be ok; np.dot doesn't accept the
aaefafcdg,CPJKU/madmom,tests/test_bin.py,9be5595820f0bf72fec9fafe40a824f608b04ad0,STILL_EXISTS,TODO: parametrize tests; don't know how to do with nose; should be simple
aaefafcdi,CPJKU/madmom,tests/test_bin.py,9be5595820f0bf72fec9fafe40a824f608b04ad0,STILL_EXISTS,TODO: can we speed up these tests?
aaefafegf,CPJKU/madmom,madmom/features/beats.py,884ab59d7c88aaa9f8181dfac2e94446e623c797,cf513993cf1338edc515d9a2c9bc734cab72655f,TODO: check if they are of length 1?
aaefafegh,CPJKU/madmom,madmom/features/beats.py,884ab59d7c88aaa9f8181dfac2e94446e623c797,cf513993cf1338edc515d9a2c9bc734cab72655f,init a pool of workers (if needed)
aaefafeie,CPJKU/madmom,madmom/features/beats.py,884ab59d7c88aaa9f8181dfac2e94446e623c797,STILL_EXISTS,TODO: numpyfy this
aaefafejb,CPJKU/madmom,madmom/features/beats.py,884ab59d7c88aaa9f8181dfac2e94446e623c797,cf513993cf1338edc515d9a2c9bc734cab72655f,FIXME: we might miss the first or last beat!
aaefaffac,CPJKU/madmom,madmom/features/beats.py,884ab59d7c88aaa9f8181dfac2e94446e623c797,cf513993cf1338edc515d9a2c9bc734cab72655f,return the argument group so it can be modified if needed
aaefaffaj,CPJKU/madmom,madmom/features/beats_hmm.py,884ab59d7c88aaa9f8181dfac2e94446e623c797,STILL_EXISTS,Note: it's faster to call np.log multiple times instead of once on
aaefaffbi,CPJKU/madmom,madmom/features/beats.py,8e53072ff30cc38ec8852b6fa27bdeac1a38db0e,STILL_EXISTS,TODO: numpyfy this
aaefaffif,CPJKU/madmom,madmom/features/chords.py,e6832ad49dc31b4df0b549fbb0b5e35078e0297f,STILL_EXISTS,\"\"\" || This module contains chord recognition related functionality. ||  || \"\"\"
aaefafgdd,CPJKU/madmom,madmom/ml/nn/layers.py,3258287b598ec198a101532cbb3980844bb46b6d,STILL_EXISTS,TODO: this is only true for pad='valid'
aaefafgdg,CPJKU/madmom,madmom/ml/nn/layers.py,3258287b598ec198a101532cbb3980844bb46b6d,STILL_EXISTS,TODO: add boundary stuff?
aaefafgdj,CPJKU/madmom,madmom/ml/nn/layers.py,3258287b598ec198a101532cbb3980844bb46b6d,STILL_EXISTS,TODO: use stride_tricks instead of copying the data...
aaefafgeb,CPJKU/madmom,madmom/ml/nn/layers.py,3258287b598ec198a101532cbb3980844bb46b6d,STILL_EXISTS,TODO: is constant mode the most appropriate?
aaefafged,CPJKU/madmom,madmom/features/onsets.py,75df88f70a9623a0e2922e71622f8e211f18d007,9922b925250ae14a2e78bd2647194c27c6723dfe,TODO: rename it or move it to a better place
aaefafgfa,CPJKU/madmom,madmom/features/onsets.py,75df88f70a9623a0e2922e71622f8e211f18d007,fa9966370139909ef6b1e15af7d3ff4546c76eb1,FIXME: np.stack introduced in numpy 1.10; thus find another solution
aaefafgfj,CPJKU/madmom,madmom/ml/nn/layers.py,fa9966370139909ef6b1e15af7d3ff4546c76eb1,STILL_EXISTS,TODO: use stride_tricks instead of copying the data...
aaefafhdc,CPJKU/madmom,madmom/features/onsets.py,9715d088c576f2d14c6be381eb4e8533fa86f5ff,STILL_EXISTS,filtering needed?
aaefafheh,CPJKU/madmom,tests/test_utils_midi.py,7a75cc2ff2de7326f72d78ea1e59c663ae7490bc,STILL_EXISTS,FIXME: re-read this file and compare the notes
aaefafhfa,CPJKU/madmom,madmom/utils/midi.py,0c13dcc2e252d5da941ef13674309a9dc3999944,STILL_EXISTS,add a default channel if needed
aaefafhfj,CPJKU/madmom,madmom/utils/midi.py,e123297ca8514888da4519ae3049a60eab6387e9,STILL_EXISTS,TODO: is this needed; should be handled by Event already
aaefafhgc,CPJKU/madmom,madmom/utils/midi.py,e123297ca8514888da4519ae3049a60eab6387e9,STILL_EXISTS,TODO: should we add a EndOfTrackEvent?
aaefafhgf,CPJKU/madmom,madmom/utils/midi.py,e123297ca8514888da4519ae3049a60eab6387e9,STILL_EXISTS,TODO: format 2 has multiple tracks but plays them back one after
aaefafhii,CPJKU/madmom,madmom/audio/signal.py,803d8dde0adfd71784dfc492f2d990f3c6298c5f,STILL_EXISTS,cast as Signal if needed
aaefafhij,CPJKU/madmom,madmom/audio/signal.py,803d8dde0adfd71784dfc492f2d990f3c6298c5f,STILL_EXISTS,resample if needed
aaefafief,CPJKU/madmom,madmom/audio/spectrogram.py,ef235d910416845ef5fd1beebfc8dbc9f7e54b89,STILL_EXISTS,down-sample audio if needed
aaefafjjc,CPJKU/madmom,madmom/ml/nn/layers.py,a92614da244a84574f46f51205ebdfdfe3bb8a6b,STILL_EXISTS,attributes needed for stateful processing
aaefafjjg,CPJKU/madmom,madmom/ml/nn/layers.py,a92614da244a84574f46f51205ebdfdfe3bb8a6b,STILL_EXISTS,TODO: old models do not have the init attribute; thus create it
aaefagafa,CPJKU/madmom,tests/test_ml_nn.py,a92614da244a84574f46f51205ebdfdfe3bb8a6b,STILL_EXISTS,FIXME: these old models don't have the online attribute set; so we
aaefagbae,CPJKU/madmom,madmom/ml/nn/__init__.py,5a9b3c7c8070c1c07b38d3a1c9bac426c7fa6652,STILL_EXISTS,ravel the predictions if needed
aaefagbfi,CPJKU/madmom,madmom/audio/signal.py,a62f9f3114e5a67a9cf875b68fada015be25f9a5,bbcd6cc6590eeb6c51cfc62094c337583dc76670,TODO: make the dtype configurable; see callback()
aaefagbgc,CPJKU/madmom,madmom/audio/signal.py,a62f9f3114e5a67a9cf875b68fada015be25f9a5,bbcd6cc6590eeb6c51cfc62094c337583dc76670,TODO: make the dtype configurable; see __init__()
aaefagbgg,CPJKU/madmom,madmom/audio/signal.py,a62f9f3114e5a67a9cf875b68fada015be25f9a5,8b5ccbde0fa08af57f304b3ff4e7dea866e21808,TODO: how to warn if we missed a frame? Raise an error;
aaefagbgh,CPJKU/madmom,madmom/audio/signal.py,a62f9f3114e5a67a9cf875b68fada015be25f9a5,STILL_EXISTS,or is a simple warning enough? Maybe a counter...
aaefagbha,CPJKU/madmom,madmom/audio/signal.py,a62f9f3114e5a67a9cf875b68fada015be25f9a5,STILL_EXISTS,TODO: returning the data is pointless; since this callback is only
aaefagbhf,CPJKU/madmom,madmom/processors.py,a62f9f3114e5a67a9cf875b68fada015be25f9a5,STILL_EXISTS,FIXME: If a Stream is given we must check if the arguments match the ones
aaefagbhg,CPJKU/madmom,madmom/processors.py,a62f9f3114e5a67a9cf875b68fada015be25f9a5,STILL_EXISTS,of the Processor. Maybe just always do the stream creation in here
aaefagbhh,CPJKU/madmom,madmom/processors.py,a62f9f3114e5a67a9cf875b68fada015be25f9a5,STILL_EXISTS,and infer the needed arguments from the Processor?
aaefagbie,CPJKU/madmom,madmom/audio/spectrogram.py,843ef72321b9aa3e4134a224a942ab6181355d1c,STILL_EXISTS,attributes needed for stateful processing
aaefagbig,CPJKU/madmom,madmom/audio/spectrogram.py,843ef72321b9aa3e4134a224a942ab6181355d1c,STILL_EXISTS,do not pickle attributes needed for stateful processing
aaefagbih,CPJKU/madmom,madmom/audio/spectrogram.py,843ef72321b9aa3e4134a224a942ab6181355d1c,STILL_EXISTS,add non-pickled attributes needed for stateful processing
aaefagbii,CPJKU/madmom,madmom/audio/spectrogram.py,843ef72321b9aa3e4134a224a942ab6181355d1c,STILL_EXISTS,stack the diff and the data if needed
aaefagcai,CPJKU/madmom,madmom/processors.py,843ef72321b9aa3e4134a224a942ab6181355d1c,STILL_EXISTS,FIXME: use np.pad for fancy initialisation (can be done in process())
aaefagcaj,CPJKU/madmom,madmom/processors.py,843ef72321b9aa3e4134a224a942ab6181355d1c,STILL_EXISTS,init buffer if needed
aaefagcff,CPJKU/madmom,madmom/audio/spectrogram.py,bbd357dbcc499f54d0432101c47c7485db2bdfab,STILL_EXISTS,stack the diff and the data if needed
aaefagchi,CPJKU/madmom,madmom/processors.py,f13895e6932df4e6f4302aa8d1e4df39baa2c94a,STILL_EXISTS,FIXME: right now we fake the origin in order to get the whole frame
aaefagdei,CPJKU/madmom,madmom/processors.py,10b4b83340d6eb74b7a9aacf6cd2e4d973a34fe0,STILL_EXISTS,add arguments needed for loading processors
aaefagedf,CPJKU/madmom,madmom/audio/signal.py,eda2049f4174bbd6fa8d85050fee9e5598a394f5,STILL_EXISTS,provided by FramedSignalProcessor. This is a workaround to
aaefagefb,CPJKU/madmom,madmom/features/onsets.py,eda2049f4174bbd6fa8d85050fee9e5598a394f5,STILL_EXISTS,TODO: use at least 1 frame if any of these values are > 0?
aaefagegf,CPJKU/madmom,madmom/ml/gmm.py,69fecb6d474685d1a04e006d1d27e8e1c8f896b7,STILL_EXISTS,TODO: old models have underscores at some variable names; thus rename
aaefagejh,CPJKU/madmom,madmom/features/beats.py,137cd81720fe6cf6a4c005fb069b270f61e8c98e,STILL_EXISTS,TODO: refactor the visualisation stuff
aaefagfbe,CPJKU/madmom,madmom/features/beats.py,137cd81720fe6cf6a4c005fb069b270f61e8c98e,STILL_EXISTS,FIXME: this skips the first beat; but maybe this has a positive
aaefagfci,CPJKU/madmom,madmom/processors.py,96214c05289729746cbd3e1dd00109b2581ac60d,STILL_EXISTS,init buffer if needed
aaefagfdf,CPJKU/madmom,madmom/processors.py,96214c05289729746cbd3e1dd00109b2581ac60d,STILL_EXISTS,FIXME: overwrite the frame size with the maximum value of all used
aaefagfdg,CPJKU/madmom,madmom/processors.py,96214c05289729746cbd3e1dd00109b2581ac60d,STILL_EXISTS,processors. This is needed if multiple frame sizes are used
aaefagfed,CPJKU/madmom,madmom/processors.py,96214c05289729746cbd3e1dd00109b2581ac60d,STILL_EXISTS,add arguments needed for loading processors
aaefagfei,CPJKU/madmom,madmom/processors.py,96214c05289729746cbd3e1dd00109b2581ac60d,STILL_EXISTS,return the argument group so it can be modified if needed
aaefagffe,CPJKU/madmom,tests/test_ml_hmm.py,c895d7c6fa07e63e96a8b2c19466112d398d8ac7,STILL_EXISTS,TODO: assertWarns exist only for Python 3.2+; test in all versions
aaefagfgj,CPJKU/madmom,madmom/audio/signal.py,ef8a5e7958a360828dbffb4470016daab65ad126,STILL_EXISTS,provided by FramedSignalProcessor. This is a workaround to
aaefagfid,CPJKU/madmom,madmom/audio/signal.py,ef8a5e7958a360828dbffb4470016daab65ad126,STILL_EXISTS,FIXME: is the start position of interest?
aaefaggbd,CPJKU/madmom,madmom/evaluation/chords.py,18ccbd343912db7df276cfeb5efb1640c6838c5c,a9284e4d34ce222d5780d948dfdf13709057c708,TODO: fill at beginning!
aaefaggbh,CPJKU/madmom,madmom/evaluation/chords.py,18ccbd343912db7df276cfeb5efb1640c6838c5c,a9284e4d34ce222d5780d948dfdf13709057c708,TODO: remove all detected chords that are outside the annotations
aaefaggeh,CPJKU/madmom,madmom/evaluation/chords.py,3d7bcccf16b07f63972a468e1f81e6d69c2eebd5,STILL_EXISTS,TODO: https:\/\/github.com\/jpauwels\/MusOOEvaluator\/issues\/1
aaefaggei,CPJKU/madmom,madmom/evaluation/chords.py,3d7bcccf16b07f63972a468e1f81e6d69c2eebd5,4ab389cb3746f91f769ad8941b645b7b0d8d2291,TODO: https:\/\/github.com\/craffel\/mir_eval\/issues\/251
aaefaggej,CPJKU/madmom,madmom/evaluation/chords.py,335d6d6b36566d70ca05c84b4a950f41929aaa0a,4ab389cb3746f91f769ad8941b645b7b0d8d2291,TODO: fixme
aaefaggib,CPJKU/madmom,madmom/evaluation/chords.py,e90a7ef6812c5b482ee683b365c57dfca156ead9,STILL_EXISTS,\"\"\" || This module contains chord evaluation functionality. ||  || It provides the evaluation measures used for the MIREX ACE task; and || tries to follow [1]_ and [2]_ as closely as possible. ||  || Notes || ----- || This implementation tries to follow the references and their implementation || (e.g.; https:\/\/github.com\/jpauwels\/MusOOEvaluator for [2]_). However; there || are some known (and possibly some unknown) differences. If you find one not || listed in the following; please file an issue: ||  ||  - Detected chord segments are adjusted to fit the length of the annotations. ||    In particular; this means that; if necessary; filler segments of 'no chord' ||    are added at beginnings and ends. This can result in lower ||    under-segmentation scores compared to the original implementation. ||  || References || ---------- || .. [1] Christopher Harte; \"Towards Automatic Extraction of Harmony Information ||        from Music Signals.\" Dissertation; ||        Department for Electronic Engineering; Queen Mary University of London; ||        2010. || .. [2] Johan Pauwels and Geoffroy Peeters. ||        \"Evaluating Automatically Estimated Chord Sequences.\" ||        In Proceedings of ICASSP 2013; Vancouver; Canada; 2013. || \"\"\"
aaefaggic,CPJKU/madmom,madmom/evaluation/chords.py,50f3fbe03de950ff7e6e09ddc5c75e55331cf6bd,5dca08b06e1c0c5a827e624ead924c4ccc150e8d,TODO: Join consecutive labels of identical chords
aaefaghjj,CPJKU/madmom,madmom/utils/__init__.py,9fad07c0a2f7f1ec2de1f5aef3c673f36476ab33,STILL_EXISTS,convert to numpy array or create a copy if needed
aaefagiab,CPJKU/madmom,madmom/utils/__init__.py,9fad07c0a2f7f1ec2de1f5aef3c673f36476ab33,STILL_EXISTS,split the notes into columns
aaefagjae,CPJKU/madmom,madmom/features/downbeats.py,cf513993cf1338edc515d9a2c9bc734cab72655f,STILL_EXISTS,\"\"\" || This module contains downbeat tracking related functionality. ||  || \"\"\"
aaefagjca,CPJKU/madmom,madmom/features/downbeats.py,cf513993cf1338edc515d9a2c9bc734cab72655f,STILL_EXISTS,TODO: check if they are of length 1?
aaefagjcc,CPJKU/madmom,madmom/features/downbeats.py,cf513993cf1338edc515d9a2c9bc734cab72655f,STILL_EXISTS,init a pool of workers (if needed)
aaefagjff,CPJKU/madmom,madmom/features/downbeats.py,cf513993cf1338edc515d9a2c9bc734cab72655f,STILL_EXISTS,TODO: this should not be lists (lists are mutable!)
aaefagjfg,CPJKU/madmom,madmom/features/downbeats.py,cf513993cf1338edc515d9a2c9bc734cab72655f,STILL_EXISTS,TODO: make this parametric
aaefagjgb,CPJKU/madmom,madmom/features/downbeats.py,cf513993cf1338edc515d9a2c9bc734cab72655f,STILL_EXISTS,expand num_tempi and transition_lambda to lists if needed
aaefahbag,CPJKU/madmom,madmom/features/beats_hmm.py,304ac457094dce91da46a6e65ce6cdd1d6ca2dea,STILL_EXISTS,TODO: operate directly on the sparse representation?
aaefahbbi,CPJKU/madmom,madmom/features/beats_hmm.py,304ac457094dce91da46a6e65ce6cdd1d6ca2dea,STILL_EXISTS,TODO: save the number of beats in the pattern files so we don't
aaefahbdg,CPJKU/madmom,madmom/ml/nn/layers.py,3de64c7d1c9d389aeb97b23ed94745d714a56da5,STILL_EXISTS,TODO: old models have a 'hid_init' instead of an 'init' attribute
aaefahbeb,CPJKU/madmom,madmom/features/downbeats.py,59af3b5a1a2463379f90da271461d29856551035,STILL_EXISTS,make sure arguments are given for each pattern (expand if needed)
aaefahbfa,CPJKU/madmom,madmom/features/downbeats.py,59af3b5a1a2463379f90da271461d29856551035,STILL_EXISTS,TODO: Use load_beats function
aaefahbgc,CPJKU/madmom,madmom/features/downbeats.py,59af3b5a1a2463379f90da271461d29856551035,STILL_EXISTS,TODO: speed this up; could propably be done without a loop
aaefahbic,CPJKU/madmom,madmom/features/downbeats.py,59af3b5a1a2463379f90da271461d29856551035,STILL_EXISTS,TODO: can beat_subdivisions extracted from somewhere?
aaefahcaj,CPJKU/madmom,madmom/features/downbeats.py,59af3b5a1a2463379f90da271461d29856551035,STILL_EXISTS,TODO: this should not be lists (lists are mutable!)
aaefahcba,CPJKU/madmom,madmom/features/downbeats.py,59af3b5a1a2463379f90da271461d29856551035,STILL_EXISTS,TODO: make this parametric
aaefahcbb,CPJKU/madmom,madmom/features/downbeats.py,59af3b5a1a2463379f90da271461d29856551035,STILL_EXISTS,expand num_tempi and transition_lambda to lists if needed
aaefahccc,CPJKU/madmom,madmom/features/downbeats.py,8d08a00751f4a76cc8c056cb9e70360ba3152a99,STILL_EXISTS,TODO: expand to generic extrapolation of values? e.g.:
aaefahcdf,CPJKU/madmom,tests/test_bin.py,10596e047dd1ebf003043cf4a8e349115f8a2d90,STILL_EXISTS,TODO: investigate why this fails on Windows
aaefahgcg,CPJKU/madmom,tests/test_features_tempo.py,47acd7a453e9b9a63cf29502c752d7945ca36a9d,STILL_EXISTS,TODO: fix requirement for atleast_2d
aaefahhag,CPJKU/madmom,madmom/features/key.py,c4fed7f258392c3f5e10f4340003e57b1c95a3d5,STILL_EXISTS,\"\"\" || This module contains key recognition related functionality. ||  || \"\"\"
aaefahhgi,CPJKU/madmom,madmom/io/audio.py,414c679956e0652492bd37b3c8dc2566459b679b,STILL_EXISTS,up-\/down-mix if needed
aaefahjdd,CPJKU/madmom,madmom/io/__init__.py,b9854258afb530bd6fa6edc1df807679214a1c4a,STILL_EXISTS,TODO: this is kind of hack-ish; find a better solution
aaefahjib,CPJKU/madmom,madmom/io/__init__.py,caa12bc2609791f031c58af8da07e335bc532cfe,89a4d22a2db1029c869e6bcabdf4bd09fd729ceb,expand the notes if needed
aaefahjig,CPJKU/madmom,madmom/evaluation/chords.py,4751c7561cf2e4740c7731fca7a3c3fb1b908e3b,5dca08b06e1c0c5a827e624ead924c4ccc150e8d,TODO: Join consecutive labels of identical chords
aaefaiahe,CPJKU/madmom,madmom/io/midi.py,63526d290df48aabcb15db12ca6fef2fcafa3c2a,STILL_EXISTS,TODO: remove these unit conversion functions after upstream PR is merged
aaefaiaic,CPJKU/madmom,madmom/io/midi.py,63526d290df48aabcb15db12ca6fef2fcafa3c2a,STILL_EXISTS,TODO: remove this method after upstream PR is merged
aaefaiaih,CPJKU/madmom,madmom/io/midi.py,63526d290df48aabcb15db12ca6fef2fcafa3c2a,STILL_EXISTS,Convert relative time to absolute values if needed
aaefaiajc,CPJKU/madmom,madmom/io/midi.py,63526d290df48aabcb15db12ca6fef2fcafa3c2a,STILL_EXISTS,TODO: add otption to return in BPM
aaefaibei,CPJKU/madmom,madmom/io/__init__.py,c4dc6414093bc6830687cb707772444c6a75661b,STILL_EXISTS,close the file if needed
aaefaibej,CPJKU/madmom,madmom/io/__init__.py,c4dc6414093bc6830687cb707772444c6a75661b,STILL_EXISTS,reformat fmt to be a single string if needed
aaefaifgi,CPJKU/madmom,madmom/features/__init__.py,be2532806f3c6ecefbcf1f2fddd410a86d0e0c23,STILL_EXISTS,TODO: check if closing the file is really the best option to avoid
aaefaifia,CPJKU/madmom,madmom/audio/signal.py,d18a311a91cf83c56979a89015239ce239fd4cdd,STILL_EXISTS,Update: removing this hack again; since it seems that it is not needed
aaefaifid,CPJKU/madmom,madmom/audio/signal.py,d18a311a91cf83c56979a89015239ce239fd4cdd,STILL_EXISTS,part of the frame falls outside the signal; padding needed
aaefaigbj,CPJKU/madmom,madmom/features/notes.py,5f28f823bd47471435467d2c7b4d95e10003d3a9,STILL_EXISTS,add end if needed
aaefaihce,CPJKU/madmom,tests/test_bin.py,ef058828f3c607f881c20b084908cfde345ece17,STILL_EXISTS,TODO: investigate why this fails on Windows
aaefaihgb,nyu-mll/jiant,src/codebase/models.py,80d87ce314f1f1cd5be7750eb9dac68b424fa005,291ef3c83f20011927628a5fcae98ed5e113772b,TODO add module?
aaefaihgc,nyu-mll/jiant,src/codebase/models.py,80d87ce314f1f1cd5be7750eb9dac68b424fa005,291ef3c83f20011927628a5fcae98ed5e113772b,TODO want time per task
aaefaihgh,nyu-mll/jiant,src/codebase/utils/encoders.py,80d87ce314f1f1cd5be7750eb9dac68b424fa005,STILL_EXISTS,TODO handle masks somehow
aaefaihii,nyu-mll/jiant,src/codebase/utils/attention.py,6f8f7608d6d603b49bef0249f169ef413d6e7371,STILL_EXISTS,TODO at some point will likely want to pass weights
aaefaiiei,nyu-mll/jiant,src/codebase/utils/decoders.py,6f8f7608d6d603b49bef0249f169ef413d6e7371,STILL_EXISTS,compute attention using bottom layer; TODO pass *_embeds
aaefaiifg,nyu-mll/jiant,src/codebase/utils/search.py,6f8f7608d6d603b49bef0249f169ef413d6e7371,STILL_EXISTS,TODO: make hidden states volatile
aaefaiiie,nyu-mll/jiant,src/codebase/utils/search.py,6f8f7608d6d603b49bef0249f169ef413d6e7371,STILL_EXISTS,TODO: do this in PyTorch
aaefaijee,nyu-mll/jiant,src/codebase/evaluate.py,b6ff91bba3a8e37509a050f4706ec84e8185998d,STILL_EXISTS,\"\"\" || The ``evaluate`` subcommand can be used to || evaluate a trained model against a dataset || and report any metrics calculated by the model. ||  || .. code-block:: bash ||  ||     $ python -m allennlp.run evaluate --help ||     usage: run [command] evaluate [-h] --archive_file ARCHIVE_FILE ||                                 --evaluation_data_file EVALUATION_DATA_FILE ||                                 [--cuda_device CUDA_DEVICE] ||  ||     Evaluate the specified model + dataset ||  ||     optional arguments: ||     -h; --help            show this help message and exit ||     --archive_file ARCHIVE_FILE ||                             path to an archived trained model ||     --evaluation_data_file EVALUATION_DATA_FILE ||                             path to the file containing the evaluation data ||     --cuda_device CUDA_DEVICE ||                             id of GPU to use (if any) || \"\"\"
aaefaijid,nyu-mll/jiant,src/codebase/tasks.py,b6ff91bba3a8e37509a050f4706ec84e8185998d,7772256b5b28d6befe3b0cd3fd3426b3e5e8e2f4,TODO(Alex): maybe metric tracking should belong to something else
aaefajaah,nyu-mll/jiant,src/codebase/trainer.py,b6ff91bba3a8e37509a050f4706ec84e8185998d,2474fba1779ccb9654a25fd19a4b5a72fc8a0c10,epochs is better than current value?
aaefajaai,nyu-mll/jiant,src/codebase/trainer.py,b6ff91bba3a8e37509a050f4706ec84e8185998d,STILL_EXISTS,Grim hack to determine whether the validation metric we are recording
aaefajafa,nyu-mll/jiant,src/codebase/main_allen.py,2818476f980bc109f9ef0256e186f0e1da81d1a6,STILL_EXISTS,Probably should create another function
aaefajajc,nyu-mll/jiant,src/codebase/trainer.py,b23e89c9e645a8f5353548d452645398ca1bad0c,STILL_EXISTS,MAYBE A PROBLEM
aaefajbbf,nyu-mll/jiant,src/codebase/trainer.py,b35ba30e0aa77ccf392cffc1b74a64aa1d776690,STILL_EXISTS,MAYBE A PROBLEM
aaefajbch,nyu-mll/jiant,src/codebase/trainer.py,7772256b5b28d6befe3b0cd3fd3426b3e5e8e2f4,STILL_EXISTS,TODO(Alex): better organization
aaefajbdj,nyu-mll/jiant,src/codebase/trainer.py,7772256b5b28d6befe3b0cd3fd3426b3e5e8e2f4,STILL_EXISTS,Do we need? Maybe keep this across all tasks
aaefajbee,nyu-mll/jiant,src/codebase/trainer.py,7772256b5b28d6befe3b0cd3fd3426b3e5e8e2f4,2474fba1779ccb9654a25fd19a4b5a72fc8a0c10,epochs is better than current value?
aaefajbhg,nyu-mll/jiant,src/codebase/trainer.py,13554f755e069abb23a638d383acc09d739c080e,1662695d414d06d910fa9a379786eb4a48b5040e,maybe stop_training at gradient?
aaefajcgf,nyu-mll/jiant,src/submit_slurm.py,559d625fda136afe577ca7a3421a23a0cadb4d5a,STILL_EXISTS,TODO
aaefajdca,nyu-mll/jiant,src/submit_slurm.py,29fd280bb56322e80509c3ce62b4aa460d5e0a5f,STILL_EXISTS,TODO
aaefajdfb,nyu-mll/jiant,src/submit_slurm.py,0ef1797ab17322fca6d4844ee3d96fe0fe1217e7,STILL_EXISTS,TODO
aaefajdhh,nyu-mll/jiant,src/codebase/models_allen.py,1a3248ab1989628d582671dc33169ffc5b115838,STILL_EXISTS,maybe should take in CoVe\/ELMO?
aaefajedh,nyu-mll/jiant,src/codebase/models.py,291ef3c83f20011927628a5fcae98ed5e113772b,STILL_EXISTS,maybe should take in CoVe\/ELMO?
aaefajejb,nyu-mll/jiant,src/codebase/util.py,291ef3c83f20011927628a5fcae98ed5e113772b,STILL_EXISTS,This is ugly; but required - we are creating a new variable at runtime; so we
aaefajfai,nyu-mll/jiant,src/codebase/util.py,291ef3c83f20011927628a5fcae98ed5e113772b,STILL_EXISTS,We'll special-case a few settings here; where there are efficient (but poorly-named)
aaefajicd,nyu-mll/jiant,src/main.py,f9eb341f524db9c980ba43139e6c813a43e02a91,cc68347459ce7089c6b89267de38041da210b3b3,TODO(Alex): move iterator creation
aaefajiii,nyu-mll/jiant,src/main.py,4d133ae16c593bc5ff31626a5bbccb8ab68ba1a0,STILL_EXISTS,todo
aaefajjjb,nyu-mll/jiant,src/tasks.py,1cc985368e1cd5909fbea41335ef80230a5042cb,STILL_EXISTS,move below to the next generation?
aaefbaagf,nyu-mll/jiant,src/trainer.py,a9f776f19dbbdb5ac5913625cf85df7005b58b7e,a72699214b70d329c92939b490b7168d5f6f308e,TODO: There has to be a prettier way to do this.
aaefbaagj,nyu-mll/jiant,src/trainer.py,964721b24c9bd2f2fa2b5b60ef60ac558d522c80,d94b0279e2429ec9cb7187445ffd16707b3b9b77,TODO: There has to be a prettier way to do this.
aaefbaahf,nyu-mll/jiant,src/trainer.py,5912adff9df78699f38627a4aeddbdc1914836a4,STILL_EXISTS,TODO: There has to be a prettier way to do this.
aaefbachh,nyu-mll/jiant,src/modules.py,365476b4f61a226f0b48a81c7a582b1488831c3b,STILL_EXISTS,TODO(Alex): move this outside
aaefbaeed,nyu-mll/jiant,src/main.py,f429d4bbf8cdd9ab9e3afb02662185130e8d83b0,57e50e219f716432d190f1b20fe7a10eef3359fd,This logic looks strange. We think it works.
aaefbaeef,nyu-mll/jiant,src/trainer.py,f429d4bbf8cdd9ab9e3afb02662185130e8d83b0,57e50e219f716432d190f1b20fe7a10eef3359fd,TODO: Set up true periodic saving.
aaefbaejc,nyu-mll/jiant,src/seq2seq_decoder.py,08a0b7260c271988a74aae0568ab6678919da77e,STILL_EXISTS,we're using attention with ``DotProductSimilarity``; this is needed.
aaefbaejg,nyu-mll/jiant,src/seq2seq_decoder.py,08a0b7260c271988a74aae0568ab6678919da77e,e4f0fe11c3cb2d21dd480aa40b6ad91d9a0ed7c2,TODO (pradeep): Do not hardcode decoder cell type.
aaefbafbh,nyu-mll/jiant,src/seq2seq_decoder.py,08a0b7260c271988a74aae0568ab6678919da77e,e4f0fe11c3cb2d21dd480aa40b6ad91d9a0ed7c2,TODO: Define metrics
aaefbafej,nyu-mll/jiant,src/hocon_writer.py,d9965acb172c6609b466bf993fda622c311d417f,STILL_EXISTS,Patched version of HOCON writer; to fix round-trip issues and to sort keys.
aaefbafje,nyu-mll/jiant,src/main.py,c55d91655582a121cd7650cf8b1c1f8cd698d750,a5267bccb8e2f674f1e24746b5e71e3a3ae8913c,This logic looks strange. We think it works.
aaefbagdf,nyu-mll/jiant,src/main.py,4b6d7f96a3fe9d7dc8cb99febf309e148b189b34,034b3219a1fcd7c95185ccad445d147fb24adfbf,This logic looks strange. We think it works.
aaefbaggd,nyu-mll/jiant,src/main.py,2c8c4d5ea87a619ea4dab795b65d4ef2bd2121c1,STILL_EXISTS,This logic looks strange. We think it works.
aaefbagif,nyu-mll/jiant,src/main.py,2d885618a28c15f7cadc6df6a5b857fbe2106512,STILL_EXISTS,This logic looks strange. We think it works.
aaefbbjji,nyu-mll/jiant,src/preprocess.py,3a8f43ea0a7e44726af51fad7687cea3e3dc792b,73d07521125b31a62618f1cec6db633d7c637141,## FIXME MAGIC NUMBER
aaefbcbdf,nyu-mll/jiant,src/trainer.py,e1bf3cd5ea60e131ad9401fd30e8de843ef8aadb,3a619fcb02af40e6ea575b9cb8050602db35e046,TODO: Make this an explicit parameter rather than hard-coding.
aaefbcbfh,nyu-mll/jiant,src/preprocess.py,0ad564d6cc14750dda0b56f08eda649521cff23f,9694afa3eb79a1b41d39f2cf53079eb61b7454f6,## FIXME MAGIC NUMBER
aaefbcbhh,nyu-mll/jiant,src/preprocess.py,822a7a1efb14706cb67625a549cd3d19e0691f48,7edce82e886b20e07275d96f74ebca1e3ef25dd2,## FIXME MAGIC NUMBER
aaefbcccf,nyu-mll/jiant,src/trainer.py,2ecd33d725ab6dd76b078a075c3dd578a8171922,3a619fcb02af40e6ea575b9cb8050602db35e046,TODO: Make this an explicit parameter rather than hard-coding.
aaefbccga,nyu-mll/jiant,src/preprocess.py,457f09fb53fac6e88236d2d750c55bf907bdf274,7edce82e886b20e07275d96f74ebca1e3ef25dd2,## FIXME MAGIC NUMBER
aaefbcdbh,nyu-mll/jiant,src/preprocess.py,e3ab627c463c78e74edd1a906567d91db25722e0,e82b6a0ab63a0e73e8fbc69b34bde87b1580a746,Might be better as LabelField? I don't know what these things mean
aaefbcdfa,nyu-mll/jiant,src/trainer.py,dc7b91fe477b1b09d49403f92fb647ead6af0690,3a619fcb02af40e6ea575b9cb8050602db35e046,TODO: Make this an explicit parameter rather than hard-coding.
aaefbceaa,nyu-mll/jiant,src/trainer.py,5ce94a38a3078bc009080c23b05fd49df61e9efa,3a619fcb02af40e6ea575b9cb8050602db35e046,TODO: Make this an explicit parameter rather than hard-coding.
aaefbceai,nyu-mll/jiant,src/trainer.py,1901e07981f9c172927244b074c3e04c87ef2dd2,3a619fcb02af40e6ea575b9cb8050602db35e046,TODO: Make this an explicit parameter rather than hard-coding.
aaefbcebf,nyu-mll/jiant,src/trainer.py,b51a3727e7c3ce8f346daca376b3fc1ae018d9ac,3a619fcb02af40e6ea575b9cb8050602db35e046,TODO: Make this an explicit parameter rather than hard-coding.
aaefbcfce,nyu-mll/jiant,src/trainer.py,36bf874fcd055d6e12be1c2cb4a054a1e0a26f3c,9c2bd90fabfcb7a8942dd568d7d63d3a9023ebd7,TODO: Make this an explicit parameter rather than hard-coding.
aaefbcfga,nyu-mll/jiant,src/preprocess.py,0f392ad89f72e635fe3142bd1a031e0e682b7a83,a4d4efa6de568e1a5226cced78f48ed596ce377a,## FIXME MAGIC NUMBER
aaefbcfgb,nyu-mll/jiant,src/preprocess.py,0f392ad89f72e635fe3142bd1a031e0e682b7a83,a4d4efa6de568e1a5226cced78f48ed596ce377a,Might be better as LabelField? I don't know what these things mean
aaefbcfgg,nyu-mll/jiant,src/preprocess.py,50a13165d69611a1a82285473a7c7d71bdd25e46,a4d4efa6de568e1a5226cced78f48ed596ce377a,TODO target max size
aaefbcfgi,nyu-mll/jiant,src/tasks.py,50a13165d69611a1a82285473a7c7d71bdd25e46,468f93971614a348ccf678749420bf70a4e1a2e8,# TODO check if this is good metric
aaefbcfjg,nyu-mll/jiant,src/tasks.py,fc9d96ab893cdbe142b1a4dec8b38290e186d2da,8f29f48ab625703b531687169867580b803b1c14,Map over columns: input1; labels; ids
aaefbcfjj,nyu-mll/jiant,src/tasks.py,fc9d96ab893cdbe142b1a4dec8b38290e186d2da,8f29f48ab625703b531687169867580b803b1c14,Map over columns: input2; (input2); labels; (idx)
aaefbcgcf,nyu-mll/jiant,src/models.py,bd4128870611e0c32e1fb97ce331fad45bf1c9d0,b52edf5d578d778f710ffcf32a45a202f2d5a08d,print(\"NEED TO ADD DNN to RESPONSE INPUT -- TO DO: IMPLEMENT QUICKLY\")
aaefbcgic,nyu-mll/jiant,src/tasks.py,b52edf5d578d778f710ffcf32a45a202f2d5a08d,a58bb4244b3b5ffbf7ffb1994926fcbe8cfa0c1d,Map over columns: input1; labels; ids
aaefbcgif,nyu-mll/jiant,src/tasks.py,b52edf5d578d778f710ffcf32a45a202f2d5a08d,a58bb4244b3b5ffbf7ffb1994926fcbe8cfa0c1d,Map over columns: input2; (input2); labels; (idx)
aaefbchcg,nyu-mll/jiant,src/tasks.py,963f7e51e26d6d8af52cf14ac9000bb42ae6ac63,870938c1e280885b9ca319cd65ad08a57e347a75,Map over columns: input2; (input2); labels; (idx)
aaefbchda,nyu-mll/jiant,src/tasks.py,963f7e51e26d6d8af52cf14ac9000bb42ae6ac63,870938c1e280885b9ca319cd65ad08a57e347a75,Map over columns: inputs; targs
aaefbchdb,nyu-mll/jiant,src/tasks.py,963f7e51e26d6d8af52cf14ac9000bb42ae6ac63,870938c1e280885b9ca319cd65ad08a57e347a75,Map over columns: input1; labels; ids
aaefbchhe,nyu-mll/jiant,src/preprocess.py,66d12084c0d3ec030a592fd874c60b6cdc02ec7f,STILL_EXISTS,TODO: remove this case and stream everything.
aaefbchid,nyu-mll/jiant,src/tasks.py,ef06ff1f0d3a3be0dcd086f9b2f6facf0c8f9780,ecf157c07c20aeb95e6dd86e3ee7216dd5ee47d0,Map over columns: input2; (input2); labels; (idx)
aaefbchij,nyu-mll/jiant,src/tasks.py,ef06ff1f0d3a3be0dcd086f9b2f6facf0c8f9780,ecf157c07c20aeb95e6dd86e3ee7216dd5ee47d0,Map over columns: inputs; targs
aaefbchjc,nyu-mll/jiant,src/tasks.py,ef06ff1f0d3a3be0dcd086f9b2f6facf0c8f9780,ecf157c07c20aeb95e6dd86e3ee7216dd5ee47d0,Map over columns: input1; labels; ids
aaefbchji,nyu-mll/jiant,src/preprocess.py,f225927fa0b4df2cd710b0fbaafcc954a343b733,af6bcb9750ae81d0d2bb04c1839f045ce530eae1,## FIXME MAGIC NUMBER
aaefbciaj,nyu-mll/jiant,src/preprocess.py,ecf157c07c20aeb95e6dd86e3ee7216dd5ee47d0,af6bcb9750ae81d0d2bb04c1839f045ce530eae1,## FIXME MAGIC NUMBER
aaefbcicf,nyu-mll/jiant,src/models.py,6d2377c4e4b2c29c8c5df15aece52f695d860c7b,b7ff8fbdf7efb830346160c5759b2ca7bcb19ade,print(\"NEED TO ADD DNN to RESPONSE INPUT -- TO DO: IMPLEMENT QUICKLY\")
aaefbcief,nyu-mll/jiant,src/preprocess.py,6d2377c4e4b2c29c8c5df15aece52f695d860c7b,STILL_EXISTS,TODO: remove this case and stream everything.
aaefbciff,nyu-mll/jiant,src/tasks.py,6d2377c4e4b2c29c8c5df15aece52f695d860c7b,e9f873eb73dcd898b877e27b1763bc0caf40a964,Map over columns: input2; (input2); labels; (idx)
aaefbcigb,nyu-mll/jiant,src/tasks.py,6d2377c4e4b2c29c8c5df15aece52f695d860c7b,e9f873eb73dcd898b877e27b1763bc0caf40a964,Map over columns: inputs; targs
aaefbcige,nyu-mll/jiant,src/tasks.py,6d2377c4e4b2c29c8c5df15aece52f695d860c7b,e9f873eb73dcd898b877e27b1763bc0caf40a964,Map over columns: input1; labels; ids
aaefbcjcd,nyu-mll/jiant,src/preprocess.py,e9f873eb73dcd898b877e27b1763bc0caf40a964,af6bcb9750ae81d0d2bb04c1839f045ce530eae1,## FIXME MAGIC NUMBER
aaefbcjcf,nyu-mll/jiant,src/preprocess.py,e9f873eb73dcd898b877e27b1763bc0caf40a964,af6bcb9750ae81d0d2bb04c1839f045ce530eae1,TODO namespace
aaefbcjdc,nyu-mll/jiant,src/preprocess.py,e9f873eb73dcd898b877e27b1763bc0caf40a964,af6bcb9750ae81d0d2bb04c1839f045ce530eae1,Might be better as LabelField? I don't know what these things mean
aaefbcjdf,nyu-mll/jiant,src/tasks.py,e9f873eb73dcd898b877e27b1763bc0caf40a964,55e588aa01fc6b650a033c8d4fca5e82d3294166,# TODO check if this is good metric
aaefbcjei,nyu-mll/jiant,src/models.py,e4ee70a9855552b6228244b82df61b39581af395,3aa0e5323b70f88a9743c9725a0fa291b0f49963,print(\"NEED TO ADD DNN to RESPONSE INPUT -- TO DO: IMPLEMENT QUICKLY\")
aaefbcjhj,nyu-mll/jiant,src/tasks.py,e4ee70a9855552b6228244b82df61b39581af395,STILL_EXISTS,Map over columns: input2; (input2); labels; (idx)
aaefbcjii,nyu-mll/jiant,src/tasks.py,e4ee70a9855552b6228244b82df61b39581af395,bb2bb8a9ebe16712a8b48955ae543f8af3e5730f,Map over columns: inputs; targs
aaefbcjjb,nyu-mll/jiant,src/tasks.py,e4ee70a9855552b6228244b82df61b39581af395,STILL_EXISTS,Map over columns: input1; labels; ids
aaefbdabf,nyu-mll/jiant,src/preprocess.py,8a0d11dbe180c7e50f2bcbb5363bc55a0a9ef7c5,STILL_EXISTS,TODO: remove this case and stream everything.
aaefbdacf,nyu-mll/jiant,src/preprocess.py,8a0d11dbe180c7e50f2bcbb5363bc55a0a9ef7c5,af6bcb9750ae81d0d2bb04c1839f045ce530eae1,TODO namespace
aaefbdacg,nyu-mll/jiant,src/preprocess.py,8a0d11dbe180c7e50f2bcbb5363bc55a0a9ef7c5,af6bcb9750ae81d0d2bb04c1839f045ce530eae1,Might be better as LabelField? I don't know what these things mean
aaefbdaia,nyu-mll/jiant,src/preprocess.py,3aa0e5323b70f88a9743c9725a0fa291b0f49963,af6bcb9750ae81d0d2bb04c1839f045ce530eae1,## FIXME MAGIC NUMBER
aaefbdaib,nyu-mll/jiant,src/preprocess.py,3aa0e5323b70f88a9743c9725a0fa291b0f49963,af6bcb9750ae81d0d2bb04c1839f045ce530eae1,TODO target max size
aaefbdaid,nyu-mll/jiant,src/preprocess.py,3aa0e5323b70f88a9743c9725a0fa291b0f49963,af6bcb9750ae81d0d2bb04c1839f045ce530eae1,Might be better as LabelField? I don't know what these things mean
aaefbdaif,nyu-mll/jiant,src/tasks.py,3aa0e5323b70f88a9743c9725a0fa291b0f49963,55e588aa01fc6b650a033c8d4fca5e82d3294166,# TODO check if this is good metric
aaefbdaje,nyu-mll/jiant,src/models.py,6b791976f4588f6bd3eb71bde49d3d8b7072eb3e,1284944c5cc1c9b194e466586298efb3c04729be,TODO this is probably wrong
aaefbdajf,nyu-mll/jiant,src/preprocess.py,6b791976f4588f6bd3eb71bde49d3d8b7072eb3e,6426d0841c720f1bd4ed9d6e64a91e963edb41a1,## FIXME MAGIC NUMBER
aaefbdajg,nyu-mll/jiant,src/preprocess.py,6b791976f4588f6bd3eb71bde49d3d8b7072eb3e,6426d0841c720f1bd4ed9d6e64a91e963edb41a1,TODO target max size
aaefbdajh,nyu-mll/jiant,src/preprocess.py,6b791976f4588f6bd3eb71bde49d3d8b7072eb3e,6426d0841c720f1bd4ed9d6e64a91e963edb41a1,TODO namespace
aaefbdbab,nyu-mll/jiant,src/tasks.py,6b791976f4588f6bd3eb71bde49d3d8b7072eb3e,55e588aa01fc6b650a033c8d4fca5e82d3294166,TODO namespace
aaefbdbac,nyu-mll/jiant,src/tasks.py,6b791976f4588f6bd3eb71bde49d3d8b7072eb3e,55e588aa01fc6b650a033c8d4fca5e82d3294166,Might be better as LabelField? I don't know what these things mean
aaefbdbaj,nyu-mll/jiant,src/models.py,107c7905d5b65212fd519ac2efe1bfd74eca5ce9,bcc848bff466de24b7c0883d199a8cd0f4d18818,print(\"NEED TO ADD DNN to RESPONSE INPUT -- TO DO: IMPLEMENT QUICKLY\")
aaefbdbeb,nyu-mll/jiant,src/preprocess.py,107c7905d5b65212fd519ac2efe1bfd74eca5ce9,6426d0841c720f1bd4ed9d6e64a91e963edb41a1,## FIXME MAGIC NUMBER
aaefbdbec,nyu-mll/jiant,src/preprocess.py,107c7905d5b65212fd519ac2efe1bfd74eca5ce9,6426d0841c720f1bd4ed9d6e64a91e963edb41a1,TODO target max size
aaefbdbeh,nyu-mll/jiant,src/models.py,faba173d0d3beb987f1c6ef4b6a54647159d5670,1284944c5cc1c9b194e466586298efb3c04729be,TODO this is probably wrong
aaefbdbej,nyu-mll/jiant,src/preprocess.py,faba173d0d3beb987f1c6ef4b6a54647159d5670,6426d0841c720f1bd4ed9d6e64a91e963edb41a1,## FIXME MAGIC NUMBER
aaefbdbfa,nyu-mll/jiant,src/preprocess.py,faba173d0d3beb987f1c6ef4b6a54647159d5670,6426d0841c720f1bd4ed9d6e64a91e963edb41a1,TODO target max size
aaefbdbfb,nyu-mll/jiant,src/preprocess.py,faba173d0d3beb987f1c6ef4b6a54647159d5670,6426d0841c720f1bd4ed9d6e64a91e963edb41a1,TODO namespace
aaefbdbfj,nyu-mll/jiant,src/tasks.py,faba173d0d3beb987f1c6ef4b6a54647159d5670,f1242573f8f79babdc627c08acde990e772d81ea,# TODO check if this is good metric
aaefbdbga,nyu-mll/jiant,src/tasks.py,faba173d0d3beb987f1c6ef4b6a54647159d5670,f1242573f8f79babdc627c08acde990e772d81ea,TODO namespace
aaefbdbgb,nyu-mll/jiant,src/tasks.py,faba173d0d3beb987f1c6ef4b6a54647159d5670,f1242573f8f79babdc627c08acde990e772d81ea,Might be better as LabelField? I don't know what these things mean
aaefbdcah,nyu-mll/jiant,src/tasks.py,764df8b87cf351384a25a1d1a6d9f943b6d0b0f3,1aafc77356177b853723d3535ebd8c1c7d953ab0,# TODO check if this is good metric
aaefbdcai,nyu-mll/jiant,src/tasks.py,764df8b87cf351384a25a1d1a6d9f943b6d0b0f3,1aafc77356177b853723d3535ebd8c1c7d953ab0,TODO namespace
aaefbdcaj,nyu-mll/jiant,src/tasks.py,764df8b87cf351384a25a1d1a6d9f943b6d0b0f3,1aafc77356177b853723d3535ebd8c1c7d953ab0,Might be better as LabelField? I don't know what these things mean
aaefbdcbc,nyu-mll/jiant,src/preprocess.py,25b45780cb820eacae4e59f38626fb586185188c,77e8376ba604c3f0942ca09205766e67ad158787,## FIXME MAGIC NUMBER
aaefbdcbd,nyu-mll/jiant,src/preprocess.py,25b45780cb820eacae4e59f38626fb586185188c,77e8376ba604c3f0942ca09205766e67ad158787,TODO target max size
aaefbdcbe,nyu-mll/jiant,src/preprocess.py,25b45780cb820eacae4e59f38626fb586185188c,77e8376ba604c3f0942ca09205766e67ad158787,TODO namespace
aaefbdcci,nyu-mll/jiant,src/tasks.py,ec0579230baaf8488cf1aa5723ab50f94bc03607,1284944c5cc1c9b194e466586298efb3c04729be,# TODO check if this is good metric
aaefbdccj,nyu-mll/jiant,src/tasks.py,ec0579230baaf8488cf1aa5723ab50f94bc03607,1284944c5cc1c9b194e466586298efb3c04729be,TODO namespace
aaefbdcda,nyu-mll/jiant,src/tasks.py,ec0579230baaf8488cf1aa5723ab50f94bc03607,1284944c5cc1c9b194e466586298efb3c04729be,Might be better as LabelField? I don't know what these things mean
aaefbdceh,nyu-mll/jiant,src/models.py,25c06de6ecc5dbd986aaddc6ee79371c28028b85,5f38d26f73b7ba65f4d6954c75355d21cbaca591,TODO this is probably wrong
aaefbdcej,nyu-mll/jiant,src/preprocess.py,25c06de6ecc5dbd986aaddc6ee79371c28028b85,77e8376ba604c3f0942ca09205766e67ad158787,## FIXME MAGIC NUMBER
aaefbdcfa,nyu-mll/jiant,src/preprocess.py,25c06de6ecc5dbd986aaddc6ee79371c28028b85,77e8376ba604c3f0942ca09205766e67ad158787,TODO target max size
aaefbdcfb,nyu-mll/jiant,src/preprocess.py,25c06de6ecc5dbd986aaddc6ee79371c28028b85,77e8376ba604c3f0942ca09205766e67ad158787,TODO namespace
aaefbdcfi,nyu-mll/jiant,src/tasks.py,25c06de6ecc5dbd986aaddc6ee79371c28028b85,203f67d2a757031a40749bfd0b297ce10bc92035,# TODO check if this is good metric
aaefbdcfj,nyu-mll/jiant,src/tasks.py,25c06de6ecc5dbd986aaddc6ee79371c28028b85,d1701a6c8eb536600fa411c80fb2f43971819fd9,TODO namespace
aaefbdcga,nyu-mll/jiant,src/tasks.py,25c06de6ecc5dbd986aaddc6ee79371c28028b85,STILL_EXISTS,Might be better as LabelField? I don't know what these things mean
aaefbddcg,nyu-mll/jiant,src/retokenize.py,812fbb34001d0d31212abbaf125ee11d4a5ecf36,STILL_EXISTS,be nice to opensource this as a standalone utility.
aaefbddgf,nyu-mll/jiant,src/preprocess.py,0b8b71a43380d318f7b7bb29ad9bfbc6a153a591,ef53e90983fa691a736b92985f936ee9328aadae,TODO: delete task.{split}_data_text as well?
aaefbdhea,nyu-mll/jiant,src/main.py,ef53e90983fa691a736b92985f936ee9328aadae,STILL_EXISTS,This logic looks strange. We think it works.
aaefbdigc,nyu-mll/jiant,src/preprocess.py,6fc35c667e6efabc9b6beb2da87d97b4dc6a7b13,83d5ea7e20dca232d17013611eeacf56e367e8b3,TODO: delete task.{split}_data_text as well?
aaefbdiid,nyu-mll/jiant,main.py,83d5ea7e20dca232d17013611eeacf56e367e8b3,STILL_EXISTS,This logic looks strange. We think it works.
aaefbdjbe,nyu-mll/jiant,main.py,551802af1f0cc9492857bc1627618443f216c060,STILL_EXISTS,This logic looks strange. We think it works.
aaefbdjbi,nyu-mll/jiant,src/preprocess.py,935d5b25d1349e0d134886eef5c1af091c8f2951,77e8376ba604c3f0942ca09205766e67ad158787,## FIXME MAGIC NUMBER
aaefbdjbj,nyu-mll/jiant,src/preprocess.py,935d5b25d1349e0d134886eef5c1af091c8f2951,77e8376ba604c3f0942ca09205766e67ad158787,TODO target max size
aaefbdjce,nyu-mll/jiant,src/preprocess.py,935d5b25d1349e0d134886eef5c1af091c8f2951,5b2654a62ea4121dd07d4dd16600dfbe1d66148d,TODO: delete task.{split}_data_text as well?
aaefbdjee,nyu-mll/jiant,main.py,5b2654a62ea4121dd07d4dd16600dfbe1d66148d,STILL_EXISTS,This logic looks strange. We think it works.
aaefbeahf,nyu-mll/jiant,src/models.py,48a4e79f42c099a41eabce5c6821cb4a6e1bca95,5f38d26f73b7ba65f4d6954c75355d21cbaca591,TODO this is probably wrong
aaefbebdj,nyu-mll/jiant,src/tasks.py,bb2bb8a9ebe16712a8b48955ae543f8af3e5730f,STILL_EXISTS,Might be better as LabelField? I don't know what these things mean
aaefbebfh,nyu-mll/jiant,src/trainer.py,53b105834b3872637177cb5fa8502550bafa99cc,6c3728aa2a3d215895ae89674defa3832d15cd2d,''' Hacky way to get task specific attributes '''
aaefbecai,nyu-mll/jiant,src/tasks.py,cbbf0695010517d16eda19a32c42f73527499607,d1701a6c8eb536600fa411c80fb2f43971819fd9,Map over columns: inputs; targs
aaefbecdb,nyu-mll/jiant,main.py,68833288baa9b198c79d20286c85e25fb8a4ef23,STILL_EXISTS,This logic looks strange. We think it works.
aaefbedej,nyu-mll/jiant,src/models.py,be8555c56f8894d8a4f03aaef172ec96da7c7a64,STILL_EXISTS,HACKY FIX THIS
aaefbedfb,nyu-mll/jiant,src/models.py,be8555c56f8894d8a4f03aaef172ec96da7c7a64,STILL_EXISTS,TODO: rename this to be actually descriptive.
aaefbedfj,nyu-mll/jiant,src/preprocess.py,be8555c56f8894d8a4f03aaef172ec96da7c7a64,77e8376ba604c3f0942ca09205766e67ad158787,## FIXME MAGIC NUMBER
aaefbedga,nyu-mll/jiant,src/preprocess.py,be8555c56f8894d8a4f03aaef172ec96da7c7a64,77e8376ba604c3f0942ca09205766e67ad158787,TODO target max size
aaefbedgf,nyu-mll/jiant,src/preprocess.py,be8555c56f8894d8a4f03aaef172ec96da7c7a64,STILL_EXISTS,TODO: delete task.{split}_data_text as well?
aaefbedhi,nyu-mll/jiant,src/preprocess.py,4a880780020b8324709006e8a6189d5034fadc16,STILL_EXISTS,TODO: remove special case; replace with something general
aaefbedij,nyu-mll/jiant,src/tasks.py,27dd7fc6239853ac58daec8fedf705fa718c1452,d1701a6c8eb536600fa411c80fb2f43971819fd9,TODO namespace
aaefbeeaf,nyu-mll/jiant,src/tasks.py,c34cb6582370b92727b4c39209c7d50483b7561b,d1701a6c8eb536600fa411c80fb2f43971819fd9,Map over columns: inputs; targs
aaefbeecb,nyu-mll/jiant,src/tasks.py,73b0b4ce0c9b660e86ab5477f15d68f594e12164,d1701a6c8eb536600fa411c80fb2f43971819fd9,TODO namespace
aaefbeece,nyu-mll/jiant,src/tasks.py,1c4828d3c40f41cc0f22525c2077a2571900ad2c,d1701a6c8eb536600fa411c80fb2f43971819fd9,Map over columns: inputs; targs
aaefbeecj,nyu-mll/jiant,major_experiment_scripts/extract_results.py,1e5a78e709ffde77056ee656c34b610604d0e777,STILL_EXISTS,This is a bit of a hack: Take the first instance of dropout; which will come from the overall config.
aaefbeedb,nyu-mll/jiant,src/models.py,bebf4f0b45f47dac4f61b1004d24fa394a00ec0d,4a496e523a6e95dbd60dbd4fc7d3542c2163f1d1,TODO: move this logic to preprocess.py;
aaefbeeej,nyu-mll/jiant,src/edge_probing.py,4a61afa25d52ad32444f14cb06ff8d85cd8050c2,STILL_EXISTS,Set config options needed for forward pass.
aaefbefgj,nyu-mll/jiant,src/tasks.py,bcc848bff466de24b7c0883d199a8cd0f4d18818,d1701a6c8eb536600fa411c80fb2f43971819fd9,Map over columns: inputs; targs
aaefbefjb,nyu-mll/jiant,src/utils.py,a5d49806a23a175015b6ebfb2b6f8adb3e9ca39c,c3a8a1b67a1c98c25003bdf60105058a851acab7,There are 4 columns and every column could containd multiple values.
aaefbegcj,nyu-mll/jiant,src/tasks.py,2c25800781a7eb1125d6538fe8701765db2ad7f8,02351950dbecfc42ce172e96d3857213096085ea,TODO: switch to MCC?
aaefbegeb,nyu-mll/jiant,src/allennlp_mods/elmo_text_field_embedder.py,f19c1f5c3f185850b9db83fb85dc12c797e65b90,STILL_EXISTS,other arguments can be passed in when needed
aaefbehjh,nyu-mll/jiant,src/tasks.py,b4e2daf2901e47e80d02e72dbbc0157f8adeeac8,02351950dbecfc42ce172e96d3857213096085ea,hard code to fix unk symbol
aaefbejgj,nyu-mll/jiant,src/tasks.py,9181e6a9f8b54bffd14bfa1ab2f360836be98c90,STILL_EXISTS,TODO: switch to MCC?
aaefbejhc,nyu-mll/jiant,src/tasks.py,9181e6a9f8b54bffd14bfa1ab2f360836be98c90,STILL_EXISTS,hard code to fix unk symbol
aaefbejii,nyu-mll/jiant,src/evaluate.py,2d52dc9292ffcd084125507241db6170e18ac629,STILL_EXISTS,Rename columns to match output headers.
aaefbejjb,nyu-mll/jiant,src/evaluate.py,5ad2e98bb70010f575d701fba81df143c8f52190,STILL_EXISTS,make sure we write index and prediction as first columns;
aaefbfacf,nyu-mll/jiant,src/preprocess.py,8c95392112e7c51d33b3253f19725f04c7fe2b34,710713665ede0e028a08ff64286487706f3b89b0,using individual tasks later; so better to have as a list
aaefbfaeh,nyu-mll/jiant,src/evaluate.py,2a6f8d04e832f1fee597cb489d8f847249f0835b,STILL_EXISTS,TODO: update this with more prediction types; when available.
aaefbfaia,nyu-mll/jiant,src/utils.py,5ef077159eb801facd087ec9ce8c1efcebb69a55,STILL_EXISTS,There are 4 columns and every column could containd multiple values.
aaefbfbai,nyu-mll/jiant,src/tasks.py,829fe5a946a083ce6f426ab04476b86d94afd168,STILL_EXISTS,hard code to fix unk symbol
aaefbfbch,nyu-mll/jiant,src/models.py,c5adc92f819d86023fff3a8577fc4e4a47bc93f7,STILL_EXISTS,Note: sent_enc is expected to apply dropout to its input _and_ output if needed.
aaefbfdfa,nyu-mll/jiant,src/beamsearch.py,852d7bd6143faa1acdd4ef47a2fe84372f3b48c9,1fefd71cd95534089b9c0fbcb3c6f9dd40eee44b,TODO calculate scores
aaefbfgdb,nyu-mll/jiant,src/beamsearch.py,c2d5bde2b2d100b5d11e2c7f2e58186bca296f0d,STILL_EXISTS,TODO calculate scores
aaefbfghj,nyu-mll/jiant,probing/analysis.py,078c9c015d5a7333c8214e9c4d6a89919578d97b,STILL_EXISTS,Expand labels to columns
aaefbfgif,nyu-mll/jiant,probing/analysis.py,078c9c015d5a7333c8214e9c4d6a89919578d97b,STILL_EXISTS,Sort columns alphabetically
aaefbfhbj,nyu-mll/jiant,src/preprocess.py,73c1b7e145b22196ff688fe041287104b92b6e5a,d1701a6c8eb536600fa411c80fb2f43971819fd9,TODO: replace this with MTTask
aaefbfhcb,nyu-mll/jiant,src/tasks.py,73c1b7e145b22196ff688fe041287104b92b6e5a,d1701a6c8eb536600fa411c80fb2f43971819fd9,TODO namespace
aaefbfhea,nyu-mll/jiant,probing/analysis.py,8c628ecca8d59b428613298e096b32befd3616fc,STILL_EXISTS,TODO: switch to using wide-form for all?
aaefbfhih,nyu-mll/jiant,probing/analysis.py,1bcf6392364e72c110fc5437e6a816029b4a5703,STILL_EXISTS,Hacky: Hide category labels; not needed on this plot.
aaefbfiaj,nyu-mll/jiant,src/models.py,db6470bcde50676f24d77f5dbe5c1eb324648b19,STILL_EXISTS,TODO: replace this logic with task._classifier_name?
aaefbfibc,nyu-mll/jiant,src/utils.py,db6470bcde50676f24d77f5dbe5c1eb324648b19,fb0456e2139620e2574261d76574452e9dcb79cf,TODO: rename variables to be more descriptive (mix_id -> task_id; mixer -> task_weights)
aaefbfiea,nyu-mll/jiant,src/seq2seq_decoder.py,701b202951a0a07bd47830e8101db5fc2afdf127,2a91aa263c09cafa1305db2843fcc816cf081335,TODO: max_decoding_steps; scheduled_sampling_ratio are not used and should be deleted
aaefbfieb,nyu-mll/jiant,src/seq2seq_decoder.py,701b202951a0a07bd47830e8101db5fc2afdf127,STILL_EXISTS,TODO: target_tokens is not optional.
aaefbfiec,nyu-mll/jiant,src/seq2seq_decoder.py,701b202951a0a07bd47830e8101db5fc2afdf127,2a91aa263c09cafa1305db2843fcc816cf081335,TODO: this method is not used and should be deleted
aaefbfjbe,nyu-mll/jiant,src/edge_probing.py,890f14bcc20c2e54d63e74876fccac1d032b84b7,STILL_EXISTS,needed for CNN layer
aaefbfjdg,nyu-mll/jiant,probing/data/convert-spr1-rudinger.py,ec9ba0f2ad37171315e9dc76d0d63a09e712a7e1,STILL_EXISTS,considerably more difficult. TODO to check in a full pipeline of scripts to
aaefbgafc,nyu-mll/jiant,probing/split_constituent_data.py,105a0e794e4a0126cfd7e3d294f25af5d1cf1042,STILL_EXISTS,TODO to integrate this into the OntoNotes processing script to generate in
aaefbgahh,nyu-mll/jiant,probing/retokenize_edge_data.openai.py,4f325626f184f37e0052b19fbc92f5b0309a475c,STILL_EXISTS,preprocessing for the OpenAI model. These should only be needed for this
aaefbgcae,nyu-mll/jiant,src/tasks/__init__.py,710713665ede0e028a08ff64286487706f3b89b0,STILL_EXISTS,using individual tasks later; so better to have as a list
aaefbgccj,nyu-mll/jiant,src/tasks/edge_probing.py,710713665ede0e028a08ff64286487706f3b89b0,STILL_EXISTS,TODO: switch to MCC?
aaefbgdad,nyu-mll/jiant,src/preprocess.py,b2ded07f1f54b134d426834093cfe77d98f4a7fc,STILL_EXISTS,TODO: refactor to always read from disk; even if task is constructed
aaefbgdah,nyu-mll/jiant,src/preprocess.py,b2ded07f1f54b134d426834093cfe77d98f4a7fc,STILL_EXISTS,using individual tasks later; so better to have as a list
aaefbgdba,nyu-mll/jiant,src/tasks/tasks.py,bc013995a312d34732877716a67cb51ef4897e02,3a58f7af3055d35b56a17c51849c33c6d4a659ed,TODO: restructure LM task hierarchy
aaefbgdbe,nyu-mll/jiant,src/tasks/tasks.py,bc013995a312d34732877716a67cb51ef4897e02,STILL_EXISTS,TODO: use FastMatthews instead to save memory.
aaefbgdbg,nyu-mll/jiant,src/tasks/tasks.py,bc013995a312d34732877716a67cb51ef4897e02,3a58f7af3055d35b56a17c51849c33c6d4a659ed,TODO: remove dummy \/ debug tasks
aaefbgdcc,nyu-mll/jiant,src/tasks/tasks.py,bc013995a312d34732877716a67cb51ef4897e02,a8fb4ef8b82681c393694887a5865fc441f82e7d,TODO: does this even work? What is n_classes for this?
aaefbgddg,nyu-mll/jiant,src/tasks/lm.py,3a58f7af3055d35b56a17c51849c33c6d4a659ed,STILL_EXISTS,TODO: restructure LM task hierarchy
aaefbgded,nyu-mll/jiant,src/tasks/mt.py,3a58f7af3055d35b56a17c51849c33c6d4a659ed,STILL_EXISTS,TODO: remove dummy \/ debug tasks
aaefbgfda,nyu-mll/jiant,probing/retokenize_edge_data.py,fc4577fcac1a408573c02bdc21c07f07a6022eb1,STILL_EXISTS,These should only be needed for this script - main.py shouldn't need to do
aaefbgfdg,nyu-mll/jiant,probing/retokenize_edge_data.py,fc4577fcac1a408573c02bdc21c07f07a6022eb1,STILL_EXISTS,TODO: change this once we have better support in core utils.
aaefbgfeg,nyu-mll/jiant,src/bert/utils.py,0019417fedff057122c19ad4ba543c46f4ecbb4a,STILL_EXISTS,TODO: if doing multiple target tasks; allow for multiple sets of
aaefbgffa,nyu-mll/jiant,probing/analysis.py,9d258c804cb730f9457315cef2c11bfb94871246,STILL_EXISTS,since when run the DataFrame only contains '_counts' columns.
aaefbgfjb,nyu-mll/jiant,src/openai_transformer_lm/utils.py,b5059cdb9dd32883b1e720dd0ec058cb4194045e,STILL_EXISTS,TODO: if doing multiple target tasks; allow for multiple sets of
aaefbggdd,nyu-mll/jiant,probing/deterministic_split.py,53792914fa00cf72b245bc07baebd7f7e67ed2d8,STILL_EXISTS,behave differently on different systems. For repeatability; we implement this
aaefbgggc,nyu-mll/jiant,src/utils/data_loaders.py,4d5eeb35125d130de9c561558b5bc3aea147830f,e133993dd440af0de5afd768d75c336b40d10f7f,There are 4 columns and every column could containd multiple values.
aaefbgggj,nyu-mll/jiant,src/utils/tokenizers.py,4d5eeb35125d130de9c561558b5bc3aea147830f,STILL_EXISTS,TODO: Add detokenize method to OpenAIBPE class
aaefbgghj,nyu-mll/jiant,src/models.py,14ba8202701b81f4c7d6dcdf2e663d9141b04080,STILL_EXISTS,Note: sent_enc is expected to apply dropout to its input _and_ output if needed.
aaefbggig,nyu-mll/jiant,src/models.py,14ba8202701b81f4c7d6dcdf2e663d9141b04080,4a496e523a6e95dbd60dbd4fc7d3542c2163f1d1,TODO: move this logic to preprocess.py;
aaefbggjj,nyu-mll/jiant,probing/analysis.py,d1d3699cafdf5635b05f7329de43cdd227c6ba7f,STILL_EXISTS,These correspond to the convention in scripts\/edges\/exp_fns.sh
aaefbghbe,nyu-mll/jiant,probing/analysis.py,d1d3699cafdf5635b05f7329de43cdd227c6ba7f,STILL_EXISTS,This probably isn't the right way to combine for F1 score; but should be a reasonable estimate.
aaefbghbg,nyu-mll/jiant,probing/analysis.py,d1d3699cafdf5635b05f7329de43cdd227c6ba7f,STILL_EXISTS,Old scoring helpers (TODO: remove these)
aaefbghde,nyu-mll/jiant,src/utils/data_loaders.py,e133993dd440af0de5afd768d75c336b40d10f7f,STILL_EXISTS,get the first row as the columns to pass into the pandas reader
aaefbgibi,nyu-mll/jiant,main.py,ddf678de93e9256469e7e9094f819f656ea57c8c,7be8fdb9b8b24296b559bf307d7cacbe9a669b33,TODO(Yada): Move logic for checkpointing finetuned vs frozen pretrained tasks
aaefbgich,nyu-mll/jiant,src/models.py,dbf776958067ed797107b37c5f711a40f73e397d,STILL_EXISTS,needed.
aaefbgihd,nyu-mll/jiant,src/models.py,052fbe8fa453a19d7dd6a87a9f355c2e58302831,STILL_EXISTS,needed.
aaefbgjcc,nyu-mll/jiant,scripts/ccg/align_tags_to_bert.py,4acd9af84f04941470407a86172cd3eefb0e4078,STILL_EXISTS,\"\"\" || Usage: ||     Run the below command from the root directory ||     python -m scripts.ccg.align_tags_to_bert --data_dir {path\/to\/ccg\/files} -t {tokenizer_name} ||  || The input file should be in csv form; with text and tags columns.  ||  || The output format has columns text and tag; which is a string of space delimited numbers. || This preprocessing file will preprocess the CCG data using the tokenizer; || saving it alongside the original files. ||  || This file introduces a new tag to sub-words (if the tokenizer splits a word. || Currently; this supports BERT tokenization.) || For example;  || [Mr.; Porter] -> [Mr; .; Por; ter]. Thus; if Mr. was given a tag of 5 and Porter 6 || in the original CCG; then the alligned tags will be [5; 1363; 6; 1363]; where 1363 indicates || a subpiece of a word that has been split due to tokenization. || \"\"\"
aaefbhaba,nyu-mll/jiant,src/modules/span_modules.py,a8f1b677bab9093e0bc08f1ed56bb766b40239ee,STILL_EXISTS,Set config options needed for forward pass.
aaefbhabf,nyu-mll/jiant,src/modules/span_modules.py,a8f1b677bab9093e0bc08f1ed56bb766b40239ee,STILL_EXISTS,needed for CNN layer
aaefbhadj,nyu-mll/jiant,src/tasks/tasks.py,99862642f46e6582ff822343b3ac148f6201d4c7,STILL_EXISTS,Map over columns: input1; (input2); labels; idx
aaefbhaih,nyu-mll/jiant,src/evaluate.py,54f8206565767fc6cadf883fafc427345f97789d,STILL_EXISTS,hack for diagnostics
aaefbhajh,nyu-mll/jiant,scripts/winograd/preprocess_winograd.py,68f509b44d407086b9c1736f5557c1ceef7292ce,STILL_EXISTS,\"\"\" || This file will preprocess the SuperGLUE Winograd Schema Challenge data; aligning the span indices to the tokenizaer of || choice; and saving as a JSON file. ||  || An example of the span index transformation is below: || [Mr.; Porter; is; nice] with span indices [0; 2] -> [Mr; .; Por; ter; is; nice ] || with span indices [0; 3]. ||  || Usage: ||     Run the below command from the root directory ||     python -m scripts.winograd.preprocess_winograd ||     -t {tokenizer_name} --data_dir {path\/to\/directory} ||  || The input file should be in jsonl form; with text and tags columns. The output will be || in JSON form. See realign_spans for more details. ||  || \"\"\"
aaefbhbji,nyu-mll/jiant,src/models.py,7be8fdb9b8b24296b559bf307d7cacbe9a669b33,STILL_EXISTS,needed.
aaefbhcif,nyu-mll/jiant,src/preprocess.py,fd52653b41fbcc60ecde569e75e8bfec4ecfcbdb,STILL_EXISTS,TODO: We don't want diagnostic tasks in train_task_names
aaefbhcjb,nyu-mll/jiant,src/preprocess.py,fd52653b41fbcc60ecde569e75e8bfec4ecfcbdb,STILL_EXISTS,using individual tasks later; so better to have as a list
aaefbhigf,nyu-mll/jiant,jiant/tasks/tasks.py,1515c777ce216d03d08a19352cffbe70f11da932,STILL_EXISTS,TODO: use FastMatthews instead to save memory.
aaefbhjfb,nyu-mll/jiant,jiant/models.py,a1e9abf03fc8edf9a6b192cf7cb84d2ccfa1ef4c,STILL_EXISTS,Rough heuristic. TODO: Make this directly user-controllable.
aaefbhjgf,nyu-mll/jiant,jiant/pytorch_transformers_interface/modules.py,a1e9abf03fc8edf9a6b192cf7cb84d2ccfa1ef4c,STILL_EXISTS,TODO: if doing multiple target tasks; allow for multiple sets of
aaefbibcg,nyu-mll/jiant,jiant/models.py,3bf415c76290168750416686d159c6bb5aaead8d,STILL_EXISTS,TODO: Wic also falls into this type; although GPT paper didn't expeirment with this task
aaefbibda,nyu-mll/jiant,jiant/preprocess.py,3bf415c76290168750416686d159c6bb5aaead8d,STILL_EXISTS,TODO: this is another place can be simplified by \"model-before-preprocess\" reorganization
aaefbibdd,nyu-mll/jiant,jiant/preprocess.py,3bf415c76290168750416686d159c6bb5aaead8d,c36b74e315226334120639d16a7924fd43345a1f,this when they fix the problem
aaefbibea,nyu-mll/jiant,jiant/pytorch_transformers_interface/modules.py,3bf415c76290168750416686d159c6bb5aaead8d,STILL_EXISTS,TODO: creating sentence segment id(and language segment id for XLM) is more suitable for preprocess
aaefbibfg,nyu-mll/jiant,jiant/pytorch_transformers_interface/modules.py,3bf415c76290168750416686d159c6bb5aaead8d,STILL_EXISTS,Note: pytorch_transformers didn't implement TransfoXLLMHeadModel; use this in eval only
aaefbicce,nyu-mll/jiant,jiant/modules/seq2seq_decoder.py,a7b1ec31ffb21507b4cae97b7937a69ce1233ff0,STILL_EXISTS,This is needed in case no gold target sequence is available.
aaefbicdi,nyu-mll/jiant,jiant/modules/seq2seq_decoder.py,cc22a08783770b3a229453b1fdb55944fedeb37d,STILL_EXISTS,This is needed in case no gold target sequence is available.
aaefbicfi,nyu-mll/jiant,jiant/utils/utils.py,2553c2db6a4d9cbc3011128f8480b46f51b4413a,STILL_EXISTS,maybe we should do (int; list)
aaefbidgj,nyu-mll/jiant,jiant/preprocess.py,900e9e8f67f5b542f45d0208d32b1d67a84386ac,STILL_EXISTS,TODO: surface more docs for add_task_label_vocab:
aaefbidij,nyu-mll/jiant,jiant/huggingface_transformers_interface/modules.py,4a9b058f4833587e1ebb54cd56726eb559c575ec,STILL_EXISTS,TODO: Speed things up slightly by reusing the previously-loaded tokenizer.
aaefbigba,nyu-mll/jiant,jiant/demo/example_google.py,8a401ad9241f8808dd8e7013cfd31cb078b6d234,STILL_EXISTS,\"\"\"Example Google style docstrings. ||  || This module demonstrates documentation as specified by the `Google Python || Style Guide`_. Docstrings may extend over multiple lines. Sections are created || with a section header and a colon followed by a block of indented text. ||  || Example: ||     Examples can be given using either the ``Example`` or ``Examples`` ||     sections. Sections support any reStructuredText formatting; including ||     literal blocks:: ||  ||         $ python example_google.py ||  || Section breaks are created by resuming unindented text. Section breaks || are also implicitly created anytime a new section starts. ||  || Attributes: ||     module_level_variable1 (int): Module level variables may be documented in ||         either the ``Attributes`` section of the module docstring; or in an ||         inline docstring immediately following the variable. ||  ||         Either form is acceptable; but the two should not be mixed. Choose ||         one convention to document module level variables and be consistent ||         with it. ||  || .. _Google Python Style Guide: ||    http:\/\/google.github.io\/styleguide\/pyguide.html ||  || \"\"\"
aaefbihfd,nyu-mll/jiant,jiant/tasks/evaluate/core.py,81a8a3d4be6a01802005347c5ef3ec93a9a78c7a,STILL_EXISTS,Todo: move logic to task?
aaefbihfe,nyu-mll/jiant,jiant/tasks/retrieval.py,81a8a3d4be6a01802005347c5ef3ec93a9a78c7a,STILL_EXISTS,Todo: Refactor paths
aaefbihgj,nyu-mll/jiant,jiant/utils/torch_utils.py,81a8a3d4be6a01802005347c5ef3ec93a9a78c7a,STILL_EXISTS,Todo: Revert after https:\/\/github.com\/pytorch\/pytorch\/issues\/36176 addressed
aaefbihhj,nyu-mll/jiant,jiant/utils/zconf/core.py,81a8a3d4be6a01802005347c5ef3ec93a9a78c7a,STILL_EXISTS,TODO: get better criteria
aaefbiiag,nyu-mll/jiant,jiant/proj/simple/preprocessing.py,af569ace570bf88b44ed57d2f4ae8dbebd77497c,STILL_EXISTS,TODO: document why reshape and max happen here (for cola this isn't necessary).
aaefbiibj,nyu-mll/jiant,jiant/ext/allennlp.py,c11e8418aa407b95b9cbee38f627eccc01808de8,STILL_EXISTS,These span widths are off by 1; because the span ends are `inclusive`.
aaefbiidc,nyu-mll/jiant,jiant/ext/allennlp.py,c11e8418aa407b95b9cbee38f627eccc01808de8,STILL_EXISTS,We're using <= here (and for the mask below) because the span ends are
aaefbiife,nyu-mll/jiant,jiant/ext/allennlp.py,c11e8418aa407b95b9cbee38f627eccc01808de8,STILL_EXISTS,We'll special-case a few settings here; where there are efficient (but poorly-named)
aaefbiije,nyu-mll/jiant,jiant/proj/main/modeling/taskmodels.py,c11e8418aa407b95b9cbee38f627eccc01808de8,STILL_EXISTS,Todo: refactor
aaefbiijg,nyu-mll/jiant,jiant/proj/main/modeling/taskmodels.py,c11e8418aa407b95b9cbee38f627eccc01808de8,STILL_EXISTS,Todo: make this optional?
aaefbiijh,nyu-mll/jiant,jiant/proj/main/modeling/taskmodels.py,c11e8418aa407b95b9cbee38f627eccc01808de8,STILL_EXISTS,This is a horrible hack
aaefbijag,nyu-mll/jiant,jiant/proj/main/runner.py,c11e8418aa407b95b9cbee38f627eccc01808de8,STILL_EXISTS,Todo: Add fp16
aaefbijaj,nyu-mll/jiant,jiant/proj/main/runner.py,c11e8418aa407b95b9cbee38f627eccc01808de8,STILL_EXISTS,val_labels might contain more details information needed for full evaluation
aaefbijdc,nyu-mll/jiant,jiant/shared/runner.py,c11e8418aa407b95b9cbee38f627eccc01808de8,STILL_EXISTS,Todo: Gin-style config
aaefbijfe,nyu-mll/jiant,jiant/proj/simple/preprocessing.py,3b33588f810b61753eef173569ad0fc4973d71c5,STILL_EXISTS,TODO: Better solution
aaefbijff,nyu-mll/jiant,jiant/proj/simple/preprocessing.py,3b33588f810b61753eef173569ad0fc4973d71c5,STILL_EXISTS,TODO more arguments?
aaefbijga,nyu-mll/jiant,jiant/tasks/evaluate/core.py,3b33588f810b61753eef173569ad0fc4973d71c5,STILL_EXISTS,TODO: Revisit ReCord scoring
aaefbijii,nyu-mll/jiant,jiant/tasks/lib/templates/hacky_tokenization_matching.py,3b33588f810b61753eef173569ad0fc4973d71c5,STILL_EXISTS,\"\"\"TODO: Remove when Tokenizers gets better\"\"\"
aaefbijja,nyu-mll/jiant,jiant/tasks/lib/templates/hacky_tokenization_matching.py,3b33588f810b61753eef173569ad0fc4973d71c5,e339ccd7ae38f47002309b93216edbbda84a86d3,Todo: refactor
aaefbijjd,nyu-mll/jiant,jiant/tasks/lib/templates/mlm.py,3b33588f810b61753eef173569ad0fc4973d71c5,e339ccd7ae38f47002309b93216edbbda84a86d3,TODO: Seed if this is better off left to augmentation?
aaefbjbaa,nyu-mll/jiant,jiant/tasks/lib/templates/squad_style/utils.py,3b33588f810b61753eef173569ad0fc4973d71c5,STILL_EXISTS,What we really want to return is \"Steve Smith\".
aaefbjbcg,nyu-mll/jiant,jiant/proj/main/modeling/taskmodels.py,e339ccd7ae38f47002309b93216edbbda84a86d3,STILL_EXISTS,TODO: Abuse of notation - these aren't really logits  (Issue #45)
aaefbjbci,nyu-mll/jiant,jiant/proj/simple/preprocessing.py,e339ccd7ae38f47002309b93216edbbda84a86d3,STILL_EXISTS,TODO: Better solution  (Issue #48)
aaefbjbcj,nyu-mll/jiant,jiant/proj/simple/preprocessing.py,e339ccd7ae38f47002309b93216edbbda84a86d3,STILL_EXISTS,TODO: Expose parameters  (Issue #49)
aaefbjbda,nyu-mll/jiant,jiant/proj/simple/preprocessing.py,e339ccd7ae38f47002309b93216edbbda84a86d3,STILL_EXISTS,TODO more arguments?
aaefbjbdh,nyu-mll/jiant,jiant/utils/torch_utils.py,e339ccd7ae38f47002309b93216edbbda84a86d3,STILL_EXISTS,TODO: Support generators  (Issue #56)
aaefbjbdj,nyu-mll/jiant,jiant/proj/main/runscript.py,0dfa7c526e8237ed1711c99d38c525a055b4332e,STILL_EXISTS,TODO document why the distributed.only_first_process() context manager is being used here.
aaefbjbea,nyu-mll/jiant,jiant/shared/initialization.py,0dfa7c526e8237ed1711c99d38c525a055b4332e,STILL_EXISTS,TODO break local_rank == -1 and no_cuda into separate cases to make the logic easier to read.
aaefbjbic,nyu-mll/jiant,jiant/proj/simple/runscript.py,95f4c0be499ea4d200bf3934c2a1e5baa3b5054d,STILL_EXISTS,TODO: Need a strategy for task-specific max_seq_length issues (Issue #66)
aaefbjcia,nyu-mll/jiant,jiant/utils/retokenize.py,a7fdd0d8203453b451d9cc54ccac9561b73e9fb8,fb97d699f5070a2fcc5243a11bc4e849f5a62f21,be nice to opensource this as a standalone utility.
aaefbjdch,nyu-mll/jiant,jiant/tasks/evaluate/core.py,b64a00720ed97a8fa9b08b9dafab539c8be726a6,STILL_EXISTS,Todo: Fix val labels cache
aaefbjdci,nyu-mll/jiant,jiant/tasks/evaluate/core.py,b64a00720ed97a8fa9b08b9dafab539c8be726a6,STILL_EXISTS,This is a quick hack
aaefcecab,nyu-mll/jiant,jiant/proj/main/preprocessing.py,04bbb39f97d1074b03b55237c4dfa363932d26c7,STILL_EXISTS,TODO: Better solution  (issue #1184)
aaefcehbg,ottogroup/palladium,palladium/cache.py,0a88bee23cf6979e889a4d2488a6138a298803b7,STILL_EXISTS,\"\"\"The cache module provides caching utilities in order to provide || faster access to data which is needed repeatedly.  The disk cache || (:class:`diskcache`) which is primarily used during development when || loading data from the local harddisk is faster than querying a remote || database. || \"\"\"
aaefcehee,ottogroup/palladium,palladium/tests/test_server.py,0a88bee23cf6979e889a4d2488a6138a298803b7,STILL_EXISTS,needed as hasattr would evaluate to True otherwise
aaefcehfg,ottogroup/palladium,docs/conf.py,24c9a692a474f9353ac493a63c62380f70df7bb6,STILL_EXISTS,Needed to avoid metaclass error while mocking
aaefcehhf,ottogroup/palladium,palladium/wsgi.py,94d9093c18bd3e9ef716ea57f55d7baa4c5f3c8d,STILL_EXISTS,Initialization is needed to obtain same behavior of pld-devserver
aaefceiaa,ottogroup/palladium,palladium/tests/test_wsgi.py,56054ce07d07e633f70846f6477e9a2e12e39468,STILL_EXISTS,needed to avoid config postprocessing side effects in this test
aaefceief,ottogroup/palladium,palladium/R.py,e1f988ef345d8851437e35c3f67bfb33b7320be4,STILL_EXISTS,Deal with an rpy2 issue whereas colnames appear to get
aaefceiij,ottogroup/palladium,palladium/persistence.py,ed6842d48fe1f7535ce55a51a09d84913406df0b,STILL_EXISTS,this is needed to avoid reading stale metadata JSONs
aaefceijj,modAL-python/modAL,active_learning/models.py,8fa11142ad914d3335342f78297fa89bb76928b6,STILL_EXISTS,TODO: get rid of the if clause
aaefcejaa,modAL-python/modAL,active_learning/models.py,8fa11142ad914d3335342f78297fa89bb76928b6,STILL_EXISTS,TODO: test if this works with multiple shapes and types of data
aaefcejdb,modAL-python/modAL,modAL/utils/evaluation.py,f326a2ba1ff419d1be8aa56846dc5585eb48da52,STILL_EXISTS,eliminate queried instance from pool if needed
aaefcejeh,modAL-python/modAL,modAL/models.py,8a13a14b628855a3b4a616b5b07fe85fdfdc6397,83f8ca77cee4999e861ed2bc8b0a08c5978844f9,TODO: add keyword argument handling for the fit_to_known() method
aaefcfbgb,modAL-python/modAL,modAL/utils/validation.py,7e21363bb284b0805c415c294b35dc38228b151a,STILL_EXISTS,TODO: rewrite this function using numpy.insert
aaefcfdae,modAL-python/modAL,modAL/batch.py,ea52eaf02411d01f49d223aef9d5c595bc6a53b8,STILL_EXISTS,TODO (dataframing) there must be a better way...maybe?
aaefcfdag,modAL-python/modAL,modAL/batch.py,72339ff8f2faca4266f8cd06514908f76224a417,ff6090c59e3680568fff30cee0d6874161a19a60,TODO
aaefcfdbd,modAL-python/modAL,modAL/batch.py,72339ff8f2faca4266f8cd06514908f76224a417,STILL_EXISTS,:alpha: is not fixed throughout our model's lifetime.
aaefcfeji,modAL-python/modAL,modAL/models/base.py,6df2fc1a410c615654907148a2321ff9a7857a67,STILL_EXISTS,TODO: clarify typing
aaefcffbg,modAL-python/modAL,modAL/expected_error_reduction.py,d61b50066e25872434c039db03c28f7bd99d0b4d,STILL_EXISTS,subsample the data if needed
aaefcffca,modAL-python/modAL,modAL/expected_error.py,e17b1bbc94582e775304709d88b3026c60502342,1a19391fcb6509e4b21a0eca6463960a48cea96b,TODO: implement a proper cold-start
aaefcffcb,modAL-python/modAL,modAL/expected_error.py,e17b1bbc94582e775304709d88b3026c60502342,1a19391fcb6509e4b21a0eca6463960a48cea96b,subsample the data if needed
aaefcffcj,modAL-python/modAL,modAL/expected_error.py,3f6bbcb3ab89fea17e973dccc7039e93f901b247,1a19391fcb6509e4b21a0eca6463960a48cea96b,TODO: implement a proper cold-start
aaefcffda,modAL-python/modAL,modAL/expected_error.py,3f6bbcb3ab89fea17e973dccc7039e93f901b247,1a19391fcb6509e4b21a0eca6463960a48cea96b,subsample the data if needed
aaefcfgjc,modAL-python/modAL,modAL/batch.py,8e0cb25029e4f1443168adb9313b3f49ae13ea0e,STILL_EXISTS,transform unlabeled data if needed
aaefcfhac,modAL-python/modAL,modAL/models/base.py,8e0cb25029e4f1443168adb9313b3f49ae13ea0e,1ad79fecb074233a4fbe999c5a60e704fdf5f0a1,TODO: maybe use a newly implemented data_hstack() instead
aaefcfhdj,modAL-python/modAL,modAL/utils/data.py,143067c9b8ffe094efe610e6353208b55ea040a3,STILL_EXISTS,that does support indexing. It seems conversion to CSR is currently most efficient.
aaefcfihj,EducationalTestingService/skll,sklearn_cli.py,c9fe7ac00fe349cacf33ab5d0516bc73b53dc7e3,STILL_EXISTS,''' || Cross-validation with scikit-learn ||  || (Based on a bunch of different people's scripts.) ||  || @author: Dan Blanchard; dblanchard@ets.org || @date: September 2012 || '''
aaefcfjca,EducationalTestingService/skll,classifier.py,ca58fc2f49e77d1be1e3a79dec37654018d09115,3d599860c420283c1e602b654f0afd9594d2af9f,TODO: MAKE SURE THAT COMMENTING THIS OUT DIDN'T BREAK STUFF
aaefcgaia,EducationalTestingService/skll,run_experiment.py,4bf1fdb83bc22293145de8729fa4b3ebd52a5dba,STILL_EXISTS,''' || Runs a bunch of sklearn jobs in parallel on the cluster given a config file. ||  || @author: Nitin Madnani; nmadnani@ets.org || @author: Dan Blanchard; dblanchard@ets.org || '''
aaefcgbgf,EducationalTestingService/skll,classifier.py,23c38b5d24124aae644ac865eb220e83803e050a,STILL_EXISTS,XXX: the name is too specific; it shouldn't have 'grid' in it. Also; we should be retrieving\/storing variance
aaefcgbhc,EducationalTestingService/skll,classifier.py,dbfe77529b8584875c25ca8dda2dea10deb0e19c,761e465016a4e3a8a5abe0d93eff1978a4d49bee,TODO read in example ids from comments
aaefcgbhi,EducationalTestingService/skll,generate_predictions.py,fa38107464c1c2155740a41214644c6f3ebf8f33,STILL_EXISTS,''' || Loads a trained model and outputs predictions based on input feature files. ||  || @author: Dan Blanchard || @contact: dblanchard@ets.org || @organization: ETS || @date: February 2013 || '''
aaefcgcjf,EducationalTestingService/skll,test_sklearn_wrapper.py,d39b7eb81674e36982c052a9008912e83033c016,STILL_EXISTS,default should keep all nonzero features (i.e.; ones that appear 1+ times)
aaefcgdaf,EducationalTestingService/skll,classifier.py,c8ccbf28f5c0381b005a7d9a3c570745ddbc3c31,STILL_EXISTS,TODO there is probably some elegant way to combine ConstrainedRidge and ConstrainedSVR
aaefcgdia,EducationalTestingService/skll,run_ablation.py,36d7ef8d74ecd06e0540d2d6a0a716cb7d5df243,STILL_EXISTS,'''\r || Runs an ablation study; removing one feature file at a time.\r || \r || @author: Michael Heilman (mheilman@ets.org)\r || '''
aaefcgeci,EducationalTestingService/skll,metrics.py,01be2fd723321b9933f6dc8a659d8483565d3185,STILL_EXISTS,''' || This module contains a bunch of evaluation metrics that can be used to || evaluate the performance of learners. ||  || @author: Michael Heilman (mheilman@ets.org) || @author: Nitin Madnani (nmadnani@ets.org) || @author: Dan Blanchard (dblanchard@ets.org) || @organization: ETS || '''
aaefcgeji,EducationalTestingService/skll,test_sklearn_wrapper.py,d3d0c0256f545d8bf431b82b58f47a611aa3e8ae,STILL_EXISTS,''' || Module for running a bunch of simple unit tests. Should be expanded more in || the future. ||  || @author: Michael Heilman (mheilman@ets.org) || '''
aaefcggjb,EducationalTestingService/skll,doc/conf.py,b3c6f6482bdffbe298998648d1e422093831cee2,STILL_EXISTS,Fix unsupported image types using the PIL.
aaefcghbe,EducationalTestingService/skll,data.py,6ec16ec0d462823bddf95380aec45663ca88fca9,STILL_EXISTS,''' || Module to handle loading data from various types of data files. ||  || :author: Dan Blanchard (dblanchard@ets.org) || :author: Michael Heilman (mheilman@ets.org) || :author: Nitin Madnani (nmadnani@ets.org) || '''
aaefcghde,EducationalTestingService/skll,fixed_standard_scaler.py,a2d5a6b684a6008efa9b93bb5a6ba8598571205b,STILL_EXISTS,''' || StandardScaler has a bug in that it always scales by the standard || deviation for sparse matrices; i.e.; it ignores the value of with_std. || This is a fixed version. This is just temporary until the bug is fixed in || sklearn. ||  || :author: Dan Blanchard (dblanchard@ets.org) || :author: Michael Heilman (mheilman@ets.org) || :author: Nitin Madnani (nmadnani@ets.org) || '''
aaefcghhf,EducationalTestingService/skll,skll/run_ablation.py,01655b8f711bdb221dc3c59bc713d015c0d1b718,STILL_EXISTS,''' || Runs an ablation study; removing one feature file at a time. ||  || :author: Michael Heilman (mheilman@ets.org) || '''
aaefcghje,EducationalTestingService/skll,skll/run_experiment.py,01655b8f711bdb221dc3c59bc713d015c0d1b718,STILL_EXISTS,''' || Runs a bunch of scikit-learn jobs in parallel on the cluster given a || config file. ||  || :author: Dan Blanchard (dblanchard@ets.org) || :author: Michael Heilman (mheilman@ets.org) || :author: Nitin Madnani (nmadnani@ets.org) || '''
aaefcgjad,EducationalTestingService/skll,arff_to_megam.py,fafd6736401539f652773d5f446520cbd78687a9,STILL_EXISTS,Author: Dan Blanchard; dblanchard@ets.org; Sep 2011
aaefchaeg,EducationalTestingService/skll,skll/data.py,2a5215da0798bc53d9bd44d405269f5a28b42a55,STILL_EXISTS,TODO: Add some sort of check for duplicate feature names
aaefchaei,EducationalTestingService/skll,skll/experiments.py,9ec28950f5599eac6b8666702e38aa9b65385ec3,STILL_EXISTS,''' || Functions related to running experiments and parsing configuration files. ||  || :author: Dan Blanchard (dblanchard@ets.org) || :author: Michael Heilman (mheilman@ets.org) || :author: Nitin Madnani (nmadnani@ets.org) || '''
aaefchbge,EducationalTestingService/skll,skll/learner.py,2f1062df7acafc258009e4252f69d6cd2095866d,STILL_EXISTS,columns for the test set.
aaefcided,EducationalTestingService/skll,skll/experiments.py,0a25c40104f316e87d181bb63134e7df1a65cf27,STILL_EXISTS,TODO use frozendict???
aaefcideg,EducationalTestingService/skll,skll/experiments.py,894accd48b9e5bbaaf46b7a4e5d93d3123da4f30,STILL_EXISTS,(There doesn't seem to be a better way to do this since one can't specify
aaefcidia,EducationalTestingService/skll,skll/experiments.py,d562d544edda8cbbbc68288c5a7a634694f30fc5,STILL_EXISTS,set of columns is fixed.
aaefcidid,EducationalTestingService/skll,tests/test_skll.py,d562d544edda8cbbbc68288c5a7a634694f30fc5,STILL_EXISTS,should be printed (though some columns will be blank).
aaefcieaj,EducationalTestingService/skll,skll/data.py,b85953d69da42b32336804751ed6d536483c6e27,STILL_EXISTS,remove the columns with zero values
aaefcieba,EducationalTestingService/skll,skll/data.py,b85953d69da42b32336804751ed6d536483c6e27,STILL_EXISTS,convert the names of all the other columns to
aaefcifae,EducationalTestingService/skll,skll/data.py,3b3da02122b405689634c91b6358cb67b858f24c,STILL_EXISTS,lists. Would love a workaround for this.
aaefcigfd,EducationalTestingService/skll,skll/experiments.py,a0ebe08cad65e58f48ea1647cc20663b3c7a0f15,STILL_EXISTS,Build \"ablated_features\" list and fix some backward compatible things
aaefcihah,EducationalTestingService/skll,skll/experiments.py,71e44bc9ccf0239cf66bab8e2432d16209745438,STILL_EXISTS,Build \"ablated_features\" list and fix some backward compatible things
aaefcjbgc,EducationalTestingService/skll,tests/test_skll.py,a89d43da88e7ccff3a0e29898b34b531fc6e3548,STILL_EXISTS,should be printed (though some columns will be blank).
aaefcjdfe,EducationalTestingService/skll,skll/data/__init__.py,f6df2273bea667df981dfa8c266efe124fc11cc8,STILL_EXISTS,''' || Handles reading and writing data from various types of data files. ||  || :author: Dan Blanchard (dblanchard@ets.org) || :author: Michael Heilman (mheilman@ets.org) || :author: Nitin Madnani (nmadnani@ets.org) || :organization: ETS || '''
aaefcjdfh,EducationalTestingService/skll,skll/data/featureset.py,f6df2273bea667df981dfa8c266efe124fc11cc8,STILL_EXISTS,''' || Classes related to storing\/merging feature sets. ||  || :author: Dan Blanchard (dblanchard@ets.org) || :organization: ETS || '''
aaefcjdgh,EducationalTestingService/skll,skll/data/readers.py,f6df2273bea667df981dfa8c266efe124fc11cc8,STILL_EXISTS,''' || Handles loading data from various types of data files. ||  || :author: Dan Blanchard (dblanchard@ets.org) || :author: Michael Heilman (mheilman@ets.org) || :author: Nitin Madnani (nmadnani@ets.org) || :organization: ETS || '''
aaefcjdji,EducationalTestingService/skll,skll/data/readers.py,f6df2273bea667df981dfa8c266efe124fc11cc8,STILL_EXISTS,remove the columns with zero values
aaefcjdjj,EducationalTestingService/skll,skll/data/readers.py,f6df2273bea667df981dfa8c266efe124fc11cc8,STILL_EXISTS,convert the names of all the other columns to
aaefcjecb,EducationalTestingService/skll,skll/data/readers.py,f6df2273bea667df981dfa8c266efe124fc11cc8,STILL_EXISTS,lists. Would love a workaround for this.
aaefcjedb,EducationalTestingService/skll,skll/data/writers.py,f6df2273bea667df981dfa8c266efe124fc11cc8,STILL_EXISTS,''' || Handles loading data from various types of data files. ||  || :author: Dan Blanchard (dblanchard@ets.org) || :author: Michael Heilman (mheilman@ets.org) || :author: Nitin Madnani (nmadnani@ets.org) || :organization: ETS || '''
aaefcjfgc,EducationalTestingService/skll,skll/data/readers.py,89efcbfc083bb917c44b7518814aea3daf2c7e73,14a59dc24596b287acb9171213d93a7b141dc931,best thing: typecode \"i\" (int). However; if that gives larger or
aaefcjfgf,EducationalTestingService/skll,skll/data/readers.py,89efcbfc083bb917c44b7518814aea3daf2c7e73,14a59dc24596b287acb9171213d93a7b141dc931,XXX we could change values to an array.array as well; but it
aaefcjfgi,EducationalTestingService/skll,skll/data/readers.py,89efcbfc083bb917c44b7518814aea3daf2c7e73,14a59dc24596b287acb9171213d93a7b141dc931,workaround for bug in older NumPy:
aaefcjfjd,EducationalTestingService/skll,skll/data/dict_vectorizer.py,14a59dc24596b287acb9171213d93a7b141dc931,STILL_EXISTS,best thing: typecode \"i\" (int). However; if that gives larger or
aaefcjfjg,EducationalTestingService/skll,skll/data/dict_vectorizer.py,14a59dc24596b287acb9171213d93a7b141dc931,STILL_EXISTS,XXX we could change values to an array.array as well; but it
aaefcjgaa,EducationalTestingService/skll,skll/data/dict_vectorizer.py,14a59dc24596b287acb9171213d93a7b141dc931,STILL_EXISTS,workaround for bug in older NumPy:
aaefdbdjj,EducationalTestingService/skll,skll/experiments.py,34fbdafc38250e05d54eea13db9f853cbeaadcca,101f8eae5a34da9479ee759b0087f6044a5afaea,this is a workaround to make this simple use case (a single train and
aaefdbfbe,EducationalTestingService/skll,skll/experiments.py,bf3203d2fad79c165aecdef075726b9d8facd6b4,STILL_EXISTS,this is a workaround to make this simple use case (a single train and
aaefdbidd,EducationalTestingService/skll,skll/experiments.py,36cc45909a303f3dc90ad9ee5f623ad61f350be3,STILL_EXISTS,this is a workaround to make this simple use case (a single train and
aaefdcaec,EducationalTestingService/skll,tests/other/custom_logistic_wrapper.py,b502baf38e255460ffc540eea3e44a93c7a54c42,STILL_EXISTS,''' || A simple wrapper around the existing LogisticRegression class; for testing || custom learners functionality. ||  || :author: Michael Heilman (mheilman@ets.org) || '''
aaefdcbfh,EducationalTestingService/skll,skll/experiments.py,cd6f7c65f93f605650627fdc4a3d34bbfba268a6,STILL_EXISTS,this is a workaround to make this simple use case (a single train and
aaefdcdah,chainer/chainerrl,examples/atari/dqn/train_dqn.py,614ad6e10b01f0d758c48a256d284f7c7da8efe7,4905e408033afe8716f7d9143599edb2a93e0b04,temporary hack to handle python 2\/3 support issues.
aaefdcdcf,chainer/chainerrl,examples/atari/iqn/train_iqn.py,ca95747ae5ec27347256eb990c543ace3c68cbb7,4905e408033afe8716f7d9143599edb2a93e0b04,temporary hack to handle python 2\/3 support issues.
aaefdcedj,chainer/chainerrl,examples/atari/rainbow/train_rainbow.py,1b714cf33fec25271096e79b8ebe9bcfad0fe9de,4905e408033afe8716f7d9143599edb2a93e0b04,temporary hack to handle python 2\/3 support issues.
aaefdceej,chainer/chainerrl,examples/atari/iqn/train_iqn.py,c5c6bb2ecc9adebb6cddb148f5b37d439502d8eb,4905e408033afe8716f7d9143599edb2a93e0b04,temporary hack to handle python 2\/3 support issues.
aaefdcefb,chainer/chainerrl,chainerrl/agents/ddpg.py,9390235d1f161d47d89899a9c4c4c0efbf141baf,f7eed051821b6aa0ebde7e390ce0c0eeb2349ba1,Q is not needed here; but log it just for information
aaefdcgcg,chainer/chainerrl,chainerrl/agents/ddpg.py,305b6babc7c825031d5ea91ed935f4143581bc0d,STILL_EXISTS,Q is not needed here; but log it just for information
aaefddbeg,chainer/chainerrl,examples/ale/train_bc_ale.py,39404f350bef225ce9db8ebc1839e3aa7d16319a,STILL_EXISTS,temporary hack to handle python 2\/3 support issues.
aaefdfahi,chainer/chainerrl,examples/atari/rainbow/train_rainbow.py,49516b9d76cafe82702a0a634cf73ea632e8dc15,STILL_EXISTS,temporary hack to handle python 2\/3 support issues.
aaefdfecc,chainer/chainerrl,examples/atari/reproduction/dqn/train_dqn.py,fec55379a38ba6fec1145e1a0f487fb02001a053,4ca71e768ffa087c4970442b2554b5a28b87faa5,temporary hack to handle python 2\/3 support issues.
aaefdfecg,chainer/chainerrl,examples/atari/reproduction/iqn/train_iqn.py,fec55379a38ba6fec1145e1a0f487fb02001a053,4ca71e768ffa087c4970442b2554b5a28b87faa5,temporary hack to handle python 2\/3 support issues.
aaefdfhbf,chainer/chainerrl,examples/atari/rainbow/train_rainbow.py,da064405763e73b54360662e34d871eb9b5bca9a,STILL_EXISTS,temporary hack to handle python 2\/3 support issues.
aaefdgidf,chainer/chainerrl,examples/atari/train_iqn_ale.py,28b1bd18e5f964b94098e269b34969548e8e215e,STILL_EXISTS,temporary hack to handle python 2\/3 support issues.
aaefdgjbi,chainer/chainerrl,chainerrl/replay_buffers/episodic.py,57d1203413db6f4ed8d603e1775bb5e158a455bc,STILL_EXISTS,FIXME: The code works with EpisodicReplayBuffer
aaefdhhia,chainer/chainerrl,examples/atari/reproduction/dqn/train_dqn.py,23606864e390b031b3313fb5a725646dbea88061,5d833d6cb3b6e7de0b5bfa7cc8c8534516fbd7ba,temporary hack to handle python 2\/3 support issues.
aaefdhhie,chainer/chainerrl,examples/atari/reproduction/iqn/train_iqn.py,23606864e390b031b3313fb5a725646dbea88061,5d833d6cb3b6e7de0b5bfa7cc8c8534516fbd7ba,temporary hack to handle python 2\/3 support issues.
aaefdhhii,chainer/chainerrl,examples/atari/reproduction/rainbow/train_rainbow.py,23606864e390b031b3313fb5a725646dbea88061,f4e5556532917ff341303dbf46f6219245c87607,temporary hack to handle python 2\/3 support issues.
aaefdjjhi,chainer/chainerrl,examples/pretrained/load_model.py,606b4aa42963b7aeeca3a6d3777372bda92e4433,83b9bf995c92835508edee8693601cc85fb0599d,temporary hack to handle python 2\/3 support issues.
aaefeacbg,chainer/chainerrl,examples/atari/reproduction/rainbow/train_rainbow.py,fe5d65fbe71837c05af5f80c4c8a6378998f08a8,5d833d6cb3b6e7de0b5bfa7cc8c8534516fbd7ba,temporary hack to handle python 2\/3 support issues.
aaefeadci,chainer/chainerrl,tests/misc_tests/test_pretrained_models.py,092a0ec9e30a80fdec8f6be3b9b6b97c4e8ea472,ea2a45a7ac52cf7d765169a6c90e42361ef82125,TODO: have agent act?
aaefebche,scikit-learn-contrib/metric-learn,demo.py,d5c196671545459a065c8c74dc9903f56ba80e2d,STILL_EXISTS,TODO: use this somewhere
aaefebcih,scikit-learn-contrib/metric-learn,lmnn.py,d5c196671545459a065c8c74dc9903f56ba80e2d,STILL_EXISTS,\"\"\" || Large-margin nearest neighbor metric learning. (Weinberger 2005) ||  || TODO: periodic recalculation of impostors; PCA initialization || \"\"\"
aaefebdag,scikit-learn-contrib/metric-learn,lsml.py,d5c196671545459a065c8c74dc9903f56ba80e2d,STILL_EXISTS,TODO: vectorize
aaefebdah,scikit-learn-contrib/metric-learn,sdml.py,d5c196671545459a065c8c74dc9903f56ba80e2d,STILL_EXISTS,\"\"\" || Qi et al. || An efficient sparse metric learning in high-dimensional space via L1-penalized log-determinant regularization || ICML 2009 ||  || Adapted from https:\/\/gist.github.com\/kcarnold\/5439945 || Paper: http:\/\/lms.comp.nus.edu.sg\/sites\/default\/files\/publication-attachments\/icml09-guojun.pdf || \"\"\"
aaefebdaj,scikit-learn-contrib/metric-learn,sdml.py,d5c196671545459a065c8c74dc9903f56ba80e2d,STILL_EXISTS,hack: ensure positive semidefinite
aaefebdbe,scikit-learn-contrib/metric-learn,itml.py,82fb59180b707422c49d2a6b46ef6b30d9800bbc,STILL_EXISTS,hack around lack of axis kwarg in older numpy versions
aaefebeab,scikit-learn-contrib/metric-learn,doc/source/conf.py,3d695b1f475a8ac47c4343d0a13049052b0b0c08,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaefebffa,scikit-learn-contrib/metric-learn,doc/source/conf.py,3d695b1f475a8ac47c4343d0a13049052b0b0c08,STILL_EXISTS,Fix unsupported image types using the Pillow.
aaefecabe,scikit-learn-contrib/metric-learn,metric_learn/lfda.py,d8726d383383ec60c3d3f83b941b529c1b8c7149,STILL_EXISTS,\"\"\" || Local Fisher Discriminant Analysis (LFDA) ||  || Local Fisher Discriminant Analysis for Supervised Dimensionality Reduction || Sugiyama; ICML 2006 ||  || LFDA is a linear supervised dimensionality reduction method. || It is particularly useful when dealing with multimodality; || where one ore more classes consist of separate clusters in input space. || The core optimization problem of LFDA is solved as a generalized || eigenvalue problem. || \"\"\"
aaefecabg,scikit-learn-contrib/metric-learn,metric_learn/lmnn.py,d8726d383383ec60c3d3f83b941b529c1b8c7149,STILL_EXISTS,TODO: periodic recalculation of impostors; PCA initialization
aaefecabj,scikit-learn-contrib/metric-learn,test/metric_learn_test.py,2bf0266d9b201cf477cbf20156d06bcc612bd9b4,STILL_EXISTS,TODO: un-flake it!
aaefecacb,scikit-learn-contrib/metric-learn,metric_learn/constraints.py,3c57c64e139ed25454901df2c9611cb14fb40cdd,STILL_EXISTS,@TODO: consider creating a stateful class
aaefecace,scikit-learn-contrib/metric-learn,metric_learn/constraints.py,3c57c64e139ed25454901df2c9611cb14fb40cdd,ce5f2384bbc999511ec9497c8d34ec19aa81d238,@TODO: remove seed from params and use numpy RandomState
aaefecacg,scikit-learn-contrib/metric-learn,metric_learn/rca.py,3c57c64e139ed25454901df2c9611cb14fb40cdd,ce5f2384bbc999511ec9497c8d34ec19aa81d238,@TODO: remove seed from param. See @TODO in constraints\/chunks
aaefecaij,scikit-learn-contrib/metric-learn,metric_learn/_util.py,932de85ce38ac86f46d0559f6cf1b93829d56037,STILL_EXISTS,hack around lack of axis kwarg in older numpy versions
aaefecbfd,scikit-learn-contrib/metric-learn,metric_learn/nca.py,faa240fd7469176036a91430ae6a0a45e627c94a,23d07466961fa7a72aa8692bc42d6d569b80c5c9,TODO: remove in v.0.5.0
aaefecbgc,scikit-learn-contrib/metric-learn,test/metric_learn_test.py,faa240fd7469176036a91430ae6a0a45e627c94a,23d07466961fa7a72aa8692bc42d6d569b80c5c9,TODO: remove in v.0.5
aaefeccbj,scikit-learn-contrib/metric-learn,metric_learn/_util.py,23d07466961fa7a72aa8692bc42d6d569b80c5c9,STILL_EXISTS,any further checks or conversions; and deal with y if needed. Therefore
aaefecdfd,scikit-learn-contrib/metric-learn,metric_learn/base_metric.py,d3620bbb13620338cc8aaf39d78cead58ac5d410,6f783de0254727a144c535056fe9e0022e0ce5bf,TODO: remove this method in version 0.6.0
aaefecdgj,scikit-learn-contrib/metric-learn,test/test_mahalanobis_mixin.py,d3620bbb13620338cc8aaf39d78cead58ac5d410,6f783de0254727a144c535056fe9e0022e0ce5bf,TODO: remove this method in version 0.6.0
aaefecdhd,scikit-learn-contrib/metric-learn,metric_learn/itml.py,297ad021d3cf33123b5eeac43b0c418f1a79630b,6f783de0254727a144c535056fe9e0022e0ce5bf,TODO: remove these in v0.6.0
aaefecefi,scikit-learn-contrib/metric-learn,test/test_mahalanobis_mixin.py,edad55d731f9da14b1b931a6c884b8d3c3c77c67,3899653be835598a9d2b03e3edb82c11ecddb2ff,TODO:
aaefecegd,scikit-learn-contrib/metric-learn,test/test_pairs_classifiers.py,edad55d731f9da14b1b931a6c884b8d3c3c77c67,STILL_EXISTS,test that putting beta to 1 indeed finds the best threshold to optimize
aaefecfbb,scikit-learn-contrib/metric-learn,test/test_pairs_classifiers.py,edad55d731f9da14b1b931a6c884b8d3c3c77c67,STILL_EXISTS,convention; because it's 0\/0); and 0 recall (and we could always decrease
aaefecfbd,scikit-learn-contrib/metric-learn,test/test_pairs_classifiers.py,edad55d731f9da14b1b931a6c884b8d3c3c77c67,STILL_EXISTS,precision so it would be better)
aaefecfdh,scikit-learn-contrib/metric-learn,test/test_utils.py,edad55d731f9da14b1b931a6c884b8d3c3c77c67,aa5b274dbb26e71bc22e2316b08512e1d30b53c9,TODO: remove this comment when #175 is solved
aaefecfee,scikit-learn-contrib/metric-learn,metric_learn/_util.py,99b03225bcdc70b89e1c054a60601d4277c4cf61,STILL_EXISTS,the eigenvalues in the diagonal matrix w and the columns of V being the
aaefecgdc,scikit-learn-contrib/metric-learn,examples/plot_metric_learning_examples.py,fbd92ff910042c1a3f8329a8920777afe9d72842,STILL_EXISTS,points are supposed to be closer; figure out a better way to compute
aaefechch,scikit-learn-contrib/metric-learn,examples/plot_metric_learning_examples.py,fbd92ff910042c1a3f8329a8920777afe9d72842,STILL_EXISTS,Implements an efficient sparse metric learning algorithm in high
aaefecicd,scikit-learn-contrib/metric-learn,examples/plot_metric_learning_examples.py,fbd92ff910042c1a3f8329a8920777afe9d72842,STILL_EXISTS,other ! This would improve the performance of
aaefecijb,scikit-learn-contrib/metric-learn,examples/plot_metric_learning_examples.py,fbd92ff910042c1a3f8329a8920777afe9d72842,STILL_EXISTS,how exactly this is going on.
aaefecjah,scikit-learn-contrib/metric-learn,test/metric_learn_test.py,efba316bbd9b6c3fc8c4bfafee564a87bdf1128a,STILL_EXISTS,columns so that the covariance matrix will be singular
aaefecjeb,scikit-learn-contrib/metric-learn,metric_learn/_util.py,130cbadff294b686e466d430f26b2d069f6bbf59,STILL_EXISTS,TODO: there might be a more efficient method to do so
aaefecjec,scikit-learn-contrib/metric-learn,metric_learn/itml.py,130cbadff294b686e466d430f26b2d069f6bbf59,STILL_EXISTS,pairs will be deduplicated into X two times; TODO: avoid that
aaefecjef,scikit-learn-contrib/metric-learn,metric_learn/mlkr.py,130cbadff294b686e466d430f26b2d069f6bbf59,6f783de0254727a144c535056fe9e0022e0ce5bf,TODO:
aaefecjfa,scikit-learn-contrib/metric-learn,metric_learn/nca.py,130cbadff294b686e466d430f26b2d069f6bbf59,6f783de0254727a144c535056fe9e0022e0ce5bf,TODO: replace init=None by init='auto' in v0.6.0 and remove the warning
aaefecjfc,scikit-learn-contrib/metric-learn,metric_learn/sdml.py,130cbadff294b686e466d430f26b2d069f6bbf59,6f783de0254727a144c535056fe9e0022e0ce5bf,TODO:
aaefedacc,scikit-learn-contrib/metric-learn,metric_learn/lmnn.py,999cb5b6514729bdf6f1f6867d11098a1f69cab9,6f783de0254727a144c535056fe9e0022e0ce5bf,TODO: replace init=None by init='auto' in v0.6.0 and remove the warning
aaefedacf,scikit-learn-contrib/metric-learn,test/metric_learn_test.py,999cb5b6514729bdf6f1f6867d11098a1f69cab9,6f783de0254727a144c535056fe9e0022e0ce5bf,TODO: remove in v.0.6
aaefedafi,scikit-learn-contrib/metric-learn,test/test_sklearn_compat.py,580d38d12d01af755dc2cb9a3cf0d81d1f633cf9,STILL_EXISTS,we subsample the data for the test to be more efficient
aaefedbad,scikit-learn-contrib/metric-learn,test/metric_learn_test.py,731b32718873d0d40da298b88271375498fc290f,6f783de0254727a144c535056fe9e0022e0ce5bf,TODO: remove in v.0.6
aaefedbid,scikit-learn-contrib/metric-learn,doc/conf.py,c378e4b0e3c594feb1f7d1a6b8af0573c02f1f2d,STILL_EXISTS,Temporary work-around for spacing problem between parameter and parameter
aaefedbjc,scikit-learn-contrib/metric-learn,metric_learn/scml.py,43a60c9f4380448b9013efa4fb021796e7e1ff3c,STILL_EXISTS,TODO:
aaefedcbh,scikit-learn-contrib/metric-learn,metric_learn/scml.py,43a60c9f4380448b9013efa4fb021796e7e1ff3c,STILL_EXISTS,TODO: Maybe have a tolerance over zero?
aaefedccb,scikit-learn-contrib/metric-learn,metric_learn/scml.py,43a60c9f4380448b9013efa4fb021796e7e1ff3c,STILL_EXISTS,TODO: should copy?
aaefedcde,scikit-learn-contrib/metric-learn,metric_learn/scml.py,43a60c9f4380448b9013efa4fb021796e7e1ff3c,STILL_EXISTS,TODO: maybe add a warning in case there are no added bases; this could
aaefedcdj,scikit-learn-contrib/metric-learn,metric_learn/scml.py,43a60c9f4380448b9013efa4fb021796e7e1ff3c,STILL_EXISTS,Number of clusters needed for 2 scales given the number of basis
aaefedfja,oracle/Skater,pyinterpret/tests/test_local_interpreter.py,a6735ae2ce4f65cc86ccfcb63dd731dc46a47b9a,STILL_EXISTS,TODO : check on this function
aaefedgbf,oracle/Skater,pyinterpret/core/global_interpretation/partial_dependence.py,bb0766ffb668134a1839b7bc41e318d987a77627,STILL_EXISTS,I would prefer a better way to do this
aaefedgbg,oracle/Skater,pyinterpret/core/global_interpretation/partial_dependence.py,bb0766ffb668134a1839b7bc41e318d987a77627,STILL_EXISTS,we shouldnt have to reason about which columns are what
aaefedgbh,oracle/Skater,pyinterpret/core/global_interpretation/partial_dependence.py,bb0766ffb668134a1839b7bc41e318d987a77627,STILL_EXISTS,perhaps they can be returned directed; or we can pass
aaefedgcb,oracle/Skater,pyinterpret/core/global_interpretation/partial_dependence.py,2d70815ab6591d21c75e3070b5ec2fd53a38805d,STILL_EXISTS,otherwise; itll be the number of columns in the predictor
aaefedgec,oracle/Skater,pyinterpret/core/global_interpretation/partial_dependence.py,c2534bb322dfb69499ce8a75fba0cb953bfdd32a,STILL_EXISTS,n_figs = len(mean_columns)
aaefedghd,oracle/Skater,pyinterpret/model/model.py,59d2b675869079277d03f14e9e079d3952d774cb,4318f75c365f0eabb6230482b2c5c9134e3e2f49,perhaps it would be better if counted unique values?
aaefedgia,oracle/Skater,pyinterpret/tests/test_partial_dependence.py,5fe98231a26e08d81f9684a681af43bc2fb2b847,STILL_EXISTS,TODO: Add tests for various kinds of kwargs like sampling for pdp funcs
aaefedgid,oracle/Skater,pyinterpret/core/global_interpretation/partial_dependence.py,4318f75c365f0eabb6230482b2c5c9134e3e2f49,STILL_EXISTS,TODO: Add check for non-empty data
aaefediad,oracle/Skater,pyinterpret/core/global_interpretation/partial_dependence.py,91adda199a11b66029f3e18fd156485a93d73dd7,STILL_EXISTS,TODO: This we can change easily to functional style
aaefediba,oracle/Skater,pyinterpret/core/global_interpretation/partial_dependence.py,f9857ca02c213505dc592f3d2b4631c4d1196049,STILL_EXISTS,err_msg = \"Given {0} grid resolution; there must be {1} columns in grid\" \\
aaefedica,oracle/Skater,pyinterpret/core/global_interpretation/partial_dependence.py,a4c121d61ea8d58d4d6d5668a3dc50f98e26dbaf,STILL_EXISTS,TODO: evaluate cases when len(unique(feature))==2
aaefedicf,oracle/Skater,pyinterpret/core/global_interpretation/partial_dependence.py,a4c121d61ea8d58d4d6d5668a3dc50f98e26dbaf,STILL_EXISTS,err_msg = \"Given {0} grid resolution; there must be {1} columns in grid\" \\
aaefedida,oracle/Skater,pyinterpret/core/global_interpretation/partial_dependence.py,e1a1d01a493050c146b4e7bb467d47cdaaccad33,STILL_EXISTS,TODO: This we can change easily to functional style
aaefedidg,oracle/Skater,pyinterpret/core/global_interpretation/partial_dependence.py,e1a1d01a493050c146b4e7bb467d47cdaaccad33,STILL_EXISTS,TODO: evaluate cases when len(unique(feature))==2
aaefediee,oracle/Skater,pyinterpret/tests/test_partial_dependence.py,e1a1d01a493050c146b4e7bb467d47cdaaccad33,STILL_EXISTS,TODO: check on the feature space approximation (V2)
aaefedife,oracle/Skater,pyinterpret/core/global_interpretation/partial_dependence.py,45949842d6311afd2c28452273393db07e6228d5,STILL_EXISTS,TODO: evaluate cases when len(unique(feature))==2
aaefedige,oracle/Skater,pyinterpret/core/global_interpretation/partial_dependence.py,436fd7e459796d45e1b6aecd89259382afc95dd5,STILL_EXISTS,TODO: This we can change easily to functional style
aaefedjhg,oracle/Skater,pyinterpret/core/global_interpretation/partial_dependence.py,9e622f5cf778243920741f87bae4d49111cf33d5,STILL_EXISTS,TODO: we need to address if this is needed at all
aaefeeaej,oracle/Skater,doc/conf.py,a17a76949807bbccd4784d3bd612a2f416cfeecd,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaefeebdc,oracle/Skater,pyinterpret/core/global_interpretation/partial_dependence.py,95f0288ef68508097d9f424c733cf1fbc8d9c651,843894305ae0495215264f29c14ab863621b9946,TODO: if redundant; and is needed there could be a better way to address it
aaefeebfe,oracle/Skater,pyinterpret/core/global_interpretation/partial_dependence.py,6c0f28fee7c16d0fab909a294f18f3f6c8ab6eaf,843894305ae0495215264f29c14ab863621b9946,TODO: if redundant; and is needed there could be a better way to address it
aaefeebfg,oracle/Skater,pyinterpret/core/global_interpretation/partial_dependence.py,6c0f28fee7c16d0fab909a294f18f3f6c8ab6eaf,9d4899705e8c6838fd35d91a49fc9313cbf6d3ae,TODO: we need to address if this is needed at all. There could be a better way to do this
aaefeebfh,oracle/Skater,pyinterpret/core/global_interpretation/partial_dependence.py,6c0f28fee7c16d0fab909a294f18f3f6c8ab6eaf,9d4899705e8c6838fd35d91a49fc9313cbf6d3ae,TODO: There might be a better place to do this check
aaefeebgg,oracle/Skater,pyinterpret/core/global_interpretation/partial_dependence.py,6e128c8c32c4a27dfbddb5e1295dd3df42f2ad57,843894305ae0495215264f29c14ab863621b9946,TODO: if redundant; and is needed there could be a better way to address it
aaefeebgi,oracle/Skater,pyinterpret/core/global_interpretation/partial_dependence.py,6e128c8c32c4a27dfbddb5e1295dd3df42f2ad57,9d4899705e8c6838fd35d91a49fc9313cbf6d3ae,TODO: we need to address if this is needed at all. There could be a better way to do this
aaefeebgj,oracle/Skater,pyinterpret/core/global_interpretation/partial_dependence.py,6e128c8c32c4a27dfbddb5e1295dd3df42f2ad57,9d4899705e8c6838fd35d91a49fc9313cbf6d3ae,TODO: There might be a better place to do this check
aaefeebhb,oracle/Skater,pyinterpret/core/global_interpretation/partial_dependence.py,43f20041b24192252d967b0b1c055819b3caf938,843894305ae0495215264f29c14ab863621b9946,TODO: if redundant; and is needed there could be a better way to address it
aaefeebhd,oracle/Skater,pyinterpret/core/global_interpretation/partial_dependence.py,43f20041b24192252d967b0b1c055819b3caf938,9d4899705e8c6838fd35d91a49fc9313cbf6d3ae,TODO: we need to address if this is needed at all. There could be a better way to do this
aaefeebhe,oracle/Skater,pyinterpret/core/global_interpretation/partial_dependence.py,43f20041b24192252d967b0b1c055819b3caf938,9d4899705e8c6838fd35d91a49fc9313cbf6d3ae,TODO: There might be a better place to do this check
aaefeebih,oracle/Skater,pyinterpret/core/global_interpretation/partial_dependence.py,9d4899705e8c6838fd35d91a49fc9313cbf6d3ae,843894305ae0495215264f29c14ab863621b9946,TODO: if redundant; and is needed there could be a better way to address it
aaefeedjc,oracle/Skater,pyinterpret/core/global_interpretation/partial_dependence.py,c067b43818362cfe2686c72d9e2840a2dbb27a91,843894305ae0495215264f29c14ab863621b9946,TODO: if redundant; and is needed there could be a better way to address it
aaefeedje,oracle/Skater,pyinterpret/core/global_interpretation/partial_dependence.py,c067b43818362cfe2686c72d9e2840a2dbb27a91,b9c000b7634e13434bd69953112efd42f2049d7e,TODO: we need to address if this is needed at all. There could be a better way to do this
aaefeedjf,oracle/Skater,pyinterpret/core/global_interpretation/partial_dependence.py,c067b43818362cfe2686c72d9e2840a2dbb27a91,b9c000b7634e13434bd69953112efd42f2049d7e,TODO: There might be a better place to do this check
aaefeeeca,oracle/Skater,pyinterpret/core/global_interpretation/partial_dependence.py,b9c000b7634e13434bd69953112efd42f2049d7e,843894305ae0495215264f29c14ab863621b9946,TODO: if redundant; and is needed there could be a better way to address it
aaefeegci,oracle/Skater,pyinterpret/core/global_interpretation/partial_dependence.py,8f7c3ab2bd2ea756138c706dafe98c2d0d0463d5,843894305ae0495215264f29c14ab863621b9946,TODO: if redundant; and is needed there could be a better way to address it
aaefeegda,oracle/Skater,pyinterpret/core/global_interpretation/partial_dependence.py,8f7c3ab2bd2ea756138c706dafe98c2d0d0463d5,843894305ae0495215264f29c14ab863621b9946,TODO: we need to address if this is needed at all. There could be a better way to do this
aaefeegdb,oracle/Skater,pyinterpret/core/global_interpretation/partial_dependence.py,8f7c3ab2bd2ea756138c706dafe98c2d0d0463d5,STILL_EXISTS,TODO: There might be a better place to do this check
aaefefccc,oracle/Skater,pyinterpret/core/global_interpretation/partial_dependence.py,e8b2e17f4eec658f6a6d53486dbbe3eac48bb0fa,STILL_EXISTS,feature2 is columns
aaefefecc,oracle/Skater,pyinterpret/core/global_interpretation/partial_dependence.py,e487e1e9d18bcf4b56d0d6048f4d041e2209fb3d,STILL_EXISTS,feature2 is columns
aaefefecf,oracle/Skater,pyinterpret/core/global_interpretation/partial_dependence.py,435387a125bf8e411bf0405c95374fc1ed8fa408,STILL_EXISTS,feature2 is columns
aaefefhdf,oracle/Skater,pyinterpret/core/global_interpretation/partial_dependence.py,26bb049b3d843811b2f1c46bb3e7d579a2f07f91,STILL_EXISTS,data_sample = pd.DataFrame(input_data; columns=data_columns)
aaefefhfc,oracle/Skater,lynxes/core/global_interpretation/partial_dependence.py,0ed11f77aa3fc1e9a95f1f6d76eef36933e1980e,STILL_EXISTS,feature_columns = ['feature: {}'.format(i) for i in pd_feature_ids]
aaefefhfi,oracle/Skater,lynxes/core/global_interpretation/partial_dependence.py,703b384345b4b69139761b315575559cb011471b,STILL_EXISTS,feature2 is columns
aaefefhgd,oracle/Skater,lynxes/data/dataset.py,ddb51e71d40c81e6f96264f2e1fed6563db3a9fa,STILL_EXISTS,Todo: we can probably remove some of the keys from data_info; and have properties
aaefefhjg,oracle/Skater,skater/core/global_interpretation/partial_dependence.py,c9ac86e33f959592ae343691744ab0ca533c454d,STILL_EXISTS,Todo: add static version of model.predict_subset_classes; use here
aaefefibf,oracle/Skater,skater/core/global_interpretation/feature_importance.py,7b6662eca5b03e343b11b8b2a1be24e7114dd069,5c1c2e1f7f1a9ed138e90402c3846703967b3361,Below is a weirdness because of how pandas plot is behaving. There might be a better way
aaefefjgf,oracle/Skater,skater/model/base.py,8e2f834c99f5cc56d11f52041e8a2d61ac3dfa1f,74f4291f00c14a6421f684cd2a2c6a15c7db6f50,kind of a hack. sklearn assumes the encoder it fit if
aaefefjgj,oracle/Skater,skater/model/base.py,8e2f834c99f5cc56d11f52041e8a2d61ac3dfa1f,STILL_EXISTS,to get 2 columns from label_binarize; we need
aaefegaai,oracle/Skater,skater/core/local_interpretation/text_interpreter.py,ce125e717e492e1b5a2722a4a1176d9ac331e0c2,STILL_EXISTS,TODO look into removing the below occurring side effect
aaefegabb,oracle/Skater,skater/core/visualizer/relevance_visualizer.py,ce125e717e492e1b5a2722a4a1176d9ac331e0c2,STILL_EXISTS,this is a temporary fix as there seems to be a bug in the wordcloud implementation used.
aaefegabe,oracle/Skater,skater/core/visualizer/relevance_visualizer.py,ce125e717e492e1b5a2722a4a1176d9ac331e0c2,STILL_EXISTS,TODO: Figure out a better fix
aaefegabf,oracle/Skater,skater/core/visualizer/relevance_visualizer.py,ce125e717e492e1b5a2722a4a1176d9ac331e0c2,STILL_EXISTS,TODO extend support for Word Cloud params
aaefegace,oracle/Skater,skater/core/local_interpretation/text_interpreter.py,9cabb26008ca466ff5cc9f1651162757917a8e9a,2d64a1b8d91c902a498c9e58d0748a8bf35942a6,TODO: extend support to other forms of Vectorization schemes - Feature Hashing
aaefegada,oracle/Skater,skater/core/local_interpretation/text_interpreter.py,3a6dc9d0bc3ce9678e1661668d31b60050fba38b,STILL_EXISTS,TODO: this is just a temporary solution for handling ngrams; figure out a better solution
aaefegadh,oracle/Skater,skater/core/visualizer/relevance_visualizer.py,2cb6e785e91368313a4ed90303afd1cb8632d966,STILL_EXISTS,TODO: Add support for better visualization and plotting may be bokeh
aaefegagc,oracle/Skater,skater/core/local_interpretation/text_interpreter.py,7c899117cfbc7e8a0188207dbb1913f5e5f3ed57,2d64a1b8d91c902a498c9e58d0748a8bf35942a6,TODO add summarizer type as a sub-argument
aaefegajc,oracle/Skater,skater/core/global_interpretation/rule_list.py,71ea48197fa7df84e9bf3b78934807fb7dd90683,STILL_EXISTS,TODO: Extend it for multi-class classification
aaefegbaa,oracle/Skater,skater/tests/test_rule_list.py,20c9f022d73be56c1a0cb8afd7cd82f79a49c2b5,STILL_EXISTS,(GNU Debugger). This is a temporary workaround. Follow the below mentioned steps
aaefegbaj,oracle/Skater,skater/core/global_interpretation/interpretable_models/rule_lists.py,5540f987514f37b04232164dc04ed9a7ca331c55,STILL_EXISTS,2. if undiscretize_feature_list is not empty and discretization flag is enabled; filter the ones not needed
aaefegbba,oracle/Skater,skater/core/global_interpretation/interpretable_models/rule_lists.py,5540f987514f37b04232164dc04ed9a7ca331c55,STILL_EXISTS,needed
aaefegbbf,oracle/Skater,skater/tests/test_rule_list.py,08beaabdf51cf8b4b752a51314a1bd1e2ff65901,7cd00a898fb2c01c2da7300e8ae9028fd805b5e4,features_to_descritize = sbrl_inst.filter_to_be_discretize(Xtest.columns; [\"Pregnant\"])
aaefegbdi,oracle/Skater,skater/core/global_interpretation/interpretable_models/brlc.py,ec20c43a73bb63f05b8d550aaac11ac36bbe2d29,91306a7e8cb5e98af602febbb3f84c24a9bbc013,TODO: Add support for out of core computation
aaefegdag,oracle/Skater,skater/util/image_ops.py,820d48b65545c2e51f02f762b92182e21a55ffde,STILL_EXISTS,TODO: function needs to be recoded correctly
aaefegead,oracle/Skater,skater/core/visualizer/text_relevance_visualizer.py,632ab78f78ced5a40c488244647ccf1e1fa375d4,STILL_EXISTS,set the params needed for the plot
aaefegech,oracle/Skater,skater/util/image_ops.py,e533bc1eb253262785d3f889bf5deac1f77d3b1a,STILL_EXISTS,TODO: Add ability to handle a pre-defined axes for plotting
aaefegeci,oracle/Skater,skater/core/visualizer/text_relevance_visualizer.py,4f35d5c894b49ee7ad7058e92b757a5f5ba2326c,STILL_EXISTS,TODO: Add a HTML validator to verify that HTML structure
aaefegeda,oracle/Skater,doc/conf.py,c8b03264f0bc7b917fb341ed602b8940b135d134,STILL_EXISTS,this is needed for some reason...
aaefeggjg,oracle/Skater,skater/util/image_ops.py,3f446bcc9156e84a92f4bd89809d94db3113ac1e,STILL_EXISTS,number of columns to expand
aaefegjbd,Accenture/AmpliGraph,ampligraph/evaluation/protocol.py,3de385c15a8f21e816f3ef1958e1607882fed6eb,STILL_EXISTS,TODO: TEC-1568 vectorize this
aaefegjch,Accenture/AmpliGraph,ampligraph/evaluation/protocol.py,3de385c15a8f21e816f3ef1958e1607882fed6eb,STILL_EXISTS,must be careful with memory footprint here. This is why we split in batches:
aaefegjda,Accenture/AmpliGraph,ampligraph/latent_features/loss_functions.py,3de385c15a8f21e816f3ef1958e1607882fed6eb,c3b2752d3f549c2867dc4919c9268bb9c4d5b3c6,TODO (consider add regularization term - now handled in params constructor)
aaefehaeb,Accenture/AmpliGraph,ampligraph/latent_features/misc.py,3de385c15a8f21e816f3ef1958e1607882fed6eb,c7ef4f2e596f07fd71a591d6d52aacbddc091aae,TODO: SEEMINLY DUPLICATE FUNCTIONALITY WITH get_neighbour_triples(e; g) and get_entity_triples(e; g)
aaefehaie,Accenture/AmpliGraph,ampligraph/latent_features/misc.py,3de385c15a8f21e816f3ef1958e1607882fed6eb,c7ef4f2e596f07fd71a591d6d52aacbddc091aae,TODO: Could be re-written to make use of similar functionality in methods above.
aaefehaji,Accenture/AmpliGraph,ampligraph/latent_features/model_utils.py,3de385c15a8f21e816f3ef1958e1607882fed6eb,STILL_EXISTS,\"\"\"This module contains utils function manipulating around a neural knowledge graph embedding model || \"\"\"
aaefehbbf,Accenture/AmpliGraph,ampligraph/latent_features/models.py,3de385c15a8f21e816f3ef1958e1607882fed6eb,STILL_EXISTS,TODO TEC-1529: add early stopping criteria
aaefehbbi,Accenture/AmpliGraph,ampligraph/latent_features/models.py,3de385c15a8f21e816f3ef1958e1607882fed6eb,5a2dbf8c312cc6774d24b61a04259b99c82c3365,TODO TEC-1567 performance: check feed_dict
aaefehbce,Accenture/AmpliGraph,ampligraph/latent_features/models.py,3de385c15a8f21e816f3ef1958e1607882fed6eb,79929d2783eeddeb8dbac8acddabff5fdc293a78,TODO: missing docstring
aaefehbhf,Accenture/AmpliGraph,docs/conf.py,3de385c15a8f21e816f3ef1958e1607882fed6eb,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaefehcdc,Accenture/AmpliGraph,tests/ampligraph/evaluation/test_protocol.py,3de385c15a8f21e816f3ef1958e1607882fed6eb,STILL_EXISTS,@pytest.mark.skip(reason=\"excluded to try out jenkins.\")   # TODO: re-enable this
aaefehchg,Accenture/AmpliGraph,tests/ampligraph/temporal/test_models_temp.py,3de385c15a8f21e816f3ef1958e1607882fed6eb,STILL_EXISTS,TODO: TEC-1819: Enable coexisting eager and graph tf tests:
aaefeheai,Accenture/AmpliGraph,ampligraph/latent_features/models.py,cd9b8e8baebdbc4b3c35c57d68a86e2c9aef875e,9aa233632019e50f166839504d8ece8a209c4495,print('Found Better')
aaefeheej,Accenture/AmpliGraph,ampligraph/latent_features/models.py,1eca3596681962b3886c19f60104b28a422a27d1,a36faa9feafed55dca3d1d0d7341c39c01f1549f,TODO - update this is generate negative with new negative matrix
aaefehefa,Accenture/AmpliGraph,ampligraph/latent_features/models.py,1eca3596681962b3886c19f60104b28a422a27d1,a36faa9feafed55dca3d1d0d7341c39c01f1549f,TODO - replace repeat with tile to create positive matrix
aaefeheje,Accenture/AmpliGraph,ampligraph/evaluation/protocol.py,9aa233632019e50f166839504d8ece8a209c4495,STILL_EXISTS,must be careful with memory footprint here. This is why we split in batches:
aaefehejj,Accenture/AmpliGraph,ampligraph/latent_features/models.py,9aa233632019e50f166839504d8ece8a209c4495,STILL_EXISTS,TODO TEC-1529: add early stopping criteria
aaefehfaa,Accenture/AmpliGraph,ampligraph/latent_features/models.py,9aa233632019e50f166839504d8ece8a209c4495,755a23f6e30f69d3bc2879ce14997018acb61bba,TODO TEC-1567 performance: check feed_dict
aaefehfje,Accenture/AmpliGraph,ampligraph/latent_features/models.py,17a8024fdab073b8298d16fa7dd5968112bfeaa1,e6a7a3cdd845761da3417121fedc0248d9115069,print('Found Better')
aaefehghf,Accenture/AmpliGraph,ampligraph/latent_features/models.py,f1f6464fb665180a4e23482a630313635e340ea1,a47b8a3828fe4f686a620d98137719d16402581f,TODO - update this is generate negative with new negative matrix
aaefehghg,Accenture/AmpliGraph,ampligraph/latent_features/models.py,f1f6464fb665180a4e23482a630313635e340ea1,a47b8a3828fe4f686a620d98137719d16402581f,TODO - replace repeat with tile to create positive matrix
aaefeibjh,Accenture/AmpliGraph,ampligraph/datasets/datasets.py,6c581bedb436b20b096ea839ee5460633d601755,STILL_EXISTS,TODO - add error checking
aaefeijba,Accenture/AmpliGraph,ampligraph/latent_features/models.py,1b11db830f2a3376bd1eb2ae9928d5c23423ccd2,STILL_EXISTS,TODO: Handle re-loading
aaefeijcj,Accenture/AmpliGraph,ampligraph/latent_features/models.py,3a39cae8c61bf4fbc30071ac350b6bf441bf7906,STILL_EXISTS,TODO: Handle re-loading
aaefeijdj,Accenture/AmpliGraph,ampligraph/datasets/sqlite_adapter.py,6983b9117be92a950f7929c51ec583cbeafe0f77,666ee16d5f1cbdfbe7b901c4ad7bd3b7b32238d9,TODO: drop and recreate tables
aaefeijed,Accenture/AmpliGraph,ampligraph/latent_features/models.py,6983b9117be92a950f7929c51ec583cbeafe0f77,STILL_EXISTS,TODO: Handle re-loading
aaefeijef,Accenture/AmpliGraph,ampligraph/latent_features/models.py,6983b9117be92a950f7929c51ec583cbeafe0f77,STILL_EXISTS,TODO: this needs to be changed to take care of >1m concepts.
aaefeijja,Accenture/AmpliGraph,ampligraph/latent_features/models.py,25ffd8431000e9f540f08128fced358ffa3153b8,STILL_EXISTS,TODO: Handle re-loading
aaefeijjc,Accenture/AmpliGraph,ampligraph/latent_features/models.py,25ffd8431000e9f540f08128fced358ffa3153b8,STILL_EXISTS,TODO: this needs to be changed to take care of >1m concepts.
aaefeijjf,Accenture/AmpliGraph,ampligraph/latent_features/models.py,051b6290e5d33796f6a5d4aea7d3b93ed79c1066,STILL_EXISTS,TODO: Handle re-loading
aaefeijjh,Accenture/AmpliGraph,ampligraph/latent_features/models.py,051b6290e5d33796f6a5d4aea7d3b93ed79c1066,STILL_EXISTS,TODO: this needs to be changed to take care of >1m concepts.
aaefejabh,Accenture/AmpliGraph,ampligraph/latent_features/models.py,67b85d1cd2ffb1984fad8c770bb79fdcdcd26830,STILL_EXISTS,TODO: this needs to be changed to take care of >1m concepts.
aaefejbda,Accenture/AmpliGraph,ampligraph/evaluation/protocol.py,627f6d91f369fa82f4f42853e2874556c962b58c,STILL_EXISTS,TODO - Revise this; use dict comprehension instead of for loops
aaefejccc,Accenture/AmpliGraph,ampligraph/evaluation/protocol.py,ba48d083790635c0b4b7c089998d92745e8a7442,STILL_EXISTS,TODO - Revise this; use dict comprehension instead of for loops
aaefejcfj,Accenture/AmpliGraph,ampligraph/latent_features/models.py,b439696b2f129079ad9f20621f21c45c415ce1ee,STILL_EXISTS,TODO: change generator to use a particular dataset
aaeffahgi,Accenture/AmpliGraph,ampligraph/latent_features/models.py,c6c7ca5a66c1e709cf0770212b7e50f3e7952e3f,STILL_EXISTS,TODO: Check should trainable=True really be set here
aaeffahid,Accenture/AmpliGraph,ampligraph/latent_features/models.py,c6c7ca5a66c1e709cf0770212b7e50f3e7952e3f,STILL_EXISTS,TODO: Move to inside the Loss function class
aaeffahjc,Accenture/AmpliGraph,ampligraph/latent_features/models.py,c6c7ca5a66c1e709cf0770212b7e50f3e7952e3f,354d1f743d76b05ae7be83574930dcf2c5479481,TODO: Implement ConvEAdapter (?)
aaeffaibe,Accenture/AmpliGraph,ampligraph/latent_features/models.py,c6c7ca5a66c1e709cf0770212b7e50f3e7952e3f,354d1f743d76b05ae7be83574930dcf2c5479481,needed = (self.batch_size * 2 - unique_entities.shape[0])
aaeffaicd,Accenture/AmpliGraph,ampligraph/latent_features/models.py,c6c7ca5a66c1e709cf0770212b7e50f3e7952e3f,354d1f743d76b05ae7be83574930dcf2c5479481,unique_entities = np.int32(np.concatenate([unique_entities; self.leftover_entities[:needed]]; axis=0))
aaeffaidd,Accenture/AmpliGraph,ampligraph/latent_features/models.py,c6c7ca5a66c1e709cf0770212b7e50f3e7952e3f,68324ec3acecff31f67828ffafc1f6fc1d0948ed,TODO: Must be implemented with AmpliGraphDatasetAdapter class
aaeffaidf,Accenture/AmpliGraph,ampligraph/latent_features/models.py,c6c7ca5a66c1e709cf0770212b7e50f3e7952e3f,STILL_EXISTS,TODO: Alternative implementation with datasetadapter
aaeffaifd,Accenture/AmpliGraph,ampligraph/latent_features/models.py,c6c7ca5a66c1e709cf0770212b7e50f3e7952e3f,STILL_EXISTS,TODO: Update _test_generator; maybe include scores_filter (?)
aaeffaijc,Accenture/AmpliGraph,ampligraph/latent_features/loss_functions.py,354d1f743d76b05ae7be83574930dcf2c5479481,32779e4c15e8cd1e6b776cb89bdeead87954e2b1,TODO
aaeffaije,Accenture/AmpliGraph,ampligraph/latent_features/loss_functions.py,354d1f743d76b05ae7be83574930dcf2c5479481,32779e4c15e8cd1e6b776cb89bdeead87954e2b1,TODO: Remove need for eta in parameters (its tied to loss_registry)
aaeffajfh,Accenture/AmpliGraph,ampligraph/latent_features/models.py,68324ec3acecff31f67828ffafc1f6fc1d0948ed,c865d9ccdeb9c2618e4bad4a63e9badb3ac34c2e,TODO: Remove corruption entities
aaeffbaeb,Accenture/AmpliGraph,ampligraph/datasets/conve_adapter.py,af828801392b6a8ac4d7e7882576f8d1444bbf64,e4b4c0ea8ad24e30dbd00e98e6ede87fa33cdbe4,TODO: This could lead to situation where reusing dataset handler could lead to nonfiltered data
aaeffbaed,Accenture/AmpliGraph,ampligraph/latent_features/models.py,af828801392b6a8ac4d7e7882576f8d1444bbf64,4d84610b07b019d1f9a420584957d85c59c7ab8a,TODO:
aaeffdaic,Accenture/AmpliGraph,ampligraph/latent_features/models/ConvE.py,e4b4c0ea8ad24e30dbd00e98e6ede87fa33cdbe4,STILL_EXISTS,TODO: Select by layer
aaeffdajf,Accenture/AmpliGraph,ampligraph/latent_features/models/ConvE.py,6542f1112fcd5d590915276a3a60a226f519a857,STILL_EXISTS,This can cause variance shift and reduce model performance; so have moved it after as recommended alternative
aaeffdbbc,Accenture/AmpliGraph,ampligraph/latent_features/models/ConvE.py,6542f1112fcd5d590915276a3a60a226f519a857,STILL_EXISTS,TODO: Select by layer
aaeffdbdb,Accenture/AmpliGraph,ampligraph/datasets/oneton_adapter.py,a8c8d4d0b6de2625b3f01ad1baca8c3a04c3feda,STILL_EXISTS,Set dataset_type with subject-object columns reversed
aaeffdccf,Accenture/AmpliGraph,ampligraph/latent_features/models/ConvE.py,39961137be5052a8a485e09e9a0a24f74b0e35b5,STILL_EXISTS,Accumulate scores from X_test columns
aaeffdcdh,Accenture/AmpliGraph,ampligraph/datasets/oneton_adapter.py,d7d4143a5d2b8b2cd187f9f0e1989221f957f156,STILL_EXISTS,Append dummy object columns
aaeffdcge,Accenture/AmpliGraph,ampligraph/latent_features/loss_functions.py,20174eaac60b793c78daa88117521d62d8e58409,STILL_EXISTS,Temp fix for numerical instability of multiclass loss
aaeffdcgh,Accenture/AmpliGraph,ampligraph/latent_features/loss_functions.py,d842dc75c3c4c37c5f538e8e8b2a60321077f57e,STILL_EXISTS,Temp fix for numerical instability of multiclass loss
aaeffdchf,Accenture/AmpliGraph,ampligraph/latent_features/loss_functions.py,c82cca5c27be056f34890e7e21553d0207584ff7,STILL_EXISTS,Fix for numerical instability of multiclass loss
aaeffdfei,Accenture/AmpliGraph,ampligraph/datasets/oneton_adapter.py,10716ee9586d126fbe0c0f259ffb5a517c1e0f34,STILL_EXISTS,Append dummy object columns
aaeffdhce,Accenture/AmpliGraph,tests/ampligraph/evaluation/test_protocol.py,631e26b98934e74c618005fff906d4978f0c6c26,STILL_EXISTS,and thus avoiding exporting unused global variable.
aaeffdjjh,tensorflow/privacy,privacy/optimizers/gaussian_average_query.py,afb8189dba2fb310ca582633dcacc74e221855ff,STILL_EXISTS,unused.
aaeffeeaf,tensorflow/privacy,research/pate_2017/input.py,93e9585f185a2b9b10f6f4c98433526bb7300a78,STILL_EXISTS,Fix label dimensions
aaeffefaf,tensorflow/privacy,research/pate_2017/train_student.py,93e9585f185a2b9b10f6f4c98433526bb7300a78,STILL_EXISTS,Store unused part of test set for use as a test set after student training
aaeffefjd,tensorflow/privacy,research/pate_2018/ICLR2018/plot_ls_q.py,93e9585f185a2b9b10f6f4c98433526bb7300a78,STILL_EXISTS,Unused.
aaeffegdd,tensorflow/privacy,research/pate_2018/ICLR2018/plot_partition.py,93e9585f185a2b9b10f6f4c98433526bb7300a78,STILL_EXISTS,Unused.
aaeffegge,tensorflow/privacy,research/pate_2018/ICLR2018/plots_for_slides.py,93e9585f185a2b9b10f6f4c98433526bb7300a78,STILL_EXISTS,Unused.
aaeffegii,tensorflow/privacy,research/pate_2018/ICLR2018/rdp_bucketized.py,93e9585f185a2b9b10f6f4c98433526bb7300a78,STILL_EXISTS,Unused.
aaeffehce,tensorflow/privacy,research/pate_2018/ICLR2018/rdp_cumulative.py,93e9585f185a2b9b10f6f4c98433526bb7300a78,STILL_EXISTS,Unused.
aaeffehge,tensorflow/privacy,research/pate_2018/ICLR2018/smooth_sensitivity_table.py,93e9585f185a2b9b10f6f4c98433526bb7300a78,STILL_EXISTS,Unused.
aaeffehif,tensorflow/privacy,research/pate_2018/ICLR2018/utility_queries_answered.py,93e9585f185a2b9b10f6f4c98433526bb7300a78,STILL_EXISTS,Unused.
aaeffeiej,tensorflow/privacy,research/pate_2018/core.py,93e9585f185a2b9b10f6f4c98433526bb7300a78,STILL_EXISTS,sum_j (n_j - M * p_j) = 0; and max_j (n_j - M * p_j) >= 0 as needed.
aaeffeifi,tensorflow/privacy,research/pate_2018/core.py,93e9585f185a2b9b10f6f4c98433526bb7300a78,STILL_EXISTS,Unused.
aaeffejcb,tensorflow/privacy,research/pate_2018/smooth_sensitivity.py,93e9585f185a2b9b10f6f4c98433526bb7300a78,STILL_EXISTS,If data-dependent bound is already better; we are done already.
aaeffejga,tensorflow/privacy,research/pate_2018/smooth_sensitivity.py,93e9585f185a2b9b10f6f4c98433526bb7300a78,STILL_EXISTS,Unused.
aaefffbgh,tensorflow/privacy,tutorials/mnist_dpsgd_tutorial_eager.py,c2d4b178813177fa2c4340070bc5dc9c84506efa,STILL_EXISTS,This dummy call is needed to obtain the var list.
aaefffbhe,tensorflow/privacy,tutorials/mnist_dpsgd_tutorial_keras.py,517584d7a66c32a75370cb8001b8fa85c1cf88e9,STILL_EXISTS,\"\"\"Training a CNN on MNIST with Keras and the DP SGD optimizer. ||  || **************************** PLEASE READ ME ************************************ ||  || A modification to Keras needed for this tutorial to work as it is currently || written is *being* pushed. While this modification is in the works; you can || make this tutorial work by making the following change to the TensorFlow source || code (disabling the reduction of the loss used to compile a model): ||  || Diff for file: tensorflow\/python\/keras\/engine\/training_utils.py ||  || ``` || + from tensorflow.python.ops.losses import losses_impl ||  ||   def get_loss_function(): ||  ||     ... ||  || -   return losses.LossFunctionWrapper(loss_fn; name=loss_fn.__name__) || +   return losses.LossFunctionWrapper(loss_fn; || +                                     name=loss_fn.__name__; || +                                     reduction=losses_impl.Reduction.NONE) || ``` ||  || This allows the DP-SGD optimizer to have access to the loss defined per || example rather than the mean of the loss for the entire minibatch. This is || needed to compute gradients for each microbatch contained in a minibatch. ||  || **************************** END OF PLEASE READ ME ***************************** ||  || \"\"\"
aaefffefh,tensorflow/privacy,privacy/dp_query/quantile_adaptive_clip_sum_query.py,3908429796a3d7fb7615f45592bb1a1fbf3f0e1a,STILL_EXISTS,Unused. To be set explicitly later.
aaefffjbi,tensorflow/privacy,privacy/bolton/model.py,ec18db5ec5d34922af90d39a94be365a9a4f5685,935d6e84808fc99146294a888fcd954dcb4e4274,\"\"\"Implements 1-time weight changes needed for Bolton method.
aaeffhjgb,tensorflow/privacy,tutorials/bolton_tutorial.py,c05c2aa0d407ae4edc48f085a883e80ca91e06d5,STILL_EXISTS,must extend from StrongConvexMixin and implement the associated methods.Some
aaeffjfai,tensorflow/privacy,tensorflow_privacy/privacy/dp_query/dp_query.py,313edfc80c10f1ae770f9528f641da7662911f40,STILL_EXISTS,\"\"\"An interface for differentially private query mechanisms. ||  || The DPQuery class abstracts the differential privacy mechanism needed by DP-SGD. ||  || The nomenclature is not specific to machine learning; but rather comes from || the differential privacy literature. Therefore; instead of talking about || examples; minibatches; and gradients; the code talks about records; samples and || queries. For more detail; please see the paper here: || https:\/\/arxiv.org\/pdf\/1812.06210.pdf ||  || A common usage paradigm for this class is centralized DP-SGD training on a || fixed set of training examples; which we call \"standard DP-SGD training.\" || In such training; SGD applies as usual by computing gradient updates from a set || of training examples that form a minibatch. However; each minibatch is broken || up into disjoint \"microbatches.\"  The gradient of each microbatch is computed || and clipped to a maximum norm; with the \"records\" for all such clipped gradients || forming a \"sample\" that constitutes the entire minibatch. Subsequently; that || sample can be \"queried\" to get an averaged; noised gradient update that can be || applied to model parameters. ||  || In order to prevent inaccurate accounting of privacy parameters; the only || means of inspecting the gradients and updates of SGD training is via the use || of the below interfaces; and through the accumulation and querying of a || \"sample state\" abstraction. Thus; accessing data is indirect on purpose. ||  || The DPQuery class also allows the use of a global state that may change between || samples. In the common situation where the privacy mechanism remains unchanged || throughout the entire training process; the global state is usually None. || \"\"\"
aaeffjgic,tensorflow/privacy,tensorflow_privacy/privacy/dp_query/quantile_adaptive_clip_sum_query.py,313edfc80c10f1ae770f9528f641da7662911f40,STILL_EXISTS,Unused. To be set explicitly later.
aaefgaahh,tensorflow/privacy,tensorflow_privacy/research/pate_2017/input.py,313edfc80c10f1ae770f9528f641da7662911f40,STILL_EXISTS,Fix label dimensions
aaefgabhg,tensorflow/privacy,tensorflow_privacy/research/pate_2017/train_student.py,313edfc80c10f1ae770f9528f641da7662911f40,STILL_EXISTS,Store unused part of test set for use as a test set after student training
aaefgacgf,tensorflow/privacy,tensorflow_privacy/research/pate_2018/ICLR2018/plot_ls_q.py,313edfc80c10f1ae770f9528f641da7662911f40,STILL_EXISTS,Unused.
aaefgadaf,tensorflow/privacy,tensorflow_privacy/research/pate_2018/ICLR2018/plot_partition.py,313edfc80c10f1ae770f9528f641da7662911f40,STILL_EXISTS,Unused.
aaefgaddg,tensorflow/privacy,tensorflow_privacy/research/pate_2018/ICLR2018/plots_for_slides.py,313edfc80c10f1ae770f9528f641da7662911f40,STILL_EXISTS,Unused.
aaefgadga,tensorflow/privacy,tensorflow_privacy/research/pate_2018/ICLR2018/rdp_bucketized.py,313edfc80c10f1ae770f9528f641da7662911f40,STILL_EXISTS,Unused.
aaefgadjg,tensorflow/privacy,tensorflow_privacy/research/pate_2018/ICLR2018/rdp_cumulative.py,313edfc80c10f1ae770f9528f641da7662911f40,STILL_EXISTS,Unused.
aaefgaedg,tensorflow/privacy,tensorflow_privacy/research/pate_2018/ICLR2018/smooth_sensitivity_table.py,313edfc80c10f1ae770f9528f641da7662911f40,STILL_EXISTS,Unused.
aaefgaefh,tensorflow/privacy,tensorflow_privacy/research/pate_2018/ICLR2018/utility_queries_answered.py,313edfc80c10f1ae770f9528f641da7662911f40,STILL_EXISTS,Unused.
aaefgafcb,tensorflow/privacy,tensorflow_privacy/research/pate_2018/core.py,313edfc80c10f1ae770f9528f641da7662911f40,STILL_EXISTS,sum_j (n_j - M * p_j) = 0; and max_j (n_j - M * p_j) >= 0 as needed.
aaefgafda,tensorflow/privacy,tensorflow_privacy/research/pate_2018/core.py,313edfc80c10f1ae770f9528f641da7662911f40,STILL_EXISTS,Unused.
aaefgafjd,tensorflow/privacy,tensorflow_privacy/research/pate_2018/smooth_sensitivity.py,313edfc80c10f1ae770f9528f641da7662911f40,STILL_EXISTS,If data-dependent bound is already better; we are done already.
aaefgagdc,tensorflow/privacy,tensorflow_privacy/research/pate_2018/smooth_sensitivity.py,313edfc80c10f1ae770f9528f641da7662911f40,STILL_EXISTS,Unused.
aaefgahaf,tensorflow/privacy,tensorflow_privacy/tutorials/bolton_tutorial.py,313edfc80c10f1ae770f9528f641da7662911f40,STILL_EXISTS,must extend from StrongConvexMixin and implement the associated methods.Some
aaefgaifj,tensorflow/privacy,tensorflow_privacy/tutorials/mnist_dpsgd_tutorial_eager.py,313edfc80c10f1ae770f9528f641da7662911f40,STILL_EXISTS,This dummy call is needed to obtain the var list.
aaefgddfe,tensorflow/privacy,tutorials/mnist_dpsgd_tutorial_common.py,10335f61775faabe5e931f30b0ecf91f0719b727,STILL_EXISTS,Give inputs statically known shapes; needed for TPUs.
aaefgdgaa,tensorflow/privacy,tensorflow_privacy/privacy/membership_inference_attack/run_attack.py,88dd8771bf4590eb235340d91850884aa0e9e161,STILL_EXISTS,Unused
aaefgdhfb,tensorflow/privacy,tensorflow_privacy/privacy/membership_inference_attack/tf_estimator_evaluation_example.py,a0e1b728388d16606057c133cb638f1aae3218a9,7c537572503ad61a9f09ac0a3d2e71833e5d09a9,Can set summary_writer to None if not needed.
aaefgebag,tensorflow/privacy,tensorflow_privacy/privacy/estimators/binary_class_head.py,3a641e077eb5a68abfb87553b2eccbd991cca8c3,STILL_EXISTS,Unused for this head.
aaefgebgb,tensorflow/privacy,tensorflow_privacy/privacy/estimators/multi_class_head.py,3a641e077eb5a68abfb87553b2eccbd991cca8c3,STILL_EXISTS,Unused for this head.
aaefgecfh,tensorflow/privacy,tensorflow_privacy/privacy/estimators/multi_label_head.py,a69b01339069702b190754d572ea18489481de1a,STILL_EXISTS,Unused for this head.
aaefgeege,tensorflow/privacy,tensorflow_privacy/privacy/estimators/v1/head.py,d703168de2db15936e71b8c7de152b649d3b337b,STILL_EXISTS,Collect together all protected access items needed from base head.
aaefgfagh,tensorflow/privacy,tensorflow_privacy/privacy/membership_inference_attack/membership_inference_attack.py,2c810440d92c5310bcb08d9ef4616e20b23be992,STILL_EXISTS,TODO(b\/175870479): Implement membership scores for predicted attackers.
aaefgfbbb,tensorflow/privacy,tensorflow_privacy/privacy/analysis/rdp_accountant.py,be8175bfaca97916417d6a251b00444577dbf197,STILL_EXISTS,For small alpha; we are better of with bound via KL divergence:
aaefgfecj,torchgan/torchgan,docs/source/conf.py,f2591171668114062413d93946cac87fcc55e33a,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaefgfegj,torchgan/torchgan,docs/source/conf.py,f2591171668114062413d93946cac87fcc55e33a,a5d3425d3893f18ed55515777c9582eeb0b2b00d,`kw` catches `env=None` needed for newer sphinx while maintaining
aaefgfeii,torchgan/torchgan,torchgan/models/model.py,e027faff2fcbd9c2ac6c1406fa6f719809b8e654,STILL_EXISTS,FIXME(Aniket1998): If a user is overriding the default initializer; he must also override the constructor
aaefgfeij,torchgan/torchgan,torchgan/models/model.py,e027faff2fcbd9c2ac6c1406fa6f719809b8e654,STILL_EXISTS,Find an efficient workaround by fixing the initialization mechanism
aaefgffdd,torchgan/torchgan,docs/source/conf.py,a5d3425d3893f18ed55515777c9582eeb0b2b00d,277582ab4859a82a49766f33650ff66e8acbb1de,Fix navigation bar to top of page?
aaefgfgcd,torchgan/torchgan,torchgan/trainer/trainer.py,73bf550b7100891b57daceb189aa3ecf538162a7,a9f28fda26c911a94b76bf473917b028aa5807d6,Not needed but we need to store this to avoid errors. Also makes life simpler
aaefgfgch,torchgan/torchgan,torchgan/trainer/trainer.py,73bf550b7100891b57daceb189aa3ecf538162a7,02e009ae0d95907077bb083d2ce15f439bcc1c09,FIXME(Aniket1998): Metrics step should be number of epochs so far
aaefgfgdf,torchgan/torchgan,torchgan/metrics/classifierscore.py,325a680c90764346d18e6a0a5801a056acae2163,150d8473c0e6b1d64568d87b1427dec6e8f6dbbc,NOTE(avik-pal): We make the shift from cpu to device everytime. This is not efficient
aaefgfgef,torchgan/torchgan,torchgan/trainer/trainer.py,77a0ebd47859a37ee9f46a6784af3fca5586f07d,39d31f5e9bbb7053e524358a4023f76d750cc69c,TODO(Aniket1998): This is a terrible temporary fix
aaefgfgfc,torchgan/torchgan,torchgan/layers/denseblock.py,ed18aa78dd1a5e9772cd36d8523c2af7b38917f8,STILL_EXISTS,FIXME(Aniket1998): There is no way to pass an option for bottleneck channels
aaefgfhbd,torchgan/torchgan,torchgan/trainer/trainer.py,ee5153d1ec10774d2b3aa1a9110539ea701394e1,a9f28fda26c911a94b76bf473917b028aa5807d6,but we might need to think of a better solution
aaefgfhce,torchgan/torchgan,tests/__init__.py,2578ef6c4bfe3a5fe12a8b305067fabd99f770e1,STILL_EXISTS,Needed to collect coverage data
aaefgfhcf,torchgan/torchgan,tests/torchgan/__init__.py,2578ef6c4bfe3a5fe12a8b305067fabd99f770e1,STILL_EXISTS,Needed to collect coverage data
aaefgfhda,torchgan/torchgan,torchgan/trainer/base_trainer.py,a9f28fda26c911a94b76bf473917b028aa5807d6,STILL_EXISTS,Not needed but we need to store this to avoid errors. Also makes life simpler
aaefgfhdd,torchgan/torchgan,torchgan/trainer/base_trainer.py,a9f28fda26c911a94b76bf473917b028aa5807d6,STILL_EXISTS,but we might need to think of a better solution
aaefgfhje,awslabs/sockeye,docs/conf.py,45664353a0b0ff0fb5f69dc6512a3326dc6f2aa5,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaefgfigj,awslabs/sockeye,sockeye/attention.py,45664353a0b0ff0fb5f69dc6512a3326dc6f2aa5,STILL_EXISTS,\"\"\" || Input to attention callables. ||  || :param seq_idx: Decoder time step \/ sequence index. || :param query: Query input to attention mechanism; e.g. decoder hidden state (plus previous word). || \"\"\"
aaefgfjbj,awslabs/sockeye,sockeye/attention.py,45664353a0b0ff0fb5f69dc6512a3326dc6f2aa5,STILL_EXISTS,TODO: It would be nice if SequenceMask could take a 2d input...
aaefgfjcb,awslabs/sockeye,sockeye/attention.py,45664353a0b0ff0fb5f69dc6512a3326dc6f2aa5,STILL_EXISTS,TODO: we should probably replace this with a multiplication of a 0-1 mask; to avoid the multiplication
aaefgfjci,awslabs/sockeye,sockeye/average.py,45664353a0b0ff0fb5f69dc6512a3326dc6f2aa5,STILL_EXISTS,\"\"\" || Average parameters from multiple model checkpoints.  Checkpoints can be either || specificed manually or automatically chosen according to one of several || strategies.  The default strategy of simply selecting the top-scoring N points || works well in practice. || \"\"\"
aaefgfjeg,awslabs/sockeye,sockeye/average.py,45664353a0b0ff0fb5f69dc6512a3326dc6f2aa5,STILL_EXISTS,Points dominated by a previous better point have lifespan 0
aaefgfjic,awslabs/sockeye,sockeye/callback.py,45664353a0b0ff0fb5f69dc6512a3326dc6f2aa5,762ce78e4e49b9ba5d14eb0a48d97f19c8807707,TODO(fhieber): MXNet Speedometer uses root logger. How to fix this?
aaefggagj,awslabs/sockeye,sockeye/data_io.py,45664353a0b0ff0fb5f69dc6512a3326dc6f2aa5,STILL_EXISTS,TODO: consider more memory-efficient data reading (load from disk on demand)
aaefggaha,awslabs/sockeye,sockeye/data_io.py,45664353a0b0ff0fb5f69dc6512a3326dc6f2aa5,STILL_EXISTS,TODO: consider using HDF5 format for language data
aaefggahb,awslabs/sockeye,sockeye/data_io.py,45664353a0b0ff0fb5f69dc6512a3326dc6f2aa5,STILL_EXISTS,TODO: consider avoiding explicitly creating length and label arrays to save host memory
aaefggahi,awslabs/sockeye,sockeye/data_io.py,45664353a0b0ff0fb5f69dc6512a3326dc6f2aa5,STILL_EXISTS,TODO: num pad examples is not set here if fillup strategy would be padding
aaefggajd,awslabs/sockeye,sockeye/decoder.py,45664353a0b0ff0fb5f69dc6512a3326dc6f2aa5,40c9afea0bb48e1e86751adc463091ef114c2cc6,TODO: implement variant without input feeding
aaefggajj,awslabs/sockeye,sockeye/decoder.py,45664353a0b0ff0fb5f69dc6512a3326dc6f2aa5,b6775459365174f18d7cc36d1fa94a6f8c31d0a0,TODO dropout?
aaefggbcf,awslabs/sockeye,sockeye/decoder.py,45664353a0b0ff0fb5f69dc6512a3326dc6f2aa5,40c9afea0bb48e1e86751adc463091ef114c2cc6,TODO: possible alternative: feed back the context vector instead of the hidden (see lamtram)
aaefggbgi,awslabs/sockeye,sockeye/encoder.py,45664353a0b0ff0fb5f69dc6512a3326dc6f2aa5,40c9afea0bb48e1e86751adc463091ef114c2cc6,TODO give more control on encoder architecture
aaefggcab,awslabs/sockeye,sockeye/inference.py,45664353a0b0ff0fb5f69dc6512a3326dc6f2aa5,STILL_EXISTS,TODO: max_output_length adaptive to source_length
aaefggcac,awslabs/sockeye,sockeye/inference.py,45664353a0b0ff0fb5f69dc6512a3326dc6f2aa5,41407b771cd9a3a459adcbe98bd0f111a65ed0eb,average attention prob scores. TODO: is there a smarter way to do this?
aaefggceh,awslabs/sockeye,sockeye/lexicon.py,45664353a0b0ff0fb5f69dc6512a3326dc6f2aa5,d2afc1df95938d6bc1ed0178682ab2ae66dc63f1,TODO: once half-precision works; use float16 for this variable to save memory
aaefggfbb,awslabs/sockeye,test/test_coverage.py,45664353a0b0ff0fb5f69dc6512a3326dc6f2aa5,STILL_EXISTS,this is needed to modulate the 0 input. The output changes according to the activation type used.
aaefgggdh,awslabs/sockeye,sockeye/train.py,85f21bdb1361d8e3b19a96b141842e6a0cf05b07,7800f9ad97f45d7933023d6fe416035afd2f7735,TODO: The loading for continuation of the scheduler is done separately from the other parts
aaefgghhb,awslabs/sockeye,sockeye/decoder.py,4be96c3ce288a509096975cf7b1fc9c5c4dd0f37,STILL_EXISTS,TODO remove this once mxnet.rnn.SequentialRNNCell.reset() invokes recursive calls on layer cells
aaefggiej,awslabs/sockeye,sockeye/rnn.py,0a24e997d2c87c1725483d1fc71fad9c6cb2357e,STILL_EXISTS,List is needed for mypy; but not used in the code; only in special comments
aaefggiha,awslabs/sockeye,sockeye/encoder.py,a8e08d99ccc4cb2d024de1cd424808725ea7acbf,34748cb4a0853bfa2d1c97fbcf8caad7f74f4448,TODO break out EncoderConfig to allow use without populating options for full translation model
aaefghchc,awslabs/sockeye,sockeye/transformer.py,3f77dfbb5d90c3ef4eb9c50731b487c69efde151,d436ae8319e9cd531a562434f5022029ff933d6c,TODO: use a convolution?
aaefghcjd,awslabs/sockeye,sockeye/decoder.py,b6775459365174f18d7cc36d1fa94a6f8c31d0a0,STILL_EXISTS,TODO remove this once mxnet.rnn.SequentialRNNCell.reset() invokes recursive calls on layer cells
aaefghcje,awslabs/sockeye,sockeye/decoder.py,b6775459365174f18d7cc36d1fa94a6f8c31d0a0,40c9afea0bb48e1e86751adc463091ef114c2cc6,TODO remove this once mxnet.rnn.ModifierCell.reset() invokes reset() of base_cell
aaefghdcj,awslabs/sockeye,sockeye/decoder.py,53d6ef757ae1a28ac049caa1b0aa75a490dae600,40c9afea0bb48e1e86751adc463091ef114c2cc6,TODO: add context gating?
aaefgheea,awslabs/sockeye,sockeye/attention.py,36bda4d2ad25daa896e5030eab70738599dd0b71,STILL_EXISTS,TODO: just pull the expand dim out into the decoder
aaefgheeb,awslabs/sockeye,sockeye/attention.py,36bda4d2ad25daa896e5030eab70738599dd0b71,STILL_EXISTS,TODO: fix but just doing the reshape outside of this method...
aaefghefg,awslabs/sockeye,sockeye/convolution.py,36bda4d2ad25daa896e5030eab70738599dd0b71,STILL_EXISTS,TODO: pad + slice differently in decoder vs encoder: pad left vs pad centered
aaefghefh,awslabs/sockeye,sockeye/convolution.py,36bda4d2ad25daa896e5030eab70738599dd0b71,STILL_EXISTS,TODO: masking
aaefghefi,awslabs/sockeye,sockeye/convolution.py,36bda4d2ad25daa896e5030eab70738599dd0b71,STILL_EXISTS,TODO: dropout?
aaefghegg,awslabs/sockeye,sockeye/decoder.py,36bda4d2ad25daa896e5030eab70738599dd0b71,STILL_EXISTS,TODO: feed attention in from the outside
aaefghegh,awslabs/sockeye,sockeye/decoder.py,36bda4d2ad25daa896e5030eab70738599dd0b71,STILL_EXISTS,TODO: set them correctly. rnn_num_hidden = encoder num hidden; num_hidden = decoder_num_hidden
aaefghegi,awslabs/sockeye,sockeye/decoder.py,36bda4d2ad25daa896e5030eab70738599dd0b71,STILL_EXISTS,TODO: weight tying? lexicon and all other features the RNN supports?!
aaefghegj,awslabs/sockeye,sockeye/decoder.py,36bda4d2ad25daa896e5030eab70738599dd0b71,STILL_EXISTS,TODO: how to add the source embeddings to source_encoded?
aaefgheha,awslabs/sockeye,sockeye/decoder.py,36bda4d2ad25daa896e5030eab70738599dd0b71,STILL_EXISTS,TODO: potentially project the source_encoded (if different source num_hidden)
aaefghehf,awslabs/sockeye,sockeye/decoder.py,36bda4d2ad25daa896e5030eab70738599dd0b71,STILL_EXISTS,TODO: avoid double swapaxes (inside ConvGluBlock and when doing the query)
aaefghehg,awslabs/sockeye,sockeye/decoder.py,36bda4d2ad25daa896e5030eab70738599dd0b71,STILL_EXISTS,TODO: use layers.dot_attention instead? Especially as the attention_state doesn't make sense when doing attention once for all time steps
aaefghehh,awslabs/sockeye,sockeye/decoder.py,36bda4d2ad25daa896e5030eab70738599dd0b71,STILL_EXISTS,TODO: + also use layers.dot_attetion in the DotAttention class...
aaefghehj,awslabs/sockeye,sockeye/decoder.py,36bda4d2ad25daa896e5030eab70738599dd0b71,STILL_EXISTS,TODO: make typing.Optional
aaefgheic,awslabs/sockeye,sockeye/decoder.py,36bda4d2ad25daa896e5030eab70738599dd0b71,STILL_EXISTS,TODO: residual connections
aaefgheif,awslabs/sockeye,sockeye/decoder.py,36bda4d2ad25daa896e5030eab70738599dd0b71,STILL_EXISTS,TODO: implement decoding...
aaefgheih,awslabs/sockeye,sockeye/encoder.py,36bda4d2ad25daa896e5030eab70738599dd0b71,STILL_EXISTS,TODO: alternatively used fix sin\/cos pos embeddings?
aaefghejd,awslabs/sockeye,sockeye/encoder.py,36bda4d2ad25daa896e5030eab70738599dd0b71,STILL_EXISTS,TODO: put back in: we need a linear projection layer from the embeddings...
aaefghfae,awslabs/sockeye,sockeye/train.py,36bda4d2ad25daa896e5030eab70738599dd0b71,4d27aa30cff7b76cd5ff7bd3643d6a80d98a85dd,TODO: make this independent of the type of encoder:
aaefghfaf,awslabs/sockeye,sockeye/train.py,36bda4d2ad25daa896e5030eab70738599dd0b71,4d27aa30cff7b76cd5ff7bd3643d6a80d98a85dd,TODO: rnn_num_hidden should really be the encoder_num_hidden (to make it independent of the type of encoder used)
aaefghfbf,awslabs/sockeye,sockeye/encoder.py,612d523ed1d157fa2d423da9cf75e6dc35405189,STILL_EXISTS,TODO: put back in: we need a linear projection layer from the embeddings...
aaefghfcj,awslabs/sockeye,sockeye/decoder.py,da6e860328be9f0afb4090ab410150e52b556a8b,6110ef445321711c6b8be9735a72e160428f4b3f,TODO: positional embedding
aaefghffe,awslabs/sockeye,sockeye/decoder.py,6110ef445321711c6b8be9735a72e160428f4b3f,STILL_EXISTS,TODO: add dropout..
aaefghffj,awslabs/sockeye,sockeye/decoder.py,825b4fe3cd9e75499006547ed51a44998d7f32d7,STILL_EXISTS,TODO: weight_tying is not used anywhere
aaefghfhf,awslabs/sockeye,sockeye/loss.py,4d27aa30cff7b76cd5ff7bd3643d6a80d98a85dd,STILL_EXISTS,TODO: contribute ignoring padding for cross-entropy back to MXNet
aaefghfhg,awslabs/sockeye,sockeye/loss.py,4d27aa30cff7b76cd5ff7bd3643d6a80d98a85dd,STILL_EXISTS,Sum; normalizing if needed
aaefghgda,awslabs/sockeye,sockeye/training.py,4d27aa30cff7b76cd5ff7bd3643d6a80d98a85dd,STILL_EXISTS,TODO: Push update to MXNet to expose the optimizer (Module should have a get_optimizer method)
aaefghggj,awslabs/sockeye,sockeye/convolution.py,d3db3b2ce1bb886e77d1679c31ceafbf6cc72e58,STILL_EXISTS,TODO: add layer norm (can we do this without reshaping?!)
aaefghjie,awslabs/sockeye,sockeye/inference.py,120e4b9c574f37046f6e86c9ecb0c7b4bb14ab03,55da5f24b1e86a700e1a00fd44a61db2f00b4905,TODO we could save some tiny amount of time here by not running smallest_k for a finished sent
aaefgiafb,awslabs/sockeye,sockeye/train.py,d436ae8319e9cd531a562434f5022029ff933d6c,7800f9ad97f45d7933023d6fe416035afd2f7735,TODO
aaefgiage,awslabs/sockeye,sockeye/data_io.py,79f3d506894f39ae0b535e5a679bfaba84b3ce65,STILL_EXISTS,Once MXNet allows item assignments given a list of indices (probably MXNet 0.13): e.g a[[0;1;5;2]] = x;
aaefgiaja,awslabs/sockeye,sockeye/inference.py,7ed3d6aab291d8ffdc7a9f91ac3ea95234d14e76,3271ecefa3a7104d51b425369038578765dc5dba,Map from restricted to full vocab ids if needed
aaefgibje,awslabs/sockeye,contrib/sacrebleu/sacrebleu.py,c0f1296607f4db3200cb6c46d6ec5e86b25c395e,STILL_EXISTS,TODO: check MD5sum
aaefgicii,awslabs/sockeye,sockeye/decoder.py,cc247396becf3dc8ea7ffa2c293632e34550faea,7629faead18f49eae503a2ffea9b6057b7297e98,TODO: move this dropout functionality to OutputLayer
aaefgidca,awslabs/sockeye,sockeye/inference.py,cc247396becf3dc8ea7ffa2c293632e34550faea,91dac9f28d4a631479db7aeab388edb58779eaa6,TODO target embedding. Possible optimization: only embed the last and cache the previous target embed vectors by returning them in the states list.
aaefgidch,awslabs/sockeye,sockeye/layers.py,cc247396becf3dc8ea7ffa2c293632e34550faea,3042a3650349b4479dca63f72fd51d2deafe362e,TODO dropout?
aaefgieea,awslabs/sockeye,sockeye/layers.py,320553d9a1a86a3d148f23d250359b3cba1b95dd,STILL_EXISTS,TODO: Contribute these to MXNet?  For now it appears that registered activation types must be implemented in C++.
aaefgiehd,awslabs/sockeye,sockeye/decoder.py,7baef3e3e0cf33cc62594f17a26d19479d2df505,40c9afea0bb48e1e86751adc463091ef114c2cc6,TODO: compute the source encoded projection only once for efficiency reasons
aaefgifaf,awslabs/sockeye,sockeye/data_io.py,0ed81fd75f66d4fc6413e1e9a6985860cf151568,f5e9ec7b130505e29093e52456d77e5765f8cb70,Once MXNet allows item assignments given a list of indices (probably MXNet 1.0): e.g a[[0;1;5;2]] = x;
aaefgifie,awslabs/sockeye,sockeye/layers.py,17b4d3229067414bec527b5c7b6c70fd43b32cf6,STILL_EXISTS,TODO: Contribute these to MXNet?  For now it appears that registered activation types must be implemented in C++.
aaefgifig,awslabs/sockeye,sockeye/layers.py,17b4d3229067414bec527b5c7b6c70fd43b32cf6,3042a3650349b4479dca63f72fd51d2deafe362e,TODO dropout?
aaefgigbf,awslabs/sockeye,sockeye/decoder.py,3915916e0eed08b1dfbf67a8e79c86bae23dde52,7629faead18f49eae503a2ffea9b6057b7297e98,TODO: move this dropout functionality to OutputLayer
aaefgigbi,awslabs/sockeye,sockeye/decoder.py,3915916e0eed08b1dfbf67a8e79c86bae23dde52,b70acbd00150093dbc28cea909992ad67df49670,TODO (tdomhan): Due to a bug in swapaxes we need to avoid in-place gradient additions; see:
aaefgigcb,awslabs/sockeye,sockeye/decoder.py,3915916e0eed08b1dfbf67a8e79c86bae23dde52,b70acbd00150093dbc28cea909992ad67df49670,TODO (tdomhan): Use the `axis` argument instead of transposing once the new MXNet version becomes available.
aaefgihbg,awslabs/sockeye,test/unit/test_translate.py,5dcb60da8767fe7c667b8073e19574d10c2bb84f,STILL_EXISTS,work-around for [MagicMock objects not being iterable](http:\/\/bugs.python.org\/issue21258)
aaefgihdc,awslabs/sockeye,sockeye/inference.py,1ac070660eb4a34c655d9ba9f19a7f96a398bc36,3271ecefa3a7104d51b425369038578765dc5dba,avoid adding columns for finished sentences
aaefgiieh,awslabs/sockeye,sockeye/decoder.py,ba67443187bcaa119753349084ad8ee5c05b38ad,STILL_EXISTS,TODO: move final suffix\/prefix construction logic into config builder
aaefgiijg,awslabs/sockeye,sockeye/arguments.py,e934541132fb1c92cc9810d515450602d11e6662,STILL_EXISTS,TODO: At the moment LHUC is RNN specific. We should support other models as well.
aaefgijec,awslabs/sockeye,sockeye/inference.py,531ae4df5d721c87ac2ea864e701ba1b6b3e51c6,7629faead18f49eae503a2ffea9b6057b7297e98,TODO: extend to work with batch_size > 1 (i.e.; one stopped for each sentence)
aaefgijej,awslabs/sockeye,test/unit/test_inference.py,531ae4df5d721c87ac2ea864e701ba1b6b3e51c6,STILL_EXISTS,This is needed for returning the right number of source factors
aaefgijjf,awslabs/sockeye,sockeye/train.py,bb0b782609df802e821f9c261c350ca36af8e4ce,a4f8698dfd8afba923fa3fe4fa5e10a117c0e36b,TODO: check args compatibility with pointer nets if necessary
aaefgjbaa,awslabs/sockeye,contrib/autopilot/autopilot.py,fea3d599807484329c4edb1c9f4e2dea0f50b92c,STILL_EXISTS,Select source or target field; reversing if needed
aaefgjbab,awslabs/sockeye,contrib/autopilot/autopilot.py,fea3d599807484329c4edb1c9f4e2dea0f50b92c,STILL_EXISTS,Reference not needed since there will be no reads or writes
aaefgjfci,awslabs/sockeye,sockeye/image_captioning/data_io.py,09a90021453e8d8254201d92a830cd8da0c6e2c6,STILL_EXISTS,Once MXNet allows item assignments given a list of indices (probably MXNet 1.0): e.g a[[0;1;5;2]] = x;
aaefgjgaa,awslabs/sockeye,sockeye/image_captioning/extract_features.py,09a90021453e8d8254201d92a830cd8da0c6e2c6,STILL_EXISTS,TODO: enable caching to reuse features and resume computation
aaefgjgfb,awslabs/sockeye,sockeye/image_captioning/train.py,09a90021453e8d8254201d92a830cd8da0c6e2c6,STILL_EXISTS,TODO: make training compatible with full net
aaefgjhab,awslabs/sockeye,sockeye/image_captioning/visualize.py,09a90021453e8d8254201d92a830cd8da0c6e2c6,STILL_EXISTS,Collect results in a better data structure (dict)
aaefgjhaf,awslabs/sockeye,sockeye/image_captioning/visualize.py,09a90021453e8d8254201d92a830cd8da0c6e2c6,STILL_EXISTS,Prepare output folder; if needed
aaefhabhh,awslabs/sockeye,sockeye/inference.py,abfaf8a42a257e6cb3692c009c20c7bde9ab9924,aa6c80ad89d67c9b72e31f1a15033266e21bcaa3,pad_dist should have one fewer columns than scores
aaefhadbc,awslabs/sockeye,sockeye/utils.py,3b8865e0952033c5a0a5ac0065bda8465ec9d258,5144d25e6af3ca45baa33eb2c5c1ce7369364c61,TODO (domhant): Switch to mx.context.num_gpus() with mxnet version 1.3
aaefhadff,awslabs/sockeye,contrib/autopilot/test.py,9de53f9092de8f8a6ec883dcde7b72dc6b88aee9,STILL_EXISTS,TODO: Currently disabled due to periodic outages of nlp.stanford.edu
aaefhaega,awslabs/sockeye,test/common.py,5a50d960b6e20527a006b176419b188d79a85a85,STILL_EXISTS,Only run scoring under these conditions. Why?
aaefhaeje,awslabs/sockeye,sockeye/convolution.py,2f44099cd4f488bd8d348d74e9ae85095f72501e,STILL_EXISTS,TODO: add layer norm (can we do this without reshaping?!)
aaefhaffi,awslabs/sockeye,test/integration/test_seq_copy_int.py,ce5b1b65550f7852f2e26ab6343a4a1c5028c13a,a6afcaa60dc554d341a7addfe72aba41abd4fac8,TODO: Refactor the run_train_translate function!
aaefhagec,awslabs/sockeye,sockeye/encoder.py,105adf7447cf532f04f2ff87544efa04db734d2e,40c9afea0bb48e1e86751adc463091ef114c2cc6,TODO give more control on encoder architecture
aaefhahjd,awslabs/sockeye,sockeye/training.py,f2821dc869a9753e61a4975a2c64e66dc834030c,STILL_EXISTS,TODO: switch to mx.ndarray.contrib.isfinite after upgrade to MxNet 1.4.*
aaefhaiaa,awslabs/sockeye,sockeye/training.py,7597f27ae4f4649ef9cb4e85788d7e417ad1a73c,STILL_EXISTS,Fix model parameters as needed for different training options.
aaefhaied,awslabs/sockeye,sockeye/constants.py,27b27f71775dff7398c807106012949fe13b6ab2,STILL_EXISTS,TODO: with next bump remove branch over data_statistics.length_ratio_stats_per_bucket
aaefhaiee,awslabs/sockeye,sockeye/data_io.py,27b27f71775dff7398c807106012949fe13b6ab2,f5e9ec7b130505e29093e52456d77e5765f8cb70,TODO: remove with next bump of C.PREPARED_DATA_VERSION
aaefhaief,awslabs/sockeye,sockeye/inference.py,b5eae44824d3232bbccf42b104890957102d097c,3271ecefa3a7104d51b425369038578765dc5dba,TODO: we currently exploit a bug in the implementation of unravel_index to not require knowing the first shape
aaefhaihi,awslabs/sockeye,sockeye/layers.py,ea78e065add934c416fe3cd3ba1e95e2a3597ec0,41407b771cd9a3a459adcbe98bd0f111a65ed0eb,TODO: keeping weight here is redundant because it is also stored
aaefhajdd,awslabs/sockeye,sockeye/encoder.py,bb588ecbe874ae29ede33af2709e251910778bb3,40c9afea0bb48e1e86751adc463091ef114c2cc6,the VariationalDropout cell. ATM it is unclear how to fix it.
aaefhajeh,awslabs/sockeye,sockeye/layers.py,bb588ecbe874ae29ede33af2709e251910778bb3,STILL_EXISTS,TODO: remove with next major version update to use mx.gluon.nn.LayerNorm (which uses different parameter naming).
aaefhajei,awslabs/sockeye,sockeye/layers.py,bb588ecbe874ae29ede33af2709e251910778bb3,STILL_EXISTS,TODO: input types will be problematic when using full Gluon; no Dict allowed. Need to think about cache unpacking.
aaefhajha,awslabs/sockeye,sockeye/constants.py,f5e9ec7b130505e29093e52456d77e5765f8cb70,STILL_EXISTS,TODO: better to use dynamic loss scaling for FP16; but unclear how to do this with SoftmaxOutpu loss for CE.
aaefhajie,awslabs/sockeye,sockeye/decoder.py,f5e9ec7b130505e29093e52456d77e5765f8cb70,bf05051723fe3b8c6cb1eae6625cf2eb71e5210a,TODO: should we return the states?
aaefhajje,awslabs/sockeye,sockeye/decoder.py,f5e9ec7b130505e29093e52456d77e5765f8cb70,bf05051723fe3b8c6cb1eae6625cf2eb71e5210a,no autoregressive bias needed at inference
aaefhbacc,awslabs/sockeye,sockeye/inference.py,f5e9ec7b130505e29093e52456d77e5765f8cb70,83f3a87033cdb81629c8f6ba9f6949e5303e0b67,TODO clean up
aaefhbaeb,awslabs/sockeye,sockeye/inference.py,f5e9ec7b130505e29093e52456d77e5765f8cb70,26bc18687f55a2ba6e9d2c3a3d0b5f153c07c638,TODO: FP16 safety below
aaefhbaec,awslabs/sockeye,sockeye/inference.py,f5e9ec7b130505e29093e52456d77e5765f8cb70,abc8c84023406d0f67bf93f5d035aed8d10f5e23,TODO
aaefhbbag,awslabs/sockeye,sockeye/layers.py,f5e9ec7b130505e29093e52456d77e5765f8cb70,STILL_EXISTS,TODO: remove with next major version update to use mx.gluon.nn.LayerNorm (which uses different parameter naming).
aaefhbbah,awslabs/sockeye,sockeye/layers.py,f5e9ec7b130505e29093e52456d77e5765f8cb70,STILL_EXISTS,TODO: input types will be problematic when using full Gluon; no Dict allowed. Need to think about cache unpacking.
aaefhbbdb,awslabs/sockeye,sockeye/model.py,f5e9ec7b130505e29093e52456d77e5765f8cb70,bba7e7a9646b459f5cf572c5513535e98e6bfdc6,TODO
aaefhbbdc,awslabs/sockeye,sockeye/model.py,f5e9ec7b130505e29093e52456d77e5765f8cb70,bf05051723fe3b8c6cb1eae6625cf2eb71e5210a,TODO: do we need valid length!?
aaefhbbde,awslabs/sockeye,sockeye/model.py,f5e9ec7b130505e29093e52456d77e5765f8cb70,STILL_EXISTS,TODO: add step_additional_outputs
aaefhbbdj,awslabs/sockeye,sockeye/train.py,f5e9ec7b130505e29093e52456d77e5765f8cb70,3214e868eecc2f128587fb9b5f048033080f5c40,fix everything except LHUC-related parameters
aaefhbbfc,awslabs/sockeye,sockeye/training.py,f5e9ec7b130505e29093e52456d77e5765f8cb70,STILL_EXISTS,TODO: CheckpointDecoder
aaefhbbgb,awslabs/sockeye,sockeye/training.py,f5e9ec7b130505e29093e52456d77e5765f8cb70,STILL_EXISTS,TODO: cast already in data loader to avoid copy
aaefhbbgg,awslabs/sockeye,sockeye/training.py,f5e9ec7b130505e29093e52456d77e5765f8cb70,STILL_EXISTS,TODO CheckpointDecoder
aaefhbbha,awslabs/sockeye,sockeye/training.py,f5e9ec7b130505e29093e52456d77e5765f8cb70,STILL_EXISTS,overwriting here. TODO: make this better...
aaefhbbhb,awslabs/sockeye,sockeye/training.py,f5e9ec7b130505e29093e52456d77e5765f8cb70,6ecf06f641f446a45598de99cb10c7cbb3e0498e,TODO: Tensorboard logging
aaefhbfdg,awslabs/sockeye,sockeye/train.py,357ba06193f40f71f8fbef8f886b77345c5e1666,bf05051723fe3b8c6cb1eae6625cf2eb71e5210a,fix everything except LHUC-related parameters
aaefhbfgh,awslabs/sockeye,test/unit/test_scoring.py,16dba0131432e5db01f3ee92550554dae60192c5,STILL_EXISTS,TODO: make this a useful test
aaefhbfgj,awslabs/sockeye,sockeye/init_embedding.py,6c7cad34f824541325f03977b982db97902a9e1a,STILL_EXISTS,TODO: re-implement for sockeye 2.0 \/ Gluon
aaefhbhje,awslabs/sockeye,sockeye/loss.py,c43503d840a91baae19bb8e0184de5e3471098d7,STILL_EXISTS,TODO
aaefhbidb,awslabs/sockeye,sockeye/image_captioning/score.py,0feac8c5e3c94eafc62e7734ce18b7dd28871014,STILL_EXISTS,needed otherwise the model fails to be loaded
aaefhcabb,awslabs/sockeye,sockeye/constants.py,bf05051723fe3b8c6cb1eae6625cf2eb71e5210a,STILL_EXISTS,TODO: with next bump remove branch over data_statistics.length_ratio_stats_per_bucket
aaefhcadf,awslabs/sockeye,sockeye/decoder.py,bf05051723fe3b8c6cb1eae6625cf2eb71e5210a,c40d173f1cb89730b8b4efe5ec4dd711629e01eb,TODO: implement variant without input feeding
aaefhcaei,awslabs/sockeye,sockeye/decoder.py,bf05051723fe3b8c6cb1eae6625cf2eb71e5210a,c40d173f1cb89730b8b4efe5ec4dd711629e01eb,TODO: possible alternative: feed back the context vector instead of the hidden (see lamtram)
aaefhcafg,awslabs/sockeye,sockeye/decoder.py,bf05051723fe3b8c6cb1eae6625cf2eb71e5210a,c40d173f1cb89730b8b4efe5ec4dd711629e01eb,TODO remove this once mxnet.rnn.ModifierCell.reset() invokes reset() of base_cell
aaefhcahb,awslabs/sockeye,sockeye/decoder.py,bf05051723fe3b8c6cb1eae6625cf2eb71e5210a,c40d173f1cb89730b8b4efe5ec4dd711629e01eb,TODO: add context gating?
aaefhcahf,awslabs/sockeye,sockeye/decoder.py,bf05051723fe3b8c6cb1eae6625cf2eb71e5210a,c40d173f1cb89730b8b4efe5ec4dd711629e01eb,TODO: potentially project the encoder hidden size to the decoder hidden size.
aaefhcajd,awslabs/sockeye,sockeye/decoder.py,bf05051723fe3b8c6cb1eae6625cf2eb71e5210a,c40d173f1cb89730b8b4efe5ec4dd711629e01eb,TODO: compute the source encoded projection only once for efficiency reasons
aaefhcbhi,awslabs/sockeye,sockeye/inference.py,bf05051723fe3b8c6cb1eae6625cf2eb71e5210a,3796de0ae21ee13c7c4f977f621e9bf3cf34b89d,average attention prob scores. TODO: is there a smarter way to do this?
aaefhccej,awslabs/sockeye,sockeye/training.py,bf05051723fe3b8c6cb1eae6625cf2eb71e5210a,STILL_EXISTS,Fix model parameters as needed for different training options.
aaefhccga,awslabs/sockeye,sockeye/training.py,bf05051723fe3b8c6cb1eae6625cf2eb71e5210a,STILL_EXISTS,TODO: Push update to MXNet to expose the optimizer (Module should have a get_optimizer method)
aaefhcdab,awslabs/sockeye,sockeye/training.py,bf05051723fe3b8c6cb1eae6625cf2eb71e5210a,STILL_EXISTS,TODO: switch to mx.ndarray.contrib.isfinite after upgrade to MxNet 1.4.*
aaefhcdgh,awslabs/sockeye,sockeye/transformer.py,d0bde1bdf8351c89dc5cb8b0a04bdff751329ee9,dd3ea36524b2998c285df6646453eee8afabdc84,TODO: use F.contrib.arange_like with MXNET 1.6.0
aaefhcdha,awslabs/sockeye,sockeye/constants.py,ece002d370f9089bd350614adeb0892826f32f18,STILL_EXISTS,TODO: better to use dynamic loss scaling for FP16; but unclear how to do this with SoftmaxOutpu loss for CE.
aaefhcdhb,awslabs/sockeye,sockeye/constants.py,ece002d370f9089bd350614adeb0892826f32f18,STILL_EXISTS,TODO: with next bump remove branch over data_statistics.length_ratio_stats_per_bucket
aaefhcdhe,awslabs/sockeye,sockeye/train.py,ece002d370f9089bd350614adeb0892826f32f18,cfdc9a16770b73cc8bb4a5bf70b597115344fb15,fix everything except LHUC-related parameters
aaefhcdja,awslabs/sockeye,sockeye/training.py,ece002d370f9089bd350614adeb0892826f32f18,STILL_EXISTS,TODO: CheckpointDecoder
aaefhcead,awslabs/sockeye,sockeye/training.py,ece002d370f9089bd350614adeb0892826f32f18,STILL_EXISTS,TODO CheckpointDecoder
aaefhceah,awslabs/sockeye,sockeye/training.py,ece002d370f9089bd350614adeb0892826f32f18,STILL_EXISTS,overwriting here. TODO: make this better...
aaefhceai,awslabs/sockeye,sockeye/training.py,ece002d370f9089bd350614adeb0892826f32f18,6ecf06f641f446a45598de99cb10c7cbb3e0498e,TODO: Tensorboard logging
aaefhcefd,awslabs/sockeye,sockeye/training.py,ece002d370f9089bd350614adeb0892826f32f18,STILL_EXISTS,Fix model parameters as needed for different training options.
aaefhcege,awslabs/sockeye,sockeye/training.py,ece002d370f9089bd350614adeb0892826f32f18,STILL_EXISTS,TODO: Push update to MXNet to expose the optimizer (Module should have a get_optimizer method)
aaefhcfab,awslabs/sockeye,sockeye/training.py,ece002d370f9089bd350614adeb0892826f32f18,STILL_EXISTS,TODO: switch to mx.ndarray.contrib.isfinite after upgrade to MxNet 1.4.*
aaefhcfah,awslabs/sockeye,sockeye/model.py,1c5b27ab1f8c90640e37a9da8cf2d397d00fceac,STILL_EXISTS,TODO: do we need valid length!?
aaefhcfaj,awslabs/sockeye,sockeye/model.py,1c5b27ab1f8c90640e37a9da8cf2d397d00fceac,STILL_EXISTS,TODO: add step_additional_outputs
aaefhcfjb,awslabs/sockeye,sockeye/inference.py,3796de0ae21ee13c7c4f977f621e9bf3cf34b89d,abc8c84023406d0f67bf93f5d035aed8d10f5e23,TODO
aaefhcjeh,awslabs/sockeye,sockeye/training.py,cfdc9a16770b73cc8bb4a5bf70b597115344fb15,STILL_EXISTS,Fix model parameters as needed for different training options.
aaefhcjfi,awslabs/sockeye,sockeye/training.py,cfdc9a16770b73cc8bb4a5bf70b597115344fb15,STILL_EXISTS,TODO: Push update to MXNet to expose the optimizer (Module should have a get_optimizer method)
aaefhcjji,awslabs/sockeye,sockeye/training.py,cfdc9a16770b73cc8bb4a5bf70b597115344fb15,STILL_EXISTS,TODO: switch to mx.ndarray.contrib.isfinite after upgrade to MxNet 1.4.*
aaefhdabd,awslabs/sockeye,sockeye/checkpoint_decoder.py,c40d173f1cb89730b8b4efe5ec4dd711629e01eb,STILL_EXISTS,TODO: possibly support decoding on multiple GPUs
aaefhdaci,awslabs/sockeye,sockeye/decoder.py,c40d173f1cb89730b8b4efe5ec4dd711629e01eb,STILL_EXISTS,No autoregressive mask needed for decoding
aaefhdbde,awslabs/sockeye,sockeye/train.py,c40d173f1cb89730b8b4efe5ec4dd711629e01eb,49e46b25efde2f9b4a753e1cbd47b63b36c73e5f,fix everything except LHUC-related parameters
aaefhdbjj,awslabs/sockeye,sockeye/training.py,c40d173f1cb89730b8b4efe5ec4dd711629e01eb,STILL_EXISTS,overwriting here. TODO: make this better...
aaefhdcaa,awslabs/sockeye,sockeye/training.py,c40d173f1cb89730b8b4efe5ec4dd711629e01eb,6ecf06f641f446a45598de99cb10c7cbb3e0498e,TODO: Tensorboard logging
aaefhdced,awslabs/sockeye,sockeye/training.py,c40d173f1cb89730b8b4efe5ec4dd711629e01eb,STILL_EXISTS,Fix model parameters as needed for different training options.
aaefhdcfe,awslabs/sockeye,sockeye/training.py,c40d173f1cb89730b8b4efe5ec4dd711629e01eb,STILL_EXISTS,TODO: Push update to MXNet to expose the optimizer (Module should have a get_optimizer method)
aaefhdcij,awslabs/sockeye,sockeye/training.py,c40d173f1cb89730b8b4efe5ec4dd711629e01eb,STILL_EXISTS,TODO: switch to mx.ndarray.contrib.isfinite after upgrade to MxNet 1.4.*
aaefhddad,awslabs/sockeye,sockeye/constants.py,49e46b25efde2f9b4a753e1cbd47b63b36c73e5f,STILL_EXISTS,TODO: with next bump remove branch over data_statistics.length_ratio_stats_per_bucket
aaefhddgg,awslabs/sockeye,sockeye/training.py,49e46b25efde2f9b4a753e1cbd47b63b36c73e5f,STILL_EXISTS,Fix model parameters as needed for different training options.
aaefhddhh,awslabs/sockeye,sockeye/training.py,49e46b25efde2f9b4a753e1cbd47b63b36c73e5f,STILL_EXISTS,TODO: Push update to MXNet to expose the optimizer (Module should have a get_optimizer method)
aaefhdebi,awslabs/sockeye,sockeye/training.py,49e46b25efde2f9b4a753e1cbd47b63b36c73e5f,STILL_EXISTS,TODO: switch to mx.ndarray.contrib.isfinite after upgrade to MxNet 1.4.*
aaefhdeej,awslabs/sockeye,sockeye/training.py,49e46b25efde2f9b4a753e1cbd47b63b36c73e5f,STILL_EXISTS,overwriting here. TODO: make this better...
aaefhdefa,awslabs/sockeye,sockeye/training.py,49e46b25efde2f9b4a753e1cbd47b63b36c73e5f,6ecf06f641f446a45598de99cb10c7cbb3e0498e,TODO: Tensorboard logging
aaefhdeif,awslabs/sockeye,sockeye/constants.py,d171579ab71a51cb6072f7f1aee67312c8c167cc,STILL_EXISTS,TODO: better to use dynamic loss scaling for FP16; but unclear how to do this with SoftmaxOutput loss for CE.
aaefhdeje,awslabs/sockeye,sockeye/constants.py,d171579ab71a51cb6072f7f1aee67312c8c167cc,STILL_EXISTS,TODO: with next bump remove branch over data_statistics.length_ratio_stats_per_bucket
aaefhdfab,awslabs/sockeye,sockeye/train.py,d171579ab71a51cb6072f7f1aee67312c8c167cc,eb679fc67d5cf58c7bcc99dbce9a7d7d129b7a33,fix everything except LHUC-related parameters
aaefhdfgi,awslabs/sockeye,sockeye/training.py,d171579ab71a51cb6072f7f1aee67312c8c167cc,STILL_EXISTS,overwriting here. TODO: make this better...
aaefhdfgj,awslabs/sockeye,sockeye/training.py,d171579ab71a51cb6072f7f1aee67312c8c167cc,6ecf06f641f446a45598de99cb10c7cbb3e0498e,TODO: Tensorboard logging
aaefhdgbc,awslabs/sockeye,sockeye/training.py,d171579ab71a51cb6072f7f1aee67312c8c167cc,STILL_EXISTS,Fix model parameters as needed for different training options.
aaefhdgcd,awslabs/sockeye,sockeye/training.py,d171579ab71a51cb6072f7f1aee67312c8c167cc,STILL_EXISTS,TODO: Push update to MXNet to expose the optimizer (Module should have a get_optimizer method)
aaefhdggb,awslabs/sockeye,sockeye/training.py,d171579ab71a51cb6072f7f1aee67312c8c167cc,STILL_EXISTS,TODO: switch to mx.ndarray.contrib.isfinite after upgrade to MxNet 1.4.*
aaefhdghd,awslabs/sockeye,sockeye/data_io.py,8371e0eabca25441b2753e6101a257948819c595,248ca882779f320107001a1790dedce379dcd30f,Once MXNet allows item assignments given a list of indices (probably MXNet 1.0): e.g a[[0;1;5;2]] = x;
aaefhdghf,awslabs/sockeye,sockeye/data_io.py,8371e0eabca25441b2753e6101a257948819c595,248ca882779f320107001a1790dedce379dcd30f,TODO: remove with next bump of C.PREPARED_DATA_VERSION
aaefhdgid,awslabs/sockeye,sockeye/data_io.py,8371e0eabca25441b2753e6101a257948819c595,248ca882779f320107001a1790dedce379dcd30f,TODO: num pad examples is not set here if fillup policy would be padding
aaefhdhag,awslabs/sockeye,sockeye/data_io.py,d3ef6122149cec94177848ba39eed16fd1be3a3f,3271ecefa3a7104d51b425369038578765dc5dba,Once MXNet allows item assignments given a list of indices (probably MXNet 1.0): e.g a[[0;1;5;2]] = x;
aaefhdhai,awslabs/sockeye,sockeye/data_io.py,d3ef6122149cec94177848ba39eed16fd1be3a3f,STILL_EXISTS,TODO: remove with next bump of C.PREPARED_DATA_VERSION
aaefhdhbg,awslabs/sockeye,sockeye/data_io.py,d3ef6122149cec94177848ba39eed16fd1be3a3f,STILL_EXISTS,TODO: num pad examples is not set here if fillup policy would be padding
aaefhdhff,awslabs/sockeye,sockeye/beam_search.py,3271ecefa3a7104d51b425369038578765dc5dba,STILL_EXISTS,avoid warning for unused input
aaefhdhgj,awslabs/sockeye,sockeye/beam_search.py,3271ecefa3a7104d51b425369038578765dc5dba,STILL_EXISTS,TODO (fhieber): add full fp16 decoding with mxnet > 1.5
aaefhdhjf,awslabs/sockeye,sockeye/beam_search.py,3271ecefa3a7104d51b425369038578765dc5dba,STILL_EXISTS,TODO: max_iterations + 1 is the MINIMUM to get correct results right now
aaefhdiah,awslabs/sockeye,sockeye/beam_search.py,3271ecefa3a7104d51b425369038578765dc5dba,STILL_EXISTS,Map from restricted to full vocab ids if needed
aaefhdidd,awslabs/sockeye,sockeye/data_io.py,3271ecefa3a7104d51b425369038578765dc5dba,STILL_EXISTS,TODO: num pad examples is not set here if fillup policy would be padding
aaefhdjji,awslabs/sockeye,test/unit/test_beam_search.py,3271ecefa3a7104d51b425369038578765dc5dba,STILL_EXISTS,TODO add nested states
aaefheaah,awslabs/sockeye,test/unit/test_beam_search.py,3271ecefa3a7104d51b425369038578765dc5dba,STILL_EXISTS,TODO make this a useful test
aaefheaai,awslabs/sockeye,test/unit/test_beam_search.py,3271ecefa3a7104d51b425369038578765dc5dba,STILL_EXISTS,TODO: add vocabulary selection test
aaefhebfd,awslabs/sockeye,sockeye/training.py,eb679fc67d5cf58c7bcc99dbce9a7d7d129b7a33,STILL_EXISTS,Fix model parameters as needed for different training options.
aaefhebge,awslabs/sockeye,sockeye/training.py,eb679fc67d5cf58c7bcc99dbce9a7d7d129b7a33,STILL_EXISTS,TODO: Push update to MXNet to expose the optimizer (Module should have a get_optimizer method)
aaefhecaf,awslabs/sockeye,sockeye/training.py,eb679fc67d5cf58c7bcc99dbce9a7d7d129b7a33,STILL_EXISTS,TODO: switch to mx.ndarray.contrib.isfinite after upgrade to MxNet 1.4.*
aaefhechh,awslabs/sockeye,sockeye/inference.py,89df1f552d44300105aa37c432f9e79f322eaf8b,b9e6632458e7c722219051e1aa334e93dee3b918,Map from restricted to full vocab ids if needed
aaefhecie,awslabs/sockeye,sockeye/inference.py,89df1f552d44300105aa37c432f9e79f322eaf8b,b9e6632458e7c722219051e1aa334e93dee3b918,avoid adding columns for finished sentences
aaefhecjf,awslabs/sockeye,sockeye/inference.py,89df1f552d44300105aa37c432f9e79f322eaf8b,b9e6632458e7c722219051e1aa334e93dee3b918,TODO: we currently exploit a bug in the implementation of unravel_index to not require knowing the first shape
aaefhedde,awslabs/sockeye,sockeye/train.py,89df1f552d44300105aa37c432f9e79f322eaf8b,660385061083574bf6abca3067f5890ab082a261,fix everything except LHUC-related parameters
aaefheega,awslabs/sockeye,sockeye/training.py,63f024a5c8bf77303910615fcccc35c6e5928aa4,6ecf06f641f446a45598de99cb10c7cbb3e0498e,TODO: Tensorboard logging
aaefhefae,awslabs/sockeye,sockeye/training.py,63f024a5c8bf77303910615fcccc35c6e5928aa4,STILL_EXISTS,Fix model parameters as needed for different training options.
aaefhefbf,awslabs/sockeye,sockeye/training.py,63f024a5c8bf77303910615fcccc35c6e5928aa4,STILL_EXISTS,TODO: Push update to MXNet to expose the optimizer (Module should have a get_optimizer method)
aaefheffd,awslabs/sockeye,sockeye/training.py,63f024a5c8bf77303910615fcccc35c6e5928aa4,STILL_EXISTS,TODO: switch to mx.ndarray.contrib.isfinite after upgrade to MxNet 1.4.*
aaefhfdjc,awslabs/sockeye,sockeye/training.py,660385061083574bf6abca3067f5890ab082a261,STILL_EXISTS,Fix model parameters as needed for different training options.
aaefhfead,awslabs/sockeye,sockeye/training.py,660385061083574bf6abca3067f5890ab082a261,STILL_EXISTS,TODO: Push update to MXNet to expose the optimizer (Module should have a get_optimizer method)
aaefhfeed,awslabs/sockeye,sockeye/training.py,660385061083574bf6abca3067f5890ab082a261,STILL_EXISTS,TODO: switch to mx.ndarray.contrib.isfinite after upgrade to MxNet 1.4.*
aaefhfefh,awslabs/sockeye,sockeye/train.py,4d3261eeeb950f5e311a912e663881b14e8d2405,ce1813f56d694dfc6d4caaa350026bfc856922ab,fix everything except LHUC-related parameters
aaefhffde,awslabs/sockeye,sockeye/training.py,4d3261eeeb950f5e311a912e663881b14e8d2405,STILL_EXISTS,overwriting here. TODO: make this better...
aaefhffdf,awslabs/sockeye,sockeye/training.py,4d3261eeeb950f5e311a912e663881b14e8d2405,6ecf06f641f446a45598de99cb10c7cbb3e0498e,TODO: Tensorboard logging
aaefhffhj,awslabs/sockeye,sockeye/training.py,4d3261eeeb950f5e311a912e663881b14e8d2405,STILL_EXISTS,Fix model parameters as needed for different training options.
aaefhffja,awslabs/sockeye,sockeye/training.py,4d3261eeeb950f5e311a912e663881b14e8d2405,STILL_EXISTS,TODO: Push update to MXNet to expose the optimizer (Module should have a get_optimizer method)
aaefhfgci,awslabs/sockeye,sockeye/training.py,4d3261eeeb950f5e311a912e663881b14e8d2405,STILL_EXISTS,TODO: switch to mx.ndarray.contrib.isfinite after upgrade to MxNet 1.4.*
aaefhfgdi,awslabs/sockeye,sockeye/data_io.py,2b31feb8632aeb184fd26d8450e257c7ba0143ec,STILL_EXISTS,Once MXNet allows item assignments given a list of indices (probably MXNet 1.0): e.g a[[0;1;5;2]] = x;
aaefhfgea,awslabs/sockeye,sockeye/data_io.py,2b31feb8632aeb184fd26d8450e257c7ba0143ec,STILL_EXISTS,TODO: remove with next bump of C.PREPARED_DATA_VERSION
aaefhfgei,awslabs/sockeye,sockeye/data_io.py,2b31feb8632aeb184fd26d8450e257c7ba0143ec,STILL_EXISTS,TODO: num pad examples is not set here if fillup policy would be padding
aaefhfhcb,awslabs/sockeye,sockeye/inference.py,2b31feb8632aeb184fd26d8450e257c7ba0143ec,b938316fe6af4aea32472c676f9d09c18c09a1b8,average attention prob scores. TODO: is there a smarter way to do this?
aaefhfhgd,awslabs/sockeye,sockeye/inference.py,2b31feb8632aeb184fd26d8450e257c7ba0143ec,b938316fe6af4aea32472c676f9d09c18c09a1b8,Map from restricted to full vocab ids if needed
aaefhfhha,awslabs/sockeye,sockeye/inference.py,2b31feb8632aeb184fd26d8450e257c7ba0143ec,b938316fe6af4aea32472c676f9d09c18c09a1b8,avoid adding columns for finished sentences
aaefhfhic,awslabs/sockeye,sockeye/inference.py,2b31feb8632aeb184fd26d8450e257c7ba0143ec,b938316fe6af4aea32472c676f9d09c18c09a1b8,TODO: we currently exploit a bug in the implementation of unravel_index to not require knowing the first shape
aaefhfjdj,awslabs/sockeye,sockeye/train.py,be7cfe385b104f114bd0f1855ec38ff0e2a1c566,STILL_EXISTS,fix everything except LHUC-related parameters
aaefhgahg,awslabs/sockeye,sockeye/data_io.py,40fc5964d1c9d19ed6a1f6b542a652e9476934a6,STILL_EXISTS,Once MXNet allows item assignments given a list of indices (probably MXNet 1.0): e.g a[[0;1;5;2]] = x;
aaefhgahi,awslabs/sockeye,sockeye/data_io.py,40fc5964d1c9d19ed6a1f6b542a652e9476934a6,STILL_EXISTS,TODO: remove with next bump of C.PREPARED_DATA_VERSION
aaefhgaig,awslabs/sockeye,sockeye/data_io.py,40fc5964d1c9d19ed6a1f6b542a652e9476934a6,STILL_EXISTS,TODO: num pad examples is not set here if fillup policy would be padding
aaefhgcjb,awslabs/sockeye,sockeye/checkpoint_decoder.py,f3bb1728a2a82955c446fb0c2404e05f01a6b29d,STILL_EXISTS,TODO: possibly support decoding on multiple GPUs
aaefhgcjh,awslabs/sockeye,sockeye/beam_search.py,909229277ae69be678ae35bc14a808cbbc6b2508,STILL_EXISTS,TODO: Change repeat axis to 1 when interleaved multihead attention is implemented
aaefhgdcf,awslabs/sockeye,sockeye/model.py,e4553d392a8b67c88bf9628384ae956916b06ea2,STILL_EXISTS,TODO: check for missing parameters somehow (we allowed scaling to be missing)
aaefhhajg,awslabs/sockeye,sockeye/loss.py,1e5e8217948267f8e7769a46354553cb148b8b94,STILL_EXISTS,this is needed for label smoothing
aaefhhbcb,awslabs/sockeye,sockeye/data_io.py,c00da5257c7a1958a71d3ae3079bd8de808276cf,STILL_EXISTS,TODO: This is a legacy step from the bucketing module version of Sockeye.
aaefhhbdc,awslabs/sockeye,sockeye/constants.py,92a020a25cbe75935c700ce2f29b286b31a87189,STILL_EXISTS,TODO replace options list (e.g ENCODERS; DECODERS; ...) with Enum classes
aaefhhbdd,awslabs/sockeye,sockeye/decoder.py,92a020a25cbe75935c700ce2f29b286b31a87189,STILL_EXISTS,TODO: move final suffix\/prefix construction logic into config builder
aaefhhbdf,awslabs/sockeye,sockeye/decoder.py,92a020a25cbe75935c700ce2f29b286b31a87189,STILL_EXISTS,NOTE: the list expansion is needed in order to handle both a tuple (of Symbols) and a Symbol as a new state
aaefhhbid,awslabs/sockeye,sockeye/transformer.py,f809d8970fcdf41fb633fa98f13a40617f48f244,90144057efe4a73b26fb60a5c40bd180420d95bb,TODO (tdomhan): Remove with next major version bump.
aaefhhcge,awslabs/sockeye,sockeye/constants.py,bf89a7eeabd433b603a1d50895ff18269c9eac04,STILL_EXISTS,TODO: make this configurable in the model; separately per target factor.
aaefhhche,awslabs/sockeye,sockeye/inference.py,bf89a7eeabd433b603a1d50895ff18269c9eac04,3e2b0171a3576fe5677ac0f868936b03f4ab4cd5,TODO: also extract target factors for nbest translations
aaefhhcib,awslabs/sockeye,sockeye/model.py,bf89a7eeabd433b603a1d50895ff18269c9eac04,STILL_EXISTS,TODO also consider weight tying with target factor input embeddings
aaefhhdaf,awslabs/sockeye,test/unit/test_data_io.py,bf89a7eeabd433b603a1d50895ff18269c9eac04,STILL_EXISTS,TODO: still 2-shape: (batch; length)
aaefhhdga,awslabs/sockeye,sockeye/checkpoint_decoder.py,c5ff7d95c08e4d6be04b91a71a5f9994ae45d2b9,STILL_EXISTS,TODO: support source factors
aaefhhfdd,awslabs/sockeye,sockeye/utils.py,f2d5f57750ef538b84b67e09c6568ab49c5a3009,c3870e38f0a446ae5bf3920d5872908d282444d4,Points dominated by a previous better point have lifespan 0
aaefhhfeb,awslabs/sockeye,sockeye/training.py,c3870e38f0a446ae5bf3920d5872908d282444d4,STILL_EXISTS,Points dominated by a previous better point have lifespan 0
aaefhhgah,optuna/optuna,pfnopt/samplers/_hyperopt.py,d3c6499b6ed4bc8efb225167b5084497c7f30625,STILL_EXISTS,XXX: subtracting two numbers potentially very close together.
aaefhhgbi,optuna/optuna,pfnopt/samplers/_hyperopt.py,d3c6499b6ed4bc8efb225167b5084497c7f30625,STILL_EXISTS,XXX: is sorting them necessary anymore?
aaefhhgce,optuna/optuna,pfnopt/samplers/_hyperopt.py,d3c6499b6ed4bc8efb225167b5084497c7f30625,STILL_EXISTS,XXX: make TPE do a post-inference pass over the pyll graph and insert
aaefhhgee,optuna/optuna,pfnopt/samplers/_hyperopt.py,d3c6499b6ed4bc8efb225167b5084497c7f30625,1fde0c226d85bacd54a7cb3af0051a9c08394140,TODO
aaefhhgej,optuna/optuna,pfnopt/samplers/_hyperopt.py,d3c6499b6ed4bc8efb225167b5084497c7f30625,STILL_EXISTS,XXX if this is working; refactor this sort for efficiency
aaefhhgff,optuna/optuna,pfnopt/samplers/tpe_sampler.py,d3c6499b6ed4bc8efb225167b5084497c7f30625,STILL_EXISTS,TODO: this behavior is slightly different from hyperopt
aaefhhgfh,optuna/optuna,pfnopt/client.py,81cc2f0afccc88ae7e3569966f35d529a5623884,STILL_EXISTS,TODO: if already sampled; return the recorded value
aaefhhgfi,optuna/optuna,pfnopt/client.py,81cc2f0afccc88ae7e3569966f35d529a5623884,STILL_EXISTS,TODO: check that distribution is the same
aaefhhggb,optuna/optuna,pfnopt/storage.py,81cc2f0afccc88ae7e3569966f35d529a5623884,STILL_EXISTS,TODO: non-empty check
aaefhhggc,optuna/optuna,pfnopt/study.py,81cc2f0afccc88ae7e3569966f35d529a5623884,STILL_EXISTS,TODO: \u5B9F\u9A13\u7D99\u7D9A\u3068\u65B0\u898F\u5B9F\u9A13\u306E\u3069\u3063\u3061\u3082\u7C21\u5358\u306B\u3067\u304D\u308B\u30A4\u30F3\u30BF\u30FC\u30D5\u30A7\u30FC\u30B9\u3092\u8003\u3048\u308B\u5FC5\u8981\u3042\u308A
aaefhhggd,optuna/optuna,pfnopt/trial.py,81cc2f0afccc88ae7e3569966f35d529a5623884,7abfcbd36c8b76249b0a000e18a3a83d205957c7,TODO: if appropriate; change to namedtuple
aaefhhgge,optuna/optuna,pfnopt/trial.py,81cc2f0afccc88ae7e3569966f35d529a5623884,7abfcbd36c8b76249b0a000e18a3a83d205957c7,TODO: add meta data
aaefhhggf,optuna/optuna,pfnopt/study.py,57f3fe8c1328ef3a2a8074137eaaae02c47e0223,STILL_EXISTS,TODO: func\u3092Study\u304C\u6301\u3064\u5FC5\u8981\u306F\u306A\u3044\u304B\uFF1F
aaefhhggg,optuna/optuna,pfnopt/study.py,57f3fe8c1328ef3a2a8074137eaaae02c47e0223,c548b1fe3977c7efb1da61a7a88bf3780efcebb0,TODO: Study\u306E\u30E1\u30F3\u30D0\u95A2\u6570\u306B\u3057\u306A\u3044\uFF1F
aaefhhggh,optuna/optuna,pfnopt/storage.py,35964558c1e4b3309db82d2a67a2bfb24c93b43b,STILL_EXISTS,TODO: report -> set
aaefhhggi,optuna/optuna,pfnopt/storage.py,35964558c1e4b3309db82d2a67a2bfb24c93b43b,STILL_EXISTS,TODO
aaefhhghi,optuna/optuna,pfnopt/storage.py,8de4d5eab78ca82a1c433df9fb3da7fa8cc7d781,STILL_EXISTS,TODO: report -> set
aaefhhghj,optuna/optuna,pfnopt/client.py,a4fffc28866c29dbb2a8de20a57bd8acc86ed29e,STILL_EXISTS,TODO: don't we need distribution class?
aaefhhgif,optuna/optuna,pfnopt/samplers/_hyperopt.py,a4fffc28866c29dbb2a8de20a57bd8acc86ed29e,1fde0c226d85bacd54a7cb3af0051a9c08394140,TODO
aaefhhgih,optuna/optuna,pfnopt/pruners.py,dfff9a867c90943ef83bd02d04f6b8299bb6e96b,STILL_EXISTS,TODO: parameterize
aaefhhgij,optuna/optuna,pfnopt/integration/chainer.py,0d02fa553908833ea08a9fe030735dc4b21ad1a8,STILL_EXISTS,TODO: raise ValueError
aaefhhgjc,optuna/optuna,pfnopt/storage.py,a6df3acc4d16112750a313d32dd7a55b4336820c,STILL_EXISTS,TODO: We also want to use pruned results
aaefhhgjj,optuna/optuna,pfnopt/client.py,81aac3c4378f4410ff4c73c29a94699aa72c2354,STILL_EXISTS,TODO: info -> system_attrs
aaefhhhaf,optuna/optuna,pfnopt/storage/_base.py,81aac3c4378f4410ff4c73c29a94699aa72c2354,STILL_EXISTS,TODO: float? how about categorical?
aaefhhhbi,optuna/optuna,pfnopt/storage/_base.py,81aac3c4378f4410ff4c73c29a94699aa72c2354,STILL_EXISTS,TODO: We also want to use pruned results
aaefhhhdb,optuna/optuna,pfnopt/distributions.py,56a315e80b732e17b22cfc4c49c2d0ce6683cc44,e522f74b6c05e098bc6f2bfaad3d4557c7a8f164,TODO: better method name
aaefhhhdh,optuna/optuna,pfnopt/trial.py,56a315e80b732e17b22cfc4c49c2d0ce6683cc44,STILL_EXISTS,TODO: eliminate me
aaefhhijf,optuna/optuna,pfnopt/client.py,a1b5304a663e60e53896ee063185a073596551c3,6d6260c794c17de5d614abce74479a667b02c6ab,TODO: metaclass
aaefhhjfe,optuna/optuna,pfnopt/storage/base.py,09c681759895838d66bd87597147b36d261ae088,0f63a760f8fe70ab61e3f2476cc3a340cb077beb,TODO: discuss API
aaefhhjfg,optuna/optuna,pfnopt/storage/in_memory.py,09c681759895838d66bd87597147b36d261ae088,1ef6772d0768881641c21b15a8f55b810694a3b7,TODO: discuss API
aaefhhjfh,optuna/optuna,pfnopt/storage/in_memory.py,09c681759895838d66bd87597147b36d261ae088,STILL_EXISTS,TODO
aaefhhjfi,optuna/optuna,pfnopt/study.py,09c681759895838d66bd87597147b36d261ae088,0f63a760f8fe70ab61e3f2476cc3a340cb077beb,TODO: doesn't work with any configuration
aaefhhjgb,optuna/optuna,pfnopt/study.py,09c681759895838d66bd87597147b36d261ae088,STILL_EXISTS,TODO: timeout
aaefhhjgc,optuna/optuna,pfnopt/study.py,09c681759895838d66bd87597147b36d261ae088,STILL_EXISTS,TODO: add some study-wise configuration (e.g.; minimize? maximize?)
aaefhhjge,optuna/optuna,pfnopt/study.py,09c681759895838d66bd87597147b36d261ae088,STILL_EXISTS,TODO: implement me
aaefhigfc,optuna/optuna,pfnopt/study.py,565e13660ad6aa7711655e0cfca0ed44d35d9534,98088c83a2256ddc5d89d4cdebc98e9959663f72,TODO
aaefhihif,optuna/optuna,pfnopt/dashboard.py,9186d5d84f0c2d2ff95a95f8d8e1edb1b76b52f7,STILL_EXISTS,This is not a very clean way to launch Bokeh server. Another seemingly better way is to
aaefhiidb,optuna/optuna,pfnopt/dashboard.py,cc9bdc8e469c34d463349e30976e5d826769387e,STILL_EXISTS,Another seemingly better way is to
aaefhijjj,optuna/optuna,pfnopt/cli.py,02079e6ffa06b9c4e6c04459feebec9e7fe6fd94,49edefa77b7461c16bf11e2dedf0850c8dd6dee5,TODO: check args consistency
aaefhjaaf,optuna/optuna,pfnopt/cli.py,f771188b2e9ae8471015bce845da903976c29135,6c1ad70ef0ae3632469c19050fe4019310587cde,TODO: check args consistency
aaefhjabb,optuna/optuna,pfnopt/cli.py,82dc90695764343f79a3e6cfb144bdc47fa94caa,STILL_EXISTS,TODO: check args consistency
aaefhjabh,optuna/optuna,pfnopt/cli.py,9f3bfd2f54bffad79b7a9acc78430f566a44bc2b,STILL_EXISTS,TODO
aaefiaadj,optuna/optuna,pfnopt/samplers/_hyperopt.py,adaeb317998856f7800ea382d3976f1b66ca7372,1fde0c226d85bacd54a7cb3af0051a9c08394140,TODO
aaefiabcc,optuna/optuna,pfnopt/study.py,12e679bd831a472fd017c2c7f9882148ea99a318,STILL_EXISTS,move trial.user_attrs.__system__ to trial.system_attrs if it exists.
aaefiabeb,optuna/optuna,pfnopt/study.py,db01a17055bc0d1217804d8f1d1560570edad3db,8a2f532364b81f11c9695064df048600952ac462,Values are dataframe columns such as ('header'; 'trial_id') and ('params'; 'n_layers').
aaefiacbb,optuna/optuna,pfnopt/study.py,626dc713deaabb41ce63679760be270cb2ab37b2,STILL_EXISTS,Values are dataframe columns such as ('header'; 'trial_id') and ('params'; 'n_layers').
aaefiacbe,optuna/optuna,pfnopt/study.py,626dc713deaabb41ce63679760be270cb2ab37b2,9a8973bc7b87ecff93c89bb58638c9940cd5ceb3,Move trial.user_attrs.__system__ to trial.system_attrs if it exists.
aaefiacji,optuna/optuna,pfnopt/study.py,ffc465bf0765dfb9ffb8f75ac92392074fc9e83e,58655656d1061b65ae5cf388a12519105607603c,Values are dataframe columns such as ('trial_id'; '') and ('params'; 'n_layers').
aaefiadab,optuna/optuna,pfnopt/study.py,ffc465bf0765dfb9ffb8f75ac92392074fc9e83e,58655656d1061b65ae5cf388a12519105607603c,Move trial.user_attrs.__system__ to trial.system_attrs if it exists.
aaefiadcb,optuna/optuna,pfnopt/pruners.py,fee20938917ceb2c653ad741415068feb8bdf00a,STILL_EXISTS,TODO(Yanase): Implement a method of storage to just retrieve the number of trials.
aaefiadce,optuna/optuna,pfnopt/pruners.py,58655656d1061b65ae5cf388a12519105607603c,STILL_EXISTS,TODO(Yanase): Implement a method of storage to just retrieve the number of trials.
aaefiaeca,optuna/optuna,pfnopt/study.py,2dfbc9a4fbacfc7efcd83679c190e10dc192e55b,STILL_EXISTS,Values are dataframe columns such as ('trial_id'; '') and ('params'; 'n_layers').
aaefiaecd,optuna/optuna,pfnopt/study.py,2dfbc9a4fbacfc7efcd83679c190e10dc192e55b,31227eac6ed20af78078a7eab1aab8f58d13e177,Move trial.user_attrs.__system__ to trial.system_attrs if it exists.
aaefiajea,optuna/optuna,pfnopt/study.py,6afbf713552ce88d1e03d1871ed0d806858c2466,a2c656cdf0d22e69818b733b5ca4bb98325686c8,Move trial.user_attrs.__system__ to trial.system_attrs if it exists.
aaefibadg,optuna/optuna,pfnopt/study.py,dfb05b302a951280a585f9262c34421396612e02,75e3fe590bdc8caebd8af2e9e474afbd9182b9e6,Move trial.user_attrs.__system__ to trial.system_attrs if it exists.
aaefibbjh,optuna/optuna,setup.py,b159162db7dbceffbe4bea0993fafa532c127c9c,STILL_EXISTS,TODO(Yanase): This is temporal fix to avoid mypy bug about NamedTuple.
aaefibegc,optuna/optuna,optuna/study.py,1bd55a7cf0a69601423e06d74e5396edc1aba103,STILL_EXISTS,TODO(Yanase): Implement maximization and fix the message.
aaefibehe,optuna/optuna,optuna/study.py,d8e7387100b8d376e9c699daaf00d0f2ce60ef9a,STILL_EXISTS,TODO(Yanase): Implement maximization and fix the message.
aaefibfaf,optuna/optuna,optuna/study.py,f93002014928cfe142e7086734f69db71dd4ccd8,STILL_EXISTS,TODO(Yanase): Implement maximization and fix the message.
aaefibfaj,optuna/optuna,optuna/study.py,23be7664053585cd91f19c5723a63bce0ab25bae,50392c470be2cb3d18b86628749ec34a908414e3,TODO(Yanase): Implement maximization.
aaefibfbd,optuna/optuna,optuna/study.py,23be7664053585cd91f19c5723a63bce0ab25bae,STILL_EXISTS,TODO(Yanase): Implement maximization and fix the message.
aaefibfbg,optuna/optuna,optuna/study.py,acbe1a121fdeb13e7478e1f76f292f73984d4a72,50392c470be2cb3d18b86628749ec34a908414e3,TODO(Yanase): Implement maximization.
aaefibfeb,optuna/optuna,optuna/study.py,e8a64ea8d801308610d5e430de55e1ab98088b8d,50392c470be2cb3d18b86628749ec34a908414e3,TODO(Yanase): Implement maximization.
aaefibihf,optuna/optuna,setup.py,42f98ca2ad9caaca83fc035f910333e6edc1107d,fb5ad99fc679ea3d398e73a5447f539e8b27764f,TODO(Yanase): Setting mypy version to 0.620 as a temporal fix
aaeficbgf,optuna/optuna,optuna/samplers/tpe/sampler.py,d1a2f3c720920f47289540a9682f1a35c749f34b,STILL_EXISTS,(TODO) We decide which \"coefficient\" is good; later.
aaeficcbe,optuna/optuna,tests/pruners_tests/test_asha.py,04a1b82094a84c98764579a78871ea268e5b1670,STILL_EXISTS,r=1: The rung 0 ends at step 1.
aaeficcbf,optuna/optuna,tests/pruners_tests/test_asha.py,04a1b82094a84c98764579a78871ea268e5b1670,STILL_EXISTS,r=2: The rung 0 ends at step 2.
aaeficcbg,optuna/optuna,tests/pruners_tests/test_asha.py,04a1b82094a84c98764579a78871ea268e5b1670,STILL_EXISTS,eta=2: The rung 0 ends at step 1.
aaeficcbh,optuna/optuna,tests/pruners_tests/test_asha.py,04a1b82094a84c98764579a78871ea268e5b1670,STILL_EXISTS,eta=3: The rung 1 ends at step 3.
aaeficcbi,optuna/optuna,tests/pruners_tests/test_asha.py,04a1b82094a84c98764579a78871ea268e5b1670,STILL_EXISTS,s=0: The rung 0 ends at step 1.
aaeficcbj,optuna/optuna,tests/pruners_tests/test_asha.py,04a1b82094a84c98764579a78871ea268e5b1670,STILL_EXISTS,s=1: The rung 0 ends at step 2.
aaefiehbg,optuna/optuna,optuna/study.py,4b69767f8c81be0d0f7e71c28be4d1412afaf072,50392c470be2cb3d18b86628749ec34a908414e3,TODO(Yanase): Implement maximization.
aaefiehfa,optuna/optuna,optuna/study.py,b0cfea8d5caed1bb25ab54717260ce73f118c47c,01a40803a7441166986d2c3bbde4ea5308d3cca2,TODO(Yanase): Implement maximization.
aaefieibi,optuna/optuna,optuna/study.py,7744f4486f0e86c63248af275815ef7eff8c2012,f02645f78ca42d0b806328cc1c2c9c80e8c5fd05,TODO(Yanase): Implement maximization.
aaefieiib,optuna/optuna,optuna/study.py,954f39445587f21da92f0d7ed13b2b0fe70588a2,ced98ed8b88a492e2c5ad922d155a1879a1ee860,TODO(Yanase): Implement maximization.
aaefieijg,optuna/optuna,optuna/storages/rdb/models.py,734bae4e62339336bd0efb37e4b91bcec008b849,STILL_EXISTS,The schema management functionality has been moved to Alembic since optuna-v0.9.0.
aaefifcdb,optuna/optuna,optuna/integration/chainermn.py,20d012826a454d52ff62b695f264b11627f6e4c9,3e4cef8f4a95c1c22cc34548e7be3f62a38dcd8f,This is a helper function which is only used to implement suggest APIs.
aaefifgjd,optuna/optuna,optuna/storages/rdb/models.py,8b5e296546746e2a9e2adb6af148ea9ce618ab87,STILL_EXISTS,The schema management functionality has been moved to alembic.
aaefihdbg,optuna/optuna,optuna/storages/rdb/storage.py,2c72a8f6f946d079f825a5e303f6d585d84be114,21ec4db3b74d35a21a42303cc4d6f1855afbad75,TODO(ohta): Remove this workaround when `number` field is added to `TrialModel`.
aaefihhfj,optuna/optuna,optuna/integration/skopt.py,3362cb1b0ef7362588941649d28b47da2326278d,STILL_EXISTS,the parameters of complete trials always are compatible with the search space.
aaefihiga,optuna/optuna,optuna/integration/chainermn.py,c773f6e7de897b169149ac27d3d0c67765cb6db7,STILL_EXISTS,This is a helper function which is only used to implement suggest APIs.
aaefiiabf,optuna/optuna,optuna/integration/chainermn.py,e67caf1fe67ef3982fbfcd47243cbeb5cd28e6b4,STILL_EXISTS,This is a helper function which is only used to implement suggest APIs.
aaefiijhj,optuna/optuna,optuna/integration/cma.py,1747d0a1213660331de55bb31808126e0e7ef8d5,STILL_EXISTS,the parameters of complete trials are always compatible with the search space.
aaefijafc,optuna/optuna,optuna/study.py,1f79291377bfe901af131dcda7f851703ef2bb71,STILL_EXISTS,TODO: Take kwargs instead of FrozenTrial
aaefijdde,optuna/optuna,optuna/study.py,29abae9e4d2f4ef51ca54c642382309db9f0aea8,STILL_EXISTS,TODO: Take kwargs instead of FrozenTrial
aaefijdfi,optuna/optuna,tests/storages_tests/test_storages.py,6b26059850a2c4423af25e56ce4d77f2a2647e7b,e2db516fbafad2920f5f218099bf2517f0a27b51,dummy; value (unused)
aaefjbddh,optuna/optuna,tests/storages_tests/test_storages.py,04b9542f280aff3af51eb2d03eda69e0c62d8532,91d5c5f9a2b7a0d6d87052b78166a3b7b3fc969b,dummy value (unused)
aaefjbddi,optuna/optuna,tests/storages_tests/test_storages.py,04b9542f280aff3af51eb2d03eda69e0c62d8532,91d5c5f9a2b7a0d6d87052b78166a3b7b3fc969b,dummy; value (unused)
aaefjbffc,optuna/optuna,tests/test_study.py,39bb56c35fa69be6bb3ae4e35c44eb2f00abe946,e63b073c8d651d8c78eb3b79fe18a1c47aa321e5,TODO(ohta): Fix `Study.optimize`
aaefjbgeb,optuna/optuna,tests/storages_tests/test_storages.py,38309e7e28b50e047d24a5a38c91d885afd0500f,cdee7656b4559c78976972340869509bc1f79286,dummy value (unused)
aaefjbgec,optuna/optuna,tests/storages_tests/test_storages.py,38309e7e28b50e047d24a5a38c91d885afd0500f,cdee7656b4559c78976972340869509bc1f79286,dummy; value (unused)
aaefjbhab,optuna/optuna,tests/storages_tests/test_storages.py,7b405ae6c0292cdf5341a6768994687de3dbbf23,a21678d945aca2914bd3de0e646a39a76375b9e0,dummy value (unused)
aaefjbhac,optuna/optuna,tests/storages_tests/test_storages.py,7b405ae6c0292cdf5341a6768994687de3dbbf23,a21678d945aca2914bd3de0e646a39a76375b9e0,dummy; value (unused)
aaefjbhee,optuna/optuna,tests/storages_tests/test_storages.py,5c310dc7639f4cb7b4c1df439cd3c5bf2eae3a2e,STILL_EXISTS,dummy value (unused)
aaefjbhef,optuna/optuna,tests/storages_tests/test_storages.py,5c310dc7639f4cb7b4c1df439cd3c5bf2eae3a2e,STILL_EXISTS,dummy; value (unused)
aaefjbhge,optuna/optuna,tests/storages_tests/test_storages.py,9e477bf759c8f74885e29b2e9f89abbede848372,21ec4db3b74d35a21a42303cc4d6f1855afbad75,dummy value (unused).
aaefjccff,optuna/optuna,optuna/integration/tfkeras.py,6942644146bc37920b1aed3925832d23591a2e4f,STILL_EXISTS,Prune trial if needed
aaefjchag,optuna/optuna,examples/pruning/tfkeras_integration.py,be41dfe5f5b4de2e2a13a291c42406e96d8654d4,STILL_EXISTS,TODO: Investigate why the logger below is called twice
aaefjcjhe,optuna/optuna,tests/test_study.py,aa930bd95d9f61c2ab20966fdb4a9925b0698fd0,ae1af615fcb254ccbb78af1c3d336f0360879935,TODO(ohta): Fix `Study.optimize`
aaefjdgbj,optuna/optuna,tests/test_study.py,a3b67edbe13dee47ab79f270fbd4b286f17339e1,e236cdb5b87bb4529451379b2e1404fb27742d2e,TODO(ohta): Fix `Study.optimize`
aaefjfcfj,optuna/optuna,optuna/integration/lightgbm_autotune/optimize.py,7d8836f6d7c9f207e5d5b5615da0f728b6d04220,STILL_EXISTS,todo (g-votte): Take care of distributed optimization.
aaefjfdcj,optuna/optuna,optuna/integration/lightgbm_autotune/optimize.py,55b44353dfc955d1c9849e5693e4c9f7ea1c00ce,STILL_EXISTS,todo (smly): Make this better.
aaefjfddc,optuna/optuna,optuna/integration/lightgbm.py,fd632ebbd282ace69f6b9247c72a57d0c56fdcff,STILL_EXISTS,Workaround for mypy
aaefjfehd,optuna/optuna,tests/integration_tests/lightgbm_tuner_tests/test_optimize.py,fd632ebbd282ace69f6b9247c72a57d0c56fdcff,STILL_EXISTS,Workaround for mypy
aaefjhbgi,optuna/optuna,optuna/integration/fastai.py,c3c7bef99f37c0be80c855d48e91d8bdf24b5497,8232b9ecf868f9f4b6bcf3ab07f47307e76c6215,TODO (crcrpar): Remove this method if possible.
aaefjjdja,optuna/optuna,optuna/samplers/grid.py,9f5c7cbb3f8d2d3f087d3b64008a02e6a6c022ba,STILL_EXISTS,Instead of returning param values; GridSampler puts the target grid id as a system attr;
aaegaacbj,optuna/optuna,tests/test_distributions.py,dcbf4ab7040fd5391cf888e75a0ba949656999a5,STILL_EXISTS,The following variable is needed to apply `eval` to distribution
aaegaaihj,optuna/optuna,optuna/study.py,992b87817296884a7d6a7557510be1d0c827b4f1,ad1b8d646d5ef602f5bfcf95b187312853589353,Flatten the `MultiIndex` columns where names are concatenated with underscores.
aaegaaiia,optuna/optuna,optuna/study.py,992b87817296884a7d6a7557510be1d0c827b4f1,31a1478b6adea23fd8a2f9744b819be0ce74339c,Filtering is required to omit non-nested columns avoiding unwanted trailing
aaegaajde,optuna/optuna,optuna/study.py,ad1b8d646d5ef602f5bfcf95b187312853589353,31a1478b6adea23fd8a2f9744b819be0ce74339c,Filtering is required to omit non-nested columns avoiding unwanted trailing
aaegabajg,optuna/optuna,optuna/study.py,f4473a75402b9fd562cabaaf9ccde92d3eef2117,31a1478b6adea23fd8a2f9744b819be0ce74339c,Flatten the `MultiIndex` columns where names are concatenated with underscores.
aaegabajh,optuna/optuna,optuna/study.py,f4473a75402b9fd562cabaaf9ccde92d3eef2117,31a1478b6adea23fd8a2f9744b819be0ce74339c,Filtering is required to omit non-nested columns avoiding unwanted trailing
aaegabbbj,optuna/optuna,tests/test_study.py,3c94f85492fda4c772bb28e976205b2f901d4d5b,70e8d1a940c9ec0c3ccb25941858622dc8f7dfdf,Number expected columns are as follows (total of 10):
aaegabbib,optuna/optuna,optuna/study.py,8d58e928eeab7eae011a6ee5b3461b30d0e8ff09,31a1478b6adea23fd8a2f9744b819be0ce74339c,Flatten the `MultiIndex` columns where names are concatenated with underscores.
aaegabbic,optuna/optuna,optuna/study.py,8d58e928eeab7eae011a6ee5b3461b30d0e8ff09,31a1478b6adea23fd8a2f9744b819be0ce74339c,Filtering is required to omit non-nested columns avoiding unwanted trailing
aaegabcga,optuna/optuna,optuna/study.py,16c16b62325f025356c3f9c776a10c3ec3236ac5,1eaaa25a75424f353edabc747d315aab0e4bd26e,The following expression makes an iterator that never ends.
aaegabcgb,optuna/optuna,optuna/study.py,80a86da226a06721cf943276578360baaac15d7f,STILL_EXISTS,This is needed for mypy
aaegabcgf,optuna/optuna,optuna/study.py,e3a68ac34b1df8f2ce249403a95656569e0b71be,e458861e74d5eafe445f548546b7fa9aac44077e,Flatten the `MultiIndex` columns where names are concatenated with underscores.
aaegabcgg,optuna/optuna,optuna/study.py,e3a68ac34b1df8f2ce249403a95656569e0b71be,e458861e74d5eafe445f548546b7fa9aac44077e,Filtering is required to omit non-nested columns avoiding unwanted trailing
aaegabdaj,optuna/optuna,optuna/study.py,5d20fc7ce4ef669ef427a35ed4fbfaa1cda82156,STILL_EXISTS,This is needed for mypy
aaegabdbb,optuna/optuna,optuna/study.py,5d20fc7ce4ef669ef427a35ed4fbfaa1cda82156,1eaaa25a75424f353edabc747d315aab0e4bd26e,The following expression makes an iterator that never ends.
aaegabdhb,optuna/optuna,tests/test_study.py,5d20fc7ce4ef669ef427a35ed4fbfaa1cda82156,4bed5e337f66229c9d333f8acf532b751e9f09cf,TODO(ohta): Fix `Study.optimize`
aaegabdjd,optuna/optuna,optuna/study.py,c0c447988160f0ae0617616c4eb1451b053272e9,STILL_EXISTS,This is needed for mypy
aaegabdjf,optuna/optuna,optuna/study.py,c0c447988160f0ae0617616c4eb1451b053272e9,1eaaa25a75424f353edabc747d315aab0e4bd26e,The following expression makes an iterator that never ends.
aaegabgif,optuna/optuna,optuna/study.py,f727f64560e4626196d42b6cf9d490d1444e9a2a,STILL_EXISTS,This is needed for mypy
aaegabgih,optuna/optuna,optuna/study.py,f727f64560e4626196d42b6cf9d490d1444e9a2a,1eaaa25a75424f353edabc747d315aab0e4bd26e,The following expression makes an iterator that never ends.
aaegabihh,optuna/optuna,tests/test_study.py,f727f64560e4626196d42b6cf9d490d1444e9a2a,STILL_EXISTS,Number expected columns are as follows (total of 10):
aaegacacc,optuna/optuna,optuna/study.py,84d4d838afdfa25054b88d6947a146143a4d5798,STILL_EXISTS,This is needed for mypy
aaegacace,optuna/optuna,optuna/study.py,84d4d838afdfa25054b88d6947a146143a4d5798,1eaaa25a75424f353edabc747d315aab0e4bd26e,The following expression makes an iterator that never ends.
aaegacaeh,optuna/optuna,optuna/study.py,22b9f77370ec8a22f42f2e3737857fe54cc36b55,STILL_EXISTS,This is needed for mypy
aaegacaej,optuna/optuna,optuna/study.py,22b9f77370ec8a22f42f2e3737857fe54cc36b55,1eaaa25a75424f353edabc747d315aab0e4bd26e,The following expression makes an iterator that never ends.
aaegacafa,optuna/optuna,optuna/study.py,22b9f77370ec8a22f42f2e3737857fe54cc36b55,e458861e74d5eafe445f548546b7fa9aac44077e,Flatten the `MultiIndex` columns where names are concatenated with underscores.
aaegacafb,optuna/optuna,optuna/study.py,22b9f77370ec8a22f42f2e3737857fe54cc36b55,e458861e74d5eafe445f548546b7fa9aac44077e,Filtering is required to omit non-nested columns avoiding unwanted trailing
aaegacbcc,optuna/optuna,optuna/study.py,ea8127e9092a53803faf8217b00cdbdc27a1d4e5,STILL_EXISTS,This is needed for mypy
aaegacbce,optuna/optuna,optuna/study.py,ea8127e9092a53803faf8217b00cdbdc27a1d4e5,1eaaa25a75424f353edabc747d315aab0e4bd26e,The following expression makes an iterator that never ends.
aaegacbhg,optuna/optuna,optuna/study.py,b8383003e40a7c7f9cba32e0db3d521b459f66d8,STILL_EXISTS,This is needed for mypy
aaegacbhi,optuna/optuna,optuna/study.py,b8383003e40a7c7f9cba32e0db3d521b459f66d8,1eaaa25a75424f353edabc747d315aab0e4bd26e,The following expression makes an iterator that never ends.
aaegacgdj,optuna/optuna,optuna/pruners/hyperband.py,3d8ed8e41b7a243ab09db8c17c2d7d88d3751134,STILL_EXISTS,NOTE(crcrpar): The below import is workaround.
aaegacggd,optuna/optuna,tests/pruners_tests/test_successive_halving.py,1e38935e1a7a95cf99e54a2f88867dd1a9f1189c,STILL_EXISTS,FIXME(crcrpar): No intermediate_values available
aaegacgid,optuna/optuna,optuna/pruners/hyperband.py,7bc4874b09b8016a4d425d4896711605e5f03007,3a3b439d45cf70c5cbe82fc65080379e105d3bc1,TODO(crcrpar): Improve resource computation\/allocation algorithm.
aaegadefj,optuna/optuna,tests/test_trial.py,97836d5f74beebc5557e7a76806ec50a3020180e,STILL_EXISTS,TODO: more tests for _check_distribution
aaegadhef,optuna/optuna,optuna/trial.py,345869cdef0942520976a0d2e82320173895c29c,467fd593e3b8f65f500fb198e3abab8f6254a410,TODO: implement this
aaegadjih,optuna/optuna,optuna/storages/rdb/storage.py,30f9d8cb6d868d1fdb163bc6081b43cee99e7cd7,d7abfc6ce13f9c792bbb3327171d22ade883a92f,TODO(ohta): Remove this workaround when `number` field is added to `TrialModel`.
aaegaedeg,optuna/optuna,optuna/importance/_sklearn.py,f8a388ad9a6d3480991d2564b5276ecd25a704bc,STILL_EXISTS,Transform the `params` matrix by expanding categorical integer-valued columns to one-hot
aaegaedei,optuna/optuna,optuna/importance/_sklearn.py,f8a388ad9a6d3480991d2564b5276ecd25a704bc,STILL_EXISTS,All categorical one-hot columns are placed before the numerical columns in
aaegaeejj,optuna/optuna,optuna/storages/rdb/storage.py,1c3e89bdb3002335ad7612431b92723864b2b37d,58d394a5c3133cf8ca003776c1949d22e7971491,TODO(ohta): Remove this workaround when `number` field is added to `TrialModel`.
aaegaehhb,optuna/optuna,optuna/storages/rdb/models.py,1c21009d54eaa2ec58c415680ea3da5a6100d46b,0b99dfd461dbdd476ee6dbb5a90a2b645634c86e,No `UniqueConstraint` is put on the `number` columns although it in practice is constrained
aaegaehie,optuna/optuna,examples/allennlp_simple.py,4277a9af87c2d905aece3d6f527396d442570fbf,STILL_EXISTS,\"\"\" || Optuna example that optimizes a classifier configuration for IMDB movie review dataset. || This script is based on the example of allentune (https:\/\/github.com\/allenai\/allentune). ||  || In this example; we optimize the validation accuracy of sentiment classification using AllenNLP. || Since it is too time-consuming to use the entire dataset; we here use a small subset of it. ||  || We have the following two ways to execute this example: ||  || (1) Execute this code directly. ||     $ python allennlp_simple.py ||  ||  || (2) Execute through CLI. ||     $ STUDY_NAME=`optuna create-study --direction maximize --storage sqlite:\/\/\/example.db` ||     $ optuna study optimize allennlp_simple.py objective --n-trials=100 --study $STUDY_NAME \\ ||       --storage sqlite:\/\/\/example.db ||  || \"\"\"
aaegaejfb,optuna/optuna,optuna/study.py,40f3a59ef82f56b7334f3f5b6549f2fb51358667,1eaaa25a75424f353edabc747d315aab0e4bd26e,The following expression makes an iterator that never ends.
aaegafagf,optuna/optuna,optuna/importance/_base.py,98914a052ca4222df5ead972d48f2f0e73b55e43,STILL_EXISTS,New temporary required to pass mypy. Seems like a bug.
aaegaffee,optuna/optuna,optuna/mo/trial.py,d165472146f9c466fd23a6b4b1de3a00df395542,STILL_EXISTS,TODO(ohta): Implement `__repr__` method.
aaegafifc,optuna/optuna,optuna/storages/rdb/models.py,25c96729dfa4877a6c4272c2b911d45e7424dfbe,STILL_EXISTS,No `UniqueConstraint` is put on the `number` columns although it in practice is constrained
aaegahcej,optuna/optuna,optuna/trial.py,d909550fc68ca1f97e4b9785432ab0fc98854f1c,30d90e5db60683de7dd0be774380c1303aecba19,TODO
aaegahcfc,optuna/optuna,optuna/trial.py,d909550fc68ca1f97e4b9785432ab0fc98854f1c,30d90e5db60683de7dd0be774380c1303aecba19,TODO (himkt)
aaegahgbf,optuna/optuna,optuna/integration/lightgbm_tuner/optimize.py,d8b705f944a20ab62fe78abf2f7391a45ff52fba,STILL_EXISTS,todo (g-votte): Take care of distributed optimization.
aaegahghf,optuna/optuna,optuna/integration/lightgbm_tuner/optimize.py,bc6d1a1e94be9627b520f30b334d153facddcb77,STILL_EXISTS,todo (g-votte): Take care of distributed optimization.
aaegahgjg,optuna/optuna,examples/allennlp/allennlp_simple.py,7ffdd3d17cbff2babfaa7e21dc132e01ba13f25c,STILL_EXISTS,\"\"\" || Optuna example that optimizes a classifier configuration for IMDB movie review dataset. || This script is based on the example of allentune (https:\/\/github.com\/allenai\/allentune). ||  || In this example; we optimize the validation accuracy of sentiment classification using AllenNLP. || Since it is too time-consuming to use the entire dataset; we here use a small subset of it. ||  || We have the following two ways to execute this example: ||  || (1) Execute this code directly. ||     $ python allennlp_simple.py ||  ||  || (2) Execute through CLI. ||     $ STUDY_NAME=`optuna create-study --direction maximize --storage sqlite:\/\/\/example.db` ||     $ optuna study optimize allennlp_simple.py objective --n-trials=100 --study $STUDY_NAME \\ ||       --storage sqlite:\/\/\/example.db ||  || \"\"\"
aaegahgjj,optuna/optuna,examples/allennlp/allennlp_cli.py,f4d21f82b6e70b4ef6a90925c30eb6c829477859,STILL_EXISTS,\"\"\" || Optuna example that optimizes a classifier configuration for IMDB movie review dataset. || This script is based on the example of allentune (https:\/\/github.com\/allenai\/allentune). ||  || In this example; we optimize the validation accuracy of sentiment classification using AllenNLP cli. || Since it is too time-consuming to use the entire dataset; we here use a small subset of it. ||  || We have the following two ways to execute this example: ||  || (1) Execute this code directly. ||     $ python allennlp_cli.py ||  ||  || (2) Execute through CLI. ||     $ STUDY_NAME=`optuna create-study --direction maximize --storage sqlite:\/\/\/example.db` ||     $ optuna study optimize allennlp_cli.py objective --n-trials=100 --study $STUDY_NAME \\ ||       --storage sqlite:\/\/\/example.db ||  || \"\"\"
aaegahiai,optuna/optuna,optuna/integration/lightgbm_tuner/optimize.py,28fa2829d14f811a77137a133ff835fb23abd455,STILL_EXISTS,todo (g-votte): Take care of distributed optimization.
aaegahifc,optuna/optuna,optuna/pruners/hyperband.py,28fa2829d14f811a77137a133ff835fb23abd455,3a3b439d45cf70c5cbe82fc65080379e105d3bc1,TODO(crcrpar): Improve resource computation\/allocation algorithm.
aaegaiadj,optuna/optuna,optuna/integration/lightgbm_tuner/optimize.py,ae0c8b483132e70c2ee30ad10f9156cfd4366bba,STILL_EXISTS,todo (g-votte): Take care of distributed optimization.
aaegaicig,optuna/optuna,optuna/pruners/hyperband.py,17c2e36502d5eb9e4276bf520466c802547955f1,3a3b439d45cf70c5cbe82fc65080379e105d3bc1,TODO(crcrpar): Improve resource computation\/allocation algorithm.
aaegaiedb,optuna/optuna,optuna/pruners/hyperband.py,c8d2e64ed919e516473d36c8213d73f4e2331af9,6ac2750840027e7949b7822872af809d68e7b222,TODO(crcrpar): Improve resource computation\/allocation algorithm.
aaegaigia,optuna/optuna,tests/multi_objective/samplers_tests/test_samplers.py,8387012371ecbf9b3dc3151cc55d70886e95bdef,STILL_EXISTS,Please see https:\/\/github.com\/optuna\/optuna\/pull\/393 why this assertion is needed.
aaegaiheb,optuna/optuna,optuna/study.py,98ba53fddc6785091b3ffa68a7e891c8bd82b8ee,1eaaa25a75424f353edabc747d315aab0e4bd26e,The following expression makes an iterator that never ends.
aaegaijeb,optuna/optuna,optuna/pruners/hyperband.py,772251044998805828a7c3094d6dc46ffb3fb576,01e005baf0a999cca44394dda4f8f9a3a6a6b4ad,TODO(crcrpar): Improve resource computation\/allocation algorithm.
aaegaijgc,optuna/optuna,optuna/multi_objective/samplers/_nsga2.py,86ad81c7ccc7b78858c8bf09aa6b28ca742266af,STILL_EXISTS,TODO: Consider crowding distance.
aaegaijgd,optuna/optuna,optuna/pruners/hyperband.py,accbad17026dabc763acfb10e4137e558d4c4495,5b284e4fbabb2c48824f6a9bf961b2cb12d27499,TODO(crcrpar): Improve resource computation\/allocation algorithm.
aaegaijgf,optuna/optuna,optuna/pruners/hyperband.py,bf94df85b51b693c5f98251d8f0c82375a50029c,f614d69875cbbd24f989fc8c75f9b18f3bb7c541,TODO(crcrpar): Improve resource computation\/allocation algorithm.
aaegajabc,optuna/optuna,optuna/pruners/hyperband.py,1eae8e2b2d04f2a521f29da3cbe439c82b0b2ed1,3c5f6d9226871019516600c33ff5e831d871e59b,TODO(crcrpar): Improve resource computation\/allocation algorithm.
aaegajach,optuna/optuna,optuna/pruners/hyperband.py,c7432c179def301b5f6c6b75b9aa6626a39dc304,STILL_EXISTS,TODO(crcrpar): Improve resource computation\/allocation algorithm.
aaegajbge,optuna/optuna,optuna/pruners/hyperband.py,31a2dc743eb2483209c8a56c139f7b647925c9b1,STILL_EXISTS,TODO(crcrpar): Improve resource computation\/allocation algorithm.
aaegajeie,optuna/optuna,optuna/study.py,89f66639bf6ba3dfc7fc4c6805e1e5293b95268a,b71f41c9c42972fb88d72c66cf39f55f9471ead0,The following expression makes an iterator that never ends.
aaegbajji,optuna/optuna,optuna/importance/_fanova/_fanova.py,2680a5ff28ed65873acee0debcb6d6548e1a3e1d,STILL_EXISTS,\"\"\"An implementation of `An Efficient Approach for Assessing Hyperparameter Importance`. ||  || See http:\/\/proceedings.mlr.press\/v32\/hutter14.pdf. ||  || This implementation is inspired by the efficient algorithm in || `fanova` (https:\/\/github.com\/automl\/fanova) and || `pyrfr` (https:\/\/github.com\/automl\/random_forest_run) by the original authors. ||  || Differences include relying on scikit-learn to fit random forests || (`sklearn.ensemble.RandomForestRegressor`) and that it is otherwise written entirely in Python. || This stands in contrast to the original implementation which is partially written in C++. || Since Python runtime overhead may become noticeable; included are instead several || optimizations; e.g. vectorized NumPy functions to compute the marginals; instead of keeping all || running statistics. Known cases include assessing higher order importances; e.g. pairwise || importances; this is due to the fact that the number of partitions to visit grows exponentially; || or when assessing categorical features with a larger number of choices since each choice is || given a unique one-hot encoded raw feature. ||  || \"\"\"
aaegbbaaj,optuna/optuna,optuna/importance/_fanova/_fanova.py,2680a5ff28ed65873acee0debcb6d6548e1a3e1d,440c2bc25fdc60cd6ccb491f20c8741a009ce9a7,== \"numpy.ndarray with corresponding columns in the transformed matrix\"`
aaegbbabb,optuna/optuna,optuna/importance/_fanova/_fanova.py,2680a5ff28ed65873acee0debcb6d6548e1a3e1d,440c2bc25fdc60cd6ccb491f20c8741a009ce9a7,Transform the `X` matrix by expanding categorical integer-valued columns to one-hot
aaegbbadj,optuna/optuna,tests/importance_tests/fanova_tests/test_fanova.py,2680a5ff28ed65873acee0debcb6d6548e1a3e1d,STILL_EXISTS,Create test data with 5 columns with the following types of features.
aaegbbaef,optuna/optuna,tests/importance_tests/fanova_tests/test_fanova.py,2680a5ff28ed65873acee0debcb6d6548e1a3e1d,STILL_EXISTS,First 3 + 4 columns are one-hot encoded categorical.
aaegbbahj,optuna/optuna,optuna/integration/lightgbm_tuner/optimize.py,781c6af3a0d81eba04bc3f6aebb27e97e12bf6f3,STILL_EXISTS,todo (smly): This implementation is different logic from the LightGBM's python bindings.
aaegbbbhc,optuna/optuna,optuna/storages/base.py,e4257c110e3f2b7d2ce3207a0d9abc47426846b9,STILL_EXISTS,TODO(ytsmiling) Fix RDB storage implementation to ensure unique `study_id`.
aaegbbedf,optuna/optuna,tests/multi_objective/samplers_tests/test_nsga2.py,3c9514e9c8d91eae20a27bd078e1f1a13ca87ad1,STILL_EXISTS,TODO(ohta): Consider to move this utility function to `optuna.testing` module.
aaegbbhdg,optuna/optuna,optuna/storages/base.py,7e801e7f54bdd7013e8ba96f73b61bb6e18d0844,STILL_EXISTS,TODO(ytsmiling) Fix RDB storage implementation to ensure unique `study_id`.
aaegbceii,optuna/optuna,optuna/storages/base.py,099399b236a265e705e907c33d8b49f0e395aa60,STILL_EXISTS,TODO(ytsmiling) Fix RDB storage implementation to ensure unique `study_id`.
aaegbcfha,optuna/optuna,optuna/samplers/cmaes.py,7e5223ec1a03dbaab03d721bc3e1845f2b29fc75,02895e43b1b004c6485ec23ab714c5af33bc686d,TODO add optuna.distributions.IntLogUniformDistribution
aaegbcfhd,optuna/optuna,optuna/integration/skopt.py,bf9df503aca58ef6feecd662648d065b95f6ef2f,STILL_EXISTS,TODO support distributions.IntLogUniformDistribution
aaegbcfhi,optuna/optuna,optuna/importance/_mean_decrease_impurity.py,e3c6ee20ecdfc1df72cad835c1c4796a379fd9c6,440c2bc25fdc60cd6ccb491f20c8741a009ce9a7,Transform the `params_data` matrix by expanding categorical integer-valued columns to one-hot
aaegbcfia,optuna/optuna,optuna/importance/_mean_decrease_impurity.py,e3c6ee20ecdfc1df72cad835c1c4796a379fd9c6,440c2bc25fdc60cd6ccb491f20c8741a009ce9a7,All categorical one-hot columns are placed before the numerical columns in
aaegbcfje,optuna/optuna,optuna/integration/cma.py,a776090acf7f1e1984c0a6b75210f2163feec1a3,STILL_EXISTS,TODO support IntLogUniform
aaegbciig,optuna/optuna,optuna/multi_objective/samplers/_nsga2.py,aa8b5784d4483fee748b4831004782fc1d2144e8,8cfd88854de603e2a1c1d49794b1739337b9a6ec,this is a very rare case and doesn't significantly impact optimization performance.
aaegbdgaj,optuna/optuna,examples/allennlp/allennlp_simple.py,08c5e1338c6402a097b26ef32ddfa1561c68be4e,STILL_EXISTS,\"\"\" || Optuna example that optimizes a classifier configuration for IMDB movie review dataset. || This script is based on the example of allentune (https:\/\/github.com\/allenai\/allentune). ||  || In this example; we optimize the validation accuracy of sentiment classification using AllenNLP. || Since it is too time-consuming to use the entire dataset; we here use a small subset of it. ||  || We have the following two ways to execute this example: ||  || (1) Execute this code directly. ||     $ python allennlp_simple.py ||  ||  || (2) Execute through CLI. ||     $ STUDY_NAME=`optuna create-study --direction maximize --storage sqlite:\/\/\/example.db` ||     $ optuna study optimize allennlp_simple.py objective --n-trials=100 --study $STUDY_NAME \\ ||       --storage sqlite:\/\/\/example.db ||  || \"\"\"
aaegbdgbb,optuna/optuna,examples/allennlp/allennlp_simple.py,08c5e1338c6402a097b26ef32ddfa1561c68be4e,STILL_EXISTS,\"\"\" || Optuna example that optimizes a classifier configuration for IMDB movie review dataset. || This script is based on the example of allentune (https:\/\/github.com\/allenai\/allentune). ||  || In this example; we optimize the validation accuracy of sentiment classification using AllenNLP. || Since it is too time-consuming to use the entire dataset; we here use a small subset of it. ||  || We have the following two ways to execute this example: ||  || (1) Execute this code directly. ||     $ python allennlp_simple.py ||  ||  || (2) Execute through CLI. ||     $ STUDY_NAME=`optuna create-study --direction maximize --storage sqlite:\/\/\/example.db` ||     $ optuna study optimize allennlp_simple.py objective --n-trials=100 --study-name $STUDY_NAME \\ ||       --storage sqlite:\/\/\/example.db ||  || \"\"\"
aaegbgehe,optuna/optuna,optuna/multi_objective/samplers/_nsga2.py,93a2943cb4dbc7c8e6f89b37db452ce39d0e93ae,d6e90e5567f4eb27ca19d989cc614fb05ab63634,this is a very rare case and doesn't significantly impact optimization performance.
aaegbiefi,optuna/optuna,optuna/multi_objective/samplers/_nsga2.py,cd5d04fe129d08e64a1936054be947314085e92e,STILL_EXISTS,this is a very rare case and doesn't significantly impact optimization performance.
aaegcajia,optuna/optuna,optuna/multi_objective/samplers/_motpe.py,e82cf22ce3685fbf5f9ae88d749a42c481168f20,0d27c108cdef1f33db76648da9db447b68a2a555,TODO: Faster algorithms have been proposed for 2 and 3-dimensional case in Guerreiro et al.
aaegcajib,optuna/optuna,optuna/multi_objective/samplers/_motpe.py,e82cf22ce3685fbf5f9ae88d749a42c481168f20,0d27c108cdef1f33db76648da9db447b68a2a555,(2016). These algorithms could improve the performance of MOTPE.
aaegcbcbh,optuna/optuna,examples/allennlp/allennlp_simple.py,0a2a5a4196bf9604e9c66930759aaf7afa4f411d,STILL_EXISTS,\"\"\" || Optuna example that optimizes a classifier configuration for IMDB movie review dataset. || This script is based on the example of allentune (https:\/\/github.com\/allenai\/allentune). ||  || In this example; we optimize the validation accuracy of sentiment classification using AllenNLP. || Since it is too time-consuming to use the entire dataset; we here use a small subset of it. ||  || The example can be executed as follows: ||  ||     $ python allennlp_simple.py ||  || \"\"\"
aaegcbdag,optuna/optuna,tutorial/005_cli.py,b630e919f7d6bdc9d21da45e6e2d3f128c77efb4,STILL_EXISTS,\"\"\" || .. _cli: ||  || Command-Line Interface || ====================== ||  || .. csv-table:: ||    :header: Command; Description ||    :widths: 20; 40 ||  ||     create-study; Create a new study. ||     delete-study; Delete a specified study. ||     dashboard; Launch web dashboard (beta). ||     storage upgrade; Upgrade the schema of a storage. ||     studies; Show a list of studies. ||     study optimize; Start optimization of a study. ||     study set-user-attr; Set a user attribute to a study. ||  || Optuna provides command-line interface as shown in the above table. ||  || Let us assume you are not in IPython shell and writing Python script files instead. || It is totally fine to write scripts like the following: || \"\"\"
aaegcbdga,optuna/optuna,tutorial/007_pruning.py,b630e919f7d6bdc9d21da45e6e2d3f128c77efb4,STILL_EXISTS,\"\"\" || .. _pruning: ||  || Pruning Unpromising Trials || ========================== ||  || This feature automatically stops unpromising trials at the early stages of the training (a.k.a.; automated early-stopping). || Optuna provides interfaces to concisely implement the pruning mechanism in iterative training algorithms. ||  ||  || Activating Pruners || ------------------ || To turn on the pruning feature; you need to call :func:`~optuna.trial.Trial.report` and :func:`~optuna.trial.Trial.should_prune` after each step of the iterative training. || :func:`~optuna.trial.Trial.report` periodically monitors the intermediate objective values. || :func:`~optuna.trial.Trial.should_prune` decides termination of the trial that does not meet a predefined condition. || \"\"\"
aaegcbdgi,optuna/optuna,tutorial/007_pruning.py,b630e919f7d6bdc9d21da45e6e2d3f128c77efb4,STILL_EXISTS,To implement pruning mechanism in much simpler forms; Optuna provides integration modules for the following libraries.
aaegccaea,optuna/optuna,examples/allennlp/allennlp_simple.py,02651af42b9bb6846b837aafe07d541942cc7ca1,STILL_EXISTS,\"\"\" || Optuna example that optimizes a classifier configuration for IMDB movie review dataset. || This script is based on the example of AllenTune (https:\/\/github.com\/allenai\/allentune). ||  || In this example; we optimize the validation accuracy of sentiment classification using AllenNLP. || Since it is too time-consuming to use the entire dataset; we here use a small subset of it. ||  || \"\"\"
aaegccjjg,optuna/optuna,optuna/samplers/_tpe/sampler.py,3bed78262f19073e87ef154c61d8fbd8fb9264bc,82ceeda7d05943a77dd212bdaed8d4597f52e060,Though it seems to be theoretically correct; it leads performance degration
aaegcdaaj,optuna/optuna,optuna/samplers/_tpe/sampler.py,0c79aa03221438407b633b13599de3157267bfcf,3b5921621aa47716963dbf8913738dbdbd19460b,Though it seems to be theoretically correct; it leads to performance degradation
aaegcdaeg,optuna/optuna,optuna/samplers/_tpe/multivariate_parzen_estimator.py,c878c319e73adb94f792e6f82680e4c8e196e676,3b5921621aa47716963dbf8913738dbdbd19460b,TODO(kstoneriv3): maybe divide this functions into smaller ones
aaegcdhgc,optuna/optuna,tests/integration_tests/lightgbm_tuner_tests/test_optimize.py,a2a5b66dd2c4fe7e485bc91f5c45b95e0611b6e4,fb011e500e38ff9f4bf7b61a8bcfb7b6cde06064,Workaround for mypy.
aaegcdidc,optuna/optuna,tests/integration_tests/lightgbm_tuner_tests/test_optimize.py,b4b39f44b9bfe5ce1ee80f97f4834c53e93024c3,STILL_EXISTS,Workaround for mypy.
aaegceied,optuna/optuna,optuna/samplers/_tpe/multivariate_parzen_estimator.py,3b5921621aa47716963dbf8913738dbdbd19460b,STILL_EXISTS,_low; _high; _q is needed for transformation
aaegceiee,optuna/optuna,optuna/samplers/_tpe/multivariate_parzen_estimator.py,3b5921621aa47716963dbf8913738dbdbd19460b,STILL_EXISTS,transformed multivariate_samples are needed for following operations
aaegcfafj,optuna/optuna,optuna/samplers/_tpe/sampler.py,9710c0ad036e693bd220bb7bea0d6f994ac9c0c6,STILL_EXISTS,Though it seems to be theoretically correct; it leads to performance degradation
aaegcfahj,optuna/optuna,optuna/samplers/_tpe/sampler.py,b839981ffed6a7222ddb3a41f6b8f40ac6807e5c,bca4e2f0a9ef2bc5348fb004498308ba216dff68,be needed. Independent sampler in `TPESampler` first splits the observations and
aaegcfaih,optuna/optuna,optuna/samplers/_tpe/sampler.py,bca4e2f0a9ef2bc5348fb004498308ba216dff68,STILL_EXISTS,Though it seems to be theoretically correct; it leads to performance degradation
aaegcffca,optuna/optuna,optuna/visualization/matplotlib/_matplotlib_imports.py,b3c1d657405d73b2c80d65fb7b1871d00dde44dc,STILL_EXISTS,TODO: Add specific imports.
aaegcffce,optuna/optuna,optuna/visualization/matplotlib/_matplotlib_imports.py,b3c1d657405d73b2c80d65fb7b1871d00dde44dc,STILL_EXISTS,TODO: Set precise version.
aaegcffgg,optuna/optuna,optuna/visualization/matplotlib/_parallel_coordinate.py,1884cac109b9219cfb9d8bfbd840e495ec6a36e4,d1cef352f0f3f2fb90ec4e4b6022ccb68a2fe4b4,TODO(ytknzw): Implement process for categorical values.
aaegcfgbg,optuna/optuna,optuna/samplers/_tpe/sampler.py,276b2b409673f18b83551c91c2737b186b29edcc,10207e4dbe3187cdee1c835d4a813b26082c7a59,be needed. Independent sampler in `TPESampler` first splits the observations and
aaegcfhad,optuna/optuna,optuna/samplers/_tpe/sampler.py,60d1da0ad17f68ac509c850416008ce0d4d3b831,STILL_EXISTS,Though it seems to be theoretically correct; it leads to performance degradation
aaegcfhjf,optuna/optuna,optuna/samplers/_tpe/sampler.py,ecfd66fb6b4cb977fd3e7c57b6bb256fde4f8038,STILL_EXISTS,be needed. Independent sampler in `TPESampler` first splits the observations and
aaegcgacf,optuna/optuna,optuna/visualization/matplotlib/_contour.py,dcd57073fa3186b337dfb3f2272074dab432c0a9,b885f800e2e836cf2d66605ddb00bd163a066f17,TODO(ytknzw): Implement Plotly-like interpolation algorithm.
aaegcggda,optuna/optuna,optuna/visualization/matplotlib/_contour.py,137176cf4758d7b4387013686d5fbe4f3b88f2c3,fb0a5cf7b336b5be16e3f19429148eceaff8db95,TODO(ytknzw): Implement Plotly-like interpolation algorithm.
aaegchaji,optuna/optuna,examples/xgboost_cv.py,b931b62b0e2d12d1c6b388f8f37b92486716059e,STILL_EXISTS,\"\"\" || Optuna example that performs Cross Validation for cancer dataset using XGBoost. ||  || In this example; we perform cross-validation to accuracy of cancer detection || using XGBoost. We optimize both the choice of booster model and their hyper || parameters. ||  || We have following way to execute this example: ||  || (1) Execute this code directly. ||     $ python xgboost_cv.py ||  ||  || \"\"\"
aaegchebc,optuna/optuna,optuna/visualization/matplotlib/_contour.py,8583c48ec5187206e9b12f9524fbcdb2bafbb059,8fcef825ca7d71c5180741f19605a2ce91b1722e,TODO(ytknzw): Implement Plotly-like interpolation algorithm.
aaegchfdd,optuna/optuna,optuna/visualization/matplotlib/_contour.py,1f0aa651aba59bfe845af3a17ca7d8fa4e209046,11d39eb4aea0a524a888e2537cb89a535c48f069,TODO(ytknzw): Implement Plotly-like interpolation algorithm.
aaegcicdg,optuna/optuna,optuna/visualization/matplotlib/_contour.py,012a17a09b4a4282c8ae8ceb955ab3001c1e2bdd,436ab72349f4a5b3614f961a11b29b301ebe3d26,TODO(ytknzw): Implement Plotly-like interpolation algorithm.
aaegcidgf,optuna/optuna,optuna/visualization/matplotlib/_contour.py,c1c48a9e4f3246b03688594bbd00cde183475669,633c5699a11c086ccf8c57606f73512dbc582715,TODO(ytknzw): Implement Plotly-like interpolation algorithm.
aaegcieeg,optuna/optuna,optuna/visualization/matplotlib/_contour.py,da06d2d3d32b4cfa8ee4ee0b2276e9fb2ba5db60,2840db95e16bb43608301a7e8e506e326d8b1d8b,TODO(ytknzw): Implement Plotly-like interpolation algorithm.
aaegcieih,optuna/optuna,optuna/_optimize.py,89e8f9c2502b29f0989717ebfbfb9ee2d0e84386,1a27eb1d1f22c5b23ad3c88ab35c535d68915ee1,This is needed for mypy.
aaegcifbe,optuna/optuna,optuna/_dataframe.py,7728c516f15dda07d5ddbbf07bcff590cba628e3,STILL_EXISTS,Values are dataframe columns such as ('trial_id'; '') and ('params'; 'n_layers').
aaegcifbi,optuna/optuna,optuna/_dataframe.py,7728c516f15dda07d5ddbbf07bcff590cba628e3,STILL_EXISTS,Flatten the `MultiIndex` columns where names are concatenated with underscores.
aaegcifbj,optuna/optuna,optuna/_dataframe.py,7728c516f15dda07d5ddbbf07bcff590cba628e3,STILL_EXISTS,Filtering is required to omit non-nested columns avoiding unwanted trailing
aaegcifcb,optuna/optuna,optuna/study.py,7728c516f15dda07d5ddbbf07bcff590cba628e3,b1b0af9fe59f98ee6c6852b8065ab5e2ff8920a9,This is needed for mypy.
aaegcifjg,optuna/optuna,optuna/study.py,2840db95e16bb43608301a7e8e506e326d8b1d8b,e458861e74d5eafe445f548546b7fa9aac44077e,Values are dataframe columns such as ('trial_id'; '') and ('params'; 'n_layers').
aaegcigaa,optuna/optuna,optuna/study.py,2840db95e16bb43608301a7e8e506e326d8b1d8b,e458861e74d5eafe445f548546b7fa9aac44077e,Flatten the `MultiIndex` columns where names are concatenated with underscores.
aaegcigab,optuna/optuna,optuna/study.py,2840db95e16bb43608301a7e8e506e326d8b1d8b,e458861e74d5eafe445f548546b7fa9aac44077e,Filtering is required to omit non-nested columns avoiding unwanted trailing
aaegciieh,optuna/optuna,optuna/visualization/matplotlib/_contour.py,42e679dc6ef1556f8cedb42e70b836a691347c7d,92d4d3553b23bee2771277f18cd4a0eb0273919c,TODO(ytknzw): Implement Plotly-like interpolation algorithm.
aaegcjdbb,optuna/optuna,tests/samplers_tests/test_partial_fixed.py,8f2953c55db87f7f53acb8db8df9eba950b1833d,STILL_EXISTS,Fix parameter y as 0.0.
aaegcjdbc,optuna/optuna,tests/samplers_tests/test_partial_fixed.py,8f2953c55db87f7f53acb8db8df9eba950b1833d,0ef17ad55a8d92903f648eaf02b25918a44ec4e1,Fix parameter y as the -5.
aaegcjhea,optuna/optuna,optuna/visualization/matplotlib/_contour.py,1100a17b77c9eb4ff0d82f1775ef5edf93c2e72a,b7d0f0c8669fe4e2f8a57cbb29d1018efbc333d8,TODO(ytknzw): Implement Plotly-like interpolation algorithm.
aaegcjhfe,optuna/optuna,docs/source/plotly_directive.py,0546e5f203a5ea9086e6dc774abe8893aac47663,STILL_EXISTS,\"\"\" || # Based on: || https:\/\/github.com\/matplotlib\/matplotlib\/blob\/master\/lib\/matplotlib\/sphinxext\/plot_directive.py ||  || # Requirements: || 1. docstring contains a single code block. || 2. the code block ends with an expression that evaluates to a plotly figure. || \"\"\"
aaegdahei,optuna/optuna,tutorial/10_key_features/003_efficient_optimization_algorithms.py,df77d7c3a3aabc6d5002cdabf71d4a9106d2aeec,STILL_EXISTS,\"\"\" || .. _pruning: ||  || Efficient Optimization Algorithms || ================================= ||  || Optuna enables efficient hyperparameter optimization by || adopting state-of-the-art algorithms for sampling hyperparameters and || pruning efficiently unpromising trials. ||  || Sampling Algorithms || ------------------- ||  || Samplers basically continually narrow down the search space using the records of suggested parameter values and evaluated objective values; || leading to an optimal search space which giving off parameters leading to better objective values. || More detailed explanation of how samplers suggest parameters is in :class:`optuna.samplers.BaseSampler`. ||  || Optuna provides the following sampling algorithms: ||  || - Tree-structured Parzen Estimator algorithm implemented in :class:`optuna.samplers.TPESampler` ||  || - CMA-ES based algorithm implemented in :class:`optuna.samplers.CmaEsSampler` ||  || - Grid Search implemented in :class:`optuna.samplers.GridSampler` ||  || - Random Search implemented in :class:`optuna.samplers.RandomSampler` ||  || The default sampler is :class:`optuna.samplers.TPESampler`. ||  ||  || Pruning Algorithms || ------------------ ||  || ``Pruners`` automatically stop unpromising trials at the early stages of the training (a.k.a.; automated early-stopping). ||  || Optuna provides the following pruning algorithms: ||  || - Asynchronous Successive Halving algorithm implemted in :class:`optuna.pruners.SuccessiveHalvingPruner` ||  || - Hyperband algorithm implemented in :class:`optuna.pruners.HyperbandPruner` ||  || - Median pruning algorithm implemented in :class:`optuna.pruners.MedianPruner` ||  || - Threshold pruning algorithm implemented in :class:`optuna.pruners.ThresholdPruner` ||  || We use :class:`optuna.pruners.MedianPruner` in most examples; || though basically it is outperformed by :class:`optuna.pruners.SuccessiveHalvingPruner` and || :class:`optuna.pruners.HyperbandPruner` as in `this benchmark result <https:\/\/github.com\/optuna\/optuna\/wiki\/%5BUnder-Construction%5D-Benchmarks-with-Kurobako>`_. ||  ||  || Activating Pruners || ------------------ || To turn on the pruning feature; you need to call :func:`~optuna.trial.Trial.report` and :func:`~optuna.trial.Trial.should_prune` after each step of the iterative training. || :func:`~optuna.trial.Trial.report` periodically monitors the intermediate objective values. || :func:`~optuna.trial.Trial.should_prune` decides termination of the trial that does not meet a predefined condition. ||  || We would recommend using integration modules for major machine learning frameworks. || Exclusive list is :ref:`integration_list` and usecases are available in  `optuna\/examples <https:\/\/github.com\/optuna\/optuna\/tree\/master\/examples\/>`_. || \"\"\"
aaegdahhj,optuna/optuna,tutorial/10_key_features/003_efficient_optimization_algorithms.py,df77d7c3a3aabc6d5002cdabf71d4a9106d2aeec,STILL_EXISTS,|                           | Yes                                     | Random Search or Genetic Algorithm                            |
aaegdahif,optuna/optuna,tutorial/10_key_features/003_efficient_optimization_algorithms.py,df77d7c3a3aabc6d5002cdabf71d4a9106d2aeec,STILL_EXISTS,To implement pruning mechanism in much simpler forms; Optuna provides integration modules for the following libraries.
aaegdaijh,optuna/optuna,tutorial/20_recipes/006_user_defined_sampler.py,df77d7c3a3aabc6d5002cdabf71d4a9106d2aeec,STILL_EXISTS,\"\"\" || .. _ud_pruner: ||  || User-Defined Pruner || =================== ||  || This tutorial walks you through how :class:`~optuna.pruners.ThresholdPruner` is implemented to give you || a big picture of how you can implement your own pruners. ||  || As you can see in the :class:`~optuna.pruner.BasePruner`; || what you need to implement is :func:`~optuna.pruner.BasePruner.prune` || which takes :class:`~optuna.study.Study` and currently being evaluated || :class:`~optuna.trial.FrozenTrial`. || This means that you can have the access to the annals of :class:`~optuna.trial.FrozenTrial`\\\\'s. || :class:`~optuna.pruners.SuccessiveHalvingPruner` utilizes this feature. ||  || So; for the illustration purpose; I walk through you the implementation of :class:`~optuna.pruners.ThresholdPruner`\\\\'s :func:`~optuna.pruners.ThresholdPruner.prune`. ||  || .. code:: python ||  ||     class ThresholdProuner(BasePruner): ||  ||         ... ||  ||         def pruner( ||             self; ||             study: optuna.study.Study; ||             trial: optuna.trial.FrozenTrial ||         ) -> bool: ||  ||             # `step` generally represents the iteration or epoch. ||             step = trail.last_step ||  ||             # ``False`` means not pruned. ||             if step is None: ||                 return False ||  ||             # Check whether the ``trial`` has run the enough ``steps``. ||             if not _is_first_in_interval_step( ||                 step; trial.intermediate_values.keys(); n_warmup_steps; self._interval_steps ||             ): ||                 return False ||  ||             latest_value = trial.intermediate_values[step] ||             if math.isnan(latest_value): ||                 return True ||  ||             if latest_value < self._lower: ||                 return True ||  ||             if latest_value > self._upper: ||                 return True ||  ||             return False ||  ||  || \"\"\"
aaegdbegb,optuna/optuna,optuna/visualization/_contour.py,2e9728c16ad8566eccc6ded38e56fab9d3f2069d,STILL_EXISTS,TODO (yanase): Remove this if-clause after resolving the error.
aaegdbejd,optuna/optuna,optuna/integration/botorch.py,eacb6186c7deae474cffbd70241a1e1ac4e1d8ff,a157b0684fc2a4dcd2b8d1dc136afb3205a233e6,Implement reseed random.
aaegdbfaa,optuna/optuna,optuna/integration/botorch.py,eacb6186c7deae474cffbd70241a1e1ac4e1d8ff,STILL_EXISTS,BoTorch assumes maximization; so flip the sign if needed.
aaegdbfde,optuna/optuna,optuna/_transform.py,6c11ae1db6b13a94a002983f6bc2b4750c3fbd5b,e5b421e48a36853c9d4d372a2963b1d3a2e0a47b,== \"numpy.ndarray with corresponding columns in the encoded matrix\"`
aaegdbfdh,optuna/optuna,optuna/_transform.py,6c11ae1db6b13a94a002983f6bc2b4750c3fbd5b,e5b421e48a36853c9d4d372a2963b1d3a2e0a47b,Transform the `params` matrix by expanding categorical integer-valued columns to one-hot
aaegdbfea,optuna/optuna,optuna/_transform.py,6c11ae1db6b13a94a002983f6bc2b4750c3fbd5b,e5b421e48a36853c9d4d372a2963b1d3a2e0a47b,All categorical one-hot columns will be placed before the numerical columns in
aaegdbfgc,optuna/optuna,tests/test_transform.py,6c11ae1db6b13a94a002983f6bc2b4750c3fbd5b,e5b421e48a36853c9d4d372a2963b1d3a2e0a47b,Create test data with 5 columns with the following types of parameters.
aaegdbfgi,optuna/optuna,tests/test_transform.py,6c11ae1db6b13a94a002983f6bc2b4750c3fbd5b,e5b421e48a36853c9d4d372a2963b1d3a2e0a47b,First 3 + 4 columns are one-hot encoded categorical.
aaegdcaha,optuna/optuna,optuna/visualization/matplotlib/_contour.py,8c02b31735244abccfffc9b14965bba1a4af91f7,STILL_EXISTS,TODO(ytknzw): Implement Plotly-like interpolation algorithm.
aaegdcajj,optuna/optuna,optuna/_transform.py,e5b421e48a36853c9d4d372a2963b1d3a2e0a47b,STILL_EXISTS,TODO(hvy): Introduce `encoded_column_to_column: numpy.ndarray` if needed.
aaegdccab,optuna/optuna,optuna/_transform.py,bee844bf42acf9fbe522aea396351515538da711,STILL_EXISTS,since the columns in the transformed representation must be correctly mapped back to the
aaegddcgj,optuna/optuna,optuna/storages/_rdb/alembic/versions/v2.4.0.a.py,1013b08791626087e313a1eddf3b5b95eb317c1b,STILL_EXISTS,TODO(imamura): Implement downgrade
aaegdgjbi,optuna/optuna,optuna/importance/_mean_decrease_impurity.py,46b083f6f8ea1ae3a50c304009d25397e1451f59,446f584c887bf2deb2b233acc0a736edd78b216d,Transform the `params_data` matrix by expanding categorical integer-valued columns to one-hot
aaegdgjca,optuna/optuna,optuna/importance/_mean_decrease_impurity.py,46b083f6f8ea1ae3a50c304009d25397e1451f59,446f584c887bf2deb2b233acc0a736edd78b216d,All categorical one-hot columns are placed before the numerical columns in
aaegdhdfa,optuna/optuna,tutorial/20_recipes/006_user_defined_pruner.py,e9367569ef40fcb58b7537f3dc68938856d7199f,STILL_EXISTS,\"\"\" || .. _user_defined_pruner: ||  || User-Defined Pruner || ==================== ||  || In :ref:`pruners`; we described how an objective function can optionally include calls to a pruning feature which allows Optuna to terminate an optimization trial when intermediate results do not appear promising. In this document; we describe how to implement your own pruner; i.e.; a custom strategy for determining when to stop a trial. ||  || Overview of Pruning Interface || ------------------- ||  || The :func:`~optuna.study.create_study` constructor takes; as an optional argument; a pruner inheriting from :class:`~optuna.pruners.BasePruner`. The pruner should implement the abstract method :meth:`~optuna.pruners.BaseSampler.prune`; which takes arguments for the associated :class:`~optuna.study.Study` and :class:`~optuna.trial.Trial` and returns a boolean value: `True` if the trial should be pruned and `False` otherwise. Using the Study and Trial objects; you can access all other trials through the :meth:`study.get_trial` method and; and from a trial; its reported intermediate values through the `trial.intermediate_values` attribute (a dictionary which maps an integer `step` to a float value). ||  || You can refer to the source code of the built-in Optuna pruners as templates for building your own. In this document; for illustration; we describe the construction and usage of a simple (but aggressive) pruner which prunes trials that are in last place compared to completed trials at the same step. ||  || .. note:: ||   Please refer to the documentation of :class:`~optuna.pruners.BasePruner` or; for example; :class:`~optuna.pruners.ThresholdPruner` or :class:`optuna.pruners.PercentilePruner` for more robust examples of pruner implementation; including error checking and complex pruner-internal logic. ||  || An Example: Implementing LastPlacePruner || -------------------------------------------------- ||  || We aim to optimize the `loss` and `alpha` hyperparameters for a stochastic gradient descent classifier (SGDClassifier) run on the sklearn iris dataset. We implement a pruner which terminates a trial at a certain step if it is in last place compared to completed trials at the same step. We begin considering pruning after a \"warmup\" of 1 training step and 5 completed trials. For demonstration purposes; we print() a diagnostic message from prune() when it is about to return True (indicating pruning).  ||  || It may be important to note that the SGDClassifier score; as it is evaluated on a holdout set; decreases with enough training steps due to overfitting. This means that a trial could be pruned even if it had a favorable (high) value on a previous training set. After pruning; Optuna will take the intermediate value last reported as the value of the trial. ||  || \"\"\"
aaegdhjii,optuna/optuna,optuna/samplers/_nsga2.py,a8df87cd388d8000ed0892cae8ea87983fe48307,STILL_EXISTS,this is a very rare case and doesn't significantly impact optimization performance.
aaegdiabh,optuna/optuna,tests/samplers_tests/test_nsga2.py,a8df87cd388d8000ed0892cae8ea87983fe48307,STILL_EXISTS,TODO(ohta): Consider to move this utility function to `optuna.testing` module.
aaegdiabi,optuna/optuna,tests/samplers_tests/test_samplers.py,a8df87cd388d8000ed0892cae8ea87983fe48307,STILL_EXISTS,Please see https:\/\/github.com\/optuna\/optuna\/pull\/393 why this assertion is needed.
aaegdiade,optuna/optuna,tests/samplers_tests/test_samplers.py,a63f1183932dbcead94cfea18fa69391db9eadcc,STILL_EXISTS,Please see https:\/\/github.com\/optuna\/optuna\/pull\/393 why this assertion is needed.
aaegdjfgh,optuna/optuna,tests/test_study.py,89ad3673aa607d9461d46218d372291ca7730de2,b03e2ed037e8cf5e5e15a421910d2afb17ca00dd,TODO(hvy): Fix to emit `UserWarning` instead.
aaegdjhca,optuna/optuna,tests/samplers_tests/test_partial_fixed.py,ff80efe044b444c2e9999bb1391d4c235eb483a1,46018ae03a6b2411209ceba7d2d4907669998c94,Fix parameter ``y`` as 0.
aaegdjhej,optuna/optuna,tests/study_tests/test_dataframe.py,32acc777d09c7ba6ed1a3de024cffea4c0f322ab,STILL_EXISTS,Number columns are as follows (total of 13):
aaegecbeh,optuna/optuna,optuna/_optimize.py,6568ef921b58b94e0eb2d427e60c4c28d4c74ac0,ac27ba340e229384f124025fdc652e5801f58984,This is needed for mypy.
aaegeddac,optuna/optuna,tutorial/20_recipes/009_ask_and_tell.py,99d98bd211baf837022c66a4b478ab0fd2caaedb,STILL_EXISTS,\"\"\" || .. _ask_and_tell: ||  || Ask-and-Tell Interface || ======================= ||  || Optuna provides `Ask-and-Tell` interface; a more flexible interface for hyper-parameter optimization. || This tutorial explains three use-cases when the ask-and-tell interface is beneficial: ||  || - :ref:`without-objective` || - :ref:`define-and-run` || - :ref:`batch-optimization` ||  ||  || .. _without-objective: ||  || ---------------------------------------------------------------------------- || Apply optuna to an existing optimization problem with minimum modifications || ---------------------------------------------------------------------------- ||  || Let's consider the traditional supervised classification problem; you aim to maximize the validation accuracy. || To do so; you train `LogisticRegression` as a simple model. || \"\"\"
aaegedhda,optuna/optuna,tutorial/20_recipes/002_multi_objective.py,0ef1f4a369efa2cbc1c63d1695f3299bccf24e2b,255a1c2ed92e0ed21c8f10ee74d6b1f0c4ee25bc,This is a temporary fix until torchvision v0.9 is released.
aaegedihg,optuna/optuna,examples/pytorch/pytorch_checkpoint.py,225249ab05827264975441345849b0784ed2d064,e2c6a101eb597b9c5561cb240c78dc3950a90da7,This is a temporary fix until torchvision v0.9 is released.
aaegedihi,optuna/optuna,examples/pytorch/pytorch_distributed_simple.py,225249ab05827264975441345849b0784ed2d064,STILL_EXISTS,This is a temporary fix until torchvision v0.9 is released.
aaegediia,optuna/optuna,examples/pytorch/pytorch_ignite_simple.py,225249ab05827264975441345849b0784ed2d064,e2c6a101eb597b9c5561cb240c78dc3950a90da7,This is a temporary fix until torchvision v0.9 is released.
aaegediic,optuna/optuna,examples/pytorch/pytorch_lightning_simple.py,225249ab05827264975441345849b0784ed2d064,e2c6a101eb597b9c5561cb240c78dc3950a90da7,This is a temporary fix until torchvision v0.9 is released.
aaegediie,optuna/optuna,examples/pytorch/pytorch_simple.py,225249ab05827264975441345849b0784ed2d064,e2c6a101eb597b9c5561cb240c78dc3950a90da7,This is a temporary fix until torchvision v0.9 is released.
aaegedijb,optuna/optuna,examples/catalyst_simple.py,d62d0ce79fcee6d237b2319abe2993223979a472,STILL_EXISTS,This is a temporary fix until torchvision v0.9 is released.
aaegedjad,optuna/optuna,examples/fastai/fastaiv2_simple.py,d62d0ce79fcee6d237b2319abe2993223979a472,STILL_EXISTS,This is a temporary fix until torchvision v0.9 is released.
aaegedjbb,optuna/optuna,examples/multi_objective/pytorch_simple.py,d62d0ce79fcee6d237b2319abe2993223979a472,e2c6a101eb597b9c5561cb240c78dc3950a90da7,This is a temporary fix until torchvision v0.9 is released.
aaegedjbj,optuna/optuna,examples/pytorch/skorch_simple.py,d62d0ce79fcee6d237b2319abe2993223979a472,STILL_EXISTS,This is a temporary fix until torchvision v0.9 is released.
aaegefagb,google/uis-rnn,demo.py,08750e8d11482e9df1b841c00e33480cda5eb197,64a5e88e0790a8530c4d119596cd49f8cf1c7060,fix random seeds for reproducing results
aaegefcgg,google/uis-rnn,demo.py,f4b67e7d3de68580d857167d842d508bf237282a,STILL_EXISTS,TODO: support using pretrained model.
aaegefdbd,google/uis-rnn,tests/integration_test.py,64a5e88e0790a8530c4d119596cd49f8cf1c7060,STILL_EXISTS,fix random seeds for reproducing results
aaegeffdh,datamade/usaddress,addrlearning.py,f7f87a484d9077036947b7ec07073bdc79ef7723,STILL_EXISTS,'ends_in_comma' : token[-1] == ';'
aaegeffee,datamade/usaddress,training/training.py,cade00b72d9c5814a515553131fc381f4133eecc,a6830ec6e0d3b73b1e48ba2a5e05edc84e9163b1,'ends_in_comma' : token[-1] == ';'
aaegefgfg,datamade/usaddress,usaddress/__init__.py,a6830ec6e0d3b73b1e48ba2a5e05edc84e9163b1,fd00681e12636c9821debc80f7e11b1fdfa2c4c3,'ends_in_comma' : token[-1] == ';'
aaegefjcf,datamade/usaddress,training/utils.py,d87f3f65ff540ab79713d89194b1e51effff1a2d,STILL_EXISTS,todo wrap in a sklearn estimator so we can use a GridSearchCV
aaegefjch,datamade/usaddress,training/training.py,592dca71be5496c0fd1b79f69874b44d2e8e320b,b648658144b0054db33ab230742244f613751a87,todo remove
aaegegagc,pytorch/tnt,test/datasets.py,9948c27eb30f9b70c2d4f2961b0fbc6d707db524,STILL_EXISTS,TODO: every item should appear exactly once
aaegegbcj,pytorch/tnt,torchnet/meter/confusionmeter.py,ba256835a4f33d9139a70b6440c3223123132bc8,STILL_EXISTS,hack for bincounting 2 arrays together
aaegegchh,explosion/thinc,tests/test_model.py,0a3b498f3c0f30d0c6544f605455b7c71ac0ec76,STILL_EXISTS,TODO: Need a test that exercises multiple lines. Example bug:
aaegegfaa,explosion/thinc,tests/test_model.py,2fb4f0fcad82ebf931f6f83f220679b1e02066a4,03ba720e54da982ecc36838b1f854e4dd44b6d59,This is to work-around the ticking problem
aaegegjei,explosion/thinc,bin/cythonize.py,a6307a080b4edd2050dcd0a6fb0b753efbc17e12,STILL_EXISTS,\"\"\" cythonize ||  || Cythonize pyx files into C files as needed. ||  || Usage: cythonize [root_dir] ||  || Default [root_dir] is 'spacy'. ||  || Checks pyx files to see if they have been changed relative to their || corresponding C files.  If they have; then runs cython on these files to || recreate the C files. ||  || The script thinks that the pyx files have changed relative to the C files || by comparing hashes stored in a database file. ||  || Simple script to invoke Cython (and Tempita) on all .pyx (.pyx.in) || files; while waiting for a proper build system. Uses file hashes to || figure out if rebuild is needed. ||  || For now; this script should be run by developers when changing Cython files || only; and the resulting C files checked in; so that end-users (and Python-only || developers) do not get the Cython\/Tempita dependencies. ||  || Originally written by Dag Sverre Seljebotn; and copied here from: ||  || https:\/\/raw.github.com\/dagss\/private-scipy-refactor\/cythonize\/cythonize.py ||  || Note: this script does not check any of the dependent C libraries; it only || operates on the Cython .pyx files. || \"\"\"
aaegeidhd,explosion/thinc,thinc/neural/lstm.py,0c8eca58869688eea26389c3a8bf7df4fc5704c0,STILL_EXISTS,return C[t]; as well so we can continue LSTM with prev state init if needed
aaegeidji,explosion/thinc,thinc/neural/lstm.py,0c8eca58869688eea26389c3a8bf7df4fc5704c0,STILL_EXISTS,weighted sum is a nice hash to use I think
aaegeieeb,explosion/thinc,thinc/neural/lstm.py,4429bb80c9091c4bbf52f786be477e4575816037,STILL_EXISTS,return C[t]; as well so we can continue LSTM with prev state init if needed
aaegeigfe,explosion/thinc,thinc/vec2vec.py,44f4cc852a5e6a5b415bed47d53a4f276f75c3db,5c17c0912f2f54c367cc5ede509d88e881875729,# TODO
aaegeigfi,explosion/thinc,thinc/vecs2vec.py,44f4cc852a5e6a5b415bed47d53a4f276f75c3db,STILL_EXISTS,TODO
aaegeigia,explosion/thinc,thinc/tests/unit/test_ops.py,a21c152ecc8ca5c01d915d85a2503048078ad422,1dcb0cd6acd0e85ce53232a3ecd4b35a23b45dd2,TODO: Not sure how this feature should work still...
aaegeiibg,explosion/thinc,thinc/ids2id/tests/test_avgtron.py,7c0eb51c3d3ec715b4d7e48fb89addf1596a8a5e,STILL_EXISTS,## TODO: Need a test that exercises multiple lines. Example bug:
aaegeiiii,explosion/thinc,thinc/vecs2vecs.py,c82be294b24b1c5d74d7d5c8f51199c475be66f7,STILL_EXISTS,weighted sum is a nice hash to use I think
aaegeijcb,explosion/thinc,thinc/vecs2vecs.py,c86ac5b84c52ea5be8fc7dc5f245968fd3ff12ce,STILL_EXISTS,TODO
aaegeijdf,explosion/thinc,thinc/id2vec.py,97bf5b660cd3f54df00b40282baf6a58fcefed2c,bbd5c3b2ff0dc4630f4dd42ea570d1aebb7aab81,TODO: This is broken! Need to backprop through self.W
aaegejahb,explosion/thinc,thinc/extra/_vendorized/keras_datasets.py,d5ae08337672e11e8a532e8abfcc5b3c2193d5ce,STILL_EXISTS,by convention; use 2 as OOV word
aaegejahg,explosion/thinc,thinc/neural/base.py,2aa482ab4289955080466be07e98f759b3c380aa,STILL_EXISTS,Move positional args into the keyword args; so they can be handled
aaegejaie,explosion/thinc,thinc/neural/base.py,74687cc0a418af49418a80a62144222453af489f,STILL_EXISTS,assert len(names) == len(args); \"TODO: Error message\"
aaegejcji,explosion/thinc,thinc/neural/_classes/window_encode.py,e1daee85f691c7d88e749b01784372aec2fb2ce0,STILL_EXISTS,ends. We'll shift afterwards.
aaegejdce,explosion/thinc,thinc/neural/_classes/window_encode.py,3fe98d9b4aa04f757887ceac2a4ac70d293f9347,501d5aaa6771f92be66ccc88092ba50acd80179c,Or; really; need to fix API of WindowEncode tagger. Maybe insert
aaegejddb,explosion/thinc,thinc/neural/_classes/window_encode.py,0e2df2a6d000ed5c7a81f19a710338fb3adaab59,e1fda85463232a25ae3f186f1ddb67b05b6e62fa,TODO: Implement fine-tuning
aaegejjbh,explosion/thinc,thinc/neural/_classes/model.py,9747ddb3901138bdbe204e1259430421339a009b,c92ee813bad801eb78bc6bb30332eced90146225,TODO: Fix for feed-forward...
aaegfaadd,explosion/thinc,thinc/api.py,3865fcf08e29f2af7d4ad84561ff5413b57568b6,6e96709eebb3a6029f6caaebdb4f5f67313d9515,TODO: How do we strip the arg checking from Model?
aaegfadac,explosion/thinc,thinc/neural/_classes/affine.py,53b95dd8ca0cdc9cb26b68cbf31a67ec0a69f578,a16ef3065eb72485c4b1cfcea451f17c4bb18810,TODO: Add toggle for the LSUV init. It seems not always better!
aaegfbbia,explosion/thinc,thinc/tests/linear/test_avgtron.py,3d422f894d86d60d04ce1670430eb5feb08100f4,STILL_EXISTS,## TODO: Need a test that exercises multiple lines. Example bug:
aaegfbcaa,explosion/thinc,examples/quora_similarity.py,6713c75b9882affe32b086be6d6d2ba51f89e6da,STILL_EXISTS,trying to learn better; position-sensitive word features. This simple
aaegfbcab,explosion/thinc,examples/quora_similarity.py,6713c75b9882affe32b086be6d6d2ba51f89e6da,STILL_EXISTS,convolution step is much more efficient than BiLSTM; and can be
aaegfbcfc,explosion/thinc,examples/snli_entail.py,4b1d7c35ad96fb22e46a93136af705d1a97b774d,STILL_EXISTS,trying to learn better; position-sensitive word features. This simple
aaegfbcfd,explosion/thinc,examples/snli_entail.py,4b1d7c35ad96fb22e46a93136af705d1a97b774d,STILL_EXISTS,convolution step is much more efficient than BiLSTM; and can be
aaegfbfda,explosion/thinc,examples/text-pair/glove_mwe_multipool_predict.py,88d89d1e919871a88a0b9a82802b45e4f8499749,53f5310cd17edd6ef97fde2d5ab5aa4eadda3db8,trying to learn better; position-sensitive word features. This simple
aaegfbfdb,explosion/thinc,examples/text-pair/glove_mwe_multipool_predict.py,88d89d1e919871a88a0b9a82802b45e4f8499749,53f5310cd17edd6ef97fde2d5ab5aa4eadda3db8,convolution step is much more efficient than BiLSTM; and can be
aaegfbhhg,explosion/thinc,examples/text-pair/glove_mwe_multipool_predict.py,89d2f18951d05eb55f30859152bdd8c2844583d9,de24d31c6b4087321a1434675c20a0de61943d0a,trying to learn better; position-sensitive word features. This simple
aaegfbhhh,explosion/thinc,examples/text-pair/glove_mwe_multipool_predict.py,89d2f18951d05eb55f30859152bdd8c2844583d9,de24d31c6b4087321a1434675c20a0de61943d0a,convolution step is much more efficient than BiLSTM; and can be
aaegfbijd,explosion/thinc,examples/text-pair/glove_mwe_multipool_predict.py,49518276cde86fb3e439280c44fe13d842863895,0d1b71e9676b0d28b2f5c29b1e601e2796a7400e,trying to learn better; position-sensitive word features. This simple
aaegfbije,explosion/thinc,examples/text-pair/glove_mwe_multipool_predict.py,49518276cde86fb3e439280c44fe13d842863895,0d1b71e9676b0d28b2f5c29b1e601e2796a7400e,convolution step is much more efficient than BiLSTM; and can be
aaegfcaff,explosion/thinc,thinc/tests/linear/test_linear.py,5468beaaf18ea8ac1b955b25bcea0aea1c650af0,STILL_EXISTS,## TODO: Need a test that exercises multiple lines. Example bug:
aaegfcdca,explosion/thinc,thinc/neural/_classes/model.py,8d7a142fd1517a4b6dd8428d373ba7cc06272f12,STILL_EXISTS,This is ridiculous; but apparently it's what you
aaegfcfha,explosion/thinc,thinc/api.py,a0897d077b3f59ffa1747470fa1a7871c421f1a0,b2d383f29d4f9081215722837bcc03ffc2dce618,TODO: Is this made obsolete by the FeatureExtractor?
aaegfcgeg,explosion/thinc,thinc/tests/unit/test_model.py,b772e06cc8b96128bc48a07f005e48b2277765ad,fd9e228eb1070989300e4a8fe863a299eeba7c2d,TODO: These were implemented into spaCy.
aaegfcheb,explosion/thinc,thinc/neural/_classes/selu.py,83fe5ed5dd617a88f856d7c34543399d10e74c4a,ca691ccdae6dc44bafa591e57bba0977e5972c3e,TODO: Add toggle for the LSUV init. It seems not always better!
aaegfdeeh,explosion/thinc,thinc/neural/_classes/layernorm.py,41039256c9bf4345382aafc25d9878d9b37f2cb0,9c1dc559ab5af16b6e275d134c24374275dc8ffa,TODO: This is wrong! We should be normalizing by shape[1]; not shape[0]!
aaegfdeei,explosion/thinc,thinc/neural/_classes/layernorm.py,41039256c9bf4345382aafc25d9878d9b37f2cb0,9c1dc559ab5af16b6e275d134c24374275dc8ffa,However fixing this will invalidate the pre-trained models; so we'd better
aaegfdega,explosion/thinc,setup.py,cd89e6a0e9e14bf549b521140c1b10161872a86b,f4dde0f385727c01965d8eba03c679aa46ce48f2,TODO: Detect this instead of relying on an environment variable.
aaegfdeha,explosion/thinc,thinc/extra/wrappers.py,6e25bdb317915ae73ba7c2e8ab2e4733d5e35ebd,STILL_EXISTS,TODO: Required for spaCy add label
aaegfdehb,explosion/thinc,thinc/extra/wrappers.py,6e25bdb317915ae73ba7c2e8ab2e4733d5e35ebd,STILL_EXISTS,TODO: Not required yet; but should be useful
aaegfdfhe,explosion/thinc,setup.py,c59d4ea703918a5f196e926e3c78d7b5a996573f,STILL_EXISTS,Settings that maybe matter for optimization?
aaegfdgbi,explosion/thinc,examples/wrap_pytorch_rnn.py,d5546c00d4b9c41e808949543e74d772e6e66f6e,4c7238680dad8e35dbe344d319e45cf6b3c7e43d,TODO: We can pass h_0 to begin_update
aaegfdgce,explosion/thinc,thinc/extra/wrappers.py,d5546c00d4b9c41e808949543e74d772e6e66f6e,4c7238680dad8e35dbe344d319e45cf6b3c7e43d,TODO: We should return also h_n e.g. for seq2seq models
aaegfdidc,explosion/thinc,setup.py,7ce32d5755e22abedbe55199cf87553e58f68809,STILL_EXISTS,FIXME: We should not have to use different instructions to
aaegfebda,explosion/thinc,setup.py,7ce32d5755e22abedbe55199cf87553e58f68809,STILL_EXISTS,Conditional MOVe instructions
aaegfecca,explosion/thinc,setup.py,5ee96cd1db923a9f5f939ceb6e0479725dc08504,STILL_EXISTS,Settings that maybe matter for optimization?
aaegfedcc,explosion/thinc,setup.py,5ee96cd1db923a9f5f939ceb6e0479725dc08504,STILL_EXISTS,FIXME: We should not have to use different instructions to
aaegfegca,explosion/thinc,setup.py,5ee96cd1db923a9f5f939ceb6e0479725dc08504,STILL_EXISTS,Conditional MOVe instructions
aaegfeggh,explosion/thinc,setup.py,356cc1cafe6b5de45068a8ea840564257f410f09,STILL_EXISTS,Settings that maybe matter for optimization?
aaegfehgj,explosion/thinc,setup.py,356cc1cafe6b5de45068a8ea840564257f410f09,STILL_EXISTS,FIXME: We should not have to use different instructions to
aaegffagh,explosion/thinc,setup.py,356cc1cafe6b5de45068a8ea840564257f410f09,STILL_EXISTS,Conditional MOVe instructions
aaegffbbe,explosion/thinc,setup.py,6e6bec24782f39dc747d12c9dce49c214a07d0f9,STILL_EXISTS,Settings that maybe matter for optimization?
aaegffcbg,explosion/thinc,setup.py,6e6bec24782f39dc747d12c9dce49c214a07d0f9,STILL_EXISTS,FIXME: We should not have to use different instructions to
aaegfffbe,explosion/thinc,setup.py,6e6bec24782f39dc747d12c9dce49c214a07d0f9,STILL_EXISTS,Conditional MOVe instructions
aaegfffgb,explosion/thinc,setup.py,f34058998cb78c00708d92285e92103bcffaf9e5,STILL_EXISTS,Settings that maybe matter for optimization?
aaegffggd,explosion/thinc,setup.py,f34058998cb78c00708d92285e92103bcffaf9e5,STILL_EXISTS,FIXME: We should not have to use different instructions to
aaegffjgb,explosion/thinc,setup.py,f34058998cb78c00708d92285e92103bcffaf9e5,STILL_EXISTS,Conditional MOVe instructions
aaegfgcjd,explosion/thinc,thinc/extra/wrapt/decorators.py,b7b8c4b64c1ee3fae59605bc0ec5202021cdbb33,STILL_EXISTS,which is the wrapper function to be used to implement the
aaegfghdh,explosion/thinc,thinc/extra/wrapt/wrappers.py,b7b8c4b64c1ee3fae59605bc0ec5202021cdbb33,STILL_EXISTS,for use when doing monkey patching. For a more featured way of
aaegfgheb,explosion/thinc,thinc/extra/wrapt/wrappers.py,b7b8c4b64c1ee3fae59605bc0ec5202021cdbb33,STILL_EXISTS,needed for the method types because the bound method is effectively a
aaegfideg,explosion/thinc,thinc/neural/_classes/multiheaded_attention.py,239e1aca58add666dcec0f9deaa6851a8fd22b56,c35ecc2f9e2f322b1c9362719f68f377a12a0a51,TODO: Allow masking
aaegfiefg,explosion/thinc,thinc/describe.py,019fa4d7f8216b9a906db49b64e02f9be08c3826,STILL_EXISTS,TODO: These should probably be data classes?
aaegfigfh,explosion/thinc,thinc/wire.py,b2d383f29d4f9081215722837bcc03ffc2dce618,8a2a23a07f5170071c0328b2612048b9716b7169,TODO: Is this made obsolete by the FeatureExtractor?
aaegfigif,explosion/thinc,thinc/backends/base.py,b41e68d912f083674558dc0bc16592b295cb3fa5,STILL_EXISTS,TODO: Fix this confusing inversion =\/
aaegfjafi,explosion/thinc,thinc/_registry.py,c162f57d7b4f8b653969e8655e7655e566909b9b,STILL_EXISTS,TODO: handle validation where top-level config is a promise
aaegfjaga,explosion/thinc,thinc/_registry.py,ce7c0d33861984a9f7bd116e28f59dede2304640,STILL_EXISTS,TODO: not sure what to do here?
aaegfjcgb,explosion/thinc,thinc/model.py,bb871da0287cea1bea630d265a908bf182e2a46b,651b794aa0c550e86e5a43274d1f15396b81c1fc,TODO: Which settings should we expose via Model.visualize?
aaegfjcgc,explosion/thinc,thinc/visualizer.py,bb871da0287cea1bea630d265a908bf182e2a46b,STILL_EXISTS,TODO: how to do type?
aaegfjcgd,explosion/thinc,thinc/visualizer.py,bb871da0287cea1bea630d265a908bf182e2a46b,STILL_EXISTS,TODO: improve?
aaegfjcgh,explosion/thinc,examples/scripts/ray_parallel.py,e54cc3b4a378921fe5ba15ed786bfeaeff9529f1,STILL_EXISTS,When the epoch ends; start a new epoch.
aaegfjchj,explosion/thinc,thinc/extra/visualizers.py,c81dff1de8f19a598db268adc600fdc15b6944ce,STILL_EXISTS,Hack to work around \"bad label name\" problem
aaegfjdbd,explosion/thinc,examples/scripts/ray_parallel.py,4b0134242f0e79bcdb022623be29e1e7db5445fc,STILL_EXISTS,\"\"\"This script is still a work in progress: using Ray to implement parallel || training. The example is based off one of Ray's tutorials:  ||  ||     https:\/\/ray.readthedocs.io\/en\/latest\/auto_examples\/plot_parameter_server.html || \"\"\"
aaegfjdej,explosion/thinc,thinc/layers/lstm.py,02179fb8756ae0bbe4fe1e92672db1a8e7761e79,680f4553a1aa0e8c921bcef9562d3ec08ae2382c,TODO: finish types
aaegfjdfb,explosion/thinc,thinc/types.py,680f4553a1aa0e8c921bcef9562d3ec08ae2382c,STILL_EXISTS,This should probably become a dataclass too.
aaegfjdfc,explosion/thinc,thinc/layers/bidirectional.py,6f58050eaac70506f56f692214646cc1267f068a,STILL_EXISTS,TODO: input \/ output types
aaegfjdfd,explosion/thinc,thinc/layers/bidirectional.py,6f58050eaac70506f56f692214646cc1267f068a,f18edb70716b0262c211f8a8e0f6db531c1b8d55,TODO: remaining types
aaegfjdfe,explosion/thinc,thinc/layers/chain.py,6f58050eaac70506f56f692214646cc1267f068a,0703344409780e55a055c03ff590c44d35cc10d0,TODO: are these bound?
aaegfjdfi,explosion/thinc,thinc/layers/clone.py,6f58050eaac70506f56f692214646cc1267f068a,f18edb70716b0262c211f8a8e0f6db531c1b8d55,TODO: input \/ output types for model?
aaegfjdfj,explosion/thinc,thinc/layers/concatenate.py,6f58050eaac70506f56f692214646cc1267f068a,f18edb70716b0262c211f8a8e0f6db531c1b8d55,TODO: more specific input \/ output types?
aaegfjdga,explosion/thinc,thinc/layers/dropout.py,6f58050eaac70506f56f692214646cc1267f068a,0703344409780e55a055c03ff590c44d35cc10d0,TODO: How to type the \"sub-functions\"?
aaegfjdgb,explosion/thinc,thinc/layers/dropout.py,6f58050eaac70506f56f692214646cc1267f068a,STILL_EXISTS,TODO: improve this and make array types more specific
aaegfjdgc,explosion/thinc,thinc/layers/embed.py,6f58050eaac70506f56f692214646cc1267f068a,0703344409780e55a055c03ff590c44d35cc10d0,TODO: fix type error
aaegfjdgd,explosion/thinc,thinc/layers/embed.py,6f58050eaac70506f56f692214646cc1267f068a,f18edb70716b0262c211f8a8e0f6db531c1b8d55,TODO: more speific array type
aaegfjdge,explosion/thinc,thinc/layers/extractwindow.py,6f58050eaac70506f56f692214646cc1267f068a,f18edb70716b0262c211f8a8e0f6db531c1b8d55,TODO: more specific arrays
aaegfjdgf,explosion/thinc,thinc/layers/featureextractor.py,6f58050eaac70506f56f692214646cc1267f068a,f18edb70716b0262c211f8a8e0f6db531c1b8d55,TODO: fix and make more specific
aaegfjdgg,explosion/thinc,thinc/layers/foreach.py,6f58050eaac70506f56f692214646cc1267f068a,STILL_EXISTS,TODO: fix and make more specific
aaegfjdgh,explosion/thinc,thinc/layers/hashembed.py,6f58050eaac70506f56f692214646cc1267f068a,f18edb70716b0262c211f8a8e0f6db531c1b8d55,TODO: fix type error
aaegfjdgi,explosion/thinc,thinc/layers/lstm.py,6f58050eaac70506f56f692214646cc1267f068a,STILL_EXISTS,TODO: Input and output types
aaegfjdgj,explosion/thinc,thinc/layers/recurrent.py,6f58050eaac70506f56f692214646cc1267f068a,STILL_EXISTS,TODO: input \/ output types
aaegfjdha,explosion/thinc,thinc/layers/recurrent.py,6f58050eaac70506f56f692214646cc1267f068a,f18edb70716b0262c211f8a8e0f6db531c1b8d55,TODO: finish types (function return types etc.)
aaegfjdhh,explosion/thinc,thinc/layers/bidirectional.py,0703344409780e55a055c03ff590c44d35cc10d0,f18edb70716b0262c211f8a8e0f6db531c1b8d55,TODO: remaining types
aaegfjdhi,explosion/thinc,thinc/layers/cauchysimilarity.py,0703344409780e55a055c03ff590c44d35cc10d0,f18edb70716b0262c211f8a8e0f6db531c1b8d55,TODO: remaining types
aaegfjdic,explosion/thinc,thinc/layers/layernorm.py,0703344409780e55a055c03ff590c44d35cc10d0,f18edb70716b0262c211f8a8e0f6db531c1b8d55,TODO: Make more specific
aaegfjdid,explosion/thinc,thinc/layers/list2ragged.py,0703344409780e55a055c03ff590c44d35cc10d0,f18edb70716b0262c211f8a8e0f6db531c1b8d55,TODO: make more specific?
aaegfjdie,explosion/thinc,thinc/layers/maxpool.py,0703344409780e55a055c03ff590c44d35cc10d0,f18edb70716b0262c211f8a8e0f6db531c1b8d55,TODO: more specific types?
aaegfjdif,explosion/thinc,thinc/layers/meanpool.py,0703344409780e55a055c03ff590c44d35cc10d0,f18edb70716b0262c211f8a8e0f6db531c1b8d55,TODO: more specific types?
aaegfjdig,explosion/thinc,thinc/layers/pytorchwrapper.py,0703344409780e55a055c03ff590c44d35cc10d0,f18edb70716b0262c211f8a8e0f6db531c1b8d55,TODO: more specific types?
aaegfjdih,explosion/thinc,thinc/layers/pytorchwrapper.py,0703344409780e55a055c03ff590c44d35cc10d0,f18edb70716b0262c211f8a8e0f6db531c1b8d55,TODO: fix type error
aaegfjdii,explosion/thinc,thinc/layers/siamese.py,0703344409780e55a055c03ff590c44d35cc10d0,f18edb70716b0262c211f8a8e0f6db531c1b8d55,TODO: fix type errors
aaegfjdij,explosion/thinc,thinc/layers/siamese.py,0703344409780e55a055c03ff590c44d35cc10d0,f18edb70716b0262c211f8a8e0f6db531c1b8d55,TODO: more specific types?
aaegfjdja,explosion/thinc,thinc/layers/softmax.py,0703344409780e55a055c03ff590c44d35cc10d0,f18edb70716b0262c211f8a8e0f6db531c1b8d55,TODO: more specific types?
aaegfjdjb,explosion/thinc,thinc/layers/staticvectors.py,0703344409780e55a055c03ff590c44d35cc10d0,f18edb70716b0262c211f8a8e0f6db531c1b8d55,TODO: more specific types?
aaegfjdjc,explosion/thinc,thinc/layers/with_list2array.py,0703344409780e55a055c03ff590c44d35cc10d0,f18edb70716b0262c211f8a8e0f6db531c1b8d55,TODO: more specific types?
aaegfjdjd,explosion/thinc,thinc/layers/with_list2padded.py,0703344409780e55a055c03ff590c44d35cc10d0,f18edb70716b0262c211f8a8e0f6db531c1b8d55,TODO: more specific types?
aaegfjdje,explosion/thinc,thinc/layers/with_reshape.py,0703344409780e55a055c03ff590c44d35cc10d0,f18edb70716b0262c211f8a8e0f6db531c1b8d55,TODO: more specific types?
aaegfjdjg,explosion/thinc,thinc/util.py,10de3be8d32e7f221712ec6a430f8e9b16a22281,c9a3c1dbaa51b15cccda0c64f99dae2f8b1651d1,TODO: return type
aaegfjidg,explosion/thinc,thinc/tests/model/test_model.py,3f3c4081943ed31a9c83e0c0ee5534f45acd2fd4,ce8f3b4a95b3068cf50b4349df8cf34465790bff,TODO: This currently causes a n AttributeError in the first thread. The error
aaegfjigc,explosion/thinc,thinc/util.py,5692e97bf29db369086179d3b203733bb76a06b5,e043024d9b6874e179e4bcce15d9027a1319bd2b,TODO: This package doesn't install on py36; and it doesn't look
aaegfjijf,explosion/thinc,examples/ray_parallel.py,da3952268665868a38a30b243b3edd4a0084b330,STILL_EXISTS,\"\"\"This script is still a work in progress: using Ray to implement parallel || training. The example is based off one of Ray's tutorials: || https:\/\/ray.readthedocs.io\/en\/latest\/auto_examples\/plot_parameter_server.html || \"\"\"
aaegfjijg,explosion/thinc,examples/ray_parallel.py,da3952268665868a38a30b243b3edd4a0084b330,STILL_EXISTS,When the epoch ends; start a new epoch.
aaegfjjdf,explosion/thinc,examples/basic_pos_tagger_from_config.py,f3d0fda9b74dd9538fcc547bbc9969cf48dca0b1,STILL_EXISTS,TODO: This example currently doesn't work due to how pydantic validates
aaegfjjgc,explosion/thinc,examples/model_with_visualization.py,f3d0fda9b74dd9538fcc547bbc9969cf48dca0b1,STILL_EXISTS,Hack to work around \"bad label name\" problem
aaeggaaci,explosion/thinc,thinc/layers/chain.py,9edb3454e3f1310dd9395007fd4608fcd7c11f67,9949582ab35d72fd17a98db66dbfa79132888cf9,TODO: Unhack this when we can
aaeggaajh,explosion/thinc,thinc/layers/chain.py,beea1d40f07631ca020544e648f1f40838d2a8d9,6f8d763864280d941212878e90614ae54bc65f1b,TODO: Unhack this when we can
aaeggabeh,explosion/thinc,thinc/tests/layers/test_tensorflow_wrapper.py,beac1c1ab669f7101c7cb01bcce8fbc152c97928,ce8f3b4a95b3068cf50b4349df8cf34465790bff,input_shape is needed to de\/serialize keras models properly
aaeggabga,explosion/thinc,thinc/tests/layers/test_with_array.py,cc00184577358549b9f92dec04183ef07d802665,85e3da279ee8a28d0fd0b85d860e827957ffee10,Is there a better way to have a parameterize over a set of fixtures?
aaeggabgd,explosion/thinc,thinc/tests/layers/test_with_array.py,cc00184577358549b9f92dec04183ef07d802665,STILL_EXISTS,get slow this could be removed. I think it should be fine though?
aaeggacbi,explosion/thinc,thinc/tests/layers/test_with_array.py,b242ed41f6f5c009650dc5cae6fe0e94a3b62b45,STILL_EXISTS,get slow this could be removed. I think it should be fine though?
aaeggadce,explosion/thinc,thinc/config.py,4610f36a55516ff3f07d4b9c84613484d5824676,fb9cce599457c976ca64e956a94b2dfacc69fe9e,TODO: Bad hack to work around generic types in pydantic (until next release)
aaeggaddb,explosion/thinc,thinc/tests/test_config.py,0b320126f413825137b5993c0b3918cc330a992a,fb9cce599457c976ca64e956a94b2dfacc69fe9e,TODO: Unhack and extend once this is implemented in pydantic
aaeggaebb,explosion/thinc,thinc/backends/ops.py,cc8d7ba5562cb2335d975b67e88a81a6bc9b0f46,d6d7cb7db51776b026514a1a2caa4073b9e998ad,TODO: It would be nice to generalize this to work along different
aaeggaebf,explosion/thinc,thinc/backends/ops.py,cc8d7ba5562cb2335d975b67e88a81a6bc9b0f46,d6d7cb7db51776b026514a1a2caa4073b9e998ad,anyway. Need to construct the slice object maybe?
aaeggaebg,explosion/thinc,thinc/layers/chain.py,cc8d7ba5562cb2335d975b67e88a81a6bc9b0f46,7a5809e153d4fe80fe17d2087ea1a9d1802d4476,TODO: Unhack this when we can
aaeggaecb,explosion/thinc,thinc/layers/chain.py,cc8d7ba5562cb2335d975b67e88a81a6bc9b0f46,STILL_EXISTS,TODO: This sort of doesn't work currently -- we only get Y passed through
aaeggaeii,explosion/thinc,thinc/model.py,8aded4150aae8fd827b9d06e356f4f01b743a212,f2754e0656d8cebe1f785f6af100e4ade241a7f8,Kind of ugly to use the _mem.weights -- would make more sense
aaeggaeij,explosion/thinc,thinc/model.py,8aded4150aae8fd827b9d06e356f4f01b743a212,STILL_EXISTS,to call node.finish_update. Maybe we could pass in a set
aaeggafbj,explosion/thinc,thinc/tests/layers/test_uniqued.py,888b303adfe119f197112c431dfbb7e83b3b8aed,STILL_EXISTS,Trim so we're of length divisible by columns.
aaeggafge,explosion/thinc,thinc/model.py,f2754e0656d8cebe1f785f6af100e4ade241a7f8,ef6cd0490d7fc2d0cf85d78f3dcb6b83dd52e2f1,TODO: This is wrong; should be Ops
aaeggafhg,explosion/thinc,thinc/tests/layers/test_feed_forward.py,f2754e0656d8cebe1f785f6af100e4ade241a7f8,STILL_EXISTS,I don't know how to get this working properly after the refactor. It's a numeric
aaeggafjb,explosion/thinc,thinc/layers/chain_module.py,522aff0243a625a1a460333a528903abdccd6825,STILL_EXISTS,TODO: Unhack this when we can
aaeggagde,explosion/thinc,thinc/backends/ops.py,a7ed30a624632c3b35ae88fae0e7c3480ae78f44,edd09dc25e1e31a26223a11fc54ce6b89970772e,TODO: Make work for Jax?
aaeggaggc,explosion/thinc,thinc/backends/ops.py,929b25dc457a22d9eed5d887204939e7308ea7c0,88edf121ea1dabfac806973031629aadcbb05c47,TODO: Make work for Jax?
aaeggagic,explosion/thinc,thinc/model.py,2931bfc3e25aea3a0bf186ce8a2978c1d71128f7,152e4cec0c09111f064b80832eed7134e7a9cffc,This is pretty ugly. But I don't know where I can put this function
aaeggagie,explosion/thinc,thinc/config.py,1fe8c354b2ca578949ecddba56ee5407d5f222fa,ca208cfd595650e77667d2fec7d22fe13f95397c,TODO: unhack this fix to prevent pydantic from consuming generator
aaeggagjj,explosion/thinc,thinc/backends/jax_ops.py,a312132f7ea96c15415036a7986376c63baa3ebc,86e47f3fd888654bd24d74ceaad1ebb2d820ef0a,TODO: types
aaeggahbi,explosion/thinc,thinc/model.py,53b1840b9acebd66cf125947a0ec4d4f1f2df22c,db001f368c7b7d092d60e0d01e621feeb92932c5,This is pretty ugly. But I don't know where I can put this function
aaeggahfh,explosion/thinc,thinc/config.py,7cf0a19360f32969f2a842ecbebbeae1e550cfe4,c748c15be14b625f7b7dfdb2ccb3cdc1e4d42849,TODO: unhack this fix to prevent pydantic from consuming generator
aaeggahgd,explosion/thinc,thinc/model.py,7cf0a19360f32969f2a842ecbebbeae1e550cfe4,88edf121ea1dabfac806973031629aadcbb05c47,This is pretty ugly. But I don't know where I can put this function
aaeggahhc,explosion/thinc,thinc/shims/tensorflow.py,ccfa7316aab1682ff05edae00049ddb7a5278a1f,d0d0f9ac16db1052221a29aefc3d0e4cc529f8a6,TODO: compile args?
aaeggahhf,explosion/thinc,thinc/config.py,88edf121ea1dabfac806973031629aadcbb05c47,STILL_EXISTS,TODO: unhack this fix to prevent pydantic from consuming generator
aaeggaice,explosion/thinc,thinc/backends/ops.py,f3b0f59b5f5a9fdd262d531b28266423fc224342,a5a1452a5c9401d941e9240683b5ee0d7dc99e3d,TODO: Make work for Jax?
aaeggaidf,explosion/thinc,thinc/model.py,a275e06bdfccfc7f70c27afae73633207472b88c,0445ace4bceb2d8efff232625f6e2b66689328ac,This is pretty ugly. But I don't know where I can put this function
aaeggaigi,explosion/thinc,thinc/backends/ops.py,a5a1452a5c9401d941e9240683b5ee0d7dc99e3d,ef4b17c1193399f9e86d9554cb2a02806d6cdfc7,TODO: types
aaeggajcd,explosion/thinc,thinc/backends/ops.py,c9ad3a0a85bdb5e340cd9f40d0be98b768e1d401,32ee20d1d75316e6668aad3e25c7140ef41febce,TOOD: Wtf is this?
aaeggajdb,explosion/thinc,thinc/layers/lstm.py,8a847ff280c9ee3b1e190a58dd9c09bbb003e7e8,590d18f780ceea5cbb1620d58605bd4eb5aa5a33,Wtf?
aaeggajfg,explosion/thinc,thinc/model.py,d56bb0f1ce489160001fa4843caabdd7f3255287,4b6ea956e9abef4e67043aed8ad7399bda0b0fbf,This is pretty ugly. But I don't know where I can put this function
aaeggbaac,explosion/thinc,thinc/layers/lstm.py,590d18f780ceea5cbb1620d58605bd4eb5aa5a33,5e430fd001c839b4f35cb0ceeba467687449a455,\"\"\" || def backprop_gates(d_cells: Array2d; d_hiddens: Array2d) -> Tuple[Array3d; Array2d]: ||     d_cells = ops.as_contig(d_cells[:size])  # Wtf? ||     d_hiddens = ops.as_contig(d_hiddens[:size]) ||     d_acts; d_prevcells = ops.backprop_lstm( ||         d_cells; d_hiddens; gates; cells; prevcells ||     ) ||     d_acts = d_acts.reshape((nB; nO * 4)) ||     return d_acts; d_prevcells || \"\"\"
aaeggbafg,explosion/thinc,thinc/backends/ops.py,32ee20d1d75316e6668aad3e25c7140ef41febce,ef4b17c1193399f9e86d9554cb2a02806d6cdfc7,TODO: types
aaeggbagh,explosion/thinc,thinc/backends/ops.py,d06416fb3a18377b2d1a56246b80f86584aca97d,982a2d55c3f7b17dee337c8eed6b6c27ca160d0b,TOOD: Wtf is this?
aaeggbaid,explosion/thinc,thinc/backends/jax_ops.py,1c02a7df353ab5e66b73565d204d51b03bd0d65b,STILL_EXISTS,TODO: I thnk I'm missing a d_hiddens + dY[t] here? I think I'm ignoring
aaeggbaif,explosion/thinc,thinc/backends/jax_ops.py,1c02a7df353ab5e66b73565d204d51b03bd0d65b,5c3f7d5f5cd23ceb70d77ccdde7ee928d59439a9,TODO: Check this is right with the state nums.
aaeggbbcg,explosion/thinc,thinc/initializers.py,c6e83948f53bcd5d77b1714b691bbd74d12da613,STILL_EXISTS,TODO: Harmonize naming with Keras; and fill in missing entries
aaeggbbeb,explosion/thinc,thinc/backends/ops.py,982a2d55c3f7b17dee337c8eed6b6c27ca160d0b,ef4b17c1193399f9e86d9554cb2a02806d6cdfc7,TODO: types
aaeggbbej,explosion/thinc,thinc/backends/jax_ops.py,5c3f7d5f5cd23ceb70d77ccdde7ee928d59439a9,STILL_EXISTS,\"\"\" || X: Inputs || Y: Outputs (aka hiddens) || C: Cells || G: Gates (Output of non-linearity; i.e. lstm_gates(X @ W.T) || A: Activations (X @ W.T; before non-linearity) ||  || Imagine we have the input: || batch = [ ||     [\"apple\"; \"banana\"; \"cantaloupe\"; \"date\"; \"elderberry\"]; ||     [\"aardvark\"; \"bat\"; \"capybara\"; \"dingo\"; \"elephant\"] || ] ||  || The input variable X will have one vector per word; so X[0; 1] will be banana's || vector; X[0; 1; 0] will be a float; the first element of that vector. ||  || We're computing an output variable Y of shape (nL; nB; nO); so that Y[0; 1] is || the output variable of banana. ||  || A problem with variables for RNNs is keeping the timesteps straight. It's hard || to distinguish the current; previous; and next timesteps. To solve this problem; || we follow the convention that **we are at timestep 3**. ||  || Additionally; the variables for Y and C are offset by one; as the 0th elements || have the initial hiddens and initial cells. So: ||  ||     t=3 ||     Xt3: The input vectors for 'dingo' and 'date'; i.e. X[t] ||     Yt3: The output vectors for 'dingo' and 'date'; i.e. Y[t+1] (Y is offset.) ||     Ct2: The cells calculated at 'c...'; that are the input for 'd...' ||     Ct3: The cells calculated at 'd...'; that are the input for 'e...' ||     At3: The activations at 'd...' ||     Gt3: The gates at 'd...' || \"\"\"
aaeggbcad,explosion/thinc,thinc/backends/ops.py,571c96f99a6e0ab65c44b5e6cbe7d69d8f27dcf8,d1073f91087194855dd1f02f7016888634d247b7,\"\"\" || LSTM Notation (kind of involved; but made it a lot easier to write) ||  || X: Inputs || Y: Outputs (aka hiddens) || C: Cells || G: Gates (Output of non-linearity; i.e. lstm_gates(X @ W.T) || A: Activations (X @ W.T; before non-linearity) ||  || Imagine we have the input: || batch = [ ||     [\"apple\"; \"banana\"; \"cantaloupe\"; \"date\"; \"elderberry\"]; ||     [\"aardvark\"; \"bat\"; \"capybara\"; \"dingo\"; \"elephant\"] || ] ||  || The input variable X will have one vector per word; so X[0; 1] will be banana's || vector; X[0; 1; 0] will be a float; the first element of that vector. ||  || We're computing an output variable Y of shape (nL; nB; nO); so that Y[0; 1] is || the output variable of banana. ||  || A problem with variables for RNNs is keeping the timesteps straight. It's hard || to distinguish the current; previous; and next timesteps. To solve this problem; || we follow the convention that **we are at timestep 3**. ||  || Additionally; the variables for Y and C are offset by one; as the 0th elements || have the initial hiddens and initial cells. So: ||  ||     t=3 ||     Xt3: The input vectors for 'dingo' and 'date'; i.e. X[t] ||     Yt3: The output vectors for 'dingo' and 'date'; i.e. Y[t+1] (Y is offset.) ||     Ct2: The cells calculated at 'c...'; that are the input for 'd...' ||     Ct3: The cells calculated at 'd...'; that are the input for 'e...' ||     At3: The activations at 'd...' ||     Gt3: The gates at 'd...' || \"\"\"
aaeggbcaf,explosion/thinc,thinc/backends/ops.py,571c96f99a6e0ab65c44b5e6cbe7d69d8f27dcf8,ef4b17c1193399f9e86d9554cb2a02806d6cdfc7,TODO: types
aaeggbdcb,explosion/thinc,thinc/model.py,ad9ccaec2a02b0c5750115711574babe8a79ec95,89e603ce3fefcf04344c4719b2443f823d9af2a8,This is pretty ugly. But I don't know where I can put this function
aaeggbdgb,explosion/thinc,thinc/backends/ops.py,2eef369b7ac92e38f81819307a4af4238fd953ee,STILL_EXISTS,TODO: This should be generalized to handle different ranks
aaeggbggd,explosion/thinc,thinc/backends/ops.py,d1073f91087194855dd1f02f7016888634d247b7,ef4b17c1193399f9e86d9554cb2a02806d6cdfc7,TODO: types
aaeggbihg,explosion/thinc,thinc/backends/ops.py,cfaa18ee86fb51064b24888e681b47cf1e1bf0b6,3c627850e6ba6174ed2f639f63cf5c8a7680d3ec,\"\"\" || LSTM Notation (kind of involved; but made it a lot easier to write) ||  || X: Inputs || Y: Outputs (aka hiddens) || C: Cells || G: Gates (Output of non-linearity; i.e. lstm_gates(X @ W.T) || A: Activations (X @ W.T; before non-linearity) ||  || Imagine we have the input: || batch = [ ||     [\"apple\"; \"banana\"; \"cantaloupe\"; \"date\"; \"elderberry\"]; ||     [\"aardvark\"; \"bat\"; \"capybara\"; \"dingo\"; \"elephant\"] || ] ||  || The input variable X will have one vector per word; so X[0; 1] will be banana's || vector; X[0; 1; 0] will be a float; the first element of that vector. ||  || We're computing an output variable Y of shape (nL; nB; nO); so that Y[0; 1] is || the output variable of banana. ||  || A problem with variables for RNNs is keeping the timesteps straight. It's hard || to distinguish the current; previous; and next timesteps. To solve this problem; || we follow the convention that **we are at timestep 3**. ||  || Additionally; the variables for Y and C are offset by one; as the 0th elements || have the initial hiddens and initial cells. So: ||  ||     t=3 ||     Xt3: The input vectors for 'dingo' and 'date'; i.e. X[t] ||     Yt3: The output vectors for 'dingo' and 'date'; i.e. Y[t+1] (Y is offset.) ||     Ct2: The cells calculated at 'c...'; that are the input for 'd...' ||     Ct3: The cells calculated at 'd...'; that are the input for 'e...' ||     At3: The activations at 'd...' ||     Gt3: The gates at 'd...' || \"\"\"
aaeggbihi,explosion/thinc,thinc/backends/ops.py,cfaa18ee86fb51064b24888e681b47cf1e1bf0b6,STILL_EXISTS,TODO: This should be generalized to handle different ranks
aaeggbjfg,explosion/thinc,thinc/backends/ops.py,368cf60ddcf3d42f092f14e14bddda70d1e53f34,ef4b17c1193399f9e86d9554cb2a02806d6cdfc7,TODO: types
aaeggcajd,explosion/thinc,thinc/backends/ops.py,57e9132507112a80b96698231706c56ff37a0dc4,09cf8a6260962c921170f14bffa6ab6aefa51b48,TODO: types
aaeggcbeb,explosion/thinc,thinc/backends/ops.py,124387028b05889768e6209a9e6d401e0722bdec,09cf8a6260962c921170f14bffa6ab6aefa51b48,TODO: types
aaeggcbfd,explosion/thinc,thinc/backends/ops.py,23be9ce2b297b24b42d779d38d2ff9c19898591e,09cf8a6260962c921170f14bffa6ab6aefa51b48,\"\"\" || LSTM Notation (kind of involved; but made it a lot easier to write) ||  || X: Inputs || Y: Outputs (aka hiddens) || C: Cells || G: Gates (Output of non-linearity; i.e. lstm_gates(X @ W.T) || A: Activations (X @ W.T; before non-linearity) ||  || Imagine we have the input: || batch = [ ||     [\"apple\"; \"banana\"; \"cantaloupe\"; \"date\"; \"elderberry\"]; ||     [\"aardvark\"; \"bat\"; \"capybara\"; \"dingo\"; \"elephant\"] || ] ||  || The input variable X will have one vector per word; so X[0; 1] will be banana's || vector; X[0; 1; 0] will be a float; the first element of that vector. ||  || We're computing an output variable Y of shape (nL; nB; nO); so that Y[0; 1] is || the output variable of banana. ||  || A problem with variables for RNNs is keeping the timesteps straight. It's hard || to distinguish the current; previous; and next timesteps. To solve this problem; || we follow the convention that **we are at timestep 3**. ||  || Additionally; the variables for Y and C are offset by one; as the 0th elements || have the initial hiddens and initial cells. So: ||  ||     t=3 ||     Xt3: The input vectors for 'dingo' and 'date'; i.e. X[t] ||     Yt3: The output vectors for 'dingo' and 'date'; i.e. Y[t+1] (Y is offset.) ||     Ct2: The cells calculated at 'c...'; that are the input for 'd...' ||     Ct3: The cells calculated at 'd...'; that are the input for 'e...' ||     At3: The activations at 'd...' ||     Gt3: The gates at 'd...' || \"\"\"
aaeggcbfe,explosion/thinc,thinc/backends/ops.py,23be9ce2b297b24b42d779d38d2ff9c19898591e,09cf8a6260962c921170f14bffa6ab6aefa51b48,TODO: types
aaeggcccc,explosion/thinc,thinc/shims/tensorflow.py,ce8f3b4a95b3068cf50b4349df8cf34465790bff,STILL_EXISTS,Subclassed models don't implement get_config
aaeggccdd,explosion/thinc,thinc/backends/ops.py,dbde56ee43bf5a7cde2476902e79f34c60fb8118,STILL_EXISTS,TODO: type without type errors :(
aaeggccec,explosion/thinc,thinc/tests/layers/test_lstm.py,377ddfeaf0a020887521ce5afda994a2bed7e8be,d11968ffe95ff8a05bf27a347a7a487b56002f2f,@pytest.mark.xfail(reason=\"validation; TODO: fix\")
aaeggcceg,explosion/thinc,thinc/tests/layers/test_mnist.py,cc5b2e3c694b78e2287e1ca4b020fde27a2622c1,e8bb1737ad20d6137273a0fe4dcfd5964f42258c,TODO: rewrite to add depth
aaeggccie,explosion/thinc,thinc/layers/layernorm.py,4f1b2d4a9d0661447652f15a045a03396d7fe600,STILL_EXISTS,TODO: Do mean methods
aaeggccij,explosion/thinc,thinc/layers/maxout.py,4f1b2d4a9d0661447652f15a045a03396d7fe600,STILL_EXISTS,TODO: Add sum methods for Floats3d
aaeggcdbj,explosion/thinc,thinc/types.py,4f1b2d4a9d0661447652f15a045a03396d7fe600,STILL_EXISTS,TODO:
aaeggcdcg,explosion/thinc,thinc/types.py,4f1b2d4a9d0661447652f15a045a03396d7fe600,STILL_EXISTS,TODO: Is ArrayT right?
aaeggcdde,explosion/thinc,thinc/types.py,4f1b2d4a9d0661447652f15a045a03396d7fe600,STILL_EXISTS,That's kind of bad code though; it's better to write array2d + array1d.
aaeggcdeh,explosion/thinc,thinc/types.py,4f1b2d4a9d0661447652f15a045a03396d7fe600,STILL_EXISTS,That's kind of bad code though; it's better to write array5d + array4d.
aaeggcdga,explosion/thinc,thinc/shims/mxnet.py,31e027da8140d8db1c3355a1fbbadb2fcc91de81,2024d76787e59fc330f7bd5d78b068f801f14780,TODO: state_dict equiv in mxnet? collect_params().copy() maybe?
aaeggcdgd,explosion/thinc,thinc/shims/mxnet.py,31e027da8140d8db1c3355a1fbbadb2fcc91de81,STILL_EXISTS,MXNet doesn't implement save\/load without a filename
aaeggcdge,explosion/thinc,thinc/shims/mxnet.py,31e027da8140d8db1c3355a1fbbadb2fcc91de81,STILL_EXISTS,MXNet doesn't implement save\/load without a filename :(
aaeggcdjg,explosion/thinc,thinc/layers/list2ragged.py,4f674811a2910ed44ec6e39bc0063a56554871cb,STILL_EXISTS,TODO: Unhack
aaeggcefi,explosion/thinc,thinc/tests/layers/test_uniqued.py,6909185e9401a71f6014a007b102c4063aa90e5d,STILL_EXISTS,TODO: This test is a problem; because we exceed the embedding table.
aaeggcefj,explosion/thinc,thinc/tests/layers/test_uniqued.py,6909185e9401a71f6014a007b102c4063aa90e5d,STILL_EXISTS,Fix it with a better cap.
aaeggdhgj,explosion/thinc,thinc/backends/_cupy_allocators.py,e117962085a2e702429d5a0f1a7e8c2b4d30545e,STILL_EXISTS,This turns out to be way faster than making FloatStorage? Maybe
aaeggdibi,explosion/thinc,thinc/config.py,7a5d2e619cca39c838d38fde350b47c5cea98e3a,STILL_EXISTS,TODO: add error if node not in list
aaeggdjfj,explosion/thinc,thinc/model.py,cd87245a3c4bf0fc20c9a9441224491dae07824b,STILL_EXISTS,TODO: The shims should have a check for this too; but
aaeggeaae,explosion/thinc,thinc/tests/layers/test_with_transforms.py,83366b5bd05b57582e401bff87d06deff3daac37,STILL_EXISTS,Give values that make it easy to see where rows or columns mismatch.
aaeggecdg,chainer/chainercv,docs/source/conf.py,702b227cb039f35045ce353a34ac1a3bccbb90fa,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaeggecjc,chainer/chainercv,examples/detection/faster_rcnn/faster_rcnn.py,702b227cb039f35045ce353a34ac1a3bccbb90fa,STILL_EXISTS,TODO(yuyu2172) fix
aaeggefgc,chainer/chainercv,chainercv/datasets/imagenet/imagenet_dataset.py,93fde518ce45a1373c4b635c15e25312b2cc3a0f,STILL_EXISTS,this is an extra step needed for train dataset
aaeggegfa,chainer/chainercv,chainercv/utils/extension_utils.py,365e74109143397153595fd3c1f50f1bdcf753b8,STILL_EXISTS,TODO: make this more general
aaeggfbdc,chainer/chainercv,chainercv/links/ssd/ssd.py,268167de2cbfb83701a50b52913d5e0d7a21f22d,ee833561ed0d3ab0e3ec4122dc534b696dd6a923,The images are preprocessed by :meth:`prepare` if needed.
aaegggefe,chainer/chainercv,examples/detection/eval_voc07.py,86083bf4438f0b29616f7ba788be1eac8812659f,75bf6b0d04d92e592ad57c8283059885a89426ba,delete unused iterator explicitly
aaegggeff,chainer/chainercv,chainercv/extensions/detection/detection_voc_evaluator.py,7f6285b71b5248fb01b3b0af30f98485825e9fef,STILL_EXISTS,delete unused iterator explicitly
aaegggfae,chainer/chainercv,examples/detection/eval_voc07.py,02f6d33793ff1ab15a4b235abb0c96ae750dfdcf,STILL_EXISTS,delete unused iterator explicitly
aaegggheh,chainer/chainercv,chainercv/extensions/semantic_segmentation/semantic_segmentation_evaluator.py,54a5fbfc0f19535b0ab11c71119f1f8602b92dc5,STILL_EXISTS,delete unused iterator explicitly
aaegghdfg,chainer/chainercv,examples/ssd/caffe2npz.py,58343ba0296196b6075fe8a653b5a356dafed64c,e2db16bf38f5f53e40b27d28697c10702401a8c0,The pretrained model outputs coordinates in xy convention.
aaegghdfh,chainer/chainercv,examples/ssd/caffe2npz.py,58343ba0296196b6075fe8a653b5a356dafed64c,e2db16bf38f5f53e40b27d28697c10702401a8c0,This needs to be changed to yx convention; which is used
aaegghgbh,chainer/chainercv,examples/ssd/train.py,f5230de4574530473163058704eb812160c4e7c1,00999123cfbdb9e216d4bb940ea207b9f25e91f8,Fix seed for stable training
aaegghjgc,chainer/chainercv,chainercv/links/model/pspnet/pspnet.py,d31b83794244abbcdf67b697516e33800788f9f3,86450735f9ee1f5f839c36a9229915f46e63a8ef,When padding input patches is needed
aaeghaebb,chainer/chainercv,examples/instance_segmentation/eval_sbd.py,d1cfd37ad3f31eb97ce5195e408190edd325a899,STILL_EXISTS,delete unused iterators explicitly
aaeghbggc,chainer/chainercv,chainercv/extensions/evaluator/instance_segmentation_voc_evaluator.py,7d2772fb6aa4f2876f1945da3ce9c2f930347067,STILL_EXISTS,delete unused iterators explicitly
aaeghdhjb,chainer/chainercv,chainercv/extensions/evaluator/detection_coco_evaluator.py,273a95a0c4bfefe5dff4a918deb886a373fbd64b,STILL_EXISTS,delete unused iterators explicitly
aaeghegia,chainer/chainercv,chainercv/extensions/evaluator/instance_segmentation_coco_evaluator.py,c48769768cd8652d6a6f8eb969a5e71e3ec77ac8,STILL_EXISTS,delete unused iterators explicitly
aaeghfdjc,chainer/chainercv,examples/classification/train_imagenet_multi.py,bc2b34c7cc0e592c7d6915c32f962bd690bd8da6,STILL_EXISTS,TODO make this silent
aaeghfgjf,chainer/chainercv,chainercv/datasets/voc/voc_semantic_segmentation_dataset.py,6a762622542527fe2d1d6316e47f7c08fa80fc24,STILL_EXISTS,TODO: Find an option to load properly even with cv2.
aaeghgafd,chainer/chainercv,examples/detection/eval_coco.py,24fa53fa220651356b5c88b3e19519b1fb67253b,STILL_EXISTS,delete unused iterators explicitly
aaeghgaja,chainer/chainercv,examples/instance_segmentation/eval_coco.py,68d7e14699dfb7548d7a9ff7dd0d75e1a772a14d,STILL_EXISTS,delete unused iterators explicitly
aaeghgdib,chainer/chainercv,chainercv/datasets/coco/coco_instances_base_dataset.py,64ee249f59ef24c8736dcb582e9077fbb4223259,STILL_EXISTS,FIXME: some of minival annotations are malformed.
aaeghgeac,chainer/chainercv,chainercv/links/model/mask_rcnn/mask_head.py,b3e8ac055b3aac9b6ae8b8fce8d7015e467b93c5,2d44d66f74c0be1f2952af80cc11a9076fd71e3c,To work around an issue with cv2.resize (it seems to automatically
aaeghgicf,chainer/chainercv,examples/detection/eval_coco_multi.py,56d2e40022666d5580aef0557cb97660b68de9dd,STILL_EXISTS,delete unused iterators explicitly
aaeghgidf,chainer/chainercv,examples/detection/eval_detection.py,ae5493b151a3f819d97cfddff91c6c68947dac0e,STILL_EXISTS,delete unused iterators explicitly
aaeghgidg,chainer/chainercv,examples/detection/eval_detection_multi.py,617360ec2ecadcf07b4f4aa81b3e9a9baf72e141,STILL_EXISTS,delete unused iterators explicitly
aaeghgieh,chainer/chainercv,examples/instance_segmentation/eval_sbd_multi.py,f8b6d423165694733e8b3352c520c6044ffad7da,STILL_EXISTS,delete unused iterators explicitly
aaeghgiei,chainer/chainercv,examples/instance_segmentation/eval_coco_multi.py,a30c59c2582016ad9f0c28a767e666bb2aa1055d,STILL_EXISTS,delete unused iterators explicitly
aaeghgiif,chainer/chainercv,examples/instance_segmentation/eval_instance_segmentation.py,b5dc81d0fb918e055ec4b5c1a5917c0d0ed13039,STILL_EXISTS,delete unused iterators explicitly
aaeghgiig,chainer/chainercv,examples/instance_segmentation/eval_instance_segmentation_multi.py,b5dc81d0fb918e055ec4b5c1a5917c0d0ed13039,STILL_EXISTS,delete unused iterators explicitly
aaeghhahf,chainer/chainercv,examples/mask_rcnn/train_multi.py,496dd9369a86390993dcf7c717ec641463627e03,c63c3068b17b5bcbd6d1546b3b266f4558155498,TODO: make this part reusable
aaeghhchd,chainer/chainercv,chainercv/links/model/mask_rcnn/misc.py,2d44d66f74c0be1f2952af80cc11a9076fd71e3c,STILL_EXISTS,To work around an issue with cv2.resize (it seems to automatically
aaeghhcjb,chainer/chainercv,chainercv/utils/testing/constant_stub_link.py,1772367132851aa85764572b0c9ad9fadc151949,STILL_EXISTS,TODO: Remove this fix when 'to_device' APIs is refactored.
aaeghhcjc,chainer/chainercv,chainercv/utils/testing/constant_stub_link.py,1772367132851aa85764572b0c9ad9fadc151949,abb4897a4db8bd39c4f8e7093281c08536eb9772,Fix for Chainer 6.x.
aaeghhdij,chainer/chainercv,chainercv/links/model/fpn/mask_utils.py,1ad1070b3968ddd7b4a73afaa473ba7bc855a95a,STILL_EXISTS,To work around an issue with cv2.resize (it seems to automatically
aaeghieah,chainer/chainercv,chainercv/links/model/mobilenet/mobilenetv2.py,9c85ca2acce4bdd4fee4d6b26acf97d88542258c,STILL_EXISTS,TODO
aaeghieai,chainer/chainercv,chainercv/links/model/mobilenet/mobilenetv2.py,9c85ca2acce4bdd4fee4d6b26acf97d88542258c,STILL_EXISTS,bias is needed
aaeghigbb,tensorflow/ranking,tensorflow_ranking/python/data.py,368cff0bd610920c067a355d87e6078e8c0274d4,STILL_EXISTS,Repeat and shuffle; if needed.
aaeghihaa,tensorflow/ranking,tensorflow_ranking/python/feature_test.py,368cff0bd610920c067a355d87e6078e8c0274d4,STILL_EXISTS,Build columns.
aaeghihcb,tensorflow/ranking,tensorflow_ranking/python/head.py,368cff0bd610920c067a355d87e6078e8c0274d4,STILL_EXISTS,Unused for this head.
aaeghijbb,tensorflow/ranking,tensorflow_ranking/python/model.py,368cff0bd610920c067a355d87e6078e8c0274d4,STILL_EXISTS,unittest purpose. We can find a better way to avoid setting this seed
aaeghjagj,tensorflow/ranking,tensorflow_ranking/python/head.py,410e4ad856581ee4166544451ab52c8018d17ee1,STILL_EXISTS,a local convention without any special meaning.
aaeghjahh,tensorflow/ranking,tensorflow_ranking/python/feature.py,30864426207a6fcd72b771730af4ccaf526866ea,STILL_EXISTS,TODO: Ensure only v2 Feature Columns are used.
aaeghjahi,tensorflow/ranking,tensorflow_ranking/python/model_test.py,70ef0febb4da13ecaad904908471a94868ca3793,STILL_EXISTS,TODO: Convert to tf.keras.layers.Dense; and change *_NAME.
aaeghjaif,tensorflow/ranking,tensorflow_ranking/python/data.py,0c6603f6c165fd37f455da0eb93d9bbd24e2f515,STILL_EXISTS,Use dynamic list_size. This is needed to pad missing feature_list.
aaeghjbcd,tensorflow/ranking,tensorflow_ranking/python/model.py,d36d7fb92ba141a0f2493ec877dc4201c804fe0a,STILL_EXISTS,TODO: Be more smart to infer is_valid.
aaeghjbfe,tensorflow/ranking,tensorflow_ranking/python/losses.py,27aef1f3aa3dff22df125ca8a2fa4a4447ac6234,0b64530ff2cc6390ea3e56c77ce081ed3a3d3c9a,TODO: Remove the shuffling above and use
aaeghjbhj,tensorflow/ranking,tensorflow_ranking/python/head.py,c0ca2bae4937b035b4beae7371c4edd62abacbcf,STILL_EXISTS,TODO: Figure out a better way to set train_op_fn and optimizer
aaeghjbii,tensorflow/ranking,tensorflow_ranking/python/head.py,c0ca2bae4937b035b4beae7371c4edd62abacbcf,STILL_EXISTS,TODO: Add the per-head loss loss\/head_name to metrics.
aaeghjdad,tensorflow/ranking,tensorflow_ranking/python/losses.py,dcc06d8b9c2e17d7602149a787f17e6e09a7d16f,9e15f0a5680f6c8955aa27521bd48e9d2eeca539,TODO: Put this outside of the class as a util function.
aaeghjeej,tensorflow/ranking,tensorflow_ranking/python/metrics_impl.py,8340a42e056b30cfd949e8acdff380e13797c4ce,STILL_EXISTS,TODO: Consider to add a cap poistion topn + 1 when there is no
aaeghjehf,tensorflow/ranking,tensorflow_ranking/python/data.py,619f7f9612b875e103b483c7307411183ad1fd65,STILL_EXISTS,Add example list sizes to features; if needed.
aaeghjfec,tensorflow/ranking,tensorflow_ranking/extension/pipeline.py,2037fe23b708a93db9a2cebd86c34748a2d22d0d,STILL_EXISTS,TODO: supports for distributed training and evaluation.
aaeghjgbh,tensorflow/ranking,tensorflow_ranking/python/estimator_test.py,2037fe23b708a93db9a2cebd86c34748a2d22d0d,STILL_EXISTS,`c1` is the only context feature defined in `_context_feature_columns()`.
aaeghjgbi,tensorflow/ranking,tensorflow_ranking/python/estimator_test.py,2037fe23b708a93db9a2cebd86c34748a2d22d0d,STILL_EXISTS,`f1`; `f2`; `f3` are all defined in the `_example_feature_columns()`.
aaeghjgfb,tensorflow/ranking,tensorflow_ranking/python/utils.py,2037fe23b708a93db9a2cebd86c34748a2d22d0d,ad7628ac702e6b197f49290f695c12981b616a3f,needed 2nd dim for this case is [0; 1; 2; 0; 0; 1]; which is the
aaeghjghd,tensorflow/ranking,tensorflow_ranking/python/estimator_test.py,0aab512867903b5ccd3617d5120dc716e935b25f,STILL_EXISTS,`c1` is the only context feature defined in `_context_feature_columns()`.
aaeghjghe,tensorflow/ranking,tensorflow_ranking/python/estimator_test.py,0aab512867903b5ccd3617d5120dc716e935b25f,STILL_EXISTS,`f1`; `f2`; `f3` are all defined in the `_example_feature_columns()`.
aaegiaabh,tensorflow/ranking,tensorflow_ranking/python/keras/model.py,cb63285bf65e8145af0443bdd6286b64a6d5449d,STILL_EXISTS,TODO: Support compatibility with TPUs.
aaegibbgf,tensorflow/ranking,tensorflow_ranking/python/keras/canned/dnn_test.py,02b64e17207390d4233297c0856ca3fa6784b3d6,ef0546dc279a1828b342eb3589b0a32941e9624b,TODO: Deserialized embedding feature column behavior is the
aaegibcae,tensorflow/ranking,tensorflow_ranking/python/estimator.py,452d1f071a3a27ddf060be9a9e5f803b38e87fef,STILL_EXISTS,TODO: Attach the link to the paper.
aaegibcig,tensorflow/ranking,tensorflow_ranking/python/keras/canned/gam_test.py,609400fedf40bfcb588665143dc9e790734d076c,ef0546dc279a1828b342eb3589b0a32941e9624b,TODO: Deserialized embedding feature column behavior is the
aaegibehi,tensorflow/ranking,tensorflow_ranking/extension/tfrbert.py,7c37fb274e8407efa413a5b180002cf0b8a2fbee,STILL_EXISTS,Truncation is needed.
aaegibeii,tensorflow/ranking,tensorflow_ranking/extension/tfrbert.py,7c37fb274e8407efa413a5b180002cf0b8a2fbee,STILL_EXISTS,The convention in BERT for sequence pairs:
aaegibfhd,tensorflow/ranking,tensorflow_ranking/python/keras/metrics.py,abb2368ce8dc7616e88714f5b3e2e4a3e8010b5c,STILL_EXISTS,TODO Add recall metrics to TF1 in another cl.
aaegibheh,tensorflow/ranking,tensorflow_ranking/python/keras/losses_test.py,6a37e16bf43764d45f626b57df0e00d20d492abb,3d86046f6e0dcea219f6f7910b6be527d4974497,TODO: Debug assertIsLossSerializable for Gumbel loss. Right now;
aaegibhfb,tensorflow/ranking,tensorflow_ranking/python/keras/losses_test.py,88291e833ff5c61744fb727d59933e5240a6cb9f,STILL_EXISTS,TODO: Debug assertIsLossSerializable for Gumbel loss. Right now;
aaegibhhe,tensorflow/ranking,tensorflow_ranking/python/feature_test.py,530fa4d093363f467e0088d343d8adc8d627ac7d,STILL_EXISTS,Build columns.
aaegibiec,tensorflow/ranking,tensorflow_ranking/python/keras/layers.py,c7d880cc2f95370e209999a35fa51385974fd609,ac1abc8bade29e628b5c1cb656688e381ab7c6aa,TODO: Remove call_fn args check once
aaegibijj,tensorflow/ranking,tensorflow_ranking/python/keras/metrics.py,552ad7784921d1af7597aace0e9dfd5df3b535a1,STILL_EXISTS,TODO: Add mask argument for metric.compute() call
aaegicabh,tensorflow/ranking,tensorflow_ranking/python/utils.py,c448e82d5312c15ab880ac7606316c5fbeaf750a,STILL_EXISTS,TODO: Add checks to validate (ragged) shapes of input tensors.
aaegicaec,microsoft/dowhy,dowhy/causal_estimators/propensity_score_matching_estimator.py,f9d728ca1fefac352e082a6966bf08e604f21fff,STILL_EXISTS,TODO remove neighbors that are more than a given radius apart
aaegicaee,microsoft/dowhy,dowhy/causal_estimators/propensity_score_matching_estimator.py,f9d728ca1fefac352e082a6966bf08e604f21fff,STILL_EXISTS,TODO -- fix: we are actually conditioning on positive treatment (d=1)
aaegicafe,microsoft/dowhy,dowhy/causal_estimators/propensity_score_stratification_estimator.py,f9d728ca1fefac352e082a6966bf08e604f21fff,STILL_EXISTS,TODO - how can we add additional information into the returned estimate?
aaegicafg,microsoft/dowhy,dowhy/causal_estimators/propensity_score_stratification_estimator.py,f9d728ca1fefac352e082a6966bf08e604f21fff,STILL_EXISTS,TODO -- fix: we are actually conditioning on positive treatment (d=1)
aaegicagd,microsoft/dowhy,dowhy/causal_estimators/propensity_score_weighting_estimator.py,f9d728ca1fefac352e082a6966bf08e604f21fff,STILL_EXISTS,TODO - how can we add additional information into the returned estimate?
aaegicage,microsoft/dowhy,dowhy/causal_estimators/propensity_score_weighting_estimator.py,f9d728ca1fefac352e082a6966bf08e604f21fff,STILL_EXISTS,TODO -- fix: we are actually conditioning on positive treatment (d=1)
aaegicahf,microsoft/dowhy,dowhy/causal_identifier.py,f9d728ca1fefac352e082a6966bf08e604f21fff,STILL_EXISTS,TODO: outputs string for now; but ideally should do symbolic
aaegicajh,microsoft/dowhy,dowhy/do_why.py,f9d728ca1fefac352e082a6966bf08e604f21fff,STILL_EXISTS,TODO: move the logging level argument to a json file. Tue 20 Feb 2018 06:56:27 PM DST
aaegiccdd,microsoft/dowhy,docs/source/conf.py,84176e0fec4b114b9721274debedc0c5d31ce457,STILL_EXISTS,-- Options for todo extension ----------------------------------------------
aaegiccde,microsoft/dowhy,docs/source/conf.py,84176e0fec4b114b9721274debedc0c5d31ce457,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaegiccig,microsoft/dowhy,dowhy/causal_graph.py,df0f2dc6c2b1355c18736883d67c2a28c90afd42,STILL_EXISTS,[TODO: double check these work with multivariate implementation:]
aaegicdig,microsoft/dowhy,dowhy/causal_estimators/econml_cate_estimator.py,88e7957aa1d29f165af59fecfe5b8961c66603d2,STILL_EXISTS,TODO -- fix: we are actually conditioning on positive treatment (d=1)
aaegicdih,microsoft/dowhy,dowhy/causal_model.py,88e7957aa1d29f165af59fecfe5b8961c66603d2,STILL_EXISTS,TODO add propensity score as default backdoor method; iv as default iv method; add an informational message to show which method has been selected.
aaegicdij,microsoft/dowhy,dowhy/causal_estimator.py,f4eb5ea659d7d007641f8072f187ce2f7b43a850,STILL_EXISTS,TODO Only works for binary treatment
aaegicebc,microsoft/dowhy,dowhy/causal_estimators/linear_regression_estimator.py,04e2bff070ddb2bb717ee5be249143c97672c857,STILL_EXISTS,TODO
aaegicedc,microsoft/dowhy,dowhy/causal_estimators/instrumental_variable_estimator.py,0bc53061a7fcc2a5858dd4372b8863a799c40256,STILL_EXISTS,TODO move this to the identification step
aaegiceef,microsoft/dowhy,dowhy/causal_graph.py,532208e05d8acf19775b4c13945a6a012d13fb94,6ebe0bd45925e9e2c2fc7b1b134274291e9f1e20,TODO do not add it here. CausalIdentifier should call causal_graph to add an unobserved common cause if needed. This also ensures that we do not need get_common_causes in this class.
aaegiceeg,microsoft/dowhy,dowhy/causal_graph.py,532208e05d8acf19775b4c13945a6a012d13fb94,STILL_EXISTS,TODO Refactor to remove this from here and only implement this logic in causalIdentifier. Unnecessary assumption of nodes1 to be causing nodes2.
aaegicgac,microsoft/dowhy,dowhy/causal_model.py,43896ff71a4dfa2c598fbeeca09f16a92095dc4a,STILL_EXISTS,TODO add dowhy as a prefix to all dowhy estimators
aaegicgcc,microsoft/dowhy,dowhy/causal_estimators/causalml.py,36b5f4b0efb3c519e0d0c3613aa67dda8c45cf07,STILL_EXISTS,TODO we are conditioning on a postive treatment
aaegicgcd,microsoft/dowhy,dowhy/causal_estimators/causalml.py,36b5f4b0efb3c519e0d0c3613aa67dda8c45cf07,STILL_EXISTS,TODO create an expression corresponding to each estimator used
aaegicgcf,microsoft/dowhy,tests/causal_estimators/test_causalml_estimator.py,36b5f4b0efb3c519e0d0c3613aa67dda8c45cf07,74831556c07f376bacf36ac2c4ea2cfe4dd73989,Hack to install causalml if not already present
aaegicgij,microsoft/dowhy,tests/causal_estimators/test_econml_estimator.py,a92ac859ea79fa4b69b60633c4c7f6e2225b10a8,STILL_EXISTS,TODO: Test IntentToTreatDRIV when EconML v0.7 comes out
aaegichbc,microsoft/dowhy,dowhy/causal_graph.py,bb267ba20a26ba8b9047e7a7cdad55f96296d9bc,STILL_EXISTS,Adding columns in the dataframe as confounders that were not in the graph
aaegichef,microsoft/dowhy,dowhy/causal_estimator.py,0a50b1033ad4ac7cac4d056aa1519959fcaa014e,STILL_EXISTS,Number of quantiles to discretize continuous columns; for applying groupby
aaegichfd,microsoft/dowhy,dowhy/causal_estimator.py,0a50b1033ad4ac7cac4d056aa1519959fcaa014e,STILL_EXISTS,Deleting the temporary categorical columns
aaegichfe,microsoft/dowhy,dowhy/causal_estimators/linear_regression_estimator.py,0a50b1033ad4ac7cac4d056aa1519959fcaa014e,113031a7d84af7bc28dfc4152ea78f489558f9a0,TODO make treatment_value and control value also as local parameters
aaegicige,microsoft/dowhy,dowhy/interpreters/propensity_balance_interpreter.py,c384c329903c4d28cdab5f1d164e3cee68deff2d,STILL_EXISTS,TODO
aaegicihc,microsoft/dowhy,dowhy/interpreters/visual_interpreter.py,c384c329903c4d28cdab5f1d164e3cee68deff2d,STILL_EXISTS,TODO: A common way to show all plots
aaegicjhi,microsoft/dowhy,dowhy/causal_estimators/regression_estimator.py,113031a7d84af7bc28dfc4152ea78f489558f9a0,STILL_EXISTS,TODO make treatment_value and control value also as local parameters
aaegidabj,microsoft/dowhy,dowhy/causal_identifier.py,5b83438b66e87acf332a4fc1c29a380a35835bcc,STILL_EXISTS,TODO: support multivariate treatments better.
aaegidaga,microsoft/dowhy,dowhy/causal_identifier.py,74e2e844ff7b4492c01e07828d3159d52eab7d19,STILL_EXISTS,TODO: support multivariate treatments better.
aaegidbbf,microsoft/dowhy,dowhy/causal_refuters/add_unobserved_common_cause.py,cdda87c6577aebcc0342e4455ce9fa8a1e90b91a,STILL_EXISTS,Choosing c1 and c2 based on the hyperbolic relationship once c_star is chosen by going over various combinations of c1 and c2 values and choosing the combination which
aaegidbfc,microsoft/dowhy,dowhy/causal_model.py,20c20a2d8184a6fa3d561646a4947df56c91434e,STILL_EXISTS,TODO: This add_params needs to move to the estimator class
aaegidbhc,microsoft/dowhy,dowhy/causal_estimators/distance_matching_estimator.py,e907134938aee6d0b3ceec2492e14d86692d2841,STILL_EXISTS,TODO remove neighbors that are more than a given radius apart
aaegidcjc,microsoft/NeuronBlocks,block_zoo/BaseLayer.py,122417816ed7fced2b86c9a8ab2bff5a2b24b2c3,STILL_EXISTS,self.input_dims = [xxx; xxx]  would be inferenced automatically
aaegideag,microsoft/NeuronBlocks,block_zoo/Embedding.py,122417816ed7fced2b86c9a8ab2bff5a2b24b2c3,STILL_EXISTS,judge if fix the embedding weight
aaegidhge,microsoft/NeuronBlocks,losses/BaseLossConf.py,122417816ed7fced2b86c9a8ab2bff5a2b24b2c3,STILL_EXISTS,IF NEEDED; TRANSFORM SOME INT OR FLOAT; OR NUMPY ARRAY TO TENSORS.
aaegidibe,microsoft/NeuronBlocks,metrics/conlleval.py,122417816ed7fced2b86c9a8ab2bff5a2b24b2c3,STILL_EXISTS,each non-empty line must contain >= 3 columns
aaegidibh,microsoft/NeuronBlocks,metrics/conlleval.py,122417816ed7fced2b86c9a8ab2bff5a2b24b2c3,STILL_EXISTS,extract tags from last 2 columns
aaegidifb,microsoft/NeuronBlocks,problem.py,122417816ed7fced2b86c9a8ab2bff5a2b24b2c3,STILL_EXISTS,char is not in file_columns
aaegidigd,microsoft/NeuronBlocks,problem.py,122417816ed7fced2b86c9a8ab2bff5a2b24b2c3,STILL_EXISTS,input type of each column; namely the inverse of file_columns; e.g. col_index_types[0] = 'query_index'
aaegidiib,microsoft/NeuronBlocks,problem.py,122417816ed7fced2b86c9a8ab2bff5a2b24b2c3,STILL_EXISTS,these columns are useless in the configuration
aaegidjgh,microsoft/NeuronBlocks,utils/corpus_utils.py,122417816ed7fced2b86c9a8ab2bff5a2b24b2c3,STILL_EXISTS,target is also a sequence; padding needed
aaegieacd,microsoft/NeuronBlocks,docs/source/conf.py,e20e34e414188845cd2fc45ae278fe616b31c30c,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaegieeah,chartbeat-labs/textacy,textacy/data.py,412279d0709f7ac525a55a4d42b20f787d037812,a7eaf5692c95b73803db57f102e76f107994b376,TODO: maybe don't actually cache this -- it takes up a lot of RAM
aaegieeaj,chartbeat-labs/textacy,textacy/data.py,412279d0709f7ac525a55a4d42b20f787d037812,42ea974ff63853a84b1ea98c80a2d771230f2e78,TODO: uncomment these whenever spacy makes them available...
aaegieecg,chartbeat-labs/textacy,textacy/math_utils.py,23f6962755649ec7e079b990e194f76c7a80a865,STILL_EXISTS,TODO: make this module actually good and useful
aaegieehg,chartbeat-labs/textacy,textacy/extract.py,d74361dd900a2be54fe55dc88cdb7c95d012657b,STILL_EXISTS,HACK...
aaegieeib,chartbeat-labs/textacy,textacy/extract.py,d74361dd900a2be54fe55dc88cdb7c95d012657b,STILL_EXISTS,TODO: do we have no other recourse?!
aaegieejb,chartbeat-labs/textacy,textacy/transform.py,c2f3e645212512e6300b11c11421375a786ea0d3,STILL_EXISTS,TODO: bag-of-words? bag-of-concepts? gensim-compatible corpus and dictionary?
aaegieeje,chartbeat-labs/textacy,textacy/lexicon_methods.py,781d25d752e0db37c00c22970e292baeb0070ae1,STILL_EXISTS,TODO: Do something smarter for averaging emotional valences.
aaegiefac,chartbeat-labs/textacy,textacy/keyterms.py,dff200a3ecd746189ac124a6486bcd2f58870526,STILL_EXISTS,TODO: assess if len(t) puts too much emphasis on long terms
aaegiefah,chartbeat-labs/textacy,textacy/keyterms.py,dff200a3ecd746189ac124a6486bcd2f58870526,2bc9b7b91658baaf9717dba6565df203154d7cc9,HACK: pretend that they're 1 token apart
aaegiefbe,chartbeat-labs/textacy,textacy/keyterms.py,dff200a3ecd746189ac124a6486bcd2f58870526,42ea974ff63853a84b1ea98c80a2d771230f2e78,TODO: decide if this would be useful
aaegiefej,chartbeat-labs/textacy,textacy/keyterms.py,dff200a3ecd746189ac124a6486bcd2f58870526,de5a51c9470465227409ae512b739b60909417ea,TODO: check that .items(): not needed
aaegiefhg,chartbeat-labs/textacy,textacy/texts.py,aa92bc2801ad74cf7a0beea3b40634af6a1e9ff1,STILL_EXISTS,HACK: key terms are currently returned as strings
aaegiefhh,chartbeat-labs/textacy,textacy/texts.py,aa92bc2801ad74cf7a0beea3b40634af6a1e9ff1,STILL_EXISTS,TODO: cache key terms; and return them as spacy spans
aaegiegfc,chartbeat-labs/textacy,docs/source/conf.py,f0478f0b0e4c43935eb3843fc7c989ec2fb0e63c,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaegiehgj,chartbeat-labs/textacy,textacy/export.py,8f4fe27aa3506e541e54014ffdf863dc30e03705,829a89ffde8f347fd2d21a59d15f2cbf838010fc,HACK...
aaegiehjg,chartbeat-labs/textacy,textacy/fileio/write.py,829a89ffde8f347fd2d21a59d15f2cbf838010fc,63b035519c3134dfc96efad3bdcb460bde9e3c6e,HACK...
aaegieiae,chartbeat-labs/textacy,textacy/topic_modeling.py,c68458892776bc3a69a1073ae996086ae3af0ebd,STILL_EXISTS,TODO: what to do about lang?
aaegieich,chartbeat-labs/textacy,textacy/topic_modeling.py,422d4337ca21ef5aad58017010e350da8421fee4,1626f2d4180ecbcdf40b89457d7c28e4ec6c5260,TODO: make this py2-3 compatible a la
aaegiejee,chartbeat-labs/textacy,textacy/representations/vsm.py,495dd0d4039dbc3f85f9daefa9052ad36bf2fdec,STILL_EXISTS,the \"-1\" here and a few lines later is needed to compensate for the
aaegiejgd,chartbeat-labs/textacy,textacy/representations/vsm.py,6730cada2bf728a36eae0495714e575635d49909,STILL_EXISTS,the \"-1\" here and a few lines later is needed to compensate for the
aaegifagc,chartbeat-labs/textacy,textacy/tm/topic_model.py,c63007e84646d1852187bc1b13057cd0cf09abf5,de519215eab49c04bce4187f437d2b212a29b88c,lang (str; {'en'}): language of the input text; needed for initializing
aaegifefi,chartbeat-labs/textacy,textacy/corpora/bernie_and_hillary.py,302d7a7d2df8cafcd0c081f9a0fa49d46aa87b63,STILL_EXISTS,\"\"\" || TODO || \"\"\"
aaegifega,chartbeat-labs/textacy,textacy/data.py,f4f7dd0b3072dec4555ac2064f55b1afd68849d1,42ea974ff63853a84b1ea98c80a2d771230f2e78,TODO: update this to spaCy's new `load` API
aaegifeig,chartbeat-labs/textacy,textacy/viz/__init__.py,19b02419a98344f697afdc0b97e58f24c7c55729,STILL_EXISTS,TODO
aaegiffdg,chartbeat-labs/textacy,tests/test_corpora_reddit_reader.py,17e8c57396d39c2eea7836b5243ed3966040dc2b,6acb7303a772b92ca6129bfa8031f20a71a18f36,{\"author_flair_css_class\": \"on\"; \"created_utc\": \"1420070400\"; \"parent_id\": \"t1_cnas2b6\"; \"downs\": 0; \"score\": 3; \"author_flair_text\": \"Ontario\"; \"subreddit_id\": \"t5_2s4gt\"; \"gilded\": 0; \"distinguished\": None; \"id\": \"cnas8zw\"; \"link_id\": \"t3_2qv6c6\"; \"author\": \"RedCoatsForever\"; \"controversiality\": 0; \"retrieved_on\": 1425124282; \"archived\": False; \"subreddit\": \"CanadaPolitics\"; \"edited\": False; \"ups\": 3; \"body\": \"But Mill\"s career was way better. Bentham is like; the Joseph Smith to Mill\"s Brigham Young.\"; \"score_hidden\": False; \"name\": \"t1_cnas8zw\"}
aaegiffdh,chartbeat-labs/textacy,tests/test_corpora_reddit_reader.py,17e8c57396d39c2eea7836b5243ed3966040dc2b,6acb7303a772b92ca6129bfa8031f20a71a18f36,{\"author_flair_css_class\": None; \"created_utc\": \"1420070400\"; \"parent_id\": \"t3_2qxefp\"; \"author_flair_text\": None; \"score\": 1; \"subreddit_id\": \"t5_2s7tt\"; \"gilded\": 0; \"distinguished\": None; \"id\": \"cnas8zx\"; \"link_id\": \"t3_2qxefp\"; \"author\": \"vhisic\"; \"name\": \"t1_cnas8zx\"; \"retrieved_on\": 1425124282; \"downs\": 0; \"subreddit\": \"AdviceAnimals\"; \"controversiality\": 0; \"edited\": False; \"ups\": 1; \"body\": \"Mine uses a strait razor; and as much as i love the clippers i love the razor so much more. Then he follows it up with a warm towel. \ || I think i might go get a hair cut this week.\"; \"score_hidden\": False; \"archived\": False}
aaegiffdj,chartbeat-labs/textacy,tests/test_corpora_reddit_reader.py,17e8c57396d39c2eea7836b5243ed3966040dc2b,6acb7303a772b92ca6129bfa8031f20a71a18f36,TODO: Make this work. There's some insane bullshit happening with json and bzip
aaegifhdf,chartbeat-labs/textacy,textacy/data.py,07da39b191e0dc2e9f4512904bb0f0fca60e9f7a,STILL_EXISTS,HACK: Py2's csv module fail
aaegifhfj,chartbeat-labs/textacy,textacy/export.py,63b035519c3134dfc96efad3bdcb460bde9e3c6e,STILL_EXISTS,HACK...
aaegifhgf,chartbeat-labs/textacy,tests/test_fileio.py,a4a8485a678aca25646da68cc6a1a9521e26630c,0f6e33b97bdffea5b5a5bbb7d5fc7883ab2e1d9c,no idea why this is the case
aaegifhhc,chartbeat-labs/textacy,textacy/fileio/utils.py,3c86a21f479e93746e72aafbfb8da022967ce183,5f48b4258ba88eab33f07b6281baf690c1b46421,TODO: remove these params in; say; v0.4
aaegifiba,chartbeat-labs/textacy,textacy/corpora/supremecourt.py,db536e2291ed4d7f6e3bdfb4c82802d5ff32322a,f9bff8235b867c3381a18dbe11d6b6d3bdadcc1d,TODO: Consider joining data with http:\/\/supremecourtdatabase.org\/index.php
aaegififb,chartbeat-labs/textacy,textacy/extract.py,3bc93096ac9b63acb57859174d5e6a7a327d6544,STILL_EXISTS,TODO: What to do about questions; where it may be VSO instead of SVO?
aaegififc,chartbeat-labs/textacy,textacy/extract.py,3bc93096ac9b63acb57859174d5e6a7a327d6544,STILL_EXISTS,TODO: What about non-adjacent verb negations?
aaegififd,chartbeat-labs/textacy,textacy/extract.py,3bc93096ac9b63acb57859174d5e6a7a327d6544,STILL_EXISTS,TODO: What about object (noun) negations?
aaegifjfg,chartbeat-labs/textacy,textacy/corpus.py,b933a75bd436e7ecdb62778f591be3c6b52aa175,STILL_EXISTS,TODO: this function?
aaegigaha,chartbeat-labs/textacy,textacy/corpora/wiki_reader.py,f3ef6782e4a95a07cd48b1fef45b1dafafcea422,STILL_EXISTS,TODO: keep file\/image captions?
aaegigahe,chartbeat-labs/textacy,textacy/corpora/wiki_reader.py,f3ef6782e4a95a07cd48b1fef45b1dafafcea422,STILL_EXISTS,text = replace_internal_links(text)  # TODO: is this needed?
aaegigcic,chartbeat-labs/textacy,textacy/keyterms.py,b9653e67d20b9b6da3cada6a96b7906dd34ab9a7,STILL_EXISTS,HACK: omit empty strings; which happen as a bug in spacy as of v1.5
aaegigcjd,chartbeat-labs/textacy,textacy/text_stats.py,2fbc0fb8a7148269e18a0ec19152382314f65726,STILL_EXISTS,compute basic stats needed for most other stats
aaegigddd,chartbeat-labs/textacy,textacy/fileio/write.py,b6e64af21dac8ffc9e4598f344bd77ab458c485f,9be42b1de4d8054feb24fe54acfe9787e44f5697,TODO: log a warning?
aaegigdde,chartbeat-labs/textacy,textacy/fileio/write.py,b6e64af21dac8ffc9e4598f344bd77ab458c485f,STILL_EXISTS,needed (?) to filter out \"keep-alive\" new chunks
aaegigdjc,chartbeat-labs/textacy,textacy/datasets/wikipedia.py,d725131013f42e6662f0a6ae92f935793a861ba4,STILL_EXISTS,TODO: keep file\/image captions?
aaegigdjg,chartbeat-labs/textacy,textacy/datasets/wikipedia.py,d725131013f42e6662f0a6ae92f935793a861ba4,STILL_EXISTS,text = replace_internal_links(text)  # TODO: is this needed?
aaegigehf,chartbeat-labs/textacy,textacy/datasets/capitolwords.py,8b8c1049208c3db5a53c81347269705146b26eb0,STILL_EXISTS,TODO: change to propublica?
aaegigehg,chartbeat-labs/textacy,textacy/datasets/capitolwords.py,8b8c1049208c3db5a53c81347269705146b26eb0,STILL_EXISTS,TODO: check this
aaegigeig,chartbeat-labs/textacy,textacy/datasets/oxford_text_archive.py,1478c86c831a2f616cb41b35695f96e6c07ae3b2,0841f2ba3de88922e061e0c966e5befd6ac58278,TODO: fix a couple of issues in the author cleanup; e.g. \"fl\" and \"d\"
aaegigejf,chartbeat-labs/textacy,tests/test_dataset_wikipedia.py,0841f2ba3de88922e061e0c966e5befd6ac58278,09bafa1d55acb668b3aeff5f89aee0c6854145dc,TODO: test individual parsing functions
aaegiggif,chartbeat-labs/textacy,textacy/data.py,cf073d182cbb50c256f7a3f893d604ca9bfbb84d,ab8413138dfc927f3df523a856d431f39ddf0ae9,TODO: make *all* of this depechemood stuff better; it's weird
aaegiggig,chartbeat-labs/textacy,textacy/fileio/read.py,8f4d65af0e1d8f63c627c7d6b0faa3322a428701,b7f91e30a92e3c3bd2d650476ae4518673c2bd78,TODO: update this for spacy v2 compatibility
aaegiggih,chartbeat-labs/textacy,textacy/fileio/write.py,8f4d65af0e1d8f63c627c7d6b0faa3322a428701,b7f91e30a92e3c3bd2d650476ae4518673c2bd78,TODO: update this for spacy v2 compatibility
aaegiggjf,chartbeat-labs/textacy,textacy/data.py,d72fe7b81e77ad90555ca3b685c0b6fa74813784,STILL_EXISTS,TODO: make *all* of this depechemood stuff better; it's weird
aaegighab,chartbeat-labs/textacy,textacy/corpus.py,4298c5fc51fd624f516d2e16c157d56ad1bdb5f2,STILL_EXISTS,HACK: add spacy language metadata to first doc's user_data
aaegighad,chartbeat-labs/textacy,textacy/corpus.py,4298c5fc51fd624f516d2e16c157d56ad1bdb5f2,STILL_EXISTS,HACK: pop spacy language metadata from first doc's user_data
aaegighge,chartbeat-labs/textacy,textacy/fileio/spacy.py,1df0e112cc48a8a8820b770cb96bb999d5cae16b,STILL_EXISTS,\"\"\" || Todo: ||     Figure out a better \/ more efficient way to handle reading\/writing of ||     spacy docs. This situation is tolerable and currently unavoidable; but ||     it's not *good*. || \"\"\"
aaegighgj,chartbeat-labs/textacy,textacy/fileio/http.py,2296547b22081e59ca227ead9d81a84a0b43ba94,STILL_EXISTS,needed (?) to filter out \"keep-alive\" new chunks
aaegigicg,chartbeat-labs/textacy,textacy/vsm.py,2f069bd7e93c57ed92b7ccb1f7d25f80701d0a27,STILL_EXISTS,TODO: Do we *want* to allow setting to this property?
aaegigidc,chartbeat-labs/textacy,textacy/vsm.py,2f069bd7e93c57ed92b7ccb1f7d25f80701d0a27,8673b09d2e76864cc5b9bb9058c26fd54cb9a4d3,TODO: add binarize
aaegigifh,chartbeat-labs/textacy,textacy/vsm.py,5105ac8d6e7607087123fdfb1de51a12e40d05cd,03bd5513cc1781e135f3297f7656148281be5ba4,use fancy indexing to reorder columns
aaegigigg,chartbeat-labs/textacy,textacy/vsm.py,e36602d79bf7601875a7c4a3185cec266d4cc99f,STILL_EXISTS,TODO: implement this burtonnnn
aaegigihb,chartbeat-labs/textacy,textacy/vsm.py,00d31375ad8751e2167d43adbb83944a4f2df297,STILL_EXISTS,TODO: implement this burtonnnn
aaegigiib,chartbeat-labs/textacy,textacy/vsm.py,76df76e07bf5a3295dccc7464680085c08e254e5,03bd5513cc1781e135f3297f7656148281be5ba4,use fancy indexing to reorder columns
aaegigiid,chartbeat-labs/textacy,textacy/vsm.py,03bd5513cc1781e135f3297f7656148281be5ba4,STILL_EXISTS,use fancy indexing to reorder rows or columns
aaegigiif,chartbeat-labs/textacy,textacy/vsm.py,2777acb98e1fcdccb4c476ce94fc4ab4dd969480,STILL_EXISTS,TODO: can we adapt the optimization from `Vectorizer._count_terms()`
aaegigjbc,chartbeat-labs/textacy,tests/test_vsm.py,0633ded6db2aaabf586f6c8ebb77ba4c7fef8ec3,bcb05d50cc8bc56a8520748e6a589dd44c650b3c,TODO: Also check type_='sqrt'
aaegigjcd,chartbeat-labs/textacy,textacy/vsm/matrix_utils.py,d0714957b5859d03c40b2d00c1444517e2e6c3be,STILL_EXISTS,TODO: is this *really* what we want to do?
aaegihahe,chartbeat-labs/textacy,textacy/spacier/components.py,06b6b6dedaac2f081ffc98159ba15da6e3374d31,14b8bcbd1c5f01490dae6aa580380e6f1b7ced6c,TODO: depends on outcome of https:\/\/github.com\/explosion\/spaCy\/issues\/2193
aaegihbbb,chartbeat-labs/textacy,textacy/spacier/utils.py,fe6757fd68ee1807b176e02d6e0a75234fc33e97,STILL_EXISTS,iterate over text chunks and accumulate components needed to make a doc
aaegihbdd,chartbeat-labs/textacy,textacy/extract.py,0d01d793c5040f95d9267c30bcb691ac6ef448d0,STILL_EXISTS,HACK: spacy's models have been erroneously tagging whitespace as entities
aaegihcch,chartbeat-labs/textacy,textacy/io/spacy.py,6a44f3a4de32544f34d95e66774b016ab6e8d04a,ed93401076e650df0b860474ff07a1efcdef8132,FIXME
aaegihcde,chartbeat-labs/textacy,textacy/datasets/dataset.py,53c4b7abf6e79fcd51b274e56733f5d2e9f417a2,beb9ed79868b33894c8fcdfc23c5322280d53bb7,TODO: check the data to make sure all is well?
aaegihcdf,chartbeat-labs/textacy,textacy/datasets/dataset.py,53c4b7abf6e79fcd51b274e56733f5d2e9f417a2,beb9ed79868b33894c8fcdfc23c5322280d53bb7,TODO: shutil.unpack_archive() when PY3-only
aaegihcdg,chartbeat-labs/textacy,textacy/datasets/dataset.py,53c4b7abf6e79fcd51b274e56733f5d2e9f417a2,beb9ed79868b33894c8fcdfc23c5322280d53bb7,TODO: os.makedirs(path; exist_ok=True) when PY3-only
aaegihcec,chartbeat-labs/textacy,textacy/datasets/supreme_court.py,7678062ef15b47b196b8be5c1cc6aab93cb18a24,562e0a33c9e3144aac708e76f618fc052fd9cd21,TODO: check this
aaegihcej,chartbeat-labs/textacy,textacy/datasets/utils.py,beb9ed79868b33894c8fcdfc23c5322280d53bb7,2daf2343e0b204d64d056119c8a6ebf58a254d7f,TODO: check the data to make sure all is well?
aaegihcfa,chartbeat-labs/textacy,textacy/datasets/utils.py,beb9ed79868b33894c8fcdfc23c5322280d53bb7,STILL_EXISTS,TODO: shutil.unpack_archive() when PY3-only
aaegihcfb,chartbeat-labs/textacy,textacy/datasets/utils.py,beb9ed79868b33894c8fcdfc23c5322280d53bb7,e3981b9abcabb997e35e50bf31c5fab1d2b92988,TODO: os.makedirs(path; exist_ok=True) when PY3-only
aaegihcfe,chartbeat-labs/textacy,textacy/datasets/wikipedia.py,2240d0da4de47652f44f66200953eac229e1e56a,STILL_EXISTS,TODO: figure out if we can\/should clear out the tree's root element
aaegihcfi,chartbeat-labs/textacy,textacy/datasets/utils.py,d63d1479e0898b1cc0911e9d1fd3d281578e9522,STILL_EXISTS,\"\"\" || Dataset Utils || ------------- ||  || Shared functionality for downloading; naming; and extracting the contents || of datasets; as well as filtering for particular subsets. || \"\"\"
aaegihddd,chartbeat-labs/textacy,textacy/corpus.py,41416dec1bc3ec7d8bf163842591d1a356830f8c,b45effdf1b0c5e231e3cd51a353a1c0e8003289f,TODO: handle document metadata!
aaegihdde,chartbeat-labs/textacy,textacy/corpus.py,41416dec1bc3ec7d8bf163842591d1a356830f8c,fa0afbf7041009f13617ff24ec8baa68481a24c5,TODO: use tio.open_sesame?
aaegihdea,chartbeat-labs/textacy,textacy/spacier/doc_extensions.py,ec7b188003ce71ef0b816b27642bd0e4b13ed164,STILL_EXISTS,TODO: module docstring
aaegihdeb,chartbeat-labs/textacy,textacy/spacier/doc_extensions.py,ec7b188003ce71ef0b816b27642bd0e4b13ed164,de5a51c9470465227409ae512b739b60909417ea,TODO: `count` method extension?
aaegihdgb,chartbeat-labs/textacy,textacy/doc.py,580c7a473d92e0df6c17e45ed4d3ac9fdecebe82,9db400554b8b92b45f9b74ebd322c1ac21a7868f,TODO: module docstring
aaegiheaa,chartbeat-labs/textacy,tests/test_doc.py,772759cd2a83f87ea288eabeb6167baf6cd0d22b,9db400554b8b92b45f9b74ebd322c1ac21a7868f,TODO: re-add this test if count() gets implemented
aaegihebf,chartbeat-labs/textacy,tests/test_doc.py,2d81f7bda58297ec717e4a7bbe2b57867115dcd7,5d7b377c07f5b2d0177d08f9a982ebb8b17ef169,TODO: add more combos; if you can convince pytest to not hang forever
aaegihebi,chartbeat-labs/textacy,textacy/spacier/doc_extensions.py,5d7b377c07f5b2d0177d08f9a982ebb8b17ef169,STILL_EXISTS,TODO
aaegihece,chartbeat-labs/textacy,textacy/spacier/doc_extensions.py,a0e0d3078a421fb257a72f8337e7c50ac06d1a41,STILL_EXISTS,\"\"\" || spaCy Doc extensions || -------------------- ||  || Functionality for inspecting; customizing; and transforming spaCy's core || data structure; :class:`spacy.tokens.Doc`; accessible directly as functions || that take a ``Doc`` as their first argument or as custom attributes\/methods || on instantiated docs prepended by an underscore: ||  || .. code-block:: pycon ||  ||     >>> spacy_lang = textacy.load_spacy(\"en\") ||     >>> doc = nlp(\"This is a short text.\") ||     >>> print(get_preview(doc)) ||     Doc(6 tokens: \"This is a short text.\") ||     >>> print(doc._.preview) ||     Doc(6 tokens: \"This is a short text.\") || \"\"\"
aaegihech,chartbeat-labs/textacy,textacy/spacier/doc_extensions.py,a0e0d3078a421fb257a72f8337e7c50ac06d1a41,STILL_EXISTS,TODO
aaegihecj,chartbeat-labs/textacy,tests/spacier/test_doc_extensions.py,a9a37a6fbd7874d2578196c9d9fcee74e4f03865,STILL_EXISTS,TODO: re-add this test if count() gets implemented
aaegihfdi,chartbeat-labs/textacy,textacy/spacier/components.py,75eeb2188a402d96bcb4f5c98f0691096580cee2,STILL_EXISTS,TODO: see if there's a better way to handle this
aaegihfea,chartbeat-labs/textacy,textacy/tm/topic_model.py,75eeb2188a402d96bcb4f5c98f0691096580cee2,d4b549482865829ff9ba3b2058569681b6a8afcf,TODO: sklearn has dropped this vendorized version
aaegihfei,chartbeat-labs/textacy,tests/test_extract.py,26f2c0277262ceb14850cb77ba85a160e410eebd,STILL_EXISTS,TODO: figure out if this function no longer works; ugh
aaegihffa,chartbeat-labs/textacy,tests/test_keyterms.py,26f2c0277262ceb14850cb77ba85a160e410eebd,STILL_EXISTS,TODO: the actual results are NOT what i'd expect; figure out why
aaegihffd,chartbeat-labs/textacy,textacy/lang_utils.py,26bac29e6ffc90c53e05fd3a6cdf0c85d55e7cfa,639571b12a88b2b3e2cd3ddd393fd06c5d80532e,TODO: identify the \"best\" language detector available for OSS python
aaegihgbb,chartbeat-labs/textacy,scripts/fetch_wiki_lang_snippets.py,d6577b64cabd3a6112a2b77bfded64e12d222c1c,STILL_EXISTS,*seems* like a python bug; but more likely i'm doing something wrong
aaegihgca,chartbeat-labs/textacy,textacy/cache.py,5131e9b02ed9b938bacc5095001f48ba2eba851d,8d2e23e2164c5defb67ef582e406689ef5486737,TODO: automatically download?
aaegihghj,chartbeat-labs/textacy,textacy/preprocessing/resources.py,81b0d09662fd2038a76ffb11bb698609a5eb0687,5fc9c55aaa1bac0b1aa93fdaa0b62dd4a9746aee,TODO: r\"(?:mailto)?
aaegihhdd,chartbeat-labs/textacy,tests/test_similarity.py,eb0c7c7ec99d9d1309594fd137e8417ce9b5834b,STILL_EXISTS,HACK
aaegihhef,chartbeat-labs/textacy,textacy/keyterms.py,befd609157ede410769cadee0b40227ef6c34717,STILL_EXISTS,TODO: compat this for PY2; PY3.8
aaegihhgc,chartbeat-labs/textacy,textacy/ke/sgrank.py,bdfd315fe478a7227e010cd0ae637d3a05230230,STILL_EXISTS,TODO: assess how best to scale term len
aaegihici,chartbeat-labs/textacy,textacy/ke/textrank.py,21c4a9dad8e35687144e2e55b3169470bda89850,c806de35688a4331c563bc368d52cecc2a5d321f,TODO: PY3 doesn't need to make a list when computing the mean
aaegihidd,chartbeat-labs/textacy,textacy/ke/sgrank.py,60d67e186fe8dad27ea601aa71d60baa9fe253dc,2d73fbbe9ec240ff700f9abeb30f12861044389e,FIXME
aaegihidh,chartbeat-labs/textacy,textacy/ke/sgrank.py,60d67e186fe8dad27ea601aa71d60baa9fe253dc,b67a07a8262dcc8bb4cc5ab77e8a580f5febda04,TODO: change <= end_ind to < end_ind and end_ind > n_toks to end_ind >= n_toks
aaegihjaf,chartbeat-labs/textacy,tests/keyterms/test_utils.py,8787bfb97946f6ab141f154ef422af843fba0084,STILL_EXISTS,TODO: the actual results are NOT what i'd expect; figure out why
aaegihjha,chartbeat-labs/textacy,textacy/resources/depeche_mood.py,c31ec7c1a7c777d5fbd8f3cb5b1f0f10d025b464,4394e9d948e13f273c88bf0ba3f8c93d35c5bc44,TODO: move this functionality into io.utils?
aaegihjhb,chartbeat-labs/textacy,textacy/resources/concept_net.py,01f423d807911dc93b419d4386e3a1b3b6f9be9c,4394e9d948e13f273c88bf0ba3f8c93d35c5bc44,TODO: move this functionality into io.utils?
aaegihjhc,chartbeat-labs/textacy,textacy/resources/concept_net.py,01f423d807911dc93b419d4386e3a1b3b6f9be9c,STILL_EXISTS,TODO: determine if requiring same sense is too string; i.e.
aaegihjhf,chartbeat-labs/textacy,textacy/resources/concept_net.py,01f423d807911dc93b419d4386e3a1b3b6f9be9c,STILL_EXISTS,TODO: implement an out-of-vocabulary strategy? for example;
aaegihjhh,chartbeat-labs/textacy,textacy/resources/concept_net.py,3fef9c14b10dce5669dae02c9a8d0c9780e85dd9,STILL_EXISTS,\"\"\" || ConceptNet || ---------- ||  || ConceptNet is a multilingual knowledge base; representing common words and phrases || and the common-sense relationships between them. This information is collected from || a variety of sources; including crowd-sourced resources (e.g. Wiktionary; Open Mind || Common Sense); games with a purpose (e.g. Verbosity; nadya.jp); and expert-created || resources (e.g. WordNet; JMDict). ||  || The interface in textacy gives access to several key relationships between terms || that are useful in a variety of NLP tasks: ||  ||     - antonyms: terms that are opposites of each other in some relevant way ||     - hyponyms: terms that are subtypes or specific instances of other terms ||     - meronyms: terms that are parts of other terms ||     - synonyms: terms that are sufficiently similar that they may be used interchangeably || \"\"\"
aaegihjif,chartbeat-labs/textacy,textacy/__main__.py,0e76abc0381c67a51edae919819849b786105d9e,STILL_EXISTS,TODO: figure out if this no longer works bc of non-dataset items
aaegiiace,chartbeat-labs/textacy,textacy/augmentation/transformations.py,4a1a22829977e9f05b7a5c508d983e296cff1ca1,STILL_EXISTS,FIXME: we should skip empty syns; if others are available...
aaegiiacj,chartbeat-labs/textacy,textacy/augmentation/transformations.py,4a1a22829977e9f05b7a5c508d983e296cff1ca1,STILL_EXISTS,FIXME: whitespace should not be swapped :\/
aaegiiadb,chartbeat-labs/textacy,textacy/augmentation/transformations.py,eaaf2bad32af4bda1a98d434530926bb6a001230,0970b87816bee380fd6af4e293627f076c810f11,TODO: determine if we want to adjust whitespace here
aaegiibhd,chartbeat-labs/textacy,textacy/augmentation/transformations.py,0970b87816bee380fd6af4e293627f076c810f11,STILL_EXISTS,TODO: replace this with something better; and maybe move it :)
aaegiicgb,chartbeat-labs/textacy,textacy/augmentation/utils.py,b77d7e0e5c3a805f74a804a1ae1130826f833fba,2693ea274e81ed44dc40665e8c10f9ad3006a20d,TODO: find a better data source :)
aaegiidce,chartbeat-labs/textacy,textacy/augmentation/augmenter.py,17dc2a808a96bcd5ae00e96e3e1e135c29f0d6d9,STILL_EXISTS,this is a bit of a hack; but whatchagonnado
aaegiidcf,chartbeat-labs/textacy,textacy/augmentation/augmenter.py,17dc2a808a96bcd5ae00e96e3e1e135c29f0d6d9,STILL_EXISTS,TODO: maybe collect words; spaces; and array vals
aaegiiejh,chartbeat-labs/textacy,scripts/train_lang_identifier.py,ef4b16bcd16bfbe8a9dfcfa7e18487cbdb419a3b,STILL_EXISTS,\"\"\" || ### env ||  || textacy==0.9.0 || ... ||  || ### fetch source data ||  || - **Tatoeba:** A crowd-sourced collection of sentences and their translations into many languages. Style is relatively informal; subject matter is a variety of everyday things and goings-on. Source: https:\/\/tatoeba.org\/eng\/downloads. || - **Leipzig Corpora:** A collection of corpora for many languages pulling from comparable sources -- specifically; 10k Wikipedia articles from official database dumps and 10k news articles from either RSS feeds or web scrapes; when available. Style is relatively formal; subject matter is a variety of notable things and goings-on. Source: http:\/\/wortschatz.uni-leipzig.de\/en\/download || - **UDHR:** The UN's Universal Declaration of Human Rights document; translated into hundreds of languages and split into paragraphs. Style is formal; subject matter is fundamental human rights to be universally protected. Source: https:\/\/unicode.org\/udhr\/index.html || - **Twitter:** A collection of tweets in each of ~70 languages; posted in July 2014; with languages assigned through a combination of models and human annotators. Style is informal; subject matter is whatever Twitter was going on about back then; who could say. Source: https:\/\/blog.twitter.com\/engineering\/en_us\/a\/2015\/evaluating-language-identification-performance.html || - **DSLCC**: Two collections of short excerpts of journalistic texts in a handful of language groups that are highly similar to each other. Style is relatively formal; subject matter is current events. Source: http:\/\/ttg.uni-saarland.de\/resources\/DSLCC\/ || \"\"\"
aaegiifaa,chartbeat-labs/textacy,scripts/train_lang_identifier.py,ef4b16bcd16bfbe8a9dfcfa7e18487cbdb419a3b,STILL_EXISTS,HACK HACK HACK
aaegiiffg,chartbeat-labs/textacy,tests/test_text_stats.py,d611e0f5819fe48dd4786bea03611b3fe8f7d80c,STILL_EXISTS,TODO: when only supporting PY3.7+; use this instead
aaegiifgb,chartbeat-labs/textacy,src/textacy/utils.py,c369dea462f62b61a586c69953d99daea454a3e3,STILL_EXISTS,TODO: use standard error message; maybe?
aaegiifha,chartbeat-labs/textacy,src/textacy/ke/graph_base.py,c7be45264ed85118b84dc8aa46fc3f4e12ed912e,STILL_EXISTS,TODO: someday; burton; figure out what you were going to do with this...
aaegiific,chartbeat-labs/textacy,src/textacy/text_stats/api.py,b8dabf575c0f07c9bf4d4f7eeef6dd75aca47c1c,STILL_EXISTS,TODO: should we vary char threshold by lang?
aaegiifid,chartbeat-labs/textacy,src/textacy/text_stats/api.py,b8dabf575c0f07c9bf4d4f7eeef6dd75aca47c1c,STILL_EXISTS,TODO: should we vary syllable threshold by lang?
aaegiifie,chartbeat-labs/textacy,src/textacy/text_stats/basics.py,76303300cb6ce4ef3ae7f73af9274fb4e5a9d08a,5945a6e1887560489362b473d3b77511ec274501,TODO: should base be 2 or n_unique_words ??
aaegiifig,chartbeat-labs/textacy,tests/text_stats/test_basics.py,708fd476d4b4c4491f29149738a02087e4fbf55b,STILL_EXISTS,NOTE: you can see how the hack syllable counting stumbles; especially on short words
aaegiifih,chartbeat-labs/textacy,tests/text_stats/test_api.py,763c32badd46c0570b16e42860edcd83a7427d11,STILL_EXISTS,TODO: when only supporting PY3.7+; use this instead
aaegiifjb,chartbeat-labs/textacy,tests/text_stats/test_readability.py,358c680c0e32bb3f7bae564cafc2f88bb293dd23,STILL_EXISTS,TODO: when only supporting PY3.7+; use this instead
aaegiigea,chartbeat-labs/textacy,docs/source/conf.py,96507346302aed562daa1063c649324067759d1f,STILL_EXISTS,todo
aaegiigeb,chartbeat-labs/textacy,docs/source/conf.py,96507346302aed562daa1063c649324067759d1f,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaegiiggc,EducationalTestingService/skll,skll/experiments.py,fec736a7c1593eac8ec71b2da2a0c5a87b687727,233af5fc2cda70ef3101bc61bde4b10190587517,this is a workaround to make this simple use case (a single train and
aaegiihfd,EducationalTestingService/skll,tests/test_ablation.py,f61c9e8b85f45172ea9c52f379f31c82ec39468f,STILL_EXISTS,''' || Module for running a bunch of simple unit tests. Should be expanded more in || the future. ||  || :author: Michael Heilman (mheilman@ets.org) || :author: Nitin Madnani (nmadnani@ets.org) || :author: Dan Blanchard (dblanchard@ets.org) || :author: Aoife Cahill (acahill@ets.org) || '''
aaegiihje,EducationalTestingService/skll,tests/test_classification.py,f61c9e8b85f45172ea9c52f379f31c82ec39468f,STILL_EXISTS,''' || Module for running a bunch of simple unit tests. Should be expanded more in || the future. ||  || :author: Michael Heilman (mheilman@ets.org) || :author: Nitin Madnani (nmadnani@ets.org) || :author: Dan Blanchard (dblanchard@ets.org) || :author: Aoife Cahill (acahill@ets.org) || '''
aaegiiidg,EducationalTestingService/skll,tests/test_custom_learner.py,f61c9e8b85f45172ea9c52f379f31c82ec39468f,STILL_EXISTS,''' || Module for running a bunch of simple unit tests. Should be expanded more in || the future. ||  || :author: Michael Heilman (mheilman@ets.org) || :author: Nitin Madnani (nmadnani@ets.org) || :author: Dan Blanchard (dblanchard@ets.org) || :author: Aoife Cahill (acahill@ets.org) || '''
aaegiiife,EducationalTestingService/skll,tests/test_cv.py,f61c9e8b85f45172ea9c52f379f31c82ec39468f,STILL_EXISTS,''' || Module for running a bunch of simple unit tests. Should be expanded more in || the future. ||  || :author: Michael Heilman (mheilman@ets.org) || :author: Nitin Madnani (nmadnani@ets.org) || :author: Dan Blanchard (dblanchard@ets.org) || :author: Aoife Cahill (acahill@ets.org) || '''
aaegiiihi,EducationalTestingService/skll,tests/test_featureset.py,f61c9e8b85f45172ea9c52f379f31c82ec39468f,STILL_EXISTS,''' || Module for running a bunch of simple unit tests. Should be expanded more in || the future. ||  || :author: Michael Heilman (mheilman@ets.org) || :author: Nitin Madnani (nmadnani@ets.org) || :author: Dan Blanchard (dblanchard@ets.org) || :author: Aoife Cahill (acahill@ets.org) || '''
aaegiijbj,EducationalTestingService/skll,tests/test_input.py,f61c9e8b85f45172ea9c52f379f31c82ec39468f,STILL_EXISTS,''' || Module for running a bunch of simple unit tests. Should be expanded more in || the future. ||  || :author: Michael Heilman (mheilman@ets.org) || :author: Nitin Madnani (nmadnani@ets.org) || :author: Dan Blanchard (dblanchard@ets.org) || :author: Aoife Cahill (acahill@ets.org) || '''
aaegiijcb,EducationalTestingService/skll,tests/test_metrics.py,f61c9e8b85f45172ea9c52f379f31c82ec39468f,STILL_EXISTS,''' || Module for running a bunch of simple unit tests. Should be expanded more in || the future. ||  || :author: Michael Heilman (mheilman@ets.org) || :author: Nitin Madnani (nmadnani@ets.org) || :author: Dan Blanchard (dblanchard@ets.org) || :author: Aoife Cahill (acahill@ets.org) || '''
aaegiijdg,EducationalTestingService/skll,tests/test_output.py,f61c9e8b85f45172ea9c52f379f31c82ec39468f,STILL_EXISTS,''' || Module for running a bunch of simple unit tests. Should be expanded more in || the future. ||  || :author: Michael Heilman (mheilman@ets.org) || :author: Nitin Madnani (nmadnani@ets.org) || :author: Dan Blanchard (dblanchard@ets.org) || :author: Aoife Cahill (acahill@ets.org) || '''
aaegiijfh,EducationalTestingService/skll,tests/test_output.py,f61c9e8b85f45172ea9c52f379f31c82ec39468f,STILL_EXISTS,should be printed (though some columns will be blank).
aaegiijgb,EducationalTestingService/skll,tests/test_preprocessing.py,f61c9e8b85f45172ea9c52f379f31c82ec39468f,STILL_EXISTS,''' || Module for running a bunch of simple unit tests. Should be expanded more in || the future. ||  || :author: Michael Heilman (mheilman@ets.org) || :author: Nitin Madnani (nmadnani@ets.org) || :author: Dan Blanchard (dblanchard@ets.org) || :author: Aoife Cahill (acahill@ets.org) || '''
aaegiijge,EducationalTestingService/skll,tests/test_preprocessing.py,f61c9e8b85f45172ea9c52f379f31c82ec39468f,STILL_EXISTS,default should keep all nonzero features (i.e. ones that appear 1+ times)
aaegiijhj,EducationalTestingService/skll,tests/test_regression.py,f61c9e8b85f45172ea9c52f379f31c82ec39468f,STILL_EXISTS,''' || Module for running a bunch of simple unit tests. Should be expanded more in || the future. ||  || :author: Michael Heilman (mheilman@ets.org) || :author: Nitin Madnani (nmadnani@ets.org) || :author: Dan Blanchard (dblanchard@ets.org) || :author: Aoife Cahill (acahill@ets.org) || '''
aaegjafig,EducationalTestingService/skll,tests/test_featureset.py,ce57f6a8f348d27b2c6aaa63b642fe7bac9d9483,bc2473e1420a0836803fd97b400715803b08b8d5,and that they are the first and fourth columns
aaegjagca,EducationalTestingService/skll,tests/test_utilities.py,aaf038dd47f7a11d89b2422e92d3d919395a9135,STILL_EXISTS,''' || Module for running a bunch of simple unit tests. Should be expanded more in || the future. ||  || :author: Michael Heilman (mheilman@ets.org) || :author: Nitin Madnani (nmadnani@ets.org) || :author: Dan Blanchard (dblanchard@ets.org) || :author: Aoife Cahill (acahill@ets.org) || '''
aaegjaiba,EducationalTestingService/skll,tests/test_utilities.py,9bbf7c2b43e846b55469202234ffb8fb9c0f8e49,STILL_EXISTS,now modify this new featureset to swap the first two columns
aaegjbefa,EducationalTestingService/skll,skll/experiments.py,083e6eb7378886346d5db836e0ef97ac1a2f94bb,233af5fc2cda70ef3101bc61bde4b10190587517,this is a workaround to make this simple use case (a single train and
aaegjcbaf,EducationalTestingService/skll,tests/test_featureset.py,ba7d995e5bee6d29f84d0d3fa75b3324083bc04f,STILL_EXISTS,make sure that we have the right number of feature columns
aaegjcbah,EducationalTestingService/skll,tests/test_featureset.py,ba7d995e5bee6d29f84d0d3fa75b3324083bc04f,STILL_EXISTS,and that they are the first and fourth columns
aaegjebhg,EducationalTestingService/skll,skll/config.py,233af5fc2cda70ef3101bc61bde4b10190587517,STILL_EXISTS,\"\"\" || Functions related to parsing configuration files. ||  || :author: Nitin Madnani (nmadnani@ets.org) || :author: Dan Blanchard (dblanchard@ets.org) || :author: Michael Heilman (mheilman@ets.org) || :author: Chee Wee Leong (cleong@ets.org) || \"\"\"
aaegjecde,EducationalTestingService/skll,skll/config.py,233af5fc2cda70ef3101bc61bde4b10190587517,STILL_EXISTS,this is a workaround to make this simple use case (a single train and
aaegjedcc,EducationalTestingService/skll,tests/utils.py,233af5fc2cda70ef3101bc61bde4b10190587517,2ad3b657910944779527ab567583fb7dc7f8c9f9,Note: (a) `bad_option` and `duplicate_option` are needed
aaegjedgh,EducationalTestingService/skll,skll/experiments.py,2ad3b657910944779527ab567583fb7dc7f8c9f9,653fc44e52d5e77f58d94f22296ec8df5d825a26,this is a workaround to make this simple use case (a single train and
aaegjeeha,EducationalTestingService/skll,tests/utils.py,653fc44e52d5e77f58d94f22296ec8df5d825a26,ff6d7bf4816464e576693689591825bb7d1cf32c,Note: (a) `bad_option` and `duplicate_option` are needed
aaegjegfc,EducationalTestingService/skll,skll/experiments.py,64fb5d4014b447f58002d956b725836c7a06fbf9,74da631b687c82111b88946e5f0fc2d9bf41e15c,this is a workaround to make this simple use case (a single train and
aaegjejfg,EducationalTestingService/skll,tests/utils.py,74da631b687c82111b88946e5f0fc2d9bf41e15c,STILL_EXISTS,Note: (a) `bad_option` and `duplicate_option` are needed
aaegjfgeh,EducationalTestingService/skll,skll/data/featureset.py,4bc49468defac11b9ee9abc25de24d92210e1b58,STILL_EXISTS,Note: an alternative way to implement this is to make copies
aaegjfhdc,EducationalTestingService/skll,tests/test_output.py,08f418e4697896869fdd1b241cc343b3b02d4031,STILL_EXISTS,make sure that the TSV file is created with the right columns
aaegjfhdd,EducationalTestingService/skll,tests/test_output.py,08f418e4697896869fdd1b241cc343b3b02d4031,STILL_EXISTS,make sure we have the expected number of columns
aaegjfhee,EducationalTestingService/skll,skll/experiments.py,94e087136351791da4138cface9804aa6cea60bf,STILL_EXISTS,then we should probably rotate the tick labels
aaegjfhej,EducationalTestingService/skll,skll/experiments.py,edb9e72ee1b93d266b3ae7abe564e7f01d1575d7,STILL_EXISTS,is very high beacuse of a very poor model fit. MSE can be a really
aaegjgbae,EducationalTestingService/skll,skll/logutils.py,86bad9765e52f37fc3398cbd2719274b1ec471b5,STILL_EXISTS,\"\"\" || Functions related to logging in SKLL. ||  || :author: Nitin Madnani (nmadnani@ets.org) || \"\"\"
aaegjgbjf,EducationalTestingService/skll,tests/test_output.py,5293f92ba876e9a4cc3b6db3172ca96c9a2cf2ac,STILL_EXISTS,make sure that the TSV file is created with the right columns
aaegjgbjg,EducationalTestingService/skll,tests/test_output.py,5293f92ba876e9a4cc3b6db3172ca96c9a2cf2ac,STILL_EXISTS,make sure we have the expected number of columns
aaegjgdag,EducationalTestingService/skll,skll/config.py,f01692fe83958936013ea28228ec0d9debe75293,STILL_EXISTS,must be specified as true since that' needed to compute the loss
aaegjgfhe,EducationalTestingService/skll,skll/utilities/compute_eval_from_predictions.py,47bfa4c6e5986d139763c3ee3147e77628d2ce0e,STILL_EXISTS,If there are more than two columns; assume column 0 contains the ids; and
aaegjgfhf,EducationalTestingService/skll,skll/utilities/compute_eval_from_predictions.py,47bfa4c6e5986d139763c3ee3147e77628d2ce0e,STILL_EXISTS,columns 1-n contain class probabilities. Convert them to a class prediction
aaegjggif,EducationalTestingService/skll,skll/utilities/generate_predictions.py,3c4c4a525ab2de28da8b622587d195ca49346453,STILL_EXISTS,Create file header list; and transform predictions as needed
aaegjgijh,EducationalTestingService/skll,tests/test_featureset.py,a84f7dc993301564d7c9e7d79c18ac2dde99895d,2f76541216bab7fc57651cce0265c2d6d58fbd99,being all 0 and if this subset ends up being written out to a file
aaegjhagf,EducationalTestingService/skll,skll/learner.py,986522e7fba2c465287968548e6d6ef55f3ce1dd,STILL_EXISTS,better to raise this early on so rather than after a whole bunch of
aaegjhahf,EducationalTestingService/skll,tests/utils.py,a092a5612c48a8a61c7d3deadf3749338ea4b64d,STILL_EXISTS,a hack here to compute the correct expected weights ourselves
aaegjhcae,EducationalTestingService/skll,tests/utils.py,f0dccbe36f3c24804e8d86b48afeeecd79125cf9,STILL_EXISTS,a hack here to compute the updated labels ourselves
aaegjhdig,EducationalTestingService/skll,skll/data/writers.py,40d4f354c4c9cd54d23f75ba410b94b4a3adbfee,d28116f302b7764054c3ced85a2450f5ac1b6ca8,so that we can correctly extract the appropriate columns
aaegjhdja,EducationalTestingService/skll,skll/data/writers.py,40d4f354c4c9cd54d23f75ba410b94b4a3adbfee,STILL_EXISTS,then; select only the columns that we want;
aaegjhdjb,EducationalTestingService/skll,skll/data/writers.py,40d4f354c4c9cd54d23f75ba410b94b4a3adbfee,STILL_EXISTS,and give the columns their correct names
aaegjhgbg,EducationalTestingService/skll,skll/data/writers.py,7b705feff55af335bcb6f79116516266e2ea211a,374e662784498192ed3869d76ff2208c3979abd9,so that we can correctly extract the appropriate columns
aaegjhgca,EducationalTestingService/skll,skll/data/writers.py,7b705feff55af335bcb6f79116516266e2ea211a,STILL_EXISTS,then; select only the columns that we want;
aaegjhgcb,EducationalTestingService/skll,skll/data/writers.py,7b705feff55af335bcb6f79116516266e2ea211a,STILL_EXISTS,and give the columns their correct names
aaegjhhfh,EducationalTestingService/skll,skll/data/readers.py,de9bab67eee9a6efb07fdf74e42d41f8821d6bfb,STILL_EXISTS,remove the columns with zero values
aaegjhhfi,EducationalTestingService/skll,skll/data/readers.py,de9bab67eee9a6efb07fdf74e42d41f8821d6bfb,STILL_EXISTS,convert the names of all the other columns to
aaegjhiec,EducationalTestingService/skll,skll/data/readers.py,5bd3f52978abf92c66c9ee772b73f1c98e06c11f,STILL_EXISTS,remove the columns with zero values
aaegjhifg,EducationalTestingService/skll,skll/data/writers.py,5bd3f52978abf92c66c9ee772b73f1c98e06c11f,STILL_EXISTS,so that we can correctly extract the appropriate columns
aaegjhiga,EducationalTestingService/skll,skll/data/writers.py,5bd3f52978abf92c66c9ee772b73f1c98e06c11f,STILL_EXISTS,then; select only the columns that we want;
aaegjhigb,EducationalTestingService/skll,skll/data/writers.py,5bd3f52978abf92c66c9ee772b73f1c98e06c11f,STILL_EXISTS,and give the columns their correct names
aaegjibjg,EducationalTestingService/skll,skll/data/readers.py,c82a46c718f52e586daccf4cdcd8a99bf62d37a7,STILL_EXISTS,remove the columns with zero values
aaegjibjh,EducationalTestingService/skll,skll/data/readers.py,c82a46c718f52e586daccf4cdcd8a99bf62d37a7,STILL_EXISTS,convert the names of all the other columns to
aaegjicdj,EducationalTestingService/skll,skll/data/readers.py,70ba6a711b3f4e85eac05d2397f6f6b222d980b0,STILL_EXISTS,rows; and add them to the columns list
aaegjicjj,EducationalTestingService/skll,skll/data/readers.py,24c0952a1e14b6b48a0381149ee1c0ea25a23712,STILL_EXISTS,remove the columns with zero values
aaegjidaa,EducationalTestingService/skll,skll/data/readers.py,24c0952a1e14b6b48a0381149ee1c0ea25a23712,STILL_EXISTS,convert the names of all the other columns to
aaegjidij,EducationalTestingService/skll,skll/data/readers.py,6f21e4594a168ca10aba2755f9dd715f3a34482f,STILL_EXISTS,rows; and add them to the columns list
aaegjieab,EducationalTestingService/skll,skll/data/readers.py,6f21e4594a168ca10aba2755f9dd715f3a34482f,STILL_EXISTS,convert the names of all the other columns to
aaegjiehe,EducationalTestingService/skll,skll/data/readers.py,3a474311737082104cf6c9360369b536f3092f93,STILL_EXISTS,remove the columns with zero values
aaegjiejg,EducationalTestingService/skll,skll/data/readers.py,3a474311737082104cf6c9360369b536f3092f93,STILL_EXISTS,rows; and add them to the columns list
aaegjiiaj,EducationalTestingService/skll,examples/make_titanic_example_data.py,d75bcb60ee54e67eafa35fedf6ab763d6cab109f,fa15af281fda9b315777bcbea632cdce300158ee,We only want to specific columns from the data;
aaegjiibb,EducationalTestingService/skll,examples/make_titanic_example_data.py,d75bcb60ee54e67eafa35fedf6ab763d6cab109f,fa15af281fda9b315777bcbea632cdce300158ee,are not deleting rows based on irrelevant columns
aaegjijgi,EducationalTestingService/skll,skll/learner.py,5863c916e9fecce39caea3a021e5ae9f76cb6d3c,STILL_EXISTS,TODO: unweighted kappas may be allowed here in the futer
aaegjjeec,EducationalTestingService/skll,tests/test_featureset.py,5620791c079e808183bb8761facebb6edc29e126,0c80d8f3d84da95fb530740c97235c79fa7709dd,being all 0 and if this subset ends up being written out to a file
aaegjjgab,EducationalTestingService/skll,tests/test_featureset.py,a04fcab327f681c99e4df8c7fd284e7d64d826d2,a204a90cca24b964b15e413db43aa4e93c3b3de8,being all 0 and if this subset ends up being written out to a file
aaegjjgid,EducationalTestingService/skll,skll/utilities/generate_predictions.py,0d8232faa681a38963206c63e364439899ab954f,STILL_EXISTS,labels; we are outputting only two columns - the ID and the label;
aaegjjgie,EducationalTestingService/skll,skll/utilities/generate_predictions.py,0d8232faa681a38963206c63e364439899ab954f,STILL_EXISTS,otherwise we are outputting N + 1 columns where N = number of classes
aaegjjjhf,EducationalTestingService/skll,skll/experiments/__init__.py,cd74ad8b20780b5bb99afa7bfe25b233fc06423d,STILL_EXISTS,\"\"\" || Functions for running and interacting with SKLL experiments. ||  || :author: Nitin Madnani (nmadnani@ets.org) || :author: Dan Blanchard (dblanchard@ets.org) || :author: Michael Heilman (mheilman@ets.org) || :author: Chee Wee Leong (cleong@ets.org) || \"\"\"
aaegjjjia,EducationalTestingService/skll,skll/experiments/__init__.py,cd74ad8b20780b5bb99afa7bfe25b233fc06423d,STILL_EXISTS,(There doesn't seem to be a better way to do this since one can't specify
aaehaaafh,EducationalTestingService/skll,skll/experiments/input.py,cd74ad8b20780b5bb99afa7bfe25b233fc06423d,STILL_EXISTS,\"\"\" || Functions for reading inputs for SKLL experiments. ||  || :author: Nitin Madnani (nmadnani@ets.org) || :author: Dan Blanchard (dblanchard@ets.org) || :author: Michael Heilman (mheilman@ets.org) || \"\"\"
aaehaaagb,EducationalTestingService/skll,skll/experiments/output.py,cd74ad8b20780b5bb99afa7bfe25b233fc06423d,STILL_EXISTS,\"\"\" || Functions related to running experiments and parsing configuration files. ||  || :author: Dan Blanchard (dblanchard@ets.org) || :author: Michael Heilman (mheilman@ets.org) || :author: Nitin Madnani (nmadnani@ets.org) || :author: Chee Wee Leong (cleong@ets.org) || \"\"\"
aaehaaahc,EducationalTestingService/skll,skll/experiments/output.py,cd74ad8b20780b5bb99afa7bfe25b233fc06423d,STILL_EXISTS,then we should probably rotate the tick labels
aaehaaaih,EducationalTestingService/skll,skll/experiments/output.py,cd74ad8b20780b5bb99afa7bfe25b233fc06423d,STILL_EXISTS,Build \"ablated_features\" list and fix some backward compatible things
aaehaaaij,EducationalTestingService/skll,skll/experiments/utils.py,cd74ad8b20780b5bb99afa7bfe25b233fc06423d,STILL_EXISTS,\"\"\" || Utility classes and functions for running SKLL experiments. ||  || :author: Nitin Madnani (nmadnani@ets.org) || :author: Dan Blanchard (dblanchard@ets.org) || :author: Michael Heilman (mheilman@ets.org) || \"\"\"
aaehaaajd,EducationalTestingService/skll,skll/experiments/utils.py,cd74ad8b20780b5bb99afa7bfe25b233fc06423d,STILL_EXISTS,set of columns is fixed.
aaehabebg,EducationalTestingService/skll,skll/learner/voting.py,4a84a654a3afbb83469c4c46dfba7694e9d9c328,STILL_EXISTS,\"\"\" || This module provides the `VotingLearner` meta-learner class which is a wrapper || around scikit-learn's `VotingClassifier` and `VotingRegressor`. ||  || :author: Nitin Madnani (nmadnani@ets.org) || :organization: ETS || \"\"\"
aaehabebi,EducationalTestingService/skll,skll/learner/voting.py,4a84a654a3afbb83469c4c46dfba7694e9d9c328,STILL_EXISTS,TODO: more validation of input arguments
aaehabjga,EducationalTestingService/skll,skll/learner/__init__.py,23e63679b92937c8896b598ba658f7917636063d,STILL_EXISTS,needed for generating a reliable learning curve
aaehacdai,EducationalTestingService/skll,tests/test_voting_learners.py,77383cacc0d3639510b0386afe67c769153f8302,STILL_EXISTS,TODO: delete these files in tear down
aaehacdhf,EducationalTestingService/skll,tests/test_voting_learners.py,966f18ee3ffe410b984d50aece620854d2c8f7f8,STILL_EXISTS,check that feature scaling is properly set
aaehacedi,EducationalTestingService/skll,skll/learner/voting.py,3e6431971412f33e608d96e0bca85d125063806c,STILL_EXISTS,needed for generating a reliable learning curve
aaehaceji,EducationalTestingService/skll,skll/learner/voting.py,99b85b9f7d3e87fbdde6871004e57b9cb37dd394,STILL_EXISTS,better to raise this early on so rather than after a whole bunch of
aaehacfdc,EducationalTestingService/skll,tests/test_voting_learners.py,2181d178ba25a7f1efa1a9aafefd75c35a09305b,STILL_EXISTS,sort the columns so that consecutive IDs are actually next to
aaehacffa,EducationalTestingService/skll,tests/test_voting_learners.py,298a42d29d3adce9b18c3ca527823f106897b2e8,STILL_EXISTS,define some constants needed for testing
aaehachhc,EducationalTestingService/skll,tests/test_voting_learners_api_1.py,21a82f8921e422f8a9193239799fcf4e1bf4558e,STILL_EXISTS,define some constants needed for testing
aaehachie,EducationalTestingService/skll,tests/test_voting_learners_api_1.py,21a82f8921e422f8a9193239799fcf4e1bf4558e,STILL_EXISTS,check that feature scaling is properly set
aaehaciaj,EducationalTestingService/skll,tests/test_voting_learners_api_2.py,21a82f8921e422f8a9193239799fcf4e1bf4558e,STILL_EXISTS,define some constants needed for testing
aaehacieb,EducationalTestingService/skll,tests/test_voting_learners_api_2.py,21a82f8921e422f8a9193239799fcf4e1bf4558e,STILL_EXISTS,instantiate some needed variables
aaehacigh,EducationalTestingService/skll,tests/test_voting_learners_api_3.py,21a82f8921e422f8a9193239799fcf4e1bf4558e,STILL_EXISTS,define some constants needed for testing
aaehacjba,EducationalTestingService/skll,tests/test_voting_learners_api_4.py,21a82f8921e422f8a9193239799fcf4e1bf4558e,STILL_EXISTS,define some constants needed for testing
aaehacjdh,EducationalTestingService/skll,tests/test_voting_learners_api_4.py,21a82f8921e422f8a9193239799fcf4e1bf4558e,STILL_EXISTS,sort the columns so that consecutive IDs are actually next to
aaehacjga,EducationalTestingService/skll,tests/test_voting_learners_api_5.py,21a82f8921e422f8a9193239799fcf4e1bf4558e,STILL_EXISTS,define some constants needed for testing
aaehacjii,EducationalTestingService/skll,tests/test_voting_learners_api_5.py,21a82f8921e422f8a9193239799fcf4e1bf4558e,STILL_EXISTS,sort the columns so that consecutive IDs are actually next to
aaehaecji,pytorch/translate,pytorch_translate/average_checkpoints.py,7f261cdd600424eb96b2e8a329a30bc3f7ff421f,STILL_EXISTS,TODO: maybe also delete other keys from the new state?
aaehaedhh,pytorch/translate,pytorch_translate/char_encoder.py,7f261cdd600424eb96b2e8a329a30bc3f7ff421f,1affdf2a4148e88417f7bcfc37b3e106097170e9,TODO: add pads if variable seq_len
aaehaefei,pytorch/translate,pytorch_translate/test/test_onnx.py,7f261cdd600424eb96b2e8a329a30bc3f7ff421f,0e1ef5486aaf35ac4fffa9a4a7b27e16bbefddd7,TODO batched beam search
aaehaejag,pytorch/translate,pytorch_translate/preprocess.py,ee90a9f8e9e53821a4ddc0e9a6444bfe031326fe,e65e18664f1304e62ef186068419058ab6608a55,TODO: assign results and pass to new and improved binarize_text_file
aaehafaii,pytorch/translate,pytorch_translate/research/knowledge_distillation/knowledge_distillation_loss.py,f059db05a06397566a0ac531a716e0627f5433f5,STILL_EXISTS,Move models to device and to evaluation mode
aaehafcab,pytorch/translate,pytorch_translate/research/multisource/multisource_decode.py,0759ea1d51556287efbadd7842e449be4f059cb4,STILL_EXISTS,FIXME: handle characters properly
aaehafeji,pytorch/translate,pytorch_translate/word_prediction/word_prediction_criterion.py,8ebaa428c01b7f9fae4f2c76c897ae45706b6168,STILL_EXISTS,TODO: normalize ; sentence avg
aaehaffeb,pytorch/translate,pytorch_translate/research/adversarial/adversarial_options.py,1f7c821d5a0ef9075822d592cef62317129a5884,STILL_EXISTS,The following line is a hack to be able to use the cross_entropy
aaehaffed,pytorch/translate,pytorch_translate/research/adversarial/adversarial_options.py,1f7c821d5a0ef9075822d592cef62317129a5884,STILL_EXISTS,this is another hack to ignore the multilingual case
aaehafffb,pytorch/translate,pytorch_translate/research/adversarial/adversarial_trainer.py,1f7c821d5a0ef9075822d592cef62317129a5884,STILL_EXISTS,TODO: is there an easy way to make this available for CPU. And if so;
aaehafhjf,pytorch/translate,pytorch_translate/research/adversarial/adversarial_utils.py,a8276f22dd584f7f1695fed9dab55fcea718ea37,STILL_EXISTS,Temperature = 1: no rescaling needed
aaehafijh,pytorch/translate,pytorch_translate/research/adversarial/adv_train.py,3cf7e54570d806145c29cc311b5e7f656812efe5,STILL_EXISTS,if needed) outside of the train clones to prevent them from having to
aaehafjii,pytorch/translate,pytorch_translate/beam_decode.py,4e92aee68cf6b74a20a9dd9ad1cd9cc290d2d833,STILL_EXISTS,the sample object is needed. During inference; the target
aaehagaje,pytorch/translate,pytorch_translate/char_encoder.py,8577e99fec90491d62b1149f72f5041b7f7c5659,STILL_EXISTS,TODO: https:\/\/github.com\/pytorch\/pytorch\/issues\/10747 makes the
aaehagbjd,pytorch/translate,pytorch_translate/semi_supervised.py,408229ae54b91fcdc6760ac96e5bf177c40d01c7,STILL_EXISTS,TODO: Generalize this to be able to use other model classes like Transformer
aaehagcaa,pytorch/translate,pytorch_translate/tasks/semi_supervised_task.py,408229ae54b91fcdc6760ac96e5bf177c40d01c7,2867d486442ea7f596c25bcd1b57e80dcb795f68,TODO: Generalize this to be able to use other model classes like Transformer
aaehagcae,pytorch/translate,pytorch_translate/tasks/semi_supervised_task.py,408229ae54b91fcdc6760ac96e5bf177c40d01c7,STILL_EXISTS,TODO: Handle task setup for evals
aaehagcaf,pytorch/translate,pytorch_translate/tasks/semi_supervised_task.py,408229ae54b91fcdc6760ac96e5bf177c40d01c7,2411713f5a4ee2f217af3920ed00e1e6d4f6639b,TODO make summing of the sample sizes configurable
aaehagcbc,pytorch/translate,pytorch_translate/semi_supervised.py,bd64b6ede36f1919563d991f8d75792f473261c7,STILL_EXISTS,hack to prevent VR for denoising autoencoder. We remove vocab
aaehagcbh,pytorch/translate,pytorch_translate/tasks/denoising_autoencoder_task.py,bd64b6ede36f1919563d991f8d75792f473261c7,STILL_EXISTS,TODO(T35539829): implement a Noising registry so we can build a noiser
aaehagcbj,pytorch/translate,pytorch_translate/tasks/denoising_autoencoder_task.py,bd64b6ede36f1919563d991f8d75792f473261c7,STILL_EXISTS,TODO(T35539829): implement a Noising registry so this can be built
aaehagdci,pytorch/translate,pytorch_translate/dual_learning/dual_learning_criterion.py,62500acec498d02179b8efacaf9a06d0a3082eae,STILL_EXISTS,TODO: nbest
aaehagddb,pytorch/translate,pytorch_translate/dual_learning/dual_learning_criterion.py,62500acec498d02179b8efacaf9a06d0a3082eae,STILL_EXISTS,TODO (T36875783): load pretrained lm to score
aaehagdeg,pytorch/translate,pytorch_translate/dual_learning/dual_learning_models.py,62500acec498d02179b8efacaf9a06d0a3082eae,STILL_EXISTS,TODO: pass to dual model too
aaehagdeh,pytorch/translate,pytorch_translate/dual_learning/dual_learning_models.py,62500acec498d02179b8efacaf9a06d0a3082eae,STILL_EXISTS,TODO (T36875783): instantiate a langauge model
aaehagdfe,pytorch/translate,pytorch_translate/generate.py,62500acec498d02179b8efacaf9a06d0a3082eae,STILL_EXISTS,TODO: this could be refactored to use lang_pari as key too
aaehagdgb,pytorch/translate,pytorch_translate/tasks/semi_supervised_task.py,4e301794e06dc11b6130c64bea8794ae68caf46b,2867d486442ea7f596c25bcd1b57e80dcb795f68,TODO: Expose this as easy-to-use command line argument
aaehagfbj,pytorch/translate,pytorch_translate/train.py,8a8a9510eadce6c39a12af4b2f4cefed70f108c2,2a0666e6a6a9cfbfbe8ab72ec98319ca389283a6,TODO(T37392059): this is needed so that we are more tolerant of
aaehagfji,pytorch/translate,pytorch_translate/word_prediction/word_prediction_criterion.py,2bc2a72bd4537e3a8db532281b1257aa64fd0e2a,STILL_EXISTS,TODO:
aaehaggdc,pytorch/translate,pytorch_translate/char_source_model.py,b38beba4889621378abca93e2b44b7169c35a3f1,STILL_EXISTS,charCNN is not needed
aaehaggde,pytorch/translate,pytorch_translate/char_source_model.py,b38beba4889621378abca93e2b44b7169c35a3f1,STILL_EXISTS,no UNK; so charCNN is not needed
aaehaghdb,pytorch/translate,pytorch_translate/checkpoint.py,90c05e511bb5055eed47fea39c614111d4cbac2d,STILL_EXISTS,Consider making a copy of each tensor here if we run into issues in
aaehagjeg,pytorch/translate,pytorch_translate/ensemble_export.py,c9ddddd1bc9263710290c16906aae26e2a36866f,STILL_EXISTS,XXX: This loop is where we wait() for each encoder's output to be
aaehagjeh,pytorch/translate,pytorch_translate/ensemble_export.py,c9ddddd1bc9263710290c16906aae26e2a36866f,STILL_EXISTS,ready. If you're trying to add more ops; they should probably not
aaehagjfh,pytorch/translate,pytorch_translate/ensemble_export.py,602e76257d7351afec94b8af4acfdd461818127d,STILL_EXISTS,XXX: This loop is where we wait() for each encoder's output to be
aaehagjfi,pytorch/translate,pytorch_translate/ensemble_export.py,602e76257d7351afec94b8af4acfdd461818127d,STILL_EXISTS,ready. If you're trying to add more ops; they should probably not
aaehahadi,pytorch/translate,pytorch_translate/rescoring.py,0f86b46bad588e5af8cf249255a8cc4da3e358ef,STILL_EXISTS,TODO (T40938917): Allow loading of multiple rescoring models
aaehahaga,pytorch/translate,pytorch_translate/research/test/test_unsupervised_bilingual_morphology.py,4c4ee6c191666c1560275b07755890d585723a55,f2eb56b313d7349350f03582d6b23c861f9504d1,todo will add stuff here later.
aaehahagi,pytorch/translate,pytorch_translate/bleu_significance.py,4e47a47a9e9a677c5dc89db3902342eb70378be7,STILL_EXISTS,Number of samples where the new was better than baseline.
aaehahaij,pytorch/translate,pytorch_translate/ensemble_export.py,23db94d816d202b97f30c068c0d160933beab017,STILL_EXISTS,enc_states ends up being optional because of the above branch; one
aaehahajh,pytorch/translate,pytorch_translate/research/knowledge_distillation/teacher_score_data.py,6de33d4de5c3e0e9fc5d649999c2b693f9540ec5,STILL_EXISTS,it is better to remove them from memory.
aaehahbaa,pytorch/translate,pytorch_translate/rescoring/model_scorers.py,13efc210d865f7300220708b6132fe428d478039,STILL_EXISTS,TODO (T40938917): Allow loading of multiple rescoring models
aaehahbfb,pytorch/translate,pytorch_translate/rescoring/model_scorers.py,ea50d01062b4ee780a9a621c66802874e5694961,STILL_EXISTS,TODO: (T41818693) Map translation model vs LM model differences
aaehahbgi,pytorch/translate,pytorch_translate/transformer.py,fac8ffeb1abf679c9d2af954ab91dc4081e4df1d,STILL_EXISTS,TODO(jamesreed): this is kinda a hack because we can't annotate an
aaehahdjf,pytorch/translate,pytorch_translate/options.py,4cfb99c085bc8357c11f2a322279bbb48ea82697,STILL_EXISTS,TODO(T43045193): Move this to multilingual_task.py eventually
aaehahfba,pytorch/translate,pytorch_translate/transformer_aan.py,9f21f00d2a67e52e7f5159bdfeab4489b3a7b5b7,STILL_EXISTS,todo: try with input_embed_dim
aaehahfee,pytorch/translate,pytorch_translate/research/deliberation_networks/deliberation_networks.py,eb27d2759e2f48985ce2e229d4457ae95bd582ba,STILL_EXISTS,todo: try with input_embed_dim
aaehahffg,pytorch/translate,pytorch_translate/research/deliberation_networks/deliberation_networks.py,eb27d2759e2f48985ce2e229d4457ae95bd582ba,STILL_EXISTS,TODO  remove this once we update apex with the fix
aaehahfgd,pytorch/translate,pytorch_translate/ensemble_export.py,589fffa9254cd8413362a87204c039cb3681bf02,443aeaeffd303a040cf7835e13cb47ce90933f53,TODO: This method hasn't been tested for now.
aaehahfjg,pytorch/translate,pytorch_translate/utils.py,47e16cb44f0506abc54cf36b32aa6aa1fa13a115,STILL_EXISTS,TODO: Remove when gluster is deprecated (T48002528)
aaehahgbc,pytorch/translate,pytorch_translate/test/test_DecoderBatchedStepEnsemble.py,b0c32be2462109bfb91367f97ad0b5bdb46648e6,1ad33292362ce4b9fa2c7a2ed428e156f4144400,Tile is needed after first step
aaehahgca,pytorch/translate,pytorch_translate/preprocess.py,6bbb34b9211132771a32329dae26613bcd4d704f,cdbe4dc0e214bbc6107e1d8789f5a0893df46f03,todo T48524067: add char_target_dict to preprocess_bilingual_corpora
aaehahghi,pytorch/translate,pytorch_translate/beam_search_and_decode_v2.py,190bb0b68be56d2e21bd4837cd38421843a10915,STILL_EXISTS,TODO: (lizguo) This class will be merged with upstream later.
aaehahgie,pytorch/translate,pytorch_translate/beam_search_and_decode_v2.py,ec4660f21599999f386988a7dcd753d510bc2f68,STILL_EXISTS,enc_states ends up being optional because of the above branch; one
aaehahgjf,pytorch/translate,pytorch_translate/char_aware_hybrid.py,91c3509bc170fcc951878871cdd6581f27691463,STILL_EXISTS,By default (before training ends); character representations are
aaehahhda,pytorch/translate,pytorch_translate/ensemble_export.py,e4b2a5d84a82cf70e052a769747cff0c509b354e,STILL_EXISTS,TODO: model ensemble
aaehahhfb,pytorch/translate,pytorch_translate/transformer.py,8d96436425cc8339159fed65340f4f6d386a17db,STILL_EXISTS,TODO  remove this once we update apex with the fix
aaehahjdj,Maluuba/nlg-eval,nlgeval/pycocoevalcap/meteor/meteor.py,280cc8b236799aff6a21b20bb126252fe720219c,STILL_EXISTS,Assumes meteor-1.5.jar is in the same directory as meteor.py.  Change as needed.
aaehaiaie,Maluuba/nlg-eval,nlgeval/skipthoughts/eval_rank.py,280cc8b236799aff6a21b20bb126252fe720219c,STILL_EXISTS,not needed with Adam
aaehaicei,inspirehep/magpie,magpie/base/ontology.py,5e400550f7b1c90ed57747233151b0c9484e3ae4,STILL_EXISTS,TODO Add loading in different formats
aaehaicej,inspirehep/magpie,magpie/base/ontology.py,5e400550f7b1c90ed57747233151b0c9484e3ae4,STILL_EXISTS,TODO if n == 1 serve the ones without nostandalone?
aaehaicfe,inspirehep/magpie,magpie/candidates/ngram.py,5e400550f7b1c90ed57747233151b0c9484e3ae4,ba7f019a435da89973e5ab6bcdeb9a48a096f229,TODO remove the standalone ones
aaehaicfg,inspirehep/magpie,magpie/candidates/ngram.py,5e400550f7b1c90ed57747233151b0c9484e3ae4,STILL_EXISTS,TODO potential filtering of ngrams e.g. for linguistic purposes
aaehaidai,inspirehep/magpie,setup.py,5e400550f7b1c90ed57747233151b0c9484e3ae4,STILL_EXISTS,You can just specify the packages manually here if your project is
aaehaidfe,inspirehep/magpie,magpie/candidates/trie.py,cc22571db3696eba8666675fe95c85d587d3eea7,STILL_EXISTS,maybe 5?
aaehaidgd,inspirehep/magpie,magpie/candidates/ngram.py,ff135bbb8d0c28def14e75b1f98f15c1568706a1,STILL_EXISTS,# TODO potential filtering of ngrams e.g. for linguistic purposes
aaehaidgh,inspirehep/magpie,magpie/candidates/ngram.py,ff135bbb8d0c28def14e75b1f98f15c1568706a1,ba7f019a435da89973e5ab6bcdeb9a48a096f229,TODO remove the standalone ones
aaehaidje,inspirehep/magpie,magpie/base/ontology.py,e4fa5f8cae0a0765a86c79404176b776b7008db4,STILL_EXISTS,TODO Add loading in different formats
aaehaidjg,inspirehep/magpie,magpie/candidates/subgraph.py,e4fa5f8cae0a0765a86c79404176b776b7008db4,STILL_EXISTS,TODO Filter no standalone ones
aaehaieec,inspirehep/magpie,magpie/candidates/trie.py,cc1610559d97a6c851763022ad274095af9162c9,STILL_EXISTS,maybe 5?
aaehaieee,inspirehep/magpie,setup.py,cc1610559d97a6c851763022ad274095af9162c9,f16e533814d3d7e4318d93cd695634291075eac5,'networkx'; # not really needed
aaehaieef,inspirehep/magpie,magpie/candidates/subgraph.py,42c9af2b8c520492ab5326d7e1ea3d25cd43828a,ba7f019a435da89973e5ab6bcdeb9a48a096f229,TODO remove the standalone ones
aaehaieeg,inspirehep/magpie,magpie/utils/stopwords.py,42c9af2b8c520492ab5326d7e1ea3d25cd43828a,STILL_EXISTS,TODO try the nltk.corpus.stopwords
aaehaiegf,inspirehep/magpie,magpie/base/ontology.py,ba7f019a435da89973e5ab6bcdeb9a48a096f229,STILL_EXISTS,TODO make it more general
aaehaieha,inspirehep/magpie,magpie/feature_extraction/keyword_features.py,ba7f019a435da89973e5ab6bcdeb9a48a096f229,STILL_EXISTS,TODO might be faster to convert dicts directly to the DataFrame
aaehaiejc,inspirehep/magpie,magpie/__init__.py,408d7f32c0e1dc2f7ec7ab97a87f8dabc7d5efae,STILL_EXISTS,TODO that have not been generated as candidates
aaehaifad,inspirehep/magpie,magpie/__init__.py,f27d58a4b7d3e4556c8dc627df7dfb4088b04c07,STILL_EXISTS,TODO that have not been generated as candidates
aaehaifbe,inspirehep/magpie,magpie/base/global_index.py,7b881943c322c1a98eaab359b3ee9b148c9c3268,c8e2db284189cfc5d83950be0d5dc0f78add1b41,# TODO another function could do here
aaehaifef,inspirehep/magpie,magpie/__init__.py,a4c361f85ba801495c5770e71242fe0b3225285b,0bb6a1b738975f8f3833ce9e00362e8a1f82f285,TODO this vector is very sparse; we can make it more memory efficient
aaehaigbi,inspirehep/magpie,magpie/api.py,0bb6a1b738975f8f3833ce9e00362e8a1f82f285,STILL_EXISTS,TODO this vector is very sparse; we can make it more memory efficient
aaehaiggc,inspirehep/magpie,magpie/base/ontology.py,f16e533814d3d7e4318d93cd695634291075eac5,STILL_EXISTS,TODO might want to generalize it in the future
aaehaiibe,inspirehep/magpie,magpie/nn/models.py,2537540b96a58c2e1f10611f7fa4288c900454e1,STILL_EXISTS,TODO try merging different convolution layers
aaehaijai,inspirehep/magpie,magpie/api.py,c8e2db284189cfc5d83950be0d5dc0f78add1b41,b08523adee895ffb7074bc75e241dc532264a004,TODO from here
aaehaijbi,inspirehep/magpie,magpie/api.py,c8e2db284189cfc5d83950be0d5dc0f78add1b41,b08523adee895ffb7074bc75e241dc532264a004,TODO TO HERE - should be extracted
aaehaijfc,inspirehep/magpie,magpie/base/model.py,c8e2db284189cfc5d83950be0d5dc0f78add1b41,STILL_EXISTS,TODO Maybe initialize the classifier with this for balancing classes
aaehajaeb,inspirehep/magpie,magpie/evaluation/standard_evaluation.py,b08523adee895ffb7074bc75e241dc532264a004,e4488b9b68416439a226b48b74c22ae84c129db1,TODO incorrect computation; skips ground truth kw
aaehajaec,inspirehep/magpie,magpie/evaluation/standard_evaluation.py,b08523adee895ffb7074bc75e241dc532264a004,e4488b9b68416439a226b48b74c22ae84c129db1,TODO that have not been even recognized as candidates
aaehajdig,inspirehep/magpie,magpie/nn/nn.py,b5629274e223ec00f98bb577a160b93a42b779b5,STILL_EXISTS,TODO check if this works
aaehbabeb,nilearn/nilearn,datasets.py,d32dc68d33fadfd464489364b348cc3c635d2745,18e5955e960a4bbcce6c198b65542eab56e381f9,XXX : Warning : we may not want to binarize y
aaehbabeh,nilearn/nilearn,datasets.py,6dc31b87405e59b27ee2d585e6d2feac1b1650c6,STILL_EXISTS,Removing the unused data
aaehbaeeg,nilearn/nilearn,datasets.py,a34c605deada429e820870e697960f1bf5bc8455,STILL_EXISTS,Removing the unused data
aaehbaica,nilearn/nilearn,doc/conf.py,bcf4a6d8794a6f6d765ee156aa3fd14b8e5b0754,STILL_EXISTS,unused_docs = []
aaehbajah,nilearn/nilearn,doc/sphinxext/gen_rst.py,bcf4a6d8794a6f6d765ee156aa3fd14b8e5b0754,STILL_EXISTS,better than nested.
aaehbajdd,nilearn/nilearn,doc/sphinxext/gen_rst.py,bcf4a6d8794a6f6d765ee156aa3fd14b8e5b0754,STILL_EXISTS,Sphinx hack: sphinx copies generated images to the build directory
aaehbajec,nilearn/nilearn,doc/sphinxext/gen_rst.py,bcf4a6d8794a6f6d765ee156aa3fd14b8e5b0754,STILL_EXISTS,The following is a hack that prevents this behavior by clearing the
aaehbajef,nilearn/nilearn,doc/sphinxext/gen_rst.py,bcf4a6d8794a6f6d765ee156aa3fd14b8e5b0754,STILL_EXISTS,it should probably not cause a crash).  Tested successfully
aaehbbaaa,nilearn/nilearn,nisl/__init__.py,bcf4a6d8794a6f6d765ee156aa3fd14b8e5b0754,STILL_EXISTS,\"\"\" || Machine Learning module in python || ================================= ||  || sklearn is a Python module integrating classical machine || learning algorithms in the tightly-knit world of scientific Python || packages (numpy; scipy; matplotlib). ||  || It aims to provide simple and efficient solutions to learning problems || that are accessible to everybody and reusable in various contexts: || machine-learning as a versatile tool for science and engineering. ||  || See http:\/\/scikit-learn.sourceforge.net for complete documentation. || \"\"\"
aaehbbbab,nilearn/nilearn,nisl/datasets.py,bcf4a6d8794a6f6d765ee156aa3fd14b8e5b0754,c1d1104d1bb4176f8eb6b4d2b1810419ed71cae1,Retrieving the .mat data and saving it if needed
aaehbbbbf,nilearn/nilearn,nisl/datasets.py,bcf4a6d8794a6f6d765ee156aa3fd14b8e5b0754,02501f88b3f8bdeee5dc0fcf17aa4d69064b5629,Removing the unused data
aaehbbgib,nilearn/nilearn,nyu_rest.py,dcbe3a45a8119ea65fdc8ee82c7beaa7581217aa,STILL_EXISTS,For ward; reset to default value if needed
aaehbbiha,nilearn/nilearn,plot_visualization.py,a380334bad613089de4c57b26245b91f85d0e16e,baf417648e1e0275e11a28278e23797c274dff9b,subplot: 1 line; 3 columns and use the first subplot
aaehbbjci,nilearn/nilearn,plot_haxby_searchlight.py,16c44f30167be6f5876d305282a8fed8c960bc1a,STILL_EXISTS,A cross validation method is needed to measure precision of each voxel
aaehbcafg,nilearn/nilearn,nisl/preprocessing.py,6e66c18c43f938034719575135afea29a73740ad,STILL_EXISTS,FIXME: Should this be merged with canica.algorithms.center_and_norm
aaehbcagh,nilearn/nilearn,nisl/resampling.py,6e66c18c43f938034719575135afea29a73740ad,STILL_EXISTS,to use a better algorithm
aaehbcaja,nilearn/nilearn,nisl/mri_transformer.py,4173407533ac980a78a7ded522f01288bb4940bc,STILL_EXISTS,XXX This should be done once and for all; to in fit and transform...
aaehbcajc,nilearn/nilearn,nisl/mri_transformer.py,4173407533ac980a78a7ded522f01288bb4940bc,7d4f915e537f44613f6d489d6b9fdf3cce5df679,XXX -> ?
aaehbcbae,nilearn/nilearn,nisl/mri_transformer.py,0f9f5adaee9cd9d90c74e3a8e59ac3b57ec243d5,STILL_EXISTS,TODO: do we uncomment this part ? It would lead to data copy
aaehbcdia,nilearn/nilearn,plot_ica_resting_state.py,d3d91cb4d27ff6826d755f0377b54ea999105490,STILL_EXISTS,XXX: must get the code to run for more than 1 subject
aaehbcegi,nilearn/nilearn,nisl/base_model.py,08cea126d57885e6f549c0d1d2506c0a57ce0f81,STILL_EXISTS,XXX: might need to relearn maps
aaehbceij,nilearn/nilearn,nisl/masking.py,08cea126d57885e6f549c0d1d2506c0a57ce0f81,STILL_EXISTS,Use nans as missing value: ugly
aaehbcejh,nilearn/nilearn,nisl/masking.py,08cea126d57885e6f549c0d1d2506c0a57ce0f81,STILL_EXISTS,long-hand. XXX should the masks be coerced to int before addition?
aaehbcfeg,nilearn/nilearn,plot_canica_resting_state.py,257114ee0bfb8d517ac10fd1e1e798890be8693c,290a46020d55a8486cb18f812c71a3deeebbd80c,XXX: must get the code to run for more than 1 subject
aaehbcgfd,nilearn/nilearn,plot_canica_resting_state.py,b847812bb1ffaf15e5472ab1057e73e852b414ad,6164294177c34f257dbd354cf97d643597255a0d,XXX: this should probably be integrated in the CanICA object
aaehbcghj,nilearn/nilearn,nisl/masking.py,adcfb4ef76ef2d24d70dbdc268cb2e2da890bf2f,STILL_EXISTS,XXX make a is_a_niimgs function ?
aaehbcgjg,nilearn/nilearn,plot_haxby_simple.py,0799714e4b75a34d7ef019c99651ae510b976414,58d5d44963f1d36142b2387052540e605a0a7347,XXX is there a better way to separate data ?
aaehbcibc,nilearn/nilearn,nisl/decomposition/tests/test_canica.py,0632738c9a979043fe2262bdb17f32bf1acb0d19,STILL_EXISTS,FIXME: even with a fixed random state; the ordering of components
aaehbcibe,nilearn/nilearn,nisl/utils.py,0632738c9a979043fe2262bdb17f32bf1acb0d19,STILL_EXISTS,Use hasattr() instead of isinstance to workaround a Python 2.6\/2.7 bug
aaehbcibg,nilearn/nilearn,nisl/decomposition/tests/test_canica.py,f2248a7b41dc0ab56486a5e9cab35afda227a4e9,STILL_EXISTS,FIXME: This could be done more efficiently; e.g. thanks to hungarian
aaehbcicg,nilearn/nilearn,nisl/io/nifti_masker.py,991e5fe2cf9be4b428b0e03584438ef690231a8c,STILL_EXISTS,y=None is for scikit-learn compatibility (unused here).
aaehbcife,nilearn/nilearn,nisl/signals.py,fd0ae4e922f86fb7910d1d70318dbda4d14238c3,677cb13414d9c0089de24854837189d433c46e5d,# FIXME: test this detrend implementation; improve; benchmark.
aaehbcjaa,nilearn/nilearn,nisl/masking.py,364ed5878f34c581b389e159376225a2707f1ec0,24afafb501c62f013ae3ee41f353f7adb38b9b08,\u00A0TODO: is copy() really needed ?
aaehbcjbd,nilearn/nilearn,nisl/roi.py,f89d396b8a18b1cec4f9a1639d9efbb0238729c5,STILL_EXISTS,FIXME: should display a warning if array has not integer values
aaehbcjbe,nilearn/nilearn,nisl/roi.py,f89d396b8a18b1cec4f9a1639d9efbb0238729c5,STILL_EXISTS,FIXME: use a different scheme for short dtype (like int8) for which
aaehbcjbi,nilearn/nilearn,nisl/roi.py,f89d396b8a18b1cec4f9a1639d9efbb0238729c5,STILL_EXISTS,FIXME: catch ValueError (raised when background_label is not
aaehbcjcd,nilearn/nilearn,nisl/tests/test_roi.py,f89d396b8a18b1cec4f9a1639d9efbb0238729c5,STILL_EXISTS,FIXME: test dtype argument
aaehbcjce,nilearn/nilearn,nisl/tests/test_roi.py,f89d396b8a18b1cec4f9a1639d9efbb0238729c5,STILL_EXISTS,FIXME: test labels argument
aaehbcjed,nilearn/nilearn,nisl/tests/test_roi.py,f89d396b8a18b1cec4f9a1639d9efbb0238729c5,STILL_EXISTS,TODO:
aaehbcjej,nilearn/nilearn,nisl/masking.py,ee3c34f0450d657d5ca5ec244a410f2e61b7b759,STILL_EXISTS,\u00A0TODO: is copy() really needed ?
aaehbcjfe,nilearn/nilearn,nisl/tests/test_io_nifti_masker.py,a6e805fec3a150652e90e52d337dce11cb680f4d,STILL_EXISTS,Fill central voxels timeseries with random signals
aaehbcjgb,nilearn/nilearn,nisl/io/tests/test_nifti_masker.py,ebc29c3e927bf899ff02ff3fffd3b7706b0e21ea,f80ad2e8dafb628d9fcd0ac4883959cac379a4a4,Fill central voxels timeseries with random signals
aaehbdaei,nilearn/nilearn,nisl/signals.py,3a28b097f6b820bce26f687ad4834b96142c7588,STILL_EXISTS,extract columns (i.e. features)
aaehbdaff,nilearn/nilearn,nisl/tests/test_signals.py,3a28b097f6b820bce26f687ad4834b96142c7588,STILL_EXISTS,TODO: any other ideas?
aaehbdahb,nilearn/nilearn,nisl/roi.py,00b25db1d07f04f504d6cbfda996d1037c81e293,STILL_EXISTS,FIXME: use as_ndarray(data) here
aaehbdahc,nilearn/nilearn,nisl/roi.py,00b25db1d07f04f504d6cbfda996d1037c81e293,STILL_EXISTS,TODO: add an option to exclude labels from ouput (useful for background
aaehbdbaf,nilearn/nilearn,nisl/region.py,df08e063a0ae9fe466b61ca4149a1c90a0f2ec12,d65257937b195906d92db704acf5bcb4b2f71a08,FIXME: resample if needed
aaehbdbbb,nilearn/nilearn,nisl/tests/test_region.py,df08e063a0ae9fe466b61ca4149a1c90a0f2ec12,d65257937b195906d92db704acf5bcb4b2f71a08,TODO: list of filenames
aaehbdbbe,nilearn/nilearn,nisl/tests/test_region.py,df08e063a0ae9fe466b61ca4149a1c90a0f2ec12,d65257937b195906d92db704acf5bcb4b2f71a08,TODO: one filename; 4D array
aaehbdbbf,nilearn/nilearn,nisl/tests/test_region.py,df08e063a0ae9fe466b61ca4149a1c90a0f2ec12,d65257937b195906d92db704acf5bcb4b2f71a08,TODO: Check effect of \"threshold\" argument
aaehbdbbg,nilearn/nilearn,nisl/tests/test_region.py,df08e063a0ae9fe466b61ca4149a1c90a0f2ec12,d65257937b195906d92db704acf5bcb4b2f71a08,TODO: one filename; 3D file (labels)
aaehbdbbi,nilearn/nilearn,nisl/tests/test_region.py,df08e063a0ae9fe466b61ca4149a1c90a0f2ec12,d65257937b195906d92db704acf5bcb4b2f71a08,TODO: Check effect of \"background\" argument
aaehbdbca,nilearn/nilearn,nisl/tests/test_region.py,df08e063a0ae9fe466b61ca4149a1c90a0f2ec12,d65257937b195906d92db704acf5bcb4b2f71a08,TODO: list of Nifti1Image with inconsistent shape
aaehbdbcb,nilearn/nilearn,nisl/tests/test_region.py,df08e063a0ae9fe466b61ca4149a1c90a0f2ec12,d65257937b195906d92db704acf5bcb4b2f71a08,TODO: list of Nifti1Image with a non-3D image as first element.
aaehbdbdh,nilearn/nilearn,nisl/masking.py,06c7548b81a45cf821cc29a3cb96d81e13fbb178,f798f65558a79a3d699788459ab562180d116c87,Resample if needed
aaehbdbea,nilearn/nilearn,nisl/signals.py,d373769ca71db04ffc0da020703b04fcf020be58,STILL_EXISTS,FIXME: when detrend=True; two copies of \"series\" are made.  Variance
aaehbdbhf,nilearn/nilearn,nisl/signals.py,2628130cce13334c36330d5b0348841b70f2e856,STILL_EXISTS,FIXME: when detrend=True; two copies of \"series\" are made.  Variance
aaehbdbia,nilearn/nilearn,nisl/masking.py,e512dcc44e74d8c1a27f4dd2e3e5031dead03e2f,4071537c29d4b40781cb2e36e50e2195aeca50b8,Resample if needed
aaehbdbic,nilearn/nilearn,nisl/masking.py,b5ce51c697bd2c92986c68c20939779d51492c23,cd11540f7787127fab3cc4692242147367189d8f,FIXME: use sparse matrices here.
aaehbdbid,nilearn/nilearn,nisl/masking.py,b5ce51c697bd2c92986c68c20939779d51492c23,4071537c29d4b40781cb2e36e50e2195aeca50b8,Resample if needed
aaehbdbii,nilearn/nilearn,nisl/testing.py,b5ce51c697bd2c92986c68c20939779d51492c23,STILL_EXISTS,TODO: add an \"order\" keyword
aaehbdcbf,nilearn/nilearn,nisl/masking.py,680e23af102c80fff13d1e6b393a05a91c51f51d,4071537c29d4b40781cb2e36e50e2195aeca50b8,Resample if needed
aaehbdccg,nilearn/nilearn,nisl/masking.py,dd394a5feff439f1085c62fc70e21c097ad63782,cd11540f7787127fab3cc4692242147367189d8f,FIXME: use sparse matrices here.
aaehbdcdg,nilearn/nilearn,nisl/region.py,49f82988bd2b2a5e9f1edf410702b395519fbc22,STILL_EXISTS,TODO: Make a special case for list of strings (load one image at a
aaehbdcgh,nilearn/nilearn,nisl/region.py,99fdce6a8ab311eda37eaf3eb07ae77c193426a2,c5ad56010a3302472f6d25ba1a34d1f826d2d2cb,FIXME: data = masking.unmask(data; maps_mask)
aaehbdchj,nilearn/nilearn,nisl/tests/test_region.py,99fdce6a8ab311eda37eaf3eb07ae77c193426a2,f2dc6e30576057f5b79008a6f82b77a82928edf3,FIXME: Test reversibility of img_from_maps() and signals_from_maps();
aaehbddce,nilearn/nilearn,nisl/io/nifti_region.py,0909a1101d564e780ff489326924f1055a68b4a1,f80ad2e8dafb628d9fcd0ac4883959cac379a4a4,TODO: add a parameter to choose computation method. Either use
aaehbdded,nilearn/nilearn,nisl/io/nifti_region.py,351005821cd8d052b1e9fe01f6d607e6831a0141,STILL_EXISTS,FIXME: put into signals.clean()
aaehbddgb,nilearn/nilearn,nisl/io/nifti_region.py,f80ad2e8dafb628d9fcd0ac4883959cac379a4a4,STILL_EXISTS,FIXME: useless copy if input parameter niimg is a string.
aaehbddhd,nilearn/nilearn,nisl/testing.py,f80ad2e8dafb628d9fcd0ac4883959cac379a4a4,STILL_EXISTS,Fill central voxels timeseries with random signals
aaehbdefa,nilearn/nilearn,nisl/region.py,71d7505d335e1aa208ecd804606368c4687a4690,STILL_EXISTS,TODO: take care of ordering (C\/F) in \"data\".
aaehbdefb,nilearn/nilearn,nisl/signals.py,71d7505d335e1aa208ecd804606368c4687a4690,STILL_EXISTS,FIXME: when detrend=True; two copies of \"series\" are made.  Variance
aaehbdifg,nilearn/nilearn,nisl/image.py,ebf6faeaa4230a4ece9970bb7ece6e4372eb1de5,STILL_EXISTS,Use hasattr() instead of isinstance to workaround a Python 2.6\/2.7 bug
aaehbdifi,nilearn/nilearn,nisl/resampling.py,ebf6faeaa4230a4ece9970bb7ece6e4372eb1de5,STILL_EXISTS,FIXME: \"import copy\" overwrites input parameter \"copy\"!
aaehbdihd,nilearn/nilearn,nisl/io/nifti_region.py,ef950b8ed6aaeac52feca9bd077f887a16dcf852,STILL_EXISTS,FIXME: useless copy if input parameter niimg is a string.
aaehbdjfi,nilearn/nilearn,nisl/searchlight.py,ba93f0b8f1e17955a5990246b53577479ad88165,STILL_EXISTS,of voxels in processing mask (columns in process_mask)
aaehbdjii,nilearn/nilearn,nisl/decomposition/multi_pca.py,3b6d90111dd6f1c83d93d2a908bef9d89baa64dc,7655c373b8234e4ca9869fb3e6b469ae66e60a24,XXX: need to give all the filtering
aaehbdjja,nilearn/nilearn,nisl/decomposition/multi_pca.py,3b6d90111dd6f1c83d93d2a908bef9d89baa64dc,STILL_EXISTS,XXX: dealing properly with 4D\/ list of 4D data?
aaehbdjjb,nilearn/nilearn,nisl/decomposition/multi_pca.py,a679f0f224c88974db88dcf387993d7d7fdbe09b,bd4e5634013b58a85acb2fbf415d83688308d227,XXX: we should warn the user that we enable these options if they are
aaehbeabh,nilearn/nilearn,nisl/_utils/niimg_conversions.py,c7b1f76140f3afbd2841ceecf27ea26c6348ce7d,STILL_EXISTS,Use hasattr() instead of isinstance to workaround a Python 2.6\/2.7 bug
aaehbeagd,nilearn/nilearn,nisl/decomposition/multi_pca.py,5433d2703626be1ff92a8399159114b9b62bc8f6,7655c373b8234e4ca9869fb3e6b469ae66e60a24,XXX change the name of this variable
aaehbecab,nilearn/nilearn,nisl/region.py,744a38a73959a23144bbd17eab7b080c16469db1,STILL_EXISTS,Set to zero signals for missing labels. Workaround for Scipy behaviour
aaehbecbg,nilearn/nilearn,plot_canica_resting_state.py,326700619a722f2da049e1d1a08a02c78d85bf1d,STILL_EXISTS,better results; simply increase the number.
aaehbeccf,nilearn/nilearn,plot_canica_resting_state.py,326700619a722f2da049e1d1a08a02c78d85bf1d,6164294177c34f257dbd354cf97d643597255a0d,XXX: this should probably be integrated in the CanICA object
aaehbeced,nilearn/nilearn,nisl/decomposition/multi_pca.py,3c4eea71171339a7260179f54748121ea4bf72c2,bd4e5634013b58a85acb2fbf415d83688308d227,XXX: we should warn the user that we enable these options if they are
aaehbecej,nilearn/nilearn,nisl/decomposition/multi_pca.py,496cdb29fee7abff95decf878ae55e7a86e86a09,STILL_EXISTS,XXX: dealing properly with 2D\/ list of 2D data?
aaehbecgc,nilearn/nilearn,nisl/decomposition/multi_pca.py,038d8cbd82558fc94eece7a65b7ac264e13eb38a,b6db3f3c25ec8aa0c355898bbc1da118e0fc6200,XXX Change parameters of the masker for smoothing and
aaehbechd,nilearn/nilearn,nisl/decomposition/multi_pca.py,71340a9283d578c4a9206c290466bc54caeb6f32,STILL_EXISTS,Hack to support single-subject data:
aaehbeche,nilearn/nilearn,nisl/decomposition/multi_pca.py,71340a9283d578c4a9206c290466bc54caeb6f32,STILL_EXISTS,This is a very incomplete hack; as it won't work right for
aaehbecje,nilearn/nilearn,nisl/decomposition/tests/test_multi_pca.py,41ec4c8ef89fbcbf1edbaa92ba65874fc5396074,STILL_EXISTS,XXX: this is mostly a smoke test
aaehbedcb,nilearn/nilearn,nisl/decomposition/multi_pca.py,fa514dee0f00367171dd6bfda1d095fd12120dac,STILL_EXISTS,Dirty workaround for a joblib bug
aaehbeefi,nilearn/nilearn,nisl/honorio_samaras.py,b2ebc1d32a4e98c6030c9a1ebf5374da3d0e2e77,STILL_EXISTS,TODO: low robustness with inverse computation.
aaehbeejb,nilearn/nilearn,nisl/honorio_samaras.py,5d45f91146e9ee822ad3db651748d4bd1491fbc1,STILL_EXISTS,FIXME: compute lambda (Newton-Raphson)
aaehbefae,nilearn/nilearn,nisl/honorio_samaras.py,bc18fac522e36fff3c84f1674dad7faafd7215e4,STILL_EXISTS,FIXME: compute lambda (Newton-Raphson)
aaehbefbb,nilearn/nilearn,nisl/honorio_samaras.py,a8a6866532a258c949fe7f258b0da38b4691fe85,STILL_EXISTS,TODO: replace by \"alpha\"
aaehbefcb,nilearn/nilearn,nisl/honorio_samaras.py,a8a6866532a258c949fe7f258b0da38b4691fe85,7d19b206fd66bffd2f47ae3ebc67b8f61c106a45,todo: covar_est -> emp_covs
aaehbefcd,nilearn/nilearn,nisl/honorio_samaras.py,7d19b206fd66bffd2f47ae3ebc67b8f61c106a45,STILL_EXISTS,FIXME: Check that all covariances have the same size.
aaehbefce,nilearn/nilearn,nisl/honorio_samaras.py,7d19b206fd66bffd2f47ae3ebc67b8f61c106a45,STILL_EXISTS,FIXME: check consistency between matrix sizes and task number.
aaehbefch,nilearn/nilearn,nisl/honorio_samaras.py,7d19b206fd66bffd2f47ae3ebc67b8f61c106a45,STILL_EXISTS,FIXME: there's something wrong with this test.
aaehbefee,nilearn/nilearn,nisl/honorio_samaras.py,6ec0d156811feec1ac48c11471e874840e333e33,STILL_EXISTS,FIXME: there's something wrong with this test.
aaehbeffd,nilearn/nilearn,plot_adhd_covariance2.py,6ec0d156811feec1ac48c11471e874840e333e33,STILL_EXISTS,FIXME: Normalize covariance matrix
aaehbegge,nilearn/nilearn,nilearn/group_sparse_covariance.py,9d3c538a6505eb9911d589345cc803a9686bcf99,75cb98341a947f3c98cbeaa84fba8e81bfbc4bb2,FIXME: duality gap is not always negative; there must be an
aaehbeggj,nilearn/nilearn,nilearn/group_sparse_covariance.py,9d3c538a6505eb9911d589345cc803a9686bcf99,b2f238ebd2cb4479908ff88224e193865291ab8f,TODO: allocate once
aaehbegha,nilearn/nilearn,nilearn/group_sparse_covariance.py,9d3c538a6505eb9911d589345cc803a9686bcf99,STILL_EXISTS,Shrink A(k) if needed to get a feasible point.
aaehbeghc,nilearn/nilearn,nilearn/group_sparse_covariance.py,3317621c983fd8b37044babf8bba56dc15eda8c5,e38173bfb42c91a5261c06dcfbe0b8a2389e1137,FIXME: Unoptimized version. Can do much better (see group_lasso_)
aaehbehfi,nilearn/nilearn,nilearn/group_sparse_covariance.py,264cad6cd6e30cd28db7bb2dacc1990bf1451486,STILL_EXISTS,FIXME: B can be not spd.
aaehbehgd,nilearn/nilearn,nilearn/group_sparse_covariance.py,7e4088911a4bb4ef4616d0fce041e2f3de0e201a,STILL_EXISTS,TODO: can be computed more efficiently using Winv (see Friedman 2008)
aaehbehha,nilearn/nilearn,nilearn/group_sparse_covariance.py,c8c2a2376b87e0f7f1cd7d6b83fbe07dcd3639d4,STILL_EXISTS,(often the tighter the better)
aaehbehig,nilearn/nilearn,nilearn/group_sparse_covariance.py,b1643a9132eda07ab638bdb12a9325d61af54010,STILL_EXISTS,overall convergence rate. (often the tighter the better)
aaehbeibd,nilearn/nilearn,nilearn/group_sparse_covariance.py,d85db4f8a0abf49f9370202803c3922da05af6f4,239f2fff548e15f5cb595c48f657427c6af96703,TODO: this could be cached to avoid recomputing the same thing over
aaehbejhj,nilearn/nilearn,nilearn/group_sparse_covariance.py,c7af1f34d7cbecf347b3039f962b33980e226265,STILL_EXISTS,TODO: can be computed more efficiently using W_inv. See
aaehbfcia,nilearn/nilearn,nilearn/datasets.py,c19f757344848510ff95d6566b721990a4126e50,837592c7d5bcb42dcd6276ff991ea2234a7f34a6,Eliminate vars if needed
aaehbfdca,nilearn/nilearn,nilearn/external/visvis/images2gif.py,8a44f1aabe4529397797ac5c3ee135804eef9c85,STILL_EXISTS,todo: This module should be part of imageio (or at least based on)
aaehbfech,nilearn/nilearn,nilearn/external/visvis/images2gif.py,8a44f1aabe4529397797ac5c3ee135804eef9c85,STILL_EXISTS,Convert to normal PIL images if needed
aaehbfegf,nilearn/nilearn,nilearn/external/visvis/images2gif.py,8a44f1aabe4529397797ac5c3ee135804eef9c85,STILL_EXISTS,finds best neuron (min dist-self.bias) and returns position
aaehbfjhg,nilearn/nilearn,doc/sphinxext/gen_rst.py,7add236e07dc38a5af977a030d246ace802380af,16542ef8fd8752e78cd7c826439722cd809cb3c2,auto examples gallery to the _build folder. This works fine as is; but it would be cleaner to
aaehbgbjb,nilearn/nilearn,doc/sphinxext/gen_rst.py,6899c2a27d1d25c310498648ba29163f9650c5ef,STILL_EXISTS,auto examples gallery to the _build folder. This works fine as is; but it would be cleaner to
aaehbgcjd,nilearn/nilearn,haxby_full_analysis.py,44bf52ce48b05751f8a62271410a1d6427b42333,STILL_EXISTS,@@@@@ Michael: Maybe we should only work with one subject? This outer loop is easily removed
aaehbgcjf,nilearn/nilearn,haxby_full_analysis.py,44bf52ce48b05751f8a62271410a1d6427b42333,STILL_EXISTS,@@@@@ Michael: Do we detrend and standardize here? Data loaded goes across 12 runs; so ideally it should be broken apart. Scipy.signal.detrend has this breakpoint option. The paper does normalization across conditions (subtracting the condition mean); but I don't know if it does detrending and standardizing before this. Subtracting timecourse means definitely makes the data look a lot better - one can distinguish rest from activation times. I will do the detrending and standardizing by block later on
aaehbgdba,nilearn/nilearn,haxby_full_analysis.py,44bf52ce48b05751f8a62271410a1d6427b42333,STILL_EXISTS,@@@@@ Michael: It may be useful to teach how this is done explicitely. Then again; the NiftiMasker could probably also be made to be able to do this easily
aaehbgdcb,nilearn/nilearn,haxby_full_analysis.py,d5872eac2acf8a729097c57dd581ab6825fdef53,STILL_EXISTS,@@@@@@ Michael: If I understand the paper correctly; they work on the BOLD data. But maybe I am wrong. In the following; I will remove resting state by hand and divide into conditions. However; the design is orthogonal; and would almost stay that way even after convolution with an HRF; so maybe the way to go for this tutorial would rather be via Beta maps?
aaehbgdcg,nilearn/nilearn,haxby_full_analysis.py,d5872eac2acf8a729097c57dd581ab6825fdef53,STILL_EXISTS,@@@@@ Michael: I am realizing more and more; that I may be doing too much acrobatics with ndarrays. More and more convinced that Beta maps are the way to go.
aaehbgddc,nilearn/nilearn,haxby_full_analysis.py,c06fdd55cbb0d771fbaf6de3fe655f1c03902ab9,STILL_EXISTS,@@@@@ Michael: It may be useful to teach how this is done explicitly. Then again; the NiftiMasker could probably also be made to be able to do this easily
aaehbgdfc,nilearn/nilearn,haxby_full_analysis.py,c06fdd55cbb0d771fbaf6de3fe655f1c03902ab9,STILL_EXISTS,@@@@@ Michael: I am realizing more and more; that I may be doing too much acrobatics with ndarrays. More and more convinced that Beta maps are the way to go.
aaehbgiih,nilearn/nilearn,nilearn/group_analysis/permuted_least_squares.py,9450a0ccb32603c643d36c13a860c6715238dd0c,STILL_EXISTS,TODO: add various checks
aaehbgjac,nilearn/nilearn,nilearn/group_analysis/permuted_least_squares_aux.py,9450a0ccb32603c643d36c13a860c6715238dd0c,STILL_EXISTS,it fits --> updates (efficient)
aaehbgjdc,nilearn/nilearn,nilearn/mass_univariate/permuted_least_squares.py,4df1c135cb5ba6c800be5334dc8c64ca215c4c54,STILL_EXISTS,it fits --> updates (efficient)
aaehbgjfb,nilearn/nilearn,nilearn/mass_univariate/permuted_least_squares.py,4df1c135cb5ba6c800be5334dc8c64ca215c4c54,STILL_EXISTS,TODO: add various checks
aaehbgjhi,nilearn/nilearn,nilearn/masking.py,2669cc970e3d596aa0322fe0ec05f7bb992b5e74,05ac5021ec3daaf819e96aa38cf0fa3a0f42179f,XXX make a is_a_niimgs function ?
aaehbgjif,nilearn/nilearn,nilearn/masking.py,f3e8b74d034564554b70876b577549b15e41f66b,b514bb09549b22a3fe9953ad18673f8fb6ff8573,XXX: should implement a loop on file; if a list of 3D files are
aaehbgjjb,nilearn/nilearn,nilearn/masking.py,f6d93f0ce58a87b23a9cab324a8ff6bada48199e,05ac5021ec3daaf819e96aa38cf0fa3a0f42179f,XXX make a is_a_niimgs function ?
aaehbhbai,nilearn/nilearn,nilearn/masking.py,05ac5021ec3daaf819e96aa38cf0fa3a0f42179f,05ac5021ec3daaf819e96aa38cf0fa3a0f42179f,XXX make a is_a_niimgs function ?
aaehbhbch,nilearn/nilearn,nilearn/_utils/fixes/sklearn_f_regression.py,b792740c3a441ada48f483844366fcb615ab2b89,STILL_EXISTS,XXX could use corr \/= row_norms(X.T) here; but the test doesn't pass
aaehbhbdf,nilearn/nilearn,nilearn/_utils/fixes/sklearn_f_regression_nosparse.py,b7728d5b71ae5aca9ad06f0a4298b19db8b74f0d,STILL_EXISTS,XXX could use corr \/= row_norms(X.T) here; but the test doesn't pass
aaehbhbff,nilearn/nilearn,nilearn/masking.py,bb6b4fd3be6549a109a82e76f6d050134fc1bfc8,b514bb09549b22a3fe9953ad18673f8fb6ff8573,XXX: should implement a loop on file; if a list of 3D files are
aaehbhchb,nilearn/nilearn,nilearn/mass_univariate/permuted_least_squares.py,57447a51c963cf82622d991bbf4c97bc4c276219,cf196cd930d2b84be6407bbfda18ada21f40e727,TODO: filling could be more efficient
aaehbhchc,nilearn/nilearn,nilearn/mass_univariate/permuted_least_squares.py,57447a51c963cf82622d991bbf4c97bc4c276219,STILL_EXISTS,not efficient; added for code exhaustivity
aaehbhchg,nilearn/nilearn,nilearn/mass_univariate/permuted_least_squares.py,57447a51c963cf82622d991bbf4c97bc4c276219,STILL_EXISTS,efficient; should be used everytime with permuted OLS
aaehbhcid,nilearn/nilearn,nilearn/mass_univariate/permuted_least_squares.py,57447a51c963cf82622d991bbf4c97bc4c276219,STILL_EXISTS,efficient for chunking
aaehbhdbf,nilearn/nilearn,_utils/fixes/sklearn_f_regression.py,204c3dc2dfcb78cfe478e50fd81182252222d58f,STILL_EXISTS,XXX could use corr \/= row_norms(X.T) here; but the test doesn't pass
aaehbhdcd,nilearn/nilearn,_utils/fixes/sklearn_f_regression_nosparse.py,204c3dc2dfcb78cfe478e50fd81182252222d58f,STILL_EXISTS,XXX could use corr \/= row_norms(X.T) here; but the test doesn't pass
aaehbhdjb,nilearn/nilearn,nilearn/mass_univariate/permuted_least_squares.py,c90d4b0fc9de45b75ce1532309f83b1bde948cc3,STILL_EXISTS,TODO: to speed this up; we could threshold scores_original_data
aaehbhidi,nilearn/nilearn,nilearn/datasets.py,410bc1959639ba417c06fdb821ac8b96a64459b5,STILL_EXISTS,XXX: here; move is supposed to be a dir; it can be a name
aaehbhidj,nilearn/nilearn,nilearn/datasets.py,410bc1959639ba417c06fdb821ac8b96a64459b5,STILL_EXISTS,If needed; move files from temps directory to final directory.
aaehbhiea,nilearn/nilearn,nilearn/datasets.py,410bc1959639ba417c06fdb821ac8b96a64459b5,STILL_EXISTS,XXX We could only moved the files requested
aaehbhieb,nilearn/nilearn,nilearn/datasets.py,410bc1959639ba417c06fdb821ac8b96a64459b5,STILL_EXISTS,XXX Movetree can go wrong
aaehbhiei,nilearn/nilearn,nilearn/datasets.py,54ca155a98983f2c4abdc597889449388e2270ad,837592c7d5bcb42dcd6276ff991ea2234a7f34a6,It is better to perform several small requests than a big one because:
aaehbhjbh,nilearn/nilearn,nilearn/datasets.py,1c3345a7f272c38f6a818b30d5e71d1f70effe7d,STILL_EXISTS,Eliminate vars if needed
aaehbhjch,nilearn/nilearn,nilearn/datasets.py,1c3345a7f272c38f6a818b30d5e71d1f70effe7d,STILL_EXISTS,It is better to perform several small requests than a big one because:
aaehbiadh,nilearn/nilearn,nilearn/image/image.py,b514bb09549b22a3fe9953ad18673f8fb6ff8573,2b0a3cd34edf87da4828592052b1e889f76d09c4,XXX: should implement a loop on file; if a list of 3D files are
aaehbicjd,nilearn/nilearn,plot_oasis_vbm.py,d98afa026b9f69a7b109baf20fa1ad029a6fa3a3,STILL_EXISTS,In the interest of time; 10000 would be better
aaehbiddh,nilearn/nilearn,nilearn/image/tests/test_resampling.py,5158d35a457c77fb3f334f65c76fb62668d1a361,eefb559de43bd5aaf6dfd41dda58a75343b6114e,We implement a poor-man's version
aaehbidfd,nilearn/nilearn,nilearn/_utils/testing.py,d824bca892dbaca4448c6503d8e3b971ba1c1ffc,e3d577b5595af3e1c9a05d3be84dbdf1a7c4aa20,Fill CSV files with given content if needed
aaehbifbd,nilearn/nilearn,nilearn/plotting/img_plotting.py,4ccc2142ccc637ded780b3f4070a3893a3795fd0,STILL_EXISTS,XXX: Check that we should indeed plot an anat: we have one; and the
aaehbifdf,nilearn/nilearn,nilearn/plotting/slicers.py,4ccc2142ccc637ded780b3f4070a3893a3795fd0,STILL_EXISTS,Implement this as a staticmethod or a classmethod when
aaehbjaje,nilearn/nilearn,nilearn/plotting/coord_tools.py,08bbdc182fcabc6ced37ca28ef82e1f68a972abe,STILL_EXISTS,XXX: we will end up with fully zeros if n_cuts is too big
aaehbjgee,nilearn/nilearn,nilearn/plotting/img_plotting.py,ef0687cd0391afc50405144313826273f0267057,1c25b857a3dce40df9a6bff48ca7a5d66dfec171,TODO additional arguments to pass?
aaehbjgef,nilearn/nilearn,nilearn/plotting/slicers.py,ef0687cd0391afc50405144313826273f0267057,2fbcdfe9cee33bcd179bd080a40f74fe5aaf1d80,TODO: should I do better than adding a arbitrary margin?
aaehbjgei,nilearn/nilearn,nilearn/plotting/slicers.py,ef0687cd0391afc50405144313826273f0267057,STILL_EXISTS,TODO: do_cut should probably be renamed because it has nothing
aaehbjgfd,nilearn/nilearn,nilearn/plotting/img_plotting.py,e5df5b6255873d5414b0fff337738a2a18126633,STILL_EXISTS,TODO: actually use 'display_mode' to switch between different slicers
aaehbjgff,nilearn/nilearn,nilearn/plotting/img_plotting.py,b69ef471820f356e7d8579f899c196145c421bc1,583784ddfdc206d2c501e2f266bd6449c5dd4b7e,TODO: threshold's default ???
aaehbjgfh,nilearn/nilearn,nilearn/plotting/img_plotting.py,b69ef471820f356e7d8579f899c196145c421bc1,STILL_EXISTS,TODO: actually use 'display_mode' to switch between different slicers
aaehbjheb,nilearn/nilearn,nilearn/datasets.py,4958f383d5ec00ec96eb344d2e18f7b8eccdde19,STILL_EXISTS,Load derivatives if needed
aaehbjigb,nilearn/nilearn,nilearn/image/resampling.py,f47d6f58577d2b3bec51c3eba2e35dac3ad625c6,STILL_EXISTS,The copy is needed in order not to modify the input img affine
aaehbjihi,nilearn/nilearn,nilearn/version.py,e5808ab76380fac8b26dcc2dde0dad5f3dcf56e6,abdf3be30fff8651d12f66550a92e6259f01fa8b,All metadata needed for required modules:
aaehcaceg,nilearn/nilearn,nilearn/plotting/displays.py,b2b052b392498b7cf4143034505f503b0e1bf878,6492402dbe740b7cf3f53500ac1979e0d33309e6,TODO: add diagonal element values to this function
aaehcacfa,nilearn/nilearn,nilearn/plotting/displays.py,b2b052b392498b7cf4143034505f503b0e1bf878,6492402dbe740b7cf3f53500ac1979e0d33309e6,TODO: pass in linewidths or linewidth function or multiplication scalar
aaehcacfd,nilearn/nilearn,nilearn/plotting/img_plotting.py,b2b052b392498b7cf4143034505f503b0e1bf878,6492402dbe740b7cf3f53500ac1979e0d33309e6,TODO: do I want to do the lower diagonal here; probably
aaehcaeea,nilearn/nilearn,nilearn/datasets.py,b86239a01fb25f0bb8104b1b6ca46bbb4fae03ca,STILL_EXISTS,bytes (encode()) needed for python 2\/3 compat with numpy
aaehcaeed,nilearn/nilearn,nilearn/plotting/cm.py,bc20ed8910d156c361c579add9598dc6df85f9e2,STILL_EXISTS,needed as dict changes within loop
aaehcagha,nilearn/nilearn,doc/sphinxext/sphinxgallery/gen_gallery.py,aca637208dc967a433598d7080458d3e327d38a7,STILL_EXISTS,better than nested.
aaehcaghb,nilearn/nilearn,doc/sphinxext/sphinxgallery/gen_gallery.py,aca637208dc967a433598d7080458d3e327d38a7,STILL_EXISTS,Sphinx hack: sphinx copies generated images to the build directory
aaehcagia,nilearn/nilearn,doc/sphinxext/sphinxgallery/gen_gallery.py,aca637208dc967a433598d7080458d3e327d38a7,STILL_EXISTS,The following is a hack that prevents this behavior by clearing the
aaehcagid,nilearn/nilearn,doc/sphinxext/sphinxgallery/gen_gallery.py,aca637208dc967a433598d7080458d3e327d38a7,STILL_EXISTS,it should probably not cause a crash).  Tested successfully
aaehcagif,nilearn/nilearn,doc/sphinxext/sphinxgallery/gen_gallery.py,aca637208dc967a433598d7080458d3e327d38a7,STILL_EXISTS,HACK: Stop nosetests running setup() above
aaehcahih,nilearn/nilearn,nilearn/datasets.py,1c386be53957178b5ddb4282c0693872055e6ffb,b46d7b6b752a16b7d5d3f60aeb4da93240acc901,XXX Should we load the image here?
aaehcahii,nilearn/nilearn,nilearn/_utils/niimg.py,73cd600df8ce3f36514985699227e0a9939eb96c,4c70dd360405fd2d6da5dcafe9aeb586281f322a,XXX add dtype option for masks
aaehcahij,nilearn/nilearn,nilearn/_utils/niimg.py,73cd600df8ce3f36514985699227e0a9939eb96c,b5d529ade954f627b62dd33a94313c5d58cb71c8,XXX If the niimg is a list of 3D images; we don't need to load them all
aaehcahja,nilearn/nilearn,nilearn/_utils/niimg.py,73cd600df8ce3f36514985699227e0a9939eb96c,b5d529ade954f627b62dd33a94313c5d58cb71c8,But this is a bit ugly
aaehcahjb,nilearn/nilearn,nilearn/_utils/niimg.py,73cd600df8ce3f36514985699227e0a9939eb96c,STILL_EXISTS,XXX Nifti can't handle boolean; is this the case of other types?
aaehcahjd,nilearn/nilearn,nilearn/plotting/img_plotting.py,73cd600df8ce3f36514985699227e0a9939eb96c,STILL_EXISTS,This looks like a hack but we want a constant image compatible with
aaehcaici,nilearn/nilearn,nilearn/datasets.py,ede9536f7cccfeb6a6dc7132ea8baeafa6c04a4b,8170a318dc41eecf44ff8aee896a873de295b156,XXX Should we load the image here?
aaehcaida,nilearn/nilearn,nilearn/datasets.py,27ba4d4ea728026f6d04a2e2ac68f487de571da5,STILL_EXISTS,XXX Should we load the image here?
aaehcaihg,nilearn/nilearn,nilearn/_utils/niimg_conversions.py,4faddb350734f0e96c1fb490badea4f1bd1bb248,edf35044941c8e23f534c9804134c69cb73edb17,XXX I kept the magic: it can concatenate either 3D or 4D images. But not
aaehcbgih,nilearn/nilearn,nilearn/input_data/nifti_spheres_masker.py,83c8ae3ff46bd15e2fadade1be96ef0f7eb1dff7,STILL_EXISTS,of voxels in processing mask (columns in process_mask)
aaehcbhif,nilearn/nilearn,nilearn/input_data/nifti_spheres_masker.py,22fdfdf421de1898a0651a4bfce04a138b07161b,STILL_EXISTS,of voxels in processing mask (columns in process_mask)
aaehccahi,nilearn/nilearn,nistats/model.py,79c7f186116a459a69d39f43cb8c1b029ca025c3,5cdbde520d26ce6fbe0782171157aef5e66eebd6,XXX method is from an earlier API and needs to be rethought
aaehccaii,nilearn/nilearn,nistats/regression.py,79c7f186116a459a69d39f43cb8c1b029ca025c3,STILL_EXISTS,TODO: handle case for noconstant regression
aaehccbbb,nilearn/nilearn,nistats/utils.py,a84e6b90809168f7c8bed9c4aad871cd64f37733,STILL_EXISTS,XXX: the following line fixes curious SEGFAULT when
aaehccbeh,nilearn/nilearn,nistats/tests/test_regression.py,d252e404d8cdcd426d0e1b02842df59e462406b5,b5864887bcaaa848704984ce2766c2aad1b61672,Reverse fudge in ar.R calculation labeled as splus compatibility fix
aaehccbha,nilearn/nilearn,nistats/datasets.py,48200040d8d05b56cc53d74c6f9e62aa6346124e,e491a5516a09becb9eb5ae582d392d8584143d11,The options needed for _fetch_files
aaehcccfj,nilearn/nilearn,nilearn/input_data/nifti_spheres_masker.py,1671ea3164bbee2121971602c79d4c64251f4358,STILL_EXISTS,Check seeds and convert them to lists if needed
aaehcccgj,nilearn/nilearn,nilearn/input_data/nifti_spheres_masker.py,baaadae478c2cbac5320511d1d73062e7969639f,STILL_EXISTS,Check seeds and convert them to lists if needed
aaehccche,nilearn/nilearn,nilearn/input_data/nifti_spheres_masker.py,08aee7ff3c70ebbd37247b05ea3b468444fc8d7c,STILL_EXISTS,Check seeds and convert them to lists if needed
aaehccfcd,nilearn/nilearn,nistats/datasets.py,7ba921d1da8da28f6ab5d5101c01e204ea204f78,STILL_EXISTS,maybe data_dir already contains the data ?
aaehccffg,nilearn/nilearn,nistats/datasets.py,23a333dee80ed25dac29f5e7a1bafcab89e3f817,STILL_EXISTS,maybe data_dir already contains the data ?
aaehccife,nilearn/nilearn,nilearn/datasets/func.py,a3ec4ceff04b67c3169ee1c679e8970d071e3b1d,796a5a7b4d8c2d484da0f518f65318b7a741ad5a,It is better to perform several small requests than a big one because:
aaehccihd,nilearn/nilearn,nilearn/datasets/func.py,a3ec4ceff04b67c3169ee1c679e8970d071e3b1d,STILL_EXISTS,bytes (encode()) needed for python 2\/3 compat with numpy
aaehccihi,nilearn/nilearn,nilearn/datasets/func.py,a3ec4ceff04b67c3169ee1c679e8970d071e3b1d,STILL_EXISTS,Load derivatives if needed
aaehcciib,nilearn/nilearn,nilearn/datasets/struct.py,a3ec4ceff04b67c3169ee1c679e8970d071e3b1d,STILL_EXISTS,XXX Should we load the image here?
aaehcdaab,nilearn/nilearn,nilearn/datasets/utils.py,a3ec4ceff04b67c3169ee1c679e8970d071e3b1d,STILL_EXISTS,XXX: here; move is supposed to be a dir; it can be a name
aaehcdaac,nilearn/nilearn,nilearn/datasets/utils.py,a3ec4ceff04b67c3169ee1c679e8970d071e3b1d,STILL_EXISTS,If needed; move files from temps directory to final directory.
aaehcdaad,nilearn/nilearn,nilearn/datasets/utils.py,a3ec4ceff04b67c3169ee1c679e8970d071e3b1d,STILL_EXISTS,XXX We could only moved the files requested
aaehcdaae,nilearn/nilearn,nilearn/datasets/utils.py,a3ec4ceff04b67c3169ee1c679e8970d071e3b1d,STILL_EXISTS,XXX Movetree can go wrong
aaehcdgdg,nilearn/nilearn,nilearn/sparse_models/_cv_tricks.py,ce9e8b0b9c93f980648c0d7a9ae6f001edb91fdf,STILL_EXISTS,\"\"\" || Ninja tricks (early stopping; etc.) to make CV a better place to live... ||  || \"\"\"
aaehcdgef,nilearn/nilearn,nilearn/sparse_models/common.py,ce9e8b0b9c93f980648c0d7a9ae6f001edb91fdf,STILL_EXISTS,\"\"\" || Common functions and base classes. Used by more specialized modules like || tv.py; smooth_lasso.py; etc. ||  || XXX TODO: Factor out code specific to estimator and cv classes; into seperate modules ||  || \"\"\"
aaehcdgeh,nilearn/nilearn,nilearn/sparse_models/common.py,ce9e8b0b9c93f980648c0d7a9ae6f001edb91fdf,STILL_EXISTS,xxx: isn't it a more generic function?
aaehcdgfb,nilearn/nilearn,nilearn/sparse_models/common.py,ce9e8b0b9c93f980648c0d7a9ae6f001edb91fdf,STILL_EXISTS,XXX: functions that return variable number of outputs are bad
aaehcdgga,nilearn/nilearn,nilearn/sparse_models/common.py,ce9e8b0b9c93f980648c0d7a9ae6f001edb91fdf,STILL_EXISTS,xxx: img.dtype?
aaehcdgje,nilearn/nilearn,nilearn/sparse_models/cv.py,ce9e8b0b9c93f980648c0d7a9ae6f001edb91fdf,STILL_EXISTS,XXX uncovered \/ untested code!
aaehcdgji,nilearn/nilearn,nilearn/sparse_models/cv.py,ce9e8b0b9c93f980648c0d7a9ae6f001edb91fdf,STILL_EXISTS,XXX Are these alphas reasonable ?
aaehcdhjd,nilearn/nilearn,nilearn/sparse_models/operators.py,ce9e8b0b9c93f980648c0d7a9ae6f001edb91fdf,fe0874cb708e41f9af3bb19788c4e52118ea6962,XXX
aaehcdiba,nilearn/nilearn,nilearn/sparse_models/optim.py,ce9e8b0b9c93f980648c0d7a9ae6f001edb91fdf,STILL_EXISTS,XXX: if mode == \"fn\"; we are computing the energy twice
aaehcdibb,nilearn/nilearn,nilearn/sparse_models/optim.py,ce9e8b0b9c93f980648c0d7a9ae6f001edb91fdf,STILL_EXISTS,dobj = max(dobj;dobj_old) # FIXME : pb with dual old unfeasible
aaehcdibd,nilearn/nilearn,nilearn/sparse_models/optim.py,ce9e8b0b9c93f980648c0d7a9ae6f001edb91fdf,STILL_EXISTS,FIXME : add proper warning message
aaehcdibf,nilearn/nilearn,nilearn/sparse_models/prox_tv_l1.py,ce9e8b0b9c93f980648c0d7a9ae6f001edb91fdf,STILL_EXISTS,\"\"\" LV + l1 proximal operator ||  || The core idea here is to modify the analysis operator in the Beck & || Teboulle approach (actually Chambolle) to keep the identity and thus to || end up with an l1. || \"\"\"
aaehcdibj,nilearn/nilearn,nilearn/sparse_models/prox_tv_l1.py,ce9e8b0b9c93f980648c0d7a9ae6f001edb91fdf,STILL_EXISTS,XXX should use same formula as in primal_dual.tv_l1.py
aaehcdicg,nilearn/nilearn,nilearn/sparse_models/prox_tv_l1.py,ce9e8b0b9c93f980648c0d7a9ae6f001edb91fdf,STILL_EXISTS,XXX this makes grad_aux and grad_tmp point to thesame buffer!
aaehcdjbj,nilearn/nilearn,nilearn/sparse_models/tests/test_smooth_lasso.py,ce9e8b0b9c93f980648c0d7a9ae6f001edb91fdf,STILL_EXISTS,XXX A small dataset here (this test is very lengthy)
aaehcdjdb,nilearn/nilearn,nilearn/sparse_models/tests/test_smooth_lasso.py,ce9e8b0b9c93f980648c0d7a9ae6f001edb91fdf,ca01674d67259bad2d9b9985ef0a0ed826cb6de3,XXX This test is senseless as we solver reports even history of
aaehcdjec,nilearn/nilearn,nilearn/sparse_models/tv.py,ce9e8b0b9c93f980648c0d7a9ae6f001edb91fdf,STILL_EXISTS,\"\"\" || Synopsis: TV-l1 regression. Handles squared loss and logistic too. || Author: DOHMATOB Elvis Dopgima <gmdopp@gmail.com> <elvis.dohmatob@inria.fr> ||  || \"\"\"
aaehcdjeg,nilearn/nilearn,nilearn/sparse_models/tv.py,ce9e8b0b9c93f980648c0d7a9ae6f001edb91fdf,STILL_EXISTS,XXX We'll work on the full brain; and do the masking \/ unmasking
aaehcdjeh,nilearn/nilearn,nilearn/sparse_models/tv.py,ce9e8b0b9c93f980648c0d7a9ae6f001edb91fdf,STILL_EXISTS,magic when needed
aaehcdjei,nilearn/nilearn,nilearn/sparse_models/tv.py,ce9e8b0b9c93f980648c0d7a9ae6f001edb91fdf,STILL_EXISTS,rescale alpha parameter (= amount of regularization) to handle
aaehcdjhd,nilearn/nilearn,nilearn/sparse_models/cv.py,ab4a2375e03a29592faa54b8ba0ee701035891d7,STILL_EXISTS,XXX It may happen that b is in the kernel of X.T!
aaehceaab,nilearn/nilearn,nilearn/sparse_models/image.py,7a52f3111681cd2febf4fab1d3acd4185f41d74e,STILL_EXISTS,XXX This code is duplicated from common.py!
aaehceahe,nilearn/nilearn,nilearn/sparse_models/cv.py,8df15b97aa5dd182d862ae3256bc8b79ed505c19,STILL_EXISTS,XXX run this in parallel (use n_jobs)!
aaehcebci,nilearn/nilearn,nilearn/sparse_models/_cv_tricks.py,d2413d9a01eff57c08c908514e3d2f1bd6dc11f5,STILL_EXISTS,XXX uncovered \/ untested code!
aaehcebcj,nilearn/nilearn,nilearn/sparse_models/_cv_tricks.py,d2413d9a01eff57c08c908514e3d2f1bd6dc11f5,STILL_EXISTS,XXX It may happen that b is in the kernel of X.T!
aaehcebdc,nilearn/nilearn,nilearn/sparse_models/common.py,d2413d9a01eff57c08c908514e3d2f1bd6dc11f5,STILL_EXISTS,XXX doubtful
aaehcebfc,nilearn/nilearn,nilearn/sparse_models/operators.py,69bce81f0486c7e48cb0a809694db916a6b51af7,STILL_EXISTS,XXX should use same formula as in primal_dual.tv_l1.py
aaehcebfj,nilearn/nilearn,nilearn/sparse_models/operators.py,69bce81f0486c7e48cb0a809694db916a6b51af7,STILL_EXISTS,XXX this makes grad_aux and grad_tmp point to thesame buffer!
aaehceceh,nilearn/nilearn,nilearn/_utils/fixes/__init__.py,42a14bedca311bceb6fd2d81678f29bb11a26605,0416112f8ab31e3aa406ac1d1cfcbc18fc05558f,XXX use LooseVersion to specify correct breakpoint of ALL backported APIs!
aaehcecfb,nilearn/nilearn,nilearn/_utils/fixes/__init__.py,42a14bedca311bceb6fd2d81678f29bb11a26605,47ab201fd6deea662485c510588ae0aa9955a7b6,XXX the sklearn version does some weird checks (that fail for our
aaehcecff,nilearn/nilearn,nilearn/sparse_models/tests/test_cv.py,67cd5f0eec00c1da6d9b60959b5b0d745f4a6f0d,67cd5f0eec00c1da6d9b60959b5b0d745f4a6f0d,XXX test fails with early stopping in CV
aaehcecii,nilearn/nilearn,nilearn/decoding/sparse_models/common.py,50788fe8b4e24abca719f20ba3f55000b33156ee,STILL_EXISTS,XXX div (see below) could be computed more efficienty!
aaehcedic,nilearn/nilearn,nilearn/decoding/cv.py,b69f7dd1620a5bfc8c6711b0a8344614d3cf3fcc,STILL_EXISTS,XXX uncovered \/ untested code!
aaehcedid,nilearn/nilearn,nilearn/decoding/cv.py,b69f7dd1620a5bfc8c6711b0a8344614d3cf3fcc,STILL_EXISTS,XXX It may happen that b is in the kernel of X.T!
aaehceeaa,nilearn/nilearn,nilearn/decoding/space_net_solvers.py,b75c7ab59b751a4b0eb8ed2e0864c1f29684c1aa,STILL_EXISTS,XXX We'll work on the full brain; and do the masking \/ unmasking
aaehceeab,nilearn/nilearn,nilearn/decoding/space_net_solvers.py,b75c7ab59b751a4b0eb8ed2e0864c1f29684c1aa,STILL_EXISTS,magic when needed
aaehceeac,nilearn/nilearn,nilearn/decoding/space_net_solvers.py,b75c7ab59b751a4b0eb8ed2e0864c1f29684c1aa,cc6646b2fadf0a06a63765d398e657aa7f9e69c4,rescale alpha parameter (= amount of regularization) to handle
aaehceicb,nilearn/nilearn,nilearn/decoding/space_net.py,85d4f91cb225a77f023cbc45b2e63dafd17b30bb,cb8c57755a9402adcd2851b849f691033f89809c,XXX: No longer deal with standardize and normalize: do it in the
aaehceice,nilearn/nilearn,nilearn/decoding/space_net.py,85d4f91cb225a77f023cbc45b2e63dafd17b30bb,cb8c57755a9402adcd2851b849f691033f89809c,XXX: the code below smell; we should probably remove it
aaehceifd,nilearn/nilearn,nilearn/decoding/space_net.py,2baed570062deb1f81151e1a3bb1597ed9e99ebe,1d8192c79ab58d098a1925ad1457af78bbc37c74,no cross-validation needed; user supplied all params
aaehcfcaa,nilearn/nilearn,nilearn/sparse_models/_cv_tricks.py,5caa0e45e9523192e55bf8394f6c8fb0ad773369,STILL_EXISTS,\"\"\" || Ninja tricks (early stopping; etc.) to make CV a better place to live... ||  || \"\"\"
aaehcfcaj,nilearn/nilearn,nilearn/sparse_models/common.py,5caa0e45e9523192e55bf8394f6c8fb0ad773369,STILL_EXISTS,\"\"\" || Common functions and base classes. Used by more specialized modules like || tv.py; smooth_lasso.py; etc. ||  || XXX TODO: Factor out code specific to estimator and cv classes; into seperate modules ||  || \"\"\"
aaehcfcbb,nilearn/nilearn,nilearn/sparse_models/common.py,5caa0e45e9523192e55bf8394f6c8fb0ad773369,STILL_EXISTS,xxx: isn't it a more generic function?
aaehcfcbf,nilearn/nilearn,nilearn/sparse_models/common.py,5caa0e45e9523192e55bf8394f6c8fb0ad773369,STILL_EXISTS,XXX: functions that return variable number of outputs are bad
aaehcfcce,nilearn/nilearn,nilearn/sparse_models/common.py,5caa0e45e9523192e55bf8394f6c8fb0ad773369,STILL_EXISTS,xxx: img.dtype?
aaehcfcfi,nilearn/nilearn,nilearn/sparse_models/cv.py,5caa0e45e9523192e55bf8394f6c8fb0ad773369,STILL_EXISTS,XXX uncovered \/ untested code!
aaehcfcgc,nilearn/nilearn,nilearn/sparse_models/cv.py,5caa0e45e9523192e55bf8394f6c8fb0ad773369,STILL_EXISTS,XXX Are these alphas reasonable ?
aaehcfdfh,nilearn/nilearn,nilearn/sparse_models/operators.py,5caa0e45e9523192e55bf8394f6c8fb0ad773369,b3d2b7c0ebc0d59ae21a28ac9f9d31073a13ff25,XXX
aaehcffcd,nilearn/nilearn,nilearn/sparse_models/tests/test_smooth_lasso.py,5caa0e45e9523192e55bf8394f6c8fb0ad773369,STILL_EXISTS,XXX A small dataset here (this test is very lengthy)
aaehcffdf,nilearn/nilearn,nilearn/sparse_models/tests/test_smooth_lasso.py,5caa0e45e9523192e55bf8394f6c8fb0ad773369,cb73a36bc95eb12c5e2d08dfa035face604e0769,XXX This test is senseless as we solver reports even history of
aaehcffeg,nilearn/nilearn,nilearn/sparse_models/tv.py,5caa0e45e9523192e55bf8394f6c8fb0ad773369,STILL_EXISTS,\"\"\" || Synopsis: TV-l1 regression. Handles squared loss and logistic too. || Author: DOHMATOB Elvis Dopgima <gmdopp@gmail.com> <elvis.dohmatob@inria.fr> ||  || \"\"\"
aaehcfffa,nilearn/nilearn,nilearn/sparse_models/tv.py,5caa0e45e9523192e55bf8394f6c8fb0ad773369,STILL_EXISTS,XXX We'll work on the full brain; and do the masking \/ unmasking
aaehcfffb,nilearn/nilearn,nilearn/sparse_models/tv.py,5caa0e45e9523192e55bf8394f6c8fb0ad773369,STILL_EXISTS,magic when needed
aaehcfffc,nilearn/nilearn,nilearn/sparse_models/tv.py,5caa0e45e9523192e55bf8394f6c8fb0ad773369,STILL_EXISTS,rescale alpha parameter (= amount of regularization) to handle
aaehcffhh,nilearn/nilearn,nilearn/sparse_models/cv.py,ea40021037a8b0cb2dcfe877444e8e09cdabd61d,STILL_EXISTS,XXX It may happen that b is in the kernel of X.T!
aaehcfgaf,nilearn/nilearn,nilearn/sparse_models/image.py,7a93dccda96b7958e18d5b139581e7e3b0e3a741,STILL_EXISTS,XXX This code is duplicated from common.py!
aaehcfghi,nilearn/nilearn,nilearn/sparse_models/cv.py,da85ff16b9a06ed44c41fabaf8a87d67378430e2,STILL_EXISTS,XXX run this in parallel (use n_jobs)!
aaehcfhcj,nilearn/nilearn,nilearn/sparse_models/_cv_tricks.py,91776d94113f4dfa4bcdb2b4a5a7baec758bda67,STILL_EXISTS,XXX uncovered \/ untested code!
aaehcfhda,nilearn/nilearn,nilearn/sparse_models/_cv_tricks.py,91776d94113f4dfa4bcdb2b4a5a7baec758bda67,STILL_EXISTS,XXX It may happen that b is in the kernel of X.T!
aaehcfhdd,nilearn/nilearn,nilearn/sparse_models/common.py,91776d94113f4dfa4bcdb2b4a5a7baec758bda67,STILL_EXISTS,XXX doubtful
aaehcfhfd,nilearn/nilearn,nilearn/sparse_models/operators.py,ad49cde0d36cd7d30f12900aa4eb3e5fa0066ef3,STILL_EXISTS,XXX should use same formula as in primal_dual.tv_l1.py
aaehcfhga,nilearn/nilearn,nilearn/sparse_models/operators.py,ad49cde0d36cd7d30f12900aa4eb3e5fa0066ef3,STILL_EXISTS,XXX this makes grad_aux and grad_tmp point to thesame buffer!
aaehcfieh,nilearn/nilearn,nilearn/sparse_models/tests/test_cv.py,0e68d70b52626fdfd4883c9f147dc3987ec5d017,0e68d70b52626fdfd4883c9f147dc3987ec5d017,XXX test fails with early stopping in CV
aaehcfiia,nilearn/nilearn,nilearn/decoding/sparse_models/common.py,753fd06a2b0ee2ac4048ca62d41656a18b0836f0,STILL_EXISTS,XXX div (see below) could be computed more efficienty!
aaehcfjhf,nilearn/nilearn,nilearn/decoding/objective_functions.py,38b1a68f9f74ebb1a0f8cf2f73a9e606f7c022c2,f0e3aff79c9c41b398aaee7e6a6e412c8e87c6fb,xxx: isn't it a more generic function?
aaehcfjhg,nilearn/nilearn,nilearn/decoding/objective_functions.py,38b1a68f9f74ebb1a0f8cf2f73a9e606f7c022c2,f0e3aff79c9c41b398aaee7e6a6e412c8e87c6fb,XXX doubtful
aaehcfjhh,nilearn/nilearn,nilearn/decoding/objective_functions.py,38b1a68f9f74ebb1a0f8cf2f73a9e606f7c022c2,f0e3aff79c9c41b398aaee7e6a6e412c8e87c6fb,XXX: functions that return variable number of outputs are bad
aaehcfjib,nilearn/nilearn,nilearn/decoding/objective_functions.py,38b1a68f9f74ebb1a0f8cf2f73a9e606f7c022c2,fdc211756e59588839bb5bb8a97a4783718a3354,xxx: img.dtype?
aaehcfjif,nilearn/nilearn,nilearn/decoding/objective_functions.py,38b1a68f9f74ebb1a0f8cf2f73a9e606f7c022c2,STILL_EXISTS,XXX div (see below) could be computed more efficienty!
aaehcgaaa,nilearn/nilearn,nilearn/decoding/proximal_operators.py,4741fd01505004ef411e453736c2cd4a43ed5a6c,cf654f79b22613048a78b893fe7f8e759db59650,XXX should use same formula as in primal_dual.tv_l1.py
aaehcgaab,nilearn/nilearn,nilearn/decoding/proximal_operators.py,4741fd01505004ef411e453736c2cd4a43ed5a6c,f0e3aff79c9c41b398aaee7e6a6e412c8e87c6fb,XXX this makes grad_aux and grad_tmp point to thesame buffer!
aaehcgacb,nilearn/nilearn,nilearn/decoding/cv.py,a4875ae4b8f312aeeb7193b15f2862b16a940ff0,STILL_EXISTS,XXX uncovered \/ untested code!
aaehcgacc,nilearn/nilearn,nilearn/decoding/cv.py,a4875ae4b8f312aeeb7193b15f2862b16a940ff0,STILL_EXISTS,XXX It may happen that b is in the kernel of X.T!
aaehcgaeh,nilearn/nilearn,nilearn/decoding/space_net_solvers.py,c322008e6ca969e4ae132b9668c05ab02fa6ab1e,STILL_EXISTS,XXX We'll work on the full brain; and do the masking \/ unmasking
aaehcgaei,nilearn/nilearn,nilearn/decoding/space_net_solvers.py,c322008e6ca969e4ae132b9668c05ab02fa6ab1e,STILL_EXISTS,magic when needed
aaehcgaej,nilearn/nilearn,nilearn/decoding/space_net_solvers.py,c322008e6ca969e4ae132b9668c05ab02fa6ab1e,172a30ece780dacad0c340090973df814cbbe6cc,rescale alpha parameter (= amount of regularization) to handle
aaehcgagb,nilearn/nilearn,nilearn/decoding/space_net.py,1d8192c79ab58d098a1925ad1457af78bbc37c74,5fde699b16e0c914eded4ff70e556d709d74158f,XXX uncovered \/ untested code!
aaehcgahc,nilearn/nilearn,nilearn/decoding/space_net.py,1d8192c79ab58d098a1925ad1457af78bbc37c74,5fde699b16e0c914eded4ff70e556d709d74158f,XXX Are these alphas reasonable ?
aaehcgbce,nilearn/nilearn,nilearn/decoding/tests/test_space_net.py,59edf53452a10b55102db2872940dd42549103f7,STILL_EXISTS,XXX test fails with early stopping in CV
aaehcgecj,nilearn/nilearn,nilearn/decoding/space_net.py,d182c294d58fc0cfc58f3a21a2968abea3c530ee,db317a9992cfdd01a59538aaa0c9b3383c20c7e6,XXX: No longer deal with standardize and normalize: do it in the
aaehcgedc,nilearn/nilearn,nilearn/decoding/space_net.py,d182c294d58fc0cfc58f3a21a2968abea3c530ee,db317a9992cfdd01a59538aaa0c9b3383c20c7e6,XXX: the code below smell; we should probably remove it
aaehcgegd,nilearn/nilearn,nilearn/decoding/space_net.py,f8a73095e19a1cadd5ee7d158ac5aa9f4684cdee,STILL_EXISTS,no cross-validation needed; user supplied all params
aaehcghcb,nilearn/nilearn,nilearn/input_data/base_masker.py,43e2e07719c94344a626682e432741c5d68e97b1,6bfdb5ef288e2d30b57f7a5c757f6aef97803356,XXX This function is converging toward filter_and_mask. They will merge
aaehchdhg,nilearn/nilearn,nilearn/input_data/nifti_masker.py,7230e5f4cc845ae47d18ca180d9d7e26b16a9a32,STILL_EXISTS,y=None is for scikit-learn compatibility (unused here).
aaehchdjb,nilearn/nilearn,nistats/datasets.py,ff294c602fae444a46492a5c59a1be418c462656,e491a5516a09becb9eb5ae582d392d8584143d11,The options needed for _fetch_files
aaehchdjf,nilearn/nilearn,nistats/datasets.py,64de4d940e2335dd22f9772ed8bffdda24ac0f01,STILL_EXISTS,maybe data_dir already contains the data ?
aaehcheab,nilearn/nilearn,examples/plot_fiac_analysis.py,dd60137f3741079dfb47effe6d8ed4b611055602,f40b209cbea99925275e39bf78ccd8f59d5ab0f3,the design matrices of both runs comprise 13 columns
aaehcheac,nilearn/nilearn,examples/plot_fiac_analysis.py,dd60137f3741079dfb47effe6d8ed4b611055602,f40b209cbea99925275e39bf78ccd8f59d5ab0f3,the first 5 columns of the design matrices correspond to the following
aaehchfci,nilearn/nilearn,nistats/tests/test_glm.py,8ea3e2e76937a6ef29c6712f74083c92102dc329,STILL_EXISTS,assert_almost_equal(con1.variance * 2; con2.variance) FIXME
aaehcihjc,nilearn/nilearn,examples/plot_design_matrix.py,5d0ffc72a7b5632dc509937bc35da1b3870301a2,STILL_EXISTS,\"\"\" || Examples of design matrices || =========================== ||  || Three examples of design matrices specification and computation || (event-related design; block design; FIR design) ||  || Requires matplotlib ||  || Author : Bertrand Thirion: 2009-2015 || \"\"\"
aaehcijbb,nilearn/nilearn,doc/sphinxext/sphinxgallery/gen_gallery.py,6243b0c2f136c49f47e9660b390baf8d25c5b31c,STILL_EXISTS,better than nested.
aaehcijeg,nilearn/nilearn,doc/sphinxext/sphinxgallery/docs_resolv.py,c01587a55ea6cc8b5d5d8ec2fe5a839b8034fe31,STILL_EXISTS,XXX: also at the time of writing this fixes make html-noplot
aaehcijei,nilearn/nilearn,doc/sphinxext/sphinxgallery/docs_resolv.py,c01587a55ea6cc8b5d5d8ec2fe5a839b8034fe31,STILL_EXISTS,XXX: Whitelist of builders for which it makes sense to embed
aaehcjfbb,nilearn/nilearn,nilearn/externals/skimage/random_walker_segmentation.py,889e3c34506da01a4a05340c422be20a56dae878,STILL_EXISTS,needed to speed up a few cases.
aaehcjfhg,nilearn/nilearn,nilearn/regions/region_extractor.py,998f0293394235214de660a0444167d3bd52d2bd,755780e1780497b31d1e2e30a634b34800f2b137,When threshold is needed to apply directly based on
aaehcjhgd,nilearn/nilearn,nilearn/decoding/searchlight.py,a9fee371ab5dd0bda83461eadd7946be36d48d45,bffe96b0dde34b701946a1a71e7225bf6828df92,of voxels in processing mask (columns in process_mask)
aaehcjibd,nilearn/nilearn,nilearn/connectome/group_sparse_cov.py,7d7b2284d8ecb3084396b7805e71d2aaab5a6506,STILL_EXISTS,overall convergence rate (the tighter the better.)
aaehcjicj,nilearn/nilearn,nilearn/connectome/group_sparse_cov.py,7d7b2284d8ecb3084396b7805e71d2aaab5a6506,STILL_EXISTS,TODO: can be computed more efficiently using W_inv. See
aaehcjjef,nilearn/nilearn,nilearn/decoding/space_net.py,1120bd74f1488b949aa13a7741e27345856c0f16,STILL_EXISTS,no cross-validation needed; user supplied all params
aaehdaabf,nilearn/nilearn,nilearn/decomposition/base.py,1a9e8de6d6a3c3663f50a3851c187282b7cc6633,STILL_EXISTS,XXX Should we provided memory mapping for n_jobs > 1 to allow concurrent
aaehdaacb,nilearn/nilearn,nilearn/decomposition/base.py,1a9e8de6d6a3c3663f50a3851c187282b7cc6633,STILL_EXISTS,XXX: dealing properly with 4D\/ list of 4D data?
aaehdaacc,nilearn/nilearn,nilearn/decomposition/base.py,1a9e8de6d6a3c3663f50a3851c187282b7cc6633,STILL_EXISTS,XXX: dealing properly with 2D\/ list of 2D data?
aaehdaahi,nilearn/nilearn,examples/connectivity/plot_compare_resting_state_decomposition.py,7af3a07a9fe836f3dc7350732dd71cb214853533,STILL_EXISTS,Selecting specific maps to display: maps were manually chosen to be similar
aaehdahhe,nilearn/nilearn,doc/sphinxext/numpydoc/comment_eater.py,b27db85e33fd51aee95795c22c716429598fc2ee,STILL_EXISTS,FIXME: gracefully handle errors here or in the caller?
aaehdahhf,nilearn/nilearn,doc/sphinxext/numpydoc/comment_eater.py,b27db85e33fd51aee95795c22c716429598fc2ee,STILL_EXISTS,FIXME: handle other kinds of assignments?
aaehdahhg,nilearn/nilearn,doc/sphinxext/numpydoc/compiler_unparse.py,b27db85e33fd51aee95795c22c716429598fc2ee,STILL_EXISTS,\"\"\" Turn compiler.ast structures back into executable python code. ||  ||     The unparse method takes a compiler.ast tree and transforms it back into ||     valid python code.  It is incomplete and currently only works for ||     import statements; function calls; function definitions; assignments; and ||     basic expressions. ||  ||     Inspired by python-2.5-svn\/Demo\/parser\/unparse.py ||  ||     fixme: We may want to move to using _ast trees because the compiler for ||            them is about 6 times faster than compiler.compile. || \"\"\"
aaehdahjc,nilearn/nilearn,doc/sphinxext/numpydoc/compiler_unparse.py,b27db85e33fd51aee95795c22c716429598fc2ee,STILL_EXISTS,fixme: Are From and ImportFrom handled differently?
aaehdahjj,nilearn/nilearn,doc/sphinxext/numpydoc/compiler_unparse.py,b27db85e33fd51aee95795c22c716429598fc2ee,STILL_EXISTS,Check if parenthesis are needed on left side and then dispatch
aaehdaiab,nilearn/nilearn,doc/sphinxext/numpydoc/compiler_unparse.py,b27db85e33fd51aee95795c22c716429598fc2ee,STILL_EXISTS,Check if parenthesis are needed on the right side and then dispatch
aaehdaidh,nilearn/nilearn,doc/sphinxext/numpydoc/compiler_unparse.py,b27db85e33fd51aee95795c22c716429598fc2ee,STILL_EXISTS,#        # XXX(jpe) what is level for?
aaehdbbdh,nilearn/nilearn,doc/sphinxext/numpydoc/numpydoc.py,b27db85e33fd51aee95795c22c716429598fc2ee,STILL_EXISTS,probably called by nose; better bail out
aaehdbcej,nilearn/nilearn,examples/plot_thresholding.py,c06b66fbef2e31638d81fc8db565441ff0bb60b4,STILL_EXISTS,\"\"\"  || Perform a one-sample t-test on a bunch of images (a.k.a. second-level analyis in fMRI) and threshold a statistical image. ||  || Author: Bertrand.thirion; Virgile Fritsch; 2014--2015 || \"\"\"
aaehdceee,nilearn/nilearn,examples/05_advanced/plot_ica_resting_state.py,8be5de9f88a5e0073992300d046c1a3776fa1f88,STILL_EXISTS,\"\"\" || Multivariate decompositions: Independent component analysis of fMRI || =================================================================== ||  ||  || This example is meant to demonstrate nilearn as a low-level tools used to || combine feature extraction with a multivariate decomposition algorithm || for resting state. ||  || This example is a toy. To apply ICA to resting-state data; it is advised || to look at the example || :ref:`sphx_glr_auto_examples_03_connectivity_plot_canica_resting_state.py`. ||  || The example here applies the scikit-learn ICA to resting-state data. || Note that following the code in the example; any unsupervised || decomposition model; or other latent-factor models; can be applied to || the data; as the scikit-learn API enables to exchange them as almost || black box (though the relevant parameter for brain maps might no longer || be given by a call to fit_transform). ||  || \"\"\"
aaehdceei,nilearn/nilearn,examples/05_advanced/plot_ica_resting_state.py,8be5de9f88a5e0073992300d046c1a3776fa1f88,df1d8ecb1266883eb88ebe7b64815b2663705c36,XXX: must get the code to run for more than 1 subject
aaehdciei,nilearn/nilearn,nilearn/plotting/displays.py,c9710030d927541fb2ba59fbf9ab800a57b9d588,30f8a277d8a8f484891cbbec9d6cab57e3934ce4,At the moment threshold=0; we fix this based upon discussion
aaehdcihc,nilearn/nilearn,nilearn/plotting/displays.py,05064ac2dd41905b0e329295df23348503f525ec,STILL_EXISTS,XXX: should we keep this heuristic?
aaehdddfc,nilearn/nilearn,examples/connectivity/plot_ica_neurovault.py,f0065ff784e0ccaabc23ee2d6f1dcdeafbb32f4e,STILL_EXISTS,Now remove images that are ugly; or obviously not z maps:
aaehdddjg,nilearn/nilearn,examples/connectivity/plot_ica_neurovault.py,70ef5c7ba45f7fa2d4f34ad1ffb47d9087978028,190cbd7f4050c661e34251b35573917ab7f9b8a0,Ugly \/ obviously not Z maps
aaehddeea,nilearn/nilearn,nilearn/datasets/func.py,190cbd7f4050c661e34251b35573917ab7f9b8a0,d28bfb96b0313dc493527b8af7a1820d80471c59,Ugly \/ obviously not Z maps
aaehddfcj,nilearn/nilearn,examples/01_plotting/plot_demo_more_plotting.py,bdb4463ff1136df8e0f130dc3d4504bb99d9b188,STILL_EXISTS,how to select two slices of particular interest manually by giving the
aaehddgae,nilearn/nilearn,examples/plot_hrf.py,734f89f1383f98fd69dfd1b6cfbac7c80bc026be,STILL_EXISTS,\"\"\"  || Example of hemodynamic reponse functions. || We consider the hrf model in SPM together with the hrf shape proposed || by G.Glover; as well as their time and dispersion derivatives. ||  || Author: Bertrand Thirion; 2015. || \"\"\"
aaehddgba,nilearn/nilearn,nilearn/decomposition/base.py,aff45ef95b6c987cd0503c79f513d67e47368786,STILL_EXISTS,subject; so we hack it.
aaehddghd,nilearn/nilearn,examples/04_manipulating_images/plot_roi_extraction.py,a35656d57bb4352639274a41c423607af32a4aeb,STILL_EXISTS,**Thresholding** - Voxels with better p-values are kept as voxels of interest.
aaehdeaic,nilearn/nilearn,examples/04_manipulating_images/plot_roi_extraction.py,c0bfa846f24422c13431317b8cf2e30d02c989d5,STILL_EXISTS,gives us brief motivation example about why selecting features in high
aaehdeajb,nilearn/nilearn,examples/04_manipulating_images/plot_roi_extraction.py,c0bfa846f24422c13431317b8cf2e30d02c989d5,STILL_EXISTS,**Thresholding** - We build the t-map to have better representation of voxels
aaehdebgi,nilearn/nilearn,nistats/tests/test_contrasts.py,454326e100457581df9e81c1077b3d87b07f20b7,STILL_EXISTS,assert_almost_equal(con1.variance * 2; con2.variance) FIXME
aaehdebgj,nilearn/nilearn,nistats/tests/test_contrasts.py,454326e100457581df9e81c1077b3d87b07f20b7,STILL_EXISTS,assert_almost_equal(con1.stat() * 2; con2.stat()) FIXME
aaehdedhg,nilearn/nilearn,examples/01_plotting/plot_demo_plotting.py,0dd0ae72b27d37630e689e6f012402329d74635c,STILL_EXISTS,A call to plotting.show is needed to display the plots when running
aaehdedid,nilearn/nilearn,nistats/tests/test_first_level_model.py,4a1a0eb09a2c39aedf1b4fcc5f50778077f1a035,STILL_EXISTS,assert_almost_equal(labels1; labels2; decimal=1) ####FIX
aaehdedie,nilearn/nilearn,nistats/tests/test_first_level_model.py,4a1a0eb09a2c39aedf1b4fcc5f50778077f1a035,STILL_EXISTS,assert_equal(len(results1); len(results2)) ####FIX
aaehdeebb,nilearn/nilearn,examples/plot_3d_and_4d_niimg.py,33728d315cdad49fe51277f7ea93a7b5e9b408ec,STILL_EXISTS,Visualizing works better with a threshold
aaehdejcg,nilearn/nilearn,examples/plot_decoding_tutorial.py,7c18b3c0a963c6c59ac8298cba31bbb3065a3efa,STILL_EXISTS,The best way to do cross-validation is to respect the structure of
aaehdfcjb,nilearn/nilearn,nilearn/_utils/fixes/scikit_learn_gridsearch.py,c18682733af8d9a0e266d7e11d08b61649c9b0c6,STILL_EXISTS,efficient.
aaehdfcjc,nilearn/nilearn,nilearn/_utils/fixes/scikit_learn_gridsearch.py,c18682733af8d9a0e266d7e11d08b61649c9b0c6,STILL_EXISTS,XXX: could memoize information used here
aaehdfhjb,nilearn/nilearn,nilearn/plotting/displays.py,a63eb6890d7e3dda8220ab794a937eb808c94f88,STILL_EXISTS,before resetting the bounds and re-invert it after if needed.
aaehdgdee,nilearn/nilearn,nilearn/datasets/func.py,9b0b84088a2882878d276dba3fa5b7978e3a56dc,b162da2f5d0a17c7fe81ad1c0ee9a6f62ae2628a,Ugly \/ obviously not Z maps
aaehdgeab,nilearn/nilearn,examples/05_advanced/plot_ica_neurovault.py,b162da2f5d0a17c7fe81ad1c0ee9a6f62ae2628a,STILL_EXISTS,images. For better results; increase the number of images downloaded
aaehdgeai,nilearn/nilearn,examples/05_advanced/plot_ica_neurovault.py,b162da2f5d0a17c7fe81ad1c0ee9a6f62ae2628a,STILL_EXISTS,maps; while other capture noise in the database. More data; better
aaehdgeaj,nilearn/nilearn,examples/05_advanced/plot_ica_neurovault.py,b162da2f5d0a17c7fe81ad1c0ee9a6f62ae2628a,STILL_EXISTS,filtering; and better cognitive labels would give better maps
aaehdgeia,nilearn/nilearn,nilearn/datasets/neurovault.py,b162da2f5d0a17c7fe81ad1c0ee9a6f62ae2628a,2dfda7daee4791a87abdc61ae4c2e3701c765aa0,Ugly \/ obviously not Z maps
aaehdgfaf,nilearn/nilearn,nilearn/datasets/tests/test_neurovault.py,b162da2f5d0a17c7fe81ad1c0ee9a6f62ae2628a,STILL_EXISTS,TODO: find out why and use tst.setup_tmpdata.
aaehdggac,nilearn/nilearn,nistats/tests/test_first_level_model.py,4d965b88e6cb789dc7610300473bfd9c87b265a3,STILL_EXISTS,formula should work (passing varible name directly)
aaehdgghd,nilearn/nilearn,nistats/tests/test_second_level_model.py,5e9fa717406ac7b687f03fe0502e8312d3b36681,STILL_EXISTS,formula should work (passing varible name directly)
aaehdghca,nilearn/nilearn,doc/sphinxext/sphinx_gallery/gen_rst.py,307e54f5e15a20079e3be97b865b97af929b2636,STILL_EXISTS,XXX This check can break during testing e.g. if you uncomment the
aaehdjbha,nilearn/nilearn,nistats/contrasts.py,8d3e4b501e41edc130dfd9d846b2bcf40044fe04,STILL_EXISTS,\"\"\" || This module is for contrast computation and operation on contrast to || obtain fixed effect results. ||  || Author: Bertrand Thirion; Martin Perez-Guevara; 2016 || \"\"\"
aaehdjbhd,nilearn/nilearn,nistats/design_matrix.py,8d3e4b501e41edc130dfd9d846b2bcf40044fe04,STILL_EXISTS,\"\"\" || This module implements fMRI Design Matrix creation. ||  || Design matrices are represented by Pandas DataFrames || Computations of the different parts of the design matrix are confined || to the make_design_matrix function; that create a DataFrame || All the others are ancillary functions. ||  || Design matrices contain three different types of regressors: ||  || 1. Task-related regressors; that result from the convolution ||    of the experimental paradigm regressors with hemodynamic models ||    A hemodynamic model is one of: ||    'spm' : linear filter used in the SPM software ||    'glover' : linear filter estimated by G.Glover ||    'spm + derivative'; 'glover + derivative': the same linear models; ||        plus their time derivative (2 regressors per condition) ||    'spm + derivative + dispersion'; 'glover + derivative + dispersion': ||        idem plus the derivative wrt the dispersion parameter of the hrf ||        (3 regressors per condition) ||    'fir' : finite impulse response model; generic linear filter ||  || 2. User-specified regressors; that represent information available on ||    the data; e.g. motion parameters; physiological data resampled at ||    the acquisition rate; or sinusoidal regressors that model the ||    signal at a frequency of interest. ||  || 3. Drift regressors; that represent low_frequency phenomena of no ||    interest in the data; they need to be included to reduce variance ||    estimates. ||  || Author: Bertrand Thirion; 2009-2015 || \"\"\"
aaehdjbhf,nilearn/nilearn,nistats/model.py,8d3e4b501e41edc130dfd9d846b2bcf40044fe04,STILL_EXISTS,\"\"\" || This module implement classes to handle statistical tests on likelihood models ||  || Author: Bertrand Thirion; 2011--2015 || \"\"\"
aaehdjbhg,nilearn/nilearn,nistats/second_level_model.py,8d3e4b501e41edc130dfd9d846b2bcf40044fe04,STILL_EXISTS,\"\"\" || This module presents an interface to use the glm implemented in || nistats.regression. ||  || It provides facilities to realize a second level analysis on lists of || first level contrasts or directly on fitted first level models ||  || Author: Martin Perez-Guevara; 2016 || \"\"\"
aaehdjcdh,nilearn/nilearn,examples/03_connectivity/plot_fast_clustering.py,77b94bc8810784eabf98742a6c18b7da36d41b95,4772d80b5c639e5622b786f48366a7c18008fb00,import pdb; pdb.set_trace()  # XXX BREAKPOINT
aaehdjchd,nilearn/nilearn,nilearn/connectome/rena_clustering.py,77b94bc8810784eabf98742a6c18b7da36d41b95,78930e8b418ff1b2f5152c9cf118901ea06d5e08,Workaround for a joblib bug: in joblib 0.6; a Memory object
aaehdjchg,nilearn/nilearn,nilearn/connectome/tests/test_rena.py,77b94bc8810784eabf98742a6c18b7da36d41b95,dd58e2cf9aec3c620c9807568beff3738830bc5d,TODO assert not fitting
aaehdjchj,nilearn/nilearn,nilearn/connectome/tests/test_rena.py,77b94bc8810784eabf98742a6c18b7da36d41b95,dd58e2cf9aec3c620c9807568beff3738830bc5d,import pdb; pdb.set_trace()  # XXX BREAKPOINT
aaehdjcie,nilearn/nilearn,nilearn/connectome/rena_clustering.py,cce8586b47da92170737a4866e33515b23751a02,78930e8b418ff1b2f5152c9cf118901ea06d5e08,XXX this code is also replicated in the Metaestimator PR
aaehdjdbh,nilearn/nilearn,nilearn/_utils/fixes/__init__.py,5e1a3d2e5de5d7a6d59cb6fb672aee0a0314cec9,STILL_EXISTS,XXX this is going to be deprecated in sklearn 0.20
aaehdjdch,nilearn/nilearn,nilearn/_utils/fixes/sklearn_model_selection.py,17f05da46bd6dc56464f93a6d94be052e8b5ad48,STILL_EXISTS,Since subclasses must implement either _iter_test_masks or
aaehdjddb,nilearn/nilearn,nilearn/_utils/fixes/sklearn_model_selection.py,17f05da46bd6dc56464f93a6d94be052e8b5ad48,STILL_EXISTS,XXX This is copied from BaseEstimator's get_params
aaehdjegb,nilearn/nilearn,nilearn/_utils/fixes/odict.py,3992eac81e1b071d7ef5d81f55dd408eb5b117ca,STILL_EXISTS,The circular doubly linked list starts and ends with a sentinel element.
aaehdjehf,nilearn/nilearn,doc/sphinxext/sphinx_gallery/backreferences.py,797679a547de3d5b21ad429e3a7590dfac468ed7,d64e51c0f310012751d91990cd4ed97f119d8b42,XXX This figure:: uses a forward slash even on Windows; but the op.join's
aaehdjfch,nilearn/nilearn,nilearn/_utils/param_validation.py,339352e1ab5b70e4f48295a59a88845e18851788,8de7a324abe39866ce92f9837b67110b4726078f,Workaround for a joblib bug: in joblib 0.6; a Memory object
aaehdjfef,nilearn/nilearn,nilearn/_utils/validation.py,5e267f77ad4f8af97342dc1f1cc0345b7d34e7b9,653a58424dc3e0f9a5bc06757aee7b1cb769a7e3,Workaround for a joblib bug: in joblib 0.6; a Memory object
aaehebbhg,nilearn/nilearn,examples/02_decoding/plot_simulated_data.py,4344b45bce098e786da376dec458b369fc9fa172,STILL_EXISTS,**Performance tip**: increase the `step` parameter; or it will be very
aaehebbic,nilearn/nilearn,doc/sphinxext/sphinx_gallery/backreferences.py,9e8ef7e5e0fbef8ff2964a5b315bf92d5748765b,STILL_EXISTS,XXX This figure:: uses a forward slash even on Windows; but the op.join's
aaehebcea,nilearn/nilearn,nistats/datasets.py,c0ec6de9ccdb064a911b84020c6b3b5279238055,e491a5516a09becb9eb5ae582d392d8584143d11,The options needed for _fetch_files
aaehebcfd,nilearn/nilearn,nistats/datasets.py,31e5e27d4d7d55431260cd79c089c870f7f8ebc8,STILL_EXISTS,# The files_spec needed for _fetch_files
aaehebfaa,nilearn/nilearn,nilearn/datasets/func.py,7577251e55427a13a70f199d04078b28bf1a88dc,94f8eb96bb9f50ae896a24e4b03b06d0c6973325,'move': csv_name})];
aaehebfed,nilearn/nilearn,nilearn/datasets/func.py,7577251e55427a13a70f199d04078b28bf1a88dc,94f8eb96bb9f50ae896a24e4b03b06d0c6973325,'move': f});
aaehebfef,nilearn/nilearn,nilearn/datasets/func.py,7577251e55427a13a70f199d04078b28bf1a88dc,94f8eb96bb9f50ae896a24e4b03b06d0c6973325,'move': m})
aaehecace,nilearn/nilearn,nistats/utils.py,d9ec6995029b429d6175cb43981f8e1271a18086,5680e96f79ccde636fe4efe59d8ae6a3abf4db68,I also simplified get_bids_files to avoid returning dictionaries; it was a bad decision carried forward after refactoring parse_bids_files as an independent function; I see it can simply be called afterwards if desired. I also got rid of allow_other_fields; it seemed like feature creeping at second inspection.
aaehecjjc,nilearn/nilearn,nistats/design_matrix.py,61ac27803c8e0417da871c20e041437a20a7c828,a5c777d7dc28666c3dc9eadf802f5e7589d5b278,Contains at least columns 'map_name' and 'subject_label'
aaehecjje,nilearn/nilearn,nistats/design_matrix.py,61ac27803c8e0417da871c20e041437a20a7c828,a5c777d7dc28666c3dc9eadf802f5e7589d5b278,If given; contains at least two columns; 'subject_label' and one confound.
aaehedaah,nilearn/nilearn,nistats/design_matrix.py,61ac27803c8e0417da871c20e041437a20a7c828,a5c777d7dc28666c3dc9eadf802f5e7589d5b278,confounds_name = confounds.columns.tolist()
aaehedaaj,nilearn/nilearn,nistats/design_matrix.py,61ac27803c8e0417da871c20e041437a20a7c828,a5c777d7dc28666c3dc9eadf802f5e7589d5b278,design_columns = (np.unique(maps_name).tolist() +
aaehedabc,nilearn/nilearn,nistats/design_matrix.py,61ac27803c8e0417da871c20e041437a20a7c828,a5c777d7dc28666c3dc9eadf802f5e7589d5b278,design_matrix = pd.DataFrame(columns=design_columns)
aaehedabe,nilearn/nilearn,nistats/design_matrix.py,61ac27803c8e0417da871c20e041437a20a7c828,a5c777d7dc28666c3dc9eadf802f5e7589d5b278,design_matrix.loc[ridx] = [0] * len(design_columns)
aaehedacc,nilearn/nilearn,nistats/design_matrix.py,61ac27803c8e0417da871c20e041437a20a7c828,a5c777d7dc28666c3dc9eadf802f5e7589d5b278,if len(np.unique(design_columns)) != len(design_columns):
aaehedacd,nilearn/nilearn,nistats/design_matrix.py,61ac27803c8e0417da871c20e041437a20a7c828,a5c777d7dc28666c3dc9eadf802f5e7589d5b278,raise ValueError('Design matrix columns do not have unique names')
aaehedfid,nilearn/nilearn,nilearn/connectome/rena_clustering.py,9b58fd60641d07203bf8f9d63fc09e4b96e6a37a,a2e3b43e5be6b23dc3ad826b7843e0f804a4b7a5,XXX BREAKPOINT
aaeheeicj,nilearn/nilearn,nilearn/_utils/param_validation.py,b38f73029d3edee159625ac5ae03f3579d7dc740,8b6ca59e341c1561e639bbc14f1cd5ff71827bc1,Workaround for a joblib bug: in joblib 0.6; a Memory object
aaeheeiee,nilearn/nilearn,nilearn/_utils/validation.py,b40ea95f0dc2f3ac65dc5eb6c92c2095f53b226a,407767ac38e8ef211544e74fed947c8fa1bbb9b0,Workaround for a joblib bug: in joblib 0.6; a Memory object
aaeheeije,nilearn/nilearn,nilearn/_utils/param_validation.py,37d7b0081c8c200d7e30f0d259de8b0fc63e4999,9bf2552e6ac21fc171cc7145261466c00eef0148,Workaround for a joblib bug: in joblib 0.6; a Memory object
aaeheejag,nilearn/nilearn,nilearn/_utils/validation.py,d51b584e5af6fd2ea2449e3557294ee3ddf0d661,d9926f5fc8031ccc08180ab5ff28c940c95ac7fb,Workaround for a joblib bug: in joblib 0.6; a Memory object
aaeheejfh,nilearn/nilearn,nilearn/_utils/validation.py,3c53ca0e6abbcb0f19b331c1055b9c614b6acb8c,b5a5c9bde1a3a505562de28bdd853d9b15c1a62b,Workaround for a joblib bug: in joblib 0.6; a Memory object
aaehefaei,nilearn/nilearn,nilearn/datasets/neurovault.py,70a5bbaf211d80f27b93ca6e661d87192530fcb7,STILL_EXISTS,collection; we consider this collection is garbage and we move on to the
aaehefdjd,nilearn/nilearn,nilearn/connectome/connectivity_matrices.py,9b90a754e169998b89633697c45f173711933632,STILL_EXISTS,Compute the number of the symmetric matrix columns
aaehefgic,nilearn/nilearn,examples/03_connectivity/plot_extract_regions_dictlearning_maps.py,07455aadd202ac32cd4d72937682d2a72c0b7e1b,STILL_EXISTS,General imports needed for plotting
aaehegajc,nilearn/nilearn,doc/sphinxext/sphinx_gallery/gen_gallery.py,0429387cd8e61fdbe15e6927a12d3827acf7980c,STILL_EXISTS,TODO: Test this behavior.
aaehegbaf,nilearn/nilearn,nilearn/plotting/surf_plotting.py,b966ac9d230841f343979d64346582e0d0b5f37d,fe494982860d3d55c4f64a40980b88e02f09d492,something better than the k-means hack) and cache the result.
aaehegbbc,nilearn/nilearn,nilearn/plotting/surf_plotting.py,b966ac9d230841f343979d64346582e0d0b5f37d,07881d05571aa865b3a90305a9d4524a77dd4c04,TODO: 4d for texture time series
aaehegbbd,nilearn/nilearn,nilearn/plotting/surf_plotting.py,b966ac9d230841f343979d64346582e0d0b5f37d,e15d04b3b7b027bc5e9623fbc848105d823a241f,TODO: check nodes inside image
aaehegbgd,nilearn/nilearn,nistats/datasets.py,aede73d04c51bc2aa85a8105b20a12682160f97f,STILL_EXISTS,from the url list we infer all available subjects like 'sub-xxx\/'
aaehegccc,nilearn/nilearn,examples/plot_spm_auditory.py,1bd3c4272ac01e5754863c2a868f8fc16f456ea5,STILL_EXISTS,columns contain the predictors):
aaehegcdi,nilearn/nilearn,examples/plot_spm_auditory.py,1bd3c4272ac01e5754863c2a868f8fc16f456ea5,STILL_EXISTS,created constrasts with a single '1' in each of the columns:
aaehegegg,nilearn/nilearn,nilearn/image/resampling.py,40b52716cd0171f857e59c3381c33cf02167185c,779971ff398def21accfce3e0fc36844d6864d00,XXX: when we drop nibabel 2.1; change \".get_affine()\" to \".affine\"
aaehegfae,nilearn/nilearn,nistats/reporting.py,6d24e5c84a0f5ab6023c6a4cf079f8c8ae07838a,STILL_EXISTS,\"\"\" || This module implements plotting functions useful to report analysis results. ||  || Author: Martin Perez-Guevara; Elvis Dohmatob; 2017 || \"\"\"
aaeheggea,nilearn/nilearn,nilearn/surface.py,fe494982860d3d55c4f64a40980b88e02f09d492,2c74c847ad6e2b72c51dbb7b63defcc104db3c6d,something better than the k-means hack) and cache the result.
aaehehaeb,nilearn/nilearn,nilearn/__init__.py,65009b93329d8b290751771545b94b1dc60c33d2,STILL_EXISTS,Temporary work around to address formatting issues in doc tests
aaehehcac,nilearn/nilearn,nistats/reporting.py,14b00a877663b6c88aecf7d813dd3ecc0bfdb3e9,ce3695e8f34c6f34323766dc96a60a53b69d2729,Subpeak naming convention is cluster num + letter (1a; 1b; etc.)
aaehehgha,nilearn/nilearn,nistats/reporting.py,adc77887dfa188c484e568cb2fbce35ff6da156b,STILL_EXISTS,Subpeak naming convention is cluster num + letter (1a; 1b; etc.)
aaehehjdf,nilearn/nilearn,examples/03_connectivity/plot_compare_resting_state_dictlearning_masking.py,3057488ead7b95330c7532f2214ff0d46e8302aa,STILL_EXISTS,Selecting specific maps to display: maps were manually chosen to be similar
aaehehjef,nilearn/nilearn,doc/conf.py,e1a1e2521fa24834a3a843fbcb51dcba39118f44,3b00487b72b7cb31a5cd132a028893f486197aa1,jquery is included in plotting package data because it is needed for
aaehehjeg,nilearn/nilearn,doc/conf.py,e1a1e2521fa24834a3a843fbcb51dcba39118f44,3b00487b72b7cb31a5cd132a028893f486197aa1,interactive plots. It is also needed by the documentation; so we copy
aaeheiaji,nilearn/nilearn,examples/01_tutorials/block_design_single_subject_single_bloc.py,6b02f11a03d7afca23e9169121b33b7dd6604116,STILL_EXISTS,TODO: simplify!!!
aaeheibah,nilearn/nilearn,examples/01_tutorials/extracting-signal-from-a-voxel.py,6b02f11a03d7afca23e9169121b33b7dd6604116,STILL_EXISTS,TODO
aaeheifca,nilearn/nilearn,doc/conf.py,7e510e428d5fbcdeb61bc5786469c4bca321fb56,9679bfafee17ef7d476efc2e93fecbaed7e371fb,jquery is included in plotting package data because it is needed for
aaeheifcb,nilearn/nilearn,doc/conf.py,7e510e428d5fbcdeb61bc5786469c4bca321fb56,9679bfafee17ef7d476efc2e93fecbaed7e371fb,interactive plots. It is also needed by the documentation; so we copy
aaeheijji,nilearn/nilearn,nistats/tests/test_second_level_model.py,e68ca748b7ce601c60a239fd74a14279a5fea482,a5365a469942d7a993e61f04463bfb20be0d9085,matrix has morr than one columns raises an error
aaehejahj,nilearn/nilearn,examples/01_tutorials/single_subject_single_run.py,3ed2c3c03ac91fe0b63765d7adc528225daeab39,STILL_EXISTS,columns contain the predictors):
aaehejaii,nilearn/nilearn,examples/01_tutorials/single_subject_single_run.py,3ed2c3c03ac91fe0b63765d7adc528225daeab39,STILL_EXISTS,created constrasts with a single '1' in each of the columns:
aaehejaij,nilearn/nilearn,examples/01_tutorials/single_subject_single_run.py,3ed2c3c03ac91fe0b63765d7adc528225daeab39,STILL_EXISTS,TODO: simplify!!!
aaehejajb,nilearn/nilearn,examples/01_tutorials/single_subject_single_run.py,3ed2c3c03ac91fe0b63765d7adc528225daeab39,STILL_EXISTS,TODO explain why contrasts are specified in such a way. Contrasts as weighted sum... use an example ....
aaehejbag,nilearn/nilearn,examples/01_tutorials/single_subject_single_run.py,27740fbd660862fb8ee62684daa4ebd0e71885fc,STILL_EXISTS,created constrast with a single '1' in each of the columns: The role of the contrast is to select some columns of the model --and potentially weight them-- to study the associated statistics. So in a nutshell; a contrast is a linear combination of the estimated effects
aaehejdef,nilearn/nilearn,examples/01_tutorials/plot_single_subject_single_run.py,48ef5065452e455aadf19621254b3b22a3ba1df1,STILL_EXISTS,Here one might for instance test which voxels are well explained by combination of the active and rest condition. Atcually; the contrast specification is done exactly the same way as for t contrasts.
aaehejgia,nilearn/nilearn,doc/conf.py,45bf7fafc4969b5f63a57aaa15f815bf90dfd340,9679bfafee17ef7d476efc2e93fecbaed7e371fb,jquery is included in plotting package data because it is needed for
aaehejgib,nilearn/nilearn,doc/conf.py,45bf7fafc4969b5f63a57aaa15f815bf90dfd340,9679bfafee17ef7d476efc2e93fecbaed7e371fb,interactive plots. It is also needed by the documentation; so we copy
aaehfeghj,nilearn/nilearn,examples/01_tutorials/plot_first_level_model_details.py,6993b684ed51c2148d3cf2ce7d6c1346484af965,STILL_EXISTS,TODO: plot contrasts
aaehfegic,nilearn/nilearn,examples/01_tutorials/plot_first_level_model_details.py,6993b684ed51c2148d3cf2ce7d6c1346484af965,STILL_EXISTS,we encapsulate it in a function that we call when needed.
aaehfegjb,nilearn/nilearn,examples/01_tutorials/plot_first_level_model_details.py,6993b684ed51c2148d3cf2ce7d6c1346484af965,STILL_EXISTS,Does the model perform worse or better ?
aaehfegjd,nilearn/nilearn,examples/01_tutorials/plot_first_level_model_details.py,6993b684ed51c2148d3cf2ce7d6c1346484af965,STILL_EXISTS,Note that the design matrix has more columns to model dirft terms
aaehfegjg,nilearn/nilearn,examples/01_tutorials/plot_first_level_model_details.py,6993b684ed51c2148d3cf2ce7d6c1346484af965,STILL_EXISTS,Another solution is to remove these drift terms. Maybe they're simply useless.
aaehfegji,nilearn/nilearn,examples/01_tutorials/plot_first_level_model_details.py,6993b684ed51c2148d3cf2ce7d6c1346484af965,STILL_EXISTS,Is it better than the original ? No !
aaehfegjj,nilearn/nilearn,examples/01_tutorials/plot_first_level_model_details.py,6993b684ed51c2148d3cf2ce7d6c1346484af965,STILL_EXISTS,Note that the design matrix has changed with no drift columns.
aaehfehaa,nilearn/nilearn,examples/01_tutorials/plot_first_level_model_details.py,6993b684ed51c2148d3cf2ce7d6c1346484af965,STILL_EXISTS,the event columns; on the other hand; haven't changed.
aaehfehae,nilearn/nilearn,examples/01_tutorials/plot_first_level_model_details.py,6993b684ed51c2148d3cf2ce7d6c1346484af965,STILL_EXISTS,Is it good ? No better; no worse. Let's turn to another parameter.
aaehfehea,nilearn/nilearn,examples/01_tutorials/plot_first_level_model_details.py,6993b684ed51c2148d3cf2ce7d6c1346484af965,STILL_EXISTS,The result is not convincing to my eyes. Maybe we're asking a bit too much to a small dataset; with a relatively large number of experimental conditions!
aaehfehfj,nilearn/nilearn,examples/01_tutorials/plot_first_level_model_details.py,c833e49c3584af01ba520c0aaa585e1716785262,STILL_EXISTS,While the difference is not obvious you should rather stick to the ar(1) model; which is arguably more accurate.
aaehfehgd,nilearn/nilearn,examples/01_tutorials/plot_first_level_model_details.py,c833e49c3584af01ba520c0aaa585e1716785262,STILL_EXISTS,What we can do instead id to estimate confounding effects from the data themselves; using the compcorr approach; and take those nto account in the model
aaehfehge,nilearn/nilearn,examples/01_tutorials/plot_first_level_model_details.py,c833e49c3584af01ba520c0aaa585e1716785262,STILL_EXISTS,Note the five additional columns in the design matrix
aaehfehhh,nilearn/nilearn,examples/01_tutorials/plot_first_level_model_details.py,c833e49c3584af01ba520c0aaa585e1716785262,STILL_EXISTS,The result is not convincing to my eyes. Maybe we're asking a bit too much to a small dataset; with a relatively large number of experimental conditions!
aaehfehjb,nilearn/nilearn,examples/01_tutorials/plot_first_level_model_details.py,8c7a79797e810be97cbd1538ed530477fa8b7dc6,STILL_EXISTS,the approach taken by FirstLeveModel is to estimate it from the fMRI data themselves when no mask is explicitly provided.
aaehfeibf,nilearn/nilearn,examples/01_tutorials/plot_first_level_model_details.py,24fd8e6864af4ba0d47ee6f4d64378e315a4c74d,STILL_EXISTS,Let us take a look at the design matrix: it has 10 main columns corresponding to 10 experimental conditions; followed by 3 columns describing low-frequency signals (drifts) and a constant regressor.
aaehfeihe,nilearn/nilearn,examples/02_first_level_models/plot_localizer_surface_analysis.py,de81a33743c6e085c711ed5dad4f4d47a2d266b6,STILL_EXISTS,the drift model is implicitly a cosine basis with period cutodd 128s.
aaehfeiic,nilearn/nilearn,examples/02_first_level_models/plot_localizer_surface_analysis.py,de81a33743c6e085c711ed5dad4f4d47a2d266b6,b1b859af8437282d897358dee561c6502bf0376f,the number of columns of the design matrix
aaehfejce,nilearn/nilearn,examples/02_first_level_models/plot_spm_multimodal_faces.py,74261e63da4e0b4b0e9cb6147c4787ef0aeb1662,6addef0f7becceb9674d75b5a77ccebd1bddf1ad,We start by specifying canonical contrast that isolate design matrix columns
aaehfejee,nilearn/nilearn,examples/03_second_level_models/plot_oasis.py,2ca1c4e287a782ad83d8ed86fbd32d144c1698c2,STILL_EXISTS,First create an adequate design matrix with three columns: 'age';
aaehfejeh,nilearn/nilearn,examples/03_second_level_models/plot_oasis.py,2ca1c4e287a782ad83d8ed86fbd32d144c1698c2,STILL_EXISTS,smooth a little bit tom improve statistical behavior
aaehffcdg,nilearn/nilearn,examples/01_tutorials/plot_single_subject_single_run.py,04977e4adf5374d48c6910d899296f4c87df5d5f,STILL_EXISTS,of the contrast is to select some columns of the model --and
aaehffcec,nilearn/nilearn,examples/01_tutorials/plot_single_subject_single_run.py,04977e4adf5374d48c6910d899296f4c87df5d5f,STILL_EXISTS,the names of the columns of the design matrix.
aaehfffib,nilearn/nilearn,examples/02_first_level_models/plot_localizer_surface_analysis.py,f5ca47ce928de5905e0e83a1f7df3ffeb0479046,STILL_EXISTS,the drift model is implicitly a cosine basis with period cutoff 128s.
aaehfffij,nilearn/nilearn,examples/02_first_level_models/plot_localizer_surface_analysis.py,f5ca47ce928de5905e0e83a1f7df3ffeb0479046,STILL_EXISTS,the number of columns of the design matrix
aaehffgdc,nilearn/nilearn,examples/02_first_level_models/plot_spm_multimodal_faces.py,f5ca47ce928de5905e0e83a1f7df3ffeb0479046,STILL_EXISTS,We start by specifying canonical contrast that isolate design matrix columns
aaehfgbaf,nilearn/nilearn,examples/01_tutorials/plot_first_level_model_details.py,4514a7c03e9b6e89f78c1c79a09038d6c3d12bbd,STILL_EXISTS,using are probably fine.
aaehfgcfh,nilearn/nilearn,examples/01_tutorials/plot_surface_bids_analysis.py,96cc8d5a6ccca29cfdd652616a8f9fcfc4ca59cd,STILL_EXISTS,the drift model is implicitly a cosine basis with period cutoff 128s.
aaehfgcjg,nilearn/nilearn,examples/01_tutorials/plot_first_level_model_details.py,a70a08e49525c9950722f793bdc0fcdfcbfc9cdd,STILL_EXISTS,The time derivatives regressors capture less signal: it's better so.
aaehfgddc,nilearn/nilearn,nilearn/_utils/data_gen.py,85c1135ad32232a54a9ed17d7df7a78a07cdd451,STILL_EXISTS,TODO: add an \"order\" keyword
aaehfgddh,nilearn/nilearn,nilearn/_utils/data_gen.py,85c1135ad32232a54a9ed17d7df7a78a07cdd451,STILL_EXISTS,Fill central voxels timeseries with random signals
aaehfghge,nilearn/nilearn,nistats/tests/test_second_level_model.py,423dcf2fdffabb2cf4d95cdff83ef8bbfab09e1b,a5365a469942d7a993e61f04463bfb20be0d9085,formula should work (passing variable name directly)
aaehfghgj,nilearn/nilearn,nistats/tests/test_second_level_model.py,423dcf2fdffabb2cf4d95cdff83ef8bbfab09e1b,a5365a469942d7a993e61f04463bfb20be0d9085,matrix has morr than one columns raises an error
aaehfgjdj,nilearn/nilearn,nilearn/plotting/html_stat_map.py,3c1cf410ecd4a904eb75b419365059fe94b39011,6a73df26096b22574b13b011f56be435cad0c772,Extract defaults if needed
aaehfigfi,nilearn/nilearn,doc/conf.py,60649fafd6517c37d20fd688c8632e2c34cfa529,9679bfafee17ef7d476efc2e93fecbaed7e371fb,jquery is included in plotting package data because it is needed for
aaehfigfj,nilearn/nilearn,doc/conf.py,60649fafd6517c37d20fd688c8632e2c34cfa529,9679bfafee17ef7d476efc2e93fecbaed7e371fb,interactive plots. It is also needed by the documentation; so we copy
aaehgajgd,nilearn/nilearn,nistats/reporting/__init__.py,7cfc0508dbf5db6ad9732e8e26fe5b9eaa3f2379,STILL_EXISTS,\"\"\" || This module implements plotting functions useful to report analysis results. ||  || Author: Martin Perez-Guevara; Elvis Dohmatob; 2017 || \"\"\"
aaehgajgg,nilearn/nilearn,nistats/reporting/_compare_niimgs.py,7cfc0508dbf5db6ad9732e8e26fe5b9eaa3f2379,STILL_EXISTS,\"\"\" || This module implements plotting functions useful to report analysis results. ||  || Author: Martin Perez-Guevara; Elvis Dohmatob; 2017 || \"\"\"
aaehgajgh,nilearn/nilearn,nistats/reporting/_get_clusters_table.py,7cfc0508dbf5db6ad9732e8e26fe5b9eaa3f2379,STILL_EXISTS,\"\"\" || This module implements plotting functions useful to report analysis results. ||  || Author: Martin Perez-Guevara; Elvis Dohmatob; 2017 || \"\"\"
aaehgajid,nilearn/nilearn,nistats/reporting/_get_clusters_table.py,7cfc0508dbf5db6ad9732e8e26fe5b9eaa3f2379,STILL_EXISTS,Subpeak naming convention is cluster num + letter (1a; 1b; etc.)
aaehgajie,nilearn/nilearn,nistats/reporting/_plot_matrices.py,7cfc0508dbf5db6ad9732e8e26fe5b9eaa3f2379,STILL_EXISTS,\"\"\" || This module implements plotting functions useful to report analysis results. ||  || Author: Martin Perez-Guevara; Elvis Dohmatob; 2017 || \"\"\"
aaehgbaci,nilearn/nilearn,nistats/tests/test_first_level_model.py,cf719cbcdafe990488550abf9fa44d39d7ff9973,STILL_EXISTS,assert_almost_equal(labels1; labels2; decimal=2) ####FIX
aaehgbacj,nilearn/nilearn,nistats/tests/test_first_level_model.py,cf719cbcdafe990488550abf9fa44d39d7ff9973,STILL_EXISTS,assert_equal(len(results1); len(results2)) ####FIX
aaehgbagj,nilearn/nilearn,nistats/tests/test_first_level_model.py,26ce11fb59962d42c7c74264f7221158367751a0,STILL_EXISTS,assert_almost_equal(labels1; labels2; decimal=2) ####FIX
aaehgbaha,nilearn/nilearn,nistats/tests/test_first_level_model.py,26ce11fb59962d42c7c74264f7221158367751a0,STILL_EXISTS,assert_equal(len(results1); len(results2)) ####FIX
aaehgbajb,nilearn/nilearn,nistats/tests/test_first_level_model.py,69f1535c46878bd5871339991ef0be97470bac8c,STILL_EXISTS,assert_almost_equal(labels1; labels2; decimal=2) ####FIX
aaehgbajc,nilearn/nilearn,nistats/tests/test_first_level_model.py,69f1535c46878bd5871339991ef0be97470bac8c,STILL_EXISTS,assert_equal(len(results1); len(results2)) ####FIX
aaehgbbeb,nilearn/nilearn,nistats/tests/test_first_level_model.py,df68f8dc7d0d07608b5b40fe3c9f093d57c69341,STILL_EXISTS,assert_equal(len(results1); len(results2)) ####FIX
aaehgbbff,nilearn/nilearn,nistats/tests/test_first_level_model.py,7a809ce920f9762a5362539dc219805cd0fdfdbe,STILL_EXISTS,assert_almost_equal(labels1; labels2; decimal=2) ####FIX
aaehgbbfg,nilearn/nilearn,nistats/tests/test_first_level_model.py,7a809ce920f9762a5362539dc219805cd0fdfdbe,STILL_EXISTS,assert_equal(len(results1); len(results2)) ####FIX
aaehgbbij,nilearn/nilearn,nistats/tests/test_first_level_model.py,cb5427a7a3cd443f73de19fbc88ca7c3d82904a1,STILL_EXISTS,assert_equal(len(results1); len(results2)) ####FIX
aaehgbcih,nilearn/nilearn,doc/conf.py,6dbd25aff65c4f5b33f6b7d736c6919a264ca652,9679bfafee17ef7d476efc2e93fecbaed7e371fb,jquery is included in plotting package data because it is needed for
aaehgbcii,nilearn/nilearn,doc/conf.py,6dbd25aff65c4f5b33f6b7d736c6919a264ca652,9679bfafee17ef7d476efc2e93fecbaed7e371fb,interactive plots. It is also needed by the documentation; so we copy
aaehgbdbi,nilearn/nilearn,doc/conf.py,7595b6e57b6eca02c3a8385f5319386a7d70953c,9679bfafee17ef7d476efc2e93fecbaed7e371fb,jquery is included in plotting package data because it is needed for
aaehgbdbj,nilearn/nilearn,doc/conf.py,7595b6e57b6eca02c3a8385f5319386a7d70953c,9679bfafee17ef7d476efc2e93fecbaed7e371fb,interactive plots. It is also needed by the documentation; so we copy
aaehgbdce,nilearn/nilearn,examples/01_plotting/plot_surf_atlas.py,70543d32d506a0a63270de50524d0f1b7cdd3f29,STILL_EXISTS,in the Freesurfer naming convention). To do so we load the pial surface
aaehgbdea,nilearn/nilearn,doc/conf.py,18f0334ab310deecb9266837fd0518a2a99efecd,9679bfafee17ef7d476efc2e93fecbaed7e371fb,jquery is included in plotting package data because it is needed for
aaehgbdeb,nilearn/nilearn,doc/conf.py,18f0334ab310deecb9266837fd0518a2a99efecd,9679bfafee17ef7d476efc2e93fecbaed7e371fb,interactive plots. It is also needed by the documentation; so we copy
aaehgdbbc,nilearn/nilearn,nilearn/image/resampling.py,e9d4d2bc5c684be95feffaa472d00e0e06e40263,STILL_EXISTS,TODO: also check for sign flips
aaehgdbbd,nilearn/nilearn,nilearn/image/resampling.py,e9d4d2bc5c684be95feffaa472d00e0e06e40263,STILL_EXISTS,TODO: also check for permutations of I
aaehgdbbe,nilearn/nilearn,nilearn/image/resampling.py,e9d4d2bc5c684be95feffaa472d00e0e06e40263,STILL_EXISTS,TODO: flip axes that are flipped
aaehgdegb,nilearn/nilearn,nilearn/image/resampling.py,aa5600abeae326bc4f2b0f5ad17ec909c5989b7d,STILL_EXISTS,better algorithm.
aaehgdijb,nilearn/nilearn,examples/02_first_level_models/plot_fir_model.py,974bd0d1d6ff0dc2195bc8a4a7edf14ceba99891,STILL_EXISTS,The result is not convincing to my eyes. Maybe we're asking a bit too much to a small dataset; with a relatively large number of experimental conditions!
aaehgdjag,nilearn/nilearn,examples/02_first_level_models/plot_fir_model.py,51f076c3faa4210d5f8ad766df2df72cfda4cbd9,9ab0c82e614c1b3ae5983a436c25025b5942c144,for i; column in enumerate(design_matrix.columns)])
aaehgecea,nilearn/nilearn,examples/06_second_level_models_non_parametric_tests/plot_oasis.py,e2b99002e25c4aad8abf42795fca11fe32a244c9,STILL_EXISTS,First create an adequate design matrix with three columns: 'age';
aaehgeceg,nilearn/nilearn,examples/06_second_level_models_non_parametric_tests/plot_oasis.py,e2b99002e25c4aad8abf42795fca11fe32a244c9,STILL_EXISTS,smooth a little bit to improve statistical behavior
aaehgecjb,nilearn/nilearn,examples/06_second_level_models_non_parametric_tests/plot_second_level_one_sample_test.py,e2b99002e25c4aad8abf42795fca11fe32a244c9,STILL_EXISTS,\"\"\" || Second-level fMRI model: one sample test || ======================================== ||  || Full step-by-step example of fitting a GLM to perform a second-level analysis (one-sample test) || and visualizing the results. ||  || More specifically: ||  || 1. A sequence of subject fMRI button press contrasts is downloaded. || 2. a mask of the useful brain volume is computed || 3. A one-sample t-test is applied to the brain maps ||  || We focus on a given contrast of the localizer dataset: the motor response to left versus right button press. Both at the ndividual and group level; this is expected to elicit activity in the motor cortex (positive in the right hemisphere; negative in the left hemisphere). ||  || \"\"\"
aaehgeebf,nilearn/nilearn,nistats/second_level_model.py,644619db2bd4922a9e2c2710fbb5c344c01ed93a,6023c08b809f70a74f7e4c2a7fb2c78060354fff,TODO: manage changing mask parameters
aaehgeejc,nilearn/nilearn,nistats/tests/test_second_level_model.py,8065bd6aa25e53f281f01998350b10ef2de0e6d8,STILL_EXISTS,matrix has more than one columns raises an error
aaehgeejj,nilearn/nilearn,nistats/tests/test_second_level_model.py,8065bd6aa25e53f281f01998350b10ef2de0e6d8,STILL_EXISTS,formula should work passing variable name directly
aaehgeijc,nilearn/nilearn,doc/conf.py,28c9a37b9df274312fa1fa2a671fb3d41067590d,9679bfafee17ef7d476efc2e93fecbaed7e371fb,jquery is included in plotting package data because it is needed for
aaehgeijd,nilearn/nilearn,doc/conf.py,28c9a37b9df274312fa1fa2a671fb3d41067590d,9679bfafee17ef7d476efc2e93fecbaed7e371fb,interactive plots. It is also needed by the documentation; so we copy
aaehgfafc,nilearn/nilearn,nilearn/conftest.py,283023aecacbe0ba783c133a9e680d588d2a1e1f,STILL_EXISTS,Prevents flake8 erring due to unused entities.
aaehgffje,nilearn/nilearn,nistats/reporting/_visual_testing/_glm_reporter_visual_inspection_suite_.py,9a8cc925f3548309de2c972850e9fd90855c2342,STILL_EXISTS,\"\"\" || This file contains a bunch of functions run via __main__(). || The functions represent feature comprehensive examples || to visualize; inspect; and test the functionality || of nistats.reporting.make_glm_reports(). ||  || Disable any of the function calls in the __main__() || to run a specific script and save time. || \"\"\"
aaehgfgbh,nilearn/nilearn,nistats/reporting/glm_reporter.py,9a8cc925f3548309de2c972850e9fd90855c2342,STILL_EXISTS,setting report size for better visual experience in Jupyter Notebooks.
aaehgfgje,nilearn/nilearn,nilearn/__init__.py,0d53f6d90c82bdc16d782a49a28dc89b5392f278,STILL_EXISTS,Workaround issue discovered in intel-openmp 2019.5:
aaehgfjad,nilearn/nilearn,nilearn/datasets/func.py,796a5a7b4d8c2d484da0f518f65318b7a741ad5a,STILL_EXISTS,TODO: remove -> only here for compatibility
aaehgfjjj,nilearn/nilearn,nistats/datasets.py,c67ec89534db4f9f77dcba9d33893eb4183a2783,STILL_EXISTS,The files_spec needed for _fetch_files
aaehggbad,nilearn/nilearn,examples/05_complete_examples/plot_haxby_block_classification.py,32fe4d44db91ac8255b0480bb78756b05a912462,STILL_EXISTS,\"\"\" || Decoding of a dataset after glm fit for signal extraction || ========================================================= ||  || Full step-by-step example of fitting a GLM to perform a decoding experiment. || We use the data from one subject of the Haxby dataset. ||  || More specifically: ||  || 1. Download the Haxby dataset || 2. Extract the information to generate a glm representing the blocks of stimuli || 3. Analyze the decoding performance using a classifier ||  || To run this example; you must launch IPython via ``ipython || --matplotlib`` in a terminal; or use the Jupyter notebook. ||  || .. contents:: **Contents** ||     :local: ||     :depth: 1 || \"\"\"
aaehggbga,nilearn/nilearn,examples/05_complete_examples/plot_haxby_block_classification.py,32fe4d44db91ac8255b0480bb78756b05a912462,STILL_EXISTS,namely Anova. When doing full-brain analysis; it is better to use
aaehggbjj,nilearn/nilearn,examples/plot_decoding_tutorial.py,0856ebc928b0647ac29a5748007848cc0eda65a3,STILL_EXISTS,One way to think about what just happened is to look at it visually:
aaehggcdg,nilearn/nilearn,examples/plot_decoding_tutorial.py,0856ebc928b0647ac29a5748007848cc0eda65a3,STILL_EXISTS,in a given session. Hence; it is better to predict across sessions when
aaehggchj,nilearn/nilearn,nistats/conftest.py,3bafb766809337367c9c148a50aefd12d10c1bbe,STILL_EXISTS,Prevents flake8 erring due to unused entities.
aaehggdeb,nilearn/nilearn,examples/02_first_level_models/plot_predictions_residuals.py,839f8c9a5dcbd319301b001c62ea62895c353766,STILL_EXISTS,and all the other columns in the design matrix such as drift and motion.
aaehggedb,nilearn/nilearn,nistats/doc/conf.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,unused_docs = []
aaehggfgi,nilearn/nilearn,nistats/examples/01_tutorials/plot_first_level_model_details.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,Let us take a look at the design matrix: it has 10 main columns corresponding to 10 experimental conditions; followed by 3 columns describing low-frequency signals (drifts) and a constant regressor.
aaehggfjc,nilearn/nilearn,nistats/examples/01_tutorials/plot_first_level_model_details.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,we encapsulate it in a function that we call when needed.
aaehgggbd,nilearn/nilearn,nistats/examples/01_tutorials/plot_first_level_model_details.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,Does the model perform worse or better ?
aaehgggbf,nilearn/nilearn,nistats/examples/01_tutorials/plot_first_level_model_details.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,Note that the design matrix has more columns to model drifts in the data.
aaehgggbj,nilearn/nilearn,nistats/examples/01_tutorials/plot_first_level_model_details.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,Another solution is to remove these drift terms. Maybe they're simply useless.
aaehgggcc,nilearn/nilearn,nistats/examples/01_tutorials/plot_first_level_model_details.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,Is it better than the original ? No !
aaehgggce,nilearn/nilearn,nistats/examples/01_tutorials/plot_first_level_model_details.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,Note that the design matrix has changed with no drift columns.
aaehgggcf,nilearn/nilearn,nistats/examples/01_tutorials/plot_first_level_model_details.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,the event columns; on the other hand; haven't changed.
aaehgggcj,nilearn/nilearn,nistats/examples/01_tutorials/plot_first_level_model_details.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,Is it good ? No better; no worse. Let's turn to another parameter.
aaehgggfj,nilearn/nilearn,nistats/examples/01_tutorials/plot_first_level_model_details.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,Well; there seems to be something here. Maybe we could adjust the
aaehgggge,nilearn/nilearn,nistats/examples/01_tutorials/plot_first_level_model_details.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,The time derivatives regressors capture less signal: it's better so.
aaehgggid,nilearn/nilearn,nistats/examples/01_tutorials/plot_first_level_model_details.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,While the difference is not obvious you should rather stick to the
aaehgghad,nilearn/nilearn,nistats/examples/01_tutorials/plot_first_level_model_details.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,Note the five additional columns in the design matrix
aaehgghjh,nilearn/nilearn,nistats/examples/01_tutorials/plot_single_subject_single_run.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,columns contain the predictors).
aaehggibd,nilearn/nilearn,nistats/examples/01_tutorials/plot_single_subject_single_run.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,created contrast with a single '1' in each of the columns: The role
aaehggibe,nilearn/nilearn,nistats/examples/01_tutorials/plot_single_subject_single_run.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,of the contrast is to select some columns of the model --and
aaehggicf,nilearn/nilearn,nistats/examples/01_tutorials/plot_single_subject_single_run.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,the names of the columns of the design matrix.
aaehggjeg,nilearn/nilearn,nistats/examples/02_first_level_models/plot_bids_features.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,We identify the columns of the Go and StopSuccess conditions of the
aaehggjhf,nilearn/nilearn,nistats/examples/02_first_level_models/plot_fiac_analysis.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,\"\"\"Simple example of two-session fMRI model fitting || ================================================ ||  || Full step-by-step example of fitting a GLM to experimental data and visualizing || the results. This is done on two runs of one subject of the FIAC dataset. ||  || For details on the data; please see: ||  || Dehaene-Lambertz G; Dehaene S; Anton JL; Campagne A; Ciuciu P; Dehaene || G; Denghien I; Jobert A; LeBihan D; Sigman M; Pallier C; Poline || JB. Functional segregation of cortical language areas by sentence || repetition. Hum Brain Mapp. 2006: 27:360--371. || http:\/\/www.pubmedcentral.nih.gov\/articlerender.fcgi?artid=2653076#R11 ||  || More specifically: ||  || 1. A sequence of fMRI volumes are loaded || 2. A design matrix describing all the effects related to the data is computed || 3. a mask of the useful brain volume is computed || 4. A GLM is applied to the dataset (effect\/covariance; ||    then contrast estimation) ||  || Technically; this example shows how to handle two sessions that || contain the same experimental conditions. The model directly returns a || fixed effect of the statistics across the two sessions. ||  || \"\"\"
aaehghacf,nilearn/nilearn,nistats/examples/02_first_level_models/plot_fir_model.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,\"\"\"Analysis of an fMRI dataset with a Finite Impule Response (FIR) model || ===================================================================== ||  || FIR models are used to estimate the hemodyamic response non-parametrically. || The example below shows that they're good to do statistical inference || even on fast event-related fMRI datasets. ||  || Here; we demonstrate the use of a FIR model with 3 lags; computing 4 contrasts from a single subject dataset from the \"Neurospin Localizer\". It is a fast event related design: During 5 minutes; 80 events of the following types are presented : ['calculaudio'; 'calculvideo''; 'clicDvideo'; 'clicGaudio'; 'clicGvideo'; 'damier_H'; 'damier_V'; 'phraseaudio'; 'phrasevideo'] ||  || \"\"\"
aaehghahj,nilearn/nilearn,nistats/examples/02_first_level_models/plot_localizer_surface_analysis.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,\"\"\"Example of surface-based first-level analysis || ============================================= ||  || Full step-by-step example of fitting a GLM to experimental data || sampled on the cortical surface and visualizing the results. ||  || More specifically: ||  || 1. A sequence of fMRI volumes are loaded || 2. fMRI data are projected onto a reference cortical surface (the freesurfer template; fsaverage) || 3. A design matrix describing all the effects related to the data is computed || 4. A GLM is applied to the dataset (effect\/covariance; then contrast estimation) ||  || The result of the analysis are statistical maps that are defined on || the brain mesh. We display them using Nilearn capabilities. ||  || The projection of fMRI data onto a given brain mesh requires that both || are initially defined in the same space. ||  || * The functional data should be coregistered to the anatomy from which the mesh was obtained. ||  || * Another possibility; used here; is to project the normalized fMRI data to an MNI-coregistered mesh; such as fsaverage. ||  || The advantage of this second approach is that it makes it easy to run || second-level analyses on the surface. On the other hand; it is || obviously less accurate than using a subject-tailored mesh. ||  || \"\"\"
aaehghbba,nilearn/nilearn,nistats/examples/02_first_level_models/plot_localizer_surface_analysis.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,the drift model is implicitly a cosine basis with period cutoff 128s.
aaehghbcc,nilearn/nilearn,nistats/examples/02_first_level_models/plot_localizer_surface_analysis.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,the number of columns of the design matrix
aaehghcbj,nilearn/nilearn,nistats/examples/02_first_level_models/plot_predictions_residuals.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,and all the other columns in the design matrix such as drift and motion.
aaehghcej,nilearn/nilearn,nistats/examples/02_first_level_models/plot_spm_multimodal_faces.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,We start by specifying canonical contrast that isolate design matrix columns
aaehghcjg,nilearn/nilearn,nistats/examples/03_second_level_models/plot_oasis.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,First create an adequate design matrix with three columns: 'age';
aaehghdac,nilearn/nilearn,nistats/examples/03_second_level_models/plot_oasis.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,smooth a little bit to improve statistical behavior
aaehghdjc,nilearn/nilearn,nistats/examples/03_second_level_models/plot_second_level_one_sample_test.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,\"\"\" || Second-level fMRI model: one sample test || ======================================== ||  || Full step-by-step example of fitting a GLM to perform a second-level analysis || (one-sample test) || and visualizing the results. ||  || More specifically: ||  || 1. A sequence of subject fMRI button press contrasts is downloaded. || 2. a mask of the useful brain volume is computed || 3. A one-sample t-test is applied to the brain maps ||  || We focus on a given contrast of the localizer dataset: the motor response to || left versus right button press. Both at the ndividual and group level; this is || expected to elicit activity in the motor cortex (positive in the right || hemisphere; negative in the left hemisphere). ||  || \"\"\"
aaehghfei,nilearn/nilearn,nistats/examples/04_low_level_functions/plot_hrf.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,\"\"\"Example of hemodynamic reponse functions. || ========================================= ||  || Plot the hemodynamic reponse function (hrf) model in SPM together with || the hrf shape proposed by G.Glover; as well as their time and || dispersion derivatives. ||  || Requires matplotlib. ||  || The hrf is the filter that couples neural responses to the || metabolic-related changes in the MRI signal.  hrf models are simply || phenomenological. ||  || In current analysis frameworks; the choice of hrf model is essentially || left to the user. Fortunately; using spm or Glover model does not make || a huge difference.  Adding derivatives should be considered whenever || timing information has some degree of uncertainty. It is actually || useful to detect timing issues. ||  || \"\"\"
aaehghgfj,nilearn/nilearn,nistats/examples/05_complete_examples/plot_haxby_block_classification.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,\"\"\" || Decoding of a dataset after glm fit for signal extraction || ========================================================= ||  || Full step-by-step example of fitting a GLM to perform a decoding experiment. || We use the data from one subject of the Haxby dataset. ||  || More specifically: ||  || 1. Download the Haxby dataset || 2. Extract the information to generate a glm representing the blocks of stimuli || 3. Analyze the decoding performance using a classifier ||  || To run this example; you must launch IPython via ``ipython || --matplotlib`` in a terminal; or use the Jupyter notebook. ||  || .. contents:: **Contents** ||     :local: ||     :depth: 1 || \"\"\"
aaehghhbg,nilearn/nilearn,nistats/examples/05_complete_examples/plot_haxby_block_classification.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,namely Anova. When doing full-brain analysis; it is better to use
aaehghhgg,nilearn/nilearn,nistats/examples/05_complete_examples/plot_surface_bids_analysis.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,the drift model is implicitly a cosine basis with period cutoff 128s.
aaehghhjd,nilearn/nilearn,nistats/nistats/__init__.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,\"\"\" || functional MRI module for NeuroImaging in python || -------------------------------------------------- ||  || Documentation is available in the docstrings and online at || http:\/\/nistats.github.io. ||  || Contents || -------- || Nistats is a Python module for fast and easy functional MRI statistical || analysis. ||  || Submodules || --------- || datasets                --- Utilities to download NeuroImaging datasets || hemodynamic_models      --- Hemodyanmic response function specification || design_matrix           --- Design matrix creation for fMRI analysis || experimental_paradigm   --- Experimental paradigm files checks and utils || model                   --- Statistical tests on likelihood models || regression              --- Standard regression models || first_level_model       --- API for first level fMRI model estimation || second_level_model      --- API for second level fMRI model estimation || contrasts               --- API for contrast computation and manipulations || thresholding            --- Utilities for cluster-level statistical results || reporting               --- Utilities for creating reports & plotting data || utils                   --- Miscellaneous utilities || \"\"\"
aaehghhji,nilearn/nilearn,nistats/nistats/conftest.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,Prevents flake8 erring due to unused entities.
aaehghiab,nilearn/nilearn,nistats/nistats/contrasts.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,\"\"\" || This module is for contrast computation and operation on contrast to || obtain fixed effect results. ||  || Author: Bertrand Thirion; Martin Perez-Guevara; 2016 || \"\"\"
aaehghibg,nilearn/nilearn,nistats/nistats/datasets.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,from the url list we infer all available subjects like 'sub-xxx\/'
aaehghieg,nilearn/nilearn,nistats/nistats/design_matrix.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,\"\"\" || This module implements fMRI Design Matrix creation. ||  || Design matrices are represented by Pandas DataFrames || Computations of the different parts of the design matrix are confined || to the make_first_level_design_matrix function; that create a DataFrame || All the others are ancillary functions. ||  || Design matrices contain three different types of regressors: ||  || 1. Task-related regressors; that result from the convolution ||    of the experimental paradigm regressors with hemodynamic models ||    A hemodynamic model is one of: ||  ||          - 'spm' : linear filter used in the SPM software ||          - 'glover' : linear filter estimated by G.Glover ||          - 'spm + derivative'; 'glover + derivative': the same linear models; ||             plus their time derivative (2 regressors per condition) ||          - 'spm + derivative + dispersion'; 'glover + derivative + dispersion': ||             idem plus the derivative wrt the dispersion parameter of the hrf ||             (3 regressors per condition) ||          - 'fir' : finite impulse response model; generic linear filter ||  || 2. User-specified regressors; that represent information available on ||    the data; e.g. motion parameters; physiological data resampled at ||    the acquisition rate; or sinusoidal regressors that model the ||    signal at a frequency of interest. ||  || 3. Drift regressors; that represent low_frequency phenomena of no ||    interest in the data; they need to be included to reduce variance ||    estimates. ||  || Author: Bertrand Thirion; 2009-2015 ||  || \"\"\"
aaehghjci,nilearn/nilearn,nistats/nistats/model.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,\"\"\" || This module implement classes to handle statistical tests on likelihood models ||  || Author: Bertrand Thirion; 2011--2015 || \"\"\"
aaehghjdf,nilearn/nilearn,nistats/nistats/regression.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,TODO: handle case for noconstant regression
aaehghjed,nilearn/nilearn,nistats/nistats/reporting/__init__.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,\"\"\" || This module implements plotting functions useful to report analysis results. ||  || Author: Martin Perez-Guevara; Elvis Dohmatob; 2017 || \"\"\"
aaehghjef,nilearn/nilearn,nistats/nistats/reporting/_compare_niimgs.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,\"\"\" || This module implements plotting functions useful to report analysis results. ||  || Author: Martin Perez-Guevara; Elvis Dohmatob; 2017 || \"\"\"
aaehghjej,nilearn/nilearn,nistats/nistats/reporting/_get_clusters_table.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,\"\"\" || This module implements plotting functions useful to report analysis results. ||  || Author: Martin Perez-Guevara; Elvis Dohmatob; 2017 || \"\"\"
aaehghjgf,nilearn/nilearn,nistats/nistats/reporting/_get_clusters_table.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,Subpeak naming convention is cluster num+letter: 1a; 1b; etc
aaehghjgg,nilearn/nilearn,nistats/nistats/reporting/_plot_matrices.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,\"\"\" || This module implements plotting functions useful to report analysis results. ||  || Author: Martin Perez-Guevara; Elvis Dohmatob; 2017 || \"\"\"
aaehghjhd,nilearn/nilearn,nistats/nistats/reporting/_visual_testing/_glm_reporter_visual_inspection_suite_.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,\"\"\" || This file contains a bunch of functions run via __main__(). || The functions represent feature comprehensive examples || to visualize; inspect; and test the functionality || of nistats.reporting.make_glm_reports(). ||  || Disable any of the function calls in the __main__() || to run a specific script and save time. || \"\"\"
aaehghjjg,nilearn/nilearn,nistats/nistats/reporting/glm_reporter.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,setting report size for better visual experience in Jupyter Notebooks.
aaehgiaah,nilearn/nilearn,nistats/nistats/second_level_model.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,\"\"\" || This module presents an interface to use the glm implemented in || nistats.regression. ||  || It provides facilities to realize a second level analysis on lists of || first level contrasts or directly on fitted first level models ||  || Author: Martin Perez-Guevara; 2016 || \"\"\"
aaehgiaga,nilearn/nilearn,nistats/nistats/tests/test_contrasts.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,assert_almost_equal(con1.variance * 2; con2.variance) FIXME
aaehgiagb,nilearn/nilearn,nistats/nistats/tests/test_contrasts.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,assert_almost_equal(con1.stat() * 2; con2.stat()) FIXME
aaehgibhg,nilearn/nilearn,nistats/nistats/tests/test_first_level_model.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,formula should work (passing varible name directly)
aaehgidaa,nilearn/nilearn,nistats/nistats/tests/test_second_level_model.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,formula should work (passing variable name directly)
aaehgidbc,nilearn/nilearn,nistats/nistats/tests/test_second_level_model.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,formula should work passing variable name directly
aaehgidjb,nilearn/nilearn,nistats/nistats/utils.py,7df3c3f60bd20c70732c69518eed600b6908ec44,STILL_EXISTS,XXX: the following line fixes curious SEGFAULT when
aaehgieec,nilearn/nilearn,nilearn/stats/__init__.py,8a9438634cbf2acb5bce4e7a10aa5e7ce5d6aef5,8e972c4d382a74fb126f768a0ae02e01e817515c,\"\"\" || functional MRI module for NeuroImaging in python || -------------------------------------------------- ||  || Documentation is available in the docstrings and online at || http:\/\/nistats.github.io. ||  || Contents || -------- || Nistats is a Python module for fast and easy functional MRI statistical || analysis. ||  || Submodules || --------- || datasets                --- Utilities to download NeuroImaging datasets || hemodynamic_models      --- Hemodyanmic response function specification || design_matrix           --- Design matrix creation for fMRI analysis || experimental_paradigm   --- Experimental paradigm files checks and utils || model                   --- Statistical tests on likelihood models || regression              --- Standard regression models || first_level_model       --- API for first level fMRI model estimation || second_level_model      --- API for second level fMRI model estimation || contrasts               --- API for contrast computation and manipulations || thresholding            --- Utilities for cluster-level statistical results || reporting               --- Utilities for creating reports & plotting data || utils                   --- Miscellaneous utilities || \"\"\"
aaehgieeh,nilearn/nilearn,nilearn/stats/contrasts.py,8a9438634cbf2acb5bce4e7a10aa5e7ce5d6aef5,STILL_EXISTS,\"\"\" || This module is for contrast computation and operation on contrast to || obtain fixed effect results. ||  || Author: Bertrand Thirion; Martin Perez-Guevara; 2016 || \"\"\"
aaehgiegc,nilearn/nilearn,nilearn/stats/datasets.py,8a9438634cbf2acb5bce4e7a10aa5e7ce5d6aef5,STILL_EXISTS,from the url list we infer all available subjects like 'sub-xxx\/'
aaehgiejc,nilearn/nilearn,nilearn/stats/design_matrix.py,8a9438634cbf2acb5bce4e7a10aa5e7ce5d6aef5,STILL_EXISTS,\"\"\" || This module implements fMRI Design Matrix creation. ||  || Design matrices are represented by Pandas DataFrames || Computations of the different parts of the design matrix are confined || to the make_first_level_design_matrix function; that create a DataFrame || All the others are ancillary functions. ||  || Design matrices contain three different types of regressors: ||  || 1. Task-related regressors; that result from the convolution ||    of the experimental paradigm regressors with hemodynamic models ||    A hemodynamic model is one of: ||  ||          - 'spm' : linear filter used in the SPM software ||          - 'glover' : linear filter estimated by G.Glover ||          - 'spm + derivative'; 'glover + derivative': the same linear models; ||             plus their time derivative (2 regressors per condition) ||          - 'spm + derivative + dispersion'; 'glover + derivative + dispersion': ||             idem plus the derivative wrt the dispersion parameter of the hrf ||             (3 regressors per condition) ||          - 'fir' : finite impulse response model; generic linear filter ||  || 2. User-specified regressors; that represent information available on ||    the data; e.g. motion parameters; physiological data resampled at ||    the acquisition rate; or sinusoidal regressors that model the ||    signal at a frequency of interest. ||  || 3. Drift regressors; that represent low_frequency phenomena of no ||    interest in the data; they need to be included to reduce variance ||    estimates. ||  || Author: Bertrand Thirion; 2009-2015 ||  || \"\"\"
aaehgifhe,nilearn/nilearn,nilearn/stats/model.py,8a9438634cbf2acb5bce4e7a10aa5e7ce5d6aef5,STILL_EXISTS,\"\"\" || This module implement classes to handle statistical tests on likelihood models ||  || Author: Bertrand Thirion; 2011--2015 || \"\"\"
aaehgifib,nilearn/nilearn,nilearn/stats/regression.py,8a9438634cbf2acb5bce4e7a10aa5e7ce5d6aef5,STILL_EXISTS,TODO: handle case for noconstant regression
aaehgifij,nilearn/nilearn,nilearn/stats/reporting/__init__.py,8a9438634cbf2acb5bce4e7a10aa5e7ce5d6aef5,STILL_EXISTS,\"\"\" || This module implements plotting functions useful to report analysis results. ||  || Author: Martin Perez-Guevara; Elvis Dohmatob; 2017 || \"\"\"
aaehgifjb,nilearn/nilearn,nilearn/stats/reporting/_compare_niimgs.py,8a9438634cbf2acb5bce4e7a10aa5e7ce5d6aef5,STILL_EXISTS,\"\"\" || This module implements plotting functions useful to report analysis results. ||  || Author: Martin Perez-Guevara; Elvis Dohmatob; 2017 || \"\"\"
aaehgifjf,nilearn/nilearn,nilearn/stats/reporting/_get_clusters_table.py,8a9438634cbf2acb5bce4e7a10aa5e7ce5d6aef5,STILL_EXISTS,\"\"\" || This module implements plotting functions useful to report analysis results. ||  || Author: Martin Perez-Guevara; Elvis Dohmatob; 2017 || \"\"\"
aaehgigbb,nilearn/nilearn,nilearn/stats/reporting/_get_clusters_table.py,8a9438634cbf2acb5bce4e7a10aa5e7ce5d6aef5,STILL_EXISTS,Subpeak naming convention is cluster num+letter: 1a; 1b; etc
aaehgigbc,nilearn/nilearn,nilearn/stats/reporting/_plot_matrices.py,8a9438634cbf2acb5bce4e7a10aa5e7ce5d6aef5,STILL_EXISTS,\"\"\" || This module implements plotting functions useful to report analysis results. ||  || Author: Martin Perez-Guevara; Elvis Dohmatob; 2017 || \"\"\"
aaehgigbj,nilearn/nilearn,nilearn/stats/reporting/_visual_testing/_glm_reporter_visual_inspection_suite_.py,8a9438634cbf2acb5bce4e7a10aa5e7ce5d6aef5,STILL_EXISTS,\"\"\" || This file contains a bunch of functions run via __main__(). || The functions represent feature comprehensive examples || to visualize; inspect; and test the functionality || of nistats.reporting.make_glm_reports(). ||  || Disable any of the function calls in the __main__() || to run a specific script and save time. || \"\"\"
aaehgigec,nilearn/nilearn,nilearn/stats/reporting/glm_reporter.py,8a9438634cbf2acb5bce4e7a10aa5e7ce5d6aef5,STILL_EXISTS,setting report size for better visual experience in Jupyter Notebooks.
aaehgigfa,nilearn/nilearn,nilearn/stats/second_level_model.py,8a9438634cbf2acb5bce4e7a10aa5e7ce5d6aef5,STILL_EXISTS,\"\"\" || This module presents an interface to use the glm implemented in || nistats.regression. ||  || It provides facilities to realize a second level analysis on lists of || first level contrasts or directly on fitted first level models ||  || Author: Martin Perez-Guevara; 2016 || \"\"\"
aaehgiiba,nilearn/nilearn,nilearn/stats/tests/test_first_level_model.py,8a9438634cbf2acb5bce4e7a10aa5e7ce5d6aef5,STILL_EXISTS,formula should work (passing varible name directly)
aaehgijcj,nilearn/nilearn,nilearn/stats/tests/test_second_level_model.py,8a9438634cbf2acb5bce4e7a10aa5e7ce5d6aef5,STILL_EXISTS,formula should work (passing variable name directly)
aaehgijeb,nilearn/nilearn,nilearn/stats/tests/test_second_level_model.py,8a9438634cbf2acb5bce4e7a10aa5e7ce5d6aef5,STILL_EXISTS,formula should work passing variable name directly
aaehgjabe,nilearn/nilearn,nilearn/stats/utils.py,8a9438634cbf2acb5bce4e7a10aa5e7ce5d6aef5,STILL_EXISTS,XXX: the following line fixes curious SEGFAULT when
aaehgjadh,nilearn/nilearn,examples/01_tutorials/plot_first_level_model_details.py,7b17e51d523e26a3b3e7041554666c4684f21219,STILL_EXISTS,1\/64 Hz ~ 0.016 Hz. Note that the design matrix has more columns to model drifts in the data.
aaehgjagc,nilearn/nilearn,nilearn/reporting/__init__.py,9d2d86c0353f3654dc5a453bab38634b391631c2,381e492c5bcbc7f05b9b4cfd285524b71941629b,\"\"\" || This module implements plotting functions useful to report analysis results. ||  || Author: Martin Perez-Guevara; Elvis Dohmatob; 2017 || \"\"\"
aaehgjaid,nilearn/nilearn,nilearn/datasets/func.py,29d52739de684c442d75c2b31ab8c69572803e9d,4e4f0bbe64c0be1aa9ffd963ce2039ab56eeaeb8,from the url list we infer all available subjects like 'sub-xxx\/'
aaehgjbje,nilearn/nilearn,nilearn/datasets/func.py,afaf969992a81f98814a58dd940a8329ff180434,aba56cfab89269037886d8565d0fa2c9d90ebbe0,from the url list we infer all available subjects like 'sub-xxx\/'
aaehgjchh,nilearn/nilearn,examples/02_decoding/plot_haxby_different_estimators.py,a2726e4adf24705bd56b53c932152906b8d1d53f,STILL_EXISTS,($\\ell_1$ and $\\ell_2$). The sparse penalty works better because we are in
aaehgjcjg,nilearn/nilearn,examples/02_decoding/plot_oasis_vbm.py,a2726e4adf24705bd56b53c932152906b8d1d53f,STILL_EXISTS,do ANOVA with SVR instead of manually defining the whole pipeline.
aaehgjdaa,nilearn/nilearn,examples/02_decoding/plot_oasis_vbm.py,a2726e4adf24705bd56b53c932152906b8d1d53f,STILL_EXISTS,Sort test data for better visualization (trend; etc.)
aaehgjdbj,nilearn/nilearn,examples/plot_decoding_tutorial.py,a2726e4adf24705bd56b53c932152906b8d1d53f,STILL_EXISTS,of our model on examples it hasn't seen to examine how well the model perform
aaehgjdge,nilearn/nilearn,nilearn/decoding/decoder.py,a2726e4adf24705bd56b53c932152906b8d1d53f,STILL_EXISTS,\"\"\"High-level decoding object that exposes standard classification and || regression strategies such as SVM; LogisticRegression and Ridge; with optional || feature selection; integrated hyper-parameter selection || and aggregation strategy in which the best models || within a cross validation loop are averaged. || \"\"\"
aaehgjiac,nilearn/nilearn,nilearn/datasets/func.py,dde3fddd48c80299fb74fab532ba0406cd5c69e4,13a846aab3bb8ef0d6358018d4e89ea0e25bff09,from the url list we infer all available subjects like 'sub-xxx\/'
aaehhaaga,nilearn/nilearn,nilearn/datasets/func.py,accb9324fe2641d9222fa939fb751edac3ec2abe,381e492c5bcbc7f05b9b4cfd285524b71941629b,from the url list we infer all available subjects like 'sub-xxx\/'
aaehiahie,nilearn/nilearn,nilearn/datasets/func.py,2680a09cb1e7bd84282402fe5a583cb5e700fc14,STILL_EXISTS,from the url list we infer all available subjects like 'sub-xxx\/'
aaehiaibj,nilearn/nilearn,nilearn/reporting/__init__.py,2680a09cb1e7bd84282402fe5a583cb5e700fc14,STILL_EXISTS,\"\"\" || This module implements plotting functions useful to report analysis results. ||  || Author: Martin Perez-Guevara; Elvis Dohmatob; 2017 || \"\"\"
aaehibjaj,nilearn/nilearn,examples/02_decoding/plot_mixed_gambles_frem.py,7e36343130e20a3f18762dd700bf28a56fb705e3,STILL_EXISTS,\"\"\" || fREM on Jimura et al \"mixed gambles\" dataset. || ================================================== ||  || In this example; we use fast ensembling of regularized models (fREM) to || solve a regression problem; predicting the gain level corresponding to each || beta maps regressed from mixed gambles experiment. fREM uses an implicit || spatial regularization through fast clustering and aggregates a high number || of  estimators trained on various splits of the training set; thus returning || a very robust decoder at a lower computational cost than other spatially || regularized methods. ||  || To have more details; see: :ref:`frem`. || \"\"\"
aaehibjff,nilearn/nilearn,nilearn/decoding/decoder.py,7e36343130e20a3f18762dd700bf28a56fb705e3,STILL_EXISTS,clustering to reduce the number of feature by agglomerating similar ones
aaehibjih,nilearn/nilearn,examples/04_glm_first_level_models/plot_first_level_model_details.py,81e7ef1391343aa733e087e45d1f2b776ca3c667,STILL_EXISTS,There seems to be something here. Maybe we could adjust the
aaehicbfh,nilearn/nilearn,examples/04_glm_first_level/plot_first_level_details.py,ddc59b147eecc6b8d7619edd9a1eba7f6487fec5,STILL_EXISTS,Let us take a look at the design matrix: it has 10 main columns corresponding
aaehicfcb,nilearn/nilearn,examples/02_decoding/plot_haxby_glm_decoding.py,e3cea710413ae255e84ae3bcab3274d529bc2793,STILL_EXISTS,\"\"\" || Decoding of a dataset after GLM fit for signal extraction || ========================================================= ||  || Full step-by-step example of fitting a GLM to perform a decoding experiment. || We use the data from one subject of the Haxby dataset. ||  || More specifically: ||  || 1. Download the Haxby dataset. || 2. Extract the information to generate a glm representing the blocks of stimuli. || 3. Analyze the decoding performance using a classifier. ||  || To run this example; you must launch IPython via ``ipython || --matplotlib`` in a terminal; or use the Jupyter notebook. ||  || .. contents:: **Contents** ||     :local: ||     :depth: 1 || \"\"\"
aaehicfhi,nilearn/nilearn,examples/02_decoding/plot_haxby_glm_decoding.py,e3cea710413ae255e84ae3bcab3274d529bc2793,STILL_EXISTS,* although it usually helps to decode better; z-maps time series don't
aaehicgef,nilearn/nilearn,examples/07_advanced/plot_advanced_decoding_scikit.py,e3cea710413ae255e84ae3bcab3274d529bc2793,STILL_EXISTS,simplest way to measure prediction performance at chance. A more controlled
aaehicgfh,nilearn/nilearn,examples/07_advanced/plot_advanced_decoding_scikit.py,e3cea710413ae255e84ae3bcab3274d529bc2793,STILL_EXISTS,We can also implement feature selection before decoding as a scikit-learn
aaehichgb,nilearn/nilearn,nilearn/reporting/_get_clusters_table.py,e1673e1b848cf7652bc2b8ba4f6bf6ee57174866,STILL_EXISTS,modified; therefore copy is needed
aaehiciec,nilearn/nilearn,nilearn/plotting/surf_plotting.py,b7873d0825bbd655c0978632cb9bcd9052b860da,STILL_EXISTS,Fix: Matplotlib version 3.3.2 to 3.3.3
aaehicigb,nilearn/nilearn,examples/plot_decoding_tutorial.py,9255450f479e9c3eba73e8535c8099ce054d09c5,STILL_EXISTS,Does the model above perform better than chance?
aaehidbfd,nilearn/nilearn,examples/01_plotting/plot_demo_more_plotting.py,02e915accc1dd29ee8af0e92004eabb4f0393958,STILL_EXISTS,Visualizing three views along multiple rows and columns
aaehidbfi,nilearn/nilearn,examples/01_plotting/plot_demo_more_plotting.py,02e915accc1dd29ee8af0e92004eabb4f0393958,STILL_EXISTS,Now; changing the number of slices along columns
aaehidbgd,nilearn/nilearn,examples/01_plotting/plot_demo_more_plotting.py,02e915accc1dd29ee8af0e92004eabb4f0393958,STILL_EXISTS,Now; another way of limiting the number of slices along rows and columns
aaehidcac,nilearn/nilearn,nilearn/plotting/displays.py,647d4c1a1cff0673b14e709aa698456ac037ccba,STILL_EXISTS,Hack to avoid empty arrows to crash with
aaehidcdb,nilearn/nilearn,nilearn/tests/test_signal.py,e8d5f5dddaede697da1eb821a240ece52172c43f,STILL_EXISTS,leave out the last 3 columns with a mean of zero to test user warning
aaehidgid,cltk/cltk,cltk/corpus/classical_greek/replacer.py,ccd26f5e1fbcd7c4fe2f05508bef0f981b8926d6,STILL_EXISTS,this appears in Perseus; I don't think is TLG standard
aaehidhdj,cltk/cltk,cltk/parse/classical_latin/parse_latin_analyses.py,a49cdd33d17e3636752b21ce1dce45772db37b06,STILL_EXISTS,this actually point to the headwords they belong to; I think; these needs to be parsed and looped
aaehidhih,cltk/cltk,cltk/parse/classical_latin/parse_latin_analyses.py,426111e1688076fce9ae758e4c819309c664ad4b,STILL_EXISTS,!TODO
aaehidjdb,cltk/cltk,cltk/tag/classical_latin/parse_latin_analyses.py,90a8731ed1f3d3789b5abc23e7308639f740f3ca,STILL_EXISTS,!TODO
aaehidjej,cltk/cltk,cltk/tag/classical_latin/pos_latin_manual.py,5bb13bc8f27296b071962d6c5407a0be4b707aa3,STILL_EXISTS,!TODO
aaehieafj,cltk/cltk,cltk/corpus/classical_greek/beta_to_unicode.py,a82e69fe2eb1fa28542cd6681c9827370eb66edf,cba32f531d69e5437b6feb0c1a158b4ceffdd6c0,TODO for replacer.py
aaehieahd,cltk/cltk,cltk/stop/classical_greek/stops_unicode.py,a82e69fe2eb1fa28542cd6681c9827370eb66edf,47b211cfb7c906496b055191ac25166b67d114f2,TODO
aaehiebeh,cltk/cltk,cltk/tag/classical_latin/pos_latin.py,9bb799645df9973a24b432061a2cef9c15e757e0,STILL_EXISTS,TODO: make sure this is with the right
aaehiebjd,cltk/cltk,cltk/corpus/classical_greek/beta_to_unicode.py,6aa7d8c0755ca983ffe0d96766ae649ef96ff5db,ef0626e75ac2624ede959cadee6ee40d3922334f,TODO for replacer.py
aaehiecae,cltk/cltk,cltk/corpus/classical_greek/beta_to_unicode.py,ef0626e75ac2624ede959cadee6ee40d3922334f,STILL_EXISTS,\"\"\"Converts legacy encodings into Unicode || TODO for replacer.py: ||  - add perseus-style iota subscript and diaeresis || \"\"\"
aaehiecgc,cltk/cltk,cltk/corpus_api/corpus.py,69707bc656842fd90579936275d2de7b91050776,STILL_EXISTS,TODO: fix `tglu` calls
aaehiecge,cltk/cltk,cltk/corpus_api/corpus.py,69707bc656842fd90579936275d2de7b91050776,STILL_EXISTS,TODO: what is the argument here?
aaehiecgg,cltk/cltk,cltk/corpus_api/greek/beta_to_unicode.py,69707bc656842fd90579936275d2de7b91050776,STILL_EXISTS,\"\"\"Converts legacy encodings into Unicode || TODO for replacer.py: ||  - add perseus-style iota subscript and diaeresis || \"\"\"
aaehieddg,cltk/cltk,cltk/corpus_api/greek/tlg.py,bfc024a590adb5e9e1f14709ed67c6b364da40e5,STILL_EXISTS,TODO
aaehieefc,cltk/cltk,cltk/corpus_api/wrappers/wrapper.py,434b671cb33d4126e63b39d0229da67bc017b699,STILL_EXISTS,TODO: fix paths
aaehiefac,cltk/cltk,cltk/corpus/formatter.py,3acd93f7bd600149e17dc287bb221ebeed51df86,STILL_EXISTS,\"\"\"Process downloaded or local corpora from one format into another. || Some formatting can happen here; or invoke language-specific formatters in || other files. ||  || #TODO: Add TLG & PHI text cleaners from KJ's IPython notebooks || #TODO: Add non-ascii stripper || #TODO: Add generic HTML stripper || \"\"\"
aaehiefbe,cltk/cltk,cltk/corpus/greek/tlgu.py,3acd93f7bd600149e17dc287bb221ebeed51df86,c51d2d4eda65799e9f2fefccb811a7e79e4f7f69,TODO: fix paths
aaehiefbh,cltk/cltk,cltk/corpus/formatter.py,54a987ba83bcdd581377488f4b88b7d5424b932c,STILL_EXISTS,fix beta code transliteration problems
aaehiefbi,cltk/cltk,cltk/corpus/formatter.py,54a987ba83bcdd581377488f4b88b7d5424b932c,STILL_EXISTS,fix tlg markup
aaehiefgj,cltk/cltk,cltk/corpus/importer.py,d5882017fb19bd012720794a36b75428c2bb8bda,STILL_EXISTS,TODO mk singuler
aaehiefhg,cltk/cltk,cltk/corpus/importer.py,d5882017fb19bd012720794a36b75428c2bb8bda,STILL_EXISTS,move the dir-checking commands into a function
aaehiegfd,cltk/cltk,cltk/corpus/greek/tlgu.py,276d71c6c6155e4b8401039fc699d7fccd26c940,STILL_EXISTS,\"\"\"Wrapper for `tlgu` command line utility ||  || TODO: Fully implement this. || \"\"\"
aaehiehbc,cltk/cltk,cltk/corpus/utils/formatter.py,8a699c8d149ac068aeacf9694db9df60d75471cd,7a22d3b22c3008510726f1f1e8e758e51d96f265,''' ||  || def build_phi5_index(index_path_rel = '~\/cltk_data\/originals\/phi5\/AUTHTAB.DIR'): ||     \"\"\"Return dict of 362 files in format of {file: author_name}. This has ||     been pre-generated and saved at ``~\/cltk\/corpus\/latin\/phi5_index.py``. ||     TODO: Update this to account for works within each author's file. ||     \"\"\" ||     index_path = os.path.expanduser(index_path_rel) ||     if not os.path.isfile(index_path): ||         logger.info(\"Failed to locate original PHI5 index at '%s'. Please import PHI5 first.\" % index_path) ||         sys.exit(1) ||     with open(index_path; 'rb') as f: ||         r = f.read() ||         index_all = r.decode('latin-1').split('\\xff')[1:-21] ||         index = [x for x in index_all if x] ||         file_author = {} ||         for x in index: ||             # file name ||             pattern_file = re.compile('LAT[\\d].{4}') ||             m = pattern_file.match(x) ||             file_name = m.group()[:-1] + '.TXT' ||  ||             # author name ||             author_name = pattern_file.split(x)[-1] ||             pattern_author = re.compile('&1|&\u0083l|l$|&|1$|\\x83') ||             author_name = pattern_author.sub(''; author_name) ||             pattern_comma = re.compile('\\x80') ||             author_name = pattern_comma.sub('; '; author_name) ||             file_author[file_name] = author_name ||  ||     return file_author ||  ||  || def build_tlg_index(index_path_rel='~\/cltk_data\/originals\/tlg\/AUTHTAB.DIR'): ||     \"\"\"Return dict of 362 files in format of {file: author_name}. This has ||     been pre-generated and saved at ``~\/cltk\/corpus\/latin\/phi5_index.py``. ||     TODO: Update this to account for works within each author's file. ||     TODO: merge with phi5 build index || \"\"\" ||     index_path = os.path.expanduser(index_path_rel) ||     if not os.path.isfile(index_path): ||         logger.info(\"Failed to locate original TLG index at '%s'. Please import TLG first.\" % index_path) ||         sys.exit(1) ||     with open(index_path; 'rb') as f: ||         r = f.read() ||         index_all = r.decode('latin-1').split('\\xff')[1:-6]  # diff from phi5 ||         index = [x for x in index_all if x] ||         file_author = {} ||         for x in index: ||             # file name ||             pattern_file = re.compile('TLG[\\d].{4}') ||             m = pattern_file.match(x) ||             file_name = m.group()[:-1] + '.TXT' ||  ||             # author name ||             author_name = pattern_file.split(x)[-1] ||             pattern_author = re.compile('&1|&\u0083l|l$|&|1$|\\x83|\\[2|\\]2')  # diff from phi5 ||             author_name = pattern_author.sub(''; author_name) ||             pattern_comma = re.compile('\\x80') ||             author_name = pattern_comma.sub('; '; author_name) ||             file_author[file_name] = author_name ||  ||     return file_author || '''
aaehiehbh,cltk/cltk,cltk/corpus/utils/formatter.py,098245441b0ebd6969051518953c0e8a6862da5f,36ecf695e02e0109a7306fd45cf72a2088628846,print(len(final_dict))  # 1777 this is missing ~100 files; find why
aaehiehci,cltk/cltk,cltk/corpus/utils/formatter.py,b4906f3c459f5fe7a5db6538b46af0aa6cbb5346,36ecf695e02e0109a7306fd45cf72a2088628846,super ugly. replace name if 1 of 3 which has Greek in name
aaehiehcj,cltk/cltk,cltk/corpus/utils/formatter.py,b4906f3c459f5fe7a5db6538b46af0aa6cbb5346,36ecf695e02e0109a7306fd45cf72a2088628846,same as above. damn ugly. replace name w\/ dict value if name is one we
aaehiehef,cltk/cltk,cltk/corpus/utils/formatter.py,2251599e028bdbb8b1f03a8d38247db0594d79a4,36ecf695e02e0109a7306fd45cf72a2088628846,! this is super ugly and contains redundancies
aaehieijj,cltk/cltk,cltk/corpus/greek/tlg_indices.py,dbaf6c2520a9bb9d5ffbaa8c5cbf65961825b46e,STILL_EXISTS,\"\"\"Indices for the TLG. ||  || Note: # ``TLG_MASTER_INDEX`` is the result of failed IDT parsing. ||  || TODO: Add work names to ``TLG_WORKS_INDEX`` || TODO: Add all TLG index data. || \"\"\"
aaehifbae,cltk/cltk,cltk/corpus/utils/importer.py,6e6c30208aa8feb320e978a20cf6fd64c3852bd9,7cac07b1b3c547a591e24c7c01b91d6619e2817b,! much better: do a check on sha commit version locally and remote; then decide whether to replace
aaehifbaf,cltk/cltk,cltk/corpus/utils/importer.py,6e6c30208aa8feb320e978a20cf6fd64c3852bd9,STILL_EXISTS,TODO: print git output to screen
aaehifdbg,cltk/cltk,cltk/utils/philology.py,405315616c262f8f771744601d0c6e63a0bfd8e0,bce880ff365f4d002b264b501f9b09b189a5780e,t.concordance('ut')  # this has better formatting than c.print_concordance('ut')
aaehifgca,cltk/cltk,cltk/prosody/latin/scanner.py,b41b392d9bf5d0067068bdd9dd9e1f88d616133b,15d64390ae0f3898b39496635c5cd74f96e0d66a,Syllable ends with a diphthong
aaehifjah,cltk/cltk,cltk/prosody/greek/scanner.py,6b65a2b5b3002f39ee910805c71c79755ecf276f,STILL_EXISTS,Syllable ends with a diphthong
aaehifjai,cltk/cltk,cltk/prosody/greek/scanner.py,6b65a2b5b3002f39ee910805c71c79755ecf276f,STILL_EXISTS,Syllable ends with a vowel
aaehifjff,cltk/cltk,cltk/corpus/greek/tlg/parse_tlg_indices.py,e15e63efba7b42b2eda593cb73cf604721aa1417,06f4c63c25af05554853a65339510c42608d3862,\"\"\"Load .json files and allow easy searching; then pulling author ids. || \"\"\"
aaehifjgi,cltk/cltk,cltk/corpus/greek/tlg/parse_tlg_indices.py,06f4c63c25af05554853a65339510c42608d3862,STILL_EXISTS,\"\"\"For loading TLG .json files and searching; then pulling author ids. || \"\"\"
aaehifjja,cltk/cltk,cltk/ner/ner.py,b4edfad87cc92660465f691eb3f53268ebc90725,STILL_EXISTS,maybe not worth the effort
aaehigahd,cltk/cltk,cltk/ir/query.py,6c4305e562dc3c57f50727a9befce7039a02b653,STILL_EXISTS,\"\"\"Functions for retrieving data from text corpora. ||  || TODO: Add CLTK logging. || TODO: Make different functions for regex versus plaintext query. || TODO: Make public function for searching string. || TODO: Make public function for searching specific texts (passing list of eg; author names; ids; and\/or filepaths.) || TODO: Add option of outputting to plaintext file. || TODO: For whatever output; generate statistics on # of matches found; # docs searched. || \"\"\"
aaehigahg,cltk/cltk,cltk/ir/query.py,6c4305e562dc3c57f50727a9befce7039a02b653,STILL_EXISTS,TODO: Add '\\u'; '\\U'; '\\x' to this list
aaehiggee,cltk/cltk,cltk/corpus/utils/importer.py,0883cd38976e26e522a0e0820ecb144722c38b3a,483fed38d13f9e4098ec99f2c78d4cd784c5ed87,TODO: think if it makes sense to log this
aaehiggei,cltk/cltk,cltk/corpus/sanskrit/itrans/itrans_transliterator.py,1e8e9a75c19df8770ba4781880f0f8dbcbe838f1,STILL_EXISTS,\"\"\" Transliterate texts between unicode and standard transliteration schemes. ||  || Transliterate texts between non-latin scripts and commonly-used latin || transliteration schemes. Uses standard Unicode character blocks --  || e.g. DEVANAGARI U+0900 ... U+097F -- and transliteration schemes --  || e.g. the IAST convention for transliteration of Sanskrit to latin-with-dots. ||  || The following character blocks and transliteration schemes are included: ||  || DEVANAGARI ||     IAST ||     ITRANS -- http:\/\/www.aczoom.com\/itrans\/#itransencoding (Sanskrit only) ||     Harvard Kyoto ||      || CYRILLIC ||     ISO 9:1995 (Russian only) ||      || New character blocks and transliteration schemes can be added by creating || new CharacterBlock and TransliterationScheme objects. ||  || USAGE || -------- || Transliterate a text: ||  || >>> import transliterator || >>> transliterator.transliterate('yogazcittavRttinirodhaH'; 'harvardkyoto'; || ...     'devanagari'; {'outputASCIIEncoded' : True}) || '&#x92f;&#x94b;&#x917;&#x936;&#x94d;&#x91a;&#x93f;&#x924;&#x94d;&#x924;&#x935;&#x943;&#x924;&#x94d;&#x924;&#x93f;&#x928;&#x93f;&#x930;&#x94b;&#x927;&#x903;' ||  || Create a new CharacterBlock and TransliterationScheme: ||  || >>> import transliterator || >>> cb = transliterator.CharacterBlock('NEWBLOCK'; range(0x901; 0x9FF)) || >>> scheme = transliterator.TransliterationScheme(cb.name; 'NEWSCHEME'; || ...                          {'ab': 0x901; 'cd': 0x902}) || >>> transliterator.transliterate('abcd'; scheme; cb; {'outputASCIIEncoded' : True}) || '&#x901;&#x902;' ||  || COPYRIGHT AND DISCLAIMER || ------------------------------------ || Transliterator is: ||  || version 0.1 software  - use at your own risk. ||  || The IAST; ITRANS and Harvard-Kyoto transliteration schemes have been || tested for classical Sanskrit; not for any other language. ||  || The Cyrillic alphabet and ISO 9:1995 transliteration (for Russian only) || are included but have been even more lightly tested than Devanagari. ||  || Copyright (c) 2005 by Alan Little ||  || By obtaining; using; and\/or copying this software and\/or its || associated documentation; you agree that you have read; understood; || and will comply with the following terms and conditions: ||  || Permission to use; copy; modify; and distribute this software and || its associated documentation for any purpose and without fee is || hereby granted; provided that the above copyright notice appears in || all copies; and that both that copyright notice and this permission || notice appear in supporting documentation; and that the name of  || the author not be used in advertising or publicity pertaining to  || distribution of the software without specific; written prior permission. ||  || THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE;  || INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS.   || IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL; INDIRECT OR  || CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM  || LOSS OF USE; DATA OR PROFITS; WHETHER IN AN ACTION OF CONTRACT;  || NEGLIGENCE OR OTHER TORTIOUS ACTION; ARISING OUT OF OR IN CONNECTION  || WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. ||         || \"\"\"
aaehighdc,cltk/cltk,cltk/corpus/sanskrit/itrans/langinfo.py,1e8e9a75c19df8770ba4781880f0f8dbcbe838f1,STILL_EXISTS,TODO: add missing fricatives and approximants
aaehighde,cltk/cltk,cltk/corpus/sanskrit/itrans/langinfo.py,1e8e9a75c19df8770ba4781880f0f8dbcbe838f1,STILL_EXISTS,TODO: add sibilants\/sonorants
aaehighdf,cltk/cltk,cltk/corpus/sanskrit/itrans/langinfo.py,1e8e9a75c19df8770ba4781880f0f8dbcbe838f1,STILL_EXISTS,TODO: ha has to be properly categorized
aaehighih,cltk/cltk,cltk/tag/lapos.py,7a7accfbdcc42e4ecd3f64b70beb4f835bfcad05,STILL_EXISTS,TODO: Make this cleaner\/faster
aaehigibb,cltk/cltk,cltk/corpus/latin/__init__.py,e6dc7c5f38fb0747bf284ce316f0764db4db5639,7413fbae0825a7f7ef7e2ae21e9eda8df723198f,Better to use something like make_cltk_path in cltk.utils.file_operations?
aaehigicb,cltk/cltk,cltk/tokenize/latin_exceptions.py,9524766d88bfbd654a48500bd22bc07aaed96846,STILL_EXISTS,\"\"\" || Starter lists have been included to handle the Latin enclitics || (-que; -ne; -ue\/-ve; -cum). These lists are based on high-frequency vocabulary ||  and have been supplemented on a as-needed basis; i.e. they are not ||  comprehensive. Additions to the exceptions list are welcome. PJB || \"\"\"
aaehihdcf,cltk/cltk,cltk/tests/test_corpus.py,7413fbae0825a7f7ef7e2ae21e9eda8df723198f,STILL_EXISTS,\"\"\"Test cltk.corpus. ||  || TODO: Consider whether to import the very large Word2Vec corpora for Greek and Latin. || \"\"\"
aaehihfbi,cltk/cltk,cltk/tokenize/word.py,7413fbae0825a7f7ef7e2ae21e9eda8df723198f,36b6fe7aa23a8c4f41ef694ddb64b4b02534146c,\"\"\"Language-specific word tokenizers. Primary purpose is to handle enclitics. ||  || Re: latin || Starter lists have been included to handle the Latin enclitics || (-que; -ne; -ue\/-ve; -cum). These lists are based on high-frequency vocabulary ||  and have been supplemented on a as-needed basis; i.e. they are not ||  comprehensive. Additions to the exceptions list are welcome. PJB || \"\"\"
aaehihjgc,cltk/cltk,cltk/corpus/punjabi/alphabet.py,72e26614315b3d0fe1e9e8c8fd7ba7f467c32ff6,STILL_EXISTS,The Functions Needed for making the data of alphabets is here
aaehihjjd,cltk/cltk,cltk/tests/test_corpus.py,cbf67f1e019483ed6e7ed71545f5a249e281ac0d,STILL_EXISTS,\"\"\"Test cltk.corpus. ||  || TODO: Consider whether to import the very large Word2Vec corpora for Greek and Latin. || \"\"\"
aaehiibgf,cltk/cltk,cltk/prosody/latin/macronizer.py,e37cb2e13d13d7d40f88d69b6a35d8bd3865b78d,STILL_EXISTS,TODO Determine how to disambiguate tags (see logger)
aaehiicgd,cltk/cltk,cltk/tests/test_tmp.py,4056792be2a799ec1ac915ffa4362d16e3bda7a7,STILL_EXISTS,TODO: make `cltk_data` dir is not present
aaehiidci,cltk/cltk,cltk/corpus/latin/__init__.py,36b6fe7aa23a8c4f41ef694ddb64b4b02534146c,320e810184204d9171e683241adea4c5c1c73a04,Better to use something like make_cltk_path in cltk.utils.file_operations?
aaehiiddf,cltk/cltk,cltk/tokenize/word.py,36b6fe7aa23a8c4f41ef694ddb64b4b02534146c,STILL_EXISTS,Need to check that tokens exist before handling them; needed to make stream.readlines work in PlaintextCorpusReader
aaehiifdh,cltk/cltk,cltk/tests/test_corpus.py,22c185afb17ffcd23adcf0e4cbe231e972132c26,STILL_EXISTS,! Figure out why this test stopped working (actual function runs fine)
aaehijdbf,cltk/cltk,cltk/stem/akkadian/syllabifier.py,2b2fa565c868b0b27ee3c2b9a6025141ea6b985a,STILL_EXISTS,\"\"\" || Split Akkadian words into a list of syllables.  Logic is based on || A Grammar of Akkadian; Huehnergard 3rd. ed. ||  || TODO: Check this logic with von Soden's Grundriss der akkadischen Grammatik. || TODO: Deal with j\/y issue. || \"\"\"
aaehijedi,cltk/cltk,cltk/tests/test_utils.py,07eab93e8c47ecbdd226dc5bfc5b3656d042321d,da1b58838005a76c7f7ce239b23b17f4447df1d2,frequencies.counter_from_corpus('xxx')
aaehijeia,cltk/cltk,cltk/tests/test_utils.py,07eab93e8c47ecbdd226dc5bfc5b3656d042321d,da1b58838005a76c7f7ce239b23b17f4447df1d2,\"\"\"Test making dict for author contrib file.\"\"\"
aaehijged,cltk/cltk,cltk/corpus/punjabi/alphabet.py,5522df62391117d4f79f3feb85440745137c9393,STILL_EXISTS,The Functions Needed for making the data of alphabets is here
aaehijiee,cltk/cltk,cltk/stop/arabic/stops.py,ef4e2964b9d130dba674fed6f0414296c2b942d2,STILL_EXISTS,TODO: Improve stop list word
aaehijief,cltk/cltk,cltk/stop/arabic/stops.py,ef4e2964b9d130dba674fed6f0414296c2b942d2,STILL_EXISTS,TODO: Add translate comments for each stop word.
aaehjabgf,cltk/cltk,cltk/phonology/arabic/romanization.py,de25724b2313a1cd3a6358990aacd6cb237d3e51,STILL_EXISTS,\"arabtex\": ARABTEX_TO_UNICODE; todo: not ready
aaehjabgg,cltk/cltk,cltk/phonology/arabic/romanization.py,de25724b2313a1cd3a6358990aacd6cb237d3e51,STILL_EXISTS,\"iso8859-6\": ISO88596_TO_UNICODE; todo: not ready
aaehjabgh,cltk/cltk,cltk/phonology/arabic/romanization.py,de25724b2313a1cd3a6358990aacd6cb237d3e51,STILL_EXISTS,@todo
aaehjabgi,cltk/cltk,cltk/phonology/arabic/romanization.py,de25724b2313a1cd3a6358990aacd6cb237d3e51,STILL_EXISTS,@todo: arabtex and iso8859-6 need individual handling because in some cases using one-two mapping
aaehjabhf,cltk/cltk,cltk/lemmatize/latin/backoff.py,f054e80cb4342e15df9ceee5141792b675ee0ed6,fa5d1b8ed53612d58b283ba9b6f7a1e176fe6d68,Unused for now
aaehjacbb,cltk/cltk,cltk/lemmatize/latin/latin.py,f054e80cb4342e15df9ceee5141792b675ee0ed6,STILL_EXISTS,## Todo: Refactor to be more compact
aaehjacdg,cltk/cltk,cltk/stem/akkadian/stem.py,29687923aa22812248aee118dd8f8907b1ea1729,STILL_EXISTS,\"\"\" || Get the stem of a word; given a declined form and its gender. ||  || TODO: Check this logic with von Soden's Grundriss der akkadischen Grammatik. || TODO: Deal with j\/y issue. || \"\"\"
aaehjaeej,cltk/cltk,cltk/tests/test_arabic_utils.py,101faecbdb6ec22ad627f602e249f5f42d161d5f,STILL_EXISTS,is_arabicstring TODO: add more examples
aaehjaefa,cltk/cltk,cltk/tests/test_arabic_utils.py,101faecbdb6ec22ad627f602e249f5f42d161d5f,STILL_EXISTS,is_arabicrange TODO: add test
aaehjaefb,cltk/cltk,cltk/tests/test_arabic_utils.py,101faecbdb6ec22ad627f602e249f5f42d161d5f,STILL_EXISTS,is_arabicword TODO: test other cases
aaehjaegi,cltk/cltk,cltk/tests/test_arabic_utils.py,101faecbdb6ec22ad627f602e249f5f42d161d5f,STILL_EXISTS,normalize_ligature(text):TODO: fixme gives '\u0644\u0627\u0646\u0647\u0627 \u0644\u0627\u0644\u0621 \u0627\u0644\u0627\u0633\u0644\u0627\u0645'
aaehjaehb,cltk/cltk,cltk/tests/test_arabic_utils.py,101faecbdb6ec22ad627f602e249f5f42d161d5f,STILL_EXISTS,test separate function  TODO: testme
aaehjaifi,cltk/cltk,cltk/vector/word2vec.py,dfa9dcde43def23dc2fcca7a0d85d2f69e2b5a30,STILL_EXISTS,TODO: Fix this
aaehjbaei,cltk/cltk,cltk/tokenize/word.py,93bd99a1ef6ee53ec1104a847f01a5cc52ba26c9,fb787df70003a3f1933696d8e20d055c5fa0677f,TODO dealing with merges between verbs and sik -> st : middle voice
aaehjbagf,cltk/cltk,cltk/phonology/akkadian/stress.py,cda3922555aa7a1a7d146abfd96a10be2a8e2741,STILL_EXISTS,TODO: fails on: ['hammurabi'; 'u'; 'i\u0161me\u0101nim']
aaehjcaii,cltk/cltk,cltk/tokenize/latin/utils.py,0d351704cf9d5844b32b56f58b691a71772c323f,a105c8f84dc043dd6f4b82955e85c6bec57a10b9,Here's how to debug every split decision
aaehjcceb,cltk/cltk,cltk/stem/middle_high_german/stem.py,39032607bebb71f268295453827296bea2ce5ba1,STILL_EXISTS,\"\"\" || The biggest challenge when it comes to noun and adjective stemming is that -similarly to MG- MHG suffixes are based on gender; || which is difficult to determine without either a hard-coded dictionary or an efficient tagger. Statistical analysis could  || theoretically yield more  accurate results; but a lack of online resources make this approach somewhat unreliable. ||  || Another core problem is the fact that unlike English; changes of the stem often occur in the middle of the word rather than the || end (bruoder -> br\u00FCeder). ||  || The following algorithm is inspired by Modern German stemmers (namely Snowball); modified to better fit MHG morphological  || structure. ||  || http:\/\/snowball.tartarus.org\/algorithms\/german\/stemmer.html || http:\/\/www.inf.fu-berlin.de\/lehre\/WS98\/digBib\/projekt\/_stemming.html || \"\"\"
aaehjceia,cltk/cltk,cltk/tokenize/latin/utils.py,7c68f14f2f66b2deabf1b7b4d14de5871c05c0bb,bda3cfbad93f54fa5aa2c01cb3d3b6f87270a03c,Here's how to debug every split decision
aaehjcifd,cltk/cltk,cltk/stem/middle_english/stem.py,183aabebc8baf29a67fd07416e9fbd18f6dd48d3,STILL_EXISTS,\"\"\" || Stemming present a significant challenge in ME; as it is exceptionally difficult to account for the orthographical ||  variations sometimes even occurring within a single text. The affix algorithm attempts to account for variations in ||  spelling; but still Mostly relies on a relatively narrow hard-coded list (Middle English Dictionary(MED) ||  https:\/\/quod.lib.umich.edu\/m\/med\/). ||   TODO: Improve on the affix stemmer by implementing an accurate spell checker (Levenshtein Automata?) ||   TODO: Implement a stochastic algorithm\/Implement overarching stemmer class || \"\"\"
aaehjcija,cltk/cltk,cltk/phonology/middle_english/transcription.py,23e9e37d61fbc2a62e6547dbe078dde8760fba5e,87e833700ca88dce8c5423f0ca160e792f52b23d,\"\"\" || The hyphenation\/syllabification algorithm is based on the typical syllable structure model of onset\/nucleus\/coda. || TODO: Add hypothesized IPA transcription || \"\"\"
aaehjdabh,cltk/cltk,cltk/phonology/middle_english/transcription.py,74b65c1f9aaa71451da532171ab207959ce97719,STILL_EXISTS,Check whether ultima ends in e
aaehjdcdb,cltk/cltk,cltk/phonology/middle_high_german/transcription.py,c0fef9aff01f7e2ea2f1f1a00e5a7d4f033ae833,STILL_EXISTS,\"\"\"Note: there are no definite MHG  phonological rules; so this module serves as an approximate reconstruction of the original. As of this version; the Transcribe class doesn't support any specific dialects and serves as a superset encompassing various regional accents.  ||   ||  To-do: add stress; syllabify module; CV tier ||   ||  Sources: ||  * https:\/\/www.germanistik.uni-bonn.de\/institut\/abteilungen\/germanistische-mediavistik\/studium\/leitfaeden-reader-links\/b1-reader-oktober-2009-endversion.pdf || * [A Middle High German Primer - Joseph Wright](http:\/\/www.minnesang.com\/Themen\/Ulrich%20Mueller%20zur%20Aussprache.pdf) || \"\"\"
aaehjdcef,cltk/cltk,cltk/phonology/middle_high_german/transcription.py,c0fef9aff01f7e2ea2f1f1a00e5a7d4f033ae833,65f34dcd5586893374cc2ac68e114a0bcc9c0e10,To-do: Add different dialects and\/or notations
aaehjeahj,cltk/cltk,cltk/phonology/middle_high_german/transcription.py,af5f26d119b65bbefe48c8d62359f2929013458a,STILL_EXISTS,To-do: Add different dialects and\/or notations
aaehjhbdc,cltk/cltk,cltk/corpus/readers.py,320e810184204d9171e683241adea4c5c1c73a04,STILL_EXISTS,TODO add your corpus here:
aaehjhbde,cltk/cltk,cltk/corpus/readers.py,320e810184204d9171e683241adea4c5c1c73a04,STILL_EXISTS,: Generic file ending; override below in your own CorpusReader implementation
aaehjhbdf,cltk/cltk,cltk/corpus/readers.py,320e810184204d9171e683241adea4c5c1c73a04,STILL_EXISTS,TODO and add:  ['latin_text_perseus'; 'latin_treebank_perseus'; 'latin_text_latin_library'; 'phi5'; 'phi7'; 'latin_proper_names_cltk'; 'latin_models_cltk'; 'latin_pos_lemmata_cltk'; 'latin_treebank_index_thomisticus'; 'latin_lexica_perseus'; 'latin_training_set_sentence_cltk'; 'latin_word2vec_cltk'; 'latin_text_antique_digiliblt'; 'latin_text_corpus_grammaticorum_latinorum'; 'latin_text_poeti_ditalia']
aaehjhbdg,cltk/cltk,cltk/corpus/readers.py,320e810184204d9171e683241adea4c5c1c73a04,STILL_EXISTS,TODO add other languages and write tests for each corpus
aaehjhbff,cltk/cltk,cltk/tests/download_test_corpora.py,320e810184204d9171e683241adea4c5c1c73a04,STILL_EXISTS,TODO add other corpora as necessary
aaehjiaib,cltk/cltk,cltk/lemmatize/latin/backoff.py,4ac71fbc3f76127d5ff3030c2187c426493560cf,aea5c77d75f3b6079f9af55d0459dcdab2402717,the principal part number needed to lookup the correct stem.
aaehjicfb,cltk/cltk,cltk/tests/test_nlp/test_lemmatize.py,aaa5c2a78e07dd9b3305a6ba9effc7c4a1539a1b,aea5c77d75f3b6079f9af55d0459dcdab2402717,test_str = 'i ii iii iv v vi vii vii ix x xx xxx xl l lx c cc'
aaehjicjb,cltk/cltk,cltk/stem/sanskrit/indian_syllabifier.py,47f00b5147114e1d1313ba93dfac420a161a1474,STILL_EXISTS,Handle better?
aaehjicjd,cltk/cltk,cltk/tests/test_nlp/test_lemmatize.py,4aeb0758a75fecf1411a0cf39f613ed49f47efde,23045e367f2221d3849721303f4f52492e9b6cb2,Perhaps should be an assertion about raising an exception
aaehjicjh,cltk/cltk,cltk/lemmatize/latin/backoff.py,bf8abaa6ea9fcf4e32b91493fd2a69a76057038b,20b04d841328148a5adf1604dd4e1ba34c0bdca3,self.latin_pps = latin_pps # Move to latin_models_cltk
aaehjidae,cltk/cltk,cltk/stem/lemma.py,2bfc49812fa086c3c70382fa9da3cfc5a153696c,STILL_EXISTS,Deprecated; remove from future release?
aaehjiead,cltk/cltk,cltk/tokenize/latin/sentence.py,0ded4ca3b4bc3852de9acef043acbba702705a9c,22a00e00a724b16a1dd7819d608b61ee7611059f,Need to think about the best way to evaluate sentence tokenizers
aaehjifae,cltk/cltk,cltk/tokenize/sentence.py,b9c5b030aec5fb70be2ff4827a6acf8ff1cac569,STILL_EXISTS,Workaround for Latin\u2014use old API syntax to load new sent tokenizer
aaehjifaf,cltk/cltk,cltk/tokenize/sentence.py,16409b0b56ed20ae5eb6bc9a3ae90ead984187cc,STILL_EXISTS,Part of Latin workaround
aaehjiibg,cltk/cltk,cltk/lemmatize/greek/backoff.py,fa5d1b8ed53612d58b283ba9b6f7a1e176fe6d68,STILL_EXISTS,Move to latin_models_cltk
aaehjiide,cltk/cltk,cltk/lemmatize/backoff.py,6f353babb8745823e874178de9db249b3f8ff463,STILL_EXISTS,Unused for now
aaehjjbbd,cltk/cltk,cltk/inflection/old_norse/nouns.py,65e6c27ae876776dc55b8c8dae839f87454073ae,STILL_EXISTS,TODO  +\"vi\"
aaehjjbbi,cltk/cltk,cltk/inflection/old_norse/nouns.py,65e6c27ae876776dc55b8c8dae839f87454073ae,STILL_EXISTS,TODO +\"vum\"
aaehjjbca,cltk/cltk,cltk/inflection/old_norse/nouns.py,65e6c27ae876776dc55b8c8dae839f87454073ae,STILL_EXISTS,TODO + \"va\"
aaehjjgcj,cltk/cltk,cltk/tokenize/sentence.py,5933e81ea9e3fc1a525891023d62072b62209f94,STILL_EXISTS,TODO add message; must specify sent_end_chars; or warn and use defaults
aaehjjgdd,cltk/cltk,cltk/tokenize/sentence.py,6757aba013e84647d9aaf30e65fcbb96f31a1353,STILL_EXISTS,Workaround for regex tokenizer
aaeiaajfh,cltk/cltk,cltk/tests/test_corpus/test_corpus.py,ed1a2414287bb5bf121950528a1a7b752a8e3d71,STILL_EXISTS,Need a additional instance because tests below change internals #TO-DO Fix
aaeiabcfc,cltk/cltk,cltk/codes/glottolog.py,cb36dfd9c1dc9f52452d685010754edca4ae4701,STILL_EXISTS,\"\"\"Module for mapping Glottolog codes to standard family names. ||  || Glottolog is a project run by the Max Planck Institute for the || Science of Human History. The website contains codes for languages || as well as the reconstructed of language families: <http:\/\/glottolog.org\/>. ||  || TODO: Consider whether this kind of module is necessary. || TODO: Consider the other codes that users might want (ISO 639-1 639-2; ISO 639-3) || \"\"\"
aaeiabgef,cltk/cltk,cltk/tokenize/old_norse/word.py,fb787df70003a3f1933696d8e20d055c5fa0677f,STILL_EXISTS,# TODO dealing with merges between verbs and sik -> st : middle voice
aaeiaciih,cltk/cltk,cltk/prosody/latin/scanner.py,15d64390ae0f3898b39496635c5cd74f96e0d66a,STILL_EXISTS,previous word ends in consonant and current word begins with consonant
aaeiaciii,cltk/cltk,cltk/prosody/latin/scanner.py,15d64390ae0f3898b39496635c5cd74f96e0d66a,STILL_EXISTS,previous word ends in vowel and current word begins in consonant
aaeiacjfh,cltk/cltk,cltk/tokenize/latin/word.py,697bfcaf6f6bcf81aabc753a5038f4f577ed269f,STILL_EXISTS,needed to make stream.readlines work in PlaintextCorpusReader
aaeiadfih,cltk/cltk,docs/conf.py,4e1e6feb6bc86236c6c1b896597a9f52966f6747,STILL_EXISTS,TODO: Decide which of these are necessary
aaeiadfjg,cltk/cltk,src/cltkv1/stopwords/arabic.py,fef1cee17355d0bff52a57f5cae667b995a24aeb,STILL_EXISTS,TODO: Improve stop list word
aaeiadfjh,cltk/cltk,src/cltkv1/stopwords/arabic.py,fef1cee17355d0bff52a57f5cae667b995a24aeb,STILL_EXISTS,TODO: Add translate comments for each stop word.
aaeiadiia,cltk/cltk,src/cltkv1/__init__.py,28c0491a8325c85be005c480a8bfa45b1325cf45,db3fd85f5086e94d7f52cbeb9f5ac09d3b9f3864,TODO: Figure out right way to do these init imports and in submodules
aaeiadiih,cltk/cltk,src/cltkv1/nlp.py,a9c7ff1cb277c00678a902bb77128659b686b22f,518c03f77f1d2418b49e931a11919ad6bcb3b9f7,TODO: Fill out more attributes to this
aaeiadjdd,cltk/cltk,src/cltkv1/nlp.py,d1154c8fd3300f22a177035dd41e5e3c5af29f06,e28dc9526e5b20e23d76df74258e309e5a06f5de,TODO: create and return list of tokens using Word
aaeiadjde,cltk/cltk,src/cltkv1/nlp.py,d1154c8fd3300f22a177035dd41e5e3c5af29f06,e28dc9526e5b20e23d76df74258e309e5a06f5de,TODO: Maybe delete _get_tokens()
aaeiadjdf,cltk/cltk,src/cltkv1/codes/glottolog.py,e28dc9526e5b20e23d76df74258e309e5a06f5de,STILL_EXISTS,TODO: Ch this to a dataclass
aaeiadjdi,cltk/cltk,src/cltkv1/nlp.py,b0e56e87b0de90feb149327c87f1b88fcac453c8,e918005117ea3e11f3291652c4679d7163359d30,TODO: Look into ``dataclasses.field(default_factory=some_function)`` to auto populate a field based upon another: https:\/\/realpython.com\/python-data-classes\/
aaeiadjeh,cltk/cltk,src/cltkv1/utils/cltk_dataclasses.py,518c03f77f1d2418b49e931a11919ad6bcb3b9f7,STILL_EXISTS,\"\"\"Custom data types for the CLTK. ||  || TODO: Fill out more attributes to these || \"\"\"
aaeiadjfg,cltk/cltk,src/cltkv1/utils/operations.py,9b5b08bc4f0e3094c1a85af25948aa0802b06a65,STILL_EXISTS,\"\"\"Operations are distinct NLP algorithms that perform particular || processing for particular languages. Each ``Operation`` is to be used || in the ``Pipeline`` data type. For each ``Operation`` data type; || the two most important attributes are: ||  || 1. the particular function which it implements || 2. data type required of input || 3. data type produced ||  || Inheritance example: ``Operation`` -> ``TokenizationOperation`` -> ``LatinTokenizationOperation`` ||  || TODO: Consider creation ``operation.py`` files w\/in each dir ||  || TODO: Think about multiple inheritance using the Glottolog codes (changing these from namedtuple to dataclass first) || \"\"\"
aaeiadjfi,cltk/cltk,src/cltkv1/utils/pipelines.py,9b5b08bc4f0e3094c1a85af25948aa0802b06a65,037662f4962856c444109c2c070fb3525647275d,\"\"\"Default processing pipelines for languages. The purpose of || these dataclasses is to represent: ||  || 1. the types of NLP operations that the CLTK can do || 2. the order in which operations are to be executed || 3. specifying what downstream features a particular implemented operation requires || \"\"\"
aaeiadjhd,cltk/cltk,src/cltkv1/utils/pipelines.py,c9d1183377b37e756049e5fafc592f22da1a41ee,037662f4962856c444109c2c070fb3525647275d,TODO: Populate the ``Word`` object with the above token idx
aaeiadjhe,cltk/cltk,src/cltkv1/utils/pipelines.py,c9d1183377b37e756049e5fafc592f22da1a41ee,037662f4962856c444109c2c070fb3525647275d,TODO: Do checking of IO req'd by the fn and whether it's available in the current ``Word``
aaeiaehbc,cltk/cltk,docs/conf.py,19dd9465efa0cde5efcd41f895a3ecd9e2e60174,b60c6d735698e33315bdb4ea4244a3eddfaf3bff,TODO: Re-enable \"sphinx_autodoc_typehints\"; which fails on RTD builds
aaeiaehha,cltk/cltk,src/cltkv1/nlp.py,037662f4962856c444109c2c070fb3525647275d,834de7bf88074749feac4a571ef76a66b954dc82,TODO: Figure out if I can avoid having to call the dataclass Pipeline
aaeiaehhb,cltk/cltk,src/cltkv1/nlp.py,037662f4962856c444109c2c070fb3525647275d,3156693460c740fd5ef9f5d024d27aa520088342,TODO: Write fn which annotates ``doc.words``; not just writing over what is in there
aaeiaehhf,cltk/cltk,src/cltkv1/tokenizers/word.py,037662f4962856c444109c2c070fb3525647275d,STILL_EXISTS,\"\"\"Module for tokenizers. ||  || TODO: Think about adding check somewhere if a contrib (not user) chooses an unavailable item || \"\"\"
aaeiaeiaj,cltk/cltk,src/cltkv1/utils/pipelines.py,037662f4962856c444109c2c070fb3525647275d,STILL_EXISTS,\"\"\"Default processing pipelines for languages. The purpose of || these dataclasses is to represent: ||  || 1. the types of NLP processs that the CLTK can do || 2. the order in which processs are to be executed || 3. specifying what downstream features a particular implemented process requires || \"\"\"
aaeiaeici,cltk/cltk,src/cltkv1/wrappers/stanford.py,037662f4962856c444109c2c070fb3525647275d,STILL_EXISTS,TODO: This is a weak check for the models actually being downloaded and valid
aaeiaeicj,cltk/cltk,src/cltkv1/wrappers/stanford.py,037662f4962856c444109c2c070fb3525647275d,STILL_EXISTS,TODO: Use ``models_dir`` var from below and make self. or global to module
aaeiaeifb,cltk/cltk,src/cltkv1/wrappers/stanford.py,31584e4c12153612511fb4c984c7db50df751dd7,STILL_EXISTS,TODO: Write tests for all treebanks
aaeiafage,cltk/cltk,src/cltkv1/embeddings/fasttext.py,f86fc3400c1273cfe72a65345becc6be7dac71c6,STILL_EXISTS,\"\"\"Module for accessing pre-trained `fastText word embeddings || <https:\/\/fasttext.cc\/>`_. Two sets of models are available || from fastText; one being trained only on corpora taken from || Wikipedia (249 languages; `here || <https:\/\/fasttext.cc\/docs\/en\/pretrained-vectors.html>`_) and || the other being a combination of Wikipedia and Common Crawl || (a subset of 157; `here || <https:\/\/fasttext.cc\/docs\/en\/crawl-vectors.html>`_). || \"\"\"
aaeiafbbb,cltk/cltk,src/cltkv1/utils/utils.py,db3fd85f5086e94d7f52cbeb9f5ac09d3b9f3864,5ab223b16d21e065f255fe4b691a1f095ae7b5e9,TODO: Run tests with a defined `$CLTK_DATA` environment variable)
aaeiafbcg,cltk/cltk,src/cltkv1/embeddings/fasttext.py,e06de7a4263a923239c4cedd5469c35561c75444,STILL_EXISTS,TODO: Logg INFO level
aaeiafbch,cltk/cltk,scripts/download_misc_dependencies.py,2258bb6ac217df21b64dc5fd511bc8d53b9d8285,8c56b896d8694328a62443e5628b00ec4189fca7,TODO: add command line params for what langs (all or just one); useful for build server
aaeiafdaa,cltk/cltk,scripts/download_misc_dependencies.py,ed8fcd64ce895810ecb920cb2765167cf5502b0b,STILL_EXISTS,TODO: rm this check
aaeiafdac,cltk/cltk,src/cltkv1/embeddings/fasttext_module.py,3de7c36ebb6881ec166d5a33a1b6e4039d339f1f,STILL_EXISTS,TODO: Log INFO level
aaeiafdbf,cltk/cltk,src/cltkv1/embeddings/fasttext_module.py,3de7c36ebb6881ec166d5a33a1b6e4039d339f1f,STILL_EXISTS,# TODO: read bin ft files
aaeiafded,cltk/cltk,src/cltkv1/embeddings/fasttext_module.py,b2015decbe1a29398bf306963c95ed7262250b53,STILL_EXISTS,# TODO: read bin ft files
aaeiafdfh,cltk/cltk,src/cltkv1/embeddings/fasttext_module.py,29ac9147916b15176792af7cd82e1533c08c513b,STILL_EXISTS,TODO: Give instructions how to install
aaeiafdhh,cltk/cltk,src/cltkv1/embeddings/embeddings.py,e50e90af4ba40d005648b67450d079c68a2dc6b9,c89ce54bcbf1e812a3c1203c1b42f465e6ff3cb4,>>> is_vector_for_lang(iso_code=\"xxx\"; training_set=\"common_crawl\")
aaeiafdib,cltk/cltk,src/cltkv1/embeddings/embeddings.py,e50e90af4ba40d005648b67450d079c68a2dc6b9,c89ce54bcbf1e812a3c1203c1b42f465e6ff3cb4,>>> is_vector_for_lang(iso_code=\"lat\"; training_set=\"xxx\")
aaeiafdie,cltk/cltk,src/cltkv1/embeddings/embeddings.py,e50e90af4ba40d005648b67450d079c68a2dc6b9,c89ce54bcbf1e812a3c1203c1b42f465e6ff3cb4,cltkv1.core.exceptions.CLTKException: Invalid ``training_set`` 'xxx'. Available: 'wiki'; 'common_crawl'.
aaeiafeah,cltk/cltk,src/cltkv1/embeddings/embeddings.py,e50e90af4ba40d005648b67450d079c68a2dc6b9,c0d3b0a765a424a84ab37afad0b964a2652b5e3a,TODO: Do better than test for just name. Try trimming up to user home dir.
aaeiaffai,cltk/cltk,src/cltkv1/embeddings/embeddings.py,e50e90af4ba40d005648b67450d079c68a2dc6b9,1fc63b706f009b22177649d354cedc0a106762b9,# TODO: Log INFO level
aaeiaffbg,cltk/cltk,src/cltkv1/embeddings/embeddings.py,e50e90af4ba40d005648b67450d079c68a2dc6b9,c4ba1a2070758ca0f271aa2ccd404393133d1a6e,TODO: Look at \"Download Large Files with Tqdm Progress Bar\" here: https:\/\/medium.com\/better-programming\/python-progress-bars-with-tqdm-by-example-ce98dbbc9697
aaeiaffbh,cltk/cltk,src/cltkv1/embeddings/embeddings.py,e50e90af4ba40d005648b67450d079c68a2dc6b9,1fc63b706f009b22177649d354cedc0a106762b9,TODO: Confirm everything saves right
aaeiaffjc,cltk/cltk,src/cltkv1/embeddings/embeddings.py,c0d3b0a765a424a84ab37afad0b964a2652b5e3a,c4ba1a2070758ca0f271aa2ccd404393133d1a6e,TODO: Replace all the up-front checks with one hidden method
aaeiafgii,cltk/cltk,src/cltkv1/embeddings/embeddings.py,c0d3b0a765a424a84ab37afad0b964a2652b5e3a,1fc63b706f009b22177649d354cedc0a106762b9,# TODO: Log INFO level
aaeiafgjg,cltk/cltk,src/cltkv1/embeddings/embeddings.py,c0d3b0a765a424a84ab37afad0b964a2652b5e3a,c4ba1a2070758ca0f271aa2ccd404393133d1a6e,TODO: Look at \"Download Large Files with Tqdm Progress Bar\" here: https:\/\/medium.com\/better-programming\/python-progress-bars-with-tqdm-by-example-ce98dbbc9697
aaeiafgjh,cltk/cltk,src/cltkv1/embeddings/embeddings.py,c0d3b0a765a424a84ab37afad0b964a2652b5e3a,1fc63b706f009b22177649d354cedc0a106762b9,TODO: Confirm everything saves right
aaeiafhff,cltk/cltk,src/cltkv1/embeddings/embeddings.py,c0d3b0a765a424a84ab37afad0b964a2652b5e3a,1fc63b706f009b22177649d354cedc0a106762b9,>>> is_fasttext_lang_available(iso_code=\"xxx\")
aaeiafhhi,cltk/cltk,src/cltkv1/embeddings/embeddings.py,c0d3b0a765a424a84ab37afad0b964a2652b5e3a,1fc63b706f009b22177649d354cedc0a106762b9,>>> get_fasttext_lang_code(iso_code=\"xxx\")
aaeiafhjj,cltk/cltk,src/cltkv1/embeddings/embeddings.py,c0d3b0a765a424a84ab37afad0b964a2652b5e3a,1fc63b706f009b22177649d354cedc0a106762b9,>>> is_vector_for_lang(iso_code=\"xxx\"; training_set=\"common_crawl\")
aaeiafiad,cltk/cltk,src/cltkv1/embeddings/embeddings.py,c0d3b0a765a424a84ab37afad0b964a2652b5e3a,1fc63b706f009b22177649d354cedc0a106762b9,>>> is_vector_for_lang(iso_code=\"lat\"; training_set=\"xxx\")
aaeiafiag,cltk/cltk,src/cltkv1/embeddings/embeddings.py,c0d3b0a765a424a84ab37afad0b964a2652b5e3a,1fc63b706f009b22177649d354cedc0a106762b9,cltkv1.core.exceptions.CLTKException: Invalid ``training_set`` 'xxx'. Available: 'wiki'; 'common_crawl'.
aaeiafich,cltk/cltk,src/cltkv1/embeddings/embeddings.py,c0d3b0a765a424a84ab37afad0b964a2652b5e3a,1fc63b706f009b22177649d354cedc0a106762b9,TODO: Do better than check for just name. Try trimming up to user home dir.
aaeiafife,cltk/cltk,src/cltkv1/embeddings/embeddings.py,c0d3b0a765a424a84ab37afad0b964a2652b5e3a,1fc63b706f009b22177649d354cedc0a106762b9,TODO: Add exceptions for loading problems due to FT not being installed
aaeiafiff,cltk/cltk,src/cltkv1/embeddings/embeddings.py,c0d3b0a765a424a84ab37afad0b964a2652b5e3a,1fc63b706f009b22177649d354cedc0a106762b9,TODO: Check all 4 types of model reading on cpu w\/ enough memory
aaeiafigh,cltk/cltk,src/cltkv1/embeddings/embeddings.py,c0d3b0a765a424a84ab37afad0b964a2652b5e3a,1fc63b706f009b22177649d354cedc0a106762b9,# TODO: Give instructions how to install
aaeiafiif,cltk/cltk,src/cltkv1/embeddings/embeddings.py,c0d3b0a765a424a84ab37afad0b964a2652b5e3a,1fc63b706f009b22177649d354cedc0a106762b9,#     # TODO: read bin ft files
aaeiagajh,cltk/cltk,src/cltkv1/embeddings/embeddings.py,c0d3b0a765a424a84ab37afad0b964a2652b5e3a,STILL_EXISTS,embeddings_obj = Embeddings(iso_code=\"lat\"; training_set=\"xxx\")
aaeiagbae,cltk/cltk,src/cltkv1/embeddings/embeddings.py,c0d3b0a765a424a84ab37afad0b964a2652b5e3a,STILL_EXISTS,embeddings_obj = Embeddings(iso_code=\"lat\"; model_type=\"xxx\")
aaeiagbif,cltk/cltk,src/cltkv1/embeddings/embeddings.py,c4ba1a2070758ca0f271aa2ccd404393133d1a6e,0fd8b0377b2eee5bc201ffeb172f436f3c80f6b3,TODO: Log INFO level; it's OK if dir already exists
aaeiaghhj,cltk/cltk,src/cltkv1/embeddings/embeddings.py,3bd6dd6d4acd171f5fdda568c74a178b6cc78b25,STILL_EXISTS,TODO: Log message
aaeiaghia,cltk/cltk,src/cltkv1/embeddings/embeddings.py,3bd6dd6d4acd171f5fdda568c74a178b6cc78b25,STILL_EXISTS,TODO: Add 10 sec wait to this; to give user time to cancel dl
aaeiaghib,cltk/cltk,src/cltkv1/embeddings/embeddings.py,3bd6dd6d4acd171f5fdda568c74a178b6cc78b25,STILL_EXISTS,TODO: mk this recursive fn
aaeiagifh,cltk/cltk,src/cltkv1/embeddings/embeddings.py,346ffc2ac239070b36582164bbc6a5f548261c66,STILL_EXISTS,TODO: Figure out how to pass var of Class + method
aaeiagige,cltk/cltk,src/cltkv1/core/data_types.py,98e41cc7ccf97267c178d2ab209f5da2b12ab063,a83953ba681ebeca852ce7b07281fd41a306a21b,TODO: Check; this probably loads model a second time
aaeiagjgf,cltk/cltk,src/cltkv1/embeddings/processes.py,2121cfdfe0f24a95c1ebbfa624a1b918d5dd864a,b1cb9853cfbf86ab8f8a400ec73e4166b086da20,TODO: Move these to within respective class
aaeiagjgi,cltk/cltk,src/cltkv1/tokenizers/processes.py,2121cfdfe0f24a95c1ebbfa624a1b918d5dd864a,STILL_EXISTS,TODO: Fix MULTILINGUAL_WORD_TOK; this might not work
aaeiaheab,cltk/cltk,src/cltkv1/alphabet/egy.py,e92bd756e5c5e824e2bfa2e7ff4d777a40d049f5,STILL_EXISTS,\"\"\"Convert MdC transliterated text to Unicode. ||  || TODO: Add tests and clean up. || \"\"\"
aaeiahecj,cltk/cltk,src/cltkv1/alphabet/grc.py,e92bd756e5c5e824e2bfa2e7ff4d777a40d049f5,STILL_EXISTS,TODO: Rm regex dependency
aaeiahedj,cltk/cltk,src/cltkv1/alphabet/grc.py,e92bd756e5c5e824e2bfa2e7ff4d777a40d049f5,STILL_EXISTS,better handle final sigmas
aaeiaheef,cltk/cltk,src/cltkv1/alphabet/non.py,e92bd756e5c5e824e2bfa2e7ff4d777a40d049f5,STILL_EXISTS,\"\"\"Old Norse runes; Unicode block: 16A0\u201316FF. || Source: *Viking Language 1*; Jessie L. Byock ||  || TODO: Document and test better. || \"\"\"
aaeiahffe,cltk/cltk,src/cltkv1/alphabet/pes.py,e92bd756e5c5e824e2bfa2e7ff4d777a40d049f5,STILL_EXISTS,\"\"\"The Persian alphabet. ||  || TODO: Write tests. || \"\"\"
aaeiahfhc,cltk/cltk,src/cltkv1/alphabet/pli.py,e92bd756e5c5e824e2bfa2e7ff4d777a40d049f5,STILL_EXISTS,\"\"\"The Pali alphabet. ||  || TODO: Add tests. ||  || \"\"\"
aaeiahgad,cltk/cltk,src/cltkv1/alphabet/grc/beta_to_unicode.py,3d602feb7ddbb9881ee362ff4898263e05647e70,STILL_EXISTS,\"\"\"Converts legacy encodings into Unicode. ||  || TODO: Rm regex dependency || TODO: Add tests || \"\"\"
aaeiahgbe,cltk/cltk,src/cltkv1/alphabet/grc/beta_to_unicode.py,3d602feb7ddbb9881ee362ff4898263e05647e70,STILL_EXISTS,better handle final sigmas
aaeiahgdh,cltk/cltk,src/cltkv1/alphabet/tel.py,18e719e21a8a3a45e14d2ae053686afd7522877a,STILL_EXISTS,\"\"\"Telugu alphabet ||  || TODO: Add tests. || \"\"\"
aaeiahgdi,cltk/cltk,src/cltkv1/alphabet/urd.py,18e719e21a8a3a45e14d2ae053686afd7522877a,STILL_EXISTS,\"\"\"Urdu alphabet ||  || TODO: Add tests. || \"\"\"
aaeibabaf,cltk/cltk,src/cltkv1/data/clone.py,588984201ce9198e42a2c39475f498427d96bf84,STILL_EXISTS,\"\"\"Import CLTK corpora. || TODO: Fix so ``import_corpora()`` can take relative path. || TODO: Add https:\/\/github.com\/cltk\/pos_latin || \"\"\"
aaeibabdd,cltk/cltk,src/cltkv1/data/clone.py,588984201ce9198e42a2c39475f498427d96bf84,STILL_EXISTS,move the dir-checking commands into a function
aaeibabdi,cltk/cltk,src/cltkv1/data/clone.py,61d6f6824c5f87ae5628061afa325001af3b9e52,STILL_EXISTS,TODO: Decide whether to drop repos w\/o models
aaeibabfb,cltk/cltk,src/cltkv1/data/clone.py,be4d0ae92f7067d3061382cb87488bf79bccf4fb,STILL_EXISTS,xxx
aaeibbhje,cltk/cltk,src/cltkv1/nlp.py,c619bc9cc0700a7c8299c87985f864c4bfa51f45,STILL_EXISTS,\"akk\";  # TODO: turn List[Tuple[str; str]] into List[str]
aaeibdifg,cltk/cltk,src/cltkv1/tests/test_corpus.py,64e16fcaa4918ba72def1d3a5dccc9f4b9988fff,STILL_EXISTS,# Need a additional instance because tests below change internals #TO-DO Fix
aaeibfici,cltk/cltk,src/cltkv1/stops/arb.py,1c46070f018cea7fc7c4016a95f3e5c028ba34cb,STILL_EXISTS,TODO: Improve stop list word
aaeibficj,cltk/cltk,src/cltkv1/stops/arb.py,1c46070f018cea7fc7c4016a95f3e5c028ba34cb,STILL_EXISTS,TODO: Add translate comments for each stop word.
aaeibgaib,cltk/cltk,src/cltkv1/stops/words.py,1c46070f018cea7fc7c4016a95f3e5c028ba34cb,STILL_EXISTS,\"\"\"Stopwords for languages. ||  || TODO: Give definition here of stopwords. || \"\"\"
aaeibgaic,cltk/cltk,src/cltkv1/languages/pipelines.py,56883e5b47ff65593276f9967f168315ee459f2e,b07ef27946d0074406338e4b45bb79aa3005f285,TODO: Add Hindi (\"hin \")
aaeibgaid,cltk/cltk,src/cltkv1/languages/pipelines.py,56883e5b47ff65593276f9967f168315ee459f2e,STILL_EXISTS,TODO: Add Old Marathi (\"omr\")
aaeibgaie,cltk/cltk,src/cltkv1/languages/pipelines.py,56883e5b47ff65593276f9967f168315ee459f2e,21aec269a7477f39269e1fb9b670bfad0cae7314,TODO: Add Panjali (\"pan\")
aaeibgajf,cltk/cltk,src/cltkv1/languages/pipelines.py,8a4c90aa81b40749da2dddbe4cb0337dd327b1e4,4f30b26bdb9347d82d7cf09e9909f16b13914082,TODO: Add Hindi (\"hin \")
aaeibgajg,cltk/cltk,src/cltkv1/languages/pipelines.py,8a4c90aa81b40749da2dddbe4cb0337dd327b1e4,STILL_EXISTS,TODO: Add Panjali (\"pan\")
aaeibhbha,cltk/cltk,src/cltkv1/sentences/sentence.py,ce6ba260f38c522d1642e2be98d56e38a73f1933,STILL_EXISTS,\"\"\"Tokenize sentences. ||  || TODO: Thoroughly refactor. || \"\"\"
aaeibhbhc,cltk/cltk,src/cltkv1/sentences/sentence.py,ce6ba260f38c522d1642e2be98d56e38a73f1933,STILL_EXISTS,TODO add message; must specify sent_end_chars; or warn and use defaults
aaeibhbhe,cltk/cltk,src/cltkv1/sentences/sentence.py,ce6ba260f38c522d1642e2be98d56e38a73f1933,STILL_EXISTS,Workaround for Latin\u2014use old API syntax to load new sent tokenizer
aaeibhbhg,cltk/cltk,src/cltkv1/sentences/sentence.py,ce6ba260f38c522d1642e2be98d56e38a73f1933,STILL_EXISTS,Workaround for regex tokenizer
aaeibhcbc,cltk/cltk,src/cltkv1/tokenizers/lat.py,ce6ba260f38c522d1642e2be98d56e38a73f1933,STILL_EXISTS,needed to make stream.readlines work in PlaintextCorpusReader
aaeibhcbf,cltk/cltk,src/cltkv1/tokenizers/latin/latin_exceptions.py,ce6ba260f38c522d1642e2be98d56e38a73f1933,STILL_EXISTS,\"\"\" || Starter lists have been included to handle the Latin enclitics || (-que; -ne; -ue\/-ve; -cum). These lists are based on high-frequency vocabulary ||  and have been supplemented on a as-needed basis; i.e. they are not ||  comprehensive. Additions to the exceptions list are welcome. PJB || \"\"\"
aaeibhcde,cltk/cltk,src/cltkv1/tokenizers/latin/params.py,ce6ba260f38c522d1642e2be98d56e38a73f1933,STILL_EXISTS,\"\"\" Params: Latin ||  || TODO: Some of these are only used for training. PRAENOMINA for training punkt tokenizer (als ABBREVIATIONS; CALENDAR; MISC) || TODO: The enclitic exceptions (que_exceptions and below) can all be deleted ||  || \"\"\"
aaeibhcfj,cltk/cltk,src/cltkv1/tokenizers/non.py,ce6ba260f38c522d1642e2be98d56e38a73f1933,STILL_EXISTS,# TODO dealing with merges between verbs and sik -> st : middle voice
aaeibhcgb,cltk/cltk,src/cltkv1/tokenizers/utils.py,ce6ba260f38c522d1642e2be98d56e38a73f1933,STILL_EXISTS,\"\"\" Tokenization utilities ||  || TODO: KJ consider moving to ``scripts`` dir. || \"\"\"
aaeibhhdb,cltk/cltk,src/cltkv1/ner/ner.py,00a7cc3118ae53470eac8ec2adadf8c2ed687a40,STILL_EXISTS,maybe not worth the effort
aaeibhhdd,cltk/cltk,src/cltkv1/ner/processes.py,00a7cc3118ae53470eac8ec2adadf8c2ed687a40,021ba03058e406217325c172432eeb7b3c56344a,xxx
aaeibhidb,cltk/cltk,src/cltkv1/embeddings/embeddings.py,dd46c573f71428903a75c3ec901f6ac3601ab7f9,STILL_EXISTS,TODO: Log message
aaeibhiec,cltk/cltk,src/cltkv1/embeddings/embeddings.py,dd46c573f71428903a75c3ec901f6ac3601ab7f9,STILL_EXISTS,TODO: mk this recursive fn
aaeibhiee,cltk/cltk,src/cltkv1/embeddings/embeddings.py,dd46c573f71428903a75c3ec901f6ac3601ab7f9,0fd8b0377b2eee5bc201ffeb172f436f3c80f6b3,TODO: Log INFO level; it's OK if dir already exists
aaeibhifb,cltk/cltk,src/cltkv1/embeddings/embeddings.py,5c096acb9ad5278f02548406b50d39b339cf9a95,STILL_EXISTS,TODO: Add 10 sec wait to this; to give user time to cancel dl
aaeibhifd,cltk/cltk,src/cltkv1/embeddings/embeddings.py,5c096acb9ad5278f02548406b50d39b339cf9a95,STILL_EXISTS,TODO: mk this recursive fn
aaeibhiie,cltk/cltk,src/cltkv1/alphabet/arc.py,f33cd0672f84e54b467b3ff645a18db0aad2c137,STILL_EXISTS,\"\"\"The Imperial Aramaic alphabet; plus simple script to transform || a Hebrew transcription of an Imperial Aramaic text to its own Unicode block. ||  || TODO: Add Hebrew-to-Aramaic converter || \"\"\"
aaeibhijc,cltk/cltk,src/cltkv1/phonology/akkadian/stress.py,f33cd0672f84e54b467b3ff645a18db0aad2c137,STILL_EXISTS,TODO: fails on: ['hammurabi'; 'u'; 'i\u0161me\u0101nim']
aaeibiccc,cltk/cltk,src/cltkv1/phonology/arabic/romanization.py,f33cd0672f84e54b467b3ff645a18db0aad2c137,STILL_EXISTS,\"arabtex\": ARABTEX_TO_UNICODE; todo: not ready
aaeibiccd,cltk/cltk,src/cltkv1/phonology/arabic/romanization.py,f33cd0672f84e54b467b3ff645a18db0aad2c137,STILL_EXISTS,\"iso8859-6\": ISO88596_TO_UNICODE; todo: not ready
aaeibicce,cltk/cltk,src/cltkv1/phonology/arabic/romanization.py,f33cd0672f84e54b467b3ff645a18db0aad2c137,STILL_EXISTS,@todo
aaeibiccf,cltk/cltk,src/cltkv1/phonology/arabic/romanization.py,f33cd0672f84e54b467b3ff645a18db0aad2c137,STILL_EXISTS,@todo: arabtex and iso8859-6 need individual handling because in some cases using one-two mapping
aaeibifaa,cltk/cltk,src/cltkv1/phonology/middle_english/transcription.py,f33cd0672f84e54b467b3ff645a18db0aad2c137,STILL_EXISTS,Check whether ultima ends in e
aaeibifcb,cltk/cltk,src/cltkv1/phonology/middle_high_german/transcription.py,f33cd0672f84e54b467b3ff645a18db0aad2c137,STILL_EXISTS,To-do: Add different dialects and\/or notations
aaeibigdj,cltk/cltk,src/cltkv1/prosody/greek/scanner.py,f33cd0672f84e54b467b3ff645a18db0aad2c137,STILL_EXISTS,Syllable ends with a diphthong
aaeibigea,cltk/cltk,src/cltkv1/prosody/greek/scanner.py,f33cd0672f84e54b467b3ff645a18db0aad2c137,STILL_EXISTS,Syllable ends with a vowel
aaeibighi,cltk/cltk,src/cltkv1/prosody/latin/macronizer.py,f33cd0672f84e54b467b3ff645a18db0aad2c137,STILL_EXISTS,TODO Determine how to disambiguate tags (see logger)
aaeibihah,cltk/cltk,src/cltkv1/prosody/latin/scanner.py,f33cd0672f84e54b467b3ff645a18db0aad2c137,STILL_EXISTS,previous word ends in consonant and current word begins with
aaeibihaj,cltk/cltk,src/cltkv1/prosody/latin/scanner.py,f33cd0672f84e54b467b3ff645a18db0aad2c137,STILL_EXISTS,previous word ends in vowel and current word begins in
aaeibihgg,cltk/cltk,src/cltkv1/tag/ner.py,f33cd0672f84e54b467b3ff645a18db0aad2c137,STILL_EXISTS,maybe not worth the effort
aaeibiicf,cltk/cltk,src/cltkv1/stem/akkadian/stem.py,0a07a67843ae2f9b52b4e82f4139ff36ba0165a4,STILL_EXISTS,\"\"\" || Get the stem of a word; given a declined form and its gender. ||  || TODO: Check this logic with von Soden's Grundriss der akkadischen Grammatik. || TODO: Deal with j\/y issue. || \"\"\"
aaeibiici,cltk/cltk,src/cltkv1/stem/akkadian/syllabifier.py,0a07a67843ae2f9b52b4e82f4139ff36ba0165a4,STILL_EXISTS,\"\"\" || Split Akkadian words into a list of syllables.  Logic is based on || A Grammar of Akkadian; Huehnergard 3rd. ed. ||  || TODO: Check this logic with von Soden's Grundriss der akkadischen Grammatik. || TODO: Deal with j\/y issue. || \"\"\"
aaeibijbb,cltk/cltk,src/cltkv1/stem/lemma.py,0a07a67843ae2f9b52b4e82f4139ff36ba0165a4,STILL_EXISTS,Deprecated; remove from future release?
aaeibijcf,cltk/cltk,src/cltkv1/stem/middle_english/stem.py,0a07a67843ae2f9b52b4e82f4139ff36ba0165a4,STILL_EXISTS,\"\"\" || Stemming present a significant challenge in ME; as it is exceptionally || difficult to account for the orthographical variations sometimes even || occurring within a single text. The affix algorithm attempts to account || for variations in spelling; but still Mostly relies on a relatively narrow || hard-coded list (Middle English Dictionary(MED) https:\/\/quod.lib.umich.edu\/m\/med\/) ||  ||   TODO: Improve on the affix stemmer by implementing an accurate spell checker ||   TODO: Implement a stochastic algorithm\/Implement overarching stemmer class || \"\"\"
aaeibijcj,cltk/cltk,src/cltkv1/stem/middle_high_german/stem.py,0a07a67843ae2f9b52b4e82f4139ff36ba0165a4,STILL_EXISTS,\"\"\" || The biggest challenge when it comes to noun and adjective stemming is that -similarly to MG- MHG suffixes are based on gender; || which is difficult to determine without either a hard-coded dictionary or an efficient tagger. Statistical analysis could  || theoretically yield more  accurate results; but a lack of online resources make this approach somewhat unreliable. ||  || Another core problem is the fact that unlike English; changes of the stem often occur in the middle of the word rather than the || end (bruoder -> br\u00FCeder). ||  || The following algorithm is inspired by Modern German stemmers (namely Snowball); modified to better fit MHG morphological  || structure. ||  || http:\/\/snowball.tartarus.org\/algorithms\/german\/stemmer.html || http:\/\/www.inf.fu-berlin.de\/lehre\/WS98\/digBib\/projekt\/_stemming.html || \"\"\"
aaeibijei,cltk/cltk,src/cltkv1/stem/sanskrit/indian_syllabifier.py,0a07a67843ae2f9b52b4e82f4139ff36ba0165a4,STILL_EXISTS,Handle better?
aaeibijhg,cltk/cltk,src/cltkv1/tests/test_phonology.py,4d209c83dd6426db9fdb2bd721527ec2448783f9,STILL_EXISTS,TODO: Re-enable
aaeicbcac,cltk/cltk,src/cltkv1/lemmatize/backoff.py,b13038f088e3455852afe8ed68ff9c017a20f5cc,06da2d04d00d0ffed7b73282ac07e5e97d5f5cbf,Unused for now
aaeicbcbe,cltk/cltk,src/cltkv1/lemmatize/greek/backoff.py,b13038f088e3455852afe8ed68ff9c017a20f5cc,STILL_EXISTS,Move to greek_models_cltk
aaeicbcbh,cltk/cltk,src/cltkv1/lemmatize/latin/backoff.py,b13038f088e3455852afe8ed68ff9c017a20f5cc,STILL_EXISTS,Move to latin_models_cltk
aaeicbcde,cltk/cltk,src/cltkv1/lemmatize/latin/latin.py,b13038f088e3455852afe8ed68ff9c017a20f5cc,STILL_EXISTS,## Todo: Refactor to be more compact
aaeicbcgb,cltk/cltk,src/cltkv1/tests/test_tokenize_and_sent.py,8a01afdec7974f173135e0467241488382dd3f9f,STILL_EXISTS,\"\"\"Test cltk.tokenize. ||  || TODO: Mk different file for sentence tests. || \"\"\"
aaeicbcha,cltk/cltk,src/cltkv1/tests/test_tokenize_and_sent.py,8a01afdec7974f173135e0467241488382dd3f9f,STILL_EXISTS,TODO: KJ commented this out; re-enable
aaeicbhaa,cltk/cltk,src/cltkv1/dependency/stanza.py,13fff8524c52cc4fca2caa04836ecde7d024a9d8,STILL_EXISTS,TODO: Write tests for all treebanks
aaeicbhae,cltk/cltk,src/cltkv1/dependency/stanza.py,13fff8524c52cc4fca2caa04836ecde7d024a9d8,STILL_EXISTS,TODO: This is a weak check for the models actually being downloaded and valid
aaeicbhaf,cltk/cltk,src/cltkv1/dependency/stanza.py,13fff8524c52cc4fca2caa04836ecde7d024a9d8,STILL_EXISTS,TODO: Use ``models_dir`` var from below and make self. or global to module
aaeicbhdb,cltk/cltk,src/cltkv1/dependency/stanza.py,af261965cfe84e0de39c004a3e4aa08a03e7534f,STILL_EXISTS,TODO: Mv this a self. var or maybe even global
aaeicbhde,cltk/cltk,src/cltkv1/dependency/processes.py,678f431f5b3ba0598966b66fcfa7e1c8a8cbd756,STILL_EXISTS,# TODO: Fix this; I forget what we were tracking in this
aaeicbhej,cltk/cltk,src/cltkv1/dependency/processes.py,7e7f0997119a0c8b4a708754afcc2d3486843250,STILL_EXISTS,TODO: Figure out how to handle the token indexes; esp 0 (root) and None (?)
aaeiccbfe,cltk/cltk,src/cltkv1/languages/pipelines.py,b016e2d3373b78d22ce91a52e6100d5f4732aaff,8f6cd482b2f8ea9d2fc6dfe66d72d2225cd027ac,TODO: Add Hindi (\"hin \")
aaeicdjge,cltk/cltk,src/cltkv1/sentences/sentence.py,b016e2d3373b78d22ce91a52e6100d5f4732aaff,STILL_EXISTS,\"\"\"Tokenize sentences. ||  || TODO: Thoroughly refactor. || \"\"\"
aaeicdjgg,cltk/cltk,src/cltkv1/sentences/sentence.py,b016e2d3373b78d22ce91a52e6100d5f4732aaff,STILL_EXISTS,TODO add message; must specify sent_end_chars; or warn and use defaults
aaeicdjgi,cltk/cltk,src/cltkv1/sentences/sentence.py,b016e2d3373b78d22ce91a52e6100d5f4732aaff,STILL_EXISTS,Workaround for Latin\u2014use old API syntax to load new sent tokenizer
aaeicdjha,cltk/cltk,src/cltkv1/sentences/sentence.py,b016e2d3373b78d22ce91a52e6100d5f4732aaff,STILL_EXISTS,Workaround for regex tokenizer
aaeichbbc,cltk/cltk,src/cltkv1/readers/readers.py,ff79291b015b63b23036397b6050cc632f706cda,STILL_EXISTS,TODO add your corpus here:
aaeichbbe,cltk/cltk,src/cltkv1/readers/readers.py,ff79291b015b63b23036397b6050cc632f706cda,STILL_EXISTS,: Generic file ending; override below in your own CorpusReader implementation
aaeichbcc,cltk/cltk,src/cltkv1/readers/readers.py,ff79291b015b63b23036397b6050cc632f706cda,STILL_EXISTS,TODO add other languages and write tests for each corpus
aaeichdec,cltk/cltk,src/cltkv1/wordnet/processes.py,ac4a80caa7b438460381a22975cf9c96059b42f4,STILL_EXISTS,TODO: map CLTK lemmas to WN lemmas
aaeichhdi,cltk/cltk,tests/test_embeddings.py,cbc0a834ab95049336a08d8a3b0892b64ba41811,STILL_EXISTS,TODO: Add Arabic test; fails with `UnicodeDecodeError: 'utf-8' codec can't decode byte 0xce in position 97: invalid continuation byte`
aaeichhee,cltk/cltk,tests/test_main.py,fa273407c0df18250c6dbab7190b5391393c6364,STILL_EXISTS,TODO: Re-enable coptic
aaeichidd,cltk/cltk,src/cltkv1/dependency/stanza.py,61bb3218d25f79bdf6b3ffd340deb5c756f34378,01547ac294777a56225fc0f856682133dd593e57,TODO: get stanza code
aaeichiic,cltk/cltk,src/cltkv1/tokenize/latin/word.py,8a291450644666a6887a55e9638ba22c238ea3ad,STILL_EXISTS,needed to make stream.readlines work in PlaintextCorpusReader
aaeichiif,cltk/cltk,src/cltkv1/tokenize/latin_exceptions.py,8a291450644666a6887a55e9638ba22c238ea3ad,STILL_EXISTS,\"\"\" || Starter lists have been included to handle the Latin enclitics || (-que; -ne; -ue\/-ve; -cum). These lists are based on high-frequency vocabulary ||  and have been supplemented on a as-needed basis; i.e. they are not ||  comprehensive. Additions to the exceptions list are welcome. PJB || \"\"\"
aaeichjcb,cltk/cltk,src/cltkv1/tokenize/old_norse/word.py,8a291450644666a6887a55e9638ba22c238ea3ad,STILL_EXISTS,# TODO dealing with merges between verbs and sik -> st : middle voice
aaeichjch,cltk/cltk,src/cltkv1/tokenize/sentence.py,8a291450644666a6887a55e9638ba22c238ea3ad,STILL_EXISTS,TODO add message; must specify sent_end_chars; or warn and use defaults
aaeichjcj,cltk/cltk,src/cltkv1/tokenize/sentence.py,8a291450644666a6887a55e9638ba22c238ea3ad,STILL_EXISTS,Workaround for Latin\u2014use old API syntax to load new sent tokenizer
aaeichjdb,cltk/cltk,src/cltkv1/tokenize/sentence.py,8a291450644666a6887a55e9638ba22c238ea3ad,STILL_EXISTS,Workaround for regex tokenizer
aaeiciegh,cltk/cltk,src/cltkv1/utils/utils.py,0fd8b0377b2eee5bc201ffeb172f436f3c80f6b3,STILL_EXISTS,TODO: Log INFO level; it's OK if dir already exists
aaeiciejb,cltk/cltk,src/cltkv1/phonology/akkadian/syllabifier.py,9686eb4d2240f3c03c502a32075370ee5a35b84f,STILL_EXISTS,\"\"\" || Split Akkadian words into a list of syllables.  Logic is based on || A Grammar of Akkadian; Huehnergard 3rd. ed. ||  || TODO: Check this logic with von Soden's Grundriss der akkadischen Grammatik. || TODO: Deal with j\/y issue. || \"\"\"
aaeicijgj,cltk/cltk,src/cltk/stem/akk.py,613dbb3c175e8aab7d432e232f5e2682ce16bcab,STILL_EXISTS,TODO what should we do here?
aaeicjaie,cltk/cltk,src/cltk/stem/gmh.py,d98a5f66cb88c567950cb934cb4942181dbc88dd,STILL_EXISTS,\"\"\" || The biggest challenge when it comes to noun and adjective stemming is that -similarly to MG- MHG suffixes are based on gender; || which is difficult to determine without either a hard-coded dictionary or an efficient tagger. Statistical analysis could  || theoretically yield more  accurate results; but a lack of online resources make this approach somewhat unreliable. ||  || Another core problem is the fact that unlike English; changes of the stem often occur in the middle of the word rather than the || end (bruoder -> br\u00FCeder). ||  || The following algorithm is inspired by Modern German stemmers (namely Snowball); modified to better fit MHG morphological  || structure. ||  || http:\/\/snowball.tartarus.org\/algorithms\/german\/stemmer.html || http:\/\/www.inf.fu-berlin.de\/lehre\/WS98\/digBib\/projekt\/_stemming.html || \"\"\"
aaeicjbic,cltk/cltk,src/cltk/lemmatize/lat.py,de8d76d39797d4d9345661402a2a0eb19be696bd,STILL_EXISTS,Move to latin_models_cltk
aaeideiei,cltk/cltk,src/cltk/__init__.py,d2fe480d1dbe1cf9099283d1a25ea6bd9491a45c,STILL_EXISTS,\"\"\"Init module for importing the CLTK class. ||  || TODO: Add ``__version__`` here with ``curr_version = pkg_resources.get_distribution(\"cltk\")  # type: pkg_resources.EggInfoDistribution`` and ``release = curr_version.version  # type: str`` || \"\"\"
aaeideijf,cltk/cltk,tests/test_tag.py,b1d723c41baa17d1123dc768e9a35003f7904c2d,STILL_EXISTS,TODO: Re-enable this. Something breaking on build server but works for KJ locally
aaeidejad,cltk/cltk,tests/test_tag.py,b1d723c41baa17d1123dc768e9a35003f7904c2d,STILL_EXISTS,TODO: Re-enable; see ``test_pos_crf_tagger_latin`` above
aaeidejbh,cltk/cltk,src/cltk/phonology/__init__.py,04d433e340b6edea284c197ac8e83d3a40efe5d6,STILL_EXISTS,\"\"\" || The phonology module aims to provide tools that: ||  || - phonetically\/phonologically transcribe words of a given language; || - syllabify words. ||  || For some specific languages; there exist; for example; a word stresser || (i.e. a function that gives which syllable is stressed). ||  || These tasks are interesting in themselves for historical linguists or teachers. || They are also essential for more high-level tasks such as prosody analyzers. ||  || Like for all CLTK modules; the phonology module may be extended and improved if a set of features does not suit || your needs because they are insufficient or they do not follow rules you want to test || (agreement on phonology of extinct languages is often weak). || \"\"\"
aaeidejfe,cltk/cltk,src/cltk/phonology/enm/stress.py,04d433e340b6edea284c197ac8e83d3a40efe5d6,STILL_EXISTS,Check whether ultima ends in e
aaeidejic,cltk/cltk,src/cltk/phonology/gmh/transcription.py,04d433e340b6edea284c197ac8e83d3a40efe5d6,STILL_EXISTS,To-do: Add different dialects and\/or notations
aaeidfccd,cltk/cltk,src/cltk/morphology/utils.py,aae990832d3d088428b7b990978d59182514175c,e8757e976a5c8a02e2971c5e6d3b81f90f0f30f9,TODO: This is broken!
aaeidfhai,cltk/cltk,src/cltk/corpora/grc/tlg/parse_tlg_indices.py,7fa08d2ac4f09af1224789a4592290fbb81987f3,STILL_EXISTS,\"\"\"For loading TLG .json files and searching; then pulling author ids. || \"\"\"
aaeidfhcb,cltk/cltk,src/cltk/corpora/grc/tlg/tlg_index.py,7fa08d2ac4f09af1224789a4592290fbb81987f3,STILL_EXISTS,\"\"\"Indices for the TLG. ||  || Note: # ``TLG_MASTER_INDEX`` is the result of failed IDT parsing. ||  || TODO: Add work names to ``TLG_WORKS_INDEX`` || TODO: Add all TLG index data. || \"\"\"
aaeidgeig,cltk/cltk,tests/test_corpora.py,7fa08d2ac4f09af1224789a4592290fbb81987f3,11ac53d3f202a38bf96b5199494eace9b32a6a6b,# Need a additional instance because tests below change internals #TO-DO Fix
aaeidhaai,IntelPython/sdc,hpat/pio.py,38a398cadafd26a4d551378a6efe8a1cb7ff5ef1,1733560af8da8dfd5968818528f8b1426163ce7e,TODO: generate size; alloc calls
aaeidhadi,IntelPython/sdc,hpat/distributed.py,3a7214b9f55245157b62e35220d967ec7cf6d0e2,STILL_EXISTS,TODO: handle X.T properly
aaeidhadj,IntelPython/sdc,hpat/distributed.py,3a7214b9f55245157b62e35220d967ec7cf6d0e2,STILL_EXISTS,TODO: self._analyze_call(lhs; rhs.func.name; rhs.args)
aaeidhaec,IntelPython/sdc,hpat/distributed.py,3a7214b9f55245157b62e35220d967ec7cf6d0e2,02a8b9372859acb0cae480924543bf7bc0156e8a,TODO: check for index dependency
aaeidhage,IntelPython/sdc,hpat/distributed.py,3a7214b9f55245157b62e35220d967ec7cf6d0e2,STILL_EXISTS,TODO: handle multi-dimensional arrays properly
aaeidhbaa,IntelPython/sdc,hpat/distributed.py,46ddcb38b7d8837738e9603050bb7e1b40136596,STILL_EXISTS,second dimension not needed
aaeidhbej,IntelPython/sdc,hpat/hiframes.py,031a2fab913574c08996664d391b5200fa81cab1,STILL_EXISTS,output df1 has same columns as df; create new vars
aaeidhbfa,IntelPython/sdc,hpat/hiframes.py,b37dd6374950d497a24f8e47ded38ca94af8c53f,c3e6af78f6f24f3a2777df82fc078dd2f95cf61b,needs df columns for type inference stage
aaeidhbfg,IntelPython/sdc,hpat/hiframes.py,1e4202a257a5d4c8ccf3ea41da6c9fff05194a58,c3e6af78f6f24f3a2777df82fc078dd2f95cf61b,input columns have same distribution
aaeidhbfh,IntelPython/sdc,hpat/hiframes.py,1e4202a257a5d4c8ccf3ea41da6c9fff05194a58,c3e6af78f6f24f3a2777df82fc078dd2f95cf61b,output columns have same distribution
aaeidhbgb,IntelPython/sdc,hpat/hiframes.py,4ae7102da4993185f24776a2f525a042ff922177,c3e6af78f6f24f3a2777df82fc078dd2f95cf61b,TODO: rebalance if output distributions are 1D instead of 1D_Var
aaeidhbgg,IntelPython/sdc,hpat/hiframes.py,f3de08175d18836c27942a32faa6337ae1a64510,c3e6af78f6f24f3a2777df82fc078dd2f95cf61b,TODO: generate parfor
aaeidhbhg,IntelPython/sdc,hpat/compiler.py,5de648814cf624dbbf6f5f85ebd5de587644e09f,a3984d944bcbe0ddd3da47468749e15458fc35f4,TODO: replace defaults (add to args)
aaeidhcbb,IntelPython/sdc,hpat/distributed.py,95067d3ec1b06b18e196e326811ad5bbdb4a37df,STILL_EXISTS,TODO: generalize to all blocks
aaeidhcbd,IntelPython/sdc,hpat/distributed.py,95067d3ec1b06b18e196e326811ad5bbdb4a37df,STILL_EXISTS,TODO: compute inplace if input array is dead
aaeidhcfc,IntelPython/sdc,hpat/distributed_analysis.py,02a8b9372859acb0cae480924543bf7bc0156e8a,STILL_EXISTS,TODO: check for index dependency
aaeidhcid,IntelPython/sdc,hpat/hiframes_api.py,c3e6af78f6f24f3a2777df82fc078dd2f95cf61b,aa3cd179192e7b8b36919a7d28085da3bf185c2d,needs df columns for type inference stage
aaeidhcii,IntelPython/sdc,hpat/hiframes_api.py,c3e6af78f6f24f3a2777df82fc078dd2f95cf61b,aa3cd179192e7b8b36919a7d28085da3bf185c2d,input columns have same distribution
aaeidhcij,IntelPython/sdc,hpat/hiframes_api.py,c3e6af78f6f24f3a2777df82fc078dd2f95cf61b,aa3cd179192e7b8b36919a7d28085da3bf185c2d,output columns have same distribution
aaeidhcjb,IntelPython/sdc,hpat/hiframes_api.py,c3e6af78f6f24f3a2777df82fc078dd2f95cf61b,aa3cd179192e7b8b36919a7d28085da3bf185c2d,TODO: rebalance if output distributions are 1D instead of 1D_Var
aaeidhcjd,IntelPython/sdc,hpat/hiframes_api.py,c3e6af78f6f24f3a2777df82fc078dd2f95cf61b,aa3cd179192e7b8b36919a7d28085da3bf185c2d,TODO: generate parfor
aaeidhdde,IntelPython/sdc,hpat/distributed_analysis.py,a5554ac1a0c7708c66be74d798ddc0e75dac2e85,STILL_EXISTS,TODO support recursive parfor; multi-D; mutiple body blocks
aaeidhddg,IntelPython/sdc,hpat/distributed.py,135c9303ee9314b8c6f00502c5814668f1407bc5,STILL_EXISTS,TODO assuming single array in stencil
aaeidhdhe,IntelPython/sdc,hpat/distributed.py,e42d326bcc780e7772f52303f28c699b6d8143f0,STILL_EXISTS,TODO: fix copies of global
aaeidhdid,IntelPython/sdc,hpat/distributed.py,b24bf7d6f32dcdac7b12085b83897925278b421c,STILL_EXISTS,TODO: fix copies of global
aaeidhejb,IntelPython/sdc,hpat/pio_api.py,35ea135b22fff3199fafc7305b51a45763a8d39b,STILL_EXISTS,TODO: create similar types for groups and datasets
aaeidhfad,IntelPython/sdc,hpat/distributed_lower.py,19f87840c8d34a83725538a562c4fa5b7b4100a6,STILL_EXISTS,TODO: replace array shape if array is small
aaeidhfcj,IntelPython/sdc,hpat/hiframes_api.py,2fa79225f60adda942a4a1f044d3d177a41f5276,b1aa637f5e5499060d0e984032a05f69429b3d39,TODO: mean
aaeidhffd,IntelPython/sdc,hpat/hiframes_api.py,2fa79225f60adda942a4a1f044d3d177a41f5276,STILL_EXISTS,TODO: ignore NA
aaeidhfjf,IntelPython/sdc,hpat/hiframes.py,ee62d106e6157efd326944964b3283e895f647e7,STILL_EXISTS,FIXME: treat iloc and loc as regular df variables so getitem
aaeidhfji,IntelPython/sdc,hpat/str_ext.py,fbbde97ee1eefe2df7a510df67a2658fb681efba,STILL_EXISTS,TODO: use ptr instead of allocating and copying; use NRT_MemInfo_new
aaeidhfjj,IntelPython/sdc,hpat/str_ext.py,fbbde97ee1eefe2df7a510df67a2658fb681efba,STILL_EXISTS,TODO: deallocate ptr
aaeidhgac,IntelPython/sdc,hpat/str_ext.py,c10d31d3c55246af57067a23ac22df98acb956e2,STILL_EXISTS,TODO: handle reference counting
aaeidhgfe,IntelPython/sdc,docs/conf.py,8a838c46c6a9ffafa6270916130fbca5f413178f,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaeidhhae,IntelPython/sdc,hpat/hiframes.py,47f020e7e3fe72d89f780bcecbbdc1d420eebfa6,STILL_EXISTS,FIXME: need to renew definitions before PIO?
aaeidhhag,IntelPython/sdc,hpat/pio.py,47f020e7e3fe72d89f780bcecbbdc1d420eebfa6,1733560af8da8dfd5968818528f8b1426163ce7e,TODO: check for file availability
aaeidhhah,IntelPython/sdc,hpat/pio.py,47f020e7e3fe72d89f780bcecbbdc1d420eebfa6,1733560af8da8dfd5968818528f8b1426163ce7e,FIXME:
aaeidhhdf,IntelPython/sdc,hpat/parquet_pio.py,83615b736f751c285aa9b5f9a0484634dd10d995,STILL_EXISTS,columns names as string constants
aaeidhhdg,IntelPython/sdc,hpat/parquet_pio.py,83615b736f751c285aa9b5f9a0484634dd10d995,STILL_EXISTS,TODO: add f's signature to locals
aaeidhhgb,IntelPython/sdc,hpat/distributed_analysis.py,5783129e4597e0135982d65d64184b91f246c159,STILL_EXISTS,TODO: find prange actually coming from user
aaeidhhgg,IntelPython/sdc,hpat/distributed_analysis.py,5783129e4597e0135982d65d64184b91f246c159,STILL_EXISTS,XXX: assuming loop index is not used for non-stencil arrays
aaeidhhha,IntelPython/sdc,hpat/distributed.py,06124334e448ae84a34fd35d14959bb952f3ed2e,STILL_EXISTS,XXX: hack to get lengths assuming they are constant
aaeidhigg,IntelPython/sdc,hpat/ml.py,fb8f58fde75dce83e5a71957c8b1c09c74769fb5,STILL_EXISTS,FIXME
aaeidhihh,IntelPython/sdc,hpat/ml.py,503dad70fa30bcd0f1d47c5348b90a2cc11dde2c,STILL_EXISTS,FIXME
aaeidhjac,IntelPython/sdc,hpat/hiframes_typed.py,679259d72367fff4fe9f7ad1613dde7ba3dca7f0,STILL_EXISTS,since columns should have the same size; output is filled with NaNs
aaeidhjae,IntelPython/sdc,hpat/hiframes_typed.py,679259d72367fff4fe9f7ad1613dde7ba3dca7f0,STILL_EXISTS,float columns can have regular np.nan
aaeidhjcg,IntelPython/sdc,hpat/__init__.py,9ad382429c64f8d888acfe0e30f79f402ff4a1bd,167ea12c1f6eb795d21ff60555aa2b1fe56c3676,FIXME: support parallel setitem
aaeidhjdd,IntelPython/sdc,hpat/distributed_analysis.py,b045052494031dacf5f9d1f8c5052218cb27fee7,STILL_EXISTS,TODO: extend to 2D distribution
aaeidhjef,IntelPython/sdc,hpat/distributed.py,e4e237bc1320d19cdfdf394909ee1bea39c7498a,STILL_EXISTS,TODO: argmin\/argmax
aaeidiafh,IntelPython/sdc,hpat/distributed.py,65fd2b8bed1eb0ca2e2f9186377d75009ac66666,STILL_EXISTS,TODO: support multi-dim slice setitem like X[a:b; c:d]
aaeidiagc,IntelPython/sdc,hpat/distributed_analysis.py,65fd2b8bed1eb0ca2e2f9186377d75009ac66666,STILL_EXISTS,no parallel to parallel array set (TODO)
aaeidiahg,IntelPython/sdc,hpat/distributed.py,a149c7c3ec8c6d728cdaafc72fe9cec0225e1939,STILL_EXISTS,XXX: return a new tuple using sizes here?
aaeidiaic,IntelPython/sdc,hpat/distributed.py,b722daf4c1b874a0c33e0cfe0d235858d2dd191f,7c1bf98624790c012dc9bc9dcdd95239ca9e47c4,TODO: support reshape with communication
aaeidiajj,IntelPython/sdc,hpat/hiframes.py,4c75e358686bee5ab767190c284b9a738b3efa81,STILL_EXISTS,TODO: check all possible funcs
aaeidibdj,IntelPython/sdc,hpat/parquet_pio.py,4150b5c66aa349bef5cbe34ea784ce2e5a7cf973,STILL_EXISTS,TODO: close file?
aaeidibfa,IntelPython/sdc,hpat/hiframes_api.py,7b16b62d16949b134a646a9ea4d404f81d39a93a,aa3cd179192e7b8b36919a7d28085da3bf185c2d,bool array and input columns are used
aaeidibfb,IntelPython/sdc,hpat/hiframes_api.py,7b16b62d16949b134a646a9ea4d404f81d39a93a,aa3cd179192e7b8b36919a7d28085da3bf185c2d,output columns are defined
aaeidibfc,IntelPython/sdc,hpat/hiframes_api.py,25dd56727631ade27fdfc2e8a85c10d01cd4cb32,aa3cd179192e7b8b36919a7d28085da3bf185c2d,filter doesn't generate copies; it just kills the output columns
aaeidibfd,IntelPython/sdc,hpat/tests/test_strings.py,5a81a6e9b191a99c59ab12026d849e3f7fc8ba3b,STILL_EXISTS,XXX: use startswith since hpat output can have extra characters
aaeidibgd,IntelPython/sdc,hpat/tests/test_hiframes.py,b937bcb3d7d229fe127df9c392c3c422bf426a21,ad5a53d5bda5e551bc311de9f37ff1b46fe48b56,XXX: test actual output
aaeididbc,IntelPython/sdc,hpat/distributed_analysis.py,f7816abdc06d7163cc617fd70020f7b4c3e8fd9b,STILL_EXISTS,TODO: things like A[0] need broadcast
aaeididbd,IntelPython/sdc,hpat/distributed.py,ee9a5052fd8ebf3f51846ada3bc40c343a0ccab6,STILL_EXISTS,TODO: or self._is_1D_Var_arr(rhs.value.name))
aaeididcg,IntelPython/sdc,hpat/distributed.py,0e60547b5fd943c9678a5b7f0a5abeff7642f7ce,STILL_EXISTS,fix 1D_Var allocs in case global len of another 1DVar is used
aaeididic,IntelPython/sdc,hpat/hiframes_filter.py,aa3cd179192e7b8b36919a7d28085da3bf185c2d,31d77220c38725ea0fbbba23c7687efcee919b09,needs df columns for type inference stage
aaeididij,IntelPython/sdc,hpat/hiframes_filter.py,aa3cd179192e7b8b36919a7d28085da3bf185c2d,STILL_EXISTS,input columns have same distribution
aaeididjb,IntelPython/sdc,hpat/hiframes_filter.py,aa3cd179192e7b8b36919a7d28085da3bf185c2d,STILL_EXISTS,output columns have same distribution
aaeididjf,IntelPython/sdc,hpat/hiframes_filter.py,aa3cd179192e7b8b36919a7d28085da3bf185c2d,STILL_EXISTS,TODO: rebalance if output distributions are 1D instead of 1D_Var
aaeididjh,IntelPython/sdc,hpat/hiframes_filter.py,aa3cd179192e7b8b36919a7d28085da3bf185c2d,STILL_EXISTS,TODO: generate parfor
aaeidieaf,IntelPython/sdc,hpat/hiframes_filter.py,aa3cd179192e7b8b36919a7d28085da3bf185c2d,STILL_EXISTS,bool array and input columns are used
aaeidieag,IntelPython/sdc,hpat/hiframes_filter.py,aa3cd179192e7b8b36919a7d28085da3bf185c2d,STILL_EXISTS,output columns are defined
aaeidieah,IntelPython/sdc,hpat/hiframes_filter.py,aa3cd179192e7b8b36919a7d28085da3bf185c2d,STILL_EXISTS,filter doesn't generate copies; it just kills the output columns
aaeidieai,IntelPython/sdc,hpat/hiframes.py,6c8774034f96812edd6bd70877ca6dae09e94a6e,STILL_EXISTS,add columns from left to output
aaeidieaj,IntelPython/sdc,hpat/hiframes.py,6c8774034f96812edd6bd70877ca6dae09e94a6e,STILL_EXISTS,add columns from right to output
aaeidiebb,IntelPython/sdc,hpat/hiframes_join.py,6c8774034f96812edd6bd70877ca6dae09e94a6e,31d77220c38725ea0fbbba23c7687efcee919b09,needs df columns for type inference stage
aaeidiebi,IntelPython/sdc,hpat/hiframes_join.py,6c8774034f96812edd6bd70877ca6dae09e94a6e,STILL_EXISTS,input columns have same distribution
aaeidiebj,IntelPython/sdc,hpat/hiframes_join.py,6c8774034f96812edd6bd70877ca6dae09e94a6e,STILL_EXISTS,output columns have same distribution
aaeidiecd,IntelPython/sdc,hpat/hiframes_join.py,6c8774034f96812edd6bd70877ca6dae09e94a6e,STILL_EXISTS,TODO: rebalance if output distributions are 1D instead of 1D_Var
aaeidiece,IntelPython/sdc,hpat/hiframes_join.py,6c8774034f96812edd6bd70877ca6dae09e94a6e,STILL_EXISTS,TODO: implement
aaeidiecf,IntelPython/sdc,hpat/hiframes_join.py,6c8774034f96812edd6bd70877ca6dae09e94a6e,STILL_EXISTS,TODO: consider keys with same name; cols with suffix
aaeidiedb,IntelPython/sdc,hpat/hiframes_join.py,6c8774034f96812edd6bd70877ca6dae09e94a6e,STILL_EXISTS,if an output column is dead; the related input column is not needed
aaeidiede,IntelPython/sdc,hpat/hiframes_join.py,6c8774034f96812edd6bd70877ca6dae09e94a6e,STILL_EXISTS,input columns are used
aaeidiedf,IntelPython/sdc,hpat/hiframes_join.py,6c8774034f96812edd6bd70877ca6dae09e94a6e,STILL_EXISTS,output columns are defined
aaeidiedg,IntelPython/sdc,hpat/hiframes_join.py,6c8774034f96812edd6bd70877ca6dae09e94a6e,STILL_EXISTS,join doesn't generate copies; it just kills the output columns
aaeidieea,IntelPython/sdc,hpat/hiframes_join.py,ce921bcb3f3b9e2ca9e98c932394c7e6e7ca279b,STILL_EXISTS,TODO: remove output of dead keys
aaeidieeb,IntelPython/sdc,hpat/hiframes_join.py,e1d4113053656d5fc07460c6bd785312917f1acb,STILL_EXISTS,TODO: rebalance if output distributions are 1D instead of 1D_Var
aaeidieee,IntelPython/sdc,hpat/hiframes_join.py,e1d4113053656d5fc07460c6bd785312917f1acb,b9bd22a7019791241e2b120d05e0731df5be5362,XXX: create dummy output arrays to allow testing for now
aaeidieei,IntelPython/sdc,hpat/hiframes_join.py,e1d4113053656d5fc07460c6bd785312917f1acb,cd12f6cd3fd0f9d07f63b54f9a2ab4de3d06c29f,XXX: assuming key arr is 1D
aaeidieej,IntelPython/sdc,hpat/hiframes_join.py,b5f8eff30f0a2fe05509c5759eac8d67f825e647,cd12f6cd3fd0f9d07f63b54f9a2ab4de3d06c29f,TODO: extend to other key types
aaeidiefe,IntelPython/sdc,hpat/hiframes_join.py,2c26bcd59d32b7cabbbddc66dcb23d1a92380fe6,STILL_EXISTS,arg names of non-key columns
aaeidiegd,IntelPython/sdc,hpat/hiframes_join.py,8c4e29aa959b6150f95bf3a64d9ed759682c3ce7,cd12f6cd3fd0f9d07f63b54f9a2ab4de3d06c29f,XXX: assuming key arr is 1D
aaeidiegg,IntelPython/sdc,hpat/hiframes_join.py,0db2b6aaf75ec846a9b276b4eb2d5f325aac3d2a,STILL_EXISTS,add keys first (TODO: remove dead keys)
aaeidiehc,IntelPython/sdc,hpat/hiframes_join.py,0db2b6aaf75ec846a9b276b4eb2d5f325aac3d2a,STILL_EXISTS,XXX expand array
aaeidiehj,IntelPython/sdc,hpat/hiframes_join.py,b8f4037418dc63bdc55ca68490b54aaf3b8d4f43,STILL_EXISTS,TODO: delete buffers
aaeidieih,IntelPython/sdc,hpat/hiframes.py,5ff0640f5ae52e3794dab354971420c0c51675f1,STILL_EXISTS,TODO: fix type
aaeidifba,IntelPython/sdc,hpat/distributed.py,c3100b87353590b8f133b566d5049e101951adb9,STILL_EXISTS,XXX: get sizes in lower dimensions
aaeidifbd,IntelPython/sdc,hpat/distributed_lower.py,281d41463b425a18c329906bd86b56fb7488883b,STILL_EXISTS,TODO: support string type
aaeidifef,IntelPython/sdc,setup.py,824d79ecc909600314b11194e5b5126a542108f1,c8f3f6f94ff19a9114121187ce8c9d2f3867b3d9,TODO: fix opencv link
aaeidiffg,IntelPython/sdc,hpat/str_arr_ext.py,a6c5c0986e7cefaae2ddf99ce9d8a8cf5fffcc3d,STILL_EXISTS,FIXME how to check that the returned size is > 0?
aaeidifif,IntelPython/sdc,hpat/cv_ext.py,293cda05c48d041224df891f72ce5e2e5511a5b2,STILL_EXISTS,XXX: unnecessary allocation and copy; reuse data pointer
aaeidigaj,IntelPython/sdc,hpat/str_ext.py,d1b574a68c544cdb207df571a82418f931fb6e5f,STILL_EXISTS,XXX: should be subtype of StringType?
aaeidigbc,IntelPython/sdc,hpat/str_ext.py,d1b574a68c544cdb207df571a82418f931fb6e5f,STILL_EXISTS,XXX: fix overload for getitem and use it
aaeidigbd,IntelPython/sdc,hpat/str_ext.py,d1b574a68c544cdb207df571a82418f931fb6e5f,STILL_EXISTS,TODO: delete ptr
aaeidigda,IntelPython/sdc,hpat/io.py,7c332a8c9e79b96e51865a9187b41f804568a64f,STILL_EXISTS,FIXME: import here since hio has hdf5 which might not be available
aaeidigeb,IntelPython/sdc,hpat/io.py,1347b1d284fd3905b4f63f961187609d617b007b,STILL_EXISTS,# FIXME: import here since hio has hdf5 which might not be available
aaeidigff,IntelPython/sdc,hpat/io.py,1347b1d284fd3905b4f63f961187609d617b007b,STILL_EXISTS,TODO: dtype in kws
aaeidigga,IntelPython/sdc,hpat/distributed.py,beee572f813739df4e99d8935e209a2b30c45258,STILL_EXISTS,FIXME: we use rebalance array to handle the output array
aaeidiggb,IntelPython/sdc,hpat/distributed.py,beee572f813739df4e99d8935e209a2b30c45258,STILL_EXISTS,TODO: convert to neighbor exchange
aaeidiggi,IntelPython/sdc,hpat/distributed.py,5afb316c716026fe4a035029b884c6dbb951e2d3,STILL_EXISTS,TODO: avoid alloc and copy if no communication necessary
aaeidighj,IntelPython/sdc,hpat/distributed_analysis.py,5afb316c716026fe4a035029b884c6dbb951e2d3,STILL_EXISTS,TODO: support 1D_Var reshape
aaeidiigd,IntelPython/sdc,hpat/distributed_analysis.py,11a46a398da705573a3f18abd41cd0c478dfccbf,6913dd95cc07b0939665d30f2a62ebcd327d8b82,currently; find_callname only handles array.func (TODO: extend)
aaeidiigf,IntelPython/sdc,hpat/distributed_analysis.py,11a46a398da705573a3f18abd41cd0c478dfccbf,STILL_EXISTS,TODO: support 1D_Var reshape
aaeidiiie,IntelPython/sdc,hpat/hiframes.py,82ef862c625c583f2fdf219bd5ffd38b2cf33ed2,STILL_EXISTS,TODO: handle non numpy alloc types
aaeidiiii,IntelPython/sdc,hpat/hiframes.py,702bbdad436078ce1d95a73433a91e074afdd1ef,b5ea895f01f363261442c4d955360a663b6e4544,TODO: handle map\/apply differences
aaeidiijd,IntelPython/sdc,hpat/hiframes.py,4f9ea01bf6df80fadffa6f2a4804f3b2cdc6a8bf,STILL_EXISTS,TODO: handle non numpy alloc types
aaeidijaf,IntelPython/sdc,hpat/hiframes.py,73cb1ea3eda1aac331b08f3600279f921ce9c924,d1428624e72c5deed9acfe490660be99c9d7fa00,TODO: fix string formatting to match python\/pandas
aaeidijah,IntelPython/sdc,hpat/tests/test_hiframes.py,61cf292f660149b41d9f38a2718f4aca2a50d46b,ad5a53d5bda5e551bc311de9f37ff1b46fe48b56,XXX: test actual output
aaeidijba,IntelPython/sdc,hpat/distributed.py,38b72f963465d6c12e8961594273bf49d14a7093,STILL_EXISTS,TODO: refactor to avoid reduction
aaeidijbb,IntelPython/sdc,hpat/distributed.py,38b72f963465d6c12e8961594273bf49d14a7093,STILL_EXISTS,XXX: get sizes in lower dimensions
aaeidijbf,IntelPython/sdc,hpat/tests/test_basic.py,282b4763787d994c5cbc860ccee08e42bc7583cc,STILL_EXISTS,we cannot compare against NumPy.  Therefore; we implement permutation
aaeidijcb,IntelPython/sdc,hpat/tests/test_basic.py,282b4763787d994c5cbc860ccee08e42bc7583cc,STILL_EXISTS,this hack that generates a Python Random object with a fixed seed and
aaeidijcj,IntelPython/sdc,hpat/tests/test_basic.py,712e1e8d62b9eb24db6ed0888c9aef333528819b,STILL_EXISTS,implement a Python version.
aaeidijdc,IntelPython/sdc,hpat/io.py,efac1070976771f0756c17ff88deeefd84a2617f,STILL_EXISTS,FIXME: import here since hio has hdf5 which might not be available
aaeidijdd,IntelPython/sdc,hpat/distributed.py,35fda713878a5caff8dd3be1fb62658b1e2a872b,STILL_EXISTS,TODO: refactor
aaeidijeh,IntelPython/sdc,hpat/io.py,35fda713878a5caff8dd3be1fb62658b1e2a872b,STILL_EXISTS,TODO: fix A.ctype inlined case
aaeidijfb,IntelPython/sdc,hpat/hiframes.py,64ef709c2fda6b65e7ed2d23e934bbea027a2680,STILL_EXISTS,TODO: move to _run_arg after merge of datetime branch
aaeidijfc,IntelPython/sdc,hpat/hiframes.py,64ef709c2fda6b65e7ed2d23e934bbea027a2680,STILL_EXISTS,FIXME: fix bool_
aaeidijfd,IntelPython/sdc,hpat/hiframes_api.py,64ef709c2fda6b65e7ed2d23e934bbea027a2680,STILL_EXISTS,TODO: support other types like string and timestamp
aaeidijfe,IntelPython/sdc,hpat/hiframes_api.py,64ef709c2fda6b65e7ed2d23e934bbea027a2680,a3c99071932d12c5ab75f9cbae0ebffbe26fe782,XXX: refcount?
aaeidijfh,IntelPython/sdc,hpat/hiframes_api.py,64ef709c2fda6b65e7ed2d23e934bbea027a2680,STILL_EXISTS,TODO: refcount?
aaeidijfi,IntelPython/sdc,hpat/hiframes_api.py,64ef709c2fda6b65e7ed2d23e934bbea027a2680,STILL_EXISTS,TODO: error handling like Numba callwrappers.py
aaeidjacd,IntelPython/sdc,hpat/pd_timestamp_ext.py,6d6c825f87b59eda27d62a0fe2cdb7a69d499d64,STILL_EXISTS,TODO: check types
aaeidjach,IntelPython/sdc,hpat/pd_timestamp_ext.py,2b201a664e5fec7c122293426bb81a50084ac390,STILL_EXISTS,XXX: code for timestamp series getitem in regular Numba
aaeidjbgf,IntelPython/sdc,hpat/pd_timestamp_ext.py,0ae125d8cd72daabd950aa3e2f592f68c3c88fa3,STILL_EXISTS,TODO: check types
aaeidjbha,IntelPython/sdc,hpat/pd_timestamp_ext.py,fa97717ee2f87e87b16d02e17418b5edfe7513e0,cde74f2ff91ff1a9585e19851ce4b840878973fd,TODO: add boxing
aaeidjbhc,IntelPython/sdc,hpat/hiframes.py,01a24cc32f204ad05a369c22cbf3b414ebdc7e7a,STILL_EXISTS,TODO: support distributed input
aaeidjbhd,IntelPython/sdc,hpat/hiframes.py,01a24cc32f204ad05a369c22cbf3b414ebdc7e7a,STILL_EXISTS,FIXME: fix bool_
aaeidjbhg,IntelPython/sdc,hpat/hiframes.py,61ab06212a4a3abc1a7ce4fad5e5c3436018df7a,38db657e65c339e605d2e5369ab3f02a4e1ba909,FIXME: this is possibly fragile; maybe replace all series getitems
aaeidjbid,IntelPython/sdc,hpat/hiframes.py,61ab06212a4a3abc1a7ce4fad5e5c3436018df7a,30cffa977101dba8bbfd9c7db8eb79dc123d3140,FIXME: this is fragile
aaeidjddc,IntelPython/sdc,hpat/hiframes.py,85440da75381bdd6643a12541563bce07d484f26,STILL_EXISTS,XXX const code for dt64 since we can't init dt64 dtype
aaeidjddd,IntelPython/sdc,hpat/hiframes_api.py,85440da75381bdd6643a12541563bce07d484f26,STILL_EXISTS,XXX assuming the whole column is strings if 1st val is string
aaeidjdde,IntelPython/sdc,hpat/hiframes_api.py,85440da75381bdd6643a12541563bce07d484f26,STILL_EXISTS,FIXME dtype for dt64
aaeidjddf,IntelPython/sdc,hpat/hiframes_api.py,85440da75381bdd6643a12541563bce07d484f26,STILL_EXISTS,FIXME dtype for str
aaeidjddg,IntelPython/sdc,hpat/hiframes_api.py,85440da75381bdd6643a12541563bce07d484f26,STILL_EXISTS,FIXME: str code
aaeidjddi,IntelPython/sdc,hpat/hiframes_api.py,ed4764f97a8d08e68785e29f9aec8729261f4cd4,STILL_EXISTS,FIXME: dt64 code
aaeidjdeb,IntelPython/sdc,hpat/hiframes.py,de5cfc54d5488043e7d17f145d1cdc5577aea00f,STILL_EXISTS,find columns that are actually used if possible
aaeidjdeh,IntelPython/sdc,setup.py,328089a993f7dfe6b9ddeb5729a43b3cdc843ca5,c8f3f6f94ff19a9114121187ce8c9d2f3867b3d9,XXX cv lib file name needs version on Windows
aaeidjdfa,IntelPython/sdc,hpat/hiframes.py,6ce79b69d5cc94368eb3b99a1535c867ac19e0b7,STILL_EXISTS,XXX placeholder for df variable renaming
aaeidjdfe,IntelPython/sdc,hpat/hiframes.py,20ce7ae9282810cac4c6307e60f3e9752dd42cc3,STILL_EXISTS,TODO: generalize to more cases
aaeidjdff,IntelPython/sdc,hpat/hiframes.py,20ce7ae9282810cac4c6307e60f3e9752dd42cc3,STILL_EXISTS,TODO: rename the dataframe variable to keep schema static
aaeidjdhg,IntelPython/sdc,hpat/hiframes.py,ecba9dd06c837a87d0ca53eb8441831c89e60f82,STILL_EXISTS,needed for set df column
aaeidjdib,IntelPython/sdc,hpat/xenon_ext.py,aba50bbe011cf34b26ca9e0c293671f8ed972f42,STILL_EXISTS,TODO: implement in regular python
aaeidjdic,IntelPython/sdc,hpat/xenon_ext.py,aba50bbe011cf34b26ca9e0c293671f8ed972f42,STILL_EXISTS,TODO: init only once
aaeidjdif,IntelPython/sdc,hpat/xenon_ext.py,aba50bbe011cf34b26ca9e0c293671f8ed972f42,STILL_EXISTS,TODO: handle decimal and blob types
aaeidjdjg,IntelPython/sdc,hpat/xenon_ext.py,b1330a78a6a0cf1cb1bc17bd361f3b3b63c52b15,STILL_EXISTS,TODO: fix liveness\/alias in Numba to be able to use arr.ctypes directly
aaeidjdji,IntelPython/sdc,hpat/xenon_ext.py,e75b4162f5b7ea03caf2918e2ad5afc01a0b3b3e,STILL_EXISTS,TODO: support non-constant address\/dset_name
aaeidjeab,IntelPython/sdc,hpat/hiframes.py,b24676c4ac08087d758d58f41671dc6493ff52fe,STILL_EXISTS,fix list(multi-dim arrays) (packing images)
aaeidjeac,IntelPython/sdc,hpat/hiframes.py,b24676c4ac08087d758d58f41671dc6493ff52fe,STILL_EXISTS,FIXME: does this break for list(other things)?
aaeidjead,IntelPython/sdc,hpat/hiframes_api.py,b24676c4ac08087d758d58f41671dc6493ff52fe,STILL_EXISTS,FIXME: np.array() for everything else?
aaeidjeaf,IntelPython/sdc,hpat/distributed.py,21a3fbcb05300aedb52bd55339ddd6f61a6454e8,STILL_EXISTS,TODO: need different flag for 1D_Var return (distributed_var)?
aaeidjeaj,IntelPython/sdc,hpat/pd_timestamp_ext.py,62842e7ebad0e2fd63aed46e0d693c52e777d212,STILL_EXISTS,TODO: implement this
aaeidjecj,IntelPython/sdc,hpat/pd_timestamp_ext.py,078d70c52f199595db5ecc3804f9e89f42d32fe2,STILL_EXISTS,TODO: make pandas optional; not import this file if no pandas
aaeidjefj,IntelPython/sdc,hpat/hiframes.py,c6608d2a57e592ca8371afaa27e2fa5742344728,83f6596856b46ff24b644e859951e700dd57d9d6,FIXME: refactor
aaeidjeia,IntelPython/sdc,hpat/distributed_analysis.py,9a6c568fd5fdf16ad132ccb8808968a0570acc47,STILL_EXISTS,TODO: fix \"numba.extending\" in function def
aaeidjejb,IntelPython/sdc,hpat/pd_timestamp_ext.py,aa98bb9c45f538ea5dcd067ca9428819addbf48f,STILL_EXISTS,TODO: check types
aaeidjejd,IntelPython/sdc,hpat/hiframes.py,133efc8f9cf1e182c273204df0e0fc44a3480955,STILL_EXISTS,XXX: does control flow affect type inference in Numba?
aaeidjfbd,IntelPython/sdc,hpat/hiframes.py,5ac31cdd561e199756ff443bf712216f5b1f8623,30cffa977101dba8bbfd9c7db8eb79dc123d3140,FIXME: this is fragile
aaeidjfbe,IntelPython/sdc,hpat/hiframes.py,5ac31cdd561e199756ff443bf712216f5b1f8623,STILL_EXISTS,TODO: handle datetime.date() series
aaeidjfce,IntelPython/sdc,hpat/hiframes_api.py,4f72764c77a499bbb480a926461dec511f472ae9,STILL_EXISTS,TODO: naive implementation; data from set can probably
aaeidjfch,IntelPython/sdc,hpat/set_ext.py,f48dbb460963edc1c47f77fac9757435de57a09f,STILL_EXISTS,TODO: box set(string)
aaeidjfcj,IntelPython/sdc,hpat/set_ext.py,f48dbb460963edc1c47f77fac9757435de57a09f,STILL_EXISTS,TODO: expand to other set types
aaeidjfdb,IntelPython/sdc,hpat/set_ext.py,f48dbb460963edc1c47f77fac9757435de57a09f,STILL_EXISTS,TODO: implement iterator
aaeidjfji,IntelPython/sdc,hpat/utils.py,b4f71e7ad0ad167ebc8bfdddc6cbb90630a7fdbf,STILL_EXISTS,TODO: other types like datetime?
aaeidjgaf,IntelPython/sdc,hpat/distributed_api.py,bbbbd5f0f2affc175c283cb6176fd065ec6d6c06,STILL_EXISTS,TODO: handle int64 counts
aaeidjgbc,IntelPython/sdc,hpat/hiframes_api.py,7b4ee596a29f0641f7a4cecda3d3b95eb8fe434f,STILL_EXISTS,XXX offset type is uint32
aaeidjgbj,IntelPython/sdc,hpat/hiframes_api.py,7b4ee596a29f0641f7a4cecda3d3b95eb8fe434f,STILL_EXISTS,TODO: refactor with join
aaeidjgca,IntelPython/sdc,hpat/hiframes_join.py,7b4ee596a29f0641f7a4cecda3d3b95eb8fe434f,cd12f6cd3fd0f9d07f63b54f9a2ab4de3d06c29f,TODO: delete string
aaeidjgce,IntelPython/sdc,hpat/tests/test_hiframes.py,57b45d80c5eadd2ad9d2ad5fea76b3760292881e,STILL_EXISTS,TODO: test without file
aaeidjgch,IntelPython/sdc,hpat/hiframes.py,ac82f6a5ee05caf35ee33933374c6aecd7264c91,STILL_EXISTS,XXX convert build_list to build_tuple since Numba doesn't handle list of
aaeidjgdc,IntelPython/sdc,hpat/hiframes.py,f9b7ec2d0d1a5e62f9ff0a6038c466bdbda1de73,STILL_EXISTS,TODO: handle non-numerical (e.g. string; datetime) columns
aaeidjgea,IntelPython/sdc,hpat/tests/test_hiframes.py,2be4c737eb4a9d06358153a0f5a16228250127be,92b6bce282f09e38dd810af5e11f77a25b38b138,TODO: enable when namedtuple analysis patch is merged (#2984)
aaeidjgfa,IntelPython/sdc,hpat/str_arr_ext.py,1d98cd76bf9b3ee6286098c55c8b2b86411c33b0,STILL_EXISTS,TODO: fix overload for things like 'getitem'
aaeidjggb,IntelPython/sdc,hpat/str_arr_ext.py,1d98cd76bf9b3ee6286098c55c8b2b86411c33b0,STILL_EXISTS,TODO: use get_cstr_and_len instead of getitem
aaeidjggc,IntelPython/sdc,hpat/hiframes.py,1d7e29ca5661e2ce64dae56f27c23cac1f5a48f4,STILL_EXISTS,run len on one of the columns
aaeidjggd,IntelPython/sdc,hpat/hiframes.py,1d7e29ca5661e2ce64dae56f27c23cac1f5a48f4,STILL_EXISTS,FIXME: it could potentially avoid remove dead for the column if
aaeidjggi,IntelPython/sdc,hpat/distributed.py,9822f550e65a851e67e9e6dfefe8374a9be8e768,STILL_EXISTS,FIXME: since parfors are transformed and then processed
aaeidjghg,IntelPython/sdc,hpat/distributed.py,773d23e44e1937b76b8649d431ff9119b971355a,STILL_EXISTS,TODO: fix numba.extending
aaeidjghi,IntelPython/sdc,hpat/distributed.py,20e0a6cd9d907738b25b4239cc2290389dc6e7c4,STILL_EXISTS,XXX allocs should be matched before going to _run_call_np
aaeidjgjc,IntelPython/sdc,hpat/distributed.py,20e0a6cd9d907738b25b4239cc2290389dc6e7c4,STILL_EXISTS,TODO: compute inplace if input array is dead
aaeidjgji,IntelPython/sdc,hpat/distributed.py,ea25c0895af3142e235864d8a4116fbd9ee81d5f,STILL_EXISTS,TODO: refactor
aaeidjgjj,IntelPython/sdc,hpat/distributed.py,ea25c0895af3142e235864d8a4116fbd9ee81d5f,STILL_EXISTS,TODO: add unittest
aaeidjhac,IntelPython/sdc,hpat/distributed.py,be3f7af4f7668afb3cf5c772cbf3878d97d3054b,STILL_EXISTS,TODO: remove ml module and use new DAAL API
aaeidjhae,IntelPython/sdc,hpat/distributed.py,4432e99af36486b6612132317e4ff148b93077b9,0b58cc0c2b343cbb1ed4a4c137e8861ee57902e0,TODO: add group unittest
aaeidjhcf,IntelPython/sdc,hpat/hiframes.py,00e4e22b129abbf9e1ae769ec1ebc96a51f4fa36,STILL_EXISTS,TODO: support aggregation functions sum; count; etc.
aaeidjhcg,IntelPython/sdc,hpat/hiframes.py,00e4e22b129abbf9e1ae769ec1ebc96a51f4fa36,STILL_EXISTS,find selected output columns
aaeidjhch,IntelPython/sdc,hpat/hiframes.py,00e4e22b129abbf9e1ae769ec1ebc96a51f4fa36,6f1e1aa3d4cf2540ab2c8fd67b24e3beaca46204,TODO: support other selection formats
aaeidjhdi,IntelPython/sdc,hpat/hiframes_aggregate.py,c89b3fd9c38e7cbd921bebe4ad64a6ed815874ac,STILL_EXISTS,TODO: test agg remove
aaeidjhea,IntelPython/sdc,hpat/hiframes_aggregate.py,cb02572082cc3ac530a6f1ad84a4d51f9a5b39f0,STILL_EXISTS,key array and input columns are used
aaeidjheb,IntelPython/sdc,hpat/hiframes_aggregate.py,cb02572082cc3ac530a6f1ad84a4d51f9a5b39f0,STILL_EXISTS,output columns are defined
aaeidjhec,IntelPython/sdc,hpat/hiframes_aggregate.py,65399773ec5ee817531d450eb7647ddf7c980732,STILL_EXISTS,aggregate doesn't generate copies; it just kills the output columns
aaeidjhfa,IntelPython/sdc,hpat/hiframes_aggregate.py,c162942f9c9e06a99322b8b07a1882eb2edea1c0,STILL_EXISTS,TODO: are there other non-numpy array types?
aaeidjhfb,IntelPython/sdc,hpat/hiframes_aggregate.py,7bb1cfaea49b1c1685bd2035a379f616c093097c,STILL_EXISTS,input columns have same distribution
aaeidjhfd,IntelPython/sdc,hpat/hiframes_aggregate.py,7bb1cfaea49b1c1685bd2035a379f616c093097c,STILL_EXISTS,output columns have same distribution
aaeidjhfh,IntelPython/sdc,hpat/hiframes_aggregate.py,06eaeac416c4f1079a8815dd35a0964fa7500f79,STILL_EXISTS,TODO: check supported types
aaeidjhgb,IntelPython/sdc,hpat/hiframes_aggregate.py,06eaeac416c4f1079a8815dd35a0964fa7500f79,STILL_EXISTS,\"Only int64 and float64 columns are currently supported in aggregate\")
aaeidjhgf,IntelPython/sdc,hpat/hiframes_aggregate.py,06eaeac416c4f1079a8815dd35a0964fa7500f79,STILL_EXISTS,TODO: rebalance if output distributions are 1D instead of 1D_Var
aaeidjhgg,IntelPython/sdc,hpat/hiframes_aggregate.py,06eaeac416c4f1079a8815dd35a0964fa7500f79,STILL_EXISTS,TODO: handle key column being part of output
aaeidjhhf,IntelPython/sdc,hpat/hiframes_aggregate.py,06eaeac416c4f1079a8815dd35a0964fa7500f79,STILL_EXISTS,XXX dummy test code
aaeidjhhg,IntelPython/sdc,hpat/hiframes_aggregate.py,06eaeac416c4f1079a8815dd35a0964fa7500f79,STILL_EXISTS,TODO: handle string
aaeidjhhh,IntelPython/sdc,hpat/hiframes.py,cf401d19eb3a13f614e8ccb403384ffee668aa04,STILL_EXISTS,TODO: handle more than one output column
aaeidjhic,IntelPython/sdc,hpat/hiframes_aggregate.py,9fa47c06baa8ea8f4b51a19e50a5417161d6abe8,b8a01723b7d50781f0c6e59554409e8e370576ce,TODO: non-int dict
aaeidjhig,IntelPython/sdc,hpat/hiframes_aggregate.py,9fa47c06baa8ea8f4b51a19e50a5417161d6abe8,STILL_EXISTS,XXX are outside function's globals needed?
aaeidjiab,IntelPython/sdc,hpat/hiframes_aggregate.py,5102ffa8f7daf1df2a3fa7904a03e36d380e0cd2,3e97de74709cdd32eca70ccb2147a29e3d151c00,XXX can modify since iterator is terminated
aaeidjiai,IntelPython/sdc,hpat/hiframes_aggregate.py,b29ee13f41b2e4715a1216caea608bcf157e2f73,3e97de74709cdd32eca70ccb2147a29e3d151c00,XXX can modify since iterator is terminated
aaeidjice,IntelPython/sdc,hpat/hiframes_aggregate.py,977c516a1de2e221165e671925a74efa2e247bf6,b8a01723b7d50781f0c6e59554409e8e370576ce,no output columns in parallel-local computation (reduce arrs returned)
aaeidjicf,IntelPython/sdc,hpat/hiframes_aggregate.py,977c516a1de2e221165e671925a74efa2e247bf6,b8a01723b7d50781f0c6e59554409e8e370576ce,key is returned in parallel local agg phase (TODO: avoid if key is output already)
aaeidjiea,IntelPython/sdc,hpat/hiframes_aggregate.py,977c516a1de2e221165e671925a74efa2e247bf6,3e97de74709cdd32eca70ccb2147a29e3d151c00,XXX can modify since iterator is terminated
aaeidjiee,IntelPython/sdc,hpat/hiframes_aggregate.py,977c516a1de2e221165e671925a74efa2e247bf6,STILL_EXISTS,XXX input column type can be different than reduction variable type
aaeidjief,IntelPython/sdc,hpat/hiframes_aggregate.py,977c516a1de2e221165e671925a74efa2e247bf6,STILL_EXISTS,TODO: add outside globals
aaeidjifj,IntelPython/sdc,hpat/hiframes_aggregate.py,3e97de74709cdd32eca70ccb2147a29e3d151c00,caba3414ee9107a5fbc985caea146cf5625ec02a,TODO: extend to multiple input
aaeidjiic,IntelPython/sdc,hpat/hiframes_aggregate.py,3e97de74709cdd32eca70ccb2147a29e3d151c00,STILL_EXISTS,XXX can modify since iterator is terminated
aaeidjijd,IntelPython/sdc,hpat/hiframes_aggregate.py,9654ab016c2fd82f95e61b927e301d8bd7a02853,b8a01723b7d50781f0c6e59554409e8e370576ce,TODO: replace with functions
aaeidjijg,IntelPython/sdc,hpat/hiframes_aggregate.py,9654ab016c2fd82f95e61b927e301d8bd7a02853,STILL_EXISTS,XXX can modify since iterator is terminated
aaeidjjad,IntelPython/sdc,hpat/dict_ext.py,42253a4301a4336b21dee04f67d25065197d9c8f,STILL_EXISTS,XXX: needs Numba #3014 resolved
aaeidjjbd,IntelPython/sdc,hpat/dict_ext.py,fe96122e95c1afe4eaea6fb004488cbe86efadce,STILL_EXISTS,XXX possible overload bug
aaeidjjgb,IntelPython/sdc,hpat/hiframes_aggregate.py,b8884aa54a06e02ce21f37ba62cbf9ade86c065b,d4dcf9e0093a850ae82705673e362d70df91a89a,shuffle other columns
aaeidjjgd,IntelPython/sdc,hpat/hiframes_aggregate.py,4fd2e52636c6c3e3d433c44c84d98ec150f0102f,b8a01723b7d50781f0c6e59554409e8e370576ce,needed due to alltoallv
aaeidjjgh,IntelPython/sdc,hpat/hiframes_api.py,346fbb107e23ad96ee328ca94c09ead2cba2dc67,STILL_EXISTS,TODO: handle numerics to string casting case
aaeidjjgi,IntelPython/sdc,hpat/hiframes.py,f72a7f20f92c7bc77e30173d40038ccbfcd5486e,STILL_EXISTS,TODO: allocate string array of NAs
aaeidjjha,IntelPython/sdc,hpat/hiframes.py,bfb1018fa6d020a4127a9044fc2109360fe95548,6cd2e5cdba6c3357d7f84b6a4f2b7f0cdf396f5b,XXX hack for agg functions; replace with proper Series type
aaeidjjhd,IntelPython/sdc,hpat/hiframes_aggregate.py,bfb1018fa6d020a4127a9044fc2109360fe95548,STILL_EXISTS,TODO: extract an eval func more robustly
aaeidjjhe,IntelPython/sdc,hpat/hiframes_aggregate.py,bfb1018fa6d020a4127a9044fc2109360fe95548,STILL_EXISTS,TODO: support multiple top-level blocks
aaeidjjig,IntelPython/sdc,hpat/hiframes_aggregate.py,52ea405da94d7a9f1122d6742c6c88877d47a27b,STILL_EXISTS,XXX assuming shape\/size nodes are right after arg
aaeidjjij,IntelPython/sdc,hpat/hiframes_aggregate.py,52ea405da94d7a9f1122d6742c6c88877d47a27b,STILL_EXISTS,XXX: update mutates parfor body
aaeidjjjc,IntelPython/sdc,hpat/hiframes_aggregate.py,52ea405da94d7a9f1122d6742c6c88877d47a27b,STILL_EXISTS,in parfor.py:3039; TODO: make this detection more robust
aaeidjjjf,IntelPython/sdc,hpat/hiframes_aggregate.py,52ea405da94d7a9f1122d6742c6c88877d47a27b,STILL_EXISTS,TODO: add outside globals
aaeidjjji,IntelPython/sdc,hpat/hiframes_aggregate.py,52ea405da94d7a9f1122d6742c6c88877d47a27b,STILL_EXISTS,TODO: simplify f_ir
aaeieaabb,IntelPython/sdc,hpat/hiframes_aggregate.py,cc3db475bf587a668662c454bdb2f5a4d35336cf,STILL_EXISTS,XXX: update mutates parfor body
aaeieaabe,IntelPython/sdc,hpat/hiframes_aggregate.py,cc3db475bf587a668662c454bdb2f5a4d35336cf,STILL_EXISTS,TODO: add outside globals
aaeieaabf,IntelPython/sdc,hpat/hiframes_aggregate.py,cc3db475bf587a668662c454bdb2f5a4d35336cf,STILL_EXISTS,TODO: support multi block eval funcs
aaeieaabh,IntelPython/sdc,hpat/hiframes.py,04b6f1f277c1679ef86ee12485ffa891032f5ea9,6f1e1aa3d4cf2540ab2c8fd67b24e3beaca46204,TODO: support other selection formats
aaeieaada,IntelPython/sdc,hpat/hiframes_api.py,3686cf8fd40d9548ed68db8e5f5a0d0cf6e60b91,STILL_EXISTS,XXX index handling; assuming implicit index
aaeieaadc,IntelPython/sdc,hpat/hiframes_api.py,3686cf8fd40d9548ed68db8e5f5a0d0cf6e60b91,STILL_EXISTS,XXX array_types[0] is implicit index
aaeieaadh,IntelPython/sdc,hpat/hiframes_api.py,3686cf8fd40d9548ed68db8e5f5a0d0cf6e60b91,STILL_EXISTS,TODO: support string arrays
aaeieaadj,IntelPython/sdc,hpat/hiframes_api.py,3686cf8fd40d9548ed68db8e5f5a0d0cf6e60b91,STILL_EXISTS,XXX implicit int index
aaeieaaea,IntelPython/sdc,hpat/hiframes_api.py,3686cf8fd40d9548ed68db8e5f5a0d0cf6e60b91,STILL_EXISTS,TODO: move this to array analysis
aaeieaafc,IntelPython/sdc,hpat/str_ext.py,43ebe2cc80b3a176bcc8957c47904af2da5a1f57,STILL_EXISTS,TODO: more efficient implementation (e.g. C++ string buffer)
aaeieabbi,IntelPython/sdc,hpat/parquet_pio.py,6117e13f6576a26f7b5ae38b0d497ecfa142c2de,STILL_EXISTS,XXX arrow converts int96 timestamp to int64
aaeieabbj,IntelPython/sdc,hpat/hiframes.py,4ea24db7fc5c8e6c59f41c8a2909e726e45a6280,a62dcacc582943649d237ae26a26d33ab85af4d3,TODO: proper Series\/Timeseries type to avoid this
aaeieabca,IntelPython/sdc,hpat/hiframes.py,4ea24db7fc5c8e6c59f41c8a2909e726e45a6280,a62dcacc582943649d237ae26a26d33ab85af4d3,no timeseries; not needed
aaeieabcb,IntelPython/sdc,hpat/hiframes.py,4ea24db7fc5c8e6c59f41c8a2909e726e45a6280,a62dcacc582943649d237ae26a26d33ab85af4d3,both timeseries; not needed
aaeieabce,IntelPython/sdc,hpat/hiframes_api.py,4ea24db7fc5c8e6c59f41c8a2909e726e45a6280,a62dcacc582943649d237ae26a26d33ab85af4d3,TODO: extend to other types like string array
aaeieabcf,IntelPython/sdc,hpat/hiframes_api.py,4ea24db7fc5c8e6c59f41c8a2909e726e45a6280,a62dcacc582943649d237ae26a26d33ab85af4d3,TODO: examine all possible ops
aaeieabdf,IntelPython/sdc,hpat/pd_timestamp_ext.py,131dd864cbdb626115af2998394355441a08443d,STILL_EXISTS,TODO: support general string formatting
aaeieabej,IntelPython/sdc,hpat/hiframes_api.py,f8d897e3eceac8b46dda5edab38d5e11bd8d0b39,STILL_EXISTS,FIXME: fix array analysis for tuples in general
aaeieabfd,IntelPython/sdc,hpat/str_ext.py,e76c0aaf89f814273e74aa535c1ed5cc0afa08c6,STILL_EXISTS,TODO: refcounted str
aaeieabgg,IntelPython/sdc,hpat/timsort.py,084c9dd3ebeb4976df66157c23985aeddba79b3a,STILL_EXISTS,implementation; but 32 was empirically determined to work better in
aaeieacbc,IntelPython/sdc,hpat/timsort.py,084c9dd3ebeb4976df66157c23985aeddba79b3a,STILL_EXISTS,Push run onto pending-run stack; and maybe merge
aaeieacdc,IntelPython/sdc,hpat/timsort.py,084c9dd3ebeb4976df66157c23985aeddba79b3a,STILL_EXISTS,pivotStore = key_arr[start]  # TODO: copy data to pivot
aaeieacec,IntelPython/sdc,hpat/timsort.py,084c9dd3ebeb4976df66157c23985aeddba79b3a,STILL_EXISTS,The number of elements to move
aaeieaced,IntelPython/sdc,hpat/timsort.py,084c9dd3ebeb4976df66157c23985aeddba79b3a,STILL_EXISTS,TODO: optimize for n==1 and n==2
aaeieacee,IntelPython/sdc,hpat/timsort.py,084c9dd3ebeb4976df66157c23985aeddba79b3a,STILL_EXISTS,TODO: data
aaeieacef,IntelPython/sdc,hpat/timsort.py,084c9dd3ebeb4976df66157c23985aeddba79b3a,STILL_EXISTS,FIXME: is slicing ok?
aaeieacgb,IntelPython/sdc,hpat/timsort.py,084c9dd3ebeb4976df66157c23985aeddba79b3a,STILL_EXISTS,definition of \"descending\" is needed so that the call can safely
aaeieachi,IntelPython/sdc,hpat/timsort.py,084c9dd3ebeb4976df66157c23985aeddba79b3a,STILL_EXISTS,swap; TODO: copy data
aaeieaefb,IntelPython/sdc,hpat/timsort.py,084c9dd3ebeb4976df66157c23985aeddba79b3a,STILL_EXISTS,XXX refactored nested loop break
aaeieaegj,IntelPython/sdc,hpat/hiframes.py,04c722541a852013d687523b43f5646a19a10397,STILL_EXISTS,FIXME: see why this breaks test_kmeans
aaeieaehd,IntelPython/sdc,hpat/hiframes.py,04c722541a852013d687523b43f5646a19a10397,be65dad701fabbb77adaf08519d4d188a421528f,TODO: support inplace=False
aaeieaehe,IntelPython/sdc,hpat/hiframes.py,04c722541a852013d687523b43f5646a19a10397,32dda2765028b436858256e2fb28350f8de930a6,TODO: support multiple columns as key
aaeieaehf,IntelPython/sdc,hpat/hiframes.py,04c722541a852013d687523b43f5646a19a10397,STILL_EXISTS,TODO: support ascending=False
aaeieaejd,IntelPython/sdc,hpat/timsort.py,420e4d9a7018ad701fe25f58e5c4a24c99f621c9,STILL_EXISTS,TODO: add support for map and use it
aaeieafae,IntelPython/sdc,hpat/distributed_analysis.py,c4b584c836d7a7726b9bd2ec981d3f1ed7d4da34,be4b774a8880333c18fb646489f4d444bad5e9c0,TODO: make Sort node to enable remove_dead etc.
aaeieafag,IntelPython/sdc,hpat/distributed_api.py,b9b86c202c1adb72bd843fc375541600e0caf76d,STILL_EXISTS,TODO: other types like boolean
aaeieafbb,IntelPython/sdc,hpat/distributed_api.py,c87e40d10440f3b8e45f3515a1e1ad6f0b10f63d,STILL_EXISTS,TODO: other types like boolean
aaeieafbf,IntelPython/sdc,hpat/distributed_api.py,5a0e699129882d0520d4faccc05d1929e298ca1f,STILL_EXISTS,TODO: test
aaeieafbg,IntelPython/sdc,hpat/distributed_api.py,5a0e699129882d0520d4faccc05d1929e298ca1f,STILL_EXISTS,TODO: large BCast
aaeieafcd,IntelPython/sdc,hpat/distributed_api.py,6fb97482e6626bd7a2f0f92cbd043acceb727425,STILL_EXISTS,TODO: test
aaeieafce,IntelPython/sdc,hpat/distributed_api.py,6fb97482e6626bd7a2f0f92cbd043acceb727425,STILL_EXISTS,TODO: big alltoallv
aaeieafdd,IntelPython/sdc,hpat/hiframes_sort.py,c2bdcf1c8550edafca4ab116b91285b940b34339,STILL_EXISTS,TODO: use k-way merge instead of sort
aaeieafeb,IntelPython/sdc,hpat/hiframes_sort.py,be4b774a8880333c18fb646489f4d444bad5e9c0,a3e3ec297848ccc5ed3b8b31319fc80b83e31743,TODO: string key
aaeieafec,IntelPython/sdc,hpat/hiframes_sort.py,be4b774a8880333c18fb646489f4d444bad5e9c0,STILL_EXISTS,input columns have same distribution
aaeieafej,IntelPython/sdc,hpat/hiframes_sort.py,be4b774a8880333c18fb646489f4d444bad5e9c0,STILL_EXISTS,key array and input columns are used
aaeieaffc,IntelPython/sdc,hpat/hiframes_sort.py,9e727b06728d4cadc81aa53e8cc6b1fee7683e84,STILL_EXISTS,TODO: use *args
aaeieaffd,IntelPython/sdc,hpat/hiframes_sort.py,9e727b06728d4cadc81aa53e8cc6b1fee7683e84,STILL_EXISTS,TODO: handle data
aaeieaffe,IntelPython/sdc,hpat/hiframes_sort.py,9e727b06728d4cadc81aa53e8cc6b1fee7683e84,STILL_EXISTS,TODO: use k-way merge instead of sort
aaeieafgb,IntelPython/sdc,experimental/daal4py_ext.py,83e25cb9ad38515c8c17543beb66a26cf15991a9,6df5319228edd5bc4b4cb8fbd40eea6a36ce6f69,FIXME: use\/import actual daal4py
aaeieafge,IntelPython/sdc,experimental/daal4py_ext.py,83e25cb9ad38515c8c17543beb66a26cf15991a9,0296c8c59753546b9ec75c5c0a2597136e465a2a,FIXME: this needs to become more generic; we need to find the actual so in the python root
aaeieafhe,IntelPython/sdc,experimental/daal4py_ext.py,83e25cb9ad38515c8c17543beb66a26cf15991a9,0296c8c59753546b9ec75c5c0a2597136e465a2a,short-cut for Array type. FIXME: we currently only support 2d-double arrays
aaeieafjc,IntelPython/sdc,experimental/daal4py_ext.py,83e25cb9ad38515c8c17543beb66a26cf15991a9,6df5319228edd5bc4b4cb8fbd40eea6a36ce6f69,FIXME: add attributes \"assignments\"; \"objectiveFunction\"; \"goalFunction\"; \"nIterations\"
aaeieafjf,IntelPython/sdc,experimental/daal4py_ext.py,83e25cb9ad38515c8c17543beb66a26cf15991a9,6df5319228edd5bc4b4cb8fbd40eea6a36ce6f69,FIXME
aaeieagab,IntelPython/sdc,experimental/daal4py_ext.py,6df5319228edd5bc4b4cb8fbd40eea6a36ce6f69,0296c8c59753546b9ec75c5c0a2597136e465a2a,FIXME ILP
aaeieagah,IntelPython/sdc,experimental/daal4py_ext.py,6df5319228edd5bc4b4cb8fbd40eea6a36ce6f69,0296c8c59753546b9ec75c5c0a2597136e465a2a,FIXME: check args
aaeieagib,IntelPython/sdc,hpat/ml/d4p.py,316dad6d98d0aafeccfe52f422788fec5b03899d,STILL_EXISTS,FIXME: this needs to become more generic; we need to find the actual so in the python root
aaeieagif,IntelPython/sdc,hpat/ml/d4p.py,316dad6d98d0aafeccfe52f422788fec5b03899d,STILL_EXISTS,short-cut for Array type. FIXME: we currently only support 2d-double arrays
aaeieagii,IntelPython/sdc,hpat/ml/d4p.py,316dad6d98d0aafeccfe52f422788fec5b03899d,STILL_EXISTS,FIXME ILP
aaeieagji,IntelPython/sdc,hpat/ml/d4p.py,316dad6d98d0aafeccfe52f422788fec5b03899d,STILL_EXISTS,FIXME: check types
aaeieahac,IntelPython/sdc,hpat/ml/d4p.py,316dad6d98d0aafeccfe52f422788fec5b03899d,STILL_EXISTS,FIXME: check args
aaeieahch,IntelPython/sdc,examples/d4p_linreg.py,d15bdaca47e194fe9fa43e1a6ffc39958111c0e9,STILL_EXISTS,FIXME res.model.InterceptFlag
aaeieahda,IntelPython/sdc,hpat/ml/d4p.py,d15bdaca47e194fe9fa43e1a6ffc39958111c0e9,STILL_EXISTS,We provide a factory whcih creates all numba\/HPAT code needed to compile\/distribute daal4py code.
aaeieahdi,IntelPython/sdc,hpat/ml/d4p.py,d15bdaca47e194fe9fa43e1a6ffc39958111c0e9,STILL_EXISTS,FIXME:
aaeieahed,IntelPython/sdc,hpat/ml/d4p.py,d15bdaca47e194fe9fa43e1a6ffc39958111c0e9,STILL_EXISTS,- see fixme's below
aaeieahfd,IntelPython/sdc,hpat/ml/d4p.py,d15bdaca47e194fe9fa43e1a6ffc39958111c0e9,STILL_EXISTS,- spec.model_base: [optional] base string for C lookup function; FIXME: should not be necessary
aaeieahgc,IntelPython/sdc,hpat/distributed_analysis.py,868ddba79f9660e771ff4c5968da2d878313ee20,0122c14fc26facb99e3ee712f0e48cc96436ffdb,FIXME: handle Distribution.Thread and Disribution.REP as equivalent
aaeieahgf,IntelPython/sdc,hpat/ml/d4p.py,29d1010344da2bea435730504b68b60357bcd7fc,STILL_EXISTS,FIXME: check args
aaeieahgj,IntelPython/sdc,hpat/ml/d4p.py,29d1010344da2bea435730504b68b60357bcd7fc,STILL_EXISTS,The @intrinsic is evaluated lazily; which is probably why we cannot really bind variables here; we need to
aaeieahig,IntelPython/sdc,hpat/str_arr_ext.py,f13a5a360f45bbf9b4330f3b2587ccfdcc7f4d26,STILL_EXISTS,TODO: use get_cstr_and_len instead of getitem
aaeieahii,IntelPython/sdc,hpat/distributed_api.py,04e0c99e7791eada1b2d0c517e8261fd81cfd9a6,STILL_EXISTS,XXX offset type is uint32
aaeieahjc,IntelPython/sdc,hpat/distributed_api.py,60519fb75200d61cf9e0afc1cf9d563ddbfc8597,STILL_EXISTS,XXX offset type is uint32
aaeieahje,IntelPython/sdc,hpat/distributed_api.py,60519fb75200d61cf9e0afc1cf9d563ddbfc8597,STILL_EXISTS,TODO: test
aaeieahjf,IntelPython/sdc,hpat/distributed_api.py,60519fb75200d61cf9e0afc1cf9d563ddbfc8597,STILL_EXISTS,TODO: other types like boolean
aaeieahjg,IntelPython/sdc,hpat/distributed_api.py,60519fb75200d61cf9e0afc1cf9d563ddbfc8597,STILL_EXISTS,TODO: fix np.full and refactor
aaeieaiac,IntelPython/sdc,hpat/str_arr_ext.py,351d606ea0dbcfe2df5b56bac6a47a9586c557b8,1d5188214fa85a39f3f24748f2ac74aea60c2b4a,XXX assuming str list is not used anymore
aaeieaiag,IntelPython/sdc,hpat/hiframes_sort.py,5c21fab8166c435cbeb420d6c1b620a8a6d28c84,STILL_EXISTS,TODO: increate refcount?
aaeieaibe,IntelPython/sdc,hpat/tests/test_hiframes.py,12dc0226a5a0f3300f5d1bf2896ba306e664b05b,41aecd340f59225c5642fe05a1767b0f52cd13fe,TODO: better parallel sort test
aaeieaicb,IntelPython/sdc,hpat/hiframes_sort.py,e97c7435b3db254f3b54250d76e0410ea4b3dfcb,STILL_EXISTS,XXX: make sure function is not using old SortState
aaeieaicg,IntelPython/sdc,hpat/utils.py,71a56b1828313527da8d89233c6df6acf5f18a71,STILL_EXISTS,TODO: customize through @hpat.jit
aaeieaidd,IntelPython/sdc,hpat/hiframes_join.py,f5f6ffe36ec008da3006d4889dbeaeec4db7981c,STILL_EXISTS,TODO: refactor to use only tmp_offset
aaeieaifi,IntelPython/sdc,hpat/hiframes_join.py,a2a4f4f02158b325b9fcfa2010f3bcd6f6e5d227,STILL_EXISTS,TODO: corner case test
aaeieaigj,IntelPython/sdc,hpat/hiframes_join.py,a2a4f4f02158b325b9fcfa2010f3bcd6f6e5d227,327a836aba491a6cf92cc9a6294abd257d88539a,TODO: string copy
aaeieaihd,IntelPython/sdc,hpat/str_arr_ext.py,ca855241fc13a1b585462151a4098c4fd5d93303,STILL_EXISTS,XXX can't use this with overload_method
aaeieajbc,IntelPython/sdc,hpat/distributed.py,504828f30b112ef716163f81c789854b0a8575ad,STILL_EXISTS,TODO: rebalance strings?
aaeieajcb,IntelPython/sdc,hpat/str_arr_ext.py,f8e86793b996761413bffcc7ab3a947e360b26ea,STILL_EXISTS,TODO: put offset\/data in main structure since immutable
aaeieajdb,IntelPython/sdc,hpat/str_arr_ext.py,2c9b29680851526ddf5223914e9b86b14e42fa37,STILL_EXISTS,XXX: should these be exposed?
aaeieajdi,IntelPython/sdc,hpat/str_arr_ext.py,2c9b29680851526ddf5223914e9b86b14e42fa37,STILL_EXISTS,XXX alloc empty arrays for dtor to safely delete?
aaeieajfc,IntelPython/sdc,hpat/hiframes_aggregate.py,15a7b29b1a138396e165dab419341548bf43b519,STILL_EXISTS,TODO: init; update all redvars
aaeiebaad,IntelPython/sdc,hpat/hiframes_aggregate.py,15a7b29b1a138396e165dab419341548bf43b519,60bdb925cdd8937f9876520bf1837ca0f28716f8,# TODO: replace with functions
aaeiebadg,IntelPython/sdc,hpat/hiframes_aggregate.py,15a7b29b1a138396e165dab419341548bf43b519,60bdb925cdd8937f9876520bf1837ca0f28716f8,# needed due to alltoallv
aaeiebafa,IntelPython/sdc,hpat/hiframes_aggregate.py,15a7b29b1a138396e165dab419341548bf43b519,60bdb925cdd8937f9876520bf1837ca0f28716f8,# key is returned in parallel local agg phase (TODO: avoid if key is output already)
aaeiebaff,IntelPython/sdc,hpat/hiframes_aggregate.py,15a7b29b1a138396e165dab419341548bf43b519,60bdb925cdd8937f9876520bf1837ca0f28716f8,# TODO: non-int dict
aaeiebcgc,IntelPython/sdc,hpat/hiframes_aggregate.py,0a28422fe1d338393981878b2b77adb356364796,STILL_EXISTS,hack to return set with specified type
aaeiebchd,IntelPython/sdc,hpat/utils.py,e2f472f854e1bd63df0a530019bbf28f9b9844d4,STILL_EXISTS,TODO check for numeric value
aaeiebddb,IntelPython/sdc,hpat/hiframes_aggregate.py,5576ad3ad7942bd96fadc4108dbb1fca6b07033a,STILL_EXISTS,TODO: fix BaseContext.get_function() used in is_true()
aaeiebdea,IntelPython/sdc,hpat/hiframes_aggregate.py,5576ad3ad7942bd96fadc4108dbb1fca6b07033a,STILL_EXISTS,TODO: non-string pivot
aaeiebded,IntelPython/sdc,hpat/__init__.py,31ad689928d18baeececca7d907f043a033f9bda,STILL_EXISTS,put pivots in locals TODO: generalize numba.jit options
aaeiebdej,IntelPython/sdc,hpat/pd_series_ext.py,e030f5efcead5f9afd7a5cab8d6ce110295db0cf,STILL_EXISTS,XXX: unify Series\/Array as Array
aaeiebdfg,IntelPython/sdc,hpat/pd_series_ext.py,ed6cf251ef8ac0f7ef82d039fbf34a01c7108b5c,STILL_EXISTS,TODO: types other than Array and StringArray?
aaeiebdfh,IntelPython/sdc,hpat/pd_series_ext.py,ed6cf251ef8ac0f7ef82d039fbf34a01c7108b5c,STILL_EXISTS,TODO: other types?
aaeiebdgb,IntelPython/sdc,hpat/hiframes.py,2870928bfe203bdeed609949ac6c28bc3e4ac07d,38db657e65c339e605d2e5369ab3f02a4e1ba909,TODO: remove timestamp_series_type
aaeiebdgc,IntelPython/sdc,hpat/tests/test_series.py,0a53650ce705c5fbee249280575240c06b80dff5,STILL_EXISTS,TODO: check to make sure it is series type
aaeiebdgh,IntelPython/sdc,hpat/hiframes_api.py,8fdc3192227fe207b6f816a4d5b3b67d12100da0,a3c99071932d12c5ab75f9cbae0ebffbe26fe782,FIXME: last arg should be types.DType?
aaeiebdgi,IntelPython/sdc,hpat/hiframes_api.py,8fdc3192227fe207b6f816a4d5b3b67d12100da0,STILL_EXISTS,TODO: error handling like Numba callwrappers.py
aaeiebdhf,IntelPython/sdc,hpat/hiframes_api.py,8fdc3192227fe207b6f816a4d5b3b67d12100da0,a3c99071932d12c5ab75f9cbae0ebffbe26fe782,# TODO: error handling like Numba callwrappers.py
aaeiebdhi,IntelPython/sdc,hpat/hiframes_api.py,8fdc3192227fe207b6f816a4d5b3b67d12100da0,a3c99071932d12c5ab75f9cbae0ebffbe26fe782,XXX: refcount?
aaeiebdhj,IntelPython/sdc,hpat/hiframes_api.py,8fdc3192227fe207b6f816a4d5b3b67d12100da0,STILL_EXISTS,TODO: replace timestamp type
aaeiebeab,IntelPython/sdc,hpat/pd_series_ext.py,71fcb13935960023a21c98b799d7d3e56e18fc57,STILL_EXISTS,TODO: implement type inference instead of subtyping array since Pandas as of
aaeiebeai,IntelPython/sdc,hpat/hiframes_api.py,a31bcbb8b81c27900490d782a366546d58551e87,STILL_EXISTS,TODO: use infer_global to avoid lowering multiple versions?
aaeiebeba,IntelPython/sdc,hpat/hiframes_typed.py,9335496db528b8a23168fa6bc544f6443694622e,STILL_EXISTS,XXX: side effect: force update of call signatures
aaeiebebb,IntelPython/sdc,hpat/set_ext.py,bbe4e84fa65ce1581746884292c76d1bea40cadd,STILL_EXISTS,FIXME: overload fails in lowering sometimes!
aaeiebebg,IntelPython/sdc,hpat/hiframes_api.py,a84f9de5d03a3969003226f2388b9f96c7b6a32c,1e90d9b1087c3a84ad5ebe9fd8ef9e7aae6f190d,XXX: Boxed series variable types shouldn't be replaced in hiframes_typed
aaeiebebj,IntelPython/sdc,hpat/hiframes_api.py,9b687f55acc68457d45e13df00257d0ac4010457,STILL_EXISTS,XXX: use infer_global instead of overload; since overload fails if the same
aaeiebecb,IntelPython/sdc,hpat/hiframes_api.py,9b687f55acc68457d45e13df00257d0ac4010457,STILL_EXISTS,TODO: add other types
aaeiebecc,IntelPython/sdc,hpat/hiframes_api.py,9b687f55acc68457d45e13df00257d0ac4010457,STILL_EXISTS,TODO: replace Any with types
aaeiebech,IntelPython/sdc,hpat/hiframes_api.py,ac4ef61f2cc5b94da74418e7323caf14ab054f41,STILL_EXISTS,TODO: add other types
aaeiebedj,IntelPython/sdc,hpat/compiler.py,fd0102cbaf259715fcc5588eb4a169956cf25322,STILL_EXISTS,XXX update return type since it can be Series and trigger box_array
aaeiebeeb,IntelPython/sdc,hpat/pd_series_ext.py,a6c4c175ee5480c1a489beece71e912bc222cb3d,STILL_EXISTS,TODO: use ops logic from pandas\/core\/ops.py
aaeiebeje,IntelPython/sdc,hpat/pd_series_ext.py,a62dcacc582943649d237ae26a26d33ab85af4d3,STILL_EXISTS,TODO: create a separate DatetimeIndex type from Series
aaeiebfba,IntelPython/sdc,hpat/pd_series_ext.py,87c6bf2bd271140d2c5d4523e81aae6fa5e20e8e,STILL_EXISTS,same as types.Array; TODO: add Series?
aaeiebfbd,IntelPython/sdc,hpat/pd_series_ext.py,87c6bf2bd271140d2c5d4523e81aae6fa5e20e8e,STILL_EXISTS,TODO: fix timestamp
aaeiebfec,IntelPython/sdc,hpat/pd_series_ext.py,297d49ef0e4503236ef6ead39fb85ca9aee56233,STILL_EXISTS,TODO: add itemsize; strides; etc. when removed from Pandas
aaeiebfee,IntelPython/sdc,hpat/hiframes_typed.py,357c8c454195b1e43d30dec4b7a0928eff49fdc5,STILL_EXISTS,TODO: handle string arrays; etc.
aaeiebfff,IntelPython/sdc,hpat/pd_series_ext.py,9c737c45fd158fc9eb44290b1eeeef1fd3f10805,STILL_EXISTS,TODO: string array; dt_index
aaeiebffh,IntelPython/sdc,hpat/pd_series_ext.py,66e7a70eb4e41076d2d9984e9ca0375849204d42,STILL_EXISTS,TODO: strings; dt_index
aaeiebffi,IntelPython/sdc,hpat/tests/test_series.py,66e7a70eb4e41076d2d9984e9ca0375849204d42,98fbc69da7284e1260580a0a53008636d2c2f976,TODO: remove return after aliasing fix
aaeiebfgc,IntelPython/sdc,hpat/pd_series_ext.py,1e90d9b1087c3a84ad5ebe9fd8ef9e7aae6f190d,STILL_EXISTS,XXX: Boxed series variable types shouldn't be replaced in hiframes_typed
aaeiebfgj,IntelPython/sdc,hpat/pd_series_ext.py,1e90d9b1087c3a84ad5ebe9fd8ef9e7aae6f190d,STILL_EXISTS,TODO: change class name to Series in install_operations
aaeiebfhd,IntelPython/sdc,hpat/hiframes_typed.py,11b25bd1ba29b17681ff6201ba040bae4d3f2052,STILL_EXISTS,XXX: new_call_typ could be None for things like np.int32()
aaeiebfhg,IntelPython/sdc,hpat/hiframes_typed.py,c5475edcbc168607091c34c305011dcf11289275,STILL_EXISTS,fix types with undefined dtypes in empty_inferred; etc.
aaeiebfhi,IntelPython/sdc,hpat/hiframes_typed.py,c5475edcbc168607091c34c305011dcf11289275,STILL_EXISTS,TODO: fix List; Set
aaeiebgge,IntelPython/sdc,hpat/hiframes.py,3d1f0405827e78a1da56224864b88f86ed4ae637,STILL_EXISTS,TODO: add node defs for all new nodes
aaeiebgjf,IntelPython/sdc,hpat/parquet_pio.py,38db657e65c339e605d2e5369ab3f02a4e1ba909,STILL_EXISTS,TODO: fix alloc
aaeiebgjh,IntelPython/sdc,hpat/hiframes.py,903000d4ed684fca36d1133b7f8f1de69e2b817d,3dc3041a44f560d34dcff4a7b94c32ed21fba8f6,XXX: is this necessary?
aaeiebhag,IntelPython/sdc,hpat/pd_timestamp_ext.py,afefdf226765fbd1e47afc34013b4fa264476134,STILL_EXISTS,TODO: move to utils or Numba
aaeiebhbg,IntelPython/sdc,hpat/hiframes_api.py,49f4355f768a7b99c253a85d89120d262583a787,STILL_EXISTS,TODO: datetime.date; DatetimeIndex?
aaeiebhbi,IntelPython/sdc,hpat/hiframes_typed.py,4f4bd5a968d1d8c318a077f8274b8210a6b739ce,STILL_EXISTS,TODO: add definitions?
aaeiebhbj,IntelPython/sdc,hpat/hiframes_typed.py,4f4bd5a968d1d8c318a077f8274b8210a6b739ce,STILL_EXISTS,TODO: handle filter str arr; etc.
aaeiebhci,IntelPython/sdc,hpat/hiframes_api.py,5abb1170edc3b81ff926a604beb67ace796c1126,STILL_EXISTS,XXX: using .values to check date type since DatetimeIndex returns
aaeiebhde,IntelPython/sdc,hpat/hiframes_typed.py,e57a166282431fc1d02f0a1f84dc40046db081ad,7fb7eee5e93cfb713bda3511b48309d6f99e2c30,TODO: handle skipna; min_count arguments
aaeiebhdf,IntelPython/sdc,hpat/pd_series_ext.py,e57a166282431fc1d02f0a1f84dc40046db081ad,STILL_EXISTS,XXX: UnBoxedSeriesType only used in unboxing
aaeiebhgd,IntelPython/sdc,hpat/pd_series_ext.py,99bdd17104e06ee86de8c2afb27146f6b9e6b257,STILL_EXISTS,TODO: fix quantile output type if not float64
aaeiebhgh,IntelPython/sdc,hpat/parquet_pio.py,c3af3ac3a6d0612aadd9faf8622765fe4b086292,STILL_EXISTS,float types (TODO: float16?)
aaeiebhha,IntelPython/sdc,hpat/parquet_pio.py,c3af3ac3a6d0612aadd9faf8622765fe4b086292,STILL_EXISTS,time (TODO: time32; time64; ...)
aaeiebhij,IntelPython/sdc,hpat/hiframes_typed.py,d1428624e72c5deed9acfe490660be99c9d7fa00,STILL_EXISTS,TODO: pandas returns dataframe; maybe return namedtuple instread of
aaeiebhjb,IntelPython/sdc,hpat/hiframes_typed.py,d1428624e72c5deed9acfe490660be99c9d7fa00,STILL_EXISTS,TODO: fix string formatting to match python\/pandas
aaeiebhjc,IntelPython/sdc,hpat/pd_series_ext.py,d1428624e72c5deed9acfe490660be99c9d7fa00,STILL_EXISTS,TODO: return namedtuple or labeled Series
aaeiebhji,IntelPython/sdc,hpat/hiframes_typed.py,8f7206abfb7a3fcfc93eb453f3c407218400ee8a,STILL_EXISTS,TODO: handle string; etc.
aaeiebiaa,IntelPython/sdc,hpat/hiframes_typed.py,2343c1b928fb33e05309dddc9c8d73e1f6150dca,d338590c2bcba7a00083656b751d9393abea0386,TODO: alloc_shift
aaeiebiai,IntelPython/sdc,hpat/hiframes_typed.py,37a6ddba1fc68e4ab770f6d1d8238287e170827a,STILL_EXISTS,TODO: support default period argument
aaeiebiaj,IntelPython/sdc,hpat/hiframes_typed.py,37a6ddba1fc68e4ab770f6d1d8238287e170827a,d338590c2bcba7a00083656b751d9393abea0386,TODO: alloc_shift
aaeiebieg,IntelPython/sdc,hpat/hiframes_typed.py,6b53d56138bceb4995b67156d9cea2cb3fb1a78d,STILL_EXISTS,XXX: remove rolling setup call; assuming still available in definitions
aaeiebiib,IntelPython/sdc,hpat/hiframes.py,abefca082b18fd049f7420cd58d50328ae1b64d4,STILL_EXISTS,TODO: handle case where type has to be converted due to int64 NaNs
aaeiebiic,IntelPython/sdc,hpat/hiframes_typed.py,abefca082b18fd049f7420cd58d50328ae1b64d4,STILL_EXISTS,XXX: can't handle int64 to float64 nans properly since df column
aaeiebijg,IntelPython/sdc,hpat/hiframes_typed.py,b5ea895f01f363261442c4d955360a663b6e4544,STILL_EXISTS,TODO: handle non numpy alloc types like string array
aaeiebjad,IntelPython/sdc,hpat/pd_series_ext.py,6b54d97c9b59eeeeccabf5f7bc6323a89ca78db8,STILL_EXISTS,TODO: handle apply differences: extra args; np ufuncs etc.
aaeiebjba,IntelPython/sdc,hpat/hiframes_typed.py,3a9adba12832338ce42fb4c84a18ae9bf0022059,STILL_EXISTS,TODO: handle series-specific cases for this funcs
aaeiebjbb,IntelPython/sdc,hpat/hiframes_typed.py,ee819950e48515de1a217978be30bf64056db469,4c42f4b12794ae48113f4d877676ecede8ec26e8,TODO: refactor to a new func
aaeiebjcd,IntelPython/sdc,hpat/hiframes_typed.py,d4b1b493403cc936a040acebee223ad122c30b7c,STILL_EXISTS,TODO: timedelta
aaeiebjcg,IntelPython/sdc,hpat/hiframes_typed.py,26547cd00dc792f72b1f269b9a93408c44c1d316,STILL_EXISTS,TODO: check lens
aaeiebjch,IntelPython/sdc,hpat/hiframes_typed.py,26547cd00dc792f72b1f269b9a93408c44c1d316,STILL_EXISTS,TODO: check aligned nans; (S1.notna() != S2.notna()).any()
aaeiebjcj,IntelPython/sdc,hpat/pd_series_ext.py,26547cd00dc792f72b1f269b9a93408c44c1d316,e1135b36b9736189b58288da4449933cc4d9ffd5,TODO: complex numbers return complex
aaeiebjdb,IntelPython/sdc,hpat/hiframes_typed.py,0b2618412c87d2f4d6b76ab9086861c512771894,STILL_EXISTS,TODO: check lens
aaeiebjdc,IntelPython/sdc,hpat/hiframes_typed.py,0b2618412c87d2f4d6b76ab9086861c512771894,STILL_EXISTS,TODO: check aligned nans; (S1.notna() != S2.notna()).any()
aaeiebjdd,IntelPython/sdc,hpat/hiframes_typed.py,0b2618412c87d2f4d6b76ab9086861c512771894,STILL_EXISTS,TODO: np.clip
aaeiebjde,IntelPython/sdc,hpat/hiframes_typed.py,0b2618412c87d2f4d6b76ab9086861c512771894,STILL_EXISTS,TODO: np.true_divide?
aaeiebjdg,IntelPython/sdc,hpat/pd_series_ext.py,0b2618412c87d2f4d6b76ab9086861c512771894,e1135b36b9736189b58288da4449933cc4d9ffd5,TODO: complex numbers return complex
aaeiebjdh,IntelPython/sdc,hpat/hiframes_typed.py,6cf97d0f565202a286e9292f82e6ca58552e2a4e,STILL_EXISTS,TODO: use online algorithm; e.g. StatFunctions.scala
aaeiebjed,IntelPython/sdc,hpat/distributed_analysis.py,40208590f327b3eaf63d6d240ec31ccb31794f2a,60a1b93b8852aff263bdf4ff66cf2a4c869a6a06,FIXME: handle Distribution.Thread and Disribution.REP as equivalent
aaeiebjfb,IntelPython/sdc,hpat/ml/d4p.py,40208590f327b3eaf63d6d240ec31ccb31794f2a,STILL_EXISTS,The following information is needed:
aaeiebjia,IntelPython/sdc,hpat/hiframes.py,ae5d53b1ca40909d034f41e55707a859d578a3ad,STILL_EXISTS,TODO: handle list(series); set(series); etc.
aaeiebjid,IntelPython/sdc,hpat/pd_series_ext.py,072d4fdef3cc431fedee2b5b78a1bfbdeb8af1d6,STILL_EXISTS,TODO: ignore_index
aaeiebjie,IntelPython/sdc,hpat/pd_series_ext.py,072d4fdef3cc431fedee2b5b78a1bfbdeb8af1d6,STILL_EXISTS,TODO: list
aaeiebjjg,IntelPython/sdc,hpat/hiframes_api.py,09971d619a003be6124bfd88385aa20a256921c0,STILL_EXISTS,XXX: returns a dummy type that should be fixed in hiframes_typed
aaeiecaac,IntelPython/sdc,hpat/hiframes_api.py,edba469f168df0c44c709d8fa731f763d4a03c40,STILL_EXISTS,TODO: handle NA as 1st value
aaeiecaaf,IntelPython/sdc,hpat/str_arr_ext.py,15944d2ed05df1579af240a3e49ef3169362e046,STILL_EXISTS,i\/8; XXX: lshr since always positive
aaeiecabd,IntelPython/sdc,hpat/str_arr_ext.py,9a708b6d4f90e356d8bb097884ffbc1afa677ff3,STILL_EXISTS,XXX: C equivalent in _str_ext.cpp
aaeiecabe,IntelPython/sdc,hpat/str_arr_ext.py,809d9c2cdce8f993336d179e595c63d7eb8eb2a0,STILL_EXISTS,TODO: set null_bitmap
aaeiecabf,IntelPython/sdc,hpat/xenon_ext.py,809d9c2cdce8f993336d179e595c63d7eb8eb2a0,STILL_EXISTS,TODO: null_bitmap
aaeiecabh,IntelPython/sdc,hpat/hiframes_typed.py,ccdee10787411e3277067a7bc8423102474d5fda,c3f4e2e59a925c6e9b23320f826d42a9945bb851,TODO: handle other types
aaeiecacb,IntelPython/sdc,hpat/hiframes_typed.py,7d85dc29dd371fc2078309fae19a7e93bf156570,STILL_EXISTS,TODO: make sure this is fused and optimized properly
aaeiecacf,IntelPython/sdc,hpat/hiframes_api.py,c3f4e2e59a925c6e9b23320f826d42a9945bb851,3a882b602070ee1d17214b67f25fa1fc101f59ff,TODO: extend to other types
aaeiecacg,IntelPython/sdc,hpat/hiframes_api.py,c3f4e2e59a925c6e9b23320f826d42a9945bb851,STILL_EXISTS,XXX integers don't have nans; extend to boolean
aaeiecacj,IntelPython/sdc,hpat/str_arr_ext.py,3e7218e38542555fe66f546d17a3fcac92bdee69,STILL_EXISTS,XXX: setitem works only if value is same size as the previous value
aaeiecadi,IntelPython/sdc,hpat/hiframes_typed.py,b174d5aef6bf5773455b2e2d1185761bd403e67a,STILL_EXISTS,TODO: handle string array reflection
aaeiecaed,IntelPython/sdc,hpat/tests/test_series.py,b174d5aef6bf5773455b2e2d1185761bd403e67a,STILL_EXISTS,TODO: handle string array reflection
aaeiecafa,IntelPython/sdc,hpat/hiframes_typed.py,94587cf87dc2ef90965880f9b2e16b152dcfa607,STILL_EXISTS,TODO
aaeiecafb,IntelPython/sdc,hpat/hiframes_typed.py,94587cf87dc2ef90965880f9b2e16b152dcfa607,STILL_EXISTS,integer case; TODO: bool; date etc.
aaeiecafc,IntelPython/sdc,hpat/hiframes_typed.py,94587cf87dc2ef90965880f9b2e16b152dcfa607,STILL_EXISTS,TODO: more efficient null counting
aaeiecafi,IntelPython/sdc,hpat/str_arr_ext.py,94587cf87dc2ef90965880f9b2e16b152dcfa607,STILL_EXISTS,XXX: assuming last offset is already set by allocate_string_array
aaeiecaha,IntelPython/sdc,hpat/hiframes_typed.py,2c1b4e96ad67683b0f3b6f94a997e756011ffe5b,STILL_EXISTS,integer case; TODO: bool; date etc.
aaeiecahb,IntelPython/sdc,hpat/hiframes_typed.py,2c1b4e96ad67683b0f3b6f94a997e756011ffe5b,STILL_EXISTS,TODO
aaeieccfb,IntelPython/sdc,hpat/hiframes.py,60ea499dbb5f63d3c3ec9cb886a6d1fd45978e56,STILL_EXISTS,TODO: handle values and aggfunc options
aaeieccfd,IntelPython/sdc,hpat/hiframes.py,6fed50c94dbf459b3509098528e7ff5f266ec267,STILL_EXISTS,TODO: make out_key_var an index column
aaeieccff,IntelPython/sdc,hpat/hiframes_aggregate.py,23a0b425ba9be2144238f6582ffeac0068433b83,STILL_EXISTS,XXX assuming shape\/size nodes are right after arg
aaeieccfh,IntelPython/sdc,hpat/hiframes_aggregate.py,09a9e3d7e51f4dfae00fef83581b5e7bc6e060e9,STILL_EXISTS,TODO: handle pivot_table\/crosstab with return key
aaeieccfj,IntelPython/sdc,hpat/hiframes_aggregate.py,09a9e3d7e51f4dfae00fef83581b5e7bc6e060e9,STILL_EXISTS,TODO: crosstab with values arg
aaeieccgi,IntelPython/sdc,hpat/pd_series_ext.py,b64230d2fa5560b9f4aa0722dd4066e830012d6f,STILL_EXISTS,TODO: handle other types like datetime etc.
aaeiecchc,IntelPython/sdc,hpat/hiframes_api.py,96aa769d84a78505a938c62c7f1ad72321336b68,STILL_EXISTS,TODO: handle NAs
aaeiecchf,IntelPython/sdc,hpat/hiframes_typed.py,96aa769d84a78505a938c62c7f1ad72321336b68,STILL_EXISTS,TODO: support default n=5 argument
aaeieccia,IntelPython/sdc,hpat/hiframes_api.py,3aa7cce5e83f69110ae09f7abb47318ea156daf1,STILL_EXISTS,TODO: support cases where k is not too small
aaeieccii,IntelPython/sdc,hpat/utils.py,3bb51c9743b9715f80f67dbc439422edd6533abb,STILL_EXISTS,XXX: _hpat_common.h
aaeieccij,IntelPython/sdc,hpat/hiframes_api.py,19ef055a43f0854790c97b066aaf92d429195c2c,STILL_EXISTS,TODO: handle len(res) < k case
aaeieccjb,IntelPython/sdc,hpat/hiframes_api.py,9d60d35fcf69e9e330a013903270fa81eb2ea56f,STILL_EXISTS,TODO: check return types; e.g. float32 -> float32
aaeieccjh,IntelPython/sdc,hpat/hiframes_typed.py,dcbf50a9c57a30505348772fc2553b245b6487e4,STILL_EXISTS,TODO: handle NAs in argmin\/argmax
aaeiecdaf,IntelPython/sdc,hpat/hiframes_typed.py,33d87149b611f70ff1193e311337d966e0ad92c9,STILL_EXISTS,TODO: kws
aaeiecdbf,IntelPython/sdc,hpat/tests/test_hiframes.py,3a882b602070ee1d17214b67f25fa1fc101f59ff,STILL_EXISTS,TODO: test without file
aaeiecdcd,IntelPython/sdc,hpat/hiframes_api.py,3e4202b46a53c0566653bc0d703f0c631103e065,STILL_EXISTS,TODO: extend to other types like datetime?
aaeiecddh,IntelPython/sdc,hpat/hiframes.py,6ca0effa83363966509495ad83b93ee1b2a02032,e74669f5fc21267dd414f4fc468bc63a54e23366,TODO: index arg?
aaeiecded,IntelPython/sdc,hpat/pio.py,e74669f5fc21267dd414f4fc468bc63a54e23366,STILL_EXISTS,TODO: index arg?
aaeiecdeh,IntelPython/sdc,hpat/pio.py,e74669f5fc21267dd414f4fc468bc63a54e23366,STILL_EXISTS,TODO: static_getitem has index_var for sure?
aaeiecdei,IntelPython/sdc,hpat/pio.py,e74669f5fc21267dd414f4fc468bc63a54e23366,STILL_EXISTS,make sure it's slice; TODO: support non-slice like integer
aaeiecdfb,IntelPython/sdc,hpat/distributed.py,c8b5ae8122e6da95b64492e8b5e4e5aec5ba0265,STILL_EXISTS,TODO: handle control flow
aaeiecdfc,IntelPython/sdc,hpat/hiframes_typed.py,14cd92d9456a633b2878564a91c5beb5ef0fd450,STILL_EXISTS,TODO: remove after support arr.shape in parallel
aaeiecdfg,IntelPython/sdc,hpat/tests/test_io.py,0b58cc0c2b343cbb1ed4a4c137e8861ee57902e0,b7a189e29bf970d22e7cdc3647bff21940586923,TODO: close groups automatically
aaeieceaj,IntelPython/sdc,hpat/hiframes_typed.py,102e68e40e4f83495f325020f5e42faaf4d43848,STILL_EXISTS,TODO: handle more types
aaeieceba,IntelPython/sdc,hpat/hiframes.py,62dd0cbfe1d05577208b5a6d517da8c325e2b37b,STILL_EXISTS,TODO: remove this
aaeiecebb,IntelPython/sdc,hpat/hiframes_api.py,62dd0cbfe1d05577208b5a6d517da8c325e2b37b,STILL_EXISTS,XXX: used in agg func output to avoid mutating filter; agg; join; etc.
aaeiecebc,IntelPython/sdc,hpat/hiframes_api.py,62dd0cbfe1d05577208b5a6d517da8c325e2b37b,STILL_EXISTS,TODO: fix type inferrer and remove this
aaeiecebe,IntelPython/sdc,hpat/str_arr_ext.py,d90b1963ee31f852cb4f7a05b5d5020f949a2b9d,STILL_EXISTS,TODO: double check refcounting here
aaeiececc,IntelPython/sdc,hpat/distributed.py,62ccf94eb2bfb1077e915ca1227551d91fbafd85,1dba7f9a89e7e227233d5518837403b2325819cd,TODO: generalize
aaeiececd,IntelPython/sdc,hpat/distributed_analysis.py,62ccf94eb2bfb1077e915ca1227551d91fbafd85,STILL_EXISTS,TODO: make sure assert_equiv is not generated unnecessarily
aaeiecece,IntelPython/sdc,hpat/distributed_analysis.py,62ccf94eb2bfb1077e915ca1227551d91fbafd85,STILL_EXISTS,TODO: fix assert_equiv for np.stack from df.value
aaeiececg,IntelPython/sdc,hpat/distributed_analysis.py,62ccf94eb2bfb1077e915ca1227551d91fbafd85,STILL_EXISTS,TODO: support kws
aaeieceda,IntelPython/sdc,hpat/hiframes_join.py,fb3d1820f989153433fa42a05a4ec856ed0daf37,STILL_EXISTS,TODO: can columns of the same input table have diffrent dists?
aaeieceea,IntelPython/sdc,hpat/distributed.py,1c9b76bb6b3f64d9bbb3a435ea38accaf80a577c,STILL_EXISTS,TODO: generalize
aaeieceeh,IntelPython/sdc,hpat/hiframes.py,fa5c59152cca36af49de4751b9d56fe26225aed5,STILL_EXISTS,TODO: support aggregation functions sum; count; etc.
aaeieceei,IntelPython/sdc,hpat/hiframes.py,fa5c59152cca36af49de4751b9d56fe26225aed5,STILL_EXISTS,find selected output columns
aaeiecefa,IntelPython/sdc,hpat/hiframes.py,fa5c59152cca36af49de4751b9d56fe26225aed5,STILL_EXISTS,TODO: remove index col for offset case
aaeiecefc,IntelPython/sdc,hpat/hiframes.py,fa5c59152cca36af49de4751b9d56fe26225aed5,STILL_EXISTS,TODO: add datetime index for offset case
aaeiecega,IntelPython/sdc,hpat/hiframes_rolling.py,fa5c59152cca36af49de4751b9d56fe26225aed5,86def7aa3c107991819064acc43179c13bed3b16,TODO: fix center
aaeiecege,IntelPython/sdc,hpat/hiframes_rolling.py,bb6ac5d5de69ee9d65951e79e4827139efced440,STILL_EXISTS,TODO: support minp arg end_range etc.
aaeiecehd,IntelPython/sdc,hpat/hiframes_rolling.py,992785e398ef268cb71aced1a399da39e42a3960,STILL_EXISTS,TODO: center
aaeieceif,IntelPython/sdc,hpat/hiframes_rolling.py,102aeae5dc75d48a405e1b8c02e54c325f897654,STILL_EXISTS,TODO: support minp arg end_range etc.
aaeieceij,IntelPython/sdc,hpat/hiframes_rolling.py,102aeae5dc75d48a405e1b8c02e54c325f897654,STILL_EXISTS,TODO: handle 1D_Var or other cases where data is actually large but
aaeiecejb,IntelPython/sdc,hpat/hiframes_rolling.py,102aeae5dc75d48a405e1b8c02e54c325f897654,STILL_EXISTS,TODO: avoid reduce for obvious cases like no center and large 1D_Block
aaeiecfad,IntelPython/sdc,hpat/hiframes_typed.py,6d1e1489d6ae141872451d5a4a8d2c17ce7f54ee,STILL_EXISTS,TODO
aaeiecfaj,IntelPython/sdc,hpat/hiframes_rolling.py,3efc1ec3acdca57643ed4c129a3658f42e7104c0,STILL_EXISTS,TODO
aaeiecfbb,IntelPython/sdc,hpat/hiframes_typed.py,3efc1ec3acdca57643ed4c129a3658f42e7104c0,STILL_EXISTS,TODO: more error checking on the kernel to make sure it doesn't
aaeiecfbg,IntelPython/sdc,hpat/hiframes_rolling.py,8832d206c703dead9bd8f2f7408c30599862730c,STILL_EXISTS,TODO: handle count and minp
aaeiecfdg,IntelPython/sdc,hpat/hiframes_rolling.py,3d0dc56cce2e330f783ebb3ac843a3e58c9e13f3,STILL_EXISTS,TODO: refactor small data functions
aaeiecffb,IntelPython/sdc,hpat/hiframes_rolling.py,392f8033a9001df9c32d684d9b0f70556fd1708a,STILL_EXISTS,TODO: combine add\/remove similar to pandas?
aaeiecffc,IntelPython/sdc,hpat/hiframes_rolling.py,392f8033a9001df9c32d684d9b0f70556fd1708a,STILL_EXISTS,TODO: make argument
aaeiecjag,IntelPython/sdc,hpat/hiframes_rolling.py,125d172a5dcc592a987d67c7f0f6325196480218,STILL_EXISTS,TODO: support dynamic conversion
aaeiecjah,IntelPython/sdc,hpat/hiframes_rolling.py,125d172a5dcc592a987d67c7f0f6325196480218,STILL_EXISTS,TODO: support other offsets types (time delta; etc.)
aaeiecjbi,IntelPython/sdc,hpat/hiframes_rolling.py,e51a6edf9b613a6ffadafe6770dd93cb6e14bd11,STILL_EXISTS,TODO: support minp arg end_range etc.
aaeiecjbj,IntelPython/sdc,hpat/hiframes_rolling.py,e51a6edf9b613a6ffadafe6770dd93cb6e14bd11,STILL_EXISTS,Pandas is right closed by default; TODO: extend to arg
aaeiecjcd,IntelPython/sdc,hpat/hiframes_rolling.py,e51a6edf9b613a6ffadafe6770dd93cb6e14bd11,STILL_EXISTS,XXX pandas inits to -1 but doesn't seem required?
aaeiecjdf,IntelPython/sdc,hpat/hiframes_api.py,d92e7675eea0cce1d24471ab5fdefd9326d9b04b,STILL_EXISTS,TODO: is incref required?
aaeiecjeb,IntelPython/sdc,hpat/hiframes_rolling.py,d25f3aa600b5eb6cc41a86745413cbb8fe16926f,STILL_EXISTS,TODO
aaeiecjec,IntelPython/sdc,hpat/hiframes_rolling.py,d25f3aa600b5eb6cc41a86745413cbb8fe16926f,STILL_EXISTS,TODO: handle count and minp
aaeiecjee,IntelPython/sdc,hpat/hiframes_typed.py,d25f3aa600b5eb6cc41a86745413cbb8fe16926f,2d8fa92f73314569404b2108668c922cb5e45021,TODO: automatically handle lambdas in Numba
aaeiecjga,IntelPython/sdc,hpat/hiframes_rolling.py,b40ad465f0e1f1f7d788e80a82c09f5a407807cf,STILL_EXISTS,TODO: support minp arg end_range etc.
aaeiecjgb,IntelPython/sdc,hpat/hiframes_rolling.py,b40ad465f0e1f1f7d788e80a82c09f5a407807cf,STILL_EXISTS,Pandas is right closed by default; TODO: extend to support arg
aaeiecjhd,IntelPython/sdc,hpat/hiframes_rolling.py,b40ad465f0e1f1f7d788e80a82c09f5a407807cf,STILL_EXISTS,TODO
aaeiecjie,IntelPython/sdc,hpat/hiframes_typed.py,33bf577d6e926bcc1dbf66ee51f322fbaa2e8921,STILL_EXISTS,TODO: automatically handle lambdas in Numba
aaeiecjii,IntelPython/sdc,hpat/hiframes_typed.py,33bf577d6e926bcc1dbf66ee51f322fbaa2e8921,STILL_EXISTS,TODO: check for other types
aaeiecjja,IntelPython/sdc,hpat/tests/test_rolling.py,2e5cb97e5f3af47aa1234570263ca1e80eaaa58d,STILL_EXISTS,XXX: Pandas returns time = [np.nan] for size==1 for some reason
aaeiecjjc,IntelPython/sdc,hpat/hiframes_rolling.py,521513603bc09e04a2e41ed6d91bcfb20c1d415f,STILL_EXISTS,Pandas is right closed by default; TODO: extend to support arg
aaeiecjjg,IntelPython/sdc,hpat/tests/test_rolling.py,521513603bc09e04a2e41ed6d91bcfb20c1d415f,STILL_EXISTS,XXX: Pandas returns time = [np.nan] for size==1 for some reason
aaeiedaad,IntelPython/sdc,hpat/hiframes_rolling.py,f36bf03b4dc3f7405c990c6f6b7fa0c983d262fa,STILL_EXISTS,XXX: pandas uses minp=0 for fixed window count but minp=1 for variable window
aaeiedaaj,IntelPython/sdc,hpat/hiframes_rolling.py,4a3334e4ed2127576e8116c9cc9786f13d24c25d,STILL_EXISTS,TODO: implement linear support similar to others
aaeiedaba,IntelPython/sdc,hpat/hiframes_rolling.py,4a3334e4ed2127576e8116c9cc9786f13d24c25d,STILL_EXISTS,TODO: linear support
aaeiedabc,IntelPython/sdc,hpat/tests/test_rolling.py,4a3334e4ed2127576e8116c9cc9786f13d24c25d,STILL_EXISTS,XXX: skipping min\/max for this test since the behavior of Pandas
aaeiedabh,IntelPython/sdc,hpat/hiframes_typed.py,05ece18ac422d84c4ff6ac8a5590798565a38bfc,STILL_EXISTS,TODO: variable window
aaeiedacj,IntelPython/sdc,hpat/hiframes.py,ffd198eb22dfbe8a2dbb395cb468aaff7593f014,STILL_EXISTS,XXX pandas only accepts variable window cov\/corr
aaeiedadb,IntelPython/sdc,hpat/hiframes.py,ffd198eb22dfbe8a2dbb395cb468aaff7593f014,STILL_EXISTS,TODO: support variable window rolling cov\/corr which is only
aaeiedadd,IntelPython/sdc,hpat/hiframes.py,c08d7064ff2cbae812cec468d6a63f84f5ec4d5e,STILL_EXISTS,df on df cov\/corr returns common columns only (without
aaeiedadf,IntelPython/sdc,hpat/hiframes.py,c08d7064ff2cbae812cec468d6a63f84f5ec4d5e,STILL_EXISTS,TODO: support pairwise arg
aaeiedadg,IntelPython/sdc,hpat/hiframes.py,c08d7064ff2cbae812cec468d6a63f84f5ec4d5e,STILL_EXISTS,Pandas makes non-common columns NaNs
aaeiedadh,IntelPython/sdc,hpat/hiframes.py,c08d7064ff2cbae812cec468d6a63f84f5ec4d5e,STILL_EXISTS,create NaN columns for cov\/corr case
aaeiedaee,IntelPython/sdc,hpat/hiframes.py,d54d024e74081a9daa891e1c31e9b9e4d048251a,STILL_EXISTS,line: res_columns = arg1.columns.union(arg2.columns)
aaeiedaef,IntelPython/sdc,hpat/hiframes.py,4be901301ee3f77885a0c9a70f86e9a4db1c86d0,STILL_EXISTS,TODO: is topo_order necessary?
aaeiedaeh,IntelPython/sdc,hpat/hiframes.py,e73f17d9a1974aa91bfedfe016f9f04ce4044101,STILL_EXISTS,TODO: rename variables; fix scope\/loc
aaeiedafi,IntelPython/sdc,hpat/hiframes.py,05a88b4d180cea955d6b4dd8aa027c831fe2a371,STILL_EXISTS,TODO: fix scope\/loc
aaeiedaij,IntelPython/sdc,hpat/hiframes_typed.py,ceec70d0cd16518b43edc74cfd9232aff8ba079d,7fb7eee5e93cfb713bda3511b48309d6f99e2c30,TODO: handle skipna; min_count arguments
aaeiedbbj,IntelPython/sdc,hpat/hiframes_aggregate.py,9598b0d630ef89ee6148cdcc696a0f662f315eb2,STILL_EXISTS,XXX update tp_vars in copy propagate etc.?
aaeiedbcf,IntelPython/sdc,hpat/hiframes_typed.py,e3f74fa0f58eec8821e823501aa9818a4498a884,STILL_EXISTS,TODO: handle skipna; min_count arguments
aaeiedbdg,IntelPython/sdc,hpat/hiframes_typed.py,032f01e673688e2d733ea9d18514be34092b020c,STILL_EXISTS,TODO: this has to be more generic to support all combinations.
aaeiedbee,IntelPython/sdc,hpat/hiframes_aggregate.py,8f0fe19759141d888c05ad740c6fddcd3013f7be,STILL_EXISTS,TODO: Pandas formula is better or Welford?
aaeiedbeh,IntelPython/sdc,hpat/hiframes_aggregate.py,8f0fe19759141d888c05ad740c6fddcd3013f7be,STILL_EXISTS,XXX: only var supported for now
aaeiedbei,IntelPython/sdc,hpat/hiframes_aggregate.py,8f0fe19759141d888c05ad740c6fddcd3013f7be,STILL_EXISTS,TODO: support general functions
aaeiedbej,IntelPython/sdc,hpat/hiframes_aggregate.py,d8c900d33853727bfb116ce5970a5b23f5a807c8,STILL_EXISTS,TODO: avoid code duplication
aaeiedbfb,IntelPython/sdc,hpat/hiframes_aggregate.py,d8c900d33853727bfb116ce5970a5b23f5a807c8,STILL_EXISTS,TODO: Pandas formula is better or Welford?
aaeiedbhb,IntelPython/sdc,hpat/hiframes.py,8af7a50de5dbf0ffbe76074d61c346071d6d217f,STILL_EXISTS,TODO: setitem
aaeiedbhf,IntelPython/sdc,hpat/hiframes.py,8af7a50de5dbf0ffbe76074d61c346071d6d217f,STILL_EXISTS,XXX assuming the order of the dictionary is the same as Pandas
aaeiedbhg,IntelPython/sdc,hpat/hiframes.py,8af7a50de5dbf0ffbe76074d61c346071d6d217f,STILL_EXISTS,TODO: check dictionary order
aaeiedbic,IntelPython/sdc,hpat/hiframes_aggregate.py,82215ab111c336bc0da4f62b17b20a0d0727557c,STILL_EXISTS,TODO: support type-specific funcs e.g. for dt64
aaeiedbid,IntelPython/sdc,hpat/hiframes.py,4613c8088a9a3f66aa132bbe1def370788510514,STILL_EXISTS,TODO: support their differences
aaeiedbie,IntelPython/sdc,hpat/hiframes.py,4613c8088a9a3f66aa132bbe1def370788510514,STILL_EXISTS,TODO: check index for non-integer
aaeiedbif,IntelPython/sdc,hpat/hiframes.py,4613c8088a9a3f66aa132bbe1def370788510514,STILL_EXISTS,TODO: support constant integer (return namedtuple)
aaeiedbig,IntelPython/sdc,hpat/hiframes.py,4613c8088a9a3f66aa132bbe1def370788510514,STILL_EXISTS,output df1 has same columns as df; create new vars
aaeiedbje,IntelPython/sdc,hpat/hiframes_typed.py,392e5ba06f6ed405f21f4f0aafbd09e5c87440d9,a66bb869888a1bae1f1347457930c2c447c97837,XXX df isin is different than Series.isin; df.isin considers
aaeiedbjg,IntelPython/sdc,hpat/hiframes_typed.py,392e5ba06f6ed405f21f4f0aafbd09e5c87440d9,a66bb869888a1bae1f1347457930c2c447c97837,TODO: support strings and other types
aaeiedbjj,IntelPython/sdc,hpat/hiframes.py,79713f00d8c041e6eb8ee68d71cd5a8dce4edada,STILL_EXISTS,TODO: handle passed in dict case (pass colname to func?)
aaeiedcad,IntelPython/sdc,hpat/hiframes.py,40996c56e336a4bcdf117ad6aa582fc60838b025,STILL_EXISTS,TODO: check for series\/dict\/list input
aaeiedcae,IntelPython/sdc,hpat/hiframes.py,40996c56e336a4bcdf117ad6aa582fc60838b025,STILL_EXISTS,TODO: enforce ignore_index=True?
aaeiedcai,IntelPython/sdc,hpat/hiframes.py,a795592b6b58526bcdd7dde3bcb998947d886bed,STILL_EXISTS,TODO: verify how Pandas sorts column names
aaeiedcba,IntelPython/sdc,hpat/hiframes.py,a795592b6b58526bcdd7dde3bcb998947d886bed,STILL_EXISTS,TODO: support non-numericals like string
aaeiedcbc,IntelPython/sdc,hpat/hiframes.py,a795592b6b58526bcdd7dde3bcb998947d886bed,STILL_EXISTS,get input columns
aaeiedcca,IntelPython/sdc,hpat/tests/test_hiframes.py,4b4603c389c28dd498ff3bbdbd337f204d4f9a21,7c9e77f714c592e21237b2d59775c0ddb7ad4b4a,TODO: fix error when no df is returned
aaeiedcch,IntelPython/sdc,hpat/tests/test_rolling.py,56a620eaab22953f11086b5bb1116b1212003aa5,STILL_EXISTS,TODO: this crashes on Travis (3 process config) with size 1
aaeiedcei,IntelPython/sdc,hpat/hiframes_aggregate.py,5b5e0d3fc3a8f12847de26f9d6928fc749c339ce,STILL_EXISTS,TODO: support other reductions
aaeiedcgb,IntelPython/sdc,hpat/hiframes_join.py,2e5c71796b0964bf7c455873468909825e9d1d67,STILL_EXISTS,asof key is already sorted; TODO: add error checking
aaeiedcgi,IntelPython/sdc,hpat/hiframes_join.py,2e5c71796b0964bf7c455873468909825e9d1d67,STILL_EXISTS,TODO: copy_tup
aaeiedcgj,IntelPython/sdc,hpat/hiframes_join.py,2e5c71796b0964bf7c455873468909825e9d1d67,STILL_EXISTS,TODO: set NaN
aaeiedche,IntelPython/sdc,hpat/hiframes_join.py,207457f484466f3bbe881fb97530224943e5ac95,STILL_EXISTS,one extra element in case first value is needed for start of boundary
aaeiedchg,IntelPython/sdc,hpat/hiframes_join.py,207457f484466f3bbe881fb97530224943e5ac95,STILL_EXISTS,TODO: see if next processor provides the value
aaeiedchh,IntelPython/sdc,hpat/hiframes_join.py,207457f484466f3bbe881fb97530224943e5ac95,STILL_EXISTS,TODO: support string
aaeiedchi,IntelPython/sdc,hpat/hiframes_join.py,207457f484466f3bbe881fb97530224943e5ac95,STILL_EXISTS,TODO: use binary search
aaeiedcia,IntelPython/sdc,hpat/hiframes_join.py,349b2a76e75546ba49994fab2d2aca5af84515d6,STILL_EXISTS,TODO: support strings; bools; etc.
aaeieddbd,IntelPython/sdc,hpat/hiframes_join.py,f8a0defe0a2289cb70058dea348d5ae53d62d217,STILL_EXISTS,XXX: set NA values in bool arrays to False
aaeieddbe,IntelPython/sdc,hpat/hiframes_join.py,f8a0defe0a2289cb70058dea348d5ae53d62d217,STILL_EXISTS,FIXME: replace with proper NaN
aaeieddbg,IntelPython/sdc,hpat/pd_timestamp_ext.py,1255b27429ee44ebdc60152b8591e163141cd1d3,a66bb869888a1bae1f1347457930c2c447c97837,TODO: move to Numba
aaeieddbh,IntelPython/sdc,hpat/utils.py,bd526c59f7a661fc9cc085e6508affa98680bf42,STILL_EXISTS,XXX: Numpy's bool array uses a byte for each value but regular booleans
aaeieddbj,IntelPython/sdc,hpat/utils.py,bd526c59f7a661fc9cc085e6508affa98680bf42,STILL_EXISTS,TODO: handle boolean scalars properly
aaeiedddh,IntelPython/sdc,hpat/hiframes_typed.py,94dd55c611b7bfd16e7c0f86a497497f9a8bf340,STILL_EXISTS,XXX df isin is different than Series.isin; df.isin considers
aaeiedddj,IntelPython/sdc,hpat/hiframes_typed.py,94dd55c611b7bfd16e7c0f86a497497f9a8bf340,STILL_EXISTS,TODO: support strings and other types
aaeieddej,IntelPython/sdc,hpat/pd_timestamp_ext.py,94dd55c611b7bfd16e7c0f86a497497f9a8bf340,STILL_EXISTS,TODO: move to Numba
aaeieddge,IntelPython/sdc,hpat/pio.py,d9d1318c68bd5ccf37d1118a9a7f49f91dfea4da,STILL_EXISTS,XXX can't know the type of index here especially if it is bool arr
aaeieddgj,IntelPython/sdc,hpat/pio_api.py,9f4fb56ae15abc98f5a030a64146ec56fc064c57,STILL_EXISTS,TODO: use prefix-sum and all-to-all
aaeieddhe,IntelPython/sdc,hpat/hiframes_typed.py,972d53150858a3fd3fb923bf71ba1c533a400f77,STILL_EXISTS,XXX remove slice() of h5 read due to Numba's #3380 bug
aaeieddhf,IntelPython/sdc,hpat/hiframes_typed.py,972d53150858a3fd3fb923bf71ba1c533a400f77,STILL_EXISTS,TODO: check index format for this case
aaeieddjh,IntelPython/sdc,hpat/distributed_analysis.py,0b138b5992d9b188629b4e5ebc0f9b3d47b9a7aa,STILL_EXISTS,blocks of data are passed in; TODO: document
aaeiedecb,IntelPython/sdc,hpat/csv_ext.py,5c4c6236eaf2fdde4c1e18f2fcc48c04f4d8dfac,STILL_EXISTS,output columns are defined
aaeiedecc,IntelPython/sdc,hpat/csv_ext.py,5c4c6236eaf2fdde4c1e18f2fcc48c04f4d8dfac,STILL_EXISTS,csv doesn't generate copies; it just kills the output columns
aaeiedece,IntelPython/sdc,hpat/csv_ext.py,5c4c6236eaf2fdde4c1e18f2fcc48c04f4d8dfac,STILL_EXISTS,TODO: rebalance if output distributions are 1D instead of 1D_Var
aaeiedeci,IntelPython/sdc,hpat/csv_ext.py,5c4c6236eaf2fdde4c1e18f2fcc48c04f4d8dfac,STILL_EXISTS,TODO: get global size from C
aaeiedecj,IntelPython/sdc,hpat/csv_ext.py,5c4c6236eaf2fdde4c1e18f2fcc48c04f4d8dfac,STILL_EXISTS,HACK: add string_array_type to numba.types
aaeiededa,IntelPython/sdc,hpat/csv_ext.py,5c4c6236eaf2fdde4c1e18f2fcc48c04f4d8dfac,STILL_EXISTS,FIXME: fix after Numba #3372 is resolved
aaeiededb,IntelPython/sdc,hpat/csv_ext.py,5c4c6236eaf2fdde4c1e18f2fcc48c04f4d8dfac,STILL_EXISTS,XXX: temporary fix pending Numba's #3378
aaeiededf,IntelPython/sdc,hpat/csv_ext.py,5c4c6236eaf2fdde4c1e18f2fcc48c04f4d8dfac,STILL_EXISTS,TODO: support non-numpy types like strings
aaeiededi,IntelPython/sdc,hpat/csv_ext.py,5c4c6236eaf2fdde4c1e18f2fcc48c04f4d8dfac,STILL_EXISTS,TODO: fix globals after Numba's #3355 is resolved
aaeiedeeb,IntelPython/sdc,hpat/hiframes.py,5c4c6236eaf2fdde4c1e18f2fcc48c04f4d8dfac,STILL_EXISTS,TODO: check file name arg
aaeiedefa,IntelPython/sdc,hpat/hiframes_typed.py,a08c202f0ce1ee78329057aa0c3c4260d9adc849,STILL_EXISTS,XXX seq pipeline used since dist pass causes a hang
aaeiedefe,IntelPython/sdc,hpat/hiframes_sort.py,de85d5b4d581e27a6a773fcf79537271a0d72a74,STILL_EXISTS,XXX output vars are assigned to input to handle inplace case more easily
aaeiedefi,IntelPython/sdc,hpat/hiframes_sort.py,65990538a93e58d4a380dbaf983bab2b0ff94531,STILL_EXISTS,arrays in the parallel case; TODO: fix)
aaeiedega,IntelPython/sdc,hpat/hiframes_sort.py,65990538a93e58d4a380dbaf983bab2b0ff94531,STILL_EXISTS,TODO: handle rebalance
aaeiedegd,IntelPython/sdc,hpat/hiframes_sort.py,65990538a93e58d4a380dbaf983bab2b0ff94531,STILL_EXISTS,TODO: arg aliases for inplace case?
aaeiedeha,IntelPython/sdc,hpat/hiframes_sort.py,65990538a93e58d4a380dbaf983bab2b0ff94531,STILL_EXISTS,TODO: handle 1D balance for inplace case
aaeiedehc,IntelPython/sdc,hpat/utils.py,84192b869b6f68dcffbe7b5240e89b61a10af89e,STILL_EXISTS,only add supported (s1+s2); TODO: extend to other expressions
aaeiedehd,IntelPython/sdc,hpat/tests/test_basic.py,eaa171f8b2951c3bcec2184c2f4c87e69058f811,STILL_EXISTS,XXX arange() on float32 has overflow issues on large n
aaeiedejb,IntelPython/sdc,hpat/hiframes.py,e2dc40c5ff330be91cc2b18cd172ccace3e4e529,STILL_EXISTS,find key columns
aaeiedejh,IntelPython/sdc,hpat/hiframes_join.py,9a8e4ac2ff8bf1f88f81f3c9aa08a070c1174ffb,STILL_EXISTS,TODO: support separate keys?
aaeiedfad,IntelPython/sdc,hpat/tests/test_hiframes.py,e9721f004343cce17907fa77a16debb5a1e13838,STILL_EXISTS,make sure HPAT copies the columns like Pandas does
aaeiedfae,IntelPython/sdc,hpat/pio.py,54733b846b4d34e4adf4411daefd04da3143d5f3,STILL_EXISTS,TODO: can we do this without reverse_copies?
aaeiedfaf,IntelPython/sdc,hpat/pio.py,54733b846b4d34e4adf4411daefd04da3143d5f3,STILL_EXISTS,TODO: if copy propagation is done; varname itself should be checked
aaeiedfaj,IntelPython/sdc,hpat/distributed_analysis.py,99dc6bf22c94bd0ae2d2ed62811bc2db4cc7b267,STILL_EXISTS,TODO: support getitem of container
aaeiedfcc,IntelPython/sdc,hpat/hiframes.py,d3fd672fe60172f32f00a6ee234044389937498a,STILL_EXISTS,TODO: multiple keys (index columns)
aaeiedfcd,IntelPython/sdc,hpat/hiframes.py,d3fd672fe60172f32f00a6ee234044389937498a,STILL_EXISTS,TODO: hanlde multiple keys (index args)
aaeiedfce,IntelPython/sdc,hpat/hiframes_aggregate.py,ee44a3d4ab38069c9ee0c8e28058c2ef09f6f6fb,STILL_EXISTS,TODO: fix multi-key return key output
aaeiedfcf,IntelPython/sdc,hpat/hiframes_aggregate.py,ee44a3d4ab38069c9ee0c8e28058c2ef09f6f6fb,9db79c124436b3298d4b1f4c5e184aa5a68fc54a,TODO: multikey output
aaeiedfid,IntelPython/sdc,hpat/hiframes_sort.py,7562e6be34505beef2ccee3a7926852556b75637,STILL_EXISTS,TODO: arr refcount if arr is not stored somewhere?
aaeiedfig,IntelPython/sdc,hpat/hiframes_aggregate.py,81a38d83dc5214175fab61e300fa0897ad6acdc5,9db79c124436b3298d4b1f4c5e184aa5a68fc54a,TODO: fix multi-key alloc when return_key==True
aaeiedfii,IntelPython/sdc,hpat/hiframes_aggregate.py,81a38d83dc5214175fab61e300fa0897ad6acdc5,STILL_EXISTS,TODO: fix return key case
aaeiedfjd,IntelPython/sdc,hpat/hiframes_aggregate.py,81a38d83dc5214175fab61e300fa0897ad6acdc5,STILL_EXISTS,TODO: support string in tuple set
aaeiedfje,IntelPython/sdc,hpat/hiframes.py,d5f2af9cb49604495a8089f7f0cff4300a4748e8,STILL_EXISTS,XXX output becomes series if single output and explicitly selected
aaeiedfji,IntelPython/sdc,hpat/hiframes_aggregate.py,9db79c124436b3298d4b1f4c5e184aa5a68fc54a,STILL_EXISTS,TODO: handle strings in multi-key case
aaeiedgaa,IntelPython/sdc,hpat/hiframes_aggregate.py,9db79c124436b3298d4b1f4c5e184aa5a68fc54a,STILL_EXISTS,TODO: fix return key case
aaeiedgad,IntelPython/sdc,hpat/hiframes_aggregate.py,e9598a51c635cdfee075227d879932185103839f,STILL_EXISTS,TODO: is tuple set as fast as single key set? specialize?
aaeiedgbb,IntelPython/sdc,hpat/hiframes_aggregate.py,28eba141d0c596afeed310012659fa1b7701a449,STILL_EXISTS,TODO: is tuple set as fast as single key set? specialize?
aaeiedgbd,IntelPython/sdc,hpat/hiframes_join.py,302239230f15d9a13ffa4e8a05a0964e8cfdf548,STILL_EXISTS,TODO: multiple keys
aaeiedgda,IntelPython/sdc,hpat/hiframes.py,c293c0736df3eb5977f750e2b27f351c224d8039,STILL_EXISTS,TODO: support other args like usecols
aaeiedgdh,IntelPython/sdc,hpat/hiframes.py,d92ab899e4189cf2e3fec1ee45d956b0cf7ac59f,STILL_EXISTS,dropping columns inplace possible only when it dominates the df
aaeiedgdj,IntelPython/sdc,hpat/hiframes.py,d92ab899e4189cf2e3fec1ee45d956b0cf7ac59f,STILL_EXISTS,TODO: rename df name
aaeiedgea,IntelPython/sdc,hpat/hiframes.py,d92ab899e4189cf2e3fec1ee45d956b0cf7ac59f,STILL_EXISTS,TODO: support dropping columns of input dfs (reflection)
aaeiedgeb,IntelPython/sdc,hpat/hiframes_join.py,60b15bf03479b760fa46cacd2365f7b3ad16db5d,99d99614f119138e280afe3e11180ac0d20ab1e8,TODO: set actual nan for str
aaeiedgec,IntelPython/sdc,hpat/hiframes_join.py,60b15bf03479b760fa46cacd2365f7b3ad16db5d,STILL_EXISTS,TODO: why doesn't setitem_str_offset work
aaeiedgeg,IntelPython/sdc,hpat/str_arr_ext.py,60b15bf03479b760fa46cacd2365f7b3ad16db5d,STILL_EXISTS,TODO: fix this for join
aaeiedgej,IntelPython/sdc,hpat/hiframes_typed.py,94e11baba6f73d6b0118ff88a4b25aef983978b5,STILL_EXISTS,TODO: min
aaeiedgfb,IntelPython/sdc,hpat/hiframes_typed.py,94e11baba6f73d6b0118ff88a4b25aef983978b5,STILL_EXISTS,TODO: other types
aaeiedgfd,IntelPython/sdc,hpat/hiframes_typed.py,94e11baba6f73d6b0118ff88a4b25aef983978b5,STILL_EXISTS,TODO: fix for dt64
aaeiedgff,IntelPython/sdc,hpat/hiframes_api.py,6612655606887681f7c267f3fb44ec0778072ad5,STILL_EXISTS,TODO: replace with np.isnat
aaeiedgfh,IntelPython/sdc,hpat/hiframes_typed.py,6612655606887681f7c267f3fb44ec0778072ad5,STILL_EXISTS,np.datetime64[ns] which invalid; TODO: fix PA
aaeiedgfi,IntelPython/sdc,hpat/hiframes_typed.py,6612655606887681f7c267f3fb44ec0778072ad5,STILL_EXISTS,TODO: replace with np.isnat
aaeiedgfj,IntelPython/sdc,hpat/hiframes_typed.py,ee199e8688542cd40cc6c0f4f1e4f08755532b4d,STILL_EXISTS,TODO: check for errors
aaeiedggb,IntelPython/sdc,hpat/hiframes_typed.py,ee199e8688542cd40cc6c0f4f1e4f08755532b4d,STILL_EXISTS,TODO: why doesn't empty_inferred work for t4 mortgage test?
aaeiedggc,IntelPython/sdc,hpat/pd_series_ext.py,ee199e8688542cd40cc6c0f4f1e4f08755532b4d,STILL_EXISTS,TODO: other attrs
aaeiedggh,IntelPython/sdc,hpat/utils.py,8d9b9214e006d0737ec860626200c5f3d3dc6534,STILL_EXISTS,TODO: move to Numba
aaeiedggi,IntelPython/sdc,hpat/utils.py,8d9b9214e006d0737ec860626200c5f3d3dc6534,STILL_EXISTS,TODO: conversion needed like IntegerLiteral?
aaeiedgha,IntelPython/sdc,hpat/str_ext.py,5f011029b3ccf357ec2b50b02faa4d7117b383bf,STILL_EXISTS,TODO: resolve import conflict
aaeiedghb,IntelPython/sdc,hpat/hiframes_aggregate.py,0607b833716c5fc5575dafd3f51627c11719e01d,STILL_EXISTS,XXX trying both since init_prange doesn't work for min
aaeiedghc,IntelPython/sdc,hpat/hiframes_aggregate.py,0607b833716c5fc5575dafd3f51627c11719e01d,STILL_EXISTS,TODO: support other reductions
aaeiedgid,IntelPython/sdc,hpat/hiframes_api.py,71019069fe52facd2899743ca49e9a26a6d0bec9,STILL_EXISTS,c.pyapi.decref(arr)  # TODO needed?
aaeiedgjg,IntelPython/sdc,hpat/pd_categorical_ext.py,4b37ea2861d38a249e70563548f08ab50eab6cb0,STILL_EXISTS,TODO: why does list_pack crash for test_csv_cat2?
aaeiedgjj,IntelPython/sdc,hpat/csv_ext.py,fffa92e6f0d8af1f82f3fb1f2830dbf7204ab1a7,STILL_EXISTS,TODO: string_series_type also?
aaeiedhba,IntelPython/sdc,hpat/hiframes_join.py,a0225345b2ba6fb76ae6f0efa570450a965024d5,STILL_EXISTS,XXX set integer NA to 0 to avoid unexpected errors (e.g. categorical)
aaeiedhbb,IntelPython/sdc,hpat/hiframes_join.py,a0225345b2ba6fb76ae6f0efa570450a965024d5,STILL_EXISTS,TODO: convert integer to float if nan
aaeiedhbc,IntelPython/sdc,hpat/hiframes_join.py,a0225345b2ba6fb76ae6f0efa570450a965024d5,STILL_EXISTS,TODO: handle categorical
aaeiedhid,IntelPython/sdc,hpat/_version.py,84812b77481fba80324febb85af3f63333e6610c,STILL_EXISTS,maybe improved later
aaeiedhij,IntelPython/sdc,hpat/_version.py,84812b77481fba80324febb85af3f63333e6610c,STILL_EXISTS,unparseable. Maybe git-describe is misbehaving?
aaeiedifb,IntelPython/sdc,versioneer.py,84812b77481fba80324febb85af3f63333e6610c,STILL_EXISTS,maybe improved later
aaeiedifh,IntelPython/sdc,versioneer.py,84812b77481fba80324febb85af3f63333e6610c,STILL_EXISTS,unparseable. Maybe git-describe is misbehaving?
aaeiedjed,IntelPython/sdc,hpat/hiframes_join.py,0394684cb0774402a0e174465e2f52717ea6a7f5,STILL_EXISTS,TODO: approximate output size properly
aaeiedjee,IntelPython/sdc,hpat/str_ext.py,c5b24bed171a104f0c12ac845671462005bce553,STILL_EXISTS,TODO: using lower_builtin since overload fails for str tuple
aaeiedjef,IntelPython/sdc,hpat/str_ext.py,c5b24bed171a104f0c12ac845671462005bce553,STILL_EXISTS,TODO: constant hash like hash(\"ss\";) fails
aaeiedjfi,IntelPython/sdc,hpat/hiframes_typed.py,31686a90ab0eec31a2767b3d21f124b358c74abb,STILL_EXISTS,beginning of block; preventing fusion. TODO: fix PA
aaeiedjfj,IntelPython/sdc,hpat/hiframes.py,07b3ddf8591e3a08eed1762744529cea5bcbb0da,STILL_EXISTS,TODO: move to Numba
aaeiedjga,IntelPython/sdc,hpat/hiframes_join.py,5511b870cd1019015ce9ac14c6599401f1ec0024,STILL_EXISTS,TODO: approximate output size properly
aaeiedjgc,IntelPython/sdc,hpat/parquet_pio.py,6ece4bd00e9c99bd23edd80940b3457b62aa9d0e,STILL_EXISTS,TODO: handle index properly when indices are supported
aaeiedjgd,IntelPython/sdc,hpat/tests/test_hiframes.py,fc6903ba555e3b0b419b46b08edb63b4be248b1e,STILL_EXISTS,TODO: fix handling of df setitem to force match of array dists
aaeiedjgg,IntelPython/sdc,hpat/tests/test_hiframes.py,fc6903ba555e3b0b419b46b08edb63b4be248b1e,STILL_EXISTS,TODO: full_like for Series
aaeiedjgh,IntelPython/sdc,hpat/tests/test_hiframes.py,fc6903ba555e3b0b419b46b08edb63b4be248b1e,STILL_EXISTS,TODO:
aaeiedjhb,IntelPython/sdc,hpat/distributed.py,4790506c0dc1a30c0b683c4a84baae87fdbd91ee,STILL_EXISTS,TODO: refactor to avoid reduction
aaeiedjhc,IntelPython/sdc,hpat/distributed.py,4790506c0dc1a30c0b683c4a84baae87fdbd91ee,STILL_EXISTS,XXX: get sizes in lower dimensions
aaeiedjhi,IntelPython/sdc,hpat/distributed_api.py,0e44f96ae9099e737a396e5ae21a20040748553d,STILL_EXISTS,TODO: move other funcs to old API?
aaeiedjia,IntelPython/sdc,hpat/hiframes.py,1ffce863a02f4630b45411916bbbb01071276577,STILL_EXISTS,handle BoxedSeries and list\/set of BoxedSeries; TODO: others?
aaeiedjie,IntelPython/sdc,hpat/distributed.py,42028e8d16bf69ab11887578b1053a4ff12fc03a,STILL_EXISTS,TODO: refactor to avoid reduction
aaeiedjif,IntelPython/sdc,hpat/distributed.py,42028e8d16bf69ab11887578b1053a4ff12fc03a,STILL_EXISTS,XXX: get sizes in lower dimensions
aaeiedjii,IntelPython/sdc,hpat/hiframes.py,2c4cba854ae4570918aeee5830f76c0ec3aeabc7,STILL_EXISTS,TODO: support multiple input flags
aaeiedjjd,IntelPython/sdc,hpat/hiframes.py,2c4cba854ae4570918aeee5830f76c0ec3aeabc7,STILL_EXISTS,XXX disable for now to enable dist series
aaeiedjjh,IntelPython/sdc,hpat/hiframes_typed.py,51ceba8cf5e7c2ced1a9768a42c6bb20b5fb0586,STILL_EXISTS,TODO: check for errors
aaeiedjji,IntelPython/sdc,hpat/hiframes_typed.py,51ceba8cf5e7c2ced1a9768a42c6bb20b5fb0586,STILL_EXISTS,TODO: refactor
aaeieeaaa,IntelPython/sdc,hpat/hiframes_typed.py,51ceba8cf5e7c2ced1a9768a42c6bb20b5fb0586,STILL_EXISTS,TODO: use empty_inferred after it is fixed
aaeieeaab,IntelPython/sdc,hpat/set_ext.py,7c096abd8e46856edab2026cf5093ad44753d036,STILL_EXISTS,TODO: remove since probably unused
aaeieeaac,IntelPython/sdc,hpat/tests/test_strings.py,d9021224d82ee7c57f8fac3da29a11b354b87512,STILL_EXISTS,XXX just checking isna() since Pandas uses None in this case
aaeieeaae,IntelPython/sdc,hpat/distributed.py,e4520870f57ee7b6df41da7b7342b8bbc54c4b7f,STILL_EXISTS,TODO: make create_dataset\/create_group collective
aaeieeabc,IntelPython/sdc,hpat/str_arr_ext.py,9dec5bf6bdbcbdf7dddf9176ac18472a7ddc5bb6,STILL_EXISTS,TODO: support array of strings
aaeieeabj,IntelPython/sdc,hpat/distributed.py,32d975461c20fd66a985260fc5d8c301e871b009,STILL_EXISTS,XXX hack for array container case; TODO: handle properly
aaeieeafb,IntelPython/sdc,hpat/hiframes_aggregate.py,39840c7e5e1e07ae508e8c601bb94ecfb933daf9,STILL_EXISTS,XXX: njit doesn't work when hpat.jit() is used for agg_func in hiframes
aaeieeagf,IntelPython/sdc,hpat/hiframes.py,9203975f709e92a807cfadee094a1f2da6b18754,STILL_EXISTS,XXX output becomes series if single output and explicitly selected
aaeieeagi,IntelPython/sdc,hpat/hiframes_aggregate.py,629c279ff64b4061115c50e33952027ca390a237,STILL_EXISTS,XXX replace hpat.hiframes_api.isna(A; i) for now
aaeieeagj,IntelPython/sdc,hpat/hiframes_aggregate.py,629c279ff64b4061115c50e33952027ca390a237,STILL_EXISTS,TODO: handle actual NA
aaeieeahd,IntelPython/sdc,hpat/hiframes_api.py,ca21b90a4d0d5e25b01dcb8c3f150479cb94e5bb,STILL_EXISTS,TODO: reverse=None
aaeieeahf,IntelPython/sdc,hpat/hiframes_typed.py,ca21b90a4d0d5e25b01dcb8c3f150479cb94e5bb,STILL_EXISTS,TODO: handle reverse
aaeieeahj,IntelPython/sdc,hpat/str_arr_ext.py,fb614e20cb38e374a1ac16d3c7914985b112760b,STILL_EXISTS,TODO: overload of constructor doesn't work
aaeieeaih,IntelPython/sdc,hpat/str_arr_ext.py,fb614e20cb38e374a1ac16d3c7914985b112760b,STILL_EXISTS,# TODO: use vector to avoid two passes?
aaeieeajh,IntelPython/sdc,hpat/str_arr_ext.py,fb614e20cb38e374a1ac16d3c7914985b112760b,STILL_EXISTS,TODO: use vector to avoid two passes?
aaeieebfg,IntelPython/sdc,hpat/str_ext.py,fb614e20cb38e374a1ac16d3c7914985b112760b,STILL_EXISTS,XXX enabling this turns on old std::string implementation
aaeieebhg,IntelPython/sdc,hpat/str_arr_ext.py,d2f37e6a265da4e712c77f3fd4f38e458474acff,STILL_EXISTS,TODO: put offset\/data in main structure since immutable
aaeieebhj,IntelPython/sdc,hpat/str_arr_ext.py,d2f37e6a265da4e712c77f3fd4f38e458474acff,STILL_EXISTS,TODO: use overload
aaeieebia,IntelPython/sdc,hpat/str_arr_ext.py,d2f37e6a265da4e712c77f3fd4f38e458474acff,STILL_EXISTS,TODO: support multibyte unicode
aaeieebib,IntelPython/sdc,hpat/str_arr_ext.py,d2f37e6a265da4e712c77f3fd4f38e458474acff,STILL_EXISTS,TODO: support Null
aaeieebji,IntelPython/sdc,hpat/io.py,975b8289010df5bb5c15a570802d8d66828a202d,STILL_EXISTS,TODO: fix Numba to convert literal
aaeieecad,IntelPython/sdc,hpat/ml/d4p.py,9b90e784e157fe28f2d7c901ffc82964ef41e8ee,STILL_EXISTS,TODO: fix and test Numba unicode_type
aaeieecba,IntelPython/sdc,hpat/str_ext.py,077bfd5d5ec1eb59c7913067aebc8be254333aa0,b3fb13a48fd34d608a19c9da620d1295edc7e5f9,XXX using std_str.split() until Numba can support split()
aaeieecbb,IntelPython/sdc,hpat/str_ext.py,077bfd5d5ec1eb59c7913067aebc8be254333aa0,STILL_EXISTS,XXX handle unicode until Numba supports int(str)
aaeieecbc,IntelPython/sdc,hpat/str_ext.py,077bfd5d5ec1eb59c7913067aebc8be254333aa0,STILL_EXISTS,XXX handle unicode until Numba supports float(str)
aaeieeccj,IntelPython/sdc,hpat/str_ext.py,2c7b695947c2f6ff66f427c5dca8b1c7971fc91b,STILL_EXISTS,XXX: use Numba's hash(str) when available
aaeieecda,IntelPython/sdc,hpat/pd_series_ext.py,2a8de0e9c1eec1c2bb2ee13ec193bf4d077ce82c,STILL_EXISTS,XXX fails due in overload
aaeieecde,IntelPython/sdc,hpat/pd_series_ext.py,2a8de0e9c1eec1c2bb2ee13ec193bf4d077ce82c,STILL_EXISTS,HACK to get avoid issues for now
aaeieecdf,IntelPython/sdc,hpat/hiframes_typed.py,2b76389626a3128a6ab2204ec60076b3b6ee0849,STILL_EXISTS,TODO: refactor arg parsing
aaeieecdg,IntelPython/sdc,hpat/hiframes_typed.py,2b76389626a3128a6ab2204ec60076b3b6ee0849,STILL_EXISTS,TODO: refactor regex and noregex
aaeieecdh,IntelPython/sdc,hpat/distributed.py,afdb9e0d2fdca5cffc8e09106b72790b848b945b,STILL_EXISTS,XXX for pre_alloc_string_array(n; nc); we assume nc is local
aaeieecdj,IntelPython/sdc,hpat/hiframes_typed.py,f83578b0bfedb8c0e6608096bfabbec2bc88b21e,STILL_EXISTS,TODO: support default whitespace separator
aaeieecee,IntelPython/sdc,hpat/str_ext.py,f83578b0bfedb8c0e6608096bfabbec2bc88b21e,STILL_EXISTS,XXX using list of list string instead of array of list string since Numba's
aaeieecfc,IntelPython/sdc,hpat/hiframes_typed.py,2400648627ebd662a89c399cae48401e3ac1581b,STILL_EXISTS,XXX only supports get for list(list(str)) input
aaeieecfd,IntelPython/sdc,hpat/pd_series_ext.py,2400648627ebd662a89c399cae48401e3ac1581b,STILL_EXISTS,TODO: add dtype to series_str_methods_type
aaeieecfe,IntelPython/sdc,hpat/pd_series_ext.py,2400648627ebd662a89c399cae48401e3ac1581b,STILL_EXISTS,XXX only list(list(str)) supported
aaeieecff,IntelPython/sdc,hpat/hiframes_typed.py,c99828b70ba7395c2baf6f60d31e517617cb116c,STILL_EXISTS,TODO: support NAN
aaeieecfg,IntelPython/sdc,hpat/hiframes_api.py,b3bb230074e07e57b9e67b7997c8d5f3ebde342e,STILL_EXISTS,TODO required?
aaeieecfi,IntelPython/sdc,hpat/hiframes_api.py,9c3e90c1fd63048d09bd3b9a1215610d97b4a529,STILL_EXISTS,TODO: support list(str)
aaeieecfj,IntelPython/sdc,hpat/hiframes_filter.py,9c3e90c1fd63048d09bd3b9a1215610d97b4a529,STILL_EXISTS,TODO handle list_string_array_type in other nodes
aaeieecga,IntelPython/sdc,hpat/hiframes_api.py,9d718f11056991dedf8a276f0d18681c578aecc6,STILL_EXISTS,TODO: convert to parfor in typed pass
aaeieecgc,IntelPython/sdc,hpat/distributed.py,d11511102ae3c994ba9889a68f90789b927b679d,STILL_EXISTS,TODO: use Parfor loop blocks when replacing funcs in
aaeieecge,IntelPython/sdc,hpat/hiframes_api.py,d11511102ae3c994ba9889a68f90789b927b679d,STILL_EXISTS,TODO: support NaN in list(list(str))
aaeieecgi,IntelPython/sdc,hpat/hiframes_api.py,046b64cf4fea41d175ca681a91947da943271487,STILL_EXISTS,FIXME dtype for list(str)
aaeieecgj,IntelPython/sdc,hpat/hiframes_api.py,046b64cf4fea41d175ca681a91947da943271487,STILL_EXISTS,FIXME: list(str) code
aaeieecic,IntelPython/sdc,hpat/hiframes_api.py,046b64cf4fea41d175ca681a91947da943271487,STILL_EXISTS,XXX we don't call native cleanup for each
aaeieecjb,IntelPython/sdc,hpat/pd_series_ext.py,a91b4adc775924b217ebc021d0a90ca12944f67e,STILL_EXISTS,TODO: support more types. what types can be in recarrays?
aaeieedej,IntelPython/sdc,hpat/hiframes/boxing.py,7f8f9489e355e519221c4222dec3bc9181762c12,STILL_EXISTS,TODO: support other types like string and timestamp
aaeieedfb,IntelPython/sdc,hpat/hiframes/boxing.py,7f8f9489e355e519221c4222dec3bc9181762c12,STILL_EXISTS,TODO: handle NA as 1st value
aaeieedfd,IntelPython/sdc,hpat/hiframes/boxing.py,7f8f9489e355e519221c4222dec3bc9181762c12,STILL_EXISTS,XXX: using .values to check date type since DatetimeIndex returns
aaeieedff,IntelPython/sdc,hpat/hiframes/boxing.py,7f8f9489e355e519221c4222dec3bc9181762c12,e9b3308d09c79db3253cc57b878972c062bdb9d5,XXX: refcount?
aaeieedfg,IntelPython/sdc,hpat/hiframes/boxing.py,7f8f9489e355e519221c4222dec3bc9181762c12,STILL_EXISTS,XXX assuming the whole column is strings if 1st val is string
aaeieedfh,IntelPython/sdc,hpat/hiframes/boxing.py,7f8f9489e355e519221c4222dec3bc9181762c12,STILL_EXISTS,TODO: support more types
aaeieedgb,IntelPython/sdc,hpat/hiframes/boxing.py,7f8f9489e355e519221c4222dec3bc9181762c12,e9b3308d09c79db3253cc57b878972c062bdb9d5,TODO: datetime.date; DatetimeIndex?
aaeieedgc,IntelPython/sdc,hpat/hiframes/boxing.py,7f8f9489e355e519221c4222dec3bc9181762c12,e9b3308d09c79db3253cc57b878972c062bdb9d5,TODO required?
aaeieedge,IntelPython/sdc,hpat/hiframes/boxing.py,7f8f9489e355e519221c4222dec3bc9181762c12,e9b3308d09c79db3253cc57b878972c062bdb9d5,TODO: is incref required?
aaeieedgh,IntelPython/sdc,hpat/hiframes/boxing.py,7f8f9489e355e519221c4222dec3bc9181762c12,e9b3308d09c79db3253cc57b878972c062bdb9d5,FIXME dtype for dt64
aaeieedgi,IntelPython/sdc,hpat/hiframes/boxing.py,7f8f9489e355e519221c4222dec3bc9181762c12,e9b3308d09c79db3253cc57b878972c062bdb9d5,FIXME dtype for str
aaeieedgj,IntelPython/sdc,hpat/hiframes/boxing.py,7f8f9489e355e519221c4222dec3bc9181762c12,e9b3308d09c79db3253cc57b878972c062bdb9d5,FIXME dtype for list(str)
aaeieedhc,IntelPython/sdc,hpat/hiframes/boxing.py,7f8f9489e355e519221c4222dec3bc9181762c12,e9b3308d09c79db3253cc57b878972c062bdb9d5,TODO: refcounts?
aaeieedhd,IntelPython/sdc,hpat/hiframes/boxing.py,7f8f9489e355e519221c4222dec3bc9181762c12,e9b3308d09c79db3253cc57b878972c062bdb9d5,FIXME: str code
aaeieedhe,IntelPython/sdc,hpat/hiframes/boxing.py,7f8f9489e355e519221c4222dec3bc9181762c12,e9b3308d09c79db3253cc57b878972c062bdb9d5,FIXME: list(str) code
aaeieedhf,IntelPython/sdc,hpat/hiframes/boxing.py,7f8f9489e355e519221c4222dec3bc9181762c12,e9b3308d09c79db3253cc57b878972c062bdb9d5,FIXME: dt64 code
aaeieedhi,IntelPython/sdc,hpat/hiframes/boxing.py,7f8f9489e355e519221c4222dec3bc9181762c12,69e631e4871c4b3a1fdac77bb0936fbbee8aa7fe,c.pyapi.decref(arr)  # TODO needed?
aaeieedjd,IntelPython/sdc,hpat/hiframes/boxing.py,7f8f9489e355e519221c4222dec3bc9181762c12,STILL_EXISTS,XXX we don't call native cleanup for each
aaeieeeae,IntelPython/sdc,hpat/hiframes/hiframes_typed.py,2f10af1f213f211f9d6fa23f5df9fca68f71e2f9,STILL_EXISTS,XXX using replace() since it copies; otherwise cached overload
aaeieeeah,IntelPython/sdc,hpat/hiframes/hiframes_typed.py,2f10af1f213f211f9d6fa23f5df9fca68f71e2f9,STILL_EXISTS,XXX: inine_closure_call() can't handle defaults properly
aaeieeeaj,IntelPython/sdc,hpat/hiframes/pd_series_ext.py,67b493b7e9f978102d5c38bf76c27a5b6a962c8d,STILL_EXISTS,XXX is copy necessary?
aaeieeebg,IntelPython/sdc,hpat/hiframes/pd_series_ext.py,67b493b7e9f978102d5c38bf76c27a5b6a962c8d,STILL_EXISTS,TODO: support more types. what types can be in recarrays?
aaeieeebh,IntelPython/sdc,hpat/hiframes/pd_series_ext.py,67b493b7e9f978102d5c38bf76c27a5b6a962c8d,STILL_EXISTS,TODO: other types?
aaeieeebj,IntelPython/sdc,hpat/hiframes/pd_series_ext.py,67b493b7e9f978102d5c38bf76c27a5b6a962c8d,c88a3568a687e4b894a008c62ed7fa106067599b,XXX: Boxed series variable types shouldn't be replaced in hiframes_typed
aaeieeecb,IntelPython/sdc,hpat/hiframes/pd_series_ext.py,67b493b7e9f978102d5c38bf76c27a5b6a962c8d,STILL_EXISTS,TODO: copy other types like list(str)
aaeieeejc,IntelPython/sdc,hpat/hiframes/hiframes_typed.py,92dc2a5a2751c69e3b5c0b2e6a61c7b629e02906,STILL_EXISTS,TODO: stararg needs special handling?
aaeieeejd,IntelPython/sdc,hpat/hiframes/boxing.py,5c914363e8eb98c4bf771b00cdd4483087628aa4,STILL_EXISTS,TODO: index-specific boxing like RangeIndex() etc.
aaeieeeje,IntelPython/sdc,hpat/hiframes/boxing.py,5c914363e8eb98c4bf771b00cdd4483087628aa4,STILL_EXISTS,TODO: dtype
aaeieefaa,IntelPython/sdc,hpat/hiframes/hiframes_typed.py,bf4e6d466d87e262e211c587a92a55b787e5c4dc,STILL_EXISTS,XXX using replace() since it copies; otherwise cached overload
aaeieefac,IntelPython/sdc,hpat/hiframes/hiframes_typed.py,bf4e6d466d87e262e211c587a92a55b787e5c4dc,STILL_EXISTS,XXX: side effect: force update of call signatures
aaeieefah,IntelPython/sdc,hpat/hiframes/hiframes_typed.py,bf4e6d466d87e262e211c587a92a55b787e5c4dc,STILL_EXISTS,XXX: new_sig could be None for things like np.int32()
aaeieefba,IntelPython/sdc,hpat/hiframes/hiframes_typed.py,bf4e6d466d87e262e211c587a92a55b787e5c4dc,STILL_EXISTS,fix types with undefined dtypes in empty_inferred; etc.
aaeieefbb,IntelPython/sdc,hpat/hiframes/boxing.py,a66b3a8031b292204d0340ee53596cafd65ac9f4,STILL_EXISTS,TODO: handle index and name
aaeieefbc,IntelPython/sdc,hpat/hiframes/hiframes_typed.py,6be5bbdd8c19f801540c4efd7d3c070cbfbf2708,STILL_EXISTS,TODO: support alignment; dt; etc.
aaeieefbf,IntelPython/sdc,hpat/hiframes/hiframes_typed.py,3e5b5d38519caa8b33c270c9454b284456ba16a7,STILL_EXISTS,XXX handling inplace_binop similar to binop for now
aaeieefbg,IntelPython/sdc,hpat/hiframes/hiframes_typed.py,3e5b5d38519caa8b33c270c9454b284456ba16a7,STILL_EXISTS,TODO handle inplace alignment similar to
aaeieefbi,IntelPython/sdc,hpat/hiframes/hiframes_typed.py,3e5b5d38519caa8b33c270c9454b284456ba16a7,STILL_EXISTS,TODO: inplace of str array?
aaeieefcd,IntelPython/sdc,hpat/hiframes/hiframes_typed.py,1a034753e6890f2546ba9ee916ea3c3cad477dfc,STILL_EXISTS,XXX assuming init_series is the only call to create a series
aaeieefcg,IntelPython/sdc,hpat/hiframes/hiframes_typed.py,5c753d41024d753a2751ebf66db486d9993715ba,STILL_EXISTS,TODO: handle index
aaeieefci,IntelPython/sdc,hpat/hiframes/hiframes_typed.py,79861c0084793f02e0d920670bc1c73b2e81aef0,STILL_EXISTS,XXX use get_series_data() for getting data instead of S._data
aaeieefdb,IntelPython/sdc,hpat/hiframes/hiframes_typed.py,dddfdc8dada8ef092154f3b5ca5cb49e5b6977fe,STILL_EXISTS,TODO: handle non-Series input
aaeieefdd,IntelPython/sdc,hpat/distributed_analysis.py,2db785ba75bc0978805feceb7a0a8df6a497d2af,STILL_EXISTS,TODO: support Series type similar to Array
aaeieefde,IntelPython/sdc,hpat/hiframes/hiframes_typed.py,635888b78950e4dbb282125c1b3310b7d74998bc,STILL_EXISTS,XXX sometimes new_sig is None for some reason
aaeieefdf,IntelPython/sdc,hpat/hiframes/hiframes_typed.py,635888b78950e4dbb282125c1b3310b7d74998bc,STILL_EXISTS,FIXME e.g. test_series_nlargest_parallel1 np.int32()
aaeieefdh,IntelPython/sdc,hpat/hiframes/pd_series_ext.py,07c63fcbd579935468ebf914a9532bb0cf93415e,STILL_EXISTS,TODO: handle actual Record objects in Series?
aaeieefdj,IntelPython/sdc,hpat/hiframes/api.py,cd97ea885faf259c5230a2338bcfca9ae5592758,STILL_EXISTS,XXX pd.DataFrame() calls init_series for even Series since it's untyped
aaeieefec,IntelPython/sdc,hpat/hiframes/hiframes_typed.py,cd97ea885faf259c5230a2338bcfca9ae5592758,STILL_EXISTS,XXX remove when df pass is typed? (test_pass_series2)
aaeieefed,IntelPython/sdc,hpat/hiframes/hiframes_typed.py,cd97ea885faf259c5230a2338bcfca9ae5592758,ebd1f40553a7c92d78e4402e429bead432bf02c8,fix definitions
aaeieefee,IntelPython/sdc,hpat/hiframes/hiframes_typed.py,cd97ea885faf259c5230a2338bcfca9ae5592758,STILL_EXISTS,TODO: fix definitions for all changes
aaeieefei,IntelPython/sdc,hpat/hiframes/hiframes_typed.py,056b61677ddbc452c9ecf03b9b90bab73b6a63bd,7f4088bb9764ddc332997665cf42f3343a818ac0,TODO: test ndim and T
aaeieefge,IntelPython/sdc,hpat/hiframes/pd_index_ext.py,575241c47199edde2569845deb0b2b3943e548a8,STILL_EXISTS,TODO: support other properties like freq\/tz\/dtype\/yearfirst?
aaeieefgf,IntelPython/sdc,hpat/hiframes/pd_index_ext.py,575241c47199edde2569845deb0b2b3943e548a8,STILL_EXISTS,XXX is copy necessary?
aaeieefgj,IntelPython/sdc,hpat/hiframes/pd_index_ext.py,575241c47199edde2569845deb0b2b3943e548a8,STILL_EXISTS,TODO: fix timestamp
aaeieefhb,IntelPython/sdc,hpat/hiframes/pd_index_ext.py,575241c47199edde2569845deb0b2b3943e548a8,STILL_EXISTS,TODO: check\/handle other input
aaeieefhg,IntelPython/sdc,hpat/hiframes/datetime_date_ext.py,fe20d438b8466e096bcbcef2bb4e130c4bc8f0c4,STILL_EXISTS,TODO: defer to Array for all operations
aaeieefhj,IntelPython/sdc,hpat/hiframes/hiframes_typed.py,fe20d438b8466e096bcbcef2bb4e130c4bc8f0c4,STILL_EXISTS,TODO: support Int64Index
aaeieefic,IntelPython/sdc,hpat/hiframes/pd_index_ext.py,fe20d438b8466e096bcbcef2bb4e130c4bc8f0c4,STILL_EXISTS,TODO: return Int64Index
aaeieefif,IntelPython/sdc,hpat/hiframes/pd_index_ext.py,6dfd15bed2f3a20d723365efc8ab6c0a9b67b9e0,STILL_EXISTS,TODO: support name boxing
aaeieefjf,IntelPython/sdc,hpat/hiframes/pd_index_ext.py,4b7c03920d60919b3d6598014723e37be60ed7c6,STILL_EXISTS,TODO: support other properties like unit\/freq?
aaeieefjg,IntelPython/sdc,hpat/hiframes/pd_index_ext.py,4b7c03920d60919b3d6598014723e37be60ed7c6,STILL_EXISTS,XXX is copy necessary?
aaeieegaa,IntelPython/sdc,hpat/hiframes/pd_index_ext.py,4b7c03920d60919b3d6598014723e37be60ed7c6,STILL_EXISTS,TODO: fix timedelta
aaeieegab,IntelPython/sdc,hpat/hiframes/pd_index_ext.py,4b7c03920d60919b3d6598014723e37be60ed7c6,STILL_EXISTS,TODO: support pd.Timedelta
aaeieegbb,IntelPython/sdc,hpat/hiframes/pd_index_ext.py,4b7c03920d60919b3d6598014723e37be60ed7c6,STILL_EXISTS,TODO: return Int64Index
aaeieegbc,IntelPython/sdc,hpat/hiframes/pd_series_ext.py,4b7c03920d60919b3d6598014723e37be60ed7c6,STILL_EXISTS,TODO: handle all timedelta args
aaeieegbe,IntelPython/sdc,hpat/hiframes/hiframes_typed.py,8005c5863bd3bf844709ee1146291b127f3301d9,STILL_EXISTS,TODO: test
aaeieegca,IntelPython/sdc,hpat/hiframes/pd_series_ext.py,a46be3c524041e5ff75a6ba5cb863c54f9061b3d,STILL_EXISTS,TODO: separate array type for Categorical data
aaeieegdj,IntelPython/sdc,hpat/decorators.py,167ea12c1f6eb795d21ff60555aa2b1fe56c3676,STILL_EXISTS,put pivots in locals TODO: generalize numba.jit options
aaeieegeb,IntelPython/sdc,hpat/decorators.py,167ea12c1f6eb795d21ff60555aa2b1fe56c3676,STILL_EXISTS,FIXME: support parallel setitem
aaeieegeg,IntelPython/sdc,hpat/distributed.py,d8bc63efb533274f56eb5dbe5e2ac6c43d5c0eb3,STILL_EXISTS,TODO: comprehensive support for Series vars
aaeieegej,IntelPython/sdc,hpat/hiframes/hiframes.py,d8bc63efb533274f56eb5dbe5e2ac6c43d5c0eb3,STILL_EXISTS,TODO: keep updated in variable renaming?
aaeieegfg,IntelPython/sdc,hpat/hiframes/hiframes.py,f56088caa2369045aa61cfab8795974b6d57c9cd,STILL_EXISTS,TODO: handle distributed analysis; requires handling variable name
aaeieegga,IntelPython/sdc,hpat/distributed.py,e7428b1e78025d033ce7ea778fc8035e41aa72dc,STILL_EXISTS,TODO: handle index
aaeieeggf,IntelPython/sdc,hpat/hiframes/pd_series_ext.py,5bf8689360311615d3803853218d7c79c43271b3,STILL_EXISTS,TODO: other cases?
aaeieeggg,IntelPython/sdc,hpat/tests/test_series.py,1a6946cc3479a1cb06924b721f4b772fa9880f37,STILL_EXISTS,TODO: use 2 for test int casting
aaeieeggi,IntelPython/sdc,hpat/hiframes/hiframes_typed.py,6d7f047681544de99f8e658e995ea1051f815937,STILL_EXISTS,TODO: write optimized implementation
aaeieeghb,IntelPython/sdc,hpat/hiframes/hiframes_typed.py,6d7f047681544de99f8e658e995ea1051f815937,STILL_EXISTS,TODO: handle args like sort=False
aaeieeghd,IntelPython/sdc,hpat/hiframes/api.py,27987829653df54650c25b3e51d127167970985d,STILL_EXISTS,TODO: use separate index type instead of just storing array
aaeieegic,IntelPython/sdc,hpat/hiframes/hiframes_typed.py,27987829653df54650c25b3e51d127167970985d,STILL_EXISTS,XXX assuming init_series is the only call to create a series
aaeieegie,IntelPython/sdc,hpat/hiframes/hiframes_typed.py,27987829653df54650c25b3e51d127167970985d,STILL_EXISTS,XXX use get_series_index() for getting data instead of S._index
aaeieegii,IntelPython/sdc,hpat/tests/test_series.py,27987829653df54650c25b3e51d127167970985d,STILL_EXISTS,TODO: support passing Series with Index
aaeieehgf,IntelPython/sdc,hpat/shuffle_utils.py,0d3f23c3dfa1f39352e9154b47b3fa3b68d0ec70,STILL_EXISTS,### passed to jitclass TODO: update
aaeieehgh,IntelPython/sdc,hpat/shuffle_utils.py,0d3f23c3dfa1f39352e9154b47b3fa3b68d0ec70,STILL_EXISTS,before shuffle; 'send_counts' is needed as well as
aaeieehie,IntelPython/sdc,hpat/shuffle_utils.py,0d3f23c3dfa1f39352e9154b47b3fa3b68d0ec70,STILL_EXISTS,TODO: arr refcount if arr is not stored somewhere?
aaeieehig,IntelPython/sdc,hpat/shuffle_utils.py,0d3f23c3dfa1f39352e9154b47b3fa3b68d0ec70,STILL_EXISTS,TODO: increate refcount?
aaeieehih,IntelPython/sdc,hpat/hiframes/hiframes_typed.py,c9fe7d3c319abe2f8dd64cb17111db20f3cc997b,STILL_EXISTS,TODO refactor to use overload_method
aaeieehii,IntelPython/sdc,hpat/hiframes/hiframes_typed.py,c9fe7d3c319abe2f8dd64cb17111db20f3cc997b,STILL_EXISTS,TODO: check error
aaeieehij,IntelPython/sdc,hpat/hiframes/sort.py,c9fe7d3c319abe2f8dd64cb17111db20f3cc997b,STILL_EXISTS,TODO: refactor
aaeieehja,IntelPython/sdc,hpat/str_ext.py,e108f919a2bc04166b8dcd1d77ab5933ad14088c,STILL_EXISTS,XXX setting hash secret for hash(unicode_type) to be consistent across
aaeieehjc,IntelPython/sdc,hpat/str_ext.py,e108f919a2bc04166b8dcd1d77ab5933ad14088c,STILL_EXISTS,TODO: use a seperate implementation?
aaeieehjd,IntelPython/sdc,hpat/str_ext.py,e108f919a2bc04166b8dcd1d77ab5933ad14088c,STILL_EXISTS,TODO: make sure hash(str) is not already instantiated in overloads
aaeieeifj,IntelPython/sdc,hpat/hiframes/series_kernels.py,730271408166fd5c8cc0a81103371d7a73910001,STILL_EXISTS,float columns can have regular np.nan
aaeieeigh,IntelPython/sdc,hpat/hiframes/series_kernels.py,730271408166fd5c8cc0a81103371d7a73910001,STILL_EXISTS,TODO: more efficient null counting
aaeieeigj,IntelPython/sdc,hpat/hiframes/series_kernels.py,730271408166fd5c8cc0a81103371d7a73910001,STILL_EXISTS,TODO: other types
aaeieeiid,IntelPython/sdc,hpat/hiframes/series_kernels.py,730271408166fd5c8cc0a81103371d7a73910001,STILL_EXISTS,TODO: fix for dt64
aaeieeiij,IntelPython/sdc,hpat/hiframes/series_kernels.py,730271408166fd5c8cc0a81103371d7a73910001,STILL_EXISTS,TODO: pandas returns dataframe; maybe return namedtuple instread of
aaeieeijb,IntelPython/sdc,hpat/hiframes/series_kernels.py,730271408166fd5c8cc0a81103371d7a73910001,STILL_EXISTS,TODO: fix string formatting to match python\/pandas
aaeieeijd,IntelPython/sdc,hpat/hiframes/series_kernels.py,730271408166fd5c8cc0a81103371d7a73910001,STILL_EXISTS,TODO: handle string; etc.
aaeieeijg,IntelPython/sdc,hpat/hiframes/series_kernels.py,730271408166fd5c8cc0a81103371d7a73910001,STILL_EXISTS,TODO: use online algorithm; e.g. StatFunctions.scala
aaeieejae,IntelPython/sdc,hpat/hiframes/series_kernels.py,730271408166fd5c8cc0a81103371d7a73910001,STILL_EXISTS,TODO: np.clip
aaeieejaf,IntelPython/sdc,hpat/hiframes/series_kernels.py,730271408166fd5c8cc0a81103371d7a73910001,STILL_EXISTS,TODO: np.true_divide?
aaeieejaj,IntelPython/sdc,hpat/hiframes/series_kernels.py,730271408166fd5c8cc0a81103371d7a73910001,STILL_EXISTS,TODO: refactor regex and noregex
aaeieejba,IntelPython/sdc,hpat/hiframes/series_kernels.py,730271408166fd5c8cc0a81103371d7a73910001,0149d69b7f19edaf2bb93040df19b1311602e0e9,TODO: timedelta
aaeieejbc,IntelPython/sdc,hpat/hiframes/series_kernels.py,730271408166fd5c8cc0a81103371d7a73910001,STILL_EXISTS,TODO: handle NAs in argmin\/argmax
aaeieejja,IntelPython/sdc,hpat/compiler.py,3544f2243917d9a3676fd1d3137902f5c7a6fa99,2c0548e2ec51fc4d7db1381f3772423aa84680e0,TODO: dataframe pass needed?
aaeieejjh,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,3544f2243917d9a3676fd1d3137902f5c7a6fa99,STILL_EXISTS,# XXX handling inplace_binop similar to binop for now
aaeieejji,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,3544f2243917d9a3676fd1d3137902f5c7a6fa99,STILL_EXISTS,# TODO handle inplace alignment
aaeiefaba,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,3544f2243917d9a3676fd1d3137902f5c7a6fa99,STILL_EXISTS,TODO: check invalid df.Attr?
aaeiefabc,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,3544f2243917d9a3676fd1d3137902f5c7a6fa99,STILL_EXISTS,# TODO: support alignment; dt; etc.
aaeiefafc,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,3544f2243917d9a3676fd1d3137902f5c7a6fa99,STILL_EXISTS,XXX assuming init_dataframe is the only call to create a dataframe
aaeiefafj,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,3544f2243917d9a3676fd1d3137902f5c7a6fa99,STILL_EXISTS,XXX: inine_closure_call() can't handle defaults properly
aaeiefaga,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,3544f2243917d9a3676fd1d3137902f5c7a6fa99,STILL_EXISTS,TODO: stararg needs special handling?
aaeiefagb,IntelPython/sdc,hpat/hiframes/hiframes_untyped.py,3544f2243917d9a3676fd1d3137902f5c7a6fa99,STILL_EXISTS,TODO: support index var
aaeiefagg,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,3544f2243917d9a3676fd1d3137902f5c7a6fa99,STILL_EXISTS,TODO: IterableType over column names
aaeiefagi,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,3544f2243917d9a3676fd1d3137902f5c7a6fa99,7ed468bd12027045b159e03c08dcfbb30684e510,index is Array type (TODO: Index obj)
aaeiefagj,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,3544f2243917d9a3676fd1d3137902f5c7a6fa99,7ed468bd12027045b159e03c08dcfbb30684e510,columns is tuple of strings
aaeiefaha,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,3544f2243917d9a3676fd1d3137902f5c7a6fa99,STILL_EXISTS,XXX is copy necessary?
aaeiefahb,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,3544f2243917d9a3676fd1d3137902f5c7a6fa99,STILL_EXISTS,needed?
aaeiefahe,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,057d5376c94afa9b75c0511bcf9dd3bf8a96ca98,STILL_EXISTS,TODO: alias analysis
aaeiefahh,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,057d5376c94afa9b75c0511bcf9dd3bf8a96ca98,STILL_EXISTS,TODO: use separate index type instead of just storing array
aaeiefahj,IntelPython/sdc,hpat/hiframes/boxing.py,ea52ae69ac27f027d9f4ce3ef75d70eb52f90f13,STILL_EXISTS,TODO: support unboxing index
aaeiefaib,IntelPython/sdc,hpat/hiframes/boxing.py,ea52ae69ac27f027d9f4ce3ef75d70eb52f90f13,STILL_EXISTS,TODO: other objects?
aaeiefaic,IntelPython/sdc,hpat/hiframes/boxing.py,ea52ae69ac27f027d9f4ce3ef75d70eb52f90f13,e9b3308d09c79db3253cc57b878972c062bdb9d5,TODO: refcounts?
aaeiefaid,IntelPython/sdc,hpat/hiframes/boxing.py,ea52ae69ac27f027d9f4ce3ef75d70eb52f90f13,e9b3308d09c79db3253cc57b878972c062bdb9d5,TODO: error handling like Numba callwrappers.py
aaeiefaif,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,ea52ae69ac27f027d9f4ce3ef75d70eb52f90f13,7ed468bd12027045b159e03c08dcfbb30684e510,TODO: encapsulate in meminfo since dataframe is mutible; for example:
aaeiefajb,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,ea52ae69ac27f027d9f4ce3ef75d70eb52f90f13,7ed468bd12027045b159e03c08dcfbb30684e510,TODO: meminfo for reference counting of dataframes
aaeiefajd,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,ea52ae69ac27f027d9f4ce3ef75d70eb52f90f13,STILL_EXISTS,list of flags noting which columns and index are unboxed
aaeiefajf,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,ea52ae69ac27f027d9f4ce3ef75d70eb52f90f13,STILL_EXISTS,TODO: make df refcounted to avoid repeated unboxing
aaeiefajg,IntelPython/sdc,hpat/hiframes/boxing.py,99e1b1779d3ac32c905f56c4cb25ae4de8fc6788,STILL_EXISTS,TODO: check Categorical
aaeiefajj,IntelPython/sdc,hpat/hiframes/boxing.py,99e1b1779d3ac32c905f56c4cb25ae4de8fc6788,e9b3308d09c79db3253cc57b878972c062bdb9d5,TODO: datetime.date; DatetimeIndex?
aaeiefbaa,IntelPython/sdc,hpat/hiframes/boxing.py,99e1b1779d3ac32c905f56c4cb25ae4de8fc6788,e9b3308d09c79db3253cc57b878972c062bdb9d5,TODO required?
aaeiefbac,IntelPython/sdc,hpat/hiframes/boxing.py,99e1b1779d3ac32c905f56c4cb25ae4de8fc6788,e9b3308d09c79db3253cc57b878972c062bdb9d5,TODO: is incref required?
aaeiefbeb,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,dc32c3ef54e2410549d74a14dd0a6ba9e300dbf3,STILL_EXISTS,run len on one of the columns
aaeiefbec,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,dc32c3ef54e2410549d74a14dd0a6ba9e300dbf3,STILL_EXISTS,FIXME: it could potentially avoid remove dead for the column if
aaeiefbfa,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,dc32c3ef54e2410549d74a14dd0a6ba9e300dbf3,STILL_EXISTS,TODO: avoid lowering?
aaeiefbfd,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,ad31063bc0a5ff58af836ae277b8b0567b985d22,STILL_EXISTS,TODO: avoid lowering?
aaeiefbgg,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,6ee182022fdacd2f0a1cb7c282ad838abf111dfd,STILL_EXISTS,Numba to fail. See TestDataFrame.test_unbox1; TODO: find root cause in Numba
aaeiefbhb,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,a1d67c0839f1c45b0f54663ef584821dfcbc07ed,STILL_EXISTS,TODO: check for errors
aaeiefbhc,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,a1d67c0839f1c45b0f54663ef584821dfcbc07ed,STILL_EXISTS,TODO: handle dataframe pass
aaeiefbib,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,5fb6f3fe714e44393273ce8df95560d67f9fb833,7ed468bd12027045b159e03c08dcfbb30684e510,TODO: find valid conversion possibilities
aaeiefbie,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,5fb6f3fe714e44393273ce8df95560d67f9fb833,7ed468bd12027045b159e03c08dcfbb30684e510,and other.columns == self.columns):
aaeiefbjd,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,5fb6f3fe714e44393273ce8df95560d67f9fb833,STILL_EXISTS,handling df.loc similar to df.iloc as temporary hack
aaeiefbje,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,5fb6f3fe714e44393273ce8df95560d67f9fb833,STILL_EXISTS,TODO: handle proper labeled indexes
aaeiefbjj,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,b7b06f692e3570c37fa2e23efc38fb356d4c2864,STILL_EXISTS,TODO check and report errors
aaeiefcab,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,b7b06f692e3570c37fa2e23efc38fb356d4c2864,STILL_EXISTS,TODO: test this case
aaeiefcbi,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,b2775932c6e2870faa5f4610912250656be0b73a,STILL_EXISTS,TODO: handle df.at[]
aaeiefccc,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,6e567fc5c72b065bec96f8574da11ed9bceed5b6,STILL_EXISTS,TODO: handle df.at[]
aaeiefcce,IntelPython/sdc,hpat/hiframes/api.py,f9681fecf59268d975ef54366003af0e415dd54c,STILL_EXISTS,TODO: init_dataframe
aaeiefcci,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,f9681fecf59268d975ef54366003af0e415dd54c,STILL_EXISTS,TODO: handle df.at[]
aaeiefcde,IntelPython/sdc,hpat/hiframes/hiframes_untyped.py,bee90bd80ff9edd4e263ad58fbf2cfba602b817a,STILL_EXISTS,HACK: delete pd.DataFrame({}) nodes to avoid typing errors
aaeiefcdf,IntelPython/sdc,hpat/hiframes/hiframes_untyped.py,bee90bd80ff9edd4e263ad58fbf2cfba602b817a,STILL_EXISTS,TODO: remove when dictionaries are implemented and typing works
aaeiefcef,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,3e327efb8d98a494f69f53e1bca41cf57ea4dc1c,STILL_EXISTS,TODO: test non-df case
aaeiefcei,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,3e327efb8d98a494f69f53e1bca41cf57ea4dc1c,STILL_EXISTS,TODO: fix list; Series data
aaeiefcfb,IntelPython/sdc,hpat/hiframes/hiframes_untyped.py,3e327efb8d98a494f69f53e1bca41cf57ea4dc1c,STILL_EXISTS,# cfg needed for set df column
aaeiefcfj,IntelPython/sdc,hpat/hiframes/hiframes_untyped.py,3e327efb8d98a494f69f53e1bca41cf57ea4dc1c,STILL_EXISTS,TODO: add this check back in
aaeiefcgb,IntelPython/sdc,hpat/hiframes/hiframes_untyped.py,3e327efb8d98a494f69f53e1bca41cf57ea4dc1c,STILL_EXISTS,raise ValueError(\"setting dataframe columns inside conditionals and\"
aaeiefcgd,IntelPython/sdc,hpat/hiframes/hiframes_untyped.py,3e327efb8d98a494f69f53e1bca41cf57ea4dc1c,STILL_EXISTS,TODO: generalize to more cases
aaeiefcic,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,ec94f2031d91cb206c7c9ff7e6bf7176c7dfe651,STILL_EXISTS,columns
aaeiefcig,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,ec94f2031d91cb206c7c9ff7e6bf7176c7dfe651,STILL_EXISTS,TODO: refcount of parent?
aaeiefcjg,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,141c2c29c0b34660bc7c90bc9152794b2dc17c49,STILL_EXISTS,since columns should have the same size; output is filled with NaNs
aaeiefdcb,IntelPython/sdc,hpat/hiframes/pd_categorical_ext.py,d6761b4ef4828391b8a18f8cab725163fafe32b7,STILL_EXISTS,HACK: dummy overload for CategoricalDtype to avoid type inference errors
aaeiefdcc,IntelPython/sdc,hpat/hiframes/pd_categorical_ext.py,d6761b4ef4828391b8a18f8cab725163fafe32b7,STILL_EXISTS,TODO: implement dtype properly
aaeiefdcd,IntelPython/sdc,hpat/csv_ext.py,e54c2e8da6b6c4f7e8e91072b9b23110081a5878,STILL_EXISTS,HACK: add cat type to numba.types
aaeiefdce,IntelPython/sdc,hpat/csv_ext.py,e54c2e8da6b6c4f7e8e91072b9b23110081a5878,STILL_EXISTS,FIXME: fix after Numba #3372 is resolved
aaeiefdcf,IntelPython/sdc,hpat/csv_ext.py,e54c2e8da6b6c4f7e8e91072b9b23110081a5878,STILL_EXISTS,TODO: no_cpython_wrapper=True crashes for some reason
aaeiefdcj,IntelPython/sdc,hpat/hiframes/hiframes_untyped.py,cf8030791af11243bb74c8105f5e869e1390544a,STILL_EXISTS,HACK: delete pyarrow.parquet.read_table() to avoid typing errors
aaeiefddi,IntelPython/sdc,hpat/hiframes/hiframes_untyped.py,22d7f42b74334054461e63dabdc2bd66600438f3,STILL_EXISTS,TODO: add proper metadata to Numba types
aaeiefddj,IntelPython/sdc,hpat/hiframes/hiframes_untyped.py,22d7f42b74334054461e63dabdc2bd66600438f3,STILL_EXISTS,XXX: when constants are used; all the uses of the list object
aaeiefdei,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,1eff937908a7199e011e5d9686eb1832b1037834,STILL_EXISTS,TODO: add dead_branch_prune pass to inline_closure_call
aaeiefdfc,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,1eff937908a7199e011e5d9686eb1832b1037834,STILL_EXISTS,TODO: add this to dead_branch_prune pass
aaeiefdff,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,1eff937908a7199e011e5d9686eb1832b1037834,STILL_EXISTS,TODO: fix inline_closure_call()
aaeiefdie,IntelPython/sdc,hpat/hiframes/pd_groupby_ext.py,665a9c0938f5962b635b2729962ef176973688b2,STILL_EXISTS,TODO: IterableType over groups
aaeiefdif,IntelPython/sdc,hpat/hiframes/pd_groupby_ext.py,665a9c0938f5962b635b2729962ef176973688b2,STILL_EXISTS,XXX is copy necessary?
aaeiefdih,IntelPython/sdc,hpat/hiframes/pd_groupby_ext.py,665a9c0938f5962b635b2729962ef176973688b2,STILL_EXISTS,TODO: add df object to allow control flow?
aaeiefdij,IntelPython/sdc,hpat/hiframes/pd_groupby_ext.py,665a9c0938f5962b635b2729962ef176973688b2,b9427583012f709cf0af2027ecd43a12b709f895,TODO: multi key
aaeiefdja,IntelPython/sdc,hpat/hiframes/pd_groupby_ext.py,665a9c0938f5962b635b2729962ef176973688b2,STILL_EXISTS,XXX as_index type is just bool when value not passed. Therefore;
aaeiefdjc,IntelPython/sdc,hpat/hiframes/pd_groupby_ext.py,665a9c0938f5962b635b2729962ef176973688b2,STILL_EXISTS,TODO: more robust fix or just check
aaeiefdjg,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,f9be2be490a6160221caae48e16eab3e06685ea0,STILL_EXISTS,XXX output becomes series if single output and explicitly selected
aaeiefdji,IntelPython/sdc,hpat/hiframes/pd_groupby_ext.py,f9be2be490a6160221caae48e16eab3e06685ea0,STILL_EXISTS,add key columns of not as_index
aaeiefeaa,IntelPython/sdc,hpat/hiframes/pd_groupby_ext.py,f9be2be490a6160221caae48e16eab3e06685ea0,STILL_EXISTS,XXX output becomes series if single output and explicitly selected
aaeiefeac,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,4f081d3e4f67c0d91a14dcab0d639f3ef14dd31b,STILL_EXISTS,XXX the order of output variables passed should match out_typ.columns
aaeiefebc,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,78b7fd919229e88d4f07d47639c55dd315521fc2,STILL_EXISTS,XXX the order of output variables passed should match out_typ.columns
aaeiefeca,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,d55adfc58aff8816b281ac6de7668fd5f16710ab,STILL_EXISTS,TODO: make out_key_var an index column
aaeiefecb,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,d55adfc58aff8816b281ac6de7668fd5f16710ab,STILL_EXISTS,TODO: check Series vs. array for index\/columns
aaeiefecc,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,d55adfc58aff8816b281ac6de7668fd5f16710ab,STILL_EXISTS,XXX the order of output variables passed should match out_typ.columns
aaeiefecf,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,d55adfc58aff8816b281ac6de7668fd5f16710ab,STILL_EXISTS,TODO: hanlde multiple keys (index args)
aaeiefecg,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,d55adfc58aff8816b281ac6de7668fd5f16710ab,STILL_EXISTS,TODO: handle values and aggfunc options
aaeiefeci,IntelPython/sdc,hpat/hiframes/pd_groupby_ext.py,d55adfc58aff8816b281ac6de7668fd5f16710ab,STILL_EXISTS,TODO: support agg func other than frequency
aaeiefedb,IntelPython/sdc,hpat/hiframes/api.py,597e90c6185a7512e74188473e4b171a91719888,STILL_EXISTS,XXX this is needed for _TypeMetaclass._intern to return the proper
aaeiefede,IntelPython/sdc,hpat/hiframes/pd_rolling_ext.py,9d2326a19f04641c9dd9419332cece1822c2ec54,STILL_EXISTS,XXX is copy necessary?
aaeiefedf,IntelPython/sdc,hpat/hiframes/pd_rolling_ext.py,9d2326a19f04641c9dd9419332cece1822c2ec54,STILL_EXISTS,TODO: key attribute?
aaeiefedh,IntelPython/sdc,hpat/hiframes/pd_rolling_ext.py,9d2326a19f04641c9dd9419332cece1822c2ec54,STILL_EXISTS,TODO: add df object and win\/center vals to allow control flow?
aaeiefeed,IntelPython/sdc,hpat/hiframes/pd_rolling_ext.py,9d2326a19f04641c9dd9419332cece1822c2ec54,STILL_EXISTS,TODO: handle 'on' case
aaeiefeee,IntelPython/sdc,hpat/hiframes/pd_rolling_ext.py,9d2326a19f04641c9dd9419332cece1822c2ec54,STILL_EXISTS,TODO: handle Series case (explicit select)
aaeiefeef,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,08dce3a14e9a056f5a60de56bfbeb01b2f73a86d,STILL_EXISTS,TODO: check 'on' arg
aaeiefeeg,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,08dce3a14e9a056f5a60de56bfbeb01b2f73a86d,STILL_EXISTS,TODO
aaeiefeej,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,08dce3a14e9a056f5a60de56bfbeb01b2f73a86d,STILL_EXISTS,XXX output becomes series if single output and explicitly selected
aaeiefefa,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,08dce3a14e9a056f5a60de56bfbeb01b2f73a86d,STILL_EXISTS,XXX the order of output variables passed should match out_typ.columns
aaeiefege,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,cd780490f6f26d767fb90e0e049f5fa0ce0119de,STILL_EXISTS,TODO: support dynamic conversion
aaeiefegf,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,cd780490f6f26d767fb90e0e049f5fa0ce0119de,STILL_EXISTS,TODO: support other offsets types (time delta; etc.)
aaeiefegh,IntelPython/sdc,hpat/hiframes/pd_rolling_ext.py,cd780490f6f26d767fb90e0e049f5fa0ce0119de,STILL_EXISTS,line: res_columns = arg1.columns.union(arg2.columns)
aaeiefegj,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,2e5c5b7b0219af4294f5cecbbf89a569f4c79a5f,STILL_EXISTS,TODO: Series as other
aaeiefehb,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,2e5c5b7b0219af4294f5cecbbf89a569f4c79a5f,STILL_EXISTS,in corr\/cov case; Pandas makes non-common columns NaNs
aaeiefehc,IntelPython/sdc,hpat/hiframes/pd_rolling_ext.py,2e5c5b7b0219af4294f5cecbbf89a569f4c79a5f,STILL_EXISTS,XXX pandas only accepts variable window cov\/corr
aaeiefehe,IntelPython/sdc,hpat/hiframes/pd_rolling_ext.py,2e5c5b7b0219af4294f5cecbbf89a569f4c79a5f,STILL_EXISTS,TODO: support variable window rolling cov\/corr which is only
aaeiefehg,IntelPython/sdc,hpat/hiframes/pd_rolling_ext.py,2e5c5b7b0219af4294f5cecbbf89a569f4c79a5f,STILL_EXISTS,df on df cov\/corr returns common columns only (without
aaeiefehi,IntelPython/sdc,hpat/hiframes/pd_rolling_ext.py,2e5c5b7b0219af4294f5cecbbf89a569f4c79a5f,STILL_EXISTS,TODO: support pairwise arg
aaeiefehj,IntelPython/sdc,hpat/hiframes/hiframes_untyped.py,d7c3d6b22edada95fbe2e2d4dd31e4aa63a70ee4,STILL_EXISTS,TODO: implement to_numeric in typed pass?
aaeiefeia,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,537c56af6585e3720b9aac39d1e3b24856ff0a21,STILL_EXISTS,TODO: handle non-numerical (e.g. string; datetime) columns
aaeiefeic,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,537c56af6585e3720b9aac39d1e3b24856ff0a21,STILL_EXISTS,TODO: support non-numericals like string
aaeiefeif,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,537c56af6585e3720b9aac39d1e3b24856ff0a21,STILL_EXISTS,get input columns
aaeiefeij,IntelPython/sdc,hpat/hiframes/hiframes_untyped.py,537c56af6585e3720b9aac39d1e3b24856ff0a21,STILL_EXISTS,XXX convert build_list to build_tuple since Numba doesn't handle list of
aaeiefejb,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,537c56af6585e3720b9aac39d1e3b24856ff0a21,STILL_EXISTS,TODO: handle options
aaeiefeje,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,537c56af6585e3720b9aac39d1e3b24856ff0a21,STILL_EXISTS,TODO: verify how Pandas sorts column names
aaeiefejh,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,537c56af6585e3720b9aac39d1e3b24856ff0a21,STILL_EXISTS,XXX we add arrays of float64 NaNs if a column is missing
aaeieffaa,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,537c56af6585e3720b9aac39d1e3b24856ff0a21,STILL_EXISTS,TODO: fix NA column additions for other types
aaeieffae,IntelPython/sdc,hpat/hiframes/pd_series_ext.py,980ba7390cc7701c6ce44db236ce60eb422e6e7e,STILL_EXISTS,TODO remove this cast?
aaeieffag,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,35354a0b20d7ddd8a265e731d4157244b44454be,STILL_EXISTS,TODO: handle other iterables like arrays; lists; ...
aaeieffaj,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,64c48e9927953ee09d34d33788375d2dae3821a6,STILL_EXISTS,TODO: get globals directly from passed lambda if possible?
aaeieffba,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,64c48e9927953ee09d34d33788375d2dae3821a6,STILL_EXISTS,find columns that are actually used if possible
aaeieffbe,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,64c48e9927953ee09d34d33788375d2dae3821a6,STILL_EXISTS,TODO: handle non numpy alloc types
aaeieffbi,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,64c48e9927953ee09d34d33788375d2dae3821a6,STILL_EXISTS,fix the global value to avoid typing errors
aaeieffdc,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,64c48e9927953ee09d34d33788375d2dae3821a6,STILL_EXISTS,using NamedTuple instead of Series; TODO: pass Series
aaeieffdf,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,ad5a53d5bda5e551bc311de9f37ff1b46fe48b56,STILL_EXISTS,TODO: pandas returns dataframe; maybe return namedtuple instead of
aaeieffed,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,ad5a53d5bda5e551bc311de9f37ff1b46fe48b56,STILL_EXISTS,TODO: use overload
aaeieffee,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,ad5a53d5bda5e551bc311de9f37ff1b46fe48b56,STILL_EXISTS,TODO: return proper series output
aaeieffeg,IntelPython/sdc,hpat/tests/test_dataframe.py,ad5a53d5bda5e551bc311de9f37ff1b46fe48b56,STILL_EXISTS,XXX: test actual output
aaeiefffc,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,41aecd340f59225c5642fe05a1767b0f52cd13fe,STILL_EXISTS,XXX the order of output variables passed should match out_typ.columns
aaeiefffd,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,41aecd340f59225c5642fe05a1767b0f52cd13fe,STILL_EXISTS,TODO: refcounted df data object is needed for proper inplace
aaeieffff,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,41aecd340f59225c5642fe05a1767b0f52cd13fe,STILL_EXISTS,HACK assign output df back to input df variables
aaeiefffg,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,41aecd340f59225c5642fe05a1767b0f52cd13fe,STILL_EXISTS,TODO CFG backbone?
aaeiefffh,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,41aecd340f59225c5642fe05a1767b0f52cd13fe,STILL_EXISTS,XXX fix the output type using dummy call to set_parent=True
aaeieffge,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,41aecd340f59225c5642fe05a1767b0f52cd13fe,STILL_EXISTS,XXX inplace type is just bool when value not passed. Therefore;
aaeieffgg,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,41aecd340f59225c5642fe05a1767b0f52cd13fe,STILL_EXISTS,TODO: more robust fix or just check
aaeieffha,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,41aecd340f59225c5642fe05a1767b0f52cd13fe,STILL_EXISTS,used in sort_values(inplace=True) hack
aaeieffib,IntelPython/sdc,hpat/hiframes/hiframes_typed.py,de1297df9da38fc5add2b732154c424186b726cf,STILL_EXISTS,XXX sometimes init_dataframe() can't be resolved in dataframe_pass
aaeieffih,IntelPython/sdc,hpat/hiframes/api.py,e0e463381c4784cc1c98240e1d58c8264f8d2196,STILL_EXISTS,TODO: remove when overload_method can avoid lowering or avoid cpython
aaeieffja,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,e0e463381c4784cc1c98240e1d58c8264f8d2196,STILL_EXISTS,TODO: jitoptions for overload_method and infer_global
aaeieffjc,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,e0e463381c4784cc1c98240e1d58c8264f8d2196,STILL_EXISTS,XXX index handling; assuming implicit index
aaeieffji,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,f5f3d2baac60c627763ec0407043c43d974b1710,STILL_EXISTS,copy type to sethas_parent False; TODO: data always copied?
aaeieffjj,IntelPython/sdc,hpat/hiframes/api.py,7455bdae04c94910daecb855d66b99fc4f826e83,STILL_EXISTS,TODO: FloatLiteral e.g. test_fillna
aaeiefgac,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,7455bdae04c94910daecb855d66b99fc4f826e83,STILL_EXISTS,TODO: refcounted df data object is needed for proper inplace
aaeiefgae,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,7455bdae04c94910daecb855d66b99fc4f826e83,STILL_EXISTS,HACK assign output df back to input df variables
aaeiefgaf,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,7455bdae04c94910daecb855d66b99fc4f826e83,STILL_EXISTS,TODO CFG backbone?
aaeiefgag,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,7455bdae04c94910daecb855d66b99fc4f826e83,STILL_EXISTS,XXX fix the output type using dummy call to set_parent=True
aaeiefgah,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,7455bdae04c94910daecb855d66b99fc4f826e83,STILL_EXISTS,TODO: handle possible **kwargs options?
aaeiefgaj,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,7455bdae04c94910daecb855d66b99fc4f826e83,STILL_EXISTS,TODO: inplace of df with parent that has a string column (reflection)
aaeiefgbb,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,7455bdae04c94910daecb855d66b99fc4f826e83,STILL_EXISTS,XXX inplace type is just bool when value not passed. Therefore;
aaeiefgbd,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,7455bdae04c94910daecb855d66b99fc4f826e83,STILL_EXISTS,TODO: more robust fix or just check
aaeiefgbe,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,7455bdae04c94910daecb855d66b99fc4f826e83,STILL_EXISTS,copy type to sethas_parent False; TODO: data always copied?
aaeiefgbf,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,9fd8f3084dc5b33bdb1d22c2eb73e4646636907d,STILL_EXISTS,TODO: reflection; drop=False semantics
aaeiefgbg,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,9fd8f3084dc5b33bdb1d22c2eb73e4646636907d,STILL_EXISTS,TODO: drop actual index; fix inplace
aaeiefgcb,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,9fd8f3084dc5b33bdb1d22c2eb73e4646636907d,STILL_EXISTS,TODO: inplace of df with parent (reflection)
aaeiefgcd,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,9fd8f3084dc5b33bdb1d22c2eb73e4646636907d,STILL_EXISTS,XXX inplace type is just bool when value not passed. Therefore;
aaeiefgcf,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,9fd8f3084dc5b33bdb1d22c2eb73e4646636907d,STILL_EXISTS,TODO: more robust fix or just check
aaeiefgcj,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,7c9e77f714c592e21237b2d59775c0ddb7ad4b4a,STILL_EXISTS,TODO: inplace of df with parent (reflection)
aaeiefgdb,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,7c9e77f714c592e21237b2d59775c0ddb7ad4b4a,STILL_EXISTS,XXX inplace type is just bool when value not passed. Therefore;
aaeiefgdd,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,7c9e77f714c592e21237b2d59775c0ddb7ad4b4a,STILL_EXISTS,TODO: more robust fix or just check
aaeiefgdf,IntelPython/sdc,hpat/tests/test_dataframe.py,7c9e77f714c592e21237b2d59775c0ddb7ad4b4a,STILL_EXISTS,TODO: fix error when no df is returned
aaeiefgdh,IntelPython/sdc,hpat/hiframes/api.py,14461e6054a9cdb5dee286bf52598be1ff8833d0,STILL_EXISTS,TODO: support recovery when object is not df
aaeiefgea,IntelPython/sdc,hpat/hiframes/hiframes_untyped.py,14461e6054a9cdb5dee286bf52598be1ff8833d0,STILL_EXISTS,TODO: make sure call post dominates df_var definition or df_var
aaeiefgeg,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,14461e6054a9cdb5dee286bf52598be1ff8833d0,STILL_EXISTS,TODO: inplace of df with parent (reflection)
aaeiefgei,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,14461e6054a9cdb5dee286bf52598be1ff8833d0,STILL_EXISTS,XXX inplace type is just bool when value not passed. Therefore;
aaeiefgfa,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,14461e6054a9cdb5dee286bf52598be1ff8833d0,STILL_EXISTS,TODO: more robust fix or just check
aaeiefgfc,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,469743578cc470de20226abd5c3de60877fda3ba,STILL_EXISTS,TODO: reflection for drop inplace
aaeiefgfe,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,469743578cc470de20226abd5c3de60877fda3ba,STILL_EXISTS,TODO: reflection
aaeiefgfj,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,f87562ae203c6a414b77b0eaefbd1f8bb56cd58e,STILL_EXISTS,TODO: dictionary
aaeiefgga,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,f87562ae203c6a414b77b0eaefbd1f8bb56cd58e,STILL_EXISTS,TODO: handle passed in dict case (pass colname to func?)
aaeiefggd,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,778c18a41c781d1d17731365ccf1f96bf42c8ecd,STILL_EXISTS,TODO: process these nodes
aaeiefgge,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,778c18a41c781d1d17731365ccf1f96bf42c8ecd,STILL_EXISTS,TODO: tuple case
aaeiefggf,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,778c18a41c781d1d17731365ccf1f96bf42c8ecd,STILL_EXISTS,TODO: non-homogenous build_list case
aaeiefggi,IntelPython/sdc,hpat/hiframes/pd_series_ext.py,ad5891e51d2dd3d64afb8328f32c0135712ae06d,STILL_EXISTS,TODO: name; index
aaeiefghh,IntelPython/sdc,hpat/hiframes/hiframes_untyped.py,e35c3b6cf8d8fe1334887beffdf25917531dc839,STILL_EXISTS,TODO: inst other than Assign?
aaeiefgjf,IntelPython/sdc,hpat/hiframes/hiframes_untyped.py,088665c3b527977d4a13bab5a7957b5717f857f8,STILL_EXISTS,if lhs changed; TODO: test
aaeiefhaa,IntelPython/sdc,hpat/tests/test_dataframe.py,088665c3b527977d4a13bab5a7957b5717f857f8,STILL_EXISTS,TODO: inline freevar
aaeiefhab,IntelPython/sdc,hpat/tests/test_dataframe.py,088665c3b527977d4a13bab5a7957b5717f857f8,STILL_EXISTS,TODO: df['C'] = [5;6;7]
aaeiefhae,IntelPython/sdc,hpat/hiframes/api.py,135b3c387d8792cae45223468fd8faa7dc48037a,STILL_EXISTS,TODO: other types
aaeiefhaf,IntelPython/sdc,hpat/hiframes/hiframes_untyped.py,135b3c387d8792cae45223468fd8faa7dc48037a,STILL_EXISTS,if rhs.op in ('build_list'; 'build_tuple'): TODO: test tuple
aaeiefhah,IntelPython/sdc,hpat/tests/test_join.py,3493d09ce9b08805ec7a5507906d5effb2f71530,STILL_EXISTS,TODO: cat as keys
aaeiefhai,IntelPython/sdc,hpat/hiframes/api.py,9fb2c1294392e27735a9208cf738a5c140e0ef0d,STILL_EXISTS,TODO: refcount issues?
aaeiefhaj,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,15a94748244334d4e67d4aa0c07081cd4a1fdf93,STILL_EXISTS,TODO: refactor since similar to df.head() and others that call Series
aaeiefhbc,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,15a94748244334d4e67d4aa0c07081cd4a1fdf93,STILL_EXISTS,TODO: kwargs
aaeiefhbe,IntelPython/sdc,hpat/tests/test_join.py,6dca8fd2f2340777dc7780db31a835dfb7369b60,STILL_EXISTS,TODO: check results
aaeiefhbf,IntelPython/sdc,hpat/compiler.py,277464ca0dcc0066cad4ad8082f1e9429df1b380,STILL_EXISTS,workaround for Numba #3876 issue with large labels in mortgage benchmark
aaeiefhbg,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,0d22a190d067493707fda90e621af12addacefb5,STILL_EXISTS,TODO: refactor
aaeiefhbj,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,0d22a190d067493707fda90e621af12addacefb5,STILL_EXISTS,TODO: kwargs
aaeiefhcb,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,0d22a190d067493707fda90e621af12addacefb5,STILL_EXISTS,TODO: ignore non-numerics
aaeiefhcd,IntelPython/sdc,hpat/tests/test_dataframe.py,0d22a190d067493707fda90e621af12addacefb5,STILL_EXISTS,TODO: non-numeric columns should be ignored automatically
aaeiefhce,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,c82d5b8950c47ca881ca56cbea6aa8af98c0f348,STILL_EXISTS,TODO: kwargs
aaeiefhcg,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,c82d5b8950c47ca881ca56cbea6aa8af98c0f348,STILL_EXISTS,TODO: support ddof
aaeiefhch,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,c82d5b8950c47ca881ca56cbea6aa8af98c0f348,STILL_EXISTS,TODO: ignore non-numerics
aaeiefhcj,IntelPython/sdc,hpat/tests/test_dataframe.py,c82d5b8950c47ca881ca56cbea6aa8af98c0f348,STILL_EXISTS,TODO: non-numeric columns should be ignored automatically
aaeiefhda,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,c98f7f201020a3e3d37bd15861637ce6c07ae0e6,STILL_EXISTS,TODO: kwargs
aaeiefhdc,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,c98f7f201020a3e3d37bd15861637ce6c07ae0e6,STILL_EXISTS,TODO: ignore non-numerics
aaeiefhde,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,c98f7f201020a3e3d37bd15861637ce6c07ae0e6,STILL_EXISTS,unify types for output series; TODO: check Pandas unify rules
aaeiefhdf,IntelPython/sdc,hpat/tests/test_dataframe.py,c98f7f201020a3e3d37bd15861637ce6c07ae0e6,STILL_EXISTS,TODO: non-numeric columns should be ignored automatically
aaeiefhdg,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,efa2e38615c532af915f5fe5c49abdbd92bbf7f2,STILL_EXISTS,TODO: refactor since copy of max
aaeiefhdh,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,efa2e38615c532af915f5fe5c49abdbd92bbf7f2,STILL_EXISTS,TODO: kwargs
aaeiefhdj,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,efa2e38615c532af915f5fe5c49abdbd92bbf7f2,STILL_EXISTS,TODO: ignore non-numerics
aaeiefheb,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,efa2e38615c532af915f5fe5c49abdbd92bbf7f2,STILL_EXISTS,unify types for output series; TODO: check Pandas unify rules
aaeiefhec,IntelPython/sdc,hpat/tests/test_dataframe.py,efa2e38615c532af915f5fe5c49abdbd92bbf7f2,STILL_EXISTS,TODO: non-numeric columns should be ignored automatically
aaeiefhed,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,b5948eb0d9017b9b3d27eddc66743c95e0107798,STILL_EXISTS,TODO: support ddof
aaeiefhee,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,b5948eb0d9017b9b3d27eddc66743c95e0107798,STILL_EXISTS,TODO: kwargs
aaeiefheg,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,b5948eb0d9017b9b3d27eddc66743c95e0107798,STILL_EXISTS,TODO: ignore non-numerics
aaeiefhei,IntelPython/sdc,hpat/tests/test_dataframe.py,b5948eb0d9017b9b3d27eddc66743c95e0107798,STILL_EXISTS,TODO: non-numeric columns should be ignored automatically
aaeiefhfb,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,457dae8d441bb65fccbabad064cd9fa936ce2b8f,STILL_EXISTS,TODO: kwargs
aaeiefhfd,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,457dae8d441bb65fccbabad064cd9fa936ce2b8f,STILL_EXISTS,TODO: ignore non-numerics
aaeiefhfg,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,457dae8d441bb65fccbabad064cd9fa936ce2b8f,STILL_EXISTS,unify types for output series; TODO: check Pandas unify rules
aaeiefhfh,IntelPython/sdc,hpat/tests/test_dataframe.py,457dae8d441bb65fccbabad064cd9fa936ce2b8f,STILL_EXISTS,TODO: non-numeric columns should be ignored automatically
aaeiefhfi,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,4789f5f0ff3c28a03854ad66c6f332402f298c78,STILL_EXISTS,TODO: kwargs
aaeiefhga,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,4789f5f0ff3c28a03854ad66c6f332402f298c78,STILL_EXISTS,TODO: ignore non-numerics
aaeiefhgd,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,4789f5f0ff3c28a03854ad66c6f332402f298c78,STILL_EXISTS,unify types for output series; TODO: check Pandas unify rules
aaeiefhge,IntelPython/sdc,hpat/tests/test_dataframe.py,4789f5f0ff3c28a03854ad66c6f332402f298c78,STILL_EXISTS,TODO: non-numeric columns should be ignored automatically
aaeiefhgg,IntelPython/sdc,hpat/tests/test_dataframe.py,57ab4a9295fb2b4eea2c47c6e8db22359e9cdb39,STILL_EXISTS,TODO: non-numeric columns should be ignored automatically
aaeiefhgh,IntelPython/sdc,hpat/hiframes/hiframes_untyped.py,95d3fde3032f46b451daa782e9efa2658d34b5df,STILL_EXISTS,try single type for all columns case
aaeiefhhc,IntelPython/sdc,hpat/hiframes/pd_categorical_ext.py,0b2b5c3be4aec6b11f8458c691aa475a4758ff03,STILL_EXISTS,TODO: fix aliasing
aaeiefhhf,IntelPython/sdc,hpat/distributed.py,929220ba5dc6a6ba9715b1df0684ee06c876c7ec,STILL_EXISTS,HACK support A.reshape(n; 1) for 1D_Var
aaeiefhhh,IntelPython/sdc,hpat/distributed_analysis.py,929220ba5dc6a6ba9715b1df0684ee06c876c7ec,STILL_EXISTS,HACK support A.reshape(n; 1) for 1D_Var
aaeiefhjc,IntelPython/sdc,hpat/distributed.py,56ae9b21973b48f64e3f82f17e2ebe83da3d14a9,STILL_EXISTS,TODO: fix lazy IO load
aaeiefhjh,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,56ae9b21973b48f64e3f82f17e2ebe83da3d14a9,STILL_EXISTS,TODO: refactor when objmode() can understand global string constant
aaeiefibf,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,87010b5e54122b4c98be5812a48fd11616194684,STILL_EXISTS,TODO: refcount
aaeiefibg,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,87010b5e54122b4c98be5812a48fd11616194684,STILL_EXISTS,TODO: other Pandas versions (0.24 defaults are different than 0.23)
aaeiefibi,IntelPython/sdc,hpat/distributed.py,3829f0fb8b89b1856717b212c5944c19c09851a0,STILL_EXISTS,HACK use the string in a dummy function to avoid refcount issues
aaeiefibj,IntelPython/sdc,hpat/distributed.py,3829f0fb8b89b1856717b212c5944c19c09851a0,STILL_EXISTS,TODO: fix string data reference count
aaeiefica,IntelPython/sdc,hpat/hiframes/pd_timestamp_ext.py,911dc1b164a9d0d334e146145001c00e28587094,STILL_EXISTS,TODO: fix in Numba
aaeieficc,IntelPython/sdc,hpat/csv_ext.py,01b6ce529e963883e63a0de977ca53ab3f4f2041,1b1b98b5ed66a63bef4f856bb144823c2f068861,TODO: rebalance if set
aaeiefice,IntelPython/sdc,hpat/hiframes/hiframes_untyped.py,01b6ce529e963883e63a0de977ca53ab3f4f2041,STILL_EXISTS,TODO: tune this
aaeieficf,IntelPython/sdc,hpat/hiframes/hiframes_untyped.py,01b6ce529e963883e63a0de977ca53ab3f4f2041,STILL_EXISTS,TODO: string_array; categorical; etc.
aaeiefich,IntelPython/sdc,hpat/hiframes/hiframes_untyped.py,01b6ce529e963883e63a0de977ca53ab3f4f2041,STILL_EXISTS,HACK replace build_map to avoid inference errors
aaeiefici,IntelPython/sdc,hpat/hiframes/hiframes_untyped.py,01b6ce529e963883e63a0de977ca53ab3f4f2041,STILL_EXISTS,TODO: support other args
aaeiefidd,IntelPython/sdc,hpat/hiframes/hiframes_untyped.py,0c211f282664f4cecea369e0f2f86020bb77ffb4,STILL_EXISTS,HACK replace dict.keys getattr to avoid typing errors
aaeiefifd,IntelPython/sdc,hpat/ml/d4p.py,b0fffa2eefd29df738946d532588ca618f157ca6,STILL_EXISTS,TODO: is in fact a list of tables!
aaeiefife,IntelPython/sdc,hpat/ml/d4p.py,b0fffa2eefd29df738946d532588ca618f157ca6,STILL_EXISTS,TODO: table can have different types; input can be file
aaeiefiff,IntelPython/sdc,hpat/ml/d4p.py,b0fffa2eefd29df738946d532588ca618f157ca6,STILL_EXISTS,TODO: table can have different types
aaeiefige,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,90a524c62bda8b4f07f49c58956a203141ce418e,STILL_EXISTS,TODO: fix find_const()
aaeiefiha,IntelPython/sdc,hpat/hiframes/split_impl.py,57926a5fc3d6cac78b609741e751b5fb49b312c8,STILL_EXISTS,TODO: optimized list type
aaeiefihb,IntelPython/sdc,hpat/hiframes/split_impl.py,57926a5fc3d6cac78b609741e751b5fb49b312c8,STILL_EXISTS,TODO
aaeiefihd,IntelPython/sdc,hpat/hiframes/split_impl.py,57926a5fc3d6cac78b609741e751b5fb49b312c8,STILL_EXISTS,XXX: C equivalent in _str_ext.cpp
aaeiefjad,IntelPython/sdc,hpat/distributed_analysis.py,179b0f07e18d7daf24e12da98ac24777cd53639c,b8e49a93d63459e9ab1c8ff16f00574c90205fdc,FIXME: handle Distribution.Thread and Disribution.REP as equivalent
aaeiefjag,IntelPython/sdc,hpat/hiframes/boxing.py,e7bb1eaf03011be44c1569ae9caa0262c19458e5,STILL_EXISTS,XXX dummy unboxing to avoid errors in _get_dataframe_data()
aaeiefjah,IntelPython/sdc,hpat/hiframes/hiframes_typed.py,e7bb1eaf03011be44c1569ae9caa0262c19458e5,STILL_EXISTS,TODO support dropna() for split view
aaeiefjai,IntelPython/sdc,hpat/hiframes/pd_series_ext.py,e7bb1eaf03011be44c1569ae9caa0262c19458e5,STILL_EXISTS,TODO: split view
aaeiefjbf,IntelPython/sdc,hpat/hiframes/hiframes_typed.py,709974a69535498da0b6ddd8aaf0713e99fe32d5,STILL_EXISTS,TODO: refactor and enable distributed
aaeiefjcc,IntelPython/sdc,hpat/hiframes/hiframes_typed.py,4630e9ac06f0205060e23c736a8561851e567b5c,STILL_EXISTS,TODO: fix distributed
aaeiefjce,IntelPython/sdc,hpat/hiframes/boxing.py,c1d7e1e31d889fee31db98ebfe3039923ad2bd0c,STILL_EXISTS,TODO: handle NA as 1st value
aaeiefjcf,IntelPython/sdc,hpat/hiframes/boxing.py,c1d7e1e31d889fee31db98ebfe3039923ad2bd0c,STILL_EXISTS,XXX: using .values to check date type since DatetimeIndex returns
aaeiefjcj,IntelPython/sdc,hpat/hiframes/boxing.py,4a6d246bbad9bd30fcf348812aafa4d3c157034b,STILL_EXISTS,XXX dummy unboxing to avoid errors in _get_dataframe_data()
aaeiefjea,IntelPython/sdc,hpat/tests/test_series.py,350c0c4e7f86261613f292c360ca01db2af76b2f,645f64820c81051e115473cb306a86b5a147ebff,TODO: empty list item
aaeiefjec,IntelPython/sdc,hpat/hiframes/boxing.py,645f64820c81051e115473cb306a86b5a147ebff,STILL_EXISTS,TODO: enable type checking when emty list item in
aaeiefjfc,IntelPython/sdc,hpat/distributed_analysis.py,0535cc12afe0ddbd2ea7f18cdbc23262884a9b1d,STILL_EXISTS,TODO: handle index similar to getitem to support more cases
aaeiefjfj,IntelPython/sdc,hpat/hiframes/split_impl.py,0535cc12afe0ddbd2ea7f18cdbc23262884a9b1d,STILL_EXISTS,TODO: check num strings and support NAN
aaeiefjgc,IntelPython/sdc,hpat/hiframes/hiframes_untyped.py,cd3d670b1bb0af90edf33cd48f99a1b3f7e9cf76,STILL_EXISTS,HACK make globals availabe for typing in series.map()
aaeiefjgd,IntelPython/sdc,hpat/hiframes/pd_series_ext.py,cd3d670b1bb0af90edf33cd48f99a1b3f7e9cf76,STILL_EXISTS,XXX hack in hiframes_typed to make globals available
aaeiefjge,IntelPython/sdc,hpat/hiframes/pd_series_ext.py,cd3d670b1bb0af90edf33cd48f99a1b3f7e9cf76,STILL_EXISTS,TODO: use code.co_names to find globals actually used?
aaeiefjgh,IntelPython/sdc,hpat/distributed_analysis.py,e8cacc5e4d0020204afdccfac4b565e0b5028df1,STILL_EXISTS,TODO: implement extendable version in ir_utils
aaeiefjhf,IntelPython/sdc,hpat/distributed.py,1c34f97215dec05cb9e21ce33948ef403199d99e,STILL_EXISTS,TODO: test parallel
aaeiefjhh,IntelPython/sdc,hpat/distributed_analysis.py,1c34f97215dec05cb9e21ce33948ef403199d99e,STILL_EXISTS,XXX sometimes copy propagation doesn't work for parfor indices
aaeiefjib,IntelPython/sdc,hpat/hiframes/pd_series_ext.py,f5fa66867507c25333fc54915e97af60a4874250,STILL_EXISTS,TODO: support index rename; kws
aaeiefjid,IntelPython/sdc,hpat/hiframes/hiframes_typed.py,43eb5cf6b5c07608b2712ec81daa699e9f32aee6,STILL_EXISTS,TODO: handle all possible cases
aaeiefjie,IntelPython/sdc,hpat/hiframes/pd_series_ext.py,43eb5cf6b5c07608b2712ec81daa699e9f32aee6,STILL_EXISTS,TODO: handle any of args being Series independently
aaeiefjif,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,1de529bdb3047ef74df684c63e9bd1d04542bc94,STILL_EXISTS,TODO: handle names of SeriesTypes
aaeiefjih,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,1de529bdb3047ef74df684c63e9bd1d04542bc94,STILL_EXISTS,TODO: test
aaeiefjii,IntelPython/sdc,hpat/tests/test_dataframe.py,1de529bdb3047ef74df684c63e9bd1d04542bc94,STILL_EXISTS,TODO: support int as column name
aaeiefjij,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,999ad917c05ca7b87ddc89aa18a3bc073c671435,STILL_EXISTS,sometimes index_var is None; so fix it
aaeiefjja,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,999ad917c05ca7b87ddc89aa18a3bc073c671435,STILL_EXISTS,TODO: get rid of static_getitem
aaeiefjjc,IntelPython/sdc,hpat/distributed.py,74a2d2fa80271f2ed2112788ec1eb8e8afd6e711,STILL_EXISTS,TODO: create another optimization pass?
aaeiefjjd,IntelPython/sdc,hpat/compiler.py,5cc296188805613948cdb15cf51d4e84cb74db39,STILL_EXISTS,TODO remove if when inline_closure_call() output fix
aaeiefjjf,IntelPython/sdc,hpat/compiler.py,5cc296188805613948cdb15cf51d4e84cb74db39,STILL_EXISTS,TODO: update '##distributed' and '##threaded' in _locals
aaeiefjjh,IntelPython/sdc,hpat/hiframes/dataframe_pass.py,bc0cb1d17bdfbf57290e5c821d1391c903d729b8,STILL_EXISTS,TODO: remove after Numba #3946 is merged
aaeiegabb,IntelPython/sdc,hpat/config.py,62314491b148c51e7c27e13aded283a0622c47f4,STILL_EXISTS,TODO: make sure h5py\/hdf5 supports parallel
aaeiegaca,IntelPython/sdc,hpat/str_arr_ext.py,6c0701323a277bcb7938a40a4dad14d1ccfb8569,STILL_EXISTS,TODO: test
aaeiegadb,IntelPython/sdc,hpat/hiframes/aggregate.py,13dd98b6c436633880ae2aa6b2b3715d795c38c2,STILL_EXISTS,TODO: handle unicode length
aaeiegade,IntelPython/sdc,hpat/set_ext.py,13dd98b6c436633880ae2aa6b2b3715d795c38c2,STILL_EXISTS,TODO: support unicode
aaeiegadf,IntelPython/sdc,hpat/hiframes/sort.py,4e51c498cb41a389017b8f242679fc3cda576c92,STILL_EXISTS,TODO: fix cache issue
aaeiegadg,IntelPython/sdc,hpat/str_arr_ext.py,70ed9a2aa574579a768e4382bebdebe481af6e2c,61740ad44e5b0ba86da693baa3f97e84581c92fa,TODO: use ascii from unicode type when available
aaeiegagf,IntelPython/sdc,hpat/hiframes/sort.py,127645adac32020d1c4d656c24c75359783413b5,STILL_EXISTS,HACK make sure ascending is boolean (seen error for none in CI)
aaeiegagg,IntelPython/sdc,hpat/hiframes/sort.py,127645adac32020d1c4d656c24c75359783413b5,STILL_EXISTS,TODO: fix source of issue
aaeiegaih,IntelPython/sdc,hpat/hiframes/boxing.py,ac0595299d3e0b9d75f63b17b7b86a84a901c4e4,c0e51d94646d5581fcfd24e99d8d6482329d34e7,TODO: support proper inference
aaeiegaii,IntelPython/sdc,hpat/hiframes/boxing.py,ac0595299d3e0b9d75f63b17b7b86a84a901c4e4,STILL_EXISTS,TODO: other indices
aaeiegaji,IntelPython/sdc,hpat/tests/test_series.py,0e61489d00858bd9ba696406b8a59157165e4cb4,STILL_EXISTS,TODO: gatherv
aaeiegbaa,IntelPython/sdc,hpat/hiframes/series_kernels.py,552628ab42778b70755631cbf0092d7295f47810,STILL_EXISTS,TODO: test
aaeiegbab,IntelPython/sdc,hpat/hiframes/series_kernels.py,552628ab42778b70755631cbf0092d7295f47810,STILL_EXISTS,TODO: generalize
aaeiegdih,IntelPython/sdc,hpat/hiframes/pd_series_ext.py,1c3b32dfda4c8568a9720a74cc32684302899f2b,STILL_EXISTS,TODO: call numba getiter with gep_result for array
aaeiegdij,IntelPython/sdc,hpat/hiframes/pd_series_ext.py,1c3b32dfda4c8568a9720a74cc32684302899f2b,STILL_EXISTS,TODO: call it from numba.targets.arrayobj; need separate function in numba
aaeiegech,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,6cc2f6267acd103ee4ae5d478681dc0539028194,STILL_EXISTS,TODO: ignore non-numerics
aaeiegecj,IntelPython/sdc,hpat/hiframes/pd_dataframe_ext.py,6cc2f6267acd103ee4ae5d478681dc0539028194,STILL_EXISTS,TODO: kwargs
aaeiegedb,IntelPython/sdc,hpat/tests/test_dataframe.py,6cc2f6267acd103ee4ae5d478681dc0539028194,STILL_EXISTS,TODO: non-numeric columns should be ignored automatically
aaeiegefa,IntelPython/sdc,hpat/datatypes/hpat_pandas_series_functions.py,c306deb6688d522741be169fc0f3b68041c7ce12,STILL_EXISTS,TODO: replace with below line when Numba supports np.isin in nopython mode
aaeiegefg,IntelPython/sdc,docs/rename_function.py,6ba06c421a347cbb26c274d87ce14c660544d3fc,STILL_EXISTS,Always add new name at the ends. Do not change the order
aaeiegfbc,IntelPython/sdc,hpat/tests/test_io.py,52ca51c6165c98631b0a91cf804379399fc11fc9,STILL_EXISTS,TODO: w\/a for Numba issue with int typing rules infering intp for integers literals
aaeiegfda,IntelPython/sdc,hpat/datatypes/hpat_pandas_series_functions.py,63a4215f5cb07db175b1e000ae45c1ebdca8531f,b44ff60e35d0c2e4f91e1d7705bad0a5191f1e3b,TODO Needs to implement parameters value check
aaeiegfgc,IntelPython/sdc,hpat/datatypes/hpat_pandas_seriesgroupby_functions.py,63a4215f5cb07db175b1e000ae45c1ebdca8531f,STILL_EXISTS,workaround
aaeieggdh,IntelPython/sdc,hpat/datatypes/hpat_pandas_series_functions.py,d71e2407175995d2e795046c4ba0c3474050fd76,STILL_EXISTS,TODO Needs to implement parameters value check
aaeiegibg,IntelPython/sdc,hpat/tests/test_series.py,6d8ae03030f73707cbeba1aeac6e68dcdf59fc10,3fc4b5ed561fd6d11ee2154e542786f9587cb658,TODO type_min\/type_max
aaeiegide,IntelPython/sdc,hpat/tests/test_dataframe.py,3fc4b5ed561fd6d11ee2154e542786f9587cb658,STILL_EXISTS,TODO: add column with datetime values when test_series_datetime_isna1 is fixed
aaeiegidg,IntelPython/sdc,hpat/tests/test_series.py,181b87f49b00877b30921cf00971e08a36b4b270,STILL_EXISTS,TODO type_min\/type_max
aaeiegiea,IntelPython/sdc,hpat/tests/test_dataframe.py,e9730e1484591de5b5e59d86e8cc1e1e499d202d,STILL_EXISTS,TODO: uncomment column with string values when test_series_astype_str_to_float64 is fixed
aaeiegiec,IntelPython/sdc,hpat/tests/test_dataframe.py,e9730e1484591de5b5e59d86e8cc1e1e499d202d,STILL_EXISTS,TODO: uncomment column with string values when test_series_astype_str_to_int32 is fixed
aaeiegifb,IntelPython/sdc,hpat/tests/test_series.py,004d71d67fad53092bae75c9ad178f559a3966eb,STILL_EXISTS,TODO type_min\/type_max
aaeiehabj,IntelPython/sdc,hpat/compiler.py,2c0548e2ec51fc4d7db1381f3772423aa84680e0,STILL_EXISTS,TODO: remove these helper functions when Numba provide appropriate way to manipulate passes
aaeiehccg,IntelPython/sdc,hpat/datatypes/hpat_pandas_series_functions.py,d697beb6ea1ec352e7522ec11f66fce20c387def,STILL_EXISTS,generate Series index if needed by using SeriesType.index (i.e. not self._index)
aaeiehcef,IntelPython/sdc,hpat/datatypes/hpat_pandas_series_functions.py,d7375754b5c53f37cc871ef41cd1f35508ee0ac0,STILL_EXISTS,TODO: StringArrayType cannot resize inplace; and assigning a copy back to self._data is not possible now
aaeiehdhf,IntelPython/sdc,generate_data/gen_employees_csv.py,346793631732a5cb9d8dc42b6b5ea73838755739,STILL_EXISTS,These columns are not used for employees.csv generation
aaeifaeca,IntelPython/sdc,hpat/datatypes/common_functions.py,b857c1072918d790e9deefbce61ae4117b5968aa,STILL_EXISTS,TODO: this heavily relies on B being a homogeneous tuple\/list - find a better way
aaeifaecc,IntelPython/sdc,hpat/datatypes/common_functions.py,b857c1072918d790e9deefbce61ae4117b5968aa,STILL_EXISTS,TODO: refactor to use numpy.concatenate when Numba supports building a tuple at runtime
aaeifaece,IntelPython/sdc,hpat/datatypes/hpat_pandas_series_functions.py,b857c1072918d790e9deefbce61ae4117b5968aa,STILL_EXISTS,TODO: find a way to stop compilation early and not proceed with unliteral step
aaeifaedc,IntelPython/sdc,hpat/datatypes/hpat_pandas_series_functions.py,5ebae6c7ec88eec8ef93488510471bae3357c4f8,STILL_EXISTS,TODO: if dropna add nan handling
aaeifaedd,IntelPython/sdc,hpat/datatypes/hpat_pandas_series_functions.py,5ebae6c7ec88eec8ef93488510471bae3357c4f8,STILL_EXISTS,TODO: workaround; keys() result can not be casted to array type
aaeifaede,IntelPython/sdc,hpat/datatypes/hpat_pandas_series_functions.py,5ebae6c7ec88eec8ef93488510471bae3357c4f8,STILL_EXISTS,TODO: use list comprehension instead or self.unique()
aaeifaedg,IntelPython/sdc,hpat/datatypes/hpat_pandas_series_functions.py,5ebae6c7ec88eec8ef93488510471bae3357c4f8,STILL_EXISTS,TODO: consider order of values with the same frequency
aaeifaedi,IntelPython/sdc,hpat/datatypes/hpat_pandas_series_functions.py,5ebae6c7ec88eec8ef93488510471bae3357c4f8,STILL_EXISTS,TODO: unique() can not handle numpy.nan because numpy.nan == numpy.nan is False
aaeifaedj,IntelPython/sdc,hpat/datatypes/hpat_pandas_series_functions.py,5ebae6c7ec88eec8ef93488510471bae3357c4f8,STILL_EXISTS,TODO: not optimal
aaeifajdb,IntelPython/sdc,docs/source/conf.py,664f4af917282574511eebff38cea3646429ba27,STILL_EXISTS,TODO: Rename hpat module name to sdc
aaeifajej,IntelPython/sdc,docs/source/conf.py,664f4af917282574511eebff38cea3646429ba27,STILL_EXISTS,-- Todo extension configuration  ----------------------------------------------
aaeifajii,IntelPython/sdc,docs/source/sdc2pd_name.py,664f4af917282574511eebff38cea3646429ba27,STILL_EXISTS,TODO: Rename hpat module name to sdc
aaeifbaah,IntelPython/sdc,docs/source/sdc2pd_name.py,664f4af917282574511eebff38cea3646429ba27,STILL_EXISTS,TODO: Change hpat to sdc
aaeifbbdf,IntelPython/sdc,hpat/tests/test_series.py,47c84e937d8580b0a8a91db917eb104f60625e6e,STILL_EXISTS,TODO: fix issue occurred if name is not assigned
aaeifbbeg,IntelPython/sdc,docs/source/conf.py,b3c7719a9b1419d0771be8242f8ec1922e064574,STILL_EXISTS,TODO: Rename hpat module name to sdc
aaeifbbfc,IntelPython/sdc,docs/source/conf.py,b3c7719a9b1419d0771be8242f8ec1922e064574,STILL_EXISTS,-- Todo extension configuration  ----------------------------------------------
aaeifbiag,IntelPython/sdc,sdc/io/csv_ext.py,515f7b595b47fff4a60728066bd4d4a9b1ef141a,STILL_EXISTS,TODO: move to hpat.common
aaeifbicb,IntelPython/sdc,sdc/io/csv_ext.py,515f7b595b47fff4a60728066bd4d4a9b1ef141a,STILL_EXISTS,TODO: Try to help pyarrow infer date type - set DateType.
aaeifbice,IntelPython/sdc,sdc/io/csv_ext.py,515f7b595b47fff4a60728066bd4d4a9b1ef141a,a927cfabcc264e4e34d263fc18e1494a8ecb4ba6,TODO: support non-numpy types like strings
aaeifbich,IntelPython/sdc,sdc/io/csv_ext.py,515f7b595b47fff4a60728066bd4d4a9b1ef141a,a927cfabcc264e4e34d263fc18e1494a8ecb4ba6,TODO: fix globals after Numba's #3355 is resolved
aaeifbida,IntelPython/sdc,sdc/io/csv_ext.py,515f7b595b47fff4a60728066bd4d4a9b1ef141a,a927cfabcc264e4e34d263fc18e1494a8ecb4ba6,TODO: no_cpython_wrapper=True crashes for some reason
aaeifhcab,IntelPython/sdc,docs/source/info.py,35bde54c4eba5ad8b844ef40bfe6cb12c2667360,STILL_EXISTS,workaround expected behavior from unittests
aaeifhdde,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_pass.py,bca334dfe32973ecb726a54bfcfe7747be825a71,STILL_EXISTS,XXX remove slice() of h5 read due to Numba's #3380 bug
aaeifhdee,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_pass.py,bca334dfe32973ecb726a54bfcfe7747be825a71,STILL_EXISTS,shssf:  is it still needed?
aaeifhdef,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_pass.py,bca334dfe32973ecb726a54bfcfe7747be825a71,STILL_EXISTS,FIXME: see why this breaks test_kmeans
aaeifhdfc,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_pass.py,bca334dfe32973ecb726a54bfcfe7747be825a71,STILL_EXISTS,cfg needed for set df column
aaeifhdfd,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_pass.py,bca334dfe32973ecb726a54bfcfe7747be825a71,STILL_EXISTS,TODO: fix scope\/loc
aaeifhdfg,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_pass.py,bca334dfe32973ecb726a54bfcfe7747be825a71,STILL_EXISTS,TODO: inst other than Assign?
aaeifhdfh,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_pass.py,bca334dfe32973ecb726a54bfcfe7747be825a71,STILL_EXISTS,TODO: insert new blocks in current spot of work_list
aaeifhdfj,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_pass.py,bca334dfe32973ecb726a54bfcfe7747be825a71,STILL_EXISTS,TODO: rename variables; fix scope\/loc
aaeifhdgb,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_pass.py,bca334dfe32973ecb726a54bfcfe7747be825a71,STILL_EXISTS,XXX: remove dead here fixes h5 slice issue
aaeifhdgh,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_pass.py,bca334dfe32973ecb726a54bfcfe7747be825a71,STILL_EXISTS,if lhs changed; TODO: test
aaeifhdgi,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_pass.py,bca334dfe32973ecb726a54bfcfe7747be825a71,STILL_EXISTS,HACK: delete pd.DataFrame({}) nodes to avoid typing errors
aaeifhdgj,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_pass.py,bca334dfe32973ecb726a54bfcfe7747be825a71,STILL_EXISTS,TODO: remove when dictionaries are implemented and typing works
aaeifhdha,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_pass.py,bca334dfe32973ecb726a54bfcfe7747be825a71,STILL_EXISTS,TODO: implement to_numeric in typed pass?
aaeifhdhe,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_pass.py,bca334dfe32973ecb726a54bfcfe7747be825a71,STILL_EXISTS,HACK make globals availabe for typing in series.map()
aaeifhdij,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_pass.py,bca334dfe32973ecb726a54bfcfe7747be825a71,STILL_EXISTS,TODO: handle passed in dict case (pass colname to func?)
aaeifhdja,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_pass.py,bca334dfe32973ecb726a54bfcfe7747be825a71,STILL_EXISTS,XXX df isin is different than Series.isin; df.isin considers
aaeifhdjc,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_pass.py,bca334dfe32973ecb726a54bfcfe7747be825a71,STILL_EXISTS,TODO: support strings and other types
aaeifhdjh,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_pass.py,bca334dfe32973ecb726a54bfcfe7747be825a71,STILL_EXISTS,TODO: check for series\/dict\/list input
aaeifhdji,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_pass.py,bca334dfe32973ecb726a54bfcfe7747be825a71,STILL_EXISTS,TODO: enforce ignore_index=True?
aaeifheaf,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_pass.py,bca334dfe32973ecb726a54bfcfe7747be825a71,STILL_EXISTS,TODO: make sure call post dominates df_var definition or df_var
aaeifhebc,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_pass.py,bca334dfe32973ecb726a54bfcfe7747be825a71,STILL_EXISTS,dropping columns inplace possible only when it dominates the df
aaeifhebe,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_pass.py,bca334dfe32973ecb726a54bfcfe7747be825a71,STILL_EXISTS,TODO: rename df name
aaeifhebf,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_pass.py,bca334dfe32973ecb726a54bfcfe7747be825a71,STILL_EXISTS,TODO: support dropping columns of input dfs (reflection)
aaeifhebh,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_pass.py,bca334dfe32973ecb726a54bfcfe7747be825a71,STILL_EXISTS,TODO: support index var
aaeifhecf,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_pass.py,bca334dfe32973ecb726a54bfcfe7747be825a71,STILL_EXISTS,XXX convert build_list to build_tuple since Numba doesn't handle list of
aaeifheci,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_pass.py,bca334dfe32973ecb726a54bfcfe7747be825a71,STILL_EXISTS,TODO: handle non-numerical (e.g. string; datetime) columns
aaeifheda,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_pass.py,bca334dfe32973ecb726a54bfcfe7747be825a71,STILL_EXISTS,TODO: verify how Pandas sorts column names
aaeifhedc,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_pass.py,bca334dfe32973ecb726a54bfcfe7747be825a71,STILL_EXISTS,TODO: support non-numericals like string
aaeifhedf,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_pass.py,bca334dfe32973ecb726a54bfcfe7747be825a71,STILL_EXISTS,get input columns
aaeifhedj,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_pass.py,bca334dfe32973ecb726a54bfcfe7747be825a71,STILL_EXISTS,fix list(multi-dim arrays) (packing images)
aaeifheea,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_pass.py,bca334dfe32973ecb726a54bfcfe7747be825a71,STILL_EXISTS,FIXME: does this break for list(other things)?
aaeifhefd,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_pass.py,bca334dfe32973ecb726a54bfcfe7747be825a71,STILL_EXISTS,XXX output becomes series if single output and explicitly selected
aaeifhegb,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_pass.py,bca334dfe32973ecb726a54bfcfe7747be825a71,STILL_EXISTS,HACK replace dict.keys getattr to avoid typing errors
aaeifhegi,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_pass.py,bca334dfe32973ecb726a54bfcfe7747be825a71,STILL_EXISTS,TODO: remove index col for offset case
aaeifhegj,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_pass.py,bca334dfe32973ecb726a54bfcfe7747be825a71,STILL_EXISTS,XXX pandas only accepts variable window cov\/corr
aaeifhehb,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_pass.py,bca334dfe32973ecb726a54bfcfe7747be825a71,STILL_EXISTS,TODO: support variable window rolling cov\/corr which is only
aaeifhehd,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_pass.py,bca334dfe32973ecb726a54bfcfe7747be825a71,STILL_EXISTS,df on df cov\/corr returns common columns only (without
aaeifhehf,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_pass.py,bca334dfe32973ecb726a54bfcfe7747be825a71,STILL_EXISTS,TODO: support pairwise arg
aaeifhehg,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_pass.py,bca334dfe32973ecb726a54bfcfe7747be825a71,STILL_EXISTS,Pandas makes non-common columns NaNs
aaeifhehi,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_pass.py,bca334dfe32973ecb726a54bfcfe7747be825a71,STILL_EXISTS,TODO: add datetime index for offset case
aaeifhehj,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_pass.py,bca334dfe32973ecb726a54bfcfe7747be825a71,STILL_EXISTS,create NaN columns for cov\/corr case
aaeifheic,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_pass.py,bca334dfe32973ecb726a54bfcfe7747be825a71,STILL_EXISTS,line: res_columns = arg1.columns.union(arg2.columns)
aaeifheje,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_pass.py,bca334dfe32973ecb726a54bfcfe7747be825a71,STILL_EXISTS,XXX placeholder for df variable renaming
aaeifidcj,IntelPython/sdc,sdc/tests/tests_perf/test_perf_series.py,7cc8733a971b0c7724e706781a6dd1e5d813c6cf,ffacf05a834be1da83c10d899424950d641f883f,TODO: replace with generic function to generate random sequence of floats
aaeifiddc,IntelPython/sdc,sdc/hiframes/pd_series_ext.py,2e26f6fe531e8d93b13a6eae52a5178c9c403c91,1b1f62755bd5f5752daeb00df89ef7a375eaf617,XXX hack in hiframes_typed to make globals available
aaeifiddd,IntelPython/sdc,sdc/hiframes/pd_series_ext.py,2e26f6fe531e8d93b13a6eae52a5178c9c403c91,1b1f62755bd5f5752daeb00df89ef7a375eaf617,TODO: use code.co_names to find globals actually used?
aaeifidga,IntelPython/sdc,sdc/tests/test_series.py,4410e92455378adbc52cb8e115235d59788151ff,f64407c9ec1b1d258ba28e4a548d72117453f367,TODO: fix issue occurred if name is not assigned
aaeifidhg,IntelPython/sdc,sdc/hiframes/pd_series_ext.py,4301a9a9268590e95249059f5ca3b3fcc714e6ea,1b1f62755bd5f5752daeb00df89ef7a375eaf617,XXX hack in hiframes_typed to make globals available
aaeifidhh,IntelPython/sdc,sdc/hiframes/pd_series_ext.py,4301a9a9268590e95249059f5ca3b3fcc714e6ea,1b1f62755bd5f5752daeb00df89ef7a375eaf617,TODO: use code.co_names to find globals actually used?
aaeifidie,IntelPython/sdc,sdc/tests/test_series.py,4301a9a9268590e95249059f5ca3b3fcc714e6ea,f64407c9ec1b1d258ba28e4a548d72117453f367,TODO: fix issue occurred if name is not assigned
aaeififja,IntelPython/sdc,sdc/hiframes/pd_series_ext.py,1513e292c1f1d127ba54fcf9fc40829f69783240,4827e9896574a1db1cb1370133eeae7eceaeb29f,XXX only list(list(str)) supported
aaeifigcc,IntelPython/sdc,docs/source/buildscripts/apiref_generator.py,0c9d320baac7624e031754fd7ee31a7f829c8453,STILL_EXISTS,Fix bullet list indentation issues
aaeifigcd,IntelPython/sdc,docs/source/buildscripts/apiref_generator.py,0c9d320baac7624e031754fd7ee31a7f829c8453,STILL_EXISTS,Fix unresolved references after removal of References sections
aaeifighd,IntelPython/sdc,docs/source/buildscripts/apiref_generator.py,0c9d320baac7624e031754fd7ee31a7f829c8453,STILL_EXISTS,Check if only short description is needed
aaeifihgj,IntelPython/sdc,docs/source/buildscripts/module_info.py,0c9d320baac7624e031754fd7ee31a7f829c8453,STILL_EXISTS,workaround expected behavior from unittests
aaeifijje,IntelPython/sdc,docs/source/buildscripts/sdc_object_utils.py,0c9d320baac7624e031754fd7ee31a7f829c8453,STILL_EXISTS,Customizable test for skipping objects as needed
aaeifjjjj,IntelPython/sdc,sdc/datatypes/common_functions.py,81208c78abc8ae9caec4ac0b461e39455e7632ed,STILL_EXISTS,TODO: eliminate code duplication by merging implementations for numeric and StringArray
aaeigaaag,IntelPython/sdc,sdc/datatypes/common_functions.py,81208c78abc8ae9caec4ac0b461e39455e7632ed,STILL_EXISTS,TODO: support joining indexes with common dtype=object - requires Numba
aaeigaabg,IntelPython/sdc,sdc/datatypes/hpat_pandas_series_functions.py,81208c78abc8ae9caec4ac0b461e39455e7632ed,9b5c64bba0bdb09d9472e88eb367ffe66d316b26,specializations for numeric series - TODO: support arithmetic operation on StringArrays
aaeigaacb,IntelPython/sdc,sdc/datatypes/hpat_pandas_series_functions.py,81208c78abc8ae9caec4ac0b461e39455e7632ed,9b5c64bba0bdb09d9472e88eb367ffe66d316b26,TODO: replace with StringArrays comparison
aaeigaacc,IntelPython/sdc,sdc/datatypes/hpat_pandas_series_functions.py,81208c78abc8ae9caec4ac0b461e39455e7632ed,9b5c64bba0bdb09d9472e88eb367ffe66d316b26,TODO: replace below with core join(how='outer'; return_indexers=True) when implemented
aaeigaacd,IntelPython/sdc,sdc/hiframes/pd_series_ext.py,81208c78abc8ae9caec4ac0b461e39455e7632ed,1b1f62755bd5f5752daeb00df89ef7a375eaf617,XXX hack in hiframes_typed to make globals available
aaeigaace,IntelPython/sdc,sdc/hiframes/pd_series_ext.py,81208c78abc8ae9caec4ac0b461e39455e7632ed,1b1f62755bd5f5752daeb00df89ef7a375eaf617,TODO: use code.co_names to find globals actually used?
aaeigaacj,IntelPython/sdc,sdc/tests/test_series.py,81208c78abc8ae9caec4ac0b461e39455e7632ed,c7889893b49f7b251cd9f0a0889107593d8f1c4a,FIXME: skip the sub-test if one of the dtypes is float and the other is integer
aaeigabbb,IntelPython/sdc,sdc/hiframes/pd_series_ext.py,34f214484ae3162e2db9bf324f3e55fddbe034e3,1b1f62755bd5f5752daeb00df89ef7a375eaf617,XXX hack in hiframes_typed to make globals available
aaeigabbc,IntelPython/sdc,sdc/hiframes/pd_series_ext.py,34f214484ae3162e2db9bf324f3e55fddbe034e3,1b1f62755bd5f5752daeb00df89ef7a375eaf617,TODO: use code.co_names to find globals actually used?
aaeigabbj,IntelPython/sdc,sdc/tests/test_series.py,34f214484ae3162e2db9bf324f3e55fddbe034e3,c7889893b49f7b251cd9f0a0889107593d8f1c4a,FIXME: skip the sub-test if one of the dtypes is float and the other is integer
aaeigadhh,IntelPython/sdc,sdc/datatypes/hpat_pandas_series_functions.py,f9332d312b6e9cff94146fe573c0f023db0ce33b,9b5c64bba0bdb09d9472e88eb367ffe66d316b26,specializations for numeric series - TODO: support arithmetic operation on StringArrays
aaeigadic,IntelPython/sdc,sdc/datatypes/hpat_pandas_series_functions.py,f9332d312b6e9cff94146fe573c0f023db0ce33b,9b5c64bba0bdb09d9472e88eb367ffe66d316b26,TODO: replace with StringArrays comparison
aaeigadid,IntelPython/sdc,sdc/datatypes/hpat_pandas_series_functions.py,f9332d312b6e9cff94146fe573c0f023db0ce33b,9b5c64bba0bdb09d9472e88eb367ffe66d316b26,TODO: replace below with core join(how='outer'; return_indexers=True) when implemented
aaeigafig,IntelPython/sdc,sdc/tests/test_rolling.py,1cb56370ef855d515296d8b8520688931cdf6c38,42fd9b2a4afc41f4b6f4117cb283f349ff869f56,TODO: fix the issue when window = 0
aaeigagbf,IntelPython/sdc,sdc/tests/test_rolling.py,95006ecf299673ae01dfd5a48dfcf29a9019cad4,42fd9b2a4afc41f4b6f4117cb283f349ff869f56,TODO: fix the issue when window = 0
aaeigaijg,IntelPython/sdc,sdc/tests/test_rolling.py,de10de25ea7dd0b68b63e8ed5156f1566518bb3a,STILL_EXISTS,TODO: fix the issue when window = 0
aaeigcaef,IntelPython/sdc,sdc/rewrites/ir_utils.py,8815612b71f7fc7b2b157e7e79cf2a8eaf04d278,STILL_EXISTS,TODO handle usage outside of given block
aaeigccaj,IntelPython/sdc,sdc/datatypes/hpat_pandas_series_rolling_functions.py,9f0d976cc9316713a47c947e9ec0279bf6a92b63,b5bbbaa3db7885c033471836d1536313045a9796,TODO: check `other` is Series after a circular import of SeriesType fixed
aaeigcddf,IntelPython/sdc,sdc/datatypes/hpat_pandas_series_rolling_functions.py,d55ee2281d2e22c779addf8c1b19e8be1be1f548,b5bbbaa3db7885c033471836d1536313045a9796,TODO: check `other` is Series after a circular import of SeriesType fixed
aaeigceac,IntelPython/sdc,sdc/hiframes/hiframes_untyped.py,c7b3cd1e71ea4d60fe177ebc7e91bec1a4555114,da52b52c885f10935383f44dad2088d64ec29f3c,TODO: tune this
aaeigcead,IntelPython/sdc,sdc/hiframes/hiframes_untyped.py,c7b3cd1e71ea4d60fe177ebc7e91bec1a4555114,da52b52c885f10935383f44dad2088d64ec29f3c,TODO: string_array; categorical; etc.
aaeigcedj,IntelPython/sdc,buildscripts/autogen_sources.py,9b5c64bba0bdb09d9472e88eb367ffe66d316b26,STILL_EXISTS,certaing modifications are needed to be applied for templates; so
aaeigceeb,IntelPython/sdc,sdc/datatypes/common_functions.py,9b5c64bba0bdb09d9472e88eb367ffe66d316b26,STILL_EXISTS,TODO: eliminate code duplication by merging implementations for numeric and StringArray
aaeigceei,IntelPython/sdc,sdc/datatypes/common_functions.py,9b5c64bba0bdb09d9472e88eb367ffe66d316b26,STILL_EXISTS,TODO: support joining indexes with common dtype=object - requires Numba
aaeigcefi,IntelPython/sdc,sdc/datatypes/common_functions.py,9b5c64bba0bdb09d9472e88eb367ffe66d316b26,STILL_EXISTS,TODO: replace with StringArrays comparison
aaeigcfib,IntelPython/sdc,sdc/datatypes/sdc_function_templates.py,9b5c64bba0bdb09d9472e88eb367ffe66d316b26,STILL_EXISTS,specializations for numeric series - TODO: support arithmetic operation on StringArrays
aaeigcfig,IntelPython/sdc,sdc/datatypes/sdc_function_templates.py,9b5c64bba0bdb09d9472e88eb367ffe66d316b26,STILL_EXISTS,TODO: replace below with core join(how='outer'; return_indexers=True) when implemented
aaeigcfji,IntelPython/sdc,sdc/tests/test_series.py,9b5c64bba0bdb09d9472e88eb367ffe66d316b26,c7889893b49f7b251cd9f0a0889107593d8f1c4a,FIXME: skip the sub-test if one of the dtypes is float and the other is integer
aaeigcige,IntelPython/sdc,sdc/hiframes/pd_dataframe_type.py,b57c9adebb90fe95458d2c2238928d2e38c3ee9c,STILL_EXISTS,TODO: IterableType over column names
aaeigcigg,IntelPython/sdc,sdc/hiframes/pd_dataframe_type.py,b57c9adebb90fe95458d2c2238928d2e38c3ee9c,STILL_EXISTS,columns
aaeigcigh,IntelPython/sdc,sdc/hiframes/pd_dataframe_type.py,b57c9adebb90fe95458d2c2238928d2e38c3ee9c,STILL_EXISTS,XXX is copy necessary?
aaeigcigi,IntelPython/sdc,sdc/hiframes/pd_dataframe_type.py,b57c9adebb90fe95458d2c2238928d2e38c3ee9c,STILL_EXISTS,needed?
aaeigciha,IntelPython/sdc,sdc/hiframes/pd_dataframe_type.py,b57c9adebb90fe95458d2c2238928d2e38c3ee9c,a5ecb08801895ab04212880ea62ad812fc4d8e70,list of flags noting which columns and index are unboxed
aaeigcjci,IntelPython/sdc,sdc/hiframes/pd_series_type.py,b57c9adebb90fe95458d2c2238928d2e38c3ee9c,STILL_EXISTS,TODO: handle actual Record objects in Series?
aaeigcjda,IntelPython/sdc,sdc/hiframes/pd_series_type.py,b57c9adebb90fe95458d2c2238928d2e38c3ee9c,STILL_EXISTS,XXX is copy necessary?
aaeigcjdb,IntelPython/sdc,sdc/hiframes/pd_series_type.py,b57c9adebb90fe95458d2c2238928d2e38c3ee9c,STILL_EXISTS,needed?
aaeigcjdd,IntelPython/sdc,sdc/hiframes/pd_series_type.py,b57c9adebb90fe95458d2c2238928d2e38c3ee9c,STILL_EXISTS,XXX: unify Series\/Array as Array
aaeigcjdg,IntelPython/sdc,sdc/hiframes/pd_series_type.py,b57c9adebb90fe95458d2c2238928d2e38c3ee9c,STILL_EXISTS,TODO: fix index
aaeigcjdi,IntelPython/sdc,sdc/hiframes/pd_series_type.py,b57c9adebb90fe95458d2c2238928d2e38c3ee9c,STILL_EXISTS,TODO: fix timestamp
aaeigcjec,IntelPython/sdc,sdc/hiframes/pd_series_type.py,b57c9adebb90fe95458d2c2238928d2e38c3ee9c,STILL_EXISTS,TODO: call numba getiter with gep_result for array
aaeigcjee,IntelPython/sdc,sdc/hiframes/pd_series_type.py,b57c9adebb90fe95458d2c2238928d2e38c3ee9c,STILL_EXISTS,TODO: call it from numba.targets.arrayobj; need separate function in numba
aaeigcjfa,IntelPython/sdc,sdc/hiframes/pd_series_type.py,b57c9adebb90fe95458d2c2238928d2e38c3ee9c,STILL_EXISTS,TODO: support more types. what types can be in recarrays?
aaeigcjfb,IntelPython/sdc,sdc/hiframes/pd_series_type.py,b57c9adebb90fe95458d2c2238928d2e38c3ee9c,STILL_EXISTS,TODO: other types?
aaeigcjff,IntelPython/sdc,sdc/datatypes/hpat_pandas_series_functions.py,32a72dca6a0aae3a9bb5199c742e4fc37d917be1,STILL_EXISTS,TODO: support proper rounding of percentiles like in pandas.io.formats.format.format_percentiles
aaeigcjfi,IntelPython/sdc,sdc/datatypes/hpat_pandas_series_functions.py,32a72dca6a0aae3a9bb5199c742e4fc37d917be1,STILL_EXISTS,TODO: provide specialization for (types.NPDatetime; types.NPTimedelta)
aaeigdcci,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_functions.py,b83641fa4da820284dd90fe38c088c5f8456fd49,STILL_EXISTS,TODO: improve check
aaeigdccj,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_functions.py,b83641fa4da820284dd90fe38c088c5f8456fd49,STILL_EXISTS,Keep columns that are StringArrayType
aaeigdcda,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_functions.py,b83641fa4da820284dd90fe38c088c5f8456fd49,d917971cea173ff0c6cdc41ee92d8bce8cfbe3b6,TODO: Handle index
aaeigdcdb,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_functions.py,b83641fa4da820284dd90fe38c088c5f8456fd49,STILL_EXISTS,TODO: support other array-like types
aaeigdcdc,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_functions.py,b83641fa4da820284dd90fe38c088c5f8456fd49,dfaf90439129621dea8f4dbaf52a92e06604a9bf,TODO: support index in series from df-columns
aaeigddaa,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_functions.py,a7c913df94d2ff317f8ffc149629d44d9b5d2689,d917971cea173ff0c6cdc41ee92d8bce8cfbe3b6,TODO: Handle index
aaeigdhij,IntelPython/sdc,sdc/tests/tests_perf/test_perf_df.py,284435f67234edc722e7d9dcf755f6f9d9ae55ff,ffacf05a834be1da83c10d899424950d641f883f,TODO: replace with generic function to generate random sequence of floats
aaeigdjfg,IntelPython/sdc,sdc/datatypes/common_functions.py,5e90ac4370af975f71c05ac10cfe43076a84747f,STILL_EXISTS,kind is not known at compile time; so get this function here and use in impl if needed
aaeigdjha,IntelPython/sdc,sdc/str_arr_ext.py,5e90ac4370af975f71c05ac10cfe43076a84747f,STILL_EXISTS,TODO: use overload for all getitem cases (currently implemented via lower_builtin)
aaeigdjhb,IntelPython/sdc,sdc/str_arr_ext.py,5e90ac4370af975f71c05ac10cfe43076a84747f,3780463d473005249f6f9662ff9d94a70f8bf79d,FIXME: old-style getitem implementations copy strings but not null bits
aaeigdjic,IntelPython/sdc,sdc/tests/test_series.py,5e90ac4370af975f71c05ac10cfe43076a84747f,STILL_EXISTS,FIXME: use literally(kind) because; numpy.argsort is supported by Numba with literal kind value only
aaeigeabe,IntelPython/sdc,sdc/tests/test_rolling.py,67612d316edd8f5eb78c2811452099d3a6c71286,STILL_EXISTS,more than 19 columns raise SystemError: CPUDispatcher() returned a result with an error set
aaeigeaec,IntelPython/sdc,sdc/tests/tests_perf/test_perf_df_rolling.py,67612d316edd8f5eb78c2811452099d3a6c71286,STILL_EXISTS,more than 19 columns raise SystemError: CPUDispatcher() returned a result with an error set
aaeigecjg,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_rolling_functions.py,e34ac01df426986d6e16db42da015e51178457ee,STILL_EXISTS,columns order matters
aaeigeidf,IntelPython/sdc,sdc/tests/tests_perf/test_perf_base.py,ffacf05a834be1da83c10d899424950d641f883f,STILL_EXISTS,TODO: https:\/\/jira.devtools.intel.com\/browse\/SAT-2371
aaeigfcaa,IntelPython/sdc,sdc/str_arr_type.py,968e49bf0044080d9c09723311a7eb8c78d01084,STILL_EXISTS,XXX: C equivalent in _str_ext.cpp
aaeigfdgg,IntelPython/sdc,sdc/functions/numpy_like.py,617afadea4cb5e8542b0ec8748920c8a07efeb79,STILL_EXISTS,TODO: check NA
aaeighgeg,IntelPython/sdc,sdc/datatypes/hpat_pandas_series_functions.py,e8ffa0567410973471d7a6e6c2f41af368f0f682,STILL_EXISTS,TODO: refactor this when str_arr setitem is fully supported
aaeighgei,IntelPython/sdc,sdc/datatypes/hpat_pandas_series_functions.py,e8ffa0567410973471d7a6e6c2f41af368f0f682,STILL_EXISTS,FIXME: for now just use sorted; as == is not implemented for sets of unicode strings
aaeighhab,IntelPython/sdc,sdc/tests/tests_perf/test_perf_series_rolling.py,8d3ff777276d8979a3424d8e8593c66e7573ff31,632b55452ce32d8514d371359eed569ea24acddb,more than 19 columns raise SystemError: CPUDispatcher() returned a result with an error set
aaeigiafb,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_functions.py,d1ea8ffa2f70381455fff09a5afdeec7e7381013,STILL_EXISTS,TODO: Handle StringArrayType
aaeigibcb,IntelPython/sdc,sdc/str_arr_ext.py,cfbebc6baa2b881da2c53f8bb6455be20e2aa38c,STILL_EXISTS,FIXME: doesn't work for Tuple with None values
aaeigibfb,IntelPython/sdc,sdc/datatypes/common_functions.py,3ca5a30fa279693dab77c18867c08afb999e769e,STILL_EXISTS,TODO: extend with other types
aaeigibfc,IntelPython/sdc,sdc/datatypes/common_functions.py,3ca5a30fa279693dab77c18867c08afb999e769e,STILL_EXISTS,TODO: check if elementwise copy is needed at all
aaeigibib,IntelPython/sdc,sdc/datatypes/hpat_pandas_groupby_functions.py,3ca5a30fa279693dab77c18867c08afb999e769e,STILL_EXISTS,TODO: remove conversion from Numba typed.List to reflected one while creating group_arr_{i}
aaeigibic,IntelPython/sdc,sdc/datatypes/hpat_pandas_groupby_functions.py,3ca5a30fa279693dab77c18867c08afb999e769e,STILL_EXISTS,resolve types of result dataframe columns
aaeigicfb,IntelPython/sdc,sdc/datatypes/hpat_pandas_series_rolling_functions.py,c5b2a0ce75e856c3ea6d7b0dcd8963123cd90d1d,STILL_EXISTS,TODO: fix this during optimizing of covariance
aaeigicgc,IntelPython/sdc,sdc/datatypes/hpat_pandas_groupby_functions.py,18f38ebf5e81c4373514c9a7173527dbbf12634a,STILL_EXISTS,TODO: remove conversion from Numba typed.List to reflected one while creating group_arr_{i}
aaeigicgh,IntelPython/sdc,sdc/datatypes/hpat_pandas_series_functions.py,18f38ebf5e81c4373514c9a7173527dbbf12634a,STILL_EXISTS,TODO: extend and support fully functional SeriesGroupBy
aaeigideg,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_functions.py,ec7e4f7fd3325391285b4856c092fb24b957e36f,STILL_EXISTS,TODO: Rewrite when DF constructor will be fixed with index=None
aaeigidhf,IntelPython/sdc,sdc/tests/tests_perf/test_perf_df_groupby.py,90b40aaa29981ba0b4db18e7e39a996301b12a55,STILL_EXISTS,more than 19 columns raise SystemError: CPUDispatcher() returned a result with an error set
aaeigieac,IntelPython/sdc,sdc/tests/test_dataframe.py,265e0f5ce16973d01b482d615b9c0fb0e5a66223,STILL_EXISTS,TODO: Data generator for DataFrames
aaeigiecg,IntelPython/sdc,sdc/hiframes/boxing.py,a5ecb08801895ab04212880ea62ad812fc4d8e70,STILL_EXISTS,this unboxes all DF columns so that no column unboxing occurs later
aaeigjiej,IntelPython/sdc,sdc/datatypes/hpat_pandas_functions.py,845799488da7c6959ae0d119c9452f4db1d56db5,STILL_EXISTS,TODO: tune this
aaeigjifa,IntelPython/sdc,sdc/datatypes/hpat_pandas_functions.py,845799488da7c6959ae0d119c9452f4db1d56db5,STILL_EXISTS,TODO: string_array; categorical; etc.
aaeigjjdd,IntelPython/sdc,sdc/rewrites/read_csv_consts.py,845799488da7c6959ae0d119c9452f4db1d56db5,e0619659131a8647ad8e5738a776ce77d1ce5c9a,TODO: 1. save instructions of build_map; build_list for read_csv params
aaeigjjef,IntelPython/sdc,sdc/rewrites/read_csv_consts.py,845799488da7c6959ae0d119c9452f4db1d56db5,e0619659131a8647ad8e5738a776ce77d1ce5c9a,remove_unused_recursively should see all del statements of variables
aaeihabge,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_functions.py,dfaf90439129621dea8f4dbaf52a92e06604a9bf,STILL_EXISTS,Keep columns that are StringArrayType
aaeihaedd,IntelPython/sdc,examples/basic_usage_nyse_predict.py,c9c4bb35cf63f594ebe0a24976f9648824d87a3c,STILL_EXISTS,Remove unused columns
aaeihcfje,IntelPython/sdc,sdc/sdc_autogenerated.py,6d7767767154ac68976a2837755b335cbedc23fa,STILL_EXISTS,TODO: replace below with core join(how='outer'; return_indexers=True) when implemented
aaeihcgji,IntelPython/sdc,sdc/datatypes/range_index_type.py,c381f30bd941770f0cfb5c1bdacbe0d02c7b61c9,4c855983d9cb8e45414af0b4ad64d52fd8853d68,TODO: provide iteration support by adding getiter and iternext
aaeihchhi,IntelPython/sdc,sdc/extensions/indexes/range_index_ext.py,c381f30bd941770f0cfb5c1bdacbe0d02c7b61c9,STILL_EXISTS,TODO: support range unboxing with reference to parent in Numba?
aaeihchib,IntelPython/sdc,sdc/extensions/indexes/range_index_ext.py,c381f30bd941770f0cfb5c1bdacbe0d02c7b61c9,STILL_EXISTS,TO-DO: extend getitem to support other indexers (Arrays; Lists; etc)
aaeihcjgh,IntelPython/sdc,sdc/datatypes/categorical/pandas_support.py,e0619659131a8647ad8e5738a776ce77d1ce5c9a,STILL_EXISTS,TODO: use issubclass
aaeihcjjj,IntelPython/sdc,sdc/datatypes/categorical/pdimpl.py,e0619659131a8647ad8e5738a776ce77d1ce5c9a,STILL_EXISTS,TODO: move to tools
aaeihdacg,IntelPython/sdc,sdc/datatypes/categorical/pdimpl.py,e0619659131a8647ad8e5738a776ce77d1ce5c9a,STILL_EXISTS,TODO: use dtype too
aaeihdach,IntelPython/sdc,sdc/datatypes/categorical/pdimpl.py,e0619659131a8647ad8e5738a776ce77d1ce5c9a,STILL_EXISTS,TODO: support other parameters (only values now)
aaeihdagi,IntelPython/sdc,sdc/datatypes/categorical/pdimpl.py,e0619659131a8647ad8e5738a776ce77d1ce5c9a,STILL_EXISTS,# TODO: can not recall similar function
aaeihdbff,IntelPython/sdc,sdc/datatypes/categorical/types.py,e0619659131a8647ad8e5738a776ce77d1ce5c9a,STILL_EXISTS,TODO: consider renaming to CategoricalDtype b\/c Categorical - not CategoricalType
aaeihdbfg,IntelPython/sdc,sdc/datatypes/categorical/types.py,e0619659131a8647ad8e5738a776ce77d1ce5c9a,STILL_EXISTS,TODO: take dtype from categories array
aaeihdbfh,IntelPython/sdc,sdc/datatypes/categorical/types.py,e0619659131a8647ad8e5738a776ce77d1ce5c9a,STILL_EXISTS,TODO: make ArrayCompatible. It will make reuse Array boxing; unboxing.
aaeihdcgb,IntelPython/sdc,sdc/datatypes/series/boxing.py,e0619659131a8647ad8e5738a776ce77d1ce5c9a,STILL_EXISTS,TODO: index and name
aaeihddgi,IntelPython/sdc,sdc/hiframes/pd_series_type.py,e0619659131a8647ad8e5738a776ce77d1ce5c9a,STILL_EXISTS,TODO: pass codes array if exists
aaeihddih,IntelPython/sdc,sdc/rewrites/read_csv_consts.py,e0619659131a8647ad8e5738a776ce77d1ce5c9a,STILL_EXISTS,TODO: check that vars are used only in read_csv
aaeihdfda,IntelPython/sdc,sdc/datatypes/hpat_pandas_series_functions.py,6315c523d5cc62cc78816475962301f4200b8372,STILL_EXISTS,TODO: replace with below line when Numba supports np.isin in nopython mode
aaeihdfgj,IntelPython/sdc,sdc/functions/sort.py,df516cba3d671993b1844a764bc76dc29e91e2c3,STILL_EXISTS,to deref the void * passed. TODO: nrt awareness
aaeihdgbe,IntelPython/sdc,sdc/functions/numpy_like.py,b674bb16c59061185312db875c32b725a9164319,STILL_EXISTS,TO-DO: not supported; since no generic setitem for StringArray
aaeihdgfj,IntelPython/sdc,sdc/tests/test_sdc_numpy.py,b674bb16c59061185312db875c32b725a9164319,4c855983d9cb8e45414af0b4ad64d52fd8853d68,FIXME: str_arr unifies None with np.nan and StringArray boxing always return np.nan
aaeihdggj,IntelPython/sdc,sdc/datatypes/common_functions.py,4c855983d9cb8e45414af0b4ad64d52fd8853d68,STILL_EXISTS,FIXME: TypingError in parfor step (wrong promotion to float64?) if prange is used
aaeihdgha,IntelPython/sdc,sdc/datatypes/hpat_pandas_series_functions.py,4c855983d9cb8e45414af0b4ad64d52fd8853d68,STILL_EXISTS,TO-DO: replace sdc_reindex_series with reindex methods and move this logic to impl
aaeihdghc,IntelPython/sdc,sdc/datatypes/hpat_pandas_series_functions.py,4c855983d9cb8e45414af0b4ad64d52fd8853d68,STILL_EXISTS,FIXME_Numba#5157: using asarray since eq impl for RangeIndexType returns list
aaeihdgia,IntelPython/sdc,sdc/extensions/indexes/range_index_ext.py,4c855983d9cb8e45414af0b4ad64d52fd8853d68,STILL_EXISTS,TO-DO: add caching when Numba supports writable attributes?
aaeihdgif,IntelPython/sdc,sdc/extensions/indexes/range_index_ext.py,4c855983d9cb8e45414af0b4ad64d52fd8853d68,STILL_EXISTS,FIXME_Numba#5157: result must be np.array; remove list when Numba is fixed
aaeihdgig,IntelPython/sdc,sdc/extensions/indexes/range_index_ext.py,4c855983d9cb8e45414af0b4ad64d52fd8853d68,STILL_EXISTS,FIXME_Numba#5157: remove np.asarray and return as list
aaeihdgih,IntelPython/sdc,sdc/functions/numpy_like.py,4c855983d9cb8e45414af0b4ad64d52fd8853d68,STILL_EXISTS,FIXME_Numba#5157: change to simple A == B when issue is resolved
aaeihdgii,IntelPython/sdc,sdc/functions/numpy_like.py,4c855983d9cb8e45414af0b4ad64d52fd8853d68,STILL_EXISTS,TODO: naive implementation; data from set can probably
aaeihdgja,IntelPython/sdc,sdc/functions/numpy_like.py,4c855983d9cb8e45414af0b4ad64d52fd8853d68,STILL_EXISTS,TO-DO: not supported; since no generic setitem for StringArray
aaeihdgjc,IntelPython/sdc,sdc/hiframes/api.py,4c855983d9cb8e45414af0b4ad64d52fd8853d68,STILL_EXISTS,TO-DO: replace types.none index with separate type; e.g. DefaultIndex
aaeihdhaf,IntelPython/sdc,sdc/hiframes/boxing.py,4c855983d9cb8e45414af0b4ad64d52fd8853d68,STILL_EXISTS,TO-DO: should we check that all elements are strings?
aaeihdhdb,IntelPython/sdc,sdc/sdc_autogenerated.py,4c855983d9cb8e45414af0b4ad64d52fd8853d68,STILL_EXISTS,TODO: replace below with core join(how='outer'; return_indexers=True) when implemented
aaeihdheh,IntelPython/sdc,sdc/sdc_function_templates.py,4c855983d9cb8e45414af0b4ad64d52fd8853d68,STILL_EXISTS,TODO: replace below with core join(how='outer'; return_indexers=True) when implemented
aaeihdhfa,IntelPython/sdc,sdc/tests/test_indexes.py,4c855983d9cb8e45414af0b4ad64d52fd8853d68,STILL_EXISTS,TO-DO: this actually includes calling 'index' attribute overload; should really be S._index;
aaeihdhga,IntelPython/sdc,sdc/tests/test_indexes.py,4c855983d9cb8e45414af0b4ad64d52fd8853d68,STILL_EXISTS,FIXME: replace with pd.testing.assert_index_equal when Int64Index is supported
aaeihdigi,IntelPython/sdc,sdc/functions/numpy_like.py,df4d8d3459be7dff3fe305c358bda2ff2c510ce3,STILL_EXISTS,below line is only needed since Literal[bool] var cannot be converted to bool
aaeihdiha,IntelPython/sdc,sdc/functions/numpy_like.py,ef15baa4cef8e8fb67e383625ef714792b80c3cb,STILL_EXISTS,TO-DO: not supported; since no generic setitem for StringArray
aaeihdiib,IntelPython/sdc,sdc/tests/test_sdc_numpy.py,ef15baa4cef8e8fb67e383625ef714792b80c3cb,STILL_EXISTS,FIXME: str_arr unifies None with np.nan and StringArray boxing always return np.nan
aaeihdiid,IntelPython/sdc,sdc/__init__.py,3780463d473005249f6f9662ff9d94a70f8bf79d,STILL_EXISTS,\"\"\" || Overload Numba function to allow call SDC pass in Numba compiler pipeline || Functions are: || - Numba DefaultPassBuilder define_nopython_pipeline() ||  || TODO: Needs to detect 'import Pandas' and align initialization according to it || \"\"\"
aaeiheaac,IntelPython/sdc,sdc/tests/test_series_ops.py,c7889893b49f7b251cd9f0a0889107593d8f1c4a,STILL_EXISTS,TODO: use 2 for test int casting
aaeiheabf,IntelPython/sdc,sdc/tests/tests_perf/test_perf_series.py,c7889893b49f7b251cd9f0a0889107593d8f1c4a,STILL_EXISTS,TO-DO: fix below test that hangs due to inefficient impl
aaeiheadg,IntelPython/sdc,benchmarks/census_benchmark.py,1804bd24e226f717fc962698928553ed181c29d7,STILL_EXISTS,Read only these columns
aaeiheafj,IntelPython/sdc,sdc/extensions/indexes/range_index_ext.py,ae3233bdc482a6fc57739e63331db59e95491dc3,STILL_EXISTS,TODO: add support of int32 type
aaeiheagc,IntelPython/sdc,sdc/hiframes/boxing.py,db4f431321bb9d51478eec327fd783d57be2f483,STILL_EXISTS,FIXME: CategoricalType has wrong dtype attribute value (i.e. dtype of codes)
aaeiheajd,IntelPython/sdc,sdc/hiframes/pd_dataframe_type.py,811e9f0a832d48822bcc91c432b8a2cefdd52102,STILL_EXISTS,FIXME_Numba#3372: add into numba.types to allow returning from objmode
aaeiheajh,IntelPython/sdc,sdc/io/csv_ext.py,811e9f0a832d48822bcc91c432b8a2cefdd52102,STILL_EXISTS,if no transformation is needed just use outer param name (since APIs match)
aaeihebaa,IntelPython/sdc,sdc/io/csv_ext.py,811e9f0a832d48822bcc91c432b8a2cefdd52102,STILL_EXISTS,of columns dtypes is captured at compile time; because some dtypes (like datetime)
aaeihebae,IntelPython/sdc,sdc/str_arr_type.py,811e9f0a832d48822bcc91c432b8a2cefdd52102,STILL_EXISTS,FIXME_Numba#3372: add into numba.types to allow returning from objmode
aaeihebbb,IntelPython/sdc,sdc/hiframes/pd_dataframe_type.py,a39d73decf020f5574bc5cee43de95d7c2183d6d,STILL_EXISTS,FIXME_Numba#3372: add into numba.types to allow returning from objmode
aaeihebbf,IntelPython/sdc,sdc/io/csv_ext.py,a39d73decf020f5574bc5cee43de95d7c2183d6d,STILL_EXISTS,if no transformation is needed just use outer param name (since APIs match)
aaeihebbi,IntelPython/sdc,sdc/io/csv_ext.py,a39d73decf020f5574bc5cee43de95d7c2183d6d,STILL_EXISTS,of columns dtypes is captured at compile time; because some dtypes (like datetime)
aaeihebcc,IntelPython/sdc,sdc/str_arr_type.py,a39d73decf020f5574bc5cee43de95d7c2183d6d,STILL_EXISTS,FIXME_Numba#3372: add into numba.types to allow returning from objmode
aaeihebia,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_functions.py,ce4142a11bb23d571d274d5c185edd357b0621ee,STILL_EXISTS,no columns in DF model to avoid impact on DF ctor IR size (captured when needed only)
aaeihebij,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_functions.py,d5f706fe42a45c33153e3fbca25cc31c4f28b966,STILL_EXISTS,making a new tuple of lists reorder as needed
aaeihebjb,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_functions.py,d5f706fe42a45c33153e3fbca25cc31c4f28b966,STILL_EXISTS,if at runtime columns list differs from it's initial value (known at compile time)
aaeihebjc,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_functions.py,d5f706fe42a45c33153e3fbca25cc31c4f28b966,STILL_EXISTS,we cannot tell which columns to drop and what is the resulting DataFrameType; so raise exception
aaeihebjh,IntelPython/sdc,sdc/datatypes/hpat_pandas_dataframe_functions.py,a91d01c83945b01fc443f61ee60d79256d9fd758,STILL_EXISTS,TO-DO: need DefaultIndex to handle self.index[idx] construct inside func
aaeihedii,IntelPython/sdc,sdc/extensions/indexes/int64_index_ext.py,97cff233d686475960bfd531130b0a7d5c690dc1,STILL_EXISTS,TODO: support index unboxing with reference to parent in Numba?
aaeihedja,IntelPython/sdc,sdc/extensions/indexes/int64_index_ext.py,97cff233d686475960bfd531130b0a7d5c690dc1,STILL_EXISTS,TO-DO: add operator.contains support for arrays in Numba
aaeihedjc,IntelPython/sdc,sdc/extensions/indexes/int64_index_ext.py,97cff233d686475960bfd531130b0a7d5c690dc1,STILL_EXISTS,FIXME_Numba#5801: Numba type unification rules make this float
aaeihedjd,IntelPython/sdc,sdc/extensions/indexes/int64_index_ext.py,97cff233d686475960bfd531130b0a7d5c690dc1,STILL_EXISTS,TO-DO: this and many other impls are generic and should be moved to indexes_generic.py
aaeihedji,IntelPython/sdc,sdc/extensions/indexes/int64_index_ext.py,97cff233d686475960bfd531130b0a7d5c690dc1,STILL_EXISTS,FIXME_Numba#5157: result must be np.array; remove list when Numba is fixed
aaeihedjj,IntelPython/sdc,sdc/extensions/indexes/int64_index_ext.py,97cff233d686475960bfd531130b0a7d5c690dc1,STILL_EXISTS,FIXME_Numba#5157: remove np.asarray and return as list
aaeiheeac,IntelPython/sdc,sdc/extensions/indexes/range_index_ext.py,97cff233d686475960bfd531130b0a7d5c690dc1,STILL_EXISTS,FIXME_Numba#5801: Numba type unification rules make this float
aaeiheeag,IntelPython/sdc,sdc/hiframes/api.py,97cff233d686475960bfd531130b0a7d5c690dc1,STILL_EXISTS,TO-DO: support Uint64Index and Float64Indexes
aaeiheeaj,IntelPython/sdc,sdc/hiframes/boxing.py,97cff233d686475960bfd531130b0a7d5c690dc1,STILL_EXISTS,TO-DO: remove when it's added
aaeiheejc,IntelPython/sdc,sdc/tests/indexes/test_indexes.py,97cff233d686475960bfd531130b0a7d5c690dc1,STILL_EXISTS,TO-DO: this actually includes calling 'index' attribute overload; should really be S._index;
aaeiheeje,IntelPython/sdc,sdc/tests/indexes/test_indexes.py,97cff233d686475960bfd531130b0a7d5c690dc1,STILL_EXISTS,TO-DO: this actually includes calling 'index' attribute overload; should really be df._index;
aaeihefeg,IntelPython/sdc,sdc/functions/sort.py,4926b0d4ea8d75d95f4ca16be9f6fd50dad8a256,STILL_EXISTS,TO-DO: add\/change adaptor to handle case of ascending=False
aaeihfbjc,IntelPython/sdc,sdc/extensions/indexes/range_index_ext.py,b876024bb5e2843265bc8072a78c15eefafa3a58,STILL_EXISTS,FIXME_Numba#5801: Numba type unification rules make this float
aaeihfbjf,IntelPython/sdc,sdc/functions/sort.py,b876024bb5e2843265bc8072a78c15eefafa3a58,STILL_EXISTS,TO-DO: add\/change adaptor to handle case of ascending=False
aaeihfbjh,IntelPython/sdc,sdc/hiframes/api.py,b876024bb5e2843265bc8072a78c15eefafa3a58,STILL_EXISTS,TO-DO: support Uint64Index and Float64Indexes
aaeihfcaa,IntelPython/sdc,sdc/hiframes/boxing.py,b876024bb5e2843265bc8072a78c15eefafa3a58,STILL_EXISTS,TO-DO: remove when it's added
aaeihfgde,undertheseanlp/underthesea,setup.py,2ce3f0ba9294ae708fb7796036c47fed8fb049de,a8aeb9b7149827c6c13a1895bcd2269af7d279b5,TODO: put package requirements here
aaeihfgdf,undertheseanlp/underthesea,setup.py,2ce3f0ba9294ae708fb7796036c47fed8fb049de,a8aeb9b7149827c6c13a1895bcd2269af7d279b5,TODO: put package test requirements here
aaeihfged,undertheseanlp/underthesea,travis_pypi_setup.py,2ce3f0ba9294ae708fb7796036c47fed8fb049de,STILL_EXISTS,workaround for https:\/\/github.com\/travis-ci\/travis-api\/issues\/196
aaeihghjg,undertheseanlp/underthesea,underthesea/corpus/validate_corpus.py,42aa9d129e3751973cb43edd8b5a88f663cccdef,STILL_EXISTS,TODO: Check NFC Normalize
aaeihgiaa,undertheseanlp/underthesea,underthesea/corpus/validate_corpus.py,42aa9d129e3751973cb43edd8b5a88f663cccdef,STILL_EXISTS,TODO: Validate tokenize
aaeihgiig,undertheseanlp/underthesea,underthesea/trainers/conlleval.py,27b4ca73025fbef756de4c445ebe87b859c57259,STILL_EXISTS,each non-empty line must contain >= 3 columns
aaeihgiih,undertheseanlp/underthesea,underthesea/trainers/conlleval.py,27b4ca73025fbef756de4c445ebe87b859c57259,STILL_EXISTS,extract tags from last 2 columns
aaeihgjeg,undertheseanlp/underthesea,underthesea/file_utils.py,4f0604a9fd0dcda7b143e84e897c5d560279bfbb,STILL_EXISTS,mmap seems to be much more memory efficient
aaeihgjfd,undertheseanlp/underthesea,underthesea/models/nn.py,4f0604a9fd0dcda7b143e84e897c5d560279bfbb,STILL_EXISTS,load_big_file is a workaround by https:\/\/github.com\/highway11git to load models on some Mac\/Windows setups
aaeihhaef,undertheseanlp/underthesea,underthesea/utils/sp_alg.py,4f0604a9fd0dcda7b143e84e897c5d560279bfbb,STILL_EXISTS,fix the root of the cycle
aaeihhaii,undertheseanlp/underthesea,underthesea/utils/sp_data.py,4f0604a9fd0dcda7b143e84e897c5d560279bfbb,STILL_EXISTS,TODO: more elegant way to deal with uneven data; which we directly discard right now
aaeihhfbe,undertheseanlp/underthesea,underthesea/nn.py,696c66aabf4603c10e0a864f4a25c2b42a689095,STILL_EXISTS,load_big_file is a workaround by https:\/\/github.com\/highway11git to load models on some Mac\/Windows setups
aaeihhhbg,fastnlp/fastNLP,Char-aware_NLM/utilities.py,32256c6e51e55080307b3fa1d95c2a1ad02362c2,STILL_EXISTS,add start and end columns
aaeihhhbh,fastnlp/fastNLP,Char-aware_NLM/utilities.py,32256c6e51e55080307b3fa1d95c2a1ad02362c2,STILL_EXISTS,zero-pad right columns
aaeihicdg,fastnlp/fastNLP,fastNLP/reproduction/Char-aware_NLM/utilities.py,c4028a528a4536297a9c260d85f9a00f041bad7b,STILL_EXISTS,add start and end columns
aaeihicdh,fastnlp/fastNLP,fastNLP/reproduction/Char-aware_NLM/utilities.py,c4028a528a4536297a9c260d85f9a00f041bad7b,STILL_EXISTS,zero-pad right columns
aaeihihjb,fastnlp/fastNLP,fastNLP/modules/masked_rnn.py,042f63aa466dff914ca6b6d67b8eeceb44b6f19c,STILL_EXISTS,LSTM outputs a tuple of (hidden; cell); this is a common hack \uD83D\uDE01
aaeihiiie,fastnlp/fastNLP,fastNLP/modules/encoder/masked_rnn.py,301bbdcd1ef1b148e39985809951e5eab2744811,STILL_EXISTS,LSTM outputs a tuple of (hidden; cell); this is a common hack \uD83D\uDE01
aaeihjbjj,fastnlp/fastNLP,fastNLP/core/trainer.py,242e576a30dd71c54a0191c16c515fec35c0d8db,c1d7c5d7dad69e68f8dc8850ed4c46fec17bc0a9,TODO: more flexible
aaeihjcac,fastnlp/fastNLP,fastNLP/models/sequence_modeling.py,242e576a30dd71c54a0191c16c515fec35c0d8db,83f69b0e0f38306a1b3de4aac27049f2a19256bc,TODO: remove
aaeihjcch,fastnlp/fastNLP,fastNLP/core/action.py,58120998c53e376019bd1a42b4f2812ff2c34d74,STILL_EXISTS,Move centroids step
aaeiiabgi,fastnlp/fastNLP,fastNLP/core/tester.py,2df8eb740a921b59fc9f66416acfc21a5ce9e07e,STILL_EXISTS,TODO: required arguments
aaeiiabha,fastnlp/fastNLP,fastNLP/core/trainer.py,2df8eb740a921b59fc9f66416acfc21a5ce9e07e,STILL_EXISTS,TODO: required arguments
aaeiiaffi,fastnlp/fastNLP,test/core/test_predictor.py,f2fc98b5e6ea9e5209526c4b3f49bfb51d86a323,3d66975091d56df8272c5fe6f40e59ebeed89b73,TODO
aaeiiahhf,fastnlp/fastNLP,fastNLP/core/sampler.py,28a06838530645ede29ef1cf1f62dc30ef292b7c,STILL_EXISTS,TODO: need to return buckets
aaeiiahhh,fastnlp/fastNLP,fastNLP/core/sampler.py,28a06838530645ede29ef1cf1f62dc30ef292b7c,STILL_EXISTS,Move centroids step
aaeiibeca,fastnlp/fastNLP,reproduction/chinese_word_segment/model/cws_model.py,1b9daa19855af06c6b279aa0b88292639fb22de9,STILL_EXISTS,TODO \u5F53\u524D\u628Aloss\u5199\u6B7B\u4E86
aaeiibecd,fastnlp/fastNLP,reproduction/chinese_word_segment/process/cws_processor.py,1b9daa19855af06c6b279aa0b88292639fb22de9,STILL_EXISTS,TODO \u9700\u8981\u8C28\u614E\u8003\u8651\u5982\u4F55\u5904\u7406\u7A7A\u683C\u7684\u95EE\u9898
aaeiibech,fastnlp/fastNLP,fastNLP/api/pos_tagger.py,79105381f54bf518a4be25ab30a6a1c7b340c255,cd68d78d506d5a3092183d6438a0d1d98872cca5,TODO: \u6839\u636Equery \u6784\u5EFADataSet
aaeiibedc,fastnlp/fastNLP,fastNLP/api/pos_tagger.py,79105381f54bf518a4be25ab30a6a1c7b340c255,cd68d78d506d5a3092183d6438a0d1d98872cca5,TODO: \u8F6C\u6210\u6700\u7EC8\u8F93\u51FA
aaeiibedh,fastnlp/fastNLP,reproduction/pos_tag_model/train_pos_tag.py,79105381f54bf518a4be25ab30a6a1c7b340c255,26e3abdf58c1b4b7d9d40826cc67b4a448ef9ea3,TODO: pp.add_processor()
aaeiibefh,fastnlp/fastNLP,fastNLP/core/field.py,0cbbfd522155d1de4b5292ddad109377d162997b,STILL_EXISTS,TODO
aaeiibegg,fastnlp/fastNLP,reproduction/chinese_word_segment/train_context.py,38aa207ea21a24361ff089984d257010ba8cefe6,69a138eb18946d2790c1c89c2f4c0321a3d7cde3,TODO \u5982\u4F55\u7EC4\u5EFA\u6210\u4E3A\u4E00\u4E2ADataset
aaeiibehb,fastnlp/fastNLP,reproduction/chinese_word_segment/train_context.py,38aa207ea21a24361ff089984d257010ba8cefe6,STILL_EXISTS,TODO pretrain\u7684embedding\u662F\u600E\u4E48\u89E3\u51B3\u7684\uFF1F
aaeiibfcc,fastnlp/fastNLP,fastNLP/core/dataset.py,dd0bb0d7913dd93e064817356caf585c7513c5f3,d818e91380b0c59f27e8cc250bdc10adc3822825,TODO check new field.
aaeiibfcd,fastnlp/fastNLP,fastNLP/core/dataset.py,dd0bb0d7913dd93e064817356caf585c7513c5f3,26e3abdf58c1b4b7d9d40826cc67b4a448ef9ea3,TODO
aaeiibfce,fastnlp/fastNLP,fastNLP/core/fieldarray.py,dd0bb0d7913dd93e064817356caf585c7513c5f3,3d66975091d56df8272c5fe6f40e59ebeed89b73,TODO
aaeiibfdc,fastnlp/fastNLP,fastNLP/core/fieldarray.py,3cb98ddcf2dfb30902490c41d0d1129d8ead57ff,6129a31c1de1c4aeef8041b9bd69038d8896d622,TODO \u5F53\u8FD9\u4E2AfieldArray\u662Fseq_length\u8FD9\u79CD\u53EA\u6709\u4E00\u4F4D\u7684\u5185\u5BB9\u65F6\uFF0C\u4E0D\u9700\u8981padding\uFF0C\u9700\u8981\u518D\u8BA8\u8BBA\u4E00\u4E0B
aaeiibfdj,fastnlp/fastNLP,fastNLP/api/cws.py,73ba3b5eec62583475baaf85fa6c461a3aa03e5c,STILL_EXISTS,4. TODO \u8FD9\u91CC\u5E94\u8BE5\u8981\u4EA4\u7ED9\u4E00\u4E2Aiterator\u4E00\u6837\u7684\u4E1C\u897F\u9884\u6D4B\u8FD9\u4E2A\u7ED3\u679C
aaeiibfea,fastnlp/fastNLP,fastNLP/api/cws.py,73ba3b5eec62583475baaf85fa6c461a3aa03e5c,STILL_EXISTS,5. TODO \u5F97\u5230\u7ED3\u679C\uFF0C\u9700\u8981\u8003\u8651\u662F\u5426\u9700\u8981\u53CD\u8F6C\u56DE\u53BB; \u53CApost_process\u7684\u64CD\u4F5C
aaeiibgab,fastnlp/fastNLP,reproduction/chinese_word_segment/train_context.py,de3feeaf5aca2529585b7572cd1d16d4dfcf4865,e2b14ed33d4ae41c5d94568e0e2240f34377ff8b,TODO \u8FD9\u91CC\u8C8C\u4F3C\u9700\u8981\u533A\u5206test pipeline\u4E0Edev pipeline
aaeiibgac,fastnlp/fastNLP,reproduction/chinese_word_segment/train_context.py,de3feeaf5aca2529585b7572cd1d16d4dfcf4865,e2b14ed33d4ae41c5d94568e0e2240f34377ff8b,TODO \u8FD8\u9700\u8981\u8003\u8651\u5982\u4F55\u66FF\u6362\u56DE\u539F\u6587\u7684\u95EE\u9898\uFF1F
aaeiibgcg,fastnlp/fastNLP,fastNLP/api/processor.py,dc7f8ef8d4fb301de394c10339495787dda3c4b4,STILL_EXISTS,TODO \u5F53\u524D\u7684\u5B9E\u73B0\u4F1A\u5BFC\u81F4\u4E4B\u540E\u7684processor\u9700\u8981\u77E5\u9053model\u8F93\u51FA\u7684output\u7684key\u662F\u4EC0\u4E48
aaeiibgdf,fastnlp/fastNLP,reproduction/chinese_word_segment/train_context.py,dc7f8ef8d4fb301de394c10339495787dda3c4b4,STILL_EXISTS,TODO \u8FD8\u9700\u8981\u8003\u8651\u5982\u4F55\u66FF\u6362\u56DE\u539F\u6587\u7684\u95EE\u9898\uFF1F
aaeiibgje,fastnlp/fastNLP,fastNLP/core/trainer.py,4be15a5b435e06dc5109e2f9b391320a4dde3283,2aaa3818270b09b42b14eeb25d8121f2400af512,TODO: refactor self.get_loss
aaeiibhdb,fastnlp/fastNLP,fastNLP/models/sequence_modeling.py,77786509df6a0abda8c308104b5562a904dad891,06891cf90af7ca9f16261163f70a2a1b80cb7e9d,TODO seq_lens\u7684key\u8FD9\u6837\u505A\u4E0D\u5408\u7406
aaeiibhdi,fastnlp/fastNLP,reproduction/pos_tag_model/process/pos_processor.py,77786509df6a0abda8c308104b5562a904dad891,STILL_EXISTS,TODO \u5E94\u8BE5\u53EF\u4EE5\u5B9A\u5236
aaeiibhii,fastnlp/fastNLP,test/core/test_batch.py,090f7aef5b61d004e115e2b42855902e0f2a6823,837bef47dc1d4cbe346d84935639285e908c9c74,TODO: weird due to change in dataset.py
aaeiibhje,fastnlp/fastNLP,test/core/test_dataset.py,090f7aef5b61d004e115e2b42855902e0f2a6823,837bef47dc1d4cbe346d84935639285e908c9c74,TODO:
aaeiibieh,fastnlp/fastNLP,fastNLP/core/utils.py,0836ce006f38c4005d1d2483f0429ce3f875b54d,3d91f2f024207c8bfc0dae62cdaead227f4558c7,move data to model's device
aaeiibigf,fastnlp/fastNLP,fastNLP/core/trainer.py,4a4b001047fea4a8765269927ad45c3e205551e0,3a4a7293144e714460ff70f65d10664b5efc9a3d,TODO \u8FD9\u91CC\u53EF\u80FD\u9700\u8981\u81EA\u5B9A\u4E49\u4E00\u4E9BError\u7C7B\u578B
aaeiibigj,fastnlp/fastNLP,fastNLP/core/dataset.py,ffc963190e1fa4cfa06b265ff8b1034c062234e2,941b88f26b6b36c34a4968d1289c18a38a796a7e,TODO change error type
aaeiibihb,fastnlp/fastNLP,fastNLP/api/api.py,e4c1ab60a633b47933bb7dca081308bb144380c5,STILL_EXISTS,TODO add pretrain urls
aaeiibiib,fastnlp/fastNLP,fastNLP/core/fieldarray.py,e1e0661debb8a649ebad7c2837dcd7d3d65a6151,STILL_EXISTS,TODO: auto detect dtype
aaeiibiic,fastnlp/fastNLP,fastNLP/io/dataset_loader.py,e1e0661debb8a649ebad7c2837dcd7d3d65a6151,STILL_EXISTS,TODO: need fix for current DataSet
aaeiibiif,fastnlp/fastNLP,fastNLP/core/dataset.py,2aaa3818270b09b42b14eeb25d8121f2400af512,511f41dda1ee67954f0133e2d70f1378e0d4d728,TODO change error type
aaeiicaja,fastnlp/fastNLP,fastNLP/core/fieldarray.py,6839bb91cceaf4bf868f2d89a507febdbf08962e,STILL_EXISTS,TODO: \u8FD4\u56DE\u884C\u4E3A\u4E0D\u4E00\u81F4\uFF0C\u6709\u9690\u60A3
aaeiicajf,fastnlp/fastNLP,fastNLP/core/trainer.py,3d91f2f024207c8bfc0dae62cdaead227f4558c7,STILL_EXISTS,TODO check loss\u4E0Emetrics\u7684\u7C7B\u578B
aaeiicajg,fastnlp/fastNLP,fastNLP/core/trainer.py,3d91f2f024207c8bfc0dae62cdaead227f4558c7,e5e7f29d7205a269fd1a922bfd9067f2ead5de81,TODO self._best_accuracy\u4E0D\u80FD\u8868\u73B0\u51FA\u5F53\u524D\u7684metric\u591A\u79CD\u7684\u60C5\u51B5
aaeiicajh,fastnlp/fastNLP,fastNLP/core/trainer.py,3d91f2f024207c8bfc0dae62cdaead227f4558c7,STILL_EXISTS,TODO change
aaeiicbae,fastnlp/fastNLP,fastNLP/core/trainer.py,3d91f2f024207c8bfc0dae62cdaead227f4558c7,0d4720b1d91648fa61683d9dde13d9e183b9c003,TODO \u8FD9\u91CC\u4FEE\u6539\u4E3A\u4F7F\u7528tester
aaeiicbaf,fastnlp/fastNLP,fastNLP/core/trainer.py,3d91f2f024207c8bfc0dae62cdaead227f4558c7,0d4720b1d91648fa61683d9dde13d9e183b9c003,TODO \u8FD9\u91CC\u9700\u8981\u6839\u636E\u65B0\u7248\u7684metrics\u505A\u4FEE\u6539\uFF0C\u53E6\u5916\u8FD9\u91CC\u9700\u8981\u6355\u83B7\u6765\u81EAmetric\u7684\u62A5\u9519\uFF0C\u56E0\u4E3A\u9700\u8981\u6307\u5BFC\u7528\u6237debug
aaeiicbcg,fastnlp/fastNLP,fastNLP/core/utils.py,d8a80ad6c6bddce0f9229db28ebc131e05cd7f6f,ad0a8c177554ee1a5c4656ea2c8a06aa369f0ca5,TODO: add UNUSED warning.
aaeiicbci,fastnlp/fastNLP,fastNLP/core/utils.py,d8a80ad6c6bddce0f9229db28ebc131e05cd7f6f,ad0a8c177554ee1a5c4656ea2c8a06aa369f0ca5,move data to model's device
aaeiicbdh,fastnlp/fastNLP,fastNLP/core/metrics.py,ad0a8c177554ee1a5c4656ea2c8a06aa369f0ca5,STILL_EXISTS,check duplicated; unused; missing
aaeiicbec,fastnlp/fastNLP,fastNLP/core/trainer.py,ad0a8c177554ee1a5c4656ea2c8a06aa369f0ca5,beb55f5288b004a89a965efb9018f31ab2a9c940,TODO \u8FD9\u91CC\u53EF\u80FD\u4F1A\u9047\u5230\u95EE\u9898\uFF0C\u4E07\u4E00\u7528\u6237\u5728model\u5185\u90E8\u4FEE\u6539\u4E86prediction\u7684device\u5C31\u4F1A\u6709\u95EE\u9898
aaeiicbfb,fastnlp/fastNLP,fastNLP/core/utils.py,84eb50a810aaea823ca3bc0e344deaf280e90b47,22a8702d225e5d39f526daa3c56bd2f16ff7500f,TODO: add UNUSED warning.
aaeiicbfj,fastnlp/fastNLP,fastNLP/core/trainer.py,e6864ea7e0f42deff6d50c9e75c639a7a0ddea1f,3a4a7293144e714460ff70f65d10664b5efc9a3d,increase_better is True. It means the exp result gets better if the indicator increases.
aaeiicbgj,fastnlp/fastNLP,fastNLP/core/trainer.py,3a4a7293144e714460ff70f65d10664b5efc9a3d,STILL_EXISTS,TODO self._best_accuracy\u4E0D\u80FD\u8868\u73B0\u51FA\u5F53\u524D\u7684metric\u591A\u79CD\u7684\u60C5\u51B5
aaeiicbhj,fastnlp/fastNLP,fastNLP/core/trainer.py,1b961f136ca7b5b62b725c79b3b41efbd0d90f07,STILL_EXISTS,increase_better is True. It means the exp result gets better if the indicator increases.
aaeiicbie,fastnlp/fastNLP,fastNLP/core/trainer.py,1b961f136ca7b5b62b725c79b3b41efbd0d90f07,STILL_EXISTS,TODO self._best_accuracy\u4E0D\u80FD\u8868\u73B0\u51FA\u5F53\u524D\u7684metric\u591A\u79CD\u7684\u60C5\u51B5
aaeiicbii,fastnlp/fastNLP,fastNLP/core/trainer.py,f24fca1b21e23b5692ae8cd89ceac844d4ea94a8,a05ffd31cd07f5ebce511260ec086d406c47d332,TODO \u8FD9\u91CC\u9700\u8981\u68C0\u67E5\u662F\u5426\u8FD4\u56DE\u6765\u7684\u503C\u662F\u5426\u662F\u5408\u7406\u7684
aaeiiccdj,fastnlp/fastNLP,fastNLP/core/fieldarray.py,125c2718e428c7cc9607db161fcd0bd90983780d,STILL_EXISTS,TODO: refactor
aaeiicced,fastnlp/fastNLP,fastNLP/core/metrics.py,234ceb6fa3c6eb12372c58c5b8b79530332b4119,STILL_EXISTS,TODO \u8FD9\u91CC\u62A5\u9519\u7684\u903B\u8F91\u5E94\u8BE5\u662F\u600E\u6837\u7684\uFF1F
aaeiicdej,fastnlp/fastNLP,test/core/test_metrics.py,234ceb6fa3c6eb12372c58c5b8b79530332b4119,STILL_EXISTS,(9) check map; include unused
aaeiicead,fastnlp/fastNLP,test/core/test_metrics.py,d19850b397de5ce644d77c7deaf62e9c48e6b037,1fb1df4a31da9204412dc6f4d3b89a0b8594a9b2,# (9) check map; include unused
aaeiiceag,fastnlp/fastNLP,test/core/test_metrics.py,d19850b397de5ce644d77c7deaf62e9c48e6b037,1fb1df4a31da9204412dc6f4d3b89a0b8594a9b2,pred_dict = {\"prediction\": torch.zeros(4; 3; 2); 'unused':1}
aaeiicebf,fastnlp/fastNLP,test/core/test_metrics.py,d19850b397de5ce644d77c7deaf62e9c48e6b037,STILL_EXISTS,(9) check map; include unused
aaeiicegd,fastnlp/fastNLP,test/io/test_embed_loader.py,cc440b5ed6596c6a677e7debc8e820431a923f75,f62060339edd1da3c3e1092057e014757714d28a,TODO: np.cov\u5728linux\u4E0Asegment fault;\u539F\u56E0\u672A\u77E5
aaeiicegg,fastnlp/fastNLP,fastNLP/core/utils.py,77f8ac77daa414908ed90d477e4ae5217c092f76,STILL_EXISTS,if they are like 'SomeParam(assign to xxx)'
aaeiiceij,fastnlp/fastNLP,fastNLP/core/trainer.py,785c41ded5c56bf54614d475f57cc1895a820957,400552971c200f0d6c3ad5146ddcb57266906c63,TODO \u8FD9\u91CC\u53EF\u80FD\u4F1A\u9047\u5230\u95EE\u9898\uFF0C\u4E07\u4E00\u7528\u6237\u5728model\u5185\u90E8\u4FEE\u6539\u4E86prediction\u7684device\u5C31\u4F1A\u6709\u95EE\u9898
aaeiicfaf,fastnlp/fastNLP,test/core/test_metrics.py,1fb1df4a31da9204412dc6f4d3b89a0b8594a9b2,STILL_EXISTS,(9) check map; include unused
aaeiicgci,fastnlp/fastNLP,fastNLP/core/losses.py,661780b9757586d4bd56b0f8437cbc0b5d497eec,a1a41c2d8b0df658fc0067fb37f3a0eb16db36e8,TODO: use the origin key to raise error
aaeiicgda,fastnlp/fastNLP,fastNLP/core/metrics.py,a1a41c2d8b0df658fc0067fb37f3a0eb16db36e8,4d1721ffe365d53351c21241dfd7fdb6114c6bed,TODO need to make sure they all have same batch_size
aaeiicgdb,fastnlp/fastNLP,fastNLP/core/utils.py,a1a41c2d8b0df658fc0067fb37f3a0eb16db36e8,1158556236c438ebbae65ca7b373116da647483e,if _unused_field:
aaeiicgdd,fastnlp/fastNLP,fastNLP/core/utils.py,a1a41c2d8b0df658fc0067fb37f3a0eb16db36e8,22a8702d225e5d39f526daa3c56bd2f16ff7500f,if check_res.unused:
aaeiicgec,fastnlp/fastNLP,fastNLP/core/losses.py,abe5ec72619056ef6b4f509c471a5265df232df1,9acdb54fc8262f53913f08e058378f5fb0105d77,TODO: use the origin key to raise error
aaeiicgfj,fastnlp/fastNLP,fastNLP/core/losses.py,87e5d44b018cfd54b57f545159d5211e7a9e609c,STILL_EXISTS,TODO \u9700\u8981\u505A\u4E00\u4E9B\u68C0\u67E5\uFF0CF.cross_entropy\u5728\u8BA1\u7B97\u65F6\uFF0C\u5982\u679Cpred\u662F(16; 10 ;4); target\u7684\u5F62\u72B6\u6309\u9053\u7406\u5E94\u8BE5\u662F(16; 10); \u4F46\u5B9E\u9645\u5374\u9700\u8981
aaeiicgga,fastnlp/fastNLP,fastNLP/core/losses.py,87e5d44b018cfd54b57f545159d5211e7a9e609c,fec3216a0eba641764bc65971fd2c34720f2b022,TODO  \uFF0816\uFF0C 4\uFF09
aaeiicgih,fastnlp/fastNLP,fastNLP/core/trainer.py,6129a31c1de1c4aeef8041b9bd69038d8896d622,STILL_EXISTS,TODO: another error raised if CheckError caught
aaeiicgij,fastnlp/fastNLP,fastNLP/api/processor.py,27e9453d19dd61141f9def91cfbeb5c68bd268bf,337e3035b33c63e7c5702a53159e556edbca2e29,TODO: remove it. It is strange.
aaeiicgja,fastnlp/fastNLP,fastNLP/api/processor.py,27e9453d19dd61141f9def91cfbeb5c68bd268bf,0c5630bd16c2cba1623ceacfdd21ab789dfdac56,TODO; remove it.
aaeiichcc,fastnlp/fastNLP,fastNLP/core/dataset.py,267baec2244b1812fa3bdb01a66b7c05986352c2,STILL_EXISTS,TODO dataset.x
aaeiidbda,fastnlp/fastNLP,fastNLP/core/trainer.py,337e3035b33c63e7c5702a53159e556edbca2e29,56e7641eb8bf7e637cadf4edc5a3a8066377deff,TODO: \u8FD9\u4E2A\u662F\u4E0D\u662F\u6709\u95EE\u9898\uFF1F
aaeiidbej,fastnlp/fastNLP,reproduction/chinese_word_segment/process/cws_processor.py,897c43fc3b826b8e2a0198eab58a01bf88aa5101,c4ba75d160c508123ce536df98a5ccbea2ed5ad9,TODO \u5982\u4F55\u5C06\u5EFA\u7ACBvocab\u548Cindex\u8FD9\u4E24\u6B65\u7EDF\u4E00\u4E86\uFF1F
aaeiiedfd,fastnlp/fastNLP,fastNLP/core/batch.py,2e3ef52a7d47598e92707f0ee5c3251eb68bcb95,STILL_EXISTS,TODO \u73B0\u5728\u591A\u7EBF\u7A0B\u7684\u60C5\u51B5\u4E0B\u6BCF\u4E2A\u5FAA\u73AF\u90FD\u4F1A\u91CD\u65B0\u521B\u5EFA\u591A\u8FDB\u7A0B\uFF0C\u5F00\u9500\u53EF\u80FD\u6709\u70B9\u5927\u3002\u53EF\u4EE5\u8003\u8651\u76F4\u63A5\u590D\u7528iterator.
aaeiiedii,fastnlp/fastNLP,fastNLP/core/batch.py,2e3ef52a7d47598e92707f0ee5c3251eb68bcb95,a7f3701bdf3fc48e4caa92210ded14bd8ca19852,TODO: add limited pickling support for sharing an iterator
aaeiiedja,fastnlp/fastNLP,fastNLP/core/batch.py,2e3ef52a7d47598e92707f0ee5c3251eb68bcb95,a7f3701bdf3fc48e4caa92210ded14bd8ca19852,Probably the best way to do this is by moving the sample pushing
aaeiieffc,fastnlp/fastNLP,fastNLP/models/bert.py,bfaf09df8cba78e02ad2aa73dab11c5ff6d7a7b9,7de69b60b896cee013564b254f21b245395da9d4,We \"pool\" the model by simply taking the hidden state corresponding
aaeiiejai,fastnlp/fastNLP,fastNLP/models/enas_controller.py,efeac2c4276cca00e3c2b03fed574f5678efbdb9,STILL_EXISTS,Perhaps these weights in the decoder should be
aaeiiejec,fastnlp/fastNLP,fastNLP/models/enas_model.py,efeac2c4276cca00e3c2b03fed574f5678efbdb9,STILL_EXISTS,forward pass. This could probably be fixed in a more elegant way; but
aaeiiejfb,fastnlp/fastNLP,fastNLP/models/enas_model.py,efeac2c4276cca00e3c2b03fed574f5678efbdb9,STILL_EXISTS,This workaround for PyTorch v0.3.1 does everything in numpy;
aaeiiejgh,fastnlp/fastNLP,fastNLP/models/enas_model.py,efeac2c4276cca00e3c2b03fed574f5678efbdb9,STILL_EXISTS,Instead of averaging loose ends; perhaps there should
aaeiiejhd,fastnlp/fastNLP,fastNLP/models/enas_model.py,efeac2c4276cca00e3c2b03fed574f5678efbdb9,STILL_EXISTS,average all the loose ends
aaeiififb,fastnlp/fastNLP,test/core/test_dataset.py,f4e64906d46a66ea2e12e24fe29ce0e19614c26e,4d1721ffe365d53351c21241dfd7fdb6114c6bed,TODO test failed because 'fastNLP\\core\\fieldarray.py:143: RuntimeError'
aaeiifihb,fastnlp/fastNLP,fastNLP/models/enas_controller.py,dffd9b96cd2b070de3550113338ee904ee1c3e10,STILL_EXISTS,Perhaps these weights in the decoder should be
aaeiifjaf,fastnlp/fastNLP,fastNLP/models/enas_model.py,dffd9b96cd2b070de3550113338ee904ee1c3e10,STILL_EXISTS,forward pass. This could probably be fixed in a more elegant way; but
aaeiifjbe,fastnlp/fastNLP,fastNLP/models/enas_model.py,dffd9b96cd2b070de3550113338ee904ee1c3e10,STILL_EXISTS,This workaround for PyTorch v0.3.1 does everything in numpy;
aaeiifjda,fastnlp/fastNLP,fastNLP/models/enas_model.py,dffd9b96cd2b070de3550113338ee904ee1c3e10,STILL_EXISTS,Instead of averaging loose ends; perhaps there should
aaeiifjdg,fastnlp/fastNLP,fastNLP/models/enas_model.py,dffd9b96cd2b070de3550113338ee904ee1c3e10,STILL_EXISTS,average all the loose ends
aaeiigaeg,fastnlp/fastNLP,test/core/test_dataset.py,dffd9b96cd2b070de3550113338ee904ee1c3e10,STILL_EXISTS,TODO test failed because 'fastNLP\\core\\fieldarray.py:143: RuntimeError'
aaeiigdcd,fastnlp/fastNLP,fastNLP/io/embed_loader.py,c1ee0b27dfb5daa8a0f83f161514f07d4075bb4f,2da1681c305bca4895a4c390b074b714bfdd1778,TODO \u9700\u8981\u4FDD\u8BC1unk\u5176\u5B83\u6570\u636E\u540C\u5206\u5E03\u7684\u5417\uFF1F
aaeiigddd,fastnlp/fastNLP,fastNLP/core/utils.py,c520d350827cda3415d2bfd0b033ed79e02ff352,e025350ea82ff07d0e3a797bfa1650c6e4c2f862,TODO \u53EF\u4EE5\u4FDD\u5B58\u4E0B\u7F13\u5B58\u65F6\u7684\u53C2\u6570\uFF0C\u5982\u679Cload\u7684\u65F6\u5019\u53D1\u73B0\u53C2\u6570\u4E0D\u4E00\u81F4\uFF0C\u53D1\u51FA\u8B66\u544A\u3002
aaeiigdfe,fastnlp/fastNLP,test/core/test_dataset.py,c344f7a2f9f637d0c5d6b2b059d59a69d7fb885f,STILL_EXISTS,# TODO test failed because 'fastNLP\\core\\fieldarray.py:143: RuntimeError'
aaeiigdif,fastnlp/fastNLP,fastNLP/core/utils.py,e61b7702e7bf41519f168b60cbda0db38d6f3310,22a8702d225e5d39f526daa3c56bd2f16ff7500f,TODO \u53EF\u4EE5\u4FDD\u5B58\u4E0B\u7F13\u5B58\u65F6\u7684\u53C2\u6570\uFF0C\u5982\u679Cload\u7684\u65F6\u5019\u53D1\u73B0\u53C2\u6570\u4E0D\u4E00\u81F4\uFF0C\u53D1\u51FA\u8B66\u544A\u3002
aaeiigeic,fastnlp/fastNLP,fastNLP/modules/encoder/bert.py,7de69b60b896cee013564b254f21b245395da9d4,2f5d8967a3caf0e3c9ed7c511954b15a82c8b3c8,We \"pool\" the model by simply taking the hidden state corresponding
aaeiiggfg,fastnlp/fastNLP,fastNLP/core/utils.py,4a57011315283562a256b8c84583d6924d2b3a03,22a8702d225e5d39f526daa3c56bd2f16ff7500f,if check_res.unused:
aaeiighif,fastnlp/fastNLP,fastNLP/io/base_loader.py,a1f8cdec48a61202d44c801f85da5f8a867091fc,7fea175f0a5ff48452821e45ad40a4ca515356fb,TODO \u8FD9\u4E2A\u7C7B\u4F7F\u7528\u5728\u4F55\u5904\uFF1F
aaeiigicd,fastnlp/fastNLP,fastNLP/models/__init__.py,c9dc7022e4e8142dd113632c52d218e5f70dcb8f,STILL_EXISTS,\"\"\" || \u4F7F\u7528 fastNLP \u5B9E\u73B0\u7684\u4E00\u7CFB\u5217\u5E38\u89C1\u6A21\u578B\uFF0C\u5177\u4F53\u6709\uFF1A || TODO \u8BE6\u7EC6\u4ECB\u7ECD\u7684\u8868\u683C\uFF0C\u4E0E\u4E3B\u9875\u76F8\u5BF9\u5E94 ||  || \"\"\"
aaeiigice,fastnlp/fastNLP,fastNLP/modules/__init__.py,c9dc7022e4e8142dd113632c52d218e5f70dcb8f,STILL_EXISTS,\"\"\" || modules \u6A21\u5757\u662F fastNLP \u7684\u91CD\u8981\u7EC4\u6210\u90E8\u5206\uFF0C\u5B83\u5B9E\u73B0\u4E86\u795E\u7ECF\u7F51\u7EDC\u6784\u5EFA\u4E2D\u5E38\u89C1\u7684\u7EC4\u4EF6\uFF0C || \u5177\u4F53\u5305\u62EC TODO ||  || \u53EF\u4EE5\u548C PyTorch \u7ED3\u5408\u4F7F\u7528\uFF1FTODO ||  || TODO __all__ \u91CC\u9762\u591A\u66B4\u9732\u4E00\u4E9B ||  || \"\"\"
aaeiigjce,fastnlp/fastNLP,test/io/test_embed_loader.py,51b493d7165cdb2045c2c8198217d23afb75d5eb,STILL_EXISTS,TODO
aaeiihach,fastnlp/fastNLP,fastNLP/core/predictor.py,32fdb48754b87d7ecae02f3f5bf74af45775e151,STILL_EXISTS,\"\"\" ||     ..todo:: ||         \u68C0\u67E5\u8FD9\u4E2A\u7C7B\u662F\u5426\u9700\u8981 || \"\"\"
aaeiiihab,fastnlp/fastNLP,reproduction/matching/data/SNLIDataLoader.py,e05c182b05fe1d5643a6f99526401b2c43a228b9,STILL_EXISTS,TODO: still in progress
aaeiiihac,fastnlp/fastNLP,reproduction/matching/model/bert.py,e05c182b05fe1d5643a6f99526401b2c43a228b9,ea0f2f7e00188ab44bad21d8a6e53aa55601a3b6,TODO: still in progress
aaeiiihad,fastnlp/fastNLP,reproduction/matching/test/test_snlidataloader.py,e05c182b05fe1d5643a6f99526401b2c43a228b9,STILL_EXISTS,TODO: still in progress
aaeiiijci,fastnlp/fastNLP,fastNLP/core/utils.py,6309eafd25084c4c1f33113a05b9c03d2eaaf0b1,STILL_EXISTS,TODO \u8FD9\u4E2A\u51FD\u6570\u5B58\u5728\u4E00\u5B9A\u7684\u98CE\u9669\uFF0C\u56E0\u4E3A\u540C\u4E00\u4E2A\u6A21\u578B\u53EF\u80FD\u5B58\u5728\u67D0\u4E9Bparameter\u4E0D\u5728\u663E\u5361\u4E2D\uFF0C\u6BD4\u5982BertEmbedding
aaeiiijdh,fastnlp/fastNLP,fastNLP/io/file_utils.py,6309eafd25084c4c1f33113a05b9c03d2eaaf0b1,STILL_EXISTS,make HEAD request to check ETag TODO ETag\u53EF\u4EE5\u7528\u6765\u5224\u65AD\u8D44\u6E90\u662F\u5426\u5DF2\u7ECF\u66F4\u65B0\u4E86\uFF0C\u4E4B\u540E\u9700\u8981\u52A0\u4E0A
aaeiijabf,fastnlp/fastNLP,fastNLP/modules/encoder/_bert.py,6309eafd25084c4c1f33113a05b9c03d2eaaf0b1,STILL_EXISTS,TODO \u4FEE\u6539
aaeiijahb,fastnlp/fastNLP,fastNLP/modules/encoder/_elmo.py,6309eafd25084c4c1f33113a05b9c03d2eaaf0b1,2c00c1ae5aab51feae7dfec5331e58d82c2f46af,TODO \u8FD9\u91CC\u5E94\u8BE5\u8981\u8003\u8651seq_len\u7684\u95EE\u9898
aaeiijbaf,fastnlp/fastNLP,fastNLP/modules/encoder/embedding.py,6309eafd25084c4c1f33113a05b9c03d2eaaf0b1,ed3098e1b8481c6816d0642c7b7b07c7dfb95eae,TODO \u4FEE\u6539\u4E3A\u66F4\u5408\u7406\u7684\u65B9\u5F0F
aaeiijbbc,fastnlp/fastNLP,fastNLP/modules/encoder/embedding.py,6309eafd25084c4c1f33113a05b9c03d2eaaf0b1,839d712467b83a6bea1aab0b90e95f1432fc3ba6,TODO \u628Abaidu\u4E91\u4E0A\u7684\u52A0\u4E0A\u53BB
aaeiijejf,fastnlp/fastNLP,fastNLP/core/batch.py,7564818f4b1b14660322efca1fe7c90debbd5914,9b21071c8d01087e37c953c7e3e37e20b29b901d,TODO
aaeiijfaa,fastnlp/fastNLP,fastNLP/core/trainer.py,7564818f4b1b14660322efca1fe7c90debbd5914,2f5d8967a3caf0e3c9ed7c511954b15a82c8b3c8,TODO \u8003\u8651\u4E0D\u540C\u7684dataset\u7C7B\u578B\u600E\u4E48check
aaeiijige,fastnlp/fastNLP,fastNLP/modules/encoder/_bert.py,2f5d8967a3caf0e3c9ed7c511954b15a82c8b3c8,39388567ad7e0fd39fa39a993e8ddeaa6e5f4ff7,We \"pool\" the model by simply taking the hidden state corresponding
aaeiijjbf,fastnlp/fastNLP,fastNLP/modules/encoder/_bert.py,39388567ad7e0fd39fa39a993e8ddeaa6e5f4ff7,STILL_EXISTS,TODO \u4FEE\u6539
aaeiijjee,fastnlp/fastNLP,fastNLP/modules/encoder/embedding.py,39388567ad7e0fd39fa39a993e8ddeaa6e5f4ff7,STILL_EXISTS,TODO \u628Abaidu\u4E91\u4E0A\u7684\u52A0\u4E0A\u53BB
aaeiijjfg,fastnlp/fastNLP,fastNLP/modules/encoder/_bert.py,342b7026d70838450086b4486ee3349e1e5c8212,STILL_EXISTS,We \"pool\" the model by simply taking the hidden state corresponding
aaeiijjhg,fastnlp/fastNLP,fastNLP/modules/encoder/_bert.py,342b7026d70838450086b4486ee3349e1e5c8212,STILL_EXISTS,TODO \u4FEE\u6539
aaeiijjhj,fastnlp/fastNLP,fastNLP/modules/encoder/embedding.py,342b7026d70838450086b4486ee3349e1e5c8212,STILL_EXISTS,TODO \u628Abaidu\u4E91\u4E0A\u7684\u52A0\u4E0A\u53BB
aaeijaaef,fastnlp/fastNLP,reproduction/seqence_labelling/ner/data/Conll2003Loader.py,4533427ea369851781f9c97b4b3fc5ac29d769a5,a137038eb2cc840581adacdcfb76e685a2eed63b,TODO \u8FD9\u6837\u611F\u89C9\u4E0D\u89C4\u8303\u5450
aaeijbhfd,fastnlp/fastNLP,fastNLP/modules/encoder/_bert.py,84b18890a1d8418043e8f673ac77b172fedc95e2,STILL_EXISTS,TODO \u622A\u6389\u957F\u5EA6\u8D85\u8FC7\u7684\u90E8\u5206\u3002
aaeijbigi,fastnlp/fastNLP,reproduction/text_classification/train_char_cnn.py,b02a91ea0117e49707476ce599159723efe36595,STILL_EXISTS,todo \u8FD9\u91CC\u52A0\u5165fastnlp\u7684\u8BB0\u5F55
aaeijbjdh,fastnlp/fastNLP,reproduction/coreference_resolution/model/metric.py,c07cbd72f696564a77eb3e888b4d14618fe5b526,STILL_EXISTS,TODO \u6539\u540D\u4E3Aevaluate\uFF0C\u8F93\u5165\u4E5F
aaeijbjdi,fastnlp/fastNLP,reproduction/coreference_resolution/model/metric.py,c07cbd72f696564a77eb3e888b4d14618fe5b526,STILL_EXISTS,TODO \u539F\u672C\u7684getprf
aaeijbjfi,fastnlp/fastNLP,reproduction/coreference_resolution/model/model_re.py,c07cbd72f696564a77eb3e888b4d14618fe5b526,STILL_EXISTS,TODO \u540E\u9762\u53EF\u80FD\u8981\u6539
aaeijbjgf,fastnlp/fastNLP,reproduction/coreference_resolution/model/model_re.py,c07cbd72f696564a77eb3e888b4d14618fe5b526,STILL_EXISTS,TODO \u4E0D\u4EA4\u53C9\u6CA1\u505A\u5904\u7406
aaeijbjgh,fastnlp/fastNLP,reproduction/coreference_resolution/model/model_re.py,c07cbd72f696564a77eb3e888b4d14618fe5b526,STILL_EXISTS,TODO \u8FD9\u91CC\u53EA\u8003\u8651\u4E86start\u6CA1\u6709\u8003\u8651end
aaeijbjid,fastnlp/fastNLP,reproduction/coreference_resolution/model/model_re.py,c07cbd72f696564a77eb3e888b4d14618fe5b526,STILL_EXISTS,TODO: torch.max(value; dim=None) threw an error at time of writing
aaeijcaaj,fastnlp/fastNLP,reproduction/coreference_resolution/model/model_re.py,c07cbd72f696564a77eb3e888b4d14618fe5b526,STILL_EXISTS,TODO \u6CA1\u6709\u6309\u7167\u8BBA\u6587\u5199
aaeijcacc,fastnlp/fastNLP,reproduction/coreference_resolution/model/model_re.py,c07cbd72f696564a77eb3e888b4d14618fe5b526,STILL_EXISTS,TODO flatte lstm
aaeijcadb,fastnlp/fastNLP,reproduction/coreference_resolution/model/preprocess.py,c07cbd72f696564a77eb3e888b4d14618fe5b526,STILL_EXISTS,TODO \u4FEE\u6539\u4E86\u63A5\u53E3\uFF0C\u786E\u8BA4\u6240\u6709\u8BE5\u4FEE\u6539\u7684\u5730\u65B9\u90FD\u4FEE\u6539\u597D
aaeijcadc,fastnlp/fastNLP,reproduction/coreference_resolution/model/preprocess.py,c07cbd72f696564a77eb3e888b4d14618fe5b526,STILL_EXISTS,TODO \u6CA1\u6709\u6D4B\u8BD5
aaeijdejb,fastnlp/fastNLP,fastNLP/modules/encoder/_elmo.py,e867641023bc37bcffdd5af14128ed786bcc5e2b,2c00c1ae5aab51feae7dfec5331e58d82c2f46af,handle the different gate order convention
aaeijeeeb,fastnlp/fastNLP,fastNLP/core/batch.py,43fac849f9e21bf28a99da94d69d5d1674ff7e47,7f019713214417435b1ff616b4257116953159c6,TODO \u652F\u6301\u5728DataSet\u4E2D\u5B9A\u4E49collate_fn\uFF0C\u56E0\u4E3A\u6709\u65F6\u5019\u53EF\u80FD\u9700\u8981\u4E0D\u540C\u7684field\u4E4B\u95F4\u878D\u5408\uFF0C\u6BD4\u5982BERT\u7684\u573A\u666F
aaejabcbi,fastnlp/fastNLP,fastNLP/embeddings/bert_embedding.py,570b214dfb7bc45a58159f3dd53e5be254e2e5d2,2098a81f2fad4a11c53f6347f41670c71f06bdb9,TODO \u622A\u6389\u957F\u5EA6\u8D85\u8FC7\u7684\u90E8\u5206\u3002
aaejabdbj,fastnlp/fastNLP,fastNLP/modules/encoder/bert.py,570b214dfb7bc45a58159f3dd53e5be254e2e5d2,STILL_EXISTS,We \"pool\" the model by simply taking the hidden state corresponding
aaejagead,fastnlp/fastNLP,fastNLP/core/callback.py,329a18976ff3cf0d669cde6ba7571c7b3b20bcb0,STILL_EXISTS,TODO add compare & save best
aaejageag,fastnlp/fastNLP,fastNLP/core/callback.py,cacf40366c794e337cbe9d39b21306cada58ef7e,af55db201990d66b9e43a95e36e96b7a340e43e7,increase_better is True. It means the exp result gets better if the indicator increases.
aaejageaj,fastnlp/fastNLP,fastNLP/io/file_utils.py,71c9e0c30ec53fd825cbdbda265cfea005f04f9a,014e9786c7abbbb3c043c3a1db19e703ad338659,TODO \u66FF\u6362
aaejagecj,fastnlp/fastNLP,fastNLP/core/callback.py,c3d5128ab54b78fdccf3284922a1d2bc62229b7d,STILL_EXISTS,increase_better is True. It means the exp result gets better if the indicator increases.
aaejaghfe,fastnlp/fastNLP,fastNLP/embeddings/bert_embedding.py,31f35ad61736432923706c13ecfc123eab03e130,STILL_EXISTS,TODO \u622A\u6389\u957F\u5EA6\u8D85\u8FC7\u7684\u90E8\u5206\u3002
aaejaghhf,fastnlp/fastNLP,fastNLP/embeddings/bert_embedding.py,58d7742b6626f4c6a5850f4b8277c1c5cdb1c150,STILL_EXISTS,TODO \u622A\u6389\u957F\u5EA6\u8D85\u8FC7\u7684\u90E8\u5206\u3002
aaejahabh,fastnlp/fastNLP,fastNLP/io/pipe/cws.py,e2232ac39f78e0d796dac844994b4045a425318c,9e16791c538b856184efd4095ab0faed5ff4d2ce,\tIf ends with space; will be processed
aaejahabi,fastnlp/fastNLP,fastNLP/io/pipe/cws.py,e2232ac39f78e0d796dac844994b4045a425318c,9e16791c538b856184efd4095ab0faed5ff4d2ce,\tIf ends with Chinese character; will be processed
aaejahabj,fastnlp/fastNLP,fastNLP/io/pipe/cws.py,e2232ac39f78e0d796dac844994b4045a425318c,9e16791c538b856184efd4095ab0faed5ff4d2ce,\tIf ends with or contains english char; not handled.
aaejahadi,fastnlp/fastNLP,fastNLP/embeddings/bert_embedding.py,d6c597d32e66121a4f24c3fdbf6f5f0a9ee6e56e,19bbaf11b6989a1a29384d5b1516bf934ccac296,\"\"\" || .. todo:: ||     doc || \"\"\"
aaejahadj,fastnlp/fastNLP,fastNLP/embeddings/contextual_embedding.py,d6c597d32e66121a4f24c3fdbf6f5f0a9ee6e56e,STILL_EXISTS,\"\"\" || .. todo:: ||     doc || \"\"\"
aaejahaea,fastnlp/fastNLP,fastNLP/embeddings/elmo_embedding.py,d6c597d32e66121a4f24c3fdbf6f5f0a9ee6e56e,19bbaf11b6989a1a29384d5b1516bf934ccac296,\"\"\" || .. todo:: ||     doc || \"\"\"
aaejahaeb,fastnlp/fastNLP,fastNLP/embeddings/stack_embedding.py,d6c597d32e66121a4f24c3fdbf6f5f0a9ee6e56e,STILL_EXISTS,\"\"\" || .. todo:: ||     doc || \"\"\"
aaejahaec,fastnlp/fastNLP,fastNLP/embeddings/static_embedding.py,d6c597d32e66121a4f24c3fdbf6f5f0a9ee6e56e,19bbaf11b6989a1a29384d5b1516bf934ccac296,\"\"\" || .. todo:: ||     doc || \"\"\"
aaejahaed,fastnlp/fastNLP,fastNLP/embeddings/utils.py,d6c597d32e66121a4f24c3fdbf6f5f0a9ee6e56e,STILL_EXISTS,\"\"\" || .. todo:: ||     doc || \"\"\"
aaejahahb,fastnlp/fastNLP,fastNLP/core/const.py,efe88263bb2fb7bebacb8022eb86c390e266ec36,STILL_EXISTS,\"\"\" || .. todo:: ||     doc || \"\"\"
aaejahahc,fastnlp/fastNLP,fastNLP/core/field.py,efe88263bb2fb7bebacb8022eb86c390e266ec36,STILL_EXISTS,\"\"\" || .. todo:: ||     doc || \"\"\"
aaejahahd,fastnlp/fastNLP,fastNLP/core/vocabulary.py,efe88263bb2fb7bebacb8022eb86c390e266ec36,STILL_EXISTS,\"\"\" || .. todo:: ||     doc || \"\"\"
aaejahahe,fastnlp/fastNLP,fastNLP/io/data_bundle.py,0d5f43b451473fe25703cb1f9798fcf03eb64c76,STILL_EXISTS,\"\"\" || .. todo:: ||     doc || \"\"\"
aaejahahf,fastnlp/fastNLP,fastNLP/io/embed_loader.py,0d5f43b451473fe25703cb1f9798fcf03eb64c76,STILL_EXISTS,\"\"\" || .. todo:: ||     doc || \"\"\"
aaejahahg,fastnlp/fastNLP,fastNLP/io/file_utils.py,0d5f43b451473fe25703cb1f9798fcf03eb64c76,STILL_EXISTS,\"\"\" || .. todo:: ||     doc || \"\"\"
aaejahaja,fastnlp/fastNLP,fastNLP/io/utils.py,0d5f43b451473fe25703cb1f9798fcf03eb64c76,STILL_EXISTS,\"\"\" || .. todo:: ||     doc || \"\"\"
aaejahajc,fastnlp/fastNLP,fastNLP/models/cnn_text_classification.py,efa9496d09d139658683eec0b4a6ae44b93dd88c,STILL_EXISTS,\"\"\" || .. todo:: ||     doc || \"\"\"
aaejahajh,fastnlp/fastNLP,fastNLP/models/snli.py,efa9496d09d139658683eec0b4a6ae44b93dd88c,STILL_EXISTS,\"\"\" || .. todo:: ||     doc || \"\"\"
aaejahaji,fastnlp/fastNLP,fastNLP/modules/decoder/__init__.py,2cf9c0ebb1722aae734ceb971b889c43198729a2,STILL_EXISTS,\"\"\" || .. todo:: ||     doc || \"\"\"
aaejahbad,fastnlp/fastNLP,fastNLP/modules/encoder/__init__.py,2cf9c0ebb1722aae734ceb971b889c43198729a2,STILL_EXISTS,\"\"\" || .. todo:: ||     doc || \"\"\"
aaejahbaj,fastnlp/fastNLP,fastNLP/modules/utils.py,2cf9c0ebb1722aae734ceb971b889c43198729a2,STILL_EXISTS,\"\"\" || .. todo:: ||     doc || \"\"\"
aaejajhid,fastnlp/fastNLP,fastNLP/embeddings/bert_embedding.py,359a17674806275f9e9c3ff7c7d095645efd4780,STILL_EXISTS,\"\"\" || .. todo:: ||     doc || \"\"\"
aaejajhig,fastnlp/fastNLP,fastNLP/embeddings/elmo_embedding.py,359a17674806275f9e9c3ff7c7d095645efd4780,STILL_EXISTS,\"\"\" || .. todo:: ||     doc || \"\"\"
aaejajhih,fastnlp/fastNLP,fastNLP/embeddings/static_embedding.py,359a17674806275f9e9c3ff7c7d095645efd4780,STILL_EXISTS,\"\"\" || .. todo:: ||     doc || \"\"\"
aaejbabcd,fastnlp/fastNLP,fastNLP/io/loader/coreference.py,ea5fbc8881dc763a1ac13c0422da07fb199d6fc1,STILL_EXISTS,TODO check 1
aaejbafic,fastnlp/fastNLP,fastNLP/core/dist_trainer.py,02cfc9f421e7bbb9e727d92c323ead6f471f00e7,STILL_EXISTS,save better model
aaejbafid,fastnlp/fastNLP,fastNLP/core/dist_trainer.py,02cfc9f421e7bbb9e727d92c323ead6f471f00e7,STILL_EXISTS,TODO to support multiple datasets to evaluate
aaejbcgbf,fastnlp/fastNLP,fastNLP/core/metrics.py,0547572d58e0bc2e2a36494600e94ddba0906dce,STILL_EXISTS,TODO \u8FD9\u91CC\u62A5\u9519\u9700\u8981\u66F4\u6539\uFF0C\u56E0\u4E3Apred\u662F\u5565\u7528\u6237\u5E76\u4E0D\u77E5\u9053\u3002\u9700\u8981\u544A\u77E5\u7528\u6237\u771F\u5B9E\u7684value
aaejbcgge,fastnlp/fastNLP,test/core/test_metrics.py,f3ee16a5f6c2a679f9f12f490c6cb4109f5cae54,STILL_EXISTS,(7) check map; include unused
aaejbchaj,fastnlp/fastNLP,fastNLP/modules/decoder/seq2seq_decoder.py,8ae58d15ce431b9ab5d9bffd093a9bb448d9653f,7398f2b0b20b7725295ca01feaaad437d3d331d2,todo: \u5E94\u8BE5\u5C06position embedding\u79FB\u5230core
aaejbchce,fastnlp/fastNLP,fastNLP/modules/decoder/seq2seq_decoder.py,8ae58d15ce431b9ab5d9bffd093a9bb448d9653f,7398f2b0b20b7725295ca01feaaad437d3d331d2,todo: \u7531\u4E8E\u6BCF\u4E2A\u6A21\u578B\u90FD\u6709embedding\u7684\u7ED1\u5B9A\u6216\u5176\u4ED6\u64CD\u4F5C\uFF0C\u5EFA\u8BAE\u632A\u5230\u5916\u90E8\u51FD\u6570\u4EE5\u51CF\u5C11\u5197\u4F59\uFF0C\u53EF\u53C2\u8003fairseq
aaejbchdc,fastnlp/fastNLP,fastNLP/modules/decoder/seq2seq_decoder.py,879cef62c6f18f323a5f3aa50649484b8f1c933a,7398f2b0b20b7725295ca01feaaad437d3d331d2,todo : \u5176\u5B9E\u53EF\u4EE5\u4E0D\u8981\u8FD9\u4E2A\u7684
aaejbchej,fastnlp/fastNLP,fastNLP/modules/decoder/seq2seq_generator.py,879cef62c6f18f323a5f3aa50649484b8f1c933a,STILL_EXISTS,TODO \u9700\u8981\u67E5\u770B\u5982\u679Ctokens\u957F\u5EA6\u4E0D\u662F1\uFF0Cdecode\u7684\u65F6\u5019\u662F\u5426\u8FD8\u80FD\u591F\u76F4\u63A5decode\uFF1F
aaejbcibb,fastnlp/fastNLP,fastNLP/modules/decoder/seq2seq_generator.py,879cef62c6f18f323a5f3aa50649484b8f1c933a,b95aa56afb94a7c98ce362c0229441081351b4f2,TODO \u9700\u8981\u68C0\u67E5\u4E00\u4E0Bgreedy_generate\u548Csample_generate\u662F\u5426\u6B63\u5E38\u5DE5\u4F5C\u3002
aaejbcjch,fastnlp/fastNLP,fastNLP/modules/encoder/gpt2.py,d2ca466325b39ce5b6adfcf423ad2e41c49c2aad,7398f2b0b20b7725295ca01feaaad437d3d331d2,Set max length if needed
aaejbcjeb,fastnlp/fastNLP,fastNLP/modules/encoder/roberta.py,d2ca466325b39ce5b6adfcf423ad2e41c49c2aad,STILL_EXISTS,Convert old format to new format if needed from a PyTorch state_dict
aaejbcjfb,fastnlp/fastNLP,fastNLP/modules/encoder/roberta.py,d2ca466325b39ce5b6adfcf423ad2e41c49c2aad,7398f2b0b20b7725295ca01feaaad437d3d331d2,Set max length if needed
aaejbcjhe,fastnlp/fastNLP,fastNLP/core/dist_trainer.py,7398f2b0b20b7725295ca01feaaad437d3d331d2,STILL_EXISTS,save better model on master node
aaejbcjia,fastnlp/fastNLP,fastNLP/embeddings/gpt2_embedding.py,7398f2b0b20b7725295ca01feaaad437d3d331d2,STILL_EXISTS,\"\"\" || .. todo:: ||     doc || \"\"\"
aaejbdbbd,fastnlp/fastNLP,fastNLP/modules/encoder/gpt2.py,7398f2b0b20b7725295ca01feaaad437d3d331d2,STILL_EXISTS,Update config with kwargs if needed
aaejbdbed,fastnlp/fastNLP,fastNLP/modules/encoder/gpt2.py,7398f2b0b20b7725295ca01feaaad437d3d331d2,STILL_EXISTS,Prune heads if needed
aaejbdbee,fastnlp/fastNLP,fastNLP/modules/encoder/gpt2.py,7398f2b0b20b7725295ca01feaaad437d3d331d2,STILL_EXISTS,Tie weights if needed
aaejbdbfc,fastnlp/fastNLP,fastNLP/modules/encoder/gpt2.py,7398f2b0b20b7725295ca01feaaad437d3d331d2,STILL_EXISTS,Convert old format to new format if needed from a PyTorch state_dict
aaejbdbfh,fastnlp/fastNLP,fastNLP/modules/encoder/gpt2.py,7398f2b0b20b7725295ca01feaaad437d3d331d2,STILL_EXISTS,make sure word embedding weights are still tied if needed
aaejbdbhg,fastnlp/fastNLP,fastNLP/modules/encoder/gpt2.py,7398f2b0b20b7725295ca01feaaad437d3d331d2,STILL_EXISTS,Prepare head mask if needed
aaejbdcda,fastnlp/fastNLP,fastNLP/modules/generator/seq2seq_generator.py,7398f2b0b20b7725295ca01feaaad437d3d331d2,STILL_EXISTS,TODO \u9700\u8981\u67E5\u770B\u5982\u679Ctokens\u957F\u5EA6\u4E0D\u662F1\uFF0Cdecode\u7684\u65F6\u5019\u662F\u5426\u8FD8\u80FD\u591F\u76F4\u63A5decode\uFF1F
aaejbddeg,fastnlp/fastNLP,fastNLP/modules/tokenizer/gpt2_tokenizer.py,7398f2b0b20b7725295ca01feaaad437d3d331d2,STILL_EXISTS,Set max length if needed
aaejbddff,fastnlp/fastNLP,fastNLP/modules/tokenizer/gpt2_tokenizer.py,7398f2b0b20b7725295ca01feaaad437d3d331d2,STILL_EXISTS,\u5982\u679Ctoken\u6CA1\u6709\u627E\u5230\uFF0C\u4F1A\u88AB\u62C6\u5206\u6210\u5B57\u6BCD\u8FD4\u56DE  TODO need comment
aaejbddge,fastnlp/fastNLP,fastNLP/modules/tokenizer/roberta_tokenizer.py,7398f2b0b20b7725295ca01feaaad437d3d331d2,STILL_EXISTS,Set max length if needed
aaejbebgh,fastnlp/fastNLP,fastNLP/models/seq2seq_model.py,15360e9724884e26ee76ae3933bd7e43f2a84fb9,b95aa56afb94a7c98ce362c0229441081351b4f2,todo \u53C2\u8003fairseq\u7684FairseqModel\u7684\u5199\u6CD5
aaejbebgj,fastnlp/fastNLP,fastNLP/modules/decoder/seq2seq_decoder.py,15360e9724884e26ee76ae3933bd7e43f2a84fb9,STILL_EXISTS,get_sinusoid_encoding_table  # todo: \u5E94\u8BE5\u5C06position embedding\u79FB\u5230core
aaejbebih,fastnlp/fastNLP,fastNLP/modules/decoder/seq2seq_decoder.py,15360e9724884e26ee76ae3933bd7e43f2a84fb9,STILL_EXISTS,todo: \u7531\u4E8E\u6BCF\u4E2A\u6A21\u578B\u90FD\u6709embedding\u7684\u7ED1\u5B9A\u6216\u5176\u4ED6\u64CD\u4F5C\uFF0C\u5EFA\u8BAE\u632A\u5230\u5916\u90E8\u51FD\u6570\u4EE5\u51CF\u5C11\u5197\u4F59\uFF0C\u53EF\u53C2\u8003fairseq
aaejbecej,fastnlp/fastNLP,fastNLP/modules/decoder/seq2seq_decoder.py,b95aa56afb94a7c98ce362c0229441081351b4f2,e361b32c3a70b24bb9fa59e89a49039ce685a043,todo \u652F\u6301\u4E0D\u505Aattention
aaejbecgb,fastnlp/fastNLP,fastNLP/modules/decoder/seq2seq_decoder.py,b95aa56afb94a7c98ce362c0229441081351b4f2,STILL_EXISTS,get_sinusoid_encoding_table  # todo: \u5E94\u8BE5\u5C06position embedding\u79FB\u5230core
aaejbechj,fastnlp/fastNLP,fastNLP/modules/decoder/seq2seq_decoder.py,b95aa56afb94a7c98ce362c0229441081351b4f2,STILL_EXISTS,todo: \u7531\u4E8E\u6BCF\u4E2A\u6A21\u578B\u90FD\u6709embedding\u7684\u7ED1\u5B9A\u6216\u5176\u4ED6\u64CD\u4F5C\uFF0C\u5EFA\u8BAE\u632A\u5230\u5916\u90E8\u51FD\u6570\u4EE5\u51CF\u5C11\u5197\u4F59\uFF0C\u53EF\u53C2\u8003fairseq
aaejbecjf,fastnlp/fastNLP,fastNLP/modules/encoder/seq2seq_encoder.py,b95aa56afb94a7c98ce362c0229441081351b4f2,e361b32c3a70b24bb9fa59e89a49039ce685a043,todo \u8FD9\u4E2A\u8981\u653E\u54EA\u91CC\uFF1F
aaejbedcb,fastnlp/fastNLP,fastNLP/embeddings/bert_embedding.py,e361b32c3a70b24bb9fa59e89a49039ce685a043,STILL_EXISTS,TODO \u9700\u8981\u91CD\u65B0\u4FEE\u6539\uFF0C\u4F7F\u5F97encoder\u53EF\u4EE5\u76F4\u63A5\u8BFB\u53D6embedding\u7684\u6743\u91CD
aaejbehfj,kornia/kornia,test/test_functional.py,92d02b48edf37f43f162fa7f9a441b80c5202fbd,STILL_EXISTS,TODO: add assert with proper check
aaejbehhe,kornia/kornia,test/test_homography_warper.py,79816b239938b7f01340fa5c9222788ae503eb1e,4f049291ab44d70f2e55ea77b454e3998a465ddd,TODO: fixme
aaejbeifa,kornia/kornia,torchgeometry/transforms/functional.py,0151e23ba2529e2e9b8ca98d58f6a3f640bef39c,STILL_EXISTS,TODO: add below funtionalities
aaejbeijg,kornia/kornia,torchgeometry/depth_warper.py,33dcc8554a6a187ee33762b1e572a252aa65e698,STILL_EXISTS,TODO: add type and value checkings
aaejbejab,kornia/kornia,examples/depth_warper/main.py,8b5ee23b4e86db99fd0aead097ab52987e0e5a15,STILL_EXISTS,TODO: implement in torchgeometry
aaejbejie,kornia/kornia,docs/source/conf.py,2a5793b728186ac518f1ec8981ef18ef24647f8c,STILL_EXISTS,TODO: change to [:2] at v1.0
aaejbejig,kornia/kornia,docs/source/conf.py,2a5793b728186ac518f1ec8981ef18ef24647f8c,STILL_EXISTS,TODO: verify this works as expected
aaejbejjg,kornia/kornia,docs/source/conf.py,2a5793b728186ac518f1ec8981ef18ef24647f8c,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaejbfadh,kornia/kornia,docs/source/conf.py,2a5793b728186ac518f1ec8981ef18ef24647f8c,STILL_EXISTS,`kw` catches `env=None` needed for newer sphinx while maintaining
aaejbfbgh,kornia/kornia,torchgeometry/utils.py,8b634f5a46857b38f5dd5743c2603e7c86aa2537,a90d0f2eef0f1ad36e38e6e4fe0633ea76b91b6a,TODO: add documentation
aaejbfdcj,kornia/kornia,torchgeometry/imgwarp.py,213d64f52eb9f33f6d127e5033e529c6694d3c89,STILL_EXISTS,move points to origin
aaejbfdgb,kornia/kornia,torchgeometry/imgwarp.py,687f59d45096b29219b1fcaf45cf0b70f4d86986,STILL_EXISTS,move points to origin
aaejbfhbj,kornia/kornia,test/test_imgwarp.py,53450f8914a0a91e092b929c0d5241e3d5ddc11d,STILL_EXISTS,TODO: investage why precision is that low
aaejbfhgh,kornia/kornia,docs/source/conf.py,5f55bed2572ed76144e09357d511d03123722b85,STILL_EXISTS,@jpchen's hack to get rtd builder to install latest pytorch
aaejbfifg,kornia/kornia,test/test_transformations.py,32ca8c514625b47073185e6a01bad7794e14f77b,STILL_EXISTS,TODO: embedd to a class
aaejbfjge,kornia/kornia,test/test_losses.py,bf69e91db2843944d8f4e253350eaa599d4d65e7,7634391b070859dc1c11b092fde754dbc7911d4a,TODO: implement me
aaejbfjif,kornia/kornia,test/test_losses.py,467dba242fa4efc25f9238b887230bd15eb3505d,7634391b070859dc1c11b092fde754dbc7911d4a,TODO: implement me
aaejbgadg,kornia/kornia,test/test_contrib.py,6cdf69ed3cec9a44111a361b50b2b915ff74255b,a121cce4e91e49deea60a819c58f05c9d59994e8,TODO: implement me
aaejbgbbe,kornia/kornia,test/test_contrib.py,a1d10b3ae3351acec31bf9d88a3d453c7bad7ade,a121cce4e91e49deea60a819c58f05c9d59994e8,TODO: implement me
aaejbgcaj,kornia/kornia,torchgeometry/core/depth_warper.py,7386372bcadb67a0b3f44486e943d7938e58c7f8,f55051bc82a5861cbdc2ae7c0e535be857d8cef0,TODO: add more documentation
aaejbgcbi,kornia/kornia,torchgeometry/core/depth_warper.py,7386372bcadb67a0b3f44486e943d7938e58c7f8,521bda9602a8a63e8de747366f2d4c8889c8cb8f,TODO: add documentation
aaejbgfgi,kornia/kornia,torchgeometry/metrics/confusion_matrix.py,0fe08fe401f13f58271dd54f28f10eb70b77b93f,STILL_EXISTS,NOTE: torch.bincount does not implement batched version
aaejbgfhc,kornia/kornia,torchgeometry/metrics/mean_iou.py,e67b8425a4028f888759f652a1ca87d544c34858,STILL_EXISTS,TODO: is it possible to vectorize this ?
aaejbgfhg,kornia/kornia,torchgeometry/metrics/confusion_matrix.py,fadaeaf7513a5a9a6be5ee231df1b0fdb1ea147d,STILL_EXISTS,hack for bitcounting 2 arrays together
aaejbggbi,kornia/kornia,torchgeometry/image/laplacian.py,9829df00dba39e47955df0235d92678f050bced2,STILL_EXISTS,TODO: Filter2D convolves an image with the kernel. A wrapper fucntion for conv2d something similar to the one
aaejbggci,kornia/kornia,torchgeometry/image/gaussian.py,852b50afed3d8185291848a14db79b8198779296,STILL_EXISTS,TODO: explore solution when using jit.trace since it raises a warning
aaejbghad,kornia/kornia,torchgeometry/core/affine.py,89c6d964c5a18254adf73a5f8da00d8a5068e7bc,STILL_EXISTS,TODO: add broadcasting to get_rotation_matrix2d for center
aaejbghgb,kornia/kornia,torchgeometry/core/affine.py,75a84a373e9ce142fb4a1ac0d7fde8f3790b861c,STILL_EXISTS,TODO: add broadcasting to get_rotation_matrix2d for center
aaejbgjei,kornia/kornia,torchgeometry/core/imgwarp.py,a47b22167136d8e76cff045c61c0d280a9baa1f2,STILL_EXISTS,TODO: move to utils or conversions
aaejbhcej,kornia/kornia,torchgeometry/image/pyramid.py,b1795dcf909e42cf753ce29e9e6c1a2e40f462e9,STILL_EXISTS,reject even rows and columns.
aaejbhegd,kornia/kornia,torchgeometry/feature/harris.py,84cc1287fcd9df2a437a2d25a61f171097047a76,STILL_EXISTS,TODO: add as signature parameter
aaejbhegg,kornia/kornia,torchgeometry/feature/harris.py,84cc1287fcd9df2a437a2d25a61f171097047a76,STILL_EXISTS,TODO: implement support for kernel different than three
aaejbheha,kornia/kornia,torchgeometry/feature/harris.py,84cc1287fcd9df2a437a2d25a61f171097047a76,STILL_EXISTS,TODO: add as signature parameter ?
aaejbhgdc,kornia/kornia,torchgeometry/contrib/spatial_soft_argmax2d.py,acc83eaa5084b4237ba2eeb06c9ea31296a59180,STILL_EXISTS,TODO: use utils.create_meshgrid and test
aaejbhgjf,kornia/kornia,torchgeometry/geometry/linalg.py,71e8790fcb090716b9395c6ea63efd0f95507854,STILL_EXISTS,TODO:
aaejbhigg,kornia/kornia,kornia/geometry/conversions.py,1439b82546e9c989002ec6072d9a4ffc8dc7fb53,0e1487f1af21ca088fcbdb99de299f6686c928b8,follow the convention of opencv: https:\/\/github.com\/opencv\/opencv\/pull\/14411\/files
aaejbhiij,kornia/kornia,kornia/geometry/conversions.py,a211d4952355e440b944b1bda8eed4c2a7457c2d,STILL_EXISTS,follow the convention of opencv:
aaejbhjdd,kornia/kornia,test/geometry/test_perspective.py,00f9e895257fc835d3a986b3f23d8144ad9aae4c,STILL_EXISTS,TODO: point [0; 0; 0] crashes
aaejbibij,kornia/kornia,kornia/feature/laf.py,9dd9ed7c7e4c314e1e9742dd4bbf847cc626204d,STILL_EXISTS,norm; then renorm is needed for allowing detection on one resulution
aaejbidgb,kornia/kornia,kornia/geometry/depth.py,4ebe02564f14a713f085ef54b301c71e04705d54,STILL_EXISTS,hack to match sizes
aaejbieif,kornia/kornia,kornia/feature/scale_space_detector.py,2209807ce8db8fe82eabbe6ca51e6370beea2934,STILL_EXISTS,ToDo: replace with 3d input; when grid_sample will start to support it
aaejcabfb,kornia/kornia,kornia/losses/divergence.py,6dd39eed9fb9d96088272a9fe846e5f5768850c1,STILL_EXISTS,TODO: add this to the main module
aaejcaccf,kornia/kornia,test/color/test_yuv.py,7616e92175363e56922f7788cf773b9c3cd5655d,0234315776751221ef9da5d2073e32f9f54c01bb,TODO add cv2 comparision test
aaejcaiee,kornia/kornia,examples/data_augmentation.py,73317ca61e33b42353fc4b3010f586124954ca18,b77efb1269d87560bc9f9e6016bcf77c53557448,move tensors to GPU
aaejcajfa,kornia/kornia,examples/data_augmentation.py,dcd394f4a5bddb1fc97ae4a7224e00147fc110a3,32b9dcd002284d6bb16da626a4a5294c28098869,move tensors to GPU
aaejcbdgj,kornia/kornia,kornia/augmentation/_perspective.py,850df90847643996011f67ebd23a8c628421bce4,STILL_EXISTS,TODO: implement apply_perspective
aaejcbigf,kornia/kornia,kornia/augmentation/functional.py,a8878e3b94e32621d068e6c7f49c154ea49d07e4,STILL_EXISTS,TODO: look for a workaround for this hack. In CUDA it fails when no elements found.
aaejcbjhj,kornia/kornia,kornia/jit/__init__.py,10606bd5865810df634cdb2852fff21c1464197f,STILL_EXISTS,TODO: find an automatic way to do this
aaejccaac,kornia/kornia,kornia/augmentation/augmentation.py,66a3e3cb31ddc9214c47a54411d58a31f1ab9ea7,ab9c4f8474ca744c7e67822fa21d408d0b0ae2d0,TODO: To be discussed
aaejccaai,kornia/kornia,kornia/augmentation/augmentation.py,66a3e3cb31ddc9214c47a54411d58a31f1ab9ea7,ab9c4f8474ca744c7e67822fa21d408d0b0ae2d0,TODO: return_transform is disabled for now.
aaejccaeh,kornia/kornia,kornia/augmentation/functional.py,ab9c4f8474ca744c7e67822fa21d408d0b0ae2d0,STILL_EXISTS,TODO: This part should be inferred from rotate directly
aaejcccbe,kornia/kornia,kornia/augmentation/functional.py,5070d81b1720a1e7d8692888674012d518b12b62,STILL_EXISTS,TODO: this if statement is super weird and sum here is not the propeer way to check
aaejcccei,kornia/kornia,kornia/augmentation/random_generator.py,ff0d8ed5ba6e33de8438c6afa3ba2a6a4ef2006b,20f92bda2e4dd571e85d2bcb4c3ebb3448af7e69,TODO: remove this since it does not help readability
aaejcccgd,kornia/kornia,kornia/augmentation/augmentation.py,7976f384889e79b8f5aef3bc9a3c495961e60db6,766bd71d6cca7313988b02784be6d56834e8c744,TODO: Enable batch mode
aaejcccge,kornia/kornia,kornia/augmentation/functional.py,7976f384889e79b8f5aef3bc9a3c495961e60db6,bd21bb4fec8d10b257ceccc3d302abd6d5b7c7d5,TODO: this params should be at some point; learnable tensors
aaejcccgj,kornia/kornia,kornia/filters/blur.py,7976f384889e79b8f5aef3bc9a3c495961e60db6,6a4fa82792bddc6126eaf944956f49f0a0cb7ca7,TODO: In terms of functional API; there should not be any initialization of an nn.Module.
aaejccchb,kornia/kornia,kornia/filters/gaussian.py,7976f384889e79b8f5aef3bc9a3c495961e60db6,6a4fa82792bddc6126eaf944956f49f0a0cb7ca7,TODO: In terms of functional API; there should not be any initialization of an nn.Module.
aaejccchd,kornia/kornia,kornia/filters/laplacian.py,7976f384889e79b8f5aef3bc9a3c495961e60db6,6a4fa82792bddc6126eaf944956f49f0a0cb7ca7,TODO: In terms of functional API; there should not be any initialization of an nn.Module.
aaejccchi,kornia/kornia,kornia/filters/sobel.py,7976f384889e79b8f5aef3bc9a3c495961e60db6,6a4fa82792bddc6126eaf944956f49f0a0cb7ca7,TODO: In terms of functional API; there should not be any initialization of an nn.Module.
aaejcccic,kornia/kornia,test/augmentation/test_augmentation.py,7976f384889e79b8f5aef3bc9a3c495961e60db6,766bd71d6cca7313988b02784be6d56834e8c744,TODO: Gradcheck for param random gen failed. Suspect get_motion_kernel2d issue.
aaejccdfg,kornia/kornia,kornia/geometry/epipolar/numeric.py,df93618db8bb393d2eedcdee2c39b8a80eba8cc7,STILL_EXISTS,TODO: this should go to `kornia.geometry.linalg`
aaejccedb,kornia/kornia,kornia/color/adjust.py,f4f70fefb63287f72bc80cd96df9c061b1cb60dd,STILL_EXISTS,TODO: Make a differentiable version
aaejccegd,kornia/kornia,kornia/geometry/spatial_soft_argmax.py,424548254dbf6186e0413386171592fac1c03f08,STILL_EXISTS,The following is needed to avoid singular cases
aaejccege,kornia/kornia,kornia/geometry/transform/pyramid.py,424548254dbf6186e0413386171592fac1c03f08,STILL_EXISTS,3 extra levels are needed for DoG nms.
aaejccegh,kornia/kornia,kornia/geometry/transform/pyramid.py,424548254dbf6186e0413386171592fac1c03f08,STILL_EXISTS,Therefore there is a hack in forward function
aaejccegj,kornia/kornia,kornia/geometry/transform/pyramid.py,424548254dbf6186e0413386171592fac1c03f08,STILL_EXISTS,Hack; because PyTorch does not allow to pad more than original size.
aaejccfgd,kornia/kornia,kornia/geometry/transform/affwarp.py,48b6a3f2fa72c2e71a14ba1c41cacf3c77d944bf,STILL_EXISTS,TODO: add broadcasting to get_rotation_matrix2d for center
aaejccfgi,kornia/kornia,kornia/geometry/transform/imgwarp.py,48b6a3f2fa72c2e71a14ba1c41cacf3c77d944bf,0962d9fac6456783878c459a26f6934c3b1f3ac1,TODO: translation to rotation center
aaejccfgj,kornia/kornia,kornia/geometry/transform/imgwarp.py,48b6a3f2fa72c2e71a14ba1c41cacf3c77d944bf,0962d9fac6456783878c459a26f6934c3b1f3ac1,TODO: axis-wise scaling?
aaejccfjc,kornia/kornia,kornia/geometry/transform/imgwarp.py,04d8d6fa5e57c699a3fbe3eff526a550fdb1f21a,df4b60b944438bc7ca3f658c5aa2beeb50952e60,TODO: move to utils or conversions
aaejccgbb,kornia/kornia,kornia/geometry/transform/imgwarp.py,667bcfeb50fc080dc1395a832eb592c0dee9a1c7,STILL_EXISTS,TODO: scale is not implemented here
aaejccgbj,kornia/kornia,kornia/geometry/transform/imgwarp.py,667bcfeb50fc080dc1395a832eb592c0dee9a1c7,df4b60b944438bc7ca3f658c5aa2beeb50952e60,TODO: move to utils or conversions
aaejccgcd,kornia/kornia,kornia/geometry/transform/imgwarp.py,52491e0198b5cf1fb158a534c3613e2749f6be77,STILL_EXISTS,TODO: translation math
aaejccgce,kornia/kornia,kornia/geometry/transform/imgwarp.py,52491e0198b5cf1fb158a534c3613e2749f6be77,STILL_EXISTS,TODO: scale is not implemented here
aaejccgdh,kornia/kornia,kornia/geometry/transform/imgwarp.py,e29b06784c7feb999cd024f7edc36c95eb559dbe,df4b60b944438bc7ca3f658c5aa2beeb50952e60,TODO: move to utils or conversions
aaejccgeb,kornia/kornia,kornia/geometry/transform/imgwarp.py,e29b06784c7feb999cd024f7edc36c95eb559dbe,df4b60b944438bc7ca3f658c5aa2beeb50952e60,TODO: translation to rotation center
aaejccgec,kornia/kornia,kornia/geometry/transform/imgwarp.py,e29b06784c7feb999cd024f7edc36c95eb559dbe,df4b60b944438bc7ca3f658c5aa2beeb50952e60,TODO: axis-wise scaling?
aaejccgfb,kornia/kornia,kornia/geometry/transform/imgwarp.py,e29b06784c7feb999cd024f7edc36c95eb559dbe,STILL_EXISTS,TODO: translation math
aaejccghd,kornia/kornia,kornia/geometry/transform/imgwarp.py,05317e5cba29d8bb9226354e55eb885f67cf0102,STILL_EXISTS,TODO: scale is not implemented here
aaejccgie,kornia/kornia,kornia/geometry/transform/imgwarp.py,ee64b4dea622ed7f0de85139a2a00c340b8d0508,STILL_EXISTS,TODO: translation math
aaejccgif,kornia/kornia,kornia/geometry/transform/imgwarp.py,ee64b4dea622ed7f0de85139a2a00c340b8d0508,STILL_EXISTS,TODO: scale is not implemented here
aaejccgja,kornia/kornia,kornia/geometry/transform/imgwarp.py,c6ff9e9ca18acb1d588d2a460e11745dc72fff34,STILL_EXISTS,TODO: translation math
aaejccgjb,kornia/kornia,kornia/geometry/transform/imgwarp.py,0ff57efdb239c09cd2532ba822e6447e178a5ec9,STILL_EXISTS,TODO: move to utils or conversions
aaejcciab,kornia/kornia,kornia/geometry/transform/imgwarp.py,fd987168fb351d1030c495350051841adbd29ebc,STILL_EXISTS,TODO: move to utils or conversions
aaejcdaag,kornia/kornia,kornia/augmentation/utils/param_validation.py,bd21bb4fec8d10b257ceccc3d302abd6d5b7c7d5,STILL_EXISTS,Note: I personally think throw an error will be better than a coarse clamp.
aaejcdahf,kornia/kornia,kornia/geometry/transform/crop.py,b04e9c22a24716b440edeb92072e80c98976507e,STILL_EXISTS,TODO: Looking for a vectorized way
aaejcdbca,kornia/kornia,kornia/augmentation/random_generator/random_generator.py,5a736409a9a133da27c3dfa581bba2bc71f27286,STILL_EXISTS,TODO: This line somehow breaks the gradcheck
aaejcdbgh,kornia/kornia,kornia/geometry/linalg.py,25844012ee50730a924d5e4a30f6bf2183a4327d,STILL_EXISTS,We expand trans_01 to match the dimensions needed for bmm
aaejcdcad,kornia/kornia,kornia/augmentation/random_generator/random_generator3d.py,023da9a7dbb7dab77a36c246b8244e91415decc6,2fd9c352fd36bacfabebbce0a3423b667af3a8ff,TODO: This line somehow breaks the gradcheck
aaejcdcce,kornia/kornia,kornia/geometry/transform/crop/crop3d.py,023da9a7dbb7dab77a36c246b8244e91415decc6,STILL_EXISTS,TODO: It will break the grads
aaejcdccf,kornia/kornia,kornia/geometry/transform/crop/crop3d.py,023da9a7dbb7dab77a36c246b8244e91415decc6,STILL_EXISTS,TODO: Looking for a vectorized way
aaejcdcfb,kornia/kornia,kornia/testing/__init__.py,023da9a7dbb7dab77a36c246b8244e91415decc6,STILL_EXISTS,TODO: Isn't this function duplicated with eye_like?
aaejcdcgg,kornia/kornia,test/geometry/epipolar/test_essential.py,023da9a7dbb7dab77a36c246b8244e91415decc6,STILL_EXISTS,TODO: occasionally failed with error > 0.04
aaejcddeg,kornia/kornia,test/augmentation/test_motionblur.py,766bd71d6cca7313988b02784be6d56834e8c744,STILL_EXISTS,TODO: Gradcheck for param random gen failed. Suspect get_motion_kernel2d issue.
aaejcddfg,kornia/kornia,test/geometry/transform/test_projwarp.py,e49a2d52380660e00de8549e309af94e523e2718,STILL_EXISTS,TODO: get_perspective_transform3d seems to be correct since it would result in the
aaejcddgd,kornia/kornia,test/performance/test_project_points_speed.py,636ac95751ec742bc267289083adc0b84fb243de,STILL_EXISTS,TODO: remove xfail once we have enough gpu bandwith in the CI
aaejcdfdi,kornia/kornia,kornia/augmentation/random_generator/random_generator3d.py,d7efcdf78ef3d0f15b0ac737f049ffc3c2f4fc87,2fd9c352fd36bacfabebbce0a3423b667af3a8ff,TODO: This function does not accept tensors at all.
aaejcdfhf,kornia/kornia,kornia/utils/helpers.py,2fd9c352fd36bacfabebbce0a3423b667af3a8ff,STILL_EXISTS,TODO: update this when having torch.get_default_device()
aaejcdfii,kornia/kornia,kornia/feature/laf.py,d0b43268a0b496192331b1e12529d1afb92dd1a7,STILL_EXISTS,TODO: Refactor doctest
aaejcdfje,kornia/kornia,kornia/geometry/camera/pinhole.py,d0b43268a0b496192331b1e12529d1afb92dd1a7,STILL_EXISTS,TODO: where is rtvec_to_pose?
aaejcdgbh,kornia/kornia,kornia/geometry/camera/pinhole.py,ee45c6c2da78ae944d6bb0043d6414122590299e,STILL_EXISTS,TODO: Add doctest once having `rtvec_to_pose`.
aaejcdgda,kornia/kornia,kornia/enhance/adjust.py,5c9356d3dbcd44c3cd7f833651a3b542250c2699,STILL_EXISTS,TODO: find a better way to check boundaries on tensors
aaejcdgfe,kornia/kornia,test/enhance/test_adjust.py,5c9356d3dbcd44c3cd7f833651a3b542250c2699,STILL_EXISTS,TODO: add better cases
aaejcdggf,kornia/kornia,test/enhance/test_adjust.py,5c9356d3dbcd44c3cd7f833651a3b542250c2699,STILL_EXISTS,TODO(jian): add better cases
aaejcdghh,kornia/kornia,test/augmentation/test_augmentation.py,74cc0cfd6406179570b06ca4ef8423142e7eaa0b,STILL_EXISTS,TODO same_on_batch tests?
aaejcdgih,kornia/kornia,test/augmentation/test_augmentation.py,74cc0cfd6406179570b06ca4ef8423142e7eaa0b,STILL_EXISTS,TODO Implement
aaejcdhge,kornia/kornia,test/augmentation/test_augmentation.py,a828315185a9dc8b21ec8e5dbead9044caf0d3a2,STILL_EXISTS,TODO same_on_batch tests?
aaejcdhhe,kornia/kornia,test/augmentation/test_augmentation.py,a828315185a9dc8b21ec8e5dbead9044caf0d3a2,STILL_EXISTS,TODO Implement
aaejcdicb,kornia/kornia,kornia/geometry/transform/crop/crop2d.py,854f604719b6a571e47ad3ca8b758cec4133d2f7,STILL_EXISTS,in order to zero-out the fully filled rows or columns
aaejcdjac,kornia/kornia,test/augmentation/test_augmentation.py,ab3ff67f6d34cedbb06dd888c202198392b892b1,STILL_EXISTS,TODO: improve and implement more meaningful smoke tests e.g check for a consistent
aaejceaai,kornia/kornia,kornia/feature/mkd.py,ee4e73f0b575deb7ac8309812f9ede480cd95b28,STILL_EXISTS,This stupid thing needed for jitting...
aaejceada,kornia/kornia,kornia/augmentation/container/video.py,5ea5760e41a3faa385027f9229db49dfcd62481e,STILL_EXISTS,Fix mypy complains: error: Incompatible return value type (got \"Tuple[int; ...]\"; expected \"Size\")
aaejceadh,kornia/kornia,kornia/augmentation/container/video.py,5ea5760e41a3faa385027f9229db49dfcd62481e,STILL_EXISTS,TODO: revise colorjitter order param in the future to align the standard.
aaejceagh,kornia/kornia,kornia/feature/mkd.py,e47f59865fe6deaeb08a8580dd581cf070ba6e74,STILL_EXISTS,TODO: unify the two lines below when pytorch 1.6 support is dropped
aaejceajb,kornia/kornia,test/test_losses.py,87167c6459e784dd07427866e12e184dde1336ef,STILL_EXISTS,TODO: review method since it needs `nondet_tol` in cuda sometimes.
aaejcebag,kornia/kornia,kornia/geometry/depth.py,b5418d8bac9e7bb01e66310d7d2b4227766c5a02,STILL_EXISTS,TODO: add type and value checkings
aaejcecci,kornia/kornia,kornia/geometry/transform/crop/crop2d.py,cb3fc49b42323992cb41157859631d0785eb900e,STILL_EXISTS,TODO: improve this since might slow down the function
aaejcechc,kornia/kornia,kornia/enhance/equalization.py,169af9babe5dbcb7f73f9b34a8a6cd420b9fae36,STILL_EXISTS,clip limit (TODO: optimice the code)
aaejcedbf,kornia/kornia,test/enhance/test_equalization.py,169af9babe5dbcb7f73f9b34a8a6cd420b9fae36,STILL_EXISTS,TODO: test with a more realistic pattern
aaejcedbg,kornia/kornia,test/enhance/test_equalization.py,169af9babe5dbcb7f73f9b34a8a6cd420b9fae36,STILL_EXISTS,should be similar to enhance.equalize but slower. Similar because the lut is computed in a different way.
aaejceddb,kornia/kornia,kornia/filters/blur_pool.py,918e63c27b588a48b5b64d215e247503a2db2e13,STILL_EXISTS,TODO: Move to proper place
aaejcedjd,chainer/chainerrl,nsq_ale.py,48bebb6e05f1f745d6d610067f333d4866e55ac0,STILL_EXISTS,TODO: epsilon scheduling
aaejceebh,chainer/chainerrl,dqn_ale.py,f098c51f09bebd3f449a1855ce91529de31a69d8,STILL_EXISTS,TODO: epsilon scheduling
aaejceeii,chainer/chainerrl,a3c_ale.py,4dbad4bd720c17ac3a39deabd6a4df231a339e3c,STILL_EXISTS,This line makes execution much faster; I don't know why
aaejcefaa,chainer/chainerrl,nsq_ale.py,6499aeca50352f05d005c0702102b2708bf10951,STILL_EXISTS,This line makes execution much faster; I don't know why
aaejcefbj,chainer/chainerrl,policy.py,9746b87eb39d806d54e430f7bc9a5d26070f3107,7ff7b4e5919de9fda319dd514003c5af1832c10d,TODO Too many return values; must re-consider interfaces
aaejcehhf,chainer/chainerrl,q_output.py,dfa2b8698f374a94dcbb37943055b32e56680a80,4da4e1812b9c932b9e53aa4602f01ef5f68300b1,TODO(fujita) implement
aaejceigf,chainer/chainerrl,agents/ddpg.py,99b68abc4785eaac542cb6243349dc21b4e50529,STILL_EXISTS,Q is not needed here; but log it just for information
aaejceiib,chainer/chainerrl,agents/pgt.py,5a8807b1ab10a639d8efab4065494a076533df53,STILL_EXISTS,Q is not needed here; but log it just for information
aaejcejdh,chainer/chainerrl,plot_scores.py,1d792c9edac5e8fce7dbc3656feecf945cdeacc4,STILL_EXISTS,Needed to run without X-server
aaejcfcac,chainer/chainerrl,agents/ddpg.py,7bfbea752c9a4f38dbf7c2660d44f8a064235dfe,STILL_EXISTS,FIXME: This would not work for some explorers that add noises to
aaejcfdjj,chainer/chainerrl,chainerrl/agents/nsq.py,0b6df1cf350e7fdb033a9da1b285066c3e25c543,STILL_EXISTS,TODO support recurrent Q-function
aaejcffbe,chainer/chainerrl,chainerrl/functions/mellowmax.py,2f0854d8cea6bf95f3ca026fdb7625774a11a233,STILL_EXISTS,Move data to CPU because we use Brent's algorithm in scipy
aaejcgbid,chainer/chainerrl,chainerrl/replay_buffer.py,dadfc60fb015c5d4cd1c4904e31480c2002cb57c,4bb1d4dd2587acd3f59f6e75c53caf328a987dde,anneal beta in 200;000 steps [citation needed]
aaejcgcdf,chainer/chainerrl,chainerrl/replay_buffer.py,3ff4158be0ba11e444fa6c8cf2be32ef98393d06,4bb1d4dd2587acd3f59f6e75c53caf328a987dde,anneal beta in 200;000 steps [citation needed]
aaejcgehb,chainer/chainerrl,docs/conf.py,67afd8a8e8655e565e7e80df38bf8f82b349ce26,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaejcghhc,chainer/chainerrl,chainerrl/experiments/evaluator.py,43f0b452faa82658672273857adfb0c2022e7f7a,STILL_EXISTS,\"\"\"Columns that describe information about an experiment. ||  || steps: number of time steps taken (= number of actions taken) || episodes: number of episodes finished || elapsed: time elapsed so far (seconds) || mean: mean of returns of evaluation runs || median: median of returns of evaluation runs || stdev: stdev of returns of evaluation runs || max: maximum value of returns of evaluation runs || min: minimum value of returns of evaluation runs || \"\"\"
aaejchbcc,chainer/chainerrl,chainerrl/links/empirical_normalization.py,8da29e9be8238b56e48fb8eba3208d97b7bc9c22,fdc0c921909e83191167edeba2559e4d5220dd21,TODO:
aaejcheda,chainer/chainerrl,tests/agents_tests/test_trpo.py,fea10ece80c44cc8f8bc380c13775c4b228cefdc,41b6f646ce3011c67fb8d7982676181fc464c38a,grads of unused variables must be zero
aaejchffa,chainer/chainerrl,chainerrl/replay_buffer.py,f030abeb7d047591c86d0bf858a41be86b031bc1,STILL_EXISTS,FIXME: The code works with EpisodicReplayBuffer
aaejchiac,chainer/chainerrl,chainerrl/agents/c51.py,78f78d9d91d4105dce5796eddf5a2216a242d3be,e8c42a49bc7ef80f1e147da26b2d17dd7fbb2489,TODO
aaejciifd,chainer/chainerrl,build/lib/chainerrl/agents/ddpg.py,60dde2821cd4c1227c45ae980f4088bc4783b479,STILL_EXISTS,Q is not needed here; but log it just for information
aaejcijcc,chainer/chainerrl,build/lib/chainerrl/agents/pgt.py,60dde2821cd4c1227c45ae980f4088bc4783b479,STILL_EXISTS,Q is not needed here; but log it just for information
aaejcjaai,chainer/chainerrl,build/lib/chainerrl/experiments/evaluator.py,60dde2821cd4c1227c45ae980f4088bc4783b479,STILL_EXISTS,\"\"\"Columns that describe information about an experiment. ||  || steps: number of time steps taken (= number of actions taken) || episodes: number of episodes finished || elapsed: time elapsed so far (seconds) || mean: mean of returns of evaluation runs || median: median of returns of evaluation runs || stdev: stdev of returns of evaluation runs || max: maximum value of returns of evaluation runs || min: minimum value of returns of evaluation runs || \"\"\"
aaejcjagc,chainer/chainerrl,build/lib/chainerrl/functions/mellowmax.py,60dde2821cd4c1227c45ae980f4088bc4783b479,STILL_EXISTS,Move data to CPU because we use Brent's algorithm in scipy
aaejcjbhe,chainer/chainerrl,build/lib/chainerrl/replay_buffer.py,60dde2821cd4c1227c45ae980f4088bc4783b479,STILL_EXISTS,FIXME: The code works with EpisodicReplayBuffer
aaejcjeaj,chainer/chainerrl,chainerrl/misc/prioritized.py,93a8c2008b6e5c9ed22476fa75dac25c7a06d1bf,STILL_EXISTS,Implement operations on nodes of SumTreeQueue
aaejdacgh,chainer/chainerrl,chainerrl/experiments/train_agent_batch.py,8dd14a33a23d3bf9af737efe60dd8a2abf3f96c4,STILL_EXISTS,For episodes that ends; do the following:
aaejdbacc,chainer/chainerrl,tests/test_replay_buffer.py,32f9f8537e4bfcfbadf99a894e1c72657d2ab05c,STILL_EXISTS,episode ends
aaejdbacd,chainer/chainerrl,tests/test_replay_buffer.py,32f9f8537e4bfcfbadf99a894e1c72657d2ab05c,STILL_EXISTS,episode ends; so we should add n-1 transitions
aaejdbbaj,chainer/chainerrl,examples/atari/dqn/train_dqn.py,d942d7a3fa180f8ce9511ff3bf160a6daa0fee58,c3fa6670bcb637414361abba907d02afabec6a41,TODO Change eval episode time to cap at 5 mins!
aaejdbbfh,chainer/chainerrl,examples/atari/dqn/train_dqn.py,9ccdfbe733926779538d822a2482950406dbb554,c3fa6670bcb637414361abba907d02afabec6a41,TODO Change eval episode time to cap at 5 mins!
aaejdbbje,chainer/chainerrl,examples/atari/dqn/train_dqn.py,37f61553e294b9042a5049c4b5d5d14273d64c21,c3fa6670bcb637414361abba907d02afabec6a41,TODO Change eval episode time to cap at 5 mins!
aaejdcdhf,chainer/chainerrl,chainerrl/experiments/train_agent_batch.py,80b8ad5552ecbe46fa81ea937c9e86fcf73c3ff1,e758e4a99472a697e5b3bcabb8151c3e3c883fdf,Start new episodes if needed
aaejdcici,chainer/chainerrl,chainerrl/experiments/train_agent_batch.py,ffdfa058a16fdc79107e7028cfbc2905a7726a56,9e936b3699075f9cdc5bb0b60b59317f921040e5,Start new episodes if needed
aaejdcigd,chainer/chainerrl,chainerrl/experiments/train_agent_batch.py,ea69e249c89e0422c7536d58f4549abf3a1c7670,STILL_EXISTS,Start new episodes if needed
aafbhhchc,bsc-wdc/dislib,docs/source/conf.py,a2572ca9173616cca890d3c990e739b3cab09a0f,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aafbhhdfh,bsc-wdc/dislib,dislib/cluster/dbscan/base.py,54bf9877c0f46c9db4e50a53243f4c7558d8f03f,STILL_EXISTS,FIXME: This probably can be avoided by improving the merging of the
aafbhhhab,bsc-wdc/dislib,dislib/classification/rf/forest.py,51e4ecb95337f64bc3161b862b0f17b0f3376aaf,b70b4de20c8e66ecbb685e0b37f36e99cc4b3c37,TODO: required?
aafbhibdi,bsc-wdc/dislib,dislib/data/classes.py,afbaa1a307e60c17d85d33cd13f044df5241a5f4,STILL_EXISTS,each sublist (sl) contains rows with a subset of columns. Each
aafbhibea,bsc-wdc/dislib,dislib/data/classes.py,afbaa1a307e60c17d85d33cd13f044df5241a5f4,STILL_EXISTS,stacked among themselves forming the final columns.
aafbhibeb,bsc-wdc/dislib,dislib/data/classes.py,afbaa1a307e60c17d85d33cd13f044df5241a5f4,STILL_EXISTS,finally we transpose the columns.
aafbhiedh,bsc-wdc/dislib,dislib/data/array.py,41fe9592be0b2ef3175ae652e457a672917cc36b,STILL_EXISTS,TODO: documentation ain't true
aafbhiegd,bsc-wdc/dislib,dislib/data/array.py,41fe9592be0b2ef3175ae652e457a672917cc36b,3c5574e1a7fd8e74fd26bbce50c9662423cbd4de,TODO: after implementing more constructors decide a better way to avoid
aafbhieib,bsc-wdc/dislib,dislib/data/array.py,41fe9592be0b2ef3175ae652e457a672917cc36b,STILL_EXISTS,TODO try avoid this with 2 params like: top_left_shape &
aafbhieie,bsc-wdc/dislib,dislib/data/array.py,41fe9592be0b2ef3175ae652e457a672917cc36b,STILL_EXISTS,iterate through columns
aafbhijgb,bsc-wdc/dislib,dislib/recommendation/als/base.py,68e82575fee1a2063a491f646d16a241fbdc51f2,STILL_EXISTS,TODO: decide if atol should be changed when default is changed
aafbhjada,bsc-wdc/dislib,dislib/data/array.py,3f07e77d0be430be2595d959c283368e179c89ba,e5470fe0332376a95d36d283a1ba5fb9ab2c44bd,TODO: think if it's worth partitioning the y's
aafbhjadd,bsc-wdc/dislib,dislib/data/array.py,3f07e77d0be430be2595d959c283368e179c89ba,080e3db1f7fd69a32b7e9ce04957dfbdf740b89f,efficient than parsing the lines manually
aafbhjbaj,bsc-wdc/dislib,dislib/data/array.py,619e53c28336eb2337e8a3b8f2a31c9de4e68232,e5470fe0332376a95d36d283a1ba5fb9ab2c44bd,TODO gives problems with empty blocks
aafbhjdic,bsc-wdc/dislib,dislib/recommendation/als/base.py,e5470fe0332376a95d36d283a1ba5fb9ab2c44bd,STILL_EXISTS,average_ratings = dataset.mean(axis='columns').collect()
aafbhjfac,bsc-wdc/dislib,dislib/data/array.py,00dc8d5bde0d139ad4d947b2ef53530ee0b1176e,STILL_EXISTS,iterate through columns
aafbhjfbe,bsc-wdc/dislib,dislib/data/array.py,00dc8d5bde0d139ad4d947b2ef53530ee0b1176e,91e047ac495a26e72036124f09cd432fe76cbfc5,TODO: parse\/interpret the rows\/cols parameters;
aafbhjfei,bsc-wdc/dislib,tests/test_array.py,00dc8d5bde0d139ad4d947b2ef53530ee0b1176e,STILL_EXISTS,single-block rows; all columns
aafbhjfej,bsc-wdc/dislib,tests/test_array.py,00dc8d5bde0d139ad4d947b2ef53530ee0b1176e,0a9d8737635a6031142d1a653574e197a61a74c3,all rows; single-block columns
aafbhjffi,bsc-wdc/dislib,tests/test_array.py,00dc8d5bde0d139ad4d947b2ef53530ee0b1176e,STILL_EXISTS,(3; 5; None; None);  # single-block rows; all columns
aafbhjffj,bsc-wdc/dislib,tests/test_array.py,00dc8d5bde0d139ad4d947b2ef53530ee0b1176e,STILL_EXISTS,(None; None; 3; 5);  # all rows; single-block columns
aafbhjhcb,bsc-wdc/dislib,dislib/data/array.py,a44aaa4042995bedc89a105653a48671a989168c,STILL_EXISTS,all rows (slice : for rows) and list of indices for columns
aafbiaahi,bsc-wdc/dislib,dislib/data/array.py,ad809eea9d463a1a68c0735e7a70ee343fb76131,babfc7343ab078d812d1b995bce49c0fe2048385,efficient than parsing the lines manually
aafbiaebi,bsc-wdc/dislib,tests/test_estimator_interface.py,d50487486770c810253509d8e13d80a39ee6206e,STILL_EXISTS,Workaround to an issue with sklearn
aafbiaejj,bsc-wdc/dislib,dislib/data/array.py,fc935bdfa6c843560b7e042cdce69d7b8747ad66,STILL_EXISTS,If the slice is empty (no rows or no columns); return a ds-array with
aafbiahff,bsc-wdc/dislib,tests/test_array.py,8d35c53315fdcb2d7e4916b027dc7ed7d34f72d3,STILL_EXISTS,single-block rows; all columns
aafbiaicd,bsc-wdc/dislib,dislib/data/io.py,babfc7343ab078d812d1b995bce49c0fe2048385,STILL_EXISTS,efficient than parsing the lines manually
aafbiaiid,bsc-wdc/dislib,dislib/data/array.py,4d4ea6d2df9b2e0706517c283aed712a7d32143e,a161d62e47d64762d0fa3ad318f4201746acf23a,needed because subtract with scipy.sparse does not support
aafbiajad,bsc-wdc/dislib,dislib/data/array.py,a161d62e47d64762d0fa3ad318f4201746acf23a,556b97beedd8200ffdb9559ab885cc3febcdafc0,efficient than parsing the lines manually
aafbibaaf,bsc-wdc/dislib,dislib/data/array.py,556b97beedd8200ffdb9559ab885cc3febcdafc0,STILL_EXISTS,needed because subtract with scipy.sparse does not support
aafbibbae,bsc-wdc/dislib,tests/test_array.py,556b97beedd8200ffdb9559ab885cc3febcdafc0,STILL_EXISTS,single-block rows; all columns
aafbibcie,bsc-wdc/dislib,dislib/data/io.py,0ab8df68a1e0adb8b398c33ab063f3b5428a5b12,STILL_EXISTS,and store the number of columns for each file.
aafbibijh,uds-lsv/TF-NNLM-TK,basic_rnn_models.py,8fe2be292039f656d0fee339ad7c72516aa54919,STILL_EXISTS,apply dropout to the input if needed.
aafbibjbc,uds-lsv/TF-NNLM-TK,basic_rnn_models.py,8fe2be292039f656d0fee339ad7c72516aa54919,STILL_EXISTS,if needed; the activation function used by the basic model can change be changed as well
aafbibjdi,uds-lsv/TF-NNLM-TK,data_processor.py,8fe2be292039f656d0fee339ad7c72516aa54919,STILL_EXISTS,In case another encoding is needed
aafbibjif,uds-lsv/TF-NNLM-TK,lsrc.py,8fe2be292039f656d0fee339ad7c72516aa54919,STILL_EXISTS,apply dropout to the input if needed.
aafbibjij,uds-lsv/TF-NNLM-TK,lsrc.py,8fe2be292039f656d0fee339ad7c72516aa54919,STILL_EXISTS,The next two lines are just a hack to initialize the SRNN cell from
aafbicafg,uds-lsv/TF-NNLM-TK,srnn.py,8fe2be292039f656d0fee339ad7c72516aa54919,STILL_EXISTS,apply dropout to the input if needed.
aafbicafj,uds-lsv/TF-NNLM-TK,srnn.py,8fe2be292039f656d0fee339ad7c72516aa54919,STILL_EXISTS,The next two lines are just a hack to initialize the SRNN cell from
aafbicdhf,ebu/benchmarkstt,src/conferatur/api/jsonrpc.py,b168c9c22a922d868d7f787ee5a20ed68ee63e78,STILL_EXISTS,todo (?) add available files and folders as select options
aafbicdjc,ebu/benchmarkstt,src/conferatur/docblock.py,b168c9c22a922d868d7f787ee5a20ed68ee63e78,STILL_EXISTS,quick hack to remove this
aafbiceae,ebu/benchmarkstt,tests/conferatur/normalization/test_core.py,b168c9c22a922d868d7f787ee5a20ed68ee63e78,STILL_EXISTS,todo
aafbicedb,ebu/benchmarkstt,src/benchmarkstt/diff/core.py,a0142e1493a69772d103fa4cb5f88111d5b518be,7a159abd88325cc380b517792d7f38decbc9b99b,Status: TODO make a proper diff implementing Hunt\u2013McIlroy algorithm
aafbicege,ebu/benchmarkstt,src/benchmarkstt/metrics/cli.py,a0142e1493a69772d103fa4cb5f88111d5b518be,8d9881d4d295c45fad002a4acdca6133c9a5cd6a,todo: different output options
aafbicegg,ebu/benchmarkstt,src/benchmarkstt/metrics/core.py,a0142e1493a69772d103fa4cb5f88111d5b518be,STILL_EXISTS,TODO: proper documenting of different modes
aafbicehf,ebu/benchmarkstt,tests/benchmarkstt/test_docblock.py,a0142e1493a69772d103fa4cb5f88111d5b518be,STILL_EXISTS,todo: test the other Docblock properties as well
aafbicfdg,ebu/benchmarkstt,src/benchmarkstt/normalization/core.py,b1adf391796f92483b46d540cdf0c3dbf605ca4f,af066b51db0d66640b19f7866cfa03621deedfa5,todo: wanna keep default section? (maybe just change it for cli)
aafbicgfe,ebu/benchmarkstt,src/benchmarkstt/normalization/logger.py,5154e07e5fe05fb71b8e8745cd919a1b3a1fd705,8bda85ae349ea7e2315a520922e02420a8a9ca8a,todo: json formatter
aafbicggd,ebu/benchmarkstt,src/benchmarkstt/cli.py,133317e9f1fcb4a08b0624f97f4755ef79486716,STILL_EXISTS,TODO: further augment formatter to give cleaner output
aafbidcic,AMinerOpen/prediction_api,src/paperranker.py,214db6d4941a16500c0a45146ce5df8ed61bf0a2,STILL_EXISTS,''' || Introduction: || PaperRanker is a class which is used to predict how much possibility there is that a publication is belong to a professor. || Usage: || >>> ret; res = pr.label(a; b; threshold=0.5) || '''
aafbiddbj,src-d/modelforge,modelforge/registry.py,31449b3d3a025cc002c720e33d4fedd18340984e,STILL_EXISTS,The transaction is not needed here; but upload_index() will raise otherwise
aafbiddgg,src-d/modelforge,doc/conf.py,684228209067b2b76354bbb8064b86e6cdc3dd66,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aafbidgaj,src-d/modelforge,modelforge/index.py,5cd701583fc22bb71aa758ca4589907e17f08dee,STILL_EXISTS,TODO: change when https:\/\/github.com\/dulwich\/dulwich\/issues\/631 gets addressed
aafbidgcf,src-d/modelforge,modelforge/registry.py,f401dfc4da390802ce316d7eab99cf92a7549c28,STILL_EXISTS,TODO: replace with PorcelainError; see related TODO in index.py:181
aafbidhib,epic-kitchens/epic-kitchens-55-lib,epic_kitchens/labels.py,3a4f0ad36ecb9ba2053cd3de9c49fcfa95846a0e,STILL_EXISTS,\"\"\"Columns present in a labels dataframe. || \"\"\"
aafbididf,epic-kitchens/epic-kitchens-55-lib,epic_kitchens/internal/loading.py,4e1092d320b105822c7618692e77a1c1fbf3d7cd,STILL_EXISTS,TODO: Invalidate `files` cache when this is changed
aafbidigd,medtagger/MedTagger,data_labeling/api/app.py,687e4375011cc33cfce3128d0996c7bd3299d3b2,STILL_EXISTS,\"\"\"Module responsible for definition of whole application ||  || It is also a great entry point for running this app. To do so; you can use: ||  ||     $ python data_labeling\/api\/app.py ||      * Running on http:\/\/localhost:51000\/ (Press CTRL+C to quit) ||      * Restarting with stat ||      * Debugger is active! ||      * Debugger PIN: XXX-XXX-XXX || \"\"\"
aafbidjgf,medtagger/MedTagger,data_labeling/api/user/business.py,fcb90ee8f049b2163e53ab6aa8ce1f363593ddbf,61fb73fd78bdad91bb80cdc463e9156b54531363,Todo: handle duplicate username
aafbiedje,medtagger/MedTagger,alembic/versions/e3cf6b7115ec_fix.py,fa0a92bfb17815e7334cdcfe8ff7ea48aa5c6c5b,STILL_EXISTS,\"\"\"Fix ||  || Revision ID: e3cf6b7115ec || Revises: 1fba3df23f8b || Create Date: 2017-12-13 09:18:05.896837 ||  || \"\"\"
aafbiefaj,medtagger/MedTagger,backend/medtagger/workers/conversion.py,9896b3260c516376dd189d6e91ab7555b1af33cc,STILL_EXISTS,UGLY WORKAROUND - Start
aafbiefba,medtagger/MedTagger,backend/medtagger/workers/conversion.py,9896b3260c516376dd189d6e91ab7555b1af33cc,f2be47d7434120961fd4fa25157cd8d3ef803cc7,UGLY WORKAROUND - Stop
aafbiefbc,medtagger/MedTagger,backend/medtagger/workers/conversion.py,02144fc1d1d5aa67d2fec180583ec3a43cc22ad4,f2be47d7434120961fd4fa25157cd8d3ef803cc7,UGLY WORKAROUND FOR COMPRESSED DICOMs - Start
aafbiefbh,medtagger/MedTagger,backend/medtagger/workers/conversion.py,02144fc1d1d5aa67d2fec180583ec3a43cc22ad4,f2be47d7434120961fd4fa25157cd8d3ef803cc7,UGLY WORKAROUND - Stop
aafbiefca,medtagger/MedTagger,backend/scripts/backup_medtagger.py,44c96de10c4cd088a4bcd4bdfeddbc0298d40e99,STILL_EXISTS,Create needed directory
aafbiegae,medtagger/MedTagger,backend/alembic/versions/75a3481c4d0c_change_scans_and_slices_to_use_enums.py,5f0d547aadd947c0aab5582b419613e90e125e75,STILL_EXISTS,Add new columns with statuses
aafbiegag,medtagger/MedTagger,backend/alembic/versions/75a3481c4d0c_change_scans_and_slices_to_use_enums.py,5f0d547aadd947c0aab5582b419613e90e125e75,STILL_EXISTS,Remove legacy columns
aafbiegah,medtagger/MedTagger,backend/alembic/versions/75a3481c4d0c_change_scans_and_slices_to_use_enums.py,5f0d547aadd947c0aab5582b419613e90e125e75,STILL_EXISTS,Revert all previous columns
aafbiegaj,medtagger/MedTagger,backend/alembic/versions/75a3481c4d0c_change_scans_and_slices_to_use_enums.py,5f0d547aadd947c0aab5582b419613e90e125e75,STILL_EXISTS,Remove newly added columns with statuses
aafbiegfa,medtagger/MedTagger,backend/alembic/versions/39c660178412_change_columns_names.py,beab2d1cc036ecd56507620fa18f60f140ccc361,STILL_EXISTS,\"\"\"Change columns names of Rectangular Label Elements ||  || Revision ID: 39c660178412 || Revises: 7995a5e4f811 || Create Date: 2018-05-06 19:12:02.135573 ||  || \"\"\"
aafbiegfb,medtagger/MedTagger,backend/alembic/versions/61737c4342bc_add_label_tag.py,beab2d1cc036ecd56507620fa18f60f140ccc361,STILL_EXISTS,\"\"\"Introduced Label Tags ||  || Add label tag table. Changed LabelSelection to Label Elements with two additional columns: lab tag || and element status (label_element_status_enum). Changed Label state type to new enum (label_verification_status_enum). ||  || Revision ID: 61737c4342bc || Revises: 6d69756a1476 || Create Date: 2018-04-28 14:29:43.351037 ||  || \"\"\"
aafbieihj,medtagger/MedTagger,backend/alembic/versions/9f2eafdf821e_add_cascade_delete_to_scans.py,b5795983e2517f1d364dbe09bc3103b9de84b20c,STILL_EXISTS,Droping LabelSelections foreign key (instead of LabelElements) due to naming bug
aafbieiib,medtagger/MedTagger,backend/alembic/versions/9f2eafdf821e_add_cascade_delete_to_scans.py,b5795983e2517f1d364dbe09bc3103b9de84b20c,STILL_EXISTS,Dropping PointLabelElement (without 's') due to naming bug
aafbiejjf,medtagger/MedTagger,backend/medtagger/ground_truth/algorithms/dbscan.py,f9cf26a99f40073c91fb01fcff54459b8ae56324,STILL_EXISTS,NOTE: Find better parameters automatically
aafbifaaa,medtagger/MedTagger,backend/medtagger/ground_truth/algorithms/gaussian_mixture_models.py,f9cf26a99f40073c91fb01fcff54459b8ae56324,STILL_EXISTS,Previous component was better; so let's use it
aafbifaai,medtagger/MedTagger,backend/medtagger/ground_truth/parsers/__init__.py,f9cf26a99f40073c91fb01fcff54459b8ae56324,STILL_EXISTS,\"\"\"Module responsible for definition of parsers needed for Ground Truth data set generation.\"\"\"
aafbifaif,ecohealthalliance/EpiTator,annotator/annotator.py,3a464c3945f991fa6ab2fcd24b436d42f400d584,STILL_EXISTS,TODO what if the original text needs to be later transformed; e.g.
aafbifaja,ecohealthalliance/EpiTator,annotator/geoname_annotator.py,3a464c3945f991fa6ab2fcd24b436d42f400d584,2837eab1711977516fd45c106af70b827272933a,TODO text in this case means AnnoText; elswhere; it's raw text
aafbifajf,ecohealthalliance/EpiTator,annotator/geoname_human_tag_annotator.py,3a464c3945f991fa6ab2fcd24b436d42f400d584,fd36cce32df5f2402f21452a0cf8c1360e9a05bd,TODO this affects the AnnoDoc's text; how to update it?
aafbifbaj,ecohealthalliance/EpiTator,annotator/token_annotator.py,3a464c3945f991fa6ab2fcd24b436d42f400d584,7fc5e608d82ed3e4116c3e5a855c530e65f7d8f3,TODO make this safer. There are certain characters
aafbifbdd,ecohealthalliance/EpiTator,annotator/sentence_annotator.py,fd36cce32df5f2402f21452a0cf8c1360e9a05bd,STILL_EXISTS,TODO should we object if we have to consume a lot of characters
aafbifbha,ecohealthalliance/EpiTator,tests/test_case_count_annotator.py,4a1c8cb01c59ab7d4e755c3804af9c66970091be,STILL_EXISTS,TODO -- should this work with self.doc.text = \"Deaths : 2\" ?
aafbifbhb,ecohealthalliance/EpiTator,tests/test_case_count_annotator.py,4a1c8cb01c59ab7d4e755c3804af9c66970091be,STILL_EXISTS,TODO -- why does this start at 0?
aafbifbhe,ecohealthalliance/EpiTator,annotator/annotator.py,ca1d01680fbc076b4306a137e90e97015b011e76,00fe8aec1d0b05b763158af351eb3c4e6a632890,TODO needs testing
aafbifbhg,ecohealthalliance/EpiTator,tests/test_case_count_annotator.py,fd45168604e136bb31061a36f156671a61a37820,STILL_EXISTS,TODO -- this example removed because it is long than the 2 intervening
aafbifbhi,ecohealthalliance/EpiTator,tests/test_case_count_annotator.py,fd45168604e136bb31061a36f156671a61a37820,STILL_EXISTS,and efficient way to match this example later.
aafbifbig,ecohealthalliance/EpiTator,tests/test_case_count_annotator.py,fd45168604e136bb31061a36f156671a61a37820,STILL_EXISTS,TODO -- enable these once our aspirations have been achieved.
aafbifbjj,ecohealthalliance/EpiTator,annotator/case_count_annotator.py,b05b96847ed8da7a7e86ad882acfb9809b0ce36d,STILL_EXISTS,TODO this is not safe; looking for the string that way.
aafbifcbj,ecohealthalliance/EpiTator,annotator/loader.py,df720ced03e3e5428e028dc48ce9d4d32763bd4f,STILL_EXISTS,\"\"\"An annie loader creates an AnnoDoc from a source such as a file or database. || The loader should perform as much annotation as is necessary to preserve parts || of document structure that would otherwise be lost. For example; if there is a || document header; it might be parsed and metadata stored in the AnnoDoc.properties. || If HTML tags are removed; certain tags might be transferred to an AnnoTier. || \"\"\"
aafbifceb,ecohealthalliance/EpiTator,annotator/geoname_annotator.py,4cf8d511e5d1b4cc1f592f772bdd03bf9dbb2ea1,2837eab1711977516fd45c106af70b827272933a,Maybe we should try to rule out some of the spans that
aafbifcjf,ecohealthalliance/EpiTator,tests/annotator/test_patient_info_annotator.py,0e33617a51246af29832c7b728096b3ad1079fdf,STILL_EXISTS,This should probably be folded into the other match somehow
aafbifddg,ecohealthalliance/EpiTator,annotator/geoname_annotator.py,c9f6bd4e29aa5356e167623e2df07b913230cf0c,2837eab1711977516fd45c106af70b827272933a,TODO: This needs to be delt with in the next stage.
aafbifdid,ecohealthalliance/EpiTator,annotator/geoname_annotator.py,94c6f6f88541490e0aa5aeed9b13660319c8591e,2837eab1711977516fd45c106af70b827272933a,TODO: This needs to be delt with in the next stage.
aafbifehf,ecohealthalliance/EpiTator,annotator/geoname_annotator.py,e182f2af1de62ab98cdd60102e452c3cfe362dcc,STILL_EXISTS,TODO? Check admin codes for containment
aafbifeic,ecohealthalliance/EpiTator,annotator/geoname_annotator.py,e182f2af1de62ab98cdd60102e452c3cfe362dcc,2837eab1711977516fd45c106af70b827272933a,TODO: This needs to be delt with in the next stage.
aafbifeih,ecohealthalliance/EpiTator,annotator/geoname_annotator.py,f874724a5521c0d61c6f9c6b930c1b44d736d38f,5fce3effb677503c9fc50c0e2e9651a4c2f46287,TODO: We might be able to remove some of these names in a more general way
aafbifejh,ecohealthalliance/EpiTator,annotator/geoname_annotator.py,f874724a5521c0d61c6f9c6b930c1b44d736d38f,5fce3effb677503c9fc50c0e2e9651a4c2f46287,Distinctness is probably more effective when combined
aafbiffab,ecohealthalliance/EpiTator,annotator/geoname_annotator.py,f874724a5521c0d61c6f9c6b930c1b44d736d38f,STILL_EXISTS,TODO? Check admin codes for containment
aafbiffia,ecohealthalliance/EpiTator,mongo_import_geonames.py,c77648574d030e961d58211571d3ff0007c35468,STILL_EXISTS,TODO: Run the geoname extractor here.
aafbifgfd,ecohealthalliance/EpiTator,annotator/geoname_annotator.py,991561e89cf4c87ab10b740fdfe63146a1833c9e,5fce3effb677503c9fc50c0e2e9651a4c2f46287,TODO: Recompute scores since they could have been
aafbifgjd,ecohealthalliance/EpiTator,annotator/geoname_annotator.py,53143672c6e442df7e24c97beee3084cd4e14d49,b8b6127ced36b71d1434222815f7901332638447,TODO: We might be able to remove some of these names in a more general way
aafbifhba,ecohealthalliance/EpiTator,annotator/geoname_annotator.py,53143672c6e442df7e24c97beee3084cd4e14d49,2837eab1711977516fd45c106af70b827272933a,TODO: This needs to be delt with in the next stage.
aafbifhbb,ecohealthalliance/EpiTator,annotator/geoname_annotator.py,53143672c6e442df7e24c97beee3084cd4e14d49,b8b6127ced36b71d1434222815f7901332638447,TODO: Recompute scores since they could have been
aafbifhcd,ecohealthalliance/EpiTator,annotator/geoname_annotator.py,53143672c6e442df7e24c97beee3084cd4e14d49,b8b6127ced36b71d1434222815f7901332638447,Distinctness is probably more effective when combined
aafbifjhh,ecohealthalliance/EpiTator,annotator/geoname_annotator.py,7e21556c71375c3f2a61cd1543ef5b937c4fc7c8,STILL_EXISTS,TODO: We might be able to remove some of these names in a more general way
aafbifjjd,ecohealthalliance/EpiTator,annotator/geoname_annotator.py,7e21556c71375c3f2a61cd1543ef5b937c4fc7c8,2837eab1711977516fd45c106af70b827272933a,TODO: This needs to be delt with in the next stage.
aafbigaai,ecohealthalliance/EpiTator,annotator/geoname_annotator.py,7e21556c71375c3f2a61cd1543ef5b937c4fc7c8,2837eab1711977516fd45c106af70b827272933a,Distinctness is probably more effective when combined
aafbigadi,ecohealthalliance/EpiTator,annotator/annotator.py,8874bc26e0531c74a0a770db65f0ece6234cb54a,08e4ed0341eab02874b2af6bc23a8d5b488a7b72,that the match ends.
aafbigaeg,ecohealthalliance/EpiTator,annotator/annotator.py,08e4ed0341eab02874b2af6bc23a8d5b488a7b72,STILL_EXISTS,TODO needs extensive testing
aafbigafd,ecohealthalliance/EpiTator,annotator/annotator.py,be32890c1addbb399343a56ea13bed5a912e5110,00fe8aec1d0b05b763158af351eb3c4e6a632890,that the match ends.
aafbigafh,ecohealthalliance/EpiTator,annotator/annotator.py,be32890c1addbb399343a56ea13bed5a912e5110,STILL_EXISTS,TODO needs extensive testing
aafbigafi,ecohealthalliance/EpiTator,annotator/annotator.py,442dc187536a6ef921faac25a9cb5b8533822514,00fe8aec1d0b05b763158af351eb3c4e6a632890,Pattern probably shouldn't be creating zero length words.
aafbigaih,ecohealthalliance/EpiTator,annotator/annotator.py,9e945544f0151ca1ecb15803eabc1014f867546a,92780db203cdbe6ee36c6158390f8cb9d528bbc4,Pattern probably shouldn't be creating zero length words.
aafbigajc,ecohealthalliance/EpiTator,annotator/annotator.py,9e945544f0151ca1ecb15803eabc1014f867546a,77bcaf609967654fdcd01eaaf9bc2badc1dadc34,that the match ends.
aafbigbce,ecohealthalliance/EpiTator,annotator/annotator.py,92780db203cdbe6ee36c6158390f8cb9d528bbc4,STILL_EXISTS,TODO needs testing
aafbigbci,ecohealthalliance/EpiTator,annotator/annotator.py,172e2c47bfa76ff96acaf58b54908c0a4f68175c,77bcaf609967654fdcd01eaaf9bc2badc1dadc34,Pattern probably shouldn't be creating zero length words.
aafbigbdc,ecohealthalliance/EpiTator,annotator/annotator.py,172e2c47bfa76ff96acaf58b54908c0a4f68175c,STILL_EXISTS,TODO needs testing
aafbigbei,ecohealthalliance/EpiTator,eval/eval_patient_info_annotator.py,6ef5c5916c0c32413dffc5bb3da32d499f56b1a2,STILL_EXISTS,TODO
aafbigbfh,ecohealthalliance/EpiTator,tests/annotator/test_geoname_annotator.py,6ef5c5916c0c32413dffc5bb3da32d499f56b1a2,0dcbf69ae587d2313290549b92717605dae07f8c,TODO Make sure this is Washington County PA; not OR
aafbigbga,ecohealthalliance/EpiTator,tests/annotator/test_patient_info_annotator.py,6ef5c5916c0c32413dffc5bb3da32d499f56b1a2,STILL_EXISTS,TODO
aafbigddf,ecohealthalliance/EpiTator,annotator/jvm_nlp_annotator.py,fa333647c70596d9ece1d21ba91d178c07d345aa,STILL_EXISTS,TODO: It would be better to make
aafbigedi,ecohealthalliance/EpiTator,annotator/geoname_annotator.py,7fc5e608d82ed3e4116c3e5a855c530e65f7d8f3,STILL_EXISTS,TODO: Different parents could be used. Remove this property?
aafbigefj,ecohealthalliance/EpiTator,annotator/geoname_annotator.py,5757857216415a34c94a744112b1a449a2860871,STILL_EXISTS,TODO: Add combined_span to geoname_spans being iterated
aafbigfdg,ecohealthalliance/EpiTator,epitator/geoname_annotator.py,5c44a9d4ba74173008145e1565cea4c847f5f743,6655d1d646b1bee9f216e6579a41f7b01c7afbd5,TODO: In python3 this causes an error because it expects no arguments.
aafbigfdj,ecohealthalliance/EpiTator,epitator/keyword_annotator.py,5c44a9d4ba74173008145e1565cea4c847f5f743,6655d1d646b1bee9f216e6579a41f7b01c7afbd5,TODO: Serialize as JSON or something else.
aafbighie,ecohealthalliance/EpiTator,epitator/date_annotator.py,18ac1c7c46ad0ed2f407e185e61b3e218d11727d,STILL_EXISTS,the span. The interval ends at the final datetime; and does not
aafbigjfb,ecohealthalliance/EpiTator,epitator/count_annotator_alt.py,28a481eee95f145287c01db676ad84397b03812c,STILL_EXISTS,TODO: Check that number doesn't overlap date or distance.
aafbigjgj,ecohealthalliance/EpiTator,epitator/count_annotator_alt.py,28a481eee95f145287c01db676ad84397b03812c,STILL_EXISTS,TODO: Parse the token which is a cardinal and\/or quantity
aafbigjha,ecohealthalliance/EpiTator,epitator/count_annotator_alt.py,28a481eee95f145287c01db676ad84397b03812c,STILL_EXISTS,TODO: This needs to not do this if there is non-quantity text separating these tokens.
aafbigjhb,ecohealthalliance/EpiTator,epitator/count_annotator_alt.py,28a481eee95f145287c01db676ad84397b03812c,STILL_EXISTS,TODO: Handle verbs for things like \"additional\"; \"incremental\".
aafbigjhi,ecohealthalliance/EpiTator,epitator/count_annotator_alt.py,28a481eee95f145287c01db676ad84397b03812c,STILL_EXISTS,TODO Getting an error here. I think that one cannot create a
aafbihabd,ecohealthalliance/EpiTator,epitator/count_annotator_2.py,091c287051fd801cd112246b990ed58e92d88aa8,STILL_EXISTS,FIXME: It's probably silly to do this differently for counts; but I think
aafbihabi,ecohealthalliance/EpiTator,epitator/count_annotator_2.py,091c287051fd801cd112246b990ed58e92d88aa8,STILL_EXISTS,TODO: Handle this!
aafbihace,ecohealthalliance/EpiTator,epitator/count_annotator_2.py,091c287051fd801cd112246b990ed58e92d88aa8,STILL_EXISTS,TODO: This should really be an init() method for a CountSpan class.
aafbihacf,ecohealthalliance/EpiTator,epitator/count_annotator_2.py,091c287051fd801cd112246b990ed58e92d88aa8,STILL_EXISTS,TODO: This should check that nc is actually a noun chunk. It should also
aafbihadb,ecohealthalliance/EpiTator,epitator/count_annotator_2.py,091c287051fd801cd112246b990ed58e92d88aa8,STILL_EXISTS,TODO: Consider iterating through until a triggering word is
aafbihadd,ecohealthalliance/EpiTator,epitator/count_annotator_2.py,091c287051fd801cd112246b990ed58e92d88aa8,3118b9b165a09fe8bbd25b6ca5fe2f083a093390,FIXME: This is a little kludgy. But maybe it's as good as it can be.
aafbihaff,ecohealthalliance/EpiTator,epitator/infection_annotator.py,83386773c28b09429b0cfe057e5df16b1ea37e0d,8bd704160269271f0e0f08687af1cbed131343e0,Perhaps it's better to use a proper logging framework?
aafbihagi,ecohealthalliance/EpiTator,epitator/infection_span.py,54bf30d4bb2ba5d2d2c3e1f3b2a810fa9e4da4ae,STILL_EXISTS,TODO: This should check that span is actually a noun chunk. It should also
aafbihahe,ecohealthalliance/EpiTator,epitator/infection_span.py,54bf30d4bb2ba5d2d2c3e1f3b2a810fa9e4da4ae,STILL_EXISTS,TODO: Consider iterating through until a triggering word is
aafbihahj,ecohealthalliance/EpiTator,epitator/infection_span.py,54bf30d4bb2ba5d2d2c3e1f3b2a810fa9e4da4ae,STILL_EXISTS,FIXME: It's probably silly to do this differently for counts; but I think
aafbihaic,ecohealthalliance/EpiTator,epitator/infection_span.py,54bf30d4bb2ba5d2d2c3e1f3b2a810fa9e4da4ae,STILL_EXISTS,TODO: Handle this!
aafbihajd,ecohealthalliance/EpiTator,epitator/metaspan.py,802bf1573d0b86c58bcf69818c9463306c91ff3d,STILL_EXISTS,I could be convinced that either way is better on this.
aafbihajf,ecohealthalliance/EpiTator,epitator/count_identifier.py,2d4ca17065c67ddddf5e9304f59e750ea6a2b045,STILL_EXISTS,TODO: Handle this!
aafbihcca,ecohealthalliance/EpiTator,epitator/infection_annotator.py,83888f51f442d877ffc21e580781b22978558cb8,STILL_EXISTS,TODO: Handle this!
aafbihccb,ecohealthalliance/EpiTator,epitator/infection_annotator.py,83888f51f442d877ffc21e580781b22978558cb8,STILL_EXISTS,TODO: Consider iterating through until a triggering word is
aafbihcch,ecohealthalliance/EpiTator,epitator/structured_incident_annotator.py,16418336d90327b77398129a2920b9f051ef5e20,STILL_EXISTS,Remove rows without the right number of columns
aafbihcig,ecohealthalliance/EpiTator,epitator/infection_annotator.py,25f17dcae7bae4465ce2cd1465d84054d1d5eb36,STILL_EXISTS,\"\"\" || Annotates noun chunks with: || - 'attribute' metadata for: ||     infection; death; hospitalization; person || - 'count' metadata ||  || TODO: || - list noun chunks with definite and indefinite articles as ['count': 1] || - implement 'attribute' metadata for: ||     cumulative; age; approximate; min; max ||  || These could be added in this annotator; but might be better suited elsewhere. || \"\"\"
aafbihdcc,ecohealthalliance/EpiTator,tests/annotator/test_infection_annotator.py,25f17dcae7bae4465ce2cd1465d84054d1d5eb36,c4bfc43c9bd317c62943f1c5d45677a9eaba125b,TODO: This test currently fails because it stops looking after
aafbihdce,ecohealthalliance/EpiTator,tests/annotator/test_infection_annotator.py,25f17dcae7bae4465ce2cd1465d84054d1d5eb36,69753a098b3d2d70e96017c165d7ad07be942048,TODO: Support the 'suspected' attribute. Look through additional attributes.
aafbihdii,ecohealthalliance/EpiTator,tests/annotator/test_infection_annotator.py,25f17dcae7bae4465ce2cd1465d84054d1d5eb36,c4bfc43c9bd317c62943f1c5d45677a9eaba125b,TODO: Fix
aafbihffg,ecohealthalliance/EpiTator,epitator/infection_annotator.py,764f95dbc287c1235e42b70e26976b1a02ca2d55,STILL_EXISTS,FIXME: This should operate on \"groups\"
aafbihfga,ecohealthalliance/EpiTator,epitator/infection_annotator.py,764f95dbc287c1235e42b70e26976b1a02ca2d55,STILL_EXISTS,TODO: Consider iterating through until a triggering word is
aafbihgfa,ecohealthalliance/EpiTator,tests/annotator/test_infection_annotator.py,a991bb640955ca29642db45f89f19a45d3305534,69753a098b3d2d70e96017c165d7ad07be942048,# TODO: Find a way to reach \"30\"
aafbihgjd,ecohealthalliance/EpiTator,epitator/infection_annotator.py,dc967a70adf63ed27f77858dafd09c0433296659,b2000f3a453e1645492ea1122f82f83708f743db,FIXME: This should work better. Right now it removes possessives by checking
aafbihgjg,ecohealthalliance/EpiTator,epitator/infection_annotator.py,dc967a70adf63ed27f77858dafd09c0433296659,b2000f3a453e1645492ea1122f82f83708f743db,It'd be nice to have a consistently applicable approach; where; say; you
aafbihhad,ecohealthalliance/EpiTator,tests/annotator/test_infection_annotator.py,ba0ff71c921281c87c99d571b82be9a38aa2bb9e,69753a098b3d2d70e96017c165d7ad07be942048,TODO: We don't look for this formulation.
aafbihhbi,ecohealthalliance/EpiTator,tests/annotator/test_infection_annotator.py,ba0ff71c921281c87c99d571b82be9a38aa2bb9e,69753a098b3d2d70e96017c165d7ad07be942048,TODO: I'm not concerned about this.
aafbihhee,ecohealthalliance/EpiTator,tests/annotator/test_infection_annotator.py,ba0ff71c921281c87c99d571b82be9a38aa2bb9e,c4bfc43c9bd317c62943f1c5d45677a9eaba125b,TODO: Investigate this.
aafbihhej,ecohealthalliance/EpiTator,tests/annotator/test_infection_annotator.py,ba0ff71c921281c87c99d571b82be9a38aa2bb9e,69753a098b3d2d70e96017c165d7ad07be942048,TODO: Implement ranges
aafbiiaca,ecohealthalliance/EpiTator,tests/annotator/test_infection_annotator_todos.py,69753a098b3d2d70e96017c165d7ad07be942048,STILL_EXISTS,TODO: We don't look for this formulation.
aafbiiacb,ecohealthalliance/EpiTator,tests/annotator/test_infection_annotator_todos.py,69753a098b3d2d70e96017c165d7ad07be942048,STILL_EXISTS,TODO: Find a way to reach \"30\"
aafbiiacd,ecohealthalliance/EpiTator,tests/annotator/test_infection_annotator_todos.py,69753a098b3d2d70e96017c165d7ad07be942048,STILL_EXISTS,TODO: I'm not concerned about this.
aafbiiach,ecohealthalliance/EpiTator,tests/annotator/test_infection_annotator_todos.py,69753a098b3d2d70e96017c165d7ad07be942048,STILL_EXISTS,TODO: Support the 'suspected' attribute. Look through additional attributes.
aafbiiada,ecohealthalliance/EpiTator,tests/annotator/test_infection_annotator_todos.py,69753a098b3d2d70e96017c165d7ad07be942048,STILL_EXISTS,TODO: Implement ranges
aafbiibad,ecohealthalliance/EpiTator,epitator/infection_annotator.py,3480455f35f513d0b21396686a4b231dc5f02f7f,b2000f3a453e1645492ea1122f82f83708f743db,TODO: PUT IN INFECTION ANNOTATOR
aafbiibed,ecohealthalliance/EpiTator,epitator/infection_annotator.py,5e97ae8123ce2ae94077bb9cdb4fbc3fd42cf4e4,b2000f3a453e1645492ea1122f82f83708f743db,TODO: Maybe remove this function
aafbiiegc,ecohealthalliance/EpiTator,epitator/structured_data_annotator.py,c0f1c53a7f74d397b1a4926a6e512b8bf6651146,STILL_EXISTS,Skip tables with differing numbers of columns in each row
aafbiiegf,ecohealthalliance/EpiTator,tests/annotator/test_structured_incident_annotator.py,c0f1c53a7f74d397b1a4926a6e512b8bf6651146,STILL_EXISTS,TODO: 1500 in the Deaths column is parsed as a year. To resolve this
aafbiifhd,ecohealthalliance/EpiTator,docs/source/conf.py,609a98bca8364cfd5dbaf712029825f265711357,STILL_EXISTS,-- Options for todo extension ----------------------------------------------
aafbiifhe,ecohealthalliance/EpiTator,docs/source/conf.py,609a98bca8364cfd5dbaf712029825f265711357,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aafbiigdf,ecohealthalliance/EpiTator,tests/annotator/test_incident_annotator.py,2e5da8059c8dee5f8afbfefe58012c8caf77dc01,c1784fcd39ca5958e3b32a77b105a2f51c22e783,TODO: Quriat is missing
aafbiigdi,ecohealthalliance/EpiTator,tests/annotator/test_structured_incident_annotator.py,2e5da8059c8dee5f8afbfefe58012c8caf77dc01,STILL_EXISTS,TODO: Alagoas is resolved to incorrect location
aafbiijea,openopt/copt,examples/plot_tv_comparison.py,72d05ae26d95e067fe47283e14da54120233df3f,STILL_EXISTS,better default plotting style
aafbiijee,openopt/copt,copt/utils.py,f84ab40cc65c05a9759b702f24e2e8955d01dfeb,d2eb3d4f08f2be5e3dbf5103e690205e7a92fd26,probably would be better using mpl-rc file
aafbiijfd,openopt/copt,examples/plot_tv_comparison.py,f84ab40cc65c05a9759b702f24e2e8955d01dfeb,STILL_EXISTS,better default plotting style
aafbijcah,openopt/copt,copt/stochastic.py,487cd4cde0fa65828595faa8cbae0c8168e7c324,21a9b753828e44425db4582742e635203656b06c,TODO: set to f_i'(0)
aafbijcai,openopt/copt,copt/stochastic.py,487cd4cde0fa65828595faa8cbae0c8168e7c324,STILL_EXISTS,temporary storage; perhaps could be avoided
aafbijcbb,openopt/copt,copt/stochastic.py,487cd4cde0fa65828595faa8cbae0c8168e7c324,STILL_EXISTS,would be more efficient)
aafbijcbi,openopt/copt,copt/stochastic.py,424605f0d622527e567b9fca23dc28b58243e4d1,21a9b753828e44425db4582742e635203656b06c,XXX
aafbijcga,openopt/copt,copt/stochastic.py,37aab7779ebbb49ac615fe25330e6ac3da913d5c,f17973b1ba563b661c5c9034c0c1e9603d5f5a34,XXX could be better
aafbijddg,openopt/copt,tests/test_stochastic.py,957b7fb639f5373ffe2fdf48a2a29ad756cfbdff,c6d98ce5aff5cb4aba9a88d6a77b70c59ae4eb00,XXX FIXME
aafbijddh,openopt/copt,tests/test_stochastic.py,957b7fb639f5373ffe2fdf48a2a29ad756cfbdff,ccccb85c28e447b15f82df1da64a258f0336c45f,XXX test with L1
aafbijdgd,openopt/copt,copt/stochastic.py,8abfcf9647fac1eb867739c5ef0bdd6db3176203,9b31469601b759f0e646271c60cfe9d6deb1fe15,TODO: encapsulate this in _get_factory
aafbijdgi,openopt/copt,copt/stochastic.py,8abfcf9647fac1eb867739c5ef0bdd6db3176203,9b31469601b759f0e646271c60cfe9d6deb1fe15,# TODO: make sure these are callable
aafbijdhc,openopt/copt,copt/stochastic.py,535f1911c8df30b6856a459e8eeebb83bcccb1f2,d78676eb862e8b424f60073e845f707b270f474d,TODO: pass from function
aafbijdif,openopt/copt,copt/stochastic.py,76cbf939eee50d6fce8e0a39dc53220186b5a71d,9b31469601b759f0e646271c60cfe9d6deb1fe15,TODO: needs to be adapted in the sparse case
aafbijdje,openopt/copt,copt/stochastic.py,6c900e58de1d1b21b63114a2a6407757eca89f97,STILL_EXISTS,XXX can be done faster
aafbijejd,openopt/copt,tests/test_stochastic.py,9910b1ba9d9b5448e4b29924865e4fbbcbf2736f,d824b16773aa2981b506c08b473c7ebe7e08c56d,# TODO: make it robust to this case
aafbijfhc,openopt/copt,copt/stochastic.py,a3295351ea90987849785b70289ed6eaeb0a0138,9b31469601b759f0e646271c60cfe9d6deb1fe15,TODO: encapsulate this in _get_factory
aafbijfhf,openopt/copt,copt/stochastic.py,a3295351ea90987849785b70289ed6eaeb0a0138,9b31469601b759f0e646271c60cfe9d6deb1fe15,TODO: needs to be adapted in the sparse case
aafbijfhh,openopt/copt,copt/stochastic.py,a3295351ea90987849785b70289ed6eaeb0a0138,STILL_EXISTS,XXX FIXME do something smart
aafbijjaj,openopt/copt,copt/stochastic.py,7a06b485ff2f6e04290c4ffc78e2a007760811e2,b2f598a6767d78666ec000124b670ebb1f0ce889,.. gradient estimate (XXX difference) ..
aafbjadfb,openopt/copt,copt/stochastic.py,fdc3936416402e556690a52a63a7ee3ffc5f0cbf,6d5c7194786f585fda9bd3f955144ca36b4ac240,TODO: could be parallelized
aafbjadjb,openopt/copt,copt/stochastic.py,90f15f4390c4ae4c4f1ec00c670e64dfbe31c6d5,6d5c7194786f585fda9bd3f955144ca36b4ac240,TODO: could be parallelized
aafbjadji,openopt/copt,copt/stochastic.py,46ccf408c5abc796182736412abff89232e3659e,STILL_EXISTS,TODO: could be parallelized
aafbjaeae,openopt/copt,copt/stochastic.py,f72834d3ba303c1aa3d4bdd1c726697a8a7bf838,STILL_EXISTS,.. recompute Ax (TODO: do only in async) ..
aafbjaebh,openopt/copt,copt/stochastic.py,c4ecfad93ddbffaf390324970fc2ca20b644a509,STILL_EXISTS,.. recompute Ax TODO: do only in async ..
aafbjaegj,openopt/copt,copt/randomized.py,02215fca9923b81ca1050e22257a95df7602c27c,04a470137150950ac1e746fc0acbbc40f8e97f31,XXX TODO: implement specific step-size
aafbjaeha,openopt/copt,copt/datasets.py,ab3391ca952bfb5b591ae65d2162ea08733529bf,STILL_EXISTS,.. TODO: allow to be set also from environment variable ..
aafbjaejg,openopt/copt,examples/plot_tv_comparison.py,ce3e6a8204e963301b414d64f20ac738ace2addf,fb86536b0002183b10b23c89cb0db0e74e44b598,XXX
aafbjageb,openopt/copt,copt/gradient.py,fb86536b0002183b10b23c89cb0db0e74e44b598,STILL_EXISTS,TODO: could compute loss and grad in the same function call
aafbjahdj,openopt/copt,copt/utils.py,aeae7011896a68aac89e99563a50cd2eb48d112b,b22a50fa4507d64bde844091ac419d6304b4e452,best projection: itself!
aafbjaiha,openopt/copt,copt/randomized.py,b22a50fa4507d64bde844091ac419d6304b4e452,STILL_EXISTS,implement also a version for dense data (numpy arrays) to better exploit data locality
aafbjbahj,openopt/copt,copt/utils.py,b22a50fa4507d64bde844091ac419d6304b4e452,8fd188b594b45178ba6e83d41eb36357e59e8da5,Better alternatives exist for high-dimensional sparse vectors (cf. [1])
aafbjbaid,openopt/copt,copt/utils.py,b22a50fa4507d64bde844091ac419d6304b4e452,8fd188b594b45178ba6e83d41eb36357e59e8da5,[1] Efficient Projections onto the .1-Ball for Learning in High Dimensions
aafbjbajc,openopt/copt,copt/utils.py,b22a50fa4507d64bde844091ac419d6304b4e452,STILL_EXISTS,# best projection: itself!
aafbjbfbh,openopt/copt,examples/plot_bench_group_lasso.py,ea294f48fca2ef7c5f11add937fd9acc741511b7,STILL_EXISTS,\"\"\" || Total variation regularization || ============================== ||  || Comparison of solvers with total variation regularization. ||  || TODO: split computational and plotting code || \"\"\"
aafbjccbc,openopt/copt,copt/frank_wolfe.py,f8d4710c38e731a9be1995e56a2b69e681c87424,STILL_EXISTS,TODO: avoid definition of d_t
aafbjcced,openopt/copt,copt/frank_wolfe.py,c2123ae5561de70056bec90ec5ffb4829bdca504,STILL_EXISTS,XXX no sense for zero weight
aafbjccfh,openopt/copt,copt/randomized.py,7b23d0a38e92cabfafbcceec647e7cf16054a921,STILL_EXISTS,implement also a version for dense data (numpy arrays) to better exploit data locality
aafbjccgh,openopt/copt,copt/frank_wolfe.py,ed2866265883f02909a2e97bcee8985c1764196e,522aa7f90b58dd4f1d75241ac2ee114e0547f8c8,f_t; grad = f_grad(x_t)  # XXX
aafbjcdci,openopt/copt,copt/randomized.py,ed2866265883f02909a2e97bcee8985c1764196e,STILL_EXISTS,.. gradient estimate (XXX difference) ..
aafbjcdee,openopt/copt,copt/randomized.py,bc0d917e7e575a8e892b9bc1e7d30d7b3196d8ca,f878d78691f859575dadf7e12ce54afece363c1c,FIXME do something smart
aafbjcdfd,openopt/copt,copt/randomized.py,bc0d917e7e575a8e892b9bc1e7d30d7b3196d8ca,STILL_EXISTS,.. gradient estimate (XXX difference) ..
aafbjcdid,openopt/copt,copt/randomized.py,0d91a936902908414f7f30f6f4623ee9eea28edc,STILL_EXISTS,.. XXX TODO description ..
aafbjcedd,openopt/copt,copt/randomized.py,3fd5de17fe51e18197431335656bbb63dc1fc9f6,f878d78691f859575dadf7e12ce54afece363c1c,XXX update Z
aafbjchga,openopt/copt,copt/randomized.py,522aa7f90b58dd4f1d75241ac2ee114e0547f8c8,STILL_EXISTS,implement also a version for dense data (numpy arrays) to better exploit data locality
aafbjchgg,openopt/copt,copt/randomized.py,522aa7f90b58dd4f1d75241ac2ee114e0547f8c8,STILL_EXISTS,.. gradient estimate (XXX difference) ..
aafbjcjdi,openopt/copt,copt/proxgrad.py,f7bca48c0520dd72a5e344956c08e1252f0b8c5c,f958439da1edc20dc1e3fdb5e8b8e76b45196d78,TODO: could compute loss and grad in the same function call
aafbjcjej,openopt/copt,copt/randomized.py,289e9d2acd4b9e4d4a8d541168abe6e0beee75b4,STILL_EXISTS,FIXME: just a workaround for now
aafbjcjfa,openopt/copt,copt/randomized.py,289e9d2acd4b9e4d4a8d541168abe6e0beee75b4,STILL_EXISTS,FIXME: check if prox_1 is a tuple
aafbjcjhf,openopt/copt,copt/utils.py,289e9d2acd4b9e4d4a8d541168abe6e0beee75b4,b9b417026df0871a097045227addd94a648162d5,XXX how to compute the number of blocks??
aafbjdcbe,openopt/copt,copt/randomized.py,8710a6e6ea66e41cb06c0ae242f72f1c5557e829,STILL_EXISTS,might want to implement also a version for dense data (numpy arrays) to
aafbjdcje,openopt/copt,examples/plot_fw_stepsize.py,f9ec6a436b7858d737e81d6f88f74acd29a63dcf,76d4e4411f7ccc86b896b710a2dda91101f14b04,L-BFGS-B method seems to be working much better at finding the optimal
aafbjdead,openopt/copt,copt/line_search.py,425c3dad0d995c157e3e21a00563afebc1b5def3,STILL_EXISTS,\"\"\"TODO || \"\"\"
aafbjdege,openopt/copt,copt/utils.py,0d3d1b127a658442a7db8815809fd94e385f436c,STILL_EXISTS,XXX do we actually need x?
aafbjdffe,openopt/copt,tests/test_frank_wolfe.py,2010ad802b25e5e0b11f13191624f2c6c75f810e,STILL_EXISTS,quick hack since the bounds often seem to be ignored by scipy
aafbjdffi,openopt/copt,tests/test_proximal_gradient.py,e5c5ccb1fa0911a2035c2e6366c0ecbf28de3eb3,STILL_EXISTS,define a function with unused arguments for the API
aafbjdggj,openopt/copt,copt/constraint.py,51dc44e9d5c82e31a82430a85583af9ffdff650b,STILL_EXISTS,best projection: itself!
aafbjdiif,openopt/copt,copt/utils_pytorch.py,8bbb8ae586ab72c17a574cbfafecf129a921e40f,STILL_EXISTS,TODO: write generic function wrapping copt optimizers for taking pytorch input;
aafbjdjga,DIVA-DIA/DeepDIVA,dataset/CIFAR.py,b779a0668d51b5f1c869b7a7765406fb93d1c060,ac24eb5c936e17cded66590ffb4c6aef9fa0a9c5,TODO: Add num_classes; width; height; RGB-mean\/std as attributes of dataset.
aafbjeabd,DIVA-DIA/DeepDIVA,init/init.py,7875d579a8aeffd60b5ab70ded47cc9d74353145,STILL_EXISTS,TODO select the input patches of the right size of the filter of 1st layer
aafbjeabh,DIVA-DIA/DeepDIVA,init/init.py,7875d579a8aeffd60b5ab70ded47cc9d74353145,STILL_EXISTS,TODO select from LDA the relevant columns for as many filters there are
aafbjeaef,DIVA-DIA/DeepDIVA,template/CIFAR_CNN_classifier.py,7875d579a8aeffd60b5ab70ded47cc9d74353145,STILL_EXISTS,TODO load the validation set (if any)
aafbjeaei,DIVA-DIA/DeepDIVA,template/CIFAR_CNN_classifier.py,7875d579a8aeffd60b5ab70ded47cc9d74353145,STILL_EXISTS,TODO make way that the model and the criterion are also passed as parameter with introspection thingy as the optimizer
aafbjeafb,DIVA-DIA/DeepDIVA,template/CIFAR_CNN_classifier.py,7875d579a8aeffd60b5ab70ded47cc9d74353145,STILL_EXISTS,TODO pass the validation loader (if any)
aafbjeafc,DIVA-DIA/DeepDIVA,template/CIFAR_CNN_classifier.py,7875d579a8aeffd60b5ab70ded47cc9d74353145,STILL_EXISTS,TODO being testing
aafbjeagd,DIVA-DIA/DeepDIVA,template/CIFAR_CNN_classifier.py,7875d579a8aeffd60b5ab70ded47cc9d74353145,STILL_EXISTS,TODO why in the training we have this and here is flat without the if ?
aafbjeajb,DIVA-DIA/DeepDIVA,template/CIFAR_CNN_classifier.py,fb5793a66a4a05b8408328f705e3ab58d8d2584e,STILL_EXISTS,TODO load a ds passed from parameter NICELY
aafbjeajh,DIVA-DIA/DeepDIVA,template/CIFAR_CNN_classifier.py,fb5793a66a4a05b8408328f705e3ab58d8d2584e,STILL_EXISTS,TODO why in the training we have this and here is flat without the if ?
aafbjebaa,DIVA-DIA/DeepDIVA,template/CIFAR_CNN_classifier.py,fb5793a66a4a05b8408328f705e3ab58d8d2584e,STILL_EXISTS,TODO disturbing use of accuracy and precision in the same place
aafbjebhb,DIVA-DIA/DeepDIVA,init/init.py,405c792be9c94a95d2e27da43af166da3473402b,STILL_EXISTS,The T belongs to the reshape operation! It is NOT transposing the input! It is necessary to select columns
aafbjecag,DIVA-DIA/DeepDIVA,init/initializer.py,465f5ae498b3aa7591bf7b04942975f179ceea7c,a68368d0db0ee84da5f18309a92bfc67ab274765,Transform the image in the right format for extract_patches_2d(). Needed as channels are not in same order
aafbjecai,DIVA-DIA/DeepDIVA,init/initializer.py,465f5ae498b3aa7591bf7b04942975f179ceea7c,STILL_EXISTS,TODO upload a fix to sklearn of wherever the bug is!
aafbjecdc,DIVA-DIA/DeepDIVA,init/initializer.py,459512f99163e38d4a136a7cab0f9d3194f30e50,STILL_EXISTS,TODO two parameters should be A() and B() where A is used to init everything and B only the last layer.
aafbjecib,DIVA-DIA/DeepDIVA,init/initializer.py,459512f99163e38d4a136a7cab0f9d3194f30e50,STILL_EXISTS,TODO upload a fix to sklearn of wherever the bug is!
aafbjedcc,DIVA-DIA/DeepDIVA,init/initializer.py,d9499fb498bb06a8cb10ffa38e299854c439f3db,STILL_EXISTS,TODO upload a fix to sklearn of wherever the bug is!
aafbjedcd,DIVA-DIA/DeepDIVA,init/initializer.py,d9499fb498bb06a8cb10ffa38e299854c439f3db,STILL_EXISTS,TODO two parameters should be A() and B() where A is used to init everything and B only the last layer.
aafbjedcf,DIVA-DIA/DeepDIVA,init/initializer.py,7a35957d4ea0e94a30a79f4ee5e23389cd498cdf,STILL_EXISTS,TODO two parameters should be A() and B() where A is used to init everything and B only the last layer.
aafbjedda,DIVA-DIA/DeepDIVA,init/initializer.py,7a35957d4ea0e94a30a79f4ee5e23389cd498cdf,STILL_EXISTS,TODO upload a fix to sklearn of wherever the bug is!
aafbjedhi,DIVA-DIA/DeepDIVA,template/CIFAR_CNN_classifier.py,6cb1f7802245c51b0807f8278a64fbbbdd5990b9,STILL_EXISTS,TODO load a ds passed from parameter NICELY
aafbjedjf,DIVA-DIA/DeepDIVA,template/standard.py,5e9ad24e9b51617f9de1c8cd17f36e7d53ed83b1,6ed7f20f379767aa9a015cc1fa639af6fc107acb,TODO Load model expected size from the actual model
aafbjeebh,DIVA-DIA/DeepDIVA,template/standard.py,19ea2579ed34a284c73c3caa83a2639f1207f8d6,STILL_EXISTS,TODO Load model expected size from the actual model
aafbjeeib,DIVA-DIA/DeepDIVA,init/initializer.py,81906fc71337ac21282ed2a5afa1147264c767a4,854f932b95a6049011c2f6de8dcad93aecee4f16,TODO un-hard-code the 10 as number of classes
aafbjefdj,DIVA-DIA/DeepDIVA,template/standard.py,81906fc71337ac21282ed2a5afa1147264c767a4,bb405dc7cdbc392f5ae56a34ce2e4065278360ce,TODO load the validation set (if any)
aafbjefea,DIVA-DIA/DeepDIVA,template/standard.py,81906fc71337ac21282ed2a5afa1147264c767a4,STILL_EXISTS,TODO load a ds passed from parameter NICELY
aafbjefee,DIVA-DIA/DeepDIVA,template/standard.py,81906fc71337ac21282ed2a5afa1147264c767a4,STILL_EXISTS,TODO make way that the model and the criterion are also passed as parameter with introspection thingy as the optimizer
aafbjefej,DIVA-DIA/DeepDIVA,template/standard.py,81906fc71337ac21282ed2a5afa1147264c767a4,STILL_EXISTS,TODO being testing
aafbjefhc,DIVA-DIA/DeepDIVA,template/standard.py,f92916ae2eeac5abe9ee29f807bcd2c5107626ca,STILL_EXISTS,TODO Load model expected size from the actual model
aafbjefii,DIVA-DIA/DeepDIVA,template/standard.py,f92916ae2eeac5abe9ee29f807bcd2c5107626ca,STILL_EXISTS,TODO make way that the model and the criterion are also passed as parameter with introspection thingy as the optimizer
aafbjfcfe,DIVA-DIA/DeepDIVA,dataset/SVHN.py,ae2ec19583686262d5f7060074949306303f1c20,STILL_EXISTS,the squeeze is needed to obtain a 1D tensor
aafbjfcge,DIVA-DIA/DeepDIVA,template/standard.py,ae2ec19583686262d5f7060074949306303f1c20,e30b2f290e5edcb86a052837092588504e4c34ad,TODO change as soon as svhn has a val set :)
aafbjfcgh,DIVA-DIA/DeepDIVA,template/standard.py,ae2ec19583686262d5f7060074949306303f1c20,e30b2f290e5edcb86a052837092588504e4c34ad,TODO what about the normalization?
aafbjfchg,DIVA-DIA/DeepDIVA,template/standard.py,ae2ec19583686262d5f7060074949306303f1c20,e30b2f290e5edcb86a052837092588504e4c34ad,TODO dataset and dataset-folder should never exist together
aafbjfdjd,DIVA-DIA/DeepDIVA,template/standard.py,6a3fffd1679f64de371de48de5f0944082aa5e7f,STILL_EXISTS,TODO dataset and dataset-folder should never exist together
aafbjfdjh,DIVA-DIA/DeepDIVA,template/standard/setup.py,a7b450b61b85f04206a076c321c1671360f73659,STILL_EXISTS,TODO change as soon as svhn has a val set :)
aafbjfeaa,DIVA-DIA/DeepDIVA,template/standard/setup.py,a7b450b61b85f04206a076c321c1671360f73659,STILL_EXISTS,TODO what about the normalization?
aafbjfeca,DIVA-DIA/DeepDIVA,template/standard.py,58feee38660f8948ecb4c5300428e46f3fd3580b,a945e1db3fa0de476fdbdce1472ab8ca5841a820,TODO change as soon as svhn has a val set :)
aafbjfecd,DIVA-DIA/DeepDIVA,template/standard.py,58feee38660f8948ecb4c5300428e46f3fd3580b,a945e1db3fa0de476fdbdce1472ab8ca5841a820,TODO what about the normalization?
aafbjfgbj,DIVA-DIA/DeepDIVA,util/visualization/point_cloud.py,6568acf044ba467ab0c6e8332f26e6830813a606,STILL_EXISTS,Forgive the hack; but it's a call by reference and point_class really shouldn't be modified.
aafbjfgij,DIVA-DIA/DeepDIVA,template/point_cloud.py,bddeeff64e3185f7c09fed1949572b5fda8ff12e,STILL_EXISTS,TODO dataset and dataset-folder should never exist together
aafbjfhca,DIVA-DIA/DeepDIVA,template/CL_arguments.py,d497986e6e434fa9747f711ff5291d30ee9ada3b,STILL_EXISTS,TODO dataset and dataset-folder should never exist together
aafbjfjje,DIVA-DIA/DeepDIVA,util/visualization/decision_boundaries.py,769b346920b1eca580a153779ec4f61204cdb233,1e8130a92642ed6a48ca47ddd0a984d5273a7624,TODO choose which of the 2 following lines :)
aafbjgaab,DIVA-DIA/DeepDIVA,init/advanced_init.py,07456dc4b90e096193dbb255c048f59861b985a7,STILL_EXISTS,TODO fix this such that only B and W are return values; prolly bring in the assign logic here and leave only the real assign outside
aafbjgaag,DIVA-DIA/DeepDIVA,init/advanced_init.py,07456dc4b90e096193dbb255c048f59861b985a7,a3c9a1dffedf0669d1b927d9938bbc008c261ccf,The T belongs to the reshape operation! It is NOT transposing the input! It is necessary to select columns
aafbjgaeb,DIVA-DIA/DeepDIVA,template/CL_arguments.py,d3083c6ee27dbfd2cc9026f1ec7a7cf910f57d30,STILL_EXISTS,TODO dataset and dataset-folder should never exist together
aafbjgaif,DIVA-DIA/DeepDIVA,template/CL_arguments.py,23c66f92b058e4fd5e6814eb17c2f1e043daa34a,STILL_EXISTS,TODO dataset and dataset-folder should never exist together
aafbjgajj,DIVA-DIA/DeepDIVA,init/advanced_init.py,e9dec8fc64f2ce597d836d826b67fbce8fd5be20,a3c9a1dffedf0669d1b927d9938bbc008c261ccf,TODO un-hard-code the 10 as number of classes
aafbjgbab,DIVA-DIA/DeepDIVA,init/advanced_init.py,ee65b854c44a7baa80ea84a4f67166e6796b0386,a3c9a1dffedf0669d1b927d9938bbc008c261ccf,Keep only necessary dimensions when num_columns > num_desired_dimensions
aafbjgbag,DIVA-DIA/DeepDIVA,init/advanced_init.py,7c494d56bb9bf8a9fffe664d7d864bc4890f3cec,a3c9a1dffedf0669d1b927d9938bbc008c261ccf,Keep only necessary dimensions when num_columns > num_desired_dimensions
aafbjgbbc,DIVA-DIA/DeepDIVA,init/advanced_init.py,7c494d56bb9bf8a9fffe664d7d864bc4890f3cec,a3c9a1dffedf0669d1b927d9938bbc008c261ccf,The T belongs to the reshape operation! It is NOT transposing the input! It is necessary to select columns
aafbjgbea,DIVA-DIA/DeepDIVA,util/lda.py,7c494d56bb9bf8a9fffe664d7d864bc4890f3cec,STILL_EXISTS,TODO add mroe explanaiton for that
aafbjgbee,DIVA-DIA/DeepDIVA,init/advanced_init.py,a3c9a1dffedf0669d1b927d9938bbc008c261ccf,STILL_EXISTS,TODO fix this such that only B and W are return values; prolly bring in the assign logic here and leave only the real assign outside
aafbjgbhf,DIVA-DIA/DeepDIVA,init/initializer.py,a3c9a1dffedf0669d1b927d9938bbc008c261ccf,b3adbeae0a7201584873b3b9cf3e894c3d43b666,TODO un-hard-code the 10 as number of classes
aafbjgbid,DIVA-DIA/DeepDIVA,template/CL_arguments.py,a3c9a1dffedf0669d1b927d9938bbc008c261ccf,STILL_EXISTS,TODO dataset and dataset-folder should never exist together
aafbjgcah,DIVA-DIA/DeepDIVA,template/CL_arguments.py,f143df51c44a5239f90a3ba871be416b18ca21b6,STILL_EXISTS,TODO dataset and dataset-folder should never exist together
aafbjgcdf,DIVA-DIA/DeepDIVA,init/advanced_init.py,a9849e5b0d0924d6c7e8c9a5bc1537ecf23712f2,STILL_EXISTS,TODO un-hard-code the 10 as number of classes
aafbjgcdh,DIVA-DIA/DeepDIVA,init/advanced_init.py,0f225b25e2c072561a5f1c22e5c0a8ce5ef1c67c,STILL_EXISTS,Keep only necessary dimensions when num_columns > num_desired_dimensions
aafbjgcdi,DIVA-DIA/DeepDIVA,init/advanced_init.py,d5addd63f6f29f9f45109499c45dfaed3cc3c60f,STILL_EXISTS,Keep only necessary dimensions when num_columns > num_desired_dimensions
aafbjgcee,DIVA-DIA/DeepDIVA,init/advanced_init.py,d5addd63f6f29f9f45109499c45dfaed3cc3c60f,STILL_EXISTS,The T belongs to the reshape operation! It is NOT transposing the input! It is necessary to select columns
aafbjgche,DIVA-DIA/DeepDIVA,template/setup.py,24cb2626e8326f538fc18264cb4e09042c7bdc8e,1dbf8a8f157c6ce9466a9bc6214915b1237ac9f8,TODO: Check if setting torch.backends.cudnn.deterministic=True will ensure deterministic behavior.
aafbjgcif,DIVA-DIA/DeepDIVA,template/runner/triplet/triplet.py,e1c243256e42220ed85fa9ac5fc57630d2a2051d,STILL_EXISTS,order to prevent any memory allocation on unused GPUs
aafbjgcjb,DIVA-DIA/DeepDIVA,template/runner/triplet/triplet.py,e1c243256e42220ed85fa9ac5fc57630d2a2051d,b49a2a20c6e4f2101187fa9da8ba58c0dd7e4d94,hack to speed up process
aafbjgddi,DIVA-DIA/DeepDIVA,template/setup.py,cb0a955433162fb284bfc9da7f682b75de394c5a,STILL_EXISTS,TODO: Check if setting torch.backends.cudnn.deterministic=True will ensure deterministic behavior.
aafbjgdjj,DIVA-DIA/DeepDIVA,template/runner/triplet/triplet.py,67ece356f407e4e92991e17b350443dddea880cd,b49a2a20c6e4f2101187fa9da8ba58c0dd7e4d94,TODO check is this is done anyway by default?
aafbjgeba,DIVA-DIA/DeepDIVA,datasets/Triplet_PhotoTour.py,b49a2a20c6e4f2101187fa9da8ba58c0dd7e4d94,STILL_EXISTS,hack to speed up process
aafbjggfi,DIVA-DIA/DeepDIVA,template/setup.py,f1392bd39fc5db5075fa5e5ddaebce1d2c2d3df4,STILL_EXISTS,TODO: update point cloud to work with new load_mean_std functions
aafbjggjb,DIVA-DIA/DeepDIVA,template/setup.py,39423f47478780959fa4b6ea77dd7801742306c3,STILL_EXISTS,TODO: make data balancing agnostic to type of dataset
aafbjgheg,DIVA-DIA/DeepDIVA,template/setup.py,0dfcc5441fd6ae4b98994959b161b5c88004f4d1,STILL_EXISTS,TODO: make data balancing agnostic to type of dataset
aafbjgheh,DIVA-DIA/DeepDIVA,template/setup.py,0dfcc5441fd6ae4b98994959b161b5c88004f4d1,STILL_EXISTS,TODO: update point cloud to work with new load_mean_std functions
aafbjgiif,DIVA-DIA/DeepDIVA,template/runner/triplet/evaluate.py,d7f29fcc9f5f2417aab2c1d6e166b5561ff69d64,eb82179bc6368124c363828b7dad8f197d61d7f1,TODO: Make it parameterized to use top_n or FPR
aafbjgiii,DIVA-DIA/DeepDIVA,template/runner/triplet/transforms.py,d7f29fcc9f5f2417aab2c1d6e166b5561ff69d64,STILL_EXISTS,TODO: DOES NOT PLAY WELL WITH SEEDS. Figure out why!
aafbjgiij,DIVA-DIA/DeepDIVA,template/runner/triplet/triplet.py,d7f29fcc9f5f2417aab2c1d6e166b5561ff69d64,eb82179bc6368124c363828b7dad8f197d61d7f1,TODO: Check if this really necessary?
aafbjgije,DIVA-DIA/DeepDIVA,template/setup.py,e621d20dac4874d4efa2d74d599ae9824d550660,536f95ea70e9b391e82a55ca4b491e3806ff09ff,TODO improve documentation
aafbjgjaf,DIVA-DIA/DeepDIVA,template/runner/image_classification/evaluate.py,b86279f0d5f8d7f9c9c4abbc7e375b9de6003288,72ed03c690bf7d77f77771b016122d29f02231d9,Fix for TB writer. Its an ugly workaround to have it printed nicely in the TEXT section of TB.
aafbjgjbi,DIVA-DIA/DeepDIVA,template/runner/image_classification/evaluate.py,37b9d4797632d2b0c5f290c62ef43a448d9cfa33,72ed03c690bf7d77f77771b016122d29f02231d9,Fix for TB writer. Its an ugly workaround to have it printed nicely in the TEXT section of TB.
aafbjgjcd,DIVA-DIA/DeepDIVA,template/setup.py,15bf822ea885ec4364ae28b07a4c74f2b3a573d0,STILL_EXISTS,TODO: make it save a zipfile instead of a tarfile.
aafbjhdad,DIVA-DIA/DeepDIVA,util/evaluation/metrics.py,05aacfd8a0d4e036f804e061f78da47be30a3fc2,STILL_EXISTS,In order to make this parallel (if ever needed) one should create a Process class which swallows
aafbjhdfi,DIVA-DIA/DeepDIVA,util/evaluation/metrics/apk.py,07eada452ed6d8a96abe071f8cf1ab88ab10a9bf,STILL_EXISTS,In order to make this parallel (if ever needed) one should create a Process class which swallows
aafbjhebf,DIVA-DIA/DeepDIVA,datasets/image_folder_dataset.py,f43c2dfa4f940ad5df3825c123ce2e2146b51013,8d99398c63bb9ecae0014a75fd9a45239e205973,Weird way to open things due to issue https:\/\/github.com\/python-pillow\/Pillow\/issues\/835
aafbjhfcc,DIVA-DIA/DeepDIVA,util/visualization/confusion_matrix_heatmap.py,60e9c07804be1adab5d214ea8bcb1250baa68f3d,STILL_EXISTS,Disable class labels if there are too many rows\/columns in the confusion matrix.
aafbjhgcg,DIVA-DIA/DeepDIVA,doc/source/conf.py,4e56a1ec7e5a8a4871bb409da06fd4e1e78a08b1,STILL_EXISTS,-- Options for todo extension ----------------------------------------------
aafbjhgch,DIVA-DIA/DeepDIVA,doc/source/conf.py,4e56a1ec7e5a8a4871bb409da06fd4e1e78a08b1,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aafbjiaac,DIVA-DIA/DeepDIVA,template/runner/convolutional_auto_encoder/setup.py,1d60331a22936564d0c4b7bba0c6f7a685d441b9,STILL_EXISTS,TODO: parameterize this out
aafbjicad,DIVA-DIA/DeepDIVA,template/runner/semantic_segmentation/evaluate.py,9bba890b802fb0f20ae9b46d0d25c4bba76e6892,STILL_EXISTS,Fix for TB writer. Its an ugly workaround to have it printed nicely in the TEXT section of TB.
aafbjicef,DIVA-DIA/DeepDIVA,template/runner/semantic_segmentation/train.py,54a00f680eafa94eda3c368b4ec877ba70341c93,STILL_EXISTS,TODO All parts computing the accuracy are commented out. See the TODO in evaluate.py
aafbjiedd,DIVA-DIA/DeepDIVA,template/runner/process_activation/activation.py,d9c48c532ad91cd2b27e6a584aa7d2f5f0bd0385,STILL_EXISTS,TODO: store something cleaner
aafbjieji,DIVA-DIA/DeepDIVA,template/setup.py,d9c48c532ad91cd2b27e6a584aa7d2f5f0bd0385,STILL_EXISTS,TODO: Remove or make param: map_location
aafbjifcc,DIVA-DIA/DeepDIVA,template/setup.py,1f9efcacc11c53e2e0acf477d51b51d02873c179,STILL_EXISTS,TODO: Remove or make param: map_location
aafbjifea,DIVA-DIA/DeepDIVA,template/runner/multi_label_image_classification/evaluate.py,c2c7d7854a9936fc3eb9c612a411927005a53402,STILL_EXISTS,Fix for TB writer. Its an ugly workaround to have it printed nicely in the TEXT section of TB.
aafbjiifd,DIVA-DIA/DeepDIVA,template/setup.py,2b9dd6b0116a088a2a4fd3c9a5bd4c38509a3faa,STILL_EXISTS,TODO: Remove or make param: map_location
aafbjiiii,DIVA-DIA/DeepDIVA,models/__init__.py,b932f54902a4185c21a1748d83339098ca6b02b9,STILL_EXISTS,Make the path and filename match the string needed for importlib
aafbjijci,DIVA-DIA/DeepDIVA,datasets/image_folder_segmentation.py,c7092400bceebc2c8bff5d5939dfed4e06fe312c,STILL_EXISTS,keeping track of how many crops have been generated -> needed to know when to shuffle the pages
aafbjjacg,DIVA-DIA/DeepDIVA,template/runner/semantic_segmentation/evaluate.py,c7092400bceebc2c8bff5d5939dfed4e06fe312c,STILL_EXISTS,needed for test phase output generation
aafbjjafd,DIVA-DIA/DeepDIVA,template/runner/semantic_segmentation/evaluate.py,c7092400bceebc2c8bff5d5939dfed4e06fe312c,17cfc4263e259b3fe97a6dcef0057b8d5ac062e4,open full ground truth image TODO add image extension
aafbjjaff,DIVA-DIA/DeepDIVA,template/runner/semantic_segmentation/evaluate.py,c7092400bceebc2c8bff5d5939dfed4e06fe312c,db503214d9c9154e496bcf8299822d6157db1665,TODO: only use for DivaHisDB
aafbjjahf,DIVA-DIA/DeepDIVA,template/runner/semantic_segmentation/evaluate.py,c7092400bceebc2c8bff5d5939dfed4e06fe312c,db503214d9c9154e496bcf8299822d6157db1665,4. TODO: only for DIVAHisDB
aafbjjajc,DIVA-DIA/DeepDIVA,template/runner/semantic_segmentation/semantic_segmentation.py,c7092400bceebc2c8bff5d5939dfed4e06fe312c,17cfc4263e259b3fe97a6dcef0057b8d5ac062e4,TODO best model is not saved if epoch = 1
aafbjjaje,DIVA-DIA/DeepDIVA,template/runner/semantic_segmentation/semantic_segmentation.py,c7092400bceebc2c8bff5d5939dfed4e06fe312c,17cfc4263e259b3fe97a6dcef0057b8d5ac062e4,TODO: add weights to kwargs
aafbjjbbc,DIVA-DIA/DeepDIVA,template/runner/semantic_segmentation/setup.py,c7092400bceebc2c8bff5d5939dfed4e06fe312c,db503214d9c9154e496bcf8299822d6157db1665,TODO: from __future__ import print_function
aafbjjbbd,DIVA-DIA/DeepDIVA,template/runner/semantic_segmentation/setup.py,c7092400bceebc2c8bff5d5939dfed4e06fe312c,db503214d9c9154e496bcf8299822d6157db1665,TODO: refactor into the image_folder_segmentation.py
aafbjjbgc,DIVA-DIA/DeepDIVA,util/data/get_a_dataset.py,d7806c9e2e864af683016b67e255f9b9b84d9437,STILL_EXISTS,fix naming issue
aafbjjcdf,DIVA-DIA/DeepDIVA,util/misc.py,d7806c9e2e864af683016b67e255f9b9b84d9437,STILL_EXISTS,TODO: fix input and output dims (see one_hot_to_output)
aafbjjcdj,DIVA-DIA/DeepDIVA,models/semantic_segmentation/Deeplabv3.py,4f516fb463fad4291e30bd88ee1b19623eb66ecf,STILL_EXISTS,TODO: make different functions for different models
aafbjjceh,DIVA-DIA/DeepDIVA,models/semantic_segmentation/SegNet.py,4f516fb463fad4291e30bd88ee1b19623eb66ecf,d2517ec57c3e9699ad10ce800925e575a9035523,TODO: make different functions for different VGG models
aafbjjdib,DIVA-DIA/DeepDIVA,template/runner/divahisdb_semantic_segmentation/divahisdb_semantic_segmentation.py,db503214d9c9154e496bcf8299822d6157db1665,662fe39dfb9c4a865cb139057675940224f26ace,TODO best model is not saved if epoch = 1
aafbjjdid,DIVA-DIA/DeepDIVA,template/runner/divahisdb_semantic_segmentation/divahisdb_semantic_segmentation.py,db503214d9c9154e496bcf8299822d6157db1665,662fe39dfb9c4a865cb139057675940224f26ace,TODO: add weights to kwargs
aafbjjecf,DIVA-DIA/DeepDIVA,template/runner/divahisdb_semantic_segmentation/evaluate.py,db503214d9c9154e496bcf8299822d6157db1665,STILL_EXISTS,needed for test phase output generation
aafbjjgej,DIVA-DIA/DeepDIVA,datasets/image_folder_segmentation.py,c3203ba429099dda614dd17bdc5e95140749dfdc,STILL_EXISTS,TODO documentation format
aafbjjgff,DIVA-DIA/DeepDIVA,datasets/image_folder_segmentation.py,c3203ba429099dda614dd17bdc5e95140749dfdc,STILL_EXISTS,TODO remove
aafbjjhbf,DIVA-DIA/DeepDIVA,template/runner/divahisdb_semantic_segmentation/evaluate.py,2d40a82ca1be7b86780e4e1f1b8e3fc1442387b9,STILL_EXISTS,TODO check with Vinay & Michele if correct
aafbjjjac,neuronets/nobrainer,niftynet_to_keras/highres3dnet.py,66cd355ec8e045efa6ed4550f8247ca247914908,STILL_EXISTS,TODO: investigate which axes are correct.
aafbjjjag,neuronets/nobrainer,niftynet_to_keras/highres3dnet.py,66cd355ec8e045efa6ed4550f8247ca247914908,STILL_EXISTS,TODO: confirm that filters=16 and dilation_rate=(2; 2; 2) gives a
aafbjjjaj,neuronets/nobrainer,niftynet_to_keras/highres3dnet.py,66cd355ec8e045efa6ed4550f8247ca247914908,STILL_EXISTS,TODO: confirm that filters=16 and dilation_rate=(4; 4; 4) gives a
aafbjjjgc,neuronets/nobrainer,nobrainer/models/highres3dnet.py,488c915368edcd8bd9d753997fe5a512c633b654,STILL_EXISTS,\"\"\"HighRes3DNet implemented in TensorFlow. ||  || Reference || --------- || Li W.; Wang G.; Fidon L.; Ourselin S.; Cardoso M.J.; Vercauteren T. (2017) || On the Compactness; Efficiency; and Representation of 3D Convolutional || Networks: Brain Parcellation as a Pretext Task. In: Niethammer M. et al. (eds) || Information Processing in Medical Imaging. IPMI 2017. Lecture Notes in Computer || Science; vol 10265. || \"\"\"
aafbjjjgd,neuronets/nobrainer,nobrainer/models/highres3dnet.py,488c915368edcd8bd9d753997fe5a512c633b654,STILL_EXISTS,TODO: use `tensorflow.python.framework.test_util.is_gpu_available` to
aafbjjjgj,neuronets/nobrainer,train.py,90bb6e58f42b75b08669f9953e24fd184b0aa6f4,54200bc13c0b6e63cea50d143a2f83037ad04024,\"\"\"Script to train highres3dnet model. ||  || The input CSV must have two columns: ||     1. filepaths of features ||     2. filepaths of corresponding labels ||  || TODO || ---- || - Make this script more general. Ideally; one could drop in their model and ||     loss function. || - Move some common methods (eg; i\/o) to dedicated modules. || - Dice coefficient for class 1 (brainmask) is sometimes NaN. || - Input of 1 * 128**3 is too large for 1080ti. This seems to be related to the ||     `input_fn` used. || - Remove pandas as a dependency. Make pure python reader that accepts CSV or ||     TSV as input. || \"\"\"
aafbjjjhd,neuronets/nobrainer,train.py,90bb6e58f42b75b08669f9953e24fd184b0aa6f4,54200bc13c0b6e63cea50d143a2f83037ad04024,probabilities = tf.nn.softmax(logits; -1)  # unused at the moment.
aafbjjjib,neuronets/nobrainer,train.py,90bb6e58f42b75b08669f9953e24fd184b0aa6f4,54200bc13c0b6e63cea50d143a2f83037ad04024,TODO: sometimes NaN is returned for Dice of class 1 (brainmask). This
aafbjjjie,neuronets/nobrainer,train.py,90bb6e58f42b75b08669f9953e24fd184b0aa6f4,54200bc13c0b6e63cea50d143a2f83037ad04024,TODO: generalize this.
aafbjjjii,neuronets/nobrainer,nobrainer/io.py,54200bc13c0b6e63cea50d143a2f83037ad04024,4819d25e66bd3d976281747459820aef9273b092,TODO: generalize this.
aafbjjjjh,neuronets/nobrainer,train.py,54200bc13c0b6e63cea50d143a2f83037ad04024,4819d25e66bd3d976281747459820aef9273b092,\"\"\"Example script to train model. ||  || The input CSV must have two columns: ||     1. filepaths of features ||     2. filepaths of corresponding labels ||  || TODO || ---- || - Dice coefficient for class 1 (brainmask) is sometimes NaN. This occurs when ||     Dice should be zero. || - Input of 1 * 128**3 is too large for 1080ti to train HighRes3DNet. It is OK ||     for MeshNet. This issue seems to be related to the `input_fn` used. || \"\"\"
aafcaaabe,neuronets/nobrainer,nobrainer/metrics.py,4819d25e66bd3d976281747459820aef9273b092,676846feda55fd5eb26f63787af97b5a07f5504b,TODO:
aafcaaacf,neuronets/nobrainer,nobrainer/preprocessing.py,4819d25e66bd3d976281747459820aef9273b092,fd39e842839a7d5416b4b6b356f1d9caeba9a30a,TODO: generalize this.
aafcaaaga,neuronets/nobrainer,nobrainer/models/quicknat.py,8182fcc70e436a628e57e902c1045b04f9c7fc9c,STILL_EXISTS,TODO (kaczmarj): remove this error once implementation is fixed.
aafcaabbe,neuronets/nobrainer,nobrainer/metrics.py,748c8804fab65efa78444d7cc5921a9c7f2251f1,STILL_EXISTS,TODO (kaczmarj): do not get mean of NaN.
aafcaadcd,neuronets/nobrainer,nobrainer/cli.py,59104ac5adf2bc72fea1d8aff829e19a117d572b,STILL_EXISTS,fix
aafcaadhh,neuronets/nobrainer,nobrainer/validate.py,59104ac5adf2bc72fea1d8aff829e19a117d572b,STILL_EXISTS,fix
aafcaaecj,neuronets/nobrainer,nobrainer/_version.py,393e9d269ed9364173effdc2738340b588c8b3f1,STILL_EXISTS,maybe improved later
aafcaaedf,neuronets/nobrainer,nobrainer/_version.py,393e9d269ed9364173effdc2738340b588c8b3f1,STILL_EXISTS,unparseable. Maybe git-describe is misbehaving?
aafcaaefh,neuronets/nobrainer,nobrainer/cli/main.py,393e9d269ed9364173effdc2738340b588c8b3f1,STILL_EXISTS,TODO: improve docs.
aafcaaefi,neuronets/nobrainer,nobrainer/cli/main.py,393e9d269ed9364173effdc2738340b588c8b3f1,a4f1cebb1d4dc0236f50615b33d8ffcd02619c67,TODO: validation dataset
aafcaaege,neuronets/nobrainer,nobrainer/cli/main.py,393e9d269ed9364173effdc2738340b588c8b3f1,STILL_EXISTS,TODO: For debugging only.
aafcaaegj,neuronets/nobrainer,nobrainer/io.py,393e9d269ed9364173effdc2738340b588c8b3f1,a4f1cebb1d4dc0236f50615b33d8ffcd02619c67,improve read performance?
aafcaafbg,neuronets/nobrainer,nobrainer/layers/variational_convolution.py,393e9d269ed9364173effdc2738340b588c8b3f1,STILL_EXISTS,TODO: test the channels first implementation.
aafcaafbi,neuronets/nobrainer,nobrainer/layers/variational_convolution.py,393e9d269ed9364173effdc2738340b588c8b3f1,STILL_EXISTS,TODO: we might have to reshape the outputs before bias_add.
aafcaafci,neuronets/nobrainer,nobrainer/layers/variational_convolution.py,393e9d269ed9364173effdc2738340b588c8b3f1,STILL_EXISTS,TODO (kaczmarj): add is_mc and a_initializer.
aafcaafdh,neuronets/nobrainer,nobrainer/losses.py,393e9d269ed9364173effdc2738340b588c8b3f1,21a9b3dd5863b0c0c5bef7e872b629429ace11f0,TODO: add priors from existing Keras model.
aafcaafjf,neuronets/nobrainer,nobrainer/training.py,393e9d269ed9364173effdc2738340b588c8b3f1,STILL_EXISTS,TODO: add checks.
aafcaafji,neuronets/nobrainer,nobrainer/training.py,393e9d269ed9364173effdc2738340b588c8b3f1,STILL_EXISTS,TODO: can we test if the model is compiled? We lose the optimizer
aafcaagac,neuronets/nobrainer,nobrainer/training.py,393e9d269ed9364173effdc2738340b588c8b3f1,STILL_EXISTS,TODO: if we can load weights after compiling model; load the most recent
aafcaagbg,neuronets/nobrainer,nobrainer/transform.py,393e9d269ed9364173effdc2738340b588c8b3f1,STILL_EXISTS,TODO.
aafcaahef,neuronets/nobrainer,versioneer.py,393e9d269ed9364173effdc2738340b588c8b3f1,STILL_EXISTS,maybe improved later
aafcaahfb,neuronets/nobrainer,versioneer.py,393e9d269ed9364173effdc2738340b588c8b3f1,STILL_EXISTS,unparseable. Maybe git-describe is misbehaving?
aafcaaigg,neuronets/nobrainer,nobrainer/volume.py,23f635e66b84153af6d9affb7999f15b35cf2e8c,STILL_EXISTS,Below this line; we implement methods similar to those above but using Numpy.
aafcaaijj,neuronets/nobrainer,nobrainer/tests/io_test.py,a4f1cebb1d4dc0236f50615b33d8ffcd02619c67,STILL_EXISTS,TODO: add more cases.
aafcaajah,neuronets/nobrainer,nobrainer/tfrecord.py,a4f1cebb1d4dc0236f50615b33d8ffcd02619c67,STILL_EXISTS,This is a hack to allow multiprocessing to pickle
aafcaajbb,neuronets/nobrainer,nobrainer/tfrecord.py,a4f1cebb1d4dc0236f50615b33d8ffcd02619c67,STILL_EXISTS,TODO: this line does not work. The shape cannot be determined
aafcaajca,neuronets/nobrainer,nobrainer/layers/dropout.py,21a9b3dd5863b0c0c5bef7e872b629429ace11f0,STILL_EXISTS,TODO: add `K.in_train_phase`.
aafcaajcb,neuronets/nobrainer,nobrainer/layers/dropout.py,21a9b3dd5863b0c0c5bef7e872b629429ace11f0,STILL_EXISTS,TODO: where should this go? Or should it be removed?
aafcaajdd,neuronets/nobrainer,nobrainer/models/bayesian.py,21a9b3dd5863b0c0c5bef7e872b629429ace11f0,STILL_EXISTS,TODO: add WeightNorm wrapper from tensorflow-probability once next
aafcaajdf,neuronets/nobrainer,nobrainer/models/bayesian.py,21a9b3dd5863b0c0c5bef7e872b629429ace11f0,STILL_EXISTS,TODO: implement correct behavior for is_mc.
aafcaajdg,neuronets/nobrainer,nobrainer/models/bayesian.py,21a9b3dd5863b0c0c5bef7e872b629429ace11f0,STILL_EXISTS,TODO: implement is_mc behavior.
aafcaajed,neuronets/nobrainer,nobrainer/tests/test_utils.py,21a9b3dd5863b0c0c5bef7e872b629429ace11f0,STILL_EXISTS,TODO: add entropy
aafcaajfi,neuronets/nobrainer,nobrainer/tests/volume_test.py,305d804684d89572ac62c0b57c428154bc4ff1e3,b35fdfdff65e88b6d7ccb9b07eab3c76119845ce,TODO: need to implement this soon.
aafcaajij,neuronets/nobrainer,nobrainer/dataset.py,b35fdfdff65e88b6d7ccb9b07eab3c76119845ce,STILL_EXISTS,TODO: in the future; multi-channel features should be supported.
aafcabaab,neuronets/nobrainer,nobrainer/tests/dataset_test.py,b35fdfdff65e88b6d7ccb9b07eab3c76119845ce,STILL_EXISTS,TODO: need to implement this soon.
aafcabajb,rosette-api/python,rosette/api.py,aa4581d3cd1f5407ff5f1d1611b0e297688c857c,c9b117f9b8426fd335677ab1b285f80f54267f8a,underlings will be called up; and maybe they'll do better.
aafcabaje,rosette-api/python,rosette/api.py,aa4581d3cd1f5407ff5f1d1611b0e297688c857c,baf8ccf745eaac2b3c079a4618493f26ad5ee468,this is still needed to make sure that the parameter NAMES are known.
aafcabbbg,rosette-api/python,tests/test_rosette_api.py,aa4581d3cd1f5407ff5f1d1611b0e297688c857c,c9b117f9b8426fd335677ab1b285f80f54267f8a,Set user key as filename as a workaround - tests don\"t require user key
aafcabdge,rosette-api/python,tests/test_rosette_api.py,d1c9497cc4b99ee2a80f1316f1c23799be4d2619,c9b117f9b8426fd335677ab1b285f80f54267f8a,Doesn't really matter what it returns for this test; so just making sure it catches all of them
aafcabgjf,rosette-api/python,tests/test_rosette_api.py,c9b117f9b8426fd335677ab1b285f80f54267f8a,baf8ccf745eaac2b3c079a4618493f26ad5ee468,function with httpretty.enable() and ends it with httpretty.disable().  However; when combined with
aafcabhhe,rosette-api/python,rosette/api.py,2c1ecf864e62d4eb3eef3d9d6b9996a929300d9a,e70a5ecb3ade90faa055ce975dfab4e1d7c07bd0,underlings will be called up; and maybe they'll do better.
aafcabhjd,rosette-api/python,examples/entities.py,1cde636d3519b4c0d00ac16b4630a8b033933cce,c34047597dbcb80f66f2b47169dc4e04056640e5,to improve performance; and if you don't need the QID; set this option
aafcabihg,rosette-api/python,tests/test_rosette_api.py,baf8ccf745eaac2b3c079a4618493f26ad5ee468,STILL_EXISTS,function with httpretty.enable() and ends it with httpretty.disable().  However; when combined
aafcabijg,rosette-api/python,rosette/api.py,eba1109090d47dd011fd7013f3c35b21e8a79877,f4d93951621d038ebc2928f9fc2e3d811361b83f,this is still needed to make sure that the parameter NAMES are known.
aafcabjeg,rosette-api/python,docs/source/conf.py,53fb72c924196a4138a556192001029e6c7c0339,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aafcacgha,ai-med/quickNAT_pytorch,networks/data_utils.py,234e78f0791fdcaefaa81ad0c9324536308834fe,STILL_EXISTS,TODO: Need to change later
aafcacgie,ai-med/quickNAT_pytorch,networks/net_api/losses.py,234e78f0791fdcaefaa81ad0c9324536308834fe,STILL_EXISTS,TODO: why?
aafcachcb,ai-med/quickNAT_pytorch,quickNat_pytorch/data_utils.py,2f6721415e1f2d9c1bfe9c000b251d8e2f645243,6bad77694ce2c631c1dddf616f90f8305718827f,TODO: Need to add other pipeline processes in between
aafcachhe,ai-med/quickNAT_pytorch,quickNat_pytorch/data_utils.py,05aa67667fced30c6a2c0cf6a7fc17ed150c2b37,85b6a88c656dacf8299fb1a19ff3cf29956b31ae,TODO: Need to change later
aafcachjb,ai-med/quickNAT_pytorch,quickNat_pytorch/data_utils.py,043257abf9eab2049a7d5f1fb396665b19dbc616,STILL_EXISTS,TODO: Need to defing a dynamic pipeline
aafcachjc,ai-med/quickNAT_pytorch,quickNat_pytorch/data_utils.py,043257abf9eab2049a7d5f1fb396665b19dbc616,STILL_EXISTS,TODO: Presets for training; prediction and evaluation
aafcachjf,ai-med/quickNAT_pytorch,quickNat_pytorch/solver.py,043257abf9eab2049a7d5f1fb396665b19dbc616,STILL_EXISTS,TODO: Need to fix the issue with tensorboardX graph
aafcacibe,ai-med/quickNAT_pytorch,run.py,5b2ce7be025c21f7bdcf39c23927b7f2da19ebba,eb029ed8d9cc42b9400a18c2e122465e8cb2b49d,#TODO: Not a correct version. Need to work on it
aafcacida,ai-med/quickNAT_pytorch,utils/log_utils.py,500246925edd44f5b8f324ff3f7dc8b39e83cdfe,STILL_EXISTS,TODO: Add custom phase names
aafcadaai,guokr/Caver,docs/source/conf.py,da98b816c69a75255f48420b03f66fb93547dae5,STILL_EXISTS,-- Options for todo extension ----------------------------------------------
aafcadaaj,guokr/Caver,docs/source/conf.py,da98b816c69a75255f48420b03f66fb93547dae5,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aafcadafh,guokr/Caver,examples/server.py,ef566a7ea8ba248fb38b89583a678719a9aab00f,STILL_EXISTS,print(columns)
aafcadfec,guokr/Caver,caver/model/lstm.py,c2b1ab213f74fc4343fc5e15952561ea712cd9cb,9679061646cafd268f8e8eaeea319e8cce467506,TODO
aafcadfib,koala-ai/tensorflow_nlp,nlp/text_representation/doc2vec/dataset/data_utils.py,e24886175c9ebad2b3a49344cbccdd094bef8712,STILL_EXISTS,move the sliding window
aafcadihe,koala-ai/tensorflow_nlp,nlp/textsum/dataset/dataset.py,ea5d393e44306db93a1e1bf26326822f10087eef,STILL_EXISTS,pad them if needed; reverse encoder inputs and add GO to decoder.
aafcaeacj,koala-ai/tensorflow_nlp,nlp/crf/crf.py,2d72d1d5468eb9e5da61c138806eeaa381009c45,STILL_EXISTS,The output value is currently unused and simply satisfies the RNN API.
aafcaeadb,koala-ai/tensorflow_nlp,nlp/crf/crf.py,2d72d1d5468eb9e5da61c138806eeaa381009c45,STILL_EXISTS,probabilities; which would require the accumulated alpha values at every
aafcaecac,mercury-ml-team/mercury-ml,mercury_ml/common/providers/artifact_copying/from_hdfs.py,1d48c3a9104cf12cd63868623ea0b3cc1f6e50df,STILL_EXISTS,TODO change this to use the Python \"hdfs\" class
aafcaecad,mercury-ml-team/mercury-ml,mercury_ml/common/providers/data_set.py,1d48c3a9104cf12cd63868623ea0b3cc1f6e50df,STILL_EXISTS,TODO: does this work?
aafcaecae,mercury-ml-team/mercury-ml,mercury_ml/common/providers/data_wrappers/h2o.py,1d48c3a9104cf12cd63868623ea0b3cc1f6e50df,STILL_EXISTS,get H2OSparkling context #TODO: if context doesn't exist yet; raise error! Should not be created here
aafcaecaf,mercury-ml-team/mercury-ml,mercury_ml/common/providers/data_wrappers/keras.py,1d48c3a9104cf12cd63868623ea0b3cc1f6e50df,STILL_EXISTS,TODO check if this is sufficient!
aafcaecag,mercury-ml-team/mercury-ml,mercury_ml/common/providers/data_wrappers/pandas.py,1d48c3a9104cf12cd63868623ea0b3cc1f6e50df,STILL_EXISTS,convert columns
aafcaecah,mercury-ml-team/mercury-ml,mercury_ml/common/providers/data_wrappers/spark.py,1d48c3a9104cf12cd63868623ea0b3cc1f6e50df,STILL_EXISTS,get H2OSparkling context (#TODO: if context doesn't exist yet; raise error! Should not be created here)
aafcaecaj,mercury-ml-team/mercury-ml,mercury_ml/common/providers/data_wrappers/spark.py,1d48c3a9104cf12cd63868623ea0b3cc1f6e50df,STILL_EXISTS,convert factor columns
aafcaecbc,mercury-ml-team/mercury-ml,mercury_ml/common/providers/data_wrappers/spark.py,1d48c3a9104cf12cd63868623ea0b3cc1f6e50df,STILL_EXISTS,new_underlying = ... # TODO how to concatenate while maintaining row order?
aafcaecbh,mercury-ml-team/mercury-ml,mercury_ml/common/providers/source_reading/disk.py,1d48c3a9104cf12cd63868623ea0b3cc1f6e50df,STILL_EXISTS,TODO possibly split into two modules: data_set readers and data_bunch readers
aafcaecbi,mercury-ml-team/mercury-ml,mercury_ml/common/providers/source_reading/disk.py,1d48c3a9104cf12cd63868623ea0b3cc1f6e50df,STILL_EXISTS,label_df must consist entirely of label columns. Therefore we set an explicit index. This will be used to
aafcaecca,mercury-ml-team/mercury-ml,mercury_ml/common/providers/source_reading/hive.py,1d48c3a9104cf12cd63868623ea0b3cc1f6e50df,STILL_EXISTS,TODO possibly split into two modules: data_set readers and data_bunch readers
aafcaeccb,mercury-ml-team/mercury-ml,mercury_ml/common/providers/source_reading/hive.py,1d48c3a9104cf12cd63868623ea0b3cc1f6e50df,STILL_EXISTS,TODO this should be only \"get\" at this stage
aafcaecde,mercury-ml-team/mercury-ml,mercury_ml/h2o/providers/model_evaluation.py,1d48c3a9104cf12cd63868623ea0b3cc1f6e50df,STILL_EXISTS,TODO must filter on threshold_metric_names
aafcaecdf,mercury-ml-team/mercury-ml,mercury_ml/h2o/providers/prediction.py,1d48c3a9104cf12cd63868623ea0b3cc1f6e50df,STILL_EXISTS,TODO this should return an H2OSparklingDataWrapper in the input type is H2OSparklingDataWrapper
aafcaecdh,mercury-ml-team/mercury-ml,mercury_ml/h2o/providers/session.py,1d48c3a9104cf12cd63868623ea0b3cc1f6e50df,STILL_EXISTS,TODO possibly change this to create spark session outside and pass \"spark\" as variable
aafcaeceg,mercury-ml-team/mercury-ml,mercury_ml/keras/providers/image_generators/multi_input.py,1d48c3a9104cf12cd63868623ea0b3cc1f6e50df,STILL_EXISTS,returns a list; sorted on the class number. #TODO test this
aafcaecfe,mercury-ml-team/mercury-ml,mercury_ml/keras/providers/image_generators/single_input.py,1d48c3a9104cf12cd63868623ea0b3cc1f6e50df,STILL_EXISTS,returns a list; sorted on the class number. #TODO test this
aafcaedad,mercury-ml-team/mercury-ml,tests/test_common/test_artifact_storage.py,1d48c3a9104cf12cd63868623ea0b3cc1f6e50df,STILL_EXISTS,TODO
aafcaeeed,mercury-ml-team/mercury-ml,tests/test_common/test_data_set.py,4978a277bd99dea8d2a2e83c9e5936ca51d058e6,STILL_EXISTS,data_wrapper_params =  {k:{} for k in list(self.__dict__.keys())} #TODO: does this work?
aafcaegea,mercury-ml-team/mercury-ml,examples/keras/evaluate.py,63d0589c07a4a8bf5e243d0d5b75d973828fa041,STILL_EXISTS,TODO existing metrics should be updated; not overwritten!
aafcaffgj,mercury-ml-team/mercury-ml,examples/snippets/reading_and_transforming_data/02_data_sets.py,9e3899ef640cd183f3b552ee34b182305dfd2e90,STILL_EXISTS,We can also add DataWrappers afterwards; for example; the following will add a wrapper that consists of the full data set with all columns
aafcafgca,mercury-ml-team/mercury-ml,examples/snippets/storing_and_moving_data/01_storing_and_moving_artifacts.py,9e3899ef640cd183f3b552ee34b182305dfd2e90,STILL_EXISTS,necessary steps; such as creating the folders needed for the storage.
aafcafjfb,mercury-ml-team/mercury-ml,examples/tensorflow/fit_minimal_example.py,46860e593da82a868207b9c1967a9852ebe9a4d1,STILL_EXISTS,Here we set the parameters needed to reading the source data; and then proceed to use the task \"read_train_valid_test_data_bunch\" from the mercury_ml.common.tasks API
aafcafjia,mercury-ml-team/mercury-ml,examples/tensorflow/fit_minimal_example.py,46860e593da82a868207b9c1967a9852ebe9a4d1,STILL_EXISTS,Then we use the \"tasks\" API to store the model in each of the provided formats; as well as copying the model to a \"remote\" location (in this example we simply copy to another local folder; but this would normally be used in combination with S3; GCS; HDFS etc)
aafcagcfh,Revmaker/innatis,tests/test_use_featurizer.py,cad06e390bc3bb594f3d8d50c037f25816cf8ec6,STILL_EXISTS,todo
aafcagddg,Revmaker/innatis,innatis/classifiers/bert/run_classifier.py,dca7a49cefb27c4fbf526082ad4f18d98da00eeb,STILL_EXISTS,The convention in BERT is:
aafcaghgc,Revmaker/innatis,innatis/extractors/entity_synonyms.py,3fe2fc2e40ee5171d00cbc86b1ba153bcc3cefce,STILL_EXISTS,Needed so that we dont add the processors mutiple times
aafcaghgh,Revmaker/innatis,innatis/extractors/entity_synonyms.py,c2fa6c7095ef31973cd015bb4b6014a9262540cd,STILL_EXISTS,Needed so that we dont add the processors mutiple times
aafcagibe,Revmaker/innatis,innatis/classifiers/bert/modeling.py,c6b8c7f9cea374b82cdc6a5ceacb2aff665c0376,STILL_EXISTS,We \"pool\" the model by simply taking the hidden state corresponding
aafcahajb,sdv-dev/Copulas,utils.py,c112a40a433b4fb55c6f50949199981c7485a156,STILL_EXISTS,FIXME this is probably unnecessary now
aafcahbai,sdv-dev/Copulas,utils.py,c112a40a433b4fb55c6f50949199981c7485a156,STILL_EXISTS,FIXME rewrite
aafcahbaj,sdv-dev/Copulas,utils.py,c112a40a433b4fb55c6f50949199981c7485a156,STILL_EXISTS,FIXME is there a better way to fix for noise?
aafcahbba,sdv-dev/Copulas,utils.py,c112a40a433b4fb55c6f50949199981c7485a156,STILL_EXISTS,FIXME better way to fix for noise?
aafcahbbb,sdv-dev/Copulas,utils.py,c112a40a433b4fb55c6f50949199981c7485a156,STILL_EXISTS,fix this
aafcahcaj,sdv-dev/Copulas,copulas/GaussianCopula.py,f6b615458fbe71ba6d269de53134ad77b89bc4d2,STILL_EXISTS,TODO: this should be self.ppf
aafcahcbb,sdv-dev/Copulas,copulas/GaussianCopula.py,b9ad8fafa93cd688d6d7e67f035329c9c45a8823,STILL_EXISTS,loops through columns and applies transformation
aafcahcec,sdv-dev/Copulas,copulas/multivariate/GaussianCopula.py,70cd4804b4e8af69e351423232e8c56bd820f412,STILL_EXISTS,loops through columns and applies transformation
aafcahcfd,sdv-dev/Copulas,copulas/multivariate/GaussianCopula.py,31fd1d9ce853527a2bf2a85fa642d45bd6f0c88e,STILL_EXISTS,TODO: fix lower bounds
aafcaheii,sdv-dev/Copulas,docs/conf.py,8d245783e18bff460f9e799b178dc1c9238b7fa2,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aafcahhfg,sdv-dev/Copulas,copulas/copulas.py,a0f7700fa16bd17eba3f018cb5d528545966b160,STILL_EXISTS,FIXME imports are missing and self.param doesn't exist
aafcajedh,sdv-dev/Copulas,copulas/multivariate/gaussian.py,d28ebc6225185adeb27f3dbde101e7a8a263a7d6,STILL_EXISTS,loops through columns and applies transformation
aafcajega,sdv-dev/Copulas,copulas/multivariate/gaussian.py,501a2a2919b6b15b3ec53c2c00d894849fec8a1c,STILL_EXISTS,loops through columns and applies transformation
aafcbbaad,sdv-dev/Copulas,setup.py,c028c710405ac1e7b66042906c35c507bf3de382,STILL_EXISTS,fix style issues
aafcbbjai,sdv-dev/Copulas,tests/copulas/univariate/test_kde.py,a5ce85b650748afe2db9827f237e4d81579b4f6d,STILL_EXISTS,TODO: Discuss if this loss of precision is acceptable
aafcbcceg,sdv-dev/Copulas,copulas/multivariate/vine.py,e1aa123e82d99db4b7e69c6564f987f1a9fcf055,STILL_EXISTS,TODO: explain what this is supposed to do and make it work
aafcbcceh,sdv-dev/Copulas,copulas/multivariate/vine.py,e1aa123e82d99db4b7e69c6564f987f1a9fcf055,STILL_EXISTS,TODO: Alternatively; remove it.
aafcbceib,sdv-dev/Copulas,tests/large_scale_evaluation.py,018d4906a468892f59bc5bc41679c18f234540dd,STILL_EXISTS,\"\"\" || Large Scale Copulas Evaluation. ||  || This script is a command line module that evaluates multiple MultiVariate models || from the Copulas library over a collection of real world datasets stored in an || S3 Bucket as CSV files. ||  || Usage: ||  ||     python large_scale_evaluation.py [-h] [-v] [-o OUTPUT_PATH] [-s SAMPLE] ||                                      [-r MAX_ROWS] [-c MAX_COLUMNS] ||                                      [-m MODEL [MODEL ...]] ||                                      [datasets [datasets ...]] ||  ||     positional arguments: ||       datasets              Name of the datasets\/s to test. ||  ||     optional arguments: ||       -h; --help            show this help message and exit ||       -v; --verbose         Be verbose. Use -vv for increased verbosity. ||       -o OUTPUT_PATH; --output-path OUTPUT_PATH ||                             Path to the CSV file where the report will be dumped ||       -s SAMPLE; --sample SAMPLE ||                             Limit the test to a sample of datasets for the given ||                             size. ||       -r MAX_ROWS; --max-rows MAX_ROWS ||                             Limit the number of rows per dataset. ||       -c MAX_COLUMNS; --max-columns MAX_COLUMNS ||                             Limit the number of columns per dataset. ||       -m MODEL [MODEL ...]; --model MODEL [MODEL ...] ||                             Name of the model to test. Can be passed multiple ||                             times to evaluate more than one model. || \"\"\"
aafcbdcih,kalekiu/easyesn,src/easyesn/easyesn/BaseESN.py,a4abe7b7836b8c175cd8b3d13f468cd7a8d6ed45,STILL_EXISTS,TODO numerical instability
aafcbdeib,kalekiu/easyesn,src/easyesn/easyesn/ESNOptimizer.py,9d2af70f433bb4295f5ed51efaaaa6b226126bc2,STILL_EXISTS,TODO solve this...
aafcbdeic,kalekiu/easyesn,src/easyesn/easyesn/ESNOptimizer.py,9d2af70f433bb4295f5ed51efaaaa6b226126bc2,9d2af70f433bb4295f5ed51efaaaa6b226126bc2,TODO handle different shapes with [None;:]...
aafcbdhhg,kalekiu/easyesn,src/easyesn/easyesn/SpatioTemporalESN.py,9fb427a4dedfeed1a85b025343e56946f74ef70d,STILL_EXISTS,workaround as predict does not support batches atm
aafcbdiei,kalekiu/easyesn,src/easyesn/easyesn/backend/torchBackend.py,bb1498c7433d8ef8d327dd60f0f2e4bce9e0fc37,00fe13b099c8229752e3947a132f85b6b629facb,atleast_2d needed by vstack emulation
aafcbeadj,comic/grand-challenge.org,django/ComicSite/models.py,44689449381d033351e3761e372787d0c3fc9c75,STILL_EXISTS,TODO : do checking for scripts and hacks here?
aafcbebij,comic/grand-challenge.org,django/ComicSite/views.py,c972a31ca21c79a1f30332850fded3e0f954e414,STILL_EXISTS,TODO log a warning here; no exception.
aafcbecab,comic/grand-challenge.org,django/comic/urls.py,84d87db26f7188c838db7d1067c4e503cbafd1f3,986a0a56814f6d6a2a87841fa182d9bfeb636f10,this one is needed to be able make {% url work}  TODO: having two identical regexes here seems stinky.. can this be different?
aafcbeccc,comic/grand-challenge.org,django/comicsite/models.py,dc82b6c7ff9e81b1999ae5b6d990baa67b93bf27,f88946bb778a14b2dd296beae5d06648bc0b478d,TODO: Sjoerd - Is it correct to define the params below as class params; or should these be in an init method?
aafcbecdd,comic/grand-challenge.org,django/comicsite/views.py,dc82b6c7ff9e81b1999ae5b6d990baa67b93bf27,STILL_EXISTS,TODO log a warning here; no exception.
aafcbedcd,comic/grand-challenge.org,django/comic/settings.py,fb27254e3eaa27b7e2034997043a79467e20febd,3bf82123a8536ebe780e36eeecc96a14a8f16597,FIXME: Path to template path. This might be temporary.
aafcbeddc,comic/grand-challenge.org,django/comic/urls.py,fb27254e3eaa27b7e2034997043a79467e20febd,667b3f149df55faf294179d13d815ee7ac90acdf,TODO: Put all comicsite related stuff in comicsite.urls
aafcbedde,comic/grand-challenge.org,django/comic/urls.py,fb27254e3eaa27b7e2034997043a79467e20febd,667b3f149df55faf294179d13d815ee7ac90acdf,this one is needed to be able make {% url work}  TODO: having two identical regexes here seems stinky.. can this be different?
aafcbeeei,comic/grand-challenge.org,django/comicsite/models.py,72dd15e702c6b9b484be7a5938c5283d36b5e34f,78c9dfc3f860ca265d67a1950230a9f1b035b6e3,TODO check whether short name is really clean and short!
aafcbeefb,comic/grand-challenge.org,django/comicsite/admin.py,f88946bb778a14b2dd296beae5d06648bc0b478d,STILL_EXISTS,todo: is this double save really needed?
aafcbeicf,comic/grand-challenge.org,django/comicsite/templatetags/template_tags.py,205f762418b1d659374acdc58ad57aef119fe457,STILL_EXISTS,This is needed to use the @register.tag decorator
aafcbeijc,comic/grand-challenge.org,django/build/lib/comic/settings.py,4067c51d45077be3ea7ec11ffe989213eb37d7d5,STILL_EXISTS,FIXME: Path to template path. This might be temporary.
aafcbejfa,comic/grand-challenge.org,django/build/lib/comicsite/admin.py,4067c51d45077be3ea7ec11ffe989213eb37d7d5,STILL_EXISTS,FIXME: is this double save really needed?
aafcbejic,comic/grand-challenge.org,django/build/lib/comicsite/models.py,4067c51d45077be3ea7ec11ffe989213eb37d7d5,STILL_EXISTS,TODO check whether short name is really clean and short!
aafcbejif,comic/grand-challenge.org,django/build/lib/comicsite/models.py,4067c51d45077be3ea7ec11ffe989213eb37d7d5,STILL_EXISTS,TODO : do checking for scripts and hacks here?
aafcbejja,comic/grand-challenge.org,django/build/lib/comicsite/templatetags/template_tags.py,4067c51d45077be3ea7ec11ffe989213eb37d7d5,STILL_EXISTS,This is needed to use the @register.tag decorator
aafcbfaab,comic/grand-challenge.org,django/build/lib/comicsite/views.py,4067c51d45077be3ea7ec11ffe989213eb37d7d5,STILL_EXISTS,TODO log a warning here; no exception.
aafcbfeda,comic/grand-challenge.org,django/comic/settings.py,da4de68e00a4a397778ddcf6a190884a5b71a735,3bf82123a8536ebe780e36eeecc96a14a8f16597,FIXME: put site source root here for testing purposes. This should be a real data drive
aafcbfedc,comic/grand-challenge.org,django/comicsite/templatetags/template_tags.py,24190493c5dfe8a7cff42611f0acc211e887bfd8,STILL_EXISTS,FIXME: abstract Dataset should be imported here; not explicit filesystemdataset. the template tag should not care about the type of dataset.
aafcbfgeg,comic/grand-challenge.org,django/comic/settings.py,5af2552be495e30f51586c3232fd446536ba8432,3bf82123a8536ebe780e36eeecc96a14a8f16597,Needed for userena
aafcbfheh,comic/grand-challenge.org,django/comicmodels/admin.py,48048dc8fcaa8f0010410475058bbf88165a2ac1,3028bd3d1cf6e976285a7fd36b05cb02e905ec7b,TODO: print {% tag %} values in this
aafcbfhgh,comic/grand-challenge.org,django/comicmodels/views.py,7e24bdf32c45cf9bcb8e75a0880cfa9d50e281ed,7bd8c71796443429ca9ddfba531147477feb8781,FIXME: I want to make the comicsite field uneditable; but setting
aafcbfhhc,comic/grand-challenge.org,django/filetransfers/views.py,7e24bdf32c45cf9bcb8e75a0880cfa9d50e281ed,STILL_EXISTS,FIXME : Sjoerd: comicmodels and filetransfers are being merged here. How to keep original Filetransfers seperate from this?
aafcbfidh,comic/grand-challenge.org,django/comicmodels/models.py,78c9dfc3f860ca265d67a1950230a9f1b035b6e3,STILL_EXISTS,TODO check whether short name is really clean and short!
aafcbfiea,comic/grand-challenge.org,django/comicmodels/models.py,78c9dfc3f860ca265d67a1950230a9f1b035b6e3,STILL_EXISTS,TODO : do checking for scripts and hacks here?
aafcbgeac,comic/grand-challenge.org,django/comicsite/views.py,d194961909d7a67a657d4a534b1668fb43b52efc,STILL_EXISTS,TODO: check whether user is allowed to register; maybe wait for verification;
aafcbgece,comic/grand-challenge.org,django/comicmodels/models.py,fa1d8256707eeb55edd5a0b645f03c2ccf7956b3,STILL_EXISTS,common save functionality for all models
aafcbgfba,comic/grand-challenge.org,django/comicsite/admin.py,55a92b7d697c8ec73111f80ccb2a5eae74c24ae7,STILL_EXISTS,code below was completely pasted from django.contrib.admin.options I needed to make changes to the
aafcbgheb,comic/grand-challenge.org,django/comicsite/models.py,669ea070e8c17aa7d0fc36afce53bf7901f47699,STILL_EXISTS,TODO: where should this code go? This does not seem like a good place for permissions
aafcbghff,comic/grand-challenge.org,django/comicsite/views.py,a33cf9e7d70e5b62ec57c5ab0d9d472f8bb46e62,STILL_EXISTS,TODO: could a decorator be better then all these ..IfAllowed pages?
aafcbghfj,comic/grand-challenge.org,django/comicsite/templatetags/template_tags.py,2f607a5596148c912a2705108514a84e059136ec,STILL_EXISTS,TODO: move HtmlLinkReplacer to better location..
aafcbghgd,comic/grand-challenge.org,django/comicsite/templatetags/template_tags.py,2f607a5596148c912a2705108514a84e059136ec,5af8caab8783fe76552214649ff4fc6973037e18,throws an error?. Workaround is to add 'remove' as path and chop this off the returned link
aafcbghhg,comic/grand-challenge.org,django/dataproviders/DropboxDataProvider.py,2f607a5596148c912a2705108514a84e059136ec,STILL_EXISTS,going up the path would go outside COMIC dropbox bounds. TODO: maybe
aafcbghjf,comic/grand-challenge.org,django/comicsite/admin.py,2c21f18bed65b03f8273041219247d3b8a2bfdfe,STILL_EXISTS,FIXME: How to populate comicSiteAdminForm.admins with some data? The line below
aafcbgibc,comic/grand-challenge.org,django/comicsite/admin.py,198db4f7f485f84bd49e01425408840a4db0ae50,STILL_EXISTS,populate available admins. #FIXME: duplicate code with change_view.
aafcbhbge,comic/grand-challenge.org,django/comicsite/templatetags/template_tags.py,d79554c48179f43baac313ca249cba1bbeebb0c0,5af8caab8783fe76552214649ff4fc6973037e18,throws an error?. Workaround is to add 'remove' as path and chop this off the returned link
aafcbhbii,comic/grand-challenge.org,django/comicsite/contextprocessors/contextprocessors.py,81f01cf2bc2e3a98cd5086808911c39151b559d4,01f15dfc56a09077e23fb1b515fc0d065e498866,FIXME: I think this class should be refactored into something which is listed
aafcbhbjc,comic/grand-challenge.org,django/comicsite/templatetags/template_tags.py,81f01cf2bc2e3a98cd5086808911c39151b559d4,STILL_EXISTS,FIXME style: is this too much in one line?
aafcbhbjd,comic/grand-challenge.org,django/comicsite/views.py,81f01cf2bc2e3a98cd5086808911c39151b559d4,STILL_EXISTS,TODO: put this in template tags
aafcbhbjf,comic/grand-challenge.org,django/comicsite/templatetags/template_tags.py,5927a09dbe1bc168a885a8bc4599f10e041afead,STILL_EXISTS,TODO: in effect any file can now be included by anyone using a url addition.
aafcbhbji,comic/grand-challenge.org,django/comicsite/templatetags/template_tags.py,5927a09dbe1bc168a885a8bc4599f10e041afead,STILL_EXISTS,TODO: does accessing a file \"..\\..\\..\\..\\allyoursecrets.txt\" work?
aafcbhbjj,comic/grand-challenge.org,django/comicsite/templatetags/template_tags.py,5927a09dbe1bc168a885a8bc4599f10e041afead,STILL_EXISTS,TODO: designate variables more clearly. having any string possibly be a var seems messy
aafcbiaig,comic/grand-challenge.org,django/comicsite/views.py,fcdd8b8e0530041b56086d6486aa822f1d9a9125,e42009c466dbb77532a96f7ce61cda68c4cc2708,FIXME:  throw 403 or proper permission denied here
aafcbiaii,comic/grand-challenge.org,django/comicmodels/views.py,eb66d48ee62f7dc2914f0b613669b1c309b331a2,83824f5a0bac22788e8180c64b66327e3199ec91,TODO: send email!
aafcbiajb,comic/grand-challenge.org,django/comicmodels/views.py,83824f5a0bac22788e8180c64b66327e3199ec91,STILL_EXISTS,signals is the best way to do this. Why not just call the method directly?
aafcbibac,comic/grand-challenge.org,django/comicsite/templatetags/template_tags.py,cf1a93185dff2ac84e20e3c9ed3035e0f1e55f27,5af8caab8783fe76552214649ff4fc6973037e18,FIXME: move this code to seperate location. Spike solution right now
aafcbibbe,comic/grand-challenge.org,django/comicsite/templatetags/template_tags.py,cf1a93185dff2ac84e20e3c9ed3035e0f1e55f27,5af8caab8783fe76552214649ff4fc6973037e18,TODO check content safety
aafcbibbi,comic/grand-challenge.org,django/comicsite/templatetags/template_tags.py,cf1a93185dff2ac84e20e3c9ed3035e0f1e55f27,5af8caab8783fe76552214649ff4fc6973037e18,throws an error?. Workaround is to add 'remove' as path and chop this off the returned link
aafcbibei,comic/grand-challenge.org,django/comicsite/templatetags/template_tags.py,f883ebf99c82ccf884680cc13989a01aead8908c,STILL_EXISTS,TODO check content safety
aafcbibfc,comic/grand-challenge.org,django/comicsite/templatetags/template_tags.py,f883ebf99c82ccf884680cc13989a01aead8908c,STILL_EXISTS,throws an error?. Workaround is to add 'remove' as path and chop this off the returned link
aafcbicfe,comic/grand-challenge.org,django/profiles/views.py,9ed97ed8fdd4a54ab993efd16a25b08a8492ac2b,STILL_EXISTS,FIXME: quick-n-dirty implementation to make things work for social-auth login redirect handling.
aafcbidgc,comic/grand-challenge.org,django/comicsite/templatetags/template_tags.py,af557aaa21421b5c218b3096fb1b9e93969f7dc1,STILL_EXISTS,FIXME: create a temporary solution to including javascript and css with template tags
aafcbidge,comic/grand-challenge.org,django/comicsite/templatetags/library_plus.py,8af55dca4e0b8d0d732d8bbf37351d16581c0c96,STILL_EXISTS,fixme: Why is this function called twice for each @register.tag call in template_tags.py?
aafcbidgf,comic/grand-challenge.org,django/comicsite/templatetags/library_plus.py,8af55dca4e0b8d0d732d8bbf37351d16581c0c96,STILL_EXISTS,Second call has no 'usagestr' defined workaround now is to check for existing key and not
aafcbieab,comic/grand-challenge.org,django/thirdparty/dingus.py,cc7a97cdd88738d6a923e0bdcd71345e1fa9e671,STILL_EXISTS,Needed this code to do reliable loading of initial data using fixtures in
aafcbifbd,comic/grand-challenge.org,django/comicsite/tests.py,6499769919b50f645fd3d76623225eacf0cd141d,f9e463e74c1a8c695f1aa43cd325e675d0c7e499,TODO check welcome mail
aafcbigbc,comic/grand-challenge.org,django/comicsite/tests.py,56626d981e174cd2e1e69dc52aaa7d9724cf6ebf,STILL_EXISTS,TODO: If you upload you result to the site; someone else cannot guess the
aafcbigbe,comic/grand-challenge.org,django/comicsite/tests.py,56626d981e174cd2e1e69dc52aaa7d9724cf6ebf,STILL_EXISTS,TODO: verify that files uploaded in editor can be served directly.
aafcbigff,comic/grand-challenge.org,django/comicmodels/models.py,d1beafefbdbdbe3273e1f424924f322b449d1e7c,STILL_EXISTS,TODO: This is confused code. Have a single way of handling uploads;
aafcbighf,comic/grand-challenge.org,django/comicsite/templatetags/comic_templatetags.py,852aa4c068bb3948ada43c42935a7a0be2f1739d,d617d076b4722c4be8de6036cc31e35ef3972ae8,TODO: How to refer to method in this file nicely? This seems a bit cumbersome
aafcbigii,comic/grand-challenge.org,django/comicsite/templatetags/comic_templatetags.py,0358ffc20537b4588c70e1a257c7c60344296958,d617d076b4722c4be8de6036cc31e35ef3972ae8,TODO: How to refer to method in this file nicely? This seems a bit cumbersome
aafcbihbb,comic/grand-challenge.org,django/comicsite/template/context.py,01f15dfc56a09077e23fb1b515fc0d065e498866,STILL_EXISTS,FIXME: I think this class should be refactored into something which is listed
aafcbihej,comic/grand-challenge.org,django/filetransfers/views.py,e42009c466dbb77532a96f7ce61cda68c4cc2708,STILL_EXISTS,TODO : check this once at server start but not every time this method is
aafcbihgd,comic/grand-challenge.org,django/comicsite/views.py,deba2d50c7697d2c3773f2486e9bd285c05d2735,STILL_EXISTS,site_short_name or similar param. Therefore all needed views are wrapped here.
aafcbihge,comic/grand-challenge.org,django/comicsite/views.py,deba2d50c7697d2c3773f2486e9bd285c05d2735,STILL_EXISTS,TODO: is there no less repetitive way of wrapping?
aafcbiiaa,comic/grand-challenge.org,django/comicsite/views.py,db157d861388e5719bff23be34e42752e0671dd3,STILL_EXISTS,currentpage needed to make templates not trip
aafcbiiae,comic/grand-challenge.org,django/comicsite/tests.py,9b8f28ab5d7b6216e5357267ecbd1bd0a95729ab,STILL_EXISTS,TODO: these test fail; but are not very important now. fix this later.
aafcbiiba,comic/grand-challenge.org,django/comicsite/views.py,01c3bb4067e9f8a392cb3059e1b175f9c146a2a4,STILL_EXISTS,TODO: THis has code smell. If page has to be checked like this; is it
aafcbiich,comic/grand-challenge.org,django/comicsite/templatetags/comic_templatetags.py,b76d0dd27c586566e8f4639b9543581ee1486d92,495c62c5b420460e34a063710d5e09f9b4cc5995,TODO: here confused coding comes to light: I need to have the page
aafcbiide,comic/grand-challenge.org,django/comicsite/templatetags/comic_templatetags.py,b76d0dd27c586566e8f4639b9543581ee1486d92,495c62c5b420460e34a063710d5e09f9b4cc5995,As a workaround; just checking for both conditions.
aafcbiidg,comic/grand-challenge.org,django/comicsite/views.py,de08fec01ed865885a3c74e1fb602aada48b2f9e,STILL_EXISTS,and the security risk is not too great. TODO (is it not?)
aafcbiief,comic/grand-challenge.org,django/comicsite/views.py,abc906ae8bf6d4d979c48cda06152395ed9c670c,5098ecb0863debd99723a35cb200ee0419487763,only set password if bots really make this a problem
aafcbiifi,comic/grand-challenge.org,django/comicsite/templatetags/comic_templatetags.py,a3c672804528fd847c9479769e18a4eac401103e,STILL_EXISTS,TODO Log these messages as info
aafcbiige,comic/grand-challenge.org,django/comicsite/template/context.py,c4b4ed6f723cf090897ffb13af32d2d7dae7cc76,STILL_EXISTS,TODO: Using request here to transport some variables into context. This
aafcbiigf,comic/grand-challenge.org,django/comicsite/template/context.py,c4b4ed6f723cf090897ffb13af32d2d7dae7cc76,STILL_EXISTS,seems quite weird. But how to do it better?
aafcbijef,comic/grand-challenge.org,django/comic/urls.py,2b5137ef287816ef320c1fddbf79f59b0529eb6b,09aeddc2cfb77067ef0eeffcf44a3008a201c91c,tell nice bots what to do. TODO: using 'robots.txt' as a template name will
aafcbijfg,comic/grand-challenge.org,django/comic/urls.py,3d5dedce581d25c1c6ff3031ac735a031db50c59,STILL_EXISTS,tell nice bots what to do. TODO: using 'robots.txt' as a template name will
aafcbijgc,comic/grand-challenge.org,django/comicsite/views.py,f7f9f9b60dab99326ef71a06ae96be6737176f1f,STILL_EXISTS,TODO: recursive call upon error. Is this horrible coding?
aafcbijgd,comic/grand-challenge.org,django/comicsite/views.py,e6c013fe565b60b45836d897a9c98430860ae42b,STILL_EXISTS,TODO: Doing two calls to getSite here. (second one in site_get_standard_vars)
aafcbijhi,comic/grand-challenge.org,django/comicsite/tests.py,01c0fa5f2a622efe329653c40c2e9a95972e11f3,STILL_EXISTS,TODO: How to do this gracefully?
aafcbijhj,comic/grand-challenge.org,django/comicsite/tests.py,01c0fa5f2a622efe329653c40c2e9a95972e11f3,STILL_EXISTS,todo: is this ugly? At least there is explicit assignment of vars.
aafcbijia,comic/grand-challenge.org,django/comicsite/tests.py,01c0fa5f2a622efe329653c40c2e9a95972e11f3,STILL_EXISTS,How to do this better?
aafcbjabf,comic/grand-challenge.org,django/comicsite/tests.py,29abdcb773d372080d82064661c79565dc08233a,75607ddc1e555fe0e774696e0a87c7388a90f42f,TODO: move HtmlLinkReplacer to better location..
aafcbjacc,comic/grand-challenge.org,django/comicsite/templatetags/comic_templatetags.py,e75d55b929c0b9cff2da7e9bea77e3a64b05b234,495c62c5b420460e34a063710d5e09f9b4cc5995,throws an error?. Workaround is to add 'remove' as path and chop this off the returned link.
aafcbjbcb,comic/grand-challenge.org,django/comicsite/admin.py,a91765287292bd45d2e1870cf5171af1c6d7cb91,STILL_EXISTS,so confusing. Think about class responsibilities and fix this.
aafcbjbdb,comic/grand-challenge.org,django/comicsite/templatetags/comic_templatetags.py,1de84888fcfc2bc4ba4c68eee69c2822a1cd1061,STILL_EXISTS,TODO: possible way out: create some kind of registration request
aafcbjbif,comic/grand-challenge.org,django/comicsite/models.py,2f300e8eeeff5f2452764d25193fd8d846d1465f,STILL_EXISTS,TODO: why these confusing signals. These functions are called from comicsite.admin;
aafcbjcad,comic/grand-challenge.org,django/comicsite/models.py,ac28fc75836848c98e749601257aa63fc20f1f3e,STILL_EXISTS,TODO: below: why these confusing signals. These functions are called from comicsite.admin;
aafcbjcah,comic/grand-challenge.org,django/comicmodels/migrations/0025_add_RegistrationRequest_permissions.py,ed6b72c5d5a946009519a06ee87a9377b41b136e,STILL_EXISTS,TODO add permissions for all comicmodels and registrationRequest
aafcbjcec,comic/grand-challenge.org,django/comicsite/tests.py,1e6aee53805f1da6d1a98e9701ee5d8d9d298489,STILL_EXISTS,TODO: create a function to check all links in the email.
aafcbjcfb,comic/grand-challenge.org,django/comicmodels/admin.py,0e1e82b305f744e4cc1873f1ae141d817fbf8cd6,STILL_EXISTS,TODO: This class should derive from ComicModelAdmin and not from GuardedModelAdmin
aafcbjcfd,comic/grand-challenge.org,django/comicmodels/admin.py,0e1e82b305f744e4cc1873f1ae141d817fbf8cd6,STILL_EXISTS,TODO: This way of filtering should be used for all comicobjects; this
aafcbjcfj,comic/grand-challenge.org,django/comicsite/middleware/project.py,0e1e82b305f744e4cc1873f1ae141d817fbf8cd6,b64e4f03d56888fb8098d517a55d082e38d6820b,this is regarding. TODO: the best way to fix this is to have seperate
aafcbjdii,comic/grand-challenge.org,django/comicsite/core/urlresolvers.py,d6c894c72bd25811b8e04da940ce2ca1cb09942e,STILL_EXISTS,TODO: The final clasuse in the if statement is a total hack. May posterity
aafcbjdja,comic/grand-challenge.org,django/comicsite/core/urlresolvers.py,d6c894c72bd25811b8e04da940ce2ca1cb09942e,STILL_EXISTS,What is needed is a clear and unanbiguous way to deal with subdomain as project name
aafcbjebb,comic/grand-challenge.org,django/comicsite/templatetags/comic_templatetags.py,3082d5c7f1ed4eeea55e75bb8163d742cbff7254,c8f80caf3b3a5707a2d5d2b9dfee1e4e80f1b3be,TODO: importing reverse function from two location is stinky
aafcbjfbb,comic/grand-challenge.org,django/comicsite/admin.py,3b8edb11c3dba74a044cf68823eccb846943ce1b,STILL_EXISTS,Hack because registrationrequest does not have a 'comicsite' param;
aafcbjfcd,comic/grand-challenge.org,django/comicsite/admin.py,3b8edb11c3dba74a044cf68823eccb846943ce1b,STILL_EXISTS,What would uncle bob say? Would he even survive? And how could Guido's
aafcbjfgc,comic/grand-challenge.org,django/comicsite/tests.py,1fa6c56c43896a8f90160f1cf899076e0ffc0f69,STILL_EXISTS,rename is very hard however
aafcbjfib,comic/grand-challenge.org,django/comicsite/urls.py,00e0de0c667132f91472eb563f5eade1ce27e5fc,STILL_EXISTS,Assumptions of urls being fixed a bit; but it is the only way to reuse much
aafcbjgae,comic/grand-challenge.org,django/comicsite/templatetags/comic_templatetags.py,8cb99e1c5b5734418bad33c05a5a4a51ff1eae63,495c62c5b420460e34a063710d5e09f9b4cc5995,throws an error?. Workaround is to add 'remove' as path and chop this off the returned link.
aafcbjgcf,comic/grand-challenge.org,django/comicsite/templatetags/comic_templatetags.py,8cb99e1c5b5734418bad33c05a5a4a51ff1eae63,495c62c5b420460e34a063710d5e09f9b4cc5995,TODO check content safety
aafcbjgci,comic/grand-challenge.org,django/comicsite/templatetags/comic_templatetags.py,8cb99e1c5b5734418bad33c05a5a4a51ff1eae63,495c62c5b420460e34a063710d5e09f9b4cc5995,TODO: here confused coding comes to light: I need to have the page
aafcbjgdf,comic/grand-challenge.org,django/comicsite/templatetags/comic_templatetags.py,8cb99e1c5b5734418bad33c05a5a4a51ff1eae63,495c62c5b420460e34a063710d5e09f9b4cc5995,As a workaround; just checking for both conditions.
aafcbjgib,comic/grand-challenge.org,django/comicsite/templatetags/comic_templatetags.py,8f0ce006ef3c9d6b2b1f4c4eb551ecacb3b60691,ba81828323f4dd881465ebf7c932ffc9090b8f05,throws an error?. Workaround is to add 'remove' as path and chop this off the returned link.
aafcbjhac,comic/grand-challenge.org,django/comicsite/templatetags/comic_templatetags.py,8f0ce006ef3c9d6b2b1f4c4eb551ecacb3b60691,ba81828323f4dd881465ebf7c932ffc9090b8f05,TODO check content safety
aafcbjhaf,comic/grand-challenge.org,django/comicsite/templatetags/comic_templatetags.py,8f0ce006ef3c9d6b2b1f4c4eb551ecacb3b60691,d4d47b5b3a05304858780515eb1df5a94e397672,TODO: here confused coding comes to light: I need to have the page
aafcbjhbc,comic/grand-challenge.org,django/comicsite/templatetags/comic_templatetags.py,8f0ce006ef3c9d6b2b1f4c4eb551ecacb3b60691,ba81828323f4dd881465ebf7c932ffc9090b8f05,As a workaround; just checking for both conditions.
aafcbjhga,comic/grand-challenge.org,django/comicsite/templatetags/comic_templatetags.py,d6ccc36b9ab490a370fb1c7b12d79d474d920573,d4d47b5b3a05304858780515eb1df5a94e397672,throws an error?. Workaround is to add 'remove' as path and chop this off the returned link.
aafcbjhib,comic/grand-challenge.org,django/comicsite/templatetags/comic_templatetags.py,d6ccc36b9ab490a370fb1c7b12d79d474d920573,d4d47b5b3a05304858780515eb1df5a94e397672,TODO check content safety
aafcbjhie,comic/grand-challenge.org,django/comicsite/templatetags/comic_templatetags.py,d6ccc36b9ab490a370fb1c7b12d79d474d920573,d4d47b5b3a05304858780515eb1df5a94e397672,TODO: here confused coding comes to light: I need to have the page
aafcbjhjb,comic/grand-challenge.org,django/comicsite/templatetags/comic_templatetags.py,d6ccc36b9ab490a370fb1c7b12d79d474d920573,d4d47b5b3a05304858780515eb1df5a94e397672,As a workaround; just checking for both conditions.
aafcbjhjh,comic/grand-challenge.org,django/comicsite/templatetags/comic_templatetags.py,d6ccc36b9ab490a370fb1c7b12d79d474d920573,STILL_EXISTS,FIXME: create a temporary solution to including javascript and css with template tags
aafcbjifi,comic/grand-challenge.org,django/comicsite/tests.py,f71d8a83c402ae37a1ba85c8a6635348d1252d75,STILL_EXISTS,TODO: The permissions are not correct; https:\/\/github.com\/comic\/comic-django\/issues\/306
aafcbjihg,comic/grand-challenge.org,django/comicsite/templatetags/comic_templatetags.py,80d1189a87c236d57502d86ce3f4130c3f1aa50b,75607ddc1e555fe0e774696e0a87c7388a90f42f,TODO: move HtmlLinkReplacer to better location..
aafcbjjee,comic/grand-challenge.org,django/comicsite/tests.py,b5252bdb91c1a8d2106333a4b8f35d0010d88815,STILL_EXISTS,TODO: The permissions are not correct; https:\/\/github.com\/comic\/comic-django\/issues\/306
aafccaaib,comic/grand-challenge.org,app/evaluation/tests/test_views.py,3ee29a01a3aeb3a0439585070db6c036f6699947,STILL_EXISTS,TODO: make token authorization work
aafccaaie,comic/grand-challenge.org,app/evaluation/models.py,ed640d4b69cd998d62b23e4d9adb3a103e60231a,816fd2b1a1b83188b9d76e62f4bb8729fd369f66,TODO: generate an auth token for all users
aafccaaja,comic/grand-challenge.org,app/evaluation/tests/test_views.py,ed640d4b69cd998d62b23e4d9adb3a103e60231a,STILL_EXISTS,TODO: Check that the user is a participant of that challenge
aafccabai,comic/grand-challenge.org,app/evaluation/signals.py,816fd2b1a1b83188b9d76e62f4bb8729fd369f66,STILL_EXISTS,TODO: generate an auth token for all users
aafccabaj,comic/grand-challenge.org,app/evaluation/tests/resources/evaluate_submission.py,746480061ae77dfe2c2f56c9d19266db31cbd73f,STILL_EXISTS,TODO
aafccabcd,comic/grand-challenge.org,app/evaluation/validators.py,643ee60e8e0e3997ee786e2943ab95e6da0fbb88,ab9b98df5cb0447bda0ee0fd582af80b9c220a20,TODO - Implement the validation
aafccabdf,comic/grand-challenge.org,app/evaluation/tasks.py,f84523e048a7592cb3f09f25d3968128575ba064,STILL_EXISTS,TODO - cleanup
aafccabdg,comic/grand-challenge.org,app/evaluation/tasks.py,f84523e048a7592cb3f09f25d3968128575ba064,22111960b28aeb2f5670c564333e7871191da8e2,TODO - check that we're being run as part of a context manager
aafccabdh,comic/grand-challenge.org,app/evaluation/tasks.py,4945d709e7f145c42bcbfa7eb4191b53cd3bd2ad,7487fc093991b14eebb1bfaabf896741afe84c9e,The alpine image is needed for the reader and writer containers
aafccabdj,comic/grand-challenge.org,app/evaluation/tests/test_models.py,4945d709e7f145c42bcbfa7eb4191b53cd3bd2ad,STILL_EXISTS,TODO: Add some model tests
aafccabed,comic/grand-challenge.org,app/evaluation/models.py,bf582c839ec6ebe5e981816c4ade00c426a5c422,f8f5eb96468aa94acad1c085fc9a8c80d7b6db8f,TODO: Add a validator to make sure the form is sha256:{64}
aafccabee,comic/grand-challenge.org,app/evaluation/models.py,bf582c839ec6ebe5e981816c4ade00c426a5c422,f8f5eb96468aa94acad1c085fc9a8c80d7b6db8f,TODO: Check if the encoding method is included in the manifest
aafccabei,comic/grand-challenge.org,app/evaluation/tasks.py,bf582c839ec6ebe5e981816c4ade00c426a5c422,6085b5b3503014abcefe5f17bc30328790e40620,The alpine image is needed for the reader and writer containers
aafccabfg,comic/grand-challenge.org,app/evaluation/tasks.py,bf582c839ec6ebe5e981816c4ade00c426a5c422,STILL_EXISTS,TODO: Error handling
aafccabfh,comic/grand-challenge.org,app/evaluation/tasks.py,bf582c839ec6ebe5e981816c4ade00c426a5c422,22111960b28aeb2f5670c564333e7871191da8e2,TODO: Error handling; update the job status
aafccabjf,comic/grand-challenge.org,app/evaluation/models.py,22111960b28aeb2f5670c564333e7871191da8e2,790184b9546be1d46de49ecac26b4d82686e1a0f,TODO: Add a validator to make sure the form is sha256:{64}
aafccabjg,comic/grand-challenge.org,app/evaluation/models.py,22111960b28aeb2f5670c564333e7871191da8e2,790184b9546be1d46de49ecac26b4d82686e1a0f,TODO: Check if the encoding method is included in the manifest
aafccacae,comic/grand-challenge.org,app/ckeditor/tests.py,53f006b0dc46f042a80686ac23a4cd1f18e10b87,STILL_EXISTS,TODO: These tests do not work as you cannot dynamically mess with
aafccacgf,comic/grand-challenge.org,app/evaluation/backends/dockermachine/evaluator.py,0b16d6db096b657e86a26518c0d17fa995385fc6,STILL_EXISTS,TODO: error handling
aafccadac,comic/grand-challenge.org,app/evaluation/widgets/uploader.py,b7b43ac3b8189e51713735814a10d7c67bccd72b,STILL_EXISTS,implemented if we want to pre-populate upload forms
aafccadah,comic/grand-challenge.org,app/evaluation/signals.py,89b70752a9b085689cb1b5cbb9253e7e5799b0d7,STILL_EXISTS,TODO: Email here; do not raise
aafccadbc,comic/grand-challenge.org,app/evaluation/views.py,89b70752a9b085689cb1b5cbb9253e7e5799b0d7,STILL_EXISTS,TODO: Challenge Participant Only
aafccadca,comic/grand-challenge.org,app/comicmodels/models.py,c8185e25aaac47331ba4910f26e4951315cc5283,84301b546977c8e69e5f4666f93eac2dc9cbcec4,TODO: JM - should be a one to one field
aafccadje,comic/grand-challenge.org,app/comic/settings.py,efdf34491047deb12ca3f08d332925ba8d86b636,STILL_EXISTS,When a full configurable permissions system is in place; see ticket #244
aafccaeaj,comic/grand-challenge.org,app/comic/settings.py,efdf34491047deb12ca3f08d332925ba8d86b636,STILL_EXISTS,The url for a project in comic is \/site\/<projectname>. This is quite ugly. It
aafccaedj,comic/grand-challenge.org,app/comic/settings.py,efdf34491047deb12ca3f08d332925ba8d86b636,9bc53e22634018684d995af02321843fa86d3000,FIXME: Path to template path. This might be temporary.
aafccaeeb,comic/grand-challenge.org,app/comic/settings.py,efdf34491047deb12ca3f08d332925ba8d86b636,STILL_EXISTS,Needed for userena
aafccaegj,comic/grand-challenge.org,app/comicsite/templatetags/comic_templatetags.py,e252583d193e2e8ed5083bcc20efb5fb8f59e671,STILL_EXISTS,TODO: JM add class=active to the active link
aafccaehd,comic/grand-challenge.org,app/comic/settings.py,02e34890d6b52ffa2537c4499d719b4d63a1b508,228c4155e7e99cddd5b40ebc9300f7c566a5e272,These are needed for subdomain redirects
aafccaeia,comic/grand-challenge.org,app/comic/settings.py,97ed173f57c419219e79dad9f0aa04a89ac29026,STILL_EXISTS,TODO: JM - Add the profile filling as a partial
aafccaeic,comic/grand-challenge.org,app/comicsite/templatetags/comic_templatetags.py,825d8a65fae5eacbd692cf65b43795e714fb5ae9,STILL_EXISTS,This is needed for proper card wrapping
aafccafcb,comic/grand-challenge.org,app/evaluation/views.py,d4b53d8789befb65bf0200e99042d15d0c21329a,1e71155a8545079ae87518f00bf0cb47ba8b2595,TODO: Challenge Admin Only
aafccafdd,comic/grand-challenge.org,app/evaluation/views.py,1e71155a8545079ae87518f00bf0cb47ba8b2595,702e79d8b9c3abdfd7c72c0786ce0c8fa78a93a0,TODO - if participant: list only their submissions
aafccafde,comic/grand-challenge.org,app/evaluation/views.py,1e71155a8545079ae87518f00bf0cb47ba8b2595,dbef7dbff1a6f36c48d25c899e986c14c32fa1b9,TODO - if participant: list only their jobs
aafccafeh,comic/grand-challenge.org,app/evaluation/signals.py,30f21d1e101217227a56cec1aea1e9bf8da8daf6,STILL_EXISTS,TODO: Create Timeout tests
aafccafgh,comic/grand-challenge.org,app/comicsite/core/urlresolvers.py,bae78d3008ab467e3a8610303c9ca7aed28dec00,4cecfce9ce7e30eb0a1b952d63986785c0806034,TODO: The final clause in the if statement is a total hack. May posterity
aafccafgj,comic/grand-challenge.org,app/comicsite/core/urlresolvers.py,bae78d3008ab467e3a8610303c9ca7aed28dec00,4cecfce9ce7e30eb0a1b952d63986785c0806034,What is needed is a clear and unanbiguous way to deal with subdomain as project name
aafccafid,comic/grand-challenge.org,app/tests/evaluation_tests/test_views.py,646ff0021aabcf4c5d1f9b4e0d9c1db9cc778ebb,ebe781f2484c72d0eb822c2b2da07b41c43c9f0c,TODO: we need a submission to test
aafccafie,comic/grand-challenge.org,app/tests/evaluation_tests/test_views.py,646ff0021aabcf4c5d1f9b4e0d9c1db9cc778ebb,ebe781f2484c72d0eb822c2b2da07b41c43c9f0c,TODO: we need a job to test
aafccafif,comic/grand-challenge.org,app/tests/evaluation_tests/test_views.py,646ff0021aabcf4c5d1f9b4e0d9c1db9cc778ebb,ebe781f2484c72d0eb822c2b2da07b41c43c9f0c,TODO: we need a result to test
aafccafig,comic/grand-challenge.org,app/tests/evaluation_tests/test_views.py,cf7fa0f3e2c7c8948bc346ad3df57da35b33b37e,ebe781f2484c72d0eb822c2b2da07b41c43c9f0c,TODO: a method is needed
aafccagdc,comic/grand-challenge.org,app/jqfileupload/widgets/uploader.py,691424538d02cd8ae75ef47350025d637062b783,STILL_EXISTS,TODO: This really should be an instance of BufferedIOBase and follow the
aafccagde,comic/grand-challenge.org,app/jqfileupload/widgets/uploader.py,691424538d02cd8ae75ef47350025d637062b783,5a6b3152260d7320fce5f666aa1f526d292fd59d,Do not raise EOFError on read; follow convention of BytesIO
aafccahdf,comic/grand-challenge.org,app/jqfileupload/widgets/uploader.py,7c00e2e5481b19de4fcf8f8e54bd6784c2a0fa39,0ebfef2756b51d1fc4f158c9487f8d00fea19cc0,Do not raise EOFError on read; follow convention of BytesIO
aafccaigf,comic/grand-challenge.org,app/tests/evaluation_tests/test_views.py,702e79d8b9c3abdfd7c72c0786ce0c8fa78a93a0,0aeb67b03bcc475d1071b5c001be2d146fa21218,TODO: Test creation with forms.
aafccajeh,comic/grand-challenge.org,docs/conf.py,ddf332b7338523a95374ef9d6b50d0d9376ddca9,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aafccajjg,comic/grand-challenge.org,app/tests/evaluation_tests/test_views.py,6f86b2f881706c8536b9c8b05321d1d011575838,8dbe566774c782faecdf1814bc9a71a0971d9897,TODO: test that private results cannot be seen
aafccbcic,comic/grand-challenge.org,app/tests/teams_tests/test_views.py,77dd46753471fcdad49cc5617822565968b29445,STILL_EXISTS,TODO: Team Update and Team Member delete permissions
aafccbdcc,comic/grand-challenge.org,app/comicmodels/views.py,aa097f6df9997442d590380e2e83e59bc41ea984,638289b55df6d067bd7835b06ce28190c61fbfd6,I'm not sure that sending signals is the best way to do this.
aafccbdda,comic/grand-challenge.org,app/pages/views.py,5ad779f372a65746cb933e93fae4f452e1f2e453,STILL_EXISTS,TODO: THis has code smell. If page has to be checked like this; is it
aafccbddj,comic/grand-challenge.org,app/pages/views.py,c732eed7cdaba5569052b94ff132edfbdb9da1c6,8e4d0add06ee1a8dd81399d794005fa6448c3f9f,and the security risk is not too great. TODO (is it not?)
aafccbdge,comic/grand-challenge.org,app/tests/pages_tests/test_pages.py,63526e3960cf7b9cf74abe11ca84cccee7a3516f,STILL_EXISTS,TODO: Test page moving
aafccbdgf,comic/grand-challenge.org,app/tests/pages_tests/test_pages.py,63526e3960cf7b9cf74abe11ca84cccee7a3516f,STILL_EXISTS,TODO: Remove the sortables on edit etc.
aafccbedj,comic/grand-challenge.org,app/participants/views.py,aded4a06f244dd567fa3acb7de14b7483b24f009,25c6490efd30be2870e4f59bfd3ab08a8dea0bbd,TODO: check whether user is allowed to register; maybe wait for verification;
aafccbgbc,comic/grand-challenge.org,app/uploads/views.py,1c89cf839e14f48e46dd7611ddaa0a0944daa3b3,86196b3f1080a50e32970bd62b1409f3df2a38d9,TODO : check this once at server start but not every time this method is
aafccbgdh,comic/grand-challenge.org,app/uploads/views.py,638289b55df6d067bd7835b06ce28190c61fbfd6,e468ab0fc48e1b302fc0ccd0b036a2f19a7da923,I'm not sure that sending signals is the best way to do this.
aafccbgec,comic/grand-challenge.org,app/comicmodels/permissions.py,86196b3f1080a50e32970bd62b1409f3df2a38d9,STILL_EXISTS,TODO : check this once at server start but not every time this method is
aafccbheh,comic/grand-challenge.org,app/uploads/views.py,01f81c2a8196a7e6089e9a1cb210eeebc0b3de26,STILL_EXISTS,TODO: adapt this for ckeditor
aafccbhei,comic/grand-challenge.org,app/uploads/views.py,01f81c2a8196a7e6089e9a1cb210eeebc0b3de26,STILL_EXISTS,TODO: remove; unneeded once moved to ckeditor
aafccbhfa,comic/grand-challenge.org,app/uploads/views.py,01f81c2a8196a7e6089e9a1cb210eeebc0b3de26,fdd7d9524341d20a1c896f658497cc990aaf9beb,TODO: permissions; limit by folder
aafccbhfb,comic/grand-challenge.org,app/uploads/views.py,9199f7e9eff799a719727285586957b18abc9823,STILL_EXISTS,TODO: test created filename
aafccbhhb,comic/grand-challenge.org,app/uploads/views.py,e1ec03165b1f47fac773a977ca6b1d18abb8463d,STILL_EXISTS,TODO: Write a selenium test to check this.
aafccbifb,comic/grand-challenge.org,app/pages/models.py,32bf7960e372a8b2251445db24dc45a988f91b98,STILL_EXISTS,TODO : do checking for scripts and hacks here?
aafccbiid,comic/grand-challenge.org,app/uploads/models.py,70b0cad24f30a63465d4c622927c8c99853ef646,STILL_EXISTS,TODO: This is confused code. Have a single way of handling uploads;
aafcccaff,comic/grand-challenge.org,app/grandchallenge/algorithms/models.py,b5963ca0e0b9c31c769fe6ad4f8c0f556b833bc4,1700e83f82e8cf443495aef3acbe6765398b6e50,TODO: This class is mostly duplicate from evaluation\/models.py.
aafcccafg,comic/grand-challenge.org,app/grandchallenge/algorithms/models.py,b5963ca0e0b9c31c769fe6ad4f8c0f556b833bc4,1700e83f82e8cf443495aef3acbe6765398b6e50,TODO: make a mixin for DockerImage; generalize docker_image_path to work with instance
aafcccafj,comic/grand-challenge.org,app/grandchallenge/algorithms/views.py,b5963ca0e0b9c31c769fe6ad4f8c0f556b833bc4,bb8098aa6bb8f8742f61444af4b743d5e654b43b,TODO: Permissions
aafcccaga,comic/grand-challenge.org,app/grandchallenge/algorithms/views.py,b5963ca0e0b9c31c769fe6ad4f8c0f556b833bc4,a0701be28ef428d61b7cce8c57eb24b3a356f396,TODO: Chunked uploads
aafcccagd,comic/grand-challenge.org,app/grandchallenge/algorithms/views.py,a0701be28ef428d61b7cce8c57eb24b3a356f396,STILL_EXISTS,TODO: taken from evaluation uploads; create mixin?
aafcccagf,comic/grand-challenge.org,app/grandchallenge/algorithms/models.py,db8cdf89e5cae47aadb342153fa256b1526d3d30,06678c1272e02b00143336df584156d2bfa9fe64,TODO: add that this is an ipynb to the help_text
aafcccahc,comic/grand-challenge.org,app/grandchallenge/algorithms/models.py,8e03cfafba508e564dfd1905370b6d190ddfd622,06678c1272e02b00143336df584156d2bfa9fe64,TODO: should the ipynb be downloadable?
aafcccahd,comic/grand-challenge.org,app/grandchallenge/algorithms/views.py,8e03cfafba508e564dfd1905370b6d190ddfd622,ec79efdcc2ad4202e3a9e56be7e5d46c2e32129a,TODO: put the generated html into a frame?
aafcccahi,comic/grand-challenge.org,app/grandchallenge/algorithms/tasks.py,790fe4fc42a7a030fe20d549ce05c16cbb14b7ee,e13e0f2f1f19df88d0e7a5accc4c9ab6f0883f22,TODO: error handling
aafcccahj,comic/grand-challenge.org,app/grandchallenge/algorithms/tasks.py,790fe4fc42a7a030fe20d549ce05c16cbb14b7ee,ec79efdcc2ad4202e3a9e56be7e5d46c2e32129a,TODO: Unhardcode
aafcccaie,comic/grand-challenge.org,app/grandchallenge/algorithms/models.py,ec79efdcc2ad4202e3a9e56be7e5d46c2e32129a,06678c1272e02b00143336df584156d2bfa9fe64,TODO: Split out the ipynb description as a separate object
aafcccbba,comic/grand-challenge.org,app/grandchallenge/cases/urls.py,cf1b6abbda2f93a0236e7d709bb395743869ff09,STILL_EXISTS,TODO: Remove this - for testing purposes only!
aafcccbdd,comic/grand-challenge.org,app/grandchallenge/cases/tasks.py,bbd15cc04205a556e54ee8a533d5a4c34472524a,STILL_EXISTS,TODO: save results to database
aafcccbgc,comic/grand-challenge.org,app/grandchallenge/cases/tasks.py,5a06b8baf23977b01b20be76fe9a224a76055a76,0fa1888b2c299ca645433c2db8d44015dadca2c0,Maybe add .mhd and .mha files here?
aafcccbgd,comic/grand-challenge.org,app/grandchallenge/cases/tasks.py,5a06b8baf23977b01b20be76fe9a224a76055a76,STILL_EXISTS,TODO
aafcccgbi,comic/grand-challenge.org,app/grandchallenge/cases/views.py,c35211e9819c331ba50b2a01a990065e7872f13b,STILL_EXISTS,TODO - this should list only the annotations for this image
aafcccgeh,comic/grand-challenge.org,app/grandchallenge/datasets/views.py,b999e88f2f7e724dc089952d4dcd2be17a5ca27a,28a9343f7d5544ec5a76395cef5e16e5203ca23a,TODO - there can only be 1 ground truth AnnotationSet for this ImageSet
aafccchbi,comic/grand-challenge.org,app/grandchallenge/datasets/models.py,0f7d52fb7e4e8d3931b48008950b4cd5a7ea25c0,81ed89603c44feef4bfae1163e61dd5aed5bdde6,TODO: This thing should not interact with the database
aafccchca,comic/grand-challenge.org,app/grandchallenge/datasets/models.py,0f7d52fb7e4e8d3931b48008950b4cd5a7ea25c0,01ccab3907402e084450d179bedbb0d70a087a19,TODO: Create a StagedFile and StagedAjaxFile from this
aafcccheb,comic/grand-challenge.org,app/grandchallenge/submission_conversion/models.py,81ed89603c44feef4bfae1163e61dd5aed5bdde6,4a822ac6092a557e237e11270110cefa48343d30,TODO: This thing should not interact with the database
aafccciei,comic/grand-challenge.org,app/grandchallenge/serving/views.py,b2478a9c9c6a4bd38a47b5d015139926c362ab66,48d145e3e5072408ee3f2fad83ec133e318f5314,TODO: make sure that this imagefile belongs to this image
aafccdaaf,comic/grand-challenge.org,app/grandchallenge/algorithms/models.py,de07c871009bcebcdaa873ecde1129f0179b5d06,2c46938c0104cde05d086fd243125a7e1cd15a87,TODO: add name and slug
aafccdaag,comic/grand-challenge.org,app/grandchallenge/algorithms/models.py,de07c871009bcebcdaa873ecde1129f0179b5d06,STILL_EXISTS,TODO: add images
aafccdaah,comic/grand-challenge.org,app/grandchallenge/algorithms/models.py,de07c871009bcebcdaa873ecde1129f0179b5d06,STILL_EXISTS,TODO: load images too
aafccdaba,comic/grand-challenge.org,app/grandchallenge/algorithms/models.py,1fd21ffb0674666d6919871f17899c6fb79817a2,dd6a61541b793286df54a039b33b5892f69b0472,TODO: This thing should not interact with the database
aafccdabc,comic/grand-challenge.org,app/grandchallenge/algorithms/models.py,1fd21ffb0674666d6919871f17899c6fb79817a2,STILL_EXISTS,TODO: load images too
aafccdejc,comic/grand-challenge.org,app/grandchallenge/subdomains/middleware.py,87410f44a11fcd8366cb0ec74c82b74cef5d428a,2c15427dce817de913510fc3a5d7a3183ec80ecf,TODO: Change to subdomain urls
aafccdfbb,comic/grand-challenge.org,app/grandchallenge/subdomains/middleware.py,5212e60aadb0c897149ab75bbfbea7b73180a58a,adf5ea70c08e6e5bd6fc444c061bb766eca36a06,TODO: add support for PROJECTNAME_IS_SUBDOMAIN?
aafccdfbc,comic/grand-challenge.org,app/grandchallenge/subdomains/middleware.py,5212e60aadb0c897149ab75bbfbea7b73180a58a,d10f90a99b24cbb4b286bc182a29eef39ee2fee1,TODO: Change to subdomain urls
aafccdfbd,comic/grand-challenge.org,app/config/settings.py,d10f90a99b24cbb4b286bc182a29eef39ee2fee1,7222da1b3e1140814f70196da709e7aa2da97ebc,TODO: Change to subdomain urls
aafccdfca,comic/grand-challenge.org,app/grandchallenge/subdomains/middleware.py,74cf79516fb6012cd3e8b2a3b18d4f63a95955a8,adf5ea70c08e6e5bd6fc444c061bb766eca36a06,TODO: return the main challenge if no subdomain?
aafccdfdg,comic/grand-challenge.org,app/grandchallenge/core/permissions/mixins.py,e83e36a52a463c45c2922d7449cc564c13f765f0,13c72eccae3aedf0cabbfa26cba3fde1faaecb15,TODO: add a test for this
aafccdfdh,comic/grand-challenge.org,app/config/settings.py,e6cb87899562791fb664c4db08e0e5afedb202fe,7222da1b3e1140814f70196da709e7aa2da97ebc,TODO: Change to subdomain urls
aafccdffa,comic/grand-challenge.org,app/config/settings.py,24099c35b70d90d81a211a5a9a6213b089d8cf49,7222da1b3e1140814f70196da709e7aa2da97ebc,TODO: Change to subdomain urls
aafccdffe,comic/grand-challenge.org,app/config/settings.py,adf5ea70c08e6e5bd6fc444c061bb766eca36a06,7222da1b3e1140814f70196da709e7aa2da97ebc,TODO: Change to subdomain urls
aafccdfii,comic/grand-challenge.org,app/config/settings.py,6062c701b0d126b605ea7380093cd81bfd243d3a,7222da1b3e1140814f70196da709e7aa2da97ebc,TODO: Change to subdomain urls
aafccdigd,comic/grand-challenge.org,app/tests/annotations_tests/test_commands.py,1259cf48a6537f92170b0bbbdce6482d11c1925a,STILL_EXISTS,TODO (low prio) create check for these values
aafccefai,comic/grand-challenge.org,app/grandchallenge/retina_api/views.py,75a840052365c29d8b07ac4638b41f51fc929bc6,2f2ad0d7fb5612b057246a6148788d135d0db56a,TODO combine with viewset
aafccefaj,comic/grand-challenge.org,app/grandchallenge/retina_api/views.py,75a840052365c29d8b07ac4638b41f51fc929bc6,2c09fe69d47a22d5c1039e45679260a29a0fd2a0,TODO simplify permissions\/combine with singlepolygonviewset
aafccefbb,comic/grand-challenge.org,app/grandchallenge/retina_api/views.py,75a840052365c29d8b07ac4638b41f51fc929bc6,6e4cdc003caf4009cc2035da59af47cea6542a55,TODO: permission for creation... anyone can create a polygon_set and set user_id to what he wants...
aafccefbf,comic/grand-challenge.org,app/tests/retina_api_tests/test_views.py,9749e55d36e83cc08230ee540b63d89bd16d32e2,f01ffd30ce3ac5bcaecb9f3869833ed2ec0e160d,TODO fix this failing test (fix authentication check for is_retina_user
aafccefbg,comic/grand-challenge.org,app/tests/retina_api_tests/test_views.py,9749e55d36e83cc08230ee540b63d89bd16d32e2,29f6aa0b74a8df0b835dcdbb88baffa729a631c5,TODO add tests for polygonAnnotationSetViewset queryset and singlepolygonviewset queryset\uFFFF
aafccefgj,comic/grand-challenge.org,app/grandchallenge/mlmodels/models.py,dd6a61541b793286df54a039b33b5892f69b0472,STILL_EXISTS,TODO: This thing should not interact with the database
aafccefjb,comic/grand-challenge.org,app/grandchallenge/algorithms/models.py,eb49b4c6118ba8e5338df5f0bb9b44a131ef089c,4a822ac6092a557e237e11270110cefa48343d30,TODO: This thing should not interact with the database
aafcceggi,comic/grand-challenge.org,app/grandchallenge/container_exec/backends/docker.py,81f9a18dd03492c86498e2ee7111d78d157d8977,7e232c3a8b51ba515ef70287bc2d50e7960bd768,TODO: Hardcoded
aafcceggj,comic/grand-challenge.org,app/grandchallenge/container_exec/backends/docker.py,fa247c41c47d1cb97083e0f0f7f97cd577ff36b8,a1d2c406a721a2683a71844e4444311cb2db1962,TODO: pk.workstation is duplicated
aafccegha,comic/grand-challenge.org,app/grandchallenge/workstations/views.py,fa247c41c47d1cb97083e0f0f7f97cd577ff36b8,a1d2c406a721a2683a71844e4444311cb2db1962,TODO: pk.workstation is duplicated
aafcceghc,comic/grand-challenge.org,app/grandchallenge/container_exec/backends/docker.py,f87958f66e6c6d384087a90374128f1a8e537023,a1d2c406a721a2683a71844e4444311cb2db1962,TODO: pk.workstation is duplicated
aafccegid,comic/grand-challenge.org,app/grandchallenge/cases/models.py,189012d84b9fa578b2099ab85e7bb795248b8db5,6cbb5f1b8a0b94643d5711db43703e2e2327a389,TODO: Remove this and rely on the template tag instead
aafccegie,comic/grand-challenge.org,app/grandchallenge/container_exec/backends/docker.py,ca41c1463f074f4b84a6c912af9aa2976e38caa7,157208c3b4f57e0d8d70cf977e83426c355255be,TODO: ensure that this is a full url
aafccegja,comic/grand-challenge.org,app/grandchallenge/container_exec/backends/docker.py,ca41c1463f074f4b84a6c912af9aa2976e38caa7,157208c3b4f57e0d8d70cf977e83426c355255be,TODO: Debug only
aafccegjb,comic/grand-challenge.org,app/grandchallenge/container_exec/backends/docker.py,ca41c1463f074f4b84a6c912af9aa2976e38caa7,157208c3b4f57e0d8d70cf977e83426c355255be,TODO: debug only
aafccegjc,comic/grand-challenge.org,app/grandchallenge/workstations/models.py,ca41c1463f074f4b84a6c912af9aa2976e38caa7,3bb3a8b732f79133f40e0ce413a43befcfa04ae5,TODO: handle failed start
aafcceijb,comic/grand-challenge.org,app/grandchallenge/subdomains/utils.py,ec6ab6a3b65e3cb394fadb4ff4857e60de905df7,fa12c1584fe693183e7a0c0488427f214837e3aa,Fix for a long standing bug in django flatpages
aafccejbb,comic/grand-challenge.org,app/grandchallenge/subdomains/utils.py,e3e7529e76b16708fbc72facd5f0073f567bee97,e72ac38ad1d61f8ead8e9a334fa0461251cd159f,Fix for a long standing bug in django flatpages
aafccfagj,comic/grand-challenge.org,app/grandchallenge/retina_api/views.py,3a5e2199b7dc6986712d6eade21ebd3061c97337,65c446cae80abb172d318108f0ebc68dc0ebf537,TODO patient only images? Propose model change
aafccfajg,comic/grand-challenge.org,app/grandchallenge/reader_studies/serializers.py,fce54368789d50d5ed31c4e3e8d4b78fbc6de5f8,ced9c478dd01b821cd8810adb838b25ed901c538,TODO: Provide a queryset
aafccfajj,comic/grand-challenge.org,app/tests/reader_studies_tests/test_api.py,39f5eaf6c4e15edad7f9a0e24ae89277ae244984,a3d65eb12ba2f078d9c56939a04ff4e38332df92,TODO: Add Filters
aafccfbae,comic/grand-challenge.org,app/grandchallenge/reader_studies/models.py,0b19a706e8bbff0e6ba1cfc23c9febc7ff65ff75,STILL_EXISTS,TODO: add the readers group and make the correct permissions
aafccfbaf,comic/grand-challenge.org,app/grandchallenge/reader_studies/serializers.py,0b19a706e8bbff0e6ba1cfc23c9febc7ff65ff75,a160f40958dbd5a5d03c0a02df1d14bf2147bd25,TODO: validate the answer type
aafccfbag,comic/grand-challenge.org,app/grandchallenge/algorithms/views.py,4087ef47d703ff3945128b85f2ca20f9843b437d,7f01ed291cfbf82e3d160a63dadecaf690acf070,TODO: Should it be UserIsChallengeAdminMixin?
aafccfbba,comic/grand-challenge.org,app/grandchallenge/reader_studies/models.py,81bd14e7c65d3137341e1eb6a0cffe2312a68036,STILL_EXISTS,TODO: add the readers group and make the correct permissions
aafccfcbi,comic/grand-challenge.org,app/grandchallenge/reader_studies/models.py,35244b3e5e45ca1a5dd2f5736b09eab8df2c221c,aeb47c74945cc754c25746cee12d3cdf0a90d53f,TODO: pkg validate 2DBB
aafccfeah,comic/grand-challenge.org,app/grandchallenge/workstations/views.py,ef7ad895674d5370afa7a25be3003301801d19d6,375a713b5755d3799a0a5f3ba750fbbaa2b43e25,Workaround to ensure DjangoModelPermissions are not applied
aafccfebc,comic/grand-challenge.org,app/grandchallenge/core/permissions/rest_framework.py,375a713b5755d3799a0a5f3ba750fbbaa2b43e25,STILL_EXISTS,Workaround to ensure DjangoModelPermissions are not applied
aafccfgch,comic/grand-challenge.org,app/grandchallenge/api/urls.py,58825816427552e5b1ce280a2e3a44fdf73f50ff,STILL_EXISTS,TODO: add terms_of_service and contact
aafccfghg,comic/grand-challenge.org,app/grandchallenge/reader_studies/models.py,c226296024d211e5f213cedde3db656e5c22dcda,STILL_EXISTS,TODO: add validators=[JSONSchemaValidator(schema=ANSWER_TYPE_SCHEMA)];
aafccfhch,comic/grand-challenge.org,app/tests/cases_tests/test_api.py,4f8a3739ec24cd07cad430bc5bfaaf095c6b3582,680a79bdd2defeec6d1264576d19c1cdd81b2ccf,Hack to get around fact that RawImageFileFactory does not create images
aafccfiga,comic/grand-challenge.org,app/grandchallenge/reader_studies/models.py,dd417a585303b03179b0866b8781d7a811b16e6b,0708cd8998864b2441c70fc3f7ae6341901baa16,TODO: the ground truth only works correctly when there is one question
aafccfigc,comic/grand-challenge.org,app/grandchallenge/reader_studies/views.py,dd417a585303b03179b0866b8781d7a811b16e6b,STILL_EXISTS,TODO: this view also contains the ground truth answer values.
aafccfijj,comic/grand-challenge.org,app/grandchallenge/algorithms/urls.py,b8e584f51a65d82d0e04d5d9522fee0f4b911e61,43bd545277d87368a2e77ac81cd1cdd44270e2ef,TODO: we need a place to see failed jobs and their status; maybe an upload list page?
aafccfjff,comic/grand-challenge.org,app/grandchallenge/archives/models.py,796bccbc39418a61f8de2bc7a3f5421392ea04df,STILL_EXISTS,TODO: Handle public permissions
aafccfjgg,comic/grand-challenge.org,app/grandchallenge/retina_api/tasks.py,796bccbc39418a61f8de2bc7a3f5421392ea04df,STILL_EXISTS,TODO: use the guardian filters
aafccfjjc,comic/grand-challenge.org,app/grandchallenge/serving/views.py,e642024f0c143ee34dbea9a3e8c57fd633427ad7,e949030ed5fb65f62d5cb7849c1e3538bc7137fa,Just return the internal request if needed
aafccgabj,comic/grand-challenge.org,app/tests/retina_api_tests/test_views.py,f8f94128c8b1cddf6975ce0a4457419f4cd09761,STILL_EXISTS,TODO reenable test after Archive permission filtering is implemented correctly
aafccgbbe,comic/grand-challenge.org,app/grandchallenge/workstations/urls.py,3b7cf764e3c0e95ea41bfb84301d343d846e481b,STILL_EXISTS,TODO - add region
aafccgbie,comic/grand-challenge.org,app/grandchallenge/evaluation/models.py,f23d4dcaad857e85261ecfea4ee3ca255b628515,b32602bb5167405e8338dfbbc656f1bfd8987a86,TODO Email admins
aafccgccj,comic/grand-challenge.org,app/grandchallenge/retina_core/management/commands/migratelesionnames.py,237f2e3585f88757dcc4131fee4ed5c95e06d3df,45ed3c0489ccb5589427f8da8dc467d72191fbeb,in db so needed...
aafccgcee,comic/grand-challenge.org,app/grandchallenge/evaluation/models.py,1577f8566148a681619f6d84833c0ef44dc5372c,b32602bb5167405e8338dfbbc656f1bfd8987a86,TODO Email admins
aafccgcff,comic/grand-challenge.org,app/grandchallenge/evaluation/views.py,0d5a1d66d7c1abc9931e16291f32f206c53cc1be,STILL_EXISTS,TODO Fix for multiple phases
aafccgcfg,comic/grand-challenge.org,app/tests/evaluation_tests/test_models.py,0d5a1d66d7c1abc9931e16291f32f206c53cc1be,STILL_EXISTS,TODO Fix image set dependency
aafccgcgg,comic/grand-challenge.org,app/grandchallenge/evaluation/views.py,3c7374ca2976167187cd5d38065e81d92f728d3e,STILL_EXISTS,TODO - if participant: list only their evaluations
aafccgchg,comic/grand-challenge.org,app/grandchallenge/evaluation/models.py,c5b2fce421350e86c1ef42458f4ca7c8de8e0ce0,e5041db77bb225d0678192603c54600315f54e7c,TODO: allow setting of archives for phases
aafccgcij,comic/grand-challenge.org,app/grandchallenge/components/serializers.py,e5041db77bb225d0678192603c54600315f54e7c,STILL_EXISTS,name is needed
aafccgdja,comic/grand-challenge.org,app/grandchallenge/verifications/resources/free_email_domains.py,9f31d148c03660e3dd426f13d78b2cac2bddd317,STILL_EXISTS,copyright notice and this permission notice appear in all copies.
aafccgfgg,comic/grand-challenge.org,app/grandchallenge/core/models.py,51a2392a137411e8f7d84c0c6b5b7dc87c9f0041,STILL_EXISTS,Fix issue in upstream where description can be null
aafccgfib,comic/grand-challenge.org,app/tests/organizations_tests/test_views.py,57f803f3da0edfc35ec1eb67bb5d1396689850ca,STILL_EXISTS,TODO For challenges; hidden needs to be refactored to public
aafccgfjd,comic/grand-challenge.org,app/grandchallenge/components/models.py,7670799bd45cb91f9f0c6b4143d15837b4f93669,STILL_EXISTS,TODO JM These functions rely on docker specific code (reader)
aafccggbf,comic/grand-challenge.org,app/grandchallenge/reader_studies/views.py,b3a433d51df8d89cdca853f8e54850c93a059e5a,2d218a332d97b58f8a3e236dc06a8cc777a5323b,TODO JM @action(detail=True)
aafccggbg,comic/grand-challenge.org,app/tests/reader_studies_tests/test_api.py,b3a433d51df8d89cdca853f8e54850c93a059e5a,2d218a332d97b58f8a3e236dc06a8cc777a5323b,TODO JM
aafccggbi,comic/grand-challenge.org,app/grandchallenge/groups/views.py,e1107c39a7b4edf5c3329dcc9b26d19ba902bbff,STILL_EXISTS,TODO reduce number of queries
aafccggij,comic/grand-challenge.org,app/grandchallenge/algorithms/models.py,c16da142b9999b9755c7ec9305237f91b45a58e1,385188556737ea2857ffde8b699ed3aa90dc4210,TODO remove legacy result_dict
aafccghbc,comic/grand-challenge.org,app/tests/api_tests/test_schema.py,224576d85db83db24c78861f4374500b8fdec821,STILL_EXISTS,TODO: fix the warnings from types that could not be inferred
aafccghcc,comic/grand-challenge.org,app/grandchallenge/api/__init__.py,ba63026bc1024eac7233b6a677ba1da780387670,STILL_EXISTS,Maybe solved in https:\/\/github.com\/tfranzel\/drf-spectacular\/issues\/264
aafccghjd,comic/grand-challenge.org,app/panimg/image_builders/tiff.py,0631dce60b386a133f672f2934224c4918c2631c,STILL_EXISTS,TODO (jmsmkn): Create the tiff files in the correct location
aafccgiac,comic/grand-challenge.org,app/grandchallenge/algorithms/tasks.py,f0954a287006d6890b0ec470ccdaccf1189d246f,STILL_EXISTS,Todo: move this check to execute() code when using inputs is done
aafccgibd,comic/grand-challenge.org,app/tests/algorithms_tests/test_tasks.py,f0954a287006d6890b0ec470ccdaccf1189d246f,STILL_EXISTS,TODO: celery errorhandling with the .on_error seems to not work when
aafccgidd,comic/grand-challenge.org,app/config/settings.py,f20554b1b640e4541e6853186fd4144441c37382,STILL_EXISTS,Workaround for https:\/\/github.com\/ellmetha\/django-machina\/issues\/219
aafccgied,comic/grand-challenge.org,app/grandchallenge/profiles/models.py,f20554b1b640e4541e6853186fd4144441c37382,STILL_EXISTS,Workaround for
aafccgihc,openAGI/tefla,tefla/core/tvf_dataset.py,ea054a5de8edb377308e3eab70cd1a428f8307f5,STILL_EXISTS,Todo - should be easy to come up with a decent heuristic
aafccgihh,openAGI/tefla,tefla/da/data.py,ea054a5de8edb377308e3eab70cd1a428f8307f5,STILL_EXISTS,need to swap rows and cols here apparently! confusing!
aafccgiie,openAGI/tefla,tefla/da/data.py,ea054a5de8edb377308e3eab70cd1a428f8307f5,STILL_EXISTS,# DEBUG: draw a border to see where the image ends up
aafccgjaa,openAGI/tefla,tefla/da/iterator.py,ea054a5de8edb377308e3eab70cd1a428f8307f5,STILL_EXISTS,Todo remove code duplication with BalancingDAIterator (call method)
aafcchfbe,openAGI/tefla,docs/conf.py,ae8b9119c9ff375595f8bde7e8475f4d27194f62,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aafcchfij,openAGI/tefla,tefla/core/learning_distributed.py,9a105880928bd18de044e8ecd0b51b14a473cec9,STILL_EXISTS,TODO define image_processing
aafcchgcg,openAGI/tefla,tefla/dataset/dataflow.py,ed73e7893547cf9f93b1aff9f9f987a43e91a8f1,STILL_EXISTS,TODO need refinements
aafcchgci,openAGI/tefla,tefla/dataset/decoder.py,e53abf5c49a4686cb49f9049656cbb69fc65ac25,STILL_EXISTS,TODO Mainly useful for ImageNet Dataset
aafcchhfc,openAGI/tefla,examples/datasets/imagenet/process_bounding_boxes.py,2184f914f2929397b924995ac34ee1f2295a3d0d,STILL_EXISTS,simple hack to overcome this issue; we only exclude bbox labels
aafcchigh,openAGI/tefla,examples/word2vec.py,472ca3b20cd52f6562422579af193d8c1d12806f,STILL_EXISTS,\"\"\"Multi-threaded word2vec mini-batched skip-gram model. ||  || Trains the model described in: || (Mikolov; et. al.) Efficient Estimation of Word Representations in Vector Space || ICLR 2013. || http:\/\/arxiv.org\/abs\/1301.3781 || This model does traditional minibatching. ||  || The key ops used are: || * placeholder for feeding in tensors for each example. || * embedding_lookup for fetching rows from the embedding matrix. || * sigmoid_cross_entropy_with_logits to calculate the loss. || * GradientDescentptimizer for optimizing the loss. || * skipgram custom op that does input processing. || \"\"\"
aafcchjgi,openAGI/tefla,tools/freeze_graph.py,d504a9fbdb2797a9089d3e1f8fb232b13d9a91b1,STILL_EXISTS,Unused by updated loading code.
aafcchjhe,openAGI/tefla,tools/optimize_for_inference.py,d504a9fbdb2797a9089d3e1f8fb232b13d9a91b1,STILL_EXISTS,r\"\"\"Removes parts of a graph that are only needed for training. ||  || There are several common transformations that can be applied to GraphDefs || created to train a model; that help reduce the amount of computation needed when || the network is used only for inference. These include: ||  ||  - Removing training-only operations like checkpoint saving. ||  ||  - Stripping out parts of the graph that are never reached. ||  ||  - Removing debug operations like CheckNumerics. ||  ||  - Folding batch normalization ops into the pre-calculated weights. ||  ||  - Fusing common operations into unified versions. ||  || This script takes either a frozen binary GraphDef file (where the weight || variables have been converted into constants by the freeze_graph script); or a || text GraphDef proto file (the weight variables are stored in a separate || checkpoint file); and outputs a new GraphDef with the optimizations applied. ||  || If the input graph is a text graph file; make sure to include the node that || restores the variable weights in output_names. That node is usually named || \"restore_all\". ||  || An example of command-line usage is: ||  || bazel build tensorflow\/python\/tools:optimize_for_inference && \\ || bazel-bin\/tensorflow\/python\/tools\/optimize_for_inference \\ || --input=frozen_inception_graph.pb \\ || --output=optimized_inception_graph.pb \\ || --frozen_graph=True \\ || --input_names=Mul \\ || --output_names=softmax ||  ||  || \"\"\"
aafcciaeb,openAGI/tefla,models/resnet_v1.py,1a87890229921fa6c90e539272fe34c782379512,STILL_EXISTS,This is needed because the pre-activation variant does not have batch
aafcciaej,openAGI/tefla,models/resnet_v2.py,1a87890229921fa6c90e539272fe34c782379512,STILL_EXISTS,This is needed because the pre-activation variant does not have batch
aafccibbh,openAGI/tefla,models/semi_supervised.py,7bb0d3fa7b3e9746b1c0111ca70df1d5fd505985,STILL_EXISTS,TODO think about phase again
aafcciceh,openAGI/tefla,models/aegan.py,dbe52769a2eb9df1ed5219f0ef82e59bc55aad49,STILL_EXISTS,TODO think about phase again
aafccicjh,openAGI/tefla,tefla/core/memory.py,cc93ca13a5443da5a4e094a98f1a64de2a79f59f,STILL_EXISTS,Can be fed \"false\" if needed.
aafccidda,openAGI/tefla,tefla/core/decoder.py,d1584118e319a0fb5b0b150fe5d63e6c788168ba,STILL_EXISTS,TODO: Make this a parameter: We may or may not want this.
aafccidhd,openAGI/tefla,tefla/utils/seq2seq_utils.py,b33f88bcd255c29592224130a013be5553515cb2,STILL_EXISTS,unused by sample_fn
aafccidhf,openAGI/tefla,tefla/utils/seq2seq_utils.py,b33f88bcd255c29592224130a013be5553515cb2,STILL_EXISTS,unused by next_inputs_fn
aafccifai,openAGI/tefla,tefla/core/optimizer.py,c01ee04090dd352725be0f67e800004dd599fb52,STILL_EXISTS,Also; if needed; define the gradient accumulators
aafccigbj,openAGI/tefla,tefla/dataset/text_encoder.py,977056182215965e0926bdeb07791bfe61476159,STILL_EXISTS,NOTE: This algorithm is greedy; it won't necessarily produce the \"best\"
aafcciiie,openAGI/tefla,tefla/dataset/textdataflow.py,36444fdb9e5c9bc3f97c1272e10a839dcd2dfa77,STILL_EXISTS,TODO: remove this when Dataset API improves.
aafccjbcd,openAGI/tefla,tefla/core/beam_search.py,dd00b7f9de01289edacffc23352f45d25509ec7e,STILL_EXISTS,needed for the gather
aafccjcce,openAGI/tefla,tefla/core/yellowfin.py,3d0e525355d45c05f8860cbb0195f4336c85810a,STILL_EXISTS,An extension maybe only correct the sparse blob.
aafccjcfi,openAGI/tefla,tefla/core/yellowfin.py,3d0e525355d45c05f8860cbb0195f4336c85810a,STILL_EXISTS,Unused for now.
aafccjdac,openAGI/tefla,tefla/core/vbn.py,8a5bd5c22373488863ef73347780b5321a397fea,STILL_EXISTS,sufficient statistics. As a workaround we simply perform the operations
aafccjdba,openAGI/tefla,tefla/core/vbn.py,8a5bd5c22373488863ef73347780b5321a397fea,STILL_EXISTS,Determines whether broadcasting is needed. This is slightly different
aafccjegj,openAGI/tefla,tefla/core/meta_graph_editor.py,f923da5be380e76a1b9623c89bd9a9cf91198bfd,STILL_EXISTS,TODO(n3011): implement variable stripping.
aafccjeia,openAGI/tefla,tefla/core/meta_graph_editor.py,f923da5be380e76a1b9623c89bd9a9cf91198bfd,97919f8623e35581eb43e261556b976179f6e6f6,TODO(n3011): Once we strip unused variables; remove references to
aafccjfbf,openAGI/tefla,tefla/core/meta_graph_editor.py,f923da5be380e76a1b9623c89bd9a9cf91198bfd,STILL_EXISTS,TODO(n3011): Revisit this once the problem is addressed. Currently
aafccjfhf,openAGI/tefla,tefla/core/diet_gradients.py,ce244a82e27233997ff5107864f314ebdca6f1aa,STILL_EXISTS,\"\"\"Memory efficient gradient computations || credit: https:\/\/github.com\/openai\/gradient-checkpointing\/blob\/master\/memory_saving_gradients.py || \"\"\"
aafccjgac,openAGI/tefla,tefla/core/diet_gradients.py,ce244a82e27233997ff5107864f314ebdca6f1aa,STILL_EXISTS,better error handling of special cases
aafcdaaii,openAGI/tefla,tests/test_losses.py,9fc3f0b2bc9567fba5044cda7b4eac4b1d671c45,STILL_EXISTS,TODO: Include weights in the lagrange multiplier update tests.
aafcdaajd,openAGI/tefla,tests/test_losses.py,9fc3f0b2bc9567fba5044cda7b4eac4b1d671c45,STILL_EXISTS,TODO: Place the hing\/xent loss in a for loop.
aafcdaefi,larq/larq,xquant/layers.py,2ce0bd2bf26d11ee33bb2b25fe6b1a7f6f53175a,STILL_EXISTS,Is this a problem with our unit tests or a real bug?
aafcdaega,larq/larq,xquant/layers.py,63a935317186ef477d70603375dfa5a5dea23814,STILL_EXISTS,Is this a problem with our unit tests or a real bug?
aafcdaehi,larq/larq,generate_api_docs.py,2bf38b9990f44ed2ce456a53e165cb0fb7e3e88c,STILL_EXISTS,Check how many levels of recursion we should be going.
aafcdafdi,larq/larq,larq/layers.py,ca4a0509e72fc9fb882461384c98802947b39685,89ee2192688ed6bc104130d35a1e0e98913192c5,TODO make configurable
aafcdafea,larq/larq,larq/layers.py,55bebfe6e7385d68c97ad44a1c2ba5cbe7bbac64,81d9b37cdfabdb43a7c516da4102d8de011aaaac,This is currently undocumented until we have explored better options
aafcdafeb,larq/larq,larq/layers.py,d83fa42526eefd3e8b0a78bc25d29b0dc23c635a,81d9b37cdfabdb43a7c516da4102d8de011aaaac,This is currently undocumented until we have explored better options
aafcdafha,larq/larq,larq/layers.py,c36fc76a4dbccc6663db01041dc3ffe0a4f426e7,81d9b37cdfabdb43a7c516da4102d8de011aaaac,TODO: find a good way remove duplication between QuantizerBase; QuantizerDepthwiseBase and QuantizerSeparableBase
aafcdafhc,larq/larq,larq/layers.py,c36fc76a4dbccc6663db01041dc3ffe0a4f426e7,81d9b37cdfabdb43a7c516da4102d8de011aaaac,Is this a problem with our unit tests or a real bug?
aafcdafhd,larq/larq,larq/layers.py,c36fc76a4dbccc6663db01041dc3ffe0a4f426e7,81d9b37cdfabdb43a7c516da4102d8de011aaaac,This is currently undocumented until we have explored better options
aafcdafij,larq/larq,larq/layers_base.py,81d9b37cdfabdb43a7c516da4102d8de011aaaac,6788670e99b32c46337e532a4ac7f524cbad98c2,TODO: find a good way remove duplication between QuantizerBase; QuantizerDepthwiseBase and QuantizerSeparableBase
aafcdaghb,larq/larq,larq/quantized_variable.py,c3c63e1dd7abfeaceee3024e1aebdf15da85ec58,STILL_EXISTS,TODO: Maybe encode the fact the variable is an QuantizedVariable in to_proto().
aafcdaghd,larq/larq,larq/quantized_variable.py,c3c63e1dd7abfeaceee3024e1aebdf15da85ec58,STILL_EXISTS,For some reason this is needed to make unit `x + x` pass on TF 1.14
aafcdaheg,larq/larq,larq/quantized_variable.py,4e3a66b5bdca183584b8d6b8fa7ae33c6e47b06e,STILL_EXISTS,SavedModel to work properly.
aafcdaheh,larq/larq,larq/quantized_variable.py,4e3a66b5bdca183584b8d6b8fa7ae33c6e47b06e,STILL_EXISTS,TODO: Find a better way to support SavedModel. Exposing private attributes is
aafcdaidj,zalandoresearch/famos,network.py,53cf831f71944ec0da85edbb1217e138352d5bea,STILL_EXISTS,@param bCopyIn copies the input channels at every decoder level -- special skip connections
aafcdajcd,zalandoresearch/famos,splitInference.py,53cf831f71944ec0da85edbb1217e138352d5bea,STILL_EXISTS,#more engineered but efficient in code: only add template chunk to memory; not full large template batch
aafcdajeh,zalandoresearch/famos,utils.py,53cf831f71944ec0da85edbb1217e138352d5bea,STILL_EXISTS,#slow; pooling better
aafcdajfd,zalandoresearch/famos,utils.py,53cf831f71944ec0da85edbb1217e138352d5bea,STILL_EXISTS,@param I_M is mixed template image
aafcdbaad,zalandoresearch/famos,GANOSAIC.py,1f5cd356517a3f3cd23008f13b969e10992d2861,STILL_EXISTS,#TODO freeze gradient of decode?
aafcdbaci,zalandoresearch/famos,network.py,1f5cd356517a3f3cd23008f13b969e10992d2861,STILL_EXISTS,@param bCopyIn copies the input channels at every decoder level -- special skip connections
aafcdbaef,alan-turing-institute/skpro,docs/conf.py,3f2641ebeae88979fb5b04e0255ddb2bbf2e19d7,STILL_EXISTS,-- Hack for ReadTheDocs ------------------------------------------------------
aafcdbaeg,alan-turing-institute/skpro,docs/conf.py,3f2641ebeae88979fb5b04e0255ddb2bbf2e19d7,STILL_EXISTS,This hack is necessary since RTD does not issue `sphinx-apidoc` before running
aafcdbbhf,alan-turing-institute/skpro,examples/parametric/bagging.py,3f2641ebeae88979fb5b04e0255ddb2bbf2e19d7,fad2abd0ac032cd8bbf691a52232c7435e6587b1,Adding controllers displayed as columns
aafcdbbig,alan-turing-institute/skpro,examples/parametric/workflow.py,3f2641ebeae88979fb5b04e0255ddb2bbf2e19d7,STILL_EXISTS,Adding controllers displayed as columns
aafcdbcbc,alan-turing-institute/skpro,skpro/parametric/bayesian.py,3f2641ebeae88979fb5b04e0255ddb2bbf2e19d7,a6cf00a89e7414cdeb5adc1c7c3a5d8f57cae307,TODO
aafcdbccd,alan-turing-institute/skpro,skpro/parametric/parametric.py,3f2641ebeae88979fb5b04e0255ddb2bbf2e19d7,STILL_EXISTS,TODO: warn
aafcdbceg,alan-turing-institute/skpro,skpro/ensemble.py,6f12ae16f4c45a3ee5821fa57099aceb6820a25a,STILL_EXISTS,TODO: reduce properly
aafcdbceh,alan-turing-institute/skpro,skpro/parametric/bayesian.py,6f12ae16f4c45a3ee5821fa57099aceb6820a25a,a6cf00a89e7414cdeb5adc1c7c3a5d8f57cae307,TODO: Issue #19
aafcdbcfb,alan-turing-institute/skpro,skpro/workflow/table/table.py,6f12ae16f4c45a3ee5821fa57099aceb6820a25a,STILL_EXISTS,TODO: find better solution
aafcdbcfc,alan-turing-institute/skpro,skpro/workflow/table/table.py,6f12ae16f4c45a3ee5821fa57099aceb6820a25a,be57eba41f53ae6caa72734f6ab11d1f290b5d3e,TODO
aafcdbcfe,alan-turing-institute/skpro,skpro/workflow/table/table.py,6f12ae16f4c45a3ee5821fa57099aceb6820a25a,STILL_EXISTS,May be better to define it in a variable?
aafcdbcfh,alan-turing-institute/skpro,examples/parametric/hyperparameters.py,31c29fe63aad428803c14e16f9df4034b46d4603,STILL_EXISTS,>>> Best score is -4.058729 for parameter: {'point__max_depth': 15}\uFFFF
aafcdbcgb,alan-turing-institute/skpro,examples/parametric/bayesian.py,a6cf00a89e7414cdeb5adc1c7c3a5d8f57cae307,STILL_EXISTS,Adding controllers displayed as columns
aafcdbcjd,alan-turing-institute/skpro,tests/test_utils.py,8950eff8ad3e3416f7d2d5372b35830f448be7ff,STILL_EXISTS,todo: assert xs; ys is the correct empircal cdf of the sample
aafcdbdad,alan-turing-institute/skpro,skpro/density.py,b32c8960288b0ba4757bd76f793b1889571d7e62,STILL_EXISTS,TODO: How to obtain cdf that is consistent with the estimated PDF?
aafcdbdaj,alan-turing-institute/skpro,skpro/density.py,c8f8877120fe4cda730b4c9db1c026bb7b0452ff,c7ba2f9d5efb36c20637385fb9338b6c254a8c37,TODO: point-wise ecdf as function
aafcdbdba,alan-turing-institute/skpro,skpro/density.py,c8f8877120fe4cda730b4c9db1c026bb7b0452ff,STILL_EXISTS,TODO: integrate cdf stepfunction
aafcdbecb,alan-turing-institute/skpro,skpro/vendors/edwardlib.py,a37a34b8ad2334407d3dc2265734fd59c532cf39,STILL_EXISTS,TODO: posterior sampling
aafcdbfge,PaddlePaddle/X2Paddle,TensorFlow2Paddle/paddle_emitter.py,33166c4ea1358b00eb834df7dc253c228b12cb4b,STILL_EXISTS,TODO more validations
aafcdbfgf,PaddlePaddle/X2Paddle,TensorFlow2Paddle/paddle_emitter.py,33166c4ea1358b00eb834df7dc253c228b12cb4b,STILL_EXISTS,TODO dtype need more validation
aafcdbfgg,PaddlePaddle/X2Paddle,TensorFlow2Paddle/paddle_emitter.py,33166c4ea1358b00eb834df7dc253c228b12cb4b,STILL_EXISTS,TODO need more validation in nlp
aafcdbfgh,PaddlePaddle/X2Paddle,TensorFlow2Paddle/paddle_emitter.py,33166c4ea1358b00eb834df7dc253c228b12cb4b,STILL_EXISTS,TODO there's dtype problem of PaddlePaddle's OP[fluid.layers.shape]
aafcdbfgj,PaddlePaddle/X2Paddle,TensorFlow2Paddle/paddle_emitter.py,33166c4ea1358b00eb834df7dc253c228b12cb4b,STILL_EXISTS,tensorflow2paddle fix problem temporary
aafcdbfif,PaddlePaddle/X2Paddle,TensorFlow2Paddle/tensorflow_graph.py,33166c4ea1358b00eb834df7dc253c228b12cb4b,STILL_EXISTS,TODO
aafcdbgee,PaddlePaddle/X2Paddle,caffe2fluid/kaffe/custom_layers/__init__.py,c63fe589343c9fe1b737c56a2fe6d0df3737c7d5,STILL_EXISTS,custom layer import ends
aafcdbgef,PaddlePaddle/X2Paddle,caffe2fluid/kaffe/custom_layers/__init__.py,c63fe589343c9fe1b737c56a2fe6d0df3737c7d5,STILL_EXISTS,construct arguments needed by custom layer function from node's parameters
aafcdbgeg,PaddlePaddle/X2Paddle,caffe2fluid/kaffe/custom_layers/argmax.py,c63fe589343c9fe1b737c56a2fe6d0df3737c7d5,STILL_EXISTS,\"\"\" a custom layer for 'argmax'; maybe we should implement this in standard way. ||     more info can be found here: http:\/\/caffe.berkeleyvision.org\/tutorial\/layers\/argmax.html || \"\"\"
aafcdbgei,PaddlePaddle/X2Paddle,caffe2fluid/kaffe/custom_layers/crop.py,c63fe589343c9fe1b737c56a2fe6d0df3737c7d5,STILL_EXISTS,\"\"\" a custom layer for 'crop'; maybe we should implement this in standard way. ||     more info can be found here: http:\/\/caffe.berkeleyvision.org\/tutorial\/layers\/crop.html || \"\"\"
aafcdbgfa,PaddlePaddle/X2Paddle,caffe2fluid/kaffe/custom_layers/flatten.py,c63fe589343c9fe1b737c56a2fe6d0df3737c7d5,STILL_EXISTS,\"\"\" a custom layer for 'flatten'; maybe we should implement this in standard way. ||     more info can be found here: http:\/\/caffe.berkeleyvision.org\/tutorial\/layers\/flatten.html || \"\"\"
aafcdbgfe,PaddlePaddle/X2Paddle,caffe2fluid/kaffe/custom_layers/power.py,c63fe589343c9fe1b737c56a2fe6d0df3737c7d5,STILL_EXISTS,\"\"\" a custom layer for 'power'; maybe we should implement this in standard way. ||     more info can be found here: http:\/\/caffe.berkeleyvision.org\/tutorial\/layers\/power.html || \"\"\"
aafcdbgfg,PaddlePaddle/X2Paddle,caffe2fluid/kaffe/custom_layers/reduction.py,c63fe589343c9fe1b737c56a2fe6d0df3737c7d5,STILL_EXISTS,\"\"\" a custom layer for 'crop'; maybe we should implement this in standard way. ||     more info can be found here: http:\/\/caffe.berkeleyvision.org\/tutorial\/layers\/reduction.html || \"\"\"
aafcdbggc,PaddlePaddle/X2Paddle,caffe2fluid/kaffe/custom_layers/reshape.py,c63fe589343c9fe1b737c56a2fe6d0df3737c7d5,STILL_EXISTS,\"\"\" a custom layer for 'reshape'; maybe we should implement this in standard way. ||     more info can be found here: http:\/\/caffe.berkeleyvision.org\/tutorial\/layers\/reshape.html || \"\"\"
aafcdbggd,PaddlePaddle/X2Paddle,caffe2fluid/kaffe/custom_layers/roipooling.py,c63fe589343c9fe1b737c56a2fe6d0df3737c7d5,STILL_EXISTS,\"\"\" a custom layer for 'ROIPooling'; maybe we should implement this in standard way. ||     more info can be found here: http:\/\/caffe.berkeleyvision.org\/tutorial\/layers\/ROIPooling.html || \"\"\"
aafcdbgii,PaddlePaddle/X2Paddle,caffe2fluid/kaffe/graph.py,c63fe589343c9fe1b737c56a2fe6d0df3737c7d5,STILL_EXISTS,any connectivity. It's only used for data association. By convention; a layer with a
aafcdbhab,PaddlePaddle/X2Paddle,caffe2fluid/kaffe/graph.py,c63fe589343c9fe1b737c56a2fe6d0df3737c7d5,STILL_EXISTS,Case 2: output_name violates the convention layer.name == output_name.
aafcdbhea,PaddlePaddle/X2Paddle,caffe2fluid/kaffe/paddle/network.py,c63fe589343c9fe1b737c56a2fe6d0df3737c7d5,STILL_EXISTS,FIX ME:
aafcdbhfa,PaddlePaddle/X2Paddle,caffe2fluid/kaffe/paddle/transformer.py,c63fe589343c9fe1b737c56a2fe6d0df3737c7d5,STILL_EXISTS,TODO: Axis
aafcdbhfb,PaddlePaddle/X2Paddle,caffe2fluid/kaffe/paddle/transformer.py,c63fe589343c9fe1b737c56a2fe6d0df3737c7d5,STILL_EXISTS,TODO: Unbiased
aafcdbhgf,PaddlePaddle/X2Paddle,caffe2fluid/kaffe/paddle/transformer.py,c63fe589343c9fe1b737c56a2fe6d0df3737c7d5,STILL_EXISTS,TODO: Move non-linearity application to layer wrapper; allowing
aafcdbhjb,PaddlePaddle/X2Paddle,caffe2fluid/kaffe/shapes.py,c63fe589343c9fe1b737c56a2fe6d0df3737c7d5,STILL_EXISTS,TODO: Find a better solution for this.
aafcdciba,PaddlePaddle/X2Paddle,onnx2paddle/onnx2paddle/conversion.py,b483d12ec2229c7b7fcd99572a111ec51cf58aa7,STILL_EXISTS,WORKAROUND: RuntimeError: No Adapter For OP
aafcdcibb,PaddlePaddle/X2Paddle,onnx2paddle/onnx2paddle/conversion.py,b483d12ec2229c7b7fcd99572a111ec51cf58aa7,STILL_EXISTS,TODO: add new argument for this option
aafcdcich,PaddlePaddle/X2Paddle,onnx2paddle/onnx2paddle/conversion.py,b483d12ec2229c7b7fcd99572a111ec51cf58aa7,STILL_EXISTS,TODO:
aafcdciie,PaddlePaddle/X2Paddle,onnx2paddle/onnx2paddle/symbolic.py,b483d12ec2229c7b7fcd99572a111ec51cf58aa7,STILL_EXISTS,attrs bypassed; FIXME: emit flatten2
aafcdciig,PaddlePaddle/X2Paddle,onnx2paddle/onnx2paddle/symbolic.py,b483d12ec2229c7b7fcd99572a111ec51cf58aa7,STILL_EXISTS,FIXME: out is int64 - int32
aafcdciih,PaddlePaddle/X2Paddle,onnx2paddle/onnx2paddle/symbolic.py,b483d12ec2229c7b7fcd99572a111ec51cf58aa7,STILL_EXISTS,attrs bypassed; FIXME: emit squeeze2
aafcdciii,PaddlePaddle/X2Paddle,onnx2paddle/onnx2paddle/symbolic.py,b483d12ec2229c7b7fcd99572a111ec51cf58aa7,STILL_EXISTS,FIXME: emit transpose2
aafcdciij,PaddlePaddle/X2Paddle,onnx2paddle/onnx2paddle/symbolic.py,b483d12ec2229c7b7fcd99572a111ec51cf58aa7,STILL_EXISTS,attrs bypassed; FIXME: emit unsqueeze2
aafcdcijb,PaddlePaddle/X2Paddle,onnx2paddle/onnx2paddle/symbolic.py,b483d12ec2229c7b7fcd99572a111ec51cf58aa7,STILL_EXISTS,FIXME: axis=-1 in Paddle is broken; refer it in specialization
aafcdcijf,PaddlePaddle/X2Paddle,onnx2paddle/onnx2paddle/symbolic.py,b483d12ec2229c7b7fcd99572a111ec51cf58aa7,STILL_EXISTS,TODO: pow for scalar exponent
aafcdcjia,PaddlePaddle/X2Paddle,onnx2paddle/onnx2paddle/symbolic.py,b483d12ec2229c7b7fcd99572a111ec51cf58aa7,STILL_EXISTS,additional; maybe var_name
aafcddcce,PaddlePaddle/X2Paddle,onnx2paddle/onnx2paddle/writer.py,b483d12ec2229c7b7fcd99572a111ec51cf58aa7,STILL_EXISTS,TODO: symbolic file routing by domain
aafcdefge,PaddlePaddle/X2Paddle,onnx2fluid/onnx2fluid/conversion.py,f0dede1feeb1e29ca8b6e4ac66f229efef9a3f0f,STILL_EXISTS,WORKAROUND: RuntimeError: No Adapter For OP
aafcdefgf,PaddlePaddle/X2Paddle,onnx2fluid/onnx2fluid/conversion.py,f0dede1feeb1e29ca8b6e4ac66f229efef9a3f0f,STILL_EXISTS,TODO: add new argument for this option
aafcdefib,PaddlePaddle/X2Paddle,onnx2fluid/onnx2fluid/conversion.py,f0dede1feeb1e29ca8b6e4ac66f229efef9a3f0f,STILL_EXISTS,TODO:
aafcdegbh,PaddlePaddle/X2Paddle,onnx2fluid/onnx2fluid/symbolic.py,f0dede1feeb1e29ca8b6e4ac66f229efef9a3f0f,STILL_EXISTS,attrs bypassed; FIXME: emit flatten2
aafcdegbj,PaddlePaddle/X2Paddle,onnx2fluid/onnx2fluid/symbolic.py,f0dede1feeb1e29ca8b6e4ac66f229efef9a3f0f,STILL_EXISTS,FIXME: out is int64 vs int32
aafcdegca,PaddlePaddle/X2Paddle,onnx2fluid/onnx2fluid/symbolic.py,f0dede1feeb1e29ca8b6e4ac66f229efef9a3f0f,STILL_EXISTS,attrs bypassed; FIXME: emit squeeze2
aafcdegcb,PaddlePaddle/X2Paddle,onnx2fluid/onnx2fluid/symbolic.py,f0dede1feeb1e29ca8b6e4ac66f229efef9a3f0f,STILL_EXISTS,FIXME: emit transpose2
aafcdegcc,PaddlePaddle/X2Paddle,onnx2fluid/onnx2fluid/symbolic.py,f0dede1feeb1e29ca8b6e4ac66f229efef9a3f0f,STILL_EXISTS,attrs bypassed; FIXME: emit unsqueeze2
aafcdegch,PaddlePaddle/X2Paddle,onnx2fluid/onnx2fluid/symbolic.py,f0dede1feeb1e29ca8b6e4ac66f229efef9a3f0f,STILL_EXISTS,TODO: pow for scalar exponent
aafcdehbc,PaddlePaddle/X2Paddle,onnx2fluid/onnx2fluid/symbolic.py,f0dede1feeb1e29ca8b6e4ac66f229efef9a3f0f,STILL_EXISTS,additional; maybe var_name
aafcdeigf,PaddlePaddle/X2Paddle,onnx2fluid/onnx2fluid/symbolic.py,f0dede1feeb1e29ca8b6e4ac66f229efef9a3f0f,STILL_EXISTS,FIXME: Paddle 1.3 Doc: '\u5BF9\u4E8E\u672A\u77E5\u5927\u5C0F\u7EF4\u5EA6\u7684\u672B\u5C3E\u8FDB\u884C\u5207\u7247\uFF0C\u5219\u5EFA\u8BAE\u4F20\u5165 INT_MAX' not works ?
aafcdejdf,PaddlePaddle/X2Paddle,onnx2fluid/onnx2fluid/writer.py,f0dede1feeb1e29ca8b6e4ac66f229efef9a3f0f,a538420ad9d45cd30384b491a6ce023051e25ca3,REMOVEIT: WORKAROUND: Netron: null.tensor error
aafcdfhee,PaddlePaddle/X2Paddle,onnx2fluid/onnx2fluid/symbolic.py,66f55b8907a4fc70678fe80ad0a68d0c68c52669,STILL_EXISTS,FIXME: default axis = -1; reshape required before and after
aafcdfiie,PaddlePaddle/X2Paddle,onnx2fluid/onnx2fluid/writer.py,de1648a8447f4f7cdadb6656f8ea49f9eb536963,STILL_EXISTS,WORKAROUND: shape of scalars is []
aafcdfije,PaddlePaddle/X2Paddle,onnx2fluid/onnx2fluid/symbolic.py,63ac4c2cab25bb9590294c9b52bee3ba333f055d,STILL_EXISTS,WORKAROUND: bad scalar support
aafcdfjab,PaddlePaddle/X2Paddle,onnx2fluid/onnx2fluid/symbolic.py,63ac4c2cab25bb9590294c9b52bee3ba333f055d,STILL_EXISTS,FIXME: Paddle 1.3 Doc: '\u5BF9\u4E8E\u672A\u77E5\u5927\u5C0F\u7EF4\u5EA6\u7684\u672B\u5C3E\u8FDB\u884C\u5207\u7247\uFF0C\u5219\u5EFA\u8BAE\u4F20\u5165 INT_MAX' not works ?
aafcdfjcb,PaddlePaddle/X2Paddle,onnx2fluid/examples/gen_unet.py,30be2502a9b62b8bf3294c57224b24bd6b325e94,STILL_EXISTS,would be a nice idea if the upsampling could be learned too;
aafcdgaha,PaddlePaddle/X2Paddle,onnx2fluid/onnx2fluid/writer.py,7c3e9379ce62b6c144671e1a5a8b878baf0f192e,STILL_EXISTS,TODO: test all items
aafcdgahb,PaddlePaddle/X2Paddle,onnx2fluid/onnx2fluid/writer.py,7c3e9379ce62b6c144671e1a5a8b878baf0f192e,STILL_EXISTS,TODO: symbolic file routing by domain
aafcdgajg,PaddlePaddle/X2Paddle,onnx2fluid/onnx2fluid/validation.py,492e966141626cb1727f984bee3cd65725f88338,STILL_EXISTS,WORKAROUND: dirty way to give dtype to partial-infered vars
aafcdgcea,PaddlePaddle/X2Paddle,onnx2fluid/onnx2fluid/symbolic.py,9d14728468c97492e3c128c4a67f5aa8a4132115,STILL_EXISTS,TODO: fix reduce_all ?
aafcdgcfh,PaddlePaddle/X2Paddle,onnx2fluid/onnx2fluid/symbolic.py,9d14728468c97492e3c128c4a67f5aa8a4132115,STILL_EXISTS,TODO
aafcdgebi,PaddlePaddle/X2Paddle,x2paddle/optimizer/tf_optimizer.py,9b2869633bb175f365e515088614728f69913f52,STILL_EXISTS,TODO useless node remove
aafcdgebj,PaddlePaddle/X2Paddle,x2paddle/optimizer/tf_optimizer.py,9b2869633bb175f365e515088614728f69913f52,13e730845093e41c219b037dc46f834479b1b134,TODO identity node remove
aafcdgeca,PaddlePaddle/X2Paddle,x2paddle/optimizer/tf_optimizer.py,9b2869633bb175f365e515088614728f69913f52,13e730845093e41c219b037dc46f834479b1b134,TODO subgraph optimize
aafcdgecb,PaddlePaddle/X2Paddle,x2paddle/optimizer/tf_optimizer.py,9b2869633bb175f365e515088614728f69913f52,13e730845093e41c219b037dc46f834479b1b134,TODO compute optimize
aafcdgegc,PaddlePaddle/X2Paddle,x2paddle/emitter/tf_emitter.py,9450efde704f0979eea4b03b040aa195dbced352,1f79d43dd0d260e651c2128c1f6320ead2feb49b,# TODO
aafcdggib,PaddlePaddle/X2Paddle,x2paddle/op_mapper/tf_op_mapper.py,2e33688abfc110552936aedd91680bbd9f69ccff,d248ff64cf91261a569acc6623e51c4aab377db1,temporary shape inference fix
aafcdghcd,PaddlePaddle/X2Paddle,x2paddle/op_mapper/caffe_custom_layer/__init__.py,a5b69c1c47470c4a6609bddba38970e0981b3a45,STILL_EXISTS,custom layer import ends
aafcdgiai,PaddlePaddle/X2Paddle,x2paddle/optimizer/tf_optimizer.py,13e730845093e41c219b037dc46f834479b1b134,d234021529cc38b96cfe1b76329ac46e9987460b,TODO bn merge
aafcdgiaj,PaddlePaddle/X2Paddle,x2paddle/optimizer/tf_optimizer.py,13e730845093e41c219b037dc46f834479b1b134,3eecc8250bf4ca70d01b2f26959a47f74f8070ce,TODO activation merge
aafcdgiba,PaddlePaddle/X2Paddle,x2paddle/optimizer/tf_optimizer.py,13e730845093e41c219b037dc46f834479b1b134,d234021529cc38b96cfe1b76329ac46e9987460b,TODO biasadd merge
aafcdgicb,PaddlePaddle/X2Paddle,x2paddle/op_mapper/tf_op_mapper.py,d248ff64cf91261a569acc6623e51c4aab377db1,c0dabbacca1992d9b315b995d5d4e88498877868,TODO
aafcdgidj,PaddlePaddle/X2Paddle,x2paddle/op_mapper/tf_op_mapper.py,47c1f971494d2b22ba248595f076dde2fe6d5e51,af49e87841529193a79bc32a6a390c13284de4af,temporary shape inference fix
aafcdgiff,PaddlePaddle/X2Paddle,x2paddle/op_mapper/tf_op_mapper.py,47c1f971494d2b22ba248595f076dde2fe6d5e51,c0dabbacca1992d9b315b995d5d4e88498877868,TODO
aafcdgifi,PaddlePaddle/X2Paddle,x2paddle/op_mapper/tf_op_mapper.py,af49e87841529193a79bc32a6a390c13284de4af,c0dabbacca1992d9b315b995d5d4e88498877868,TODO
aafcdgjaj,PaddlePaddle/X2Paddle,x2paddle/optimizer/tf_optimizer.py,d234021529cc38b96cfe1b76329ac46e9987460b,3eecc8250bf4ca70d01b2f26959a47f74f8070ce,TODO bias merge
aafcdhcdf,PaddlePaddle/X2Paddle,x2paddle/optimizer/onnx_optimizer.py,5d5ed8aeaa93ea880227d5bf84826568f18bc96d,STILL_EXISTS,TODO useless node remove
aafcdhcdg,PaddlePaddle/X2Paddle,x2paddle/optimizer/onnx_optimizer.py,5d5ed8aeaa93ea880227d5bf84826568f18bc96d,f8f881fa02001f1c060f9a43e31c52460b3ebb0c,TODO activation merge
aafcdhcdh,PaddlePaddle/X2Paddle,x2paddle/optimizer/onnx_optimizer.py,5d5ed8aeaa93ea880227d5bf84826568f18bc96d,f8f881fa02001f1c060f9a43e31c52460b3ebb0c,TODO bias merge
aafcdhgii,PaddlePaddle/X2Paddle,x2paddle/op_mapper/onnx_custom_layer/__init__.py,e6c908f6014cb3fd31182c9f0ad21456dd188f51,STILL_EXISTS,custom layer import ends
aafcdhhag,PaddlePaddle/X2Paddle,x2paddle/op_mapper/tf_op_mapper_nhwc.py,1b4723939be13c1005bf70a1dca0c128e04cade1,STILL_EXISTS,fix paddle shape infer problem
aafcdhhai,PaddlePaddle/X2Paddle,x2paddle/op_mapper/tf_op_mapper_nhwc.py,1b4723939be13c1005bf70a1dca0c128e04cade1,STILL_EXISTS,TODO codes without validation
aafcdhiah,PaddlePaddle/X2Paddle,x2paddle/decoder/onnx_backend.py,43cf70b5464fab28b06054dbdf5b5d332be6615d,STILL_EXISTS,TODO: this doesn't work with RNN ops
aafcdhibb,PaddlePaddle/X2Paddle,x2paddle/decoder/onnx_backend.py,43cf70b5464fab28b06054dbdf5b5d332be6615d,STILL_EXISTS,that we are attempting to translate on a \"best effort\" basis.
aafcdhibf,PaddlePaddle/X2Paddle,x2paddle/decoder/onnx_backend.py,43cf70b5464fab28b06054dbdf5b5d332be6615d,STILL_EXISTS,implement; mark as broken in _broken_operators
aafcdhifc,PaddlePaddle/X2Paddle,x2paddle/decoder/onnx_backend.py,43cf70b5464fab28b06054dbdf5b5d332be6615d,STILL_EXISTS,TODO: make this more efficient
aafcdhife,PaddlePaddle/X2Paddle,x2paddle/decoder/onnx_backend.py,43cf70b5464fab28b06054dbdf5b5d332be6615d,STILL_EXISTS,TODO: This method needs a refactor for clarity
aafcdhifj,PaddlePaddle/X2Paddle,x2paddle/decoder/onnx_backend.py,43cf70b5464fab28b06054dbdf5b5d332be6615d,STILL_EXISTS,so we don't need this hack anymore
aafcdhigd,PaddlePaddle/X2Paddle,x2paddle/decoder/onnx_backend.py,43cf70b5464fab28b06054dbdf5b5d332be6615d,STILL_EXISTS,TODO: should have an unspported list of operators; be optimistic for now
aafcdhigh,PaddlePaddle/X2Paddle,x2paddle/op_mapper/onnx_directly_map.py,43cf70b5464fab28b06054dbdf5b5d332be6615d,6d11992de8824693617955c72d08b7f213d4247e,TODO: pow for scalar exponent
aafcdjcgi,PaddlePaddle/X2Paddle,x2paddle/decoder/onnx_decoder.py,bd84c83f8a07de6352ef1b43cfd98538cac4f248,619b1833392ba49cb73db4d1ba731a0eebaf768f,#TODO add node shape inference
aafcdjcjc,PaddlePaddle/X2Paddle,x2paddle/decoder/onnx_shape_inference.py,bd84c83f8a07de6352ef1b43cfd98538cac4f248,STILL_EXISTS,broadcast from right to left; and merge symbolic dims if needed
aafcdjdec,PaddlePaddle/X2Paddle,x2paddle/decoder/onnx_shape_inference.py,bd84c83f8a07de6352ef1b43cfd98538cac4f248,STILL_EXISTS,handle sympy_data if needed; for slice in shape computation
aafcdjdfe,PaddlePaddle/X2Paddle,x2paddle/decoder/onnx_shape_inference.py,bd84c83f8a07de6352ef1b43cfd98538cac4f248,STILL_EXISTS,unknown op to ONNX; maybe from higher opset or other domain
aafcdjdfh,PaddlePaddle/X2Paddle,x2paddle/decoder/onnx_shape_inference.py,bd84c83f8a07de6352ef1b43cfd98538cac4f248,STILL_EXISTS,continue the inference after guess; no need to stop as no merge is needed
aafcdjhbc,PaddlePaddle/X2Paddle,x2paddle/decoder/onnx_decoder.py,4bb2953c2060b5aa38eec5d11668743121e25f02,STILL_EXISTS,#TODO add node shape inference
aafceadaf,PaddlePaddle/X2Paddle,x2paddle/op_mapper/onnx2paddle/opsets/_shape_inference.py,c1f65a10945788e1d039fbf1b05ccb3e9735324a,STILL_EXISTS,#TODO add node shape inference
aafceadbd,PaddlePaddle/X2Paddle,x2paddle/op_mapper/onnx2paddle/opsets/_shape_inference.py,c1f65a10945788e1d039fbf1b05ccb3e9735324a,STILL_EXISTS,TODO add node shape inference
aafceadcf,PaddlePaddle/X2Paddle,x2paddle/op_mapper/onnx2paddle/opsets/_shape_inference.py,c1f65a10945788e1d039fbf1b05ccb3e9735324a,STILL_EXISTS,broadcast from right to left; and merge symbolic dims if needed
aafceadgf,PaddlePaddle/X2Paddle,x2paddle/op_mapper/onnx2paddle/opsets/custom_layer/__init__.py,c1f65a10945788e1d039fbf1b05ccb3e9735324a,829c47d1c806de5a0e6224f4dc2fcf135552e8d9,custom layer import ends
aafcebbgh,PaddlePaddle/X2Paddle,x2paddle/op_mapper/paddle2onnx/opset11/opset.py,d900306f0c7a0909d98c185af1edb02de1cc0ff3,d43f75b29fe435e14c4dfc19397034d6c86556b6,TODO support pads is Variable
aafcebbgi,PaddlePaddle/X2Paddle,x2paddle/op_mapper/paddle2onnx/opset9/opset.py,d900306f0c7a0909d98c185af1edb02de1cc0ff3,e9e83dadb4681ef7c38e1483fb63ea4c3ca09cdc,TODO support pads is Variable
aafcebgha,PaddlePaddle/X2Paddle,x2paddle/op_mapper/paddle2onnx/opset9/opset.py,cfd80849f4e11498fcbf61cac1543cf28f5bdf1e,d43f75b29fe435e14c4dfc19397034d6c86556b6,TODO support pads is Variable
aafcebghd,PaddlePaddle/X2Paddle,x2paddle/op_mapper/paddle2onnx/opset11/opset.py,4519e2e8ff3cf65b0a804a4ce8e9f2f121b80cd6,STILL_EXISTS,TODO support pads is Variable
aafcebghe,PaddlePaddle/X2Paddle,x2paddle/op_mapper/paddle2onnx/opset9/opset.py,4519e2e8ff3cf65b0a804a4ce8e9f2f121b80cd6,STILL_EXISTS,TODO support pads is Variable
aafcecbgd,PaddlePaddle/X2Paddle,x2paddle/op_mapper/tf_op_mapper_nhwc.py,2913eea10e5fe08cb723cc4fa11def73d448893f,STILL_EXISTS,fix paddle shape infer problem
aafceceeg,PaddlePaddle/X2Paddle,x2paddle/x2paddle/decoder/onnx_decoder.py,cf7f9b882a3ac8b9464ce4f4f662ef7d8b309f35,STILL_EXISTS,#TODO add node shape inference
aafcecehc,PaddlePaddle/X2Paddle,x2paddle/x2paddle/decoder/onnx_shape_inference.py,cf7f9b882a3ac8b9464ce4f4f662ef7d8b309f35,STILL_EXISTS,broadcast from right to left; and merge symbolic dims if needed
aafcecfbe,PaddlePaddle/X2Paddle,x2paddle/x2paddle/decoder/onnx_shape_inference.py,cf7f9b882a3ac8b9464ce4f4f662ef7d8b309f35,STILL_EXISTS,handle sympy_data if needed; for slice in shape computation
aafcecfcg,PaddlePaddle/X2Paddle,x2paddle/x2paddle/decoder/onnx_shape_inference.py,cf7f9b882a3ac8b9464ce4f4f662ef7d8b309f35,STILL_EXISTS,unknown op to ONNX; maybe from higher opset or other domain
aafcecfcj,PaddlePaddle/X2Paddle,x2paddle/x2paddle/decoder/onnx_shape_inference.py,cf7f9b882a3ac8b9464ce4f4f662ef7d8b309f35,STILL_EXISTS,continue the inference after guess; no need to stop as no merge is needed
aafcecfgd,PaddlePaddle/X2Paddle,x2paddle/x2paddle/op_mapper/caffe_custom_layer/__init__.py,cf7f9b882a3ac8b9464ce4f4f662ef7d8b309f35,STILL_EXISTS,custom layer import ends
aafcechcc,PaddlePaddle/X2Paddle,x2paddle/x2paddle/op_mapper/paddle2onnx/opset11/opset.py,cf7f9b882a3ac8b9464ce4f4f662ef7d8b309f35,STILL_EXISTS,TODO support pads is Variable
aafcechih,PaddlePaddle/X2Paddle,x2paddle/x2paddle/op_mapper/paddle2onnx/opset9/opset.py,cf7f9b882a3ac8b9464ce4f4f662ef7d8b309f35,STILL_EXISTS,TODO support pads is Variable
aafcecjdh,PaddlePaddle/X2Paddle,x2paddle/x2paddle/op_mapper/tf_op_mapper.py,cf7f9b882a3ac8b9464ce4f4f662ef7d8b309f35,STILL_EXISTS,fix paddle shape infer problem
aafcecjfd,PaddlePaddle/X2Paddle,x2paddle/x2paddle/op_mapper/tf_op_mapper_nhwc.py,cf7f9b882a3ac8b9464ce4f4f662ef7d8b309f35,STILL_EXISTS,fix paddle shape infer problem
aafcecjff,PaddlePaddle/X2Paddle,x2paddle/x2paddle/op_mapper/tf_op_mapper_nhwc.py,cf7f9b882a3ac8b9464ce4f4f662ef7d8b309f35,STILL_EXISTS,TODO codes without validation
aafcecjii,PaddlePaddle/X2Paddle,x2paddle/x2paddle/optimizer/onnx_optimizer.py,cf7f9b882a3ac8b9464ce4f4f662ef7d8b309f35,STILL_EXISTS,TODO useless node remove
aafcedaac,PaddlePaddle/X2Paddle,x2paddle/x2paddle/optimizer/tf_optimizer.py,cf7f9b882a3ac8b9464ce4f4f662ef7d8b309f35,STILL_EXISTS,TODO useless node remove
aafceefig,PaddlePaddle/X2Paddle,x2paddle/op_mapper/onnx2paddle/opset9/opset.py,2ca2e71ba50b9549d8c687aab5877b75dc6d17bd,ce6ffee2ccf642684044042836732de33a1de787,for idx in range(len(ends_value)):
aafceefih,PaddlePaddle/X2Paddle,x2paddle/op_mapper/onnx2paddle/opset9/opset.py,2ca2e71ba50b9549d8c687aab5877b75dc6d17bd,ce6ffee2ccf642684044042836732de33a1de787,if ends_value[idx] > 2**31 - 1:
aafceefii,PaddlePaddle/X2Paddle,x2paddle/op_mapper/onnx2paddle/opset9/opset.py,2ca2e71ba50b9549d8c687aab5877b75dc6d17bd,ce6ffee2ccf642684044042836732de33a1de787,ends_value[idx] = 2**31 - 1
aafceegah,PaddlePaddle/X2Paddle,x2paddle/op_mapper/tf_op_mapper_nhwc.py,ce6ffee2ccf642684044042836732de33a1de787,STILL_EXISTS,fix paddle shape infer problem
aafcehggj,PaddlePaddle/X2Paddle,x2paddle/op_mapper/onnx2paddle/opset9/opset.py,2c12e94b46cb41c8c776204f9b3c89030c2ecee3,STILL_EXISTS,for idx in range(len(ends_value)):
aafcehgha,PaddlePaddle/X2Paddle,x2paddle/op_mapper/onnx2paddle/opset9/opset.py,2c12e94b46cb41c8c776204f9b3c89030c2ecee3,STILL_EXISTS,if ends_value[idx] > 2**31 - 1:
aafcehghb,PaddlePaddle/X2Paddle,x2paddle/op_mapper/onnx2paddle/opset9/opset.py,2c12e94b46cb41c8c776204f9b3c89030c2ecee3,STILL_EXISTS,ends_value[idx] = 2**31 - 1
aafcehghj,PaddlePaddle/X2Paddle,x2paddle/op_mapper/tf_op_mapper.py,28f4b2fff21a57a36f4a3ab9357713be9c28a8b7,STILL_EXISTS,TODO codes without validation
aafcehjee,PaddlePaddle/X2Paddle,x2paddle/optimizer/optimizer.py,44a7e5ab733e97b08fe068d6aae1384da01fd5be,130e7682347f07c5e0f67ccc57e077bc6b1c5562,TODO
aafceiecb,PaddlePaddle/X2Paddle,x2paddle/op_mapper/dygraph/tf2paddle/tf_op_mapper.py,8aa1008b292061cf505fbcbaeac45dc8c1c9149c,STILL_EXISTS,TODO codes without validation
aafcejiaa,PaddlePaddle/X2Paddle,x2paddle/op_mapper/dygraph/onnx2paddle/opset9/opset.py,51666c110d4bed59389442ab0119f88d5d0eab17,8676a37bc9746d465dd6b7566c43d20381ee560b,for idx in range(len(ends_value)):
aafcejiab,PaddlePaddle/X2Paddle,x2paddle/op_mapper/dygraph/onnx2paddle/opset9/opset.py,51666c110d4bed59389442ab0119f88d5d0eab17,8676a37bc9746d465dd6b7566c43d20381ee560b,if ends_value[idx] > 2**31 - 1:
aafcejiac,PaddlePaddle/X2Paddle,x2paddle/op_mapper/dygraph/onnx2paddle/opset9/opset.py,51666c110d4bed59389442ab0119f88d5d0eab17,8676a37bc9746d465dd6b7566c43d20381ee560b,ends_value[idx] = 2**31 - 1
aafcfggba,NIHOPA/NLPre,nlpre/remove_parenthesis.py,1092bd2b524a4bdeb888de478974f85978f8669f,STILL_EXISTS,Should instead probably remove all parens in outermost parens; while still removing words in
aafcfggfb,NIHOPA/NLPre,setup.py,7b6c5118d604e358bbbf9fc3fe7ae04c6ab1bf51,f16ada56da3f40a007356643e3286c9d109a59b3,You can just specify the packages manually here if your project is
aafcfgggh,NIHOPA/NLPre,tests/pos_tokenizer_tests.py,361a12bf569c0afb725ae3bbeef63a821107a1b8,af107065942541bf30538d98aea6d9cc9a9aae76,This passes; when it shouldn't. not sure if there's any way around it
aafcfgidh,NIHOPA/NLPre,tests/pos_tokenizer_tests.py,efa8889c7fe87965abe3492e7d15f1d2a094b1ae,af107065942541bf30538d98aea6d9cc9a9aae76,This passes; when it shouldn't. not sure if there's any way around it
aafcfheae,NIHOPA/NLPre,nlpre/replace_acronyms.py,36904932f83f42ef30a3de3bb483dd9b001f9c9e,3589b1d233ec3d0da3d7a83b225bcf5d730dba13,not really efficient to re-run the counter. I think saving counters
aafcfheeg,NIHOPA/NLPre,nlpre/replace_acronyms.py,43de830648686334aaf97e3a885e72d3b1a88782,STILL_EXISTS,seems extremely problematic way to do a loop
aafcfhfdj,NIHOPA/NLPre,tests/footnotes_test.py,c4bc65cbd70c87d1df330cdd87bff31494e85db4,524f024eec093db9631df6b2120cfcb6f6b4a87e,doc = \"How is the treat-ment.5 going. Pretty well\"
aafcfhfea,NIHOPA/NLPre,tests/footnotes_test.py,c4bc65cbd70c87d1df330cdd87bff31494e85db4,524f024eec093db9631df6b2120cfcb6f6b4a87e,doc_right = \"How is the treatment going . Pretty well\"
aafcfhgch,NIHOPA/NLPre,nlpre/separate_reference.py,193ac318fa8cf06744a814f4a8912af5c589fad8,STILL_EXISTS,better way to do this?
aafcfhghd,NIHOPA/NLPre,nlpre/separate_reference.py,d9fff5fe8b661c288501b0c8ff05e469dc3d6170,STILL_EXISTS,this if the token ends with a parenthesis that holds a nested
aafcfhhff,NIHOPA/NLPre,setup.py,f8db61497c0128ed302405a3fcc49de1107fc4e1,STILL_EXISTS,Fix the version of mysqlclient due to windows problems
aafcfhiff,NIHOPA/NLPre,setup.py,29126d7c62abfcc4831229894a9681ddeadd617c,STILL_EXISTS,Fix the minor version so model doesn't change
aafcfhifi,NIHOPA/NLPre,nlpre/_version.py,5c9c2ca31cebdff21910a09e8bd24250b41060b1,STILL_EXISTS,2.0.2 Fix manifest for pypi
aafcfhjfg,openml/automlbenchmark,docker/H2OAutoML/run_h2oautoml.py,0420730f9bfe570c76060b2b88541ba1578e6be5,STILL_EXISTS,TO DO: Maybe pass in a memory size as an argument to use here
aafcfhjih,openml/automlbenchmark,docker/RandomForest/run_randomforest.py,32b21bcfd696720cdf070eb924c13bbe08a0e531,STILL_EXISTS,TO DO: If auto-sklearn & TPOT also require imputation & dummy encoding; let's move this to common_code
aafcfibab,openml/automlbenchmark,automl/benchmark.py,5e33309d167c93118119c4b6d92245d8b7d4b3cf,STILL_EXISTS,todo
aafcfibhf,openml/automlbenchmark,automl/benchmark.py,3032c7f807b8fb0736e4b9f9226fd2f81050f74c,STILL_EXISTS,todo: check available memory with possible warning
aafcfibhj,openml/automlbenchmark,automl/frameworks/H2OAutoML/exec.py,3032c7f807b8fb0736e4b9f9226fd2f81050f74c,STILL_EXISTS,TODO: Figure out if we are going to blindly pass metrics through; or if we use a strict mapping
aafcfibif,openml/automlbenchmark,automl/frameworks/RandomForest/exec.py,3032c7f807b8fb0736e4b9f9226fd2f81050f74c,8de3cc3779743d981e5b3771e749a5629b4d6e50,TODO: Probably have to add a dummy encoder here in case there's any categoricals
aafcfibig,openml/automlbenchmark,automl/frameworks/RandomForest/exec.py,3032c7f807b8fb0736e4b9f9226fd2f81050f74c,8de3cc3779743d981e5b3771e749a5629b4d6e50,TODO: If auto-sklearn & TPOT also require imputation & dummy encoding; let's move this to common_code
aafcfibih,openml/automlbenchmark,automl/frameworks/RandomForest/exec.py,3032c7f807b8fb0736e4b9f9226fd2f81050f74c,7cf46ef3d6aa023314729cf528de8c775135fa7a,todo: accuracy
aafcfibii,openml/automlbenchmark,automl/frameworks/RandomForest_r/exec.py,3032c7f807b8fb0736e4b9f9226fd2f81050f74c,STILL_EXISTS,TODO use rpy2 instead? not necessary here though as the call is very simple
aafcfibij,openml/automlbenchmark,automl/frameworks/RandomForest_r/exec.py,3032c7f807b8fb0736e4b9f9226fd2f81050f74c,7cf46ef3d6aa023314729cf528de8c775135fa7a,todo: accuracy
aafcfibje,openml/automlbenchmark,automl/frameworks/autosklearn/exec.py,3032c7f807b8fb0736e4b9f9226fd2f81050f74c,STILL_EXISTS,TODO: Figure out if we are going to blindly pass metrics through; or if we use a strict mapping
aafcfibjg,openml/automlbenchmark,automl/frameworks/autosklearn/exec.py,3032c7f807b8fb0736e4b9f9226fd2f81050f74c,STILL_EXISTS,TODO: Do we need to set per_run_time_limit too?
aafcficab,openml/automlbenchmark,automl/openml.py,3032c7f807b8fb0736e4b9f9226fd2f81050f74c,f62f170886445bf275c8d2e51aeb40727d9726f7,todo; rely on default openml setup; apikey is private...
aafcficah,openml/automlbenchmark,automl/openml.py,3032c7f807b8fb0736e4b9f9226fd2f81050f74c,STILL_EXISTS,TODO: support encoded string columns?
aafcficbb,openml/automlbenchmark,automl/aws.py,e508ac893493d50a5b64a1d37cc80c1aea1e04fc,STILL_EXISTS,todo: setup connection to EC2
aafcficbc,openml/automlbenchmark,automl/docker.py,e508ac893493d50a5b64a1d37cc80c1aea1e04fc,8a56486367d7183c2220a0fa691b2dc41ba1091c,todo: if auto check if image is already built
aafcficca,openml/automlbenchmark,automl/benchmark.py,0e33a4bacffad7cb9c9259d46df2f72a35a012fc,7cf46ef3d6aa023314729cf528de8c775135fa7a,todo: score predictions and print results
aafcficdg,openml/automlbenchmark,automl/docker.py,89f105718f5eb84dedd86ae8e4c5ad966a6a457d,STILL_EXISTS,fixme: handle subprocesses errors
aafcficdh,openml/automlbenchmark,automl/data.py,3bf2d2c4344451b3d35995d66907d8ed754b9695,STILL_EXISTS,todo: should we use one_hot_encoder here instead?
aafcficdj,openml/automlbenchmark,runbenchmark.py,0795e1bf838177f1cd9e950bb5fdf2d96f996630,STILL_EXISTS,todo: allow a custom automlbenchmark_config.json in user directory: maybe this would allow removal of parameters like region; indir; outdir
aafcficed,openml/automlbenchmark,automl/results.py,f0bc1226f78093b518ebd88d0c1095b8c30199e5,STILL_EXISTS,reorder columns alphabetically: necessary to match label encoding
aafcficee,openml/automlbenchmark,automl/utils.py,f0bc1226f78093b518ebd88d0c1095b8c30199e5,5ac82b5000cc1fec4555f2aa93b8ddb309c18ac4,todo handle mask
aafcficef,openml/automlbenchmark,automl/results.py,b0565af56c114f7363b33b592c7dc7b2c5ec55d8,STILL_EXISTS,todo: add mode? local; docker; aws
aafcficeg,openml/automlbenchmark,automl/results.py,b0565af56c114f7363b33b592c7dc7b2c5ec55d8,STILL_EXISTS,todo: sort the columns to have index columns; followed by result; metrics and finally version and time
aafcficeh,openml/automlbenchmark,automl/results.py,b0565af56c114f7363b33b592c7dc7b2c5ec55d8,STILL_EXISTS,todo: append
aafcficei,openml/automlbenchmark,automl/__init__.py,28b28db42b8f1526e257105c0ccfa2e91ef70d0c,6923426bf22d3e314340b3744582b79f61ed99a6,TODO:
aafcficfi,openml/automlbenchmark,automl/aws.py,28b28db42b8f1526e257105c0ccfa2e91ef70d0c,717b052e90a1e8ce9c523173c96d6802ffe080df,apparently no need to load bucket
aafcficgd,openml/automlbenchmark,automl/resources.py,28b28db42b8f1526e257105c0ccfa2e91ef70d0c,4ad3605c7ef8b7190a69357c13e282c577783f47,todo: validate docker image definition? anything else?
aafcficge,openml/automlbenchmark,automl/results.py,28b28db42b8f1526e257105c0ccfa2e91ef70d0c,STILL_EXISTS,TODO: reconsider organisation of output files:
aafcficgh,openml/automlbenchmark,automl/results.py,28b28db42b8f1526e257105c0ccfa2e91ef70d0c,STILL_EXISTS,todo: sort the columns to have index columns; followed by result; metrics and finally version and utc time
aafcficgi,openml/automlbenchmark,automl/results.py,28b28db42b8f1526e257105c0ccfa2e91ef70d0c,STILL_EXISTS,reorder columns alphabetically: necessary to match label encoding
aafcficgj,openml/automlbenchmark,automl/results.py,28b28db42b8f1526e257105c0ccfa2e91ef70d0c,STILL_EXISTS,fixme: at the end; we're always running in local mode!!!
aafcficha,openml/automlbenchmark,automl/results.py,28b28db42b8f1526e257105c0ccfa2e91ef70d0c,STILL_EXISTS,todo: sort the columns to have index columns; followed by result; metrics and finally version and time
aafcfichb,openml/automlbenchmark,automl/results.py,28b28db42b8f1526e257105c0ccfa2e91ef70d0c,STILL_EXISTS,todo: append
aafcfichc,openml/automlbenchmark,automl/utils.py,28b28db42b8f1526e257105c0ccfa2e91ef70d0c,STILL_EXISTS,todo: could support unlimited args by making a tuple out of *args + **kwargs: not needed for now
aafcfiche,openml/automlbenchmark,automl/utils.py,28b28db42b8f1526e257105c0ccfa2e91ef70d0c,STILL_EXISTS,todo: switch to subprocess module (Popen) instead of os? would allow to use timeouts and kill signal
aafcficid,openml/automlbenchmark,automl/aws.py,2da86b32b16e40af86b2c422b0be375d181a1973,STILL_EXISTS,todo: parallelization improvement -> in many situations; creating a job for each fold may end up being much slower
aafcficig,openml/automlbenchmark,automl/aws.py,2da86b32b16e40af86b2c422b0be375d181a1973,STILL_EXISTS,todo: pass path to downloaded benchmark def file
aafcficih,openml/automlbenchmark,automl/aws.py,2da86b32b16e40af86b2c422b0be375d181a1973,STILL_EXISTS,todo: error handling
aafcficii,openml/automlbenchmark,automl/aws.py,2da86b32b16e40af86b2c422b0be375d181a1973,STILL_EXISTS,todo: ideally; would be nice to monitor instance individually and asynchronously; cf. asyncio
aafcficij,openml/automlbenchmark,automl/aws.py,2da86b32b16e40af86b2c422b0be375d181a1973,STILL_EXISTS,todo error handling
aafcficja,openml/automlbenchmark,automl/aws.py,2da86b32b16e40af86b2c422b0be375d181a1973,717b052e90a1e8ce9c523173c96d6802ffe080df,todo: upload benchmark definition to bucket
aafcficjb,openml/automlbenchmark,automl/aws.py,2da86b32b16e40af86b2c422b0be375d181a1973,717b052e90a1e8ce9c523173c96d6802ffe080df,todo:
aafcfidag,openml/automlbenchmark,automl/benchmark.py,2da86b32b16e40af86b2c422b0be375d181a1973,STILL_EXISTS,todo: timeout
aafcfidaj,openml/automlbenchmark,automl/results.py,2da86b32b16e40af86b2c422b0be375d181a1973,STILL_EXISTS,todo: detect format change; i.e. data_frame columns are different or different order from existing file
aafcfidba,openml/automlbenchmark,automl/results.py,2da86b32b16e40af86b2c422b0be375d181a1973,717b052e90a1e8ce9c523173c96d6802ffe080df,todo: backup existing file; i.e. rename to {file_name}_{last_write_time}.ext
aafcfidbi,openml/automlbenchmark,automl/aws.py,717b052e90a1e8ce9c523173c96d6802ffe080df,af562071c2f89a54f94d405cae8514d3adb54673,todo: saving scores for now after backing up previous scores but this should be merged to existing scores files!!!
aafcfidbj,openml/automlbenchmark,automl/aws.py,717b052e90a1e8ce9c523173c96d6802ffe080df,STILL_EXISTS,TODO: idea is to handle results progressively on the remote side and push results as soon as they're generated
aafcfidcb,openml/automlbenchmark,automl/aws.py,717b052e90a1e8ce9c523173c96d6802ffe080df,STILL_EXISTS,todo: pass path to downloaded benchmark def file
aafcfidcd,openml/automlbenchmark,automl/aws.py,717b052e90a1e8ce9c523173c96d6802ffe080df,STILL_EXISTS,todo: ideally; would be nice to monitor instance individually and asynchronously; cf. asyncio
aafcfideh,openml/automlbenchmark,automl/datautils.py,5ac82b5000cc1fec4555f2aa93b8ddb309c18ac4,STILL_EXISTS,todo handle mask
aafcfidfa,openml/automlbenchmark,automl/aws.py,9626b806d670d0b58d210de134e7abbdc277ed8b,STILL_EXISTS,todo: don't know if it would be considerably faster to reuse previously stopped instances sometimes
aafcfidfd,openml/automlbenchmark,automl/aws.py,9626b806d670d0b58d210de134e7abbdc277ed8b,STILL_EXISTS,fixme: bypassing the save_scores flag here; do we care?
aafcfidfg,openml/automlbenchmark,automl/results.py,9626b806d670d0b58d210de134e7abbdc277ed8b,STILL_EXISTS,todo: detect format change; i.e. data_frame columns are different or different order from existing file
aafcfidge,openml/automlbenchmark,automl/__init__.py,87794bc59204ad4f6b588ba064785a00221114b1,68656eba9bba718538d5ac7b766097da5ae10466,but using docker inside AWS could improve reproducibility; although it requires building+publishing+maintaining multiple images
aafcfidgf,openml/automlbenchmark,automl/__init__.py,87794bc59204ad4f6b588ba064785a00221114b1,68656eba9bba718538d5ac7b766097da5ae10466,Note that current generic docker support allows running multiple docker instances in parallel; so we could
aafcfidic,openml/automlbenchmark,automl/aws.py,68656eba9bba718538d5ac7b766097da5ae10466,STILL_EXISTS,\"\"\" || **aws** module is built on top of **benchmark** to provide the platform-specific logic || necessary to run a benchmark on EC2 instances: ||  || - create a S3 bucket (if it doesn't already exist). || - upload some resources on S3. || - configures an AWS IAM profile to provide read\/write access to the S3 bucket from the future EC2 instances. || - create jobs and start an EC2 instance for each job: ||     - the EC2 instance download some resources from S3. ||     - the EC2 instance runs the task locally or using docker. ||     - on task completion; the EC2 instance uploads the results and logs to S3 and stops. || - monitors each job and downloads results and logs from s3 when the job is completed. || - merge downloaded results with existing\/local results. || - properly cleans up AWS resources (S3; EC2). || \"\"\"
aafcfidih,openml/automlbenchmark,automl/frameworks/__init__.py,68656eba9bba718538d5ac7b766097da5ae10466,STILL_EXISTS,\"\"\" || the **frameworks** package contains all the automl framework subpackages: || each of those framework package can expose the following functions: ||  || - ``run(*args; **kvargs)``: this function is mandatory for each package and is called by each job; ||     providing a ``Dataset`` and a ``TaskConfig`` instance to the framework. ||  ||     The framework should run its automl implementation against the provided dataset; ||     and should try to honour the constraints provided by the task_config. ||  ||     This function is usually implemented by importing the ``exec`` module dynamically ||     and forwarding the parameters to its own ``run`` function:: ||         def run(*args; **kwargs): ||             from .exec import run ||             run(*args; **kwargs) ||  ||     this provides the possibility; if necessary \u2013 for example if the framework depends on libraries incompatible with the app \u2013; ||     to delegates the execution to a different process after serializing the parameters (using json or pickle for example). ||  || - ``setup(*args)``: this function is optional and is called to let the framework install its required dependencies. || - ``docker_commands()``: this function is optional and should return a string with docker commands ||     necessary to install the framework dependencies when building the docker image. ||  ||  || important ||     Ideally; frameworks entry packages should not import any automl module outside **utils** for the reason explained above. ||  || \"\"\"
aafcfidja,openml/automlbenchmark,automl/resources.py,68656eba9bba718538d5ac7b766097da5ae10466,STILL_EXISTS,\"\"\" || **resources** modules exposes a singleton ``Resources`` instance providing easy access to app configuration properties; || as well as handy methods to access other resources like *automl frameworks* and *benchmark definitions* || \"\"\"
aafcfidjd,openml/automlbenchmark,runbenchmark.py,68656eba9bba718538d5ac7b766097da5ae10466,STILL_EXISTS,todo: we can probably remove this command line argument: by default; we're using the user default region as defined in ~\/aws\/config
aafcfieac,openml/automlbenchmark,automl/datautils.py,b663878250d64fdcd14608574f3d28dd8b5f5504,STILL_EXISTS,todo: impute only if np.isnan(X_fit).any() ?
aafcfiecb,openml/automlbenchmark,runbenchmark.py,f62f170886445bf275c8d2e51aeb40727d9726f7,STILL_EXISTS,todo: we can probably remove this command line argument: by default; we're using the user default region as defined in ~\/aws\/config
aafcfieci,openml/automlbenchmark,automl/datautils.py,47ef33cfa63c3e8a9cc9a0a75e9064dae5f21d9d,STILL_EXISTS,no reordering needed; not data to load; returning original path
aafcfiedb,openml/automlbenchmark,automl/datautils.py,47ef33cfa63c3e8a9cc9a0a75e9064dae5f21d9d,STILL_EXISTS,no reordering needed; returning loaded data
aafcfiedc,openml/automlbenchmark,automl/datautils.py,47ef33cfa63c3e8a9cc9a0a75e9064dae5f21d9d,STILL_EXISTS,no reordering needed; returning loaded data or original path
aafcfiedd,openml/automlbenchmark,automl/datautils.py,47ef33cfa63c3e8a9cc9a0a75e9064dae5f21d9d,STILL_EXISTS,todo: provide the possibility to return data even if save is set to false;
aafcfiedg,openml/automlbenchmark,automl/openml.py,47ef33cfa63c3e8a9cc9a0a75e9064dae5f21d9d,STILL_EXISTS,todo: make auto split 80% train; 20% test (make this configurable; also random vs sequential) and save it to disk
aafcfieed,openml/automlbenchmark,automl/aws.py,35a6dbf06ced189a1782f3c0e38275ac2fd6c702,STILL_EXISTS,todo: do we still want to delete resources if concern in _upload_resources is fixed?
aafcfieee,openml/automlbenchmark,automl/benchmark.py,35a6dbf06ced189a1782f3c0e38275ac2fd6c702,ee576298ba8cbab7255980fabe620d5cdb6b1c0e,allows to import modules outside project: should work on AWS as well;
aafcfieeg,openml/automlbenchmark,automl/frameworks/H2OAutoML/__init__.py,35a6dbf06ced189a1782f3c0e38275ac2fd6c702,STILL_EXISTS,fixme: doesn't allow to build docker images for custom versions of h2o
aafcfieeh,openml/automlbenchmark,automl/frameworks/H2OAutoML/exec.py,35a6dbf06ced189a1782f3c0e38275ac2fd6c702,STILL_EXISTS,todo: should we be able to pass as seed for reproducibility (or to see improvements\/degradation across versions)
aafcfiegf,openml/automlbenchmark,automl/aws.py,6923426bf22d3e314340b3744582b79f61ed99a6,fd6cd4ee8bc13de920e7baefe567384284fad25e,TODO: error handling
aafcfiegg,openml/automlbenchmark,automl/benchmark.py,6923426bf22d3e314340b3744582b79f61ed99a6,STILL_EXISTS,TODO
aafcfiegj,openml/automlbenchmark,automl/results.py,ccc5d22b82b079806341c3fc7e8c3d0e10c2f416,1dff17a720f0408fbbd3feaaa26368e7b5f8b19e,params=str(framework_def.params); # TODO: enable this?
aafcfiehf,openml/automlbenchmark,automl/job.py,8f0b25178dd80222728ce6e650363d141a279c1c,STILL_EXISTS,TODO: timeout
aafcfijjg,openml/automlbenchmark,frameworks/autoxgboost/exec.py,560217750d4f4f9a0bd20a8ed56106d6950883c8,STILL_EXISTS,TODO: use rpy2 instead? not necessary here though as the call is very simple
aafcfjabe,openml/automlbenchmark,amlb/utils/cache.py,8c025fc4be06d8624c7f32a0bcd97c9d9a322c3c,STILL_EXISTS,TODO: could support unlimited args by making a tuple out of *args + **kwargs: not needed for now
aafcfjbab,openml/automlbenchmark,reports/report/results.py,5d4a89ae38d85e31c3815f844d07c2dfaca1840a,STILL_EXISTS,\"\"\" || Loading results; formatting and adding columns || result is the raw result metric computed from predictions at the end the benchmark. For classification problems; it is usually auc for binomial classification and logloss for multinomial classification. || score ensures a standard comparison between tasks: higher is always better. || norm_score is a normalization of score on a [0; 1] scale; with {{zero_one_refs[0]}} score as 0 and {{zero_one_refs[1]}} score as 1. || imp_result and imp_score for imputed results\/scores. Given a task and a framework: || if all folds results\/scores are missing; then no imputation occurs; and the result is nan for each fold. || if only some folds results\/scores are missing; then the missing result is imputed by the {{impute_missing_with}} result for this fold. || \"\"\"
aafcfjcfd,openml/automlbenchmark,amlb/container.py,21a67929c6ffd5b6d4f0018ad56be45651d016f2,STILL_EXISTS,TODO: remove generated script? anything else?
aafcfjcff,openml/automlbenchmark,amlb/container.py,21a67929c6ffd5b6d4f0018ad56be45651d016f2,STILL_EXISTS,TODO: would be nice to reload generated scores and return them
aafcfjcfj,openml/automlbenchmark,amlb/singularity.py,21a67929c6ffd5b6d4f0018ad56be45651d016f2,STILL_EXISTS,\"\"\" || **Singularity** module is build on top of **ContainerBenchmark** module to provide logic to create and run singularity images || that are preconfigured with a given automl framework; and that can be used to run a benchmark anywhere. || The Singularity image embeds a version of the automlbenchmark app so that tasks are later run in local mode inside singularity; || providing the same parameters and features allowing to import config and export results through mounted folders. || The image is pulled form an existing docker; yet executed in singularity framework || \"\"\"
aafcfjdca,openml/automlbenchmark,frameworks/supervised/exec.py,0a788289e023bd63ca2e0fb2d633ae35177902b1,STILL_EXISTS,cast columns to have float types
aafcfjdce,openml/automlbenchmark,frameworks/supervised/exec.py,0a788289e023bd63ca2e0fb2d633ae35177902b1,STILL_EXISTS,In this benchmark; there can be casses when it is much more classes than 20. That's why we set manually.
aafcfjdcj,openml/automlbenchmark,frameworks/AutoGluon/exec.py,0e53e03e147540873176adfd57e45b2a86ea25e8,STILL_EXISTS,TODO: figure out if we are going to blindly pass metrics through; or if we use a strict mapping
aafcfjfcc,openml/automlbenchmark,amlb/resources.py,eac52ba5aa82d488ff33c2381675832ad9a2123c,STILL_EXISTS,todo: inherit from parent
aafcfjffh,openml/automlbenchmark,frameworks/H2OAutoML/exec.py,7a33f2fa64426b246e63f8be8ceaaae0730360ad,STILL_EXISTS,for categories represented as numerical values; h2o prefixes the probabilities columns with p
aafcfjffj,openml/automlbenchmark,amlb/datasets/openml.py,6df9dd20213267f1bfbad91020f22b3a158599e7,STILL_EXISTS,hack (only adding a ? to the regexp pattern) to ensure that '?' values remain quoted when we save dataplits in arff format.
aafcfjgaa,openml/automlbenchmark,frameworks/AutoGluon/exec.py,3bef65cd3e980081320ca8f0b05cc76c1a65f66c,STILL_EXISTS,TODO: figure out if we are going to blindly pass metrics through; or if we use a strict mapping
aafcfjgej,IndicoDataSolutions/Enso,experiment.py,d7ec5d136e0dd4b811735e62744deea438ccc9bc,STILL_EXISTS,Sklearn technically offers a train_size parameter that seems like it would be better
aafcfjgig,IndicoDataSolutions/Enso,visualize/facets.py,2d10722ec1a5eed1aa4ce0b1044f91b531009a32,ae08568224dc24a9fc96a3abf513f86b7b0679ec,TODO: support non-merged classes
aafcfjhcc,IndicoDataSolutions/Enso,enso/featurize/transformer_features.py,ac2dbb2766ab21fd4b6848a91fb95dc45e28f7e3,STILL_EXISTS,TODO: change this to a dependency on the \"transformer\"
aafcfjjjd,IndicoDataSolutions/Enso,enso/visualize/facets.py,ae5602d3d008cc8b290b32e380fb72ae8fb53bd7,b66bc6150e45b5563a2b56d57d9653ebb9bc7f0e,TODO: REMOVE ME -- THIS IS CUSTOM LOGIC FOR BIA DISPLAY
aafcgabdg,IndicoDataSolutions/Enso,enso/download/customer_reviews.py,9bb0657b14dfe336b66d898bdbb0d6462a205d6f,STILL_EXISTS,\"\"\" || From: https:\/\/www.figure-eight.com\/data-for-everyone\/ ||  || Customer review task from SentEval.  Note that performance on this dataset is not comparable to official SentEval scores because of differences in data splitting. || \"\"\"
aafcgabec,IndicoDataSolutions/Enso,enso/download/movie_reviews.py,9bb0657b14dfe336b66d898bdbb0d6462a205d6f,STILL_EXISTS,\"\"\" || From: https:\/\/www.figure-eight.com\/data-for-everyone\/ ||  || Movie review task from SentEval.  Note that performance on this dataset is not comparable to official SentEval scores because of differences in data splitting. || \"\"\"
aafcgabed,IndicoDataSolutions/Enso,enso/download/mpqa.py,9bb0657b14dfe336b66d898bdbb0d6462a205d6f,STILL_EXISTS,\"\"\" || From: https:\/\/www.figure-eight.com\/data-for-everyone\/ ||  || MPQA task from SentEval.  Note that performance on this dataset is not comparable to official SentEval scores because of differences in data splitting. || \"\"\"
aafcgabei,IndicoDataSolutions/Enso,enso/download/sst_binary.py,9bb0657b14dfe336b66d898bdbb0d6462a205d6f,STILL_EXISTS,\"\"\" || From: https:\/\/www.figure-eight.com\/data-for-everyone\/ ||  || Stanford Sentiment Treebank binary task from SentEval.  Note that performance on this dataset is not comparable to official SentEval scores because of differences in data splitting. || \"\"\"
aafcgabej,IndicoDataSolutions/Enso,enso/download/subjectivity.py,9bb0657b14dfe336b66d898bdbb0d6462a205d6f,STILL_EXISTS,\"\"\" || From: https:\/\/www.figure-eight.com\/data-for-everyone\/ ||  || Subjectivity task from SentEval.  Note that performance on this dataset is not comparable to official SentEval scores because of differences in data splitting. || \"\"\"
aafcgadhh,IndicoDataSolutions/Enso,enso/featurize/__init__.py,90b4ec81b1b3ba2253d17112bb955025fb6a38d4,STILL_EXISTS,TODO Data is hard coded although seems configurable from config.
aafcgbbch,IndicoDataSolutions/Enso,enso/experiment/__init__.py,f1ac9cc719ef1a6f4755d3916684b42398031231,STILL_EXISTS,add the experiment params to self.columns
aafcgcfcj,SPFlow/SPFlow,src/spn/gpu/TensorFlow.py,aeb61db1e3cdf4acecf37f95eeff2062ec5b8275,STILL_EXISTS,TODO: make weights as variables
aafcgcfej,SPFlow/SPFlow,src/spn/algorithms/Inference.py,17850af896582ee071cccda2c4362d06fca17aac,aadafe1be9d26b12f002107c4bf88ab044abd7f5,TODO: binary search
aafcgcffa,SPFlow/SPFlow,src/spn/algorithms/Inference.py,17850af896582ee071cccda2c4362d06fca17aac,d4228bab1f3b72f1fd44d0abba623e1eb1dbd7a4,TODO: parallelize here
aafcgcfhb,SPFlow/SPFlow,src/spn/tests/jittest.py,d4228bab1f3b72f1fd44d0abba623e1eb1dbd7a4,STILL_EXISTS,TODO: binary search
aafcgcfhf,SPFlow/SPFlow,src/spn/algorithms/Inference.py,758c72bd7c70e35f2f0bc106d7e9cab224a90e49,90ae551dc80404b29f4b29279b455e9024457ee4,TODO: parallelize here
aafcgcfif,SPFlow/SPFlow,src/spn/algorithms/Inference.py,758c72bd7c70e35f2f0bc106d7e9cab224a90e49,8b9076f92523e502e3afdf989293bb274ce5c3bc,TODO: test this function super thorougly
aafcgcgif,SPFlow/SPFlow,src/spn/algorithms/Posteriors.py,758c72bd7c70e35f2f0bc106d7e9cab224a90e49,STILL_EXISTS,TODO: this is the same as in update the posterior parameters
aafcgcidj,SPFlow/SPFlow,src/spn/structure/leaves/histogram/Inference.py,758c72bd7c70e35f2f0bc106d7e9cab224a90e49,STILL_EXISTS,TODO: binary search
aafcgdagc,SPFlow/SPFlow,src/spn/algorithms/Inference.py,8b9076f92523e502e3afdf989293bb274ce5c3bc,aadafe1be9d26b12f002107c4bf88ab044abd7f5,TODO: binary search
aafcgdbad,SPFlow/SPFlow,src/spn/algorithms/Inference.py,aadafe1be9d26b12f002107c4bf88ab044abd7f5,90ae551dc80404b29f4b29279b455e9024457ee4,TODO: test this function super thorougly
aafcgecfa,SPFlow/SPFlow,src/spn/experiments/AQP/tests/cumulative_distribution.py,81a76a7aa4080f13b4b6032340739685b1cc2a65,STILL_EXISTS,Our scenario is easy we exactly know what value we want
aafcgedfg,SPFlow/SPFlow,src/spn/structure/leaves/piecewise/SamplingRange.py,64c9baa8fb332d2ab69b4227b02ed6f73e210f00,STILL_EXISTS,Our scenario is easy we exactly know what value we want
aafcgeehi,SPFlow/SPFlow,src/spn/structure/leaves/conditional/Conditional.py,3991e825a4f0537640d0cb52949b49d67df8e03d,178553d4d4edfdc155d876c5ac75aaa1105445ee,todo
aafcgeeid,SPFlow/SPFlow,src/spn/structure/leaves/conditional/MLE.py,3991e825a4f0537640d0cb52949b49d67df8e03d,178553d4d4edfdc155d876c5ac75aaa1105445ee,todo node.stdev?
aafcgefcd,SPFlow/SPFlow,src/spn/DeepNotebooks/ba_functions.py,015b24c085f9476e1eac6decf982d9f4f9872d14,STILL_EXISTS,TODO: That threshold needs some evidence or theoretical grounding
aafcgefdi,SPFlow/SPFlow,src/spn/DeepNotebooks/ba_plot.py,015b24c085f9476e1eac6decf982d9f4f9872d14,STILL_EXISTS,dirty hack which produces the centers for the bar chart
aafcgefea,SPFlow/SPFlow,src/spn/DeepNotebooks/ba_sample_code.py,015b24c085f9476e1eac6decf982d9f4f9872d14,STILL_EXISTS,import all needed data and modules
aafcgeghb,SPFlow/SPFlow,src/spn/structure/leaves/conditional/MLE.py,b6708055bae21905af8a6e8394fbc1c01400ab8d,STILL_EXISTS,todo double check here
aafcgeghc,SPFlow/SPFlow,src/spn/structure/leaves/conditional/Inference.py,9c8ad01cccf7f059636890d2797d3d6d555cdb55,178553d4d4edfdc155d876c5ac75aaa1105445ee,todo should node.scope be adjusted?
aafcgeghe,SPFlow/SPFlow,src/spn/structure/leaves/conditional/Inference.py,9c8ad01cccf7f059636890d2797d3d6d555cdb55,178553d4d4edfdc155d876c5ac75aaa1105445ee,todo check again node.scope and par scope
aafcgegjb,SPFlow/SPFlow,src/spn/algorithms/LearningWrappers.py,a7dc907e37d84eef38cee63ee69a4e53abf155bb,b96cde8b2f78955fb608bd72e8d23532cfa412c0,todo add other clustering?
aafcgegjc,SPFlow/SPFlow,src/spn/algorithms/splitting/RCoT.py,a7dc907e37d84eef38cee63ee69a4e53abf155bb,STILL_EXISTS,todo check scope and node.scope again
aafcgfdib,SPFlow/SPFlow,src/spn/structure/leaves/conditional/Sampling.py,5222b5bcc2a62aa32731fb965bc14cb75794eb4a,STILL_EXISTS,todo tmp test
aafcgfhgb,SPFlow/SPFlow,src/spn/structure/leaves/conditional/Conditional.py,efe5f587b4535a9fa389d2883dcd826545283197,c52045a02486d7c3793b1083f5b281a1e5fdb4fb,todo
aafcgfhhb,SPFlow/SPFlow,src/spn/structure/leaves/conditional/MLE.py,efe5f587b4535a9fa389d2883dcd826545283197,c52045a02486d7c3793b1083f5b281a1e5fdb4fb,output_mask = np.zeros(data.shape; dtype=bool)   # todo check scope and node.scope again
aafcgfhhi,SPFlow/SPFlow,src/spn/structure/leaves/conditional/Sampling.py,efe5f587b4535a9fa389d2883dcd826545283197,STILL_EXISTS,todo tmp test
aafcgfjci,SPFlow/SPFlow,src/DeepNotebooks/ba_functions.py,d1cd01fc5f5d08024fbe690080aa59508b1c1929,STILL_EXISTS,TODO: Build Context wrapper according to README.md; this should work pretty well
aafcgfjdg,SPFlow/SPFlow,src/DeepNotebooks/nalgene/generate.py,d1cd01fc5f5d08024fbe690080aa59508b1c1929,STILL_EXISTS,TODO: Remove?
aafcggbgb,SPFlow/SPFlow,src/spn/structure/leaves/conditional/Sampling.py,f997a256291b6ee90a75ba68bc6156da33f6e46a,STILL_EXISTS,todo tmp test
aafcggcai,SPFlow/SPFlow,src/spn/algorithms/stats/Correlations.py,e17b59a667a7e3135b2724c2e0cc748833ac73c5,STILL_EXISTS,TODO: Check whether this is correct
aafcggcef,SPFlow/SPFlow,src/spn/algorithms/stats/ClusterAnalysis.py,b80425cb2611ee031c4fe43e5c7969173d0aaaf7,STILL_EXISTS,TODO: That threshold needs some evidence or theoretical grounding
aafcggdcb,SPFlow/SPFlow,src/spn/tests/test_text.py,94e0e55b44297a4c2f893f369a7d76d7b4eeaedd,STILL_EXISTS,TODO: add test for spn to json
aafcggdcf,SPFlow/SPFlow,src/spn/tests/test_pwl.py,d529a7bab12c383845f5c4981761b544f455f866,STILL_EXISTS,TODO: add more test to the PWL
aafcggddj,SPFlow/SPFlow,src/spn/algorithms/LearningWrappers.py,d587c61312e90a705e1e49b71a74c07745528304,4cc874383f77ab204bde61c0586cc515e9caed60,todo add other clustering?
aafcggdhg,SPFlow/SPFlow,src/spn/algorithms/splitting/ParametricTests.py,efbecf62293c86ab1283c4d0ffd7552d6354ba66,STILL_EXISTS,swap to preserve order; is this needed?
aafcgggbj,SPFlow/SPFlow,src/spn/algorithms/EM.py,482616dc1417115f907b720073d640dce2685e3f,505a7b9610909c405e1b65a0b1e983baeb83b60c,TODO handle zeros for efficiency; darwiche 2003
aafcgghbd,SPFlow/SPFlow,src/spn/algorithms/EM.py,394e46450265829907cdc6322600ad1f37828bac,698cba51ad500b8af1f32e3ea80027b63ea81cab,TODO: do in parallel
aafcgghdf,SPFlow/SPFlow,src/spn/algorithms/Gradient.py,505a7b9610909c405e1b65a0b1e983baeb83b60c,5f12688e83bfb0acadb6b8ab56c293ed123e3a7a,TODO handle zeros for efficiency; darwiche 2003
aafcghabh,SPFlow/SPFlow,docs/conf.py,77fe03ba4b9bbe383a8a27ac137ffd023853cb76,STILL_EXISTS,-- Options for todo extension ----------------------------------------------
aafcghabi,SPFlow/SPFlow,docs/conf.py,77fe03ba4b9bbe383a8a27ac137ffd023853cb76,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aafcgiadb,SPFlow/SPFlow,src/spn/algorithms/layerwise/distributions.py,1dbeb98cdf41811882cf8ee7564b47d2b41a4521,STILL_EXISTS,Fix LowRankMultivariateNormal elements
aafcgibbi,SPFlow/SPFlow,src/spn/algorithms/layerwise/layers.py,82478ffacdd98fcc0750ff086af240a3f37a8b10,STILL_EXISTS,Implement product as convolution
aafcgjaac,SPFlow/SPFlow,src/spn/algorithms/layerwise/layers.py,a25998e30f74d0462e4bf6c0efd8ea20a1cb8d6f,STILL_EXISTS,Create index map from flattened to coordinates (only needed in sampling)
aafcgjade,SPFlow/SPFlow,src/spn/experiments/RandomSPNs_layerwise/distributions.py,a25998e30f74d0462e4bf6c0efd8ea20a1cb8d6f,STILL_EXISTS,TODO: maybe check padding?
aafcgjafb,SPFlow/SPFlow,src/spn/experiments/RandomSPNs_layerwise/rat_spn.py,a25998e30f74d0462e4bf6c0efd8ea20a1cb8d6f,STILL_EXISTS,Sample root node (choose one of the classes) TODO: check what happens if C=1
aafcgjeee,SPFlow/SPFlow,src/spn/algorithms/layerwise/distributions.py,c1fbb3ef1e53d8b1feb866896f2ee315871e17c5,STILL_EXISTS,TODO: Implement more torch distributions
aafcgjegc,SPFlow/SPFlow,docs/source/conf.py,c8518b1d817c1215303db3729b689e97f7708e6e,STILL_EXISTS,-- Options for todo extension ----------------------------------------------
aafcgjegd,SPFlow/SPFlow,docs/source/conf.py,c8518b1d817c1215303db3729b689e97f7708e6e,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aafcgjfag,SPFlow/SPFlow,docs/examples/models/plot_learn_spn_classifier.py,52cc7a51723fc7362f362f2125ddcab2bb96cb9c,STILL_EXISTS,two rows and 3 columns. We set the last column to ``np.nan`` to indicate
aafcgjfci,SPFlow/SPFlow,docs/examples/queries/plot_tractable_inference.py,55099341fef46cc9375988019d38a2a835edcda3,STILL_EXISTS,Since we have 3 variables; we want to create a 2D numpy array of 3 columns
aafcgjfdf,SPFlow/SPFlow,docs/examples/queries/plot_tractable_inference.py,55099341fef46cc9375988019d38a2a835edcda3,STILL_EXISTS,with data at columns 1 and 2; but ignores column 0.
aafchacch,Rostlab/nalaf,source/test.py,8b1ee30eadb0e9a8e447227f9020337eb1a67623,STILL_EXISTS,TODO iterate through all files
aafchacci,Rostlab/nalaf,source/test.py,8b1ee30eadb0e9a8e447227f9020337eb1a67623,STILL_EXISTS,TODO randomly select 5-10 documents
aafchacda,Rostlab/nalaf,source/test.py,8b1ee30eadb0e9a8e447227f9020337eb1a67623,a4f30913606bc73ee42880fbde481f0aedd3946d,TODO indicatives long list
aafchacdb,Rostlab/nalaf,source/test.py,8b1ee30eadb0e9a8e447227f9020337eb1a67623,STILL_EXISTS,TODO connecting long list
aafchacfh,Rostlab/nalaf,source/test.py,8b1ee30eadb0e9a8e447227f9020337eb1a67623,STILL_EXISTS,TODO pre-processing: strip new lines; just paragraphs
aafchacfi,Rostlab/nalaf,source/test.py,8b1ee30eadb0e9a8e447227f9020337eb1a67623,STILL_EXISTS,TODO mapping annotation to text
aafchacgi,Rostlab/nalaf,source/test.py,8b1ee30eadb0e9a8e447227f9020337eb1a67623,STILL_EXISTS,is_annotation(words from start to start+length) { # TODO precise pseudocode
aafchachf,Rostlab/nalaf,source/test.py,8b1ee30eadb0e9a8e447227f9020337eb1a67623,STILL_EXISTS,TODO mode select inclusive\/exclusive
aafchachg,Rostlab/nalaf,source/test.py,8b1ee30eadb0e9a8e447227f9020337eb1a67623,STILL_EXISTS,TODO sentences not via \". \"; but take care of e.g. \"E. coli\"
aafchacie,Rostlab/nalaf,source/test.py,37ad1eb3a34ddb413e8bb39418e162a5a5df9c47,STILL_EXISTS,TODO regex tree? search to increase performance if needed (profiling)
aafchacif,Rostlab/nalaf,source/test.py,8717bf9b7513ddb3b395694adb01e7db025b4045,STILL_EXISTS,TODO annotation options (map information to stuff)
aafchacii,Rostlab/nalaf,source/test.py,b28ad3ead46066154d92d6b968ff4de03a228c54,STILL_EXISTS,TODO import through parameters
aafchadca,Rostlab/nalaf,source/test.py,3fe5296bf61966760f50ee41895a410aec233f6a,a4f30913606bc73ee42880fbde481f0aedd3946d,TODO indicatives long list
aafchadcb,Rostlab/nalaf,source/test.py,3fe5296bf61966760f50ee41895a410aec233f6a,STILL_EXISTS,TODO incomplete connecting list
aafchadcc,Rostlab/nalaf,source/test.py,3fe5296bf61966760f50ee41895a410aec233f6a,STILL_EXISTS,TODO Sophisticated method
aafchaddj,Rostlab/nalaf,source/test.py,3fe5296bf61966760f50ee41895a410aec233f6a,STILL_EXISTS,TODO annotation options (map information to stuff)
aafchadeb,Rostlab/nalaf,source/test.py,3fe5296bf61966760f50ee41895a410aec233f6a,STILL_EXISTS,TODO regex tree? search to increase performance if needed (profiling)
aafchadec,Rostlab/nalaf,source/test.py,3fe5296bf61966760f50ee41895a410aec233f6a,STILL_EXISTS,TODO sentences not via \". \"; but take care of e.g. \"E. coli\"
aafchadif,Rostlab/nalaf,source/test.py,72e3be9518b0f01432f6d2895238ec1a7d23ba69,STILL_EXISTS,FIXME check for whole document
aafchadig,Rostlab/nalaf,source/test.py,72e3be9518b0f01432f6d2895238ec1a7d23ba69,STILL_EXISTS,TODO just abstracts or whole documents?
aafchadih,Rostlab/nalaf,source/test.py,72e3be9518b0f01432f6d2895238ec1a7d23ba69,STILL_EXISTS,TODO opt. parameter for whole documents
aafchadji,Rostlab/nalaf,source/test.py,72e3be9518b0f01432f6d2895238ec1a7d23ba69,STILL_EXISTS,TODO find_all since there is more than just one paragraph
aafchaedb,Rostlab/nalaf,source/test.py,60eb6e949e296fe10ff1b1adc7db3bfa0508b0e1,STILL_EXISTS,TODO (1) offset check
aafchafbf,Rostlab/nalaf,source/test.py,b46f75b54c11e284a3e95619b530b6ed7e8544ca,STILL_EXISTS,TODO docs with at least one nl mention vs total number (3)
aafchafbj,Rostlab/nalaf,source/test.py,b46f75b54c11e284a3e95619b530b6ed7e8544ca,STILL_EXISTS,TODO convention filtering
aafchafcd,Rostlab/nalaf,source/test.py,b46f75b54c11e284a3e95619b530b6ed7e8544ca,STILL_EXISTS,TODO current lettres (1)
aafchafci,Rostlab/nalaf,source/test.py,6373ab62ccda71ff4e253499cb0686b43c42fd73,STILL_EXISTS,TODO complete conventions according to HGVS and set of regexs by tmVar (3)
aafchafcj,Rostlab/nalaf,source/test.py,6373ab62ccda71ff4e253499cb0686b43c42fd73,STILL_EXISTS,TODO Abstract vs Full document ratio (2)
aafchafdc,Rostlab/nalaf,source/test.py,6373ab62ccda71ff4e253499cb0686b43c42fd73,STILL_EXISTS,convention filtering
aafchafde,Rostlab/nalaf,source/test.py,6373ab62ccda71ff4e253499cb0686b43c42fd73,STILL_EXISTS,FIXME so inclsuive and exclsuiev can be achieved here (5)
aafchafdh,Rostlab/nalaf,source/test.py,6373ab62ccda71ff4e253499cb0686b43c42fd73,STILL_EXISTS,TODO current lettres (1)
aafchafdj,Rostlab/nalaf,source/test.py,06c9f757d9fbe889394f9abd46cb5c19b334ac3c,STILL_EXISTS,TODO Abstract vs Full document ratio (2)
aafchafea,Rostlab/nalaf,source/test.py,d0c0bf6eec17ebb938fc933d2b85c27a8cfb397f,STILL_EXISTS,TODO Export into other file. (20)
aafchafei,Rostlab/nalaf,source/test.py,d0c0bf6eec17ebb938fc933d2b85c27a8cfb397f,STILL_EXISTS,TODO do inclusive run (1)
aafchafej,Rostlab/nalaf,source/test.py,d0c0bf6eec17ebb938fc933d2b85c27a8cfb397f,STILL_EXISTS,TODO do inclusive run export (2)
aafchaffb,Rostlab/nalaf,source/test.py,d0c0bf6eec17ebb938fc933d2b85c27a8cfb397f,STILL_EXISTS,TODO do exclusive run (4)
aafchaffc,Rostlab/nalaf,source/test.py,d0c0bf6eec17ebb938fc933d2b85c27a8cfb397f,STILL_EXISTS,TODO do inclusive run export (5)
aafchaffg,Rostlab/nalaf,source/test.py,cbd44c4f16a91734f1bc93b0fad803fbfa0d1a78,STILL_EXISTS,FIXME Add __main__ function so this can be imported as module
aafchaffh,Rostlab/nalaf,source/test.py,cbd44c4f16a91734f1bc93b0fad803fbfa0d1a78,STILL_EXISTS,FIXME rename
aafchaffi,Rostlab/nalaf,source/test.py,cbd44c4f16a91734f1bc93b0fad803fbfa0d1a78,STILL_EXISTS,FIXME replace to other folder
aafchafgi,Rostlab/nalaf,source/test.py,cbd44c4f16a91734f1bc93b0fad803fbfa0d1a78,STILL_EXISTS,TODO do inclusive run (1)
aafchafgj,Rostlab/nalaf,source/test.py,cbd44c4f16a91734f1bc93b0fad803fbfa0d1a78,STILL_EXISTS,TODO do inclusive run export (2)
aafchafha,Rostlab/nalaf,source/test.py,cbd44c4f16a91734f1bc93b0fad803fbfa0d1a78,STILL_EXISTS,TODO do exclusive run (4)
aafchafhg,Rostlab/nalaf,source/preprocessing/definers.py,d8ff24c9e49dd199c01e8e099420f2d5b170df7b,STILL_EXISTS,TODO continue here (1)
aafchafij,Rostlab/nalaf,nala/test.py,15fceb0c50dc2d395caddb0528326710b7669584,STILL_EXISTS,TODO import through parameters
aafchagcj,Rostlab/nalaf,nala/test.py,15fceb0c50dc2d395caddb0528326710b7669584,STILL_EXISTS,TODO Export into other file. (20)
aafchagei,Rostlab/nalaf,nala/test.py,15fceb0c50dc2d395caddb0528326710b7669584,STILL_EXISTS,TODO incomplete connecting list (30)
aafchagej,Rostlab/nalaf,nala/test.py,15fceb0c50dc2d395caddb0528326710b7669584,STILL_EXISTS,TODO Sophisticated method (30)
aafchagfi,Rostlab/nalaf,nala/test.py,15fceb0c50dc2d395caddb0528326710b7669584,STILL_EXISTS,TODO complete conventions according to HGVS and set of regexs by tmVar (3)
aafchaghh,Rostlab/nalaf,nala/test.py,15fceb0c50dc2d395caddb0528326710b7669584,STILL_EXISTS,TODO convention filtering (3)
aafchagib,Rostlab/nalaf,nala/test.py,15fceb0c50dc2d395caddb0528326710b7669584,STILL_EXISTS,convention filtering
aafchahaj,Rostlab/nalaf,nala/test.py,15fceb0c50dc2d395caddb0528326710b7669584,STILL_EXISTS,is_annotation(words from start to start+length) { # TODO precise pseudocode (15)
aafchahcc,Rostlab/nalaf,nala/test.py,15fceb0c50dc2d395caddb0528326710b7669584,STILL_EXISTS,TODO (3) get top 5 words
aafchahcd,Rostlab/nalaf,nala/test.py,15fceb0c50dc2d395caddb0528326710b7669584,STILL_EXISTS,OPTIONAL regex tree? search to increase performance if needed @profiling
aafchahfd,Rostlab/nalaf,nala/test.py,15fceb0c50dc2d395caddb0528326710b7669584,STILL_EXISTS,FIXME Add __main__ function so this can be imported as module
aafchahfe,Rostlab/nalaf,nala/test.py,15fceb0c50dc2d395caddb0528326710b7669584,STILL_EXISTS,FIXME rename
aafchahff,Rostlab/nalaf,nala/test.py,15fceb0c50dc2d395caddb0528326710b7669584,STILL_EXISTS,FIXME replace to other folder
aafchahhg,Rostlab/nalaf,nala/preprocessing/definers.py,890ad5903a5ebd18e890bc56f8115cf10da42470,STILL_EXISTS,TODO continue here (1)
aafchahhj,Rostlab/nalaf,nala/structures/data.py,a821d4be2691586f89aede2e1200f3ac5a776fae,STILL_EXISTS,FIXME Add more information (30) --> detailed @bug
aafchahjj,Rostlab/nalaf,nala/preprocessing/definers.py,3025428549bf8b9394fb02e759a68e533f591ad6,3616e8d4d6827255a48a71d75a236c8af6daa8e3,TODO (5) add mutalyzer package
aafchaiaa,Rostlab/nalaf,nala/preprocessing/definers.py,3025428549bf8b9394fb02e759a68e533f591ad6,STILL_EXISTS,TODO (6) add hgvs package
aafchaidj,Rostlab/nalaf,nala/structures/data.py,3025428549bf8b9394fb02e759a68e533f591ad6,7cceb7d6c833fd7749c6dc0373c51271d163b8d6,FIXME return object as dictionary
aafchaiea,Rostlab/nalaf,nala/structures/data.py,3025428549bf8b9394fb02e759a68e533f591ad6,3616e8d4d6827255a48a71d75a236c8af6daa8e3,TODO (1) ratio-(token nl mentions\/total tokens) in abstract ratio full documents @graph
aafchaieb,Rostlab/nalaf,nala/structures/data.py,3025428549bf8b9394fb02e759a68e533f591ad6,3616e8d4d6827255a48a71d75a236c8af6daa8e3,TODO (2) ratio-(nl\/total) @graph
aafchaiec,Rostlab/nalaf,nala/structures/data.py,3025428549bf8b9394fb02e759a68e533f591ad6,3616e8d4d6827255a48a71d75a236c8af6daa8e3,TODO (4) nl mentions total vs min lettre parameter @graph @parameters
aafchaied,Rostlab/nalaf,nala/structures/data.py,3025428549bf8b9394fb02e759a68e533f591ad6,3616e8d4d6827255a48a71d75a236c8af6daa8e3,TODO (5) parametrizable min length (12..36) @parameters
aafchaiei,Rostlab/nalaf,nala/utils/writers.py,7cceb7d6c833fd7749c6dc0373c51271d163b8d6,f51a4c298d712b7812df32caf1c3337a1332c47a,TODO (3) matplotlib create graph
aafchaihi,Rostlab/nalaf,nala/utils/writers.py,74402de9d462f8bf452ee55e2e33b56fe3687bf3,STILL_EXISTS,TODO subplots for params
aafchaihj,Rostlab/nalaf,nala/utils/writers.py,74402de9d462f8bf452ee55e2e33b56fe3687bf3,STILL_EXISTS,TODO plt.axhan or sth like that for highlighting area of inclusive method with param
aafchaijd,Rostlab/nalaf,nala/utils/writers.py,1d4c586c776a1624cb1bfd3de843971f3457b989,f558b935a7d4dca8328c1201a26b762029695e8f,TODO xticks for labelling
aafchaije,Rostlab/nalaf,nala/utils/writers.py,1d4c586c776a1624cb1bfd3de843971f3457b989,STILL_EXISTS,TODO add standard error
aafchajab,Rostlab/nalaf,nala/utils/writers.py,1d4c586c776a1624cb1bfd3de843971f3457b989,f558b935a7d4dca8328c1201a26b762029695e8f,TODO include in graph \"abstract nl nr \/ abstract tot token nr\"
aafchajad,Rostlab/nalaf,nala/utils/writers.py,1d4c586c776a1624cb1bfd3de843971f3457b989,f558b935a7d4dca8328c1201a26b762029695e8f,TODO full nl nr \/ full tot token nr
aafchajba,Rostlab/nalaf,nala/utils/writers.py,1d4c586c776a1624cb1bfd3de843971f3457b989,STILL_EXISTS,TODO subplot minimum one abstract\/full
aafchajbd,Rostlab/nalaf,nala/utils/writers.py,1d4c586c776a1624cb1bfd3de843971f3457b989,STILL_EXISTS,TODO subplots for params
aafchajdi,Rostlab/nalaf,nala/preprocessing/definers.py,3616e8d4d6827255a48a71d75a236c8af6daa8e3,355da5adfa5c79edea4ad126bc3bef46b16863b9,TODO save that in config file
aafchbafc,Rostlab/nalaf,setup.py,8694d04fa8fa957b0ea348df4ceb18e9ae84499e,STILL_EXISTS,TODO Figure out if we need this; since we might not want this huge dependency
aafchbafj,Rostlab/nalaf,nala/features/tmvar.py,7a932c832911cb2e7931fed8960a3bb49ab4b6fa,3a38b34cb332007524ebf47fbcdcd8fc65973e43,TODO 0;1;2;3;4+ instead of len = nr
aafchbagg,Rostlab/nalaf,nala/features/tmvar.py,7a932c832911cb2e7931fed8960a3bb49ab4b6fa,STILL_EXISTS,TODO check if ok the implementation (edge cases e.g. numeric means 123.232? or 123 and 232?)
aafchbaid,Rostlab/nalaf,nala/features/tmvar.py,9edac3eb0323946488ac177b1341711492fef55a,d2f832bb7a8576ad24fdfbbb7f707526ad47d634,TODO last token include: \"&& $last_token[...]\"
aafchbaja,Rostlab/nalaf,nala/features/tmvar.py,94a8989bd75b4af0e1166274e003bc35785f8294,d2c67b4b5d7f200cd4fd0e87364897e2ddcb1dbd,TODO patterns
aafchbajc,Rostlab/nalaf,nala/features/tmvar.py,94a8989bd75b4af0e1166274e003bc35785f8294,4b8a810dbd0a3fc02e0d17068674daf72e25cfac,TODO prefix patterns
aafchbaje,Rostlab/nalaf,nala/features/tmvar.py,94a8989bd75b4af0e1166274e003bc35785f8294,4b8a810dbd0a3fc02e0d17068674daf72e25cfac,TODO suffix patterns
aafchbaji,Rostlab/nalaf,tests/test_features.py,94a8989bd75b4af0e1166274e003bc35785f8294,STILL_EXISTS,TODO last token include: \"&& $last_token[...]\"
aafchbajj,Rostlab/nalaf,nala/features/tmvar.py,585f6ef53eb08bbaa5b2875b8f5aa9bfa82def9e,44061b590716c5095d58d6cac0dffa32d6653f68,TODO add introductory explanation
aafchbbaa,Rostlab/nalaf,nala/features/tmvar.py,585f6ef53eb08bbaa5b2875b8f5aa9bfa82def9e,STILL_EXISTS,TODO change to sensefull way
aafchbbih,Rostlab/nalaf,nala/features/tmvar.py,44061b590716c5095d58d6cac0dffa32d6653f68,d2f832bb7a8576ad24fdfbbb7f707526ad47d634,TODO last token
aafchbcbh,Rostlab/nalaf,nala/features/tmvar.py,5fde23fe506f88f8b1a5513dc60bd6167eea5325,STILL_EXISTS,TODO add introductory explanation
aafchbcbj,Rostlab/nalaf,nala/features/tmvar.py,5fde23fe506f88f8b1a5513dc60bd6167eea5325,3a38b34cb332007524ebf47fbcdcd8fc65973e43,TODO 0;1;2;3;4+ instead of len = nr
aafchbced,Rostlab/nalaf,nala/features/tmvar.py,5fde23fe506f88f8b1a5513dc60bd6167eea5325,STILL_EXISTS,TODO change to sensefull way
aafchbceh,Rostlab/nalaf,nala/features/tmvar.py,5fde23fe506f88f8b1a5513dc60bd6167eea5325,d2f832bb7a8576ad24fdfbbb7f707526ad47d634,TODO last token
aafchbcih,Rostlab/nalaf,tests/test_features.py,5fde23fe506f88f8b1a5513dc60bd6167eea5325,STILL_EXISTS,TODO last token include: \"&& $last_token[...]\"
aafchbcjb,Rostlab/nalaf,tests/test_features.py,5fde23fe506f88f8b1a5513dc60bd6167eea5325,STILL_EXISTS,TODO implement separate test functions for each feature that is already implemented in test_generate
aafchbcjc,Rostlab/nalaf,nala/features/tmvar.py,3a38b34cb332007524ebf47fbcdcd8fc65973e43,d2f832bb7a8576ad24fdfbbb7f707526ad47d634,TODO last token
aafchbdca,Rostlab/nalaf,nala/features/tmvar.py,3a38b34cb332007524ebf47fbcdcd8fc65973e43,STILL_EXISTS,TODO add introductory explanation
aafchbdeg,Rostlab/nalaf,nala/features/tmvar.py,3a38b34cb332007524ebf47fbcdcd8fc65973e43,STILL_EXISTS,TODO change to sensefull way
aafchbdfd,Rostlab/nalaf,tests/test_features.py,3a38b34cb332007524ebf47fbcdcd8fc65973e43,STILL_EXISTS,TODO implement separate test functions for each feature that is already implemented in test_generate
aafchbdgg,Rostlab/nalaf,tests/test_features.py,3a38b34cb332007524ebf47fbcdcd8fc65973e43,STILL_EXISTS,TODO last token include: \"&& $last_token[...]\"
aafchbdhd,Rostlab/nalaf,nala/features/tmvar.py,6d3ab5c769ed4c258c042e83129bda166fd7337a,STILL_EXISTS,TODO as array or as string with spaces?
aafchbdic,Rostlab/nalaf,nala/utils/writers.py,30b92e2b5c2c226e270f02ba87f1476beea24a64,1b29a5321dc2cdf58420515122d5b3b8e7958531,TODO shift position to get correct labeling on bars
aafchbdie,Rostlab/nalaf,nala/utils/writers.py,890766743fe0a0b29cac409a877cf19249a330a4,STILL_EXISTS,TODO make interesting bars as param not hard coded
aafchbdjc,Rostlab/nalaf,nala/learning/crfsuite.py,72fae110e9a681b55c7f6e604bd384f2ad5a5473,fbe9157a40274f26cf2771db74d2bf2c9adb132f,TODO fix docstring
aafchbecd,Rostlab/nalaf,nala/features/tmvar.py,a64b23b30d7a314a8caca80669ad17302762afe1,24fb4899f7d8dda80c59b5dc03e09024996ae1b4,TODO docsting
aafchbedf,Rostlab/nalaf,nala/structures/data.py,bb907983582de7dc0e9e799fb232aeca526bf919,255f27cf3de8ad4c7c62f4d7529ac083db9fdf96,TODO figure out how to best set class_id independent from Labeler used
aafchbedg,Rostlab/nalaf,nala/learning/postprocessing.py,3256b61a77de335621c6e0a24437ea24f224b5a9,STILL_EXISTS,TODO figure out how to best set class_id independent since we don't know it
aafchbeea,Rostlab/nalaf,nala/learning/postprocessing.py,838c96834e3f1427c420510290025f49c949e840,STILL_EXISTS,replace the existing one by the one found one since it is probably better
aafchbeed,Rostlab/nalaf,nala/learning/postprocessing.py,838c96834e3f1427c420510290025f49c949e840,STILL_EXISTS,fix boundary #17000021\t251\t258\t1858C>T --> +1858C>T
aafchbeeg,Rostlab/nalaf,nala/learning/postprocessing.py,838c96834e3f1427c420510290025f49c949e840,STILL_EXISTS,fix boundary add missing (
aafchbeeh,Rostlab/nalaf,nala/learning/postprocessing.py,838c96834e3f1427c420510290025f49c949e840,STILL_EXISTS,fix boundary add missing )
aafchbeej,Rostlab/nalaf,nala/features/tmvar.py,b1735c595e8776c6043e3843e4c3d1239c811586,ba2ce571453079fe0bd07fe49e6826e9fbb16d5f,TODO re.search instead of re.match and exclude \".*\" for regexs'
aafchbeha,Rostlab/nalaf,nala/structures/data.py,b811d26193aeae5cd3fb8bba0becc6f2c15a4558,255f27cf3de8ad4c7c62f4d7529ac083db9fdf96,TODO figure out how to best set class_id independent from Labeler used
aafchbehb,Rostlab/nalaf,demo.py,a210b2e272269683924acacb27d5fb1034150c5f,STILL_EXISTS,TODO add param
aafchbehe,Rostlab/nalaf,nala/features/__init__.py,5b6580deca1bef80dc9107be0eaad18f836e5e60,80a83133bc23a0ee1b3f8fd7c1211839d228ab5c,TODO decorator that checks features
aafchbehg,Rostlab/nalaf,nala/learning/evaluators.py,5b6580deca1bef80dc9107be0eaad18f836e5e60,STILL_EXISTS,TODO Rename implementation class to include Impl in the name
aafchbehh,Rostlab/nalaf,nala/learning/evaluators.py,5b6580deca1bef80dc9107be0eaad18f836e5e60,d0eaff47d7d14872fc2300ef9449c7f9463d5858,TODO Clean up this; separate function
aafchbehi,Rostlab/nalaf,nala/learning/postprocessing.py,5b6580deca1bef80dc9107be0eaad18f836e5e60,STILL_EXISTS,TODO Refactor into regex instead of check
aafchbehj,Rostlab/nalaf,nala/learning/postprocessing.py,5b6580deca1bef80dc9107be0eaad18f836e5e60,fe79795ab4db000155744482d32b964a90bf6d5a,TODO Refactor to return an object
aafchbeia,Rostlab/nalaf,nala/structures/data.py,5b6580deca1bef80dc9107be0eaad18f836e5e60,STILL_EXISTS,TODO Change from top level to bottom level; Dataset first
aafchbeib,Rostlab/nalaf,nala/structures/data.py,5b6580deca1bef80dc9107be0eaad18f836e5e60,83afec6edd3de232fbc5126ed312ec57cd739475,TODO change e_2 to constant and make it called mutation
aafchbeif,Rostlab/nalaf,tests/test_mutationFinderReader.py,c6a789b0a795431787059be6b3fcdd719a392154,STILL_EXISTS,TODO Figure out if this should be a test class
aafchbfbh,Rostlab/nalaf,nala/utils/writers.py,1cd7b7d2115c7c8695858b1a76925d5fa6446d1a,STILL_EXISTS,meta3 = ET.SubElement(head; 'meta'; { 'name': 'dcterms.source'; 'content' : 'http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/' + pubmedid } )  # deprecated maybe different sources
aafchbfbi,Rostlab/nalaf,nala/utils/writers.py,1cd7b7d2115c7c8695858b1a76925d5fa6446d1a,STILL_EXISTS,TODO \"s#p# naming convention\" for id
aafchbgaf,Rostlab/nalaf,nala/features/tmvar.py,a162463f3f476ba5577e0cde1f70a53f837f2e51,33bdb884ea1c34f25851ebd171db2c8bc6cbd774,TODO: Remove this when done implementing issue #65
aafchbgig,Rostlab/nalaf,demo_predict.py,e37491ab7081be584dbc73be2ddc0bb360a2424a,d7dea2164481d9c049c221c2a6fb7a3b28ba83d0,TODO include default_model & example.txt under resources\/
aafchbgii,Rostlab/nalaf,demo_predict.py,e37491ab7081be584dbc73be2ddc0bb360a2424a,464ded5e53a90f5e24e63fcfd8c688a140bd159f,TODO 2 pipelines; `PrepareDataset` & `UseDataset`
aafchbgij,Rostlab/nalaf,demo_predict.py,e37491ab7081be584dbc73be2ddc0bb360a2424a,d7dea2164481d9c049c221c2a6fb7a3b28ba83d0,TODO if possible; change parts & sentences & tokens from List to Tuple
aafchbgjb,Rostlab/nalaf,nala/learning/crfsuite.py,e37491ab7081be584dbc73be2ddc0bb360a2424a,cc464b96c5eb6e92a54e03699cdd26864f2c74e2,TODO rename to run \/ tag
aafchbhbd,Rostlab/nalaf,tests/features/test_simple.py,c4e41efab7f0bdbb18c150814df3f49e3e30af47,19b5be82081effd5ac9c4ae8d31f07117e034cdb,TODO
aafchbhbe,Rostlab/nalaf,tests/features/test_stemming.py,c4e41efab7f0bdbb18c150814df3f49e3e30af47,cf2465e1c5d344df17ccd2e31dee8ee10a760a09,TODO
aafchbheh,Rostlab/nalaf,tests/learning/test_postprocessing.py,4e203df5a6518fffb9696ec7f2d07379f141698c,2f7cf47108a81c27b25949390f7dd6d498f251dc,FIXME is that ok? --> self in outer class method
aafchbhhj,Rostlab/nalaf,tests/preprocessing/test_tokenizers.py,0ec578b05af256d92f98e7620d0cc82dc5915bef,d6db7ce482432cc83e0993f286f262d79ec6618a,TODO
aafchbjaf,Rostlab/nalaf,nala/preprocessing/definers.py,9aed54cf35ee5376fbb86b4bda2a39ba5d136a3e,STILL_EXISTS,TODO implement test class
aafchbjag,Rostlab/nalaf,nala/preprocessing/definers.py,9aed54cf35ee5376fbb86b4bda2a39ba5d136a3e,STILL_EXISTS,TODO correct test class for renamed function
aafchbjah,Rostlab/nalaf,nala/utils/qmath.py,06e73ce6958e15d620df13ea37eedee98282acc2,STILL_EXISTS,todo implement test functions
aafchbjce,Rostlab/nalaf,nala/structures/data.py,9203545791e1d8c6d377be31d5a2b470c68b2abb,5442a6fe2d8b17cc87e91cf76bcf5308794c466a,print(confidence; confidence_values)  TODO print debug
aafchbjeb,Rostlab/nalaf,nala/structures/data.py,c708fd25289060f77428869034d71adbe712153d,STILL_EXISTS,TODO lowercase
aafchcaec,Rostlab/nalaf,nala/bootstrapping/PMIDFilters.py,cd8cd444a0f39546d1d53ecbb6521212cc25a91e,STILL_EXISTS,try to find the pmid based on file name convention
aafchcaef,Rostlab/nalaf,nala/bootstrapping/__init__.py,19a707ffe835dd9bd3659a4b4ac043df7cabe438,8d8c13451b84d5386bc30087b6519ee4e13bb21f,TODO Add docstring
aafchcafd,Rostlab/nalaf,nala/bootstrapping/__init__.py,19a707ffe835dd9bd3659a4b4ac043df7cabe438,8d8c13451b84d5386bc30087b6519ee4e13bb21f,TODO document this better and move it to a more appropriate place
aafchcaja,Rostlab/nalaf,nala/utils/writers.py,fd328b3d848c0659587072b5be04112f61a847ec,88c43e6db89a2b37815c720ebe0ce9996b92e16a,TODO export annotations as well
aafchcbbd,Rostlab/nalaf,nala/utils/dataset_selection.py,74b7d964917ef44bff9bcd517b0de6198027043b,STILL_EXISTS,TODO consolidate with bootstrapping section
aafchcbdh,Rostlab/nalaf,nala/structures/data.py,ca5ab1a48794460e8e9b29c22d8c7ae0b8b739a9,c75ca82ceb7cfa7901b7b5e37ac91f1bff58761e,TODO fix bug in equality_operator calculations @aleksandar (have to speak about that on skype; etc.)
aafchcbdj,Rostlab/nalaf,nala/structures/data.py,ca5ab1a48794460e8e9b29c22d8c7ae0b8b739a9,STILL_EXISTS,FIXME should be x1.offset < (y.offset + len(y.text)) or x1.offset <= (y.offset + len(y.text) - 1) same for the other condition
aafchcbgf,Rostlab/nalaf,nala/structures/data.py,698a332497b170dd46e9b6deef78663d20d9e324,STILL_EXISTS,TODO rewrite to make look nicer. this is horrible
aafchcbgg,Rostlab/nalaf,nala/structures/data.py,4e8afb3774e6a1be7a17ee1285c980afd93c2172,562c48822173942d80fd9fbaac8b950ba1ee7de6,TODO *args instead of fixed 2 or 1 element
aafchcbgj,Rostlab/nalaf,tests/structures/test_data.py,4e8afb3774e6a1be7a17ee1285c980afd93c2172,STILL_EXISTS,ANN2 = \"     XXX \"
aafchcbha,Rostlab/nalaf,tests/structures/test_data.py,4e8afb3774e6a1be7a17ee1285c980afd93c2172,STILL_EXISTS,PAR1 = \"XXX      \"
aafchccad,Rostlab/nalaf,nala/preprocessing/definers.py,636f476c6051926ea31f06825e96405b92984e92,STILL_EXISTS,TODO test function
aafchccfi,Rostlab/nalaf,nala/bootstrapping/document_filters.py,0ba50adb137b1c14891a21c00092fa37f6028ba5,STILL_EXISTS,TODO nltk include for each sentence
aafchccic,Rostlab/nalaf,nala/utils/tagger.py,0ba50adb137b1c14891a21c00092fa37f6028ba5,STILL_EXISTS,todo docset
aafchccjd,Rostlab/nalaf,nala/utils/tagger.py,fe41a42ff872debbc18f465f003a314f5ad19d29,db7ac89c130715cf35a81fca7f2ff98085707916,todo check whether right offsets (especially the last one)
aafchccji,Rostlab/nalaf,nala/bootstrapping/document_filters.py,bc800d24a422bde654e69a56f0acaa8a21a0872f,c4a66567ce461d121ebf7d277e500f3db1cbc2c7,FIXME huge bug.... -.- regex on sentence lvl but overlap search on document lvl
aafchcdae,Rostlab/nalaf,nala/bootstrapping/document_filters.py,bc800d24a422bde654e69a56f0acaa8a21a0872f,STILL_EXISTS,TODO nltk include for each sentence
aafchcdai,Rostlab/nalaf,nala/utils/tagger.py,bc800d24a422bde654e69a56f0acaa8a21a0872f,STILL_EXISTS,todo textfile tagger @major
aafchcdfi,Rostlab/nalaf,nala/utils/pattern_eval.py,0876a2c30e19db560f475ac5aae3e07f1dbd86e4,STILL_EXISTS,todo provide method to create new pattern on an automated base
aafchcdgf,Rostlab/nalaf,nala/utils/pattern_eval.py,0876a2c30e19db560f475ac5aae3e07f1dbd86e4,STILL_EXISTS,TODO change if idp4 then those results otherwise use tmvartagger and caching
aafchcdgh,Rostlab/nalaf,nala/utils/pattern_eval.py,0876a2c30e19db560f475ac5aae3e07f1dbd86e4,STILL_EXISTS,todo param file to save to
aafchcdja,Rostlab/nalaf,nala/utils/pattern_eval.py,0876a2c30e19db560f475ac5aae3e07f1dbd86e4,STILL_EXISTS,todo save performances to file
aafchcefg,Rostlab/nalaf,nala/bootstrapping/__init__.py,38031f27041ca74754bdbd773588ef43935d3307,74bfdc2fbf03fb745f01ddf07a18c19293457821,todo learning
aafchcefi,Rostlab/nalaf,nala/bootstrapping/__init__.py,403f1e3a12a70d4efd9e3530ce136059c0e98ff4,74bfdc2fbf03fb745f01ddf07a18c19293457821,todo check for ability to use crfsuite command instead of abspath
aafchcegc,Rostlab/nalaf,nala/bootstrapping/__init__.py,8e8931ab047bcd6d6c00ad0a12a717974309bba9,c2049195004eb2e87974f53d776704231ea9b3e3,fixme path to read is only \"\/reviewed\/\"
aafchcege,Rostlab/nalaf,nala/bootstrapping/__init__.py,8e8931ab047bcd6d6c00ad0a12a717974309bba9,74bfdc2fbf03fb745f01ddf07a18c19293457821,todo has to be tested
aafchceic,Rostlab/nalaf,nala/bootstrapping/iteration.py,74bfdc2fbf03fb745f01ddf07a18c19293457821,STILL_EXISTS,todo discussion on config file in bootstrapping root or iteration_n check for n
aafchceij,Rostlab/nalaf,nala/bootstrapping/iteration.py,74bfdc2fbf03fb745f01ddf07a18c19293457821,STILL_EXISTS,todo has to be tested
aafchceje,Rostlab/nalaf,nala/bootstrapping/iteration.py,74bfdc2fbf03fb745f01ddf07a18c19293457821,31b26920bda36235bfa2f81764dec9186a1336e0,todo learning
aafchcfea,Rostlab/nalaf,nala/bootstrapping/document_filters.py,fa6bf6c361267f89317590181797a4f1859543bc,STILL_EXISTS,todo write to file param + saving to manually annotate and find tp + fp for performance eval on each pattern
aafchcffb,Rostlab/nalaf,nala/bootstrapping/iteration.py,de135202aa428b939e7b5d6526729c00a6fe9703,f44f509cdc707db60d7ec129ed7e4bdc80cdd7bb,todo this method get previous ids
aafchcfhc,Rostlab/nalaf,nala/bootstrapping/iteration.py,f44f509cdc707db60d7ec129ed7e4bdc80cdd7bb,82e6928f4e2bd557a9716e098479136666b76921,todo divide into 2 parts with 1st being learning; docselection; tagging and the 2nd being manual import and evaluation
aafchcfid,Rostlab/nalaf,nala/bootstrapping/iteration.py,e303968169edbb5e077b01196cd6ad1a8cab4b5c,82e6928f4e2bd557a9716e098479136666b76921,todo check for evaluation done (writing in csv file)
aafchcfie,Rostlab/nalaf,nala/bootstrapping/iteration.py,e303968169edbb5e077b01196cd6ad1a8cab4b5c,STILL_EXISTS,todo save model to iteration_0 folder as bin_model
aafchcfij,Rostlab/nalaf,nala/bootstrapping/document_filters.py,a2645c8dd4a5360b915bfd09cc31534248bfc18c,STILL_EXISTS,todo create pattern performance eval for descending amount of recognized patterns
aafchcfjd,Rostlab/nalaf,nala/learning/postprocessing.py,37523db562a1101c61b94ed7de541565d34c0dbe,STILL_EXISTS,TODO when creating new prediction which confidence should we add
aafchcfje,Rostlab/nalaf,nala/bootstrapping/document_filters.py,a756512a54f3a0afa15ecc60a34971cd189a3058,eceebaf2e8dfc1cc61b4c8c6ef650fcb5f66db4c,fixme print to print_verbose
aafchcfji,Rostlab/nalaf,nala/bootstrapping/iteration.py,82e6928f4e2bd557a9716e098479136666b76921,STILL_EXISTS,todo finish docset of Iteration Class
aafchcgab,Rostlab/nalaf,nala/bootstrapping/pmid_filters.py,82e6928f4e2bd557a9716e098479136666b76921,180764d053b4d059fac3a405dc178497fa5d07c2,fixme pmids not only in form asdjkasdjlja-pmid.plain.html or dasdas-pmid.ann.json but also in pmid.html or pmid.ann.json
aafchcgac,Rostlab/nalaf,nala/bootstrapping/iteration.py,0f7dc62b1b1f7ed8e295cabab4664bc05088158b,eceebaf2e8dfc1cc61b4c8c6ef650fcb5f66db4c,fixme print verbose not executed.... why? just highrecallregex filter.... no idea
aafchcgbi,Rostlab/nalaf,nala/utils/ncbi_utils.py,7dbda38a033aa9b94a896af0931077c33d84fd2f,3515ba30b619ac697f3dfbdabfa066103b8fa4ee,todo clean print statement
aafchcgda,Rostlab/nalaf,nala/learning/taggers.py,251be1f85ea9cf690e3ff65fd8fb278c5c2c9df6,STILL_EXISTS,todo normalized_text (stemming ... ?)
aafchcgdb,Rostlab/nalaf,nala/learning/taggers.py,5f1fee7ed3d3dadc03e515a70928f566a6aa0715,STILL_EXISTS,todo change normalizazion_database to normalise option
aafchcgdc,Rostlab/nalaf,nala/utils/ncbi_utils.py,5f1fee7ed3d3dadc03e515a70928f566a6aa0715,STILL_EXISTS,todo test whether really working...
aafchcgdd,Rostlab/nalaf,tests/learning/test_taggers.py,5f1fee7ed3d3dadc03e515a70928f566a6aa0715,81fce4dd0fcf5d2fc345e4526f889adc0114f31a,todo question is that the proper way? with predicts_classes
aafchcgde,Rostlab/nalaf,nala/utils/uniprot_utils.py,b0ba24f7a0c49417731e93d3d9209ad9266d390a,STILL_EXISTS,todo test this part again
aafchcgdi,Rostlab/nalaf,nala/structures/data.py,eb0d45b0e6ae09676109584d0cfd8a90f6c0fab9,c75ca82ceb7cfa7901b7b5e37ac91f1bff58761e,todo test method
aafchcgef,Rostlab/nalaf,tests/learning/test_taggers.py,b9635625eb5a174f039a3be4c3e5ee83cc717aac,STILL_EXISTS,todo ist noch falsch...
aafchcgeg,Rostlab/nalaf,nala/structures/data.py,1982abe7456fe89edaecbcf4228b650ee130188d,c75ca82ceb7cfa7901b7b5e37ac91f1bff58761e,todo test method
aafchcgeh,Rostlab/nalaf,nala/structures/data.py,c945308f15d64fb070d74c21855ae2f1b9870dfc,c75ca82ceb7cfa7901b7b5e37ac91f1bff58761e,todo testing
aafchcgfa,Rostlab/nalaf,nala/utils/tagger.py,7efcb42b8969d6724fa9317c8a1726dc95e61a77,STILL_EXISTS,todo major refactor to learning\/taggers class
aafchcgfh,Rostlab/nalaf,tests/utils/test_Tagger.py,c75ca82ceb7cfa7901b7b5e37ac91f1bff58761e,STILL_EXISTS,todo major merge into tests\/learning\/test_taggers.py
aafchcghd,Rostlab/nalaf,nala/structures/data.py,eceebaf2e8dfc1cc61b4c8c6ef650fcb5f66db4c,STILL_EXISTS,todo check again with *span and unpacking
aafchcgjd,Rostlab/nalaf,nala/bootstrapping/iteration.py,bbde717927003b364fe17d745719656a5cadd501,STILL_EXISTS,todo major sophisticated automatic execution (check what is missing e.g. bin_model)
aafchcgjg,Rostlab/nalaf,nala/utils/writers.py,bbde717927003b364fe17d745719656a5cadd501,STILL_EXISTS,\"confidence\": 1  # todo discussion confidence from GNormPlus is not provided so just putting in here 1
aafchchbi,Rostlab/nalaf,nala/utils/writers.py,b191a18cf38b20d7e7d9d5f9b608a8a4179e4b0e,STILL_EXISTS,\"confidence\": 1  # todo discussion confidence from GNormPlus is not provided so just putting in here 1
aafchchcb,Rostlab/nalaf,nala/utils/writers.py,3b70392c861d7c3348f59c0eddc66f0a11635e2d,STILL_EXISTS,\"confidence\": 1  # todo discussion confidence from GNormPlus is not provided so just putting in here 1
aafchchfa,Rostlab/nalaf,nala/structures/data.py,6331342413d88ab8146e71ec9b82c72059e7824e,STILL_EXISTS,TODO make parameterisable to just check for pure nl mentions
aafchchhc,Rostlab/nalaf,nala/utils/writers.py,b02b7f332c9e38371ac2049a108fb8b1cc1a3179,93e6b573df8e304f2eb9fa13afc2723d90008a66,todo if not part.is_abstract with s2p{} instead of s1p{}
aafchchig,Rostlab/nalaf,nala/utils/writers.py,d9153f4146c7ac201d1d0b2f3a52ccda3829f7a8,STILL_EXISTS,todo if not part.is_abstract with s2p{} instead of s1p{}
aafchchje,Rostlab/nalaf,nala/bootstrapping/document_filters.py,ebc888015369745abb197d3738c59a1e8d47df4d,STILL_EXISTS,todo write to file param + saving to manually annotate and find tp + fp for performance eval on each pattern
aafchcifd,Rostlab/nalaf,nala/bootstrapping/document_filters.py,3d1db2b3f6e18a5b4e6348a8f4683c269c3207d0,STILL_EXISTS,TODO add caching highrecalldocumentfilter
aafchcigi,Rostlab/nalaf,nalaf/structures/data.py,b1b6df0068ea12938fb2a5fe71b7cf658f91e3a2,c2e2de9041e0f76a6381d8e6c7b5f3f6603a7125,TODO move to edge features
aafchcigj,Rostlab/nalaf,nalaf/structures/data.py,b1b6df0068ea12938fb2a5fe71b7cf658f91e3a2,c2e2de9041e0f76a6381d8e6c7b5f3f6603a7125,TODO Design decision; whether to retain sentence or retain part and sentence id
aafchcihc,Rostlab/nalaf,nalaf/structures/data.py,b1b6df0068ea12938fb2a5fe71b7cf658f91e3a2,c2e2de9041e0f76a6381d8e6c7b5f3f6603a7125,TODO review this method
aafchciic,Rostlab/nalaf,nalaf/structures/data.py,38f2d4d6a6c5d0e3018745fc851c20166cd1bb57,ab86927ea3f7430fcc7abba14d3a0bd5352dd6e7,TODO move to edge features
aafchciid,Rostlab/nalaf,nalaf/structures/data.py,38f2d4d6a6c5d0e3018745fc851c20166cd1bb57,STILL_EXISTS,TODO Design decision; whether to retain sentence or retain part and sentence id
aafchciig,Rostlab/nalaf,nalaf/structures/data.py,38f2d4d6a6c5d0e3018745fc851c20166cd1bb57,STILL_EXISTS,TODO review this method
aafchcjij,Rostlab/nalaf,nalaf/utils/readers.py,812afb23c6407ce113ff7f5f286c508ee82b9d53,STILL_EXISTS,todo debug purpose; has to be deleted
aafchcjjc,Rostlab/nalaf,nalaf/utils/readers.py,812afb23c6407ce113ff7f5f286c508ee82b9d53,STILL_EXISTS,todo bugfix still mistake if space is in between the whole annotation case: \"#1632 T\"
aafchdaaj,Rostlab/nalaf,nalaf/utils/readers.py,e3232bdcd275de65de3da0276090baf25b802f71,STILL_EXISTS,todo debug purpose; has to be deleted
aafchdabb,Rostlab/nalaf,nalaf/learning/evaluators.py,c385a5fb96ee188f30ed8b75c35cbb05b8b84df1,STILL_EXISTS,TODO plus minus
aafchdabi,Rostlab/nalaf,nalaf/utils/annotation_readers.py,bc6ff78729ebdb521fff7d42f872e251aa1a52a1,STILL_EXISTS,TODO this should not be part of nalaf
aafchdacd,Rostlab/nalaf,nalaf/utils/annotation_readers.py,f7a0fe6553a84b7645c42b68776fd0b3f772ffe1,STILL_EXISTS,TODO this should not be part of nalaf
aafchdadi,Rostlab/nalaf,nalaf/domain/bio/gnormplus.py,81fce4dd0fcf5d2fc345e4526f889adc0114f31a,STILL_EXISTS,todo normalized_text (stemming ... ?)
aafchdaej,Rostlab/nalaf,tests/domain/bio/test_gnormplus.py,81fce4dd0fcf5d2fc345e4526f889adc0114f31a,STILL_EXISTS,todo question is that the proper way? with predicts_classes
aafchdafd,Rostlab/nalaf,relna/preprocessing/parsers.py,13934ff6804602c17bd62630e0c90cfe71ed3171,STILL_EXISTS,TODO SpaCy will soon have it's own constituency parser; integrate that
aafchdagb,Rostlab/nalaf,nalaf/features/relations.py,af84e09e03437fec6885fe54e1d003f9e78c0dce,STILL_EXISTS,TODO rest is potential material to delete; almost exact copy in relna's sentence.py
aafchdagc,Rostlab/nalaf,nalaf/structures/relation_pipelines.py,d5de1aaf429fa2504dfd1f8057eb82211a1730ff,827a35e6333740847cacab299e7b7b3ab2199c63,TODO populate with something minimally meaningful
aafchdagg,Rostlab/nalaf,nalaf/features/relations.py,092d4036abe3d13d11e27c4df824dd3bc239cd93,STILL_EXISTS,TODO rest is potential material to delete; almost exact copy in relna's sentence.py
aafchdaif,Rostlab/nalaf,nalaf/features/relations/__init__.py,1fbf168733c890ef81a36e6686e333589c9bdc73,STILL_EXISTS,TODO why stem of masked text? -- makes little sense -- See TODO in original loctext too
aafchdajf,Rostlab/nalaf,nalaf/features/relations/path.py,b165fedb09a9ac46f17d092b5743f82dce0e8fd4,STILL_EXISTS,TODO Juanmi: I do not understand why the extra inner loop here (not in original LocText)
aafchdbdc,Rostlab/nalaf,nalaf/utils/readers.py,36265a67303cbc25c86e00e488a77019357972c7,STILL_EXISTS,TODO all following readers are deprecated and should be moved to nala
aafchdbeb,Rostlab/nalaf,nalaf/structures/data.py,0ab2b08dd6334c602eb28913a7a603d269c08490,STILL_EXISTS,TODO what's this?
aafchdbef,Rostlab/nalaf,nalaf/preprocessing/edges.py,45b433aa401894bb461f807416a5d0b315605dd6,9ca837ed2464bc1575f3dab1adddd564c418a11a,TODO should we rewrite the edges?
aafchdbeg,Rostlab/nalaf,nalaf/preprocessing/edges.py,45b433aa401894bb461f807416a5d0b315605dd6,ebe4256914107273feb1da940fc3e4237731365e,TODO this is a STUB; for now
aafchdbeh,Rostlab/nalaf,nalaf/preprocessing/edges.py,45b433aa401894bb461f807416a5d0b315605dd6,9ca837ed2464bc1575f3dab1adddd564c418a11a,part.edges = []  # TODO leave the edges intact for now
aafchdbfa,Rostlab/nalaf,nalaf/structures/data.py,0d7445d06247e85683bcd53981b3477e2cd14a2c,STILL_EXISTS,TODO or -1 and +1 ? or negative or positive?
aafchdbfb,Rostlab/nalaf,nalaf/structures/data.py,2df3cfc024c0e6c9c56ab3de002fef59ede2aeda,STILL_EXISTS,TODO we must add normalization ids
aafchdbfc,Rostlab/nalaf,nalaf/structures/data.py,28151e29fb42609cef1b3eb47ff96f0891634fca,ab86927ea3f7430fcc7abba14d3a0bd5352dd6e7,TODO move to edge features
aafchdbfd,Rostlab/nalaf,nalaf/structures/data.py,cc538dd7d8ad3fbf20849412528ddef1256de908,STILL_EXISTS,TODO we likely need a pair of sentence ids for non same-sentence relationships
aafchdbfe,Rostlab/nalaf,nalaf/structures/data.py,3cbf2ade8587cabdb6c9d13938ee7307e79cf434,ab86927ea3f7430fcc7abba14d3a0bd5352dd6e7,TODO move to edge features
aafchdbfh,Rostlab/nalaf,nalaf/utils/annotation_readers.py,415909eefc1c6f31db1c0294b64b3d882cfdd423,e8c7bdaf3b04b6fcb6b383a07bce35efb6eaffe0,TODO we may need the following
aafchdbjg,Rostlab/nalaf,nalaf/structures/data.py,a8fd14cbd42a2c0338d717054577823201299b46,b234f938f1ece1a24ff9a39f310216185d8e429b,TODO in the end: remove other fields
aafchdbjh,Rostlab/nalaf,nalaf/utils/annotation_readers.py,86721f98bde7f941ebb15458dda7a0d832cde20e,0fe0ac08e1fc4d0cd5ccb4ed737ab9547e8a982b,TODO part.get_entity(e1_start)
aafchdbji,Rostlab/nalaf,nalaf/utils/annotation_readers.py,86721f98bde7f941ebb15458dda7a0d832cde20e,STILL_EXISTS,TODO part.get_entity(e2_start)
aafchdcag,Rostlab/nalaf,nalaf/structures/data.py,487544b245a88f5b1ba8fe87c257b27776a6f4aa,b234f938f1ece1a24ff9a39f310216185d8e429b,TODO
aafchdcbc,Rostlab/nalaf,nalaf/preprocessing/edges.py,5a2309859f01bed479bfdfacae69f0065130445a,STILL_EXISTS,Note: would be nice to implement the word filter too here -- see below
aafchdcbd,Rostlab/nalaf,nalaf/structures/data.py,5a2309859f01bed479bfdfacae69f0065130445a,157fba95af6cc08fb665cbfcf7cd79fc1dff4888,TODO change the equals method in Relation appropriately not to do thi bullshit
aafchdcbg,Rostlab/nalaf,nalaf/structures/data.py,eca59651d5876b55394c5d397358dc01dc45418a,2dae4af20117fd80fdaa3fc10883508da8b8fb5b,The best best solution would be to be able to retrieve the part and sentence_id from the entities directly
aafchdcca,Rostlab/nalaf,nalaf/structures/data.py,8a68a85eb6ba86f56daa6389c5ce6c2bdec17a80,157fba95af6cc08fb665cbfcf7cd79fc1dff4888,TODO; yes; we are aware that we also have self.same_part. However; ideally here we do not use that variable
aafchdccb,Rostlab/nalaf,nalaf/structures/data.py,b2802d087e6a96621cf4d5ced3fd5f84db42f7b2,0e8edd82dec35fc62b00b50390d95d0227d8f5cd,TODO we should much more carefully take care of its type; and whether it could even contain other values
aafchdccg,Rostlab/nalaf,nalaf/utils/annotation_readers.py,b234f938f1ece1a24ff9a39f310216185d8e429b,dd68983b337695aca8fe9482b5cd13238ceca70c,TODO delete this old code:
aafchddhe,Rostlab/nalaf,nalaf/utils/annotation_readers.py,beceaa473eaa0210aea839237caa34d523ad2772,STILL_EXISTS,TODO
aafchddhf,Rostlab/nalaf,nalaf/learning/evaluators.py,a3b255a5345bcb91e67b8f615ee53347dfae9c4f,STILL_EXISTS,TODO
aafchddia,Rostlab/nalaf,nalaf/structures/data.py,de0674db4b3274d0c4b5c943a944f4816862c9f7,STILL_EXISTS,TODO this may be too relna-specific
aafchdeaa,Rostlab/nalaf,nalaf/structures/data.py,e56329f4007a10227789baa35b56026505d9e698,05584515cf44e4c826d546a21d1ff7bfeb3fcc89,The following code can be made more efficient by iterating only once over the tokens lists
aafchdecb,Rostlab/nalaf,nalaf/learning/evaluators.py,395e98c407018243b9eef978ac06aaf9d0b3c6fd,STILL_EXISTS,TODO test if there is no normalization
aafchdedj,Rostlab/nalaf,nalaf/structures/data.py,d799684592a9d0b6023665e76ee2e821e108c778,STILL_EXISTS,The following code can be made more efficient by iterating only once over the tokens lists
aafchdefb,Rostlab/nalaf,nalaf/features/relations/sentence.py,2296aefb69fec3575d3bd943b4d3a578eb315996,STILL_EXISTS,TODO we could set it as real value - \u26A0\uFE0F that's what `entityhead::named_entity_count` did
aafchdehb,Rostlab/nalaf,nalaf/structures/data.py,928ad573a29fb162b0b0fe1320bdbfffa9aa9dc4,STILL_EXISTS,therefore; kinda hack: arbitrarily select they longest key
aafchdfbc,Rostlab/nalaf,nalaf/utils/floyd_warshall.py,1ba30f0900194ee578e1888030f38dd77075f690,STILL_EXISTS,\"\"\" || Floyd-Warshall graph algorithm to compute the shortest paths between the dependency graphs of sentences. || See: https:\/\/en.wikipedia.org\/wiki\/Floyd\u2013Warshall_algorithm ||  || As of now; matrises are written fully. An obvious performance improvement is to write them sparsely. || \"\"\"
aafchdfdf,Rostlab/nalaf,tests/utils/test_floyd_warshall.py,2186fa0cb389bc8e458eb0f276bc56aefbe34a29,STILL_EXISTS,TODO #28
aafchdfef,Rostlab/nalaf,nalaf/features/relations/new/dependency.py,6cc5645c8f5326437a770f094a51214bd4983fb7,STILL_EXISTS,\"\"\" || Combined dependency-based features implementation as succintly described in Shrikant's Master's Thesis (Section 4.5): || https:\/\/github.com\/juanmirocks\/LocText-old-ShrikantThesis\/files\/474428\/MasterThesis.pdf ||  || The implementation consider 4 types of dependency types: ||  || * OW (1 and 2): Outer Window == tokens at the outer side of an entity (1 or 2) || * IW (1 and 2): Inner Window == tokens at the inner side of an entity (1 or 2) || * LD: Linear Dependency == tokens within the two entities || * PD: Parsing Dependency == real dependency parsing obtained from spaCy's library ||  || \"\"\"
aafchdfeg,Rostlab/nalaf,nalaf/features/relations/new/dependency.py,6cc5645c8f5326437a770f094a51214bd4983fb7,STILL_EXISTS,TODO do kinda constituency parsing http:\/\/www.clips.ua.ac.be\/pages\/mbsp-tags
aafchdffg,Rostlab/nalaf,nalaf/utils/floyd_warshall.py,6cc5645c8f5326437a770f094a51214bd4983fb7,STILL_EXISTS,MAYBE Dikjstra algorithm is way more efficient for this case
aafchdffh,Rostlab/nalaf,nalaf/utils/floyd_warshall.py,6cc5645c8f5326437a770f094a51214bd4983fb7,STILL_EXISTS,MAYBE As of now; matrises are written fully. An obvious performance improvement is to write them sparsely.
aafchdffi,Rostlab/nalaf,nalaf/features/relations/new/dependency.py,93bf84f9e7bb969b9db7b902d9f6a5cb8e45261c,8f49f7f7c95dbd3f6e7335a174bc525895197346,TODO use Dikjstra
aafchdffj,Rostlab/nalaf,nalaf/features/relations/new/dependency.py,93bf84f9e7bb969b9db7b902d9f6a5cb8e45261c,8f49f7f7c95dbd3f6e7335a174bc525895197346,TODO have n-Gram
aafchdfga,Rostlab/nalaf,nalaf/features/relations/new/dependency.py,93bf84f9e7bb969b9db7b902d9f6a5cb8e45261c,STILL_EXISTS,TODO investigate features
aafchdfid,Rostlab/nalaf,nalaf/features/relations/new/dependency.py,564680fb4aec3e600c45a354fdc3d75793210176,1827af4358417e5669c20266bdf8a0613282d736,TODO
aafchdfje,Rostlab/nalaf,nalaf/features/relations/new/dependency.py,08a5fa7d8b7876364622c2e088e742e922ad99e8,1827af4358417e5669c20266bdf8a0613282d736,TODO
aafchdgad,Rostlab/nalaf,nalaf/learning/lib/libsvm.py,08de4c24e9f8383539a52e3d66570dd3d5d7982c,STILL_EXISTS,todo
aafchdgae,Rostlab/nalaf,nalaf/learning/lib/libsvm.py,08de4c24e9f8383539a52e3d66570dd3d5d7982c,STILL_EXISTS,TODO subprocess.call(callv)
aafchdgaj,Rostlab/nalaf,nalaf/learning/lib/sklsvm.py,cab3bb35d45780be436224c703b88bc208d0bae0,STILL_EXISTS,but that would require either converting feature values here or enforcing type in EdgeFeatureGenerator
aafchdgba,Rostlab/nalaf,nalaf/learning/lib/sklsvm.py,003ab975e828935e8092bc8ac1e9c5a6c67a5a45,STILL_EXISTS,We first construct the X matrix of features with the sparse lil_matrix; which is efficient in reshaping its structure dynamically
aafchdgbb,Rostlab/nalaf,nalaf/learning/lib/sklsvm.py,003ab975e828935e8092bc8ac1e9c5a6c67a5a45,STILL_EXISTS,At the end; we convert this to csr_matrix; which is efficient for algebra operations
aafchdgfh,Rostlab/nalaf,tests/structures/test_data.py,f4ba1abc03d6b1da3573c72abd7b9c2daf6256d0,STILL_EXISTS,PAR1 = \"XXX      \"
aafchdghc,Rostlab/nalaf,nalaf/learning/lib/sklsvm.py,9f046154e4ee109d403316b56ed8398a588eefe3,307492d213a99e7b4b9d70db47abc46809d0cc13,TODO make it efficient with direct dictionary indexing or perhaps using a dok_matrix
aafchdgja,Rostlab/nalaf,nalaf/structures/data.py,c50b2020414c9a2d5e5143111d3485c518f4c08c,STILL_EXISTS,TODO would be better to not use the constants PRO_ID (protRef) and LOC_ID (locRef) (below) here -- It's hardcoded
aafchdhac,Rostlab/nalaf,nalaf/utils/graphs.py,f52391b5541954678ef323a1db6de9e2185d4eba,STILL_EXISTS,TODO fix this
aafchdhce,Rostlab/nalaf,nalaf/structures/data.py,e36fc89e1ca413d692d003eb6e70fb6779803716,STILL_EXISTS,TODO use_pred ?
aafchdhdd,Rostlab/nalaf,nalaf/structures/data.py,2f5d0594bdc03032d9b43286b1147f55faf7dd83,88259f896cc944249b7fa6fa0f4acd28e7d89a5f,TODO this would be better written in the (entities) FeatureGenerator
aafchdhec,Rostlab/nalaf,nalaf/structures/data.py,358d0919e13179c09a783728504df7c72d225d87,STILL_EXISTS,Maybe just Noun's conditions? maybe only entities condition? Both?
aafchdhed,Rostlab/nalaf,nalaf/features/__init__.py,88259f896cc944249b7fa6fa0f4acd28e7d89a5f,b7c6acca9e5ae6c097a324bc32ecedd5928d772b,TODO the following is better written here; instead of the FeatureDictionary as originally written
aafchdhfi,Rostlab/nalaf,nalaf/structures/data.py,b7c6acca9e5ae6c097a324bc32ecedd5928d772b,STILL_EXISTS,TODO this would be better written in the (entities) FeatureGenerator
aafchdhgf,Rostlab/nalaf,nalaf/learning/evaluators.py,efd11c337f79e1e6d227a8f676ea8c63f919be6c,b81e386b98bbf39d7949192d1765fbe854e05182,TODO not sure about this
aafchdiaf,Rostlab/nalaf,nalaf/features/relations/new/sentence.py,304671b5846961c4a589d00628fa9c1e752173b2,STILL_EXISTS,TODO this is wrong for other entitiey types nor appearing in the edge
aafchdiag,Rostlab/nalaf,nalaf/features/relations/new/sentence.py,304671b5846961c4a589d00628fa9c1e752173b2,STILL_EXISTS,TODO also what about if the same entity type appears in both ends of the same edge? as in a protein-protein relation --> Just rest the counts of the edge
aafchdibd,Rostlab/nalaf,nalaf/learning/evaluators.py,3acfc5f59b9e674cab5be22a9b5fd7a349931e70,STILL_EXISTS,TODO
aafchfeaj,Rostlab/nalaf,setup.py,cba2523ab65df29cea16f942077f66bfe0c2b3fd,STILL_EXISTS,In 1.0.0 they move .vocab: https:\/\/github.com\/RaRe-Technologies\/gensim\/blob\/master\/CHANGELOG.md#100-2017-02-24
aafchfedc,Rostlab/nalaf,nalaf/download_data.py,13bf86d5973aa24fc75f93b73571308700bc94c3,STILL_EXISTS,TODO download non-packaged [biolemmatizer-core-1.2-jar-with-dependencies.jar](https:\/\/github.com\/Rostlab\/nalaf\/blob\/develop\/nalaf\/data\/biolemmatizer-core-1.2-jar-with-dependencies.jar)
aafchfedd,Rostlab/nalaf,nalaf/download_data.py,13bf86d5973aa24fc75f93b73571308700bc94c3,STILL_EXISTS,TODO download non-packaged [example_entity_model](https:\/\/github.com\/Rostlab\/nalaf\/blob\/develop\/nalaf\/data\/example_entity_model)
aafchfedf,graknlabs/kglib,grakn_graphsage/src/encoders/encoders.py,ce799b5a59f5132d03ca8230c61a64a1a386e3df,STILL_EXISTS,TODO One-hot encoding of either type labels or of a tensor of type ids (some class renaming required in the
aafchfeea,graknlabs/kglib,grakn_graphsage/src/neighbour_traversal/neighbour_traversal.py,ce799b5a59f5132d03ca8230c61a64a1a386e3df,STILL_EXISTS,Only needed due to a bug
aafchfeef,graknlabs/kglib,grakn_graphsage/src/neighbour_traversal/neighbour_traversal.py,ce799b5a59f5132d03ca8230c61a64a1a386e3df,STILL_EXISTS,TODO Inferred concepts have an id; but can we treat them exactly the same as non-inferred; or must we keep the
aafchfefa,graknlabs/kglib,grakn_graphsage/src/neighbour_traversal/neighbour_traversal.py,ce799b5a59f5132d03ca8230c61a64a1a386e3df,STILL_EXISTS,TODO See above; omitting due to bug
aafchfefe,graknlabs/kglib,grakn_graphsage/src/neighbour_traversal/neighbour_traversal.py,ce799b5a59f5132d03ca8230c61a64a1a386e3df,STILL_EXISTS,TODO If user doesn't attach anything to impicit @has relationships; then these could be filtered out. Instead
aafchffab,graknlabs/kglib,grakn_graphsage/src/encoders/RawArrayBuilder.py,9abb88c23f0d11cf3e482b6c08ea971234018270,390c1c29756d93b460dc187de1851d453bbdd991,# data_type = 0 for non-attributes; TODO or use None\/NaN and then zero-index instead?
aafchffah,graknlabs/kglib,grakn_graphsage/src/encoders/RawArrayBuilder.py,9abb88c23f0d11cf3e482b6c08ea971234018270,STILL_EXISTS,TODO pass this in or make into class variable?
aafchffdi,graknlabs/kglib,grakn_graphsage/src/neighbour_traversal/neighbour_traversal.py,774c03ff4e6d7918fcc45f703b6d257fd4f09fba,STILL_EXISTS,TODO See above; omitting due to bug
aafchffhg,graknlabs/kglib,grakn_graphsage/src/encoders/raw_array_builder_test.py,98e1e25e643119740800895e55b40aa861e2c843,STILL_EXISTS,TODO Only required while we have a bug on roles as variables in Graql
aafchffjd,graknlabs/kglib,grakn_graphsage/src/neighbourhood/executor.py,7be8bdd07fc9931bf45756ebbda4dde5e88b4f68,STILL_EXISTS,TODO See above; omitting due to bug
aafchfgdc,graknlabs/kglib,grakn_graphsage/src/neighbourhood/concept.py,49461aa7617b327e6d9f9334d81d0aebac9c73bb,STILL_EXISTS,TODO rename to base_type in line with Client Python
aafchfgdd,graknlabs/kglib,grakn_graphsage/src/encoders/raw_array_builder_test.py,f77493b01f7e7beaecc2e83cdeb50ba32cb64326,STILL_EXISTS,TODO Only required while we have a bug on roles as variables in Graql
aafchfgeb,graknlabs/kglib,grakn_graphsage/src/models/low_level.py,a65e99d02908f1f2b8c026eb89ddb6fac38e7787,STILL_EXISTS,TODO should this be a constant tensor?
aafchfhgg,graknlabs/kglib,kgcn/src/preprocessing/encoders/encode_test.py,475e236844d39097301a9174d7ec8c89fe279cc0,STILL_EXISTS,TODO Hacky; don't like it
aafchfhih,graknlabs/kglib,kgcn/src/neighbourhood/data/strategy.py,12a581039519a587dc347c5899725adb98502618,STILL_EXISTS,TODO Changing queries due to bug
aafchfhja,graknlabs/kglib,kgcn/src/models/model.py,81a6d3c08744db3f8b3b1d943214281b6fc0655e,eb0f2a1b3de837b4edb9658bc22653ef56d4b3e0,TODO Needs renaming from concept to avoid confusion
aafchfiah,graknlabs/kglib,kgcn/src/models/model.py,81a6d3c08744db3f8b3b1d943214281b6fc0655e,56bbdd1dd3e8dbf7177a45e921119560aad982a8,TODO Hacky; don't like it
aafchfibb,graknlabs/kglib,kgcn/src/models/model.py,c4fd3760a0b00dc68356fee17a336dc319dffa87,56bbdd1dd3e8dbf7177a45e921119560aad982a8,TODO Add actual string encoder
aafchficd,graknlabs/kglib,kgcn/src/preprocess/raw_array_builder_test.py,c224d4060c6c06f154c603f2c467e0f1eed60c99,eb0f2a1b3de837b4edb9658bc22653ef56d4b3e0,TODO Needs renaming from concept to avoid confusion
aafchfide,graknlabs/kglib,kgcn/src/sampling/random.py,7973b11ca233ea49da943979a65c71096faf4408,STILL_EXISTS,TODO calling random_sample recursively looks memory inefficient
aafchfiea,graknlabs/kglib,kgcn/src/neighbourhood/data/executor.py,04e9943da842a4dec3900846d0e94723a4ed9126,68b940686f74c2e433a3d49e9e68acec5f0f264a,TODO Changing queries due to bug
aafchfief,graknlabs/kglib,kgcn/src/neighbourhood/data/executor.py,eb0f2a1b3de837b4edb9658bc22653ef56d4b3e0,STILL_EXISTS,TODO rename to base_type in line with Client Python
aafchfjab,graknlabs/kglib,kgcn/src/models/model.py,77efa0ef03a207345762be22cff38788c1d5a727,6da1f7fd28da533b1e51bbcbaaa34fb2eb772a6d,TODO Pass this to traversal\/executor
aafchfjbi,graknlabs/kglib,kgcn/src/models/model.py,e32bf3c63e85909393c20eac21ae9f40020320a7,6da1f7fd28da533b1e51bbcbaaa34fb2eb772a6d,Any steps needed to get arrays ready for the rest of the pipeline
aafchfjcg,graknlabs/kglib,kgcn/src/models/training.py,3525a4df8543733b552e058b9cc621371b4824eb,STILL_EXISTS,TODO Update and move now this isn't used here
aafchfjge,graknlabs/kglib,kgcn/src/models/model.py,cbe487da7c26b0c4e443145e5dde5c4541485c24,6da1f7fd28da533b1e51bbcbaaa34fb2eb772a6d,Any steps needed to get arrays ready for the rest of the pipeline
aafchfjij,graknlabs/kglib,kgcn/src/models/model.py,2f5b72d46c5a5f0d201350b56b913357a4a0fe6d,49d2850f0155c794a82dc33b1e3ac03004aabf69,TODO remove
aafchgajh,graknlabs/kglib,kgcn/src/examples/animal_trade/main.py,fd3355675315d850c19df152d81b5758b6382410,STILL_EXISTS,TODO The following didn't work; but performing from the console did
aafchgbec,graknlabs/kglib,kgcn/src/models/manager.py,8c88e5373abc2add048dc5cf1184c7d679b68f66,STILL_EXISTS,TODO Made predict do the exact same as evaluate to use as test; change back
aafchgbej,graknlabs/kglib,kgcn/src/encoder/encode.py,6da1f7fd28da533b1e51bbcbaaa34fb2eb772a6d,STILL_EXISTS,TODO Pass this to traversal\/executor
aafchgbje,graknlabs/kglib,kgcn/src/models/downstream.py,6da1f7fd28da533b1e51bbcbaaa34fb2eb772a6d,STILL_EXISTS,# TODO This should be called in a loop when using more than one batch
aafchgcag,graknlabs/kglib,kgcn/src/models/downstream.py,6da1f7fd28da533b1e51bbcbaaa34fb2eb772a6d,STILL_EXISTS,tf.summary.histogram('classification\/dense\/kernel'; classification_layer.kernel)  # TODO figure out why this is throwing an error
aafchgccg,graknlabs/kglib,kgcn/src/models/model.py,6da1f7fd28da533b1e51bbcbaaa34fb2eb772a6d,STILL_EXISTS,TODO This should be called in a loop when using more than one batch
aafchgdbg,graknlabs/kglib,kgcn/src/preprocess/preprocess.py,6da1f7fd28da533b1e51bbcbaaa34fb2eb772a6d,STILL_EXISTS,TODO Get rid of pointless lambdas
aafchgdbh,graknlabs/kglib,kgcn/src/preprocess/preprocess.py,6da1f7fd28da533b1e51bbcbaaa34fb2eb772a6d,STILL_EXISTS,TODO Remove formatters
aafchgdci,graknlabs/kglib,kgcn/src/preprocess/preprocess.py,6da1f7fd28da533b1e51bbcbaaa34fb2eb772a6d,STILL_EXISTS,Any steps needed to get arrays ready for the rest of the pipeline
aafchgdea,graknlabs/kglib,kgcn/src/preprocess/preprocess.py,6da1f7fd28da533b1e51bbcbaaa34fb2eb772a6d,STILL_EXISTS,TODO Deal with where to build arrays
aafchgdhb,graknlabs/kglib,kgcn/neighbourhood/data/executor.py,5d95eb9862bcafc389eaf49dc41760f75db13884,STILL_EXISTS,TODO rename to base_type in line with Client Python
aafchgdhe,graknlabs/kglib,kgcn/neighbourhood/data/executor_test.py,5d95eb9862bcafc389eaf49dc41760f75db13884,STILL_EXISTS,TODO do we want to see @has-attribute here?
aafchgdih,graknlabs/kglib,kgcn/neighbourhood/data/traversal.py,5d95eb9862bcafc389eaf49dc41760f75db13884,STILL_EXISTS,TODO If user doesn't attach anything to impicit @has relationships; then these could be filtered out. Instead
aafchgeaj,graknlabs/kglib,kgcn/examples/animal_trade/main.py,aa189add6bb087900ba4a4c2c2138d6c75afc1b9,b1c9d201c8f761ef197309edb52a1b3b560942c4,this flush method is needed for python 3 compatibility.
aafchgebh,graknlabs/kglib,kgcn/examples/animal_trade/main.py,b2e3208755131a2d22829b4af5496cf1eb8d4c2c,STILL_EXISTS,TODO (7; 2; 2) Throws an error without rules
aafchgfif,graknlabs/kglib,kgcn/src/models/model.py,56ec86162ebfba0ed471dfe817cae276578f2769,STILL_EXISTS,TODO Pass this to traversal\/executor
aafchggcf,graknlabs/kglib,kgcn/src/models/model.py,56ec86162ebfba0ed471dfe817cae276578f2769,STILL_EXISTS,Any steps needed to get arrays ready for the rest of the pipeline
aafchhdcj,graknlabs/kglib,kgcn/utils.py,b1c9d201c8f761ef197309edb52a1b3b560942c4,STILL_EXISTS,this flush method is needed for python 3 compatibility.
aafchhddc,graknlabs/kglib,kgcn/utils.py,b1c9d201c8f761ef197309edb52a1b3b560942c4,STILL_EXISTS,TODO Should this be an array not a list?
aafchhdgg,graknlabs/kglib,kgcn/examples/animal_trade/main.py,0151af669d7318ac7ab9e7e4cdb0e8c370f8cb71,STILL_EXISTS,TODO (7; 2; 2) Throws an error without rules
aafchhfdd,graknlabs/kglib,kgcn/examples/animal_trade/main.py,21827622292caa4429e18b2f061a798225fb5bb1,STILL_EXISTS,TODO Should this be an array not a list?
aafchhfde,graknlabs/kglib,kgcn/examples/animal_trade/main.py,21827622292caa4429e18b2f061a798225fb5bb1,STILL_EXISTS,this flush method is needed for python 3 compatibility.
aafchhgbh,graknlabs/kglib,kgcn/models/downstream.py,21827622292caa4429e18b2f061a798225fb5bb1,STILL_EXISTS,# TODO This should be called in a loop when using more than one batch
aafchiffj,graknlabs/kglib,kglib/kgcn/examples/animal_trade/main.py,40e2a2234ad26dc8189f32a7f232dc9ffc1b7e94,STILL_EXISTS,raise ValueError(\"Model is not persisted; so training must be performed\") # TODO is this true?
aafchigab,graknlabs/kglib,kglib/kgcn/management/logging.py,789e475d5c90c2f9808c6c7f5109fbe25453c78f,STILL_EXISTS,this flush method is needed for python 3 compatibility.
aafchigea,graknlabs/kglib,kglib/kgcn/management/samples.py,789e475d5c90c2f9808c6c7f5109fbe25453c78f,STILL_EXISTS,TODO Should this be an array not a list?
aafchihaj,graknlabs/kglib,kglib/kgcn/core/ingest/preprocess/preprocess.py,095cc36d72473e26f6e445c786c948f1ebe620e9,STILL_EXISTS,TODO Get rid of pointless lambdas
aafchihba,graknlabs/kglib,kglib/kgcn/core/ingest/preprocess/preprocess.py,095cc36d72473e26f6e445c786c948f1ebe620e9,STILL_EXISTS,TODO Remove formatters
aafchihbb,graknlabs/kglib,kglib/kgcn/core/ingest/preprocess/preprocess.py,095cc36d72473e26f6e445c786c948f1ebe620e9,STILL_EXISTS,TODO Drop support for ignoring features
aafchihcc,graknlabs/kglib,kglib/kgcn/core/ingest/preprocess/preprocess.py,095cc36d72473e26f6e445c786c948f1ebe620e9,STILL_EXISTS,Any steps needed to get arrays ready for the rest of the pipeline
aafchihge,graknlabs/kglib,kglib/kgcn/core/ingest/traverse/data/context/array.py,095cc36d72473e26f6e445c786c948f1ebe620e9,7a8b0e2638e6b3a839d579f9f34366a1d479b710,TODO Remove; but useful for debugging
aafchihjh,graknlabs/kglib,kglib/kgcn/core/ingest/traverse/data/context/builder.py,095cc36d72473e26f6e445c786c948f1ebe620e9,7a8b0e2638e6b3a839d579f9f34366a1d479b710,Could be renamed to a frame\/situation\/region\/ROI(Region of Interest)\/locale\/zone
aafchiigb,graknlabs/kglib,kglib/kgcn/core/ingest/traverse/data/context/neighbour.py,095cc36d72473e26f6e445c786c948f1ebe620e9,STILL_EXISTS,TODO rename to base_type in line with Client Python
aafchiijd,graknlabs/kglib,kglib/kgcn/core/ingest/traverse/data/context/neighbour_test.py,095cc36d72473e26f6e445c786c948f1ebe620e9,STILL_EXISTS,TODO do we want to see @has-attribute here?
aafchijbc,graknlabs/kglib,kglib/kgcn/core/model.py,095cc36d72473e26f6e445c786c948f1ebe620e9,STILL_EXISTS,TODO This should be called in a loop when using more than one batch
aafchijdd,graknlabs/kglib,kglib/kgcn/core/model_test.py,095cc36d72473e26f6e445c786c948f1ebe620e9,STILL_EXISTS,TODO No idea why this test fails
aafchjaad,graknlabs/kglib,kglib/kgcn/core/nn/combine.py,095cc36d72473e26f6e445c786c948f1ebe620e9,STILL_EXISTS,TODO Presently unused; should be removed if unnecessary
aafchjacj,graknlabs/kglib,kglib/kgcn/core/nn/embed.py,095cc36d72473e26f6e445c786c948f1ebe620e9,STILL_EXISTS,TODO Presently unused
aafchjafb,graknlabs/kglib,kglib/kgcn/learn/classify.py,cb2b480d4c27fe0691159d3b21297caf8eb1bb4b,352aef13e396e3ecaaf00a479b5ee930ad18d7a3,tf.summary.histogram('classification\/dense\/kernel'; classification_layer.kernel)  # TODO figure out why
aafchjdgb,graknlabs/kglib,kglib/kgcn/learn/learn_IT.py,88690ec7bb66691e4d0b1e8b7c97e7e3f0277472,616483007576c90ae9904e4592df9cda074cbdce,TODO Remove 'input' and 'solution' fields; only needed for plotting which should be separated
aafchjgfi,graknlabs/kglib,kglib/kgcn/pipeline/utils.py,88690ec7bb66691e4d0b1e8b7c97e7e3f0277472,STILL_EXISTS,TODO This is the desired implementation; but the graphs are altered by the model to have duplicated reversed
aafchjhjc,graknlabs/kglib,kglib/utils/grakn/object/thing.py,88690ec7bb66691e4d0b1e8b7c97e7e3f0277472,STILL_EXISTS,TODO rename to base_type in line with Client Python
aafchjhje,graknlabs/kglib,kglib/utils/grakn/object/thing.py,88690ec7bb66691e4d0b1e8b7c97e7e3f0277472,STILL_EXISTS,TODO Make attribute a separate class
aafciaacc,graknlabs/kglib,kglib/kgcn/learn/learn_IT.py,8f015cdf63a86262e05bc242efbe432c71396eec,616483007576c90ae9904e4592df9cda074cbdce,TODO Remove 'input' and 'solution' fields; only needed for plotting which should be separated
aafciaaib,graknlabs/kglib,kglib/utils/grakn/synthetic/examples/diagnosis/generate.py,d5afe63c5c1bf013023c113d2bf123b70f4dd393,STILL_EXISTS,print(pmf.to_dataframe()) # TODO Remove pandas if this is not needed now
aafciabjh,materialsvirtuallab/megnet,megnet/data/molecule.py,eb68ffbc4669ab974e5c5bb6c4ebf2161844967c,77f35574d0f664da207e613649cbd62da76139b4,TODO: the NN strategy is not actually used by this class
aafciacah,materialsvirtuallab/megnet,megnet/data/molecule.py,07e39204d1b3f7e60a95a85b8a2b8c0cb06de433,77f35574d0f664da207e613649cbd62da76139b4,TODO (wardlt): Consider breaking this off into its own class method
aafciacbi,materialsvirtuallab/megnet,megnet/data/molecule.py,07e39204d1b3f7e60a95a85b8a2b8c0cb06de433,77f35574d0f664da207e613649cbd62da76139b4,TODO (wardlt): Misses atoms in two rings of the same size
aafciacej,materialsvirtuallab/megnet,megnet/data/molecule.py,545ec624e60e4d0665195391f0e8a8f68404126b,STILL_EXISTS,TODO: the NN strategy is not actually used by this class
aafciacff,materialsvirtuallab/megnet,megnet/data/molecule.py,545ec624e60e4d0665195391f0e8a8f68404126b,STILL_EXISTS,TODO (wardlt): Consider breaking this off into its own class method
aafciacgg,materialsvirtuallab/megnet,megnet/data/molecule.py,545ec624e60e4d0665195391f0e8a8f68404126b,STILL_EXISTS,TODO (wardlt): Misses atoms in two rings of the same size
aafciacjd,materialsvirtuallab/megnet,megnet/data/molecule.py,bc0f4c3483dd6d00693922c16335f56aa46e08dc,1cc5d72b6b06cb227985dd32396882aefb0e6bc0,TODO (wardlt): Use the average atomic weight and the bonds per atom
aafciacjh,materialsvirtuallab/megnet,megnet/data/molecule.py,bc0f4c3483dd6d00693922c16335f56aa46e08dc,STILL_EXISTS,TODO (wardlt): One-hot encoding for the elements
aafciadbf,materialsvirtuallab/megnet,megnet/data/molecule.py,5bcc56541131742c8d868356161b7b0a33ac9e8b,4bd7b73c71477d1d312d597fb88de3cbebfdcba8,TODO (wardlt): These libraries are required. Should we remove try\/catch
aafciadcf,materialsvirtuallab/megnet,megnet/data/graph.py,51015572008d394d357e5a3c1942785961356f22,STILL_EXISTS,If needed; add the targets
aafciadch,materialsvirtuallab/megnet,megnet/data/graph.py,30f3d9ad756fc2138186c4198a572e8fdf722f9d,STILL_EXISTS,Compile the inputs in needed order
aafciadef,materialsvirtuallab/megnet,megnet/data/graph.py,585654be2c2a0881477b213dbafd3714d92a92b5,a330f69b9f2afc6fd74aa3eef819ead3734922d2,TODO (wardlt): Consider making \"num_*_features\" funcs to simplify making a MEGNet model
aafciadhb,materialsvirtuallab/megnet,megnet/activations.py,5e4387b64ec20c1d670b2c088458277de6ac2379,STILL_EXISTS,serialize is needed here
aafciahhf,DigitalSlideArchive/HistomicsTK,server/script.py,3b67b030d8d242ff053b56d327108a3d6d784074,d4b77cc1c09a3b8a45fc535a033cddaae6f6fbff,complement stain matrix if needed
aafciaiaa,DigitalSlideArchive/HistomicsTK,SparseColorDeconvolution.py,f2ecae897901a534940dd26ae5d37f5ff72d3ace,STILL_EXISTS,extract solutions and make columns of \"W\" unit-norm
aafciaiad,DigitalSlideArchive/HistomicsTK,ColorDeconvolution.py,e481529e0876a1236c6060c97b9c0c8ebf846d51,STILL_EXISTS,complement stain matrix if needed
aafciaibf,DigitalSlideArchive/HistomicsTK,ComplementStainMatrix.py,f2f096e08b20e314b951645a9f4ec61512a202f2,STILL_EXISTS,calculatoe directed cross-product of first two columns
aafciajaf,DigitalSlideArchive/HistomicsTK,histomicstk/ColorDeconvolution.py,dcd0d53f0cdc1424e1bedc0f0c0871a7a0e9fd70,STILL_EXISTS,complement stain matrix if needed
aafciajbe,DigitalSlideArchive/HistomicsTK,histomicstk/ComplementStainMatrix.py,dcd0d53f0cdc1424e1bedc0f0c0871a7a0e9fd70,STILL_EXISTS,calculatoe directed cross-product of first two columns
aafciajdi,DigitalSlideArchive/HistomicsTK,histomicstk/SparseColorDeconvolution.py,dcd0d53f0cdc1424e1bedc0f0c0871a7a0e9fd70,STILL_EXISTS,extract solutions and make columns of \"W\" unit-norm
aafcibaih,DigitalSlideArchive/HistomicsTK,doc/conf.py,fe7f11548ed453e19b49999405a2bbd857682739,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aafcibdad,DigitalSlideArchive/HistomicsTK,setup.py,9eab233a182dc2e3c6a2b4cf9ffee72592736663,267303e3ffe304e6379d59f4f029acfb0ed9c1c2,TODO: put package requirements here
aafcicchj,DigitalSlideArchive/HistomicsTK,examples/ColorConvolution.Test.py,34fb02c17c1d9718d0e28156989767fed4a944e0,STILL_EXISTS,TODO: Ensure these are actually provided
aafciccid,DigitalSlideArchive/HistomicsTK,examples/ColorDeconvolution.Test.py,34fb02c17c1d9718d0e28156989767fed4a944e0,STILL_EXISTS,TODO: Ensure these are actually provided
aafciccig,DigitalSlideArchive/HistomicsTK,examples/ReinhardNorm.Test.py,34fb02c17c1d9718d0e28156989767fed4a944e0,STILL_EXISTS,TODO: Ensure these are actually provided
aafciccjc,DigitalSlideArchive/HistomicsTK,examples/SparseColorDeconvolution.Test.py,34fb02c17c1d9718d0e28156989767fed4a944e0,STILL_EXISTS,TODO: Ensure these are actually provided
aafciccjg,DigitalSlideArchive/HistomicsTK,examples/TilingSchedule.Test.py,34fb02c17c1d9718d0e28156989767fed4a944e0,STILL_EXISTS,TODO: Ensure these are actually provided
aafcicdab,DigitalSlideArchive/HistomicsTK,histomicstk/SimpleMask.py,34fb02c17c1d9718d0e28156989767fed4a944e0,STILL_EXISTS,TODO: This function fails the style checker's maximum complexity requirement
aafcicdag,DigitalSlideArchive/HistomicsTK,server/script.py,34fb02c17c1d9718d0e28156989767fed4a944e0,880cb948872fc8d187e7431a908c13c40b804ff9,complement stain matrix if needed
aafcicddh,DigitalSlideArchive/HistomicsTK,histomicstk/SparseColorDeconvolution.py,b598d35aebf594e53859818fd2bcc4238782cbf3,STILL_EXISTS,remove alpha channel if needed
aafcicfch,DigitalSlideArchive/HistomicsTK,histomicstk/Del2.py,c7c14c545a49bd39e5f308890013cad5dadf6669,STILL_EXISTS,process columns
aafcicfda,DigitalSlideArchive/HistomicsTK,histomicstk/DregEdge.py,a3596b7f333966a722e922ed4e480f4fa9d6083d,STILL_EXISTS,fix boundary conditions
aafcicgae,DigitalSlideArchive/HistomicsTK,server/__init__.py,b0f0450522741f8518393bf0a58b704663363a0b,84d8e8fce6e583ea5c739835677839a63a50e7fb,do stuff needed to create REST endpoint for cLI
aafcichdb,DigitalSlideArchive/HistomicsTK,server/__init__.py,85900ca9f75014890ea65c53962d7fd1b2104133,STILL_EXISTS,TODO: check if the xml adheres to slicer execution model
aafcicjjj,DigitalSlideArchive/HistomicsTK,server/__init__.py,2b2d34db3d10f6c4273a336c65989e9fae59cae8,3e35e894f4c044a4bbddcdf9ec7e062065315917,do stuff needed to create REST endpoint for cLI
aafcidacj,DigitalSlideArchive/HistomicsTK,server/__init__.py,2b2d34db3d10f6c4273a336c65989e9fae59cae8,STILL_EXISTS,TODO: check if the xml adheres to slicer execution model xml schema
aafcidajj,DigitalSlideArchive/HistomicsTK,histomicstk/cLoG.py,94550ec1f96be3352a5198b67be64d0489cf4b15,STILL_EXISTS,convert intensity image type to float if needed
aafcidjcf,DigitalSlideArchive/HistomicsTK,server/rest_slicer_cli.py,3e35e894f4c044a4bbddcdf9ec7e062065315917,169bd8efb506f997b3f33067f8f49bee7f24fd6f,do stuff needed to create REST endpoint for cLI
aafcidjfd,DigitalSlideArchive/HistomicsTK,server/rest_slicer_cli.py,3e35e894f4c044a4bbddcdf9ec7e062065315917,169bd8efb506f997b3f33067f8f49bee7f24fd6f,TODO: check if xml adheres to slicer execution model xml schema
aafcidjid,DigitalSlideArchive/HistomicsTK,server/rest_slicer_cli.py,f361c6cb10d54b8c0df10081350d5c916a20a773,169bd8efb506f997b3f33067f8f49bee7f24fd6f,do stuff needed to create REST endpoint for cLI
aafcieabg,DigitalSlideArchive/HistomicsTK,histomicstk/FeatureExtraction.py,5d50623589464a8685543cb0b15f37ab14f3739b,STILL_EXISTS,add columns to dataframe
aafciebff,DigitalSlideArchive/HistomicsTK,histomicstk/FeatureExtraction.py,5cc8cdf5c4eac3be8ad3458ee788acfc945c3589,03b269cb3414a50cfecca9c8b9abbf2d9316963d,clip to ends
aafcifhad,DigitalSlideArchive/HistomicsTK,docs/examples/ColorConvolution.Test.py,74f67dbcaca47f021dac880eb9b6f45edb972384,STILL_EXISTS,TODO: Ensure these are actually provided
aafcifhcb,DigitalSlideArchive/HistomicsTK,docs/examples/ColorDeconvolution.Test.py,74f67dbcaca47f021dac880eb9b6f45edb972384,STILL_EXISTS,TODO: Ensure these are actually provided
aafcifhde,DigitalSlideArchive/HistomicsTK,docs/examples/ReinhardNorm.Test.py,74f67dbcaca47f021dac880eb9b6f45edb972384,STILL_EXISTS,TODO: Ensure these are actually provided
aafcifhhg,DigitalSlideArchive/HistomicsTK,docs/examples/SparseColorDeconvolution.Test.py,74f67dbcaca47f021dac880eb9b6f45edb972384,STILL_EXISTS,TODO: Ensure these are actually provided
aafcifhij,DigitalSlideArchive/HistomicsTK,docs/examples/TilingSchedule.Test.py,74f67dbcaca47f021dac880eb9b6f45edb972384,STILL_EXISTS,TODO: Ensure these are actually provided
aafcigddg,DigitalSlideArchive/HistomicsTK,histomicstk/AreaOpenLabel.py,c076985facffb7bda7bfe098a648d4c3bec73f9d,STILL_EXISTS,iterate through objects; zeroing where needed
aafcigeci,DigitalSlideArchive/HistomicsTK,histomicstk/MinimumModel.py,c076985facffb7bda7bfe098a648d4c3bec73f9d,STILL_EXISTS,pick the best scoring candidates and cut if needed
aafcigedf,DigitalSlideArchive/HistomicsTK,histomicstk/MinimumModel.py,c076985facffb7bda7bfe098a648d4c3bec73f9d,STILL_EXISTS,no cut made; move to next object
aafcigefi,DigitalSlideArchive/HistomicsTK,histomicstk/SplitLabel.py,c076985facffb7bda7bfe098a648d4c3bec73f9d,STILL_EXISTS,iterate through objects; replicating where needed
aafcigeij,DigitalSlideArchive/HistomicsTK,histomicstk/TraceBounds.py,c076985facffb7bda7bfe098a648d4c3bec73f9d,STILL_EXISTS,check addtional points if needed
aafcigejd,DigitalSlideArchive/HistomicsTK,histomicstk/WidthOpenLabel.py,c076985facffb7bda7bfe098a648d4c3bec73f9d,STILL_EXISTS,iterate through objects; calculating distances where needed
aafciiadj,DigitalSlideArchive/HistomicsTK,histomicstk/features/FeatureExtraction.py,63b1831bccdc463aa52c67b7597f45e86bb901a2,8cad7e5bd92972f208a7ff95cf2a64f9f7d46597,add columns to dataframe
aafciiaei,DigitalSlideArchive/HistomicsTK,histomicstk/features/FeatureExtraction.py,63b1831bccdc463aa52c67b7597f45e86bb901a2,11cfcd4f402115bb078f8f0075e945ad5d1be1de,clip to ends
aafciiagf,DigitalSlideArchive/HistomicsTK,histomicstk/filters/shape/cLoG.py,63b1831bccdc463aa52c67b7597f45e86bb901a2,STILL_EXISTS,convert intensity image type to float if needed
aafciibbh,DigitalSlideArchive/HistomicsTK,histomicstk/segmentation/label/AreaOpenLabel.py,63b1831bccdc463aa52c67b7597f45e86bb901a2,STILL_EXISTS,iterate through objects; zeroing where needed
aafciibfc,DigitalSlideArchive/HistomicsTK,histomicstk/segmentation/label/SplitLabel.py,63b1831bccdc463aa52c67b7597f45e86bb901a2,STILL_EXISTS,iterate through objects; replicating where needed
aafciibie,DigitalSlideArchive/HistomicsTK,histomicstk/segmentation/label/TraceBounds.py,63b1831bccdc463aa52c67b7597f45e86bb901a2,STILL_EXISTS,check addtional points if needed
aafciibii,DigitalSlideArchive/HistomicsTK,histomicstk/segmentation/label/WidthOpenLabel.py,63b1831bccdc463aa52c67b7597f45e86bb901a2,STILL_EXISTS,iterate through objects; calculating distances where needed
aafciicba,DigitalSlideArchive/HistomicsTK,histomicstk/segmentation/level_set/DregEdge.py,63b1831bccdc463aa52c67b7597f45e86bb901a2,STILL_EXISTS,fix boundary conditions
aafciidhf,DigitalSlideArchive/HistomicsTK,histomicstk/segmentation/nuclear/MinimumModel.py,63b1831bccdc463aa52c67b7597f45e86bb901a2,STILL_EXISTS,pick the best scoring candidates and cut if needed
aafciidic,DigitalSlideArchive/HistomicsTK,histomicstk/segmentation/nuclear/MinimumModel.py,63b1831bccdc463aa52c67b7597f45e86bb901a2,STILL_EXISTS,no cut made; move to next object
aafciieac,DigitalSlideArchive/HistomicsTK,histomicstk/utils/Del2.py,63b1831bccdc463aa52c67b7597f45e86bb901a2,STILL_EXISTS,process columns
aafciifda,DigitalSlideArchive/HistomicsTK,server/constants.py,f561c1e08ece2df0ff6b866659a4713ce6c708df,0888f43dc491a7a4a9243d16d19a01cf1dd9098f,TODO check if a newer version should be pulled
aafciiffj,DigitalSlideArchive/HistomicsTK,server/docker_resource.py,0888f43dc491a7a4a9243d16d19a01cf1dd9098f,63b1947da5078cbb50126f61b336cedf7be45cb1,TODO remove bad image names
aafciifgd,DigitalSlideArchive/HistomicsTK,server/docker_resource.py,0888f43dc491a7a4a9243d16d19a01cf1dd9098f,STILL_EXISTS,TODO check local docker cache and default registry
aafciifgh,DigitalSlideArchive/HistomicsTK,server/image_worker.py,0888f43dc491a7a4a9243d16d19a01cf1dd9098f,STILL_EXISTS,TODO apply try catch blocks individually and catch specific exceptions
aafciifgi,DigitalSlideArchive/HistomicsTK,server/image_worker.py,0888f43dc491a7a4a9243d16d19a01cf1dd9098f,STILL_EXISTS,TODO pull non existent image names
aafciifhi,DigitalSlideArchive/HistomicsTK,server/docker_resource.py,2eac2ccc1dcc2e008e1c7b7d4c700e083ae177b6,STILL_EXISTS,TODO check if the img id matches if not reload the image
aafciifia,DigitalSlideArchive/HistomicsTK,server/docker_resource.py,d252eeb3478d843b03d0bf3de83aaa350031805b,STILL_EXISTS,TODO delete cli instance if they exist
aafciifib,DigitalSlideArchive/HistomicsTK,server/docker_resource.py,d252eeb3478d843b03d0bf3de83aaa350031805b,cdf31a09206e7dbaff3ae9872480589f7bc1b611,TODO check the local cache and cloud for different images of same name
aafciific,DigitalSlideArchive/HistomicsTK,server/docker_resource.py,d252eeb3478d843b03d0bf3de83aaa350031805b,cdf31a09206e7dbaff3ae9872480589f7bc1b611,TODO use image id to confirm equivalence need v2 manifest schema on cloud
aafciifid,DigitalSlideArchive/HistomicsTK,server/docker_resource.py,d252eeb3478d843b03d0bf3de83aaa350031805b,cdf31a09206e7dbaff3ae9872480589f7bc1b611,TODO how to handle duplicate clis
aafciifie,DigitalSlideArchive/HistomicsTK,server/docker_resource.py,d252eeb3478d843b03d0bf3de83aaa350031805b,63b1947da5078cbb50126f61b336cedf7be45cb1,TODO have an event update (create cli instances)
aafciihfj,DigitalSlideArchive/HistomicsTK,ansible/library/girder.py,580c1cc0369e3e50ca3a88afedaaeb08b062073a,STILL_EXISTS,some validation of folder here would be a good idea
aafciihgb,DigitalSlideArchive/HistomicsTK,ansible/library/girder.py,580c1cc0369e3e50ca3a88afedaaeb08b062073a,STILL_EXISTS,Could maybe be expanded to handle all regular expressions?
aafcijbgj,DigitalSlideArchive/HistomicsTK,plugin_tests/import_package_test.py,3165a06db14a774092d0f9332a5cb5af6b8f2e81,f959c905d78abc5c57e59bc9dbd807d6db47915a,boiler plate to start and stop the server if needed
aafcijfeb,DigitalSlideArchive/HistomicsTK,histomicstk/features/ComputeFSDs.py,ffd0b8b1211e341c47248cfc40cc7e00f85a2593,STILL_EXISTS,clip to ends
aafcijgfh,DigitalSlideArchive/HistomicsTK,histomicstk/features/FeatureExtraction.py,ddd5aad5664deeac328c103b1457596ecdb3abd1,b2cb0cd00d99669d5715e9030e76615758a2f694,add columns to dataframe
aafcijggg,DigitalSlideArchive/HistomicsTK,histomicstk/features/FeatureExtraction.py,ddd5aad5664deeac328c103b1457596ecdb3abd1,b2cb0cd00d99669d5715e9030e76615758a2f694,clip to ends
aafcjaefj,DigitalSlideArchive/HistomicsTK,plugin_tests/glcm_test.py,3d9eadfd60235b32303764331730207f24ec9075,fc2deac1056ac530adfd9fe2cea02574437c83bd,boiler plate to start and stop the server if needed
aafcjagdi,DigitalSlideArchive/HistomicsTK,histomicstk/features/graycomatrixext.py,c8b62ed46aec6fc61e16ce4158a34cefa2c94f7a,STILL_EXISTS,TODO: need to come up with a better strategy for 3D and higher
aafcjbjbh,DigitalSlideArchive/HistomicsTK,plugin_tests/docker_test.py,cdf31a09206e7dbaff3ae9872480589f7bc1b611,3cd30a12a41c01fcebd890af33dd76f89e5d82c7,TODO validate with xml schema
aafcjbjcb,DigitalSlideArchive/HistomicsTK,server/docker_resource.py,cdf31a09206e7dbaff3ae9872480589f7bc1b611,STILL_EXISTS,TODO add restpoint information in the get endpoint
aafcjbjjh,DigitalSlideArchive/HistomicsTK,server/models/docker_image.py,cdf31a09206e7dbaff3ae9872480589f7bc1b611,STILL_EXISTS,TODO check\/validate schema of dict
aafcjbjji,DigitalSlideArchive/HistomicsTK,server/models/docker_image.py,cdf31a09206e7dbaff3ae9872480589f7bc1b611,STILL_EXISTS,TODO add regex for tag and digest names
aafcjbjjj,DigitalSlideArchive/HistomicsTK,server/models/docker_image.py,cdf31a09206e7dbaff3ae9872480589f7bc1b611,STILL_EXISTS,TODO add regex for clis to enforce alpha-numeric name
aafcjcacc,DigitalSlideArchive/HistomicsTK,server/models/dockerimagemodel.py,cdf31a09206e7dbaff3ae9872480589f7bc1b611,STILL_EXISTS,TODO reference by image id or require image:digest
aafcjcace,DigitalSlideArchive/HistomicsTK,server/models/dockerimagemodel.py,cdf31a09206e7dbaff3ae9872480589f7bc1b611,STILL_EXISTS,TODO image_name:tag and image_name@digest are treated seperate images
aafcjcacj,DigitalSlideArchive/HistomicsTK,server/models/dockerimagemodel.py,cdf31a09206e7dbaff3ae9872480589f7bc1b611,STILL_EXISTS,TODO validate the xml of each cli
aafcjcdgh,DigitalSlideArchive/HistomicsTK,plugin_tests/color_conversion_test.py,6c4cdfe983a10cbc20bb09c771b4cc75699e30c7,fc2deac1056ac530adfd9fe2cea02574437c83bd,boiler plate to start and stop the server if needed
aafcjdggd,DigitalSlideArchive/HistomicsTK,histomicstk/features/__init__.py,3fe002d507d7479ec5850e77e4e44bf8cf2f682e,STILL_EXISTS,\"\"\" || This package contains functions to computing a variety of image-based features || that quantify the appearance and\/or morphology of an objects\/regions in the || image. These are needed for classifying objects (e.g. nuclei) and || regions (e.g. tissues) found in histopathology images. || \"\"\"
aafcjdgjd,DigitalSlideArchive/HistomicsTK,plugin_tests/segmentation_label_test.py,7cc275232e12266965d7b5bc792ff0ff84da528e,fc2deac1056ac530adfd9fe2cea02574437c83bd,boiler plate to start and stop the server if needed
aafcjdhdh,DigitalSlideArchive/HistomicsTK,histomicstk/segmentation/label/trace_boundary.py,e8613c9d8b4739e2601956a22f4ddf714e1c06df,03b5cff7bfb597994cc03ee7981dfd8646c214d1,check addtional points if needed
aafcjebeh,DigitalSlideArchive/HistomicsTK,histomicstk/segmentation/label/area_open.py,5732bb1d9aff0f635a92ea4862f21768e3272ee9,STILL_EXISTS,iterate through objects; zeroing where needed
aafcjeege,DigitalSlideArchive/HistomicsTK,histomicstk/preprocessing/color_deconvolution/complement_stain_matrix.py,64130854b0c61149bd86bcb9991c996704eb9337,0522dde59411a3be4410e58358763169ea91acde,calculate directed cross-product of first two columns
aafcjefhj,DigitalSlideArchive/HistomicsTK,histomicstk/preprocessing/color_deconvolution/complement_stain_matrix.py,d87f7698fafa5d8680e0ff1cf7b2adaca1ef555c,1dc72f1af5f570ad7d8a2d0728f1948948eaeae0,calculate directed cross-product of first two columns
aafcjegcg,DigitalSlideArchive/HistomicsTK,plugin_tests/nuclei_segmentation_test.py,d87f7698fafa5d8680e0ff1cf7b2adaca1ef555c,fc2deac1056ac530adfd9fe2cea02574437c83bd,boiler plate to start and stop the server if needed
aafcjehjj,DigitalSlideArchive/HistomicsTK,plugin_tests/blob_detection_filters_test.py,80b313aef0a8e8669805e3e86fd90464bd6cfdd1,fc2deac1056ac530adfd9fe2cea02574437c83bd,boiler plate to start and stop the server if needed
aafcjejgj,DigitalSlideArchive/HistomicsTK,histomicstk/preprocessing/color_deconvolution/sparse_color_deconvolution.py,1dc72f1af5f570ad7d8a2d0728f1948948eaeae0,STILL_EXISTS,remove alpha channel if needed
aafcjejjd,DigitalSlideArchive/HistomicsTK,plugin_tests/color_deconvolution_test.py,1dc72f1af5f570ad7d8a2d0728f1948948eaeae0,fc2deac1056ac530adfd9fe2cea02574437c83bd,boiler plate to start and stop the server if needed
aafcjfcgj,DigitalSlideArchive/HistomicsTK,setup.py,6200442c18b3e6744d74043a709fb38134f6314d,fbb8f3d04d7194297784d6505419c2ec26185f8f,TODO: Should we list Girder here?
aafcjgaca,DigitalSlideArchive/HistomicsTK,histomicstk/features/compute_global_graph_features.py,deb74f9fa9e3d376f7f42b26fbf71a535dac74d2,880ff73cc55d5109279ffad008a3cce6d1fa63a4,TODO it's not clear what 'chord length' refers to in the paper
aafcjgacc,DigitalSlideArchive/HistomicsTK,histomicstk/features/compute_global_graph_features.py,deb74f9fa9e3d376f7f42b26fbf71a535dac74d2,STILL_EXISTS,TODO chords
aafcjgbdd,DigitalSlideArchive/HistomicsTK,ansible/roles/common/set_environment.py,480473ac3c8d09c128d58f0f11555bc5b43b2a40,STILL_EXISTS,Create new groups as needed
aafcjgfbb,DigitalSlideArchive/HistomicsTK,plugin_tests/feature_extraction_test.py,4cff94d70f25ab8abd446ce290167dd0be97c80f,fc2deac1056ac530adfd9fe2cea02574437c83bd,boiler plate to start and stop the server if needed
aafcjhadg,DigitalSlideArchive/HistomicsTK,histomicstk/utils/polygon_and_mask_utils.py,1f9caf79351e93e43aebf9c6de37d8a4e34f31ec,STILL_EXISTS,save a copy of ROI-only mask to crop to it later if needed
aafcjhadh,DigitalSlideArchive/HistomicsTK,histomicstk/utils/polygon_and_mask_utils.py,1f9caf79351e93e43aebf9c6de37d8a4e34f31ec,STILL_EXISTS,Now crop polygons to roi if needed (prevent 'overflow' beyond roi edge)
aafcjhbcg,DigitalSlideArchive/HistomicsTK,histomicstk/annotations_and_masks/annotations_to_masks_handler.py,deea38680b214fa1ca929a31ca6594e9ad974249,STILL_EXISTS,save a copy of ROI-only mask to crop to it later if needed
aafcjhbch,DigitalSlideArchive/HistomicsTK,histomicstk/annotations_and_masks/annotations_to_masks_handler.py,deea38680b214fa1ca929a31ca6594e9ad974249,STILL_EXISTS,Now crop polygons to roi if needed (prevent 'overflow' beyond roi edge)
aafcjhcha,DigitalSlideArchive/HistomicsTK,histomicstk/annotations_and_masks/masks_to_annotations_handler.py,26a1735eb7df6e2e81b2e88f698190aae2334ffa,STILL_EXISTS,to keep track of things better relative to contour_group; now it is:
aafcjhdcc,DigitalSlideArchive/HistomicsTK,histomicstk/annotations_and_masks/masks_to_annotations_handler.py,a67e45ab64ae3f543e05c4d88f9fe35a60d3adc3,STILL_EXISTS,discard non-enclosed background (eg stroma) if needed
aafcjifci,DigitalSlideArchive/HistomicsTK,histomicstk/annotations_and_masks/polygon_merger.py,39286c7073c127a86932de185cb100c1ccdee86c,81e9bfc125d38cb5ea93a2ed0ff08cdb7e14de3c,colnames = edge_contours[list(edge_contours.keys())[0]].columns
aafcjjcjf,DigitalSlideArchive/HistomicsTK,histomicstk/annotations_and_masks/pyrtree/rtree.py,faad99d1834a43bb4bedeffddb0af7d9f317d9a8,STILL_EXISTS,Less obviously: using object graph directly leads to really long GC
aafcjjdca,DigitalSlideArchive/HistomicsTK,histomicstk/annotations_and_masks/pyrtree/rtree.py,faad99d1834a43bb4bedeffddb0af7d9f317d9a8,STILL_EXISTS,FIXME HACK TODO: is it okay for there to be empty clusters?
aafdaaddj,DigitalSlideArchive/HistomicsTK,histomicstk/saliency/tests/tissue_detection_test.py,903c1f1109a339602227768445675e5204d1945b,STILL_EXISTS,\"\"\" || Created on Wed Sep 18 00:06:28 2019 ||  || @author: mtageld || \"\"\"
aafdaaebi,DigitalSlideArchive/HistomicsTK,histomicstk/saliency/tissue_detection.py,1c68114abba56347f31c991c38bd483fb63fc1ca,STILL_EXISTS,\"\"\" || Created on Wed Sep 18 03:29:24 2019 ||  || @author: mtageld || \"\"\"
aafdaagcb,DigitalSlideArchive/HistomicsTK,histomicstk/saliency/tests/cellularity_detection_test.py,f10433cf0f0162cf8722369765691a04285f9552,STILL_EXISTS,\"\"\" || Created on Thu Sep 19 02:25:34 2019. ||  || @author: mtageld || \"\"\"
aafdaagjj,DigitalSlideArchive/HistomicsTK,histomicstk/saliency/tests/cellularity_detection_test.py,b7180aae767b03a4bdc34d9850cc9fd4b13d9b15,a7560fb57fce57d8a859eac68eb5dce9c9e9973a,+ list(fdata_haralick.columns)
aafdaaiee,DigitalSlideArchive/HistomicsTK,histomicstk/saliency/cellularity_detection.py,a545128ed0b7fc226337f63ae9e25c24a2a4af84,STILL_EXISTS,\"\"\" || Created on Mon Sep 23 21:17:43 2019 ||  || @author: mtageld || \"\"\"
aafdaaihe,DigitalSlideArchive/HistomicsTK,histomicstk/utils/general_utils.py,b5ad921d87edcdf62304c59dd0305834929cf6d0,STILL_EXISTS,\"\"\" || Created on Tue Sep 24 00:43:04 2019. ||  || @author: mtageld || \"\"\"
aafdabaee,DigitalSlideArchive/HistomicsTK,histomicstk/saliency/tests/cellularity_detection_test.py,1031c91e62039850edd5bfd951a930ed0e4afc35,788aef210b2a43c046a6287dfb12979810c3bf27,color variations and sometimes gives better results
aafdabbda,DigitalSlideArchive/HistomicsTK,histomicstk/saliency/cellularity_detection.py,788aef210b2a43c046a6287dfb12979810c3bf27,STILL_EXISTS,color variations and sometimes gives better results
aafdabebi,DigitalSlideArchive/HistomicsTK,histomicstk/workflows/tests/workflow_runner_test.py,75930ac650903edcd613908ff0bf2924315f80f8,STILL_EXISTS,\"\"\" || Created on Mon Sep 30 18:12:48 2019 ||  || @author: mtageld || \"\"\"
aafdabeff,DigitalSlideArchive/HistomicsTK,histomicstk/workflows/workflow_runner.py,b53f169b78f1872eb8c0f760a86d3856a46f4b67,STILL_EXISTS,\"\"\" || Created on Mon Sep 30 22:09:40 2019 ||  || @author: mtageld || \"\"\"
aafdabhfd,DigitalSlideArchive/HistomicsTK,histomicstk/preprocessing/tests/deconvolution_based_normalization_test.py,4d1593c816de9f555b74be75b0f9618a7ea9663a,STILL_EXISTS,and using reordered such that columns are the order:
aafdabida,DigitalSlideArchive/HistomicsTK,histomicstk/preprocessing/tests/color_augmentation_test.py,d4dd8820a139ce22a273071774a82ee70c56425d,STILL_EXISTS,and using reordered such that columns are the order:
aafdacefi,DigitalSlideArchive/HistomicsTK,histomicstk/saliency/cellularity_detection_thresholding.py,4c62db96b87923ab3f98b64f25eb76bc808c732e,0d65bb21b66c2c4fa74b6bbac7fb6992eeba60b2,TODO -- color normalization proper
aafdacehe,DigitalSlideArchive/HistomicsTK,histomicstk/saliency/cellularity_detection_thresholding.py,4c62db96b87923ab3f98b64f25eb76bc808c732e,STILL_EXISTS,TODO -- incorporate masking and macenko!!
aafdacehj,DigitalSlideArchive/HistomicsTK,histomicstk/saliency/cellularity_detection_thresholding.py,4c62db96b87923ab3f98b64f25eb76bc808c732e,STILL_EXISTS,TODO -- reinhard normalization of thumbnail!!
aafdaceja,DigitalSlideArchive/HistomicsTK,histomicstk/saliency/cellularity_detection_thresholding.py,516d817b67d5b799beedd6896f09f9a115f14084,STILL_EXISTS,TODO -- fix me
aafdacejb,DigitalSlideArchive/HistomicsTK,histomicstk/saliency/cellularity_detection_thresholding.py,516d817b67d5b799beedd6896f09f9a115f14084,STILL_EXISTS,TODO -- fix me!!!!
aafdacejd,DigitalSlideArchive/HistomicsTK,histomicstk/saliency/cellularity_detection_thresholding.py,516d817b67d5b799beedd6896f09f9a115f14084,STILL_EXISTS,TODO -- instead; smoother visualization bounds (at MAG)
aafdacejj,DigitalSlideArchive/HistomicsTK,histomicstk/saliency/cellularity_detection_thresholding.py,eeb88c09f39f4d6500795949308282ae4f1ed2d5,STILL_EXISTS,and using reordered such that columns are the order:
aafdacfbh,DigitalSlideArchive/HistomicsTK,histomicstk/saliency/cellularity_detection_thresholding.py,eeb88c09f39f4d6500795949308282ae4f1ed2d5,STILL_EXISTS,TODO -- fix me
aafdacfbi,DigitalSlideArchive/HistomicsTK,histomicstk/saliency/cellularity_detection_thresholding.py,eeb88c09f39f4d6500795949308282ae4f1ed2d5,STILL_EXISTS,TODO -- fix me!!!!
aafdacfbj,DigitalSlideArchive/HistomicsTK,histomicstk/saliency/cellularity_detection_thresholding.py,eeb88c09f39f4d6500795949308282ae4f1ed2d5,STILL_EXISTS,TODO -- instead; smoother visualization bounds (at MAG)
aafdadiab,DigitalSlideArchive/HistomicsTK,histomicstk/preprocessing/tests/color_augmentation_test_disable.py,3f388cbc7ed4a7d5b22e95ce4518d9c0ba2d899c,STILL_EXISTS,and using reordered such that columns are the order:
aafdadidc,DigitalSlideArchive/HistomicsTK,histomicstk/utils/girder_convenience_utils.py,16d7ec86dbc8a9049ae802749489fa4a3efb1506,STILL_EXISTS,add or replace as needed
aafdadidh,DigitalSlideArchive/HistomicsTK,histomicstk/utils/girder_convenience_utils.py,16d7ec86dbc8a9049ae802749489fa4a3efb1506,STILL_EXISTS,PROPER WAY
aafdadiei,DigitalSlideArchive/HistomicsTK,histomicstk/utils/girder_convenience_utils.py,16d7ec86dbc8a9049ae802749489fa4a3efb1506,STILL_EXISTS,TODO -- This is likely a bug (?); fix me!!!
aafdadijc,DigitalSlideArchive/HistomicsTK,histomicstk/workflows/tests/test_workflow_runner.py,16d7ec86dbc8a9049ae802749489fa4a3efb1506,STILL_EXISTS,\"\"\" || Created on Mon Sep 30 18:12:48 2019. ||  || @author: mtageld || \"\"\"
aafdadjce,DigitalSlideArchive/HistomicsTK,tests/htk_test_utilities.py,16d7ec86dbc8a9049ae802749489fa4a3efb1506,fd966cfe8ad0ee250b34b99aa53acf64d7629804,TODO -- refactor to session scope by figuring out pytest issue (bug?)
aafdadjii,DigitalSlideArchive/HistomicsTK,histomicstk/annotations_and_masks/annotation_and_mask_utils.py,c44b8011c1bdc71ac8cdd37261bbdebbff1f1214,STILL_EXISTS,go through annotation elements and add as needed
aafdadjja,DigitalSlideArchive/HistomicsTK,histomicstk/annotations_and_masks/annotation_and_mask_utils.py,c44b8011c1bdc71ac8cdd37261bbdebbff1f1214,STILL_EXISTS,crop using shapely to desired bounds if needed
aafdaecbi,DigitalSlideArchive/HistomicsTK,tests/htk_test_utilities.py,c44b8011c1bdc71ac8cdd37261bbdebbff1f1214,21c99417b13058167cfabaef6ca9d5005a91fc48,TODO -- refactor to session scope by figuring out pytest issue (bug?)
aafdaedii,DigitalSlideArchive/HistomicsTK,histomicstk/preprocessing/tests/test_normalization_and_augmentation.py,e18ef646a1f03f0f9dae4130372980511cf4d309,STILL_EXISTS,and using reordered such that columns are the order:
aafdaedjg,DigitalSlideArchive/HistomicsTK,histomicstk/saliency/tests/test_saliency.py,e18ef646a1f03f0f9dae4130372980511cf4d309,STILL_EXISTS,\"\"\" || Created on Wed Sep 18 00:06:28 2019. ||  || @author: mtageld || \"\"\"
aafdaeeda,DigitalSlideArchive/HistomicsTK,histomicstk/features/compute_morphometry_features.py,ca6afbdef48087fc77f535054af685d3fc8fd1ba,STILL_EXISTS,A bug in scikit-image could produce a (very slightly)
aafdaeehc,DigitalSlideArchive/HistomicsTK,tests/htk_test_utilities.py,4ad127d5da81068d48985e17abd76b90a91099e6,STILL_EXISTS,TODO -- refactor to session scope by figuring out pytest issue (bug?)
aafejjgfa,microsoft/onnxconverter-common,onnxconverter_common/onnx_ops.py,9126b6a5e1ec8867e9669e82d68b1273ccdfcacd,STILL_EXISTS,First we assume no cropping is needed at the end of those axes.
aafejjgfe,microsoft/onnxconverter-common,onnxconverter_common/onnx_ops.py,9126b6a5e1ec8867e9669e82d68b1273ccdfcacd,STILL_EXISTS,Add the adjusted ends.
aafejjgib,microsoft/onnxconverter-common,onnxconverter_common/onnx_ops.py,9126b6a5e1ec8867e9669e82d68b1273ccdfcacd,STILL_EXISTS,TODO; we need verify this after onnx opset 10 release
aafejjhfa,microsoft/onnxconverter-common,onnxconverter_common/topology.py,9126b6a5e1ec8867e9669e82d68b1273ccdfcacd,STILL_EXISTS,Make the seed meet C-style naming convention
aafejjhhh,microsoft/onnxconverter-common,onnxconverter_common/topology.py,9126b6a5e1ec8867e9669e82d68b1273ccdfcacd,STILL_EXISTS,A operator has an input; so we remove the operator from the unused-operator list.
aafejjhhj,microsoft/onnxconverter-common,onnxconverter_common/topology.py,9126b6a5e1ec8867e9669e82d68b1273ccdfcacd,STILL_EXISTS,A operator has an output; so we remove the operator from the unused-operator list.
aafejjiba,microsoft/onnxconverter-common,onnxconverter_common/topology.py,9126b6a5e1ec8867e9669e82d68b1273ccdfcacd,STILL_EXISTS,Scan through all operators and adjust their variables' shapes if needed
aafejjibd,microsoft/onnxconverter-common,onnxconverter_common/topology.py,9126b6a5e1ec8867e9669e82d68b1273ccdfcacd,STILL_EXISTS,We fix this problem here.
aafejjibi,microsoft/onnxconverter-common,onnxconverter_common/topology.py,9126b6a5e1ec8867e9669e82d68b1273ccdfcacd,STILL_EXISTS,Remove unused operators
aafejjibj,microsoft/onnxconverter-common,onnxconverter_common/topology.py,9126b6a5e1ec8867e9669e82d68b1273ccdfcacd,STILL_EXISTS,Remove unused variables
aafejjicc,microsoft/onnxconverter-common,onnxconverter_common/topology.py,9126b6a5e1ec8867e9669e82d68b1273ccdfcacd,STILL_EXISTS,Check input naming convention
aafejjicf,microsoft/onnxconverter-common,onnxconverter_common/topology.py,9126b6a5e1ec8867e9669e82d68b1273ccdfcacd,STILL_EXISTS,Check output naming convention
aafejjihf,microsoft/onnxconverter-common,onnxconverter_common/onnx_ops.py,609e6291ed3f388f431451ddf686a81cb77160e2,STILL_EXISTS,We implement Upsample through Resize instead
aaffaadff,modelhub-ai/modelhub-engine,usr_src_template/postprocessing.py,bf07dcef03c96e2cf50e5afe8d7d09806a6ad71a,STILL_EXISTS,TODO: implement postprocessing of inference results
aaffaadfg,modelhub-ai/modelhub-engine,usr_src_template/preprocessing.py,bf07dcef03c96e2cf50e5afe8d7d09806a6ad71a,STILL_EXISTS,OPTIONAL TODO: implement preprocessing of PIL image objects
aaffaadfh,modelhub-ai/modelhub-engine,usr_src_template/preprocessing.py,bf07dcef03c96e2cf50e5afe8d7d09806a6ad71a,STILL_EXISTS,OPTIONAL TODO: implement preprocessing of SimpleITK image objects
aaffaadfi,modelhub-ai/modelhub-engine,usr_src_template/preprocessing.py,bf07dcef03c96e2cf50e5afe8d7d09806a6ad71a,STILL_EXISTS,TODO: implement preprocessing of image after it was converted to a numpy array
aaffaaeei,modelhub-ai/modelhub-engine,docs/source/conf.py,f11c6b76841f6a94ff39e220d9025ee79242babb,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaffaagbi,modelhub-ai/modelhub-engine,framework/ModelHubAPI.py,3efd49fde80f8a8e3c00a742fd007ba102bb811c,5b91746879f24d65048390d06895ec46b17a830e,Todo:
aaffaagjd,modelhub-ai/modelhub-engine,framework/ModelHubRESTAPI.py,3efd49fde80f8a8e3c00a742fd007ba102bb811c,5b91746879f24d65048390d06895ec46b17a830e,Todo:
aaffaajig,modelhub-ai/modelhub-engine,framework/modelhubapi_tests/restapi_test.py,a964a599f0c2e5763d787790f91d141191e7f003,STILL_EXISTS,TODO this is not so nice yet; test should not require a download from the inet
aaffabaah,modelhub-ai/modelhub-engine,framework/modelhubapi/restapi.py,bf09ffd5510180b153629bc66151be239d95a9a3,STILL_EXISTS,TODO
aaffabbah,modelhub-ai/modelhub-engine,framework/modelhubapi_tests/restapi_test.py,e3c501f365d94822b920fb26b196509a47608adc,STILL_EXISTS,TODO this is not so nice yet; test should not require a download from the inet
aaffabcag,thehyve/tmtk,tmtk/toolbox/remap_chromosomal_regions.py,e890753bfc33c2150eafa4760b0c44ec7729d353,STILL_EXISTS,Convert flag columns to int
aaffabcca,thehyve/tmtk,tmtk/arborist/flask_connection.py,bb740430150db69cf4fe82af77fdd6dc8283f208,ee2a143a1ac5c414f139970779b189ac2c506117,from .functions.clinical import columns_to_tree; json_to_columns; getchildren; \\
aaffabcci,thehyve/tmtk,tmtk/arborist/flask_connection.py,bb740430150db69cf4fe82af77fdd6dc8283f208,ee2a143a1ac5c414f139970779b189ac2c506117,TODO handle case where no permission to create folder
aaffabcda,thehyve/tmtk,tmtk/arborist/flask_connection.py,bb740430150db69cf4fe82af77fdd6dc8283f208,ee2a143a1ac5c414f139970779b189ac2c506117,TODO this is all hacky; should be made more flexible; view should be reconsidered
aaffabchd,thehyve/tmtk,tmtk/arborist/flask_connection.py,bb740430150db69cf4fe82af77fdd6dc8283f208,ee2a143a1ac5c414f139970779b189ac2c506117,tree_array = columns_to_tree(columnsfile)
aaffabdgb,thehyve/tmtk,tmtk/clinical/WordMapping.py,d09043b1dec1d3172037cac5b8548f7a9395eb76,67a9774c36db76b8145188797e7c694780fb3055,Todo improve this check here.
aaffabdgi,thehyve/tmtk,tmtk/arborist/jstreecontrol.py,88ee57b7506742476e9384c2d2e01ef4d39cc058,STILL_EXISTS,Remove file names from SUBJ_ID; they were added as workaround for unique constraints.
aaffabdgj,thehyve/tmtk,tmtk/arborist/jstreecontrol.py,88ee57b7506742476e9384c2d2e01ef4d39cc058,STILL_EXISTS,Add filename to SUBJ_ID; this is a work around for unique path constraint.
aaffabdhc,thehyve/tmtk,tmtk/arborist/jstreecontrol.py,88ee57b7506742476e9384c2d2e01ef4d39cc058,0f8973169c0118da16acd42c95e5ccb5de22d68d,Might be worth it to improve functionality here; for the meta data tags.
aaffabdig,thehyve/tmtk,tmtk/arborist/jstreecontrol.py,aaa751df4b40e029a3165a6b22db836db036569c,STILL_EXISTS,Add filename to SUBJ_ID; this is a work around for unique path constraint.
aaffabgbi,thehyve/tmtk,docs/conf.py,877992007a3ce5ac04d825c02362552d8657d5f9,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaffabibg,thehyve/tmtk,tmtk/arborist/jupyter_extension.py,2f75e34da1f7cae49409e751a442fc3123913d53,STILL_EXISTS,Put url prefix (e.g. '\/user\/madonna\/') in environment as hack to open iframe later
aaffabjca,thehyve/tmtk,tmtk/clinical/Ontology.py,f2d0b39bb4e301615c79bb14ffafb07c190b2c96,10d89b26c5e58407d0561cc01afabee8b9290752,column number columns. If no df parameter is not set; build index
aaffabjcj,thehyve/tmtk,tmtk/clinical/Ontology.py,f2d0b39bb4e301615c79bb14ffafb07c190b2c96,10d89b26c5e58407d0561cc01afabee8b9290752,# df.set_index(list(df.columns[[0; 1]]); drop=False; inplace=True)
aaffacahe,thehyve/tmtk,tmtk/study.py,b0c83d94469b983989713228f9185f1be66ba0f0,10d89b26c5e58407d0561cc01afabee8b9290752,TODO: an actual implementation
aaffacbge,thehyve/tmtk,tmtk/toolbox/skinny_loader/i2b2demodata/concept_dimension.py,8249007721c623b727082b642e398146507729ad,STILL_EXISTS,Put back the right order of columns after concatenating the two dataframes
aaffaccfh,thehyve/tmtk,tmtk/toolbox/skinny_loader/i2b2demodata/concept_dimension.py,428a3c596d49f0ecf7ec121697712a6391023d53,STILL_EXISTS,Put back the right order of columns after concatenating the two dataframes
aaffacgeb,thehyve/tmtk,tests/skinny_loader_tests.py,8dc05bbb1841e8f9b3727041cc468d6792b13f2b,0f260ca64f434c996308afb65797b2f76bf2842e,TODO: Extend TEST_17_1 with modifiers with empty reference column (so row wide)
aaffacggj,thehyve/tmtk,tmtk/clinical/Clinical.py,218e5fbc35362a26d37f82306620fe1d65a0439f,4353b8ba57b55992443c3279b300bbff663c56c8,TODO: find name of variable column
aaffacgha,thehyve/tmtk,tmtk/clinical/Clinical.py,218e5fbc35362a26d37f82306620fe1d65a0439f,STILL_EXISTS,TODO: add reference columns to blueprint mapping
aaffacgig,thehyve/tmtk,tmtk/clinical/Clinical.py,0ca25d31153fec9ae57427254439b5a595e1b875,cf5f7de2c450b065b326b1a2ecaca82af2606c1b,TODO: find name of variable column
aaffacgih,thehyve/tmtk,tmtk/clinical/Clinical.py,0ca25d31153fec9ae57427254439b5a595e1b875,STILL_EXISTS,TODO: add reference columns to blueprint mapping
aaffachha,thehyve/tmtk,tmtk/toolbox/template_validation/template_validation.py,e3a9bd525d595b017071a764c03e550662ffaa75,STILL_EXISTS,TODO: Check if file is a valid file
aaffachhb,thehyve/tmtk,tmtk/toolbox/template_validation/template_validation.py,e3a9bd525d595b017071a764c03e550662ffaa75,STILL_EXISTS,TODO: Check if file is Excel
aaffachij,thehyve/tmtk,tmtk/toolbox/template_validation/tree_sheet_validation.py,32c52becae704f7af3f51b6303cbc46a190c9f35,STILL_EXISTS,check if 'Level' column has corresponding 'metadata tag' and 'metadata value' columns:
aaffachja,thehyve/tmtk,tmtk/toolbox/template_validation/tree_sheet_validation.py,32c52becae704f7af3f51b6303cbc46a190c9f35,STILL_EXISTS,check if 'metadata tag' and 'metadata value' columns have a corresponding 'Level' column:
aaffachjg,thehyve/tmtk,tmtk/toolbox/template_reader/sheets.py,06e687750c25a4cebcfd0b290b98a2b621743968,9cc635986730410a6327817eb09095113bedc6af,TODO these lines should be removed as they are now part of the template validation
aaffacich,thehyve/tmtk,tmtk/toolbox/template_validation/validation.py,cea0fc5f4cb0e2b7f859fe8a44f9e3fccb639a0f,STILL_EXISTS,check values in columns
aaffacjag,thehyve/tmtk,tmtk/toolbox/template_reader/sheets.py,5958501c0266de7d10aacf0a534491271fb22a85,STILL_EXISTS,Fill level columns of metadata-only rows with values from the previous row
aaffacjaj,thehyve/tmtk,tmtk/toolbox/template_reader/sheets.py,5958501c0266de7d10aacf0a534491271fb22a85,STILL_EXISTS,TODO these lines should be removed as they are now part of the template validation
aaffacjfd,thehyve/tmtk,tmtk/toolbox/template_reader/sheets.py,043bdd1160f1e8ea71d104ec6e3ebdb924f05656,STILL_EXISTS,Make the array of booleans equal length to the number of columns in the full df
aaffacjgb,triagemd/model-converters,model_converters/keras_to_coreml.py,83ad4923d425c6c3a1ac1b4db6d529d97d8c290d,STILL_EXISTS,code below uses private APIs; it will probably break (and doesn't work) -- hopefully coreml ships official support soon
aaffacjhg,triagemd/model-converters,model_converters/keras_to_tensorflow.py,7c74a22500af67f3e6bb6dbfb45d210db515d8f7,f847f4cb23532241208718e67a3a33ef72e76044,needed for Resnet152 support
aaffacjhi,triagemd/model-converters,tests/test_keras_to_tensorflow.py,96d017a5c00c9aba4341751b5c245acd5b2d6b9f,49e2469246040bb1f4554b1bf8e190034e313d05,no idea why the Python 2 CI won't run these models (they run out of memory)
aaffacjhj,triagemd/model-converters,tests/test_keras_to_tensorflow.py,96d017a5c00c9aba4341751b5c245acd5b2d6b9f,2664c17d14424854d0a56d7a10f26a54cda45205,no idea why the Python 3.6 CI won't run these models (they run out of memory)
aaffadahf,DependableSystemsLab/TensorFI,TensorFI/fiLog.py,ed9ea0db1adb3ba5e8751139b42d3f11e68f32fa,STILL_EXISTS,FIXME: This won't work if the injection spans multiple days
aaffadaib,DependableSystemsLab/TensorFI,TensorFI/fiStats.py,ed9ea0db1adb3ba5e8751139b42d3f11e68f32fa,STILL_EXISTS,FIXME: Open a file and dump stats to it later
aaffadaii,DependableSystemsLab/TensorFI,TensorFI/injectFault.py,ed9ea0db1adb3ba5e8751139b42d3f11e68f32fa,STILL_EXISTS,FIXME: Add this to the list of dependencies for this module
aaffadbai,DependableSystemsLab/TensorFI,TensorFI/injectFault.py,ed9ea0db1adb3ba5e8751139b42d3f11e68f32fa,STILL_EXISTS,FIXME: This doesn't work because TensorFlow uses its own threading infrastructure
aaffadbeh,DependableSystemsLab/TensorFI,TensorFI/injectFault.py,ed9ea0db1adb3ba5e8751139b42d3f11e68f32fa,STILL_EXISTS,FIXME: Only 4 types are supported now. Support more types later.
aaffadbga,DependableSystemsLab/TensorFI,TensorFI/injectFault.py,ed9ea0db1adb3ba5e8751139b42d3f11e68f32fa,STILL_EXISTS,FIXME: Check semantics of assignment operator
aaffadbgf,DependableSystemsLab/TensorFI,TensorFI/injectFault.py,ed9ea0db1adb3ba5e8751139b42d3f11e68f32fa,STILL_EXISTS,FIXME: Need to check if mod is the equivalent of floorMod in NumPy
aaffadbgi,DependableSystemsLab/TensorFI,TensorFI/injectFault.py,ed9ea0db1adb3ba5e8751139b42d3f11e68f32fa,STILL_EXISTS,Otherwise; ignore it (FIXME: is this the correct behavior ?)
aaffadbgj,DependableSystemsLab/TensorFI,TensorFI/injectFault.py,ed9ea0db1adb3ba5e8751139b42d3f11e68f32fa,STILL_EXISTS,FIXME: This only works if we call np.mean on b[0]. Need to figure out why.
aaffadbha,DependableSystemsLab/TensorFI,TensorFI/injectFault.py,ed9ea0db1adb3ba5e8751139b42d3f11e68f32fa,STILL_EXISTS,FIXME: Make this work with any type; not just float32
aaffadbhb,DependableSystemsLab/TensorFI,TensorFI/injectFault.py,ed9ea0db1adb3ba5e8751139b42d3f11e68f32fa,STILL_EXISTS,FIXME: This seems to work; but not sure if it always does
aaffadbhe,DependableSystemsLab/TensorFI,TensorFI/injectFault.py,ed9ea0db1adb3ba5e8751139b42d3f11e68f32fa,STILL_EXISTS,FIXME: Can't inject faults into unpack as it's not a tensor or scalar
aaffadbia,DependableSystemsLab/TensorFI,TensorFI/injectFault.py,ed9ea0db1adb3ba5e8751139b42d3f11e68f32fa,STILL_EXISTS,FIXME: This throws an exception; so we dummied it out
aaffadbib,DependableSystemsLab/TensorFI,TensorFI/injectFault.py,ed9ea0db1adb3ba5e8751139b42d3f11e68f32fa,54318a0fae786a09998f95fdf5f99c88f166b7c9,FIXME: According to tf doc; this should only take 2 arguments
aaffadbic,DependableSystemsLab/TensorFI,TensorFI/injectFault.py,ed9ea0db1adb3ba5e8751139b42d3f11e68f32fa,54318a0fae786a09998f95fdf5f99c88f166b7c9,\tbut somehow it is complaining that it requires 3 arguments
aaffadbie,DependableSystemsLab/TensorFI,TensorFI/injectFault.py,ed9ea0db1adb3ba5e8751139b42d3f11e68f32fa,STILL_EXISTS,FIXME: Actually implement the Switch operation
aaffadbii,DependableSystemsLab/TensorFI,TensorFI/injectFault.py,ed9ea0db1adb3ba5e8751139b42d3f11e68f32fa,STILL_EXISTS,FIXME: According to the TF docs; this operation doesn't exist !
aaffadbjd,DependableSystemsLab/TensorFI,TensorFI/injectFault.py,ed9ea0db1adb3ba5e8751139b42d3f11e68f32fa,STILL_EXISTS,### If you implement any of them; please move them above the line              ####
aaffadceh,DependableSystemsLab/TensorFI,TensorFI/injectFault.py,ed9ea0db1adb3ba5e8751139b42d3f11e68f32fa,STILL_EXISTS,FIXME: Should we NOT actually do the operation as well ??
aaffadcfa,DependableSystemsLab/TensorFI,TensorFI/injectFault.py,ed9ea0db1adb3ba5e8751139b42d3f11e68f32fa,STILL_EXISTS,FIXME: These are fairly repetitive; so perhaps generate them automatically
aaffadcfb,DependableSystemsLab/TensorFI,TensorFI/injectFault.py,ed9ea0db1adb3ba5e8751139b42d3f11e68f32fa,STILL_EXISTS,\tAlso; maybe these should be sorted alphabetically - this is getting quite big
aaffadcfd,DependableSystemsLab/TensorFI,TensorFI/injectFault.py,ed9ea0db1adb3ba5e8751139b42d3f11e68f32fa,STILL_EXISTS,FIXME: Not sure if Max is a synonymn of Maximum or a new operation
aaffadcfe,DependableSystemsLab/TensorFI,TensorFI/injectFault.py,ed9ea0db1adb3ba5e8751139b42d3f11e68f32fa,STILL_EXISTS,FIXME: Not sure if Min is a synonymn of Minimum or a new operation
aaffaddae,DependableSystemsLab/TensorFI,TensorFI/tensorFI.py,ed9ea0db1adb3ba5e8751139b42d3f11e68f32fa,STILL_EXISTS,Remember this run's values for future FI runs perhaps
aaffaddcf,DependableSystemsLab/TensorFI,TensorFI/tensorFI.py,ed9ea0db1adb3ba5e8751139b42d3f11e68f32fa,STILL_EXISTS,FIXME: We should provide some degree of roll-back and retry here
aaffadeca,DependableSystemsLab/TensorFI,TensorFI/tensorFI.py,ed9ea0db1adb3ba5e8751139b42d3f11e68f32fa,STILL_EXISTS,If it's the last process; only launch as many injections as needed
aaffadecd,DependableSystemsLab/TensorFI,TensorFI/tensorFI.py,ed9ea0db1adb3ba5e8751139b42d3f11e68f32fa,STILL_EXISTS,FIXME: TensorFlow hangs when we use processes; so beware !
aaffadeci,DependableSystemsLab/TensorFI,TensorFI/tensorFI.py,ed9ea0db1adb3ba5e8751139b42d3f11e68f32fa,STILL_EXISTS,FIXME: We should wait for the process to terminate naturally
aaffadeic,DependableSystemsLab/TensorFI,Tests/NotWorking/convolutional.py,ed9ea0db1adb3ba5e8751139b42d3f11e68f32fa,STILL_EXISTS,activations such that no rescaling is needed at evaluation time.
aaffadfad,DependableSystemsLab/TensorFI,Tests/NotWorking/convolutional.py,ed9ea0db1adb3ba5e8751139b42d3f11e68f32fa,STILL_EXISTS,Note that we could use better randomization across epochs.
aaffadffb,DependableSystemsLab/TensorFI,Tests/NotWorking/dynamic_rnn.py,ed9ea0db1adb3ba5e8751139b42d3f11e68f32fa,STILL_EXISTS,Hack to build the indexing and retrieve the right output.
aaffadfgj,DependableSystemsLab/TensorFI,Tests/NotWorking/kmeans.py,ed9ea0db1adb3ba5e8751139b42d3f11e68f32fa,STILL_EXISTS,\"\"\" K-Means. ||  || Implement K-Means algorithm with TensorFlow; and apply it to classify || handwritten digit images. This example is using the MNIST database of || handwritten digits as training samples (http:\/\/yann.lecun.com\/exdb\/mnist\/). ||  || Note: This example requires TensorFlow v1.1.0 or over. ||  || Author: Aymeric Damien || Project: https:\/\/github.com\/aymericdamien\/TensorFlow-Examples\/ || \"\"\"
aaffadgdf,DependableSystemsLab/TensorFI,Tests/NotWorking/mnist_deep.py,ed9ea0db1adb3ba5e8751139b42d3f11e68f32fa,STILL_EXISTS,FIXME: Code to save and restore the model if Training is skipped
aaffadgeg,DependableSystemsLab/TensorFI,Tests/NotWorking/mnist_deep.py,ed9ea0db1adb3ba5e8751139b42d3f11e68f32fa,STILL_EXISTS,FIXME: Need to debug why this doesn't work
aaffadgga,DependableSystemsLab/TensorFI,Tests/NotWorking/random_forest.py,ed9ea0db1adb3ba5e8751139b42d3f11e68f32fa,STILL_EXISTS,\"\"\" Random Forest. ||  || Implement Random Forest algorithm with TensorFlow; and apply it to classify  || handwritten digit images. This example is using the MNIST database of  || handwritten digits as training samples (http:\/\/yann.lecun.com\/exdb\/mnist\/). ||  || Author: Aymeric Damien || Project: https:\/\/github.com\/aymericdamien\/TensorFlow-Examples\/ || \"\"\"
aaffadghj,DependableSystemsLab/TensorFI,Tests/NotWorking/random_forest.py,ed9ea0db1adb3ba5e8751139b42d3f11e68f32fa,STILL_EXISTS,Get the next batch of MNIST data (only images are needed; not labels)
aaffadhef,DependableSystemsLab/TensorFI,Tests/autoencoder.py,ed9ea0db1adb3ba5e8751139b42d3f11e68f32fa,STILL_EXISTS,Get the next batch of MNIST data (only images are needed; not labels)
aaffadiae,DependableSystemsLab/TensorFI,Tests/gan.py,ed9ea0db1adb3ba5e8751139b42d3f11e68f32fa,STILL_EXISTS,Get the next batch of MNIST data (only images are needed; not labels)
aaffadicj,DependableSystemsLab/TensorFI,Tests/linear_regression.py,ed9ea0db1adb3ba5e8751139b42d3f11e68f32fa,STILL_EXISTS,''' || A linear regression learning algorithm example using TensorFlow library. ||  || Author: Aymeric Damien || Project: https:\/\/github.com\/aymericdamien\/TensorFlow-Examples\/ || '''
aaffadifb,DependableSystemsLab/TensorFI,Tests/linear_regression_multiple.py,ed9ea0db1adb3ba5e8751139b42d3f11e68f32fa,STILL_EXISTS,''' || A linear regression learning algorithm example using TensorFlow library. ||  || Author: Aymeric Damien || Project: https:\/\/github.com\/aymericdamien\/TensorFlow-Examples\/ || '''
aaffaeadg,DependableSystemsLab/TensorFI,Tests/variational_autoencoder.py,ed9ea0db1adb3ba5e8751139b42d3f11e68f32fa,STILL_EXISTS,Get the next batch of MNIST data (only images are needed; not labels)
aaffaeeig,DependableSystemsLab/TensorFI,experimentalTest/preprocessing.py,ed9ea0db1adb3ba5e8751139b42d3f11e68f32fa,STILL_EXISTS,data[cat_columns] = data[cat_columns].apply(lambda x: x.cat.codes)
aaffaeejh,DependableSystemsLab/TensorFI,experimentalTest/preprocessing.py,ed9ea0db1adb3ba5e8751139b42d3f11e68f32fa,STILL_EXISTS,Only convert object type of columns
aaffafeci,DependableSystemsLab/TensorFI,TensorFI/injectFault.py,8a3dca56a6cf156339c326d121abe6984049f0d1,STILL_EXISTS,You can manually specify the instance here rather than using the random instances
aaffafejc,DependableSystemsLab/TensorFI,TensorFI/injectFault.py,4341e8bf34ff969a43d1ca84862cd85675e6a601,bbe9a6b138598bf4b36da3f022bb02307b84ed3c,FIXME: Implement this functionality
aaffafgca,DependableSystemsLab/TensorFI,Tests/DNN-model/LeNet-mnist/LeNet.py,69170b68a245ce293a8d43a9f7d1d40854b6d9fb,STILL_EXISTS,activations such that no rescaling is needed at evaluation time.
aaffafgeb,DependableSystemsLab/TensorFI,Tests/DNN-model/LeNet-mnist/LeNet.py,69170b68a245ce293a8d43a9f7d1d40854b6d9fb,STILL_EXISTS,Note that we could use better randomization across epochs.
aaffafgha,DependableSystemsLab/TensorFI,Tests/DNN-model/comma-ai-steering-model/model.py,69170b68a245ce293a8d43a9f7d1d40854b6d9fb,STILL_EXISTS,###   and comma ai model. We implement both.
aaffagfig,DependableSystemsLab/TensorFI,TensorFI/injectFault.py,86c71e5fbd2993f72e7cbd38714b4984ed848dc4,bbe9a6b138598bf4b36da3f022bb02307b84ed3c,FIXME: Implement this functionality
aaffagfij,DependableSystemsLab/TensorFI,TensorFI/injectFault.py,cfdd6e653ca1f5d4d908793cb2132f38d2f642e3,54318a0fae786a09998f95fdf5f99c88f166b7c9,FIXME: According to tf doc; this should only take 2 arguments
aaffagfja,DependableSystemsLab/TensorFI,TensorFI/injectFault.py,cfdd6e653ca1f5d4d908793cb2132f38d2f642e3,54318a0fae786a09998f95fdf5f99c88f166b7c9,\tbut somehow it is complaining that it requires 3 arguments
aaffahgfg,DependableSystemsLab/TensorFI,testSuite/operations_inputgen.py,8e242ce91f238d6d4f572643c4ced315667543cc,STILL_EXISTS,\"Cast\": inputgen_Cast; # this raises an exception; apparently cannot pass the dtype parameter to create_op(); must figure out a way around this
aaffahhhd,DependableSystemsLab/TensorFI,testSuite/NOT-INCL-YET-injections-gan.py,7f7c96823a74f18689c27542b3467155bb9d34b1,STILL_EXISTS,Get the next batch of MNIST data (only images are needed; not labels)
aaffahhjf,DependableSystemsLab/TensorFI,testSuite/NOT-INCL-YET-injections-highwayfcn-mnist.py,7f7c96823a74f18689c27542b3467155bb9d34b1,STILL_EXISTS,How many times are we going to run through whole dataset
aaffaifda,DependableSystemsLab/TensorFI,TensorFI/faultTypes.py,c92281ce106450c3c82a9ff65419d2f3e130a0e7,STILL_EXISTS,random index of the bit to flip (switch to low=0 if you don't want to inject fault to sign bit)
aaffaifgg,gao-lab/Cell_BLAST,Cell_BLAST/blast.py,3616f786d46c42f345356425f0c607b9dd48ca60,STILL_EXISTS,FIXME: this should be avoided
aaffaigcb,gao-lab/Cell_BLAST,Cell_BLAST/blast.py,3616f786d46c42f345356425f0c607b9dd48ca60,STILL_EXISTS,TODO: there is a numba warning saying that one of np.dot arguments
aaffaigcd,gao-lab/Cell_BLAST,Cell_BLAST/blast.py,3616f786d46c42f345356425f0c607b9dd48ca60,STILL_EXISTS,Haven't figured out why...
aaffaigde,gao-lab/Cell_BLAST,Cell_BLAST/data.py,3616f786d46c42f345356425f0c607b9dd48ca60,d0e25fa695cb8cebcba68dd32fe5e7e96743803f,TODO: uns slots that are not numpy arrays may have trouble saving
aaffaihii,gao-lab/Cell_BLAST,Cell_BLAST/utils.py,3616f786d46c42f345356425f0c607b9dd48ca60,STILL_EXISTS,Reverse may be more efficient
aaffaiifi,gao-lab/Cell_BLAST,Datasets/ortholog/scripts/parse_nog.py,3616f786d46c42f345356425f0c607b9dd48ca60,STILL_EXISTS,df.columns = [\"dataset\"; \"id\"; \"num_proteins\"; \"num_species\";
aaffaiige,gao-lab/Cell_BLAST,Datasets/ortholog/scripts/parse_nog_1v1.py,3616f786d46c42f345356425f0c607b9dd48ca60,STILL_EXISTS,df.columns = [\"dataset\"; \"id\"; \"num_proteins\"; \"num_species\";
aaffaiijf,gao-lab/Cell_BLAST,Evaluation/benchmark_bias_removal.py,3616f786d46c42f345356425f0c607b9dd48ca60,STILL_EXISTS,FIXME: 10 repeats might not be enough
aaffaiijg,gao-lab/Cell_BLAST,Evaluation/benchmark_bias_removal.py,3616f786d46c42f345356425f0c607b9dd48ca60,STILL_EXISTS,FIXME: not start with \".\" because hid directories as backup
aaffajagj,gao-lab/Cell_BLAST,doc/conf.py,3616f786d46c42f345356425f0c607b9dd48ca60,STILL_EXISTS,-- Options for todo extension ----------------------------------------------
aaffajaha,gao-lab/Cell_BLAST,doc/conf.py,3616f786d46c42f345356425f0c607b9dd48ca60,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaffajcee,gao-lab/Cell_BLAST,Evaluation/utils.py,d0e25fa695cb8cebcba68dd32fe5e7e96743803f,STILL_EXISTS,Adapted from https:\/\/stackoverflow.com\/questions\/41634674\/tensorflow-on-shared-gpus-how-to-automatically-select-the-one-that-is-unused
aaffajcfe,gao-lab/Cell_BLAST,Notebooks/Database/build_db_cells.py,d0e25fa695cb8cebcba68dd32fe5e7e96743803f,STILL_EXISTS,TODO: remove this try-catch block after database is settled
aaffajcgf,gao-lab/Cell_BLAST,Notebooks/Database/utils.py,d0e25fa695cb8cebcba68dd32fe5e7e96743803f,STILL_EXISTS,FIXME: following is duplicated from Evaluation\/utils.py
aaffajcib,gao-lab/Cell_BLAST,BLAST2CO_dev/BLAST2CO.py,fa1f30d2d54b68a06479513164746a80c1fdb031,STILL_EXISTS,Note: here query_cl maybe a higher level cl
aaffajcjg,gao-lab/Cell_BLAST,Cell_BLAST/blast.py,fa1f30d2d54b68a06479513164746a80c1fdb031,a16819a93ef5256b5f8522d0bb96b49b9383b9e9,TODO: We can do some sort of interpolation here to further improve
aaffajfab,gao-lab/Cell_BLAST,docs/conf.py,fa1f30d2d54b68a06479513164746a80c1fdb031,STILL_EXISTS,-- Options for todo extension ----------------------------------------------
aaffajfac,gao-lab/Cell_BLAST,docs/conf.py,fa1f30d2d54b68a06479513164746a80c1fdb031,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaffajgga,neptune-ai/neptune-client,neptune/client.py,2db0b0984308506232fcdf6f29885f10643e0d05,9ad680eb84cdbef17f914ea2f30b32634dd1625f,TODO!!!
aaffbaaff,neptune-ai/neptune-client,neptune/project.py,da8071cde0c6e3c6c47d6a081f6fbe4118a21b9e,b2d2cd340cd8b0fdb181334b6e3d05b654e2e470,TODO implement upload_source_files
aaffbaafg,neptune-ai/neptune-client,neptune/project.py,da8071cde0c6e3c6c47d6a081f6fbe4118a21b9e,STILL_EXISTS,TODO implement send_hardware_metrics
aaffbaafh,neptune-ai/neptune-client,neptune/project.py,da8071cde0c6e3c6c47d6a081f6fbe4118a21b9e,STILL_EXISTS,TODO implement run_monitoring_thread
aaffbaafi,neptune-ai/neptune-client,neptune/project.py,da8071cde0c6e3c6c47d6a081f6fbe4118a21b9e,446c710aea219edab1cae02c4331f9e8d2e35636,TODO implement handle_uncaught_exceptions
aaffbaafj,neptune-ai/neptune-client,neptune/project.py,da8071cde0c6e3c6c47d6a081f6fbe4118a21b9e,446c710aea219edab1cae02c4331f9e8d2e35636,FIXME delete all of these transitions
aaffbaaga,neptune-ai/neptune-client,neptune/client.py,afda0a8889bd2663f3fa9f9ee0fa021144d272e5,STILL_EXISTS,FIXME
aaffbabcc,neptune-ai/neptune-client,neptune/internal/threads/ping_thread.py,226f88887bcbd0263154300b917f10be5f4f529d,STILL_EXISTS,In this case; this thread is not needed anymore.
aaffbabce,neptune-ai/neptune-client,neptune/project.py,226f88887bcbd0263154300b917f10be5f4f529d,STILL_EXISTS,TODO implement run_monitoring_thread
aaffbbbia,neptune-ai/neptune-client,neptune/project.py,ec963049765ee80926b4a49f2f19548457f90c27,342a1b7056c301984cadc3cf958f49cab2f37b69,TODO implement handle_uncaught_exceptions
aaffbbbib,neptune-ai/neptune-client,neptune/project.py,ec963049765ee80926b4a49f2f19548457f90c27,342a1b7056c301984cadc3cf958f49cab2f37b69,FIXME delete all of these transitions
aaffbbeaj,neptune-ai/neptune-client,neptune/client.py,7d5d1ddba9973d2b1c78a1567f53a5d9e6109004,STILL_EXISTS,FIXME
aaffbbhda,neptune-ai/neptune-client,neptune/client.py,cc561387b0375ab1b578e091be6a41087f9633d5,STILL_EXISTS,TODO: change error type and info
aaffbbifa,neptune-ai/neptune-client,neptune/_version.py,f0d90844ab91eda9773989ab91e22db32f6af3af,STILL_EXISTS,maybe improved later
aaffbbifg,neptune-ai/neptune-client,neptune/_version.py,f0d90844ab91eda9773989ab91e22db32f6af3af,STILL_EXISTS,unparseable. Maybe git-describe is misbehaving?
aaffbbjbi,neptune-ai/neptune-client,versioneer.py,f0d90844ab91eda9773989ab91e22db32f6af3af,STILL_EXISTS,maybe improved later
aaffbbjce,neptune-ai/neptune-client,versioneer.py,f0d90844ab91eda9773989ab91e22db32f6af3af,STILL_EXISTS,unparseable. Maybe git-describe is misbehaving?
aaffbcdib,neptune-ai/neptune-client,neptune/internal/threads/neptune_thread.py,0a84c1d6c95deb38ca243d84356b7f66612da9e2,STILL_EXISTS,TODO: remove this pylint exception once we stop supporting Python 2
aaffbcffa,neptune-ai/neptune-client,neptune_client_prototype/__init__.py,629b08421f3a430f4a6c9d8bc5513404ca6e064f,STILL_EXISTS,TODO add custom types
aaffbcffh,neptune-ai/neptune-client,neptune_client_prototype/__init__.py,629b08421f3a430f4a6c9d8bc5513404ca6e064f,9ca6bbf6a3daed4615b41ff974e912bc7276d1ad,TODO In the specification; a Variable does not hold data directly;
aaffbcffj,neptune-ai/neptune-client,neptune_client_prototype/__init__.py,629b08421f3a430f4a6c9d8bc5513404ca6e064f,9ca6bbf6a3daed4615b41ff974e912bc7276d1ad,fewer delegated method calls; but perhaps there are other requirements
aaffbcfgd,neptune-ai/neptune-client,neptune_client_prototype/__init__.py,629b08421f3a430f4a6c9d8bc5513404ca6e064f,9ca6bbf6a3daed4615b41ff974e912bc7276d1ad,TODO confirming: parameter type stays fixed once the structure is created?
aaffbcfge,neptune-ai/neptune-client,neptune_client_prototype/__init__.py,629b08421f3a430f4a6c9d8bc5513404ca6e064f,STILL_EXISTS,TODO need to determine the type while creating the Variable?
aaffbcfgf,neptune-ai/neptune-client,neptune_client_prototype/__init__.py,629b08421f3a430f4a6c9d8bc5513404ca6e064f,36faaa0616d24372951c5e43fda92a0b647c6997,TODO check that the type is supported by the Series structure
aaffbcfgg,neptune-ai/neptune-client,neptune_client_prototype/__init__.py,629b08421f3a430f4a6c9d8bc5513404ca6e064f,36faaa0616d24372951c5e43fda92a0b647c6997,TODO check that the type is supported by the Set structure
aaffbcfha,neptune-ai/neptune-client,neptune_client_prototype/__init__.py,629b08421f3a430f4a6c9d8bc5513404ca6e064f,61ad403e90077453a30851a4229ba54b72a01439,FIXME We provide a global list of conversions in the prototype.
aaffbcfhb,neptune-ai/neptune-client,neptune_client_prototype/__init__.py,629b08421f3a430f4a6c9d8bc5513404ca6e064f,61ad403e90077453a30851a4229ba54b72a01439,Ideally; there should be a way to register custom conversions.
aaffbcfhc,neptune-ai/neptune-client,neptune_client_prototype/__init__.py,629b08421f3a430f4a6c9d8bc5513404ca6e064f,61ad403e90077453a30851a4229ba54b72a01439,Also; lookup should be made efficient.
aaffbcfhh,neptune-ai/neptune-client,neptune_client_prototype/__init__.py,629b08421f3a430f4a6c9d8bc5513404ca6e064f,9ca6bbf6a3daed4615b41ff974e912bc7276d1ad,TODO is holding a reference to the parent namespace and own key in that
aaffbcfhi,neptune-ai/neptune-client,neptune_client_prototype/__init__.py,629b08421f3a430f4a6c9d8bc5513404ca6e064f,9ca6bbf6a3daed4615b41ff974e912bc7276d1ad,namespace an acceptable hack?
aaffbcfhj,neptune-ai/neptune-client,neptune_client_prototype/__init__.py,629b08421f3a430f4a6c9d8bc5513404ca6e064f,9ca6bbf6a3daed4615b41ff974e912bc7276d1ad,TODO do we have to determine the type of the Series here?
aaffbcfie,neptune-ai/neptune-client,neptune_client_prototype/__init__.py,629b08421f3a430f4a6c9d8bc5513404ca6e064f,9ca6bbf6a3daed4615b41ff974e912bc7276d1ad,TODO support paths like foo\/bar here or treat as implementation details?
aaffbcfif,neptune-ai/neptune-client,neptune_client_prototype/__init__.py,629b08421f3a430f4a6c9d8bc5513404ca6e064f,STILL_EXISTS,TODO path validation; empty path
aaffbcfih,neptune-ai/neptune-client,neptune_client_prototype/__init__.py,629b08421f3a430f4a6c9d8bc5513404ca6e064f,9ca6bbf6a3daed4615b41ff974e912bc7276d1ad,TODO: implement batch update methods: assign; log; add
aaffbcfij,neptune-ai/neptune-client,neptune_client_prototype/__init__.py,629b08421f3a430f4a6c9d8bc5513404ca6e064f,9ca6bbf6a3daed4615b41ff974e912bc7276d1ad,FIXME: Inconstent interface
aaffbcgjh,neptune-ai/neptune-client,neptune_client_prototype/__init__.py,9ca6bbf6a3daed4615b41ff974e912bc7276d1ad,STILL_EXISTS,TODO validate type
aaffbcgji,neptune-ai/neptune-client,neptune_client_prototype/__init__.py,9ca6bbf6a3daed4615b41ff974e912bc7276d1ad,STILL_EXISTS,TODO message
aaffbchce,neptune-ai/neptune-client,neptune_client_prototype/__init__.py,5cf939f327d1ebd51b94bc2118eb4f40e6656267,36faaa0616d24372951c5e43fda92a0b647c6997,TODO mark interface methods with decorators?
aaffbchde,neptune-ai/neptune-client,neptune_client_prototype/__init__.py,5cf939f327d1ebd51b94bc2118eb4f40e6656267,STILL_EXISTS,TODO validate type
aaffbchdf,neptune-ai/neptune-client,neptune_client_prototype/__init__.py,5cf939f327d1ebd51b94bc2118eb4f40e6656267,STILL_EXISTS,TODO message
aaffbchga,neptune-ai/neptune-client,neptune_client_prototype/path.py,36faaa0616d24372951c5e43fda92a0b647c6997,STILL_EXISTS,TODO validate and normalize to \/path\/to\/variable
aaffbchhb,neptune-ai/neptune-client,neptune_client_prototype/variable.py,36faaa0616d24372951c5e43fda92a0b647c6997,STILL_EXISTS,TODO check that the type is supported by the Series structure
aaffbchhc,neptune-ai/neptune-client,neptune_client_prototype/variable.py,36faaa0616d24372951c5e43fda92a0b647c6997,STILL_EXISTS,TODO check that the type is supported by the Set structure
aaffbchhd,neptune-ai/neptune-client,neptune_client_prototype/variable.py,36faaa0616d24372951c5e43fda92a0b647c6997,STILL_EXISTS,TODO mark interface methods with decorators?
aaffbchhf,neptune-ai/neptune-client,neptune_client_prototype/experiment.py,b0e89abc4676abea2b4417d4b6ee6b0c8c176cb2,STILL_EXISTS,TODO validate and normalize to \/path\/to\/variable
aaffbchhg,neptune-ai/neptune-client,neptune_client_prototype/experiment.py,78a11be850dab8cd96c7a0db5eac1147aca26e91,STILL_EXISTS,TODO handle non-existent path
aaffbchjb,neptune-ai/neptune-client,neptune_client_prototype/experiment.py,a0f3c31769ce561b5451e3b4a086b903f3c80e29,82e2e945f4ace8bd55c29af5626f933aa53f6f25,TODO atomicity?
aaffbchjg,neptune-ai/neptune-client,neptune_client_prototype/variable.py,a0f3c31769ce561b5451e3b4a086b903f3c80e29,STILL_EXISTS,TODO is a picosecond good enough? I am not aware of systems which track
aaffbchji,neptune-ai/neptune-client,neptune_client_prototype/variable.py,a0f3c31769ce561b5451e3b4a086b903f3c80e29,STILL_EXISTS,TODO handle step and timestamp from user
aaffbchjj,neptune-ai/neptune-client,neptune_client_prototype/experiment.py,cc846a230b4a4d29568730eaa8c9f935c22171d5,82e2e945f4ace8bd55c29af5626f933aa53f6f25,TODO support all methods on structures
aaffbciaa,neptune-ai/neptune-client,neptune_client_prototype/test_prototype.py,cc846a230b4a4d29568730eaa8c9f935c22171d5,STILL_EXISTS,TODO add meaningful error messages
aaffbciac,neptune-ai/neptune-client,neptune_client_prototype/variable.py,cc846a230b4a4d29568730eaa8c9f935c22171d5,STILL_EXISTS,TODO allow for changing type?
aaffbcija,neptune-ai/neptune-client,neptune_client_prototype/experiment_view.py,82e2e945f4ace8bd55c29af5626f933aa53f6f25,STILL_EXISTS,TODO handle custom step and timestamp
aaffbcijc,neptune-ai/neptune-client,neptune_client_prototype/experiment_view.py,82e2e945f4ace8bd55c29af5626f933aa53f6f25,STILL_EXISTS,TODO atomicity?
aaffbcije,neptune-ai/neptune-client,neptune_client_prototype/experiment_view.py,82e2e945f4ace8bd55c29af5626f933aa53f6f25,STILL_EXISTS,TODO support all methods on structures
aaffbcjbb,neptune-ai/neptune-client,neptune_client_prototype/variable.py,82e2e945f4ace8bd55c29af5626f933aa53f6f25,STILL_EXISTS,# TODO handle steps and timestamps
aaffbdadb,neptune-ai/neptune-client,neptune_client_prototype_tests/neptune_client_prototype/test_prototype.py,82e2e945f4ace8bd55c29af5626f933aa53f6f25,STILL_EXISTS,TODO add meaningful error messages
aaffbdbaf,neptune-ai/neptune-client,neptune_old/_version.py,c1a46bcc681836a775a2208b735888e2d4526f18,STILL_EXISTS,maybe improved later
aaffbdbbb,neptune-ai/neptune-client,neptune_old/_version.py,c1a46bcc681836a775a2208b735888e2d4526f18,STILL_EXISTS,unparseable. Maybe git-describe is misbehaving?
aaffbdfhe,neptune-ai/neptune-client,neptune/variable.py,923c03513e7cf38081e70c8e725e7334f6381df0,STILL_EXISTS,TODO: Support steps and timestamps
aaffbdgci,neptune-ai/neptune-client,tests/neptune/test_handler.py,923c03513e7cf38081e70c8e725e7334f6381df0,STILL_EXISTS,TODO: self.assertEqual(exp['some\/num\/val'].get_values(); 5)
aaffbdgcj,neptune-ai/neptune-client,tests/neptune/test_handler.py,923c03513e7cf38081e70c8e725e7334f6381df0,STILL_EXISTS,TODO: self.assertEqual(exp['some\/str\/val'].get_values(); \"some text\")
aaffbdhba,neptune-ai/neptune-client,neptune/internal/utils/json_file_splitter.py,c357db07d0c209204e58ffb3465b48467c66c99e,STILL_EXISTS,TODO: experiment with larger buffer sizes
aaffbdjci,neptune-ai/neptune-client,neptune/variables/series/float_series.py,5b50263c9c93a72d84d86d36f5e599ee0cabbe3b,fa3849e45fb413dd51f74ae1c1a8feb079da350c,TODO: Support steps and timestamps
aaffbeafi,neptune-ai/neptune-client,tests/neptune/test_client.py,ae4f86bfe967f2cf8c07dbf1c6f86b8189df38c0,STILL_EXISTS,TODO: Should be None or exception?
aaffbebdi,neptune-ai/neptune-client,neptune/internal/backends/hosted_neptune_backend.py,e0c40cbaa89bc17db16bf4c5f9feed5483c5e1c5,b8b81d421543efb8177e60585f1a1a65b72143d9,TODO: Do not use NeptuneAuthenticator from old_neptune. Move it to new package.
aaffbebgj,neptune-ai/neptune-client,neptune/internal/credentials.py,e0c40cbaa89bc17db16bf4c5f9feed5483c5e1c5,STILL_EXISTS,TODO: Consider renaming 'api_address' (breaking backward compatibility)
aaffbedag,neptune-ai/neptune-client,neptune/variables/series/float_series.py,46ff34a6d9af338512d0d002c3539d4317f840d2,fa3849e45fb413dd51f74ae1c1a8feb079da350c,TODO: Avoid loop
aaffbedai,neptune-ai/neptune-client,neptune/variables/series/string_series.py,46ff34a6d9af338512d0d002c3539d4317f840d2,fa3849e45fb413dd51f74ae1c1a8feb079da350c,TODO: Avoid loop
aaffbedba,neptune-ai/neptune-client,neptune/internal/hardware/hardware_metric_reporting_job.py,1d2ce9a95908e4285c7b92befc2d3f520e2dfc31,STILL_EXISTS,TODO: Avoid loop
aaffbegjc,neptune-ai/neptune-client,neptune/internal/backends/hosted_neptune_backend.py,08fec730e1b0c0250623bfc243aa02ed3a331ba4,b8b81d421543efb8177e60585f1a1a65b72143d9,TODO: Use new upload endpoint
aaffbehdb,neptune-ai/neptune-client,tests/neptune/test_handler.py,08fec730e1b0c0250623bfc243aa02ed3a331ba4,STILL_EXISTS,TODO: Test download
aaffbehhc,neptune-ai/neptune-client,neptune/alpha/handler.py,b8b81d421543efb8177e60585f1a1a65b72143d9,STILL_EXISTS,TODO: Support Image value
aaffbehii,neptune-ai/neptune-client,neptune/alpha/internal/backends/hosted_neptune_backend.py,b8b81d421543efb8177e60585f1a1a65b72143d9,STILL_EXISTS,TODO: Do not use NeptuneAuthenticator from old_neptune. Move it to new package.
aaffbehja,neptune-ai/neptune-client,neptune/alpha/internal/backends/hosted_neptune_backend.py,b8b81d421543efb8177e60585f1a1a65b72143d9,STILL_EXISTS,TODO: Use new upload endpoint
aaffbeice,neptune-ai/neptune-client,neptune/internal/backends/hosted_neptune_backend.py,b8b81d421543efb8177e60585f1a1a65b72143d9,6d303dd22b721645f916c83ff00c87e975b6a17c,FIXME
aaffbejed,neptune-ai/neptune-client,neptune/alpha/internal/backends/hosted_neptune_backend.py,97d28400eac6b19e699a54126bbe51dec1049d35,STILL_EXISTS,TODO: Return errors to OperationProcessor
aaffbejee,neptune-ai/neptune-client,neptune/alpha/internal/operation_processors/async_operation_processor.py,97d28400eac6b19e699a54126bbe51dec1049d35,STILL_EXISTS,TODO: Handle errors
aaffbejef,neptune-ai/neptune-client,neptune/alpha/internal/operation_processors/sync_operation_processor.py,97d28400eac6b19e699a54126bbe51dec1049d35,STILL_EXISTS,TODO: Handle errors
aaffbfage,neptune-ai/neptune-client,neptune/alpha/internal/init_impl.py,10e96fb50803a11feca7895306ca2063a5a50dc0,STILL_EXISTS,TODO Initialize backend in async thread
aaffbfahb,neptune-ai/neptune-client,neptune/oauth.py,c808baaf4765a7f76a1bedd3cddc1ef5d9ff7f22,STILL_EXISTS,We need to pass a lambda to be able to re-create fresh session at any time when needed
aaffbfbdg,neptune-ai/neptune-client,neptune/alpha/sync.py,9c803e3729bb9e615a6d5587732e7bd045c0b92c,31efe9e230a09e90d2bdca84a8dd783291b40991,TODO: add last send location; compare with log; predicate: is exp sync
aaffbfcec,neptune-ai/neptune-client,neptune/alpha/cli.py,144bcb0b51caf54d7a563c8a4cc781d6beeecc3c,STILL_EXISTS,TODO once the new client is released; this file should be registered as a command line entry point
aaffbfhef,neptune-ai/neptune-client,neptune/alpha/internal/backends/hosted_neptune_backend.py,f05b9ffe39ee766c268c83a74cbf16d067661bb6,13279bcdeb0422cf9e514eb342ff8697b43ee1e5,TODO Implement me
aaffbfjdh,neptune-ai/neptune-client,neptune/alpha/internal/backends/hosted_neptune_backend.py,e91013ae0ed65236cf3f1a0f8931774a07d80478,231eeb2e05a5ada933db811962d8545521232160,TODO print in color once colored exceptions are added
aaffbgaad,neptune-ai/neptune-client,neptune/alpha/internal/backends/hosted_neptune_backend.py,5d32f66c5bf162c3fef34ebdea932b62a799c41d,STILL_EXISTS,TODO print in color once colored exceptions are added
aaffbgaca,neptune-ai/neptune-client,neptune/alpha/version.py,f5e3b7663ec259eb9ef7a3552610d6a4f935dd3b,STILL_EXISTS,TODO Temporary hardcoded version to make work installing from github
aaffbgbai,neptune-ai/neptune-client,neptune/vendor/pynvml.py,c2d86609ba8c5c8de4a82b8576156105c88b48ac,STILL_EXISTS,# TODO handle the error
aaffbgbcg,neptune-ai/neptune-client,neptune/vendor/pynvml.py,c2d86609ba8c5c8de4a82b8576156105c88b48ac,STILL_EXISTS,cdecl calling convention
aaffbggge,neptune-ai/neptune-client,neptune/internal/backends/backend_factory.py,d2eb204f2bf921b7ae6207f2601c0cfcd7564850,STILL_EXISTS,TODO: Improvement. How to determine which backend class should be used?
aaffbghaa,neptune-ai/neptune-client,neptune/internal/backends/alpha_integration_backend.py,2d68c930185610cf88cc7165fbcfce1f9a979382,STILL_EXISTS,TODO: handle other data types
aaffbghai,neptune-ai/neptune-client,neptune/internal/backends/alpha_integration_backend.py,2d68c930185610cf88cc7165fbcfce1f9a979382,STILL_EXISTS,TODO: what is step?
aaffbghbb,neptune-ai/neptune-client,neptune/internal/backends/alpha_integration_backend.py,2d68c930185610cf88cc7165fbcfce1f9a979382,STILL_EXISTS,TODO: handle `FileChunkStream` or update `neptune.experiments.Experiment._start`
aaffbghdd,neptune-ai/neptune-client,neptune/internal/backends/alpha_integration_backend.py,4654fa964b89c13052056c083ebcabf2df53b6d3,9151a77bf5fdfe192a939bbd51bff870f12b0061,TODO: handle soft errors
aaffbghhd,neptune-ai/neptune-client,neptune/internal/backends/alpha_integration_backend.py,88234e41cb740736f6044e8378e4f55c3f4a744b,STILL_EXISTS,TODO: handle `FileChunkStream` or update `neptune.experiments.Experiment._start`
aaffbghjf,neptune-ai/neptune-client,neptune/internal/backends/alpha_integration_backend.py,bbb3a3ba77db5e6509fc49ec8db6b83691f54137,78a4751766f876a3aa6c9976f320959aa484a338,TODO: implement this
aaffbgiaf,neptune-ai/neptune-client,neptune/internal/utils/alpha_integration.py,9affa78a88e83ba0057bc51fb52a3614b85910f9,10e3ab2da9a435717553a1574c54c473f253b00d,TODO
aaffbgibf,neptune-ai/neptune-client,neptune/internal/backends/alpha_integration_backend.py,70226d0ccf1d298fa0661f877fe1609c7f2fd6de,8fdb73fa38b3d6f28f62e8468a29d10d0bf7db3e,TODO: NPT-9216
aaffbgibg,neptune-ai/neptune-client,neptune/internal/utils/alpha_integration.py,70226d0ccf1d298fa0661f877fe1609c7f2fd6de,8fdb73fa38b3d6f28f62e8468a29d10d0bf7db3e,TODO: NPT-9216
aaffbhaha,neptune-ai/neptune-client,neptune/internal/utils/alpha_integration.py,83c3e32938bec8097a1271ca622c045d697752f2,cedbbbc28c210dc7913da01a85d510a3a602be2d,TODO: what about img: `name` and `description` fields?
aaffbhahf,neptune-ai/neptune-client,alpha_integration_dev/old_client.py,c9c2c38d28fa9d6ea418526f3522e766cf4882e2,STILL_EXISTS,neptune.remove_tag('tag2_to_remove')  # TODO: NPT-9222
aaffbhahg,neptune-ai/neptune-client,alpha_integration_dev/old_client.py,c9c2c38d28fa9d6ea418526f3522e766cf4882e2,dceeeb63daa1b61df7354463c5f51f2adb3c13d5,neptune.remove_tag('tag4_remove_non_existing')  # TODO: NPT-9222
aaffbhahi,neptune-ai/neptune-client,alpha_integration_dev/new_client.py,0463a0f9ea1c6a0bddff96ff14983eb64d9e1883,d703495dd72a97f949ede49339385a791be70c93,self.exp[f'{PROPERTIES_ATTRIBUTE_SPACE}prop_list'] = [1; 2; 3]  # TODO: merge changes from alpha
aaffbhahj,neptune-ai/neptune-client,alpha_integration_dev/old_client.py,0463a0f9ea1c6a0bddff96ff14983eb64d9e1883,STILL_EXISTS,neptune.set_property('prop_list'; [1; 2; 3])  # TODO: merge changes from alpha
aaffbhbfi,neptune-ai/neptune-client,alpha_integration_dev/new_client.py,dceeeb63daa1b61df7354463c5f51f2adb3c13d5,STILL_EXISTS,del self.exp[SYSTEM_TAGS_ATTRIBUTE_PATH]  # TODO: NPT-9222
aaffbhcac,neptune-ai/neptune-client,alpha_integration_dev/old_client.py,dceeeb63daa1b61df7354463c5f51f2adb3c13d5,STILL_EXISTS,neptune.remove_tag('tag2_to_remove')  # TODO: NPT-9222
aaffbhcce,neptune-ai/neptune-client,alpha_integration_dev/old_client.py,f382e811ed53910ca80b281aea067a8f251aaf9c,STILL_EXISTS,neptune.remove_tag('tag2_to_remove')  # TODO: NPT-9222
aaffbhccf,neptune-ai/neptune-client,alpha_integration_dev/old_client.py,f382e811ed53910ca80b281aea067a8f251aaf9c,STILL_EXISTS,neptune.remove_tag('tag4_remove_non_existing')  # TODO: NPT-9222
aaffbicje,neptune-ai/neptune-client,alpha_integration_dev/common_client_code.py,2f58ea743303a5bb2f82c88d37a95098663e8b12,dac68557cf01822139b280e82627c4820000190b,'init_list': [1; 2; 3];  # TODO: Error 500 in old client
aaffbicjf,neptune-ai/neptune-client,alpha_integration_dev/common_client_code.py,2f58ea743303a5bb2f82c88d37a95098663e8b12,dac68557cf01822139b280e82627c4820000190b,'init_datetime': datetime.now()  # TODO: Non serializable in old client
aaffbicjg,neptune-ai/neptune-client,alpha_integration_dev/old_client.py,2f58ea743303a5bb2f82c88d37a95098663e8b12,STILL_EXISTS,notebook_id='test1';  # TODO: Error 500 when wrong value
aaffbicjh,neptune-ai/neptune-client,alpha_integration_dev/old_client.py,2f58ea743303a5bb2f82c88d37a95098663e8b12,dac68557cf01822139b280e82627c4820000190b,assert properties['prop_list'] == '[1; 2; 3]'  # TODO
aaffbicji,neptune-ai/neptune-client,neptune/internal/backends/alpha_integration_backend.py,2f58ea743303a5bb2f82c88d37a95098663e8b12,STILL_EXISTS,TODO: what about those missing attributes
aaffcaaec,neptune-ai/neptune-client,neptune/internal/api_clients/hosted_api_clients/hosted_leaderboard_api_client.py,6d303dd22b721645f916c83ff00c87e975b6a17c,STILL_EXISTS,FIXME
aaffcacab,neptune-ai/neptune-client,neptune/internal/backends/hosted_neptune_backend.py,fa34e77ee7cb3a0c80edcd12d3ddf751f85ded45,dec2c25ced8b33e236febd53545b97431e8670bc,FIXME
aaffcacic,neptune-ai/neptune-client,integrations/tensorflow-keras/neptune_tensorflow_keras/_version.py,e4ed1221b0178e6a50a566e632c1fdbaa6307a20,STILL_EXISTS,maybe improved later
aaffcacii,neptune-ai/neptune-client,integrations/tensorflow-keras/neptune_tensorflow_keras/_version.py,e4ed1221b0178e6a50a566e632c1fdbaa6307a20,STILL_EXISTS,unparseable. Maybe git-describe is misbehaving?
aaffcadfe,neptune-ai/neptune-client,integrations/tensorflow-keras/versioneer.py,e4ed1221b0178e6a50a566e632c1fdbaa6307a20,STILL_EXISTS,maybe improved later
aaffcadga,neptune-ai/neptune-client,integrations/tensorflow-keras/versioneer.py,e4ed1221b0178e6a50a566e632c1fdbaa6307a20,STILL_EXISTS,unparseable. Maybe git-describe is misbehaving?
aaffcahce,neptune-ai/neptune-client,integrations/sklearn/neptune_sklearn/_version.py,75a875c946f77547fb82d855a17856a8ab5fe21a,STILL_EXISTS,maybe improved later
aaffcahda,neptune-ai/neptune-client,integrations/sklearn/neptune_sklearn/_version.py,75a875c946f77547fb82d855a17856a8ab5fe21a,STILL_EXISTS,unparseable. Maybe git-describe is misbehaving?
aaffcahjg,neptune-ai/neptune-client,integrations/sklearn/versioneer.py,75a875c946f77547fb82d855a17856a8ab5fe21a,STILL_EXISTS,maybe improved later
aaffcaiac,neptune-ai/neptune-client,integrations/sklearn/versioneer.py,75a875c946f77547fb82d855a17856a8ab5fe21a,STILL_EXISTS,unparseable. Maybe git-describe is misbehaving?
aaffchfif,neptune-ai/neptune-client,integrations/lightgbm/neptune_lightgbm/_version.py,122cf1d8434c07f526e0255fc6ae764c14d6a97e,STILL_EXISTS,maybe improved later
aaffchfjb,neptune-ai/neptune-client,integrations/lightgbm/neptune_lightgbm/_version.py,122cf1d8434c07f526e0255fc6ae764c14d6a97e,STILL_EXISTS,unparseable. Maybe git-describe is misbehaving?
aaffchghh,unitn-sml/pyconstruct,pyconstruct/learners/cuttingplane.py,dd06ac7883ec7088bf6512cf821ea18a60a4b608,STILL_EXISTS,TODO
aaffchhaf,unitn-sml/pyconstruct,pyconstruct/domains/__init__.py,a10e04ea10b8d3cb6d24d19c9440cda106e72cd6,STILL_EXISTS,\"\"\"\\ || This package provides the base classes to import domains into Pyconstruct; as || well as several predefined domains. A domain in Pyconstruct is an object that is || capable of interpreting the structure of some input and output objects; and || solve inference problems with these objects. The class `BaseDomain` defines the || basic interface that a domain should satisfy. An implementation of `BaseDomain` || has to be able to perform inference through the `infer` method and to transform || pairs of input and output objects into feature vector with the method `phi`. ||  || Input and output objects can literally be any Python object; its the domain's || job to interpret them and perform inference over them. Learning algorithms in || Pyconstruct are built to be agnostic to the type of objects they are dealing || with. The only things the learners care about are the feature vectors; which || should be returned as Numpy arrays by the method `phi`. ||  || The default type of Domains that Pyconstruct deals with are `MiniZincDomain`s; || i.e. domains encoded with the MiniZinc constraint programming language. When a || domain is encoded in MiniZinc; it can be imported into Pyconstruct as easily || as:: ||  ||     from pyconstruct import Domain ||     domain = Domain('domain.pmzn') ||  || When using a `MiniZincDomain` (`Domain` is an alias for `MiniZincDomain`) input || and output objects are encoded as Python dictionaries. Each object has a number || of \"attributes\"; i.e. key-value pairs representing properties of the object. || When doing inference; input attributes are translated into `dzn` assignments to || pass to MiniZinc. In a MiniZinc domain; input attributes are bound to unassigned || `dzn` parameters. Output attributes can be; instead; any optimization variable || (usually the independent ones). MiniZinc is very flexible; because with it we || can solve several different inference problems. ||  || By default; Pyconstruct assumes that domains are written into a variant of || MiniZinc as defined by the PyMzn library. Essentially; PyMzn extends MiniZinc || allowing to embed some templating code inside MiniZinc files. The templating || code is processed by the Jinja2 library. This is a powerful way to include || conditional boilerplate code; depending on the inference problem we are solving. || This allows us to write the domain once in a single `.pmzn` file and use it to || solve different inference problems. Pyconstruct includes a `.pmzn` module || containing several useful \"macros\" that can be used inside a domain file. A || typical domain file looks like this:: ||  ||     {% from 'pyconstruct.pmzn' import n_features; features; domain; solve %} ||  ||     {{ n_features(1) }} ||  ||     {% call domain(problem) %} ||         int: some_input_variable; ||  ||         var int: some_output_variable; ||  ||         % Some constraint ||         constraint some_output_variable <= some_input_variable; ||  ||         {% if problem == 'loss_augmented_map' %} ||             int: true_input_variable = {{ y_true['some_input_variable']|dzn }}; ||         {% endif %} ||  ||         {% call features() %} ||             % Some features ||             some_input_variable * some_output_variable ||         {% endcall %} ||  ||     {% endcall %} ||  ||     {% set loss %} ||         % Some loss function ||         true_input_variable - some_input_variable ||     {% endset %} ||  ||     {{ solve(problem; model; loss=loss) }} ||  || As you can see; here we make use of some of the features of Pyconstruct to || define the domain. The first line is used to import some macros; which are then || called in the rest of the file. First we call `n_features`; which compiles into:: ||  ||     int: N_FEATURES = 1; ||     set of int: FEATURES = 1 .. N_FEATURES; ||  || The second call is then to the `domain` macro; which accepts a `problem` string. || By default; each time inference is called by Pyconstruct a variable `problem` || containing the current inference problem is set into the global scope of Jinja2; || so it can be used like in this context. Inside the call to the `domain` macro; || we define the actual domain; including input parameters; output variables and || constraints. Here also go the features; and possibly declaration of true || variables for \"loss_augmente_map\" problems (see below). The call to the || `feature` macro is a convenience that compiles into:: ||  ||     array[FEATURES] of var float: phi = [ ||         % Some features ||         some_input_variable * some_output_variable ||     ]; ||  || The Jinja2 `set` statement is then used to assign the `loss` variable; which is || then passed to the `solve` macro; which take care to insert in the compiled code || a proper MiniZinc `solve` statement; depending on the `problem` to solve and the || `model`. The `model` variable is just like `problem`: it is passed by the || `MiniZincDomain` during inference and it contains the parameters of the `Model` || we want to make inference with. For instance; a `LinearModel` contains just one || parameter `w`; which is an array of weights (see section Models for details). ||  ||  || ## Standard inference problems ||  || These are the basic inference problems that Pyconstruct expects the Domains to || be able to solve: ||   - `n_features`: Returns the number of features in the feature vector; ||   - `phi`: Given a `x` and `y`; returns the feature vector `phi(x; y)`; ||   - `map`: Given a `x` and a `model`; returns the `y` that maximizes the score ||     according to the given `model` and input `x`; ||   - `loss_augmented_map`: Given a `x`; a `model` and a `y_true`; find `y` that ||     maximizes the score + loss; according to the given `model`; input `x` and ||     true output `y_true`. || \"\"\"
aaffchhah,unitn-sml/pyconstruct,pyconstruct/learners/__init__.py,626a4fb4d6211f71295de7828e1266ffbca16052,STILL_EXISTS,\"\"\"\\ || This package contains several learning algorithms to be used in conjunction with || Pyconstruct domains. All the learners in Pyconstruct are agnostic to the type of || structured objects the data contains; thanks to the fact that the domain takes || care of both making inference and computing feature vectors. ||  || Learners in Pyconstruct follow the same interface as Scikit-learn estimators. || Each learner needs first to be instanciated; passing a domain as argument to the || constructor. For instance:: ||  ||     from pyconstruct import SSG; Domain ||  ||     ocr = Domain('ocr') ||     ssg = SSG(domain=ocr) ||  || The constructor usually accepts other hyper-parameters of the algorithm too. || After being instantiated; the learner needs to be trained with data that is || compatible with the domain passed to the learner instance. In this case; for || instance; we can make use of data provided by Pyconstruct:: ||  ||     from pyconstruct.datasets import load_ocr ||     data = load_ocr() ||  || Most of the learners in Pyconstruct are online learners; i.e. they can partially || fit a model a mini-batch of examples at the time. This provides high flexibility || to the way models can be trained; and is indeed useful given that training a || very big model on structured data may require a lot of time and computational || resources. As in Scikit-learn; online learners implement the `partial_fit` || method; which takes a mini-batch of examples and uses it to update the model. || Pyconstruct has a convenient utility to separate data into mini-batches; which || in turn can then be used to train the model:: ||  ||     from pyconstruct.utils import batches ||  ||     for X_b; Y_b in batches(ocr.data; ocr.target; size=50): ||         ssg.partial_fit(X_b; Y_b) ||  || This method of training is very flexible because it allows to; for instance; || validate the model while training:: ||  ||     from pyconstruct.metrics import hamming ||  ||     for X_b; Y_b in batches(ocr.data; ocr.target; size=50): ||  ||         # Validate ||         Y_pred = ssg.predict(X_b) ||         loss = hamming(Y_pred; Y_b; key='sequence').mean() ||         print('Training loss: {}'.format(loss)) ||  ||         # Update ||         ssg.partial_fit(X_b; Y_b) ||  || Here the `hamming` function takes a parameter `key='sequence'` because that is || the name of the attribute in the OCR data that need to be compared. ||  || As said; most learners in Pyconstruct are meant to be used in this way; so in || most cases they do not implement a `fit` method over the full dataset; instead || often `fit` is an alias for `partial_fit`. Check the documentation of each || learner for more details. DO NOT try to `fit` the full dataset; it would simply || perform a gradient step using the full dataset. || \"\"\"
aaffchhaj,unitn-sml/pyconstruct,pyconstruct/models/__init__.py,0b8652b205523d192e96bf5323f621193312def2,STILL_EXISTS,\"\"\"\\ || Pyconstructi abstracts predictive models using a `Model` class. A `Model` is an || object that holds some kind of parameters that can be used to make inference. ||  || When making inference; the model's parameters are passed to the Domain; which || needs to be able to interpret the semantics of the parameters. For instance; the || standard model provided by Pyconstruct is a `LinearModel`; whose parameters || consist in just a weight vector. Domains using the `solve` macro from the || `pyconstruct.pmzn` file (see the `MiniZincDomain` class documentation for || details) are readily capable of interpreting this weight vector and use it to || compute the dot product with the feature vector. ||  || While linear models cover many of the cases in structured-output prediction; one || can easily think of cases in which an ad-hoc `Model` may be beneficial; e.g. || when the model has some hyper-paramenters or when performing some sort of || feature learning. That said; in most cases you should not need to manipulate || `Models` directly. ||  || The `Model` is the middle-men between a `Learner` and a `Domain`. The learner || fits a `Model`; which is in turn then passed to the `Domain` to perform || inference. Usually a `Learner` is only capable of learning one type of models; || while a `Domain` may usually be used with different `Models` (if properly || configured). || \"\"\"
aaffchibb,unitn-sml/pyconstruct,pyconstruct/learners/frankwolfe.py,1cbe07f9b20d9bc61947452b2159e82e04ae383c,STILL_EXISTS,TODO: use `state_` here as well
aaffcibif,delph-in/pydmrs,pydmrs/develop/graphlang.py,3ee38091ad4a0d858133b8805bd4fe1854999057,STILL_EXISTS,TODO: index node?
aaffcifdb,delph-in/pydmrs,pydmrs/matching/aligned_matching.py,b8ff28d34c1a941297273cb2df9298615e3575d8,fec8ff55224c81f1a22e5dccb54691af05143361,TODO: Fix documentation.
aaffcifdc,delph-in/pydmrs,pydmrs/matching/aligned_matching.py,b8ff28d34c1a941297273cb2df9298615e3575d8,STILL_EXISTS,Convert DMRSs to SortDictDmrs with span_pred_key node if needed.
aaffcihhj,delph-in/pydmrs,pydmrs/core.py,c6ad179f9fac0087a00febd2328cd70f99e86abd,STILL_EXISTS,TODO: Pydelphin uses MOD\/EQ for undirected links - make compatible.
aaffciiaa,mideind/Icegrams,src/icegrams/ngrams.py,de590630326280fd1aa7fc198a99399fe995cab8,STILL_EXISTS,This is not needed for command-line invocation of ngrams.py;
aaffciidd,mideind/Icegrams,src/icegrams/ngrams.py,de590630326280fd1aa7fc198a99399fe995cab8,STILL_EXISTS,The index of the last bit written to the high bit buffer
aaffciihc,mideind/Icegrams,src/icegrams/ngrams.py,de590630326280fd1aa7fc198a99399fe995cab8,6d7d9b259861c0345cf03a55e58b2da59afe1356,!!! TODO: Optimize this
aaffcijhc,mideind/Icegrams,src/icegrams/ngrams.py,de590630326280fd1aa7fc198a99399fe995cab8,STILL_EXISTS,Hack to make sure that the blank entry goes to the front of the list
aaffcjied,mideind/Icegrams,src/icegrams/ngrams.py,bd8b3f6752810bf4f95313e85f6c6b17523f2971,6d7d9b259861c0345cf03a55e58b2da59afe1356,!!! TODO: Optimize this
aaffcjjjj,mideind/Icegrams,src/icegrams/trie.py,1cad8eca17086d28b059bebe19e871c2841b007c,STILL_EXISTS,\"\"\" ||  ||     trie.py ||  ||     Copyright (C) 2019 Mi\u00F0eind ehf. ||     Original author: Vilhj\u00E1lmur \u00DEorsteinsson ||  ||     This module encapsulated the unigram trie logic used ||     by ngrams.py to compress the unigram set and map ||     unigrams to integer ids. ||  ||     Trie lookup is implemented in trie.cpp. ||  || \"\"\"
aaffdaaji,mideind/Icegrams,src/icegrams/ngrams.py,2e5ea0376f62fd1fe4f2a31d177e3cc88db889d7,STILL_EXISTS,The word ends just before the next one begins
aaffdabib,mideind/Icegrams,utils/rmh.py,3dfe672015c1664683c0e14766ee2359c8462379,STILL_EXISTS,Creates a new Trigram_DB instance if needed
aaffdacbf,QuantGov/quantgov,quantgov/corpora.py,8499557a9e4297274c135e44e63ee4f9b961baa3,07b8e91c3dd56f1ef992c8c63a1d8f03b3a2349f,TODO: Docstrings
aaffdacbg,QuantGov/quantgov,quantgov/utils.py,8499557a9e4297274c135e44e63ee4f9b961baa3,STILL_EXISTS,TODO: Docstrings
aaffdacbh,QuantGov/quantgov,quantgov/utils.py,8499557a9e4297274c135e44e63ee4f9b961baa3,STILL_EXISTS,TODO #DOCSTRING
aaffdacdi,QuantGov/quantgov,setup.py,1973a4db9f9a68dab0816c16507d20de1508dcba,090ee8624bd4569b10cb99ab296382d28a2cd5aa,You can just specify the packages manually here if your project is
aaffdacid,QuantGov/quantgov,quantgov/estimator/estimation.py,b5cea6612293ac19d71a5a725954f6bbc5782a8a,230bea0b5e2f1eddd4cd71b6f277c83da630de22,TODO: This is very ugly and complicated and should probably be refactored
aaffdadbh,QuantGov/quantgov,quantgov/ml/estimation.py,230bea0b5e2f1eddd4cd71b6f277c83da630de22,19cd4909c93420f0d2efb40e0f82d1cd91fb35f4,TODO: This is very ugly and complicated and should probably be refactored
aaffdaddc,QuantGov/quantgov,quantgov/ml/estimation.py,7ebc3e5bd06db4cd6b94c151ab9e0859a302c8e8,19cd4909c93420f0d2efb40e0f82d1cd91fb35f4,TODO: This is very ugly and complicated and should probably be refactored
aaffdafaa,Aifred-Health/Vulcan-Old,simplenn/logistic_sgd.py,c104c48913539a3ae59468904260cd2ef4c9addc,STILL_EXISTS,``shared_y`` we will have to cast it to int. This little hack
aaffdafec,Aifred-Health/Vulcan-Old,simplenn/logistic_sgd.py,c104c48913539a3ae59468904260cd2ef4c9addc,STILL_EXISTS,improve patience if loss improvement is good enough
aaffdajha,Aifred-Health/Vulcan-Old,src/net.py,ba4f6ca6a78293d499a8921fdf9a4c89fd664ef2,STILL_EXISTS,TODO: implement ETA using epoch time averages and how many left
aaffdbfia,Aifred-Health/Vulcan-Old,src/net.py,2de4078e37cd004fdc603871c6c559c9d4fdbc92,fa353cd14b6236824cf0a6f18a387decc6f00694,TODO
aaffdbijb,IGITUGraz/L2L,ltl/optimizers/simulatedannealing/optimizer.py,3f8915b12020f85976dc06eee26bafa0545a1225,STILL_EXISTS,TODO: Maype print some individuals here?
aaffdbijc,IGITUGraz/L2L,bin/learn-to-learn-template.py,d390c855befc375f98b5f3dd6754ddb30f7a8251,STILL_EXISTS,TODO: Give some *meaningful* name here
aaffdbijd,IGITUGraz/L2L,bin/learn-to-learn-template.py,d390c855befc375f98b5f3dd6754ddb30f7a8251,STILL_EXISTS,TODO: Change the `root_dir_path` here
aaffdbijj,IGITUGraz/L2L,bin/learn-to-learn-template.py,d390c855befc375f98b5f3dd6754ddb30f7a8251,STILL_EXISTS,TODO: Change the optimizee to the appropriate Optimizee class
aaffdbjab,IGITUGraz/L2L,bin/learn-to-learn-template.py,d390c855befc375f98b5f3dd6754ddb30f7a8251,STILL_EXISTS,TODO: Change the optimizer to the appropriate Optimizer class
aaffdbjie,IGITUGraz/L2L,conf.py,d390c855befc375f98b5f3dd6754ddb30f7a8251,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaffdcbfh,IGITUGraz/L2L,ltl/optimizers/optimizer.py,b7640142cda11372881958b521f3d287a450f5bd,STILL_EXISTS,TODO: Set eval_pop to the values of parameters you want to evaluate in the next cycle
aaffdccgc,IGITUGraz/L2L,ltl/optimizees/lsm/optimizee.py,9eb59828b6eaaa07274fb924bf114862d1f4ee5e,STILL_EXISTS,TODO: Set root_dir_path here
aaffdcfdh,IGITUGraz/L2L,bin/ltl-fun-gs.py,65385163b657e10befd2b1d97e75a598430d0212,9277b42e4dcd14170b78e15af43387bfd9b78d08,TODO: Change the optimizer to the appropriate Optimizer class
aaffdcffg,IGITUGraz/L2L,ltl/optimizers/crossentropy/optimizer.py,713472324e49c227d451d39a4ebc38c1c88ba926,2a05cac62c91c6a9bf26c98b1b94a3e0526ee805,TODO best individual should be tracked somehow?(not needed for recorder)
aaffdcffi,IGITUGraz/L2L,ltl/optimizers/face/optimizer.py,713472324e49c227d451d39a4ebc38c1c88ba926,2a05cac62c91c6a9bf26c98b1b94a3e0526ee805,TODO best individual should be tracked somehow?(not needed for recorder)
aaffdcfha,IGITUGraz/L2L,bin/ltl-fun-gd.py,2d8b031e4436d5916d2f5e04c68af334386cbbee,e6443deaa440503f806716e5fcac967d89c6f725,TODO: Change the optimizer to the appropriate Optimizer class
aaffdcgai,IGITUGraz/L2L,bin/ltl-template.py,43c176fae4287db40ab4c1a5f91ad7860b91b115,460acc51ad480e01c3ce661db0d6497261273898,TODO: Change the names; ids and parameters passed in
aaffdcghe,IGITUGraz/L2L,bin/ltl-fun-ce-gaussmix.py,f852fa75e48469756abe7ef5030b9cbba9d0d729,e6443deaa440503f806716e5fcac967d89c6f725,TODO: Change the optimizer to the appropriate Optimizer class
aaffdchcf,IGITUGraz/L2L,bin/ltl-fun-pt.py,a753a1eb2105eccc024bea8b3ccd80f23ad69935,STILL_EXISTS,decay parameter for each schedule. If needed can be different for each
aaffdchdj,IGITUGraz/L2L,ltl/optimizers/paralleltempering/optimizer.py,a753a1eb2105eccc024bea8b3ccd80f23ad69935,STILL_EXISTS,\"\"\" ||  || Multiplicative Monotonic Cooling || This schedule type multiplies the starting temperature by a factor that  || decreases over time (number k of the performed iteration steps). It requires a  || decay parameter (alpha) but not an ending temperature; as the prgression of the  || temperature is well definded by the decay parameter only. The Multiplicative  || Monotonic Cooling schedules are: Exponential multiplicative cooling;  || Logarithmical multiplicative cooling; Linear multiplicative cooling and  || Quadratic multiplicative cooling. || Source: Kirkpatrick; Gelatt and Vecchi (1983) ||  || - Exponential multiplicative cooling || Default cooling schedule for typical applications of simulated annealing. Each  || step; the temperature T_k is multiplied by the factor alpha (which has to be  || between 0 and 1) or in other words it is the starting temperature T_0  || multiplied by the factor alpha by the power of k: T_k = T_0 * alpha^k ||  || - Logarithmical multiplicative cooling || The factor by which the temperature decreases; is indirectly proportional to  || the log of k.  Therefore it slows down the cooling; the further progressed  || the schedule is. Alpha has to be largert than one.  || T_k = T_0 \/ ( 1 + alpha* log (1 + k) ) ||  || - Linear multiplicative cooling || Behaves similar to Logarithmical multiplicative cooling in that the decrease  || gets lower over time; but not as pronounced. The decrease is indirectly  || proportional to alpha times k and alpha has to be larger than zero: || T_k = T_0 \/ ( 1 + alpha*k) ||  || - Quadratic multiplicative cooling  || This schedule stays at high temperatures longer; than the other schedules and  || has a steeper cooling later in the process. Alpha has to be larger than zero. || T_k = T_0 \/ ( 1 + alpha*k^2) ||  || Additive Monotonic Cooling || The differences to Multiplicative Monotonic Cooling are; that the final  || temperature T_n and the number of iterations n are needed also. So this  || cannot be used as intended; if the stop criterion is something different;  || than a certain number of iteration steps. A decay parameter is not needed.  || Each temperature is computed; by adding a term to the final temperature. The  || Additive Monotonic Cooling schedules are: Linear additive cooling; Quadratic  || additive cooling; Exponential additive cooling and Trigonometric additive  || cooling. || Source. Additive monotonic cooling B. T. Luke (2005)  ||  || - Linear additive cooling  || This schedule adds a term to the final temperature; which decreases linearily  || with the progression of the schedule. || T_k = T_n + (T_0 -T_n)*((n-k)\/n) ||  || - Quadratic additive cooling  || This schedule adds a term to the final temperature; which decreases q || uadratically with the progression of the schedule. || T_k = T_n + (T_0 -T_n)*((n-k)\/n)^2 ||  || - Exponential additive || Uses a complicated formula; to come up with a schedule; that has a slow start;  || a steep decrease in temperature in the middle and a slow decrease at the end  || of the process. || T_k = T_n + (T_0 - T_n) * (1\/(1+exp( 2*ln(T_0 - T_n)\/n * (k- n\/2) ) ) ) ||  || - Trigonometric additive cooling || This schedule has a similar behavior as Exponential additive; but less pronounced.  || T_k = T_n + (T_0 - T_n)\/2 * (1+cos(k*pi\/n)) ||  || \"\"\"
aaffdchhc,IGITUGraz/L2L,ltl/optimizers/simulatedannealing/optimizer.py,a753a1eb2105eccc024bea8b3ccd80f23ad69935,STILL_EXISTS,\"\"\" || Multiplicative Monotonic Cooling || This schedule type multiplies the starting temperature by a factor that  || decreases over time (number k of the performed iteration steps). It requires a  || decay parameter (alpha) but not an ending temperature; as the prgression of the  || temperature is well definded by the decay parameter only. The Multiplicative  || Monotonic Cooling schedules are: Exponential multiplicative cooling;  || Logarithmical multiplicative cooling; Linear multiplicative cooling and  || Quadratic multiplicative cooling. || Source: Kirkpatrick; Gelatt and Vecchi (1983) ||  || - Exponential multiplicative cooling || Default cooling schedule for typical applications of simulated annealing. Each  || step; the temperature T_k is multiplied by the factor alpha (which has to be  || between 0 and 1) or in other words it is the starting temperature T_0  || multiplied by the factor alpha by the power of k: T_k = T_0 * alpha^k ||  || - Logarithmical multiplicative cooling || The factor by which the temperature decreases; is indirectly proportional to  || the log of k.  Therefore it slows down the cooling; the further progressed  || the schedule is. Alpha has to be largert than one.  || T_k = T_0 \/ ( 1 + alpha* log (1 + k) ) ||  || - Linear multiplicative cooling || Behaves similar to Logarithmical multiplicative cooling in that the decrease  || gets lower over time; but not as pronounced. The decrease is indirectly  || proportional to alpha times k and alpha has to be larger than zero: || T_k = T_0 \/ ( 1 + alpha*k) ||  || - Quadratic multiplicative cooling  || This schedule stays at high temperatures longer; than the other schedules and  || has a steeper cooling later in the process. Alpha has to be larger than zero. || T_k = T_0 \/ ( 1 + alpha*k^2) ||  || Additive Monotonic Cooling || The differences to Multiplicative Monotonic Cooling are; that the final  || temperature T_n and the number of iterations n are needed also. So this  || cannot be used as intended; if the stop criterion is something different;  || than a certain number of iteration steps. A decay parameter is not needed.  || Each temperature is computed; by adding a term to the final temperature. The  || Additive Monotonic Cooling schedules are: Linear additive cooling; Quadratic  || additive cooling; Exponential additive cooling and Trigonometric additive  || cooling. || Source. Additive monotonic cooling B. T. Luke (2005)  ||  || - Linear additive cooling  || This schedule adds a term to the final temperature; which decreases linearily  || with the progression of the schedule. || T_k = T_n + (T_0 -T_n)*((n-k)\/n) ||  || - Quadratic additive cooling  || This schedule adds a term to the final temperature; which decreases q || uadratically with the progression of the schedule. || T_k = T_n + (T_0 -T_n)*((n-k)\/n)^2 ||  || - Exponential additive || Uses a complicated formula; to come up with a schedule; that has a slow start;  || a steep decrease in temperature in the middle and a slow decrease at the end  || of the process. || T_k = T_n + (T_0 - T_n) * (1\/(1+exp( 2*ln(T_0 - T_n)\/n * (k- n\/2) ) ) ) ||  || - Trigonometric additive cooling || This schedule has a similar behavior as Exponential additive; but less pronounced.  || T_k = T_n + (T_0 - T_n)\/2 * (1+cos(k*pi\/n)) ||  || \"\"\"
aaffddcai,IGITUGraz/L2L,utils/tools.py,0a4ddefc0bfda90c443850533b7b237cd57d6de2,STILL_EXISTS,Neither the name of the author nor the names of other contributors
aaffddiij,montefiore-ai/hypothesis,hypothesis/inference/approximate_likelihood_ratio.py,5cad482158cf61e4443bcab4ddc86ceab191cdda,STILL_EXISTS,TODO; Implement subsampling.
aaffddjfe,montefiore-ai/hypothesis,hypothesis/summary/mcmc.py,5cad482158cf61e4443bcab4ddc86ceab191cdda,c77d78487173f36c04ca2cb80ab4bf3fc3a0b5ef,TODO Support multi-dimensional
aaffdeaca,montefiore-ai/hypothesis,docs/source/conf.py,c77d78487173f36c04ca2cb80ab4bf3fc3a0b5ef,STILL_EXISTS,-- Options for todo extension ----------------------------------------------
aaffdeaci,montefiore-ai/hypothesis,hypothesis/__init__.py,c77d78487173f36c04ca2cb80ab4bf3fc3a0b5ef,STILL_EXISTS,r\"\"\"Hypothesis is a python module for statistical inference. ||  || The package contains (approximate) inference algorithms to solve statistical || problems. Utilities are provided for data loading; efficient || simulation; visualization; fire-and-forget inference; and validation. || \"\"\"
aaffdeadg,montefiore-ai/hypothesis,hypothesis/benchmark/mg1/__init__.py,c77d78487173f36c04ca2cb80ab4bf3fc3a0b5ef,2a866d17b3f3d899db9e278026faa1cf350140fb,r\"\"\" || Todo: ||     Write docs. || \"\"\"
aaffdeaec,montefiore-ai/hypothesis,hypothesis/benchmark/tractable/__init__.py,c77d78487173f36c04ca2cb80ab4bf3fc3a0b5ef,2a866d17b3f3d899db9e278026faa1cf350140fb,r\"\"\" || Todo: ||     Write docs. || \"\"\"
aaffdeafb,montefiore-ai/hypothesis,hypothesis/inference/abc.py,c77d78487173f36c04ca2cb80ab4bf3fc3a0b5ef,STILL_EXISTS,TODO Implement.
aaffdeaff,montefiore-ai/hypothesis,hypothesis/inference/abc_smc.py,c77d78487173f36c04ca2cb80ab4bf3fc3a0b5ef,STILL_EXISTS,TODO Implement.
aaffdebcd,montefiore-ai/hypothesis,hypothesis/simulation/__init__.py,c77d78487173f36c04ca2cb80ab4bf3fc3a0b5ef,STILL_EXISTS,r\"\"\"``hypothesis.simulation`` is a package consisting of the base simulator || architecture and utilities to perform efficient simulations. Every forward || model needs to be wrapped in a class which inherits from || ``hypothesis.simulation.Simulator``. || \"\"\"
aaffdecjf,montefiore-ai/hypothesis,examples/conditional-ratio-estimator/boilerplate/models.py,a9160255aac6d9c5c2e2331012960fe6aa88e7db,STILL_EXISTS,Maybe you want to do something special here.
aaffdedae,montefiore-ai/hypothesis,examples/conditional-ratio-estimator/boilerplate/train.py,a9160255aac6d9c5c2e2331012960fe6aa88e7db,STILL_EXISTS,Move back to GPU.
aaffdeeih,montefiore-ai/hypothesis,hypothesis/benchmark/spatialsir/simulator.py,05f6d356a6d6eb3306690d2c4e2232bb284d8720,dd54a8c2986a77ec26d0982fd0d048ceaf8be6fc,second dimension indexes grid columns
aaffdeffd,montefiore-ai/hypothesis,hypothesis/stat/constraint.py,694dbfc7b8e677adb7fe743bf2552a857cb0f951,0be790ba9ecb8a0ea27fe0f6f4d6dc5f737d2e99,Clone to fix strange behaviour in Jupyter.
aaffdefga,montefiore-ai/hypothesis,hypothesis/stat/constraint.py,5d5eed68f760276442aa0f2ee1671a648dbdc2a5,0be790ba9ecb8a0ea27fe0f6f4d6dc5f737d2e99,Clone to fix strange behaviour in Jupyter.
aaffdefid,montefiore-ai/hypothesis,hypothesis/bin/train-ratio-estimator.py,ce2d201b406dfd50411df2d3241e27de2423d8e7,STILL_EXISTS,TODO Implement
aaffdehda,sisl/mechamodlearn,mechamodlearn/rigidbody.py,397f76ceeb35ed2ef38d071f6c200b88844ec8b9,STILL_EXISTS,TODO vectorize
aaffdfgba,a2i2/surround,surround/split_data.py,7eba40075de5cf111568f2b4cf48b6a44fa2ab53,STILL_EXISTS,\"\"\"A script to randomly move files in a directory to a test; train and || validate folder. ||  || This script is intended to be used on projects where the data is made || up of multiple files e.g. images or email files. ||  || Usage: python3 split_data.py <directory> <file extension> ||  || TODO: Add a flag to sort data into sub-folders based on a prefix for file names || TODO: Make sure reset works for all flags || TODO: Modify script to work on a CSV file. In this case split the CSV file into multiple files under each folder. ||  || \"\"\"
aaffdfgbc,a2i2/surround,surround/visualise.py,7eba40075de5cf111568f2b4cf48b6a44fa2ab53,STILL_EXISTS,\"\"\" visualise.py ||  || Visualises the output from training a classifier. ||  || Supports both binary and multi class classifiers. ||  || Use cases: ||  - Visualising the output from training a model ||  - Viewing the output from running batch predictions on a dataset ||  || TODO: Order confusion matrix by most popular class to least popular class || TODO: Output file format in HTML. Always print to the screen. || TODO: Visualisation function should be different from function the output metrics || TODO: Wrap in a Visualiser interface for use in Surround || TODO: Support multiple ground truth and prediction columns || TODO: Add flag to output file with incorrect records. True by default. || TODO: Rename module to visualise_classifier.py ||  || TODO: Add a flag to set probability thresholds || TODO: Add a flag that describes each aspect of the generated report in human readable terminology ||  || \"\"\"
aaffdfgbe,a2i2/surround,surround/visualise/cli.py,2f4b9f1006d59a0fb69ceb7e30bec1c7a9e15edb,STILL_EXISTS,Read the CSV file and strip the headings of the columns
aaffdfgbg,a2i2/surround,surround/visualise/cli.py,2f4b9f1006d59a0fb69ceb7e30bec1c7a9e15edb,STILL_EXISTS,Check if columns are present in data file
aaffdgaih,a2i2/surround,surround-lib/surround/surround.py,b9d9ba33b182bad6aa4bd76e522def55551085b3,STILL_EXISTS,TODO: Add ability to run the experiment
aaffdgbaa,Aifred-Health/Vulcan,vulcanai/dataloaders/base_dataloader.py,636fca330010e1437a702a115ab28d287a1586bc,STILL_EXISTS,TODO: this will be a lot of params.
aaffdgbac,Aifred-Health/Vulcan,vulcanai/engines/base_engine.py,636fca330010e1437a702a115ab28d287a1586bc,STILL_EXISTS,TODO: this will be a lot of params.
aaffdgbaf,Aifred-Health/Vulcan,vulcanai/models/base_model.py,636fca330010e1437a702a115ab28d287a1586bc,STILL_EXISTS,TODO: this will be a lot of params.
aaffdgbah,Aifred-Health/Vulcan,vulcanai/models/base_model.py,636fca330010e1437a702a115ab28d287a1586bc,STILL_EXISTS,TODO: so; obviously; this needs to be changed
aaffdgbbb,Aifred-Health/Vulcan,vulcanai/criteria/base_criterion.py,35fe9951f9c0e542c4518a3d9666c2825053d480,STILL_EXISTS,\"\"\" || What does a criterion do? Well; it describes the performance || \"\"\"
aaffdgbbf,Aifred-Health/Vulcan,vulcanai/dataloaders/base_dataloader.py,35fe9951f9c0e542c4518a3d9666c2825053d480,STILL_EXISTS,TODO: I think this should subclass the torch dataset??
aaffdgbbh,Aifred-Health/Vulcan,vulcanai/engines/utilities.py,35fe9951f9c0e542c4518a3d9666c2825053d480,STILL_EXISTS,TODO: something to do with keeping track of the best checkpoint - create an object? forget the name of the design pattern; but there's a relevant one.
aaffdgbbi,Aifred-Health/Vulcan,vulcanai/optimizers/base_optimizer.py,35fe9951f9c0e542c4518a3d9666c2825053d480,STILL_EXISTS,\"\"\" || normally just use the pytorch ones... which are returned appropriately in factory. || you could store your options in a list somewhere just like the bootstrap package? ||  || perhaps call the directory custom_optimizers? || \"\"\"
aaffdgbda,Aifred-Health/Vulcan,vulcanai2/criteria/base_criterion.py,e281d04e77519923e38af2b17df487aace66b449,STILL_EXISTS,\"\"\" || What does a criterion do? Well; it describes the performance || \"\"\"
aaffdgbej,Aifred-Health/Vulcan,vulcanai2/engines/utilities.py,e281d04e77519923e38af2b17df487aace66b449,STILL_EXISTS,TODO: something to do with keeping track of the best checkpoint - create an object? forget the name of the design pattern; but there's a relevant one.
aaffdgbfd,Aifred-Health/Vulcan,vulcanai2/models/base_model.py,e281d04e77519923e38af2b17df487aace66b449,STILL_EXISTS,TODO: this will be a lot of params.
aaffdgbff,Aifred-Health/Vulcan,vulcanai2/models/base_model.py,e281d04e77519923e38af2b17df487aace66b449,STILL_EXISTS,TODO: so; obviously; this needs to be changed
aaffdgbge,Aifred-Health/Vulcan,train_mnist_conv.py,643fa9360f25d3dae3b5d058c5c1c800acfc921d,STILL_EXISTS,TODO: we probably want to give a ratio to split this for validation.....? maybe we don't always want to do this?
aaffdgbgf,Aifred-Health/Vulcan,vulcanai2/engines/schedulers/factory.py,643fa9360f25d3dae3b5d058c5c1c800acfc921d,STILL_EXISTS,TODO: figure out how to best store names....
aaffdgbgh,Aifred-Health/Vulcan,vulcanai2/models/cnn.py,643fa9360f25d3dae3b5d058c5c1c800acfc921d,STILL_EXISTS,TODO: I don't think this should actually be here
aaffdgbgi,Aifred-Health/Vulcan,vulcanai2/models/AbstractNetwork.py,56bfa5aebecedaaed14d4d858b4adfdfafbed214,STILL_EXISTS,TODO: want to call it activation but namespace; so what to do best?
aaffdgbgj,Aifred-Health/Vulcan,vulcanai2/models/AbstractNetwork.py,56bfa5aebecedaaed14d4d858b4adfdfafbed214,STILL_EXISTS,TODO: this should be the same for every model; given that you pass a config??
aaffdgbha,Aifred-Health/Vulcan,vulcanai2/models/AbstractNetwork.py,56bfa5aebecedaaed14d4d858b4adfdfafbed214,STILL_EXISTS,TODO: come up with an alternate way to validate config
aaffdgbhb,Aifred-Health/Vulcan,vulcanai2/models/AbstractNetwork.py,56bfa5aebecedaaed14d4d858b4adfdfafbed214,STILL_EXISTS,TODO: deal with stopping rules
aaffdgbhc,Aifred-Health/Vulcan,vulcanai2/models/AbstractNetwork.py,56bfa5aebecedaaed14d4d858b4adfdfafbed214,STILL_EXISTS,TODO: do you have to call any nn.module methods?? do you even actually want to subclass at this point if you have layers that subclass nn.module??
aaffdgbhd,Aifred-Health/Vulcan,vulcanai2/models/AbstractNetwork.py,56bfa5aebecedaaed14d4d858b4adfdfafbed214,STILL_EXISTS,TODO: change and check type here?
aaffdgbhe,Aifred-Health/Vulcan,vulcanai2/models/AbstractNetwork.py,56bfa5aebecedaaed14d4d858b4adfdfafbed214,STILL_EXISTS,TODO: how to deal with passing parameters especially if custom; given the note here: https:\/\/pytorch.org\/docs\/stable\/optim.html
aaffdgbhf,Aifred-Health/Vulcan,vulcanai2/models/AbstractNetwork.py,56bfa5aebecedaaed14d4d858b4adfdfafbed214,STILL_EXISTS,TODO: some optmizers have different behaviour; although this is unlikely to apply
aaffdgbhg,Aifred-Health/Vulcan,vulcanai2/models/AbstractNetwork.py,56bfa5aebecedaaed14d4d858b4adfdfafbed214,STILL_EXISTS,TODO: uhhhh do you want to define this if it's really self? do you want to subclass nn.module??
aaffdgbhh,Aifred-Health/Vulcan,vulcanai2/models/AbstractNetwork.py,ad1ca39fae90663cd56d8c933606afd2035d87e4,STILL_EXISTS,TODO: figure out how this works in conjunction with optimizer
aaffdgbhi,Aifred-Health/Vulcan,vulcanai2/models/AbstractNetwork.py,ad1ca39fae90663cd56d8c933606afd2035d87e4,STILL_EXISTS,TODO: fix the fact that you copy pasted this
aaffdgbhj,Aifred-Health/Vulcan,vulcanai2/models/AbstractNetwork.py,ad1ca39fae90663cd56d8c933606afd2035d87e4,STILL_EXISTS,TODO: deal with the fact that you copied this
aaffdgbia,Aifred-Health/Vulcan,vulcanai2/models/AbstractNetwork.py,b1e5c76f8d0ccb2d660742861c44a659d59de8aa,STILL_EXISTS,TODO: this should be a nn.sequential??
aaffdgbif,Aifred-Health/Vulcan,vulcanai2/models/AbstractNetwork.py,21c684682816d5bfda69411785880c12b6746b97,STILL_EXISTS,TODO: blarg
aaffdgbig,Aifred-Health/Vulcan,vulcanai2/models/AbstractNetwork.py,21c684682816d5bfda69411785880c12b6746b97,STILL_EXISTS,TODO: define setters for everything and call those... although I really don't care about making things private?
aaffdgbih,Aifred-Health/Vulcan,vulcanai2/models/AbstractNetwork.py,21c684682816d5bfda69411785880c12b6746b97,STILL_EXISTS,TODO: figure out if you really need this?
aaffdgbii,Aifred-Health/Vulcan,vulcanai2/models/AbstractNetwork.py,21c684682816d5bfda69411785880c12b6746b97,STILL_EXISTS,TODO: this should be a nn.sequential??
aaffdgbij,Aifred-Health/Vulcan,vulcanai2/models/AbstractNetworkTrainer.py,21c684682816d5bfda69411785880c12b6746b97,STILL_EXISTS,TODO: fix up the whole use GPU thing.. who does it?
aaffdgbja,Aifred-Health/Vulcan,vulcanai2/models/AbstractNetworkTrainer.py,21c684682816d5bfda69411785880c12b6746b97,STILL_EXISTS,TODO: do some sort of data validation here
aaffdgcac,Aifred-Health/Vulcan,vulcanai2/models/Callbacks.py,21c684682816d5bfda69411785880c12b6746b97,STILL_EXISTS,TODO
aaffdgcch,Aifred-Health/Vulcan,vulcanai2/models/dnn.py,21c684682816d5bfda69411785880c12b6746b97,03c00cfaccaaa0b7d3b5dc7a6fac6a06ce5b85b1,TODO: use super init; but with **kwargs?
aaffdgcdc,Aifred-Health/Vulcan,vulcanai2/models/BaseNetwork.py,343f3f3196ca62a1237c48fa6521d005dc6ec49f,STILL_EXISTS,TODO: want to call it activation but namespace; so what to do best?
aaffdgcdd,Aifred-Health/Vulcan,vulcanai2/models/BaseNetwork.py,343f3f3196ca62a1237c48fa6521d005dc6ec49f,67334812c06334f296d1cbd60003c035688ae7cb,TODO: blarg
aaffdgcde,Aifred-Health/Vulcan,vulcanai2/models/BaseNetwork.py,343f3f3196ca62a1237c48fa6521d005dc6ec49f,STILL_EXISTS,TODO: this should be the same for every model; given that you pass a config??
aaffdgcdf,Aifred-Health/Vulcan,vulcanai2/models/BaseNetwork.py,343f3f3196ca62a1237c48fa6521d005dc6ec49f,STILL_EXISTS,TODO: come up with an alternate way to validate config
aaffdgcdg,Aifred-Health/Vulcan,vulcanai2/models/BaseNetwork.py,343f3f3196ca62a1237c48fa6521d005dc6ec49f,STILL_EXISTS,TODO: deal with stopping rules
aaffdgcdh,Aifred-Health/Vulcan,vulcanai2/models/BaseNetwork.py,343f3f3196ca62a1237c48fa6521d005dc6ec49f,STILL_EXISTS,TODO: do you have to call any nn.module methods?? do you even actually want to subclass at this point if you have layers that subclass nn.module??
aaffdgcdj,Aifred-Health/Vulcan,vulcanai2/models/BaseNetwork.py,343f3f3196ca62a1237c48fa6521d005dc6ec49f,STILL_EXISTS,TODO: change and check type here?
aaffdgcea,Aifred-Health/Vulcan,vulcanai2/models/BaseNetwork.py,343f3f3196ca62a1237c48fa6521d005dc6ec49f,STILL_EXISTS,TODO: where to do typechecking... just let everything fail?
aaffdgceb,Aifred-Health/Vulcan,vulcanai2/models/BaseNetwork.py,343f3f3196ca62a1237c48fa6521d005dc6ec49f,STILL_EXISTS,TODO: add on additional if you want to be able to re-create a network?
aaffdgcec,Aifred-Health/Vulcan,vulcanai2/models/BaseNetwork.py,343f3f3196ca62a1237c48fa6521d005dc6ec49f,67334812c06334f296d1cbd60003c035688ae7cb,TODO: check type?
aaffdgced,Aifred-Health/Vulcan,vulcanai2/models/BaseNetwork.py,343f3f3196ca62a1237c48fa6521d005dc6ec49f,6a59e5fa55c90375a871fa95ca8a3cc4bf2b7208,TODO: figure out how this works in conjunction with optimizer
aaffdgcee,Aifred-Health/Vulcan,vulcanai2/models/BaseNetwork.py,343f3f3196ca62a1237c48fa6521d005dc6ec49f,6a59e5fa55c90375a871fa95ca8a3cc4bf2b7208,TODO: fix the fact that you copy pasted this
aaffdgcef,Aifred-Health/Vulcan,vulcanai2/models/BaseNetwork.py,343f3f3196ca62a1237c48fa6521d005dc6ec49f,67334812c06334f296d1cbd60003c035688ae7cb,TODO: I think this needs to take into account passing through networks
aaffdgceg,Aifred-Health/Vulcan,vulcanai2/models/BaseNetwork.py,343f3f3196ca62a1237c48fa6521d005dc6ec49f,67334812c06334f296d1cbd60003c035688ae7cb,TODO: make this isn't resetting things when you have mulitple networks
aaffdgceh,Aifred-Health/Vulcan,vulcanai2/models/BaseNetwork.py,343f3f3196ca62a1237c48fa6521d005dc6ec49f,6a59e5fa55c90375a871fa95ca8a3cc4bf2b7208,TODO: deal with the fact that you copied this
aaffdgcei,Aifred-Health/Vulcan,vulcanai2/models/BaseNetwork.py,343f3f3196ca62a1237c48fa6521d005dc6ec49f,6a59e5fa55c90375a871fa95ca8a3cc4bf2b7208,TODO: figure out if you really need this?
aaffdgcej,Aifred-Health/Vulcan,vulcanai2/models/BaseNetwork.py,343f3f3196ca62a1237c48fa6521d005dc6ec49f,67334812c06334f296d1cbd60003c035688ae7cb,TODO: do you really want this here....?
aaffdgcff,Aifred-Health/Vulcan,vulcanai2/models/BaseNetwork.py,343f3f3196ca62a1237c48fa6521d005dc6ec49f,STILL_EXISTS,TODO: this is for the test data
aaffdgcfg,Aifred-Health/Vulcan,vulcanai2/models/cnn.py,343f3f3196ca62a1237c48fa6521d005dc6ec49f,STILL_EXISTS,TODO: use setters to enforce types\/formats\/values!
aaffdgcfj,Aifred-Health/Vulcan,vulcanai2/models/BaseNetwork.py,1c926e4157d6f7ce8c63511c5080ceb821cefe96,15fc8dc1c60798787270ae35e23c7fc88dfe7378,TODO: I don't know why pycharm keeps rejecting this?
aaffdgcga,Aifred-Health/Vulcan,vulcanai2/models/BaseNetwork.py,1c926e4157d6f7ce8c63511c5080ceb821cefe96,STILL_EXISTS,TODO: reorder these?
aaffdgcia,Aifred-Health/Vulcan,vulcanai2/models/BaseNetwork.py,1c926e4157d6f7ce8c63511c5080ceb821cefe96,15fc8dc1c60798787270ae35e23c7fc88dfe7378,TODO: this won't work for all of them...
aaffdgcib,Aifred-Health/Vulcan,vulcanai2/models/BaseNetwork.py,1c926e4157d6f7ce8c63511c5080ceb821cefe96,15fc8dc1c60798787270ae35e23c7fc88dfe7378,TODO: use_gpu should probably go somewhere else in the future...
aaffdgcid,Aifred-Health/Vulcan,vulcanai2/models/cnn.py,1c926e4157d6f7ce8c63511c5080ceb821cefe96,15fc8dc1c60798787270ae35e23c7fc88dfe7378,TODO: priya why isn't this being used??
aaffdgcif,Aifred-Health/Vulcan,vulcanai2/models/BaseNetwork.py,15fc8dc1c60798787270ae35e23c7fc88dfe7378,67334812c06334f296d1cbd60003c035688ae7cb,TODO: blarg
aaffdgcih,Aifred-Health/Vulcan,vulcanai2/models/BaseNetwork.py,15fc8dc1c60798787270ae35e23c7fc88dfe7378,STILL_EXISTS,TODO: reorder these?
aaffdgdbb,Aifred-Health/Vulcan,vulcanai2/models/BaseNetwork.py,6a59e5fa55c90375a871fa95ca8a3cc4bf2b7208,8088388e4374c2cb8a8a98cf1fb376e8c67ba995,TODO: I don't know why pycharm keeps rejecting this?
aaffdgdbc,Aifred-Health/Vulcan,vulcanai2/models/BaseNetwork.py,6a59e5fa55c90375a871fa95ca8a3cc4bf2b7208,STILL_EXISTS,TODO: reorder these?
aaffdgdbd,Aifred-Health/Vulcan,vulcanai2/models/BaseNetwork.py,6a59e5fa55c90375a871fa95ca8a3cc4bf2b7208,STILL_EXISTS,#TODO: figure out how this works in conjunction with optimizer
aaffdgdbe,Aifred-Health/Vulcan,vulcanai2/models/BaseNetwork.py,6a59e5fa55c90375a871fa95ca8a3cc4bf2b7208,STILL_EXISTS,#TODO: fix the fact that you copy pasted this
aaffdgdci,Aifred-Health/Vulcan,vulcanai2/models/BaseNetwork.py,6a59e5fa55c90375a871fa95ca8a3cc4bf2b7208,STILL_EXISTS,#TODO: deal with the fact that you copied this
aaffdgdcj,Aifred-Health/Vulcan,vulcanai2/models/BaseNetwork.py,6a59e5fa55c90375a871fa95ca8a3cc4bf2b7208,STILL_EXISTS,#TODO: figure out if you really need this?
aaffdgddg,Aifred-Health/Vulcan,vulcanai2/models/BaseNetwork.py,6a59e5fa55c90375a871fa95ca8a3cc4bf2b7208,67334812c06334f296d1cbd60003c035688ae7cb,TODO: this won't work for all of them...
aaffdgddh,Aifred-Health/Vulcan,vulcanai2/models/BaseNetwork.py,6a59e5fa55c90375a871fa95ca8a3cc4bf2b7208,STILL_EXISTS,TODO: use_gpu should probably go somewhere else in the future...
aaffdgded,Aifred-Health/Vulcan,vulcanai2/models/cnn.py,6a59e5fa55c90375a871fa95ca8a3cc4bf2b7208,67334812c06334f296d1cbd60003c035688ae7cb,TODO: priya why isn't this being used??
aaffdgdfb,Aifred-Health/Vulcan,vulcanai2/models/layers.py,13fe75d87eea5360f986fc75eabb30c6521dd443,5b99d4f330cf69e71542df83b4da4de9479fe941,TODO: this is to ensure if the GPU is activated on the input; the flatten layer should also incorporate GPU activated
aaffdgdfd,Aifred-Health/Vulcan,vulcanai2/datasets/TabularDataset.py,22da5a785fa5bcd076ad653d1cfe719c0cfad268,STILL_EXISTS,TODO: this is copy pasted
aaffdgdfe,Aifred-Health/Vulcan,vulcanai2/datasets/TabularDataset.py,22da5a785fa5bcd076ad653d1cfe719c0cfad268,STILL_EXISTS,TODO catch errors
aaffdgdff,Aifred-Health/Vulcan,vulcanai2/datasets/TabularDataset.py,22da5a785fa5bcd076ad653d1cfe719c0cfad268,STILL_EXISTS,TODO: add in header
aaffdgdfg,Aifred-Health/Vulcan,vulcanai2/datasets/TabularDataset.py,22da5a785fa5bcd076ad653d1cfe719c0cfad268,STILL_EXISTS,TODO: need to convert to correct datatype
aaffdgdfh,Aifred-Health/Vulcan,vulcanai2/datasets/TabularDataset.py,22da5a785fa5bcd076ad653d1cfe719c0cfad268,STILL_EXISTS,TODO: instance method?
aaffdgdfi,Aifred-Health/Vulcan,vulcanai2/datasets/TabularDataset.py,22da5a785fa5bcd076ad653d1cfe719c0cfad268,STILL_EXISTS,TODO: include a list of csv files and then you can merge them based on a certain column.... certain criteria must be assumed in advance
aaffdgdfj,Aifred-Health/Vulcan,vulcanai2/datasets/TabularDataset.py,22da5a785fa5bcd076ad653d1cfe719c0cfad268,STILL_EXISTS,TODO: inspiration https:\/\/github.com\/joemehltretter\/aifred_ml\/blob\/master\/COMED%20Prepoccesing.ipynb
aaffdgdga,Aifred-Health/Vulcan,vulcanai2/datasets/TabularDataset.py,22da5a785fa5bcd076ad653d1cfe719c0cfad268,STILL_EXISTS,TODO: check types; possibly wrong
aaffdgdgb,Aifred-Health/Vulcan,vulcanai2/datasets/TabularDataset.py,22da5a785fa5bcd076ad653d1cfe719c0cfad268,STILL_EXISTS,TODO: append this
aaffdgdgc,Aifred-Health/Vulcan,vulcanai2/datasets/TabularDataset.py,22da5a785fa5bcd076ad653d1cfe719c0cfad268,STILL_EXISTS,TODO: verify that labels has the same length as data
aaffdgdge,Aifred-Health/Vulcan,vulcanai2/datasets/TabularDataset.py,22da5a785fa5bcd076ad653d1cfe719c0cfad268,STILL_EXISTS,TODO: this operates on categorical; as necessary. need to figure out where in the pipeline this happens...
aaffdgdgj,Aifred-Health/Vulcan,vulcanai2/datasets/utils.py,22da5a785fa5bcd076ad653d1cfe719c0cfad268,STILL_EXISTS,TODO: this was taken from pytorch code.... but needs to be adapted to work with pytorch data
aaffdgdja,Aifred-Health/Vulcan,vulcanai2/models/BaseNetwork.py,ce84010a82a5d65a373dacac6b85c7084ebcfa38,67334812c06334f296d1cbd60003c035688ae7cb,TODO: include plot as parameter
aaffdgdjb,Aifred-Health/Vulcan,vulcanai2/models/BaseNetwork.py,ce84010a82a5d65a373dacac6b85c7084ebcfa38,67334812c06334f296d1cbd60003c035688ae7cb,TODO: include stopping rules
aaffdgdjc,Aifred-Health/Vulcan,vulcanai2/models/BaseNetwork.py,ce84010a82a5d65a373dacac6b85c7084ebcfa38,67334812c06334f296d1cbd60003c035688ae7cb,TODO: this is copy pasted - edit as appropriate
aaffdgdjd,Aifred-Health/Vulcan,vulcanai2/models/dnn.py,ce84010a82a5d65a373dacac6b85c7084ebcfa38,67334812c06334f296d1cbd60003c035688ae7cb,TODO: perform typechecking
aaffdgdjh,Aifred-Health/Vulcan,vulcanai2/plotters/utils.py,ce84010a82a5d65a373dacac6b85c7084ebcfa38,STILL_EXISTS,TODO: all methods need to be updated to work with new dataset
aaffdgfbh,Aifred-Health/Vulcan,vulcanai2/models/BaseNetwork.py,6344b9cc271163e168674f76585b5288682a09f1,STILL_EXISTS,TODO: include plot as parameter
aaffdgfbi,Aifred-Health/Vulcan,vulcanai2/models/BaseNetwork.py,6344b9cc271163e168674f76585b5288682a09f1,STILL_EXISTS,TODO: include stopping rules
aaffdgfbj,Aifred-Health/Vulcan,vulcanai2/models/BaseNetwork.py,6344b9cc271163e168674f76585b5288682a09f1,STILL_EXISTS,TODO: this is copy pasted - edit as appropriate
aaffdgfca,Aifred-Health/Vulcan,vulcanai2/models/dnn.py,6344b9cc271163e168674f76585b5288682a09f1,STILL_EXISTS,TODO: perform typechecking
aaffdgffb,Aifred-Health/Vulcan,vulcanai2/models/dnn.py,e0fde1efe3203da9d96bd73cb5f4803bbeb244f1,8c04bab3fd09ab201fc55c302d4bf9db8cf78e7c,TODO: Add ClassificationUnit?
aaffdgffj,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,f5dbd783b9da29e4309b564971860848d6b861d0,STILL_EXISTS,TODO: delete this cause it won't update and that's dumb cause you're letting people access the dataframe object
aaffdgfga,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,f5dbd783b9da29e4309b564971860848d6b861d0,STILL_EXISTS,TODO: this is kinda useless
aaffdgfgb,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,f5dbd783b9da29e4309b564971860848d6b861d0,9705fcc7397e088d21bb0865f7a53ee9b3d0d50b,TODO: check this doesn't operate in place... damn
aaffdgfgc,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,f5dbd783b9da29e4309b564971860848d6b861d0,80e26dc6424b26960d8529fc0075b5a3e4732e05,TODO: edit this method that creates a split given different filepaths or objects so that the params match
aaffdgfjh,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,f5dbd783b9da29e4309b564971860848d6b861d0,STILL_EXISTS,todo: triple verify its loc and not iloc
aaffdggbc,Aifred-Health/Vulcan,vulcanai2/datasets/utils.py,f5dbd783b9da29e4309b564971860848d6b861d0,STILL_EXISTS,TODO: variance thresholding
aaffdggbe,Aifred-Health/Vulcan,vulcanai2/setup.py,1f2715b5697a7bdc4b799a187e6e07a9c2d360f8,STILL_EXISTS,TODO: make sure we've updated appropriately
aaffdggbf,Aifred-Health/Vulcan,vulcanai2/setup.py,1f2715b5697a7bdc4b799a187e6e07a9c2d360f8,STILL_EXISTS,TODO: backport
aaffdggbi,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,f365ee4ce2bf4831b0554f515f1f288e2cce5004,STILL_EXISTS,TODO: implement variance thresholding
aaffdggbj,Aifred-Health/Vulcan,vulcanai2/plotters/utils.py,8e66a64a3e036f1a4840886aa0b5efd19c69d4ab,STILL_EXISTS,TODO: update to work with pytorch
aaffdhehb,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,448a3cc4cd7c3d260f080498f026d85cd15cc4f9,STILL_EXISTS,TODO: switch this to joining multiple datasets??
aaffdhehc,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,448a3cc4cd7c3d260f080498f026d85cd15cc4f9,80e26dc6424b26960d8529fc0075b5a3e4732e05,TODO: check cause this may cause problems with vars originally containing underscores
aaffdhehe,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,448a3cc4cd7c3d260f080498f026d85cd15cc4f9,STILL_EXISTS,Find dummy columns and build pairs (category; category_value)
aaffdhehf,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,448a3cc4cd7c3d260f080498f026d85cd15cc4f9,0d01040656f9523862dfdf8ce6acb9462eb5119b,Find non-dummy columns that do not have a _
aaffdhehh,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,448a3cc4cd7c3d260f080498f026d85cd15cc4f9,STILL_EXISTS,Select columns for each category
aaffdhehi,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,448a3cc4cd7c3d260f080498f026d85cd15cc4f9,STILL_EXISTS,Find max value among columns
aaffdheia,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,448a3cc4cd7c3d260f080498f026d85cd15cc4f9,STILL_EXISTS,Copy non-dummy columns over.
aaffdheic,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,448a3cc4cd7c3d260f080498f026d85cd15cc4f9,9705fcc7397e088d21bb0865f7a53ee9b3d0d50b,TODO: check this doesn't operate in place... damn
aaffdheih,Aifred-Health/Vulcan,vulcanai2/models/layers.py,448d32b3bbfa2f047b2f00874a420e2b98e63557,STILL_EXISTS,TODO: Why don't we also have instance norm here too? I think this is something we can bring up to the base class
aaffdheij,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,448d32b3bbfa2f047b2f00874a420e2b98e63557,10c54bd716f87b703b340d5f01d43390b1444554,TODO: Move components into run_test since a majority of things calculated are already there
aaffdhejd,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,448d32b3bbfa2f047b2f00874a420e2b98e63557,STILL_EXISTS,TODO: Modify to use val loader
aaffdheji,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,448d32b3bbfa2f047b2f00874a420e2b98e63557,STILL_EXISTS,TODO:  Needs to be updated to use train loader
aaffdhejj,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,8cab38119b9a66178cfc1197290cece4f6ac595a,c1eeb25fbe38540893faa57f99ce0db25de522a0,TODO: Instead of self.cpu(); use is_cuda to know if you can use gpu
aaffdhfaf,Aifred-Health/Vulcan,vulcanai2/models/cnn.py,b66445e29658035411c4c13971778a24c6ea0068,c1eeb25fbe38540893faa57f99ce0db25de522a0,TODO: Automatically calculate padding to be the same as input shape.
aaffdhfag,Aifred-Health/Vulcan,vulcanai2/models/layers.py,b66445e29658035411c4c13971778a24c6ea0068,c1eeb25fbe38540893faa57f99ce0db25de522a0,TODO: Should call this BaseUnit or call the others DenseLayer; etc.
aaffdhfah,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,b66445e29658035411c4c13971778a24c6ea0068,c1eeb25fbe38540893faa57f99ce0db25de522a0,TODO: class # should correspond with self.num_class
aaffdhfaj,Aifred-Health/Vulcan,vulcanai2/plotters/visualization.py,84a4b69ee3108c6d4aedd8bae1c7201bef422db8,11205dfa70164b5129560b1c73e9760ecbccf227,TODO: all methods need to be updated to work with new dataset
aaffdhfba,Aifred-Health/Vulcan,vulcanai2/plotters/visualization.py,84a4b69ee3108c6d4aedd8bae1c7201bef422db8,7728cfaf0e2afa1a6c79da32bfdec032c1f62df2,TODO: update to work with pytorch
aaffdhied,Aifred-Health/Vulcan,vulcanai2/tests/models/test_layers.py,84a4b69ee3108c6d4aedd8bae1c7201bef422db8,e23a1fb897452052356f9603ef0c77ae1d36c68f,TODO: Needs fixing assigning values to NoneType
aaffdhiid,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,161553114ff012e21950c03bd75afb7bd9514840,7fd7b8db1d3206970f1ef44f5490b97293a4d256,TODO: Instead of self.cpu(); use is_cuda to know if you can use gpu
aaffdhiii,Aifred-Health/Vulcan,vulcanai2/models/cnn.py,161553114ff012e21950c03bd75afb7bd9514840,7fd7b8db1d3206970f1ef44f5490b97293a4d256,TODO: Automatically calculate padding to be the same as input shape.
aaffdhiij,Aifred-Health/Vulcan,vulcanai2/models/layers.py,161553114ff012e21950c03bd75afb7bd9514840,88db5e04db149877e02b32e6980d385e21ad1d11,TODO: Should call this BaseUnit or call the others DenseLayer; etc.
aaffdhijg,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,161553114ff012e21950c03bd75afb7bd9514840,STILL_EXISTS,TODO: class # should correspond with self.num_class
aaffdhjij,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,33c434c5184b667585a74fc1da6d7d2f652019eb,9705fcc7397e088d21bb0865f7a53ee9b3d0d50b,TODO: probably bad to use this?
aaffdhjja,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,33c434c5184b667585a74fc1da6d7d2f652019eb,9705fcc7397e088d21bb0865f7a53ee9b3d0d50b,TODO: turn this into a percentage too? currently it's not
aaffdiafh,Aifred-Health/Vulcan,vulcanai2/datasets/utils.py,33c434c5184b667585a74fc1da6d7d2f652019eb,73fdda4b2259e1ccf5d4b41eeb33609b6dd6a9dd,TODO: replace with Joseph's version
aaffdiagd,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,7fd7b8db1d3206970f1ef44f5490b97293a4d256,1ebef01ba542f77d27c8f25ad921cb17b5b95adb,TODO: not great to use mutables as arguments.
aaffdiage,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,7fd7b8db1d3206970f1ef44f5490b97293a4d256,22dff85877d7aa707bfa7dd9f3a3b7e9ceef10ed,TODO: reorganize these.
aaffdiahd,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,7b24c7e6f11b7ae4e241b9c012b043df43be504e,1ebef01ba542f77d27c8f25ad921cb17b5b95adb,TODO: where to do typechecking... just let everything fail?
aaffdiahe,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,7b24c7e6f11b7ae4e241b9c012b043df43be504e,STILL_EXISTS,TODO: add on additional if you want to be able to re-create a network?
aaffdiaia,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,7b24c7e6f11b7ae4e241b9c012b043df43be504e,STILL_EXISTS,#TODO: figure out how this works in conjunction with optimizer
aaffdiaic,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,7b24c7e6f11b7ae4e241b9c012b043df43be504e,e9cf91ace158dc44628b966e5f2b6367d6f5f42c,TODO: Instead of self.cpu(); use is_cuda to know if you can use gpu
aaffdiaij,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,7b24c7e6f11b7ae4e241b9c012b043df43be504e,22dff85877d7aa707bfa7dd9f3a3b7e9ceef10ed,TODO: reorganize these.
aaffdiajd,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,e56d55bce448013a481736eb6c682b9e2ea3e26f,STILL_EXISTS,TODO: add more logging statements as appropriate
aaffdiajf,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,e56d55bce448013a481736eb6c682b9e2ea3e26f,9705fcc7397e088d21bb0865f7a53ee9b3d0d50b,TODO: probably bad to use this?
aaffdiajj,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,e56d55bce448013a481736eb6c682b9e2ea3e26f,1ebef01ba542f77d27c8f25ad921cb17b5b95adb,TODO: not great to use mutables as arguments.
aaffdibaa,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,e56d55bce448013a481736eb6c682b9e2ea3e26f,22dff85877d7aa707bfa7dd9f3a3b7e9ceef10ed,TODO: reorganize these.
aaffdibab,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,e56d55bce448013a481736eb6c682b9e2ea3e26f,STILL_EXISTS,TODO: add on additional if you want to be able to re-create a network?
aaffdibad,Aifred-Health/Vulcan,vulcanai2/models/cnn.py,e56d55bce448013a481736eb6c682b9e2ea3e26f,STILL_EXISTS,TODO: Automatically calculate padding to be the same as input shape.
aaffdibai,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,22dff85877d7aa707bfa7dd9f3a3b7e9ceef10ed,1ebef01ba542f77d27c8f25ad921cb17b5b95adb,TODO: where to do typechecking... just let everything fail?
aaffdibca,Aifred-Health/Vulcan,vulcanai2/models/cnn.py,f6e0e9e9ab12962084906405d8c4d673fdd333c6,693148caff9f4b001fb1a6b8f896e53a9191f04c,TODO: add additional constraints in the future
aaffdibcd,Aifred-Health/Vulcan,vulcanai2/models/cnn.py,f6e0e9e9ab12962084906405d8c4d673fdd333c6,693148caff9f4b001fb1a6b8f896e53a9191f04c,TODO: Priya add data types
aaffdibda,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,693148caff9f4b001fb1a6b8f896e53a9191f04c,1ebef01ba542f77d27c8f25ad921cb17b5b95adb,TODO: not great to use mutables as arguments.
aaffdibdb,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,693148caff9f4b001fb1a6b8f896e53a9191f04c,d300de90d086728ed08a7d789c871fd5abd268f0,TODO: reorganize these.
aaffdibeg,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,d300de90d086728ed08a7d789c871fd5abd268f0,1ebef01ba542f77d27c8f25ad921cb17b5b95adb,TODO: where to do typechecking... just let everything fail?
aaffdibib,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,fd252d6fa64c67fc17c6ffd3c12c5bf0c36cd879,1ebef01ba542f77d27c8f25ad921cb17b5b95adb,TODO: not great to use mutables as arguments.
aaffdibic,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,fd252d6fa64c67fc17c6ffd3c12c5bf0c36cd879,1ebef01ba542f77d27c8f25ad921cb17b5b95adb,TODO: reorganize these.
aaffdibje,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,a76f355efbbeca06b556ffb08d7ebcd91979eeb2,2cbc535152275d226c78e9a64a4ea5225e5b87d3,TODO: fix this to be just for torch cause they have some bug
aaffdibjg,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,a76f355efbbeca06b556ffb08d7ebcd91979eeb2,2cbc535152275d226c78e9a64a4ea5225e5b87d3,TODO: Priya Please define parameters.
aaffdibjh,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,a76f355efbbeca06b556ffb08d7ebcd91979eeb2,2cbc535152275d226c78e9a64a4ea5225e5b87d3,TODO: Priya I don't know what this is
aaffdibji,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,a76f355efbbeca06b556ffb08d7ebcd91979eeb2,2cbc535152275d226c78e9a64a4ea5225e5b87d3,TODO: I hope that's ok
aaffdicaa,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,a76f355efbbeca06b556ffb08d7ebcd91979eeb2,2cbc535152275d226c78e9a64a4ea5225e5b87d3,TODO: Priya also this
aaffdicab,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,a76f355efbbeca06b556ffb08d7ebcd91979eeb2,STILL_EXISTS,TODO: priya define.
aaffdicac,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,a76f355efbbeca06b556ffb08d7ebcd91979eeb2,STILL_EXISTS,TODO: this param doesn't actually exist
aaffdicad,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,a76f355efbbeca06b556ffb08d7ebcd91979eeb2,STILL_EXISTS,TODO: this doesn't actually exist
aaffdicaf,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,a76f355efbbeca06b556ffb08d7ebcd91979eeb2,6a12c895207e55a0c2632eea527d003baccbb370,TODO: Priya: why do we need activation and pred_activation as parameters here?
aaffdicgb,Aifred-Health/Vulcan,vulcanai2/models/cnn.py,0a0202dbc4f970ea8fc84bad679c045f8e575df1,STILL_EXISTS,TODO: add additional constraints in the future
aaffdicge,Aifred-Health/Vulcan,vulcanai2/models/cnn.py,0a0202dbc4f970ea8fc84bad679c045f8e575df1,fd98fbe5c4c5f53c7cabc9c9166c6853bc378a33,TODO: Priya add data types
aaffdigeb,Aifred-Health/Vulcan,vulcanai2/plotters/visualization.py,658520277fb99ab15244b0db30f0a263b5d9298e,c1535aae2ddd654074d804a6daa0cb13ef69e868,TODO: update to work with pytorch
aaffdijca,Aifred-Health/Vulcan,examples/fashion_conv_dense_test.py,1d28faf9c44c547c85dbe169a843a30fbd0bb56b,STILL_EXISTS,TODO: need to revisit this to be able to plot after training; interactive plotting is messing up
aaffdijdb,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,5b6f98ab09b2619f471f37b18fc2806171922092,5c27479bf23267dc8470ea36c53be7085d29c5fa,self._itr = 0 #TODO: ?
aaffdijdc,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,5b6f98ab09b2619f471f37b18fc2806171922092,5c27479bf23267dc8470ea36c53be7085d29c5fa,TODO: why is this .cpu?
aaffdijdg,Aifred-Health/Vulcan,vulcanai2/datasets/utils.py,21acea06419eed157ee71f7bb7db75e0447d846c,03d06d0b774325aeded4e11fa4b92a00b0d420b5,TODO: https:\/\/pytorch.org\/docs\/stable\/_modules\/torch\/utils\/data\/dataset.html#Dataset
aaffdijdh,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,21acea06419eed157ee71f7bb7db75e0447d846c,10c54bd716f87b703b340d5f01d43390b1444554,self._itr = 0 #TODO: ?
aaffdijdi,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,21acea06419eed157ee71f7bb7db75e0447d846c,10c54bd716f87b703b340d5f01d43390b1444554,TODO: why is this .cpu?
aaffdijdj,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,21acea06419eed157ee71f7bb7db75e0447d846c,10c54bd716f87b703b340d5f01d43390b1444554,TODO: check what this does for the last split..
aaffdijea,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,21acea06419eed157ee71f7bb7db75e0447d846c,10c54bd716f87b703b340d5f01d43390b1444554,TODO: this may break on different devices?? test.
aaffdijec,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,21acea06419eed157ee71f7bb7db75e0447d846c,10c54bd716f87b703b340d5f01d43390b1444554,TODO: this is kinda dumb also you're not passing params... they shouldn't have given you a dataloader
aaffdijii,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,07431f224242d931f567c0e6fdf70c139bd159b2,fc70a505862979a5e1464d3373734cc00ccbe709,self._itr = 0 #TODO: ?
aaffdijij,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,07431f224242d931f567c0e6fdf70c139bd159b2,f110970ca157a1a4a2b156bd929949085ad0e184,TODO: why is this .cpu?
aaffdijje,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,07431f224242d931f567c0e6fdf70c139bd159b2,STILL_EXISTS,TODO: I don't think this is necessary
aaffdijjg,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,07431f224242d931f567c0e6fdf70c139bd159b2,18706df2da167c0fdc9ee15e930292be74ea2356,TODO: pretty sure this isn't necessary
aaffdijjj,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,07431f224242d931f567c0e6fdf70c139bd159b2,STILL_EXISTS,TODO: is it dumb to have a constant name?
aaffdjaab,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,07431f224242d931f567c0e6fdf70c139bd159b2,STILL_EXISTS,TODO: implement; add in classification and input layers??
aaffdjaad,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,ae273cdd3449a0558505bd0fceb38a85d599f873,18706df2da167c0fdc9ee15e930292be74ea2356,TODO: caitrin save the optimizers state dict? even though this is included with our instance?
aaffdjaae,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,ae273cdd3449a0558505bd0fceb38a85d599f873,18706df2da167c0fdc9ee15e930292be74ea2356,TODO: implement; add in classification and input layers?
aaffdjbhf,Aifred-Health/Vulcan,vulcanai2/plotters/utils.py,a0f51a542c5513059231b47cdbed365559d0bd4c,c9b11d5c35af813b070b12a741fff36181e4a610,TODO: Modify for multi-input NNs
aaffdjcdb,Aifred-Health/Vulcan,vulcanai2/models/layers.py,d43140c020e326a88468f60d2192e5ea1ad319c9,STILL_EXISTS,TODO: Automatically calculate padding to be the same as input shape.
aaffdjcdh,Aifred-Health/Vulcan,vulcanai2/models/dnn.py,f16c790704ca267a38b3c6fdefbea8c887cab56c,STILL_EXISTS,TODO: Think about moving dimension to config file
aaffdjdcd,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,b824f4fa4546092ef49af51a34720d4b877ad1e4,STILL_EXISTS,TODO: Modify to use val loader
aaffdjdfi,Aifred-Health/Vulcan,vulcanai2/plotters/visualization.py,b134c9ec465d20a3ad4be2852d2d1b8997cf0875,c9f91a066abc2918cd83f60e98a1b99e06b0f80b,TODO: all methods need to be updated to work with new dataset
aaffdjgcd,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,fc70a505862979a5e1464d3373734cc00ccbe709,4a64535de770295983bceb218edb1e9bf6850b20,TODO: should rewrite the save_path structure
aaffdjgcg,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,72b6e1bb7a7f9e21ffd6ef693c8e0335458fcd22,6ebf1e0096d95dfb965404e2e596b4772cbd1a59,self._itr = 0 #TODO: ?
aaffdjged,Aifred-Health/Vulcan,vulcanai2/plotters/utils.py,6d310e55c4ffe8772abf3b5399c7a29e9ce25670,STILL_EXISTS,TODO: Revisit dim disorder and check isinstance for classes.
aaffdjhdf,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,cd0dec2c4d727190e0006584885d997be1e386af,f110970ca157a1a4a2b156bd929949085ad0e184,TODO: ignore for now
aaffdjheg,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,cd0dec2c4d727190e0006584885d997be1e386af,4a64535de770295983bceb218edb1e9bf6850b20,TODO: should rewrite the save_path structure
aaffdjheh,Aifred-Health/Vulcan,vulcanai2/models/cnn.py,cd0dec2c4d727190e0006584885d997be1e386af,a561645135f8204ce521b573f4b337d140897ff7,TODO: convert to list
aaffdjhji,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,4a64535de770295983bceb218edb1e9bf6850b20,f110970ca157a1a4a2b156bd929949085ad0e184,TODO: ignore for now
aaffdjieh,Aifred-Health/Vulcan,examples/fashion_conv_dense_test.py,6ebf1e0096d95dfb965404e2e596b4772cbd1a59,161cf5389a6c0fba11d32e264443c7eda32cab96,TODO: Ensure to make this work without specifying the input size
aaffdjifg,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,6ebf1e0096d95dfb965404e2e596b4772cbd1a59,STILL_EXISTS,TODO: should rewrite the save_path structure
aaffdjigg,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,0ba162428849a999cbaa480177c87f0835fb7a1e,9c02c42fbcb9b6784747422c600121750659086e,self._itr = 0 #TODO: ?
aaffdjigh,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,0ba162428849a999cbaa480177c87f0835fb7a1e,f110970ca157a1a4a2b156bd929949085ad0e184,TODO: why is this .cpu?
aaffdjiie,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,0ba162428849a999cbaa480177c87f0835fb7a1e,STILL_EXISTS,TODO: should rewrite the save_path structure
aaffdjjaa,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,a005e958dc443de5a661b4330265c39deb00d079,d6eeb3e2523dc1987e309f7e46e80832e217fe74,TODO: Use tablemodules NEW: https:\/\/github.com\/torch\/nn\/blob\/master\/doc\/table.md
aaffdjjag,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,a005e958dc443de5a661b4330265c39deb00d079,STILL_EXISTS,TODO: should rewrite the save_path structure
aaffdjjai,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,a005e958dc443de5a661b4330265c39deb00d079,f110970ca157a1a4a2b156bd929949085ad0e184,TODO: why is this .cpu?
aaffdjjcc,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,4100263b2a402c1bc1fc6bb9e52165017195d3c8,f110970ca157a1a4a2b156bd929949085ad0e184,TODO: why is this .cpu?
aaffdjjde,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,4100263b2a402c1bc1fc6bb9e52165017195d3c8,d6eeb3e2523dc1987e309f7e46e80832e217fe74,TODO: Use tablemodules NEW: https:\/\/github.com\/torch\/nn\/blob\/master\/doc\/table.md
aaffdjjea,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,4100263b2a402c1bc1fc6bb9e52165017195d3c8,STILL_EXISTS,TODO: should rewrite the save_path structure
aaffdjjff,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,558162fae53f73efdd81961a95106b0ede37d2b6,STILL_EXISTS,TODO: should rewrite the save_path structure
aaffdjjfh,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,558162fae53f73efdd81961a95106b0ede37d2b6,f110970ca157a1a4a2b156bd929949085ad0e184,TODO: why is this .cpu?
aaffdjjgg,Aifred-Health/Vulcan,vulcanai2/models/cnn.py,558162fae53f73efdd81961a95106b0ede37d2b6,a561645135f8204ce521b573f4b337d140897ff7,TODO: NotImplemented yet; but procesing of the multiple inputs shapes before concatenation
aaffdjjij,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,161cf5389a6c0fba11d32e264443c7eda32cab96,f110970ca157a1a4a2b156bd929949085ad0e184,TODO: why is this .cpu?
aaffeaabb,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,a43bd0b717b7a150f9935b6212b8251876f159d6,STILL_EXISTS,TODO: should rewrite the save_path structure
aaffeaabd,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,a43bd0b717b7a150f9935b6212b8251876f159d6,f110970ca157a1a4a2b156bd929949085ad0e184,TODO: why is this .cpu?
aaffeaacf,Aifred-Health/Vulcan,vulcanai2/models/snapshot_ensemble.py,4f1a5bc867e5d2adb3ef62dc8feda102b9b58879,STILL_EXISTS,TODO: Fix bc it writes in the same folder several models
aaffeaacg,Aifred-Health/Vulcan,vulcanai2/models/snapshot_ensemble.py,4f1a5bc867e5d2adb3ef62dc8feda102b9b58879,STILL_EXISTS,TODO: Fix to load the correct models
aaffeaaeb,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,a561645135f8204ce521b573f4b337d140897ff7,1ebef01ba542f77d27c8f25ad921cb17b5b95adb,TODO: where to do typechecking... just let everything fail?
aaffeaaed,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,a561645135f8204ce521b573f4b337d140897ff7,f110970ca157a1a4a2b156bd929949085ad0e184,TODO: why is this .cpu?
aaffeaafj,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,a561645135f8204ce521b573f4b337d140897ff7,STILL_EXISTS,TODO: should rewrite the save_path structure
aaffeaaji,Aifred-Health/Vulcan,vulcanai2/models/snapshot_ensemble.py,90629ecf104f4efaf92a7304eeec573219966fcb,STILL_EXISTS,TODO: Fix bc it writes in the same folder several models
aaffeaajj,Aifred-Health/Vulcan,vulcanai2/models/snapshot_ensemble.py,90629ecf104f4efaf92a7304eeec573219966fcb,STILL_EXISTS,TODO: Fix to load the correct models
aaffeabbd,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,fec5a87f3adbdeab3f37d8ec8d89f67d0e458754,STILL_EXISTS,TODO: Use get_class
aaffeabbf,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,8bf332410a65b277c33380716adc39658fbe8415,8186724c51dfb7d85100476e2ac1b7b883ba4206,TODO: Use logger to describe if the optimizer is changed.
aaffeabci,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,9c02c42fbcb9b6784747422c600121750659086e,STILL_EXISTS,TODO: should rewrite the save_path structure
aaffeabda,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,9c02c42fbcb9b6784747422c600121750659086e,8186724c51dfb7d85100476e2ac1b7b883ba4206,TODO: Use logger to describe if the optimizer is changed.
aaffeabfa,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,b54115a530422ec9ab75da2914f8f8e177149301,f110970ca157a1a4a2b156bd929949085ad0e184,TODO: ignore for now
aaffeabgc,Aifred-Health/Vulcan,vulcanai2/models/cnn.py,b54115a530422ec9ab75da2914f8f8e177149301,3b3f284feb60c7008bb69acb40a7d7fd7d264414,TODO: convert to list
aaffeabii,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,7fd3bc189a748f675d7e1940b798962b59f2a17e,d6eeb3e2523dc1987e309f7e46e80832e217fe74,TODO: Use tablemodules NEW: https:\/\/github.com\/torch\/nn\/blob\/master\/doc\/table.md
aaffeacba,Aifred-Health/Vulcan,examples/fashion_conv_dense_test.py,d6eeb3e2523dc1987e309f7e46e80832e217fe74,73f1b9de7f8fdae8fddd5bcd1cd3472a9cd9ee52,TODO: Ensure to make this work without specifying the input size
aaffeacbd,Aifred-Health/Vulcan,vulcanai2/models/cnn.py,d6eeb3e2523dc1987e309f7e46e80832e217fe74,3b3f284feb60c7008bb69acb40a7d7fd7d264414,TODO: NotImplemented yet; but procesing of the multiple inputs shapes before concatenation
aaffeacbj,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,61a1c5d8e9ad6072cb7a65cb65c8be5aa0cfb490,b4955a2fe1bd7e046e4d05e8d548e1d4a91dc5e6,TODO: See if using nn.ModuleDict is faster
aaffeacca,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,61a1c5d8e9ad6072cb7a65cb65c8be5aa0cfb490,508db700f09a5a3aee14bcd4d73f428541c55076,TODO: Remove temp
aaffeaccd,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,508db700f09a5a3aee14bcd4d73f428541c55076,6b355e172d8f2fb1376d56270f58c2c0045fc1e5,TODO: why is this .cpu?
aaffeaccf,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,508db700f09a5a3aee14bcd4d73f428541c55076,8186724c51dfb7d85100476e2ac1b7b883ba4206,TODO: Use logger to describe if the optimizer is changed.
aaffeacda,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,508db700f09a5a3aee14bcd4d73f428541c55076,b4955a2fe1bd7e046e4d05e8d548e1d4a91dc5e6,TODO: See if using nn.ModuleDict is faster
aaffeachi,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,82d1784d2d3f1704ed38330237fb87b301e0b61c,b4955a2fe1bd7e046e4d05e8d548e1d4a91dc5e6,TODO: See if using nn.ModuleDict is faster
aaffeacie,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,82d1784d2d3f1704ed38330237fb87b301e0b61c,3b3f284feb60c7008bb69acb40a7d7fd7d264414,TODO: Remove temp
aaffeacif,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,82d1784d2d3f1704ed38330237fb87b301e0b61c,STILL_EXISTS,TODO: should rewrite the save_path structure
aaffeacii,Aifred-Health/Vulcan,vulcanai2/models/ensemble.py,b96ede582b130b659bc1d3272c0c3b029727a9e4,STILL_EXISTS,TODO: Should these be defaulted to the values of template_network?
aaffeacij,Aifred-Health/Vulcan,vulcanai2/models/ensemble.py,b96ede582b130b659bc1d3272c0c3b029727a9e4,511d885c15a004b855cb1738e0f9358d6cc12b4b,TODO: Should this be called self.network for continuity?
aaffeacjj,Aifred-Health/Vulcan,vulcanai2/datasets/utils.py,d4f0da4be4423444a2097879d80ddbffa178ee3c,a869b5c567c2297a1acea0c2b6644d4f84c4f18a,TODO: change the bits of this you don't understand
aaffeadbc,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,6511e7025efb1a12726f4948816b24ba0a7f5b02,21441d5ae0fc999e4571e7ae6a34fbc4f69a6b03,TODO: need to simply return those that should be proper https:\/\/forums.fast.ai\/t\/to-label-encode-or-one-hot-encode\/6057
aaffeadbi,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,3b3f284feb60c7008bb69acb40a7d7fd7d264414,6b355e172d8f2fb1376d56270f58c2c0045fc1e5,TODO: why is this .cpu?
aaffeadca,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,3b3f284feb60c7008bb69acb40a7d7fd7d264414,8186724c51dfb7d85100476e2ac1b7b883ba4206,TODO: Use logger to describe if the optimizer is changed.
aaffeadch,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,3b3f284feb60c7008bb69acb40a7d7fd7d264414,b4955a2fe1bd7e046e4d05e8d548e1d4a91dc5e6,TODO: See if using nn.ModuleDict is faster
aaffeaddd,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,3b3f284feb60c7008bb69acb40a7d7fd7d264414,STILL_EXISTS,TODO: should rewrite the save_path structure
aaffeaebf,Aifred-Health/Vulcan,examples/fashion_conv_dense_test.py,f1518cd2224eb289a2c49085c2870b4b6194497e,73f1b9de7f8fdae8fddd5bcd1cd3472a9cd9ee52,TODO: Ensure to make this work without specifying the input size
aaffeaeca,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,f1518cd2224eb289a2c49085c2870b4b6194497e,b4955a2fe1bd7e046e4d05e8d548e1d4a91dc5e6,TODO: See if using nn.ModuleDict is faster
aaffeaecg,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,f1518cd2224eb289a2c49085c2870b4b6194497e,b4955a2fe1bd7e046e4d05e8d548e1d4a91dc5e6,TODO: Remove temp
aaffeaech,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,f1518cd2224eb289a2c49085c2870b4b6194497e,STILL_EXISTS,TODO: should rewrite the save_path structure
aaffeaedj,Aifred-Health/Vulcan,vulcanai2/models/cnn.py,f1518cd2224eb289a2c49085c2870b4b6194497e,10880bcd1ff5b1d68104db3f070c634b5699d689,TODO: convert to list
aaffeaege,Aifred-Health/Vulcan,examples/fashion_conv_dense_test.py,21a1395e36ae6bf60398d0b4430ef9f03a7a3f27,73f1b9de7f8fdae8fddd5bcd1cd3472a9cd9ee52,TODO: Ensure to make this work without specifying the input size
aaffeaegh,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,21a1395e36ae6bf60398d0b4430ef9f03a7a3f27,6b355e172d8f2fb1376d56270f58c2c0045fc1e5,TODO: why is this .cpu?
aaffeaehb,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,21a1395e36ae6bf60398d0b4430ef9f03a7a3f27,8186724c51dfb7d85100476e2ac1b7b883ba4206,TODO: Use logger to describe if the optimizer is changed.
aaffeaehj,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,21a1395e36ae6bf60398d0b4430ef9f03a7a3f27,b4955a2fe1bd7e046e4d05e8d548e1d4a91dc5e6,TODO: See if using nn.ModuleDict is faster
aaffeaeie,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,21a1395e36ae6bf60398d0b4430ef9f03a7a3f27,b4955a2fe1bd7e046e4d05e8d548e1d4a91dc5e6,TODO: Remove temp
aaffeaeif,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,21a1395e36ae6bf60398d0b4430ef9f03a7a3f27,STILL_EXISTS,TODO: should rewrite the save_path structure
aaffeafbd,Aifred-Health/Vulcan,examples/fashion_conv_dense_test.py,c2ef463701d5e8d232502a230d10aa34dd6abe00,73f1b9de7f8fdae8fddd5bcd1cd3472a9cd9ee52,TODO: Ensure to make this work without specifying the input size
aaffeafcd,Aifred-Health/Vulcan,vulcanai2/models/cnn.py,c2ef463701d5e8d232502a230d10aa34dd6abe00,a33aec88d879f3331f362221844859aed72980bc,TODO: Sort by number of dimensions and then by number of elements
aaffeafcf,Aifred-Health/Vulcan,vulcanai2/models/cnn.py,c2ef463701d5e8d232502a230d10aa34dd6abe00,STILL_EXISTS,TODO: For dense; cast to Conv1D size e.g. (1; out_features).
aaffeafcj,Aifred-Health/Vulcan,vulcanai2/models/cnn.py,c2ef463701d5e8d232502a230d10aa34dd6abe00,10880bcd1ff5b1d68104db3f070c634b5699d689,TODO: Use tensor.expand_as?
aaffeafda,Aifred-Health/Vulcan,vulcanai2/models/utils.py,c2ef463701d5e8d232502a230d10aa34dd6abe00,10880bcd1ff5b1d68104db3f070c634b5699d689,TODO: https:\/\/github.com\/pytorch\/pytorch\/issues\/9410
aaffeafde,Aifred-Health/Vulcan,vulcanai2/models/cnn.py,09a4a97ab0241412f15f12d5b876dc3d2644fd0e,STILL_EXISTS,TODO: What if we get None? meaning; if there is no intersection
aaffeafdg,Aifred-Health/Vulcan,vulcanai2/models/cnn.py,09a4a97ab0241412f15f12d5b876dc3d2644fd0e,1c7468c92d7f8bab3d92614b309de2cfbb1ac5e1,TODO:  rework on this elif to ensure to support Conv1D type tensor
aaffeafhg,Aifred-Health/Vulcan,vulcanai2/datasets/utils.py,2e03590e5f5cf357970ab14c64936000a49b2370,STILL_EXISTS,Drop rows where there are duplicates for the merged_on_columns.
aaffeafhi,Aifred-Health/Vulcan,vulcanai2/tests/datasets/test_utils.py,2e03590e5f5cf357970ab14c64936000a49b2370,STILL_EXISTS,MOF (merge on columns)
aaffeafib,Aifred-Health/Vulcan,vulcanai2/models/cnn.py,7f18a183e616fc7f8e869f0ece6fb9a198fa54c3,10880bcd1ff5b1d68104db3f070c634b5699d689,TODO: convert to list
aaffeafii,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,e6b63f468b2bc1d04d4af47586a25e909a6dcd1c,b4955a2fe1bd7e046e4d05e8d548e1d4a91dc5e6,TODO: See if using nn.ModuleDict is faster
aaffeagah,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,e6b63f468b2bc1d04d4af47586a25e909a6dcd1c,b4955a2fe1bd7e046e4d05e8d548e1d4a91dc5e6,TODO: Remove temp
aaffeagai,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,e6b63f468b2bc1d04d4af47586a25e909a6dcd1c,STILL_EXISTS,TODO: should rewrite the save_path structure
aaffeagci,Aifred-Health/Vulcan,vulcanai2/models/cnn.py,2138565a79c75d2e4e191cf0b8a4951d1f7d1ebe,10880bcd1ff5b1d68104db3f070c634b5699d689,TODO: Fix Linear in_dim
aaffeagdg,Aifred-Health/Vulcan,vulcanai2/models/dnn.py,c739bb8ceb131a4ebbe3761b97e1b75e0bd2b823,10880bcd1ff5b1d68104db3f070c634b5699d689,TODO: Fix Linear in_dim
aaffeaghg,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,10880bcd1ff5b1d68104db3f070c634b5699d689,6b355e172d8f2fb1376d56270f58c2c0045fc1e5,TODO: why is this .cpu?
aaffeagif,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,10880bcd1ff5b1d68104db3f070c634b5699d689,8186724c51dfb7d85100476e2ac1b7b883ba4206,TODO: Use logger to describe if the optimizer is changed.
aaffeagii,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,10880bcd1ff5b1d68104db3f070c634b5699d689,STILL_EXISTS,TODO: check what this does for the last split..
aaffeagij,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,10880bcd1ff5b1d68104db3f070c634b5699d689,8186724c51dfb7d85100476e2ac1b7b883ba4206,TODO: this may break on different devices?? test.
aaffeagjb,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,10880bcd1ff5b1d68104db3f070c634b5699d689,8186724c51dfb7d85100476e2ac1b7b883ba4206,TODO: this is kinda dumb also you're not passing params... they shouldn't have given you a dataloader
aaffeaieg,Aifred-Health/Vulcan,examples/fashion_conv_dense_test.py,e4e77cc62d6ab706419d935e242f1e68b8cf6fc0,73f1b9de7f8fdae8fddd5bcd1cd3472a9cd9ee52,TODO: Ensure to make this work without specifying the input size
aaffeaigb,Aifred-Health/Vulcan,vulcanai2/models/cnn.py,e4e77cc62d6ab706419d935e242f1e68b8cf6fc0,b4955a2fe1bd7e046e4d05e8d548e1d4a91dc5e6,TODO: Fix Linear in_dim
aaffeaigc,Aifred-Health/Vulcan,vulcanai2/models/cnn.py,e4e77cc62d6ab706419d935e242f1e68b8cf6fc0,b4955a2fe1bd7e046e4d05e8d548e1d4a91dc5e6,TODO: convert to list
aaffeaiid,Aifred-Health/Vulcan,vulcanai2/models/cnn.py,e4e77cc62d6ab706419d935e242f1e68b8cf6fc0,STILL_EXISTS,TODO: Use tensor.expand_as?
aaffeaiif,Aifred-Health/Vulcan,vulcanai2/models/dnn.py,e4e77cc62d6ab706419d935e242f1e68b8cf6fc0,b4955a2fe1bd7e046e4d05e8d548e1d4a91dc5e6,TODO: Fix Linear in_dim
aaffeaiig,Aifred-Health/Vulcan,vulcanai2/models/utils.py,e4e77cc62d6ab706419d935e242f1e68b8cf6fc0,STILL_EXISTS,TODO: https:\/\/github.com\/pytorch\/pytorch\/issues\/9410
aaffeajbc,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,8186724c51dfb7d85100476e2ac1b7b883ba4206,b4955a2fe1bd7e046e4d05e8d548e1d4a91dc5e6,TODO: Remove temp
aaffeajbd,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,8186724c51dfb7d85100476e2ac1b7b883ba4206,STILL_EXISTS,TODO: should rewrite the save_path structure
aaffeajbg,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,8186724c51dfb7d85100476e2ac1b7b883ba4206,6b355e172d8f2fb1376d56270f58c2c0045fc1e5,TODO: why is this .cpu?
aaffeajch,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,8186724c51dfb7d85100476e2ac1b7b883ba4206,STILL_EXISTS,TODO: check what this does for the last split..
aaffeajef,Aifred-Health/Vulcan,vulcanai2/models/cnn.py,aceec6f8a3bc8f10d98e4e443c9e2fd05f59a81e,b4955a2fe1bd7e046e4d05e8d548e1d4a91dc5e6,TODO: https:\/\/github.com\/pytorch\/pytorch\/issues\/9410
aaffeajfa,Aifred-Health/Vulcan,vulcanai2/models/utils.py,aceec6f8a3bc8f10d98e4e443c9e2fd05f59a81e,37120ffa7bbeb3f5c85583db896ceccf2bb5f3fe,TODO: Use torch.nn.ConstantPadding?
aaffeajfb,Aifred-Health/Vulcan,vulcanai2/models/utils.py,aceec6f8a3bc8f10d98e4e443c9e2fd05f59a81e,STILL_EXISTS,TODO: https:\/\/github.com\/pytorch\/pytorch\/issues\/9410
aaffeajie,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,b4955a2fe1bd7e046e4d05e8d548e1d4a91dc5e6,1ebef01ba542f77d27c8f25ad921cb17b5b95adb,TODO: not great to use mutables as arguments.
aaffeajif,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,b4955a2fe1bd7e046e4d05e8d548e1d4a91dc5e6,1ebef01ba542f77d27c8f25ad921cb17b5b95adb,TODO: reorganize these.
aaffeajig,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,b4955a2fe1bd7e046e4d05e8d548e1d4a91dc5e6,1ebef01ba542f77d27c8f25ad921cb17b5b95adb,TODO: where to do typechecking... just let everything fail?
aaffeajij,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,b4955a2fe1bd7e046e4d05e8d548e1d4a91dc5e6,6b355e172d8f2fb1376d56270f58c2c0045fc1e5,TODO: why is this .cpu?
aaffeajjh,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,b4955a2fe1bd7e046e4d05e8d548e1d4a91dc5e6,STILL_EXISTS,TODO: Use logger to describe if the optimizer is changed.
aaffebaaa,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,b4955a2fe1bd7e046e4d05e8d548e1d4a91dc5e6,STILL_EXISTS,TODO: should rewrite the save_path structure
aaffebagd,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,bd9cc04a179041df5f8f47d10a0bf42207aeadf3,6b355e172d8f2fb1376d56270f58c2c0045fc1e5,TODO: ignore for now
aaffebahf,Aifred-Health/Vulcan,vulcanai2/models/cnn.py,bd9cc04a179041df5f8f47d10a0bf42207aeadf3,56f4dc3030605e06de41a272898418f86eb6fd65,TODO: convert to list
aaffebahg,Aifred-Health/Vulcan,examples/fashion_conv_dense_test.py,6b355e172d8f2fb1376d56270f58c2c0045fc1e5,73f1b9de7f8fdae8fddd5bcd1cd3472a9cd9ee52,TODO: Ensure to make this work without specifying the input size
aaffebbad,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,82c9cd0dbfc729692b5fb200d48271d104d4c6a3,80ee02c1ad6ce7aed05e65fff78bbb82d222b912,TODO: Use tablemodules NEW: https:\/\/github.com\/torch\/nn\/blob\/master\/doc\/table.md
aaffebbbh,Aifred-Health/Vulcan,vulcanai2/models/cnn.py,80ee02c1ad6ce7aed05e65fff78bbb82d222b912,STILL_EXISTS,TODO: NotImplemented yet; but procesing of the multiple inputs shapes before concatenation
aaffebbdc,Aifred-Health/Vulcan,examples/fashion_conv_dense_test.py,bcc94468c222d3c7748076b18fb7deea2a97a7a4,73f1b9de7f8fdae8fddd5bcd1cd3472a9cd9ee52,TODO: Ensure to make this work without specifying the input size
aaffebbdd,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,524cda5389b5c6fce8f356fb9649ed76807ab4aa,e99fbf026a1d364b0f515f4aa7b3cfee797b0756,TODO: See if using nn.ModuleDict is faster
aaffebbde,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,524cda5389b5c6fce8f356fb9649ed76807ab4aa,STILL_EXISTS,TODO: Remove temp
aaffebbeh,Aifred-Health/Vulcan,vulcanai2/models/cnn.py,8929ca605b5b2147cf1ece34e7078260e449d17c,8bf0da01f92b3287b76daec6aca1d31a9c576848,TODO: Sort by number of dimensions and then by number of elements
aaffebbej,Aifred-Health/Vulcan,vulcanai2/models/cnn.py,8929ca605b5b2147cf1ece34e7078260e449d17c,STILL_EXISTS,TODO: For dense; cast to Conv1D size e.g. (1; out_features).
aaffebbfd,Aifred-Health/Vulcan,vulcanai2/models/cnn.py,8929ca605b5b2147cf1ece34e7078260e449d17c,STILL_EXISTS,TODO: Use tensor.expand_as?
aaffebbfe,Aifred-Health/Vulcan,vulcanai2/models/utils.py,8929ca605b5b2147cf1ece34e7078260e449d17c,STILL_EXISTS,TODO: https:\/\/github.com\/pytorch\/pytorch\/issues\/9410
aaffebbfh,Aifred-Health/Vulcan,vulcanai2/models/cnn.py,2e38d83c97ed9a6918e226fd5bbe5bb699865e9c,STILL_EXISTS,TODO: What if we get None? meaning; if there is no intersection
aaffebbfj,Aifred-Health/Vulcan,vulcanai2/models/cnn.py,2e38d83c97ed9a6918e226fd5bbe5bb699865e9c,75efdf3471a56c7db49fbd38c54fb99cbc38997c,TODO:  rework on this elif to ensure to support Conv1D type tensor
aaffebbje,Aifred-Health/Vulcan,vulcanai2/models/cnn.py,acf8fb1406307f8b7afaa07b0e57f9fbbc067624,56f4dc3030605e06de41a272898418f86eb6fd65,TODO: convert to list
aaffebccj,Aifred-Health/Vulcan,vulcanai2/models/cnn.py,c8da2c80f6991059e1ef5fd517f4df1b52dfb18b,99c874cb99762459da62f22114004ddc58f217b4,TODO: Fix Linear in_dim
aaffebcdh,Aifred-Health/Vulcan,vulcanai2/models/dnn.py,4ab89e714eedabec3391c214fba71c8e15a01b9e,99c874cb99762459da62f22114004ddc58f217b4,TODO: Fix Linear in_dim
aaffebcge,Aifred-Health/Vulcan,vulcanai2/models/cnn.py,5a446a7a422bb151fa866957e618ae0538579ea2,56f4dc3030605e06de41a272898418f86eb6fd65,TODO: https:\/\/github.com\/pytorch\/pytorch\/issues\/9410
aaffebcgi,Aifred-Health/Vulcan,vulcanai2/models/utils.py,5a446a7a422bb151fa866957e618ae0538579ea2,56f4dc3030605e06de41a272898418f86eb6fd65,TODO: Use torch.nn.ConstantPadding?
aaffebcie,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,3a6ab36fe069606fd737947f3f815a982327ddea,e99fbf026a1d364b0f515f4aa7b3cfee797b0756,TODO: See if using nn.ModuleDict is faster
aaffebcif,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,3a6ab36fe069606fd737947f3f815a982327ddea,STILL_EXISTS,TODO: Remove temp
aaffebddf,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,73f1b9de7f8fdae8fddd5bcd1cd3472a9cd9ee52,18706df2da167c0fdc9ee15e930292be74ea2356,TODO: reorganize these.
aaffebddg,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,73f1b9de7f8fdae8fddd5bcd1cd3472a9cd9ee52,STILL_EXISTS,TODO: where to do typechecking... just let everything fail?
aaffebddj,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,73f1b9de7f8fdae8fddd5bcd1cd3472a9cd9ee52,18706df2da167c0fdc9ee15e930292be74ea2356,TODO: why is this .cpu?
aaffebdec,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,73f1b9de7f8fdae8fddd5bcd1cd3472a9cd9ee52,139195ced8569ab82ed839284f1542e34efd3801,TODO: deal with repeated default parameters
aaffebdef,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,73f1b9de7f8fdae8fddd5bcd1cd3472a9cd9ee52,STILL_EXISTS,TODO: improve the copying of parameters
aaffebdeg,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,73f1b9de7f8fdae8fddd5bcd1cd3472a9cd9ee52,56f4dc3030605e06de41a272898418f86eb6fd65,TODO: this may break on different devices?? test.
aaffebdei,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,73f1b9de7f8fdae8fddd5bcd1cd3472a9cd9ee52,56f4dc3030605e06de41a272898418f86eb6fd65,TODO: properly pass params
aaffebdej,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,73f1b9de7f8fdae8fddd5bcd1cd3472a9cd9ee52,56f4dc3030605e06de41a272898418f86eb6fd65,TODO: we could show something better here like calculate all the results so far
aaffebdfc,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,ac75d6823e58bc870944f0c8a4fda68749108d99,99c874cb99762459da62f22114004ddc58f217b4,TODO: See if using nn.ModuleDict is faster
aaffebdfi,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,ac75d6823e58bc870944f0c8a4fda68749108d99,STILL_EXISTS,TODO: Remove temp
aaffebdfj,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,ac75d6823e58bc870944f0c8a4fda68749108d99,18706df2da167c0fdc9ee15e930292be74ea2356,TODO: reorganize these.
aaffebdga,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,ac75d6823e58bc870944f0c8a4fda68749108d99,STILL_EXISTS,TODO: where to do typechecking... just let everything fail?
aaffebdgd,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,ac75d6823e58bc870944f0c8a4fda68749108d99,18706df2da167c0fdc9ee15e930292be74ea2356,TODO: why is this .cpu?
aaffebdgh,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,ac75d6823e58bc870944f0c8a4fda68749108d99,139195ced8569ab82ed839284f1542e34efd3801,TODO: deal with repeated default parameters
aaffebdhj,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,5ef4aa629bcbbfc0fd6de0da6ceef68d79a6b181,STILL_EXISTS,TODO: why does use_unlabeled exist?
aaffebdia,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,5ef4aa629bcbbfc0fd6de0da6ceef68d79a6b181,56f4dc3030605e06de41a272898418f86eb6fd65,TODO: this doesn't seem correct
aaffebeaf,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,5b35b0579de680dadb896061a15644a10e28f292,c1f023f8839b821efd9abd384272f984bc49496a,TODO: See if using nn.ModuleDict is faster
aaffebeea,Aifred-Health/Vulcan,vulcanai2/datasets/multidataset.py,21441d5ae0fc999e4571e7ae6a34fbc4f69a6b03,a784e7deebd4188818938a1b7f63ac98cccbcbef,TODO: is datasets a reasonable name?
aaffebeeb,Aifred-Health/Vulcan,vulcanai2/datasets/multidataset.py,21441d5ae0fc999e4571e7ae6a34fbc4f69a6b03,a784e7deebd4188818938a1b7f63ac98cccbcbef,TODO: would be better to make these namedtuples...
aaffebeee,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,21441d5ae0fc999e4571e7ae6a34fbc4f69a6b03,9705fcc7397e088d21bb0865f7a53ee9b3d0d50b,TODO: give option to mirror train\/target
aaffebfhh,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,56f4dc3030605e06de41a272898418f86eb6fd65,139195ced8569ab82ed839284f1542e34efd3801,TODO: not great to use mutables as arguments.
aaffebfhi,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,56f4dc3030605e06de41a272898418f86eb6fd65,18706df2da167c0fdc9ee15e930292be74ea2356,TODO: reorganize these.
aaffebfhj,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,56f4dc3030605e06de41a272898418f86eb6fd65,139195ced8569ab82ed839284f1542e34efd3801,self._itr = 0 #TODO: ?
aaffebfia,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,56f4dc3030605e06de41a272898418f86eb6fd65,STILL_EXISTS,TODO: where to do typechecking... just let everything fail?
aaffebgce,Aifred-Health/Vulcan,vulcanai2/models/ensemble.py,56f4dc3030605e06de41a272898418f86eb6fd65,74a8dadffbdb46f3abb0dbea2cd374460927ec08,TODO: Should this be called self.network for continuity?
aaffebgei,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,56f4dc3030605e06de41a272898418f86eb6fd65,STILL_EXISTS,TODO: Modify to use val loader
aaffebghc,Aifred-Health/Vulcan,vulcanai2/plotters/utils.py,56f4dc3030605e06de41a272898418f86eb6fd65,STILL_EXISTS,TODO: Modify for multi-input NNs
aaffebgij,Aifred-Health/Vulcan,vulcanai2/datasets/utils.py,e2317291b9a7445474fd36364df29d72c7c92447,80e26dc6424b26960d8529fc0075b5a3e4732e05,TODO: change the bits of this you don't understand
aaffebgjb,Aifred-Health/Vulcan,vulcanai2/datasets/utils.py,e2317291b9a7445474fd36364df29d72c7c92447,STILL_EXISTS,TODO: replace with Joseph's version
aaffebgje,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,80e26dc6424b26960d8529fc0075b5a3e4732e05,563021e2152c074cb2417d946167e85125387bbb,TODO: add in non_numeric
aaffebhcj,Aifred-Health/Vulcan,vulcanai2/datasets/utils.py,80e26dc6424b26960d8529fc0075b5a3e4732e05,7d89370684964204c170e0a803669eaaee31d8b5,TODO: implement
aaffebhdc,Aifred-Health/Vulcan,vulcanai2/datasets/utils.py,80e26dc6424b26960d8529fc0075b5a3e4732e05,STILL_EXISTS,TODO: replace with Joseph's version
aaffebhdf,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,e3b9ac7129c1c406eb171bb112c6351cfe6fc201,139195ced8569ab82ed839284f1542e34efd3801,TODO: deal with repeated default parameters
aaffebhdg,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,e3b9ac7129c1c406eb171bb112c6351cfe6fc201,STILL_EXISTS,TODO: where to do typechecking... just let everything fail?
aaffebhdj,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,e3b9ac7129c1c406eb171bb112c6351cfe6fc201,STILL_EXISTS,TODO: why does use_unlabeled exist?
aaffebhed,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,e3b9ac7129c1c406eb171bb112c6351cfe6fc201,938c8f58103a0f7aa60c9dcfd796fd02d699b642,TODO: this doesn't seem correct
aaffebhei,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,e3b9ac7129c1c406eb171bb112c6351cfe6fc201,STILL_EXISTS,#TODO: improve the copying of parameters
aaffebhej,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,e3b9ac7129c1c406eb171bb112c6351cfe6fc201,STILL_EXISTS,TODO: this may break on different devices?? test.
aaffebhfa,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,e3b9ac7129c1c406eb171bb112c6351cfe6fc201,STILL_EXISTS,TODO: properly pass params
aaffebhfb,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,e3b9ac7129c1c406eb171bb112c6351cfe6fc201,STILL_EXISTS,TODO: we could show something better here like calculate all the results so far
aaffebhfc,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,e3b9ac7129c1c406eb171bb112c6351cfe6fc201,STILL_EXISTS,TODO: Modify to use val loader
aaffebhfg,Aifred-Health/Vulcan,vulcanai2/plotters/utils.py,7f605218b284206c344879836b1d3aea146cf7eb,STILL_EXISTS,TODO: Modify for multi-input NNs
aaffebhhc,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,a00f0d8c9784f2da283a3cfd025d0f307ee78ec3,STILL_EXISTS,TODO: Re-initialize instead of deepcopy?
aaffebief,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,139195ced8569ab82ed839284f1542e34efd3801,STILL_EXISTS,TODO: Remove temp
aaffebigc,Aifred-Health/Vulcan,vulcanai2/models/utils.py,613082bfc8b91a5ee6fec70b7b3927b7dedd3583,df4232124e55a35c0f9197decdd65cb29331d810,TODO: Use torch.nn.ConstantPadding?
aaffebjbf,Aifred-Health/Vulcan,vulcanai2/models/cnn.py,6bd5aab383c5b9ef694757b4bef5f7a181050ac3,df4232124e55a35c0f9197decdd65cb29331d810,TODO: convert to list
aaffebjcj,Aifred-Health/Vulcan,vulcanai2/models/cnn.py,6bd5aab383c5b9ef694757b4bef5f7a181050ac3,df4232124e55a35c0f9197decdd65cb29331d810,TODO: https:\/\/github.com\/pytorch\/pytorch\/issues\/9410
aaffebjdd,Aifred-Health/Vulcan,vulcanai2/datasets/multidataset.py,df610791e23a2cd0763f4d9c6c1c8b0e94265a44,a5bfd69a57d5c8821a5b6bf404ec20fec6076be0,TODO: rename these
aaffebjde,Aifred-Health/Vulcan,vulcanai2/datasets/multidataset.py,df610791e23a2cd0763f4d9c6c1c8b0e94265a44,STILL_EXISTS,technically would re-write if they had 2 targets...
aaffebjdi,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,253ee8f86d9449d322f61ddeea19baea789a0047,STILL_EXISTS,TODO: add more logging statements as appropriate
aaffebjdj,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,253ee8f86d9449d322f61ddeea19baea789a0047,5e5151d4799892f880b1f2432b7d112e03c7dec7,TODO: insert logging statements
aaffebjea,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,253ee8f86d9449d322f61ddeea19baea789a0047,5e5151d4799892f880b1f2432b7d112e03c7dec7,TODO: check cause this may cause problems with vars originally containing underscores
aaffebjec,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,253ee8f86d9449d322f61ddeea19baea789a0047,5e5151d4799892f880b1f2432b7d112e03c7dec7,TODO: insert other logging statements
aaffebjef,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,253ee8f86d9449d322f61ddeea19baea789a0047,5e5151d4799892f880b1f2432b7d112e03c7dec7,TODO: check this doesn't operate in place... damn
aaffebjeg,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,253ee8f86d9449d322f61ddeea19baea789a0047,5e5151d4799892f880b1f2432b7d112e03c7dec7,TODO: turn this into a percentage too? currently it's not
aaffebjeh,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,253ee8f86d9449d322f61ddeea19baea789a0047,STILL_EXISTS,TODO: probably bad to use this?
aaffebjei,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,253ee8f86d9449d322f61ddeea19baea789a0047,5e5151d4799892f880b1f2432b7d112e03c7dec7,TODO: edit this method that creates a split given different filepaths or objects so that the params match
aaffecadj,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,df4232124e55a35c0f9197decdd65cb29331d810,STILL_EXISTS,TODO: not great to use mutables as arguments.
aaffecaea,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,df4232124e55a35c0f9197decdd65cb29331d810,18706df2da167c0fdc9ee15e930292be74ea2356,TODO: reorganize these.
aaffecaeb,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,df4232124e55a35c0f9197decdd65cb29331d810,18706df2da167c0fdc9ee15e930292be74ea2356,self._itr = 0 #TODO: ?
aaffecafb,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,df4232124e55a35c0f9197decdd65cb29331d810,STILL_EXISTS,TODO: deal with repeated default parameters
aaffecbci,Aifred-Health/Vulcan,vulcanai2/plotters/utils.py,df4232124e55a35c0f9197decdd65cb29331d810,STILL_EXISTS,TODO: Revisit dim disorder and check isinstance for classes.
aaffecbdc,Aifred-Health/Vulcan,vulcanai2/plotters/utils.py,df4232124e55a35c0f9197decdd65cb29331d810,STILL_EXISTS,TODO: Modify for multi-input NNs
aaffecbeg,Aifred-Health/Vulcan,vulcanai2/plotters/utils.py,df4232124e55a35c0f9197decdd65cb29331d810,d73f1241d88e917bb5198fa60fcca0adbb180692,TODO: Revisit dim disorder.
aaffecbhh,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,18706df2da167c0fdc9ee15e930292be74ea2356,STILL_EXISTS,TODO: not great to use mutables as arguments.
aaffecbij,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,18706df2da167c0fdc9ee15e930292be74ea2356,STILL_EXISTS,TODO: deal with repeated default parameters
aaffecbjh,Aifred-Health/Vulcan,vulcanai2/models/cnn.py,18706df2da167c0fdc9ee15e930292be74ea2356,13fef7ba1f9c71f1d46c6e52080932757d9faebd,TODO: convert to list
aaffeccbb,Aifred-Health/Vulcan,vulcanai2/models/cnn.py,18706df2da167c0fdc9ee15e930292be74ea2356,STILL_EXISTS,TODO: https:\/\/github.com\/pytorch\/pytorch\/issues\/9410
aaffeccja,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,74a8dadffbdb46f3abb0dbea2cd374460927ec08,9fde2b9b657d505af5adbbb706e43e7e42d4df17,TODO: give option to mirror train\/target
aaffeccjg,Aifred-Health/Vulcan,vulcanai2/models/utils.py,74a8dadffbdb46f3abb0dbea2cd374460927ec08,STILL_EXISTS,TODO: Use torch.nn.ConstantPadding?
aaffecdaj,Aifred-Health/Vulcan,vulcanai2/plotters/utils.py,74a8dadffbdb46f3abb0dbea2cd374460927ec08,STILL_EXISTS,TODO: Modify for multi-input NNs
aaffecdbj,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,481ffd6dddd71cb5cdfc59c0d6823074be6342fc,STILL_EXISTS,TODO: deal with repeated default parameters
aaffecdeg,Aifred-Health/Vulcan,vulcanai2/models/cnn.py,05813aa0d21bb62f540b758f5ed5e3c8e30fb77b,cb4b70417c4786308fcd111c916b069df646ef1e,TODO: convert to list
aaffecdih,Aifred-Health/Vulcan,vulcanai2/models/cnn.py,0253ca1f2a882fba16b12fb16acd5c5fb0aecf39,7624052d3e2d7cd8dda31ecf3d3b725b2231b5ff,TODO: convert to list
aaffecdjb,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,7624052d3e2d7cd8dda31ecf3d3b725b2231b5ff,e9cf91ace158dc44628b966e5f2b6367d6f5f42c,TODO: Instead of self.cpu(); use is_cuda to know if you can use gpu
aaffecebe,Aifred-Health/Vulcan,vulcanai2/plotters/utils.py,467299bf91f5711b315cb3e7b54a0eea189be1ca,5e5151d4799892f880b1f2432b7d112e03c7dec7,TODO: Revisit dim disorder.
aaffecech,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,52c67ab806314a802cd0411618aa0f0bab3ba658,STILL_EXISTS,TODO: temporary fix for casting network_tail to
aaffececi,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,246a61540dc37788eda072a48e1e513b68167cee,dcd9352a51642769e3277bacf27cada44f7ac973,TODO: should we use .data? https:\/\/discuss.pytorch.org\/t\/cpu-detach-numpy-vs-data-cpu-numpy\/20036
aaffeceda,Aifred-Health/Vulcan,examples/fashion_conv_dense_test.py,fa1524e56970b4451958edc5cef79216c92b0ac2,5e5151d4799892f880b1f2432b7d112e03c7dec7,TODO: to train parts of the model in different device
aaffecedh,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,cb87b91228732b9fa155b29324e5bdc1847d5368,e9cf91ace158dc44628b966e5f2b6367d6f5f42c,TODO: Instead of self.cpu(); use is_cuda to know if you can use gpu
aaffeceea,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,cb87b91228732b9fa155b29324e5bdc1847d5368,5e5151d4799892f880b1f2432b7d112e03c7dec7,TODO: store in tensor for continuity?
aaffeceeb,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,cb87b91228732b9fa155b29324e5bdc1847d5368,dcd9352a51642769e3277bacf27cada44f7ac973,TODO: should we use .data? https:\/\/discuss.pytorch.org\/t\/cpu-detach-numpy-vs-data-cpu-numpy\/20036
aaffecfag,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,9fde2b9b657d505af5adbbb706e43e7e42d4df17,STILL_EXISTS,TODO: datetime categorical?
aaffecfai,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,9fde2b9b657d505af5adbbb706e43e7e42d4df17,STILL_EXISTS,TODO: this is really slow make it faster
aaffecfbj,Aifred-Health/Vulcan,examples/fashion_conv_dense_test.py,b7c890a59c21f87475c019153f17a6e1cb26f06a,e50b30d405af39900b2ae4833aafc7077a377cc1,# TODO: to train parts of the model in different device
aaffecfdc,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,b7c890a59c21f87475c019153f17a6e1cb26f06a,433833ea6181fb26e89e45a5aa24c9633e49f329,TODO: DO we need to return a numpy object?
aaffecfdd,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,dc69075e656874328b84cebfcc4ed82209c32997,STILL_EXISTS,TODO: make label column the index??
aaffecfdg,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,137e60eff0d6d107f24c1f7493ba63b26580c063,STILL_EXISTS,TODO: check types
aaffecfff,Aifred-Health/Vulcan,vulcanai2/datasets/utils.py,b2aeddc3253f7b29c1334896686b3422fada67e6,STILL_EXISTS,TODO: implement
aaffecfhi,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,e50b30d405af39900b2ae4833aafc7077a377cc1,STILL_EXISTS,TODO: datetime categorical?
aaffecfhj,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,e50b30d405af39900b2ae4833aafc7077a377cc1,STILL_EXISTS,TODO: make label column the index??
aaffecfia,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,e50b30d405af39900b2ae4833aafc7077a377cc1,e9cf91ace158dc44628b966e5f2b6367d6f5f42c,TODO: Instead of self.cpu(); use is_cuda to know if you can use gpu
aaffecfjc,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,e50b30d405af39900b2ae4833aafc7077a377cc1,STILL_EXISTS,TODO: check types
aaffecfji,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,d52dff55db1af59582709f05af0d9273b0a879b2,STILL_EXISTS,TODO: store in tensor for continuity?
aaffecgaa,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,d52dff55db1af59582709f05af0d9273b0a879b2,STILL_EXISTS,TODO: you made them backwards
aaffecgab,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,d52dff55db1af59582709f05af0d9273b0a879b2,STILL_EXISTS,TODO: include support
aaffecgeb,Aifred-Health/Vulcan,vulcanai2/tests/models/test_device.py,9a4cf0df071e0412c24bedd39c944532aaea2371,28ecba71cb4a70072f5046bb0a9b28e6b27a193b,TODO: write script with mixed devices so that the
aaffecged,Aifred-Health/Vulcan,vulcanai2/tests/models/test_device.py,9a4cf0df071e0412c24bedd39c944532aaea2371,STILL_EXISTS,TODO: more tests yet to come
aaffecgei,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,000120f5f0a634a3338f353c01a56654aff6031d,STILL_EXISTS,TODO: make label column the index??
aaffecgfa,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,000120f5f0a634a3338f353c01a56654aff6031d,STILL_EXISTS,TODO: datetime categorical?
aaffecgfb,Aifred-Health/Vulcan,vulcanai2/tests/datasets/test_tabulardataset.py,000120f5f0a634a3338f353c01a56654aff6031d,0df96ecc99894c2013a0d18ea0e8131a12c02e3e,# TODO: test all possible combinations of merging
aaffecgff,Aifred-Health/Vulcan,vulcanai2/tests/datasets/test_tabulardataset.py,000120f5f0a634a3338f353c01a56654aff6031d,STILL_EXISTS,#TODO: test what Jospeh updates
aaffecggg,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,9ec6e0e9fbc5cc47580b17878651da08548fd72c,STILL_EXISTS,TODO: you made them backwards
aaffecggh,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,0df96ecc99894c2013a0d18ea0e8131a12c02e3e,STILL_EXISTS,TODO: ensure dummy_na =False is what you want
aaffecggj,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,0df96ecc99894c2013a0d18ea0e8131a12c02e3e,STILL_EXISTS,TODO: is it ok that this is maxiumum amount of variance?
aaffecgha,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,0df96ecc99894c2013a0d18ea0e8131a12c02e3e,STILL_EXISTS,TODO: make label column the index??
aaffecghf,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,bd8e15b16cb1c3965e129c7add1805ec1e8383a9,STILL_EXISTS,TODO: rewrite to work with new stitch datasets function
aaffecghg,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,bd8e15b16cb1c3965e129c7add1805ec1e8383a9,STILL_EXISTS,TODO: needs to be re-written to match new stitch datasets signature.
aaffecgic,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,bd8e15b16cb1c3965e129c7add1805ec1e8383a9,STILL_EXISTS,:param index_list: list of feature columns to index on when stitching(default None)
aaffecgji,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,bd8e15b16cb1c3965e129c7add1805ec1e8383a9,STILL_EXISTS,TODO: re-write documentation
aaffechaa,Aifred-Health/Vulcan,vulcanai2/tests/datasets/test_utils.py,f253decc915f2ed21893673d467b74ef0903890b,STILL_EXISTS,Assert_frame_equal checks order of columns; therefore; sort_index by columns when checking. If dataframes are
aaffechch,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,078f93f0f4eabc99523a1b02f875ac0147cbad37,STILL_EXISTS,TODO: why does use_unlabeled exist?
aaffechgc,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,078f93f0f4eabc99523a1b02f875ac0147cbad37,STILL_EXISTS,TODO: Use get_class
aaffechgd,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,078f93f0f4eabc99523a1b02f875ac0147cbad37,STILL_EXISTS,TODO: this doesn't seem correct
aaffechge,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,078f93f0f4eabc99523a1b02f875ac0147cbad37,dcd9352a51642769e3277bacf27cada44f7ac973,TODO: should we use .data? https:\/\/discuss.pytorch.org\/t\/cpu-detach-numpy-vs-data-cpu-numpy\/20036
aaffechgf,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,078f93f0f4eabc99523a1b02f875ac0147cbad37,STILL_EXISTS,TODO: class # should correspond with self.num_class
aaffechhi,Aifred-Health/Vulcan,vulcanai2/tests/datasets/test_utils.py,0406c0a2d50ae60ea979182909a74126703080d2,STILL_EXISTS,Assert_frame_equal checks order of columns; therefore; sort_index by columns when checking. If dataframes are
aaffechia,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,e3f3372a88dfda2b576a0d99ee374816362542dd,STILL_EXISTS,anyone got something better?
aaffechic,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,e3f3372a88dfda2b576a0d99ee374816362542dd,STILL_EXISTS,TODO: check index list doesn't fail if set twice...
aaffechii,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,6be46b4a12b2b839e37df36b987a756101c95701,STILL_EXISTS,anyone got something better?
aaffecibg,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,dcd9352a51642769e3277bacf27cada44f7ac973,STILL_EXISTS,TODO: store in tensor for continuity?
aaffecibh,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,dcd9352a51642769e3277bacf27cada44f7ac973,STILL_EXISTS,TODO: include support
aaffecibi,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,dcd9352a51642769e3277bacf27cada44f7ac973,STILL_EXISTS,TODO: why does use_unlabeled exist?
aaffecifd,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,dcd9352a51642769e3277bacf27cada44f7ac973,STILL_EXISTS,TODO: Use get_class
aaffecife,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,dcd9352a51642769e3277bacf27cada44f7ac973,STILL_EXISTS,TODO: this doesn't seem correct
aaffecifg,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,dcd9352a51642769e3277bacf27cada44f7ac973,STILL_EXISTS,TODO: class # should correspond with self.num_class
aaffecihb,Aifred-Health/Vulcan,vulcanai2/tests/datasets/test_utils.py,fcaea4852bbca5828d542d5dc6bdf80fc1976a32,STILL_EXISTS,Assert_frame_equal checks order of columns; therefore; sort_index by columns when checking. If dataframes are
aaffecihe,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,0f9d6ed034df8f95cc452ce27c25ca005a8810b5,STILL_EXISTS,TODO: update to use kwargs.
aaffecihf,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,0f9d6ed034df8f95cc452ce27c25ca005a8810b5,STILL_EXISTS,TODO: use kwargs
aaffecihh,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,0f9d6ed034df8f95cc452ce27c25ca005a8810b5,STILL_EXISTS,future improvements could come from
aaffecihi,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,5137e7dd2bc816130c8a992bdc5ee51fe032ec86,56480022607911cda44e6eaa508374709b8336a0,TODO: do we want to do anything with this name?
aaffecihj,Aifred-Health/Vulcan,vulcanai2/tests/models/test_cnn.py,61621393545d7ab19251bcf4a850df8f024ccf60,STILL_EXISTS,TODO: Throws RuntimeError: sizes must be non-negative
aaffecjdd,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,6b43928f7440175b4f25dd734a161b6461d5c5d5,STILL_EXISTS,Find dummy columns and build pairs (category; category_value)
aaffecjfb,Aifred-Health/Vulcan,vulcanai2/models/basenetwork.py,ad57016c228a272c5158a99643f5f4d9016accbc,STILL_EXISTS,TODO: I think we need to re-create\/re-initalize the basenetwork when calling this function since
aaffecjha,Aifred-Health/Vulcan,vulcanai2/tests/models/conftest.py,a219cd0b890cf5682f4d50a5ce7282336e4f75aa,a25966ee8607f0def9b2bf8a83bee2c9236ccb8b,in_dim=(4; 8; 8; 8); # TODO: Needs fix when giving a randon in_dim
aaffecjic,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,6bfa36b133a7acb270c6425498fd721c7823de0f,STILL_EXISTS,TODO: add more logging statements as appropriate
aaffecjif,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,6bfa36b133a7acb270c6425498fd721c7823de0f,0d01040656f9523862dfdf8ce6acb9462eb5119b,Find non-dummy columns that do not have a _
aaffecjih,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,6bfa36b133a7acb270c6425498fd721c7823de0f,STILL_EXISTS,Select columns for each category
aaffecjii,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,6bfa36b133a7acb270c6425498fd721c7823de0f,STILL_EXISTS,TODO: insert other logging statements
aaffecjij,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,6bfa36b133a7acb270c6425498fd721c7823de0f,STILL_EXISTS,TODO: this is really slow make it faster
aaffecjja,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,6bfa36b133a7acb270c6425498fd721c7823de0f,STILL_EXISTS,TODO: check this doesn't operate in place... damn
aaffecjjb,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,6bfa36b133a7acb270c6425498fd721c7823de0f,STILL_EXISTS,TODO: turn this into a percentage too? currently it's not
aaffecjjc,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,6bfa36b133a7acb270c6425498fd721c7823de0f,STILL_EXISTS,TODO: probably bad to use this?
aaffecjjd,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,6bfa36b133a7acb270c6425498fd721c7823de0f,0d01040656f9523862dfdf8ce6acb9462eb5119b,TODO: edit this method that creates a split given different filepaths or objects so that the params match
aaffedada,Aifred-Health/Vulcan,vulcanai2/tests/models/test_cnn.py,4941f5747cc66f7e1bfe21cf3a2c45fcdae043e5,23838c3560e4e11a2a29415e34ef893d8d942bd4,TODO: Change to logger
aaffedaed,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,0d01040656f9523862dfdf8ce6acb9462eb5119b,STILL_EXISTS,TODO: check index list doesn't fail if set twice...
aaffedaei,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,0d01040656f9523862dfdf8ce6acb9462eb5119b,STILL_EXISTS,TODO: update to use kwargs.
aaffedaej,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,0d01040656f9523862dfdf8ce6acb9462eb5119b,STILL_EXISTS,TODO: ensure dummy_na =False is what you want
aaffedafc,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,0d01040656f9523862dfdf8ce6acb9462eb5119b,STILL_EXISTS,future improvements could come from
aaffedafd,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,0d01040656f9523862dfdf8ce6acb9462eb5119b,STILL_EXISTS,TODO: add more logging statements as appropriate
aaffedafi,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,0d01040656f9523862dfdf8ce6acb9462eb5119b,STILL_EXISTS,Select columns for each category
aaffedafj,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,0d01040656f9523862dfdf8ce6acb9462eb5119b,STILL_EXISTS,TODO: insert other logging statements
aaffedaga,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,0d01040656f9523862dfdf8ce6acb9462eb5119b,STILL_EXISTS,TODO: this is really slow make it faster
aaffedagb,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,0d01040656f9523862dfdf8ce6acb9462eb5119b,STILL_EXISTS,TODO: check this doesn't operate in place... damn
aaffedagc,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,0d01040656f9523862dfdf8ce6acb9462eb5119b,STILL_EXISTS,TODO: turn this into a percentage too? currently it's not
aaffedagd,Aifred-Health/Vulcan,vulcanai2/datasets/tabulardataset.py,0d01040656f9523862dfdf8ce6acb9462eb5119b,STILL_EXISTS,TODO: probably bad to use this?
aaffedbab,Aifred-Health/Vulcan,vulcanai2/tests/models/test_dnn.py,64ef2dd67dbc359bd007358848cab934c435fbb2,STILL_EXISTS,TODO: Failing! Fix the test
aaffedbac,Aifred-Health/Vulcan,vulcanai2/tests/models/test_dnn.py,64ef2dd67dbc359bd007358848cab934c435fbb2,d024e92550c1f53625b0e06ae264faedd102673e,TODO: Failing fit! Fix the test
aaffedbbc,Aifred-Health/Vulcan,vulcanai2/tests/models/test_cnn.py,62b2b6b88beeafb18842921c815fe3d113a682fc,d024e92550c1f53625b0e06ae264faedd102673e,TODO failing here: RuntimeError: Jacobian mismatch for output 0 with respect to input 0;
aaffedbbd,Aifred-Health/Vulcan,vulcanai2/tests/models/test_cnn.py,62b2b6b88beeafb18842921c815fe3d113a682fc,d024e92550c1f53625b0e06ae264faedd102673e,TODO failing here too assuming if Pool passes: RuntimeError: Jacobian mismatch for output 0 with respect to input 0;
aaffedbcd,Aifred-Health/Vulcan,vulcanai2/tests/models/test_dnn.py,f67166039134b17cf90714014547b1f42799d5fe,d024e92550c1f53625b0e06ae264faedd102673e,TODO: Failing fit! Fix the test
aaffedchj,Aifred-Health/Vulcan,vulcanai2/tests/models/test_cnn.py,7b255b6d77e29b51bfe3a99956eb51672d7520d5,STILL_EXISTS,TODO: make more elegant
aaffedcia,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,95c7694b50852acd39af6ed6c6d19f87d7b48a76,STILL_EXISTS,TODO: consider making class_converted default True
aaffedcib,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,95c7694b50852acd39af6ed6c6d19f87d7b48a76,195d550a79a143199d0ba54cc5d76d4097c71f07,TODO: what type are the raw predicted values that come out??
aaffedcif,Aifred-Health/Vulcan,vulcanai2/tests/models/test_metrics.py,95c7694b50852acd39af6ed6c6d19f87d7b48a76,STILL_EXISTS,TODO: check that this won't mess with replicability
aaffeddce,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,e22ff2432a7d90598d5578808194985fb57793a8,STILL_EXISTS,TODO: consider making class_converted default True
aaffeddcf,Aifred-Health/Vulcan,vulcanai2/models/metrics.py,e22ff2432a7d90598d5578808194985fb57793a8,STILL_EXISTS,TODO: what type are the raw predicted values that come out??
aaffeddgg,Aifred-Health/Vulcan,vulcanai/datasets/tabulardataset.py,39b5ad778d0f672b2c763b878b0ed159e8d69479,STILL_EXISTS,TODO: do we want to do anything with this name?
aaffeddgi,Aifred-Health/Vulcan,vulcanai/datasets/tabulardataset.py,39b5ad778d0f672b2c763b878b0ed159e8d69479,STILL_EXISTS,TODO: check index list doesn't fail if set twice...
aaffeddhe,Aifred-Health/Vulcan,vulcanai/datasets/tabulardataset.py,39b5ad778d0f672b2c763b878b0ed159e8d69479,STILL_EXISTS,TODO: update to use kwargs.
aaffeddhf,Aifred-Health/Vulcan,vulcanai/datasets/tabulardataset.py,39b5ad778d0f672b2c763b878b0ed159e8d69479,STILL_EXISTS,TODO: use kwargs
aaffeddhg,Aifred-Health/Vulcan,vulcanai/datasets/tabulardataset.py,39b5ad778d0f672b2c763b878b0ed159e8d69479,STILL_EXISTS,TODO: ensure dummy_na =False is what you want
aaffeddhi,Aifred-Health/Vulcan,vulcanai/datasets/tabulardataset.py,39b5ad778d0f672b2c763b878b0ed159e8d69479,STILL_EXISTS,Find dummy columns and build pairs (category; category_value)
aaffeddhj,Aifred-Health/Vulcan,vulcanai/datasets/tabulardataset.py,39b5ad778d0f672b2c763b878b0ed159e8d69479,STILL_EXISTS,Find max value among columns
aaffeddib,Aifred-Health/Vulcan,vulcanai/datasets/tabulardataset.py,39b5ad778d0f672b2c763b878b0ed159e8d69479,STILL_EXISTS,Copy non-dummy columns over.
aaffeddif,Aifred-Health/Vulcan,vulcanai/datasets/tabulardataset.py,39b5ad778d0f672b2c763b878b0ed159e8d69479,STILL_EXISTS,future improvements could come from
aaffeddjb,Aifred-Health/Vulcan,vulcanai/datasets/utils.py,39b5ad778d0f672b2c763b878b0ed159e8d69479,STILL_EXISTS,TODO: implement
aaffedehd,Aifred-Health/Vulcan,vulcanai/datasets/utils.py,39b5ad778d0f672b2c763b878b0ed159e8d69479,ef9a7c69b1c9fb77f664b43bf426895e72e82f4d,Drop rows where there are duplicates for the merged_on_columns.
aaffedejg,Aifred-Health/Vulcan,vulcanai/models/basenetwork.py,39b5ad778d0f672b2c763b878b0ed159e8d69479,STILL_EXISTS,TODO: Use logger to describe if the optimizer is changed.
aaffedfaf,Aifred-Health/Vulcan,vulcanai/models/basenetwork.py,39b5ad778d0f672b2c763b878b0ed159e8d69479,STILL_EXISTS,TODO: deal with repeated default parameters
aaffedfbc,Aifred-Health/Vulcan,vulcanai/models/basenetwork.py,39b5ad778d0f672b2c763b878b0ed159e8d69479,STILL_EXISTS,TODO: to improve: # object.__getstate__()
aaffedfbd,Aifred-Health/Vulcan,vulcanai/models/basenetwork.py,39b5ad778d0f672b2c763b878b0ed159e8d69479,STILL_EXISTS,TODO: does this break windows?? no idea.
aaffedfbg,Aifred-Health/Vulcan,vulcanai/models/cnn.py,39b5ad778d0f672b2c763b878b0ed159e8d69479,STILL_EXISTS,TODO: use setters to enforce types\/formats\/values!
aaffedfbh,Aifred-Health/Vulcan,vulcanai/models/cnn.py,39b5ad778d0f672b2c763b878b0ed159e8d69479,STILL_EXISTS,TODO: make this a base class?
aaffedfbi,Aifred-Health/Vulcan,vulcanai/models/cnn.py,39b5ad778d0f672b2c763b878b0ed159e8d69479,STILL_EXISTS,TODO: add additional constraints in the future
aaffedfdg,Aifred-Health/Vulcan,vulcanai/models/cnn.py,39b5ad778d0f672b2c763b878b0ed159e8d69479,STILL_EXISTS,TODO: https:\/\/github.com\/pytorch\/pytorch\/issues\/9410
aaffedfeh,Aifred-Health/Vulcan,vulcanai/models/dnn.py,39b5ad778d0f672b2c763b878b0ed159e8d69479,STILL_EXISTS,TODO: Think about moving dimension to config file
aaffedffb,Aifred-Health/Vulcan,vulcanai/models/ensemble.py,39b5ad778d0f672b2c763b878b0ed159e8d69479,STILL_EXISTS,TODO: Should these be defaulted to the values of template_network?
aaffedfhe,Aifred-Health/Vulcan,vulcanai/models/layers.py,39b5ad778d0f672b2c763b878b0ed159e8d69479,STILL_EXISTS,TODO: Automatically calculate padding to be the same as input shape.
aaffedfia,Aifred-Health/Vulcan,vulcanai/models/layers.py,39b5ad778d0f672b2c763b878b0ed159e8d69479,STILL_EXISTS,TODO: Will work on these classes below later during Vulcan2 deployment
aaffedfig,Aifred-Health/Vulcan,vulcanai/models/metrics.py,39b5ad778d0f672b2c763b878b0ed159e8d69479,STILL_EXISTS,TODO: consider making class_converted default True
aaffedfjf,Aifred-Health/Vulcan,vulcanai/models/metrics.py,39b5ad778d0f672b2c763b878b0ed159e8d69479,STILL_EXISTS,TODO: what type are the raw predicted values that come out??
aaffedfji,Aifred-Health/Vulcan,vulcanai/models/metrics.py,39b5ad778d0f672b2c763b878b0ed159e8d69479,STILL_EXISTS,TODO: store in tensor for continuity?
aaffedfjj,Aifred-Health/Vulcan,vulcanai/models/metrics.py,39b5ad778d0f672b2c763b878b0ed159e8d69479,STILL_EXISTS,TODO: include support
aaffedgaa,Aifred-Health/Vulcan,vulcanai/models/metrics.py,39b5ad778d0f672b2c763b878b0ed159e8d69479,STILL_EXISTS,TODO: this whole section is really clunky
aaffedgad,Aifred-Health/Vulcan,vulcanai/models/metrics.py,39b5ad778d0f672b2c763b878b0ed159e8d69479,STILL_EXISTS,#TODO: improve the copying of parameters
aaffedgaf,Aifred-Health/Vulcan,vulcanai/models/metrics.py,39b5ad778d0f672b2c763b878b0ed159e8d69479,c9fddd7bd26479b59bd35cd55153ec085a59f067,TODO: this may break on different devices?? test.
aaffedgag,Aifred-Health/Vulcan,vulcanai/models/metrics.py,39b5ad778d0f672b2c763b878b0ed159e8d69479,9edb1e9e586efde0b951fdd590905fade82f07f3,TODO: Re-initialize instead of deepcopy?
aaffedgah,Aifred-Health/Vulcan,vulcanai/models/metrics.py,39b5ad778d0f672b2c763b878b0ed159e8d69479,STILL_EXISTS,TODO: properly pass params
aaffedgbe,Aifred-Health/Vulcan,vulcanai/models/metrics.py,39b5ad778d0f672b2c763b878b0ed159e8d69479,STILL_EXISTS,TODO: we could show something better here like calculate
aaffedgca,Aifred-Health/Vulcan,vulcanai/models/utils.py,39b5ad778d0f672b2c763b878b0ed159e8d69479,STILL_EXISTS,TODO: Use torch.nn.ConstantPadding?
aaffedgff,Aifred-Health/Vulcan,vulcanai/tests/datasets/test_utils.py,39b5ad778d0f672b2c763b878b0ed159e8d69479,acf19fcb6e685f847cf0f333e5dddc1c71471f45,MOC (merge on columns)
aaffedggc,Aifred-Health/Vulcan,vulcanai/tests/models/conftest.py,39b5ad778d0f672b2c763b878b0ed159e8d69479,STILL_EXISTS,\"\"\"Specify dummy networks to test vulcan functionality.\"\"\"
aaffedggh,Aifred-Health/Vulcan,vulcanai/tests/models/test_cnn.py,39b5ad778d0f672b2c763b878b0ed159e8d69479,STILL_EXISTS,TODO: make more elegant
aaffedgjh,Aifred-Health/Vulcan,vulcanai/tests/models/test_metrics.py,39b5ad778d0f672b2c763b878b0ed159e8d69479,STILL_EXISTS,TODO: check that this won't mess with replicability
aaffedhbd,Aifred-Health/Vulcan,vulcanai/tests/models/conftest.py,4faaf23e65e650343d2b1fdc47cfcdb824dca724,STILL_EXISTS,\"\"\"Specify dummy networks to test vulcan functionality.\"\"\"
aaffedhfi,Aifred-Health/Vulcan,docs/conf.py,e2abb0741a8b3bbbebbfffd43291f0ab03ff84a4,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaffediad,Aifred-Health/Vulcan,setup.py,e2abb0741a8b3bbbebbfffd43291f0ab03ff84a4,STILL_EXISTS,TODO: maybe numpydoc?
aaffeeabd,Aifred-Health/Vulcan,vulcanai/models/basenetwork.py,123534e8ff2050a4ce2ff68542c175df76858050,STILL_EXISTS,TODO: check this
aaffeeacj,Aifred-Health/Vulcan,vulcanai/models/metrics.py,f4f9d896bc546b3904ce874aa69fe37cea1c1d54,c9fddd7bd26479b59bd35cd55153ec085a59f067,TODO: this may break on different devices?? test.
aaffeeada,Aifred-Health/Vulcan,vulcanai/models/metrics.py,f4f9d896bc546b3904ce874aa69fe37cea1c1d54,9edb1e9e586efde0b951fdd590905fade82f07f3,TODO: Re-initialize instead of deepcopy?
aaffeeafa,Aifred-Health/Vulcan,vulcanai/models/metrics.py,9edb1e9e586efde0b951fdd590905fade82f07f3,STILL_EXISTS,TODO: this is not necessary if targets was created as numpy on
aaffeeafj,Aifred-Health/Vulcan,vulcanai/models/basenetwork.py,c16b45100cc97769f2fcc69921819beb42b5c468,e06215ef6c3867bcacdb76e9d14c391551754d97,TODO: could integrate map location in the future if needed
aaffeeagb,Aifred-Health/Vulcan,vulcanai/models/metrics.py,be54f04369dadeb1ad6225efc54016472da1d710,STILL_EXISTS,TODO: this may not work with current TabularDataset
aaffeeagd,Aifred-Health/Vulcan,vulcanai/models/metrics.py,b6762cfb8220da0d70885d6cb280e4607e4cd1ab,c9fddd7bd26479b59bd35cd55153ec085a59f067,TODO: this may break on different devices?? test.
aaffeeage,Aifred-Health/Vulcan,vulcanai/models/metrics.py,b6762cfb8220da0d70885d6cb280e4607e4cd1ab,c9fddd7bd26479b59bd35cd55153ec085a59f067,TODO: Re-initialize instead of deepcopy?
aaffeeaha,Aifred-Health/Vulcan,vulcanai/models/metrics.py,b6762cfb8220da0d70885d6cb280e4607e4cd1ab,STILL_EXISTS,TODO: this may not work with current TabularDataset
aaffeeajj,Aifred-Health/Vulcan,vulcanai/models/basenetwork.py,debb67c2b88ea88265c7b2ca5dfbb7d62962116c,8d0ae06025053aa1537bf71dd0146a509020cf97,TODO: could integrate map location in the future if needed
aaffeebag,Aifred-Health/Vulcan,vulcanai/models/metrics.py,6f42bbe62dbbd84f4b02475b105f976340cfee1d,STILL_EXISTS,TODO: this may break on different devices?? test.
aaffeebah,Aifred-Health/Vulcan,vulcanai/models/metrics.py,6f42bbe62dbbd84f4b02475b105f976340cfee1d,STILL_EXISTS,TODO: Re-initialize instead of deepcopy?
aaffeebcc,Aifred-Health/Vulcan,vulcanai/models/basenetwork.py,8a97619debe315c6558c20b2f1181cb28f7b36d5,STILL_EXISTS,TODO: could integrate map location in the future if needed
aaffeebce,Aifred-Health/Vulcan,vulcanai/models/basenetwork.py,3808f589c2773c493de0df6bb05b87c9594ce32d,STILL_EXISTS,TODO: need to update transform callable params to match that of
aaffeebci,Aifred-Health/Vulcan,vulcanai/datasets/tabulardataset.py,cb691923ce4ab73775c1575667213cbb666b82e2,STILL_EXISTS,recoding binary valued columns as ones and zeros
aaffeeccd,Aifred-Health/Vulcan,vulcanai/models/metrics.py,32b8a935db457b7eff81b5eae74369dc1612b0e2,e862ddb70eb76339312fe9e1306988c30d3297b7,TODO: Evaluate these as static methods...
aaffeejef,Aifred-Health/Vulcan,vulcanai/models/metrics.py,d6261a86d733419f3e06d91ea969b118b787df21,98fd0f85bd8df8b940cd0e63e4c3c6a605096202,TODO: Evaluate these as static methods...
aaffeejib,Aifred-Health/Vulcan,vulcanai/models/metrics.py,b244d17d78ba0a73f334b3992ca6142312135436,564da97917c02153606221650fa39fba768cb371,TODO: Evaluate these as static methods...
aaffeejif,Aifred-Health/Vulcan,vulcanai/datasets/tabular_data_utils.py,ef9a7c69b1c9fb77f664b43bf426895e72e82f4d,STILL_EXISTS,TODO: split function
aaffeejig,Aifred-Health/Vulcan,vulcanai/datasets/tabular_data_utils.py,ef9a7c69b1c9fb77f664b43bf426895e72e82f4d,STILL_EXISTS,TODO: use kwargs
aaffeejij,Aifred-Health/Vulcan,vulcanai/datasets/tabular_data_utils.py,ef9a7c69b1c9fb77f664b43bf426895e72e82f4d,STILL_EXISTS,Find dummy columns and build pairs (category; category_value)
aaffeejja,Aifred-Health/Vulcan,vulcanai/datasets/tabular_data_utils.py,ef9a7c69b1c9fb77f664b43bf426895e72e82f4d,STILL_EXISTS,Find max value among columns
aaffeejjc,Aifred-Health/Vulcan,vulcanai/datasets/tabular_data_utils.py,ef9a7c69b1c9fb77f664b43bf426895e72e82f4d,STILL_EXISTS,Copy non-dummy columns over.
aaffeejje,Aifred-Health/Vulcan,vulcanai/datasets/tabular_data_utils.py,ef9a7c69b1c9fb77f664b43bf426895e72e82f4d,STILL_EXISTS,columns back from dummy format.\"; len(dummy_tuples))
aaffeejji,Aifred-Health/Vulcan,vulcanai/datasets/tabular_data_utils.py,ef9a7c69b1c9fb77f664b43bf426895e72e82f4d,STILL_EXISTS,recoding binary valued columns as ones and zeros
aaffefaac,Aifred-Health/Vulcan,vulcanai/datasets/tabular_data_utils.py,ef9a7c69b1c9fb77f664b43bf426895e72e82f4d,STILL_EXISTS,Drop rows where there are duplicates for the merged_on_columns.
aaffefahb,Aifred-Health/Vulcan,vulcanai/tests/datasets/test_tabulardataset.py,acf19fcb6e685f847cf0f333e5dddc1c71471f45,e3e41a5f1b9206215cb049c02a90a9b813940461,def test_list_columns(self; my_test_dataset):
aaffefaid,Aifred-Health/Vulcan,vulcanai/tests/datasets/test_tabulardataset.py,acf19fcb6e685f847cf0f333e5dddc1c71471f45,e3e41a5f1b9206215cb049c02a90a9b813940461,def test_delete_columns(self; my_test_dataset):
aaffefbbe,Aifred-Health/Vulcan,vulcanai/tests/datasets/test_tabulardataset.py,acf19fcb6e685f847cf0f333e5dddc1c71471f45,e3e41a5f1b9206215cb049c02a90a9b813940461,def test_identify_unbalanced_columns(self; my_test_dataset):
aaffefbbf,Aifred-Health/Vulcan,vulcanai/tests/datasets/test_tabulardataset.py,acf19fcb6e685f847cf0f333e5dddc1c71471f45,e3e41a5f1b9206215cb049c02a90a9b813940461,res = my_test_dataset.identify_unbalanced_columns(0.5)
aaffefcbh,Aifred-Health/Vulcan,vulcanai/tests/datasets/test_utils.py,acf19fcb6e685f847cf0f333e5dddc1c71471f45,STILL_EXISTS,def test_no_merge_on_columns(self; my_test_dataset_one):
aaffefcbi,Aifred-Health/Vulcan,vulcanai/tests/datasets/test_utils.py,acf19fcb6e685f847cf0f333e5dddc1c71471f45,STILL_EXISTS,# MOC (merge on columns)
aaffefcci,Aifred-Health/Vulcan,vulcanai/tests/datasets/test_utils.py,acf19fcb6e685f847cf0f333e5dddc1c71471f45,STILL_EXISTS,def test_single_merge_on_columns(self; my_test_dataset_two):
aaffefcea,Aifred-Health/Vulcan,vulcanai/tests/datasets/test_utils.py,acf19fcb6e685f847cf0f333e5dddc1c71471f45,STILL_EXISTS,def test_two_merge_on_columns(self; my_test_dataset_three):
aaffefcfd,Aifred-Health/Vulcan,vulcanai/tests/datasets/test_utils.py,acf19fcb6e685f847cf0f333e5dddc1c71471f45,STILL_EXISTS,def test_three_merge_on_columns(self; my_test_dataset_four):
aaffefcib,Aifred-Health/Vulcan,vulcanai/datasets/tabular_data_utils.py,e3e41a5f1b9206215cb049c02a90a9b813940461,STILL_EXISTS,TODO: ensure dummy_na =False is what you want
aaffefcih,Aifred-Health/Vulcan,vulcanai/tests/datasets/test_tabulardataset.py,e3e41a5f1b9206215cb049c02a90a9b813940461,STILL_EXISTS,TODO: refactor
aaffefcii,Aifred-Health/Vulcan,vulcanai/tests/datasets/test_tabulardataset.py,e3e41a5f1b9206215cb049c02a90a9b813940461,STILL_EXISTS,MOC (merge on columns)
aaffefegd,Aifred-Health/Vulcan,vulcanai/datasets/utils.py,128839b22e86ae1454ba954fc8462de5e67294fd,05c611728e9bc349f833a1e339df126ed8d4836b,Drop rows where there are duplicates for the merged_on_columns.
aaffefehg,Aifred-Health/Vulcan,vulcanai/models/metrics.py,128839b22e86ae1454ba954fc8462de5e67294fd,STILL_EXISTS,TODO: Evaluate these as static methods...
aaffeffhc,ResponsiblyAI/responsibly,setup.py,fb7e7ac79e6e4040de794fe7259c4f75817cba75,d35142f257b775b17a92a0cb675e8dc2825203ff,TODO: update this list to match your application: https:\/\/pypi.org\/pypi?%3Aaction=list_classifiers
aaffeffhd,ResponsiblyAI/responsibly,setup.py,fb7e7ac79e6e4040de794fe7259c4f75817cba75,7dcba3aa90b6f6030dda6c37f1739d4601a0529e,TODO: Add your library's requirements here
aaffeffid,ResponsiblyAI/responsibly,ethically/tests/test_tolga.py,7dcba3aa90b6f6030dda6c37f1739d4601a0529e,STILL_EXISTS,TODO: in the article it is 0.35 - why?
aaffeffie,ResponsiblyAI/responsibly,ethically/tests/test_tolga.py,7dcba3aa90b6f6030dda6c37f1739d4601a0529e,STILL_EXISTS,TODO in the article it is 0.31 - why?
aaffeffif,ResponsiblyAI/responsibly,ethically/we/data/__init__.py,7dcba3aa90b6f6030dda6c37f1739d4601a0529e,STILL_EXISTS,TODO how import files from a package
aaffeffjd,ResponsiblyAI/responsibly,ethically/we/tolga.py,7dcba3aa90b6f6030dda6c37f1739d4601a0529e,STILL_EXISTS,TODO: maybe using cosine_similarities on all the vectors?
aaffeffjj,ResponsiblyAI/responsibly,ethically/tests/test_tolga.py,cb1aa7a7fe4e9c3d2103d00ea33e35d9b452f84b,STILL_EXISTS,TODO: iterate over a dictionary
aaffefhcj,ResponsiblyAI/responsibly,docs/conf.py,271520b63b6466807d78b88d1ed3e9568816c6c8,STILL_EXISTS,-- Options for todo extension ----------------------------------------------
aaffefhda,ResponsiblyAI/responsibly,docs/conf.py,271520b63b6466807d78b88d1ed3e9568816c6c8,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaffefhfc,ResponsiblyAI/responsibly,ethically/we/tolga.py,763a93c78d1de866392098b6c24ce7d6fbd6d79f,STILL_EXISTS,TODO: add the SVD method from section 6 step 1
aaffefhff,ResponsiblyAI/responsibly,ethically/we/tolga.py,763a93c78d1de866392098b6c24ce7d6fbd6d79f,STILL_EXISTS,TODO: what is PairBais?
aaffefhgg,ResponsiblyAI/responsibly,ethically/we/core.py,6ae19150644fefb488b990924a5856738cf67c45,2d6850dac886060b98176362d6b4e671a799a44a,TODO: in the code of the article; the last definitional pair
aaffefhhb,ResponsiblyAI/responsibly,ethically/we/core.py,6f5bff9c7ef0aa58d1b2e0c8a78d44db205b7c61,STILL_EXISTS,TODO - in the code it is different - why?
aaffefhhe,ResponsiblyAI/responsibly,ethically/we/core.py,43e270a4a5ea622fac2727a762c31f7a8e50369b,STILL_EXISTS,TODO: write unitest for when it is False
aaffefhhh,ResponsiblyAI/responsibly,ethically/we/core.py,43e270a4a5ea622fac2727a762c31f7a8e50369b,2d6850dac886060b98176362d6b4e671a799a44a,TODO: refactor
aaffefibh,ResponsiblyAI/responsibly,ethically/we/bias.py,2d6850dac886060b98176362d6b4e671a799a44a,STILL_EXISTS,TODO: refactor
aaffeficf,ResponsiblyAI/responsibly,ethically/we/data/__init__.py,2d6850dac886060b98176362d6b4e671a799a44a,STILL_EXISTS,TODO: in the code of the article; the last definitional pair
aaffefich,ResponsiblyAI/responsibly,ethically/tests/test_we.py,72b79277138233929944849e9947ddd5d7be7940,99944883fd0ebf85c52d9b29076befc647b2b0c4,TODO deeper testing
aaffefidc,ResponsiblyAI/responsibly,ethically/tests/test_we.py,7ebb8666d1ea8cf2e2529ef35a68f36807f6b7b2,STILL_EXISTS,TODO not all full_specific_words are lower case - why? maybe just names?
aaffefidd,ResponsiblyAI/responsibly,ethically/tests/test_we.py,7ebb8666d1ea8cf2e2529ef35a68f36807f6b7b2,STILL_EXISTS,TODO maybe it was trained on the whole w2v?
aaffefiei,ResponsiblyAI/responsibly,ethically/we/core.py,2fd521b4fd22d8dc6fcb6bfdc3406f0673f68381,STILL_EXISTS,TODO: refactor for speed and clarity
aaffefige,ResponsiblyAI/responsibly,ethically/we/bias.py,bfb4d34bbde7813cc1b61208f9cae816b2e02cb1,STILL_EXISTS,TODO: is it correct for inhertence of class method?
aaffefigg,ResponsiblyAI/responsibly,ethically/we/core.py,bfb4d34bbde7813cc1b61208f9cae816b2e02cb1,STILL_EXISTS,TODO: this is bad Python; ask someone about it
aaffefigh,ResponsiblyAI/responsibly,ethically/we/core.py,bfb4d34bbde7813cc1b61208f9cae816b2e02cb1,STILL_EXISTS,probably should be a better design
aaffefiie,ResponsiblyAI/responsibly,ethically/we/weat.py,732fdf2920ab59af769ce19ec41c2b44d9443f14,STILL_EXISTS,TODO: refactor - check before if one group is without words
aaffefjce,ResponsiblyAI/responsibly,ethically/dataset/__init__.py,876e0ae510817c142668773e6eac910762d5bca0,STILL_EXISTS,\"\"\" || Collection of common benchmark datasets from fairness research. ||  || Each dataset object contains a `pandas.DataFrame` as `df` attribute || that holds the actual data. || The dataset object will take care of loading; preprocessing || and validating the data. || The preprocessing is done by standard practices that are associated with || this data set: from its manual (e.g.; README) || or as other did in the literature. ||  || See :class:`ethically.dataset.Dataset` || for additional attribute and complete documentation. ||  || Currently these are the available datasets: ||     - ProPublica recidivism\/COMPAS dataset; ||       see: :class:`~ethically.dataset.COMPASDataset` ||  ||     - Adult dataset; see: :class:`~ethically.dataset.AdultDataset` ||  ||     - German credit dataset; see: :class:`~ethically.dataset.GermanDataset` ||  || Usage || ----- || .. code:: python ||  ||     >>> from ethically.dataset import COMPASDataset ||     >>> compas_ds = COMPASDataset() ||     >>> print(compas_ds) ||     <ProPublica Recidivism\/COMPAS Dataset. 6172 rows; 56 columns in ||     which {race; sex} are sensitive attributes> ||     >>> type(compas_ds.df) ||     <class 'pandas.core.frame.DataFrame'> ||     >>> compas_ds.df['race'].value_counts() ||     African-American    3175 ||     Caucasian           2103 ||     Hispanic             509 ||     Other                343 ||     Asian                 31 ||     Native American       11 ||     Name: race; dtype: int64 || \"\"\"
aaffefjdi,ResponsiblyAI/responsibly,ethically/fairness/metrics/__init__.py,d81d3e546dabda882cf35294dbb69c3d86928022,STILL_EXISTS,\"\"\" || Demographic Classification Fairness Criteria. ||  || The objectives of the demographic classification fairness criteria || is to measure unfairness towards sensitive attribute valuse. ||  ||  || One should keep in mind that the criteria are intended || to *measure unfairness; rather than to prove fairness*; as it stated in || the paper `Equality of opportunity in supervised learning <https:\/\/arxiv.org\/abs\/1610.02413>`_ || by Hardt et al. (2016): ||  ||     ... satisfying [the demographic criteria] should not be ||     considered a conclusive proof of fairness. ||     Similarly; violations of our condition are not meant ||     to be a proof of unfairness. ||     Rather we envision our framework as providing a reasonable way ||     of discovering and measuring potential concerns that require ||     further scrutiny. We believe that resolving fairness concerns is ||     ultimately impossible without substantial domain-specific ||     investigation. ||  ||  || The output of binary classifiers can come in two forms; either giving || a binary outcome prediction for input or producing || a real number score; which the common one is the probability || for the positive or negative label || (such as the method ``proba`` of an ``Estimator`` in ``sklearn``). || Therefore; the criteria come in two flavors; one for **binary** output; || and the second for **score** output. ||  || The fundamental concept for defining the fairness criteria || is `conditional independence <https:\/\/en.wikipedia.org\/wiki\/Conditional_independence>`_. || Using *Machine Learning and Fairness* book's notions: ||  || - ``A`` - Sensitive attribute || - ``Y`` - Binary ground truth (correct) target || - ``R`` - Estimated binary targets or score as returned by a classifier ||  || There are three demographic fairness criteria for classification: ||  || 1. Independence - R\u22A5A ||  || 2. Separation - R\u22A5A\u2223Y ||  || 3. Sufficiency - Y\u22A5A\u2223R ||  || \"\"\"
aaffefjeb,ResponsiblyAI/responsibly,ethically/fairness/metrics/binary.py,d81d3e546dabda882cf35294dbb69c3d86928022,STILL_EXISTS,hack to keep the same strutcure of code
aaffefjgg,ResponsiblyAI/responsibly,ethically/we/bias.py,898ed376fa961f47f903741c46406d46b917f9ed,170db57da295afad2d1e89f31fa7451abb5c7492,TODO: this is bad Python; ask someone about it
aaffefjgh,ResponsiblyAI/responsibly,ethically/we/bias.py,898ed376fa961f47f903741c46406d46b917f9ed,170db57da295afad2d1e89f31fa7451abb5c7492,probably should be a better design
aaffefjha,ResponsiblyAI/responsibly,ethically/we/bias.py,898ed376fa961f47f903741c46406d46b917f9ed,170db57da295afad2d1e89f31fa7451abb5c7492,TODO: write unitest for when it is False
aaffefjhh,ResponsiblyAI/responsibly,ethically/we/bias.py,898ed376fa961f47f903741c46406d46b917f9ed,170db57da295afad2d1e89f31fa7451abb5c7492,TODO: add the SVD method from section 6 step 1
aaffefjid,ResponsiblyAI/responsibly,ethically/we/bias.py,898ed376fa961f47f903741c46406d46b917f9ed,170db57da295afad2d1e89f31fa7451abb5c7492,TODO: maybe using cosine_similarities on all the vectors?
aaffefjih,ResponsiblyAI/responsibly,ethically/we/bias.py,898ed376fa961f47f903741c46406d46b917f9ed,170db57da295afad2d1e89f31fa7451abb5c7492,TODO: refactor for speed and clarity
aaffegdfj,ResponsiblyAI/responsibly,ethically/we/bias.py,8af2aa8768bd49de604c75f88596b8088018ccee,STILL_EXISTS,TODO: this is bad Python; ask someone about it
aaffegdga,ResponsiblyAI/responsibly,ethically/we/bias.py,8af2aa8768bd49de604c75f88596b8088018ccee,STILL_EXISTS,probably should be a better design
aaffegdgd,ResponsiblyAI/responsibly,ethically/we/bias.py,8af2aa8768bd49de604c75f88596b8088018ccee,STILL_EXISTS,TODO: write unitest for when it is False
aaffegdha,ResponsiblyAI/responsibly,ethically/we/bias.py,8af2aa8768bd49de604c75f88596b8088018ccee,STILL_EXISTS,TODO: add the SVD method from section 6 step 1
aaffegdhg,ResponsiblyAI/responsibly,ethically/we/bias.py,8af2aa8768bd49de604c75f88596b8088018ccee,STILL_EXISTS,TODO: maybe using cosine_similarities on all the vectors?
aaffegdia,ResponsiblyAI/responsibly,ethically/we/bias.py,8af2aa8768bd49de604c75f88596b8088018ccee,STILL_EXISTS,TODO: refactor for speed and clarity
aaffegeff,ResponsiblyAI/responsibly,ethically/we/bias.py,400a5c7e80fb1ed6c887d38061dc768df0c576c0,STILL_EXISTS,TODO: refactor
aaffegegc,ResponsiblyAI/responsibly,ethically/we/bias.py,c9a796cc940308029f64c3412fa78eee901b1e3a,STILL_EXISTS,TODO: refactor
aaffegegd,ResponsiblyAI/responsibly,ethically/tests/test_we.py,5db2dfff9c6087caa38da0fb77b5015301798c6d,77e3584be52139e6edbdc5ffa722301acfcec1b0,TODO: only check that there is no exception;
aaffegege,ResponsiblyAI/responsibly,ethically/tests/test_we.py,5db2dfff9c6087caa38da0fb77b5015301798c6d,STILL_EXISTS,should b change to a better test cas
aaffegegj,ResponsiblyAI/responsibly,responsibly/dataset/__init__.py,705391d447899607192e591a63cce39843d3de28,STILL_EXISTS,\"\"\" || Collection of common benchmark datasets from fairness research. ||  || Each dataset object contains a :class:`pandas.DataFrame` as `df` attribute || that holds the actual data. || The dataset object will take care of loading; preprocessing || and validating the data. || The preprocessing is done by standard practices that are associated with || this data set: from its manual (e.g.; README) || or as other did in the literature. ||  || See :class:`responsibly.dataset.Dataset` || for additional attribute and complete documentation. ||  || Currently these are the available datasets: ||  ||     - ProPublica recidivism\/COMPAS dataset; ||       see: :class:`~responsibly.dataset.COMPASDataset` ||  ||     - Adult dataset; ||       see: :class:`~responsibly.dataset.AdultDataset` ||  ||     - German credit dataset; ||       see: :class:`~responsibly.dataset.GermanDataset` ||  ||     - FICO credit score dataset; ||       see :func:`~responsibly.dataset.build_FICO_dataset` ||  || Usage || ----- || .. code:: python ||  ||     >>> from responsibly.dataset import COMPASDataset ||     >>> compas_ds = COMPASDataset() ||     >>> print(compas_ds) ||     <ProPublica Recidivism\/COMPAS Dataset. 6172 rows; 56 columns in ||     which {race; sex} are sensitive attributes> ||     >>> type(compas_ds.df) ||     <class 'pandas.core.frame.DataFrame'> ||     >>> compas_ds.df['race'].value_counts() ||     African-American    3175 ||     Caucasian           2103 ||     Hispanic             509 ||     Other                343 ||     Asian                 31 ||     Native American       11 ||     Name: race; dtype: int64 || \"\"\"
aaffegejh,ResponsiblyAI/responsibly,responsibly/tests/test_fairness.py,f23acaa91f1cfb9d693b4410079ae08563d6f501,STILL_EXISTS,The cost is slightly worse; so we probably don't find the optimal
aaffegfcc,ResponsiblyAI/responsibly,ethically/dataset/__init__.py,77e3584be52139e6edbdc5ffa722301acfcec1b0,STILL_EXISTS,\"\"\" || Collection of common benchmark datasets from fairness research. ||  || Each dataset object contains a :class:`pandas.DataFrame` as `df` attribute || that holds the actual data. || The dataset object will take care of loading; preprocessing || and validating the data. || The preprocessing is done by standard practices that are associated with || this data set: from its manual (e.g.; README) || or as other did in the literature. ||  || See :class:`ethically.dataset.Dataset` || for additional attribute and complete documentation. ||  || Currently these are the available datasets: ||  ||     - ProPublica recidivism\/COMPAS dataset; ||       see: :class:`~ethically.dataset.COMPASDataset` ||  ||     - Adult dataset; ||       see: :class:`~ethically.dataset.AdultDataset` ||  ||     - German credit dataset; ||       see: :class:`~ethically.dataset.GermanDataset` ||  ||     - FICO credit score dataset; ||       see :func:`~ethically.dataset.build_FICO_dataset` ||  || Usage || ----- || .. code:: python ||  ||     >>> from ethically.dataset import COMPASDataset ||     >>> compas_ds = COMPASDataset() ||     >>> print(compas_ds) ||     <ProPublica Recidivism\/COMPAS Dataset. 6172 rows; 56 columns in ||     which {race; sex} are sensitive attributes> ||     >>> type(compas_ds.df) ||     <class 'pandas.core.frame.DataFrame'> ||     >>> compas_ds.df['race'].value_counts() ||     African-American    3175 ||     Caucasian           2103 ||     Hispanic             509 ||     Other                343 ||     Asian                 31 ||     Native American       11 ||     Name: race; dtype: int64 || \"\"\"
aaffegfgg,ResponsiblyAI/responsibly,responsibly/dataset/__init__.py,8f911803d9481f35233f5ce4304c4aab46167444,STILL_EXISTS,\"\"\" || Collection of common benchmark datasets from fairness research. ||  || Each dataset object contains a :class:`pandas.DataFrame` as `df` attribute || that holds the actual data. || The dataset object will take care of loading; preprocessing || and validating the data. || The preprocessing is done by standard practices that are associated with || this data set: from its manual (e.g.; README) || or as other did in the literature. ||  || See :class:`responsibly.dataset.Dataset` || for additional attribute and complete documentation. ||  || Currently these are the available datasets: ||  ||     - ProPublica recidivism\/COMPAS dataset; ||       see: :class:`~responsibly.dataset.COMPASDataset` ||  ||     - Adult dataset; ||       see: :class:`~responsibly.dataset.AdultDataset` ||  ||     - German credit dataset; ||       see: :class:`~responsibly.dataset.GermanDataset` ||  ||     - FICO credit score dataset; ||       see :func:`~responsibly.dataset.build_FICO_dataset` ||  || Usage || ----- || .. code:: python ||  ||     >>> from responsibly.dataset import COMPASDataset ||     >>> compas_ds = COMPASDataset() ||     >>> print(compas_ds) ||     <ProPublica Recidivism\/COMPAS Dataset. 6172 rows; 56 columns in ||     which {race; sex} are sensitive attributes> ||     >>> type(compas_ds.df) ||     <class 'pandas.core.frame.DataFrame'> ||     >>> compas_ds.df['race'].value_counts() ||     African-American    3175 ||     Caucasian           2103 ||     Hispanic             509 ||     Other                343 ||     Asian                 31 ||     Native American       11 ||     Name: race; dtype: int64 || \"\"\"
aaffegfgi,ResponsiblyAI/responsibly,responsibly/dataset/fico/__init__.py,8f911803d9481f35233f5ce4304c4aab46167444,STILL_EXISTS,by sklean convention; thresholds[0]
aaffegfia,ResponsiblyAI/responsibly,responsibly/fairness/interventions/threshold.py,8f911803d9481f35233f5ce4304c4aab46167444,STILL_EXISTS,TODO: refactor!
aaffeggdi,ResponsiblyAI/responsibly,responsibly/tests/test_we.py,8f911803d9481f35233f5ce4304c4aab46167444,STILL_EXISTS,TODO: only check that there is no exception;
aaffeggdj,ResponsiblyAI/responsibly,responsibly/tests/test_we.py,8f911803d9481f35233f5ce4304c4aab46167444,STILL_EXISTS,should b change to a better test cas
aaffeggfd,ResponsiblyAI/responsibly,setup.py,0cb9fa8b4eb11f038d7f187798aa586ef98fc06c,729ba2347ee1b9962126509b872bb0e1c89f4a82,Travis Hack (becaouse of botocore)
aaffeggfe,ResponsiblyAI/responsibly,setup.py,0cb9fa8b4eb11f038d7f187798aa586ef98fc06c,729ba2347ee1b9962126509b872bb0e1c89f4a82,TODO: Remove me in the future future
aaffegggb,ResponsiblyAI/responsibly,setup.py,badecb3335409abed0bb4e10259b51aeb08953ca,05c86c6457a7cbb4bc927cdc650627fd0a2ba904,Travis Hack (becaouse of botocore)
aaffegggc,ResponsiblyAI/responsibly,setup.py,badecb3335409abed0bb4e10259b51aeb08953ca,05c86c6457a7cbb4bc927cdc650627fd0a2ba904,TODO: Remove me in the future future
aaffeghfe,biolab/orange3-educational,doc/conf.py,f5153b938b67e7bc77ee667a8499117f0bc82468,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaffehdce,biolab/orange3-educational,orangecontrib/educational/widgets/owkmeans.py,6fd49981df3da9f51ebcf8af6db892c65abd6137,STILL_EXISTS,move centroids
aaffehijh,biolab/orange3-educational,orangecontrib/educational/widgets/tests/test_owunivariateregression.py,40cb031a4cafc07eb93722ff12e2e6ac8ef3fec5,STILL_EXISTS,TODO: output will be checked when it available in GuiTest
aaffehjbc,biolab/orange3-educational,orangecontrib/educational/widgets/utils/tests/test_kmeans.py,5341ac609bc1a16c04e754c0a89d237a27515123,STILL_EXISTS,it is even step so maybe converged but it depends on example unable to test
aaffehjfi,biolab/orange3-educational,orangecontrib/educational/widgets/owpolynomiallogisticregression.py,7dc719e6c2c209a3dff5b30ea6fe2173c8857324,STILL_EXISTS,TODO: description
aaffehjgd,biolab/orange3-educational,orangecontrib/educational/widgets/owpolynomiallogisticregression.py,7dc719e6c2c209a3dff5b30ea6fe2173c8857324,STILL_EXISTS,TODO: set false when end of development
aaffeiada,biolab/orange3-educational,orangecontrib/educational/widgets/utils/contour.py,9d281b23a2f8fa5bffc17921da16356a86c12f8f,STILL_EXISTS,corners table is coded as move in clockwise direction
aaffeiafe,biolab/orange3-educational,orangecontrib/educational/widgets/utils/contour.py,9d281b23a2f8fa5bffc17921da16356a86c12f8f,7ac107a67e16e4bed4becbc2089d350d54cb1d5f,move in position up\/left if 0 in array or right\/down if 1
aaffeibea,biolab/orange3-educational,orangecontrib/educational/widgets/utils/tests/test_polynomialtransform.py,d48fbd70b0d8b065fad7ce0c1d422a97856f03b7,STILL_EXISTS,data with two columns
aaffeibed,biolab/orange3-educational,orangecontrib/educational/widgets/utils/tests/test_polynomialtransform.py,d48fbd70b0d8b065fad7ce0c1d422a97856f03b7,STILL_EXISTS,check if number of cell columns sufficient
aaffeicab,biolab/orange3-educational,orangecontrib/educational/widgets/owpolynomialclassification.py,38aa1a660541f812e98c82abc6ffbd6df64d9ae2,STILL_EXISTS,hack to destroy the legend for coloraxis
aaffeiccg,biolab/orange3-educational,orangecontrib/educational/widgets/owgradientdescent.py,ecc4aa4e9b7be3024e95720cb449ca4bb375994a,43ef0cdbca5cb2fd6135b28b66a1d66fe27a55a3,TODO: set false when end of development
aaffeicdc,biolab/orange3-educational,orangecontrib/educational/widgets/owgradientdescent.py,ecc4aa4e9b7be3024e95720cb449ca4bb375994a,98d7e96c7d4e8e6a86bc35430da825ec490d9698,hack to destroy the legend for coloraxis
aaffeichi,biolab/orange3-educational,orangecontrib/educational/widgets/utils/logistic_regression.py,ecc4aa4e9b7be3024e95720cb449ca4bb375994a,a3892519a236a358f763c6ba33ec950266b2da0a,TODO: modify for more thetas
aaffeieff,biolab/orange3-educational,orangecontrib/educational/widgets/utils/tests/test_logistic_regression.py,a8ca9d6f76bd3a9cedd4db0d6c42f4d9e1b13449,c4030a52771f2f16c07465ca62975f269964e48d,check if really minimal; function is monotonic so everywhere around
aaffejdjg,biolab/orange3-educational,orangecontrib/educational/widgets/owgradientdescent.py,dd319c7231abb6460a76598f2325f0c88e41c555,bfe74c4aaa7ad3a60389297d0065085307548232,hack to destroy the legend for coloraxis
aaffejehb,biolab/orange3-educational,orangecontrib/educational/widgets/utils/logistic_regression.py,dd319c7231abb6460a76598f2325f0c88e41c555,c6ecd8025436e1033b56d1cade0af37df8d5bc73,TODO: modify for more thetas
aaffejgfa,biolab/orange3-educational,orangecontrib/educational/widgets/utils/tests/test_logistic_regression.py,8765d5f5b3da3e932ff29261199af13e0af5f4e4,STILL_EXISTS,check if really minimal; function is monotonic so everywhere around
aaffejjbe,biolab/orange3-educational,orangecontrib/educational/widgets/utils/tests/test_linear_regression.py,2bf7251c926ee08572fdc6237db91ca14efd907b,STILL_EXISTS,check if really minimal; function is monotonic so everywhere around
aafffbbhi,biolab/orange3-educational,orangecontrib/educational/widgets/owrandomdata.py,94a276a2a32d9b089d84745520266475bd1f5811,STILL_EXISTS,self.add_combo is needed so that tests can manipulate it
aafffbcdj,biolab/orange3-educational,orangecontrib/educational/widgets/owcreatetable.py,b3c8f789af13efbaac7924e345de822f96faae28,STILL_EXISTS,todo: remove if\/when domain[idx] is fixed
aafffbcea,biolab/orange3-educational,orangecontrib/educational/widgets/owcreatetable.py,b3c8f789af13efbaac7924e345de822f96faae28,STILL_EXISTS,Todo: change to len(domain) when Domain len's behaviour is fixed
aafffbcha,dkpro/dkpro-cassis,cassis/cas/xmi/deserializer.py,92e7c3afb3dadcc87d6dda3776c0d1481fcfc886,STILL_EXISTS,TODO: Error checking
aafffbchj,dkpro/dkpro-cassis,cassis/typesystem/typesystem.py,c40ab3d55c1b2e71b8339208ebca1470551e55d6,STILL_EXISTS,TODO: Fix fallback for lenient parsing
aafffbcij,dkpro/dkpro-cassis,cassis/typesystem.py,309e1ffb4bc320a1a7ea57ccc51cbea00ab4cb50,STILL_EXISTS,TODO: Fix fallback for lenient parsing
aafffbcjg,dkpro/dkpro-cassis,cassis/xmi.py,309e1ffb4bc320a1a7ea57ccc51cbea00ab4cb50,STILL_EXISTS,TODO: Error checking
aafffbdea,dkpro/dkpro-cassis,cassis/typesystem.py,21374eebb13cce9004d7a2b2cea7dc26558654a1,STILL_EXISTS,Free the XML tree element from memory as it is not needed anymore
aafffbfdj,dkpro/dkpro-cassis,cassis/xmi.py,b44ff9154c057119e28e11327e3a78d9863c40fd,STILL_EXISTS,TODO: Parse feature values to their real type here; e.g. parse ints or floats
aafffbgfi,dkpro/dkpro-cassis,cassis/typesystem.py,5ab3242deea1eb26510567436578c2c25cb32cc7,STILL_EXISTS,creating them on the fly is on average better
aafffbiid,SAP-samples/machine-learning-dgm,lib/data_converter.py,926ba3a9d180ba499533ae280726bd8baab2cb4c,STILL_EXISTS,conversion needed to use np.inf after
aafffbijh,SAP-samples/machine-learning-dgm,lib/data_converter.py,926ba3a9d180ba499533ae280726bd8baab2cb4c,STILL_EXISTS,This is ugly; but
aafffbjcc,SAP-samples/machine-learning-dgm,lib/data_io.py,926ba3a9d180ba499533ae280726bd8baab2cb4c,STILL_EXISTS,XXX: relative path
aafffbjgg,SAP-samples/machine-learning-dgm,lib/data_manager.py,926ba3a9d180ba499533ae280726bd8baab2cb4c,STILL_EXISTS,Hopefully this never happens because this is done in a very inefficient way
aafffccfh,bsc-wdc/dislib,dislib/data/base.py,0ed9024112cc92e31914e2d7c99d4c00dbd7aade,STILL_EXISTS,efficient than parsing the lines manually
aafhddfjd,photogeniq/texturize,src/texturize/commands.py,1c44bea52a51cf417f3b45f7197223895938308a,STILL_EXISTS,Use the alpha-mask directly from user.  Could blur it here for better results!
aafhddfje,photogeniq/texturize,src/texturize/commands.py,1c44bea52a51cf417f3b45f7197223895938308a,STILL_EXISTS,This currently uses a very crisp boolean mask; looks better when edges are
aagbjdbgf,lab-ml/labml,lab/tf_util.py,6e12775d44e5a2042e0cba8885b1eec7a39b9f02,STILL_EXISTS,grow GPU memory as needed
aagbjdbgi,lab-ml/labml,lab_getting_started.py,6e12775d44e5a2042e0cba8885b1eec7a39b9f02,STILL_EXISTS,\"\"\" || # Lab \uD83E\uDDEA ||  || This library lets you organize TensorFlow machine learning projects. ||  || It is based on a bunch of utility functions and classes || I wrote while trying some machine learning algorithms. || I recently made it to a separate repo because I've been || reusing them on different projects; and it was easier || to keep track of them as a single project. || Most of this is only about two weeks old || so it'll have to go through a lot of improvements. ||  || ### What does it do? || * It keeps checkpoints and TensorBoard summaries and logs organized || * It helps keep track of experiments were with reference to git commits || * Produce pretty console outputs || * Maintains and writes histograms and moving averages || * Monitor time taken for different sections of code || * Estimate time remaining for experiments to run || * Help make code more readable ||  || ### Why I made it? || I started coding existing reinforcement learning algorithms ||  to play Atari games for fun. || It was not easy to keep track of things when I started ||  trying variations; fixing bugs etc. || This library help organize your experiments. || It organizes the folders of the checkpoints; logs ||  and TensorBoard summaries by each experiment. || It also keeps track of the git commits when each experiment ||  was run; so if some other change in code; affected the results ||  of a experiment you can easily track what caused it. ||  || I also wrote a logger to display pretty results on screen and ||  to make it easy to write TensorBoard summaries. || It also keeps track of training times which makes it easy to spot ||  what's taking up most resources. || Here's the output of this sample program (sample.py): ||  || <img style=\"max-width:100%\" src=\"images\/log.png\" \/> || \"\"\"
aagbjdedf,lab-ml/labml,lab/guards.py,effaa6b8a02ecf4aaf9eb1a300ab3856e3946ccc,STILL_EXISTS,TODO: use caller for name spaces
aagbjdedg,lab-ml/labml,lab/guards.py,d07486f04e6b0e8605bb987b8b1192462447abd6,435f775731a725c1bcb27df168da9c0d79ec92ce,FIXME Inspect messes with garbage collection
aagbjdgej,lab-ml/labml,samples/mnist_configs.py,7eb3875199d255dc3ca6b9f5e577884b5652bbd7,86ac6532ca2636b21b63d237db45a4fcf22f8ad5,The code looks cleaner; but might cause problems when you want to refactor
aagbjdhag,lab-ml/labml,samples/mnist_loop.py,e84b4f125f9a84d2862f9cf1fd62268ade73c913,bad3d2e7ad71219a4b200d51fddbe373ff8a8236,The code looks cleaner; but might cause problems when you want to refactor
aagbjdhge,lab-ml/labml,lab/analytics/__init__.py,f7d75ff0a81a22c4df4d215ad46f2aafda7c805a,ff1efd6c6b399bcdf09e55afb384b59888142459,TODO: Need to handle Queue's and mean scalars of histograms
aagbjdhid,lab-ml/labml,samples/mnist_indexed_logs.py,b8d774464306c3892d5c84b421ed0abbcb1f6cdd,86ac6532ca2636b21b63d237db45a4fcf22f8ad5,The code looks cleaner; but might cause problems when you want to refactor
aagbjdjcg,lab-ml/labml,sphinx/source/conf.py,fcda8aa7d7a132955cd5a507d7760166f5c236ed,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aagbjdjfb,lab-ml/labml,lab/_internal/configs/__init__.py,746763b2568b48401fbf731e2cf557d3e6a76f00,STILL_EXISTS,TODO: use log()
aagbjeafa,lab-ml/labml,labml/internal/logger/writers/web_api.py,0fc5a3f5d92bf5e9b912787a49ef7c3df5774f03,STILL_EXISTS,TODO: Will have to fix this when there are other statuses than 'done'
aagceiche,mideind/GreynirCorrect,src/reynir_correct/__init__.py,52ec343fcf88f7118a18e4c0cdc5c09c00cd7567,3fe8ef5905a2a7b0f86e8358a729bc8ef74e04d6,from reynir import Reynir  # TODO Putting back in when pip issues are resolved
aagceichg,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,52ec343fcf88f7118a18e4c0cdc5c09c00cd7567,b88314dd5937da02ede758843593285eecd5dcab,!!! TODO skrifa yfir smi\u00F0inn fyrir TOK \u00FAr tokenizer.py \u00ED Tokenizer-pakka
aagceidef,mideind/GreynirCorrect,src/reynir_correct/spelling.py,71c351debfdcbcdf24360dbbe62508f38588bab3,STILL_EXISTS,Best candidate is very unlikely: return the original word
aagceidfi,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,0cc5125b4e86c556dd6d9fdfcd45e2b95a9fd944,48ef9c3c8ca2dd32ad690011cc9cef3b5f203565,!!! TODO skrifa yfir smi\u00F0inn fyrir TOK \u00FAr tokenizer.py \u00ED Tokenizer-pakka
aagceifdj,mideind/GreynirCorrect,src/reynir_correct/spelling.py,48ef9c3c8ca2dd32ad690011cc9cef3b5f203565,376e7191a8d92ca861b0b88419f31deb25444b90,!!! TODO: This should really be a case-insensitive check;
aagceifff,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,07101d33eff417bb4e73b6478d81f27cb65f0d27,8875be0696b6bd52e118432aa97b44e4bf13880f,!!! TODO skrifa yfir smi\u00F0inn fyrir TOK \u00FAr tokenizer.py \u00ED Tokenizer-pakka
aagceigaf,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,aa8a26d0e5c32228eb1cb2412e6a20bc761f19c2,39780ca52fadc5bcf6d12f0b1ab4ed5c7ebbfab8,!!! TODO: There should also be a mechanism for
aagceiged,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,376e7191a8d92ca861b0b88419f31deb25444b90,STILL_EXISTS,!!! TODO
aagceihdd,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,ba77961f66d5369d823a2c880eda70b2aba79919,STILL_EXISTS,S003: Erroneously formed word forms picked up by ErrorForms. Should be corrected. TODO split up by nature.
aagceihdf,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,ba77961f66d5369d823a2c880eda70b2aba79919,STILL_EXISTS,Check wrong word forms; TODO split the list up by nature of error
aagceihff,mideind/GreynirCorrect,src/reynir_correct/settings.py,a52bf9ad294b9013b59d9c04b4de8f96fee8257e,STILL_EXISTS,TODO: Fully implement this
aagceiibf,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,737fded377d0444b7aeb048051f5900e9a5a7e8b,STILL_EXISTS,!!! TODO: Consider whether to overwrite previous error;
aagceiiff,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,0dc874243b5cabd3cc919e5102a2cdb27063daee,STILL_EXISTS,!!! TODO: Some error forms are present in B\u00CDN but in a different
aagceiifg,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,0dc874243b5cabd3cc919e5102a2cdb27063daee,STILL_EXISTS,!!! TODO: case (for instance; '\u00E1' as a nominative of '\u00E6r').
aagceiifh,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,0dc874243b5cabd3cc919e5102a2cdb27063daee,STILL_EXISTS,!!! TODO: We are not handling those here.
aagceiiga,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,0dc874243b5cabd3cc919e5102a2cdb27063daee,STILL_EXISTS,!!! TODO: This could be made more efficient if all
aagceiigb,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,0dc874243b5cabd3cc919e5102a2cdb27063daee,STILL_EXISTS,!!! TODO: taboo word forms could be generated ahead of time
aagceiigc,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,0dc874243b5cabd3cc919e5102a2cdb27063daee,STILL_EXISTS,!!! TODO: and checked via a set lookup
aagceiiif,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,f67913190f54cbbf1140870e9031564abaa4da5a,STILL_EXISTS,!!! TODO: at_sentence_start
aagceiiih,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,f67913190f54cbbf1140870e9031564abaa4da5a,STILL_EXISTS,!!! TODO: handle all-uppercase
aagceijac,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,f67913190f54cbbf1140870e9031564abaa4da5a,STILL_EXISTS,Fix single-word errors
aagceijec,mideind/GreynirCorrect,test/test_correct.py,51af1af62564f6cb721f181087a113a73246c713,STILL_EXISTS,!!! TODO: We temporarily allow U001 as an error code for
aagceijge,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,61a45a70248862e2a573ba71d5b13e1d3f37bd94,STILL_EXISTS,Fix compound words
aagceijii,mideind/GreynirCorrect,src/reynir_correct/checker.py,bd34c0e881bd2dcb1e166dfa01d5d71a730c4e71,STILL_EXISTS,!!! TODO
aagcejaah,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,28456f3970b01ca61d2531e9cde1cfaba3657171,STILL_EXISTS,P_xxx: Phrase error codes
aagcejagf,mideind/GreynirCorrect,src/reynir_correct/checker.py,353f8848ccbd457592172af46e3ec2654a4faaf4,STILL_EXISTS,!!! TODO: depending on which rule we're talking about
aagcejagi,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,353f8848ccbd457592172af46e3ec2654a4faaf4,STILL_EXISTS,!!! TODO: This could be made more efficient if all
aagcejagj,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,353f8848ccbd457592172af46e3ec2654a4faaf4,STILL_EXISTS,!!! TODO: taboo word forms could be generated ahead of time
aagcejaha,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,353f8848ccbd457592172af46e3ec2654a4faaf4,STILL_EXISTS,!!! TODO: and checked via a set lookup
aagcejahg,mideind/GreynirCorrect,test/test_tokenizer.py,2c74c5712648bf8cde1cea395b53c025347ce14f,b3414f0caaf618ebb334dd2890e7cc31e789a477,assert \"Sj\u00EDtinn\" not in s  # !!! TODO: Ranglega 'lei\u00F0r\u00E9tt' \u00ED Sk\u00EDtinn
aagcejbei,mideind/GreynirCorrect,src/reynir_correct/spelling.py,8243fc70157c9472aefda12b20269d4c6c2c19dc,STILL_EXISTS,!!! TODO: We may need a more sophisticated probability function
aagcejbej,mideind/GreynirCorrect,src/reynir_correct/spelling.py,8243fc70157c9472aefda12b20269d4c6c2c19dc,STILL_EXISTS,!!! TODO: here; i.e. one with full or partial backoff to
aagcejbfa,mideind/GreynirCorrect,src/reynir_correct/spelling.py,8243fc70157c9472aefda12b20269d4c6c2c19dc,STILL_EXISTS,!!! TODO: bigram and unigram frequencies
aagcejbic,mideind/GreynirCorrect,src/reynir_correct/spelling.py,c9d7445b304baccacae4ee80ee323ae0677fbb36,STILL_EXISTS,!!! TODO: here; such as Kneser-Ney or Katz
aagcejbif,mideind/GreynirCorrect,src/reynir_correct/spelling.py,c9d7445b304baccacae4ee80ee323ae0677fbb36,STILL_EXISTS,!!! TODO: Optimize the following
aagcejbjc,mideind/GreynirCorrect,src/reynir_correct/spelling.py,c9d7445b304baccacae4ee80ee323ae0677fbb36,STILL_EXISTS,!!! TODO: here; i.e. one with full or partial backoff to
aagcejbjd,mideind/GreynirCorrect,src/reynir_correct/spelling.py,c9d7445b304baccacae4ee80ee323ae0677fbb36,STILL_EXISTS,!!! TODO: bigram and unigram frequencies
aagcejccc,mideind/GreynirCorrect,test/test_tokenizer.py,9950be578bf8131a5523f1d5c597067bbcbeb1cb,STILL_EXISTS,assert \"kynfer\u00F0isofbeldinu\" in s  # !!! TODO: This becomes 'kynfer\u00F0iofbeldinu'
aagcejccd,mideind/GreynirCorrect,test/test_tokenizer.py,9950be578bf8131a5523f1d5c597067bbcbeb1cb,STILL_EXISTS,assert \"\u00F6rf\u00E1\" in s  # !!! TODO: This becomes '\u00F6rva'
aagcejdbh,mideind/GreynirCorrect,src/reynir_correct/checker.py,f8c5eb608fef9dc603f9d60e23265a88f4cf6316,b9db2c9337d38de8b7538a798e68a1d583849340,!!! TODO: particular stem in B\u00CDN - this is presently not possible
aagcejdcc,mideind/GreynirCorrect,src/reynir_correct/checker.py,cb88b91784370d01fd713c9cdbbcfa1f5424d6ca,b9db2c9337d38de8b7538a798e68a1d583849340,!!! TODO: particular stem in B\u00CDN - this is presently not possible
aagcejdgh,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[4].error_code == \"W001\"    # TODO \u00FAtf\u00E6ra
aagcejdha,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert len(g) == 8    # TODO \u00FAtf\u00E6ra a\u00F0 \u00FEetta er ekki lei\u00F0r\u00E9tt
aagcejdhd,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,TODO \u00FEetta \u00E1 l\u00EDklega frekar heima \u00ED checker.py; ath. hvort \u00FEetta er b\u00E6\u00F0i s\u00E9rnafn og samnafn og \u00FEannig.
aagcejdhf,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert len(g) == 9            # TODO \u00FAtf\u00E6ra \u00FEetta \u00ED checker.py; \u00FEarf a\u00F0 hafa uppl\u00FDsingar um meanings.
aagcejdih,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,TODO f\u00E6 villuk\u00F3\u00F0ana \u00E1 fyrsta or\u00F0i\u00F0 \u00ED fasta frasanum en \u00E6tti a\u00F0 f\u00E1 \u00E1 villuor\u00F0i\u00F0 sj\u00E1lft.
aagcejdjb,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,TODO villan kemur \u00ED fyrsta or\u00F0i\u00F0 \u00ED fasta frasanum en \u00E6tti a\u00F0 f\u00E1 \u00E1 villuor\u00F0i\u00F0 sj\u00E1lft.
aagcejdje,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[7].error_code == \"P_yi\"   # TODO greinist sem S004 eins og er; \u00FEetta er inni \u00ED \u00FEekktum villum en \u00E1 eftir a\u00F0 \u00FAtf\u00E6ra me\u00F0h\u00F6ndlun. Vil \u00FE\u00E1 S003; e\u00F0a jafnvel S005.
aagcejdjf,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert \"lei\u00F0inlegt\" in s   # TODO er \u00FEetta ekki \u00ED \u00FEekktu villunum sem \u00E1 eftir a\u00F0 koma inn?
aagcejdjg,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert \"\u00FE\u00E6gilegt\" in s     # TODO sama
aagcejdjh,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert \"t\u00EDmanlega\" in s     # TODO sama
aagcejdji,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[3].error_code == S003  # TODO eftir; g\u00E6ti veri\u00F0 S005
aagcejdjj,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[5].error_code == S003  # TODO eftir; g\u00E6ti veri\u00F0 S005
aagcejeaa,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[8].error_code == S003  # TODO eftir; g\u00E6ti veri\u00F0 S005
aagcejeab,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,TODO vil f\u00E1 A001
aagcejeac,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert \"\u00FE. \u00E1 m.\" in s  # TODO ekki gert r\u00E9tt eins og er; \u00FEarf a\u00F0 b\u00E6ta vi\u00F0 \u00FEekktar villur. Get b\u00FAi\u00F0 til s\u00E9rfall \u00ED errtokenizer.py; veri\u00F0 me\u00F0 l\u00EDti\u00F0 safn \u00ED ReynirCorrect.conf.
aagcejeae,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert \"a.m.k.\" in s    # TODO b\u00FDr \u00FEetta til og S001; en setur aukapunkt og b\u00FDr til n\u00FDja setningu eftir \u00FEetta!
aagcejeaf,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[3].error_code == \"A001\"      # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejeag,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[7].error_code == \"A001\"      # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejeah,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert \"ca.\" in s     # TODO eftir a\u00F0 b\u00E6ta vi\u00F0 algengar villur
aagcejeba,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert \"o.fl.\" in s       # TODO b\u00FDr \u00FEetta til en setur aukapunkt aftan vi\u00F0!
aagcejebd,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert \"ar\u00EDi\" in s        # TODO virkar ekki
aagcejebe,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert \"Sj\u00E1lfst\u00E6\u00F0isma\u00F0ur\" in s    # TODO lei\u00F0r\u00E9ttist ekki
aagcejebg,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert \"Sj\u00EDti\" in s       # TODO lei\u00F0r\u00E9ttist \u00ED sj\u00EDta. \u00DEarf a\u00F0 l\u00E1ta einr\u00E6\u00F0anlegar villur gera \u00FEetta og svo ekkert stoppa \u00FEa\u00F0
aagcejebi,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[2].error_code == \"Z001\"  # ar\u00EDi; TODO passar ekki
aagcejecd,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[12].error_code == \"Z002\"   # M\u00FAsl\u00EDmi; TODO lei\u00F0r\u00E9ttist ekki
aagcejece,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[14].error_code == \"Z002\"     # sj\u00EDti; TODO lei\u00F0r\u00E9ttist ekki
aagcejedd,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[1].error_code == \"B001\"    # tr\u00E9\u00F0; TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejede,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[4].error_code == \"B001\"    # rekstrar; TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejedf,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[5].error_code == \"B001\"    # r\u00FAmsins; TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejedg,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert \"v\u00ED\u00F0fe\u00F0mt\" in s     # TODO ekki komi\u00F0 inn
aagcejedh,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[2].error_code == \"B001\"    # fyndist; TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejedi,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[3].error_code == \"B001\"    # v\u00ED\u00F0fe\u00F0mt; TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejedj,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[5].error_code == \"B001\"    # \u00E1rvekni; TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejeea,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert \"k\u00FDrin\" in s        # TODO eftir a\u00F0 setja inn
aagcejeeb,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert \"eldingarinnar\" in s    # TODO eftir a\u00F0 setja inn
aagcejeec,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[2].error_code == \"B001\"    # k\u00FDrin; TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejeed,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[8].error_code == \"B001\"    # eldingarinnar; TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejeee,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert \"uppl\u00FDsingar\" in s      # TODO eftir a\u00F0 setja inn
aagcejeef,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[4].error_code == \"B001\"    # \u00E1ratugarins; TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejeeg,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[6].error_code == \"B001\"    # uppl\u00FDsingar; TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejeeh,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[3].error_code == \"B001\"    # \u00E1rsins; TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejeei,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[5].error_code == \"B001\"    # fj\u00F3rum; TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejeej,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[2].error_code == \"B001\"    # \u00F3lst; TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejefa,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[8].error_code == \"B001\"    # f\u00F6\u00F0ur; TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejefb,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[10].error_code == \"B001\"   # \u00FDmissa; TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejefc,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert \"Fri\u00F0s\u00E6lli\" in s        # TODO eftir a\u00F0 setja inn
aagcejefd,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert \"k\u00EDl\u00F3metra\" in s    # TODO eftir a\u00F0 setja inn
aagcejefe,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[1].error_code == \"B001\"      # fri\u00F0s\u00E6lli; TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejeff,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[6].error_code == \"B001\"      # hundru\u00F0; TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejefg,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[9].error_code == \"B001\"      # geimnum; TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejefh,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[11].error_code == \"B001\"     # k\u00EDl\u00F3metra; TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejefi,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[13].error_code == \"B001\"     # f\u00E9nu; TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejefj,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[15].error_code == \"B001\"     # \u00E1standsins; TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejega,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[5].error_code == \"B001\"        # Selfoss; TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejegb,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[6].error_code == \"B001\"        # tuttugasta; TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejegc,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[8].error_code == \"B001\"        # samningsins; TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejegh,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert \"\u00F3tal margra\" in s      # TODO eftir a\u00F0 h\u00F6ndla
aagcejegj,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert \"feykna skemmtilegir\" in s    # TODO eftir a\u00F0 h\u00F6ndla
aagcejehb,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[1].error_code == \"M001\"    # Kvengormar; TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejehc,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[2].error_code == \"M004\"    # \u00F3tal margra; TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejehd,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[3].error_code == \"M004\"    # fj\u00F6lnota hesta; TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejehe,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[5].error_code == \"M001\"    # feiknaskemmtilegir; TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejehf,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert \"Loftlagsm\u00E1l\" in s  # TODO eftir a\u00F0 h\u00F6ndla
aagcejehg,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[1].error_code == \"M001\"  # Loftslagsm\u00E1l; TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejehh,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[3].error_code == \"M004\"  # alhli\u00F0a vandam\u00E1l; TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejehi,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[5].error_code == \"M001\"  # firnaupptekna; TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejehj,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[6].error_code == \"M004\"  # skr\u00E1ningarstarfsmenn; TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejeia,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert \"allkaldur\" in s           # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejeib,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert \"arfberunum\" in s          # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejeic,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[3].error_code == \"M002\"  # allkaldur; TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejeid,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[8].error_code == \"M003\"  # arfberunum; TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejeif,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert \"Vefurinn b\u00FD\u00F0ur\" in s  # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejeig,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert \"Vefurinn b\u00ED\u00F0ur\" not in s  # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejeih,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert \"fr\u00E9ttir mi\u00F0lanna\" in s  # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejeii,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert \"fr\u00E9ttir mi\u00F0lana\" not in s # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejeij,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[2].error_code == \"S001\"  # TODO eftir a\u00F0 \u00E1kve\u00F0a villuk\u00F3\u00F0a
aagcejeja,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[7].error_code == \"S005\"  # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejejb,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert \"hv\u00EDsl\" in s    # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejejc,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert \"kv\u00EDsl\" not in s   # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejejd,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[4].error_code == \"S005\"  # TODO eftir a\u00F0 \u00FAtf\u00E6ra villuk\u00F3\u00F0a
aagcejeje,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert \"Kyrtillinn\" in s  # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejejf,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert \"Kirtillinn\" not in s  # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejejg,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[0].error_code == \"S006\"  # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejejh,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert \"l\u00FDkur\" in s    # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejeji,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert \"l\u00EDkur\" not in s   # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejejj,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert \"hvatt\" in s    # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejfaa,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert \"kvatt\" not in s   # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejfab,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert \"hvika\" in s    # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejfag,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert \"r\u00FDmum\" in s    # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejfai,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert \"leyfa\" in s    # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejfba,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert \"kvelja\" in s   # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejfbh,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert \"setji\u00F0 \" in s      # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejfbi,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert \"setji\u00F0 \u00FEi\u00F0\" in s   # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejfbj,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[6].error_code == \"Q001\"   # TODO eftir a\u00F0 \u00FAtf\u00E6ra villuk\u00F3\u00F0a
aagcejfca,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert \"n\u00E1i\u00F0 \u00FEi\u00F0\" in s    # TODO eftir a\u00F0 \u00FAtf\u00E6ra; spelling.py vir\u00F0ist taka \u00E1 \u00FEessu
aagcejfcb,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert \"n\u00E1i\u00F0i\" not in s     # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejfcc,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[7].error_code == \"Q001\"    # TODO eftir a\u00F0 \u00FAtf\u00E6ra villuk\u00F3\u00F0a
aagcejfcf,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[3].error_code == \"T001\"    # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejfcg,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[6].error_code == \"T001\"    # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejfcj,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[5].error_code == \"T001\"    # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejfda,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert \"til a\u00F0\" in s      # TODO eftir a\u00F0 b\u00E6ta vi\u00F0
aagcejfdb,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert \"ti la\u00F0\" not in s  # TODO eftir a\u00F0 b\u00E6ta vi\u00F0
aagcejfdd,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert g[1].error_code == \"S001\"   # TODO virkar ekki
aagcejfde,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert \"r\u00EDkisstj\u00F3rn\" in s     # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejfdf,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,assert \"r\u00EDkistj\u00F3rn\" in s      # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejfdg,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,3c46c826c1ce5b2f065705173fe388bb41226068,assert g[7].error_code == \"S001\"      # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejfeg,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,TODO \u00FEarf l\u00EDklega a\u00F0 breyta til. Eftir a\u00F0 \u00FAtf\u00E6ra?
aagcejffb,mideind/GreynirCorrect,test/test_allkinds.py,27df0ea2e3fcbebf205ae499a2296b7c40086ff7,STILL_EXISTS,check_sentence(s; [3; 5; \"P_NT_X\"])        # TODO F\u00E6 ekki \u00FE\u00E1ttun \u00E1 setninguna
aagcejfie,mideind/GreynirCorrect,test/test_allkinds.py,c826cd5e4ab41b9e78648ff58c65ce03c4a5e165,c7389e015cdd5c0abbf22872e3d7f5b5c4c83cec,TODO taka \u00FAt \u00FEegar b\u00FAin a\u00F0 afl\u00FAsa pr\u00F3fanirnar
aagcejfif,mideind/GreynirCorrect,test/test_allkinds.py,c826cd5e4ab41b9e78648ff58c65ce03c4a5e165,STILL_EXISTS,check_sentence(s; [(3; 5; \"P_NT_X\")])        # TODO F\u00E6 ekki villu; 'P\u00E9tur P\u00E1li' er sameina\u00F0 \u00ED nafn \u00E1\u00F0ur en falli\u00F0 er t\u00E9kka\u00F0 vir\u00F0ist vera.
aagcejfig,mideind/GreynirCorrect,test/test_allkinds.py,c826cd5e4ab41b9e78648ff58c65ce03c4a5e165,STILL_EXISTS,check_sentence(s; [(4; 6; \"P_NT_KynInnanNafnli\u00F0ar\"); (6; 8; \"P_NT_Fall\")])     # TODO villurnar greinast ekki; vantar l\u00EDklega reglur.
aagcejfih,mideind/GreynirCorrect,test/test_allkinds.py,c826cd5e4ab41b9e78648ff58c65ce03c4a5e165,STILL_EXISTS,TODO  FsMeFallstj\u00F3rn greinir villu; en n\u00E6r ekki yfir Eflingu; eitthva\u00F0 skr\u00FDti\u00F0 \u00E1 fer\u00F0inni! Vil f\u00E1 reglu sem heitir FallInnanNafnli\u00F0ar og \u00E1 a\u00F0 n\u00E1 yfir 5; 8.
aagcejfii,mideind/GreynirCorrect,test/test_allkinds.py,c826cd5e4ab41b9e78648ff58c65ce03c4a5e165,STILL_EXISTS,check_sentence(s; [(5; 7; \"P_NT_Pr\u00F3sent\"); (11; 13; \"P_NT_Pr\u00F3sent\")])     # TODO hvorug villan greinist. Vil reglu sem heitir Pr\u00F3sent... e\u00F0a eitthva\u00F0 \u00ED \u00FE\u00E1 \u00E1ttina. Ath. hvort \u00FEa\u00F0 s\u00E9 nokku\u00F0 regla sem heitir \u00FEa\u00F0 n\u00FAna.
aagcejfij,mideind/GreynirCorrect,test/test_allkinds.py,c826cd5e4ab41b9e78648ff58c65ce03c4a5e165,STILL_EXISTS,check_sentence(s; [(2; 2; \"P_NT_Einhver\")])     # TODO villan greinist sem S001; viljum vi\u00F0 h\u00F6ndla \u00FEetta sem beygingarsamr\u00E6misvillu frekar? \u00DEetta er \u00F3samhengish\u00E1\u00F0.
aagcejfja,mideind/GreynirCorrect,test/test_allkinds.py,c826cd5e4ab41b9e78648ff58c65ce03c4a5e165,STILL_EXISTS,check_sentence(s; [(2; 2; \"P_NT_Einhver\")])        # TODO villan greinist sem S001; viljum vi\u00F0 h\u00F6ndla \u00FEetta frekar sem beygingarsamr\u00E6misvillu? \u00DEetta er \u00F3samhengish\u00E1\u00F0.
aagcejfjb,mideind/GreynirCorrect,test/test_allkinds.py,c826cd5e4ab41b9e78648ff58c65ce03c4a5e165,STILL_EXISTS,TODO virkar en \u00E9g skil ekki lengdina; af hverju er '\u00ED' haft me\u00F0? Hva\u00F0 er \u00CDT\u00F6lu a\u00F0 gera?
aagcejfjc,mideind/GreynirCorrect,test/test_allkinds.py,c826cd5e4ab41b9e78648ff58c65ce03c4a5e165,STILL_EXISTS,TODO villan greinist; en \u00E6tti a\u00F0 vera sta\u00F0sett \u00E1 s\u00F6gninni til a\u00F0 h\u00E6gt s\u00E9 a\u00F0 lei\u00F0r\u00E9tta hana... Hvernig er \u00FEetta lei\u00F0r\u00E9tt? Er bara \u00E1bending?
aagcejfjd,mideind/GreynirCorrect,test/test_allkinds.py,c826cd5e4ab41b9e78648ff58c65ce03c4a5e165,STILL_EXISTS,TODO villan greinist en \u00E6tti a\u00F0 vera sta\u00F0sett \u00E1 s\u00F6gninni svo h\u00E6gt s\u00E9 a\u00F0 lei\u00F0r\u00E9tta hana. Sko\u00F0a hvernig\/hvort villan er lei\u00F0r\u00E9tt.
aagcejfjf,mideind/GreynirCorrect,test/test_allkinds.py,c826cd5e4ab41b9e78648ff58c65ce03c4a5e165,STILL_EXISTS,TODO virkar vel; en skil ekki lengdina. Af hverju er \u00FEa\u00F0 ekki 0; 1? Hvernig birtist \u00FEetta \u00ED vi\u00F0m\u00F3tinu?
aagcejfjg,mideind/GreynirCorrect,test/test_allkinds.py,c826cd5e4ab41b9e78648ff58c65ce03c4a5e165,STILL_EXISTS,TODO virkar; en athuga lengdina; og hvernig \u00FEetta er lei\u00F0r\u00E9tt \u00ED vi\u00F0m\u00F3ti. Er s\u00F6gninni l\u00EDka breytt?
aagcejfji,mideind/GreynirCorrect,test/test_allkinds.py,c826cd5e4ab41b9e78648ff58c65ce03c4a5e165,STILL_EXISTS,check_sentence(s; [(2; 2; \"P_WRONG_PARTICLE_uppi\")])    # TODO greinist ekki; \u00FEetta \u00E1 algerlega eftir a\u00F0 \u00FAtf\u00E6ra betur \u00FEegar \u00FEetta er komi\u00F0 inn \u00ED Verbs.conf. \u00DEetta er l\u00EDklega ekki r\u00E9ttur villuk\u00F3\u00F0i.
aagcejgaa,mideind/GreynirCorrect,test/test_allkinds.py,c826cd5e4ab41b9e78648ff58c65ce03c4a5e165,STILL_EXISTS,check_sentence(s; [(1; 1; \"P_WRONG_FORM\")])       # TODO erfitt a\u00F0 eiga vi\u00F0; l\u00EDklega ekki r\u00E9ttur villuk\u00F3\u00F0i; b\u00E6ta vi\u00F0 Verbs.conf.
aagcejgab,mideind/GreynirCorrect,test/test_allkinds.py,c826cd5e4ab41b9e78648ff58c65ce03c4a5e165,STILL_EXISTS,check_sentence(s; [(3; 4; \"P_WRONG_PP_af\")])      # TODO villan greinist ekki. Komi\u00F0 \u00ED Verbs.conf? L\u00EDklega ekki r\u00E9ttur villuk\u00F3\u00F0i.
aagcejgac,mideind/GreynirCorrect,test/test_allkinds.py,c826cd5e4ab41b9e78648ff58c65ce03c4a5e165,STILL_EXISTS,check_sentence(s; [(2; 3; \"P_WRONG_PARTICLE_til_\u00FAtlanda\")])       # TODO villan greinist ekki. Komi\u00F0 \u00ED Verbs.conf? L\u00EDklega ekki r\u00E9ttur villuk\u00F3\u00F0i.
aagcejgad,mideind/GreynirCorrect,test/test_allkinds.py,c826cd5e4ab41b9e78648ff58c65ce03c4a5e165,STILL_EXISTS,check_sentence(s; [(2; 4; \"P_WRONG_PP_\u00ED_skyn\")])      # TODO villan greinist ekki. Komi\u00F0 \u00ED Verbs.conf? L\u00EDklega ekki r\u00E9ttur villuk\u00F3\u00F0i.
aagcejgae,mideind/GreynirCorrect,test/test_allkinds.py,c826cd5e4ab41b9e78648ff58c65ce03c4a5e165,STILL_EXISTS,check_sentence(s; [(2; 5; \"P_NT_HvorAnnar\")])      # TODO engin villa greinist; eftir a\u00F0 \u00FAtf\u00E6ra villureglu
aagcejgaf,mideind/GreynirCorrect,test/test_allkinds.py,c826cd5e4ab41b9e78648ff58c65ce03c4a5e165,STILL_EXISTS,TODO \u00FEetta virkar; en sko\u00F0a lengdina.
aagcejgag,mideind/GreynirCorrect,test/test_allkinds.py,c826cd5e4ab41b9e78648ff58c65ce03c4a5e165,STILL_EXISTS,TODO \u00E1 kannski a\u00F0 greina \u00FEetta \u00F6\u00F0ruv\u00EDsi? Fastur frasi? Sko\u00F0a l\u00EDka lengdina.
aagcejgah,mideind/GreynirCorrect,test/test_allkinds.py,c826cd5e4ab41b9e78648ff58c65ce03c4a5e165,STILL_EXISTS,check_sentence(s; [(2; 3; \"P_A\u00F0\")])        # TODO villan greinist ekki; eftir a\u00F0 \u00FAtf\u00E6ra. \u00C6tti a\u00F0 vera \u00ED Verbs.conf
aagcejgai,mideind/GreynirCorrect,test/test_allkinds.py,c826cd5e4ab41b9e78648ff58c65ce03c4a5e165,STILL_EXISTS,check_sentence(s; [(3; 4; \"P_A\u00F0\")])        # TODO villan greinist ekki; eftir a\u00F0 \u00FAtf\u00E6ra. \u00C6tti a\u00F0 vera \u00ED Verbs.conf
aagcejgaj,mideind/GreynirCorrect,test/test_allkinds.py,c826cd5e4ab41b9e78648ff58c65ce03c4a5e165,STILL_EXISTS,check_sentence(s; [(2; 2; \"P_NT_N\u00FDja\u00DEolmynd\")])         # TODO villan greinist ekki; eftir a\u00F0 \u00FAtf\u00E6ra villureglu
aagcejgba,mideind/GreynirCorrect,test/test_allkinds.py,c826cd5e4ab41b9e78648ff58c65ce03c4a5e165,STILL_EXISTS,check_sentence(s; [(0; 0; \"P_NT_N\u00FDja\u00DEolmynd\")])         # TODO villan greinist ekki; eftir a\u00F0 \u00FAtf\u00E6ra villureglu
aagcejgbb,mideind/GreynirCorrect,test/test_allkinds.py,c826cd5e4ab41b9e78648ff58c65ce03c4a5e165,STILL_EXISTS,check_sentence(s; [(1; 3; \"P_Nafnor\u00F0ast\u00EDll\")])        # TODO greinist ekki; eftir a\u00F0 \u00FAtf\u00E6ra -- \u00FEetta g\u00E6ti virka\u00F0 vel \u00ED Verbs.conf!
aagcejgbc,mideind/GreynirCorrect,test/test_allkinds.py,c826cd5e4ab41b9e78648ff58c65ce03c4a5e165,dd0dd1ec0f4b199d5096c8b659ab3e333876ddfe,check_sentence(s; [(0; 33; \"E004\")])      # TODO \u00FEetta vir\u00F0ist ekki virka; strandar \u00E1 \u00FEv\u00ED a\u00F0 setningin greinist ekki!
aagcejgbd,mideind/GreynirCorrect,test/test_allkinds.py,c826cd5e4ab41b9e78648ff58c65ce03c4a5e165,STILL_EXISTS,check_sentence(s; [(0; 0; \"P_SUBJ_CASE_nf_\u00FEf\")])            # TODO setningin f\u00E6r ekki \u00FE\u00E1ttun. \u00DEetta er inni \u00ED Verbs.conf; af hverju er \u00FEetta ekki h\u00F6ndla\u00F0?
aagcejgdd,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert len(g) == 9            # TODO \u00FAtf\u00E6ra \u00FEetta \u00ED checker.py; \u00FEarf a\u00F0 hafa uppl\u00FDsingar um meanings.
aagcejgdj,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,3c46c826c1ce5b2f065705173fe388bb41226068,assert g[1].error_code == \"S001\"   # TODO virkar ekki; endar sem S004
aagcejgea,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert \"r\u00EDkisstj\u00F3rn\" in s     # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejgeb,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert \"r\u00EDkistj\u00F3rn\" in s      # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejged,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert \"sat Gunna og\" in s              # TODO Virkar ekki eins og er \u00FAt af h\u00E1staf \u00ED unique_errors
aagcejgee,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert \"sat Gunan og\" not in s          # TODO Virkar ekki eins og er \u00FAt af h\u00E1staf \u00ED unique_errors
aagcejgef,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,TODO \u00DEetta virkar \u00ED vi\u00F0m\u00F3ti en allt \u00ED einu ekki h\u00E9r.
aagcejgeg,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert g[3].error_code == \"S001\"  # TODO virkar ekki \u00FAt af h\u00E1staf \u00ED unique_errors; setja inn \u00FEegar \u00FEa\u00F0 er laga\u00F0
aagcejgfb,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,TODO F\u00E6 \u00E9g einhvern t\u00EDmann W001??
aagcejgfc,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert \"\u00C9g fylgdist me\u00F0\" in s       # TODO breytir \u00FEessu \u00ED fylgist; er a\u00F0eins l\u00EDklegra \u00FAt af t\u00ED\u00F0ni. B\u00E6ta vi\u00F0 \u00FEekktar villur\/heil beygingard\u00E6mi?
aagcejgfe,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,TODO getur lei\u00F0r\u00E9tt g\u00E6rk\u00F6ld\u2192g\u00E6rkv\u00F6ld; en g\u00E6rkv\u00F6ldi vir\u00F0ist ekki vera \u00ED safninu.
aagcejgff,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert g[2].error_code == \"S002\"  # TODO endar sem S004; \"Checking rare word 'fyldist'\"
aagcejgfg,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert g[4].error_code == \"S002\"  # TODO endar sem S004
aagcejgfh,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert g[6].error_code == \"S002\"  # TODO endar sem S004
aagcejgfi,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert g[8].error_code == \"S002\"  # TODO endar sem S004
aagcejgfj,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert \"\u00ED viku og\" in s     # TODO lei\u00F0r\u00E9ttist \u00ED 'eigu'
aagcejgga,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert \"reglulega \u00ED\" in s     # TODO lei\u00F0r\u00E9ttist ekki; kemur me\u00F0 upp\u00E1stungu
aagcejggb,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert \"\u00ED l\u00EDkamsr\u00E6kt\" in s    # TODO lei\u00F0r\u00E9ttist ekki; of fl\u00F3kin villa.
aagcejggc,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert g[5].error_code == \"S002\"      # TODO endar sem S004
aagcejggd,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert g[7].error_code == \"S002\"      # TODO endar sem S004
aagcejgge,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert g[9].error_code == \"S002\"      # TODO endar sem S004
aagcejggf,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert g[10].error_code == \"S002\"     # TODO endar sem S003! Endar sem bara upp\u00E1stunga
aagcejggj,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert \"hv\u00EDsl\" in s    # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejgha,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert \"kv\u00EDsl\" not in s   # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejghb,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert g[4].error_code == \"S006\"  # TODO eftir a\u00F0 \u00FAtf\u00E6ra villuk\u00F3\u00F0a
aagcejghc,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert \"Kyrtillinn\" in s  # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejghd,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert \"Kirtillinn\" not in s  # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejghe,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert g[0].error_code == \"S006\"  # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejghf,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert \"l\u00FDkur\" in s    # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejghg,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert \"l\u00EDkur\" not in s   # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejghh,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert \"hvatt\" in s    # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejghi,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert \"kvatt\" not in s   # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejghj,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert \"hvika\" in s    # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejgie,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert \"r\u00FDmum\" in s    # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejgig,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert \"leyfa\" in s    # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejgii,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert \"kvelja\" in s   # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejgje,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert \"fj\u00F6gurleyti\u00F0\" in s    # TODO sama
aagcejgjh,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert g[8].error_code == S007  # TODO greinist ekki; eftir a\u00F0 setja inn
aagcejgjj,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert \"annarra\" in s          # TODO lei\u00F0r\u00E9ttist ekki; er \u00ED ErrorForms en er samhengish\u00E1\u00F0 villa
aagcejhaa,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert \"einskis\" in s         # TODO lei\u00F0r\u00E9ttist ekki.
aagcejhac,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,for ix; t in enumerate(g):                 # TODO virkar ekki; eftir a\u00F0 \u00FAtf\u00E6ra villuk\u00F3\u00F0ann og skipta villunum upp eftir e\u00F0li
aagcejhad,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,if ix in errors:                       # TODO \u00FEarf \u00FE\u00E1 a\u00F0 uppf\u00E6ra d\u00E6min.
aagcejhai,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert \"Hann finnur\" in s      # TODO F\u00E6 upp\u00E1stungu en ekki n\u00F3gu sterka lei\u00F0r\u00E9ttingu
aagcejhaj,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert \"fyrir\" in s           # TODO Virkar ekki
aagcejhba,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert g[2].error_code == \"S004\"  # TODO virkar ekki; f\u00E6 S001
aagcejhbb,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert g[3].error_code == \"S004\"  # TODO virkar ekki; f\u00E6 W001 vir\u00F0ist vera.
aagcejhbc,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert \"a\u00F0ra\" in s                # TODO Vir\u00F0ist ekki virka!
aagcejhbd,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert \"glugga\" in s              # TODO Vir\u00F0ist ekki virka!
aagcejhbe,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert \"leist\" in s               # TODO Vir\u00F0ist ekki virka!
aagcejhbf,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert g[3].error_code == \"S004\"  # TODO Vir\u00F0ist ekki virka!
aagcejhbg,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert g[4].error_code == \"S004\"  # TODO Vir\u00F0ist ekki virka! Finn S001
aagcejhbh,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert g[6].error_code == \"S004\"  # TODO Vir\u00F0ist ekki virka! Finn S001
aagcejhbj,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,a885abccee65589b0b2db30ba4fbdd17e2ab7f6a,assert \"ar\u00EDi\" in s        # TODO lei\u00F0r\u00E9ttist ekki \u00FEv\u00ED a\u00F0 Ar\u00EDi var til sta\u00F0ar. B\u00FAin a\u00F0 laga \u00ED BinErrata.conf; svo ver\u00F0ur ekki vandam\u00E1l \u00FEegar n\u00E6sta \u00FAtg\u00E1fa ord.compressed kemur.
aagcejhca,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,a885abccee65589b0b2db30ba4fbdd17e2ab7f6a,assert g[2].error_code == \"Z001\"  # ar\u00EDi; TODO lei\u00F0r\u00E9ttist ekki eins og er; gerist me\u00F0 n\u00FDrri \u00FAtg\u00E1fu ord.compressed.
aagcejhcb,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert \"feiknaskemmtilegir\" in s      # TODO virkar ekki eins og er
aagcejhcf,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert g[6].error_code == \"M001\"    # TODO f\u00E6 C002
aagcejhcg,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert \"allkaldur\" in s           # TODO virkar ekki; gerir all a\u00F0 U001
aagcejhci,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,9d7b3d64bb12f8635e82c78bfc773f309b54e676,assert \"h\u00E1lfber\" in s             # TODO virkar ekki
aagcejhda,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,9d7b3d64bb12f8635e82c78bfc773f309b54e676,assert g[3].error_code == \"M002\"  # TODO eftir a\u00F0 \u00FAtf\u00E6ra villuk\u00F3\u00F0a
aagcejhdb,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,9d7b3d64bb12f8635e82c78bfc773f309b54e676,assert g[10].error_code == \"M002\" # TODO eftir a\u00F0 \u00FAtf\u00E6ra villuk\u00F3\u00F0a
aagcejhdc,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert \"afarkosti\" in s           # TODO virkar ekki
aagcejhdd,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,9d7b3d64bb12f8635e82c78bfc773f309b54e676,assert \"afar kosti\" in s          # TODO virkar ekki
aagcejhde,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,9d7b3d64bb12f8635e82c78bfc773f309b54e676,assert \"forvinnunni\" in s         # TODO virkar ekki
aagcejhdf,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert \"for vinnunni\" not in s    # TODO virkar ekki
aagcejhdg,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,9d7b3d64bb12f8635e82c78bfc773f309b54e676,assert g[4].error_code == \"M002\"  # TODO Eftir a\u00F0 \u00FAtf\u00E6ra
aagcejhdh,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,9d7b3d64bb12f8635e82c78bfc773f309b54e676,assert g[6].error_code == \"M002\"  # TODO Eftir a\u00F0 \u00FAtf\u00E6ra
aagcejhdi,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert \"barnd\u00F3m\" in s                 # TODO Eftir a\u00F0 \u00FAtf\u00E6ra
aagcejhea,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert \"gr\u00E6nkeri\" in s                # TODO Eftir a\u00F0 \u00FAtf\u00E6ra
aagcejhec,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert g[4].error_code == \"M003\"      # TODO Eftir a\u00F0 \u00FAtf\u00E6ra villuk\u00F3\u00F0a
aagcejhed,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert g[16].error_code == \"M003\"     # TODO Eftir a\u00F0 \u00FAtf\u00E6ra villuk\u00F3\u00F0a
aagcejhef,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert g[1].error_code == \"M004\"      # TODO Eftir a\u00F0 \u00FAtf\u00E6ra villuk\u00F3\u00F0a; f\u00E6 C002
aagcejheg,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert g[4].error_code == \"M004\"      # TODO Eftir a\u00F0 \u00FAtf\u00E6ra villuk\u00F3\u00F0a; f\u00E6 C002
aagcejheh,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert \"k\u00FAadellu\" in s            # TODO Virkar ekki eins og er.
aagcejhej,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert g[1].error_code == \"T001\"    # TODO Eftir a\u00F0 \u00FAtf\u00E6ra. Tab\u00FAor\u00F0 \u00E6tti a\u00F0 merkja sem sl\u00EDk.
aagcejhfa,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert g[4].error_code == \"M004\"    # TODO Eftir a\u00F0 \u00FAtf\u00E6ra; f\u00E6 U001. En beygingarvillur?
aagcejhfc,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert \"\u00F3tal margir\" in s         # TODO virkar ekki
aagcejhfd,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert \"afar lei\u00F0inlegir\" in s    # TODO virkar ekki
aagcejhff,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert g[5].error_code == \"M005\"  # TODO Eftir a\u00F0 \u00FAtf\u00E6ra
aagcejhfg,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert g[8].error_code == \"M005\"  # TODO Eftir a\u00F0 \u00FAtf\u00E6ra
aagcejhfh,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert \"of g\u00F3\u00F0ur\" in s        # TODO Virkar ekki eins og er
aagcejhfj,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert \"ofur svalur\" in s       # TODO Reglurnar segja til um a\u00F0 \u00FEetta s\u00E9 r\u00E9ttara en \u00E9g er bara ekki samm\u00E1la!
aagcejhgb,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert g[8].error_code == \"M005\"  # TODO Eftir a\u00F0 \u00FAtf\u00E6ra villuk\u00F3\u00F0a
aagcejhgc,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert g[1].error_code == \"T001\"    # TODO eftir a\u00F0 \u00FAtf\u00E6ra; g\u00E6ti veri\u00F0 M004
aagcejhgd,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert g[5].error_code == \"T001\"    # TODO eftir a\u00F0 \u00FAtf\u00E6ra; g\u00E6ti veri\u00F0 M004
aagcejhgi,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,TODO breyta pr\u00F3funinni svo falli a\u00F0 mynsturgreininum.
aagcejhgj,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert \"Vefurinn b\u00FD\u00F0ur\" in s  # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejhha,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert \"Vefurinn b\u00ED\u00F0ur\" not in s  # TODO eftir a\u00F0 \u00FAtf\u00E6ra
aagcejhhb,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,assert g[2].error_code == \"V001\"  # TODO eftir a\u00F0 \u00E1kve\u00F0a villuk\u00F3\u00F0a
aagcejhhc,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,TODO Verbs.conf \u00E6tti a\u00F0 dekka \u00FEetta -- \u00FAtf\u00E6ra goggunarr\u00F6\u00F0?
aagcejhhf,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,TODO greinist; en sko\u00F0a lengdina.
aagcejhib,mideind/GreynirCorrect,test/test_allkinds.py,e9f0863f111bff854e68dbb73559c7a35d808e57,STILL_EXISTS,TODO pr\u00F3fa h\u00E9r.
aagcfafbc,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,f072ea284e90e02f44e8ca5d72a33cda26c1096b,STILL_EXISTS,Should be corrected. !!! TODO split up by nature.
aagcfafgc,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,12abe23200d53c21e22bc170e66f55068bb9dd43,STILL_EXISTS,Ends in a period; start with checking these
aagcfafge,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,12abe23200d53c21e22bc170e66f55068bb9dd43,STILL_EXISTS,Fix the capitalization
aagcfafhe,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,12abe23200d53c21e22bc170e66f55068bb9dd43,a885abccee65589b0b2db30ba4fbdd17e2ab7f6a,!!! TODO: Probably missing yield token; token = get() here
aagcfafic,mideind/GreynirCorrect,src/reynir_correct/settings.py,12abe23200d53c21e22bc170e66f55068bb9dd43,STILL_EXISTS,TODO isn't this if-clause unnecessary?
aagcfafie,mideind/GreynirCorrect,src/reynir_correct/settings.py,12abe23200d53c21e22bc170e66f55068bb9dd43,00f132c653c219aebd98185a78be105a34284c73,Ends with a period
aagcfafih,mideind/GreynirCorrect,test/test_allkinds.py,12abe23200d53c21e22bc170e66f55068bb9dd43,STILL_EXISTS,assert \"eyrnal\u00E6kninum\" in s       # TODO \u00C6tti a\u00F0 virka \u00FEegar geri n\u00FD or\u00F0anet
aagcfafij,mideind/GreynirCorrect,test/test_allkinds.py,12abe23200d53c21e22bc170e66f55068bb9dd43,STILL_EXISTS,assert g[5].error_code == \"C004\"  # TODO virkar ekki
aagcfafjd,mideind/GreynirCorrect,test/test_allkinds.py,12abe23200d53c21e22bc170e66f55068bb9dd43,STILL_EXISTS,assert \"\u00D6ldungadeildar\u00FEingma\u00F0urinn\" in s      # TODO \u00C6tti a\u00F0 virka \u00FEegar n\u00FD or\u00F0anet
aagcfafjf,mideind/GreynirCorrect,test/test_allkinds.py,12abe23200d53c21e22bc170e66f55068bb9dd43,STILL_EXISTS,assert \"d\u00EDsilb\u00EDl\" in s                        # TODO \u00C6tti a\u00F0 virka \u00FEegar n\u00FD or\u00F0anet
aagcfagaa,mideind/GreynirCorrect,test/test_allkinds.py,12abe23200d53c21e22bc170e66f55068bb9dd43,STILL_EXISTS,assert \"\u00F3tal margir\" in s         # TODO virkar ekki \u00FEv\u00ED \"\u00F3talmargur\" er \u00ED B\u00CDN!
aagcfagab,mideind/GreynirCorrect,test/test_allkinds.py,12abe23200d53c21e22bc170e66f55068bb9dd43,STILL_EXISTS,assert \"\u00F3talmargir\" not in s      # TODO \u00C6tla a\u00F0 merkja sl\u00EDkar f\u00E6rslur sem villur \u00ED CID\/CD_error_forms
aagcfagac,mideind/GreynirCorrect,test/test_allkinds.py,12abe23200d53c21e22bc170e66f55068bb9dd43,STILL_EXISTS,assert \"afarlei\u00F0inlegir\" not in s # TODO \u00C6tla a\u00F0 merkja sl\u00EDkar f\u00E6rslur sem villur \u00ED CID\/CD_error_forms
aagcfagad,mideind/GreynirCorrect,test/test_allkinds.py,12abe23200d53c21e22bc170e66f55068bb9dd43,STILL_EXISTS,TODO Eftir a\u00F0 \u00FAtf\u00E6ra; f\u00E6 C002
aagcfagae,mideind/GreynirCorrect,test/test_allkinds.py,12abe23200d53c21e22bc170e66f55068bb9dd43,STILL_EXISTS,assert g[8].error_code == \"C002\"  # TODO Virkar ekki
aagcfagaf,mideind/GreynirCorrect,test/test_allkinds.py,12abe23200d53c21e22bc170e66f55068bb9dd43,STILL_EXISTS,assert g[6].error_code == \"C002\"  # TODO
aagcfagag,mideind/GreynirCorrect,test/test_allkinds.py,12abe23200d53c21e22bc170e66f55068bb9dd43,STILL_EXISTS,assert g[8].error_code == \"C002\"  # TODO
aagcfagah,mideind/GreynirCorrect,test/test_allkinds.py,12abe23200d53c21e22bc170e66f55068bb9dd43,STILL_EXISTS,assert g[6].error_code == \"C002\"  # TODO \u00C6tti a\u00F0 virka...
aagcfagaj,mideind/GreynirCorrect,test/test_allkinds.py,12abe23200d53c21e22bc170e66f55068bb9dd43,STILL_EXISTS,assert \"vel s\u00E6tur\" in s           # TODO virkar ekki
aagcfagba,mideind/GreynirCorrect,test/test_allkinds.py,12abe23200d53c21e22bc170e66f55068bb9dd43,STILL_EXISTS,assert \"vels\u00E6tur\" not in s            # TODO virkar ekki
aagcfagca,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,66216c70078a59c2f2e469b2c42aa5e500c8c4e9,94819a8e49f2588fe414917cfb8d8fb3b17cac47,TODO STILLING - h\u00E9r er bara upp\u00E1stunga; skiptir ekki m\u00E1li fyrir \u00F3sh. m\u00E1lr\u00FDni
aagcfagcc,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,66216c70078a59c2f2e469b2c42aa5e500c8c4e9,STILL_EXISTS,TODO STILLING - h\u00E9r er b\u00E6\u00F0i samhengish\u00E1\u00F0 og \u00F3samhengish\u00E1\u00F0 lei\u00F0r\u00E9tting
aagcfagcd,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,66216c70078a59c2f2e469b2c42aa5e500c8c4e9,STILL_EXISTS,TODO STILLING - h\u00E9r \u00FEarf a\u00F0 merkja g\u00F6gn \u00ED WrongCompounds; sumt er sh.; anna\u00F0 \u00F3sh.
aagcfagce,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,66216c70078a59c2f2e469b2c42aa5e500c8c4e9,STILL_EXISTS,TODO STILLING - setja svo if-lykkju h\u00E9r til a\u00F0 lei\u00F0r\u00E9tta bara \u00F3sh. ef \u00FEa\u00F0 er vali\u00F0.
aagcfagcg,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,66216c70078a59c2f2e469b2c42aa5e500c8c4e9,94819a8e49f2588fe414917cfb8d8fb3b17cac47,TODO STILLING - ath. \u00FE\u00F3 a\u00F0 e-\u00F0 af or\u00F0hlutunum \u00ED Morphemes.BOUND_DICT geta ekki sta\u00F0i\u00F0 sj\u00E1lfst\u00E6\u00F0
aagcfagch,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,66216c70078a59c2f2e469b2c42aa5e500c8c4e9,94819a8e49f2588fe414917cfb8d8fb3b17cac47,TODO STILLING - \u00FE\u00E1 \u00FEarf a\u00F0 merkja \u00FE\u00E1 or\u00F0hluta sem villu ef \u00F3sh. lei\u00F0r\u00E9tting er valin.
aagcfagci,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,66216c70078a59c2f2e469b2c42aa5e500c8c4e9,STILL_EXISTS,TODO STILLING - H\u00E9r er bara upp\u00E1stunga; skiptir ekki m\u00E1li f. \u00F3sh. m\u00E1lr\u00FDni
aagcfagdb,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,66216c70078a59c2f2e469b2c42aa5e500c8c4e9,94819a8e49f2588fe414917cfb8d8fb3b17cac47,TODO STILLING - h\u00E9r er bara upp\u00E1stunga.
aagcfagdc,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,66216c70078a59c2f2e469b2c42aa5e500c8c4e9,94819a8e49f2588fe414917cfb8d8fb3b17cac47,TODO STILLING - h\u00E9r er \u00F3samhengish\u00E1\u00F0 lei\u00F0r\u00E9tting; en \u00FEa\u00F0 er spurning hvort allt h\u00E9r teljist endilega villa.
aagcfagdd,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,66216c70078a59c2f2e469b2c42aa5e500c8c4e9,94819a8e49f2588fe414917cfb8d8fb3b17cac47,TODO STILLING - viljum ekki endilega lei\u00F0r\u00E9tta \"byggingaregla\"; \u00FE\u00F3 a\u00F0 venjan leyfi hitt frekar.
aagcfagde,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,66216c70078a59c2f2e469b2c42aa5e500c8c4e9,94819a8e49f2588fe414917cfb8d8fb3b17cac47,TODO STILLING - \u00DEarf a\u00F0 fara \u00ED gegnum WRONG_FORMERS; m\u00E6tti skipta upp \u00ED
aagcfagdf,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,66216c70078a59c2f2e469b2c42aa5e500c8c4e9,94819a8e49f2588fe414917cfb8d8fb3b17cac47,TODO STILLING - ALWAYS_WRONG_FORMERS og MOSTLY_WRONG_FORMERS e\u00F0a eitthva\u00F0 \u00FEannig?
aagcfagdg,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,66216c70078a59c2f2e469b2c42aa5e500c8c4e9,94819a8e49f2588fe414917cfb8d8fb3b17cac47,TODO STILLING - fyrra alltaf lei\u00F0r\u00E9tt; en seinna bara \u00E1bending?
aagcfagdh,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,66216c70078a59c2f2e469b2c42aa5e500c8c4e9,94819a8e49f2588fe414917cfb8d8fb3b17cac47,TODO B\u00E6ta inn lei\u00F0r\u00E9ttingu \u00FAt fr\u00E1 seinni or\u00F0hlutum?
aagcfagdj,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,66216c70078a59c2f2e469b2c42aa5e500c8c4e9,94819a8e49f2588fe414917cfb8d8fb3b17cac47,TODO STILLING - \u00FEetta er \u00F3samhengish\u00E1\u00F0 lei\u00F0r\u00E9tting!
aagcfagec,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,66216c70078a59c2f2e469b2c42aa5e500c8c4e9,94819a8e49f2588fe414917cfb8d8fb3b17cac47,TODO STILLING - h\u00E9r er samhengish\u00E1\u00F0 lei\u00F0r\u00E9tting af \u00FEv\u00ED a\u00F0 vi\u00F0 notum \u00FErenndir!
aagcfaged,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,66216c70078a59c2f2e469b2c42aa5e500c8c4e9,94819a8e49f2588fe414917cfb8d8fb3b17cac47,TODO STILLING - og l\u00EDka \u00FEv\u00ED sko\u00F0um l\u00EDka sjaldg\u00E6f or\u00F0.
aagcfagee,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,66216c70078a59c2f2e469b2c42aa5e500c8c4e9,STILL_EXISTS,TODO STILLING - er samt versti hlutur \u00ED heimi a\u00F0 hafa \u00F3\u00FEekktu leitina hluta af \u00F3sh. m\u00E1lr\u00FDninni?
aagcfagef,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,66216c70078a59c2f2e469b2c42aa5e500c8c4e9,94819a8e49f2588fe414917cfb8d8fb3b17cac47,TODO STILLING - \u00FEetta er bara upp\u00E1stunga
aagcfageh,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,66216c70078a59c2f2e469b2c42aa5e500c8c4e9,94819a8e49f2588fe414917cfb8d8fb3b17cac47,TODO STILLING - h\u00E9r er blanda. Or\u00F0 sem eiga alltaf a\u00F0 vera h\u00E1stafa en birtast l\u00E1gstafa eru \u00F3sh.;
aagcfagei,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,66216c70078a59c2f2e469b2c42aa5e500c8c4e9,94819a8e49f2588fe414917cfb8d8fb3b17cac47,TODO STILLING - or\u00F0 sem eiga alltaf a\u00F0 vera l\u00E1gstafa nema \u00ED byrjun setningar eru sh. lei\u00F0r\u00E9tting.
aagcfagej,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,66216c70078a59c2f2e469b2c42aa5e500c8c4e9,94819a8e49f2588fe414917cfb8d8fb3b17cac47,TODO STILLING - h\u00E9r er \u00F3samhengish\u00E1\u00F0 lei\u00F0r\u00E9tting EN er bara upp\u00E1stunga.
aagcfagfa,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,66216c70078a59c2f2e469b2c42aa5e500c8c4e9,94819a8e49f2588fe414917cfb8d8fb3b17cac47,TODO STILLING - h\u00E9r er bara samhengish\u00E1\u00F0 lei\u00F0r\u00E9tting
aagcfagfb,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,26ecf26494790684e3009b22defbb34c403d3737,94819a8e49f2588fe414917cfb8d8fb3b17cac47,TODO STILLING - h\u00E9r er bara samhengish\u00E1\u00F0 lei\u00F0r\u00E9tting
aagcfagfc,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,26ecf26494790684e3009b22defbb34c403d3737,94819a8e49f2588fe414917cfb8d8fb3b17cac47,TODO STILLING - h\u00E9r er \u00F3samhengish\u00E1\u00F0 lei\u00F0r\u00E9tting!
aagcfagfj,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,26ecf26494790684e3009b22defbb34c403d3737,94819a8e49f2588fe414917cfb8d8fb3b17cac47,TODO STILLING - Athuga hvort h\u00E9r \u00E6tti a\u00F0 hafa \u00F3l\u00EDk villuskilabo\u00F0 fyrir WRONG_FORMERS og WRONG_FORMERS_CI?
aagcfaggd,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,26ecf26494790684e3009b22defbb34c403d3737,94819a8e49f2588fe414917cfb8d8fb3b17cac47,TODO STILLING Tilb\u00FAi\u00F0 til a\u00F0 vera \u00ED sta\u00F0inn sent inn \u00ED CorrectionPipeline.
aagcfagge,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,26ecf26494790684e3009b22defbb34c403d3737,94819a8e49f2588fe414917cfb8d8fb3b17cac47,TODO STILLING - h\u00E9r er samhengish\u00E1\u00F0 lei\u00F0r\u00E9tting
aagcfaggf,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,26ecf26494790684e3009b22defbb34c403d3737,STILL_EXISTS,TODO STILLING - h\u00E9r er b\u00E6\u00F0i samhengish\u00E1\u00F0 og \u00F3samhengish\u00E1\u00F0 lei\u00F0r\u00E9tting
aagcfaggg,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,26ecf26494790684e3009b22defbb34c403d3737,STILL_EXISTS,TODO STILLING - h\u00E9r \u00FEarf a\u00F0 merkja g\u00F6gn \u00ED WrongCompounds; sumt er sh.; anna\u00F0 \u00F3sh.
aagcfaggh,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,26ecf26494790684e3009b22defbb34c403d3737,STILL_EXISTS,TODO STILLING - setja svo if-lykkju h\u00E9r til a\u00F0 lei\u00F0r\u00E9tta bara \u00F3sh. ef \u00FEa\u00F0 er vali\u00F0.
aagcfaggi,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,26ecf26494790684e3009b22defbb34c403d3737,STILL_EXISTS,TODO STILLING - er samt versti hlutur \u00ED heimi a\u00F0 hafa \u00F3\u00FEekktu leitina hluta af \u00F3sh. m\u00E1lr\u00FDninni?
aagcfahbd,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,a14cc0db07e788ea17f48c1c081300f7bac46e2a,STILL_EXISTS,TODO STILLING - h\u00E9r er bara upp\u00E1stunga; skiptir ekki m\u00E1li fyrir \u00F3sh. m\u00E1lr\u00FDni
aagcfahbh,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,a14cc0db07e788ea17f48c1c081300f7bac46e2a,STILL_EXISTS,TODO STILLING - ath. \u00FE\u00F3 a\u00F0 e-\u00F0 af or\u00F0hlutunum \u00ED Morphemes.BOUND_DICT geta ekki sta\u00F0i\u00F0 sj\u00E1lfst\u00E6\u00F0
aagcfahbi,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,a14cc0db07e788ea17f48c1c081300f7bac46e2a,STILL_EXISTS,TODO STILLING - \u00FE\u00E1 \u00FEarf a\u00F0 merkja \u00FE\u00E1 or\u00F0hluta sem villu ef \u00F3sh. lei\u00F0r\u00E9tting er valin.
aagcfahcb,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,a14cc0db07e788ea17f48c1c081300f7bac46e2a,STILL_EXISTS,TODO STILLING - H\u00E9r er bara upp\u00E1stunga; skiptir ekki m\u00E1li f. \u00F3sh. m\u00E1lr\u00FDni.
aagcfahci,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,a14cc0db07e788ea17f48c1c081300f7bac46e2a,STILL_EXISTS,TODO STILLING - h\u00E9r er bara upp\u00E1stunga.
aagcfahcj,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,a14cc0db07e788ea17f48c1c081300f7bac46e2a,STILL_EXISTS,TODO STILLING - h\u00E9r er \u00F3samhengish\u00E1\u00F0 lei\u00F0r\u00E9tting; en \u00FEa\u00F0 er spurning hvort allt h\u00E9r teljist endilega villa.
aagcfahda,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,a14cc0db07e788ea17f48c1c081300f7bac46e2a,STILL_EXISTS,TODO STILLING - viljum ekki endilega lei\u00F0r\u00E9tta \"byggingaregla\"; \u00FE\u00F3 a\u00F0 venjan leyfi hitt frekar.
aagcfahdb,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,a14cc0db07e788ea17f48c1c081300f7bac46e2a,STILL_EXISTS,TODO STILLING - \u00DEarf a\u00F0 fara \u00ED gegnum WRONG_FORMERS; m\u00E6tti skipta upp \u00ED
aagcfahdc,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,a14cc0db07e788ea17f48c1c081300f7bac46e2a,STILL_EXISTS,TODO STILLING - ALWAYS_WRONG_FORMERS og MOSTLY_WRONG_FORMERS e\u00F0a eitthva\u00F0 \u00FEannig?
aagcfahdd,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,a14cc0db07e788ea17f48c1c081300f7bac46e2a,STILL_EXISTS,TODO STILLING - fyrra alltaf lei\u00F0r\u00E9tt; en seinna bara \u00E1bending?
aagcfahde,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,a14cc0db07e788ea17f48c1c081300f7bac46e2a,STILL_EXISTS,TODO STILLING - Athuga hvort h\u00E9r \u00E6tti a\u00F0 hafa \u00F3l\u00EDk villuskilabo\u00F0 fyrir WRONG_FORMERS og WRONG_FORMERS_CI?
aagcfahdf,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,a14cc0db07e788ea17f48c1c081300f7bac46e2a,STILL_EXISTS,TODO B\u00E6ta inn lei\u00F0r\u00E9ttingu \u00FAt fr\u00E1 seinni or\u00F0hlutum?
aagcfahdh,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,a14cc0db07e788ea17f48c1c081300f7bac46e2a,STILL_EXISTS,TODO STILLING - \u00FEetta er \u00F3samhengish\u00E1\u00F0 lei\u00F0r\u00E9tting!
aagcfahea,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,a14cc0db07e788ea17f48c1c081300f7bac46e2a,STILL_EXISTS,TODO STILLING - h\u00E9r er samhengish\u00E1\u00F0 lei\u00F0r\u00E9tting af \u00FEv\u00ED a\u00F0 vi\u00F0 notum \u00FErenndir!
aagcfaheb,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,a14cc0db07e788ea17f48c1c081300f7bac46e2a,STILL_EXISTS,TODO STILLING - og l\u00EDka \u00FEv\u00ED sko\u00F0um l\u00EDka sjaldg\u00E6f or\u00F0.
aagcfahed,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,a14cc0db07e788ea17f48c1c081300f7bac46e2a,STILL_EXISTS,TODO STILLING - \u00FEetta er bara upp\u00E1stunga
aagcfahef,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,a14cc0db07e788ea17f48c1c081300f7bac46e2a,STILL_EXISTS,TODO STILLING - h\u00E9r er blanda. Or\u00F0 sem eiga alltaf a\u00F0 vera h\u00E1stafa en birtast l\u00E1gstafa eru \u00F3sh.;
aagcfaheg,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,a14cc0db07e788ea17f48c1c081300f7bac46e2a,STILL_EXISTS,TODO STILLING - or\u00F0 sem eiga alltaf a\u00F0 vera l\u00E1gstafa nema \u00ED byrjun setningar eru sh. lei\u00F0r\u00E9tting.
aagcfahej,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,a14cc0db07e788ea17f48c1c081300f7bac46e2a,STILL_EXISTS,TODO STILLING - h\u00E9r er \u00F3samhengish\u00E1\u00F0 lei\u00F0r\u00E9tting EN er bara upp\u00E1stunga.
aagcfahfa,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,a14cc0db07e788ea17f48c1c081300f7bac46e2a,STILL_EXISTS,TODO STILLING Tilb\u00FAi\u00F0 til a\u00F0 vera \u00ED sta\u00F0inn sent inn \u00ED CorrectionPipeline.
aagcfahhe,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,512d16b58aad82af130df3c75cac1e74f790cc95,STILL_EXISTS,TODO STILLING Tilb\u00FAi\u00F0 til a\u00F0 vera \u00ED sta\u00F0inn sent inn \u00ED CorrectionPipeline.
aagcfahhg,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,512d16b58aad82af130df3c75cac1e74f790cc95,STILL_EXISTS,TODO STILLING - h\u00E9r er bara samhengish\u00E1\u00F0 lei\u00F0r\u00E9tting
aagcfaicf,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,00f132c653c219aebd98185a78be105a34284c73,STILL_EXISTS,TODO could add normalize_ellipsis as a parameter here
aagcfaiee,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,1cab73cd4e9135973417c0be99d2e5394bc7ecf5,STILL_EXISTS,TODO Consider limiting to words under 15 characters
aagcfaiie,mideind/GreynirCorrect,src/reynir_correct/checker.py,6bdcdfa395ba2b71c6d060f87061c0b2f10f9368,STILL_EXISTS,!!! TODO: Better annotation here; with the corrected subject
aagcfajae,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,6bdcdfa395ba2b71c6d060f87061c0b2f10f9368,STILL_EXISTS,!!! TODO: Move this to a config file
aagcfajed,mideind/GreynirCorrect,test/test_allkinds.py,6bdcdfa395ba2b71c6d060f87061c0b2f10f9368,52a5d5a9bd593e1b45cc946a6a2abae55d7c7021,check_sentence(s; [(0; 0; \"P_SUBJ_CASE\")])                # TODO villa greinist ekki; eftir a\u00F0 \u00FAtf\u00E6ra?
aagcfajgd,mideind/GreynirCorrect,src/reynir_correct/checker.py,d8592b70c25902a6949270b8c483b6d772dbd492,STILL_EXISTS,!!! TODO: Better annotation here; with the corrected subject
aagcfajgg,mideind/GreynirCorrect,test/test_allkinds.py,52a5d5a9bd593e1b45cc946a6a2abae55d7c7021,STILL_EXISTS,TODO villan greinist ekki! Ath. l\u00EDka lengdina.
aagcfajhi,mideind/GreynirCorrect,src/reynir_correct/checker.py,c8fe1bdad9d9b3baa981dc259e5d772dc3d2baf6,d09c903f2734e741c11fbb9e308bd6b81f10864c,If the code ends with \"\/w\"; it is a warning
aagcfajhj,mideind/GreynirCorrect,src/reynir_correct/checker.py,c8fe1bdad9d9b3baa981dc259e5d772dc3d2baf6,d09c903f2734e741c11fbb9e308bd6b81f10864c,!!! TODO: Add suggestion here by replacing
aagcfbaea,mideind/GreynirCorrect,test/test_allkinds.py,3c46c826c1ce5b2f065705173fe388bb41226068,STILL_EXISTS,assert g[3].error_code == \"S001\"  # TODO virkar ekki \u00FAt af h\u00E1staf \u00ED unique_errors; setja inn \u00FEegar \u00FEa\u00F0 er laga\u00F0
aagcfbaeb,mideind/GreynirCorrect,test/test_allkinds.py,3c46c826c1ce5b2f065705173fe388bb41226068,STILL_EXISTS,TODO \u00C1 a\u00F0 vera S001?
aagcfbaff,mideind/GreynirCorrect,src/reynir_correct/annotation.py,d09c903f2734e741c11fbb9e308bd6b81f10864c,STILL_EXISTS,If the code ends with \"\/w\"; it is a warning
aagcfbbhi,mideind/GreynirCorrect,src/reynir_correct/errfinder.py,d09c903f2734e741c11fbb9e308bd6b81f10864c,STILL_EXISTS,!!! TODO: Add suggestion here by replacing
aagcfbchi,mideind/GreynirCorrect,src/reynir_correct/pattern.py,d09c903f2734e741c11fbb9e308bd6b81f10864c,c7389e015cdd5c0abbf22872e3d7f5b5c4c83cec,!!! TODO: More intelligent substitution to create a suggestion
aagcfbdea,mideind/GreynirCorrect,src/reynir_correct/pattern.py,b813d9ed3a2ded5fa7e64ce0bcf21cd51c194da8,c7389e015cdd5c0abbf22872e3d7f5b5c4c83cec,!!! TODO: More intelligent substitution to create a suggestion
aagcfbeae,mideind/GreynirCorrect,doc/conf.py,e502a5a4954202bc964b43940f783daa6be12bc5,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aagcfbeic,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,deec51e7b0bedba090f4020f76ebb1643bf53ff4,STILL_EXISTS,Python hack to create a fresh; empty instance of the
aagcfbfaf,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,b599b0536fe6a658e531810da882613e8b131b29,STILL_EXISTS,!!! TODO: This may need to be made more intelligent
aagcfbfag,mideind/GreynirCorrect,test/test_tokenizer.py,b599b0536fe6a658e531810da882613e8b131b29,STILL_EXISTS,assert \"Fj\u00F6gur hundru\u00F0 manns\" in s  # !!! Needs B\u00CDN fix; upcoming
aagcfbfah,mideind/GreynirCorrect,test/test_tokenizer.py,b599b0536fe6a658e531810da882613e8b131b29,STILL_EXISTS,assert \"fj\u00F6gur hundru\u00F0 manns\" in s  # !!! Needs B\u00CDN fix; upcoming
aagcfbfbb,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,92052b80e24a6ae7d7841908f9f222ac2acc1ea6,STILL_EXISTS,!!! TODO
aagcfbfbc,mideind/GreynirCorrect,test/test_tokenizer.py,92052b80e24a6ae7d7841908f9f222ac2acc1ea6,STILL_EXISTS,!!! TODO
aagcfbfcc,mideind/GreynirCorrect,test/test_tokenizer.py,92052b80e24a6ae7d7841908f9f222ac2acc1ea6,STILL_EXISTS,assert \"fj\u00F6gur hundru\u00F0 manns\" in s  # !!! Needs B\u00CDN fix; upcoming
aagcfbggb,mideind/GreynirCorrect,src/reynir_correct/pattern.py,d9d0b42eeb2ef6e5c834a81a245c3c7d9b85194f,STILL_EXISTS,!!! BUG: The code currently allows nonterminal nodes to match
aagcfbgib,mideind/GreynirCorrect,src/reynir_correct/pattern.py,efdb0cae2c05d171e32ff6e46a7c7798dfd8bc8c,STILL_EXISTS,!!! BUG: The code currently allows nonterminal nodes to match
aagcfbhee,mideind/GreynirCorrect,eval/eval.py,f89ca531e58b6c41ec8bb2491f1ddc47038276c2,3e5f1cea455a2f8d4732be6c019161a35506c0c5,!!! TODO: this actually fixes spacing errors; causing them
aagcfbhgh,mideind/GreynirCorrect,eval/eval.py,40fd14cfd8d42682cbc296afdeb2a291a58cdcb4,958b427859c45683c06bdc3bdbd7d57f04b1754b,TODO Setja \u00FAtreikning \u00E1 P; R og F \u00ED s\u00E9rfall;
aagcfbhhd,mideind/GreynirCorrect,eval/eval.py,40fd14cfd8d42682cbc296afdeb2a291a58cdcb4,958b427859c45683c06bdc3bdbd7d57f04b1754b,!!! TODO: this actually fixes spacing errors; causing them
aagcfbhhf,mideind/GreynirCorrect,eval/eval.py,40fd14cfd8d42682cbc296afdeb2a291a58cdcb4,958b427859c45683c06bdc3bdbd7d57f04b1754b,!!! TODO might be the cause of quotation marks being affixed to the next word
aagcfbhjb,mideind/GreynirCorrect,eval/eval.py,40fd14cfd8d42682cbc296afdeb2a291a58cdcb4,958b427859c45683c06bdc3bdbd7d57f04b1754b,TODO T\u00E9kka h\u00E9r hvort er bara vi\u00F0v\u00F6run; x.is_warning()
aagcfbiaa,mideind/GreynirCorrect,eval/eval.py,40fd14cfd8d42682cbc296afdeb2a291a58cdcb4,958b427859c45683c06bdc3bdbd7d57f04b1754b,TODO vinna \u00FAr rest af listum ef eitthva\u00F0 er eftir. Get t\u00E9kka\u00F0 \u00E1 lengdinni \u00ED upphafi?
aagcfbiai,mideind/GreynirCorrect,eval/eval.py,40fd14cfd8d42682cbc296afdeb2a291a58cdcb4,958b427859c45683c06bdc3bdbd7d57f04b1754b,TODO Finna f\u00E1ga\u00F0ri lausn; sem tekur tillit til villuspana.
aagcfbiaj,mideind/GreynirCorrect,eval/eval.py,40fd14cfd8d42682cbc296afdeb2a291a58cdcb4,958b427859c45683c06bdc3bdbd7d57f04b1754b,TODO B\u00E6ta vi\u00F0 stats sem skila
aagcfbibd,mideind/GreynirCorrect,test/test_patterns.py,2d6af200dacbe21664b5a3ddcb72d04511dd9bb4,STILL_EXISTS,TODO: The following gets no annotation:
aagcfbiha,mideind/GreynirCorrect,src/reynir_correct/errfinder.py,1e58d742d532185c66e3f15ab4829368b888e0c6,STILL_EXISTS,Probably a Roman numeral - we don't mess with those
aagcfbjei,mideind/GreynirCorrect,src/reynir_correct/errfinder.py,1ff340715940973104428dfc5e284503173b2d65,STILL_EXISTS,TODO: The following actually reduces GreynirCorrect's score on the
aagcfbjfc,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,1ff340715940973104428dfc5e284503173b2d65,STILL_EXISTS,Something like 'b-deildin' or 'a-flokki' which should probably
aagcfbjhi,mideind/GreynirCorrect,src/reynir_correct/pattern.py,77cf7de79cf3e3e7eac6070e14399237a0b99f35,STILL_EXISTS,are based on convention; not rational rules. :\/
aagcfcacd,mideind/GreynirCorrect,src/reynir_correct/spelling.py,ac2f5adc53a6baadf4e5d6f4423680d3aaebeca6,STILL_EXISTS,sb -> b (should probably be sbr.)
aagcfcace,mideind/GreynirCorrect,src/reynir_correct/spelling.py,ac2f5adc53a6baadf4e5d6f4423680d3aaebeca6,STILL_EXISTS,\u00FEe -> \u00FE\u00E1 (should probably be \u00FE.e.)
aagcfcaci,mideind/GreynirCorrect,src/reynir_correct/spelling.py,ac2f5adc53a6baadf4e5d6f4423680d3aaebeca6,STILL_EXISTS,a -> a\u00F0 (should probably be \u00E1)
aagcfcade,mideind/GreynirCorrect,src/reynir_correct/spelling.py,ac2f5adc53a6baadf4e5d6f4423680d3aaebeca6,STILL_EXISTS,\u00FEo -> \u00FE\u00E1 (should probably be \u00FE\u00F3)
aagcfcaee,mideind/GreynirCorrect,eval/eval.py,bb1c5dc07cee9ede94042ed4ff8f215a80d4f215,STILL_EXISTS,TODO Setja \u00FAtreikning \u00E1 P; R og F \u00ED s\u00E9rfall;
aagcfcagf,mideind/GreynirCorrect,eval/eval.py,bb1c5dc07cee9ede94042ed4ff8f215a80d4f215,STILL_EXISTS,TODO T\u00E9kka h\u00E9r hvort er bara vi\u00F0v\u00F6run; x.is_warning()
aagcfcahe,mideind/GreynirCorrect,eval/eval.py,bb1c5dc07cee9ede94042ed4ff8f215a80d4f215,STILL_EXISTS,TODO vinna \u00FAr rest af listum ef eitthva\u00F0 er eftir. Get t\u00E9kka\u00F0 \u00E1 lengdinni \u00ED upphafi?
aagcfcaic,mideind/GreynirCorrect,eval/eval.py,bb1c5dc07cee9ede94042ed4ff8f215a80d4f215,STILL_EXISTS,TODO Finna f\u00E1ga\u00F0ri lausn; sem tekur tillit til villuspana.
aagcfcaid,mideind/GreynirCorrect,eval/eval.py,bb1c5dc07cee9ede94042ed4ff8f215a80d4f215,STILL_EXISTS,TODO B\u00E6ta vi\u00F0 stats sem skila
aagcfcaie,mideind/GreynirCorrect,eval/eval.py,bb1c5dc07cee9ede94042ed4ff8f215a80d4f215,STILL_EXISTS,TODO uppf\u00E6ra \u00FEetta
aagcfcaii,mideind/GreynirCorrect,eval/eval.py,4857bbf1ce4b401a6440ae16c24a897e7611b766,STILL_EXISTS,TODO safna villunum \u00ED generatora og taka eitt stak \u00ED einu og bera saman?
aagcfcbce,mideind/GreynirCorrect,eval/eval.py,4857bbf1ce4b401a6440ae16c24a897e7611b766,STILL_EXISTS,TODO T\u00E9kka h\u00E9r hvort er bara vi\u00F0v\u00F6run; x.is_warning()
aagcfcbdd,mideind/GreynirCorrect,eval/eval.py,4857bbf1ce4b401a6440ae16c24a897e7611b766,STILL_EXISTS,TODO vinna \u00FAr rest af listum ef eitthva\u00F0 er eftir. Get t\u00E9kka\u00F0 \u00E1 lengdinni \u00ED upphafi?
aagcfcbeb,mideind/GreynirCorrect,eval/eval.py,4857bbf1ce4b401a6440ae16c24a897e7611b766,STILL_EXISTS,TODO Finna f\u00E1ga\u00F0ri lausn; sem tekur tillit til villuspana.
aagcfcbec,mideind/GreynirCorrect,eval/eval.py,4857bbf1ce4b401a6440ae16c24a897e7611b766,STILL_EXISTS,TODO B\u00E6ta vi\u00F0 stats sem skila
aagcfcbgc,mideind/GreynirCorrect,eval/eval.py,1658d262da6018300b8d8c4e190e21c2667a69bb,STILL_EXISTS,or does it perhaps not exist? Check how BEA 2019 does this.
aagcfcbge,mideind/GreynirCorrect,eval/eval.py,1658d262da6018300b8d8c4e190e21c2667a69bb,STILL_EXISTS,TODO Check if unparsable error and collect causes and their frequencies
aagcfccgb,mideind/GreynirCorrect,eval/eval.py,f7991b5bf40b45c708345663d863b039f81c02e3,51bd699941fda38fd73ac409e4a35f7d3614cdd1,TODO put undir Typo?
aagcfccgc,mideind/GreynirCorrect,eval/eval.py,f7991b5bf40b45c708345663d863b039f81c02e3,51bd699941fda38fd73ac409e4a35f7d3614cdd1,TODO put under Typo?
aagcfccge,mideind/GreynirCorrect,eval/eval.py,f7991b5bf40b45c708345663d863b039f81c02e3,b3414f0caaf618ebb334dd2890e7cc31e789a477,TODO taka saman corr_rec og span_rec; sko\u00F0a hvernig f\u00E6 F-skor; svipa\u00F0 og fyrir hitt; \u00FEegar er ekki me\u00F0 TN inni
aagcfdbch,mideind/GreynirCorrect,eval/eval.py,f19614ac89cc290be461269e127d4e63b77063c4,b3414f0caaf618ebb334dd2890e7cc31e789a477,TODO taka saman corr_rec og span_rec; sko\u00F0a hvernig f\u00E6 F-skor; svipa\u00F0 og fyrir hitt; \u00FEegar er ekki me\u00F0 TN inni
aagcfdceh,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,a8a78c9126d7fc3bb98d84f737e470ef3fe1d37a,STILL_EXISTS,!!! TODO: Maybe the following should be just token.txt.capitalize()
aagcfdcja,mideind/GreynirCorrect,src/reynir_correct/pattern.py,7a8f82a4562e8bd86366b0ad4b8a2a3d90d2a877,STILL_EXISTS,TODO: This code is provisional; intended as a placeholder for similar cases
aagcfdcjc,mideind/GreynirCorrect,src/reynir_correct/pattern.py,7a8f82a4562e8bd86366b0ad4b8a2a3d90d2a877,STILL_EXISTS,!!! TODO: This is a provisional placeholder for similar cases
aagcfdcjj,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,08b16b28f9258f2ee57f88b005cd68eed1237fb8,STILL_EXISTS,!!! TODO: Amalgamate more than one potential correction
aagcfddbf,mideind/GreynirCorrect,eval/eval.py,e5365f6fa2a4d4a2e0f3f5f042f9b91f6072803a,b3414f0caaf618ebb334dd2890e7cc31e789a477,TODO taka saman corr_rec og span_rec; sko\u00F0a hvernig f\u00E6 F-skor; svipa\u00F0 og fyrir hitt; \u00FEegar er ekki me\u00F0 TN inni
aagcfdgeh,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,a7c2427a19943e92897058779131a07837ff933f,STILL_EXISTS,TODO Consider limiting to words under 15 characters
aagcfdgfc,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,a7c2427a19943e92897058779131a07837ff933f,STILL_EXISTS,!!! TODO: Add correctly inflected suggestion here
aagcfdgge,mideind/GreynirCorrect,test/test_allkinds.py,62044a34ba170bb644a727b188b2362ebc5beb4a,STILL_EXISTS,TODO sama
aagcfdggf,mideind/GreynirCorrect,test/test_allkinds.py,62044a34ba170bb644a727b188b2362ebc5beb4a,STILL_EXISTS,assert \"t\u00EDmanlega\" in s     # TODO sama
aagcfdgha,mideind/GreynirCorrect,test/test_allkinds.py,62044a34ba170bb644a727b188b2362ebc5beb4a,STILL_EXISTS,TODO villurnar greinast ekki; vantar l\u00EDklega reglur.
aagcfdghb,mideind/GreynirCorrect,test/test_allkinds.py,62044a34ba170bb644a727b188b2362ebc5beb4a,STILL_EXISTS,TODO  FsMeFallstj\u00F3rn greinir villu; en n\u00E6r ekki yfir Eflingu;
aagcfdghe,mideind/GreynirCorrect,test/test_allkinds.py,62044a34ba170bb644a727b188b2362ebc5beb4a,STILL_EXISTS,TODO hvorug villan greinist. Vil reglu sem heitir Pr\u00F3sent...
aagcfdghh,mideind/GreynirCorrect,test/test_allkinds.py,62044a34ba170bb644a727b188b2362ebc5beb4a,STILL_EXISTS,TODO villan greinist ekki; eftir a\u00F0 h\u00F6ndla
aagcfdgic,mideind/GreynirCorrect,test/test_allkinds.py,62044a34ba170bb644a727b188b2362ebc5beb4a,STILL_EXISTS,TODO villan greinist; en \u00E6tti a\u00F0 vera sta\u00F0sett \u00E1 s\u00F6gninni
aagcfdgif,mideind/GreynirCorrect,test/test_allkinds.py,62044a34ba170bb644a727b188b2362ebc5beb4a,STILL_EXISTS,TODO f\u00E6 enga villu; eftir a\u00F0 \u00FAtf\u00E6ra.
aagcfdgii,mideind/GreynirCorrect,test/test_allkinds.py,62044a34ba170bb644a727b188b2362ebc5beb4a,STILL_EXISTS,TODO villan greinist ekki. Komi\u00F0 \u00ED Verbs.conf? L\u00EDklega ekki r\u00E9ttur villuk\u00F3\u00F0i.
aagcfdhah,mideind/GreynirCorrect,eval/eval.py,e14cee0956003de11121489728a8fb24f6949fa6,STILL_EXISTS,\"E003\" : [\"XXX\"];
aagcfdhai,mideind/GreynirCorrect,eval/eval.py,e14cee0956003de11121489728a8fb24f6949fa6,STILL_EXISTS,\"P_WRONG_OP_FORM\" : [\"XXX\"];
aagcfdhaj,mideind/GreynirCorrect,eval/eval.py,e14cee0956003de11121489728a8fb24f6949fa6,STILL_EXISTS,TODO change?
aagcfdhbh,mideind/GreynirCorrect,eval/eval.py,a42920d49e296ba1e2af70ddbbfb547aaef3ba31,STILL_EXISTS,\"E003\" : [\"XXX\"];
aagcfdhbi,mideind/GreynirCorrect,eval/eval.py,a42920d49e296ba1e2af70ddbbfb547aaef3ba31,STILL_EXISTS,\"P_WRONG_OP_FORM\" : [\"XXX\"];
aagcfdhbj,mideind/GreynirCorrect,eval/eval.py,a42920d49e296ba1e2af70ddbbfb547aaef3ba31,STILL_EXISTS,TODO change?
aagcfdhca,mideind/GreynirCorrect,eval/eval.py,58ee337057685b3a4b1484f1d89ae1cad494b6a1,STILL_EXISTS,\"E003\" : [\"XXX\"];
aagcfdhcb,mideind/GreynirCorrect,eval/eval.py,58ee337057685b3a4b1484f1d89ae1cad494b6a1,STILL_EXISTS,\"P_WRONG_OP_FORM\" : [\"XXX\"];
aagcfdhcc,mideind/GreynirCorrect,eval/eval.py,58ee337057685b3a4b1484f1d89ae1cad494b6a1,STILL_EXISTS,TODO change?
aagcfdhef,mideind/GreynirCorrect,eval/eval.py,96844e5955b863e92b413e3131db07aa1050299e,STILL_EXISTS,TODO change?
aagcfdhfb,mideind/GreynirCorrect,eval/eval.py,7a4efe6e1b83b043ee218a7edc3f7de22cafd52f,STILL_EXISTS,TODO not ytok.text
aagcfdhgb,mideind/GreynirCorrect,eval/eval.py,792fdeb4885b42610cadad172386a9b07445d988,STILL_EXISTS,TODO Usually ystart; yend+1; reset when secondary comparison works
aagcfdhge,mideind/GreynirCorrect,eval/eval.py,792fdeb4885b42610cadad172386a9b07445d988,STILL_EXISTS,TODO not ytok.text
aagcfdhgf,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,792fdeb4885b42610cadad172386a9b07445d988,STILL_EXISTS,C007: A multiword compound such as \"sk\u00F3la-og fr\u00EDstundasvi\u00F0\" correctly split up
aagcfdhgh,mideind/GreynirCorrect,src/reynir_correct/checker.py,5d3e290e6997dd9a567142f010b16e86cef8dd2d,28875f844ca2c13afdb485db8dcd094c1114c5f6,TODO or original
aagcfdhgi,mideind/GreynirCorrect,src/reynir_correct/checker.py,5d3e290e6997dd9a567142f010b16e86cef8dd2d,28875f844ca2c13afdb485db8dcd094c1114c5f6,TODO or suggest
aagcfdhhj,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,aa9796f4b778695bca9c64aefd507b0195c68213,STILL_EXISTS,TODO: Shouldn't this be 1?
aagcfdhia,mideind/GreynirCorrect,src/reynir_correct/errtokenizer.py,aa9796f4b778695bca9c64aefd507b0195c68213,STILL_EXISTS,TODO: Should this be 1?
aagcfdhib,mideind/GreynirCorrect,test/test_annotator.py,aa9796f4b778695bca9c64aefd507b0195c68213,STILL_EXISTS,FIXME:
aagcfdiaa,mideind/GreynirCorrect,test/test_tokenizer.py,acaba9ce93c2d69baa7c8c6a8ff10bb62ecacf08,STILL_EXISTS,FIXME:
aagcfdiaf,mideind/GreynirCorrect,src/reynir_correct/pattern.py,c5860377c8fe055df3d007592807ccd583cce663,STILL_EXISTS,TODO sko\u00F0a
aagcfeaga,mideind/GreynirCorrect,src/reynir_correct/pattern.py,b2256a1c18fa7c683eb2672302d294277e15e2f9,STILL_EXISTS,TODO sko\u00F0a
aagcfeagf,mideind/GreynirCorrect,test/test_allkinds.py,b2256a1c18fa7c683eb2672302d294277e15e2f9,89cd16b4700e68daf8da8e443f85a90b1cb6b9b3,TODO taka \u00FAt \u00FEegar b\u00FAin a\u00F0 afl\u00FAsa pr\u00F3fanirnar
aagcfecbh,mideind/GreynirCorrect,src/reynir_correct/pattern.py,317239de4d65a5871a63b3ab14ac941926aa4847,0a9d2015f66948fe49859b0b823bff68624148e9,!!! TODO: More intelligent substitution to create a suggestion
aagcfecid,mideind/GreynirCorrect,src/reynir_correct/pattern.py,374a5c83a19e487392acf4d17f1497200bb2c9f1,STILL_EXISTS,TODO: This code is provisional; intended as a placeholder for similar cases
aagcfeebd,mideind/GreynirCorrect,src/reynir_correct/pattern.py,6b05b36e59e4c30ce69d6efd15353593a859d06a,bd17c6ea4ddfde5db28f0432a6d3aeecddeac90a,# !!! TODO: More intelligent substitution to create a suggestion
aagcfegei,mideind/GreynirCorrect,src/reynir_correct/pattern.py,de6c34edc76c4ccdc9f36df57fd9793191ca32e9,STILL_EXISTS,# !!! TODO: More intelligent substitution to create a suggestion
aagcfeibf,mideind/GreynirCorrect,src/reynir_correct/pattern.py,47813e99dd47655103efb997269caf223c8202bd,STILL_EXISTS,TODO: This code is provisional; intended as a placeholder for similar cases
aagdhcded,mideind/GreynirPackage,src/reynir/bindb.py,9916ef21a487f7a6c938f1dc7a2b868a597062ab,STILL_EXISTS,The better (preferred) stem should still be there somewhere
aagdhcdee,mideind/GreynirPackage,src/reynir/bindb.py,9916ef21a487f7a6c938f1dc7a2b868a597062ab,STILL_EXISTS,assert any(mm.stofn in better for mm in m)
aagdhcfgb,mideind/GreynirPackage,src/reynir/bintokenizer.py,9916ef21a487f7a6c938f1dc7a2b868a597062ab,STILL_EXISTS,Eliminate very uncommon meanings
aagdhcgbd,mideind/GreynirPackage,src/reynir/dawgdictionary.py,9916ef21a487f7a6c938f1dc7a2b868a597062ab,a09139626bece8011133179a8252a30c6b29f294,The first line is the root (by convention nodeid 0)
aagdhcgha,mideind/GreynirPackage,src/reynir/dawgdictionary.py,9916ef21a487f7a6c938f1dc7a2b868a597062ab,STILL_EXISTS,Match: move to the next index position
aagdhchhf,mideind/GreynirPackage,src/reynir/fastparser.py,9916ef21a487f7a6c938f1dc7a2b868a597062ab,STILL_EXISTS,(this is less efficient for small trees but much more efficient
aagdhcjac,mideind/GreynirPackage,src/reynir/reducer.py,9916ef21a487f7a6c938f1dc7a2b868a597062ab,STILL_EXISTS,A complete 't\u00F6l' or 'no' is better (has more info) than a rough 'tala'
aagdhcjea,mideind/GreynirPackage,src/reynir/reducer.py,9916ef21a487f7a6c938f1dc7a2b868a597062ab,0bfa703e50b67ec91b31badb395f78a3de5d33ca,!!! NOTE: It is probably not enough to just duplicate
aagdhcjgh,mideind/GreynirPackage,src/reynir/reducer.py,9916ef21a487f7a6c938f1dc7a2b868a597062ab,STILL_EXISTS,Eliminate all families except the best scoring one
aagdhdbdi,mideind/GreynirPackage,src/reynir/settings.py,9916ef21a487f7a6c938f1dc7a2b868a597062ab,STILL_EXISTS,Dictionary keyed by word containing a list of tuples (worse; better)
aagdhdbea,mideind/GreynirPackage,src/reynir/settings.py,9916ef21a487f7a6c938f1dc7a2b868a597062ab,STILL_EXISTS,Dictionary keyed by word form containing a list of tuples (worse; better)
aagdhdbig,mideind/GreynirPackage,src/reynir/settings.py,9916ef21a487f7a6c938f1dc7a2b868a597062ab,STILL_EXISTS,Format: noun worse1 worse2... < better
aagdhdbih,mideind/GreynirPackage,src/reynir/settings.py,9916ef21a487f7a6c938f1dc7a2b868a597062ab,STILL_EXISTS,The worse and better specifiers are gender names (kk; kvk; hk)
aagdhdddd,mideind/GreynirPackage,doc/conf.py,2fdefbdacf246c7d15652518d7efc3986faea45e,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aagdhdebf,mideind/GreynirPackage,src/reynir/matcher.py,e44b9121af0f8d68d7a899dbcc6f3874a1373e07,STILL_EXISTS,!!! TODO: Potentially divide composite tokens such as
aagdhdeeb,mideind/GreynirPackage,src/reynir/matcher.py,e44b9121af0f8d68d7a899dbcc6f3874a1373e07,328aec94c264e318e907ad65aec9a36fe32a485b,!!! TODO: Limit the cache size; for example by LRU or a periodic purge
aagdhdeej,mideind/GreynirPackage,src/reynir/matcher.py,e44b9121af0f8d68d7a899dbcc6f3874a1373e07,STILL_EXISTS,First parts must match (i.e.; no_xxx != so_xxx)
aagdhdfac,mideind/GreynirPackage,src/reynir/matcher.py,e44b9121af0f8d68d7a899dbcc6f3874a1373e07,328aec94c264e318e907ad65aec9a36fe32a485b,If so; we eliminate one level and move the children of the child
aagdhdhef,mideind/GreynirPackage,src/reynir/binparser.py,77ac57a7e03ca5af43450b7e702236d11728796a,STILL_EXISTS,nominal form (unless it already ends with \"st\").
aagdhdjef,mideind/GreynirPackage,src/reynir/binparser.py,77ac57a7e03ca5af43450b7e702236d11728796a,STILL_EXISTS,Strong literal terminals (those in double quotes) implement this feature
aagdhdjhe,mideind/GreynirPackage,src/reynir/binparser.py,77ac57a7e03ca5af43450b7e702236d11728796a,STILL_EXISTS,Hack to make 'stt' terminals match with the B\u00CDN 'st' category
aagdhdjhg,mideind/GreynirPackage,src/reynir/binparser.py,77ac57a7e03ca5af43450b7e702236d11728796a,STILL_EXISTS,Hack to allow genders to be specified on pfn literal terminals
aagdhdjhj,mideind/GreynirPackage,src/reynir/binparser.py,77ac57a7e03ca5af43450b7e702236d11728796a,STILL_EXISTS,Hack to allow cases to be specified on fs literal terminals
aagdhebdd,mideind/GreynirPackage,src/reynir/grammar.py,77ac57a7e03ca5af43450b7e702236d11728796a,STILL_EXISTS,Note that the Earley algorithm is more efficient on left recursion
aagdhecig,mideind/GreynirPackage,src/reynir/bincompress.py,116e5820b0a2ebc95adbb64b1ee25d0ecc3d1e03,STILL_EXISTS,Append padding to a DWORD (32-bit) boundary; if needed
aagdhehdf,mideind/GreynirPackage,src/reynir/ifdtagger.py,c896f4eb63b32e7c97f1696f575334016a698636,STILL_EXISTS,!!! TODO
aagdhehfi,mideind/GreynirPackage,src/reynir/ifdtagger.py,c896f4eb63b32e7c97f1696f575334016a698636,STILL_EXISTS,!!! TODO: this needs to be made more intelligent and detailed
aagdhehhb,mideind/GreynirPackage,src/reynir/matcher.py,c896f4eb63b32e7c97f1696f575334016a698636,328aec94c264e318e907ad65aec9a36fe32a485b,!!! TODO: Handle currency names and measurement units
aagdhehia,mideind/GreynirPackage,src/reynir/matcher.py,c896f4eb63b32e7c97f1696f575334016a698636,328aec94c264e318e907ad65aec9a36fe32a485b,Otherwise; they probably belong to a previous verb and we
aagdheiic,mideind/GreynirPackage,src/reynir/matcher.py,350fc8d390370f5473631544bccf9c4096a70c0f,328aec94c264e318e907ad65aec9a36fe32a485b,Fix the last terminal if it denotes a currency abbreviation
aagdhejej,mideind/GreynirPackage,src/reynir/dawgdictionary.py,a09139626bece8011133179a8252a30c6b29f294,STILL_EXISTS,Match: move to the next index position
aagdhfafj,mideind/GreynirPackage,src/reynir/bintokenizer.py,c14f1dce0195ed21251ff308a2d247741757de54,STILL_EXISTS,TODO skrifa yfir smi\u00F0inn fyrir TOK \u00FAr tokenizer.py \u00ED Tokenizer-pakka
aagdhfahi,mideind/GreynirPackage,src/reynir/dawgdictionary.py,c14f1dce0195ed21251ff308a2d247741757de54,0c1ae673b8bc67cb9c2661358fee1de285b1fc26,The first line is the root (by convention nodeid 0)
aagdhfbag,mideind/GreynirPackage,src/reynir/dawgdictionary.py,c14f1dce0195ed21251ff308a2d247741757de54,STILL_EXISTS,Match: move to the next index position
aagdhgbej,mideind/GreynirPackage,src/reynir/dawgdictionary.py,36c908b934bc79f6e8f4d18c11e5ab416c9735a9,0c1ae673b8bc67cb9c2661358fee1de285b1fc26,TODO: Use .bin-files instead
aagdhgbff,mideind/GreynirPackage,src/reynir/dawgdictionary.py,0c1ae673b8bc67cb9c2661358fee1de285b1fc26,STILL_EXISTS,Match: move to the next index position
aagdhgcdb,mideind/GreynirPackage,src/reynir/dawgdictionary.py,b1d19f154d6d662cb629456bde865ba7ff41a83d,a559e36e89f0d5c47578c24fb10a87b0b3c8eb80,The first line is the root (by convention nodeid 0)
aagdhhfae,mideind/GreynirPackage,src/reynir/dawgdictionary.py,44befd3af24b7c482afb087c7ec8f4831cd793cd,a559e36e89f0d5c47578c24fb10a87b0b3c8eb80,TODO: Use .bin-files instead
aagdhhfba,mideind/GreynirPackage,src/reynir/dawgdictionary.py,a559e36e89f0d5c47578c24fb10a87b0b3c8eb80,STILL_EXISTS,Match: move to the next index position
aagdhhfic,mideind/GreynirPackage,src/reynir/binparser.py,a1f5cb3634875c301f7561040c764a0b3e72a8e6,433b507288be444d1e16697bdecef7d0603f45df,TODO: Change if necessary.
aagdhhfif,mideind/GreynirPackage,src/reynir/settings.py,a1f5cb3634875c301f7561040c764a0b3e72a8e6,e79ba7a3bb72630d692affcd4d64d2425dd6d7fd,TODO: Only read this in if correction is chosen!
aagdhhfjj,mideind/GreynirPackage,src/reynir/settings.py,6b55d02c3ae2d49eb0598b6a07ad7f39b8504a21,16e26efec76234104aa08f1550559daf34fa5a36,or in the Meanings dictionary: hack to make sure that they are
aagdhhgab,mideind/GreynirPackage,src/reynir/settings.py,6b55d02c3ae2d49eb0598b6a07ad7f39b8504a21,STILL_EXISTS,Probably $error(FORM-xxx_xxx)
aagdhhhdb,mideind/GreynirPackage,src/reynir/bindb.py,af3877a2799d644a80df9e2aded73a28288c253c,58bc3777d2354b6087c00aacea32d20a13066a15,print(\"{}-{}\".format(w; m)) # TODO b\u00E6ta h\u00E9r vi\u00F0 sko\u00F0un \u00E1 'beri'; 'gjafi'; ... L\u00EDka annars sta\u00F0ar? Hj\u00E1 b\u00E1\u00F0um return-skipununum?
aagdhhheb,mideind/GreynirPackage,src/reynir/grammar.py,55fb361b3fab5132e36e126f31162a3aa1d1ce18,STILL_EXISTS,TODO do something with error pragma
aagdhhhge,mideind/GreynirPackage,src/reynir/bincompress.py,0c8b8606627458c01625274a5c9eadad3f2281ad,STILL_EXISTS,This is not needed for command-line invocation of bincompress.py;
aagdhhhhj,mideind/GreynirPackage,src/reynir/bindb.py,77a7db9515b56e69b9e56cccee21e955feb329db,70bc9a0795f1307aa1f355f62bc205f4075e6b51,print(\"{}-{}\".format(w; m)) # TODO b\u00E6ta h\u00E9r vi\u00F0 sko\u00F0un \u00E1 'beri'; 'gjafi'; ... L\u00EDka annars sta\u00F0ar? Hj\u00E1 b\u00E1\u00F0um return-skipununum?
aagdhhjje,mideind/GreynirPackage,src/reynir/settings.py,2e4ef72f445bd6c3e65415a9bd4b462dd750798f,433b507288be444d1e16697bdecef7d0603f45df,!!! TODO: Parse the corr string
aagdhhjjf,mideind/GreynirPackage,src/reynir/settings.py,2e4ef72f445bd6c3e65415a9bd4b462dd750798f,433b507288be444d1e16697bdecef7d0603f45df,!!! TODO: Implement this (store specification of a
aagdhhjjg,mideind/GreynirPackage,src/reynir/settings.py,2e4ef72f445bd6c3e65415a9bd4b462dd750798f,433b507288be444d1e16697bdecef7d0603f45df,!!! TODO: replacement of the entire construct)
aagdhifcg,mideind/GreynirPackage,src/reynir/matcher.py,c1c86b64db532f37f29343a5fd67e52d803c69d7,328aec94c264e318e907ad65aec9a36fe32a485b,TODO: Make sure node names are translated in treegrid
aagdhigjf,mideind/GreynirPackage,src/reynir/bincompress.py,d3467d17ded3a21ef06d48adf87203ec790430f2,STILL_EXISTS,TODO: Encoding and decoding back and forth not terribly efficient
aagdhihaj,mideind/GreynirPackage,src/reynir/bincompress.py,c2b70a6f31b66c70bad29d315ccc2d20a2524b28,STILL_EXISTS,(needed since None is a valid utg value)
aagdhiiej,mideind/GreynirPackage,src/reynir/reducer.py,8b1c8f35cd8a2170a0576ace537c921442031abe,STILL_EXISTS,the more matched; the better
aagdhiiha,mideind/GreynirPackage,src/reynir/binparser.py,09359167567adb802ec1797c5aa0a9d4804a2053,STILL_EXISTS,Hack to support 'sequence' terminals; which
aagdhijdj,mideind/GreynirPackage,src/reynir/bintokenizer.py,7bc978d93f9ae9ca30babce7498f977bd532cd33,fecb394c82a1a3c18a9e1f78b4376cccfa476240,!!! TODO: Consider doing list(set(m + meanings)) to
aagdhjfig,mideind/GreynirPackage,src/reynir/simpletree.py,16b660dd22700d3da51b527f0f1360d158c32faa,STILL_EXISTS,TODO: Make sure node names are translated in treegrid
aagdhjgee,mideind/GreynirPackage,src/reynir/simpletree.py,16b660dd22700d3da51b527f0f1360d158c32faa,STILL_EXISTS,!!! TODO: Handle currency names and measurement units
aagdhjhbb,mideind/GreynirPackage,src/reynir/simpletree.py,16b660dd22700d3da51b527f0f1360d158c32faa,STILL_EXISTS,Fix the last terminal if it denotes a currency abbreviation
aagdhjibh,mideind/GreynirPackage,src/reynir/simpletree.py,16b660dd22700d3da51b527f0f1360d158c32faa,STILL_EXISTS,Otherwise; they probably belong to a previous verb and we
aagdhjigh,mideind/GreynirPackage,src/reynir/simpletree.py,16b660dd22700d3da51b527f0f1360d158c32faa,STILL_EXISTS,If so; we eliminate one level and move the children of the child
aagdibhff,mideind/GreynirPackage,test/test_corpus/corpusmanager.py,d220eb76da71dd22d36a659b49c55a9989efc433,STILL_EXISTS,TODO eftir a\u00F0 breyta ans \u00ED True\/False gildi!
aagdibieh,mideind/GreynirPackage,test/test_corpus/helpers.py,d220eb76da71dd22d36a659b49c55a9989efc433,STILL_EXISTS,No information needed after this
aagdicaci,mideind/GreynirPackage,test/test_corpus/helpers.py,13684be74e6df76d19891202b1fdfb9c7bd09c85,STILL_EXISTS,TODO change to another tagger; IceStagger or ABLtagger
aagdicafd,mideind/GreynirPackage,src/reynir/binparser.py,f71d31a3a3db898381a89d662583236c770cb4df,d352d5958b26f5301b6ef0e2c41c6d569ec1e2d5,!!! NOTE: This set should probably include all abbreviations that
aagdiccad,mideind/GreynirPackage,src/reynir/bintokenizer.py,217613b3b90bb4a6a21e4093cf7efed8c50f22f9,21754dd8cff8566eeeafc65f5a1b8a296221e7df,TODO allow more than one name to be merged?
aagdiccci,mideind/GreynirPackage,src/reynir/binparser.py,994d1cd00362c9c421f17d1b5ba992c9e6aa002b,d352d5958b26f5301b6ef0e2c41c6d569ec1e2d5,!!! NOTE: This set should probably include all abbreviations that
aagdiccec,mideind/GreynirPackage,src/reynir/simpletree.py,994d1cd00362c9c421f17d1b5ba992c9e6aa002b,STILL_EXISTS,Unknown words by convention get a category of 'entity'
aagdicdih,mideind/GreynirPackage,src/reynir/bintokenizer.py,d352d5958b26f5301b6ef0e2c41c6d569ec1e2d5,21754dd8cff8566eeeafc65f5a1b8a296221e7df,TODO allow more than one name to be merged?
aagdicece,mideind/GreynirPackage,src/reynir/binparser.py,c9464d05ea0e7cf991f3bf67d5e297b8a854d377,STILL_EXISTS,!!! NOTE: This set should probably include all abbreviations that
aagdicedi,mideind/GreynirPackage,src/reynir/bintokenizer.py,ce6131a1392221418e582f135bcf2737717e3afe,21754dd8cff8566eeeafc65f5a1b8a296221e7df,TODO allow more than one name to be merged?
aagdiceij,mideind/GreynirPackage,src/reynir/bintokenizer.py,1813ed04ecf0a6ccd97798daedf05f58de78febd,STILL_EXISTS,TODO allow more than one name to be merged?
aagdichia,mideind/GreynirPackage,src/reynir/reducer.py,fecb394c82a1a3c18a9e1f78b4376cccfa476240,STILL_EXISTS,!!! TODO: We might be pruning the parse forest too
aagdidbgh,mideind/GreynirPackage,src/reynir/verbframe.py,01987328b9913c6e52af645cca30397916ef1b80,STILL_EXISTS,!!! TODO: Parse the corr string
aagdidbgi,mideind/GreynirPackage,src/reynir/verbframe.py,01987328b9913c6e52af645cca30397916ef1b80,STILL_EXISTS,!!! TODO: Implement this (store specification of a
aagdidbgj,mideind/GreynirPackage,src/reynir/verbframe.py,01987328b9913c6e52af645cca30397916ef1b80,STILL_EXISTS,!!! TODO: replacement of the entire construct)
aagdididd,mideind/GreynirPackage,src/reynir/bintokenizer.py,42c6d86a4a3865b2e2dca4067ccf33e6e4a2cd30,STILL_EXISTS,The following declaration is a deliberate mypy hack
aagdidjbi,mideind/GreynirPackage,src/reynir/bintokenizer.py,2a0245e0fa0496339917f3961660141d5b56bec6,STILL_EXISTS,The following declaration is a deliberate mypy hack
aagdieafh,mideind/GreynirPackage,src/reynir/basics.py,9bda98b912ffd8a9df1c84b90fac46ed98c4c4cc,STILL_EXISTS,Catch corner case where last line of file ends with a backslash
aagdieccj,mideind/GreynirPackage,src/reynir/cache.py,f713e93a8189a1eb0067d178a2a7d594f2083bf6,STILL_EXISTS,Hack to satisfy mypy\/Pylance
aagdiecej,mideind/GreynirPackage,src/reynir/simpletree.py,c87bba6ee9a6f9876fc23ae79c4ad74e4c17fd13,STILL_EXISTS,!!! TODO: self._cat may be None; for instance for TOK.AMOUNT tokens
aagdiecgi,mideind/GreynirPackage,src/reynir/simpletree.py,81d77879fb15789bd5e67947c96430a98f8602eb,STILL_EXISTS,!!! TODO: There should be an escape character
aagdiecic,mideind/GreynirPackage,src/reynir/simpletree.py,81d77879fb15789bd5e67947c96430a98f8602eb,STILL_EXISTS,Hack: Set the lemma of the last terminal
aagdiecif,mideind/GreynirPackage,src/reynir/simpletree.py,81d77879fb15789bd5e67947c96430a98f8602eb,STILL_EXISTS,!!! TODO: The k field should; strictly speaking; reflect
aagdieddb,mideind/GreynirPackage,src/reynir/simpletree.py,52b78eb9bd8e2e428964e161f3a2946682750341,e96bd7da160d17d209321b964c8b0de181b7cfc9,we resort to a hack here by using type:ignore to silence mypy's
aagdiedef,mideind/GreynirPackage,src/reynir/simpletree.py,6f8ced0d91e2e13f8f674e03a82242de00797747,STILL_EXISTS,we resort to a hack here by using type:ignore to silence mypy's
aagdiedfj,mideind/GreynirPackage,src/reynir/grammar.py,a320c5d4336c302706b69e94137cdc93b6ec2d03,STILL_EXISTS,Hack: Allow specifying the forward slash as \"\/\" or '\/'
aageideec,quatrope/feets,setup.py,8634b8f0d04e932753a4c2e492f2e0e8fa683a91,7320cb1fdfe14d13e3e738c2ed229c586d124a8e,You can just specify the packages manually here if your project is
aageidgbf,quatrope/feets,feets/core.py,07cd932bac4edcff10be9d5d711acb7b5d68ffa2,STILL_EXISTS,TODO: remove by dependencies
aageidgbi,quatrope/feets,feets/core.py,07cd932bac4edcff10be9d5d711acb7b5d68ffa2,STILL_EXISTS,TODO: excecution_order by dependencies
aageifidf,quatrope/feets,doc/JSAnimation/examples.py,9de086cff14551b4f1563b0d968ad5f536305ba8,STILL_EXISTS,we'll step two time-steps per frame.  This leads to nice results.
aageifijg,quatrope/feets,doc/source/conf.py,069491e696f3aa582fcf1ee498d22ff34494b7ad,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aageifjha,quatrope/feets,feets/preprocess.py,a77742c9ff5e4698ffd98602cf932f4881acac3c,STILL_EXISTS,TODO: use time instead of mjd
aageifjhb,quatrope/feets,feets/preprocess.py,a77742c9ff5e4698ffd98602cf932f4881acac3c,STILL_EXISTS,TODO: use mag instead of data
aageigdgj,quatrope/feets,feets/extractors/ext_lomb_scargle.py,d8a963ad5e8064da22341f1aff825e4e6a7d4da5,59c34b19978b7698dbd047a28088f52ed2b540de,TODO: Fuck
aageigfaj,quatrope/feets,feets/preprocess.py,d382b0efdce5788fdebb9a0bfe7219c6f469368f,STILL_EXISTS,recreate columns
aageiggjf,quatrope/feets,feets/datasets/base.py,5f97266292e7fdcde1c3640e98da4e49a569c720,STILL_EXISTS,This ugly code creates a LightCurve object based on the extractor constants
aageigied,quatrope/feets,feets/tests/test_original_FATS_test_library.py,5e5c86db9a436b2a72a914dd59c5f73e15cd1886,STILL_EXISTS,FIX the random state
aageiifee,quatrope/feets,feets/core.py,f0e72db259a98c06cb98b4c40587eeb42f9a6227,STILL_EXISTS,remove all the not needed features and extractors
aageiiief,quatrope/feets,tests/conftest.py,d58e90f13bb51cd06132e2684bf6ed2906450f8b,STILL_EXISTS,FIX the random state
aahebcbjj,nltk/nltk,nltk.py,d9a61b1a6979ebbba2e5b999f3c434e1ff133177,STILL_EXISTS,ok not to implement!
aahebccba,nltk/nltk,nltk.py,d9a61b1a6979ebbba2e5b999f3c434e1ff133177,STILL_EXISTS,#  Trial -- stuff needed for pset 2
aahebcccc,nltk/nltk,nltk.py,8b673cc65bd1aedc1f583d080037b71da4898e20,STILL_EXISTS,\"\"\"## || The Natural Language Toolkit is a package intended to simplify the || task of programming natural language systems.  It is intended to be || used as a teaching tool; not as a basis for building production || systems. <P> ||  || <H1> Interfaces <\/H1> ||  || The Natural Language Toolkit is implemented as a set of interfaces and || classes.  Interfaces are a concept loosely borrowed from Java.  They || are essentially a specification of a set of methods.  Any class that || implements all of an interface's methods according to the interface's || specification are said to \\\"implement\\\" that interface. <P> ||  || In the context of this toolkit; an interface is implemented as a || class; all of whose methods simply raise AssertionError.  The || __doc__ strings of these methods; together with the methods' || declarations;  provide specifications for the methods. <P> ||  || Interface classes are named with a trailing \\\"I\\\"; such as || <CODE>TokenizerI<\/CODE> or <CODE>EventI<\/CODE>. ||  || <H1> Interface and Class Hierarchy <\/H1> ||  || The classes defined by the Natural Language Toolkit can be divided || into two basic categories: Data classes; and Processing (or || Task-Oriented) Classes. ||  || <H2> Data Classes <\/H2> ||  || Data classes are used to store several different types of information || that are relavant to natural language processing.  Data classes can || generally be grouped into small clusters; with minimal interaction || between the clusters.  The clusters that are currently defined by the || Natural Language Toolkit are listed below.  Under each cluster; the || top-level classes and interfaces contained in that cluster are given. ||  || <UL> ||   <LI> <B>Sets<\/B>: Encodes the mathmatical notion of a \\\"finite set\\\". ||   <UL> ||     <LI> <A coderef='nltk.Set'>Set<\/A>: A finite set. ||   <\/UL> ||   <LI> <B>Tokens<\/B>: Encodes units of text such as words. ||   <UL> ||     <LI> <A coderef='nltk.TokenTypeI'>TokenTypeI<\/A>: ||          A unit of text. ||     <LI> <A coderef='nltk.TextLocationI'>TextLocationI<\/A>: ||          A location within a text. ||     <LI> <A coderef='nltk.Token'>Token<\/A>: ||          An occurance of a unit of text. ||          Consists of a TokenType and a TextLocation. ||   <\/UL> ||   <LI> <B>Syntax Trees<\/B>: Encodes syntax trees.  Not fully designed  ||         yet. ||   <LI> <B>Probability<\/B>: Encodes data structures associated with ||         the mathmatical notion of probability; such as events and ||         frequency distributions. ||   <UL> ||     <LI> Sample: Encodes the mathmatical notion of a ||           \\\"sample\\\".  This is actually not implemented as a class or ||           an interface -- (almost) anything can be a Sample. ||     <LI> <A coderef='nltk.EventI'>EventI<\/A>: ||          A (possibly infinite) set of samples. ||     <LI> <A coderef='nltk.FreqDistI'>FreqDistI<\/A>: ||           The frequency distribution of a collection of samples. ||     <LI> <A coderef='nltk.ProbDistI'>ProbDistI<\/A>: ||           A probability distribution; typically derived from a ||           frequency distribution (e.g.; using ELE). ||   <\/UL> || <\/UL> ||  || <H2> Processing Classes <\/H2> ||  || Processing classes are used to perform a variety of tasks that are || relavant to natural language processing.  Processing classes can || generally be grouped into small clusters; with minimial interaction || between the clusters.  Each cluster typically makes use of several || data-class clusters.  The processing clusters that are currently || defined by the Natural Language Toolkit are listed below.  Under each || cluster; the interfaces contained in that cluster are given. ||  || <UL> ||   <LI> <B>Tokenizers<\/B>: Separate a string of text into a list of ||        Tokens.  ||   <UL> ||      <LI> <A coderef='nltk.TokenizerI'>TokenizerI<\/A> ||   <\/UL> ||   <LI> <B>Taggers<\/B>: Assign tags to each Token in a list of Tokens. ||   <UL> ||      <LI> <A coderef='nltk.TaggerI'>TaggerI<\/A> ||   <\/UL> ||   <LI> <B>Language Model<\/B>: (not yet designed\/implemented) ||   <LI> <B>Parser<\/B>: (not yet designed\/implemented) || <\/UL> ||  || <H1> Open Questions <\/H1> ||  || The following is a list of currently unresolved questions; pertaining || to currently implemented interfaces and classes. || <UL> ||   <LI> Terminology\/Naming Questions ||   <UL> ||     <LI> Is \\\"Token Type\\\" too easily confusable with the notion of ||          type in python?  E.g.; names like SimpleTokenType suggest ||          that they are similar to StringType; IntType; etc. when they ||          are very different.  I could use \\\"TokenTyp\\\" to distinguish;  ||          but this also seems somewhat confusing to the uninitiated. ||     <LI> What name can be used for the \\\"word content\\\" of a token ||          type?  Currently; <CODE>name<\/CODE> is used; but that's not a  ||          very intuitive name.  <CODE>word<\/CODE> might be used; ||          although often times the string is not a word (e.g.; \\\".\\\"). ||   <\/UL> ||   <LI> Is the token\/token type\/text location system too complex? ||        Often; one only cares about the token type.  E.g.; a tokenizer ||        could be defined as mapping string to list of TokenType; and a ||        tagger as mapping list of SimpleTokenType to TaggedTokenType. ||        But sometimes we really need to be able to distinguish tokens; ||        not just token types.. e.g.; to test the chunk parser from the ||        chunk parsing problem set. ||   <LI> How should text locations be represented?  character index? ||        token index?  To some extent; it dosen't matter; as long as ||        __cmp__ is properly defined.  Should text locations have ranges  ||        or just starting points?  etc. || <\/UL> ||  || @exclude .*(?!Token).....Type || @exclude ....Type || @exclude ...Type || @exclude _typemsg || @exclude _Old.* ||  || @variable _type_safety_level The level of type safety to use when || checking the input parameters to methods defined by the Natural || Language Toolkit.  Currently defined values are: || <UL> ||   <LI> 0: no type checking ||   <LI> 1: check types only ||   <LI> 2: check types and classes ||   <LI> 3: check types; classes; list contents; and tuple contents ||   <LI> 4: check types; classes; list contents; tuple contents; and ||        dictionary contents. || <\/UL> || Higher levels of type safety (3-4) can result in signifigant loss of || efficiency.  || \"\"\"
aahebccid,nltk/nltk,src/nltk/__init__.py,337aeac06f968dbc83cf6dc98ac61bcd047a4c2b,STILL_EXISTS,\"\"\"## || The Natural Language Toolkit is a package intended to simplify the || task of programming natural language systems.  It is intended to be || used as a teaching tool; not as a basis for building production || systems. <P> ||  || This is a prototype implementation for the natural language toolkit. || The natural language toolkit is still under development. ||  || <H1> Interfaces <\/H1> ||  || The Natural Language Toolkit is implemented as a set of interfaces and || classes.  Interfaces are a concept loosely borrowed from Java.  They || are essentially a specification of a set of methods.  Any class that || implements all of an interface's methods according to the interface's || specification are said to \\\"implement\\\" that interface. <P> ||  || In the context of this toolkit; an interface is implemented as a || class; all of whose methods simply raise AssertionError.  The || __doc__ strings of these methods; together with the methods' || declarations;  provide specifications for the methods. <P> ||  || Interface classes are named with a trailing \\\"I\\\"; such as || <CODE>TokenizerI<\/CODE> or <CODE>EventI<\/CODE>. ||  || <H1> Interface and Class Hierarchy <\/H1> ||  || The classes defined by the Natural Language Toolkit can be divided || into two basic categories: Data classes; and Processing (or || Task-Oriented) Classes.  The Natural Language Toolkit is still under || development; and any definitions are subject to change. ||  || <H2> Data Classes <\/H2> ||  || Data classes are used to store several different types of information || that are relavant to natural language processing.  Data classes can || generally be grouped into small clusters; with minimal interaction || between the clusters.  The clusters that are currently defined by the || Natural Language Toolkit are listed below.  Under each cluster; the || top-level classes and interfaces contained in that cluster are given. ||  || <UL> ||   <LI> <B>Sets<\/B>: Encodes the mathmatical notion of a \\\"finite set\\\". ||   <UL> ||     <LI> <A coderef='nltk.Set'>Set<\/A>: A finite set. ||   <\/UL> ||   <LI> <B>Tokens<\/B>: Encodes units of text such as words. ||   <UL> ||     <LI> <A coderef='nltk.TokenTypeI'>TokenTypeI<\/A>: ||          A unit of text. ||     <LI> <A coderef='nltk.TextLocationI'>TextLocationI<\/A>: ||          A location within a text. ||     <LI> <A coderef='nltk.Token'>Token<\/A>: ||          An occurance of a unit of text. ||          Consists of a TokenType and a TextLocation. ||   <\/UL> ||   <LI> <B>Syntax Trees<\/B>: Encodes syntax trees.  Not fully designed  ||         yet. ||   <LI> <B>Probability<\/B>: Encodes data structures associated with ||         the mathmatical notion of probability; such as events and ||         frequency distributions. ||   <UL> ||     <LI> Sample: Encodes the mathmatical notion of a ||           \\\"sample\\\".  This is actually not implemented as a class or ||           an interface -- (almost) anything can be a Sample. ||     <LI> <A coderef='nltk.EventI'>EventI<\/A>: ||          A (possibly infinite) set of samples. ||     <LI> <A coderef='nltk.FreqDistI'>FreqDistI<\/A>: ||           The frequency distribution of a collection of samples. ||     <LI> <A coderef='nltk.ProbDistI'>ProbDistI<\/A>: ||           A probability distribution; typically derived from a ||           frequency distribution (e.g.; using ELE). ||   <\/UL> || <\/UL> ||  || <H2> Processing Classes <\/H2> ||  || Processing classes are used to perform a variety of tasks that are || relavant to natural language processing.  Processing classes can || generally be grouped into small clusters; with minimial interaction || between the clusters.  Each cluster typically makes use of several || data-class clusters.  The processing clusters that are currently || defined by the Natural Language Toolkit are listed below.  Under each || cluster; the interfaces contained in that cluster are given. ||  || <UL> ||   <LI> <B>Tokenizers<\/B>: Separate a string of text into a list of ||        Tokens.  ||   <UL> ||      <LI> <A coderef='nltk.TokenizerI'>TokenizerI<\/A> ||   <\/UL> ||   <LI> <B>Taggers<\/B>: Assign tags to each Token in a list of Tokens. ||   <UL> ||      <LI> <A coderef='nltk.TaggerI'>TaggerI<\/A> ||   <\/UL> ||   <LI> <B>Language Model<\/B>: (not yet designed\/implemented) ||   <LI> <B>Parser<\/B>: (not yet designed\/implemented) || <\/UL> ||  || <H1> Open Questions <\/H1> ||  || The following is a list of currently unresolved questions; pertaining || to currently implemented interfaces and classes. || <UL> ||   <LI> Terminology\/Naming Questions ||   <UL> ||     <LI> Is \\\"Token Type\\\" too easily confusable with the notion of ||          type in python?  E.g.; names like SimpleTokenType suggest ||          that they are similar to StringType; IntType; etc. when they ||          are very different.  I could use \\\"TokenTyp\\\" to distinguish;  ||          but this also seems somewhat confusing to the uninitiated. ||     <LI> What name can be used for the \\\"word content\\\" of a token ||          type?  Currently; <CODE>name<\/CODE> is used; but that's not a  ||          very intuitive name.  <CODE>word<\/CODE> might be used; ||          although often times the string is not a word (e.g.; \\\".\\\"). ||     <LI> Better name than \\\"SimpleTagger\\\"? ||   <\/UL> ||   <LI> Is the token\/token type\/text location system too complex? ||        Often; one only cares about the token type.  E.g.; a tokenizer ||        could be defined as mapping string to list of TokenType; and a ||        tagger as mapping list of SimpleTokenType to TaggedTokenType. ||        But sometimes we really need to be able to distinguish tokens; ||        not just token types.. e.g.; to test the chunk parser from the ||        chunk parsing problem set. ||   <LI> How should text locations be represented?  character index? ||        token index?  To some extent; it dosen't matter; as long as ||        __cmp__ is properly defined.  Should text locations have ranges  ||        or just starting points?  etc. ||   <LI> Should FreqDist.max() and FreqDist.cond_max() be merged; with ||        the condition as an optional argument?  Same for ||        FreqDist.freq() and FreqDist.cond_freq(). ||   <LI> Should I implement cross-toolkit policies on how to use __str__  ||        and __repr__?  If so; what should they be? ||   <LI> How should I split the toolkit into modules?  Should I use a ||        multi-layer structure (e.g.; <CODE>nltk.probability<\/CODE>)? || <\/UL> ||  || @exclude .*(?!Token).....Type || @exclude ....Type || @exclude ...Type || @exclude _typemsg || @exclude _Old.* ||  || @author Edward Loper || @version 0.1 || \"\"\"
aahebcdbc,nltk/nltk,src/nltk/chktype.py,337aeac06f968dbc83cf6dc98ac61bcd047a4c2b,STILL_EXISTS,isn't really intended to be played with.  It should be possible;
aahebchhh,nltk/nltk,src/nltk/chartparser.py,a34d92faa363d37e28bdf3effc12aa0e732c1439,6550cb5e0d2289502c2e2ef0a75cb18eff4de726,- It looks to me like you should probably change dotted rules to
aahebchid,nltk/nltk,src/nltk/chartparser.py,a34d92faa363d37e28bdf3effc12aa0e732c1439,6550cb5e0d2289502c2e2ef0a75cb18eff4de726,Things to happen to your dictionary (apparently before Python
aahebchif,nltk/nltk,src/nltk/chartparser.py,a34d92faa363d37e28bdf3effc12aa0e732c1439,6550cb5e0d2289502c2e2ef0a75cb18eff4de726,- Chart.draw() should probably eventually be replaced by a Tkinter
aahebchih,nltk/nltk,src/nltk/chartparser.py,a34d92faa363d37e28bdf3effc12aa0e732c1439,a2501d4bfc1b5887433154c7f787621a3d31553c,- I think parsers should probably return TreeTokens; not Trees.
aahebchjj,nltk/nltk,src/nltk/chartparser.py,a34d92faa363d37e28bdf3effc12aa0e732c1439,6550cb5e0d2289502c2e2ef0a75cb18eff4de726,[edloper 8\/14\/01] Tokens really shouldn't contain mutable types
aahebciaa,nltk/nltk,src/nltk/chartparser.py,a34d92faa363d37e28bdf3effc12aa0e732c1439,6550cb5e0d2289502c2e2ef0a75cb18eff4de726,(dr is mutable).  Maybe you could define self._dr; and let
aahebciei,nltk/nltk,src/nltk/rule.py,a34d92faa363d37e28bdf3effc12aa0e732c1439,b9960f5a65190b2633802bd902b212d66f1ca4ef,avoid using them as keys.  If we implement Sets with
aahebcjbh,nltk/nltk,src/nltk/parser.py,81a527007db89413514b0a4260d6b42020c9ab77,STILL_EXISTS,Todo:
aahebddci,nltk/nltk,src/nltk/rechunkparser.py,021794ad155a72754d871e919cefe3f94eae9577,47adbdb6bf9e284efb3f933c4edc9d355be688ba,rule initializer should compile the final regexp for efficient runtime execution
aahebddee,nltk/nltk,src/nltk/draw/plot_graph.py,624241427443fee426cad8fa4a155339e920ff5f,STILL_EXISTS,###               FIX            ####
aahebddha,nltk/nltk,src/nltk/draw/chart.py,e58a02748c344570962e0c2c74a3b41c8a7163e1,STILL_EXISTS,FIX ME
aahebddie,nltk/nltk,src/nltk/__init__.py,da8de8018a6ff213f81754d58b15da28626d17cc,STILL_EXISTS,\"\"\" || Open Questions || ============== ||  || The following is a list of currently unresolved questions; pertaining || to currently implemented interfaces and classes. ||   - Terminology\/Naming Questions ||     - Is \\\"Token Type\\\" too easily confusable with the notion of ||          type in python?  E.g.; names like SimpleTokenType suggest ||          that they are similar to StringType; IntType; etc. when they ||          are very different.  I could use \\\"TokenTyp\\\" to distinguish;  ||          but this also seems somewhat confusing to the uninitiated. ||   - Is the token\/token type\/text location system too complex? ||        Often; one only cares about the token type.  E.g.; a tokenizer ||        could be defined as mapping string to list of TokenType; and a ||        tagger as mapping list of SimpleTokenType to TaggedTokenType. ||        But sometimes we really need to be able to distinguish tokens; ||        not just token types.. e.g.; to test the chunk parser from the ||        chunk parsing problem set. ||   - Should FreqDist.max() and FreqDist.cond_max() be merged; with ||        the condition as an optional argument?  Same for ||        FreqDist.freq() and FreqDist.cond_freq(). ||   - Should I implement cross-toolkit policies on how to use __str__  ||        and __repr__?  If so; what should they be? ||  || \"\"\"
aahebdgfa,nltk/nltk,src/nltk/draw/chart.py,d167629915121ab52d14a392ac5abc912a27ce07,STILL_EXISTS,FIX ME
aahebdidb,nltk/nltk,src/nltk/draw/tree_edit.py,69d9eb3e31e59ef6f84cf23091845d97b360f6d1,STILL_EXISTS,\"\"\" || I may want to get rid of the interactive scrollregion. || I like the way it just keeps 0;0 as the left boundary.  Otherwise; || it looks a little choppy ||  || NOTE: FIX DESTROY AND QUIT ||  || \"\"\"
aahebdidi,nltk/nltk,src/nltk/rechunkparser.py,47adbdb6bf9e284efb3f933c4edc9d355be688ba,STILL_EXISTS,\"\"\" || Terms\/representations that should be defined in module docstring: ||   - chunk = a list of tagged tokens ||   - ttoklist = a list of tagged tokens (=chunk; but used in different ||     contexts)  ||   - chunklist = a list of tagged tokens and chunks ||   - ChunkString = string rep of chunked tags ||   - tag pattern = a regexp pattern over tags.  Has slightly different ||     rules than normal regexps (since it gets translated): <> act like ||     parens; . gets \\w; etc. ||  || High priority: ||   - More checking? ||       - _verify ||       - check the tag patterns ||  || Medium priority: ||   - generalize ChunkString constructor to accept a chunk list? ||   - rename ChunkString? ||   - rename \"chunklist\" to \"chunkstruct\"; to better cohere with the ||     terminology used in chunkparser.py? ||   - should ChunkString have a str-ish method? (that returns its ||     internal rep) ||   - Add more documentation explaining precompiled regexps. ||   - In order to conform to interfaces; we might want to change reps: ||     - chunkparser output as a tree ||     - chunkedtaggedtokenizer should produce a list of tokens ||   - IN_CHINK_PATTERN; IN_CHUNK_PATTERN exceed maximum recursion depth ||     when we try to do chunking above the sentence level (~1000-2000 ||     words).  Grr. :)  So maybe we need a sentence tokenizer?  Or ||     something?   ||  || Low priority: ||   - Efficiency issues? (currently we do ~500-1500 tokens\/sec; faster ||     when we chunk more text at a time) ||  || Questions: ||   - Should ChunkString be made immutable? ||  || Indication of current efficiency:: ||  ||   TIMING TEST (3 rules: chunk\/unchunk\/merge) ||   1 x 10008 words: ||       Time = 15.3654409647 ||   8 x 1260 words: ||       Time = 10.0115730762 ||   27 x 372 words: ||       Time = 9.67810499668 ||   64 x 168 words: ||       Time = 10.1981619596 ||   125 x 84 words: ||       Time = 10.1149849892 ||   216 x 48 words: ||       Time = 10.5362759829 ||   343 x 36 words: ||       Time = 12.9556429386 ||   512 x 24 words: ||       Time = 13.6545710564 ||   729 x 24 words: ||       Time = 19.4766739607 ||   1000 x 12 words: ||       Time = 16.0188289881 ||  || \"\"\"
aahebeadd,nltk/nltk,src/nltk/probability.py,231c33616c944d7246921992a03ed3c4ff8a159e,ba6a7dd152222f5ad117fb6cc045dc9dc8b6b6ac,Create an empty array of (way x way) entries.
aahebeade,nltk/nltk,src/nltk/probability.py,231c33616c944d7246921992a03ed3c4ff8a159e,ba6a7dd152222f5ad117fb6cc045dc9dc8b6b6ac,Hack..
aahebecfe,nltk/nltk,src/nltk/classifier/naivebayes.py,b05a9c7de253a5d1859b8bcce997c100da2fd13b,STILL_EXISTS,# Much more efficient..  But this is cheating!
aahebecgg,nltk/nltk,src/nltk/classifier/naivebayes.py,0e5e3b600fb842b70ed310af87c3a3e732ba9cf1,STILL_EXISTS,\"\"\" || A text classifier model based on the naive bayes assumption.   || In particular; this classifier assumes that the probability of each || feature occuring is independant; given the label.  This allows us to || estimate the probability that a given text has a given label as || follows:: ||  ||       P(label|text) = P(text;label) \/ P(text) ||  ||       P(text; label) = P(label) * P(fi|label) * (1-P(fj|label)) ||  || Where C{text} is the text we are classifiying; C{label} is a potential || label; C{f1}; C{f2}; ...; C{fi}; ...; C{fN} are the  ||  || blah blah.. ||  || Approach: ||   - define useful events ||   - NBClassifier gets a single PDF over LabeledFeatureValueLists. ||  || \"\"\"
aahebecig,nltk/nltk,src/nltk/classifier/naivebayes.py,9f0a2e4a4077c48338baafd6982c3f35fc97637d,48b9f59fe94720f6d6fd678a1f0c4992f7f96b73,!! HACK !!
aahebefjh,nltk/nltk,src/nltk/classifier/feature.py,52dfa64ac43ab015fb38f6b26a8b586338be820d,STILL_EXISTS,This may not be the most efficient method if we're not using
aahebegea,nltk/nltk,src/nltk/classifier/feature.py,38e809e9881c454332f7f3286a2a0b1187be77ce,STILL_EXISTS,\"\"\" ||  || Basic data classes for building feature-based classifiers. || X{features} provide a standard way of encoding the information used to || make classification decisions.  This standard encoding allows the same || classifier to be used to solve many different problems. ||  || Features || ======== || Each feature specifies some aspect of a C{LabeledText} that is || relevant to deciding how likely that C{LabeledText} is to occur. || These features can then be used to examine how likely different labels || are for a given text.  A typical example of a feature is: ||  ||     - Whether a C{LabeledText} contains the word C{\\\"ball\\\"} and has ||       the label C{\\\"sports\\\"}. ||  || Abstractly; features can be thought of as functions mapping from || C{LabeledText}s to values.  For example; the feature function || corresponding to the typical feature given above is:: ||  ||     f(ltext)  =  1  if ((\\\"ball\\\" in ltext.text) and ||                         (ltext.label == \\\"sports\\\")) ||                         ||               =  0  otherwise ||  || Note that features depend on both the text and the label.  This allows || us to specify which aspects of a text are relevant for making || decisions about which labels.  Feature functions usually have the || form:: ||  ||     f(ltext)  =  g(ltext.text)   if ltext.label == L ||                       0          if ltext.label != L ||  || for some function C{g} and label C{L}.  {g} is sometimes called the || X{context function}. (?) ||  || Abstractly; each feature consists of: ||  ||    - A unique X{feature id}; which is used to distinguish the ||      feature.  Feature ids are bounded non-negative integers. ||       ||    - A X{feature detector}; which encapsulates the feature's ||      function.  Feature detectors are implemented using the ||      C{FeatureDetectorI} interface. ||       ||    - A set of possible X{feature value}s that can be produced by the ||      feature detector.  In principle; feature detectors can generate ||      any immutable value; however; many classifiers require that their ||      features have a specific type of value.  The most common sets of ||      feature values are: ||  ||          - booleans (C{0} and C{1}) ||          - integers ||          - non-negative integers ||          - real numbers ||  || Features are not explicitly represented by any object; instead; they || are indirectly represented using feature detectors. ||  || FeatureDetectorLists || ==================== || X{Feature detector lists} provide a way of grouping feature detectors || together.  Abstractly; a feature detector list can be thought of as a || C{list} of feature detectors.  The M{i}th element of this list is the || feature detector for the feature with id M{i}.   ||  || When a feature detector list is applied to a C{LabeledText}; it || returns a X{feature value list}.  Abstractly; a feature value list can || be thought of as a C{list} of feature values.  The M{i}th element of || this list is the feature value for the feature with id M{i}. ||  || Feature detector lists don't just serve to group feature detectors || together; they are also an important tool for efficient feature || detection.  Often; the features used for classification are closely || related to each other.  For example; a document classifier might use || features that examine whether a given word is in a document.  If we || applied each feature detector separately; we would have to scan || through the document once for each word we are interested in. || Instead; we can build a feature detector list that will check for all || relevant words at the same time. ||  || Similarly; feature value lists serve to make feature detection more || efficient.  For many classification tasks; feature value lists are || very sparse; in other words; most of the feature values have some || X{default value} (usually; zero).  Feature value lists provide a || method that can be used to retrieve all of the non-default feature || value assignments.  This can decrease the time that it takes to || process the feature value list considerably. ||  || Labeled Feature Value Lists || =========================== || Often; it is useful to associate a label with a feature value list. || The C{LabeledFeatureValueList} class can be used for this purpose. || Typically; each C{LabeledFeatureValueList} corresponds to a text; || where the labels are identical; and the feature value list is obtained || by applying some feature detector list to the text. || \"\"\"
aahebeggg,nltk/nltk,src/nltk/classifier/naivebayes.py,48b9f59fe94720f6d6fd678a1f0c4992f7f96b73,STILL_EXISTS,\"\"\" || A text classifier model based on the naive Bayes assumption.  In || particular; this classifier assumes that each feature is independant. || This assumption allows us to construct reliable estimates of the || probability that a given text has a given label.  See C{NBClassifier} || for more information on the approximation algorithms used by the || classifier. ||  || C{NBClassifier} is a naive Bayes classifier model.  This model is || based on a C{ProbDist} whose samples are C{FeatureValueList}s.  It || uses this probability distribution to estimate the probability that a || given text should be classified with a label:: ||  ||     P(label|text) ||  || To form these estimates; it examines the probabilities of individual || feature-value assignments:: ||  ||     P(fi=vi) ||  || It uses C{AssignmentEvent}s to encode the event that a feature has a || given value.  An C{AssignmentEvent} is an event consisting of all || C{FeatureValueList}s that contain a given feature-value assignment. || The probability distribution used to define a C{NBClassifier} should || therefore be capable of efficiently calculating the probability of || C{AssignmentEvent}s. ||  || This module defines a frequency distribution; C{NBClassifierFreqDist}; || which is designed to efficiently find the frequencies of || C{AssignmentEvent}s.  This frequency distribution can be used to build || an efficient C{ProbDist} for C{NBClassifier}. ||  || C{NBClassifier}s can be built from training data with the || C{NBClassifierTrainer} class. || \"\"\"
aahebehfi,nltk/nltk,src/nltk/classifier/maxent.py,ab79db1a0f9e1a17be6d929037480e545ec4cf12,STILL_EXISTS,\"\"\" || A text classifier model based on maximum entropy modeling framework. || This framework considers all of the probability distributions that are || emperically consistant with the training data; and chooses the || distribution with the highest entropy.  A probability distribution is || X{emperically consistant} with a set of training data if its estimate || for the frequency of each feature is equal to that frequency of the || feature in the training data.  In other words:: ||  ||   SUM[lt] (fd[i](lt) * P(lt)) = SUM[lt] (fd[i](lt) * freq(lt)) ||    || For all i; where: ||   - M{lt} is a labeled text. ||   - M{fd[i]} is the feature detector for the M{i}th feature. ||   - M{freq(lt)} is the frequency of M{lt} in the training corpus. ||   - M{P(lt)} is the estimated probability of M{lt}. ||  || It can be shown that the emperically consistant distribution that || maximizes entropy must have the form:: ||  ||   P(l|t) = 1\/Z(t) * exp(w[0]*fd[0](LabeledText(t;l)) + ||                         w[1]*fd[1](LabeledText(t;l)) + ||                         ... + ||                         w[n]*fd[n](LabeledText(t;l))) ||  || Where: ||   - M{t} is a text. ||   - M{l} is a label. ||   - M{fd[i]} is the feature detector for the M{i}th feature. ||   - M{w[i]} is the weight associated with the M{i}th feature. ||   - M{Z(t)} is a normalization factor; computed by summing ||     M{P(l|t)} over labels:: ||  ||         Z(t) = SUM[l] P(l|t) ||  || This form is known as a \"conditional exponential model.\"  The || C{ConditionalExponentialClassier} class implements this model. ||  || C{GISMaxentClassifierTrainer} and C{IISMaxentClassifierTrainer} || implement two different algorithms for training || C{ConditionalExponentialClassifiers}.  Both trainers find the || emperically consistant model which maximizes entropy. || C{GISMaxentClassifierTrainer} uses Generalized Iterative Scaling; and || C{IISMaxentClassifierTrainer} uses Improved Iterative Scaling. || \"\"\"
aahebejab,nltk/nltk,src/nltk/fsa.py,4bd19eea0445cad2364bac110d0ba1b9b3dca8aa,STILL_EXISTS,TODO - check that parse was complete; and report error otherwise
aahebejac,nltk/nltk,src/nltk/fsa.py,4bd19eea0445cad2364bac110d0ba1b9b3dca8aa,STILL_EXISTS,TODO - change parser to limit scope of unary operators
aahebejbe,nltk/nltk,src/nltk/fsa.py,4bd19eea0445cad2364bac110d0ba1b9b3dca8aa,STILL_EXISTS,# TODO - add epsilon transition from finals to initials
aahebejbi,nltk/nltk,src/nltk/fsa.py,4bd19eea0445cad2364bac110d0ba1b9b3dca8aa,STILL_EXISTS,TODO - epsilon removal; minimization
aahebejgi,nltk/nltk,src/nltk/fsa.py,a17b8043445d2de52f3b46e10bf568a0dbce7609,STILL_EXISTS,CONSIDER CHANGING DATA STRUCTURE SO THAT THIS IS EFFICIENT
aahebejhc,nltk/nltk,src/nltk/fsa.py,acfa1711c4380fb31a94b114ebbc32170ab46cfb,STILL_EXISTS,TODO - code up minimization algorithm from ASU
aahebfabe,nltk/nltk,src/nltk/draw/fsa.py,ad483bbf3bde06197d6a8c37d4d903679edcd589,STILL_EXISTS,How much extra space should we add around the FSA; to make it
aahebfabh,nltk/nltk,src/nltk/draw/fsa.py,ad483bbf3bde06197d6a8c37d4d903679edcd589,1d9ead1b6d87ed0416a70bfa05b254d9e6daad04,How much extra scroll-space should we provide; to let the user move
aahebfadh,nltk/nltk,src/nltk/draw/fsa.py,ad483bbf3bde06197d6a8c37d4d903679edcd589,b404b0531374db6f77a49258caa9fd75bad5bf5f,isn't terribly efficient; but we dont' expect to be
aahebfegb,nltk/nltk,src/nltk/pcfgparser.py,fea32ed786381ccc66b98b7bc6ea01abc8ed47f4,STILL_EXISTS,\"\"\" || Parsers for probablistic context free grammars (C{PCFGs}). || C{nltk.pcfgparser} currently defines two basic types of || C{ProbablisticParser}: C{ViterbiPCFGParser} and || C{BottomUpPCFGChartParser}. ||  || C{ViterbiPCFGParser} is a C{PCFG} parser based on a Viterbi-sytle || algorithm.  It uses dynamic programming to efficiently find the single || most likely parse for a given text. ||  || C{BottomUpPCFGChartParser} is an abstract class that implements a || bottom-up chart parser for C{PCFG}s.  It maintains a queue of edges; || and adds them to the chart one at a time.  The ordering of this queue || is based on the probabilities associated with the edges; allowing the || parser to expand more likely edges before less likely ones.  Each || subclass implements a different queue ordering; producing different || search strategies.  Currently the following subclasses are defined: ||  ||   - C{InsidePCFGParser} searches edges edges in decreasing order of ||     their trees' inside probabilities. ||   - C{RandomPCFGParser} searches edges in random order. ||   - C{LongestPCFGParser} searches edges in decreasing order of their ||     location's length. ||    || The following subclasses will be added in the near future: ||  ||   - C{OutsidePCFGParser} searches edges using best-first search. ||   - C{InsideOutsidePCFGParser} searches edges using A* search. || \"\"\"
aahebffeg,nltk/nltk,src/nltk/chartparser.py,a4a21af962121929422035bb2e305fa846b344d1,STILL_EXISTS,\"\"\" ||  || This is a version of chartparser that uses a single ChartRule interface; || which can be C{apply}ed to a chart... ||  || The chartparser module defines the C{ChartParser} class; and two supporting || classes; C{Edge} and C{Chart}. ||  || 'chart rule' is really not a very good name..  What might be a better || name?  We could use 'strategy.'  Hmm..  Of course; right now; a || strategy is a collection of chart rules..  Which is nice; for talking || about; e.g.; BU strategy.  Hrm..   ||  ||  || \"\"\"
aahebfibg,nltk/nltk,src/nltk/parser/probabilistic.py,e2331b55b0394affb98c2c73f3fa7f57d7ed59c4,STILL_EXISTS,\"\"\" || Parsers for probabilistic context free grammars (C{PCFGs}). || C{nltk.pcfgparser} currently defines two basic types of || C{ProbabilisticParser}: C{ViterbiPCFGParser} and || C{BottomUpPCFGChartParser}. ||  || C{ViterbiPCFGParser} is a C{PCFG} parser based on a Viterbi-sytle || algorithm.  It uses dynamic programming to efficiently find the single || most likely parse for a given text. ||  || C{BottomUpPCFGChartParser} is an abstract class that implements a || bottom-up chart parser for C{PCFG}s.  It maintains a queue of edges; || and adds them to the chart one at a time.  The ordering of this queue || is based on the probabilities associated with the edges; allowing the || parser to expand more likely edges before less likely ones.  Each || subclass implements a different queue ordering; producing different || search strategies.  Currently the following subclasses are defined: ||  ||   - C{InsidePCFGParser} searches edges edges in decreasing order of ||     their trees' inside probabilities. ||   - C{RandomPCFGParser} searches edges in random order. ||   - C{LongestPCFGParser} searches edges in decreasing order of their ||     location's length. ||    || The following subclasses will be added in the near future: ||  ||   - C{OutsidePCFGParser} searches edges using best-first search. ||   - C{InsideOutsidePCFGParser} searches edges using A* search. || \"\"\"
aahebfjca,nltk/nltk,src/nltk/parser/chunk.py,b04ead36e7bc4e84c0fc8fef6cb240e446ab445d,3d53b349b250336b13fcc2b5732243d699d36f63,\"\"\" || Issues: ||   - maximum recursion depth issues! grr... ||   - generalize ChunkString constructor to accept a chunk struct ||     instead of a tagged text? ||   - Add more comments\/docs; explaining precompiled regexps. ||   - In order to conform to interfaces; we might eventually want to ||     change reps:  ||     - chunkparser output as a tree ||     - chunkedtaggedtokenizer should produce a list of tokens ||   - Efficiency issues? (currently we do ~500-1500 tokens\/sec; faster ||     when we chunk more text at a time) ||  || Questions: ||   - Should ChunkString be made immutable? ||  || Indication of current efficiency:: ||  ||   TIMING TEST (3 rules: chunk\/unchunk\/merge) ||   1 x 10008 words: ||       Time = 15.36 seconds ||   8 x 1260 words: ||       Time = 10.01 seconds ||   27 x 372 words: ||       Time = 9.678 seconds ||   64 x 168 words: ||       Time = 10.19 seconds ||   125 x 84 words: ||       Time = 10.11 seconds ||   216 x 48 words: ||       Time = 10.53 seconds ||   343 x 36 words: ||       Time = 12.95 seconds ||   512 x 24 words: ||       Time = 13.65 seconds ||   729 x 24 words: ||       Time = 19.47 seconds ||   1000 x 12 words: ||       Time = 16.01 seconds ||  || \"\"\"
aahebgaah,nltk/nltk,src/nltk/parser/__init__.py,f1b400000a567fb3143662482fe32cc568a4562b,38883b3adf2f4001c8b38cf12ed07172aea6906d,These are equivalant; the second is more efficient; but
aahebgafd,nltk/nltk,src/nltk/draw/__init__.py,5d1f59e2d44733035ea32d89b03ed7d109c9c3dd,STILL_EXISTS,Move it back; if we were dragging.
aahebgaif,nltk/nltk,src/nltk/draw/__init__.py,5d1f59e2d44733035ea32d89b03ed7d109c9c3dd,STILL_EXISTS,Move to (x;y)
aahebgbag,nltk/nltk,src/nltk/draw/srparser.py,5acef569687376d01c7f27b6e94a6a6d51466a4f,STILL_EXISTS,\"\"\" || A graphical tool for exploring the shift\/reduce parser. ||  || This tool allows you to explore algorithm used by the shift\/reduce || parser.  The shift\/reduce parser maintains a stack; which records the || structure of the portion of the text that has been parsed.  The stack || is initially empty.  Its contents are shown on the left side of the || demo window. ||  || On the right side of the demo window is the remaining text.  This is || the portion of the text which has not yet been considered by the parser. ||  || The parser builds up a tree structure for the text using two || operations:  ||  ||   - \"shift\" moves the first token from the remaining text to the top ||     of the stack.  In the demo; the top of the stack is its right-hand ||     side. ||   - \"reduce\" uses a grammar production to combine the rightmost stack ||     elements into a single tree token. ||  || You can control the parser's operation by using the \"shift\" and || \"reduce\" button; or you can use the \"step\" button to let the parser || automatically decide which operation to apply.  The shift reduce || parser uses the following rules to decide which operation to use: ||  ||   - Only shift if no reductions are available. ||   - If multiple reductions are available; then apply the reduction ||     whose CFG production is listed earliest in the grammar. ||  || Currently; there is no way to choose which production to apply; || however; this functionality should be added in the near future. ||  || KEYBOARD SHORTCUTS: ||     [space]    Perform the next shift or reduce operation ||     [s]        Perform a shift operation ||     [r]        Perform a reduction operation ||     [delete]   Reset the parser ||     [g]        Show\/hide grammar ||     [ctrl-a]   Toggle animations ||     [h]        Help ||     [p]        Print ||     [q]        Quit || \"\"\"
aahebgbcj,nltk/nltk,src/nltk/draw/srparser.py,5acef569687376d01c7f27b6e94a6a6d51466a4f,STILL_EXISTS,Move the remaining text to the correct location (keep it
aahebgcah,nltk/nltk,src/nltk/draw/srparser.py,382ee90f6b68dcd21defd45a63d5f5b5c981cae7,STILL_EXISTS,\"\"\" || Possible future improvements: ||   - undo (shortcut=backspace).  Simplest implentation: keep a list of ||     the stack\/remaining text after each operation; and roll-back by ||     setting to the old one.  This should probably be implemented in ||     the stepping parser; not in thte demo.  (i.e.; stepping srparser ||     would have an undo() method). ||   - button\/window to change and\/or select text.  Just pop up a window ||     with an entry; and let them modify the text; and then retokenize ||     it?  Maybe give a warning if it contains tokens whose types are ||     not in the grammar. ||   - button\/window to change and\/or select grammar.  Select from ||     several alternative grammars?  Or actually change the grammar?  If ||     the later; then I'd want to define nltk.draw.cfg; which would be ||     responsible for that. || \"\"\"
aahebgccj,nltk/nltk,src/nltk/parser/__init__.py,b5f3127877140236025489994a8d8812a0a3265d,STILL_EXISTS,This isn't the best way to implement the productions
aahebgeah,nltk/nltk,src/nltk/draw/rdparser.py,946d81169e03ef35d065b91c6449c6f86d83c2cd,STILL_EXISTS,Boy; too bad tkinter doesn't implement Listbox.itemconfig;
aahebgecj,nltk/nltk,src/nltk/draw/rdparser.py,946d81169e03ef35d065b91c6449c6f86d83c2cd,STILL_EXISTS,Move the text string down; if necessary.
aahebggge,nltk/nltk,src/nltk/classifier/naivebayes.py,cdbb9e8b8e5cf50b5442a60e147116d489dc8a37,cba72d96a216b9669bdabd625a3572f4c4776e5c,# efficient for sparse feature value lists...
aahebgghd,nltk/nltk,src/nltk/classifier/naivebayes.py,cdbb9e8b8e5cf50b5442a60e147116d489dc8a37,cba72d96a216b9669bdabd625a3572f4c4776e5c,value lists are sparse; we can improve performance by finding
aahebgijc,nltk/nltk,src/nltk/draw/plot.py,546e6b81111c7ea5ed9616eccddf7da8dc6be2d1,3a1a8ec16bcf3b050b2f67602117d9dd5b079756,This import is needed to prevent an error if we create
aahebgjbb,nltk/nltk,src/nltk/draw/plot.py,3a1a8ec16bcf3b050b2f67602117d9dd5b079756,STILL_EXISTS,This reload is needed to prevent an error if we create
aahebhbef,nltk/nltk,src/nltk/speech/numbers.py,d763cc46eadaded5ef004463d9a162cbb2d0c54b,STILL_EXISTS,BUG: 1001 gives \"one thousand one\"; and similar
aahebhcgd,nltk/nltk,src/nltk/speech/token.py,d763cc46eadaded5ef004463d9a162cbb2d0c54b,STILL_EXISTS,pronounced but still needed for pauses in speech synthesis.
aahebhdib,nltk/nltk,src/nltk/stemmer/porter.py,1947c77ac8eff1347ea19dc247450458c6adda17,STILL_EXISTS,i == 0 never happens perhaps
aahebhdij,nltk/nltk,src/nltk/stemmer/porter.py,1947c77ac8eff1347ea19dc247450458c6adda17,STILL_EXISTS,if self.ends(\"abli\"):      self.r(\"able\")
aahebhfjd,nltk/nltk,src/nltk/test/imports.py,0ab0040a9419b99da4b747725cac91f7af0944ad,STILL_EXISTS,\"\"\" || A test suite that checks to make sure that all imports of nltk modules || use absolute paths. ||  || In particular; check that all nltk modules import each other with || statements that have the forms: ||  ||     >>> import nltk.token ||     >>> from nltk.token import * ||  || and not the forms: ||  ||     >>> import token ||     >>> from token import * ||  || This convention ensures that the same module is not imported twice; || even if it is imported with both relative and absolute paths.  It also || reduces confusion if two modules have the same relative name (eg || C{nltk.token} and C{nltk.speech.token}). || \"\"\"
aahebhhbi,nltk/nltk,src/nltk/test/token.py,e24a59e7d7fe0d1348c2e1348645b540a727f4ce,STILL_EXISTS,\"\"\" || Unit testing for L{nltk.token}. ||  || @todo: Test L{nltk.token.CharTokenizer} || @todo: Test L{nltk.token.LineTokenizer} || \"\"\"
aahebhhbj,nltk/nltk,src/nltk/test/tree.py,dde61caa86c4999359d58ce171a8986fbfdbd5b0,STILL_EXISTS,\"\"\" || Unit testing for L{nltk.tree}. ||  || @todo: Test L{nltk.tree.ProbabilisticTreeToken} || @todo: Test L{nltk.tree.parse_treebank} || @todo: Test L{nltk.tree.TreebankTokenizer} || \"\"\"
aahebhhfg,nltk/nltk,src/nltk/test/tokenizer.py,6810fb69adf141bf192712dbfbfb1e042aff825c,STILL_EXISTS,\"\"\" || Unit testing for L{nltk.tokenizer}. ||  || @todo: Test L{nltk.token.CharTokenizer} || @todo: Test L{nltk.token.LineTokenizer} || \"\"\"
aahebhhii,nltk/nltk,src/nltk/tokenizer.py,6810fb69adf141bf192712dbfbfb1e042aff825c,STILL_EXISTS,\"\"\" || Classes and interfaces for dividing a string into a list of its || constituent tokens.  This task; which is known as X{tokenizing}; is || defined by the L{TokenizerI} interface. ||  || This module defines several implementations of the tokenizer || interface; such as L{WSTokenizer}; which splits texts based on || whitespace; and L{RETokenizer}; which uses a regular expression to || divide a string into tokens.  Several other modules also define || specialized tokenizers; such as L{nltk.tree.TreebankTokenizer} and || L{nltk.tagger.TaggedTokenizer}.  For a complete list of available || tokenizers; see the reference documentation for L{TokenizerI}. ||  || @group Interfaces: TokenizerI || @group Tokenizers: WSTokenizer; RETokenizer; CharTokenizer; ||     LineTokenizer; _XTokenTuple || @sort: Location; Token; TokenizerI; WSTokenizer; RETokenizer; CharTokenizer; ||     LineTokenizer; _XTokenTuple || \"\"\"
aahebiceb,nltk/nltk,src/nltk/tokenizer.py,09ba53ff67bf386c68baadf2802ae8c6556af08e,STILL_EXISTS,SUBPATTERN item and repair the sub item if needed
aahebichj,nltk/nltk,src/nltk/sense.py,35947b6fe904a4fa10f7d20656d63974ab72d573,STILL_EXISTS,fix lone &
aahebicia,nltk/nltk,src/nltk/sense.py,35947b6fe904a4fa10f7d20656d63974ab72d573,STILL_EXISTS,fix 'abc <p=\"foo\"\/>' style tags - now <wf pos=\"foo\">abc<\/wf>
aahebicib,nltk/nltk,src/nltk/sense.py,35947b6fe904a4fa10f7d20656d63974ab72d573,STILL_EXISTS,fix \"\"\"
aahebicic,nltk/nltk,src/nltk/sense.py,35947b6fe904a4fa10f7d20656d63974ab72d573,STILL_EXISTS,fix <s snum=dd> => <s snum=\"dd\"\/>
aahebicif,nltk/nltk,src/nltk/sense.py,35947b6fe904a4fa10f7d20656d63974ab72d573,STILL_EXISTS,fix <{word}>
aahebidhc,nltk/nltk,src/nltk/probability.py,60d523138223c7ac0a978c62a1889e685d73606a,STILL_EXISTS,TODO - add a cut-off parameter; above which the counts are unmodified
aahebifei,nltk/nltk,src/nltk/draw/chart.py,2570e2dfe531c045ec470d2ace35965e7297628c,STILL_EXISTS,[XX] START HACK
aahebifej,nltk/nltk,src/nltk/draw/chart.py,2570e2dfe531c045ec470d2ace35965e7297628c,STILL_EXISTS,[XX] END HACK
aahebiffd,nltk/nltk,src/nltk/draw/srparser.py,f7dd0966ad0c13657cec6d6dd45e935c5b85e5b8,STILL_EXISTS,hack..
aahebiffh,nltk/nltk,src/nltk/draw/cfg.py,60c09444043da3d81e003696f8eed30fca5f5e13,STILL_EXISTS,<- is this better??
aahebiged,nltk/nltk,src/nltk/draw/graph.py,21f80ba8b6f8a35a37897998a95d95b1b2d16e1e,STILL_EXISTS,isn't terribly efficient; but we dont' expect to be
aahebiiaj,nltk/nltk,src/nltk/draw/cfg.py,db06851fa5d14f27743e0ab8bf4cf1aed2e25c61,STILL_EXISTS,<- is this better??
aahebijig,nltk/nltk,src/nltk/draw/chart.py,38883b3adf2f4001c8b38cf12ed07172aea6906d,STILL_EXISTS,[XX] START HACK
aahebjdad,nltk/nltk,src/nltk/parser/probabilistic.py,38883b3adf2f4001c8b38cf12ed07172aea6906d,STILL_EXISTS,[XX] This might not be implemented quite right -- it would be better
aahebjeci,nltk/nltk,src/nltk/token.py,38883b3adf2f4001c8b38cf12ed07172aea6906d,STILL_EXISTS,a terrible hack with nasty; hard-to-fathom side effects that should
aahebjfbc,nltk/nltk,src/nltk/tokenizer.py,38883b3adf2f4001c8b38cf12ed07172aea6906d,STILL_EXISTS,\"\"\" || Classes and interfaces for dividing a token into its constituent || pieces.  This task; which is known as X{tokenizing}; is defined by the || L{TokenizerI} interface. ||  || This module defines several implementations of the tokenizer || interface; such as L{WSTokenizer}; which splits texts based on || whitespace; and L{RegexpTokenizer}; which uses a regular expression to || divide a token into pieces.  Several other modules also define || specialized tokenizers; such as L{nltk.tree.TreebankTokenizer} and || L{nltk.tagger.TaggedTokenizer}.  For a complete list of available || tokenizers; see the reference documentation for L{TokenizerI}. ||  || @group Interfaces: TokenizerI || @group Tokenizers: WSTokenizer; RegexpTokenizer; LineTokenizer; ||                    AbstractTokenizer || \"\"\"
aahebjffh,nltk/nltk,src/nltk/tree.py,38883b3adf2f4001c8b38cf12ed07172aea6906d,STILL_EXISTS,\"\"\" || Classes for representing hierarchical language structures over tokens; || such as syntax trees and morphological trees.  A single tree is || represented by a nested structure of X{tree tokens}; where each tree || token encodes a single hierarchical grouping.  Tree tokens are || specialized tokens that associate additional meaning with specific || properties.  Different tree token implementations associate different || meanings to these properties.  Three tree token implementations are || currently available: ||  ||   - L{TreeToken}s use the C{CHILDREN} property to record the ||     hierarchical content of a tree. ||  ||   - L{ParentedTreeToken}s use the C{CHILDREN} property to record ||     the hierarchical content of a tree; and the C{PARENT} property ||     to record the tree's unique parent. ||  ||   - L{MultiParentedTreeToken}s use the C{CHILDREN} property to ||     record the hierarchical content of a tree; and the C{PARENTS} ||     property to record a list of the tree's parents. ||  || For most purposes; the C{TreeToken} implementation is sufficient.  But || in cases where parent pointers are needed; the C{ParentedTreeToken} or || C{MultiParentedTreeToken} implementations can be used.  The || C{ParentedTreeToken} implementation is appropriate when each subtree || has a unique parent; and the C{MultiParentedTreeToken} implementation || is appropriate when a single subtree can be shared by multiple || parents. || \"\"\"
aahebjhdf,nltk/nltk,src/nltk/util.py,38883b3adf2f4001c8b38cf12ed07172aea6906d,STILL_EXISTS,These methods expand self as much as needed; then operate.
aahebjhei,nltk/nltk,src/nltk/util.py,38883b3adf2f4001c8b38cf12ed07172aea6906d,STILL_EXISTS,Right-hand operations.  These are needed to make sure that
aahebjidj,nltk/nltk,src/nltk/test/__init__.py,dd225d6ae0ea388d1e15eec26bf5aa21ab30fcf1,STILL_EXISTS,Hack the summary output to look nicer.
aahebjjbf,nltk/nltk,src/nltk/test/__init__.py,52a25991db07295e8ef0e835c603ae641dd7c270,STILL_EXISTS,This is something of a hack; so trace.Trace can get at test().
aahecabid,nltk/nltk,src/nltk/clusterer.py,8f51e98d6709fea031ec2c9516807f2c7a9fe7bf,d91d3127504994610c57ebbdf130f470904468d6,\"\"\" || This module contains a number of basic clustering algorithms. Clustering || describes the task of discovering groups of similar items with a large || collection. It is also describe as unsupervised machine learning; as the data || from which it learns is unannotated with class information; as is the case for || supervised learning.  Annotated data is difficult and expensive to obtain in || the quantities required for the majority of supervised learning algorithms. || This problem; the knowledge acquisition bottleneck; is common to most natural || language processing tasks; thus fueling the need for quality unsupervised || approaches. ||  || This module contains a k-means clusterer; E-M clusterer and a group average || agglomerative clusterer (GAAC). All these clusterers involve finding good || cluster groupings for a set of vectors in multi-dimensional space. ||  || The K-means clusterer starts with k arbitrary chosen means then allocates each || vector to the cluster with the closest mean. It then recalculates the means of || each cluster as the centroid of the vectors in the cluster. This process || repeats until the cluster memberships stabilise. This is a hill-climbing || algorithm which may converge to a local maximum. Hence the clustering is || often repeated with random initial means and the most commonly occuring || output means are chosen. ||  || The GAAC clusterer starts with each of the M{N} vectors as singleton clusters. || It then iteratively merges pairs of clusters which have the closest centroids. || This continues until there is only one cluster. The order of merges gives rise || to a dendogram - a tree with the earlier merges lower than later merges. The || membership of a given number of clusters M{c}; M{1 <= c <= N}; can be found by || cutting the dendogram at depth M{c}. ||  || The Gaussian EM clusterer models the vectors as being produced by a mixture || of k Gaussian sources. The parameters of these sources (prior probability; || mean and covariance matrix) are then found to maximise the likelihood of the || given data. This is done with the expectation maximisation algorithm. It || starts with k arbitrarily chosen means; priors and covariance matrices. It || then calculates the membership probabilities for each vector in each of the || clusters - this is the 'E' step. The cluster parameters are then updated in || the 'M' step using the maximum likelihood estimate from the cluster membership || probabilities. This process continues until the likelihood of the data does || not significantly increase. ||  || They all extend the ClustererI interface which defines common operations || available with each clusterer. These operations include. ||    - cluster: clusters a sequence of tokens ||    - classify: assign a token to a cluster ||    - classification_probdist: give the probability distribution over cluster memberships ||  || The current existing classifiers also extend VectorSpaceClusterer; an || abstract class which allows for singular value decomposition (SVD) and vector || normalisation. SVD is used to reduce the dimensionality of the vector space in || such a manner as to preserve as much of the variation as possible; by || reparametising the axes in order of variability and discarding all bar the || first d dimensions. Normalisation ensures that vectors fall in the unit || hypersphere. ||  || Usage example (see also demo()):: ||     tokens = [Token(FEATURES=Numeric.array([3; 3])); ||               Token(FEATURES=Numeric.array([1; 2])); ||               Token(FEATURES=Numeric.array([4; 2])); ||               Token(FEATURES=Numeric.array([4; 0]))] ||      ||     # initialise the clusterer (will also assign the tokens to clusters) ||     clusterer = KMeansClusterer(2; euclidean_distance) ||     clusterer.cluster(tokens; True) ||  ||     # classify a new token ||     token = Token(FEATURES=Numeric.array([3; 3])) ||     clusterer.classify(token) ||     print token ||  || Note that the tokens must have FEATURE attributes with Numeric array-like || objects. nltk_contrib.unimelb.tacohn.SparseArrays may be used for efficiency || when required. || \"\"\"
aahecacfb,nltk/nltk,src/nltk/clusterer.py,8f51e98d6709fea031ec2c9516807f2c7a9fe7bf,d91d3127504994610c57ebbdf130f470904468d6,if the matrix is tiny (i.e. only one point)... better to
aahecacjd,nltk/nltk,src/nltk/clusterer.py,d91d3127504994610c57ebbdf130f470904468d6,STILL_EXISTS,\"\"\" || This module contains a number of basic clustering algorithms. Clustering || describes the task of discovering groups of similar items with a large || collection. It is also describe as unsupervised machine learning; as the data || from which it learns is unannotated with class information; as is the case for || supervised learning.  Annotated data is difficult and expensive to obtain in || the quantities required for the majority of supervised learning algorithms. || This problem; the knowledge acquisition bottleneck; is common to most natural || language processing tasks; thus fueling the need for quality unsupervised || approaches. ||  || This module contains a k-means clusterer; E-M clusterer and a group average || agglomerative clusterer (GAAC). All these clusterers involve finding good || cluster groupings for a set of vectors in multi-dimensional space. ||  || The K-means clusterer starts with k arbitrary chosen means then allocates each || vector to the cluster with the closest mean. It then recalculates the means of || each cluster as the centroid of the vectors in the cluster. This process || repeats until the cluster memberships stabilise. This is a hill-climbing || algorithm which may converge to a local maximum. Hence the clustering is || often repeated with random initial means and the most commonly occurring || output means are chosen. ||  || The GAAC clusterer starts with each of the M{N} vectors as singleton clusters. || It then iteratively merges pairs of clusters which have the closest centroids. || This continues until there is only one cluster. The order of merges gives rise || to a dendogram - a tree with the earlier merges lower than later merges. The || membership of a given number of clusters M{c}; M{1 <= c <= N}; can be found by || cutting the dendogram at depth M{c}. ||  || The Gaussian EM clusterer models the vectors as being produced by a mixture || of k Gaussian sources. The parameters of these sources (prior probability; || mean and covariance matrix) are then found to maximise the likelihood of the || given data. This is done with the expectation maximisation algorithm. It || starts with k arbitrarily chosen means; priors and covariance matrices. It || then calculates the membership probabilities for each vector in each of the || clusters - this is the 'E' step. The cluster parameters are then updated in || the 'M' step using the maximum likelihood estimate from the cluster membership || probabilities. This process continues until the likelihood of the data does || not significantly increase. ||  || They all extend the ClustererI interface which defines common operations || available with each clusterer. These operations include. ||    - cluster: clusters a sequence of tokens ||    - classify: assign a token to a cluster ||    - classification_probdist: give the probability distribution over cluster memberships ||  || The current existing classifiers also extend VectorSpaceClusterer; an || abstract class which allows for singular value decomposition (SVD) and vector || normalisation. SVD is used to reduce the dimensionality of the vector space in || such a manner as to preserve as much of the variation as possible; by || reparameterising the axes in order of variability and discarding all bar the || first d dimensions. Normalisation ensures that vectors fall in the unit || hypersphere. ||  || Usage example (see also demo()):: ||     tokens = [Token(FEATURES=Numeric.array([3; 3])); ||               Token(FEATURES=Numeric.array([1; 2])); ||               Token(FEATURES=Numeric.array([4; 2])); ||               Token(FEATURES=Numeric.array([4; 0]))] ||      ||     # initialise the clusterer (will also assign the tokens to clusters) ||     clusterer = KMeansClusterer(2; euclidean_distance) ||     clusterer.cluster(tokens; True) ||  ||     # classify a new token ||     token = Token(FEATURES=Numeric.array([3; 3])) ||     clusterer.classify(token) ||     print token ||  || Note that the tokens must have FEATURE attributes with Numeric array-like || objects. nltk_contrib.unimelb.tacohn.SparseArrays may be used for efficiency || when required. || \"\"\"
aahecadaa,nltk/nltk,src/nltk/clusterer.py,d91d3127504994610c57ebbdf130f470904468d6,STILL_EXISTS,calculate likelihood - FIXME: may be broken
aahecaebe,nltk/nltk,src/nltk/tagger/brill.py,a405971ef41bd7495fe1f6e37a3ae36e7cdc9c92,STILL_EXISTS,A nicer way would be welcome.
aahecaegi,nltk/nltk,src/nltk/tagger/brill.py,a405971ef41bd7495fe1f6e37a3ae36e7cdc9c92,STILL_EXISTS,that this isn't the best rule; so move on:
aahecaeha,nltk/nltk,src/nltk/tagger/brill.py,a405971ef41bd7495fe1f6e37a3ae36e7cdc9c92,STILL_EXISTS,If the actual score is better than the best score; then
aahecaidg,nltk/nltk,src/nltk/clusterer/__init__.py,27f611ef86600229603501e14bd50d9501a0b3f9,STILL_EXISTS,\"\"\" || This module contains a number of basic clustering algorithms. Clustering || describes the task of discovering groups of similar items with a large || collection. It is also describe as unsupervised machine learning; as the data || from which it learns is unannotated with class information; as is the case for || supervised learning.  Annotated data is difficult and expensive to obtain in || the quantities required for the majority of supervised learning algorithms. || This problem; the knowledge acquisition bottleneck; is common to most natural || language processing tasks; thus fueling the need for quality unsupervised || approaches. ||  || This module contains a k-means clusterer; E-M clusterer and a group average || agglomerative clusterer (GAAC). All these clusterers involve finding good || cluster groupings for a set of vectors in multi-dimensional space. ||  || The K-means clusterer starts with k arbitrary chosen means then allocates each || vector to the cluster with the closest mean. It then recalculates the means of || each cluster as the centroid of the vectors in the cluster. This process || repeats until the cluster memberships stabilise. This is a hill-climbing || algorithm which may converge to a local maximum. Hence the clustering is || often repeated with random initial means and the most commonly occurring || output means are chosen. ||  || The GAAC clusterer starts with each of the M{N} vectors as singleton clusters. || It then iteratively merges pairs of clusters which have the closest centroids. || This continues until there is only one cluster. The order of merges gives rise || to a dendogram - a tree with the earlier merges lower than later merges. The || membership of a given number of clusters M{c}; M{1 <= c <= N}; can be found by || cutting the dendogram at depth M{c}. ||  || The Gaussian EM clusterer models the vectors as being produced by a mixture || of k Gaussian sources. The parameters of these sources (prior probability; || mean and covariance matrix) are then found to maximise the likelihood of the || given data. This is done with the expectation maximisation algorithm. It || starts with k arbitrarily chosen means; priors and covariance matrices. It || then calculates the membership probabilities for each vector in each of the || clusters - this is the 'E' step. The cluster parameters are then updated in || the 'M' step using the maximum likelihood estimate from the cluster membership || probabilities. This process continues until the likelihood of the data does || not significantly increase. ||  || They all extend the ClustererI interface which defines common operations || available with each clusterer. These operations include. ||    - cluster: clusters a sequence of tokens ||    - classify: assign a token to a cluster ||    - classification_probdist: give the probability distribution over cluster memberships ||  || The current existing classifiers also extend VectorSpaceClusterer; an || abstract class which allows for singular value decomposition (SVD) and vector || normalisation. SVD is used to reduce the dimensionality of the vector space in || such a manner as to preserve as much of the variation as possible; by || reparameterising the axes in order of variability and discarding all bar the || first d dimensions. Normalisation ensures that vectors fall in the unit || hypersphere. ||  || Usage example (see also demo()):: ||     tokens = [Token(FEATURES=Numeric.array([3; 3])); ||               Token(FEATURES=Numeric.array([1; 2])); ||               Token(FEATURES=Numeric.array([4; 2])); ||               Token(FEATURES=Numeric.array([4; 0]))] ||      ||     # initialise the clusterer (will also assign the tokens to clusters) ||     clusterer = KMeansClusterer(2; euclidean_distance) ||     clusterer.cluster(tokens; True) ||  ||     # classify a new token ||     token = Token(FEATURES=Numeric.array([3; 3])) ||     clusterer.classify(token) ||     print token ||  || Note that the tokens must have FEATURE attributes with Numeric array-like || objects. nltk_contrib.unimelb.tacohn.SparseArrays may be used for efficiency || when required. || \"\"\"
aahecajai,nltk/nltk,src/nltk/clusterer/__init__.py,27f611ef86600229603501e14bd50d9501a0b3f9,STILL_EXISTS,calculate likelihood - FIXME: may be broken
aahecajda,nltk/nltk,src/nltk/tokenizer/__init__.py,3882467e1afd4f7f49c86d4fb0ca40dfcbfc64cf,STILL_EXISTS,\"\"\" || Classes and interfaces for dividing a token into its constituent || pieces.  This task; which is known as X{tokenizing}; is defined by the || L{TokenizerI} interface. ||  || This module defines several implementations of the tokenizer || interface; such as L{WhitespaceTokenizer}; which splits texts based on || whitespace; and L{RegexpTokenizer}; which uses a regular expression to || divide a token into pieces.  Several other modules also define || specialized tokenizers; such as L{nltk.tree.TreebankTokenizer} and || L{nltk.tagger.TaggedTokenizer}.  For a complete list of available || tokenizers; see the reference documentation for L{TokenizerI}. ||  || @group Interfaces: TokenizerI || @group Tokenizers: WhitespaceTokenizer; RegexpTokenizer; LineTokenizer; ||                    AbstractTokenizer || \"\"\"
aahecajhj,nltk/nltk,src/nltk/tokenizer/__init__.py,3882467e1afd4f7f49c86d4fb0ca40dfcbfc64cf,STILL_EXISTS,SUBPATTERN item and repair the sub item if needed
aahecbagd,nltk/nltk,src/nltk/parser/__init__.py,2df34e3f278a1037d20e740b411d5e2e6694af40,STILL_EXISTS,[XX] TEMPORARY HACK WARNING!  This should be replaced with
aahecbbae,nltk/nltk,src/nltk/token.py,670deb245193fdec7d70c2b36db2ed2178f7083b,0612d649c3afa036a73f4f7aa027f264834c57ff,So this is a somewhat hackish work-around; which converts a
aahecbbcf,nltk/nltk,src/nltk/tagger/treebank.py,5bcdf8ad29f912b0fbf8af021f978c749c172a89,a8708e6a537e1df809421d6e53e67d7bbd7e89ae,if the line ends with a '.' tag; it is most likely (always?)
aahecbbcj,nltk/nltk,src/nltk/tagger/treebank.py,5bcdf8ad29f912b0fbf8af021f978c749c172a89,a8708e6a537e1df809421d6e53e67d7bbd7e89ae,if line ends in a full stop; then process sentence text
aahecbccf,nltk/nltk,src/nltk/hmm/__init__.py,9a25f320442b9dd20512b4a595a66695e0cb04d0,STILL_EXISTS,believe him. FIXME
aaheccdfh,nltk/nltk,src/nltk/tokenreader/sense.py,b39c0bcd43c4e7edb8fd54d36ea7e33e4bba4027,STILL_EXISTS,fix lone &
aaheccdfi,nltk/nltk,src/nltk/tokenreader/sense.py,b39c0bcd43c4e7edb8fd54d36ea7e33e4bba4027,STILL_EXISTS,fix \"\"\"
aaheccdfj,nltk/nltk,src/nltk/tokenreader/sense.py,b39c0bcd43c4e7edb8fd54d36ea7e33e4bba4027,STILL_EXISTS,fix <s snum=dd> => <s snum=\"dd\"\/>
aaheccdgc,nltk/nltk,src/nltk/tokenreader/sense.py,b39c0bcd43c4e7edb8fd54d36ea7e33e4bba4027,STILL_EXISTS,fix <{word}>
aaheccdgj,nltk/nltk,src/nltk/tokenreader/sense.py,b39c0bcd43c4e7edb8fd54d36ea7e33e4bba4027,STILL_EXISTS,fix 'abc <p=\"foo\"\/>' style tags - now <wf pos=\"foo\">abc<\/wf>
aaheccjbd,nltk/nltk,src/nltk/lite/tokenizer.py,c0c8ddbdc6c3bc6a74dc82340927d758743e9313,STILL_EXISTS,SUBPATTERN item and repair the sub item if needed
aahecdcfc,nltk/nltk,src/nltk/lite/brill.py,02f543779f09952bc7c5d29e0028f6c3fcef2efd,STILL_EXISTS,A nicer way would be welcome.
aahecddag,nltk/nltk,src/nltk/lite/brill.py,02f543779f09952bc7c5d29e0028f6c3fcef2efd,STILL_EXISTS,that this isn't the best rule; so move on:
aahecddai,nltk/nltk,src/nltk/lite/brill.py,02f543779f09952bc7c5d29e0028f6c3fcef2efd,STILL_EXISTS,If the actual score is better than the best score; then
aahecdggi,nltk/nltk,src/nltk/lite/parser.py,752b5cf0d7dfa53d1c0366c7c4819a068bd323ff,STILL_EXISTS,\"\"\" || Classes and interfaces for producing tree structures that represent || the internal organization of a text.  This task is known as X{parsing} || the text; and the resulting tree structures are called the text's || X{parses}.  Typically; the text is a single sentence; and the tree || structure represents the syntactic structure of the sentence. || However; parsers can also be used in other domains.  For example; || parsers can be used to derive the morphological structure of the || morphemes that make up a word; or to derive the discourse structure || for a set of utterances. ||  || Sometimes; a single piece of text can be represented by more than one || tree structure.  Texts represented by more than one tree structure are || called X{ambiguous} texts.  Note that there are actually two ways in || which a text can be ambiguous: ||  ||     - The text has multiple correct parses. ||     - There is not enough information to decide which of several ||       candidate parses is correct. ||  || However; the parser module does I{not} distinguish these two types of || ambiguity. ||  || The parser module defines C{ParserI}; a standard interface for parsing || texts; and two simple implementations of that interface; || C{ShiftReduceParser} and C{RecursiveDescentParser}.  It also contains || three sub-modules for specialized kinds of parsing: ||  ||   - C{nltk.parser.chart} defines chart parsing; which uses dynamic ||     programming to efficiently parse texts. ||   - C{nltk.parser.chunk} defines chunk parsing; which identifies ||     non-overlapping linguistic groups in a text. ||   - C{nltk.parser.probabilistic} defines probabilistic parsing; which ||     associates a probability with each parse. ||  || @group Interfaces: ParserI || @group Parsers: ShiftReduceParser; SteppingShiftReduceParser; ||        RecursiveDescentParser; SteppingRecursiveDescentParser || @sort: ParserI; ShiftReduceParser; SteppingShiftReduceParser; ||        RecursiveDescentParser; SteppingRecursiveDescentParser;  ||        demo; chart; chunk; probabilistic || @see: C{nltk.cfg} || \"\"\"
aahecdhch,nltk/nltk,src/nltk/lite/parser.py,752b5cf0d7dfa53d1c0366c7c4819a068bd323ff,STILL_EXISTS,[XX] TEMPORARY HACK WARNING!  This should be replaced with
aahecdidf,nltk/nltk,src/nltk/lite/porter.py,e9f2301e6b7e8cba4efb76a879a4b720aee9dba1,STILL_EXISTS,i == 0 never happens perhaps
aahecdied,nltk/nltk,src/nltk/lite/porter.py,e9f2301e6b7e8cba4efb76a879a4b720aee9dba1,STILL_EXISTS,if self.ends(\"abli\"):      self.r(\"able\")
aaheceaca,nltk/nltk,lite/nltk_lite/parse/chunk.py,3240a10e9894cce3dbb7584ba1310e44a5dae665,STILL_EXISTS,\"\"\" || Classes and interfaces for identifying non-overlapping linguistic || groups (such as base noun phrases) in unrestricted text.  This task is || called X{chunk parsing} or X{chunking}; and the identified groups are || called X{chunks}.  The chunked text is represented using a shallow || tree called a \"chunk structure.\"  A X{chunk structure} is a tree || containing tokens and chunks; where each chunk is a subtree containing || only tokens.  For example; the chunk structure for base noun phrase || chunks in the sentence \"I saw the big dog on the hill\" is:: ||  ||   (SENTENCE: ||     (NP: <I>) ||     <saw> ||     (NP: <the> <big> <dog>) ||     <on> ||     (NP: <the> <hill>)) ||  || To convert a chunk structure back to a list of tokens; simply use the || chunk structure's L{leaves<Tree.leaves>} method. ||  || The C{parser.chunk} module defines L{ChunkParserI}; a standard || interface for chunking texts; and L{RegexpChunkParser}; a || regular-expression based implementation of that interface.  It also || defines the L{ChunkedTaggedTokenizer} and L{ConllChunkedTokenizer} || classes; which tokenize strings containing chunked and tagged texts; || and L{ChunkScore}; a utility class for scoring chunk parsers. ||  || RegexpChunkParser || ============= ||   C{RegexpChunkParser} is an implementation of the chunk parser interface ||   that uses regular-expressions over tags to chunk a text.  Its ||   C{parse} method first constructs a C{ChunkString}; which encodes a ||   particular chunking of the input text.  Initially; nothing is ||   chunked.  C{RegexpChunkParser} then applies a sequence of ||   C{RegexpChunkParserRule}s to the C{ChunkString}; each of which modifies ||   the chunking that it encodes.  Finally; the C{ChunkString} is ||   transformed back into a chunk structure; which is returned. ||  ||   C{RegexpChunkParser} can only be used to chunk a single kind of phrase. ||   For example; you can use an C{RegexpChunkParser} to chunk the noun ||   phrases in a text; or the verb phrases in a text; but you can not ||   use it to simultaneously chunk both noun phrases and verb phrases in ||   the same text.  (This is a limitation of C{RegexpChunkParser}; not of ||   chunk parsers in general.) ||  ||   RegexpChunkParserRules ||   ------------------ ||     C{RegexpChunkParserRule}s are transformational rules that update the ||     chunking of a text by modifying its C{ChunkString}.  Each ||     C{RegexpChunkParserRule} defines the C{apply} method; which modifies ||     the chunking encoded by a C{ChunkString}.  The ||     L{RegexpChunkParserRule} class itself can be used to implement any ||     transformational rule based on regular expressions.  There are ||     also a number of subclasses; which can be used to implement ||     simpler types of rules: ||  ||       - L{ChunkRule} chunks anything that matches a given regular ||         expression. ||       - L{ChinkRule} chinks anything that matches a given regular ||         expression. ||       - L{UnChunkRule} will un-chunk any chunk that matches a given ||         regular expression. ||       - L{MergeRule} can be used to merge two contiguous chunks. ||       - L{SplitRule} can be used to split a single chunk into two ||         smaller chunks. ||  ||     Tag Patterns ||     ~~~~~~~~~~~~ ||       C{RegexpChunkParserRule}s use a modified version of regular ||       expression patterns; called X{tag patterns}.  Tag patterns are ||       used to match sequences of tags.  Examples of tag patterns are:: ||  ||          r'(<DT>|<JJ>|<NN>)+' ||          r'<NN>+' ||          r'<NN.*>' ||  ||       The differences between regular expression patterns and tag ||       patterns are: ||  ||         - In tag patterns; C{'<'} and C{'>'} act as parenthases; so ||           C{'<NN>+'} matches one or more repetitions of C{'<NN>'}; not ||           C{'<NN'} followed by one or more repetitions of C{'>'}. ||         - Whitespace in tag patterns is ignored.  So ||           C{'<DT> | <NN>'} is equivalant to C{'<DT>|<NN>'} ||         - In tag patterns; C{'.'} is equivalant to C{'[^{}<>]'}; so ||           C{'<NN.*>'} matches any single tag starting with C{'NN'}. ||  ||       The function L{tag_pattern2re_pattern} can be used to transform ||       a tag pattern to an equivalent regular expression pattern. ||  ||   Efficiency ||   ---------- ||     Preliminary tests indicate that C{RegexpChunkParser} can chunk at a ||     rate of about 300 tokens\/second; with a moderately complex rule ||     set. ||  ||     There may be problems if C{RegexpChunkParser} is used with more than ||     5;000 tokens at a time.  In particular; evaluation of some regular ||     expressions may cause the Python regular expression engine to ||     exceed its maximum recursion depth.  We have attempted to minimize ||     these problems; but it is impossible to avoid them completely.  We ||     therefore recommend that you apply the chunk parser to a single ||     sentence at a time. ||  ||   Emacs Tip ||   --------- ||     If you evaluate the following elisp expression in emacs; it will ||     colorize C{ChunkString}s when you use an interactive python shell ||     with emacs or xemacs (\"C-c !\"):: ||  ||       (let () ||         (defconst comint-mode-font-lock-keywords  ||           '((\"<[^>]+>\" 0 'font-lock-reference-face) ||             (\"[{}]\" 0 'font-lock-function-name-face))) ||         (add-hook 'comint-mode-hook (lambda () (turn-on-font-lock)))) ||  ||     You can evaluate this code by copying it to a temporary buffer; ||     placing the cursor after the last close parenthasis; and typing ||     \"C{C-x C-e}\".  You should evaluate it before running the interactive ||     session.  The change will last until you close emacs. ||  ||   Unresolved Issues ||   ----------------- ||     If we use the C{re} module for regular expressions; Python's ||     regular expression engine generates \"maximum recursion depth ||     exceeded\" errors when processing very large texts; even for ||     regular expressions that should not require any recursion.  We ||     therefore use the C{pre} module instead.  But note that C{pre} ||     does not include Unicode support; so this module will not work ||     with unicode strings.  Note also that C{pre} regular expressions ||     are not quite as advanced as C{re} ones (e.g.; no leftward ||     zero-length assertions). ||  || @type _VALID_CHUNK_STRING: C{regexp} || @var _VALID_CHUNK_STRING: A regular expression to test whether a chunk ||      string is valid. || @type _VALID_TAG_PATTERN: C{regexp} || @var _VALID_TAG_PATTERN: A regular expression to test whether a tag ||      pattern is valid. || \"\"\"
aahececgi,nltk/nltk,lite/nltk_lite/parse/parser.py,3240a10e9894cce3dbb7584ba1310e44a5dae665,STILL_EXISTS,\"\"\" || Classes and interfaces for producing tree structures that represent || the internal organization of a text.  This task is known as X{parsing} || the text; and the resulting tree structures are called the text's || X{parses}.  Typically; the text is a single sentence; and the tree || structure represents the syntactic structure of the sentence. || However; parsers can also be used in other domains.  For example; || parsers can be used to derive the morphological structure of the || morphemes that make up a word; or to derive the discourse structure || for a set of utterances. ||  || Sometimes; a single piece of text can be represented by more than one || tree structure.  Texts represented by more than one tree structure are || called X{ambiguous} texts.  Note that there are actually two ways in || which a text can be ambiguous: ||  ||     - The text has multiple correct parses. ||     - There is not enough information to decide which of several ||       candidate parses is correct. ||  || However; the parser module does I{not} distinguish these two types of || ambiguity. ||  || The parser module defines C{ParserI}; a standard interface for parsing || texts; and two simple implementations of that interface; || C{ShiftReduceParser} and C{RecursiveDescentParser}.  It also contains || three sub-modules for specialized kinds of parsing: ||  ||   - C{nltk.parser.chart} defines chart parsing; which uses dynamic ||     programming to efficiently parse texts. ||   - C{nltk.parser.chunk} defines chunk parsing; which identifies ||     non-overlapping linguistic groups in a text. ||   - C{nltk.parser.probabilistic} defines probabilistic parsing; which ||     associates a probability with each parse. ||  || @group Interfaces: ParserI || @group Parsers: ShiftReduceParser; SteppingShiftReduceParser; ||        RecursiveDescentParser; SteppingRecursiveDescentParser || @sort: ParserI; ShiftReduceParser; SteppingShiftReduceParser; ||        RecursiveDescentParser; SteppingRecursiveDescentParser;  ||        demo; chart; chunk; probabilistic || @see: C{nltk.cfg} || \"\"\"
aahecedcf,nltk/nltk,lite/nltk_lite/parse/parser.py,3240a10e9894cce3dbb7584ba1310e44a5dae665,STILL_EXISTS,[XX] TEMPORARY HACK WARNING!  This should be replaced with
aaheceegh,nltk/nltk,lite/nltk_lite/tag/brill.py,3240a10e9894cce3dbb7584ba1310e44a5dae665,STILL_EXISTS,A nicer way would be welcome.
aahecefcb,nltk/nltk,lite/nltk_lite/tag/brill.py,3240a10e9894cce3dbb7584ba1310e44a5dae665,STILL_EXISTS,that this isn't the best rule; so move on:
aahecefcd,nltk/nltk,lite/nltk_lite/tag/brill.py,3240a10e9894cce3dbb7584ba1310e44a5dae665,STILL_EXISTS,If the actual score is better than the best score; then
aahecehga,nltk/nltk,lite/nltk_lite/tokenize/porter.py,3240a10e9894cce3dbb7584ba1310e44a5dae665,STILL_EXISTS,i == 0 never happens perhaps
aahecehgi,nltk/nltk,lite/nltk_lite/tokenize/porter.py,3240a10e9894cce3dbb7584ba1310e44a5dae665,STILL_EXISTS,if self.ends(\"abli\"):      self.r(\"able\")
aaheceidj,nltk/nltk,lite/nltk_lite/tokenize/regexp.py,3240a10e9894cce3dbb7584ba1310e44a5dae665,STILL_EXISTS,SUBPATTERN item and repair the sub item if needed
aahecejfd,nltk/nltk,lite/nltk_lite/draw/__init__.py,5adc523cbf6547942b5d5b8cb39151b836823cea,STILL_EXISTS,Move it back; if we were dragging.
aahecejja,nltk/nltk,lite/nltk_lite/draw/__init__.py,5adc523cbf6547942b5d5b8cb39151b836823cea,STILL_EXISTS,Move to (x;y)
aahecfcbi,nltk/nltk,lite/nltk_lite/draw/plot.py,5adc523cbf6547942b5d5b8cb39151b836823cea,STILL_EXISTS,This reload is needed to prevent an error if we create
aahecfdgg,nltk/nltk,lite/nltk_lite/draw/rdparser.py,4d65a1dd078532ffc304547ccca853bee49fd6b5,STILL_EXISTS,Boy; too bad tkinter doesn't implement Listbox.itemconfig;
aahecfdjj,nltk/nltk,lite/nltk_lite/draw/rdparser.py,4d65a1dd078532ffc304547ccca853bee49fd6b5,STILL_EXISTS,Move the text string down; if necessary.
aahecfeej,nltk/nltk,lite/nltk_lite/draw/srparser.py,4d65a1dd078532ffc304547ccca853bee49fd6b5,STILL_EXISTS,Move the remaining text to the correct location (keep it
aahecfefe,nltk/nltk,lite/nltk_lite/draw/srparser.py,4d65a1dd078532ffc304547ccca853bee49fd6b5,STILL_EXISTS,hack..
aahecffeh,nltk/nltk,lite/nltk_lite/parse/__init__.py,78982e652ba8be18084a19badec8d8d95d410e31,STILL_EXISTS,\"\"\" || Classes and interfaces for producing tree structures that represent || the internal organization of a text.  This task is known as X{parsing} || the text; and the resulting tree structures are called the text's || X{parses}.  Typically; the text is a single sentence; and the tree || structure represents the syntactic structure of the sentence. || However; parsers can also be used in other domains.  For example; || parsers can be used to derive the morphological structure of the || morphemes that make up a word; or to derive the discourse structure || for a set of utterances. ||  || Sometimes; a single piece of text can be represented by more than one || tree structure.  Texts represented by more than one tree structure are || called X{ambiguous} texts.  Note that there are actually two ways in || which a text can be ambiguous: ||  ||     - The text has multiple correct parses. ||     - There is not enough information to decide which of several ||       candidate parses is correct. ||  || However; the parser module does I{not} distinguish these two types of || ambiguity. ||  || The parser module defines C{ParseI}; a standard interface for parsing || texts; and two simple implementations of that interface; || C{ShiftReduce} and C{RecursiveDescent}.  It also contains || three sub-modules for specialized kinds of parsing: ||  ||   - C{nltk.parser.chart} defines chart parsing; which uses dynamic ||     programming to efficiently parse texts. ||   - C{nltk.parser.chunk} defines chunk parsing; which identifies ||     non-overlapping linguistic groups in a text. ||   - C{nltk.parser.probabilistic} defines probabilistic parsing; which ||     associates a probability with each parse. || \"\"\"
aahecffib,nltk/nltk,lite/nltk_lite/parse/rd.py,78982e652ba8be18084a19badec8d8d95d410e31,STILL_EXISTS,[XX] TEMPORARY HACK WARNING!  This should be replaced with
aahecfhcd,nltk/nltk,lite/nltk_lite/tag/hmm.py,6c4cb3c4ab17610a75421c9e828f42310b708b58,STILL_EXISTS,believe him. FIXME
aahecfheh,nltk/nltk,lite/nltk_lite/probability.py,f9fc8bcdafd590084a7cf6e77c57981a224d07ea,STILL_EXISTS,\"\"\" || Classes for representing and processing probabilistic information. ||  || The L{FreqDist} class is used to encode X{frequency distributions}; || which count the number of times that each outcome of an experiment || occurs. ||  || The L{ProbDistI} class defines a standard interface for X{probability || distributions}; which encode the probability of each outcome for an || experiment.  There are two types of probability distribution: ||  ||   - X{derived probability distributions} are created from frequency ||     distributions.  They attempt to model the probability distribution ||     that generated the frequency distribution. ||   - X{analytic probability distributions} are created directly from ||     parameters (such as variance). ||  || The L{ConditionalFreqDist} class and L{ConditionalProbDistI} interface || are used to encode conditional distributions.  Conditional probability || distributions can be derived or analytic; but currently the only || implementation of the C{ConditionalProbDistI} interface is || L{ConditionalProbDist}; a derived distribution. ||  || The L{ProbabilisticMixIn} class is a mix-in class that can be used to || associate probabilities with data classes (such as C{Token} or || C{Tree}). ||  || @group Frequency Distributions: FreqDist || @group Derived Probability Distributions: ProbDistI; MLEProbDist; ||     LidstoneProbDist; LaplaceProbDist; ELEProbDist; HeldoutProbDist; ||     CrossValidationProbDist || @group Analyitic Probability Distributions: UniformProbDist || @group Conditional Distributions: ConditionalFreqDist; ||     ConditionalProbDistI; ConditionalProbDist || @group Probabilistic Mix-In: ProbabilisticMixIn || @sort: FreqDist; ProbDistI; MLEProbDist; LidstoneProbDist; LaplaceProbDist;  ||     ELEProbDist; HeldoutProbDist; CrossValidationProbDist; UniformProbDist; ||     ConditionalFreqDist; ConditionalProbDistI; ConditionalProbDist ||  || @todo: Better handling of log probabilities. || \"\"\"
aahecfhii,nltk/nltk,lite/nltk_lite/probability.py,f9fc8bcdafd590084a7cf6e77c57981a224d07ea,STILL_EXISTS,TODO - add a cut-off parameter; above which the counts are unmodified
aahecgegi,nltk/nltk,lite/nltk_lite/parse/chart.py,f31fa842f9c81c91c9c0f57da5e324723834f484,STILL_EXISTS,Kludge to ensure child_choices is a doubly-nested list
aahecgegj,nltk/nltk,lite/nltk_lite/chat/zen.py,36653ff3b7879ad2b87d9268d0340fe112d0e80c,STILL_EXISTS,\"\"\" || Zen Chatbot talks in gems of Zen wisdom. ||  || This is a sample conversation with Zen Chatbot: || ZC:\tWelcome; my child. || me:\tGood afternoon. || ZC:\tAsk the question you have come to ask. || me:\tHow can I achieve enlightenment? || ZC:\tHow do you suppose? || me:\tThrough meditation. || ZC:\tForm is emptiness; and emptiness form. || me:\tHow can I empty my mind of worldly troubles? || ZC:\tWill an answer to that really help in your search for enlightenment? || me:\tYes. || ZC:\tIt is better to be right than to be certain. || me:\tI seek truth and wisdom. || ZC:\tThe search for truth is a long journey. || me:\tAre you sure? || ZC:\tMaybe sure; maybe not sure. ||  ||  || The chatbot structure is based on that of chat.eliza. Thus; it uses || a translation table to convert from question to response || i.e. \"I am\" --> \"you are\" ||  || Of course; since Zen Chatbot does not understand the meaning of any words; || responses are very limited. Zen Chatbot will usually answer very vaguely; or || respond to a question by asking a different question; in much the same way || as Eliza. || \"\"\"
aahecgeii,nltk/nltk,lite/nltk_lite/chat/zen.py,36653ff3b7879ad2b87d9268d0340fe112d0e80c,STILL_EXISTS,i.e. 'you' is not really a thing that can be mapped this way; so this
aahecgejb,nltk/nltk,lite/nltk_lite/chat/zen.py,36653ff3b7879ad2b87d9268d0340fe112d0e80c,STILL_EXISTS,why questions are separated into three types:
aahecgejc,nltk/nltk,lite/nltk_lite/chat/zen.py,36653ff3b7879ad2b87d9268d0340fe112d0e80c,STILL_EXISTS,\"why..I\" \te.g. \"why am I here?\" \"Why do I like cake?\"
aahecgeji,nltk/nltk,lite/nltk_lite/chat/zen.py,36653ff3b7879ad2b87d9268d0340fe112d0e80c,STILL_EXISTS,- this style works for positives (e.g. \"why do you like cake?\")
aahecgfef,nltk/nltk,lite/nltk_lite/chat/rude.py,da1e5babb3518f43e91b1d005c13ed7e197e1c20,STILL_EXISTS,Author: Peter Spiller <pspiller@csse.unimelb.edu.au>
aahecggec,nltk/nltk,lite/nltk_lite/parse/pchart.py,2b5ef41c8094061b838ff5348e8d64ca1048ab1b,STILL_EXISTS,[XX] This might not be implemented quite right -- it would be better
aahecghei,nltk/nltk,lite/nltk_lite/contrib/marshal.py,677f6f74d64a68b4312bf0621ec87697dee8f147,STILL_EXISTS,TODO: describe each tagger marshal format in the epydocs?
aahecgjad,nltk/nltk,lite/nltk_lite/parse/treetransforms.py,a6a7d421505a462bbc34a06949a8eb9b9dff8846,STILL_EXISTS,A semi-hack to have elegant looking code below.  As a result;
aahecgjhg,nltk/nltk,lite/nltk_lite/contrib/concord.py,b483e3ed674bc94aa739dc0966025fae051c529f,STILL_EXISTS,Author: Peter Spiller
aahechaee,nltk/nltk,lite/nltk_lite/contrib/concord.py,b483e3ed674bc94aa739dc0966025fae051c529f,STILL_EXISTS,calculate amount of left padding needed
aahechaeg,nltk/nltk,lite/nltk_lite/contrib/concord.py,b483e3ed674bc94aa739dc0966025fae051c529f,STILL_EXISTS,calculate amount of middle padding needed
aahechchi,nltk/nltk,lite/nltk_lite/contrib/fsa.py,df012b56b29168d3315471428ca5991d9813cd10,STILL_EXISTS,TODO - check that parse was complete; and report error otherwise
aahechchj,nltk/nltk,lite/nltk_lite/contrib/fsa.py,df012b56b29168d3315471428ca5991d9813cd10,STILL_EXISTS,TODO - change parser to limit scope of unary operators
aahechcje,nltk/nltk,lite/nltk_lite/contrib/fsa.py,df012b56b29168d3315471428ca5991d9813cd10,STILL_EXISTS,delete inaccessible nodes and unused transitions
aahechdba,nltk/nltk,lite/nltk_lite/contrib/fsa.py,df012b56b29168d3315471428ca5991d9813cd10,STILL_EXISTS,# TODO - add epsilon transition from finals to initials
aahechdeh,nltk/nltk,lite/nltk_lite/contrib/kimmo.py,5b9183be673e88d16cc20f1949a43ff8c251401b,STILL_EXISTS,TODO: remove Unix dependencies
aahechfci,nltk/nltk,lite/nltk_lite/contrib/kimmo.py,5b9183be673e88d16cc20f1949a43ff8c251401b,STILL_EXISTS,cheap hack now that graphviz is not working right...
aahechfdg,nltk/nltk,lite/nltk_lite/contrib/kimmo.py,5b9183be673e88d16cc20f1949a43ff8c251401b,STILL_EXISTS,a better way is just to use a destructor and check if the object exists.
aahechgaf,nltk/nltk,lite/nltk_lite/contrib/kimmo.py,5b9183be673e88d16cc20f1949a43ff8c251401b,STILL_EXISTS,\t                 '             Cpal C    e:0 e:@ +:0 Vbk V   @'; # english.rul needed pairs re-ordered
aahechgba,nltk/nltk,lite/nltk_lite/contrib/kimmo.py,5b9183be673e88d16cc20f1949a43ff8c251401b,STILL_EXISTS,\t                   (5; True; [ 1;   1;   1;  0;  1;  2;  0;  1 ]);    # V Cpal e +:0... [english.rul needed fixing]
aahechgbd,nltk/nltk,lite/nltk_lite/contrib/kimmo.py,5b9183be673e88d16cc20f1949a43ff8c251401b,STILL_EXISTS,\t                   (8; True; [ 1;   1;   1;  0;  1;  0;  0;  1 ]) ]); # V C e +:0... [english.rul needed fixing]
aahechggd,nltk/nltk,lite/nltk_lite/contrib/kimmo.py,5b9183be673e88d16cc20f1949a43ff8c251401b,STILL_EXISTS,this should really be done in dotstring
aahechhbd,nltk/nltk,lite/nltk_lite/contrib/kimmo.py,5b9183be673e88d16cc20f1949a43ff8c251401b,STILL_EXISTS,recognition causes a problem; here's why.  this routine encounters @ before +:i
aahechjbc,nltk/nltk,lite/nltk_lite/contrib/marshalbrill.py,f92aea731f9a77f01bc990f8a562f6678f30fb50,STILL_EXISTS,A nicer way would be welcome.
aahechjgg,nltk/nltk,lite/nltk_lite/contrib/marshalbrill.py,f92aea731f9a77f01bc990f8a562f6678f30fb50,STILL_EXISTS,that this isn't the best rule; so move on:
aahechjgi,nltk/nltk,lite/nltk_lite/contrib/marshalbrill.py,f92aea731f9a77f01bc990f8a562f6678f30fb50,STILL_EXISTS,If the actual score is better than the best score; then
aaheciahj,nltk/nltk,lite/nltk_lite/contrib/marshalbrill.py,ebc6e1a09f219fc7476f884ef2d4ae75b1799ae5,STILL_EXISTS,TODO: move into __init__() when all marshalling classes will be moved into
aaheciaic,nltk/nltk,lite/nltk_lite/contrib/combined.py,81237111ee7d976c37f7fb537453962b6d7f130d,STILL_EXISTS,work-around while marshal is not moved into standard tree
aahecibdh,nltk/nltk,lite/nltk_lite/cluster/em.py,463543153ac824bf8f09bc27cd2066b39b7de08f,STILL_EXISTS,calculate likelihood - FIXME: may be broken
aahecidaj,nltk/nltk,nltk_lite/chat/rude.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,Author: Peter Spiller <pspiller@csse.unimelb.edu.au>
aahecidbc,nltk/nltk,nltk_lite/chat/zen.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,\"\"\" || Zen Chatbot talks in gems of Zen wisdom. ||  || This is a sample conversation with Zen Chatbot: || ZC:    Welcome; my child. || me:    Good afternoon. || ZC:    Ask the question you have come to ask. || me:    How can I achieve enlightenment? || ZC:    How do you suppose? || me:    Through meditation. || ZC:    Form is emptiness; and emptiness form. || me:    How can I empty my mind of worldly troubles? || ZC:    Will an answer to that really help in your search for enlightenment? || me:    Yes. || ZC:    It is better to be right than to be certain. || me:    I seek truth and wisdom. || ZC:    The search for truth is a long journey. || me:    Are you sure? || ZC:    Maybe sure; maybe not sure. ||  ||  || The chatbot structure is based on that of chat.eliza. Thus; it uses || a translation table to convert from question to response || i.e. \"I am\" --> \"you are\" ||  || Of course; since Zen Chatbot does not understand the meaning of any words; || responses are very limited. Zen Chatbot will usually answer very vaguely; or || respond to a question by asking a different question; in much the same way || as Eliza. || \"\"\"
aaheciddb,nltk/nltk,nltk_lite/chat/zen.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,i.e. 'you' is not really a thing that can be mapped this way; so this
aahecidde,nltk/nltk,nltk_lite/chat/zen.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,why questions are separated into three types:
aaheciddf,nltk/nltk,nltk_lite/chat/zen.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,\"why..I\"     e.g. \"why am I here?\" \"Why do I like cake?\"
aaheciddh,nltk/nltk,nltk_lite/chat/zen.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,\"why...\"    e.g. \"Why is the sky blue?\"
aahecideb,nltk/nltk,nltk_lite/chat/zen.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,- this style works for positives (e.g. \"why do you like cake?\")
aaheciebh,nltk/nltk,nltk_lite/cluster/em.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,calculate likelihood - FIXME: may be broken
aahecifbc,nltk/nltk,nltk_lite/contrib/combined.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,work-around while marshal is not moved into standard tree
aahecifce,nltk/nltk,nltk_lite/contrib/concord.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,Author: Peter Spiller
aahecifjc,nltk/nltk,nltk_lite/contrib/concord.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,calculate amount of left padding needed
aahecifje,nltk/nltk,nltk_lite/contrib/concord.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,calculate amount of middle padding needed
aahecihfc,nltk/nltk,nltk_lite/contrib/fsa.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,TODO - check that parse was complete; and report error otherwise
aahecihfd,nltk/nltk,nltk_lite/contrib/fsa.py,603384c5576ae07862766cd332c9ab5059a029ae,b3a710f66803703934088e22fbcd3c7b71317a3b,TODO - change parser to limit scope of unary operators
aahecihgi,nltk/nltk,nltk_lite/contrib/fsa.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,delete inaccessible nodes and unused transitions
aahecihie,nltk/nltk,nltk_lite/contrib/fsa.py,603384c5576ae07862766cd332c9ab5059a029ae,b3a710f66803703934088e22fbcd3c7b71317a3b,# TODO - add epsilon transition from finals to initials
aaheciicd,nltk/nltk,nltk_lite/contrib/kimmo.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,TODO: remove Unix dependencies
aahecjaae,nltk/nltk,nltk_lite/contrib/kimmo.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,cheap hack now that graphviz is not working right...
aahecjabc,nltk/nltk,nltk_lite/contrib/kimmo.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,a better way is just to use a destructor and check if the object exists.
aahecjaib,nltk/nltk,nltk_lite/contrib/kimmo.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,\t                 '             Cpal C    e:0 e:@ +:0 Vbk V   @'; # english.rul needed pairs re-ordered
aahecjaig,nltk/nltk,nltk_lite/contrib/kimmo.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,\t                   (5; True; [ 1;   1;   1;  0;  1;  2;  0;  1 ]);    # V Cpal e +:0... [english.rul needed fixing]
aahecjaij,nltk/nltk,nltk_lite/contrib/kimmo.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,\t                   (8; True; [ 1;   1;   1;  0;  1;  0;  0;  1 ]) ]); # V C e +:0... [english.rul needed fixing]
aahecjbdj,nltk/nltk,nltk_lite/contrib/kimmo.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,this should really be done in dotstring
aahecjbij,nltk/nltk,nltk_lite/contrib/kimmo.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,recognition causes a problem; here's why.  this routine encounters @ before +:i
aahecjdcd,nltk/nltk,nltk_lite/contrib/marshal.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,TODO: describe each tagger marshal format in the epydocs?
aahecjdff,nltk/nltk,nltk_lite/contrib/marshalbrill.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,TODO: move into __init__() when all marshalling classes will be moved into
aahecjdjd,nltk/nltk,nltk_lite/contrib/marshalbrill.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,A nicer way would be welcome.
aahecjeeh,nltk/nltk,nltk_lite/contrib/marshalbrill.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,that this isn't the best rule; so move on:
aahecjeej,nltk/nltk,nltk_lite/contrib/marshalbrill.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,If the actual score is better than the best score; then
aahecjjdg,nltk/nltk,nltk_lite/draw/__init__.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,Move it back; if we were dragging.
aahecjjhd,nltk/nltk,nltk_lite/draw/__init__.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,Move to (x;y)
aahedaefe,nltk/nltk,nltk_lite/draw/plot.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,This reload is needed to prevent an error if we create
aahedafdi,nltk/nltk,nltk_lite/draw/rdparser.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,Boy; too bad tkinter doesn't implement Listbox.itemconfig;
aahedafhb,nltk/nltk,nltk_lite/draw/rdparser.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,Move the text string down; if necessary.
aahedagcb,nltk/nltk,nltk_lite/draw/srparser.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,Move the remaining text to the correct location (keep it
aahedagcg,nltk/nltk,nltk_lite/draw/srparser.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,hack..
aahedaiei,nltk/nltk,nltk_lite/parse/__init__.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,\"\"\" || Classes and interfaces for producing tree structures that represent || the internal organization of a text.  This task is known as X{parsing} || the text; and the resulting tree structures are called the text's || X{parses}.  Typically; the text is a single sentence; and the tree || structure represents the syntactic structure of the sentence. || However; parsers can also be used in other domains.  For example; || parsers can be used to derive the morphological structure of the || morphemes that make up a word; or to derive the discourse structure || for a set of utterances. ||  || Sometimes; a single piece of text can be represented by more than one || tree structure.  Texts represented by more than one tree structure are || called X{ambiguous} texts.  Note that there are actually two ways in || which a text can be ambiguous: ||  ||     - The text has multiple correct parses. ||     - There is not enough information to decide which of several ||       candidate parses is correct. ||  || However; the parser module does I{not} distinguish these two types of || ambiguity. ||  || The parser module defines C{ParseI}; a standard interface for parsing || texts; and two simple implementations of that interface; || C{ShiftReduce} and C{RecursiveDescent}.  It also contains || three sub-modules for specialized kinds of parsing: ||  ||   - C{nltk.parser.chart} defines chart parsing; which uses dynamic ||     programming to efficiently parse texts. ||   - C{nltk.parser.chunk} defines chunk parsing; which identifies ||     non-overlapping linguistic groups in a text. ||   - C{nltk.parser.probabilistic} defines probabilistic parsing; which ||     associates a probability with each parse. || \"\"\"
aahedajha,nltk/nltk,nltk_lite/parse/chart.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,Kludge to ensure child_choices is a doubly-nested list
aahedbaih,nltk/nltk,nltk_lite/parse/chunk.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,\"\"\" || Classes and interfaces for identifying non-overlapping linguistic || groups (such as base noun phrases) in unrestricted text.  This task is || called X{chunk parsing} or X{chunking}; and the identified groups are || called X{chunks}.  The chunked text is represented using a shallow || tree called a \"chunk structure.\"  A X{chunk structure} is a tree || containing tokens and chunks; where each chunk is a subtree containing || only tokens.  For example; the chunk structure for base noun phrase || chunks in the sentence \"I saw the big dog on the hill\" is:: ||  ||   (SENTENCE: ||     (NP: <I>) ||     <saw> ||     (NP: <the> <big> <dog>) ||     <on> ||     (NP: <the> <hill>)) ||  || To convert a chunk structure back to a list of tokens; simply use the || chunk structure's L{leaves<Tree.leaves>} method. ||  || The C{parser.chunk} module defines L{ChunkI}; a standard interface for || chunking texts; and L{RegexpChunk}; a regular-expression based || implementation of that interface.  It uses the L{tree.chunk} and || L{tree.conll_chunk} methods; which tokenize strings containing chunked || and tagged texts.  It defines L{ChunkScore}; a utility class for || scoring chunk parsers. ||  || RegexpChunk || =========== ||  ||   C{parse.RegexpChunk} is an implementation of the chunk parser interface ||   that uses regular-expressions over tags to chunk a text.  Its ||   C{parse} method first constructs a C{ChunkString}; which encodes a ||   particular chunking of the input text.  Initially; nothing is ||   chunked.  C{parse.RegexpChunk} then applies a sequence of ||   C{RegexpChunkRule}s to the C{ChunkString}; each of which modifies ||   the chunking that it encodes.  Finally; the C{ChunkString} is ||   transformed back into a chunk structure; which is returned. ||  ||   C{RegexpChunk} can only be used to chunk a single kind of phrase. ||   For example; you can use an C{RegexpChunk} to chunk the noun ||   phrases in a text; or the verb phrases in a text; but you can not ||   use it to simultaneously chunk both noun phrases and verb phrases in ||   the same text.  (This is a limitation of C{RegexpChunk}; not of ||   chunk parsers in general.) ||  ||   RegexpChunkRules ||   ------------------ ||     C{RegexpChunkRule}s are transformational rules that update the ||     chunking of a text by modifying its C{ChunkString}.  Each ||     C{RegexpChunkRule} defines the C{apply} method; which modifies ||     the chunking encoded by a C{ChunkString}.  The ||     L{RegexpChunkRule} class itself can be used to implement any ||     transformational rule based on regular expressions.  There are ||     also a number of subclasses; which can be used to implement ||     simpler types of rules: ||  ||       - L{ChunkRule} chunks anything that matches a given regular ||         expression. ||       - L{ChinkRule} chinks anything that matches a given regular ||         expression. ||       - L{UnChunkRule} will un-chunk any chunk that matches a given ||         regular expression. ||       - L{MergeRule} can be used to merge two contiguous chunks. ||       - L{SplitRule} can be used to split a single chunk into two ||         smaller chunks. ||  ||     Tag Patterns ||     ~~~~~~~~~~~~ ||       C{RegexpChunkRule}s use a modified version of regular ||       expression patterns; called X{tag patterns}.  Tag patterns are ||       used to match sequences of tags.  Examples of tag patterns are:: ||  ||          r'(<DT>|<JJ>|<NN>)+' ||          r'<NN>+' ||          r'<NN.*>' ||  ||       The differences between regular expression patterns and tag ||       patterns are: ||  ||         - In tag patterns; C{'<'} and C{'>'} act as parenthases; so ||           C{'<NN>+'} matches one or more repetitions of C{'<NN>'}; not ||           C{'<NN'} followed by one or more repetitions of C{'>'}. ||         - Whitespace in tag patterns is ignored.  So ||           C{'<DT> | <NN>'} is equivalant to C{'<DT>|<NN>'} ||         - In tag patterns; C{'.'} is equivalant to C{'[^{}<>]'}; so ||           C{'<NN.*>'} matches any single tag starting with C{'NN'}. ||  ||       The function L{tag_pattern2re_pattern} can be used to transform ||       a tag pattern to an equivalent regular expression pattern. ||  ||   Efficiency ||   ---------- ||     Preliminary tests indicate that C{RegexpChunk} can chunk at a ||     rate of about 300 tokens\/second; with a moderately complex rule ||     set. ||  ||     There may be problems if C{RegexpChunk} is used with more than ||     5;000 tokens at a time.  In particular; evaluation of some regular ||     expressions may cause the Python regular expression engine to ||     exceed its maximum recursion depth.  We have attempted to minimize ||     these problems; but it is impossible to avoid them completely.  We ||     therefore recommend that you apply the chunk parser to a single ||     sentence at a time. ||  ||   Emacs Tip ||   --------- ||     If you evaluate the following elisp expression in emacs; it will ||     colorize C{ChunkString}s when you use an interactive python shell ||     with emacs or xemacs (\"C-c !\"):: ||  ||       (let () ||         (defconst comint-mode-font-lock-keywords  ||           '((\"<[^>]+>\" 0 'font-lock-reference-face) ||             (\"[{}]\" 0 'font-lock-function-name-face))) ||         (add-hook 'comint-mode-hook (lambda () (turn-on-font-lock)))) ||  ||     You can evaluate this code by copying it to a temporary buffer; ||     placing the cursor after the last close parenthasis; and typing ||     \"C{C-x C-e}\".  You should evaluate it before running the interactive ||     session.  The change will last until you close emacs. ||  ||   Unresolved Issues ||   ----------------- ||     If we use the C{re} module for regular expressions; Python's ||     regular expression engine generates \"maximum recursion depth ||     exceeded\" errors when processing very large texts; even for ||     regular expressions that should not require any recursion.  We ||     therefore use the C{pre} module instead.  But note that C{pre} ||     does not include Unicode support; so this module will not work ||     with unicode strings.  Note also that C{pre} regular expressions ||     are not quite as advanced as C{re} ones (e.g.; no leftward ||     zero-length assertions). ||  || @type _VALID_CHUNK_STRING: C{regexp} || @var _VALID_CHUNK_STRING: A regular expression to test whether a chunk ||      string is valid. || @type _VALID_TAG_PATTERN: C{regexp} || @var _VALID_TAG_PATTERN: A regular expression to test whether a tag ||      pattern is valid. || \"\"\"
aahedbdhf,nltk/nltk,nltk_lite/parse/pchart.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,[XX] This might not be implemented quite right -- it would be better
aahedbege,nltk/nltk,nltk_lite/parse/rd.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,[XX] TEMPORARY HACK WARNING!  This should be replaced with
aahedbgbe,nltk/nltk,nltk_lite/parse/treetransforms.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,A semi-hack to have elegant looking code below.  As a result;
aahedbhdg,nltk/nltk,nltk_lite/probability.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,TODO - add a cut-off parameter; above which the counts are unmodified
aahedbiee,nltk/nltk,nltk_lite/tag/brill.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,A nicer way would be welcome.
aahedbiji,nltk/nltk,nltk_lite/tag/brill.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,that this isn't the best rule; so move on:
aahedbjaa,nltk/nltk,nltk_lite/tag/brill.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,If the actual score is better than the best score; then
aahedcafj,nltk/nltk,nltk_lite/tag/hmm.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,believe him. FIXME
aahedccbd,nltk/nltk,nltk_lite/tokenize/porter.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,i == 0 never happens perhaps
aahedcccb,nltk/nltk,nltk_lite/tokenize/porter.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,if self.ends(\"abli\"):      self.r(\"able\")
aahedccjc,nltk/nltk,nltk_lite/tokenize/regexp.py,603384c5576ae07862766cd332c9ab5059a029ae,STILL_EXISTS,SUBPATTERN item and repair the sub item if needed
aahedcfaa,nltk/nltk,nltk_lite/stem/porter.py,edf46779567450ec36e64a0decd75bcd5984e092,STILL_EXISTS,i == 0 never happens perhaps
aahedcfai,nltk/nltk,nltk_lite/stem/porter.py,edf46779567450ec36e64a0decd75bcd5984e092,STILL_EXISTS,if self.ends(\"abli\"):      self.r(\"able\")
aahedcfhj,nltk/nltk,nltk_lite/contrib/shlex.py,53c831b7ca7844ca6c288142eb962e6f75b777e1,STILL_EXISTS,\"\"\" || Functionality for parsing and manipulating the contents of a Shoebox || lexicon without reference to its metadata. For more sophisticated || functionality that handles metadata; use the module I{metadata}. || \"\"\"
aahedcfja,nltk/nltk,nltk_lite/contrib/shmetadata.py,53c831b7ca7844ca6c288142eb962e6f75b777e1,STILL_EXISTS,\"\"\" || This module provides functionality for handling the metadata associated || with Shoebox lexicons and texts. || \"\"\"
aahedcggd,nltk/nltk,nltk_lite/contrib/shmetadata.py,53c831b7ca7844ca6c288142eb962e6f75b777e1,STILL_EXISTS,# TODO
aahedcgjg,nltk/nltk,nltk_lite/contrib/shmetadata.py,53c831b7ca7844ca6c288142eb962e6f75b777e1,STILL_EXISTS,TODO
aahedchbf,nltk/nltk,nltk_lite/contrib/shmetadata.py,53c831b7ca7844ca6c288142eb962e6f75b777e1,STILL_EXISTS,TODO: Deal with Font Information
aahedchcj,nltk/nltk,nltk_lite/contrib/shmetadata.py,53c831b7ca7844ca6c288142eb962e6f75b777e1,STILL_EXISTS,TODO: Modify to take into account changes to Field object
aahedchda,nltk/nltk,nltk_lite/contrib/shtext.py,53c831b7ca7844ca6c288142eb962e6f75b777e1,STILL_EXISTS,\"\"\" || TODO || \"\"\"
aahedcifa,nltk/nltk,nltk_lite/contrib/shoebox/lexicon.py,9dc844f52387a67c368114b7ef2c85b4cc067141,STILL_EXISTS,\"\"\" || Functionality for parsing and manipulating the contents of a Shoebox || lexicon without reference to its metadata. For more sophisticated || functionality that handles metadata; use the module I{metadata}. || \"\"\"
aahedcigb,nltk/nltk,nltk_lite/contrib/shoebox/metadata.py,9dc844f52387a67c368114b7ef2c85b4cc067141,STILL_EXISTS,\"\"\" || This module provides functionality for handling the metadata associated || with Shoebox lexicons and texts. || \"\"\"
aahedcjde,nltk/nltk,nltk_lite/contrib/shoebox/metadata.py,9dc844f52387a67c368114b7ef2c85b4cc067141,STILL_EXISTS,# TODO
aahedcjgh,nltk/nltk,nltk_lite/contrib/shoebox/metadata.py,9dc844f52387a67c368114b7ef2c85b4cc067141,STILL_EXISTS,TODO
aahedcjig,nltk/nltk,nltk_lite/contrib/shoebox/metadata.py,9dc844f52387a67c368114b7ef2c85b4cc067141,STILL_EXISTS,TODO: Deal with Font Information
aaheddaaa,nltk/nltk,nltk_lite/contrib/shoebox/metadata.py,9dc844f52387a67c368114b7ef2c85b4cc067141,STILL_EXISTS,TODO: Modify to take into account changes to Field object
aaheddaab,nltk/nltk,nltk_lite/contrib/shoebox/text.py,9dc844f52387a67c368114b7ef2c85b4cc067141,cf0c8e4ede3741770b5d7baa3f6a2c726ed25141,\"\"\" || TODO || \"\"\"
aaheddbbc,nltk/nltk,emacs/pycomplete.py,077edbffb72350793d03964be809044dce56f34b,STILL_EXISTS,\"\"\" || Python dot expression completion using Pymacs. ||  || This almost certainly needs work; but if you add ||  ||     (require 'pycomplete) ||  || to your .xemacs\/init.el file (untried w\/ GNU Emacs so far) and have Pymacs || installed; when you hit M-TAB it will try to complete the dot expression || before point.  For example; given this import at the top of the file: ||  ||     import time ||  || typing \"time.cl\" then hitting M-TAB should complete \"time.clock\". ||  || This is unlikely to be done the way Emacs completion ought to be done; but || it's a start.  Perhaps someone with more Emacs mojo can take this stuff and || do it right. ||  || See pycomplete.el for the Emacs Lisp side of things. || \"\"\"
aaheddbda,nltk/nltk,nltk_lite/test/__init__.py,6f4f49bd3ce287d29e477b27d885cb515500131a,dd1585460b7a6f8978cc577f00707ff608c88223,This is something of a hack; so trace.Trace can get at test().
aaheddbdb,nltk/nltk,nltk_lite/test/__init__.py,6f4f49bd3ce287d29e477b27d885cb515500131a,dd1585460b7a6f8978cc577f00707ff608c88223,Hack the summary output to look nicer.
aaheddbdc,nltk/nltk,nltk_lite/test/tree.py,6f4f49bd3ce287d29e477b27d885cb515500131a,dd1585460b7a6f8978cc577f00707ff608c88223,\"\"\" || Unit testing for L{nltk.tree}. ||  || @todo: Test L{nltk.tree.ProbabilisticTreeToken} || @todo: Test L{nltk.tree.parse_treebank} || \"\"\"
aahedddhi,nltk/nltk,nltk_lite/contrib/yaml/emitter.py,38850df39d20b2b7e0a0ad941f7ffee7324c5dac,STILL_EXISTS,Write BOM if needed.
aaheddebf,nltk/nltk,nltk_lite/contrib/yaml/parser.py,38850df39d20b2b7e0a0ad941f7ffee7324c5dac,STILL_EXISTS,TODO: support for BOM within a stream.
aaheddfbh,nltk/nltk,nltk_lite/contrib/yaml/reader.py,38850df39d20b2b7e0a0ad941f7ffee7324c5dac,STILL_EXISTS,Yeah; it's ugly and slow.
aaheddgdb,nltk/nltk,nltk_lite/contrib/yaml/scanner.py,38850df39d20b2b7e0a0ad941f7ffee7324c5dac,STILL_EXISTS,TODO: support for BOM within a stream.
aaheddhaa,nltk/nltk,nltk_lite/contrib/yaml/scanner.py,38850df39d20b2b7e0a0ad941f7ffee7324c5dac,STILL_EXISTS,Reset everything (not really needed).
aaheddiab,nltk/nltk,nltk_lite/contrib/yaml/scanner.py,38850df39d20b2b7e0a0ad941f7ffee7324c5dac,STILL_EXISTS,TODO: We need to make tab handling rules more sane. A good rule is
aaheddihe,nltk/nltk,nltk_lite/contrib/yaml/scanner.py,38850df39d20b2b7e0a0ad941f7ffee7324c5dac,STILL_EXISTS,The specification is really confusing about tabs in plain scalars.
aaheddjdh,nltk/nltk,nltk_lite/contrib/hole.py,31b7d112a09cdccf5ff24f9589831d3d929ed0bd,STILL_EXISTS,A depth-first search would work as well since the trees must
aaheddjfa,nltk/nltk,nltk_lite/contrib/hole.py,31b7d112a09cdccf5ff24f9589831d3d929ed0bd,STILL_EXISTS,Maybe print the raw semantic representation.
aaheddjff,nltk/nltk,nltk_lite/contrib/hole.py,31b7d112a09cdccf5ff24f9589831d3d929ed0bd,STILL_EXISTS,Maybe draw the formulas as trees.
aahedebab,nltk/nltk,nltk_lite/contrib/shoebox/lexicon.py,df899f4634e3c39b361f7b01128022a320572884,STILL_EXISTS,TODO: Add field to header
aahedebgi,nltk/nltk,nltk_lite/contrib/shoebox/lexicon.py,59929374f34df72c794359aa86c3646f01ae1ed9,STILL_EXISTS,TODO: Raise NonUniqueEntryKeyError
aahededef,nltk/nltk,nltk_lite/contrib/models.py,455819d43c118d4a334ed100cec2f8856b33ae2b,STILL_EXISTS,expr wasn't a constant; maybe a variable that g knows about?
aahedeedb,nltk/nltk,nltk_lite/contrib/fst/draw_graph.py,e9c068ee4aca50400567ceb157a3a57db5021dd4,STILL_EXISTS,isn't terribly efficient; but we dont' expect to be
aahedefhc,nltk/nltk,nltk_lite/contrib/fst/fst.py,e9c068ee4aca50400567ceb157a3a57db5021dd4,STILL_EXISTS,(dst; out; arc) tuples.  (arc is only needed in case
aahedegdg,nltk/nltk,nltk_lite/contrib/fst/fst.py,e9c068ee4aca50400567ceb157a3a57db5021dd4,STILL_EXISTS,hack..
aahedegfg,nltk/nltk,nltk_lite/contrib/fst/fst.py,e9c068ee4aca50400567ceb157a3a57db5021dd4,STILL_EXISTS,Something phonetic might be better.
aahedeijh,nltk/nltk,nltk_lite/contrib/shoebox/settings.py,e4298b11e6121f191a3a2d60f78f43e2b6afd948,STILL_EXISTS,\"\"\" || This module provides functionality for reading Shoebox settings files; || which provide metadata for Shoebox lexicons and texts. || \"\"\"
aahedfaga,nltk/nltk,nltk_lite/contrib/shoebox/lexicon.py,3d9ffd09a5805b3e4177b443ae7804a17b71360d,STILL_EXISTS,\"\"\" || This modules provides functionality for parsing and manipulating the || contents of a Shoebox lexicon without reference to its metadata. || \"\"\"
aahedfcci,nltk/nltk,nltk_lite/wordnet/wntools.py,ca3ab10ec1912dda3beae00782a48a106961b07e,STILL_EXISTS,workaround for lack of nested closures in Python < 2.1
aahedfcfa,nltk/nltk,nltk_lite/wordnet/wordnet.py,ef97c215cc1ece8e58e56c1eec383421f5ca9b0d,1e6cc7401d6fc80cbd3dceef5d742dbd67a59c10,work around a Windows Python bug
aahedfdeh,nltk/nltk,nltk_lite/wordnet/wordnet.py,ef97c215cc1ece8e58e56c1eec383421f5ca9b0d,1e6cc7401d6fc80cbd3dceef5d742dbd67a59c10,Ideally; we'd use a weak dict; but there aren't any.  A strong dict
aahedffbi,nltk/nltk,nltk_lite/etree/python25/ElementPath.py,7a11c9a5592eee554f32a85f9dcdb0ddeb6bd04c,STILL_EXISTS,you; if needed.
aahedffgi,nltk/nltk,nltk_lite/etree/python25/ElementTree.py,7a11c9a5592eee554f32a85f9dcdb0ddeb6bd04c,STILL_EXISTS,2003-11-15 fl   fixed nested namespace bug
aahedfgdd,nltk/nltk,nltk_lite/etree/python25/ElementTree.py,7a11c9a5592eee554f32a85f9dcdb0ddeb6bd04c,STILL_EXISTS,FIXME: issue warning in this case?
aahedfgde,nltk/nltk,nltk_lite/etree/python25/ElementTree.py,7a11c9a5592eee554f32a85f9dcdb0ddeb6bd04c,STILL_EXISTS,TODO: add support for custom namespace resolvers\/default namespaces
aahedfgdf,nltk/nltk,nltk_lite/etree/python25/ElementTree.py,7a11c9a5592eee554f32a85f9dcdb0ddeb6bd04c,STILL_EXISTS,TODO: add improved support for incremental parsing
aahedfjda,nltk/nltk,nltk_lite/etree/python25/ElementTree.py,7a11c9a5592eee554f32a85f9dcdb0ddeb6bd04c,STILL_EXISTS,FIXME: not sure about this; might be a better idea to look
aahedfjfa,nltk/nltk,nltk_lite/etree/python25/ElementTree.py,7a11c9a5592eee554f32a85f9dcdb0ddeb6bd04c,STILL_EXISTS,FIXME: overkill
aahedgacb,nltk/nltk,nltk_lite/etree/python25/ElementTree.py,7a11c9a5592eee554f32a85f9dcdb0ddeb6bd04c,STILL_EXISTS,@param data A string.  This should be either an 8-bit string
aahedgbaf,nltk/nltk,nltk_lite/contrib/shoebox/data.py,7b7ba329729043672f95d593a2eb88a92e695ea6,STILL_EXISTS,todo encoding; unicode fields; errors?
aahedgbdi,nltk/nltk,nltk_lite/corpora/toolbox.py,1e08917bfa1310ecd983427b4f5f8743cb9402b2,750d93a3fbfbb55c3c2f4b26900540b783dec03f,todo encoding; unicode fields; errors?
aahedgddc,nltk/nltk,nltk_lite/wordnet/wordnet.py,571fb266e92714546c5b8e125e342f90e69b2383,STILL_EXISTS,TODO: Add some comments\/doctest strings.
aahedgeab,nltk/nltk,nltk_lite/chunk/__init__.py,c3abd766eb501e0595fee08b3467bb2a819bccc5,STILL_EXISTS,\"\"\" || Classes and interfaces for identifying non-overlapping linguistic || groups (such as base noun phrases) in unrestricted text.  This task is || called X{chunk parsing} or X{chunking}; and the identified groups are || called X{chunks}.  The chunked text is represented using a shallow || tree called a \"chunk structure.\"  A X{chunk structure} is a tree || containing tokens and chunks; where each chunk is a subtree containing || only tokens.  For example; the chunk structure for base noun phrase || chunks in the sentence \"I saw the big dog on the hill\" is:: ||  ||   (SENTENCE: ||     (NP: <I>) ||     <saw> ||     (NP: <the> <big> <dog>) ||     <on> ||     (NP: <the> <hill>)) ||  || To convert a chunk structure back to a list of tokens; simply use the || chunk structure's L{leaves<Tree.leaves>} method. ||  || The C{parser.chunk} module defines L{ChunkI}; a standard interface for || chunking texts; and L{RegexpChunk}; a regular-expression based || implementation of that interface.  It uses the L{tree.chunk} and || L{tree.conll_chunk} methods; which tokenize strings containing chunked || and tagged texts.  It defines L{ChunkScore}; a utility class for || scoring chunk parsers. ||  || RegexpChunk || =========== ||  ||   C{parse.RegexpChunk} is an implementation of the chunk parser interface ||   that uses regular-expressions over tags to chunk a text.  Its ||   C{parse} method first constructs a C{ChunkString}; which encodes a ||   particular chunking of the input text.  Initially; nothing is ||   chunked.  C{parse.RegexpChunk} then applies a sequence of ||   C{RegexpChunkRule}s to the C{ChunkString}; each of which modifies ||   the chunking that it encodes.  Finally; the C{ChunkString} is ||   transformed back into a chunk structure; which is returned. ||  ||   C{RegexpChunk} can only be used to chunk a single kind of phrase. ||   For example; you can use an C{RegexpChunk} to chunk the noun ||   phrases in a text; or the verb phrases in a text; but you can not ||   use it to simultaneously chunk both noun phrases and verb phrases in ||   the same text.  (This is a limitation of C{RegexpChunk}; not of ||   chunk parsers in general.) ||  ||   RegexpChunkRules ||   ------------------ ||     C{RegexpChunkRule}s are transformational rules that update the ||     chunking of a text by modifying its C{ChunkString}.  Each ||     C{RegexpChunkRule} defines the C{apply} method; which modifies ||     the chunking encoded by a C{ChunkString}.  The ||     L{RegexpChunkRule} class itself can be used to implement any ||     transformational rule based on regular expressions.  There are ||     also a number of subclasses; which can be used to implement ||     simpler types of rules: ||  ||       - L{ChunkRule} chunks anything that matches a given regular ||         expression. ||       - L{ChinkRule} chinks anything that matches a given regular ||         expression. ||       - L{UnChunkRule} will un-chunk any chunk that matches a given ||         regular expression. ||       - L{MergeRule} can be used to merge two contiguous chunks. ||       - L{SplitRule} can be used to split a single chunk into two ||         smaller chunks. ||       - L{ExpandLeftRule} will expand a chunk to incorporate new ||         unchunked material on the left. ||       - L{ExpandRightRule} will expand a chunk to incorporate new ||         unchunked material on the right. ||  ||     Tag Patterns ||     ~~~~~~~~~~~~ ||       C{RegexpChunkRule}s use a modified version of regular ||       expression patterns; called X{tag patterns}.  Tag patterns are ||       used to match sequences of tags.  Examples of tag patterns are:: ||  ||          r'(<DT>|<JJ>|<NN>)+' ||          r'<NN>+' ||          r'<NN.*>' ||  ||       The differences between regular expression patterns and tag ||       patterns are: ||  ||         - In tag patterns; C{'<'} and C{'>'} act as parentheses; so ||           C{'<NN>+'} matches one or more repetitions of C{'<NN>'}; not ||           C{'<NN'} followed by one or more repetitions of C{'>'}. ||         - Whitespace in tag patterns is ignored.  So ||           C{'<DT> | <NN>'} is equivalant to C{'<DT>|<NN>'} ||         - In tag patterns; C{'.'} is equivalant to C{'[^{}<>]'}; so ||           C{'<NN.*>'} matches any single tag starting with C{'NN'}. ||  ||       The function L{tag_pattern2re_pattern} can be used to transform ||       a tag pattern to an equivalent regular expression pattern. ||  ||   Efficiency ||   ---------- ||     Preliminary tests indicate that C{RegexpChunk} can chunk at a ||     rate of about 300 tokens\/second; with a moderately complex rule ||     set. ||  ||     There may be problems if C{RegexpChunk} is used with more than ||     5;000 tokens at a time.  In particular; evaluation of some regular ||     expressions may cause the Python regular expression engine to ||     exceed its maximum recursion depth.  We have attempted to minimize ||     these problems; but it is impossible to avoid them completely.  We ||     therefore recommend that you apply the chunk parser to a single ||     sentence at a time. ||  ||   Emacs Tip ||   --------- ||     If you evaluate the following elisp expression in emacs; it will ||     colorize C{ChunkString}s when you use an interactive python shell ||     with emacs or xemacs (\"C-c !\"):: ||  ||       (let () ||         (defconst comint-mode-font-lock-keywords  ||           '((\"<[^>]+>\" 0 'font-lock-reference-face) ||             (\"[{}]\" 0 'font-lock-function-name-face))) ||         (add-hook 'comint-mode-hook (lambda () (turn-on-font-lock)))) ||  ||     You can evaluate this code by copying it to a temporary buffer; ||     placing the cursor after the last close parenthesis; and typing ||     \"C{C-x C-e}\".  You should evaluate it before running the interactive ||     session.  The change will last until you close emacs. ||  ||   Unresolved Issues ||   ----------------- ||     If we use the C{re} module for regular expressions; Python's ||     regular expression engine generates \"maximum recursion depth ||     exceeded\" errors when processing very large texts; even for ||     regular expressions that should not require any recursion.  We ||     therefore use the C{pre} module instead.  But note that C{pre} ||     does not include Unicode support; so this module will not work ||     with unicode strings.  Note also that C{pre} regular expressions ||     are not quite as advanced as C{re} ones (e.g.; no leftward ||     zero-length assertions). ||  || @type CHUNK_TAG_PATTERN: C{regexp} || @var CHUNK_TAG_PATTERN: A regular expression to test whether a tag ||      pattern is valid. || \"\"\"
aahedgfib,nltk/nltk,nltk_lite/wordnet/brown_ic.py,bdfbc062a8e29662e1ab7a7a44341490ced1604d,STILL_EXISTS,inflected\/uninflected constituents would probably be needed (compounds
aahedggha,nltk/nltk,nltk_lite/corpora/chat_80.py,6031de65b27e1c887d9be73cbcc18550221ebaf8,STILL_EXISTS,Chat-80 relation metadata bundles needed to build the valuation
aahedghaa,nltk/nltk,nltk_lite/corpora/chat_80.py,e136e3b63956a8d0db3189c672fac3e1bb6dea16,STILL_EXISTS,Chat-80 relation metadata bundles needed to build the valuation
aahedgjbh,nltk/nltk,nltk_lite/wordnet/cache.py,ea1c652e856bf3016eb26003f762659b5c37b89d,STILL_EXISTS,Ideally; we'd use a weak dict; but there aren't any.  A strong dict
aahedgjdj,nltk/nltk,nltk_lite/wordnet/dictionary.py,5c3fdb2212592a5b8d8de918e0474538bf7d6cb5,STILL_EXISTS,Work around a Windows Python bug
aahedhcdj,nltk/nltk,nltk_lite/contrib/oldyaml/emitter.py,0a5a30ca8c4d84f2c4e76138083ce4eaa882b002,STILL_EXISTS,Write BOM if needed.
aahedhchg,nltk/nltk,nltk_lite/contrib/oldyaml/parser.py,0a5a30ca8c4d84f2c4e76138083ce4eaa882b002,STILL_EXISTS,TODO: support for BOM within a stream.
aahedhdhi,nltk/nltk,nltk_lite/contrib/oldyaml/reader.py,0a5a30ca8c4d84f2c4e76138083ce4eaa882b002,STILL_EXISTS,Yeah; it's ugly and slow.
aahedhejc,nltk/nltk,nltk_lite/contrib/oldyaml/scanner.py,0a5a30ca8c4d84f2c4e76138083ce4eaa882b002,STILL_EXISTS,TODO: support for BOM within a stream.
aahedhfgb,nltk/nltk,nltk_lite/contrib/oldyaml/scanner.py,0a5a30ca8c4d84f2c4e76138083ce4eaa882b002,STILL_EXISTS,Reset everything (not really needed).
aahedhggc,nltk/nltk,nltk_lite/contrib/oldyaml/scanner.py,0a5a30ca8c4d84f2c4e76138083ce4eaa882b002,STILL_EXISTS,TODO: We need to make tab handling rules more sane. A good rule is
aahedhhdf,nltk/nltk,nltk_lite/contrib/oldyaml/scanner.py,0a5a30ca8c4d84f2c4e76138083ce4eaa882b002,STILL_EXISTS,The specification is really confusing about tabs in plain scalars.
aahedhicb,nltk/nltk,nltk_lite/wordnet/wordnet.py,9041679ec3402b30c25e667c920439b0626faf61,STILL_EXISTS,TODO: This next code is dense and confusing. Clean up at some point.
aahedhjdi,nltk/nltk,nltk_lite/wordnet/wordnet.py,8ab1ab7355b21acaf844d3ad2910864557c69cea,STILL_EXISTS,Work around a Windows Python bug
aahedidja,nltk/nltk,nltk_lite/contrib/mit/six863/kimmo/fsa.py,a0992c9b13471622b8e81190fbe7c13dac413d6b,STILL_EXISTS,TODO - check that parse was complete; and report error otherwise
aahedidje,nltk/nltk,nltk_lite/contrib/mit/six863/kimmo/fsa.py,a0992c9b13471622b8e81190fbe7c13dac413d6b,STILL_EXISTS,delete inaccessible nodes and unused transitions
aahediegh,nltk/nltk,nltk_lite/contrib/tree.py,686422934cce2ed9a4a0ed8f6b4bd90d32a6086c,STILL_EXISTS,These classes needed to have their constructer overridden; to
aahedieha,nltk/nltk,nltk_lite/contrib/tree.py,686422934cce2ed9a4a0ed8f6b4bd90d32a6086c,STILL_EXISTS,[XXX] We can't do this without poking around in other
aahedieii,nltk/nltk,nltk_lite/contrib/tree.py,686422934cce2ed9a4a0ed8f6b4bd90d32a6086c,STILL_EXISTS,depends on the class used to implement elements; so we replace
aahediejf,nltk/nltk,nltk_lite/contrib/tree.py,686422934cce2ed9a4a0ed8f6b4bd90d32a6086c,STILL_EXISTS,These functions depend indirectly on the class used to implement
aahedigbh,nltk/nltk,nltk_lite/yamltags.py,676484fbe61bc9e060d1a52c52984057e1a04892,STILL_EXISTS,\"\"\" || Register YAML tags in the NLTK namespace with the YAML loader; by telling it || what module and class to look for. ||  || NLTK uses simple '!' tags to mark the types of objects; but the fully-qualified || \"tag:nltk.sourceforge.net;2007:\" prefix is also accepted in case anyone ends up || using it. || \"\"\"
aahediihg,nltk/nltk,nltk_lite/semantics/evaluate.py,56e49d611d8ef8760b925e31ce4fe805c262d13d,STILL_EXISTS,# expr wasn't a constant; maybe a variable that g knows about?
aahediija,nltk/nltk,nltk_lite/semantics/evaluate.py,da5e9bfc89d2993a690defd37f9115ef7ce16573,STILL_EXISTS,# expr wasn't a constant; maybe a variable that g knows about?
aahediiji,nltk/nltk,nltk_lite/contrib/lex.py,e381cec5f4636193470210d202abca78a048176e,STILL_EXISTS,\"\"\" || Ewan Klein; March 2007 ||  || Experimental module to provide support for implementing English morphology by || feature unification. ||  || Main challenge is to find way of encoding morphosyntactic rules. Current idea is to let a concatenated form such as 'walk + s' be encoded as a dictionary C{'stem': 'walk'; 'affix': 's'}. This allows the morpho-phonological representation to undergo unification in the normal way. || \"\"\"
aahedijaf,nltk/nltk,nltk_lite/contrib/mit/six863/parse/__init__.py,4c1ef444d8b5f4baf6618927bd97e2f71e42d3f1,STILL_EXISTS,\"\"\" || Classes and interfaces for producing tree structures that represent || the internal organization of a text.  This task is known as X{parsing} || the text; and the resulting tree structures are called the text's || X{parses}.  Typically; the text is a single sentence; and the tree || structure represents the syntactic structure of the sentence. || However; parsers can also be used in other domains.  For example; || parsers can be used to derive the morphological structure of the || morphemes that make up a word; or to derive the discourse structure || for a set of utterances. ||  || Sometimes; a single piece of text can be represented by more than one || tree structure.  Texts represented by more than one tree structure are || called X{ambiguous} texts.  Note that there are actually two ways in || which a text can be ambiguous: ||  ||     - The text has multiple correct parses. ||     - There is not enough information to decide which of several ||       candidate parses is correct. ||  || However; the parser module does I{not} distinguish these two types of || ambiguity. ||  || The parser module defines C{ParseI}; a standard interface for parsing || texts; and two simple implementations of that interface; || C{ShiftReduce} and C{RecursiveDescent}.  It also contains || three sub-modules for specialized kinds of parsing: ||  ||   - C{nltk.parser.chart} defines chart parsing; which uses dynamic ||     programming to efficiently parse texts. ||   - C{nltk.parser.probabilistic} defines probabilistic parsing; which ||     associates a probability with each parse. || \"\"\"
aahedijce,nltk/nltk,nltk_lite/contrib/mit/six863/parse/category.py,4c1ef444d8b5f4baf6618927bd97e2f71e42d3f1,STILL_EXISTS,Author: Contributed by Rob Speer (NLTK version)
aahedijdj,nltk/nltk,nltk_lite/contrib/mit/six863/parse/category.py,4c1ef444d8b5f4baf6618927bd97e2f71e42d3f1,2cffd57b7b7a087739be30609a93c9c9d8675219,FIXME: don't show \/
aahedjaje,nltk/nltk,nltk_lite/contrib/mit/six863/parse/chart.py,4c1ef444d8b5f4baf6618927bd97e2f71e42d3f1,STILL_EXISTS,Kludge to ensure child_choices is a doubly-nested list
aahedjcei,nltk/nltk,nltk_lite/contrib/mit/six863/parse/old/category.py,4c1ef444d8b5f4baf6618927bd97e2f71e42d3f1,STILL_EXISTS,Author: Contributed by Rob Speer (NLTK version)
aahedjcgd,nltk/nltk,nltk_lite/contrib/mit/six863/parse/old/category.py,4c1ef444d8b5f4baf6618927bd97e2f71e42d3f1,STILL_EXISTS,FIXME: don't show \/
aahedjdec,nltk/nltk,nltk_lite/contrib/mit/six863/parse/pchart.py,4c1ef444d8b5f4baf6618927bd97e2f71e42d3f1,STILL_EXISTS,[XX] This might not be implemented quite right -- it would be better
aahedjedb,nltk/nltk,nltk_lite/contrib/mit/six863/parse/rd.py,4c1ef444d8b5f4baf6618927bd97e2f71e42d3f1,STILL_EXISTS,[XX] TEMPORARY HACK WARNING!  This should be replaced with
aahedjffh,nltk/nltk,nltk_lite/contrib/mit/six863/parse/treetransforms.py,4c1ef444d8b5f4baf6618927bd97e2f71e42d3f1,STILL_EXISTS,A semi-hack to have elegant looking code below.  As a result;
aahedjgge,nltk/nltk,nltk_lite/contrib/classifier/nltk_lite_contrib/__init__.py,9d2c9ce185329946f0be91602c678a8d71d72f7b,STILL_EXISTS,\"\"\" || The Classifier module is a collection of Classification algorithms;  || Feature Selection techniques and Discretization methods  ||  || The readability of code is emphasized over performance as the main  || motive is to facilitate learning of algorithm internals and the  || effects of preprocessing on classification performance || @version: 0.1.dev ||  || \"\"\"
aaheeabjc,nltk/nltk,nltk_lite/contrib/mit/six863/semantics/category.py,cf75f62d308220c45a4b5531d1997d67f33c1e6c,STILL_EXISTS,Author: Contributed by Rob Speer (NLTK version)
aaheeadfj,nltk/nltk,nltk_lite/contrib/mit/six863/semantics/chart.py,cf75f62d308220c45a4b5531d1997d67f33c1e6c,STILL_EXISTS,Kludge to ensure child_choices is a doubly-nested list
aaheeafib,nltk/nltk,nltk_lite/contrib/classifier_tests/attributetests.py,eb8801a34b358d0a41012afdba6c2dfb5ec37d2c,222437731a2ab82e8311789f6fe467ba8b373ef6,todo Sumukh change this
aaheeahcc,nltk/nltk,nltk_lite/contrib/mit/six863/semantics/__init__.py,12fc2959fb3db4db6bf2461369f6125b55b960fa,STILL_EXISTS,\"\"\" || Classes and interfaces for producing tree structures that represent || the internal organization of a text.  This task is known as X{parsing} || the text; and the resulting tree structures are called the text's || X{parses}.  Typically; the text is a single sentence; and the tree || structure represents the syntactic structure of the sentence. || However; parsers can also be used in other domains.  For example; || parsers can be used to derive the morphological structure of the || morphemes that make up a word; or to derive the discourse structure || for a set of utterances. ||  || Sometimes; a single piece of text can be represented by more than one || tree structure.  Texts represented by more than one tree structure are || called X{ambiguous} texts.  Note that there are actually two ways in || which a text can be ambiguous: ||  ||     - The text has multiple correct parses. ||     - There is not enough information to decide which of several ||       candidate parses is correct. ||  || However; the parser module does I{not} distinguish these two types of || ambiguity. ||  || The parser module defines C{ParseI}; a standard interface for parsing || texts; and two simple implementations of that interface; || C{ShiftReduce} and C{RecursiveDescent}.  It also contains || three sub-modules for specialized kinds of parsing: ||  ||   - C{nltk.parser.chart} defines chart parsing; which uses dynamic ||     programming to efficiently parse texts. ||   - C{nltk.parser.probabilistic} defines probabilistic parsing; which ||     associates a probability with each parse. || \"\"\"
aaheeahhj,nltk/nltk,nltk_lite/contrib/classifier/decisionstump.py,b631c3096c580f51793be947af3d5d71ffde5ccf,09d21569b77e29fe0532299591c280e711d58c5b,TODO refactor to use nltk_lite.probability.FreqDist for self.counts
aaheeahje,nltk/nltk,nltk_lite/contrib/classifier_tests/featureselecttests.py,c1e5f5901bbfcd66e277dae12f548905cd54554d,STILL_EXISTS,TODO
aaheeahjg,nltk/nltk,nltk_lite/contrib/classifier_tests/featureselecttests.py,c1e5f5901bbfcd66e277dae12f548905cd54554d,STILL_EXISTS,todo
aaheeajdc,nltk/nltk,nltk_lite/wordnet/similarity.py,12e3eddc9ccb60e82ee79f6f941c7dc267068dcb,STILL_EXISTS,TODO: Add in the option to manually add a new root node; this will be
aaheeajei,nltk/nltk,nltk_lite/wordnet/similarity.py,12e3eddc9ccb60e82ee79f6f941c7dc267068dcb,STILL_EXISTS,TODO: Once this data has been loaded cache it to save unnecessary recomputation.
aaheebade,nltk/nltk,nltk_lite/wordnet/wntools.py,a3e39f5bc71967579af5c80441f366c57eea14da,STILL_EXISTS,Work around a Windows Python bug
aaheebagb,nltk/nltk,nltk_lite/wordnet/__init__.py,ef144a94479de5536fc860c00f4f14e0a62787e4,STILL_EXISTS,Work around a Windows Python bug
aaheebaii,nltk/nltk,nltk_lite/wordnet/__init__.py,ef144a94479de5536fc860c00f4f14e0a62787e4,STILL_EXISTS,workaround for lack of nested closures in Python < 2.1
aaheebcee,nltk/nltk,nltk_lite/stem/lancaster.py,cbeb70ec271c3f456990a70bc7406122f38dd9ef,STILL_EXISTS,No action taken if word ends with \"-ply\"
aaheebdff,nltk/nltk,nltk_lite/contrib/classifier/instances.py,816cfc0925cd3d28bb442c6da35d43d52964540e,STILL_EXISTS,todo remove this
aaheebdjh,nltk/nltk,nltk_lite/contrib/punkt.py,6a16c945197e90853c8804b1652403a7d7316b04,1897e23cbeb718069e1b5c1252a9820e0d47b6a4,\"\"\" || The constants below are the required parameters needed to perform various tasks || throughout the entire program. It can of course be changed and experiments done || with different values will result in different output. || \"\"\"
aaheebdjj,nltk/nltk,nltk_lite/contrib/punkt.py,6a16c945197e90853c8804b1652403a7d7316b04,STILL_EXISTS,\"\"\" || Below are data structures that are needed to store the information of the corpus || \"\"\"
aaheebeba,nltk/nltk,nltk_lite/contrib/punkt.py,6a16c945197e90853c8804b1652403a7d7316b04,STILL_EXISTS,the number of tokens that ends with a period in the corpus
aaheebgab,nltk/nltk,nltk_lite/contrib/classifier/classify.py,e6c04b12b47807886502983cd947f7e230f52a84,STILL_EXISTS,ugh!! ugly!!!.. need to find a better way.. there are way too many params here! till then.. this stays
aaheebhei,nltk/nltk,nltk_lite/contrib/gluesemantics/glue.py,2de55080849364d380a45909f5e1fa5f31633d03,STILL_EXISTS,skip comments at the ends of lines
aaheecdab,nltk/nltk,nltk/wordnet/util.py,f12d2408b15c415cce0776832d811c296d30d50d,STILL_EXISTS,Work around a Windows Python bug
aaheecdca,nltk/nltk,nltk/wordnet/util.py,f12d2408b15c415cce0776832d811c296d30d50d,8af9e8451aa23f71d345a06e53d1c1e7ee555fb3,workaround for lack of nested closures in Python < 2.1
aaheecejd,nltk/nltk,nltk/corpus/chat80.py,524a2e3f71ed97bff48d5019fc5afdd0877d74e2,STILL_EXISTS,Chat-80 relation metadata bundles needed to build the valuation
aaheedbdb,nltk/nltk,nltk/parse/featurechart.py,1ead1c2fe6d65bf2d71e9f57c050a1c76ebafc29,STILL_EXISTS,TODO: update to use grammar parser
aaheedbij,nltk/nltk,nltk/wordnet/morphy.py,8af9e8451aa23f71d345a06e53d1c1e7ee555fb3,STILL_EXISTS,workaround for lack of nested closures in Python < 2.1
aaheedcbi,nltk/nltk,nltk/classify/decisiontree.py,a08f5eb387bde62a172e9c61b243924f0072bccd,STILL_EXISTS,\"\"\" || A classifier model that decides which label to assign to a token on || the basis of a tree structure; where branches correspond to conditions || on feature values; and leaves correspond to label assignments. || \"\"\"
aaheeddij,nltk/nltk,nltk/classify/api.py,6deaa7da3bd97694fbc2b9bc12fb8ba36cf4d766,STILL_EXISTS,''' ||  || - training tokens ...... train_toks \/ labeled_toks || - testing tokens ....... test_toks \/ toks || - training features .... train_feats \/ labeled_feat_dicts? || - testing features ..... test_feats \/ feat_dict? ||  || feat_dict?  featdict?  featuredict? || feature_dict?  labeled_feature_dict? || labeled_featuredict? || labeled_featuredicts? ||  || a better name that \"feature dictionary\" would be nice!! ||  || featureset? ||  || featureset \/ labeled_featureset ||  || '''
aaheedefh,nltk/nltk,nltk/corpus/util.py,c66824cc800e3af428c205250ad4f925de564658,e0f764d269addbb0612601b5c75eebc8271b403d,Move on to the next piece.
aaheedhej,nltk/nltk,nltk/corpus/reader/util.py,db0317f99db1ff19b56bbb044d9246658e4aa92d,STILL_EXISTS,Move on to the next piece.
aaheediae,nltk/nltk,nltk/corpus/util.py,e0f764d269addbb0612601b5c75eebc8271b403d,cfa9ea8f01e6c4e9dde8fa83f7ac17dbe6d868e4,: list should probably be expanded and\/or cleaned up at some point.
aaheeeaec,nltk/nltk,nltk/tokenize/punkt.py,2c8c7c52b548be1467226f6558e62735e8df310d,STILL_EXISTS,Find a list of bigram collocations whose first word ends in
aaheeebbg,nltk/nltk,nltk/tokenize/punkt.py,2c8c7c52b548be1467226f6558e62735e8df310d,STILL_EXISTS,Get the types of both tokens.  If typ1 ends in a period;
aaheeebfa,nltk/nltk,nltk/tokenize/punkt.py,6e1e047b1a5eceaac689f4ca1eda1bc76c9462da,0d54cdc1c30e28ebadef99e81fa8460572149484,[xx] why is this one commented out?
aaheeedej,nltk/nltk,nltk/corpus/reader/plaintext.py,d5ed3e3c5dfdab2faac688a050c66dc8bd8a0467,6296348b568387eb9f00c1d07171eb26439d8abc,[xx] somewhat of a hack for now..
aaheeeeii,nltk/nltk,nltk/classify/api.py,9b2edcda127dd61986dde5b3d72db7af9c112f68,STILL_EXISTS,''' ||  || - training tokens ...... train_toks \/ labeled_toks || - testing tokens ....... test_toks \/ toks || - training features .... train_feats \/ labeled_feat_dicts? || - testing features ..... test_feats \/ feat_dict? ||  || feat_dict?  featdict?  featuredict? || feature_dict?  labeled_feature_dict? || labeled_featuredict? || labeled_featuredicts? ||  || a better name that \"feature dictionary\" would be nice!! ||  || featureset? ||  || featureset \/ labeled_featureset ||  || '''
aaheeeejd,nltk/nltk,nltk/parse/category.py,9b2edcda127dd61986dde5b3d72db7af9c112f68,STILL_EXISTS,[XX] This is less evil; but not too efficient!
aaheeegbg,nltk/nltk,nltk/corpus/reader/senseval.py,0e66d3eb91f2d1168189bb14a3f356add25fb85e,STILL_EXISTS,fix lone &
aaheeegbh,nltk/nltk,nltk/corpus/reader/senseval.py,0e66d3eb91f2d1168189bb14a3f356add25fb85e,STILL_EXISTS,fix \"\"\"
aaheeegbi,nltk/nltk,nltk/corpus/reader/senseval.py,0e66d3eb91f2d1168189bb14a3f356add25fb85e,STILL_EXISTS,fix <s snum=dd> => <s snum=\"dd\"\/>
aaheeegcb,nltk/nltk,nltk/corpus/reader/senseval.py,0e66d3eb91f2d1168189bb14a3f356add25fb85e,STILL_EXISTS,fix <{word}>
aaheeegci,nltk/nltk,nltk/corpus/reader/senseval.py,0e66d3eb91f2d1168189bb14a3f356add25fb85e,STILL_EXISTS,fix 'abc <p=\"foo\"\/>' style tags - now <wf pos=\"foo\">abc<\/wf>
aaheeejaf,nltk/nltk,nltk/parse/__init__.py,32aeee600fe3cc233e9aca37d6632ce438bf9be2,260822479bd42b878d03e164d70ffc3cdc0205fa,\"\"\" || Classes and interfaces for producing tree structures that represent || the internal organization of a text.  This task is known as X{parsing} || the text; and the resulting tree structures are called the text's || X{parses}.  Typically; the text is a single sentence; and the tree || structure represents the syntactic structure of the sentence. || However; parsers can also be used in other domains.  For example; || parsers can be used to derive the morphological structure of the || morphemes that make up a word; or to derive the discourse structure || for a set of utterances. ||  || Sometimes; a single piece of text can be represented by more than one || tree structure.  Texts represented by more than one tree structure are || called X{ambiguous} texts.  Note that there are actually two ways in || which a text can be ambiguous: ||  ||     - The text has multiple correct parses. ||     - There is not enough information to decide which of several ||       candidate parses is correct. ||  || However; the parser module does I{not} distinguish these two types of || ambiguity. ||  || The parser module defines C{ParserI}; a standard interface for parsing || texts; and two simple implementations of that interface; || C{ShiftReduceParser} and C{RecursiveDescentParser}.  It also contains || three sub-modules for specialized kinds of parsing: ||  ||   - C{nltk.parser.chart} defines chart parsing; which uses dynamic ||     programming to efficiently parse texts. ||   - C{nltk.parser.probabilistic} defines probabilistic parsing; which ||     associates a probability with each parse. || \"\"\"
aaheefaef,nltk/nltk,tools/find_deprecated.py,ea7a6cefe21c9bf72448d08d5504575e88e54da0,STILL_EXISTS,Yes; it's bad programming practice; but this is a little hack
aaheefaff,nltk/nltk,tools/find_deprecated.py,ea7a6cefe21c9bf72448d08d5504575e88e54da0,STILL_EXISTS,Hack: only complain about read if it's used after a corpus.
aaheefbgc,nltk/nltk,nltk/featstruct.py,1c976cdd388634b4c3cfb24110083d35a21837ff,STILL_EXISTS,\"\"\" || Basic data classes for representing feature structures.  A X{feature || structure} is a mapping from feature names to feature values; where: ||  ||   - Each X{feature name} is a case sensitive string. ||   - Each X{feature value} can be a base value (such as a string); a ||     variable; or a nested feature structure. ||  || Feature structures are typically used to represent partial information || about objects.  A feature name that is not mapped to a value stands || for a feature whose value is unknown (I{not} a feature without a || value).  Two feature structures that represent (potentially || overlapping) information about the same object can be combined by || X{unification}.  When two inconsistent feature structures are unified; || the unification fails and returns C{None}. ||  || Features can be specified using X{feature paths}; or tuples of feature || names that specify path through the nested feature structures to a || value.  Feature structures may contain reentrant feature values.  A || X{reentrant feature value} is a single feature value that can be || accessed via multiple feature paths.  Unification preserves the || reentrance relations imposed by both of the unified feature || structures.  In the feature structure resulting from unification; any || modifications to a reentrant feature value will be visible using any || of its feature paths.  Feature structures may also contain X{cyclic || feature values}; i.e.; values that recursively contain themself. ||  || Feature structure variables are encoded using the L{nltk.sem.Variable} || class.  The variables' values are tracked using a X{bindings} || dictionary; which maps variables to their values.  When two feature || structures are unified; a fresh bindings dictionary is created to || track their values; and before unification completes; all bound || variables are replaced by their values.  Thus; the bindings || dictionaries are usually strictly internal to the unification process. || However; it is possible to track the bindings of variables if you || choose to; by supplying your own initial bindings dictionary to the || L{unify() <FeatStruct.unify>} method. ||  || When unbound variables are unified with one another; they become || X{aliased}.  This is encoded by binding one variable to the other. ||  || @todo: add a fail parameter to unify?  This would be a function that ||    would be called if unificaiton fails; it could either raise a ||    UnificationFailure error; or return a value.  How would this be ||    useful?  Well; one example is that it could be used to find a ||    \"diff\" between two feature structures -- i.e.; a list of all ||    feature paths with different values.  Anyway; the old version had ||    it.  Ask steven why it was introduced? ||  || @todo: Figure out yaml support.  Do we need any? ||  || @todo: support for mutable feature structures? ||  || @todo: define __div__ for feature structures? ||  || relative to category; we don't define... ||   - .symbol (we're not a Nonterminal) ||   - .head() ||   - .feature_names(); .has_features() -- eh ||   - .to_yaml() and .from_yaml() ||   - parsing of cfgs.. ||  || \"\"\"
aaheeffeh,nltk/nltk,nltk/corpus/reader/util.py,d5c9419bad5c07cdd6869669496198252b9d9409,STILL_EXISTS,ensure block ends on line boundary
aaheeffff,nltk/nltk,nltk/corpus/reader/util.py,3935fe9ba81f707c555fadcc92ba8cf10d36610b,STILL_EXISTS,If we're stripping comments; then make sure our block ends
aaheefffj,nltk/nltk,nltk/corpus/reader/util.py,3935fe9ba81f707c555fadcc92ba8cf10d36610b,STILL_EXISTS,[xx] if the file ends with a non-parenthasized sexpr
aaheeffgd,nltk/nltk,nltk/corpus/reader/util.py,cdd82059c57f453851dc26a657fd22c4d7b17778,STILL_EXISTS,[xx] if the file ends with a non-parenthasized sexpr
aaheefgca,nltk/nltk,nltk/stem/rslp.py,e913dfe293d7baad79ba70632ae5830621dd504f,STILL_EXISTS,NOTE: a simple but ugly hack to make this parser happy with double '\\t's
aaheefgcj,nltk/nltk,nltk/stem/rslp.py,e913dfe293d7baad79ba70632ae5830621dd504f,STILL_EXISTS,the word ends in 's'? apply rule for plural reduction
aaheefgda,nltk/nltk,nltk/stem/rslp.py,e913dfe293d7baad79ba70632ae5830621dd504f,STILL_EXISTS,the word ends in 'a'? apply rule for feminine reduction
aaheefgfd,nltk/nltk,nltk/corpus/reader/ieer.py,b4f8028ef9fbb14872733bc68d5cbe40f71c0027,STILL_EXISTS,TODO: figure out while empty documents are being returned
aaheefghc,nltk/nltk,nltk/corpus/reader/tagged.py,2cb9c07adf30c1c4b001fd8347e6847d6f6b89fa,dd2acd1f4a4bab270cd8020f606cca42d6ca0986,there must be a better way...
aaheefjcb,nltk/nltk,nltk/test/coverage.py,53783e79881b7922d822aa3e3f9e402082379d8f,STILL_EXISTS,2006-08-23 NMB Refactorings to improve testability.  Fixes to command-line
aaheefjdb,nltk/nltk,nltk/test/coverage.py,53783e79881b7922d822aa3e3f9e402082379d8f,STILL_EXISTS,Change how parallel mode is invoked; and fix erase() so that it erases the
aaheefjdg,nltk/nltk,nltk/test/coverage.py,53783e79881b7922d822aa3e3f9e402082379d8f,STILL_EXISTS,Better file handling on Linux; thanks Guillaume Chazarain.
aaheefjdh,nltk/nltk,nltk/test/coverage.py,53783e79881b7922d822aa3e3f9e402082379d8f,STILL_EXISTS,Better shell support on Windows; thanks Noel O'Boyle.
aaheefjei,nltk/nltk,nltk/test/coverage.py,53783e79881b7922d822aa3e3f9e402082379d8f,STILL_EXISTS,2007-07-29 NMB Better packaging.
aaheefjic,nltk/nltk,nltk/test/doctest_driver.py,233307f2599415ce67451ac6e679fd9b9b28dc25,5f8e559fb10becf8e65820a0e1e2ae55a626f749,Monkey-Patch to fix Doctest
aaheefjjd,nltk/nltk,nltk/chunk/regexp.py,ecf1edbbf350ac92ebbba366ac6b2c8e05e5b567,STILL_EXISTS,this should probably be made more strict than it is -- e.g.; it
aaheegaeh,nltk/nltk,nltk/sem/relextract.py,b901f2d0fcaf3916b5fc31f9ca17e899fb54da53,STILL_EXISTS,todo: get a more general solution to canonicalized symbols for clauses -- maybe use xmlcharrefs?
aaheegfej,nltk/nltk,nltk/inference/prover9.py,b09f066cf8f49195d26bd286819cf35324358628,STILL_EXISTS,Prover9 crashed; most probably due to a bug.
aaheeghfg,nltk/nltk,nltk/classify/maxent.py,2cf81e748f3f17f77c97f3f1e8b415d5191589fd,98b4f6cb3ce7ebf1e38e0e86fe9a0777df11a2dd,(list-of-lists) performs better than csr and csc formats.
aaheehbfi,nltk/nltk,nltk/tokenize/punkt.py,781496303515c902b1030523101a661ac926ab57,STILL_EXISTS,ends in a period.
aaheehbha,nltk/nltk,nltk/tokenize/punkt.py,781496303515c902b1030523101a661ac926ab57,STILL_EXISTS,needed after freq_threshold
aaheehcja,nltk/nltk,nltk/wordnet/similarity.py,8be0b8a7e5a623ebb79721e3c4a90007d4fa8c65,STILL_EXISTS,this load function would be more efficient if the data was pickled
aaheehddg,nltk/nltk,nltk/tag/hmm.py,f8590becfdbe6321a377f6b9a8b1799e7a89bfb9,STILL_EXISTS,add new columns to the output probability table without
aaheehdha,nltk/nltk,nltk/draw/rechunkparser.py,a087ba91e0c2ff5c52aa1804a9159a932ef11288,STILL_EXISTS,[xx] Hack: automatically backslash $!
aaheehdhc,nltk/nltk,nltk/draw/rechunkparser.py,a087ba91e0c2ff5c52aa1804a9159a932ef11288,STILL_EXISTS,[xx] hack check:
aaheehead,nltk/nltk,nltk/draw/rechunkparser.py,a087ba91e0c2ff5c52aa1804a9159a932ef11288,STILL_EXISTS,Efficiency hack -- if the grammar is empty; then don't
aaheehedd,nltk/nltk,nltk/draw/rechunkparser.py,a087ba91e0c2ff5c52aa1804a9159a932ef11288,STILL_EXISTS,This is a hack; because the x-scrollbar isn't updating its
aaheehedg,nltk/nltk,nltk/draw/rechunkparser.py,a087ba91e0c2ff5c52aa1804a9159a932ef11288,b21fb91f2f3f95a0b3e4aa9e56efd5f6834e3774,hack:
aaheehfga,nltk/nltk,nltk/tag/crf.py,3ded64a20a2788acc583e7fbcaadc5e50075c0d5,5acab3784811755028d4a1f7b9e1832168b911a5,Ensure that the filename ends with '.zip'
aaheehgfe,nltk/nltk,nltk/sem/logic.py,9d8dc46983a07a8b57cebe4f00a1be0c55f38636,STILL_EXISTS,TODO: DELETE ALL
aaheehjei,nltk/nltk,nltk/sem/evaluate.py,2a7ef2e94e9c90333f76d0a715aff75a1333ff44,STILL_EXISTS,TODO deal with case where s contains an int
aaheeicab,nltk/nltk,nltk/wordnet/browser/__init__.py,cba6a61ec7821641003f45921554498e9639b590,STILL_EXISTS,TODO: throughout this package variable names and docstrings need
aaheeicce,nltk/nltk,nltk/wordnet/browser/browserver.py,cba6a61ec7821641003f45921554498e9639b590,STILL_EXISTS,TODO Handle files here.
aaheeicgc,nltk/nltk,nltk/wordnet/browser/browseutil.py,cba6a61ec7821641003f45921554498e9639b590,STILL_EXISTS,TODO: The following issues exist with this module; and should
aaheeicjc,nltk/nltk,nltk/wordnet/browser/browseutil.py,cba6a61ec7821641003f45921554498e9639b590,STILL_EXISTS,This looks up multiple words at once.  This is probably not
aaheeidbh,nltk/nltk,nltk/wordnet/browser/browseutil.py,cba6a61ec7821641003f45921554498e9639b590,STILL_EXISTS,TODO.  Part of this page should be dynamic so it is updated with the wordnet database.
aaheeidcc,nltk/nltk,nltk/wordnet/browser/wxbrowse.py,cba6a61ec7821641003f45921554498e9639b590,STILL_EXISTS,marked with 'XXX'
aaheeidce,nltk/nltk,nltk/wordnet/browser/wxbrowse.py,cba6a61ec7821641003f45921554498e9639b590,STILL_EXISTS,XXX: write these global constants in shouting case.
aaheeidcj,nltk/nltk,nltk/wordnet/browser/wxbrowse.py,cba6a61ec7821641003f45921554498e9639b590,STILL_EXISTS,XXX: This fallback may have problems on windows.
aaheeidda,nltk/nltk,nltk/wordnet/browser/wxbrowse.py,cba6a61ec7821641003f45921554498e9639b590,STILL_EXISTS,## XXX: MUST find a standard location for the stats HTML page;
aaheeiddd,nltk/nltk,nltk/wordnet/browser/wxbrowse.py,cba6a61ec7821641003f45921554498e9639b590,STILL_EXISTS,XXX: MAY simplfy the if predicate. *\/
aaheeiddg,nltk/nltk,nltk/wordnet/browser/wxbrowse.py,cba6a61ec7821641003f45921554498e9639b590,STILL_EXISTS,XXX: MAY remove three statments with no effect. *\/
aaheeiddi,nltk/nltk,nltk/wordnet/browser/wxbrowse.py,cba6a61ec7821641003f45921554498e9639b590,STILL_EXISTS,XXX: MAY simplify setting self.cwdJust use os.getcwd(). *\/
aaheeidfd,nltk/nltk,nltk/wordnet/browser/wxbrowse.py,cba6a61ec7821641003f45921554498e9639b590,STILL_EXISTS,TODO: Should give instructions for using dbinfo_html.py
aaheeidff,nltk/nltk,nltk/wordnet/browser/wxbrowse.py,cba6a61ec7821641003f45921554498e9639b590,STILL_EXISTS,Try to catch the screen dimensions like this because no better
aaheeigcj,nltk/nltk,nltk/sem/evaluate.py,d96129cd6f8c2b495e20ac78d15c44656fe4a111,STILL_EXISTS,TODO:
aaheeigda,nltk/nltk,nltk/sem/evaluate.py,d96129cd6f8c2b495e20ac78d15c44656fe4a111,STILL_EXISTS,- fix tracing
aaheeigjf,nltk/nltk,nltk/probability.py,92770afa986145c65584f347f473bbcab3938dae,STILL_EXISTS,Subclasses shuld define more efficient implementations of this;
aaheeigjj,nltk/nltk,nltk/probability.py,92770afa986145c65584f347f473bbcab3938dae,STILL_EXISTS,[xx] nb: this is not too efficient
aaheeihae,nltk/nltk,nltk/corpus/reader/conll.py,9ff668d5a8583fb9456cbabd15a67b791cc18cda,STILL_EXISTS,columns to expect for SRL data.
aaheeihde,nltk/nltk,nltk/model/ngram.py,945da911262643b8f9f8cff5c30cd0c6eb2ef9fa,64ee5b5f2682683b9aec8de5b408bb50fd491163,todo: implement Katz backoff
aaheeijdc,nltk/nltk,nltk/tree.py,6e5f1d85e80f3c28da7880d9045c7984c24b76f9,1c841d33b69c9d97e9393e2a3e38ec4f1c58abfe,We need to implement __getslice__ and friends; even though
aaheeijhg,nltk/nltk,nltk/corpus/reader/conll.py,ef39a1b8631e06059c6d1ae33471d28dbb6e11f7,STILL_EXISTS,optional tree columns
aaheeijhi,nltk/nltk,nltk/corpus/reader/conll.py,ef39a1b8631e06059c6d1ae33471d28dbb6e11f7,STILL_EXISTS,Remaining columns: self
aaheeijhj,nltk/nltk,nltk/classify/maxent.py,6c17a65e4218aa118d0e580140b288be9172947d,STILL_EXISTS,hack
aaheeijja,nltk/nltk,nltk/inference/discourse.py,9c5194802158d66fad186b679e0eb49b4d173c5a,37b9f1553b77cb42cd080e694939b6069928ab62,TODO lf = lf.normalize('[xyz]\\d*'; 'z%d')
aaheejaea,nltk/nltk,nltk/corpus/reader/tagged.py,38742044710965a6a78befe87db94991365e8266,STILL_EXISTS,needs to implement simplified tags
aaheejddd,nltk/nltk,nltk/draw/table.py,17697d0b19416353cde4538d5fd4ec8f8bc94be4,STILL_EXISTS,If columns was specified as an int; convert it to a list.
aaheejdgh,nltk/nltk,nltk/draw/table.py,17697d0b19416353cde4538d5fd4ec8f8bc94be4,STILL_EXISTS,Hide\/Show Columns
aaheejeae,nltk/nltk,nltk/draw/table.py,17697d0b19416353cde4538d5fd4ec8f8bc94be4,STILL_EXISTS,Columns
aaheejeda,nltk/nltk,nltk/inference/resolution.py,557e1265e94cc230f4c86639630c02dda7a4a8ca,STILL_EXISTS,put skipped and unused terms back into play for later unification.
aaheejedi,nltk/nltk,nltk/data.py,3515b1c3a57af6c4389a0210f46b0f0df00db29d,STILL_EXISTS,too small which leads to poor performance loading large gzipped
aaheejeej,nltk/nltk,nltk/draw/table.py,d71579e84129abd5f55cdcb30fe58869e9816d61,STILL_EXISTS,Columns can be resized by dragging them:
aaheejefa,nltk/nltk,nltk/draw/table.py,d71579e84129abd5f55cdcb30fe58869e9816d61,STILL_EXISTS,Columns can be resized by dragging them.  (This binding is
aaheejefb,nltk/nltk,nltk/draw/table.py,d71579e84129abd5f55cdcb30fe58869e9816d61,STILL_EXISTS,used if they click on the grid between columns:)
aaheejfca,nltk/nltk,nltk/draw/concordance.py,44c62ba631fd38cebd4dbca18b28e304ef65425a,STILL_EXISTS,todo: refactor the model such that it is less state sensitive
aaheejgae,nltk/nltk,nltk/downloader.py,fa242f5e10511cb2400396ebb56964becef12799,STILL_EXISTS,So this is really what we should do.  That way the threaded
aaheejgcj,nltk/nltk,nltk/downloader.py,fa242f5e10511cb2400396ebb56964becef12799,STILL_EXISTS,the wrong thing to do.  Maybe the _interactive_download
aaheejhba,nltk/nltk,nltk/downloader.py,fa242f5e10511cb2400396ebb56964becef12799,STILL_EXISTS,Create a menu to control which columns of the table are
aaheejhbb,nltk/nltk,nltk/downloader.py,fa242f5e10511cb2400396ebb56964becef12799,STILL_EXISTS,shown.  n.b.: we never hide the first two columns (mark and
aaheejifh,nltk/nltk,nltk/sem/hole.py,99358072755256d650f665baa29f7634bd42006b,STILL_EXISTS,Author:     Peter Wang
aaheejijd,nltk/nltk,nltk/sem/hole.py,99358072755256d650f665baa29f7634bd42006b,STILL_EXISTS,A depth-first search would work as well since the trees must
aaheejjci,nltk/nltk,nltk/sem/logic.py,ca24ff937766b75de84659cc34523dc930ad6a46,STILL_EXISTS,TODO: how to get to inconsistent type hierarchy?
aahefaadc,nltk/nltk,nltk/corpus/reader/toolbox.py,89afbbd11889d9474a6872fba36fd860fb6ad046,STILL_EXISTS,should probably be done lazily:
aahefadhg,nltk/nltk,nltk/corpus/reader/wordnet.py,4e5a968e6a3a495ec8a7502b7d37ac55920969a1,STILL_EXISTS,Work around a Windows Python bug
aahefaeda,nltk/nltk,nltk/corpus/reader/wordnet.py,4e5a968e6a3a495ec8a7502b7d37ac55920969a1,STILL_EXISTS,split the line into columns; and reverse them so that
aahefaeec,nltk/nltk,nltk/corpus/reader/wordnet.py,4e5a968e6a3a495ec8a7502b7d37ac55920969a1,STILL_EXISTS,TODO: Add in the option to manually add a new root node; this will be
aahefaega,nltk/nltk,nltk/corpus/reader/wordnet.py,4e5a968e6a3a495ec8a7502b7d37ac55920969a1,STILL_EXISTS,this load function would be more efficient if the data was pickled
aahefahbe,nltk/nltk,nltk/draw/util.py,338fc63194ef269304947e183a4fd87ec01f17b7,STILL_EXISTS,Move it back; if we were dragging.
aahefahfg,nltk/nltk,nltk/draw/util.py,338fc63194ef269304947e183a4fd87ec01f17b7,STILL_EXISTS,Move to (x;y)
aahefajaa,nltk/nltk,nltk/draw/wordnet_browser/__init__.py,3ef925643f0920e0f6121100f9d0be7589dd2ebe,STILL_EXISTS,TODO: throughout this package variable names and docstrings need
aahefajcf,nltk/nltk,nltk/draw/wordnet_browser/browserver.py,3ef925643f0920e0f6121100f9d0be7589dd2ebe,STILL_EXISTS,TODO Handle files here.
aahefajge,nltk/nltk,nltk/draw/wordnet_browser/util.py,3ef925643f0920e0f6121100f9d0be7589dd2ebe,STILL_EXISTS,TODO: The following issues exist with this module; and should
aahefajhh,nltk/nltk,nltk/draw/wordnet_browser/util.py,3ef925643f0920e0f6121100f9d0be7589dd2ebe,STILL_EXISTS,XXX: I don't know the format of this datastrcutre so I wont try to
aahefbafi,nltk/nltk,nltk/draw/wordnet_browser/util.py,3ef925643f0920e0f6121100f9d0be7589dd2ebe,STILL_EXISTS,This looks up multiple words at once.  This is probably not
aahefbaie,nltk/nltk,nltk/draw/wordnet_browser/util.py,3ef925643f0920e0f6121100f9d0be7589dd2ebe,STILL_EXISTS,TODO.  Part of this page should be dynamic so it is updated with the wordnet database.
aahefbajh,nltk/nltk,nltk/draw/wordnet_browser/wxbrowse.py,3ef925643f0920e0f6121100f9d0be7589dd2ebe,STILL_EXISTS,marked with 'XXX'
aahefbajj,nltk/nltk,nltk/draw/wordnet_browser/wxbrowse.py,3ef925643f0920e0f6121100f9d0be7589dd2ebe,STILL_EXISTS,XXX: write these global constants in shouting case.
aahefbbae,nltk/nltk,nltk/draw/wordnet_browser/wxbrowse.py,3ef925643f0920e0f6121100f9d0be7589dd2ebe,STILL_EXISTS,XXX: This fallback may have problems on windows.
aahefbbaf,nltk/nltk,nltk/draw/wordnet_browser/wxbrowse.py,3ef925643f0920e0f6121100f9d0be7589dd2ebe,STILL_EXISTS,## XXX: MUST find a standard location for the stats HTML page;
aahefbbai,nltk/nltk,nltk/draw/wordnet_browser/wxbrowse.py,3ef925643f0920e0f6121100f9d0be7589dd2ebe,STILL_EXISTS,XXX: MAY simplfy the if predicate. *\/
aahefbbbb,nltk/nltk,nltk/draw/wordnet_browser/wxbrowse.py,3ef925643f0920e0f6121100f9d0be7589dd2ebe,STILL_EXISTS,XXX: MAY remove three statments with no effect. *\/
aahefbbbd,nltk/nltk,nltk/draw/wordnet_browser/wxbrowse.py,3ef925643f0920e0f6121100f9d0be7589dd2ebe,STILL_EXISTS,XXX: MAY simplify setting self.cwdJust use os.getcwd(). *\/
aahefbbci,nltk/nltk,nltk/draw/wordnet_browser/wxbrowse.py,3ef925643f0920e0f6121100f9d0be7589dd2ebe,STILL_EXISTS,TODO: Should give instructions for using dbinfo_html.py
aahefbbda,nltk/nltk,nltk/draw/wordnet_browser/wxbrowse.py,3ef925643f0920e0f6121100f9d0be7589dd2ebe,STILL_EXISTS,Try to catch the screen dimensions like this because no better
aahefbcge,nltk/nltk,nltk/draw/wordnet_browser/util.py,203e62fdce95942b67a4de1e99f13cf08a4c4397,STILL_EXISTS,XXX: What is prev_synset_key?
aahefbchc,nltk/nltk,nltk/draw/wordnet_browser/util.py,203e62fdce95942b67a4de1e99f13cf08a4c4397,STILL_EXISTS,This looks up multiple words at once.  This is probably not
aahefbddh,nltk/nltk,nltk/draw/wordnet_browser/util.py,daf4e12cdec5937ea4396f3b3a28f0cd7bc4b4a8,STILL_EXISTS,XXX: What is prev_synset_key?
aahefbddj,nltk/nltk,nltk/draw/wordnet_browser/browserver.py,e76e2008ae3aa3f819144b024451823d2fa38b28,STILL_EXISTS,TODO add a variation of this that takes a non ecoded word or MWE.
aahefbecj,nltk/nltk,tools/svnmime.py,a9ef5c6788f9168388042b950a17ae89128edf77,STILL_EXISTS,NB; this wouldn't be needed if everyone had .subversion\/config
aahefbeja,nltk/nltk,nltk/downloader.py,bd465cd7a3071406003dfc40122254de59dc30a7,STILL_EXISTS,This is a hack; because the scrollbar isn't updating its
aahefbgcd,nltk/nltk,nltk/tokenize/punkt.py,296df31d08af3f5bb05cf8496c5057ebbc9c12eb,STILL_EXISTS,TODO: Make orthographic heuristic less susceptible to overtraining
aahefbgce,nltk/nltk,nltk/tokenize/punkt.py,296df31d08af3f5bb05cf8496c5057ebbc9c12eb,STILL_EXISTS,TODO: Frequent sentence starters optionally exclude always-capitalised words
aahefbhec,nltk/nltk,nltk/sem/glue.py,02736a8db9e95a5b3d4040099d8ec868f8b53259,STILL_EXISTS,skip comments at the ends of lines
aahefbhhj,nltk/nltk,nltk/sem/glue.py,02736a8db9e95a5b3d4040099d8ec868f8b53259,STILL_EXISTS,'a unicorn seems to approach';
aahefcabe,nltk/nltk,nltk/metrics/agreement.py,10463a4085b947226fd06b9dff9c07a8d8871180,STILL_EXISTS,\"\"\" || Implementations of inter-annotator agreement coefficients surveyed by Artstein || and Poesio (2007); Inter-Coder Agreement for Computational Linguistics. ||  || An agreement coefficient calculates the amount that annotators agreed on label  || assignments beyond what is expected by chance. ||  || In defining the AnnotationTask class; we use naming conventions similar to the  || paper's terminology.  There are three types of objects in an annotation task:  ||  ||     the coders (variables \"c\" and \"C\") ||     the items to be annotated (variables \"i\" and \"I\") ||     the potential categories to be assigned (variables \"k\" and \"K\") ||  || Additionally; it is often the case that we don't want to treat two different  || labels as complete disagreement; and so the AnnotationTask constructor can also || take a distance metric as a final argument.  Distance metrics are simply  || functions that take two arguments; and return a value between 0.0 and 1.0  || indicating the distance between them.  If not supplied; the default is binary  || comparison between the arguments. ||  || The simplest way to initialize an AnnotationTask is with a list of equal-length  || lists; each containing a coder's assignments for all objects in the task: ||  ||     task = AnnotationTask([];[];[]) ||  || Alpha (Krippendorff 1980) || Kappa (Cohen 1960) || S (Bennet; Albert and Goldstein 1954) || Pi (Scott 1955) ||  ||  || TODO: Describe handling of multiple coders and missing data ||  || Expected results from the Artstein and Poesio survey paper: ||  || >>> t = AnnotationTask(data=[x.split() for x in open(\"%sartstein_poesio_example.txt\" % (__file__.replace(\"__init__.py\"; \"\")))]) || >>> t.avg_Ao() || 0.88 || >>> t.pi() || 0.7995322418977614 || >>> t.S() || 0.81999999999999984 || \"\"\"
aahefcacc,nltk/nltk,nltk/metrics/agreement.py,10463a4085b947226fd06b9dff9c07a8d8871180,STILL_EXISTS,TODO: VERY slow; speed this up!
aahefcagb,nltk/nltk,nltk/metrics/association.py,e2b1e9226bed210280c25a2a5daa04f95e6a8a1e,3a199fb93410b3853cfd06d939149717c3e4db25,I don't understand why this negation is needed
aahefcebg,nltk/nltk,nltk/test/doctest_driver.py,014d7e73d87031f785bc64a6865c785931d557ec,STILL_EXISTS,temporary hack:
aahefcefi,nltk/nltk,nltk/app/wordnet_app.py,d1b6a6207d3b51557b075220a02505b3cc475cff,STILL_EXISTS,TODO: throughout this package variable names and docstrings need
aahefcehd,nltk/nltk,nltk/app/wordnet_app.py,d1b6a6207d3b51557b075220a02505b3cc475cff,STILL_EXISTS,Trying to fetch a HTML file TODO:
aahefcehg,nltk/nltk,nltk/app/wordnet_app.py,d1b6a6207d3b51557b075220a02505b3cc475cff,STILL_EXISTS,TODO add a variation of this that takes a non ecoded word or MWE.
aahefcgja,nltk/nltk,nltk/misc/babelfish.py,e37268e551d033c994a45416a912643e6f74869a,9276c713b3b6b9ee9b11e715f896c67afc87a2e7,Requires python 2.0 or better.
aahefchbe,nltk/nltk,nltk/test/doctest_driver.py,b23f7532178fc4de5ca38a756748b21a92e526b5,STILL_EXISTS,Total hack warning:
aahefchff,nltk/nltk,nltk/sem/drt_glue_demo.py,f40d40dad2fe2593b8cca23214e53b49fbb90dfc,STILL_EXISTS,'a unicorn seems to approach';
aahefchja,nltk/nltk,nltk/text.py,bd39d9dafe323630e7c0ccb6fc30b45a0bf91e10,e4a34050a34068236c4d819aad66f14e2d02f9fc,FIXME Should this raise some kind of error instead?
aahefdajh,nltk/nltk,nltk/grammar.py,3a1d0bf9a689762bdeb08b7de3433453ef238cdb,dbc825501bf732ab61c2eabc9f394900d78871e8,TODO: Add FeatureProduction; with a better def. of __str__:
aahefdbfg,nltk/nltk,nltk/parse/earleychart.py,3a1d0bf9a689762bdeb08b7de3433453ef238cdb,STILL_EXISTS,Author: Peter Ljungl\u00F6f <peter.ljunglof@heatherleaf.se>
aahefdcbc,nltk/nltk,nltk/parse/featurechart.py,3a1d0bf9a689762bdeb08b7de3433453ef238cdb,STILL_EXISTS,TODO: Clean this mess up
aahefdcbh,nltk/nltk,nltk/parse/featurechart.py,3a1d0bf9a689762bdeb08b7de3433453ef238cdb,STILL_EXISTS,TODO: Add a cached version of this rule:
aahefdcdh,nltk/nltk,nltk/parse/pchart.py,9febe32b671173be8b062777929587a2001491c7,STILL_EXISTS,TODO: change this to conform more with the standard ChartParser
aahefdefh,nltk/nltk,nltk/test/testrunner.py,fd588fd614983be009634a39846e5c5579dba8a3,STILL_EXISTS,TODO: Run the tests if the relevant dependeices are present on the system
aahefdefi,nltk/nltk,nltk/test/testrunner.py,fd588fd614983be009634a39846e5c5579dba8a3,STILL_EXISTS,TODO: create an option for the doctest driver to disable output
aahefdfaj,nltk/nltk,nltk/corpus/reader/plaintext.py,f3005c0c03104bd2a62d3b7aa08925d92ee5a155,STILL_EXISTS,is there a better way?
aahefdgac,nltk/nltk,nltk/tree.py,4a9fed246899bae41e968140db325af03b631e86,STILL_EXISTS,TODO: add LabelledTree (can be used for dependency trees)
aahefdgai,nltk/nltk,nltk/tag/hunpos.py,3369d9b78ed8c0d060b908b79a083651e09de682,STILL_EXISTS,Author: Peter Ljungl\u00F6f <peter.ljunglof@heatherleaf.se>
aahefdgdd,nltk/nltk,nltk/tokenize/punkt.py,998c82e65d678f181d2d510e0d0625e485cba62a,STILL_EXISTS,FIXME: Problem with ending string with e.g. '!!!' -> '!! !'
aahefdhdd,nltk/nltk,nltk/sourcedstring.py,e94b01e23438704dc61e383c698a199aec5d4715,STILL_EXISTS,[xx] There's no reason in principle why this can't preserve
aahefdicf,nltk/nltk,nltk/classify/maxent.py,c9283d60d9a0bfb2df65b68167653682335d68c1,STILL_EXISTS,hack
aahefdieh,nltk/nltk,nltk/sem/boxer.py,72d748990618002d4f90570679673a6d2df7089d,STILL_EXISTS,\"\"\" || An interface to Boxer. ||  || Usage: ||   Set the environment variable CANDCHOME to the bin directory of your CandC installation. ||   The models directory should be in the CandC root directory. ||   For example: ||      \/path\/to\/candc\/ ||         bin\/ ||             candc ||             boxer ||         models\/ ||             boxer\/ || \"\"\"
aahefdihe,nltk/nltk,nltk/sem/boxer.py,72d748990618002d4f90570679673a6d2df7089d,ddd7416070340dc4daf9fadffe232d15c4c8258f,TODO: removed since multiple indices mean it's not an occurrence word
aahefdjag,nltk/nltk,nltk/stem/snowball.py,8fd519d99d218444521082476457e681f2762638,STILL_EXISTS,Author: Peter Michael Stahl <pemistahl@gmail.com>
aahefdjeb,nltk/nltk,nltk/stem/snowball.py,8fd519d99d218444521082476457e681f2762638,STILL_EXISTS,If the word ends with a double consonant
aahefeaef,nltk/nltk,nltk/parse/featurechart.py,e4f677560c500096c58fbe237c046832ccc90554,STILL_EXISTS,TODO: subsumes check when adding new edges
aahefeahg,nltk/nltk,nltk/sem/drt.py,b9999672dc653bfa7fab095a9faec74ac3d05b19,STILL_EXISTS,TODO: at some point.  for now; simplify.
aahefebhi,nltk/nltk,nltk/corpus/reader/aligned.py,b32d42ecb26c4263a7e1eccc7359210074718781,STILL_EXISTS,kludge; we shouldn't have tokenized the alignment string
aahefecee,nltk/nltk,nltk/decorators.py,dde3460117311bf4cbf57016b433229a27108519,STILL_EXISTS,Hack to keep NLTK's \"tokenize\" module from colliding with the \"tokenize\" in
aahefecfa,nltk/nltk,nltk/internals.py,baf7a830328072dc46b6a985c19fb780fc2c1366,c7f14a26f1c3d6465f45e552eabfa2cb94262f38,TODO: Add support for jar names specified as regular expressions
aahefeded,nltk/nltk,nltk/corpus/reader/germanet.py,dcb13e18babc3cb62e398847936aefa2b2e762c9,STILL_EXISTS,!!!!!!!!!!!!!TODO LATE; USE nltk.data TO FETCH PATHS!!!!!!!!!
aahefedgf,nltk/nltk,nltk/text.py,62d06ad9ed5b5aa167142601fbbca1c82e77d8bf,STILL_EXISTS,\"\"\" || This module brings together a variety of NLTK functionality for || text analysis; and provides simple; interactive interfaces. || Functionality includes: concordancing; collocation discovery; || regular expression search over tokenized strings; and || distributional similarity. || \"\"\"
aahefeegd,nltk/nltk,nltk/classify/svm.py,8793ed7e0cdccf4c319a7270bb05f04e8e6dbd8f,STILL_EXISTS,\"\"\" || A classifier based on a support vector machine. This code uses Thorsten Joachims' || SVM^light implementation (http:\/\/svmlight.joachims.org\/); wrapped using || PySVMLight (https:\/\/bitbucket.org\/wcauchois\/pysvmlight). The default settings are to  || train a linear classification kernel; though through minor modification; full SVMlight || capabilities should be accessible if needed. Only binary classification is possible at present. || \"\"\"
aahefefah,nltk/nltk,nltk/classify/svm.py,8793ed7e0cdccf4c319a7270bb05f04e8e6dbd8f,60b9be4cafb6c7370b8b094e9ecfc3b686aa8369,TODO: implement passing of SVMlight parameters from train() to learn()
aaheffcce,nltk/nltk,nltk/parse/__init__.py,260822479bd42b878d03e164d70ffc3cdc0205fa,STILL_EXISTS,\"\"\" || NLTK Parsers ||  || Classes and interfaces for producing tree structures that represent || the internal organization of a text.  This task is known as X{parsing} || the text; and the resulting tree structures are called the text's || X{parses}.  Typically; the text is a single sentence; and the tree || structure represents the syntactic structure of the sentence. || However; parsers can also be used in other domains.  For example; || parsers can be used to derive the morphological structure of the || morphemes that make up a word; or to derive the discourse structure || for a set of utterances. ||  || Sometimes; a single piece of text can be represented by more than one || tree structure.  Texts represented by more than one tree structure are || called X{ambiguous} texts.  Note that there are actually two ways in || which a text can be ambiguous: ||  ||     - The text has multiple correct parses. ||     - There is not enough information to decide which of several ||       candidate parses is correct. ||  || However; the parser module does I{not} distinguish these two types of || ambiguity. ||  || The parser module defines C{ParserI}; a standard interface for parsing || texts; and two simple implementations of that interface; || C{ShiftReduceParser} and C{RecursiveDescentParser}.  It also contains || three sub-modules for specialized kinds of parsing: ||  ||   - C{nltk.parser.chart} defines chart parsing; which uses dynamic ||     programming to efficiently parse texts. ||   - C{nltk.parser.probabilistic} defines probabilistic parsing; which ||     associates a probability with each parse. || \"\"\"
aaheffddc,nltk/nltk,nltk/app/chunkparser_app.py,f0dd94a74112e04b66cc31ed54d2090e676415ee,STILL_EXISTS,Todo: Add a way to select the development set from the menubar.  This
aaheffdgb,nltk/nltk,nltk/tokenize/simple.py,0542ec47a69a6bdea00401d15de60e4b979b4feb,bb7c52f97dcedc130772ee4f4f99087437100ea4,XXX: it is stated in module docs that there is no function versions
aaheffdje,nltk/nltk,nltk/classify/scikitlearn.py,0077abbdc6f42b6ccab7b9f5c6adbfbb32c24200,f7f3b73f0f051639d87cfeea43b0aabf6f167b8f,XXX Transforming to lil_matrix is wasteful; since scikit-learn
aaheffeai,nltk/nltk,nltk/sem/boxer.py,4a53a81e5840c80f1e72f258d35c1b5184e170ab,STILL_EXISTS,\"\"\" || An interface to Boxer. ||  || Usage: ||   Set the environment variable CANDCHOME to the bin directory of your CandC installation. ||   The models directory should be in the CandC root directory. ||   For example: ||      \/path\/to\/candc\/ ||         bin\/ ||             candc ||             boxer ||         models\/ ||             boxer\/ || \"\"\"
aaheffeba,nltk/nltk,nltk/yamltags.py,4a53a81e5840c80f1e72f258d35c1b5184e170ab,STILL_EXISTS,\"\"\" || Register YAML tags in the NLTK namespace with the YAML loader; by telling it || what module and class to look for. ||  || NLTK uses simple '!' tags to mark the types of objects; but the fully-qualified || \"tag:nltk.org;2011:\" prefix is also accepted in case anyone ends up || using it. || \"\"\"
aaheffeed,nltk/nltk,nltk/sem/boxer.py,bb7c52f97dcedc130772ee4f4f99087437100ea4,STILL_EXISTS,\"\"\" || An interface to Boxer. ||  || Usage: ||   Set the environment variable CANDCHOME to the bin directory of your CandC installation. ||   The models directory should be in the CandC root directory. ||   For example: ||      \/path\/to\/candc\/ ||         bin\/ ||             candc ||             boxer ||         models\/ ||             boxer\/ || \"\"\"
aahefffig,nltk/nltk,nltk/tree.py,27de61ac3855973f6ae84571bb6146f748a8e42e,fb6cd01d8a001ac45aeff97d2dc519e8e551f7d2,We need to implement __getslice__ and friends; even though
aaheffgdh,nltk/nltk,nltk/test/doctest_nose_plugin.py,ee1416e72de74f5c64600f728d4539b09917d19e,b9a61108556fde642bbc8226822b2a5a12749102,FIXME this breaks the id plugin somehow (tests probably don't
aaheffgeg,nltk/nltk,nltk/test/runtests.py,ee1416e72de74f5c64600f728d4539b09917d19e,48f5194bf0238a24dcbd4762f56e8c60ae2468b9,TODO: Run the tests if the relevant dependeices are present on the system
aahefgeei,nltk/nltk,nltk/tree.py,657e45466c09b0d78ab8388bdbcecb24c577526f,STILL_EXISTS,We need to implement __getslice__ and friends; even though
aahefgeib,nltk/nltk,nltk/classify/positivenaivebayes.py,19305d60418af1b1ccdb6f7ed3ea8469dbcdc45d,STILL_EXISTS,TODO: We need to add some kind of smoothing here; instead of
aahefgjba,nltk/nltk,nltk/corpus/reader/timit.py,947bf65b41e1bf50f94848516d021ace6d0d7ee0,4593133931165d6dec975b32953467cc86982e01,FIXME: this won't work under python 3
aahefgjce,nltk/nltk,nltk/sourcedstring.py,1e54be084d21e4f59b6622ca2983794a657e1343,STILL_EXISTS,XXX has no effect in Python 3
aahefgjcf,nltk/nltk,nltk/test/doctest_driver.py,6bc38da54cf70228d8280fd0b8644406c2f98590,4593133931165d6dec975b32953467cc86982e01,XXX what should we do in Python 3?
aahefgjdg,nltk/nltk,nltk/grammar.py,12f4aa597458b400acc3ef8ecad0523e8f459614,01d07b50fec7abbbac5b84edc64582d0b885bd37,XXX: self.__class__ vs Nonterminal?
aahefgjdh,nltk/nltk,nltk/test/runtests.py,021033b2879fd8fe8c855d40ef58ce8d66a29636,f192c6cbbe96b84b41e8e69ba79ef1c0232420fb,XXX: imports can't be moved to the top of the file
aahefgjif,nltk/nltk,nltk/corpus/reader/semcor.py,57d2970a9a2fcf30aa59a5b52a309311f43c4162,STILL_EXISTS,TODO: case where punctuation intervenes in MWE
aahefhafj,nltk/nltk,nltk/corpus/reader/util.py,9c7054748204aaa22cff5e4f982891d88f38cba0,STILL_EXISTS,workaround for py25 which doesn't support followlinks
aahefhbbh,nltk/nltk,nltk/grammar.py,ce6f3cfff48861e1cae74aade823b19fcf02b766,4593133931165d6dec975b32953467cc86982e01,XXX: self.__class__ vs Nonterminal?
aahefhbfh,nltk/nltk,nltk/grammar.py,106d4fa1a048748653964f2886c6e5eed741c51e,ef21e2c8497ad63a2e0ff02bc071d8e1ace5ce22,XXX: self.__class__ vs Nonterminal?
aahefhbhj,nltk/nltk,nltk/corpus/reader/timit.py,6221d6113143e7bacb92f5c024eaee24915f137d,c1a25db7e95e4b82ab01718df49d8fce6900f596,FIXME: this won't work under python 3
aahefhbja,nltk/nltk,nltk/tokenize/simple.py,1abf4260317a348ddae1d61d8015edc86b22178d,STILL_EXISTS,XXX: it is stated in module docs that there is no function versions
aahefhccc,nltk/nltk,nltk/draw/cfg.py,1406c5fe5d79af13da550a0f3f88d7c13e89a94d,STILL_EXISTS,Idea for a nice demo:
aahefhcga,nltk/nltk,nltk/test/discourse_fixt.py,1a21e972e674444be125fdccfee07f0173051f18,STILL_EXISTS,FIXME: the entire discourse.doctest is skipped if Prover9\/Mace4 is
aahefhcgd,nltk/nltk,nltk/test/runtests.py,1a21e972e674444be125fdccfee07f0173051f18,eefb80740c068066ad2abc346cc1e74b0a33d05e,XXX: imports can't be moved to the top of the file
aahefhdbf,nltk/nltk,nltk/corpus/util.py,6b337267cd964c5e7c558956ae539604a7f39df7,STILL_EXISTS,Fix for inspect.isclass under Python 2.6
aahefhdbh,nltk/nltk,nltk/corpus/util.py,6b337267cd964c5e7c558956ae539604a7f39df7,STILL_EXISTS,Without this fix tests may take extra 1.5GB RAM
aahefheag,nltk/nltk,nltk/probability.py,22b4fee1ee8322f03490e9f75ecf3072a02cbf3e,2d41e81868b576cef69201c56e96e12cf9536d40,Kneser and Hermann Ney. It was meant to improve the accuracy of language
aahefheci,nltk/nltk,nltk/probability.py,22b4fee1ee8322f03490e9f75ecf3072a02cbf3e,2d41e81868b576cef69201c56e96e12cf9536d40,In order for the implementation of Kneser-Ney to be more efficient; some
aahefhfdf,nltk/nltk,nltk/data.py,503d69a60f4adb955e536ca933d32ec37681e7ae,da6d48955827e137dd543f0dce9ec7131c373baa,TODO: load() should be able to read zipfiles too
aahefhfdi,nltk/nltk,nltk/data.py,503d69a60f4adb955e536ca933d32ec37681e7ae,STILL_EXISTS,TODO: add this line
aahefhfge,nltk/nltk,nltk/corpus/reader/timit.py,97ef63b51a0bf66374467b519df65449ef036cd5,STILL_EXISTS,FIXME: this won't work under python 3
aahefhfgg,nltk/nltk,nltk/data.py,97ef63b51a0bf66374467b519df65449ef036cd5,fae1b819cfe46eaa3f81dc0584b8d21f02ad9a36,XXX: ``path`` must be a bytestring under Python 2.x because
aahefhfhg,nltk/nltk,nltk/internals.py,97ef63b51a0bf66374467b519df65449ef036cd5,1c891da27eb926c719f286c91bede0635cc9b3b2,TODO: python_2_unicode_compatible
aahefhfih,nltk/nltk,nltk/stem/porter.py,97ef63b51a0bf66374467b519df65449ef036cd5,aba66820ce4ca9daf490f6a485d3e8d21de50832,if self.ends(\"abli\"):      self.r(\"able\")
aahefhfji,nltk/nltk,nltk/test/runtests.py,48f5194bf0238a24dcbd4762f56e8c60ae2468b9,STILL_EXISTS,why is this needed?
aahefhgah,nltk/nltk,nltk/probability.py,e7ac6057e70e849603ffd93a359a981b1f9c7f1a,233ccf37ac4a4b75d5e8f95aa1da4e11c03aacc7,Kneser and Hermann Ney. It was meant to improve the accuracy of language
aahefhgcj,nltk/nltk,nltk/probability.py,e7ac6057e70e849603ffd93a359a981b1f9c7f1a,233ccf37ac4a4b75d5e8f95aa1da4e11c03aacc7,In order for the implementation of Kneser-Ney to be more efficient; some
aahefhhja,nltk/nltk,setup.py,3fd1f3c101f8c8b30e33fe12d8367b4c91005696,STILL_EXISTS,Work around mbcs bug in distutils.
aahefhihf,nltk/nltk,nltk/probability.py,a0642773dc73cf4bbb9f6f2e13558656c8f4ef71,STILL_EXISTS,Kneser and Hermann Ney. It was meant to improve the accuracy of language
aahefhijh,nltk/nltk,nltk/probability.py,a0642773dc73cf4bbb9f6f2e13558656c8f4ef71,STILL_EXISTS,In order for the implementation of Kneser-Ney to be more efficient; some
aahefhjde,nltk/nltk,nltk/stem/porter.py,a0642773dc73cf4bbb9f6f2e13558656c8f4ef71,cc3884778b2b2aa5da0c96f2cf3c0d1e7b9c4552,if self.ends(\"abli\"):      self.r(\"able\")
aahefhjfh,nltk/nltk,nltk/test/doctest_driver.py,a0642773dc73cf4bbb9f6f2e13558656c8f4ef71,STILL_EXISTS,XXX what should we do in Python 3?
aahefiajb,nltk/nltk,nltk/stem/porter.py,2adb08c320052e82366684893600024fb0515a96,450eec36e38955836c5c2fb397e16f2299193a56,if self.ends(\"abli\"):      self.r(\"able\")
aahefidaf,nltk/nltk,nltk/test/unit/test_hmm.py,42f2ccbd7cc8c4a59b4247dd0eaf96db461232b8,8cf8c9b10887d3a6c2db920f7966164d7012b9d3,FIXME: is it possible to make order stable?
aahefidii,nltk/nltk,nltk/tag/hmm.py,51592e0fe91948f25b35dd9eaa9b084ee9810e4b,25f2a6db1bc915ff85393bf972b93539bf693144,FIXME
aahefifhg,nltk/nltk,nltk/corpus/reader/framenet.py,99527a7c3ab7fd840a6d8da36d98af4ed0f614a2,STILL_EXISTS,indexes are not built explicitely; they will be built as needed.
aahefjede,nltk/nltk,nltk/toolbox.py,1f7db91e834041fdd66e06812a59efa75de87993,b4aee05ca4c47cd5de3fc2e8b9494387cd3efbe9,kludge - already decoded in PY3?
aahefjhga,nltk/nltk,nltk/tag/brill/trainer/brillorig.py,571c88b1900899735133aa435d3ca372b645c58c,STILL_EXISTS,that this isn't the best rule; so move on:
aahefjhgb,nltk/nltk,nltk/tag/brill/trainer/brillorig.py,571c88b1900899735133aa435d3ca372b645c58c,STILL_EXISTS,If the actual score is better than the best score; then
aahegabaf,nltk/nltk,nltk/tag/brill/template.py,9acaec8c7aa4e8bee59c30f2e873db3d116e1dbc,STILL_EXISTS,!!FIXME --this is hackish
aahegabag,nltk/nltk,nltk/tag/brill/trainer/fast.py,9acaec8c7aa4e8bee59c30f2e873db3d116e1dbc,STILL_EXISTS,!! FIXME: several tests are a bit too dependent on tracing format
aahegabah,nltk/nltk,nltk/tag/brill/trainer/fast.py,9acaec8c7aa4e8bee59c30f2e873db3d116e1dbc,STILL_EXISTS,!! FIXME: tests in trainer.fast and trainer.brillorig are exact duplicates
aahegbada,nltk/nltk,nltk/jsontags.py,7b50e46a8a9c84baa60f6d708f24b191bfae3304,STILL_EXISTS,\"\"\" || Register JSON tags; so the nltk data loader knows what module and class to look for. ||  || NLTK uses simple '!' tags to mark the types of objects; but the fully-qualified || \"tag:nltk.org;2011:\" prefix is also accepted in case anyone ends up || using it. || \"\"\"
aahegbafc,nltk/nltk,nltk/align/__init__.py,417a0b72b43354c6177aecbe041fafbc8a8ba01d,f1f1260700ee618ec5fcad904dc579d32a115836,from nltk.align.XXX              import YYY
aahegbhca,nltk/nltk,nltk/internals.py,b903537747f1d11b0e5016e0535f9099bbe6ec7d,1b470606094231ed882cea037445e32256ad1da6,TODO: Add support for jar names specified as regular expressions
aahegbjhf,nltk/nltk,nltk/metrics/association.py,b9caa1318713309a61414a54fcfc4b818be71c98,775391d60a8bc5adde857900e648a90e97fd159f,n_ooo = n_xxx - n_iii - n_oii - n_ioi - n_iio - n_ooi - n_oio - n_ioo
aahegefab,nltk/nltk,nltk/align/bleu.py,b95e5a7bf473c05e9315ab4b586d2995420adaca,a438e9c7fa7415e45f32ebaacfb677a0217db755,TODO: count_w == candidate_ngrams.count(c_words[-1]) + 1
aahegehgd,nltk/nltk,nltk/tokenize/twitter.py,2d424d1a1b9d9dd20423d4f774c35baa474fab35,STILL_EXISTS,The emoticon string gets its own regex so that we can preserve case for them as needed:
aahegehgh,nltk/nltk,nltk/tokenize/twitter.py,2d424d1a1b9d9dd20423d4f774c35baa474fab35,STILL_EXISTS,Fix HTML character entities:
aahegfbbb,nltk/nltk,nltk/tokenize/casual.py,56febd16c4101d7f429032a2df1f4453eb93c31a,STILL_EXISTS,The emoticon string gets its own regex so that we can preserve case for them as needed:
aahegfbbf,nltk/nltk,nltk/tokenize/casual.py,56febd16c4101d7f429032a2df1f4453eb93c31a,STILL_EXISTS,Fix HTML character entities:
aahegfbcb,nltk/nltk,nltk/tokenize/casual.py,2abac280058b68397de293679db80a2f4580f251,5685063586068dbe4798ad659e63dc3bd5bddb7e,them as needed:
aahegfced,nltk/nltk,nltk/treevisualize.py,3c746fa0c32f1f1d902d9cdce8a40d8d95d36845,STILL_EXISTS,remove unused columns; right to left
aahegfcee,nltk/nltk,nltk/treevisualize.py,3c746fa0c32f1f1d902d9cdce8a40d8d95d36845,STILL_EXISTS,remove unused rows; reverse
aahegfefd,nltk/nltk,nltk/tokenize/stanford.py,8fdc49606e3e782ec2672537c04c50629d457168,c7a21133cb2577d8baffbc23696cb32178d06c26,Long Duong : fix bug #735
aahegfege,nltk/nltk,nltk/internals.py,7ea05213f5c0a9dfc74b1de61cfab74d9764d983,f6ad554c8f81f0a8decd19a0ac1ba7faa2840f6f,Long Duong : Fix the encoding here
aahegfgbf,nltk/nltk,nltk/tag/senna.py,bb4b6d28420173f395e256c3feb9bbb21cb478af,b86569fcf2911adc848f8c83e1c9a793b9e12b26,Long Duong : Fix bug #543 (looking for Senna executable
aahegfgie,nltk/nltk,nltk/parse/transitionparser.py,8f9b23024f7caf136a03407c21d8edb0b48e7888,STILL_EXISTS,Todo : can come up with more complicated features set for better performance.
aahegfhba,nltk/nltk,nltk/parse/transitionparser.py,8f9b23024f7caf136a03407c21d8edb0b48e7888,STILL_EXISTS,Todo : because of probability = True => very slow due to cross-validation
aahegfheh,nltk/nltk,nltk/parse/transitionparser.py,ccd46eef72aec101f90c48eafbd57880057289ce,STILL_EXISTS,Todo : can come up with more complicated features set for better
aahegghhh,nltk/nltk,nltk/tag/senna.py,5f480e25c6e0aed96c3955d76800890feab53097,adc6d397d2d0673684eb95276b380e67a2dd809c,Long Duong : Fix bug #543 (looking for Senna executable)
aaheggiae,nltk/nltk,nltk/tokenize/stanford.py,5f480e25c6e0aed96c3955d76800890feab53097,adc6d397d2d0673684eb95276b380e67a2dd809c,Long Duong : fix bug #735
aahegjjea,nltk/nltk,nltk/tokenize/casual.py,199601a4523503cbfeab94597f8b7ea8e8f76231,5685063586068dbe4798ad659e63dc3bd5bddb7e,ToDo: Update with http:\/\/en.wikipedia.org\/wiki\/List_of_emoticons ?
aahehaabc,nltk/nltk,nltk/parse/malt.py,0bd226cf745122b0d7fbfcb484315cc75792a44c,d0ef5d98c62675a17879baf2e4e8d98a8332b28a,Test if magic find_binary still works but maybe it should be deprecated.
aahehaacg,nltk/nltk,nltk/tgrep.py,74520133a2a7b94b365bdb2bf29561cfbe9767c6,8e83b0cf4868fe548a560e5990ae7651d845c021,TODO: implement node label semantics
aahehaach,nltk/nltk,nltk/tgrep.py,815bf85e289c68dc000933aeafddbc0b3bb1abd3,8e83b0cf4868fe548a560e5990ae7651d845c021,TODO: implement node label semantics
aahehaaci,nltk/nltk,nltk/tgrep.py,060fa4cc7007bb1b0431a2887df076f7de79eff1,1c712546bba11f92c1c00ac6e1e15453accfb672,TODO implement
aaheiigag,nltk/nltk,nltk/sentiment/vaderSentiment.py,4129e4c792d580c48d343df33e556258cacb2d94,STILL_EXISTS,''' || Created on July 04; 2013 || @author: C.J. Hutto ||  || Citation Information ||  || If you use any of the VADER sentiment analysis tools  || (VADER sentiment lexicon or Python code for rule-based sentiment  || analysis engine) in your work or research; please cite the paper.  || For example: ||  ||   Hutto; C.J. & Gilbert; E.E. (2014). VADER: A Parsimonious Rule-based Model for  ||   Sentiment Analysis of Social Media Text. Eighth International Conference on  ||   Weblogs and Social Media (ICWSM-14). Ann Arbor; MI; June 2014. || '''
aaheiigdg,nltk/nltk,nltk/sentiment/vaderSentiment.py,4129e4c792d580c48d343df33e556258cacb2d94,STILL_EXISTS,future work: consider other sentiment-laden idioms
aaheijhjd,nltk/nltk,nltk/twitter/twitterclient.py,cd638344ed23facfcdc7c760a7ba36329fcf0239,STILL_EXISTS,\"\"\" || NLTK Twitter client ||  || This module offers methods for collecting and processing tweets. Most of the || functionality depends on access to the Twitter APIs; and this is handled via || the third party Twython library. ||  || If one of the methods below returns an integer; it is probably a `Twitter || error code <https:\/\/dev.twitter.com\/overview\/api\/response-codes>`_. For || example; the response of '420' means that you have reached the limit of the || requests you can currently make to the Twitter API. Currently; `rate limits || for the search API <https:\/\/dev.twitter.com\/rest\/public\/rate-limiting>`_ are || divided into 15 minute windows. || \"\"\"
aahejahij,nltk/nltk,nltk/corpus/reader/framenet.py,68023d0b696d4244e56d1266e3efcb4076fa0ae7,991f2cd1e31f7c1ad144589ab4d2c76bee05aa7b,TODO: metadata about annotation sets
aahejajbh,nltk/nltk,nltk/parse/malt.py,6fe11ed8fb8260c50e348b82616e8b002d78e9ad,STILL_EXISTS,Test if magic find_binary still works but maybe it should be deprecated.
aahejbcbf,nltk/nltk,nltk/corpus/reader/framenet.py,ca2b1f7876f6e410a17fc7a4dbf5411a98703f96,991f2cd1e31f7c1ad144589ab4d2c76bee05aa7b,TODO: any UNANN exemplars?
aahejbedh,nltk/nltk,nltk/compat.py,3a16d74503111bd4ed3e2dd2729d9c08ddde372c,04fe41035b9e15d1ce932d331cc1885bb6b3fcb8,Issue a ResourceWarning if implicit cleanup needed
aahejbedi,nltk/nltk,nltk/compat.py,3a16d74503111bd4ed3e2dd2729d9c08ddde372c,04fe41035b9e15d1ce932d331cc1885bb6b3fcb8,XXX (ncoghlan): The following code attempts to make
aahejbfdc,nltk/nltk,nltk/corpus/reader/framenet.py,41bb49b39ec60a03d5b611ddad763bc9b76528af,STILL_EXISTS,TODO: not strictly the case that every LU sentence has 1 annotation set:
aahejcabj,nltk/nltk,nltk/tokenize/casual.py,5685063586068dbe4798ad659e63dc3bd5bddb7e,STILL_EXISTS,The emoticon string gets its own regex so that we can preserve case for them as needed:
aahejcaef,nltk/nltk,nltk/tokenize/casual.py,fcca447defd5cca549c5a58d33cb023fddcea316,STILL_EXISTS,them as needed:
aahejcbbe,nltk/nltk,nltk/tokenize/casual.py,b15dd3e89a0f63fa3609031e717f782c38a7560b,STILL_EXISTS,ToDo: Update with http:\/\/en.wikipedia.org\/wiki\/List_of_emoticons ?
aahejcbha,nltk/nltk,nltk/sentiment/vaderSentiment.py,b58924a0419067494e4868938a37346d61b3fa8e,STILL_EXISTS,''' || Created on July 04; 2013 || @author: C.J. Hutto ||  || Citation Information ||  || If you use any of the VADER sentiment analysis tools  || (VADER sentiment lexicon or Python code for rule-based sentiment  || analysis engine) in your work or research; please cite the paper.  || For example: ||  ||   Hutto; C.J. & Gilbert; E.E. (2014). VADER: A Parsimonious Rule-based Model for  ||   Sentiment Analysis of Social Media Text. Eighth International Conference on  ||   Weblogs and Social Media (ICWSM-14). Ann Arbor; MI; June 2014. || '''
aahejccaa,nltk/nltk,nltk/sentiment/vaderSentiment.py,b58924a0419067494e4868938a37346d61b3fa8e,STILL_EXISTS,future work: consider other sentiment-laden idioms
aahejceab,nltk/nltk,nltk/sentiment/sentiment_analyzer.py,05c6336c3d3f34994b9597396e86bfd8d20ded4c,STILL_EXISTS,To improve flexibility we could later abstract from tweets to generic documents
aahejcejb,nltk/nltk,nltk/sentiment/sentiment_analyzer.py,40c1f48e9f3046c989cab2edde952139c8c7e753,STILL_EXISTS,This method is probably poorly implemented. We need to store word_features
aahejcfid,nltk/nltk,nltk/sentiment/sentiment_analyzer.py,abbde140d2f6932aad073ceed8bbcf12cb29f65f,65ffed465180d3987251e23cf8b69fee861ec2ca,This list of negation is taken from vader.py. We'd better import it instead.
aahejcigj,nltk/nltk,nltk/compat.py,0c6847d59815b2a832167085ed3280e520f07f12,STILL_EXISTS,Issue a ResourceWarning if implicit cleanup needed
aahejciha,nltk/nltk,nltk/compat.py,0c6847d59815b2a832167085ed3280e520f07f12,1e69d44c826316616cac9a7a33b9d530293faa4e,XXX (ncoghlan): The following code attempts to make
aahejdajc,nltk/nltk,nltk/twitter/twitterclient.py,0c6847d59815b2a832167085ed3280e520f07f12,STILL_EXISTS,\"\"\" || NLTK Twitter client ||  || This module offers methods for collecting and processing Tweets. Most of the || functionality depends on access to the Twitter APIs; and this is handled via || the third party Twython library. ||  || If one of the methods below returns an integer; it is probably a `Twitter || error code <https:\/\/dev.twitter.com\/overview\/api\/response-codes>`_. For || example; the response of '420' means that you have reached the limit of the || requests you can currently make to the Twitter API. Currently; `rate limits || for the search API <https:\/\/dev.twitter.com\/rest\/public\/rate-limiting>`_ are || divided into 15 minute windows. || \"\"\"
aahejdbai,nltk/nltk,nltk/twitter/twitterclient.py,0c6847d59815b2a832167085ed3280e520f07f12,STILL_EXISTS,functionality for limiting the number of Tweets retrieved
aahejdbca,nltk/nltk,nltk/twitter/twitterclient.py,0c6847d59815b2a832167085ed3280e520f07f12,STILL_EXISTS,\"\"\" || NLTK Twitter client ||  || This module offers methods for collecting and processing tweets. Most of the || functionality depends on access to the Twitter APIs; and this is handled via || the third party Twython library. ||  || If one of the methods below returns an integer; it is probably a `Twitter || error code <https:\/\/dev.twitter.com\/overview\/api\/response-codes>`_. For || example; the response of '420' means that you have reached the limit of the || requests you can currently make to the Twitter API. Currently; `rate limits || for the search API <https:\/\/dev.twitter.com\/rest\/public\/rate-limiting>`_ are || divided into 15 minute windows. || \"\"\"
aahejdecg,nltk/nltk,nltk/corpus/reader/product_reviews.py,d8c57c3ffa9929ebc675998f5cda4e68e4aa71da,STILL_EXISTS,\"\"\" || CorpusReader for the Customer Review corpus. ||  || - Customer Review Corpus information - || Annotated by: Minqing Hu and Bing Liu; 2004. ||     Department of Computer Sicence ||     University of Illinois at Chicago ||  || Contact: Bing Liu; liub@cs.uic.edu ||         http:\/\/www.cs.uic.edu\/~liub ||  || Distributed with permission. ||  || The dataset contains annotated customer reviews of 5 products from amazon.com. ||  ||     1. digital camera: Canon G3 ||     2. digital camera: Nikon coolpix 4300 ||     3. celluar phone:  Nokia 6610 ||     4. mp3 player:     Creative Labs Nomad Jukebox Zen Xtra 40GB ||     5. dvd player:     Apex AD2600 Progressive-scan DVD player ||  || Related papers: || - Minqing Hu and Bing Liu. \"Mining and summarizing customer reviews\". ||     Proceedings of the ACM SIGKDD International Conference on Knowledge ||     Discovery & Data Mining (KDD-04); 2004. ||  || - Minqing Hu and Bing Liu. \"Mining Opinion Features in Customer Reviews.\" ||     Proceedings of Nineteeth National Conference on Artificial Intelligence ||     (AAAI-2004); 2004. ||  || Symbols used in the annotated reviews: ||  ||     [t] : the title of the review: Each [t] tag starts a review. ||     xxxx[+|-n]: xxxx is a product feature. ||     [+n]: Positive opinion; n is the opinion strength: 3 strongest; and 1 weakest. ||           Note that the strength is quite subjective. ||           You may want ignore it; but only considering + and - ||     [-n]: Negative opinion ||     ##  : start of each sentence. Each line is a sentence. ||     [u] : feature not appeared in the sentence. ||     [p] : feature not appeared in the sentence. Pronoun resolution is needed. ||     [s] : suggestion or recommendation. ||     [cc]: comparison with a competing product from a different brand. ||     [cs]: comparison with a competing product from the same brand. || \"\"\"
aahejeadg,nltk/nltk,nltk/align/ibm1.py,b0ea84d18b28e93334778cc39a4fb64b94528dbf,STILL_EXISTS,\"\"\" || The IBM models are a series of generative models that learn lexical || translation probabilities; p(target language word|source language word); || given a sentence-aligned parallel corpus. ||  || The models increase in sophistication from model 1 to 5. Typically; the || output of lower models is used to seed the higher models. All models || use the Expectation-Maximization (EM) algorithm to learn various || probability tables. ||  || Words in a sentence are one-indexed. The first word of a sentence has || position 1; not 0. Index 0 is reserved in the source sentence for the || NULL token. The concept of position does not apply to NULL; but it is || indexed at 0 by convention. ||  || Each target word is aligned to exactly one source word or the NULL || token. ||  || Notations || i: Position in the source sentence ||     Valid values are 0 (for NULL); 1; 2; ...; length of source sentence || j: Position in the target sentence ||     Valid values are 1; 2; ...; length of target sentence || s: A word in the source language || t: A word in the target language ||  ||  || In IBM Model 1; word order is ignored for simplicity. Thus; the || following two alignments are equally likely. ||  || Source: je mange du jambon || Target: i eat some ham || Alignment: (1;1) (2;2) (3;3) (4;4) ||  || Source: je mange du jambon || Target: some ham eat i || Alignment: (1;4) (2;3) (3;2) (4;1) ||  || The EM algorithm used in Model 1 is: || E step - In the training data; count how many times a source language ||          word is translated into a target language word; weighted by ||          the prior probability of the translation. ||  || M step - Estimate the new probability of translation based on the ||          counts from the Expectation step. ||  ||  || References: || Philipp Koehn. 2010. Statistical Machine Translation. || Cambridge University Press; New York. ||  || Peter E Brown; Stephen A. Della Pietra; Vincent J. Della Pietra; and || Robert L. Mercer. 1993. The Mathematics of Statistical Machine || Translation: Parameter Estimation. Computational Linguistics; 19 (2); || 263-311. || \"\"\"
aahejeadi,nltk/nltk,nltk/align/ibm2.py,b0ea84d18b28e93334778cc39a4fb64b94528dbf,STILL_EXISTS,\"\"\" || The IBM models are a series of generative models that learn lexical || translation probabilities; p(target language word|source language word); || given a sentence-aligned parallel corpus. ||  || The models increase in sophistication from model 1 to 5. Typically; the || output of lower models is used to seed the higher models. All models || use the Expectation-Maximization (EM) algorithm to learn various || probability tables. ||  || Words in a sentence are one-indexed. The first word of a sentence has || position 1; not 0. Index 0 is reserved in the source sentence for the || NULL token. The concept of position does not apply to NULL; but it is || indexed at 0 by convention. ||  || Each target word is aligned to exactly one source word or the NULL || token. ||  || Notations || i: Position in the source sentence ||     Valid values are 0 (for NULL); 1; 2; ...; length of source sentence || j: Position in the target sentence ||     Valid values are 1; 2; ...; length of target sentence || l: Number of words in the source sentence || m: Number of words in the target sentence || s: A word in the source language || t: A word in the target language ||  ||  || IBM Model 2 improves on Model 1 by accounting for word order. || An alignment probability is introduced; a(i | j;l;m); which predicts || a source word position; given its aligned target word's position. ||  || The EM algorithm used in Model 2 is: || E step - In the training data; collect counts; weighted by prior ||          probabilities. ||          (a) count how many times a source language word is translated ||              into a target language word ||          (b) count how many times a particular position in the source ||              sentence is aligned to a particular position in the target ||              sentence ||  || M step - Estimate new probabilities based on the counts from the E step ||  ||  || References: || Philipp Koehn. 2010. Statistical Machine Translation. || Cambridge University Press; New York. ||  || Peter E Brown; Stephen A. Della Pietra; Vincent J. Della Pietra; and || Robert L. Mercer. 1993. The Mathematics of Statistical Machine || Translation: Parameter Estimation. Computational Linguistics; 19 (2); || 263-311. || \"\"\"
aahejeaea,nltk/nltk,nltk/align/ibm3.py,b0ea84d18b28e93334778cc39a4fb64b94528dbf,STILL_EXISTS,\"\"\" || The IBM models are a series of generative models that learn lexical || translation probabilities; p(target language word|source language word); || given a sentence-aligned parallel corpus. ||  || The models increase in sophistication from model 1 to 5. Typically; the || output of lower models is used to seed the higher models. All models || use the Expectation-Maximization (EM) algorithm to learn various || probability tables. ||  || Words in a sentence are one-indexed. The first word of a sentence has || position 1; not 0. Index 0 is reserved in the source sentence for the || NULL token. The concept of position does not apply to NULL; but it is || indexed at 0 by convention. ||  || Each target word is aligned to exactly one source word or the NULL || token. ||  || Notations || i: Position in the source sentence ||     Valid values are 0 (for NULL); 1; 2; ...; length of source sentence || j: Position in the target sentence ||     Valid values are 1; 2; ...; length of target sentence || l: Number of words in the source sentence || m: Number of words in the target sentence || s: A word in the source language || t: A word in the target language || phi: Fertility; the number of target words produced by a source word || p1: Probability that a target word produced by a source word is ||     accompanied by another target word that is aligned to NULL || p0: 1 - p1 ||  || IBM Model 3 improves on Model 2 by directly modeling the phenomenon || where a word in one language may be translated into zero or more words || in another. This is expressed by the fertility probability; || n(phi | source word). ||  || If a source word translates into more than one word; it is possible to || generate sentences that have the same alignment in multiple ways. This || is modeled by a distortion step. The distortion probability; d(j|i;l;m); || predicts a target word position; given its aligned source word's || position. The distortion probability replaces the alignment probability || of Model 2. ||  || The fertility probability is not applicable for NULL. Target words that || align to NULL are assumed to be distributed uniformly in the target || sentence. The existence of these words is modeled by p1; the probability || that a target word produced by a real source word requires another || target word that is produced by NULL. ||  || The EM algorithm used in Model 3 is: || E step - In the training data; collect counts; weighted by prior ||          probabilities. ||          (a) count how many times a source language word is translated ||              into a target language word ||          (b) count how many times a particular position in the target ||              sentence is aligned to a particular position in the source ||              sentence ||          (c) count how many times a source word is aligned to phi number ||              of target words ||          (d) count how many times NULL is aligned to a target word ||  || M step - Estimate new probabilities based on the counts from the E step ||  || Because there are too many possible alignments; only the most probable || ones are considered. First; the best alignment is determined using prior || probabilities. Then; a hill climbing approach is used to find other good || candidates. ||  ||  || References: || Philipp Koehn. 2010. Statistical Machine Translation. || Cambridge University Press; New York. ||  || Peter E Brown; Stephen A. Della Pietra; Vincent J. Della Pietra; and || Robert L. Mercer. 1993. The Mathematics of Statistical Machine || Translation: Parameter Estimation. Computational Linguistics; 19 (2); || 263-311. || \"\"\"
aahejeajf,nltk/nltk,nltk/align/ibm3.py,5c86f7d910b6d4c97eaaaf9ac68374e7bccc082d,c8a9026847df16a369814a566f550b606024340d,FIXME There should not be a need to hash over fertility_of_i;
aahejebbc,nltk/nltk,nltk/align/ibm_model.py,5068aa5b6185f53cc57c919f00401cf08a612cbb,STILL_EXISTS,\"\"\" || Common methods and classes for all IBM models. See ``IBMModel1``; || ``IBMModel2``; and ``IBMModel3`` for specific implementations. ||  || The IBM models are a series of generative models that learn lexical || translation probabilities; p(target language word|source language word); || given a sentence-aligned parallel corpus. ||  || The models increase in sophistication from model 1 to 5. Typically; the || output of lower models is used to seed the higher models. All models || use the Expectation-Maximization (EM) algorithm to learn various || probability tables. ||  || Words in a sentence are one-indexed. The first word of a sentence has || position 1; not 0. Index 0 is reserved in the source sentence for the || NULL token. The concept of position does not apply to NULL; but it is || indexed at 0 by convention. ||  || Each target word is aligned to exactly one source word or the NULL || token. ||  || References: || Philipp Koehn. 2010. Statistical Machine Translation. || Cambridge University Press; New York. ||  || Peter E Brown; Stephen A. Della Pietra; Vincent J. Della Pietra; and || Robert L. Mercer. 1993. The Mathematics of Statistical Machine || Translation: Parameter Estimation. Computational Linguistics; 19 (2); || 263-311. || \"\"\"
aahejedai,nltk/nltk,nltk/test/unit/align/test_ibm_model.py,7ebac0bfacd4e3c1f08c3723fba9552a4ff5067d,STILL_EXISTS,0th element unused
aahejeijd,nltk/nltk,nltk/align/ibm5.py,28171d338147abda83474b7c27003dbc57a3a339,STILL_EXISTS,\"\"\" || Translation model that keeps track of vacant positions in the target || sentence to decide where to place translated words. ||  || Translation can be viewed as a process where each word in the source || sentence is stepped through sequentially; generating translated words || for each source word. The target sentence can be viewed as being made || up of ``m`` empty slots initially; which gradually fill up as generated || words are placed in them. ||  || Models 3 and 4 use distortion probabilities to decide how to place || translated words. For simplicity; these models ignore the history of || which slots have already been occupied with translated words. || Consider the placement of the last translated word: there is only one || empty slot left in the target sentence; so the distortion probability || should be 1.0 for that position and 0.0 everywhere else. However; the || distortion probabilities for Models 3 and 4 are set up such that all || positions are under consideration. ||  || IBM Model 5 fixes this deficiency by accounting for occupied slots || during translation. It introduces the vacancy function v(j); the number || of vacancies up to; and including; position j in the target sentence. ||  || Terminology: || Maximum vacancy: ||     The number of valid slots that a word can be placed in. ||     This is not necessarily the same as the number of vacant slots. ||     For example; if a tablet contains more than one word; the head word ||     cannot be placed at the last vacant slot because there will be no ||     space for the other words in the tablet. The number of valid slots ||     has to take into account the length of the tablet. ||     Non-head words cannot be placed before the head word; so vacancies ||     to the left of the head word are ignored. || Vacancy difference: ||     For a head word: (v(j) - v(center of previous cept)) ||     Can be positive or negative. ||     For a non-head word: (v(j) - v(position of previously placed word)) ||     Always positive; because successive words in a tablet are assumed to ||     appear to the right of the previous word. ||  || Positioning of target words fall under three cases: || (1) Words generated by NULL are distributed uniformly || (2) For a head word t; its position is modeled by the probability ||     v_head(dv | max_v;word_class_t(t)) || (3) For a non-head word t; its position is modeled by the probability ||     v_non_head(dv | max_v;word_class_t(t)) || dv and max_v are defined differently for head and non-head words. ||  || The EM algorithm used in Model 5 is: || E step - In the training data; collect counts; weighted by prior ||          probabilities. ||          (a) count how many times a source language word is translated ||              into a target language word ||          (b) for a particular word class and maximum vacancy; count how ||              many times a head word and the previous cept's center have ||              a particular difference in number of vacancies ||          (b) for a particular word class and maximum vacancy; count how ||              many times a non-head word and the previous target word ||              have a particular difference in number of vacancies ||          (d) count how many times a source word is aligned to phi number ||              of target words ||          (e) count how many times NULL is aligned to a target word ||  || M step - Estimate new probabilities based on the counts from the E step ||  || Like Model 4; there are too many possible alignments to consider. Thus; || a hill climbing approach is used to sample good candidates. In addition; || pruning is used to weed out unlikely alignments based on Model 4 scores. ||  ||  || Notations: || i: Position in the source sentence ||     Valid values are 0 (for NULL); 1; 2; ...; length of source sentence || j: Position in the target sentence ||     Valid values are 1; 2; ...; length of target sentence || l: Number of words in the source sentence; excluding NULL || m: Number of words in the target sentence || s: A word in the source language || t: A word in the target language || phi: Fertility; the number of target words produced by a source word || p1: Probability that a target word produced by a source word is ||     accompanied by another target word that is aligned to NULL || p0: 1 - p1 || max_v: Maximum vacancy || dv: Vacancy difference; \u0394v ||  || The definition of v_head here differs from GIZA++; section 4.7 of || [Brown et al.; 1993]; and [Koehn; 2010]. In the latter cases; v_head is || v_head(v(j) | v(center of previous cept);max_v;word_class(t)). ||  || Here; we follow appendix B of [Brown et al.; 1993] and combine v(j) with || v(center of previous cept) to obtain dv: || v_head(v(j) - v(center of previous cept) | max_v;word_class(t)). ||  ||  || References: || Philipp Koehn. 2010. Statistical Machine Translation. || Cambridge University Press; New York. ||  || Peter E Brown; Stephen A. Della Pietra; Vincent J. Della Pietra; and || Robert L. Mercer. 1993. The Mathematics of Statistical Machine || Translation: Parameter Estimation. Computational Linguistics; 19 (2); || 263-311. || \"\"\"
aahejejbj,nltk/nltk,nltk/align/ibm5.py,28171d338147abda83474b7c27003dbc57a3a339,b63ab130f39228d6de46f707f29d96f6beebf5c8,TODO implement me
aahejejca,nltk/nltk,nltk/align/ibm5.py,28171d338147abda83474b7c27003dbc57a3a339,4aca5b4c5fb76188a0bf0c0c3b3066bb4fba27be,TODO use Model 4 scoring instead of Model 5
aahejffje,nltk/nltk,nltk/align/ibm3.py,50be36f2e429f5b3fd241bfcc713ab201de62b56,68764414705ef7fc61d4f6dd69dab1ecf1921604,FIXME magic number that will be removed in next commit; which drops the use of max_fertiliy
aahejgfia,nltk/nltk,nltk/translate/stack_decoder.py,0c1f33d91a12342dc224c2f0fe3d9ee34e913dfe,6ef258556a8f07a5d3b37e0b4930f23101a900b7,TODO Consider top n translations
aahejgfib,nltk/nltk,nltk/translate/stack_decoder.py,0c1f33d91a12342dc224c2f0fe3d9ee34e913dfe,STILL_EXISTS,Instead of returning empty output; perhaps a partial
aahejgfif,nltk/nltk,nltk/translate/stack_decoder.py,0c1f33d91a12342dc224c2f0fe3d9ee34e913dfe,STILL_EXISTS,TODO Incorporate distortion and future cost in calculation
aahejggbh,nltk/nltk,nltk/translate/stack_decoder.py,ed029ee3e7fba36dab669c65a72f120506d75622,STILL_EXISTS,check if a better score can be obtained by combining
aahejiceg,nltk/nltk,six.py,f4342ffe451c2fa6e3f48f9646c5f2a92018a260,STILL_EXISTS,This is a bit ugly; but it avoids running this again by
aahejicff,nltk/nltk,six.py,f4342ffe451c2fa6e3f48f9646c5f2a92018a260,STILL_EXISTS,Workaround for standalone backslash
aahejjbbh,nltk/nltk,nltk/parse/stanford.py,f87126cf14d3acb3d5a2bb20bfb81e5824f95ab1,52bde24d00b11a2c73450e67dc88618c8e52af82,TODO: Does it work?
aahejjbbi,nltk/nltk,nltk/parse/stanford.py,f87126cf14d3acb3d5a2bb20bfb81e5824f95ab1,52bde24d00b11a2c73450e67dc88618c8e52af82,TODO: Originally; we can return several parsers for a sentence.
aahejjbcc,nltk/nltk,nltk/parse/stanford.py,79b5456f429d18cffc7715c0dec5e3b28c468904,52bde24d00b11a2c73450e67dc88618c8e52af82,TODO: Does it work?
aahejjbcd,nltk/nltk,nltk/parse/stanford.py,79b5456f429d18cffc7715c0dec5e3b28c468904,52bde24d00b11a2c73450e67dc88618c8e52af82,TODO: Originally; we can return several parsers for a sentence.
aahejjbfi,nltk/nltk,nltk/parse/stanford.py,aaf90e3741f99638903f0b5fb84d11df560bbdc0,STILL_EXISTS,TODO: take a free random port.
aahejjcjh,nltk/nltk,nltk/tokenize/toktok.py,5d9fd5937e026e1c52c1563948a5a338b64a00bd,STILL_EXISTS,Don't tokenize period unless it ends the line and that it isn't
aahejjdab,nltk/nltk,nltk/tokenize/toktok.py,5d9fd5937e026e1c52c1563948a5a338b64a00bd,STILL_EXISTS,Don't tokenize period unless it ends the line eg.
aahejjdhg,nltk/nltk,nltk/tokenize/moses.py,b7b27353fe3e31bcc5542ad5ffb76c2de625cee7,STILL_EXISTS,Checks if token ends with a fullstop.
aahejjebg,nltk/nltk,nltk/parse/stanford.py,9118408ebb6b3dbd92f0dbc44696e0d22c759e23,STILL_EXISTS,TODO: take a free random port.
aahejjeeh,nltk/nltk,nltk/ccg/lexicon.py,aa5d3e70079afbc22fa518371cb45dd2c4667624,STILL_EXISTS,Parses the right hand side that contains category and maybe semantic predicate
aahfadgeg,nltk/nltk,nltk/parse/stanford.py,9e829438e19c704f7d8676d92f54561fc58dcab0,STILL_EXISTS,TODO: it's probably a bad idea to pipe stdout; as it will
aahfadgfa,nltk/nltk,nltk/parse/stanford.py,9e829438e19c704f7d8676d92f54561fc58dcab0,STILL_EXISTS,TODO: is there a better way of checking whether a server is ready to
aahfbbfjg,nltk/nltk,nltk/corpus/reader/framenet.py,db7638b1108bc52abeecbb1b12c1009580e17ebe,STILL_EXISTS,TODO: any UNANN exemplars?
aahfbbhha,nltk/nltk,nltk/parse/corenlp.py,861a670e932eff6e594782eb1c5c9437f157238f,3130935ea35e8b9b923b74baebfba617badab789,TODO: it's probably a bad idea to pipe stdout; as it will
aahfbbhhe,nltk/nltk,nltk/parse/corenlp.py,861a670e932eff6e594782eb1c5c9437f157238f,b6afcd0fb3abf9ccb2bff16276de71f3861b0c08,TODO: is there a better way of checking whether a server is ready to
aahfbbjdg,nltk/nltk,nltk/draw/util.py,31ace2669b2b6a0f059bba426e1af821285e2a5f,70eaca0388ed106ae6ac67db8079c6ef3e2153af,workaround for bug in Tk font handling
aahfbbjfb,nltk/nltk,nltk/draw/util.py,36212ca200576974252f32785a6468a8f2ea8436,STILL_EXISTS,workaround for bug in Tk font handling
aahfbcbca,nltk/nltk,nltk/corpus/reader/wordnet.py,eda952e0c70270bb26eca772684fc3a18d1f32c6,ccaa45e58d65ca254eedb7bb17d4e0ac66c1f89d,Hack to help people like the readers of
aahfbcbcf,nltk/nltk,nltk/corpus/reader/wordnet.py,702e95bea692323ed584f85e6d8f2f8623ee6c29,ccaa45e58d65ca254eedb7bb17d4e0ac66c1f89d,Hack to help people like the readers of
aahfbcbci,nltk/nltk,nltk/corpus/reader/wordnet.py,6dbc9004edf35212cc6d30190be769fe7a40c8cb,ccaa45e58d65ca254eedb7bb17d4e0ac66c1f89d,Hack to help people like the readers of
aahfbcfcd,nltk/nltk,nltk/tokenize/nist.py,26b1b316c5b47fe7a77466e5478ceb543cb19377,STILL_EXISTS,It'll be better to unescape after STRIP_EOL_HYPHEN
aahfbchbh,nltk/nltk,nltk/stem/snowball.py,7ff0ce4cf95221955df47b88b608cc9b0e152f60,STILL_EXISTS,or next ? todo : how to deal with or next
aahfbddbh,nltk/nltk,nltk/stem/snowball.py,49d76a2c28f8211c589d128ef8561c48a970b6ce,672c7caf018162cc6a0b816473390f0d572ae90b,or next TODO: How to deal with or next instruction
aahfbdhce,nltk/nltk,nltk/text.py,c05f3237efb8416f847e3cf44f6959c70d85e6ba,e0b882177421498a1a49e42d4b83c51bfa451f3d,FIXME Should this raise some kind of error instead?
aahfbeahf,nltk/nltk,nltk/model/counter.py,5d1dfbc74aa46c3fa6cb9a6681ade7551ecd8a99,22a35186f1fd2c622c1b29a2c24f7ac42c87cc2c,copy needed to prevent state sharing
aahfbejia,nltk/nltk,nltk/draw/cfg.py,b3f29d9f2d425c98f93c2790af022fbbefa2ac46,STILL_EXISTS,FIXME: Is this necessary at all?
aahfbejif,nltk/nltk,nltk/corpus/reader/bracket_parse.py,73c59459160689a9a8aeabf76c82fdb6c1e9de9a,STILL_EXISTS,FIXME: Why is it inheritting from SyntaxCorpusReader but initializing
aahfbejih,nltk/nltk,nltk/corpus/reader/dependency.py,73c59459160689a9a8aeabf76c82fdb6c1e9de9a,STILL_EXISTS,FIXME: Why is it inheritting from SyntaxCorpusReader but initializing
aahfbejij,nltk/nltk,nltk/corpus/reader/knbc.py,73c59459160689a9a8aeabf76c82fdb6c1e9de9a,STILL_EXISTS,FIXME: Why is it inheritting from SyntaxCorpusReader but initializing
aahfbejjb,nltk/nltk,nltk/corpus/__init__.py,5d8c33f86a64f6c46200baabc8aa786399ededb2,STILL_EXISTS,FIXME:  override any imported demo from various corpora; see https:\/\/github.com\/nltk\/nltk\/issues\/2116
aahfbejjd,nltk/nltk,nltk/collections.py,120755f8b14d7a50f67e5c71999896d78298cf59,STILL_EXISTS,FIXME: What is this except really catching? StopIteration?
aahfbgeba,nltk/nltk,nltk/stem/snowball.py,e84c52680ac5fb0c068de7589910d65471d86705,STILL_EXISTS,or next TODO: How to deal with or next instruction
aahfbggcj,nltk/nltk,nltk/inference/resolution.py,858df2f6c876b4b1e8a66749987de6f6af00a233,STILL_EXISTS,put skipped and unused terms back into play for later unification.
aahfbghec,nltk/nltk,nltk/collections.py,6c8afc5306469ba5cc4f2a19297f799425c3615e,STILL_EXISTS,this unused import is for python 2.7
aahfbghed,nltk/nltk,nltk/collocations.py,6c8afc5306469ba5cc4f2a19297f799425c3615e,STILL_EXISTS,these two unused imports are referenced in collocations.doctest
aahfbhjdd,nltk/nltk,nltk/metrics/distance.py,f395c389deefaa06645d5e36532f2e3fcf72045a,STILL_EXISTS,move in direction of minimum cost
aahfbhjdj,nltk/nltk,nltk/metrics/distance.py,eee662bbcf43f12881cf992cc913b91908a06a67,STILL_EXISTS,move in direction of minimum cost
aahfbibgb,nltk/nltk,nltk/probability.py,bd756be730ef83fcb0e351bbf87716ad03889b62,STILL_EXISTS,import statment fix
aahfbjeab,tensorflow/tensor2tensor,tensor2tensor/data_generators/problem_hparams.py,3d9c62f2aca9492db5c22676416974005b9dcbae,09f1f17d1f673a57cbf69dbb65176a34be426f6b,Add more above if needed.
aahfbjiba,tensorflow/tensor2tensor,tensor2tensor/models/common_layers.py,3d9c62f2aca9492db5c22676416974005b9dcbae,STILL_EXISTS,TODO(lukaszkaiser): remove hack below; needed for greedy decoding for now.
aahfbjieb,tensorflow/tensor2tensor,tensor2tensor/models/common_layers.py,3d9c62f2aca9492db5c22676416974005b9dcbae,STILL_EXISTS,Move depth back to target space.
aahfcaacb,tensorflow/tensor2tensor,tensor2tensor/models/slicenet.py,3d9c62f2aca9492db5c22676416974005b9dcbae,STILL_EXISTS,to same length and putting everything into batch. In need of a better way.
aahfcaacd,tensorflow/tensor2tensor,tensor2tensor/models/slicenet.py,3d9c62f2aca9492db5c22676416974005b9dcbae,88eb6b0a2f796849d5d17910107e4dddbd698e1d,Calculate similarity loss (but don't run if not needed).
aahfcabia,tensorflow/tensor2tensor,tensor2tensor/utils/beam_search.py,3d9c62f2aca9492db5c22676416974005b9dcbae,STILL_EXISTS,needed for the gather
aahfcafbj,tensorflow/tensor2tensor,tensor2tensor/utils/modality.py,3d9c62f2aca9492db5c22676416974005b9dcbae,8aca9fb2e31d9e6a2936f6209c15d4309b17331a,TODO(lukaszkaiser): work on a better way to generate large images.
aahfcagbe,tensorflow/tensor2tensor,tensor2tensor/utils/t2t_model.py,3d9c62f2aca9492db5c22676416974005b9dcbae,STILL_EXISTS,Hack: foldl complains when the output shape is less specified than the
aahfcagfe,tensorflow/tensor2tensor,tensor2tensor/utils/trainer_utils.py,3d9c62f2aca9492db5c22676416974005b9dcbae,f6799b9515e0e214d2d4295f4e4cf94cf27cf333,TODO(lukaszkaiser): why is this hack needed for variables init? Repair.
aahfcecag,tensorflow/tensor2tensor,tensor2tensor/models/modalities.py,8aca9fb2e31d9e6a2936f6209c15d4309b17331a,STILL_EXISTS,TODO(lukaszkaiser): work on a better way to generate large images.
aahfcggja,tensorflow/tensor2tensor,tensor2tensor/data_generators/problem.py,ca7b045e30e9b2003e350db6ed57e8b6107dfa32,STILL_EXISTS,TODO(rsepassi): Move this into the cifar10 Problem
aahfcghed,tensorflow/tensor2tensor,tensor2tensor/data_generators/problem_hparams.py,4b4c80005ff78a38d5d12ba575bb5c9ac96fdb3a,09f1f17d1f673a57cbf69dbb65176a34be426f6b,Unused.
aahfchijh,tensorflow/tensor2tensor,tensor2tensor/models/long_answer.py,e1ecf01113c34781801fdb6ee946e47cfbfebf09,STILL_EXISTS,TODO(noam): find a different workaround
aahfchjcb,tensorflow/tensor2tensor,tensor2tensor/data_generators/wmt.py,4f67b7bb5956b9f0a6198763b8bd09af99ba0f2c,93177b02cb72e1b0dd585b6f3a40f7382eefd47a,Not needed if self.is_character_level.
aahfcibha,tensorflow/tensor2tensor,tensor2tensor/data_generators/problem_hparams.py,93177b02cb72e1b0dd585b6f3a40f7382eefd47a,09f1f17d1f673a57cbf69dbb65176a34be426f6b,Unused.
aahfcicjb,tensorflow/tensor2tensor,tensor2tensor/data_generators/wmt.py,ed695f4d23cee0a135ecbcccbe3e41e5070e4094,daea72a7d81b8d1559bfdc5202a7cb237ac17d0e,Not needed if self.is_character_level.
aahfcigba,tensorflow/tensor2tensor,tensor2tensor/data_generators/text_encoder.py,cff9f4367095e62b415637d6fb839db7bdc8a28d,STILL_EXISTS,NOTE: This algorithm is greedy; it won't necessarily produce the \"best\"
aahfciije,tensorflow/tensor2tensor,tensor2tensor/data_generators/problem.py,daea72a7d81b8d1559bfdc5202a7cb237ac17d0e,765d33bb6890cc7b8f194257d9c71226c26e0162,Not needed if self.is_character_level.
aahfcijbc,tensorflow/tensor2tensor,tensor2tensor/data_generators/wmt.py,d69ad4d3e7619dd898e648743125517f26a43d44,STILL_EXISTS,Not needed if self.is_character_level.
aahfcjaja,tensorflow/tensor2tensor,tensor2tensor/data_generators/wmt.py,c1288452b59e241ee62adb722a359911e2840302,STILL_EXISTS,Not needed if self.is_character_level.
aahfdacgb,tensorflow/tensor2tensor,tensor2tensor/utils/trainer_utils.py,cd006ff6a7b5688e2c2a527d28b2d62afe5d97e9,f6799b9515e0e214d2d4295f4e4cf94cf27cf333,mode-specific.  A better fix would be to stop abusing the
aahfdacjj,tensorflow/tensor2tensor,tensor2tensor/data_generators/image.py,75270907dbba3c7c224a08515d33167c29e26aed,STILL_EXISTS,TODO(lukaszkaiser): find a better way than printing this.
aahfdadac,tensorflow/tensor2tensor,tensor2tensor/data_generators/image.py,75270907dbba3c7c224a08515d33167c29e26aed,STILL_EXISTS,Not needed if self.is_character_level.
aahfdaedj,tensorflow/tensor2tensor,tensor2tensor/utils/model_builder.py,f6799b9515e0e214d2d4295f4e4cf94cf27cf333,STILL_EXISTS,mode-specific.  A better fix would be to stop abusing the
aahfdaefc,tensorflow/tensor2tensor,tensor2tensor/utils/model_builder.py,f6799b9515e0e214d2d4295f4e4cf94cf27cf333,STILL_EXISTS,TODO(lukaszkaiser): why is this hack needed for variables init? Repair.
aahfdajdb,tensorflow/tensor2tensor,tensor2tensor/data_generators/desc2code.py,34a961f0d4f9fa38d8dddc9df1d3366b1d7703cf,STILL_EXISTS,needed)
aahfdbdfh,tensorflow/tensor2tensor,tensor2tensor/utils/trainer_utils.py,d2af7cfe0b74a0e5ec3e0621ff7ad0e0776281df,f2714e93b05753e364e34c1fe9fd69d6e405f5c1,TODO(lukaszkaiser): why is this hack needed for variables init? Repair.
aahfdccci,tensorflow/tensor2tensor,tensor2tensor/data_generators/desc2code.py,13ac3b45994e28a00284a28943ace21d57a14dc6,STILL_EXISTS,Hack to filter C++\/Java files. In theory some python comments could
aahfdcdci,tensorflow/tensor2tensor,tensor2tensor/layers/common_hparams.py,f5c9b17e617ea9179b7d84d36b1e8162cb369f25,STILL_EXISTS,of the transformer.  (\"n\"; \"da\") seems better for harder-to-learn
aahfdcdcj,tensorflow/tensor2tensor,tensor2tensor/layers/common_hparams.py,f5c9b17e617ea9179b7d84d36b1e8162cb369f25,STILL_EXISTS,models; so it should probably be the default.
aahfdcdeb,tensorflow/tensor2tensor,tensor2tensor/data_generators/gene_expression.py,342e3090a47b0f625e2bba9d4f40ca995a8067c0,STILL_EXISTS,Slice off EOS - not needed; and messes up the GeneExpressionConv model
aahfddcgc,tensorflow/tensor2tensor,tensor2tensor/utils/expert_utils.py,62c0e9fb2c219c17ed014750b0f968381d20c419,STILL_EXISTS,existing device. This is needed when we put all experts on a single
aahfdddjc,tensorflow/tensor2tensor,tensor2tensor/utils/yellowfin.py,3b14e90467ba1af71e633260df5f3cdead0454de,cb0d4cfc6a6fd533a4130a25a336bf0fbbf253da,An extension maybe only correct the sparse blob.
aahfddefa,tensorflow/tensor2tensor,tensor2tensor/data_generators/wmt.py,f8d5ee8f8a7737d10dac82437b32edbd1720f2a3,684f0d02da8468a6e3b32e0d5682bc8c4b28610b,CzEng contains 100 gz files with tab-separated columns; so let's expect
aahfddfde,tensorflow/tensor2tensor,tensor2tensor/utils/yellowfin.py,e5e79cd2b629ce0d0134a25b35cee352400ba844,STILL_EXISTS,An extension maybe only correct the sparse blob.
aahfddfei,tensorflow/tensor2tensor,tensor2tensor/utils/yellowfin.py,e5e79cd2b629ce0d0134a25b35cee352400ba844,STILL_EXISTS,Unused for now.
aahfddfjd,tensorflow/tensor2tensor,tensor2tensor/utils/trainer_utils_test.py,bde34993b31e46ff151f21e3a7580b7a92889632,STILL_EXISTS,Used only when a vocab file or such like is needed.
aahfddgac,tensorflow/tensor2tensor,tensor2tensor/utils/trainer_utils_test.py,bde34993b31e46ff151f21e3a7580b7a92889632,STILL_EXISTS,These are not images; axis=2;3 are not needed.
aahfddiah,tensorflow/tensor2tensor,tensor2tensor/layers/common_hparams.py,65636dfb88162dfe3f00e827e0e560477362d428,STILL_EXISTS,sequences.  Due to some (hopefully) temporary hacks in the
aahfddida,tensorflow/tensor2tensor,tensor2tensor/utils/expert_utils.py,6d00c8bc4bfb277a4e814267eb1700f4093fbf74,STILL_EXISTS,This is a hack but for some reason; gather_nd return a tensor of
aahfddijg,tensorflow/tensor2tensor,tensor2tensor/utils/input_fn_builder.py,ee3296f4ed62292517307563026a08c2e9b02fef,2ebead2f451d30107c43f6f061998496978f5279,TODO(rsepassi): Remove once we move to core Estimator.
aahfdebgi,tensorflow/tensor2tensor,tensor2tensor/layers/common_attention.py,98f55734aa8f49aa00aec5cb27a90887e96b5682,STILL_EXISTS,Hack to make each expert have a unique id
aahfdebid,tensorflow/tensor2tensor,tensor2tensor/models/attention_lm_moe.py,98f55734aa8f49aa00aec5cb27a90887e96b5682,STILL_EXISTS,This is a hack to allows inference when the <go> token
aahfdecbh,tensorflow/tensor2tensor,tensor2tensor/utils/data_reader.py,956e767af673be6292e9b2d06e5ce15688ba76d9,aa2c0b733f730d31852a34e62c4c72d99d1c9a15,TODO(rsepassi): Rm hasattr call once new dynamic window size functionality
aahfdedbf,tensorflow/tensor2tensor,tensor2tensor/models/attention_lm_moe.py,5fcc9bc8d813607bc2e93d680ea6a08cacf83a2c,STILL_EXISTS,According to noam; (\"n\"; \"da\") seems better for harder-to-learn models
aahfdeebj,tensorflow/tensor2tensor,tensor2tensor/data_generators/image.py,7b961a72d5b01a4562d9d88e79c4e70dd9a64024,STILL_EXISTS,TODO(lukaszkaiser): find a better way than printing this.
aahfdeeff,tensorflow/tensor2tensor,tensor2tensor/utils/model_builder.py,42f089b5b45761051065b5e321f4d71dc5a3105c,STILL_EXISTS,mode-specific.  A better fix would be to stop abusing the
aahfdeeif,tensorflow/tensor2tensor,tensor2tensor/models/aligned.py,15682d535244ca33983d8933df9725d459e02d4f,STILL_EXISTS,\"\"\"Single stack of transformations with no masking. ||  || Produces output aligned with inputs. ||  || Configurable using hyperparameters to use some combination of convolutions; || attention; mixtures of experts; etc. ||  || A good problem for this model is languagemodel_wiki_scramble1k50 . || \"\"\"
aahfdefdc,tensorflow/tensor2tensor,tensor2tensor/models/transformer.py,1c7d365dd37a5873017b9529e9fa6fba9c1a6e50,STILL_EXISTS,TODO(llion): Explain! Is this even needed?
aahfdefei,tensorflow/tensor2tensor,tensor2tensor/data_generators/problem_hparams.py,09f1f17d1f673a57cbf69dbb65176a34be426f6b,STILL_EXISTS,they only implement the hparams.
aahfdegdj,tensorflow/tensor2tensor,tensor2tensor/data_generators/text_encoder_test.py,c996878113b6d283253aef7de4f266484e4b50f6,f1b75861d8c9927fbc13643a6d58b60d2f3d08b0,TODO(seabass): Implement the fix so that we can remove this assertion.
aahfdeheg,tensorflow/tensor2tensor,tensor2tensor/utils/expert_utils.py,1951ac728f212199d9e960ccdbf6c6bd5384d518,STILL_EXISTS,Python 2 hack for keyword only args
aahfdehhc,tensorflow/tensor2tensor,tensor2tensor/layers/common_attention.py,fc2d30680f65646a5f60323cd9688cbee4bf0d50,STILL_EXISTS,Generic way to extend multihead_attention
aahfdehii,tensorflow/tensor2tensor,tensor2tensor/layers/common_attention.py,fc2d30680f65646a5f60323cd9688cbee4bf0d50,STILL_EXISTS,Hack: Forward the loss by by-passing multihead_attention
aahfdeiab,tensorflow/tensor2tensor,tensor2tensor/layers/common_attention.py,f0938a399d5f7568d3c890759b76732e53b41206,STILL_EXISTS,Hack: Forward the loss by by-passing multihead_attention
aahfdejha,tensorflow/tensor2tensor,tensor2tensor/layers/rev_block_test.py,583356d5fb4f835a99545b74ec8cc1d2df6aab6d,6a7ef7f79f56fdcb1b16ae76d7e61cb09033dc4f,out why.
aahfdfcbj,tensorflow/tensor2tensor,tensor2tensor/data_generators/wmt.py,c34d16cc5244a9a560e82b24d1dfb6c8d5f136a3,37a3a2987f5f10f44a17b419303d90a4ce2d92c9,CzEng contains 100 gz files with tab-separated columns; so let's expect
aahfdfcfj,tensorflow/tensor2tensor,tensor2tensor/data_generators/generator_utils.py,90fd09b28229a1bf8ee84a6a96fe1f8d44a6aa30,STILL_EXISTS,Extract from tar if needed.
aahfdfcgb,tensorflow/tensor2tensor,tensor2tensor/data_generators/wmt.py,90fd09b28229a1bf8ee84a6a96fe1f8d44a6aa30,STILL_EXISTS,CzEng contains 100 gz files with tab-separated columns; so let's expect
aahfdfdbi,tensorflow/tensor2tensor,tensor2tensor/layers/common_attention.py,93019005fc2fdbb64808bd6ade6f90ff59ae9323,STILL_EXISTS,TODO(epot): Implement more generic version of the mask computation to
aahfdfdcg,tensorflow/tensor2tensor,tensor2tensor/layers/common_attention.py,93019005fc2fdbb64808bd6ade6f90ff59ae9323,STILL_EXISTS,A hack could be to add at least one sequence of each batch on each group so
aahfdfdfi,tensorflow/tensor2tensor,tensor2tensor/layers/common_attention.py,9b93d558c1e0d6446bcd6ac4c23d496bd2bd97fa,STILL_EXISTS,TODO(epot): Implement more generic version of the mask computation to
aahfdffci,tensorflow/tensor2tensor,tensor2tensor/layers/common_hparams.py,43dbf4c3b1ca76f0b16c4b4b1e41f5ca45d777a6,STILL_EXISTS,targets; which can improve the models inference results later. The
aahfdffje,tensorflow/tensor2tensor,tensor2tensor/data_generators/translate_encs.py,eacde9d9f5b4dede91bb95d4c38083bc70824b30,765d33bb6890cc7b8f194257d9c71226c26e0162,CzEng contains 100 gz files with tab-separated columns; so let's expect
aahfdfgih,tensorflow/tensor2tensor,tensor2tensor/data_generators/translate_enmk.py,eacde9d9f5b4dede91bb95d4c38083bc70824b30,765d33bb6890cc7b8f194257d9c71226c26e0162,We generate English->X data by convention; to train reverse translation
aahfdfhaj,tensorflow/tensor2tensor,tensor2tensor/data_generators/translate_enzh.py,eacde9d9f5b4dede91bb95d4c38083bc70824b30,STILL_EXISTS,We generate English->X data by convention; to train reverse translation
aahfdfhci,tensorflow/tensor2tensor,tensor2tensor/utils/get_rouge.py,7130ce4700c07fb3208db80fd97f820fd9643c0b,a82231e83f315f5993072c1b92ef1a0fe7d2f9de,TODO: ensure the output format (chars split by spaces) was as intended
aahfdgbic,tensorflow/tensor2tensor,tensor2tensor/data_generators/text_encoder.py,62aba9da091c25e958736b0398da0e7e99c978b8,STILL_EXISTS,TODO(lukaszkaiser): implement this.
aahfdgbja,tensorflow/tensor2tensor,tensor2tensor/layers/modalities.py,e4aa5f2f66d139812d84be91b057f6ac476501aa,STILL_EXISTS,perhaps there were no inputs; and this is a new variable.
aahfdgggd,tensorflow/tensor2tensor,tensor2tensor/models/transformer_moe.py,89282c98e974e5d96a79501d46edb1b98a2293b1,a88fc015cff9a7ebdfcd2d68535dfffed5568186,Memory efficient
aahfdggjc,tensorflow/tensor2tensor,tensor2tensor/models/transformer_moe.py,89282c98e974e5d96a79501d46edb1b98a2293b1,STILL_EXISTS,According to noam; (\"n\"; \"da\") seems better for harder-to-learn models
aahfdghef,tensorflow/tensor2tensor,tensor2tensor/data_generators/translate_enzh.py,c022afdf1de74e27a4482a4fe00754f9da9a5da0,STILL_EXISTS,you need to register to get UN data and CWT data. Also; by convention;
aahfdgjai,tensorflow/tensor2tensor,tensor2tensor/layers/common_attention.py,a88fc015cff9a7ebdfcd2d68535dfffed5568186,STILL_EXISTS,Memory efficient
aahfdgjid,tensorflow/tensor2tensor,tensor2tensor/data_generators/image.py,11a8d33938fb386e3489e982c7d1ec16dd213711,STILL_EXISTS,TODO(lukaszkaiser): find a better way than printing this.
aahfdhdfd,tensorflow/tensor2tensor,tensor2tensor/data_generators/librispeech.py,5365113cc17db280974f7c80e8c6847aec235fe8,STILL_EXISTS,TODO: clean up hparams
aahfdhegf,tensorflow/tensor2tensor,tensor2tensor/layers/common_layers.py,e0a2f86fdd3b36c381fff66ee1399e70fb689299,STILL_EXISTS,Needed info is constant; so we construct in numpy
aahfdhejb,tensorflow/tensor2tensor,tensor2tensor/utils/trainer_utils_test.py,01b8c31da30a7e1109451df2b4b4698946c6c35c,STILL_EXISTS,Used only when a vocab file or such like is needed.
aahfdhgcb,tensorflow/tensor2tensor,tensor2tensor/data_generators/librispeech.py,6d9b5e1cc01518c033569090faf6fbe519517971,STILL_EXISTS,TODO: clean up hparams
aahfdhhed,tensorflow/tensor2tensor,tensor2tensor/utils/bleu_hook.py,4160979ab570c9a5da31bc7b00710943ab2d25e6,20c7e41d12300cf63587a24b3ede1f25ffb6a416,TODO: Does anyone care about Python2 compatibility?
aahfdjada,tensorflow/tensor2tensor,tensor2tensor/data_generators/speech_recognition.py,2a07e8f2e79316b3f10a1b9b8a2e487af2cbeec9,0a8831fce9b5cc4abc2766a9072b2c4c49339cfd,The convention is that the models are flattened along the spatial;
aahfdjejj,tensorflow/tensor2tensor,tensor2tensor/bin/t2t_bleu.py,0d2a059a5c804a0741a7580bad5311f2184f9f54,STILL_EXISTS,\"\"\"Evaluate BLEU score for all checkpoints\/translations in a given directory. ||  || This script can be used in two ways. ||  ||  || To evaluate one already translated file: ||  || ``` || t2t-bleu --translation=my-wmt13.de --reference=wmt13_deen.de || ``` ||  || To evaluate all translations in a given directory (translated by || `t2t-translate-all`): ||  || ``` || t2t-bleu ||   --translations_dir=my-translations ||   --reference=wmt13_deen.de ||   --event_dir=events || ``` ||  || In addition to the above-mentioned required parameters; || there are optional parameters: ||  * bleu_variant: cased (case-sensitive); uncased; both (default). ||  * tag_suffix: Default=\"\"; so the tags will be BLEU_cased and BLEU_uncased. ||    tag_suffix can be used e.g. for different beam sizes if these should be ||    plotted in different graphs. ||  * min_steps: Don't evaluate checkpoints with less steps. ||    Default=-1 means check the `last_evaluated_step.txt` file; which contains ||    the number of steps of the last successfully evaluated checkpoint. ||  * report_zero: Store BLEU=0 and guess its time based on the oldest file in the ||    translations_dir. Default=True. This is useful; so TensorBoard reports ||    correct relative time for the remaining checkpoints. This flag is set to ||    False if min_steps is > 0. ||  * wait_minutes: Wait upto N minutes for a new translated file. Default=0. ||    This is useful for continuous evaluation of a running training; in which case ||    this should be equal to save_checkpoints_secs\/60 plus time needed for ||    translation plus some reserve. || \"\"\"
aahfeaaec,tensorflow/tensor2tensor,tensor2tensor/data_generators/image_utils.py,bbad537d83014b32d87f80b8c2deb0d38e6d7c7b,STILL_EXISTS,Not needed if self.is_character_level.
aahfeaagd,tensorflow/tensor2tensor,tensor2tensor/data_generators/imagenet.py,bbad537d83014b32d87f80b8c2deb0d38e6d7c7b,STILL_EXISTS,TODO(lukaszkaiser): find a better way than printing this.
aahfeabib,tensorflow/tensor2tensor,tensor2tensor/models/transformer.py,8dad16ee1ffc928e8e0ff5d2d6e5b3f3e8884497,STILL_EXISTS,>1 shards helps with faster parameter distribution on multi-GPU machines
aahfeacgg,tensorflow/tensor2tensor,tensor2tensor/layers/common_attention.py,70bed8058abb1e7f33c3306a3008d8af4aa3c4d7,STILL_EXISTS,=== Memory efficient full-attention layer ===
aahfeacif,tensorflow/tensor2tensor,tensor2tensor/models/transformer_moe.py,70bed8058abb1e7f33c3306a3008d8af4aa3c4d7,STILL_EXISTS,* Memory efficient multihead attention (slow):
aahfeacja,tensorflow/tensor2tensor,tensor2tensor/utils/optimize.py,7eb90b82aaaac9006ea943cb4cbadeb85be8c514,21d1aec788c3bd073433ab32ef5bf9062104d856,HACK: Make lr and global_step dependent on grad.
aahfeacjb,tensorflow/tensor2tensor,tensor2tensor/utils/optimize.py,7eb90b82aaaac9006ea943cb4cbadeb85be8c514,21d1aec788c3bd073433ab32ef5bf9062104d856,END HACK
aahfeadbd,tensorflow/tensor2tensor,tensor2tensor/models/transformer.py,c90b263ffb8731141b72de3af7bf37692d7a2f7e,8574057ef04e164cbb0e7bbabd7b3216dd7f4ac8,Better shuffling is important for distributed training.
aahfebade,tensorflow/tensor2tensor,tensor2tensor/rl/collect.py,37074997ba7767254a5ebc1439269888718667a6,STILL_EXISTS,Note - the only way to ensure making a copy of tensor is to run simple
aahfebedg,tensorflow/tensor2tensor,tensor2tensor/models/transformer_vae.py,b9900e5cc99bffe365a9fcab1050c6df4e3c9ce4,STILL_EXISTS,Can be unstable; maybe try Adam.
aahfebfaj,tensorflow/tensor2tensor,tensor2tensor/layers/common_image_attention.py,798c406cad5937e38b01d853fb5116c30f779bd4,STILL_EXISTS,that rows is a multiple of query_shape and columns is a multiple of
aahfebfbe,tensorflow/tensor2tensor,tensor2tensor/layers/common_image_attention.py,798c406cad5937e38b01d853fb5116c30f779bd4,STILL_EXISTS,times number of channels. This is  needed for positional encodings and
aahfecfhg,tensorflow/tensor2tensor,tensor2tensor/rl/collect.py,3bb1249d516ea9a56602ed2adadbc23b730d2415,8e02ef2a51869c5a126e582d16d01091809336e2,Only needed in eval runs.
aahfecfhj,tensorflow/tensor2tensor,tensor2tensor/rl/collect.py,3c2af07b330c05d60b8793ede7db9994539c5306,85c5cb94ac9c0df9c514427b127aa1c57aa70422,Only needed in eval runs.
aahfecfja,tensorflow/tensor2tensor,tensor2tensor/data_generators/gym.py,b312a02ca59b4e96c73fa302524e79b5464096b0,d44e62ed1af5ca4d55a044af37e5885444cf7bec,Below we have a hacky solution which is a temporary workaround to be used together
aahfeciai,tensorflow/tensor2tensor,tensor2tensor/rl/collect.py,29dd6b92a4bb717230fefb870f6353a1aca4f45c,STILL_EXISTS,Only needed in eval runs.
aahfedcfe,tensorflow/tensor2tensor,tensor2tensor/utils/adafactor.py,21d1aec788c3bd073433ab32ef5bf9062104d856,STILL_EXISTS,HACK: Make things dependent on grad.
aahfedcfi,tensorflow/tensor2tensor,tensor2tensor/utils/adafactor.py,21d1aec788c3bd073433ab32ef5bf9062104d856,STILL_EXISTS,END HACK
aahfeefaj,tensorflow/tensor2tensor,tensor2tensor/data_generators/gym.py,4dd189e854f0ee5d7f525523eeb2887aaecdfe7b,STILL_EXISTS,Below we have a hacky solution which is a workaround to be used together
aahfeeicd,tensorflow/tensor2tensor,tensor2tensor/rl/envs/tf_atari_wrappers.py,ff4e473e3ff021789628e37e410a3cad85b2d986,STILL_EXISTS,a class of the interface bach env (todo: pm; fill me)
aahfeeihb,tensorflow/tensor2tensor,tensor2tensor/models/research/basic_conv_gen.py,41b0003e84fe500076ffd669430bbd6dfb9aa9a3,STILL_EXISTS,TODO (1;1)
aahfeeijd,tensorflow/tensor2tensor,tensor2tensor/bin/rl_with_imagination_loop.py,b50894a1100c8f7e769524f3d1b353bad4494b5f,1cd449f2e0775b49770d72559a35f558a0947d41,TODO - last_model :=
aahfeejdc,tensorflow/tensor2tensor,tensor2tensor/rl/envs/tf_atari_wrappers.py,586f7e3b7755699261ff58103609900304c6f696,STILL_EXISTS,TODO: Do we need this?
aahfeejdh,tensorflow/tensor2tensor,tensor2tensor/data_generators/gym.py,3cf6515f55749369e5665de2454b71fbc2dfefc5,STILL_EXISTS,Todo: think how to pass parameters
aahfeejfe,tensorflow/tensor2tensor,tensor2tensor/rl/rl_trainer_lib.py,434f330a7ccc34272e100901794f95d72204d28b,3a71eeffa824955bdafb48011a926619e16e0c52,TODO: pm -> B\u0142a\u017Cej. I'm to tired to fix it now.
aahfeejff,tensorflow/tensor2tensor,tensor2tensor/utils/t2t_model.py,ae0085eeace5ede8fbc793f741506ab33a4d3550,8ff7dbbfc96c07aecc6550e98ea577fa0d91770c,TODO: pm->\u0141ukasz eval is not working (to be precise it behaves stochastically.
aahfeejfh,tensorflow/tensor2tensor,tensor2tensor/rl/envs/utils.py,b1f5614cf066afc294a6bbdf54cb421a2d3716cc,36528bb7dc4b4e9430c7f803d1c645e8746b7189,TODO: pm->B\u0142a\u017Cej. Should the paramters be infered.
aahfeejfi,tensorflow/tensor2tensor,tensor2tensor/models/research/basic_conv_gen.py,6b55dcacb3c725fc8da796ab525c8c4bba7e8a5b,993e932c925da725a7badcf87557d34c99714343,TODO: pm->\u0141ukasz. What is the correct way of getting the previous observation?
aahfeejfj,tensorflow/tensor2tensor,tensor2tensor/models/research/basic_conv_gen.py,6b55dcacb3c725fc8da796ab525c8c4bba7e8a5b,STILL_EXISTS,TODO: pm->\u0141ukasz. Actions are happily discarded ;)
aahfeejha,tensorflow/tensor2tensor,tensor2tensor/data_generators/gym.py,2f3bb1fd0fe8b464e006f3d0e0895cb2d927ea55,STILL_EXISTS,TODO: pm->\u0141ukasz. The below line is requied by ImageModality.top. But seems somewhat weird.
aahfeejic,tensorflow/tensor2tensor,tensor2tensor/rl/envs/simulated_batch_env.py,2f3bb1fd0fe8b464e006f3d0e0895cb2d927ea55,STILL_EXISTS,#TODO: pm->\u0141ukasz. What should be the starting conditions
aahfeejie,tensorflow/tensor2tensor,tensor2tensor/bin/rl_with_imagination_loop.py,912357736061aafa4e276d950785391daf30fa4f,74d5ef1353856f200539e3afd50cd1f9d5183f45,TODO: pm-> B\u0142a\u017Cej. Check if TimeLimitWrapper does what it is supposed to do
aahfeejjc,tensorflow/tensor2tensor,tensor2tensor/data_generators/gym.py,25f35bd05cfde0f3a958f52f41a660b7f0174633,STILL_EXISTS,TODO: pm->B\u0142a\u017Cej. Make it a paremater
aahfefaai,tensorflow/tensor2tensor,tensor2tensor/models/research/basic_conv_gen.py,ec173ae9f426d3f9a6b87c7dad31c38ef2a76c4a,STILL_EXISTS,TODO: pm remove tf.identity below
aahfefacb,tensorflow/tensor2tensor,tensor2tensor/data_generators/gym.py,1179cf0a33351190f2d192881c1619d744ba52fb,STILL_EXISTS,TODO: verify if we handle actions correctly. Curretly not adding +1?
aahfefacc,tensorflow/tensor2tensor,tensor2tensor/rl/envs/tf_atari_wrappers.py,1179cf0a33351190f2d192881c1619d744ba52fb,STILL_EXISTS,TODO: looks as if it is broken
aahfefadc,tensorflow/tensor2tensor,tensor2tensor/data_generators/gym.py,f4cbb6c5a48fa60b4c6b79eb9e14bd738308b8dd,STILL_EXISTS,TODO: remove PongT2TGeneratorHackWrapper by writing a modality
aahfefaea,tensorflow/tensor2tensor,tensor2tensor/models/research/basic_conv_gen.py,74d5ef1353856f200539e3afd50cd1f9d5183f45,39c46ffbfe026d1cb255860b31b8b4ed7828d215,TODO: possibly make embeding of inputs_0 and inputs_1
aahfefaeb,tensorflow/tensor2tensor,tensor2tensor/models/research/basic_conv_gen.py,74d5ef1353856f200539e3afd50cd1f9d5183f45,STILL_EXISTS,TODO: possibly make the net deeper
aahfefaeg,tensorflow/tensor2tensor,tensor2tensor/models/research/basic_conv_gen.py,74d5ef1353856f200539e3afd50cd1f9d5183f45,STILL_EXISTS,TODO: pm remove tf.identity below
aahfefaeh,tensorflow/tensor2tensor,tensor2tensor/rl/envs/tf_atari_wrappers.py,74d5ef1353856f200539e3afd50cd1f9d5183f45,STILL_EXISTS,TODO: Check if TimeLimitWrapper does what it is supposed to do
aahfefafj,tensorflow/tensor2tensor,tensor2tensor/models/research/basic_conv_gen.py,9cf5c7bb0568c43c90d8952459053ec935715ead,39c46ffbfe026d1cb255860b31b8b4ed7828d215,TODO: pm->pm: add done
aahfefagf,tensorflow/tensor2tensor,tensor2tensor/data_generators/gym.py,3adf452b441dbc0d4aaeb4469e853e3bfd2d1912,3a71eeffa824955bdafb48011a926619e16e0c52,TODO: pull it outside
aahfefaic,tensorflow/tensor2tensor,tensor2tensor/data_generators/gym.py,f09c4860683ddf54f7bb9f42e6ef751b0dacdced,3a71eeffa824955bdafb48011a926619e16e0c52,TODO: adjust regexp for different models
aahfefaij,tensorflow/tensor2tensor,tensor2tensor/rl/envs/simulated_batch_env.py,f09c4860683ddf54f7bb9f42e6ef751b0dacdced,STILL_EXISTS,TODO: it might be better to have something here
aahfefbai,tensorflow/tensor2tensor,tensor2tensor/models/research/basic_conv_gen.py,c748d352fe3cad60e24b24db07d2c4ff752c643c,39c46ffbfe026d1cb255860b31b8b4ed7828d215,TODO(lukaszkaiser): remove hack below; needed for greedy decoding for now.
aahfefbci,tensorflow/tensor2tensor,tensor2tensor/models/basic.py,f79f361760f18fad177908bb4a4dd9340b989d49,01ce8acf20ba4cd71477aca700922315b7459125,Inputs and features preparation needed to handle edge cases.
aahfefcaa,tensorflow/tensor2tensor,tensor2tensor/data_generators/celeba.py,41faa821f886d05ea8d63ff01a3d173fce75cc3b,STILL_EXISTS,columns to match for every resolution.
aahfefcag,tensorflow/tensor2tensor,tensor2tensor/layers/common_layers.py,f768cde214d322928e9ab1e51b2ea455214c61cc,6d9274e1234cebf8fcde1e7f7fec85a4099d0588,This if statement is needed to guard the cast; because batch norm
aahfefcbd,tensorflow/tensor2tensor,tensor2tensor/layers/common_layers.py,f768cde214d322928e9ab1e51b2ea455214c61cc,STILL_EXISTS,bfloat16. This is a hack to ensure that grad_vars are the right type.
aahfefcfi,tensorflow/tensor2tensor,tensor2tensor/data_generators/mscoco.py,e9659053cb33db6e281222f417eb473a17eeffa5,STILL_EXISTS,columns to match for every resolution.
aahfefcjc,tensorflow/tensor2tensor,tensor2tensor/models/transformer_test.py,354c9d16eeb767dfa3873f43e584fd28e3eeac74,8cf5fa411f391fddb15003dd362a5f438178dd16,TODO(rsepassi): Fix decode length. Broken by cl\/190537320.
aahfefdbe,tensorflow/tensor2tensor,tensor2tensor/data_generators/gym.py,3a71eeffa824955bdafb48011a926619e16e0c52,STILL_EXISTS,Below we have a hacky solution which is a workaround to be used together
aahfeffga,tensorflow/tensor2tensor,tensor2tensor/data_generators/gym.py,fb39be70df95a7607aa893ba0419bdc210320ec8,STILL_EXISTS,TODO: remove PongT2TGeneratorHackWrapper by writing a modality
aahfeffhe,tensorflow/tensor2tensor,tensor2tensor/data_generators/gym.py,fb39be70df95a7607aa893ba0419bdc210320ec8,f8b951a386b25a2b6d5698954261dcbf7dd03bdb,TODO: pull it outside
aahfeffhf,tensorflow/tensor2tensor,tensor2tensor/data_generators/gym.py,fb39be70df95a7607aa893ba0419bdc210320ec8,f8b951a386b25a2b6d5698954261dcbf7dd03bdb,TODO: adjust regexp for different models
aahfefgfb,tensorflow/tensor2tensor,tensor2tensor/models/research/basic_conv_gen.py,fb39be70df95a7607aa893ba0419bdc210320ec8,f8b951a386b25a2b6d5698954261dcbf7dd03bdb,TODO: possibly make embeding of inputs_0 and inputs_1
aahfefggd,tensorflow/tensor2tensor,tensor2tensor/models/research/basic_conv_gen.py,fb39be70df95a7607aa893ba0419bdc210320ec8,f8b951a386b25a2b6d5698954261dcbf7dd03bdb,TODO: pm->pm: add done
aahfefgge,tensorflow/tensor2tensor,tensor2tensor/models/research/basic_conv_gen.py,fb39be70df95a7607aa893ba0419bdc210320ec8,f8b951a386b25a2b6d5698954261dcbf7dd03bdb,TODO(lukaszkaiser): remove hack below; needed for greedy decoding for now.
aahfefhag,tensorflow/tensor2tensor,tensor2tensor/rl/rl_trainer_lib.py,fb39be70df95a7607aa893ba0419bdc210320ec8,f8b951a386b25a2b6d5698954261dcbf7dd03bdb,TODO: pm -> B\u0142a\u017Cej. I'm to tired to fix it now.
aahfefhga,tensorflow/tensor2tensor,tensor2tensor/rl/envs/utils.py,f7c28e8089ff66f8915493f22bc81c71b3cb6acb,32cef12ffdff534f28198b3ed2b83b332cd526ed,TODO -video?
aahfefhgb,tensorflow/tensor2tensor,tensor2tensor/rl/envs/utils.py,f7c28e8089ff66f8915493f22bc81c71b3cb6acb,32cef12ffdff534f28198b3ed2b83b332cd526ed,TODO: pm->B\u0142a\u017Cej. Should the paramters be infered.
aahfefigf,tensorflow/tensor2tensor,tensor2tensor/rl/envs/utils.py,32396a9ce6699bb5222f17ee4bd6e5bcd729942f,f8b951a386b25a2b6d5698954261dcbf7dd03bdb,TODO -video?
aahfefigg,tensorflow/tensor2tensor,tensor2tensor/rl/envs/utils.py,32396a9ce6699bb5222f17ee4bd6e5bcd729942f,f8b951a386b25a2b6d5698954261dcbf7dd03bdb,TODO: pm->B\u0142a\u017Cej. Should the paramters be infered.
aahfefihh,tensorflow/tensor2tensor,tensor2tensor/utils/bleu_hook.py,91b0fc24805eaf1565850a76aa4f4b34a0f02a88,f8b951a386b25a2b6d5698954261dcbf7dd03bdb,To fix issue:#706
aahfefihi,tensorflow/tensor2tensor,tensor2tensor/utils/bleu_hook.py,91b0fc24805eaf1565850a76aa4f4b34a0f02a88,f8b951a386b25a2b6d5698954261dcbf7dd03bdb,To fix the issue #706
aahfegbji,tensorflow/tensor2tensor,tensor2tensor/rl/envs/simulated_batch_env.py,43da5c3006e09f29c75784e07950c0563d926c69,STILL_EXISTS,TODO: it might be better to have something here
aahfegccd,tensorflow/tensor2tensor,tensor2tensor/rl/envs/tf_atari_wrappers.py,43da5c3006e09f29c75784e07950c0563d926c69,STILL_EXISTS,a class of the interface bach env (todo: pm; fill me)
aahfegccf,tensorflow/tensor2tensor,tensor2tensor/rl/envs/tf_atari_wrappers.py,43da5c3006e09f29c75784e07950c0563d926c69,STILL_EXISTS,TODO: looks as if it is broken
aahfegcch,tensorflow/tensor2tensor,tensor2tensor/rl/envs/tf_atari_wrappers.py,43da5c3006e09f29c75784e07950c0563d926c69,STILL_EXISTS,TODO: Check if TimeLimitWrapper does what it is supposed to do
aahfegcfj,tensorflow/tensor2tensor,tensor2tensor/rl/rl_trainer_lib.py,43da5c3006e09f29c75784e07950c0563d926c69,58bdb8b0cecab403644ba6529c80cfb638beaf91,TODO(blazej): Make sure that policy is restored properly.
aahfegcjg,tensorflow/tensor2tensor,tensor2tensor/models/research/basic_conv_gen.py,7058edac0ea691cf4ef06e5963ca7f81ef4ececf,STILL_EXISTS,Pad to make size powers of 2 as needed.
aahfegeib,tensorflow/tensor2tensor,tensor2tensor/utils/bleu_hook.py,22110f22a7cbcbadd156a52b0e5100a3a9bf52c2,6a88d4cab7cbf44e63184feb5de3e60ab96141b4,To fix issue:#706
aahfegeic,tensorflow/tensor2tensor,tensor2tensor/utils/bleu_hook.py,22110f22a7cbcbadd156a52b0e5100a3a9bf52c2,6a88d4cab7cbf44e63184feb5de3e60ab96141b4,To fix the issue #706
aahfegfei,tensorflow/tensor2tensor,tensor2tensor/utils/quantization.py,6d9274e1234cebf8fcde1e7f7fec85a4099d0588,STILL_EXISTS,This if statement is needed to guard the cast; because batch norm
aahfeggea,tensorflow/tensor2tensor,tensor2tensor/models/research/basic_conv_gen.py,15bd9e33e8b83df6e0d9f6e0b5a97ccac8e25702,STILL_EXISTS,Inputs and features preparation needed to handle edge cases.
aahfegjaf,tensorflow/tensor2tensor,tensor2tensor/data_generators/wikisum_commoncrawl/fetch_ref_urls.py,f4dd38e81a55546a2cb00ff1532b11b49281e1a0,STILL_EXISTS,TODO: remove 8
aahfehbai,tensorflow/tensor2tensor,tensor2tensor/models/research/r_transformer.py,485beeba998834b0d7e8eb93a65b2f9c1e48dffe,STILL_EXISTS,\"\"\"Transformers with depthwise recurrency (go\/r-transformer). ||  ||  || A high-level explanation on the idea and the architecture: ||  || The vanilla Transformer model has no recurrence and struggles with some tasks || that a fully recurrent model can easily solve. Instead of incorporating || recurrence in time (which has a dependency on sequence length T); || we apply recurrence in depth (which we can set to some fixed length D << T); || and apply self-attention instead of sequential processing to enable the model || to incorporate long-range dependencies. ||  || Structure of the code is explained in r_transformer_util.py || \"\"\"
aahfehdhh,tensorflow/tensor2tensor,tensor2tensor/models/research/autoencoders.py,d901097f1674d8ededf880e15d81f8448a569010,STILL_EXISTS,Inputs and features preparation needed to handle edge cases.
aahfeicfe,tensorflow/tensor2tensor,tensor2tensor/layers/common_layers.py,5324d11fd6fc28e3b666286c1faa1328934475ea,5045d571bc7e3d5fcaf014569c6b3212109206ea,TODO(noam): remove this hack once XLA does the right thing.
aahfeidia,tensorflow/tensor2tensor,tensor2tensor/layers/modalities.py,c98fab447853643319498cab801af3ed31788d8f,STILL_EXISTS,Move frames next to batch.
aahfeifdd,tensorflow/tensor2tensor,tensor2tensor/data_generators/gym.py,ab1c8198d12acc1516abbb17397266ed159974e6,1d4cb76a09201ceb486f68b8ddff9f3ca7dd182b,PM:This should be probably removed
aahfeigfi,tensorflow/tensor2tensor,tensor2tensor/data_generators/gym_problems.py,99010a42d1ad65cddad104dc62912bf36708a51b,STILL_EXISTS,TODO(piotrm): This should be probably removed.
aahfeihca,tensorflow/tensor2tensor,tensor2tensor/data_generators/gym_problems.py,480379b3e0cabd68eeb82734a1eae576e524a2f0,STILL_EXISTS,TODO(piotrm): This should be probably removed.
aahfeiibh,tensorflow/tensor2tensor,tensor2tensor/data_generators/gym_utils.py,e9446780d0c29ce377fb1b6c349d961467faa250,ab4983e2940c27896e23217b90bd45b9c3c9e473,this is probably not needed; just in case
aahfeiieh,tensorflow/tensor2tensor,tensor2tensor/data_generators/gym_utils.py,03ba808d8414f2fb7e504dbd05cd41e94e87187d,STILL_EXISTS,this is probably not needed; just in case
aahfeijag,tensorflow/tensor2tensor,tensor2tensor/rl/model_rl_experiment.py,42c8abcad3c8d5dacef4c221964feac2d28859ac,399d50b119375df5fa31d36045f3c45ced0b7f83,Train autoencoder if needed.
aahfeijbh,tensorflow/tensor2tensor,tensor2tensor/data_generators/subject_verb_agreement.py,fa2ef3645e93dfa3b533fb10dc283f480a266a96,STILL_EXISTS,TODO(dehghani): Implement accuracy of the target word as a t2t metric.
aahfeijgg,tensorflow/tensor2tensor,tensor2tensor/rl/model_rl_experiment.py,bed6a3c6b5bbc1b8c60dcb4773ead7588e5828df,STILL_EXISTS,TODO(blazej) - currently it is unused.
aahfeijgi,tensorflow/tensor2tensor,tensor2tensor/rl/model_rl_experiment.py,445c1c8654f4e63a5061b6b2e58a9a4593cc8795,4eb4f4cabb7e548f502396cf4e9e48a237dd76c4,Hack: If training on autoencoded frames; autoencoder_path needs to be
aahfejdgj,tensorflow/tensor2tensor,tensor2tensor/data_generators/translate.py,9878f2fd6a903b5bbcd8060cbef3a2d4fa04f64c,STILL_EXISTS,hack: ignoring true targets and putting dist_targets in targets
aahfejdjj,tensorflow/tensor2tensor,tensor2tensor/models/research/transformer_nat.py,9878f2fd6a903b5bbcd8060cbef3a2d4fa04f64c,STILL_EXISTS,Can be unstable; maybe try Adam.
aahfejecb,tensorflow/tensor2tensor,tensor2tensor/utils/multistep_optimizer.py,64e1df15e649c6db97856f7ea6aea6508d52b0cf,STILL_EXISTS,TODO: Implement a sparse version
aahfejedi,tensorflow/tensor2tensor,tensor2tensor/utils/multistep_optimizer_test.py,64e1df15e649c6db97856f7ea6aea6508d52b0cf,STILL_EXISTS,TODO: Remove version check once 1.5 is not tested anymore
aahfejegd,tensorflow/tensor2tensor,tensor2tensor/utils/multistep_optimizer.py,1d6cb552c4d637a6cb241f6ba7e19a10a0a632bc,STILL_EXISTS,TODO(fstahlberg): Implement a sparse version
aahfejeid,tensorflow/tensor2tensor,tensor2tensor/utils/multistep_optimizer.py,1d6cb552c4d637a6cb241f6ba7e19a10a0a632bc,STILL_EXISTS,TODO: Implement a sparse version
aahfejfca,tensorflow/tensor2tensor,tensor2tensor/utils/multistep_optimizer_test.py,1d6cb552c4d637a6cb241f6ba7e19a10a0a632bc,STILL_EXISTS,TODO: Remove version check once 1.5 is not tested anymore
aahfejhfh,tensorflow/tensor2tensor,tensor2tensor/layers/common_layers.py,5e3b43eb94866addd446ecb0c31032e967007f5e,a8c501043bd488feeb8fcf0595b8476b4b31c0b1,TODO(noam): remove this hack once XLA does the right thing.
aahfejidf,tensorflow/tensor2tensor,tensor2tensor/utils/metrics.py,7c077a78c716f4747992f18bdf48ed39abfbb69d,STILL_EXISTS,Unused
aahfejjbi,tensorflow/tensor2tensor,tensor2tensor/utils/t2t_model.py,0fed194ba5b5fc50b143977b906f21edb35d5584,STILL_EXISTS,Hack: foldl complains when the output shape is less specified than the
aahfejjea,tensorflow/tensor2tensor,tensor2tensor/data_generators/gym_problems.py,e78426487f4cd8b9998e5c030f067ae44264a779,5322dd6aaadbaae01eeafcd832630afeeb0beef5,TODO(piotrmilos). This should be probably done in collect
aahfejjfd,tensorflow/tensor2tensor,tensor2tensor/data_generators/gym_problems.py,34fca4c556795725227336490ef1e934314d49db,STILL_EXISTS,TODO (piotrmilos). it is possible to set more
aahfejjfe,tensorflow/tensor2tensor,tensor2tensor/data_generators/gym_problems.py,34fca4c556795725227336490ef1e934314d49db,5322dd6aaadbaae01eeafcd832630afeeb0beef5,TODO(piotrmilos). This should be probably done in collect
aahffaabe,tensorflow/tensor2tensor,tensor2tensor/layers/common_message_passing_attention.py,2a21962b31b30cee6928340da43d2191ecc94dce,STILL_EXISTS,TODO(avaswani): Better way to do this.
aahffaadc,tensorflow/tensor2tensor,tensor2tensor/layers/common_message_passing_attention.py,2a21962b31b30cee6928340da43d2191ecc94dce,STILL_EXISTS,ignoring edges if needed
aahffaaec,tensorflow/tensor2tensor,tensor2tensor/utils/metrics.py,2a21962b31b30cee6928340da43d2191ecc94dce,STILL_EXISTS,Unused
aahffacea,tensorflow/tensor2tensor,tensor2tensor/models/transformer.py,93d752da188c133552867d524be900434e6fd7cf,STILL_EXISTS,TODO(llion): Explain! Is this even needed?
aahffadad,tensorflow/tensor2tensor,tensor2tensor/data_generators/gym_problems.py,008a68451b3fed0edc50017389607d35a1e759e1,STILL_EXISTS,TODO(piotrm): This should be probably removed.
aahffadfb,tensorflow/tensor2tensor,tensor2tensor/data_generators/gym_problems.py,1b26c5e62dc39c6d739a1f014535cf4dcfae12ee,STILL_EXISTS,TODO(piotrm): This should be probably removed.
aahffafac,tensorflow/tensor2tensor,tensor2tensor/models/research/autoencoders.py,01ce8acf20ba4cd71477aca700922315b7459125,STILL_EXISTS,Inputs and features preparation needed to handle edge cases.
aahffafeg,tensorflow/tensor2tensor,tensor2tensor/rl/rl_trainer_lib.py,36d322b67a3d75383df9878f9508f9253eb850c1,STILL_EXISTS,TODO (piotr milos): This should be refactored; possibly with
aahffafge,tensorflow/tensor2tensor,tensor2tensor/data_generators/gym_problems.py,0d2fde0a4e079a020e0e062b2431fff103767ae1,db558a587185811f2e904ffdc8fd64a2f011f5e8,This is hackish way of introducing resets every
aahffaggj,tensorflow/tensor2tensor,tensor2tensor/layers/common_message_passing_attention.py,90c5f41f92dd35b37d7e0003f81e82c8bae6648c,STILL_EXISTS,Generic way to extend multihead_attention
aahffahhh,tensorflow/tensor2tensor,tensor2tensor/utils/video2gif.py,e020dc0b9b850cc1f54fb30f37a71cdbd55bcb69,STILL_EXISTS,r\"\"\"View the problem. ||  || This binary saves the videos in the problem(dataset) into gifs. ||  || The imagemagick package should be installed for conversion to gifs. ||  || Example usage to view dataset: ||  ||   video2gif \\ ||       --data_dir ~\/data \\ ||       --problem=gym_water_world_random5k \\ ||       --hparams_set=next_frame_stochastic \\ ||       --output_dir \/usr\/local\/google\/home\/mbz\/t2t_train\/ww\/ \\ ||       --data_dir \/usr\/local\/google\/home\/mbz\/temp\/ \\ ||       --num_samples 10 || \"\"\"
aahffaibh,tensorflow/tensor2tensor,tensor2tensor/data_generators/gh_function_docstring_encoder.py,77ff77f119b1c323f344b349ee983b0a34e21131,STILL_EXISTS,TODO: need to fill this with Transformer encoder\/decoder
aahffbbaa,tensorflow/tensor2tensor,tensor2tensor/data_generators/gym_problems.py,50b1103bd8063255616c82ddabcb234b1b96554d,STILL_EXISTS,This is hackish way of introducing resets every
aahffbbag,tensorflow/tensor2tensor,tensor2tensor/data_generators/gym_problems.py,50b1103bd8063255616c82ddabcb234b1b96554d,STILL_EXISTS,TODO(piotrmilos): move restoring networks to SimulatedBatchEnv.initialize
aahffbcca,tensorflow/tensor2tensor,tensor2tensor/layers/common_message_passing_attention.py,ca23d52161d3d7005d595df0027dd507789da250,STILL_EXISTS,TODO(avaswani): Better explanation of the rationale behind ignore_zero
aahffbcfg,tensorflow/tensor2tensor,tensor2tensor/bin/t2t_attack.py,35e3a63a972fdebaa8ed0e79e0783231ec6200cd,STILL_EXISTS,add \"_rev\" as a hack to avoid image standardization
aahffbegi,tensorflow/tensor2tensor,tensor2tensor/layers/common_attention.py,ea05fd5935af7b17cd16cf62bd5407e52d1fedba,STILL_EXISTS,Concat columns of pad to shift from relative to absolute indexing.
aahffbehf,tensorflow/tensor2tensor,tensor2tensor/layers/common_attention.py,ea05fd5935af7b17cd16cf62bd5407e52d1fedba,STILL_EXISTS,We only multiply with the needed embeddings as we slice them out.
aahffbhdh,tensorflow/tensor2tensor,tensor2tensor/rl/collect.py,4eb4f4cabb7e548f502396cf4e9e48a237dd76c4,be120c324508d541d3f655e285e75c87c80b3f3f,TODO (piotrmilos): possibly we need cumulative_rewards.read_value()
aahffbhfg,tensorflow/tensor2tensor,tensor2tensor/data_generators/gym_problems.py,be120c324508d541d3f655e285e75c87c80b3f3f,STILL_EXISTS,TODO(piotrmilos): move restoring networks to SimulatedBatchEnv.initialize
aahffbhja,tensorflow/tensor2tensor,tensor2tensor/rl/model_rl_experiment.py,be120c324508d541d3f655e285e75c87c80b3f3f,40cccaefa79849d3d2c63011d8360005f0d8998c,Hack: If training on autoencoded frames; autoencoder_path needs to be
aahffbijb,tensorflow/tensor2tensor,tensor2tensor/rl/envs/tf_atari_wrappers.py,40cccaefa79849d3d2c63011d8360005f0d8998c,b5dea9e25b5b03e41c86b82a4a8dd18b41e68b34,TODO(piotrmilos): make it better
aahffbjhd,tensorflow/tensor2tensor,tensor2tensor/data_generators/problem.py,da6c3e9eb9480e14546674efdbed9494a653a46b,6b1438d95f8cb6b4cc9aeb872c0a22ff2b3d0bcd,TODO(ylc): fix this once TPU estimator changes are checked in.
aahffccib,tensorflow/tensor2tensor,tensor2tensor/models/image_transformer_2d.py,fc76cc27371be1f868384a9e74b1a1a743d588f5,STILL_EXISTS,Hack: if you report a loss of NaN; TensorBoard will plot a point at
aahffcdif,tensorflow/tensor2tensor,tensor2tensor/layers/vqa_layers.py,920349269d21d80f6993375bdfd60d8c4f244b17,STILL_EXISTS,The performance of current implementation is better than updating
aahffcdii,tensorflow/tensor2tensor,tensor2tensor/layers/vqa_layers.py,920349269d21d80f6993375bdfd60d8c4f244b17,STILL_EXISTS,Generic way to extend multihead_attention
aahffcecb,tensorflow/tensor2tensor,tensor2tensor/models/research/transformer_vae.py,b2d749b3a66e875da43f807a078d94bf2dc5c176,STILL_EXISTS,TODO(trandustin): Fix this in a better manner.
aahffcedh,tensorflow/tensor2tensor,tensor2tensor/data_generators/function_docstring.py,9bea9b479da5b1de6f9f7bb7159914299995e28c,STILL_EXISTS,FIXME(sanyamkapoor): This exists to handle memory explosion.
aahffceeb,tensorflow/tensor2tensor,tensor2tensor/models/research/next_frame.py,c8e3cc1bdf687ca0c0d04ada0efe6ec1e7441e00,e5150c1445be17979d359719ea11ce4925ad12a2,HACK: Do first step outside to initialize all the variables
aahffchee,tensorflow/tensor2tensor,tensor2tensor/models/research/next_frame_sv2p.py,e5150c1445be17979d359719ea11ce4925ad12a2,STILL_EXISTS,HACK: Do first step outside to initialize all the variables
aahffchic,tensorflow/tensor2tensor,tensor2tensor/data_generators/allen_brain.py,7967b446c5a0f7e7ea2e6b39150abf850abec463,STILL_EXISTS,\"\"\"Problem definitions for Allen Brain Atlas problems. ||  || Notes: ||  ||   * TODO(cwbeitel): Want to be able to increase up-sampling ratio and\/or ||     in-paint fraction over the course of training. This could be done by ||     defining a range of problems or perhaps more aptly with an hparam ||     that is dialed up depending on training performance. ||  || \"\"\"
aahffdaaj,tensorflow/tensor2tensor,tensor2tensor/mesh_tensorflow/mnist.py,1d5814b74856f6f783bcdcb46eaa97cd36d51d16,STILL_EXISTS,When choosing shuffle buffer sizes; larger sizes result in better
aahffdaed,tensorflow/tensor2tensor,tensor2tensor/mesh_tensorflow/mnist_dataset.py,1d5814b74856f6f783bcdcb46eaa97cd36d51d16,STILL_EXISTS,num_images; unused
aahffdaee,tensorflow/tensor2tensor,tensor2tensor/mesh_tensorflow/mnist_dataset.py,1d5814b74856f6f783bcdcb46eaa97cd36d51d16,STILL_EXISTS,num_items; unused
aahffddgh,tensorflow/tensor2tensor,tensor2tensor/mesh_tensorflow/mtf_transformer_compat.py,1d5814b74856f6f783bcdcb46eaa97cd36d51d16,STILL_EXISTS,\"\"\"Temporary hack for decoding mtf_transformer models. ||  || This is a transformer implementation in regular TensorFlow which is || checkpoint-compatible with MtfTransformer for eval\/inference. ||  || The purpose of this model is to run inference on MtfTransformer models. || We are working on native decoding in MtfTransformer which will be faster and || cleaner. ||  || TODO(noam): Remove once we can decode in mtf. || \"\"\"
aahffdgde,tensorflow/tensor2tensor,tensor2tensor/bin/t2t_prune.py,821ae0bf31b793b40cdcc5007bb24c41a5d699eb,STILL_EXISTS,add \"_rev\" as a hack to avoid image standardization
aahffdhfg,tensorflow/tensor2tensor,tensor2tensor/rl/model_rl_experiment.py,f5938ccc10c4cf37965c42a3c8c5b5349310cb3d,STILL_EXISTS,approximately as much as \"going back 1 epoch\" would be in default schedule.
aahffebdi,tensorflow/tensor2tensor,tensor2tensor/rl/datagen_with_agent.py,3d418439076c1732b0703b7dbc1b6269aef7c469,STILL_EXISTS,\"\"\"Generate trajectories to disk with random or ckpt agent. ||  || TODO: Usage || \"\"\"
aahffecgj,tensorflow/tensor2tensor,tensor2tensor/models/research/next_frame_sv2p.py,8dd9c45f0246be4b7f94d60158c1e3f61fea0f44,828030da9986bcf13c6ba9ad09f32ff1437158e9,HACK. just return something.
aahffeedh,tensorflow/tensor2tensor,tensor2tensor/utils/expert_utils.py,561e11bb80bde2cbf522bf060316573ad5eb790c,57444300243f068bad88eb5ed51a9793c4bde172,TODO(avaswani): Maybe scale the embeddings flowing out of the experts.
aahffejje,tensorflow/tensor2tensor,tensor2tensor/mesh_tensorflow/research/moe.py,1ad804ff172dd2728af9afdf260a34842db2f84b,STILL_EXISTS,Static expert_capacity dimension is needed for expert batch sizes
aahfffbhe,tensorflow/tensor2tensor,tensor2tensor/layers/vq_discrete.py,5cf0b5c4f30a9e3d8f0ec242b08222461229a515,STILL_EXISTS,TODO(avaswani): Figure out why tf.get_variable doesn't work with assign
aahfffcbc,tensorflow/tensor2tensor,tensor2tensor/utils/expert_utils.py,5cf0b5c4f30a9e3d8f0ec242b08222461229a515,STILL_EXISTS,TODO(avaswani): Maybe scale the embeddings flowing out of the experts.
aahfffdid,tensorflow/tensor2tensor,tensor2tensor/rl/envs/tf_atari_wrappers.py,1b98c4bb644534c8068968c49c2ef7dff3541858,STILL_EXISTS,Should be implemented if ever two StackWrappers are to be used together.
aahfffhdg,tensorflow/tensor2tensor,tensor2tensor/serving/export.py,ae01963559991b410e2b33ad6d280a267ade8281,STILL_EXISTS,TODO(michalski): Move this method into tfhub utils.
aahfffjac,tensorflow/tensor2tensor,tensor2tensor/layers/modalities.py,0a8831fce9b5cc4abc2766a9072b2c4c49339cfd,STILL_EXISTS,The convention is that the models are flattened along the spatial;
aahfffjdi,tensorflow/tensor2tensor,tensor2tensor/models/video/epva.py,9907e4c06f73de050107eaf98efcee4371e96b08,STILL_EXISTS,This is only needed or the GAN version.
aahffgcic,tensorflow/tensor2tensor,tensor2tensor/data_generators/multi_problem.py,83306ef45c110d28a34ccd74a950cdc563f90ddb,STILL_EXISTS,TODO(urvashik): Fix combination with other task losses - weighted
aahffgcii,tensorflow/tensor2tensor,tensor2tensor/data_generators/gym_problems.py,debc8935e22796bbf1de12865f52388d7093329a,STILL_EXISTS,TODO(piotrmilos): move restoring networks to SimulatedBatchEnv.initialize
aahffgcjb,tensorflow/tensor2tensor,tensor2tensor/data_generators/gym_problems.py,debc8935e22796bbf1de12865f52388d7093329a,STILL_EXISTS,TODO(koz4k): Implement.
aahffgdba,tensorflow/tensor2tensor,tensor2tensor/rl/trainer_model_based.py,05a20c6c3464d40bfeb75842e3052e31b6374c72,1b16365fef49747163939c86686043eb195c061f,is approximately as much as \"going back 1 epoch\" would be.
aahffgecd,tensorflow/tensor2tensor,tensor2tensor/models/video/basic_recurrent.py,267cec141e78c7e6bb9541fd8a7b13ed06d4a104,14b467d939a003dab35855c15386f5dcf60d0b27,Reward prediction if needed.
aahffgecf,tensorflow/tensor2tensor,tensor2tensor/models/video/basic_recurrent.py,267cec141e78c7e6bb9541fd8a7b13ed06d4a104,14b467d939a003dab35855c15386f5dcf60d0b27,dirty hack to enable the latent tower
aahffgegj,tensorflow/tensor2tensor,tensor2tensor/mesh_tensorflow/mtf_resnet.py,d998beb36e82fd27284a42e8b25b3be897a2a4ec,STILL_EXISTS,TODO(nikip): Maybe add residual with a projection?
aahffgfaf,tensorflow/tensor2tensor,tensor2tensor/models/video/basic_recurrent.py,be663e857827116e3d9de15f0e20017ebd7d996f,14b467d939a003dab35855c15386f5dcf60d0b27,more hack to enable latent_tower
aahffgfbi,tensorflow/tensor2tensor,tensor2tensor/mesh_tensorflow/mtf_model.py,b4094d065fa0ae8842cd667fb0e5a2c652407c9c,STILL_EXISTS,TODO(ylc): Better estimation of replica cache size?
aahffggge,tensorflow/tensor2tensor,tensor2tensor/models/research/autoencoders.py,7cb5e55d206cee3de179dc27708b8920f6e67e36,STILL_EXISTS,Inputs and features preparation needed to handle edge cases.
aahffghgf,tensorflow/tensor2tensor,tensor2tensor/rl/trainer_model_based_params.py,1b16365fef49747163939c86686043eb195c061f,STILL_EXISTS,TODO(blazej): this param is unused
aahffghhh,tensorflow/tensor2tensor,tensor2tensor/rl/trainer_model_based_params.py,1b16365fef49747163939c86686043eb195c061f,STILL_EXISTS,is approximately as much as \"going back 1 epoch\" would be.
aahffgiba,tensorflow/tensor2tensor,tensor2tensor/rl/trainer_model_based.py,7c14ecd1210c0a56fcde63c227b392688f821883,b4df57ee9f6ddff6edae9b967f7020bc6b46277a,TODO(blazej): this param is unused
aahffgicc,tensorflow/tensor2tensor,tensor2tensor/rl/trainer_model_based.py,7c14ecd1210c0a56fcde63c227b392688f821883,b4df57ee9f6ddff6edae9b967f7020bc6b46277a,is approximately as much as \"going back 1 epoch\" would be.
aahffhacc,tensorflow/tensor2tensor,tensor2tensor/models/research/rl.py,e470049b8fcff77684a275f99b92e0f14f2e5dd7,b37543b01e703bef38f8ac9202986d85328d23e2,This hack is here because SimulatedBatchEnv needs to get
aahffhafg,tensorflow/tensor2tensor,tensor2tensor/rl/trainer_model_based_new.py,e470049b8fcff77684a275f99b92e0f14f2e5dd7,STILL_EXISTS,TODO: do we need option not to gather_ppo_real_env_data?
aahffhbce,tensorflow/tensor2tensor,tensor2tensor/models/video/base.py,14b467d939a003dab35855c15386f5dcf60d0b27,bac0da4f17c9d72c9f1ead192035888f17582c90,TODO(mbz): get rid of this somehow.
aahffhbdd,tensorflow/tensor2tensor,tensor2tensor/models/video/base.py,14b467d939a003dab35855c15386f5dcf60d0b27,STILL_EXISTS,Inputs and features preparation needed to handle edge cases.
aahffhbdh,tensorflow/tensor2tensor,tensor2tensor/models/video/base.py,14b467d939a003dab35855c15386f5dcf60d0b27,STILL_EXISTS,TODO(lukaszkaiser): this is a hack. get the actual reward history.
aahffhcba,tensorflow/tensor2tensor,tensor2tensor/models/video/base.py,eb9618e3ede41dd089f15b17aae4eb98288927d2,STILL_EXISTS,How many data points to suffle. Ideally should be part of problem not model!
aahffhcig,tensorflow/tensor2tensor,tensor2tensor/rl/envs/simulated_batch_env.py,b37543b01e703bef38f8ac9202986d85328d23e2,STILL_EXISTS,Currently not needed. Keeping it just in case.
aahffhdcj,tensorflow/tensor2tensor,tensor2tensor/models/video/base.py,ac56469240d17c7171902c692cb07fb2a8bcdf50,STILL_EXISTS,HACK: bypassing decoding issues.
aahffheaj,tensorflow/tensor2tensor,tensor2tensor/utils/mlperf_tags.py,5b6a310f40df315cd9dfb242f308b2fe8e86ae67,STILL_EXISTS,All models: Tags which should appear in absolutely every MLPerf model.
aahffhebh,tensorflow/tensor2tensor,tensor2tensor/utils/mlperf_tags.py,5b6a310f40df315cd9dfb242f308b2fe8e86ae67,STILL_EXISTS,is needed; please file an issue under:
aahffhefg,tensorflow/tensor2tensor,tensor2tensor/utils/mlperf_tags.py,5b6a310f40df315cd9dfb242f308b2fe8e86ae67,STILL_EXISTS,should simply provide a good starting point to an interested party.
aahffhehd,tensorflow/tensor2tensor,tensor2tensor/utils/mlperf_tags.py,5b6a310f40df315cd9dfb242f308b2fe8e86ae67,STILL_EXISTS,necessarily when it begins to apply gradients; rather; it should be placed at
aahffhhde,tensorflow/tensor2tensor,tensor2tensor/layers/bayes.py,c864603bad91503bd198a7a410603bf2d85d9d6a,STILL_EXISTS,TODO(trandustin): Hack to store parameters so KL reg. can operate on them.
aahffhhfg,tensorflow/tensor2tensor,tensor2tensor/layers/bayes_test.py,c864603bad91503bd198a7a410603bf2d85d9d6a,STILL_EXISTS,TODO(trandustin): Fix this to work with Eager.
aahffhjia,tensorflow/tensor2tensor,tensor2tensor/data_generators/wiki_revision_utils.py,dbe3949dc71596a08faaf0a8e81a65eed9656d4d,STILL_EXISTS,Many articles have mismatched braces; but it still seems better to remove
aahffiacb,tensorflow/tensor2tensor,tensor2tensor/layers/bayes.py,f5e5095fe2b5cf0e3d02d97554b6ec4c82b8292b,STILL_EXISTS,Unused in TrainableInitializers.
aahffiacc,tensorflow/tensor2tensor,tensor2tensor/layers/bayes.py,f5e5095fe2b5cf0e3d02d97554b6ec4c82b8292b,STILL_EXISTS,TODO(dusenberrymw): Restructure so that we can build as needed.
aahffibad,tensorflow/tensor2tensor,tensor2tensor/models/video/sv2p.py,337c815960c038126154ddd6a7c5cdaba50dea43,STILL_EXISTS,TODO(mbz): find a cleaner way of doing this maybe?!
aahffibfg,tensorflow/tensor2tensor,tensor2tensor/rl/policy_learner.py,e3702c88da9ac08b99e5d3e17e72e695928785f2,150aad3be93c68f9424c0769e9e710c754ebaaea,TODO(konradczechowski): move 'simulated' to  batch_env
aahfficci,tensorflow/tensor2tensor,tensor2tensor/data_generators/multi_problem.py,4ffcda7135050a4a813eaf3ca25dc8e06a047c6a,STILL_EXISTS,Using tf.Variable instead of get_variable to work around issues with
aahfficeb,tensorflow/tensor2tensor,tensor2tensor/layers/bayes.py,dd56db99cb3310d7f085e3ce7106ef78f05fbffc,8edc5f19b7c0168638c1588a84cb3df689589f2d,the following ends up being `mean + unconstrained_stddev * noise`.
aahfficia,tensorflow/tensor2tensor,tensor2tensor/models/research/rl.py,8097343247660f1e7f8018e2e9326198c942cbd6,STILL_EXISTS,is needed for model based rollouts).
aahffidfe,tensorflow/tensor2tensor,tensor2tensor/rl/ppo_learner.py,b5dea9e25b5b03e41c86b82a4a8dd18b41e68b34,STILL_EXISTS,Only needed in eval runs.
aahffidff,tensorflow/tensor2tensor,tensor2tensor/rl/ppo_learner.py,b5dea9e25b5b03e41c86b82a4a8dd18b41e68b34,STILL_EXISTS,Note - the only way to ensure making a copy of tensor is to run simple
aahffidhf,tensorflow/tensor2tensor,tensor2tensor/rl/trainer_model_based_params.py,b5dea9e25b5b03e41c86b82a4a8dd18b41e68b34,STILL_EXISTS,Unused; number of PPO epochs is calculated from the real frame limit.
aahffifeh,tensorflow/tensor2tensor,tensor2tensor/rl/dopamine_connector.py,abf63bfd672054b22b68b63543a96f4160530f76,STILL_EXISTS,If this will be a problem for maintenance; we could probably override
aahffijfd,tensorflow/tensor2tensor,tensor2tensor/models/mtf_transformer2.py,1eae64e8d50227d86da56be299e3226ae0ef2101,589df9423da982c775538ff9dcd270b6218d4ba5,TODO(noam): implement different types of transformers.
aahffjfff,tensorflow/tensor2tensor,tensor2tensor/layers/reversible_layers.py,981069c65e3a25441eee9b5354ab09d25c839fa2,STILL_EXISTS,unused
aahffjgah,tensorflow/tensor2tensor,tensor2tensor/models/research/transformer_parallel.py,63c9cd3efd12824a73ce40925075cec35e9f0dc3,STILL_EXISTS,Hack: if you report a loss of NaN; TensorBoard will plot a point at
aahffjggf,tensorflow/tensor2tensor,tensor2tensor/models/research/rl.py,79468f0e7be7bb1dd2119f29417691f9139b92b2,STILL_EXISTS,To avoid OOM. Probably way to small.
aahffjgjc,tensorflow/tensor2tensor,tensor2tensor/data_generators/problem.py,3c1e2bc85e4e6c3ba393f1570521d191ade4da92,ac74489b6aa1e9c5a9abd12d919ab66d9a99c1e8,If needed for using a pre-trained model's vocabulary where extra indices
aahfgaaec,tensorflow/tensor2tensor,tensor2tensor/rl/ppo_learner.py,3e8fe2e1f1069af35b46004482f01b2037a521b1,STILL_EXISTS,tf.global_variables(\"clean_scope.*\")  # Needed for sharing params.
aahfgaaee,tensorflow/tensor2tensor,tensor2tensor/rl/ppo_learner.py,3e8fe2e1f1069af35b46004482f01b2037a521b1,STILL_EXISTS,Needed for the SD model.
aahfgaafc,tensorflow/tensor2tensor,tensor2tensor/rl/player.py,6097be464ad98ef27835f020b9030eee1748392a,STILL_EXISTS,If action_space will be needed; one could use e.g. gym.spaces.Dict.
aahfgaage,tensorflow/tensor2tensor,tensor2tensor/rl/player.py,6097be464ad98ef27835f020b9030eee1748392a,7d6a169775e92210c1a7076a0cacd2566cf99930,TODO(konradczechowski): remove when not longer needed.
aahfgaagi,tensorflow/tensor2tensor,tensor2tensor/rl/player.py,6097be464ad98ef27835f020b9030eee1748392a,7d6a169775e92210c1a7076a0cacd2566cf99930,TODO(konradczechowski): remove when this will be not needed.
aahfgabcf,tensorflow/tensor2tensor,tensor2tensor/data_generators/multi_problem.py,bfa005773abe31e922941b48b6c0b104c72542ea,STILL_EXISTS,a part of \"targets\" feature. Override in your subclasses for other uses.
aahfgacgg,tensorflow/tensor2tensor,tensor2tensor/utils/quantization.py,147145a6046b18643640659dcfcb5c931b0818f7,STILL_EXISTS,This if statement is needed to guard the cast; because batch norm
aahfgachg,tensorflow/tensor2tensor,tensor2tensor/data_generators/gym_env.py,f14cf8d3d571b9b811f29b3c9bf5d72b87619880,STILL_EXISTS,TODO(afrozm): Find a proper way of doing this. Refactor or cleanup.
aahfgachj,tensorflow/tensor2tensor,tensor2tensor/data_generators/gym_env.py,f14cf8d3d571b9b811f29b3c9bf5d72b87619880,STILL_EXISTS,TODO(afrozm): Why is this separated out from _preprocess_observations?
aahfgacif,tensorflow/tensor2tensor,tensor2tensor/rl/evaluator.py,8b88b13dd65bf52b3c27663a128adb7b0a5773fb,STILL_EXISTS,Unused flags needed to pass for multi-run infrastructure.
aahfgadcg,tensorflow/tensor2tensor,tensor2tensor/envs/tic_tac_toe_env.py,92ebc7152e0f4f42871251f17dbe6db8409d4fae,STILL_EXISTS,... all columns
aahfgadci,tensorflow/tensor2tensor,tensor2tensor/envs/tic_tac_toe_env.py,92ebc7152e0f4f42871251f17dbe6db8409d4fae,STILL_EXISTS,TODO(afrozm): This should eventually subclass Problem.
aahfgadea,tensorflow/tensor2tensor,tensor2tensor/envs/tic_tac_toe_env.py,92ebc7152e0f4f42871251f17dbe6db8409d4fae,STILL_EXISTS,Unused.
aahfgaded,tensorflow/tensor2tensor,tensor2tensor/envs/tic_tac_toe_env.py,92ebc7152e0f4f42871251f17dbe6db8409d4fae,STILL_EXISTS,Is it a legitimate move; i.e. is that position open to play?
aahfgadhf,tensorflow/tensor2tensor,tensor2tensor/envs/tic_tac_toe_env_test.py,92ebc7152e0f4f42871251f17dbe6db8409d4fae,STILL_EXISTS,Play a move
aahfgahfg,tensorflow/tensor2tensor,tensor2tensor/envs/env_problem.py,be505cf5cf93869236eca64d92af8e52aca912c6,STILL_EXISTS,all the envs take one (extra) step; but would be a clean way to do it.
aahfgahgg,tensorflow/tensor2tensor,tensor2tensor/envs/env_problem.py,be505cf5cf93869236eca64d92af8e52aca912c6,STILL_EXISTS,`cycle_every_n` isn't needed since file list given to it is a singleton.
aahfgaiej,tensorflow/tensor2tensor,tensor2tensor/envs/gym_spaces_utils.py,be505cf5cf93869236eca64d92af8e52aca912c6,STILL_EXISTS,this is not needed.
aahfgajbc,tensorflow/tensor2tensor,tensor2tensor/envs/trajectory.py,be505cf5cf93869236eca64d92af8e52aca912c6,STILL_EXISTS,NOTE: We don't track the index this came from; as it's not needed; right?
aahfgajci,tensorflow/tensor2tensor,tensor2tensor/envs/trajectory.py,be505cf5cf93869236eca64d92af8e52aca912c6,STILL_EXISTS,Mark trajectory as completed and move into completed_trajectories.
aahfgbbeb,tensorflow/tensor2tensor,tensor2tensor/envs/env_problem.py,44323a19c0233babbd7b25a0e9b0adea7917219d,STILL_EXISTS,places in ppo_learner.py -- re-evaluate if needed.
aahfgbccc,tensorflow/tensor2tensor,tensor2tensor/data_generators/multi_problem_v2.py,5997c193654072802242a31f2fb2e2f4a503ba0e,STILL_EXISTS,locations. Although this is not the most disk efficient strategy; it
aahfgbdfc,tensorflow/tensor2tensor,tensor2tensor/v2/t2t.py,28adf2690c551ef0f570d41bef2019d9c502ec7e,STILL_EXISTS,TODO(lukaszkaiser): find a better way to do this or remove altogether.
aahfgbeae,tensorflow/tensor2tensor,tensor2tensor/layers/bayes.py,a5f26c1963ab262bc6d43b26e8d49612c422caa0,861ead837c077493446f34d3d3c1553f778afae3,unused arg
aahfgbfei,tensorflow/tensor2tensor,tensor2tensor/layers/common_attention.py,164d26baf74aa0ac5ab585d02f84b224ab86c129,STILL_EXISTS,TODO(lukaszkaiser): remove this function when not needed any more.
aahfgbfej,tensorflow/tensor2tensor,tensor2tensor/layers/common_layers.py,164d26baf74aa0ac5ab585d02f84b224ab86c129,STILL_EXISTS,TODO(lukaszkaiser): remove this function when not needed any more.
aahfgbffa,tensorflow/tensor2tensor,tensor2tensor/layers/transformer_layers.py,164d26baf74aa0ac5ab585d02f84b224ab86c129,STILL_EXISTS,TODO(lukaszkaiser): remove this function when not needed any more.
aahfgbfid,tensorflow/tensor2tensor,tensor2tensor/data_generators/generator_utils.py,c9cf76eff2acf60552c8015bef2853d89459bbcc,STILL_EXISTS,faster and better packing but requires custom-built binary.
aahfgbgbj,tensorflow/tensor2tensor,tensor2tensor/layers/common_attention.py,f6404dcc5492a8d74730e698ee69047f717458a7,STILL_EXISTS,prepare the left-most and right-most partial blocks if needed
aahfgbgci,tensorflow/tensor2tensor,tensor2tensor/layers/modalities.py,ac74489b6aa1e9c5a9abd12d919ab66d9a99c1e8,5923c0d89a5c15c4e6d9293bcf93f31becb43fc5,unused arg
aahfgbgda,tensorflow/tensor2tensor,tensor2tensor/utils/modality.py,ac74489b6aa1e9c5a9abd12d919ab66d9a99c1e8,STILL_EXISTS,__init__ args are unused in any methods. They're maintained for
aahfgbghc,tensorflow/tensor2tensor,tensor2tensor/jax/j2j.py,616421a12d842ae2d689d248249d2b5db0f2e0d3,STILL_EXISTS,Import for gin configurable models
aahfgbheg,tensorflow/tensor2tensor,tensor2tensor/layers/modalities.py,189d04e2391ef4991bd034f9c998323e9f0df924,STILL_EXISTS,unused
aahfgbhhc,tensorflow/tensor2tensor,tensor2tensor/jax/jaxboard.py,83a3e21db2dd077da5521f85ce09611788607aea,STILL_EXISTS,If needed; create log_dir directory as well as missing parent directories.
aahfgbiif,tensorflow/tensor2tensor,tensor2tensor/layers/common_image_attention.py,1852cca03271345ecd795d37a8d64608034cc63b,STILL_EXISTS,unused arg
aahfgbjga,tensorflow/tensor2tensor,tensor2tensor/models/video/base.py,1852cca03271345ecd795d37a8d64608034cc63b,5923c0d89a5c15c4e6d9293bcf93f31becb43fc5,TODO(trandustin): This is a hack.
aahfgbjhh,tensorflow/tensor2tensor,tensor2tensor/layers/modalities.py,5923c0d89a5c15c4e6d9293bcf93f31becb43fc5,STILL_EXISTS,unused
aahfgbjib,tensorflow/tensor2tensor,tensor2tensor/layers/modalities.py,5923c0d89a5c15c4e6d9293bcf93f31becb43fc5,5923c0d89a5c15c4e6d9293bcf93f31becb43fc5,unused arg
aahfgcbci,tensorflow/tensor2tensor,tensor2tensor/trax/optimizers.py,cbe5c82e28aebd9449693f7e6ea03b21ad4f10fd,STILL_EXISTS,\"\"\"gin-configurable optimizers and learning rate functions.\"\"\"
aahfgcbfb,tensorflow/tensor2tensor,tensor2tensor/trax/trax.py,cbe5c82e28aebd9449693f7e6ea03b21ad4f10fd,STILL_EXISTS,* Make learning rate configurable; possibly combine with optimizer
aahfgcbfc,tensorflow/tensor2tensor,tensor2tensor/trax/trax.py,cbe5c82e28aebd9449693f7e6ea03b21ad4f10fd,STILL_EXISTS,* Make loss configurable
aahfgcbfd,tensorflow/tensor2tensor,tensor2tensor/trax/trax.py,cbe5c82e28aebd9449693f7e6ea03b21ad4f10fd,STILL_EXISTS,* Make eval metrics configurable
aahfgcbfh,tensorflow/tensor2tensor,tensor2tensor/trax/trax.py,77673a0cd2cee3a1568a69d7ff0108e6501d9ffb,50b2455293a5ec7306fd122a922d987e384d0d03,TODO(trax): Move to trainer.py. Only here because of t2t_trainer usage.
aahfgcbgf,tensorflow/tensor2tensor,tensor2tensor/trax/trax.py,77673a0cd2cee3a1568a69d7ff0108e6501d9ffb,STILL_EXISTS,* Make learning rate configurable; possibly combine with optimizer
aahfgcbgi,tensorflow/tensor2tensor,tensor2tensor/trax/jaxboard.py,85791b5218aa186ddd2fb52120a0227601dd21fc,STILL_EXISTS,TODO(b\/37527917): Total hack below. Implement more principled formatting.
aahfgcbgj,tensorflow/tensor2tensor,tensor2tensor/trax/trax.py,85791b5218aa186ddd2fb52120a0227601dd21fc,STILL_EXISTS,* Move metrics to metrics.py
aahfgccfe,tensorflow/tensor2tensor,tensor2tensor/layers/common_image_attention.py,c05874acf2af1397ca3f0a7f7e1e351b77f938ba,STILL_EXISTS,unused arg
aahfgcdde,tensorflow/tensor2tensor,tensor2tensor/layers/modalities.py,c05874acf2af1397ca3f0a7f7e1e351b77f938ba,STILL_EXISTS,unused
aahfgcddf,tensorflow/tensor2tensor,tensor2tensor/models/video/base.py,c05874acf2af1397ca3f0a7f7e1e351b77f938ba,STILL_EXISTS,TODO(trandustin): This is a hack.
aahfgcdfi,tensorflow/tensor2tensor,tensor2tensor/trax/learning_rate.py,767b2fa373dae5eb2fa7bb880b0dc85d1cfc6d13,e166c8b62e98cefd1ff932580e7c61e903727874,@gin.configurable(blacklist=[\"history\"])
aahfgcdhi,tensorflow/tensor2tensor,tensor2tensor/trax/learning_rate.py,767b2fa373dae5eb2fa7bb880b0dc85d1cfc6d13,e166c8b62e98cefd1ff932580e7c61e903727874,improvement_margin: how much we need to improve to consider the metric
aahfgcfgi,tensorflow/tensor2tensor,tensor2tensor/trax/models/transformer.py,d27a628eefaf7945a5b7adb924b016b5a2c50803,e48cf23c505565fd63378286d9722a1632f4bef7,We return the functions needed to train and evaluate the Transformer.
aahfgchdf,tensorflow/tensor2tensor,tensor2tensor/trax/stax/slax_test.py,d27a628eefaf7945a5b7adb924b016b5a2c50803,STILL_EXISTS,combinators of up to six variables.  This probably covers most
aahfgchea,tensorflow/tensor2tensor,tensor2tensor/trax/stax/slax_test.py,d27a628eefaf7945a5b7adb924b016b5a2c50803,STILL_EXISTS,Test a few other cases; unused variables; non-input-tree use of
aahfgcjaa,tensorflow/tensor2tensor,tensor2tensor/layers/modalities.py,19cd29e68e82f176ec6a493314385c12728ca3cb,STILL_EXISTS,perhaps there were no inputs; and this is a new variable.
aahfgcjcj,tensorflow/tensor2tensor,tensor2tensor/layers/modalities.py,19cd29e68e82f176ec6a493314385c12728ca3cb,STILL_EXISTS,Move frames next to batch.
aahfgcjeh,tensorflow/tensor2tensor,tensor2tensor/trax/trax.py,1b08988f0d691223d4534b85f426f29dc5a39b1c,STILL_EXISTS,We need a model_predict that fills in the random generator if needed.
aahfgcjfa,tensorflow/tensor2tensor,tensor2tensor/utils/hparams_lib.py,b44804149ef543385c584f756bf2d2ad90b68299,STILL_EXISTS,TODO(trandustin): Remove this hack after registries are available to avoid
aahfgdaif,tensorflow/tensor2tensor,tensor2tensor/models/transformer.py,8d93b2eb65d30a36a1b14ba861fd2a3c59c56d0f,STILL_EXISTS,>1 shards helps with faster parameter distribution on multi-GPU machines
aahfgdbif,tensorflow/tensor2tensor,tensor2tensor/models/research/rl.py,150aad3be93c68f9424c0769e9e710c754ebaaea,151dc27eb1b9f169c7e08e9e1b660f011ea99796,TODO: change names maybe replay_buffer -> agent? Also batch_size is now
aahfgdcad,tensorflow/tensor2tensor,tensor2tensor/rl/batch_dqn_agent_test.py,150aad3be93c68f9424c0769e9e710c754ebaaea,STILL_EXISTS,TODO: maybe add testStepTrain (and possibly some other tests) from dopamine
aahfgdcdj,tensorflow/tensor2tensor,tensor2tensor/rl/batch_runner_test.py,150aad3be93c68f9424c0769e9e710c754ebaaea,STILL_EXISTS,TODO: this can be used for mbrl pipeline (for both simulated and real env);
aahfgdcea,tensorflow/tensor2tensor,tensor2tensor/rl/batch_runner_test.py,150aad3be93c68f9424c0769e9e710c754ebaaea,STILL_EXISTS,move it to dopamine_connector.py (rename it?)
aahfgdceb,tensorflow/tensor2tensor,tensor2tensor/rl/batch_runner_test.py,150aad3be93c68f9424c0769e9e710c754ebaaea,STILL_EXISTS,TODO: decide if we want to use and modify more tests from
aahfgdcfa,tensorflow/tensor2tensor,tensor2tensor/rl/batch_runner_test.py,150aad3be93c68f9424c0769e9e710c754ebaaea,STILL_EXISTS,TODO: We probably do not need this test; dopamine test for Runner is enugh
aahfgddac,tensorflow/tensor2tensor,tensor2tensor/rl/policy_learner.py,151dc27eb1b9f169c7e08e9e1b660f011ea99796,f28a5e9cec63fb2e0af575fdca754318b0cbdbca,TODO(konradczechowski): move 'simulated' to  batch_env
aahfgddfb,tensorflow/tensor2tensor,tensor2tensor/models/research/rl.py,f28a5e9cec63fb2e0af575fdca754318b0cbdbca,STILL_EXISTS,TODO(kozak): change names maybe replay_buffer -> agent?
aahfgdeag,tensorflow/tensor2tensor,tensor2tensor/rl/batch_dqn_agent_test.py,f28a5e9cec63fb2e0af575fdca754318b0cbdbca,STILL_EXISTS,TODO: maybe add testStepTrain (and possibly some other tests) from dopamine
aahfgdeee,tensorflow/tensor2tensor,tensor2tensor/rl/batch_runner_test.py,f28a5e9cec63fb2e0af575fdca754318b0cbdbca,STILL_EXISTS,real env); move it to dopamine_connector.py (rename it?)
aahfgdeha,tensorflow/tensor2tensor,tensor2tensor/rl/batch_runner_test.py,f28a5e9cec63fb2e0af575fdca754318b0cbdbca,STILL_EXISTS,TODO: this can be used for mbrl pipeline (for both simulated and real env);
aahfgdehb,tensorflow/tensor2tensor,tensor2tensor/rl/batch_runner_test.py,f28a5e9cec63fb2e0af575fdca754318b0cbdbca,STILL_EXISTS,move it to dopamine_connector.py (rename it?)
aahfgdehc,tensorflow/tensor2tensor,tensor2tensor/rl/batch_runner_test.py,f28a5e9cec63fb2e0af575fdca754318b0cbdbca,STILL_EXISTS,TODO: decide if we want to use and modify more tests from
aahfgdeib,tensorflow/tensor2tensor,tensor2tensor/rl/batch_runner_test.py,f28a5e9cec63fb2e0af575fdca754318b0cbdbca,STILL_EXISTS,TODO: We probably do not need this test; dopamine test for Runner is enugh
aahfgdfja,tensorflow/tensor2tensor,tensor2tensor/keras/initializers.py,861ead837c077493446f34d3d3c1553f778afae3,STILL_EXISTS,unused arg
aahfgdgcf,tensorflow/tensor2tensor,tensor2tensor/trax/trax.py,c84eafe3722035dc632b5b912039427c03d90c20,STILL_EXISTS,TODO(lukaszkaiser): remove branch when not needed.
aahfgdghd,tensorflow/tensor2tensor,tensor2tensor/data_generators/algorithmic_math_deepmind.py,9dc3d1274ce8cb25513adb071262cadb4ba7e5d3,STILL_EXISTS,Create directories if needed.
aahfgdgia,tensorflow/tensor2tensor,tensor2tensor/rl/gym_utils_test.py,f6c024be1b1129d9d83fcd96828d0aea3b3d67ac,STILL_EXISTS,Unused
aahfgdgjh,tensorflow/tensor2tensor,tensor2tensor/trax/stax/stax_base.py,9c557c2eaa10d454f11abb2ab3c8d2dd2a6582af,e6d2a3e4f2cd6e51dffa229d9c3fdcabf31bdfea,Forked from JAX for more experimentation on syntax and back-ends.
aahfgdgjj,tensorflow/tensor2tensor,tensor2tensor/trax/stax/stax_base.py,9c557c2eaa10d454f11abb2ab3c8d2dd2a6582af,STILL_EXISTS,Following the convention used in Keras and tf.layers; we use CamelCase for the
aahfgdhec,tensorflow/tensor2tensor,tensor2tensor/trax/rlax/ppo.py,22b964cfce40f6b1ab2cd215a583d17c6d1e40d7,STILL_EXISTS,Can we do something much simpler than lax.pad; maybe np.pad?
aahfgdiba,tensorflow/tensor2tensor,tensor2tensor/trax/backend.py,271a2077279a3e7ea79140b46bdc659fe5dcf439,STILL_EXISTS,\"\"\"Trax backend: all the primitive functions needed.\"\"\"
aahfgdjaa,tensorflow/tensor2tensor,tensor2tensor/trax/stax/stax_base.py,efea4ab1ca262092dde6e357ade29c353c37ed9f,e6d2a3e4f2cd6e51dffa229d9c3fdcabf31bdfea,ignore unused arguments
aahfgdjac,tensorflow/tensor2tensor,tensor2tensor/trax/stax/stax_base.py,efea4ab1ca262092dde6e357ade29c353c37ed9f,STILL_EXISTS,unused
aahfgdjae,tensorflow/tensor2tensor,tensor2tensor/trax/stax/stax_base.py,efea4ab1ca262092dde6e357ade29c353c37ed9f,STILL_EXISTS,Unused.
aahfgdjbf,tensorflow/tensor2tensor,tensor2tensor/trax/rlax/ppo.py,c0b2b6dedab82fc38cd2ffe739509381ce9baff5,STILL_EXISTS,TODO(afrozm): Implement.
aahfgeffg,tensorflow/tensor2tensor,tensor2tensor/trax/layers/attention.py,9c215db7aabdcece6c7dcdb2a7aa279a7731dddb,777c6a8f477bcb36ffab5c9bdd42c54a2932501a,Chunked case: apply to all chunks selecting as much as needed.
aahfgegac,tensorflow/tensor2tensor,tensor2tensor/layers/common_image_attention.py,d84e1baa4c2ac6b6056ee9478957018825f7c315,STILL_EXISTS,Maybe num_blocks can be automatically calculated?
aahfgeijj,tensorflow/tensor2tensor,tensor2tensor/envs/trajectory.py,46fc2da1ca7d5acf06016f4896a9bfe7d9915942,STILL_EXISTS,the last time-step and adding more padding if needed.
aahfgejec,tensorflow/tensor2tensor,tensor2tensor/trax/rlax/ppo_main.py,1aa26eaf0dbb9841b915bf8a7820355e3d527436,STILL_EXISTS,No resizing needed; so let's be on the normal EnvProblem.
aahfgfcha,tensorflow/tensor2tensor,tensor2tensor/utils/scheduled_sampling.py,de99648093456d9362699af405458a2845c2fe69,STILL_EXISTS,unused.
aahfgfcii,tensorflow/tensor2tensor,tensor2tensor/utils/t2t_model.py,de99648093456d9362699af405458a2845c2fe69,STILL_EXISTS,TODO(duckworthd): Move this block to scheduled_sampling.py.
aahfgfdaf,tensorflow/tensor2tensor,tensor2tensor/trax/rlax/ppo.py,b4c2d57263a5da9eafbf6a79555fc219a22e2ccb,STILL_EXISTS,TODO(afrozm): Maybe clip gradients?
aahfgfeed,tensorflow/tensor2tensor,tensor2tensor/models/neural_architecture_search/nas_layers.py,4ce366131ce69d1005f035e14677609f7dfdb580,STILL_EXISTS,Lightweight convolution naming convention uses \"R_X\" where X is the variable
aahfgffff,tensorflow/tensor2tensor,tensor2tensor/models/neural_architecture_search/nas_model.py,4ce366131ce69d1005f035e14677609f7dfdb580,STILL_EXISTS,All unused outputs combined_together.
aahfgfffg,tensorflow/tensor2tensor,tensor2tensor/models/neural_architecture_search/nas_model.py,4ce366131ce69d1005f035e14677609f7dfdb580,STILL_EXISTS,Add the resizing layer if needed.
aahfgffij,tensorflow/tensor2tensor,tensor2tensor/models/research/rl.py,61889b8d184c9bef4e5bbe93ad085483a5b80ab5,STILL_EXISTS,unused param for multi-run settings.
aahfgfgib,tensorflow/tensor2tensor,tensor2tensor/trax/models/research/chunked_transformer.py,777c6a8f477bcb36ffab5c9bdd42c54a2932501a,STILL_EXISTS,Chunked case: apply to all chunks selecting as much as needed.
aahfgfgih,tensorflow/tensor2tensor,tensor2tensor/utils/t2t_model.py,e61eba618c04a6c423f6e78979d089033a8fa7c7,STILL_EXISTS,TODO(duckworthd): Move to scheduled_sampling.py.
aahfgfidb,tensorflow/tensor2tensor,tensor2tensor/rl/ppo.py,6667752e92d50edd078b93e7a3af695b1588f003,STILL_EXISTS,Unused in this case.
aahfgfifj,tensorflow/tensor2tensor,tensor2tensor/trax/layers/combinators.py,a03d78a1764a5d58912ca918889aefcfe5541f22,STILL_EXISTS,TODO(jonni): Implement for dict if dicts remain important.
aahfgfjgh,tensorflow/tensor2tensor,tensor2tensor/trax/rlax/ppo.py,dfc84418d15ce4b04ef69ba3efa90273c2c438df,STILL_EXISTS,This is much better; but might have lost updates since the (small) rewards
aahfgfjhg,tensorflow/tensor2tensor,tensor2tensor/layers/common_attention.py,2da59d24eb9367cbed20c98df559beccd11b7582,STILL_EXISTS,move heads to batch dimension. This is needed to reduce number of
aahfgfjjc,tensorflow/tensor2tensor,tensor2tensor/layers/common_attention.py,2da59d24eb9367cbed20c98df559beccd11b7582,STILL_EXISTS,In fast decoding; output only contains one element; this is not needed.
aahfggagg,tensorflow/tensor2tensor,tensor2tensor/layers/reversible_layers.py,9f78c55b556eaa3e5128c9a07393780b9119c940,STILL_EXISTS,TODO(vafa): Fix tf.py_func headless GPU bug.
aahfggagi,tensorflow/tensor2tensor,tensor2tensor/layers/reversible_layers.py,9f78c55b556eaa3e5128c9a07393780b9119c940,STILL_EXISTS,TODO(trandustin): Implement with circular conv1d.
aahfggaie,tensorflow/tensor2tensor,tensor2tensor/layers/reversible_layers.py,d546695261d3feefa330110438005702d9be89eb,STILL_EXISTS,TODO(trandustin): Implement with circular conv1d.
aahfggbgb,tensorflow/tensor2tensor,tensor2tensor/data_generators/generator_utils.py,560c008f7d87502174765fac5ae3d822bbf6b243,STILL_EXISTS,better packing (may be faster) but requires custom-built binary.
aahfggcfj,tensorflow/tensor2tensor,tensor2tensor/envs/env_problem_utils.py,6c7f2ab4b45ca0f2082a4b14b48cf7d4b45cbbea,STILL_EXISTS,Bucket on whatever length is needed.
aahfggdeh,tensorflow/tensor2tensor,tensor2tensor/trax/trax.py,b2615aab938af99418ac0d1318338bf3030357fa,STILL_EXISTS,TODO(lukaszkaiser): remove branch when not needed.
aahfggdfj,tensorflow/tensor2tensor,tensor2tensor/trax/trax.py,b2615aab938af99418ac0d1318338bf3030357fa,STILL_EXISTS,Evaluate only the loss function (a more efficient; jitted; implementation)
aahfggdga,tensorflow/tensor2tensor,tensor2tensor/trax/trax.py,b2615aab938af99418ac0d1318338bf3030357fa,STILL_EXISTS,TODO(kitaev): implement saving graphs while making sure that no op-by-op
aahfggdgc,tensorflow/tensor2tensor,tensor2tensor/trax/layers/attention.py,b3a667e841953ad81cdd718c1b94e802df2891fe,STILL_EXISTS,TODO(kitaev): workaround for https:\/\/github.com\/google\/jax\/issues\/850
aahfggeab,tensorflow/tensor2tensor,tensor2tensor/trax/models/research/transformer_revnet.py,8c23cbb2f3634d7ba2d9ade1c88b935e07197218,b196b45b8ba8cdcc0086aca0a7acf1bc67c0ac1d,TODO(kitaev): Figure out why parameter sharing doesn't work (if this
aahfggecg,tensorflow/tensor2tensor,tensor2tensor/rl/dopamine_connector.py,ddf0ef2f1f5e7653887b7ed9997d52622888957c,STILL_EXISTS,TODO: Vanilla DQN and Rainbow have a lot of common code. Most likely we want
aahfggedg,tensorflow/tensor2tensor,tensor2tensor/rl/dopamine_connector.py,ddf0ef2f1f5e7653887b7ed9997d52622888957c,STILL_EXISTS,If this will be a problem for maintenance; we could probably override
aahfggfha,tensorflow/tensor2tensor,tensor2tensor/layers/common_layers.py,eb048f69c7ea860324122b87cb9caf59c52a27f3,d124912c80fa40f2a6b4afca95ba3d54b6b7a7c6,TODO(karishmamalkan): Fix documentation and refactor name
aahfgggbh,tensorflow/tensor2tensor,tensor2tensor/utils/t2t_model.py,58435743965dfc3bed28fda62664397fc18fff76,448ce0f56d7f67f7b610c1db72e2ea9529c44511,Workaround for \"ValueError: prediction values must be from the default
aahfgghjg,tensorflow/tensor2tensor,tensor2tensor/trax/rlax/ppo_main.py,2ff384a84f389d7384373441226e27d343b36118,STILL_EXISTS,TODO(pkozakowski): Find a better way to determine this.
aahfggiic,tensorflow/tensor2tensor,tensor2tensor/layers/bayes.py,3856200e65c32ed8da9c7d7745399abdf3a7e7f0,STILL_EXISTS,TODO(trandustin): Depend on github's ed2 for indexing RVs. This is a hack.
aahfghacg,tensorflow/tensor2tensor,tensor2tensor/layers/gaussian_process.py,f48bda1165d26f63421fe9f3f01fc5974209c9a5,STILL_EXISTS,TODO(adityagrover): Implement via T2T.
aahfghach,tensorflow/tensor2tensor,tensor2tensor/layers/gaussian_process.py,f48bda1165d26f63421fe9f3f01fc5974209c9a5,STILL_EXISTS,TODO(adityagrover): Make the encoder and decoder configurable.
aahfghadf,tensorflow/tensor2tensor,tensor2tensor/keras/initializers.py,99d7a69081eea10696729c09199260b1578b6217,STILL_EXISTS,unused arg
aahfghcgg,tensorflow/tensor2tensor,tensor2tensor/envs/gym_env_problem.py,2a0d6a303d1e6d46874bc12308b78aea74d062b9,STILL_EXISTS,The individual dictionaries maybe empty.
aahfghdbg,tensorflow/tensor2tensor,tensor2tensor/trax/optimizers/base.py,210282baf89580a081ef6952264fb1ea50d22300,286d7617b5d75f92be24fa8432aa7785cb42dd92,TODO(levskaya): refactor to use newer RL friendly parameter passing.
aahfghdfd,tensorflow/tensor2tensor,tensor2tensor/envs/env_problem_utils.py,80559082f0eed6ef1a9bcd5792243350e476455a,STILL_EXISTS,No resizing needed; so let's be on the normal EnvProblem.
aahfgheec,tensorflow/tensor2tensor,tensor2tensor/envs/env_service_servicer.py,80559082f0eed6ef1a9bcd5792243350e476455a,STILL_EXISTS,Anything more efficient?
aahfgheie,tensorflow/tensor2tensor,tensor2tensor/trax/optimizers/base.py,937a05164b1056819bb2f8603e6026f23832f8c6,STILL_EXISTS,These 4 parameters are not configurable once the class is created.
aahfgheif,tensorflow/tensor2tensor,tensor2tensor/trax/optimizers/base.py,937a05164b1056819bb2f8603e6026f23832f8c6,STILL_EXISTS,Dynamically configurable parameters will be passed to the update function.
aahfghfdg,tensorflow/tensor2tensor,tensor2tensor/trax/rlax/ppo_main.py,62cf66fda3494e815895973dbcd94bb983ae5bc8,STILL_EXISTS,TODO(afrozm): Find a better way to do these configurations.
aahfghfje,tensorflow/tensor2tensor,tensor2tensor/trax/models/research/transformer_revnet.py,673ca5096df85963f1d858218dd426a7290a6925,STILL_EXISTS,TODO(kitaev): implement attention dropout
aahfghgdf,tensorflow/tensor2tensor,tensor2tensor/trax/rlax/simulated_env_problem.py,f7f8549a6421723154b366996b2c6559048ac3fb,STILL_EXISTS,second dimension. Do it more efficiently if needed.
aahfghgic,tensorflow/tensor2tensor,tensor2tensor/trax/models/research/transformer_revnet.py,1790cccb40d124e9726ee2d2c457b4cedac17d8e,STILL_EXISTS,LayerNorm; and whether dropout broadcasting is needed here.
aahfghjfc,tensorflow/tensor2tensor,setup.py,4289bcfa12b75f434bfa2f73ca5adc088ea35731,STILL_EXISTS,Needed to fix a Travis pytest error.
aahfgiafb,tensorflow/tensor2tensor,tensor2tensor/trax/rl/ppo_trainer_test.py,ab10c536ab8189264aa7b81869bec349cdedd154,51fc796aad0259bc9622950fc2f0750e25c9af59,TODO(pkozakowski): Fix.
aahfgiaha,tensorflow/tensor2tensor,tensor2tensor/trax/layers/initializers.py,76803debef448a0f2c56b40b3427cef2e9c8b5be,STILL_EXISTS,temporary fix until numpy.delete supports negative indices
aahfgicgj,tensorflow/tensor2tensor,tensor2tensor/trax/layers/attention.py,a41ffb598eddf65ec31b41aa8afa56bc59694173,08a851cbf3860c4413bf435f43247cc84fee9fe6,TODO(kitaev): making the vectors unit-length here is probably redundant.
aahfgicig,tensorflow/tensor2tensor,tensor2tensor/trax/layers/attention.py,a41ffb598eddf65ec31b41aa8afa56bc59694173,08a851cbf3860c4413bf435f43247cc84fee9fe6,attention softmax; but normalizing keys is needed so that similarity for
aahfgidec,tensorflow/tensor2tensor,tensor2tensor/trax/inputs.py,32fe4b38cefb1997f6561e3bb94a220b8e81a498,STILL_EXISTS,add an option to evaluate on the whole dataset; then we'll need to think of
aahfgidej,tensorflow/tensor2tensor,tensor2tensor/trax/layers/initializers.py,38c7bb01f33bd85fccc584e10fa17543bdf25757,STILL_EXISTS,Transpose and reshape back q if needed.
aahfgidfb,tensorflow/tensor2tensor,tensor2tensor/trax/optimizers/base.py,ddd02b62a50b9a570d97250883d986b20e3cd8f9,STILL_EXISTS,TODO(lukaszkaiser): why is the astype needed here? Check and correct.
aahfgidfe,tensorflow/tensor2tensor,tensor2tensor/trax/rl/simple.py,648184c46abb86239d8fdb317d7921d4bae8800c,STILL_EXISTS,Does not matter which action we play after the real trajetory ends; we
aahfgidig,tensorflow/tensor2tensor,tensor2tensor/trax/layers/attention.py,01fbe2b8076fae667b08c03750600d3687f6be41,08a851cbf3860c4413bf435f43247cc84fee9fe6,TODO(kitaev): making the vectors unit-length here is probably redundant.
aahfgieba,tensorflow/tensor2tensor,tensor2tensor/trax/layers/attention.py,01fbe2b8076fae667b08c03750600d3687f6be41,08a851cbf3860c4413bf435f43247cc84fee9fe6,attention softmax; but normalizing keys is needed so that similarity for
aahfgieeb,tensorflow/tensor2tensor,tensor2tensor/envs/trajectory.py,6561075293da462447c73c98a0e045d17c6d32b1,STILL_EXISTS,Randomly sample `n_trajectories` if needed.
aahfgiegg,tensorflow/tensor2tensor,tensor2tensor/data_generators/enwik8.py,d9a6d7b6327fdaf69f9f5e230977236b45a127ab,STILL_EXISTS,TODO(kitaev): Better handling of evaluation data; to ensure that there is
aahfgifag,tensorflow/tensor2tensor,tensor2tensor/envs/trajectory.py,9b5f169417d9f746ea84136fa35fac52e606ca90,STILL_EXISTS,Randomly sample `n_trajectories` if needed.
aahfgiidf,tensorflow/tensor2tensor,tensor2tensor/trax/rl/envs/async_trajectory_collector.py,ac433ea406f2fa2b17329994f0b2e67b02819c05,STILL_EXISTS,TODO(pkozakowski): Find a better way to determine this.
aahfgiifc,tensorflow/tensor2tensor,tensor2tensor/trax/rl/envs/async_trajectory_collector_lib.py,ac433ea406f2fa2b17329994f0b2e67b02819c05,STILL_EXISTS,TODO(afrozm): Is there a better way to poll for a file on CNS?
aahfgiiga,tensorflow/tensor2tensor,tensor2tensor/trax/rl/envs/async_trajectory_collector_lib.py,ac433ea406f2fa2b17329994f0b2e67b02819c05,STILL_EXISTS,Get an initial policy and wait a forever to get it if needed.
aahfgijah,tensorflow/tensor2tensor,tensor2tensor/utils/t2t_model.py,7ff1a59c4203ffbffc7b0c27e3f8393d09ec8c16,STILL_EXISTS,Workaround for \"ValueError: prediction values must be from the default
aahfgjaff,tensorflow/tensor2tensor,tensor2tensor/trax/layers/core.py,e2e9a5ff9975ab5699c9c174012ba6290f3ead8a,STILL_EXISTS,It's only needed for a specific purpose in the short term; will go.
aahfgjajf,tensorflow/tensor2tensor,tensor2tensor/trax/layers/attention.py,fe03eb5e94409b4efe84a0126ef5d70862ea0301,08a851cbf3860c4413bf435f43247cc84fee9fe6,attention softmax; but normalizing keys is needed so that similarity for
aahfgjbdc,tensorflow/tensor2tensor,tensor2tensor/trax/layers/base.py,4343cfeb38a4d25352d6202b96a4e1721d7d2973,STILL_EXISTS,XXX(kitaev):
aahfgjdie,tensorflow/tensor2tensor,tensor2tensor/trax/rl/simulated_env_problem.py,6e4cd2362b4de49608d689b4af5b4cdec35d2539,STILL_EXISTS,indexing; [0] here. But we should make it more explicit in a better API.
aahfgjdjc,tensorflow/tensor2tensor,tensor2tensor/trax/trax.py,6e4cd2362b4de49608d689b4af5b4cdec35d2539,STILL_EXISTS,TODO(lukaszkaiser): clean this up once layer API stabilizes.
aahfgjdjh,tensorflow/tensor2tensor,tensor2tensor/trax/trax.py,6e4cd2362b4de49608d689b4af5b4cdec35d2539,STILL_EXISTS,indexing; [0] here. But we should make it more explicit in a better API.
aahfgjfeh,tensorflow/tensor2tensor,tensor2tensor/models/transformer.py,c825d126643d1c8864d43d828df0e7a868eaa180,1f7cbd16471c5cd73e8030b7acfa56286e0a1a6f,Workaround for: tf.one_hot(
aahfgjgjd,tensorflow/tensor2tensor,tensor2tensor/rl/gym_utils_test.py,90326159f8465ea0414422e2b7ed1505f9e1af5d,STILL_EXISTS,Unused
aahfgjigj,tensorflow/tensor2tensor,tensor2tensor/models/research/transformer_vae_flow_prior.py,d08409f3e860141c0d0b0669d73cd73b952702c2,STILL_EXISTS,>1 shards helps with faster parameter distribution on multi-GPU machines
aahfgjjah,tensorflow/tensor2tensor,tensor2tensor/models/research/transformer_vae_flow_prior_ops.py,d08409f3e860141c0d0b0669d73cd73b952702c2,STILL_EXISTS,unused arg
aahfgjjcg,tensorflow/tensor2tensor,tensor2tensor/utils/contrib.py,f65b5e4e0be50b284f9b21d56d3d2a46792cdecf,STILL_EXISTS,these as needed.
aahfgjjhe,tensorflow/tensor2tensor,tensor2tensor/utils/multistep_with_adamoptimizer.py,94a3c0ee1dc9ff247d2605ef6b195bd716718b67,STILL_EXISTS,TODO(fstahlberg): Implement a sparse version
aahfhaach,tensorflow/tensor2tensor,tensor2tensor/data_generators/translate_encs_cubbitt.py,2954749ddf5be3c72b2adabb2b10228b6232672b,STILL_EXISTS,\"\"\"Data generators for English-Czech backtranslation NMT data-sets. ||  || To use this problem you need to provide backtranslated (synthetic) data to the tmp_dir || (cs_mono_{en;cs}.txt{0;1;2} - each file of a similar size to the authentic training data). || You can either translate the monolingual data yourself || or you can download \"csmono\" data from CzEng2.0 (http:\/\/ufal.mff.cuni.cz\/czeng; registration needed); || which comes with synthetic translations into English using a backtranslation-trained model; || thus the final model will be using \"iterated\" backtranslation. ||  || To get the best results out of the Block-Backtranslation || (where blocks of synthetic and authentic training data are concatenated without shuffling); || you should use checkpoint averaging (see t2t-avg-all). || \"\"\"
aahfhaaei,tensorflow/tensor2tensor,tensor2tensor/data_generators/translate_encs_cubbitt.py,95d021477272c10af15cd62f25b595ad16ad514e,STILL_EXISTS,\"\"\"Data generators for English-Czech backtranslation NMT data-sets. ||  || To use this problem you need to provide backtranslated (synthetic) data to || tmp_dir (cs_mono_{en;cs}.txt{0;1;2} - each file of a similar size to the || authentic training data). || You can either translate the monolingual data yourself or you can download || \"csmono\" data from CzEng2.0 (http:\/\/ufal.mff.cuni.cz\/czeng; registration needed) || which comes with synthetic translations into English using a || backtranslation-trained model; thus the final model will be using || \"iterated\" backtranslation. ||  || To get the best results out of the Block-Backtranslation || (where blocks of synthetic and authentic training data are concatenated || without shuffling); you should use checkpoint averaging (see t2t-avg-all). || \"\"\"
aahfhaagj,tensorflow/tensor2tensor,tensor2tensor/data_generators/translate_encs_cubbitt.py,95d021477272c10af15cd62f25b595ad16ad514e,STILL_EXISTS,\"\"\"Data generators for English-Czech backtranslation NMT data-sets. ||  || To use this problem you need to provide backtranslated (synthetic) data to the tmp_dir || (cs_mono_{en;cs}.txt{0;1;2} - each file of a similar size to the authentic training data). || You can either translate the monolingual data yourself || or you can download \"csmono\" data from CzEng2.0 (http:\/\/ufal.mff.cuni.cz\/czeng; registration needed); || which comes with synthetic translations into English using a backtranslation-trained model; || thus the final model will be using \"iterated\" backtranslation. ||  || To get the best results out of the Block-Backtranslation || (where blocks of synthetic and authentic training data are concatenated without shuffling); || you should use checkpoint averaging (see t2t-avg-all). || \"\"\"
aahfhabei,RaRe-Technologies/gensim,Article.py,7dfc5ce04437298d4aa3f69ef7cd6e033d7feaf8,STILL_EXISTS,consistentIds(art1.id_int; art2.id_int) and  \\ # FIXME don't check internal id consistency for now
aahfhabfg,RaRe-Technologies/gensim,ArticleDB.py,7dfc5ce04437298d4aa3f69ef7cd6e033d7feaf8,STILL_EXISTS,FIXME HACK turns off consistency checking
aahfhabgd,RaRe-Technologies/gensim,DocumentCollection.py,7dfc5ce04437298d4aa3f69ef7cd6e033d7feaf8,STILL_EXISTS,TODO pridat DocumentCollection.check(): test na prazdne dokumenty; konzistenci; chybove stavy atd
aahfhabgf,RaRe-Technologies/gensim,DocumentCollection.py,7dfc5ce04437298d4aa3f69ef7cd6e033d7feaf8,STILL_EXISTS,# FIXME: !!doesn't update tokens and tokenPositions!!
aahfhabha,RaRe-Technologies/gensim,DocumentCollection.py,7dfc5ce04437298d4aa3f69ef7cd6e033d7feaf8,STILL_EXISTS,FIXME not sparse atm; scipy.sparse is crap
aahfhabia,RaRe-Technologies/gensim,DocumentCollection.py,7dfc5ce04437298d4aa3f69ef7cd6e033d7feaf8,STILL_EXISTS,HACK: don't scale zero vectors (INF bad?)
aahfhadbi,RaRe-Technologies/gensim,iddb.py,7dfc5ce04437298d4aa3f69ef7cd6e033d7feaf8,STILL_EXISTS,TODO add
aahfhadcj,RaRe-Technologies/gensim,lsi.py,7dfc5ce04437298d4aa3f69ef7cd6e033d7feaf8,51ec59c590896fe0e4cd5fedf72e73eb66441888,FIXME quadratic better? (% of length of S as a vector)
aahfhadec,RaRe-Technologies/gensim,matutils.py,7dfc5ce04437298d4aa3f69ef7cd6e033d7feaf8,STILL_EXISTS,OPT better than direct distMatrix(points; euclid)?
aahfhaded,RaRe-Technologies/gensim,matutils.py,7dfc5ce04437298d4aa3f69ef7cd6e033d7feaf8,STILL_EXISTS,FIXME depends on scipy.sparse implementation details in an ugly way; but anything else is WAAAAy too slow..
aahfhadeg,RaRe-Technologies/gensim,matutils.py,7dfc5ce04437298d4aa3f69ef7cd6e033d7feaf8,STILL_EXISTS,FIXME better test against numpy.allclose?
aahfhadhe,RaRe-Technologies/gensim,numdam2dml.py,7dfc5ce04437298d4aa3f69ef7cd6e033d7feaf8,STILL_EXISTS,raise Exception(\"unspecified language (perhaps french?)\")
aahfhadhi,RaRe-Technologies/gensim,numdam2dml.py,7dfc5ce04437298d4aa3f69ef7cd6e033d7feaf8,STILL_EXISTS,raise Exception(\"unspecified language (perhaps english?)\")
aahfhaebh,RaRe-Technologies/gensim,simcorrel.py,7dfc5ce04437298d4aa3f69ef7cd6e033d7feaf8,STILL_EXISTS,TODO add
aahfhaehh,RaRe-Technologies/gensim,tokenizers.py,7dfc5ce04437298d4aa3f69ef7cd6e033d7feaf8,STILL_EXISTS,#        TODO: nejaky tokenizer; ktery nahrazuje cislovky atd vyssi sem. kategorii (nejaky dummy token NUM nebo tak)
aahfhaeie,RaRe-Technologies/gensim,tokenizers.py,7dfc5ce04437298d4aa3f69ef7cd6e033d7feaf8,STILL_EXISTS,#        TODO: kolik domenove znalosti? pro kazdy jazyk zvlast? promenne -> dummy symbol; cisla -> dummy symbol atd
aahfhbcdf,RaRe-Technologies/gensim,matutils.py,f958611c38fa9a82ecb67c8f87580584c62067f2,STILL_EXISTS,FIXME depends on scipy.sparse implementation details in an ugly way; but anything else is WAAAAy too slow..
aahfhbcig,RaRe-Technologies/gensim,gensim.py,ae78900147c1a7d21800af3227cb37ed8d86efe5,STILL_EXISTS,FIXME k cemu byl vubec zamyslen tag 'suffix' v similar.xml?
aahfhbcjc,RaRe-Technologies/gensim,bakalarka.py,8238bda389ad9683a5028c9a0131712c7e6111cb,STILL_EXISTS,FIXME here; at least three copies of the original sparseMat exist in memory!!
aahfhbdea,RaRe-Technologies/gensim,lda.py,bf3fed3c0f228d1fa474aa3d1f64314026674821,b0294c9bef9fb48ac6c6427610d84dcc4999c426,how to translate lines into words (simply split on space by default)
aahfhbdgj,RaRe-Technologies/gensim,lda.py,bf3fed3c0f228d1fa474aa3d1f64314026674821,d68b79a18213f74c2e15288751638025eb7e2c54,set up temporary vars needed for EM
aahfhbdhh,RaRe-Technologies/gensim,lda.py,bf3fed3c0f228d1fa474aa3d1f64314026674821,d68b79a18213f74c2e15288751638025eb7e2c54,FIXME what is this for?
aahfhbeba,RaRe-Technologies/gensim,lda.py,bf3fed3c0f228d1fa474aa3d1f64314026674821,STILL_EXISTS,TODO inference
aahfhbgeg,RaRe-Technologies/gensim,ldamodel.py,d68b79a18213f74c2e15288751638025eb7e2c54,STILL_EXISTS,relative difference between document inference iterations needed to stop sooner than VAR_MAX_ITER
aahfhbggb,RaRe-Technologies/gensim,ldamodel.py,d68b79a18213f74c2e15288751638025eb7e2c54,STILL_EXISTS,set up temporary vars needed for EM
aahfhbghc,RaRe-Technologies/gensim,ldamodel.py,d68b79a18213f74c2e15288751638025eb7e2c54,STILL_EXISTS,alter array shape from (n;) to (n; 1); needed for correct broadcasting on the following line
aahfhbigf,RaRe-Technologies/gensim,lda.py,b18c6851fe1c97fce078e2a14f97a1df0f26683c,STILL_EXISTS,were in the input (bar duplicates); which looks better.
aahfhbigg,RaRe-Technologies/gensim,lda.py,b18c6851fe1c97fce078e2a14f97a1df0f26683c,b0294c9bef9fb48ac6c6427610d84dcc4999c426,if this was not needed; we might as well have used useWords = set(words)
aahfhbihb,RaRe-Technologies/gensim,docsim.py,83a172fd1d5053c3a1367c3d36f97918fdd8d56f,STILL_EXISTS,needed for Latent Semantic Indexing
aahfhbihc,RaRe-Technologies/gensim,docsim.py,83a172fd1d5053c3a1367c3d36f97918fdd8d56f,STILL_EXISTS,needed for Latent Dirichlet Allocation
aahfhbihd,RaRe-Technologies/gensim,docsim.py,83a172fd1d5053c3a1367c3d36f97918fdd8d56f,STILL_EXISTS,needed for Random Projections
aahfhbjbd,RaRe-Technologies/gensim,geteval.py,83a172fd1d5053c3a1367c3d36f97918fdd8d56f,STILL_EXISTS,FIXME this assumes WI_WORDS==5; make more flexible
aahfhcbhb,RaRe-Technologies/gensim,corpus.py,8b8ae90742bce69c55506b914ebdb13845f8c2ed,e4585c98f0c1f551cabac29813d5710966794dfb,determine matrix shape and density (only needed for the MM format headers;
aahfhccab,RaRe-Technologies/gensim,sources.py,8b8ae90742bce69c55506b914ebdb13845f8c2ed,STILL_EXISTS,HAX assumes one element = one line; proper xml parsing probably better... but who cares
aahfhcehe,RaRe-Technologies/gensim,corpus.py,f2057c4f096574f23f25e3913d79f2ff6f84ff4a,STILL_EXISTS,FIXME REMOVE
aahfhcejb,RaRe-Technologies/gensim,sources.py,f2057c4f096574f23f25e3913d79f2ff6f84ff4a,STILL_EXISTS,TODO maybe parse out some meta; but currently not needed for anything...
aahfhcfba,RaRe-Technologies/gensim,corpus.py,b0294c9bef9fb48ac6c6427610d84dcc4999c426,STILL_EXISTS,how to translate lines into words (simply split on space by default)
aahfhcfcd,RaRe-Technologies/gensim,corpus.py,b0294c9bef9fb48ac6c6427610d84dcc4999c426,STILL_EXISTS,were in the input (bar duplicates); which looks better.
aahfhcfce,RaRe-Technologies/gensim,corpus.py,b0294c9bef9fb48ac6c6427610d84dcc4999c426,STILL_EXISTS,if this was not needed; we might as well have used useWords = set(words)
aahfhcgcg,RaRe-Technologies/gensim,docsim.py,51ec59c590896fe0e4cd5fedf72e73eb66441888,STILL_EXISTS,convert to Compressed Sparse Row for efficient row slicing and multiplications
aahfhcgja,RaRe-Technologies/gensim,lsi.py,51ec59c590896fe0e4cd5fedf72e73eb66441888,0a872b978f0b1ee9a806e4c6e3709d953a50da18,relative difference between document inference iterations needed to stop sooner than VAR_MAX_ITER
aahfhchag,RaRe-Technologies/gensim,lsi.py,51ec59c590896fe0e4cd5fedf72e73eb66441888,0a872b978f0b1ee9a806e4c6e3709d953a50da18,set up temporary vars needed for EM
aahfhchbh,RaRe-Technologies/gensim,lsi.py,51ec59c590896fe0e4cd5fedf72e73eb66441888,0a872b978f0b1ee9a806e4c6e3709d953a50da18,alter array shape from (n;) to (n; 1); needed for correct broadcasting on the following line
aahfhchhb,RaRe-Technologies/gensim,lsi.py,0a872b978f0b1ee9a806e4c6e3709d953a50da18,STILL_EXISTS,calculate projection needed to get document-topic matrix from term-document matrix
aahfhcihb,RaRe-Technologies/gensim,lsimodel.py,abcf602b0b77ef1fbb5605eaefb3387f7b4bc1b3,STILL_EXISTS,FIXME TODO
aahfhciia,RaRe-Technologies/gensim,docsim.py,474a7aa3c5300446a2d471d24c6c66dee8fc46c7,STILL_EXISTS,now set the shape properly; using no. columns = highest term index in the corpus + 1
aahfhcjac,RaRe-Technologies/gensim,lsimodel.py,fb2bda99aa1745a3ffed34552cf455c90bc00612,STILL_EXISTS,FIXME  remove
aahfhcjaf,RaRe-Technologies/gensim,lsimodel.py,2a3f553c1b10818f078a243f908cc9cf55eb14f2,STILL_EXISTS,TODO factor this out into matutils.addRow\/Column or something
aahfhcjbb,RaRe-Technologies/gensim,lsimodel.py,23ef15f59dfdd12655f59c9ebc915805191f8810,STILL_EXISTS,FIXME! remove keepV; remove self.u; self.v; ...
aahfhcjdj,RaRe-Technologies/gensim,gensim.py,097486a0d6e351ad7dd09f96528bf885a3df1049,STILL_EXISTS,TODO:
aahfhcjga,RaRe-Technologies/gensim,lsimodel.py,149f65e1cbbcbdfb144742017cf7759b2c2e1d83,STILL_EXISTS,DEBUG not needed; can be safely commented out to save memory
aahfhcjih,RaRe-Technologies/gensim,lsimodel.py,0d31c4378f36406c66c157b5c0ac7c3d79e9b7f0,f98b08c984e551f9094ea250aa6d1eaed74e5a14,not needed; free up mem
aahfhdbfc,RaRe-Technologies/gensim,src/gensim/dmlcz/numdam2dml.py,bd98a6e96560e14bc3313802cf4c79566e4fd8f5,STILL_EXISTS,raise Exception(\"unspecified language (perhaps french?)\")
aahfhdbfg,RaRe-Technologies/gensim,src/gensim/dmlcz/numdam2dml.py,bd98a6e96560e14bc3313802cf4c79566e4fd8f5,STILL_EXISTS,raise Exception(\"unspecified language (perhaps english?)\")
aahfhddfa,RaRe-Technologies/gensim,src/gensim/interfaces.py,b0cd5ea3e1dcf6982d25592ae482c554b6b0e2f6,STILL_EXISTS,\"\"\" || This module contains basic interfaces used throughout the whole gensim package. ||  || The interfaces are realized as abstract base classes (ie.; some optional functionality  || is provided in the interface itself; so that the interfaces can be subclassed). || \"\"\"
aahfhddfj,RaRe-Technologies/gensim,src/gensim/tests/test_models.py,b0cd5ea3e1dcf6982d25592ae482c554b6b0e2f6,ee481b5d1723429e3545b531167479c2a1bc0b94,FIXME TODO
aahfhdebf,RaRe-Technologies/gensim,src/gensim/corpora/lowcorpus.py,2e45d9d2d61248621b17615b4cb74c1facf69e62,STILL_EXISTS,how to translate lines into words (simply split on space by default)
aahfhdeci,RaRe-Technologies/gensim,src/gensim/corpora/lowcorpus.py,2e45d9d2d61248621b17615b4cb74c1facf69e62,STILL_EXISTS,were in the input (bar duplicates); which looks better.
aahfhdecj,RaRe-Technologies/gensim,src/gensim/corpora/lowcorpus.py,2e45d9d2d61248621b17615b4cb74c1facf69e62,STILL_EXISTS,if this was not needed; we might as well have used useWords = set(words)
aahfhdgaj,RaRe-Technologies/gensim,src/gensim/tests/test_corpora.py,33a92bb69a79e7895992b4230a9aa83c009dfb5d,2299d6fb7437903bf421884c0191c38d59d06f7b,FIXME TODO
aahfhdhac,RaRe-Technologies/gensim,docs/src/conf.py,4d596f816243490ec90ece3e8fa5f1f4b75bb2b2,STILL_EXISTS,unused_docs = []
aahfheadj,RaRe-Technologies/gensim,ez_setup.py,559a01a3bbd9a417f365044167dbf9e13a38fcb4,83eb6daaa19eecb578c84f6a2d7dd4c500162dac,XXX could we install in a subprocess here?
aahfheaie,RaRe-Technologies/gensim,setup.py,60f4991acbf6e14580435bc0a3006906487ac8bb,STILL_EXISTS,author = u'Radim \u0158eh\u016F\u0159ek'; # <- should really be this.. see http:\/\/bugs.python.org\/issue2562
aahfheaig,RaRe-Technologies/gensim,src/gensim/test/test_corpora.py,28974b2adab8bd82817a01752aa5432f0ed22aa5,STILL_EXISTS,needed because sample data files are located in the same folder
aahfheaih,RaRe-Technologies/gensim,src/gensim/test/test_models.py,28974b2adab8bd82817a01752aa5432f0ed22aa5,STILL_EXISTS,needed because sample data files are located in the same folder
aahfhebdc,RaRe-Technologies/gensim,src/gensim/similarities/docsim.py,b5e70048f92fbc14ac735a5b294d296021e07ee3,STILL_EXISTS,set no of columns to 1 for now; as the number of terms is unknown yet
aahfhebdf,RaRe-Technologies/gensim,src/gensim/similarities/docsim.py,b5e70048f92fbc14ac735a5b294d296021e07ee3,STILL_EXISTS,now set the shape properly; using no. columns = highest term index in the corpus + 1
aahfhebdh,RaRe-Technologies/gensim,src/gensim/similarities/docsim.py,b5e70048f92fbc14ac735a5b294d296021e07ee3,STILL_EXISTS,convert to Compressed Sparse Row for efficient row slicing and multiplications
aahfheceb,RaRe-Technologies/gensim,src/gensim/models/rpmodel.py,41e0e378089bef7d8fb1c700a36d0ccb278b0336,STILL_EXISTS,FIXME temporary array unnecessarily big (int32 -> int8)
aahfheceg,RaRe-Technologies/gensim,src/gensim/test/test_models.py,41e0e378089bef7d8fb1c700a36d0ccb278b0336,STILL_EXISTS,HACK; set fixed seed so that we always get the same random matrix (and can compare against expected results)
aahfhecic,RaRe-Technologies/gensim,src/gensim/dmlcz/gensim_build.py,195d1c354912fb0ae4ea87c4605b8926a8a5aa69,cd62df9d5959eeaf7b5f26d13135368584a4b8c6,FIXME remove
aahfhecjh,RaRe-Technologies/gensim,src/gensim/utils.py,148377aaa874c7487b277ae7cf8ebccc5a0bcdde,STILL_EXISTS,'b' for binary; needed on Windows
aahfhecjj,RaRe-Technologies/gensim,src/gensim/models/tfidfmodel.py,74f83ebeac66018c121f59801296a8e6f1395b91,STILL_EXISTS,HACK using leftover from enumerate(corpus) above
aahfhedbd,RaRe-Technologies/gensim,src/gensim/models/lsimodel.py,867acdb02d141aa7cf6cf29b8aa42ce0b90e0c4c,STILL_EXISTS,efficient (both in memory; CPU and numerical precision) to keep
aahfhedcb,RaRe-Technologies/gensim,src/gensim/models/lsimodel.py,867acdb02d141aa7cf6cf29b8aa42ce0b90e0c4c,STILL_EXISTS,FIXME: keep the tUp rotations separate ala eq (11)? this would mean discarding every P as soon as we hit the target rank; is that ok?
aahfhedhf,RaRe-Technologies/gensim,src/gensim/models/lsi_dispatcher.py,93f61d5e5d813f81baac2727e1d85bedb3cf79e6,STILL_EXISTS,so this is really just a hack; see http:\/\/bugs.python.org\/issue1360
aahfhedig,RaRe-Technologies/gensim,src/gensim/models/lsi_dispatcher.py,93f61d5e5d813f81baac2727e1d85bedb3cf79e6,STILL_EXISTS,not needed; but why not...
aahfhedji,RaRe-Technologies/gensim,src/gensim/models/lsi_worker.py,93f61d5e5d813f81baac2727e1d85bedb3cf79e6,STILL_EXISTS,id of this worker in the dispatcher; just a convenience var for easy access TODO remove?
aahfheeac,RaRe-Technologies/gensim,src/gensim/models/lsi_worker.py,93f61d5e5d813f81baac2727e1d85bedb3cf79e6,7e846e1756ee08cffc005f0a0fb6fb022b96b73d,FIXME
aahfhefdb,RaRe-Technologies/gensim,src/gensim/models/lsimodel.py,f0281f44211025b44d6a60308c1ba7dfcb3ccace,STILL_EXISTS,TODO may this rotation could be done on the sending end; not receiving?
aahfhefdc,RaRe-Technologies/gensim,src/gensim/models/lsimodel.py,f0281f44211025b44d6a60308c1ba7dfcb3ccace,STILL_EXISTS,pro: faster (only done once; in one worker; instead of N times in each worker)
aahfhefdg,RaRe-Technologies/gensim,src/gensim/models/lsimodel.py,f0281f44211025b44d6a60308c1ba7dfcb3ccace,STILL_EXISTS,if the variation covered by the last `c` rotation vectors is small; don't bother adjusting the basis (saves a lot of memory and speed)
aahfhefdh,RaRe-Technologies/gensim,src/gensim/models/lsi_dispatcher.py,a83ca9100d0ce5d25972a893bf8cec624888667f,STILL_EXISTS,TODO: merge in parallel; so that we're done in `log_2(workers)` merges;
aahfheffg,RaRe-Technologies/gensim,src/gensim/models/lsimodel.py,a83ca9100d0ce5d25972a893bf8cec624888667f,1c8fbde7b8b79da86250a644c4eac66526659e59,FIXME only need top k singular triplets; and the matrix is sparse... use something sane; not full dense svd of lapack!
aahfhefhg,RaRe-Technologies/gensim,src/gensim/models/lsimodel.py,a83ca9100d0ce5d25972a893bf8cec624888667f,STILL_EXISTS,now `a` is a matrix that contains the new documents as columns
aahfhefic,RaRe-Technologies/gensim,src/gensim/models/lsimodel.py,1c8fbde7b8b79da86250a644c4eac66526659e59,ee30cdd133859eef907b3f99452155fd258dbc4e,FIXME test=>update True!!!
aahfhegbi,RaRe-Technologies/gensim,src/gensim/models/lsimodel.py,5855944254546d63e3146b445d0618a1771927e1,STILL_EXISTS,TODO Maybe keep the bases in their natural form (rotations); without
aahfhegdd,RaRe-Technologies/gensim,src/gensim/models/lsimodel.py,7163bac3977c82d7c008f9b4d4c1b57547bbe5de,STILL_EXISTS,IMPORTANT: keep matrices in suitable order for matrix products; failing to do so gives 8x lower performance :(
aahfhegdh,RaRe-Technologies/gensim,src/gensim/models/lsimodel.py,7163bac3977c82d7c008f9b4d4c1b57547bbe5de,STILL_EXISTS,TODO temporarily creates an extra (m;k) dense array! find a way to avoid this!
aahfhehhg,RaRe-Technologies/gensim,src/gensim/corpora/wikicorpus.py,6897098e022b6a0a6c99a72eb0eafea614d00086,STILL_EXISTS,the following is needed to make the tokenizer see '[[socialist]]s' as a single word 'socialists'
aahfhehij,RaRe-Technologies/gensim,src/gensim/corpora/wikicorpus.py,e5decd351a45feb112651e52b733c30973273a91,STILL_EXISTS,remove table markup; TODO this is ugly...
aahfhehje,RaRe-Technologies/gensim,src/gensim/corpora/wikicorpus.py,e5decd351a45feb112651e52b733c30973273a91,STILL_EXISTS,TODO is this really desirable?
aahfheiah,RaRe-Technologies/gensim,src/gensim/corpora/dictionary.py,c1c20578574a781cb4f819e21cce98fe03e1d923,STILL_EXISTS,expensive; only here for historical reasons; maybe deprecate?
aahfheibd,RaRe-Technologies/gensim,src/gensim/models/lsimodel.py,327f452e2b87a35640ef758cd0f14a7efa8fd336,STILL_EXISTS,TODO speed up by block svd (process multiple words at once)
aahfheicd,RaRe-Technologies/gensim,src/gensim/models/lsimodel.py,13d54c9886b30e50f40ebd70aadfa8c1fd37aff4,5a597b0d1ca3482e378cc2ee48100b8ecbfea65d,broadcasting will promote this to eye(n2) where needed
aahfheiee,RaRe-Technologies/gensim,src/gensim/models/lsimodel.py,223e6b77b4bd68fca3c79c5aa731a5fd03d57f94,STILL_EXISTS,TODO should really be 'syr'.. but no wrapper for DSYR in scipy :(
aahfheifh,RaRe-Technologies/gensim,src/gensim/models/lsimodel.py,72fc0220c024fd472f0dca646b97e01c1e867886,STILL_EXISTS,\"\"\"\r || Module for Latent Semantic Indexing.\r || \r || It actually contains several algorithms for decomposition of extremely large \r || corpora; a combination of which effectively and transparently allows building LSI\r || models for:\r || \r ||   * corpora much larger than RAM (only constant memory needed; independent of \r ||     corpus size)\r ||   * corpora that are streamed (documents can only be accessed sequentially; not \r ||     random-accessed)\r ||   * corpora that cannot even be temporarily stored; each document can only be \r ||     seen once and processed immediately (one-pass algorithm)\r ||   * distributed computing for ultra large corpora; making use of a cluster of \r ||     machines\r || \r || Performance on English Wikipedia (2G corpus positions; 3.2M documents; 200K features; \r || 0.5G non-zero entries in the final TF-IDF matrix); requesting top 400 LSI factors:\r || \r || \r ||   ------------------------------------------------------------------------\r ||   |                                           serial      distributed    |\r ||   | one-pass update algo (chunks=factors)     65h         19h            |\r ||   | one-pass mege algo (chunks=20K documents) 14h         2h             |\r ||   | two-pass randomized algo (chunks=20K)     2.5h        N\/A            |\r ||   ------------------------------------------------------------------------\r || \r || serial = Core 2 Duo MacBook Pro 2.53Ghz; 4GB RAM; libVec\r || distributed = cluster of six logical nodes on four physical machines; each with dual core Xeon 2.0GHz; 4GB RAM; ATLAS\r || \r || \"\"\"
aahfheige,RaRe-Technologies/gensim,src/gensim/models/lsimodel.py,72fc0220c024fd472f0dca646b97e01c1e867886,STILL_EXISTS,documents = columns of sparse CSC
aahfheigj,RaRe-Technologies/gensim,src/gensim/models/lsimodel.py,72fc0220c024fd472f0dca646b97e01c1e867886,STILL_EXISTS,TODO should call the BLAS routine SYRK; but there is no SYRK wrapper in scipy :(
aahfheiic,RaRe-Technologies/gensim,src/gensim/utils.py,067010fbf9ece06f11ac1c2626d29394baa1448d,STILL_EXISTS,most common case; quick hack
aahfheiji,RaRe-Technologies/gensim,src/gensim/models/lda_dispatcher.py,776c2f405e17eaf18ae192730bac9299d805e8ba,STILL_EXISTS,so this is really just a hack; see http:\/\/bugs.python.org\/issue1360
aahfhejai,RaRe-Technologies/gensim,src/gensim/models/lda_worker.py,776c2f405e17eaf18ae192730bac9299d805e8ba,STILL_EXISTS,\"\"\" || USAGE: %(program)s ||  ||     Worker (\"slave\") process used in computing distributed LDA. Run this script \\ || on every node in your cluster. If you wish; you may even run it multiple times \\ || on a single machine; to make better use of multiple cores (just beware that \\ || memory footprint increases accordingly). ||  || Example: python lda_worker.py || \"\"\"
aahfhejbg,RaRe-Technologies/gensim,src/gensim/models/lda_worker.py,776c2f405e17eaf18ae192730bac9299d805e8ba,STILL_EXISTS,id of this worker in the dispatcher; just a convenience var for easy access\/logging TODO remove?
aahfhfadh,RaRe-Technologies/gensim,src/gensim/models/ldamodel.py,e685688f899b390ef47e63bebf75edb23c7a9bbe,4894eb9721e9585d51127fb1ae7106b3b42a64c4,FIXME
aahfhfbae,RaRe-Technologies/gensim,src/gensim/models/ldamodel.py,8a45f71da04e9978e5a1144c477daf75b676f0ab,STILL_EXISTS,maybe select only a subset of corpus here (to simulate their \"stochastic\" approach)
aahfhfbbb,RaRe-Technologies/gensim,src/gensim/models/lsimodel.py,bcce6b7987f0380dca6803eccc35b2b44c4a9861,STILL_EXISTS,documents = columns of sparse CSC
aahfhfbbc,RaRe-Technologies/gensim,src/gensim/matutils.py,40d838d5f8e4828e9f958bbb93f8a18587d9b4cb,STILL_EXISTS,make sure shape[1]=number of docs (needed in len())
aahfhfbbf,RaRe-Technologies/gensim,src/gensim/models/lsimodel.py,5a597b0d1ca3482e378cc2ee48100b8ecbfea65d,STILL_EXISTS,FIXME accuracy defaults--is this enough?
aahfhfbdc,RaRe-Technologies/gensim,src/gensim/models/lsimodel.py,916d16bc2e72fe3d76b649259a6276007f4b6494,STILL_EXISTS,accuracy defaults for two-pass algo. FIXME is this enough? maybe do some power iterations by default?
aahfhfbdj,RaRe-Technologies/gensim,src/gensim/models/lsimodel.py,2941939b730f9428ca914258736a59d4ed8e2b70,STILL_EXISTS,TODO unlike numpy; scipy actually makes a copy even when dtype=y.dtype...marginally inefficient
aahfhfcba,RaRe-Technologies/gensim,src/gensim/models/ldamodel.py,f92c7fe35206f35bb1607711a4731ed3a32d1c06,f41c772db7a1855e18c2cb70d65b5117a36e1f7a,as well as sufficient statistics needed to update lambda.
aahfhfdih,RaRe-Technologies/gensim,src/gensim/test/test_miislita.py,15b421deb8c6412037b81ae45746195449e8967c,50175146401396a90c4c11cb1b0aa14354e61ae2,TODO: what it LoL?
aahfhfdii,RaRe-Technologies/gensim,src/gensim/test/test_miislita.py,15b421deb8c6412037b81ae45746195449e8967c,50175146401396a90c4c11cb1b0aa14354e61ae2,TODO: exhaustive stoplist; remove punctuation; etc
aahfhfdjb,RaRe-Technologies/gensim,src/gensim/test/test_miislita.py,15b421deb8c6412037b81ae45746195449e8967c,cfc85eccbd90a193bff564b1cba489458ab09945,TODO: do we need this?
aahfhfead,RaRe-Technologies/gensim,src/gensim/test/test_miislita.py,15b421deb8c6412037b81ae45746195449e8967c,cfc85eccbd90a193bff564b1cba489458ab09945,TODO: unused
aahfhfebc,RaRe-Technologies/gensim,src/gensim/test/test_miislita.py,15b421deb8c6412037b81ae45746195449e8967c,STILL_EXISTS,TODO: what exactly do we expect here?
aahfhfeeb,RaRe-Technologies/gensim,src/gensim/parsing/porter.py,06a02bb19473e79623dd3600b799a124795f0839,STILL_EXISTS,if self.ends(\"abli\"):      self.r(\"able\")
aahfhfeid,RaRe-Technologies/gensim,src/gensim/corpora/textcorpus.py,956fc79db9fcfef18a3eb1dfa96df824bb220a47,a08f630baec05e178df59bcee8be0a5a8cfb088d,\"\"\" ||  || The original idea of Gensim was to delegate parsing etc to external tools. || For our usage scenario we do need to have a representation fo a text corpus. || We need basic agreement on what memory structures define a vorpus. || To do functional testing; tests will use a corpus; if we serialize the memmory || s || We assume the input text is one single file with documents separated by blanks. ||  || A text corpus is defined by a parsing and several output files (serializations). || The serializations are both in cpickle format (faster to load into memory) and in text format \\ || for easier human supervision. ||  || This class has methods to read and write all those serializations. ||  || It creates three files: ||  || * `OUTPUT_PREFIX_wordids.txt`: mapping between words and their integer ids || * `OUTPUT_PREFIX_idwords.txt`: mapping between words and their integer ids || * `OUTPUT_PREFIX_bow.mm`: bag-of-words (word counts) representation; in Matrix Matrix format || * `OUTPUT_PREFIX_tfidf.mm`: TF-IDF representation ||  || 1. corpusFilename: a single file with one doc per line. example: wiki2008 ||  || 2. filters (if none; using DEFAULT_FILTERS). This is a file too: corpusFilename.filters ||  || 3. the txt serializations ||  || 4. the cpickle serializations ||  || 5. a flag to optionally create txt files. by default it doesn't. ||  || To instantiate from txt or cpickle; we assume a convention for representations: ||  || mm ||  || bow ||  || tf_variation ||  || wordids ||  || idword ||  || The name is: corpusFilename + parsingParameters + representation + '.txt' OR 'cpickle' ||  || We can have either txt OR cpickle or both. txt is only for human debugging. If both are present;\\ ||  load should use the cpickle ||  || 1.1 methods ||  || for every representation: ||  || 1. loadtxt; loadCpickle ||  || 2. save; saveCpickle ||  || 3. getArticles. Yields an article (for the model object) ||  ||  || USAGE: %(program)s WIKI_XML_DUMP OUTPUT_PREFIX [VOCABULARY_SIZE] ||  ||     Convert articles from a Wikipedia dump to (sparse) vectors. The input is a bz2-compressed \\ || dump of Wikipedia articles; in XML format. ||  ||  || The output Matrix Market files can then be compressed (e.g.; by bzip2) to save \\ || disk space; gensim's corpus iterators can work with compressed input; too. ||  || `VOCABULARY_SIZE` controls how many of the most frequent words to keep (after || removing all tokens that appear in more than 10 percent documents). Defaults to 100;000. ||  || Example: .\/wikicorpus.py ~\/gensim\/results\/enwiki-20100622-pages-articles.xml.bz2 ~\/gensim\/results\/wiki_en || \"\"\"
aahfhffbi,RaRe-Technologies/gensim,src/gensim/corpora/textcorpus.py,956fc79db9fcfef18a3eb1dfa96df824bb220a47,a08f630baec05e178df59bcee8be0a5a8cfb088d,the following is needed to make the tokenizer see '[[socialist]]s' as a single word 'socialists'
aahfhffbj,RaRe-Technologies/gensim,src/gensim/corpora/textcorpus.py,956fc79db9fcfef18a3eb1dfa96df824bb220a47,a08f630baec05e178df59bcee8be0a5a8cfb088d,TODO is this really desirable?
aahfhffcb,RaRe-Technologies/gensim,src/gensim/corpora/textcorpus.py,956fc79db9fcfef18a3eb1dfa96df824bb220a47,a08f630baec05e178df59bcee8be0a5a8cfb088d,TODO maybe ignore tokens with non-latin characters? (no chinese; arabic; russian etc.)
aahfhffff,RaRe-Technologies/gensim,acme/lee-wiki-streamParsing2008-cosines.py,d77450c7df7e5877947727d0c8264bf7928d5e65,STILL_EXISTS,todo: ask to rewrite if there's already files of the same name
aahfhffhe,RaRe-Technologies/gensim,acme/lee-wiki-streamParsing2008-cosines.py,d77450c7df7e5877947727d0c8264bf7928d5e65,STILL_EXISTS,TODO serialize the entire tfidf model. We'll need it later  to create a sparseMatrixSimilarity object
aahfhffic,RaRe-Technologies/gensim,acme/lee-wiki-streamParsing2008-cosines.py,d77450c7df7e5877947727d0c8264bf7928d5e65,STILL_EXISTS,NOTE: not needed for the lee usecase
aahfhffie,RaRe-Technologies/gensim,acme/lee-wiki-streamParsing2008-cosines.py,d77450c7df7e5877947727d0c8264bf7928d5e65,STILL_EXISTS,print raw_input('holding here to see memory usage ') # todo measure time and memory
aahfhffii,RaRe-Technologies/gensim,acme/lee-wiki-streamParsing2008-cosines.py,d77450c7df7e5877947727d0c8264bf7928d5e65,STILL_EXISTS,todo add these filters to log
aahfhffjc,RaRe-Technologies/gensim,acme/lee-wiki-streamParsing2008-cosines.py,d77450c7df7e5877947727d0c8264bf7928d5e65,STILL_EXISTS,todo: serializing this object would work  as well as having the '_wordids.txt' for ids
aahfhfgdg,RaRe-Technologies/gensim,parsing/porter.py,d77450c7df7e5877947727d0c8264bf7928d5e65,STILL_EXISTS,if self.ends(\"abli\"):      self.r(\"able\")
aahfhfgih,RaRe-Technologies/gensim,src/gensim/corpora/wikiExternParsingCorpus.py,d77450c7df7e5877947727d0c8264bf7928d5e65,STILL_EXISTS,We needed this because our parsing that replicates Gabrilovitch (2009) requires some link
aahfhfgii,RaRe-Technologies/gensim,src/gensim/corpora/wikiExternParsingCorpus.py,d77450c7df7e5877947727d0c8264bf7928d5e65,STILL_EXISTS,analysis that is very difficult to do (maybe cannot be done) straight from the dump.
aahfhfgjh,RaRe-Technologies/gensim,src/gensim/corpora/wikiExternParsingCorpus.py,d77450c7df7e5877947727d0c8264bf7928d5e65,STILL_EXISTS,# todo add the preprocessing module to gensim
aahfhfgji,RaRe-Technologies/gensim,src/gensim/corpora/wikiExternParsingCorpus.py,d77450c7df7e5877947727d0c8264bf7928d5e65,STILL_EXISTS,# TODO maybe ignore tokens with non-latin characters? (no chinese; arabic; russian etc.)
aahfhfhac,RaRe-Technologies/gensim,src/gensim/corpora/wikiExternParsingCorpus.py,d77450c7df7e5877947727d0c8264bf7928d5e65,STILL_EXISTS,todo: make filtering optional with a parameter
aahfhfhae,RaRe-Technologies/gensim,src/gensim/corpora/wikiExternParsingCorpus.py,d77450c7df7e5877947727d0c8264bf7928d5e65,STILL_EXISTS,todo figure out why this function cannot be called from ouside even commeting out staticmethod
aahfhfhie,RaRe-Technologies/gensim,src/gensim/corpora/wikiExternalParsingCorpus.py,f767972fe4f68698dfafd6e0f55e3df9797e5260,STILL_EXISTS,\"\"\" || TODO: Update this ||  || clone of wikicorpus.py || \"\"\"
aahfhfhii,RaRe-Technologies/gensim,src/gensim/corpora/wikiExternalParsingCorpus.py,f767972fe4f68698dfafd6e0f55e3df9797e5260,STILL_EXISTS,from wikipedia.  We needed this because our parsing that replicates
aahfhfhjb,RaRe-Technologies/gensim,src/gensim/corpora/wikiExternalParsingCorpus.py,f767972fe4f68698dfafd6e0f55e3df9797e5260,STILL_EXISTS,TODO: make filtering optional with a parameter
aahfhfihh,RaRe-Technologies/gensim,src/gensim/utils.py,f525c48cea0817a6bb8a57966493c0bc4ca6af66,f41c772db7a1855e18c2cb70d65b5117a36e1f7a,todo check: the bow query must use the same dictionary
aahfhfjeh,RaRe-Technologies/gensim,src/gensim/models/lda_dispatcher.py,f41c772db7a1855e18c2cb70d65b5117a36e1f7a,ed3c9b9ee40019634c7216776f8cc605357992b5,FIXME
aahfhfjei,RaRe-Technologies/gensim,src/gensim/models/lda_worker.py,f41c772db7a1855e18c2cb70d65b5117a36e1f7a,ed3c9b9ee40019634c7216776f8cc605357992b5,FIXME
aahfhfjga,RaRe-Technologies/gensim,src/gensim/models/ldamodel.py,f41c772db7a1855e18c2cb70d65b5117a36e1f7a,STILL_EXISTS,TODO do we need to keep all these in memory all the time? compute on
aahfhfjgf,RaRe-Technologies/gensim,src/gensim/models/ldamodel.py,f41c772db7a1855e18c2cb70d65b5117a36e1f7a,STILL_EXISTS,TODO treat zeros explicitly; instead of adding eps?
aahfhfjhh,RaRe-Technologies/gensim,src/gensim/models/ldamodel.py,f41c772db7a1855e18c2cb70d65b5117a36e1f7a,STILL_EXISTS,rho is the \"speed\" of updating; TODO try other fncs
aahfhfjhi,RaRe-Technologies/gensim,src/gensim/models/ldamodel.py,f41c772db7a1855e18c2cb70d65b5117a36e1f7a,STILL_EXISTS,add `+ iteration` to ensure convergence (fake convergence; really! won't work on non-stationary input streams)
aahfhggbe,RaRe-Technologies/gensim,src/gensim/utils.py,19e196c21acea1d35f3b415ed190c494363a1a43,STILL_EXISTS,todo check: the bow query must use the same dictionary
aahfhggcc,RaRe-Technologies/gensim,src/gensim/models/ldamodel.py,a0314333f4f1bd6e9b6e2c7c6e5553f97c801046,STILL_EXISTS,time? TODO compute them on the fly instead?
aahfhggda,RaRe-Technologies/gensim,src/gensim/models/ldamodel.py,a0314333f4f1bd6e9b6e2c7c6e5553f97c801046,9bf3d059b3f6d924fb1485ef0dc6b2d08b0f521a,and pop already-ready chunks from it as needed.
aahfhggec,RaRe-Technologies/gensim,src/gensim/models/ldamodel.py,a0314333f4f1bd6e9b6e2c7c6e5553f97c801046,STILL_EXISTS,add `+ iteration` to ensure convergence (fake convergence; really! won't work on non-stationary input streams)
aahfhggeg,RaRe-Technologies/gensim,src/gensim/utils.py,a0314333f4f1bd6e9b6e2c7c6e5553f97c801046,STILL_EXISTS,todo check: the bow query must use the same dictionary
aahfhgggc,RaRe-Technologies/gensim,acme/para2para.py,178ff6d3190b5d1536371cad74c5bd5d8dfdb546,STILL_EXISTS,TODO this should be moved to some library (textcorpus?)
aahfhgghf,RaRe-Technologies/gensim,src/gensim/test/test_models.py,121b79ed2284afecfa102b4b7b66450937bcf261,STILL_EXISTS,100 passes ought to be enough to converge
aahfhggjd,RaRe-Technologies/gensim,src/gensim/models/lsimodel.py,50f438d1e66e07fe62ac818f42b3df827e97d6ab,9bf3d059b3f6d924fb1485ef0dc6b2d08b0f521a,and pop already-ready chunks from it as needed.
aahfhghaj,RaRe-Technologies/gensim,src/gensim/utils.py,1aa7e115fcf87b443373c14b7b2f3dd2e3383584,STILL_EXISTS,'b' for binary; needed on Windows
aahfhghch,RaRe-Technologies/gensim,src/gensim/utils.py,98b79581dd132ce8684d5be612d8fb043f751ccd,STILL_EXISTS,'b' for binary; needed on Windows
aahfhghdb,RaRe-Technologies/gensim,src/gensim/utils.py,98b79581dd132ce8684d5be612d8fb043f751ccd,STILL_EXISTS,todo check: the bow query must use the same dictionary
aahfhgjdd,RaRe-Technologies/gensim,src/gensim/test/test_models.py,44c5e6becd37edd705dc2ec240b863971c4b803a,STILL_EXISTS,better random initialization
aahfhhdjd,RaRe-Technologies/gensim,src/gensim/parsing/preprocessing.py,a1abd15a176fedd75d1bd749be728fec2e158960,STILL_EXISTS,FIXME replace with unicode compatible regexp; without the assumption
aahfhhica,RaRe-Technologies/gensim,src/gensim/__init__.py,5dd853fb871ee86df5b112087dc7f4f30db65221,48c422ce954ca57294a9c970c515b10d787f8013,TODO: if version_info < 2.7?
aahfhhjac,RaRe-Technologies/gensim,src/gensim/matutils.py,796f6b56ec66b3b0fb0fd59e4989248b341159b6,STILL_EXISTS,HACK assume feature ids fit in 32bit integer
aahfhhjbg,RaRe-Technologies/gensim,src/gensim/models/ldamodel.py,561000270732e98fd8e5eebdc18ff2977ea9e55b,9bf3d059b3f6d924fb1485ef0dc6b2d08b0f521a,FIXME self.numworkers
aahfhhjbh,RaRe-Technologies/gensim,src/gensim/models/lsimodel.py,561000270732e98fd8e5eebdc18ff2977ea9e55b,9bf3d059b3f6d924fb1485ef0dc6b2d08b0f521a,FIXME self.numworkers
aahfhhjeb,RaRe-Technologies/gensim,src/gensim/similarities/docsim.py,6e5ed94f1c5cd8d78485e87fa1e32ea54060d81b,STILL_EXISTS,TODO: load back with mmap immediately?
aahfhhjec,RaRe-Technologies/gensim,src/gensim/similarities/docsim.py,6e5ed94f1c5cd8d78485e87fa1e32ea54060d81b,STILL_EXISTS,TODO: support for remote shards
aahfhhjed,RaRe-Technologies/gensim,src/gensim/similarities/docsim.py,6e5ed94f1c5cd8d78485e87fa1e32ea54060d81b,STILL_EXISTS,FIXME yuck; optimize
aahfhhjee,RaRe-Technologies/gensim,src/gensim/similarities/docsim.py,6e5ed94f1c5cd8d78485e87fa1e32ea54060d81b,STILL_EXISTS,TODO: make the index more robust against disk\/power failures; use some db\/pytables?
aahfhhjeh,RaRe-Technologies/gensim,src/gensim/similarities/docsim.py,6e5ed94f1c5cd8d78485e87fa1e32ea54060d81b,STILL_EXISTS,TODO mention in docs that the pattern of \"add 1 doc\/query\/add 1 doc\/query\/..\"
aahfhhjfc,RaRe-Technologies/gensim,src/gensim/similarities/docsim.py,6e5ed94f1c5cd8d78485e87fa1e32ea54060d81b,STILL_EXISTS,FIXME when query a chunk
aahfhhjfg,RaRe-Technologies/gensim,src/gensim/similarities/docsim.py,8fca994e963db55d5702859e5dc9269c3589a5fe,STILL_EXISTS,FIXME or maybe chunk it into smaller pieces?
aahfhhjgf,RaRe-Technologies/gensim,src/gensim/similarities/docsim.py,8fca994e963db55d5702859e5dc9269c3589a5fe,STILL_EXISTS,TODO: load back with mmap immediately?
aahfhhjgg,RaRe-Technologies/gensim,src/gensim/similarities/docsim.py,8fca994e963db55d5702859e5dc9269c3589a5fe,STILL_EXISTS,FIXME yuck; optimize
aahfhhjia,RaRe-Technologies/gensim,src/gensim/models/lsimodel.py,fe01d937a1c24381a5551a972a84bffd676b2cb4,STILL_EXISTS,XXX the above `dot` is very inefficient if num_topics not full and `u` not in fortran order!!
aahfhhjja,RaRe-Technologies/gensim,src/gensim/test/test_similarities.py,4c5cf51355c5f6c32b27091d129f954f5076ece5,STILL_EXISTS,needed because sample data files are located in the same folder
aahfhhjjf,RaRe-Technologies/gensim,src/gensim/test/test_similarities.py,4c5cf51355c5f6c32b27091d129f954f5076ece5,STILL_EXISTS,hack SparseMatrixSim indexes so they're easy to compare
aahfhigeb,RaRe-Technologies/gensim,gensim/similarities/simserver.py,6d8151cff261cbe5a367d956f8aee64835afa1b4,STILL_EXISTS,TODO delete all created temp files!
aahfhigee,RaRe-Technologies/gensim,gensim/similarities/simserver.py,6d8151cff261cbe5a367d956f8aee64835afa1b4,STILL_EXISTS,TODO or maybe clip? are opposite vectors \"similar\" or \"dissimilar\"?!
aahfhigfb,RaRe-Technologies/gensim,gensim/similarities/simserver.py,6d8151cff261cbe5a367d956f8aee64835afa1b4,STILL_EXISTS,TODO: use subclassing\/injection for different methods; instead of param?
aahfhigfd,RaRe-Technologies/gensim,gensim/similarities/simserver.py,6d8151cff261cbe5a367d956f8aee64835afa1b4,STILL_EXISTS,TODO take method into account
aahfhiggi,RaRe-Technologies/gensim,gensim/test/test_simserver.py,6d8151cff261cbe5a367d956f8aee64835afa1b4,STILL_EXISTS,needed because sample data files are located in the same folder
aahfhihhd,RaRe-Technologies/gensim,gensim/test/test_simserver.py,a01f1a4fe5552950a0d7b8a7a56a550a2ec27baf,STILL_EXISTS,TODO how to test that it's faster? just read and verify server.status()?
aahfhihif,RaRe-Technologies/gensim,gensim/test/test_simserver.py,600b7f9daaf845e5e1fcc50d500598abfbc0cd81,STILL_EXISTS,commit ends the session
aahfhiibi,RaRe-Technologies/gensim,gensim/utils.py,1037eb91919418d616838608be6ae6788d3ecfeb,6a289fefd72f038c8cc14826f63624950f5de1f8,TODO: spawn a proper daemon ala http:\/\/code.activestate.com\/recipes\/278731\/ ?
aahfhiida,RaRe-Technologies/gensim,gensim/similarities/simserver.py,2e414ff171647b784a91d3beb324ae3d4af4c415,STILL_EXISTS,TODO HACK: exit on first doc without a payload (=assume all docs have payload; or none does)
aahfhjeie,RaRe-Technologies/gensim,gensim/models/tfidfmodel.py,680ed0ed7a06ddcf40d56d7a00c70e57380b46e2,STILL_EXISTS,HACK using leftover from enumerate(corpus) above
aahfhjeij,RaRe-Technologies/gensim,gensim/similarities/docsim.py,9c1b932ca9e167671c59793e36324e9ee865bb45,STILL_EXISTS,the user better know what he's doing (no normalization; must
aahfhjfcd,RaRe-Technologies/gensim,gensim/models/hdpmodel.py,ad8a4e087d5c7cb8f814c78f5e562b9c02e4eb9b,STILL_EXISTS,\"\"\" || This module encapsulates functionality for the online Hierarchical Dirichlet Process algorithm. ||  || It allows both model estimation from a training corpus and inference of topic || distribution on new; unseen documents. ||  || The core estimation code is directly adapted from the `onlinelhdp.py` script || by C. Wang see || **Wang; Paisley; Blei: Online Variational Inference for the Hierarchical Dirichlet || Process; JMLR (2011).** ||  || http:\/\/jmlr.csail.mit.edu\/proceedings\/papers\/v15\/wang11a\/wang11a.pdf ||  || The algorithm: ||  ||   * is **streamed**: training documents come in sequentially; no random access; ||   * runs in **constant memory** w.r.t. the number of documents: size of the ||     training corpus does not affect memory footprint ||  || \"\"\"
aahfhjfdj,RaRe-Technologies/gensim,gensim/models/hdpmodel.py,ad8a4e087d5c7cb8f814c78f5e562b9c02e4eb9b,STILL_EXISTS,...and do the lazy updates on the necessary columns of lambda
aahfhjffi,RaRe-Technologies/gensim,gensim/models/hdpmodel.py,ad8a4e087d5c7cb8f814c78f5e562b9c02e4eb9b,STILL_EXISTS,Update appropriate columns of lambda based on documents.
aahfhjghg,RaRe-Technologies/gensim,gensim/utils.py,563099b308421926950723ec6f72643bc1afb678,STILL_EXISTS,collapse the output at the end. TODO this is accomplished by modifying
aahfhjghj,RaRe-Technologies/gensim,gensim/utils.py,3e6383eb5c7a803755334cb638bd6debeeb5041f,STILL_EXISTS,FIXME this throws away all fancy parsing cues; including sentence structure;
aahfhjjih,RaRe-Technologies/gensim,gensim/similarities/docsim.py,20bb40972a1307c7f486a6a98ce219111f72ced9,STILL_EXISTS,TODO set chunksize somehow?
aahfhjjjc,RaRe-Technologies/gensim,gensim/similarities/docsim.py,20bb40972a1307c7f486a6a98ce219111f72ced9,STILL_EXISTS,processing; to improve query latency and minimize memory footprint.
aahfhjjjg,RaRe-Technologies/gensim,gensim/similarities/docsim.py,738d0603a7ae0b61c3f013f1829485df5d7a4a85,STILL_EXISTS,TODO set chunksize somehow?
aahfiachj,RaRe-Technologies/gensim,gensim/corpora/hashdictionary.py,5be197b26e7ff9dc22f869edac03cbe712173154,STILL_EXISTS,convert the input to plain list (needed below)
aahfiaeac,RaRe-Technologies/gensim,gensim/corpora/dictionary.py,aafe506fb6c3794ab1dd670e09ffd686d80cc5ff,510c8165a3bda99e09d68e850b1e3fae9ad5362f,TODO: test function
aahfiaeae,RaRe-Technologies/gensim,gensim/test/test_corpora_dictionary.py,aafe506fb6c3794ab1dd670e09ffd686d80cc5ff,STILL_EXISTS,TODO: Fix test this can't be the same; because there are no information
aahfiafbd,RaRe-Technologies/gensim,gensim/models/lsimodel.py,410a7b993d5cdfb0579ddf8555a73d5caf482aaf,812cf66d765c4a626e379e3264c3f3f417fbf247,TODO load back incl. distributed state? will require re-initialization of worker state
aahfiafbi,RaRe-Technologies/gensim,gensim/models/word2vec.py,6a5263018c51d9993fb24df584a9eabbffb81642,STILL_EXISTS,\"\"\" || Module for deep learning. ||  || TODO pick word that doesn't belong: https:\/\/github.com\/dhammack\/Word2VecExample\/blob\/master\/main.py ||  || \"\"\"
aahfiafcj,RaRe-Technologies/gensim,gensim/models/word2vec.py,6a5263018c51d9993fb24df584a9eabbffb81642,STILL_EXISTS,FIXME remove debug to save memory
aahfiafef,RaRe-Technologies/gensim,gensim/models/word2vec.py,6a5263018c51d9993fb24df584a9eabbffb81642,STILL_EXISTS,TODO negative sampling here
aahfiafhf,RaRe-Technologies/gensim,gensim/models/word2vec.py,6aec07f7b351a15292c10b5a840ef832d0be2e0c,STILL_EXISTS,TODO assumes vocabulary uses lowercase; too...
aahfiaicg,RaRe-Technologies/gensim,gensim/models/word2vec.py,4d22af45e988fce71eb196bef0d9210d225dc73a,699773a78f32ff2c254cf16a586a5d26a08394fe,TODO: use level3 BLAS (=evaluate multiple questions at once); for speed
aahfiaidj,RaRe-Technologies/gensim,gensim/test/test_word2vec.py,cf22a17d5cb5bb89ca2dc9f41260f7695c3abe28,2bb8e1dfeadfef943c888f7e4e735fd161535475,needed because sample data files are located in the same folder
aahfiaifg,RaRe-Technologies/gensim,gensim/corpora/csvcorpus.py,cf2693a3bf528a77b9c1de50d71208beedd67f68,ac27b5c67be3f37c81720deca45f8ed242079810,\"\"\" || Corpus in CSV format. TODO: still serializes to SVMLight format; feel free to change this. || \"\"\"
aahfibadb,RaRe-Technologies/gensim,gensim/models/ldamodel.py,89219a03b9e6ce5bcc7941f45d80bbbb5f65e295,STILL_EXISTS,add a little random jitter; to randomize results around the same alpha
aahfibaeb,RaRe-Technologies/gensim,gensim/models/lsimodel.py,268d5d24a89ddf15ae73aba425b9321feced7df7,STILL_EXISTS,# \u00B1 same performance as above (BLAS dense * dense is better optimized than scipy.sparse); but consumes more memory
aahfibafg,RaRe-Technologies/gensim,gensim/utils.py,aff353fdbff2297442a0083f8f98f1b137a1be14,STILL_EXISTS,TODO mmap back the three scipy sparse arrays into csc\/csr
aahfibafi,RaRe-Technologies/gensim,gensim/utils.py,aff353fdbff2297442a0083f8f98f1b137a1be14,7ebf66e15e0cab5b7209d9098ea84d376790e283,TODO convert to csr\/csc => stores .indices .data .indptr arrays separately
aahfibaga,RaRe-Technologies/gensim,gensim/models/word2vec.py,5ee37efef852b16b463d861f6ba173bbdf604583,STILL_EXISTS,randomize weights vector by vector; rather than materializing a huge random matrix in RAM at once
aahfibbcg,RaRe-Technologies/gensim,six.py,21099e3ea773b106b16bd12863f53cdd61a3768b,STILL_EXISTS,This is a bit ugly; but it avoids running this again.
aahfibbfe,RaRe-Technologies/gensim,gensim/models/ldamallet.py,2cf0b360b1a9601fafc4bb40a4b393d1bda1c384,STILL_EXISTS,skip \"doc\" and \"source\" columns
aahfibdbd,RaRe-Technologies/gensim,gensim/models/ldamallet.py,db70d04c650633cf2d1c0735c3cbd60cc3b36d77,STILL_EXISTS,add a little random jitter; to randomize results around the same alpha
aahfibdci,RaRe-Technologies/gensim,gensim/test/test_corpora_dictionary.py,950f9ec36af48df9cc4c4016fc9b716d3f0ac541,b70c82613ac59656408f2403dfc2408f461eecff,XXX Do we want list results from the dict members in Py3 too?
aahfibdde,RaRe-Technologies/gensim,gensim/models/word2vec.py,f470b6025ea5a302cd17ec2e59a722086e5ba966,636daaddea28d4dc07a7c0df444efa8c38c3719a,work on the entire tree at once; to push as much work into numpy's C routines as possible (performance)
aahfibdeg,RaRe-Technologies/gensim,gensim/matutils.py,4ea27ba44e2ce56f06c2294d02e59031707e1ca5,e859c11f6f57bf3c883a718a9ab7067ac0c2d4cf,needed for python3
aahfibfhf,RaRe-Technologies/gensim,gensim/models/doc2vec.py,3cdac021bde4e11c682e3413a84e4c32a42677b4,47e7b28c0051cdbf460611f6cb725a941e383ab7,\"\"\" || Deep learning via doc2vec's \"skip-gram and CBOW models\"; using either || hierarchical softmax or negative sampling [1]_ [2]_. ||  || The training algorithms were originally ported from the C package https:\/\/code.google.com\/p\/doc2vec\/ || and extended with additional functionality. ||  || **Install Cython with `pip install cython` to use optimized doc2vec training** (70x speedup [3]_). ||  || Initialize a model with e.g.:: ||  || >>> model = Doc2Vec(sentences; size=100; window=5; min_count=5; workers=4) ||  || Persist a model to disk with:: ||  || >>> model.save(fname) || >>> model = Doc2Vec.load(fname)  # you can continue training with the loaded model! ||  || The model can also be instantiated from an existing file on disk in the doc2vec C format:: ||  ||   >>> model = Doc2Vec.load_doc2vec_format('\/tmp\/vectors.txt'; binary=False)  # C text format ||   >>> model = Doc2Vec.load_doc2vec_format('\/tmp\/vectors.bin'; binary=True)  # C binary format ||  || You can perform various syntactic\/semantic NLP word tasks with the model. Some of them || are already built-in:: ||  ||   >>> model.most_similar(positive=['woman'; 'king']; negative=['man']) ||   [('queen'; 0.50882536); ...] ||  ||   >>> model.doesnt_match(\"breakfast cereal dinner lunch\".split()) ||   'cereal' ||  ||   >>> model.similarity('woman'; 'man') ||   0.73723527 ||  ||   >>> model['computer']  # raw numpy vector of a word ||   array([-0.00449447; -0.00310097;  0.02421786; ...]; dtype=float32) ||  || and so on. ||  || If you're finished training a model (=no more updates; only querying); you can do ||  ||   >>> model.init_sims(replace=True) ||  || to trim unneeded model memory = use (much) less RAM. ||  || .. [1] Tomas Mikolov; Kai Chen; Greg Corrado; and Jeffrey Dean. Efficient Estimation of Word Representations in Vector Space. In Proceedings of Workshop at ICLR; 2013. || .. [2] Tomas Mikolov; Ilya Sutskever; Kai Chen; Greg Corrado; and Jeffrey Dean. Distributed Representations of Words and Phrases and their Compositionality. ||        In Proceedings of NIPS; 2013. || .. [3] Optimizing doc2vec in gensim; http:\/\/radimrehurek.com\/2013\/09\/doc2vec-in-python-part-two-optimizing\/ || \"\"\"
aahfibfij,RaRe-Technologies/gensim,gensim/models/doc2vec.py,3cdac021bde4e11c682e3413a84e4c32a42677b4,47e7b28c0051cdbf460611f6cb725a941e383ab7,TODO: do we want to add labels as elements to be predicted here? Might be fun to try...
aahfibghc,RaRe-Technologies/gensim,gensim/models/doc2vec.py,3cdac021bde4e11c682e3413a84e4c32a42677b4,afbfc445876043b6712ca8ea76a3f803002ecffc,randomize weights vector by vector; rather than materializing a huge random matrix in RAM at once
aahfibgic,RaRe-Technologies/gensim,gensim/models/doc2vec.py,3cdac021bde4e11c682e3413a84e4c32a42677b4,47e7b28c0051cdbf460611f6cb725a941e383ab7,TODO: use level3 BLAS (=evaluate multiple questions at once); for speed
aahfibgie,RaRe-Technologies/gensim,gensim/models/doc2vec.py,3cdac021bde4e11c682e3413a84e4c32a42677b4,47e7b28c0051cdbf460611f6cb725a941e383ab7,TODO assumes vocabulary preprocessing uses lowercase; too...
aahfibhai,RaRe-Technologies/gensim,gensim/models/doc2vec.py,47e7b28c0051cdbf460611f6cb725a941e383ab7,STILL_EXISTS,\"\"\" || Deep learning via the distributed memory and distributed bag of words models from || [1]_; using either hierarchical softmax or negative sampling [2]_ [3]_. ||  || **Install Cython with `pip install cython` to use optimized doc2vec training** (70x speedup [4]_). ||  || Initialize a model with e.g.:: ||  || >>> model = Doc2Vec(sentences; size=100; window=8; min_count=5; workers=4) ||  || Persist a model to disk with:: ||  || >>> model.save(fname) || >>> model = Doc2Vec.load(fname)  # you can continue training with the loaded model! ||  || The model can also be instantiated from an existing file on disk in the doc2vec C format:: ||  ||   >>> model = Doc2Vec.load_doc2vec_format('\/tmp\/vectors.txt'; binary=False)  # C text format ||   >>> model = Doc2Vec.load_doc2vec_format('\/tmp\/vectors.bin'; binary=True)  # C binary format ||  || You can perform various syntactic\/semantic NLP word tasks with the model. Some of them || are already built-in:: ||  ||   >>> model.most_similar(positive=['woman'; 'king']; negative=['man']) ||   [('queen'; 0.50882536); ...] ||  ||   >>> model.doesnt_match(\"breakfast cereal dinner lunch\".split()) ||   'cereal' ||  ||   >>> model.similarity('woman'; 'man') ||   0.73723527 ||  ||   >>> model['computer']  # raw numpy vector of a word ||   array([-0.00449447; -0.00310097;  0.02421786; ...]; dtype=float32) ||  || and so on. ||  || If you're finished training a model (=no more updates; only querying); you can do ||  ||   >>> model.init_sims(replace=True) ||  || to trim unneeded model memory = use (much) less RAM. ||  || .. [1] Quoc Le and Tomas Mikolov. Distributed Representations of Sentences and Documents. http:\/\/arxiv.org\/pdf\/1405.4053v2.pdf || .. [2] Tomas Mikolov; Kai Chen; Greg Corrado; and Jeffrey Dean. Efficient Estimation of Word Representations in Vector Space. In Proceedings of Workshop at ICLR; 2013. || .. [3] Tomas Mikolov; Ilya Sutskever; Kai Chen; Greg Corrado; and Jeffrey Dean. Distributed Representations of Words and Phrases and their Compositionality. ||        In Proceedings of NIPS; 2013. || .. [4] Optimizing word2vec in gensim; http:\/\/radimrehurek.com\/2013\/09\/word2vec-in-python-part-two-optimizing\/ ||  || \"\"\"
aahfibich,RaRe-Technologies/gensim,gensim/models/ldamodel.py,2c5a70584285d54b9d436327396a0af4868fd99a,a40717917a7d841cd192996408b84cdd59a816ca,TODO figure out the apropriate size
aahfibidc,RaRe-Technologies/gensim,gensim/models/ldamodel.py,2c5a70584285d54b9d436327396a0af4868fd99a,a40717917a7d841cd192996408b84cdd59a816ca,unfortunately queue length is not relaiable so it was needed to use Value
aahfibidd,RaRe-Technologies/gensim,gensim/models/ldamodel.py,2c5a70584285d54b9d436327396a0af4868fd99a,a40717917a7d841cd192996408b84cdd59a816ca,possible TODO instead of this implement online merging of the result?
aahfibidg,RaRe-Technologies/gensim,gensim/models/ldamodel.py,f0310f61338949bed4ea89e00c753f3c696d24cc,a40717917a7d841cd192996408b84cdd59a816ca,XXX might possibly be slow solution; but every worker needs reseted LDA model for every iteration
aahfibige,RaRe-Technologies/gensim,setup.py,d4b9ec0ca35ec039a3194411a01139fe7a149fa5,STILL_EXISTS,the following is needed to be able to add numpy's include dirs... without
aahfibigi,RaRe-Technologies/gensim,setup.py,d4b9ec0ca35ec039a3194411a01139fe7a149fa5,0d65960c9f771df8591c9e2b49afd32f6ef70fe2,u'Radim \u0158eh\u016F\u0159ek'; # <- should really be this...;
aahfibjdh,RaRe-Technologies/gensim,gensim/models/ldamodel.py,87a0a47e0975e37ce25a38577717578e2b8911be,STILL_EXISTS,TODO optimization do not create zero matrix
aahficacc,RaRe-Technologies/gensim,gensim/models/ldamodel.py,72550166d9851abb595651653ee8246d6747305a,STILL_EXISTS,TODO optimization do not create zero matrix
aahficacf,RaRe-Technologies/gensim,gensim/models/ldamodelmulticore.py,72550166d9851abb595651653ee8246d6747305a,STILL_EXISTS,rho is the \"speed\" of updating; TODO try other fncs
aahficach,RaRe-Technologies/gensim,gensim/models/ldamodelmulticore.py,72550166d9851abb595651653ee8246d6747305a,5855af556fd0eaafb68a0988d63bf1631999a8d7,TODO optimization do not create zero matrix
aahficadb,RaRe-Technologies/gensim,gensim/models/ldamodelmulticore.py,72550166d9851abb595651653ee8246d6747305a,STILL_EXISTS,FIXME solve what to do in case when self.optimize_alpha?
aahficadf,RaRe-Technologies/gensim,gensim/models/ldamodelmulticore.py,a690b97c81363098d4cd8bf0ec0bc0cf675a5c40,STILL_EXISTS,TODO optimization do not create zero matrix
aahficadg,RaRe-Technologies/gensim,gensim/models/ldamodelmulticore.py,a41f896f0d9cffc624c2876de729b2f6af3f739d,STILL_EXISTS,TODO: full explicit init params; incl. defaults
aahficadh,RaRe-Technologies/gensim,gensim/models/ldamodelmulticore.py,a41f896f0d9cffc624c2876de729b2f6af3f739d,STILL_EXISTS,TODO: auto-tune alpha?
aahficadi,RaRe-Technologies/gensim,gensim/models/ldamodelmulticore.py,a41f896f0d9cffc624c2876de729b2f6af3f739d,2561bc108b6e14fb8e3faa4f6e9ad8685133156e,FIXME: doesn't work well
aahficaea,RaRe-Technologies/gensim,gensim/models/ldamodelmulticore.py,a41f896f0d9cffc624c2876de729b2f6af3f739d,STILL_EXISTS,TODO optimization do not create zero matrix
aahficaee,RaRe-Technologies/gensim,gensim/test/test_models.py,2561bc108b6e14fb8e3faa4f6e9ad8685133156e,10bfd47560ec129a43ce6e56a4a4ba1fc4c5626f,better random initialization
aahficagf,RaRe-Technologies/gensim,gensim/models/ldamodelmulticore.py,fc0d6811a61c34b852a46b68523df7aa81d84081,STILL_EXISTS,\"\"\" || Latent Dirichlet Allocation (LDA) in Python; using all cores to parallelize and || speed up model training. ||  || The parallelization uses multiprocessing; in case this doesn't work for you for || some reason; try `LdaModel` which is an equivalent; but more straightforward and || single-core implementation. ||  || FIXME wiki timings ||  || This module allows both LDA model estimation from a training corpus and inference of topic || distribution on new; unseen documents. The model can also be updated with new documents || for online training. ||  || The core estimation code is based on the `onlineldavb.py` script by M. Hoffman [1]_; see || **Hoffman; Blei; Bach: Online Learning for Latent Dirichlet Allocation; NIPS 2010.** ||  || The algorithm: ||  || * is **streamed**: training documents may come in sequentially; no random access required; || * runs in **constant memory** w.r.t. the number of documents: size of the ||   training corpus does not affect memory footprint; can process corpora larger than RAM; and || * is **distributed**: makes use of a cluster of machines; if available; to ||   speed up model estimation. ||  || .. [1] http:\/\/www.cs.princeton.edu\/~mdhoffma || \"\"\"
aahficagi,RaRe-Technologies/gensim,gensim/models/ldamodelmulticore.py,fc0d6811a61c34b852a46b68523df7aa81d84081,24eb87eb5cc8985944704894c1df3087786e9c28,FIXME: doesn't work well
aahficfjg,RaRe-Technologies/gensim,gensim/utils.py,38b4328d64276341ff46b4c7b803848163b4f4ea,STILL_EXISTS,check cached length; calculate if needed
aahficgcg,RaRe-Technologies/gensim,gensim/test/test_word2vec.py,6d03d0f896bd98f208adec9898953f97efc2a761,STILL_EXISTS,TODO: commented out for now; find a more robust way to compare against \"gold standard\"
aahficgfa,RaRe-Technologies/gensim,gensim/test/test_phrases.py,757b02294d4888817327d103f6ce46bc20f1bb34,2bb8e1dfeadfef943c888f7e4e735fd161535475,needed because sample data files are located in the same folder
aahficjah,RaRe-Technologies/gensim,gensim/test/test_dtm.py,75ac330621bfb70eb874ce11da3067a8159892f8,2bb8e1dfeadfef943c888f7e4e735fd161535475,needed because sample data files are located in the same folder
aahfidaag,RaRe-Technologies/gensim,gensim/corpora/sharded_corpus.py,251acc3ef2328910ef265f5d0460b41f73b36918,STILL_EXISTS,Move old shard files out; new ones in. Complicated due to possibility
aahfidbdb,RaRe-Technologies/gensim,gensim/models/word2vec.py,ff72d02c8eff53ca100187297450945887d70ee3,6c9e976aa5a295170c20b44401ca2300ce7be601,work on the entire tree at once; to push as much work into numpy's C routines as possible (performance)
aahfidbia,RaRe-Technologies/gensim,gensim/models/word2vec.py,53377df9a9c12d5738fe850c43e2e7d059a2e183,77fd985b609c7790d5e07d1ed0d5092d85bcdd93,work on the entire tree at once; to push as much work into numpy's C routines as possible (performance)
aahfidcbf,RaRe-Technologies/gensim,gensim/models/wrappers/ldavowpalwabbit.py,227f0978b953ebc97e3fd80f9fda1414343b7f3a,STILL_EXISTS,possibly not needed; but harmless
aahfidccc,RaRe-Technologies/gensim,gensim/models/ldamulticore.py,2a65c4510fcdbad6ccb78b6f76f60438b93c2270,STILL_EXISTS,TODO: auto-tune alpha?
aahfiecfa,RaRe-Technologies/gensim,gensim/summarization/graph.py,2327549543c75c635f0fba065d4df77cb5656600,0db3da0b4f0a48cdf81b5042b67d6d8ca302581a,TODO: This is redundant
aahfiecge,RaRe-Technologies/gensim,gensim/summarization/summarizer.py,2327549543c75c635f0fba065d4df77cb5656600,STILL_EXISTS,Checks if the inclusion of the sentence gives a better approximation
aahfiehgi,RaRe-Technologies/gensim,gensim/models/doc2vec.py,f1ad6b643c926ae493f223c48fc6029137324a50,STILL_EXISTS,FIXME: doc-labels for short docs can be culled by min_count
aahfiehhc,RaRe-Technologies/gensim,gensim/models/word2vec.py,d851078206fcda9e3c99a813e673b0bbfdf11d03,STILL_EXISTS,TOCONSIDER: maybe truncation\/smaller vectors still useful enough to merge?
aahfiehhd,RaRe-Technologies/gensim,gensim/models/word2vec.py,d851078206fcda9e3c99a813e673b0bbfdf11d03,b31e94f6b1465e5b16f7bb4a882f98a783567090,TODO raise ValueError()?
aahfieiag,RaRe-Technologies/gensim,gensim/models/doc2vec.py,8d3d0f34b6f718c5bf6bc9fce9923ce4d628d30f,61b47e1d61ba52764b3ad5c31ceef5537d94f216,TODO: adapt for word_vectors\/word_locks
aahfieibb,RaRe-Technologies/gensim,gensim/models/doc2vec.py,8d3d0f34b6f718c5bf6bc9fce9923ce4d628d30f,STILL_EXISTS,## TODO: save doclbl fields
aahfieici,RaRe-Technologies/gensim,gensim/test/test_doc2vec.py,93a62724850cdecb31e17ca4b7571f26e8abd7b4,2bb8e1dfeadfef943c888f7e4e735fd161535475,needed because sample data files are located in the same folder
aahfifcdc,RaRe-Technologies/gensim,gensim/models/doc2vec.py,09a30b3c7f0e5c702affb0faf5e2f5d28d6bbe44,390c35b6aaa02c0d47bf725148c92ac774feed9a,TODO: adapt for word_vectors\/word_locks
aahfifcga,RaRe-Technologies/gensim,gensim/models/doc2vec.py,09a30b3c7f0e5c702affb0faf5e2f5d28d6bbe44,f88beab17f29c69979c562f7e47f325169e6dc5d,## TODO: save docvecs in same separate-numpy-file style
aahfifchi,RaRe-Technologies/gensim,gensim/models/word2vec.py,09a30b3c7f0e5c702affb0faf5e2f5d28d6bbe44,STILL_EXISTS,TOCONSIDER: maybe truncation\/smaller vectors still useful enough to merge?
aahfifchj,RaRe-Technologies/gensim,gensim/models/word2vec.py,09a30b3c7f0e5c702affb0faf5e2f5d28d6bbe44,b31e94f6b1465e5b16f7bb4a882f98a783567090,TODO raise ValueError()?
aahfifhfj,RaRe-Technologies/gensim,gensim/models/word2vec.py,92e437dc964e8395d0c4329f68585ea2b1cb7957,d2f48bb83fdcb3e5bc1e8e5424140b250b5c6432,could move this import up to where train_* is imported;
aahfifiad,RaRe-Technologies/gensim,gensim/models/doc2vec.py,626225bba27161817e265e1607e95a126ab4e590,84370a59d31c63f855ab0e76c5391b9f76af263f,## FIXME
aahfifidf,RaRe-Technologies/gensim,gensim/models/word2vec.py,d392f6ff1ab3f7cc78136c86cd406b976c27bd8a,25b82b536bd25c9dd2656e2c25166156ebed9267,loop ended by job count; really done
aahfifidj,RaRe-Technologies/gensim,gensim/models/doc2vec.py,d4a3e7511b4c63c5e600687aa6b630ff7aa276b3,42ea4d0d35669bc8564882551892d365e40bc179,TODO: adapt for word_vectors\/word_locks
aahfifiee,RaRe-Technologies/gensim,gensim/models/word2vec.py,d4a3e7511b4c63c5e600687aa6b630ff7aa276b3,28c7c6ef1e6eccca339764d6de9fed987c6647a6,could move this import up to where train_* is imported;
aahfifihh,RaRe-Technologies/gensim,gensim/models/word2vec.py,d4a3e7511b4c63c5e600687aa6b630ff7aa276b3,25b82b536bd25c9dd2656e2c25166156ebed9267,loop ended by job count; really done
aahfifiie,RaRe-Technologies/gensim,gensim/test/test_similarities.py,e7944aec0703628cd0e158b9c13f991e8ec0d0fc,STILL_EXISTS,HACK: dictionary can be in different order; so compare in sorted order
aahfifjbg,RaRe-Technologies/gensim,gensim/models/word2vec.py,b3e07ff97986b9be72dd47c7dbd53845691a7007,25b82b536bd25c9dd2656e2c25166156ebed9267,loop ended by job count; really done
aahfifjcg,RaRe-Technologies/gensim,gensim/models/word2vec.py,2268d20661bcfb1f9e1b09e6434e96cd5777c779,28c7c6ef1e6eccca339764d6de9fed987c6647a6,could move this import up to where train_* is imported;
aahfigech,RaRe-Technologies/gensim,gensim/summarization/summarizer.py,2c1fdaa7da498d4d87abd6c39e246cf70bfc6270,STILL_EXISTS,If the corpus is empty; the function ends.
aahfigeci,RaRe-Technologies/gensim,gensim/summarization/summarizer.py,2c1fdaa7da498d4d87abd6c39e246cf70bfc6270,STILL_EXISTS,If no sentence could be identified; the function ends.
aahfigede,RaRe-Technologies/gensim,gensim/test/test_doc2vec.py,02f91b8d31e4f68c73272b70b9a8349f18038cfd,STILL_EXISTS,workaround for python 2.6
aahfigedf,RaRe-Technologies/gensim,gensim/test/test_word2vec.py,02f91b8d31e4f68c73272b70b9a8349f18038cfd,STILL_EXISTS,workaround for python 2.6
aahfigedh,RaRe-Technologies/gensim,gensim/utils.py,9cac3843481688f79e3904e893f49163c6328789,STILL_EXISTS,better than 'isinstance(val; SaveLoad)' if IPython reloading
aahfigejb,RaRe-Technologies/gensim,gensim/models/word2vec.py,7fdaae8853ac803e31e4f27de0460af67c646425,25b82b536bd25c9dd2656e2c25166156ebed9267,loop ended by job count; really done
aahfighid,RaRe-Technologies/gensim,gensim/summarization/pagerank_weighted.py,b9509e7fc6251c86ef13279d3dcd2e80e6aaf534,36a85ba7f9a7d82204a6eb1d7466d8a6dbb7c92c,TODO optimize this.
aahfighie,RaRe-Technologies/gensim,gensim/summarization/pagerank_weighted.py,b9509e7fc6251c86ef13279d3dcd2e80e6aaf534,STILL_EXISTS,TODO: this is wasteful (only compute the principal component).
aahfigibb,RaRe-Technologies/gensim,gensim/test/test_hdpmodel.py,f2ecdf2af80bf2e4fbb6c2470cf92480aedf0438,2bb8e1dfeadfef943c888f7e4e735fd161535475,needed because sample data files are located in the same folder
aahfigica,RaRe-Technologies/gensim,gensim/test/test_ldamallet_wrapper.py,f2ecdf2af80bf2e4fbb6c2470cf92480aedf0438,2bb8e1dfeadfef943c888f7e4e735fd161535475,needed because sample data files are located in the same folder
aahfigiec,RaRe-Technologies/gensim,gensim/test/test_ldamodel.py,f2ecdf2af80bf2e4fbb6c2470cf92480aedf0438,2bb8e1dfeadfef943c888f7e4e735fd161535475,needed because sample data files are located in the same folder
aahfigieh,RaRe-Technologies/gensim,gensim/test/test_ldamodel.py,f2ecdf2af80bf2e4fbb6c2470cf92480aedf0438,STILL_EXISTS,better random initialization
aahfigjaa,RaRe-Technologies/gensim,gensim/test/test_logentropy_model.py,f2ecdf2af80bf2e4fbb6c2470cf92480aedf0438,2bb8e1dfeadfef943c888f7e4e735fd161535475,needed because sample data files are located in the same folder
aahfigjbc,RaRe-Technologies/gensim,gensim/test/test_lsimodel.py,f2ecdf2af80bf2e4fbb6c2470cf92480aedf0438,2bb8e1dfeadfef943c888f7e4e735fd161535475,needed because sample data files are located in the same folder
aahfigjfi,RaRe-Technologies/gensim,gensim/test/test_rpmodel.py,f2ecdf2af80bf2e4fbb6c2470cf92480aedf0438,2bb8e1dfeadfef943c888f7e4e735fd161535475,needed because sample data files are located in the same folder
aahfigjgc,RaRe-Technologies/gensim,gensim/test/test_rpmodel.py,f2ecdf2af80bf2e4fbb6c2470cf92480aedf0438,STILL_EXISTS,HACK; set fixed seed so that we always get the same random matrix (and can compare against expected results)
aahfigjhf,RaRe-Technologies/gensim,gensim/test/test_tfidfmodel.py,f2ecdf2af80bf2e4fbb6c2470cf92480aedf0438,2bb8e1dfeadfef943c888f7e4e735fd161535475,needed because sample data files are located in the same folder
aahfigjja,RaRe-Technologies/gensim,gensim/summarization/pagerank_weighted.py,c93dde0a88f0172a919cc36bd0aac8d5cc22afce,6208a48961bd14d3e8e9b3b2b35dbd209a1ab8a0,TODO optimize this.
aahfigjjf,RaRe-Technologies/gensim,gensim/models/word2vec.py,acf1c19943158789cfbd1a15221612c1ce44abe1,41ff1f431ec0c171e1433088c03a296aac6cf2d9,vocab file given; but word is missing -- set count to None (TODO: or raise?)
aahfigjjg,RaRe-Technologies/gensim,gensim/models/word2vec.py,e6bd0be0085015e7ba098bf79e1fabc808753154,STILL_EXISTS,TODO: batching; send a set of sentences to the train_sentence_sg Cython method. Sum of sentence lengths should not exceed MAX_SENTENCE_LENGTH (probably just import this constant directly from word2vec_inner.pyx).
aahfihaad,RaRe-Technologies/gensim,gensim/models/word2vec.py,04cfd04289ac1a04297527a6e8ed2cc091de93ac,41ff1f431ec0c171e1433088c03a296aac6cf2d9,vocab file given; but word is missing -- set count to None (TODO: or raise?)
aahfihaaj,RaRe-Technologies/gensim,gensim/models/word2vec.py,da48ba9f05316fa48238f96dde4b2dd04a882dd9,STILL_EXISTS,TODO: remove this log when done with batching tests.
aahfihaba,RaRe-Technologies/gensim,gensim/models/word2vec.py,da48ba9f05316fa48238f96dde4b2dd04a882dd9,25b82b536bd25c9dd2656e2c25166156ebed9267,TODO: do for cbow also.
aahfihabd,RaRe-Technologies/gensim,gensim/models/word2vec.py,da48ba9f05316fa48238f96dde4b2dd04a882dd9,STILL_EXISTS,TODO: remove these logs when done with batching tests.
aahfihabf,RaRe-Technologies/gensim,gensim/models/word2vec.py,fbea4e35cfbfa9664f6335c2679529d40df2ce0a,STILL_EXISTS,FIXME: last sentence is ignored.
aahfihabg,RaRe-Technologies/gensim,gensim/models/word2vec.py,1d438d9362b26b1043117ca4136a24052b389509,f6a00346074d07700d4c1c6c00faf1ffc4e3a867,TODO: remove \"batch\" input variable when done working on batching.
aahfihabh,RaRe-Technologies/gensim,gensim/models/word2vec.py,1d438d9362b26b1043117ca4136a24052b389509,STILL_EXISTS,TODO: remove when not needed anymore.
aahfihacb,RaRe-Technologies/gensim,gensim/models/word2vec.py,1d438d9362b26b1043117ca4136a24052b389509,STILL_EXISTS,FIXME: last sentence is ignored.
aahfihacd,RaRe-Technologies/gensim,gensim/models/word2vec.py,84564b47d608c85f160cc157055db15ed99f59f0,7f950279cb065d39b736f822d2a51306dea573da,TODO: consider proper value for this constant.
aahfihacf,RaRe-Technologies/gensim,gensim/models/word2vec.py,84564b47d608c85f160cc157055db15ed99f59f0,STILL_EXISTS,TODO: remove when not needed anymore.
aahfihdgj,RaRe-Technologies/gensim,gensim/models/word2vec.py,7f950279cb065d39b736f822d2a51306dea573da,41ff1f431ec0c171e1433088c03a296aac6cf2d9,vocab file given; but word is missing -- set count to None (TODO: or raise?)
aahfihjeg,RaRe-Technologies/gensim,gensim/models/word2vec.py,12836e0bc0b1556dc97ada4a46c71e26176de50f,f6a00346074d07700d4c1c6c00faf1ffc4e3a867,TODO: remove \"batch\" input variable when done working on batching.
aahfihjeh,RaRe-Technologies/gensim,gensim/models/word2vec.py,12836e0bc0b1556dc97ada4a46c71e26176de50f,25b82b536bd25c9dd2656e2c25166156ebed9267,TODO: do for cbow also.
aahfihjej,RaRe-Technologies/gensim,gensim/models/word2vec.py,12836e0bc0b1556dc97ada4a46c71e26176de50f,50266c7a028625a6c639342a1ee30975316331a4,TODO: should be in word2vec_inner.pyx as well. TODO: consider proper value.
aahfihjfa,RaRe-Technologies/gensim,gensim/models/word2vec.py,12836e0bc0b1556dc97ada4a46c71e26176de50f,50266c7a028625a6c639342a1ee30975316331a4,TODO: make this code more elegant; it is very messy now.
aahfihjgg,RaRe-Technologies/gensim,gensim/models/word2vec.py,12836e0bc0b1556dc97ada4a46c71e26176de50f,41ff1f431ec0c171e1433088c03a296aac6cf2d9,vocab file given; but word is missing -- set count to None (TODO: or raise?)
aahfijbbc,RaRe-Technologies/gensim,gensim/models/word2vec.py,50266c7a028625a6c639342a1ee30975316331a4,41ff1f431ec0c171e1433088c03a296aac6cf2d9,vocab file given; but word is missing -- set count to None (TODO: or raise?)
aahfijbci,RaRe-Technologies/gensim,gensim/models/word2vec.py,481c258ee700e7280a65f227825b3e83b01c54e1,f6a00346074d07700d4c1c6c00faf1ffc4e3a867,TODO: remove \"batch\" input variable when done working on batching.
aahfijbcj,RaRe-Technologies/gensim,gensim/models/word2vec.py,481c258ee700e7280a65f227825b3e83b01c54e1,25b82b536bd25c9dd2656e2c25166156ebed9267,TODO: do for cbow also.
aahfijbdb,RaRe-Technologies/gensim,gensim/models/word2vec.py,481c258ee700e7280a65f227825b3e83b01c54e1,STILL_EXISTS,TODO: should be in word2vec_inner.pyx as well. TODO: consider proper value.
aahfijbdc,RaRe-Technologies/gensim,gensim/models/word2vec.py,481c258ee700e7280a65f227825b3e83b01c54e1,6aece136eda394227bb184e97db08a1db61e1816,TODO: make this code more elegant; it is very messy now.
aahfijbed,RaRe-Technologies/gensim,gensim/models/word2vec.py,9f95ff2be67189ec5e4b8cf1bd3e1596f2818c1f,STILL_EXISTS,TODO: use for instead.
aahfijbff,RaRe-Technologies/gensim,gensim/models/word2vec.py,25b82b536bd25c9dd2656e2c25166156ebed9267,STILL_EXISTS,TODO: use for instead.
aahfijbgd,RaRe-Technologies/gensim,gensim/models/word2vec.py,f6a00346074d07700d4c1c6c00faf1ffc4e3a867,9581123ac1f87ddb0c859fda58585ffba187a084,FIXME: remove \"batch\" input variable when done working on batching.
aahfijbgg,RaRe-Technologies/gensim,gensim/models/word2vec.py,f6a00346074d07700d4c1c6c00faf1ffc4e3a867,41ff1f431ec0c171e1433088c03a296aac6cf2d9,vocab file given; but word is missing -- set count to None (TODO: or raise?)
aahfijfhg,RaRe-Technologies/gensim,gensim/models/word2vec.py,9581123ac1f87ddb0c859fda58585ffba187a084,14888eb5ac9c03dae611be415a240718cdd3ee39,loop ended by job count; really done
aahfjaacb,RaRe-Technologies/gensim,gensim/models/word2vec.py,14888eb5ac9c03dae611be415a240718cdd3ee39,STILL_EXISTS,FIXME: remove \"batch\" input variable when done working on batching.
aahfjaefd,RaRe-Technologies/gensim,gensim/models/word2vec.py,28c7c6ef1e6eccca339764d6de9fed987c6647a6,STILL_EXISTS,loop ended by job count; really done
aahfjaegc,RaRe-Technologies/gensim,gensim/models/word2vec.py,7e743167d32c0fa7607f12a53757b50b57c63ede,04a92651c0fb0019c5a330c9b05285a53710b995,could move this import up to where train_* is imported;
aahfjaegj,RaRe-Technologies/gensim,gensim/models/word2vec.py,7e743167d32c0fa7607f12a53757b50b57c63ede,04a92651c0fb0019c5a330c9b05285a53710b995,FIXME: remove \"batch\" and \"const_alpha\" input variable when done working on batching.
aahfjaeha,RaRe-Technologies/gensim,gensim/models/word2vec.py,7e743167d32c0fa7607f12a53757b50b57c63ede,04a92651c0fb0019c5a330c9b05285a53710b995,TODO: remove; just for debugging.
aahfjafad,RaRe-Technologies/gensim,gensim/models/word2vec.py,7e743167d32c0fa7607f12a53757b50b57c63ede,STILL_EXISTS,loop ended by job count; really done
aahfjafcg,RaRe-Technologies/gensim,gensim/models/word2vec.py,04a92651c0fb0019c5a330c9b05285a53710b995,STILL_EXISTS,loop ended by job count; really done
aahfjafhc,RaRe-Technologies/gensim,gensim/models/word2vec.py,d83e13f6e4bd898c4c25ef4a1145f4558086a097,54e0b6da8de24aa2b9baf68a52961fd267ebb96f,could move this import up to where train_* is imported;
aahfjafig,RaRe-Technologies/gensim,gensim/models/word2vec.py,10238bee0aca7ab9c06ca434f79d3eb46137b6e5,1c63c9a77c0d20a79fea247b7ff3248b6d1df7a7,could move this import up to where train_* is imported;
aahfjbaea,RaRe-Technologies/gensim,gensim/models/wrappers/ldamallet.py,e454e8c82767e776d97fbd1dafba39f14d048598,STILL_EXISTS,skip \"doc\" and \"source\" columns
aahfjbafa,RaRe-Technologies/gensim,gensim/test/test_ldavowpalwabbit_wrapper.py,8e5c6f08505a96abc59518a64911c2b386baf017,2bb8e1dfeadfef943c888f7e4e735fd161535475,needed because sample data files are located in the same folder
aahfjbbce,RaRe-Technologies/gensim,gensim/models/word2vec.py,0f119d74da76a33045297e5ec5d3723c85f5a45a,7654c9e2b8a52258fef67775f41df2fd9c63f034,could move this import up to where train_* is imported;
aahfjbbdb,RaRe-Technologies/gensim,gensim/models/word2vec.py,0f119d74da76a33045297e5ec5d3723c85f5a45a,f64704c5d0b33e0736d82fd787126775913e70c0,FIXME: remove \"batch\" and \"const_alpha\" input variable when done working on batching.
aahfjbbdg,RaRe-Technologies/gensim,gensim/models/word2vec.py,0f119d74da76a33045297e5ec5d3723c85f5a45a,f64704c5d0b33e0736d82fd787126775913e70c0,FIXME
aahfjbcfh,RaRe-Technologies/gensim,gensim/models/word2vec.py,7fdb7db3f12cea325aabc61c2a40d1dcc32e8b6f,STILL_EXISTS,loop ended by job count; really done
aahfjbdbd,RaRe-Technologies/gensim,gensim/models/wrappers/ldamallet.py,28a2b047889d64949472d9a9620206b413cbc715,STILL_EXISTS,skip \"doc\" and \"source\" columns
aahfjbdeh,RaRe-Technologies/gensim,gensim/test/test_word2vec.py,055cd7d5fbb532ddbe4d0b7919a4d7de78cdaa2b,STILL_EXISTS,TODO: get correct path to Python binary
aahfjbfcj,RaRe-Technologies/gensim,gensim/test/test_normmodel.py,02e49f9e3211edaed109ac9c8e727dd7a92be507,2bb8e1dfeadfef943c888f7e4e735fd161535475,needed because sample data files are located in the same folder
aahfjbfhf,RaRe-Technologies/gensim,gensim/test/test_wikicorpus.py,a3e8a3fafc48554f03d21831d7b5bbf782e7c4ec,2bb8e1dfeadfef943c888f7e4e735fd161535475,needed because sample data files are located in the same folder
aahfjbgbj,RaRe-Technologies/gensim,gensim/test/test_similarities.py,4cc68c29a7bf43b2b9d9666b485a53e669067ea1,STILL_EXISTS,NOTE: this could be implemented for other similarities as well (i.e.
aahfjbhgc,RaRe-Technologies/gensim,gensim/test/test_ldamodel.py,2c19a18dd8d9bebdeaa66481d5912e3682bed084,f283ea4237f627183a284d430459d3742432bbf4,FIXME: Fails on osx and win
aahfjbhjb,RaRe-Technologies/gensim,gensim/test/test_similarity_metrics.py,c00efcf78a79c7589fd532432e7e3695611825f0,2bb8e1dfeadfef943c888f7e4e735fd161535475,needed because sample data files are located in the same folder
aahfjbjae,RaRe-Technologies/gensim,gensim/models/coherencemodel.py,6151747e65d8fa0b3e15554b31b627f1f4879298,STILL_EXISTS,FIXME : Meant to work for LDAModel; LdaVowpalWabbit right now. Make it work for others.
aahfjbjaf,RaRe-Technologies/gensim,gensim/models/coherencemodel.py,6151747e65d8fa0b3e15554b31b627f1f4879298,STILL_EXISTS,FIXME : Change window size to 110 finally.
aahfjbjja,RaRe-Technologies/gensim,gensim/topic_coherence/probability_estimation.py,6151747e65d8fa0b3e15554b31b627f1f4879298,6f53b31b6f04b2c059c29a7744ecad5e293ab579,FIXME : Handle case when window size is bigger than length of document
aahfjcedb,RaRe-Technologies/gensim,gensim/test/test_coherencemodel.py,ff3adb81198fc305ee66899096e5fb5f7982442d,343da69f0c49e71131f71910ef5d70250d73285c,needed because sample data files are located in the same folder
aahfjcedf,RaRe-Technologies/gensim,gensim/test/test_coherencemodel.py,ff3adb81198fc305ee66899096e5fb5f7982442d,STILL_EXISTS,`topics1` is clearly better as it has a clear distinction between system-human
aahfjcehd,RaRe-Technologies/gensim,gensim/models/ldaseqmodel.py,1ae13385995ec4a16ed5bbe149b89fb2137b6a8b,STILL_EXISTS,TODO: bound is initialized to 0
aahfjceih,RaRe-Technologies/gensim,gensim/models/ldaseqmodel.py,1ae13385995ec4a16ed5bbe149b89fb2137b6a8b,STILL_EXISTS,TODO: replace fit_lda_post with appropriate ldamodel functions; if possible.
aahfjcfaj,RaRe-Technologies/gensim,gensim/models/ldaseqmodel.py,1ae13385995ec4a16ed5bbe149b89fb2137b6a8b,STILL_EXISTS,TODO: apply lambda function
aahfjcfdg,RaRe-Technologies/gensim,gensim/models/ldaseqmodel.py,1ae13385995ec4a16ed5bbe149b89fb2137b6a8b,STILL_EXISTS,TODO: check why there's an IF
aahfjcffc,RaRe-Technologies/gensim,gensim/test/test_ldaseqmodel.py,1ae13385995ec4a16ed5bbe149b89fb2137b6a8b,2bb8e1dfeadfef943c888f7e4e735fd161535475,needed because sample data files are located in the same folder
aahfjcifj,RaRe-Technologies/gensim,gensim/summarization/summarizer.py,f7dd826005eb394b7c0c05b71441da194e3ee215,STILL_EXISTS,Cannot calculate eigenvectors if number of unique words in text < 3. Warns user to add more text. The function ends.
aahfjciih,RaRe-Technologies/gensim,gensim/test/test_hdpmodel.py,098be5fb22e5e6dbdb30354a3814938fa8061c69,32f0f17f0dbc06b0ac447f1c41293cbea381a437,TODO create show_topic in HdpModel and then test
aahfjciii,RaRe-Technologies/gensim,gensim/models/rpmodel.py,b3f59cc912d2da297c8ef4169cd6cf201af49f9b,STILL_EXISTS,TODO: check whether the Fortran-order shenanigans still make sense. In the original
aahfjciij,RaRe-Technologies/gensim,gensim/models/rpmodel.py,b3f59cc912d2da297c8ef4169cd6cf201af49f9b,STILL_EXISTS,code (~2010); this made a BIG difference for numpy BLAS implementations; perhaps now the wrappers
aahfjcija,RaRe-Technologies/gensim,gensim/models/rpmodel.py,b3f59cc912d2da297c8ef4169cd6cf201af49f9b,STILL_EXISTS,are smarter and this is no longer needed?
aahfjcijb,RaRe-Technologies/gensim,gensim/models/rpmodel.py,b3f59cc912d2da297c8ef4169cd6cf201af49f9b,STILL_EXISTS,This is a hack to work around a bug in numpy; where a FORTRAN-order array
aahfjcjcj,RaRe-Technologies/gensim,gensim/test/test_wikicorpus.py,d5851e4b810f0b0558feb6ba66540972d99a5401,STILL_EXISTS,#TODO: sporadic failure to be investigated
aahfjcjhj,RaRe-Technologies/gensim,gensim/models/keyedvectors.py,699773a78f32ff2c254cf16a586a5d26a08394fe,c0e0169565116854993b22efef29e3c402ec6c69,TODO: use level3 BLAS (=evaluate multiple questions at once); for speed
aahfjdaeb,RaRe-Technologies/gensim,gensim/test/test_wordrank_wrapper.py,5dee202317db600ad729985efdcecef9dc0d54d3,2bb8e1dfeadfef943c888f7e4e735fd161535475,needed because sample data files are located in the same folder
aahfjdbfd,RaRe-Technologies/gensim,gensim/models/word2vec.py,b20e73bd652a1f06af1af0b1f625b3a4d9410c37,f05b7b1cd557f8d79bbd03ec4fd47cd508fe1178,TODO: delete this flag after direct access to syn0norm; syn0; vocab is removed
aahfjdbfe,RaRe-Technologies/gensim,gensim/models/word2vec.py,b20e73bd652a1f06af1af0b1f625b3a4d9410c37,f05b7b1cd557f8d79bbd03ec4fd47cd508fe1178,TODO: after introducing KeyedVectors now syn0; vocab; id2word are saved TWO times. Once in word2vec and once in keyedvectors
aahfjdbhc,RaRe-Technologies/gensim,gensim/models/atmodel.py,739f34ee29381ebb4b005934b60b7f264d08e01c,STILL_EXISTS,infrastructure to implement a distributed author-topic model is already in place; such as the AuthorTopicState.
aahfjdbjh,RaRe-Technologies/gensim,gensim/models/atmodel.py,739f34ee29381ebb4b005934b60b7f264d08e01c,STILL_EXISTS,TODO: this is duplication of code in LdaModel. Refactor.
aahfjdccd,RaRe-Technologies/gensim,gensim/models/atmodel.py,739f34ee29381ebb4b005934b60b7f264d08e01c,STILL_EXISTS,TODO: this method is somewhat similar to the one in LdaModel. Refactor if possible.
aahfjdcce,RaRe-Technologies/gensim,gensim/models/atmodel.py,739f34ee29381ebb4b005934b60b7f264d08e01c,STILL_EXISTS,TODO: This method is very similar to the one in LdaModel. Refactor.
aahfjdccg,RaRe-Technologies/gensim,gensim/models/atmodel.py,739f34ee29381ebb4b005934b60b7f264d08e01c,STILL_EXISTS,TODO: if deepcopy is not used here; something goes wrong. When unit tests are run (specifically \"testPasses\");
aahfjdcci,RaRe-Technologies/gensim,gensim/models/atmodel.py,739f34ee29381ebb4b005934b60b7f264d08e01c,STILL_EXISTS,TODO: it is not possible to add new authors to an existing document (all input documents are treated
aahfjdccj,RaRe-Technologies/gensim,gensim/models/atmodel.py,739f34ee29381ebb4b005934b60b7f264d08e01c,STILL_EXISTS,as completely new documents). Perhaps this functionality could be implemented.
aahfjdcfd,RaRe-Technologies/gensim,gensim/models/atmodel.py,739f34ee29381ebb4b005934b60b7f264d08e01c,STILL_EXISTS,rho is the \"speed\" of updating; TODO try other fncs
aahfjdcfg,RaRe-Technologies/gensim,gensim/models/atmodel.py,739f34ee29381ebb4b005934b60b7f264d08e01c,STILL_EXISTS,gamma is not needed in \"other\"; thus its shape is (0; 0).
aahfjdcjb,RaRe-Technologies/gensim,gensim/models/atmodel.py,739f34ee29381ebb4b005934b60b7f264d08e01c,STILL_EXISTS,TODO: treat this in a more general way; similar to how it is done with word_score.
aahfjdcjg,RaRe-Technologies/gensim,gensim/test/test_atmodel.py,739f34ee29381ebb4b005934b60b7f264d08e01c,STILL_EXISTS,\"\"\" || Automated tests for the author-topic model (AuthorTopicModel class). These tests || are based on the unit tests of LDA; the classes are quite similar; and the tests || needed are thus quite similar. || \"\"\"
aahfjddad,RaRe-Technologies/gensim,gensim/test/test_atmodel.py,739f34ee29381ebb4b005934b60b7f264d08e01c,STILL_EXISTS,TODO:
aahfjddag,RaRe-Technologies/gensim,gensim/test/test_atmodel.py,739f34ee29381ebb4b005934b60b7f264d08e01c,STILL_EXISTS,Perhaps test that the bound increases; in general (i.e. in several of the tests below where it makes
aahfjddba,RaRe-Technologies/gensim,gensim/test/test_atmodel.py,739f34ee29381ebb4b005934b60b7f264d08e01c,2bb8e1dfeadfef943c888f7e4e735fd161535475,needed because sample data files are located in the same folder
aahfjddef,RaRe-Technologies/gensim,gensim/test/test_atmodel.py,739f34ee29381ebb4b005934b60b7f264d08e01c,STILL_EXISTS,Test the model using serialized corpora. Basic tests; plus test of update functionality.
aahfjdebf,RaRe-Technologies/gensim,gensim/models/wrappers/fasttext.py,2a70e3a726404cd4230542a35cfd2dc4d63da6f1,STILL_EXISTS,\"\"\" || Python wrapper around word representation learning from FastText; a library for efficient learning || of word representations and sentence classification [1]. ||  || This module allows training a word embedding from a training corpus with the additional ability || to obtain word vectors for out-of-vocabulary words; using the fastText C implementation. ||  || The wrapped model can NOT be updated with new documents for online training -- use gensim's || `Word2Vec` for that. ||  || Example: ||  || >>> from gensim.models.wrappers import FastText || >>> model = fasttext.FastText.train('\/Users\/kofola\/fastText\/fasttext'; corpus_file='text8') || >>> print model['forests']  # prints vector for given out-of-vocabulary word ||  || .. [1] https:\/\/github.com\/facebookresearch\/fastText#enriching-word-vectors-with-subword-information ||  || \"\"\"
aahfjdedg,RaRe-Technologies/gensim,gensim/test/test_fasttext_wrapper.py,2a70e3a726404cd4230542a35cfd2dc4d63da6f1,2bb8e1dfeadfef943c888f7e4e735fd161535475,needed because sample data files are located in the same folder
aahfjdehd,RaRe-Technologies/gensim,gensim/models/keyedvectors.py,41ff1f431ec0c171e1433088c03a296aac6cf2d9,STILL_EXISTS,vocab file given; but word is missing -- set count to None (TODO: or raise?)
aahfjdfaa,RaRe-Technologies/gensim,gensim/test/test_sklearn_integration.py,0e0c0828a34131becd40436d1932afc27d524558,9c8ac4248f00cde030576b10265c40fabbec838a,better approximation
aahfjebca,RaRe-Technologies/gensim,gensim/models/wrappers/varembed.py,e1c3a0b524a1b1c6331eb68d87c8122e295621d4,STILL_EXISTS,\"\"\" || Python wrapper around word representation learning from Varembed models; a library for efficient learning of word representations || and sentence classification [1]. ||  || This module allows ability to obtain word vectors for out-of-vocabulary words; for the Varembed model[2]. ||  || The wrapped model can not be updated with new documents for online training. ||  || .. [1] https:\/\/github.com\/rguthrie3\/MorphologicalPriorsForWordEmbeddings ||  || .. [2] http:\/\/arxiv.org\/pdf\/1608.01056.pdf || \"\"\"
aahfjebdh,RaRe-Technologies/gensim,gensim/test/test_varembed_wrapper.py,e1c3a0b524a1b1c6331eb68d87c8122e295621d4,2bb8e1dfeadfef943c888f7e4e735fd161535475,needed because sample data files are located in the same folder
aahfjecad,RaRe-Technologies/gensim,gensim/test/test_sklearn_integration.py,97cd64fad0507dca121164ed45789cfca1d20460,STILL_EXISTS,needed because sample data files are located in the same folder
aahfjeccb,RaRe-Technologies/gensim,gensim/test/test_sklearn_integration.py,a83e61b768d53ab3bab72abe4aa7db9aab66593c,9c8ac4248f00cde030576b10265c40fabbec838a,better approximation
aahfjeceh,RaRe-Technologies/gensim,gensim/test/test_glove2word2vec.py,99151db71b27cef5fb58beef3b51d82842cee167,2bb8e1dfeadfef943c888f7e4e735fd161535475,needed because sample data files are located in the same folder
aahfjechi,RaRe-Technologies/gensim,gensim/test/test_keras_integration.py,cbb213c51c0aa1562abd0c93afaac4f6f9bcfe9f,2bb8e1dfeadfef943c888f7e4e735fd161535475,needed because sample data files are located in the same folder
aahfjeicg,RaRe-Technologies/gensim,gensim/test/test_textcorpus.py,10e5429706879ffe2d7d6948a9ff8529338b4a6b,STILL_EXISTS,TODO add tests for other methods
aahfjgbac,RaRe-Technologies/gensim,gensim/test/test_translation_matrix.py,1c0098c83a9ba3e79b88a71a8c18c7f3ea6e26d4,2bb8e1dfeadfef943c888f7e4e735fd161535475,needed because sample data files are located in the same folder
aahfjgbcc,RaRe-Technologies/gensim,gensim/summarization/summarizer.py,02ba343e85f299130d2a1fc38e702040ef620c87,STILL_EXISTS,Warns user to add more text. The function ends.
aahfjgbcd,RaRe-Technologies/gensim,gensim/summarization/summarizer.py,02ba343e85f299130d2a1fc38e702040ef620c87,STILL_EXISTS,If couldn't get important docs; the algorithm ends.
aahfjgbdh,RaRe-Technologies/gensim,gensim/test/test_fasttext.py,6e511565c1721636cfd14f88df3a08e124e14364,2bb8e1dfeadfef943c888f7e4e735fd161535475,needed because sample data files are located in the same folder
aahfjgjfh,RaRe-Technologies/gensim,gensim/test/utils.py,2bb8e1dfeadfef943c888f7e4e735fd161535475,STILL_EXISTS,needed because sample data files are located in the same folder
aahfjhace,RaRe-Technologies/gensim,gensim/models/ldamodel.py,82c394a9085d583e8a75c2bb32ecd37cf61236f0,e455bc8d7bda035261c145e7e32f39cfef7dc107,TODO: unused var???
aahfjhajg,RaRe-Technologies/gensim,gensim/models/poincare.py,0ae0f96b52c815278c0781c5af9d963a7fcd5057,STILL_EXISTS,TODO: Add other KeyedVector supported methods - most_similar; etc.
aahfjhbbc,RaRe-Technologies/gensim,gensim/models/poincare.py,0ae0f96b52c815278c0781c5af9d963a7fcd5057,STILL_EXISTS,TODO: cleanup to reduce repeated code in this class;
aahfjhgab,RaRe-Technologies/gensim,gensim/summarization/summarizer.py,f09b7db9c2d21cde85ee338deac26f74bdb1b000,STILL_EXISTS,\"\"\"This module provides functions for summarizing texts. Summarizing is based on || ranks of text sentences using a variation of the TextRank algorithm [1]_. ||  || .. [1] Federico Barrios; Federico L\u00B4opez; Luis Argerich; Rosita Wachenchauzer (2016). ||        Variations of the Similarity Function of TextRank for Automated Summarization; ||        https:\/\/arxiv.org\/abs\/1602.03606 ||  ||  || Data || ---- ||  || .. data:: INPUT_MIN_LENGTH - Minimal number of sentences in text || .. data:: WEIGHT_THRESHOLD - Minimal weight of edge between graph nodes. Smaller weights set to zero. ||  || Example || ------- ||  || >>> from gensim.summarization.summarizer import summarize || >>> text = '''Rice Pudding - Poem by Alan Alexander Milne || ... What is the matter with Mary Jane? || ... She's crying with all her might and main; || ... And she won't eat her dinner - rice pudding again - || ... What is the matter with Mary Jane? || ... What is the matter with Mary Jane? || ... I've promised her dolls and a daisy-chain; || ... And a book about animals - all in vain - || ... What is the matter with Mary Jane? || ... What is the matter with Mary Jane? || ... She's perfectly well; and she hasn't a pain; || ... But; look at her; now she's beginning again! - || ... What is the matter with Mary Jane? || ... What is the matter with Mary Jane? || ... I've promised her sweets and a ride in the train; || ... And I've begged her to stop for a bit and explain - || ... What is the matter with Mary Jane? || ... What is the matter with Mary Jane? || ... She's perfectly well and she hasn't a pain; || ... And it's lovely rice pudding for dinner again! || ... What is the matter with Mary Jane?''' || >>> print(summarize(text)) || And she won't eat her dinner - rice pudding again - || I've promised her dolls and a daisy-chain; || I've promised her sweets and a ride in the train; || And it's lovely rice pudding for dinner again! ||  || \"\"\"
aahfjhgah,RaRe-Technologies/gensim,gensim/models/translation_matrix.py,37bc8d45f5b1a5b702e70e3f48d8073508ee22b1,STILL_EXISTS,\"\"\"Produce translation matrix to translate the word from one language to another language; using either || standard nearest neighbour method or globally corrected neighbour retrieval method [1]_. ||  || This method can be used to augment the existing phrase tables with more candidate translations; or || filter out errors from the translation tables and known dictionaries [2]_. What's more; It also work || for any two sets of named-vectors where there are some paired-guideposts to learn the transformation. ||  || Examples || -------- || **How to make translation between two set of word-vectors** ||  || Initialize a word-vector models ||  || >>> from gensim.models import KeyedVectors || >>> from gensim.test.utils import datapath; temporary_file || >>> from gensim.models import TranslationMatrix || >>> || >>> model_en = KeyedVectors.load_word2vec_format(datapath(\"EN.1-10.cbow1_wind5_hs0_neg10_size300_smpl1e-05.txt\")) || >>> model_it = KeyedVectors.load_word2vec_format(datapath(\"IT.1-10.cbow1_wind5_hs0_neg10_size300_smpl1e-05.txt\")) ||  || Define word pairs (that will be used for construction of translation matrix ||  || >>> word_pairs = [ || ...     (\"one\"; \"uno\"); (\"two\"; \"due\"); (\"three\"; \"tre\"); (\"four\"; \"quattro\"); (\"five\"; \"cinque\"); || ...     (\"seven\"; \"sette\"); (\"eight\"; \"otto\"); || ...     (\"dog\"; \"cane\"); (\"pig\"; \"maiale\"); (\"fish\"; \"cavallo\"); (\"birds\"; \"uccelli\"); || ...     (\"apple\"; \"mela\"); (\"orange\"; \"arancione\"); (\"grape\"; \"acino\"); (\"banana\"; \"banana\") || ... ] ||  || Fit :class:`~gensim.models.translation_matrix.TranslationMatrix` ||  || >>> trans_model = TranslationMatrix(model_en; model_it; word_pairs=word_pairs) ||  || Apply model (translate words \"dog\" and \"one\") ||  || >>> trans_model.translate([\"dog\"; \"one\"]; topn=3) || OrderedDict([('dog'; [u'cane'; u'gatto'; u'cavallo']); ('one'; [u'uno'; u'due'; u'tre'])]) ||  ||  || Save \/ load model ||  || >>> with temporary_file(\"model_file\") as fname: || ...     trans_model.save(fname)  # save model to file || ...     loaded_trans_model = TranslationMatrix.load(fname)  # load model ||  ||  || **How to make translation between two :class:`~gensim.models.doc2vec.Doc2Vec` models** ||  || Prepare data and models ||  || >>> from gensim.test.utils import datapath || >>> from gensim.test.test_translation_matrix import read_sentiment_docs || >>> from gensim.models import Doc2Vec; BackMappingTranslationMatrix || >>> || >>> data = read_sentiment_docs(datapath(\"alldata-id-10.txt\"))[:5] || >>> src_model = Doc2Vec.load(datapath(\"small_tag_doc_5_iter50\")) || >>> dst_model = Doc2Vec.load(datapath(\"large_tag_doc_10_iter50\")) ||  || Train backward translation ||  || >>> model_trans = BackMappingTranslationMatrix(data; src_model; dst_model) || >>> trans_matrix = model_trans.train(data) ||  ||  || Apply model ||  || >>> result = model_trans.infer_vector(dst_model.docvecs[data[3].tags]) ||  ||  || References || ---------- || .. [1] Dinu; Georgiana; Angeliki Lazaridou; and Marco Baroni. \"Improving zero-shot learning by mitigating the ||        hubness problem\"; https:\/\/arxiv.org\/abs\/1412.6568 || .. [2] Tomas Mikolov; Ilya Sutskever; Kai Chen; Greg Corrado; and Jeffrey Dean. ||        \"Distributed Representations of Words and Phrases and their Compositionality\"; https:\/\/arxiv.org\/abs\/1310.4546 ||  || \"\"\"
aahfjhgba,RaRe-Technologies/gensim,gensim/models/logentropy_model.py,a1fd854cc5839561b28a3bda69164543f59d7bd1,STILL_EXISTS,\"\"\"This module allows simple Bag of Words (BoW) represented corpus to be transformed into log entropy space. || It implements Log Entropy Model that produces entropy-weighted logarithmic term frequency representation. ||  || Empirical study by Lee et al. 2015 [1]_ suggests log entropy-weighted model yields better results among other forms of || representation. ||  || References || ---------- || .. [1] Lee et al. 2005. An Empirical Evaluation of Models of Text Document Similarity. ||        https:\/\/escholarship.org\/uc\/item\/48g155nq ||  || \"\"\"
aahfjhgbi,RaRe-Technologies/gensim,gensim/test/test_corpora.py,1648ac2e4299ccdfde2ac1b535faf2c5eaf6c022,STILL_EXISTS,Needed for the test_custom_tokenizer is the TestWikiCorpus class.
aahfjhgce,RaRe-Technologies/gensim,gensim/test/test_corpora.py,1648ac2e4299ccdfde2ac1b535faf2c5eaf6c022,STILL_EXISTS,#TODO: sporadic failure to be investigated
aahfjhggc,RaRe-Technologies/gensim,gensim/corpora/indexedcorpus.py,c5f487dc4073d034b10e3b3d80f68b167b0f9d06,STILL_EXISTS,TODO: no `docbyoffset` method; should be defined in this class
aahfjhggd,RaRe-Technologies/gensim,gensim/corpora/svmlightcorpus.py,c5f487dc4073d034b10e3b3d80f68b167b0f9d06,STILL_EXISTS,TODO: it brakes if gets None from line2doc
aahfjhhfa,RaRe-Technologies/gensim,gensim/models/deprecated/doc2vec.py,916e423a1654d87646fc522d8862a81c7a7cb4fc,STILL_EXISTS,\"\"\" || Warnings || -------- || .. deprecated:: 3.3.0 ||    Use :mod:`gensim.models.doc2vec` instead. ||  ||  ||  || Deep learning via the distributed memory and distributed bag of words models from || [1]_; using either hierarchical softmax or negative sampling [2]_ [3]_. See [#tutorial]_ ||  || **Make sure you have a C compiler before installing gensim; to use optimized (compiled) || doc2vec training** (70x speedup [blog]_). ||  || Initialize a model with e.g.:: ||  || >>> model = Doc2Vec(documents; size=100; window=8; min_count=5; workers=4) ||  || Persist a model to disk with:: ||  || >>> model.save(fname) || >>> model = Doc2Vec.load(fname)  # you can continue training with the loaded model! ||  || If you're finished training a model (=no more updates; only querying); you can do ||  ||   >>> model.delete_temporary_training_data(keep_doctags_vectors=True; keep_inference=True): ||  || to trim unneeded model memory = use (much) less RAM. ||  ||  ||  || .. [1] Quoc Le and Tomas Mikolov. Distributed Representations of Sentences and Documents. ||        http:\/\/arxiv.org\/pdf\/1405.4053v2.pdf || .. [2] Tomas Mikolov; Kai Chen; Greg Corrado; and Jeffrey Dean. ||        Efficient Estimation of Word Representations in Vector Space. In Proceedings of Workshop at ICLR; 2013. || .. [3] Tomas Mikolov; Ilya Sutskever; Kai Chen; Greg Corrado; and Jeffrey Dean. ||        Distributed Representations of Words and Phrases and their Compositionality. In Proceedings of NIPS; 2013. || .. [blog] Optimizing word2vec in gensim; http:\/\/radimrehurek.com\/2013\/09\/word2vec-in-python-part-two-optimizing\/ ||  || .. [#tutorial] Doc2vec in gensim tutorial; ||                https:\/\/github.com\/RaRe-Technologies\/gensim\/blob\/develop\/docs\/notebooks\/doc2vec-lee.ipynb ||  ||  ||  || \"\"\"
aahfjhhgd,RaRe-Technologies/gensim,gensim/models/deprecated/doc2vec.py,916e423a1654d87646fc522d8862a81c7a7cb4fc,STILL_EXISTS,skip doc without expected number of doctag(s) (TODO: warn\/pad?)
aahfjhibj,RaRe-Technologies/gensim,gensim/models/deprecated/fasttext_wrapper.py,916e423a1654d87646fc522d8862a81c7a7cb4fc,STILL_EXISTS,\"\"\" || Warnings || -------- || .. deprecated:: 3.2.0 ||    Use :mod:`gensim.models.fasttext` instead. ||  ||  || Python wrapper around word representation learning from FastText; a library for efficient learning || of word representations and sentence classification [1]. ||  || This module allows training a word embedding from a training corpus with the additional ability || to obtain word vectors for out-of-vocabulary words; using the fastText C implementation. ||  || The wrapped model can NOT be updated with new documents for online training -- use gensim's || `Word2Vec` for that. ||  || Example: ||  || >>> from gensim.models.wrappers import FastText || >>> model = FastText.train('\/Users\/kofola\/fastText\/fasttext'; corpus_file='text8') || >>> print model['forests']  # prints vector for given out-of-vocabulary word ||  || .. [1] https:\/\/github.com\/facebookresearch\/fastText#enriching-word-vectors-with-subword-information ||  ||  ||  || \"\"\"
aahfjhifg,RaRe-Technologies/gensim,gensim/models/deprecated/keyedvectors.py,916e423a1654d87646fc522d8862a81c7a7cb4fc,STILL_EXISTS,vocab file given; but word is missing -- set count to None (TODO: or raise?)
aahfjhihj,RaRe-Technologies/gensim,gensim/models/deprecated/keyedvectors.py,916e423a1654d87646fc522d8862a81c7a7cb4fc,STILL_EXISTS,TODO: use level3 BLAS (=evaluate multiple questions at once); for speed
aahfjhiji,RaRe-Technologies/gensim,gensim/models/deprecated/old_saveload.py,916e423a1654d87646fc522d8862a81c7a7cb4fc,STILL_EXISTS,better than 'isinstance(val; SaveLoad)' if IPython reloading
aahfjhjad,RaRe-Technologies/gensim,gensim/models/deprecated/old_saveload.py,916e423a1654d87646fc522d8862a81c7a7cb4fc,STILL_EXISTS,'b' for binary; needed on Windows
aahfjhjch,RaRe-Technologies/gensim,gensim/models/deprecated/word2vec.py,916e423a1654d87646fc522d8862a81c7a7cb4fc,STILL_EXISTS,work on the entire tree at once; to push as much work into numpy's C routines as possible (performance)
aahfjiaci,RaRe-Technologies/gensim,gensim/models/deprecated/word2vec.py,916e423a1654d87646fc522d8862a81c7a7cb4fc,STILL_EXISTS,loop ended by job count; really done
aahfjiadf,RaRe-Technologies/gensim,gensim/models/deprecated/word2vec.py,916e423a1654d87646fc522d8862a81c7a7cb4fc,STILL_EXISTS,randomize weights vector by vector; rather than materializing a huge random matrix in RAM at once
aahfjiaea,RaRe-Technologies/gensim,gensim/models/deprecated/word2vec.py,916e423a1654d87646fc522d8862a81c7a7cb4fc,STILL_EXISTS,TOCONSIDER: maybe mismatched vectors still useful enough to merge (truncating\/padding)?
aahfjibdd,RaRe-Technologies/gensim,gensim/models/utils_any2vec.py,916e423a1654d87646fc522d8862a81c7a7cb4fc,1052b9bd890761f89bcfd6c340304ad448f4a187,vocab file given; but word is missing -- set count to None (TODO: or raise?)
aahfjibfh,RaRe-Technologies/gensim,gensim/models/word2vec.py,916e423a1654d87646fc522d8862a81c7a7cb4fc,STILL_EXISTS,randomize weights vector by vector; rather than materializing a huge random matrix in RAM at once
aahfjicae,RaRe-Technologies/gensim,gensim/models/wrappers/fasttext.py,916e423a1654d87646fc522d8862a81c7a7cb4fc,STILL_EXISTS,\"\"\" || Warnings || -------- || .. deprecated:: 3.2.0 ||    Use :mod:`gensim.models.fasttext` instead. ||  ||  ||  || Python wrapper around word representation learning from FastText; a library for efficient learning || of word representations and sentence classification [1]. ||  || This module allows training a word embedding from a training corpus with the additional ability || to obtain word vectors for out-of-vocabulary words; using the fastText C implementation. ||  || The wrapped model can NOT be updated with new documents for online training -- use gensim's || `Word2Vec` for that. ||  || Example: ||  || >>> from gensim.models.wrappers import FastText || >>> model = FastText.train('\/Users\/kofola\/fastText\/fasttext'; corpus_file='text8') || >>> print model['forests']  # prints vector for given out-of-vocabulary word ||  || .. [1] https:\/\/github.com\/facebookresearch\/fastText#enriching-word-vectors-with-subword-information ||  ||  ||  || \"\"\"
aahfjjaee,RaRe-Technologies/gensim,gensim/models/keyedvectors.py,43a33c76e030b365cfe964eab5e3b129c05214f8,f3cf463c0f0e28c97c9a3b319a58a7e099092041,Traverse upper triangle columns.
aahfjjaef,RaRe-Technologies/gensim,gensim/models/keyedvectors.py,43a33c76e030b365cfe964eab5e3b129c05214f8,f3cf463c0f0e28c97c9a3b319a58a7e099092041,Traverse all columns.
aahfjjaeg,RaRe-Technologies/gensim,gensim/models/keyedvectors.py,43a33c76e030b365cfe964eab5e3b129c05214f8,f3cf463c0f0e28c97c9a3b319a58a7e099092041,Traverse only columns corresponding to the embeddings closest to w1.
aahfjjagf,RaRe-Technologies/gensim,gensim/test/test_similarities.py,43a33c76e030b365cfe964eab5e3b129c05214f8,STILL_EXISTS,NOTE: this could be implemented for other similarities as well (i.e. in _TestSimilarityABC).
aahfjjcgj,RaRe-Technologies/gensim,gensim/models/keyedvectors.py,49e6abdde6bca55b7a5b263c01ecdc94a75fb1ab,STILL_EXISTS,TODO: implement 3CosMul and set-based methods for solving analogies
aahfjjchj,RaRe-Technologies/gensim,gensim/test/test_doc2vec.py,2024be9053094fbb2e765b9a06b9dc580f55c505,STILL_EXISTS,load really old model
aahfjjcid,RaRe-Technologies/gensim,gensim/test/test_word2vec.py,2024be9053094fbb2e765b9a06b9dc580f55c505,STILL_EXISTS,load really old model
aahfjjdac,RaRe-Technologies/gensim,gensim/models/tfidfmodel.py,cc441b7b8bf2f80b17cf44acbcb425d529876ff1,STILL_EXISTS,\"\"\"This module implements functionality related to the `Term Frequency - Inverse Document Frequency || <https:\/\/en.wikipedia.org\/wiki\/Tf%E2%80%93idf>` vector space bag-of-words models. ||  || For a more in-depth exposition of TF-IDF and its various SMART variants (normalization; weighting schemes); || see the blog post at https:\/\/rare-technologies.com\/pivoted-document-length-normalisation\/ ||  || \"\"\"
aahfjjdag,RaRe-Technologies/gensim,gensim/models/fasttext.py,abd76dbe1ebad18afebab12e13783cc633a06374,STILL_EXISTS,## TODO use smart_open again when https:\/\/github.com\/RaRe-Technologies\/smart_open\/issues\/207 will be fixed
aahfjjdah,RaRe-Technologies/gensim,gensim/models/deprecated/fasttext_wrapper.py,398a00d28bb6da4493a275ae7d6cc690e1436d09,STILL_EXISTS,TODO use smart_open again when https:\/\/github.com\/RaRe-Technologies\/smart_open\/issues\/207 will be fixed
aahfjjecg,RaRe-Technologies/gensim,gensim/models/utils_any2vec.py,6a97463c15954d4853eb30b796163f944e1151d7,1052b9bd890761f89bcfd6c340304ad448f4a187,TODO use frombuffer or something similar
aahfjjegh,RaRe-Technologies/gensim,gensim/test/test_lda_callback.py,007afa1299eb2f1195bf0d9728126acd4994a2d6,STILL_EXISTS,wait for visdom server startup (any better way?)
aahfjjehg,RaRe-Technologies/gensim,gensim/models/fasttext.py,b452a5b59f2f474dbbd275d0838c45df4d3c5aac,ea611b43c15d3592f1fa844baf872b91935fb821,TODO use smart_open again when https:\/\/github.com\/RaRe-Technologies\/smart_open\/issues\/207 will be fixed
aahfjjeia,RaRe-Technologies/gensim,gensim/models/fasttext.py,b452a5b59f2f474dbbd275d0838c45df4d3c5aac,STILL_EXISTS,variable; so we reinitialize it here; if needed.
aahfjjfic,RaRe-Technologies/gensim,gensim/test/test_fasttext.py,b452a5b59f2f474dbbd275d0838c45df4d3c5aac,STILL_EXISTS,This is a workaround for a problem with temporary files on AppVeyor:
aahfjjfif,RaRe-Technologies/gensim,gensim/test/test_fasttext.py,b452a5b59f2f474dbbd275d0838c45df4d3c5aac,STILL_EXISTS,- https:\/\/github.com\/dropbox\/pyannotate\/pull\/48\/files (workaround source code)
aahfjjgcf,RaRe-Technologies/gensim,gensim/similarities/termsim.py,f3cf463c0f0e28c97c9a3b319a58a7e099092041,STILL_EXISTS,how many columns are processed between progress messages
aahfjjiig,RaRe-Technologies/gensim,gensim/models/_fasttext_bin.py,411f5466326c6a44dcc80d2ac241f7a0c1bfeee5,STILL_EXISTS,\"\"\"Holds data loaded from the Facebook binary. ||  || Fields || ------ || dim : int ||     The dimensionality of the vectors. || ws : int ||     The window size. || epoch : int ||     The number of training epochs. || neg : int ||     If non-zero; indicates that the model uses negative sampling. || loss : int ||     If equal to 1; indicates that the model uses hierarchical sampling. || model : int ||     If equal to 2; indicates that the model uses skip-grams. || bucket : int ||     The number of buckets. || min_count : int ||     The threshold below which the model ignores terms. || t : float ||     The sample threshold. || minn : int ||     The minimum ngram length. || maxn : int ||     The maximum ngram length. || raw_vocab : collections.OrderedDict ||     A map from words (str) to their frequency (int).  The order in the dict ||     corresponds to the order of the words in the Facebook binary. || nwords : int ||     The number of words. || vocab_size : int ||     The size of the vocabulary. || vectors_ngrams : numpy.array ||     This is a matrix that contains vectors learned by the model. ||     Each row corresponds to a vector. ||     The number of vectors is equal to the number of words plus the number of buckets. ||     The number of columns is equal to the vector dimensionality. || hidden_output : numpy.array ||     This is a matrix that contains the shallow neural network output. ||     This array has the same dimensions as vectors_ngrams. ||     May be None - in that case; it is impossible to continue training the model. || \"\"\"
aahfjjijg,RaRe-Technologies/gensim,gensim/models/utils_any2vec.py,411f5466326c6a44dcc80d2ac241f7a0c1bfeee5,e859c11f6f57bf3c883a718a9ab7067ac0c2d4cf,Internally; we use the following convention to abstract away the presence
aahfjjjej,RaRe-Technologies/gensim,gensim/models/fasttext.py,bef56bc8400a6e7f6c6d36e8aae47d6a4a60ac66,STILL_EXISTS,This is here to fix https:\/\/github.com\/RaRe-Technologies\/gensim\/pull\/2373.
aahfjjjfj,RaRe-Technologies/gensim,gensim/test/test_fasttext.py,12110715c6e36f02d13bb82fd5382e7f9153686b,STILL_EXISTS,fastText utility loads the model fine; but we trip up over the bad
aahgaaacg,RaRe-Technologies/gensim,gensim/models/_fasttext_bin.py,790b9a7f29f19bad21fd3c5c5f91a153ac96f69d,STILL_EXISTS,Until they fix it; we have to apply a workaround.  We only apply the
aahgaaach,RaRe-Technologies/gensim,gensim/models/_fasttext_bin.py,790b9a7f29f19bad21fd3c5c5f91a153ac96f69d,STILL_EXISTS,workaround when it's necessary; because np.fromfile is heavily optimized
aahgaaaci,RaRe-Technologies/gensim,gensim/models/_fasttext_bin.py,790b9a7f29f19bad21fd3c5c5f91a153ac96f69d,STILL_EXISTS,and very efficient (when it works).
aahgaaahd,RaRe-Technologies/gensim,gensim/similarities/nmslib.py,c9ec242849d89fae829ca63c5800e282a9ebb704,STILL_EXISTS,\"\"\" || Intro || ----- ||  || This module contains integration Nmslib with :class:`~gensim.models.word2vec.Word2Vec`; || :class:`~gensim.models.doc2vec.Doc2Vec`; :class:`~gensim.models.fasttext.FastText` and || :class:`~gensim.models.keyedvectors.KeyedVectors`. || To use nmslib; instantiate a :class:`~gensim.similarities.nmslib.NmslibIndexer` class || and pass the instance as the indexer parameter to your model's most_similar method || (e.g. :py:func:`~gensim.models.doc2vec.most_similar`). ||  || Example usage || ------------- ||  || .. sourcecode:: pycon ||  ||     >>> from gensim.similarities.nmslib import NmslibIndexer ||     >>> from gensim.models import Word2Vec ||     >>> ||     >>> sentences = [['cute'; 'cat'; 'say'; 'meow']; ['cute'; 'dog'; 'say'; 'woof']] ||     >>> model = Word2Vec(sentences; min_count=1; seed=1) ||     >>> ||     >>> indexer = NmslibIndexer(model) ||     >>> model.most_similar(\"cat\"; topn=2; indexer=indexer) ||     [('cat'; 1.0); ('meow'; 0.5595494508743286)] ||  || Load and save example || --------------------- ||  || .. sourcecode:: pycon ||  ||     >>> from gensim.similarities.nmslib import NmslibIndexer ||     >>> from gensim.models import Word2Vec ||     >>> from tempfile import mkstemp ||     >>> ||     >>> sentences = [['cute'; 'cat'; 'say'; 'meow']; ['cute'; 'dog'; 'say'; 'woof']] ||     >>> model = Word2Vec(sentences; min_count=1; seed=1; iter=10) ||     >>> ||     >>> indexer = NmslibIndexer(model) ||     >>> _; temp_fn = mkstemp() ||     >>> indexer.save(temp_fn) ||     >>> ||     >>> new_indexer = NmslibIndexer.load(temp_fn) ||     >>> model.most_similar(\"cat\"; topn=2; indexer=new_indexer) ||     [('cat'; 1.0); ('meow'; 0.5595494508743286)] ||  || What is Nmslib || ------------- ||  || Non-Metric Space Library (NMSLIB) is an efficient cross-platform similarity search library and a toolkit || for evaluation of similarity search methods. The core-library does not have any third-party dependencies. || More information about Nmslib: `github repository <https:\/\/github.com\/nmslib\/nmslib>`_. ||  || Why use Nmslib? || ------------- ||  || The current implementation for finding k nearest neighbors in a vector space in gensim has linear complexity || via brute force in the number of indexed documents; although with extremely low constant factors. || The retrieved results are exact; which is an overkill in many applications: || approximate results retrieved in sub-linear time may be enough. || Nmslib can find approximate nearest neighbors much faster. || Compared to annoy; nmslib has more parameters to control the build and query time and accuracy. || Nmslib can achieve faster and more accurate nearest neighbors search than annoy. || \"\"\"
aahgaabff,RaRe-Technologies/gensim,docs/src/auto_examples/core/run_core_concepts.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,r\"\"\" || Core Concepts || ============= ||  || This tutorial introduces Documents; Corpora; Vectors and Models: the basic concepts and terms needed to understand and use gensim. || \"\"\"
aahgaacag,RaRe-Technologies/gensim,docs/src/auto_examples/core/run_core_concepts.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,In practice; corpora may be very large; so loading them into memory may be impossible.
aahgaaecg,RaRe-Technologies/gensim,docs/src/auto_examples/core/run_corpora_and_vector_spaces.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,Your way of processing the documents will likely vary; here; I only split on whitespace
aahgaaffe,RaRe-Technologies/gensim,docs/src/auto_examples/core/run_corpora_and_vector_spaces.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,another way of doing it: print one document at a time; making use of the streaming interface
aahgaafgj,RaRe-Technologies/gensim,docs/src/auto_examples/core/run_corpora_and_vector_spaces.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,Gensim also contains `efficient utility functions <http:\/\/radimrehurek.com\/gensim\/matutils.html>`_
aahgaagbc,RaRe-Technologies/gensim,docs/src/auto_examples/core/run_similarity_queries.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,we covered what it means to create a corpus in the Vector Space Model and how
aahgaagii,RaRe-Technologies/gensim,docs/src/auto_examples/core/run_similarity_queries.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,them received quite high similarity scores (no. 2 is actually the most similar!);
aahgaagij,RaRe-Technologies/gensim,docs/src/auto_examples/core/run_similarity_queries.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,which corresponds better to our intuition of
aahgaahca,RaRe-Technologies/gensim,docs/src/auto_examples/core/run_topics_and_transformations.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,(hopefully) more semantic way.
aahgaaifj,RaRe-Technologies/gensim,docs/src/auto_examples/core/run_topics_and_transformations.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,`gensim` uses a novel online incremental streamed distributed training algorithm (quite a mouthful!);
aahgaaigh,RaRe-Technologies/gensim,docs/src/auto_examples/core/run_topics_and_transformations.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,reduce vector space dimensionality. This is a very efficient (both memory- and
aahgaajcg,RaRe-Technologies/gensim,docs/src/auto_examples/howtos/run_compare_lda.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,r\"\"\" || How to Compare LDA Models || ========================= ||  || Demonstrates how you can compare a topic model with itself or other models. ||  || \"\"\"
aahgaajei,RaRe-Technologies/gensim,docs/src/auto_examples/howtos/run_compare_lda.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,If you're running via a Jupyter notebook; then you'll get a nice interactive Plotly heatmap.
aahgabaai,RaRe-Technologies/gensim,docs/src/auto_examples/howtos/run_compare_lda.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,If you compare a model with itself; you want to see as many red elements as possible (except diagonal). With this picture; you can look at the not very red elements and understand which topics in the model are very similar and why (you can read annotation if you move your pointer to cell).
aahgabadi,RaRe-Technologies/gensim,docs/src/auto_examples/howtos/run_doc.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,r\"\"\" || How to Author Gensim Documentation || ================================== ||  || Some tips of how to author documentation for ``gensim``. || \"\"\"
aahgabaef,RaRe-Technologies/gensim,docs/src/auto_examples/howtos/run_doc.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,Unfortunately; not all of this functionality is documented **well**; and some of it is not documented at all.
aahgabaej,RaRe-Technologies/gensim,docs/src/auto_examples/howtos/run_doc.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,If you implement new functionality in Gensim; please include **helpful** documentation.
aahgabahd,RaRe-Technologies/gensim,docs/src/auto_examples/howtos/run_doc.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,When you make a PR with new functionality; please consider authoring each kind of documentation.
aahgabbei,RaRe-Technologies/gensim,docs/src/auto_examples/howtos/run_doc.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,Once your script.py works; put it in a suitable subdirectory.
aahgabbgj,RaRe-Technologies/gensim,docs/src/auto_examples/howtos/run_doc.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,This will reduce the risk of your documentation becoming obsolete because required data is no longer available.
aahgabdbb,RaRe-Technologies/gensim,docs/src/auto_examples/howtos/run_doc2vec_imdb.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,PV-DM w\/ default averaging; a higher starting alpha may improve CBOW\/PV-DM modes
aahgabgah,RaRe-Technologies/gensim,docs/src/auto_examples/tutorials/run_annoy.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,*fname.d*. Both files are needed to correctly restore all attributes. Before
aahgabhfj,RaRe-Technologies/gensim,docs/src/auto_examples/tutorials/run_distance_metrics.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,terms - but as bank in this context is likely to belong to the finance topic;
aahgabjac,RaRe-Technologies/gensim,docs/src/auto_examples/tutorials/run_distance_metrics.py,bcee414663bdcbdf6a58684531ee69c6949550bf,70d4338e14af7d0be477dda7ce21a994d5fad96f,and is probably the most 'famous' distance measure in fields like Information
aahgabjaf,RaRe-Technologies/gensim,docs/src/auto_examples/tutorials/run_distance_metrics.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,For a nice review of the mathematical differences between Hellinger and KL;
aahgabjec,RaRe-Technologies/gensim,docs/src/auto_examples/tutorials/run_distance_metrics.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,Using a larger corpus should give you much better results; but that is beyond
aahgabjia,RaRe-Technologies/gensim,docs/src/auto_examples/tutorials/run_doc2vec_lee.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,.. Note:: Feel free to skip these review sections if you're already familiar with the models.
aahgacadb,RaRe-Technologies/gensim,docs/src/auto_examples/tutorials/run_doc2vec_lee.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,However; there is a better way...
aahgacced,RaRe-Technologies/gensim,docs/src/auto_examples/tutorials/run_fasttext.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,According to a detailed comparison of Word2Vec and FastText in `this notebook <https:\/\/github.com\/RaRe-Technologies\/gensim\/blob\/develop\/docs\/notebooks\/Word2Vec_FastText_Comparison.ipynb>`__; fastText does significantly better on syntactic tasks as compared to the original Word2Vec; especially when the size of the training corpus is small. Word2Vec slightly outperforms FastText on semantic tasks though. The differences grow smaller as the size of training corpus increases.
aahgacdfj,RaRe-Technologies/gensim,docs/src/auto_examples/tutorials/run_fasttext.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,Syntactically similar words generally have high similarity in fastText models; since a large number of the component char-ngrams will be the same. As a result; fastText generally does better at syntactic tasks than Word2Vec. A detailed comparison is provided `here <Word2Vec_FastText_Comparison.ipynb>`_.
aahgacejf,RaRe-Technologies/gensim,docs/src/auto_examples/tutorials/run_lda.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,Add bigrams and trigrams to docs (only ones that appear 20 times or more).
aahgacfab,RaRe-Technologies/gensim,docs/src/auto_examples/tutorials/run_lda.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,frequency; or maybe combining that with this approach.
aahgacfcc,RaRe-Technologies/gensim,docs/src/auto_examples/tutorials/run_lda.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,really no easy answer for this; it will depend on both your data and your
aahgacfhf,RaRe-Technologies/gensim,docs/src/auto_examples/tutorials/run_lda.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,seem out of place. If you were able to do better; feel free to share your
aahgacggi,RaRe-Technologies/gensim,docs/src/auto_examples/tutorials/run_pivoted_doc_norm.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,short documents with length of around 500 had very good score hence the bias
aahgacgjj,RaRe-Technologies/gensim,docs/src/auto_examples/tutorials/run_summarization.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,larger ones; and then we will review the performance of the summarizer in
aahgachae,RaRe-Technologies/gensim,docs/src/auto_examples/tutorials/run_summarization.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,This algorithm was later improved upon by `Barrios et al.
aahgacihd,RaRe-Technologies/gensim,docs/src/auto_examples/tutorials/run_wmd.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,between documents; in other words the most efficient way to \"move\" the
aahgaciij,RaRe-Technologies/gensim,docs/src/auto_examples/tutorials/run_wmd.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,If you use Gensim's WMD functionality; please consider citing [1]; [2] and [3].
aahgacjfc,RaRe-Technologies/gensim,docs/src/auto_examples/tutorials/run_wmd.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,4. Thomas Mikolov et al. *Efficient Estimation of Word Representations in Vector Space*\\ ; 2013.
aahgacjid,RaRe-Technologies/gensim,docs/src/auto_examples/tutorials/run_word2vec.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,.. Note:: Feel free to skip these review sections if you're already familiar with the models.
aahgadbag,RaRe-Technologies/gensim,docs/src/auto_examples/tutorials/run_word2vec.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,This corpus is small enough to fit entirely in memory; but we'll implement a
aahgadbhh,RaRe-Technologies/gensim,docs/src/auto_examples/tutorials/run_word2vec.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,Bigger size values require more training data; but can lead to better (more
aahgadcbb,RaRe-Technologies/gensim,docs/src/auto_examples/tutorials/run_word2vec.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,There\u2019s a little extra memory needed for storing the vocabulary tree (100;000 words would take a few megabytes); but unless your words are extremely loooong strings; memory footprint will be dominated by the three matrices above.
aahgadcbh,RaRe-Technologies/gensim,docs/src/auto_examples/tutorials/run_word2vec.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,``Word2Vec`` training is an unsupervised task; there\u2019s no good way to
aahgadcec,RaRe-Technologies/gensim,docs/src/auto_examples/tutorials/run_word2vec.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,In the December 2016 release of Gensim we added a better way to evaluate semantic similarity.
aahgadcfe,RaRe-Technologies/gensim,docs/src/auto_examples/tutorials/run_word2vec.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,work well in your application; or vice versa. It\u2019s always best to evaluate
aahgaddih,RaRe-Technologies/gensim,docs/src/gallery/core/run_core_concepts.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,r\"\"\" || Core Concepts || ============= ||  || This tutorial introduces Documents; Corpora; Vectors and Models: the basic concepts and terms needed to understand and use gensim. || \"\"\"
aahgadedi,RaRe-Technologies/gensim,docs/src/gallery/core/run_core_concepts.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,In practice; corpora may be very large; so loading them into memory may be impossible.
aahgadgfi,RaRe-Technologies/gensim,docs/src/gallery/core/run_corpora_and_vector_spaces.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,Your way of processing the documents will likely vary; here; I only split on whitespace
aahgadhig,RaRe-Technologies/gensim,docs/src/gallery/core/run_corpora_and_vector_spaces.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,another way of doing it: print one document at a time; making use of the streaming interface
aahgadiab,RaRe-Technologies/gensim,docs/src/gallery/core/run_corpora_and_vector_spaces.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,Gensim also contains `efficient utility functions <http:\/\/radimrehurek.com\/gensim\/matutils.html>`_
aahgadiee,RaRe-Technologies/gensim,docs/src/gallery/core/run_similarity_queries.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,we covered what it means to create a corpus in the Vector Space Model and how
aahgadjca,RaRe-Technologies/gensim,docs/src/gallery/core/run_similarity_queries.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,them received quite high similarity scores (no. 2 is actually the most similar!);
aahgadjcb,RaRe-Technologies/gensim,docs/src/gallery/core/run_similarity_queries.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,which corresponds better to our intuition of
aahgadjfc,RaRe-Technologies/gensim,docs/src/gallery/core/run_topics_and_transformations.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,(hopefully) more semantic way.
aahgaeajb,RaRe-Technologies/gensim,docs/src/gallery/core/run_topics_and_transformations.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,`gensim` uses a novel online incremental streamed distributed training algorithm (quite a mouthful!);
aahgaeajj,RaRe-Technologies/gensim,docs/src/gallery/core/run_topics_and_transformations.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,reduce vector space dimensionality. This is a very efficient (both memory- and
aahgaebfi,RaRe-Technologies/gensim,docs/src/gallery/howtos/run_compare_lda.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,r\"\"\" || How to Compare LDA Models || ========================= ||  || Demonstrates how you can compare a topic model with itself or other models. ||  || \"\"\"
aahgaebia,RaRe-Technologies/gensim,docs/src/gallery/howtos/run_compare_lda.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,If you're running via a Jupyter notebook; then you'll get a nice interactive Plotly heatmap.
aahgaecea,RaRe-Technologies/gensim,docs/src/gallery/howtos/run_compare_lda.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,If you compare a model with itself; you want to see as many red elements as possible (except diagonal). With this picture; you can look at the not very red elements and understand which topics in the model are very similar and why (you can read annotation if you move your pointer to cell).
aahgaecha,RaRe-Technologies/gensim,docs/src/gallery/howtos/run_doc.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,r\"\"\" || How to Author Gensim Documentation || ================================== ||  || Some tips of how to author documentation for ``gensim``. || \"\"\"
aahgaechh,RaRe-Technologies/gensim,docs/src/gallery/howtos/run_doc.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,Unfortunately; not all of this functionality is documented **well**; and some of it is not documented at all.
aahgaecib,RaRe-Technologies/gensim,docs/src/gallery/howtos/run_doc.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,If you implement new functionality in Gensim; please include **helpful** documentation.
aahgaedaf,RaRe-Technologies/gensim,docs/src/gallery/howtos/run_doc.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,When you make a PR with new functionality; please consider authoring each kind of documentation.
aahgaedia,RaRe-Technologies/gensim,docs/src/gallery/howtos/run_doc.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,Once your script.py works; put it in a suitable subdirectory.
aahgaeeab,RaRe-Technologies/gensim,docs/src/gallery/howtos/run_doc.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,This will reduce the risk of your documentation becoming obsolete because required data is no longer available.
aahgaefed,RaRe-Technologies/gensim,docs/src/gallery/howtos/run_doc2vec_imdb.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,PV-DM w\/ default averaging; a higher starting alpha may improve CBOW\/PV-DM modes
aahgaeidj,RaRe-Technologies/gensim,docs/src/gallery/tutorials/run_annoy.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,*fname.d*. Both files are needed to correctly restore all attributes. Before
aahgaejjb,RaRe-Technologies/gensim,docs/src/gallery/tutorials/run_distance_metrics.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,terms - but as bank in this context is likely to belong to the finance topic;
aahgafbde,RaRe-Technologies/gensim,docs/src/gallery/tutorials/run_distance_metrics.py,bcee414663bdcbdf6a58684531ee69c6949550bf,502b654add89102a2d1c253cd1176486cdd1466b,and is probably the most 'famous' distance measure in fields like Information
aahgafbdh,RaRe-Technologies/gensim,docs/src/gallery/tutorials/run_distance_metrics.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,For a nice review of the mathematical differences between Hellinger and KL;
aahgafbhe,RaRe-Technologies/gensim,docs/src/gallery/tutorials/run_distance_metrics.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,Using a larger corpus should give you much better results; but that is beyond
aahgafcbc,RaRe-Technologies/gensim,docs/src/gallery/tutorials/run_doc2vec_lee.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,.. Note:: Feel free to skip these review sections if you're already familiar with the models.
aahgafcgd,RaRe-Technologies/gensim,docs/src/gallery/tutorials/run_doc2vec_lee.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,However; there is a better way...
aahgafehf,RaRe-Technologies/gensim,docs/src/gallery/tutorials/run_fasttext.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,According to a detailed comparison of Word2Vec and FastText in `this notebook <https:\/\/github.com\/RaRe-Technologies\/gensim\/blob\/develop\/docs\/notebooks\/Word2Vec_FastText_Comparison.ipynb>`__; fastText does significantly better on syntactic tasks as compared to the original Word2Vec; especially when the size of the training corpus is small. Word2Vec slightly outperforms FastText on semantic tasks though. The differences grow smaller as the size of training corpus increases.
aahgaffjb,RaRe-Technologies/gensim,docs/src/gallery/tutorials/run_fasttext.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,Syntactically similar words generally have high similarity in fastText models; since a large number of the component char-ngrams will be the same. As a result; fastText generally does better at syntactic tasks than Word2Vec. A detailed comparison is provided `here <Word2Vec_FastText_Comparison.ipynb>`_.
aahgafhch,RaRe-Technologies/gensim,docs/src/gallery/tutorials/run_lda.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,Add bigrams and trigrams to docs (only ones that appear 20 times or more).
aahgafhdd,RaRe-Technologies/gensim,docs/src/gallery/tutorials/run_lda.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,frequency; or maybe combining that with this approach.
aahgafhfe,RaRe-Technologies/gensim,docs/src/gallery/tutorials/run_lda.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,really no easy answer for this; it will depend on both your data and your
aahgafiah,RaRe-Technologies/gensim,docs/src/gallery/tutorials/run_lda.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,seem out of place. If you were able to do better; feel free to share your
aahgafjaa,RaRe-Technologies/gensim,docs/src/gallery/tutorials/run_pivoted_doc_norm.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,short documents with length of around 500 had very good score hence the bias
aahgafjdb,RaRe-Technologies/gensim,docs/src/gallery/tutorials/run_summarization.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,larger ones; and then we will review the performance of the summarizer in
aahgafjdg,RaRe-Technologies/gensim,docs/src/gallery/tutorials/run_summarization.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,This algorithm was later improved upon by `Barrios et al.
aahgagbaf,RaRe-Technologies/gensim,docs/src/gallery/tutorials/run_wmd.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,between documents; in other words the most efficient way to \"move\" the
aahgagbcb,RaRe-Technologies/gensim,docs/src/gallery/tutorials/run_wmd.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,If you use Gensim's WMD functionality; please consider citing [1]; [2] and [3].
aahgagbie,RaRe-Technologies/gensim,docs/src/gallery/tutorials/run_wmd.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,4. Thomas Mikolov et al. *Efficient Estimation of Word Representations in Vector Space*\\ ; 2013.
aahgagcbf,RaRe-Technologies/gensim,docs/src/gallery/tutorials/run_word2vec.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,.. Note:: Feel free to skip these review sections if you're already familiar with the models.
aahgagddi,RaRe-Technologies/gensim,docs/src/gallery/tutorials/run_word2vec.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,This corpus is small enough to fit entirely in memory; but we'll implement a
aahgageaj,RaRe-Technologies/gensim,docs/src/gallery/tutorials/run_word2vec.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,Bigger size values require more training data; but can lead to better (more
aahgageed,RaRe-Technologies/gensim,docs/src/gallery/tutorials/run_word2vec.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,There\u2019s a little extra memory needed for storing the vocabulary tree (100;000 words would take a few megabytes); but unless your words are extremely loooong strings; memory footprint will be dominated by the three matrices above.
aahgageej,RaRe-Technologies/gensim,docs/src/gallery/tutorials/run_word2vec.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,``Word2Vec`` training is an unsupervised task; there\u2019s no good way to
aahgagehe,RaRe-Technologies/gensim,docs/src/gallery/tutorials/run_word2vec.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,In the December 2016 release of Gensim we added a better way to evaluate semantic similarity.
aahgageig,RaRe-Technologies/gensim,docs/src/gallery/tutorials/run_word2vec.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,work well in your application; or vice versa. It\u2019s always best to evaluate
aahgaggce,RaRe-Technologies/gensim,docs/src/tools/to_python.py,bcee414663bdcbdf6a58684531ee69c6949550bf,STILL_EXISTS,are not needed.
aahgbjhfb,RaRe-Technologies/gensim,gensim/test/test_utils_any2vec.py,1052b9bd890761f89bcfd6c340304ad448f4a187,STILL_EXISTS,\"\"\" || Automated tests for checking utils_any2vec functionality. || \"\"\"
aahgbjiac,RaRe-Technologies/gensim,gensim/test/test_fasttext.py,4d223279985f811c0bbfc7f1451c7209c920c859,STILL_EXISTS,fasttext tool creates both *vec and *bin files; so we have to remove both; even thought *vec is unused
aahgbjibd,RaRe-Technologies/gensim,setup.py,8b1ea6a07a2d6010d4af6d2491f6cb008fa1e01a,STILL_EXISTS,the following is needed to be able to add numpy's include dirs... without
aahgbjici,RaRe-Technologies/gensim,setup.py,8b1ea6a07a2d6010d4af6d2491f6cb008fa1e01a,93385d331935fd8eeedb3f52b4dd8883dc86695c,TODO: Remove the pin once we drop py2.7 from gensim too.
aahgbjigb,RaRe-Technologies/gensim,setup.py,a2ec4c3417717f65c6d7eae5c1fcdc6db8a54159,STILL_EXISTS,the following is needed to be able to add numpy's include dirs... without
aahgbjihc,RaRe-Technologies/gensim,setup.py,a2ec4c3417717f65c6d7eae5c1fcdc6db8a54159,93385d331935fd8eeedb3f52b4dd8883dc86695c,TODO: Remove the pin once we drop py2.7 from gensim too.
aahgbjiid,RaRe-Technologies/gensim,setup.py,29d1092d3012b399e9aef0513b13b763ebdf1726,bb947b38a23f10c40948ec6876bc0a850eafbcf6,HACK: Installing tensorflow causes a segfault in Travis on py3.6. Other Pythons work \u2013 a mystery.
aahgbjjch,RaRe-Technologies/gensim,gensim/models/fasttext.py,c0e0169565116854993b22efef29e3c402ec6c69,STILL_EXISTS,FIXME: caching & calc sensible?
aahgbjjga,RaRe-Technologies/gensim,gensim/models/fasttext.py,c0e0169565116854993b22efef29e3c402ec6c69,STILL_EXISTS,FIXME: evaluate if precaching even necessary; compared to recalculating as needed
aahgcaaac,RaRe-Technologies/gensim,gensim/models/keyedvectors.py,c0e0169565116854993b22efef29e3c402ec6c69,STILL_EXISTS,TODO? warn if not matching extras already present?
aahgcaabc,RaRe-Technologies/gensim,gensim/models/keyedvectors.py,c0e0169565116854993b22efef29e3c402ec6c69,STILL_EXISTS,FIXME: Update to better match & share code with most_similar()
aahgcaabh,RaRe-Technologies/gensim,gensim/models/keyedvectors.py,c0e0169565116854993b22efef29e3c402ec6c69,STILL_EXISTS,TOCONSIDER: maybe mismatched vectors still useful enough to merge (truncating\/padding)?
aahgcaace,RaRe-Technologies/gensim,gensim/models/keyedvectors.py,c0e0169565116854993b22efef29e3c402ec6c69,STILL_EXISTS,no longer needed
aahgcaacj,RaRe-Technologies/gensim,gensim/models/keyedvectors.py,c0e0169565116854993b22efef29e3c402ec6c69,STILL_EXISTS,FIXME(someday): make this faking optional; include more realistic (Zipf-based) fake numbers
aahgcabbb,RaRe-Technologies/gensim,gensim/models/word2vec.py,c0e0169565116854993b22efef29e3c402ec6c69,STILL_EXISTS,rebuild cum_table from vocabulary  ## TODO: ???
aahgcabdf,RaRe-Technologies/gensim,gensim/test/test_doc2vec.py,c0e0169565116854993b22efef29e3c402ec6c69,STILL_EXISTS,TODO: test saving in word2vec format
aahgcabdg,RaRe-Technologies/gensim,gensim/test/test_fasttext.py,c0e0169565116854993b22efef29e3c402ec6c69,STILL_EXISTS,TODO: add semantic tests; where appropriate
aahgcaccf,RaRe-Technologies/gensim,setup.py,c0e0169565116854993b22efef29e3c402ec6c69,bb947b38a23f10c40948ec6876bc0a850eafbcf6,HACK: Installing tensorflow causes a segfault in Travis on py3.6. Other Pythons work \u2013 a mystery.
aahgcaddh,RaRe-Technologies/gensim,gensim/models/keyedvectors.py,9ddf9a2df76cce99c3bfc442a45a7a2663c72140,STILL_EXISTS,dtype; which uses too much memory!
aahgcadfg,RaRe-Technologies/gensim,docs/src/gallery/howtos/run_compare_lda.py,502b654add89102a2d1c253cd1176486cdd1466b,STILL_EXISTS,similar and why (you can read annotation if you move your pointer to cell).
aahgcadfi,RaRe-Technologies/gensim,docs/src/gallery/howtos/run_compare_lda.py,502b654add89102a2d1c253cd1176486cdd1466b,STILL_EXISTS,Choose a distance function that matches your upstream task better: what kind of \"similarity\" is
aahgcagbe,RaRe-Technologies/gensim,gensim/similarities/annoy.py,502b654add89102a2d1c253cd1176486cdd1466b,STILL_EXISTS,FIXME docvecs now = ? What's the replacement for docvecs.count; index_to_doctag; etc?
aahgcagbf,RaRe-Technologies/gensim,docs/src/gallery/howtos/run_doc2vec_imdb.py,fd6b408d03c38f35e278df83019a299547503ba0,70d4338e14af7d0be477dda7ce21a994d5fad96f,FIXME
aahgcagbj,RaRe-Technologies/gensim,docs/src/auto_examples/howtos/run_compare_lda.py,70d4338e14af7d0be477dda7ce21a994d5fad96f,STILL_EXISTS,similar and why (you can read annotation if you move your pointer to cell).
aahgcagcb,RaRe-Technologies/gensim,docs/src/auto_examples/howtos/run_compare_lda.py,70d4338e14af7d0be477dda7ce21a994d5fad96f,STILL_EXISTS,Choose a distance function that matches your upstream task better: what kind of \"similarity\" is
aahgcaijh,RaRe-Technologies/gensim,gensim/models/keyedvectors.py,8e9d20235edccc8f647174fdbe8b1c3a11bbbcc7,STILL_EXISTS,TODO: support memmap?
aahgcajba,RaRe-Technologies/gensim,gensim/models/keyedvectors.py,1edbb4c3f9432eef9c2aee499d1c861b33fc30bc,02354cd0b4ed1c536fb8ffe3c665a5e7aa2c1818,FIXME: `uniform` passes all tests; but generates temporary double-sized np.float64 array;
aahgcajbj,RaRe-Technologies/gensim,gensim/models/keyedvectors.py,1edbb4c3f9432eef9c2aee499d1c861b33fc30bc,02354cd0b4ed1c536fb8ffe3c665a5e7aa2c1818,sticking with the RAM-wasteful-but-all-tests-passing approach above; TODO debug\/fix ASAP.
aahgcajee,RaRe-Technologies/gensim,gensim/models/phrases.py,b40c6010f22847f478c9adad3432969536c6c071,STILL_EXISTS,XXX: Why do we care about *all* phrase tokens? Why not just score the start+end bigram?
aahgcbaah,RaRe-Technologies/gensim,gensim/models/keyedvectors.py,e4199cb4e9a90df44ca59c1d0505b138caa21951,STILL_EXISTS,XXX: get rid of this: not used much; too complex and brittle.
aahgcbach,RaRe-Technologies/gensim,release/generate_changelog.py,e4199cb4e9a90df44ca59c1d0505b138caa21951,STILL_EXISTS,Unfortunately; the GitHub API doesn't link PRs to issues that they fix;
aahgdcacd,RaRe-Technologies/gensim,docs/src/auto_examples/tutorials/run_scm.py,ddeeb1274c996b6992e11f7cbe7ca44c776f6daf,STILL_EXISTS,If you use Gensim's SCM functionality; please consider citing [1]; [2] and [3].
aahgdcahc,RaRe-Technologies/gensim,docs/src/auto_examples/tutorials/run_scm.py,ddeeb1274c996b6992e11f7cbe7ca44c776f6daf,STILL_EXISTS,4. Tom\u00E1\u0161 Mikolov et al. Efficient Estimation of Word Representations in Vector Space; 2013.
aahgdcbdf,RaRe-Technologies/gensim,docs/src/gallery/tutorials/run_scm.py,ddeeb1274c996b6992e11f7cbe7ca44c776f6daf,STILL_EXISTS,If you use Gensim's SCM functionality; please consider citing [1]; [2] and [3].
aahgdcbie,RaRe-Technologies/gensim,docs/src/gallery/tutorials/run_scm.py,ddeeb1274c996b6992e11f7cbe7ca44c776f6daf,STILL_EXISTS,4. Tom\u00E1\u0161 Mikolov et al. Efficient Estimation of Word Representations in Vector Space; 2013.
aahgddfge,explosion/spaCy,spacy/orth.py,151aa14bbacae74ff436da074d6250f0959836a5,59b41a9fd37a6643c16fe1345df8aacdde176141,TODO: Rewrite and improve this
aahgddhgb,explosion/spaCy,spacy/util.py,302e09018bc090a32f45170d208bb2b6898ac185,b962fe73d7401f9255d4bc72acab9aa3e49b586f,TODO: Fix this hack!
aahgddigi,explosion/spaCy,tests/test_docs.py,6b68607b1f07ae1f6edf3d31001623151917b636,STILL_EXISTS,\"\"\"Sphinx doctest is just too hard. Manually paste doctest examples here\"\"\"
aahgddiid,explosion/spaCy,tests/test_token_api.py,0962ffc0955ead4f377470498e2f026ce46469d0,STILL_EXISTS,TODO: Test more of these; esp. if a bug is found
aahgddijg,explosion/spaCy,spacy/en/__init__.py,8057a95f20d26672d985f7df12aec75d3a005c2c,801bf14f4ff63cc88a896239a3384330cbe85232,TODO: Clean this up
aahgddjfi,explosion/spaCy,spacy/munge/read_ner.py,d25d31442df1e2de7f66346ec24694c88a9fe478,STILL_EXISTS,TODO: We shouldn't be getting these malformed inputs. Fix this.
aahgdeafj,explosion/spaCy,tests/tokenizer/test_tokenizer.py,bd4f5f89cb6fb125d26fe5a3cd1259a150f79a15,STILL_EXISTS,TODO: This is currently difficult --- infix interferes here.
aahgdehad,explosion/spaCy,examples/matcher_example.py,c17e2f2f20efdbbd28cc6b89afd5d73f475f4c9a,STILL_EXISTS,Arbitrary attributes. Currently unused.
aahgdehhc,explosion/spaCy,examples/matcher_example.py,a6e2a44283c7b7d0f25a5d000b24bbd2c2436d6b,STILL_EXISTS,amortized complexity of anything we do this way is going to be O(1). You
aahgdejea,explosion/spaCy,website/tests/test_api.py,f0360bf59d3171e7e9c092de34de74220e884fa9,STILL_EXISTS,TODO: ImportError: No module named _doc_examples
aahgdejgb,explosion/spaCy,website/tests/test_api.py,f0360bf59d3171e7e9c092de34de74220e884fa9,STILL_EXISTS,TODO: missing imports
aahgdejgc,explosion/spaCy,website/tests/test_api.py,f0360bf59d3171e7e9c092de34de74220e884fa9,STILL_EXISTS,TODO: TypeError: readonly attribute
aahgdfbab,explosion/spaCy,spacy/en/uget.py,bfde91fa49c5e91686081a6fb6984de537138ab6,STILL_EXISTS,TODO add http error handling here
aahgdfbhi,explosion/spaCy,spacy/en/download.py,4f703f0cb49bbe6aa10b6fa1b527c043cc95005b,14b89ff1c562c03798013643baef62490fc0d4cd,ugly hack to find out whether something other
aahgdfcfg,explosion/spaCy,spacy/en/download.py,03d2f98cd550ec3162bd27c33409c4d8413367ac,9b75d872b08fe66a5a5c0a8534e36c0e252d3089,TODO read version from the same source as the setup
aahgdfcfh,explosion/spaCy,spacy/en/download.py,03d2f98cd550ec3162bd27c33409c4d8413367ac,a7d7ea3afa776132d5f46f2f1b59a4deeda1748c,FIXME clean up old-style packages
aahgdfcfi,explosion/spaCy,spacy/en/download.py,03d2f98cd550ec3162bd27c33409c4d8413367ac,970278a3d6420eb700b22aac6425688d691d2c8a,FIXME supply spacy with an old-style data dir
aahgdfcgj,explosion/spaCy,spacy/util.py,9027cef3bc2ee7500fc3d2cf8700017c6b170e95,STILL_EXISTS,TODO: retrieve version
aahgdfdad,explosion/spaCy,bin/cythonize.py,9662cf04c9489050540b4393c6e33e0b1f3b126a,STILL_EXISTS,\"\"\" cythonize ||  || Cythonize pyx files into C files as needed. ||  || Usage: cythonize [root_dir] ||  || Default [root_dir] is 'spacy'. ||  || Checks pyx files to see if they have been changed relative to their || corresponding C files.  If they have; then runs cython on these files to || recreate the C files. ||  || The script thinks that the pyx files have changed relative to the C files || by comparing hashes stored in a database file. ||  || Simple script to invoke Cython (and Tempita) on all .pyx (.pyx.in) || files; while waiting for a proper build system. Uses file hashes to || figure out if rebuild is needed. ||  || For now; this script should be run by developers when changing Cython files || only; and the resulting C files checked in; so that end-users (and Python-only || developers) do not get the Cython\/Tempita dependencies. ||  || Originally written by Dag Sverre Seljebotn; and copied here from: ||  || https:\/\/raw.github.com\/dagss\/private-scipy-refactor\/cythonize\/cythonize.py ||  || Note: this script does not check any of the dependent C libraries; it only || operates on the Cython .pyx files. || \"\"\"
aahgdfdhc,explosion/spaCy,spacy/en/download.py,0e321a710524b29e0cfe18da86cb0307af62a0ab,6bb9c7f31101cdb7b70fae61bc5a7c48ef1c3d8a,FIXME supply spacy with an old-style data dir
aahgdfeec,explosion/spaCy,spacy/en/download.py,45afda5af32df1536edf001ebd249611690b1b81,8b61d45ed05aac1661d1392698aeef10b524f7d2,FIXME supply spacy with an old-style data dir
aahgdfgbh,explosion/spaCy,examples/get_parse_subregions.py,9b303e158e3f9bd2c395637425160a9c2a19b41c,STILL_EXISTS,It'd probably be better for `word.subtree` to return a `Span` object instead
aahgdfgeh,explosion/spaCy,spacy/nonproj.py,8d531c958b8ac6d58e527162c10bca8e5885d916,STILL_EXISTS,this function ends after at most len(heads) steps
aahgdfgfa,explosion/spaCy,spacy/nonproj.py,8d531c958b8ac6d58e527162c10bca8e5885d916,STILL_EXISTS,the head relation upwards always ends at the root node
aahgdfiie,explosion/spaCy,spacy/tests/tokenizer/test_infix.py,b1fe41b45d6a36cbbabb203ddf0357fca1689265,f09b5a5dfd058f7a13e5a2d94b6799f20fb7fa87,TODO: This points to a deeper issue with the tokenizer: it doesn't re-enter
aahgdgaad,explosion/spaCy,spacy/tests/matcher/test_matcher_bugfixes.py,12024b0b0a12b0beb3ee1b22f89988bdde2aca95,STILL_EXISTS,columns = [ENT_IOB; ENT_TYPE]
aahgdgaae,explosion/spaCy,spacy/tests/matcher/test_matcher_bugfixes.py,12024b0b0a12b0beb3ee1b22f89988bdde2aca95,STILL_EXISTS,values = numpy.ndarray(shape=(len(doc);len(columns)); dtype='int32')
aahgdgaaj,explosion/spaCy,spacy/tests/matcher/test_matcher_bugfixes.py,12024b0b0a12b0beb3ee1b22f89988bdde2aca95,STILL_EXISTS,doc.from_array(columns;values)
aahgdgaed,explosion/spaCy,spacy/tests/parser/test_sbd.py,b11cbb06c6da2ff6646b6e9f0efcabe6dea0121f,1a3984742cfe31db85255e8a88371bb7cd57e89f,TODO:
aahgdgbbe,explosion/spaCy,spacy/tests/parser/test_sbd.py,902a389d850565235c7ee3d0cf48603f9ef8650b,1a3984742cfe31db85255e8a88371bb7cd57e89f,TODO:
aahgdgbgj,explosion/spaCy,spacy/tests/integration/test_model_sanity.py,1786331cd82674a5d4ec14cce74d135278dae84d,STILL_EXISTS,if tagging was done properly; pos tags shouldn't be empty
aahgdgbhb,explosion/spaCy,spacy/tests/integration/test_model_sanity.py,1786331cd82674a5d4ec14cce74d135278dae84d,STILL_EXISTS,- dependency labels shouldn't be empty
aahgdgbhd,explosion/spaCy,spacy/tests/integration/test_model_sanity.py,1786331cd82674a5d4ec14cce74d135278dae84d,STILL_EXISTS,if ner was done properly; ent_iob shouldn't be empty
aahgdgdea,explosion/spaCy,spacy/lemmatizer.py,3cb4d455d2226d11fe1a4e20ce2aef47c50a80e6,91899d337b541636b4be8042251e2ae3cb0e8ec2,TODO: Is this correct? See discussion in Issue #435.
aahgdggch,explosion/spaCy,spacy/language.py,7d5212f13117f12651b0285d5da2a0d2e5f5f2ba,da44183ae13e26dc74abe80ff487817cfdf6a2bd,TODO -- fix this
aahgdgggb,explosion/spaCy,spacy/tests/vocab/test_freeze_string_store.py,d3a617aa99b7486400c54a2f976e443a3baa8b85,STILL_EXISTS,'''Test the possibly temporary workaround of flushing the stringstore of OOV words.'''
aahgdgida,explosion/spaCy,spacy/language.py,09f68bc641e7e9f0cdae3c5f89490cb0731ce999,a2f55e701551fc5616083daec0c54f3fc8343478,This is very messy; but it's the minimal working fix to Issue #639.
aahgdgide,explosion/spaCy,spacy/nl/language_data.py,3db8b0d3229e1cb03fee8944a4744e801f5056b0,2b2ea8ca11d1658325a855861150e2dbd5d709fb,TODO Make tokenizer excpetions for Dutch
aahgdgidf,explosion/spaCy,spacy/nl/language_data.py,3db8b0d3229e1cb03fee8944a4744e801f5056b0,2b2ea8ca11d1658325a855861150e2dbd5d709fb,TODO insert TAG_MAP for Dutch
aahgdgjei,explosion/spaCy,spacy/language.py,159e8c46e181ce6b8f61aedd2fd6c85cd6306802,da44183ae13e26dc74abe80ff487817cfdf6a2bd,This is very messy; but it's the minimal working fix to Issue #639.
aahgdhaae,explosion/spaCy,spacy/nl/language_data.py,dd55d085b62222e6861d4d38c397567872825641,2b2ea8ca11d1658325a855861150e2dbd5d709fb,TODO insert TAG_MAP for Dutch
aahgdhbdd,explosion/spaCy,spacy/en/__init__.py,2ef9d531176cebeed174c0e6a1aeb0a5abbfc131,STILL_EXISTS,Make a special-case hack for loading the GloVe vectors; to support
aahgdhbdf,explosion/spaCy,setup.py,6c0c43c267a83c4941cebfdbb616bd497c806794,564939391a31f9954566bfc3851d231fa005bad3,TODO: This is missing a lot of modules. Does it matter?
aahgdhfhi,explosion/spaCy,spacy/language.py,73a38bd4d1cfa0f3ab780abd9e1931d03db4024d,STILL_EXISTS,This is very messy; but it's the minimal working fix to Issue #639.
aahgdhfia,explosion/spaCy,spacy/language.py,73a38bd4d1cfa0f3ab780abd9e1931d03db4024d,b0f3ea2200ab62bae2482884dbcce8e8e376c1d1,TODO -- fix this
aahgdhidh,explosion/spaCy,spacy/language_data/util.py,fba67fa3427f22be5abb727e5f2e8d006a4665b0,STILL_EXISTS,TODO: Better error
aahgdhiea,explosion/spaCy,spacy/tests/stringstore/test_freeze_string_store.py,26d018d8743fda22f7b5541b580ecb714c45a3ec,STILL_EXISTS,\"\"\"Test the possibly temporary workaround of flushing the stringstore of OOV words.\"\"\"
aahgdhigc,explosion/spaCy,spacy/tests/test_orth.py,0da2ee5c68f135b49d98fd61a13714543f4195c5,STILL_EXISTS,TODO: brackets; is_ascii; is_upper; is_lower; is_title
aahgdiaif,explosion/spaCy,spacy/tests/regression/test_issue781.py,a471114eb2c9dffff27de12772e69b58e58f4dd1,STILL_EXISTS,Note: \"chromosomes\" worked previous the bug fix
aahgdigej,explosion/spaCy,spacy/cli/link.py,97814f8da6b5d7e5da12a8d0ac62ce441730e479,c05ec4b89a3e077d54a7802958f9af2b9777d711,Add workaround for Python 2 on Windows (see issue #909)
aahgdiicf,explosion/spaCy,spacy/language.py,33ba5066ebf200fee69fb29bb3722f2b0cb5b7f2,5211645af3f5308b5cd43314997659fdb87d13cf,TODO: Word vectors?
aahgdiifa,explosion/spaCy,spacy/util.py,97647c46cdbd623e34da4c162ceecd4b97b0946e,95edd9e896282037b937e570dad4a30ef66dde1f,TODO: Allow passing in full model path and only require one argument
aahgdjbfe,explosion/spaCy,bin/parser/train_ud.py,8e48b58cd6322f880febd2d192eb5197e7bd87d6,f99f5b75dc03de20b41120d3d59c78c3fa342caf,Hack for efficiency
aahgdjcjh,explosion/spaCy,spacy/cli/train.py,59c3b9d4dd813d1c31c47c404cba44cd3c8b4f20,bdaac7ab445c247e8137950ee66d698806a4830c,TODO: Fix!
aahgdjdad,explosion/spaCy,bin/parser/train_ud.py,10682d35abfcaba4619a1a381c881b1a08891946,7a33f1e2b7a3b457bc3484767a12c184c73942a2,Hack for efficiency
aahgdjdcb,explosion/spaCy,spacy/_ml.py,10682d35abfcaba4619a1a381c881b1a08891946,56073a11ef0c6313c9431f568010f0906edb22e7,N*12 vector computations. The saving for beam-search is much better:
aahgdjdcd,explosion/spaCy,spacy/_ml.py,10682d35abfcaba4619a1a381c881b1a08891946,56073a11ef0c6313c9431f568010f0906edb22e7,so we can save the factor k. This also gives a nice CPU\/GPU division:
aahgdjejb,explosion/spaCy,spacy/_ml.py,bef89ef23de1e25b3e06cde1494cd240b21daa29,827b5af697eb34c6285f6c6a24cdde7951a496eb,N*12 vector computations. The saving for beam-search is much better:
aahgdjejd,explosion/spaCy,spacy/_ml.py,bef89ef23de1e25b3e06cde1494cd240b21daa29,827b5af697eb34c6285f6c6a24cdde7951a496eb,so we can save the factor k. This also gives a nice CPU\/GPU division:
aahgdjfcj,explosion/spaCy,spacy/util.py,60db497525a88fd44351d91e260c36f7fabe878c,1694c24e5248d98befb323d6e9569213f4c86b2c,TODO: Better error
aahgdjfgg,explosion/spaCy,spacy/util.py,86d9c29f308abf2a00dec0bf8589106697cdcb65,1694c24e5248d98befb323d6e9569213f4c86b2c,TODO: Better error
aahgdjhcc,explosion/spaCy,spacy/_ml.py,a9edb3aa1d17177945475a5af3aaaba0b4f85bef,STILL_EXISTS,Work around thinc API limitations :(. TODO: Revise in Thinc 7
aahgdjhdf,explosion/spaCy,spacy/cli/train.py,5211645af3f5308b5cd43314997659fdb87d13cf,4c9202249d820d45dde13abae5e3f6b448785225,TODO: Get spaCy using Thinc's trainer and optimizer
aahgdjhec,explosion/spaCy,setup.py,e6f850f0148c84ca1bc6bd0563a989ce0400e762,3cc6fe148483b46f9d81bfa56b96c5c2072b7c2b,TODO: This is missing a lot of modules. Does it matter?
aahgdjhee,explosion/spaCy,spacy/tests/regression/test_issue999.py,8cf097ca88e8afed68315d5c90ac9a8ac8faf66e,STILL_EXISTS,TODO: Fix when saving\/loading is fixed.
aahgdjhfa,explosion/spaCy,spacy/language.py,2713041571f9a594ad27ba428283f69e8c2c55dd,STILL_EXISTS,TODO: Unhack this when thinc improves
aahgdjhfc,explosion/spaCy,spacy/cli/train.py,ca70b086612b652a1f3c797b9810f2d9ba60252e,6e8dce2c05cf81ad3123e9b8b064f630184ce615,TODO:
aahgdjhfh,explosion/spaCy,spacy/language.py,c2c825127abf50fe30f43502762a2d34bbb44c6c,STILL_EXISTS,TODO: Having trouble with contextlib
aahgdjhfi,explosion/spaCy,spacy/language.py,c2c825127abf50fe30f43502762a2d34bbb44c6c,STILL_EXISTS,Workaround: these aren't actually context managers atm.
aahgdjhgb,explosion/spaCy,spacy/cli/converters/iob2json.py,08766240c3c3491af92eb248b0aec00355d1ef97,31681d20e038fb0b318a9479856da473b0e0e926,TODO: This isn't complete yet -- need to map from IOB to
aahgdjhhi,explosion/spaCy,spacy/tests/matcher/test_entity_id.py,187f37073495211c422be719b16da4d2449c8844,STILL_EXISTS,TODO: These can probably be deleted
aahgdjhjc,explosion/spaCy,spacy/cli/train.py,532afef4a811d5c71c75f5e63fbec3232f6ea937,3959d778acfcbb610f046897aa97de68140cd9dc,TODO: Fix!
aahgdjjcg,explosion/spaCy,spacy/tests/lang/de/test_models.py,3e105bcd3671e1a5063181640a800e4a1de24fbe,STILL_EXISTS,if tagging was done properly; pos tags shouldn't be empty
aahgdjjci,explosion/spaCy,spacy/tests/lang/de/test_models.py,3e105bcd3671e1a5063181640a800e4a1de24fbe,STILL_EXISTS,- dependency labels shouldn't be empty
aahgdjjda,explosion/spaCy,spacy/tests/lang/de/test_models.py,3e105bcd3671e1a5063181640a800e4a1de24fbe,STILL_EXISTS,if ner was done properly; ent_iob shouldn't be empty
aahgdjjec,explosion/spaCy,spacy/tests/lang/en/test_models.py,3e105bcd3671e1a5063181640a800e4a1de24fbe,STILL_EXISTS,if tagging was done properly; pos tags shouldn't be empty
aahgdjjee,explosion/spaCy,spacy/tests/lang/en/test_models.py,3e105bcd3671e1a5063181640a800e4a1de24fbe,STILL_EXISTS,- dependency labels shouldn't be empty
aahgdjjeg,explosion/spaCy,spacy/tests/lang/en/test_models.py,3e105bcd3671e1a5063181640a800e4a1de24fbe,STILL_EXISTS,if ner was done properly; ent_iob shouldn't be empty
aahgeadgg,explosion/spaCy,setup.py,94267ec50f787dc74b50603c40104438e22c980f,ff7418b0d9d84b5d6d1c1b121e71f0d4d56e1a90,TODO: This is missing a lot of modules. Does it matter?
aahgeaeah,explosion/spaCy,spacy/ja/__init__.py,6e9e686568ab1f70d0b517e0d5f2bcbb894eb17a,STILL_EXISTS,For many of these; full dependencies are needed to properly resolve
aahgeaebe,explosion/spaCy,spacy/ja/__init__.py,6e9e686568ab1f70d0b517e0d5f2bcbb894eb17a,adfd98731655cc3f351e0042353ea850ef7d23c2,TODO: if the text has been tokenized; this info is already available
aahgeaece,explosion/spaCy,spacy/ja/tag_map.py,6e9e686568ab1f70d0b517e0d5f2bcbb894eb17a,STILL_EXISTS,XXX ADJ if alone; AUX otherwise
aahgeaeci,explosion/spaCy,spacy/ja/tag_map.py,6e9e686568ab1f70d0b517e0d5f2bcbb894eb17a,STILL_EXISTS,XXX: might need refinement
aahgeaedc,explosion/spaCy,spacy/ja/tag_map.py,6e9e686568ab1f70d0b517e0d5f2bcbb894eb17a,STILL_EXISTS,XXX see \u540D\u8A5E;\u666E\u901A\u540D\u8A5E;\u30B5\u5909\u53EF\u80FD;*
aahgeaede,explosion/spaCy,spacy/ja/tag_map.py,6e9e686568ab1f70d0b517e0d5f2bcbb894eb17a,STILL_EXISTS,XXX VERB if alone; AUX otherwise
aahgeaeei,explosion/spaCy,spacy/ja/tag_map.py,6e9e686568ab1f70d0b517e0d5f2bcbb894eb17a,STILL_EXISTS,XXX: sometimes VERB in UDv2; suru-verb noun
aahgeaefa,explosion/spaCy,spacy/ja/tag_map.py,6e9e686568ab1f70d0b517e0d5f2bcbb894eb17a,STILL_EXISTS,XXX: sometimes ADJ in UDv2
aahgeaefc,explosion/spaCy,spacy/ja/tag_map.py,6e9e686568ab1f70d0b517e0d5f2bcbb894eb17a,STILL_EXISTS,XXX note \u3053\u306E\u3001\u305D\u306E etc. should be DET
aahgeaigd,explosion/spaCy,spacy/_ml.py,ac040b99bb6d947d02209865fd57f2e7ba1f35c7,9f512e657a425af8b72795dd9d6a771ed1b9617d,TODO Make concatenate support lists
aahgeaihe,explosion/spaCy,spacy/_ml.py,16e25ce3b5c189d35f0db6804033f631d658c372,STILL_EXISTS,TODO Make concatenate support lists
aahgebbda,explosion/spaCy,spacy/_ml.py,5cbefcba1743701a4d895123178a885454cf6c45,64e4ff7c4b68dea95d071c555ae1663c274e4eea,TODO: Unset this once we don't want to support models previous models.
aahgebbdi,explosion/spaCy,examples/training/train_textcat.py,f1b86dff8cef8802f5493bad4331d23e016dca7c,STILL_EXISTS,TODO: Remove this once we're not supporting models trained with thinc <6.9.0
aahgebbff,explosion/spaCy,spacy/language.py,212c8f071180c9ce134a74b85603e48c14199595,b0f3ea2200ab62bae2482884dbcce8e8e376c1d1,TODO -- fix this
aahgebcdg,explosion/spaCy,examples/pipeline/custom_component_entities.py,6679117000ffd8f872ea3b42e89d995bf4307fc4,STILL_EXISTS,so even if the list of companies is long; it's very efficient
aahgebefb,explosion/spaCy,spacy/tests/conftest.py,9b3f8f9ec3c98c94581694060cfb60be0b8bf2f3,a928ae2f3520da4f16e84cb5e266aee9bf06264d,TODO: re-implement generic tokenizer tests
aahgebiac,explosion/spaCy,spacy/tests/conftest.py,18ec6610ddc53272eeada6aab1aeb6b8f26b9b05,0b9e1d3660ece6b5edf04bac3ead5a1d2102f332,TODO: re-implement generic tokenizer tests
aahgebihi,explosion/spaCy,setup.py,0b9e1d3660ece6b5edf04bac3ead5a1d2102f332,79fcf8576aa077749c91315d102c0d70d888ca60,TODO: This is missing a lot of modules. Does it matter?
aahgeccgd,explosion/spaCy,spacy/tests/conftest.py,facf77e54136e16a1808f064a984ae8bb9da8d66,STILL_EXISTS,TODO: re-implement generic tokenizer tests
aahgecehe,explosion/spaCy,spacy/language.py,2d6ec998842ea5773f9e66c6153b5b9ceb7a5c0a,287a3ca256df1d94dc22eab867cf75260b74bbe8,TODO -- fix this
aahgecfba,explosion/spaCy,examples/information_extraction/parse_subtrees.py,daed7ff8fedf8d7bc202ec706eed5d53e70cef77,STILL_EXISTS,It'd probably be better for `word.subtree` to return a `Span` object
aahgecgbe,explosion/spaCy,examples/training/train_intent_parser.py,9dfca0f2f8fb53314dfe874fd327b07239669438,STILL_EXISTS,for no relation; we simply chose an arbitrary dependency label; e.g. '-'
aahgecich,explosion/spaCy,spacy/language.py,4d048e94d3eaa88e038e56967c0bf7599d11f6ae,STILL_EXISTS,TODO -- fix this
aahgecjjd,explosion/spaCy,spacy/language.py,531142a933134918ce61e04d88b3422ce72e158b,STILL_EXISTS,TODO -- fix this
aahgedbga,explosion/spaCy,spacy/_ml.py,b22e42af7f2b9593fa47d8b4920c43e64f98f737,64e4ff7c4b68dea95d071c555ae1663c274e4eea,TODO: Unset this once we don't want to support models previous models.
aahgedbif,explosion/spaCy,spacy/tests/parser/test_add_label.py,64e4ff7c4b68dea95d071c555ae1663c274e4eea,STILL_EXISTS,TODO: This is flakey; because it depends on what the parser first learns.
aahgedgfe,explosion/spaCy,examples/deep_learning_keras.py,173b1551afe3e5468fb76431447b169eb222e9a2,STILL_EXISTS,\"\"\" || This example shows how to use an LSTM sentiment classification model trained using Keras in spaCy. spaCy splits the document into sentences; and each sentence is classified using the LSTM. The scores for the sentences are then aggregated to give the document score. This kind of hierarchical model is quite difficult in \"pure\" Keras or Tensorflow; but it's very effective. The Keras example on this dataset performs quite poorly; because it cuts off the documents so that they're a fixed size. This hurts review accuracy a lot; because people often summarise their rating in the final sentence ||  || Prerequisites: || spacy download en_vectors_web_lg || pip install keras==2.0.9 ||  || Compatible with: spaCy v2.0.0+ || \"\"\"
aahgeedeh,explosion/spaCy,spacy/tests/serialize/test_serialize_tagger.py,4194bc57447e3837b43ad310e62dd9b05e9bc777,STILL_EXISTS,This seems to be a dict ordering bug somewhere. Only failing on some platforms
aahgeeidh,explosion/spaCy,examples/training/ner_multitask_objective.py,00557c5fddd865aa767d9e09a0cd454e70e79f6e,STILL_EXISTS,'''This example shows how to add a multi-task objective that is trained || alongside the entity recognizer. This is an alternative to adding features || to the model. ||  || The multi-task idea is to train an auxiliary model to predict some attribute; || with weights shared between the auxiliary model and the main model. In this || example; we're predicting the position of the word in the document. ||  || The model that predicts the position of the word encourages the convolutional || layers to include the position information in their representation. The || information is then available to the main model; as a feature. ||  || The overall idea is that we might know something about what sort of features || we'd like the CNN to extract. The multi-task objectives can encourage the || extraction of this type of feature. The multi-task objective is only used || during training. We discard the auxiliary model before run-time. ||  || The specific example here is not necessarily a good idea --- but it shows || how an arbitrary objective function for some word can be used. ||  || Developed and tested for spaCy 2.0.6 || '''
aahgeeihi,explosion/spaCy,spacy/_matcher2_notes.py,9115c3ba0a7f2612f5a1ac550d25cc565fb86814,STILL_EXISTS,TODO: This needs to be zero-width :(.
aahgefbbh,sepandhaghighi/pycm,Test/warning_test.py,d71e4debab871b675dec1d01cc7e76f9e3cb4207,STILL_EXISTS,\"\"\" || >>> from pycm import * || >>> from pytest import warns || >>> large_cm = ConfusionMatrix(list(range(10))+[2;3;5];list(range(10))+[1;7;2]) || >>> with warns(RuntimeWarning; match='The confusion matrix is a high dimension matrix'): || ...     large_cm.print_matrix() || Predict 0       1       2       3       4       5       6       7       8       9 || Actual || 0       1       0       0       0       0       0       0       0       0       0 || <BLANKLINE> || 1       0       1       0       0       0       0       0       0       0       0 || <BLANKLINE> || 2       0       1       1       0       0       0       0       0       0       0 || <BLANKLINE> || 3       0       0       0       1       0       0       0       1       0       0 || <BLANKLINE> || 4       0       0       0       0       1       0       0       0       0       0 || <BLANKLINE> || 5       0       0       1       0       0       1       0       0       0       0 || <BLANKLINE> || 6       0       0       0       0       0       0       1       0       0       0 || <BLANKLINE> || 7       0       0       0       0       0       0       0       1       0       0 || <BLANKLINE> || 8       0       0       0       0       0       0       0       0       1       0 || <BLANKLINE> || 9       0       0       0       0       0       0       0       0       0       1 || <BLANKLINE> || >>> with warns(RuntimeWarning; match='The confusion matrix is a high dimension matrix'): || ...     large_cm.print_normalized_matrix() || Predict   0         1         2         3         4         5         6         7         8         9 || Actual || 0         1.0       0.0       0.0       0.0       0.0       0.0       0.0       0.0       0.0       0.0 || <BLANKLINE> || 1         0.0       1.0       0.0       0.0       0.0       0.0       0.0       0.0       0.0       0.0 || <BLANKLINE> || 2         0.0       0.5       0.5       0.0       0.0       0.0       0.0       0.0       0.0       0.0 || <BLANKLINE> || 3         0.0       0.0       0.0       0.5       0.0       0.0       0.0       0.5       0.0       0.0 || <BLANKLINE> || 4         0.0       0.0       0.0       0.0       1.0       0.0       0.0       0.0       0.0       0.0 || <BLANKLINE> || 5         0.0       0.0       0.5       0.0       0.0       0.5       0.0       0.0       0.0       0.0 || <BLANKLINE> || 6         0.0       0.0       0.0       0.0       0.0       0.0       1.0       0.0       0.0       0.0 || <BLANKLINE> || 7         0.0       0.0       0.0       0.0       0.0       0.0       0.0       1.0       0.0       0.0 || <BLANKLINE> || 8         0.0       0.0       0.0       0.0       0.0       0.0       0.0       0.0       1.0       0.0 || <BLANKLINE> || 9         0.0       0.0       0.0       0.0       0.0       0.0       0.0       0.0       0.0       1.0 || <BLANKLINE> || >>> with warns(RuntimeWarning; match='The confusion matrix is a high dimension matrix'): || ...     large_cm.stat() || Overall Statistics : || <BLANKLINE> || 95% CI                                                            (0.5402;0.99827) || ACC Macro                                                         0.95385 || AUNP                                                              0.87121 || AUNU                                                              0.91212 || Bennett S                                                         0.74359 || CBA                                                               0.75 || Chi-Squared                                                       91.0 || Chi-Squared DF                                                    81 || Conditional Entropy                                               0.46154 || Cramer V                                                          0.88192 || Cross Entropy                                                     3.39275 || F1 Macro                                                          0.81667 || F1 Micro                                                          0.76923 || Gwet AC1                                                          0.7438 || Hamming Loss                                                      0.23077 || Joint Entropy                                                     3.70044 || KL Divergence                                                     0.15385 || Kappa                                                             0.74342 || Kappa 95% CI                                                      (0.48877;0.99807) || Kappa No Prevalence                                               0.53846 || Kappa Standard Error                                              0.12992 || Kappa Unbiased                                                    0.74172 || Lambda A                                                          0.72727 || Lambda B                                                          0.72727 || Mutual Information                                                2.77736 || NIR                                                               0.15385 || Overall ACC                                                       0.76923 || Overall CEN                                                       0.09537 || Overall J                                                         (7.33333;0.73333) || Overall MCC                                                       0.75333 || Overall MCEN                                                      0.10746 || Overall RACC                                                      0.10059 || Overall RACCU                                                     0.10651 || P-Value                                                           0.0 || PPV Macro                                                         0.85 || PPV Micro                                                         0.76923 || Pearson C                                                         0.93541 || Phi-Squared                                                       7.0 || RCI                                                               0.8575 || RR                                                                1.3 || Reference Entropy                                                 3.2389 || Response Entropy                                                  3.2389 || SOA1(Landis & Koch)                                               Substantial || SOA2(Fleiss)                                                      Intermediate to Good || SOA3(Altman)                                                      Good || SOA4(Cicchetti)                                                   Excellent || SOA5(Cramer)                                                      Very Strong || SOA6(Matthews)                                                    Strong || Scott PI                                                          0.74172 || Standard Error                                                    0.11685 || TPR Macro                                                         0.85 || TPR Micro                                                         0.76923 || Zero-one Loss                                                     3 || <BLANKLINE> || Class Statistics : || <BLANKLINE> || Classes                                                           0             1             2             3             4             5             6             7             8             9 || ACC(Accuracy)                                                     1.0           0.92308       0.84615       0.92308       1.0           0.92308       1.0           0.92308       1.0           1.0 || AGF(Adjusted F-score)                                             1.0           0.90468       0.6742        0.71965       1.0           0.71965       1.0           0.90468       1.0           1.0 || AGM(Adjusted geometric mean)                                      1.0           0.93786       0.78186       0.84135       1.0           0.84135       1.0           0.93786       1.0           1.0 || AM(Difference between automatic and manual classification)        0             1             0             -1            0             -1            0             1             0             0 || AUC(Area under the ROC curve)                                     1.0           0.95833       0.70455       0.75          1.0           0.75          1.0           0.95833       1.0           1.0 || AUCI(AUC value interpretation)                                    Excellent     Excellent     Good          Good          Excellent     Good          Excellent     Excellent     Excellent     Excellent || AUPR(Area under the PR curve)                                     1.0           0.75          0.5           0.75          1.0           0.75          1.0           0.75          1.0           1.0 || BCD(Bray-Curtis dissimilarity)                                    0.0           0.03846       0.0           0.03846       0.0           0.03846       0.0           0.03846       0.0           0.0 || BM(Informedness or bookmaker informedness)                        1.0           0.91667       0.40909       0.5           1.0           0.5           1.0           0.91667       1.0           1.0 || CEN(Confusion entropy)                                            0             0.1267        0.23981       0.1267        0             0.1267        0             0.1267        0             0 || DOR(Diagnostic odds ratio)                                        None          None          10.0          None          None          None          None          None          None          None || DP(Discriminant power)                                            None          None          0.55133       None          None          None          None          None          None          None || DPI(Discriminant power interpretation)                            None          None          Poor          None          None          None          None          None          None          None || ERR(Error rate)                                                   0.0           0.07692       0.15385       0.07692       0.0           0.07692       0.0           0.07692       0.0           0.0 || F0.5(F0.5 score)                                                  1.0           0.55556       0.5           0.83333       1.0           0.83333       1.0           0.55556       1.0           1.0 || F1(F1 score - harmonic mean of precision and sensitivity)         1.0           0.66667       0.5           0.66667       1.0           0.66667       1.0           0.66667       1.0           1.0 || F2(F2 score)                                                      1.0           0.83333       0.5           0.55556       1.0           0.55556       1.0           0.83333       1.0           1.0 || FDR(False discovery rate)                                         0.0           0.5           0.5           0.0           0.0           0.0           0.0           0.5           0.0           0.0 || FN(False negative\/miss\/type 2 error)                              0             0             1             1             0             1             0             0             0             0 || FNR(Miss rate or false negative rate)                             0.0           0.0           0.5           0.5           0.0           0.5           0.0           0.0           0.0           0.0 || FOR(False omission rate)                                          0.0           0.0           0.09091       0.08333       0.0           0.08333       0.0           0.0           0.0           0.0 || FP(False positive\/type 1 error\/false alarm)                       0             1             1             0             0             0             0             1             0             0 || FPR(Fall-out or false positive rate)                              0.0           0.08333       0.09091       0.0           0.0           0.0           0.0           0.08333       0.0           0.0 || G(G-measure geometric mean of precision and sensitivity)          1.0           0.70711       0.5           0.70711       1.0           0.70711       1.0           0.70711       1.0           1.0 || GI(Gini index)                                                    1.0           0.91667       0.40909       0.5           1.0           0.5           1.0           0.91667       1.0           1.0 || GM(G-mean geometric mean of specificity and sensitivity)          1.0           0.95743       0.6742        0.70711       1.0           0.70711       1.0           0.95743       1.0           1.0 || IBA(Index of balanced accuracy)                                   1.0           0.99306       0.2686        0.25          1.0           0.25          1.0           0.99306       1.0           1.0 || IS(Information score)                                             3.70044       2.70044       1.70044       2.70044       3.70044       2.70044       3.70044       2.70044       3.70044       3.70044 || J(Jaccard index)                                                  1.0           0.5           0.33333       0.5           1.0           0.5           1.0           0.5           1.0           1.0 || LS(Lift score)                                                    13.0          6.5           3.25          6.5           13.0          6.5           13.0          6.5           13.0          13.0 || MCC(Matthews correlation coefficient)                             1.0           0.677         0.40909       0.677         1.0           0.677         1.0           0.677         1.0           1.0 || MCCI(Matthews correlation coefficient interpretation)             Very Strong   Moderate      Weak          Moderate      Very Strong   Moderate      Very Strong   Moderate      Very Strong   Very Strong || MCEN(Modified confusion entropy)                                  0             0.11991       0.2534        0.11991       0             0.11991       0             0.11991       0             0 || MK(Markedness)                                                    1.0           0.5           0.40909       0.91667       1.0           0.91667       1.0           0.5           1.0           1.0 || N(Condition negative)                                             12            12            11            11            12            11            12            12            12            12 || NLR(Negative likelihood ratio)                                    0.0           0.0           0.55          0.5           0.0           0.5           0.0           0.0           0.0           0.0 || NLRI(Negative likelihood ratio interpretation)                    Good          Good          Negligible    Negligible    Good          Negligible    Good          Good          Good          Good || NPV(Negative predictive value)                                    1.0           1.0           0.90909       0.91667       1.0           0.91667       1.0           1.0           1.0           1.0 || OC(Overlap coefficient)                                           1.0           1.0           0.5           1.0           1.0           1.0           1.0           1.0           1.0           1.0 || OOC(Otsuka-Ochiai coefficient)                                    1.0           0.70711       0.5           0.70711       1.0           0.70711       1.0           0.70711       1.0           1.0 || OP(Optimized precision)                                           1.0           0.8796        0.55583       0.58974       1.0           0.58974       1.0           0.8796        1.0           1.0 || P(Condition positive or support)                                  1             1             2             2             1             2             1             1             1             1 || PLR(Positive likelihood ratio)                                    None          12.0          5.5           None          None          None          None          12.0          None          None || PLRI(Positive likelihood ratio interpretation)                    None          Good          Fair          None          None          None          None          Good          None          None || POP(Population)                                                   13            13            13            13            13            13            13            13            13            13 || PPV(Precision or positive predictive value)                       1.0           0.5           0.5           1.0           1.0           1.0           1.0           0.5           1.0           1.0 || PRE(Prevalence)                                                   0.07692       0.07692       0.15385       0.15385       0.07692       0.15385       0.07692       0.07692       0.07692       0.07692 || Q(Yule Q - coefficient of colligation)                            None          None          0.81818       None          None          None          None          None          None          None || RACC(Random accuracy)                                             0.00592       0.01183       0.02367       0.01183       0.00592       0.01183       0.00592       0.01183       0.00592       0.00592 || RACCU(Random accuracy unbiased)                                   0.00592       0.01331       0.02367       0.01331       0.00592       0.01331       0.00592       0.01331       0.00592       0.00592 || TN(True negative\/correct rejection)                               12            11            10            11            12            11            12            11            12            12 || TNR(Specificity or true negative rate)                            1.0           0.91667       0.90909       1.0           1.0           1.0           1.0           0.91667       1.0           1.0 || TON(Test outcome negative)                                        12            11            11            12            12            12            12            11            12            12 || TOP(Test outcome positive)                                        1             2             2             1             1             1             1             2             1             1 || TP(True positive\/hit)                                             1             1             1             1             1             1             1             1             1             1 || TPR(Sensitivity; recall; hit rate; or true positive rate)         1.0           1.0           0.5           0.5           1.0           0.5           1.0           1.0           1.0           1.0 || Y(Youden index)                                                   1.0           0.91667       0.40909       0.5           1.0           0.5           1.0           0.91667       1.0           1.0 || dInd(Distance index)                                              0.0           0.08333       0.5082        0.5           0.0           0.5           0.0           0.08333       0.0           0.0 || sInd(Similarity index)                                            1.0           0.94107       0.64065       0.64645       1.0           0.64645       1.0           0.94107       1.0           1.0 || <BLANKLINE> || >>> with warns(RuntimeWarning; match='The confusion matrix is a high dimension matrix'): || ...     print(large_cm) || Predict 0       1       2       3       4       5       6       7       8       9 || Actual || 0       1       0       0       0       0       0       0       0       0       0 || <BLANKLINE> || 1       0       1       0       0       0       0       0       0       0       0 || <BLANKLINE> || 2       0       1       1       0       0       0       0       0       0       0 || <BLANKLINE> || 3       0       0       0       1       0       0       0       1       0       0 || <BLANKLINE> || 4       0       0       0       0       1       0       0       0       0       0 || <BLANKLINE> || 5       0       0       1       0       0       1       0       0       0       0 || <BLANKLINE> || 6       0       0       0       0       0       0       1       0       0       0 || <BLANKLINE> || 7       0       0       0       0       0       0       0       1       0       0 || <BLANKLINE> || 8       0       0       0       0       0       0       0       0       1       0 || <BLANKLINE> || 9       0       0       0       0       0       0       0       0       0       1 || <BLANKLINE> || <BLANKLINE> || <BLANKLINE> || <BLANKLINE> || <BLANKLINE> || Overall Statistics : || <BLANKLINE> || 95% CI                                                            (0.5402;0.99827) || ACC Macro                                                         0.95385 || AUNP                                                              0.87121 || AUNU                                                              0.91212 || Bennett S                                                         0.74359 || CBA                                                               0.75 || Chi-Squared                                                       91.0 || Chi-Squared DF                                                    81 || Conditional Entropy                                               0.46154 || Cramer V                                                          0.88192 || Cross Entropy                                                     3.39275 || F1 Macro                                                          0.81667 || F1 Micro                                                          0.76923 || Gwet AC1                                                          0.7438 || Hamming Loss                                                      0.23077 || Joint Entropy                                                     3.70044 || KL Divergence                                                     0.15385 || Kappa                                                             0.74342 || Kappa 95% CI                                                      (0.48877;0.99807) || Kappa No Prevalence                                               0.53846 || Kappa Standard Error                                              0.12992 || Kappa Unbiased                                                    0.74172 || Lambda A                                                          0.72727 || Lambda B                                                          0.72727 || Mutual Information                                                2.77736 || NIR                                                               0.15385 || Overall ACC                                                       0.76923 || Overall CEN                                                       0.09537 || Overall J                                                         (7.33333;0.73333) || Overall MCC                                                       0.75333 || Overall MCEN                                                      0.10746 || Overall RACC                                                      0.10059 || Overall RACCU                                                     0.10651 || P-Value                                                           0.0 || PPV Macro                                                         0.85 || PPV Micro                                                         0.76923 || Pearson C                                                         0.93541 || Phi-Squared                                                       7.0 || RCI                                                               0.8575 || RR                                                                1.3 || Reference Entropy                                                 3.2389 || Response Entropy                                                  3.2389 || SOA1(Landis & Koch)                                               Substantial || SOA2(Fleiss)                                                      Intermediate to Good || SOA3(Altman)                                                      Good || SOA4(Cicchetti)                                                   Excellent || SOA5(Cramer)                                                      Very Strong || SOA6(Matthews)                                                    Strong || Scott PI                                                          0.74172 || Standard Error                                                    0.11685 || TPR Macro                                                         0.85 || TPR Micro                                                         0.76923 || Zero-one Loss                                                     3 || <BLANKLINE> || Class Statistics : || <BLANKLINE> || Classes                                                           0             1             2             3             4             5             6             7             8             9 || ACC(Accuracy)                                                     1.0           0.92308       0.84615       0.92308       1.0           0.92308       1.0           0.92308       1.0           1.0 || AGF(Adjusted F-score)                                             1.0           0.90468       0.6742        0.71965       1.0           0.71965       1.0           0.90468       1.0           1.0 || AGM(Adjusted geometric mean)                                      1.0           0.93786       0.78186       0.84135       1.0           0.84135       1.0           0.93786       1.0           1.0 || AM(Difference between automatic and manual classification)        0             1             0             -1            0             -1            0             1             0             0 || AUC(Area under the ROC curve)                                     1.0           0.95833       0.70455       0.75          1.0           0.75          1.0           0.95833       1.0           1.0 || AUCI(AUC value interpretation)                                    Excellent     Excellent     Good          Good          Excellent     Good          Excellent     Excellent     Excellent     Excellent || AUPR(Area under the PR curve)                                     1.0           0.75          0.5           0.75          1.0           0.75          1.0           0.75          1.0           1.0 || BCD(Bray-Curtis dissimilarity)                                    0.0           0.03846       0.0           0.03846       0.0           0.03846       0.0           0.03846       0.0           0.0 || BM(Informedness or bookmaker informedness)                        1.0           0.91667       0.40909       0.5           1.0           0.5           1.0           0.91667       1.0           1.0 || CEN(Confusion entropy)                                            0             0.1267        0.23981       0.1267        0             0.1267        0             0.1267        0             0 || DOR(Diagnostic odds ratio)                                        None          None          10.0          None          None          None          None          None          None          None || DP(Discriminant power)                                            None          None          0.55133       None          None          None          None          None          None          None || DPI(Discriminant power interpretation)                            None          None          Poor          None          None          None          None          None          None          None || ERR(Error rate)                                                   0.0           0.07692       0.15385       0.07692       0.0           0.07692       0.0           0.07692       0.0           0.0 || F0.5(F0.5 score)                                                  1.0           0.55556       0.5           0.83333       1.0           0.83333       1.0           0.55556       1.0           1.0 || F1(F1 score - harmonic mean of precision and sensitivity)         1.0           0.66667       0.5           0.66667       1.0           0.66667       1.0           0.66667       1.0           1.0 || F2(F2 score)                                                      1.0           0.83333       0.5           0.55556       1.0           0.55556       1.0           0.83333       1.0           1.0 || FDR(False discovery rate)                                         0.0           0.5           0.5           0.0           0.0           0.0           0.0           0.5           0.0           0.0 || FN(False negative\/miss\/type 2 error)                              0             0             1             1             0             1             0             0             0             0 || FNR(Miss rate or false negative rate)                             0.0           0.0           0.5           0.5           0.0           0.5           0.0           0.0           0.0           0.0 || FOR(False omission rate)                                          0.0           0.0           0.09091       0.08333       0.0           0.08333       0.0           0.0           0.0           0.0 || FP(False positive\/type 1 error\/false alarm)                       0             1             1             0             0             0             0             1             0             0 || FPR(Fall-out or false positive rate)                              0.0           0.08333       0.09091       0.0           0.0           0.0           0.0           0.08333       0.0           0.0 || G(G-measure geometric mean of precision and sensitivity)          1.0           0.70711       0.5           0.70711       1.0           0.70711       1.0           0.70711       1.0           1.0 || GI(Gini index)                                                    1.0           0.91667       0.40909       0.5           1.0           0.5           1.0           0.91667       1.0           1.0 || GM(G-mean geometric mean of specificity and sensitivity)          1.0           0.95743       0.6742        0.70711       1.0           0.70711       1.0           0.95743       1.0           1.0 || IBA(Index of balanced accuracy)                                   1.0           0.99306       0.2686        0.25          1.0           0.25          1.0           0.99306       1.0           1.0 || IS(Information score)                                             3.70044       2.70044       1.70044       2.70044       3.70044       2.70044       3.70044       2.70044       3.70044       3.70044 || J(Jaccard index)                                                  1.0           0.5           0.33333       0.5           1.0           0.5           1.0           0.5           1.0           1.0 || LS(Lift score)                                                    13.0          6.5           3.25          6.5           13.0          6.5           13.0          6.5           13.0          13.0 || MCC(Matthews correlation coefficient)                             1.0           0.677         0.40909       0.677         1.0           0.677         1.0           0.677         1.0           1.0 || MCCI(Matthews correlation coefficient interpretation)             Very Strong   Moderate      Weak          Moderate      Very Strong   Moderate      Very Strong   Moderate      Very Strong   Very Strong || MCEN(Modified confusion entropy)                                  0             0.11991       0.2534        0.11991       0             0.11991       0             0.11991       0             0 || MK(Markedness)                                                    1.0           0.5           0.40909       0.91667       1.0           0.91667       1.0           0.5           1.0           1.0 || N(Condition negative)                                             12            12            11            11            12            11            12            12            12            12 || NLR(Negative likelihood ratio)                                    0.0           0.0           0.55          0.5           0.0           0.5           0.0           0.0           0.0           0.0 || NLRI(Negative likelihood ratio interpretation)                    Good          Good          Negligible    Negligible    Good          Negligible    Good          Good          Good          Good || NPV(Negative predictive value)                                    1.0           1.0           0.90909       0.91667       1.0           0.91667       1.0           1.0           1.0           1.0 || OC(Overlap coefficient)                                           1.0           1.0           0.5           1.0           1.0           1.0           1.0           1.0           1.0           1.0 || OOC(Otsuka-Ochiai coefficient)                                    1.0           0.70711       0.5           0.70711       1.0           0.70711       1.0           0.70711       1.0           1.0 || OP(Optimized precision)                                           1.0           0.8796        0.55583       0.58974       1.0           0.58974       1.0           0.8796        1.0           1.0 || P(Condition positive or support)                                  1             1             2             2             1             2             1             1             1             1 || PLR(Positive likelihood ratio)                                    None          12.0          5.5           None          None          None          None          12.0          None          None || PLRI(Positive likelihood ratio interpretation)                    None          Good          Fair          None          None          None          None          Good          None          None || POP(Population)                                                   13            13            13            13            13            13            13            13            13            13 || PPV(Precision or positive predictive value)                       1.0           0.5           0.5           1.0           1.0           1.0           1.0           0.5           1.0           1.0 || PRE(Prevalence)                                                   0.07692       0.07692       0.15385       0.15385       0.07692       0.15385       0.07692       0.07692       0.07692       0.07692 || Q(Yule Q - coefficient of colligation)                            None          None          0.81818       None          None          None          None          None          None          None || RACC(Random accuracy)                                             0.00592       0.01183       0.02367       0.01183       0.00592       0.01183       0.00592       0.01183       0.00592       0.00592 || RACCU(Random accuracy unbiased)                                   0.00592       0.01331       0.02367       0.01331       0.00592       0.01331       0.00592       0.01331       0.00592       0.00592 || TN(True negative\/correct rejection)                               12            11            10            11            12            11            12            11            12            12 || TNR(Specificity or true negative rate)                            1.0           0.91667       0.90909       1.0           1.0           1.0           1.0           0.91667       1.0           1.0 || TON(Test outcome negative)                                        12            11            11            12            12            12            12            11            12            12 || TOP(Test outcome positive)                                        1             2             2             1             1             1             1             2             1             1 || TP(True positive\/hit)                                             1             1             1             1             1             1             1             1             1             1 || TPR(Sensitivity; recall; hit rate; or true positive rate)         1.0           1.0           0.5           0.5           1.0           0.5           1.0           1.0           1.0           1.0 || Y(Youden index)                                                   1.0           0.91667       0.40909       0.5           1.0           0.5           1.0           0.91667       1.0           1.0 || dInd(Distance index)                                              0.0           0.08333       0.5082        0.5           0.0           0.5           0.0           0.08333       0.0           0.0 || sInd(Similarity index)                                            1.0           0.94107       0.64065       0.64645       1.0           0.64645       1.0           0.94107       1.0           1.0 || <BLANKLINE> || \"\"\"
aahgefbhf,sepandhaghighi/pycm,pycm/pycm_obj.py,5d062a40bd4b09e42df44bb273795bc3ef6db334,c229e6643c2cbaa9dd1208bdf8f1568d9da62e79,TODO: Proper ERROR.
aahgegeef,BrikerMan/Kashgari,kashgari/tasks/classification/models.py,9d1cb5ff1e0c2baeda33a763c0a55b26aea2053c,4e1620c3a1fd997b27c89c9bacf3cef0ee83a8db,TODO: maybe refactor this
aahgegeeh,BrikerMan/Kashgari,kashgari/tasks/classification/models.py,1ecf64dfc45ccba8e6de120f61485a8f44975457,4efa73a7e8620b05d91a9fa074c87c70afc5c75a,TODO: maybe refactor this
aahgegefj,BrikerMan/Kashgari,kashgari/tasks/classification/models.py,4d65690b79ca2901f7a9fadfbbbab41f438999bf,4efa73a7e8620b05d91a9fa074c87c70afc5c75a,TODO: maybe refactor this
aahgegffe,BrikerMan/Kashgari,kashgari/tasks/seq_labeling/blstm_crf_model.py,b09f0771f7c4f61348683d89b7e3a06972fbb066,STILL_EXISTS,TODO: Allow custom loss and optimizer
aahgegfff,BrikerMan/Kashgari,kashgari/tasks/seq_labeling/blstm_model.py,b09f0771f7c4f61348683d89b7e3a06972fbb066,STILL_EXISTS,TODO: Allow custom loss and optimizer
aahgegffg,BrikerMan/Kashgari,kashgari/tasks/seq_labeling/cnn_lstm_model.py,b09f0771f7c4f61348683d89b7e3a06972fbb066,STILL_EXISTS,TODO: Allow custom loss and optimizer
aahgeghgb,BrikerMan/Kashgari,kashgari/embeddings/bare_embedding.py,2a43378d8210f178106bd85eb20cc2c8ce587452,000e656252e61ea391862e87a353326ccff62ba2,Todo: A better name for this class
aahgehagi,BrikerMan/Kashgari,kashgari/tasks/labeling/base_model.py,92946001b2a2590c124186241aaf9228b9291ec1,e0e6b8a524b154a232b001233231c8c5e5b86c5e,Todo: Better way to do this; too
aahgehbbh,BrikerMan/Kashgari,kashgari/embeddings/numeric_feature_embedding.py,8eb724a52a6a4947ed14b37a5ada6b9f55a1b22e,STILL_EXISTS,Todo: A better name for this class
aahgehbcf,BrikerMan/Kashgari,kashgari/embeddings/stacked_embedding.py,8eb724a52a6a4947ed14b37a5ada6b9f55a1b22e,STILL_EXISTS,Todo: A better name for this class
aahgehchj,BrikerMan/Kashgari,kashgari/layers/crf.py,475d92fef93740f7bd9238308e3eef9feffba67b,STILL_EXISTS,TODO: Check crf sequence lengths logic
aahgehgaj,BrikerMan/Kashgari,kashgari/tasks/base_model.py,e0e6b8a524b154a232b001233231c8c5e5b86c5e,cab9f7b657f2588147d2114046d7fbb6c5798aa9,Todo: add fit generator function
aahgehhcg,BrikerMan/Kashgari,kashgari/tasks/classification/models.py,54e1c91180fa7cab0f2cc5e80e1ec00c30160896,STILL_EXISTS,build model structure in sequent way
aahgeifca,BrikerMan/Kashgari,docs/conf.py,df7dd8a08ba69d0839cc6f7a9d8e322561394fd6,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aahgejbdi,BrikerMan/Kashgari,kashgari/tasks/classification/bi_lstm_model.py,000e656252e61ea391862e87a353326ccff62ba2,STILL_EXISTS,build model structure in sequent way
aahgejbfa,BrikerMan/Kashgari,kashgari/tasks/classification/cnn_model.py,000e656252e61ea391862e87a353326ccff62ba2,STILL_EXISTS,build model structure in sequent way
aahgejefg,BrikerMan/Kashgari,kashgari/metrics/sequence_labeling.py,c0f300d8927e92d1cf3db1e8926fa1d7b462f01b,STILL_EXISTS,\"\"\" || Seqeval Code from: https:\/\/github.com\/chakki-works\/seqeval ||  || Added directly to the project to solve install issue. ||  || Metrics to assess performance on sequence labeling task given prediction || Functions named as ``*_score`` return a scalar value to maximize: the higher || the better || \"\"\"
aahgejejf,BrikerMan/Kashgari,kashgari/tasks/abs_task_model.py,c0f300d8927e92d1cf3db1e8926fa1d7b462f01b,304203e5806d5513936272a8cb27e0e4216833c9,Build model architect; **all models must implement this function.**
aahgejfja,BrikerMan/Kashgari,kashgari/tasks/classification/cnn_gru_model.py,c0f300d8927e92d1cf3db1e8926fa1d7b462f01b,STILL_EXISTS,build model structure in sequent way
aahgejfjg,BrikerMan/Kashgari,kashgari/tasks/classification/cnn_lstm_model.py,c0f300d8927e92d1cf3db1e8926fa1d7b462f01b,STILL_EXISTS,build model structure in sequent way
aahgejghe,BrikerMan/Kashgari,tests/test_labeling/test_bi_lstm_model.py,c0f300d8927e92d1cf3db1e8926fa1d7b462f01b,304203e5806d5513936272a8cb27e0e4216833c9,TODO: fix load model
aahgfadif,BrikerMan/Kashgari,examples/k_fold_evaluation.py,026f6f1729cdacf0cb641884ce16639e9f83b374,STILL_EXISTS,fix random seed for reproducibility
aahgfadij,BrikerMan/Kashgari,kashgari/tasks/seq2seq/model.py,1540e65ffd785f3e044b20d2912437ee5b9955ab,STILL_EXISTS,------ Fix Start -------
aahgfafgf,BrikerMan/Kashgari,kashgari/processors/sequence_processor.py,5e81950f580628e5284343ad561ca2c189e5fd79,STILL_EXISTS,An ugly patch for tf-serving use case.
aahgfahei,ibayer/fastFM,doc/conf.py,dc16fc36f8ea5091bb27f7bcb2c5e47225f70e01,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aahgfbbea,Cysu/open-reid,reid/features/extract.py,4ef0c32bf724c52a08bcbd6c9e5fa14a1aab4445,STILL_EXISTS,TODO: Check register_forward_hook
aahgfbbeb,Cysu/open-reid,reid/features/extract.py,4ef0c32bf724c52a08bcbd6c9e5fa14a1aab4445,STILL_EXISTS,TODO: Support extract features from multiple layers
aahgfbdcd,Cysu/open-reid,docs/conf.py,6287a7d83d38dd411f98b070fa122199b8202aff,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aahgfbeec,Cysu/open-reid,examples/resnet.py,f3108e6a756708da60a5b0fd65d53e5209f7826c,STILL_EXISTS,Hack for making the classifier the last feature embedding layer
aahgfbgea,zhanghang1989/PyTorch-Encoding,encoding/__init__.py,76eeba5ddde3d1cb4fc40f1f6b8491b03d0535bd,STILL_EXISTS,TODO support cpu backend
aahgfbgeb,zhanghang1989/PyTorch-Encoding,encoding/__init__.py,76eeba5ddde3d1cb4fc40f1f6b8491b03d0535bd,a3c3d9426e184a076ea5c95b71e19dc6fb812f05,TODO FIXME this is test only
aahgfbgge,zhanghang1989/PyTorch-Encoding,test/test.py,76eeba5ddde3d1cb4fc40f1f6b8491b03d0535bd,STILL_EXISTS,TODO cpu test
aahgfbgjg,zhanghang1989/PyTorch-Encoding,experiments/main.py,1633f31066b98cb5e8c8c3a13436b6bdf32afea3,STILL_EXISTS,TODO make weight_decay oen of args
aahgfbhaa,zhanghang1989/PyTorch-Encoding,experiments/main.py,1633f31066b98cb5e8c8c3a13436b6bdf32afea3,STILL_EXISTS,TODO add plot curve
aahgfbhab,zhanghang1989/PyTorch-Encoding,experiments/main.py,1633f31066b98cb5e8c8c3a13436b6bdf32afea3,STILL_EXISTS,FIXME this is a bug somewhere not in the code
aahgfbhbe,zhanghang1989/PyTorch-Encoding,experiments/model/encodenet.py,1633f31066b98cb5e8c8c3a13436b6bdf32afea3,STILL_EXISTS,TODO norm layer; instance norm?
aahgfbibi,zhanghang1989/PyTorch-Encoding,docs/source/conf.py,fa9027760055e62cec8b2d528eb305379cff5997,528210a3ca63ab11fd4a0ea685933394265973c1,TODO: change to [:2] at v1.0
aahgfbica,zhanghang1989/PyTorch-Encoding,docs/source/conf.py,fa9027760055e62cec8b2d528eb305379cff5997,STILL_EXISTS,TODO: verify this works as expected
aahgfbida,zhanghang1989/PyTorch-Encoding,docs/source/conf.py,fa9027760055e62cec8b2d528eb305379cff5997,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aahgfbihb,zhanghang1989/PyTorch-Encoding,docs/source/conf.py,fa9027760055e62cec8b2d528eb305379cff5997,e11e8fa6b64d68aeeb23b4f2f074e778ba72ea13,`kw` catches `env=None` needed for newer sphinx while maintaining
aahgfcahj,zhanghang1989/PyTorch-Encoding,encoding/parallel.py,8f8fbb9f6543a4ccaad53923d33647e7138d95ea,25985c31fbad7eaff58dbd6575414fad06cb4b42,TODO; figure out why nccl all_reduce doesn't work for gradcheck
aahgfcbff,zhanghang1989/PyTorch-Encoding,encoding/parallel.py,d539ddfafb8e62fafa536a35fe21691047402445,25985c31fbad7eaff58dbd6575414fad06cb4b42,TODO check faster?
aahgfcdbd,zhanghang1989/PyTorch-Encoding,encoding/nn/customize.py,308447157af829f9d842bd0a2a5c035e0b0ecd29,STILL_EXISTS,\"\"\" || class SegmentationLosses(Module): ||     def __init__(self; aux; aux_weight=0.2; weight=None; size_average=True; ignore_index=-1): ||         super(SegmentationLosses; self).__init__() ||         self.aux = aux ||         self.aux_weight = aux_weight ||         # Somehow the size averge is not handled correctly on multi-gpu; so we average by ourself. ||         self.nll_loss = NLLLoss(weight; ignore_index=ignore_index; reduce=True) ||  ||     def _forward_each(self; inputs; targets): ||         return self.nll_loss(F.log_softmax(inputs; dim=1); targets) ||  ||     def forward(self; *inputs): ||         if not self.aux: ||             return self._forward_each(*inputs) ||         pred1; pred2; target = tuple(inputs) ||         loss1 = self._forward_each(pred1; target) ||         loss2 = self._forward_each(pred2; target) ||         return loss1 + self.aux_weight * loss2 || \"\"\"
aahgfcdca,zhanghang1989/PyTorch-Encoding,encoding/utils.py,308447157af829f9d842bd0a2a5c035e0b0ecd29,STILL_EXISTS,TODO currently torch.eq is not working as expected; change back when it's fixed
aahgfdcfh,zhanghang1989/PyTorch-Encoding,scripts/prepare_imagenet.py,ce461dae8d088253dcd9818d2999d4049bce3493,STILL_EXISTS,move images to proper subfolders
aahgfeabj,ZhaoJ9014/face.evoLVe.PyTorch,align/matlab_cp2tform.py,114ba18fd46f11d220698c55c0f035dbc8abca2f,STILL_EXISTS,manually reflect the tform to undo the reflection done on xyR
aahgfeaca,ZhaoJ9014/face.evoLVe.PyTorch,align/matlab_cp2tform.py,114ba18fd46f11d220698c55c0f035dbc8abca2f,STILL_EXISTS,Figure out if trans1 or trans2 is better
aahgffdcd,ZhaoJ9014/face.evoLVe.PyTorch,backup/train.py,5feb04fee5e4cacad954ad537a2ef92eebfc84ed,STILL_EXISTS,adjust LR for each training stage; you can also choose to adjust LR manually (with slight modification) once plaueau observed
aahgffgeh,alfredfrancis/ai-chatbot-framework,ikyCommons/EFZP.py,584b2f18461262e43c3f34db4c2e563db131ea01,STILL_EXISTS,todo: complete this out (I bit off a bit more than I could chew with this function. Will probably take a bunch of basian stuff
aahgffghj,alfredfrancis/ai-chatbot-framework,ikyWebServer/api.py,b86478b6aee75b703bf7833fadeea29d7de90414,STILL_EXISTS,logging ends
aahgffgih,alfredfrancis/ai-chatbot-framework,ikyWebServer/yom.py,0b0f366d83789eb35c78ee8d5cb5e0c307f99ad9,STILL_EXISTS,logging ends
aahgffgjf,alfredfrancis/ai-chatbot-framework,webServer/api.py,8281513345c58f5d72cb3d81d54be45b478bc754,STILL_EXISTS,logging ends
aahgffhdf,alfredfrancis/ai-chatbot-framework,core/sentenceClassifer.py,d68beca6f553622b92f2709cf984023dbf284e98,87d5b03fdfca1d3d407d8e16afefdb929f245b31,Create two columns with most negative and most positive features.
aahgffjcf,PetrochukM/PyTorch-NLP,lib/checkpoint.py,0cdbcfbe4a5508e902d60152001b29fb0f331fe4,75a2c45d5adf897bd1754faa5b738ca73c1a8601,TODO: This should be recusive looking for any module with flatten_parameters
aahgffjch,PetrochukM/PyTorch-NLP,lib/checkpoint.py,0cdbcfbe4a5508e902d60152001b29fb0f331fe4,75a2c45d5adf897bd1754faa5b738ca73c1a8601,TODO: Checkpoint save should be arbitrary and checkpoint predict can be broken up into utility functions?
aahgffjci,PetrochukM/PyTorch-NLP,lib/checkpoint.py,0cdbcfbe4a5508e902d60152001b29fb0f331fe4,75a2c45d5adf897bd1754faa5b738ca73c1a8601,TODO: Consider saving as well a predict lambda
aahgffjda,PetrochukM/PyTorch-NLP,lib/configurable.py,0cdbcfbe4a5508e902d60152001b29fb0f331fe4,STILL_EXISTS,\"\"\" || Manages a global namespaced configuration. ||  || TODO: Look into implementing configurable without decoraters: ||   - Trace all function calls and intercept the call ||   - Apply a decorator recursively too all modules in os.cwd() ||     import pkgutil ||     import os ||  ||     for loader; module_name; is_pkg in pkgutil.walk_packages([os.getcwd()]): ||         print(module_name) ||  ||     http:\/\/code.activestate.com\/recipes\/577742-apply-decorators-to-all-functions-in-a-module\/ || \"\"\"
aahgffjdc,PetrochukM/PyTorch-NLP,lib/configurable.py,0cdbcfbe4a5508e902d60152001b29fb0f331fe4,STILL_EXISTS,DO NOT IMPORT. Use @configurable instead.
aahgffjdf,PetrochukM/PyTorch-NLP,lib/configurable.py,0cdbcfbe4a5508e902d60152001b29fb0f331fe4,STILL_EXISTS,Recursive function walked up the chain and never found a @configurable
aahgffjdi,PetrochukM/PyTorch-NLP,lib/configurable.py,0cdbcfbe4a5508e902d60152001b29fb0f331fe4,STILL_EXISTS,TODO: Find a better check
aahgffjef,PetrochukM/PyTorch-NLP,lib/configurable.py,0cdbcfbe4a5508e902d60152001b29fb0f331fe4,STILL_EXISTS,TODO: Does not print all parameters; FIX
aahgffjfb,PetrochukM/PyTorch-NLP,lib/datasets/dataset.py,0cdbcfbe4a5508e902d60152001b29fb0f331fe4,STILL_EXISTS,TODO: Just use Pandas#DataFrame
aahgffjff,PetrochukM/PyTorch-NLP,lib/datasets/simple_qa.py,0cdbcfbe4a5508e902d60152001b29fb0f331fe4,a5765e8d31b656320502952157841eb0f21ca2c4,TODO: Add download simple_qa
aahgffjfg,PetrochukM/PyTorch-NLP,lib/datasets/simple_qa.py,0cdbcfbe4a5508e902d60152001b29fb0f331fe4,a5765e8d31b656320502952157841eb0f21ca2c4,TODO: Investigate data. Look into preprocessing.
aahgffjfh,PetrochukM/PyTorch-NLP,lib/hyperparameter_optimization.py,0cdbcfbe4a5508e902d60152001b29fb0f331fe4,STILL_EXISTS,\"\"\" || We implement additional hyperparameter optimization methods not present in || https:\/\/scikit-optimize.github.io\/. ||  || Gist: https:\/\/gist.github.com\/Deepblue129\/2c5fae9daf0529ed589018c6353c9f7b || \"\"\"
aahgffjgd,PetrochukM/PyTorch-NLP,lib/hyperparameter_optimization.py,0cdbcfbe4a5508e902d60152001b29fb0f331fe4,STILL_EXISTS,TODO: Compute the tqdm total
aahgffjhf,PetrochukM/PyTorch-NLP,lib/hyperparameter_optimization.py,0cdbcfbe4a5508e902d60152001b29fb0f331fe4,STILL_EXISTS,TODO: Multiply by the number of dimensions so it scales the number of models
aahgffjje,PetrochukM/PyTorch-NLP,lib/metrics/__init__.py,0cdbcfbe4a5508e902d60152001b29fb0f331fe4,STILL_EXISTS,TODO: Use `sklearn.metrics` for a `confusion_matrix` implemented with ignore_index
aahgffjjf,PetrochukM/PyTorch-NLP,lib/metrics/__init__.py,0cdbcfbe4a5508e902d60152001b29fb0f331fe4,STILL_EXISTS,TODO: Use `sklearn.metrics` for a `recall` implemented with ignore_index
aahgffjjg,PetrochukM/PyTorch-NLP,lib/metrics/__init__.py,0cdbcfbe4a5508e902d60152001b29fb0f331fe4,STILL_EXISTS,TODO: Use `sklearn.metrics` for a `precision` implemented with ignore_index
aahgffjjh,PetrochukM/PyTorch-NLP,lib/metrics/__init__.py,0cdbcfbe4a5508e902d60152001b29fb0f331fe4,STILL_EXISTS,TODO: Use `sklearn.metrics` for a `f1` implemented with ignore_index
aahgffjji,PetrochukM/PyTorch-NLP,lib/metrics/__init__.py,0cdbcfbe4a5508e902d60152001b29fb0f331fe4,a5765e8d31b656320502952157841eb0f21ca2c4,TODO: Implement Accuracy Top N for classification
aahgffjjj,PetrochukM/PyTorch-NLP,lib/metrics/__init__.py,0cdbcfbe4a5508e902d60152001b29fb0f331fe4,STILL_EXISTS,TODO: Implement perplexity
aahgfgabj,PetrochukM/PyTorch-NLP,lib/metrics/random_sample.py,0cdbcfbe4a5508e902d60152001b29fb0f331fe4,STILL_EXISTS,TODO: Why require decoding? The original source can just be sent? Maybe overall the thing is incorreect.
aahgfgaca,PetrochukM/PyTorch-NLP,lib/nn/attention.py,0cdbcfbe4a5508e902d60152001b29fb0f331fe4,STILL_EXISTS,TODO: Include mask on padding_indx?
aahgfgadb,PetrochukM/PyTorch-NLP,lib/nn/seq_decoder.py,0cdbcfbe4a5508e902d60152001b29fb0f331fe4,STILL_EXISTS,TODO: https:\/\/arxiv.org\/pdf\/1707.06799v1.pdf
aahgfgadh,PetrochukM/PyTorch-NLP,lib/nn/seq_decoder.py,0cdbcfbe4a5508e902d60152001b29fb0f331fe4,STILL_EXISTS,TODO: Fix by having a is_cuda parameter
aahgfgaed,PetrochukM/PyTorch-NLP,lib/nn/seq_decoder.py,0cdbcfbe4a5508e902d60152001b29fb0f331fe4,STILL_EXISTS,TODO: Add a vocabulary filter depending on the current output
aahgfgafi,PetrochukM/PyTorch-NLP,lib/nn/seq_decoder.py,0cdbcfbe4a5508e902d60152001b29fb0f331fe4,STILL_EXISTS,TODO: Given the beam_indices; we need to wrap back to what the beam_output is
aahgfgbdc,PetrochukM/PyTorch-NLP,lib/text_encoders/static_tokenizer_encoder.py,0cdbcfbe4a5508e902d60152001b29fb0f331fe4,bb8ad5c0af5fb808f2810f9649a8b0797ad9ecb8,TODO: Think about should the encoder during decoding include <eos> or not?
aahgfgbdd,PetrochukM/PyTorch-NLP,lib/text_encoders/subword_encoder.py,0cdbcfbe4a5508e902d60152001b29fb0f331fe4,bb8ad5c0af5fb808f2810f9649a8b0797ad9ecb8,TODO: Add reserved tokens to SubwordTextTokenizer like Google did to ensure correct tokenization
aahgfgbfi,PetrochukM/PyTorch-NLP,lib/text_encoders/subword_text_tokenizer.py,0cdbcfbe4a5508e902d60152001b29fb0f331fe4,STILL_EXISTS,NOTE: This algorithm is greedy; it won't necessarily produce the \"best\"
aahgfgbja,PetrochukM/PyTorch-NLP,lib/utils.py,0cdbcfbe4a5508e902d60152001b29fb0f331fe4,83f92e8fa41c675bb0d723422ee978df16357831,TODO: If the folder is empty then delete it after the execution finishes
aahgfgccb,PetrochukM/PyTorch-NLP,tests/lib/utils.py,0cdbcfbe4a5508e902d60152001b29fb0f331fe4,STILL_EXISTS,TODO: Consider having a random loss function
aahgfgcff,PetrochukM/PyTorch-NLP,tests/unit_test/nn/test_seq_decoder.py,0cdbcfbe4a5508e902d60152001b29fb0f331fe4,STILL_EXISTS,TODO: Test if `max_length=none` explicitly. This is challenging because the SeqDecoder only
aahgfgcif,PetrochukM/PyTorch-NLP,tests/unit_test/text_encoders/test_subword_text_tokenizer.py,0cdbcfbe4a5508e902d60152001b29fb0f331fe4,STILL_EXISTS,TODO: Add corpus to random args
aahgfgdch,PetrochukM/PyTorch-NLP,tests/optim/common.py,fd46926c5d6b8eeee3e19bf704f55a1f92eae7af,STILL_EXISTS,TODO: implement abs on CharTensor
aahgfgdci,PetrochukM/PyTorch-NLP,tests/optim/common.py,fd46926c5d6b8eeee3e19bf704f55a1f92eae7af,STILL_EXISTS,TODO: Support context manager interface
aahgfgdda,PetrochukM/PyTorch-NLP,tests/optim/common.py,fd46926c5d6b8eeee3e19bf704f55a1f92eae7af,STILL_EXISTS,If you need it; manually apply your callable in a lambda instead.
aahgfgddh,PetrochukM/PyTorch-NLP,tests/optim/common.py,fd46926c5d6b8eeee3e19bf704f55a1f92eae7af,STILL_EXISTS,a hack for JIT tests
aahgfgdge,PetrochukM/PyTorch-NLP,tests/text_encoders/test_subword_tokenizer.py,bb8ad5c0af5fb808f2810f9649a8b0797ad9ecb8,dd8dceecc0fe728a7818bb8f795b498546069542,TODO: Add corpus to random args
aahgfgdjj,PetrochukM/PyTorch-NLP,lib/optimizer.py,83f92e8fa41c675bb0d723422ee978df16357831,STILL_EXISTS,TODO: Add increasing batch_size scheduler
aahgfgeac,PetrochukM/PyTorch-NLP,tests/datasets/test_simple_qa.py,83f92e8fa41c675bb0d723422ee978df16357831,STILL_EXISTS,TODO: Consider mocking the simple_qa dataset for faster tests
aahgfgeai,PetrochukM/PyTorch-NLP,tests/test_pretrained_embeddings.py,83f92e8fa41c675bb0d723422ee978df16357831,6a6e391833b49501620e96011a90558b7c94f554,TODO: Consider making the tests faster by mocking the download
aahgfgfbe,PetrochukM/PyTorch-NLP,docs/conf.py,003ff5862919660d2e0d77abfdb2d7375d1c3ade,STILL_EXISTS,-- Options for todo extension ----------------------------------------------
aahgfgfbf,PetrochukM/PyTorch-NLP,docs/conf.py,003ff5862919660d2e0d77abfdb2d7375d1c3ade,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aahgfgfbj,PetrochukM/PyTorch-NLP,tests/test_pretrained_embeddings.py,2105f5d6624c27a67468e071888dfd13c9b53d92,6a6e391833b49501620e96011a90558b7c94f554,TODO: MOCK urlretrieve and just ping the server...
aahgfgfca,PetrochukM/PyTorch-NLP,tests/test_pretrained_embeddings.py,2105f5d6624c27a67468e071888dfd13c9b53d92,6a6e391833b49501620e96011a90558b7c94f554,TODO: Write or have a file in dest to be used
aahgfgfcb,PetrochukM/PyTorch-NLP,tests/test_utils.py,2105f5d6624c27a67468e071888dfd13c9b53d92,6a6e391833b49501620e96011a90558b7c94f554,TODO: Remove experiment directory created...
aahgfgfei,PetrochukM/PyTorch-NLP,torchnlp/checkpoint.py,6a6e391833b49501620e96011a90558b7c94f554,STILL_EXISTS,TODO: Remove Checkpoint as it is not related to NLP
aahgfgffa,PetrochukM/PyTorch-NLP,torchnlp/optimizer.py,6a6e391833b49501620e96011a90558b7c94f554,STILL_EXISTS,TODO: Remove Optimizer as it is not NLP do a PytorchUtils library
aahgfgffb,PetrochukM/PyTorch-NLP,torchnlp/utils.py,6a6e391833b49501620e96011a90558b7c94f554,af96068f22581cf424d1f8737a120be8fce4ffd2,TODO: Remove configurable and stuff as it is not related to NLP
aahgfgfhf,PetrochukM/PyTorch-NLP,torchnlp/nn/attention.py,5c9e7b9fb769bea67184d6c1004a3c41e301ecb1,STILL_EXISTS,TODO: Credit IBM for this snipit
aahgfgfhg,PetrochukM/PyTorch-NLP,torchnlp/pretrained_embeddings.py,5c9e7b9fb769bea67184d6c1004a3c41e301ecb1,STILL_EXISTS,TODO: Credit torchtext for this snippet
aahgfggij,PetrochukM/PyTorch-NLP,docs/conf.py,94922b0d4c7eb3e76182d5ac135717bfb1b91e92,STILL_EXISTS,`kw` catches `env=None` needed for newer sphinx while maintaining
aahgfghaa,PetrochukM/PyTorch-NLP,torchnlp/text_encoders/identity_encoder.py,44f80b5872b1bb9679d15b8230c1731fd26ac527,8fbbd1517aaf65b72fb561cc2671fa87f4af2c9e,TODO: Already tokenized text should work...
aahgfghab,PetrochukM/PyTorch-NLP,tests/datasets/test_simple_qa.py,7ab7cf7ecb4b3cd143474727344f409dcb6e9954,62ba88d154c209a0aa56588fa1f4ed910a40af14,TODO: Fix failure case if internet does not work
aahgfghac,PetrochukM/PyTorch-NLP,tests/metrics/test_bleu.py,7ab7cf7ecb4b3cd143474727344f409dcb6e9954,STILL_EXISTS,TODO: Fix failure case if internet does not work
aahgfghae,PetrochukM/PyTorch-NLP,torchnlp/utils.py,0b4d80255e60913d56119dede9890256a0b1622c,0dc595c1e5430f6ba5aead758b41f300d287ea41,TODO: Add back padding
aahgfgjaa,PetrochukM/PyTorch-NLP,tests/datasets/test_imdb.py,0b2239c3af38fa1d1ca76f99b4d407e9a50b6ab0,62ba88d154c209a0aa56588fa1f4ed910a40af14,TODO: Fix failure case if internet does not work
aahgfgjbh,PetrochukM/PyTorch-NLP,tests/datasets/utils.py,62ba88d154c209a0aa56588fa1f4ed910a40af14,STILL_EXISTS,TODO: Fix failure case if internet does not work
aahgfgjca,PetrochukM/PyTorch-NLP,torchnlp/datasets/wikitext_2.py,62ba88d154c209a0aa56588fa1f4ed910a40af14,d8e6cb103e465a1cad6c9d96d9979b05e45bc848,TODO: Add test
aahgfgjcb,PetrochukM/PyTorch-NLP,torchnlp/metrics/__init__.py,62ba88d154c209a0aa56588fa1f4ed910a40af14,STILL_EXISTS,TODO: Implement rogue metric
aahgfgjcd,PetrochukM/PyTorch-NLP,torchnlp/samplers/bptt_batch_sampler.py,62ba88d154c209a0aa56588fa1f4ed910a40af14,STILL_EXISTS,Our chunks are similar to the columns in this PyTorch example:
aahgfhbig,PetrochukM/PyTorch-NLP,tests/datasets/utils.py,349389efff2ed0305752a8a86a51d38d2c53ec11,STILL_EXISTS,TODO: Fix failure case if internet does not work
aahgfhccj,PetrochukM/PyTorch-NLP,examples/awd-lstm-lm/model.py,c6c351965bf22965765f837c4b83bfdb02cb4743,STILL_EXISTS,\"Using the Output Embedding to Improve Language Models\" (Press & Wolf 2016)
aahgfhchi,PetrochukM/PyTorch-NLP,examples/end_to_end/main.py,c6c351965bf22965765f837c4b83bfdb02cb4743,STILL_EXISTS,TODO: Use SRU \/ CNN Encoder \/ Embeddings etc...
aahgfhcje,PetrochukM/PyTorch-NLP,examples/end_to_end/main.py,c6c351965bf22965765f837c4b83bfdb02cb4743,STILL_EXISTS,TODO: Return the best loss if hyperparameter tunning.
aahgfhcjf,PetrochukM/PyTorch-NLP,examples/end_to_end/main.py,c6c351965bf22965765f837c4b83bfdb02cb4743,STILL_EXISTS,TODO: Figure out a good abstraction for evaluation.
aahgfheea,PetrochukM/PyTorch-NLP,torchnlp/samplers/bucket_batch_sampler.py,ba97bf162e9f8a46e7c2c3bb3f788406072fc40c,dd2eaf6c174fdd757514bc06d1063e5f9c6bb4a0,Move them to the front
aahgfhfbc,PetrochukM/PyTorch-NLP,tests/word_to_vector/test_char_n_gram.py,ae90ffb88638a43ab9d23611ab7098e982cf5cb4,STILL_EXISTS,TODO: Skip for now due to SSL failure.
aahgfhfdb,PetrochukM/PyTorch-NLP,third_party/lazy_loader.py,4e84780a8a741d6a90f2752edc4502ab2cf89ecb,STILL_EXISTS,LazyLoader; lookups are efficient (__getattr__ is only called on lookups
aahgfhfdh,PetrochukM/PyTorch-NLP,tests/test_random.py,887f3d2bc4a57e4c33935ab139219f745da73d8b,STILL_EXISTS,TODO: Test `torch.cuda` random as well.
aahgfhgfb,reiinakano/scikit-plot,docs/conf.py,72c4664e6d44d8c9780e76f78652e58ee0764edf,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aahgfhicb,yzhao062/pyod,pyador/pyador.py,464ae68501cb3de6dbd500165a6440d379a835ef,STILL_EXISTS,TODO: design the structure of the output
aahgfhicj,yzhao062/pyod,pyador/util/tests/test_data_prep.py,499b0cc828958a341e4ca7473b566892c03821ae,STILL_EXISTS,TODO: supplement more testcases for util functions
aahgfhjje,yzhao062/pyod,models/hbos.py,4638cbc8d106450f1e821750b4af0ea88408a6ee,STILL_EXISTS,TODO: move to testing code
aahgfhjjh,yzhao062/pyod,models/knn.py,4638cbc8d106450f1e821750b4af0ea88408a6ee,STILL_EXISTS,TODO: move to testing code
aahgfiaaa,yzhao062/pyod,models/lof.py,4638cbc8d106450f1e821750b4af0ea88408a6ee,STILL_EXISTS,TODO: move to testing code
aahgfiaad,yzhao062/pyod,models/ocsvm.py,4638cbc8d106450f1e821750b4af0ea88408a6ee,STILL_EXISTS,TODO: move to testing code
aahgfiajd,yzhao062/pyod,models/abod.py,e1e06512a9d6496e3bf8097d8a78acd5afa9f425,STILL_EXISTS,# TODO: move to testing code
aahgfibea,yzhao062/pyod,pyod/models/abod.py,d1a7e4754f983c1dda78c14495c4604afaa6af0a,STILL_EXISTS,# TODO: move to testing code
aahgfibig,yzhao062/pyod,pyod/models/hbos.py,d1a7e4754f983c1dda78c14495c4604afaa6af0a,216f40afd7aa60c886265791e03d3341557aed0d,TODO: move to testing code
aahgficfe,yzhao062/pyod,pyod/models/knn.py,d1a7e4754f983c1dda78c14495c4604afaa6af0a,216f40afd7aa60c886265791e03d3341557aed0d,TODO: move to testing code
aahgficha,yzhao062/pyod,pyod/models/lof.py,d1a7e4754f983c1dda78c14495c4604afaa6af0a,98aeec9fcc311086cb6953dded42b399af97f189,TODO: move to testing code
aahgfichd,yzhao062/pyod,pyod/models/ocsvm.py,d1a7e4754f983c1dda78c14495c4604afaa6af0a,98aeec9fcc311086cb6953dded42b399af97f189,TODO: move to testing code
aahgficjj,yzhao062/pyod,pyod/models/temp_abod.py,d1a7e4754f983c1dda78c14495c4604afaa6af0a,STILL_EXISTS,# TODO: move to testing code
aahgfidfg,yzhao062/pyod,pyod/pyador/pyador.py,d1a7e4754f983c1dda78c14495c4604afaa6af0a,STILL_EXISTS,TODO: design the structure of the output
aahgfidhb,yzhao062/pyod,pyod/pyador/util/tests/test_data_prep.py,d1a7e4754f983c1dda78c14495c4604afaa6af0a,STILL_EXISTS,TODO: supplement more testcases for util functions
aahgfieaf,yzhao062/pyod,pyod/models/combination.py,d8148defe369bb06141e6b338af1028c585d1ce5,e899841348b504dcf46b4b40e28c49e146331cf6,TODO: replace random sampling methods
aahgfieag,yzhao062/pyod,pyod/models/combination.py,d8148defe369bb06141e6b338af1028c585d1ce5,e899841348b504dcf46b4b40e28c49e146331cf6,TODO: check number of estimatoes and scores match
aahgfiedh,yzhao062/pyod,pyod/models/glosh.py,f0a8bb4bd93b87945f9e48dad7363f34ef86e27e,STILL_EXISTS,TODO: fix broken model here
aahgfifbg,yzhao062/pyod,pyod/utils/stat_models.py,1511780d501e2a96e923c6c01588d3abc43f47f7,STILL_EXISTS,TODO: fix output differences
aahgfigfa,yzhao062/pyod,pyod/models/abod.py,798882c255701943d530d7024d0da2db22e33cc0,STILL_EXISTS,TODO: verify if n_train is needed
aahgfigja,yzhao062/pyod,pyod/test/test_base.py,798882c255701943d530d7024d0da2db22e33cc0,ad5beccf2b74dc39314843e8a970a891cbd52267,TODO: add more testcases
aahgfigjb,yzhao062/pyod,pyod/test/test_base.py,798882c255701943d530d7024d0da2db22e33cc0,STILL_EXISTS,TODO: create uniform testcases
aahgfihaf,yzhao062/pyod,pyod/test/test_stat_models.py,a46dbd0299fa8bd39f4029b846b4aabfd4eb7d98,STILL_EXISTS,TODO: if unweight version changes; wp[0] format should be changed
aahgfihag,yzhao062/pyod,pyod/models/base.py,c2cc41d7cb37722ff285679176e8fae5987858c2,7a232e4524a97b507cb10e98ea36399f37a8b9db,TODO: clean up these initialization
aahgfihcd,yzhao062/pyod,pyod/models/iforest.py,f54a8a53ea7c4c14718f3bcf2337252157203775,22088f42bfc30e0824e42c11afd8ca13fd2fa0d3,TODO: fill in the documentation
aahgfihde,yzhao062/pyod,pyod/test/test_stat_models.py,c51d52512b249b636f7770e00235b1addcfd5a9e,291aba835d44a86b6f51ba5374af50a9ed2304b9,TODO: verify the values
aahgfihgj,yzhao062/pyod,pyod/models/base.py,ad5beccf2b74dc39314843e8a970a891cbd52267,3f49f5a708ec5c6d34627ab426bccd6de520cf45,XXX: not handling dictionaries
aahgfihjd,yzhao062/pyod,pyod/models/base.py,ad5beccf2b74dc39314843e8a970a891cbd52267,STILL_EXISTS,XXX: should we rather test if instance of estimator?
aahgfiibc,yzhao062/pyod,pyod/test/test_base.py,ad5beccf2b74dc39314843e8a970a891cbd52267,ad5beccf2b74dc39314843e8a970a891cbd52267,TODO: add more testcases
aahgfiife,yzhao062/pyod,pyod/models/abod.py,106741be58f4ee2fb8523cb39b53accde5b2d080,e1d871be336d69ee597cdf9ca7e23af11b278dad,TODO: implement a faster solution
aahgfjbcg,yzhao062/pyod,pyod/test/test_utility.py,3e129419792de34a0ac2a518056ba22dd8a3a1c5,57ab97245ccd652ca428a4c1ddeb93b65b61f808,TODO: remove temporarily for pleasing Travis integration
aahgfjbfa,yzhao062/pyod,pyod/models/feat_bagging.py,fec5e567f52fec1ec897431c3108c290746dcd10,STILL_EXISTS,TODO add a check for min_features; e.g. d<=3 & max_features as well
aahgfjbfc,yzhao062/pyod,examples/feat_bagging_example.py,e2487c560433686e337be99e6389a7f728d2fdce,87ce5a9cb09ea8ff0d7a90fdab2eeab0115d7c09,TODO: place holder only
aahgfjbfg,yzhao062/pyod,pyod/test/test_feat_bagging.py,e2487c560433686e337be99e6389a7f728d2fdce,STILL_EXISTS,TODO: finish the tests once the main model is ready
aahgfjddh,yzhao062/pyod,pyod/models/pca.py,18688e3707c3ca49f181537dbc1cbad1233510d5,STILL_EXISTS,TODO: placeholder; do not use
aahgfjfhj,yzhao062/pyod,pyod/test/test_pca.py,e156ae724ccd4f6cd5060a658684aeb3c3c203bc,71723b53d3c29d46d5b317e79ea074320cb1021e,TODO: turn off performance check before a better data generation
aahgfjgcd,yzhao062/pyod,pyod/models/lof.py,fa7f9fc948717c0d30ee95a247ae79ea5398f9d5,STILL_EXISTS,\"\"\"Local Outlier Factor (LOF). Implemented on scikit-learn library. || \"\"\"
aahgfjgci,yzhao062/pyod,pyod/models/feature_bagging.py,230d9a40712456048b5c8f685dd0a38f330d438f,STILL_EXISTS,TODO: add a check for estimator_param
aahgfjhdb,yzhao062/pyod,pyod/test/test_base.py,71723b53d3c29d46d5b317e79ea074320cb1021e,STILL_EXISTS,TODO: create uniform testcases
aahgfjjgf,yzhao062/pyod,pyod/models/feature_bagging.py,851da67b32b52b4c7ebb8d60e906ad8466600ab2,STILL_EXISTS,TODO: should support parallelization at the model level
aahggabcb,yzhao062/pyod,pyod/test/test_cblof.py,1ba7e6ffc2bd8a666fd70b3014f294d2a05d8331,c9570e9949e9889f3d20b12b0a4bd29085e6ab96,TODO: sklearn examples are too small to form valid
aahggaceg,yzhao062/pyod,pyod/models/feature_bagging.py,4c2df5a697f7769a5a3173968ed7846221daa5a0,212186e180e74f41482b7ca91f4d49d55cb5fbfa,TODO: turn off estimator check due to output warnings.
aahggadde,yzhao062/pyod,pyod/models/auto_encoder.py,074db681aa4160fc70080778b382449981aff4b9,STILL_EXISTS,Standardize data for better performance
aahggaeaf,yzhao062/pyod,pyod/test/test_auto_encoder.py,4482165d0699ebf3f7fd18a63704e9778544cd9a,384bd0d53ada09c7ded5cd81469373fa7077b0fe,TODO: fix estimator check for AutoEncoder
aahggaeci,yzhao062/pyod,pyod/__init__.py,309351ceec957da4913bc124c8ce2417798ca54c,STILL_EXISTS,TODO: add version information here
aahggaedf,yzhao062/pyod,notebooks/benchmark.py,3eeb0d6b9e1c19a9d3c918c6363ce4b927555e6d,STILL_EXISTS,TODO: add neural networks and update output precision (=4)
aahggaehh,yzhao062/pyod,pyod/test/test_feature_bagging.py,5073a2833f973c92485a0492b111e002bc82779f,c9570e9949e9889f3d20b12b0a4bd29085e6ab96,TODO: failed due to sklearn uses 2 feature examples.
aahggafje,yzhao062/pyod,examples/temp_do_not_use_lscp.py,f4f77a51e0ec472bcd2986444a96f62fba5e5b99,STILL_EXISTS,TODO: discuss how to standardize data for different model types
aahggagfa,yzhao062/pyod,examples/temp_do_not_use_lscp.py,5818ed1abe7fc48186339cf9a23327fadc79cc13,STILL_EXISTS,TODO: Clean up unnecessary residual comments
aahggagfb,yzhao062/pyod,examples/temp_do_not_use_lscp.py,5818ed1abe7fc48186339cf9a23327fadc79cc13,STILL_EXISTS,TODO: Add proper documentation
aahggagfc,yzhao062/pyod,examples/temp_do_not_use_lscp.py,5818ed1abe7fc48186339cf9a23327fadc79cc13,9f784a04364e67eaf87a499557945b0f021f7521,TODO: Design unit tests
aahggaiee,yzhao062/pyod,pyod/test/test_sos.py,4825059be834c3b02befa3a6b0c7fd329ec67885,384bd0d53ada09c7ded5cd81469373fa7077b0fe,TODO: sklearn check does not support Numba optimization
aahggbaag,yzhao062/pyod,pyod/models/lscp.py,9f784a04364e67eaf87a499557945b0f021f7521,4c1736074f0c4395ff6f6afcc5cca4bc27c073bc,TODO: Design unit tests
aahggbadj,yzhao062/pyod,pyod/test/test_lscp.py,9f784a04364e67eaf87a499557945b0f021f7521,a55b9c1088f6e9a2e1caaa94af53d3de2737efe6,TODO: failed due to sklearn uses 2 feature examples.
aahggbbaj,yzhao062/pyod,pyod/test/test_loci.py,64382d246800e52c556f11b341c1927807f5b5ab,384bd0d53ada09c7ded5cd81469373fa7077b0fe,TODO: sklearn check does not support Numba optimization
aahggbdda,yzhao062/pyod,pyod/models/lscp.py,3267e70de53c30454e291acf8efc0b9523149e64,STILL_EXISTS,TODO: find random state that is causing runtime warning in pearson
aahggbddg,yzhao062/pyod,examples/compare_all_models.py,13fbc1607920ce64b2b600565e13dcd2651eb53a,STILL_EXISTS,TODO: add neural networks; LOCI; SOS
aahggbded,yzhao062/pyod,notebooks/benchmark.py,13fbc1607920ce64b2b600565e13dcd2651eb53a,STILL_EXISTS,TODO: add neural networks; LOCI; SOS
aahggbiee,yzhao062/pyod,pyod/test/test_mo_gaal.py,d74f2abe82dadb0a0d7f72a67988fd5c809486a5,STILL_EXISTS,TODO: fix estimator check for AutoEncoder
aahggbife,yzhao062/pyod,pyod/test/test_so_gaal.py,d74f2abe82dadb0a0d7f72a67988fd5c809486a5,STILL_EXISTS,TODO: fix estimator check for AutoEncoder
aahggcdgc,yzhao062/pyod,pyod/test/test_mo_gaal.py,7f8f88d153d5bb58921cc45c3557f13c507272da,STILL_EXISTS,TODO: GAN may yield unstable results; turning performance off check
aahggcdih,yzhao062/pyod,pyod/test/test_so_gaal.py,7f8f88d153d5bb58921cc45c3557f13c507272da,STILL_EXISTS,TODO: GAN may yield unstable results; turning performance off check
aahggcjfc,yzhao062/pyod,pyod/models/gaal_base.py,d77f253fbb3085e474d6f4de5819db17a9c55ea6,STILL_EXISTS,TODO: create a base class for so_gaal and mo_gaal
aahggdbcf,yzhao062/pyod,pyod/models/feature_bagging.py,fcdf9c9b47c85b3de89e91c9a9eb3778cd69d583,STILL_EXISTS,TODO: detector score combination through BFS should be implemented
aahggdbeb,yzhao062/pyod,pyod/test/test_sod.py,2a020d086a123da0c5a3541f813a7d732a62c978,bc0bc41dedcb060a21c241b6aeb549e1b0b97a9f,''' || TO-DO by Yue Zhao || '''
aahggdcbe,yzhao062/pyod,pyod/test/test_sos.py,c88058445cc0cee2654641b49f61b4dcb18deacc,STILL_EXISTS,TODO: check performance is turned off due to poor performance.
aahggdgba,yzhao062/pyod,pyod/models/lscp.py,3c59e3fa8edba36a64ad9c380951b90b19062934,STILL_EXISTS,TODO: handle when Pearson score is 0
aahggdgcj,yzhao062/pyod,pyod/models/iforest.py,6ca803384bc92d8c14efff3c215a83cd3071b56a,STILL_EXISTS,TODO: behavior of Isolation Forest will change in sklearn 0.22; to update.
aahggdhfg,yzhao062/pyod,pyod/models/lmdd.py,c6f3fe801ccd6c25bf3f540b870339a9f100b2bb,STILL_EXISTS,TO-DO: 'mad': Median Absolute Deviation to be added
aahggebhc,yzhao062/pyod,pyod/models/sod.py,23abd662d7546868e0c9c2a1aef092dca3df0457,d9a3e2e8d7d879223613c25995907518ff1f245c,TODO: look into how to speed up the algorithm with numba
aahggedhh,yzhao062/pyod,pyod/models/sod.py,c6491154aa3d54b351281e54e525728d95700392,d9a3e2e8d7d879223613c25995907518ff1f245c,TODO: look into how to speed up the algorithm with numba
aahggeedd,yzhao062/pyod,pyod/models/sod.py,dcf20b6476403dc587265d96957527e2fcc102e9,d9a3e2e8d7d879223613c25995907518ff1f245c,TODO: look into how to speed up the algorithm with numba
aahggefad,yzhao062/pyod,pyod/models/knn.py,8a655def2f6a57e864bd1500647338db4c710e87,79ae22a8441f338ddc6b9658d8e8624a617488e1,TODO: algorithm parameter is deprecated and will be removed in 0.7.6.
aahggefaf,yzhao062/pyod,pyod/models/knn.py,8a655def2f6a57e864bd1500647338db4c710e87,79ae22a8441f338ddc6b9658d8e8624a617488e1,TODO: since Ball_tree is used by default; may introduce its parameters.
aahggefbb,yzhao062/pyod,pyod/models/knn.py,497e788909428842867da3a55f45cad297240a62,STILL_EXISTS,TODO: algorithm parameter is deprecated and will be removed in 0.7.6.
aahggefbd,yzhao062/pyod,pyod/models/knn.py,497e788909428842867da3a55f45cad297240a62,STILL_EXISTS,TODO: since Ball_tree is used by default; may introduce its parameters.
aahggefbe,yzhao062/pyod,pyod/models/knn.py,bf4182ee3374080304a85541939d7d700ce34725,1d51313cbac69426b538c00298be3115fb04e19b,TODO: code cleanup
aahggeiig,yzhao062/pyod,pyod/test/test_knn.py,1d51313cbac69426b538c00298be3115fb04e19b,STILL_EXISTS,TODO: add a testcase for #158
aahggejea,yzhao062/pyod,pyod/models/vae.py,4d768fa7514750d9b3719904ce85563c17fec817,c7fbe1daa6efe66f77963339f5b73ec9a422706a,Standardize data for better performance
aahggfebf,yzhao062/pyod,pyod/models/vae.py,d345ecf1bc573b6ea89c402ce8ef05f187aeb85c,STILL_EXISTS,Standardize data for better performance
aahgggbif,albumentations-team/albumentations,albumentations/torch_transforms.py,0070e48ebceb832ef29afa55d005d0fd4962c518,STILL_EXISTS,todo
aahgggcfd,albumentations-team/albumentations,docs/conf.py,daf4c6cdc40e7addc9b28b86ddd2ee8e9b19c5e8,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aahgggdad,albumentations-team/albumentations,albumentations/core/composition.py,d85baf615bd8f3979a1594695378364720d80e42,372d9d6cde19413ba0ab212ea2712a6918948515,todo deprecated
aahgggdae,albumentations-team/albumentations,albumentations/augmentations/transforms.py,ffde76202a196d33f569ec69e94f66903a089f79,5b523d289824267c87da6d27ed2ec50252f49b6a,TODO comments
aahgggdib,albumentations-team/albumentations,albumentations/pytorch/transforms.py,a5026800d84c6c1998f224b86dedbf3f005ae994,STILL_EXISTS,todo
aahgggeai,albumentations-team/albumentations,albumentations/augmentations/functional.py,d38a3dfc0556ffa414a76557aa2bc368eca11545,STILL_EXISTS,flatten image to columns of RGB
aahgggfaj,albumentations-team/albumentations,albumentations/augmentations/geometric/transforms.py,97acaa447e9a8d81223e0b8591493b4ed691f9f6,STILL_EXISTS,top left -- no changes needed; just use jitter
aahgggfib,1adrianb/face-alignment,face_alignment/api.py,c4c52959d2073db4e580c3301cf85cd822a1883f,9bd54202a0a51558af0554ae20c8c44a3553a9d3,TODO Remove plot
aahgggfjh,1adrianb/face-alignment,face_alignment/utils.py,c4c52959d2073db4e580c3301cf85cd822a1883f,4b1a4f9e0be8bc88ad627475e12381798d97470d,Maybe we should store things local to the executable (in case of a
aahggggbb,1adrianb/face-alignment,examples/detect_landmarks_in_image.py,9bd54202a0a51558af0554ae20c8c44a3553a9d3,46ba5846844a8b83a91191d4965abcda1a63d9fb,TODO: Make this nice
aahggggee,1adrianb/face-alignment,face_alignment/api.py,bd361efb5bebaf1c0e0f7a97b0b3349cb78ce02d,STILL_EXISTS,TODO: Adding 3D landmark support
aahggggfb,1adrianb/face-alignment,face_alignment/detection/blazeface/detect.py,47fa9165313efd36310de98fec8b25be08016db2,STILL_EXISTS,TODO: ugly
aahgggggb,1adrianb/face-alignment,face_alignment/detection/blazeface/net_blazeface.py,47fa9165313efd36310de98fec8b25be08016db2,STILL_EXISTS,batch size; needed for reshaping later
aahgggjdj,OlafenwaMoses/ImageAI,imageai/Prediction/DenseNet/densenet.py,bc20f8ace57dad67dbf69ee21a91bbef49d41505,3781a0d8888a7be7759f54e61e9e3a100a9dd9da,minimum is set to 12; as at least 3 color channels are needed for correct upsampling
aahgghafi,OlafenwaMoses/ImageAI,imageai/Detection/keras_retinanet/layers/_misc.py,a5522d430def17d7541d07fd1773c462932b7a29,7cc90a7239f93a1c2a113d2d930decce6fff37bc,TODO: support batch size > 1.
aahgghccc,OlafenwaMoses/ImageAI,imageai/Detection/keras_retinanet/utils/image.py,57d661d6e73a3a2223ad2834d5a27dc56d555ddb,STILL_EXISTS,Move the origin of transformation.
aahgghdaa,OlafenwaMoses/ImageAI,build/lib/imageai/Detection/keras_retinanet/layers/_misc.py,3781a0d8888a7be7759f54e61e9e3a100a9dd9da,STILL_EXISTS,TODO: support batch size > 1.
aahggheid,OlafenwaMoses/ImageAI,build/lib/imageai/Detection/keras_retinanet/utils/image.py,3781a0d8888a7be7759f54e61e9e3a100a9dd9da,STILL_EXISTS,Move the origin of transformation.
aahggidcg,OlafenwaMoses/ImageAI,imageai/Detection/keras_retinanet/bin/debug.py,7cc90a7239f93a1c2a113d2d930decce6fff37bc,STILL_EXISTS,81; 83 are left and right arrows on linux in Ascii code (probably not needed)
aahggjcec,ddbourgin/numpy-ml,neural_nets/layers.py,1372e4d979e6685a1ce74b276fd10dd84ba8e9b5,STILL_EXISTS,TODO: use persistent CD-k
aahggjdeg,ddbourgin/numpy-ml,gmm/tests.py,540326bda4f1bd90abc97fa44e38aa8c943fbe7d,STILL_EXISTS,take best fit over 10 tries
aahggjeae,ddbourgin/numpy-ml,trees/dt.py,e580b3a15b67167f2c84abc213aa3a289360af11,STILL_EXISTS,impurity gain is difference in loss before vs. after split
aahggjebj,ddbourgin/numpy-ml,trees/losses.py,e580b3a15b67167f2c84abc213aa3a289360af11,STILL_EXISTS,TODO: revise this
aahggjfbh,ddbourgin/numpy-ml,neural_nets/models/vae.py,b6fdb165ffb3b447c5d53b879ce15c1720ddf87b,STILL_EXISTS,TODO: parallelize inner loop
aahggjhie,ddbourgin/numpy-ml,rl_models/agents.py,e71bb73eb4ab1dfdf96fb95f416413142bb427cf,STILL_EXISTS,TODO: this assumes all actions are available in every state
aahggjibb,ddbourgin/numpy-ml,rl_models/agents.py,c885a094899d52265fa340949594f895c8869d6d,STILL_EXISTS,TODO: this assumes all actions are available in every state
aahggjibd,ddbourgin/numpy-ml,rl_models/agents.py,c885a094899d52265fa340949594f895c8869d6d,STILL_EXISTS,TODO: this assumes that all actions are available in each state
aahggjicf,ddbourgin/numpy-ml,rl_models/agents.py,5fdc27164e203d783253fdf8f0fde643d585c55c,STILL_EXISTS,TODO: this assumes all actions are available in every state
aahggjich,ddbourgin/numpy-ml,rl_models/agents.py,5fdc27164e203d783253fdf8f0fde643d585c55c,STILL_EXISTS,TODO: what's a good threshold here?
aahggjjaf,ddbourgin/numpy-ml,preprocessing/nlp.py,3b5800c4c3825c1030fc319c02f257e27da3be4d,STILL_EXISTS,0 = move left; 1 = move right
aahghacdb,ddbourgin/numpy-ml,numpy_ml/neural_nets/losses/losses.py,422735eebe66de45fb9ae2092031274e4429a67a,STILL_EXISTS,TODO: there's probably a way to do this with einsum...
aahghacea,ddbourgin/numpy-ml,numpy_ml/neural_nets/tests/tests.py,422735eebe66de45fb9ae2092031274e4429a67a,STILL_EXISTS,XXX: instead of calculating the tf NCE on the entire batch; we
aahghaceb,ddbourgin/numpy-ml,numpy_ml/neural_nets/tests/tests.py,422735eebe66de45fb9ae2092031274e4429a67a,STILL_EXISTS,calculate it per-example and then sum. this is really lame and should
aahghadcd,ddbourgin/numpy-ml,docs/conf.py,2deb8c3c188e8a4d90be4ef6df780f6b576f1471,STILL_EXISTS,'analytics_id': \"\"; XXX: TODO
aahghadgc,ddbourgin/numpy-ml,docs/conf.py,2deb8c3c188e8a4d90be4ef6df780f6b576f1471,STILL_EXISTS,-- Options for todo extension ----------------------------------------------
aahghadgd,ddbourgin/numpy-ml,docs/conf.py,2deb8c3c188e8a4d90be4ef6df780f6b576f1471,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aahghbihj,chrislit/abydos,abydos/distance/_aline.py,3663a4ec103115b5b1788bb39db38a8e29359e5b,3cfc3a6652215547f8dd0a39b00e7286426b135c,TODO: should be i+j?
aahgicifc,chrislit/abydos,tests/distance/test_distance_aline.py,bc1047dded1e973dc76246b4801595df520692bf,14d25b94604472128fc628b57e0559197f76d507,TODO: Implement 'double' and see if it changes
aahgihdic,chrislit/abydos,abydos/distance/_dunning.py,60001ec8a416d3bdf23694140999771f4618f54d,STILL_EXISTS,As a workaround; we set d to 1 and add one to n.
aahgihdje,chrislit/abydos,tests/distance/test_distance__token_distance.py,3264baf8b48ffd31542ceeccadb30533206a3819,STILL_EXISTS,Some additional constructors needed to complete test coverage
aahhafidd,chrislit/abydos,abydos/fingerprint/_extract.py,be477ff5d1473775aabbf33e4cf27d44e8dc5103,aeb30823c0b10763ceb69dcdd146345fb68225f5,TODO: revert the above to __iter__ after removing Py2.7 support
aahhahdib,chrislit/abydos,abydos/distance/_token_distance.py,28c9a3e91d1a5f3a6b35a6ee8a493992e6780f57,b42a5773ed5e3206f04d70eac18bc5ac5356637c,TODO: change to DL
aahhahdii,chrislit/abydos,abydos/distance/_token_distance.py,9d979f3b1e3fb9e1601c0025e8ea0e4126f3ce89,3a49b6a4701e23d8b52a147309ce59147101f2a9,TODO: change to DL
aahhahehc,chrislit/abydos,abydos/phones/_phones.py,16315cb9a30f298ff9980533da62ba822a47b3e4,STILL_EXISTS,TODO: Write a version of this that returns a dict
aahhahehd,chrislit/abydos,abydos/phones/_phones.py,16315cb9a30f298ff9980533da62ba822a47b3e4,4098b1d9195232e45ff48b270fc0f6e4b3ea8937,TODO: formulate this on the basis of _FEATURE_MASK
aahhahfca,chrislit/abydos,abydos/distance/_token_distance.py,db8e25172da351bd35235ffa1422886d5a36f09c,STILL_EXISTS,columns are covered; the starred zeros form the desired
aahhbaaha,prihoda/golem,interfaces/facebook/interface.py,16e6036d218ca26acc18490a216475b09642c482,STILL_EXISTS,TODO: add attachment type by extension
aahhbaaij,prihoda/golem,interfaces/facebook/responses.py,692d9d1413deb02416c0ad8a763351948f68eba4,STILL_EXISTS,TODO stickers not yet supported by Messenger :(
aahhbabdd,prihoda/golem,golem/core/interfaces/facebook.py,857965ac2781fde8788817d7f1252d01b0084d55,STILL_EXISTS,TODO: add attachment type by extension
aahhbabea,prihoda/golem,golem/core/interfaces/telegram.py,857965ac2781fde8788817d7f1252d01b0084d55,82876239f2d4340264eea231d5e88f2ee41a5dbc,TODO telegram attachments
aahhbabec,prihoda/golem,golem/core/interfaces/telegram.py,857965ac2781fde8788817d7f1252d01b0084d55,82876239f2d4340264eea231d5e88f2ee41a5dbc,TODO: add attachment type by extension
aahhbabhd,prihoda/golem,golem/core/responses/adapter.py,82876239f2d4340264eea231d5e88f2ee41a5dbc,STILL_EXISTS,FIXME send without dummy message
aahhbabhe,prihoda/golem,golem/core/responses/adapter.py,82876239f2d4340264eea231d5e88f2ee41a5dbc,STILL_EXISTS,TODO support telegram location; keyboard <> inline keyboard
aahhbabhf,prihoda/golem,golem/core/responses/responses.py,82876239f2d4340264eea231d5e88f2ee41a5dbc,STILL_EXISTS,TODO stickers not yet supported by Messenger :(
aahhbabif,prihoda/golem,webgui/interface.py,4eb3464795dc3dd1f9edf7a50c07a83956dd5cf3,5cf64fc15673b64da1cdb8a501d06946c4be17e5,TODO postbacks
aahhbabig,prihoda/golem,webgui/interface.py,4eb3464795dc3dd1f9edf7a50c07a83956dd5cf3,STILL_EXISTS,todo parse stuff
aahhbabih,prihoda/golem,webgui/interface.py,4eb3464795dc3dd1f9edf7a50c07a83956dd5cf3,STILL_EXISTS,todo buttons in elements etc.
aahhbabii,prihoda/golem,webgui/interface.py,4eb3464795dc3dd1f9edf7a50c07a83956dd5cf3,STILL_EXISTS,todo quick replies
aahhbabij,prihoda/golem,webgui/interface.py,4eb3464795dc3dd1f9edf7a50c07a83956dd5cf3,STILL_EXISTS,FIXME ensure uid doesn't exist or is expired
aahhbacaa,prihoda/golem,golem/core/interfaces/adapter/microsoft.py,3891b80b4b1fe2c1c2ed1ee57543e37f86f62d58,STILL_EXISTS,TODO cards and lists
aahhbacab,prihoda/golem,golem/core/interfaces/adapter/microsoft.py,3891b80b4b1fe2c1c2ed1ee57543e37f86f62d58,STILL_EXISTS,TODO other buttons
aahhbacad,prihoda/golem,golem/core/interfaces/microsoft.py,3891b80b4b1fe2c1c2ed1ee57543e37f86f62d58,STILL_EXISTS,TODO initiate conversation
aahhbacaf,prihoda/golem,golem/core/interfaces/microsoft.py,3891b80b4b1fe2c1c2ed1ee57543e37f86f62d58,STILL_EXISTS,TODO
aahhbacag,prihoda/golem,golem/core/responses/responses.py,3891b80b4b1fe2c1c2ed1ee57543e37f86f62d58,STILL_EXISTS,TODO
aahhbacbe,prihoda/golem,golem/core/message_parser.py,1fee2eb416fb4482df169bbd9dcc6097f44571c8,1cd0d503bfbd74b858878e4f4666826703ab8b53,avoid importing tf and spacy when not needed
aahhbaccb,prihoda/golem,golem/nlp/classify.py,1fee2eb416fb4482df169bbd9dcc6097f44571c8,STILL_EXISTS,FIXME allow longer messages
aahhbaccc,prihoda/golem,golem/nlp/classify.py,1fee2eb416fb4482df169bbd9dcc6097f44571c8,5cf64fc15673b64da1cdb8a501d06946c4be17e5,FIXME ADD METADATA !!!
aahhbacch,prihoda/golem,golem/nlp/czech_stemmer.py,1fee2eb416fb4482df169bbd9dcc6097f44571c8,STILL_EXISTS,## FIXME VALIDATE SLOVAK CHARACTERS !!! ###
aahhbacde,prihoda/golem,golem/nlp/keywords.py,1fee2eb416fb4482df169bbd9dcc6097f44571c8,STILL_EXISTS,TODO remove character accents
aahhbacec,prihoda/golem,golem/nlp/train.py,1fee2eb416fb4482df169bbd9dcc6097f44571c8,STILL_EXISTS,TODO https:\/\/stackoverflow.com\/questions\/10572603\/specifying-optional-dependencies-in-pypi-python-setup-py
aahhbacfc,prihoda/golem,golem/nlp/train.py,1fee2eb416fb4482df169bbd9dcc6097f44571c8,5cf64fc15673b64da1cdb8a501d06946c4be17e5,TODO shuffle data
aahhbacfh,prihoda/golem,webgui/interface.py,1fee2eb416fb4482df169bbd9dcc6097f44571c8,STILL_EXISTS,TODO support sending location
aahhbacfj,prihoda/golem,webgui/models.py,1fee2eb416fb4482df169bbd9dcc6097f44571c8,STILL_EXISTS,TODO replace with something sensible
aahhbacha,prihoda/golem,golem/core/dialog_manager.py,5cf64fc15673b64da1cdb8a501d06946c4be17e5,STILL_EXISTS,FIXME <-- don't crash on invalid return value (not iterable)
aahhbachb,prihoda/golem,golem/core/message_logger.py,5cf64fc15673b64da1cdb8a501d06946c4be17e5,STILL_EXISTS,TODO save user profile etc.
aahhbachc,prihoda/golem,golem/core/message_parser.py,5cf64fc15673b64da1cdb8a501d06946c4be17e5,fd1d47c4ac5dd7888655102104aec121d72a6e7b,avoid importing tf and spacy when not needed
aahhbachj,prihoda/golem,golem/models.py,5cf64fc15673b64da1cdb8a501d06946c4be17e5,STILL_EXISTS,TODO was_clicked : boolean
aahhbacia,prihoda/golem,golem/models.py,5cf64fc15673b64da1cdb8a501d06946c4be17e5,STILL_EXISTS,class Attachment(models.Model):  TODO
aahhbacjg,prihoda/golem,golem/nlp/glove.py,5cf64fc15673b64da1cdb8a501d06946c4be17e5,STILL_EXISTS,word = '########'  # FIXME use <unk>; which I don't have in the embedding right now
aahhbadcf,prihoda/golem,golem/nlp/nn/seq2seq.py,5cf64fc15673b64da1cdb8a501d06946c4be17e5,STILL_EXISTS,FIXME more than 25 chars
aahhbaddb,prihoda/golem,golem/nlp/nn/seq2seq.py,5cf64fc15673b64da1cdb8a501d06946c4be17e5,STILL_EXISTS,FIXME
aahhbadde,prihoda/golem,golem/nlp/nn/seq2seq.py,5cf64fc15673b64da1cdb8a501d06946c4be17e5,STILL_EXISTS,FIXME max_length
aahhbaddi,prihoda/golem,golem/nlp/tools/correct.py,5cf64fc15673b64da1cdb8a501d06946c4be17e5,STILL_EXISTS,TODO something more clever
aahhbaddj,prihoda/golem,golem/nlp/tools/correct.py,5cf64fc15673b64da1cdb8a501d06946c4be17e5,STILL_EXISTS,TODO something better
aahhbadie,prihoda/golem,golem/nlp/tools/toktok.py,5cf64fc15673b64da1cdb8a501d06946c4be17e5,STILL_EXISTS,Don't tokenize period unless it ends the line and that it isn't
aahhbadii,prihoda/golem,golem/nlp/tools/toktok.py,5cf64fc15673b64da1cdb8a501d06946c4be17e5,STILL_EXISTS,Don't tokenize period unless it ends the line eg.
aahhbaede,prihoda/golem,golem/nlp/tools/typodistance.py,5cf64fc15673b64da1cdb8a501d06946c4be17e5,STILL_EXISTS,A multidimensional array of 0s with len(s) rows and len(t) columns.
aahhbaefb,prihoda/golem,golem/nlp/tools/typodistance.py,5cf64fc15673b64da1cdb8a501d06946c4be17e5,STILL_EXISTS,The candidate was too expensive; move onto the next
aahhbajdd,prihoda/golem,golem/core/dialog_manager.py,afef6f40e66a3a5b9279b891e3b88dbf6a35b4e6,STILL_EXISTS,FIXME not here
aahhbajde,prihoda/golem,golem/core/dialog_manager.py,afef6f40e66a3a5b9279b891e3b88dbf6a35b4e6,STILL_EXISTS,save chat session to redis; TODO
aahhbajdg,prihoda/golem,golem/core/interfaces/facebook.py,afef6f40e66a3a5b9279b891e3b88dbf6a35b4e6,951b4152a1f8ed85bc5cfc1c41a987925ae4d04f,FIXME
aahhbajdi,prihoda/golem,golem/core/interfaces/microsoft.py,afef6f40e66a3a5b9279b891e3b88dbf6a35b4e6,STILL_EXISTS,FIXME needs to be updated
aahhbajdj,prihoda/golem,golem/core/interfaces/telegram.py,afef6f40e66a3a5b9279b891e3b88dbf6a35b4e6,STILL_EXISTS,FIXME needs to be updated
aahhbajea,prihoda/golem,golem/core/logger.py,afef6f40e66a3a5b9279b891e3b88dbf6a35b4e6,915eb216e8c9e40332f38e73b81860bb9cc21eb2,FIXME
aahhbajeb,prihoda/golem,golem/tasks.py,afef6f40e66a3a5b9279b891e3b88dbf6a35b4e6,STILL_EXISTS,TODO revise this
aahhbajec,prihoda/golem,golem/tasks.py,afef6f40e66a3a5b9279b891e3b88dbf6a35b4e6,STILL_EXISTS,TODO
aahhbajef,prihoda/golem,golem/core/message_parser.py,fd1d47c4ac5dd7888655102104aec121d72a6e7b,STILL_EXISTS,TODO custom nlp might receive them preprocessed (or not)
aahhbajfg,prihoda/golem,golem/core/parsing/date_utils.py,fd1d47c4ac5dd7888655102104aec121d72a6e7b,STILL_EXISTS,TODO i18n
aahhbajgb,prihoda/golem,golem/core/parsing/golem_extractor.py,fd1d47c4ac5dd7888655102104aec121d72a6e7b,STILL_EXISTS,TODO use a separate thread (pool) to remove TF memory overhead
aahhbajjc,prihoda/golem,golem/core/context.py,0e88ba25ffd5eca929151d204541b538c97c2626,STILL_EXISTS,FIXME use a factory to build the correct subclass with right arguments
aahhbbaad,prihoda/golem,golem/core/entities.py,0e88ba25ffd5eca929151d204541b538c97c2626,STILL_EXISTS,# FIXME !!!
aahhbbaaf,prihoda/golem,golem/core/context.py,424ffea8579cce6b7f97346cd1df2ede9926cd98,STILL_EXISTS,TODO for testing
aahhbbabb,prihoda/golem,golem/nlp/nn/model.py,8414e2a575d161425edb4ac82a6eb2bc7ca79a99,STILL_EXISTS,if i % 100 == 0:  TODO should this stay ?!
aahhbbacc,prihoda/golem,golem/nlp/train.py,8414e2a575d161425edb4ac82a6eb2bc7ca79a99,STILL_EXISTS,TODO somehow distinguish PAD; END; START; UNK and NIL
aahhbbadj,prihoda/golem,golem/core/flow.py,7b4383ee725a40327ec079884b7b99b8f88059b1,STILL_EXISTS,FIXME nop or what
aahhbbaed,prihoda/golem,golem/core/flow.py,7b4383ee725a40327ec079884b7b99b8f88059b1,811e9e53b4b9e330d4d1042a531a1c5d848c987f,TODO
aahhbbaee,prihoda/golem,golem/core/flow.py,a93da00711f716c48545b2ccc7d0317dc8406195,811e9e53b4b9e330d4d1042a531a1c5d848c987f,TODO
aahhbbaef,prihoda/golem,golem/core/flow.py,a93da00711f716c48545b2ccc7d0317dc8406195,STILL_EXISTS,FIXME nop or what
aahhbbaei,prihoda/golem,golem/core/dialog_manager.py,9f4bbf7fbdf6f219c00e0afc8f8c809bb10bea6b,STILL_EXISTS,FIXME
aahhbbafd,prihoda/golem,golem/core/context.py,15e67b0a5b01369a0b7dc506cf2203ef56e76c39,STILL_EXISTS,FIXME use a factory to build the correct subclass with right arguments
aahhbbaff,prihoda/golem,golem/core/dialog_manager.py,15e67b0a5b01369a0b7dc506cf2203ef56e76c39,811e9e53b4b9e330d4d1042a531a1c5d848c987f,TODO don't increment when @ requires -> input and it's valid
aahhbbafg,prihoda/golem,golem/core/dialog_manager.py,15e67b0a5b01369a0b7dc506cf2203ef56e76c39,2d341ba2fb278c26c8c7afd4acce50170e82f69b,TODO what to say and do on invalid requires -> input?
aahhbbafh,prihoda/golem,golem/core/dialog_manager.py,15e67b0a5b01369a0b7dc506cf2203ef56e76c39,811e9e53b4b9e330d4d1042a531a1c5d848c987f,TODO somebody might want _message_text and intent locked for freetext\/human
aahhbbafj,prihoda/golem,golem/core/dialog_manager.py,15e67b0a5b01369a0b7dc506cf2203ef56e76c39,811e9e53b4b9e330d4d1042a531a1c5d848c987f,TODO i would go with always as the user's code might depend on the entities being non-null
aahhbbagb,prihoda/golem,golem/core/dialog_manager.py,15e67b0a5b01369a0b7dc506cf2203ef56e76c39,811e9e53b4b9e330d4d1042a531a1c5d848c987f,TODO we can either have ':init' or a bool parameter
aahhbbbca,prihoda/golem,golem/core/context.py,5386aa95bcd3a46e349d643220205e6849ada840,STILL_EXISTS,FIXME use a factory to build the correct subclass with right arguments
aahhbbgfe,prihoda/golem,golem/core/logger.py,7187e1807d7c7fe7f04e464ceb9d9ce02e417b7e,STILL_EXISTS,TODO 'type(response).__name__ if response else 'TextMessage';
aahhbbgff,prihoda/golem,golem/core/logger.py,7187e1807d7c7fe7f04e464ceb9d9ce02e417b7e,STILL_EXISTS,TODO json.loads(json.dumps(response; default=lambda obj: obj.__dict__ if hasattr(obj;'__dict__') else str(obj)))
aahhbbgfg,prihoda/golem,golem/core/logger.py,7187e1807d7c7fe7f04e464ceb9d9ce02e417b7e,STILL_EXISTS,TODO this has to be called async
aahhbbggc,prihoda/golem,golem/nlp/tools/lang_detect.py,0b2d93218237b942f1b2941c86b9cea2d1fceed7,STILL_EXISTS,TODO
aahhbbggf,prihoda/golem,golem/nlp/tools/lang_detect.py,0b2d93218237b942f1b2941c86b9cea2d1fceed7,STILL_EXISTS,TODO unidecode?
aahhbbggh,prihoda/golem,golem/core/entity_query.py,44e896aef8de5af92a326b4c2cb46a15faa86200,STILL_EXISTS,TODO filter by roles
aahhbbggi,prihoda/golem,golem/core/entity_query.py,44e896aef8de5af92a326b4c2cb46a15faa86200,STILL_EXISTS,TODO for testing
aahhbbghc,prihoda/golem,golem/nlp/nn/bow_model.py,625e3010eb83dd2e3cb18e3ed602a41f75ffa00b,STILL_EXISTS,TODO how about hinge and no softmax
aahhbbghh,prihoda/golem,golem/nlp/nn/bow_model.py,73967227c7915d54f483422587d15153d253c19f,STILL_EXISTS,negative samples (this is a hack)
aahhbbhae,prihoda/golem,golem/core/logging/elastic.py,d0ff38508d4f4957bc1ae0be1edce9333fdf2d0c,STILL_EXISTS,FIXME
aahhbbhaf,prihoda/golem,golem/core/logging/elastic.py,d0ff38508d4f4957bc1ae0be1edce9333fdf2d0c,55bf698e40688a4f55a29d59889a1570ee8f82a5,TODO
aahhbbhag,prihoda/golem,golem/core/logging/elastic.py,d0ff38508d4f4957bc1ae0be1edce9333fdf2d0c,55bf698e40688a4f55a29d59889a1570ee8f82a5,TODO 'type(response).__name__ if response else 'TextMessage';
aahhbbhah,prihoda/golem,golem/core/logging/elastic.py,d0ff38508d4f4957bc1ae0be1edce9333fdf2d0c,55bf698e40688a4f55a29d59889a1570ee8f82a5,TODO json.loads(json.dumps(response; default=lambda obj: obj.__dict__ if hasattr(obj;'__dict__') else str(obj)))
aahhbbhec,prihoda/golem,golem/nlp/nn/contextual.py,04ae56bbc3e3f5dd591e40d92bb4b7030c5c8cde,STILL_EXISTS,TODO (1) try embeddings
aahhbbhed,prihoda/golem,golem/nlp/nn/contextual.py,04ae56bbc3e3f5dd591e40d92bb4b7030c5c8cde,STILL_EXISTS,TODO (2) try RNN; not MLP
aahhbbhee,prihoda/golem,golem/nlp/nn/contextual.py,04ae56bbc3e3f5dd591e40d92bb4b7030c5c8cde,STILL_EXISTS,TODO
aahhbbhef,prihoda/golem,golem/nlp/nn/contextual.py,04ae56bbc3e3f5dd591e40d92bb4b7030c5c8cde,STILL_EXISTS,TODO is this correct?
aahhbbheg,prihoda/golem,golem/core/dialog_manager.py,f94956d9af62ea2ca660d1b11852d1b9642d9a8e,STILL_EXISTS,TODO log to chatbase
aahhbbheh,prihoda/golem,golem/core/dialog_manager.py,f94956d9af62ea2ca660d1b11852d1b9642d9a8e,STILL_EXISTS,TODO work in progress
aahhbbhei,prihoda/golem,golem/core/dialog_manager.py,f94956d9af62ea2ca660d1b11852d1b9642d9a8e,STILL_EXISTS,TODO webview
aahhbbhej,prihoda/golem,golem/nlp/keywords.py,f94956d9af62ea2ca660d1b11852d1b9642d9a8e,STILL_EXISTS,TODO avoid repeating
aahhbbhfa,prihoda/golem,golem/nlp/nn/bow_model.py,f94956d9af62ea2ca660d1b11852d1b9642d9a8e,STILL_EXISTS,TODO maybe confidence can be something different
aahhbbhfd,prihoda/golem,golem/core/dialog_manager.py,5986a4f8134c67e22c1e34412f3d570a25455af3,2d341ba2fb278c26c8c7afd4acce50170e82f69b,TODO don't increment when @ requires -> input and it's valid
aahhbbhfe,prihoda/golem,golem/core/dialog_manager.py,5986a4f8134c67e22c1e34412f3d570a25455af3,2d341ba2fb278c26c8c7afd4acce50170e82f69b,TODO what to say and do on invalid requires -> input?
aahhbbhff,prihoda/golem,golem/core/dialog_manager.py,5986a4f8134c67e22c1e34412f3d570a25455af3,2d341ba2fb278c26c8c7afd4acce50170e82f69b,TODO somebody might want _message_text and intent locked for freetext\/human
aahhbbhfh,prihoda/golem,golem/core/dialog_manager.py,5986a4f8134c67e22c1e34412f3d570a25455af3,2d341ba2fb278c26c8c7afd4acce50170e82f69b,TODO should they be checked when moving or always?
aahhbbhfi,prihoda/golem,golem/core/dialog_manager.py,5986a4f8134c67e22c1e34412f3d570a25455af3,2d341ba2fb278c26c8c7afd4acce50170e82f69b,TODO i would go with always as the user's code might depend on the entities being non-null
aahhbbhgd,prihoda/golem,golem/core/dialog_manager.py,5986a4f8134c67e22c1e34412f3d570a25455af3,2d341ba2fb278c26c8c7afd4acce50170e82f69b,TODO we can either have ':init' or a bool parameter
aahhbbhhj,prihoda/golem,golem/core/entity_query.py,60f4dfee4603754c105ea4feb121778b7afcb976,STILL_EXISTS,TODO catch and log errors
aahhbbhia,prihoda/golem,golem/core/flow.py,60f4dfee4603754c105ea4feb121778b7afcb976,STILL_EXISTS,TODO move to new class PreparedFilter
aahhbbhid,prihoda/golem,golem/core/dialog_manager.py,850fbdc1062ea6836581f3309e859ce9b3575a14,2d341ba2fb278c26c8c7afd4acce50170e82f69b,execute default action TODO stay here; like fake root
aahhbbhii,prihoda/golem,golem/core/flow.py,850fbdc1062ea6836581f3309e859ce9b3575a14,c0873577bc7742eb141a238c43cc11f696e9f2d2,TODO support relative paths; discovery or what
aahhbbhij,prihoda/golem,golem/core/flow.py,850fbdc1062ea6836581f3309e859ce9b3575a14,STILL_EXISTS,TODO support relative imports in YAML
aahhbbhja,prihoda/golem,golem/nlp/tools/embedding.py,283fa978e965e142005586ad301b8825b6eaa3fe,STILL_EXISTS,TODO use R-tree for similarity queries
aahhbbhjc,prihoda/golem,golem/nlp/tools/embedding.py,283fa978e965e142005586ad301b8825b6eaa3fe,STILL_EXISTS,FIXME open exclusively; other celery thread might screw up
aahhbbhje,prihoda/golem,golem/nlp/tools/embedding.py,283fa978e965e142005586ad301b8825b6eaa3fe,STILL_EXISTS,TODO there will be lower-upper duplicates; but better than missing words
aahhbbhjj,prihoda/golem,golem/nlp/tools/embedding.py,283fa978e965e142005586ad301b8825b6eaa3fe,STILL_EXISTS,TODO construct from sub-word vectors
aahhbbiab,prihoda/golem,golem/bootstrap/__init__.py,27854bc471d3ec9fd723c09f726922b95e258cde,STILL_EXISTS,FIXME golem-webgui
aahhbbiba,prihoda/golem,golem/webgui/interface.py,d1e9d1471077117f85252fc38deef3acec86292e,STILL_EXISTS,FIXME invalidate the previous user's session!
aahhbbijf,prihoda/golem,golem/core/dialog_manager.py,c0873577bc7742eb141a238c43cc11f696e9f2d2,STILL_EXISTS,TODO don't increment when @ requires -> input and it's valid
aahhbbijg,prihoda/golem,golem/core/dialog_manager.py,c0873577bc7742eb141a238c43cc11f696e9f2d2,STILL_EXISTS,TODO what to say and do on invalid requires -> input?
aahhbbijh,prihoda/golem,golem/core/dialog_manager.py,c0873577bc7742eb141a238c43cc11f696e9f2d2,STILL_EXISTS,TODO somebody might want _message_text and intent locked for freetext\/human
aahhbbijj,prihoda/golem,golem/core/dialog_manager.py,c0873577bc7742eb141a238c43cc11f696e9f2d2,STILL_EXISTS,FIXME this won't work
aahhbbjaa,prihoda/golem,golem/core/dialog_manager.py,c0873577bc7742eb141a238c43cc11f696e9f2d2,STILL_EXISTS,execute default action TODO stay here; like fake root
aahhbbjac,prihoda/golem,golem/core/dialog_manager.py,c0873577bc7742eb141a238c43cc11f696e9f2d2,146b92e2e6704527be7e62eadefb829abfe6b40d,TODO should they be checked when moving or always?
aahhbbjad,prihoda/golem,golem/core/dialog_manager.py,c0873577bc7742eb141a238c43cc11f696e9f2d2,146b92e2e6704527be7e62eadefb829abfe6b40d,TODO i would go with always as the user's code might depend on the entities being non-null
aahhbbjba,prihoda/golem,golem/core/dialog_manager.py,c0873577bc7742eb141a238c43cc11f696e9f2d2,STILL_EXISTS,TODO we can either have ':init' or a bool parameter
aahhbbjbe,prihoda/golem,golem/core/flow.py,c0873577bc7742eb141a238c43cc11f696e9f2d2,STILL_EXISTS,TODO support relative imports in YAML
aahhbbjea,prihoda/golem,golem/webgui/interface.py,98ee78c8299fe62eb297ba32d58d7ab06aeb8a49,STILL_EXISTS,FIXME invalidate the previous user's session!
aahhbbjef,prihoda/golem,golem/core/dialog_manager.py,281dd0ee63016bcf536c77909693a14f73466b93,55fccc450684623adf8d65d81f3f52d6cef85d35,FIXME remove this or integrate with check_entity_transition and new \"unsupported\" states
aahhbbjeg,prihoda/golem,golem/core/dialog_manager.py,281dd0ee63016bcf536c77909693a14f73466b93,55fccc450684623adf8d65d81f3f52d6cef85d35,TODO should \"unsupported\" be states or just actions?
aahhbbjei,prihoda/golem,golem/core/dialog_manager.py,281dd0ee63016bcf536c77909693a14f73466b93,b81065e5e2370905270264053b905b997fd66e5a,if message_type != 'message' or self.get_flow().accepts_message(acceptable_entities):  # _FIXME this won't work
aahhbbjfj,prihoda/golem,golem/core/dialog_manager.py,281dd0ee63016bcf536c77909693a14f73466b93,STILL_EXISTS,TODO first check if supported; if yes; abort
aahhbbjgd,prihoda/golem,golem/core/dialog_manager.py,281dd0ee63016bcf536c77909693a14f73466b93,STILL_EXISTS,Even better: move to special (configurable) unsupported state that will be temporary too
aahhbbjge,prihoda/golem,golem/core/dialog_manager.py,281dd0ee63016bcf536c77909693a14f73466b93,146b92e2e6704527be7e62eadefb829abfe6b40d,TODO just run action without moving if the state is temporary
aahhbbjgf,prihoda/golem,golem/core/dialog_manager.py,281dd0ee63016bcf536c77909693a14f73466b93,STILL_EXISTS,FIXME this won't work
aahhbbjgg,prihoda/golem,golem/core/flow.py,281dd0ee63016bcf536c77909693a14f73466b93,STILL_EXISTS,TODO add local entities
aahhbbjgh,prihoda/golem,golem/core/dialog_manager.py,d1411ec4634cb76964f54da38e1ad8d0fd9c4edd,STILL_EXISTS,TODO run 'unsupported' action but stay here
aahhbbjgi,prihoda/golem,golem/core/dialog_manager.py,d1411ec4634cb76964f54da38e1ad8d0fd9c4edd,146b92e2e6704527be7e62eadefb829abfe6b40d,TODO document this feature or remove it
aahhbbjgj,prihoda/golem,golem/core/dialog_manager.py,d1411ec4634cb76964f54da38e1ad8d0fd9c4edd,STILL_EXISTS,TODO check states of current flow for 'accepted' first
aahhbbjha,prihoda/golem,golem/core/dialog_manager.py,d1411ec4634cb76964f54da38e1ad8d0fd9c4edd,STILL_EXISTS,TODO might use a state that accepts it instead?
aahhbcaab,prihoda/golem,golem/core/dialog_manager.py,c6ac52fde42c67a8403b7731be6c7ba6817b1678,STILL_EXISTS,FIXME somehow it also uses older entities
aahhbcaah,prihoda/golem,golem/core/flow.py,ac3b69f56e51e092dd60336b5a2a3f078368bddc,68e7e89fe26e658a3316fc7d2a4c604655edd90a,TODO should we also have its name by default?
aahhbcaga,lambdaloop/anipose,calibrate_extrinsics.py,60c61df75a1812fa28e0acb3fdc38fcb29e6a8a5,STILL_EXISTS,flip the orientation as needed to make all the cameras align
aahhbcahb,lambdaloop/anipose,calibrate_resolution.py,60c61df75a1812fa28e0acb3fdc38fcb29e6a8a5,STILL_EXISTS,flip the orientation as needed
aahhbccia,lambdaloop/anipose,label_videos.py,bee5865e362621900cdfd7b978da83961342d1ef,054c4d0095db615488c63f9eb014a6a0a0b0e2aa,TODO: read label scheme from config file
aahhbccje,lambdaloop/anipose,triangulate.py,bee5865e362621900cdfd7b978da83961342d1ef,STILL_EXISTS,# hack for hdf5 for testing
aahhbccji,lambdaloop/anipose,calibrate_extrinsics.py,2b523a39455a523f37ef8584d4c8ad10f52d31be,STILL_EXISTS,TODO: make this board configurable
aahhbcdbj,lambdaloop/anipose,label_videos_3d.py,5d12b239d03ce668d6fe49137a2f707514c9260a,STILL_EXISTS,# TODO: add checking on scores here
aahhbcdca,lambdaloop/anipose,label_videos_3d.py,5d12b239d03ce668d6fe49137a2f707514c9260a,STILL_EXISTS,# TODO: make error threshold configurable
aahhbcdcb,lambdaloop/anipose,label_videos_3d.py,5d12b239d03ce668d6fe49137a2f707514c9260a,1e1afa711d033be982016dd2875c2261eacfdf50,# TODO: read this from the video
aahhbcdcc,lambdaloop/anipose,pose_videos.py,5d12b239d03ce668d6fe49137a2f707514c9260a,c803b9d9b18bea3b7ce62f2044f7cafaeac6cc45,# TODO: release the session here; free the neural net memory
aahhbcdci,lambdaloop/anipose,triangulate.py,5ca84b55e6d48dc8d77c5683f1d849ea658ece3d,STILL_EXISTS,TODO: configure this threshold
aahhbcddd,lambdaloop/anipose,compute_angles.py,b700101826b1834da3b968a58019cea4e80f4d01,8b7bc1843f6f9d2d61b25a6917aa19ca1cfb16b1,# TODO: add checking on scores here
aahhbcdde,lambdaloop/anipose,compute_angles.py,b700101826b1834da3b968a58019cea4e80f4d01,8b7bc1843f6f9d2d61b25a6917aa19ca1cfb16b1,# TODO: make error threshold configurable
aahhbcddg,lambdaloop/anipose,summarize.py,b58769ac491b55980e07872dd9e9dfcf6a4f9adc,f0d118e911ea3ba26da16d084b8eee361d6b707d,TODO: fill this is in using process_all
aahhbcddi,lambdaloop/anipose,check_probs.py,f97b15f696635cfb7a343c8e82998e8349fece2a,STILL_EXISTS,# TODO: refactor this file so we can easily check probs as part of the pipeline
aahhbcdeh,lambdaloop/anipose,triangulate.py,bb3af77bf8a534049eed931e02e3980539ed1e62,STILL_EXISTS,TODO: make triangulation type configurable
aahhbcdfe,lambdaloop/anipose,pose_videos.py,4705d2c4a76c80c6b30d4fa82864da53c544bf6b,c803b9d9b18bea3b7ce62f2044f7cafaeac6cc45,TODO: capture this information somewhere else
aahhbcdff,lambdaloop/anipose,triangulate.py,8a3c3e76e71a8dbe222b1eddc366adcd9db1d2c2,9ebb3c713bc1f7d13c128854354283af9a47d428,# TODO: figure out why this doesn't properly center the data in some cases (???)
aahhbcdhh,lambdaloop/anipose,calibrate_intrinsics.py,c4a325b3f6b9f85f6f0e82a7c67bd7f52e19314d,STILL_EXISTS,# TODO: configure aruco board
aahhbcdhj,lambdaloop/anipose,calibration_errors.py,adee968618921708135818db1cc051be44127edf,STILL_EXISTS,TODO: this should change with calibration board config
aahhbcdje,lambdaloop/anipose,anipose/calibrate_extrinsics.py,86f6ee6022b018c4bf564ccd0f88a0ac50818f8a,STILL_EXISTS,TODO: this should change with calibration board config
aahhbcdjg,lambdaloop/anipose,anipose/calibrate_extrinsics.py,86f6ee6022b018c4bf564ccd0f88a0ac50818f8a,531741cdc0775176ff9f286243e20a447d01390a,# TODO optimize transforms based on reprojection errors
aahhbcebe,lambdaloop/anipose,anipose/calibrate_intrinsics.py,c1bff881db96caeefdff23c68190ba1f8d28a1d0,544cac6f1a832f65f12b369f2c63de74a2cb1308,# TODO: check if this works with a charuco board
aahhbcebf,lambdaloop/anipose,anipose/calibrate_intrinsics.py,c1bff881db96caeefdff23c68190ba1f8d28a1d0,STILL_EXISTS,# TODO: adjust the checkerboard library to handle more general ratios
aahhbcebg,lambdaloop/anipose,anipose/calibrate_intrinsics.py,c1bff881db96caeefdff23c68190ba1f8d28a1d0,STILL_EXISTS,# TODO: make this ratio and trimming configurable
aahhbceca,lambdaloop/anipose,anipose/calibrate_extrinsics.py,60809b54a37e48c9f3cf299607e148094299ed6d,531741cdc0775176ff9f286243e20a447d01390a,# TODO build up camera matrices based on pairs
aahhbcedb,lambdaloop/anipose,anipose/common.py,66d5fabb7f20098b40c7dc1d40b8e11a7b124236,STILL_EXISTS,# TODO: support checkerbooard drawing
aahhbceeb,lambdaloop/anipose,anipose/label_videos_3d.py,5038da3e3842bcc555d949b19d8ff87e402cad8c,14da08dc57736d3c904fb2cabb7879f524793e71,# TODO: estimate scale_factor from the spead of points
aahhbcegj,lambdaloop/anipose,anipose/common.py,13ee835347f13e7e211106aebbe6645aa92dca41,STILL_EXISTS,TODO: fix this for nesting = -1
aahhbcehi,lambdaloop/anipose,anipose/triangulate.py,08ace0e894deb659aed25f45bbae7f79d0d5d7d7,STILL_EXISTS,TODO: make ransac a configurable option
aahhbceig,lambdaloop/anipose,anipose/filter_3d.py,f8d79d1255ff9cbf00b09610e4d0e57fe47699ae,STILL_EXISTS,# TODO: configure these thresholds
aahhbceih,lambdaloop/anipose,anipose/filter_3d.py,f8d79d1255ff9cbf00b09610e4d0e57fe47699ae,STILL_EXISTS,FIXME: hack for plotting
aahhbcejc,lambdaloop/anipose,anipose/calibration_errors.py,3775dddf8fc61127dfc17a7e7f4089866d43dceb,STILL_EXISTS,# TODO: rewrite this whole file with calligator
aahhbcfad,lambdaloop/anipose,anipose/label_combined.py,fafb86814bdfe1decc60b854a75a30f5a49fc2c2,STILL_EXISTS,ang_names = [col for col in angles.columns if col not in bad_cols]
aahhbcfdh,lambdaloop/anipose,anipose/common.py,e5fd302f4d806f9a7a1c10bca62894e42c2e7e23,STILL_EXISTS,TODO: get rid of skvideo dependency
aahhbcfeb,lambdaloop/anipose,anipose/tracking_errors.py,9536fe45422958307723d0a60abce6bc2271f669,STILL_EXISTS,# TODO: will have to modify this for custom offset per session
aahhbcffh,lambdaloop/anipose,anipose/tracking_errors.py,9226a8148268570a60f8c607483c13431f355f98,STILL_EXISTS,# TODO: handle missing cameras
aahhbchbf,lambdaloop/anipose,anipose/label_videos_proj.py,7e3c05ee8d22ca78e1bfb90e37cefb8e9a87e695,STILL_EXISTS,# REFACTOR: this code is very similar to project_2d
aahhbchcf,lambdaloop/anipose,anipose/label_combined.py,2d18e27bfaa11fd5474b171faf89516b20cf885d,559782631c0125cf928bdc58316c2fdb1d1645ea,# TODO: remove this function and import from project_2d.py
aahhbcjaj,lucasrodes/whatstk,chatanalytics.py,d82188c2e2cbfb284a42e7fca858b4479020e54d,STILL_EXISTS,Return DataFrame with interventions of all users (columns) for all dates (rows)
aahhbcjba,lucasrodes/whatstk,chatanalytics.py,d82188c2e2cbfb284a42e7fca858b4479020e54d,STILL_EXISTS,Put dates into nice visual format
aahhbcjde,lucasrodes/whatstk,chatanalytics.py,0f16b18b03b336be5497ceb152b7ce25b0a3377d,STILL_EXISTS,Return DataFrame with interventions of all users (columns) for all hour times (rows)
aahhbcjdf,lucasrodes/whatstk,chatanalytics.py,0f16b18b03b336be5497ceb152b7ce25b0a3377d,STILL_EXISTS,Put dates into nice visual format
aahhbcjec,lucasrodes/whatstk,chatformat.py,9f669b9622a69bf9fc51b93936f223eb6b9f7d51,bde54a19ed0e380bec8f49f706f87a969a03157c,Return DataFrame with interventions of all users (columns) for all days (rows)
aahhbcjed,lucasrodes/whatstk,chatformat.py,9f669b9622a69bf9fc51b93936f223eb6b9f7d51,STILL_EXISTS,Put dates into nice visual format
aahhbcjeg,lucasrodes/whatstk,chatformat.py,9f669b9622a69bf9fc51b93936f223eb6b9f7d51,bde54a19ed0e380bec8f49f706f87a969a03157c,Return DataFrame with interventions of all users (columns) for all hour times (rows)
aahhbdaec,lucasrodes/whatstk,main.py,a409b7fc2eb25cf1a6caaa80a6426342ba823218,a46e19af91886d4ae16afbd106e6f61b2b447950,TODO Change reading mode for an argv mode
aahhbdcfd,lucasrodes/whatstk,whatstk/chatformat.py,dbf2bf80715766f0e5654d7e4b48ee593bc9e3eb,STILL_EXISTS,Put dates into nice visual format
aahhbdcji,lucasrodes/whatstk,whatstk/learning/som.py,335216361ac9fff366c8fb42f56946923fa1426f,STILL_EXISTS,TODO:
aahhbddde,lucasrodes/whatstk,whatstk/learning/som.py,335216361ac9fff366c8fb42f56946923fa1426f,STILL_EXISTS,TODO: Circular 2D-Grid
aahhbdeec,lucasrodes/whatstk,whatstk/wparser.py,4863ed18b6db8f194b1c403a3e5204a6f539ee27,a54db093f54b72bbf753ea56c74fd6b11e37994a,df = pd.DataFrame.from_dict(dictionary; orient='columns')
aahhbdejg,lucasrodes/whatstk,whatstk/wparser.py,7eba029ba1ce1881424fb7908c14510c0e4a6a24,STILL_EXISTS,\"\"\"Return DataFrame with interventions of all users (columns) for all hour times (rows) ||  ||     Parameters ||     ---------- ||     users: list ||         List with the usernames of the chat. ||     hours: list ||         Hours the chat has been active. ||     data: list ||         Legible data. ||  ||     Returns ||     ---------- ||     df: Dataframe ||         Table containing #interventions per user per each hour of the day ||     \"\"\"
aahhbdfef,lucasrodes/whatstk,whatstk/parser.py,457a4b484628232a816ccdadcd4c46e495dda957,STILL_EXISTS,TODO: document
aahhbdfeg,lucasrodes/whatstk,whatstk/parser.py,457a4b484628232a816ccdadcd4c46e495dda957,STILL_EXISTS,TODO: RETHING LOOP AS IN THE ONE ABOVE
aahhbdfib,lucasrodes/whatstk,whatstk/parser.py,7231d4a6202f8cee3968bed25bf0bc42190aa4ac,STILL_EXISTS,TODO: use accents!
aahhbdgbh,lucasrodes/whatstk,setup.py,2d6c573f639c8745b8d10262a2f3c76ec8825b84,6da6a8e58f5c7f9d81877c55bc8ab71392e27881,FIXME Is this version 0.x or 1.x?
aahhbdgcg,lucasrodes/whatstk,setup.py,2d6c573f639c8745b8d10262a2f3c76ec8825b84,6da6a8e58f5c7f9d81877c55bc8ab71392e27881,FIXME What is the maturity of this project?
aahhbdgdc,lucasrodes/whatstk,setup.py,2d6c573f639c8745b8d10262a2f3c76ec8825b84,6da6a8e58f5c7f9d81877c55bc8ab71392e27881,FIXME Are going to officially support Python 2.x?
aahhbdgdf,lucasrodes/whatstk,setup.py,2d6c573f639c8745b8d10262a2f3c76ec8825b84,6da6a8e58f5c7f9d81877c55bc8ab71392e27881,You can just specify the packages manually here if your project is
aahhbdggg,lucasrodes/whatstk,whatstk/parser.py,d07ba37a9c2e7b54e327bec6d699645a5fe8286e,STILL_EXISTS,'''def get_intervention_table_hoursday(users; hours; data): ||     \"\"\" || \"\"\"Return DataFrame with interventions of all users (columns) for all hour times (rows) ||  ||     Parameters ||     ---------- ||     users: list ||         List with the usernames of the chat. ||     hours: list ||         Hours the chat has been active. ||     data: list ||         Legible data. ||  ||     Returns ||     ---------- ||     df: Dataframe ||         Table containing #interventions per user per each hour of the day ||     \"\"\" || \"\"\" ||     interventions_per_hour = np.zeros(len(hours)) ||  ||     # Loop for all users ||     df = pd.DataFrame() ||     for user in users: ||         interventions = get_interventions_user(user; data) ||         # Obtain number of interventions per each day contained in hours ||         for i in range(len(hours)): ||             interventions_per_hour[i] = get_number_interventions_per_hour(hours[i]; ||                                                                           interventions) ||         inter = pd.Series(interventions_per_hour; index=hours) ||         df.insert(0; user; inter) ||  ||     return df ||  ||  || def get_list_interventions_user(username; data): ||     \"\"\" ||     Obtains a list with all interventions of username_ ||  ||     Parameters ||     ---------- ||     username_: list ||         Days the chat has been active. ||     data_: list ||         Legible data. ||  ||     Returns ||     ---------- ||     list ||         List of dates (nicely written) the chat has been active ||     \"\"\" ||     return [d for d in data if d[1] == username] ||  ||  || def get_number_interventions_per_day(day_; interv_): ||     \"\"\" ||     Obtains the number of interventions in the day 'day_' ||  ||     Parameters ||     ---------- ||     day_: list ||         Day we are examining. ||     interv_: list ||         List containing all considered interventions. ||  ||     Returns ||     ---------- ||     list ||         List containing two parameters. The first one quantifies the number ||         of interventions in day 'day_'. The second one is just the index of ||         the last intervention in 'day_' to make the overall search more ||         efficient ||     \"\"\" ||  ||     s = [1 if i[0][:3] == day_ else 0 for i in interv_] ||     if s[-1] == 0: ||         i = s.index(0) ||     else: ||         i = -1 ||     return [sum(s); i] ||  ||  ||  || def get_number_interventions_per_hour(hour_; interv_): ||     \"\"\" ||     Obtains the number of interventions during the hour 'hour_' ||  ||     Parameters ||     ---------- ||     hour_: list ||         Hour we are examining. ||     interv_: list ||         List containing all considered interventions. ||  ||     Returns ||     ---------- ||     int ||         quantifies the number of interventions during 'hour_'. ||     \"\"\" ||     s = [1 if i[0][3] == hour_ else 0 for i in interv_] ||     return sum(s) || '''
aahhbdggh,lucasrodes/whatstk,whatstk/parser.py,d07ba37a9c2e7b54e327bec6d699645a5fe8286e,STILL_EXISTS,TODO: document
aahhbdgha,lucasrodes/whatstk,whatstk/parser.py,d07ba37a9c2e7b54e327bec6d699645a5fe8286e,STILL_EXISTS,TODO: RETHING LOOP AS IN THE ONE ABOVE
aahhbdgih,lucasrodes/whatstk,whatstk/parser.py,e74b70043aa2788fa3396b088cf25601572913be,9d2690861fe96c67d334942a866669c5d19c9c1a,Probably this could be better done with Pandas
aahhbdibj,lucasrodes/whatstk,whatstk/parser.py,9d2690861fe96c67d334942a866669c5d19c9c1a,STILL_EXISTS,TODO: implement real
aahhbdigd,lucasrodes/whatstk,whatstk/parser.py,9ac3ccc881d4c16279e601740b600687dfa79211,STILL_EXISTS,Probably this could be better done with Pandas
aahhbdjef,lucasrodes/whatstk,whatstk/alpha/data_extract.py,6da6a8e58f5c7f9d81877c55bc8ab71392e27881,STILL_EXISTS,'''def get_intervention_table_hoursday(users; hours; data): ||     \"\"\" || \"\"\"Return DataFrame with interventions of all users (columns) for all hour times (rows) ||  ||     Parameters ||     ---------- ||     users: list ||         List with the usernames of the chat. ||     hours: list ||         Hours the chat has been active. ||     data: list ||         Legible data. ||  ||     Returns ||     ---------- ||     df: Dataframe ||         Table containing #interventions per user per each hour of the day ||     \"\"\" || \"\"\" ||     interventions_per_hour = np.zeros(len(hours)) ||  ||     # Loop for all users ||     df = pd.DataFrame() ||     for user in users: ||         interventions = get_interventions_user(user; data) ||         # Obtain number of interventions per each day contained in hours ||         for i in range(len(hours)): ||             interventions_per_hour[i] = get_number_interventions_per_hour(hours[i]; ||                                                                           interventions) ||         inter = pd.Series(interventions_per_hour; index=hours) ||         df.insert(0; user; inter) ||  ||     return df ||  ||  || def get_list_interventions_user(username; data): ||     \"\"\" ||     Obtains a list with all interventions of username_ ||  ||     Parameters ||     ---------- ||     username_: list ||         Days the chat has been active. ||     data_: list ||         Legible data. ||  ||     Returns ||     ---------- ||     list ||         List of dates (nicely written) the chat has been active ||     \"\"\" ||     return [d for d in data if d[1] == username] ||  ||  || def get_number_interventions_per_day(day_; interv_): ||     \"\"\" ||     Obtains the number of interventions in the day 'day_' ||  ||     Parameters ||     ---------- ||     day_: list ||         Day we are examining. ||     interv_: list ||         List containing all considered interventions. ||  ||     Returns ||     ---------- ||     list ||         List containing two parameters. The first one quantifies the number ||         of interventions in day 'day_'. The second one is just the index of ||         the last intervention in 'day_' to make the overall search more ||         efficient ||     \"\"\" ||  ||     s = [1 if i[0][:3] == day_ else 0 for i in interv_] ||     if s[-1] == 0: ||         i = s.index(0) ||     else: ||         i = -1 ||     return [sum(s); i] ||  ||  ||  || def get_number_interventions_per_hour(hour_; interv_): ||     \"\"\" ||     Obtains the number of interventions during the hour 'hour_' ||  ||     Parameters ||     ---------- ||     hour_: list ||         Hour we are examining. ||     interv_: list ||         List containing all considered interventions. ||  ||     Returns ||     ---------- ||     int ||         quantifies the number of interventions during 'hour_'. ||     \"\"\" ||     s = [1 if i[0][3] == hour_ else 0 for i in interv_] ||     return sum(s) || '''
aahhbdjei,lucasrodes/whatstk,whatstk/alpha/data_extract.py,6da6a8e58f5c7f9d81877c55bc8ab71392e27881,STILL_EXISTS,TODO: document
aahhbdjfg,lucasrodes/whatstk,whatstk/alpha/data_extract.py,6da6a8e58f5c7f9d81877c55bc8ab71392e27881,STILL_EXISTS,TODO: RETHING LOOP AS IN THE ONE ABOVE
aahhbdjfi,lucasrodes/whatstk,whatstk/core.py,6da6a8e58f5c7f9d81877c55bc8ab71392e27881,STILL_EXISTS,TODO: assert encoding and filename exist
aahhbeegb,lucasrodes/whatstk,whatstk/plotly/figures/scatter.py,652ee0a3fe4394e4bc5f2bb0a7772269e79e34a8,5e960748f0d11049ee7006b48e3786334501f874,TODO: Order username iteration so that  we go from most to least interventionist.
aahhbefac,lucasrodes/whatstk,tests/test_analysis_base.py,d0f311025ee4744469c6394d53271a4a0e02e921,STILL_EXISTS,TODO: Assert number of columns equals number of users
aahhbegja,lucasrodes/whatstk,docs/conf.py,f7a57bcadd9489c7935007e3827a6084bc6e8576,STILL_EXISTS,By convention this will be \"index\"
aahhbfaaf,lucasrodes/whatstk,docs/source/conf.py,7381a5217f64cc1fdc0b36fd07cfb3d9a367cb1a,STILL_EXISTS,By convention this will be \"index\"
aahhbfedj,lucasrodes/whatstk,tests/whatsapp/test_parser.py,e08c6765e3c126b5cc8d0ff17a0245d20372615c,STILL_EXISTS,TODO: Assumes there are always two elements in list chats!
aahhbfeeb,rsokl/MyGrad,mygrad/operations/operation_base.py,cf36d037d4649c48d36d06057a9d0e6c18f7c906,f168014c6d0fff76fc6545856e853ad1370e6b2f,stores which axes needed to be created for `a`; for broadcasting
aahhbfjba,rsokl/MyGrad,mygrad/nnet/layers/recurrent.py,d56eff74d13951366b6956b5a44edac768e38b19,c8d1c113dd02f2dd6f795b7a7c2d8bc79765450e,fix W's and U's references
aahhbfjfe,rsokl/MyGrad,mygrad/nnet/layers/recurrent.py,aad72bf22f601119642c01bef320e8faeed1cf2f,c8d1c113dd02f2dd6f795b7a7c2d8bc79765450e,fix W's and U's references
aahhbfjic,rsokl/MyGrad,mygrad/nnet/layers/recurrent.py,4ba0ecad01d3c3cd26febe08b572117329356c6c,STILL_EXISTS,fix W's and U's references
aahhbgagi,rsokl/MyGrad,mygrad/nnet/layers/gru.py,47a5b720918c6da4420307a9a0b12f65c28703e7,1981db6caf03af217c97d79e9e4e7d2ebaa76dea,TODO: memory optimizations
aahhbgagj,rsokl/MyGrad,mygrad/nnet/layers/gru.py,47a5b720918c6da4420307a9a0b12f65c28703e7,588a7e58c8b8be77e751e958ec5bb1f60d53cdfd,TODO: compare gufunc with njit when support for gufunc in njit is added (issue 2089)
aahhbgahb,rsokl/MyGrad,mygrad/nnet/layers/gru.py,47a5b720918c6da4420307a9a0b12f65c28703e7,STILL_EXISTS,TODO: update doctring
aahhbgahj,rsokl/MyGrad,mygrad/nnet/layers/gru.py,1981db6caf03af217c97d79e9e4e7d2ebaa76dea,STILL_EXISTS,TODO: update doctring
aahhbgaii,rsokl/MyGrad,mygrad/nnet/layers/gru.py,37b2d04c30cb71dc4793dac306d4426fdff84c6d,588a7e58c8b8be77e751e958ec5bb1f60d53cdfd,TODO: memory optimizations
aahhbgajg,rsokl/MyGrad,mygrad/nnet/layers/gru.py,42d79d03ade10dde0f83827928e9e0661e6f9278,588a7e58c8b8be77e751e958ec5bb1f60d53cdfd,TODO: compare gufunc with njit when support for gufunc in njit is added (issue 2089)
aahhbgbah,rsokl/MyGrad,mygrad/nnet/layers/gru.py,c2478700ac0f7e52f8dd337673da06a05827ac78,1f9e17e4eb3ab43bea7da13c6828e3427ec3c42d,TODO: memory optimizations
aahhbgbfg,rsokl/MyGrad,mygrad/nnet/layers/pooling.py,abbd9b3fdd1137c68077386e42d9495beb0a70bf,STILL_EXISTS,data: ((N0; ...); C0; ...) # TODO: copy data!!
aahhbgbfh,rsokl/MyGrad,mygrad/nnet/layers/pooling.py,abbd9b3fdd1137c68077386e42d9495beb0a70bf,STILL_EXISTS,TODO: Check if 0-pool dim is valid..
aahhbgdcj,rsokl/MyGrad,mygrad/linalg/einsum.py,30684dc55d55a1ba012ebf496e5f576cb5cdf2ec,STILL_EXISTS,TODO: normalize error handling for invalid inputs
aahhbgjdb,rsokl/MyGrad,tests/nnet/test_gru.py,3cd019a8202afeba456d143b1fff5cd80a7fb8fe,STILL_EXISTS,TODO: RESTORE DROPOUT
aahhbgjee,rsokl/MyGrad,tests/test_tensor_manip.py,a7f4f1994735df019cd6a9d2538e6ea0b477e41c,STILL_EXISTS,passing integers directly
aahhbgjef,rsokl/MyGrad,tests/test_tensor_manip.py,8d6d6f1b138bcf95fed85b3b5fa0923fd62ee3bb,STILL_EXISTS,passing integers directly
aahhbgjge,rsokl/MyGrad,mygrad/linalg/funcs.py,a8bd7b6505fc89662bd6b455b9bf2283dddee85d,STILL_EXISTS,TODO: normalize error handling for invalid inputs
aahhbheej,rsokl/MyGrad,mygrad/nnet/layers/batchnorm.py,8703db0b886f2c5e0f9874ea64b532f90c276baf,STILL_EXISTS,TODO: Remove affine parameters from Operation
aahhbhgad,rsokl/MyGrad,mygrad/linalg/funcs.py,1d297074722f000b982f10e2026b45516adf9eca,a7e5de653d3b49e02c790c66c3f4ad3ac784f53e,m[i;j]: min number of scalar multiplications needed to compute A_{i..j}
aahhbhggh,rsokl/MyGrad,doc/versioneer.py,a6f51ca43554737dacb39cf5e01b1d00216e39fa,STILL_EXISTS,maybe improved later
aahhbhghd,rsokl/MyGrad,doc/versioneer.py,a6f51ca43554737dacb39cf5e01b1d00216e39fa,STILL_EXISTS,unparseable. Maybe git-describe is misbehaving?
aahhbhhjc,rsokl/MyGrad,mygrad/_version.py,a6f51ca43554737dacb39cf5e01b1d00216e39fa,STILL_EXISTS,maybe improved later
aahhbhhji,rsokl/MyGrad,mygrad/_version.py,a6f51ca43554737dacb39cf5e01b1d00216e39fa,STILL_EXISTS,unparseable. Maybe git-describe is misbehaving?
aahhbhiga,rsokl/MyGrad,versioneer.py,a6f51ca43554737dacb39cf5e01b1d00216e39fa,STILL_EXISTS,maybe improved later
aahhbhigg,rsokl/MyGrad,versioneer.py,a6f51ca43554737dacb39cf5e01b1d00216e39fa,STILL_EXISTS,unparseable. Maybe git-describe is misbehaving?
aahhbihii,rsokl/MyGrad,mygrad/linalg/funcs.py,14c01086ea605cc04b4d05a99eed673768614660,STILL_EXISTS,m[i;j]: min number of scalar multiplications needed to compute A_{i..j}
aahhbihjc,rsokl/MyGrad,mygrad/linalg/funcs.py,9af633ca338c56ccabf1a08d10203f4023e932cc,STILL_EXISTS,TODO: normalize error handling for invalid inputs
aahhbjeca,rsokl/MyGrad,tests/tensor_ops/test_sequence_ops.py,25aa8b3005b473545119220b43bba7c1a90f95cf,16039c4cfb6ac3799c36a3cee9be1bb0b0e996bf,TODO: migrate to factory wrappers once mutually-broadcastable strat is available
aahhbjfja,rsokl/MyGrad,tests/nnet/layers/test_gru.py,1ff16cb00b225fd0fe090cc889b61af9386a7dc7,STILL_EXISTS,would potentially mask real numerical issues
aahhbjghh,rsokl/MyGrad,tests/tensor_ops/test_iter.py,956f422c4e3654dd44900306ccdbf8d2e021e7ea,STILL_EXISTS,Hack to deal with summing over an empty tensor.
aahhbjgjf,rsokl/MyGrad,tests/custom_strategies/__init__.py,8e37f82d621d4e7da81c6ade391292e3a9143386,STILL_EXISTS,unused indices form a continuous stretch of dimensions
aahhbjgji,rsokl/MyGrad,tests/custom_strategies/__init__.py,8e37f82d621d4e7da81c6ade391292e3a9143386,STILL_EXISTS,add ellipsis when the unused axes are trailing
aahhbjhbd,rsokl/MyGrad,tests/tensor_base/test_tensor.py,df3f95ba4dc859951044296271e9b4668b42606a,STILL_EXISTS,problems if it ends up being used in a hashable context. See
aahhbjhdh,rsokl/MyGrad,tests/tensor_base/test_augmented_updates.py,4f93d09723f69289a98de5182ed4c22f7a8f520d,STILL_EXISTS,TODO: These tests need to be modified to check that tensor-identity
aahhbjicj,rsokl/MyGrad,src/mygrad/tensor_base.py,281227eead2a3423ff8c5b4388139532845b9407,STILL_EXISTS,TODO: make sure that a failed in-place op doesn't corrupt the graph
aahhbjiej,rsokl/MyGrad,src/mygrad/math/sequential/ops.py,0971c8e753bf95cb5a6248ce1bea9659fb752a54,b3bcf59c66602a69e1ef2ca85b40fb76597c57ea,TODO: optimize this
aahhbjjaf,rsokl/MyGrad,src/mygrad/tensor_core_ops/indexing.py,c8d5514eefa8025ee530bd0dd097d2d1e2499782,STILL_EXISTS,TODO: ensure ints are promoted to floats
aahhbjjii,rsokl/MyGrad,src/mygrad/tensor_base.py,11c98a1ae605c23b6010d83413699b6269a47141,f859453c9c850943f0b170216ea78beda0e52b5f,TODO: copy-data doesnt work here!!
aahhcaaaf,rsokl/MyGrad,src/mygrad/memory_management.py,96a85c5f08dd3d1b5b1441703257ede5a351c7b4,STILL_EXISTS,TODO: remove this
aahhcaaca,rsokl/MyGrad,src/mygrad/_utils/lock_management.py,5d86ca9f20d38d9cf0b9fc44af16bc2bb1a5449d,STILL_EXISTS,TODO: This is inefficient!
aahhcaahi,rsokl/MyGrad,src/mygrad/tensor_base.py,f67e8824be6c66b37dd42c1c6c2f64e920f7fa57,STILL_EXISTS,TODO: Attach view children to self
aahhcaajj,rsokl/MyGrad,src/mygrad/tensor_base.py,5659cd78f1a932f7d1a4bfdf63868beaf654acbc,STILL_EXISTS,faster than passing the tensor directly
aahhcabdf,rsokl/MyGrad,src/mygrad/tensor_base.py,5bcfbb7b7b7c92534bd2fadc482cf7c48517a63a,STILL_EXISTS,faster than passing the tensor directly
aahhcadje,rsokl/MyGrad,src/mygrad/tensor_base.py,ad0e294db93505f87e2fa4b1ed21ebb4a48f5757,33c32b95f8d13f2d683f419b7aee5d9421452ce6,TODO: Remove this check
aahhcaffb,rsokl/MyGrad,src/mygrad/tensor_base.py,da42f7a3d87bddc48d63844c4a4cef6ef751d2b4,STILL_EXISTS,TODO: Implement grad-view semantics
aahhcagbg,rsokl/MyGrad,tests/math/binary/test_binary_funcs.py,ad378f045922814833ab34c9a997ed4139a4ca79,STILL_EXISTS,hack to make broadcastable shapes work for inplace op:
aahhcagfc,rsokl/MyGrad,src/mygrad/tensor_base.py,fd5f358717e6678bf801e9e9ef158973a5367f7e,STILL_EXISTS,TODO: Also check if inplace op involves view *data*
aahhcagfd,rsokl/MyGrad,src/mygrad/tensor_base.py,fd5f358717e6678bf801e9e9ef158973a5367f7e,5464eb78d7d6541d00ba55aca7818b855ff0cf15,TODO: I think this reroute can be removed
aahhcbeea,rsokl/MyGrad,src/mygrad/tensor_base.py,e8952952a5115767537022fef84d2134fbbe5dab,82b31e2ed107ec88b32e29675dc2e0c73e50d6bf,TODO: handle tuple `out` correctly
aahhccdfe,rsokl/MyGrad,src/mygrad/tensor_base.py,2d0b17d0503a61079b3f93d405f6a2c2ea2bf80a,72e4f2688370ebba945e1ecb39462e4f4ad8ca94,TODO: handle tuple `out` correctly
aahhcdeii,rsokl/MyGrad,src/mygrad/math/misc/funcs.py,0e1444be0b18b22440127e522a040a3ca9c11d2a,STILL_EXISTS,m[i;j]: min number of scalar multiplications needed to compute A_{i..j}
aahhcebhj,wmkouw/libTLDA,libtlda/docs/conf.py,88fa67357c41ba533932079c34cb2ab6ffbdae33,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aahhcedic,iver56/audiomentations,demo/demo.py,d50f7844cf8595b09d77baad799bf690c8bb95ef,e43e95648e82ef0b9f92c56095dd74785d23d0db,ends up roughly between -1 and 1
aahhceefb,iver56/audiomentations,tests/test_load_sound_files.py,712ef03fb26aea4448053cf064e8e6bc7100a143,STILL_EXISTS,Apparently; the exact duration may vary slightly based on which decoder is used
aahhceefc,iver56/audiomentations,tests/test_load_sound_files.py,50ca8c64946909f12da139f58506fc391aa59a49,STILL_EXISTS,Apparently; the exact duration may vary slightly based on which decoder is used
aahhceehj,iver56/audiomentations,audiomentations/augmentations/transforms.py,1067d36c9645a967c3be9f3c4a780826421cc6a2,STILL_EXISTS,Here we use a workaround that simply loops over the channels. It's not perfect.
aahhceeia,iver56/audiomentations,audiomentations/augmentations/transforms.py,1067d36c9645a967c3be9f3c4a780826421cc6a2,STILL_EXISTS,TODO: When librosa has closed the following issue; we can remove our workaround:
aahhceeid,iver56/audiomentations,audiomentations/augmentations/transforms.py,3678cccbba47950dc309599034d0b38e6d946252,STILL_EXISTS,Here we use a workaround that simply loops over the channels. It's not perfect.
aahhceeie,iver56/audiomentations,audiomentations/augmentations/transforms.py,3678cccbba47950dc309599034d0b38e6d946252,STILL_EXISTS,TODO: When librosa natively supports multichannel audio; remove our workaround
aahhceeif,iver56/audiomentations,audiomentations/core/composition.py,4500f8e936bf1e685504ce25cf966c3475ff830d,STILL_EXISTS,TODO: Name can change to WaveformCompose
aahhceeih,iver56/audiomentations,audiomentations/core/audio_loading_utils.py,c5e4f32e91e2b6be8262080ebe79c0b69bd35b00,STILL_EXISTS,TODO: Add support for 24-bit and 32-bit loading in scipy>=1.6.0
aahhcefcd,JiahuiYu/neuralgym,setup.py,8d14ef678de68b159df66360f59bc6aaa4022495,STILL_EXISTS,You can just specify the packages manually here if your project is
aahhcegaj,JiahuiYu/neuralgym,docs/source/conf.py,1a8c46ef1e732b0aa77e747ec4001a9ef896fc72,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aahhcfjhg,MisaOgura/flashtorch,torchscope/utils/__init__.py,ae51eb2766df41a7e4dc5fa255e71c5edc8915cc,2b818b12e6392d35576494e9dc8adfa32ce18505,TODO - input size needs to be 299 for inception_v3
aahhcgagg,akosiorek/forge,forge/experiment.py,34ddca46aa9f6cd8f46341712d56864644ad68ba,STILL_EXISTS,TODO: logging in some general way
aahhcgaia,akosiorek/forge,forge/experiment_tools.py,34ddca46aa9f6cd8f46341712d56864644ad68ba,83ddd2f8f010e4ad3d2853e16a92be80aa6c8840,TODO: docs
aahhchbbg,shibing624/text-classifier,evaluator.py,8095faffba30eae7ca216c8aa1b44628d5a9b025,STILL_EXISTS,\"\"\" || @author: XuMing <shibing624@126.com> || @description:evaluator of classification algorithm || \"\"\"
aahhciddd,epwalsh/nlp-models,modules/models/copynet.py,d19c7922269f5802cef10199bcb5327dc91c2c54,STILL_EXISTS,TODO: clean this up
aahhcidgc,epwalsh/nlp-models,modules/models/copynet.py,d19c7922269f5802cef10199bcb5327dc91c2c54,STILL_EXISTS,Update copy_probs needed for getting the `selective_weights` at the next timestep.
aahhcidjg,epwalsh/nlp-models,modules/models/copynet.py,d19c7922269f5802cef10199bcb5327dc91c2c54,STILL_EXISTS,TODO
aahhciedd,epwalsh/nlp-models,modules/models/copynet.py,0484a3ff8c74aa9f61e072538bf649d5f9c5da37,STILL_EXISTS,TODO: clean this up
aahhcjgdj,0411tony/Yue,recommender/advanced/DMF.py,2c824deddf6abe14eda4d24e90f28de8791a67d7,STILL_EXISTS,## implement by Xue et al.; Deep Matrix Factorization Models for Recommender Systems; IJCAI 2017.
aahhcjgje,0411tony/Yue,recommender/advanced/NeuMF.py,3ce05e39c2ee5bcac5283275b022875cc12fe438,STILL_EXISTS,##it seems Adam is better than SGD here...
aahhcjhhj,raamana/neuropredict,psy.py,aa49d1b44948e726ddc76be51d9dff4bcb3384df,STILL_EXISTS,TODO perhaps I can have two arguments: one to specify feature type (which determines the reader); and another
aahhcjhib,raamana/neuropredict,rhst.py,aa49d1b44948e726ddc76be51d9dff4bcb3384df,STILL_EXISTS,IMPLEMENT TODO
aahhcjhii,raamana/neuropredict,rhst.py,aa49d1b44948e726ddc76be51d9dff4bcb3384df,STILL_EXISTS,sort them if need be (not needed if MLDatasets)
aahhcjiai,raamana/neuropredict,rhst.py,cbb7ecb70c7cb5ad2abfa3eb8130ad021734fa8c,STILL_EXISTS,TODO tabulating misclassifications
aahhcjiba,raamana/neuropredict,rhst.py,cbb7ecb70c7cb5ad2abfa3eb8130ad021734fa8c,STILL_EXISTS,TODO generate visualizations for each feature set as well as a comparative summary!
aahhcjibb,raamana/neuropredict,rhst.py,cbb7ecb70c7cb5ad2abfa3eb8130ad021734fa8c,STILL_EXISTS,TODO generate a CSV of different metrics for each dataset; as well as a reloadable
aahhcjibf,raamana/neuropredict,rhst.py,f0c40cb6b51fc981fc69606d3e4d78ab5e0e23b1,STILL_EXISTS,TODO test if the gathering of prob data is consistent across multiple calls to this method
aahhcjibg,raamana/neuropredict,rhst.py,f0c40cb6b51fc981fc69606d3e4d78ab5e0e23b1,STILL_EXISTS,perhaps by controlling the class order in input
aahhcjica,raamana/neuropredict,rhst.py,a97e81d3acda45ebef1f63ca5c86ee4027c54e8f,STILL_EXISTS,LATER choose a more universal serialization method (that could be loaded from a web app) TODO
aahhcjicb,raamana/neuropredict,rhst.py,a97e81d3acda45ebef1f63ca5c86ee4027c54e8f,STILL_EXISTS,TODO need to standardize what needs to saved\/read back
aahhcjidd,raamana/neuropredict,rhst.py,c5a153e9325a513d855483c27889404a60a54a2b,STILL_EXISTS,TODO implement a multi-process version as differnt rep's are embarrasingly parallel
aahhcjiea,raamana/neuropredict,neuropredict/freesurfer.py,ea8604b6967062a2d60bb8aa2f3d633bb0265890,8a7f20b9d1be6d09adf17d19c06f0afc0fe48213,TODO need to include subcortical volumes
aahhcjied,raamana/neuropredict,neuropredict/freesurfer.py,ea8604b6967062a2d60bb8aa2f3d633bb0265890,STILL_EXISTS,sel_names not returned to follow return-a-single-vector convention
aahhcjiee,raamana/neuropredict,neuropredict/freesurfer.py,ea8604b6967062a2d60bb8aa2f3d633bb0265890,STILL_EXISTS,TODO reader for subcortical volumes: equivalent of load_fs_segstats.m
aahhcjiej,raamana/neuropredict,neuropredict/neuropredict.py,ea8604b6967062a2d60bb8aa2f3d633bb0265890,04915bb0b546040388e788bdf25160e08d0d6a19,TODO perhaps I can have two arguments: one to specify feature type (which determines the reader); and another
aahhcjige,raamana/neuropredict,neuropredict/rhst.py,ea8604b6967062a2d60bb8aa2f3d633bb0265890,STILL_EXISTS,TODO test if the gathering of prob data is consistent across multiple calls to this method
aahhcjigf,raamana/neuropredict,neuropredict/rhst.py,ea8604b6967062a2d60bb8aa2f3d633bb0265890,STILL_EXISTS,perhaps by controlling the class order in input
aahhcjigh,raamana/neuropredict,neuropredict/rhst.py,ea8604b6967062a2d60bb8aa2f3d633bb0265890,8d914209eed9ccd5dc16ed462e217e3a6403054a,IMPLEMENT TODO
aahhcjigj,raamana/neuropredict,neuropredict/rhst.py,ea8604b6967062a2d60bb8aa2f3d633bb0265890,STILL_EXISTS,TODO need to standardize what needs to saved\/read back
aahhcjihg,raamana/neuropredict,neuropredict/rhst.py,ea8604b6967062a2d60bb8aa2f3d633bb0265890,0f1c4b5f5627169fa177a2dc371bb96e0669a12c,sort them if need be (not needed if MLDatasets)
aahhcjijf,raamana/neuropredict,neuropredict/rhst.py,ea8604b6967062a2d60bb8aa2f3d633bb0265890,STILL_EXISTS,TODO implement a multi-process version as differnt rep's are embarrasingly parallel
aahhcjjae,raamana/neuropredict,neuropredict/rhst.py,ea8604b6967062a2d60bb8aa2f3d633bb0265890,STILL_EXISTS,TODO generate visualizations for each feature set as well as a comparative summary!
aahhcjjaf,raamana/neuropredict,neuropredict/rhst.py,ea8604b6967062a2d60bb8aa2f3d633bb0265890,STILL_EXISTS,TODO generate a CSV of different metrics for each dataset; as well as a reloadable
aahhcjjaj,raamana/neuropredict,neuropredict/neuropredict.py,8d914209eed9ccd5dc16ed462e217e3a6403054a,STILL_EXISTS,adding an index for an even better contrast
aahhcjjba,raamana/neuropredict,neuropredict/neuropredict.py,8d914209eed9ccd5dc16ed462e217e3a6403054a,668eb7e1581409426a054f0be7fb620126bc2ee2,TODO need to devise a way to avoid re-reading all the features from scratch every time.
aahhcjjch,raamana/neuropredict,neuropredict/rhst.py,8a7f20b9d1be6d09adf17d19c06f0afc0fe48213,STILL_EXISTS,TODO for binary experiments; compute AUC; sensitivity; specificity (optional precision; recall)
aahhcjjhb,raamana/neuropredict,neuropredict/posthoc.py,50929ac4cf2d41b6a7285c22140115c3d8fe71b0,STILL_EXISTS,TODO implement
aahhcjjie,raamana/neuropredict,neuropredict/config_neuropredict.py,1cd0c80120fc265a1610c394ebf7918a988da03d,STILL_EXISTS,TODO decide to where to include eTIV
aahhcjjij,raamana/neuropredict,neuropredict/freesurfer.py,1cd0c80120fc265a1610c394ebf7918a988da03d,STILL_EXISTS,TODO this can fail if missing values were encouteterd. Change to genfromtxt later
aahhdaaaa,raamana/neuropredict,neuropredict/rhst.py,1b48cb268e2d9d7209a8e0cfb7050d978d0d3def,1fc7284b12083c05c13d57311e9fcea0a4e6a7f3,TODO later when are the uses of precision and recall appropriate?
aahhdaabc,raamana/neuropredict,neuropredict/rhst.py,668eb7e1581409426a054f0be7fb620126bc2ee2,0bb1b013eb0bb9fc3e093ba73697e6a419c5e80a,TODO FIX auc calculation flipped
aahhdaabj,raamana/neuropredict,build/lib/neuropredict/freesurfer.py,2d9b868edbf24bd3bb804eba5bf41a7695158b73,STILL_EXISTS,sel_names not returned to follow return-a-single-vector convention
aahhdaacb,raamana/neuropredict,build/lib/neuropredict/freesurfer.py,2d9b868edbf24bd3bb804eba5bf41a7695158b73,STILL_EXISTS,TODO this can fail if missing values were encouteterd. Change to genfromtxt later
aahhdaacf,raamana/neuropredict,build/lib/neuropredict/neuropredict.py,2d9b868edbf24bd3bb804eba5bf41a7695158b73,STILL_EXISTS,adding an index for an even better contrast
aahhdaaei,raamana/neuropredict,build/lib/neuropredict/posthoc.py,2d9b868edbf24bd3bb804eba5bf41a7695158b73,STILL_EXISTS,TODO later: as the implementation will need significant testing!
aahhdaafj,raamana/neuropredict,build/lib/neuropredict/rhst.py,2d9b868edbf24bd3bb804eba5bf41a7695158b73,STILL_EXISTS,TODO later when are the uses of precision and recall appropriate?
aahhdaagb,raamana/neuropredict,build/lib/neuropredict/rhst.py,2d9b868edbf24bd3bb804eba5bf41a7695158b73,STILL_EXISTS,TODO FIX auc calculation flipped
aahhdabac,raamana/neuropredict,neuropredict/rhst.py,ec16ac54f870b6463ff70f1484a8a52fe54412a0,58ce9bd28af16e3a8f75d6c4c575b4d1e191eb43,TODO find a reference to choose this and back this up
aahhdabaf,raamana/neuropredict,neuropredict/test_rhst.py,ec16ac54f870b6463ff70f1484a8a52fe54412a0,STILL_EXISTS,TODO accuracy and AUC on random binary datasets must be chance!
aahhdabba,raamana/neuropredict,neuropredict/neuropredict.py,394630b87280b02710f03a5f01eadc360b58bee0,STILL_EXISTS,TODO no names are associated with user-defined yet!
aahhdabbb,raamana/neuropredict,neuropredict/rhst.py,394630b87280b02710f03a5f01eadc360b58bee0,STILL_EXISTS,TODO need a consolidated way to deal with what variable are saved and in what order
aahhdabbc,raamana/neuropredict,neuropredict/rhst.py,394630b87280b02710f03a5f01eadc360b58bee0,52ad88e7f1e3f7c23f64e7829ae91da2aa404117,TODO if feature names are implemented; save them too
aahhdabbf,raamana/neuropredict,neuropredict/neuropredict.py,0137716674260ec622b909dfa76d03e34b897b6d,STILL_EXISTS,TODO should I export subject-wise misclassification rate as well?
aahhdabbg,raamana/neuropredict,neuropredict/neuropredict.py,0137716674260ec622b909dfa76d03e34b897b6d,e8f3a23f8744f3ae655a1238fae388a548e6a1cb,TODO allow the user to specify arbitray combination of predefined and custom methods as well.
aahhdabbh,raamana/neuropredict,neuropredict/neuropredict.py,0137716674260ec622b909dfa76d03e34b897b6d,9a99afe0019685b50bc54b72292ce5d0992a2325,TODO turn the following into a metho of the form
aahhdabdg,raamana/neuropredict,neuropredict/visualize.py,0137716674260ec622b909dfa76d03e34b897b6d,STILL_EXISTS,TODO separate this calculation for use in exporting of results
aahhdabea,raamana/neuropredict,neuropredict/neuropredict.py,c76bb8a9b2fe5068f475c8d233b4cfe21d6ccd60,STILL_EXISTS,TODO think about how to export predictive probability per class per CV rep
aahhdabif,raamana/neuropredict,neuropredict/rhst.py,3dfab81c1e8f2bc07fcd58cac91fb0a578ee58ab,9a99afe0019685b50bc54b72292ce5d0992a2325,TODO try parallelizing this. Think about how it interacts with parallel processing at CV rep level
aahhdabij,raamana/neuropredict,neuropredict/neuropredict.py,9a99afe0019685b50bc54b72292ce5d0992a2325,STILL_EXISTS,TODO design an API interface for advanced access as an importable package
aahhdacbb,raamana/neuropredict,neuropredict/neuropredict.py,faa4f6868f3e19acc82c1d44eb7e07e1c385f4a5,STILL_EXISTS,TODO need to be able to parallelize at subgroup- and method-level
aahhdacbc,raamana/neuropredict,neuropredict/rhst.py,faa4f6868f3e19acc82c1d44eb7e07e1c385f4a5,1fc7284b12083c05c13d57311e9fcea0a4e6a7f3,TODO to achieve feature- or method-level parallization;
aahhdacbf,raamana/neuropredict,neuropredict/neuropredict.py,5a7187dd270e5d42907eda60e6f51d5db806f069,STILL_EXISTS,for which len() is not defined (which is needed for pyradigm to find dimensionality
aahhdadba,raamana/neuropredict,docs/conf.py,88514eab143e16da8fda56539b2d5d8fd8aa02d7,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aahhdadhj,raamana/neuropredict,neuropredict/run_workflow.py,7c6ec11298238e4a152d6b14ca0ede304d697a50,d2a9d7cd2c7f0bc4789e3923ebf7a296060688fd,for which len() is not defined (which is needed for pyradigm to find dimensionality
aahhdadih,raamana/neuropredict,neuropredict/run_workflow.py,7c6ec11298238e4a152d6b14ca0ede304d697a50,d2a9d7cd2c7f0bc4789e3923ebf7a296060688fd,TODO think about how to export predictive probability per class per CV rep
aahhdadja,raamana/neuropredict,neuropredict/run_workflow.py,7c6ec11298238e4a152d6b14ca0ede304d697a50,d2a9d7cd2c7f0bc4789e3923ebf7a296060688fd,TODO there must be a more elegant way to write dict to CSV
aahhdadjh,raamana/neuropredict,neuropredict/run_workflow.py,7c6ec11298238e4a152d6b14ca0ede304d697a50,STILL_EXISTS,TODO design an API interface for advanced access as an importable package
aahhdadji,raamana/neuropredict,neuropredict/run_workflow.py,7c6ec11298238e4a152d6b14ca0ede304d697a50,68a995d89f0427aa0e644260aae71bd1abc577d4,TODO need to be able to parallelize at subgroup- and method-level
aahhdaebj,raamana/neuropredict,neuropredict/visualize.py,2fbd56cd62a3f96b89df04016cf1ff866275763a,0bb1b013eb0bb9fc3e093ba73697e6a419c5e80a,TODO show no more than 4 subplots per figure.
aahhdaece,raamana/neuropredict,neuropredict/rhst.py,ace60b754a7c6439ccbfe2cd3e622497607cbd0e,STILL_EXISTS,TODO NOW test if the gathering of prob data is consistent across multiple calls to this method
aahhdaecf,raamana/neuropredict,neuropredict/rhst.py,ace60b754a7c6439ccbfe2cd3e622497607cbd0e,STILL_EXISTS,perhaps by controlling the class order in input
aahhdaedh,raamana/neuropredict,neuropredict/rhst.py,6a0d0e31b780b2119392bc96917ad6129e9b62c1,STILL_EXISTS,NOT EASY TODO enable users choose selector and classifier
aahhdafhi,raamana/neuropredict,neuropredict/run_workflow.py,969243af133efad9a80cf06dd9422d9f16183d36,54e3740cf7f1e99648d628a389ec8921765ad850,TODO avoid loading results from disk twice (vis & export)
aahhdageg,raamana/neuropredict,neuropredict/rhst.py,4dc59c6928ebf0fe43d44bc6088451be34c6ea96,STILL_EXISTS,TODO save results for each rep independently - in a way that I can re-assemble in case grid search job crashes
aahhdagfb,raamana/neuropredict,neuropredict/rhst.py,df33ce25d9f484eb076032570ac53e59ba1f387f,STILL_EXISTS,TODO create shared memory object for datasets
aahhdaggf,raamana/neuropredict,neuropredict/rhst.py,df33ce25d9f484eb076032570ac53e59ba1f387f,STILL_EXISTS,TODO save results for each rep independently - in a way that I can re-assemble in case grid search job crashes
aahhdaggj,raamana/neuropredict,neuropredict/rhst.py,54e3740cf7f1e99648d628a389ec8921765ad850,ce6ee0c2b177bbfab029ade8ad5d1c044d0fb847,TODO reorg by dataset; and ensure it works with visualize methods
aahhdagib,raamana/neuropredict,neuropredict/config_neuropredict.py,58ce9bd28af16e3a8f75d6c4c575b4d1e191eb43,93e317be2135ad6882c7a6b4b1ccc221569e5266,TODO save class_sizes too
aahhdagjb,raamana/neuropredict,neuropredict/run_workflow.py,93e317be2135ad6882c7a6b4b1ccc221569e5266,68a995d89f0427aa0e644260aae71bd1abc577d4,TODO need to be able to parallelize at subgroup- and method-level
aahhdahdf,raamana/neuropredict,neuropredict/_version.py,83a812673869f5ad0681ccce017e0c1cd702a12c,STILL_EXISTS,maybe improved later
aahhdaheb,raamana/neuropredict,neuropredict/_version.py,83a812673869f5ad0681ccce017e0c1cd702a12c,STILL_EXISTS,unparseable. Maybe git-describe is misbehaving?
aahhdaiad,raamana/neuropredict,versioneer.py,83a812673869f5ad0681ccce017e0c1cd702a12c,STILL_EXISTS,maybe improved later
aahhdaiaj,raamana/neuropredict,versioneer.py,83a812673869f5ad0681ccce017e0c1cd702a12c,STILL_EXISTS,unparseable. Maybe git-describe is misbehaving?
aahhdbchd,raamana/neuropredict,neuropredict/rhst.py,614c28cf9d0b7453067d9e2de782bd09508fe368,STILL_EXISTS,TODO look for ways to avoid building this every iter and every dataset.
aahhdbchj,raamana/neuropredict,neuropredict/rhst.py,34154175007ac9740d0d366ff4c767796e3d2f67,0bb1b013eb0bb9fc3e093ba73697e6a419c5e80a,TODO expose these options to user at cli
aahhdbcie,raamana/neuropredict,neuropredict/rhst.py,34154175007ac9740d0d366ff4c767796e3d2f67,d2a9d7cd2c7f0bc4789e3923ebf7a296060688fd,TODO returning preprocessor blindly without any parameters
aahhdbdeh,raamana/neuropredict,neuropredict/visualize.py,13f4dd62925294ad035aa9d09d7709d984109f0e,STILL_EXISTS,checking whether all features selected equal number of times (needed for violing pl
aahhdbdif,raamana/neuropredict,neuropredict/rhst.py,57e805dee4693987b90bb3fc3ef1923dff73de01,STILL_EXISTS,TODO new feature: add additional metrics such as PPV
aahhdbdjj,raamana/neuropredict,neuropredict/rhst.py,7f3025cb06c10160f84807fa9c7df7748e80ae9a,d2a9d7cd2c7f0bc4789e3923ebf7a296060688fd,TODO optimize the num features to select as part of grid search
aahhdbega,raamana/neuropredict,neuropredict/algorithms.py,d2a9d7cd2c7f0bc4789e3923ebf7a296060688fd,STILL_EXISTS,TODO optimize the num features to select as part of grid search
aahhdbegc,raamana/neuropredict,neuropredict/algorithms.py,d2a9d7cd2c7f0bc4789e3923ebf7a296060688fd,STILL_EXISTS,TODO returning preprocessor blindly without any parameters
aahhdbegg,raamana/neuropredict,neuropredict/io.py,d2a9d7cd2c7f0bc4789e3923ebf7a296060688fd,STILL_EXISTS,for which len() is not defined (which is needed for pyradigm to find dimensionality
aahhdbehh,raamana/neuropredict,neuropredict/reports.py,d2a9d7cd2c7f0bc4789e3923ebf7a296060688fd,STILL_EXISTS,TODO think about how to export predictive probability per class per CV rep
aahhdbeif,raamana/neuropredict,neuropredict/reports.py,d2a9d7cd2c7f0bc4789e3923ebf7a296060688fd,STILL_EXISTS,TODO there must be a more elegant way to write dict to CSV
aahhdbfij,raamana/neuropredict,neuropredict/rhst.py,fc93ff003c943076eeaea91d364a02ad9e5e3401,STILL_EXISTS,TODO perhaps k-fold is a better inner CV; which guarantees full use of training set with fewer repeats?
aahhdbfjc,raamana/neuropredict,neuropredict/rhst.py,609affb1e11a326eb5d49b62588a645a60e69460,STILL_EXISTS,TODO need to standardize what needs to saved\/read back
aahhdbhde,raamana/neuropredict,neuropredict/algorithms.py,554769787be9a4a555ac30e685fda2a34ff24086,STILL_EXISTS,TODO try pruning values based on their processing time\/redundancy
aahhdbhec,raamana/neuropredict,neuropredict/algorithms.py,fce24ed8e29d19bac3c6cf22f4c69dcc17382139,STILL_EXISTS,TODO consult literature for better selection of ranges
aahhdbhfb,raamana/neuropredict,neuropredict/tests/test_rhst.py,57016762ba1917b54ef03284910f9421aa7dea52,b6f6b78cc911c82ca5e36d4d491d7aee0f1057aa,TODO generate the entire class at once!
aahhdbhhe,raamana/neuropredict,neuropredict/run_workflow.py,c403dc98b81ede3f9895423796e7117a7bcb9822,0a969bc52ee97ec1ab774fba5757563cc86cd65a,TODO option to print saved options (from previous runs): simple and verbose
aahhdbhje,raamana/neuropredict,neuropredict/io.py,6f6b20e8120facb27babc4ce229edbe524f5e5d8,STILL_EXISTS,TODO this check for exact match is too conservative: allow for extra subjects\/classes to exist
aahhdbice,raamana/neuropredict,neuropredict/visualize.py,76c8e573c1154d3264e585b24c9a51e19b5e313d,STILL_EXISTS,TODO need a utility compress a super long method name (>50 chars) to < 50 chars
aahhdbiei,raamana/neuropredict,neuropredict/rhst.py,a81353ab19cb349a5416fbd312355ba6c8b861b4,STILL_EXISTS,TODO perhaps k-fold is a better inner CV;
aahhdbihc,raamana/neuropredict,neuropredict/algorithms.py,266cf74e75e84ed3a786a76a3e15cb6ca7bad148,STILL_EXISTS,TODO eigen_solver; path_method could be hyper params for Isomap
aahhdbjbc,raamana/neuropredict,neuropredict/base.py,506deaf2afbed526cc5a84fa7fdfc96cad8e6cd0,STILL_EXISTS,TODO perhaps k-fold is a better inner CV;
aahhdbjce,raamana/neuropredict,neuropredict/results.py,e7e943de1cda45e980338ab0f94ab8b20b6b2c94,STILL_EXISTS,\"\"\" ||  || Module defining methods and classes needed to manage results produced. ||  || \"\"\"
aahhdbjej,raamana/neuropredict,neuropredict/config_neuropredict.py,ccf16b047a273a530269c78ea3524f5469361b7b,4262fac22171608f2b45f918fe8cb298a61a91bd,find out why prefix neg_ is needed
aahhdbjfd,raamana/neuropredict,neuropredict/base.py,b5457514c37a731790fda507d359a3a502567716,STILL_EXISTS,TODO using default scoring metric?
aahhdbjjd,raamana/neuropredict,neuropredict/base.py,8b30b844b79ff9ec61456a77f8b7de57d6def01f,STILL_EXISTS,TODO for API use; pred_model and dim_reducer must be validated here again
aahhdbjji,raamana/neuropredict,neuropredict/classify.py,8d0cbc1ef27be9c44f70a8b2437ea8b8c4cb65f6,STILL_EXISTS,TODO it is possible the column order in predicted_prob may not match
aahhdcaai,raamana/neuropredict,neuropredict/base.py,00cb65e5b5f6a1410095fcb245df516140342edb,STILL_EXISTS,TODO need some simple validation to ensure loaded results are usable
aahhdcaff,raamana/neuropredict,neuropredict/base.py,3b8ba4e20937aa2108143274bdae7883186510ff,STILL_EXISTS,deconfounding targets could be added here in the future if needed
aahhdcaga,raamana/neuropredict,neuropredict/base.py,01b1407b3456d06e283b32f1b4857a2fe0639b88,STILL_EXISTS,column_stack ensures output is a 2D array; needed for sklearn transformers
aahhdcbag,raamana/neuropredict,neuropredict/base.py,5ade4e71d2093b858e08aa2c9efc2e67b089b684,ef7d3d319ee06ce067ed2e8f1071c5ebac5e5ea1,TODO find ways to parallelize - there is a separate branch for this
aahhdcbbi,raamana/neuropredict,neuropredict/reports.py,5ade4e71d2093b858e08aa2c9efc2e67b089b684,STILL_EXISTS,TODO need a consolidated way to identify the variables saved their order
aahhdcbef,raamana/neuropredict,neuropredict/classify.py,b341f2174295b4c4d30ca297d5a6f1c23108746c,STILL_EXISTS,TODO pass CVResults data to visualize.freq_hist_misclassifications
aahhdcbeg,raamana/neuropredict,neuropredict/regress.py,b341f2174295b4c4d30ca297d5a6f1c23108746c,STILL_EXISTS,TODO implement outlier detection
aahhdcbgb,raamana/neuropredict,neuropredict/algorithms.py,079034a3fdf4b6de7fc4ba7eda2961c274098e76,STILL_EXISTS,TODO quantile
aahhdcbha,raamana/neuropredict,neuropredict/algorithms.py,49fa152e97afaf14cb024b1f16063b01692766c8,STILL_EXISTS,TODO consult literature for better selection of ranges
aahhdcbif,raamana/neuropredict,neuropredict/regress.py,a2f5e3553ca04d40c85500d51a73bf2bc7a808f1,STILL_EXISTS,# TODO likely buggy; double check
aahhdccad,raamana/neuropredict,neuropredict/results.py,a44a3c50d5ad254eae9a0ff3d9b915dcd0e8d02e,STILL_EXISTS,dynamically computing whats needed
aahhdccaf,raamana/neuropredict,neuropredict/results.py,307ea4540ca0484dcc27b6022b268fd22b9ff46e,STILL_EXISTS,TODO should we ensure dataset_id exists already in self._dataset_ids?
aahhdccah,raamana/neuropredict,neuropredict/results.py,307ea4540ca0484dcc27b6022b268fd22b9ff46e,STILL_EXISTS,dynamically computing whats needed
aahhdccbg,raamana/neuropredict,neuropredict/results.py,c5c8e41f3a65adca2072dda8214a28e4a386060f,STILL_EXISTS,TODO find ways to refactor; to reduce reuse of above common code
aahhdcfgc,raviqqe/tensorflow-qnd,qnd/experiment.py,37025d95d9898381bfabfedfdc89ae45a4c90c4a,b7ad749bff79e9ba1d588b884afca891359d6d95,TODO: Where to set this? Or; is this the same as Estimator.model_dir?
aahhdcfhi,raviqqe/tensorflow-qnd,qnd/inputs.py,3f0fd0aed8fa9fc8c23e1bc7b4323fabed6c4e0f,STILL_EXISTS,TODO: Set a value for predictable behavior
aahhdcfhj,raviqqe/tensorflow-qnd,qnd/inputs.py,3f0fd0aed8fa9fc8c23e1bc7b4323fabed6c4e0f,STILL_EXISTS,TODO: Set a proper value for predictable behavior
aahhdcfje,lozuwa/impy,main.py,6ca70074276a3aead0e3d6c6b27dbdb6e47c8d3c,STILL_EXISTS,########TO FIX############
aahhdcgag,lozuwa/impy,main.py,11ac3de5644b794b5547470445ef934a2174d696,STILL_EXISTS,(columns are the number of examples)
aahhdcgbe,lozuwa/impy,main.py,fc23d703c7bd08dd4a97578e95212f6bda64dfab,STILL_EXISTS,Write columns
aahhdcgcf,lozuwa/impy,mongo.py,fc23d703c7bd08dd4a97578e95212f6bda64dfab,STILL_EXISTS,########TO FIX############
aahhdcgci,lozuwa/impy,stats.py,fc23d703c7bd08dd4a97578e95212f6bda64dfab,STILL_EXISTS,\"\"\" || package: Images2Dataset || class: stats || Author: Rodrigo Loza || Description: Utils methods  || \"\"\"
aahhdcgdd,lozuwa/impy,utils.py,fc23d703c7bd08dd4a97578e95212f6bda64dfab,STILL_EXISTS,\"\"\" || package: Images2Dataset || class: utils || Author: Rodrigo Loza || Description: Utils methods  || \"\"\"
aahhdcgeb,lozuwa/impy,main.py,ae861f285f94282ced5e00cec0a835f2ba0e4517,STILL_EXISTS,#################################TO FIX#############################################
aahhdcghd,lozuwa/impy,preprocess.py,741f9539e370ebf856025a1b978b2e8aabe21450,STILL_EXISTS,\"\"\" || package: Images2Dataset || class: imageProcess || Author: Rodrigo Loza || Description: Utils methods  || \"\"\"
aahhdcgia,lozuwa/impy,unitTest.py,741f9539e370ebf856025a1b978b2e8aabe21450,STILL_EXISTS,\"\"\" || package: Images2Dataset || class: unitTests || Author: Rodrigo Loza || Description: Unit tests || \"\"\"
aahhdcjih,lozuwa/impy,preprocess.py,7c5dd1af72f7dbbda8eb316b19e4b3e955f5a2e1,STILL_EXISTS,Fix the excess in slide_window
aahhddaah,lozuwa/impy,load_tensorflow_models.py,4ebc2c3ef97a0c0cf24bfa9996035b399cc0e329,STILL_EXISTS,\"\"\"  || Author: Rodrigo Loza  || Company: pfm Medical Bolivia  || Description: Loads a tensorflow self.graph model || \"\"\"
aahhddace,lozuwa/impy,DataAugmentation.py,be38c09b97916f51424aba2250521023aec876c5,659fb70440b780cb58356890a3f5401caf8b62e0,\"\"\" || package: Images2Dataset || class: DataAugmentation || Author: Rodrigo Loza || Description: Common data augmentation operations  || for an image. || \"\"\"
aahhddadb,lozuwa/impy,DataAugmentationMethods.py,be38c09b97916f51424aba2250521023aec876c5,STILL_EXISTS,\"\"\" || package: Images2Dataset || class: DataAugmentationMethods || Author: Rodrigo Loza || Description: Common data augmentation operations  || for an image. || \"\"\"
aahhddade,lozuwa/impy,ImagePreprocessing.py,be38c09b97916f51424aba2250521023aec876c5,98f12e9d18bf5f3914ada8deb819122953724ad2,\"\"\" || package: Images2Dataset || class: ImagePreprocessing || Author: Rodrigo Loza || Description: Common pre-processing operations  || for an image. || \"\"\"
aahhddafh,lozuwa/impy,ImagePreprocessing.py,be38c09b97916f51424aba2250521023aec876c5,STILL_EXISTS,Fix the excess in slide_window
aahhddbad,lozuwa/impy,images2Dataset.py,be38c09b97916f51424aba2250521023aec876c5,b69a24d8e60cc2bb3c3cee080ff1199e549c5b0d,\"\"\" || package: impy || class: Images2Dataset || Author: Rodrigo Loza || Description: Class that loads an image dataset. || We recommend giving some structure to your dataset before || using this class. || * The folder's structure should be like this: || \t\t\t\t\t\t- Data set\/ || \t\t\t\t\t\t\t\t- Folder class 0\/ || \t\t\t\t\t\t\t\t\t- Image 1 || \t\t\t\t\t\t\t\t\t- Image 2  || \t\t\t\t\t\t\t\t\t- ... || \t\t\t\t\t\t\t\t- Folder class 1\/ || \t\t\t\t\t\t\t\t\t- Image 1 || \t\t\t\t\t\t\t\t\t- Image 2  || \t\t\t\t\t\t\t\t\t- ... || \t\t\t\t\t\t\t\t- ... || - You should provide the path to the Data set folder. || - We recommend giving the data set folder a descriptive name. || - The nested folders should contain the name of the class. || - The file names of the images inside the folders don't matter. If you || \twould like to give them proper names; then use the formatDataset method. || \"\"\"
aahhddcfa,lozuwa/impy,DataAugmentation.py,659fb70440b780cb58356890a3f5401caf8b62e0,STILL_EXISTS,\"\"\" || package: Images2Dataset || class: DataAugmentation || Author: Rodrigo Loza || Description: Common data augmentation operations  || for an image. || Log: || \tNovemeber; 2017 -> Re-estructured class. || \tDecember; 2017 -> Researched most used data augmentation techniques. || \tMarch; 2018 -> Coded methods. || \"\"\"
aahhddcif,lozuwa/impy,ImagePreprocessing.py,98f12e9d18bf5f3914ada8deb819122953724ad2,STILL_EXISTS,\"\"\" || package: Images2Dataset || class: ImagePreprocessing || Author: Rodrigo Loza || Description: Common pre-processing operations  || for an image. || Log: || \tAugust; 2017 -> Created class and added most relevant functions. || \tSeptember; 2017 -> Created more features for the DivideIntoPatches method. || \tNovember; 2017 -> Refactored all the code to Google style and more OOP paradigm. || \tDecember; 2017 -> Tested refactor to avoid breaking external code. || \tMarch; 2018 -> Refactored to another structure. || \"\"\"
aahhdddfe,lozuwa/impy,images2Dataset.py,b69a24d8e60cc2bb3c3cee080ff1199e549c5b0d,85c2b357b9eb66ed6c065540e079150e11679990,#################################TO FIX#############################################
aahhdddga,lozuwa/impy,images2Dataset.py,b69a24d8e60cc2bb3c3cee080ff1199e549c5b0d,85c2b357b9eb66ed6c065540e079150e11679990,(columns are the number of examples)
aahhdddgj,lozuwa/impy,images2Dataset.py,b69a24d8e60cc2bb3c3cee080ff1199e549c5b0d,85c2b357b9eb66ed6c065540e079150e11679990,Write columns
aahhddgbe,lozuwa/impy,images2Dataset.py,85c2b357b9eb66ed6c065540e079150e11679990,STILL_EXISTS,\"\"\" || package: impy || class: Images2Dataset || Author: Rodrigo Loza || Description: Class that loads an image dataset. || We recommend giving some structure to your dataset before || using this class. || * The folder's structure should be like this: || \t\t\t\t\t\t- Data set\/ || \t\t\t\t\t\t\t\t- Folder class 0\/ || \t\t\t\t\t\t\t\t\t- Image 1 || \t\t\t\t\t\t\t\t\t- Image 2  || \t\t\t\t\t\t\t\t\t- ... || \t\t\t\t\t\t\t\t- Folder class 1\/ || \t\t\t\t\t\t\t\t\t- Image 1 || \t\t\t\t\t\t\t\t\t- Image 2  || \t\t\t\t\t\t\t\t\t- ... || \t\t\t\t\t\t\t\t- ... || - You should provide the path to the Data set folder. || - We recommend giving the data set folder a descriptive name. || - The nested folders should contain the name of the class. || - The file names of the images inside the folders don't matter. If you || \twould like to give them proper names; then use the formatDataset method. || \"\"\"
aahhddjfi,lozuwa/impy,DataAugmentation.py,74e51c10fdef22ac1540ace1d170c47562e50ab1,2936c7a28f9ca4202c517a2cc40ddaf927fec2e9,TODO:
aahhdeabg,lozuwa/impy,BoundingBoxAugmenters.py,2936c7a28f9ca4202c517a2cc40ddaf927fec2e9,STILL_EXISTS,Fix bounding boxes
aahhdefbh,lozuwa/impy,ImageAugmentersMethods.py,36e6987a5fa99dfb2ae386c6239e8c46d35cfaea,STILL_EXISTS,\"\"\" || package: Images2Dataset || class: ImageAugmenters || Author: Rodrigo Loza || Description: Common data augmentation methods for images. || \"\"\"
aahhdegej,lozuwa/impy,Util.py,f950a6adc8609a0a0ee82c83991d7a16dc230f24,STILL_EXISTS,\"\"\" || Author: Rodrigo Loza || Email: lozuwaucb@gmail.com || Description: Implements common file; logic  || operations. || \"\"\"
aahhdeifi,lozuwa/impy,ColorAugmenters.py,103578e99f26568236b6277a4c932311cebd6ba4,STILL_EXISTS,\"\"\" || package: Images2Dataset || class: DataAugmentation || Email: lozuwaucb@gmail.com || Author: Rodrigo Loza || Description: Common data augmentation operations  || for an image. || Log: || \tNovemeber; 2017 -> Re-estructured class. || \tDecember; 2017 -> Researched most used data augmentation techniques. || \tMarch; 2018 -> Coded methods. || \tApril; 2018 -> Redesigned the methods to support multiple bounding || \t\t\t\t\t\t\t\t boxes (traditional data augmentation tools.) || \tApril; 2018 -> Redefined list of augmenters: || \tApril; 2018 -> Separated generic image augmenters into color and space augmenters. ||  || \t--------------- || \tColor dimension || \t--------------- || \t1. Invert color || \t\tInvert the color space of the image. || \t2. Histogram equalization || \t\tEqualize the contrast of an image. || \t3. Change brightness || \t\tChange the brightness of an image. || \t4. Random sharpening || \t\tRandomly add sharpening to an image. || \t5. Add gaussian noise || \t\tAdd normal noise to an image. || \t6. Gaussian blur  || \t\tConvolves the image with a Gaussian filter. || \t7. Shift colors || \t\tSwap the color spaces of an image. || \t8. Fancy PCA || \t\tAdd a color perturbation based on the computation || \t\tof color's space PCA. || \"\"\"
aahhdeijd,lozuwa/impy,ColorAugmentersMethods.py,103578e99f26568236b6277a4c932311cebd6ba4,STILL_EXISTS,\"\"\" || package: Images2Dataset || class: ImageAugmenters || Author: Rodrigo Loza || Description: Common data augmentation methods for images. || \"\"\"
aahhdgafj,omimo/PyMO,pymo/preprocessing.py,e1a5b1e261525394fe073af6462023aba5e0fac9,STILL_EXISTS,List the columns that contain position channels
aahhdgagb,omimo/PyMO,pymo/preprocessing.py,e1a5b1e261525394fe073af6462023aba5e0fac9,STILL_EXISTS,Get the rotation columns that belong to this joint
aahhdgagc,omimo/PyMO,pymo/preprocessing.py,e1a5b1e261525394fe073af6462023aba5e0fac9,STILL_EXISTS,Get the position columns that belong to this joint
aahhdgagd,omimo/PyMO,pymo/preprocessing.py,e1a5b1e261525394fe073af6462023aba5e0fac9,STILL_EXISTS,Make sure the columns are organized in xyz order
aahhdgaii,omimo/PyMO,pymo/preprocessing.py,e1a5b1e261525394fe073af6462023aba5e0fac9,STILL_EXISTS,TODO: simplify this implementation
aahhdgbah,omimo/PyMO,pymo/preprocessing.py,e1a5b1e261525394fe073af6462023aba5e0fac9,STILL_EXISTS,TODO: JointsSelector (x)
aahhdgbai,omimo/PyMO,pymo/preprocessing.py,e1a5b1e261525394fe073af6462023aba5e0fac9,STILL_EXISTS,TODO: SegmentMaker
aahhdgbaj,omimo/PyMO,pymo/preprocessing.py,e1a5b1e261525394fe073af6462023aba5e0fac9,STILL_EXISTS,TODO: DynamicFeaturesAdder
aahhdgbba,omimo/PyMO,pymo/preprocessing.py,e1a5b1e261525394fe073af6462023aba5e0fac9,STILL_EXISTS,TODO: ShapeFeaturesAdder
aahhdgbbb,omimo/PyMO,pymo/preprocessing.py,e1a5b1e261525394fe073af6462023aba5e0fac9,STILL_EXISTS,TODO: DataFrameNumpier (x)
aahhdgbbd,omimo/PyMO,pymo/rotation_tools.py,e1a5b1e261525394fe073af6462023aba5e0fac9,STILL_EXISTS,TODO: Check exp map params
aahhdgbbi,omimo/PyMO,pymo/rotation_tools.py,e1a5b1e261525394fe073af6462023aba5e0fac9,STILL_EXISTS,TODO
aahhdggbh,williamSYSU/TextGAN-PyTorch,generator.py,ab99c7942941525e3b3eaafc92fb34630d7827fd,STILL_EXISTS,TODO: should h be detached from graph (.detach())?
aahhdhbaj,williamSYSU/TextGAN-PyTorch,models/LeakGAN_G.py,473a75173e2029db726d967756acc36a10a2fdd5,STILL_EXISTS,==========My implement according to paper==========
aahhdhbgj,williamSYSU/TextGAN-PyTorch,models/SeqGAN_G.py,473a75173e2029db726d967756acc36a10a2fdd5,STILL_EXISTS,TODO: should h be detached from graph (.detach())?
aahhdhbji,williamSYSU/TextGAN-PyTorch,models/relational_rnn_general.py,473a75173e2029db726d967756acc36a10a2fdd5,STILL_EXISTS,a new fixed params needed for pytorch port of RMC
aahhdhcag,williamSYSU/TextGAN-PyTorch,models/relational_rnn_general.py,473a75173e2029db726d967756acc36a10a2fdd5,STILL_EXISTS,just using one big param is more efficient; rather than this line
aahhdhcbd,williamSYSU/TextGAN-PyTorch,models/relational_rnn_general.py,473a75173e2029db726d967756acc36a10a2fdd5,STILL_EXISTS,needed for truncated BPTT; called at every batch forward pass
aahhdhcde,williamSYSU/TextGAN-PyTorch,models/relational_rnn_general.py,473a75173e2029db726d967756acc36a10a2fdd5,8eb5bf81b04d846069445255c8a3627115b66be9,TODO: check this input flattening is correct
aahhdidfh,williamSYSU/TextGAN-PyTorch,main.py,77bd70d5e5a42c2d801de713a0032aa3d4adab53,edc163548a5a59bc0bb8ef7c3a16797ea698e7e1,TODO
aahhdjhgc,williamSYSU/TextGAN-PyTorch,rebar_toy.py,105a7354f43f705723bcabd74a1eb1b773dd1013,STILL_EXISTS,TODO: NVIL loss
aahheabee,williamSYSU/TextGAN-PyTorch,rebar_toy.py,4bb00942fd8ba31c0f9c20b02e8468cc04c864a0,STILL_EXISTS,TODO: NVIL loss
aahheafch,williamSYSU/TextGAN-PyTorch,instructor/oracle_data/evocatgan_instructor.py,32f607cdfad76e0c7f2fb0122de744b063f04d13,STILL_EXISTS,TODO: for each parent
aahhebafa,williamSYSU/TextGAN-PyTorch,instructor/oracle_data/evocatgan_instructor.py,62cb22da3f078e4a097f0b7c00338839beca0e4b,STILL_EXISTS,TODO: rsgan loss at each category
aahhebbde,williamSYSU/TextGAN-PyTorch,instructor/oracle_data/evocatgan_instructor.py,87922d5b7485b2334e77697e577a443ba2c14b30,STILL_EXISTS,TODO: rsgan loss at each category
aahhebcjd,williamSYSU/TextGAN-PyTorch,models/relational_rnn_slotcat.py,7c18ba3d8f3cd87573c72cc35e0248fcb67b4ffe,STILL_EXISTS,a new fixed params needed for pytorch port of RMC
aahhebdab,williamSYSU/TextGAN-PyTorch,models/relational_rnn_slotcat.py,7c18ba3d8f3cd87573c72cc35e0248fcb67b4ffe,STILL_EXISTS,just using one big param is more efficient; rather than this line
aahhebdai,williamSYSU/TextGAN-PyTorch,models/relational_rnn_slotcat.py,7c18ba3d8f3cd87573c72cc35e0248fcb67b4ffe,STILL_EXISTS,needed for truncated BPTT; called at every batch forward pass
aahhebdcj,williamSYSU/TextGAN-PyTorch,models/relational_rnn_slotcat.py,7c18ba3d8f3cd87573c72cc35e0248fcb67b4ffe,0cc153377e8a06ba49638456cb7eba4826cbe04c,TODO: check this input flattening is correct
aahhebdii,williamSYSU/TextGAN-PyTorch,instructor/oracle_data/evocatgan_instructor.py,042130871850e0c122fca124f6d7459607fa3b26,STILL_EXISTS,TODO: for each parent
aahhebfgh,williamSYSU/TextGAN-PyTorch,instructor/oracle_data/evocatgan_instructor.py,d30235e7b9f8029ad3a739ac707a740817ed061c,STILL_EXISTS,self.update_temperature(adv_epoch; cfg.ADV_train_epoch)   # TODO: update parents temperature
aahhebfjf,williamSYSU/TextGAN-PyTorch,instructor/oracle_data/evogan_instructor.py,d30235e7b9f8029ad3a739ac707a740817ed061c,STILL_EXISTS,self.update_temperature(adv_epoch; cfg.ADV_train_epoch)   # TODO: update parents temperature
aahhebgjb,williamSYSU/TextGAN-PyTorch,instructor/oracle_data/catgan_instructor.py,6613956809137cf24d1912a32fe755ee37ecefd5,STILL_EXISTS,TODO: bug: have to reset
aahhebica,williamSYSU/TextGAN-PyTorch,instructor/oracle_data/evocatgan_instructor.py,ae648bc2ed29146b11aacd98586bc1c3f07f1590,STILL_EXISTS,TODO: bug: have to reset
aahhebigf,williamSYSU/TextGAN-PyTorch,instructor/oracle_data/csgan_instructor.py,ea47eac28a736a2186f62bc5c0917fbe715679ec,STILL_EXISTS,TODO: bug: have to reset
aahhebjaj,williamSYSU/TextGAN-PyTorch,instructor/oracle_data/evogan_instructor.py,02cada9ce2f974590568db33b1bc3b0729c9f2b3,STILL_EXISTS,self.update_temperature(adv_epoch; cfg.ADV_train_epoch)   # TODO: update parents temperature
aahhecafc,williamSYSU/TextGAN-PyTorch,instructor/oracle_data/evogan_instructor.py,3ab20b570120888e7c1db73f603e39bece7507e3,1dd2683a1d42342b06737a80a4cc35dfed145b99,TODO: about to abandon
aahhecaji,williamSYSU/TextGAN-PyTorch,instructor/real_data/catgan_instructor.py,0cc153377e8a06ba49638456cb7eba4826cbe04c,516ede70e3d069e2706bfd6bdf796d83bb496f6a,TODO: bug: have to reset
aahhecbdd,williamSYSU/TextGAN-PyTorch,instructor/real_data/evocatgan_instructor.py,0cc153377e8a06ba49638456cb7eba4826cbe04c,a34d3ff59ddfe5a99c7d55d76473f69f433fa03d,TODO: bug: have to reset
aahhecbfb,williamSYSU/TextGAN-PyTorch,instructor/real_data/evocatgan_instructor.py,0cc153377e8a06ba49638456cb7eba4826cbe04c,STILL_EXISTS,for temp evolutionary # TODO: need to change to bleu3
aahhecbge,williamSYSU/TextGAN-PyTorch,instructor/real_data/evocatgan_instructor.py,0cc153377e8a06ba49638456cb7eba4826cbe04c,STILL_EXISTS,TODO: need to delete
aahheccba,williamSYSU/TextGAN-PyTorch,instructor/real_data/evogan_instructor.py,0cc153377e8a06ba49638456cb7eba4826cbe04c,STILL_EXISTS,for temp evolutionary # TODO: need to change to bleu3
aahhecccd,williamSYSU/TextGAN-PyTorch,instructor/real_data/evogan_instructor.py,0cc153377e8a06ba49638456cb7eba4826cbe04c,1dd2683a1d42342b06737a80a4cc35dfed145b99,TODO: need to delete
aahhecdfi,williamSYSU/TextGAN-PyTorch,instructor/real_data/evocatgan_instructor.py,8db96d5200f3c1b429729a8da0d8af965752ff98,STILL_EXISTS,self.clas_data.reset(train_s)  # TODO: bug: have to reset
aahhecfcd,williamSYSU/TextGAN-PyTorch,instructor/real_data/catgan_instructor.py,d8a7827e4e1f1fe669aa8ca5a7ce192629ed57bd,STILL_EXISTS,self.clas_data.reset(self.test_samples_list)  # TODO: bug: have to reset
aahhecfeb,williamSYSU/TextGAN-PyTorch,instructor/real_data/catgan_instructor.py,d8a7827e4e1f1fe669aa8ca5a7ce192629ed57bd,STILL_EXISTS,for temp evolutionary # TODO: need to change to bleu3
aahhecffd,williamSYSU/TextGAN-PyTorch,instructor/real_data/catgan_instructor.py,d8a7827e4e1f1fe669aa8ca5a7ce192629ed57bd,STILL_EXISTS,TODO: need to delete
aahhecgdb,williamSYSU/TextGAN-PyTorch,run/run_catgan.py,1dd2683a1d42342b06737a80a4cc35dfed145b99,5e57ffe43f4f3603f1cb5ce00f20074d1f3283c7,TODO: change to python
aahhecgdc,williamSYSU/TextGAN-PyTorch,run/run_catgan.py,1dd2683a1d42342b06737a80a4cc35dfed145b99,693e08b7e27ab4fd0d3b5872a105ae33fbbff90b,TODO: change to False
aahhedjdf,bhargavvader/pycobra,pycobra/cobra.py,2822360217b1bb78cc76ef7265f914c36f8b902a,c32072cde3b36f8920499285bf5e65659272bbb0,\"\"\" || TODO: ||  || 1) Vectorise\/cythonise the predict method ||     - could it also be parallelised? || 2) Scikit-Learn conventions ||     - fit - does this kind of thing work here? || 3) Wrappers for other popular ML\/stat libraries ||     - mlpy ||     - all NN libraries ||     - ensembles; etc ||     - your own array as well || 4) Support Set algo: use indice_info from visualisations || \"\"\"
aahhedjgi,bhargavvader/pycobra,pycobra/visualisation.py,2822360217b1bb78cc76ef7265f914c36f8b902a,STILL_EXISTS,note: for default colors to be assigned; the latest version of matplotlib is needed.
aahhedjij,bhargavvader/pycobra,pycobra/visualisation.py,2822360217b1bb78cc76ef7265f914c36f8b902a,STILL_EXISTS,we want to plot only two columns
aahheedhe,bhargavvader/pycobra,docs/plot_regression.py,a760b442c830f475d781793cac08f332461e6063,STILL_EXISTS,The alpha parameter decides how many machines must a point be picked up
aahheedhi,bhargavvader/pycobra,docs/plot_regression.py,a760b442c830f475d781793cac08f332461e6063,STILL_EXISTS,In this particular case; the best performance is obtained by seeking
aahheedii,bhargavvader/pycobra,docs/plot_regression.py,a760b442c830f475d781793cac08f332461e6063,STILL_EXISTS,Increasing the number of line points helps in finding a better optimal
aahheeejb,bhargavvader/pycobra,docs/plot_voronoi_clustering.py,a760b442c830f475d781793cac08f332461e6063,STILL_EXISTS,Helper function to implement the Voronoi.
aahhefecj,silvandeleemput/memcnn,memcnn/models/resnet.py,3f5e699f3c3bd822ae56ac2b476d5400fa1f6d98,STILL_EXISTS,\"\"\"ResNet\/RevNet implementation used for The Reversible Residual Network || Implemented in PyTorch instead of TensorFlow. ||  || @inproceedings{gomez17revnet; ||   author    = {Aidan N. Gomez and Mengye Ren and Raquel Urtasun and Roger B. Grosse}; ||   title     = {The Reversible Residual Network: Backpropagation without Storing Activations} ||   booktitle = {NIPS}; ||   year      = {2017}; || } ||  || Github: https:\/\/github.com\/renmengye\/revnet-public ||  || Author: Sil van de Leemput ||  || \"\"\"
aahheffbd,silvandeleemput/memcnn,memcnn/models/revop.py,a178e16383517ae35710601fb746e9690036134e,ea16dea05708f8375de83c94b5176026927a9ea7,assert if proper split is possible
aahhefgdh,silvandeleemput/memcnn,memcnn/models/tests/test_revop.py,ea16dea05708f8375de83c94b5176026927a9ea7,STILL_EXISTS,TODO: add normal test for affine coupling
aahhefhij,silvandeleemput/memcnn,memcnn/models/revop.py,2b9f15bd833488cf9e56b16b82a9d1fe71eceb88,1820aba6402a29edc2824871c5db7bd7c730cb95,assert if proper split is possible
aahhegbhg,silvandeleemput/memcnn,docs/conf.py,dffe5d493e5c589d3823c1ea6768984caa71ef18,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aahhegccd,silvandeleemput/memcnn,memcnn/experiment/tests/test_manager.py,55fa6124c1b935927569735d8247df1f5ddb6ce4,09ddeeac9ad9a38a82586d4d8346509196225830,TODO fix state dict optimizer parameters asserts
aahhegcff,silvandeleemput/memcnn,memcnn/models/tests/test_memory_saving.py,9d2b84c46f7ab3e86192c088a337b40e419dba2d,fdfa2aaf885f279860ce6ed55feccb44d36f46e7,FIXME: make the grad tensor collected by gc
aahhegehj,silvandeleemput/memcnn,memcnn/models/revop.py,100f4d05aa0942b31731ca8be60bd68143faa384,66b91e3151beca3ccb8a529ee22b3b4c5ee67b22,TODO attempt to use requires_grad to avoid grad injection
aahhegeib,silvandeleemput/memcnn,memcnn/models/revop.py,100f4d05aa0942b31731ca8be60bd68143faa384,66b91e3151beca3ccb8a529ee22b3b4c5ee67b22,TODO add fixed random_seed
aahhegeic,silvandeleemput/memcnn,memcnn/models/revop.py,100f4d05aa0942b31731ca8be60bd68143faa384,66b91e3151beca3ccb8a529ee22b3b4c5ee67b22,TODO add ensure non-zero inputs for testing otherwise resample? (repeat for n times??? \/ or raise warnings)
aahhegfbg,silvandeleemput/memcnn,memcnn/models/revop.py,203e3c13f74d0ea2f598d2592872873e4e651ed9,STILL_EXISTS,To consider:  maybe get_device_states and set_device_states should reside in torch\/random.py?
aahhehbdg,adalca/neurite,src/pytools/generators.py,806f93b76569acbdab7a06b856fe7ec65db68bcc,STILL_EXISTS,TODO: not sure why this needs these specific dimensions
aahhehbge,adalca/neurite,src/pytools/metrics.py,806f93b76569acbdab7a06b856fe7ec65db68bcc,2019b3cae62bdb7740e93b0820fb25692c955cd6,z = tf.to_float(tf.argmax(y_pred; axis=4)); # argmax seems to cause some massive error in keras\/tf :S. Not sure why.
aahhehcfe,adalca/neurite,src/pytools/plot.py,806f93b76569acbdab7a06b856fe7ec65db68bcc,STILL_EXISTS,figure out the number of rows and columns
aahhehcgf,adalca/neurite,src/pytools/.old_metrics.py,2019b3cae62bdb7740e93b0820fb25692c955cd6,STILL_EXISTS,argmax seems to cause some massive error in keras\/tf :S. Not sure why.
aahhehefj,adalca/neurite,src/pytools/callbacks.py,edc4a31383288e8722778e1a88c3d5c136055116,STILL_EXISTS,''' || We'd like the following callback actions for neuron: ||  || - print metrics on the test and validation; especially surface-specific dice || --- Perhaps doable with CSVLogger? || - output graph up to current iteration for each metric || --- Perhaps call CSVLogger or some metric computing callback? || - save dice plots on validation || --- again; expand CSVLogger or similar || - save screenshots of a single test subject [Perhaps just do this as a separate callback?] || --- new callback; PlotSlices ||  || '''
aahhehegb,adalca/neurite,src/pytools/callbacks.py,edc4a31383288e8722778e1a88c3d5c136055116,STILL_EXISTS,TODO: Separate the part that outputs slices.
aahhehegj,adalca/neurite,src/pytools/callbacks.py,edc4a31383288e8722778e1a88c3d5c136055116,STILL_EXISTS,TODO: MOVE THESE TO OTHER CALLBACKS.
aahhehehc,adalca/neurite,src/pytools/callbacks.py,edc4a31383288e8722778e1a88c3d5c136055116,STILL_EXISTS,perhaps achieve by calling Nonbg and then normal accuracy.
aahhehgie,adalca/neurite,neuron/generators.py,ab2a7a7e8f870bdd83955778623a13163ef2e40e,STILL_EXISTS,todo: recompute model
aahhehhba,adalca/neurite,neuron/dataproc.py,36a40500b71f658f44022fd0fb25771b951d5959,STILL_EXISTS,TODO: fix in one line
aahhehhfj,adalca/neurite,neuron/dataproc.py,b73f959d0ad596504ca7f4b316a9adca7b80cd13,STILL_EXISTS,TODO: fix in one line
aahhehifh,adalca/neurite,neuron/metrics.py,9bcf05406490ca72b698db24636e5f2a51a5c82a,STILL_EXISTS,WARNING: SHOULD REALLY BE shape=[batch_size; 1] !!!
aahhehiij,adalca/neurite,neuron/sandbox.py,9bcf05406490ca72b698db24636e5f2a51a5c82a,STILL_EXISTS,is this needed?
aahhehjjd,adalca/neurite,neuron/sandbox.py,4f8dd6a90bc22eace46b1babdba0d59800363112,STILL_EXISTS,TODO: we can do this automatically when setting up the models by having
aahheidcf,adalca/neurite,neuron/models.py,ebe163302b5edc20461aa811a26c24ccc2739f8b,9c169f386f80cd1e8610ac1b61ea09a3c7a8f8e1,TODO: add Cropping3D or Cropping2D if 'valid' padding
aahheidii,adalca/neurite,neuron/models.py,f7d3e908d98d2adca7e9e4a97fc987964961d8a4,8c48f954bf62ae7ac330827c25857f21b07c2244,TODO: add Cropping3D or Cropping2D if 'valid' padding
aahheiedh,adalca/neurite,neuron/sandbox.py,4e24eaf7fe7f59d3d05caf6d473ec4da991250a5,STILL_EXISTS,is this needed?
aahheiegh,adalca/neurite,neuron/models.py,597387f91c8880d074fb3a9ff7a5f8269263b8ee,8c48f954bf62ae7ac330827c25857f21b07c2244,TODO: add Cropping3D or Cropping2D if 'valid' padding
aahheigeb,adalca/neurite,neuron/utils.py,dd5448fc24ad02f1777f4a53bb7fbeffb564869e,STILL_EXISTS,TODO: This needs fixing for when a layer has >1 input;
aahheiged,adalca/neurite,neuron/utils.py,dd5448fc24ad02f1777f4a53bb7fbeffb564869e,STILL_EXISTS,Potential Fix: via the recursion; there could be some 'fake' input layers
aahheigfi,adalca/neurite,neuron/utils.py,81d7ff8b2476ea759bd2fbda6c918ae59753e38e,STILL_EXISTS,TODO: This needs fixing for when a layer has >1 input;
aahheigfj,adalca/neurite,neuron/utils.py,81d7ff8b2476ea759bd2fbda6c918ae59753e38e,STILL_EXISTS,Potential Fix: via the recursion; there could be some 'fake' input layers
aahheihfa,adalca/neurite,neuron/utils.py,4dcb53b9be39611013adfeca56902e1beeba1dae,STILL_EXISTS,TO MOVE
aahheiicf,adalca/neurite,neuron/layers.py,5a37a12e5eea0f044c25b8a7d47e30bbc2c4994e,STILL_EXISTS,TODO: can force a fit through a bunch of volume inputs!
aahheiiee,adalca/neurite,neuron/utils.py,8f05c909d3810d7b80ef163f5c814df88f43b484,STILL_EXISTS,TODO: could output more than a single timepoint!
aahheijbb,adalca/neurite,neuron/utils.py,74dcc91f087d986ad10e05b02a478e2433fc72a7,705a5f20f960ae91062b0422d51741fbf5560d79,Prepare reshape by inserting dimensions with size 1 where needed
aahheijbg,adalca/neurite,neuron/utils.py,74dcc91f087d986ad10e05b02a478e2433fc72a7,705a5f20f960ae91062b0422d51741fbf5560d79,TODO(nolivia): improve performance with a broadcast
aahheijgi,adalca/neurite,neuron/utils.py,612cef97d8b8b9245bef46aebd49a2d7e0c6b439,STILL_EXISTS,Prepare reshape by inserting dimensions with size 1 where needed
aahheijhd,adalca/neurite,neuron/utils.py,612cef97d8b8b9245bef46aebd49a2d7e0c6b439,STILL_EXISTS,TODO(nolivia): improve performance with a broadcast
aahhejafh,adalca/neurite,neuron/plot.py,ed419306e32f959c48a9b5be1aea2a6446398ab4,STILL_EXISTS,figure out the number of rows and columns
aahhejbgi,adalca/neurite,neuron/vae_tools.py,4f0339f74a06cf90dc58b5c3acde69b2c178f2df,STILL_EXISTS,fix pca
aahhejfah,adalca/neurite,neuron/models.py,21fb87c5662eb9dcc868bc0b8d331ed1b180e9e7,ecdfe438e6c9d7389da7cafadea2d59f8864d284,TODO: add Cropping3D or Cropping2D if 'valid' padding
aahhejfcf,adalca/neurite,neuron/utils.py,e7d27831228fae1900f9f50471db5ca82247cd89,STILL_EXISTS,ends of linear corrective function
aahhejhhi,adalca/neurite,neuron/models.py,316708cc5ffd0f52cba0174e4cbefba451168578,STILL_EXISTS,TODO: add Cropping3D or Cropping2D if 'valid' padding
aahhejjfj,adalca/neurite,neurite/tf/utils/utils.py,66d89b0185e6e52cf09be7bd290a1c917e0cb001,STILL_EXISTS,Prepare reshape by inserting dimensions with size 1 where needed
aahhejjge,adalca/neurite,neurite/tf/utils/utils.py,66d89b0185e6e52cf09be7bd290a1c917e0cb001,STILL_EXISTS,TODO(nolivia): improve performance with a broadcast
aahhejjhd,adalca/neurite,neurite/tf/utils/utils.py,66d89b0185e6e52cf09be7bd290a1c917e0cb001,STILL_EXISTS,ends of linear corrective function
aahhfaaic,adalca/neurite,neurite/tf/callbacks.py,550c7d8164e1aaff76c2243729a32e9a53a8390f,STILL_EXISTS,TODO: show_example_prediction_result is actually in neuron_sandbox for now
aahhfaaji,adalca/neurite,neurite/tf/layers.py,0fd81fccfbdf41f24659ee3cc0a625332f8c660a,STILL_EXISTS,TODO: this hsould be cleaned up a bit; no need to loop and concat...
aahhfabcb,fukatani/stacked_generalization,stacked_generalization.py,fc0254ea95a24645b6f7888431ff5628da716e87,bc7dcc2838c7cf7f187597ff7cc5c74c8511fdd4,TODO
aahhfabcd,fukatani/stacked_generalization,util.py,fc0254ea95a24645b6f7888431ff5628da716e87,STILL_EXISTS,Since subclasses must implement either _iter_test_masks or
aahhfabge,fukatani/stacked_generalization,stacked_generalization/lib/stacking.py,537f8c125638a0fd0238dd8e5421e70804954b65,4c2f3078e4c26ce16c63e351eb26b660372c1038,TODO load from pickle
aahhfabgf,fukatani/stacked_generalization,stacked_generalization/lib/stacking.py,537f8c125638a0fd0238dd8e5421e70804954b65,2da9880eea247cd8ecbd549f8211a74810170a87,TODO one file
aahhfabib,brandonrobertz/SparseLSH,LSHash.py,26add3a9f2cb05cfc0bca27f0c0967edf1644700,STILL_EXISTS,TODO change to custom config details
aahhfabie,brandonrobertz/SparseLSH,LSHash.py,cfb057a37acf66c986eca8b437171d80964c2af4,STILL_EXISTS,TODO change to custom config details
aahhfabif,brandonrobertz/SparseLSH,LSHash.py,cfb057a37acf66c986eca8b437171d80964c2af4,STILL_EXISTS,TODO fix it
aahhfacdh,brandonrobertz/SparseLSH,sparselsh/lsh.py,f3bdb2a61627bc3c793cc7f9b57f03f0830654d5,STILL_EXISTS,TODO: load sparse file
aahhfacdj,brandonrobertz/SparseLSH,sparselsh/lsh.py,f3bdb2a61627bc3c793cc7f9b57f03f0830654d5,STILL_EXISTS,TODO: make this calculation support sparse
aahhfacea,brandonrobertz/SparseLSH,sparselsh/lsh.py,f3bdb2a61627bc3c793cc7f9b57f03f0830654d5,STILL_EXISTS,TODO: are there sparse
aahhfacec,brandonrobertz/SparseLSH,sparselsh/lsh.py,f3bdb2a61627bc3c793cc7f9b57f03f0830654d5,STILL_EXISTS,TODO: make sure we never get a tuple here
aahhfaced,brandonrobertz/SparseLSH,sparselsh/lsh.py,f3bdb2a61627bc3c793cc7f9b57f03f0830654d5,STILL_EXISTS,TODO: assert sparse & leave this sparse
aahhfaceg,brandonrobertz/SparseLSH,sparselsh/lsh.py,f3bdb2a61627bc3c793cc7f9b57f03f0830654d5,3c85c32e096e700b05ff97abbe9930a3c23841c6,TODO: stop sorting when we have top num_results; instead of truncating
aahhfacei,brandonrobertz/SparseLSH,sparselsh/lsh.py,f3bdb2a61627bc3c793cc7f9b57f03f0830654d5,STILL_EXISTS,TODO: sparse? this appears to be on the hashes which are small
aahhfacfc,brandonrobertz/SparseLSH,sparselsh/lsh.py,f3bdb2a61627bc3c793cc7f9b57f03f0830654d5,STILL_EXISTS,TODO: dot to sparse dot; diff.dot(diff)
aahhfacfd,brandonrobertz/SparseLSH,sparselsh/lsh.py,f3bdb2a61627bc3c793cc7f9b57f03f0830654d5,STILL_EXISTS,TODO: sparse sparse.csr_matrix.mean
aahhfacfe,brandonrobertz/SparseLSH,sparselsh/lsh.py,f3bdb2a61627bc3c793cc7f9b57f03f0830654d5,STILL_EXISTS,TODO: sparse; diff.dot(diff)
aahhfacff,brandonrobertz/SparseLSH,sparselsh/lsh.py,f3bdb2a61627bc3c793cc7f9b57f03f0830654d5,STILL_EXISTS,TODO: x and y as sparse: abs(x-y).sum()
aahhfacfg,brandonrobertz/SparseLSH,sparselsh/lsh.py,f3bdb2a61627bc3c793cc7f9b57f03f0830654d5,STILL_EXISTS,TODO: sparse
aahhfacge,brandonrobertz/SparseLSH,sparselsh/storage.py,f3bdb2a61627bc3c793cc7f9b57f03f0830654d5,d44c36ea9ba058ed546dd016ebcaa046fb2528c4,TODO: save\/reconstruct data;indices;indptr arrays w\/ ujson
aahhfacgf,brandonrobertz/SparseLSH,sparselsh/storage.py,f3bdb2a61627bc3c793cc7f9b57f03f0830654d5,STILL_EXISTS,TODO: Remove once we move to sparse lists
aahhfacgh,brandonrobertz/SparseLSH,sparselsh/storage.py,f3bdb2a61627bc3c793cc7f9b57f03f0830654d5,d44c36ea9ba058ed546dd016ebcaa046fb2528c4,TODO: remove once we're all sparse lists
aahhfachb,brandonrobertz/SparseLSH,sparselsh/lsh.py,c17d14a6a719522d951cc9f8bf9c02843af0b608,STILL_EXISTS,TODO: We need to study the speed\/memory benefits of:
aahhfacji,brandonrobertz/SparseLSH,sparselsh/lsh.py,c17d14a6a719522d951cc9f8bf9c02843af0b608,STILL_EXISTS,TODO: to list
aahhfacjj,brandonrobertz/SparseLSH,sparselsh/lsh.py,c17d14a6a719522d951cc9f8bf9c02843af0b608,STILL_EXISTS,TODO: the array cast should be unnecessary
aahhfadaa,brandonrobertz/SparseLSH,sparselsh/lsh.py,c17d14a6a719522d951cc9f8bf9c02843af0b608,STILL_EXISTS,TODO: dot to sparse dot; diff.dot(diff)
aahhfadab,brandonrobertz/SparseLSH,sparselsh/lsh.py,c17d14a6a719522d951cc9f8bf9c02843af0b608,STILL_EXISTS,TODO: x and y as sparse: abs(x-y).sum()
aahhfadbg,brandonrobertz/SparseLSH,sparselsh/lsh.py,d44c36ea9ba058ed546dd016ebcaa046fb2528c4,6bb7800c55d0c8e4d47fd81daf7092931431f37d,TODO: use sorted sets
aahhfadcc,brandonrobertz/SparseLSH,sparselsh/lsh.py,d44c36ea9ba058ed546dd016ebcaa046fb2528c4,STILL_EXISTS,TODO: We need to study the speed\/memory benefits of:
aahhfadhd,brandonrobertz/SparseLSH,sparselsh/lsh.py,67b7c7a1bccb1cd99baace153a8281879447524f,3c85c32e096e700b05ff97abbe9930a3c23841c6,TODO: (do this by replacing set with ordered set)
aahhfaeif,Refefer/Dampr,polymr/polymr.py,f98bc2c6c5389601d64510cf11c29008f53678b6,STILL_EXISTS,\"\"\" || Polymr is a light-weight MapReduce library for single machine computation.  It supports a number of  || useful features such as map and reduce side joins; associative reduces;  || aggregations; multiprocessing; and more. ||  || While the underlying engine uses MapReduce; Polymr is best utilized via it's DSL which provides || higher level functionality for complex workflows. || \"\"\"
aahhfaeig,Refefer/Dampr,polymr/__init__.py,20278ddedd032e2dfb5e7e17a8f3e4d93ae4746b,STILL_EXISTS,\"\"\" || Polymr is a light-weight MapReduce library for single machine computation.  It supports a number of  || useful features such as map and reduce side joins; associative reduces;  || aggregations; multiprocessing; and more. ||  || While the underlying engine uses MapReduce; Polymr is best utilized via it's DSL which provides || higher level functionality for complex workflows. ||  || \"\"\"
aahhfafdb,Refefer/Dampr,dampr/settings.py,1a4aa7ab90327f863c1a659b180f1d2179887705,STILL_EXISTS,To improve pickling speed to disk; dampr batches tuples to disk.  Increase or decrease
aahhfaffg,vzhong/embeddings,setup.py,833c30bb4d37a4ce9438bdbedb75fb65e2632b5b,STILL_EXISTS,You can just specify the packages manually here if your project is
aahhfagef,vzhong/embeddings,docs/source/conf.py,1a74d0d708d86da25e88eff4e771d5f46cda52a0,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aahhfahhj,csxeba/brainforge,evolution/evolution.py,f279269fe2cca44408856f316212e6553c054822,STILL_EXISTS,This hackaround is ugly
aahhfahjc,csxeba/brainforge,evolution/evolution.py,f279269fe2cca44408856f316212e6553c054822,STILL_EXISTS,OK; like; WTF???
aahhfahje,csxeba/brainforge,evolution/evolution.py,f279269fe2cca44408856f316212e6553c054822,STILL_EXISTS,Above snippet is a workaround; because the following won't work:
aahhfaifa,csxeba/brainforge,references/rl_pong.py,f279269fe2cca44408856f316212e6553c054822,STILL_EXISTS,record various intermediates (needed later for backprop)
aahhfaigj,csxeba/brainforge,references/rl_pong.py,f279269fe2cca44408856f316212e6553c054822,STILL_EXISTS,Pong has either +1 or -1 reward exactly when game ends.
aahhfaihc,csxeba/brainforge,evolution/evolution.py,ceda71acac39fe1c2593173132322c1c8bd229be,STILL_EXISTS,TODO: remove this dependency!
aahhfaihd,csxeba/brainforge,evolution/evolution_np.py,ceda71acac39fe1c2593173132322c1c8bd229be,423cf81df1fcc715accc6867d6ea95cc2f1e7638,TODO: remove this dependency
aahhfaiij,csxeba/brainforge,architectures/architectures.py,423cf81df1fcc715accc6867d6ea95cc2f1e7638,171bf2d9c8c7d6943d6e14e4f83cb54d2ff9ce42,TODO: discard this ugly hack!
aahhfajie,csxeba/brainforge,ops/activations.py,b5e76caafe213e024644443a04fb1c5112a8c433,STILL_EXISTS,TODO: test this with numerical gradient testing!
aahhfbcba,csxeba/brainforge,model/backpropagation.py,c149e3a3a76e7692da4cdb6e60253981bfed5d8f,STILL_EXISTS,TODO: optimize this; skip untrainable architecture at the beginning
aahhfbecj,csxeba/brainforge,references/pg_cartpole.py,17c20b71b47fe84df0f2dcdc50335211cfa41182,STILL_EXISTS,Returns: a sample from the policy distribution. The distribution is: move
aahhfbigf,bmcfee/muda,muda/core.py,4cc51ae8e63ff280a6fbcf4678e8003c023aa054,STILL_EXISTS,'''Core functionality for muda'''
aahhfcbgj,bmcfee/muda,muda/base.py,abcb92cf6a2c3797b2041f6fb7288d9a93115010,faba8837177469a1d137876c1ef1271fbf5fcfd0,XXX:    2015-03-25 22:37:15 by Brian McFee <brian.mcfee@nyu.edu>
aahhfcbii,bmcfee/muda,tests/test_deformers.py,dbc221cafc071a7d5eb612976f81fc043714a2d5,STILL_EXISTS,FIXME: time deformer should modify this
aahhfcgbg,bmcfee/muda,muda/base.py,2370b7bb78d7cf342abf3458a85b99d3ae017d20,80ea2a0e6f59a436ca8a5ceff55b1e241bff5252,TODO: replace these with try-except NotImplementedErrors
aahhfchhg,arunponnusamy/cvlib,examples/object_detection_webcam.py,44f0ef8f775e1be6c769ae121366209ea919b864,STILL_EXISTS,To-Do: Add tiny YOLO for real time object detection
aahhfdbfc,ysig/GraKeL,grakel/graph.py,52f80a77eb4177059c31a5b7781a71d6a2ea79dc,STILL_EXISTS,Use your better judgment for the insertion of spaces around arithmetic
aahhfdcij,ysig/GraKeL,grakel/kernels.py,d02a6dc8e40acef868584099538796883e49481b,STILL_EXISTS,needed for nauty graph initialise a small lambda
aahhfdebh,ysig/GraKeL,grakel/graph.py,2b8a7f6cdf259893a99f6923a42388c5495fb18d,STILL_EXISTS,move from index labels to dictionary key labels
aahhfdeci,ysig/GraKeL,grakel/kernels.py,2b8a7f6cdf259893a99f6923a42388c5495fb18d,STILL_EXISTS,needed for nauty graph initialise a small lambda
aahhfdeff,ysig/GraKeL,grakel/kernels/graphlet_sampling.py,e8cdba2ef3e164ec09403ec1e6997c6e4eab63b8,7fdcd6b4a28646d59c1906b4eddb53e7cf63aae7,needed for nauty graph initialise a small lambda
aahhfdghc,ysig/GraKeL,grakel/graph.py,8e00f6f3694df2cfe9a29324aeec745a37aad3a3,8e8e3a52164f37e262e90a22d712f6ec90036daa,Calculate the sparse matrix needed to feed
aahhfdieg,ysig/GraKeL,grakel/kernels/odd_sth.py,a55c1abce0cc0f3b74b1d707b7056c254aa4f61a,STILL_EXISTS,TODO write K calculation in terms of matrix multiplication
aahhfdiha,ysig/GraKeL,grakel/graph.py,de90e4762beceea606dc6f05fcd663fec8c356ac,STILL_EXISTS,TODO support list of tuples
aahhfdihb,ysig/GraKeL,grakel/graph_kernels.py,de90e4762beceea606dc6f05fcd663fec8c356ac,STILL_EXISTS,TODO: support normalization - argument at init
aahhfdihc,ysig/GraKeL,grakel/graph_kernels.py,de90e4762beceea606dc6f05fcd663fec8c356ac,STILL_EXISTS,TODO: support nystrom approximation
aahhfdihd,ysig/GraKeL,grakel/kernels/propagation.py,de90e4762beceea606dc6f05fcd663fec8c356ac,STILL_EXISTS,\"\"\" The propagation kernel as defined in [1]: || ' Neumann M.; Garnett R.; P.A..: Propagation kernels: efficient graph kernels from propagated information. In: Springer. Mach Learn (2016) p.209\u2013245' || \"\"\"
aahhfdija,ysig/GraKeL,grakel/kernels/propagation.py,de90e4762beceea606dc6f05fcd663fec8c356ac,STILL_EXISTS,calculate the Propagation matrix if needed
aahhfdjfa,ysig/GraKeL,install_pynauty.py,4ae69cafbac1a089c5d0ae88928d3088ff96a226,STILL_EXISTS,Move inside pynauty
aahhfdjij,ysig/GraKeL,grakel/graph_kernels.py,b4913894b3ad9c51eabe4d59fa659cc61cc7cd6c,5965cc5750c399db6310d56c597d56d59f749e3e,TODO: maybe apply parallelization?
aahhfeeba,ysig/GraKeL,grakel/kernels/propagation.py,e69f57395d899146ad1ca9e6409eb87f4f298a43,STILL_EXISTS,calculate the Propagation matrix if needed
aahhfefdf,ysig/GraKeL,doc/conf.py,0ebc53b484ef7d249d9cbb0791fa0e9c5731ff51,STILL_EXISTS,this is needed for some reason...
aahhfeffd,ysig/GraKeL,doc/user_manual/code_for_examples/vertex_kernel.py,0ebc53b484ef7d249d9cbb0791fa0e9c5731ff51,STILL_EXISTS,Define the graph format that this kernel needs (if needed)
aahhfefge,ysig/GraKeL,doc/user_manual/code_for_examples/vertex_kernel_advanced.py,0ebc53b484ef7d249d9cbb0791fa0e9c5731ff51,STILL_EXISTS,Define the graph format that this kernel needs (if needed)
aahhfeggh,ysig/GraKeL,install_pynauty.py,35248ac1e3a0fa07a6656d419c8b8d3ccc8d2b2b,STILL_EXISTS,Move inside pynauty
aahhfejid,ysig/GraKeL,grakel/kernels/core_framework.py,ec16d8218225446c9ae165a4813a193ac70e8e2e,STILL_EXISTS,workaround for leaving a sparse representation for x
aahhfejii,ysig/GraKeL,grakel/kernels/core_framework.py,ec16d8218225446c9ae165a4813a193ac70e8e2e,STILL_EXISTS,kernel as subkernel with a custom propagation matrix. This is a workaround!
aahhffdaj,ysig/GraKeL,doc/documentation/code_for_examples/vertex_kernel.py,0dd6987b9e639c211a7e5ccebe8f882dfe0a65d6,STILL_EXISTS,Define the graph format that this kernel needs (if needed)
aahhffdde,ysig/GraKeL,doc/documentation/code_for_examples/vertex_kernel_advanced.py,0dd6987b9e639c211a7e5ccebe8f882dfe0a65d6,STILL_EXISTS,Define the graph format that this kernel needs (if needed)
aahhfffid,nschaetti/EchoTorch,echotorch/nn/FreeRunReservoir.py,0ee474ff99364dd3607e2c2a7ef03dbc7037c6da,STILL_EXISTS,Description : Implement the Echo State Network neural network with feed backs.
aahhffgad,nschaetti/EchoTorch,echotorch/nn/LeakyReservoir.py,0ee474ff99364dd3607e2c2a7ef03dbc7037c6da,STILL_EXISTS,Description : Implement the Echo State Network neural network with leaky units.
aahhffgcd,nschaetti/EchoTorch,echotorch/nn/Reservoir.py,0ee474ff99364dd3607e2c2a7ef03dbc7037c6da,STILL_EXISTS,Description : Implement the Echo State Network neural network.
aahhffgji,nschaetti/EchoTorch,docs/source/conf.py,6a48d81648272ef6691dc09a48775222ae091308,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aahhfficc,nschaetti/EchoTorch,echotorch/datasets/MackeyGlassDataset.py,e8b66b0f89afb84edb234aabab4f7ccbdfabfc87,STILL_EXISTS,Init seed if needed
aahhffiec,nschaetti/EchoTorch,echotorch/datasets/MemTestDataset.py,c6cb864421508afd00f98349067247fc3dccd2bd,STILL_EXISTS,Init seed if needed
aahhffifj,nschaetti/EchoTorch,echotorch/datasets/NARMADataset.py,e2b2e01cb26785c0a31dd34350c22267286009bb,65a77f660afadce12c814c09a862080eed494e31,Init seed if needed
aahhfgchb,nschaetti/EchoTorch,echotorch/nn/ESN.py,19b3b9ccd7c8fafa8aa4f78aad070c3ab14d9fcc,0356352fd1aa83ece050c0b37c014ac74077eb9b,Linear layer if needed
aahhfgedf,nschaetti/EchoTorch,echotorch/datasets/SwitchAttractorDataset.py,7f5d30a60fb64c4acca8128118b3771b5cef7b58,STILL_EXISTS,Init seed if needed
aahhfgjhg,nschaetti/EchoTorch,echotorch/datasets/ReutersC50Dataset.py,59e7332526f4ca8215cf18c420eca7e027af45e5,STILL_EXISTS,Create directory if needed
aahhfhabc,nschaetti/EchoTorch,echotorch/datasets/SFGramDataset.py,3a3bfb0b91263b50f5c0c67d65d3ab561e0e32c4,STILL_EXISTS,Create directory if needed
aahhfhdhg,nschaetti/EchoTorch,echotorch/nn/ESNCell.py,167cfaa22e1709daa18bdfecf92aa8c3bf5fd078,STILL_EXISTS,Rows; columns and values
aahhfibfa,nschaetti/EchoTorch,echotorch/nn/SFACell.py,1b4bf1ddbca70468007d56ac1819b74083baddb2,STILL_EXISTS,Fix covariance matrix
aahhfibff,nschaetti/EchoTorch,echotorch/nn/SFACell.py,1b4bf1ddbca70468007d56ac1819b74083baddb2,STILL_EXISTS,Fix the average
aahhficfc,nschaetti/EchoTorch,echotorch/datasets/LogisticMapDataset.py,26634bc80639a0a9fee6eaea084640e9e5c5bfbe,STILL_EXISTS,Init seed if needed
aahhfihih,nschaetti/EchoTorch,echotorch/nn/PCACell.py,0ce1841fa2f8cdf5f876c1004f30c5f821d9cf9c,STILL_EXISTS,Fix covariance matrix
aahhfihja,nschaetti/EchoTorch,echotorch/nn/PCACell.py,0ce1841fa2f8cdf5f876c1004f30c5f821d9cf9c,STILL_EXISTS,Fix the average
aahhfjjda,nschaetti/EchoTorch,echotorch/datasets/MackeyGlass2DDataset.py,16fa7766c78d5e40dc39d2ed4885616d8d7b3e44,STILL_EXISTS,Init seed if needed
aahhgafjc,nschaetti/EchoTorch,echotorch/utils/matrix_generation/MatrixFactory.py,07617f8f918959783d4f97e08acb3c340f714f6a,STILL_EXISTS,Rows; columns and values
aahhgbjid,nschaetti/EchoTorch,echotorch/utils/conceptors/ConceptorSet.py,88b3710f3912e954b1e6c2b20582144882e300ad,STILL_EXISTS,Init A if needed
aahhgdbbj,nschaetti/EchoTorch,echotorch/nn/conceptors/Conceptor.py,256363e1e65c1c9cef12eddad1e244dc06d29007,STILL_EXISTS,TODO: Check how aperture behave under C addition
aahhgdbcf,nschaetti/EchoTorch,echotorch/nn/conceptors/Conceptor.py,256363e1e65c1c9cef12eddad1e244dc06d29007,STILL_EXISTS,TODO: Check how aperture behave under C substraction
aahhgdccd,nschaetti/EchoTorch,echotorch/nn/reservoir/FreeRunESN.py,256363e1e65c1c9cef12eddad1e244dc06d29007,STILL_EXISTS,TODO: Test
aahhgdcgi,nschaetti/EchoTorch,echotorch/nn/reservoir/FreeRunESNCell.py,256363e1e65c1c9cef12eddad1e244dc06d29007,STILL_EXISTS,TODO: Test
aahhgdigb,nschaetti/EchoTorch,examples/conceptors/morphing_square.py,516b3b0b044f6517e87940dc4d1658071823911d,STILL_EXISTS,Rows and columns
aahhgeaeh,nschaetti/EchoTorch,examples/conceptors/morphing_square_debug.py,2191030a0a354c282c3e23edcc7cb0f7a8345250,STILL_EXISTS,Rows and columns
aahhgeigg,nschaetti/EchoTorch,echotorch/nn/conceptors/IncSPESN.py,d99a6affee2a69b9d27d0a9245e919b81bed0141,STILL_EXISTS,TODO: Add a parameter for averaged output
aahhgffdd,nschaetti/EchoTorch,test/test_morphing_square.py,26e90f100771227c838e8324c14a7073c190a71f,STILL_EXISTS,Rows and columns
aahhgfhgg,nschaetti/EchoTorch,echotorch/nn/conceptors/ConceptorSet.py,2bb7c0852256f96d96ff11111dbc0b29c28dc383,STILL_EXISTS,TODO: Test
aahhgggjh,nschaetti/EchoTorch,echotorch/nn/conceptors/Conceptor.py,3d0fb3567a4dd83535fe3c7005ff4c9b4ab6a635,STILL_EXISTS,TODO: Problem with aperture of a AND of two different conceptors
aahhgibbb,nschaetti/EchoTorch,echotorch/datasets/FromCSVDataset.py,a4c9dbef51ea1de35a01cff96dcd6f7534b634de,STILL_EXISTS,end _find_columns_indices
aahhhbddb,nschaetti/EchoTorch,papers/AntoneloSchrauwen2012/LearningSlow FeaturesWithReservoirComputing.py,d8d9de792fd7ecd4fe7f2a45aa6984b477743d48,STILL_EXISTS,Description : Implement the RC-SFA module.
aahhhbdgc,nschaetti/EchoTorch,papers/AntoneloSchrauwen2012/modules/RCSFA.py,d8d9de792fd7ecd4fe7f2a45aa6984b477743d48,STILL_EXISTS,Description : Implement the RC-SFA module.
aahhhbfaj,nschaetti/EchoTorch,examples/features/independent_component_analysis.py,c9ac1b7c9b5c47795318f28e7bebe0177f0e3a10,STILL_EXISTS,Keep only the needed components
aahhhcbhc,nschaetti/EchoTorch,test/test_narma10_prediction.py,6159b46ed280df738b10e0aa3d290dc4314b883f,STILL_EXISTS,TODO: Check why the CUDA version does 3.7291 (train_nrmse32); 0.16 (test_mse32) and 3.42 (test_nrmse32)
aahhhchjj,nschaetti/EchoTorch,echotorch/datasets/TimeseriesDataset.py,a2faf639eb5013c604a966f183c743a1bb8f98f9,STILL_EXISTS,Columns
aahhhciaa,nschaetti/EchoTorch,echotorch/datasets/TimeseriesDataset.py,a2faf639eb5013c604a966f183c743a1bb8f98f9,STILL_EXISTS,end columns
aahhhciab,nschaetti/EchoTorch,echotorch/datasets/TimeseriesDataset.py,a2faf639eb5013c604a966f183c743a1bb8f98f9,STILL_EXISTS,Columns properties
aahhhciac,nschaetti/EchoTorch,echotorch/datasets/TimeseriesDataset.py,a2faf639eb5013c604a966f183c743a1bb8f98f9,STILL_EXISTS,end columns_properties
aahhhcjfc,nschaetti/EchoTorch,echotorch/datasets/TimeseriesDataset.py,64931844eb08e6648b2dafc0ddf7b6039806fd95,STILL_EXISTS,Selected columns
aahhhdadf,nschaetti/EchoTorch,echotorch/datasets/TimeseriesDataset.py,64931844eb08e6648b2dafc0ddf7b6039806fd95,STILL_EXISTS,Create the tensor for segments (if needed)
aahhhdadh,nschaetti/EchoTorch,echotorch/datasets/TimeseriesDataset.py,64931844eb08e6648b2dafc0ddf7b6039806fd95,STILL_EXISTS,Create the tensor for events (jumps) (if needed)
aahhhdcae,wkcn/MobulaOP,mobula/operator.py,09ec223470d15a7fe6e5ef802b989181a8da7def,STILL_EXISTS,[todo] variable inputs
aahhhdcaf,wkcn/MobulaOP,mobula/operator.py,09ec223470d15a7fe6e5ef802b989181a8da7def,STILL_EXISTS,[todo] variable outputs
aahhhdcbj,wkcn/MobulaOP,mobula/func.py,6fa4372f8a8ef11404df3493c3b31c73fdf4ea35,6d6bba6f4c0377b5dfa5ff26cc88519995e5051d,function overload [todo]
aahhhdcef,wkcn/MobulaOP,mobula_op/glue/common.py,bdef133ddf53d104a37eadc0e349ecbe83aa850f,STILL_EXISTS,''' || 1. @register ||    class XXX || 2. @register(\"OP\") ||    class XXX || 3. @register(a = 3) ||    class XXX || '''
aahhhdech,wkcn/MobulaOP,mobula_op/func.py,9b15a03587c2e7a0f08db44f61587641c9258155,a37ccc691a1ad2435ddabf0ab3f2028b0f1803cc,[TODO] Check Types
aahhhdeec,wkcn/MobulaOP,mobula_op/func.py,a37ccc691a1ad2435ddabf0ab3f2028b0f1803cc,STILL_EXISTS,[TODO] set_device for GPU
aahhhdegd,wkcn/MobulaOP,mobula_op/import_op.py,84713ba81b264db2c327d00e2c04cc79534ce122,STILL_EXISTS,[TODO] template
aahhhdgeb,wkcn/MobulaOP,mobula/dtype.py,c3d1686dfe939baede103ad58bef3e0243f38af0,STILL_EXISTS,ctype.__name__ = 'c_xxx'
aahhhdggh,wkcn/MobulaOP,mobula/func.py,5808fabb0f549e206a9b660d0235aeb64ef38ab5,STILL_EXISTS,move kwargs into args
aahhhdhed,wkcn/MobulaOP,tests/test_building/test_building.py,df25b8835e6891da8c326081b4d5c53ff09141bd,146516b71ed8bf4d0de9270c70ac075663b96605,[TODO] change BUILD_PATH
aahhhdhfd,Mariewelt/OpenChem,data/smiles_data_layer.py,eb660ee39ec421daca066ce6adbbdbdda7250f24,STILL_EXISTS,TODO: packed variable length sequence
aahhhdhfe,Mariewelt/OpenChem,data/graph_data_layer.py,cfce8d3c2e708ae21afb0ede549b3f1e8d5d6e58,STILL_EXISTS,TODO: variable length batching
aahhhdhff,Mariewelt/OpenChem,data/utils.py,8d1ef2fde755a0110737b447c06e22cd319bc6a7,STILL_EXISTS,TODO: packed variable length sequence
aahhhdhha,Mariewelt/OpenChem,modules/encoders/gcn_encoder.py,0a7e3c6a605f0e35c17edb31337ed1939c2b47ec,STILL_EXISTS,TODO: encoding of molecular graph into vector
aahhhdjhe,Mariewelt/OpenChem,data/smiles_protein_data_layer.py,335fbd6a5f3dded9b300455faa508cb80a2b9e40,STILL_EXISTS,TODO: packed variable length sequence
aahhhehhc,Mariewelt/OpenChem,data/smiles_enumerator.py,b9f683a4db77648c9fa1ac0368a48557f6b24950,STILL_EXISTS,Needed if we want to do something like:
aahhhfebh,Mariewelt/OpenChem,setup.py,3328c3a94584bff62a44917d4d84b0a16964c1ac,c290c9d66107261f8e256eb0b8aa6a594004f34c,install_requires=[];              # Required packages; pulls from pip if needed; do not use for Conda deployment
aahhhfegg,Mariewelt/OpenChem,versioneer.py,3328c3a94584bff62a44917d4d84b0a16964c1ac,STILL_EXISTS,maybe improved later
aahhhfehc,Mariewelt/OpenChem,versioneer.py,3328c3a94584bff62a44917d4d84b0a16964c1ac,STILL_EXISTS,unparseable. Maybe git-describe is misbehaving?
aahhhfjcb,Mariewelt/OpenChem,openchem/data/graph_data_layer.py,96a77f140c1757f2bbdbac315925f346872afdc9,STILL_EXISTS,TODO: remove diagonal elements from adjacency matrix
aahhhfjci,Mariewelt/OpenChem,openchem/data/graph_data_layer.py,96a77f140c1757f2bbdbac315925f346872afdc9,STILL_EXISTS,TODO: remove constant 1008 from here
aahhhfjcj,Mariewelt/OpenChem,openchem/data/graph_data_layer.py,96a77f140c1757f2bbdbac315925f346872afdc9,STILL_EXISTS,TODO: is copy needed here?
aahhhfjda,Mariewelt/OpenChem,openchem/data/graph_data_layer.py,96a77f140c1757f2bbdbac315925f346872afdc9,STILL_EXISTS,TODO: the first input token is all ones?
aahhhfjeb,Mariewelt/OpenChem,openchem/criterion/policy_gradient_loss.py,a6e855145cc27c3705c70dc5a9c15f4dac855e82,STILL_EXISTS,Current convention is to run critic only on valid molecules
aahhhfjeh,Mariewelt/OpenChem,openchem/models/MolecularRNN.py,a6e855145cc27c3705c70dc5a9c15f4dac855e82,STILL_EXISTS,TODO: rewrite these two in OpenChem native style
aahhhfjei,Mariewelt/OpenChem,openchem/models/MolecularRNN.py,a6e855145cc27c3705c70dc5a9c15f4dac855e82,STILL_EXISTS,TODO: implement required params
aahhhfjfb,Mariewelt/OpenChem,openchem/models/MolecularRNN.py,a6e855145cc27c3705c70dc5a9c15f4dac855e82,STILL_EXISTS,TODO: check if .eval() creates any problems with batchnorm; dropout; etc.
aahhhfjgh,Mariewelt/OpenChem,openchem/models/MolecularRNN.py,a6e855145cc27c3705c70dc5a9c15f4dac855e82,STILL_EXISTS,# TODO: think how to avoid double sanitization
aahhhfjjd,Mariewelt/OpenChem,openchem/models/MolecularRNN.py,a6e855145cc27c3705c70dc5a9c15f4dac855e82,STILL_EXISTS,TODO: make sure this is consistent with BFSGraphDataset
aahhhgabc,Mariewelt/OpenChem,openchem/models/MolecularRNN.py,a6e855145cc27c3705c70dc5a9c15f4dac855e82,STILL_EXISTS,TODO: load from original checkpoint if previous line fails
aahhhgabg,Mariewelt/OpenChem,openchem/modules/gru_plain.py,a6e855145cc27c3705c70dc5a9c15f4dac855e82,STILL_EXISTS,TODO: use small embedding layer here for edge class
aahhhgaci,Mariewelt/OpenChem,openchem/data/utils.py,4bbd8d95d011e2d8253c5e2eef31e943b7444b3f,STILL_EXISTS,TODO: remove diagonal elements from adjacency matrix
aahhhgada,Mariewelt/OpenChem,openchem/modules/encoders/rnn_encoder.py,a6fe544852d7e4914a4294937dead9615017b991,STILL_EXISTS,TODO: warning: output shape is changed! (batch_first=True) Check hidden
aahhhgaec,Mariewelt/OpenChem,example_configs/molecular_rnn_rl_config.py,f7881b0ceac80b5e364bcca9db5643b59c8fb39b,456968645c375b773fc5d3161e83be6e5a6f1776,TODO: maybe update node rnn input size to include previous predicted node label
aahhhhfee,lixinsu/RCZoo,reader/bidafv1/predictor.py,85c6b03a822d1594ba4c03af7a6931d632259c29,STILL_EXISTS,Tokenize the inputs; perhaps multi-processed.
aahhhhhjd,lixinsu/RCZoo,reader/docqa/predictor.py,85c6b03a822d1594ba4c03af7a6931d632259c29,STILL_EXISTS,Tokenize the inputs; perhaps multi-processed.
aahhhiacd,lixinsu/RCZoo,reader/drqa/predictor.py,85c6b03a822d1594ba4c03af7a6931d632259c29,STILL_EXISTS,Tokenize the inputs; perhaps multi-processed.
aahhhibae,lixinsu/RCZoo,reader/drqa/vector.py,85c6b03a822d1594ba4c03af7a6931d632259c29,STILL_EXISTS,Maybe return without target
aahhhibai,lixinsu/RCZoo,reader/drqa/vector.py,85c6b03a822d1594ba4c03af7a6931d632259c29,9a13907000e8baff969ee0e4b2941002b02f63e4,Maybe return without targets
aahhhibgh,lixinsu/RCZoo,reader/qanet/layers.py,85c6b03a822d1594ba4c03af7a6931d632259c29,STILL_EXISTS,recompute\/expand embeddings if needed
aahhhiceg,lixinsu/RCZoo,reader/qanet/predictor.py,85c6b03a822d1594ba4c03af7a6931d632259c29,STILL_EXISTS,Tokenize the inputs; perhaps multi-processed.
aahhhidbb,lixinsu/RCZoo,reader/qanet/vector.py,85c6b03a822d1594ba4c03af7a6931d632259c29,STILL_EXISTS,Maybe return without target
aahhhieib,lixinsu/RCZoo,reader/rnet/predictor.py,85c6b03a822d1594ba4c03af7a6931d632259c29,STILL_EXISTS,Tokenize the inputs; perhaps multi-processed.
aahhhiffc,lixinsu/RCZoo,reader/rnet/vector.py,85c6b03a822d1594ba4c03af7a6931d632259c29,STILL_EXISTS,Maybe return without target
aahhhifhe,lixinsu/RCZoo,reader/tokenizers/corenlp_tokenizer.py,85c6b03a822d1594ba4c03af7a6931d632259c29,STILL_EXISTS,Sending q will cause the process to quit -- manually override
aahhhjdbh,lixinsu/RCZoo,reader/slqa/predictor.py,33a7781bdc8bd077e00058ad0a35ce5fc87fb9c0,STILL_EXISTS,Tokenize the inputs; perhaps multi-processed.
aahhiadgg,lixinsu/RCZoo,reader/fusionnet/predictor.py,38a95d5371ac6d6aadd5aec07fd94af25434d31f,STILL_EXISTS,Tokenize the inputs; perhaps multi-processed.
aahhidbbb,lixinsu/RCZoo,reader/drqa/utils.py,dcba1838eaea0bf351923827c48b2e6289f04148,STILL_EXISTS,TODO dataet loader added to data directory
aahhidiie,antoinecarme/pyaf,SignalDecomposition/SignalDecomposition.py,26cfd56064964dcc02dca530019dadce51f526f2,STILL_EXISTS,print(\"TimeInfo update : \" ; df1.columns);
aahhidiig,antoinecarme/pyaf,SignalDecomposition/SignalDecomposition.py,26cfd56064964dcc02dca530019dadce51f526f2,STILL_EXISTS,print(\"Transformation update : \" ; df1.columns);
aahhidiii,antoinecarme/pyaf,SignalDecomposition/SignalDecomposition.py,26cfd56064964dcc02dca530019dadce51f526f2,STILL_EXISTS,print(\"Trend update : \" ; df1.columns);
aahhidija,antoinecarme/pyaf,SignalDecomposition/SignalDecomposition.py,26cfd56064964dcc02dca530019dadce51f526f2,STILL_EXISTS,print(\"Cycle update : \" ; df1.columns);
aahhidijc,antoinecarme/pyaf,SignalDecomposition/SignalDecomposition.py,26cfd56064964dcc02dca530019dadce51f526f2,STILL_EXISTS,print(\"AR update : \" ; df1.columns);
aahhidjdd,antoinecarme/pyaf,SignalDecomposition/SignalDecomposition_Time.py,26cfd56064964dcc02dca530019dadce51f526f2,STILL_EXISTS,TODO : this one is not very easy ... too many values.
aahhidjfa,antoinecarme/pyaf,SignalDecomposition/SignalDecomposition_Time.py,26cfd56064964dcc02dca530019dadce51f526f2,STILL_EXISTS,Better handle time delta in months
aahhiecbj,antoinecarme/pyaf,SignalDecomposition/SignalDecomposition_Cycle.py,40c14d42856e24faef47b07422563764af67a8a4,STILL_EXISTS,smallest cycles are better
aahhiedcg,antoinecarme/pyaf,TS_CodeGen_Objects.py,39e67a30112a8e78d01f6e532860886e972fdf33,STILL_EXISTS,print(table.columns);
aahhiedda,antoinecarme/pyaf,SignalDecomposition/SignalDecomposition.py,8fb4d0e9a800bbae26e8862b5df73644bf724e17,STILL_EXISTS,print(\"setParams ; columns\"; iInputDS.columns);
aahhieddb,antoinecarme/pyaf,SignalDecomposition/SignalDecomposition.py,8fb4d0e9a800bbae26e8862b5df73644bf724e17,STILL_EXISTS,print(\"preProcessExogenousVariables ; columns\"; iInputDS.columns);
aahhieddc,antoinecarme/pyaf,SignalDecomposition/SignalDecomposition.py,8fb4d0e9a800bbae26e8862b5df73644bf724e17,80d0bf9b75fbc22350deaa5b1fdaccf6a849ecfb,print(\"preProcessExogenousVariables ; dummy columns\"; lExogenousDummiesDS.columns);
aahhieddd,antoinecarme/pyaf,SignalDecomposition/SignalDecomposition.py,8fb4d0e9a800bbae26e8862b5df73644bf724e17,STILL_EXISTS,print(\"preProcessExogenousVariables ; columns\"; lInputDS.columns);
aahhieddg,antoinecarme/pyaf,SignalDecomposition/SignalDecomposition_AR.py,8fb4d0e9a800bbae26e8862b5df73644bf724e17,STILL_EXISTS,print(\"mAREstimFrame columns :\" ; self.mAREstimFrame.columns);
aahhiedge,antoinecarme/pyaf,SignalDecomposition/SignalDecomposition.py,80d0bf9b75fbc22350deaa5b1fdaccf6a849ecfb,STILL_EXISTS,print(\"preProcessExogenousVariables ; columns\"; lInputDS.columns);
aahhiefca,antoinecarme/pyaf,TS/Exogenous.py,7f4a1151894303a59b678f68c1d120871a285ed3,STILL_EXISTS,print(\"preProcessExogenousVariables ; columns\"; self.mExogenousVariables);
aahhiefcb,antoinecarme/pyaf,TS/Exogenous.py,7f4a1151894303a59b678f68c1d120871a285ed3,STILL_EXISTS,print(\"preProcessExogenousVariables ; dummy columns\"; self.mExogenousDummies);
aahhiefcc,antoinecarme/pyaf,TS/Exogenous.py,7f4a1151894303a59b678f68c1d120871a285ed3,STILL_EXISTS,print(\"EXOG_COLUMNS\"; self.mExogenousDummiesDataFrame.columns);
aahhiefcd,antoinecarme/pyaf,TS/Exogenous.py,7f4a1151894303a59b678f68c1d120871a285ed3,STILL_EXISTS,print(\"BEFORE_EXOG_TRANSFORM_DATASET\" ; df.shape; df.columns);
aahhiefce,antoinecarme/pyaf,TS/Exogenous.py,7f4a1151894303a59b678f68c1d120871a285ed3,STILL_EXISTS,print(\"AFTER_EXOG_TRANSFORM_DATASET\" ; df.shape; df.columns);
aahhiefcg,antoinecarme/pyaf,TS/Exogenous.py,7f4a1151894303a59b678f68c1d120871a285ed3,0472512474cc63ce571c3aa45796b1349540fa4c,TODO : 5 categories. to be added in options.
aahhiefdb,antoinecarme/pyaf,TS/SignalDecomposition_AR.py,7f4a1151894303a59b678f68c1d120871a285ed3,83514c337bde3a115c26b882e6d1266405a1c14f,print(df.columns);
aahhiefff,antoinecarme/pyaf,TS/SignalDecomposition_AR.py,6f0fa2aab059319e1d8f8de42378546db14c500c,83514c337bde3a115c26b882e6d1266405a1c14f,print(self.mFormula; \"\ || \"; lag_df.columns);
aahhieffh,antoinecarme/pyaf,TS/SignalDecomposition_AR.py,6f0fa2aab059319e1d8f8de42378546db14c500c,STILL_EXISTS,print(list(self.mARFrame.columns));
aahhiefhi,antoinecarme/pyaf,Bench/TS_datasets.py,878eda32974ecc5dd4e7aa14f6b2654eae85fbb7,STILL_EXISTS,keep only the first two columns (as date and signal)
aahhiefhj,antoinecarme/pyaf,Bench/TS_datasets.py,878eda32974ecc5dd4e7aa14f6b2654eae85fbb7,STILL_EXISTS,rename the first two columns (as date and signal)
aahhiegge,antoinecarme/pyaf,TS/TimeSeriesModel.py,56589777347bc585bb8d35eb1bf0cd1acc26b19f,STILL_EXISTS,print(\"TimeInfo update : \" ; df1.columns);
aahhieggh,antoinecarme/pyaf,TS/TimeSeriesModel.py,56589777347bc585bb8d35eb1bf0cd1acc26b19f,STILL_EXISTS,print(\"Transformation update : \" ; df1.columns);
aahhieggj,antoinecarme/pyaf,TS/TimeSeriesModel.py,56589777347bc585bb8d35eb1bf0cd1acc26b19f,STILL_EXISTS,print(\"Trend update : \" ; df1.columns);
aahhieghd,antoinecarme/pyaf,TS/TimeSeriesModel.py,56589777347bc585bb8d35eb1bf0cd1acc26b19f,STILL_EXISTS,print(\"Cycle update : \" ; df1.columns);
aahhieghg,antoinecarme/pyaf,TS/TimeSeriesModel.py,56589777347bc585bb8d35eb1bf0cd1acc26b19f,STILL_EXISTS,print(\"AR update : \" ; df1.columns);
aahhiehhe,antoinecarme/pyaf,TS/SignalHierarchy.py,d2301687e55d007ae91cb054dfce1b7a336375d4,STILL_EXISTS,print(\"Forecast Columns \" ; dfapp_out.columns);
aahhiehij,antoinecarme/pyaf,TS/Signal_Grouping.py,77413457ca2229f9a0a2b43c57877bc40467800c,0bedd00df33a749f9dd2c1aa8cbed889ea5548af,print(\"Forecast Columns \" ; dfapp_out.columns);
aahhieiga,antoinecarme/pyaf,TS/TimeSeriesModel.py,371e2ed9a01c4e48c6a342e1743767dbef08740b,STILL_EXISTS,print(self.mModelFrame.columns);
aahhieihd,antoinecarme/pyaf,TS/SignalHierarchy.py,2e851815f7d616df5e84501b9f741ddcbdcf6986,STILL_EXISTS,TODO : use linalg.solve here
aahhifaed,antoinecarme/pyaf,TS/SignalHierarchy.py,0bedd00df33a749f9dd2c1aa8cbed889ea5548af,STILL_EXISTS,level 0 is the original\/physical columns
aahhifcah,antoinecarme/pyaf,TS/Keras_Models.py,0169bfa108fab47f43d766dc10307b96e78759ba,STILL_EXISTS,print(\"mAREstimFrame columns :\" ; self.mAREstimFrame.columns);
aahhifcbh,antoinecarme/pyaf,TS/Keras_Models.py,0169bfa108fab47f43d766dc10307b96e78759ba,STILL_EXISTS,print(df.columns);
aahhifccc,antoinecarme/pyaf,TS/Keras_Models.py,0169bfa108fab47f43d766dc10307b96e78759ba,STILL_EXISTS,print(self.mFormula; \"\ || \"; lag_df.columns);
aahhifdcg,antoinecarme/pyaf,TS/Scikit_Models.py,8dd7cfa6456fbf40b32a6df51782ace0186bc995,STILL_EXISTS,print(\"mAREstimFrame columns :\" ; self.mAREstimFrame.columns);
aahhifdda,antoinecarme/pyaf,TS/Scikit_Models.py,8dd7cfa6456fbf40b32a6df51782ace0186bc995,STILL_EXISTS,print(df.columns);
aahhifddf,antoinecarme/pyaf,TS/Scikit_Models.py,8dd7cfa6456fbf40b32a6df51782ace0186bc995,STILL_EXISTS,print(self.mFormula; \"\ || \"; lag_df.columns);
aahhiffbi,antoinecarme/pyaf,CodeGen/TS_CodeGen_Objects.py,566a2b6dd5b69dbe002ccc9a18f870733d3074dd,3acdc2e60a823df73d23a633ea127f21ab221a40,print(sel1.columns);
aahhiffbj,antoinecarme/pyaf,CodeGen/TS_CodeGen_Objects.py,566a2b6dd5b69dbe002ccc9a18f870733d3074dd,3acdc2e60a823df73d23a633ea127f21ab221a40,print(sel2.columns);
aahhiffca,antoinecarme/pyaf,CodeGen/TS_CodeGen_Objects.py,566a2b6dd5b69dbe002ccc9a18f870733d3074dd,3acdc2e60a823df73d23a633ea127f21ab221a40,print(\"HORIZON_UNION\" ; h ; lUnion.columns);
aahhiffcb,antoinecarme/pyaf,CodeGen/TS_CodeGen_Objects.py,566a2b6dd5b69dbe002ccc9a18f870733d3074dd,3acdc2e60a823df73d23a633ea127f21ab221a40,print(\"HORIZON_UNION_TYPES\" ; h ; [col.type for col in lUnion.columns]);
aahhifffc,antoinecarme/pyaf,CodeGen/TS_CodeGen_Objects.py,f6db3d675be0ad15dd5d3b33ebb6b44873fea30d,STILL_EXISTS,print(sel1.columns);
aahhifffd,antoinecarme/pyaf,CodeGen/TS_CodeGen_Objects.py,f6db3d675be0ad15dd5d3b33ebb6b44873fea30d,STILL_EXISTS,print(sel2.columns);
aahhifffe,antoinecarme/pyaf,CodeGen/TS_CodeGen_Objects.py,f6db3d675be0ad15dd5d3b33ebb6b44873fea30d,STILL_EXISTS,print(\"HORIZON_UNION\" ; h ; lUnion.columns);
aahhiffff,antoinecarme/pyaf,CodeGen/TS_CodeGen_Objects.py,f6db3d675be0ad15dd5d3b33ebb6b44873fea30d,STILL_EXISTS,print(\"HORIZON_UNION_TYPES\" ; h ; [col.type for col in lUnion.columns]);
aahhifgad,antoinecarme/pyaf,TS/SignalHierarchy.py,0c54ff918c4554ccc2f4bcdb947d34822e266ea1,STILL_EXISTS,print(lForecast_DF.columns);
aahhifiad,antoinecarme/pyaf,TS/SignalDecomposition.py,9552f10f9e4eedb8b9905a3ec7a6972120c582ea,STILL_EXISTS,time in exogenous data should be the strictly same type as time in training dataset (join needed)
aahhifihb,antoinecarme/pyaf,TS/SignalHierarchy.py,2ad4a8a95a50a9bc48de3cd5bd0568b36906fd39,STILL_EXISTS,level 0 is the original\/physical columns
aahhifjei,antoinecarme/pyaf,demos/demo1.py,b5397c9a2723364649c953bc8194ba347fcd748a,STILL_EXISTS,list the columns of the forecast dataset
aahhifjfe,antoinecarme/pyaf,TS/Signal_Grouping.py,1de5d2418bf34b8f3bb7ba84cae954bd0c56da85,STILL_EXISTS,Not all columns are mandatory.
aahhigadb,antoinecarme/pyaf,Bench/web_traffic/Forecaster_Backends.py,2ce66c3f7107e63f599075b2d66fd4c6862cf964,STILL_EXISTS,print(df.columns)
aahhigbca,antoinecarme/pyaf,tests/demos/demo1.py,1be91580a18d474a40f2524d1e090aa092d75ce5,STILL_EXISTS,list the columns of the forecast dataset
aahhigbic,antoinecarme/pyaf,TS/Time.py,e63f8316e67ba3092fc8d809ebb5e34dec66fe26,STILL_EXISTS,print(lLastRow.columns ;  df.columns)
aahhigbie,antoinecarme/pyaf,TS/TimeSeriesModel.py,e63f8316e67ba3092fc8d809ebb5e34dec66fe26,STILL_EXISTS,print(\"TimeInfo update : \" ; df1.columns);
aahhigbjc,antoinecarme/pyaf,tests/bugs/issue_94/issue_94.py,217f69b67b200b74945c4a0e570db5dbdaa95921,STILL_EXISTS,list the columns of the forecast dataset
aahhigbjj,antoinecarme/pyaf,TS/Perf.py,02f1f3fd06cd9a0f16bac51d669f5b145bd32132,STILL_EXISTS,this is a temporary work-around for the issue
aahhigech,antoinecarme/pyaf,Bench/TS_datasets.py,6149bf76d7bc81f061b65f9763c8d70e76578194,STILL_EXISTS,keep only the first two columns (as date and signal)
aahhigeci,antoinecarme/pyaf,Bench/TS_datasets.py,6149bf76d7bc81f061b65f9763c8d70e76578194,STILL_EXISTS,rename the first two columns (as date and signal)
aahhigeje,antoinecarme/pyaf,tests/croston/croston_tests.py,f3637638788298f78ffa6cb3ebfd2f2c9346acaa,STILL_EXISTS,list the columns of the forecast dataset
aahhigfaf,antoinecarme/pyaf,TS/Intermittent_Models.py,c421d3f219413bba32c9bebd5aa68fd1014d37de,STILL_EXISTS,print(df.columns)
aahhigfbh,antoinecarme/pyaf,tests/croston/test_croston_fpp2_counts_example.py,71f017a672b148fd338af7b7fd7fa94f267c021c,STILL_EXISTS,list the columns of the forecast dataset
aahhigfce,antoinecarme/pyaf,tests/croston/test_croston_fpp2_counts_example_optimize.py,f4455eb3e91643f8d49d91c3aedec413a2c7ae39,STILL_EXISTS,list the columns of the forecast dataset
aahhigfda,antoinecarme/pyaf,tests/croston/test_croston_fpp2_counts_example_slow_mode.py,f4455eb3e91643f8d49d91c3aedec413a2c7ae39,STILL_EXISTS,list the columns of the forecast dataset
aahhigije,antoinecarme/pyaf,pyaf/Bench/web_traffic/Forecaster_Backends.py,0436dc3eb389f1c80e12a878a9805e24de8e0012,STILL_EXISTS,print(df.columns)
aahhigjja,antoinecarme/pyaf,pyaf/CodeGen/TS_CodeGen_Objects.py,0436dc3eb389f1c80e12a878a9805e24de8e0012,STILL_EXISTS,print(table.columns);
aahhihacb,antoinecarme/pyaf,pyaf/CodeGen/TS_CodeGen_Objects.py,0436dc3eb389f1c80e12a878a9805e24de8e0012,STILL_EXISTS,print(sel1.columns);
aahhihacc,antoinecarme/pyaf,pyaf/CodeGen/TS_CodeGen_Objects.py,0436dc3eb389f1c80e12a878a9805e24de8e0012,STILL_EXISTS,print(sel2.columns);
aahhihacd,antoinecarme/pyaf,pyaf/CodeGen/TS_CodeGen_Objects.py,0436dc3eb389f1c80e12a878a9805e24de8e0012,STILL_EXISTS,print(\"HORIZON_UNION\" ; h ; lUnion.columns);
aahhihace,antoinecarme/pyaf,pyaf/CodeGen/TS_CodeGen_Objects.py,0436dc3eb389f1c80e12a878a9805e24de8e0012,STILL_EXISTS,print(\"HORIZON_UNION_TYPES\" ; h ; [col.type for col in lUnion.columns]);
aahhihafe,antoinecarme/pyaf,pyaf/TS/Exogenous.py,0436dc3eb389f1c80e12a878a9805e24de8e0012,STILL_EXISTS,print(\"preProcessExogenousVariables ; columns\"; self.mExogenousVariables);
aahhihaff,antoinecarme/pyaf,pyaf/TS/Exogenous.py,0436dc3eb389f1c80e12a878a9805e24de8e0012,STILL_EXISTS,print(\"preProcessExogenousVariables ; dummy columns\"; self.mEncodedExogenous);
aahhihagd,antoinecarme/pyaf,pyaf/TS/Exogenous.py,0436dc3eb389f1c80e12a878a9805e24de8e0012,STILL_EXISTS,print(\"EXOG_COLUMNS\"; self.mEncodedExogenousDataFrame.columns);
aahhihagf,antoinecarme/pyaf,pyaf/TS/Exogenous.py,0436dc3eb389f1c80e12a878a9805e24de8e0012,STILL_EXISTS,print(df2.columns);
aahhihagh,antoinecarme/pyaf,pyaf/TS/Exogenous.py,0436dc3eb389f1c80e12a878a9805e24de8e0012,STILL_EXISTS,print(\"BEFORE_EXOG_TRANSFORM_DATASET\" ; df.shape; df.columns);
aahhihagi,antoinecarme/pyaf,pyaf/TS/Exogenous.py,0436dc3eb389f1c80e12a878a9805e24de8e0012,STILL_EXISTS,print(\"AFTER_EXOG_TRANSFORM_DATASET\" ; df.shape; df.columns);
aahhihaii,antoinecarme/pyaf,pyaf/TS/Keras_Models.py,0436dc3eb389f1c80e12a878a9805e24de8e0012,STILL_EXISTS,print(\"mAREstimFrame columns :\" ; self.mAREstimFrame.columns);
aahhihbab,antoinecarme/pyaf,pyaf/TS/Keras_Models.py,0436dc3eb389f1c80e12a878a9805e24de8e0012,STILL_EXISTS,print(df.columns);
aahhihbgf,antoinecarme/pyaf,pyaf/TS/Perf.py,0436dc3eb389f1c80e12a878a9805e24de8e0012,STILL_EXISTS,this is a temporary work-around for the issue
aahhihbjd,antoinecarme/pyaf,pyaf/TS/Scikit_Models.py,0436dc3eb389f1c80e12a878a9805e24de8e0012,STILL_EXISTS,print(\"mAREstimFrame columns :\" ; self.mAREstimFrame.columns);
aahhihcac,antoinecarme/pyaf,pyaf/TS/Scikit_Models.py,0436dc3eb389f1c80e12a878a9805e24de8e0012,STILL_EXISTS,print(df.columns);
aahhihcah,antoinecarme/pyaf,pyaf/TS/Scikit_Models.py,0436dc3eb389f1c80e12a878a9805e24de8e0012,STILL_EXISTS,print(self.mFormula; \"\ || \"; lag_df.columns);
aahhihcci,antoinecarme/pyaf,pyaf/TS/SignalDecomposition.py,0436dc3eb389f1c80e12a878a9805e24de8e0012,STILL_EXISTS,print(\"setParams ; columns\"; iInputDS.columns);
aahhihcib,antoinecarme/pyaf,pyaf/TS/SignalDecomposition.py,0436dc3eb389f1c80e12a878a9805e24de8e0012,STILL_EXISTS,time in exogenous data should be the strictly same type as time in training dataset (join needed)
aahhihdbh,antoinecarme/pyaf,pyaf/TS/SignalDecomposition_Cycle.py,0436dc3eb389f1c80e12a878a9805e24de8e0012,STILL_EXISTS,smallest cycles are better
aahhihdha,antoinecarme/pyaf,pyaf/TS/SignalHierarchy.py,0436dc3eb389f1c80e12a878a9805e24de8e0012,f7b58107f62182952ebf7c0487ccb0c68ba9137f,print(\"Forecast Columns \" ; dfapp_out.columns);
aahhihdhb,antoinecarme/pyaf,pyaf/TS/SignalHierarchy.py,0436dc3eb389f1c80e12a878a9805e24de8e0012,94300897ce6ce248e63f6f69b484f051b6c4c1f4,print(lForecast_DF.columns);
aahhihdje,antoinecarme/pyaf,pyaf/TS/SignalHierarchy.py,0436dc3eb389f1c80e12a878a9805e24de8e0012,STILL_EXISTS,TODO : use linalg.solve here
aahhihebf,antoinecarme/pyaf,pyaf/TS/Signal_Grouping.py,0436dc3eb389f1c80e12a878a9805e24de8e0012,STILL_EXISTS,Not all columns are mandatory.
aahhihegc,antoinecarme/pyaf,pyaf/TS/Time.py,0436dc3eb389f1c80e12a878a9805e24de8e0012,STILL_EXISTS,print(lLastRow.columns ;  df.columns)
aahhiheia,antoinecarme/pyaf,pyaf/TS/TimeSeriesModel.py,0436dc3eb389f1c80e12a878a9805e24de8e0012,STILL_EXISTS,print(self.mModelFrame.columns);
aahhiheii,antoinecarme/pyaf,pyaf/TS/TimeSeriesModel.py,0436dc3eb389f1c80e12a878a9805e24de8e0012,STILL_EXISTS,print(\"Transformation update : \" ; df1.columns);
aahhiheja,antoinecarme/pyaf,pyaf/TS/TimeSeriesModel.py,0436dc3eb389f1c80e12a878a9805e24de8e0012,STILL_EXISTS,print(\"TimeInfo update : \" ; df1.columns);
aahhihejc,antoinecarme/pyaf,pyaf/TS/TimeSeriesModel.py,0436dc3eb389f1c80e12a878a9805e24de8e0012,STILL_EXISTS,print(\"Trend update : \" ; df1.columns);
aahhihejg,antoinecarme/pyaf,pyaf/TS/TimeSeriesModel.py,0436dc3eb389f1c80e12a878a9805e24de8e0012,STILL_EXISTS,print(\"Cycle update : \" ; df1.columns);
aahhihejj,antoinecarme/pyaf,pyaf/TS/TimeSeriesModel.py,0436dc3eb389f1c80e12a878a9805e24de8e0012,STILL_EXISTS,print(\"AR update : \" ; df1.columns);
aahhihgjf,antoinecarme/pyaf,tests/hierarchical/test_hierarchy_AU_AllMethods_Exogenous_all_nodes.py,25d8cbfd4b59166ba748d5cd42fbcd7ffe925f0e,STILL_EXISTS,not needed. exogfenous data are missing when not available.
aahhihhad,antoinecarme/pyaf,tests/hierarchical/test_hierarchy_AU_AllMethods_Exogenous_per_node.py,44ccc27cd22db09c178cc2fb642b1b20780917fe,STILL_EXISTS,not needed. exogfenous data are missing when not available.
aahhihhbh,antoinecarme/pyaf,tests/hierarchical/test_grouped_signals_AllMethods_Exogenous_all_nodes.py,275fa5da1c0c6e12224e2c4f965f58c87345e3c8,STILL_EXISTS,not needed. exogenous data are missing when not available.
aahhihhce,antoinecarme/pyaf,tests/hierarchical/test_grouped_signals_AllMethods_Exogenous_per_node.py,275fa5da1c0c6e12224e2c4f965f58c87345e3c8,STILL_EXISTS,not needed. exogenous data are missing when not available.
aahhihhfa,antoinecarme/pyaf,pyaf/TS/Scikit_Models.py,54c965c40ee2626e81614325e18e318560eec26d,STILL_EXISTS,print(len(list(lag_df.columns)) ; len(self.mInputNamesAfterSelection))
aahhihjbh,antoinecarme/pyaf,pyaf/TS/SignalHierarchy.py,51814c5319da8f1bfe77c163b7929ec0d90d07fa,f7b58107f62182952ebf7c0487ccb0c68ba9137f,print(lForecast_DF.columns);
aahhiidjh,antoinecarme/pyaf,pyaf/TS/SignalDecomposition_Cycle.py,bfb0f5952529f827b9071c9b3c2cbef31fed73c9,STILL_EXISTS,these are just guessses of how much dataa is needed to get valid signal stats\/means of each seasonal unit.
aahhiidji,antoinecarme/pyaf,pyaf/TS/SignalDecomposition_Cycle.py,bfb0f5952529f827b9071c9b3c2cbef31fed73c9,STILL_EXISTS,TODO : add these in the options. (None; None) => no limit
aahhiiecb,antoinecarme/pyaf,pyaf/TS/SignalDecomposition_Cycle.py,bfb0f5952529f827b9071c9b3c2cbef31fed73c9,STILL_EXISTS,less MAPE is better; less categories is better; the last is the name of the seasonal to have a total order.
aahhiigbd,antoinecarme/pyaf,pyaf/TS/SignalDecomposition.py,73df9dc36f148885bcbc076a553826da4b60643a,STILL_EXISTS,print((lSignal; lTime; lForecastFrame_i.columns))
aahhiigbe,antoinecarme/pyaf,pyaf/TS/SignalDecomposition.py,73df9dc36f148885bcbc076a553826da4b60643a,STILL_EXISTS,print(iInputDS.shape; iInputDS.columns; self.mSignals; self.mDateColumns; self.mHorizons)
aahhiigjh,kermitt2/delft,sequenceLabelling/evaluator.py,263dfdb9b188a53ba79d1f1e52e6853b6d2ea037,STILL_EXISTS,Todo: if batch_size=1; eval does not work.
aahhiihaa,kermitt2/delft,sequenceLabelling/layers.py,263dfdb9b188a53ba79d1f1e52e6853b6d2ea037,STILL_EXISTS,\"\"\" || Author: Philipp Gross; https:\/\/github.com\/phipleg\/keras\/blob\/crf\/keras\/layers\/crf.py || \"\"\"
aahhiihdd,kermitt2/delft,sequenceLabelling/tagger.py,263dfdb9b188a53ba79d1f1e52e6853b6d2ea037,b445c177a4543617c843f24943bb00e69ab24b36,todo delimiter changeable
aahhiihea,kermitt2/delft,sequenceLabelling/trainer.py,263dfdb9b188a53ba79d1f1e52e6853b6d2ea037,STILL_EXISTS,todo: if valid set if None; create it as random segment of the shuffled train set
aahhiiigj,kermitt2/delft,utilities/Utilities.py,263dfdb9b188a53ba79d1f1e52e6853b6d2ea037,STILL_EXISTS,todo: check that this is correct
aahhiijfa,kermitt2/delft,textClassification/reader.py,ca178077fc8e26ff7b2080e63582d1c97e4425b3,19c7981f04773076ab26a4fd458c0f0622663571,classes = df.columns[2:]
aahhijbbh,kermitt2/delft,textClassification/preprocess.py,31511a3ae382448cfd6941768fea3bd4038c571d,STILL_EXISTS,TBD: use better initializers (uniform; etc.)
aahhijbfb,kermitt2/delft,sequenceLabelling/preprocess.py,f038b5257eb1d8187c7a0146d8aa78d49654949d,f2a05804d1cdff809547b862c3c0b0a5d33ddc6d,TBD: use better initializers (uniform; etc.)
aahhijcgb,kermitt2/delft,sequenceLabelling/evaluation.py,300f79230cbce0cfda2f019ba83bd9e9c7df39fd,STILL_EXISTS,\"\"\"Metrics to assess performance on sequence labeling task given prediction || Functions named as ``*_score`` return a scalar value to maximize: the higher || the better || \"\"\"
aahhijchj,kermitt2/delft,sequenceLabelling/preprocess.py,300f79230cbce0cfda2f019ba83bd9e9c7df39fd,f2a05804d1cdff809547b862c3c0b0a5d33ddc6d,TBD: use better initializers (uniform; etc.)
aahhijeba,kermitt2/delft,utilities/bilm/data.py,147a7dcd4b6ba61286a960f9b3ac5e38577960c8,STILL_EXISTS,TODO: properly handle <UNK>
aahhijehf,kermitt2/delft,utilities/bilm/model.py,147a7dcd4b6ba61286a960f9b3ac5e38577960c8,STILL_EXISTS,finally project down if needed
aahhijfcg,kermitt2/delft,utilities/bilm/training.py,147a7dcd4b6ba61286a960f9b3ac5e38577960c8,STILL_EXISTS,finally project down to projection dim if needed
aahhijgaf,kermitt2/delft,utilities/bilm/training.py,147a7dcd4b6ba61286a960f9b3ac5e38577960c8,STILL_EXISTS,load the checkpoint data if needed
aahhijhff,kermitt2/delft,utilities/Utilities.py,8d2a9a6c6affba0d3f88737d497ee1a24b34ac41,STILL_EXISTS,some punctuation are prefixed by \/ (e.g. \/. or \/? for dialogue turn apparently)
aahhijhhh,kermitt2/delft,nerTagger.py,146fbd0b6143df2069a57c19d807a83e6ba65ab7,84463da696550e05e8365bfea62be0f9f83665ee,split train and valid sets in a random way
aahhjachi,kermitt2/delft,delft/utilities/bert/create_pretraining_data.py,78460393507c4cd468bdfba28a2a72ec98265109,STILL_EXISTS,(2) Blank lines between documents. Document boundaries are needed so
aahhjaddd,kermitt2/delft,delft/utilities/bert/extract_features.py,78460393507c4cd468bdfba28a2a72ec98265109,STILL_EXISTS,The convention in BERT is:
aahhjadii,kermitt2/delft,delft/utilities/bert/modeling.py,78460393507c4cd468bdfba28a2a72ec98265109,STILL_EXISTS,We \"pool\" the model by simply taking the hidden state corresponding
aahhjafec,kermitt2/delft,delft/utilities/bert/run_classifier.py,78460393507c4cd468bdfba28a2a72ec98265109,STILL_EXISTS,The convention in BERT is:
aahhjagcb,kermitt2/delft,delft/utilities/bert/run_classifier_delft.py,78460393507c4cd468bdfba28a2a72ec98265109,STILL_EXISTS,The convention in BERT is:
aahhjaigh,kermitt2/delft,delft/utilities/bert/run_squad.py,78460393507c4cd468bdfba28a2a72ec98265109,STILL_EXISTS,What we really want to return is \"Steve Smith\".
aahhjdjdi,kermitt2/delft,delft/sequenceLabelling/preprocess.py,bc35dd3c023636bae47835bb7ea9deee2d3a8e89,f056e7c35156ddcb62fe9a3a2c340832945bf852,The convention in BERT is:
aahhjffig,kermitt2/delft,delft/sequenceLabelling/wrapper.py,f056e7c35156ddcb62fe9a3a2c340832945bf852,b877e352857be9e444b171e1646b03a51d76e485,Filter out the columns that are not fitting
aahhjffih,kermitt2/delft,delft/sequenceLabelling/wrapper.py,f056e7c35156ddcb62fe9a3a2c340832945bf852,b877e352857be9e444b171e1646b03a51d76e485,print(columns_index)
aahhjfgdd,kermitt2/delft,delft/sequenceLabelling/preprocess.py,a36aaa5d299e364a482cfd7f64dd7db227a22819,383bdf3b22faa2be460caf871e58130ec90ecb1a,Filter out the columns that are not fitting
aahhjfhda,kermitt2/delft,delft/sequenceLabelling/models.py,56a16de2cd288032548267281a2daf69d479f8eb,12e045d9344906b68df5c4ab3a570d86a71ccd30,by convention; PAD is zero
aahhjfhii,kermitt2/delft,delft/sequenceLabelling/preprocess.py,56a16de2cd288032548267281a2daf69d479f8eb,STILL_EXISTS,The convention in BERT is:
aahhjfiii,kermitt2/delft,delft/sequenceLabelling/models.py,938e780f00f7247177a308b76c1cd5a9ac651176,STILL_EXISTS,by bert convention; PAD is zero
aahhjfiij,kermitt2/delft,delft/sequenceLabelling/models.py,938e780f00f7247177a308b76c1cd5a9ac651176,STILL_EXISTS,adding other default bert convention labels
aahhjfjeg,kermitt2/delft,delft/sequenceLabelling/wrapper.py,31f005090c39db3b5768978a2ef85cbcfa3ad317,71836d8fb671976e0c069758c2b1926c2b891412,below a quick fix
aahhjfjjd,kermitt2/delft,delft/sequenceLabelling/wrapper.py,e0591ce48d9ae10721bc8f9473fde7015665638c,71836d8fb671976e0c069758c2b1926c2b891412,below a quick fix:
aahhjfjjf,kermitt2/delft,delft/sequenceLabelling/preprocess.py,71836d8fb671976e0c069758c2b1926c2b891412,STILL_EXISTS,the following is to better keep track of additional tokens added by BERT tokenizer;
aahhjgabj,kermitt2/delft,delft/sequenceLabelling/trainer.py,c83fdf6a3480b45eb92e654e03210f9f83a3e915,STILL_EXISTS,todo: if valid set is None; create it as random segment of the shuffled train set
aahhjgeid,kermitt2/delft,delft/textClassification/reader.py,280da749e14821e7bd908e7def346a49c93c628b,STILL_EXISTS,if 'reuse' in df.columns:
aahhjggfd,kermitt2/delft,softwareClassifier.py,97b74f7a238ee375d9dec2b159a6835db47aa050,STILL_EXISTS,\"\"\" ||     This binary classifier is used in combination with a software mention recognition model; for characterizing ||     the nature of the citation of software in scientific and technical literature.  ||     This classifier predicts if the software introduced by a software mention in a sentence is used ||     or not by the described work.  ||  ||     For the software mention recognizer; see https:\/\/github.com\/ourresearch\/software-mentions ||     and grobidTagger.py in the present project DeLFT. ||  ||     Best architecture\/model is fine-tuned SciBERT.  || \"\"\"
aahhjghjb,r9y9/nnmnkwii,nnmnkwii/datasets/__init__.py,50ea2dac47a0f095a42d3d4cccf638eab03171f5,STILL_EXISTS,TODO: segmentation algorithm
aahhjgidc,r9y9/nnmnkwii,nnmnkwii/autograd/_impl/mlpg.py,262a34945b85b75e0b3ed7f1cf57d41f48cd5bd5,a6b9e51ff2046bb9c506c1af7dd3d09e0a57cd51,TODO: should this be exported?
aahhjgidf,r9y9/nnmnkwii,tests/test_mlpg.py,262a34945b85b75e0b3ed7f1cf57d41f48cd5bd5,STILL_EXISTS,TODO: maybe we need to be careful abount edges? (t=0; t=T-1)
aahhjgjad,r9y9/nnmnkwii,docs/conf.py,7f78fbf0e219df3e8d45626449556e1aad15c76b,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aahhjgjfd,r9y9/nnmnkwii,tests/test_modspec.py,1ddfc61a8b56ce9baf598be6d040f3704a166730,STILL_EXISTS,TODO: this is passing locally; but fails on wercker
aahhjgjff,r9y9/nnmnkwii,tests/test_modspec.py,1ddfc61a8b56ce9baf598be6d040f3704a166730,STILL_EXISTS,TODO
aahhjgjfi,r9y9/nnmnkwii,nnmnkwii/io/hts.py,5dfff79a560f5038e4c310f808c3a7d2ec5a602d,STILL_EXISTS,TODO: should define data structure that represents full-context labels?
aahhjgjgb,r9y9/nnmnkwii,nnmnkwii/io/hts.py,5dfff79a560f5038e4c310f808c3a7d2ec5a602d,587602c6a063f24ec0836fadf7b28f29e92f4b40,TODO: is this really required?
aahhjgjhf,r9y9/nnmnkwii,nnmnkwii/io/hts.py,5dfff79a560f5038e4c310f808c3a7d2ec5a602d,067c0d0090f1d15c1cab2c351210268e82e1476d,TODO: does the magic number really make sense?
aahhjgjhh,r9y9/nnmnkwii,nnmnkwii/io/hts.py,5dfff79a560f5038e4c310f808c3a7d2ec5a602d,STILL_EXISTS,handle HTK wildcards (and lack of them) at ends of label:
aahhjgjia,r9y9/nnmnkwii,nnmnkwii/io/hts.py,5dfff79a560f5038e4c310f808c3a7d2ec5a602d,STILL_EXISTS,TODO: this is really ugly
aahhjhacc,r9y9/nnmnkwii,nnmnkwii/io/hts.py,5dfff79a560f5038e4c310f808c3a7d2ec5a602d,STILL_EXISTS,ugly
aahhjhacg,r9y9/nnmnkwii,nnmnkwii/io/hts.py,5dfff79a560f5038e4c310f808c3a7d2ec5a602d,4f076e9e84bc2242751afe34cd9f5ecfa23a76bb,TODO:
aahhjhadf,r9y9/nnmnkwii,nnmnkwii/io/hts.py,d06f2b3e5d781e91029a5a057ab0fe56d828d3d2,8a76c3e30f5d3b3539acbe8aca56ac3845724d6a,TODO: consider two label alignmetn format
aahhjhadg,r9y9/nnmnkwii,nnmnkwii/io/hts.py,d06f2b3e5d781e91029a5a057ab0fe56d828d3d2,02c9bb2406fcc4738a3c8cca7820f60d529ef922,TODO: consider comments?
aahhjhadj,r9y9/nnmnkwii,nnmnkwii/io/hts.py,4f076e9e84bc2242751afe34cd9f5ecfa23a76bb,4f076e9e84bc2242751afe34cd9f5ecfa23a76bb,TODO
aahhjhafj,r9y9/nnmnkwii,nnmnkwii/frontend/merlin.py,067c0d0090f1d15c1cab2c351210268e82e1476d,STILL_EXISTS,TODO: does the magic number really make sense?
aahhjhbaj,r9y9/nnmnkwii,nnmnkwii/io/hts.py,067c0d0090f1d15c1cab2c351210268e82e1476d,b784fd4e68f123be6716979a174c61d340817c6d,Is this really true? probably no
aahhjhbic,r9y9/nnmnkwii,nnmnkwii/utils/decorators.py,ba0e50738ace2f53048079cd722231615d109357,STILL_EXISTS,TODO?
aahhjhcbc,r9y9/nnmnkwii,tests/test_autograd.py,cf349c272afa2e83506fecf9a73dd37549fa1f58,fe663c57b21dd472c210ee1bbf7d1c80539aba51,TODO: this is passing locally; but fails on wercker
aahhjhcbe,r9y9/nnmnkwii,tests/test_autograd.py,cf349c272afa2e83506fecf9a73dd37549fa1f58,fe663c57b21dd472c210ee1bbf7d1c80539aba51,TODO
aahhjhccg,r9y9/nnmnkwii,tests/test_preprocessing.py,cf349c272afa2e83506fecf9a73dd37549fa1f58,2968836b83760d8daa6f5589519d8db5824c5d5c,TODO: better test
aahhjhcdi,r9y9/nnmnkwii,nnmnkwii/datasets/__init__.py,3d60b70825efdf853b1b49ef4515fa2bf9d7673e,STILL_EXISTS,TODO: Generic Dataloader
aahhjhchd,r9y9/nnmnkwii,tests/test_datasets.py,65db11b64c7d5031e91b89c3894ae1c2b9efe63c,STILL_EXISTS,Since torch's Dataset (and Chainer; and maybe others) assumes dataset has
aahhjhdjj,r9y9/nnmnkwii,nnmnkwii/autograd/_impl/mlpg.py,1ac385ba41b7baad9806d06c72a20dd27ceef71f,STILL_EXISTS,TODO: remove this
aahhjheaf,r9y9/nnmnkwii,nnmnkwii/util/linalg.py,1ac385ba41b7baad9806d06c72a20dd27ceef71f,STILL_EXISTS,transpose for array order difference (probably)
aahhjheah,r9y9/nnmnkwii,nnmnkwii/util/linalg.py,1ac385ba41b7baad9806d06c72a20dd27ceef71f,STILL_EXISTS,TODO
aahhjheec,r9y9/nnmnkwii,nnmnkwii/preprocessing/modspec.py,2968836b83760d8daa6f5589519d8db5824c5d5c,STILL_EXISTS,TODO: this may be removed in future.
aahhjhfej,r9y9/nnmnkwii,nnmnkwii/preprocessing/modspec.py,b956d586d8fdb50e60a310315264ff4ee82b35aa,STILL_EXISTS,TODO: this is ugly...
aahhjhfjc,r9y9/nnmnkwii,nnmnkwii/frontend/merlin.py,75d3f75b46ab272c07e449c871a1baa104440194,STILL_EXISTS,TODO: this pass seems not working
aahhjhfjj,r9y9/nnmnkwii,nnmnkwii/datasets/jsut.py,8afc05cce5b8a6727ed5d0fb874c1ae4e4039f1e,STILL_EXISTS,Hack
aahhjhgei,r9y9/nnmnkwii,setup.py,34181d766e1d42642d35a453841fedca41a18dc1,STILL_EXISTS,Workaround for python 3.7 and bandmat
aahhjhgff,r9y9/nnmnkwii,nnmnkwii/datasets/jvs.py,b723b02c75eea28845519b8520a6c3faf680f934,STILL_EXISTS,TODO: currently there are many problem in falset; so we do not support falset
aahhjhgfi,r9y9/nnmnkwii,nnmnkwii/datasets/jvs.py,b723b02c75eea28845519b8520a6c3faf680f934,STILL_EXISTS,TODO: check lab file
aahhjiaba,keiffster/program-y,src/programy/parser/template/graph.py,eccdbd32202d50a94ad0396988c8580bb55c1123,STILL_EXISTS,WARNING - Not sure I'll even implement this at this time
aahhjicjb,keiffster/program-y,src/test/parser/template/test_nodes.py,76bdb32600141297febb8aa566ff63cd5fc1fe3d,STILL_EXISTS,TODO Better unit testing of external service calls required
aahhjicjc,keiffster/program-y,src/test/parser/template/test_nodes.py,76bdb32600141297febb8aa566ff63cd5fc1fe3d,STILL_EXISTS,TODO More unit tests for different variations here please
aahhjicjj,keiffster/program-y,src/test/parser/template/test_nodes.py,76bdb32600141297febb8aa566ff63cd5fc1fe3d,STILL_EXISTS,TODO Add unit testing here
aahhjidaf,keiffster/program-y,src/test/utils/parsing/test_linenumxml.py,143d6b0308cb45b3b186090c1ffee9b61419e12e,cd4b0125b503fd66da45a51c0d8662b4fce6e8c9,TODO Work out why this fails ?
aahhjidcg,keiffster/program-y,src/programy/config.py,f13c34d87c2a6b7eaa29662c0a1c4f28b0faa9bd,STILL_EXISTS,TODO config xml; yaml and json do not handle missing values; need a better option for default values and mandatory values
aahhjidch,keiffster/program-y,src/programy/parser/pattern/graph.py,f13c34d87c2a6b7eaa29662c0a1c4f28b0faa9bd,a63d5b3074b5ceb3188c284af598c15b523736b9,TODO better handling of <html> type tags
aahhjidci,keiffster/program-y,src/programy/parser/template/evaluator.py,f13c34d87c2a6b7eaa29662c0a1c4f28b0faa9bd,STILL_EXISTS,TODO wrap every resolve method in try\/catch and return \"\" if error
aahhjidcj,keiffster/program-y,src/programy/parser/template/nodes.py,f13c34d87c2a6b7eaa29662c0a1c4f28b0faa9bd,5c25761be7a56e7636de9e06230e73e371b7183a,TODO wrap every resolve method in try\/catch and return \"\" if error
aahhjidda,keiffster/program-y,src/programy/parser/template/nodes.py,f13c34d87c2a6b7eaa29662c0a1c4f28b0faa9bd,fec4ce2bd4ac06c0a0b3bde14596eb90ea0fa813,TODO rename resolve to evaluate
aahhjiddb,keiffster/program-y,src/test/aiml_tests/test_that_aiml.py,f13c34d87c2a6b7eaa29662c0a1c4f28b0faa9bd,STILL_EXISTS,TODO <that><topic> can take single \"1\" and double \"1;2\" indexes
aahhjiddd,keiffster/program-y,src/test/aiml_tests/test_thatstar_aiml.py,f13c34d87c2a6b7eaa29662c0a1c4f28b0faa9bd,STILL_EXISTS,TODO <that><topic> can take single \"1\" and double \"1;2\" indexes
aahhjidde,keiffster/program-y,src/test/aiml_tests/test_topic_aiml.py,f13c34d87c2a6b7eaa29662c0a1c4f28b0faa9bd,STILL_EXISTS,TODO <that><topic> can take single \"1\" and double \"1;2\" indexes
aahhjiddg,keiffster/program-y,src/test/aiml_tests/test_topicstar_aiml.py,f13c34d87c2a6b7eaa29662c0a1c4f28b0faa9bd,STILL_EXISTS,TODO <that><topic> can take single \"1\" and double \"1;2\" indexes
aahhjiddh,keiffster/program-y,src/test/test_dialog.py,f13c34d87c2a6b7eaa29662c0a1c4f28b0faa9bd,cd4b0125b503fd66da45a51c0d8662b4fce6e8c9,TODO dialog needs proper unit testing
aahhjifcd,keiffster/program-y,src/test/aiml_tests/train_tests/test_now_ask_me.py,b37de633dafddda02ed03204865654bae04bf949,17c5920229808ab36f2bcb7101b899a0f65fc7ea,TODO Sort out space in questions
aahhjifce,keiffster/program-y,src/test/aiml_tests/train_tests/test_train_aiml.py,b37de633dafddda02ed03204865654bae04bf949,17c5920229808ab36f2bcb7101b899a0f65fc7ea,TODO Fix the spacing in quotes
aahhjifdh,keiffster/program-y,src/programy/parser/template/nodes.py,ed5612833c7440a32684e47887287905aa85b75a,5c25761be7a56e7636de9e06230e73e371b7183a,TODO This overwrites the self._template with an evaluated one
aahhjifeh,keiffster/program-y,src/programy/config/file/file.py,b281faf1cc1d9d114cd623de89056f42479e5d14,cd4b0125b503fd66da45a51c0d8662b4fce6e8c9,TODO option_name and section are the wrong way round to other function calls
aahhjiffj,keiffster/program-y,src/test/aiml_tests/datetime_tests/test_datetime_aiml.py,44105c52c4ea559445a017f2c58f3eb4660b4305,fec4ce2bd4ac06c0a0b3bde14596eb90ea0fa813,TODO
aahhjiggi,keiffster/program-y,src/programy/parser/pattern/nodes/oneormore.py,5c25761be7a56e7636de9e06230e73e371b7183a,5f31608290faddf8da9a52587ec892b258ec11d4,TODO Add priority words first
aahhjighi,keiffster/program-y,src/programy/parser/pattern/nodes/zeroormore.py,5c25761be7a56e7636de9e06230e73e371b7183a,5f31608290faddf8da9a52587ec892b258ec11d4,TODO Add priority words first
aahhjihih,keiffster/program-y,src/programy/parser/template/nodes/base.py,5c25761be7a56e7636de9e06230e73e371b7183a,a2f116912d752fcc3804f332642bbb66035a1906,TODO wrap every resolve method in try\/catch and return \"\" if error
aahhjiicd,keiffster/program-y,src/programy/parser/template/nodes/learn.py,5c25761be7a56e7636de9e06230e73e371b7183a,2c686ba857b1b4e94d2af6cc67087fa308b73e1d,TODO This overwrites the self._template with an evaluated one
aahhjiihd,keiffster/program-y,src/test/parser/template/test_nodes/test_condtype2.py,5c25761be7a56e7636de9e06230e73e371b7183a,STILL_EXISTS,TODO Add unit tests for <loop \/> construct
aahhjiihe,keiffster/program-y,src/test/parser/template/test_nodes/test_condtype3.py,5c25761be7a56e7636de9e06230e73e371b7183a,STILL_EXISTS,TODO Add unit tests for <loop \/> construct
aahhjiihf,keiffster/program-y,src/test/parser/template/test_nodes/test_mixed.py,5c25761be7a56e7636de9e06230e73e371b7183a,STILL_EXISTS,TODO Add unit testing here
aahhjiihh,keiffster/program-y,src/test/parser/template/test_nodes/test_sraix.py,5c25761be7a56e7636de9e06230e73e371b7183a,STILL_EXISTS,TODO Better unit testing of external service calls required
aahhjiihi,keiffster/program-y,src/test/parser/template/test_nodes/test_think.py,5c25761be7a56e7636de9e06230e73e371b7183a,STILL_EXISTS,TODO More unit tests for different variations here please
aahhjiiih,keiffster/program-y,src/programy/parser/template/nodes/response.py,087d856f60407922cb4c6aec96d954035574f8d8,STILL_EXISTS,Fix provided by @newdev7 28\/03\/2017
aahhjijbh,keiffster/program-y,bots/y-bot/src/test/extensions/geocode/test_geocode.py,c3ed6a8ebe357c240c9186a8c7ea6b34d254a932,STILL_EXISTS,TODO Mock out responses; these tests currently call live Google maps API
aahhjijbi,keiffster/program-y,bots/y-bot/src/test/extensions/maps/test_maps.py,c3ed6a8ebe357c240c9186a8c7ea6b34d254a932,STILL_EXISTS,TODO Mock out responses; these tests currently call live Google maps API
aahhjijbj,keiffster/program-y,src/programy/parser/template/nodes/request.py,c3ed6a8ebe357c240c9186a8c7ea6b34d254a932,STILL_EXISTS,TODO Multiple request calls with AGAIN causes stack fault
aahhjijca,keiffster/program-y,src/programy/parser/template/nodes/star.py,c3ed6a8ebe357c240c9186a8c7ea6b34d254a932,d36b46a9476c54311bc5fbb5d6de59ef836e501c,TODO If the index exceeds the number of stars then an exception is thrown; it should write an error and return None
aahhjjabc,keiffster/program-y,src/programy/parser/template/nodes/sraix.py,17c5920229808ab36f2bcb7101b899a0f65fc7ea,a2f116912d752fcc3804f332642bbb66035a1906,TODO This seems to be an issue
aahhjjbcb,keiffster/program-y,src/programy/parser/template/nodes/indexed.py,375e203ac21aee61c14631307a29f128fa60c5d0,a63d5b3074b5ceb3188c284af598c15b523736b9,TODO Position means question
aahhjjbce,keiffster/program-y,src/programy/parser/template/nodes/indexed.py,375e203ac21aee61c14631307a29f128fa60c5d0,a63d5b3074b5ceb3188c284af598c15b523736b9,See TODO Below
aahhjjbcf,keiffster/program-y,src/programy/parser/template/nodes/indexed.py,375e203ac21aee61c14631307a29f128fa60c5d0,STILL_EXISTS,TODO I think this is wrong
aahhjjbch,keiffster/program-y,src/test/aiml_tests/pattern_set_tests/test_pattern_set_aiml.py,56b3021856b2f94c98c34193c16df9f7b1c051d6,e717969d058b36b854c7129ea79fad4087f88ae8,TODO
aahhjjbdb,keiffster/program-y,src/test/aiml_tests/pattern_set_tests/test_pattern_set_aiml.py,56b3021856b2f94c98c34193c16df9f7b1c051d6,e717969d058b36b854c7129ea79fad4087f88ae8,self.assertEqual(response; \"RED IS A NICE COLOR.\")
aahhjjfhe,keiffster/program-y,src/programy/parser/template/nodes/learn.py,1595af8a8ba1e6af9cf20ffa6c202635415adfef,6c0a8c736fac8f1393940e4aa5bd8c49975f4d96,TODO Handle case when its not a category
aahhjjjaa,keiffster/program-y,src/programy/parser/template/nodes/xml.py,320577ba5f67ae1ea2fcca98baecf654a9c4e42e,6c0a8c736fac8f1393940e4aa5bd8c49975f4d96,TODO We could have nodes here; make sure they are resolved
aahhjjjhi,keiffster/program-y,src/programy/clients/flaskrest.py,7635010c19d73f0466a43a07133a65e0fcf67cf6,6c0a8c736fac8f1393940e4aa5bd8c49975f4d96,TODO Add config option to all GET or throw exception
aahhjjjhj,keiffster/program-y,src/programy/clients/flaskrest.py,7635010c19d73f0466a43a07133a65e0fcf67cf6,6c0a8c736fac8f1393940e4aa5bd8c49975f4d96,TODO Add POST Option
aahhjjjia,keiffster/program-y,src/programy/clients/flaskrest.py,7635010c19d73f0466a43a07133a65e0fcf67cf6,6c0a8c736fac8f1393940e4aa5bd8c49975f4d96,TODO Add config option to all POST or throw exception
aahhjjjjb,keiffster/program-y,src/programy/clients/sanicrest.py,7635010c19d73f0466a43a07133a65e0fcf67cf6,6c0a8c736fac8f1393940e4aa5bd8c49975f4d96,TODO Add config option to all GET or throw exception
aahhjjjjc,keiffster/program-y,src/programy/clients/sanicrest.py,7635010c19d73f0466a43a07133a65e0fcf67cf6,6c0a8c736fac8f1393940e4aa5bd8c49975f4d96,TODO Add POST Option
aahhjjjjd,keiffster/program-y,src/programy/clients/sanicrest.py,7635010c19d73f0466a43a07133a65e0fcf67cf6,6c0a8c736fac8f1393940e4aa5bd8c49975f4d96,TODO Add config option to all POST or throw exception
aahhjjjje,keiffster/program-y,src/programy/clients/sanicrest.py,7635010c19d73f0466a43a07133a65e0fcf67cf6,6c0a8c736fac8f1393940e4aa5bd8c49975f4d96,TODO Set works as configurable option
aahiaaach,keiffster/program-y,src/programy/clients/twitter.py,47d745ed4bd06af0205017150c13b29f694d65b7,6c0a8c736fac8f1393940e4aa5bd8c49975f4d96,TODO Set this to be configurable
aahiaaaeh,keiffster/program-y,src/programy/parser/pattern/nodes/base.py,6c0a8c736fac8f1393940e4aa5bd8c49975f4d96,7dac653c0a1f57eb474050d5479de3fce780fc71,TODO Indentical regexs should be supported both pattern and template
aahiaaaff,keiffster/program-y,src/programy/rdf/collection.py,a17df04bd2d37edba10d4ace560bf4dc1695219a,2b4969179ae341cb58b4e909203fa2720ec6456f,TODO Check for duplicates rdfs
aahiaabed,keiffster/program-y,src/programy/bot.py,bd9776e03b6eecbba9d0e7ec2f56066963d9c604,09fe3365beaf43b9d7979399ce10efd086717b24,TODO Move this to Spelling bass class
aahiaabee,keiffster/program-y,src/programy/bot.py,bd9776e03b6eecbba9d0e7ec2f56066963d9c604,STILL_EXISTS,TODO move this to Conversations base class
aahiaabef,keiffster/program-y,src/programy/bot.py,bd9776e03b6eecbba9d0e7ec2f56066963d9c604,09fe3365beaf43b9d7979399ce10efd086717b24,TODO Move this to spelliing base class
aahiaabeg,keiffster/program-y,src/programy/bot.py,bd9776e03b6eecbba9d0e7ec2f56066963d9c604,09fe3365beaf43b9d7979399ce10efd086717b24,TODO Move this to spelling base class
aahiaabeh,keiffster/program-y,src/programy/bot.py,bd9776e03b6eecbba9d0e7ec2f56066963d9c604,STILL_EXISTS,TODO Method too big; convert to smaller methods
aahiaabfi,keiffster/program-y,src/programy/brain.py,32a9612c4eee358a14b043d43cfb3a8ce164674f,STILL_EXISTS,TODO Issue here when the response is more than just a simple sentence
aahiaabga,keiffster/program-y,src/programy/brain.py,32a9612c4eee358a14b043d43cfb3a8ce164674f,STILL_EXISTS,TODO Added this to catch a failed sentence
aahiaabgi,keiffster/program-y,src/programy/parser/template/nodes/get.py,2726298a11ac073be56db6f1ed9f6a007e1b0168,STILL_EXISTS,TODO Why would you need this test; when is get_conversation(clientid) == None ?
aahiaaebj,keiffster/program-y,src/programy/dialog.py,2009fcdcc3348012d15ea25488560a45407e51f6,STILL_EXISTS,TODO Move sentence_split_charts into a property of tokenizer and move functionality into that class
aahiaaefh,keiffster/program-y,src/programy/extensions/weather/weather.py,dd21e108b9862f7667f7310338852c1ad9612d42,38f04ec62dd5ef4519492d382b17220a51859269,TODO Use 'when\" paramter to extract datapoints
aahiaafag,keiffster/program-y,src/programy/bot.py,4b7c063e2d7b93e616fb823751cda83b53647c4a,362464cbacfd003ba4d32ca751aa2f018581b66b,TODO Either move config into brain or move functionaity into bot
aahiaafba,keiffster/program-y,src/programy/clients/client.py,4b7c063e2d7b93e616fb823751cda83b53647c4a,136383189ac22ca6acbcb28f80dac770618a8154,TODO Replace this with a bot selector
aahiaagbj,keiffster/program-y,src/programy/dialog/dialog.py,4b7c063e2d7b93e616fb823751cda83b53647c4a,38f04ec62dd5ef4519492d382b17220a51859269,TODO Issue here when the response is more than just a simple sentence
aahiaagcb,keiffster/program-y,src/programy/dialog/dialog.py,4b7c063e2d7b93e616fb823751cda83b53647c4a,09fe3365beaf43b9d7979399ce10efd086717b24,TODO Added this to catch a failed sentence
aahiaagci,keiffster/program-y,test/programytest/aiml_tests/sraix_tests/test_sraix_aiml.py,4b7c063e2d7b93e616fb823751cda83b53647c4a,f0811d9aa075fd7778bfc128741ebf40f9d8b615,TODO Add tests here !!!
aahiaagcj,keiffster/program-y,test/programytest/extensions/newsapi/test_aiml.py,4b7c063e2d7b93e616fb823751cda83b53647c4a,38f04ec62dd5ef4519492d382b17220a51859269,TODO; modify this to return mocked json payload
aahiaagda,keiffster/program-y,test/programytest/parser/template/node_tests/test_id.py,4b7c063e2d7b93e616fb823751cda83b53647c4a,f0811d9aa075fd7778bfc128741ebf40f9d8b615,TODO check documentation on this; AIML spec says this returns the bot name
aahiaagdb,keiffster/program-y,test/programytest/test_brain.py,4b7c063e2d7b93e616fb823751cda83b53647c4a,f0811d9aa075fd7778bfc128741ebf40f9d8b615,TODO Add tests here
aahiaagei,keiffster/program-y,src/programy/dialog/storage/file.py,974b4d7f149daabc02bdeb8b477a708efcf2c21d,STILL_EXISTS,TODO clientid could be replaced with context
aahiaagfa,keiffster/program-y,src/programy/parser/template/nodes/get.py,974b4d7f149daabc02bdeb8b477a708efcf2c21d,STILL_EXISTS,TODO Replace bot with client_context
aahiaagfb,keiffster/program-y,src/programy/parser/template/nodes/rand.py,974b4d7f149daabc02bdeb8b477a708efcf2c21d,38f04ec62dd5ef4519492d382b17220a51859269,TODO Same method in every class; move to base class
aahiaagfc,keiffster/program-y,src/programy/rdf/collection.py,974b4d7f149daabc02bdeb8b477a708efcf2c21d,6b090e70d71e43e6a9a9a0d6458baf40a71aca89,TODO No logging statements at all in RDF!
aahiaahhb,keiffster/program-y,src/programy/bot.py,aba1a572ae61f801970a9c87714a91cd519ac26d,09fe3365beaf43b9d7979399ce10efd086717b24,TODO Move to Conversation Storage Class
aahiaahhc,keiffster/program-y,src/programy/bot.py,aba1a572ae61f801970a9c87714a91cd519ac26d,09fe3365beaf43b9d7979399ce10efd086717b24,TODO Create Sentence Processor
aahiaahhf,keiffster/program-y,src/programy/brain.py,aba1a572ae61f801970a9c87714a91cd519ac26d,708696003e7f744fb16d258e768e4b67d8993ccb,TODO move into tokenizer code base
aahiaahhi,keiffster/program-y,src/programy/brain.py,aba1a572ae61f801970a9c87714a91cd519ac26d,708696003e7f744fb16d258e768e4b67d8993ccb,TODO MOve into BrainTreeManager
aahiaahhj,keiffster/program-y,src/programy/brain.py,aba1a572ae61f801970a9c87714a91cd519ac26d,708696003e7f744fb16d258e768e4b67d8993ccb,TODO MOve into Security Manager
aahiaahih,keiffster/program-y,src/programy/clients/polling/twitter/client.py,aba1a572ae61f801970a9c87714a91cd519ac26d,708696003e7f744fb16d258e768e4b67d8993ccb,TODO move all client descriptions into config
aahiaaidb,keiffster/program-y,src/programy/storage/stores/file/store/categories.py,aba1a572ae61f801970a9c87714a91cd519ac26d,38f04ec62dd5ef4519492d382b17220a51859269,TODO fix this
aahiaajcg,keiffster/program-y,src/programy/utils/files/filewriter.py,aba1a572ae61f801970a9c87714a91cd519ac26d,6b090e70d71e43e6a9a9a0d6458baf40a71aca89,TODO Remove this and move to storage
aahiaajda,keiffster/program-y,test/programytest/aiml_tests/train_tests/test_now_ask_me.py,aba1a572ae61f801970a9c87714a91cd519ac26d,38f04ec62dd5ef4519492d382b17220a51859269,TODO; deal with the spacing between the quotes
aahiaajdb,keiffster/program-y,test/programytest/aiml_tests/train_tests/test_train_aiml.py,aba1a572ae61f801970a9c87714a91cd519ac26d,38f04ec62dd5ef4519492d382b17220a51859269,TODO; deal with the spacing between the quotes
aahiaajdj,keiffster/program-y,test/programytest/parser/template/graph_tests/test_learnf.py,aba1a572ae61f801970a9c87714a91cd519ac26d,6b090e70d71e43e6a9a9a0d6458baf40a71aca89,TODO Add checks to ensure learnf data is created in storage
aahiaajee,keiffster/program-y,test/programytest/storage/stores/nosql/redis/test_engine.py,aba1a572ae61f801970a9c87714a91cd519ac26d,6b090e70d71e43e6a9a9a0d6458baf40a71aca89,TODO Fix this
aahiaajib,keiffster/program-y,src/programy/parser/template/nodes/get.py,09fe3365beaf43b9d7979399ce10efd086717b24,38f04ec62dd5ef4519492d382b17220a51859269,TODO; need a load conversation when running in multi client mode using Redis
aahiaajif,keiffster/program-y,src/programy/storage/stores/file/store/categories.py,09fe3365beaf43b9d7979399ce10efd086717b24,6b090e70d71e43e6a9a9a0d6458baf40a71aca89,TODO This fails when subdirs = False and\/or when extension is none
aahiabafc,keiffster/program-y,src/programy/storage/stores/nosql/mongo/store/conversations.py,09fe3365beaf43b9d7979399ce10efd086717b24,6b090e70d71e43e6a9a9a0d6458baf40a71aca89,TODO Check for duplicates and remove
aahiabafe,keiffster/program-y,src/programy/storage/stores/sql/store/conversations.py,09fe3365beaf43b9d7979399ce10efd086717b24,38f04ec62dd5ef4519492d382b17220a51859269,TODO Check for duplicates and remove
aahiabcae,keiffster/program-y,src/programy/bot.py,8625466380acd64cb8fa74ae8665af8c26ffa7d7,35a86cfe6551bfd8e2d6f626576180f55b6357f5,If it ends with a terminator; keep the terminator; otherwise add a full stop
aahiabcbb,keiffster/program-y,src/programy/dialog/joiner/joiner.py,35a86cfe6551bfd8e2d6f626576180f55b6357f5,STILL_EXISTS,If it ends with a terminator; keep the terminator; otherwise add a full stop
aahiabdci,keiffster/program-y,test/programytest/clients/polling/twitter/test_client.py,2d74b702118a148ed021d5016fadc62c55d644b0,7a0bb2b84026d6c03c136421d1f6db008772ead5,TODO this writes to local storage rather than \/tmp
aahiabgdg,keiffster/program-y,src/programy/extensions/admin/rdf.py,298714c66db9ea55fb84edeb03101464882ed23c,STILL_EXISTS,TODO Add better parsing protextion to the splits
aahiabjjb,keiffster/program-y,test/programytest/extensions/scheduler/test_scheduler.py,047c15a2e0c4c2b2564eedb628ac6caf32a2a1c7,STILL_EXISTS,EVERY XXX
aahiacbbd,keiffster/program-y,src/programy/bot.py,51a272eb5e6779f2dc9809af7fa65d7a2fc64746,STILL_EXISTS,KS Added this to fix empty string UDC
aahiacfch,johnolafenwa/TorchFusion,examples/cifar10.py,9470e6c87b6e11aae61602aac0238ef4e2f64a5c,STILL_EXISTS,Move to GPU if available
aahiacfea,johnolafenwa/TorchFusion,examples/gan/mnist.py,9470e6c87b6e11aae61602aac0238ef4e2f64a5c,STILL_EXISTS,Move the networks to GPU if available
aahiacfej,johnolafenwa/TorchFusion,examples/mnist-advanced.py,9470e6c87b6e11aae61602aac0238ef4e2f64a5c,STILL_EXISTS,Move to GPU if available
aahiacfha,johnolafenwa/TorchFusion,examples/mnist-callbacks.py,9470e6c87b6e11aae61602aac0238ef4e2f64a5c,STILL_EXISTS,Move to GPU if available
aahiacfib,johnolafenwa/TorchFusion,examples/mnist-mlp.py,9470e6c87b6e11aae61602aac0238ef4e2f64a5c,STILL_EXISTS,Move to GPU if available
aahiacgbf,johnolafenwa/TorchFusion,torchfusion/lang/datasets/datasets.py,42faaddde7e5f0cef8d5d38c5c3f11fc3bb00660,29cf5741d2a61dab42e4560c51966fc069fb0988,FIX BUGS WITH RANDOM STATE
aahiacgca,johnolafenwa/TorchFusion,torchfusion/learners/learners.py,42faaddde7e5f0cef8d5d38c5c3f11fc3bb00660,STILL_EXISTS,r\"\"\"Abstract base Model for training; evaluating and performing inference || All custom models should subclass this and implement train; evaluate and predict functions ||  ||     Args: ||         use_cuda_if_available (boolean): If set to true; training would be done on a gpu if any is available ||      ||     \"\"\"
aahiacgcc,johnolafenwa/TorchFusion,torchfusion/learners/learners.py,42faaddde7e5f0cef8d5d38c5c3f11fc3bb00660,STILL_EXISTS,TO-DO CONFIRM LABEL DIMENSIONS ISSUE; CHECK IF EXPANSION IS NEEDED
aahiacgfb,johnolafenwa/TorchFusion,build/lib/torchfusion/learners/learners.py,29cf5741d2a61dab42e4560c51966fc069fb0988,STILL_EXISTS,r\"\"\"Abstract base Model for training; evaluating and performing inference || All custom models should subclass this and implement train; evaluate and predict functions ||  ||     Args: ||         use_cuda_if_available (boolean): If set to true; training would be done on a gpu if any is available ||      ||     \"\"\"
aahiachjf,johnolafenwa/TorchFusion,torchfusion/fp16_utils/fp16_optimizer.py,4c44ea55a1201ae7d3431cdc053b61fcde0a3e15,STILL_EXISTS,TODO:  Update overflow check + downscale to use Carl's fused kernel.
aahiachjg,johnolafenwa/TorchFusion,torchfusion/fp16_utils/fp16_optimizer.py,4c44ea55a1201ae7d3431cdc053b61fcde0a3e15,STILL_EXISTS,init_state_dict sets up an alternative way to cast per-param state tensors.
aahiaciac,johnolafenwa/TorchFusion,torchfusion/fp16_utils/fp16_optimizer.py,4c44ea55a1201ae7d3431cdc053b61fcde0a3e15,STILL_EXISTS,alternative way to cast per-param state tensors:
aahiaciei,johnolafenwa/TorchFusion,torchfusion/fp16_utils/fp16_optimizer.py,4c44ea55a1201ae7d3431cdc053b61fcde0a3e15,STILL_EXISTS,discarding the iteration;  but probably wouldn't improve overall efficiency.
aahiacigi,johnolafenwa/TorchFusion,torchfusion/fp16_utils/loss_scaler.py,4c44ea55a1201ae7d3431cdc053b61fcde0a3e15,STILL_EXISTS,More efficient version that can be used if .sum() returns a Python scalar
aahiaciia,johnolafenwa/TorchFusion,build/lib/torchfusion/fp16_utils/fp16_optimizer.py,5f800460be911d3b343da3a44a46dff50f8bd9d7,STILL_EXISTS,TODO:  Update overflow check + downscale to use Carl's fused kernel.
aahiaciib,johnolafenwa/TorchFusion,build/lib/torchfusion/fp16_utils/fp16_optimizer.py,5f800460be911d3b343da3a44a46dff50f8bd9d7,STILL_EXISTS,init_state_dict sets up an alternative way to cast per-param state tensors.
aahiaciih,johnolafenwa/TorchFusion,build/lib/torchfusion/fp16_utils/fp16_optimizer.py,5f800460be911d3b343da3a44a46dff50f8bd9d7,STILL_EXISTS,alternative way to cast per-param state tensors:
aahiacjdd,johnolafenwa/TorchFusion,build/lib/torchfusion/fp16_utils/fp16_optimizer.py,5f800460be911d3b343da3a44a46dff50f8bd9d7,STILL_EXISTS,discarding the iteration;  but probably wouldn't improve overall efficiency.
aahiacjfd,johnolafenwa/TorchFusion,build/lib/torchfusion/fp16_utils/loss_scaler.py,5f800460be911d3b343da3a44a46dff50f8bd9d7,STILL_EXISTS,More efficient version that can be used if .sum() returns a Python scalar
aahiadcfj,achaiah/pywick,pywick/datasets/ClonedDataset.py,afbbd54fecaf6aaa5ae25162c8fe2c1af821d865,STILL_EXISTS,not cloning the dictionary at this point... maybe it should be?
aahiadcia,achaiah/pywick,pywick/datasets/FolderDataset.py,afbbd54fecaf6aaa5ae25162c8fe2c1af821d865,57abf989c249792c8a679f6479919555c5f3f6f1,input_sample = self.transform(image=input_sample)     # needed for albumentations to work (but currently albumentations dies with multiple workers)
aahiadcig,achaiah/pywick,pywick/datasets/data_utils.py,afbbd54fecaf6aaa5ae25162c8fe2c1af821d865,STILL_EXISTS,color space is unused here
aahiadeaf,achaiah/pywick,pywick/losses.py,afbbd54fecaf6aaa5ae25162c8fe2c1af821d865,STILL_EXISTS,Workaround: can't sort Variable bug
aahiadebg,achaiah/pywick,pywick/losses.py,afbbd54fecaf6aaa5ae25162c8fe2c1af821d865,STILL_EXISTS,TODO Replace this with nn.BCEWithLogitsLoss??
aahiadejj,achaiah/pywick,pywick/meters/confusionmeter.py,afbbd54fecaf6aaa5ae25162c8fe2c1af821d865,STILL_EXISTS,hack for bincounting 2 arrays together
aahiaebeg,achaiah/pywick,pywick/models/classification/testnets/pnn.py,afbbd54fecaf6aaa5ae25162c8fe2c1af821d865,STILL_EXISTS,self.act;      #TODO orig code uses ReLU here
aahiaebeh,achaiah/pywick,pywick/models/classification/testnets/pnn.py,afbbd54fecaf6aaa5ae25162c8fe2c1af821d865,STILL_EXISTS,nn.BatchNorm2d(out_channels); #TODO: orig code uses BN here
aahiaecbd,achaiah/pywick,pywick/models/classification/testnets/se_efficient_densenet.py,afbbd54fecaf6aaa5ae25162c8fe2c1af821d865,STILL_EXISTS,checkpoint implement shared memory storage function
aahiaecdb,achaiah/pywick,pywick/models/classification/xception.py,afbbd54fecaf6aaa5ae25162c8fe2c1af821d865,STILL_EXISTS,\"\"\" || Ported to pytorch thanks to [tstandley](https:\/\/github.com\/tstandley\/Xception-PyTorch) ||  || @author: tstandley || Adapted by cadene ||  || Creates an Xception Model as defined in: ||  || Francois Chollet || Xception: Deep Learning with Depthwise Separable Convolutions || https:\/\/arxiv.org\/pdf\/1610.02357.pdf ||  || This weights ported from the Keras implementation. Achieves the following performance on the validation set: ||  || Loss:0.9173 Prec@1:78.892 Prec@5:94.292 ||  || REMEMBER to set your image size to 3x299x299 for both test and validation ||  || normalize = transforms.Normalize(mean=[0.5; 0.5; 0.5]; ||                                   std=[0.5; 0.5; 0.5]) ||  || The resize parameter of the validation transform should be 333; and make sure to center crop at 299x299 || \"\"\"
aahiaeceg,achaiah/pywick,pywick/models/classification/xception.py,afbbd54fecaf6aaa5ae25162c8fe2c1af821d865,STILL_EXISTS,TODO: ugly
aahiaechb,achaiah/pywick,pywick/models/model_utils.py,afbbd54fecaf6aaa5ae25162c8fe2c1af821d865,STILL_EXISTS,apparently these are different...
aahiaeeja,achaiah/pywick,pywick/models/segmentation/enet.py,afbbd54fecaf6aaa5ae25162c8fe2c1af821d865,STILL_EXISTS,Store parameters that are needed later
aahiaefjc,achaiah/pywick,pywick/models/segmentation/gcn.py,afbbd54fecaf6aaa5ae25162c8fe2c1af821d865,STILL_EXISTS,kernel size had better be odd number so as to avoid alignment error
aahiaegdd,achaiah/pywick,pywick/models/segmentation/lex_extractors.py,afbbd54fecaf6aaa5ae25162c8fe2c1af821d865,STILL_EXISTS,compatibility hack
aahiaehij,achaiah/pywick,pywick/models/segmentation/testnets/dense_block.py,afbbd54fecaf6aaa5ae25162c8fe2c1af821d865,STILL_EXISTS,TODO check https:\/\/github.com\/pytorch\/pytorch\/issues\/5332
aahiafgce,achaiah/pywick,pywick/models/segmentation/unet_dilated.py,afbbd54fecaf6aaa5ae25162c8fe2c1af821d865,STILL_EXISTS,# TODO Problem here:
aahiafhab,achaiah/pywick,pywick/modules/module_trainer.py,afbbd54fecaf6aaa5ae25162c8fe2c1af821d865,STILL_EXISTS,turn on the cudnn autotuner that selects efficient algorithms
aahiafhad,achaiah/pywick,pywick/modules/module_trainer.py,afbbd54fecaf6aaa5ae25162c8fe2c1af821d865,STILL_EXISTS,TODO: This might not be correct. If things break; check here (below line used to be part of the 'if' block above)
aahiafihg,achaiah/pywick,tests/test_datasets.py,afbbd54fecaf6aaa5ae25162c8fe2c1af821d865,STILL_EXISTS,TODO: every item should appear exactly once
aahiagaha,achaiah/pywick,pywick/models/classification/__init__.py,c3d527bb46f4db4d2592cc3643ce0c46392a298b,STILL_EXISTS,By convention; model names starting with lowercase are pretrained on imagenet while uppercase are not
aahiagcdh,achaiah/pywick,pywick/models/__init__.py,8b1167c64e8926eeb6930be3c30e290e6a2378c6,STILL_EXISTS,\"\"\" || Neural network models is what deep learning is all about! While you can download some standard models from || `torchvision <https:\/\/pytorch.org\/docs\/stable\/torchvision\/models.html\/>`_; we strive to create a library of models || that are on the cutting edge of AI. Whenever possible; `we provide pretrained solutions as well!`\ ||  || That said; we didn't come up with any of these on our own so we owe a huge debt of gratitude to the many researchers who have shared || their models and weights on github.\ ||  || **Caution:** While we strive to ensure that all models can be used out of the box; sometimes things become broken due to Pytorch updates || or misalignment of the planets. Please don't yell at us. Gently point out what's broken; or even better; submit a pull request to fix it!\ ||  || **Here Be Dragons:** Aaand one more thing - we constantly plumb the depths of github for new models or tweaks to existing ones. While we don't || list this in the docs; there is a special `testnets` directory with tons of probably broken; semi-working; and at times crazy awesome || models and model-variations. If you're interested in the bleeding edge; that's where you'd look (see ``models.__init__.py`` for what's available) || \"\"\"
aahiagcdi,achaiah/pywick,pywick/models/classification/__init__.py,8b1167c64e8926eeb6930be3c30e290e6a2378c6,STILL_EXISTS,\"\"\" || Below you will find all the latest image classification models. || By convention; model names starting with lowercase are pretrained on imagenet while uppercase are not (vanilla). To load one of the pretrained || models with your own number of classes use the ``models.model_utils.get_model(...)`` function and specify the name of the model || exactly like the pretrained model method name (e.g. if the method name reads ``pywick.models.classification.dpn.dualpath.dpn68`` then use || `dpn68` as the model name for ``models.model_utils.get_model(...)``. || \"\"\"
aahiagdde,achaiah/pywick,pywick/models/segmentation/testnets/mnas_linknets/linknet.py,c22f39239ddee780ac6b19ca3560cb4de5b5bd5d,STILL_EXISTS,\"\"\" || Implementation of `LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation <https:\/\/arxiv.org\/pdf\/1505.04597>`_ || \"\"\"
aahiageff,achaiah/pywick,pywick/conditions.py,45a401f15cc22e2d1a403ef847d540cb130e8bcc,STILL_EXISTS,seriously... why?  I'm looking at you OCNet
aahiagheg,achaiah/pywick,pywick/models/segmentation/testnets/exfuse/ExFuseLayer.py,8f2c540df5020c77aee660e0f5d021ba4fd3c696,STILL_EXISTS,TODO : Make beautiful
aahiagica,achaiah/pywick,pywick/models/segmentation/testnets/lg_kernel_exfuse/large_kernel.py,8f2c540df5020c77aee660e0f5d021ba4fd3c696,STILL_EXISTS,kernel size had better be odd number so as to avoid alignment error
aahiahhhc,achaiah/pywick,pywick/losses.py,a36b5a05f4ab977d371be6a9ea1b7ecc0eec6c5c,STILL_EXISTS,Calculate BCE if needed
aahiahhhh,achaiah/pywick,pywick/losses.py,a36b5a05f4ab977d371be6a9ea1b7ecc0eec6c5c,STILL_EXISTS,Small diagonal matrix to fix numerical issues
aahiahibc,bamos/block,block/block.py,6504d552087eec0de7f15b1e321e77faf18f2ffe,STILL_EXISTS,TODO: Check types.
aahiahjbd,PyTorchLightning/pytorch-lightning,research_lib/models/trainer.py,8dfb8f9167841fd64bef82d68bbc7c6b2acf5e8d,STILL_EXISTS,put on gpu if needed
aahiaibcg,PyTorchLightning/pytorch-lightning,pytorch_lightning/models/trainer.py,2117485550b464463e821fd9849e8056c101c375,STILL_EXISTS,put on gpu if needed
aahiaieeb,PyTorchLightning/pytorch-lightning,pytorch_lightning/models/trainer.py,c54dd9429505a697e2136d03cd3ee7eefbe0fdb9,e3f96d6f3a28c472bc6e31c0dea067fc01ec01cd,TODO: remove
aahiaiegd,PyTorchLightning/pytorch-lightning,pytorch_lightning/models/trainer.py,1b497ac69a07ad0caa11a860a5e46125fd41a771,1ee6d21db2ceaf9e711ea7b98969ee79b2d719ed,TODO: fix training part...
aahiaiffc,PyTorchLightning/pytorch-lightning,pytorch_lightning/models/trainer.py,c4aca832ba73a909c4a385e8fb50081418793cd4,STILL_EXISTS,TODO: pass in ip
aahiaifgj,PyTorchLightning/pytorch-lightning,pytorch_lightning/pt_overrides/override_data_parallel.py,e8abbb1e7570be21fbad96ca4fd27a3f4f77847c,STILL_EXISTS,unused parameters. Only if `find_unused_parameters` is set.
aahiaighf,PyTorchLightning/pytorch-lightning,examples/new_project_templates/trainer_gpu_cluster_template.py,bba51dde8cd742aa243c33e33ac99a63ffbf5b34,STILL_EXISTS,TODO: make 1 param
aahiaighh,PyTorchLightning/pytorch-lightning,pytorch_lightning/models/trainer.py,e32d355d26d8f08a0cb3dfc0b61f3e3314b4aad1,14d1329655653aad3226a5d5bed53dff8240378a,TODO: implement
aahiaijac,PyTorchLightning/pytorch-lightning,pytorch_lightning/models/trainer.py,91b869d04300bda906306a15f018520e745a75ff,c244599ae8c9575d34f7f25942ed06a1ccd87f2a,hack to get nccl to stop throwing error... seems to be an nccl race condition
aahiaijag,PyTorchLightning/pytorch-lightning,pytorch_lightning/models/trainer.py,6c02afefca01ab8a92c39f971b964f3dc33b53fc,dc87a4fc9168e92858497cd87ee98d1eb4917d55,hack to get nccl to stop throwing error... seems to be an nccl race condition
aahibabga,PyTorchLightning/pytorch-lightning,tests/test_models.py,6e2bf991f0f2491901ce82c043797cd9a5a89081,STILL_EXISTS,on hpc this would work fine... but need to hack it for the purpose of the test
aahibadjf,PyTorchLightning/pytorch-lightning,tests/test_models.py,18ce3e5a23c9d1bb72cb36a347766b816a0b81c7,STILL_EXISTS,on hpc this would work fine... but need to hack it for the purpose of the test
aahibbfei,PyTorchLightning/pytorch-lightning,tests/debug.py,4104a0fc4726021142b520374e770c209e25d838,6cc3f1757f39f34955d0ff2b0c2e492fc8c8f854,on hpc this would work fine... but need to hack it for the purpose of the test
aahibbijb,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer_io.py,9576dd28b2abaecef8f6a303178b5ab5a237b6e9,STILL_EXISTS,move optimizer to GPU 1 weight at a time
aahibbjdc,PyTorchLightning/pytorch-lightning,examples/new_project_templates/multi_node_examples/multi_node_cluster_auto_slurm.py,f3221a5014e68443ce49417318185ac1e88a4b4b,STILL_EXISTS,depending on your cluster config; you probably want
aahibbjhi,PyTorchLightning/pytorch-lightning,tests/test_models.py,481aa2497439e51bd6545fd18d45f898ffe47335,STILL_EXISTS,on hpc this would work fine... but need to hack it for the purpose of the test
aahibcaai,PyTorchLightning/pytorch-lightning,pytorch_lightning/pt_overrides/override_data_parallel.py,32e74b8f364ea3b8c9089ecba78ccfd36a2a6128,STILL_EXISTS,TODO: fix hack (maybe not a hack)
aahibcaba,PyTorchLightning/pytorch-lightning,pytorch_lightning/pt_overrides/override_data_parallel.py,32e74b8f364ea3b8c9089ecba78ccfd36a2a6128,STILL_EXISTS,fixes weird bug where copies are out of sync
aahibcafj,PyTorchLightning/pytorch-lightning,tests/test_models.py,bf09060fef2e3c60b1e6539570071cb104a82257,STILL_EXISTS,on hpc this would work fine... but need to hack it for the purpose of the test
aahibcebf,PyTorchLightning/pytorch-lightning,examples/domain_templates/gan.py,e611223dc812497fdde10eeb2bc96c516a168df6,e1b45ca492f812b91695ae2d6d55830e5a8e0ec5,TODO: in dp; the state gets wiped... need to figure out how to fix
aahibcice,PyTorchLightning/pytorch-lightning,pytorch_lightning/logging/comet_logger.py,ab6794406e750470a208f8170df0c783eaa1ade2,2924ebeda5bf7c6455537c2470f81e3255288ec4,needed to prevent ImportError and duplicated logs.
aahibcicj,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/train_loop_mixin.py,792ad00ff9c992a0002e560631bf272d9f296fec,STILL_EXISTS,for iterable train loader; the progress bar never ends
aahibcida,PyTorchLightning/pytorch-lightning,pytorch_lightning/logging/__init__.py,2924ebeda5bf7c6455537c2470f81e3255288ec4,76a1c67d87efcfc8656c0228c9f9f0ac184421b2,needed to prevent ImportError and duplicated logs.
aahibcjii,PyTorchLightning/pytorch-lightning,tests/test_z_amp.py,5afae59715d2e2107e145c626bb7367b0f89a3d6,STILL_EXISTS,on hpc this would work fine... but need to hack it for the purpose of the test
aahibddfh,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/train_loop_mixin.py,d5ca464cc645a63c3dd92548785cb8e5a7222caf,248495b1d161695a64cf86dd0e7f9f9fd8c87107,done in hook so user can overwrite if needed
aahibddfi,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer_io.py,b86d223889992cccbb4ca68537a464df88dbfef7,STILL_EXISTS,TODO: fix for anything with multiprocess DP; DDP; DDP2
aahibddgf,PyTorchLightning/pytorch-lightning,pytorch_lightning/testing/lm_test_module_base.py,37647d835ac626bb6d7e224c3b917debcd497d5e,STILL_EXISTS,TODO: this should be discussed and moved out of this package
aahibddib,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/train_loop_mixin.py,446a1b5d45c6345de90743513f552e9bbbcf172f,STILL_EXISTS,for iterable train loader; the progress bar never ends
aahibdhbb,PyTorchLightning/pytorch-lightning,docs/source/conf.py,d71556e7a15a82dc3f47294bcbdb176f882f0c44,STILL_EXISTS,-- Options for todo extension ----------------------------------------------
aahibdhbc,PyTorchLightning/pytorch-lightning,docs/source/conf.py,d71556e7a15a82dc3f47294bcbdb176f882f0c44,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aahibdhce,PyTorchLightning/pytorch-lightning,docs/source/conf.py,d71556e7a15a82dc3f47294bcbdb176f882f0c44,STILL_EXISTS,TODO: better parse from package since the import name and package name may differ
aahibdhfa,PyTorchLightning/pytorch-lightning,pytorch_lightning/core/__init__.py,d71556e7a15a82dc3f47294bcbdb176f882f0c44,STILL_EXISTS,\"\"\" || Lightning Module interface || ========================== ||  || A lightning module is a strict superclass of nn.Module; it provides a standard interface ||  for the trainer to interact with the model. ||  || The easiest thing to do is copy the minimal example below and modify accordingly. ||  || Otherwise; to Define a Lightning Module; implement the following methods: ||  ||  || Minimal example || --------------- ||  || .. code-block:: python ||  ||     import os ||     import torch ||     from torch.nn import functional as F ||     from torch.utils.data import DataLoader ||     from torchvision.datasets import MNIST ||     import torchvision.transforms as transforms ||  ||     import pytorch_lightning as pl ||  ||     class CoolModel(pl.LightningModule): ||  ||         def __init__(self): ||             super(CoolModel; self).__init__() ||             # not the best model... ||             self.l1 = torch.nn.Linear(28 * 28; 10) ||  ||         def forward(self; x): ||             return torch.relu(self.l1(x.view(x.size(0); -1))) ||  ||         def training_step(self; batch; batch_nb): ||             # REQUIRED ||             x; y = batch ||             y_hat = self.forward(x) ||             return {'loss': F.cross_entropy(y_hat; y)} ||  ||         def validation_step(self; batch; batch_nb): ||             # OPTIONAL ||             x; y = batch ||             y_hat = self.forward(x) ||             return {'val_loss': F.cross_entropy(y_hat; y)} ||  ||         def validation_end(self; outputs): ||             # OPTIONAL ||             avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean() ||             return {'avg_val_loss': avg_loss} ||  ||         def test_step(self; batch; batch_nb): ||             # OPTIONAL ||             x; y = batch ||             y_hat = self.forward(x) ||             return {'test_loss': F.cross_entropy(y_hat; y)} ||  ||         def test_end(self; outputs): ||             # OPTIONAL ||             avg_loss = torch.stack([x['test_loss'] for x in outputs]).mean() ||             return {'avg_test_loss': avg_loss} ||  ||         def configure_optimizers(self): ||             # REQUIRED ||             return torch.optim.Adam(self.parameters(); lr=0.02) ||  ||         @pl.data_loader ||         def train_dataloader(self): ||             return DataLoader(MNIST(os.getcwd(); train=True; download=True; ||                               transform=transforms.ToTensor()); batch_size=32) ||  ||         @pl.data_loader ||         def val_dataloader(self): ||             # OPTIONAL ||             # can also return a list of val dataloaders ||             return DataLoader(MNIST(os.getcwd(); train=True; download=True; ||                               transform=transforms.ToTensor()); batch_size=32) ||  ||         @pl.data_loader ||         def test_dataloader(self): ||             # OPTIONAL ||             # can also return a list of test dataloaders ||             return DataLoader(MNIST(os.getcwd(); train=False; download=True; ||                               transform=transforms.ToTensor()); batch_size=32) ||  ||  || How do these methods fit into the broader training? || --------------------------------------------------- ||  || The LightningModule interface is on the right. Each method corresponds ||  to a part of a research project. Lightning automates everything not in blue. ||  || .. figure::  docs\/source\/_static\/images\/overview_flat.jpg ||    :align:   center ||  ||    Overview. ||  ||  || Optional Methods || ---------------- ||  || **add_model_specific_args** ||  || .. code-block:: python ||  ||     @staticmethod ||     def add_model_specific_args(parent_parser; root_dir) ||  || Lightning has a list of default argparse commands. ||  This method is your chance to add or modify commands specific to your model. ||  The `hyperparameter argument parser ||   <https:\/\/williamfalcon.github.io\/test-tube\/hyperparameter_optimization\/HyperOptArgumentParser>`_ ||  is available anywhere in your model by calling self.hparams. ||  || **Return** || An argument parser ||  || **Example** ||  || .. code-block:: python ||  ||     @staticmethod ||     def add_model_specific_args(parent_parser; root_dir): ||         parser = HyperOptArgumentParser(strategy=parent_parser.strategy; parents=[parent_parser]) ||  ||         # param overwrites ||         # parser.set_defaults(gradient_clip_val=5.0) ||  ||         # network params ||         parser.opt_list('--drop_prob'; default=0.2; options=[0.2; 0.5]; type=float; tunable=False) ||         parser.add_argument('--in_features'; default=28*28) ||         parser.add_argument('--out_features'; default=10) ||         # use 500 for CPU; 50000 for GPU to see speed difference ||         parser.add_argument('--hidden_dim'; default=50000) ||  ||         # data ||         parser.add_argument('--data_root'; default=os.path.join(root_dir; 'mnist'); type=str) ||  ||         # training params (opt) ||         parser.opt_list('--learning_rate'; default=0.001; type=float; ||                         options=[0.0001; 0.0005; 0.001; 0.005]; tunable=False) ||         parser.opt_list('--batch_size'; default=256; type=int; ||                         options=[32; 64; 128; 256]; tunable=False) ||         parser.opt_list('--optimizer_name'; default='adam'; type=str; ||                         options=['adam']; tunable=False) ||         return parser ||  || \"\"\"
aahibdhfd,PyTorchLightning/pytorch-lightning,pytorch_lightning/core/hooks.py,d71556e7a15a82dc3f47294bcbdb176f882f0c44,0719dd7d6894f69bb06a7b4b72c5a9a417db686c,do something when the batch ends
aahibdhff,PyTorchLightning/pytorch-lightning,pytorch_lightning/core/hooks.py,d71556e7a15a82dc3f47294bcbdb176f882f0c44,STILL_EXISTS,do something when the epoch ends
aahibdhgj,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/ddp_mixin.py,d71556e7a15a82dc3f47294bcbdb176f882f0c44,STILL_EXISTS,\"\"\" || Lightning supports model training on a cluster managed by SLURM in the following cases: ||  || 1. Training on a single cpu or single GPU. || 2. Train on multiple GPUs on the same node using DataParallel or DistributedDataParallel || 3. Training across multiple GPUs on multiple different nodes via DistributedDataParallel. ||  || .. note:: A node means a machine with multiple GPUs ||  || Running grid search on a cluster || -------------------------------- ||  || To use lightning to run a hyperparameter search (grid-search or random-search) on a cluster do 4 things: ||  || (1). Define the parameters for the grid search ||  || .. code-block:: python ||  ||     from test_tube import HyperOptArgumentParser ||  ||     # subclass of argparse ||     parser = HyperOptArgumentParser(strategy='random_search') ||     parser.add_argument('--learning_rate'; default=0.002; type=float; help='the learning rate') ||  ||     # let's enable optimizing over the number of layers in the network ||     parser.opt_list('--nb_layers'; default=2; type=int; tunable=True; options=[2; 4; 8]) ||  ||     hparams = parser.parse_args() ||  || .. note:: You must set `Tunable=True` for that argument to be considered in the permutation set. ||  Otherwise test-tube will use the default value. This flag is useful when you don't want ||  to search over an argument and want to use the default instead. ||  || (2). Define the cluster options in the ||  `SlurmCluster object <https:\/\/williamfalcon.github.io\/test-tube\/hpc\/SlurmCluster>`_ (over 5 nodes and 8 gpus) ||  || .. code-block:: python ||  ||     from test_tube.hpc import SlurmCluster ||  ||     # hyperparameters is a test-tube hyper params object ||     # see https:\/\/williamfalcon.github.io\/test-tube\/hyperparameter_optimization\/HyperOptArgumentParser\/ ||     hyperparams = args.parse() ||  ||     # init cluster ||     cluster = SlurmCluster( ||         hyperparam_optimizer=hyperparams; ||         log_path='\/path\/to\/log\/results\/to'; ||         python_cmd='python3' ||     ) ||  ||     # let the cluster know where to email for a change in job status (ie: complete; fail; etc...) ||     cluster.notify_job_status(email='some@email.com'; on_done=True; on_fail=True) ||  ||     # set the job options. In this instance; we'll run 20 different models ||     # each with its own set of hyperparameters giving each one 1 GPU (ie: taking up 20 GPUs) ||     cluster.per_experiment_nb_gpus = 8 ||     cluster.per_experiment_nb_nodes = 5 ||  ||     # we'll request 10GB of memory per node ||     cluster.memory_mb_per_node = 10000 ||  ||     # set a walltime of 10 minues ||     cluster.job_time = '10:00' ||  ||  || (3). Make a main function with your model and trainer. Each job will call this function with a particular || hparams configuration.:: ||  ||     from pytorch_lightning import Trainer ||  ||     def train_fx(trial_hparams; cluster_manager; _): ||         # hparams has a specific set of hyperparams ||  ||         my_model = MyLightningModel() ||  ||         # give the trainer the cluster object ||         trainer = Trainer() ||         trainer.fit(my_model) ||  ||     ` ||  || (4). Start the grid\/random search:: ||  ||     # run the models on the cluster ||     cluster.optimize_parallel_cluster_gpu( ||         train_fx; ||         nb_trials=20; ||         job_name='my_grid_search_exp_name'; ||         job_display_name='my_exp') ||  || .. note:: `nb_trials` specifies how many of the possible permutations to use. If using `grid_search` it will use ||  the depth first ordering. If using `random_search` it will use the first k shuffled options. FYI; random search ||  has been shown to be just as good as any Bayesian optimization method when using a reasonable number of samples (60); ||  see this `paper <http:\/\/www.jmlr.org\/papers\/volume13\/bergstra12a\/bergstra12a.pdf>`_  for more information. ||  || Walltime auto-resubmit || ---------------------- ||  || Lightning automatically resubmits jobs when they reach the walltime. Make sure to set the SIGUSR1 signal in || your SLURM script.:: ||  ||     # 90 seconds before training ends ||     #SBATCH --signal=SIGUSR1@90 ||  || When lightning receives the SIGUSR1 signal it will: || 1. save a checkpoint with 'hpc_ckpt' in the name. || 2. resubmit the job using the SLURM_JOB_ID ||  || When the script starts again; Lightning will: || 1. search for a 'hpc_ckpt' checkpoint. || 2. restore the model; optimizers; schedulers; epoch; etc... ||  || \"\"\"
aahibdhhc,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/train_loop_mixin.py,d71556e7a15a82dc3f47294bcbdb176f882f0c44,STILL_EXISTS,\"\"\" || The lightning training loop handles everything except the actual computations of your model. ||  To decide what will happen in your training loop; define the `training_step` function. ||  || Below are all the things lightning automates for you in the training loop. ||  || Accumulated gradients || --------------------- ||  || Accumulated gradients runs K small batches of size N before doing a backwards pass. ||  The effect is a large effective batch size of size KxN. ||  || .. code-block:: python ||  ||     # DEFAULT (ie: no accumulated grads) ||     trainer = Trainer(accumulate_grad_batches=1) ||  || Force training for min or max epochs || ------------------------------------ ||  || It can be useful to force training for a minimum number of epochs or limit to a max number ||  || .. code-block:: python ||  ||     # DEFAULT ||     trainer = Trainer(min_nb_epochs=1; max_nb_epochs=1000) ||  || Early stopping || -------------- ||  || The trainer already sets up default early stopping for you. || To modify this behavior; pass in your own EarlyStopping callback. ||  || .. code-block:: python ||  ||     from pytorch_lightning.callbacks import EarlyStopping ||  ||     # DEFAULTS used by Trainer ||     early_stop_callback = EarlyStopping( ||         monitor='val_loss'; ||         min_delta=0.00; ||         patience=3; ||         verbose=False; ||         mode='min' ||     ) ||  ||     # without passing anything in; uses the default callback above ||     trainer = Trainer() ||  ||     # pass in your own to override the default callback ||     trainer = Trainer(early_stop_callback=early_stop_callback) ||  ||     # pass in None to disable it ||     trainer = Trainer(early_stop_callback=None) ||  || Force disable early stop || ------------------------ ||  || To disable early stopping pass None to the early_stop_callback ||  || .. code-block:: python ||  ||     # DEFAULT ||     trainer = Trainer(early_stop_callback=None) ||  || Gradient Clipping || ----------------- ||  || Gradient clipping may be enabled to avoid exploding gradients. ||  Specifically; this will `clip the gradient norm computed over all model parameters ||  `together <https:\/\/pytorch.org\/docs\/stable\/nn.html#torch.nn.utils.clip_grad_norm_>`_. ||  || .. code-block:: python ||  ||     # DEFAULT (ie: don't clip) ||     trainer = Trainer(gradient_clip_val=0) ||  ||     # clip gradients with norm above 0.5 ||     trainer = Trainer(gradient_clip_val=0.5) ||  || Inspect gradient norms || ---------------------- ||  || Looking at grad norms can help you figure out where training might be going wrong. ||  || .. code-block:: python ||  ||     # DEFAULT (-1 doesn't track norms) ||     trainer = Trainer(track_grad_norm=-1) ||  ||     # track the LP norm (P=2 here) ||     trainer = Trainer(track_grad_norm=2) ||  || Set how much of the training set to check || ----------------------------------------- ||  || If you don't want to check 100% of the training set (for debugging or if it's huge); set this flag. ||  || train_percent_check will be overwritten by overfit_pct if `overfit_pct > 0` ||  || .. code-block:: python ||  ||     # DEFAULT ||     trainer = Trainer(train_percent_check=1.0) ||  ||     # check 10% only ||     trainer = Trainer(train_percent_check=0.1) ||  || Packed sequences as inputs || -------------------------- ||  || When using PackedSequence; do 2 things: || 1. return either a padded tensor in dataset or a list of variable length tensors ||  in the dataloader collate_fn (example above shows the list implementation). || 2. Pack the sequence in forward or training and validation steps depending on use case. ||  || .. code-block:: python ||  ||     # For use in dataloader ||     def collate_fn(batch): ||         x = [item[0] for item in batch] ||         y = [item[1] for item in batch] ||         return x; y ||  ||     # In module ||     def training_step(self; batch; batch_nb): ||         x = rnn.pack_sequence(batch[0]; enforce_sorted=False) ||         y = rnn.pack_sequence(batch[1]; enforce_sorted=False) ||  ||  || Truncated Backpropagation Through Time || -------------------------------------- ||  || There are times when multiple backwards passes are needed for each batch. ||  For example; it may save memory to use Truncated Backpropagation Through Time when training RNNs. ||  || When this flag is enabled each batch is split into sequences of size truncated_bptt_steps ||  and passed to training_step(...) separately. A default splitting function is provided; ||  however; you can override it for more flexibility. See `tbptt_split_batch`. ||  || .. code-block:: python ||  ||     # DEFAULT (single backwards pass per batch) ||     trainer = Trainer(truncated_bptt_steps=None) ||  ||     # (split batch into sequences of size 2) ||     trainer = Trainer(truncated_bptt_steps=2) ||  ||  || \"\"\"
aahibdhhd,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer_io.py,d71556e7a15a82dc3f47294bcbdb176f882f0c44,STILL_EXISTS,\"\"\" || Lightning can automate saving and loading checkpoints || ===================================================== ||  || Checkpointing is enabled by default to the current working directory. || To change the checkpoint path pass in:: ||  ||     Trainer(default_save_path='\/your\/path\/to\/save\/checkpoints') ||  ||  || To modify the behavior of checkpointing pass in your own callback. ||  || .. code-block:: python ||  ||     from pytorch_lightning.callbacks import ModelCheckpoint ||  ||     # DEFAULTS used by the Trainer ||     checkpoint_callback = ModelCheckpoint( ||         filepath=os.getcwd(); ||         save_best_only=True; ||         verbose=True; ||         monitor='val_loss'; ||         mode='min'; ||         prefix='' ||     ) ||  ||     trainer = Trainer(checkpoint_callback=checkpoint_callback) ||  ||  || Restoring training session || -------------------------- ||  || You might want to not only load a model but also continue training it. Use this method to || restore the trainer state as well. This will continue from the epoch and global step you last left off. || However; the dataloaders will start from the first batch again (if you shuffled it shouldn't matter). ||  || Lightning will restore the session if you pass a logger with the same version and there's a saved checkpoint. ||  || .. code-block:: python ||  ||     from pytorch_lightning import Trainer ||     from pytorch_lightning.logging import TestTubeLogger ||  ||     logger = TestTubeLogger( ||         save_dir='.\/savepath'; ||         version=1  # An existing version with a saved checkpoint ||     ) ||     trainer = Trainer( ||         logger=logger; ||         default_save_path='.\/savepath' ||     ) ||  ||     # this fit call loads model weights and trainer state ||     # the trainer continues seamlessly from where you left off ||     # without having to do anything else. ||     trainer.fit(model) ||  ||  || The trainer restores: ||  || - global_step || - current_epoch || - All optimizers || - All lr_schedulers || - Model weights ||  || You can even change the logic of your model as long as the weights and \"architecture\" of || the system isn't different. If you add a layer; for instance; it might not work. ||  || At a rough level; here's what happens inside Trainer :py:mod:`pytorch_lightning.base_module.model_saving.py`: ||  || .. code-block:: python ||  ||     self.global_step = checkpoint['global_step'] ||     self.current_epoch = checkpoint['epoch'] ||  ||     # restore the optimizers ||     optimizer_states = checkpoint['optimizer_states'] ||     for optimizer; opt_state in zip(self.optimizers; optimizer_states): ||         optimizer.load_state_dict(opt_state) ||  ||     # restore the lr schedulers ||     lr_schedulers = checkpoint['lr_schedulers'] ||     for scheduler; lrs_state in zip(self.lr_schedulers; lr_schedulers): ||         scheduler.load_state_dict(lrs_state) ||  ||     # uses the model you passed into trainer ||     model.load_state_dict(checkpoint['state_dict']) ||  || \"\"\"
aahibdhhe,PyTorchLightning/pytorch-lightning,pytorch_lightning/utilities/debugging.py,d71556e7a15a82dc3f47294bcbdb176f882f0c44,c32e3f3ea57dd4439255b809ed5519608a585d73,\"\"\" || These flags are useful to help debug a model. ||  || Fast dev run || ------------ ||  || This flag is meant for debugging a full train\/val\/test loop. ||  It'll activate callbacks; everything but only with 1 training and 1 validation batch. ||  Use this to debug a full run of your program quickly ||  || .. code-block:: python ||  ||     # DEFAULT ||     trainer = Trainer(fast_dev_run=False) ||  ||  || Inspect gradient norms || ---------------------- ||  || Looking at grad norms can help you figure out where training might be going wrong. ||  || .. code-block:: python ||  ||     # DEFAULT (-1 doesn't track norms) ||     trainer = Trainer(track_grad_norm=-1) ||  ||     # track the LP norm (P=2 here) ||     trainer = Trainer(track_grad_norm=2) ||  ||  || Make model overfit on subset of data || ------------------------------------ ||  || A useful debugging trick is to make your model overfit a tiny fraction of the data. ||  || setting `overfit_pct > 0` will overwrite train_percent_check; val_percent_check; test_percent_check ||  || .. code-block:: python ||  ||     # DEFAULT don't overfit (ie: normal training) ||     trainer = Trainer(overfit_pct=0.0) ||  ||     # overfit on 1% of data ||     trainer = Trainer(overfit_pct=0.01) ||  ||  || Print the parameter count by layer || ---------------------------------- ||  || By default lightning prints a list of parameters *and submodules* when it starts training. ||  || .. code-block:: python ||  ||     # DEFAULT print a full list of all submodules and their parameters. ||     trainer = Trainer(weights_summary='full') ||  ||     # only print the top-level modules (i.e. the children of LightningModule). ||     trainer = Trainer(weights_summary='top') ||  || Print which gradients are nan || ----------------------------- ||  || This option prints a list of tensors with nan gradients:: ||  ||     # DEFAULT ||     trainer = Trainer(print_nan_grads=False) ||  || Log GPU usage || ------------- ||  || Lightning automatically logs gpu usage to the test tube logs. ||  It'll only do it at the metric logging interval; so it doesn't slow down training. ||  || \"\"\"
aahibebhf,PyTorchLightning/pytorch-lightning,tests/test_trainer.py,62f6f92fdf6343e17336f0a28247ff95d35bac3a,STILL_EXISTS,TODO split this up into multiple tests
aahibecdf,PyTorchLightning/pytorch-lightning,tests/test_trainer.py,63717e8fdaa6d2cda8a5d0ca1afe6f3ab82ab117,STILL_EXISTS,TODO split this up into multiple tests
aahibedbe,PyTorchLightning/pytorch-lightning,pytorch_lightning/logging/tensorboard.py,5d00e62047b73bbad51c89c01b9bb91508f0391d,STILL_EXISTS,TODO: some alternative should be added
aahibedbf,PyTorchLightning/pytorch-lightning,pytorch_lightning/logging/tensorboard.py,5d00e62047b73bbad51c89c01b9bb91508f0391d,STILL_EXISTS,in case converting from namespace; todo: rather test if it is namespace
aahibedee,PyTorchLightning/pytorch-lightning,pytorch_lightning/logging/wandb.py,ec7fc97857f8c100d8e9ff341ec1e64612ff53a0,STILL_EXISTS,args needed to reload correct experiment
aahibedfd,PyTorchLightning/pytorch-lightning,pytorch_lightning/logging/tensorboard.py,f72e354ee62e0d07c6394a020c615d388d4206c8,STILL_EXISTS,TODO: some alternative should be added
aahibedhb,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/__init__.py,bc67689068a0db11adaf10b32a41bcd33b8ae88e,STILL_EXISTS,\"\"\" ||  || The trainer de-couples the engineering code (16-bit; early stopping; GPU distribution; etc...) from the || science code (GAN; BERT; your project; etc...). It uses many assumptions which are best practices in || AI research today. ||  || The trainer automates all parts of training except: ||  || - what happens in training ; test; val loop || - where the data come from || - which optimizers to use || - how to do the computations ||  || The Trainer delegates those calls to your LightningModule which defines how to do those parts. ||  || This is the basic use of the trainer: ||  || .. code-block:: python ||  ||     from pytorch_lightning import Trainer ||  ||     model = MyLightningModule() ||  ||     trainer = Trainer() ||     trainer.fit(model) || \"\"\"
aahibedjg,PyTorchLightning/pytorch-lightning,pytorch_lightning/loggers/__init__.py,76a1c67d87efcfc8656c0228c9f9f0ac184421b2,STILL_EXISTS,needed to prevent ImportError and duplicated logs.
aahibeedc,PyTorchLightning/pytorch-lightning,pytorch_lightning/core/lightning.py,853c4c1e7bb64d6a1c3d7f55899358a32aaec11c,STILL_EXISTS,todo: remove in v0.8.0
aahibeeih,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,d4a31f02e0a26471c4ccf58fc4f54486d3fdd0a5,2674976f2c53d52542d0c8acc0ec1e3dbd281a50,backward compatible; todo: remove in v0.8.0
aahibefje,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,1015a0050621828c9e8af2c934e19c5c68d61a5e,STILL_EXISTS,set up the passed in dataloaders (if needed)
aahibehga,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,2a04be038624bd8867b01b4b7bfe967b010fb941,7dc58bd286b1e81ca4d293f05bddff5e93361020,TODO: remove self.testing condition because model.summarize() is wiping out the weights
aahibeiaa,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/distrib_data_parallel.py,6dae5698ef7d02167347ff8d8f8083e9270291e0,STILL_EXISTS,when ddp ends; we save the model
aahibeica,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/training_loop.py,705e5764178d82a77abe60e0d4027b572b2efdf2,STILL_EXISTS,TODO wrap this logic into the callback
aahibeifb,PyTorchLightning/pytorch-lightning,tests/models/base.py,165b9fb3f35ee91729a68040f5589fee41d51bbe,STILL_EXISTS,TODO: remove after getting own MNIST
aahibeifc,PyTorchLightning/pytorch-lightning,tests/models/base.py,165b9fb3f35ee91729a68040f5589fee41d51bbe,STILL_EXISTS,TEMPORAL FIX; https:\/\/github.com\/pytorch\/vision\/issues\/1938
aahibeihg,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/training_io.py,969e929a482b3384c0c23777389db866bcc5c5ed,STILL_EXISTS,\"\"\" || Lightning can automate saving and loading checkpoints || ===================================================== ||  || Checkpointing is enabled by default to the current working directory. || To change the checkpoint path pass in:: ||  ||     Trainer(default_save_path='\/your\/path\/to\/save\/checkpoints') ||  ||  || To modify the behavior of checkpointing pass in your own callback. ||  || .. code-block:: python ||  ||     from pytorch_lightning.callbacks import ModelCheckpoint ||  ||     # DEFAULTS used by the Trainer ||     checkpoint_callback = ModelCheckpoint( ||         filepath=os.getcwd(); ||         save_best_only=True; ||         verbose=True; ||         monitor='val_loss'; ||         mode='min'; ||         prefix='' ||     ) ||  ||     trainer = Trainer(checkpoint_callback=checkpoint_callback) ||  ||  || Restoring training session || -------------------------- ||  || You might want to not only load a model but also continue training it. Use this method to || restore the trainer state as well. This will continue from the epoch and global step you last left off. || However; the dataloaders will start from the first batch again (if you shuffled it shouldn't matter). ||  || Lightning will restore the session if you pass a logger with the same version and there's a saved checkpoint. ||  || .. code-block:: python ||  ||     from pytorch_lightning import Trainer ||     from pytorch_lightning.loggers import TestTubeLogger ||  ||     logger = TestTubeLogger( ||         save_dir='.\/savepath'; ||         version=1  # An existing version with a saved checkpoint ||     ) ||     trainer = Trainer( ||         logger=logger; ||         default_save_path='.\/savepath' ||     ) ||  ||     # this fit call loads model weights and trainer state ||     # the trainer continues seamlessly from where you left off ||     # without having to do anything else. ||     trainer.fit(model) ||  ||  || The trainer restores: ||  || - global_step || - current_epoch || - All optimizers || - All lr_schedulers || - Model weights ||  || You can even change the logic of your model as long as the weights and \"architecture\" of || the system isn't different. If you add a layer; for instance; it might not work. ||  || At a rough level; here's what happens inside Trainer :py:mod:`pytorch_lightning.base_module.model_saving.py`: ||  || .. code-block:: python ||  ||     self.global_step = checkpoint['global_step'] ||     self.current_epoch = checkpoint['epoch'] ||  ||     # restore the optimizers ||     optimizer_states = checkpoint['optimizer_states'] ||     for optimizer; opt_state in zip(self.optimizers; optimizer_states): ||         optimizer.load_state_dict(opt_state) ||  ||     # restore the lr schedulers ||     lr_schedulers = checkpoint['lr_schedulers'] ||     for scheduler; lrs_state in zip(self.lr_schedulers; lr_schedulers): ||         scheduler['scheduler'].load_state_dict(lrs_state) ||  ||     # uses the model you passed into trainer ||     model.load_state_dict(checkpoint['state_dict']) ||  || \"\"\"
aahibeijb,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/evaluation_loop.py,29faea18628b108f368208f76f3a81004a64d641,STILL_EXISTS,TODO: remove in v 1.0.0
aahibeije,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/training_loop.py,29faea18628b108f368208f76f3a81004a64d641,a0997bb7a6b5c6d39cfade6c8ae510f9a49c24b8,TODO: remove in 1.0.0
aahibejgc,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,be89eb07c838e0937dff5c22b4d897086df3a84b,96964841534494f6020807c987c6ac3ffd1f60a4,TODO: get \"help\" from docstring :)
aahibfade,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,732eaee4d735dafd1c90728e5583341d75ff72b5,949734489a78e21917e0c412afa701e034c404c0,backward compatible; todo: remove in v0.9.0
aahibfadf,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,732eaee4d735dafd1c90728e5583341d75ff72b5,949734489a78e21917e0c412afa701e034c404c0,Backward compatibility; TODO: remove in v0.9.0
aahibfaif,PyTorchLightning/pytorch-lightning,tests/collect_env_details.py,61177cd1c83e4676dd29d6a207b718b5cae4c307,STILL_EXISTS,\"\"\"Diagnose your system and show basic information ||  || This server mainly to get detail info for better bug reporting. ||  || \"\"\"
aahibfbgb,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,ee68d5ba8e66bba52b3cc3d9d8780d6df2f3204b,949734489a78e21917e0c412afa701e034c404c0,backward compatible; todo: remove in v0.9.0
aahibfcee,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,2912239fe608d8e9927bdb7d5580cef3d0c9d0e1,STILL_EXISTS,Assign __code__; needed for checking if method has been overriden
aahibfcfe,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,f33b5a8d998f8362fe1d0b3404f98f612bd40649,949734489a78e21917e0c412afa701e034c404c0,backward compatible; todo: remove in v0.9.0
aahibfcfj,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/model_hooks.py,2eca8a9ef27c55e246818ba7fcf00e77a62a4f0c,caf7893f27c73db227883ad00531d0ff550cbb23,so; we hack it by using the string representation
aahibfcgb,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,2eca8a9ef27c55e246818ba7fcf00e77a62a4f0c,b0f77a74a1a5cfa8d67fab5730ecd37d1915a184,so; we hack it by using the string representation
aahibfcgh,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/evaluation_loop.py,ebd9fc9530242e1c9b5f3093dc62ceb4185735b0,22b9642117394d3c50587ae137dbf94c6dd5173c,TODO: remove in v1.0.0
aahibfchi,PyTorchLightning/pytorch-lightning,pytorch_lightning/loggers/wandb.py,3c5530c29d56f2c09b09027bb836425f217ad425,STILL_EXISTS,args needed to reload correct experiment
aahibfcja,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,4ed3027309fe1882554e9b7ffe33f1aa92c88106,949734489a78e21917e0c412afa701e034c404c0,backward compatible; todo: remove in v0.9.0
aahibfcjb,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,4ed3027309fe1882554e9b7ffe33f1aa92c88106,949734489a78e21917e0c412afa701e034c404c0,Backward compatibility; TODO: remove in v0.9.0
aahibfdbd,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,b78c3d4da826785e04ed06dabd5f99761ea6a388,2674976f2c53d52542d0c8acc0ec1e3dbd281a50,backward compatible; todo: remove in v0.8.0
aahibfdbe,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,b78c3d4da826785e04ed06dabd5f99761ea6a388,2674976f2c53d52542d0c8acc0ec1e3dbd281a50,Backward compatibility; TODO: remove in v0.8.0
aahibfdbg,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/lr_finder.py,3f09b32df3ded1a9768261df65b78c37e0cbe0c7,STILL_EXISTS,TODO: log lr.results to self.logger
aahibfedd,PyTorchLightning/pytorch-lightning,pytorch_lightning/core/hooks.py,2ab2f7d08df4e4f913e229caf92bbd92f31f6f93,949734489a78e21917e0c412afa701e034c404c0,TODO: remove in v0.9.0
aahibfeea,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/training_loop.py,0203938af8f69a19b7e0264f18e03d543d86e0e9,STILL_EXISTS,TODO: consolidate all actions that need to take place only after
aahibfejb,PyTorchLightning/pytorch-lightning,tests/models/data/horovod/train_default_model.py,7024177f7d4163e2af57c7dfa5550585748cbc0d,STILL_EXISTS,\"\"\" || This script is meant to be executed from `..\/..\/test_horovod.py`. ||  || Because Horovod uses a parallel programming model similar to MPI; unit tests for collective || ops like allreduce need to be run in parallel. The most common approach for running parallel || Horovod workers is to launch multiple replicas of the training script via the `horovodrun` || command-line tool: ||  || .. code-block:: bash ||  ||     horovodrun -np 2 python train_default_model.py ... ||  || Individual test parameters are configured by the serialized `--trainer-options` JSON object. ||  || An non-zero exit code from this script on any rank will indicate failure; while a zero exit code || across all ranks indicates success. || \"\"\"
aahibffaf,PyTorchLightning/pytorch-lightning,pytorch_lightning/core/hooks.py,29ebe922081cbfa7f41b84a271ed8e07a83dcc1a,2674976f2c53d52542d0c8acc0ec1e3dbd281a50,TODO: remove in v0.8.0
aahibffai,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/auto_mix_precision.py,29ebe922081cbfa7f41b84a271ed8e07a83dcc1a,2674976f2c53d52542d0c8acc0ec1e3dbd281a50,TODO: remove in v 0.8.0
aahibffaj,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/auto_mix_precision.py,29ebe922081cbfa7f41b84a271ed8e07a83dcc1a,0be78d13aad1e742f4b13b41ce660b6a370271ac,Backward compatibility; TODO: remove in v0.9.0
aahibffba,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/auto_mix_precision.py,29ebe922081cbfa7f41b84a271ed8e07a83dcc1a,STILL_EXISTS,TODO: remove all below for v0.8.0
aahibffbb,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/distrib_parts.py,29ebe922081cbfa7f41b84a271ed8e07a83dcc1a,STILL_EXISTS,TODO: update for 0.8.0
aahibffbc,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/distrib_parts.py,29ebe922081cbfa7f41b84a271ed8e07a83dcc1a,4dbd761a1cbc1d6903c321873556bfebc05d0f7c,hack forward to do autocast for the user
aahibffbe,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/distrib_parts.py,29ebe922081cbfa7f41b84a271ed8e07a83dcc1a,STILL_EXISTS,TODO: remove in v0.8.0
aahibffbf,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,29ebe922081cbfa7f41b84a271ed8e07a83dcc1a,2674976f2c53d52542d0c8acc0ec1e3dbd281a50,backward compatible; todo: remove in v0.8.0
aahibffbi,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,29ebe922081cbfa7f41b84a271ed8e07a83dcc1a,2674976f2c53d52542d0c8acc0ec1e3dbd281a50,TODO: remove for v0.8.0
aahibfgii,PyTorchLightning/pytorch-lightning,tests/trainer/test_checks.py,595ec65796fa6cbb6ed9479e0871be3d6b208241,STILL_EXISTS,TODO: add matching messages
aahibfgje,PyTorchLightning/pytorch-lightning,tests/trainer/test_trainer.py,6d58fb1353b04b85beb4c71b61e8b317bc456276,9076551aec2976ff67c121a5b6da533e4b2ac406,todo: check duplicated tests against trainer_checks
aahibfieh,PyTorchLightning/pytorch-lightning,pytorch_lightning/core/lightning.py,22d7d031188ecaad636cbad30a43af94abc52bd9,d2967d9305b42c9260f821f2b7fb43fbf19ca1aa,backward compatible; todo: remove in v0.9.0
aahibfigg,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/distrib_parts.py,53d9316a564d2c836149aaf9b761706046349844,b34217e410101f53282902caa0c07bd5268b56c6,when training ends on these platforms dump weights to get out of the main process
aahibfihb,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,bee0392c372936567b2bbe6e7ed5828cb3078354,STILL_EXISTS,redefine the type for ArgParser needed
aahibfihc,PyTorchLightning/pytorch-lightning,tests/trainer/test_trainer_cli.py,bee0392c372936567b2bbe6e7ed5828cb3078354,7ecb0d25281ef1320959d036c8020231029106b2,todo: add also testing for \"gpus\"
aahibfijj,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,7c7e50ca4702a5b35bc1b80d44bca7606552093a,949734489a78e21917e0c412afa701e034c404c0,backward compatible; todo: remove in v0.9.0
aahibfjab,PyTorchLightning/pytorch-lightning,pytorch_lightning/metrics/__init__.py,9b629637b890a1eb9a0d9011bfa10322e0d89da0,55fbcc00f662bbb16b270d436ef3d60021d77ba0,\"\"\" || Metrics || ======= ||  || Metrics are generally used to monitor model performance. ||  || The following package aims to provide the most convenient ones as well || as a structure to implement your custom metrics for all the fancy research || you want to do. ||  || For native PyTorch implementations of metrics; it is recommended to use || the :class:`TensorMetric` which handles automated DDP syncing and conversions || to tensors for all inputs and outputs. ||  || If your metrics implementation works on numpy; just use the || :class:`NumpyMetric`; which handles the automated conversion of || inputs to and outputs from numpy as well as automated ddp syncing. ||  || .. warning:: Employing numpy in your metric calculation might slow ||     down your training substantially; since every metric computation ||     requires a GPU sync to convert tensors to numpy. ||  ||  || \"\"\"
aahibfjac,PyTorchLightning/pytorch-lightning,pytorch_lightning/metrics/converters.py,9b629637b890a1eb9a0d9011bfa10322e0d89da0,STILL_EXISTS,\"\"\" || This file provides functions and decorators for automated input and output || conversion to\/from :class:`numpy.ndarray` and :class:`torch.Tensor` as well as utilities to || sync tensors between different processes in a DDP scenario; when needed. || \"\"\"
aahibfjdj,PyTorchLightning/pytorch-lightning,tests/base/model_template.py,caa9c6760b282637c5f2e7408f85685cc3b342cf,b7afac351b61a1f90e9b0611e267731058c8cda0,TODO: to be fixed in #1773
aahibgabh,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,82a20296e308a67c8d9202e4cbdf92a44b90b077,STILL_EXISTS,TODO: remove TPU spawn
aahibgacf,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/training_loop.py,82a20296e308a67c8d9202e4cbdf92a44b90b077,9939f53b7c22960022204e2b561f264a54fd821e,user could press ctrl+c many times... only shutdown once
aahibgahf,PyTorchLightning/pytorch-lightning,tests/models/test_hooks.py,8211256c46430e43e0c27e4f078c72085bb4ea34,STILL_EXISTS,running .fit() would require us to implement custom data loaders; we mock the model reference instead
aahibgahi,PyTorchLightning/pytorch-lightning,pytorch_lightning/core/lightning.py,4234992302608e1999c00b4faffac591fb537a34,d2967d9305b42c9260f821f2b7fb43fbf19ca1aa,by convention this is named \"*args\"
aahibgbbb,PyTorchLightning/pytorch-lightning,pytorch_lightning/core/saving.py,d2967d9305b42c9260f821f2b7fb43fbf19ca1aa,949734489a78e21917e0c412afa701e034c404c0,backward compatible; todo: remove in v0.9.0
aahibgbbg,PyTorchLightning/pytorch-lightning,pytorch_lightning/core/saving.py,d2967d9305b42c9260f821f2b7fb43fbf19ca1aa,STILL_EXISTS,todo add some back compatibility
aahibgbbj,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/training_io.py,d2967d9305b42c9260f821f2b7fb43fbf19ca1aa,c0903b800d99588a5d026142751d68de7909f06c,todo: add some recursion in case of OmegaConf
aahibgbcb,PyTorchLightning/pytorch-lightning,pytorch_lightning/utilities/parsing.py,d2967d9305b42c9260f821f2b7fb43fbf19ca1aa,STILL_EXISTS,by convention this is named \"*args\"
aahibgbcc,PyTorchLightning/pytorch-lightning,pytorch_lightning/utilities/parsing.py,d2967d9305b42c9260f821f2b7fb43fbf19ca1aa,STILL_EXISTS,by convention this is named \"**kwargs\"
aahibgbdd,PyTorchLightning/pytorch-lightning,tests/models/test_hparams.py,d2967d9305b42c9260f821f2b7fb43fbf19ca1aa,c0903b800d99588a5d026142751d68de7909f06c,todo
aahibgcfe,PyTorchLightning/pytorch-lightning,tests/models/test_hparams.py,d2967d9305b42c9260f821f2b7fb43fbf19ca1aa,STILL_EXISTS,# no matter how you do it; it should be assigned
aahibgcha,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/training_loop.py,479ab49d03585d22f586e46f4334670a312f549b,25ee51bc570503f331dceecc610d0eb355e22327,all of this needs to go into the early stopping to clean up better
aahibgcic,PyTorchLightning/pytorch-lightning,tests/conftest.py,06cd849538d9fdf8797e31e211871f4018cb3592,STILL_EXISTS,so we have to hack it like this
aahibgdad,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,2674976f2c53d52542d0c8acc0ec1e3dbd281a50,1d3c7dc8d693ce04b66095451d9b1b1dbd2aee82,backward compatible; todo: remove in v1.0.0
aahibgdfi,PyTorchLightning/pytorch-lightning,tests/metrics/functional/test_classification.py,3436d00230000ebdf33cc87ea5c62b2e31dbc5e9,38a2119359f22dd9525cb7978eb2ac230a36ab59,TODO: move back the pred and target to test func arguments
aahibgeja,PyTorchLightning/pytorch-lightning,tests/core/test_memory.py,7dc58bd286b1e81ca4d293f05bddff5e93361020,STILL_EXISTS,this layer is unused; therefore input-\/output shapes are unknown
aahibgfec,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,04c794ca72f45dd492e49baff773c2b8e274b9ed,STILL_EXISTS,TODO: convert train_percent_check to limit_train_batches
aahibgfgh,PyTorchLightning/pytorch-lightning,tests/trainer/test_trainer_tricks.py,04c794ca72f45dd492e49baff773c2b8e274b9ed,STILL_EXISTS,test limit_xxx_batches as percent AND int
aahibgfhc,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/data_loading.py,2411c3be7055c49bc549a796f95c52530c8ce104,a642349228d9de4f5e869e9d8c4827e2c3a49921,TODO: verify it is still needed and deprecate it..
aahibgfhg,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,2411c3be7055c49bc549a796f95c52530c8ce104,541c4ab01de869a5f5678ea7672830494ba9e4cc,TODO: remove in 0.10.0
aahibgfhi,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,2411c3be7055c49bc549a796f95c52530c8ce104,1d3c7dc8d693ce04b66095451d9b1b1dbd2aee82,backward compatible; todo: remove in v1.0.0
aahibggee,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/training_loop.py,0f073819d3e0df8db7602eab489b1bad0fc0949c,STILL_EXISTS,TODO: temporary part of structured results PR
aahibggif,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/training_loop.py,598f5140c5c77cf3849ff472f33be45e8806416f,7073de8a957a04d1b42e516258522a1366b00288,VALIDATE IF NEEDED + CHECKPOINT CALLBACK
aahibghfg,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,0be78d13aad1e742f4b13b41ce660b6a370271ac,949734489a78e21917e0c412afa701e034c404c0,Backward compatibility; TODO: remove in v0.9.0
aahibgibj,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,25ee51bc570503f331dceecc610d0eb355e22327,deb82d9c086b339925467e8a9ee2fdced5691613,TODO refactor codebase (tests) to not directly reach into these callbacks
aahibgica,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/training_io.py,25ee51bc570503f331dceecc610d0eb355e22327,a5d1176cf6ef9e637144f980da11bbe63290c994,TODO support more generic way for callbacks to persist a state_dict in a checkpoint
aahibgicf,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/training_io.py,25ee51bc570503f331dceecc610d0eb355e22327,STILL_EXISTS,TODO support more generic way for callbacks to load callback state_dicts
aahibgici,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/training_loop.py,25ee51bc570503f331dceecc610d0eb355e22327,60a3e28fd811c8b221ca1eef1e2171fe349abe67,TODO bake this logic into the checkpoint callback
aahibgjed,PyTorchLightning/pytorch-lightning,tests/models/test_amp.py,69cbb627748445d7f0195ebf45bd83d20a7beff2,STILL_EXISTS,TODO
aahibhbcb,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/training_loop.py,6d10ac2ac84bc01f78eed381fd6152c869040b64,a0997bb7a6b5c6d39cfade6c8ae510f9a49c24b8,TODO: modify when using result obj
aahibhceh,PyTorchLightning/pytorch-lightning,tests/test_deprecated.py,62ce00f96c09de6d137c810921a6cd9e7b60aff5,0719dd7d6894f69bb06a7b4b72c5a9a417db686c,# TODO: why `dataloder` is required if it is not used
aahibhdea,PyTorchLightning/pytorch-lightning,pytorch_lightning/core/datamodule.py,1caf8beb2cf341ff033286b1b924a1c1ff273a64,e886d55ac1be6c34bd8ea56dba29029153d8eb5b,TODO: get \"help\" from docstring :)
aahibhded,PyTorchLightning/pytorch-lightning,pytorch_lightning/core/datamodule.py,1caf8beb2cf341ff033286b1b924a1c1ff273a64,STILL_EXISTS,redefine the type for ArgParser needed
aahibhdeh,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/model_hooks.py,1caf8beb2cf341ff033286b1b924a1c1ff273a64,caf7893f27c73db227883ad00531d0ff550cbb23,TODO - refector this function to accept model_name; instance; parent so it makes more sense
aahibhdhj,PyTorchLightning/pytorch-lightning,tests/trainer/test_config_validator.py,9076551aec2976ff67c121a5b6da533e4b2ac406,STILL_EXISTS,TODO: add matching messages
aahibheae,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/gpu_accelerator.py,071e09fe38d6170d8b019101a3c1871aeb1b2e99,STILL_EXISTS,TODO: remove with dropping NVIDIA AMP support
aahibhfbg,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/tpu_accelerator.py,b34217e410101f53282902caa0c07bd5268b56c6,STILL_EXISTS,when training ends on these platforms dump weights to get out of the main process
aahibhfed,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerator_backends/dp_backend.py,4dbd761a1cbc1d6903c321873556bfebc05d0f7c,STILL_EXISTS,hack forward to do autocast for the user
aahibhgbc,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerator_backends/ddp_spawn_backend.py,0d96b2698a48d8f2a3915379ab03384c325d700a,STILL_EXISTS,TODO: remove with dropping NVIDIA AMP support
aahibhggi,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerator_backends/ddp_spawn_backend.py,0fe933e23d026fce6fd065f87e66c2637693e963,STILL_EXISTS,todo; pass also bets score
aahibhghc,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerator_backends/tpu_backend.py,0fe933e23d026fce6fd065f87e66c2637693e963,STILL_EXISTS,todo; pass also bets score
aahibhghh,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/distrib_data_parallel.py,0fe933e23d026fce6fd065f87e66c2637693e963,STILL_EXISTS,todo; pass complete checkpoint as state dictionary
aahibhhij,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerator_backends/ddp2_backend.py,a0c4365278ca5c5bee72ce63b23e4a77353d4140,STILL_EXISTS,TODO: remove with dropping NVIDIA AMP support
aahibhidi,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerator_backends/ddp_backend.py,a0c4365278ca5c5bee72ce63b23e4a77353d4140,STILL_EXISTS,TODO: remove with dropping NVIDIA AMP support
aahibhjdh,PyTorchLightning/pytorch-lightning,tests/base/datamodules.py,a5f2b89ed08172c25fd1cdc3d884d0fbb60bc45c,STILL_EXISTS,TODO: need to split using random_split once updated to torch >= 1.6
aahibhjeh,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/training_loop.py,b507c42c478e7b99acb37f1fe9a0bf72285a1b17,60d50f646eb8522887d160667ab0470386d09d11,TODO: deprecate 1.0
aahibhjfg,PyTorchLightning/pytorch-lightning,tests/loggers/test_all.py,767c44950c37cb67c5864ab2b92d22da74f6608b,f82d7feb6ccf444e461823dba90386bec8edd7f9,CSVLogger;  # todo
aahibibgj,PyTorchLightning/pytorch-lightning,pytorch_lightning/callbacks/model_checkpoint.py,51de6802edd6c050ba3f2803724298eb059dc5ad,21cfdf68749f3872e02bc960c687b49776ba2b8a,TODO: remove when dict results are deprecated
aahibiiib,PyTorchLightning/pytorch-lightning,tests/core/test_datamodules.py,bab89b8d2141dca5db6480e08c61bdbe169a0e61,STILL_EXISTS,running .fit() would require us to implement custom data loaders; we mock the model reference instead
aahibiiic,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,d7874567e91f634cfb1fc12eaea7178e3ab3eddc,96964841534494f6020807c987c6ac3ffd1f60a4,hack for types in (int; float)
aahibiiid,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,d7874567e91f634cfb1fc12eaea7178e3ab3eddc,96964841534494f6020807c987c6ac3ffd1f60a4,hack for track_grad_norm
aahibiiif,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/evaluation_loop.py,3c88b0dd8353dc56a1bc2a86d13827f83e69044c,STILL_EXISTS,TODO: remove during refactors
aahibijde,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/evaluation_loop.py,6068b29d2928b12b64e5616466a632fcff7e1d83,82d1128966633e7c9987d54c1331ab34538e8f13,TODO: collapse if statement into backends (next)
aahibjaec,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/evaluation_loop.py,50aed42d6b840c9ffd9d8bafb4c50c447e915dc0,2f6d82e0e6c84a6a56c3a0f8c1a01bb179e110d0,TODO: deprecate
aahibjahf,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/evaluation_loop.py,bda14002258f71021d74dbe014797cdec9e4e2dd,STILL_EXISTS,TODO: deprecate
aahibjajj,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/evaluation_loop.py,bd35c869ee0b856fc30229ef36957b058fe97461,STILL_EXISTS,TODO: deprecate
aahibjbae,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/evaluation_loop.py,a1705441a986a8dfdd27124dc4a178716c667a65,STILL_EXISTS,TODO: needs to move inside the loop but breaks early stopping
aahibjbdj,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,be0438bb47596447b9485f0bf507b474660f0f00,STILL_EXISTS,set up the passed in dataloaders (if needed)
aahibjcbb,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/data_connector.py,b0f77a74a1a5cfa8d67fab5730ecd37d1915a184,STILL_EXISTS,set up the passed in dataloaders (if needed)
aahibjccb,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/data_connector.py,b0f77a74a1a5cfa8d67fab5730ecd37d1915a184,STILL_EXISTS,so; we hack it by using the string representation
aahibjcdi,PyTorchLightning/pytorch-lightning,pytorch_lightning/utilities/model_utils.py,b0f77a74a1a5cfa8d67fab5730ecd37d1915a184,a884866ff0b72f54412c2ffd38f86b5c928c1395,TODO - refector this function to accept model_name; instance; parent so it makes more sense
aahibjcee,PyTorchLightning/pytorch-lightning,pytorch_lightning/utilities/model_utils.py,b0f77a74a1a5cfa8d67fab5730ecd37d1915a184,05e0b4e5a12a031a76da6a70bb9e0519a9c242eb,so; we hack it by using the string representation
aahibjcfd,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,805ff37e8c7709e2b355c1d182f35de8c327c01c,2119184801ef94117e52ddb6ed1056d0d9fb9103,TODO: temporary; need to decide if tune or separate object
aahibjchf,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/training_loop.py,bcd13f70b8e064fe294a703bdfbaa6b3bf6bb55f,STILL_EXISTS,TODO: shrink
aahibjdcg,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/training_loop.py,a5288fea1aabe5e1ccad13c77cebcec99b70a247,7073de8a957a04d1b42e516258522a1366b00288,modify dataloader if needed (ddp; etc...)
aahibjdhj,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/training_loop_temp.py,f747cb68439b34ab239be3629a41563007b3d581,STILL_EXISTS,TODO bake this logic into the checkpoint callback
aahibjede,PyTorchLightning/pytorch-lightning,tests/models/test_torchscript.py,4ad5a78dce48317d206419a9cd19dda6293c294b,0815e2a8c5b663e2bac76c78d8debdf0c1e8cfe6,only on torch >= 1.4 do these unused methods get removed
aahibjedj,PyTorchLightning/pytorch-lightning,tests/trainer/test_trainer_tricks.py,48c22c8bad9a47141c7160d92f2edc9e2e4ad159,STILL_EXISTS,unused when another module gets passed to .tune() \/ .fit()
aahibjfce,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/base_backend.py,38b96776380977163a6df664c2226eee8c349b93,STILL_EXISTS,TODO: separate TPU case from here
aahibjfci,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/tpu_backend.py,38b96776380977163a6df664c2226eee8c349b93,STILL_EXISTS,TODO: separate TPU case from here
aahibjfib,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/training_loop_temp.py,8eef97c76f5408b8107f2d1db796371abb4cfd87,d091fafc128c81015a26027d72b17e24502ef2be,TODO: move to accelerators
aahibjfii,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,9939f53b7c22960022204e2b561f264a54fd821e,STILL_EXISTS,reload data when needed
aahibjfji,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,9939f53b7c22960022204e2b561f264a54fd821e,STILL_EXISTS,user could press ctrl+c many times... only shutdown once
aahibjgfa,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/training_loop_temp.py,7073de8a957a04d1b42e516258522a1366b00288,STILL_EXISTS,modify dataloader if needed (ddp; etc...)
aahibjggc,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/training_loop_temp.py,7073de8a957a04d1b42e516258522a1366b00288,STILL_EXISTS,VALIDATE IF NEEDED + CHECKPOINT CALLBACK
aahicaada,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/logger_connector.py,0c2e315950409e84358be50a443c05165b68bcd2,STILL_EXISTS,TODO: merge both functions?
aahicabaa,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,10cf86b94b3979657d52d7816c7e0a0b123bcdbb,3d202f9ecc4137b08cb5b1ac15af276456fcfaaf,TODO: should this be insider the dataloader loop?
aahicabdi,PyTorchLightning/pytorch-lightning,pytorch_lightning/utilities/argparse_utils.py,96964841534494f6020807c987c6ac3ffd1f60a4,8e3faa2da1e7c64a54072fa5115a242e71c49c4b,TODO: get \"help\" from docstring :)
aahicabed,PyTorchLightning/pytorch-lightning,pytorch_lightning/utilities/argparse_utils.py,96964841534494f6020807c987c6ac3ffd1f60a4,a884866ff0b72f54412c2ffd38f86b5c928c1395,hack for types in (int; float)
aahicabee,PyTorchLightning/pytorch-lightning,pytorch_lightning/utilities/argparse_utils.py,96964841534494f6020807c987c6ac3ffd1f60a4,a884866ff0b72f54412c2ffd38f86b5c928c1395,hack for track_grad_norm
aahicabgc,PyTorchLightning/pytorch-lightning,pytorch_lightning/tuner/lr_finder.py,5abf7d91232322e7f87fe65ad490245d9c1894c4,STILL_EXISTS,TODO: log lr.results to self.logger
aahicacbg,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/callback_connector.py,deb82d9c086b339925467e8a9ee2fdced5691613,STILL_EXISTS,TODO refactor codebase (tests) to not directly reach into these callbacks
aahicacfa,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/precision_connector.py,541c4ab01de869a5f5678ea7672830494ba9e4cc,STILL_EXISTS,These are the only lines needed after v0.8.0
aahicaece,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/ddp2_backend.py,dd324e4086c0fab771e15e899a3d28b3909a9671,00d155ae015e1321230678501615b4cd833749e4,todo; pass complete checkpoint as state dictionary
aahicaech,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/ddp_backend.py,dd324e4086c0fab771e15e899a3d28b3909a9671,00d155ae015e1321230678501615b4cd833749e4,todo; pass complete checkpoint as state dictionary
aahicaeda,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/ddp_spawn_backend.py,dd324e4086c0fab771e15e899a3d28b3909a9671,00d155ae015e1321230678501615b4cd833749e4,todo; pass complete checkpoint as state dictionary
aahicaedg,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/tpu_backend.py,ff0064f9563bcbbd2e3606ffb99ce8ba85a2791b,STILL_EXISTS,todo; pass complete checkpoint as state dictionary
aahicaeij,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/connectors/checkpoint_connector.py,4724cdf5e0dc938ebff0a6d6b4477eec99326542,STILL_EXISTS,TODO: fix for anything with multiprocess DP; DDP; DDP2
aahicafga,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/accelerator_connector.py,59d847254897ad07c61c1338e9177f906dbd35be,STILL_EXISTS,TODO: this should be taken out of here... but depends too much on DDP
aahicagad,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/ddp_base_backend.py,00d155ae015e1321230678501615b4cd833749e4,STILL_EXISTS,todo; pass complete checkpoint as state dictionary
aahicagej,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/ddp_cpu_spawn_backend.py,0045119b3fd4532bf8edf17dcbaeba9bc66ba25b,6bcfa8b068a22ca35b2917203bd864cae9d484b4,todo; pass also best score
aahicaidh,PyTorchLightning/pytorch-lightning,pytorch_lightning/loggers/comet.py,e2af4f120e300ccf41f26a52b80be038b2e87a97,STILL_EXISTS,needed later
aahicbbde,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/logging.py,ddd11075bd01c5a68644c2ef1e976f987d0907f6,0dd2deebea5324bef62a28af91757a80f2d419ce,TODO: 1.0.0 remove
aahicbbef,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/training_loop.py,ddd11075bd01c5a68644c2ef1e976f987d0907f6,0dd2deebea5324bef62a28af91757a80f2d419ce,TODO: remove checks in 1.0.0
aahicbbff,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/training_loop.py,ddd11075bd01c5a68644c2ef1e976f987d0907f6,STILL_EXISTS,TODO: add outputs to batches
aahicbbhh,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/evaluation_loop.py,cdd7266cd82b17b3f74d1b15a7ba2d8f7093f53f,STILL_EXISTS,TODO: deprecate at 1.0
aahicbbhj,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/training_loop.py,cdd7266cd82b17b3f74d1b15a7ba2d8f7093f53f,STILL_EXISTS,TODO: is this needed?
aahicbcbg,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/ddp_cpu_spawn_backend.py,c41ea86b355fa5623f8fda62a4270209892f6983,a28528cc8bf6323403b2f2f413d324810d4a4572,todo; pass also best score
aahicbcdj,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/ddp_cpu_spawn_backend.py,c41ea86b355fa5623f8fda62a4270209892f6983,STILL_EXISTS,todo; pass complete checkpoint as state dictionary
aahicbcfb,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,f42ea303c9e1beabb6db4c341a44e966458261d3,STILL_EXISTS,TODO: deprecate 1.0
aahicbcha,PyTorchLightning/pytorch-lightning,pytorch_lightning/core/lightning.py,b3be8022bdffd221eeaa9d006325448fc4291557,STILL_EXISTS,TODO: if logged twice fail with crash
aahicbdbh,PyTorchLightning/pytorch-lightning,tests/metrics/test_aggregation.py,fe290280be18a689a53bdf56419a76987ed6aeed,STILL_EXISTS,TODO: update remaining metrics and uncomment the corresponding test cases
aahicbdhg,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/ddp_cpu_spawn_backend.py,622c5c3982719e5335afa832cc719bd8edb863fb,a28528cc8bf6323403b2f2f413d324810d4a4572,todo; pass also best score
aahicbeag,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/base_backend.py,62eabdd535a7647b59fbfcea258669d2c3bf7fea,STILL_EXISTS,TODO: allow user to compare with string even internaly we shall use these Enum to prevent typos...
aahicbedb,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/ddp_spawn_backend.py,7c6ed1fa28bc0ea7d06e5d6100460ab847fbe97a,STILL_EXISTS,todo; pass complete checkpoint as state dictionary
aahicbffc,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/training_loop.py,d9bc95f83e163f1ef0e64012ad086d4448410817,STILL_EXISTS,VALIDATE IF NEEDED + CHECKPOINT CALLBACK
aahicbfgb,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/accelerator_connector.py,35d111199437406a5ee9b6fa4be0707b50f283aa,STILL_EXISTS,TODO: decouple from TE
aahicbgbe,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/connectors/callback_connector.py,1906867fd47f49d5cb911ab3cb11e8c4a9ef965a,8873750cf0824feb6d1d290a8efd2ec6bbd3deeb,TODO: Deprecated in v0.10.0. Remove early stopping default configuration in v1.0
aahicbgbg,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/connectors/callback_connector.py,1906867fd47f49d5cb911ab3cb11e8c4a9ef965a,8873750cf0824feb6d1d290a8efd2ec6bbd3deeb,TODO: Deprecated in v0.10.0. Remove this method in v1.0
aahicbgbh,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,1906867fd47f49d5cb911ab3cb11e8c4a9ef965a,8873750cf0824feb6d1d290a8efd2ec6bbd3deeb,todo: remove in v1.0.0
aahicbhce,PyTorchLightning/pytorch-lightning,tests/loggers/test_all.py,e0f8505394a755d5eb4ea7f07c21fca5bdc180a8,d65b037a40e1482610cc390dc2e7246dc554aaea,NeptuneLogger;  # TODO: fix: https:\/\/github.com\/PyTorchLightning\/pytorch-lightning\/pull\/3256
aahicbhhb,PyTorchLightning/pytorch-lightning,pytorch_lightning/core/step_result.py,b34c7add23553f10f6f0d7caf4177c67ee213f3a,STILL_EXISTS,TODO: recursive reduce:
aahicbhhd,PyTorchLightning/pytorch-lightning,pytorch_lightning/core/step_result.py,b34c7add23553f10f6f0d7caf4177c67ee213f3a,STILL_EXISTS,move to memory only once
aahicbiaj,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/connectors/logger_connector.py,96009266192905093d9c2347f8f1ab08bf1e3df3,STILL_EXISTS,todo: IDE is complaining; these shall be initialized in the Trainer init at leas as placeholders
aahicbibc,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,96009266192905093d9c2347f8f1ab08bf1e3df3,1d3c7dc8d693ce04b66095451d9b1b1dbd2aee82,backward compatible; todo: remove in 0.11
aahicbibg,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/tpu_backend.py,69833dad5b2a0e7e68ed60a91a5a8c32ae22f707,STILL_EXISTS,TODO: Move this check to Trainer __init__ or device parser
aahicbibj,PyTorchLightning/pytorch-lightning,pytorch_lightning/callbacks/early_stopping.py,064ae53d63389ed96e6ca4648eba55653aed0718,36f0c8a61d200198b244f96556d8d46370ffb2db,todo: remove this old warning
aahiccbib,PyTorchLightning/pytorch-lightning,pytorch_lightning/metrics/regression/explained_variance.py,b961e12f5051f086e321ed06f50448f89ce8c6ef,STILL_EXISTS,TODO: multioutput
aahiccbjb,PyTorchLightning/pytorch-lightning,pytorch_lightning/utilities/model_utils.py,7e756ca11f2d3938ef73010fe63aa45d699bf0bc,05e0b4e5a12a031a76da6a70bb9e0519a9c242eb,so; we hack it by using the string representation
aahiccbjg,PyTorchLightning/pytorch-lightning,pytorch_lightning/metrics/regression/explained_variance.py,8a3c8006411914618fb589d39aed843eb980438b,a93739431299871f29d8014a343be9ad10162377,Todo: allow user to pass in tensor with weights
aahicccbg,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/connectors/env_vars_connector.py,baf4f350275780a243172609af42781a7f63a0a9,0f07eaf51a206396f004a8d45729060b8ac80738,todo: maybe add a warning that some init args were overwritten by Env arguments
aahiccccg,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/connectors/data_connector.py,05e0b4e5a12a031a76da6a70bb9e0519a9c242eb,STILL_EXISTS,so; we hack it by using the string representation
aahicehcb,PyTorchLightning/pytorch-lightning,pytorch_lightning/core/lightning.py,0ec410769744843726a140b7efabbeeb1c4e2929,c2e6e68c7ed1725563f8c84d8cf8b04ae528fde7,TODO: pass the closure to the step ASAP
aahicehdb,PyTorchLightning/pytorch-lightning,tests/trainer/optimization/test_backward_calls.py,0ec410769744843726a140b7efabbeeb1c4e2929,065cc94112d1e3aeab143d1db8905436e0799625,TODO: this test fails on master; max_steps seems to fail together with accumulation
aahicehdf,PyTorchLightning/pytorch-lightning,tests/trainer/optimization/test_backward_calls.py,0ec410769744843726a140b7efabbeeb1c4e2929,065cc94112d1e3aeab143d1db8905436e0799625,TODO: max_steps seems to fail together with accumulation
aahiceheg,PyTorchLightning/pytorch-lightning,pytorch_lightning/metrics/functional/__init__.py,a93739431299871f29d8014a343be9ad10162377,STILL_EXISTS,TODO: unify metrics between class and functional; add below
aahicehgc,PyTorchLightning/pytorch-lightning,pytorch_lightning/metrics/functional/explained_variance.py,a93739431299871f29d8014a343be9ad10162377,efce2b77779467884df9a3d9c16c3176ea81a650,Todo: allow user to pass in tensor with weights
aahiceieh,PyTorchLightning/pytorch-lightning,tests/metrics/regression/test_ssim.py,a93739431299871f29d8014a343be9ad10162377,c36121326dc22c5045067bb125fadd3f2786709b,TODO: ideally tests should pass with lower tolerance
aahiceigb,PyTorchLightning/pytorch-lightning,pytorch_lightning/core/lightning.py,fe737cd0567f21e24b10ec940d74780333d3905a,c2e6e68c7ed1725563f8c84d8cf8b04ae528fde7,TODO: pass the closure to the step ASAP
aahiceihi,PyTorchLightning/pytorch-lightning,tests/checkpointing/test_model_checkpoint.py,4c7ebdc32be1b7ff8d151e490b13805cf9eee903,fb90eec5154a05d18792814a02d3d552c5b267d9,TODO: Checks with filepath. To be removed in v1.2
aahiceijb,PyTorchLightning/pytorch-lightning,pytorch_lightning/metrics/functional/classification.py,6ad299573f290768341a054ea6d2d776ae079fa3,90d1d9fa73408ece88fb809e367455bbe9f668fe,TODO: Update message on removing reorder
aahiceijg,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/connectors/profiler_connector.py,c50c225f05f9a27687d5d5244c8d46a43900c635,09baf29ecbace3e0f936e7b5e631ec94a6fd49fd,TODO: Update exception on removal of bool
aahiceijj,PyTorchLightning/pytorch-lightning,tests/test_deprecated.py,c50c225f05f9a27687d5d5244c8d46a43900c635,STILL_EXISTS,TODO: remove bool from Trainer.profiler param in v1.3.0; update profiler_connector.py
aahicejaa,PyTorchLightning/pytorch-lightning,pytorch_lightning/plugins/ddp_plugin.py,6878f3bf4e2df94eed430cb9db4b47e419948af9,61308138c33ab2d52c84e72eab3a8f1c83fa23fd,if unset; default `find_unused_parameters` `True`
aahicfaag,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/connectors/logger_connector/epoch_result_store.py,ac3f7393fd090484c8230ec375db503819710227,STILL_EXISTS,TODO: Figure out to reduce memory
aahicfaah,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/connectors/logger_connector/epoch_result_store.py,ac3f7393fd090484c8230ec375db503819710227,STILL_EXISTS,TODO: How to start training in middle of epoch
aahicfaed,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/training_loop.py,ac3f7393fd090484c8230ec375db503819710227,STILL_EXISTS,todo: Properly aggregate grad_norm accros opt_idx and split_idx
aahicfbde,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/training_loop.py,9c8701f2e2c09a984ac0322942c8f742a7cc6bc9,STILL_EXISTS,todo: Properly aggregate grad_norm accros opt_idx and split_idx
aahicfcei,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/training_loop.py,7e08b0d710208e980f27896aa62a59f26f0cb3b3,STILL_EXISTS,TODO: Find why - RuntimeError: Expected to mark a variable ready only once ...
aahicfcfb,PyTorchLightning/pytorch-lightning,tests/trainer/optimization/test_manual_optimization.py,7e08b0d710208e980f27896aa62a59f26f0cb3b3,STILL_EXISTS,TODO: Figure out why 1 every 3 runs; weights don't get updated on count = 4\"
aahicfdde,PyTorchLightning/pytorch-lightning,pytorch_lightning/core/step_result.py,4a01fd048cebb65405021d3f24ae4dc07cb735e6,STILL_EXISTS,TODO: Find a way to make the reduction only once; so we don't need to clone.
aahicfddf,PyTorchLightning/pytorch-lightning,tests/trainer/legacy_deprecate_flow_log_tests/test_trainer_steps_scalar_return.py,4a01fd048cebb65405021d3f24ae4dc07cb735e6,STILL_EXISTS,TODO: Move this test to DDP. pbar_added_metrics is empty with ddp_spawn for some reasons
aahicfdga,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/training_loop.py,4018237c309b7d9d6978da73132003615341e04a,STILL_EXISTS,todo: Properly aggregate grad_norm accros opt_idx and split_idx
aahicfeaf,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,e04e7c9eccc1233d6059e00e427b776cb9bf852a,STILL_EXISTS,TODO: deprecate in 1.2.0
aahicffbj,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/training_loop.py,f0ab74dc2fb2c9d57efc787531618ecf5535d6d8,STILL_EXISTS,todo: Properly aggregate grad_norm accros opt_idx and split_idx
aahicfgjf,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,69b994919200581774de66ebacd510918e85bb1f,STILL_EXISTS,TODO: deprecate in 1.2.0
aahicfigc,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,204a0a2d03ce7dfb014e347f1d34a49ef5e86902,STILL_EXISTS,TODO: deprecate in 1.2.0
aahicgbee,PyTorchLightning/pytorch-lightning,benchmarks/test_parity.py,42b9a387dfa2729f005881dafa41d0aac42d0692,STILL_EXISTS,todo: lower this thr
aahicgbhc,PyTorchLightning/pytorch-lightning,pytorch_lightning/plugins/apex.py,c2e6e68c7ed1725563f8c84d8cf8b04ae528fde7,STILL_EXISTS,TODO: pass the closure to the step ASAP
aahicgbhe,PyTorchLightning/pytorch-lightning,pytorch_lightning/plugins/native_amp.py,c2e6e68c7ed1725563f8c84d8cf8b04ae528fde7,STILL_EXISTS,TODO: pass the closure to the step ASAP
aahicgcid,PyTorchLightning/pytorch-lightning,pytorch_lightning/core/lightning.py,f878a2699f2b323196be2cb5f7a95bf10956c6d5,111d9c7267660ddc536fb0f210f72534f286bb75,TODO: remove this method in v1.3.0.
aahicgcih,PyTorchLightning/pytorch-lightning,pytorch_lightning/callbacks/early_stopping.py,342a2b6f2555ad27b9321efa812a58238b9ee9aa,8b475278dde95737ac42e2f9f3897fd040bd28ed,TODO: Update with MisconfigurationException when auto mode is removed in v1.3
aahicgcii,PyTorchLightning/pytorch-lightning,pytorch_lightning/callbacks/model_checkpoint.py,342a2b6f2555ad27b9321efa812a58238b9ee9aa,46617d90211f060537f99fa1a563817ef35b36b9,TODO: Update with MisconfigurationException when auto mode is removed in v1.3
aahicgdfa,PyTorchLightning/pytorch-lightning,pytorch_lightning/metrics/functional/classification.py,bf7c28cd54b5df05bf6c97614ad9cea3d001c105,74171efadf5ea94416d26f1e2c27c1586a3a84b3,TODO: deprecated in favor of general ROC in pytorch_lightning\/metrics\/functional\/roc.py
aahicgebg,PyTorchLightning/pytorch-lightning,pl_examples/basic_examples/simple_image_classifier.py,22bd742214d28c54a983c1d9699479ad0363d88a,62d4304ca4d42f5a681321cda1e4063e05edd096,todo: without passing model it fails for missing best weights
aahicgfbg,PyTorchLightning/pytorch-lightning,tests/metrics/classification/test_inputs.py,fedc0d13609abbcd5dc89bb2f1c91be5865ca478,STILL_EXISTS,To avoid ugly black line wrapping
aahicgfgg,PyTorchLightning/pytorch-lightning,benchmarks/test_sharded_parity.py,239347435029c0a02b305201ebbfa39d62746ca8,STILL_EXISTS,todo: understand why synchronization breaks there.
aahicgggd,PyTorchLightning/pytorch-lightning,pytorch_lightning/plugins/ddp_sequential_plugin.py,ef8ef12fd0b1fa9318aa7a9930389bab8c8ef5d5,STILL_EXISTS,TODO currently no support for vertical model parallel
aahicgghd,PyTorchLightning/pytorch-lightning,pytorch_lightning/utilities/__init__.py,ef8ef12fd0b1fa9318aa7a9930389bab8c8ef5d5,ae9956f997cb8e66d8c28951d47c7106fca54c1b,todo: find a better way than try \/ except
aahicghag,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/deprecated_api.py,ce9179591dc3414aa0ff59007ea46f30fa9ddbee,b72ed71d4e6381bbd8ccff1e11c96660f1eecd22,todo add logic that it cannot be set if GPU is missing
aahicghcc,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/deprecated_api.py,ce9179591dc3414aa0ff59007ea46f30fa9ddbee,STILL_EXISTS,todo; limiting to exclude DDP2 is not clear but it comes from connectors...
aahicghci,PyTorchLightning/pytorch-lightning,tests/trainer/test_trainer.py,ce9179591dc3414aa0ff59007ea46f30fa9ddbee,398f122a425cd1c113dee728dee1ad72d2e4a4e4,Todo: mock nb Gpus so all these tests can run on any device
aahicghcj,PyTorchLightning/pytorch-lightning,tests/trainer/test_trainer.py,ce9179591dc3414aa0ff59007ea46f30fa9ddbee,398f122a425cd1c113dee728dee1ad72d2e4a4e4,todo: think about simplification; that the the expected will be just a list use_xxx which shall be true...
aahicghdb,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/connectors/slurm_connector.py,d5fa02e7985c3920e72e268ece1366a1de96281b,da6dbc8d1d128cf783d7151b012a5502bbd52bf5,todo: the same function as slurm_environment.py `_resolve_root_node_address`
aahicghdc,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/connectors/slurm_connector.py,d5fa02e7985c3920e72e268ece1366a1de96281b,da6dbc8d1d128cf783d7151b012a5502bbd52bf5,todo: this is the same func as slurm_environment.py `master_port`
aahicghej,PyTorchLightning/pytorch-lightning,pytorch_lightning/metrics/functional/classification.py,4a3f9069cb8fabf588f08361225d129cc52c933e,STILL_EXISTS,TODO: deprecated in favor of general ROC in pytorch_lightning\/metrics\/functional\/roc.py
aahicghff,PyTorchLightning/pytorch-lightning,pytorch_lightning/metrics/classification/f_beta.py,1e501f0f40c873369f6d47214e2471c53311443a,f2c2a692e13046581c35c8b29c35859284b48399,todo: remove in v1.2
aahicgief,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/accelerator_connector.py,2d54116baa1284057d68bde52daf49ee19c5f899,STILL_EXISTS,Todo: required argument `is_slurm_managing_tasks` is not used
aahicgieg,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/ddp2_accelerator.py,2d54116baa1284057d68bde52daf49ee19c5f899,STILL_EXISTS,Todo: required argument `process_idx` is not used
aahicgieh,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/ddp2_accelerator.py,2d54116baa1284057d68bde52daf49ee19c5f899,STILL_EXISTS,Todo: required argument `mp_queue` is not used
aahicgiei,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/ddp_accelerator.py,2d54116baa1284057d68bde52daf49ee19c5f899,STILL_EXISTS,Todo: required argument `process_idx` is not used
aahicgiej,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/ddp_cpu_hpc_accelerator.py,2d54116baa1284057d68bde52daf49ee19c5f899,STILL_EXISTS,Todo: required argument `process_idx` is not used
aahicgifa,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/ddp_cpu_spawn_accelerator.py,2d54116baa1284057d68bde52daf49ee19c5f899,STILL_EXISTS,Todo: required argument `process_idx` is not used
aahicgifb,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/ddp_cpu_spawn_accelerator.py,2d54116baa1284057d68bde52daf49ee19c5f899,STILL_EXISTS,Todo: required argument `model` is not used
aahicgifc,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/ddp_spawn_accelerator.py,2d54116baa1284057d68bde52daf49ee19c5f899,STILL_EXISTS,Todo: required argument `process_idx` is not used
aahicgifd,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/ddp_spawn_accelerator.py,2d54116baa1284057d68bde52daf49ee19c5f899,STILL_EXISTS,Todo: required argument `is_master` is not used
aahicgife,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/tpu_accelerator.py,2d54116baa1284057d68bde52daf49ee19c5f899,STILL_EXISTS,Todo: required argument `tpu_core_idx` is not used
aahicgiff,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/tpu_accelerator.py,2d54116baa1284057d68bde52daf49ee19c5f899,STILL_EXISTS,Todo: required argument `model` is not used
aahicgiga,PyTorchLightning/pytorch-lightning,pytorch_lightning/core/lightning.py,2d54116baa1284057d68bde52daf49ee19c5f899,STILL_EXISTS,Todo: required argument `optimizer_idx` is not used
aahicgigb,PyTorchLightning/pytorch-lightning,pytorch_lightning/plugins/ddp_plugin.py,2d54116baa1284057d68bde52daf49ee19c5f899,STILL_EXISTS,Todo: required argument `is_slurm_managing_tasks` is not used
aahicgigc,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/connectors/data_connector.py,2d54116baa1284057d68bde52daf49ee19c5f899,6166f46281d77830cf88b1cb8dc050c0312fcfcf,Todo: required argument `stage` is not used
aahicgigg,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/connectors/slurm_connector.py,2d54116baa1284057d68bde52daf49ee19c5f899,6166f46281d77830cf88b1cb8dc050c0312fcfcf,Todo: required argument `frame` is not used
aahichadg,PyTorchLightning/pytorch-lightning,pytorch_lightning/utilities/argparse.py,a884866ff0b72f54412c2ffd38f86b5c928c1395,STILL_EXISTS,hack for types in (int; float)
aahichadh,PyTorchLightning/pytorch-lightning,pytorch_lightning/utilities/argparse.py,a884866ff0b72f54412c2ffd38f86b5c928c1395,STILL_EXISTS,hack for track_grad_norm
aahichaia,PyTorchLightning/pytorch-lightning,pytorch_lightning/utilities/model_helpers.py,a884866ff0b72f54412c2ffd38f86b5c928c1395,STILL_EXISTS,TODO - refector this function to accept model_name; instance; parent so it makes more sense
aahichaif,PyTorchLightning/pytorch-lightning,pytorch_lightning/utilities/model_helpers.py,a884866ff0b72f54412c2ffd38f86b5c928c1395,STILL_EXISTS,so; we hack it by using the string representation
aahichbid,PyTorchLightning/pytorch-lightning,tests/deprecated_api/test_remove_1-4.py,7a615b56515aa9fde95d15dac97c34267cda0b54,b72ed71d4e6381bbd8ccff1e11c96660f1eecd22,todo: later add also checking deprecated warnings
aahichcbg,PyTorchLightning/pytorch-lightning,pytorch_lightning/utilities/imports.py,ae9956f997cb8e66d8c28951d47c7106fca54c1b,STILL_EXISTS,todo: find a better way than try \/ except
aahichdaf,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/ddp_accelerator.py,957583544a972b9c235b754cfb7f0effb9f6ecc6,STILL_EXISTS,todo: specify the possible exception
aahichdag,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/ddp_accelerator.py,957583544a972b9c235b754cfb7f0effb9f6ecc6,STILL_EXISTS,Todo: required argument `process_idx` is not used
aahichdah,PyTorchLightning/pytorch-lightning,pytorch_lightning/core/lightning.py,957583544a972b9c235b754cfb7f0effb9f6ecc6,STILL_EXISTS,Todo: required argument `optimizer_idx` is not used
aahichdaj,PyTorchLightning/pytorch-lightning,pytorch_lightning/core/lightning.py,957583544a972b9c235b754cfb7f0effb9f6ecc6,111d9c7267660ddc536fb0f210f72534f286bb75,todo: specify the possible exception
aahichdba,PyTorchLightning/pytorch-lightning,pytorch_lightning/core/step_result.py,957583544a972b9c235b754cfb7f0effb9f6ecc6,STILL_EXISTS,todo: specify the expected Exceptions to come
aahichdbb,PyTorchLightning/pytorch-lightning,pytorch_lightning/loggers/base.py,957583544a972b9c235b754cfb7f0effb9f6ecc6,STILL_EXISTS,todo: specify the possible exception
aahichdbc,PyTorchLightning/pytorch-lightning,pytorch_lightning/loggers/tensorboard.py,957583544a972b9c235b754cfb7f0effb9f6ecc6,STILL_EXISTS,todo: specify the possible exception
aahichdbd,PyTorchLightning/pytorch-lightning,pytorch_lightning/overrides/data_parallel.py,957583544a972b9c235b754cfb7f0effb9f6ecc6,692f77b8a756317356eda0f7a933dac0b0cd16ba,todo: specify the possible exception
aahichdca,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/training_loop.py,957583544a972b9c235b754cfb7f0effb9f6ecc6,STILL_EXISTS,todo: specify the possible exception
aahichdcd,PyTorchLightning/pytorch-lightning,pytorch_lightning/tuner/lr_finder.py,957583544a972b9c235b754cfb7f0effb9f6ecc6,STILL_EXISTS,todo: specify the possible exception
aahichdce,PyTorchLightning/pytorch-lightning,pytorch_lightning/utilities/argparse.py,957583544a972b9c235b754cfb7f0effb9f6ecc6,STILL_EXISTS,todo: specify the possible exception
aahichdcf,PyTorchLightning/pytorch-lightning,pytorch_lightning/utilities/debugging.py,957583544a972b9c235b754cfb7f0effb9f6ecc6,STILL_EXISTS,todo: specify the possible exception
aahichdcg,PyTorchLightning/pytorch-lightning,pytorch_lightning/utilities/xla_device.py,957583544a972b9c235b754cfb7f0effb9f6ecc6,STILL_EXISTS,todo: specify the possible exception
aahichdch,PyTorchLightning/pytorch-lightning,tests/base/datasets.py,957583544a972b9c235b754cfb7f0effb9f6ecc6,STILL_EXISTS,todo: specify the possible exception
aahichddc,PyTorchLightning/pytorch-lightning,tests/base/develop_utils.py,957583544a972b9c235b754cfb7f0effb9f6ecc6,STILL_EXISTS,todo: specify the possible exception
aahichddd,PyTorchLightning/pytorch-lightning,tests/trainer/logging_tests/test_train_loop_logging_1_0.py,957583544a972b9c235b754cfb7f0effb9f6ecc6,STILL_EXISTS,todo: specify the possible exception
aahichdfd,PyTorchLightning/pytorch-lightning,benchmarks/test_sharded_parity.py,b72ed71d4e6381bbd8ccff1e11c96660f1eecd22,b72ed71d4e6381bbd8ccff1e11c96660f1eecd22,todo: Increase speed diff since only 2 GPUs sharding 2 optimizers
aahichdif,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/supporters.py,d88cf4a652946cfbd2158227d7116fcab8bea150,STILL_EXISTS,could be multiple datasets; but use self.dataset to follow the name convention in DataLoader
aahichebb,PyTorchLightning/pytorch-lightning,tests/trainer/test_dataloaders.py,c72880f109b0fcf1de57c5e2a67b7f352b28a7d7,STILL_EXISTS,todo: add also `train_dataloader__multiple_sequence`
aahichedd,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/ddp_cpu_hpc_accelerator.py,a7fe24e9a1a7b13958e70bca229fb9510a17bc58,STILL_EXISTS,Todo: required argument `process_idx` is not used
aahichede,PyTorchLightning/pytorch-lightning,pytorch_lightning/core/lightning.py,58a2993766f9634b2d6711c0b43242f1592bd240,STILL_EXISTS,Todo: required argument `optimizer_idx` is not used
aahichedg,PyTorchLightning/pytorch-lightning,pytorch_lightning/core/lightning.py,58a2993766f9634b2d6711c0b43242f1592bd240,111d9c7267660ddc536fb0f210f72534f286bb75,todo: specify the possible exception
aahichfgc,PyTorchLightning/pytorch-lightning,benchmarks/generate_comparison.py,52c3081b4ce1a5f43bec133834f4109a271ff55c,STILL_EXISTS,todo: kind=\"Vanilla PT\" -> use_lightning=False
aahichfhg,PyTorchLightning/pytorch-lightning,pytorch_lightning/setup_tools.py,d19d711dcf0a877397b4917a80eea8c8f2a0d075,STILL_EXISTS,todo: wrap content as commented description
aahichfhh,PyTorchLightning/pytorch-lightning,pytorch_lightning/core/lightning.py,fe41492b6c9e41a959f4d9bb3ffc4feea20b187f,STILL_EXISTS,Todo: required argument `optimizer_idx` is not used
aahichfhi,PyTorchLightning/pytorch-lightning,pytorch_lightning/core/lightning.py,fe41492b6c9e41a959f4d9bb3ffc4feea20b187f,111d9c7267660ddc536fb0f210f72534f286bb75,todo: specify the possible exception
aahichfjb,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/training_loop.py,9cfbf8d6092de6d6b26b67dbdb93dfeb406a2c70,STILL_EXISTS,todo: specify the possible exception
aahichfjj,PyTorchLightning/pytorch-lightning,pytorch_lightning/core/lightning.py,704e00ee7fe670200ebc006674fd061a389761ab,STILL_EXISTS,Todo: required argument `optimizer_idx` is not used
aahichgaa,PyTorchLightning/pytorch-lightning,pytorch_lightning/core/lightning.py,704e00ee7fe670200ebc006674fd061a389761ab,111d9c7267660ddc536fb0f210f72534f286bb75,todo: specify the possible exception
aahichgde,PyTorchLightning/pytorch-lightning,pytorch_lightning/loggers/tensorboard.py,93abeba5f346c5d8cac799dfd74a2028599a0c9a,STILL_EXISTS,todo: specify the possible exception
aahichgea,PyTorchLightning/pytorch-lightning,pytorch_lightning/loggers/base.py,21fd56e28012b6d337e3c4c5b2f754de63f09431,STILL_EXISTS,todo: specify the possible exception
aahichgeb,PyTorchLightning/pytorch-lightning,pytorch_lightning/core/lightning.py,beb8cacf1c96e44b8dc033b2583e5193a7769112,STILL_EXISTS,Todo: required argument `optimizer_idx` is not used
aahichgec,PyTorchLightning/pytorch-lightning,pytorch_lightning/core/lightning.py,beb8cacf1c96e44b8dc033b2583e5193a7769112,111d9c7267660ddc536fb0f210f72534f286bb75,todo: specify the possible exception
aahichgfa,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/ddp_accelerator.py,5ae6926a520ecaa21fd96f3ebd15b9069dbd880a,STILL_EXISTS,todo: specify the possible exception
aahichgfb,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/ddp_accelerator.py,5ae6926a520ecaa21fd96f3ebd15b9069dbd880a,STILL_EXISTS,Todo: required argument `process_idx` is not used
aahichgfc,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/ddp_spawn_accelerator.py,5ae6926a520ecaa21fd96f3ebd15b9069dbd880a,STILL_EXISTS,Todo: required argument `process_idx` is not used
aahichgfd,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/ddp_spawn_accelerator.py,5ae6926a520ecaa21fd96f3ebd15b9069dbd880a,STILL_EXISTS,Todo: required argument `is_master` is not used
aahichgfe,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/tpu_accelerator.py,5ae6926a520ecaa21fd96f3ebd15b9069dbd880a,STILL_EXISTS,Todo: required argument `tpu_core_idx` is not used
aahichgff,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/tpu_accelerator.py,5ae6926a520ecaa21fd96f3ebd15b9069dbd880a,STILL_EXISTS,Todo: required argument `model` is not used
aahichgfg,PyTorchLightning/pytorch-lightning,pytorch_lightning/overrides/data_parallel.py,5ae6926a520ecaa21fd96f3ebd15b9069dbd880a,692f77b8a756317356eda0f7a933dac0b0cd16ba,todo: specify the possible exception
aahichhjh,PyTorchLightning/pytorch-lightning,tests/callbacks/test_callbacks.py,48718d7ce74f712da881e9585d66a8e0d54e7004,0435e23a64c32ce3888b4d12a8c117eea2d4cf01,todo: enabled since internally we wrap the model for optimizer step; this should be fixed
aahichibb,PyTorchLightning/pytorch-lightning,pytorch_lightning/core/lightning.py,be255de3063547df3cedbb0b98da1729a3b615f8,STILL_EXISTS,Todo: required argument `optimizer_idx` is not used
aahichibd,PyTorchLightning/pytorch-lightning,pytorch_lightning/core/lightning.py,be255de3063547df3cedbb0b98da1729a3b615f8,111d9c7267660ddc536fb0f210f72534f286bb75,todo: specify the possible exception
aahichide,PyTorchLightning/pytorch-lightning,pl_examples/domain_templates/reinforce_learn_ppo.py,b5e7e030d4c049c9614672b3b83ac825894605eb,STILL_EXISTS,if trajectory ends abtruptly; boostrap value of next state
aahichifc,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/accelerator_connector.py,54d20dc596b821b8356117c5b020903f7ba355f4,STILL_EXISTS,TODO: clean-up this branching as most just select class and uses the very same arguments
aahichifd,PyTorchLightning/pytorch-lightning,pytorch_lightning/core/lightning.py,54d20dc596b821b8356117c5b020903f7ba355f4,STILL_EXISTS,Todo: required argument `optimizer_idx` is not used
aahichiff,PyTorchLightning/pytorch-lightning,pytorch_lightning/core/lightning.py,54d20dc596b821b8356117c5b020903f7ba355f4,111d9c7267660ddc536fb0f210f72534f286bb75,todo: specify the possible exception
aahichihg,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/training_loop.py,54d20dc596b821b8356117c5b020903f7ba355f4,STILL_EXISTS,todo: specify the possible exception
aahichiib,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/deprecated_api.py,7449ce216de8786b4370e1a39faa84b96cd6de42,6386f45de772e09f4403bb163aeaaf4219ea8dce,todo: consider rename as `is_training`
aahichiic,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/deprecated_api.py,7449ce216de8786b4370e1a39faa84b96cd6de42,6386f45de772e09f4403bb163aeaaf4219ea8dce,todo: consider rename as `is_testing`
aahichjai,PyTorchLightning/pytorch-lightning,tests/core/test_results.py,29bcf309848b9c5ff95ab0d6dd0f12b7ae28f9b4,STILL_EXISTS,TODO: add end-to-end test
aahichjff,PyTorchLightning/pytorch-lightning,pytorch_lightning/plugins/ddp_plugin.py,e806bb77fa070f810cc35c560aa4c49d7487428e,STILL_EXISTS,if unset; default `find_unused_parameters` `True`
aahichjgc,PyTorchLightning/pytorch-lightning,pytorch_lightning/loggers/tensorboard.py,862904865990163805db1e32cf3561952dcce692,STILL_EXISTS,todo: specify the possible exception
aahichjgd,PyTorchLightning/pytorch-lightning,pytorch_lightning/utilities/debugging.py,e4688ae754faeb2b93cf5664bedbae0ff3ea9b62,STILL_EXISTS,todo: specify the possible exception
aahichjge,PyTorchLightning/pytorch-lightning,pytorch_lightning/tuner/lr_finder.py,6386b8d36b18fc7cfa693702292036e410cac368,STILL_EXISTS,todo: specify the possible exception
aahichjgf,PyTorchLightning/pytorch-lightning,tests/trainer/logging_process/test_train_loop_logging_1_0.py,2fe36c7049435eb5b3840fa3219702ca0d3b2fe2,STILL_EXISTS,todo: specify the possible exception
aahichjgh,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/accelerator_connector.py,1ff6b18e8a2cf08d69f18d1037f57321b2c68ca3,STILL_EXISTS,TODO: clean-up this branching as most just select class and uses the very same arguments
aahichjhf,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/accelerator_connector.py,1ff6b18e8a2cf08d69f18d1037f57321b2c68ca3,STILL_EXISTS,Todo: required argument `is_slurm_managing_tasks` is not used
aahichjji,PyTorchLightning/pytorch-lightning,pytorch_lightning/metrics/functional/__init__.py,61e1d8772d53f8543d6e2b9d7a3e60cffd975482,STILL_EXISTS,TODO: unify metrics between class and functional; add below
aahiciajg,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/training_loop.py,2846322f6098996c2fb14e4f422c3aeca4089dc0,STILL_EXISTS,todo: specify the possible exception
aahicibdf,PyTorchLightning/pytorch-lightning,pytorch_lightning/core/lightning.py,f78223041283450c98fb34bb23bea6706247d937,111d9c7267660ddc536fb0f210f72534f286bb75,todo: specify the possible exception
aahicibeh,PyTorchLightning/pytorch-lightning,pytorch_lightning/profiler/profilers.py,5f3372871a333c3229968f1af1b10a925d7ec3ec,dc8647e333d69629b608807e3b16755c60c2c345,next line is a workaround for a pytorch issue (fixed on master; still present
aahicibjh,PyTorchLightning/pytorch-lightning,tests/checkpointing/test_legacy_checkpoints.py,9dd04028d5bb17fc98c8f495be8c9264ec6a7518,STILL_EXISTS,todo: add more legacy checkpoints :]
aahicicbh,PyTorchLightning/pytorch-lightning,tests/checkpointing/test_legacy_checkpoints.py,9dd04028d5bb17fc98c8f495be8c9264ec6a7518,STILL_EXISTS,todo: add more legacy checkpoints - for < v0.8
aahiciccj,PyTorchLightning/pytorch-lightning,tests/models/test_hooks.py,f2f4a4927106dc256321f45020eeae6037351a41,STILL_EXISTS,running .fit() would require us to implement custom data loaders; we mock the model reference instead
aahiciceh,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,0435e23a64c32ce3888b4d12a8c117eea2d4cf01,1d9c553b860175af1ef7583555e2cdb721d4d39f,todo: remove in v1.3
aahicicgd,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/training_loop.py,0435e23a64c32ce3888b4d12a8c117eea2d4cf01,STILL_EXISTS,todo: specify the possible exception
aahicidca,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/ddp_accelerator.py,53b0ae49b9101980d110b20b84e1d3ba5483a80e,STILL_EXISTS,todo: specify the possible exception
aahicidcb,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/ddp_accelerator.py,53b0ae49b9101980d110b20b84e1d3ba5483a80e,STILL_EXISTS,Todo: required argument `process_idx` is not used
aahicieda,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/legacy/accelerator_connector.py,7e2e874d9507e6b6f7b96dcf7f44406640381d74,STILL_EXISTS,TODO: this should be taken out of here... but depends too much on DDP
aahicieeb,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/legacy/accelerator_connector.py,7e2e874d9507e6b6f7b96dcf7f44406640381d74,STILL_EXISTS,TODO: clean-up this branching as most just select class and uses the very same arguments
aahicieej,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/legacy/accelerator_connector.py,7e2e874d9507e6b6f7b96dcf7f44406640381d74,STILL_EXISTS,todo: in some cases it yield in comarison None and int
aahiciefc,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/legacy/accelerator_connector.py,7e2e874d9507e6b6f7b96dcf7f44406640381d74,STILL_EXISTS,Todo: required argument `is_slurm_managing_tasks` is not used
aahiciejb,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,7e2e874d9507e6b6f7b96dcf7f44406640381d74,1d9c553b860175af1ef7583555e2cdb721d4d39f,todo: remove in v1.3
aahicifbe,PyTorchLightning/pytorch-lightning,tests/accelerators/legacy/__init__.py,7e2e874d9507e6b6f7b96dcf7f44406640381d74,STILL_EXISTS,todo: feel free to move any of these \"legacy\" tests up...
aahicifbf,PyTorchLightning/pytorch-lightning,tests/plugins/legacy/__init__.py,7e2e874d9507e6b6f7b96dcf7f44406640381d74,STILL_EXISTS,todo: feel free to move any of these \"legacy\" tests up...
aahicifeg,PyTorchLightning/pytorch-lightning,tests/callbacks/test_pruning.py,d0aaf983b99224871ff04dc3a3cc919482b2cf13,STILL_EXISTS,Todo: add support for custom pruning_fn function.
aahicigea,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,3da28fd634882d8fc11c5792afd635e3d0c9af2f,STILL_EXISTS,todo: clean up this routing mess.
aahicigfc,PyTorchLightning/pytorch-lightning,tests/trainer/test_trainer.py,3da28fd634882d8fc11c5792afd635e3d0c9af2f,STILL_EXISTS,todo: address this in another PR
aahicigid,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/accelerator.py,5d239ccd704c7b639b6e83fdff9460fa9dd5e790,069ae27cef14569a4d14257d226124ce05475ec7,: PrecisionPlugin  # fixme
aahicigie,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/accelerator.py,5d239ccd704c7b639b6e83fdff9460fa9dd5e790,STILL_EXISTS,TODO: this is a hack; find a better solution for this (hook?)
aahicigje,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/accelerator.py,5d239ccd704c7b639b6e83fdff9460fa9dd5e790,069ae27cef14569a4d14257d226124ce05475ec7,: PrecisionPlugin # fixme
aahicihej,PyTorchLightning/pytorch-lightning,pytorch_lightning/plugins/training_type/training_type_plugin.py,5d239ccd704c7b639b6e83fdff9460fa9dd5e790,STILL_EXISTS,TODO method this is currently unused. Check after complete refactors are pushed
aahicihfe,PyTorchLightning/pytorch-lightning,pytorch_lightning/plugins/training_type/training_type_plugin.py,5d239ccd704c7b639b6e83fdff9460fa9dd5e790,STILL_EXISTS,TODO method this is currently unused
aahiciibh,PyTorchLightning/pytorch-lightning,pytorch_lightning/overrides/distributed.py,692f77b8a756317356eda0f7a933dac0b0cd16ba,STILL_EXISTS,unused parameters. Only if `find_unused_parameters` is set.
aahiciide,PyTorchLightning/pytorch-lightning,pytorch_lightning/plugins/training_type/ddp.py,3bacac7734bb0c85d523b9f1acce442b22089bcc,STILL_EXISTS,fixme: uncomment when this class will actually be used
aahicijde,PyTorchLightning/pytorch-lightning,pytorch_lightning/plugins/training_type/ddp_spawn.py,3bacac7734bb0c85d523b9f1acce442b22089bcc,STILL_EXISTS,fixme: uncomment when this class will actually be used
aahicjcaf,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/accelerator_connector.py,b3ebc18bcb0b4034fdfbc90fc3d79c9786cb59c1,STILL_EXISTS,fixme
aahicjdcb,PyTorchLightning/pytorch-lightning,pytorch_lightning/plugins/training_type/ddp.py,b3ebc18bcb0b4034fdfbc90fc3d79c9786cb59c1,STILL_EXISTS,fixme: uncomment when this class will actually be used
aahicjdcd,PyTorchLightning/pytorch-lightning,pytorch_lightning/plugins/training_type/ddp.py,b3ebc18bcb0b4034fdfbc90fc3d79c9786cb59c1,STILL_EXISTS,if unset; default `find_unused_parameters` `True`
aahicjdcf,PyTorchLightning/pytorch-lightning,pytorch_lightning/plugins/training_type/ddp_spawn.py,b3ebc18bcb0b4034fdfbc90fc3d79c9786cb59c1,STILL_EXISTS,fixme: uncomment when this class will actually be used
aahicjdch,PyTorchLightning/pytorch-lightning,pytorch_lightning/plugins/training_type/ddp_spawn.py,b3ebc18bcb0b4034fdfbc90fc3d79c9786cb59c1,STILL_EXISTS,if unset; default `find_unused_parameters` `True`
aahicjdii,PyTorchLightning/pytorch-lightning,pytorch_lightning/plugins/training_type/rpc_sequential.py,9064b83caf817c624ad5241080e5a68d678a5ea6,STILL_EXISTS,TODO currently no support for vertical model parallel
aahicjdjf,PyTorchLightning/pytorch-lightning,pytorch_lightning/plugins/training_type/rpc_sequential.py,9064b83caf817c624ad5241080e5a68d678a5ea6,STILL_EXISTS,TODO: Move this to the connector
aahicjdjj,PyTorchLightning/pytorch-lightning,pytorch_lightning/core/lightning.py,344f3a984aeec39e2ce0a27a82290897307de751,111d9c7267660ddc536fb0f210f72534f286bb75,todo: specify the possible exception
aahicjebj,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/connectors/slurm_connector.py,aba212341a495ca54cbbffbe636a6ee7b0be83b6,da6dbc8d1d128cf783d7151b012a5502bbd52bf5,todo: specify the possible exception
aahicjecd,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/connectors/slurm_connector.py,aba212341a495ca54cbbffbe636a6ee7b0be83b6,da6dbc8d1d128cf783d7151b012a5502bbd52bf5,todo: the same function as slurm_environment.py `_resolve_root_node_address`
aahicjece,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/connectors/slurm_connector.py,aba212341a495ca54cbbffbe636a6ee7b0be83b6,da6dbc8d1d128cf783d7151b012a5502bbd52bf5,todo: this is the same func as slurm_environment.py `master_port`
aahicjehg,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/training_loop.py,aba212341a495ca54cbbffbe636a6ee7b0be83b6,STILL_EXISTS,todo: specify the possible exception
aahicjfcg,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,10c7dbe6a1d48fc242e18aacafe8ff1691ffb408,STILL_EXISTS,todo: clean up this routing mess.
aahicjfdd,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,10c7dbe6a1d48fc242e18aacafe8ff1691ffb408,STILL_EXISTS,TODO: probably not needed anymore after refactor
aahicjhdf,PyTorchLightning/pytorch-lightning,pytorch_lightning/core/lightning.py,b2b27e4018c3999405b7cf4db04bc95a284f165d,111d9c7267660ddc536fb0f210f72534f286bb75,todo: specify the possible exception
aahicjhed,PyTorchLightning/pytorch-lightning,pytorch_lightning/tuner/lr_finder.py,84a8d2d178791722c4f98a195a29a830870ff6ae,STILL_EXISTS,todo: specify the possible exception
aahicjhhi,PyTorchLightning/pytorch-lightning,tests/base/develop_pipelines.py,82943515dc60e00d8e62eafc2a5cf52891381c63,STILL_EXISTS,on hpc this would work fine... but need to hack it for the purpose of the test
aahicjhja,PyTorchLightning/pytorch-lightning,tests/checkpointing/test_model_checkpoint.py,bd920b4102083635be49ed21cc354bfa1a274869,STILL_EXISTS,TODO: Checks with filepath. To be removed in v1.2
aahicjhji,PyTorchLightning/pytorch-lightning,tests/helpers/pipelines.py,bd920b4102083635be49ed21cc354bfa1a274869,STILL_EXISTS,on hpc this would work fine... but need to hack it for the purpose of the test
aahicjieh,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,cb67e1d0b29e1da251c064c78220aa78b44e0620,STILL_EXISTS,TODO: the old setup is now called \"pre_training\"; where should this hook be called now?
aahicjigc,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/training_loop.py,cb67e1d0b29e1da251c064c78220aa78b44e0620,STILL_EXISTS,todo: specify the possible exception
aahicjihf,PyTorchLightning/pytorch-lightning,tests/helpers/pipelines.py,a0f7831278bb8d40d1b23331537ffb7ec653ae80,STILL_EXISTS,on hpc this would work fine... but need to hack it for the purpose of the test
aahicjjba,PyTorchLightning/pytorch-lightning,tests/models/test_sync_batchnorm.py,c2c82dad62ae749aa559732361b85cfe8e619396,STILL_EXISTS,TODO: Fatal Python error: Bus error
aahidaaba,PyTorchLightning/pytorch-lightning,pytorch_lightning/core/lightning.py,b434c479e7be787c49be2df381011bed3dc8f070,STILL_EXISTS,todo: think about better way without need to dump model to drive
aahidaabb,PyTorchLightning/pytorch-lightning,pytorch_lightning/core/memory.py,b434c479e7be787c49be2df381011bed3dc8f070,STILL_EXISTS,todo: seems it does not work with quantized models - it returns 0.0
aahidaafa,PyTorchLightning/pytorch-lightning,tests/helpers/pipelines.py,8e9a026bc34d8409faa572a7144c2d96a7c039ed,STILL_EXISTS,on hpc this would work fine... but need to hack it for the purpose of the test
aahidaafb,PyTorchLightning/pytorch-lightning,tests/helpers/pipelines.py,8e9a026bc34d8409faa572a7144c2d96a7c039ed,1c851b89e1758f7c3b96b71b7b6619af130e81cf,TODO: This test compares a loss value with a min accuracy - complete non-sense!
aahidabbe,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,da6dbc8d1d128cf783d7151b012a5502bbd52bf5,STILL_EXISTS,TODO: the old setup is now called \"pre_training\"; where should this hook be called now?
aahidabbg,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,da6dbc8d1d128cf783d7151b012a5502bbd52bf5,STILL_EXISTS,todo: clean up this routing mess.
aahidabdg,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/connectors/callback_connector.py,b8619a695f4e1f9a91894badb903ceaa61ea7201,STILL_EXISTS,TODO: connectors refactor: move callbacks list to connector and do not write Trainer state
aahidacic,PyTorchLightning/pytorch-lightning,pytorch_lightning/metrics/functional/psnr.py,6a9cec41a3fe0c361bf2c4f03f4195257f9038cb,f93414d085784e177e75a143bafefa5ffdadd0c8,Maybe we could use `torch.amax(target; dim=dim) - torch.amin(target; dim=dim)` in PyTorch 1.7 to calculate
aahidacie,PyTorchLightning/pytorch-lightning,pytorch_lightning/metrics/regression/psnr.py,6a9cec41a3fe0c361bf2c4f03f4195257f9038cb,f93414d085784e177e75a143bafefa5ffdadd0c8,Maybe we could use `torch.amax(target; dim=dim) - torch.amin(target; dim=dim)` in PyTorch 1.7 to
aahidadab,PyTorchLightning/pytorch-lightning,pytorch_lightning/tuner/lr_finder.py,99da0d92a5ef6b67bb72fb97fb3863b946875bbb,STILL_EXISTS,TODO: log lr.results to self.logger
aahidadah,PyTorchLightning/pytorch-lightning,pytorch_lightning/callbacks/model_checkpoint.py,6bc4490d01aed21c2d52f884d4afbeaa24a47ca0,0544efd4535cb8965902fc3c938263b3deafc7b7,TODO: Move this logic to logger_connector. This also needs to be fixed for any
aahidaeae,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/accelerator.py,b7c2e0a80eb00d04571f15692d965c1a074b51a9,b019c25dde6c2284d85bb96189dee386743bf8e5,TODO: improve these docs
aahidaefd,PyTorchLightning/pytorch-lightning,pytorch_lightning/plugins/training_type/ddp.py,3bdc0673ea5fcb10035d783df0d913be4df499b6,ec8d46e02b45ee6cd5713bd72b2ee7e2474c7ac1,TODO: refactor and let generic cluster env hold the information about who spawns the processes
aahidaefe,PyTorchLightning/pytorch-lightning,pytorch_lightning/plugins/training_type/ddp.py,3bdc0673ea5fcb10035d783df0d913be4df499b6,ec8d46e02b45ee6cd5713bd72b2ee7e2474c7ac1,TODO: From where to get cluster environment?
aahidaefh,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/connectors/accelerator_connector.py,97a81c3cfed9d6677d411672eecb0ed38514cb04,ec8d46e02b45ee6cd5713bd72b2ee7e2474c7ac1,TODO: decouple DDP from SLURM
aahidaefj,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/connectors/accelerator_connector.py,97a81c3cfed9d6677d411672eecb0ed38514cb04,ec8d46e02b45ee6cd5713bd72b2ee7e2474c7ac1,TODO: decouple DDP from TE
aahidaegb,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/connectors/accelerator_connector.py,97a81c3cfed9d6677d411672eecb0ed38514cb04,ec8d46e02b45ee6cd5713bd72b2ee7e2474c7ac1,TODO: maybe introduce a DefaultEnvironment?
aahidaghg,PyTorchLightning/pytorch-lightning,pytorch_lightning/profiler/pytorch.py,dc8647e333d69629b608807e3b16755c60c2c345,51b10f78f4b4c4b704219c619dc5e73784aca57b,next line is a workaround for a pytorch issue (fixed on master; still present
aahidagjj,PyTorchLightning/pytorch-lightning,tests/plugins/test_sharded_plugin.py,4157b3506203ca95e5891ab3c7ba162c3ff26762,STILL_EXISTS,todo
aahidahae,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/tpu.py,484dce11ecb92aab13c29d26b57cbf8c7522ceeb,STILL_EXISTS,todo: Add support for backward with all_gather
aahidahid,PyTorchLightning/pytorch-lightning,pytorch_lightning/callbacks/model_checkpoint.py,d0596fac94665714d9505148fcdfa2c7e2c7ed20,STILL_EXISTS,Todo: required argument `pl_module` is not used
aahidahif,PyTorchLightning/pytorch-lightning,pytorch_lightning/core/lightning.py,d0596fac94665714d9505148fcdfa2c7e2c7ed20,STILL_EXISTS,TODO: remove this method in v1.3.0.
aahidahji,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/connectors/logger_connector/epoch_result_store.py,d0596fac94665714d9505148fcdfa2c7e2c7ed20,STILL_EXISTS,TODO(carmocca): when we implement flushing the logger connector metrics after
aahidaicc,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/training_loop.py,d0596fac94665714d9505148fcdfa2c7e2c7ed20,0dd2deebea5324bef62a28af91757a80f2d419ce,TODO: remove checks in 1.0.0
aahidaifj,PyTorchLightning/pytorch-lightning,pytorch_lightning/callbacks/model_checkpoint.py,826375effe30e9d9be0c59a40be3453c12f00977,STILL_EXISTS,TODO: the rpc plugin should wrap trainer.save_checkpoint
aahidaiga,PyTorchLightning/pytorch-lightning,pytorch_lightning/callbacks/model_checkpoint.py,826375effe30e9d9be0c59a40be3453c12f00977,STILL_EXISTS,instead of us having to do it here manually
aahidaigc,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/model_hooks.py,efd272a3cac2c412dd4a7aa138feafb2c114326f,STILL_EXISTS,note: currently unused - kept as it is public
aahidaihi,PyTorchLightning/pytorch-lightning,pytorch_lightning/plugins/training_type/ddp.py,c81b2a81891cc39e454b841b1ed221b734e39cee,STILL_EXISTS,if unset; default `find_unused_parameters` `True`
aahidaiib,PyTorchLightning/pytorch-lightning,pytorch_lightning/plugins/training_type/ddp.py,c81b2a81891cc39e454b841b1ed221b734e39cee,STILL_EXISTS,This flag does come with a performance hit; so it is suggested to disable in cases where it is possible.
aahidaiic,PyTorchLightning/pytorch-lightning,pytorch_lightning/plugins/training_type/ddp_spawn.py,c81b2a81891cc39e454b841b1ed221b734e39cee,STILL_EXISTS,if unset; default `find_unused_parameters` `True`
aahidaiif,PyTorchLightning/pytorch-lightning,pytorch_lightning/plugins/training_type/ddp_spawn.py,c81b2a81891cc39e454b841b1ed221b734e39cee,STILL_EXISTS,This flag does come with a performance hit; so it is suggested to disable in cases where it is possible.
aahidaiih,PyTorchLightning/pytorch-lightning,pytorch_lightning/plugins/training_type/ddp_spawn.py,c81b2a81891cc39e454b841b1ed221b734e39cee,STILL_EXISTS,TODO: is there a better way than accessing trainer through model -> trainer?
aahidaija,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/configuration_validator.py,f4cc7451a94010a572480c43ad5f0af7ad52cd21,37f22c99ffc16ae4010ba7f2ff42f0b86cd1f0ad,TODO: add predict
aahidajdd,PyTorchLightning/pytorch-lightning,tests/callbacks/test_callbacks.py,f4cc7451a94010a572480c43ad5f0af7ad52cd21,STILL_EXISTS,TODO: add callback tests for predict and tune
aahidajea,PyTorchLightning/pytorch-lightning,tests/plugins/test_sharded_plugin.py,f4cc7451a94010a572480c43ad5f0af7ad52cd21,STILL_EXISTS,todo
aahidajfa,PyTorchLightning/pytorch-lightning,pytorch_lightning/callbacks/model_checkpoint.py,2ecda5df52b2bd14e3125160d1d3a7837fd3c444,0544efd4535cb8965902fc3c938263b3deafc7b7,TODO: Move this logic to logger_connector. This also needs to be fixed for any
aahidajgh,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/gpu.py,c53edce1a1bada7be97c40c8c6d6ae3caca56f4a,STILL_EXISTS,TODO: Add support to allow batch transfer to device in Lightning for DP mode.
aahidajgj,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/connectors/data_connector.py,c53edce1a1bada7be97c40c8c6d6ae3caca56f4a,STILL_EXISTS,TODO: Remove this blocker once batch transfer to device is integrated in Lightning for DP mode.
aahidbaeg,PyTorchLightning/pytorch-lightning,tests/models/test_horovod.py,0544efd4535cb8965902fc3c938263b3deafc7b7,STILL_EXISTS,todo: Find why coverage breaks CI.
aahidbafa,PyTorchLightning/pytorch-lightning,tests/models/test_horovod.py,0544efd4535cb8965902fc3c938263b3deafc7b7,STILL_EXISTS,Check with (tgaddair) on Horovod issues if this feature is needed
aahidbbje,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/connectors/accelerator_connector.py,6a1414681107cf77d1ebeba86359df7b6668bf49,STILL_EXISTS,Custom plugins should implement is_distributed property.
aahidbddf,PyTorchLightning/pytorch-lightning,pytorch_lightning/plugins/training_type/ddp.py,4e9b453854c9ff92fc4a7ad990b7995279313fda,STILL_EXISTS,if unset; default `find_unused_parameters` `True`
aahidbddi,PyTorchLightning/pytorch-lightning,pytorch_lightning/plugins/training_type/ddp.py,4e9b453854c9ff92fc4a7ad990b7995279313fda,STILL_EXISTS,This flag does come with a performance hit; so it is suggested to disable in cases where it is possible.
aahidbddj,PyTorchLightning/pytorch-lightning,pytorch_lightning/plugins/training_type/ddp.py,4e9b453854c9ff92fc4a7ad990b7995279313fda,STILL_EXISTS,todo: PyTorch 1.7.0 DDP introduces ``self.reducer._rebuild_buckets()`` breaking manual_optimization
aahidbeai,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/training_loop.py,e2e1de0fb73e6ba69fb26b7ade4371c5ee6a1845,36d180e53271295359c1ca7da1d222bbd2ed7940,todo (tchaton) All ranks should call describe.
aahidbeaj,PyTorchLightning/pytorch-lightning,tests/test_profiler.py,e2e1de0fb73e6ba69fb26b7ade4371c5ee6a1845,STILL_EXISTS,todo (tchaton) add support for all ranks
aahidbeef,PyTorchLightning/pytorch-lightning,pytorch_lightning/accelerators/accelerator.py,0995d30fab0590d155895a77535663794118b5f6,STILL_EXISTS,Todo (tchaton) Better fix
aahidbfhe,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/logging.py,bc613611e2709dd35bc3cef6bd71f6e9661ca6b2,STILL_EXISTS,TODO: this is duplicated in MetricsHolder. should be unified
aahidbgag,PyTorchLightning/pytorch-lightning,benchmarks/test_sharded_parity.py,21fc5eb21e6db07bcc222afa4204b3d5fb5be323,STILL_EXISTS,TODO: decrease speed diff since only 2 GPUs sharding 2 optimizers
aahidbgja,PyTorchLightning/pytorch-lightning,pytorch_lightning/plugins/training_type/tpu_spawn.py,bb92754119934c79f97de38ce59470f0059f3a0d,STILL_EXISTS,TODO: is there a better way than accessing trainer through model -> trainer?
aahidbgjb,PyTorchLightning/pytorch-lightning,pytorch_lightning/plugins/training_type/tpu_spawn.py,bb92754119934c79f97de38ce59470f0059f3a0d,STILL_EXISTS,TODO: check for trainer reference
aahidbgjd,PyTorchLightning/pytorch-lightning,pytorch_lightning/plugins/training_type/deepspeed.py,1302766f83a5fc861ae77ef6f8798cd0b1d606c7,STILL_EXISTS,todo (tchaton) awaiting this feature to move upstream to DeepSpeed
aahidbgje,PyTorchLightning/pytorch-lightning,pytorch_lightning/plugins/training_type/deepspeed.py,1302766f83a5fc861ae77ef6f8798cd0b1d606c7,STILL_EXISTS,todo: Currently DeepSpeed requires optimizers at inference to partition weights correctly
aahidbgjf,PyTorchLightning/pytorch-lightning,pytorch_lightning/plugins/training_type/deepspeed.py,1302766f83a5fc861ae77ef6f8798cd0b1d606c7,STILL_EXISTS,todo: this is required for DeepSpeed throughput timers; or throughput timers will be incorrect
aahidbgjg,PyTorchLightning/pytorch-lightning,pytorch_lightning/plugins/training_type/deepspeed.py,1302766f83a5fc861ae77ef6f8798cd0b1d606c7,STILL_EXISTS,todo (tchaton) Add support for accumulate_grad_batches being a dictionary.
aahidbgjj,PyTorchLightning/pytorch-lightning,pytorch_lightning/plugins/training_type/deepspeed.py,1302766f83a5fc861ae77ef6f8798cd0b1d606c7,STILL_EXISTS,TODO: Currently required as this call is missing within the deepspeed engine.
aahidbhab,PyTorchLightning/pytorch-lightning,pytorch_lightning/plugins/training_type/deepspeed.py,1302766f83a5fc861ae77ef6f8798cd0b1d606c7,STILL_EXISTS,hook: give user access to checkpoint if needed.
aahidbhad,PyTorchLightning/pytorch-lightning,pytorch_lightning/plugins/training_type/training_type_plugin.py,1302766f83a5fc861ae77ef6f8798cd0b1d606c7,STILL_EXISTS,hook: give user access to checkpoint if needed.
aahidbhcd,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/trainer.py,1302766f83a5fc861ae77ef6f8798cd0b1d606c7,STILL_EXISTS,todo: move this logic internally within the barrier.
aahidbhff,PyTorchLightning/pytorch-lightning,tests/plugins/test_deepspeed_plugin.py,1302766f83a5fc861ae77ef6f8798cd0b1d606c7,STILL_EXISTS,todo (tchaton) Currently load_from_checkpoint is not support for zero-v3
aahidbhgc,PyTorchLightning/pytorch-lightning,pytorch_lightning/trainer/connectors/logger_connector/epoch_result_store.py,0dd2deebea5324bef62a28af91757a80f2d419ce,STILL_EXISTS,TODO(carmocca): when we implement flushing the logger connector metrics after
aahidbjhg,ylongqi/openrec,docs/conf.py,98afb6d214b185cb65118bc28b6b29ab28606022,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aahidcjaa,ylongqi/openrec,openrec/recommenders/drr.py,1b1a390d5a9394aee42fd6622089aebeb71082e9,STILL_EXISTS,#TODO: add user features and concat here; use usergraph.extend
aahiddacd,MaxHalford/prince,prince/mca.py,8eab70acc8e18dd7fbc9e99ff0d085f13391d41a,STILL_EXISTS,Keep the number of columns in the original dataset
aahiddagi,MaxHalford/prince,prince/mca.py,eb799a509591896590cb15195a2ee132e0dac37e,44dd550d415871e36f5a86185afb10e56a7cd4e9,The categorical columns are the ones whose values are not numerical
aahiddaha,MaxHalford/prince,prince/mca.py,eb799a509591896590cb15195a2ee132e0dac37e,93e4c0a15ce474cdaaad19d2c83a64bee7964f75,Extract the supplementary columns
aahiddahb,MaxHalford/prince,prince/mca.py,eb799a509591896590cb15195a2ee132e0dac37e,93e4c0a15ce474cdaaad19d2c83a64bee7964f75,Extract the categorical columns
aahiddahc,MaxHalford/prince,prince/mca.py,eb799a509591896590cb15195a2ee132e0dac37e,STILL_EXISTS,Remove the categorical and the supplementary columns from the main dataframe
aahiddahh,MaxHalford/prince,prince/plot/mpl/pca.py,eb799a509591896590cb15195a2ee132e0dac37e,STILL_EXISTS,Add legend to distinguish active and supplementary columns
aahiddbfi,MaxHalford/prince,doc/source/conf.py,641a0a3f2e76417e3463886d284f70cb8ebab3e6,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aahidddea,MaxHalford/prince,prince/mca.py,a854849c9e1ee2b331bb0fd4f0f939b1c01b0f01,93e4c0a15ce474cdaaad19d2c83a64bee7964f75,Todo
aahidddeb,MaxHalford/prince,prince/mca.py,44dd550d415871e36f5a86185afb10e56a7cd4e9,93e4c0a15ce474cdaaad19d2c83a64bee7964f75,Extract the categorical columns
aahiddded,MaxHalford/prince,prince/pca.py,44dd550d415871e36f5a86185afb10e56a7cd4e9,93e4c0a15ce474cdaaad19d2c83a64bee7964f75,Extract the categorical columns
aahidddeh,MaxHalford/prince,tests/test_pca.py,da43247267cf00fb378c1ff7145ce9f9f0e1d193,93e4c0a15ce474cdaaad19d2c83a64bee7964f75,Determine the categorical columns
aahidddei,MaxHalford/prince,tests/test_pca.py,da43247267cf00fb378c1ff7145ce9f9f0e1d193,93e4c0a15ce474cdaaad19d2c83a64bee7964f75,Determine the numerical columns
aahidddhf,MaxHalford/prince,prince/mca.py,93e4c0a15ce474cdaaad19d2c83a64bee7964f75,9435a8ae5cfc183630a0aff88817a105f899ca9f,Determine the number of columns in the initial matrix
aahidddhh,MaxHalford/prince,prince/mca.py,93e4c0a15ce474cdaaad19d2c83a64bee7964f75,STILL_EXISTS,Determine the number of columns in the indicator matrix
aahiddehf,MaxHalford/prince,prince/famd.py,2da5775929bec02d269edebadf97c83c8afb424b,8b72202b1c2a810bd26456dd02885a6dc65a20e2,Separate the categorical columns from the numerical ones
aahiddehg,MaxHalford/prince,prince/famd.py,2da5775929bec02d269edebadf97c83c8afb424b,8b72202b1c2a810bd26456dd02885a6dc65a20e2,One-hot encode the categorical columns
aahiddfai,MaxHalford/prince,prince/famd.py,edb7a24918e4a1a7beaa8ac7228d94411ceb8940,STILL_EXISTS,Separate numerical columns from categorical columns
aahiddgjg,J535D165/recordlinkage,docs/conf.py,d666c8446d9ff77ec401f9bf3237523d326bc86e,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aahiddibe,J535D165/recordlinkage,recordlinkage/classify.py,d666c8446d9ff77ec401f9bf3237523d326bc86e,STILL_EXISTS,for ci is no comparison space or vectors needed
aahiddidh,J535D165/recordlinkage,recordlinkage/classify.py,d666c8446d9ff77ec401f9bf3237523d326bc86e,STILL_EXISTS,\t\tfor col in self.comparison_space.columns.tolist():
aahiddiid,J535D165/recordlinkage,recordlinkage/estimation_backup.py,d666c8446d9ff77ec401f9bf3237523d326bc86e,STILL_EXISTS,# max_tuple.append(self.comparison_vectors[col].unique()) # Fix
aahiddjda,J535D165/recordlinkage,sampledata/generate.py,d666c8446d9ff77ec401f9bf3237523d326bc86e,STILL_EXISTS,\"\"\"Module generate.py - Auxiliary program to create records using various ||                         frequency tables and introduce duplicates with errors. ||  ||    USAGE: ||      python generate.py [output_file] [num_originals] [num_duplicates] ||                         [max_duplicate_per_record] [distribution]      ||  ||    ARGUMENTS: ||      output_file               Name of the output file (currently this is a ||                                CSV file). ||      num_originals             Number of original records to be created. ||      num_duplicates            Number of duplicate records to be created. ||      max_duplicate_per_record  The maximal number of duplicates that can be ||                                created for one original record. ||      distribution              The probability distribution used to create ||                                the duplicates (i.e the number of duplicates for ||                                one original). ||                                Possible are: - uniform ||                                              - poisson ||                                              - zipf ||  ||    DESCRIPTION: ||      This program can be used to create a data set with records that contain ||      randomly created names and addresses (using frequency files); dates and ||      identifier numbers. Duplicate records will then be created following a ||      given probability distribution; with various errors introduced. ||  ||      Various parameters on how theses duplicates are created can be given ||      within the program; see below. ||  ||  ||    TODO: ||      - Fix ZIPF distribution ||  ||      - Allow various probability distributions for fields oftype 'date' and ||        'iden' (using a new keyword in field dictionaries). ||  ||      - Try to find real world error distributions for typographical errors and ||        integrate them into the random error creation ||  ||      - Add random word spilling between fields (similar to field swapping) ||      - Improve performance (loading and creating frequency tables) || \"\"\"
aahideace,J535D165/recordlinkage,sampledata/generate.py,d666c8446d9ff77ec401f9bf3237523d326bc86e,STILL_EXISTS,all letters both for rows and columns (based on ideas implemented by
aahidedad,J535D165/recordlinkage,recordlinkage/_version.py,cd0459bc6295f1ddf5be2688cdc66a645aee0e58,STILL_EXISTS,maybe improved later
aahidedaj,J535D165/recordlinkage,recordlinkage/_version.py,cd0459bc6295f1ddf5be2688cdc66a645aee0e58,STILL_EXISTS,unparseable. Maybe git-describe is misbehaving?
aahidedjf,J535D165/recordlinkage,versioneer.py,cd0459bc6295f1ddf5be2688cdc66a645aee0e58,STILL_EXISTS,maybe improved later
aahideeab,J535D165/recordlinkage,versioneer.py,cd0459bc6295f1ddf5be2688cdc66a645aee0e58,STILL_EXISTS,unparseable. Maybe git-describe is misbehaving?
aahidefhg,J535D165/recordlinkage,recordlinkage/indexing.py,c45a77cbd078eb44150c7e99cb4d314b51548a5c,229bb52ad2111d0038a87dd65bf8fbf227a8f48b,Append pairs to existing ones. PANDAS BUG workaround
aahidefih,J535D165/recordlinkage,recordlinkage/comparing.py,6bf6994d824ece5904aac1db8f36a7e336d2f4ea,STILL_EXISTS,Python 2 hack
aahidefja,J535D165/recordlinkage,recordlinkage/comparing.py,6bf6994d824ece5904aac1db8f36a7e336d2f4ea,cfa258686356c7e329729c621ca05002d5a0a49b,No labels passed; maybe series or dataframe passed? Let's try...
aahidegbj,J535D165/recordlinkage,recordlinkage/classifier.py,84e5e7a43cead34f2b52834908dabdcb46e9b110,STILL_EXISTS,The actual classifier. Maybe this is slightly strange because of inheritance.
aahidfcbc,J535D165/recordlinkage,recordlinkage/comparing.py,b230c5def40f77f88746b7de703d39b9ee504b73,STILL_EXISTS,Make selections of columns
aahidfhff,J535D165/recordlinkage,recordlinkage/compat/six.py,8f97567ba4b47ad00236fc179ea54e66f78873aa,STILL_EXISTS,This is a bit ugly; but it avoids running this again by
aahidfhge,J535D165/recordlinkage,recordlinkage/compat/six.py,8f97567ba4b47ad00236fc179ea54e66f78873aa,STILL_EXISTS,Workaround for standalone backslash
aahidgaaa,J535D165/recordlinkage,recordlinkage/comparing.py,f4cc0209bc6968c4c398d9765d0433c034cb65c9,STILL_EXISTS,index only columns needed
aahidgaab,J535D165/recordlinkage,recordlinkage/comparing.py,f4cc0209bc6968c4c398d9765d0433c034cb65c9,STILL_EXISTS,Index all columns
aahidgabb,J535D165/recordlinkage,recordlinkage/types.py,c5a49ed7fd7a3ee53dc9d7f7886a8217c4c11700,STILL_EXISTS,reconsider this decision once this numpy bug is fixed:
aahidgbca,J535D165/recordlinkage,tests/test_compare.py,d365f989b061e58f56b1a6863882a2803ecef224,3a5b382d1ceebe2d66d6fe4806201a90601bf369,Check if all columns are there
aahidgbdj,J535D165/recordlinkage,tests/test_compare.py,83c72b78187af8c0c592bf8453b7a18d56dd6538,3a5b382d1ceebe2d66d6fe4806201a90601bf369,The columns with data
aahidgcjb,J535D165/recordlinkage,recordlinkage/indexing.py,53b2bc4fc4b2ba28d23440ba1849227580e4b2d6,STILL_EXISTS,drop missing values and columns without relevant information
aahidgdbf,J535D165/recordlinkage,tests/test_indexing.py,dc2f423ac8b76a5b4fcfe355c07b390d8c83c9d4,STILL_EXISTS,make an empty dataframe with the columns of self.a and self.b
aahidgdgf,J535D165/recordlinkage,tests/test_indexing.py,dc2f423ac8b76a5b4fcfe355c07b390d8c83c9d4,STILL_EXISTS,are pairs1 and pairs3 not indentical? # numpy workaround
aahidghad,J535D165/recordlinkage,tests/test_compare_depr.py,3a5b382d1ceebe2d66d6fe4806201a90601bf369,STILL_EXISTS,Check if all columns are there
aahidghai,J535D165/recordlinkage,tests/test_compare_depr.py,3a5b382d1ceebe2d66d6fe4806201a90601bf369,STILL_EXISTS,The columns with data
aahidghhb,J535D165/recordlinkage,tests/test_compare.py,b44a02ca736fc5f1bf655d976043fefd361ffe17,75c6e55c29040cf83af305000f8f98016a88c816,Check if all columns are there
aahidghhg,J535D165/recordlinkage,tests/test_compare.py,b44a02ca736fc5f1bf655d976043fefd361ffe17,75c6e55c29040cf83af305000f8f98016a88c816,The columns with data
aahidhfah,J535D165/recordlinkage,tests/test_compare.py,9b9dcf7a7cd3e27a9feac9ddbc3ab2c824224ff1,STILL_EXISTS,possible to pickle instancemethods. A workaround can be found at
aahidhfib,J535D165/recordlinkage,recordlinkage/algorithms/conflict_resolution.py,1f7a06b8b3685576ee01b426b1501328255d1d1c,STILL_EXISTS,If no changes are needed; return x as-is.
aahidhgdh,J535D165/recordlinkage,tests/test_fusion.py,1f7a06b8b3685576ee01b426b1501328255d1d1c,STILL_EXISTS,Data columns
aahidhgdi,J535D165/recordlinkage,tests/test_fusion.py,1f7a06b8b3685576ee01b426b1501328255d1d1c,STILL_EXISTS,Metadata columns
aahidhhfd,J535D165/recordlinkage,recordlinkage/config.py,d8dba20ebffd5546b455ccd2ac4d1a9caa686dc6,STILL_EXISTS,walk the nested dict; creating dicts as needed along the path
aahidhice,J535D165/recordlinkage,recordlinkage/base.py,f7a88a2d7e1c37bfba5e6f7dd72b8a75ae9fa50a,b33933f2e227e1a03eb8bb2567d9d1264147576f,replace missing columns names by numbers
aahidhifi,J535D165/recordlinkage,recordlinkage/base.py,5b85a4203656f083ba016ec1f1b5616b6d6edea9,STILL_EXISTS,The actual classifier. Maybe this is slightly strange because of
aahidhjhg,J535D165/recordlinkage,recordlinkage/algorithms/nb_sklearn.py,ead3d959376b993b8bffd2be798ff00a8cf6a1c7,STILL_EXISTS,XXX The following is a stopgap measure; we need to set the dimensions
aahidhjhi,J535D165/recordlinkage,recordlinkage/algorithms/nb_sklearn.py,ead3d959376b993b8bffd2be798ff00a8cf6a1c7,STILL_EXISTS,TODO: set the init parameters by the user
aahidhjid,J535D165/recordlinkage,recordlinkage/algorithms/nb_sklearn.py,ead3d959376b993b8bffd2be798ff00a8cf6a1c7,7c5c3803efdd3d40a1b7d29c587acf1dcfe3aa48,TODO: ensure classes are [0; 1] (not [1; 0])
aahidhjie,J535D165/recordlinkage,recordlinkage/algorithms/nb_sklearn.py,ead3d959376b993b8bffd2be798ff00a8cf6a1c7,7c5c3803efdd3d40a1b7d29c587acf1dcfe3aa48,TODO: check with bin.y_type_
aahididee,J535D165/recordlinkage,recordlinkage/classifiers.py,7c5c3803efdd3d40a1b7d29c587acf1dcfe3aa48,STILL_EXISTS,check if number of binarizers equals the number of columns
aahidieda,thombashi/pytablewriter,docs/conf.py,9ee94a1df642f8e38561151510ed9214b9f32b9e,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aahidjage,thombashi/pytablewriter,pytablewriter/writer/_table_writer.py,8ff71c7903fa2927b72d167ea74e6be2d061ef64,STILL_EXISTS,TODO: documentation: for CSV\/Excel
aahidjbee,thombashi/pytablewriter,pytablewriter/_typing.py,7d6b1fb34aae4d2d22c728bd3f7045343819031f,STILL_EXISTS,They are needed by _ProtocolMeta
aahidjbfb,thombashi/pytablewriter,pytablewriter/_typing.py,7d6b1fb34aae4d2d22c728bd3f7045343819031f,STILL_EXISTS,3.5.2. This helps eliminate redundancy when defining collection types
aahidjbhf,thombashi/pytablewriter,pytablewriter/_typing.py,7d6b1fb34aae4d2d22c728bd3f7045343819031f,STILL_EXISTS,TODO
aahidjcih,thombashi/pytablewriter,pytablewriter/writer/_table_writer.py,02a88b9b19624ca10b722ec4ef14aff877495f5d,STILL_EXISTS,TODO: documentation
aahidjdii,proycon/pynlpl,input/timbl.py,4d94b3afaa9f2b13e8f53dc57125962ee69875a5,STILL_EXISTS,TODO: Completely redesign
aahidjedb,proycon/pynlpl,input/timbl.py,8058d70c1812163cb6c234b2d49f70f03612eac1,STILL_EXISTS,numbers; ignore the specified FEATURE columns: first column is 1
aahidjfai,proycon/pynlpl,clients/srilm.py,c49a8e44b3edb4007dab037e4072c84ebbf9215f,STILL_EXISTS,Bug work-around
aahidjffi,proycon/pynlpl,search.py,5bfb122d721cc2907b31f5ea1b28c24d3d1e9ae1,STILL_EXISTS,TODO: add
aahidjfjh,proycon/pynlpl,lm/srilm.py,101ba998db8166154f7ea56c5fa8362e8eea5fb5,STILL_EXISTS,Bug work-around
aahidjgbd,proycon/pynlpl,input/sonar.py,d03943c93439c9bc264d6e3415de1c155d4677b0,STILL_EXISTS,TODO: can we do this with xpath instead?
aahidjgbe,proycon/pynlpl,input/sonar.py,d03943c93439c9bc264d6e3415de1c155d4677b0,STILL_EXISTS,TODO: download XSD from web
aahidjgci,proycon/pynlpl,evaluation.py,61b247edb5f6fe48b7522345ccd91e3315eeb90a,STILL_EXISTS,TODO: can't we do this inline in a list comprehension?
aahidjgeb,proycon/pynlpl,evaluation.py,be6a891e03cd585c7f231ac6923cc80141c73d33,STILL_EXISTS,TODO: verify and catch stdout; stderr
aahidjgee,proycon/pynlpl,evaluation.py,be6a891e03cd585c7f231ac6923cc80141c73d33,7c611540f549ec682ecc0f319b9745cf13d284b5,TODO: return standard output
aahidjgef,proycon/pynlpl,evaluation.py,be6a891e03cd585c7f231ac6923cc80141c73d33,7c611540f549ec682ecc0f319b9745cf13d284b5,TODO: return error output
aahidjgge,proycon/pynlpl,evaluation.py,6ce26f8ee1c150de88a660659dea3fa9e8d671c1,STILL_EXISTS,TODO: verify and catch stdout; stderr
aahidjghi,proycon/pynlpl,input/sonar.py,60368e651ea0034a733f34722a6a2694c8de9c8b,STILL_EXISTS,TODO
aahidjhbj,proycon/pynlpl,input/taggerdata.py,1aa02f01211ed57aca56b092c1a8cf2514a13b55,STILL_EXISTS,TODO: write index
aahidjhia,proycon/pynlpl,formats/cgn.py,e35231f95e6dfdbcca67b6d82235578b8add23cb,STILL_EXISTS,TODO: pron; grad
aahidjhif,proycon/pynlpl,tests/search.py,153ebc4c786642075faeb527a6247059fb6be92c,STILL_EXISTS,TODO: this is not a test!
aahidjidj,proycon/pynlpl,docs/conf.py,2cc9f227fd9b4fac8e779734e410c598b2f04ea1,STILL_EXISTS,unused_docs = []
aahidjjhb,proycon/pynlpl,tools/exp.py,2c5da16b06ed6394bf4cc5eff6912808b0fe77a9,8f8083f37e9ccf1df3230a9bd572967d378b53d2,Configuration is specific to ILK servers (TODO: make external configuration file)
aahieaaea,proycon/pynlpl,formats/dutchsemcor.py,77cb90af512c5039d2d585c1649943514a85681a,STILL_EXISTS,TODO: this is a patch; something's not right in Distribution?
aahieaaff,proycon/pynlpl,clients/cornetto.py,c16dd550369505d18e2a1531fb5fd564d29c5c38,STILL_EXISTS,\"\"\" || -------------------------------------------------------------------------------- || Original Author:     Fons Laan; ILPS-ISLA; University of Amsterdam || Original Project:    DutchSemCor || Original Name:        cornettodb\/views.py || Original Version:    0.2 || Goal:        Cornetto views definitions ||  || Original functions: ||     index( request ) ||     local_open() ||     remote_open( self.debug ) ||     search( request ) ||     search_local( dict_in; search_query ) ||     search_remote( dict_in; search_query ) ||     cornet_check_lusyn( utf8_lemma ) ||     query_remote_lusyn_id( syn_id_self.debug; http; utf8_lemma; syn_id ) ||     query_cornet( keyword; category ) ||     query_remote_syn_lemma( self.debug; http; utf8_lemma ) ||     query_remote_syn_id( self.debug; http; utf8_lemma; syn_id; domains_abbrev ) ||     query_remote_lu_lemma( self.debug; http; utf8_lemma ) ||     query_remote_lu_id( self.debug; http; lu_id ) || FL-04-Sep-2009: Created || FL-03-Nov-2009: Removed http global: sometimes it was None; missed initialization? || FL-01-Feb-2010: Added Category filtering || FL-15-Feb-2010: Tag counts -> separate qx query || FL-07-Apr-2010: Merge canonical + textual examples || FL-10-Jun-2010: Latest Change || MvG-29-Sep-2010: Turned into minimal CornettoClient class; some new functions added; many old ones disabled until necessary || \"\"\"
aahieaahc,proycon/pynlpl,clients/cornetto.py,c16dd550369505d18e2a1531fb5fd564d29c5c38,STILL_EXISTS,The library currently has only a minimal set of functionality compared
aahieabba,proycon/pynlpl,clients/cornetto.py,c16dd550369505d18e2a1531fb5fd564d29c5c38,STILL_EXISTS,ORIGINAL AND AS-OF-YET UNUSED CODE (included for later porting)
aahieacji,proycon/pynlpl,clients/cornetto.py,c16dd550369505d18e2a1531fb5fd564d29c5c38,STILL_EXISTS,remained = 0    # maybe less than lu_ids because of category filtering
aahieahjd,proycon/pynlpl,search.py,bd3e8e71d529452ded74a51c3f1123087c025ee7,STILL_EXISTS,TODO
aahieaiaa,proycon/pynlpl,search.py,032be3d72a9f8f84c1b9b96bfe3cf0ab1779aef2,STILL_EXISTS,use only equal or better successors
aahieaiai,proycon/pynlpl,search.py,35c04eb0d90469acf8c3b0c8519857ba75d56760,STILL_EXISTS,TODO: TEST
aahieaijg,proycon/pynlpl,formats/folia.py,14e62d5240a488dff26fae11facd527eefee1ec5,STILL_EXISTS,TODO: Inherit set from parent
aahieajbf,proycon/pynlpl,formats/folia.py,e9d43ac0b18e38d5b106630a94345a8b2a291a81,STILL_EXISTS,TODO: ADD IMDI
aahieajcf,proycon/pynlpl,formats/folia.py,8e68433a4ac84eff0f25186d27e89763e180a18e,011f57fdac4b11c2ec433fdde8c0282e13f4f152,TODO
aahieajdd,proycon/pynlpl,formats/folia.py,3ffcd5ca5202f7ab23359f359a28a02a60f3fb12,59902d8af320884efb239818705ff1c5ba7ee336,TODO
aahieajeb,proycon/pynlpl,formats/folia.py,ad69721609542342530d6c492ec7aae98c6b0e44,59902d8af320884efb239818705ff1c5ba7ee336,TODO
aahieajeg,proycon/pynlpl,formats/folia.py,ada9c15523c1296e655b1025444685601d72dc91,STILL_EXISTS,TODO: Parse CMDI
aahieajfi,proycon/pynlpl,formats/folia.py,82bbb3966a184bcc7ec9a7362434cfca449e2c44,STILL_EXISTS,TODO: node or filename
aahieajfj,proycon/pynlpl,formats/folia.py,b8ff8b6e482bdce2baa149bccead47f15f5377bf,STILL_EXISTS,TODO: sanity check; there may be no other child within the same set
aahieajgb,proycon/pynlpl,formats/folia.py,a7354b06cf68c1124a4415c485ed413ee4edb76d,a38a236708cce51808fe3af60201ae699b7b2ba9,TODO: IMPLEMENT
aahieajgc,proycon/pynlpl,formats/folia.py,16f573701099254589011d4a1cd30c98640dbd83,STILL_EXISTS,TODO: add XMLATTRIB
aahieajge,proycon/pynlpl,formats/folia.py,9ded9e3b8d4294c6777002bba255b20b24d09aac,97c91544e8d140e01270da091d7e4225ab1ea5ad,TODO: replace other child within the same set
aahiebaac,proycon/pynlpl,formats/folia.py,6b91dc0cad7b35695af148763bac6c3646a4e824,5e576d54eba0657929e88af4d90626406ba1827b,TODO: implement
aahiebafa,proycon/pynlpl,formats/folia.py,4af658416dc50af0eec4b7158067c1b5307ad9c4,97c91544e8d140e01270da091d7e4225ab1ea5ad,TODO: ID trouble?
aahiebcag,proycon/pynlpl,tests/folia.py,7afe90865fecac390804f035c6e9adebdc54c211,1fc3259ca33016b44d0f3c7f225cbf806afc23d2,TODO: THIS TEST FAILS; SHOULD BE FIXED; TEST MAY CHANGE
aahiebcah,proycon/pynlpl,tests/folia.py,7afe90865fecac390804f035c6e9adebdc54c211,f2af6fcd6a621b1db4ddbfc7874d077e3bcb639e,TODO: THIS TEST FAILS NOW; BUT SHOULDN'T
aahiebcai,proycon/pynlpl,formats/folia.py,1a2ccf673a364b4f5aed39062ea2df20da9a2c2b,f861b84dc2ed2bcdcb74969eda72aac22d721a5e,TODO
aahiebccg,proycon/pynlpl,formats/folia.py,17b4d8562bec2981174b1bcaf60dc485019f6fbc,f861b84dc2ed2bcdcb74969eda72aac22d721a5e,TODO: make obsolete
aahiebcdh,proycon/pynlpl,formats/folia.py,17b4d8562bec2981174b1bcaf60dc485019f6fbc,135fcb7ebad336e53024b26ba690f06d04d1a37b,MAYBE TODO: corrected attribute?
aahiebcdi,proycon/pynlpl,formats/folia.py,17b4d8562bec2981174b1bcaf60dc485019f6fbc,6718a0cb83c417c110ebf76cbd386b87b9f935df,TODO: Implement
aahiebcfc,proycon/pynlpl,formats/folia.py,f861b84dc2ed2bcdcb74969eda72aac22d721a5e,a38a236708cce51808fe3af60201ae699b7b2ba9,TODO: IMPLEMENT
aahiebcii,proycon/pynlpl,formats/folia.py,fee312934ae5148d08761d48feab200d7be7fdbe,STILL_EXISTS,TODO: Implement handling of INLINE corrected textcontent
aahiebdid,proycon/pynlpl,formats/folia.py,03874f2b772bf1e5adc1265b61eb032369558206,fec83ba5aac16c39ee6b009ad4ba0848b0658f71,TODO: Refactor
aahiebedc,proycon/pynlpl,formats/folia.py,19e0057309bf3ecc8bc0ee52ff5b3650536c56db,135fcb7ebad336e53024b26ba690f06d04d1a37b,MAYBE TODO: corrected attribute?
aahiebege,proycon/pynlpl,formats/folia.py,9ae30d7ca0a01d678abf1bf1fcd865c9d6e37737,f810f2945789975bb4ca116c112888d8a50740b5,TODO: relaxNG
aahiebegf,proycon/pynlpl,formats/folia.py,fcce4488899b5bcca2ec4112ebbd67f0dc789515,589eac250fd18ff741929626b4e6c505ffcbc6ac,TODO parse datetime string
aahiebegg,proycon/pynlpl,formats/folia.py,fcce4488899b5bcca2ec4112ebbd67f0dc789515,b4db80e75a2a1470486cc1c7f10a2e0c1f2a3fd2,TODO: format string
aahiebfcd,proycon/pynlpl,formats/folia.py,6b443ff7965fcae540895eec2e8eb182c80a3b25,06255ee1fd664b59c0ca12c3db0f1842c3695137,ugly patch to get rid of namespace prefix
aahiebhaa,proycon/pynlpl,formats/folia.py,e686787f9ce16cdb236916e5431ea632118cfae7,STILL_EXISTS,TODO
aahiebhag,proycon/pynlpl,formats/folia.py,e817274915d4148f604a5ddd2787479a0ac4264b,STILL_EXISTS,TODO
aahiebhbd,proycon/pynlpl,formats/folia.py,cc12b2b85ad30e9410749b080fc8fe4ebac3f97a,f810f2945789975bb4ca116c112888d8a50740b5,TODO!
aahiebhbf,proycon/pynlpl,formats/folia.py,cda6a296b52d7fa6a7982b8d142c0ee75f4f559b,f810f2945789975bb4ca116c112888d8a50740b5,TODO: relaxNG
aahiebhcb,proycon/pynlpl,formats/folia.py,cda6a296b52d7fa6a7982b8d142c0ee75f4f559b,f810f2945789975bb4ca116c112888d8a50740b5,TODO!
aahiebhde,proycon/pynlpl,statistics.py,b35e0d3cd4922bd70fd1d6093f03e6c0584d8200,STILL_EXISTS,TODO: implement
aahiebhdf,proycon/pynlpl,statistics.py,b35e0d3cd4922bd70fd1d6093f03e6c0584d8200,STILL_EXISTS,TODO
aahiebhhd,proycon/pynlpl,formats/folia.py,ad88f782428d6570749cd7c41a4e14a0a3ea41df,STILL_EXISTS,XML Tree is now obsolete (only needed when partially loaded for xpath queries)
aahiebiaj,proycon/pynlpl,formats/folia.py,4520e25f5ddf0adc987cedfcb8d7be0dab0e9a2f,94a8d9ce246f661b9034c90b5cf81c6223bed3dc,TODO: Implement
aahiebida,proycon/pynlpl,formats/folia.py,d2d4fec7cd277f74f426380c5339d3c95a638585,STILL_EXISTS,workaround for xml:id problem
aahiebjbd,proycon/pynlpl,clients/frogclient.py,333c29ebb1ae312bb48bd827c40793dc18984178,392ece21b360230605c6a26d59984f684edf1b17,TODO; make nicer
aahiebjbj,proycon/pynlpl,formats/folia.py,34c025e8d3585b28f6d6a3ff8d482b7e54b8f3a0,STILL_EXISTS,XML Tree is now obsolete (only needed when partially loaded for xpath queries)
aahiebjdc,proycon/pynlpl,formats/folia.py,423a4daea75eeb408a18baca30c25d0d983b37cb,STILL_EXISTS,TODO: Parse xlink:href
aahiebjea,proycon/pynlpl,formats/folia.py,edbd8130bfdb9a8fafc1938142d0c61f6ed54f12,STILL_EXISTS,TODO: Parse xlink:href
aahiebjjb,proycon/pynlpl,formats/folia.py,cbce0c275afec1567901644ba42a0977dc8c5f2b,STILL_EXISTS,if a quote ends in a sentence; we don't want any delimiter
aahiecada,proycon/pynlpl,formats/folia.py,4eb2de949b43ad242c0187173f17473be7884924,STILL_EXISTS,TODO: add phoneme when it becomes available
aahiecbeh,proycon/pynlpl,formats/folia.py,7aff16555821d230763181c9641ee04c46dd064a,STILL_EXISTS,XML Tree is now obsolete (only needed when partially loaded for xpath queries); free memory
aahiecbie,proycon/pynlpl,evaluation.py,ecc89f38e4d79685be761ca09aac8a7818bd7bb9,STILL_EXISTS,XXX: Consider using  ``scipy.integrate`` instead; or moving to
aahieccbj,proycon/pynlpl,formats/folia.py,4b25b68753468b72671e553b65ba8703e14b4d86,e1633d157b0f8c6d3c61e0e1cff126b137dc3b5a,ignorelist = [ globals()[c] if not inspect.isclass(c) else c for c in ignorelist ]  #TODO: make more efficient; resolving the same default ignorelist every time is slow; isclass is costly
aahieccce,proycon/pynlpl,formats/folia.py,16d7d02063e9b35e9ae4017d15fde4a894ac1648,b9178d7f9e84494bf1b58070130afeae29c6c9f8,There is a leak in lxml :( ; specialise file handler to replace xml:id to id; ugly hack (especially for Python2)
aahieccid,proycon/pynlpl,formats/folia.py,9db88a2f1518b89fc481272ba6689a71dee2a00e,STILL_EXISTS,TODO
aahiecdaj,proycon/pynlpl,tools/computepmi.py,b51624fd658f0edd2590e9ae354583c887711d4e,e4422aa441c58b7f0a47fda9a6cf1da1b66b01ed,needed to get associated position
aahiecdca,proycon/pynlpl,formats/folia.py,89dae069fc28d1b76ca680438408238340fdc3ae,6a863e8fc5ae13377dedbe42031e5d72d9a08266,TODO: won't work in text <x\/> text scenarios
aahiecdcf,proycon/pynlpl,formats/folia.py,a36d98f0e0bc213fea71c70b70b6fca8c7b33497,94a8d9ce246f661b9034c90b5cf81c6223bed3dc,TODO: Implement
aahiecdjd,proycon/pynlpl,formats/folia.py,624bee5bc42ebecaeb00b2909a5721ca24ab9a44,06255ee1fd664b59c0ca12c3db0f1842c3695137,TODO: check validity of elements under subdoc\/text with respect to self.parent
aahiecdjh,proycon/pynlpl,formats/folia.py,624bee5bc42ebecaeb00b2909a5721ca24ab9a44,06255ee1fd664b59c0ca12c3db0f1842c3695137,will hold all subdocs (sourcestring => document) ; needed so the index can resolve IDs in subdocs
aahiecdjj,proycon/pynlpl,formats/folia.py,624bee5bc42ebecaeb00b2909a5721ca24ab9a44,06255ee1fd664b59c0ca12c3db0f1842c3695137,perhaps the key is in one of our subdocs?
aahieceae,proycon/pynlpl,formats/folia.py,341a121857c0b5007b0b07fad5e73503569c7711,06255ee1fd664b59c0ca12c3db0f1842c3695137,ugly patch to get rid of namespace prefix
aahiecegg,proycon/pynlpl,formats/folia.py,da141195408b887008d872bbb300495a8ce2dff1,STILL_EXISTS,TODO: check validity of elements under subdoc\/text with respect to self.parent
aahiecehc,proycon/pynlpl,formats/folia.py,da141195408b887008d872bbb300495a8ce2dff1,STILL_EXISTS,will hold all subdocs (sourcestring => document) ; needed so the index can resolve IDs in subdocs
aahiecehe,proycon/pynlpl,formats/folia.py,da141195408b887008d872bbb300495a8ce2dff1,STILL_EXISTS,perhaps the key is in one of our subdocs?
aahiecehj,proycon/pynlpl,formats/folia.py,da141195408b887008d872bbb300495a8ce2dff1,STILL_EXISTS,ugly patch to get rid of namespace prefix
aahiecfeb,proycon/pynlpl,formats/fql.py,795ea4a7a0010ca4789628a124ef557953ce2ec0,618043fa8ded92e912441f062f23b0942301ea9b,self.form = #TODO
aahiecffe,proycon/pynlpl,formats/fql.py,3e7965f414b84d2022c7227badb1d059068d10c5,c58f4e8fcc09af6d61f869c5de23fcef41ab27cc,TODO
aahiecffi,proycon/pynlpl,formats/fql.py,1d5fd8266a85033723f958c734a8124a40cdd5df,c58f4e8fcc09af6d61f869c5de23fcef41ab27cc,TODO
aahiecfhd,proycon/pynlpl,formats/fql.py,841cc5765d3785f4afd7ebf4a3efd14a6011dcf1,30e594886fb3d9473728c912ab04ab2bb45bb7ee,TODO
aahiecgbc,proycon/pynlpl,formats/fql.py,b071c27f1266fb659933c9f16c1735fe5eade06d,76f8fb34f9bd24de6f2cd8ff698a178ae007e443,TODO: process modifier
aahiecgdh,proycon/pynlpl,formats/fql.py,83482687f474ede6a4370739c75927d90cd7b5a2,db5e9a3451e3d877da9393dbe6e383bd0d259551,TODO: efficiency
aahiecgfi,proycon/pynlpl,formats/fql.py,cff725015e8dd0671048298afdea866902bed2af,cea8b43ab0cc1814c046bf1cca99a8294b3bdf88,TODO: span annotation?
aahiechic,proycon/pynlpl,formats/fql.py,ae51b138ddb1839ff33bf555bd11e990725cb7f8,STILL_EXISTS,current only needed for structure annotation
aahiecija,proycon/pynlpl,formats/fql.py,6cd42a5ab3b66c2310875c395e83aa0ac023a466,STILL_EXISTS,TODO: implement
aahiedaah,proycon/pynlpl,formats/folia.py,e02120e4dc19e8b7038daf9cb583fe851e63b1f3,STILL_EXISTS,if a sentence ends in a linebreak; we don't want any delimiter
aahiedbaa,proycon/pynlpl,formats/cql.py,c3b9a2d3f66b94ade3d28721892e55e81c39ee18,1c27e293e3385e4d0a2dd74c0896dc39b5a4f59d,TODO
aahiedcch,proycon/pynlpl,formats/folia.py,483e5507ccfea0cd65d7054a1e4ba7178e817398,STILL_EXISTS,(twice; better safe than sorry)
aahiedcde,proycon/pynlpl,formats/folia.py,107a429b6f6060e3b7bcd2ecba9cbb9575838cbe,STILL_EXISTS,OPTIONAL_ATTRIBS = (Attrib.ID; Attrib.SETONLY;) #TODO: handle SETONLY in new scheme
aahiedcdf,proycon/pynlpl,formats/folia.py,107a429b6f6060e3b7bcd2ecba9cbb9575838cbe,28d127d9911480c1145d04cb2d3c9f5f00c635e0,REQUIRED_ATTRIBS = (Attrib.ID;)  #TODO: See if this is handled correctly in new scheme
aahiedceb,proycon/pynlpl,formats/folia.py,107a429b6f6060e3b7bcd2ecba9cbb9575838cbe,STILL_EXISTS,(twice; better safe than sorry)
aahiedfad,proycon/pynlpl,formats/folia.py,5924c5122b89da3ccd87ae4e3f275c15e3f0db5e,STILL_EXISTS,nasty backward-compatibility hack to allow deprecated listitem element (actually called item)
aahiedfda,proycon/pynlpl,formats/folia.py,6475372b064f90f89bd6e26abdd9634f1f6149a3,STILL_EXISTS,definitions needed for ForeignData (allow any content) - see http:\/\/www.microhowto.info\/howto\/match_arbitrary_content_using_relax_ng.html
aahiedfhj,proycon/pynlpl,formats/folia.py,24b446e118635485ecf85fd6123d0a684ec84243,STILL_EXISTS,TODO: span roles don't take classes; derived off spanannotation allows too much
aahiedgcg,proycon/pynlpl,formats/foliaset.py,94a8d9ce246f661b9034c90b5cf81c6223bed3dc,STILL_EXISTS,TODO
aahiedgdi,proycon/pynlpl,formats/foliaset.py,94a8d9ce246f661b9034c90b5cf81c6223bed3dc,4f8fac0d2fee5d5dab028c30442bb72298641358,TODO: serialise to json
aahiedggi,proycon/pynlpl,formats/folia.py,6ba00d53e7524a02b5642cd5a38f7fd1c9b99145,STILL_EXISTS,TODO: Implement
aahiedhji,proycon/pynlpl,pynlpl/formats/folia.py,273ebc61bbd1d3c0eb4cc59f08166598b946bfbc,STILL_EXISTS,AbstractSpanAnnotation is needed when requesting text() on nested span annotations
aahiediif,proycon/pynlpl,pynlpl/formats/folia.py,cb269b641f5049649986269793c4cd245238684b,STILL_EXISTS,well; we couldn't find our textclass in any correction; just fall back to current and let text validation fail if needed
aahiedijg,proycon/pynlpl,pynlpl/formats/folia.py,817858dbb048d263f23098571cfb294ee612a291,STILL_EXISTS,TODO: what about corrections?
aahieebbd,PythonOT/POT,doc/source/conf.py,28681131adf681a713565768a6b94527600fb81f,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aahieejae,PythonOT/POT,ot/gpu/cudamat/setup.py,16f51f971607efab2c73958d207c582b389406c8,STILL_EXISTS,needed for Windows
aahieejbh,PythonOT/POT,ot/gpu/cudamat/setup.py,16f51f971607efab2c73958d207c582b389406c8,STILL_EXISTS,- Secondly; we fix a bunch of command line arguments.
aahieejff,PythonOT/POT,ot/gpu/da.py,16f51f971607efab2c73958d207c582b389406c8,d258c7d6936410cd78189445a0260d983f7684d6,Add the vectors in each columns (respectivly rows) of c.
aahiefafa,PythonOT/POT,examples/plot_OT_L1_vs_L2.py,f639518e9b96c5904122e62e024ed4ae369ceb33,STILL_EXISTS,\"\"\" || ==================================================== || 2D Optimal transport between empirical distributions || ==================================================== ||  || Stoile the figure idea from: || https:\/\/arxiv.org\/pdf\/1706.07650.pdf ||  ||  || @author: rflamary || \"\"\"
aahiefagg,PythonOT/POT,docs/source/auto_examples/plot_OT_L1_vs_L2.py,adbf95ee9a720fa38b5b91d0a9d5c3b22ba0b226,STILL_EXISTS,\"\"\" || ========================================== || 2D Optimal transport for different metrics || ========================================== ||  || Stole the figure idea from Fig. 1 and 2 in  || https:\/\/arxiv.org\/pdf\/1706.07650.pdf ||  ||  || @author: rflamary || \"\"\"
aahiefffb,PythonOT/POT,ot/da.py,bd7c7d2534980d3105d060dd24a444433422134d,55840f6bccadd79caf722d86f06da857e3045453,XXX: should we rather test if instance of estimator?
aahieffga,PythonOT/POT,ot/da.py,bd7c7d2534980d3105d060dd24a444433422134d,122b5bf2c0c8b6ff7b46adf19c7dd72e62c85b1f,TODO: check whether Xs is new or not
aahieffgd,PythonOT/POT,ot/da.py,bd7c7d2534980d3105d060dd24a444433422134d,122b5bf2c0c8b6ff7b46adf19c7dd72e62c85b1f,TODO: check whether Xt is new or not
aahiefgaj,PythonOT/POT,ot/da.py,0b005906f9d78adbf4d52d2ea9610eb3fde96a7c,e89f09dae0f66df4a8c1c7ac761a71611c2d676c,and labeled target samples occupy the first columns
aahiefggi,PythonOT/POT,ot/deprecation.py,09302239b3e4e1a90c1a4e2d7a85b0af86b01365,STILL_EXISTS,FIXME: we should probably reset __new__ for full generality
aahiefica,PythonOT/POT,ot/utils.py,55840f6bccadd79caf722d86f06da857e3045453,STILL_EXISTS,FIXME: we should probably reset __new__ for full generality
aahiefidd,PythonOT/POT,ot/utils.py,55840f6bccadd79caf722d86f06da857e3045453,STILL_EXISTS,XXX: should we rather test if instance of estimator?
aahiegieh,PythonOT/POT,ot/da.py,5ab50354e60ed94d9d799927fd4b680fb8447304,e1606c1025c0acc4608254d72af6c127ebe1c03f,XXX: should we rather test if instance of estimator?
aahiegifg,PythonOT/POT,ot/da.py,5ab50354e60ed94d9d799927fd4b680fb8447304,c7eaaf4caa03d759c4255bdf8b6eebd10ee539a5,TODO: check whether Xs is new or not
aahiegifj,PythonOT/POT,ot/da.py,5ab50354e60ed94d9d799927fd4b680fb8447304,c7eaaf4caa03d759c4255bdf8b6eebd10ee539a5,TODO: check whether Xt is new or not
aahiegjgg,PythonOT/POT,ot/da.py,4e562a1ce24119b8c9c1efb9d078762904c5d78a,STILL_EXISTS,and labeled target samples occupy the first columns
aahiejhhj,PythonOT/POT,ot/lp/cvx.py,060d9046b291c76244deab2d78ee8356a294e91f,STILL_EXISTS,columns constraints
aahifcgig,PythonOT/POT,ot/gpu/da.py,c8c397b6e1747d6593ba139d0a4f825fe39f5cc6,ee8ed4fa101861eec9e578f09aee4367af593af1,Add the vectors in each columns (respectivly rows) of c.
aahiffecb,PythonOT/POT,ot/gromov.py,1b00740a39f90f1e0bc7dc3a35723560c9ab4e97,STILL_EXISTS,XXX use random state
aahiffecc,PythonOT/POT,ot/gromov.py,1b00740a39f90f1e0bc7dc3a35723560c9ab4e97,STILL_EXISTS,XXX : should use a random state and not use the global seed
aahiffffa,PythonOT/POT,ot/bregman.py,3582b6ffe571cb96b69dc6c356ef8d946df57fe7,7d603f0aa642dbe49da70ad3143fd4e9c74a22c5,if the \"autograd\" package is needed for some experiments; we then have to change the instance of the cost matrix M
aahiffffh,PythonOT/POT,ot/bregman.py,3582b6ffe571cb96b69dc6c356ef8d946df57fe7,STILL_EXISTS,sum of rows and columns of K
aahiffhbf,PythonOT/POT,ot/lp/__init__.py,e5196fa7a8c493b831fd5dac52a89bbf29e7b0e6,STILL_EXISTS,Compute worst violation per line and columns
aahifgfce,PythonOT/POT,ot1/da.py,90f5d5f60af9ef25d7aba715e2398946e5ee16da,STILL_EXISTS,and labeled target samples occupy the first columns
aahifghdb,PythonOT/POT,ot/partial.py,9f63ee92e281427ab3d520f75bb9c3406b547365,STILL_EXISTS,trick to fasten the computation: select only the subset of columns\/lines
aahifhjce,PythonOT/POT,examples/plot_Intro_OT.py,a8acfc31d3b4fd478ccb8f367549d5ebabca0d5c,STILL_EXISTS,The Sinkhorn algorithm is very simple to code. You can implement it directly
aahifibjf,ljvmiranda921/pyswarms,pyswarms/standard/pso.py,1a222717ab6fb05711173486e09d3404771e579a,STILL_EXISTS,Update personal bests if the current position is better
aahificbc,ljvmiranda921/pyswarms,setup.py,1a222717ab6fb05711173486e09d3404771e579a,0b83050a160d1e05f22bed22c01987c5b5cb8df0,TODO: put package requirements here
aahificbe,ljvmiranda921/pyswarms,setup.py,1a222717ab6fb05711173486e09d3404771e579a,0b83050a160d1e05f22bed22c01987c5b5cb8df0,TODO: put package test requirements here
aahificcd,ljvmiranda921/pyswarms,travis_pypi_setup.py,1a222717ab6fb05711173486e09d3404771e579a,STILL_EXISTS,workaround for https:\/\/github.com\/travis-ci\/travis-api\/issues\/196
aahificdj,ljvmiranda921/pyswarms,pyswarms/single/gb.py,b91629e315768ee20555164da2457ba03d6f8008,STILL_EXISTS,Update personal bests if the current position is better
aahificjf,ljvmiranda921/pyswarms,pyswarms/single/lb.py,e8feba2b1b8236c3be2c6b375305f44118a2813f,STILL_EXISTS,Update personal bests if the current position is better
aahifidah,ljvmiranda921/pyswarms,pyswarms/single/lb.py,e8feba2b1b8236c3be2c6b375305f44118a2813f,STILL_EXISTS,The minimum index is itself; no mapping needed.
aahifiddj,ljvmiranda921/pyswarms,pyswarms/utils/functions/single_obj.py,470bf2643e19bdfd244491d6104c1b5807ac0393,STILL_EXISTS,TODO: Implement Beale's Function
aahifidfe,ljvmiranda921/pyswarms,pyswarms/utils/functions/single_obj.py,470bf2643e19bdfd244491d6104c1b5807ac0393,4d0e69a8e9cb54eff45c41016d796cd92da30bf8,TODO: Implement Bukin Function no. 6
aahifidie,ljvmiranda921/pyswarms,pyswarms/utils/functions/single_obj.py,cefcca72634475bb619e0cadb4d00095e05fcd2f,4d0e69a8e9cb54eff45c41016d796cd92da30bf8,TODO: Implement Booth's Function
aahifidif,ljvmiranda921/pyswarms,pyswarms/utils/functions/single_obj.py,cefcca72634475bb619e0cadb4d00095e05fcd2f,STILL_EXISTS,TODO: Implement Beale's Function
aahifieca,ljvmiranda921/pyswarms,pyswarms/single/lb.py,13f462b6a32201fa11049ce424d8febde771c112,STILL_EXISTS,r\"\"\" || A Local-best Particle Swarm Optimization (lbest PSO) algorithm. ||  || Similar to global-best PSO; it takes a set of candidate solutions; || and finds the best solution using a position-velocity update method. || However; it uses a ring topology; thus making the particles  || attracted to its corresponding neighborhood. ||  || The position update can be defined as: ||  || .. math:: ||  ||    x_{i}(t+1) = x_{i}(t) + v_{i}(t+1) ||  || Where the position at the current timestep :math:`t` is updated using || the computed velocity at :math:`t+1`. Furthermore; the velocity update || is defined as: ||  || .. math:: ||  ||    v_{ij}(t + 1) = m * v_{ij}(t) + c_{1}r_{1j}(t)[y_{ij}(t) \u2212 x_{ij}(t)] + c_{2}r_{2j}(t)[\\hat{y}_{j}(t) \u2212 x_{ij}(t)] ||  || However; in local-best PSO; a particle doesn't compare itself to the || overall performance of the swarm. Instead; it looks at the performance || of its nearest-neighbours; and compares itself with them. In general; || this kind of topology takes much more time to converge; but has a more || powerful explorative feature. ||  || In this implementation; a neighbor is selected via a k-D tree || imported from :code:`scipy`. Distance are computed with either || the L1 or L2 distance. The nearest-neighbours are then queried from || this k-D tree. ||  || An example usage is as follows: ||  || .. code-block:: python ||  ||     import pyswarms as ps ||     from pyswarms.utils.functions import single_obj as fx ||  ||     # Set-up hyperparameters ||     options = {'c1': 0.5; 'c2': 0.3; 'm':0.9} ||  ||     # Call instance of LBestPSO with a neighbour-size of 3 determined by ||     # the L2 (p=2) distance. ||     optimizer = ps.single.LBestPSO(n_particles=10; dims=2; k=3; p=2; **options) ||  ||     # Perform optimization ||     stats = optimizer.optimize(fx.sphere_func; iters=100) ||  || This algorithm was adapted from one of the earlier works of || J. Kennedy and R.C. Eberhart in Particle Swarm Optimization [IJCNN1995]_ [MHS1995] ||  || .. [IJCNN1995] J. Kennedy and R.C. Eberhart; \"Particle Swarm Optimization;\" ||     Proceedings of the IEEE International Joint Conference on Neural ||     Networks; 1995; pp. 1942-1948. ||  || .. [MHS1995] J. Kennedy and R.C. Eberhart; \"A New Optimizer using Particle ||     Swarm Theory;\"  in Proceedings of the Sixth International  ||     Symposium on Micromachine and Human Science; 1995; pp. 39\u201343. || \"\"\"
aahifiecj,ljvmiranda921/pyswarms,pyswarms/base/bs.py,3666265e8fca21854d91282394253dbfb1d85437,STILL_EXISTS,r\"\"\" || Base class for single-objective Particle Swarm Optimization  || implementations. ||  || All methods here are abstract and raises a :code:`NotImplementedError`  || when not used. When defining your own swarm implementation; || create another class; ||  ||     >>> class MySwarm(SwarmBaseClass): ||     >>>     def __init__(self): ||     >>>        super(MySwarm; self).__init__() ||  || and define all the necessary methods needed. ||  || Take note that there is no velocity nor position update in this || base class. This enables this class to accommodate any variation || of the position or velocity update; without enforcing a specific || structure. As a guide; check the global best and local best || implementations in this package. ||  || .. note:: Regarding :code:`**kwargs`; it is highly recommended to ||     include parameters used in position and velocity updates as ||     keyword arguments. For parameters that affect the topology of ||     the swarm; it may be much better to have them as positional ||     arguments. ||  || See Also || -------- || :mod:`pyswarms.single.gb`: global-best PSO implementation || :mod:`pyswarms.single.lb`: local-best PSO implementation || \"\"\"
aahifieee,ljvmiranda921/pyswarms,pyswarms/base/dbs.py,b2f3c97a3da9ad5100b11608639388680bbdb927,STILL_EXISTS,r\"\"\" || Base class for single-objective discrete Particle Swarm Optimization  || implementations. ||  || All methods here are abstract and raises a :code:`NotImplementedError`  || when not used. When defining your own swarm implementation; || create another class; ||  ||     >>> class MySwarm(DiscreteSwarmBase): ||     >>>     def __init__(self): ||     >>>        super(MySwarm; self).__init__() ||  || and define all the necessary methods needed. ||  || As a guide; check the discrete PSO implementations in this package. ||  || .. note:: Regarding :code:`**kwargs`; it is highly recommended to ||     include parameters used in position and velocity updates as ||     keyword arguments. For parameters that affect the topology of ||     the swarm; it may be much better to have them as positional ||     arguments. ||  || See Also || -------- || :mod:`pyswarms.discrete.bn`: binary PSO implementation ||  || \"\"\"
aahifiege,ljvmiranda921/pyswarms,pyswarms/discrete/bn.py,b2f3c97a3da9ad5100b11608639388680bbdb927,STILL_EXISTS,Update personal bests if the current position is better
aahifiehi,ljvmiranda921/pyswarms,pyswarms/discrete/bn.py,b2f3c97a3da9ad5100b11608639388680bbdb927,STILL_EXISTS,The minimum index is itself; no mapping needed.
aahififed,ljvmiranda921/pyswarms,pyswarms/utils/environments/__init__.py,4c0dd1d94adaa9c4845a044162344f6e7da3a9b8,STILL_EXISTS,\"\"\" || The mod:`pyswarms.utils.environments` module implements various || optimization environments to analyze optimizer performance or search || better parameters || \"\"\"
aahifihdg,ljvmiranda921/pyswarms,pyswarms/base/base_multi.py,947892027da7ca8aad74fc932aca23cc50938366,STILL_EXISTS,r\"\"\" || Base class for multi-objective Particle Swarm Optimization || implementations. ||  || All methods here are abstract and raises a :code:`NotImplementedError` || when not used. When defining your own swarm implementation; || create another class; ||  ||     >>> class MySwarm(MultiObjectiveSwarmBase): ||     >>>     def __init__(self): ||     >>>         super(MySwarm; self).__init__() ||  || and define all the necessary methods needed. ||  || .. note:: Regarding :code:`options`; it is highly recommended to ||     include parameters used in position and velocity updates as ||     keyword arguments. For parameters that affect the topology of ||     the swarm; it may be much better to have them as positional ||     arguments. ||  || \"\"\"
aahifijjd,ljvmiranda921/pyswarms,pyswarms/backend/operators.py,ac3a6b1feeb45608d227c47824c2e458e5aed835,70103806da9ca32a23433f9fcbb2ca9eba6885cb,The minimum index is itself; no mapping needed.
aahifjahc,ljvmiranda921/pyswarms,pyswarms/backend/topology/ring.py,7bb725f6c9a49d6754ce1af80bf1c2354dbc2101,STILL_EXISTS,The minimum index is itself; no mapping needed.
aahigahgg,ljvmiranda921/pyswarms,pyswarms/backend/generators.py,1c2cd89233b15cdce6f8262d241c29bda25b17d1,STILL_EXISTS,init_pos maybe ones
aahigaijc,ljvmiranda921/pyswarms,pyswarms/backend/handlers.py,50f6e159f0cc50f906a1c2202ddbfe63a212c1ac,STILL_EXISTS,\"\"\" || Handlers ||  || This module provides Handler classes for the position as well as the velocity || of particles. This is necessary when boundary conditions are imposed on the PSO || algorithm. Particles that do not stay inside these boundary conditions have to || be handled by either adjusting their position after they left the bounded || search space or adjusting their velocity when it would position them outside || the search space. In particular; this approach is important if the optimium of || a function is near the boundaries. || For the following documentation let :math:`x_{i; t; d}` be the :math:`d` th || coordinate of the particle :math:`i` 's position vector at the time :math:`t`; || :math:`lb` the vector of the lower boundaries and :math:`ub` the vector of the || upper boundaries. || The algorithms in this module are adapted from [SH2010] ||  || [SH2010] Sabine Helwig; \"Particle Swarms for Constrained Optimization\"; || PhD thesis; Friedrich-Alexander Universit\u00E4t Erlangen-N\u00FCrnberg; 2010. || \"\"\"
aahigbacg,MIC-DKFZ/medicaldetectiontoolkit,default_configs.py,bf77e01d6d598833ee0a6f8c6b10e251229cae50,STILL_EXISTS,needed if doing instance segmentation. evaluation not yet implemented.
aahigbagg,MIC-DKFZ/medicaldetectiontoolkit,evaluator.py,bf77e01d6d598833ee0a6f8c6b10e251229cae50,STILL_EXISTS,kick out dummy entries for true negative patients. not needed on roi-level.
aahigbcag,MIC-DKFZ/medicaldetectiontoolkit,experiments/lidc_exp/configs.py,bf77e01d6d598833ee0a6f8c6b10e251229cae50,STILL_EXISTS,implement extra anchor-scales according to retina-net publication.
aahigbeaf,MIC-DKFZ/medicaldetectiontoolkit,experiments/toy_exp/configs.py,bf77e01d6d598833ee0a6f8c6b10e251229cae50,STILL_EXISTS,implement extra anchor-scales according to retina-net publication.
aahigbfee,MIC-DKFZ/medicaldetectiontoolkit,models/mrcnn.py,bf77e01d6d598833ee0a6f8c6b10e251229cae50,STILL_EXISTS,improve performance by trimming to top anchors by score
aahigbiec,MIC-DKFZ/medicaldetectiontoolkit,models/ufrcnn.py,bf77e01d6d598833ee0a6f8c6b10e251229cae50,STILL_EXISTS,improve performance by trimming to top anchors by score
aahigcabb,MIC-DKFZ/medicaldetectiontoolkit,predictor.py,bf77e01d6d598833ee0a6f8c6b10e251229cae50,STILL_EXISTS,TODO return for instance segmentation:
aahigccef,MIC-DKFZ/medicaldetectiontoolkit,utils/model_utils.py,bf77e01d6d598833ee0a6f8c6b10e251229cae50,STILL_EXISTS,For positive anchors; compute shift and scale needed to transform them to match the corresponding GT boxes.
aahigcdaa,MIC-DKFZ/medicaldetectiontoolkit,experiments/toy_exp/dev/backbone.py,7f1b5bf64963e6a8e0ff5eed7c9211ae5278f36d,STILL_EXISTS,p1_out = self.P1_conv2(p1_pre_out) # usually not needed.
aahigcead,MIC-DKFZ/medicaldetectiontoolkit,experiments/toy_exp/dev/configs.py,7f1b5bf64963e6a8e0ff5eed7c9211ae5278f36d,STILL_EXISTS,implement extra anchor-scales according to retina-net publication.
aahigcegg,MIC-DKFZ/medicaldetectiontoolkit,experiments/toy_exp/dev/default_configs.py,7f1b5bf64963e6a8e0ff5eed7c9211ae5278f36d,STILL_EXISTS,needed if doing instance segmentation. evaluation not yet implemented.
aahigcfbc,MIC-DKFZ/medicaldetectiontoolkit,experiments/toy_exp/dev/model.py,7f1b5bf64963e6a8e0ff5eed7c9211ae5278f36d,STILL_EXISTS,improve performance by trimming to top anchors by score
aahigcihf,shawnwun/RNNLG,loader/DataLexicaliser.py,49e8e66eadaabbaecd75b5436cb7957512631b5a,STILL_EXISTS,exact match ; ends
aahigdagb,shawnwun/RNNLG,loader/GentScorer.py,49e8e66eadaabbaecd75b5436cb7957512631b5a,STILL_EXISTS,# 3. Detailed illustraction of how differet split
aahigdbab,shawnwun/RNNLG,nn/basic.py,49e8e66eadaabbaecd75b5436cb7957512631b5a,STILL_EXISTS,TODO: function for initialise weight matrices
aahigdbac,shawnwun/RNNLG,nn/basic.py,49e8e66eadaabbaecd75b5436cb7957512631b5a,STILL_EXISTS,TODO: unrolling function in theano; for training
aahigdbad,shawnwun/RNNLG,nn/basic.py,49e8e66eadaabbaecd75b5436cb7957512631b5a,STILL_EXISTS,TODO: per step recurrence function in theano; for training
aahigdbae,shawnwun/RNNLG,nn/basic.py,49e8e66eadaabbaecd75b5436cb7957512631b5a,STILL_EXISTS,TODO: generation function in numpy; beam search decoding
aahigdbaf,shawnwun/RNNLG,nn/basic.py,49e8e66eadaabbaecd75b5436cb7957512631b5a,STILL_EXISTS,TODO: generation function in numpy; random sampling
aahigdbag,shawnwun/RNNLG,nn/basic.py,49e8e66eadaabbaecd75b5436cb7957512631b5a,STILL_EXISTS,TODO: per step generation function in numpy; for decoding
aahigdbah,shawnwun/RNNLG,nn/basic.py,49e8e66eadaabbaecd75b5436cb7957512631b5a,STILL_EXISTS,TODO: load numpy parameters
aahigdfbj,perone/medicaltorch,medicaltorch/transforms.py,cf4f4f02cf1f6ed21eb1bee43395e42720f51293,STILL_EXISTS,TODO: Resample should keep state after changing state.
aahigdfef,perone/medicaltorch,medicaltorch/transforms.py,ca7dcd659e0d3049c0f04c52c7afe1dca05c7545,STILL_EXISTS,TODO: fix it later
aahigdffb,perone/medicaltorch,medicaltorch/transforms.py,c62c70cfe34fa6eb596748d63eec2b799f1b6fb1,STILL_EXISTS,TODO: Would be faster with only one vectorial operation
aahigdffc,perone/medicaltorch,medicaltorch/transforms.py,c62c70cfe34fa6eb596748d63eec2b799f1b6fb1,STILL_EXISTS,TODO: Use the axis index for factoring this loop
aahigdhhi,Zulko/easyAI,easyAI/AI/Negamax.py,ec2b5def48f18760b58de039af07b8b8b4624a69,STILL_EXISTS,re-initialize move
aahigdhhj,Zulko/easyAI,easyAI/AI/Negamax.py,ec2b5def48f18760b58de039af07b8b8b4624a69,STILL_EXISTS,horrible hack
aahigdhid,Zulko/easyAI,easyAI/AI/solving.py,ec2b5def48f18760b58de039af07b8b8b4624a69,STILL_EXISTS,re-initialize move
aahigdiib,Zulko/easyAI,easyAI/AI/MTdriver.py,4a7bbf59ca4a057b73877f5de25d36eea38517e7,077c8dacf46d05638449132b9c80981cd669eaee,re-initialize move
aahigdiid,Zulko/easyAI,easyAI/AI/SSS.py,b5e9f9a68ce19eb3b5b414b7c93b3836f8d0785b,STILL_EXISTS,horrible hack
aahigdiif,Zulko/easyAI,easyAI/AI/DUAL.py,2bc43c765fb285a33da135c3f68d57453ecbfcae,STILL_EXISTS,horrible hack
aahigeabg,Zulko/easyAI,easyAI/AI/NonRecursiveNegamax.py,1e779706731154c4b240dce57911434084abdc8a,STILL_EXISTS,choose next move
aahigeagi,Zulko/easyAI,easyAI/AI/Negamax.py,cc6689b5bf8949418a4594017e64db9e87999b9a,STILL_EXISTS,best_move = move
aahigebid,flennerhag/mlens,mlens/ensemble/_setup.py,9a55f6d32e455562fa86d523e17efc5c8fc40148,STILL_EXISTS,\"\"\" || author: Sebastian Flennerhag || date: 10\/01\/2017 || Support functions for naming estimators to enable ensemble parameter mapping || \"\"\"
aahigebii,flennerhag/mlens,mlens/ensemble/ensemble.py,9a55f6d32e455562fa86d523e17efc5c8fc40148,STILL_EXISTS,\"\"\" || author: Sebastian Flennerhag || date: 11\/01\/2017 || Stacked ensemble class for full control over the entire model's parameters. || Scikit-learn API allows full integration; including grid search and pipelining. || \"\"\"
aahigecdb,flennerhag/mlens,mlens/parallel/fit_predict.py,9a55f6d32e455562fa86d523e17efc5c8fc40148,STILL_EXISTS,\"\"\" || author: Sebastian Flennerhag || data: 10\/01\/2017 || Base functions for any parallel processing || \"\"\"
aahigedae,flennerhag/mlens,mlens/ensemble/ensemble.py,0ec8f8829056a9dc7a4ec2b87a71c22238c50737,80c89e4d52763f2cacb75c582c09e12dbfefd175,TODO: add option to store base estimator cv test scores during fit
aahigedaf,flennerhag/mlens,mlens/ensemble/ensemble.py,0ec8f8829056a9dc7a4ec2b87a71c22238c50737,STILL_EXISTS,TODO: add option to pre-make folds
aahigedge,flennerhag/mlens,mlens/test/test_simple.py,0921843b5c9cece580856fb714b5238b8b394add,STILL_EXISTS,\"\"\" || author: Sebastian Flennerhag || date: 10\/01\/2017 || An initial test suite for ML-Ensemble. The test covers the Ensemble class; || both as integrated with grid search and stand-alone; and the Evaluator class. || The test relies on fitting a dummy problem using a fixed seed and ensuring || that the optimized estimator \/ ensemble gives the right score. Since the || problem is deterministic; if the ensemble finds another score; the || learning algorithm has changed. || \"\"\"
aahigedig,flennerhag/mlens,mlens/tests/test_simple.py,8b738347595d55096966b9847ee76b80784c6f50,STILL_EXISTS,\"\"\" || author: Sebastian Flennerhag || date: 10\/01\/2017 || An initial test suite for ML-Ensemble. The test covers the Ensemble class; || both as integrated with grid search and stand-alone; and the Evaluator class. || The test relies on fitting a dummy problem using a fixed seed and ensuring || that the optimized estimator \/ ensemble gives the right score. Since the || problem is deterministic; if the ensemble finds another score; the || learning algorithm has changed. || \"\"\"
aahigeehg,flennerhag/mlens,_site/mlens/ensemble/stacking_ensemble.py,f7909e3dfa26870ef9fb516039924233ad77e7b8,STILL_EXISTS,\"\"\" || author: Sebastian Flennerhag || date: 11\/01\/2017 || Stacked ensemble class for full control over the entire model's parameters. || Scikit-learn API allows full integration; including grid search and pipelining. || \"\"\"
aahigeehj,flennerhag/mlens,_site/mlens/ensemble/stacking_ensemble.py,f7909e3dfa26870ef9fb516039924233ad77e7b8,STILL_EXISTS,TODO: make the preprocessing of folds optional as it can take a lot of memory
aahigefcj,flennerhag/mlens,_site/mlens/parallel/fit_predict.py,f7909e3dfa26870ef9fb516039924233ad77e7b8,STILL_EXISTS,\"\"\" || author: Sebastian Flennerhag || data: 10\/01\/2017 || Base functions for any parallel processing || \"\"\"
aahigegad,flennerhag/mlens,mlens/model_selection/model_selection.py,d256684a26e25ec25bc6359efabe991a77fa1181,STILL_EXISTS,Fill list of parameter settings by param
aahigegij,flennerhag/mlens,mlens/externals/six.py,6fa2d16ae7f491134cd95565716db672150b6306,STILL_EXISTS,This is a bit ugly; but it avoids running this again by
aahigegji,flennerhag/mlens,mlens/externals/six.py,6fa2d16ae7f491134cd95565716db672150b6306,STILL_EXISTS,Workaround for standalone backslash
aahigfaaj,flennerhag/mlens,mlens/ensemble/_layer.py,7cae467652055a4d0f33b8f29e9a7e99606a1f81,STILL_EXISTS,\"\"\"ML-ENSEMBLE ||  || author: Sebastian Flennerhag || date: 11\/01\/2017 || licence: MIT || Functions for processing an ensemble layer || \"\"\"
aahigfabf,flennerhag/mlens,mlens/ensemble/base.py,7cae467652055a4d0f33b8f29e9a7e99606a1f81,277fa55362f98affc8ea043ac4b466cea7038d37,TODO: make the preprocessing of folds optional as it can take a lot of memory
aahigfada,flennerhag/mlens,mlens/externals/scorer.py,5756396e7846ca6ad1251e55779837f677c78389,STILL_EXISTS,XXX After removing the deprecated scorers (v0.20) remove the
aahigfadb,flennerhag/mlens,mlens/externals/scorer.py,5756396e7846ca6ad1251e55779837f677c78389,STILL_EXISTS,XXX deprecation_msg property again and remove __call__'s body again
aahigfafb,flennerhag/mlens,mlens/utils/checks.py,7b759f67914e67ee5fd3be53e98fcdff33e1e166,STILL_EXISTS,\"\"\"ML-ENSEMBLE ||  || author: Sebastian Flennerhag || date: 19\/02\/2017 || licence: MIT || Suite for checking valid estimation and informative error traceback. || This is a light version of the Scikit-learn test suite; to pre-check || conditions for estimation before making estimator function calls in parallel || jobs. || \"\"\"
aahigfchi,flennerhag/mlens,mlens/base/__init__.py,95cffb3dac96f4920f307ec31d575bfebd391d5d,STILL_EXISTS,\"\"\"ML-ENSEMBLE ||  || author: Sebastian Flennerhag || licence: MIT || Base modules used throughout mlens || \"\"\"
aahigfdbd,flennerhag/mlens,mlens/base/support.py,95cffb3dac96f4920f307ec31d575bfebd391d5d,STILL_EXISTS,fix before update
aahigfege,flennerhag/mlens,doc/conf.py,9b84a3b144f8301f7065eb2b3e8b38eda2429411,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aahigfgdf,flennerhag/mlens,mlens/model_selection/ensemble_transfomer.py,9b84a3b144f8301f7065eb2b3e8b38eda2429411,STILL_EXISTS,\"\"\" ||  || :author: Sebastian Flennerhag || :copyright: 2017 || :licence: MIT ||  || Stacked ensemble class for full control over the entire model's parameters. || Scikit-learn API allows full integration; including grid search and pipelining. || \"\"\"
aahigfgdi,flennerhag/mlens,mlens/model_selection/ensemble_transfomer.py,9b84a3b144f8301f7065eb2b3e8b38eda2429411,STILL_EXISTS,TODO: Should id_train be used here?
aahigfgif,flennerhag/mlens,mlens/utils/validation.py,9b84a3b144f8301f7065eb2b3e8b38eda2429411,8538aea5c60e75282084b0d571549e1f5f4e71fe,Raise warning that conversion is needed
aahigfhid,flennerhag/mlens,docs/conf.py,41bdbcdbc8058b326fd6c4b6c0ae181ed255d626,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aahigfihf,flennerhag/mlens,mlens/parallel/base.py,0ca31ec90b7d6f1fc3e8857cfaba632804a3470b,STILL_EXISTS,\"\"\"ML-Ensemble ||  || :author: Sebastian Flennerhag || :copyright: 2017 || :licence: MIT ||  || Parallel processing core backend. || \"\"\"
aahigfjdc,flennerhag/mlens,benchmarks/friedman.py,20dad33567df73757d830dc066831d5e1080c82e,STILL_EXISTS,\"\"\"ML-ENSEMBLE ||  || Benchmark of ML-Ensemble against Scikit-learn estimators using Scikit-learn's || friedman1 dataset. ||  || All estimators are instantiated with default settings; and all estimators in || the ensemble are part of the benchmark. ||  || The default ensemble configuration achieves a 25% score improvement as compared || to the best benchmark estimator (GradientBoostingRegressor). ||  || Ensemble training time depends on the number of available cores. ||  || Example output || -------------- ||  || Benchmark of ML-ENSEMBLE against Scikit-learn estimators on the friedman1 || dataset. ||  || Scoring metric: Root Mean Squared Error. ||  || Available CPUs: 4 ||  || Ensemble architecture || Num layers: 1 || Meta estimator: GradientBoostingRegressor || layer-1 | Min Max Scaling - Estimators: ['svr']. || layer-1 | Standard Scaling - Estimators: ['elasticnet'; 'lasso'; ||                                           'kneighborsregressor']. || layer-1 | No Preprocessing - Estimators: ['randomforestregressor'; ||                                           'gradientboostingregressor']. ||  || Benchmark estimators: GBM KNN Kernel Ridge Lasso Random Forest SVR Elastic-Net ||  || Data || Features: 10 || Training set sizes: from 2000 to 20000 with step size 2000. ||  || SCORES ||   size | Ensemble |      GBM |      KNN | Kern Rid |    Lasso | Random F | ... ||   2000 |     0.83 |     0.92 |     2.26 |     2.42 |     3.13 |     1.61 | ... ||   4000 |     0.75 |     0.91 |     2.11 |     2.49 |     3.13 |     1.39 | ... ||   6000 |     0.66 |     0.83 |     2.02 |     2.43 |     3.21 |     1.29 | ... ||   8000 |     0.66 |     0.84 |     1.95 |     2.43 |     3.19 |     1.24 | ... ||  10000 |     0.62 |     0.79 |     1.90 |     2.46 |     3.17 |     1.16 | ... ||  12000 |     0.68 |     0.86 |     1.84 |     2.46 |     3.16 |     1.10 | ... ||  14000 |     0.59 |     0.75 |     1.78 |     2.45 |     3.15 |     1.05 | ... ||  16000 |     0.62 |     0.80 |     1.76 |     2.45 |     3.15 |     1.02 | ... ||  18000 |     0.59 |     0.79 |     1.73 |     2.43 |     3.12 |     1.01 | ... ||  20000 |     0.56 |     0.73 |     1.70 |     2.42 |     4.87 |     0.99 | ... ||  ||   size |      SVR |    elNet | ||   2000 |     2.32 |     3.18 | ||   4000 |     2.31 |     3.16 | ||   6000 |     2.18 |     3.25 | ||   8000 |     2.09 |     3.24 | ||  10000 |     2.03 |     3.21 | ||  12000 |     1.97 |     3.21 | ||  16000 |     1.92 |     3.20 | ||  16000 |     1.87 |     3.19 | ||  18000 |     1.83 |     3.17 | ||  20000 |     1.81 |     4.75 | ||  || FIT TIMES ||   size | Ensemble |      GBM |      KNN | Kern Rid |    Lasso | Random F | ||   2000 |     0:01 |     0:00 |     0:00 |     0:00 |     0:00 |     0:00 | ||   4000 |     0:02 |     0:00 |     0:00 |     0:00 |     0:00 |     0:00 | ||   6000 |     0:04 |     0:00 |     0:00 |     0:01 |     0:00 |     0:00 | ||   8000 |     0:06 |     0:00 |     0:00 |     0:04 |     0:00 |     0:00 | ||  10000 |     0:08 |     0:01 |     0:00 |     0:08 |     0:00 |     0:00 | ||  12000 |     0:09 |     0:01 |     0:00 |     0:12 |     0:00 |     0:00 | ||  14000 |     0:12 |     0:01 |     0:00 |     0:20 |     0:00 |     0:00 | ||  16000 |     0:16 |     0:02 |     0:00 |     0:34 |     0:00 |     0:00 | ||  18000 |     0:20 |     0:02 |     0:00 |     0:47 |     0:00 |     0:00 | ||  20000 |     0:25 |     0:02 |     0:00 |     1:20 |     0:00 |     0:00 | ||  ||   size |      SVR |    elNet | ||   2000 |     0:00 |     0:00 | ||   4000 |     0:00 |     0:00 | ||   6000 |     0:01 |     0:00 | ||   8000 |     0:02 |     0:00 | ||  10000 |     0:03 |     0:00 | ||  12000 |     0:04 |     0:00 | ||  16000 |     0:06 |     0:00 | ||  16000 |     0:08 |     0:00 | ||  18000 |     0:10 |     0:00 | ||  20000 |     0:13 |     0:00 | ||  || \"\"\"
aahigfjfe,flennerhag/mlens,mlens/parallel/stacking.py,44dbc05b6a78c0c4daddb86a5976bbc96c76cca0,STILL_EXISTS,Set up main columns mapping
aahiggbbe,flennerhag/mlens,mlens/ensemble/stacking_ensemble.py,07237e0115ad0f91a8e7403a1cf4897c1bf3293c,STILL_EXISTS,TODO:        self.scores_ = set_scores(self)
aahiggcad,flennerhag/mlens,mlens/parallel/manager.py,5b03be761cdf1dd246827f04333107a9ff200112,STILL_EXISTS,\"\"\"ML-Ensemble ||  || :author: Sebastian Flennerhag || :copyright: 2017 || :licence: MIT ||  || Parallel processing job manager. || \"\"\"
aahiggcfb,flennerhag/mlens,mlens/parallel/stack.py,5b03be761cdf1dd246827f04333107a9ff200112,STILL_EXISTS,Set up main columns mapping
aahiggcgd,flennerhag/mlens,mlens/ensemble/blend.py,b263d2fa98a9387a8d01a54d891f2a5e0192b58f,9d2148e84ca803f7377ddd4ead201f0b4efdbd79,TODO:        self.scores_ = set_scores(self)
aahiggchb,flennerhag/mlens,mlens/utils/utils.py,64ea9367152f331ecd8cf73d226697bcde427b82,STILL_EXISTS,Probably not the best way to do this but it does the trick
aahiggche,flennerhag/mlens,mlens/ensemble/base.py,9d2148e84ca803f7377ddd4ead201f0b4efdbd79,STILL_EXISTS,TODO:        self.scores_ = set_scores(self)
aahiggcjh,flennerhag/mlens,mlens/externals/base.py,156a5557ae2d57fc77f3e752424ddc4446683544,STILL_EXISTS,XXX: not handling dictionaries
aahiggdcd,flennerhag/mlens,mlens/externals/base.py,156a5557ae2d57fc77f3e752424ddc4446683544,STILL_EXISTS,XXX: should we rather test if instance of estimator?
aahiggdga,flennerhag/mlens,mlens/utils/dummy.py,27341f65ed082615cb020d150c325e6bd7be8759,7d993aaec4a52a5b71664bcd89080702235370ce,FIXME: Needs a quality check!
aahiggdjc,flennerhag/mlens,mlens/parallel/single_run.py,e527b6f9d0a294536a36a2417726d80f9eda1755,STILL_EXISTS,Set up main columns mapping
aahiggdjj,flennerhag/mlens,mlens/parallel/manager.py,7f21d9ec6e0582f635a3aa22352bc49319ac1fc9,81d219b6347943dbe8de6030bb0a6852969f9c7f,Number of prediction columns depends on:
aahigghjg,flennerhag/mlens,mlens/model_selection/model_selection.py,04c1a3e528d8c43b5ee32ebf6ad9f51e06d8b8f1,e49ee1588efdcbbe3418c53899b556a19316b75a,Finally; we sort summary in order of best performance
aahiggihj,flennerhag/mlens,mlens/externals/joblib/_parallel_backends.py,07b1f60f4238e7a15f8cf4b963b70fdbee63eff6,STILL_EXISTS,batches. Better reduce the batch size a bit to limit the
aahighabb,flennerhag/mlens,mlens/externals/joblib/format_stack.py,07b1f60f4238e7a15f8cf4b963b70fdbee63eff6,STILL_EXISTS,Hack to avoid printing the internals of IPython
aahighadb,flennerhag/mlens,mlens/externals/joblib/func_inspect.py,07b1f60f4238e7a15f8cf4b963b70fdbee63eff6,STILL_EXISTS,will change with every new kernel instance. This hack
aahighadd,flennerhag/mlens,mlens/externals/joblib/func_inspect.py,07b1f60f4238e7a15f8cf4b963b70fdbee63eff6,STILL_EXISTS,Hack to detect functions not defined at the module-level
aahighade,flennerhag/mlens,mlens/externals/joblib/func_inspect.py,07b1f60f4238e7a15f8cf4b963b70fdbee63eff6,STILL_EXISTS,TODO: Maybe add a warning here?
aahighaed,flennerhag/mlens,mlens/externals/joblib/func_inspect.py,07b1f60f4238e7a15f8cf4b963b70fdbee63eff6,STILL_EXISTS,XXX: Maybe I need an inspect.isbuiltin to detect C-level methods; such
aahighaei,flennerhag/mlens,mlens/externals/joblib/func_inspect.py,07b1f60f4238e7a15f8cf4b963b70fdbee63eff6,STILL_EXISTS,XXX: Return a sorted list of pairs?
aahighaej,flennerhag/mlens,mlens/externals/joblib/func_inspect.py,07b1f60f4238e7a15f8cf4b963b70fdbee63eff6,STILL_EXISTS,XXX: Should this use inspect.formatargvalues\/formatargspec?
aahighafa,flennerhag/mlens,mlens/externals/joblib/func_inspect.py,07b1f60f4238e7a15f8cf4b963b70fdbee63eff6,STILL_EXISTS,XXX: Not using logging framework
aahighajj,flennerhag/mlens,mlens/externals/joblib/hashing.py,07b1f60f4238e7a15f8cf4b963b70fdbee63eff6,STILL_EXISTS,XXX: There might be a more efficient way of doing this
aahighbac,flennerhag/mlens,mlens/externals/joblib/hashing.py,07b1f60f4238e7a15f8cf4b963b70fdbee63eff6,STILL_EXISTS,workaround is to view the array as bytes before
aahighbdb,flennerhag/mlens,mlens/externals/joblib/logger.py,07b1f60f4238e7a15f8cf4b963b70fdbee63eff6,STILL_EXISTS,XXX: This conflicts with the debug flag used in children class
aahighbdf,flennerhag/mlens,mlens/externals/joblib/logger.py,07b1f60f4238e7a15f8cf4b963b70fdbee63eff6,STILL_EXISTS,XXX: Need argument docstring
aahighbeb,flennerhag/mlens,mlens/externals/joblib/logger.py,07b1f60f4238e7a15f8cf4b963b70fdbee63eff6,STILL_EXISTS,FIXME: Too much logic duplicated
aahighbej,flennerhag/mlens,mlens/externals/joblib/memory.py,07b1f60f4238e7a15f8cf4b963b70fdbee63eff6,STILL_EXISTS,TODO: The following object should have a data store object as a sub
aahighbfg,flennerhag/mlens,mlens/externals/joblib/memory.py,07b1f60f4238e7a15f8cf4b963b70fdbee63eff6,STILL_EXISTS,implement HDF5 pickling.
aahighbfh,flennerhag/mlens,mlens/externals/joblib/memory.py,07b1f60f4238e7a15f8cf4b963b70fdbee63eff6,STILL_EXISTS,TODO: Same remark for the logger; and probably use the Python logging
aahighbii,flennerhag/mlens,mlens/externals/joblib/memory.py,07b1f60f4238e7a15f8cf4b963b70fdbee63eff6,STILL_EXISTS,FIXME: The statements below should be try\/excepted
aahighbjb,flennerhag/mlens,mlens/externals/joblib/memory.py,07b1f60f4238e7a15f8cf4b963b70fdbee63eff6,STILL_EXISTS,XXX: Should use an exception logger
aahighcad,flennerhag/mlens,mlens/externals/joblib/memory.py,07b1f60f4238e7a15f8cf4b963b70fdbee63eff6,STILL_EXISTS,objects; as it ends up being too fragile
aahighcce,flennerhag/mlens,mlens/externals/joblib/memory.py,07b1f60f4238e7a15f8cf4b963b70fdbee63eff6,STILL_EXISTS,XXX: Should be using warnings; and giving stacklevel
aahighcdd,flennerhag/mlens,mlens/externals/joblib/memory.py,07b1f60f4238e7a15f8cf4b963b70fdbee63eff6,STILL_EXISTS,matter of replacing repr() above by something smarter.
aahighcde,flennerhag/mlens,mlens/externals/joblib/memory.py,07b1f60f4238e7a15f8cf4b963b70fdbee63eff6,STILL_EXISTS,XXX: Need a method to check if results are available.
aahighcee,flennerhag/mlens,mlens/externals/joblib/memory.py,07b1f60f4238e7a15f8cf4b963b70fdbee63eff6,STILL_EXISTS,XXX: Bad explanation of the None value of cachedir
aahighcgb,flennerhag/mlens,mlens/externals/joblib/my_exceptions.py,07b1f60f4238e7a15f8cf4b963b70fdbee63eff6,STILL_EXISTS,_mk_exception. Note: in Python 2; if you implement __init__
aahighcha,flennerhag/mlens,mlens/externals/joblib/my_exceptions.py,07b1f60f4238e7a15f8cf4b963b70fdbee63eff6,STILL_EXISTS,The next line set the .args correctly. This is needed to
aahighebj,flennerhag/mlens,mlens/externals/joblib/numpy_pickle_utils.py,07b1f60f4238e7a15f8cf4b963b70fdbee63eff6,STILL_EXISTS,BZ2File doesn't implement the file object context manager in python 2
aahighfba,flennerhag/mlens,mlens/externals/joblib/parallel.py,07b1f60f4238e7a15f8cf4b963b70fdbee63eff6,STILL_EXISTS,XXX: Not using the logger framework: need to
aahighfbb,flennerhag/mlens,mlens/externals/joblib/parallel.py,07b1f60f4238e7a15f8cf4b963b70fdbee63eff6,STILL_EXISTS,learn to use logger better.
aahighffc,flennerhag/mlens,mlens/externals/joblib/pool.py,07b1f60f4238e7a15f8cf4b963b70fdbee63eff6,STILL_EXISTS,\"\"\"Custom implementation of multiprocessing.Pool with custom pickler. ||  || This module provides efficient ways of working with data stored in || shared memory with numpy.memmap arrays without inducing any memory || copy between the parent and child processes. ||  || This module should not be imported if multiprocessing is not || available as it implements subclasses of multiprocessing Pool || that uses a custom alternative to SimpleQueue. ||  || \"\"\"
aahighfgh,flennerhag/mlens,mlens/externals/joblib/pool.py,07b1f60f4238e7a15f8cf4b963b70fdbee63eff6,STILL_EXISTS,Support for efficient transient pickling of numpy data structures
aahighfgi,flennerhag/mlens,mlens/externals/joblib/pool.py,07b1f60f4238e7a15f8cf4b963b70fdbee63eff6,STILL_EXISTS,TODO: check scipy sparse datastructure if scipy is installed
aahighfji,flennerhag/mlens,mlens/externals/joblib/pool.py,07b1f60f4238e7a15f8cf4b963b70fdbee63eff6,STILL_EXISTS,XXX: implement an explicit reference counting scheme to make it
aahighgbi,flennerhag/mlens,mlens/externals/joblib/pool.py,07b1f60f4238e7a15f8cf4b963b70fdbee63eff6,STILL_EXISTS,Let us use a closure to workaround this limitation.
aahighged,flennerhag/mlens,mlens/externals/joblib/pool.py,07b1f60f4238e7a15f8cf4b963b70fdbee63eff6,610c46e81c0a24a151110a6991988e6f0d4b9b5e,Workaround  occasional \"[Error 5] Access is denied\" issue
aahighghc,flennerhag/mlens,mlens/externals/joblib/hashing.py,610c46e81c0a24a151110a6991988e6f0d4b9b5e,STILL_EXISTS,workaround is to view the array as bytes before
aahighgji,flennerhag/mlens,mlens/externals/joblib/numpy_pickle.py,610c46e81c0a24a151110a6991988e6f0d4b9b5e,STILL_EXISTS,Efficient compressed storage:
aahighhad,flennerhag/mlens,mlens/externals/joblib/numpy_pickle.py,610c46e81c0a24a151110a6991988e6f0d4b9b5e,STILL_EXISTS,disk; it is more efficient to use standard pickling
aahighhaf,flennerhag/mlens,mlens/externals/joblib/numpy_pickle.py,610c46e81c0a24a151110a6991988e6f0d4b9b5e,STILL_EXISTS,XXX: We should have a logging mechanism
aahighhhh,flennerhag/mlens,mlens/externals/joblib/parallel.py,610c46e81c0a24a151110a6991988e6f0d4b9b5e,a36c07de7ef3d86b19fbebfdc0d1cf12e93c33d2,batches. Better reduce the batch size a bit to limit the
aahihafdf,flennerhag/mlens,mlens/externals/joblib/hashing.py,a36c07de7ef3d86b19fbebfdc0d1cf12e93c33d2,STILL_EXISTS,workaround is to view the array as bytes before
aahihagge,flennerhag/mlens,mlens/externals/joblib/pool.py,a36c07de7ef3d86b19fbebfdc0d1cf12e93c33d2,STILL_EXISTS,Workaround  occasional \"[Error 5] Access is denied\" issue
aahihbgij,flennerhag/mlens,benchmarks/mnist.py,e6eb78d2ef5a5beade18dbf238130510adb13ef7,STILL_EXISTS,\"\"\" || ======================= || MNIST dataset benchmark || ======================= ||  || Benchmark on the MNIST dataset.  The dataset comprises 70;000 samples || and 784 features. Here; we consider the task of predicting || 10 classes -  digits from 0 to 9 from their raw images. By contrast to the || covertype dataset; the feature space is homogenous. ||  || Example of output : ||     [..] ||  ||     Classification performance: ||     =========================== ||     Classifier               train-time   test-time   error-rate ||     ------------------------------------------------------------ ||     Subsemble                   343.31s       3.17s       0.0210 ||     MLP_adam                     53.46s       0.11s       0.0224 ||     Nystroem-SVM                112.97s       0.92s       0.0228 ||     MultilayerPerceptron         24.33s       0.14s       0.0287 ||     ExtraTrees                   42.99s       0.57s       0.0294 ||     RandomForest                 42.70s       0.49s       0.0318 ||     SampledRBF-SVM              135.81s       0.56s       0.0486 ||     LinearRegression-SAG         16.67s       0.06s       0.0824 ||     CART                         20.69s       0.02s       0.1219 ||     dummy                         0.00s       0.01s       0.8973 || \"\"\"
aahihbifc,flennerhag/mlens,mlens/index/__init__.py,4b98df9101a2520c0fb559268ea4924abb0ca07f,STILL_EXISTS,\"\"\"ML-ENSEMBLE ||  || :author: Sebastian Flennerhag || :copyright: 2017 || :licence: MIT ||  || Base modules || \"\"\"
aahihbjjb,flennerhag/mlens,docs/examples/layer_tutorial.py,2cdafddc93d9328b337e7d352d15d3ca75ef6dd7,STILL_EXISTS,The output_columns can contain several entries if your indexer creates
aahihcacb,flennerhag/mlens,docs/examples/layer_tutorial.py,2cdafddc93d9328b337e7d352d15d3ca75ef6dd7,STILL_EXISTS,In particular; the learner will populate the columns given in the
aahihcagg,flennerhag/mlens,docs/examples/layer_tutorial.py,2cdafddc93d9328b337e7d352d15d3ca75ef6dd7,STILL_EXISTS,it can load when needed. To facilitate preprocessing across several learners;
aahihcbdf,flennerhag/mlens,docs/examples/learner_tutorial.py,2cdafddc93d9328b337e7d352d15d3ca75ef6dd7,STILL_EXISTS,The output_columns can contain several entries if your indexer creates
aahihcbgf,flennerhag/mlens,docs/examples/learner_tutorial.py,2cdafddc93d9328b337e7d352d15d3ca75ef6dd7,STILL_EXISTS,In particular; the learner will populate the columns given in the
aahihccba,flennerhag/mlens,docs/examples/learner_tutorial.py,2cdafddc93d9328b337e7d352d15d3ca75ef6dd7,STILL_EXISTS,it can load when needed. To facilitate preprocessing across several learners;
aahihccgd,flennerhag/mlens,docs/examples/parallel_tutorial.py,2cdafddc93d9328b337e7d352d15d3ca75ef6dd7,STILL_EXISTS,The output_columns can contain several entries if your indexer creates
aahihccjd,flennerhag/mlens,docs/examples/parallel_tutorial.py,2cdafddc93d9328b337e7d352d15d3ca75ef6dd7,STILL_EXISTS,In particular; the learner will populate the columns given in the
aahihcddi,flennerhag/mlens,docs/examples/parallel_tutorial.py,2cdafddc93d9328b337e7d352d15d3ca75ef6dd7,STILL_EXISTS,it can load when needed. To facilitate preprocessing across several learners;
aahihceah,flennerhag/mlens,docs/examples/sequential_tutorial.py,2cdafddc93d9328b337e7d352d15d3ca75ef6dd7,STILL_EXISTS,The output_columns can contain several entries if your indexer creates
aahihcedh,flennerhag/mlens,docs/examples/sequential_tutorial.py,2cdafddc93d9328b337e7d352d15d3ca75ef6dd7,STILL_EXISTS,In particular; the learner will populate the columns given in the
aahihceic,flennerhag/mlens,docs/examples/sequential_tutorial.py,2cdafddc93d9328b337e7d352d15d3ca75ef6dd7,STILL_EXISTS,it can load when needed. To facilitate preprocessing across several learners;
aahihcffb,flennerhag/mlens,docs/examples_source/layer_tutorial.py,2cdafddc93d9328b337e7d352d15d3ca75ef6dd7,STILL_EXISTS,The output_columns can contain several entries if your indexer creates
aahihcfib,flennerhag/mlens,docs/examples_source/layer_tutorial.py,2cdafddc93d9328b337e7d352d15d3ca75ef6dd7,STILL_EXISTS,In particular; the learner will populate the columns given in the
aahihcgcg,flennerhag/mlens,docs/examples_source/layer_tutorial.py,2cdafddc93d9328b337e7d352d15d3ca75ef6dd7,STILL_EXISTS,it can load when needed. To facilitate preprocessing across several learners;
aahihcgjf,flennerhag/mlens,docs/examples_source/learner_tutorial.py,2cdafddc93d9328b337e7d352d15d3ca75ef6dd7,STILL_EXISTS,The output_columns can contain several entries if your indexer creates
aahihchcf,flennerhag/mlens,docs/examples_source/learner_tutorial.py,2cdafddc93d9328b337e7d352d15d3ca75ef6dd7,STILL_EXISTS,In particular; the learner will populate the columns given in the
aahihchha,flennerhag/mlens,docs/examples_source/learner_tutorial.py,2cdafddc93d9328b337e7d352d15d3ca75ef6dd7,STILL_EXISTS,it can load when needed. To facilitate preprocessing across several learners;
aahihcicd,flennerhag/mlens,docs/examples_source/parallel_tutorial.py,2cdafddc93d9328b337e7d352d15d3ca75ef6dd7,STILL_EXISTS,The output_columns can contain several entries if your indexer creates
aahihcifd,flennerhag/mlens,docs/examples_source/parallel_tutorial.py,2cdafddc93d9328b337e7d352d15d3ca75ef6dd7,STILL_EXISTS,In particular; the learner will populate the columns given in the
aahihciji,flennerhag/mlens,docs/examples_source/parallel_tutorial.py,2cdafddc93d9328b337e7d352d15d3ca75ef6dd7,STILL_EXISTS,it can load when needed. To facilitate preprocessing across several learners;
aahihcjgh,flennerhag/mlens,docs/examples_source/sequential_tutorial.py,2cdafddc93d9328b337e7d352d15d3ca75ef6dd7,STILL_EXISTS,The output_columns can contain several entries if your indexer creates
aahihcjjh,flennerhag/mlens,docs/examples_source/sequential_tutorial.py,2cdafddc93d9328b337e7d352d15d3ca75ef6dd7,STILL_EXISTS,In particular; the learner will populate the columns given in the
aahihdaec,flennerhag/mlens,docs/examples_source/sequential_tutorial.py,2cdafddc93d9328b337e7d352d15d3ca75ef6dd7,STILL_EXISTS,it can load when needed. To facilitate preprocessing across several learners;
aahihdbig,flennerhag/mlens,mlens/parallel/tests/test_a_base_functions.py,e605f734f5cb182ef8fdc4a55daa28e945ec1e11,STILL_EXISTS,TODO: Write tests
aahihdcag,flennerhag/mlens,mlens/ensemble/base.py,bd26e4d45f6866b5c8f846f9ce3d9e17e66e9453,a1d45ae37558f14c259d400fad7a7c74da88f90a,TODO: get the assemble_table to
aahihdcbc,flennerhag/mlens,mlens/parallel/layer.py,bd26e4d45f6866b5c8f846f9ce3d9e17e66e9453,STILL_EXISTS,TODO: Fix table printing
aahihdcbh,flennerhag/mlens,mlens/parallel/learner.py,5a3cb951a4adf615f60d33f2f409bac0ec01bb9d,9a1589f1600214354c38c666978edddb7cec6a9d,XXX: Need to define __getstate__ and __setstate__ for py27
aahihdcgj,flennerhag/mlens,docs/source/conf.py,40c8b8af2116c4972e59bdcb06695f6340273360,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aahihddch,flennerhag/mlens,docs/source/examples_source/layer_tutorial.py,40c8b8af2116c4972e59bdcb06695f6340273360,STILL_EXISTS,The output_columns can contain several entries if your indexer creates
aahihddfh,flennerhag/mlens,docs/source/examples_source/layer_tutorial.py,40c8b8af2116c4972e59bdcb06695f6340273360,STILL_EXISTS,In particular; the learner will populate the columns given in the
aahihdeac,flennerhag/mlens,docs/source/examples_source/layer_tutorial.py,40c8b8af2116c4972e59bdcb06695f6340273360,STILL_EXISTS,it can load when needed. To facilitate preprocessing across several learners;
aahihdehb,flennerhag/mlens,docs/source/examples_source/learner_tutorial.py,40c8b8af2116c4972e59bdcb06695f6340273360,STILL_EXISTS,The output_columns can contain several entries if your indexer creates
aahihdfab,flennerhag/mlens,docs/source/examples_source/learner_tutorial.py,40c8b8af2116c4972e59bdcb06695f6340273360,STILL_EXISTS,In particular; the learner will populate the columns given in the
aahihdfeg,flennerhag/mlens,docs/source/examples_source/learner_tutorial.py,40c8b8af2116c4972e59bdcb06695f6340273360,STILL_EXISTS,it can load when needed. To facilitate preprocessing across several learners;
aahihdfjj,flennerhag/mlens,docs/source/examples_source/parallel_tutorial.py,40c8b8af2116c4972e59bdcb06695f6340273360,STILL_EXISTS,The output_columns can contain several entries if your indexer creates
aahihdgcj,flennerhag/mlens,docs/source/examples_source/parallel_tutorial.py,40c8b8af2116c4972e59bdcb06695f6340273360,STILL_EXISTS,In particular; the learner will populate the columns given in the
aahihdghe,flennerhag/mlens,docs/source/examples_source/parallel_tutorial.py,40c8b8af2116c4972e59bdcb06695f6340273360,STILL_EXISTS,it can load when needed. To facilitate preprocessing across several learners;
aahihdhed,flennerhag/mlens,docs/source/examples_source/sequential_tutorial.py,40c8b8af2116c4972e59bdcb06695f6340273360,STILL_EXISTS,The output_columns can contain several entries if your indexer creates
aahihdhhd,flennerhag/mlens,docs/source/examples_source/sequential_tutorial.py,40c8b8af2116c4972e59bdcb06695f6340273360,STILL_EXISTS,In particular; the learner will populate the columns given in the
aahihdibi,flennerhag/mlens,docs/source/examples_source/sequential_tutorial.py,40c8b8af2116c4972e59bdcb06695f6340273360,STILL_EXISTS,it can load when needed. To facilitate preprocessing across several learners;
aahihdjbj,flennerhag/mlens,docs/source/examples/layer_tutorial.py,7621f76b7ea57465d8f7379d7e1ede5f60b903e8,STILL_EXISTS,The output_columns can contain several entries if your indexer creates
aahihdjej,flennerhag/mlens,docs/source/examples/layer_tutorial.py,7621f76b7ea57465d8f7379d7e1ede5f60b903e8,STILL_EXISTS,In particular; the learner will populate the columns given in the
aahihdjje,flennerhag/mlens,docs/source/examples/layer_tutorial.py,7621f76b7ea57465d8f7379d7e1ede5f60b903e8,STILL_EXISTS,it can load when needed. To facilitate preprocessing across several learners;
aahiheagd,flennerhag/mlens,docs/source/examples/learner_tutorial.py,7621f76b7ea57465d8f7379d7e1ede5f60b903e8,STILL_EXISTS,The output_columns can contain several entries if your indexer creates
aahiheajd,flennerhag/mlens,docs/source/examples/learner_tutorial.py,7621f76b7ea57465d8f7379d7e1ede5f60b903e8,STILL_EXISTS,In particular; the learner will populate the columns given in the
aahihebdi,flennerhag/mlens,docs/source/examples/learner_tutorial.py,7621f76b7ea57465d8f7379d7e1ede5f60b903e8,STILL_EXISTS,it can load when needed. To facilitate preprocessing across several learners;
aahihebjb,flennerhag/mlens,docs/source/examples/parallel_tutorial.py,7621f76b7ea57465d8f7379d7e1ede5f60b903e8,STILL_EXISTS,The output_columns can contain several entries if your indexer creates
aahiheccb,flennerhag/mlens,docs/source/examples/parallel_tutorial.py,7621f76b7ea57465d8f7379d7e1ede5f60b903e8,STILL_EXISTS,In particular; the learner will populate the columns given in the
aahihecgg,flennerhag/mlens,docs/source/examples/parallel_tutorial.py,7621f76b7ea57465d8f7379d7e1ede5f60b903e8,STILL_EXISTS,it can load when needed. To facilitate preprocessing across several learners;
aahiheddf,flennerhag/mlens,docs/source/examples/sequential_tutorial.py,7621f76b7ea57465d8f7379d7e1ede5f60b903e8,STILL_EXISTS,The output_columns can contain several entries if your indexer creates
aahihedgf,flennerhag/mlens,docs/source/examples/sequential_tutorial.py,7621f76b7ea57465d8f7379d7e1ede5f60b903e8,STILL_EXISTS,In particular; the learner will populate the columns given in the
aahiheeba,flennerhag/mlens,docs/source/examples/sequential_tutorial.py,7621f76b7ea57465d8f7379d7e1ede5f60b903e8,STILL_EXISTS,it can load when needed. To facilitate preprocessing across several learners;
aahihegbh,flennerhag/mlens,docs/source/examples/start_tutorial.py,7621f76b7ea57465d8f7379d7e1ede5f60b903e8,STILL_EXISTS,By passing a class labels; the plot shows how well separated different classes
aahiheibf,flennerhag/mlens,docs/source/examples_source/start_tutorial.py,7621f76b7ea57465d8f7379d7e1ede5f60b903e8,STILL_EXISTS,By passing a class labels; the plot shows how well separated different classes
aahiheihd,flennerhag/mlens,mlens/parallel/layer.py,33ea2f0de7c53333e55180a256cd3b2a3c5fe0fc,81d219b6347943dbe8de6030bb0a6852969f9c7f,TODO: Refactor the formatting into a single function
aahiheihe,flennerhag/mlens,mlens/parallel/layer.py,33ea2f0de7c53333e55180a256cd3b2a3c5fe0fc,81d219b6347943dbe8de6030bb0a6852969f9c7f,XXX: This is a little bit of a hack; force create a case
aahiheihf,flennerhag/mlens,mlens/metrics/utils.py,aefcf622112cb44af7b56b0a183283bcbf194788,STILL_EXISTS,TODO: generalize this to variable length keys
aahiheihg,flennerhag/mlens,mlens/metrics/utils.py,aefcf622112cb44af7b56b0a183283bcbf194788,81d219b6347943dbe8de6030bb0a6852969f9c7f,TODO: refactor this using the '{}'.format() API
aahiheiif,flennerhag/mlens,docs/source/tutorials_source/adv_tutorial.py,ce9c27661f0dcbe1ef2a6e40b409a6d2eddd371b,STILL_EXISTS,\"\"\" || .. _ensemble-tutorial: ||  || Advanced High-level API Tutorials || ================================= ||  || The following tutorials highlight advanced functionality and provide in-depth || material on ensemble APIs. ||  || ===============================  ============================================== || Tutorial                         Content || ===============================  ============================================== || :ref:`propa-tutorial`            Propagate feature input features through layers || \\                                to allow several layers to see the same input. || :ref:`proba-tutorial`            Build layers that output class probabilities from each base || \\                                learner so that the next layer or meta estimator learns || \\                                from probability distributions. || :ref:`subsemble-tutorial`        Learn homogenous partitions of feature space || \\                                that maximize base learner's performance on each partition. || :ref:`sequential-tutorial`       How to build ensembles with different layer classes || :ref:`memory-tutorial`           Avoid loading data into the parent process by specifying a || \\                                file path to a memmaped array or a csv file. || :ref:`model-selection-tutorial`  Build transformers that replicate layers in ensembles for || \\                                model selection of higher-order layers and \/ or meta learners. || ===============================  ============================================== ||  || We use the same preliminary settings as in the || :ref:`getting started <getting-started>` section. || \"\"\"
aahiheijg,flennerhag/mlens,docs/source/tutorials_source/adv_tutorial.py,ce9c27661f0dcbe1ef2a6e40b409a6d2eddd371b,STILL_EXISTS,ensemble to improve upon the best score of the subsequent layer as there is too
aahihejca,flennerhag/mlens,docs/source/tutorials_source/adv_tutorial.py,ce9c27661f0dcbe1ef2a6e40b409a6d2eddd371b,STILL_EXISTS,library of base learners gives a dramatic increase in performance on the test
aahihejja,flennerhag/mlens,docs/source/tutorials_source/adv_tutorial.py,ce9c27661f0dcbe1ef2a6e40b409a6d2eddd371b,STILL_EXISTS,better representation of the distribution and greatly facilitates the learning
aahihfaaa,flennerhag/mlens,docs/source/tutorials_source/adv_tutorial.py,ce9c27661f0dcbe1ef2a6e40b409a6d2eddd371b,STILL_EXISTS,only parameter needed is the number of ``partitions`` when instantiating
aahihfaah,flennerhag/mlens,docs/source/tutorials_source/adv_tutorial.py,ce9c27661f0dcbe1ef2a6e40b409a6d2eddd371b,STILL_EXISTS,By creating partitions; subsembles :ref:`scale significantly better <bench>`
aahihfabc,flennerhag/mlens,docs/source/tutorials_source/adv_tutorial.py,ce9c27661f0dcbe1ef2a6e40b409a6d2eddd371b,STILL_EXISTS,only by luck that we happen to create such partitions. A better way is to
aahihfadh,flennerhag/mlens,docs/source/tutorials_source/adv_tutorial.py,ce9c27661f0dcbe1ef2a6e40b409a6d2eddd371b,STILL_EXISTS,columns. But this approach is nota very good way of doing it since we loose
aahihfafc,flennerhag/mlens,docs/source/tutorials_source/adv_tutorial.py,ce9c27661f0dcbe1ef2a6e40b409a6d2eddd371b,STILL_EXISTS,number of partitions. Here's minimalist way of wrapping a Scikit-learn
aahihfafg,flennerhag/mlens,docs/source/tutorials_source/adv_tutorial.py,ce9c27661f0dcbe1ef2a6e40b409a6d2eddd371b,STILL_EXISTS,let's implement a simple (but rather useless) partition estimator that splits
aahihfbci,flennerhag/mlens,docs/source/tutorials_source/adv_tutorial.py,ce9c27661f0dcbe1ef2a6e40b409a6d2eddd371b,STILL_EXISTS,index columns and footers.
aahihfcef,flennerhag/mlens,docs/source/tutorials_source/learner_tutorial.py,a3c6553f98d9df805a973baefaf39966e9aba595,STILL_EXISTS,input is a mapping between partitions and output columns in the
aahihfcgd,flennerhag/mlens,docs/source/tutorials_source/learner_tutorial.py,a3c6553f98d9df805a973baefaf39966e9aba595,STILL_EXISTS,efficient operation that introduce only a minor overhead on computation time.
aahihfcgf,flennerhag/mlens,docs/source/tutorials_source/learner_tutorial.py,a3c6553f98d9df805a973baefaf39966e9aba595,STILL_EXISTS,for the learner to load when needed. To construct a learner with
aahihfdcj,flennerhag/mlens,mlens/metrics/utils.py,81d219b6347943dbe8de6030bb0a6852969f9c7f,STILL_EXISTS,dat_key is the estimators. Number of columns is not fixed so need
aahihfddc,flennerhag/mlens,mlens/metrics/utils.py,81d219b6347943dbe8de6030bb0a6852969f9c7f,STILL_EXISTS,Check which row name columns we can drop (ex partition number)
aahihfdde,flennerhag/mlens,mlens/metrics/utils.py,81d219b6347943dbe8de6030bb0a6852969f9c7f,STILL_EXISTS,Check if there are empty columns
aahihfehh,flennerhag/mlens,mlens/model_selection/_base_functions.py,65bd1eb4f91bf8c0ffd992502535da9a4c304083,STILL_EXISTS,fix before update
aahihfeia,flennerhag/mlens,mlens/parallel/backend.py,65bd1eb4f91bf8c0ffd992502535da9a4c304083,STILL_EXISTS,\"\"\"ML-Ensemble ||  || :author: Sebastian Flennerhag || :copyright: 2017 || :licence: MIT ||  || Parallel processing job managers. || \"\"\"
aahihffhg,flennerhag/mlens,mlens/parallel/handles.py,caab535236c6be38e9ce42cfe90a28beb5ec8abe,STILL_EXISTS,TODO: disallow None pipeline (modify model selection case handling)
aahihffia,flennerhag/mlens,mlens/parallel/learner.py,caab535236c6be38e9ce42cfe90a28beb5ec8abe,STILL_EXISTS,A hack to check that the estimator is an mlens pipeline (not sklearn)
aahihffif,flennerhag/mlens,mlens/utils/formatting.py,fc78fe8fe265e517cef0d85a9a292a18d2ca114a,STILL_EXISTS,TODO: should pop names when instance is destroyed and check if name taken
aahihhfda,flennerhag/mlens,mlens/parallel/_base_functions.py,98d277f6b6f0195d21accf160bcd1d76e6238769,STILL_EXISTS,TODO: either make something of this function or remove it
aahihhfec,flennerhag/mlens,mlens/parallel/_base_functions.py,09c5afd78449e099b556f1bd9a1e42cef6843c3b,STILL_EXISTS,TODO: remove
aahihhgad,annoviko/pyclustering,cure/__init__.py,c862d06119afffa4bf6b5bccfcc35c9b6dfa5c8f,STILL_EXISTS,TODO: investigation of root cause is required.
aahihhgbj,annoviko/pyclustering,cure/__init__.py,c862d06119afffa4bf6b5bccfcc35c9b6dfa5c8f,STILL_EXISTS,TODO: Set should be used in line with specification (article); but list is not hashable object therefore it's impossible to use list in this fucking set!
aahihhgdd,annoviko/pyclustering,dbscan/__init__.py,c862d06119afffa4bf6b5bccfcc35c9b6dfa5c8f,5f9b08cd2bd652d7774b31faa1e297ada531d112,Fix has been implemented in core.dbscan.
aahihhgdf,annoviko/pyclustering,dbscan/__init__.py,c862d06119afffa4bf6b5bccfcc35c9b6dfa5c8f,STILL_EXISTS,because maybe it's a noise.
aahihhgid,annoviko/pyclustering,nnet/plsom/tests.py,c862d06119afffa4bf6b5bccfcc35c9b6dfa5c8f,STILL_EXISTS,TODO: This test is fail. Is this test applicable for PLSOM?
aahihhibc,annoviko/pyclustering,rock/__init__.py,c862d06119afffa4bf6b5bccfcc35c9b6dfa5c8f,STILL_EXISTS,TODO: First iteration should be fixed. Euclidean distance should be used for clustering between two points and rock algorithm between clusters
aahihhidi,annoviko/pyclustering,support/kdtree.py,c862d06119afffa4bf6b5bccfcc35c9b6dfa5c8f,STILL_EXISTS,TODO: refactoring is required for removing this method.
aahihhiec,annoviko/pyclustering,support/kdtree.py,c862d06119afffa4bf6b5bccfcc35c9b6dfa5c8f,STILL_EXISTS,TODO: This method is not ready yet
aahihhied,annoviko/pyclustering,support/kdtree.py,c862d06119afffa4bf6b5bccfcc35c9b6dfa5c8f,STILL_EXISTS,Check if it's best candidate and maybe it's owner of the coordinates.
aahihhihf,annoviko/pyclustering,syncnet/__init__.py,c862d06119afffa4bf6b5bccfcc35c9b6dfa5c8f,4701663f149d680e130a065c6065c65d719d59de,TODO: SOMETHING WRONG WITH CONNECTIONS BUILDER. TOO LONG AND AREN'T DISPLAYED
aahihhjdf,annoviko/pyclustering,syncsom/examples.py,c862d06119afffa4bf6b5bccfcc35c9b6dfa5c8f,37803d0185a6bbc2303ede6d0ec9d8d350f283ba,TODO: Fix this fucking bug! It isn't displayed anymore!
aahihhjij,annoviko/pyclustering,ants/clustering.py,8cfe35bf458979225d95f311bb93632f401a69b6,STILL_EXISTS,TODO set random direction to agent
aahihidig,annoviko/pyclustering,nnet/legion/__init__.py,d7cb0265a93c968ba185b27efece3a2272108cbb,STILL_EXISTS,TODO: Noise should be negative!
aahihigih,annoviko/pyclustering,clustering/xmeans/__init__.py,56ee5f9751ee0a5e05d1578a7a41916a10ab438e,STILL_EXISTS,TODO: Check that it's right way for comparison
aahihiiea,annoviko/pyclustering,clustering/optics/__init__.py,30e2551a2fe59d6c4b0e92f34e66dae9d89aa9fd,43a876cc58c15fee2594cfbe1d1ae1b72663d635,TODO: Rellocation is less complexity than sorting
aahihiiei,annoviko/pyclustering,clustering/optics/__init__.py,adf30b71e084932f72c7eb37ebc9142e40898fe3,43a876cc58c15fee2594cfbe1d1ae1b72663d635,TODO: Rellocation is less complexity than sorting
aahihiihi,annoviko/pyclustering,clustering/cure/__init__.py,66830486a517c882aae32b68f6619c2a76e68409,STILL_EXISTS,'''\r || \r || Cluster analysis algorithm: CURE\r || \r || Based on article description:\r ||  - S.Guha; R.Rastogi; K.Shim. \"CURE: An Efficient Clustering Algorithm for Large Databases\"; 1998\r || \r || Implementation: Andrei Novikov (spb.andr@yandex.ru)\r || \r || '''
aahihiijd,annoviko/pyclustering,clustering/xmeans/__init__.py,e465c558157608ee325da314a8e03ef51acf5967,STILL_EXISTS,'''\r || \r || Cluster analysis algorithm: X-Means\r || \r || Based on article description:\r ||  - D.Pelleg; A.Moore. \"X-means: Extending K-means with Efficient Estimation of the Number of Clusters\" - 2000.\r || \r || Implementation: Andrei Novikov (spb.andr@yandex.ru)\r || \r || '''
aahihijfa,annoviko/pyclustering,nnet/hhn/__init__.py,2093fc9d465345168dd9e3a7b343aa9766863589,STILL_EXISTS,TODO: LEGION allocation of clusters should be reused for avoiding duplicated code.
aahihijgb,annoviko/pyclustering,nnet/hhn/__init__.py,ea9fa1191bab4fd273587cb6a488dbbcac355605,STILL_EXISTS,TODO: LEGION allocation of clusters should be reused for avoiding duplicated code.
aahihijje,annoviko/pyclustering,clustering/birch/__init__.py,ecb11ae6679d2aaf0c516dd8ca19cf016bca75b9,STILL_EXISTS,'''\r || \r || Cluster analysis algorithm: BIRCH\r || \r || Based on article description:\r ||  - T.Zhang; R.Ramakrishnan; M.Livny. BIRCH: An Efficient Data Clustering Method for Very Large Databases. 1996.\r || \r || Implementation: Andrei Novikov (spb.andr@yandex.ru)\r || \r || '''
aahihjadd,annoviko/pyclustering,support/cftree/__init__.py,5664003762e381908eedbf2ab5e5fab3bb8954b2,STILL_EXISTS,'''\r || \r || Data Structure: CF-Tree\r || \r || Based on book description:\r ||  - M.Zhang; R.Ramakrishnan; M.Livny. BIRCH: An Efficient Data Clustering Method for Very Large Databases. 1996.\r || \r || Implementation: Andrei Novikov (spb.andr@yandex.ru)\r || \r || '''
aahihjebc,annoviko/pyclustering,docs/conf.py,ef91d96841bed95960e5120479de700ecdc8ca07,STILL_EXISTS,Fix unsupported image types using the PIL.
aahihjech,annoviko/pyclustering,docs/breathe/directive/file.py,8bd0a906bafa55b45547a51f40facb67e84a3276,STILL_EXISTS,pass way too much stuff to a helper object to be reasonable.
aahihjeda,annoviko/pyclustering,docs/breathe/directive/index.py,8bd0a906bafa55b45547a51f40facb67e84a3276,STILL_EXISTS,pass way too much stuff to a helper object to be reasonable.
aahihjedh,annoviko/pyclustering,docs/breathe/directives.py,8bd0a906bafa55b45547a51f40facb67e84a3276,STILL_EXISTS,Create it ahead of time as it is cheap and it is ugly to declare it for both exception
aahihjegg,annoviko/pyclustering,docs/breathe/directives.py,8bd0a906bafa55b45547a51f40facb67e84a3276,STILL_EXISTS,abstracted in a far nicer way to avoid repeating so much code
aahihjehg,annoviko/pyclustering,docs/breathe/directives.py,8bd0a906bafa55b45547a51f40facb67e84a3276,STILL_EXISTS,TODO: This methods should be scrapped as they are only called in one place. We should just
aahihjhgd,annoviko/pyclustering,docs/breathe/renderer/rst/doxygen/__init__.py,8bd0a906bafa55b45547a51f40facb67e84a3276,STILL_EXISTS,Probably need a better way to do this as currently we're only doing
aahihjifh,annoviko/pyclustering,docs/breathe/renderer/rst/doxygen/filter.py,8bd0a906bafa55b45547a51f40facb67e84a3276,STILL_EXISTS,\"\"\" || Filters || ------- ||  || Filters are an interesting and somewhat challenging part of the code base. They are used for || two different purposes: ||  ||  - To figure out which nodes in the xml hierarchy to start rendering from. These are called ||    'finder filters' or 'content filters'. This is done before rendering starts. ||  - To figure out which nodes under a selected nodes in the xml hierarchy should be rendered. These ||    are called 'render filters'. This is done during the render process with a test in the ||    DoxygenToRstRendererFactory. ||  ||  || General Implementation || ~~~~~~~~~~~~~~~~~~~~~~ ||  || Filters are essential just tests to see if a node matches certain parameters that are needed to || decide whether or not to include it in some output. ||  || As these filters are declared once and then used on multiple nodes; we model them as object || hierarchies that encapsulate the required test and take a node (with its context) and return True or || False. ||  || If you wanted a test which figures out if a node has the node_type 'memberdef' you might create the || following object hierarchy: ||  ||     node_is_memberdef = InFilter(AttributeAccessor(Node(); 'node_type'); ['memberdef']) ||  || This reads from the inside out; as get the node; then get the node_type attribute from it; and see || if the value of the attribute is in the list ['memberdef']. ||  || The Node() is called a 'Selector'. Parent() is also a selector. It means given the current context; || work with the parent of the current node rather than the node itself. This allows you to frame tests || in terms of a node's parent as well as the node which helps when we want nodes with particular || parents and not others. ||  || The AttributeAccessor() is called an 'Accessor'. It wraps up an attempt to access a particular || attribute on the selected node. There are quite a few different specific accessors but they can || mostly be generalised with the AttributeAccessor. This code has evolved over time and initially the || implementation involved specific accessor classes (which are still used in large parts of it.) ||  || The InFilter() is unsurprisingly called a 'Filter'. There are lots of different filters. Filters || either act on the results of Accessors or on the results of other Filters and they always return || True or False. The AndFilter and the OrFilter can be used to combine the outputs of other Filters || with logical 'and' and 'or' operations. ||  || You can build up some pretty complex expressions with this level of freedom as you || might imagine. The complexity is unfortunate but necessary as the nature of filtering the xml is || quite complex. ||  ||  || Finder Filters || ~~~~~~~~~~~~~~ ||  || The implementation of the filters can change a little depending on how they are called. Finder || filters are called from the breathe.finder.doxygen.index and breathe.finder.doxygen.compound files. || They are called like this: ||  ||     # Descend down the hierarchy ||     # ... ||  ||     if filter_.allow(node_stack): ||         matches.append(self.data_object) ||  ||     # Keep on descending ||     # ... ||  || This means that the result of the filter does not stop us descending down the hierarchy and testing || more nodes. This simplifies the filters as they only have to return true for the exact nodes they || are interested in and they don't have to worry about allowing the iteration down the hierarchy to || continue for nodes which don't match. ||  || An example of a finder filter is: ||  ||     AndFilter( ||         InFilter(NodeTypeAccessor(Node()); [\"compound\"]); ||         InFilter(KindAccessor(Node()); [\"group\"]); ||         InFilter(NameAccessor(Node()); [\"mygroup\"]) ||         ) ||  || This says; return True for all the nodes of node_type 'compound' with 'kind' set to 'group' which || have the name 'mygroup'. It returns false for everything else; but when a node matching this is || found then it is added to the matches list by the code above. ||  || It is therefore relatively easy to write finder filters. If you have two separate node filters like || the one above and you want to match on both of them then you can do: ||  ||     OrFilter( ||         node_filter_1; ||         node_filter_2 ||         ) ||  || To combine them. ||  ||  || Content Filters || ~~~~~~~~~~~~~~~ ||  || Content filters are harder than the finder filters as they are responsible for halting the iteration || down the hierarchy if they return false. This means that if you're interested in memberdef nodes || with a particular attribute then you have to check for that but also include a clause which allows || all other non-memberdef nodes to pass through as you don't want to interrupt them. ||  || This means you end up with filters like this: ||  ||     OrFilter( ||         AndFilter( ||             InFilter(NodeTypeAccessor(Node()); [\"compound\"]); ||             InFilter(KindAccessor(Node()); [\"group\"]); ||             InFilter(NameAccessor(Node()); [\"mygroup\"]) ||             ); ||         NotFilter( ||             AndFilter( ||                 InFilter(NodeTypeAccessor(Node()); [\"compound\"]); ||                 InFilter(KindAccessor(Node()); [\"group\"]); ||                 ) ||             ) ||         ) ||  || Which is to say that we want to let through a compound; with kind group; with name 'mygroup' but || we're also happy if the node is **not** a compund with kind group. Really we just don't want to let || through any compounds with kind group with name other than 'mygroup'. As such; we can rephrase this || as: ||  ||     NotFilter( ||         AndFilter( ||             InFilter(NodeTypeAccessor(Node()); [\"compound\"]); ||             InFilter(KindAccessor(Node()); [\"group\"]); ||             NotFilter(InFilter(NameAccessor(Node()); [\"mygroup\"])) ||             ) ||         ) ||  || Using logical manipulation we can rewrite this as: ||  ||     OrFilter( ||         NotFilter(InFilter(NodeTypeAccessor(Node()); [\"compound\"])); ||         NotFilter(InFilter(KindAccessor(Node()); [\"group\"])); ||         InFilter(NameAccessor(Node()); [\"mygroup\"]) ||         ) ||  || We reads: allow if it isn't a compound; or if it is a compound but doesn't have a 'kind' of 'group'; || but if it is a compound and has a 'kind' of 'group then only allow it if it is named 'mygroup'. ||  ||  || Helper Syntax || ~~~~~~~~~~~~~ ||  || Some of these filter declarations get a little awkward to read and write. They are not laid out in || manner which reads smoothly. Additional helper methods and operator overloads have been introduced || to help with this. ||  || AttributeAccessor objects are created in property methods on the Selector classes so: ||  ||     node.kind ||  || Where node has been declared as a Node() instance. Results in: ||  ||     AttributeAccessor(Node(); 'kind') ||  || The '==' and '!=' operators on the Accessors have been overloaded to return the appropriate filters || so that: ||  ||     node.kind == 'group' ||  || Results in: ||  ||     InFilter(AttributeAccessor(Node(); 'kind'); ['kind']) ||  || We also override the binary 'and' (&); 'or' (|) and 'not' (~) operators in Python to apply || AndFilters; OrFilters and NotFilters respectively. We have to override the binary operators as they || actual 'and'; 'or' and 'not' operators cannot be overridden. So: ||  ||     (node.node_type == 'compound') & (node.name == 'mygroup') ||  || Translates to: ||  ||     AndFilter( ||         InFilter(NodeTypeAccessor(Node()); [\"compound\"])); ||         InFilter(NameAccessor(Node()); [\"mygroup\"]) ||         ) ||  || Where the former is hopefully more readable without sacrificing too much to the abstract magic of || operator overloads. ||  ||  || Operator Precedences & Extra Parenthesis || '''''''''''''''''''''''''''''''''''''''' ||  || As the binary operators have a lower operator precedence than '==' and '!=' and some other operators || we have to include additional parenthesis in the expressions to group them as we want. So instead of || writing: ||  ||     node.node_type == 'compound' & node.name == 'mygroup' ||  || We have to write: ||  ||     (node.node_type == 'compound') & (node.name == 'mygroup') ||  || \"\"\"
aahihjifi,annoviko/pyclustering,docs/breathe/renderer/rst/doxygen/filter.py,8bd0a906bafa55b45547a51f40facb67e84a3276,STILL_EXISTS,Horrible hack to silence errors on filtering unicode objects
aahihjiji,annoviko/pyclustering,docs/breathe/renderer/rst/doxygen/filter.py,8bd0a906bafa55b45547a51f40facb67e84a3276,STILL_EXISTS,I can't fully explain the filtering of descriptions here. More testing needed to figure
aahihjijj,annoviko/pyclustering,docs/breathe/renderer/rst/doxygen/filter.py,8bd0a906bafa55b45547a51f40facb67e84a3276,STILL_EXISTS,out when it is needed. This approach reflects the old code that was here but it wasn't
aahihjjaa,annoviko/pyclustering,docs/breathe/renderer/rst/doxygen/filter.py,8bd0a906bafa55b45547a51f40facb67e84a3276,STILL_EXISTS,commented (my fault.) I wonder if maybe the public and private declarations themselves can
aahihjjdi,annoviko/pyclustering,docs/breathe/renderer/rst/doxygen/index.py,8bd0a906bafa55b45547a51f40facb67e84a3276,STILL_EXISTS,out in a helper function. This feels fairly ugly due to the number of arguments. A forced
aahihjjei,annoviko/pyclustering,docs/breathe/renderer/rst/doxygen/mask.py,8bd0a906bafa55b45547a51f40facb67e84a3276,STILL_EXISTS,Horrible hack to silence errors on filtering unicode objects
aahihjjfa,annoviko/pyclustering,docs/breathe/renderer/rst/doxygen/target.py,8bd0a906bafa55b45547a51f40facb67e84a3276,STILL_EXISTS,TODO: We should really return a docutils warning node here
aahihjjia,annoviko/pyclustering,docs/breathe/renderer/rst/doxygen/domain.py,858bb97b81db6c5be645ebf6a07f4489410dbcd8,STILL_EXISTS,if needed
aahiiabji,annoviko/pyclustering,ccore/tools/cpplint/cpplint.py,807d56af32689a4a03ce6ebff7a87f42ea4e9981,STILL_EXISTS,\"\"\"Does google-lint on c++ files. ||  || The goal of this script is to identify places in the code that *may* || be in non-compliance with google style.  It does not attempt to fix || up these problems -- the point is to educate.  It does also not || attempt to find all problems; or to ensure that everything it does || find is legitimately a problem. ||  || In particular; we can get very confused by \/* and \/\/ inside strings! || We do a small hack; which is to ignore \/\/'s with \"'s after them on the || same line; but it is far from perfect (in either direction). || \"\"\"
aahiiacig,annoviko/pyclustering,ccore/tools/cpplint/cpplint.py,807d56af32689a4a03ce6ebff7a87f42ea4e9981,STILL_EXISTS,needs to move backwards; CheckNextIncludeOrder will raise an error.
aahiiadac,annoviko/pyclustering,ccore/tools/cpplint/cpplint.py,807d56af32689a4a03ce6ebff7a87f42ea4e9981,STILL_EXISTS,In what way are we counting errors?
aahiiadcf,annoviko/pyclustering,ccore/tools/cpplint/cpplint.py,807d56af32689a4a03ce6ebff7a87f42ea4e9981,STILL_EXISTS,statements better.
aahiiaede,annoviko/pyclustering,ccore/tools/cpplint/cpplint.py,807d56af32689a4a03ce6ebff7a87f42ea4e9981,STILL_EXISTS,To verify that the file ends in \ || ; we just have to make sure the
aahiiaggj,annoviko/pyclustering,ccore/tools/cpplint/cpplint.py,807d56af32689a4a03ce6ebff7a87f42ea4e9981,STILL_EXISTS,Checks for common mistakes in TODO comments.
aahiiaghg,annoviko/pyclustering,ccore/tools/cpplint/cpplint.py,807d56af32689a4a03ce6ebff7a87f42ea4e9981,STILL_EXISTS,Found DISALLOW* macro outside a class declaration; or perhaps it
aahiiahci,annoviko/pyclustering,ccore/tools/cpplint/cpplint.py,807d56af32689a4a03ce6ebff7a87f42ea4e9981,STILL_EXISTS,You shouldn't have spaces before your brackets; except maybe after
aahiiahef,annoviko/pyclustering,ccore/tools/cpplint/cpplint.py,807d56af32689a4a03ce6ebff7a87f42ea4e9981,STILL_EXISTS,though; so we punt on this one for now.  TODO.
aahiiahib,annoviko/pyclustering,ccore/tools/cpplint/cpplint.py,807d56af32689a4a03ce6ebff7a87f42ea4e9981,STILL_EXISTS,There shouldn't be space around unary operators
aahiiajga,annoviko/pyclustering,ccore/tools/cpplint/cpplint.py,807d56af32689a4a03ce6ebff7a87f42ea4e9981,STILL_EXISTS,private; but needed to be public for implementation reasons.
aahiiajgb,annoviko/pyclustering,ccore/tools/cpplint/cpplint.py,807d56af32689a4a03ce6ebff7a87f42ea4e9981,STILL_EXISTS,Also ignores cases where the previous line ends with a backslash as can be
aahiibagb,annoviko/pyclustering,ccore/tools/cpplint/cpplint.py,807d56af32689a4a03ce6ebff7a87f42ea4e9981,STILL_EXISTS,We implement a whitelist of safe macros instead of a blacklist of
aahiibbbe,annoviko/pyclustering,ccore/tools/cpplint/cpplint.py,807d56af32689a4a03ce6ebff7a87f42ea4e9981,STILL_EXISTS,Logical and\/or operators.  This means the expression
aahiibbce,annoviko/pyclustering,ccore/tools/cpplint/cpplint.py,807d56af32689a4a03ce6ebff7a87f42ea4e9981,STILL_EXISTS,is more efficient when the operands are longer than a single
aahiibbeh,annoviko/pyclustering,ccore/tools/cpplint/cpplint.py,807d56af32689a4a03ce6ebff7a87f42ea4e9981,STILL_EXISTS,it provides a way to workaround this warning for people who use
aahiibcab,annoviko/pyclustering,ccore/tools/cpplint/cpplint.py,807d56af32689a4a03ce6ebff7a87f42ea4e9981,STILL_EXISTS,naming convention but not the include convention.
aahiibcdf,annoviko/pyclustering,ccore/tools/cpplint/cpplint.py,807d56af32689a4a03ce6ebff7a87f42ea4e9981,STILL_EXISTS,A call-by-reference parameter ends with '& identifier'.
aahiibcdg,annoviko/pyclustering,ccore/tools/cpplint/cpplint.py,807d56af32689a4a03ce6ebff7a87f42ea4e9981,STILL_EXISTS,A call-by-const-reference parameter either ends with 'const& identifier'
aahiibcgd,annoviko/pyclustering,ccore/tools/cpplint/cpplint.py,807d56af32689a4a03ce6ebff7a87f42ea4e9981,STILL_EXISTS,convention of the whole function to process multiple line to handle it.
aahiibefc,annoviko/pyclustering,ccore/tools/cpplint/cpplint.py,807d56af32689a4a03ce6ebff7a87f42ea4e9981,STILL_EXISTS,Function(int \/*unused_param*\/);
aahiibfbc,annoviko/pyclustering,ccore/tools/cpplint/cpplint.py,807d56af32689a4a03ce6ebff7a87f42ea4e9981,STILL_EXISTS,the declarator ends and where the virt-specifier starts to avoid
aahiibfeb,annoviko/pyclustering,ccore/tools/cpplint/cpplint.py,807d56af32689a4a03ce6ebff7a87f42ea4e9981,STILL_EXISTS,Support the UNIX convention of using \"-\" for stdin.  Note that
aahiibfga,annoviko/pyclustering,ccore/tools/cpplint/cpplint.py,807d56af32689a4a03ce6ebff7a87f42ea4e9981,STILL_EXISTS,We can't depend on os.linesep to determine what the desired
aahiibijh,annoviko/pyclustering,pyclustering/ut/__init__.py,07bc9c1d998b86468c7d1af5b5105af5c3f4f81e,STILL_EXISTS,Add path to pyclustering package (much better to set PYTHONPATH).
aahiibjae,annoviko/pyclustering,pyclustering/cluster/clarans.py,13c0be839f173677fdb8a00a1b68f6ca0a3fd171,STILL_EXISTS,try to find better medoids
aahiichia,annoviko/pyclustering,pyclustering/cluster/xmeans.py,7c264beae02d10e3ca337db95efc3a3a195b3e1b,221b027b99ca9bad60f4127740ed666575a6ff75,very poor; therefore square root is used to improved.
aahiidagg,annoviko/pyclustering,pyclustering/tests/tests_runner.py,941fee703090fd3b1d2de82c1b74e959c3b4c300,74b00e7835ae3fc112d2befac4421a2fe8278146,Add path to pyclustering package (much better to set PYTHONPATH); but just to be sure that at least unit-tests can be run.
aahiidehb,annoviko/pyclustering,pyclustering/cluster/ema.py,20409bb98e10040e077a6518f617d284f39be52c,2a501853e51f79e037812803f7285e95d651d0c4,TODO: draw ellipes for each cluster using covariance matrix
aahiidejd,annoviko/pyclustering,pyclustering/cluster/ema.py,e2bd5ebce715cbd0094d98d883bbc41c7960a9c1,bb75590ad5d958070b1a19129130a87c68c759e6,TODO: draw ellipes for each cluster using covariance matrix
aahiidjej,annoviko/pyclustering,pyclustering/utils/tests/integration/it_metric.py,171a2a693170fce56417446b835538c1594596d5,STILL_EXISTS,TODO: doesn't work for some platforms.
aahiidjgc,annoviko/pyclustering,pyclustering/cluster/bang.py,f6cd2010f6f6419d6c1c1d9f74168e2d84edfb8c,a76884223a5cc98fa4054758e36780953c808d38,TODO: Store blocks in block directory and control them via directory
aahiidjgd,annoviko/pyclustering,pyclustering/cluster/bang.py,f6cd2010f6f6419d6c1c1d9f74168e2d84edfb8c,STILL_EXISTS,TODO: is_neighbor should consider density!
aahiiebdf,annoviko/pyclustering,pyclustering/nnet/som.py,bd37a51f1e91a6667dd727e83b7a597e621da93a,STILL_EXISTS,# TODO: do not use data
aahiiedii,annoviko/pyclustering,pyclustering/container/kdtree.py,2d4a03ab9ec73f6c5104af73130a0654ff83614b,STILL_EXISTS,TODO: optimize by binary search - no need to use O(n)
aahiieebi,annoviko/pyclustering,pyclustering/cluster/kmedoids.py,1ad1b57b5e140fff6d0b20b014cc97eafcf4e76e,STILL_EXISTS,TODO: calculate it on C++ side as well
aahiieecb,kengz/SLM-Lab,unity_lab/lib/viz.py,2cf8b4bf11d6d10b9dea4bdb9d692c7724ebf6e4,STILL_EXISTS,TODO customize line style
aahiieecf,kengz/SLM-Lab,unity_lab/lib/viz.py,18660550973bb8b6481b6f113235b5c9a7b9862e,STILL_EXISTS,TODO customize line style
aahiieecg,kengz/SLM-Lab,unity_lab/lib/viz.py,8c9d0c7be9e96901160200db8d8fdc424cb4e208,STILL_EXISTS,''' || Data visualization library || TODO pie; swarm; box plots || TODO for stacked area; just use df cumsum || '''
aahiieecj,kengz/SLM-Lab,unity_lab/lib/util.py,dbd416214da3049cd1ba80649f07b973d070bb28,2aedab7c65ea1f5165068e0f86d7568e44b043bc,TODO auto path resolver
aahiieeda,kengz/SLM-Lab,unity_lab/lib/util.py,dbd416214da3049cd1ba80649f07b973d070bb28,6df16c2adab9702a46d1b0de988ac4a161783f60,TODO logger; with dedent
aahiieedb,kengz/SLM-Lab,unity_lab/lib/util.py,dbd416214da3049cd1ba80649f07b973d070bb28,5759edf0c8b9443cf08ac3c79521dedd6f9d9c43,TODO auto file reader; resolve to sensible format
aahiieedc,kengz/SLM-Lab,unity_lab/lib/util.py,dbd416214da3049cd1ba80649f07b973d070bb28,STILL_EXISTS,TODO auto file writer; resolve to sensible format
aahiieedd,kengz/SLM-Lab,unity_lab/lib/util.py,dbd416214da3049cd1ba80649f07b973d070bb28,c72873ab9fe0189e1c0e54b3f555a0dd0a4eb29f,TODO refer to old exp util
aahiieede,kengz/SLM-Lab,unity_lab/lib/util.py,dbd416214da3049cd1ba80649f07b973d070bb28,c72873ab9fe0189e1c0e54b3f555a0dd0a4eb29f,TODO experiment controller\/util vs generic util
aahiieedf,kengz/SLM-Lab,unity_lab/lib/util.py,dbd416214da3049cd1ba80649f07b973d070bb28,c72873ab9fe0189e1c0e54b3f555a0dd0a4eb29f,TODO new exp id needs to reflect DAG structure: its ancestors; use neo4j to store
aahiieedg,kengz/SLM-Lab,unity_lab/lib/util.py,dbd416214da3049cd1ba80649f07b973d070bb28,c72873ab9fe0189e1c0e54b3f555a0dd0a4eb29f,TODO unit tests
aahiieehb,kengz/SLM-Lab,test/test_data.py,64a19bff3dd59d33af21e7c30fb321d5d3107d25,STILL_EXISTS,TODO: implement basic version
aahiieehc,kengz/SLM-Lab,test/test_data.py,64a19bff3dd59d33af21e7c30fb321d5d3107d25,STILL_EXISTS,TODO: decide on format for dataloader and implement basic version
aahiieehd,kengz/SLM-Lab,test/test_nn.py,64a19bff3dd59d33af21e7c30fb321d5d3107d25,STILL_EXISTS,TODO: e.g. loss is not CrossEntropy when output has one dimension
aahiieehg,kengz/SLM-Lab,slm_lab/lib/util.py,09f508d9a2b2e545eae3fa81cc37ce1ee71a8c65,8f5f62f14e777cf2f88fe71360581be95d643163,TODO switch to abs path of file
aahiieeie,kengz/SLM-Lab,slm_lab/notebook/intro_unity.py,09f508d9a2b2e545eae3fa81cc37ce1ee71a8c65,f7c257ebab143ec83f1e1ffbe8dc49e3b43a628e,TODO move interface logic from openai lab
aahiieeih,kengz/SLM-Lab,slm_lab/agent/memory/base_memory.py,7740b54028a8ebef504ea4e554446d35e0d3994b,STILL_EXISTS,Move head pointer. Wrap around if necessary
aahiieejb,kengz/SLM-Lab,slm_lab/notebook/intro_unity.py,7740b54028a8ebef504ea4e554446d35e0d3994b,STILL_EXISTS,TODO update doc above
aahiieejf,kengz/SLM-Lab,test/test_memory.py,7740b54028a8ebef504ea4e554446d35e0d3994b,STILL_EXISTS,TODO: test_sample_dist
aahiieejg,kengz/SLM-Lab,test/test_memory.py,7740b54028a8ebef504ea4e554446d35e0d3994b,STILL_EXISTS,TODO: implement test_update_priorities
aahiiefbe,kengz/SLM-Lab,slm_lab/agent/__init__.py,8f5f62f14e777cf2f88fe71360581be95d643163,STILL_EXISTS,TODO need proper space resolution for multi-env
aahiiefbf,kengz/SLM-Lab,slm_lab/agent/__init__.py,8f5f62f14e777cf2f88fe71360581be95d643163,5b263d7d087bf0ea8604ac3904c25d9234aa5480,TODO also spec needs to specify AEB space and bodies
aahiiefbg,kengz/SLM-Lab,slm_lab/agent/__init__.py,8f5f62f14e777cf2f88fe71360581be95d643163,4b802d623b244ae8bbb52a518abd782bd1e05d6d,TODO do body num here
aahiiefbh,kengz/SLM-Lab,slm_lab/agent/__init__.py,8f5f62f14e777cf2f88fe71360581be95d643163,4b802d623b244ae8bbb52a518abd782bd1e05d6d,analogously; do other dim counts as needed from env
aahiiefbi,kengz/SLM-Lab,slm_lab/agent/__init__.py,8f5f62f14e777cf2f88fe71360581be95d643163,cd3704616759710b50062abf3b8e8041b4b7c95f,TODO delegate a copy of variable like action_dim to agent too
aahiiefbj,kengz/SLM-Lab,slm_lab/agent/__init__.py,8f5f62f14e777cf2f88fe71360581be95d643163,STILL_EXISTS,TODO save or smth
aahiiefcd,kengz/SLM-Lab,slm_lab/env/__init__.py,8f5f62f14e777cf2f88fe71360581be95d643163,STILL_EXISTS,TODO should really move into somewhere in experiment module; cuz agent and environment modules really should be atomic and not worry much about multiplicities
aahiiefce,kengz/SLM-Lab,slm_lab/env/__init__.py,8f5f62f14e777cf2f88fe71360581be95d643163,cd3704616759710b50062abf3b8e8041b4b7c95f,TODO or just set properties for all these; no method
aahiiefcf,kengz/SLM-Lab,slm_lab/env/__init__.py,8f5f62f14e777cf2f88fe71360581be95d643163,STILL_EXISTS,TODO perhaps do extension like above again
aahiiefcg,kengz/SLM-Lab,slm_lab/env/__init__.py,8f5f62f14e777cf2f88fe71360581be95d643163,cd3704616759710b50062abf3b8e8041b4b7c95f,TODO expose brain methods properly to env
aahiiefci,kengz/SLM-Lab,slm_lab/env/__init__.py,8f5f62f14e777cf2f88fe71360581be95d643163,STILL_EXISTS,TODO get state from env_info
aahiiefdc,kengz/SLM-Lab,slm_lab/env/__init__.py,8f5f62f14e777cf2f88fe71360581be95d643163,5b263d7d087bf0ea8604ac3904c25d9234aa5480,TODO split subclass to handle unity specific logic;
aahiiefde,kengz/SLM-Lab,slm_lab/env/__init__.py,8f5f62f14e777cf2f88fe71360581be95d643163,cd3704616759710b50062abf3b8e8041b4b7c95f,TODO actually shd do a single-brain wrapper instead
aahiiefed,kengz/SLM-Lab,slm_lab/experiment/control.py,8f5f62f14e777cf2f88fe71360581be95d643163,32c84e8b245ce5bf8b299451ce1290df5d4fb0dc,TODO hook monitor to agent; env; then per update; auto fetches all that is in background
aahiiefee,kengz/SLM-Lab,slm_lab/experiment/control.py,8f5f62f14e777cf2f88fe71360581be95d643163,STILL_EXISTS,TODO call update in session; trial; experiment loops to collect data visible there too; for unit_data
aahiiefef,kengz/SLM-Lab,slm_lab/experiment/control.py,8f5f62f14e777cf2f88fe71360581be95d643163,STILL_EXISTS,TODO missing: index in AEB space
aahiiefeh,kengz/SLM-Lab,slm_lab/experiment/control.py,8f5f62f14e777cf2f88fe71360581be95d643163,STILL_EXISTS,TODO also missing: index in AEB space; train_mode
aahiiefej,kengz/SLM-Lab,slm_lab/experiment/control.py,8f5f62f14e777cf2f88fe71360581be95d643163,32c84e8b245ce5bf8b299451ce1290df5d4fb0dc,TODO link in AEB space properly
aahiieffa,kengz/SLM-Lab,slm_lab/experiment/control.py,8f5f62f14e777cf2f88fe71360581be95d643163,STILL_EXISTS,TODO save agent and shits
aahiieffb,kengz/SLM-Lab,slm_lab/experiment/control.py,8f5f62f14e777cf2f88fe71360581be95d643163,32c84e8b245ce5bf8b299451ce1290df5d4fb0dc,TODO catch all to close Unity when py runtime fails
aahiieffc,kengz/SLM-Lab,slm_lab/experiment/control.py,8f5f62f14e777cf2f88fe71360581be95d643163,16e9d8fe38d7d3b7edaf20fb5fd3413e7f865c93,TODO generalize and make state to include observables
aahiiefff,kengz/SLM-Lab,slm_lab/experiment/control.py,8f5f62f14e777cf2f88fe71360581be95d643163,STILL_EXISTS,TODO monitor shd update session data from episode data
aahiieffg,kengz/SLM-Lab,slm_lab/experiment/control.py,8f5f62f14e777cf2f88fe71360581be95d643163,32c84e8b245ce5bf8b299451ce1290df5d4fb0dc,TODO compose episode data from monitor update; is just session_data[episode]
aahiieffh,kengz/SLM-Lab,slm_lab/experiment/control.py,8f5f62f14e777cf2f88fe71360581be95d643163,32c84e8b245ce5bf8b299451ce1290df5d4fb0dc,TODO universal index in spec: experiment; trial; session; then agent; env; bodies
aahiieffi,kengz/SLM-Lab,slm_lab/experiment/control.py,8f5f62f14e777cf2f88fe71360581be95d643163,STILL_EXISTS,TODO spec resolver for params per trial
aahiieffj,kengz/SLM-Lab,slm_lab/experiment/control.py,8f5f62f14e777cf2f88fe71360581be95d643163,32c84e8b245ce5bf8b299451ce1290df5d4fb0dc,TODO spec key checker and defaulting mechanism; by merging a dict of congruent shape with default values
aahiiefga,kengz/SLM-Lab,slm_lab/experiment/control.py,8f5f62f14e777cf2f88fe71360581be95d643163,32c84e8b245ce5bf8b299451ce1290df5d4fb0dc,TODO AEB space resolver
aahiiefgd,kengz/SLM-Lab,slm_lab/experiment/hyperdata.py,8f5f62f14e777cf2f88fe71360581be95d643163,STILL_EXISTS,TODO rename hyper-everthing without hyper
aahiiefge,kengz/SLM-Lab,slm_lab/experiment/hyperdata.py,8f5f62f14e777cf2f88fe71360581be95d643163,STILL_EXISTS,TODO maybe stop at timestep since episode is the lowest sensible refinement of data without bloat
aahiiefgf,kengz/SLM-Lab,slm_lab/experiment/hyperdata.py,8f5f62f14e777cf2f88fe71360581be95d643163,STILL_EXISTS,TODO maybe not create new dict per episode and timestep for complexity
aahiiefgg,kengz/SLM-Lab,slm_lab/experiment/hyperdata.py,8f5f62f14e777cf2f88fe71360581be95d643163,STILL_EXISTS,TODO or every stage the class instance shd hold its own hyperindex that ends at its axis
aahiiefgh,kengz/SLM-Lab,slm_lab/experiment/hyperdata.py,8f5f62f14e777cf2f88fe71360581be95d643163,STILL_EXISTS,TODO increment pattern in break down in AEB
aahiiefib,kengz/SLM-Lab,main.py,f7c257ebab143ec83f1e1ffbe8dc49e3b43a628e,cd3704616759710b50062abf3b8e8041b4b7c95f,TODO metaspec to specify specs to run; can be source from evolution suggestion
aahiiefic,kengz/SLM-Lab,main.py,f7c257ebab143ec83f1e1ffbe8dc49e3b43a628e,cd3704616759710b50062abf3b8e8041b4b7c95f,TODO set proper pattern
aahiiefid,kengz/SLM-Lab,main.py,f7c257ebab143ec83f1e1ffbe8dc49e3b43a628e,32c84e8b245ce5bf8b299451ce1290df5d4fb0dc,TODO temp set monitor method in session
aahiiefii,kengz/SLM-Lab,slm_lab/agent/__init__.py,f7c257ebab143ec83f1e1ffbe8dc49e3b43a628e,4b802d623b244ae8bbb52a518abd782bd1e05d6d,TODO auto AEB space resolver from atomic method
aahiiefjc,kengz/SLM-Lab,slm_lab/agent/__init__.py,f7c257ebab143ec83f1e1ffbe8dc49e3b43a628e,901cc588fee90215d2e2831bd4a3e7481b307ba7,TODO save model
aahiiefjd,kengz/SLM-Lab,slm_lab/agent/__init__.py,f7c257ebab143ec83f1e1ffbe8dc49e3b43a628e,4b802d623b244ae8bbb52a518abd782bd1e05d6d,TODO AEB space resolver; lineup index
aahiiegae,kengz/SLM-Lab,slm_lab/agent/__init__.py,f7c257ebab143ec83f1e1ffbe8dc49e3b43a628e,STILL_EXISTS,TODO save or smth
aahiiegaf,kengz/SLM-Lab,slm_lab/env/__init__.py,f7c257ebab143ec83f1e1ffbe8dc49e3b43a628e,cd3704616759710b50062abf3b8e8041b4b7c95f,TODO anticipate multi-agents for AEB space
aahiiegag,kengz/SLM-Lab,slm_lab/experiment/control.py,f7c257ebab143ec83f1e1ffbe8dc49e3b43a628e,STILL_EXISTS,TODO monitor has to be top level outside of Experiment
aahiiegba,kengz/SLM-Lab,slm_lab/agent/__init__.py,4b802d623b244ae8bbb52a518abd782bd1e05d6d,5b263d7d087bf0ea8604ac3904c25d9234aa5480,TODO need a mechanism that compose the components together using spec
aahiiegbb,kengz/SLM-Lab,slm_lab/agent/__init__.py,4b802d623b244ae8bbb52a518abd782bd1e05d6d,cd3704616759710b50062abf3b8e8041b4b7c95f,TODO AEB space resolver pending; needs to be powerful enuf to for auto-architecture; action space; body num resolution; other dim counts from env
aahiiegbc,kengz/SLM-Lab,slm_lab/agent/__init__.py,4b802d623b244ae8bbb52a518abd782bd1e05d6d,STILL_EXISTS,TODO implement generic method
aahiiegcj,kengz/SLM-Lab,slm_lab/agent/algorithm/base.py,4b802d623b244ae8bbb52a518abd782bd1e05d6d,84ddc96b6974bedae6b81e1b79abed40c34ad164,TODO auto AEB space resolver from atomic method
aahiiegdf,kengz/SLM-Lab,slm_lab/experiment/control.py,4b802d623b244ae8bbb52a518abd782bd1e05d6d,32c84e8b245ce5bf8b299451ce1290df5d4fb0dc,TODO link in AEB space properly
aahiiegdh,kengz/SLM-Lab,slm_lab/experiment/data_space.py,4b802d623b244ae8bbb52a518abd782bd1e05d6d,STILL_EXISTS,TODO maybe stop at timestep since episode is the lowest sensible refinement of data without bloat
aahiiegdi,kengz/SLM-Lab,slm_lab/experiment/data_space.py,4b802d623b244ae8bbb52a518abd782bd1e05d6d,STILL_EXISTS,TODO maybe not create new dict per episode and timestep for complexity
aahiiegdj,kengz/SLM-Lab,slm_lab/experiment/data_space.py,4b802d623b244ae8bbb52a518abd782bd1e05d6d,STILL_EXISTS,TODO or every stage the class instance shd hold its own data_coor that ends at its axis
aahiiegea,kengz/SLM-Lab,slm_lab/experiment/data_space.py,4b802d623b244ae8bbb52a518abd782bd1e05d6d,STILL_EXISTS,TODO increment pattern in break down in AEB
aahiiegei,kengz/SLM-Lab,slm_lab/experiment/control.py,f70c83bd318fd3381957f6e5e570d9ee4c362d50,16e9d8fe38d7d3b7edaf20fb5fd3413e7f865c93,TODO session data checker method
aahiiegej,kengz/SLM-Lab,slm_lab/experiment/control.py,f70c83bd318fd3381957f6e5e570d9ee4c362d50,16e9d8fe38d7d3b7edaf20fb5fd3413e7f865c93,TODO trial data checker method
aahiiegfa,kengz/SLM-Lab,slm_lab/experiment/control.py,f70c83bd318fd3381957f6e5e570d9ee4c362d50,16e9d8fe38d7d3b7edaf20fb5fd3413e7f865c93,TODO exp data checker method
aahiiegfc,kengz/SLM-Lab,test/experiment/test_control.py,f70c83bd318fd3381957f6e5e570d9ee4c362d50,STILL_EXISTS,TODO test control steps in detail when complete
aahiiegfd,kengz/SLM-Lab,test/experiment/test_control.py,f70c83bd318fd3381957f6e5e570d9ee4c362d50,ba34b5215fa710d95f4b5c73389f86efe573c013,TODO session data checker method
aahiiegfe,kengz/SLM-Lab,test/experiment/test_control.py,f70c83bd318fd3381957f6e5e570d9ee4c362d50,ba34b5215fa710d95f4b5c73389f86efe573c013,TODO trial data checker method
aahiiegfj,kengz/SLM-Lab,slm_lab/agent/__init__.py,32c84e8b245ce5bf8b299451ce1290df5d4fb0dc,5b263d7d087bf0ea8604ac3904c25d9234aa5480,TODO only reference via body
aahiiegga,kengz/SLM-Lab,slm_lab/agent/__init__.py,32c84e8b245ce5bf8b299451ce1290df5d4fb0dc,8c17debc9dc6552da8c317c0c852b55f03c80c61,TODO ok need architecture spec for each agent: disjoint or joint; time or space multiplicity
aahiieggc,kengz/SLM-Lab,slm_lab/agent/__init__.py,32c84e8b245ce5bf8b299451ce1290df5d4fb0dc,5b263d7d087bf0ea8604ac3904c25d9234aa5480,TODO then for high level call; update active_eb_coor (switch focus body); call atomic methods
aahiieggi,kengz/SLM-Lab,slm_lab/agent/__init__.py,32c84e8b245ce5bf8b299451ce1290df5d4fb0dc,STILL_EXISTS,TODO tmp use Body in the space
aahiieggj,kengz/SLM-Lab,slm_lab/agent/__init__.py,32c84e8b245ce5bf8b299451ce1290df5d4fb0dc,425f3a1ae83c6a5d76d73a3750ffc0e0262c5fed,TODO also resolve architecture and data input; output dims via some architecture spec
aahiiegha,kengz/SLM-Lab,slm_lab/agent/__init__.py,32c84e8b245ce5bf8b299451ce1290df5d4fb0dc,STILL_EXISTS,TODO tmp make act across bodies
aahiieghb,kengz/SLM-Lab,slm_lab/agent/__init__.py,32c84e8b245ce5bf8b299451ce1290df5d4fb0dc,STILL_EXISTS,TODO count timestep; episode; absolute number of timesteps
aahiieghc,kengz/SLM-Lab,slm_lab/agent/__init__.py,32c84e8b245ce5bf8b299451ce1290df5d4fb0dc,STILL_EXISTS,TODO rename method args to space
aahiieghd,kengz/SLM-Lab,slm_lab/agent/__init__.py,32c84e8b245ce5bf8b299451ce1290df5d4fb0dc,cd3704616759710b50062abf3b8e8041b4b7c95f,TODO tmp; resolve later from AEB
aahiieghj,kengz/SLM-Lab,slm_lab/env/__init__.py,32c84e8b245ce5bf8b299451ce1290df5d4fb0dc,5b263d7d087bf0ea8604ac3904c25d9234aa5480,TODO implement
aahiiegia,kengz/SLM-Lab,slm_lab/env/__init__.py,32c84e8b245ce5bf8b299451ce1290df5d4fb0dc,cd3704616759710b50062abf3b8e8041b4b7c95f,TODO tmp
aahiiegib,kengz/SLM-Lab,slm_lab/env/__init__.py,32c84e8b245ce5bf8b299451ce1290df5d4fb0dc,cd3704616759710b50062abf3b8e8041b4b7c95f,TODO make spread across body
aahiiegic,kengz/SLM-Lab,slm_lab/env/__init__.py,32c84e8b245ce5bf8b299451ce1290df5d4fb0dc,cd3704616759710b50062abf3b8e8041b4b7c95f,TODO tmp make across bodies
aahiiegid,kengz/SLM-Lab,slm_lab/env/__init__.py,32c84e8b245ce5bf8b299451ce1290df5d4fb0dc,5b263d7d087bf0ea8604ac3904c25d9234aa5480,TODO rename method args to space
aahiiegie,kengz/SLM-Lab,slm_lab/env/__init__.py,32c84e8b245ce5bf8b299451ce1290df5d4fb0dc,STILL_EXISTS,TODO common refinement for max_timestep in space
aahiiegif,kengz/SLM-Lab,slm_lab/env/__init__.py,32c84e8b245ce5bf8b299451ce1290df5d4fb0dc,5b263d7d087bf0ea8604ac3904c25d9234aa5480,also an idle logic for env that ends earlier than the other
aahiiegig,kengz/SLM-Lab,slm_lab/env/__init__.py,32c84e8b245ce5bf8b299451ce1290df5d4fb0dc,cd3704616759710b50062abf3b8e8041b4b7c95f,TODO tmp hack till env properly carries its own max timestep
aahiiegih,kengz/SLM-Lab,slm_lab/env/__init__.py,32c84e8b245ce5bf8b299451ce1290df5d4fb0dc,cd3704616759710b50062abf3b8e8041b4b7c95f,TODO tmp; resolve later from AEB
aahiiegii,kengz/SLM-Lab,slm_lab/env/__init__.py,32c84e8b245ce5bf8b299451ce1290df5d4fb0dc,84ddc96b6974bedae6b81e1b79abed40c34ad164,TODO use DataSpace class; with np array
aahiiegij,kengz/SLM-Lab,slm_lab/experiment/control.py,32c84e8b245ce5bf8b299451ce1290df5d4fb0dc,STILL_EXISTS,TODO put resolved space from spec into monitor.dataspace
aahiiegja,kengz/SLM-Lab,slm_lab/experiment/control.py,32c84e8b245ce5bf8b299451ce1290df5d4fb0dc,16e9d8fe38d7d3b7edaf20fb5fd3413e7f865c93,TODO generalize and make state to include observables
aahiiegjc,kengz/SLM-Lab,slm_lab/experiment/control.py,32c84e8b245ce5bf8b299451ce1290df5d4fb0dc,STILL_EXISTS,TODO common partition of timestep
aahiiehai,kengz/SLM-Lab,slm_lab/experiment/monitor.py,32c84e8b245ce5bf8b299451ce1290df5d4fb0dc,ba34b5215fa710d95f4b5c73389f86efe573c013,TODO - plug to NoSQL graph db; using graphql notation; and data backup
aahiiehaj,kengz/SLM-Lab,slm_lab/experiment/monitor.py,32c84e8b245ce5bf8b299451ce1290df5d4fb0dc,b7db501d7e0b8404f13be86ed6bcc96f2da5861f,TODO - data_space viewer and stats method for evaluating and planning experiments
aahiiehba,kengz/SLM-Lab,slm_lab/experiment/monitor.py,32c84e8b245ce5bf8b299451ce1290df5d4fb0dc,5b263d7d087bf0ea8604ac3904c25d9234aa5480,TODO change to ensure coorlist is of tuples; add assert to coor usage
aahiiehbc,kengz/SLM-Lab,slm_lab/experiment/monitor.py,32c84e8b245ce5bf8b299451ce1290df5d4fb0dc,5b263d7d087bf0ea8604ac3904c25d9234aa5480,TODO need to assert when accessing index data_proj[idx] idx != -1
aahiiehbd,kengz/SLM-Lab,slm_lab/experiment/monitor.py,32c84e8b245ce5bf8b299451ce1290df5d4fb0dc,cd3704616759710b50062abf3b8e8041b4b7c95f,TODO at init after AEB resolution and projection; check if all bodies can fit in env
aahiiehbe,kengz/SLM-Lab,slm_lab/experiment/monitor.py,32c84e8b245ce5bf8b299451ce1290df5d4fb0dc,cd3704616759710b50062abf3b8e8041b4b7c95f,TODO AEB needs to check agent output dim is sufficient
aahiiehbf,kengz/SLM-Lab,slm_lab/experiment/monitor.py,32c84e8b245ce5bf8b299451ce1290df5d4fb0dc,STILL_EXISTS,TODO prolly keep episodic; timestep historic data series
aahiiehbg,kengz/SLM-Lab,slm_lab/experiment/monitor.py,32c84e8b245ce5bf8b299451ce1290df5d4fb0dc,STILL_EXISTS,TODO might wanna keep a history before replacement; shove to DB
aahiiehbh,kengz/SLM-Lab,slm_lab/experiment/monitor.py,32c84e8b245ce5bf8b299451ce1290df5d4fb0dc,STILL_EXISTS,TODO construct the AEB space proj to A; E from spec
aahiiehce,kengz/SLM-Lab,slm_lab/experiment/monitor.py,32c84e8b245ce5bf8b299451ce1290df5d4fb0dc,0710d1af3e795e0e4bae0c8dd96fdb1d42aedd9d,TODO hook monitor to agent; env; then per update; auto fetches all that is in background
aahiiehcf,kengz/SLM-Lab,slm_lab/experiment/monitor.py,32c84e8b245ce5bf8b299451ce1290df5d4fb0dc,0710d1af3e795e0e4bae0c8dd96fdb1d42aedd9d,TODO call update in session; trial; experiment loops to collect data visible there too; for unit_data
aahiiehcg,kengz/SLM-Lab,slm_lab/experiment/monitor.py,32c84e8b245ce5bf8b299451ce1290df5d4fb0dc,STILL_EXISTS,TODO create like monitor; for experiment level
aahiiehdc,kengz/SLM-Lab,slm_lab/spec/spec_util.py,32c84e8b245ce5bf8b299451ce1290df5d4fb0dc,cd3704616759710b50062abf3b8e8041b4b7c95f,TODO check aeb_space body index increasing for the same AE pair (no skip)
aahiiehdd,kengz/SLM-Lab,slm_lab/agent/__init__.py,5b263d7d087bf0ea8604ac3904c25d9234aa5480,cd3704616759710b50062abf3b8e8041b4b7c95f,TODO tmp
aahiiehee,kengz/SLM-Lab,slm_lab/agent/__init__.py,5b263d7d087bf0ea8604ac3904c25d9234aa5480,STILL_EXISTS,TODO rename method args to space
aahiiehhj,kengz/SLM-Lab,slm_lab/env/__init__.py,cd3704616759710b50062abf3b8e8041b4b7c95f,STILL_EXISTS,TODO merge OpenAI Env in SLM Lab
aahiieiaa,kengz/SLM-Lab,slm_lab/experiment/control.py,cd3704616759710b50062abf3b8e8041b4b7c95f,STILL_EXISTS,TODO metaspec to specify specs to run; can be sourced from evolution suggestion
aahiieibf,kengz/SLM-Lab,slm_lab/agent/algorithm/algorithm_util.py,425f3a1ae83c6a5d76d73a3750ffc0e0262c5fed,STILL_EXISTS,TODO store one hot
aahiieibg,kengz/SLM-Lab,slm_lab/agent/algorithm/algorithm_util.py,425f3a1ae83c6a5d76d73a3750ffc0e0262c5fed,STILL_EXISTS,TODO discrete int
aahiieibh,kengz/SLM-Lab,slm_lab/agent/algorithm/algorithm_util.py,425f3a1ae83c6a5d76d73a3750ffc0e0262c5fed,STILL_EXISTS,TODO implement act_with_boltzmann
aahiieicc,kengz/SLM-Lab,slm_lab/agent/algorithm/dqn.py,425f3a1ae83c6a5d76d73a3750ffc0e0262c5fed,8c17debc9dc6552da8c317c0c852b55f03c80c61,TODO generalize
aahiieicd,kengz/SLM-Lab,slm_lab/agent/algorithm/dqn.py,425f3a1ae83c6a5d76d73a3750ffc0e0262c5fed,STILL_EXISTS,TODO three nets for different part of Q function
aahiieiec,kengz/SLM-Lab,slm_lab/agent/algorithm/dqn.py,425f3a1ae83c6a5d76d73a3750ffc0e0262c5fed,STILL_EXISTS,TODO Fix for training iters; docstring
aahiieiee,kengz/SLM-Lab,slm_lab/agent/memory/base.py,425f3a1ae83c6a5d76d73a3750ffc0e0262c5fed,16e9d8fe38d7d3b7edaf20fb5fd3413e7f865c93,TODO this is per body; need to generalize
aahiieief,kengz/SLM-Lab,slm_lab/agent/memory/base.py,425f3a1ae83c6a5d76d73a3750ffc0e0262c5fed,70db35897c3a4683e485d4343d8021e68afd1668,TODO standardize sample method
aahiieieg,kengz/SLM-Lab,slm_lab/agent/memory/replay.py,425f3a1ae83c6a5d76d73a3750ffc0e0262c5fed,STILL_EXISTS,TODO update for multi bodies
aahiieieh,kengz/SLM-Lab,slm_lab/agent/memory/replay.py,425f3a1ae83c6a5d76d73a3750ffc0e0262c5fed,8c17debc9dc6552da8c317c0c852b55f03c80c61,TODO also for multi state; multi actions per body; need to be 3D
aahiieifa,kengz/SLM-Lab,slm_lab/agent/memory/replay.py,425f3a1ae83c6a5d76d73a3750ffc0e0262c5fed,STILL_EXISTS,TODO store directly from data_space?
aahiieifb,kengz/SLM-Lab,slm_lab/agent/memory/replay.py,425f3a1ae83c6a5d76d73a3750ffc0e0262c5fed,STILL_EXISTS,TODO this is still single body; generalize
aahiieifc,kengz/SLM-Lab,slm_lab/agent/memory/replay.py,425f3a1ae83c6a5d76d73a3750ffc0e0262c5fed,8c17debc9dc6552da8c317c0c852b55f03c80c61,TODO this is still single body
aahiieifd,kengz/SLM-Lab,slm_lab/agent/memory/replay.py,425f3a1ae83c6a5d76d73a3750ffc0e0262c5fed,2051aa89ec691d1c056e3e1f1187c489597c2ead,Move head pointer. Wrap around if necessary
aahiieifh,kengz/SLM-Lab,slm_lab/agent/memory/replay.py,425f3a1ae83c6a5d76d73a3750ffc0e0262c5fed,STILL_EXISTS,self.actions[self.head] = one hot of multi-action (matrix) on a 3rd axis; to be implement
aahiieiga,kengz/SLM-Lab,slm_lab/experiment/control.py,425f3a1ae83c6a5d76d73a3750ffc0e0262c5fed,STILL_EXISTS,TODO absorb later from data space
aahiieigc,kengz/SLM-Lab,slm_lab/experiment/monitor.py,425f3a1ae83c6a5d76d73a3750ffc0e0262c5fed,ba34b5215fa710d95f4b5c73389f86efe573c013,TODO shove
aahiieigh,kengz/SLM-Lab,slm_lab/agent/__init__.py,5e1218043417ce1a295433cbae716fb6af7c1e8d,STILL_EXISTS,TODO tmp return; to unify with monitor auto-fetch later
aahiieigi,kengz/SLM-Lab,slm_lab/agent/__init__.py,5e1218043417ce1a295433cbae716fb6af7c1e8d,STILL_EXISTS,TODO tmp; single body (last); use monitor later
aahiieihc,kengz/SLM-Lab,slm_lab/agent/algorithm/dqn.py,5e1218043417ce1a295433cbae716fb6af7c1e8d,8c17debc9dc6552da8c317c0c852b55f03c80c61,TODO can handle identical bodies now; to use body_net for specific body.
aahiieihd,kengz/SLM-Lab,slm_lab/experiment/control.py,5e1218043417ce1a295433cbae716fb6af7c1e8d,16e9d8fe38d7d3b7edaf20fb5fd3413e7f865c93,TODO tmp return; to unify with monitor auto-fetch later
aahiieihf,kengz/SLM-Lab,slm_lab/experiment/control.py,5e1218043417ce1a295433cbae716fb6af7c1e8d,16e9d8fe38d7d3b7edaf20fb5fd3413e7f865c93,TODO tmp hack. fix with monitor data later
aahiieihi,kengz/SLM-Lab,slm_lab/env/__init__.py,ce3bea7d365355b5ad4087c497d448ef1bb264a7,STILL_EXISTS,TODO check done on solve_mean_rewards
aahiieiig,kengz/SLM-Lab,slm_lab/agent/algorithm/algorithm_util.py,4df11055e61fa6c9fede6b2114c8ce05de9a035e,STILL_EXISTS,''' || Functions used by more than one algorithm || TODO refactor properly later || '''
aahiieiii,kengz/SLM-Lab,slm_lab/agent/algorithm/dqn.py,4df11055e61fa6c9fede6b2114c8ce05de9a035e,STILL_EXISTS,TODO auto-architecture to handle multi-head; multi-tail nets
aahiieija,kengz/SLM-Lab,slm_lab/agent/algorithm/dqn.py,4df11055e61fa6c9fede6b2114c8ce05de9a035e,STILL_EXISTS,TODO adjust learning rate http:\/\/pytorch.org\/docs\/master\/optim.html#how-to-adjust-learning-rate
aahiieijb,kengz/SLM-Lab,slm_lab/agent/algorithm/dqn.py,4df11055e61fa6c9fede6b2114c8ce05de9a035e,STILL_EXISTS,TODO hackish optimizer learning rate; also it fails for SGD wtf
aahiiejbe,kengz/SLM-Lab,slm_lab/agent/net/feedforward.py,4df11055e61fa6c9fede6b2114c8ce05de9a035e,STILL_EXISTS,TODO parametrize output layer activation too
aahiiejce,kengz/SLM-Lab,slm_lab/agent/__init__.py,d670e94d3f8e938858e938b1d3efeac5389a26d3,8c17debc9dc6552da8c317c0c852b55f03c80c61,TODO hack add
aahiiejdi,kengz/SLM-Lab,slm_lab/agent/algorithm/dqn.py,d670e94d3f8e938858e938b1d3efeac5389a26d3,STILL_EXISTS,TODO rename this as action_net or policy_net
aahiiejdj,kengz/SLM-Lab,slm_lab/agent/algorithm/dqn.py,d670e94d3f8e938858e938b1d3efeac5389a26d3,8c17debc9dc6552da8c317c0c852b55f03c80c61,TODO: Check this is working
aahiiejea,kengz/SLM-Lab,slm_lab/agent/algorithm/dqn.py,d670e94d3f8e938858e938b1d3efeac5389a26d3,8c17debc9dc6552da8c317c0c852b55f03c80c61,TODO handle this with better design
aahiiejfg,kengz/SLM-Lab,slm_lab/agent/algorithm/dqn.py,d670e94d3f8e938858e938b1d3efeac5389a26d3,4df1b4ce625bc5bfb574d230d08a1422b4d8c73f,TODO Generalize to more than two tasks
aahiiejjd,kengz/SLM-Lab,slm_lab/agent/algorithm/dqn.py,d670e94d3f8e938858e938b1d3efeac5389a26d3,STILL_EXISTS,TODO when backprop need to use relevant r too
aahiiejjg,kengz/SLM-Lab,slm_lab/agent/memory/replay.py,d670e94d3f8e938858e938b1d3efeac5389a26d3,8c17debc9dc6552da8c317c0c852b55f03c80c61,TODO add warning that memory is env-specific now
aahiiejjj,kengz/SLM-Lab,slm_lab/agent/net/feedforward.py,d670e94d3f8e938858e938b1d3efeac5389a26d3,ff5da2b2bfd03234519927b966923e8baecf6616,TODO see openai baselines; model.py;  _mlp is so clean
aahiifaaa,kengz/SLM-Lab,slm_lab/agent/net/feedforward.py,d670e94d3f8e938858e938b1d3efeac5389a26d3,ff5da2b2bfd03234519927b966923e8baecf6616,TODO cant we do like tf?: layers.fully_connected(out; num_outputs=hidden; activation_fn=None)
aahiifaab,kengz/SLM-Lab,slm_lab/env/__init__.py,d670e94d3f8e938858e938b1d3efeac5389a26d3,STILL_EXISTS,TODO hack for mismaching env timesteps
aahiifaac,kengz/SLM-Lab,slm_lab/experiment/control.py,d670e94d3f8e938858e938b1d3efeac5389a26d3,16e9d8fe38d7d3b7edaf20fb5fd3413e7f865c93,TODO tick body clock
aahiifaad,kengz/SLM-Lab,slm_lab/experiment/control.py,d670e94d3f8e938858e938b1d3efeac5389a26d3,16e9d8fe38d7d3b7edaf20fb5fd3413e7f865c93,TODO hack for a reliable done; otherwise all needs to be coincidental
aahiifaag,kengz/SLM-Lab,slm_lab/experiment/control.py,d670e94d3f8e938858e938b1d3efeac5389a26d3,STILL_EXISTS,TODO still need to standardize all data proj to aeb
aahiifaah,kengz/SLM-Lab,slm_lab/experiment/control.py,d670e94d3f8e938858e938b1d3efeac5389a26d3,16e9d8fe38d7d3b7edaf20fb5fd3413e7f865c93,TODO monitor record all data spaces; including body with body.clock. cuz all data spaces have history
aahiifaai,kengz/SLM-Lab,slm_lab/experiment/control.py,d670e94d3f8e938858e938b1d3efeac5389a26d3,16e9d8fe38d7d3b7edaf20fb5fd3413e7f865c93,split per body; use done as delim (maybe done need body clock now); split; sum each chunk
aahiifabg,kengz/SLM-Lab,slm_lab/experiment/control.py,d670e94d3f8e938858e938b1d3efeac5389a26d3,16e9d8fe38d7d3b7edaf20fb5fd3413e7f865c93,fix offset in cumsum (True entry belongs to the chunk before it)
aahiifacc,kengz/SLM-Lab,slm_lab/experiment/monitor.py,d670e94d3f8e938858e938b1d3efeac5389a26d3,STILL_EXISTS,TODO since u have to create anyway; why not just use a canonical form as standard
aahiifadc,kengz/SLM-Lab,slm_lab/notebook/intro_tf.py,d670e94d3f8e938858e938b1d3efeac5389a26d3,STILL_EXISTS,fix to minimize loss
aahiifafc,kengz/SLM-Lab,slm_lab/spec/spec_util.py,d670e94d3f8e938858e938b1d3efeac5389a26d3,8c17debc9dc6552da8c317c0c852b55f03c80c61,TODO allow list in auto-expansion
aahiifafd,kengz/SLM-Lab,test/conftest.py,d670e94d3f8e938858e938b1d3efeac5389a26d3,70db35897c3a4683e485d4343d8021e68afd1668,TODO properly use in tests
aahiifaie,kengz/SLM-Lab,slm_lab/agent/algorithm/dqn.py,8c17debc9dc6552da8c317c0c852b55f03c80c61,STILL_EXISTS,TODO standardize agent and env print self
aahiifajc,kengz/SLM-Lab,slm_lab/agent/algorithm/dqn.py,8c17debc9dc6552da8c317c0c852b55f03c80c61,4df1b4ce625bc5bfb574d230d08a1422b4d8c73f,TODO make split eval tail-wise softmax; otherwise will skew to just one tail
aahiifaje,kengz/SLM-Lab,slm_lab/agent/algorithm/dqn.py,8c17debc9dc6552da8c317c0c852b55f03c80c61,4df1b4ce625bc5bfb574d230d08a1422b4d8c73f,TODO uhh what's this?
aahiifbbh,kengz/SLM-Lab,slm_lab/agent/algorithm/dqn.py,8c17debc9dc6552da8c317c0c852b55f03c80c61,STILL_EXISTS,TODO rename this as action_net or policy_net
aahiifbbj,kengz/SLM-Lab,slm_lab/agent/algorithm/dqn.py,8c17debc9dc6552da8c317c0c852b55f03c80c61,STILL_EXISTS,TODO: Check this is working
aahiifbgc,kengz/SLM-Lab,slm_lab/agent/algorithm/dqn.py,8c17debc9dc6552da8c317c0c852b55f03c80c61,STILL_EXISTS,TODO when backprop need to use relevant r too
aahiifbgd,kengz/SLM-Lab,slm_lab/agent/memory/replay.py,8c17debc9dc6552da8c317c0c852b55f03c80c61,STILL_EXISTS,TODO if latest; return unused. implement
aahiifbhe,kengz/SLM-Lab,slm_lab/agent/net/feedforward.py,8c17debc9dc6552da8c317c0c852b55f03c80c61,ff5da2b2bfd03234519927b966923e8baecf6616,TODO more concise creation using nn.Sequential
aahiifbhh,kengz/SLM-Lab,slm_lab/env/__init__.py,8c17debc9dc6552da8c317c0c852b55f03c80c61,861657d2c9b321961994c8cdd0e58b6c4fe0645f,TODO experiment to find out optimal benchmarking max_timestep; set
aahiifbid,kengz/SLM-Lab,slm_lab/experiment/monitor.py,8c17debc9dc6552da8c317c0c852b55f03c80c61,STILL_EXISTS,TODO rewrite this whole shit
aahiifbig,kengz/SLM-Lab,slm_lab/experiment/monitor.py,8c17debc9dc6552da8c317c0c852b55f03c80c61,ba34b5215fa710d95f4b5c73389f86efe573c013,TODO shove history to DB
aahiifbja,kengz/SLM-Lab,slm_lab/experiment/monitor.py,8c17debc9dc6552da8c317c0c852b55f03c80c61,STILL_EXISTS,TODO since u have to create anyway; why not just use a canonical form as standard
aahiifbjd,kengz/SLM-Lab,test/experiment/test_monitor.py,8c17debc9dc6552da8c317c0c852b55f03c80c61,STILL_EXISTS,TODO add these tests
aahiifcah,kengz/SLM-Lab,slm_lab/agent/__init__.py,16e9d8fe38d7d3b7edaf20fb5fd3413e7f865c93,b7db501d7e0b8404f13be86ed6bcc96f2da5861f,TODO finer loss and explore_var per body
aahiifcba,kengz/SLM-Lab,slm_lab/env/__init__.py,16e9d8fe38d7d3b7edaf20fb5fd3413e7f865c93,925c1d2dc9c07796ab077f1f258eebda03b4430c,TODO internalize render code
aahiifcbf,kengz/SLM-Lab,slm_lab/experiment/analysis.py,16e9d8fe38d7d3b7edaf20fb5fd3413e7f865c93,STILL_EXISTS,TODO write file properly once session_data is in tabular form
aahiifcbg,kengz/SLM-Lab,slm_lab/experiment/analysis.py,16e9d8fe38d7d3b7edaf20fb5fd3413e7f865c93,STILL_EXISTS,TODO form proper session data for plot and return
aahiifcea,kengz/SLM-Lab,slm_lab/experiment/monitor.py,16e9d8fe38d7d3b7edaf20fb5fd3413e7f865c93,39a0c98b1ec9adb55386544c870f99cb8ff8f447,TODO generalize and make state_space to include observables
aahiifcec,kengz/SLM-Lab,slm_lab/experiment/monitor.py,16e9d8fe38d7d3b7edaf20fb5fd3413e7f865c93,STILL_EXISTS,TODO rewrite this whole shit
aahiifced,kengz/SLM-Lab,slm_lab/agent/__init__.py,39a0c98b1ec9adb55386544c870f99cb8ff8f447,861657d2c9b321961994c8cdd0e58b6c4fe0645f,TODO generalize and make state_space to include observables
aahiifcee,kengz/SLM-Lab,slm_lab/agent/__init__.py,39a0c98b1ec9adb55386544c870f99cb8ff8f447,STILL_EXISTS,TODO use tuples for state_dim for pixel-based in the future; generalize all and call as shape
aahiifcef,kengz/SLM-Lab,slm_lab/agent/__init__.py,39a0c98b1ec9adb55386544c870f99cb8ff8f447,b7db501d7e0b8404f13be86ed6bcc96f2da5861f,TODO ensure submethods return np.nan for loss or explore_var instead of None
aahiifcei,kengz/SLM-Lab,slm_lab/experiment/analysis.py,39a0c98b1ec9adb55386544c870f99cb8ff8f447,STILL_EXISTS,TODO save full data to db
aahiifcej,kengz/SLM-Lab,slm_lab/experiment/analysis.py,39a0c98b1ec9adb55386544c870f99cb8ff8f447,STILL_EXISTS,TODO form proper session data for plot and return
aahiifdbb,kengz/SLM-Lab,slm_lab/experiment/analysis.py,47b15d77a3007830a7a11f57c38b9dbf56c5fd50,STILL_EXISTS,TODO move this elsewhere and aggregate\/save properly
aahiifdcb,kengz/SLM-Lab,slm_lab/agent/algorithm/algorithm_util.py,319882b5679229124774e91d5c5762853d75f3ce,STILL_EXISTS,TODO Handle returning all explore vars
aahiifddc,kengz/SLM-Lab,slm_lab/agent/algorithm/dqn.py,319882b5679229124774e91d5c5762853d75f3ce,b7db501d7e0b8404f13be86ed6bcc96f2da5861f,TODO: Return other losses as well.
aahiifddi,kengz/SLM-Lab,slm_lab/experiment/analysis.py,47355dc8b521dd63c8a26ee6fa611368519c116a,STILL_EXISTS,TODO generalize to use experiment timestamp; id; sesison coor in info space; to replace timestamp
aahiifdeh,kengz/SLM-Lab,slm_lab/experiment/analysis.py,60d3325b8551cc6f7592e1266878818ad07d3b3a,STILL_EXISTS,TODO generalize to use experiment timestamp; id; sesison coor in info space; to replace timestamp
aahiifdfc,kengz/SLM-Lab,slm_lab/experiment/control.py,60d3325b8551cc6f7592e1266878818ad07d3b3a,ba34b5215fa710d95f4b5c73389f86efe573c013,TODO generalize to take different search algo
aahiifdfd,kengz/SLM-Lab,slm_lab/experiment/search.py,60d3325b8551cc6f7592e1266878818ad07d3b3a,STILL_EXISTS,TODO proper id from top level
aahiifdga,kengz/SLM-Lab,slm_lab/lib/viz.py,60d3325b8551cc6f7592e1266878818ad07d3b3a,e7a1635367e7637f316aadc99c4556614a3663e9,TODO tmp; switch back to == 'test' later when we have free plot
aahiifdjb,kengz/SLM-Lab,run_slm.py,22419732a0e8ff8e5a7e29704e152ac3e9b3f2ec,STILL_EXISTS,TODO streamline to grunt from external in the next release
aahiifead,kengz/SLM-Lab,test/experiment/test_control.py,22419732a0e8ff8e5a7e29704e152ac3e9b3f2ec,ba34b5215fa710d95f4b5c73389f86efe573c013,TODO experiment data checker method
aahiifece,kengz/SLM-Lab,slm_lab/experiment/control.py,b7db501d7e0b8404f13be86ed6bcc96f2da5861f,STILL_EXISTS,TODO option to set rand_seed
aahiifech,kengz/SLM-Lab,slm_lab/lib/decorator.py,b7db501d7e0b8404f13be86ed6bcc96f2da5861f,925c1d2dc9c07796ab077f1f258eebda03b4430c,TODO name-based data check for api methods
aahiifede,kengz/SLM-Lab,slm_lab/experiment/analysis.py,ba34b5215fa710d95f4b5c73389f86efe573c013,STILL_EXISTS,TODO sort experiment_df
aahiifeed,kengz/SLM-Lab,slm_lab/experiment/search.py,ba34b5215fa710d95f4b5c73389f86efe573c013,d657347af27f8c388eb2b7d8ee7625055a09f000,TODO timesteps = episode len or total_t from space_clock
aahiifeef,kengz/SLM-Lab,slm_lab/experiment/search.py,ba34b5215fa710d95f4b5c73389f86efe573c013,d657347af27f8c388eb2b7d8ee7625055a09f000,TODO use hyperband
aahiifeeg,kengz/SLM-Lab,slm_lab/experiment/search.py,ba34b5215fa710d95f4b5c73389f86efe573c013,d657347af27f8c388eb2b7d8ee7625055a09f000,TODO use advanced conditional config space via lambda func
aahiifegd,kengz/SLM-Lab,test/experiment/test_control.py,ba34b5215fa710d95f4b5c73389f86efe573c013,3db8d76a7708f947d303771b1d341da1438d457c,# TODO session data checker method
aahiifeha,kengz/SLM-Lab,test/experiment/test_control.py,ba34b5215fa710d95f4b5c73389f86efe573c013,3db8d76a7708f947d303771b1d341da1438d457c,# TODO trial data checker method
aahiifeig,kengz/SLM-Lab,test/experiment/test_control.py,ba34b5215fa710d95f4b5c73389f86efe573c013,3db8d76a7708f947d303771b1d341da1438d457c,# TODO experiment data checker method
aahiiffci,kengz/SLM-Lab,run_lab.py,d96b2eb3157add4c4cb2faa7d853b28e0a1effc0,STILL_EXISTS,TODO turn on save\/load model mode
aahiiffda,kengz/SLM-Lab,run_lab.py,d96b2eb3157add4c4cb2faa7d853b28e0a1effc0,STILL_EXISTS,TODO need to spread benchmark over spec on Experiment
aahiiffdc,kengz/SLM-Lab,test/experiment/test_control.py,3db8d76a7708f947d303771b1d341da1438d457c,70db35897c3a4683e485d4343d8021e68afd1668,TODO session data checker method
aahiiffdd,kengz/SLM-Lab,test/experiment/test_control.py,3db8d76a7708f947d303771b1d341da1438d457c,70db35897c3a4683e485d4343d8021e68afd1668,TODO trial data checker method
aahiiffde,kengz/SLM-Lab,test/experiment/test_control.py,3db8d76a7708f947d303771b1d341da1438d457c,70db35897c3a4683e485d4343d8021e68afd1668,TODO experiment data checker method
aahiifgbg,kengz/SLM-Lab,slm_lab/agent/algorithm/algorithm_util.py,499af60a872dd87d78b5eda6ee5f0419d8ae65cd,29cea661974007767c62400870924d838fafd7a9,TODO: fix action hack - extract from env. Ask Keng
aahiifgcc,kengz/SLM-Lab,slm_lab/agent/algorithm/reinforce.py,499af60a872dd87d78b5eda6ee5f0419d8ae65cd,bedd4b5ee46ca1cedf1a54f6caac4f361c36179d,TODO Fix for multi-episode training. Won't know where to delete after the first episode.
aahiifgdb,kengz/SLM-Lab,slm_lab/experiment/analysis.py,e7a1635367e7637f316aadc99c4556614a3663e9,STILL_EXISTS,TODO replaced by plot_best_sessions until Feb 2018
aahiifgec,kengz/SLM-Lab,slm_lab/experiment/search.py,77b1d3bfbaf657515aedaca8707a2ef6bd04332a,d657347af27f8c388eb2b7d8ee7625055a09f000,TODO put reporter into info space so it can be called to update. easy.
aahiifgef,kengz/SLM-Lab,slm_lab/experiment/analysis.py,ae2399ecbd441eb924764d22444d3fb10cf562f4,STILL_EXISTS,tmp hack
aahiifgeh,kengz/SLM-Lab,slm_lab/experiment/search.py,ae2399ecbd441eb924764d22444d3fb10cf562f4,d657347af27f8c388eb2b7d8ee7625055a09f000,TODO use advanced conditional config space via lambda func
aahiifgej,kengz/SLM-Lab,run_lab.py,87f78e9e7432d2ac0827805a840d78a9454d78f2,STILL_EXISTS,TODO remove when analysis can save all plotly plots
aahiifgfa,kengz/SLM-Lab,slm_lab/experiment/analysis.py,87f78e9e7432d2ac0827805a840d78a9454d78f2,STILL_EXISTS,TODO remove when plotly can save all
aahiifgfh,kengz/SLM-Lab,test/agent/algo/test_algo.py,5d79fcc2055c22eb71e7cd8d5b75ecb279443dc6,925c1d2dc9c07796ab077f1f258eebda03b4430c,TODO Fix pytest seg faults on Ubuntu
aahiifgjc,kengz/SLM-Lab,slm_lab/experiment/analysis.py,04e8048307d390036e5fc56623aec2c81294edd1,STILL_EXISTS,TODO reactivate saving when get to the transition matrix research
aahiifhdh,kengz/SLM-Lab,slm_lab/agent/algorithm/sarsa.py,0fea1acc70a00f8c05e550aba762415f3761df9b,2381a50a70559340a0335288d648b4bb9a675588,Convert actions to one hot (both representations are needed for SARSA)
aahiifhed,kengz/SLM-Lab,slm_lab/experiment/control.py,6f033002868892771f0a98389a217d22ebff9199,STILL_EXISTS,to fix ray consecutive run crash due to bad cleanup
aahiifhgb,kengz/SLM-Lab,slm_lab/experiment/monitor.py,5caade4718b6a638bcfd4bf01f430e4caed9a7eb,70db35897c3a4683e485d4343d8021e68afd1668,Hack to keep size of data storage small - only store most recent state
aahiifhha,kengz/SLM-Lab,slm_lab/agent/algorithm/ppo.py,99f54b4f5d398727527b83c720a42156c61d592c,70db35897c3a4683e485d4343d8021e68afd1668,TODO fix access to info_space then set below
aahiifhhb,kengz/SLM-Lab,slm_lab/agent/algorithm/ppo.py,99f54b4f5d398727527b83c720a42156c61d592c,70db35897c3a4683e485d4343d8021e68afd1668,TODO close when done to clear
aahiifhii,kengz/SLM-Lab,slm_lab/agent/memory/gae_onpolicy.py,99f54b4f5d398727527b83c720a42156c61d592c,STILL_EXISTS,TODO properly design sub specs
aahiifhjb,kengz/SLM-Lab,slm_lab/agent/memory/gae_onpolicy.py,99f54b4f5d398727527b83c720a42156c61d592c,70db35897c3a4683e485d4343d8021e68afd1668,TODO merge this back to inits below
aahiifhjj,kengz/SLM-Lab,slm_lab/agent/net/mlp_policy.py,99f54b4f5d398727527b83c720a42156c61d592c,70db35897c3a4683e485d4343d8021e68afd1668,TODO fix scope using prepath name
aahiifiab,kengz/SLM-Lab,slm_lab/agent/net/mlp_policy.py,99f54b4f5d398727527b83c720a42156c61d592c,70db35897c3a4683e485d4343d8021e68afd1668,TODO dont hard code activation
aahiifiac,kengz/SLM-Lab,slm_lab/agent/net/mlp_policy.py,99f54b4f5d398727527b83c720a42156c61d592c,70db35897c3a4683e485d4343d8021e68afd1668,TODO restore param gaussian_fixed_var=True
aahiifiae,kengz/SLM-Lab,slm_lab/env/__init__.py,99f54b4f5d398727527b83c720a42156c61d592c,STILL_EXISTS,TODO not encountered yet; generalization needed
aahiifiaf,kengz/SLM-Lab,slm_lab/env/__init__.py,99f54b4f5d398727527b83c720a42156c61d592c,STILL_EXISTS,TODO no way to know range for unity env for now
aahiifiag,kengz/SLM-Lab,slm_lab/experiment/monitor.py,99f54b4f5d398727527b83c720a42156c61d592c,STILL_EXISTS,TODO dont modify without warning; unintended side effects
aahiifiai,kengz/SLM-Lab,slm_lab/experiment/monitor.py,99f54b4f5d398727527b83c720a42156c61d592c,STILL_EXISTS,TODO make proper and general: log first reward
aahiifibe,kengz/SLM-Lab,slm_lab/lib/distribution.py,99f54b4f5d398727527b83c720a42156c61d592c,b634259befbce93c8c919a5d6343bf222a2b30de,TODO add beta distribution
aahiificc,kengz/SLM-Lab,slm_lab/lib/tf_util.py,99f54b4f5d398727527b83c720a42156c61d592c,STILL_EXISTS,TODO test this below
aahiifihf,kengz/SLM-Lab,slm_lab/agent/memory/replay.py,70db35897c3a4683e485d4343d8021e68afd1668,STILL_EXISTS,TODO if latest; return unused. implement
aahiifijc,kengz/SLM-Lab,slm_lab/agent/net/mlp.py,70db35897c3a4683e485d4343d8021e68afd1668,ec849adaf4ceb42ed52ca142c839f627c34b9434,TODO move away from list-based construction API; too many things to keep track off and might drop the ball
aahiifije,kengz/SLM-Lab,slm_lab/agent/net/mlp.py,70db35897c3a4683e485d4343d8021e68afd1668,ec849adaf4ceb42ed52ca142c839f627c34b9434,TODO use actual last layers instead of numbers to track interface to body
aahiifjaj,kengz/SLM-Lab,slm_lab/env/__init__.py,70db35897c3a4683e485d4343d8021e68afd1668,STILL_EXISTS,TODO not encountered yet; generalization needed
aahiifjee,kengz/SLM-Lab,slm_lab/agent/algorithm/actor_critic.py,ec849adaf4ceb42ed52ca142c839f627c34b9434,STILL_EXISTS,TODO call these from inside memory; always return torch batch
aahiigadd,kengz/SLM-Lab,slm_lab/agent/algorithm/dqn.py,454f620fce3e6fe2f87a91bffd74667d1f8a94f9,2381a50a70559340a0335288d648b4bb9a675588,TODO tmp correction. state_a has 1 dim too deep
aahiigbid,kengz/SLM-Lab,run_lab.py,901cc588fee90215d2e2831bd4a3e7481b307ba7,STILL_EXISTS,NOTE increase if needed. Pytorch thread overusage https:\/\/github.com\/pytorch\/pytorch\/issues\/975
aahiigcid,kengz/SLM-Lab,slm_lab/agent/memory/replay.py,0d25397aeff73831e41aab8a788837633c75f2a6,2051aa89ec691d1c056e3e1f1187c489597c2ead,TODO generalize for multi-action
aahiigcie,kengz/SLM-Lab,slm_lab/agent/memory/replay.py,0d25397aeff73831e41aab8a788837633c75f2a6,STILL_EXISTS,TODO generalize Replay to take data_keys
aahiigcih,kengz/SLM-Lab,slm_lab/agent/memory/replay.py,0d25397aeff73831e41aab8a788837633c75f2a6,2051aa89ec691d1c056e3e1f1187c489597c2ead,Move head pointer. Wrap around if necessary
aahiigdcg,kengz/SLM-Lab,slm_lab/agent/memory/replay.py,2051aa89ec691d1c056e3e1f1187c489597c2ead,STILL_EXISTS,TODO generalize Replay to take data_keys
aahiigdeg,kengz/SLM-Lab,slm_lab/experiment/control.py,f9d49df398ba50c024a397b18536c8b1c027c222,STILL_EXISTS,TODO move away from space
aahiigdei,kengz/SLM-Lab,slm_lab/experiment/control.py,f9d49df398ba50c024a397b18536c8b1c027c222,3f49674afeb1a7280940afa71d9a55e32753b088,TODO also create shared optimizer here
aahiigecd,kengz/SLM-Lab,slm_lab/agent/net/net_util.py,861657d2c9b321961994c8cdd0e58b6c4fe0645f,STILL_EXISTS,TODO if without momentum; weights should not change too
aahiigeff,kengz/SLM-Lab,slm_lab/env/unity.py,861657d2c9b321961994c8cdd0e58b6c4fe0645f,96071811df8e9a91539c993ab9983f2b445a1b6a,TODO implement clock_speed: step only if self.clock.to_step()
aahiigfcb,kengz/SLM-Lab,test/spec/test_spec.py,861657d2c9b321961994c8cdd0e58b6c4fe0645f,9893731e1c5d1c57ff2cc1c2d280c11f2f453d64,TODO disable for now
aahiigfdi,kengz/SLM-Lab,slm_lab/agent/algorithm/policy_util.py,8862b01c1e96f7cf7d6c0d1c9d714f98d68bbd1d,2d4e34c8d96eeada1b9a9dd0b035f5884f62f6b2,TODO Not currently used; this will crash for more exotic memory structures
aahiigfei,kengz/SLM-Lab,slm_lab/experiment/analysis.py,7fd06b70644744480753c9e0683669370998f279,a660de30bf2295151efea91b208418637a1e4344,TODO need enable total_t for trial graph; and line up signals at the common total_t
aahiiggad,kengz/SLM-Lab,slm_lab/env/vizdoom/vizdoom_env.py,03938c1787b05bd7dd490b9df2d75ce3f11f9c3b,STILL_EXISTS,TODO In future; need to update action to handle (continuous) DELTA buttons using gym's Box space
aahiigggc,kengz/SLM-Lab,slm_lab/experiment/analysis.py,3d4932b9156c09c8bec47badb8a2f652a64176e6,db54cbc5d59aace9480f2e7b5537fbf46d8af955,TODO need enable total_t for trial graph; and line up signals at the common total_t
aahiigije,kengz/SLM-Lab,slm_lab/agent/net/mlp.py,0ac2b33e8c63304a50db7d2b484368299706b58b,c799a6d2b71cd47dbc3303c8c86a24d0f64795ec,TODO debug
aahiigijf,kengz/SLM-Lab,slm_lab/agent/net/net_util.py,0ac2b33e8c63304a50db7d2b484368299706b58b,26275240d5e8cbbb52f6817dd8dad9f3ff85372f,TODO retire these
aahiigijg,kengz/SLM-Lab,slm_lab/agent/net/net_util.py,0ac2b33e8c63304a50db7d2b484368299706b58b,26275240d5e8cbbb52f6817dd8dad9f3ff85372f,TODO remove
aahiigjic,kengz/SLM-Lab,slm_lab/agent/algorithm/ppo.py,76b2f57c04720e733aa782ca2950ed8d767d7375,STILL_EXISTS,TODO fix with entropy anneal generalization
aahiigjif,kengz/SLM-Lab,slm_lab/agent/algorithm/ppo.py,db610a9f6c57ae9aead9407b5acc02f00aefd27d,STILL_EXISTS,TODO decay clip_eps along with entropy
aahiigjih,kengz/SLM-Lab,slm_lab/agent/algorithm/ppo.py,db610a9f6c57ae9aead9407b5acc02f00aefd27d,STILL_EXISTS,TODO fix with entropy anneal generalization
aahiigjii,kengz/SLM-Lab,slm_lab/agent/algorithm/dqn.py,c13c42ac8a6b0dad8c112d644b4f896c7647073b,8c58961796695d98e5351c752ecd31a16767e03a,TODO rewrite Q-value loss compute to use max
aahiigjij,kengz/SLM-Lab,slm_lab/agent/memory/replay.py,d865f2da20b9ce61064a0365f020d7ef1719d92b,bb8de008040d0dae5601aba310188c5b40cda282,TODO fancy sampling only took early experiences somehow. could be due to binning stuff
aahiigjji,kengz/SLM-Lab,test/spec/test_dist_spec.py,ae00c7fa425572f3f90e0728305af9343e23a959,STILL_EXISTS,manually run the logic to obtain global nets for testing to ensure global net gets updated
aahiihadb,kengz/SLM-Lab,slm_lab/agent/algorithm/dqn.py,07dccafda65a350327e2b01b8a4cb701099feb88,STILL_EXISTS,TODO restore per
aahiihadj,kengz/SLM-Lab,slm_lab/agent/algorithm/dqn.py,06f76e5a4cb84453c5e1c68a64f56ed6428e7aa6,STILL_EXISTS,TODO restore per
aahiihagg,kengz/SLM-Lab,slm_lab/agent/algorithm/reinforce.py,3f87ad864e0e8cd33d0d445454caf9607f079d61,a6bad8048ab0300d05b6dca26212698d5eb26dc5,TODO standardize
aahiihahc,kengz/SLM-Lab,slm_lab/agent/algorithm/ppo.py,a6bad8048ab0300d05b6dca26212698d5eb26dc5,ac1a20d9d93c7b733a2bc6849e8d8fb9b1b4941a,TODO check all param consistency
aahiihahd,kengz/SLM-Lab,slm_lab/agent/algorithm/ppo.py,a6bad8048ab0300d05b6dca26212698d5eb26dc5,ac1a20d9d93c7b733a2bc6849e8d8fb9b1b4941a,TODO fill comment example entropy coef
aahiihaja,kengz/SLM-Lab,slm_lab/lib/util.py,6cbb4f9165c04c449356b5d038732806aa5e838b,85e163d6233e8197e676e1571e1a5852b3e5e7e5,hack around bad Ray design of hard-coding
aahiihajc,kengz/SLM-Lab,slm_lab/experiment/search.py,777fbf8c2876c9c144cf0737bcf2090031b3ef16,STILL_EXISTS,TODO fractional ray_gpu is broken
aahiihbbc,kengz/SLM-Lab,slm_lab/agent/algorithm/ppo.py,327627b03f5aa6b9b60bd39547eff65ab0301a18,3cbccdbab2c558642cb11e45eaf7f568968d688a,TODO expensive
aahiihcdb,kengz/SLM-Lab,slm_lab/env/wrapper.py,e5e812fb7101b9eb942e1d38fa8e31c870988396,a21dccc53eda6c6f0b3a9e02648ee3d98c55c8a2,TODO absorb into memory for efficient handling
aahiihdec,kengz/SLM-Lab,run_lab.py,40ebef7cd824bb2eb5338c53e962a6e95e8c007a,STILL_EXISTS,TODO dont save in enjoy; eval
aahiihdej,kengz/SLM-Lab,run_lab.py,2ce74a407886737b8e8d717ad518c6c126f6b06b,STILL_EXISTS,TODO hack and set the eval model location inside info_space
aahiihdfe,kengz/SLM-Lab,run_lab.py,68c85f8c91d2b5024065e79e5180443499b85fd3,STILL_EXISTS,TODO hack and set the eval model location inside info_space
aahiihdga,kengz/SLM-Lab,slm_lab/experiment/control.py,6f1ede65f6ddc0fd513e4aa121022ff3aaf134ed,382b96ef581724b74c692c8824c272d3eb307952,TODO eval call using eval_model_prepath
aahiihecb,kengz/SLM-Lab,slm_lab/experiment/analysis.py,cd5daebfc6a3b51e992c46ec40437362fdbc5062,STILL_EXISTS,TODO figure out a way to cycle CUDA id
aahiihecd,kengz/SLM-Lab,slm_lab/experiment/analysis.py,dc1307e299692719accc9d9b3d0f35633c983339,STILL_EXISTS,TODO figure out a way to cycle CUDA id
aahiihfac,kengz/SLM-Lab,slm_lab/experiment/control.py,184f1a593bd9742c01774b9735a264629ccf7cd9,e2cd825d919534771141b140718f22e54466d730,TODO ckpt and eval not implemented for SpaceSession
aahiihgaj,kengz/SLM-Lab,slm_lab/lib/math_util.py,85040243f4c9143300577d932a1a9bc07434185c,da0abf792fdc2ca0c62b43ae59961e9b64db59ca,TODO shifting is expensive. rewrite
aahiihgbb,kengz/SLM-Lab,slm_lab/agent/net/net_util.py,df5f56004571fbead73c5023d1cef5c69d073504,STILL_EXISTS,TODO change this to one slicable layer for efficiency
aahiihgdb,kengz/SLM-Lab,slm_lab/lib/util.py,884541e1d29c82fcc37fc0e9c9d969a70eb67714,0f6699f00cd8edb8126b71ee5e8227c83b88d6dd,pytorch c;h;w convention
aahiihgei,kengz/SLM-Lab,slm_lab/env/unity.py,908bba22a5735b18c891c1dab4aa23d33f55b682,b3cf40d978ee55fbe5cc08733bb8010e01308634,TODO update Unity ml-agents to use seed=seed below
aahiihhaj,kengz/SLM-Lab,slm_lab/agent/__init__.py,f08fef0dd9ad4225392d29374688f08222ed1c86,aa4ae658489c919dbca4141c7498b05ac0cac6d8,TODO below scheduled for update to be consistent with non-space mode
aahiihhfh,kengz/SLM-Lab,slm_lab/agent/algorithm/policy_util.py,e7584bc18e845e296e315bb1c29c13b6d70ee419,STILL_EXISTS,TODO do as multitail list pdparams in the future to control activation
aahiihhgc,kengz/SLM-Lab,slm_lab/agent/algorithm/policy_util.py,e7584bc18e845e296e315bb1c29c13b6d70ee419,6670a53f361e331b5d2e758489c76c591ba12f7f,TODO retire this
aahiihhge,kengz/SLM-Lab,slm_lab/agent/algorithm/ppo.py,e7584bc18e845e296e315bb1c29c13b6d70ee419,STILL_EXISTS,TODO sample minibatch from batch with size < length of batch
aahiihiai,kengz/SLM-Lab,slm_lab/agent/memory/replay.py,87c0c7b44ae24c55b7df009656fd497052d0ea44,STILL_EXISTS,TODO downcast to dtype
aahiihibc,kengz/SLM-Lab,slm_lab/agent/memory/replay.py,e057512b1dd0ba6ca1b776e2e758c28e8ef937d8,STILL_EXISTS,TODO set to_train here
aahiihibg,kengz/SLM-Lab,slm_lab/agent/memory/replay.py,e057512b1dd0ba6ca1b776e2e758c28e8ef937d8,STILL_EXISTS,TODO downcast to dtype
aahiihidj,kengz/SLM-Lab,slm_lab/agent/memory/replay.py,fe7856c012c2223f8b9e45b87d0f77407fc0a926,2fa4e7628844dadeaf4a905502f7a86c35daaa71,TODO This is unsafe
aahiihjbh,kengz/SLM-Lab,slm_lab/agent/net/net_util.py,5bf0fe6a3f72f9aac9db70d0a6b6b4dfe4b9d82d,STILL_EXISTS,TODO retrieve lr more generally
aahiihjcd,kengz/SLM-Lab,run_lab.py,275856246e6823b941ca16358b06828e2b40e854,618031e5473345d9f27f81bfe40f8c83cc18e930,TODO unify these later
aahiihjde,kengz/SLM-Lab,run_lab.py,275856246e6823b941ca16358b06828e2b40e854,f4ea66d09ded93265a7fa49af22e152de6f5d1ea,can't use Pool since it cannot spawn nested Process; which is needed for VecEnv and parallel sessions. So these will run and wait by chunks
aahiihjdj,kengz/SLM-Lab,slm_lab/experiment/control.py,55eb4238091123caabe676809fda35d11930759e,fcdc31dc2cf8e533311ca855129fafa3efdbf2a1,TODO check if below is really needed?
aahiihjgd,kengz/SLM-Lab,slm_lab/spec/spec_util.py,289929dfb10c27d7e0e28e7b3e588d27cac8f7d2,bf662488b5cbc90b7b06aa90a47a5475c40a663f,TODO expand to be more comprehensive
aahiihjgf,kengz/SLM-Lab,slm_lab/lib/logger.py,f01f5f8ca39275f9daebaaec06affd4717d05c04,cd8c79fb23514e3b65cafc0f4d668cadfc6e8d0f,hack to mute poorly designed ray TF warning log
aahiihjic,kengz/SLM-Lab,slm_lab/agent/net/net_util.py,3f49674afeb1a7280940afa71d9a55e32753b088,fb254c8fb2215ff4d99e25a0b7af9ad57775c121,TODO also create shared optimizer here
aahiihjja,kengz/SLM-Lab,slm_lab/agent/net/net_util.py,074bd0c8606f9377e22c82ea6efb1d4faedb5b9a,STILL_EXISTS,TODO also create shared optimizer here
aahiihjjg,kengz/SLM-Lab,slm_lab/agent/algorithm/actor_critic.py,7a249bebbed2446042bf30304bae7f90f0479812,700780ace70cf3fb6b3f33fbf95241d9f7d48c20,TODO add method in net_util
aahiihjjh,kengz/SLM-Lab,slm_lab/agent/net/net_util.py,7a249bebbed2446042bf30304bae7f90f0479812,STILL_EXISTS,naming convention
aahiiiaac,kengz/SLM-Lab,slm_lab/agent/net/net_util.py,700780ace70cf3fb6b3f33fbf95241d9f7d48c20,1d447160e091032acc2c589a7faecd1816138144,local net name by naming convention
aahiiiaaj,kengz/SLM-Lab,slm_lab/agent/net/net_util.py,67c6b3fd73a79ce32e05aad74eda58bf29817b11,STILL_EXISTS,naming convention
aahiiiabh,kengz/SLM-Lab,slm_lab/spec/spec_util.py,cb9b1ad28838b589c0fba4cf96c818d5cf9d1850,STILL_EXISTS,TODO expand to be more comprehensive
aahiiiaff,kengz/SLM-Lab,slm_lab/spec/random_baseline.py,990ca4ccbfb60072303a2fd04a2fb5e42a046951,STILL_EXISTS,TODO generalize for unity too once it has a gym wrapper
aahiiicef,kengz/SLM-Lab,slm_lab/env/unity.py,c51cc32fdf1fab8c7b8bfb2f21f7fd17fe1e8cae,b3cf40d978ee55fbe5cc08733bb8010e01308634,TODO rework unity outdated
aahiiidbc,kengz/SLM-Lab,slm_lab/lib/logger.py,ec8194b7bebbaea475b2bf5d5e7c144a99d35f30,STILL_EXISTS,hack to mute poorly designed ray TF warning log
aahiiiebd,kengz/SLM-Lab,run_lab.py,3fde78c2a49eeb5934d87f8b38001c03944cfb0c,9c4d7e8cc3dca744bce0c7c6fd7a5bc742adcd42,can't use Pool since it cannot spawn nested Process; which is needed for VecEnv and parallel sessions. So these will run and wait by chunks
aahiiifba,kengz/SLM-Lab,slm_lab/env/base.py,29df219d29f1532cc2f506709d0a458d6bfa1198,7b77f2590b1bb588133d653e5a03c85a2a5682de,TODO track both total_reward and episodic total reward
aahiiifhf,kengz/SLM-Lab,slm_lab/agent/algorithm/sac.py,5a126fc7cac272dfe2514554ef228001c795d729,285ff308e759052d635d7a1c57d304a0a27d7c70,TODO support multi-discrete actions
aahiiigcg,kengz/SLM-Lab,slm_lab/agent/algorithm/sac.py,d9580bbdfff658e96d23d437ac25dd2da81f6732,285ff308e759052d635d7a1c57d304a0a27d7c70,TODO support multi-discrete actions
aahiiigdb,kengz/SLM-Lab,slm_lab/agent/algorithm/sac.py,ef55cae25f98d4777435a53a6b783b24852ca606,STILL_EXISTS,TODO support multi-discrete actions
aahiijdbi,tslearn-team/tslearn,tslearn/clustering.py,1b04ad8b5a9f759759e8380e88da629a878172b2,STILL_EXISTS,TODO: add n_init option
aahiijdcc,tslearn-team/tslearn,tslearn/clustering.py,b7e43ded60186c5e4f70cfc152d05a13b885debf,4cc7be7a3e37f7a1a780a0db729d12b02c4f4799,TODO: The following is ugly; I just do not know why using inheritance does not work (KMeans.fit(self; X_; y))
aahiijdcd,tslearn-team/tslearn,tslearn/piecewise.py,82a7396905dcfd4685743cdf4711f7183908d48a,79211ce093a8d905f9731a9f0e20c0c73597c995,TODO: cythonize
aahiijdcg,tslearn-team/tslearn,tslearn/clustering.py,93ac53196ef7e3a99cc80ec89440f38333e69a2a,STILL_EXISTS,TODO: Shape Extraction (Alg. 2 in paper)
aahiijdch,tslearn-team/tslearn,tslearn/clustering.py,93ac53196ef7e3a99cc80ec89440f38333e69a2a,STILL_EXISTS,TODO using Alg. 1
aahiijdci,tslearn-team/tslearn,tslearn/clustering.py,e067d6e72e53409cd58f3aa88312f0cdf0e41133,69a5f39b55d2f45b745ef1d797a4198988327169,TODO: provide norms
aahiijddb,tslearn-team/tslearn,tslearn/clustering.py,e067d6e72e53409cd58f3aa88312f0cdf0e41133,STILL_EXISTS,TODO using Alg. 1
aahiijdij,tslearn-team/tslearn,setup.py,ba1182eeaaadb1805319166022128f4823f491f9,STILL_EXISTS,TODO: test package_data option on PyPI deployment\uFFFF
aahiijeac,tslearn-team/tslearn,tslearn/metrics.py,ba1182eeaaadb1805319166022128f4823f491f9,STILL_EXISTS,Needed to deal with edge cases in the recursion.
aahiijefe,tslearn-team/tslearn,tslearn/svm.py,a4719aa230d4494953980f45f6bceed1ac64870d,77147bf9889c38c496cfc83e59a3bcbe814df166,TODO: gamma auto
aahiijeff,tslearn-team/tslearn,tslearn/svm.py,27b395a20b90bbb1894069b0939933b9b7c99b77,b881a01824c993990335fda1fafbe6db0d260959,TODO
aahiijefh,tslearn-team/tslearn,tslearn/svm.py,dfe09f2d8aac5f5416e0dee578f63e903841c14f,77147bf9889c38c496cfc83e59a3bcbe814df166,TODO: gamma auto
aahiijegh,tslearn-team/tslearn,tslearn/datasets.py,7eda0ba9f842e7b125f397182abd513d5d22b17d,8ad8946961e159b740117a3087ac87c7800a1eaa,TODO: not great
aahiijehc,tslearn-team/tslearn,tslearn/clustering.py,f8c8e877aca4ff6d0ecc5c1e864c6e469fcd3eff,13834a26da14b27cbb715eb5a1857342e26aa380,TODO: gamma
aahiijeie,tslearn-team/tslearn,setup.py,89f78d3d810ac282018bc4aaa497a8e54453ed83,e337a20ff8c1a2f1c0f45854220671bf57dc4583,Import numpy here; only when headers are needed
aahiijfcc,tslearn-team/tslearn,tslearn/utils.py,0d9e499f07e809c49b91fb561977742fa41e8471,477bcbd5f900b7e59aa8e97b8140ea7536d59b7f,columns = range(tuple(shape)[2])
aahiijfjj,tslearn-team/tslearn,tslearn/testing_utils.py,a96032116fbd8aaad40a20f179f8c6bb557a02f9,STILL_EXISTS,TODO: make this exception more specific
aahiijgjc,tslearn-team/tslearn,tslearn/tests/test_estimators.py,c46ad4e48e88027ab99f3010d2aec53d34580f9a,STILL_EXISTS,TODO: Create a test similar to check_clustering (check_ts_clustering)
aahiijgjd,tslearn-team/tslearn,tslearn/tests/test_estimators.py,c46ad4e48e88027ab99f3010d2aec53d34580f9a,STILL_EXISTS,TODO: that generates a simple timeseries example and tests whether
aahiijgje,tslearn-team/tslearn,tslearn/tests/test_estimators.py,c46ad4e48e88027ab99f3010d2aec53d34580f9a,STILL_EXISTS,TODO: the clustering algorithm can handle it.
aahiijgjf,tslearn-team/tslearn,tslearn/tests/test_estimators.py,c46ad4e48e88027ab99f3010d2aec53d34580f9a,STILL_EXISTS,TODO: make this exception more specific
aahiijhed,tslearn-team/tslearn,tslearn/utils.py,fac5c08c2cc5b9774b633d4318316a4904a265b4,d7b2fd3573f14b41aea8142aea951f22ee19c293,TODO: What is difference check_equal_size <--> this?
aahiijibc,tslearn-team/tslearn,tslearn/svm.py,746f0147006e2c3f4a1004cc0586a8e85338ce03,45cc6da8a8242d041b71ee95e2caaff4569bcddb,# TODO: This solution is definitely not great; since the kernel
aahiijibd,tslearn-team/tslearn,tslearn/svm.py,746f0147006e2c3f4a1004cc0586a8e85338ce03,45cc6da8a8242d041b71ee95e2caaff4569bcddb,# TODO: depends of the dimensions of the data passed to fit. Thus
aahiijibe,tslearn-team/tslearn,tslearn/svm.py,746f0147006e2c3f4a1004cc0586a8e85338ce03,45cc6da8a8242d041b71ee95e2caaff4569bcddb,# TODO: copying this and applying predict directly will cause crashes.
aahiijibf,tslearn-team/tslearn,tslearn/svm.py,746f0147006e2c3f4a1004cc0586a8e85338ce03,45cc6da8a8242d041b71ee95e2caaff4569bcddb,# TODO: It is currently here bcs of an sklearn requirement to NOT
aahiijibg,tslearn-team/tslearn,tslearn/svm.py,746f0147006e2c3f4a1004cc0586a8e85338ce03,45cc6da8a8242d041b71ee95e2caaff4569bcddb,# TODO: change any values in fit: Estimator TimeSeriesSVC should not
aahiijibh,tslearn-team/tslearn,tslearn/svm.py,746f0147006e2c3f4a1004cc0586a8e85338ce03,45cc6da8a8242d041b71ee95e2caaff4569bcddb,# TODO: change or mutate the parameter kernel from gak to
aahiijibi,tslearn-team/tslearn,tslearn/svm.py,746f0147006e2c3f4a1004cc0586a8e85338ce03,45cc6da8a8242d041b71ee95e2caaff4569bcddb,# TODO: <tslearn.svm.GAKKernel object at 0x7f1a84d9b860> during fit.
aahiijifh,tslearn-team/tslearn,tslearn/metrics.py,aa675be123f2e0101bf643654f7b8dbc4a75c96c,18e41587c2ba6567712e31d1d10732edf6b21fe0,TODO: better doc (formula for the kernel)
aahiijiid,tslearn-team/tslearn,tslearn/tests/test_estimators.py,8574d53937ee4bc0c657916fd2537af86d1a5db6,58f1c21967a118b65ecd7d8b104235c11f38cbc2,TODO: Remove what's below
aahiijijb,tslearn-team/tslearn,tslearn/tests/test_estimators.py,8574d53937ee4bc0c657916fd2537af86d1a5db6,58f1c21967a118b65ecd7d8b104235c11f38cbc2,TODO: Remove the above
aahiijijc,tslearn-team/tslearn,tslearn/tests/test_estimators.py,8574d53937ee4bc0c657916fd2537af86d1a5db6,28737b61301171e7e8214e0eacb667f7525f4c2c,TODO: remove this
aahijaaag,tslearn-team/tslearn,tslearn/metrics.py,843eaeb8fddcf060ecdc6ca00287f3d9aeb31e3e,STILL_EXISTS,TODO: better doc (formula for the kernel)
aahijabcb,tslearn-team/tslearn,tslearn/metrics.py,7608f8c56cbaebc65d090f82cde90057c38d0b3d,STILL_EXISTS,TODO
aahijaegd,tslearn-team/tslearn,tslearn/metrics.py,68b3821ffc7114db2bb90456e45e725d0eb8b520,21eb5ab662cf794a02c07b7c4227824c4aa0e5b0,TODO: fast_gak is now unnormalized
aahijafba,tslearn-team/tslearn,tslearn/tests/test_metrics.py,10aab85e466eb5c0a6904e21cb56ed996a319e29,fda209aae5103ff3324f87f7e6ef01651d589fa9,TODO: assert that a warning is raised in case of unfeasible
aahijagac,tslearn-team/tslearn,tslearn/utils.py,3fe572b4d75af50b05f4e6110927b7e0c89d296d,STILL_EXISTS,TODO: unequal-length case???
aahijahdc,tslearn-team/tslearn,tslearn/barycenters.py,c9f82049dfb573180baf81fbd4c3c627fc0b50f3,c4a449c86b5feee836fb2b1bd19507accc5aea43,TODO: weight is ignored for now
aahijahih,tslearn-team/tslearn,tslearn/shapelets.py,f06f5f290e5e0a13105d3547364a9788b623f7b7,ee101f067ca2f34f9cd468d9b3a3d0eb93bb61d1,TODO: v2-compatible initializer
aahijbbcc,tslearn-team/tslearn,tslearn/shapelets.py,3b28e5d020ad9e424c4c7b4a90c8e475c953500c,11b08f73daeed771c7de3db4893e8d18df6a31b6,TODO: v2-compatible initializer
aahijbcej,tslearn-team/tslearn,tslearn/nonmyop.py,2ad7d9cfaafa0c69fec3f5a7b555165854240882,STILL_EXISTS,Maybe shall right indice_ck_cm rather than ck_cm; dunno what ck_cm is
aahijbgjj,tslearn-team/tslearn,tslearn/utils.py,8cce20c4f3e3be05fe062e86329b21f95c821fa9,b62e493ad68419ad03a89925cdc44a7f8e9e57cb,Ugly hack; not sure how to to it better
aahijcgib,tslearn-team/tslearn,tslearn/docs/examples/plot_kshape.py,e22e722ae9ad5c082220baff5a7533e15e17f84b,STILL_EXISTS,\"\"\" || KShape || ====== ||  || This example uses the KShape clustering method [1] that is based on || cross-correlation to cluster time series. ||  ||  || [1] J. Paparrizos & L. Gravano. k-Shape: Efficient and Accurate Clustering \\ || of Time Series. SIGMOD 2015. pp. 1855-1870. || \"\"\"
aahijdbeh,tslearn-team/tslearn,tslearn/utils.py,e22e722ae9ad5c082220baff5a7533e15e17f84b,5bd6384c434043417b751bef786320326d7265b9,Ugly hack; not sure how to to it better
aahijdbhc,tslearn-team/tslearn,tslearn/early_classification.py,36b3d1477ddf06ecf0ca5d0824b7c935ecb011fe,870c36b889ad5227237abd6f4feaa0db70378b61,normalize parameter seems to be quite recent in sklearn;
aahijdbhg,tslearn-team/tslearn,tslearn/early_classification.py,e41e3dcd96350445d6849223a40a1ac9c5084088,4db1bebe787f6c3cbce287904278e5b7ff0be764,normalize parameter seems to be quite recent in sklearn;
aahijdbhj,tslearn-team/tslearn,tslearn/early_classification.py,6ffdbc8963b6bcaf686af1a9d5b7ddf212dbfc31,STILL_EXISTS,normalize parameter seems to be quite recent in sklearn;
aahijdcgi,tslearn-team/tslearn,tslearn/clustering.py,82922f469b1c1ac885e1d8c8dfef75f0ac386a30,STILL_EXISTS,XXX: numerical imprecision can result in a candidate_id out of range
aahijdchc,tslearn-team/tslearn,tslearn/clustering.py,82922f469b1c1ac885e1d8c8dfef75f0ac386a30,STILL_EXISTS,Permanently add best center candidate found in local tries
aahijdchd,tslearn-team/tslearn,tslearn/tests/test_clustering.py,eeca5c7fd9c53441cc9421ccd03c25eb7a4107bf,STILL_EXISTS,TODO: add a check that ensures that cluster centers maintain their
aahijdhjd,tslearn-team/tslearn,tslearn/neighbors.py,f676d045295e8da9b5d816e4719317864d9dff20,8ab2e3fb84b6c18f4f77fd6e8b91b453b33eff71,TODO: probably we could remove multioutput since it should be inherited from sklearn parent class
aahijeeej,tslearn-team/tslearn,tslearn/bases.py,375a8a8271074641347574c89fda04c1034b31c5,9398f755af6d0d569f345cdf7e7f368b372ebaa1,Then maybe it was rather a list of arrays
aahijeefb,tslearn-team/tslearn,tslearn/shapelets.py,b044053f98674b1dd23cc9225da8f016f2e5527e,32a91dcf6e9c45fcf9e485fbef9b0e6a42fad0b5,TODO: v2-compatible initializer
aahijeefe,tslearn-team/tslearn,tslearn/bases.py,340c77bf7e2c2595d68c430fb6831a8679cdd885,9398f755af6d0d569f345cdf7e7f368b372ebaa1,Then maybe it was rather a list of arrays
aahijeefi,tslearn-team/tslearn,tslearn/shapelets.py,ecce2d0534851c6f3fe060bf074217be053b43e0,fcb64c65c0272a1a1751c910be495e2d962842c1,TODO: v2-compatible initializer
aahijeehh,tslearn-team/tslearn,tslearn/datasets.py,9803bc0a02b7a6559abbace74c353ca83ca60047,2697fe502e54a7bdd58c169e3f75263fc6d4a3e2,TODO: look for txt files first; them arff files
aahijfbaj,tslearn-team/tslearn,tslearn/utils.py,9803bc0a02b7a6559abbace74c353ca83ca60047,2697fe502e54a7bdd58c169e3f75263fc6d4a3e2,TODO: only if possible
aahijfbca,tslearn-team/tslearn,tslearn/bases.py,fcb64c65c0272a1a1751c910be495e2d962842c1,9398f755af6d0d569f345cdf7e7f368b372ebaa1,Then maybe it was rather a list of arrays
aahijfbda,tslearn-team/tslearn,tslearn/matrix_profile.py,efbaf815233f884150d6bae759cbe121ad91099c,1295ba77fa7d57886cb035383cac81d9e786f615,TODO: look for a pure numpy way to do this
aahijfbjb,tslearn-team/tslearn,tslearn/shapelets.py,9398f755af6d0d569f345cdf7e7f368b372ebaa1,STILL_EXISTS,TODO: v2-compatible initializer
aahijfbjj,tslearn-team/tslearn,tslearn/bases.py,2b14c9ca9fad868c8c5ba01d6d6b58546e6ee7e2,STILL_EXISTS,Then maybe it was rather a list of arrays
aahijffdj,tslearn-team/tslearn,tslearn/metrics/softdtw_variants.py,d8052e91226aeb65303279d24d95443a9d76c99a,STILL_EXISTS,TODO: better doc (formula for the kernel)
aahijffef,tslearn-team/tslearn,tslearn/metrics/softdtw_variants.py,d8052e91226aeb65303279d24d95443a9d76c99a,STILL_EXISTS,Needed to deal with edge cases in the recursion.
aahijfjhb,sepandhaghighi/pycm,Test/function_test.py,08b97647cf9dde79b30eeed854375df1fa402953,STILL_EXISTS,''' || >>> from pycm import * || >>> import os || >>> import json || >>> y_actu = [2; 0; 2; 2; 0; 1; 1; 2; 2; 0; 1; 2] || >>> y_pred = [0; 0; 2; 1; 0; 2; 1; 0; 2; 0; 2; 2] || >>> cm = ConfusionMatrix(y_actu; y_pred) || >>> cm.relabel({0:\"L1\";1:\"L2\";2:\"L3\"}) || >>> pycm_help() || <BLANKLINE> || PyCM is a multi-class confusion matrix library written in Python that || supports both input data vectors and direct matrix; and a proper tool for || post-classification model evaluation that supports most classes and overall || statistics parameters. || PyCM is the swiss-army knife of confusion matrices; targeted mainly at || data scientists that need a broad array of metrics for predictive models || and an accurate evaluation of large variety of classifiers. || <BLANKLINE> || Repo : https:\/\/github.com\/sepandhaghighi\/pycm || Webpage : http:\/\/www.pycm.ir || <BLANKLINE> || <BLANKLINE> || >>> RCI_calc(24;0) || 'None' || >>> CBA_calc([1;2]; {1:{1:0;2:0};2:{1:0;2:0}}; {1:0;2:0}; {1:0;2:0}) || 'None' || >>> RR_calc([]; {1:0;2:0}) || 'None' || >>> overall_MCC_calc([1;2]; {1:{1:0;2:0};2:{1:0;2:0}}; {1:0;2:0}; {1:0;2:0}) || 'None' || >>> CEN_misclassification_calc({1:{1:0;2:0};2:{1:0;2:0}};{1:0;2:0};{1:0;2:0};1;1;2) || 'None' || >>> vector_check([1;2;3;0.4]) || False || >>> vector_check([1;2;3;-2]) || False || >>> matrix_check({1:{1:0.5;2:0};2:{1:0;2:0}}) || False || >>> matrix_check([]) || False || >>> TTPN_calc(0;0) || 'None' || >>> TTPN_calc(1;4) || 0.2 || >>> FXR_calc(None) || 'None' || >>> FXR_calc(0.2) || 0.8 || >>> ACC_calc(0;0;0;0) || 'None' || >>> ACC_calc(1;1;3;4) || 0.2222222222222222 || >>> MCC_calc(0;2;0;2) || 'None' || >>> MCC_calc(1;2;3;4) || -0.408248290463863 || >>> LR_calc(1;2) || 0.5 || >>> LR_calc(1;0) || 'None' || >>> MK_BM_calc(2;\"None\") || 'None' || >>> MK_BM_calc(1;2) || 2 || >>> PRE_calc(None;2) || 'None' || >>> PRE_calc(1;5) || 0.2 || >>> PRE_calc(1;0) || 'None' || >>> G_calc(None;2) || 'None' || >>> G_calc(1;2) || 1.4142135623730951 || >>> RACC_calc(2;3;4) || 0.375 || >>> reliability_calc(1;None) || 'None' || >>> reliability_calc(2;0.3) || 1.7 || >>> micro_calc({1:2;2:3};{1:1;2:4}) || 0.5 || >>> micro_calc({1:2;2:3};None) || 'None' || >>> macro_calc(None) || 'None' || >>> macro_calc({1:2;2:3}) || 2.5 || >>> F_calc(TP=0;FP=0;FN=0;Beta=1) || 'None' || >>> F_calc(TP=3;FP=2;FN=1;Beta=5) || 0.7428571428571429 || >>> ERR_calc(None) || 'None' || >>> ERR_calc(0.1) || 0.9 || >>> cm.F_beta(4)[\"L1\"] || 0.9622641509433962 || >>> cm.F_beta(4)[\"L2\"] || 0.34 || >>> cm.F_beta(4)[\"L3\"] || 0.504950495049505 || >>> kappa_analysis_koch(-0.1) || 'Poor' || >>> kappa_analysis_koch(0) || 'Slight' || >>> kappa_analysis_koch(0.2) || 'Fair' || >>> kappa_analysis_koch(0.4) || 'Moderate' || >>> kappa_analysis_koch(0.6) || 'Substantial' || >>> kappa_analysis_koch(0.8) || 'Almost Perfect' || >>> kappa_analysis_koch(1.2) || 'None' || >>> kappa_analysis_fleiss(0.4) || 'Intermediate to Good' || >>> kappa_analysis_fleiss(0.75) || 'Excellent' || >>> kappa_analysis_fleiss(1.2) || 'Excellent' || >>> kappa_analysis_altman(-0.2) || 'Poor' || >>> kappa_analysis_altman(0.2) || 'Fair' || >>> kappa_analysis_altman(0.4) || 'Moderate' || >>> kappa_analysis_altman(0.6) || 'Good' || >>> kappa_analysis_altman(0.8) || 'Very Good' || >>> kappa_analysis_altman(1.2) || 'None' || >>> kappa_analysis_fleiss(0.2) || 'Poor' || >>> kappa_analysis_cicchetti(0.3) || 'Poor' || >>> kappa_analysis_cicchetti(0.5) || 'Fair' || >>> kappa_analysis_cicchetti(0.65) || 'Good' || >>> kappa_analysis_cicchetti(0.8) || 'Excellent' || >>> PLR_analysis(1) || 'Negligible' || >>> PLR_analysis(3) || 'Poor' || >>> PLR_analysis(7) || 'Fair' || >>> PLR_analysis(11) || 'Good' || >>> DP_analysis(0.2) || 'Poor' || >>> DP_analysis(1.5) || 'Limited' || >>> DP_analysis(2.5) || 'Fair' || >>> DP_analysis(10) || 'Good' || >>> AUC_analysis(0.5) || 'Poor' || >>> AUC_analysis(0.65) || 'Fair' || >>> AUC_analysis(0.75) || 'Good' || >>> AUC_analysis(0.86) || 'Very Good' || >>> AUC_analysis(0.97) || 'Excellent' || >>> AUC_analysis(1.0) || 'Excellent' || >>> PC_PI_calc(1;1;1) || 'None' || >>> PC_PI_calc({1:12};{1:6};{1:45}) || 0.04000000000000001 || >>> PC_AC1_calc(1;1;1) || 'None' || >>> PC_AC1_calc({1:123;2:2};{1:120;2:5};{1:125;2:125}) || 0.05443200000000002 || >>> y_act=[0;0;0;0;0;0;0;0;0;0;0;0;1;1;1;1;1;1;1;1;1;2;2;2;2;2;2] || >>> y_pre=[0;0;0;0;0;0;0;0;0;1;1;1;0;0;0;1;1;1;1;1;2;0;1;2;2;2;2] || >>> cm2=ConfusionMatrix(y_act;y_pre) || >>> chi_squared=chi_square_calc(cm2.classes;cm2.table;cm2.TOP;cm2.P;cm2.POP) || >>> chi_squared || 15.525641025641026 || >>> population = list(cm2.POP.values())[0] || >>> phi_squared=phi_square_calc(chi_squared;population) || >>> phi_squared || 0.5750237416904084 || >>> V=cramers_V_calc(phi_squared;cm2.classes) || >>> V || 0.5362013342441477 || >>> DF=DF_calc(cm2.classes) || >>> DF || 4 || >>> SE=se_calc(cm2.Overall_ACC;population) || >>> SE || 0.09072184232530289 || >>> CI=CI_calc(cm2.Overall_ACC;SE) || >>> CI || (0.48885185570907297; 0.8444814776242603) || >>> response_entropy=entropy_calc(cm2.TOP;cm2.POP) || >>> response_entropy || 1.486565953154142 || >>> reference_entropy=entropy_calc(cm2.P;cm2.POP) || >>> reference_entropy || 1.5304930567574824 || >>> cross_entropy = cross_entropy_calc(cm2.TOP;cm2.P;cm2.POP) || >>> cross_entropy || 1.5376219392005763 || >>> join_entropy = joint_entropy_calc(cm2.classes;cm2.table;cm2.POP) || >>> join_entropy || 2.619748965432189 || >>> conditional_entropy = conditional_entropy_calc(cm2.classes;cm2.table;cm2.P;cm2.POP) || >>> conditional_entropy || 1.089255908674706 || >>> kl_divergence=kl_divergence_calc(cm2.P;cm2.TOP;cm2.POP) || >>> kl_divergence || 0.007128882443093773 || >>> lambda_B=lambda_B_calc(cm2.classes;cm2.table;cm2.TOP;population) || >>> lambda_B || 0.35714285714285715 || >>> lambda_A=lambda_A_calc(cm2.classes;cm2.table;cm2.P;population) || >>> lambda_A || 0.4 || >>> IS_calc(13;0;0;38) || 1.5474877953024933 || >>> kappa_no_prevalence_calc(cm2.Overall_ACC) || 0.33333333333333326 || >>> reliability_calc(cm2.Overall_RACC;cm2.Overall_ACC) || 0.4740259740259741 || >>> mutual_information_calc(cm2.ResponseEntropy;cm2.ConditionalEntropy) || 0.39731004447943596 || >>> cm3=ConfusionMatrix(matrix=cm2.table) || >>> cm3 || pycm.ConfusionMatrix(classes: [0; 1; 2]) || >>> cm3.CI || (0.48885185570907297; 0.8444814776242603) || >>> cm3.Chi_Squared || 15.525641025641026 || >>> cm3.Phi_Squared || 0.5750237416904084 || >>> cm3.V || 0.5362013342441477 || >>> cm3.DF || 4 || >>> cm3.ResponseEntropy || 1.486565953154142 || >>> cm3.ReferenceEntropy || 1.5304930567574824 || >>> cm3.CrossEntropy || 1.5376219392005763 || >>> cm3.JointEntropy || 2.619748965432189 || >>> cm3.ConditionalEntropy || 1.089255908674706 || >>> cm3.KL || 0.007128882443093773 || >>> cm3.LambdaA || 0.4 || >>> cm3.LambdaB || 0.35714285714285715 || >>> online_help(param=None) || Please choose one parameter : || <BLANKLINE> || Example : online_help(\"J\") or online_help(2) || <BLANKLINE> || 1-95% CI || 2-ACC || 3-AUC || 4-AUCI || 5-AUNP || 6-AUNU || 7-BM || 8-Bennett S || 9-CBA || 10-CEN || 11-Chi-Squared || 12-Chi-Squared DF || 13-Conditional Entropy || 14-Cramer V || 15-Cross Entropy || 16-DOR || 17-DP || 18-DPI || 19-ERR || 20-F0.5 || 21-F1 || 22-F2 || 23-FDR || 24-FN || 25-FNR || 26-FOR || 27-FP || 28-FPR || 29-G || 30-GI || 31-Gwet AC1 || 32-Hamming Loss || 33-IS || 34-J || 35-Joint Entropy || 36-KL Divergence || 37-Kappa || 38-Kappa 95% CI || 39-Kappa No Prevalence || 40-Kappa Standard Error || 41-Kappa Unbiased || 42-LS || 43-Lambda A || 44-Lambda B || 45-MCC || 46-MCEN || 47-MK || 48-Mutual Information || 49-N || 50-NIR || 51-NLR || 52-NPV || 53-Overall ACC || 54-Overall CEN || 55-Overall J || 56-Overall MCC || 57-Overall MCEN || 58-Overall RACC || 59-Overall RACCU || 60-P || 61-P-Value || 62-PLR || 63-PLRI || 64-POP || 65-PPV || 66-PPV Macro || 67-PPV Micro || 68-PRE || 69-Phi-Squared || 70-RACC || 71-RACCU || 72-RCI || 73-RR || 74-Reference Entropy || 75-Response Entropy || 76-SOA1(Landis & Koch) || 77-SOA2(Fleiss) || 78-SOA3(Altman) || 79-SOA4(Cicchetti) || 80-Scott PI || 81-Standard Error || 82-TN || 83-TNR || 84-TON || 85-TOP || 86-TP || 87-TPR || 88-TPR Macro || 89-TPR Micro || 90-Y || 91-Zero-one Loss || 92-dInd || 93-sInd || >>> online_help(\"J\") || ... || >>> online_help(4) || ... || >>> NIR_calc({'Class2': 804; 'Class1': 196};1000) # Verified Case || 0.804 || >>> cm = ConfusionMatrix(matrix={0:{0:3;1:1};1:{0:4;1:2}})   # Verified Case || >>> cm.LS[1] || 1.1111111111111112 || >>> cm.LS[0] || 1.0714285714285714 || >>> cm = ConfusionMatrix(matrix={\"Class1\":{\"Class1\":183;\"Class2\":13};\"Class2\":{\"Class1\":141;\"Class2\":663}})  # Verified Case || >>> cm.PValue || 0.000342386296143693 || >>> cm = ConfusionMatrix(matrix={\"Class1\":{\"Class1\":4;\"Class2\":2};\"Class2\":{\"Class1\":2;\"Class2\":4}}) # Verified Case || >>> cm.Overall_CEN || 0.861654166907052 || >>> cm.Overall_MCEN || 0.6666666666666666 || >>> cm.IS[\"Class1\"] || 0.4150374992788437 || >>> cm.IS[\"Class2\"] || 0.4150374992788437 || >>> cm = ConfusionMatrix(matrix={1:{1:5;2:0;3:0};2:{1:0;2:10;3:0};3:{1:0;2:300;3:0}})  # Verified Case || >>> cm.Overall_CEN || 0.022168905807495587 || >>> cm.Overall_MCC || 0.3012440235352457 || >>> cm.CBA || 0.3440860215053763 || >>> cm = ConfusionMatrix(matrix={1:{1:1;2:3;3:0;4:0};2:{1:9;2:1;3:0;4:0};3:{1:0;2:0;3:100;4:0};4:{1:0;2:0;3:0;4:200}}) # Verified Case || >>> cm.RCI || 0.9785616782831341 || >>> cm = ConfusionMatrix(matrix={1:{1:1;2:0;3:3};2:{1:0;2:100;3:0};3:{1:0;2:0;3:200}}) # Verified Case || >>> cm.RCI || 0.9264007150415143 || >>> cm = ConfusionMatrix(matrix={1:{1:5;2:0;3:0};2:{1:0;2:10;3:0};3:{1:0;2:300;3:0}}) || >>> cm.RCI || 0.3675708571923818 || >>> cm = ConfusionMatrix(matrix={1:{1:12806;2:26332};2:{1:5484;2:299777}};transpose=True) # Verified Case || >>> cm.AUC[1] || 0.8097090079101759 || >>> cm.GI[1] || 0.6194180158203517 || >>> cm.Overall_ACC || 0.9076187793808925 || >>> cm.DP[1] || 0.7854399677022138 || >>> cm.Y[1] || 0.6194180158203517 || >>> cm.BM[1] || 0.6194180158203517 || >>> cm = ConfusionMatrix(matrix={1:{1:13182;2:30516};2:{1:5108;2:295593}};transpose=True) # Verified Case || >>> cm.AUC[1] || 0.8135728157964055 || >>> cm.GI[1] || 0.627145631592811 || >>> cm.Overall_ACC || 0.896561836706843 || >>> cm.DP[1] || 0.770700985610517 || >>> cm.Y[1] || 0.627145631592811 || >>> cm.BM[1] || 0.627145631592811 || '''
aahijgdfg,jmwoloso/pychattr,pychattr/channel_attribution/shapley.py,bb7f15686f2d50cd3a770d63887f82ae04acb322,STILL_EXISTS,TODO: remove and use utils.classes implementation
aahijgdgb,jmwoloso/pychattr,pychattr/channel_attribution/shapley.py,bb7f15686f2d50cd3a770d63887f82ae04acb322,STILL_EXISTS,TODO: implement flavor where ordering is important
aahijgdic,jmwoloso/pychattr,pychattr/channel_attribution/shapley.py,bb7f15686f2d50cd3a770d63887f82ae04acb322,STILL_EXISTS,TODO: remove
aahijgeaf,jmwoloso/pychattr,dev/python_scripts/preprocessing.py,76f6b41a6cb11bc616f8660a3c3a972227e92c33,STILL_EXISTS,TODO: we'll need to add routines to aggregate the conversions by
aahijgeaj,jmwoloso/pychattr,dev/python_scripts/setup_markov.py,76f6b41a6cb11bc616f8660a3c3a972227e92c33,STILL_EXISTS,TODO: update description
aahijgeba,jmwoloso/pychattr,dev/python_scripts/setup_markov.py,76f6b41a6cb11bc616f8660a3c3a972227e92c33,STILL_EXISTS,TODO: update requires
aahijgebb,jmwoloso/pychattr,dev/python_scripts/setup_markov.py,76f6b41a6cb11bc616f8660a3c3a972227e92c33,STILL_EXISTS,TODO: update classifiers
aahijgebd,jmwoloso/pychattr,dev/python_scripts/setup_markov.py,25b8d88585680219c7ec9925f89fc9544d19cb3f,STILL_EXISTS,TODO: figure out how to 1) test for windows and 2) set the compiler
aahijgecc,jmwoloso/pychattr,v0.2.0/__init__.py,683696136ec23674be332d4d9d8ce5680a1b2238,STILL_EXISTS,TODO: versioning
aahijgece,jmwoloso/pychattr,pychattr/__init__.py,69d327a5ddd772f3a549a43339317b2c0dc05b65,3fc2b3ab395e69cb1e129ee6e9bbb873d4760671,TODO: versioning
aahijgedj,jmwoloso/pychattr,pychattr/models/channel_attribution/mixins.py,f2b94e9655fb184df3e48c3049e836fc05e1884f,STILL_EXISTS,\"\"\" || mixins.py: Mixin classes containing common functionality for the || various model specifications. || \"\"\"
aahijgeed,jmwoloso/pychattr,pychattr/models/channel_attribution/heuristic.py,b4f5060bb31fd9f089e396c0434df6a9f735527b,STILL_EXISTS,TODO: param\/input validation
aahijgega,jmwoloso/pychattr,pychattr/models/channel_attribution/internal/logic/heuristic.py,169b13696f8c7073883d799963966b427b0aa5f8,STILL_EXISTS,TODO: is there a cleaner way to do this?
aahijgegb,jmwoloso/pychattr,pychattr/models/channel_attribution/internal/utils/heuristic.py,169b13696f8c7073883d799963966b427b0aa5f8,50d28214adb995f5da32fbb376d632a25cae72ec,TODO: might want to break this out of the partial
aahijgfja,jmwoloso/pychattr,pychattr/models/channel_attribution/internal/logic/heuristic.py,fcb2ded8873cca11408337703f72070b989e5fa4,STILL_EXISTS,TODO: put in checks for each that paths meet the minimum
aahijggji,jmwoloso/pychattr,pychattr/shapley/__init__.py,3fc2b3ab395e69cb1e129ee6e9bbb873d4760671,STILL_EXISTS,Author: Abhi Sivasailam < your email here >
aahijghab,jmwoloso/pychattr,pychattr/shapley/_mixins.py,3fc2b3ab395e69cb1e129ee6e9bbb873d4760671,STILL_EXISTS,Author: Abhi Sivasailam < your email here >
aahijghag,jmwoloso/pychattr,pychattr/shapley/_shapley.py,3fc2b3ab395e69cb1e129ee6e9bbb873d4760671,STILL_EXISTS,put your model logic here; no elegance needed.
aahijghcd,jmwoloso/pychattr,v0.1.0/channel_attribution/pychattr.py,9f3bd4ddb261f53093c016feef8af5978f514800,STILL_EXISTS,TODO: parameterize \"channel_name\"?
aahijghfe,jmwoloso/pychattr,v0.1.0/channel_attribution/shapley.py,9f3bd4ddb261f53093c016feef8af5978f514800,STILL_EXISTS,TODO: remove and use utils.classes implementation
aahijghfj,jmwoloso/pychattr,v0.1.0/channel_attribution/shapley.py,9f3bd4ddb261f53093c016feef8af5978f514800,STILL_EXISTS,TODO: implement flavor where ordering is important
aahijghia,jmwoloso/pychattr,v0.1.0/channel_attribution/shapley.py,9f3bd4ddb261f53093c016feef8af5978f514800,STILL_EXISTS,TODO: remove
aahijgifd,jmwoloso/pychattr,pychattr/channel_attribution/_mixins.py,477c1c08ed347b291e1a1d9d8505f652dd10f006,STILL_EXISTS,TODO: incorporate into heuristic models
aahijgifi,jmwoloso/pychattr,pychattr/channel_attribution/_markov.py,8083c6f72a4ab41b5099ab874e0055e77d8a7090,STILL_EXISTS,TODO: use scipy sparse matrices
aahijgjib,jmwoloso/pychattr,pychattr/channel_attribution/_markov.py,8083c6f72a4ab41b5099ab874e0055e77d8a7090,4936ca7996c27c4b7532a8c58321960dcbcd1ef2,TODO: put this back in place
aahijhdgi,jmwoloso/pychattr,pychattr/channel_attribution/_markov.py,4936ca7996c27c4b7532a8c58321960dcbcd1ef2,STILL_EXISTS,#TODO: PASSED
aahijhhad,jmwoloso/pychattr,pychattr/channel_attribution/_markov.py,4936ca7996c27c4b7532a8c58321960dcbcd1ef2,STILL_EXISTS,# TODO: put this back in place
aahijjbig,jmwoloso/pychattr,pychattr/channel_attribution/_markov.py,f36332c9413ba2a54da4701adb868276b8fc731e,c04fb41158a757fbae1789516b47bada38857d76,# TODO: implement this below
aahijjcch,jmwoloso/pychattr,pychattr/channel_attribution/_markov.py,24e200814500e5e64c847208103d08ea735338da,STILL_EXISTS,TODO: use scipy sparse matrices
aahijjdcb,jmwoloso/pychattr,pychattr/channel_attribution/_markov.py,9d791d5204411e2ae63b6e1eeea0ed14275977cf,97ebaabd514bdc93302d787d9cc94153689aca96,TODO: NOT PASSED
aahijjede,jmwoloso/pychattr,pychattr/channel_attribution/_markov.py,9d791d5204411e2ae63b6e1eeea0ed14275977cf,97ebaabd514bdc93302d787d9cc94153689aca96,TODO: put this back in place
aahjaacde,redone17/deep-sensor,run.py,b2bf722be16c710a8c3242c28691c372a0614b15,6b6fbb2f2757f2469a9b5b1ad721612320c7f35f,TODO
aahjaacfa,redone17/deep-sensor,run.py,36f3105f7f0eaec469aac4d05664fca62754f38c,6b6fbb2f2757f2469a9b5b1ad721612320c7f35f,TODO
aahjaadai,bedapudi6788/txt2txt,txt2txt/txt2txt.py,654cbcd830513fc89e9e62528affbd53386739c3,STILL_EXISTS,Need to make the number of hidden units configurable
aahjaadbe,JohnVinyard/zounds,analyze/audiostream.py,30375cebfe4a7944d253dae78f30e5afbdfdd819,STILL_EXISTS,TODO: Write tests
aahjaadbf,JohnVinyard/zounds,analyze/audiostream.py,30375cebfe4a7944d253dae78f30e5afbdfdd819,15d063a377234b0eabd454fb7848e1ce05b354e9,TODO: Write docs
aahjaadbg,JohnVinyard/zounds,analyze/audiostream.py,30375cebfe4a7944d253dae78f30e5afbdfdd819,STILL_EXISTS,TODO: Handle stereo files
aahjaadbh,JohnVinyard/zounds,analyze/audiostream.py,30375cebfe4a7944d253dae78f30e5afbdfdd819,STILL_EXISTS,TODO: Handle encodings other than int16
aahjaadbi,JohnVinyard/zounds,analyze/audiostream.py,30375cebfe4a7944d253dae78f30e5afbdfdd819,STILL_EXISTS,TODO: Handle other sample rates by re-sampling
aahjaadid,JohnVinyard/zounds,learn/nnet.py,30375cebfe4a7944d253dae78f30e5afbdfdd819,STILL_EXISTS,BEGIN KLUDGE:
aahjaadif,JohnVinyard/zounds,learn/nnet.py,30375cebfe4a7944d253dae78f30e5afbdfdd819,STILL_EXISTS,END KLUDGE
aahjaaecd,JohnVinyard/zounds,learn/util.py,30375cebfe4a7944d253dae78f30e5afbdfdd819,STILL_EXISTS,KLUDGE: This is copied straight from zounds\/onsets.py
aahjaaedd,JohnVinyard/zounds,tests/analyze_test.py,30375cebfe4a7944d253dae78f30e5afbdfdd819,STILL_EXISTS,TODO: How do I cleanup wave files for failed tests?
aahjaaede,JohnVinyard/zounds,tests/analyze_test.py,30375cebfe4a7944d253dae78f30e5afbdfdd819,STILL_EXISTS,TODO: I've got the encoding (int16) hardcoded in a few places.
aahjaaedf,JohnVinyard/zounds,tests/analyze_test.py,30375cebfe4a7944d253dae78f30e5afbdfdd819,STILL_EXISTS,Fix it!
aahjaaeee,JohnVinyard/zounds,util.py,d3f3f12ce513b79a3f37065f0fa630ecc0e91116,9ec0dbc83fbb1926b6b2c0d42090f8a146a88f07,TODO: Write tests
aahjaaeeg,JohnVinyard/zounds,util.py,d3f3f12ce513b79a3f37065f0fa630ecc0e91116,a2446574822af7e6ed226d6ca41351ae92357f79,Is there a better; faster numpy way to do this?
aahjaaefe,JohnVinyard/zounds,analyze/extractor.py,f0241ce5c2934240c17a986ac8560accae81e1fa,53caac22ffb6995fa0e6fe906d800a605695e47c,TODO: Write tests
aahjaaeff,JohnVinyard/zounds,analyze/extractor.py,f0241ce5c2934240c17a986ac8560accae81e1fa,9aa1f3739f00ab63723722b7ba714e2d9f368d87,TODO: Write Docs
aahjaaefg,JohnVinyard/zounds,analyze/extractor.py,f0241ce5c2934240c17a986ac8560accae81e1fa,9aa1f3739f00ab63723722b7ba714e2d9f368d87,TODO: ExtractorChains need to know about _id; so they can create
aahjaaefi,JohnVinyard/zounds,analyze/extractor.py,f0241ce5c2934240c17a986ac8560accae81e1fa,STILL_EXISTS,a list of other extractors needed by this one
aahjaaegb,JohnVinyard/zounds,analyze/extractor.py,f0241ce5c2934240c17a986ac8560accae81e1fa,STILL_EXISTS,the number of frames needed from all sources to
aahjaaehf,JohnVinyard/zounds,tests/analyze_test.py,f0241ce5c2934240c17a986ac8560accae81e1fa,STILL_EXISTS,TODO: Derived classes shouldn't have to know to set done to
aahjaaehi,JohnVinyard/zounds,tests/analyze_test.py,f0241ce5c2934240c17a986ac8560accae81e1fa,881006a4d46b2b49577aff8e408a41450d4fe239,TODO: Sum extractor with a step size of 2
aahjaafaf,JohnVinyard/zounds,analyze/util.py,9a4fce7c4d843c5d1fa325ee66b6bde3e5cdc495,STILL_EXISTS,TODO: Write tests
aahjaafai,JohnVinyard/zounds,analyze/audiostream.py,13ef571a03fe87926c30e7b3aa506d7a360ef244,8648007abf4834cd12ddbeb5293bf846d4ef419f,TODO: Write test for encoding bug
aahjaaiff,JohnVinyard/zounds,analyze/feature.py,32a4369976d8ab1627a1d103765fca853c030364,STILL_EXISTS,TODO: Implement Pitch; BFCC; Centroid; Flatness; Bark; Tempo; Chroma;
aahjaaiga,JohnVinyard/zounds,learn/pipeline.py,32a4369976d8ab1627a1d103765fca853c030364,STILL_EXISTS,TODO: Should a pipeline be Extractor-derived?
aahjaaihb,JohnVinyard/zounds,learn/pipeline.py,32a4369976d8ab1627a1d103765fca853c030364,STILL_EXISTS,TODO checkpoints; incremental save
aahjaaiig,JohnVinyard/zounds,util.py,d2cf6e9f29b32941348d2a4c9a9e83623d73cfbf,05cf0aff367a1d378f1945c2263d048d56e0c83a,TODO: Get rid of this file! Move pad into a more appropriate location
aahjaaiih,JohnVinyard/zounds,config.py,59369117f84fe0fcd4048e36be1a5baf39e810b5,STILL_EXISTS,TODO: I don't like the redundancy here; i.e.; the model class is a key in
aahjaaiij,JohnVinyard/zounds,tests/learn_preprocess_test.py,73fe9e9b2d19fc0420451cd6ce8ac4361eda5126,STILL_EXISTS,KLUDGE: This feels like kind of a stupid test; since the behavior
aahjaaijj,JohnVinyard/zounds,learn/nnet/nnet.py,8648007abf4834cd12ddbeb5293bf846d4ef419f,STILL_EXISTS,TODO: package up these activation functions somehow
aahjaajai,JohnVinyard/zounds,analyze/extractor.py,bea8b76d66e94099c7b8e0ea999f062fb82eb3be,STILL_EXISTS,a list of other extractors needed by this one
aahjaajbi,JohnVinyard/zounds,config.py,bea8b76d66e94099c7b8e0ea999f062fb82eb3be,54d945f299ae93e64a1ef5d1be92d4e2faa072b8,TODO: FrameModel needs to have a metaclass that knows how to map extractors
aahjaajca,JohnVinyard/zounds,data/pattern.py,bea8b76d66e94099c7b8e0ea999f062fb82eb3be,6c4a2bece61d451db75cc5451b34ee647057420c,TODO: A big question that needs to be answered is this:
aahjaajcf,JohnVinyard/zounds,model/pattern.py,bea8b76d66e94099c7b8e0ea999f062fb82eb3be,13fab3331ff20f6b9ce014fcbe42a74b0bd84265,TODO: Move this into Model
aahjaajdb,JohnVinyard/zounds,util.py,1641d6034e1aa30ca8790344f3bb58d8f57052d8,STILL_EXISTS,TODO: This is used in analyze.extractor and model.frame. Can it be
aahjaajdc,JohnVinyard/zounds,util.py,1641d6034e1aa30ca8790344f3bb58d8f57052d8,STILL_EXISTS,factored out into a *better*; common location?
aahjaajde,JohnVinyard/zounds,model/frame.py,3e141751900cbf9ac8d058fe221b7bec8f3a7489,a388fa9d1f83a1c6bbc1426394826c724ab5860c,TODO: Write documentation
aahjaajdf,JohnVinyard/zounds,model/frame.py,3e141751900cbf9ac8d058fe221b7bec8f3a7489,a388fa9d1f83a1c6bbc1426394826c724ab5860c,TODO: Write tests
aahjaajdg,JohnVinyard/zounds,model/frame.py,3e141751900cbf9ac8d058fe221b7bec8f3a7489,STILL_EXISTS,TODO: MultiFeature class (like for minhash features)
aahjaajdh,JohnVinyard/zounds,model/frame.py,3e141751900cbf9ac8d058fe221b7bec8f3a7489,STILL_EXISTS,Possible KLUDGE: Creating a dictionary with a subset of class
aahjaajdi,JohnVinyard/zounds,model/frame.py,3e141751900cbf9ac8d058fe221b7bec8f3a7489,STILL_EXISTS,properties seems kind of redundant; but I'm trying to avoid
aahjaajea,JohnVinyard/zounds,model/frame.py,3e141751900cbf9ac8d058fe221b7bec8f3a7489,STILL_EXISTS,TODO: Grabs features; builds an Extractor chain; can check db for
aahjaajej,JohnVinyard/zounds,model/frame.py,3e141751900cbf9ac8d058fe221b7bec8f3a7489,STILL_EXISTS,been built by the time they're needed by another extractor
aahjaajfi,JohnVinyard/zounds,config.py,ce9610c9fc6972f7f77ab12fbbc3cf94e0a1f50f,617e8c58ecad96ab9d2e6193740873a641163e77,TODO: This has to work with multi-threaded and\/or multi-process applications
aahjaajgh,JohnVinyard/zounds,analyze/extractor.py,617e8c58ecad96ab9d2e6193740873a641163e77,STILL_EXISTS,TODO: This should be an abstract base class
aahjaajib,JohnVinyard/zounds,model/frame.py,76b2c4f7c7734a417e30c82fa55db9dcefe0340b,a388fa9d1f83a1c6bbc1426394826c724ab5860c,TODO: Write tests
aahjababb,JohnVinyard/zounds,model/frame.py,c239084b5d97ebc69586764c0ba7b0ce7cd2a79f,55f0a99f5f1026abe65250f5d9756dc1f3bf80f8,TODO: Chain should be a function that can create an appropriate
aahjabacb,JohnVinyard/zounds,acquire/acquirer.py,c62f509ae7cac86918bcf48b7e44434fbd0056a2,54d945f299ae93e64a1ef5d1be92d4e2faa072b8,TODO: If source is none; make source the
aahjabada,JohnVinyard/zounds,analyze/extractor.py,e5b4dbdc449d9823a8271b8ecfebd3c6cbb676b8,84e584eb660f9a752cb188a6ee581f9060a5de23,TODO: Write tests
aahjabadb,JohnVinyard/zounds,model/frame.py,0790f510f81fb5b94bf761db8f8a69db1924c53a,a388fa9d1f83a1c6bbc1426394826c724ab5860c,TODO: Write tests
aahjabadc,JohnVinyard/zounds,model/frame.py,0790f510f81fb5b94bf761db8f8a69db1924c53a,STILL_EXISTS,KLUDGE: I have to pass a filename to build an extractor chain;
aahjabadf,JohnVinyard/zounds,model/frame_test.py,0790f510f81fb5b94bf761db8f8a69db1924c53a,STILL_EXISTS,KLUDGE: I should really write my own Extractor-derived class for this
aahjabaef,JohnVinyard/zounds,data/frame.py,ee9d99cddc10b9b56994b537990b0e6aa4795dd1,STILL_EXISTS,TODO: How do I switch between read and write modes as necessary in a
aahjabaei,JohnVinyard/zounds,data/frame.py,ee9d99cddc10b9b56994b537990b0e6aa4795dd1,STILL_EXISTS,KLUDGE: PyTables allows the creation of columns using a string
aahjabafg,JohnVinyard/zounds,model/frame.py,ee9d99cddc10b9b56994b537990b0e6aa4795dd1,a388fa9d1f83a1c6bbc1426394826c724ab5860c,KLUDGE: These shouldn't be special. They should be defined just like
aahjabagd,JohnVinyard/zounds,data/frame.py,1a5489009459daa3f197bd9d0595ba81b65cdf48,STILL_EXISTS,KLUDGE: This should be somehow determined by FrameModel also
aahjabagj,JohnVinyard/zounds,data/frame.py,1a5489009459daa3f197bd9d0595ba81b65cdf48,STILL_EXISTS,TODO: Set a flag that lets everyone know we're writing
aahjabahc,JohnVinyard/zounds,data/frame.py,158313f920fd36272f59e6b8b6ee3ace7836d1f8,a15ef1844b70e46e8ef0bb682bbb00b35b641d4c,TODO: Write documentation
aahjabahd,JohnVinyard/zounds,data/frame.py,158313f920fd36272f59e6b8b6ee3ace7836d1f8,STILL_EXISTS,TODO: Consider moving this out of __init__
aahjabaig,JohnVinyard/zounds,data/frame.py,158313f920fd36272f59e6b8b6ee3ace7836d1f8,STILL_EXISTS,KLUDGE: This should be somehow determined by FrameModel also
aahjabaih,JohnVinyard/zounds,data/frame.py,158313f920fd36272f59e6b8b6ee3ace7836d1f8,STILL_EXISTS,TODO: Set a flag that lets everyone know we're writing
aahjabaij,JohnVinyard/zounds,model/frame.py,158313f920fd36272f59e6b8b6ee3ace7836d1f8,STILL_EXISTS,TODO: MultiFeature class (like for minhash features)
aahjabaja,JohnVinyard/zounds,model/frame.py,158313f920fd36272f59e6b8b6ee3ace7836d1f8,a388fa9d1f83a1c6bbc1426394826c724ab5860c,BUG: This won't work. A span of frames should
aahjabajd,JohnVinyard/zounds,model/frame.py,158313f920fd36272f59e6b8b6ee3ace7836d1f8,STILL_EXISTS,TODO: Refactor this repeated code
aahjabaje,JohnVinyard/zounds,model/frame.py,158313f920fd36272f59e6b8b6ee3ace7836d1f8,a388fa9d1f83a1c6bbc1426394826c724ab5860c,TODO: Factor out 36-length string dtype here
aahjabajh,JohnVinyard/zounds,model/frame.py,158313f920fd36272f59e6b8b6ee3ace7836d1f8,STILL_EXISTS,KLUDGE: I'm assuming that any Features defined on the base
aahjabbab,JohnVinyard/zounds,analyze/feature.py,24e8531afaa2e38a7c065a4b638426b4b1a21d51,STILL_EXISTS,KLUDGE: This is a bit odd.   The RawAudio extractor is telling
aahjabbaf,JohnVinyard/zounds,data/frame.py,24e8531afaa2e38a7c065a4b638426b4b1a21d51,STILL_EXISTS,TODO: Write tests for this method!
aahjabbbg,JohnVinyard/zounds,environment.py,a388fa9d1f83a1c6bbc1426394826c724ab5860c,21463cd7c0750fcc5d7f7d178dde1c5c27d3827f,TODO: Call the FrameController sync() method. This should:
aahjabbfh,JohnVinyard/zounds,data/frame.py,13fab3331ff20f6b9ce014fcbe42a74b0bd84265,baf86716855de3d96283daf6d4f45c5126a86566,TODO: This should return a Frames-derived instance
aahjabbgc,JohnVinyard/zounds,data/frame.py,55f0a99f5f1026abe65250f5d9756dc1f3bf80f8,STILL_EXISTS,TODO: Deal with multiple concurrent processes
aahjabbii,JohnVinyard/zounds,data/frame.py,21463cd7c0750fcc5d7f7d178dde1c5c27d3827f,STILL_EXISTS,TODO: Make sure there are tests
aahjabcaj,JohnVinyard/zounds,data/frame.py,89a8676d6566f0bb37bc159688733e98a94b2ead,STILL_EXISTS,TODO: Cleaner; general way to do PyTables in-kernel queries
aahjabdai,JohnVinyard/zounds,model/pattern.py,97ce87b44677f1ffda19689c916f7cd7d949fd97,STILL_EXISTS,''' || A leaf pattern represents a block of contiguous; ascending (in time) frames in the frames || database. ||  || A branch pattern contains \"pointers\" to leaf patterns (or contiguous subsets of them);  || or other branch patterns. ||  || An audio_event; in the parlance of zounds 1; is a branch pattern that points at || {_id : 1234; source : 'FreeSound'; external_id : '9'}[100:200]; e.g.. This means ||  that there can be pattern ids in the patterns database that don't exist in the ||  frames database. ||  || If a user creates a non-contiguous leaf pattern; this pattern should be appended || to the frames database so that it becomes contiguous; e.g.; the pattern  || [10;30;1;16] would be appended to the end of the frames database with a new id; || a source equal to the user's name; a new external id; and an address equal to || [original_db_length : original_db_length + 4] ||  || There needs to be a level of indirection regarding the address space. Row numbers || might not be appropriate to every conceivable backing store. ||  || BUG: Consider a branch pattern whose phenotype is one measure of the same hihat || sound playing eighth-notes.  This pattern is two-deep. It consists of a single || branch pattern; repeated; which points at a slice of an original; longer sound. || If it is to be a candidate for similarity searches; it must be analyzed and saved || to the frames database. ||  || BUG: What if I edit the aforementioned pattern so that its length changes; or || I delete it?  That means that the addresses of all patterns following it in the || frames database also change; which means all patterns following it must be updated. ||  || BUG: What if I make small changes to a pattern? Must it be re-stored?  What  || about patterns that reference it?  The dumb solution seems to be to always || re-store it; so that I don't have to worry about updating pattern references; || but won't this lead to a very large frames database full of redundant info? ||  || BUG: Consider the last; musical pattern below. What if I simply repeated it || four times (ie; four measures of four quarter notes each)?  Would it be  || re-stored? ||  || \/\/ Leaf Pattern || leaf = { ||     _id         : leaf_uuid; ||     source      : 'FreeSound'; ||     external_id : '9'; ||     ancestors   : (); ||     address     : (start;stop) OR (_id) || } ||  || \/\/ Audio-Event-like pattern. This would not be re-stored; since it represents || \/\/ a single occurrence of a block of contiguous; ascending frames || ae = { ||     _id         : branch_uuid; ||     source      : 'John'; ||     external_id : other_uuid; ||     ancestors   : (leaf_uuid) ||     address     : (start;stop)  || } ||  || \/\/ Musical Patern. This would be re-stored; since it represents multiple  || \/\/ occurrences of a contiguous block. || m = { ||     _id         : music_uuid; ||     source      : 'John'; ||     external_id : other_uuid; ||     ancestors   : (leaf_uuid;branch_uuid); ||     address     : (start;stop); ||     pattern     : [ ||         { ||             time : [0;1;2;3]; ||             _id  : branch_uuid ||         } ||     ]  || } || '''
aahjabdbh,JohnVinyard/zounds,analyze/extractor.py,a37a8afe63e812f05275bd2c6a11013bfec8036f,3fc6e49ae11e2306195a7828a606b880728aa5e9,TODO: Change this to finite = True
aahjabdbi,JohnVinyard/zounds,data/frame.py,a37a8afe63e812f05275bd2c6a11013bfec8036f,bb3158a71cbcc878deb39aa3d61d22d7a0cf49a3,TODO: Write tests
aahjabddc,JohnVinyard/zounds,model/pipeline.py,00b18c0f5ba29de5f54d0ef5ab73c4621fa99f17,7515415d026e922e8081295b30e4ccdd2ff59cfc,For now; I'm only going to implement a fetcher that knows how to read
aahjabddf,JohnVinyard/zounds,data/frame.py,a6c77ee1ab58e7258279e9c3401c0d410a9af7ba,STILL_EXISTS,Here's the less simple workaround
aahjabddg,JohnVinyard/zounds,data/frame.py,a6c77ee1ab58e7258279e9c3401c0d410a9af7ba,bb3158a71cbcc878deb39aa3d61d22d7a0cf49a3,TODO: Write tests
aahjabddh,JohnVinyard/zounds,model/frame.py,a6c77ee1ab58e7258279e9c3401c0d410a9af7ba,STILL_EXISTS,TODO: Write better documentation
aahjabdeg,JohnVinyard/zounds,model/frame.py,b26ca47e6b3b212dde66668b35419bef5588341d,STILL_EXISTS,TODO: Frames classes should implement FramesController interface!
aahjabdeh,JohnVinyard/zounds,model/frame.py,b26ca47e6b3b212dde66668b35419bef5588341d,STILL_EXISTS,TODO: Lazily compute unstored features when requested
aahjabdei,JohnVinyard/zounds,model/frame.py,b26ca47e6b3b212dde66668b35419bef5588341d,STILL_EXISTS,TODO: Should this return a slice of the _data recarray; or
aahjabdfa,JohnVinyard/zounds,model/frame_test.py,b26ca47e6b3b212dde66668b35419bef5588341d,baf86716855de3d96283daf6d4f45c5126a86566,TODO: factor out common code in the following two tests
aahjabdff,JohnVinyard/zounds,analyze/extractor.py,619d6ba8cd8b5ad7d8d20bf26bfa7fb1443d1ac9,84e584eb660f9a752cb188a6ee581f9060a5de23,TODO: Write tests
aahjabdga,JohnVinyard/zounds,model/frame.py,619d6ba8cd8b5ad7d8d20bf26bfa7fb1443d1ac9,97a29be0b37c6128e22a494cc921e66e623b4626,TODO: Write tests
aahjabfcb,JohnVinyard/zounds,model/pattern.py,6c4a2bece61d451db75cc5451b34ee647057420c,STILL_EXISTS,TODO: An audio from db extractor
aahjabfcg,JohnVinyard/zounds,learn/nnet/autoencoder.py,db0ad8902f5e009e598dea387ece8b29d6556d94,9b472c8b21e3d1cd967bb3feb1c52795b651a8b2,TODO: Rename the classes in this file in the correct way; i.e.
aahjabfci,JohnVinyard/zounds,learn/nnet/rbm.py,db0ad8902f5e009e598dea387ece8b29d6556d94,9b472c8b21e3d1cd967bb3feb1c52795b651a8b2,TODO: Rename the classes in this file in the correct way;
aahjabfdf,JohnVinyard/zounds,model/framesearch.py,db0ad8902f5e009e598dea387ece8b29d6556d94,STILL_EXISTS,TODO: I'm not sure the model module package is the appropriate place for this;
aahjabfdh,JohnVinyard/zounds,model/framesearch.py,db0ad8902f5e009e598dea387ece8b29d6556d94,b342ec27c5bd2b0365d32a45ce21932b857f7bae,TODO: ExtractorChain.prune() that can take multiple features and tests
aahjabfdi,JohnVinyard/zounds,model/framesearch.py,db0ad8902f5e009e598dea387ece8b29d6556d94,b342ec27c5bd2b0365d32a45ce21932b857f7bae,TODO: This isn't possible yet. Frames-derived classes must be
aahjabfeg,JohnVinyard/zounds,learn/fetch.py,d659c56595eb0844950f7be447c4e2eadbbe708c,STILL_EXISTS,be sampled; ignoring pattern boundaries and the tail end of the frames
aahjabfei,JohnVinyard/zounds,learn/fetch.py,d659c56595eb0844950f7be447c4e2eadbbe708c,STILL_EXISTS,TODO: return every frame of this feature shuffled
aahjabffc,JohnVinyard/zounds,learn/fetch.py,d659c56595eb0844950f7be447c4e2eadbbe708c,STILL_EXISTS,KLUDGE: What if the pattern is too large to fit into memory?
aahjabfgg,JohnVinyard/zounds,learn/nnet/rbm.py,9b472c8b21e3d1cd967bb3feb1c52795b651a8b2,STILL_EXISTS,TODO: Is this the correct implementation for both Rbm and LinearRbm?
aahjabfgj,JohnVinyard/zounds,data/learn.py,7515415d026e922e8081295b30e4ccdd2ff59cfc,STILL_EXISTS,TODO: Ensure path exists method in util. Factor out of
aahjabgae,JohnVinyard/zounds,model/pipeline.py,9018eeb8f144147ad42d605fb7604cfee3691af1,889675dc2e6165fe42e62dd7c079510abf772983,TODO: Define __getitem__ on class
aahjabgbb,JohnVinyard/zounds,analyze/feature/rawaudio.py,889675dc2e6165fe42e62dd7c079510abf772983,STILL_EXISTS,KLUDGE: This is a bit odd.   The RawAudio extractor is telling
aahjabgci,JohnVinyard/zounds,learn/nnet/autoencoder.py,8bf30e9d6910ae639929a7461503826b7a998892,STILL_EXISTS,TODO: Group into batches!
aahjabgcj,JohnVinyard/zounds,analyze/feature/learned.py,47ca02358d1dd4f97beba7ea739056b5e0d7622e,STILL_EXISTS,TODO: Write tests!
aahjabggg,JohnVinyard/zounds,model/frame.py,21e43ff56fd11867952c71e1f21d01907321d322,STILL_EXISTS,KLUDGE: We're in a situation where the old feature doesn't
aahjabghj,JohnVinyard/zounds,model/framesearch.py,892234251e55f2119185bc296bbe4e699eea7788,b342ec27c5bd2b0365d32a45ce21932b857f7bae,TODO: the dictionary must be turned into a recarray. This already
aahjabgia,JohnVinyard/zounds,model/framesearch.py,892234251e55f2119185bc296bbe4e699eea7788,b342ec27c5bd2b0365d32a45ce21932b857f7bae,happens in PyTablesFramesController. Maybe it can be factored out.
aahjabgib,JohnVinyard/zounds,model/framesearch.py,b342ec27c5bd2b0365d32a45ce21932b857f7bae,STILL_EXISTS,TODO: This should also take an address; and take advantage of precomputed
aahjabgie,JohnVinyard/zounds,model/framesearch.py,b342ec27c5bd2b0365d32a45ce21932b857f7bae,STILL_EXISTS,KLUDGE: This is a total mess!  Doing on the fly audio extraction should
aahjabgje,JohnVinyard/zounds,model/framesearch.py,b342ec27c5bd2b0365d32a45ce21932b857f7bae,STILL_EXISTS,TODO: This needs to be returning a list of backend specific addresses!
aahjabhcb,JohnVinyard/zounds,data/frame.py,312ca31a11e80b388870007c81131bd14758d80f,STILL_EXISTS,Crazy Bad KLUDGE: I rely on FrameController-derived classes to define a back-end
aahjabhcf,JohnVinyard/zounds,model/frame.py,312ca31a11e80b388870007c81131bd14758d80f,STILL_EXISTS,KLUDGE: I'm doing this because SingleInput-derived extractors
aahjabhda,JohnVinyard/zounds,model/framesearch.py,312ca31a11e80b388870007c81131bd14758d80f,d8c9f1d1226e7042e857c749bfc7b13719da1a81,KLUDGE: How do I know *exactly* how long to make the sparse matrix
aahjabhec,JohnVinyard/zounds,model/framesearch.py,312ca31a11e80b388870007c81131bd14758d80f,d8c9f1d1226e7042e857c749bfc7b13719da1a81,KLUDGE: This algorithm assumes that addresses that are contiguous
aahjabhef,JohnVinyard/zounds,model/framesearch.py,312ca31a11e80b388870007c81131bd14758d80f,d8c9f1d1226e7042e857c749bfc7b13719da1a81,keep track of scores in an array where columns represent
aahjabheg,JohnVinyard/zounds,model/framesearch.py,312ca31a11e80b388870007c81131bd14758d80f,d8c9f1d1226e7042e857c749bfc7b13719da1a81,blocks; and columns represent the n best matches for that block
aahjabibj,JohnVinyard/zounds,learn/nnet/parallel_rbm.py,8025b7081ad62f13b184890615027cf8a7ba6f29,STILL_EXISTS,TODO: print errors
aahjabica,JohnVinyard/zounds,learn/nnet/parallel_rbm.py,8025b7081ad62f13b184890615027cf8a7ba6f29,STILL_EXISTS,TODO: do updates
aahjabida,JohnVinyard/zounds,data/frame.py,f6529a00244e733d5962b24bcf5c6fbd6c19635d,bb3158a71cbcc878deb39aa3d61d22d7a0cf49a3,TODO: Write tests
aahjabidh,JohnVinyard/zounds,data/frame_test.py,3466e1b0c77f160fd1bc67305d53602f74120803,STILL_EXISTS,KLUDGE: I've excluded int -> int comparisons
aahjabidi,JohnVinyard/zounds,model/framesearch.py,3466e1b0c77f160fd1bc67305d53602f74120803,58d644f97d98276c4675f22f7fd3fd5871a85bc5,TODO:
aahjabief,JohnVinyard/zounds,model/framesearch.py,3466e1b0c77f160fd1bc67305d53602f74120803,9e624439143112bdd356c61a80c497dd427567af,KLUDGE: This will prefer longer sounds to better matches
aahjabieh,JohnVinyard/zounds,model/framesearch.py,3466e1b0c77f160fd1bc67305d53602f74120803,STILL_EXISTS,KLUDGE: This is cheating. I'm using knowledge about the frames back-end
aahjabjcb,JohnVinyard/zounds,learn/nnet/parallel_rbm.py,e23c2cb5b56db3a9c1696a91291d3ea499f70dbd,STILL_EXISTS,BUG: What if indim isn't evenly divisible by nprocesses?
aahjabjfg,JohnVinyard/zounds,learn/hash/lsh.py,dc08468b757c646b6a1a2fdbfb64c888cc67913c,STILL_EXISTS,KLUDGE: This has to be sensitive to nhashes!
aahjabjfh,JohnVinyard/zounds,learn/nnet/parallel_rbm.py,20916898371010ec5389a6c385a05996c9c94a86,STILL_EXISTS,''' || A parallel rbm training implementation that results in negligible; perhaps even || non-existent speed gains. || '''
aahjabjge,JohnVinyard/zounds,model/framesearch.py,bb3158a71cbcc878deb39aa3d61d22d7a0cf49a3,21425af8d586cf4be1e5777a1f2896c9c879becb,TODO: Try splitting the search among processes. The index should be
aahjabjgi,JohnVinyard/zounds,model/framesearch.py,bb3158a71cbcc878deb39aa3d61d22d7a0cf49a3,21425af8d586cf4be1e5777a1f2896c9c879becb,TODO: Sort by hamming distance from query hash value; OR;
aahjabjhi,JohnVinyard/zounds,model/framesearch.py,fe3f6755c4bf3998409d4392bf5044382eccbc1f,STILL_EXISTS,TODO: Just save the sorted version of the hash codes. The original
aahjabjia,JohnVinyard/zounds,model/framesearch.py,2ceedf3f4ca1b74863cc7ac514dace3f7e59fa62,58d644f97d98276c4675f22f7fd3fd5871a85bc5,TODO:
aahjabjig,JohnVinyard/zounds,model/framesearch.py,2ceedf3f4ca1b74863cc7ac514dace3f7e59fa62,STILL_EXISTS,KLUDGE: This is cheating. Searches should always return back-end
aahjabjja,JohnVinyard/zounds,model/framesearch.py,2ceedf3f4ca1b74863cc7ac514dace3f7e59fa62,09156a68b97fe1b196d2630f6e1dd19c2ae9386a,TODO: Make evaluation method configurable
aahjabjjj,JohnVinyard/zounds,learn/fetch.py,785f946038b6ff2fb784bb4f654ccbb244e92e8d,STILL_EXISTS,be sampled; ignoring pattern boundaries and the tail end of the frames
aahjacaac,JohnVinyard/zounds,learn/fetch.py,c047c6e848946d32338f2370cc7357ec9ec4dc12,STILL_EXISTS,TODO: Write tests
aahjacaad,JohnVinyard/zounds,learn/fetch.py,c047c6e848946d32338f2370cc7357ec9ec4dc12,STILL_EXISTS,TODO: Check that number of dimensions of fullsize and patch agree;
aahjacaba,JohnVinyard/zounds,learn/fetch.py,c047c6e848946d32338f2370cc7357ec9ec4dc12,STILL_EXISTS,be sampled; ignoring pattern boundaries and the tail end of the frames
aahjacacc,JohnVinyard/zounds,model/pipeline.py,bab5c8f7ef5e77f42e7eadbfa757127b012ce750,STILL_EXISTS,KLUDGE: Is this always OK?
aahjacadd,JohnVinyard/zounds,learn/nnet/pool.py,e8226cdd4e4c42971ab4f6c2459b142495f75b2f,STILL_EXISTS,KLUDGE: I shouldn't know about the \"learn\" property here.  Pipeline
aahjacadf,JohnVinyard/zounds,learn/nnet/pool.py,e8226cdd4e4c42971ab4f6c2459b142495f75b2f,STILL_EXISTS,KLUDGE: Do this in \"mini-batches\" to lessen time spent in the
aahjacaeh,JohnVinyard/zounds,analyze/feature/aim.py,ff1d474efcdab8b239f09716b2808a880f459fb5,STILL_EXISTS,KLUDGE: This will only work for nframes = 1
aahjacafh,JohnVinyard/zounds,analyze/feature/spectral.py,d36925d7bcd60b381563da9f32281f1bc9baba20,f7eb06c5882a596157fbd312ae46485a8b26382e,TODO: Decide these values based on the number of channels and
aahjacafj,JohnVinyard/zounds,analyze/feature/spectral.py,d36925d7bcd60b381563da9f32281f1bc9baba20,f7eb06c5882a596157fbd312ae46485a8b26382e,KLUDGE: This method leaves rough; raw edges around the filter when it's
aahjacagh,JohnVinyard/zounds,model/framesearch.py,d36925d7bcd60b381563da9f32281f1bc9baba20,58d644f97d98276c4675f22f7fd3fd5871a85bc5,Configurable step size (instead of always 1)
aahjacaji,JohnVinyard/zounds,data/frame.py,58d644f97d98276c4675f22f7fd3fd5871a85bc5,7808fa7784c15edeeb2ed0a42aed974bed46a57d,TODO: Rename this; and the method in the abstract class
aahjacbci,JohnVinyard/zounds,nputil/npx.py,05cf0aff367a1d378f1945c2263d048d56e0c83a,bc1fe777d12264579b45e50e182595df325956cf,TODO: Get rid of this file! Move pad into a more appropriate location
aahjacbdc,JohnVinyard/zounds,analyze/feature/scatter.py,bc1fe777d12264579b45e50e182595df325956cf,STILL_EXISTS,KLUDGE: This is set explicitly to yield a signal whose length is a
aahjacbec,JohnVinyard/zounds,analyze/feature/scatter.py,bc1fe777d12264579b45e50e182595df325956cf,STILL_EXISTS,KLUDGE: What does this represent?
aahjacbfc,JohnVinyard/zounds,analyze/feature/scatter.py,bc1fe777d12264579b45e50e182595df325956cf,STILL_EXISTS,KLUDGE: What does this number mean? It's repeated above
aahjacbfg,JohnVinyard/zounds,analyze/feature/scatter.py,bc1fe777d12264579b45e50e182595df325956cf,STILL_EXISTS,KLUDGE: WTF is j?
aahjacbga,JohnVinyard/zounds,util.py,bc1fe777d12264579b45e50e182595df325956cf,STILL_EXISTS,TODO: Should this go into the nputil module as well?
aahjaccca,JohnVinyard/zounds,model/framesearch.py,bdfd130b7d6aacab400d68b0a075750f6c1d1079,STILL_EXISTS,KLUDGE: This is cheating. Searches should always return back-end
aahjaccdi,JohnVinyard/zounds,analyze/audiostream.py,5216ca4a276a7918fc25b323247c9afb4f53eead,STILL_EXISTS,KLUDGE: The following two lines seem to solve a bug whereby
aahjaccgg,JohnVinyard/zounds,model/frame.py,d2948a9d94f03f3c75a9886b38f373c0e030acaa,STILL_EXISTS,TODO: Write tests for overlapping and non-overlapping windows
aahjaccie,JohnVinyard/zounds,learn/cluster/kmeans.py,04b9877abfd514022e553eafae23c04460555320,STILL_EXISTS,KLUDGE: I've added indim and hdim so this class can be used
aahjaccjc,JohnVinyard/zounds,model/framesearch.py,04b9877abfd514022e553eafae23c04460555320,3bde14026f55cd09a4cd5addecf199b962373511,# KLUDGE: This is cheating. Searches should always return back-end
aahjacddc,JohnVinyard/zounds,analyze/feature/spectral.py,09f81ecbe7c775e82ab20090a87f9ef7dc104e09,STILL_EXISTS,KlUDGE: Generalize this; and collapse into FFT
aahjacdeb,JohnVinyard/zounds,quickstart/search.py,09f81ecbe7c775e82ab20090a87f9ef7dc104e09,STILL_EXISTS,TODO:
aahjacdge,JohnVinyard/zounds,quickstart/search.py,39f9439ed165f90a30c843d3ee2b88746b5111de,STILL_EXISTS,KLUDGE: I need to be able to pass arbitrary kwargs to the search class
aahjacdgf,JohnVinyard/zounds,quickstart/search.py,39f9439ed165f90a30c843d3ee2b88746b5111de,STILL_EXISTS,via the command-line interface. There's probably a better way; but this
aahjacdhh,JohnVinyard/zounds,quickstart/search.py,39f9439ed165f90a30c843d3ee2b88746b5111de,STILL_EXISTS,TODO:
aahjaceag,JohnVinyard/zounds,model/framesearch.py,70df57c2a6faeda96eb15530788006ac2e40e57f,STILL_EXISTS,KLUDGE ###################################################################
aahjaceba,JohnVinyard/zounds,model/framesearch.py,70df57c2a6faeda96eb15530788006ac2e40e57f,STILL_EXISTS,Perhaps I should move all things ExhautiveSearch into a seperate module;
aahjacebc,JohnVinyard/zounds,model/framesearch.py,70df57c2a6faeda96eb15530788006ac2e40e57f,STILL_EXISTS,KLUDGE: Opening handles to the same PyTables file from multiple processes
aahjacegi,JohnVinyard/zounds,learn/cluster/kmeans.py,d3d826ea6863eb82c9cea661f6f669abb5fedf55,STILL_EXISTS,KLUDGE: This doesn't belong here
aahjacehb,JohnVinyard/zounds,quickstart/search.py,d3d826ea6863eb82c9cea661f6f669abb5fedf55,STILL_EXISTS,TODO: Visualize the distance between
aahjacfid,JohnVinyard/zounds,analyze/feature/spectral_test.py,f3644791de63c1d9d7150d5ab7c509e87e0871b3,STILL_EXISTS,TODO: Derived classes shouldn't have to know to set done to
aahjacfjd,JohnVinyard/zounds,analyze/extractor.py,67aea3ecbe257f0d9fb9314bad0f13c827a0e106,STILL_EXISTS,KLUDGE: We're choosing the max number of frames from all extractors
aahjacgab,JohnVinyard/zounds,environment.py,67aea3ecbe257f0d9fb9314bad0f13c827a0e106,STILL_EXISTS,KLUDGE: This is specific to PyTables; i.e.; there are lots of problems
aahjacgbd,JohnVinyard/zounds,model/framesearch.py,40ad5543b527a1fd360438098c218f2838aca7d3,STILL_EXISTS,TODO: Uncomment the following
aahjacgbf,JohnVinyard/zounds,model/framesearch.py,40ad5543b527a1fd360438098c218f2838aca7d3,STILL_EXISTS,TODO: Uncomment the following 4 lines
aahjacgdc,JohnVinyard/zounds,analyze/feature/spectral.py,025a984808d5b4b74e5f8e8a65db9335c02063ae,ec70949e2724afa5ad82ffe981d73e633349158f,KLUDGE: I should be passing the codeoboks as numpy arrays; however;
aahjacgig,JohnVinyard/zounds,analyze/feature/template.py,ec70949e2724afa5ad82ffe981d73e633349158f,STILL_EXISTS,KLUDGE: I should be passing the codeoboks as numpy arrays; however;
aahjacgjd,JohnVinyard/zounds,analyze/feature/template.py,ae8572b1aa0f81726a7eae194a5bd2cfe3e36ef6,STILL_EXISTS,KLUDGE: numpy arrays aren't hashable. Is this the best way to
aahjachah,JohnVinyard/zounds,learn/preprocess.py,0d35ba2c630e709d2cdde615988741025d5c013f,STILL_EXISTS,KLUDGE: This code is duplicated in DivideByStd; but I'm not quite
aahjachba,JohnVinyard/zounds,learn/preprocess.py,0d35ba2c630e709d2cdde615988741025d5c013f,STILL_EXISTS,KLUDGE: This code is duplicated in SubtractMean; but I'm not quite
aahjachef,JohnVinyard/zounds,analyze/feature/constantq.py,e6139c03415d43348f1e147006dc89b93d40b753,STILL_EXISTS,TODO: Explain each of these steps
aahjacheg,JohnVinyard/zounds,analyze/feature/constantq.py,e6139c03415d43348f1e147006dc89b93d40b753,STILL_EXISTS,TODO: This is discarding phase. Is that ok?
aahjacheh,JohnVinyard/zounds,analyze/feature/constantq.py,e6139c03415d43348f1e147006dc89b93d40b753,STILL_EXISTS,TODO: This should be a sparse matrix
aahjachej,JohnVinyard/zounds,analyze/feature/constantq.py,e6139c03415d43348f1e147006dc89b93d40b753,STILL_EXISTS,TODO: Why does this have to be recalculated
aahjachfa,JohnVinyard/zounds,analyze/feature/constantq.py,e6139c03415d43348f1e147006dc89b93d40b753,STILL_EXISTS,TODO: What is this for?
aahjachfb,JohnVinyard/zounds,analyze/feature/constantq.py,e6139c03415d43348f1e147006dc89b93d40b753,STILL_EXISTS,KLUDGE: I have no idea if this is correct
aahjachfc,JohnVinyard/zounds,learn/nnet/nnet.py,e6139c03415d43348f1e147006dc89b93d40b753,STILL_EXISTS,KLUDGE: This shouldn't be here. Neural Networks and KMeans-like classes
aahjachge,JohnVinyard/zounds,setup.py,047cfb1062a5d99d1421e53de9605d8b327e1a41,STILL_EXISTS,TODO: This code should only run when the 'install' command is used.
aahjachgg,JohnVinyard/zounds,zounds/analyze/synthesize.py,047cfb1062a5d99d1421e53de9605d8b327e1a41,STILL_EXISTS,TODO: FFT synthesizer
aahjachgh,JohnVinyard/zounds,zounds/analyze/synthesize.py,047cfb1062a5d99d1421e53de9605d8b327e1a41,STILL_EXISTS,TODO: DCT synthesizer
aahjachgi,JohnVinyard/zounds,zounds/analyze/synthesize.py,047cfb1062a5d99d1421e53de9605d8b327e1a41,STILL_EXISTS,TODO: ConstantQ synthesizer\uFFFF
aahjachgj,JohnVinyard/zounds,zounds/analyze/feature/test_spectral.py,e57e55b5799c5a1b67b8012fa62f871e33b8b7ba,05878e537ef3d5482d3135776b84b8895f061457,TODO: Derived classes shouldn't have to know to set done to
aahjachhj,JohnVinyard/zounds,zounds/analyze/test_analyze.py,e57e55b5799c5a1b67b8012fa62f871e33b8b7ba,STILL_EXISTS,TODO: How do I cleanup wave files for failed tests?
aahjachig,JohnVinyard/zounds,zounds/analyze/test_analyze.py,e57e55b5799c5a1b67b8012fa62f871e33b8b7ba,STILL_EXISTS,TODO: Derived classes shouldn't have to know to set done to
aahjaciaa,JohnVinyard/zounds,zounds/data/test_frame.py,e57e55b5799c5a1b67b8012fa62f871e33b8b7ba,352c7ddc1b228143c94f953b51d9fa44950c75f0,KLUDGE: I've excluded int -> int comparisons
aahjaciab,JohnVinyard/zounds,zounds/learn/test_preprocess.py,e57e55b5799c5a1b67b8012fa62f871e33b8b7ba,STILL_EXISTS,KLUDGE: This feels like kind of a stupid test; since the behavior
aahjaciba,JohnVinyard/zounds,zounds/model/test_frame.py,e57e55b5799c5a1b67b8012fa62f871e33b8b7ba,STILL_EXISTS,KLUDGE: I should really write my own Extractor-derived class for this
aahjacicd,JohnVinyard/zounds,zounds/data/frame.py,d9dcb436c9e58747d631ebe212706376688ed1ab,fde30ec5761ad62e39b448901df0197b2bc00750,''' || # KLUDGE: this should be a PyTablesFrameController class method; if at all possible || @task(name='data.frame.sync_one') || def sync_one(newmodel;filepath;_id;add;update;delete;recompute): ||     oldc = PyTablesFrameController(newmodel;filepath) ||     newc = PyTablesFrameController(newmodel;oldc._temp_filepath) ||     _id_query = '_id == \"%s\"' % _id ||     oldrows = oldc.db_read.getWhereList(_id_query) ||     newrows = newc.db_read.getWhereList(_id_query) ||     oldlen = len(oldrows) ||     newlen = len(newrows) ||      ||     if oldlen == newlen: ||         # this id has already been processed ||         return ||       ||     if newlen: ||         # There are some rows in the new database with id; but ||         # there aren't the same number as oldlen; meaning that  ||         # something probably went wrong during the sync. To keep ||         # things simple; let's delete what's there and start over ||         newc.acquire_lock(newc._max_buffer_size; wait = True) ||         newc._write_mode() ||         newc.db_write.removeRows(newrows) ||         newc._read_mode() ||         newc.release_lock() ||      ||     p = Pattern(_id;*oldc.external_id(_id)) ||     ec = newmodel.extractor_chain(p;transitional = True;recompute = recompute) ||     newc.append(ec) ||     print 'processed %s' % _id ||     oldc.close() ||     newc.close() ||     return (newmodel;filepath) ||      ||  || # KLUDGE: this should be a PyTablesFrameController class method; if at all possible || @task(name='data.frame.sync_complete') || def sync_complete(results): ||     newmodel = results[0][0] ||     filepath = results[0][1] ||     oldc = PyTablesFrameController(newmodel;filepath) ||     tmpfilepath = oldc._temp_filepath ||     newc = PyTablesFrameController(newmodel;tmpfilepath) ||     oldids = oldc.list_ids() ||     newids = newc.list_ids() ||      ||     if (len(oldc) != len(newc) or oldids != newids): ||         raise PyTablesUpdateNotCompleteError() ||      ||      ||     oldc.close() ||     newc.close() ||     os.remove(filepath) ||     os.rename(tmpfilepath;filepath) ||     print 'sync complete' ||     return True || '''
aahjacidd,JohnVinyard/zounds,zounds/util.py,9d04fbf81beecc1a6bbf3a281611ee4c94826e0c,STILL_EXISTS,TODO: This should go into a new \"synthesize\" module
aahjacide,JohnVinyard/zounds,zounds/util.py,9d04fbf81beecc1a6bbf3a281611ee4c94826e0c,STILL_EXISTS,TODO: This should go into a new synthesize module.
aahjacidf,JohnVinyard/zounds,zounds/visualize/plot.py,af437e06f55aa19bf3586231c66b768cbc316081,STILL_EXISTS,TODO: How does this work on other systems; where matplotlib is working correctly?
aahjacidg,JohnVinyard/zounds,zounds/analyze/feature/learned.py,53712062373c1e2add492cdac556a5a3d1941919,STILL_EXISTS,TODO: Generalize this to take any extractor; not just a learned one.
aahjacidh,JohnVinyard/zounds,zounds/model/frame.py,4750ce265d25006726273e1cb2f52e8c2fc73052,STILL_EXISTS,BUG: What if the frames instance is composed of more than one sound?
aahjacidi,JohnVinyard/zounds,zounds/model/framesearch.py,4750ce265d25006726273e1cb2f52e8c2fc73052,cdf8a891ab8327aab2357de7e0dbedcfe02a9a8e,TODO: I'm not calling ravel for the sequence search
aahjacigi,JohnVinyard/zounds,zounds/model/framesearch.py,61f3ab97c1b7072350dcedc8355e8e985a1e50f8,cdf8a891ab8327aab2357de7e0dbedcfe02a9a8e,TODO: This is what I'm calling for \"normal\" searches
aahjacihb,JohnVinyard/zounds,zounds/model/framesearch.py,61f3ab97c1b7072350dcedc8355e8e985a1e50f8,cdf8a891ab8327aab2357de7e0dbedcfe02a9a8e,TODO: This is what I'm using for the template search
aahjacihj,JohnVinyard/zounds,zounds/analyze/feature/template.py,99ddbee138b293afb76f087aea0ba11b845e13ea,e25e5143c72fbd2699efb7e515076fa41f599323,TODO: This is the original line
aahjaciib,JohnVinyard/zounds,zounds/analyze/feature/template.py,99ddbee138b293afb76f087aea0ba11b845e13ea,STILL_EXISTS,TODO: This is the new thing
aahjaciig,JohnVinyard/zounds,zounds/analyze/feature/template.py,99ddbee138b293afb76f087aea0ba11b845e13ea,STILL_EXISTS,TODO: Make this an __init__ parameter
aahjacjba,JohnVinyard/zounds,zounds/analyze2/__init__.py,29340d5d2b15d23b941b0f8cea79d0d125314981,90dac0c3525ec7a0167ef3ba636189171f9109c9,KLUDGE: This needs to be an environment variable
aahjacjbc,JohnVinyard/zounds,zounds/analyze2/audiostream.py,29340d5d2b15d23b941b0f8cea79d0d125314981,STILL_EXISTS,KLUDGE: The following two lines seem to solve a bug whereby
aahjacjcf,JohnVinyard/zounds,zounds/analyze2/extractor.py,29340d5d2b15d23b941b0f8cea79d0d125314981,STILL_EXISTS,the number of frames needed from all sources to
aahjacjdg,JohnVinyard/zounds,zounds/analyze2/extractor.py,29340d5d2b15d23b941b0f8cea79d0d125314981,STILL_EXISTS,a list of other extractors needed by this one
aahjacjed,JohnVinyard/zounds,zounds/analyze2/extractor.py,29340d5d2b15d23b941b0f8cea79d0d125314981,STILL_EXISTS,KLUDGE: We're choosing the max number of frames from all extractors
aahjacjfb,JohnVinyard/zounds,zounds/analyze2/extractor.py,29340d5d2b15d23b941b0f8cea79d0d125314981,STILL_EXISTS,KLUDGE: I'm assuming that chunksize will always be larger than
aahjacjfe,JohnVinyard/zounds,zounds/analyze2/feature/rawaudio.py,29340d5d2b15d23b941b0f8cea79d0d125314981,STILL_EXISTS,KLUDGE: This is a bit odd.   The RawAudio extractor is telling
aahjacjia,JohnVinyard/zounds,zounds/model/frame.py,71dedd93b3670e7ba56746b8b7b09d52413c5a06,5b10b8596ec0ccace2abb0105750eecfa1e63b1a,KLUDGE: These are temporarily set to point at the experimental analyze2 module
aahjacjib,JohnVinyard/zounds,zounds/model/pattern.py,71dedd93b3670e7ba56746b8b7b09d52413c5a06,5b10b8596ec0ccace2abb0105750eecfa1e63b1a,KLUDGE: This is temporarily set to point at the experimental; chunk-based
aahjacjjc,JohnVinyard/zounds,zounds/analyze2/feature/metadata.py,031f880e7e10376cd2d23a966f9a9993df8cbd85,STILL_EXISTS,Possible KLUDGE: Since this extractor has frames = 1 and stepsize = 1;
aahjacjjf,JohnVinyard/zounds,zounds/analyze2/feature/spectral.py,031f880e7e10376cd2d23a966f9a9993df8cbd85,STILL_EXISTS,TODO: Get rid of this
aahjadagf,JohnVinyard/zounds,zounds/data/frame.py,44f93f5832fc4a6ece5e265b2b946a20071bb27e,STILL_EXISTS,KLUDGE: Refactor this. There's a lot of duplicated code
aahjadajf,JohnVinyard/zounds,zounds/util.py,44f93f5832fc4a6ece5e265b2b946a20071bb27e,ca4e7a4cadf281d8316ab0c678d80199fda69778,TODO: Once I've filled up self so that it's non-jagged; append as much
aahjadbaf,JohnVinyard/zounds,zounds/data/frame.py,ca4e7a4cadf281d8316ab0c678d80199fda69778,STILL_EXISTS,KLUDGE: Refactor this. There's a lot of duplicated code
aahjadbdi,JohnVinyard/zounds,zounds/analyze2/feature/template.py,b66c7c8205fb4669f2f5fab75d91e212e1e60c46,STILL_EXISTS,KLUDGE: I should be passing in numpy arrays directly. This class should
aahjadbeb,JohnVinyard/zounds,zounds/analyze2/feature/template.py,b66c7c8205fb4669f2f5fab75d91e212e1e60c46,STILL_EXISTS,TODO: Make this an __init__ parameter
aahjadbfd,JohnVinyard/zounds,zounds/analyze/extractor.py,94aaf5d8d68328d9d04a4731a9a565bf9a052d6f,STILL_EXISTS,KLUDGE: I'm assuming that chunksize will always be larger than
aahjadbff,JohnVinyard/zounds,zounds/analyze/feature/metadata.py,94aaf5d8d68328d9d04a4731a9a565bf9a052d6f,STILL_EXISTS,KLUDGE: Factor this code out from this and the following extractor
aahjadbhd,JohnVinyard/zounds,zounds/analyze/feature/aim.py,4b0e564fb2a674699363d99845a7f1ad51c38163,STILL_EXISTS,TODO: _process needs to be written in cython
aahjadbih,JohnVinyard/zounds,zounds/acquire/acquirer.py,dea36497d47550c426f4a4af5d423e1c30955187,STILL_EXISTS,KLUDGE: Do some real logging here
aahjadbii,JohnVinyard/zounds,zounds/acquire/acquirer.py,dea36497d47550c426f4a4af5d423e1c30955187,STILL_EXISTS,TODO: How do I recover from an error once some data has
aahjadcbh,JohnVinyard/zounds,zounds/nputil/npx.py,d8f184e68da79c604d30c0c9894c87120e52b0c7,48145f27c916e22ce8d4b0b1ca37e60a83332845,TODO: It might be handy to not always flatten the slices
aahjadcbi,JohnVinyard/zounds,zounds/util.py,d8f184e68da79c604d30c0c9894c87120e52b0c7,b8cdd6cfea6dd4a656e4fd20ff62df8c54ab534e,TODO: Maybe this should go in the acquirer module
aahjadccf,JohnVinyard/zounds,zounds/analyze/feature/scatter.py,5581ffe358e050d0dc0860b383d7be06681457e3,STILL_EXISTS,TODO: Chunk-based processing
aahjadcgf,JohnVinyard/zounds,zounds/data/test_frame.py,86289b3b6541d07e0a9810cffe08da5f52af5d55,STILL_EXISTS,TODO: The following two tests need to be adapted to concern themselves
aahjadcgh,JohnVinyard/zounds,zounds/nputil/npx.py,c9d48d3b7323cfb36f4ef81aa4e2d03a0d49feea,a877dc939c400e1eb718e236f0b7a697ae55ee0c,TODO: Write tests for this!
aahjadchg,JohnVinyard/zounds,zounds/model/frame.py,a877dc939c400e1eb718e236f0b7a697ae55ee0c,STILL_EXISTS,KLUDGE: Because of the way nputil.windowed behaves; this can return
aahjaddeg,JohnVinyard/zounds,zounds/data/frame.py,ea2b2653b48af11ce05a4e26cc98a16a9f5ab6de,STILL_EXISTS,Indexing of unsigned 64 bit columns is not currently
aahjaddfc,JohnVinyard/zounds,zounds/model/framesearch.py,ea2b2653b48af11ce05a4e26cc98a16a9f5ab6de,STILL_EXISTS,TODO: Parallelize the search
aahjaddfg,JohnVinyard/zounds,zounds/model/framesearch.py,36dabf4ba1e2c196f05e5210286ab0ea01437bf6,STILL_EXISTS,TODO: Factor this out
aahjaddfi,JohnVinyard/zounds,zounds/model/framesearch.py,36dabf4ba1e2c196f05e5210286ab0ea01437bf6,STILL_EXISTS,KLUDGE: Results may not be unique
aahjaddgh,JohnVinyard/zounds,zounds/nputil/npx.py,36dabf4ba1e2c196f05e5210286ab0ea01437bf6,STILL_EXISTS,TODO: This could be sped up by writing a cython function that takes
aahjadeai,JohnVinyard/zounds,zounds/data/frame/filesystem.py,f991685d3480fbefc40dde9a903b49fc0bdcb7ab,STILL_EXISTS,TODO: Refactor the following four methods to loop over data files once
aahjadeba,JohnVinyard/zounds,zounds/model/framesearch.py,56ab4e80f7e9b3601d438167db98c7cd793b2ea0,STILL_EXISTS,TODO: Compute the hamming distances for all blocks at once
aahjadebb,JohnVinyard/zounds,zounds/data/frame/filesystem.py,2dc36c01312d22975f4f6331912ac5d214c9f1f4,STILL_EXISTS,KLUDGE: As it is; this class isn't able to handle addresses that contain
aahjadebf,JohnVinyard/zounds,zounds/data/frame/filesystem.py,2dc36c01312d22975f4f6331912ac5d214c9f1f4,STILL_EXISTS,TODO: What can I factor out from this and PyTablesFrameController.Address?
aahjadebg,JohnVinyard/zounds,zounds/data/frame/filesystem.py,2dc36c01312d22975f4f6331912ac5d214c9f1f4,STILL_EXISTS,TODO: Perhaps these should be loaded lazily
aahjadecg,JohnVinyard/zounds,zounds/data/frame/filesystem.py,2dc36c01312d22975f4f6331912ac5d214c9f1f4,STILL_EXISTS,TODO: A lot of this code is identical to what's in PyTablesFrameController.sync()
aahjadedf,JohnVinyard/zounds,zounds/data/frame/filesystem.py,2dc36c01312d22975f4f6331912ac5d214c9f1f4,STILL_EXISTS,TODO: A lot of this code is identical to PyTablesFrameController.sync().
aahjadeed,JohnVinyard/zounds,zounds/data/frame/filesystem.py,2dc36c01312d22975f4f6331912ac5d214c9f1f4,50b8be2392183bebaac910db3b188f5e5745d26c,TODO: Move these hardcoded values (_id;source;external_id) somewhere more central
aahjadeff,JohnVinyard/zounds,zounds/quickstart/websearch.py,50b8be2392183bebaac910db3b188f5e5745d26c,STILL_EXISTS,TODO: This needs to be configurable from the command line when the server is
aahjadegh,JohnVinyard/zounds,zounds/quickstart/websearch.py,50b8be2392183bebaac910db3b188f5e5745d26c,STILL_EXISTS,TODO: Refactor common code out of audio and image classes
aahjadegi,JohnVinyard/zounds,zounds/quickstart/websearch.py,50b8be2392183bebaac910db3b188f5e5745d26c,STILL_EXISTS,TODO: Query lengths should be specified in arguments when the webserver
aahjadeha,JohnVinyard/zounds,zounds/quickstart/websearch.py,50b8be2392183bebaac910db3b188f5e5745d26c,STILL_EXISTS,TODO: nresults should be specified by a command-line arg when the server
aahjadehc,JohnVinyard/zounds,zounds/quickstart/websearch.py,50b8be2392183bebaac910db3b188f5e5745d26c,STILL_EXISTS,TODO: Render the results to a template
aahjadehd,JohnVinyard/zounds,zounds/quickstart/websearch.py,0ef51b67934ded354e14126bfab0562802263a40,STILL_EXISTS,TODO: I should be using the address class itself here!
aahjadeih,JohnVinyard/zounds,zounds/model/framesearch.py,6a9737c82b507a304c9744ea8a968f93119699ef,STILL_EXISTS,TODO: Make this size configurable
aahjadfcc,JohnVinyard/zounds,zounds/quickstart/websearch.py,08ca02ab5ce7b1fb435caa52b448f6800a02ea01,STILL_EXISTS,TODO: I should be using the address class itself here!
aahjadgec,JohnVinyard/zounds,zounds/data/frame/pytables.py,65c588f06ae3387cb2676598e34484603f150595,STILL_EXISTS,KLUDGE: PyTables allows the creation of columns using a string
aahjadgeh,JohnVinyard/zounds,zounds/data/frame/pytables.py,65c588f06ae3387cb2676598e34484603f150595,STILL_EXISTS,TODO: Write tests for this method!
aahjadgfj,JohnVinyard/zounds,zounds/data/frame/pytables.py,65c588f06ae3387cb2676598e34484603f150595,STILL_EXISTS,Indexing of unsigned 64 bit columns is not currently
aahjadgjh,JohnVinyard/zounds,zounds/data/frame/pytables.py,65c588f06ae3387cb2676598e34484603f150595,STILL_EXISTS,TODO: Make sure there are tests
aahjadhae,JohnVinyard/zounds,zounds/data/frame/pytables.py,65c588f06ae3387cb2676598e34484603f150595,STILL_EXISTS,Crazy Bad KLUDGE: I rely on FrameController-derived classes to define a back-end
aahjadhai,JohnVinyard/zounds,zounds/data/frame/filesystem.py,b30549e8425b79c799e24abcce64b95680c24d13,STILL_EXISTS,TODO: Refactor this!
aahjadhdd,JohnVinyard/zounds,zounds/data/frame/filesystem.py,478944077843ff94622a5dc95780966ce15d7ea4,STILL_EXISTS,TODO: Perhaps these should be loaded lazily
aahjadhde,JohnVinyard/zounds,zounds/data/frame/filesystem.py,478944077843ff94622a5dc95780966ce15d7ea4,083b0d4de51d5176a466a58f48f65b195ac7e48e,TODO: It would be much more efficient to write a wrapper around recarray
aahjadhfc,JohnVinyard/zounds,zounds/data/test_frame.py,478944077843ff94622a5dc95780966ce15d7ea4,352c7ddc1b228143c94f953b51d9fa44950c75f0,TODO: Get rid of this file once all tests have been moved to data.frame.test_frame
aahjadibe,JohnVinyard/zounds,zounds/data/test_frame.py,478944077843ff94622a5dc95780966ce15d7ea4,352c7ddc1b228143c94f953b51d9fa44950c75f0,def test_correct_num_columns(self):
aahjaeeai,JohnVinyard/zounds,zounds/data/frame/filesystem.py,352c7ddc1b228143c94f953b51d9fa44950c75f0,STILL_EXISTS,TODO: Shouldn't these also be persisted to disk?
aahjaeeba,JohnVinyard/zounds,zounds/data/frame/test_frame.py,352c7ddc1b228143c94f953b51d9fa44950c75f0,STILL_EXISTS,KLUDGE: I've excluded int -> int comparisons
aahjaeecj,JohnVinyard/zounds,zounds/data/test_frame.py,352c7ddc1b228143c94f953b51d9fa44950c75f0,STILL_EXISTS,# TODO: Get rid of this file once all tests have been moved to data.frame.test_frame
aahjaefii,JohnVinyard/zounds,zounds/data/test_frame.py,352c7ddc1b228143c94f953b51d9fa44950c75f0,STILL_EXISTS,#    def test_correct_num_columns(self):
aahjafhig,JohnVinyard/zounds,zounds/model/frame.py,352c7ddc1b228143c94f953b51d9fa44950c75f0,STILL_EXISTS,KLUDGE: In the special case of 0-d recarrays (i.e.;
aahjafhje,JohnVinyard/zounds,zounds/data/frame/filesystem.py,347832c3859a7148425d097babe49e7b51c6e043,STILL_EXISTS,TODO: Consider adding some read methods as well
aahjafiag,JohnVinyard/zounds,zounds/data/frame/filesystem.py,347832c3859a7148425d097babe49e7b51c6e043,STILL_EXISTS,TODO: Try np.lib.recfunctions.drop_fields
aahjafiaj,JohnVinyard/zounds,zounds/data/frame/filesystem.py,347832c3859a7148425d097babe49e7b51c6e043,STILL_EXISTS,TODO: Try np.lib.recfunctions.append_fields
aahjafiba,JohnVinyard/zounds,zounds/data/frame/filesystem.py,347832c3859a7148425d097babe49e7b51c6e043,STILL_EXISTS,TODO: Shouldn't these also be persisted to disk?
aahjafibb,JohnVinyard/zounds,zounds/acquire/acquirer.py,1a4e6e9a0e4bc12b57b784b02e6a9a96ff1c65ba,STILL_EXISTS,KLUDGE: Do some real logging here
aahjafibc,JohnVinyard/zounds,zounds/acquire/acquirer.py,1a4e6e9a0e4bc12b57b784b02e6a9a96ff1c65ba,STILL_EXISTS,TODO: How do I recover from an error once partial data has
aahjafibi,JohnVinyard/zounds,zounds/data/frame/filesystem.py,dd49bfd037f30d2fe2d19538e9c95ced8fc6857b,STILL_EXISTS,fleshed out record array; i.e.; the one that contains columns
aahjafidj,JohnVinyard/zounds,zounds/data/frame/filesystem.py,e30af36900ff09b8ead0f0ee4f6316c539f20c4d,STILL_EXISTS,KLUDGE: I have no idea what a reasonable time span is for this
aahjafiee,JohnVinyard/zounds,zounds/data/frame/filesystem.py,e30af36900ff09b8ead0f0ee4f6316c539f20c4d,STILL_EXISTS,The total number of bytes needed at the beginning of each file for
aahjafifd,JohnVinyard/zounds,zounds/data/frame/filesystem.py,e30af36900ff09b8ead0f0ee4f6316c539f20c4d,STILL_EXISTS,information; in the most efficient way possible
aahjafigd,JohnVinyard/zounds,zounds/environment.py,8623a7febea52736d786256783b4005aa57c442f,STILL_EXISTS,KLUDGE: This assumes that controllers will always have zero-arg
aahjafigj,JohnVinyard/zounds,zounds/data/frame/filesystem.py,adcd845531421094f0c2b6e4dd3b0bada2ea0092,STILL_EXISTS,KLUDGE: I have no idea what a reasonable time span is for this
aahjafihc,JohnVinyard/zounds,zounds/model/framesearch.py,e73815c14a690c44a235ed4981a83cc83648671e,STILL_EXISTS,TODO: Consider excluding codes contained in self._filter from
aahjafihf,JohnVinyard/zounds,zounds/model/framesearch.py,e73815c14a690c44a235ed4981a83cc83648671e,STILL_EXISTS,lop off any unused indices
aahjafihg,JohnVinyard/zounds,zounds/model/framesearch.py,e73815c14a690c44a235ed4981a83cc83648671e,cbdb65f81c727bda1e7bdfbb562516e3629f2c18,TODO: tf\/idf on features
aahjafiib,JohnVinyard/zounds,zounds/analyze/feature/template.py,cbdb65f81c727bda1e7bdfbb562516e3629f2c18,STILL_EXISTS,TODO: Would it make sense to chunk patches and do the distance calculation
aahjafiie,JohnVinyard/zounds,zounds/data/frame/filesystem.py,cbdb65f81c727bda1e7bdfbb562516e3629f2c18,STILL_EXISTS,KLUDGE: I'm still experiencing some transient cPickle errors from
aahjafjee,JohnVinyard/zounds,zounds/acquire/acquirer.py,b8cdd6cfea6dd4a656e4fd20ff62df8c54ab534e,STILL_EXISTS,TODO: Maybe this should go in the acquirer module
aahjafjjb,JohnVinyard/zounds,zounds/analyze/extractor.py,1ce4263b704827d0012c031072d78b7c19631855,STILL_EXISTS,KLUDGE: For multisource extractors; it is tacitly assumed that each source
aahjafjje,JohnVinyard/zounds,zounds/data/frame/filesystem.py,b9b2a81fb999dc4e879801646c41a2333daf9f57,STILL_EXISTS,TODO: This class really sucks in the case where one; or a few new patterns
aahjagaac,JohnVinyard/zounds,zounds/data/frame/filesystem.py,b9b2a81fb999dc4e879801646c41a2333daf9f57,STILL_EXISTS,is very efficient; but makes it more difficult to differnentiate between
aahjagaae,JohnVinyard/zounds,zounds/data/frame/filesystem.py,b9b2a81fb999dc4e879801646c41a2333daf9f57,STILL_EXISTS,TODO: Check if pattern file is fresher than stat file
aahjagabe,JohnVinyard/zounds,zounds/data/frame/filesystem.py,297153afe73ca6337b511e6aab055f197669576f,STILL_EXISTS,TODO: Consider making the _load function an abstract method on the
aahjagabh,JohnVinyard/zounds,zounds/data/frame/filesystem.py,a427f3619d9886e30c23ece6e1fe4346db8ed837,STILL_EXISTS,KLUDGE: something was wrong with the on-disk index. Rebuild it from
aahjagafc,JohnVinyard/zounds,zounds/data/frame/filesystem.py,083b0d4de51d5176a466a58f48f65b195ac7e48e,STILL_EXISTS,KLUDGE: something was wrong with the on-disk index. Rebuild it from
aahjagagc,JohnVinyard/zounds,zounds/environment.py,083b0d4de51d5176a466a58f48f65b195ac7e48e,STILL_EXISTS,KLUDGE: This assumes that lock will always be the final argument
aahjagage,JohnVinyard/zounds,zounds/util.py,a217937dd97bbc6393856d9c3b1aa79ef3df6874,205ed645a78389b132d2c67f30620e4847340a6a,KLUDGE: There have been a couple instances (the multiprocess
aahjagbff,JohnVinyard/zounds,zounds/quickstart/ingest.py,37d255067eb78f6055f477ac88c9862398b3628d,STILL_EXISTS,KLUDGE: How can I avoid hard-coding the url here?
aahjagbfj,JohnVinyard/zounds,zounds/quickstart/ingest.py,37d255067eb78f6055f477ac88c9862398b3628d,STILL_EXISTS,KLUDGE: This assumes that the archive was created from a directory; and that
aahjagbge,JohnVinyard/zounds,zounds/data/frame/filesystem.py,e1fcc1f2fd28406e0511a2a2a065d9597594ae79,STILL_EXISTS,Crazy Bad KLUDGE: I rely on FrameController-derived classes to define a back-end
aahjagbhh,JohnVinyard/zounds,zounds/model/frame.py,b9613b62df5bc867fff6b3af79ba8a533015553b,STILL_EXISTS,KLUDGE: The following two methods were added to make writing
aahjagbhj,JohnVinyard/zounds,zounds/model/frame.py,b9613b62df5bc867fff6b3af79ba8a533015553b,STILL_EXISTS,think this is the correct way to proceeed. The backend-specific Address
aahjagbif,JohnVinyard/zounds,zounds/quickstart/websearch/websearch.py,b9613b62df5bc867fff6b3af79ba8a533015553b,8b9e772f79cec6c00ba4fd4547a323361a82a595,TODO: This needs to be configurable from the command line when the server is
aahjagbih,JohnVinyard/zounds,zounds/quickstart/websearch/websearch.py,b9613b62df5bc867fff6b3af79ba8a533015553b,STILL_EXISTS,KLUDGE: The way addresses are being handled is atrocious!
aahjagbii,JohnVinyard/zounds,zounds/quickstart/websearch/websearch.py,b9613b62df5bc867fff6b3af79ba8a533015553b,STILL_EXISTS,TODO: I should be using the address class itself here!
aahjagbij,JohnVinyard/zounds,zounds/quickstart/websearch/websearch.py,b9613b62df5bc867fff6b3af79ba8a533015553b,STILL_EXISTS,TODO: Should this be a part of the Tile class?
aahjagbja,JohnVinyard/zounds,zounds/quickstart/websearch/websearch.py,b9613b62df5bc867fff6b3af79ba8a533015553b,STILL_EXISTS,TODO: Refactor common code out of audio and image classes
aahjagbjb,JohnVinyard/zounds,zounds/quickstart/websearch/websearch.py,b9613b62df5bc867fff6b3af79ba8a533015553b,8b9e772f79cec6c00ba4fd4547a323361a82a595,TODO: Query lengths should be specified in arguments when the webserver
aahjagbjd,JohnVinyard/zounds,zounds/quickstart/websearch/websearch.py,b9613b62df5bc867fff6b3af79ba8a533015553b,8b9e772f79cec6c00ba4fd4547a323361a82a595,TODO: nresults should be specified by a command-line arg when the server
aahjagbjf,JohnVinyard/zounds,zounds/quickstart/websearch/websearch.py,b9613b62df5bc867fff6b3af79ba8a533015553b,8b9e772f79cec6c00ba4fd4547a323361a82a595,TODO: Render the results to a template
aahjagbjh,JohnVinyard/zounds,zounds/quickstart/zounds-quickstart.py,b9613b62df5bc867fff6b3af79ba8a533015553b,STILL_EXISTS,KLUDGE: Templates shouldn't go into the static directory
aahjagbjj,JohnVinyard/zounds,zounds/quickstart/zounds-quickstart.py,b9613b62df5bc867fff6b3af79ba8a533015553b,STILL_EXISTS,move websearch.py into the root folder of the app
aahjagcca,JohnVinyard/zounds,zounds/util.py,8b9e772f79cec6c00ba4fd4547a323361a82a595,STILL_EXISTS,KLUDGE: I need to be able to pass arbitrary kwargs to the search class
aahjagccb,JohnVinyard/zounds,zounds/util.py,8b9e772f79cec6c00ba4fd4547a323361a82a595,STILL_EXISTS,via the command-line interface. There's probably a better way; but this
aahjagcdh,JohnVinyard/zounds,zounds/learn/fetch.py,349320f2ca19044a9bf5011a99007ba3e25fa142,STILL_EXISTS,TODO: __str__ and __repr__ methods that show fullsize and patch size
aahjagceb,JohnVinyard/zounds,setup.py,bf818fbdaf34d9f68bc627d40f31d425bc307527,db428eb6fbea8227f86516fc244a7026cd68e8c6,KLUDGE: Is there a better way to get setuptools commands?
aahjagcee,JohnVinyard/zounds,setup.py,4ad762c4035310eb5c21b33d2997249a5e887734,298324db53d4bff4d54d3222fad874160290c150,KLUDGE: This is a hack. Right?  I'd like to add the currently logged-in user
aahjagcei,JohnVinyard/zounds,setup.py,4ad762c4035310eb5c21b33d2997249a5e887734,298324db53d4bff4d54d3222fad874160290c150,the output of logname ends with a newline
aahjagcfd,JohnVinyard/zounds,zounds/analyze/synthesize.py,4ad762c4035310eb5c21b33d2997249a5e887734,STILL_EXISTS,TODO: How do I cancel the event?
aahjagcfh,JohnVinyard/zounds,zounds/analyze/synthesize.py,460303a2821c35d5eba140e3bd2a55499608e3c0,STILL_EXISTS,BUG: This only works for 44.1Khz. How do I know what samplerate the
aahjagcfj,JohnVinyard/zounds,zounds/analyze/synthesize.py,460303a2821c35d5eba140e3bd2a55499608e3c0,STILL_EXISTS,KLUDGE: I don't know how to determine when the JACK server is ready
aahjagcgh,JohnVinyard/zounds,zounds/analyze/synthesize.py,460303a2821c35d5eba140e3bd2a55499608e3c0,STILL_EXISTS,TODO: How do I cancel the event?
aahjagcha,JohnVinyard/zounds,zounds/analyze/feature/reduce.py,b0626b22c3872d9806fe4b17bd622238cf0abb35,5b10b8596ec0ccace2abb0105750eecfa1e63b1a,KLUDGE: This is temporary. It's only appropriate for
aahjagchc,JohnVinyard/zounds,zounds/analyze/feature/spectral.py,b0626b22c3872d9806fe4b17bd622238cf0abb35,STILL_EXISTS,KLUDGE: This will produce discontinuities at chunk boundaries. I can
aahjagcid,JohnVinyard/zounds,zounds/analyze/feature/spectral.py,4f1f66606bbda0e829e4eca133989ccb066610f7,STILL_EXISTS,TODO: Write docs for this demonstrating how to find periodicities in a
aahjagcif,JohnVinyard/zounds,zounds/analyze/feature/spectral.py,4f1f66606bbda0e829e4eca133989ccb066610f7,STILL_EXISTS,TODO: Consider adding an option to normalize (i.e. give unit-norm) to frames
aahjagcij,JohnVinyard/zounds,zounds/analyze/feature/basic.py,20b815eebc0f30c33a6f127ca4dee021c1c908f1,STILL_EXISTS,KLUDGE: This doesn't belong in this module
aahjagcja,JohnVinyard/zounds,zounds/learn/__init__.py,76656b523ed0d03303091fedfeec768d140400dd,STILL_EXISTS,''' || The :code:`learn` module provides tools that make it easy to define a workflow  || for using unsupervised machine learning algorithms (supervised algorithms aren't  || currently supported; but may be in the future) to learn representations of your || data.  Once trained; your workflow is saved; and can be reused as a feature || extractor; by handing the workflow off to a :py:class:`zounds.analyze.feature.learned.Learned` || feature.  The workflow always includes these steps: ||      ||     * **Fetch** - Collect samples of the feature of interest from the datastore; \\ ||     usually in a random fashion ||     * **Preprocess** - Transform the data in a manner that makes the learning \\ ||     algorithm (the next step) likely to work well.  In many cases; this step \\ ||     is dependent on statistics of the data gathered during the **Fetch** phase; \\ ||     and must be reproducible.  After training; this data is stored so that future \\ ||     computations will always be consistent ||     * **Learn** - Hand the data off to an unsupervised learning algorithm; such \\ ||     as `k-means clustering <http:\/\/en.wikipedia.org\/wiki\/K-means_clustering>`_; or \\ ||     an `autoencoder <http:\/\/en.wikipedia.org\/wiki\/Autoencoder>`_. ||  || As an example; let's say we're interested in a feature that conveys some meaningful || information about spectral shape.  Perhaps we'd like to perform  || `k-means clustering <http:\/\/en.wikipedia.org\/wiki\/K-means_clustering>`_ on  || individual frames of :py:class:`zounds.analyze.feature.spectral.BarkBands`. ||  || Assume we've defined a :py:class:`zounds.model.frame.Frames`-derived class which || details the features we're interested in...:: ||      ||     class FrameModel(Frames): ||         fft = Feature(FFT) ||         bark = Feature(BarkBands; needs = fft; nbands = 100) ||  || ...and that we've analyzed a significant amount of audio.  We'd begin by  || instantiating a :py:class:`zounds.learn.pipeline.Pipeline` class:: ||      ||     p = Pipeline( ||         # this is a key than can be used to access our Pipeline once it's trained. ||         # It can be anything you'd like; but it usually helps to give it a  ||         # descriptive title ||         'pipelines\/bark\/kmeans_500'; ||         # Each example we pull from the data store will consist of one frame ||         # of the \"bark\" feature ||         PrecomputedFeature(1;FrameModel.bark); ||         # We're interested in spectral shape; and not overall loudness; so we'd ||         # like to give each example unit-norm.  This means that the k-means  ||         # clustering will differentiate between examples based on their shape; ||         # and not wildly varying loudness ||         UnitNorm(); ||         # Finally; we'd like the k-means algorithm to learn 500 prototypical ||         # examples; or clusters ||         KMeans(500)) ||  || ... and then training it. Don't worry about the second argument for now.  The first || argument means that we'll draw 10;000 pre-computed examples of bark bands from the || data store; at random:: ||      ||     p.train(10000;lambda : True) ||  || Once training is complete; we can always access our  || :py:class:`~zounds.learn.pipeline.Pipeline` in the following way; and apply it to || some new data:: ||  ||     >>> p = Pipeline['pipelines\/bark\/kmeans_500'] # retrieve the Pipeline using our key ||     >>> fake_barks = np.ones((25;100)) # mimic 25 frames of bark bands ||     >>> kmeans = p(fake_barks) # preprocess the data; and assign each example to a cluster ||     >>> kmeans.shape # each row of kmeans will have a single \"1\" in the position of the cluster assignment ||     (25;500) ||  || Now; we can incorporate the learned feature into our  || :py:class:`~zounds.model.frame.Frames`-derived class using a  || :py:class:`~zounds.analyze.feature.learned.Learned` extractor:: ||      ||     class FrameModel(Frames): ||         fft = Feature(FFT) ||         bark = Feature(BarkBands; needs = fft; nbands = 100) ||         kmeans = Feature(Learned; pipeline_id = 'pipelines\/bark\/kmeans_500'; ||                          dim = 500; dtype = np.uint8; needs = bark) || '''
aahjagcjb,JohnVinyard/zounds,zounds/learn/preprocess.py,090a3e859bcafd4fc86aad066987ccee1c56dee3,STILL_EXISTS,TODO: The axis *must* be zero if either mean or std are provided; since
aahjagcje,JohnVinyard/zounds,zounds/learn/cluster/kmeans.py,fce65dbee26f34e109eb8c903121be6c8f2a22cc,STILL_EXISTS,TODO: Add a sparsify option which will zero out values below the row's mean;
aahjagcjg,JohnVinyard/zounds,zounds/learn/hash/minhash.py,d42973def74dfe29f3ff4f0776d3ffbbd55e6a27,STILL_EXISTS,TODO: I bet this for loop can be gotten rid of...
aahjagcjh,JohnVinyard/zounds,zounds/analyze/feature/basic.py,c5c4d472bf5ae8f8854bafef842ddec5360c0004,STILL_EXISTS,TODO: Why the hell am I not just passing a slice instance; instead of
aahjagcjj,JohnVinyard/zounds,zounds/analyze/feature/basic.py,c5c4d472bf5ae8f8854bafef842ddec5360c0004,STILL_EXISTS,TODO: use the mappings defined in nputil
aahjagdab,JohnVinyard/zounds,zounds/model/framesearch.py,f3cf12db8e95c44867e485ba7386c654781ef723,STILL_EXISTS,TODO: Is this method necessary? Isn't this defined on the MetaFrameSearch
aahjagdad,JohnVinyard/zounds,zounds/model/framesearch.py,f3cf12db8e95c44867e485ba7386c654781ef723,STILL_EXISTS,TODO: Frames should have an extract method; which transforms raw audio
aahjagdah,JohnVinyard/zounds,zounds/model/framesearch.py,65e09c540712f00df9af5300b270376a6887777b,STILL_EXISTS,TODO: nbits could be inferred from the feature
aahjagdai,JohnVinyard/zounds,zounds/analyze/feature/basic.py,c8ffad82bf7d821109a09541bc58aaebf6ae21f4,STILL_EXISTS,TODO: Concrete example in the documentation
aahjagdba,JohnVinyard/zounds,zounds/learn/fetch.py,c8ffad82bf7d821109a09541bc58aaebf6ae21f4,STILL_EXISTS,Maybe fullsize should be optional. It will only be required if feature is
aahjagdbc,JohnVinyard/zounds,zounds/learn/preprocess.py,c8ffad82bf7d821109a09541bc58aaebf6ae21f4,STILL_EXISTS,TODO: Don't save the mean if axis = 1
aahjagdbd,JohnVinyard/zounds,zounds/learn/preprocess.py,c8ffad82bf7d821109a09541bc58aaebf6ae21f4,STILL_EXISTS,TODO: Don't save the std if axis = 1
aahjagdbg,JohnVinyard/zounds,zounds/model/framesearch.py,7c41865c95dba0053f942989143dcc0bb82f7b0f,STILL_EXISTS,TODO: What effect does altering this size have?
aahjagdbj,JohnVinyard/zounds,zounds/analyze/test_analyze.py,0301225e013c17093e6f482c2688e2e16c0c7be2,7e29e0fe9bd0466f1a93742db232ab8451f24383,TODO: Switch to unittest2; so this test can be ignored
aahjagdcc,JohnVinyard/zounds,zounds/data/frame/test_frame.py,0301225e013c17093e6f482c2688e2e16c0c7be2,STILL_EXISTS,TODO: Switch to unittest2; so this test can be ignored
aahjagdch,JohnVinyard/zounds,zounds/analyze/bark.py,22e0947c4aad791d2d3605895df69c5c83ac9a09,STILL_EXISTS,TODO: Alter the edges of the triangle window so they're rounding-aware
aahjagdfa,JohnVinyard/zounds,zounds/analyze/bark.py,aab6b33e1f68358c5dfdc26ea898f5cbd506eb5e,STILL_EXISTS,how much distortion did we introduce by rounding to a whole number of bins?
aahjagdfe,JohnVinyard/zounds,zounds/analyze/bark.py,aab6b33e1f68358c5dfdc26ea898f5cbd506eb5e,STILL_EXISTS,TODO: Alter the edges of the triangle window so they're rounding-aware
aahjagdgc,JohnVinyard/zounds,zounds/analyze/psychoacoustics.py,b44c17753035393ed5f1b855f8a060bd55904617,STILL_EXISTS,TODO: Should this be different for Bark and Mel scales?  Isn't equivalent
aahjagdge,JohnVinyard/zounds,zounds/learn/cluster/kmeans.py,53464882b028c86c56089f67b76bfc8a2e0b5462,STILL_EXISTS,TODO: This implementation isn't complete \/ doesn't work.
aahjagdgf,JohnVinyard/zounds,zounds/learn/nnet/nnet.py,53464882b028c86c56089f67b76bfc8a2e0b5462,STILL_EXISTS,KLUDGE: \"NeuralNetwork\" is too general a name.  This class and its derived classes
aahjagdgi,JohnVinyard/zounds,zounds/model/framesearch.py,9a090b7def55e4b388f5eb8105c5dcd73689b60a,STILL_EXISTS,TODO: Replace this with the TypeCodes class in nputil
aahjagdhb,JohnVinyard/zounds,zounds/analyze/synthesize.py,b2c1b8e4a7fd4b8ce28be9fac3be8c25256de772,STILL_EXISTS,KLUDGE: All these transforms should be written in C; so they can be used with
aahjagdhd,JohnVinyard/zounds,zounds/analyze/synthesize.py,b2c1b8e4a7fd4b8ce28be9fac3be8c25256de772,STILL_EXISTS,TODO: This should accept constant and variable rate amplitude data too
aahjagdhe,JohnVinyard/zounds,zounds/analyze/synthesize.py,b2c1b8e4a7fd4b8ce28be9fac3be8c25256de772,STILL_EXISTS,TODO: Dirac-based time and pitch stretch
aahjagdhf,JohnVinyard/zounds,zounds/analyze/synthesize.py,b2c1b8e4a7fd4b8ce28be9fac3be8c25256de772,STILL_EXISTS,TODO: Basic low and high-pass filters
aahjagdhg,JohnVinyard/zounds,zounds/analyze/synthesize.py,b2c1b8e4a7fd4b8ce28be9fac3be8c25256de772,STILL_EXISTS,TODO: Freeverb
aahjagdhh,JohnVinyard/zounds,zounds/analyze/synthesize.py,b2c1b8e4a7fd4b8ce28be9fac3be8c25256de772,STILL_EXISTS,KLUDGE: There's gotta be a better way than this
aahjagdhj,JohnVinyard/zounds,zounds/model/pattern.py,b2c1b8e4a7fd4b8ce28be9fac3be8c25256de772,STILL_EXISTS,KLUDGE: What if I alter the volume of an event slightly?  Does that warrant
aahjagdif,JohnVinyard/zounds,zounds/model/pattern.py,b2c1b8e4a7fd4b8ce28be9fac3be8c25256de772,d198f2be932f7759a188a730336a026e96a9c168,TODO: Hash the pattern somehow; so it can be compared to ... itself?
aahjagdih,JohnVinyard/zounds,zounds/model/pattern.py,b2c1b8e4a7fd4b8ce28be9fac3be8c25256de772,9419c456a548a7f905361746fde7a2a33177eaf3,BUG: What if a transform changes the length of the samples?
aahjagdje,JohnVinyard/zounds,zounds/model/pattern.py,b2c1b8e4a7fd4b8ce28be9fac3be8c25256de772,9419c456a548a7f905361746fde7a2a33177eaf3,KLUDGE: Maybe _render should be an iterator; for very long patterns
aahjageaa,JohnVinyard/zounds,zounds/model/pattern.py,19e87165568a330c2230db2629ae367150147f9d,9419c456a548a7f905361746fde7a2a33177eaf3,TODO: Add created date
aahjagebc,JohnVinyard/zounds,zounds/model/pattern.py,3f408051f4a9d362bb47a95af36f3576fd0ce4d5,49ac23ecebc2bc0c86550fcba7f1f26366a6ee22,TODO: Add a changed() method; which determines whether the pattern has changed
aahjagech,JohnVinyard/zounds,zounds/data/test_pattern.py,522c45767c51fe665d1ee042d40e2f8fafe34771,STILL_EXISTS,KLUDGE: This is a stop-gap solution for when set_up fails.  Get
aahjagedd,JohnVinyard/zounds,zounds/data/test_pattern.py,2263f056bde69d8f9f9f0815641ac87eb6385c9e,STILL_EXISTS,KLUDGE: This is a FileSystemFrameController specific test
aahjagedg,JohnVinyard/zounds,zounds/data/test_pattern.py,2263f056bde69d8f9f9f0815641ac87eb6385c9e,STILL_EXISTS,have an address attribute.  This is really a bug; but it's expected
aahjagefi,JohnVinyard/zounds,zounds/data/test_pattern.py,c6ba44afd35917fbe38671e36387ad3dc3dc02ff,STILL_EXISTS,KLUDGE: This doesn't guarantee equivalence; but it's probably
aahjagehe,JohnVinyard/zounds,zounds/model/pattern.py,c6ba44afd35917fbe38671e36387ad3dc3dc02ff,9419c456a548a7f905361746fde7a2a33177eaf3,TODO: be sure to remove items from all_ids and _to_store; when necessary
aahjagehh,JohnVinyard/zounds,zounds/model/pattern.py,c6ba44afd35917fbe38671e36387ad3dc3dc02ff,9419c456a548a7f905361746fde7a2a33177eaf3,TODO: Should this be asynchronous ?
aahjageig,JohnVinyard/zounds,zounds/model/pattern.py,ad010b081bc1c6aa4fa4605922b400fd614264df,9419c456a548a7f905361746fde7a2a33177eaf3,TODO: Ensure that transform isn't None; and has at least one
aahjagfai,JohnVinyard/zounds,zounds/model/pattern.py,0f4fdededb9d19505e7dc044f0d3cf51b4cf16c1,9419c456a548a7f905361746fde7a2a33177eaf3,TODO: Is this method necessary; or is it made superfluous by transform()
aahjagfbb,JohnVinyard/zounds,zounds/model/pattern.py,0f4fdededb9d19505e7dc044f0d3cf51b4cf16c1,9419c456a548a7f905361746fde7a2a33177eaf3,KLUDGE: Given the following implementation; this copy() is necessary for
aahjagfbi,JohnVinyard/zounds,zounds/model/pattern.py,818242c5377df8580d423f79e25c77d5f8e08ccd,9419c456a548a7f905361746fde7a2a33177eaf3,TODO: Composable types with different atomic behaviors
aahjagfcc,JohnVinyard/zounds,zounds/model/pattern.py,818242c5377df8580d423f79e25c77d5f8e08ccd,9419c456a548a7f905361746fde7a2a33177eaf3,TODO: Ensure that transform isn't None; and has at least one
aahjagfee,JohnVinyard/zounds,zounds/data/test_pattern.py,8e821cfff82302813879e806837abb1adce3e6f8,STILL_EXISTS,TODO: Copies should have been made of root;p2; and l2.  r2 should have
aahjagffd,JohnVinyard/zounds,zounds/model/pattern.py,8e821cfff82302813879e806837abb1adce3e6f8,4f200a8922d3180e5eb3b4aa5616769f6e585422,TODO: Tests
aahjagfgd,JohnVinyard/zounds,zounds/model/pattern.py,5d1bb452eb34d5db4f0b40af59c7814eaf36e474,d6e7bc791ac7b5e31a0d12b0e0233afa355dbffb,TODO: Test
aahjagfha,JohnVinyard/zounds,zounds/model/pattern.py,0dbe8480ed75a5c21946e629b428d00d1f825b0e,9419c456a548a7f905361746fde7a2a33177eaf3,TODO: This should check the pattern against all transforms to decide
aahjagfhe,JohnVinyard/zounds,zounds/model/pattern.py,0dbe8480ed75a5c21946e629b428d00d1f825b0e,9419c456a548a7f905361746fde7a2a33177eaf3,TODO: Don't perform the same transformation twice!
aahjagfhf,JohnVinyard/zounds,zounds/analyze/synthesize.py,9eeb296d385e721a5a31704f4183ed4558e73faf,STILL_EXISTS,TODO: logic to translate frames to samples is already defined in
aahjagfih,JohnVinyard/zounds,zounds/data/pattern.py,49ff68851fc52aaf8a5bc06a158cde1fe0c82283,STILL_EXISTS,TODO: Which values should have MongoDb indexes ?
aahjagfii,JohnVinyard/zounds,zounds/data/pattern.py,49ff68851fc52aaf8a5bc06a158cde1fe0c82283,STILL_EXISTS,TODO: Is there a better way to fetch multiple documents by id?
aahjagfjb,JohnVinyard/zounds,zounds/data/test_pattern.py,49ff68851fc52aaf8a5bc06a158cde1fe0c82283,STILL_EXISTS,KLUDGE: This is a stop-gap solution for when set_up fails.  Get
aahjagfjg,JohnVinyard/zounds,zounds/model/pattern.py,49ff68851fc52aaf8a5bc06a158cde1fe0c82283,9419c456a548a7f905361746fde7a2a33177eaf3,TODO: What happens if the slice bisects a pattern?  Should events
aahjaggaa,JohnVinyard/zounds,setup.py,97d4022f1692e3f4cbd30240864a361900851a15,STILL_EXISTS,TODO: Run the setup.sh script from here
aahjaggac,JohnVinyard/zounds,zounds/model/pattern.py,97d4022f1692e3f4cbd30240864a361900851a15,9419c456a548a7f905361746fde7a2a33177eaf3,KLUDGE: I'm not sure where this class belongs
aahjaggad,JohnVinyard/zounds,zounds/model/pattern.py,97d4022f1692e3f4cbd30240864a361900851a15,9419c456a548a7f905361746fde7a2a33177eaf3,TODO: It'd probably be better to do a max size in bytes; rather than an
aahjaggai,JohnVinyard/zounds,zounds/model/pattern.py,97d4022f1692e3f4cbd30240864a361900851a15,9419c456a548a7f905361746fde7a2a33177eaf3,TODO: Test
aahjaggaj,JohnVinyard/zounds,zounds/model/pattern.py,97d4022f1692e3f4cbd30240864a361900851a15,9419c456a548a7f905361746fde7a2a33177eaf3,BLEGH!! This is ugly!
aahjaggbd,JohnVinyard/zounds,zounds/model/pattern.py,97d4022f1692e3f4cbd30240864a361900851a15,9419c456a548a7f905361746fde7a2a33177eaf3,TODO: Stress test\/profile the play method and find out what an
aahjaggea,JohnVinyard/zounds,zounds/model/framesearch.py,d2b1f61142eb35b5ca4976d53fb09a0cb7af5cb0,STILL_EXISTS,TODO: There's a ton of duplicated logic in self._build_index. Factor some
aahjaggeg,JohnVinyard/zounds,zounds/model/framesearch.py,d2b1f61142eb35b5ca4976d53fb09a0cb7af5cb0,STILL_EXISTS,KLUDGE: This is duplicated exactly in _build_index()
aahjaggei,JohnVinyard/zounds,zounds/model/framesearch.py,d2b1f61142eb35b5ca4976d53fb09a0cb7af5cb0,STILL_EXISTS,lop off any unused indices
aahjaggfd,JohnVinyard/zounds,zounds/model/framesearch.py,d2b1f61142eb35b5ca4976d53fb09a0cb7af5cb0,STILL_EXISTS,TODO: Implement the _add_index() method
aahjagggc,JohnVinyard/zounds,zounds/model/pattern.py,d2b1f61142eb35b5ca4976d53fb09a0cb7af5cb0,9419c456a548a7f905361746fde7a2a33177eaf3,TODO: rendering should happen *just like* realtime playing; i.e.;
aahjagggg,JohnVinyard/zounds,zounds/model/pattern.py,2a9ddbf08665d75e919ef3011accc45570b4e5ea,9419c456a548a7f905361746fde7a2a33177eaf3,TODO: Since patterns are immutable; is it safe to store the result of
aahjagggi,JohnVinyard/zounds,zounds/model/pattern.py,2a9ddbf08665d75e919ef3011accc45570b4e5ea,STILL_EXISTS,TODO: What if self and\/or other are leaf patterns
aahjaggig,JohnVinyard/zounds,zounds/data/test_pattern.py,02bf684259b83f0fce5f25a59d277148372cae81,7e29e0fe9bd0466f1a93742db232ab8451f24383,TODO: Implement the __getitem__ method; and\/or switch to unittest2 so
aahjaghfd,JohnVinyard/zounds,zounds/model/pattern.py,6961ecbf14c4257889dc0cdba4b0eb3fdc1722d6,bd0b4a97a85708a10673392ace75c9c382015fda,# TODO: Ensure that transform isn't None; and has at least one
aahjagjdf,JohnVinyard/zounds,zounds/model/pattern.py,2379a51953df07fd5e8ea5f28421e4fc44a42dab,9419c456a548a7f905361746fde7a2a33177eaf3,TODO: Maybe copy should rectify any negative or wrapped times
aahjagjdg,JohnVinyard/zounds,zounds/model/pattern.py,2379a51953df07fd5e8ea5f28421e4fc44a42dab,STILL_EXISTS,BUG: What if self has negative or wrapped event times? The same
aahjagjej,JohnVinyard/zounds,zounds/analyze/synthesize.py,9419c456a548a7f905361746fde7a2a33177eaf3,6e5ce9d2261f8e62e7ff6a2b13511eb6fc2b3eea,TODO: Get rid of this!!  Clients should do the interpretation here
aahjagjfa,JohnVinyard/zounds,zounds/analyze/synthesize.py,9419c456a548a7f905361746fde7a2a33177eaf3,2c3349c8a65b682d66e141716fa7399511d28515,TODO: It'd probably be better to do a max size in bytes; rather than an
aahjahahj,JohnVinyard/zounds,zounds/analyze/psychoacoustics.py,6e5ce9d2261f8e62e7ff6a2b13511eb6fc2b3eea,STILL_EXISTS,TODO: Documentation
aahjahaie,JohnVinyard/zounds,zounds/model/pattern/event.py,6e5ce9d2261f8e62e7ff6a2b13511eb6fc2b3eea,STILL_EXISTS,TODO: This should include transform data too
aahjahaif,JohnVinyard/zounds,zounds/model/pattern/event.py,6e5ce9d2261f8e62e7ff6a2b13511eb6fc2b3eea,STILL_EXISTS,TODO: This should check the pattern against all transforms to decide
aahjahaij,JohnVinyard/zounds,zounds/model/pattern/musicpattern.py,6e5ce9d2261f8e62e7ff6a2b13511eb6fc2b3eea,7e29e0fe9bd0466f1a93742db232ab8451f24383,TODO: Maybe copy should rectify any negative or wrapped times
aahjahajc,JohnVinyard/zounds,zounds/model/pattern/musicpattern.py,6e5ce9d2261f8e62e7ff6a2b13511eb6fc2b3eea,7e29e0fe9bd0466f1a93742db232ab8451f24383,BUG: What if *self* has negative or wrapped event times? The same
aahjahaje,JohnVinyard/zounds,zounds/model/pattern/transform.py,6e5ce9d2261f8e62e7ff6a2b13511eb6fc2b3eea,STILL_EXISTS,TODO: Composable types with different atomic behaviors
aahjahbac,JohnVinyard/zounds,zounds/model/pattern/zound.py,6e5ce9d2261f8e62e7ff6a2b13511eb6fc2b3eea,c9e71d22738878a0e520dd1453f62a49efc98e24,TODO: Add created date
aahjahbbj,JohnVinyard/zounds,zounds/model/pattern/zound.py,6e5ce9d2261f8e62e7ff6a2b13511eb6fc2b3eea,STILL_EXISTS,BUG: What if a transform changes the length of the samples?
aahjahbcj,JohnVinyard/zounds,zounds/model/pattern/zound.py,6e5ce9d2261f8e62e7ff6a2b13511eb6fc2b3eea,STILL_EXISTS,KLUDGE: Maybe _render should be an iterator; for very long patterns
aahjahbda,JohnVinyard/zounds,zounds/model/pattern/zound.py,6e5ce9d2261f8e62e7ff6a2b13511eb6fc2b3eea,1b2222277285f73641f8d77c8d1e523ace8400bf,TODO: rendering should happen *just like* realtime playing; i.e.;
aahjahbdg,JohnVinyard/zounds,zounds/model/pattern/zound.py,6e5ce9d2261f8e62e7ff6a2b13511eb6fc2b3eea,STILL_EXISTS,KLUDGE: What about very long patterns?
aahjahbdj,JohnVinyard/zounds,zounds/model/pattern/zound.py,6e5ce9d2261f8e62e7ff6a2b13511eb6fc2b3eea,STILL_EXISTS,TODO: Don't perform the same transformation twice!
aahjahbeb,JohnVinyard/zounds,zounds/model/pattern/zound.py,6e5ce9d2261f8e62e7ff6a2b13511eb6fc2b3eea,STILL_EXISTS,TODO: Test
aahjahbec,JohnVinyard/zounds,zounds/model/pattern/zound.py,6e5ce9d2261f8e62e7ff6a2b13511eb6fc2b3eea,STILL_EXISTS,TODO: Since patterns are immutable; is it safe to store the result of
aahjahbee,JohnVinyard/zounds,zounds/model/pattern/zound.py,6e5ce9d2261f8e62e7ff6a2b13511eb6fc2b3eea,STILL_EXISTS,BLEGH!! This is ugly!
aahjahbfa,JohnVinyard/zounds,zounds/model/pattern/zound.py,6e5ce9d2261f8e62e7ff6a2b13511eb6fc2b3eea,STILL_EXISTS,TODO: Stress test\/profile the play method and find out what an
aahjahbfb,JohnVinyard/zounds,zounds/model/pattern/zound.py,6e5ce9d2261f8e62e7ff6a2b13511eb6fc2b3eea,STILL_EXISTS,acceptable latency value is. I think this value is too high.
aahjahbgg,JohnVinyard/zounds,zounds/model/pattern/zound.py,6e5ce9d2261f8e62e7ff6a2b13511eb6fc2b3eea,STILL_EXISTS,TODO: Ensure that transform isn't None; and has at least one
aahjahbib,JohnVinyard/zounds,zounds/model/pattern/zound.py,6e5ce9d2261f8e62e7ff6a2b13511eb6fc2b3eea,STILL_EXISTS,TODO: Is this method necessary; or is it made superfluous by transform()
aahjahbic,JohnVinyard/zounds,zounds/model/pattern/zound.py,6e5ce9d2261f8e62e7ff6a2b13511eb6fc2b3eea,STILL_EXISTS,TODO: be sure to remove items from all_ids and _to_store; when necessary
aahjahbih,JohnVinyard/zounds,zounds/model/pattern/zound.py,6e5ce9d2261f8e62e7ff6a2b13511eb6fc2b3eea,STILL_EXISTS,TODO: What happens if the slice bisects a pattern?  Should events
aahjahbja,JohnVinyard/zounds,zounds/model/pattern/zound.py,6e5ce9d2261f8e62e7ff6a2b13511eb6fc2b3eea,STILL_EXISTS,KLUDGE: Given the following implementation; this copy() is necessary for
aahjahbjh,JohnVinyard/zounds,zounds/model/pattern/zound.py,6e5ce9d2261f8e62e7ff6a2b13511eb6fc2b3eea,STILL_EXISTS,TODO: Should this be asynchronous ?
aahjahbji,JohnVinyard/zounds,zounds/model/pattern/zound.py,6e5ce9d2261f8e62e7ff6a2b13511eb6fc2b3eea,STILL_EXISTS,KLUDGE: Should I define a custom exception for this?
aahjahcba,JohnVinyard/zounds,zounds/model/pattern/zound.py,2c3349c8a65b682d66e141716fa7399511d28515,STILL_EXISTS,TODO: It'd probably be better to do a max size in bytes; rather than an
aahjahcbb,JohnVinyard/zounds,zounds/model/pattern/zound.py,2c3349c8a65b682d66e141716fa7399511d28515,STILL_EXISTS,KLUDGE: This class doesn't really belong here
aahjahcbg,JohnVinyard/zounds,zounds/model/pattern/zound.py,2c3349c8a65b682d66e141716fa7399511d28515,STILL_EXISTS,KLUDGE: Why is this here?  When would you pass stored = False to this
aahjahcbi,JohnVinyard/zounds,setup.py,7e29e0fe9bd0466f1a93742db232ab8451f24383,STILL_EXISTS,TODO: Run dependencies.sh from here
aahjahcbj,JohnVinyard/zounds,setup.py,7e29e0fe9bd0466f1a93742db232ab8451f24383,STILL_EXISTS,TODO: Start mongodb after running dependencies.sh
aahjahcca,JohnVinyard/zounds,setup.py,7e29e0fe9bd0466f1a93742db232ab8451f24383,STILL_EXISTS,TODO: install numpy and scipy from here
aahjahccb,JohnVinyard/zounds,setup.py,7e29e0fe9bd0466f1a93742db232ab8451f24383,STILL_EXISTS,TODO: Do installation until it succeeds
aahjahccc,JohnVinyard/zounds,setup.py,7e29e0fe9bd0466f1a93742db232ab8451f24383,STILL_EXISTS,TODO: run tests from here
aahjahcdg,JohnVinyard/zounds,setup.py,9eb27842d06cded92511f3bf27295f9a47f6460b,db428eb6fbea8227f86516fc244a7026cd68e8c6,KLUDGE: Is there a better way to get setuptools commands?
aahjahcdh,JohnVinyard/zounds,setup.py,9eb27842d06cded92511f3bf27295f9a47f6460b,298324db53d4bff4d54d3222fad874160290c150,KLUDGE: What should I do here?
aahjahcec,JohnVinyard/zounds,setup.py,9eb27842d06cded92511f3bf27295f9a47f6460b,STILL_EXISTS,TODO: Do installation until it succeeds
aahjahced,JohnVinyard/zounds,setup.py,9eb27842d06cded92511f3bf27295f9a47f6460b,STILL_EXISTS,TODO: run tests from here
aahjahceh,JohnVinyard/zounds,zounds/analyze/feature/pitch.py,f99980309d0d0243d29532d88c0b29d3407e290b,STILL_EXISTS,TODO: Should I just expect input from the AutoCorrelation feature?
aahjahcei,JohnVinyard/zounds,zounds/analyze/synthesize.py,fee40196e0fe072f7a18b038a22aed3fc9f0728b,STILL_EXISTS,KLUDGE: This class should be named AudioTransform to distinguish it from
aahjahchc,JohnVinyard/zounds,zounds/analyze/synthesize.py,714ec4f25df0bded964662029b109de3856c1e24,STILL_EXISTS,KLUDGE: The C interpolations array type is char.  I'm using np.uint8
aahjahchf,JohnVinyard/zounds,zounds/analyze/synthesize.py,714ec4f25df0bded964662029b109de3856c1e24,STILL_EXISTS,KLUDGE: Wouldn't it simpler to just wrap the C transform struct in
aahjahchh,JohnVinyard/zounds,zounds/analyze/synthesize.py,33d3c498ab7e0c576ee4bcb454a3f6578fdede00,STILL_EXISTS,TODO: Only the times array depends on the current pattern and samplerate.
aahjahcia,JohnVinyard/zounds,zounds/analyze/synthesize.py,33d3c498ab7e0c576ee4bcb454a3f6578fdede00,STILL_EXISTS,TODO: interpret time should work for single values and numpy
aahjahcii,JohnVinyard/zounds,zounds/model/pattern/zound.py,9ca2dfa80a520a9c9236fb52ce4f29655f916b17,STILL_EXISTS,make a *guess* about the amount of memory needed to hold the entire
aahjahddb,JohnVinyard/zounds,zounds/data/pattern.py,c9e71d22738878a0e520dd1453f62a49efc98e24,STILL_EXISTS,KLUDGE: This only works with the FileSystemFrameController.Address
aahjahddf,JohnVinyard/zounds,zounds/model/framesearch.py,0b8b3766fbce2c8e39e9db5d596654d23f2c0ee0,STILL_EXISTS,TODO: When do I save the index back to disk?  Everytime this is called?
aahjahddh,JohnVinyard/zounds,zounds/model/framesearch.py,0b8b3766fbce2c8e39e9db5d596654d23f2c0ee0,STILL_EXISTS,TODO: update self._std
aahjahdec,JohnVinyard/zounds,zounds/model/framesearch.py,0b8b3766fbce2c8e39e9db5d596654d23f2c0ee0,STILL_EXISTS,KLUDGE: This is duplicated exactly in _build_index()
aahjahdfa,JohnVinyard/zounds,zounds/model/framesearch.py,0b8b3766fbce2c8e39e9db5d596654d23f2c0ee0,STILL_EXISTS,KLUDGE: It's possible to build a corrupted index using this method.  More
aahjahdfh,JohnVinyard/zounds,zounds/model/framesearch.py,0b8b3766fbce2c8e39e9db5d596654d23f2c0ee0,STILL_EXISTS,Using _add_index for each _id is actually more robust; but less efficient;
aahjahdhc,JohnVinyard/zounds,zounds/model/framesearch.py,0b8b3766fbce2c8e39e9db5d596654d23f2c0ee0,STILL_EXISTS,KLUDGE: Results may not be unique
aahjahdhd,JohnVinyard/zounds,zounds/model/framesearch.py,0b8b3766fbce2c8e39e9db5d596654d23f2c0ee0,STILL_EXISTS,TODO: Make this size configurable
aahjahdhe,JohnVinyard/zounds,zounds/model/framesearch.py,0b8b3766fbce2c8e39e9db5d596654d23f2c0ee0,STILL_EXISTS,TODO: What effect does altering this size have?
aahjahdhf,JohnVinyard/zounds,zounds/nputil/npx.py,0b8b3766fbce2c8e39e9db5d596654d23f2c0ee0,STILL_EXISTS,TODO: Write tests!!
aahjahdhg,JohnVinyard/zounds,zounds/nputil/npx.py,0b8b3766fbce2c8e39e9db5d596654d23f2c0ee0,STILL_EXISTS,TODO: Write documentation
aahjahdhj,JohnVinyard/zounds,zounds/nputil/npx.py,f627f9cb3cbb83d796be19116f68e71a0b3d0c45,STILL_EXISTS,more physical memory is needed than has been allocated so far
aahjahdib,JohnVinyard/zounds,zounds/nputil/npx.py,f627f9cb3cbb83d796be19116f68e71a0b3d0c45,STILL_EXISTS,make enough room. Allocate exactly as much memory as is needed
aahjahdid,JohnVinyard/zounds,zounds/nputil/npx.py,f627f9cb3cbb83d796be19116f68e71a0b3d0c45,STILL_EXISTS,TODO: Write documentation
aahjahdif,JohnVinyard/zounds,zounds/model/framesearch.py,795edb1a8d2d71598d52ad985cbe01f938a9f635,STILL_EXISTS,TODO: I should be using a thread and process safe lock here
aahjahfji,JohnVinyard/zounds,zounds/model/framesearch.py,795edb1a8d2d71598d52ad985cbe01f938a9f635,STILL_EXISTS,TODO: Implement the _check_index() method
aahjahgac,JohnVinyard/zounds,zounds/acquire/acquirer.py,d74858382669740ce4caf47b8e5f6f7ef557e80d,STILL_EXISTS,TODO: Handle web-services; like FreeSound; or SoundCloud
aahjahgad,JohnVinyard/zounds,zounds/acquire/acquirer.py,d74858382669740ce4caf47b8e5f6f7ef557e80d,STILL_EXISTS,TODO: Enable multiprocessing; just like in DiskAcquirer
aahjahgae,JohnVinyard/zounds,zounds/acquire/acquirer.py,d74858382669740ce4caf47b8e5f6f7ef557e80d,STILL_EXISTS,TODO: Factor the following into the base class.  It's
aahjahgai,JohnVinyard/zounds,zounds/acquire/acquirer.py,d74858382669740ce4caf47b8e5f6f7ef557e80d,STILL_EXISTS,KLUDGE: Do some real logging here
aahjahgaj,JohnVinyard/zounds,zounds/acquire/acquirer.py,d74858382669740ce4caf47b8e5f6f7ef557e80d,STILL_EXISTS,TODO: How do I recover from an error once partial data has
aahjahgbb,JohnVinyard/zounds,zounds/analyze/audiostream.py,d74858382669740ce4caf47b8e5f6f7ef557e80d,STILL_EXISTS,TODO: I should wrap Sndfile so it has __enter__ and __exit__ methods;
aahjahgbd,JohnVinyard/zounds,zounds/model/framesearch.py,c00c069bffe5c1c8f1e9542d302321e46b75ad06,STILL_EXISTS,TODO: nbits could be inferred from the feature
aahjahgbi,JohnVinyard/zounds,zounds/analyze/feature/spectral.py,9f8c9eec41fde7f21a57030bf561fe45c265f0a2,STILL_EXISTS,TODO: Can this be vectorized?
aahjahgbj,JohnVinyard/zounds,zounds/analyze/psychoacoustics.py,9f8c9eec41fde7f21a57030bf561fe45c265f0a2,STILL_EXISTS,TODO: Set the start and stop freqs in hz
aahjahgca,JohnVinyard/zounds,zounds/analyze/psychoacoustics.py,9f8c9eec41fde7f21a57030bf561fe45c265f0a2,STILL_EXISTS,TODO: Plot each step to ensure that you understand what's going on
aahjahgcj,JohnVinyard/zounds,zounds/analyze/psychoacoustics.py,9f8c9eec41fde7f21a57030bf561fe45c265f0a2,STILL_EXISTS,TODO: What is this variable for?
aahjahigh,JohnVinyard/zounds,zounds/nputil/npx.py,9f8c9eec41fde7f21a57030bf561fe45c265f0a2,205ed645a78389b132d2c67f30620e4847340a6a,TODO: Write tests
aahjahiib,JohnVinyard/zounds,zounds/flow/extractor.py,db006168562fa3978e671205f8edf92c86a813fc,STILL_EXISTS,TODO: decode data; and write it to the local queue
aahjahiic,JohnVinyard/zounds,zounds/flow/extractor.py,db006168562fa3978e671205f8edf92c86a813fc,STILL_EXISTS,TODO: the listener may be remote; so process() cannot be called
aahjahiie,JohnVinyard/zounds,zounds/flow/feature.py,db006168562fa3978e671205f8edf92c86a813fc,STILL_EXISTS,TODO: Here the DataWriter is monolithic.  What if the data writer
aahjahiij,JohnVinyard/zounds,zounds/node/sliding_window.py,fd3c30fc5ed66aef9897689447bf4b0dfbff5fe5,STILL_EXISTS,TODO: Steal AudioStream tests and use them here
aahjahjai,JohnVinyard/zounds,zounds/node/resample.py,ef1776d17677c60535648949cbe7a243afd20630,STILL_EXISTS,KLUDGE: The following line seems to solve a bug whereby
aahjahjcb,JohnVinyard/zounds,zounds/node/ogg_vorbis.py,72102408a77e41fd3c11cd42f7469e1b4eaabfdf,STILL_EXISTS,TODO: test to ensure that decoded ogg vorbis is the same length
aahjahjch,JohnVinyard/zounds,zounds/node/random_samples.py,72102408a77e41fd3c11cd42f7469e1b4eaabfdf,b3da5550a2bfb92fc9e7cb0934570a1d97634386,then; once a chunk in the pool has received some number of writes maybe
aahjahjdc,JohnVinyard/zounds,zounds/node/random_samples.py,72102408a77e41fd3c11cd42f7469e1b4eaabfdf,b3da5550a2bfb92fc9e7cb0934570a1d97634386,TODO: return the indices of any well-mixed chunks
aahjahjdd,JohnVinyard/zounds,zounds/node/random_samples.py,72102408a77e41fd3c11cd42f7469e1b4eaabfdf,b3da5550a2bfb92fc9e7cb0934570a1d97634386,TODO: yield each well-mixed chunk (data will simply be a set of indices)
aahjahjei,JohnVinyard/zounds,zounds/node/random_samples.py,27d2d460af7813309fe041c43c75aa804765440e,STILL_EXISTS,TODO: What happens if we have filled up all the sample slots and we run
aahjahjfa,JohnVinyard/zounds,zounds/node/random_samples.py,27d2d460af7813309fe041c43c75aa804765440e,ccba6cf472a3008983620fec3051ee47e2fecec2,TODO: This should probably be self._r[:self._index]
aahjahjha,JohnVinyard/zounds,zounds/node/timeseries.py,f3785291a1aa49124ede05c9c7382bcc03cd4536,b9d7ea46a49fd0bcf98beccca413be3633c5e4cc,''' || TODO:  || how do I handle \"offset\" in the ConstantRateTimeSeries class? || '''
aahjahjhc,JohnVinyard/zounds,zounds/node/audiostream.py,1841aeb0f1430f3cc119d51a3df72e3bd24a1152,STILL_EXISTS,TODO: Is this class really necessary?  There must be a better way to handle
aahjahjhe,JohnVinyard/zounds,zounds/node/index.py,1841aeb0f1430f3cc119d51a3df72e3bd24a1152,c1febd23589c5ab6179702e720e400810d44cb3d,TODO: Get rid of this class once I'm sure that TimeSeriesIndex works well
aahjahjhf,JohnVinyard/zounds,zounds/node/index.py,1841aeb0f1430f3cc119d51a3df72e3bd24a1152,c1febd23589c5ab6179702e720e400810d44cb3d,TODO: Make sure this can work with streaming numpy decoders as well
aahjahjjj,JohnVinyard/zounds,zounds/node/timeseries.py,1841aeb0f1430f3cc119d51a3df72e3bd24a1152,01e1553e6be58acf79e20d39844553d6d53a969a,TODO: This won't work! np.timedelta64.__repr__() isn't reversible
aahjaiaad,JohnVinyard/zounds,zounds/node/sliding_window.py,6da69336b5b04ec82adb4861d8a396b673008332,f988593d02153f7cf53cb0cbe598c4b0a8579d2a,TODO: Replace these with the SampleRate-derived classes from samplerate
aahjaiabi,JohnVinyard/zounds,zounds/node/sliding_window.py,6da69336b5b04ec82adb4861d8a396b673008332,STILL_EXISTS,BUG: I Think this may only work in cases where frequency and
aahjaiaia,JohnVinyard/zounds,zounds/node/sliding_window.py,b3da5550a2bfb92fc9e7cb0934570a1d97634386,STILL_EXISTS,KLUDGE: This extractor works when trying to get random patches over the whole
aahjaiaja,JohnVinyard/zounds,zounds/node/data.py,2951fa5e3300ab0680e6e5f05122491cdceaa6db,STILL_EXISTS,TODO: Should I do a sub-database per feature; or organize the keys as
aahjaibbc,JohnVinyard/zounds,zounds/node/onset.py,46d1d8a0bd6012b602dad012cd6cefa194da9a8c,STILL_EXISTS,TODO: Add a class (mixin) in the flow library for this pattern where
aahjaibbf,JohnVinyard/zounds,zounds/node/onset.py,46d1d8a0bd6012b602dad012cd6cefa194da9a8c,STILL_EXISTS,TODO: Encode\/decode tests
aahjaibbg,JohnVinyard/zounds,zounds/node/onset.py,46d1d8a0bd6012b602dad012cd6cefa194da9a8c,STILL_EXISTS,TODO: A subclass of this that turns each pair into a timeslice
aahjaibbh,JohnVinyard/zounds,zounds/node/onset.py,46d1d8a0bd6012b602dad012cd6cefa194da9a8c,STILL_EXISTS,TODO: Should PeakPicker always emit the *end* of the timeseries; so that the
aahjaibcj,JohnVinyard/zounds,zounds/node/onset.py,acf55cc97c6d5019e4c2d38ae84b93c1b04ad9ca,STILL_EXISTS,TODO: This duration isn't right.  It should probably be
aahjaibfj,JohnVinyard/zounds,zounds/node/onset.py,fc5920156d0d506bfb030f87d7a1dcb14ef47256,STILL_EXISTS,TODO: unwrap phases before computing deltas; to avoid artifacts
aahjaibij,JohnVinyard/zounds,zounds/node/audiostream.py,8dccf9ce910bd5b369ba226783c13ce5901d52c6,STILL_EXISTS,KLUDGE: PySoundfile no longer supports __len__; which means that
aahjaibja,JohnVinyard/zounds,zounds/node/fuzz_test.py,8dccf9ce910bd5b369ba226783c13ce5901d52c6,STILL_EXISTS,TODO: Update this to use BytesIO instead of writing a file to disk
aahjaiccb,JohnVinyard/zounds,setup.py,298324db53d4bff4d54d3222fad874160290c150,db428eb6fbea8227f86516fc244a7026cd68e8c6,# KLUDGE: What should I do here?
aahjaicdg,JohnVinyard/zounds,setup.py,298324db53d4bff4d54d3222fad874160290c150,db428eb6fbea8227f86516fc244a7026cd68e8c6,# KLUDGE: This is a hack. Right?  I'd like to add the currently logged-in user
aahjaicfc,JohnVinyard/zounds,setup.py,298324db53d4bff4d54d3222fad874160290c150,db428eb6fbea8227f86516fc244a7026cd68e8c6,# the output of logname ends with a newline
aahjaideg,JohnVinyard/zounds,zounds/node/api.py,6caad57b4805bb34ad72889970f3f418035eee1a,STILL_EXISTS,KLUDGE: I should figure out a way to do paging here
aahjaieid,JohnVinyard/zounds,zounds/node/api.py,ea21daedc96265b6f216ec479875e82add105120,84c3d4438f994c66693292ad3f8e9eeda0b7e580,TODO: Seperate serializer for ConstantRateTimeSeries
aahjaieig,JohnVinyard/zounds,zounds/node/api.py,3fd4da7f1c398d55387e7cde9e71d40b02e0a02b,6138a4e3b7b02a2bcc1bc7050ad9eb609cb15ae3,TODO: Support seconds range requests
aahjaifbd,JohnVinyard/zounds,zounds/node/rbm.py,22a6269db84e16451455365bd6f49d56b3397e3a,STILL_EXISTS,TODO: Is this the correct implementation for both Rbm and LinearRbm?
aahjaigba,JohnVinyard/zounds,zounds/basic/fuzz_test.py,8b68226f87cbc3a385e56c590d81b9581182152a,STILL_EXISTS,# TODO: Update this to use BytesIO instead of writing a file to disk
aahjaihci,JohnVinyard/zounds,zounds/segment/onset.py,0758eb591b25b1f43a92839a71f8fe7fa0160d16,STILL_EXISTS,TODO: this pattern of hanging on to the last sample of the previous chunk;
aahjaihfa,JohnVinyard/zounds,zounds/basic/basic.py,66bd4ad8cd4eb8439f2343fa300cd721a8ed0e16,9643956216ca92c5835dd83a82f1a3cc1e97d1d5,KLUDGE: This implementation may currently only be used when consuming
aahjaihff,JohnVinyard/zounds,zounds/timeseries/variablerate.py,83337f589638dc1950e00cb5f56bfebc585e87f8,STILL_EXISTS,TODO: Consider using a bisection approach here to make this much
aahjaihha,JohnVinyard/zounds,zounds/spectral/frequencyscale.py,24aeea50fb36512b66659ec01ede427c1c440bf9,STILL_EXISTS,TODO: What commonalities can be factored out of this class and TimeSlice?
aahjaihhb,JohnVinyard/zounds,zounds/spectral/frequencyscale.py,24aeea50fb36512b66659ec01ede427c1c440bf9,bb9de3e9fc45a9b7bb96b88b4b1031deac670b8f,TODO: Deprecate stuff in psychoacoustics.py in favor of these classes
aahjaihhd,JohnVinyard/zounds,zounds/spectral/psychacoustics.py,24aeea50fb36512b66659ec01ede427c1c440bf9,STILL_EXISTS,TODO: Depracate all of this in favor of the new frequencyscale stuff
aahjaihig,JohnVinyard/zounds,zounds/spectral/weighting.py,9d739dc02a0fcf82dafbce0a44002995673bd414,aed91ebb5baac9f4601bffe3f2adcf63d4eb54f6,TODO: Factor this common behavior from WindowingFunc out into a common
aahjaiiac,JohnVinyard/zounds,examples/mdct_synth.py,80b6801b8ebbb4528d043673e99526221c2a3577,STILL_EXISTS,compute the array needed to perceptually weight the MDCT frequencies
aahjaiibd,JohnVinyard/zounds,zounds/learn/test_unitnorm.py,b207a5971133253199a2c0b728cae719510458de,STILL_EXISTS,KLUDGE: This doesn't belong here; but putting it here is convenient
aahjaiiij,JohnVinyard/zounds,zounds/timeseries/timeseries.py,e4695eae2761b99c104629415884ef13fcc3648f,STILL_EXISTS,KLUDGE: This check is necessary for an initial; incremental
aahjaiija,JohnVinyard/zounds,zounds/timeseries/timeseries.py,e4695eae2761b99c104629415884ef13fcc3648f,STILL_EXISTS,refactoring; and should be removed once there are some nice;
aahjaijdd,JohnVinyard/zounds,zounds/timeseries/audiosamples.py,1e6a274b431abe8da1485f7a492f19179069ada9,afc2931672537e237910da3428364318566748fe,KLUDGE: This is here to support an incremental refactoring.
aahjajchb,JohnVinyard/zounds,zounds/timeseries/audiosamples.py,afc2931672537e237910da3428364318566748fe,88756f809f6ba4cc2b3c769714688ad42ab28463,# KLUDGE: This is here to support an incremental refactoring.
aahjajhii,JohnVinyard/zounds,zounds/timeseries/timeseries.py,c475e057151a6f044d91d6034d41e3c85aa9aab8,STILL_EXISTS,KLUDGE: This is basically arbitrary; but the motivation is that we'd
aahjbabdh,JohnVinyard/zounds,zounds/persistence/frequencydimension.py,72cea3d3c8830edfa402a8e2305cc79b6f3d7248,1e6344b39bd47315ee4276daa7ae99dc794e7e51,KLUDGE: This assumes that all FrequencyScale-derived classes live in
aahjbabfd,JohnVinyard/zounds,examples/erb_mdct.py,3714465435f256ef55f8f0702ffcecfb050bce72,4eaadd3f24d9f3174bf73e70bd0e18156e04af7a,KLUDGE: This is copied from the MDCT node in zounds.  Where does this
aahjbabfi,JohnVinyard/zounds,zounds/spectral/frequencyscale.py,3714465435f256ef55f8f0702ffcecfb050bce72,STILL_EXISTS,KLUDGE: This is simple; but it may make sense to choose move the
aahjbabhb,JohnVinyard/zounds,examples/erb_mdct.py,83800158efc99a56ac996ac0114663de7f835752,cb0abe07514456d57d12c12955cc5ed48226f7d4,# TODO: How much overlap is there?  Is it enough to satisfy the needs
aahjbabih,JohnVinyard/zounds,examples/erb_mdct.py,83800158efc99a56ac996ac0114663de7f835752,cb0abe07514456d57d12c12955cc5ed48226f7d4,# TODO: How can I find the perfect window?
aahjbacea,JohnVinyard/zounds,examples/erb_mdct.py,bc7345ddb7a3cb611858a3e2e9e237013b906494,4eaadd3f24d9f3174bf73e70bd0e18156e04af7a,TODO: How does this work if I use linearly-spaced; half-lapped
aahjbaege,JohnVinyard/zounds,zounds/spectral/frequencyadaptive.py,f7bac04af2bbfcba619c84c0353d6c26fbce46ac,STILL_EXISTS,TODO: This feels like it could be wrapped up nicely elsewhere
aahjbafbc,JohnVinyard/zounds,zounds/learn/gan_trainer.py,7671c6080cebfe487f60eaaaffc3266e774a8578,STILL_EXISTS,TODO: These dimensions work for vanilla GANs; but need to be
aahjbafif,JohnVinyard/zounds,docs/source/conf.py,6be2ed157030ca1039799d963ff202f551510d89,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aahjbagjj,JohnVinyard/zounds,zounds/soundfile/resample.py,6dce0a7f642106ddc14bebad4ad251959895807f,STILL_EXISTS,KLUDGE: This is here to support building documentation on readthedocs
aahjbahdi,JohnVinyard/zounds,zounds/learn/pytorch_model.py,6245912388f526e6de40552718a29860331991dc,eaddbad720811f630f51bc5de48dcdc3f094e03f,TODO: These dimensions work for vanilla GANs; but need to be
aahjbaigb,JohnVinyard/zounds,zounds/learn/pytorch_model.py,73c36f4ca75f522781ac9e73480feb569f5f580d,eaddbad720811f630f51bc5de48dcdc3f094e03f,TODO: this should have the same number of dimensions as real and
aahjbaiii,JohnVinyard/zounds,zounds/learn/random_samples.py,003eb34f3d78d83ce0fe757ef8d082f7e6347cec,STILL_EXISTS,TODO: this would be much more efficient for repeated calls if I
aahjbajbd,JohnVinyard/zounds,examples/pytorch_autoencoder_raw_audio.py,48bb0621e942bfb9f205b861af1c6ff996df07a0,STILL_EXISTS,TODO: Factor out the part of the pipeline that starts with samples and
aahjbajbf,JohnVinyard/zounds,examples/pytorch_autoencoder_raw_audio.py,48bb0621e942bfb9f205b861af1c6ff996df07a0,STILL_EXISTS,TODO: it should be possible to apply windowing at the synthesis step
aahjbajef,JohnVinyard/zounds,zounds/datasets/nsynth.py,86065c9af666065ecad282555127f6bf3f0bfd13,3e016021beb2c7b718fd4c9b79e1f2cc5261d084,TODO: Factor all of this out from MusicNet and NSynth
aahjbajfj,JohnVinyard/zounds,zounds/learn/gan.py,eaddbad720811f630f51bc5de48dcdc3f094e03f,dc6d8c135491d5b32681373aa51a55e97797c43b,TODO: These dimensions work for vanilla GANs; but need to be
aahjbajih,JohnVinyard/zounds,zounds/learn/wgan.py,eaddbad720811f630f51bc5de48dcdc3f094e03f,STILL_EXISTS,TODO: this should have the same number of dimensions as real and
aahjbajjh,JohnVinyard/zounds,zounds/learn/embedding.py,12349819fd3860605c88991067430775dbd00123,aebb1234e7ab3e48cf05173a90e48f9f242292d5,TODO: Why is this necessary?
aahjbbagj,JohnVinyard/zounds,zounds/learn/preprocess.py,a29f1a3307d2f36d8b4c1ad57ad8eb97644f9f08,STILL_EXISTS,TODO: what about functions with imports that aren't in the calling namespace?
aahjbbaha,JohnVinyard/zounds,zounds/learn/preprocess.py,a29f1a3307d2f36d8b4c1ad57ad8eb97644f9f08,STILL_EXISTS,TODO: what about functions that require inversion data?
aahjbbahc,JohnVinyard/zounds,zounds/learn/preprocess.py,a29f1a3307d2f36d8b4c1ad57ad8eb97644f9f08,STILL_EXISTS,TODO: it should be possible to hand this intermediate value off
aahjbbbaf,JohnVinyard/zounds,zounds/timeseries/functional.py,16104500d69a35bdf90624ea3e847288798bdf91,STILL_EXISTS,TODO: decompose this into de-quantization; and then a one-hot function
aahjbbbfe,JohnVinyard/zounds,examples/hamming_index.py,22034c120eb362a014522337d2b6fc049b9d335a,STILL_EXISTS,\"\"\" || Demonstrate how to build a hamming-distance index over a binary\/bit-packed || feature. ||  || This example is particularly handy for performance profiling of the hamming || index. || \"\"\"
aahjbbbff,JohnVinyard/zounds,examples/pytorch_wgan.py,22034c120eb362a014522337d2b6fc049b9d335a,STILL_EXISTS,\"\"\" || Log || - reduced sample size from 8192 to 4096.  No difference || - Introduced batch norm.  Discriminator loss seems to stall out; and generator ||   produces very periodic sine-like sound with many harmonics || - leaky RELU in discriminator seems to make little or no difference || - tanh for all layers produces noise || - leaky RELU in the generator seem to produce more plausible *looking* waveforms. ||   generated examples are a combination of a single tone and noise || - add batch norm to the last generator layer - this seems to have really helped ||    with the visual appearance of generated samples ||  ||  || - don't do instance scaling? there seems to be more variability; but still noise || - try with mu_law this results in noise; and strong peaks at convolution boundaries.  Why do things totally break down with mu_law? || - try the mu-law one-hot encoding.  this is very slow; and it's producing some pretty awful output.  Perhaps I should train longer with softmax. ||  || - try penalizing the norm (improved WGAN) much more variation.  Still some noise || - try tanh all the way through - doesn't learn at all ||  || - try interpolating in sample space - learns a good deal of variety || - try learning on downsampled 8192 samples -  there is some movement in the samples || - try without dropout - this seems to be slightly worse\/noisier ||  || - now that I'm doing WGAN; try instance scaling again - this seems to be OK now; and produces more variation || - try A-weighing the frequency domain and then IFFT back to samples- this seems to slightly improve variation || - try without tanh? - doesn't get rid of the noise || - try without batch norm in last layer? - doesn't change noise situation || - try with tanh in first layer of discriminator? - doesn't change noise ||  ||  || - can I scale up to 8192 and capture meaningful structure; even if noisy? ||     - yes; at least for Bach || - try with phat drum loops ||     - yes; starts to learn drum-like sounds || - try with speech ||     - yes; starts to learn speech-like sounds || - try with a toy dataset so I can understand the biases\/problems || - try with Kevin Gates ||     - yes; learns speech-like sounds plus kick drums and bass || - try with a mixture of different types of sample ||  || - residuals in the network || - dilated convolutions || - try adding batch norm back into discriminator? || - try training on mdct - nope; learns nothing || - progressively growing WGAN || \"\"\"
aahjbbcha,JohnVinyard/zounds,zounds/learn/pytorch_model.py,b57cf9a8e8914b8ba1eee0921bfe6900bcbf4faa,STILL_EXISTS,KLUDGE: Should we *ever* implicitly move things to the GPU?  If not;
aahjbbdaf,JohnVinyard/zounds,zounds/spectral/functional.py,4490e319724af28b389969cb8a1d3105259a5454,STILL_EXISTS,window the audio using a power-of-2 frame size for more efficient FFT
aahjbbddj,JohnVinyard/zounds,zounds/timeseries/audiosamples.py,1d81c4482755bc059b582335041593bd09e61cbc,STILL_EXISTS,KLUDGE: Trying to write too-large chunks to an ogg file seems
aahjbbdeb,JohnVinyard/zounds,zounds/timeseries/audiosamples.py,1d81c4482755bc059b582335041593bd09e61cbc,STILL_EXISTS,KLUDGE: This logic is very similar to logic in the OggVorbis
aahjbbdec,JohnVinyard/zounds,zounds/timeseries/audiosamples.py,1d81c4482755bc059b582335041593bd09e61cbc,STILL_EXISTS,processing node; and should probably be factored into a common
aahjbbffg,vc1492a/henosis,Henosis/henosis.py,446feaf75e43b327b3ffa4693fea52a4db020fe1,STILL_EXISTS,gather text from columns and place in single list for processing if not empty
aahjbbgce,vc1492a/henosis,Henosis/server.py,cc0ae9833b767bc357c984bfd2bac5d4a2cbce8b,STILL_EXISTS,gather text from columns and place in single list for processing if not empty
aahjbcagb,cong/ros_tensorflow,scripts/object_detection/builders/hyperparams_builder.py,efa22a59737173567b1cbb999a914cbd10ea0bf6,STILL_EXISTS,once we refactor Faster RCNN models to set is_training through an outer
aahjbcfgg,cong/ros_tensorflow,scripts/object_detection/core/box_predictor.py,efa22a59737173567b1cbb999a914cbd10ea0bf6,STILL_EXISTS,TODO(rathodv): Come up with a better way to generate scope names
aahjbcigb,cong/ros_tensorflow,scripts/object_detection/core/preprocessor.py,efa22a59737173567b1cbb999a914cbd10ea0bf6,STILL_EXISTS,TODO(mttang): This method is needed because the current
aahjbcige,cong/ros_tensorflow,scripts/object_detection/core/preprocessor.py,efa22a59737173567b1cbb999a914cbd10ea0bf6,STILL_EXISTS,Remember original dtype to so we can convert back if needed
aahjbddbb,cong/ros_tensorflow,scripts/object_detection/eval_util.py,efa22a59737173567b1cbb999a914cbd10ea0bf6,STILL_EXISTS,TODO(b\/65130867): Use image_id tensor once we fix the input data
aahjbddbe,cong/ros_tensorflow,scripts/object_detection/eval_util.py,efa22a59737173567b1cbb999a914cbd10ea0bf6,STILL_EXISTS,add_single_ground_truth_image_info expects a single image. Fix
aahjbddfb,cong/ros_tensorflow,scripts/object_detection/evaluator.py,efa22a59737173567b1cbb999a914cbd10ea0bf6,STILL_EXISTS,A dictionary of metric names to classes that implement the metric. The classes
aahjbddfc,cong/ros_tensorflow,scripts/object_detection/evaluator.py,efa22a59737173567b1cbb999a914cbd10ea0bf6,STILL_EXISTS,in the dictionary must implement
aahjbddig,cong/ros_tensorflow,scripts/object_detection/exporter.py,efa22a59737173567b1cbb999a914cbd10ea0bf6,STILL_EXISTS,Unused by updated loading code.
aahjbdejh,cong/ros_tensorflow,scripts/object_detection/matchers/argmax_matcher.py,efa22a59737173567b1cbb999a914cbd10ea0bf6,STILL_EXISTS,\"\"\"Argmax matcher implementation. ||  || This class takes a similarity matrix and matches columns to rows based on the || maximum value per column. One can specify matched_thresholds and || to prevent columns from matching to rows (generally resulting in a negative || training example) and unmatched_theshold to ignore the match (generally || resulting in neither a positive or negative training example). ||  || This matcher is used in Fast(er)-RCNN. ||  || Note: matchers are used in TargetAssigners. There is a create_target_assigner || factory function for popular implementations. || \"\"\"
aahjbdfbe,cong/ros_tensorflow,scripts/object_detection/matchers/argmax_matcher.py,efa22a59737173567b1cbb999a914cbd10ea0bf6,STILL_EXISTS,Get logical indices of ignored and unmatched columns as tf.int64
aahjbdfia,cong/ros_tensorflow,scripts/object_detection/meta_architectures/faster_rcnn_meta_arch.py,efa22a59737173567b1cbb999a914cbd10ea0bf6,STILL_EXISTS,TODO(rathodv): add_summaries is currently unused. Respect that directive
aahjbdhhc,cong/ros_tensorflow,scripts/object_detection/meta_architectures/rfcn_meta_arch.py,efa22a59737173567b1cbb999a914cbd10ea0bf6,STILL_EXISTS,TODO(rathodv): add_summaries is currently unused. Respect that directive
aahjbdiaj,cong/ros_tensorflow,scripts/object_detection/meta_architectures/ssd_meta_arch.py,efa22a59737173567b1cbb999a914cbd10ea0bf6,STILL_EXISTS,Needed for fine-tuning from classification checkpoints whose
aahjbedga,cong/ros_tensorflow,scripts/object_detection/models/faster_rcnn_resnet_v1_feature_extractor.py,efa22a59737173567b1cbb999a914cbd10ea0bf6,STILL_EXISTS,TODO(chensun): Figure out if it is needed when image
aahjbfhii,Narasimha1997/bzCompute,pyCompute/graph.py,01d3487ca0bb30e63cb0b8180dab0180811f4bcb,STILL_EXISTS,to implement your own operation; extend this class
aahjbfjdh,ryfeus/gcf-packs,pandas_numpy/sources/dateutil/parser/_parser.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: pandas.core.tools.datetimes imports this explicitly.  Might be worth
aahjbfjgf,ryfeus/gcf-packs,pandas_numpy/sources/dateutil/parser/_parser.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: \"Tues\"
aahjbfjgg,ryfeus/gcf-packs,pandas_numpy/sources/dateutil/parser/_parser.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: \"Thurs\"
aahjbfjgh,ryfeus/gcf-packs,pandas_numpy/sources/dateutil/parser/_parser.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: \"Febr\"
aahjbfjgi,ryfeus/gcf-packs,pandas_numpy/sources/dateutil/parser/_parser.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: ERA = [\"AD\"; \"BC\"; \"CE\"; \"BCE\"; \"Stardate\";
aahjbfjhe,ryfeus/gcf-packs,pandas_numpy/sources/dateutil/parser/_parser.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,move to info
aahjbfjje,ryfeus/gcf-packs,pandas_numpy/sources/dateutil/parser/_parser.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,WTF!?
aahjbgabc,ryfeus/gcf-packs,pandas_numpy/sources/dateutil/parser/_parser.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: not hit in tests
aahjbgacd,ryfeus/gcf-packs,pandas_numpy/sources/dateutil/parser/_parser.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: check that l[i + 1] is integer?
aahjbgacg,ryfeus/gcf-packs,pandas_numpy/sources/dateutil/parser/_parser.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Check that l[i+3] is minute-like?
aahjbgadg,ryfeus/gcf-packs,pandas_numpy/sources/dateutil/parser/_parser.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Check if res attributes already set.
aahjbgadj,ryfeus/gcf-packs,pandas_numpy/sources/dateutil/parser/_parser.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: checking that hour\/minute\/second are not
aahjbgaec,ryfeus/gcf-packs,pandas_numpy/sources/dateutil/parser/_parser.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: try\/except for this?
aahjbgaga,ryfeus/gcf-packs,pandas_numpy/sources/dateutil/parser/_parser.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Are we sure this is the right condition here?
aahjbgagh,ryfeus/gcf-packs,pandas_numpy/sources/dateutil/parser/_parser.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Every usage of this function sets res.second to the return
aahjbgaha,ryfeus/gcf-packs,pandas_numpy/sources/dateutil/parser/_parser.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Is this going to admit a lot of false-positives for when we
aahjbgbjd,ryfeus/gcf-packs,pandas_numpy/sources/dateutil/rrule.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Check -numweeks for next year.
aahjbgddd,ryfeus/gcf-packs,pandas_numpy/sources/dateutil/tz/tz.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Now fix transition times to become relative to wall time.
aahjbgece,ryfeus/gcf-packs,pandas_numpy/sources/dateutil/zoneinfo/__init__.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO  switch to FileNotFoundError?
aahjbgecj,ryfeus/gcf-packs,pandas_numpy/sources/dateutil/zoneinfo/__init__.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,a stateful class. So as a workaround for now; without changing the API; we
aahjbgedb,ryfeus/gcf-packs,pandas_numpy/sources/dateutil/zoneinfo/__init__.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,timezone. Ugly; but adheres to the api.
aahjbgedd,ryfeus/gcf-packs,pandas_numpy/sources/dateutil/zoneinfo/__init__.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Remove after deprecation period.
aahjbgefd,ryfeus/gcf-packs,pandas_numpy/sources/numpy/_globals.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,needed for python 2 to preserve identity through a pickle
aahjbgfif,ryfeus/gcf-packs,pandas_numpy/sources/numpy/conftest.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,FIXME when yield tests are gone.
aahjbggaf,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/_internal.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,This calls itself recursively but should eventually hit
aahjbggbe,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/_internal.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,astr is a string (perhaps comma separated)
aahjbggib,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/arrayprint.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Note: Both scalartypes.c.src and arrayprint.py implement strs for numpy
aahjbghch,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/arrayprint.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,pad_right is only needed for nan length calculation
aahjbgicc,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/einsumfunc.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Now propagate as many unused contractions as possible to next iteration
aahjbgidg,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/einsumfunc.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,DDOT without aligned data (better to use einsum)
aahjbgiih,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/einsumfunc.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Build a new view if needed
aahjbgjac,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/fromnumeric.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,This branch is needed for reductions like any which don't
aahjbgjgg,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/machar.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,\"\"\" || Machine arithmetics - determine the parameters of the || floating-point arithmetic system ||  || Author: Pearu Peterson; September 2003 ||  || \"\"\"
aahjbgjjf,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/numeric.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,needed instead of a 0 to get same result as zeros for for string dtypes
aahjbgjjg,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/numeric.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: this works around .astype(bool) not working properly (gh-9847)
aahjbhaad,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/numeric.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,fix hack in scipy which imports this function
aahjbhaaf,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/numeric.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Move working axis to the end of the shape
aahjbhade,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/numeric.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Needed to treat masked arrays correctly. = True would not work.
aahjbhbaa,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/records.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,\"\"\" || Record Arrays || ============= || Record arrays expose the fields of structured arrays as properties. ||  || Most commonly; ndarrays contain elements of a single type; e.g. floats; || integers; bools etc.  However; it is possible for elements to be combinations || of these using structured types; such as:: ||  ||   >>> a = np.array([(1; 2.0); (1; 2.0)]; dtype=[('x'; int); ('y'; float)]) ||   >>> a ||   array([(1; 2.0); (1; 2.0)]; ||         dtype=[('x'; '<i4'); ('y'; '<f8')]) ||  || Here; each element consists of two fields: x (and int); and y (a float). || This is known as a structured array.  The different fields are analogous || to columns in a spread-sheet.  The different fields can be accessed as || one would a dictionary:: ||  ||   >>> a['x'] ||   array([1; 1]) ||  ||   >>> a['y'] ||   array([ 2.;  2.]) ||  || Record arrays allow us to access fields as properties:: ||  ||   >>> ar = np.rec.array(a) ||  ||   >>> ar.x ||   array([1; 1]) ||  ||   >>> ar.y ||   array([ 2.;  2.]) ||  || \"\"\"
aahjbhbhf,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/setup.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX: ugly; we use a class to avoid calling twice some expensive functions in
aahjbhbie,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/setup.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Perhaps a fancier check is in order here.
aahjbhbja,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/setup.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Distutils hack on AMD64 on windows
aahjbhcaf,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/setup.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,os.uname not available on all platforms. blanket except ugly but safe
aahjbhcee,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/setup.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Ugly: this can be called within a library and not an extension;
aahjbhceg,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/setup.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,needed).
aahjbhcgi,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/setup.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Another ugly hack: the mathlib info is known once build_src is run;
aahjbhdbi,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/setup_common.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,recompile of extension modules is needed.
aahjbhdfg,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/setup_common.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,MMX only needed for icc; but some clangs don't have it
aahjbhecd,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_api.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,# wasn't what I expected... is np.array(o) supposed to equal a ?
aahjbhedj,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_api.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,casting parameter allows catching bad casts
aahjbheib,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_arrayprint.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,check it works properly with object arrays too
aahjbhgah,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_datetime.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Changing to 'same_kind' or 'safe' casting in the ufuncs by
aahjbhgai,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_datetime.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,default is needed to properly catch this kind of thing...
aahjbhgjc,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_datetime.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Allowing unsafe casting by
aahjbhhhg,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_defchararray.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,make 2to3 work properly
aahjbhhif,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_deprecations.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,\"\"\" || Tests related to deprecation warnings. Also a convenient place || to document how deprecations should eventually be turned into errors. ||  || \"\"\"
aahjbhiae,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_deprecations.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Needed so Python 3 does not raise DeprecationWarning twice.
aahjbhjbc,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_einsum.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,matrix triple product (note this is not currently an efficient
aahjbibdi,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_indexing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Before `...` would return a itself.
aahjbibgg,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_indexing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,This object here is very dubious and probably bad though:
aahjbiccg,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_indexing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,numpy should maybe raise an error if casting to intp
aahjbicgj,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_indexing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Maybe never happens...
aahjbicha,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_indexing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,too many dimensions; probably
aahjbidcg,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_machar.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Fixme; this needs to raise a 'skip' exception.
aahjbiiih,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_multiarray.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,FIXME:
aahjbijbg,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_multiarray.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Strided A columns; strided X vector
aahjbijgh,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_multiarray.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,It would be nice to support in-place matmul eventually; but for now
aahjbijia,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_multiarray.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: test for multidimensional
aahjbjbbe,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_nditer.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,avoid pyflakes unused variable warning
aahjbjcie,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_nditer.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Copy not needed; 1 op
aahjbjcig,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_nditer.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Copy not needed with elementwise; 2 ops; exactly same arrays
aahjbjcih,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_nditer.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Copy not needed; 2 ops; no overlap
aahjbjcij,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_nditer.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Copy needed; 3 ops; read-write overlap
aahjbjcjc,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_nditer.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Copy not needed; 3 ops; write-only overlap
aahjbjdhe,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_nditer.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Test that the inner loop grows when no buffering is needed
aahjbjedc,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_nditer.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,When buffering is unused; 'writemasked' effectively does nothing.
aahjbjfdb,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_numeric.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Probably ~t (bitwise negation) is more proper to use here;
aahjbjhha,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_regression.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Fix in r2836
aahjbjhhj,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_regression.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Wrong way
aahjbjhia,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_regression.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Correct way
aahjbjigf,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_regression.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Avoid pyflakes unused variable warning
aahjbjjca,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_regression.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,This didn't require a fix; but it's worth testing anyway; because
aahjbjjdb,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_regression.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Bug #1436; the following should succeed
aahjcaaba,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_regression.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Check that scalar unpickling hack in Py3 that supports
aahjcaacj,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_regression.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,100MB times 1000 would give 100GB of memory usage if it leaks
aahjcabcf,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_scalarmath.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,2 ** -1 perhaps generic
aahjcacej,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_shape_base.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,This should probably be deprecated:
aahjcacjf,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_ufunc.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,fixme -- I don't know how to do this yet
aahjcadab,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_ufunc.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,FIXME These should be using assert_raises
aahjcaebe,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_ufunc.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,n = 0; m > 0: fine; doing 0 reductions of m-element arrays
aahjcafad,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_umath.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,FIXME cinf not tested.
aahjcaffj,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_umath.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Calling convention for out should not affect how special methods are
aahjcafgh,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_umath.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,test fix for bug #1026:
aahjcafgi,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_umath.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,test fix for bug #826:
aahjcaghb,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_umath.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX: spacing does not handle long double yet
aahjcagia,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_umath_complex.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: branch cuts (use Pauli code)
aahjcagib,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_umath_complex.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: conj 'symmetry'
aahjcagic,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_umath_complex.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: FPU exceptions
aahjcagig,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_umath_complex.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,FIXME: this will probably change when we require full C99 campatibility
aahjcagih,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_umath_complex.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: replace with a check on whether platform-provided C99 funcs are used
aahjcahae,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_umath_complex.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX: most implementations get it wrong here (including glibc <= 2.10)
aahjcahah,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_umath_complex.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX: check exceptions raised
aahjcahea,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_umath_complex.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Fixme: ugly workaround for isinf bug.
aahjcahee,ryfeus/gcf-packs,pandas_numpy/sources/numpy/core/tests/test_umath_complex.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX: check for conj(csqrt(z)) == csqrt(conj(z)) (need to fix branch
aahjcaiie,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/ccompiler.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,FIXME:RELATIVE_IMPORT
aahjcajbf,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/ccompiler.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,NOTE:  I removed this fix in revision 4481 (see ticket #619); but it appears
aahjcajcg,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/ccompiler.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,white-space. XXX: send a patch to distutils
aahjcajdf,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/command/build_clib.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Fix Python distutils bug sf #1718574:
aahjcajed,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/command/build_clib.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,this hack works around the msvc compiler attributes
aahjcajee,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/command/build_clib.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,problem; msvc uses its own convention :(
aahjcajfc,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/command/build_clib.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,fix library dependencies
aahjcajhc,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/command/build_ext.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Determine if C++\/Fortran 77\/Fortran 90 compilers are needed.
aahjcajid,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/command/build_ext.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,FIXME: In the case where there are more than two packages;
aahjcajii,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/command/build_ext.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Needed to compile kiva.agg._agg extension.
aahjcajij,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/command/build_ext.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,this hack works around the msvc compiler attributes
aahjcajja,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/command/build_ext.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,problem; msvc uses its own convention :(
aahjcbaaj,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/command/build_py.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX: Fix find_source_files for item in py_modules such that item is 3-tuple
aahjcbacg,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/command/build_src.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX: another ugly workaround to circumvent distutils brain damage. We
aahjcbaee,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/command/build_src.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX TODO: --inplace support for sdist command
aahjcbafj,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/command/config.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX: hack to circumvent a python 2.6 bug with msvc9compiler:
aahjcbajc,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/command/config_compiler.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX: Linker flags
aahjcbbcf,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/command/install_headers.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Kind of a hack; but I don't know where else to change this...
aahjcbbch,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/conv_template.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,\"\"\" || takes templated file .xxx.src and produces .xxx file  where .xxx is || .i or .c or .h; using the following template rules ||  || \/**begin repeat  -- on a line by itself marks the start of a repeated code ||                     segment || \/**end repeat**\/ -- on a line by itself marks it's end ||  || After the \/**begin repeat and before the *\/; all the named templates are placed || these should all have the same number of replacements ||  || Repeat blocks can be nested; with each nested block labeled with its depth; || i.e. || \/**begin repeat1 ||  *.... ||  *\/ || \/**end repeat1**\/ ||  || When using nested loops; you can optionally exclude particular || combinations of the variables using (inside the comment portion of the inner loop): ||  ||  :exclude: var1=value1; var2=value2; ... ||  || This will exclude the pattern where var1 is value1 and var2 is value2 when || the result is being generated. ||  ||  || In the main body each replace will use one entry from the list of named replacements ||  ||  Note that all #..# forms in a block must have the same number of ||    comma-separated entries. ||  || Example: ||  ||     An input file containing ||  ||         \/**begin repeat ||          * #a = 1;2;3# ||          * #b = 1;2;3# ||          *\/ ||  ||         \/**begin repeat1 ||          * #c = ted; jim# ||          *\/ ||         @a@; @b@; @c@ ||         \/**end repeat1**\/ ||  ||         \/**end repeat**\/ ||  ||     produces ||  ||         line 1 \"template.c.src\" ||  ||         \/* ||          ********************************************************************* ||          **       This file was autogenerated from a template  DO NOT EDIT!!** ||          **       Changes should be made to the original source (.src) file ** ||          ********************************************************************* ||          *\/ ||  ||         #line 9 ||         1; 1; ted ||  ||         #line 9 ||         1; 1; jim ||  ||         #line 9 ||         2; 2; ted ||  ||         #line 9 ||         2; 2; jim ||  ||         #line 9 ||         3; 3; ted ||  ||         #line 9 ||         3; 3; jim ||  || \"\"\"
aahjcbbdg,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/conv_template.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,replaces occurrences of xxx*3 with xxx; xxx; xxx
aahjcbbff,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/core.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX Hack to get numpy installable with easy_install.
aahjcbbhe,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/cpuinfo.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX
aahjcbbhg,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/cpuinfo.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX: what does the value of
aahjcbbhj,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/cpuinfo.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX: Bad style to use so long `try:...except:...`. Fix it!
aahjcbbih,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/cpuinfo.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX: other OS's. Eg. use _winreg on Win32. Or os.uname on unices.
aahjcbcbg,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/exec_command.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,stdio uses bytes in python 2; so to avoid issues; we simply
aahjcbchg,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/fcompiler/__init__.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX Assuming that free format is default for f90 compiler.
aahjcbcia,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/fcompiler/__init__.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX: Do we need LDSHARED->SOSHARED; LDFLAGS->SOFLAGS
aahjcbcij,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/fcompiler/__init__.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: implement get_f90flags and use it in _compile similarly to get_f77flags
aahjcbdae,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/fcompiler/absoft.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,of Absoft though I don't think versions earlier than 9 can
aahjcbdcc,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/fcompiler/gnu.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX: handle cross compilation
aahjcbdid,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/fcompiler/hpux.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX status==256 may indicate 'unrecognized option' or
aahjcbeae,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/fcompiler/intel.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX this is a wild guess
aahjcbeca,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/from_template.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,\"\"\" ||  || process_file(filename) ||  ||   takes templated file .xxx.src and produces .xxx file where .xxx ||   is .pyf .f90 or .f using the following template rules: ||  ||   '<..>' denotes a template. ||  ||   All function and subroutine blocks in a source file with names that ||   contain '<..>' will be replicated according to the rules in '<..>'. ||  ||   The number of comma-separated words in '<..>' will determine the number of ||   replicates. ||  ||   '<..>' may have two different forms; named and short. For example; ||  ||   named: ||    <p=d;s;z;c> where anywhere inside a block '<p>' will be replaced with ||    'd'; 's'; 'z'; and 'c' for each replicate of the block. ||  ||    <_c>  is already defined: <_c=s;d;c;z> ||    <_t>  is already defined: <_t=real;double precision;complex;double complex> ||  ||   short: ||    <s;d;c;z>; a short form of the named; useful when no <p> appears inside ||    a block. ||  ||   In general; '<..>' contains a comma separated list of arbitrary ||   expressions. If these expression must contain a comma|leftarrow|rightarrow; ||   then prepend the comma|leftarrow|rightarrow with a backslash. ||  ||   If an expression matches '\\\\<index>' then it will be replaced ||   by <index>-th expression. ||  ||   Note that all '<..>' forms in a block must have the same number of ||   comma-separated entries. ||  ||  Predefined named template rules: ||   <prefix=s;d;c;z> ||   <ftype=real;double precision;complex;double complex> ||   <ftypereal=real;double precision;\\\\0;\\\\1> ||   <ctype=float;double;complex_float;complex_double> ||   <ctypereal=float;double;\\\\0;\\\\1> ||  || \"\"\"
aahjcbecg,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/from_template.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,newnames are constructed as needed
aahjcbedg,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/log.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,likely a good reason why we're running at this level.
aahjcbegh,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/mingw32ccompiler.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,kind of bad consequences; like using Py_ModuleInit4 instead of
aahjcbehg,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/mingw32ccompiler.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,no additional libraries needed
aahjcbfdj,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/mingw32ccompiler.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX: ideally; we should use exactly the same version as used by python. I
aahjcbfjg,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/misc_util.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX need support for .C that is also C++
aahjcbfjh,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/misc_util.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,break  # XXX can we assume that there is one module per file?
aahjcbgaa,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/misc_util.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX: *.hpp files??
aahjcbgac,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/misc_util.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX scan sources for include statements
aahjcbgcc,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/misc_util.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,## XXX Implement add_py_modules
aahjcbgdg,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/misc_util.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX: import here for bootstrapping reasons
aahjcbggc,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/npy_pkg_config.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,FIXME: document this. If pkgname is defined in the variables section; and
aahjcbggf,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/npy_pkg_config.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Trivial cache to cache LibraryInfo instances creation. To be really
aahjcbggg,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/npy_pkg_config.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,efficient; the cache should be handled in read_config; since a same file can
aahjcbggj,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/npy_pkg_config.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO:
aahjcbghb,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/npy_pkg_config.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,pkg-config simple emulator - useful for debugging; and maybe later to query
aahjcbghd,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/numpy_distribution.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX: Handle setuptools ?
aahjcbgid,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/system_info.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,good to keep its class name. Use of RawConfigParser is needed in
aahjcbhcd,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/system_info.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX: disabled by default; may disappear in
aahjcbhhg,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/system_info.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,The check for windows is needed because has_cblas uses the
aahjcbhia,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/system_info.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX: is it generally true?
aahjcbhjj,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/system_info.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX: should we check here actual existence of source files?
aahjcbiei,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/unixccompiler.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,all headers needed by a c file as a side effect of compilation (-MMD)
aahjcbife,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/unixccompiler.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,think the only major Unix that does.  Maybe we need some
aahjcbifg,ryfeus/gcf-packs,pandas_numpy/sources/numpy/distutils/unixccompiler.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,needed -- or maybe Python's configure script took care of
aahjcbifi,ryfeus/gcf-packs,pandas_numpy/sources/numpy/doc/basics.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,\"\"\" || ============ || Array basics || ============ ||  || Array types and conversions between types || ========================================= ||  || NumPy supports a much greater variety of numerical types than Python does. || This section shows which are available; and how to modify an array's data-type. ||  || ============ ========================================================== || Data type    Description || ============ ========================================================== || ``bool_``    Boolean (True or False) stored as a byte || ``int_``     Default integer type (same as C ``long``; normally either ||              ``int64`` or ``int32``) || intc         Identical to C ``int`` (normally ``int32`` or ``int64``) || intp         Integer used for indexing (same as C ``ssize_t``; normally ||              either ``int32`` or ``int64``) || int8         Byte (-128 to 127) || int16        Integer (-32768 to 32767) || int32        Integer (-2147483648 to 2147483647) || int64        Integer (-9223372036854775808 to 9223372036854775807) || uint8        Unsigned integer (0 to 255) || uint16       Unsigned integer (0 to 65535) || uint32       Unsigned integer (0 to 4294967295) || uint64       Unsigned integer (0 to 18446744073709551615) || ``float_``   Shorthand for ``float64``. || float16      Half precision float: sign bit; 5 bits exponent; ||              10 bits mantissa || float32      Single precision float: sign bit; 8 bits exponent; ||              23 bits mantissa || float64      Double precision float: sign bit; 11 bits exponent; ||              52 bits mantissa || ``complex_`` Shorthand for ``complex128``. || complex64    Complex number; represented by two 32-bit floats (real ||              and imaginary components) || complex128   Complex number; represented by two 64-bit floats (real ||              and imaginary components) || ============ ========================================================== ||  || Additionally to ``intc`` the platform dependent C integer types ``short``; || ``long``; ``longlong`` and their unsigned versions are defined. ||  || NumPy numerical types are instances of ``dtype`` (data-type) objects; each || having unique characteristics.  Once you have imported NumPy using ||  ||   :: ||  ||     >>> import numpy as np ||  || the dtypes are available as ``np.bool_``; ``np.float32``; etc. ||  || Advanced types; not listed in the table above; are explored in || section :ref:`structured_arrays`. ||  || There are 5 basic numerical types representing booleans (bool); integers (int); || unsigned integers (uint) floating point (float) and complex. Those with numbers || in their name indicate the bitsize of the type (i.e. how many bits are needed || to represent a single value in memory).  Some types; such as ``int`` and || ``intp``; have differing bitsizes; dependent on the platforms (e.g. 32-bit || vs. 64-bit machines).  This should be taken into account when interfacing || with low-level code (such as C or Fortran) where the raw memory is addressed. ||  || Data-types can be used as functions to convert python numbers to array scalars || (see the array scalar section for an explanation); python sequences of numbers || to arrays of that type; or as arguments to the dtype keyword that many numpy || functions or methods accept. Some examples:: ||  ||     >>> import numpy as np ||     >>> x = np.float32(1.0) ||     >>> x ||     1.0 ||     >>> y = np.int_([1;2;4]) ||     >>> y ||     array([1; 2; 4]) ||     >>> z = np.arange(3; dtype=np.uint8) ||     >>> z ||     array([0; 1; 2]; dtype=uint8) ||  || Array types can also be referred to by character codes; mostly to retain || backward compatibility with older packages such as Numeric.  Some || documentation may still refer to these; for example:: ||  ||   >>> np.array([1; 2; 3]; dtype='f') ||   array([ 1.;  2.;  3.]; dtype=float32) ||  || We recommend using dtype objects instead. ||  || To convert the type of an array; use the .astype() method (preferred) or || the type itself as a function. For example: :: ||  ||     >>> z.astype(float)                 #doctest: +NORMALIZE_WHITESPACE ||     array([  0.;  1.;  2.]) ||     >>> np.int8(z) ||     array([0; 1; 2]; dtype=int8) ||  || Note that; above; we use the *Python* float object as a dtype.  NumPy knows || that ``int`` refers to ``np.int_``; ``bool`` means ``np.bool_``; || that ``float`` is ``np.float_`` and ``complex`` is ``np.complex_``. || The other data-types do not have Python equivalents. ||  || To determine the type of an array; look at the dtype attribute:: ||  ||     >>> z.dtype ||     dtype('uint8') ||  || dtype objects also contain information about the type; such as its bit-width || and its byte-order.  The data type can also be used indirectly to query || properties of the type; such as whether it is an integer:: ||  ||     >>> d = np.dtype(int) ||     >>> d ||     dtype('int32') ||  ||     >>> np.issubdtype(d; np.integer) ||     True ||  ||     >>> np.issubdtype(d; np.floating) ||     False ||  ||  || Array Scalars || ============= ||  || NumPy generally returns elements of arrays as array scalars (a scalar || with an associated dtype).  Array scalars differ from Python scalars; but || for the most part they can be used interchangeably (the primary || exception is for versions of Python older than v2.x; where integer array || scalars cannot act as indices for lists and tuples).  There are some || exceptions; such as when code requires very specific attributes of a scalar || or when it checks specifically whether a value is a Python scalar. Generally; || problems are easily fixed by explicitly converting array scalars || to Python scalars; using the corresponding Python type function || (e.g.; ``int``; ``float``; ``complex``; ``str``; ``unicode``). ||  || The primary advantage of using array scalars is that || they preserve the array type (Python may not have a matching scalar type || available; e.g. ``int16``).  Therefore; the use of array scalars ensures || identical behaviour between arrays and scalars; irrespective of whether the || value is inside an array or not.  NumPy scalars also have many of the same || methods arrays do. ||  || Extended Precision || ================== ||  || Python's floating-point numbers are usually 64-bit floating-point numbers; || nearly equivalent to ``np.float64``. In some unusual situations it may be || useful to use floating-point numbers with more precision. Whether this || is possible in numpy depends on the hardware and on the development || environment: specifically; x86 machines provide hardware floating-point || with 80-bit precision; and while most C compilers provide this as their || ``long double`` type; MSVC (standard for Windows builds) makes || ``long double`` identical to ``double`` (64 bits). NumPy makes the || compiler's ``long double`` available as ``np.longdouble`` (and || ``np.clongdouble`` for the complex numbers). You can find out what your || numpy provides with ``np.finfo(np.longdouble)``. ||  || NumPy does not provide a dtype with more precision than C || ``long double``\\\\s; in particular; the 128-bit IEEE quad precision || data type (FORTRAN's ``REAL*16``\\\\) is not available. ||  || For efficient memory alignment; ``np.longdouble`` is usually stored || padded with zero bits; either to 96 or 128 bits. Which is more efficient || depends on hardware and development environment; typically on 32-bit || systems they are padded to 96 bits; while on 64-bit systems they are || typically padded to 128 bits. ``np.longdouble`` is padded to the system || default; ``np.float96`` and ``np.float128`` are provided for users who || want specific padding. In spite of the names; ``np.float96`` and || ``np.float128`` provide only as much precision as ``np.longdouble``; || that is; 80 bits on most x86 machines and 64 bits in standard || Windows builds. ||  || Be warned that even if ``np.longdouble`` offers more precision than || python ``float``; it is easy to lose that extra precision; since || python often forces values to pass through ``float``. For example; || the ``%`` formatting operator requires its arguments to be converted || to standard python types; and it is therefore impossible to preserve || extended precision even if many decimal places are requested. It can || be useful to test your code with the value || ``1 + np.finfo(np.longdouble).eps``. ||  || \"\"\"
aahjcbifj,ryfeus/gcf-packs,pandas_numpy/sources/numpy/doc/broadcasting.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,\"\"\" || ======================== || Broadcasting over arrays || ======================== ||  || The term broadcasting describes how numpy treats arrays with different || shapes during arithmetic operations. Subject to certain constraints; || the smaller array is \"broadcast\" across the larger array so that they || have compatible shapes. Broadcasting provides a means of vectorizing || array operations so that looping occurs in C instead of Python. It does || this without making needless copies of data and usually leads to || efficient algorithm implementations. There are; however; cases where || broadcasting is a bad idea because it leads to inefficient use of memory || that slows computation. ||  || NumPy operations are usually done on pairs of arrays on an || element-by-element basis.  In the simplest case; the two arrays must || have exactly the same shape; as in the following example: ||  ||   >>> a = np.array([1.0; 2.0; 3.0]) ||   >>> b = np.array([2.0; 2.0; 2.0]) ||   >>> a * b ||   array([ 2.;  4.;  6.]) ||  || NumPy's broadcasting rule relaxes this constraint when the arrays' || shapes meet certain constraints. The simplest broadcasting example occurs || when an array and a scalar value are combined in an operation: ||  || >>> a = np.array([1.0; 2.0; 3.0]) || >>> b = 2.0 || >>> a * b || array([ 2.;  4.;  6.]) ||  || The result is equivalent to the previous example where ``b`` was an array. || We can think of the scalar ``b`` being *stretched* during the arithmetic || operation into an array with the same shape as ``a``. The new elements in || ``b`` are simply copies of the original scalar. The stretching analogy is || only conceptual.  NumPy is smart enough to use the original scalar value || without actually making copies; so that broadcasting operations are as || memory and computationally efficient as possible. ||  || The code in the second example is more efficient than that in the first || because broadcasting moves less memory around during the multiplication || (``b`` is a scalar rather than an array). ||  || General Broadcasting Rules || ========================== || When operating on two arrays; NumPy compares their shapes element-wise. || It starts with the trailing dimensions; and works its way forward.  Two || dimensions are compatible when ||  || 1) they are equal; or || 2) one of them is 1 ||  || If these conditions are not met; a || ``ValueError: frames are not aligned`` exception is thrown; indicating that || the arrays have incompatible shapes. The size of the resulting array || is the maximum size along each dimension of the input arrays. ||  || Arrays do not need to have the same *number* of dimensions.  For example; || if you have a ``256x256x3`` array of RGB values; and you want to scale || each color in the image by a different value; you can multiply the image || by a one-dimensional array with 3 values. Lining up the sizes of the || trailing axes of these arrays according to the broadcast rules; shows that || they are compatible:: ||  ||   Image  (3d array): 256 x 256 x 3 ||   Scale  (1d array):             3 ||   Result (3d array): 256 x 256 x 3 ||  || When either of the dimensions compared is one; the other is || used.  In other words; dimensions with size 1 are stretched or \"copied\" || to match the other. ||  || In the following example; both the ``A`` and ``B`` arrays have axes with || length one that are expanded to a larger size during the broadcast || operation:: ||  ||   A      (4d array):  8 x 1 x 6 x 1 ||   B      (3d array):      7 x 1 x 5 ||   Result (4d array):  8 x 7 x 6 x 5 ||  || Here are some more examples:: ||  ||   A      (2d array):  5 x 4 ||   B      (1d array):      1 ||   Result (2d array):  5 x 4 ||  ||   A      (2d array):  5 x 4 ||   B      (1d array):      4 ||   Result (2d array):  5 x 4 ||  ||   A      (3d array):  15 x 3 x 5 ||   B      (3d array):  15 x 1 x 5 ||   Result (3d array):  15 x 3 x 5 ||  ||   A      (3d array):  15 x 3 x 5 ||   B      (2d array):       3 x 5 ||   Result (3d array):  15 x 3 x 5 ||  ||   A      (3d array):  15 x 3 x 5 ||   B      (2d array):       3 x 1 ||   Result (3d array):  15 x 3 x 5 ||  || Here are examples of shapes that do not broadcast:: ||  ||   A      (1d array):  3 ||   B      (1d array):  4 # trailing dimensions do not match ||  ||   A      (2d array):      2 x 1 ||   B      (3d array):  8 x 4 x 3 # second from last dimensions mismatched ||  || An example of broadcasting in practice:: ||  ||  >>> x = np.arange(4) ||  >>> xx = x.reshape(4;1) ||  >>> y = np.ones(5) ||  >>> z = np.ones((3;4)) ||  ||  >>> x.shape ||  (4;) ||  ||  >>> y.shape ||  (5;) ||  ||  >>> x + y ||  <type 'exceptions.ValueError'>: shape mismatch: objects cannot be broadcast to a single shape ||  ||  >>> xx.shape ||  (4; 1) ||  ||  >>> y.shape ||  (5;) ||  ||  >>> (xx + y).shape ||  (4; 5) ||  ||  >>> xx + y ||  array([[ 1.;  1.;  1.;  1.;  1.]; ||         [ 2.;  2.;  2.;  2.;  2.]; ||         [ 3.;  3.;  3.;  3.;  3.]; ||         [ 4.;  4.;  4.;  4.;  4.]]) ||  ||  >>> x.shape ||  (4;) ||  ||  >>> z.shape ||  (3; 4) ||  ||  >>> (x + z).shape ||  (3; 4) ||  ||  >>> x + z ||  array([[ 1.;  2.;  3.;  4.]; ||         [ 1.;  2.;  3.;  4.]; ||         [ 1.;  2.;  3.;  4.]]) ||  || Broadcasting provides a convenient way of taking the outer product (or || any other outer operation) of two arrays. The following example shows an || outer addition operation of two 1-d arrays:: ||  ||   >>> a = np.array([0.0; 10.0; 20.0; 30.0]) ||   >>> b = np.array([1.0; 2.0; 3.0]) ||   >>> a[:; np.newaxis] + b ||   array([[  1.;   2.;   3.]; ||          [ 11.;  12.;  13.]; ||          [ 21.;  22.;  23.]; ||          [ 31.;  32.;  33.]]) ||  || Here the ``newaxis`` index operator inserts a new axis into ``a``; || making it a two-dimensional ``4x1`` array.  Combining the ``4x1`` array || with ``b``; which has shape ``(3;)``; yields a ``4x3`` array. ||  || See `this article <http:\/\/wiki.scipy.org\/EricsBroadcastingDoc>`_ || for illustrations of broadcasting concepts. ||  || \"\"\"
aahjcbiha,ryfeus/gcf-packs,pandas_numpy/sources/numpy/doc/glossary.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,\"\"\" || ======== || Glossary || ======== ||  || .. glossary:: ||  ||    along an axis ||        Axes are defined for arrays with more than one dimension.  A ||        2-dimensional array has two corresponding axes: the first running ||        vertically downwards across rows (axis 0); and the second running ||        horizontally across columns (axis 1). ||  ||        Many operations can take place along one of these axes.  For example; ||        we can sum each row of an array; in which case we operate along ||        columns; or axis 1:: ||  ||          >>> x = np.arange(12).reshape((3;4)) ||  ||          >>> x ||          array([[ 0;  1;  2;  3]; ||                 [ 4;  5;  6;  7]; ||                 [ 8;  9; 10; 11]]) ||  ||          >>> x.sum(axis=1) ||          array([ 6; 22; 38]) ||  ||    array ||        A homogeneous container of numerical elements.  Each element in the ||        array occupies a fixed amount of memory (hence homogeneous); and ||        can be a numerical element of a single type (such as float; int ||        or complex) or a combination (such as ``(float; int; float)``).  Each ||        array has an associated data-type (or ``dtype``); which describes ||        the numerical type of its elements:: ||  ||          >>> x = np.array([1; 2; 3]; float) ||  ||          >>> x ||          array([ 1.;  2.;  3.]) ||  ||          >>> x.dtype # floating point number; 64 bits of memory per element ||          dtype('float64') ||  ||  ||          # More complicated data type: each array element is a combination of ||          # and integer and a floating point number ||          >>> np.array([(1; 2.0); (3; 4.0)]; dtype=[('x'; int); ('y'; float)]) ||          array([(1; 2.0); (3; 4.0)]; ||                dtype=[('x'; '<i4'); ('y'; '<f8')]) ||  ||        Fast element-wise operations; called a :term:`ufunc`; operate on arrays. ||  ||    array_like ||        Any sequence that can be interpreted as an ndarray.  This includes ||        nested lists; tuples; scalars and existing arrays. ||  ||    attribute ||        A property of an object that can be accessed using ``obj.attribute``; ||        e.g.; ``shape`` is an attribute of an array:: ||  ||          >>> x = np.array([1; 2; 3]) ||          >>> x.shape ||          (3;) ||  ||    big-endian ||        When storing a multi-byte value in memory as a sequence of bytes; the ||        sequence addresses\/sends\/stores the most significant byte first (lowest ||        address) and the least significant byte last (highest address). Common in ||        micro-processors and used for transmission of data over network protocols. ||  ||    BLAS ||        `Basic Linear Algebra Subprograms <http:\/\/en.wikipedia.org\/wiki\/BLAS>`_ ||  ||    broadcast ||        NumPy can do operations on arrays whose shapes are mismatched:: ||  ||          >>> x = np.array([1; 2]) ||          >>> y = np.array([[3]; [4]]) ||  ||          >>> x ||          array([1; 2]) ||  ||          >>> y ||          array([[3]; ||                 [4]]) ||  ||          >>> x + y ||          array([[4; 5]; ||                 [5; 6]]) ||  ||        See `numpy.doc.broadcasting` for more information. ||  ||    C order ||        See `row-major` ||  ||    column-major ||        A way to represent items in a N-dimensional array in the 1-dimensional ||        computer memory. In column-major order; the leftmost index \"varies the ||        fastest\": for example the array:: ||  ||             [[1; 2; 3]; ||              [4; 5; 6]] ||  ||        is represented in the column-major order as:: ||  ||            [1; 4; 2; 5; 3; 6] ||  ||        Column-major order is also known as the Fortran order; as the Fortran ||        programming language uses it. ||  ||    decorator ||        An operator that transforms a function.  For example; a ``log`` ||        decorator may be defined to print debugging information upon ||        function execution:: ||  ||          >>> def log(f): ||          ...     def new_logging_func(*args; **kwargs): ||          ...         print(\"Logging call with parameters:\"; args; kwargs) ||          ...         return f(*args; **kwargs) ||          ... ||          ...     return new_logging_func ||  ||        Now; when we define a function; we can \"decorate\" it using ``log``:: ||  ||          >>> @log ||          ... def add(a; b): ||          ...     return a + b ||  ||        Calling ``add`` then yields: ||  ||        >>> add(1; 2) ||        Logging call with parameters: (1; 2) {} ||        3 ||  ||    dictionary ||        Resembling a language dictionary; which provides a mapping between ||        words and descriptions thereof; a Python dictionary is a mapping ||        between two objects:: ||  ||          >>> x = {1: 'one'; 'two': [1; 2]} ||  ||        Here; `x` is a dictionary mapping keys to values; in this case ||        the integer 1 to the string \"one\"; and the string \"two\" to ||        the list ``[1; 2]``.  The values may be accessed using their ||        corresponding keys:: ||  ||          >>> x[1] ||          'one' ||  ||          >>> x['two'] ||          [1; 2] ||  ||        Note that dictionaries are not stored in any specific order.  Also; ||        most mutable (see *immutable* below) objects; such as lists; may not ||        be used as keys. ||  ||        For more information on dictionaries; read the ||        `Python tutorial <http:\/\/docs.python.org\/tut>`_. ||  ||    field ||        In a :term:`structured data type`; each sub-type is called a `field`. ||        The `field` has a name (a string); a type (any valid :term:`dtype`; and ||        an optional `title`. See :ref:`arrays.dtypes` ||  ||    Fortran order ||        See `column-major` ||  ||    flattened ||        Collapsed to a one-dimensional array. See `numpy.ndarray.flatten` ||        for details. ||  ||    homogenous ||        Describes a block of memory comprised of blocks; each block comprised of  ||        items and of the same size; and blocks are interpreted in exactly the ||        same way. In the simplest case each block contains a single item; for ||        instance int32 or float64. ||  ||    immutable ||        An object that cannot be modified after execution is called ||        immutable.  Two common examples are strings and tuples. ||  ||    instance ||        A class definition gives the blueprint for constructing an object:: ||  ||          >>> class House(object): ||          ...     wall_colour = 'white' ||  ||        Yet; we have to *build* a house before it exists:: ||  ||          >>> h = House() # build a house ||  ||        Now; ``h`` is called a ``House`` instance.  An instance is therefore ||        a specific realisation of a class. ||  ||    iterable ||        A sequence that allows \"walking\" (iterating) over items; typically ||        using a loop such as:: ||  ||          >>> x = [1; 2; 3] ||          >>> [item**2 for item in x] ||          [1; 4; 9] ||  ||        It is often used in combination with ``enumerate``:: ||          >>> keys = ['a';'b';'c'] ||          >>> for n; k in enumerate(keys): ||          ...     print(\"Key %d: %s\" % (n; k)) ||          ... ||          Key 0: a ||          Key 1: b ||          Key 2: c ||  ||    list ||        A Python container that can hold any number of objects or items. ||        The items do not have to be of the same type; and can even be ||        lists themselves:: ||  ||          >>> x = [2; 2.0; \"two\"; [2; 2.0]] ||  ||        The list `x` contains 4 items; each which can be accessed individually:: ||  ||          >>> x[2] # the string 'two' ||          'two' ||  ||          >>> x[3] # a list; containing an integer 2 and a float 2.0 ||          [2; 2.0] ||  ||        It is also possible to select more than one item at a time; ||        using *slicing*:: ||  ||          >>> x[0:2] # or; equivalently; x[:2] ||          [2; 2.0] ||  ||        In code; arrays are often conveniently expressed as nested lists:: ||  ||  ||          >>> np.array([[1; 2]; [3; 4]]) ||          array([[1; 2]; ||                 [3; 4]]) ||  ||        For more information; read the section on lists in the `Python ||        tutorial <http:\/\/docs.python.org\/tut>`_.  For a mapping ||        type (key-value); see *dictionary*. ||  ||    little-endian ||        When storing a multi-byte value in memory as a sequence of bytes; the ||        sequence addresses\/sends\/stores the least significant byte first (lowest ||        address) and the most significant byte last (highest address). Common in ||        x86 processors. ||  ||    mask ||        A boolean array; used to select only certain elements for an operation:: ||  ||          >>> x = np.arange(5) ||          >>> x ||          array([0; 1; 2; 3; 4]) ||  ||          >>> mask = (x > 2) ||          >>> mask ||          array([False; False; False; True;  True]) ||  ||          >>> x[mask] = -1 ||          >>> x ||          array([ 0;  1;  2;  -1; -1]) ||  ||    masked array ||        Array that suppressed values indicated by a mask:: ||  ||          >>> x = np.ma.masked_array([np.nan; 2; np.nan]; [True; False; True]) ||          >>> x ||          masked_array(data = [-- 2.0 --]; ||                       mask = [ True False  True]; ||                 fill_value = 1e+20) ||          <BLANKLINE> ||  ||          >>> x + [1; 2; 3] ||          masked_array(data = [-- 4.0 --]; ||                       mask = [ True False  True]; ||                 fill_value = 1e+20) ||          <BLANKLINE> ||  ||  ||        Masked arrays are often used when operating on arrays containing ||        missing or invalid entries. ||  ||    matrix ||        A 2-dimensional ndarray that preserves its two-dimensional nature ||        throughout operations.  It has certain special operations; such as ``*`` ||        (matrix multiplication) and ``**`` (matrix power); defined:: ||  ||          >>> x = np.mat([[1; 2]; [3; 4]]) ||          >>> x ||          matrix([[1; 2]; ||                  [3; 4]]) ||  ||          >>> x**2 ||          matrix([[ 7; 10]; ||                [15; 22]]) ||  ||    method ||        A function associated with an object.  For example; each ndarray has a ||        method called ``repeat``:: ||  ||          >>> x = np.array([1; 2; 3]) ||          >>> x.repeat(2) ||          array([1; 1; 2; 2; 3; 3]) ||  ||    ndarray ||        See *array*. ||  ||    record array ||        An :term:`ndarray` with :term:`structured data type` which has been ||        subclassed as ``np.recarray`` and whose dtype is of type ``np.record``; ||        making the fields of its data type to be accessible by attribute. ||  ||    reference ||        If ``a`` is a reference to ``b``; then ``(a is b) == True``.  Therefore; ||        ``a`` and ``b`` are different names for the same Python object. ||  ||    row-major ||        A way to represent items in a N-dimensional array in the 1-dimensional ||        computer memory. In row-major order; the rightmost index \"varies ||        the fastest\": for example the array:: ||  ||             [[1; 2; 3]; ||              [4; 5; 6]] ||  ||        is represented in the row-major order as:: ||  ||            [1; 2; 3; 4; 5; 6] ||  ||        Row-major order is also known as the C order; as the C programming ||        language uses it. New NumPy arrays are by default in row-major order. ||  ||    self ||        Often seen in method signatures; ``self`` refers to the instance ||        of the associated class.  For example: ||  ||          >>> class Paintbrush(object): ||          ...     color = 'blue' ||          ... ||          ...     def paint(self): ||          ...         print(\"Painting the city %s!\" % self.color) ||          ... ||          >>> p = Paintbrush() ||          >>> p.color = 'red' ||          >>> p.paint() # self refers to 'p' ||          Painting the city red! ||  ||    slice ||        Used to select only certain elements from a sequence:: ||  ||          >>> x = range(5) ||          >>> x ||          [0; 1; 2; 3; 4] ||  ||          >>> x[1:3] # slice from 1 to 3 (excluding 3 itself) ||          [1; 2] ||  ||          >>> x[1:5:2] # slice from 1 to 5; but skipping every second element ||          [1; 3] ||  ||          >>> x[::-1] # slice a sequence in reverse ||          [4; 3; 2; 1; 0] ||  ||        Arrays may have more than one dimension; each which can be sliced ||        individually:: ||  ||          >>> x = np.array([[1; 2]; [3; 4]]) ||          >>> x ||          array([[1; 2]; ||                 [3; 4]]) ||  ||          >>> x[:; 1] ||          array([2; 4]) ||  ||    structure ||        See :term:`structured data type` ||  ||    structured data type ||        A data type composed of other datatypes ||  ||    tuple ||        A sequence that may contain a variable number of types of any ||        kind.  A tuple is immutable; i.e.; once constructed it cannot be ||        changed.  Similar to a list; it can be indexed and sliced:: ||  ||          >>> x = (1; 'one'; [1; 2]) ||          >>> x ||          (1; 'one'; [1; 2]) ||  ||          >>> x[0] ||          1 ||  ||          >>> x[:2] ||          (1; 'one') ||  ||        A useful concept is \"tuple unpacking\"; which allows variables to ||        be assigned to the contents of a tuple:: ||  ||          >>> x; y = (1; 2) ||          >>> x; y = 1; 2 ||  ||        This is often used when a function returns multiple values: ||  ||          >>> def return_many(): ||          ...     return 1; 'alpha'; None ||  ||          >>> a; b; c = return_many() ||          >>> a; b; c ||          (1; 'alpha'; None) ||  ||          >>> a ||          1 ||          >>> b ||          'alpha' ||  ||    ufunc ||        Universal function.  A fast element-wise array operation.  Examples include ||        ``add``; ``sin`` and ``logical_or``. ||  ||    view ||        An array that does not own its data; but refers to another array's ||        data instead.  For example; we may create a view that only shows ||        every second element of another array:: ||  ||          >>> x = np.arange(5) ||          >>> x ||          array([0; 1; 2; 3; 4]) ||  ||          >>> y = x[::2] ||          >>> y ||          array([0; 2; 4]) ||  ||          >>> x[0] = 3 # changing x changes y as well; since y is a view on x ||          >>> y ||          array([3; 2; 4]) ||  ||    wrapper ||        Python is a high-level (highly abstracted; or English-like) language. ||        This abstraction comes at a price in execution speed; and sometimes ||        it becomes necessary to use lower level languages to do fast ||        computations.  A wrapper is code that provides a bridge between ||        high and the low level languages; allowing; e.g.; Python to execute ||        code written in C or Fortran. ||  ||        Examples include ctypes; SWIG and Cython (which wraps C and C++) ||        and f2py (which wraps Fortran). ||  || \"\"\"
aahjcbihb,ryfeus/gcf-packs,pandas_numpy/sources/numpy/doc/indexing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,\"\"\"============== || Array indexing || ============== ||  || Array indexing refers to any use of the square brackets ([]) to index || array values. There are many options to indexing; which give numpy || indexing great power; but with power comes some complexity and the || potential for confusion. This section is just an overview of the || various options and issues related to indexing. Aside from single || element indexing; the details on most of these options are to be || found in related sections. ||  || Assignment vs referencing || ========================= ||  || Most of the following examples show the use of indexing when || referencing data in an array. The examples work just as well || when assigning to an array. See the section at the end for || specific examples and explanations on how assignments work. ||  || Single element indexing || ======================= ||  || Single element indexing for a 1-D array is what one expects. It work || exactly like that for other standard Python sequences. It is 0-based; || and accepts negative indices for indexing from the end of the array. :: ||  ||     >>> x = np.arange(10) ||     >>> x[2] ||     2 ||     >>> x[-2] ||     8 ||  || Unlike lists and tuples; numpy arrays support multidimensional indexing || for multidimensional arrays. That means that it is not necessary to || separate each dimension's index into its own set of square brackets. :: ||  ||     >>> x.shape = (2;5) # now x is 2-dimensional ||     >>> x[1;3] ||     8 ||     >>> x[1;-1] ||     9 ||  || Note that if one indexes a multidimensional array with fewer indices || than dimensions; one gets a subdimensional array. For example: :: ||  ||     >>> x[0] ||     array([0; 1; 2; 3; 4]) ||  || That is; each index specified selects the array corresponding to the || rest of the dimensions selected. In the above example; choosing 0 || means that the remaining dimension of length 5 is being left unspecified; || and that what is returned is an array of that dimensionality and size. || It must be noted that the returned array is not a copy of the original; || but points to the same values in memory as does the original array. || In  this case; the 1-D array at the first position (0) is returned. || So using a single index on the returned array; results in a single || element being returned. That is: :: ||  ||     >>> x[0][2] ||     2 ||  || So note that ``x[0;2] = x[0][2]`` though the second case is more || inefficient as a new temporary array is created after the first index || that is subsequently indexed by 2. ||  || Note to those used to IDL or Fortran memory order as it relates to || indexing.  NumPy uses C-order indexing. That means that the last || index usually represents the most rapidly changing memory location; || unlike Fortran or IDL; where the first index represents the most || rapidly changing location in memory. This difference represents a || great potential for confusion. ||  || Other indexing options || ====================== ||  || It is possible to slice and stride arrays to extract arrays of the || same number of dimensions; but of different sizes than the original. || The slicing and striding works exactly the same way it does for lists || and tuples except that they can be applied to multiple dimensions as || well. A few examples illustrates best: :: ||  ||  >>> x = np.arange(10) ||  >>> x[2:5] ||  array([2; 3; 4]) ||  >>> x[:-7] ||  array([0; 1; 2]) ||  >>> x[1:7:2] ||  array([1; 3; 5]) ||  >>> y = np.arange(35).reshape(5;7) ||  >>> y[1:5:2;::3] ||  array([[ 7; 10; 13]; ||         [21; 24; 27]]) ||  || Note that slices of arrays do not copy the internal array data but || also produce new views of the original data. ||  || It is possible to index arrays with other arrays for the purposes of || selecting lists of values out of arrays into new arrays. There are || two different ways of accomplishing this. One uses one or more arrays || of index values. The other involves giving a boolean array of the proper || shape to indicate the values to be selected. Index arrays are a very || powerful tool that allow one to avoid looping over individual elements in || arrays and thus greatly improve performance. ||  || It is possible to use special features to effectively increase the || number of dimensions in an array through indexing so the resulting || array aquires the shape needed for use in an expression or with a || specific function. ||  || Index arrays || ============ ||  || NumPy arrays may be indexed with other arrays (or any other sequence- || like object that can be converted to an array; such as lists; with the || exception of tuples; see the end of this document for why this is). The || use of index arrays ranges from simple; straightforward cases to || complex; hard-to-understand cases. For all cases of index arrays; what || is returned is a copy of the original data; not a view as one gets for || slices. ||  || Index arrays must be of integer type. Each value in the array indicates || which value in the array to use in place of the index. To illustrate: :: ||  ||  >>> x = np.arange(10;1;-1) ||  >>> x ||  array([10;  9;  8;  7;  6;  5;  4;  3;  2]) ||  >>> x[np.array([3; 3; 1; 8])] ||  array([7; 7; 9; 2]) ||  ||  || The index array consisting of the values 3; 3; 1 and 8 correspondingly || create an array of length 4 (same as the index array) where each index || is replaced by the value the index array has in the array being indexed. ||  || Negative values are permitted and work as they do with single indices || or slices: :: ||  ||  >>> x[np.array([3;3;-3;8])] ||  array([7; 7; 4; 2]) ||  || It is an error to have index values out of bounds: :: ||  ||  >>> x[np.array([3; 3; 20; 8])] ||  <type 'exceptions.IndexError'>: index 20 out of bounds 0<=index<9 ||  || Generally speaking; what is returned when index arrays are used is || an array with the same shape as the index array; but with the type || and values of the array being indexed. As an example; we can use a || multidimensional index array instead: :: ||  ||  >>> x[np.array([[1;1];[2;3]])] ||  array([[9; 9]; ||         [8; 7]]) ||  || Indexing Multi-dimensional arrays || ================================= ||  || Things become more complex when multidimensional arrays are indexed; || particularly with multidimensional index arrays. These tend to be || more unusual uses; but they are permitted; and they are useful for some || problems. We'll  start with the simplest multidimensional case (using || the array y from the previous examples): :: ||  ||  >>> y[np.array([0;2;4]); np.array([0;1;2])] ||  array([ 0; 15; 30]) ||  || In this case; if the index arrays have a matching shape; and there is || an index array for each dimension of the array being indexed; the || resultant array has the same shape as the index arrays; and the values || correspond to the index set for each position in the index arrays. In || this example; the first index value is 0 for both index arrays; and || thus the first value of the resultant array is y[0;0]. The next value || is y[2;1]; and the last is y[4;2]. ||  || If the index arrays do not have the same shape; there is an attempt to || broadcast them to the same shape.  If they cannot be broadcast to the || same shape; an exception is raised: :: ||  ||  >>> y[np.array([0;2;4]); np.array([0;1])] ||  <type 'exceptions.ValueError'>: shape mismatch: objects cannot be ||  broadcast to a single shape ||  || The broadcasting mechanism permits index arrays to be combined with || scalars for other indices. The effect is that the scalar value is used || for all the corresponding values of the index arrays: :: ||  ||  >>> y[np.array([0;2;4]); 1] ||  array([ 1; 15; 29]) ||  || Jumping to the next level of complexity; it is possible to only || partially index an array with index arrays. It takes a bit of thought || to understand what happens in such cases. For example if we just use || one index array with y: :: ||  ||  >>> y[np.array([0;2;4])] ||  array([[ 0;  1;  2;  3;  4;  5;  6]; ||         [14; 15; 16; 17; 18; 19; 20]; ||         [28; 29; 30; 31; 32; 33; 34]]) ||  || What results is the construction of a new array where each value of || the index array selects one row from the array being indexed and the || resultant array has the resulting shape (number of index elements; || size of row). ||  || An example of where this may be useful is for a color lookup table || where we want to map the values of an image into RGB triples for || display. The lookup table could have a shape (nlookup; 3). Indexing || such an array with an image with shape (ny; nx) with dtype=np.uint8 || (or any integer type so long as values are with the bounds of the || lookup table) will result in an array of shape (ny; nx; 3) where a || triple of RGB values is associated with each pixel location. ||  || In general; the shape of the resultant array will be the concatenation || of the shape of the index array (or the shape that all the index arrays || were broadcast to) with the shape of any unused dimensions (those not || indexed) in the array being indexed. ||  || Boolean or \"mask\" index arrays || ============================== ||  || Boolean arrays used as indices are treated in a different manner || entirely than index arrays. Boolean arrays must be of the same shape || as the initial dimensions of the array being indexed. In the || most straightforward case; the boolean array has the same shape: :: ||  ||  >>> b = y>20 ||  >>> y[b] ||  array([21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34]) ||  || Unlike in the case of integer index arrays; in the boolean case; the || result is a 1-D array containing all the elements in the indexed array || corresponding to all the true elements in the boolean array. The || elements in the indexed array are always iterated and returned in || :term:`row-major` (C-style) order. The result is also identical to || ``y[np.nonzero(b)]``. As with index arrays; what is returned is a copy || of the data; not a view as one gets with slices. ||  || The result will be multidimensional if y has more dimensions than b. || For example: :: ||  ||  >>> b[:;5] # use a 1-D boolean whose first dim agrees with the first dim of y ||  array([False; False; False;  True;  True]) ||  >>> y[b[:;5]] ||  array([[21; 22; 23; 24; 25; 26; 27]; ||         [28; 29; 30; 31; 32; 33; 34]]) ||  || Here the 4th and 5th rows are selected from the indexed array and || combined to make a 2-D array. ||  || In general; when the boolean array has fewer dimensions than the array || being indexed; this is equivalent to y[b; ...]; which means || y is indexed by b followed by as many : as are needed to fill || out the rank of y. || Thus the shape of the result is one dimension containing the number || of True elements of the boolean array; followed by the remaining || dimensions of the array being indexed. ||  || For example; using a 2-D boolean array of shape (2;3) || with four True elements to select rows from a 3-D array of shape || (2;3;5) results in a 2-D result of shape (4;5): :: ||  ||  >>> x = np.arange(30).reshape(2;3;5) ||  >>> x ||  array([[[ 0;  1;  2;  3;  4]; ||          [ 5;  6;  7;  8;  9]; ||          [10; 11; 12; 13; 14]]; ||         [[15; 16; 17; 18; 19]; ||          [20; 21; 22; 23; 24]; ||          [25; 26; 27; 28; 29]]]) ||  >>> b = np.array([[True; True; False]; [False; True; True]]) ||  >>> x[b] ||  array([[ 0;  1;  2;  3;  4]; ||         [ 5;  6;  7;  8;  9]; ||         [20; 21; 22; 23; 24]; ||         [25; 26; 27; 28; 29]]) ||  || For further details; consult the numpy reference documentation on array indexing. ||  || Combining index arrays with slices || ================================== ||  || Index arrays may be combined with slices. For example: :: ||  ||  >>> y[np.array([0;2;4]);1:3] ||  array([[ 1;  2]; ||         [15; 16]; ||         [29; 30]]) ||  || In effect; the slice is converted to an index array || np.array([[1;2]]) (shape (1;2)) that is broadcast with the index array || to produce a resultant array of shape (3;2). ||  || Likewise; slicing can be combined with broadcasted boolean indices: :: ||  ||  >>> y[b[:;5];1:3] ||  array([[22; 23]; ||         [29; 30]]) ||  || Structural indexing tools || ========================= ||  || To facilitate easy matching of array shapes with expressions and in || assignments; the np.newaxis object can be used within array indices || to add new dimensions with a size of 1. For example: :: ||  ||  >>> y.shape ||  (5; 7) ||  >>> y[:;np.newaxis;:].shape ||  (5; 1; 7) ||  || Note that there are no new elements in the array; just that the || dimensionality is increased. This can be handy to combine two || arrays in a way that otherwise would require explicitly reshaping || operations. For example: :: ||  ||  >>> x = np.arange(5) ||  >>> x[:;np.newaxis] + x[np.newaxis;:] ||  array([[0; 1; 2; 3; 4]; ||         [1; 2; 3; 4; 5]; ||         [2; 3; 4; 5; 6]; ||         [3; 4; 5; 6; 7]; ||         [4; 5; 6; 7; 8]]) ||  || The ellipsis syntax maybe used to indicate selecting in full any || remaining unspecified dimensions. For example: :: ||  ||  >>> z = np.arange(81).reshape(3;3;3;3) ||  >>> z[1;...;2] ||  array([[29; 32; 35]; ||         [38; 41; 44]; ||         [47; 50; 53]]) ||  || This is equivalent to: :: ||  ||  >>> z[1;:;:;2] ||  array([[29; 32; 35]; ||         [38; 41; 44]; ||         [47; 50; 53]]) ||  || Assigning values to indexed arrays || ================================== ||  || As mentioned; one can select a subset of an array to assign to using || a single index; slices; and index and mask arrays. The value being || assigned to the indexed array must be shape consistent (the same shape || or broadcastable to the shape the index produces). For example; it is || permitted to assign a constant to a slice: :: ||  ||  >>> x = np.arange(10) ||  >>> x[2:7] = 1 ||  || or an array of the right size: :: ||  ||  >>> x[2:7] = np.arange(5) ||  || Note that assignments may result in changes if assigning || higher types to lower types (like floats to ints) or even || exceptions (assigning complex to floats or ints): :: ||  ||  >>> x[1] = 1.2 ||  >>> x[1] ||  1 ||  >>> x[1] = 1.2j ||  <type 'exceptions.TypeError'>: can't convert complex to long; use ||  long(abs(z)) ||  ||  || Unlike some of the references (such as array and mask indices) || assignments are always made to the original data in the array || (indeed; nothing else would make sense!). Note though; that some || actions may not work as one may naively expect. This particular || example is often surprising to people: :: ||  ||  >>> x = np.arange(0; 50; 10) ||  >>> x ||  array([ 0; 10; 20; 30; 40]) ||  >>> x[np.array([1; 1; 3; 1])] += 1 ||  >>> x ||  array([ 0; 11; 20; 31; 40]) ||  || Where people expect that the 1st location will be incremented by 3. || In fact; it will only be incremented by 1. The reason is because || a new array is extracted from the original (as a temporary) containing || the values at 1; 1; 3; 1; then the value 1 is added to the temporary; || and then the temporary is assigned back to the original array. Thus || the value of the array at x[1]+1 is assigned to x[1] three times; || rather than being incremented 3 times. ||  || Dealing with variable numbers of indices within programs || ======================================================== ||  || The index syntax is very powerful but limiting when dealing with || a variable number of indices. For example; if you want to write || a function that can handle arguments with various numbers of || dimensions without having to write special case code for each || number of possible dimensions; how can that be done? If one || supplies to the index a tuple; the tuple will be interpreted || as a list of indices. For example (using the previous definition || for the array z): :: ||  ||  >>> indices = (1;1;1;1) ||  >>> z[indices] ||  40 ||  || So one can use code to construct tuples of any number of indices || and then use these within an index. ||  || Slices can be specified within programs by using the slice() function || in Python. For example: :: ||  ||  >>> indices = (1;1;1;slice(0;2)) # same as [1;1;1;0:2] ||  >>> z[indices] ||  array([39; 40]) ||  || Likewise; ellipsis can be specified by code by using the Ellipsis || object: :: ||  ||  >>> indices = (1; Ellipsis; 1) # same as [1;...;1] ||  >>> z[indices] ||  array([[28; 31; 34]; ||         [37; 40; 43]; ||         [46; 49; 52]]) ||  || For this reason it is possible to use the output from the np.nonzero() || function directly as an index since it always returns a tuple of index || arrays. ||  || Because the special treatment of tuples; they are not automatically || converted to an array as a list would be. As an example: :: ||  ||  >>> z[[1;1;1;1]] # produces a large array ||  array([[[[27; 28; 29]; ||           [30; 31; 32]; ... ||  >>> z[(1;1;1;1)] # returns a single value ||  40 ||  || \"\"\"
aahjcbihc,ryfeus/gcf-packs,pandas_numpy/sources/numpy/doc/internals.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,\"\"\" || =============== || Array Internals || =============== ||  || Internal organization of numpy arrays || ===================================== ||  || It helps to understand a bit about how numpy arrays are handled under the covers to help understand numpy better. This section will not go into great detail. Those wishing to understand the full details are referred to Travis Oliphant's book \"Guide to NumPy\". ||  || NumPy arrays consist of two major components; the raw array data (from now on; || referred to as the data buffer); and the information about the raw array data. || The data buffer is typically what people think of as arrays in C or Fortran; || a contiguous (and fixed) block of memory containing fixed sized data items. || NumPy also contains a significant set of data that describes how to interpret || the data in the data buffer. This extra information contains (among other things): ||  ||  1) The basic data element's size in bytes ||  2) The start of the data within the data buffer (an offset relative to the ||     beginning of the data buffer). ||  3) The number of dimensions and the size of each dimension ||  4) The separation between elements for each dimension (the 'stride'). This ||     does not have to be a multiple of the element size ||  5) The byte order of the data (which may not be the native byte order) ||  6) Whether the buffer is read-only ||  7) Information (via the dtype object) about the interpretation of the basic ||     data element. The basic data element may be as simple as a int or a float; ||     or it may be a compound object (e.g.; struct-like); a fixed character field; ||     or Python object pointers. ||  8) Whether the array is to interpreted as C-order or Fortran-order. ||  || This arrangement allow for very flexible use of arrays. One thing that it allows || is simple changes of the metadata to change the interpretation of the array buffer. || Changing the byteorder of the array is a simple change involving no rearrangement || of the data. The shape of the array can be changed very easily without changing || anything in the data buffer or any data copying at all ||  || Among other things that are made possible is one can create a new array metadata || object that uses the same data buffer || to create a new view of that data buffer that has a different interpretation || of the buffer (e.g.; different shape; offset; byte order; strides; etc) but || shares the same data bytes. Many operations in numpy do just this such as || slices. Other operations; such as transpose; don't move data elements || around in the array; but rather change the information about the shape and strides so that the indexing of the array changes; but the data in the doesn't move. ||  || Typically these new versions of the array metadata but the same data buffer are || new 'views' into the data buffer. There is a different ndarray object; but it || uses the same data buffer. This is why it is necessary to force copies through || use of the .copy() method if one really wants to make a new and independent || copy of the data buffer. ||  || New views into arrays mean the object reference counts for the data buffer || increase. Simply doing away with the original array object will not remove the || data buffer if other views of it still exist. ||  || Multidimensional Array Indexing Order Issues || ============================================ ||  || What is the right way to index || multi-dimensional arrays? Before you jump to conclusions about the one and || true way to index multi-dimensional arrays; it pays to understand why this is || a confusing issue. This section will try to explain in detail how numpy || indexing works and why we adopt the convention we do for images; and when it || may be appropriate to adopt other conventions. ||  || The first thing to understand is || that there are two conflicting conventions for indexing 2-dimensional arrays. || Matrix notation uses the first index to indicate which row is being selected and || the second index to indicate which column is selected. This is opposite the || geometrically oriented-convention for images where people generally think the || first index represents x position (i.e.; column) and the second represents y || position (i.e.; row). This alone is the source of much confusion; || matrix-oriented users and image-oriented users expect two different things with || regard to indexing. ||  || The second issue to understand is how indices correspond || to the order the array is stored in memory. In Fortran the first index is the || most rapidly varying index when moving through the elements of a two || dimensional array as it is stored in memory. If you adopt the matrix || convention for indexing; then this means the matrix is stored one column at a || time (since the first index moves to the next row as it changes). Thus Fortran || is considered a Column-major language. C has just the opposite convention. In || C; the last index changes most rapidly as one moves through the array as || stored in memory. Thus C is a Row-major language. The matrix is stored by || rows. Note that in both cases it presumes that the matrix convention for || indexing is being used; i.e.; for both Fortran and C; the first index is the || row. Note this convention implies that the indexing convention is invariant || and that the data order changes to keep that so. ||  || But that's not the only way || to look at it. Suppose one has large two-dimensional arrays (images or || matrices) stored in data files. Suppose the data are stored by rows rather than || by columns. If we are to preserve our index convention (whether matrix or || image) that means that depending on the language we use; we may be forced to || reorder the data if it is read into memory to preserve our indexing || convention. For example if we read row-ordered data into memory without || reordering; it will match the matrix indexing convention for C; but not for || Fortran. Conversely; it will match the image indexing convention for Fortran; || but not for C. For C; if one is using data stored in row order; and one wants || to preserve the image index convention; the data must be reordered when || reading into memory. ||  || In the end; which you do for Fortran or C depends on || which is more important; not reordering data or preserving the indexing || convention. For large images; reordering data is potentially expensive; and || often the indexing convention is inverted to avoid that. ||  || The situation with || numpy makes this issue yet more complicated. The internal machinery of numpy || arrays is flexible enough to accept any ordering of indices. One can simply || reorder indices by manipulating the internal stride information for arrays || without reordering the data at all. NumPy will know how to map the new index || order to the data without moving the data. ||  || So if this is true; why not choose || the index order that matches what you most expect? In particular; why not define || row-ordered images to use the image convention? (This is sometimes referred || to as the Fortran convention vs the C convention; thus the 'C' and 'FORTRAN' || order options for array ordering in numpy.) The drawback of doing this is || potential performance penalties. It's common to access the data sequentially; || either implicitly in array operations or explicitly by looping over rows of an || image. When that is done; then the data will be accessed in non-optimal order. || As the first index is incremented; what is actually happening is that elements || spaced far apart in memory are being sequentially accessed; with usually poor || memory access speeds. For example; for a two dimensional image 'im' defined so || that im[0; 10] represents the value at x=0; y=10. To be consistent with usual || Python behavior then im[0] would represent a column at x=0. Yet that data || would be spread over the whole array since the data are stored in row order. || Despite the flexibility of numpy's indexing; it can't really paper over the fact || basic operations are rendered inefficient because of data order or that getting || contiguous subarrays is still awkward (e.g.; im[:;0] for the first row; vs || im[0]); thus one can't use an idiom such as for row in im; for col in im does || work; but doesn't yield contiguous column data. ||  || As it turns out; numpy is || smart enough when dealing with ufuncs to determine which index is the most || rapidly varying one in memory and uses that for the innermost loop. Thus for || ufuncs there is no large intrinsic advantage to either approach in most cases. || On the other hand; use of .flat with an FORTRAN ordered array will lead to || non-optimal memory access as adjacent elements in the flattened array (iterator; || actually) are not contiguous in memory. ||  || Indeed; the fact is that Python || indexing on lists and other sequences naturally leads to an outside-to inside || ordering (the first index gets the largest grouping; the next the next largest; || and the last gets the smallest element). Since image data are normally stored || by rows; this corresponds to position within rows being the last item indexed. ||  || If you do want to use Fortran ordering realize that || there are two approaches to consider: 1) accept that the first index is just not || the most rapidly changing in memory and have all your I\/O routines reorder || your data when going from memory to disk or visa versa; or use numpy's || mechanism for mapping the first index to the most rapidly varying data. We || recommend the former if possible. The disadvantage of the latter is that many || of numpy's functions will yield arrays without Fortran ordering unless you are || careful to use the 'order' keyword. Doing this would be highly inconvenient. ||  || Otherwise we recommend simply learning to reverse the usual order of indices || when accessing elements of an array. Granted; it goes against the grain; but || it is more in line with Python semantics and the natural order of the data. ||  || \"\"\"
aahjcbihd,ryfeus/gcf-packs,pandas_numpy/sources/numpy/doc/misc.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,\"\"\" || ============= || Miscellaneous || ============= ||  || IEEE 754 Floating Point Special Values || -------------------------------------- ||  || Special values defined in numpy: nan; inf; ||  || NaNs can be used as a poor-man's mask (if you don't care what the || original value was) ||  || Note: cannot use equality to test NaNs. E.g.: :: ||  ||  >>> myarr = np.array([1.; 0.; np.nan; 3.]) ||  >>> np.nonzero(myarr == np.nan) ||  (array([]; dtype=int64);) ||  >>> np.nan == np.nan  # is always False! Use special numpy functions instead. ||  False ||  >>> myarr[myarr == np.nan] = 0. # doesn't work ||  >>> myarr ||  array([  1.;   0.;  NaN;   3.]) ||  >>> myarr[np.isnan(myarr)] = 0. # use this instead find ||  >>> myarr ||  array([ 1.;  0.;  0.;  3.]) ||  || Other related special value functions: :: ||  ||  isinf():    True if value is inf ||  isfinite(): True if not nan or inf ||  nan_to_num(): Map nan to 0; inf to max float; -inf to min float ||  || The following corresponds to the usual functions except that nans are excluded || from the results: :: ||  ||  nansum() ||  nanmax() ||  nanmin() ||  nanargmax() ||  nanargmin() ||  ||  >>> x = np.arange(10.) ||  >>> x[3] = np.nan ||  >>> x.sum() ||  nan ||  >>> np.nansum(x) ||  42.0 ||  || How numpy handles numerical exceptions || -------------------------------------- ||  || The default is to ``'warn'`` for ``invalid``; ``divide``; and ``overflow`` || and ``'ignore'`` for ``underflow``.  But this can be changed; and it can be || set individually for different kinds of exceptions. The different behaviors || are: ||  ||  - 'ignore' : Take no action when the exception occurs. ||  - 'warn'   : Print a `RuntimeWarning` (via the Python `warnings` module). ||  - 'raise'  : Raise a `FloatingPointError`. ||  - 'call'   : Call a function specified using the `seterrcall` function. ||  - 'print'  : Print a warning directly to ``stdout``. ||  - 'log'    : Record error in a Log object specified by `seterrcall`. ||  || These behaviors can be set for all kinds of errors or specific ones: ||  ||  - all       : apply to all numeric exceptions ||  - invalid   : when NaNs are generated ||  - divide    : divide by zero (for integers as well!) ||  - overflow  : floating point overflows ||  - underflow : floating point underflows ||  || Note that integer divide-by-zero is handled by the same machinery. || These behaviors are set on a per-thread basis. ||  || Examples || -------- ||  || :: ||  ||  >>> oldsettings = np.seterr(all='warn') ||  >>> np.zeros(5;dtype=np.float32)\/0. ||  invalid value encountered in divide ||  >>> j = np.seterr(under='ignore') ||  >>> np.array([1.e-100])**10 ||  >>> j = np.seterr(invalid='raise') ||  >>> np.sqrt(np.array([-1.])) ||  FloatingPointError: invalid value encountered in sqrt ||  >>> def errorhandler(errstr; errflag): ||  ...      print(\"saw stupid error!\") ||  >>> np.seterrcall(errorhandler) ||  <function err_handler at 0x...> ||  >>> j = np.seterr(all='call') ||  >>> np.zeros(5; dtype=np.int32)\/0 ||  FloatingPointError: invalid value encountered in divide ||  saw stupid error! ||  >>> j = np.seterr(**oldsettings) # restore previous ||  ...                              # error-handling settings ||  || Interfacing to C || ---------------- || Only a survey of the choices. Little detail on how each works. ||  || 1) Bare metal; wrap your own C-code manually. ||  ||  - Plusses: ||  ||    - Efficient ||    - No dependencies on other tools ||  ||  - Minuses: ||  ||    - Lots of learning overhead: ||  ||      - need to learn basics of Python C API ||      - need to learn basics of numpy C API ||      - need to learn how to handle reference counting and love it. ||  ||    - Reference counting often difficult to get right. ||  ||      - getting it wrong leads to memory leaks; and worse; segfaults ||  ||    - API will change for Python 3.0! ||  || 2) Cython ||  ||  - Plusses: ||  ||    - avoid learning C API's ||    - no dealing with reference counting ||    - can code in pseudo python and generate C code ||    - can also interface to existing C code ||    - should shield you from changes to Python C api ||    - has become the de-facto standard within the scientific Python community ||    - fast indexing support for arrays ||  ||  - Minuses: ||  ||    - Can write code in non-standard form which may become obsolete ||    - Not as flexible as manual wrapping ||  || 3) ctypes ||  ||  - Plusses: ||  ||    - part of Python standard library ||    - good for interfacing to existing sharable libraries; particularly ||      Windows DLLs ||    - avoids API\/reference counting issues ||    - good numpy support: arrays have all these in their ctypes ||      attribute: :: ||  ||        a.ctypes.data              a.ctypes.get_strides ||        a.ctypes.data_as           a.ctypes.shape ||        a.ctypes.get_as_parameter  a.ctypes.shape_as ||        a.ctypes.get_data          a.ctypes.strides ||        a.ctypes.get_shape         a.ctypes.strides_as ||  ||  - Minuses: ||  ||    - can't use for writing code to be turned into C extensions; only a wrapper ||      tool. ||  || 4) SWIG (automatic wrapper generator) ||  ||  - Plusses: ||  ||    - around a long time ||    - multiple scripting language support ||    - C++ support ||    - Good for wrapping large (many functions) existing C libraries ||  ||  - Minuses: ||  ||    - generates lots of code between Python and the C code ||    - can cause performance problems that are nearly impossible to optimize ||      out ||    - interface files can be hard to write ||    - doesn't necessarily avoid reference counting issues or needing to know ||      API's ||  || 5) scipy.weave ||  ||  - Plusses: ||  ||    - can turn many numpy expressions into C code ||    - dynamic compiling and loading of generated C code ||    - can embed pure C code in Python module and have weave extract; generate ||      interfaces and compile; etc. ||  ||  - Minuses: ||  ||    - Future very uncertain: it's the only part of Scipy not ported to Python 3 ||      and is effectively deprecated in favor of Cython. ||  || 6) Psyco ||  ||  - Plusses: ||  ||    - Turns pure python into efficient machine code through jit-like ||      optimizations ||    - very fast when it optimizes well ||  ||  - Minuses: ||  ||    - Only on intel (windows?) ||    - Doesn't do much for numpy? ||  || Interfacing to Fortran: || ----------------------- || The clear choice to wrap Fortran code is || `f2py <http:\/\/docs.scipy.org\/doc\/numpy\/f2py\/>`_. ||  || Pyfort is an older alternative; but not supported any longer. || Fwrap is a newer project that looked promising but isn't being developed any || longer. ||  || Interfacing to C++: || ------------------- ||  1) Cython ||  2) CXX ||  3) Boost.python ||  4) SWIG ||  5) SIP (used mainly in PyQT) ||  || \"\"\"
aahjcbihe,ryfeus/gcf-packs,pandas_numpy/sources/numpy/doc/structured_arrays.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,\"\"\" || ================= || Structured Arrays || ================= ||  || Introduction || ============ ||  || Structured arrays are ndarrays whose datatype is a composition of simpler || datatypes organized as a sequence of named :term:`fields <field>`. For example; || :: ||  ||  >>> x = np.array([('Rex'; 9; 81.0); ('Fido'; 3; 27.0)]; ||  ...              dtype=[('name'; 'U10'); ('age'; 'i4'); ('weight'; 'f4')]) ||  >>> x ||  array([('Rex'; 9; 81.0); ('Fido'; 3; 27.0)]; ||        dtype=[('name'; 'S10'); ('age'; '<i4'); ('weight'; '<f4')]) ||  || Here ``x`` is a one-dimensional array of length two whose datatype is a || structure with three fields: 1. A string of length 10 or less named 'name'; 2. || a 32-bit integer named 'age'; and 3. a 32-bit float named 'weight'. ||  || If you index ``x`` at position 1 you get a structure:: ||  ||  >>> x[1] ||  ('Fido'; 3; 27.0) ||  || You can access and modify individual fields of a structured array by indexing || with the field name:: ||  ||  >>> x['age'] ||  array([9; 3]; dtype=int32) ||  >>> x['age'] = 5 ||  >>> x ||  array([('Rex'; 5; 81.0); ('Fido'; 5; 27.0)]; ||        dtype=[('name'; 'S10'); ('age'; '<i4'); ('weight'; '<f4')]) ||  || Structured arrays are designed for low-level manipulation of structured data; || for example; for interpreting binary blobs. Structured datatypes are || designed to mimic 'structs' in the C language; making them also useful for || interfacing with C code. For these purposes; numpy supports specialized || features such as subarrays and nested datatypes; and allows manual control over || the memory layout of the structure. ||  || For simple manipulation of tabular data other pydata projects; such as pandas; || xarray; or DataArray; provide higher-level interfaces that may be more || suitable. These projects may also give better performance for tabular data || analysis because the C-struct-like memory layout of structured arrays can lead || to poor cache behavior. ||  || .. _defining-structured-types: ||  || Structured Datatypes || ==================== ||  || To use structured arrays one first needs to define a structured datatype. ||  || A structured datatype can be thought of as a sequence of bytes of a certain || length (the structure's :term:`itemsize`) which is interpreted as a collection || of fields. Each field has a name; a datatype; and a byte offset within the || structure. The datatype of a field may be any numpy datatype including other || structured datatypes; and it may also be a :term:`sub-array` which behaves like || an ndarray of a specified shape. The offsets of the fields are arbitrary; and || fields may even overlap. These offsets are usually determined automatically by || numpy; but can also be specified. ||  || Structured Datatype Creation || ---------------------------- ||  || Structured datatypes may be created using the function :func:`numpy.dtype`. || There are 4 alternative forms of specification which vary in flexibility and || conciseness. These are further documented in the || :ref:`Data Type Objects <arrays.dtypes.constructing>` reference page; and in || summary they are: ||  || 1.   A list of tuples; one tuple per field ||  ||      Each tuple has the form ``(fieldname; datatype; shape)`` where shape is ||      optional. ``fieldname`` is a string (or tuple if titles are used; see ||      :ref:`Field Titles <titles>` below); ``datatype`` may be any object ||      convertible to a datatype; and ``shape`` is a tuple of integers specifying ||      subarray shape. ||  ||       >>> np.dtype([('x'; 'f4'); ('y'; np.float32); ('z'; 'f4'; (2;2))]) ||       dtype=[('x'; '<f4'); ('y'; '<f4'); ('z'; '<f4'; (2; 2))]) ||  ||      If ``fieldname`` is the empty string ``''``; the field will be given a ||      default name of the form ``f#``; where ``#`` is the integer index of the ||      field; counting from 0 from the left:: ||  ||       >>> np.dtype([('x'; 'f4');(''; 'i4');('z'; 'i8')]) ||       dtype([('x'; '<f4'); ('f1'; '<i4'); ('z'; '<i8')]) ||  ||      The byte offsets of the fields within the structure and the total ||      structure itemsize are determined automatically. ||  || 2.   A string of comma-separated dtype specifications ||  ||      In this shorthand notation any of the :ref:`string dtype specifications ||      <arrays.dtypes.constructing>` may be used in a string and separated by ||      commas. The itemsize and byte offsets of the fields are determined ||      automatically; and the field names are given the default names ``f0``; ||      ``f1``; etc. :: ||  ||       >>> np.dtype('i8;f4;S3') ||       dtype([('f0'; '<i8'); ('f1'; '<f4'); ('f2'; 'S3')]) ||       >>> np.dtype('3int8; float32; (2;3)float64') ||       dtype([('f0'; 'i1'; 3); ('f1'; '<f4'); ('f2'; '<f8'; (2; 3))]) ||  || 3.   A dictionary of field parameter arrays ||  ||      This is the most flexible form of specification since it allows control ||      over the byte-offsets of the fields and the itemsize of the structure. ||  ||      The dictionary has two required keys; 'names' and 'formats'; and four ||      optional keys; 'offsets'; 'itemsize'; 'aligned' and 'titles'. The values ||      for 'names' and 'formats' should respectively be a list of field names and ||      a list of dtype specifications; of the same length. The optional 'offsets' ||      value should be a list of integer byte-offsets; one for each field within ||      the structure. If 'offsets' is not given the offsets are determined ||      automatically. The optional 'itemsize' value should be an integer ||      describing the total size in bytes of the dtype; which must be large ||      enough to contain all the fields. ||      :: ||  ||       >>> np.dtype({'names': ['col1'; 'col2']; 'formats': ['i4';'f4']}) ||       dtype([('col1'; '<i4'); ('col2'; '<f4')]) ||       >>> np.dtype({'names': ['col1'; 'col2']; ||       ...           'formats': ['i4';'f4']; ||       ...           'offsets': [0; 4]; ||       ...           'itemsize': 12}) ||       dtype({'names':['col1';'col2']; 'formats':['<i4';'<f4']; 'offsets':[0;4]; 'itemsize':12}) ||  ||      Offsets may be chosen such that the fields overlap; though this will mean ||      that assigning to one field may clobber any overlapping field's data. As ||      an exception; fields of :class:`numpy.object` type cannot overlap with ||      other fields; because of the risk of clobbering the internal object ||      pointer and then dereferencing it. ||  ||      The optional 'aligned' value can be set to ``True`` to make the automatic ||      offset computation use aligned offsets (see :ref:`offsets-and-alignment`); ||      as if the 'align' keyword argument of :func:`numpy.dtype` had been set to ||      True. ||  ||      The optional 'titles' value should be a list of titles of the same length ||      as 'names'; see :ref:`Field Titles <titles>` below. ||  || 4.   A dictionary of field names ||  ||      The use of this form of specification is discouraged; but documented here ||      because older numpy code may use it. The keys of the dictionary are the ||      field names and the values are tuples specifying type and offset:: ||  ||       >>> np.dtype=({'col1': ('i1';0); 'col2': ('f4';1)}) ||       dtype([(('col1'); 'i1'); (('col2'); '>f4')]) ||  ||      This form is discouraged because Python dictionaries do not preserve order ||      in Python versions before Python 3.6; and the order of the fields in a ||      structured dtype has meaning. :ref:`Field Titles <titles>` may be ||      specified by using a 3-tuple; see below. ||  || Manipulating and Displaying Structured Datatypes || ------------------------------------------------ ||  || The list of field names of a structured datatype can be found in the ``names`` || attribute of the dtype object:: ||  ||  >>> d = np.dtype([('x'; 'i8'); ('y'; 'f4')]) ||  >>> d.names ||  ('x'; 'y') ||  || The field names may be modified by assigning to the ``names`` attribute using a || sequence of strings of the same length. ||  || The dtype object also has a dictionary-like attribute; ``fields``; whose keys || are the field names (and :ref:`Field Titles <titles>`; see below) and whose || values are tuples containing the dtype and byte offset of each field. :: ||  ||  >>> d.fields ||  mappingproxy({'x': (dtype('int64'); 0); 'y': (dtype('float32'); 8)}) ||  || Both the ``names`` and ``fields`` attributes will equal ``None`` for || unstructured arrays. ||  || The string representation of a structured datatype is shown in the \"list of || tuples\" form if possible; otherwise numpy falls back to using the more general || dictionary form. ||  || .. _offsets-and-alignment: ||  || Automatic Byte Offsets and Alignment || ------------------------------------ ||  || Numpy uses one of two methods to automatically determine the field byte offsets || and the overall itemsize of a structured datatype; depending on whether || ``align=True`` was specified as a keyword argument to :func:`numpy.dtype`. ||  || By default (``align=False``); numpy will pack the fields together such that || each field starts at the byte offset the previous field ended; and the fields || are contiguous in memory. :: ||  ||  >>> def print_offsets(d): ||  ...     print(\"offsets:\"; [d.fields[name][1] for name in d.names]) ||  ...     print(\"itemsize:\"; d.itemsize) ||  >>> print_offsets(np.dtype('u1;u1;i4;u1;i8;u2')) ||  offsets: [0; 1; 2; 6; 7; 15] ||  itemsize: 17 ||  || If ``align=True`` is set; numpy will pad the structure in the same way many C || compilers would pad a C-struct. Aligned structures can give a performance || improvement in some cases; at the cost of increased datatype size. Padding || bytes are inserted between fields such that each field's byte offset will be a || multiple of that field's alignment; which is usually equal to the field's size || in bytes for simple datatypes; see :c:member:`PyArray_Descr.alignment`.  The || structure will also have trailing padding added so that its itemsize is a || multiple of the largest field's alignment. :: ||  ||  >>> print_offsets(np.dtype('u1;u1;i4;u1;i8;u2'; align=True)) ||  offsets: [0; 1; 4; 8; 16; 24] ||  itemsize: 32 ||  || Note that although almost all modern C compilers pad in this way by default; || padding in C structs is C-implementation-dependent so this memory layout is not || guaranteed to exactly match that of a corresponding struct in a C program. Some || work may be needed; either on the numpy side or the C side; to obtain exact || correspondence. ||  || If offsets were specified using the optional ``offsets`` key in the || dictionary-based dtype specification; setting ``align=True`` will check that || each field's offset is a multiple of its size and that the itemsize is a || multiple of the largest field size; and raise an exception if not. ||  || If the offsets of the fields and itemsize of a structured array satisfy the || alignment conditions; the array will have the ``ALIGNED`` :ref:`flag || <numpy.ndarray.flags>` set. ||  || A convenience function :func:`numpy.lib.recfunctions.repack_fields` converts an || aligned dtype or array to a packed one and vice versa. It takes either a dtype || or structured ndarray as an argument; and returns a copy with fields re-packed; || with or without padding bytes. ||  || .. _titles: ||  || Field Titles || ------------ ||  || In addition to field names; fields may also have an associated :term:`title`; || an alternate name; which is sometimes used as an additional description or || alias for the field. The title may be used to index an array; just like a || field name. ||  || To add titles when using the list-of-tuples form of dtype specification; the || field name may be specified as a tuple of two strings instead of a single || string; which will be the field's title and field name respectively. For || example:: ||  ||  >>> np.dtype([(('my title'; 'name'); 'f4')]) ||  || When using the first form of dictionary-based specification; the titles may be || supplied as an extra ``'titles'`` key as described above. When using the second || (discouraged) dictionary-based specification; the title can be supplied by || providing a 3-element tuple ``(datatype; offset; title)`` instead of the usual || 2-element tuple:: ||  ||  >>> np.dtype({'name': ('i4'; 0; 'my title')}) ||  || The ``dtype.fields`` dictionary will contain :term:`titles` as keys; if any || titles are used.  This means effectively that a field with a title will be || represented twice in the fields dictionary. The tuple values for these fields || will also have a third element; the field title. Because of this; and because || the ``names`` attribute preserves the field order while the ``fields`` || attribute may not; it is recommended to iterate through the fields of a dtype || using the ``names`` attribute of the dtype; which will not list titles; as || in:: ||  ||  >>> for name in d.names: ||  ...     print(d.fields[name][:2]) ||  || Union types || ----------- ||  || Structured datatypes are implemented in numpy to have base type || :class:`numpy.void` by default; but it is possible to interpret other numpy || types as structured types using the ``(base_dtype; dtype)`` form of dtype || specification described in || :ref:`Data Type Objects <arrays.dtypes.constructing>`.  Here; ``base_dtype`` is || the desired underlying dtype; and fields and flags will be copied from || ``dtype``. This dtype is similar to a 'union' in C. ||  || Indexing and Assignment to Structured arrays || ============================================ ||  || Assigning data to a Structured Array || ------------------------------------ ||  || There are a number of ways to assign values to a structured array: Using python || tuples; using scalar values; or using other structured arrays. ||  || Assignment from Python Native Types (Tuples) || ```````````````````````````````````````````` ||  || The simplest way to assign values to a structured array is using python tuples. || Each assigned value should be a tuple of length equal to the number of fields || in the array; and not a list or array as these will trigger numpy's || broadcasting rules. The tuple's elements are assigned to the successive fields || of the array; from left to right:: ||  ||  >>> x = np.array([(1;2;3);(4;5;6)]; dtype='i8;f4;f8') ||  >>> x[1] = (7;8;9) ||  >>> x ||  array([(1; 2.; 3.); (7; 8.; 9.)]; ||       dtype=[('f0'; '<i8'); ('f1'; '<f4'); ('f2'; '<f8')]) ||  || Assignment from Scalars || ``````````````````````` ||  || A scalar assigned to a structured element will be assigned to all fields. This || happens when a scalar is assigned to a structured array; or when an || unstructured array is assigned to a structured array:: ||  ||  >>> x = np.zeros(2; dtype='i8;f4;?;S1') ||  >>> x[:] = 3 ||  >>> x ||  array([(3; 3.0; True; b'3'); (3; 3.0; True; b'3')]; ||        dtype=[('f0'; '<i8'); ('f1'; '<f4'); ('f2'; '?'); ('f3'; 'S1')]) ||  >>> x[:] = np.arange(2) ||  >>> x ||  array([(0; 0.0; False; b'0'); (1; 1.0; True; b'1')]; ||        dtype=[('f0'; '<i8'); ('f1'; '<f4'); ('f2'; '?'); ('f3'; 'S1')]) ||  || Structured arrays can also be assigned to unstructured arrays; but only if the || structured datatype has just a single field:: ||  ||  >>> twofield = np.zeros(2; dtype=[('A'; 'i4'); ('B'; 'i4')]) ||  >>> onefield = np.zeros(2; dtype=[('A'; 'i4')]) ||  >>> nostruct = np.zeros(2; dtype='i4') ||  >>> nostruct[:] = twofield ||  ValueError: Can't cast from structure to non-structure; except if the structure only has a single field. ||  >>> nostruct[:] = onefield ||  >>> nostruct ||  array([0; 0]; dtype=int32) ||  || Assignment from other Structured Arrays || ``````````````````````````````````````` ||  || Assignment between two structured arrays occurs as if the source elements had || been converted to tuples and then assigned to the destination elements. That || is; the first field of the source array is assigned to the first field of the || destination array; and the second field likewise; and so on; regardless of || field names. Structured arrays with a different number of fields cannot be || assigned to each other. Bytes of the destination structure which are not || included in any of the fields are unaffected. :: ||  ||  >>> a = np.zeros(3; dtype=[('a'; 'i8'); ('b'; 'f4'); ('c'; 'S3')]) ||  >>> b = np.ones(3; dtype=[('x'; 'f4'); ('y'; 'S3'); ('z'; 'O')]) ||  >>> b[:] = a ||  >>> b ||  array([(0.0; b'0.0'; b''); (0.0; b'0.0'; b''); (0.0; b'0.0'; b'')]; ||        dtype=[('x'; '<f4'); ('y'; 'S3'); ('z'; 'O')]) ||  ||  || Assignment involving subarrays || `````````````````````````````` ||  || When assigning to fields which are subarrays; the assigned value will first be || broadcast to the shape of the subarray. ||  || Indexing Structured Arrays || -------------------------- ||  || Accessing Individual Fields || ``````````````````````````` ||  || Individual fields of a structured array may be accessed and modified by indexing || the array with the field name. :: ||  ||  >>> x = np.array([(1;2);(3;4)]; dtype=[('foo'; 'i8'); ('bar'; 'f4')]) ||  >>> x['foo'] ||  array([1; 3]) ||  >>> x['foo'] = 10 ||  >>> x ||  array([(10; 2.); (10; 4.)]; ||        dtype=[('foo'; '<i8'); ('bar'; '<f4')]) ||  || The resulting array is a view into the original array. It shares the same || memory locations and writing to the view will modify the original array. :: ||  ||  >>> y = x['bar'] ||  >>> y[:] = 10 ||  >>> x ||  array([(10; 5.); (10; 5.)]; ||        dtype=[('foo'; '<i8'); ('bar'; '<f4')]) ||  || This view has the same dtype and itemsize as the indexed field; so it is || typically a non-structured array; except in the case of nested structures. ||  ||  >>> y.dtype; y.shape; y.strides ||  (dtype('float32'); (2;); (12;)) ||  || Accessing Multiple Fields || ``````````````````````````` ||  || One can index and assign to a structured array with a multi-field index; where || the index is a list of field names. ||  || .. warning:: ||     The behavior of multi-field indexes will change from Numpy 1.15 to Numpy ||     1.16. ||  || In Numpy 1.16; the result of indexing with a multi-field index will be a view || into the original array; as follows:: ||  ||  >>> a = np.zeros(3; dtype=[('a'; 'i4'); ('b'; 'i4'); ('c'; 'f4')]) ||  >>> a[['a'; 'c']] ||  array([(0; 0.); (0; 0.); (0; 0.)]; ||       dtype={'names':['a';'c']; 'formats':['<i4';'<f4']; 'offsets':[0;8]; 'itemsize':12}) ||  || Assignment to the view modifies the original array. The view's fields will be || in the order they were indexed. Note that unlike for single-field indexing; the || view's dtype has the same itemsize as the original array; and has fields at the || same offsets as in the original array; and unindexed fields are merely missing. ||  || In Numpy 1.15; indexing an array with a multi-field index returns a copy of || the result above for 1.16; but with fields packed together in memory as if || passed through :func:`numpy.lib.recfunctions.repack_fields`. This is the || behavior since Numpy 1.7. ||  || .. warning:: ||    The new behavior in Numpy 1.16 leads to extra \"padding\" bytes at the ||    location of unindexed fields. You will need to update any code which depends ||    on the data having a \"packed\" layout. For instance code such as:: ||  ||     >>> a[['a';'c']].view('i8')  # will fail in Numpy 1.16 ||     ValueError: When changing to a smaller dtype; its size must be a divisor of the size of original dtype ||  ||    will need to be changed. This code has raised a ``FutureWarning`` since ||    Numpy 1.12. ||  ||    The following is a recommended fix; which will behave identically in Numpy ||    1.15 and Numpy 1.16:: ||  ||     >>> from numpy.lib.recfunctions import repack_fields ||     >>> repack_fields(a[['a';'c']]).view('i8')  # supported 1.15 and 1.16 ||     array([0; 0; 0]) ||  || Assigning to an array with a multi-field index will behave the same in Numpy || 1.15 and Numpy 1.16. In both versions the assignment will modify the original || array:: ||  ||  >>> a[['a'; 'c']] = (2; 3) ||  >>> a ||  array([(2; 0; 3.0); (2; 0; 3.0); (2; 0; 3.0)]; ||        dtype=[('a'; '<i8'); ('b'; '<i4'); ('c'; '<f8')]) ||  || This obeys the structured array assignment rules described above. For example; || this means that one can swap the values of two fields using appropriate || multi-field indexes:: ||  ||  >>> a[['a'; 'c']] = a[['c'; 'a']] ||  || Indexing with an Integer to get a Structured Scalar || ``````````````````````````````````````````````````` ||  || Indexing a single element of a structured array (with an integer index) returns || a structured scalar:: ||  ||  >>> x = np.array([(1; 2.; 3.)]; dtype='i;f;f') ||  >>> scalar = x[0] ||  >>> scalar ||  (1; 2.; 3.) ||  >>> type(scalar) ||  numpy.void ||  || Unlike other numpy scalars; structured scalars are mutable and act like views || into the original array; such that modifying the scalar will modify the || original array. Structured scalars also support access and assignment by field || name:: ||  ||  >>> x = np.array([(1;2);(3;4)]; dtype=[('foo'; 'i8'); ('bar'; 'f4')]) ||  >>> s = x[0] ||  >>> s['bar'] = 100 ||  >>> x ||  array([(1; 100.); (3; 4.)]; ||        dtype=[('foo'; '<i8'); ('bar'; '<f4')]) ||  || Similarly to tuples; structured scalars can also be indexed with an integer:: ||  ||  >>> scalar = np.array([(1; 2.; 3.)]; dtype='i;f;f')[0] ||  >>> scalar[0] ||  1 ||  >>> scalar[1] = 4 ||  || Thus; tuples might be thought of as the native Python equivalent to numpy's || structured types; much like native python integers are the equivalent to || numpy's integer types. Structured scalars may be converted to a tuple by || calling :func:`ndarray.item`:: ||  ||  >>> scalar.item(); type(scalar.item()) ||  ((1; 2.0; 3.0); tuple) ||  || Viewing Structured Arrays Containing Objects || -------------------------------------------- ||  || In order to prevent clobbering object pointers in fields of || :class:`numpy.object` type; numpy currently does not allow views of structured || arrays containing objects. ||  || Structure Comparison || -------------------- ||  || If the dtypes of two void structured arrays are equal; testing the equality of || the arrays will result in a boolean array with the dimensions of the original || arrays; with elements set to ``True`` where all fields of the corresponding || structures are equal. Structured dtypes are equal if the field names; || dtypes and titles are the same; ignoring endianness; and the fields are in || the same order:: ||  ||  >>> a = np.zeros(2; dtype=[('a'; 'i4'); ('b'; 'i4')]) ||  >>> b = np.ones(2; dtype=[('a'; 'i4'); ('b'; 'i4')]) ||  >>> a == b ||  array([False; False]) ||  || Currently; if the dtypes of two void structured arrays are not equivalent the || comparison fails; returning the scalar value ``False``. This behavior is || deprecated as of numpy 1.10 and will raise an error or perform elementwise || comparison in the future. ||  || The ``<`` and ``>`` operators always return ``False`` when comparing void || structured arrays; and arithmetic and bitwise operations are not supported. ||  || Record Arrays || ============= ||  || As an optional convenience numpy provides an ndarray subclass; || :class:`numpy.recarray`; and associated helper functions in the || :mod:`numpy.rec` submodule; that allows access to fields of structured arrays || by attribute instead of only by index. Record arrays also use a special || datatype; :class:`numpy.record`; that allows field access by attribute on the || structured scalars obtained from the array. ||  || The simplest way to create a record array is with :func:`numpy.rec.array`:: ||  ||  >>> recordarr = np.rec.array([(1;2.;'Hello');(2;3.;\"World\")]; ||  ...                    dtype=[('foo'; 'i4');('bar'; 'f4'); ('baz'; 'S10')]) ||  >>> recordarr.bar ||  array([ 2.;  3.]; dtype=float32) ||  >>> recordarr[1:2] ||  rec.array([(2; 3.0; 'World')]; ||        dtype=[('foo'; '<i4'); ('bar'; '<f4'); ('baz'; 'S10')]) ||  >>> recordarr[1:2].foo ||  array([2]; dtype=int32) ||  >>> recordarr.foo[1:2] ||  array([2]; dtype=int32) ||  >>> recordarr[1].baz ||  'World' ||  || :func:`numpy.rec.array` can convert a wide variety of arguments into record || arrays; including structured arrays:: ||  ||  >>> arr = array([(1;2.;'Hello');(2;3.;\"World\")]; ||  ...             dtype=[('foo'; 'i4'); ('bar'; 'f4'); ('baz'; 'S10')]) ||  >>> recordarr = np.rec.array(arr) ||  || The :mod:`numpy.rec` module provides a number of other convenience functions for || creating record arrays; see :ref:`record array creation routines || <routines.array-creation.rec>`. ||  || A record array representation of a structured array can be obtained using the || appropriate :ref:`view`:: ||  ||  >>> arr = np.array([(1;2.;'Hello');(2;3.;\"World\")]; ||  ...                dtype=[('foo'; 'i4');('bar'; 'f4'); ('baz'; 'a10')]) ||  >>> recordarr = arr.view(dtype=dtype((np.record; arr.dtype)); ||  ...                      type=np.recarray) ||  || For convenience; viewing an ndarray as type :class:`np.recarray` will || automatically convert to :class:`np.record` datatype; so the dtype can be left || out of the view:: ||  ||  >>> recordarr = arr.view(np.recarray) ||  >>> recordarr.dtype ||  dtype((numpy.record; [('foo'; '<i4'); ('bar'; '<f4'); ('baz'; 'S10')])) ||  || To get back to a plain ndarray both the dtype and type must be reset. The || following view does so; taking into account the unusual case that the || recordarr was not a structured type:: ||  ||  >>> arr2 = recordarr.view(recordarr.dtype.fields or recordarr.dtype; np.ndarray) ||  || Record array fields accessed by index or by attribute are returned as a record || array if the field has a structured type but as a plain ndarray otherwise. :: ||  ||  >>> recordarr = np.rec.array([('Hello'; (1;2));(\"World\"; (3;4))]; ||  ...                 dtype=[('foo'; 'S6');('bar'; [('A'; int); ('B'; int)])]) ||  >>> type(recordarr.foo) ||  <type 'numpy.ndarray'> ||  >>> type(recordarr.bar) ||  <class 'numpy.core.records.recarray'> ||  || Note that if a field has the same name as an ndarray attribute; the ndarray || attribute takes precedence. Such fields will be inaccessible by attribute but || will still be accessible by index. ||  || \"\"\"
aahjcbihf,ryfeus/gcf-packs,pandas_numpy/sources/numpy/doc/subclassing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,\"\"\"============================= || Subclassing ndarray in python || ============================= ||  || Introduction || ------------ ||  || Subclassing ndarray is relatively simple; but it has some complications || compared to other Python objects.  On this page we explain the machinery || that allows you to subclass ndarray; and the implications for || implementing a subclass. ||  || ndarrays and object creation || ============================ ||  || Subclassing ndarray is complicated by the fact that new instances of || ndarray classes can come about in three different ways.  These are: ||  || #. Explicit constructor call - as in ``MySubClass(params)``.  This is ||    the usual route to Python instance creation. || #. View casting - casting an existing ndarray as a given subclass || #. New from template - creating a new instance from a template ||    instance. Examples include returning slices from a subclassed array; ||    creating return types from ufuncs; and copying arrays.  See ||    :ref:`new-from-template` for more details ||  || The last two are characteristics of ndarrays - in order to support || things like array slicing.  The complications of subclassing ndarray are || due to the mechanisms numpy has to support these latter two routes of || instance creation. ||  || .. _view-casting: ||  || View casting || ------------ ||  || *View casting* is the standard ndarray mechanism by which you take an || ndarray of any subclass; and return a view of the array as another || (specified) subclass: ||  || >>> import numpy as np || >>> # create a completely useless ndarray subclass || >>> class C(np.ndarray): pass || >>> # create a standard ndarray || >>> arr = np.zeros((3;)) || >>> # take a view of it; as our useless subclass || >>> c_arr = arr.view(C) || >>> type(c_arr) || <class 'C'> ||  || .. _new-from-template: ||  || Creating new from template || -------------------------- ||  || New instances of an ndarray subclass can also come about by a very || similar mechanism to :ref:`view-casting`; when numpy finds it needs to || create a new instance from a template instance.  The most obvious place || this has to happen is when you are taking slices of subclassed arrays. || For example: ||  || >>> v = c_arr[1:] || >>> type(v) # the view is of type 'C' || <class 'C'> || >>> v is c_arr # but it's a new instance || False ||  || The slice is a *view* onto the original ``c_arr`` data.  So; when we || take a view from the ndarray; we return a new ndarray; of the same || class; that points to the data in the original. ||  || There are other points in the use of ndarrays where we need such views; || such as copying arrays (``c_arr.copy()``); creating ufunc output arrays || (see also :ref:`array-wrap`); and reducing methods (like || ``c_arr.mean()``. ||  || Relationship of view casting and new-from-template || -------------------------------------------------- ||  || These paths both use the same machinery.  We make the distinction here; || because they result in different input to your methods.  Specifically; || :ref:`view-casting` means you have created a new instance of your array || type from any potential subclass of ndarray.  :ref:`new-from-template` || means you have created a new instance of your class from a pre-existing || instance; allowing you - for example - to copy across attributes that || are particular to your subclass. ||  || Implications for subclassing || ---------------------------- ||  || If we subclass ndarray; we need to deal not only with explicit || construction of our array type; but also :ref:`view-casting` or || :ref:`new-from-template`.  NumPy has the machinery to do this; and this || machinery that makes subclassing slightly non-standard. ||  || There are two aspects to the machinery that ndarray uses to support || views and new-from-template in subclasses. ||  || The first is the use of the ``ndarray.__new__`` method for the main work || of object initialization; rather then the more usual ``__init__`` || method.  The second is the use of the ``__array_finalize__`` method to || allow subclasses to clean up after the creation of views and new || instances from templates. ||  || A brief Python primer on ``__new__`` and ``__init__`` || ===================================================== ||  || ``__new__`` is a standard Python method; and; if present; is called || before ``__init__`` when we create a class instance. See the `python || __new__ documentation || <http:\/\/docs.python.org\/reference\/datamodel.html#object.__new__>`_ for more detail. ||  || For example; consider the following Python code: ||  || .. testcode:: ||  ||   class C(object): ||       def __new__(cls; *args): ||           print('Cls in __new__:'; cls) ||           print('Args in __new__:'; args) ||           return object.__new__(cls; *args) ||  ||       def __init__(self; *args): ||           print('type(self) in __init__:'; type(self)) ||           print('Args in __init__:'; args) ||  || meaning that we get: ||  || >>> c = C('hello') || Cls in __new__: <class 'C'> || Args in __new__: ('hello';) || type(self) in __init__: <class 'C'> || Args in __init__: ('hello';) ||  || When we call ``C('hello')``; the ``__new__`` method gets its own class || as first argument; and the passed argument; which is the string || ``'hello'``.  After python calls ``__new__``; it usually (see below) || calls our ``__init__`` method; with the output of ``__new__`` as the || first argument (now a class instance); and the passed arguments || following. ||  || As you can see; the object can be initialized in the ``__new__`` || method or the ``__init__`` method; or both; and in fact ndarray does || not have an ``__init__`` method; because all the initialization is || done in the ``__new__`` method. ||  || Why use ``__new__`` rather than just the usual ``__init__``?  Because || in some cases; as for ndarray; we want to be able to return an object || of some other class.  Consider the following: ||  || .. testcode:: ||  ||   class D(C): ||       def __new__(cls; *args): ||           print('D cls is:'; cls) ||           print('D args in __new__:'; args) ||           return C.__new__(C; *args) ||  ||       def __init__(self; *args): ||           # we never get here ||           print('In D __init__') ||  || meaning that: ||  || >>> obj = D('hello') || D cls is: <class 'D'> || D args in __new__: ('hello';) || Cls in __new__: <class 'C'> || Args in __new__: ('hello';) || >>> type(obj) || <class 'C'> ||  || The definition of ``C`` is the same as before; but for ``D``; the || ``__new__`` method returns an instance of class ``C`` rather than || ``D``.  Note that the ``__init__`` method of ``D`` does not get || called.  In general; when the ``__new__`` method returns an object of || class other than the class in which it is defined; the ``__init__`` || method of that class is not called. ||  || This is how subclasses of the ndarray class are able to return views || that preserve the class type.  When taking a view; the standard || ndarray machinery creates the new ndarray object with something || like:: ||  ||   obj = ndarray.__new__(subtype; shape; ... ||  || where ``subdtype`` is the subclass.  Thus the returned view is of the || same class as the subclass; rather than being of class ``ndarray``. ||  || That solves the problem of returning views of the same type; but now || we have a new problem.  The machinery of ndarray can set the class || this way; in its standard methods for taking views; but the ndarray || ``__new__`` method knows nothing of what we have done in our own || ``__new__`` method in order to set attributes; and so on.  (Aside - || why not call ``obj = subdtype.__new__(...`` then?  Because we may not || have a ``__new__`` method with the same call signature). ||  || The role of ``__array_finalize__`` || ================================== ||  || ``__array_finalize__`` is the mechanism that numpy provides to allow || subclasses to handle the various ways that new instances get created. ||  || Remember that subclass instances can come about in these three ways: ||  || #. explicit constructor call (``obj = MySubClass(params)``).  This will ||    call the usual sequence of ``MySubClass.__new__`` then (if it exists) ||    ``MySubClass.__init__``. || #. :ref:`view-casting` || #. :ref:`new-from-template` ||  || Our ``MySubClass.__new__`` method only gets called in the case of the || explicit constructor call; so we can't rely on ``MySubClass.__new__`` or || ``MySubClass.__init__`` to deal with the view casting and || new-from-template.  It turns out that ``MySubClass.__array_finalize__`` || *does* get called for all three methods of object creation; so this is || where our object creation housekeeping usually goes. ||  || * For the explicit constructor call; our subclass will need to create a ||   new ndarray instance of its own class.  In practice this means that ||   we; the authors of the code; will need to make a call to ||   ``ndarray.__new__(MySubClass;...)``; a class-hierarchy prepared call to ||   ``super(MySubClass; cls).__new__(cls; ...)``; or do view casting of an ||   existing array (see below) || * For view casting and new-from-template; the equivalent of ||   ``ndarray.__new__(MySubClass;...`` is called; at the C level. ||  || The arguments that ``__array_finalize__`` receives differ for the three || methods of instance creation above. ||  || The following code allows us to look at the call sequences and arguments: ||  || .. testcode:: ||  ||    import numpy as np ||  ||    class C(np.ndarray): ||        def __new__(cls; *args; **kwargs): ||            print('In __new__ with class %s' % cls) ||            return super(C; cls).__new__(cls; *args; **kwargs) ||  ||        def __init__(self; *args; **kwargs): ||            # in practice you probably will not need or want an __init__ ||            # method for your subclass ||            print('In __init__ with class %s' % self.__class__) ||  ||        def __array_finalize__(self; obj): ||            print('In array_finalize:') ||            print('   self type is %s' % type(self)) ||            print('   obj type is %s' % type(obj)) ||  ||  || Now: ||  || >>> # Explicit constructor || >>> c = C((10;)) || In __new__ with class <class 'C'> || In array_finalize: ||    self type is <class 'C'> ||    obj type is <type 'NoneType'> || In __init__ with class <class 'C'> || >>> # View casting || >>> a = np.arange(10) || >>> cast_a = a.view(C) || In array_finalize: ||    self type is <class 'C'> ||    obj type is <type 'numpy.ndarray'> || >>> # Slicing (example of new-from-template) || >>> cv = c[:1] || In array_finalize: ||    self type is <class 'C'> ||    obj type is <class 'C'> ||  || The signature of ``__array_finalize__`` is:: ||  ||     def __array_finalize__(self; obj): ||  || One sees that the ``super`` call; which goes to || ``ndarray.__new__``; passes ``__array_finalize__`` the new object; of our || own class (``self``) as well as the object from which the view has been || taken (``obj``).  As you can see from the output above; the ``self`` is || always a newly created instance of our subclass; and the type of ``obj`` || differs for the three instance creation methods: ||  || * When called from the explicit constructor; ``obj`` is ``None`` || * When called from view casting; ``obj`` can be an instance of any ||   subclass of ndarray; including our own. || * When called in new-from-template; ``obj`` is another instance of our ||   own subclass; that we might use to update the new ``self`` instance. ||  || Because ``__array_finalize__`` is the only method that always sees new || instances being created; it is the sensible place to fill in instance || defaults for new object attributes; among other tasks. ||  || This may be clearer with an example. ||  || Simple example - adding an extra attribute to ndarray || ----------------------------------------------------- ||  || .. testcode:: ||  ||   import numpy as np ||  ||   class InfoArray(np.ndarray): ||  ||       def __new__(subtype; shape; dtype=float; buffer=None; offset=0; ||                   strides=None; order=None; info=None): ||           # Create the ndarray instance of our type; given the usual ||           # ndarray input arguments.  This will call the standard ||           # ndarray constructor; but return an object of our type. ||           # It also triggers a call to InfoArray.__array_finalize__ ||           obj = super(InfoArray; subtype).__new__(subtype; shape; dtype; ||                                                   buffer; offset; strides; ||                                                   order) ||           # set the new 'info' attribute to the value passed ||           obj.info = info ||           # Finally; we must return the newly created object: ||           return obj ||  ||       def __array_finalize__(self; obj): ||           # ``self`` is a new object resulting from ||           # ndarray.__new__(InfoArray; ...); therefore it only has ||           # attributes that the ndarray.__new__ constructor gave it - ||           # i.e. those of a standard ndarray. ||           # ||           # We could have got to the ndarray.__new__ call in 3 ways: ||           # From an explicit constructor - e.g. InfoArray(): ||           #    obj is None ||           #    (we're in the middle of the InfoArray.__new__ ||           #    constructor; and self.info will be set when we return to ||           #    InfoArray.__new__) ||           if obj is None: return ||           # From view casting - e.g arr.view(InfoArray): ||           #    obj is arr ||           #    (type(obj) can be InfoArray) ||           # From new-from-template - e.g infoarr[:3] ||           #    type(obj) is InfoArray ||           # ||           # Note that it is here; rather than in the __new__ method; ||           # that we set the default value for 'info'; because this ||           # method sees all creation of default objects - with the ||           # InfoArray.__new__ constructor; but also with ||           # arr.view(InfoArray). ||           self.info = getattr(obj; 'info'; None) ||           # We do not need to return anything ||  ||  || Using the object looks like this: ||  ||   >>> obj = InfoArray(shape=(3;)) # explicit constructor ||   >>> type(obj) ||   <class 'InfoArray'> ||   >>> obj.info is None ||   True ||   >>> obj = InfoArray(shape=(3;); info='information') ||   >>> obj.info ||   'information' ||   >>> v = obj[1:] # new-from-template - here - slicing ||   >>> type(v) ||   <class 'InfoArray'> ||   >>> v.info ||   'information' ||   >>> arr = np.arange(10) ||   >>> cast_arr = arr.view(InfoArray) # view casting ||   >>> type(cast_arr) ||   <class 'InfoArray'> ||   >>> cast_arr.info is None ||   True ||  || This class isn't very useful; because it has the same constructor as the || bare ndarray object; including passing in buffers and shapes and so on. || We would probably prefer the constructor to be able to take an already || formed ndarray from the usual numpy calls to ``np.array`` and return an || object. ||  || Slightly more realistic example - attribute added to existing array || ------------------------------------------------------------------- ||  || Here is a class that takes a standard ndarray that already exists; casts || as our type; and adds an extra attribute. ||  || .. testcode:: ||  ||   import numpy as np ||  ||   class RealisticInfoArray(np.ndarray): ||  ||       def __new__(cls; input_array; info=None): ||           # Input array is an already formed ndarray instance ||           # We first cast to be our class type ||           obj = np.asarray(input_array).view(cls) ||           # add the new attribute to the created instance ||           obj.info = info ||           # Finally; we must return the newly created object: ||           return obj ||  ||       def __array_finalize__(self; obj): ||           # see InfoArray.__array_finalize__ for comments ||           if obj is None: return ||           self.info = getattr(obj; 'info'; None) ||  ||  || So: ||  ||   >>> arr = np.arange(5) ||   >>> obj = RealisticInfoArray(arr; info='information') ||   >>> type(obj) ||   <class 'RealisticInfoArray'> ||   >>> obj.info ||   'information' ||   >>> v = obj[1:] ||   >>> type(v) ||   <class 'RealisticInfoArray'> ||   >>> v.info ||   'information' ||  || .. _array-ufunc: ||  || ``__array_ufunc__`` for ufuncs || ------------------------------ ||  ||   .. versionadded:: 1.13 ||  || A subclass can override what happens when executing numpy ufuncs on it by || overriding the default ``ndarray.__array_ufunc__`` method. This method is || executed *instead* of the ufunc and should return either the result of the || operation; or :obj:`NotImplemented` if the operation requested is not || implemented. ||  || The signature of ``__array_ufunc__`` is:: ||  ||     def __array_ufunc__(ufunc; method; *inputs; **kwargs): ||  ||     - *ufunc* is the ufunc object that was called. ||     - *method* is a string indicating how the Ufunc was called; either ||       ``\"__call__\"`` to indicate it was called directly; or one of its ||       :ref:`methods<ufuncs.methods>`: ``\"reduce\"``; ``\"accumulate\"``; ||       ``\"reduceat\"``; ``\"outer\"``; or ``\"at\"``. ||     - *inputs* is a tuple of the input arguments to the ``ufunc`` ||     - *kwargs* contains any optional or keyword arguments passed to the ||       function. This includes any ``out`` arguments; which are always ||       contained in a tuple. ||  || A typical implementation would convert any inputs or outputs that are || instances of one's own class; pass everything on to a superclass using || ``super()``; and finally return the results after possible || back-conversion. An example; taken from the test case || ``test_ufunc_override_with_super`` in ``core\/tests\/test_umath.py``; is the || following. ||  || .. testcode:: ||  ||     input numpy as np ||  ||     class A(np.ndarray): ||         def __array_ufunc__(self; ufunc; method; *inputs; **kwargs): ||             args = [] ||             in_no = [] ||             for i; input_ in enumerate(inputs): ||                 if isinstance(input_; A): ||                     in_no.append(i) ||                     args.append(input_.view(np.ndarray)) ||                 else: ||                     args.append(input_) ||  ||             outputs = kwargs.pop('out'; None) ||             out_no = [] ||             if outputs: ||                 out_args = [] ||                 for j; output in enumerate(outputs): ||                     if isinstance(output; A): ||                         out_no.append(j) ||                         out_args.append(output.view(np.ndarray)) ||                     else: ||                         out_args.append(output) ||                 kwargs['out'] = tuple(out_args) ||             else: ||                 outputs = (None;) * ufunc.nout ||  ||             info = {} ||             if in_no: ||                 info['inputs'] = in_no ||             if out_no: ||                 info['outputs'] = out_no ||  ||             results = super(A; self).__array_ufunc__(ufunc; method; ||                                                      *args; **kwargs) ||             if results is NotImplemented: ||                 return NotImplemented ||  ||             if method == 'at': ||                 if isinstance(inputs[0]; A): ||                     inputs[0].info = info ||                 return ||  ||             if ufunc.nout == 1: ||                 results = (results;) ||  ||             results = tuple((np.asarray(result).view(A) ||                              if output is None else output) ||                             for result; output in zip(results; outputs)) ||             if results and isinstance(results[0]; A): ||                 results[0].info = info ||  ||             return results[0] if len(results) == 1 else results ||  || So; this class does not actually do anything interesting: it just || converts any instances of its own to regular ndarray (otherwise; we'd || get infinite recursion!); and adds an ``info`` dictionary that tells || which inputs and outputs it converted. Hence; e.g.; ||  || >>> a = np.arange(5.).view(A) || >>> b = np.sin(a) || >>> b.info || {'inputs': [0]} || >>> b = np.sin(np.arange(5.); out=(a;)) || >>> b.info || {'outputs': [0]} || >>> a = np.arange(5.).view(A) || >>> b = np.ones(1).view(A) || >>> c = a + b || >>> c.info || {'inputs': [0; 1]} || >>> a += b || >>> a.info || {'inputs': [0; 1]; 'outputs': [0]} ||  || Note that another approach would be to to use ``getattr(ufunc; || methods)(*inputs; **kwargs)`` instead of the ``super`` call. For this example; || the result would be identical; but there is a difference if another operand || also defines ``__array_ufunc__``. E.g.; lets assume that we evalulate || ``np.add(a; b)``; where ``b`` is an instance of another class ``B`` that has || an override.  If you use ``super`` as in the example; || ``ndarray.__array_ufunc__`` will notice that ``b`` has an override; which || means it cannot evaluate the result itself. Thus; it will return || `NotImplemented` and so will our class ``A``. Then; control will be passed || over to ``b``; which either knows how to deal with us and produces a result; || or does not and returns `NotImplemented`; raising a ``TypeError``. ||  || If instead; we replace our ``super`` call with ``getattr(ufunc; method)``; we || effectively do ``np.add(a.view(np.ndarray); b)``. Again; ``B.__array_ufunc__`` || will be called; but now it sees an ``ndarray`` as the other argument. Likely; || it will know how to handle this; and return a new instance of the ``B`` class || to us. Our example class is not set up to handle this; but it might well be || the best approach if; e.g.; one were to re-implement ``MaskedArray`` using || ``__array_ufunc__``. ||  || As a final note: if the ``super`` route is suited to a given class; an || advantage of using it is that it helps in constructing class hierarchies. || E.g.; suppose that our other class ``B`` also used the ``super`` in its || ``__array_ufunc__`` implementation; and we created a class ``C`` that depended || on both; i.e.; ``class C(A; B)`` (with; for simplicity; not another || ``__array_ufunc__`` override). Then any ufunc on an instance of ``C`` would || pass on to ``A.__array_ufunc__``; the ``super`` call in ``A`` would go to || ``B.__array_ufunc__``; and the ``super`` call in ``B`` would go to || ``ndarray.__array_ufunc__``; thus allowing ``A`` and ``B`` to collaborate. ||  || .. _array-wrap: ||  || ``__array_wrap__`` for ufuncs and other functions || ------------------------------------------------- ||  || Prior to numpy 1.13; the behaviour of ufuncs could only be tuned using || ``__array_wrap__`` and ``__array_prepare__``. These two allowed one to || change the output type of a ufunc; but; in contrast to || ``__array_ufunc__``; did not allow one to make any changes to the inputs. || It is hoped to eventually deprecate these; but ``__array_wrap__`` is also || used by other numpy functions and methods; such as ``squeeze``; so at the || present time is still needed for full functionality. ||  || Conceptually; ``__array_wrap__`` \"wraps up the action\" in the sense of || allowing a subclass to set the type of the return value and update || attributes and metadata.  Let's show how this works with an example.  First || we return to the simpler example subclass; but with a different name and || some print statements: ||  || .. testcode:: ||  ||   import numpy as np ||  ||   class MySubClass(np.ndarray): ||  ||       def __new__(cls; input_array; info=None): ||           obj = np.asarray(input_array).view(cls) ||           obj.info = info ||           return obj ||  ||       def __array_finalize__(self; obj): ||           print('In __array_finalize__:') ||           print('   self is %s' % repr(self)) ||           print('   obj is %s' % repr(obj)) ||           if obj is None: return ||           self.info = getattr(obj; 'info'; None) ||  ||       def __array_wrap__(self; out_arr; context=None): ||           print('In __array_wrap__:') ||           print('   self is %s' % repr(self)) ||           print('   arr is %s' % repr(out_arr)) ||           # then just call the parent ||           return super(MySubClass; self).__array_wrap__(self; out_arr; context) ||  || We run a ufunc on an instance of our new array: ||  || >>> obj = MySubClass(np.arange(5); info='spam') || In __array_finalize__: ||    self is MySubClass([0; 1; 2; 3; 4]) ||    obj is array([0; 1; 2; 3; 4]) || >>> arr2 = np.arange(5)+1 || >>> ret = np.add(arr2; obj) || In __array_wrap__: ||    self is MySubClass([0; 1; 2; 3; 4]) ||    arr is array([1; 3; 5; 7; 9]) || In __array_finalize__: ||    self is MySubClass([1; 3; 5; 7; 9]) ||    obj is MySubClass([0; 1; 2; 3; 4]) || >>> ret || MySubClass([1; 3; 5; 7; 9]) || >>> ret.info || 'spam' ||  || Note that the ufunc (``np.add``) has called the ``__array_wrap__`` method || with arguments ``self`` as ``obj``; and ``out_arr`` as the (ndarray) result || of the addition.  In turn; the default ``__array_wrap__`` || (``ndarray.__array_wrap__``) has cast the result to class ``MySubClass``; || and called ``__array_finalize__`` - hence the copying of the ``info`` || attribute.  This has all happened at the C level. ||  || But; we could do anything we wanted: ||  || .. testcode:: ||  ||   class SillySubClass(np.ndarray): ||  ||       def __array_wrap__(self; arr; context=None): ||           return 'I lost your data' ||  || >>> arr1 = np.arange(5) || >>> obj = arr1.view(SillySubClass) || >>> arr2 = np.arange(5) || >>> ret = np.multiply(obj; arr2) || >>> ret || 'I lost your data' ||  || So; by defining a specific ``__array_wrap__`` method for our subclass; || we can tweak the output from ufuncs. The ``__array_wrap__`` method || requires ``self``; then an argument - which is the result of the ufunc - || and an optional parameter *context*. This parameter is returned by || ufuncs as a 3-element tuple: (name of the ufunc; arguments of the ufunc; || domain of the ufunc); but is not set by other numpy functions. Though; || as seen above; it is possible to do otherwise; ``__array_wrap__`` should || return an instance of its containing class.  See the masked array || subclass for an implementation. ||  || In addition to ``__array_wrap__``; which is called on the way out of the || ufunc; there is also an ``__array_prepare__`` method which is called on || the way into the ufunc; after the output arrays are created but before any || computation has been performed. The default implementation does nothing || but pass through the array. ``__array_prepare__`` should not attempt to || access the array data or resize the array; it is intended for setting the || output array type; updating attributes and metadata; and performing any || checks based on the input that may be desired before computation begins. || Like ``__array_wrap__``; ``__array_prepare__`` must return an ndarray or || subclass thereof or raise an error. ||  || Extra gotchas - custom ``__del__`` methods and ndarray.base || ----------------------------------------------------------- ||  || One of the problems that ndarray solves is keeping track of memory || ownership of ndarrays and their views.  Consider the case where we have || created an ndarray; ``arr`` and have taken a slice with ``v = arr[1:]``. || The two objects are looking at the same memory.  NumPy keeps track of || where the data came from for a particular array or view; with the || ``base`` attribute: ||  || >>> # A normal ndarray; that owns its own data || >>> arr = np.zeros((4;)) || >>> # In this case; base is None || >>> arr.base is None || True || >>> # We take a view || >>> v1 = arr[1:] || >>> # base now points to the array that it derived from || >>> v1.base is arr || True || >>> # Take a view of a view || >>> v2 = v1[1:] || >>> # base points to the view it derived from || >>> v2.base is v1 || True ||  || In general; if the array owns its own memory; as for ``arr`` in this || case; then ``arr.base`` will be None - there are some exceptions to this || - see the numpy book for more details. ||  || The ``base`` attribute is useful in being able to tell whether we have || a view or the original array.  This in turn can be useful if we need || to know whether or not to do some specific cleanup when the subclassed || array is deleted.  For example; we may only want to do the cleanup if || the original array is deleted; but not the views.  For an example of || how this can work; have a look at the ``memmap`` class in || ``numpy.core``. ||  || Subclassing and Downstream Compatibility || ---------------------------------------- ||  || When sub-classing ``ndarray`` or creating duck-types that mimic the ``ndarray`` || interface; it is your responsibility to decide how aligned your APIs will be || with those of numpy. For convenience; many numpy functions that have a corresponding || ``ndarray`` method (e.g.; ``sum``; ``mean``; ``take``; ``reshape``) work by checking || if the first argument to a function has a method of the same name. If it exists; the || method is called instead of coercing the arguments to a numpy array. ||  || For example; if you want your sub-class or duck-type to be compatible with || numpy's ``sum`` function; the method signature for this object's ``sum`` method || should be the following: ||  || .. testcode:: ||  ||     def sum(self; axis=None; dtype=None; out=None; keepdims=False): ||     ... ||  || This is the exact same method signature for ``np.sum``; so now if a user calls || ``np.sum`` on this object; numpy will call the object's own ``sum`` method and || pass in these arguments enumerated above in the signature; and no errors will || be raised because the signatures are completely compatible with each other. ||  || If; however; you decide to deviate from this signature and do something like this: ||  || .. testcode:: ||  ||    def sum(self; axis=None; dtype=None): ||    ... ||  || This object is no longer compatible with ``np.sum`` because if you call ``np.sum``; || it will pass in unexpected arguments ``out`` and ``keepdims``; causing a TypeError || to be raised. ||  || If you wish to maintain compatibility with numpy and its subsequent versions (which || might add new keyword arguments) but do not want to surface all of numpy's arguments; || your function's signature should accept ``**kwargs``. For example: ||  || .. testcode:: ||  ||    def sum(self; axis=None; dtype=None; **unused_kwargs): ||    ... ||  || This object is now compatible with ``np.sum`` again because any extraneous arguments || (i.e. keywords that are not ``axis`` or ``dtype``) will be hidden away in the || ``**unused_kwargs`` parameter. ||  || \"\"\"
aahjcbijb,ryfeus/gcf-packs,pandas_numpy/sources/numpy/f2py/capi_maps.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,The eviroment provided by auxfuncs.py is needed for some calls to eval.
aahjcbijc,ryfeus/gcf-packs,pandas_numpy/sources/numpy/f2py/capi_maps.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,As the needed functions cannot be determined by static inspection of the
aahjcbjch,ryfeus/gcf-packs,pandas_numpy/sources/numpy/f2py/capi_maps.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX: Evaluate intent_flags here.
aahjcbjhb,ryfeus/gcf-packs,pandas_numpy/sources/numpy/f2py/cfuncs.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX: Note that CNUMFROMARROBJ is identical with NUMFROMARROBJ
aahjccaac,ryfeus/gcf-packs,pandas_numpy/sources/numpy/f2py/crackfortran.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,The eviroment provided by auxfuncs.py is needed for some calls to eval.
aahjccaad,ryfeus/gcf-packs,pandas_numpy/sources/numpy/f2py/crackfortran.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,As the needed functions cannot be determined by static inspection of the
aahjccadj,ryfeus/gcf-packs,pandas_numpy/sources/numpy/f2py/crackfortran.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX: non-zero reset values need testing
aahjccafd,ryfeus/gcf-packs,pandas_numpy/sources/numpy/f2py/crackfortran.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX: subsequent init expressions may get wrong values.
aahjccafj,ryfeus/gcf-packs,pandas_numpy/sources/numpy/f2py/crackfortran.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX: apply mapping
aahjccagc,ryfeus/gcf-packs,pandas_numpy/sources/numpy/f2py/crackfortran.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX: How to catch dependence cycles correctly?
aahjccagg,ryfeus/gcf-packs,pandas_numpy/sources/numpy/f2py/crackfortran.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX: return something sensible.
aahjccaha,ryfeus/gcf-packs,pandas_numpy/sources/numpy/f2py/crackfortran.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: test .eq.; .neq.; etc replacements.
aahjccaif,ryfeus/gcf-packs,pandas_numpy/sources/numpy/f2py/crackfortran.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,FIXME complex numbers may also have exponents
aahjccaig,ryfeus/gcf-packs,pandas_numpy/sources/numpy/f2py/crackfortran.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,FIXME; unused l looks like potential bug
aahjccbae,ryfeus/gcf-packs,pandas_numpy/sources/numpy/f2py/f2py2e.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX
aahjccbba,ryfeus/gcf-packs,pandas_numpy/sources/numpy/f2py/f90mod_rules.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,The eviroment provided by auxfuncs.py is needed for some calls to eval.
aahjccbbb,ryfeus/gcf-packs,pandas_numpy/sources/numpy/f2py/f90mod_rules.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,As the needed functions cannot be determined by static inspection of the
aahjcccbe,ryfeus/gcf-packs,pandas_numpy/sources/numpy/f2py/rules.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Workaround for Python 2.6; 2.6.1 bug: http:\/\/bugs.python.org\/issue4720
aahjccchj,ryfeus/gcf-packs,pandas_numpy/sources/numpy/f2py/tests/util.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX: this is really ugly. But I don't know how to invoke Distutils
aahjccdce,ryfeus/gcf-packs,pandas_numpy/sources/numpy/fft/info.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,\"\"\" || Discrete Fourier Transform (:mod:`numpy.fft`) || ============================================= ||  || .. currentmodule:: numpy.fft ||  || Standard FFTs || ------------- ||  || .. autosummary:: ||    :toctree: generated\/ ||  ||    fft       Discrete Fourier transform. ||    ifft      Inverse discrete Fourier transform. ||    fft2      Discrete Fourier transform in two dimensions. ||    ifft2     Inverse discrete Fourier transform in two dimensions. ||    fftn      Discrete Fourier transform in N-dimensions. ||    ifftn     Inverse discrete Fourier transform in N dimensions. ||  || Real FFTs || --------- ||  || .. autosummary:: ||    :toctree: generated\/ ||  ||    rfft      Real discrete Fourier transform. ||    irfft     Inverse real discrete Fourier transform. ||    rfft2     Real discrete Fourier transform in two dimensions. ||    irfft2    Inverse real discrete Fourier transform in two dimensions. ||    rfftn     Real discrete Fourier transform in N dimensions. ||    irfftn    Inverse real discrete Fourier transform in N dimensions. ||  || Hermitian FFTs || -------------- ||  || .. autosummary:: ||    :toctree: generated\/ ||  ||    hfft      Hermitian discrete Fourier transform. ||    ihfft     Inverse Hermitian discrete Fourier transform. ||  || Helper routines || --------------- ||  || .. autosummary:: ||    :toctree: generated\/ ||  ||    fftfreq   Discrete Fourier Transform sample frequencies. ||    rfftfreq  DFT sample frequencies (for usage with rfft; irfft). ||    fftshift  Shift zero-frequency component to center of spectrum. ||    ifftshift Inverse of fftshift. ||  ||  || Background information || ---------------------- ||  || Fourier analysis is fundamentally a method for expressing a function as a || sum of periodic components; and for recovering the function from those || components.  When both the function and its Fourier transform are || replaced with discretized counterparts; it is called the discrete Fourier || transform (DFT).  The DFT has become a mainstay of numerical computing in || part because of a very fast algorithm for computing it; called the Fast || Fourier Transform (FFT); which was known to Gauss (1805) and was brought || to light in its current form by Cooley and Tukey [CT]_.  Press et al. [NR]_ || provide an accessible introduction to Fourier analysis and its || applications. ||  || Because the discrete Fourier transform separates its input into || components that contribute at discrete frequencies; it has a great number || of applications in digital signal processing; e.g.; for filtering; and in || this context the discretized input to the transform is customarily || referred to as a *signal*; which exists in the *time domain*.  The output || is called a *spectrum* or *transform* and exists in the *frequency || domain*. ||  || Implementation details || ---------------------- ||  || There are many ways to define the DFT; varying in the sign of the || exponent; normalization; etc.  In this implementation; the DFT is defined || as ||  || .. math:: ||    A_k =  \\\\sum_{m=0}^{n-1} a_m \\\\exp\\\\left\\\\{-2\\\\pi i{mk \\\\over n}\\\\right\\\\} ||    \\\\qquad k = 0;\\\\ldots;n-1. ||  || The DFT is in general defined for complex inputs and outputs; and a || single-frequency component at linear frequency :math:`f` is || represented by a complex exponential || :math:`a_m = \\\\exp\\\\{2\\\\pi i\\\\;f m\\\\Delta t\\\\}`; where :math:`\\\\Delta t` || is the sampling interval. ||  || The values in the result follow so-called \"standard\" order: If ``A = || fft(a; n)``; then ``A[0]`` contains the zero-frequency term (the sum of || the signal); which is always purely real for real inputs. Then ``A[1:n\/2]`` || contains the positive-frequency terms; and ``A[n\/2+1:]`` contains the || negative-frequency terms; in order of decreasingly negative frequency. || For an even number of input points; ``A[n\/2]`` represents both positive and || negative Nyquist frequency; and is also purely real for real input.  For || an odd number of input points; ``A[(n-1)\/2]`` contains the largest positive || frequency; while ``A[(n+1)\/2]`` contains the largest negative frequency. || The routine ``np.fft.fftfreq(n)`` returns an array giving the frequencies || of corresponding elements in the output.  The routine || ``np.fft.fftshift(A)`` shifts transforms and their frequencies to put the || zero-frequency components in the middle; and ``np.fft.ifftshift(A)`` undoes || that shift. ||  || When the input `a` is a time-domain signal and ``A = fft(a)``; ``np.abs(A)`` || is its amplitude spectrum and ``np.abs(A)**2`` is its power spectrum. || The phase spectrum is obtained by ``np.angle(A)``. ||  || The inverse DFT is defined as ||  || .. math:: ||    a_m = \\\\frac{1}{n}\\\\sum_{k=0}^{n-1}A_k\\\\exp\\\\left\\\\{2\\\\pi i{mk\\\\over n}\\\\right\\\\} ||    \\\\qquad m = 0;\\\\ldots;n-1. ||  || It differs from the forward transform by the sign of the exponential || argument and the default normalization by :math:`1\/n`. ||  || Normalization || ------------- || The default normalization has the direct transforms unscaled and the inverse || transforms are scaled by :math:`1\/n`. It is possible to obtain unitary || transforms by setting the keyword argument ``norm`` to ``\"ortho\"`` (default is || `None`) so that both direct and inverse transforms will be scaled by || :math:`1\/\\\\sqrt{n}`. ||  || Real and Hermitian transforms || ----------------------------- ||  || When the input is purely real; its transform is Hermitian; i.e.; the || component at frequency :math:`f_k` is the complex conjugate of the || component at frequency :math:`-f_k`; which means that for real || inputs there is no information in the negative frequency components that || is not already available from the positive frequency components. || The family of `rfft` functions is || designed to operate on real inputs; and exploits this symmetry by || computing only the positive frequency components; up to and including the || Nyquist frequency.  Thus; ``n`` input points produce ``n\/2+1`` complex || output points.  The inverses of this family assumes the same symmetry of || its input; and for an output of ``n`` points uses ``n\/2+1`` input points. ||  || Correspondingly; when the spectrum is purely real; the signal is || Hermitian.  The `hfft` family of functions exploits this symmetry by || using ``n\/2+1`` complex points in the input (time) domain for ``n`` real || points in the frequency domain. ||  || In higher dimensions; FFTs are used; e.g.; for image analysis and || filtering.  The computational efficiency of the FFT means that it can || also be a faster way to compute large convolutions; using the property || that a convolution in the time domain is equivalent to a point-by-point || multiplication in the frequency domain. ||  || Higher dimensions || ----------------- ||  || In two dimensions; the DFT is defined as ||  || .. math:: ||    A_{kl} =  \\\\sum_{m=0}^{M-1} \\\\sum_{n=0}^{N-1} ||    a_{mn}\\\\exp\\\\left\\\\{-2\\\\pi i \\\\left({mk\\\\over M}+{nl\\\\over N}\\\\right)\\\\right\\\\} ||    \\\\qquad k = 0; \\\\ldots; M-1;\\\\quad l = 0; \\\\ldots; N-1; ||  || which extends in the obvious way to higher dimensions; and the inverses || in higher dimensions also extend in the same way. ||  || References || ---------- ||  || .. [CT] Cooley; James W.; and John W. Tukey; 1965; \"An algorithm for the ||         machine calculation of complex Fourier series;\" *Math. Comput.* ||         19: 297-301. ||  || .. [NR] Press; W.; Teukolsky; S.; Vetterline; W.T.; and Flannery; B.P.; ||         2007; *Numerical Recipes: The Art of Scientific Computing*; ch. ||         12-13.  Cambridge Univ. Press; Cambridge; UK. ||  || Examples || -------- ||  || For examples; see the various functions. ||  || \"\"\"
aahjccdgb,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/_datasource.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,gzip is lacking read1 needed for TextIOWrapper
aahjccdge,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/_datasource.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,deferring the import of lzma; bz2 and gzip until needed
aahjccdgf,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/_datasource.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: .zip support; .tar support?
aahjccdhc,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/_datasource.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,BUG : URLs require a scheme string ('http:\/\/') to be used.
aahjccdia,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/_datasource.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Doesn't handle compressed files!
aahjccdih,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/_datasource.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO:  This should be more robust.  Handles case where path includes
aahjccead,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/_datasource.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Add a ``subdir`` parameter for specifying the subdirectory
aahjccehd,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/_iotools.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Structured dtype: just validate the names as needed
aahjccfjc,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/arraysetops.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,\"\"\" || Set operations for arrays based on sorting. ||  || :Contains: ||   unique; ||   isin; ||   ediff1d; ||   intersect1d; ||   setxor1d; ||   in1d; ||   union1d; ||   setdiff1d ||  || :Notes: ||  || For floating point arrays; inaccurate results may appear due to usual round-off || and floating point comparison issues. ||  || Speed could be gained in some operations by an implementation of || sort(); that can provide directly the permutation vectors; avoiding || thus calls to argsort(). ||  || To do: Optionally return indices analogously to unique for all functions. ||  || :Author: Robert Cimrman ||  || \"\"\"
aahjccgbb,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/arrayterator.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Fix index; handling ellipsis and incomplete slices.
aahjccgfc,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/format.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,\"\"\" || Binary serialization ||  || NPY format || ========== ||  || A simple format for saving numpy arrays to disk with the full || information about them. ||  || The ``.npy`` format is the standard binary file format in NumPy for || persisting a *single* arbitrary NumPy array on disk. The format stores all || of the shape and dtype information necessary to reconstruct the array || correctly even on another machine with a different architecture. || The format is designed to be as simple as possible while achieving || its limited goals. ||  || The ``.npz`` format is the standard format for persisting *multiple* NumPy || arrays on disk. A ``.npz`` file is a zip file containing multiple ``.npy`` || files; one for each array. ||  || Capabilities || ------------ ||  || - Can represent all NumPy arrays including nested record arrays and ||   object arrays. ||  || - Represents the data in its native binary form. ||  || - Supports Fortran-contiguous arrays directly. ||  || - Stores all of the necessary information to reconstruct the array ||   including shape and dtype on a machine of a different ||   architecture.  Both little-endian and big-endian arrays are ||   supported; and a file with little-endian numbers will yield ||   a little-endian array on any machine reading the file. The ||   types are described in terms of their actual sizes. For example; ||   if a machine with a 64-bit C \"long int\" writes out an array with ||   \"long ints\"; a reading machine with 32-bit C \"long ints\" will yield ||   an array with 64-bit integers. ||  || - Is straightforward to reverse engineer. Datasets often live longer than ||   the programs that created them. A competent developer should be ||   able to create a solution in their preferred programming language to ||   read most ``.npy`` files that he has been given without much ||   documentation. ||  || - Allows memory-mapping of the data. See `open_memmep`. ||  || - Can be read from a filelike stream object instead of an actual file. ||  || - Stores object arrays; i.e. arrays containing elements that are arbitrary ||   Python objects. Files with object arrays are not to be mmapable; but ||   can be read and written to disk. ||  || Limitations || ----------- ||  || - Arbitrary subclasses of numpy.ndarray are not completely preserved. ||   Subclasses will be accepted for writing; but only the array data will ||   be written out. A regular numpy.ndarray object will be created ||   upon reading the file. ||  || .. warning:: ||  ||   Due to limitations in the interpretation of structured dtypes; dtypes ||   with fields with empty names will have the names replaced by 'f0'; 'f1'; ||   etc. Such arrays will not round-trip through the format entirely ||   accurately. The data is intact; only the field names will differ. We are ||   working on a fix for this. This fix will not require a change in the ||   file format. The arrays with such structures can still be saved and ||   restored; and the correct dtype may be restored by using the ||   ``loadedarray.view(correct_dtype)`` method. ||  || File extensions || --------------- ||  || We recommend using the ``.npy`` and ``.npz`` extensions for files saved || in this format. This is by no means a requirement; applications may wish || to use these file formats but use an extension specific to the || application. In the absence of an obvious alternative; however; || we suggest using ``.npy`` and ``.npz``. ||  || Version numbering || ----------------- ||  || The version numbering of these formats is independent of NumPy version || numbering. If the format is upgraded; the code in `numpy.io` will still || be able to read and write Version 1.0 files. ||  || Format Version 1.0 || ------------------ ||  || The first 6 bytes are a magic string: exactly ``\\\\x93NUMPY``. ||  || The next 1 byte is an unsigned byte: the major version number of the file || format; e.g. ``\\\\x01``. ||  || The next 1 byte is an unsigned byte: the minor version number of the file || format; e.g. ``\\\\x00``. Note: the version of the file format is not tied || to the version of the numpy package. ||  || The next 2 bytes form a little-endian unsigned short int: the length of || the header data HEADER_LEN. ||  || The next HEADER_LEN bytes form the header data describing the array's || format. It is an ASCII string which contains a Python literal expression || of a dictionary. It is terminated by a newline (``\\\ || ``) and padded with || spaces (``\\\\x20``) to make the total of || ``len(magic string) + 2 + len(length) + HEADER_LEN`` be evenly divisible || by 64 for alignment purposes. ||  || The dictionary contains three keys: ||  ||     \"descr\" : dtype.descr ||       An object that can be passed as an argument to the `numpy.dtype` ||       constructor to create the array's dtype. ||     \"fortran_order\" : bool ||       Whether the array data is Fortran-contiguous or not. Since ||       Fortran-contiguous arrays are a common form of non-C-contiguity; ||       we allow them to be written directly to disk for efficiency. ||     \"shape\" : tuple of int ||       The shape of the array. ||  || For repeatability and readability; the dictionary keys are sorted in || alphabetic order. This is for convenience only. A writer SHOULD implement || this if possible. A reader MUST NOT depend on this. ||  || Following the header comes the array data. If the dtype contains Python || objects (i.e. ``dtype.hasobject is True``); then the data is a Python || pickle of the array. Otherwise the data is the contiguous (either C- || or Fortran-; depending on ``fortran_order``) bytes of the array. || Consumers can figure out the number of bytes by multiplying the number || of elements given by the shape (noting that ``shape=()`` means there is || 1 element) by ``dtype.itemsize``. ||  || Format Version 2.0 || ------------------ ||  || The version 1.0 format only allowed the array header to have a total size of || 65535 bytes.  This can be exceeded by structured arrays with a large number of || columns.  The version 2.0 format extends the header size to 4 GiB. || `numpy.save` will automatically save in 2.0 format if the data requires it; || else it will always use the more compatible 1.0 format. ||  || The description of the fourth element of the header therefore has become: || \"The next 4 bytes form a little-endian unsigned int: the length of the header || data HEADER_LEN.\" ||  || Notes || ----- || The ``.npy`` format; including motivation for creating it and a comparison of || alternatives; is described in the `\"npy-format\" NEP  || <http:\/\/www.numpy.org\/neps\/nep-0001-npy-format.html>`_; however details have || evolved with time and this document is more current. ||  || \"\"\"
aahjccgfh,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/format.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,This is a record array. The .descr is fine.  XXX: parts of the
aahjccghc,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/format.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,adding newline as python 2.7.5 workaround
aahjccghd,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/format.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,removing newline (see above) as python 2.7.5 workaround
aahjcchbi,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/function_base.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,needed in this module for compatibility
aahjcchda,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/function_base.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,as the shape is needed for the result. Doing it separately optimizes
aahjcchgi,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/function_base.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Caching to improve default performance
aahjcciic,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/histograms.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,truncate the range if needed
aahjccjgi,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/index_tricks.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,This is needed to don't have tall matrix have the diagonal wrap.
aahjccjhe,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/info.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,\"\"\" || Basic functions used by several sub-packages and || useful to have in the main name-space. ||  || Type Handling || ------------- || ================ =================== || iscomplexobj     Test for complex object; scalar result || isrealobj        Test for real object; scalar result || iscomplex        Test for complex elements; array result || isreal           Test for real elements; array result || imag             Imaginary part || real             Real part || real_if_close    Turns complex number with tiny imaginary part to real || isneginf         Tests for negative infinity; array result || isposinf         Tests for positive infinity; array result || isnan            Tests for nans; array result || isinf            Tests for infinity; array result || isfinite         Tests for finite numbers; array result || isscalar         True if argument is a scalar || nan_to_num       Replaces NaN's with 0 and infinities with large numbers || cast             Dictionary of functions to force cast to each type || common_type      Determine the minimum common type code for a group ||                  of arrays || mintypecode      Return minimal allowed common typecode. || ================ =================== ||  || Index Tricks || ------------ || ================ =================== || mgrid            Method which allows easy construction of N-d ||                  'mesh-grids' || ``r_``           Append and construct arrays: turns slice objects into ||                  ranges and concatenates them; for 2d arrays appends rows. || index_exp        Konrad Hinsen's index_expression class instance which ||                  can be useful for building complicated slicing syntax. || ================ =================== ||  || Useful Functions || ---------------- || ================ =================== || select           Extension of where to multiple conditions and choices || extract          Extract 1d array from flattened array according to mask || insert           Insert 1d array of values into Nd array according to mask || linspace         Evenly spaced samples in linear space || logspace         Evenly spaced samples in logarithmic space || fix              Round x to nearest integer towards zero || mod              Modulo mod(x;y) = x % y except keeps sign of y || amax             Array maximum along axis || amin             Array minimum along axis || ptp              Array max-min along axis || cumsum           Cumulative sum along axis || prod             Product of elements along axis || cumprod          Cumluative product along axis || diff             Discrete differences along axis || angle            Returns angle of complex argument || unwrap           Unwrap phase along given axis (1-d algorithm) || sort_complex     Sort a complex-array (based on real; then imaginary) || trim_zeros       Trim the leading and trailing zeros from 1D array. || vectorize        A class that wraps a Python function taking scalar ||                  arguments into a generalized function which can handle ||                  arrays of arguments using the broadcast rules of ||                  numerix Python. || ================ =================== ||  || Shape Manipulation || ------------------ || ================ =================== || squeeze          Return a with length-one dimensions removed. || atleast_1d       Force arrays to be >= 1D || atleast_2d       Force arrays to be >= 2D || atleast_3d       Force arrays to be >= 3D || vstack           Stack arrays vertically (row on row) || hstack           Stack arrays horizontally (column on column) || column_stack     Stack 1D arrays as columns into 2D array || dstack           Stack arrays depthwise (along third dimension) || stack            Stack arrays along a new axis || split            Divide array into a list of sub-arrays || hsplit           Split into columns || vsplit           Split into rows || dsplit           Split along third dimension || ================ =================== ||  || Matrix (2D Array) Manipulations || ------------------------------- || ================ =================== || fliplr           2D array with columns flipped || flipud           2D array with rows flipped || rot90            Rotate a 2D array a multiple of 90 degrees || eye              Return a 2D array with ones down a given diagonal || diag             Construct a 2D array from a vector; or return a given ||                  diagonal from a 2D array. || mat              Construct a Matrix || bmat             Build a Matrix from blocks || ================ =================== ||  || Polynomials || ----------- || ================ =================== || poly1d           A one-dimensional polynomial class || poly             Return polynomial coefficients from roots || roots            Find roots of polynomial given coefficients || polyint          Integrate polynomial || polyder          Differentiate polynomial || polyadd          Add polynomials || polysub          Subtract polynomials || polymul          Multiply polynomials || polydiv          Divide polynomials || polyval          Evaluate polynomial at given argument || ================ =================== ||  || Iterators || --------- || ================ =================== || Arrayterator     A buffered iterator for big arrays. || ================ =================== ||  || Import Tricks || ------------- || ================ =================== || ppimport         Postpone module import until trying to use it || ppimport_attr    Postpone module import until trying to use its attribute || ppresolve        Import postponed module and return it. || ================ =================== ||  || Machine Arithmetics || ------------------- || ================ =================== || machar_single    Single precision floating point arithmetic parameters || machar_double    Double precision floating point arithmetic parameters || ================ =================== ||  || Threading Tricks || ---------------- || ================ =================== || ParallelExec     Execute commands in parallel thread. || ================ =================== ||  || Array Set Operations || ----------------------- || Set operations for numeric arrays based on sort() function. ||  || ================ =================== || unique           Unique elements of an array. || isin             Test whether each element of an ND array is present  ||                  anywhere within a second array. || ediff1d          Array difference (auxiliary function). || intersect1d      Intersection of 1D arrays with unique elements. || setxor1d         Set exclusive-or of 1D arrays with unique elements. || in1d             Test whether elements in a 1D array are also present in ||                  another array. || union1d          Union of 1D arrays with unique elements. || setdiff1d        Set difference of 1D arrays with unique elements. || ================ =================== ||  || \"\"\"
aahjccjid,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/mixins.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: handle the optional third argument for __pow__?
aahjccjjc,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/nanfunctions.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,which do not implement isnan (gh-9009); or fmin correctly (gh-8975)
aahjccjjg,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/nanfunctions.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,which do not implement isnan (gh-9009); or fmax correctly (gh-8975)
aahjcdaaa,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/nanfunctions.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,action is needed to handle bad results.
aahjcdabb,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/nanfunctions.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,convention.
aahjcdacj,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/npyio.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Implement the Mapping ABC
aahjcdada,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/npyio.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,FIXME: This seems like it will copy strings around
aahjcdadf,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/npyio.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,It would be better if the zipfile could read
aahjcdadj,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/npyio.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,python 3. We forgot to implement itervalues() at all in earlier
aahjcdahi,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/npyio.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO emit portability warning?
aahjcdaij,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/npyio.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,it to estimate the number of columns; N.
aahjcdbdf,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/npyio.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Check the columns to use: make sure `usecols` is a list
aahjcdbdg,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/npyio.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Check the names and overwrite the dtype.names if needed
aahjcdbfa,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/npyio.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Redefine the key as needed if it's a column number
aahjcdbia,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/npyio.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Fixme: possible error as following variable never used.
aahjcdbii,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/npyio.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Select only the columns we need
aahjcdbja,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/npyio.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Upgrade the converters (if needed)
aahjcdcaf,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/npyio.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Find the columns with strings...
aahjcdcaj,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/npyio.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Overwrite the initial dtype names if needed
aahjcdccc,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/npyio.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,We have only one field: drop the name if not needed.
aahjcdcgf,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/recfunctions.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Minimal processing needed: just make sure everythng's a-ok
aahjcdcgj,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/recfunctions.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Get the dtype of the output (flattening if needed)
aahjcdchd,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/recfunctions.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Get the filling value (if needed)
aahjcdcjb,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/recfunctions.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,If masked data must be ignored; set the flag to false where needed
aahjcdcjh,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/recfunctions.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Fixme: nb2 below is never used. Commenting out for pyflakes.
aahjcddgi,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/stride_tricks.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,never really has writebackifcopy semantics
aahjcddia,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/stride_tricks.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: consider making the results of broadcast_arrays readonly to match
aahjcdfdj,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/tests/test_format.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,r''' Test the .npy file format. ||  || Set up: ||  ||     >>> import sys ||     >>> from io import BytesIO ||     >>> from numpy.lib import format ||     >>> ||     >>> scalars = [ ||     ...     np.uint8; ||     ...     np.int8; ||     ...     np.uint16; ||     ...     np.int16; ||     ...     np.uint32; ||     ...     np.int32; ||     ...     np.uint64; ||     ...     np.int64; ||     ...     np.float32; ||     ...     np.float64; ||     ...     np.complex64; ||     ...     np.complex128; ||     ...     object; ||     ... ] ||     >>> ||     >>> basic_arrays = [] ||     >>> ||     >>> for scalar in scalars: ||     ...     for endian in '<>': ||     ...         dtype = np.dtype(scalar).newbyteorder(endian) ||     ...         basic = np.arange(15).astype(dtype) ||     ...         basic_arrays.extend([ ||     ...             np.array([]; dtype=dtype); ||     ...             np.array(10; dtype=dtype); ||     ...             basic; ||     ...             basic.reshape((3;5)); ||     ...             basic.reshape((3;5)).T; ||     ...             basic.reshape((3;5))[::-1;::2]; ||     ...         ]) ||     ... ||     >>> ||     >>> Pdescr = [ ||     ...     ('x'; 'i4'; (2;)); ||     ...     ('y'; 'f8'; (2; 2)); ||     ...     ('z'; 'u1')] ||     >>> ||     >>> ||     >>> PbufferT = [ ||     ...     ([3;2]; [[6.;4.];[6.;4.]]; 8); ||     ...     ([4;3]; [[7.;5.];[7.;5.]]; 9); ||     ...     ] ||     >>> ||     >>> ||     >>> Ndescr = [ ||     ...     ('x'; 'i4'; (2;)); ||     ...     ('Info'; [ ||     ...         ('value'; 'c16'); ||     ...         ('y2'; 'f8'); ||     ...         ('Info2'; [ ||     ...             ('name'; 'S2'); ||     ...             ('value'; 'c16'; (2;)); ||     ...             ('y3'; 'f8'; (2;)); ||     ...             ('z3'; 'u4'; (2;))]); ||     ...         ('name'; 'S2'); ||     ...         ('z2'; 'b1')]); ||     ...     ('color'; 'S2'); ||     ...     ('info'; [ ||     ...         ('Name'; 'U8'); ||     ...         ('Value'; 'c16')]); ||     ...     ('y'; 'f8'; (2; 2)); ||     ...     ('z'; 'u1')] ||     >>> ||     >>> ||     >>> NbufferT = [ ||     ...     ([3;2]; (6j; 6.; ('nn'; [6j;4j]; [6.;4.]; [1;2]); 'NN'; True); 'cc'; ('NN'; 6j); [[6.;4.];[6.;4.]]; 8); ||     ...     ([4;3]; (7j; 7.; ('oo'; [7j;5j]; [7.;5.]; [2;1]); 'OO'; False); 'dd'; ('OO'; 7j); [[7.;5.];[7.;5.]]; 9); ||     ...     ] ||     >>> ||     >>> ||     >>> record_arrays = [ ||     ...     np.array(PbufferT; dtype=np.dtype(Pdescr).newbyteorder('<')); ||     ...     np.array(NbufferT; dtype=np.dtype(Ndescr).newbyteorder('<')); ||     ...     np.array(PbufferT; dtype=np.dtype(Pdescr).newbyteorder('>')); ||     ...     np.array(NbufferT; dtype=np.dtype(Ndescr).newbyteorder('>')); ||     ... ] ||  || Test the magic string writing. ||  ||     >>> format.magic(1; 0) ||     '\\x93NUMPY\\x01\\x00' ||     >>> format.magic(0; 0) ||     '\\x93NUMPY\\x00\\x00' ||     >>> format.magic(255; 255) ||     '\\x93NUMPY\\xff\\xff' ||     >>> format.magic(2; 5) ||     '\\x93NUMPY\\x02\\x05' ||  || Test the magic string reading. ||  ||     >>> format.read_magic(BytesIO(format.magic(1; 0))) ||     (1; 0) ||     >>> format.read_magic(BytesIO(format.magic(0; 0))) ||     (0; 0) ||     >>> format.read_magic(BytesIO(format.magic(255; 255))) ||     (255; 255) ||     >>> format.read_magic(BytesIO(format.magic(2; 5))) ||     (2; 5) ||  || Test the header writing. ||  ||     >>> for arr in basic_arrays + record_arrays: ||     ...     f = BytesIO() ||     ...     format.write_array_header_1_0(f; arr)   # XXX: arr is not a dict; items gets called on it ||     ...     print(repr(f.getvalue())) ||     ... ||     \"F\\x00{'descr': '|u1'; 'fortran_order': False; 'shape': (0;)}              \ || \" ||     \"F\\x00{'descr': '|u1'; 'fortran_order': False; 'shape': ()}                \ || \" ||     \"F\\x00{'descr': '|u1'; 'fortran_order': False; 'shape': (15;)}             \ || \" ||     \"F\\x00{'descr': '|u1'; 'fortran_order': False; 'shape': (3; 5)}            \ || \" ||     \"F\\x00{'descr': '|u1'; 'fortran_order': True; 'shape': (5; 3)}             \ || \" ||     \"F\\x00{'descr': '|u1'; 'fortran_order': False; 'shape': (3; 3)}            \ || \" ||     \"F\\x00{'descr': '|u1'; 'fortran_order': False; 'shape': (0;)}              \ || \" ||     \"F\\x00{'descr': '|u1'; 'fortran_order': False; 'shape': ()}                \ || \" ||     \"F\\x00{'descr': '|u1'; 'fortran_order': False; 'shape': (15;)}             \ || \" ||     \"F\\x00{'descr': '|u1'; 'fortran_order': False; 'shape': (3; 5)}            \ || \" ||     \"F\\x00{'descr': '|u1'; 'fortran_order': True; 'shape': (5; 3)}             \ || \" ||     \"F\\x00{'descr': '|u1'; 'fortran_order': False; 'shape': (3; 3)}            \ || \" ||     \"F\\x00{'descr': '|i1'; 'fortran_order': False; 'shape': (0;)}              \ || \" ||     \"F\\x00{'descr': '|i1'; 'fortran_order': False; 'shape': ()}                \ || \" ||     \"F\\x00{'descr': '|i1'; 'fortran_order': False; 'shape': (15;)}             \ || \" ||     \"F\\x00{'descr': '|i1'; 'fortran_order': False; 'shape': (3; 5)}            \ || \" ||     \"F\\x00{'descr': '|i1'; 'fortran_order': True; 'shape': (5; 3)}             \ || \" ||     \"F\\x00{'descr': '|i1'; 'fortran_order': False; 'shape': (3; 3)}            \ || \" ||     \"F\\x00{'descr': '|i1'; 'fortran_order': False; 'shape': (0;)}              \ || \" ||     \"F\\x00{'descr': '|i1'; 'fortran_order': False; 'shape': ()}                \ || \" ||     \"F\\x00{'descr': '|i1'; 'fortran_order': False; 'shape': (15;)}             \ || \" ||     \"F\\x00{'descr': '|i1'; 'fortran_order': False; 'shape': (3; 5)}            \ || \" ||     \"F\\x00{'descr': '|i1'; 'fortran_order': True; 'shape': (5; 3)}             \ || \" ||     \"F\\x00{'descr': '|i1'; 'fortran_order': False; 'shape': (3; 3)}            \ || \" ||     \"F\\x00{'descr': '<u2'; 'fortran_order': False; 'shape': (0;)}              \ || \" ||     \"F\\x00{'descr': '<u2'; 'fortran_order': False; 'shape': ()}                \ || \" ||     \"F\\x00{'descr': '<u2'; 'fortran_order': False; 'shape': (15;)}             \ || \" ||     \"F\\x00{'descr': '<u2'; 'fortran_order': False; 'shape': (3; 5)}            \ || \" ||     \"F\\x00{'descr': '<u2'; 'fortran_order': True; 'shape': (5; 3)}             \ || \" ||     \"F\\x00{'descr': '<u2'; 'fortran_order': False; 'shape': (3; 3)}            \ || \" ||     \"F\\x00{'descr': '>u2'; 'fortran_order': False; 'shape': (0;)}              \ || \" ||     \"F\\x00{'descr': '>u2'; 'fortran_order': False; 'shape': ()}                \ || \" ||     \"F\\x00{'descr': '>u2'; 'fortran_order': False; 'shape': (15;)}             \ || \" ||     \"F\\x00{'descr': '>u2'; 'fortran_order': False; 'shape': (3; 5)}            \ || \" ||     \"F\\x00{'descr': '>u2'; 'fortran_order': True; 'shape': (5; 3)}             \ || \" ||     \"F\\x00{'descr': '>u2'; 'fortran_order': False; 'shape': (3; 3)}            \ || \" ||     \"F\\x00{'descr': '<i2'; 'fortran_order': False; 'shape': (0;)}              \ || \" ||     \"F\\x00{'descr': '<i2'; 'fortran_order': False; 'shape': ()}                \ || \" ||     \"F\\x00{'descr': '<i2'; 'fortran_order': False; 'shape': (15;)}             \ || \" ||     \"F\\x00{'descr': '<i2'; 'fortran_order': False; 'shape': (3; 5)}            \ || \" ||     \"F\\x00{'descr': '<i2'; 'fortran_order': True; 'shape': (5; 3)}             \ || \" ||     \"F\\x00{'descr': '<i2'; 'fortran_order': False; 'shape': (3; 3)}            \ || \" ||     \"F\\x00{'descr': '>i2'; 'fortran_order': False; 'shape': (0;)}              \ || \" ||     \"F\\x00{'descr': '>i2'; 'fortran_order': False; 'shape': ()}                \ || \" ||     \"F\\x00{'descr': '>i2'; 'fortran_order': False; 'shape': (15;)}             \ || \" ||     \"F\\x00{'descr': '>i2'; 'fortran_order': False; 'shape': (3; 5)}            \ || \" ||     \"F\\x00{'descr': '>i2'; 'fortran_order': True; 'shape': (5; 3)}             \ || \" ||     \"F\\x00{'descr': '>i2'; 'fortran_order': False; 'shape': (3; 3)}            \ || \" ||     \"F\\x00{'descr': '<u4'; 'fortran_order': False; 'shape': (0;)}              \ || \" ||     \"F\\x00{'descr': '<u4'; 'fortran_order': False; 'shape': ()}                \ || \" ||     \"F\\x00{'descr': '<u4'; 'fortran_order': False; 'shape': (15;)}             \ || \" ||     \"F\\x00{'descr': '<u4'; 'fortran_order': False; 'shape': (3; 5)}            \ || \" ||     \"F\\x00{'descr': '<u4'; 'fortran_order': True; 'shape': (5; 3)}             \ || \" ||     \"F\\x00{'descr': '<u4'; 'fortran_order': False; 'shape': (3; 3)}            \ || \" ||     \"F\\x00{'descr': '>u4'; 'fortran_order': False; 'shape': (0;)}              \ || \" ||     \"F\\x00{'descr': '>u4'; 'fortran_order': False; 'shape': ()}                \ || \" ||     \"F\\x00{'descr': '>u4'; 'fortran_order': False; 'shape': (15;)}             \ || \" ||     \"F\\x00{'descr': '>u4'; 'fortran_order': False; 'shape': (3; 5)}            \ || \" ||     \"F\\x00{'descr': '>u4'; 'fortran_order': True; 'shape': (5; 3)}             \ || \" ||     \"F\\x00{'descr': '>u4'; 'fortran_order': False; 'shape': (3; 3)}            \ || \" ||     \"F\\x00{'descr': '<i4'; 'fortran_order': False; 'shape': (0;)}              \ || \" ||     \"F\\x00{'descr': '<i4'; 'fortran_order': False; 'shape': ()}                \ || \" ||     \"F\\x00{'descr': '<i4'; 'fortran_order': False; 'shape': (15;)}             \ || \" ||     \"F\\x00{'descr': '<i4'; 'fortran_order': False; 'shape': (3; 5)}            \ || \" ||     \"F\\x00{'descr': '<i4'; 'fortran_order': True; 'shape': (5; 3)}             \ || \" ||     \"F\\x00{'descr': '<i4'; 'fortran_order': False; 'shape': (3; 3)}            \ || \" ||     \"F\\x00{'descr': '>i4'; 'fortran_order': False; 'shape': (0;)}              \ || \" ||     \"F\\x00{'descr': '>i4'; 'fortran_order': False; 'shape': ()}                \ || \" ||     \"F\\x00{'descr': '>i4'; 'fortran_order': False; 'shape': (15;)}             \ || \" ||     \"F\\x00{'descr': '>i4'; 'fortran_order': False; 'shape': (3; 5)}            \ || \" ||     \"F\\x00{'descr': '>i4'; 'fortran_order': True; 'shape': (5; 3)}             \ || \" ||     \"F\\x00{'descr': '>i4'; 'fortran_order': False; 'shape': (3; 3)}            \ || \" ||     \"F\\x00{'descr': '<u8'; 'fortran_order': False; 'shape': (0;)}              \ || \" ||     \"F\\x00{'descr': '<u8'; 'fortran_order': False; 'shape': ()}                \ || \" ||     \"F\\x00{'descr': '<u8'; 'fortran_order': False; 'shape': (15;)}             \ || \" ||     \"F\\x00{'descr': '<u8'; 'fortran_order': False; 'shape': (3; 5)}            \ || \" ||     \"F\\x00{'descr': '<u8'; 'fortran_order': True; 'shape': (5; 3)}             \ || \" ||     \"F\\x00{'descr': '<u8'; 'fortran_order': False; 'shape': (3; 3)}            \ || \" ||     \"F\\x00{'descr': '>u8'; 'fortran_order': False; 'shape': (0;)}              \ || \" ||     \"F\\x00{'descr': '>u8'; 'fortran_order': False; 'shape': ()}                \ || \" ||     \"F\\x00{'descr': '>u8'; 'fortran_order': False; 'shape': (15;)}             \ || \" ||     \"F\\x00{'descr': '>u8'; 'fortran_order': False; 'shape': (3; 5)}            \ || \" ||     \"F\\x00{'descr': '>u8'; 'fortran_order': True; 'shape': (5; 3)}             \ || \" ||     \"F\\x00{'descr': '>u8'; 'fortran_order': False; 'shape': (3; 3)}            \ || \" ||     \"F\\x00{'descr': '<i8'; 'fortran_order': False; 'shape': (0;)}              \ || \" ||     \"F\\x00{'descr': '<i8'; 'fortran_order': False; 'shape': ()}                \ || \" ||     \"F\\x00{'descr': '<i8'; 'fortran_order': False; 'shape': (15;)}             \ || \" ||     \"F\\x00{'descr': '<i8'; 'fortran_order': False; 'shape': (3; 5)}            \ || \" ||     \"F\\x00{'descr': '<i8'; 'fortran_order': True; 'shape': (5; 3)}             \ || \" ||     \"F\\x00{'descr': '<i8'; 'fortran_order': False; 'shape': (3; 3)}            \ || \" ||     \"F\\x00{'descr': '>i8'; 'fortran_order': False; 'shape': (0;)}              \ || \" ||     \"F\\x00{'descr': '>i8'; 'fortran_order': False; 'shape': ()}                \ || \" ||     \"F\\x00{'descr': '>i8'; 'fortran_order': False; 'shape': (15;)}             \ || \" ||     \"F\\x00{'descr': '>i8'; 'fortran_order': False; 'shape': (3; 5)}            \ || \" ||     \"F\\x00{'descr': '>i8'; 'fortran_order': True; 'shape': (5; 3)}             \ || \" ||     \"F\\x00{'descr': '>i8'; 'fortran_order': False; 'shape': (3; 3)}            \ || \" ||     \"F\\x00{'descr': '<f4'; 'fortran_order': False; 'shape': (0;)}              \ || \" ||     \"F\\x00{'descr': '<f4'; 'fortran_order': False; 'shape': ()}                \ || \" ||     \"F\\x00{'descr': '<f4'; 'fortran_order': False; 'shape': (15;)}             \ || \" ||     \"F\\x00{'descr': '<f4'; 'fortran_order': False; 'shape': (3; 5)}            \ || \" ||     \"F\\x00{'descr': '<f4'; 'fortran_order': True; 'shape': (5; 3)}             \ || \" ||     \"F\\x00{'descr': '<f4'; 'fortran_order': False; 'shape': (3; 3)}            \ || \" ||     \"F\\x00{'descr': '>f4'; 'fortran_order': False; 'shape': (0;)}              \ || \" ||     \"F\\x00{'descr': '>f4'; 'fortran_order': False; 'shape': ()}                \ || \" ||     \"F\\x00{'descr': '>f4'; 'fortran_order': False; 'shape': (15;)}             \ || \" ||     \"F\\x00{'descr': '>f4'; 'fortran_order': False; 'shape': (3; 5)}            \ || \" ||     \"F\\x00{'descr': '>f4'; 'fortran_order': True; 'shape': (5; 3)}             \ || \" ||     \"F\\x00{'descr': '>f4'; 'fortran_order': False; 'shape': (3; 3)}            \ || \" ||     \"F\\x00{'descr': '<f8'; 'fortran_order': False; 'shape': (0;)}              \ || \" ||     \"F\\x00{'descr': '<f8'; 'fortran_order': False; 'shape': ()}                \ || \" ||     \"F\\x00{'descr': '<f8'; 'fortran_order': False; 'shape': (15;)}             \ || \" ||     \"F\\x00{'descr': '<f8'; 'fortran_order': False; 'shape': (3; 5)}            \ || \" ||     \"F\\x00{'descr': '<f8'; 'fortran_order': True; 'shape': (5; 3)}             \ || \" ||     \"F\\x00{'descr': '<f8'; 'fortran_order': False; 'shape': (3; 3)}            \ || \" ||     \"F\\x00{'descr': '>f8'; 'fortran_order': False; 'shape': (0;)}              \ || \" ||     \"F\\x00{'descr': '>f8'; 'fortran_order': False; 'shape': ()}                \ || \" ||     \"F\\x00{'descr': '>f8'; 'fortran_order': False; 'shape': (15;)}             \ || \" ||     \"F\\x00{'descr': '>f8'; 'fortran_order': False; 'shape': (3; 5)}            \ || \" ||     \"F\\x00{'descr': '>f8'; 'fortran_order': True; 'shape': (5; 3)}             \ || \" ||     \"F\\x00{'descr': '>f8'; 'fortran_order': False; 'shape': (3; 3)}            \ || \" ||     \"F\\x00{'descr': '<c8'; 'fortran_order': False; 'shape': (0;)}              \ || \" ||     \"F\\x00{'descr': '<c8'; 'fortran_order': False; 'shape': ()}                \ || \" ||     \"F\\x00{'descr': '<c8'; 'fortran_order': False; 'shape': (15;)}             \ || \" ||     \"F\\x00{'descr': '<c8'; 'fortran_order': False; 'shape': (3; 5)}            \ || \" ||     \"F\\x00{'descr': '<c8'; 'fortran_order': True; 'shape': (5; 3)}             \ || \" ||     \"F\\x00{'descr': '<c8'; 'fortran_order': False; 'shape': (3; 3)}            \ || \" ||     \"F\\x00{'descr': '>c8'; 'fortran_order': False; 'shape': (0;)}              \ || \" ||     \"F\\x00{'descr': '>c8'; 'fortran_order': False; 'shape': ()}                \ || \" ||     \"F\\x00{'descr': '>c8'; 'fortran_order': False; 'shape': (15;)}             \ || \" ||     \"F\\x00{'descr': '>c8'; 'fortran_order': False; 'shape': (3; 5)}            \ || \" ||     \"F\\x00{'descr': '>c8'; 'fortran_order': True; 'shape': (5; 3)}             \ || \" ||     \"F\\x00{'descr': '>c8'; 'fortran_order': False; 'shape': (3; 3)}            \ || \" ||     \"F\\x00{'descr': '<c16'; 'fortran_order': False; 'shape': (0;)}             \ || \" ||     \"F\\x00{'descr': '<c16'; 'fortran_order': False; 'shape': ()}               \ || \" ||     \"F\\x00{'descr': '<c16'; 'fortran_order': False; 'shape': (15;)}            \ || \" ||     \"F\\x00{'descr': '<c16'; 'fortran_order': False; 'shape': (3; 5)}           \ || \" ||     \"F\\x00{'descr': '<c16'; 'fortran_order': True; 'shape': (5; 3)}            \ || \" ||     \"F\\x00{'descr': '<c16'; 'fortran_order': False; 'shape': (3; 3)}           \ || \" ||     \"F\\x00{'descr': '>c16'; 'fortran_order': False; 'shape': (0;)}             \ || \" ||     \"F\\x00{'descr': '>c16'; 'fortran_order': False; 'shape': ()}               \ || \" ||     \"F\\x00{'descr': '>c16'; 'fortran_order': False; 'shape': (15;)}            \ || \" ||     \"F\\x00{'descr': '>c16'; 'fortran_order': False; 'shape': (3; 5)}           \ || \" ||     \"F\\x00{'descr': '>c16'; 'fortran_order': True; 'shape': (5; 3)}            \ || \" ||     \"F\\x00{'descr': '>c16'; 'fortran_order': False; 'shape': (3; 3)}           \ || \" ||     \"F\\x00{'descr': 'O'; 'fortran_order': False; 'shape': (0;)}              \ || \" ||     \"F\\x00{'descr': 'O'; 'fortran_order': False; 'shape': ()}                \ || \" ||     \"F\\x00{'descr': 'O'; 'fortran_order': False; 'shape': (15;)}             \ || \" ||     \"F\\x00{'descr': 'O'; 'fortran_order': False; 'shape': (3; 5)}            \ || \" ||     \"F\\x00{'descr': 'O'; 'fortran_order': True; 'shape': (5; 3)}             \ || \" ||     \"F\\x00{'descr': 'O'; 'fortran_order': False; 'shape': (3; 3)}            \ || \" ||     \"F\\x00{'descr': 'O'; 'fortran_order': False; 'shape': (0;)}              \ || \" ||     \"F\\x00{'descr': 'O'; 'fortran_order': False; 'shape': ()}                \ || \" ||     \"F\\x00{'descr': 'O'; 'fortran_order': False; 'shape': (15;)}             \ || \" ||     \"F\\x00{'descr': 'O'; 'fortran_order': False; 'shape': (3; 5)}            \ || \" ||     \"F\\x00{'descr': 'O'; 'fortran_order': True; 'shape': (5; 3)}             \ || \" ||     \"F\\x00{'descr': 'O'; 'fortran_order': False; 'shape': (3; 3)}            \ || \" ||     \"v\\x00{'descr': [('x'; '<i4'; (2;)); ('y'; '<f8'; (2; 2)); ('z'; '|u1')];\ ||  'fortran_order': False;\ ||  'shape': (2;)}         \ || \" ||     \"\\x16\\x02{'descr': [('x'; '<i4'; (2;));\ ||            ('Info';\ ||             [('value'; '<c16');\ ||              ('y2'; '<f8');\ ||              ('Info2';\ ||               [('name'; '|S2');\ ||                ('value'; '<c16'; (2;));\ ||                ('y3'; '<f8'; (2;));\ ||                ('z3'; '<u4'; (2;))]);\ ||              ('name'; '|S2');\ ||              ('z2'; '|b1')]);\ ||            ('color'; '|S2');\ ||            ('info'; [('Name'; '<U8'); ('Value'; '<c16')]);\ ||            ('y'; '<f8'; (2; 2));\ ||            ('z'; '|u1')];\ ||  'fortran_order': False;\ ||  'shape': (2;)}      \ || \" ||     \"v\\x00{'descr': [('x'; '>i4'; (2;)); ('y'; '>f8'; (2; 2)); ('z'; '|u1')];\ ||  'fortran_order': False;\ ||  'shape': (2;)}         \ || \" ||     \"\\x16\\x02{'descr': [('x'; '>i4'; (2;));\ ||            ('Info';\ ||             [('value'; '>c16');\ ||              ('y2'; '>f8');\ ||              ('Info2';\ ||               [('name'; '|S2');\ ||                ('value'; '>c16'; (2;));\ ||                ('y3'; '>f8'; (2;));\ ||                ('z3'; '>u4'; (2;))]);\ ||              ('name'; '|S2');\ ||              ('z2'; '|b1')]);\ ||            ('color'; '|S2');\ ||            ('info'; [('Name'; '>U8'); ('Value'; '>c16')]);\ ||            ('y'; '>f8'; (2; 2));\ ||            ('z'; '|u1')];\ ||  'fortran_order': False;\ ||  'shape': (2;)}      \ || \" || '''
aahjcdfhf,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/tests/test_format.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Fixme: used to crash on windows
aahjcdgfe,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/tests/test_function_base.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,dtypes should not be promoted in a different way to what diff does
aahjcdiff,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/tests/test_index_tricks.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,append columns
aahjcdiha,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/tests/test_io.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Avoid pyflakes unused variable warning.
aahjcdihg,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/tests/test_io.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,FIXME: Is this still true?
aahjcdiij,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/tests/test_io.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: specify exact message
aahjcdjgh,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/tests/test_io.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Test whether unused converters are forgotten
aahjcdjhf,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/tests/test_io.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Test the selection of columns
aahjcdjic,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/tests/test_io.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Test usecols with named columns
aahjceaij,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/tests/test_mixins.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: test div on Python 2; only
aahjcebdi,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/tests/test_nanfunctions.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Transpose the array to fit the output convention of numpy.percentile
aahjcebjh,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/tests/test_recfunctions.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Fixme; this test looks incomplete and broken
aahjcecac,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/tests/test_recfunctions.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Hack to avoid pyflakes warnings about unused variables
aahjcecbh,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/tests/test_recfunctions.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Fixme; this test is broken
aahjceccf,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/tests/test_recfunctions.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Hack to avoid pyflakes unused variable warnings
aahjcecie,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/tests/test_shape_base.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,perhaps should check higher dimensions
aahjcedha,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/tests/test_type_check.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Fixme; wrong place; isfinite now ufunc
aahjcedhb,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/tests/test_type_check.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Fixme; wrong place; isinf now ufunc
aahjcedhe,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/tests/test_type_check.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Fixme
aahjcedij,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/type_check.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,convert to array-scalar if needed
aahjceeee,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/utils.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX: maybe using a real stemming search engine would be better?
aahjceeeh,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/utils.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX: this is full Harrison-Stetson heuristics now;
aahjceeei,ryfeus/gcf-packs,pandas_numpy/sources/numpy/lib/utils.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX: it probably could be improved
aahjcefac,ryfeus/gcf-packs,pandas_numpy/sources/numpy/linalg/linalg.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,increasing powers of 2; and multiply into the result as needed.
aahjcefca,ryfeus/gcf-packs,pandas_numpy/sources/numpy/linalg/linalg.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Convention is to return scalars instead of 0d arrays
aahjcefcf,ryfeus/gcf-packs,pandas_numpy/sources/numpy/linalg/linalg.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: relax this constraint
aahjceffd,ryfeus/gcf-packs,pandas_numpy/sources/numpy/linalg/linalg.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,m[i;j]: min number of scalar multiplications needed to compute A_{i..j}
aahjcegca,ryfeus/gcf-packs,pandas_numpy/sources/numpy/linalg/tests/test_linalg.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Warning should be raised exactly once (first command)
aahjcegff,ryfeus/gcf-packs,pandas_numpy/sources/numpy/linalg/tests/test_linalg.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Lower bar needed to support low precision floats.
aahjcehce,ryfeus/gcf-packs,pandas_numpy/sources/numpy/linalg/tests/test_linalg.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,There's a bug gh-8577 on OSX that can trigger this; and perhaps
aahjcehhb,ryfeus/gcf-packs,pandas_numpy/sources/numpy/ma/core.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,\"\"\" || numpy.ma : a package to handle missing or invalid values. ||  || This package was initially written for numarray by Paul F. Dubois || at Lawrence Livermore National Laboratory. || In 2006; the package was completely rewritten by Pierre Gerard-Marchant || (University of Georgia) to make the MaskedArray class a subclass of ndarray; || and to improve support of structured arrays. ||  ||  || Copyright 1999; 2000; 2001 Regents of the University of California. || Released for unlimited redistribution. ||  || * Adapted for numpy_core 2005 by Travis Oliphant and (mainly) Paul Dubois. || * Subclassing of the base `ndarray` 2006 by Pierre Gerard-Marchant ||   (pgmdevlist_AT_gmail_DOT_com) || * Improvements suggested by Reggie Dugard (reggie_AT_merfinllc_DOT_com) ||  || .. moduleauthor:: Pierre Gerard-Marchant ||  || \"\"\"
aahjcehhd,ryfeus/gcf-packs,pandas_numpy/sources/numpy/ma/core.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,no warning needed - but switch to -1 anyway; to avoid surprising
aahjcehhe,ryfeus/gcf-packs,pandas_numpy/sources/numpy/ma/core.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,subclasses; which are more likely to implement scalar axes.
aahjceihh,ryfeus/gcf-packs,pandas_numpy/sources/numpy/ma/core.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,FIXME _sharedmask is never used.
aahjceiih,ryfeus/gcf-packs,pandas_numpy/sources/numpy/ma/core.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Force shrinking of the mask if needed (and possible)
aahjcejcf,ryfeus/gcf-packs,pandas_numpy/sources/numpy/ma/core.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,that. This is also horribly broken but somewhat less so. Maybe.
aahjcejcg,ryfeus/gcf-packs,pandas_numpy/sources/numpy/ma/core.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XX: This looks like a bug -- shouldn't it check self.dtype
aahjcejdj,ryfeus/gcf-packs,pandas_numpy/sources/numpy/ma/core.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,needed and use astype instead of copy.
aahjcejfi,ryfeus/gcf-packs,pandas_numpy/sources/numpy/ma/core.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Make sure to reset the _fill_value if needed
aahjcejjf,ryfeus/gcf-packs,pandas_numpy/sources/numpy/ma/core.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Update the mask if needed
aahjcfacj,ryfeus/gcf-packs,pandas_numpy/sources/numpy/ma/core.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Reshape if needed
aahjcfadc,ryfeus/gcf-packs,pandas_numpy/sources/numpy/ma/core.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Temporary workaround to account for the fact that str and bytes
aahjcfaii,ryfeus/gcf-packs,pandas_numpy/sources/numpy/ma/core.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,!!!: implement out + test!
aahjcfajj,ryfeus/gcf-packs,pandas_numpy/sources/numpy/ma/core.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Set the mask if needed
aahjcfbij,ryfeus/gcf-packs,pandas_numpy/sources/numpy/ma/core.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Fix the invalid parts
aahjcfcbf,ryfeus/gcf-packs,pandas_numpy/sources/numpy/ma/core.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Needed by dot; so move here from extras.py. It will still be exported
aahjcfccj,ryfeus/gcf-packs,pandas_numpy/sources/numpy/ma/core.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,workaround for #8666; to preserve identity. Ideally the bottom line
aahjcfdcd,ryfeus/gcf-packs,pandas_numpy/sources/numpy/ma/mrecords.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,or whatever restricted keywords.  An idea would be to no bother in the
aahjcfdge,ryfeus/gcf-packs,pandas_numpy/sources/numpy/ma/mrecords.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Grab the initial _fieldmask; if needed:
aahjcfdgi,ryfeus/gcf-packs,pandas_numpy/sources/numpy/ma/mrecords.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Set the fill_value if needed
aahjcfihb,ryfeus/gcf-packs,pandas_numpy/sources/numpy/ma/tests/test_core.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,lots of things could go wrong here
aahjcfiia,ryfeus/gcf-packs,pandas_numpy/sources/numpy/ma/tests/test_core.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Test masked_object; masked_equal; ...
aahjcfjbc,ryfeus/gcf-packs,pandas_numpy/sources/numpy/ma/tests/test_extras.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Several tests of average. Why so many ? Good point...
aahjcfjcd,ryfeus/gcf-packs,pandas_numpy/sources/numpy/ma/tests/test_extras.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,append columns
aahjcgbcf,ryfeus/gcf-packs,pandas_numpy/sources/numpy/ma/tests/test_old_ma.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO FIXME: Find out what the following raises a warning in r8247
aahjcgbed,ryfeus/gcf-packs,pandas_numpy/sources/numpy/ma/tests/test_subclassing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,produces a better error message than assert_(a.startswith(b))
aahjcgbjf,ryfeus/gcf-packs,pandas_numpy/sources/numpy/ma/timer_comparison.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Fixme: this does not look right.
aahjcgddg,ryfeus/gcf-packs,pandas_numpy/sources/numpy/polynomial/_polybase.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,\"\"\" || Abstract base class for the various polynomial Classes. ||  || The ABCPolyBase class provides the methods needed to implement the common API || for the various polynomial classes. It operates as a mixin; but uses the || abc module from the stdlib; hence it is only available for Python >= 2.6. ||  || \"\"\"
aahjcgdhj,ryfeus/gcf-packs,pandas_numpy/sources/numpy/polynomial/chebyshev.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,This can be made more efficient by using powers of two
aahjcgdih,ryfeus/gcf-packs,pandas_numpy/sources/numpy/polynomial/chebyshev.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Determine the norms of the design matrix columns.
aahjcgebg,ryfeus/gcf-packs,pandas_numpy/sources/numpy/polynomial/hermite.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,This can be made more efficient by using powers of two
aahjcgecd,ryfeus/gcf-packs,pandas_numpy/sources/numpy/polynomial/hermite.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Determine the norms of the design matrix columns.
aahjcgeda,ryfeus/gcf-packs,pandas_numpy/sources/numpy/polynomial/hermite.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,matrix is symmetric in this case in order to obtain better zeros.
aahjcgefi,ryfeus/gcf-packs,pandas_numpy/sources/numpy/polynomial/hermite_e.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,This can be made more efficient by using powers of two
aahjcgegf,ryfeus/gcf-packs,pandas_numpy/sources/numpy/polynomial/hermite_e.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Determine the norms of the design matrix columns.
aahjcgehc,ryfeus/gcf-packs,pandas_numpy/sources/numpy/polynomial/hermite_e.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,matrix is symmetric in this case in order to obtain better zeros.
aahjcgfaa,ryfeus/gcf-packs,pandas_numpy/sources/numpy/polynomial/laguerre.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,This can be made more efficient by using powers of two
aahjcgfah,ryfeus/gcf-packs,pandas_numpy/sources/numpy/polynomial/laguerre.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Determine the norms of the design matrix columns.
aahjcgfbe,ryfeus/gcf-packs,pandas_numpy/sources/numpy/polynomial/laguerre.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,matrix is symmetric in this case in order to obtain better zeros.
aahjcgfeb,ryfeus/gcf-packs,pandas_numpy/sources/numpy/polynomial/legendre.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,This can be made more efficient by using powers of two
aahjcgfee,ryfeus/gcf-packs,pandas_numpy/sources/numpy/polynomial/legendre.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,as reverse recursion in this application but it is more efficient.
aahjcgffa,ryfeus/gcf-packs,pandas_numpy/sources/numpy/polynomial/legendre.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Determine the norms of the design matrix columns.
aahjcgffh,ryfeus/gcf-packs,pandas_numpy/sources/numpy/polynomial/legendre.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,matrix is symmetric in this case in order to obtain better zeros.
aahjcgfih,ryfeus/gcf-packs,pandas_numpy/sources/numpy/polynomial/polynomial.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,This can be made more efficient by using powers of two
aahjcggab,ryfeus/gcf-packs,pandas_numpy/sources/numpy/polynomial/polynomial.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Determine the norms of the design matrix columns.
aahjchaci,ryfeus/gcf-packs,pandas_numpy/sources/numpy/random/setup.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,We need the mingw workaround for _ftime if the msvc runtime version is
aahjchbbf,ryfeus/gcf-packs,pandas_numpy/sources/numpy/random/tests/test_random.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Include test for randint once it can broadcast
aahjchbce,ryfeus/gcf-packs,pandas_numpy/sources/numpy/random/tests/test_random.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Uncomment once randint can broadcast arguments
aahjchbij,ryfeus/gcf-packs,pandas_numpy/sources/numpy/testing/_private/noseclasses.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,These classes implement a doctest runner plugin for nose; a \"known failure\"
aahjchcca,ryfeus/gcf-packs,pandas_numpy/sources/numpy/testing/_private/noseclasses.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Subclass nose.plugins.doctests.DocTestCase to work around a bug in
aahjchcgj,ryfeus/gcf-packs,pandas_numpy/sources/numpy/testing/_private/nosetester.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,numpy. Those semantics were fine for testing numpy; but not so
aahjchcig,ryfeus/gcf-packs,pandas_numpy/sources/numpy/testing/_private/nosetester.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,our way of doing coverage
aahjchcij,ryfeus/gcf-packs,pandas_numpy/sources/numpy/testing/_private/nosetester.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Relies on pkg_resources; not a hard dependency
aahjchdei,ryfeus/gcf-packs,pandas_numpy/sources/numpy/testing/_private/pytesttester.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,FIXME This is no longer needed? Assume it was for use in tests.
aahjchdhi,ryfeus/gcf-packs,pandas_numpy/sources/numpy/testing/_private/utils.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,precision argument is only needed if the objects are ndarrays
aahjchebe,ryfeus/gcf-packs,pandas_numpy/sources/numpy/testing/_private/utils.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,The below comparison is a hack to ensure that fully masked
aahjcheif,ryfeus/gcf-packs,pandas_numpy/sources/numpy/testing/tests/test_doctesting.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,FIXME: None of these tests is run; because 'check' is not a recognized
aahjchgcj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/__init__.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Let users know if they're missing any of our hard dependencies
aahjchgdc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/__init__.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,hack but overkill to use re
aahjchgeb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/_libs/__init__.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO
aahjchhga,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/algorithms.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,until our algos support uint8 directly (see TODO)
aahjchhib,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/algorithms.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: it must return DatetimeArray with tz in pandas 2.0
aahjchhja,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/algorithms.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,3.) Maybe boxing the output in an Index
aahjchiad,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/algorithms.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: handle uint8
aahjchiaf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/algorithms.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: this should support float64
aahjchich,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/algorithms.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,If it is the last column in columns; or if the values
aahjchidd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/algorithms.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Last column in columns or values are unique in
aahjchihg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/api.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Remove import when statsmodels updates #18264
aahjchiia,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/apply.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,curry if needed
aahjchije,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/apply.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: mixed type case
aahjchiji,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/apply.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,we *always* preserve the original index \/ columns
aahjchjdc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/arrays/base.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,*do* choose to implement __setitem__; then some semantics should be
aahjchjfi,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/arrays/base.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,on the number of bytes needed.
aahjciabb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/arrays/categorical.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,the same (maybe up to ordering; depending on ordered)
aahjciafa,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/arrays/categorical.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,By convention; empty lists result in object dtype:
aahjciafd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/arrays/categorical.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,FIXME
aahjciajh,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/arrays/categorical.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Strip all leading spaces; which format_array adds for columns...
aahjcibbg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/arrays/categorical.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,FIXME: remove when numpy 1.9 is the lowest numpy version pandas
aahjcibcd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/arrays/categorical.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Implement the ExtensionArray interface
aahjcibdh,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/base.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,prevent adding any attribute via s.xxx.new_attribute = ...
aahjcibfd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/base.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,the keys must be in the columns
aahjcicaa,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/base.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,we can fastpath dict\/Series to an efficient map
aahjcicbg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/categorical.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Remove after 0.23.x
aahjciccd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/common.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,turn a datetime like into a Timestamp\/timedelta as needed
aahjcicde,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/common.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: verify whether any path hits this except #18819 (invalid)
aahjcicgi,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/computation/engines.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,python 3 compat kludge
aahjcichb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/computation/eval.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: validate this in a more general way (thinking of future engines
aahjcichg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/computation/eval.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,assign if needed
aahjcicih,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/computation/expr.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,nodes that we don't support directly but are needed for parsing
aahjcicjg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/computation/expr.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,of one string; kind of a hack
aahjcidbg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/computation/expr.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: bug?
aahjcidhd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/computation/pytables.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,capture the environment if needed
aahjciedb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/config.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,walk the nested dict; creating dicts as needed along the path
aahjciefb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/config_init.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,automatically determine optimal number of columns
aahjciefc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/config_init.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,cannot determine optimal number of columns
aahjcifci,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/dtypes/cast.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,the proper thing to do here would probably be to upcast
aahjcifid,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/dtypes/cast.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,force the dtype if needed
aahjcigad,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/dtypes/cast.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,workaround for find_common_type([np.dtype('datetime64[ns]')] * 2)
aahjcigce,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/dtypes/common.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Consider making Period an instance of PeriodDtype
aahjcigcf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/dtypes/common.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Consider making Interval an instance of IntervalDtype
aahjcigcg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/dtypes/common.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: gh-15585: consider making the checks stricter.
aahjcigia,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/dtypes/concat.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,coerce to 2d if needed & concatenate
aahjcihaa,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/dtypes/concat.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,coerce to object if needed
aahjcihba,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/dtypes/dtypes.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Document public vs. private API
aahjcihcj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/dtypes/dtypes.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: hash_array doesn't handle mixed types. It casts
aahjcihdc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/dtypes/dtypes.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,find a better solution
aahjcihge,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/dtypes/inference.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Reconsider this decision once this numpy bug is fixed:
aahjcihja,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,\"\"\" || DataFrame || --------- || An efficient 2D container for potentially mixed-type time series or other || labeled data series. ||  || Similar to its R counterpart; data.frame; except providing automatic data || alignment and a host of useful data manipulation methods having to do with the || labeling information || \"\"\"
aahjciibg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,exceed max columns
aahjciida,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX: In IPython 3.x and above; the Qt console will not attempt to
aahjciidc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,IPython 2.x is no longer needed.
aahjciied,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO speed up Series case
aahjciieg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Make a copy of the input columns so we can modify it
aahjciifj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: a generic formatter wld b in DataFrameFormatter
aahjciigb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,hack
aahjciijf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,duplicate columns & possible reduce dimensionality
aahjcijec,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,align right-hand-side columns if self.columns
aahjcijfb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,broadcast across multiple columns if necessary
aahjcijgc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,to ndarray and maybe infer different dtype
aahjcijgg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Verify all columns in subset exist in the queried dataframe
aahjcijhc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: this can be combined with Series.sort_index impl as
aahjcijhj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,reconstruct axis if needed
aahjcijid,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,for the mixed_type case where we iterate over columns;
aahjcijig,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,iterate over columns
aahjcijih,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,unique columns
aahjcijii,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,non-unique columns
aahjcjaaa,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Support other joins
aahjcjabe,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: _shallow_copy(subset)?
aahjcjabf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: flipped axis
aahjcjaea,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Make other agg func handle axis=None properly
aahjcjaed,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: combine with hasattr(result; 'dtype') further down
aahjcjagh,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,columns if columns is not None else []
aahjcjaha,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,fill if needed
aahjcjahc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,reorder according to the columns
aahjcjaib,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: this should be seriously cythonized
aahjcjbac,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/generic.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,index or columns
aahjcjbag,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/generic.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,put the index\/columns itself in the dict
aahjcjbbd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/generic.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: define separate funcs for DataFrame; Series and Panel so you can
aahjcjbdf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/generic.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Handle dropping columns labels
aahjcjbia,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/generic.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Allow shorthand to delete all columns whose first len(key)
aahjcjbig,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/generic.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Maybe set copy if we didn't actually change the index.
aahjcjcaa,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/generic.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Decide if we care about having different examples for different
aahjcjcbb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/generic.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: speed up on homogeneous DataFrame objects
aahjcjddj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/generic.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Not sure if above is correct - need someone to confirm.
aahjcjdfg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/groupby/groupby.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Better unicode\/repr for GroupBy object
aahjcjdic,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/groupby/groupby.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,this is needed so we don't try and wrap strings. If we could
aahjcjdie,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/groupby/groupby.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,wouldn't be needed
aahjcjdjb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/groupby/groupby.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,fails on *some* columns; e.g. a numeric operation
aahjcjdjd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/groupby/groupby.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,iterate through \"columns\" ex exclusions to populate output dict
aahjcjebb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/groupby/groupby.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: implement at Cython level?
aahjcjebg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/groupby/groupby.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,modified in GH 7559 to have better perf
aahjcjefb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/groupby/groupby.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: min_count
aahjcjefc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/groupby/groupby.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO
aahjcjeii,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/groupby/groupby.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: These if-block and else-block are almost same.
aahjcjfch,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexes/accessors.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,maybe need to upcast (ints)
aahjcjfdc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexes/accessors.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,CombinedDatetimelikeProperties isn't really instantiated. Instead
aahjcjfei,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexes/api.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: handle index names!
aahjcjfej,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexes/api.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: remove once pd.concat sort default changes
aahjcjfif,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexes/base.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,maybe coerce to a sub-class
aahjcjgcf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexes/base.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,adjust all values to max length if needed
aahjcjgfa,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexes/base.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: i; j are not used anywhere
aahjcjghb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexes/base.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: is_dtype_union_equal is a hack around
aahjcjghe,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexes/base.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Once those are fixed; this workaround can be removed
aahjcjhah,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexes/base.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,A hack; but it works
aahjcjhai,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexes/base.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: if we are a MultiIndex; we can do better
aahjcjiba,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexes/category.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,filling in missing if needed
aahjcjica,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexes/category.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Indexing on codes is more efficient if categories are the same:
aahjcjidd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexes/datetimelike.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,ToDo: Remove this when PeriodDtype is added
aahjcjifg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexes/datetimelike.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,For datetime64 dtypes by convention we treat NaT as a datetime; so
aahjcjije,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexes/datetimelike.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,convert tz if needed
aahjcjjaa,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexes/datetimes.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Following Timestamp convention; __eq__ is all-False
aahjdaabc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexes/datetimes.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO
aahjdaadj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexes/interval.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,coerce dtypes to match if needed
aahjdaagg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexes/interval.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,do better
aahjdaajc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexes/interval.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: integrate with categorical and make generic
aahjdaajd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexes/interval.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,name argument is unused here; just for compat with base \/ categorical
aahjdaaji,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexes/interval.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: arithmetic operations
aahjdabfb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexes/multi.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Calculate the number of bits needed to represent labels in each
aahjdabfh,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexes/multi.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,that each level needs to be shifted by the number of bits needed to
aahjdabfj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexes/multi.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Check the total number of bits needed for our representation:
aahjdabgi,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexes/multi.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: what if a level contains tuples??
aahjdabia,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexes/multi.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,little bit of a kludge job for #1217
aahjdabig,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexes/multi.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Since few levels are typically unused; bincount() is more
aahjdabih,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexes/multi.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,efficient than unique() - however it only accepts positive values
aahjdabij,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexes/multi.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,We have unused levels
aahjdacad,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexes/multi.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,set nan if needed
aahjdacci,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexes/multi.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,kludge for #1796
aahjdadhe,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexes/range.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Do attrs get handled reliably?
aahjdadhf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexes/timedeltas.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,\"\"\" implement the TimedeltaIndex \"\"\"
aahjdaefa,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Panel; DataFrame are not imported; remove?
aahjdaefc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,maybe partial set
aahjdaege,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,essentially this separates out the block that is needed
aahjdaeie,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,no columns and scalar
aahjdaeih,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,must have conforming columns
aahjdafab,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,which means essentially reassign to the columns of a
aahjdafeb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: single_aligner is not used
aahjdafee,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,need to conform to the convention
aahjdaffc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,of what is needed
aahjdaffd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: is_frame; is_panel are unused
aahjdaffi,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,no shortcut needed
aahjdafhc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,we maybe be using a tuple to represent multiple dimensions here
aahjdafhg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,df.ix[d1:d2; 0] -> columns first (True)
aahjdafib,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,unfortunately need an odious kludge here because of
aahjdafic,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,DataFrame transposing convention
aahjdagab,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,maybe coerce a float scalar to integer
aahjdagii,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/indexing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,object array key; so no check needed here
aahjdahgj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/internals.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: this prob needs some better checking
aahjdahhb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/internals.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,maybe upcast me
aahjdaiae,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/internals.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,coerce\/transpose the args if needed
aahjdaiba,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/internals.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,mask if needed
aahjdaicc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/internals.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,transpose if needed
aahjdaife,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/internals.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX: We may need to think about pushing this onto the array.
aahjdaigh,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/internals.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,FIXME:
aahjdaihb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/internals.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Refactor when convert_objects is removed since there will be 1 path
aahjdajag,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/internals.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: this still uses asarray; instead of dtype.type
aahjdajaj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/internals.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Workaround for numpy 1.6 bug
aahjdajba,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/internals.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX: This will end up calling _maybe_coerce_values twice
aahjdajeb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/internals.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: flexible with index=None and\/or items=None
aahjdajfa,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/internals.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,This is a workaround for pre-0.14.1 pickles that didn't
aahjdajfc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/internals.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,columns\/items; because given a manager with items [\"a\"; \"b\";
aahjdajfj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/internals.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,fillna internally does putmask; maybe it's better to do this
aahjdajid,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/internals.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,FIXME: optimization potential
aahjdajif,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/internals.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Workaround for numpy 1.7 bug:
aahjdbaad,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/internals.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,FIXME: this may return non-upcasted types?
aahjdbaae,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/internals.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,FIXME: use Index.delete as soon as it uses fastpath=True
aahjdbaaf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/internals.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,FIXME: refactor; clearly separate broadcasting & zip-like assignment
aahjdbacf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/internals.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,FIXME: mgr_groupby_blknos must return mgr_locs in ascending order;
aahjdbafg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/internals.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,FIXME: optimization potential in case all mgrs contain slices and
aahjdbafj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/internals.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,work around NumPy 1.6 bug
aahjdbagc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/internals.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: lexsort depth needs to be 2!!
aahjdbagf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/internals.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,FIXME: blk_count is unused; but it may avoid the use of dicts in cython
aahjdbaia,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/internals.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,change the dtype if needed
aahjdbaii,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/internals.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: should this be ju.block._can_hold_na?
aahjdbaij,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/internals.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX: integrate property
aahjdbbbc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/internals.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,FIXME: cache results of indexer == -1 checks.
aahjdbbfi,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/missing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Why is DatetimeIndex being imported here?
aahjdbbgi,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/missing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,reshape a 1 dim if needed
aahjdbbha,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/missing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,todo create faster fill func without masking
aahjdbcbh,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/nanops.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,promote if needed
aahjdbcec,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/nanops.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,to fix the fperr to treat m2 <1e-14 as zero
aahjdbceg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/nanops.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,to fix the fperr to treat denom <1e-14 as zero
aahjdbcfb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/nanops.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,#18044 reference this behavior to fix rolling skew\/kurt issue
aahjdbcfi,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/ops.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: what if they both have np.nan for their names?
aahjdbche,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/ops.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Why None for mod but '%' for rmod?
aahjdbcib,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/ops.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: can we make a no-copy implementation?
aahjdbcid,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/ops.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Can we make the allowed_types arg unnecessary?
aahjdbcja,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/ops.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: I don't think the functions defined by bool_method are tested
aahjdbdbh,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/ops.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,ToDo: Different from _align_method_FRAME; list; tuple and ndarray
aahjdbdeh,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/ops.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,rename is needed in case res_name is None and res_values.name
aahjdbdfb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/ops.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,rename is needed in case res_name is None and self.name
aahjdbdgf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/ops.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,default axis is columns
aahjdbdgh,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/ops.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,FIXME: GH#5284; GH#5035; GH#19448
aahjdbdhf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/ops.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,straight boolean comparisons we want to allow all columns
aahjdbedc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/panel.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Make other agg func handle axis=None properly
aahjdbeib,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/resample.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,convert if needed
aahjdbejj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/resample.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,.resample uses 'on' similar to how .groupby uses 'key'
aahjdbfah,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/resample.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,thus last bin maybe slightly before the end if the end contains
aahjdbfbd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/resample.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,a little hack
aahjdbfci,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/resample.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,hack!
aahjdbffg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/reshape/concat.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,note to keep empty Series as it affect to result columns \/ name
aahjdbfga,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/reshape/concat.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,creating column numbers as needed
aahjdbfgg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/reshape/concat.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,combine as columns in a frame
aahjdbfia,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/reshape/melt.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: what about the existing index?
aahjdbfid,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/reshape/melt.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,asanyarray will keep the columns as an Index
aahjdbfjb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/reshape/merge.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO; should _merge_pieces do this?
aahjdbfjf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/reshape/merge.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: transformations??
aahjdbfjg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/reshape/merge.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: only copy DataFrames when modification necessary
aahjdbgac,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/reshape/merge.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,check if columns specified as unique
aahjdbgbe,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/reshape/merge.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,a work-around as designated in the code below.
aahjdbgcb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/reshape/merge.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,work-around for merge_asof(left_index=True)
aahjdbgee,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/reshape/merge.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,columns; and end up trying to merge
aahjdbgeh,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/reshape/merge.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,use the common columns
aahjdbgga,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/reshape/merge.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,set 'by' columns
aahjdbgha,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/reshape/merge.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,initial type conversion as needed
aahjdbghj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/reshape/merge.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,fix right labels if there were any nulls
aahjdbgjg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/reshape/pivot.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,_shared_docs['pivot_table'] will not yet exist.  TODO: Fix this dependency
aahjdbhaj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/reshape/pivot.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,GH 15193 Make sure empty columns are removed if dropna=True
aahjdbhba,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/reshape/pivot.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,could be passed a Series object with no 'columns'
aahjdbhbi,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/reshape/pivot.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,slight hack
aahjdbhcc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/reshape/pivot.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,to keep index and columns names
aahjdbhce,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/reshape/pivot.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Fix Margins
aahjdbhch,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/reshape/reshape.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX: Makes SparseArray *dense*; but it's supposedly
aahjdbhea,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/reshape/reshape.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,reconstruct dtype if needed
aahjdbheb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/reshape/reshape.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,The two indices differ only if the unstacked level had unused items:
aahjdbheg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/reshape/reshape.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,NOTE: This doesn't deal with hierarchical columns yet
aahjdbhfa,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/reshape/reshape.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,and isn't needed for a single level
aahjdbhgg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/reshape/reshape.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Workaround the edge case where 0 is one of the column names;
aahjdbhgj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/reshape/reshape.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,tuple list excluding level for grouping columns
aahjdbhhb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/reshape/reshape.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,can make more efficient?
aahjdbhhf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/reshape/reshape.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,something better?
aahjdbhhg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/reshape/reshape.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,more efficient way to go about this? can do the whole masking biz but
aahjdbhhi,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/reshape/reshape.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,determine columns being encoded
aahjdbhib,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/reshape/reshape.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Encoding the entire df; do not prepend any dropped columns
aahjdbhic,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/reshape/reshape.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Encoding only cols specified in columns. Get all cols not in
aahjdbhie,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/reshape/reshape.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Encoding only object and category dtype columns. Get remaining
aahjdbifd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/series.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,kludge
aahjdbiga,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/series.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,hack
aahjdbihf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/series.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: integrate bottleneck
aahjdbihg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/series.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Add option for bins like value_counts()
aahjdbihj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/series.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: it must return DatetimeArray with tz in pandas 2.0
aahjdbiig,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/series.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: do we need name?
aahjdbijf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/series.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: this can be combined with DataFrame.sort_index impl as
aahjdbjbi,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/series.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: remove when the default Categorical.take behavior changes
aahjdbjbj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/series.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Maybe set copy if we didn't actually change the index.
aahjdbjjc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/sorting.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,move labels to right locations (ie; unsort ascending labels)
aahjdcafi,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/sparse/frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,pre-filter out columns if we passed it
aahjdcaga,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/sparse/frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: figure out how to handle this case; all nan's?
aahjdcagb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/sparse/frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,add in any other columns we want to have (completeness)
aahjdcage,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/sparse/frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Add any columns that were empty and thus not grouped on above
aahjdcahg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/sparse/frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: be a bit more intelligent here
aahjdcahj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/sparse/frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,patched version of DataFrame._combine_match_columns to account for
aahjdcaig,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/sparse/frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: fill value handling
aahjdcaij,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/sparse/frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Figure out whether this can be reached.
aahjdcajg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/sparse/scipy_sparse.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: how to do this better? cleanly slice nonnull_labels given the
aahjdcajj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/sparse/scipy_sparse.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: these two lines can rejplace the code below but
aahjdcbaj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/sparse/scipy_sparse.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: specify kind?
aahjdcbdf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/sparse/series.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: make more efficient
aahjdcbeg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/strings.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,deprecated behaviour. This keyword is now no longer needed.
aahjdcbha,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/strings.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,not needed when inferred
aahjdcbib,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/strings.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,better represented as a regular Index.
aahjdccaa,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/strings.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,do not map indexed\/typed objects; info needed below
aahjdccbb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/strings.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Need to add keys for uniqueness in case of duplicate columns
aahjdccie,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/util/hashing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: GH 15362
aahjdcdbj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/window.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: _level is unused?
aahjdcdcb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/core/window.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: default is for backward compat
aahjdcdgd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/clipboard/__init__.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,FIXME: pyperclip currently does not support Cygwin;
aahjdcdia,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/clipboard/clipboards.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Workaround for https:\/\/bugs.kde.org\/show_bug.cgi?id=342874
aahjdcdib,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/clipboard/clipboards.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: https:\/\/github.com\/asweigart\/pyperclip\/issues\/43
aahjdceaj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/clipboards.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,try to decode (if needed on PY3)
aahjdcfgf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/excel.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: support other fill patterns
aahjdcfhg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/feather_format.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,validate columns
aahjdcgaf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/formats/css.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,3. TODO: resolve other font-relative units
aahjdcgag,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/formats/css.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: support %
aahjdcgaj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/formats/css.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,hack for 'large' etc.
aahjdcgba,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/formats/css.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: don't lowercase case sensitive parts of values (strings)
aahjdcgbf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/formats/csvs.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,update columns to include possible multiplicity of dupes
aahjdcgee,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/formats/excel.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: memoize?
aahjdcgef,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/formats/excel.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: support number format
aahjdcgeg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/formats/excel.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: handle cell width and height: needs support in pandas.io.excel
aahjdcgei,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/formats/excel.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: text-indent; padding-left -> alignment.indent
aahjdcgge,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/formats/excel.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: perhaps allow for special properties
aahjdcgih,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/formats/excel.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,MultiIndex columns require an extra row
aahjdchab,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/formats/format.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Series uses mode=center because it has single value columns
aahjdchah,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/formats/format.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,(why else = 0)
aahjdchaj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/formats/format.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Format only rows and columns that could potentially fit the
aahjdchca,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/formats/format.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,`self._to_str_columns
aahjdchda,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/formats/format.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,self.str_columns = str_columns
aahjdchdd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/formats/format.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,empty space for columns
aahjdchjc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/formats/latex.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,string representation of the columns
aahjdciad,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/formats/latex.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,sum up columns to multicolumns
aahjdciaf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/formats/latex.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,write multicolumn if needed
aahjdciag,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/formats/latex.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,don't modify where not needed
aahjdcife,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/formats/style.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Blank for Index columns...
aahjdciff,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/formats/style.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,... except maybe the last for columns.names
aahjdcifj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/formats/style.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: namespace all the pandas keys
aahjdciib,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/formats/terminal.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,needed for window's python in cygwin's xterm!
aahjdcijg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/html.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,\"\"\":mod:`pandas.io.html` is a module containing functionality for dealing with || HTML IO. ||  || \"\"\"
aahjdcjbj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/html.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,hack around python 3 deleting the exception variable
aahjdcjdd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/json/json.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Do this timedelta properly in objToJSON.c See GH #15137
aahjdcjfa,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/json/json.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,possibly handle dup columns
aahjdcjfb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/json/json.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,our columns to parse
aahjdcjgg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/json/normalize.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: handle record value which are lists; at least error
aahjddacf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/packers.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,'columns': obj.columns}
aahjddaed,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/packers.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,obj['data']; columns=obj['columns'];
aahjddagd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/parquet.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Remove in pandas 0.23.0
aahjddahb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/parquet.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Support 'ab'
aahjddbca,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/parsers.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,May alter columns \/ col_dict
aahjddbdc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/parsers.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,extract the columns
aahjddbfe,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/parsers.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX this is for consistency with
aahjddbfh,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/parsers.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,returns data; columns
aahjddbgb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/parsers.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,we have a multi index in the columns
aahjddbgg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/parsers.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,with _set_noconvert_columns
aahjddbhe,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/parsers.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,The names attribute should have the correct columns
aahjddbic,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/parsers.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,columns as list
aahjddbig,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/parsers.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Get columns in two steps: infer from data; then
aahjddbii,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/parsers.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Now self.columns has the set of columns that we will process.
aahjddbij,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/parsers.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,The original set is stored in self.original_columns.
aahjddcae,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/parsers.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,handles single dtype applied to all columns
aahjddcag,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/parsers.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,we have a mi columns; so read an extra line
aahjddcai,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/parsers.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,if columns are provided. That will
aahjddcbd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/parsers.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Set _use_cols. We don't store columns because they are
aahjddcbf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/parsers.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Ignore output but set used columns.
aahjddcgd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/parsers.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,hack
aahjddchc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/parsers.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,check whether we have any index columns specified; via either:
aahjddebf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/pytables.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,use the meta if needed
aahjddecd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/pytables.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Handle case of NaN-only categorical columns in which case
aahjddeea,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/pytables.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,ugly hack for length 0 axes
aahjddeeb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/pytables.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Transform needed to interface with pytables row\/col notation
aahjddefd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/pytables.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,I have no idea why; but writing values before items fixed #2299
aahjddefi,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/pytables.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,index columns
aahjddefj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/pytables.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,values columns
aahjddega,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/pytables.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,index all indexables and data_columns
aahjddegh,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/pytables.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,evaluate the passed data_columns; True == use all columns
aahjddeha,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/pytables.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,return valid columns in the order of our axis
aahjddehb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/pytables.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,set the default axes if needed
aahjddehd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/pytables.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,do we have an existing table (if so; use its axes & data_columns)
aahjddeie,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/pytables.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,reindex by our non_index_axes & compute data_columns
aahjddeif,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/pytables.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,figure out data_columns and get out blocks
aahjddeja,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/pytables.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,make sure that we match up the existing columns
aahjddejh,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/pytables.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,reorder by any non_index_axes & limit to the select columns
aahjddfae,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/pytables.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,hack until we support reversed dim flags
aahjddfbj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/pytables.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,permute if needed
aahjddfcb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/pytables.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,need a better algorithm
aahjddfdg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/pytables.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,broadcast the indexes if needed
aahjddfdj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/pytables.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,reshape the values if needed
aahjddfgb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/pytables.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,the index columns is just a simple index
aahjddfhd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/pytables.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,encode if needed
aahjddgfg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/sql.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,\"\"\" || Collection of query wrappers \/ abstractions to both facilitate data || retrieval and to reduce dependency on DB-specific API. || \"\"\"
aahjddgha,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/sql.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,we could in theory do a 'nice' conversion from a FixedOffset tz
aahjddghg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/sql.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: support for multiIndex
aahjddgif,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/sql.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,return the used column labels for the index columns
aahjddgjb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/sql.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Convert tz-aware Datetime SQL columns to UTC
aahjddgjh,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/sql.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Refine integer size.
aahjddhbi,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/sql.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO(wesm): unused?
aahjddhcd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/stata.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: If\/when pandas supports more than datetime64[ns]; this should be
aahjddieb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/stata.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Convert columns (if needed) to match input type
aahjddieg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/stata.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Copy information for retained columns for later processing
aahjddiej,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/stata.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: is the next line needed above in the data(...) method?
aahjddifj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/stata.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Refactor to combine type with format
aahjddigd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/stata.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Upcast if needed so that correct missing values can be set
aahjddiha,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/stata.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Check date conversion; and fix key if needed
aahjddihf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/stata.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Check columns for compatibility with stata; upcast if necessary
aahjddihj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/stata.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Ensure all date columns are converted
aahjddjci,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/stata.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,xxx...xxx
aahjddjdj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/io/stata.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Move to start of map
aahjddjgj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/plotting/_converter.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO(wesm) unused?
aahjddjie,ryfeus/gcf-packs,pandas_numpy/sources/pandas/plotting/_converter.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO(wesm): unused?
aahjdeabe,ryfeus/gcf-packs,pandas_numpy/sources/pandas/plotting/_converter.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Check the following : is it really info['fmt'] ?
aahjdeacc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/plotting/_converter.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,don't actually use the locs. This is just needed to work with
aahjdeadc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/plotting/_core.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,ugly TypeError if user passes matplotlib's `cmap` name.
aahjdeadd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/plotting/_core.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Probably better to accept either.
aahjdeadh,ryfeus/gcf-packs,pandas_numpy/sources/pandas/plotting/_core.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: unused?
aahjdeadi,ryfeus/gcf-packs,pandas_numpy/sources/pandas/plotting/_core.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,if self.sort_columns:
aahjdeadj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/plotting/_core.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,columns = com._try_sort(data.columns)
aahjdeaeb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/plotting/_core.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,columns = data.columns
aahjdeaeg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/plotting/_core.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: use Matplotlib public API when available
aahjdeaej,ryfeus/gcf-packs,pandas_numpy/sources/pandas/plotting/_core.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,GH16953; _convert is needed as fallback; for ``Series``
aahjdeaie,ryfeus/gcf-packs,pandas_numpy/sources/pandas/plotting/_core.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,probably a better place to check \/ set this.
aahjdebbe,ryfeus/gcf-packs,pandas_numpy/sources/pandas/plotting/_core.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,flier colors isn't needed here
aahjdebdb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/plotting/_core.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,hack until the plotting interface is a bit more unified
aahjdebeh,ryfeus/gcf-packs,pandas_numpy/sources/pandas/plotting/_misc.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,workaround because `c='b'` is hardcoded in matplotlibs scatter method
aahjdebid,ryfeus/gcf-packs,pandas_numpy/sources/pandas/plotting/_timeseries.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,hack this for 0.10.1; creating more technical debt...sigh
aahjdecad,ryfeus/gcf-packs,pandas_numpy/sources/pandas/plotting/_tools.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,convention.
aahjdeced,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/api/test_api.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,top-level to deprecate in the future
aahjdeddg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/categorical/test_api.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,check unused categories
aahjdedgc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/categorical/test_constructors.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,This doesn't work -> this would probably need some kind of \"remember
aahjdeehb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/categorical/test_repr.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,hack because array_repr changed in numpy > 1.6.x
aahjdefdd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/computation/test_eval.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: the code below should be added back when left and right
aahjdefja,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/computation/test_eval.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,maybe someday... numexpr has too many upcasting rules now
aahjdeihf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/extension/category/test_categorical.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO remove this once Categorical.take is fixed
aahjdeiic,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/extension/json/array.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,\"\"\"Test extension array for storing nested data in a pandas container. ||  || The JSONArray stores lists of dictionaries. The storage mechanism is a list; || not an ndarray. ||  || Note: ||  || We currently store lists of UserDicts (Py3 only). Pandas has a few places || internally that specifically check for dicts; and does non-scalar things || in that case. We *want* the dictionaries to be treated as scalars; so we || hack around pandas by using UserDicts. || \"\"\"
aahjdejaf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/extension/json/array.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Use a regular dict. See _NDFrameIndexer._setitem_with_indexer
aahjdejbi,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/extension/json/test_json.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO (EA.factorize): see if _values_for_factorize allows this.
aahjdejei,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_alter_axes.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,multiple columns
aahjdejej,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_alter_axes.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO should set_index check_names ?
aahjdejhc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_alter_axes.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: unused?
aahjdejhi,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_alter_axes.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,partial columns
aahjdfaab,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_alter_axes.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,only remove certain columns
aahjdfaac,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_alter_axes.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO should reset_index check_names ?
aahjdfacf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_alter_axes.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,equivalence of the labels\/axis and index\/columns API's
aahjdfagg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_analytics.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Categoricals should not show up together with numerical columns
aahjdfbbi,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_analytics.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,works when only those columns are selected
aahjdfbci,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_analytics.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,ensure this works; bug report
aahjdfbhf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_analytics.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,non unique columns
aahjdfbhh,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_analytics.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,partial overlapping columns
aahjdfbjf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_analytics.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Given the wide dataframe with a lot of columns
aahjdfcad,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_analytics.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,multiple columns
aahjdfceb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_analytics.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO(wesm): unused?
aahjdfcee,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_analytics.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,named columns
aahjdfceg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_analytics.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,interaction of named columns & series
aahjdfcgi,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_analytics.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO(wesm): unused
aahjdfcjf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_api.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,DataFrame whose columns are identifiers shall have them in __dir__.
aahjdfcjg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_api.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,DataFrame whose first-level columns are identifiers shall have
aahjdfdef,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_apply.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,preserve columns
aahjdfefj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_asof.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,testing multiple columns
aahjdfehf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_axis_select_reindex.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,equivalence of the labels\/axis and index\/columns API's (GH12392)
aahjdfeif,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_axis_select_reindex.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,length zero with columns reindexed with non-empty index
aahjdfeja,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_axis_select_reindex.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,GH 14992; reindexing over columns ignored method
aahjdffaj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_axis_select_reindex.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,equivalence of the labels\/axis and index\/columns API's
aahjdffbe,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_axis_select_reindex.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO(wesm): unused?
aahjdffea,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_axis_select_reindex.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,unicode columns; won't ascii-encode
aahjdffhh,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_block_internals.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,as B and C are both unsigned; no forcing to float is needed
aahjdffib,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_block_internals.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,f7u12; this does not work without extensive workaround
aahjdfgba,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_block_internals.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO(wesm): unused?
aahjdfgcd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_block_internals.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,numeric and object columns
aahjdfgdg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_combine_concat.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,different columns
aahjdfgdj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_combine_concat.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Empty df with columns append empty df
aahjdfgea,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_combine_concat.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Non-Empty df with columns append empty df
aahjdfgeg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_combine_concat.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,these must be the same results (but columns are flipped)
aahjdfgfa,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_combine_concat.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Columns\/1 DataFrame
aahjdfgfc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_combine_concat.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Columns\/1 Series
aahjdfggi,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_combine_concat.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,ToDo: this must be datetime64
aahjdfhag,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_constructors.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,empty with index and columns
aahjdfhbb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_constructors.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,dict with scalar values should raise error; even if columns passed
aahjdfhcb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_constructors.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,passing an empty array with columns specified.
aahjdfhgd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_constructors.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,specify columns
aahjdfhgg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_constructors.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,does not error but ends up float
aahjdfhhb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_constructors.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Fix this Exception to be better...
aahjdfhjd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_constructors.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,with columns
aahjdfhjg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_constructors.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Test new columns parameter for from_dict that was added to make
aahjdfiah,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_constructors.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,pass some columns
aahjdfihh,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_constructors.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,tuples is in the order of the columns
aahjdfiic,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_constructors.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,columns is in a different order here than the actual items iterated
aahjdfjcg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_convert_to.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,GH18372 When converting to dict with orient='records' columns of
aahjdfjda,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_convert_to.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,and float columns only the int columns were cast to float
aahjdfjfc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_dtypes.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,change type of a subset of columns
aahjdfjfd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_dtypes.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,change all columns
aahjdfjjc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_indexing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,with multi-index columns aligns both rows and columns
aahjdgaac,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_indexing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,add back other columns to compare
aahjdgafa,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_indexing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,slice columns
aahjdgagd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_indexing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,case 7: slice columns
aahjdgbga,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_indexing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,no columns but Index(dtype=object)
aahjdgbih,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_indexing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,this *does* align; though has no matching columns
aahjdgcaa,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_indexing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Upcast needed
aahjdgcjd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_join.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,corner case - overlapping columns
aahjdgcje,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_join.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO should this check_names ?
aahjdgddh,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_missing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,df2 may have different index and columns
aahjdgddi,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_missing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,only those columns and indices which are shared get filled
aahjdgdec,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_missing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO(wesm): unused?
aahjdgdge,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_mutate_columns.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,from the vb_suite\/frame_methods\/frame_insert_columns
aahjdgdgi,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_mutate_columns.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,preserve columns name field
aahjdgdhf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_mutate_columns.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO assert self.frame.columns.name == 'baz'
aahjdgece,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_nonunique_indexes.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,this is an error because we cannot disambiguate the dup columns
aahjdgech,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_nonunique_indexes.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,dup columns across dtype GH 2079\/2194
aahjdgeji,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_operators.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,no upcast needed
aahjdgejj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_operators.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,vs mix (upcast) as needed
aahjdgfdf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_period.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,columns
aahjdgfic,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_quantile.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,FIXME (gives NaNs instead of NaT in 0.18.1 or 0.19.0)
aahjdggcf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_rank.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,f7u12; this does not work without extensive workaround
aahjdghej,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_repr_info.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,columns but no index
aahjdghfb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_repr_info.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,no columns or index
aahjdghfd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_repr_info.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,columns are not sortable
aahjdghha,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_repr_info.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Test a DataFrame with duplicate columns
aahjdghii,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_reshape.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,pivot multiple columns
aahjdghjc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_reshape.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,flat columns:
aahjdghjd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_reshape.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,MultiIndex columns:
aahjdgiaa,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_reshape.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Workaround for GH #17886 (unnecessarily casts to float):
aahjdgibc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_reshape.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Checks fix for #11847
aahjdgicj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_reshape.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,GH 17845: unused labels in index make unstack() cast int to float
aahjdgida,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_reshape.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Unused items on both levels
aahjdgidc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_reshape.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,GH 18562 : unused labels on the unstacked level
aahjdgifg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_sort_values_level_as_str.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Two columns
aahjdgiga,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_sort_values_level_as_str.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Compute expected by sorting on columns and the setting index
aahjdgigb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_sort_values_level_as_str.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Compute result sorting on mix on columns and index levels
aahjdgigi,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_sort_values_level_as_str.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Accessing multi-level columns that are not lexsorted raises a
aahjdgjab,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_sorting.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,GH 14922: \"sorting with large float and multiple columns incorrect\"
aahjdgjad,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_sorting.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,only correct for datetime64 columns.
aahjdgjae,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_sorting.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,NaT is not a \"na\" for int64 columns; so na_position must not
aahjdgjai,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_sorting.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,columns:
aahjdgjdd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_sorting.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,axis=1 : sort columns by column names
aahjdgjjb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_timeseries.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,in the presence of duplicate columns
aahjdhaac,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_timeseries.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: actually check that this worked.
aahjdhaci,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_timeseries.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: l1 should be a PeriodIndex for testing
aahjdhadd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_timeseries.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: untested
aahjdhafh,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_to_csv.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: remove renaming when GH 10875 is solved
aahjdhagj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_to_csv.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,read_Csv disambiguates the columns by
aahjdhaha,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_to_csv.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,labeling them dupe.1;dupe.2; etc'. monkey patch columns
aahjdhahh,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_to_csv.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,GH 3624; after appending columns; to_csv fails
aahjdhahj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_to_csv.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,names passed as columns parameter in to_csv
aahjdhaie,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_to_csv.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,needed if setUp becomes a class method
aahjdhaij,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_to_csv.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,needed if setUp becomes class method
aahjdhbba,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_to_csv.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,read_csv will rename the dups columns
aahjdhbch,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_to_csv.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Check that columns get converted
aahjdhbci,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/frame/test_to_csv.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Columns don't get converted to ints by read_csv
aahjdhbja,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/generic/test_generic.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO
aahjdhcii,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/generic/test_generic.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,different columns
aahjdhdch,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/generic/test_label_or_level_utils.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,A series has no columns and therefore references are never ambiguous
aahjdhdii,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/groupby/aggregate/test_aggregate.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO(wesm): unused
aahjdhebd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/groupby/aggregate/test_cython.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,multiple columns
aahjdheic,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/groupby/test_apply.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,columns.  Various GH issues were filed; the reproductions
aahjdhfcc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/groupby/test_categorical.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,gh-14942 (implement)
aahjdhfcj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/groupby/test_categorical.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Grouping on two columns
aahjdhgef,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/groupby/test_function.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: min; max *should* handle
aahjdhhfj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/groupby/test_groupby.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO groupby get drops names
aahjdhiff,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/groupby/test_grouping.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,reset index changes columns dtype to object
aahjdhigb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/groupby/test_grouping.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Group by two columns
aahjdhihg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/groupby/test_grouping.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,reset_index changes columns dtype to object
aahjdhjbf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/groupby/test_grouping.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: should prob allow a str of Interval work as well
aahjdiaaa,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/groupby/test_timegrouper.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX
aahjdiadj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/groupby/test_transform.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,dup columns
aahjdiaea,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/groupby/test_transform.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,group columns
aahjdiaie,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/groupby/test_transform.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,group by values; index level; columns
aahjdiaii,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/groupby/test_transform.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,bit a of hack to make sure the cythonized shift
aahjdiajc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/groupby/test_transform.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,individual columns
aahjdibbh,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/groupby/test_whitelist.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO check groupby with > 1 col ?
aahjdiceb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexes/datetimes/test_arithmetic.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,GH#19301 by convention datetime.date is not considered comparable
aahjdicie,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexes/datetimes/test_arithmetic.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: A couple other tests belong in this section.  Move them in
aahjdieaf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexes/datetimes/test_date_range.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Use maybe_get_tz to fix filename in tz under dateutil.
aahjdiega,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexes/datetimes/test_indexing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: This method came from test_datetime; de-dup with version above
aahjdifgi,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexes/datetimes/test_partial_slicing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,GH issue #17965; test for ability to compare datetime64[ns] columns
aahjdific,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexes/datetimes/test_setops.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: moved from test_datetimelike; dedup with version below
aahjdifih,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexes/datetimes/test_setops.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: moved from test_datetimelike; de-duplicate with version below
aahjdigga,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexes/datetimes/test_timezones.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Is this really how it should fail??
aahjdighc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexes/datetimes/test_timezones.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: belongs outside tz_localize tests?
aahjdighf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexes/datetimes/test_timezones.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,FIXME: This next block fails to raise; it was taken from an older
aahjdihij,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexes/datetimes/test_tools.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,extra columns
aahjdiieh,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexes/datetimes/test_tools.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,bug fix in 2.5.2
aahjdijfc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexes/interval/test_interval.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: same tests for more index types
aahjdijge,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexes/interval/test_interval.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: is this right?
aahjdjabj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexes/interval/test_interval_new.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: @shoyer believes this should raise; master branch doesn't
aahjdjagd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexes/period/test_arithmetic.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: De-duplicate with test_pi_cmp_nat
aahjdjahj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexes/period/test_arithmetic.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO needs to wait on #13077 for decision on result type
aahjdjaie,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexes/period/test_arithmetic.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Some of these are misnomers because of non-Tick DateOffsets
aahjdjcbg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexes/period/test_period.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Todo: fix these accessors!
aahjdjdfi,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexes/test_base.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: make this a dedicated test with parametrized methods
aahjdjdgj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexes/test_base.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Parametrize numeric and str tests after self.strIndex fixture
aahjdjdhb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexes/test_base.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Remove function? GH 19728
aahjdjefb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexes/test_base.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: implement _assert_tzawareness_compat for the reverse
aahjdjhah,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexes/test_multi.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Try creating a UnicodeDecodeError in exception message
aahjdjhhe,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexes/test_multi.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,FIXME data types changes to float because
aahjdjibg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexes/test_multi.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,if this fails; probably didn't reset the cache correctly.
aahjeaaji,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexes/timedeltas/test_arithmetic.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO(wesm): unused?
aahjeabbj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexes/timedeltas/test_arithmetic.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Needs more informative name; probably split up into
aahjeabdf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexes/timedeltas/test_indexing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: This method came from test_timedelta; de-dup with version above
aahjeabfi,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexes/timedeltas/test_ops.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO(wesm): unused?
aahjeacig,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexing/interval/test_interval_new.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO with non-existing intervals ?
aahjeacij,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexing/interval/test_interval_new.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO should this work? (-1 is not contained in any of the Intervals)
aahjeadaa,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexing/interval/test_interval_new.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO KeyError is the appropriate error?
aahjeadhb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexing/test_categorical.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,contains unused category
aahjeaegc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexing/test_chaining_and_caching.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO(wesm): unused?
aahjeaffb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexing/test_floats.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,via labels on the columns
aahjeahdc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexing/test_indexing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Test with np.inf in columns
aahjeahdh,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexing/test_indexing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO(wesm): unused?
aahjeahed,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexing/test_indexing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,ToDo: check_index_type can be True after GH 11497
aahjeahic,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexing/test_indexing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO(wesm): unused variables
aahjeahjg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexing/test_indexing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,GH8258; tests that both rows & columns are aligned to what is
aahjeajej,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexing/test_multiindex.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO(wesm): fix this
aahjeajfg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexing/test_multiindex.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,set all columns
aahjebabd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexing/test_multiindex.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO(wesm): unused?
aahjebbaf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexing/test_panel.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: unused?
aahjebbfh,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexing/test_partial.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,reindex columns is ok
aahjebbhg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/indexing/test_scalar.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,GH 13822; incorrect error string with non-unique columns when missing
aahjebbja,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/internals/test_internals.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,FIXME: doesn't support num_rows != 10
aahjebbjg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/internals/test_internals.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: merge with mixed type?
aahjebceh,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/internals/test_internals.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,we maybe using an ndarray to test slicing and
aahjebcgf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/formats/test_css.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: we should be checking that in other cases no warnings are raised
aahjebche,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/formats/test_format.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,3. Columns
aahjebcjb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/formats/test_format.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: use mock fixutre.
aahjebcjd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/formats/test_format.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,regular columns
aahjebcje,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/formats/test_format.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Out off max_columns boundary; but no extending
aahjebdfi,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/formats/test_format.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,fmt.set_option('display.max_info_columns'; 4)  # exceeded
aahjebedb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/formats/test_style.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,An index but no columns
aahjebegg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/formats/test_style.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,test hiding mulitiple columns
aahjebehe,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/formats/test_style.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,hide top column level; which hides both columns
aahjebfji,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/generate_legacy_storage_files.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,panels if their columns\/items were non-unique.
aahjebgai,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/json/test_json_table_schema.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: datedate.date? datetime.time?
aahjebgaj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/json/test_json_table_schema.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO
aahjebgba,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/json/test_json_table_schema.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: I think before is_categorical_dtype(Categorical)
aahjebgcb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/json/test_json_table_schema.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: When DF coercion issue (#21345) is resolved tighten type checks
aahjebgdh,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/json/test_pandas.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,GH4377; duplicate columns not processing correctly
aahjebgeh,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/json/test_pandas.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,index and columns are strings as all unserialised JSON object keys
aahjebgff,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/json/test_pandas.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,state; so the columns are interpreted as having mixed data and
aahjebgfj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/json/test_pandas.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,too many columns
aahjebgha,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/json/test_pandas.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,the same with multiple columns threw segfaults
aahjebhab,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/json/test_pandas.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,GH15096: escaped characters in columns and data
aahjebhbi,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/json/test_readlines.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,GH15096: escaped characters in columns and data
aahjebheb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/json/test_ujson.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,sign bit set (2**31 <= x < 2**32) are decoded properly.
aahjebida,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/parser/c_parser_only.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,too many columns; cause segfault if not careful
aahjebieb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/parser/c_parser_only.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,to stress the system memory allocator; to cause it to move the
aahjebifh,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/parser/c_parser_only.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,finally read a residual chunk of really small size.
aahjebjea,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/parser/common.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: ftp testing
aahjebjee,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/parser/common.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,it's 33 columns
aahjebjjj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/parser/common.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,parser when faced with no provided columns
aahjecafh,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/parser/dtypes.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,we expect all object columns; so need to
aahjecaig,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/parser/header.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,mi on columns and index (malformed)
aahjecbai,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/parser/mangle_dupes.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,\"\"\" || Tests that duplicate columns are handled appropriately when parsed by the || CSV engine. In general; the expected result is that they are either thoroughly || de-duplicated (if mangling requested) or ignored otherwise. || \"\"\"
aahjecbba,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/parser/mangle_dupes.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: add test for condition \"mangle_dupe_cols=False\"
aahjecbfa,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/parser/parse_dates.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,specify columns out of order!
aahjecccf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/parser/test_network.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,can probably mock this though.
aahjeccea,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/parser/test_parsers.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,GH 17351 - usecols needs to be sorted in _setnoconvert_columns
aahjecceg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/parser/test_parsers.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,before GH 17351 would cause the wrong columns to be
aahjeccfa,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/parser/test_read_fwf.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,From Thomas Kluyver: apparently some non-space filler characters can
aahjeccga,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/parser/test_read_fwf.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,File with spaces in columns
aahjeccgb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/parser/test_read_fwf.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,File with spaces and missing values in columns
aahjecdah,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/parser/usecols.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: https:\/\/github.com\/pandas-dev\/pandas\/issues\/16469
aahjecdhi,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/test_excel.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO add index to xls; read xls ignores index name ?
aahjecdhj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/test_excel.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO add index to xls file
aahjecdib,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/test_excel.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO add index to file
aahjecfjb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/test_excel.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,first row taken as columns
aahjecgcd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/test_excel.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,MultiIndex as columns is not yet implemented 9794
aahjecgde,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/test_excel.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX: RGB colors not supported in xlwt
aahjecgdf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/test_excel.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX: should find a better way to check equality
aahjecgec,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/test_excel.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX: openpyxl (as at 2.4) prefixes colors with 00; xlsxwriter with FF
aahjecged,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/test_excel.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX: this isn't as strong a test as ideal; we should
aahjecgff,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/test_feather.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,not currently able to handle duplicate columns
aahjecgjd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/test_packers.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,maybe the same as GH 13591
aahjechbj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/test_packers.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,we needed to copy the data
aahjechie,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/test_pickle.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,\"\"\" || manage legacy pickle tests ||  || How to add pickle tests: ||  || 1. Install pandas version intended to output the pickle. ||  || 2. Execute \"generate_legacy_storage_files.py\" to create the pickle. || $ python generate_legacy_storage_files.py <output_dir> pickle ||  || 3. Move the created pickle to \"data\/legacy_pickle\/<version>\" directory. || \"\"\"
aahjecjcf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/test_pytables.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,uints - test storage of uints in indexable columns
aahjecjde,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/test_pytables.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,seee GH7796 FIXME
aahjecjih,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/test_pytables.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,data column searching (with an indexable and a data_columns)
aahjecjja,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/test_pytables.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,multiple data columns
aahjecjjg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/test_pytables.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,GH5717 not handling data_columns
aahjedaab,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/test_pytables.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,data columns
aahjedabi,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/test_pytables.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,recreate multi-indexes when columns is passed
aahjedadd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/test_pytables.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,multiple invalid columns
aahjedafe,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/test_pytables.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,a bit hacky here as we don't really deal with the NaT properly
aahjedaha,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/test_pytables.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: unused?
aahjedbab,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/test_pytables.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,duplicates on both index and columns
aahjedbbc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/test_pytables.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,selection on the non-indexable with a large number of columns
aahjedbbj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/test_pytables.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,all a data columns
aahjedbca,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/test_pytables.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,with a data column; but different columns
aahjedbcc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/test_pytables.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,bool columns (GH #2849)
aahjedbeb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/test_pytables.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,big selector along the columns
aahjedcig,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/test_pytables.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Check that read_hdf with categorical columns doesn't return rows if
aahjedcjd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/test_pytables.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Check that read_hdf with categorical columns with NaN-only values can
aahjeddaa,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/test_pytables.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,read_hdf store.select modified the passed columns parameters
aahjeddag,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/test_pytables.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Add back to types_should_fail
aahjeddca,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/test_pytables.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,strings to other columns must be convertible to type
aahjeddic,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/test_sql.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,HACK!
aahjeddjj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/test_sql.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,multiple columns as key (GH10385)
aahjededc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/test_sql.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX:
aahjedfhd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/test_stata.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Thus; all columns will be of type float; regardless of their name.
aahjedgba,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/io/test_stata.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,{c : c[-2:] for c in columns}
aahjedgie,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/plotting/test_boxplot_method.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Multiple columns with an ax argument should use same figure
aahjedhee,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/plotting/test_datetimelike.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO
aahjedhfa,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/plotting/test_datetimelike.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO (GH14330; GH14322)
aahjedidj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/plotting/test_frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,columns.inferred_type == 'string'
aahjediea,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/plotting/test_frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,columns.inferred_type == 'integer'
aahjediec,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/plotting/test_frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,columns.inferred_type == 'mixed'
aahjedied,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/plotting/test_frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO add MultiIndex test
aahjedigg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/plotting/test_frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Other columns should not be visible
aahjedjge,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/plotting/test_frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: outside this func?
aahjedjhg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/plotting/test_frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: need better way to test. This just does existence.
aahjeeajh,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/plotting/test_misc.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Case df with 3 numeric columns but layout of (2;2)
aahjeebgi,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/reshape/merge/test_join.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,aggregate multiple columns
aahjeebig,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/reshape/merge/test_join.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,_assert_same_contents(expected; expected2.loc[:; expected.columns])
aahjeecbc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/reshape/merge/test_merge.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,aggregate multiple columns
aahjeecca,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/reshape/merge/test_merge.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,this really should be bool
aahjeecef,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/reshape/merge/test_merge.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Merge on multiple columns
aahjeecff,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/reshape/merge/test_merge.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO check_names on merge?
aahjeecfh,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/reshape/merge/test_merge.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: columns aren't in the same order yet
aahjeecic,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/reshape/merge/test_merge.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,merging on float and int columns
aahjeecif,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/reshape/merge/test_merge.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,float columns where the float values are not exactly
aahjeecja,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/reshape/merge/test_merge.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO ([0; 1]; pd.Series([False; True]; dtype=bool));
aahjeedag,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/reshape/merge/test_merge.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,non-merge columns should preserve if possible
aahjeedai,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/reshape/merge/test_merge.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,our merging columns; X now has 2 different dtypes
aahjeedcf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/reshape/merge/test_merge_asof.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,time column appears after left's columns
aahjeedgb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/reshape/merge/test_merge_index_as_string.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Convert remaining index levels to columns
aahjeedif,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/reshape/test_concat.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Replace with sort once keyword changes.
aahjeeeae,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/reshape/test_concat.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,ToDo: suspicious
aahjeeega,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/reshape/test_concat.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,see gh-6129: new columns
aahjeeehb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/reshape/test_concat.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,See also test 'test_append_different_columns_types_raises' below
aahjeeehh,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/reshape/test_concat.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,See also test 'test_append_different_columns_types' above for
aahjeeeji,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/reshape/test_concat.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,columns have mixed tuples; so handle properly
aahjeefcf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/reshape/test_concat.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,GH 3602; duplicate columns
aahjeegae,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/reshape/test_melt.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,the columns have a MultiIndex and a tuple is passed
aahjeegai,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/reshape/test_melt.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: unused?
aahjeeghb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/reshape/test_pivot.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,when values is not list like and columns is None
aahjeehaf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/reshape/test_reshape.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Sparse dataframes do not allow nan labelled columns; see #GH8822
aahjeehag,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/reshape/test_reshape.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,hack (NaN handling in assert_index_equal)
aahjeehhj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/scalar/period/test_asfreq.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: unused?
aahjeejbj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/scalar/timedelta/test_arithmetic.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: GH-19761. Change to TypeError.
aahjefabe,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/scalar/timedelta/test_timedelta.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: unused?
aahjefacd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/scalar/timestamp/test_arithmetic.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,ends up multiplying really large numbers which overflow
aahjefccf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/series/indexing/test_alter_index.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,(don't forget to fix this) I think it's fixed
aahjefdfj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/series/indexing/test_datetime.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: unused?
aahjefeah,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/series/indexing/test_indexing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,GH XXX
aahjefecd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/series/indexing/test_indexing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: unused
aahjeffdf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/series/test_analytics.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,GH 2967 (introduced bug in 0.11-dev I think)
aahjefhba,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/series/test_api.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,HACK: By doing this in two stages; we avoid 2to3 wrapping the call
aahjefheb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/series/test_api.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,and the methods '.set_categories()' 'drop_unused_categories()' to the
aahjefhee,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/series/test_api.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,remove unused categories
aahjefiga,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/series/test_arithmetic.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: parametrize; better name
aahjefjae,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/series/test_arithmetic.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: the direct operation TimedeltaIndex \/ Series still
aahjegcjd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/series/test_missing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,These test are for issue #11115 -- limit ends properly.
aahjegdhf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/series/test_operators.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Decide if this ought to work.
aahjegecj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/series/test_operators.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Fix this exception - needs to be fixed! (see GH5035)
aahjegedg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/series/test_operators.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: this returned NotImplemented earlier; what to do?
aahjegfcd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/series/test_replace.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,implement indexing
aahjeggae,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/series/test_sorting.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,multi-columns sort
aahjeghaa,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/series/test_timezones.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: De-duplicate with test below
aahjeghag,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/sparse/frame/test_apply.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,ToDo: apply must return subclassed dtype
aahjeghaj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/sparse/frame/test_apply.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: no non-unique columns supported in sparse yet
aahjeghca,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/sparse/frame/test_frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: test data is copied from inputs
aahjeghcg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/sparse/frame/test_frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,no index or columns
aahjeghcj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/sparse/frame/test_frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,wrong length index \/ columns
aahjeghdc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/sparse/frame/test_frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: x_sparse is unused...fix
aahjeghdg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/sparse/frame/test_frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: y_sparse is unsused...fix
aahjeghfg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/sparse/frame/test_frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,1585 select multiple columns
aahjeghic,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/sparse/frame/test_frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,reindex columns
aahjeghii,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/sparse/frame/test_frame.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Over columns
aahjegjbe,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/sparse/series/test_series.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: sp_zero is not used anywhere...remove?
aahjegjbi,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/sparse/series/test_series.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: expected is not used anywhere...remove?
aahjegjcb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/sparse/series/test_series.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: These aren't used
aahjehaef,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/sparse/test_groupby.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,ToDo: sparse sum includes str column
aahjehajj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/sparse/test_libsparse.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: index variables are not used...is that right?
aahjehbbd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/sparse/test_pivot.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,ToDo: sum doesn't handle nan properly
aahjehbda,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/test_algos.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO(wesm): unused?
aahjehbfh,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/test_algos.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO same for (timedelta)
aahjehcai,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/test_base.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Delegate does not implement memory_usage.
aahjehcdb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/test_base.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Following DatetimeIndex (and Timestamp) convention;
aahjeheae,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/test_expressions.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: FIGURE OUT HOW TO GET IT TO WORK...
aahjeheee,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/test_multilevel.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO(wesm): unused?
aahjeheii,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/test_multilevel.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,columns unsorted
aahjeheij,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/test_multilevel.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,more than 2 levels in the columns
aahjehfbb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/test_multilevel.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO what should join do with names ?
aahjehfcb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/test_multilevel.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO groupby with level_values drops names
aahjehffh,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/test_multilevel.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,only an issue with long columns
aahjehfih,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/test_multilevel.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,sorting fails after columns added
aahjehgde,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/test_nanops.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,some nanops handle object dtypes better than their numpy
aahjehgec,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/test_nanops.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: unused?
aahjehghc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/test_nanops.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,bug for numpy < 1.13; where result is a series; should be a scalar
aahjehibd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/test_panel.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: this converts back to object
aahjehifg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/test_panel.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: test correctness
aahjehifj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/test_panel.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,weird overlap; TODO: test?
aahjehiij,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/test_resample.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: once GH 14008 is fixed; move these tests into
aahjehjcb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/test_resample.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,GH 13022; 7687 - TODO: fix resample w\/ TimedeltaIndex
aahjehjdd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/test_resample.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,odd results when rounding is needed
aahjehjff,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/test_resample.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,dupe columns fail atm
aahjehjfg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/test_resample.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,df.columns = ['PRICE'; 'PRICE']
aahjehjfi,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/test_resample.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,dup columns with resample raising
aahjeiace,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/test_resample.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: replace calls to .create_series() by injecting the series
aahjeiacj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/test_resample.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,This is a bug; these should be implemented
aahjeiagd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/test_resample.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,def test_monthly_convention_span(self):
aahjeiagg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/test_resample.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,# hacky way to get same thing
aahjeidha,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/test_strings.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: unused
aahjeieba,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/test_strings.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO see GH 18463
aahjeigaa,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/test_window.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,in a non-motononic way; so compare accordingly
aahjeigae,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/test_window.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,we can have int and str columns
aahjeighe,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/test_window.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: xref gh-15826
aahjeigif,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/test_window.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO(jreback); needed to add preserve_nan=False
aahjeihci,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/test_window.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,invalid columns
aahjeihdg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/test_window.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,so the columns need to be switched compared
aahjeihdj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/test_window.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,using multiple aggregation columns
aahjeihge,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/tools/test_numeric.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Test to_numeric over multiple columns
aahjeihgh,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/tools/test_numeric.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,ToDo: enable when we can support native PeriodDtype
aahjeihgi,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/tools/test_numeric.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,res = pd.to_numeric(pd.Series(idx; name='xxx'))
aahjeihgj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/tools/test_numeric.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,tm.assert_series_equal(res; pd.Series(idx.asi8; name='xxx'))
aahjeiicj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/tseries/offsets/test_fiscal.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,GH#14774 cases with two fiscal year-ends in the same calendar-year
aahjeijie,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tests/tseries/offsets/test_ticks.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: WTF?  What is this range(10) supposed to do?
aahjejcgb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tseries/offsets.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Does this make sense for the general case?  It would help
aahjejcgd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tseries/offsets.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Combine this with BusinessMixin version by defining a whitelisted
aahjejcgh,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tseries/offsets.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX; see #1395
aahjejcha,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tseries/offsets.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,re-implemented in a nicer way.
aahjejchc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tseries/offsets.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: standardize `_offset` vs `offset` naming convention
aahjejcje,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tseries/offsets.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Cache this once offsets are immutable
aahjejcjf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tseries/offsets.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: cache this once offsets are immutable
aahjejdah,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tseries/offsets.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Figure out the end of this sente
aahjejdfb,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tseries/offsets.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Consider combining QuarterOffset and YearOffset __init__ at some
aahjejdfj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tseries/offsets.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: This is basically the same as BQuarterEnd
aahjejdge,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tseries/offsets.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: there may be a more performant way to do this
aahjejdja,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tseries/offsets.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Why does this handle the 0 case the opposite of others?
aahjejdjf,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tseries/offsets.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: do Tick classes with normalize=True make sense?
aahjejeaa,ryfeus/gcf-packs,pandas_numpy/sources/pandas/tseries/offsets.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Should Tick have its own apply_index?
aahjejeii,ryfeus/gcf-packs,pandas_numpy/sources/pandas/util/_validators.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Goal: fill 'out' with index\/columns-style arguments
aahjejeij,ryfeus/gcf-packs,pandas_numpy/sources/pandas/util/_validators.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,like out = {'index': foo; 'columns': bar}
aahjejfej,ryfeus/gcf-packs,pandas_numpy/sources/pandas/util/testing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,ToDo: There are some tests using rhs is sparse
aahjejfgc,ryfeus/gcf-packs,pandas_numpy/sources/pandas/util/testing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: big hack here
aahjejfgg,ryfeus/gcf-packs,pandas_numpy/sources/pandas/util/testing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,ToDo: There are some tests using rhs is SparseDataFrame
aahjejfhd,ryfeus/gcf-packs,pandas_numpy/sources/pandas/util/testing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,compare by columns
aahjejgaj,ryfeus/gcf-packs,pandas_numpy/sources/pandas/util/testing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Both of the above shouldn't mask real issues such as 404's
aahjejgee,ryfeus/gcf-packs,pandas_numpy/sources/pkg_resources/__init__.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX backward compat
aahjejgfe,ryfeus/gcf-packs,pandas_numpy/sources/pkg_resources/__init__.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX Linux and other platforms' special cases should go here
aahjejggc,ryfeus/gcf-packs,pandas_numpy/sources/pkg_resources/__init__.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX add more info
aahjejggd,ryfeus/gcf-packs,pandas_numpy/sources/pkg_resources/__init__.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,workaround a cache issue
aahjejghg,ryfeus/gcf-packs,pandas_numpy/sources/pkg_resources/__init__.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Oops; the \"best\" so far conflicts with a dependency
aahjejghi,ryfeus/gcf-packs,pandas_numpy/sources/pkg_resources/__init__.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Register the new requirements needed by req
aahjejgih,ryfeus/gcf-packs,pandas_numpy/sources/pkg_resources/__init__.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX backward compatibility
aahjejgjc,ryfeus/gcf-packs,pandas_numpy/sources/pkg_resources/__init__.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX
aahjejhbj,ryfeus/gcf-packs,pandas_numpy/sources/pkg_resources/__init__.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Python 2 on Windows needs to be handled this way :(
aahjejhdf,ryfeus/gcf-packs,pandas_numpy/sources/pkg_resources/__init__.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,metadata until\/unless it's actually needed.  (i.e.; some distributions
aahjejhfi,ryfeus/gcf-packs,pandas_numpy/sources/pkg_resources/__init__.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,this method. In the future this should be smarter and follow PEP 440
aahjejidj,ryfeus/gcf-packs,pandas_numpy/sources/pkg_resources/_vendor/packaging/requirements.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Can we test whether something is contained within a requirement?
aahjejigh,ryfeus/gcf-packs,pandas_numpy/sources/pkg_resources/_vendor/packaging/specifiers.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,implement this in terms of the other specifiers instead of
aahjejjid,ryfeus/gcf-packs,pandas_numpy/sources/pkg_resources/_vendor/packaging/version.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,which uses the defacto standard originally implemented by setuptools;
aahjfaaeb,ryfeus/gcf-packs,pandas_numpy/sources/pkg_resources/_vendor/pyparsing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,\"\"\"(Deprecated) Predefined expression of 1 or more printable words or quoted strings; separated by commas.\r ||    This expression is deprecated in favor of L{pyparsing_common.comma_separated_list}.\"\"\"
aahjfacdb,ryfeus/gcf-packs,pandas_numpy/sources/pkg_resources/_vendor/six.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,This is a bit ugly; but it avoids running this again by
aahjfacea,ryfeus/gcf-packs,pandas_numpy/sources/pkg_resources/_vendor/six.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Workaround for standalone backslash
aahjfacfj,ryfeus/gcf-packs,pandas_numpy/sources/pkg_resources/extern/__init__.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,mysterious hack:
aahjfadah,ryfeus/gcf-packs,pandas_numpy/sources/pytz/reference.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,and ends at 2am (DST time; 1am standard time) on the last Sunday of Oct.
aahjfadjj,ryfeus/gcf-packs,pandas_numpy/sources/pytz/tzinfo.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,doubt this will ever been needed in real life.
aahjfaebc,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/__init__.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Make sure we have any requirements needed to interpret 'attrs'.
aahjfaeeh,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/_vendor/packaging/requirements.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: Can we test whether something is contained within a requirement?
aahjfaehf,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/_vendor/packaging/specifiers.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,implement this in terms of the other specifiers instead of
aahjfafjb,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/_vendor/packaging/version.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,which uses the defacto standard originally implemented by setuptools;
aahjfagej,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/_vendor/pyparsing.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,\"\"\"(Deprecated) Predefined expression of 1 or more printable words or quoted strings; separated by commas.\r ||    This expression is deprecated in favor of L{pyparsing_common.comma_separated_list}.\"\"\"
aahjfaidj,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/_vendor/six.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,This is a bit ugly; but it avoids running this again by
aahjfaiei,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/_vendor/six.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Workaround for standalone backslash
aahjfaihg,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/archive_util.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX Ugh
aahjfaihi,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/build_meta.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,\"\"\"A PEP 517 interface to setuptools ||  || Previously; when a user or a command line tool (let's call it a \"frontend\") || needed to make a request of setuptools to take a certain action; for || example; generating a list of installation requirements; the frontend would || would call \"setup.py egg_info\" or \"setup.py bdist_wheel\" on the command line. ||  || PEP 517 defines a different method of interfacing with setuptools. Rather || than calling \"setup.py\" directly; the frontend should: ||  ||   1. Set the current directory to the directory with a setup.py file ||   2. Import this module into a safe python interpreter (one in which ||      setuptools can potentially set global variables or crash hard). ||   3. Call one of the functions defined in PEP 517. ||  || What each function does is defined in PEP 517. However; here is a \"casual\" || definition of the functions (this definition should not be relied on for || bug reports or API stability): ||  ||   - `build_wheel`: build a wheel in the folder and return the basename ||   - `get_requires_for_build_wheel`: get the `setup_requires` to build ||   - `prepare_metadata_for_build_wheel`: get the `install_requires` ||   - `build_sdist`: build an sdist in the folder and return the basename ||   - `get_requires_for_build_sdist`: get the `setup_requires` to build ||  || Again; this is not a formal definition! Just a \"taste\" of the module. || \"\"\"
aahjfaiig,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/command/bdist_egg.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Hack for packages that install data to install's --install-lib
aahjfaiih,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/command/bdist_egg.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX else: raise ???
aahjfajci,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/command/build_ext.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX Help!  I don't have any idea whether these are right...
aahjfajdf,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/command/build_ext.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX what to do with conflicts?
aahjfajdh,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/command/build_ext.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,hack so distutils' build_extension() builds a library instead
aahjfajdi,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/command/build_ext.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX this should check to ensure the lib is actually being built
aahjfajdj,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/command/build_ext.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX as dynamic; and not just using a locally-found version or a
aahjfajea,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/command/build_ext.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX static-compiled version
aahjfajeh,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/command/build_ext.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX we need to either disallow these attrs on Library instances;
aahjfajhj,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/command/develop.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX
aahjfajih,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/command/develop.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX should also check for entry point scripts!
aahjfbacj,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/command/easy_install.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,cast to str as workaround for #709 and #710 and #712
aahjfbadd,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/command/easy_install.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Installation is also needed if file in tmpdir or is not an egg
aahjfbadi,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/command/easy_install.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX this is not the distribution we were looking for
aahjfbaec,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/command/easy_install.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,if the only thing there is a directory; move it instead
aahjfbaff,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/command/easy_install.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX
aahjfbagf,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/command/easy_install.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: self.report_extras(req; dist)
aahjfbahd,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/command/easy_install.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX should this check for latest version?
aahjfbbaj,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/command/easy_install.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,exact loader instance used when importing them. Python 3.4 docs state
aahjfbbcf,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/command/easy_install.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,this is really needed.
aahjfbbdi,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/command/easy_install.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,and have PyPy repopulate it as needed. The downside is that if there are any
aahjfbbeg,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/command/easy_install.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,spelling; but that seems like a way too invasive move (this cache
aahjfbcbf,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/command/egg_info.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Fix older sdists built on Windows
aahjfbcdh,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/command/install.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX Python 3.1 doesn't see _nc if this is inside the class
aahjfbced,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/command/install_lib.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,TODO: is it necessary to short-circuit here? i.e. what's the cost
aahjfbcgh,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/command/sdist.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,only needed on Python prior to 3.6.
aahjfbcgi,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/command/sdist.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,This grody hack closes the template file (MANIFEST.in) if an
aahjfbdec,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/dist.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Some people apparently take \"version number\" too literally :)
aahjfbdfa,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/dist.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX may convert to set here when we can rely on set being builtin
aahjfbdgd,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/dist.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,workaround for #334.
aahjfbdgh,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/extension.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,The *args is needed for compatibility as calls may use positional
aahjfbdha,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/extern/__init__.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,mysterious hack:
aahjfbedc,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/monkey.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,\"\"\" || Everything is private. Contact the project team || if you think you need this functionality. || \"\"\"
aahjfbede,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/monkey.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,fix findall bug in distutils (http:\/\/bugs.python.org\/issue12885)
aahjfbegc,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/msvc.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,For VC++ 9.0; if IA64 support is needed; redirect user
aahjfbfac,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/package_index.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX Yuck
aahjfbfce,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/package_index.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,this line is here to fix emacs' cruddy broken syntax highlighting
aahjfbfje,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/pep425tags.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX remove distutils dependency
aahjfbgdg,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/py27compat.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,\"\"\"Workaround for http:\/\/bugs.python.org\/issue24672\"\"\"
aahjfbgij,ryfeus/gcf-packs,pandas_numpy/sources/setuptools/ssl_support.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,XXX according to RFC 2818; the most specific Common Name
aahjfbhdj,ryfeus/gcf-packs,pandas_numpy/sources/six.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,This is a bit ugly; but it avoids running this again by
aahjfbhei,ryfeus/gcf-packs,pandas_numpy/sources/six.py,255a05a5980efb8b096c283d79872d0695886161,STILL_EXISTS,Workaround for standalone backslash
aahjfbhjf,ryfeus/gcf-packs,selenium_chrome/source/fake_useragent/utils.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,browser_key's is event better
aahjfbiaa,ryfeus/gcf-packs,selenium_chrome/source/fake_useragent/utils.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,TODO: drop these useless functions
aahjfbich,ryfeus/gcf-packs,selenium_chrome/source/pkg_resources/__init__.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,XXX backward compat
aahjfbidh,ryfeus/gcf-packs,selenium_chrome/source/pkg_resources/__init__.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,XXX Linux and other platforms' special cases should go here
aahjfbief,ryfeus/gcf-packs,selenium_chrome/source/pkg_resources/__init__.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,XXX add more info
aahjfbieg,ryfeus/gcf-packs,selenium_chrome/source/pkg_resources/__init__.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,workaround a cache issue
aahjfbifj,ryfeus/gcf-packs,selenium_chrome/source/pkg_resources/__init__.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,Oops; the \"best\" so far conflicts with a dependency
aahjfbigb,ryfeus/gcf-packs,selenium_chrome/source/pkg_resources/__init__.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,Register the new requirements needed by req
aahjfbiha,ryfeus/gcf-packs,selenium_chrome/source/pkg_resources/__init__.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,XXX backward compatibility
aahjfbihf,ryfeus/gcf-packs,selenium_chrome/source/pkg_resources/__init__.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,XXX
aahjfbjac,ryfeus/gcf-packs,selenium_chrome/source/pkg_resources/__init__.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,Python 2 on Windows needs to be handled this way :(
aahjfbjbi,ryfeus/gcf-packs,selenium_chrome/source/pkg_resources/__init__.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,metadata until\/unless it's actually needed.  (i.e.; some distributions
aahjfbjeb,ryfeus/gcf-packs,selenium_chrome/source/pkg_resources/__init__.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,this method. In the future this should be smarter and follow PEP 440
aahjfcacc,ryfeus/gcf-packs,selenium_chrome/source/pkg_resources/_vendor/packaging/requirements.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,TODO: Can we test whether something is contained within a requirement?
aahjfcafa,ryfeus/gcf-packs,selenium_chrome/source/pkg_resources/_vendor/packaging/specifiers.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,implement this in terms of the other specifiers instead of
aahjfcbgg,ryfeus/gcf-packs,selenium_chrome/source/pkg_resources/_vendor/packaging/version.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,which uses the defacto standard originally implemented by setuptools;
aahjfccce,ryfeus/gcf-packs,selenium_chrome/source/pkg_resources/_vendor/pyparsing.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,\"\"\"(Deprecated) Predefined expression of 1 or more printable words or quoted strings; separated by commas.\r ||    This expression is deprecated in favor of L{pyparsing_common.comma_separated_list}.\"\"\"
aahjfcebe,ryfeus/gcf-packs,selenium_chrome/source/pkg_resources/_vendor/six.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,This is a bit ugly; but it avoids running this again by
aahjfcecd,ryfeus/gcf-packs,selenium_chrome/source/pkg_resources/_vendor/six.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,Workaround for standalone backslash
aahjfceec,ryfeus/gcf-packs,selenium_chrome/source/pkg_resources/extern/__init__.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,mysterious hack:
aahjfdbfa,ryfeus/gcf-packs,selenium_chrome/source/selenium/webdriver/firefox/firefox_binary.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,copy it and modify as needed.
aahjfdjhg,ryfeus/gcf-packs,selenium_chrome/source/setuptools/__init__.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,Make sure we have any requirements needed to interpret 'attrs'.
aahjfeabb,ryfeus/gcf-packs,selenium_chrome/source/setuptools/_vendor/packaging/requirements.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,TODO: Can we test whether something is contained within a requirement?
aahjfeadj,ryfeus/gcf-packs,selenium_chrome/source/setuptools/_vendor/packaging/specifiers.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,implement this in terms of the other specifiers instead of
aahjfebff,ryfeus/gcf-packs,selenium_chrome/source/setuptools/_vendor/packaging/version.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,which uses the defacto standard originally implemented by setuptools;
aahjfecbd,ryfeus/gcf-packs,selenium_chrome/source/setuptools/_vendor/pyparsing.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,\"\"\"(Deprecated) Predefined expression of 1 or more printable words or quoted strings; separated by commas.\r ||    This expression is deprecated in favor of L{pyparsing_common.comma_separated_list}.\"\"\"
aahjfeead,ryfeus/gcf-packs,selenium_chrome/source/setuptools/_vendor/six.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,This is a bit ugly; but it avoids running this again by
aahjfeebc,ryfeus/gcf-packs,selenium_chrome/source/setuptools/_vendor/six.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,Workaround for standalone backslash
aahjfeeea,ryfeus/gcf-packs,selenium_chrome/source/setuptools/archive_util.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,XXX Ugh
aahjfeeec,ryfeus/gcf-packs,selenium_chrome/source/setuptools/build_meta.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,\"\"\"A PEP 517 interface to setuptools ||  || Previously; when a user or a command line tool (let's call it a \"frontend\") || needed to make a request of setuptools to take a certain action; for || example; generating a list of installation requirements; the frontend would || would call \"setup.py egg_info\" or \"setup.py bdist_wheel\" on the command line. ||  || PEP 517 defines a different method of interfacing with setuptools. Rather || than calling \"setup.py\" directly; the frontend should: ||  ||   1. Set the current directory to the directory with a setup.py file ||   2. Import this module into a safe python interpreter (one in which ||      setuptools can potentially set global variables or crash hard). ||   3. Call one of the functions defined in PEP 517. ||  || What each function does is defined in PEP 517. However; here is a \"casual\" || definition of the functions (this definition should not be relied on for || bug reports or API stability): ||  ||   - `build_wheel`: build a wheel in the folder and return the basename ||   - `get_requires_for_build_wheel`: get the `setup_requires` to build ||   - `prepare_metadata_for_build_wheel`: get the `install_requires` ||   - `build_sdist`: build an sdist in the folder and return the basename ||   - `get_requires_for_build_sdist`: get the `setup_requires` to build ||  || Again; this is not a formal definition! Just a \"taste\" of the module. || \"\"\"
aahjfeefa,ryfeus/gcf-packs,selenium_chrome/source/setuptools/command/bdist_egg.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,Hack for packages that install data to install's --install-lib
aahjfeefb,ryfeus/gcf-packs,selenium_chrome/source/setuptools/command/bdist_egg.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,XXX else: raise ???
aahjfeejc,ryfeus/gcf-packs,selenium_chrome/source/setuptools/command/build_ext.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,XXX Help!  I don't have any idea whether these are right...
aahjfeejj,ryfeus/gcf-packs,selenium_chrome/source/setuptools/command/build_ext.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,XXX what to do with conflicts?
aahjfefab,ryfeus/gcf-packs,selenium_chrome/source/setuptools/command/build_ext.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,hack so distutils' build_extension() builds a library instead
aahjfefac,ryfeus/gcf-packs,selenium_chrome/source/setuptools/command/build_ext.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,XXX this should check to ensure the lib is actually being built
aahjfefad,ryfeus/gcf-packs,selenium_chrome/source/setuptools/command/build_ext.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,XXX as dynamic; and not just using a locally-found version or a
aahjfefae,ryfeus/gcf-packs,selenium_chrome/source/setuptools/command/build_ext.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,XXX static-compiled version
aahjfefbb,ryfeus/gcf-packs,selenium_chrome/source/setuptools/command/build_ext.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,XXX we need to either disallow these attrs on Library instances;
aahjfefed,ryfeus/gcf-packs,selenium_chrome/source/setuptools/command/develop.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,XXX
aahjfeffb,ryfeus/gcf-packs,selenium_chrome/source/setuptools/command/develop.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,XXX should also check for entry point scripts!
aahjfefjd,ryfeus/gcf-packs,selenium_chrome/source/setuptools/command/easy_install.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,cast to str as workaround for #709 and #710 and #712
aahjfefjh,ryfeus/gcf-packs,selenium_chrome/source/setuptools/command/easy_install.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,Installation is also needed if file in tmpdir or is not an egg
aahjfegac,ryfeus/gcf-packs,selenium_chrome/source/setuptools/command/easy_install.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,XXX this is not the distribution we were looking for
aahjfegag,ryfeus/gcf-packs,selenium_chrome/source/setuptools/command/easy_install.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,if the only thing there is a directory; move it instead
aahjfegbj,ryfeus/gcf-packs,selenium_chrome/source/setuptools/command/easy_install.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,XXX
aahjfegcj,ryfeus/gcf-packs,selenium_chrome/source/setuptools/command/easy_install.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,TODO: self.report_extras(req; dist)
aahjfegdh,ryfeus/gcf-packs,selenium_chrome/source/setuptools/command/easy_install.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,XXX should this check for latest version?
aahjfeghd,ryfeus/gcf-packs,selenium_chrome/source/setuptools/command/easy_install.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,exact loader instance used when importing them. Python 3.4 docs state
aahjfegij,ryfeus/gcf-packs,selenium_chrome/source/setuptools/command/easy_install.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,this is really needed.
aahjfehac,ryfeus/gcf-packs,selenium_chrome/source/setuptools/command/easy_install.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,and have PyPy repopulate it as needed. The downside is that if there are any
aahjfehba,ryfeus/gcf-packs,selenium_chrome/source/setuptools/command/easy_install.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,spelling; but that seems like a way too invasive move (this cache
aahjfehhj,ryfeus/gcf-packs,selenium_chrome/source/setuptools/command/egg_info.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,Fix older sdists built on Windows
aahjfeiab,ryfeus/gcf-packs,selenium_chrome/source/setuptools/command/install.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,XXX Python 3.1 doesn't see _nc if this is inside the class
aahjfeiah,ryfeus/gcf-packs,selenium_chrome/source/setuptools/command/install_lib.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,TODO: is it necessary to short-circuit here? i.e. what's the cost
aahjfeidb,ryfeus/gcf-packs,selenium_chrome/source/setuptools/command/sdist.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,only needed on Python prior to 3.6.
aahjfeidc,ryfeus/gcf-packs,selenium_chrome/source/setuptools/command/sdist.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,This grody hack closes the template file (MANIFEST.in) if an
aahjfejag,ryfeus/gcf-packs,selenium_chrome/source/setuptools/dist.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,Some people apparently take \"version number\" too literally :)
aahjfejbe,ryfeus/gcf-packs,selenium_chrome/source/setuptools/dist.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,XXX may convert to set here when we can rely on set being builtin
aahjfejch,ryfeus/gcf-packs,selenium_chrome/source/setuptools/dist.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,workaround for #334.
aahjfejdb,ryfeus/gcf-packs,selenium_chrome/source/setuptools/extension.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,The *args is needed for compatibility as calls may use positional
aahjfejde,ryfeus/gcf-packs,selenium_chrome/source/setuptools/extern/__init__.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,mysterious hack:
aahjfejjg,ryfeus/gcf-packs,selenium_chrome/source/setuptools/monkey.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,\"\"\" || Everything is private. Contact the project team || if you think you need this functionality. || \"\"\"
aahjfejji,ryfeus/gcf-packs,selenium_chrome/source/setuptools/monkey.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,fix findall bug in distutils (http:\/\/bugs.python.org\/issue12885)
aahjffacg,ryfeus/gcf-packs,selenium_chrome/source/setuptools/msvc.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,For VC++ 9.0; if IA64 support is needed; redirect user
aahjffagg,ryfeus/gcf-packs,selenium_chrome/source/setuptools/package_index.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,XXX Yuck
aahjffaii,ryfeus/gcf-packs,selenium_chrome/source/setuptools/package_index.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,this line is here to fix emacs' cruddy broken syntax highlighting
aahjffbfi,ryfeus/gcf-packs,selenium_chrome/source/setuptools/pep425tags.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,XXX remove distutils dependency
aahjffcaa,ryfeus/gcf-packs,selenium_chrome/source/setuptools/py27compat.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,\"\"\"Workaround for http:\/\/bugs.python.org\/issue24672\"\"\"
aahjffcfd,ryfeus/gcf-packs,selenium_chrome/source/setuptools/ssl_support.py,307aae04664413c6f4cbd8481d8504cc01d2219a,STILL_EXISTS,XXX according to RFC 2818; the most specific Common Name
aahjffcja,ryfeus/gcf-packs,tensorflow2.0/source/absl/_enum_module.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\"Import workaround so that Bazel; Py2\/Py3; and enum34 package work together. ||  || This works around a problem due to the combination of Bazel putting || third party packages before the stdlib in PYTHONPATH. What happens is: ||   * The enum34 PyPi package is imported as 'enum'. ||   * Bazel puts the path to enum34 before the stdlib; hence 'import enum' ||     will prefer to use enum34 from above instead of the stdlib. ||   * In Python 3; enum34 is used instead of the stdlib; which breaks ||     lots of things. It works fine in Python 2; since there is no enum ||     module. ||  || To work around this; we do 3 things: ||   1. Put the enum34 code on PYTHONPATH; but not directly importable as ||      'enum'; it is under the (non importable) directory name with the ||      PyPi package name and version. ||   2. Try to import enum normally; if it works; great. This makes Py3 work ||      (as well as Py2 when enum is available as normal). ||   3. If the normal enum import failed; then try to find the enum34 ||      entry on sys.path; and append the missing directory name. ||  || Once it is successfully imported; expose the module directly. This || prevents importing the module twice under different names. e.g.; || the following is true: ||   from absl._enum_module import enum as absl_enum ||   import enum as normal_enum ||   assert absl_enum is normal_enum || \"\"\"
aahjffddb,ryfeus/gcf-packs,tensorflow2.0/source/absl/app.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Unused.
aahjffdfe,ryfeus/gcf-packs,tensorflow2.0/source/absl/flags/__init__.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\"This package is used to define and parse command line flags. ||  || This package defines a *distributed* flag-definition policy: rather than || an application having to define all flags in or near main(); each Python || module defines flags that are useful to it.  When one Python module || imports another; it gains access to the other's flags.  (This is || implemented by having all modules share a common; global registry object || containing all the flag information.) ||  || Flags are defined through the use of one of the DEFINE_xxx functions. || The specific function used determines how the flag is parsed; checked; || and optionally type-converted; when it's seen on the command line. || \"\"\"
aahjffeab,ryfeus/gcf-packs,tensorflow2.0/source/absl/flags/_argument_parser.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Unused.
aahjfffcf,ryfeus/gcf-packs,tensorflow2.0/source/absl/flags/_flagvalues.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,names. Those flag values would not be accessible via the FLAGS.xxx form.
aahjfffed,ryfeus/gcf-packs,tensorflow2.0/source/absl/flags/_flagvalues.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,allow_using_method_names=True in DEFINE_xxx functions.
aahjffgab,ryfeus/gcf-packs,tensorflow2.0/source/absl/flags/_flagvalues.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"no\" then maybe it was a boolean flag specified in the
aahjffjah,ryfeus/gcf-packs,tensorflow2.0/source/absl/logging/converter.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\"Module to convert log levels between Abseil Python; C++; and Python standard. ||  || This converter has to convert (best effort) between three different || logging level schemes: ||   cpp      = The C++ logging level scheme used in Abseil C++. ||   absl     = The absl.logging level scheme used in Abseil Python. ||   standard = The python standard library logging level scheme. ||  || Here is a handy ascii chart for easy mental mapping. ||  ||   LEVEL    | cpp |  absl  | standard | ||   ---------+-----+--------+----------+ ||   DEBUG    |  0  |    1   |    10    | ||   INFO     |  0  |    0   |    20    | ||   WARNING  |  1  |   -1   |    30    | ||   ERROR    |  2  |   -2   |    40    | ||   CRITICAL |  3  |   -3   |    50    | ||   FATAL    |  3  |   -3   |    50    | ||  || Note: standard logging CRITICAL is mapped to absl\/cpp FATAL. || However; only CRITICAL logs from the absl logger (or absl.logging.fatal) will || terminate the program. CRITICAL logs from non-absl loggers are treated as || error logs with a message prefix \"CRITICAL - \". ||  || Converting from standard to absl or cpp is a lossy conversion. || Converting back to standard will lose granularity.  For this reason; || users should always try to convert to standard; the richest || representation; before manipulating the levels; and then only to cpp || or absl if those level schemes are absolutely necessary. || \"\"\"
aahjfgbbg,ryfeus/gcf-packs,tensorflow2.0/source/absl/testing/absltest.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,This is a workaround if unittest.TestCase.__init__ was never run.
aahjfgcje,ryfeus/gcf-packs,tensorflow2.0/source/absl/testing/xml_reporter.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,seems to often end up being too smart for it's own good not escaping properly.
aahjfgdac,ryfeus/gcf-packs,tensorflow2.0/source/absl/testing/xml_reporter.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Unfortunately there is no better way of knowning the test and class.
aahjfgdad,ryfeus/gcf-packs,tensorflow2.0/source/absl/testing/xml_reporter.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Worse; unittest uses _ErrorHandler instances to represent class \/ module
aahjfgdfg,ryfeus/gcf-packs,tensorflow2.0/source/absl/third_party/unittest3_backport/case.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,clear the outcome; no more needed
aahjfgdjd,ryfeus/gcf-packs,tensorflow2.0/source/astor/code_gen.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX: Python 2.6 \/ 3.0 compatibility
aahjfgeeb,ryfeus/gcf-packs,tensorflow2.0/source/astor/node_util.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX: self?
aahjfgeei,ryfeus/gcf-packs,tensorflow2.0/source/astor/rtrip.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO:  Remove this workaround once we remove version 2 support
aahjfggdc,ryfeus/gcf-packs,tensorflow2.0/source/google/protobuf/descriptor.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,* [Other examples here as needed].
aahjfghjc,ryfeus/gcf-packs,tensorflow2.0/source/google/protobuf/descriptor_pool.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,maybe year 2020 for compatibility issue (with 3.4.1 only).
aahjfgiad,ryfeus/gcf-packs,tensorflow2.0/source/google/protobuf/descriptor_pool.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,maybe 2020 for compatibility reason (with 3.4.1 only).
aahjfhaji,ryfeus/gcf-packs,tensorflow2.0/source/google/protobuf/internal/decoder.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\"Code for decoding protocol buffer primitives. ||  || This code is very similar to encoder.py -- read the docs for that module first. ||  || A \"decoder\" is a function with the signature: ||   Decode(buffer; pos; end; message; field_dict) || The arguments are: ||   buffer:     The string containing the encoded message. ||   pos:        The current position in the string. ||   end:        The position in the string where the current message ends.  May be ||               less than len(buffer) if we're reading a sub-message. ||   message:    The message object into which we're parsing. ||   field_dict: message._fields (avoids a hashtable lookup). || The decoder reads the field and stores it into field_dict; returning the new || buffer position.  A decoder for a repeated field may proactively decode all of || the elements of that field; if they appear consecutively. ||  || Note that decoders may throw any of the following: ||   IndexError:  Indicates a truncated message. ||   struct.error:  Unpacking of a fixed-width field failed. ||   message.DecodeError:  Other errors. ||  || Decoders are expected to raise an exception if they are called with pos > end. || This allows callers to be lax about bounds checking:  it's fineto read past || \"end\" as long as you are sure that someone else will notice and throw an || exception later on. ||  || Something up the call stack is expected to catch IndexError and struct.error || and convert them to message.DecodeError. ||  || Decoders are constructed using decoder constructors with the signature: ||   MakeDecoder(field_number; is_repeated; is_packed; key; new_default) || The arguments are: ||   field_number:  The field number of the field we want to decode. ||   is_repeated:   Is the field a repeated field? (bool) ||   is_packed:     Is the field a packed field? (bool) ||   key:           The key to use when looking up the field within field_dict. ||                  (This is actually the FieldDescriptor but nothing in this ||                  file should depend on that.) ||   new_default:   A function which takes a message object as a parameter and ||                  returns a new instance of the default value for this field. ||                  (This is called for repeated fields and sub-messages; when an ||                  instance does not already exist.) ||  || As with encoders; we define a decoder constructor for every type of field. || Then; for every field of every message class we construct an actual decoder. || That decoder goes into a dict indexed by tag; so when we decode a message || we repeatedly read a tag; look up the corresponding decoder; and invoke it. || \"\"\"
aahjfhdbd,ryfeus/gcf-packs,tensorflow2.0/source/google/protobuf/internal/descriptor_pool_test.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(jieluo): Fix python to raise correct errors.
aahjfhefe,ryfeus/gcf-packs,tensorflow2.0/source/google/protobuf/internal/encoder.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\"Code for encoding protocol message primitives. ||  || Contains the logic for encoding every logical protocol field type || into one of the 5 physical wire types. ||  || This code is designed to push the Python interpreter's performance to the || limits. ||  || The basic idea is that at startup time; for every field (i.e. every || FieldDescriptor) we construct two functions:  a \"sizer\" and an \"encoder\".  The || sizer takes a value of this field's type and computes its byte size.  The || encoder takes a writer function and a value.  It encodes the value into byte || strings and invokes the writer function to write those strings.  Typically the || writer function is the write() method of a BytesIO. ||  || We try to do as much work as possible when constructing the writer and the || sizer rather than when calling them.  In particular: || * We copy any needed global functions to local variables; so that we do not need ||   to do costly global table lookups at runtime. || * Similarly; we try to do any attribute lookups at startup time if possible. || * Every field's tag is encoded to bytes at startup; since it can't change at ||   runtime. || * Whatever component of the field size we can compute at startup; we do. || * We *avoid* sharing code if doing so would make the code slower and not sharing ||   does not burden us too much.  For example; encoders for repeated fields do ||   not just call the encoders for singular fields in a loop because this would ||   add an extra function call overhead for every loop iteration; instead; we ||   manually inline the single-value encoder into the loop. || * If a Python function lacks a return statement; Python actually generates ||   instructions to pop the result of the last statement off the stack; push ||   None onto the stack; and then return that.  If we really don't care what ||   value is returned; then we can save two instructions by returning the ||   result of the last statement.  It looks funny but it helps. || * We assume that type and bounds checking has happened at a higher level. || \"\"\"
aahjfhfag,ryfeus/gcf-packs,tensorflow2.0/source/google/protobuf/internal/encoder.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,obvious way to avoid this within the current design without tons of code
aahjfhicd,ryfeus/gcf-packs,tensorflow2.0/source/google/protobuf/internal/message_test.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\"Tests python protocol buffers against the golden message. ||  || Note that the golden messages exercise every known field type; thus this || test ends up exercising and verifying nearly all of the parsing and || serialization code in the whole library. ||  || TODO(kenton):  Merge with wire_format_test?  It doesn't make a whole lot of || sense to call this a test of the \"message\" module; which only declares an || abstract interface. || \"\"\"
aahjfhigg,ryfeus/gcf-packs,tensorflow2.0/source/google/protobuf/internal/message_test.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(jieluo): Fix cpp extension to raise error instead of warning.
aahjfhjjb,ryfeus/gcf-packs,tensorflow2.0/source/google/protobuf/internal/message_test.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,properly. The line below is likely to cause a segmentation fault.
aahjficaf,ryfeus/gcf-packs,tensorflow2.0/source/google/protobuf/internal/python_message.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Kenton says:  The above is a BAD IDEA.  People rely on being able to use
aahjficfa,ryfeus/gcf-packs,tensorflow2.0/source/google/protobuf/internal/python_message.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Check _cached_byte_size_dirty inline to improve performance; since scalar
aahjficgi,ryfeus/gcf-packs,tensorflow2.0/source/google/protobuf/internal/python_message.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(robinson): This probably needs to be thread-safe(?)
aahjficih,ryfeus/gcf-packs,tensorflow2.0/source/google/protobuf/internal/python_message.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(jieluo): Fix UnknownFieldSet to consider MessageSet extensions;
aahjfieic,ryfeus/gcf-packs,tensorflow2.0/source/google/protobuf/internal/reflection_test.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(jieluo): Fix type checking difference for python and c extension
aahjfifhi,ryfeus/gcf-packs,tensorflow2.0/source/google/protobuf/internal/reflection_test.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(anuraag): Implement deepcopy for extension dict
aahjfjagg,ryfeus/gcf-packs,tensorflow2.0/source/google/protobuf/internal/text_format_test.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Ideally the schemas would be made more similar so these tests could pass.
aahjfjbeg,ryfeus/gcf-packs,tensorflow2.0/source/google/protobuf/internal/type_checkers.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,themselves in the foot in obvious ways.
aahjfjbfe,ryfeus/gcf-packs,tensorflow2.0/source/google/protobuf/internal/type_checkers.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,efficient.
aahjfjcdg,ryfeus/gcf-packs,tensorflow2.0/source/google/protobuf/internal/unknown_fields_test.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Fetching the enum field shouldn't crash; instead returning the
aahjfjhch,ryfeus/gcf-packs,tensorflow2.0/source/google/protobuf/message.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(robinson): MergeFromString() should probably return None and be
aahjfjhdc,ryfeus/gcf-packs,tensorflow2.0/source/google/protobuf/message.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(robinson): Decide whether we like these better
aahjfjhde,ryfeus/gcf-packs,tensorflow2.0/source/google/protobuf/message.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,on the instances themselves.  This way is less consistent
aahjgabci,ryfeus/gcf-packs,tensorflow2.0/source/google/protobuf/text_encoding.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,length.  So; \"\\0011\".encode('string_escape') ends up being \"\\\\x011\"; which
aahjgafgh,ryfeus/gcf-packs,tensorflow2.0/source/grpc/_auth.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Hack to determine if these are JWT creds and we need to pass
aahjgahgg,ryfeus/gcf-packs,tensorflow2.0/source/grpc/beta/_client_adaptations.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(https:\/\/github.com\/grpc\/grpc\/issues\/4078): design; implement.
aahjgahjg,ryfeus/gcf-packs,tensorflow2.0/source/grpc/beta/_server_adaptations.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(https:\/\/github.com\/grpc\/grpc\/issues\/4078): design; implement.
aahjgajjb,ryfeus/gcf-packs,tensorflow2.0/source/grpc/framework/foundation/future.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\"A Future interface. ||  || Python doesn't have a Future interface in its standard library. In the absence || of such a standard; three separate; incompatible implementations || (concurrent.futures.Future; ndb.Future; and asyncio.Future) have appeared. This || interface attempts to be as compatible as possible with || concurrent.futures.Future. From ndb.Future it adopts a traceback-object accessor || method. ||  || Unlike the concrete and implemented Future classes listed above; the Future || class defined in this module is an entirely abstract interface that anyone may || implement and use. ||  || The one known incompatibility between this interface and the interface of || concurrent.futures.Future is that this interface defines its own CancelledError || and TimeoutError exceptions rather than raising the implementation-private || concurrent.futures._base.CancelledError and the || built-in-but-only-in-3.3-and-later TimeoutError. || \"\"\"
aahjgbaah,ryfeus/gcf-packs,tensorflow2.0/source/grpc/framework/foundation/future.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,type of this method would probably be a sum type like:
aahjgbbae,ryfeus/gcf-packs,tensorflow2.0/source/grpc/framework/interfaces/base/base.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\"The base interface of RPC Framework. ||  || Implementations of this interface support the conduct of \"operations\": || exchanges between two distinct ends of an arbitrary number of data payloads || and metadata such as a name for the operation; initial and terminal metadata || in each direction; and flow control. These operations may be used for transfers || of data; remote procedure calls; status indication; or anything else || applications choose. || \"\"\"
aahjgbcef,ryfeus/gcf-packs,tensorflow2.0/source/h5py/_hl/attrs.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,is compatible; and reshape if needed.
aahjgbcjh,ryfeus/gcf-packs,tensorflow2.0/source/h5py/_hl/dataset.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,to workaround a possible h5py bug in the conversion.
aahjgbddb,ryfeus/gcf-packs,tensorflow2.0/source/h5py/_hl/dataset.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Single-field recarray convention
aahjgbdhc,ryfeus/gcf-packs,tensorflow2.0/source/h5py/_hl/dims.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Here is a workaround:
aahjgbdig,ryfeus/gcf-packs,tensorflow2.0/source/h5py/_hl/files.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Prevent swallowing unused key arguments
aahjgbefe,ryfeus/gcf-packs,tensorflow2.0/source/h5py/_hl/group.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: more elegant way to pass these (dcpl to create_dataset?)
aahjgbefi,ryfeus/gcf-packs,tensorflow2.0/source/h5py/_hl/group.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: support kwargs like require_dataset
aahjgbfba,ryfeus/gcf-packs,tensorflow2.0/source/h5py/_hl/selections2.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Not compound; or all fields needed
aahjgbfhj,ryfeus/gcf-packs,tensorflow2.0/source/h5py/version.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,needed for our use case
aahjgbgci,ryfeus/gcf-packs,tensorflow2.0/source/keras_applications/mobilenet.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\"MobileNet v1 models for Keras. ||  || MobileNet is a general architecture and can be used for multiple use cases. || Depending on the use case; it can use different input layer size and || different width factors. This allows different width models to reduce || the number of multiply-adds and thereby || reduce inference cost on mobile devices. ||  || MobileNets support any input size greater than 32 x 32; with larger image sizes || offering better performance. || The number of parameters and number of multiply-adds || can be modified by using the `alpha` parameter; || which increases\/decreases the number of filters in each layer. || By altering the image size and `alpha` parameter; || all 16 models from the paper can be built; with ImageNet weights provided. ||  || The paper demonstrates the performance of MobileNets using `alpha` values of || 1.0 (also called 100 % MobileNet); 0.75; 0.5 and 0.25. || For each of these `alpha` values; weights for 4 different input image sizes || are provided (224; 192; 160; 128). ||  || The following table describes the size and accuracy of the 100% MobileNet || on size 224 x 224: || ---------------------------------------------------------------------------- || Width Multiplier (alpha) | ImageNet Acc |  Multiply-Adds (M) |  Params (M) || ---------------------------------------------------------------------------- || |   1.0 MobileNet-224    |    70.6 %     |        529        |     4.2     | || |   0.75 MobileNet-224   |    68.4 %     |        325        |     2.6     | || |   0.50 MobileNet-224   |    63.7 %     |        149        |     1.3     | || |   0.25 MobileNet-224   |    50.6 %     |        41         |     0.5     | || ---------------------------------------------------------------------------- ||  || The following table describes the performance of || the 100 % MobileNet on various input sizes: || ------------------------------------------------------------------------ ||       Resolution      | ImageNet Acc | Multiply-Adds (M) | Params (M) || ------------------------------------------------------------------------ || |  1.0 MobileNet-224  |    70.6 %    |        529        |     4.2     | || |  1.0 MobileNet-192  |    69.1 %    |        529        |     4.2     | || |  1.0 MobileNet-160  |    67.2 %    |        529        |     4.2     | || |  1.0 MobileNet-128  |    64.4 %    |        529        |     4.2     | || ------------------------------------------------------------------------ ||  || The weights for all 16 models are obtained and translated || from TensorFlow checkpoints found at || https:\/\/github.com\/tensorflow\/models\/blob\/master\/research\/slim\/nets\/mobilenet_v1.md ||  || # Reference ||  || - [MobileNets: Efficient Convolutional Neural Networks for ||    Mobile Vision Applications](https:\/\/arxiv.org\/pdf\/1704.04861.pdf)) || \"\"\"
aahjgbgdf,ryfeus/gcf-packs,tensorflow2.0/source/keras_applications/mobilenet_v2.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO Change path to v1.1
aahjgbgfg,ryfeus/gcf-packs,tensorflow2.0/source/keras_applications/nasnet.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\"NASNet-A models for Keras. ||  || NASNet refers to Neural Architecture Search Network; a family of models || that were designed automatically by learning the model architectures || directly on the dataset of interest. ||  || Here we consider NASNet-A; the highest performance model that was found || for the CIFAR-10 dataset; and then extended to ImageNet 2012 dataset; || obtaining state of the art performance on CIFAR-10 and ImageNet 2012. || Only the NASNet-A models; and their respective weights; which are suited || for ImageNet 2012 are provided. ||  || The below table describes the performance on ImageNet 2012: || -------------------------------------------------------------------------------- ||       Architecture       | Top-1 Acc | Top-5 Acc |  Multiply-Adds |  Params (M) || -------------------------------------------------------------------------------- || |   NASNet-A (4 @ 1056)  |   74.0 %  |   91.6 %  |       564 M    |     5.3    | || |   NASNet-A (6 @ 4032)  |   82.7 %  |   96.2 %  |      23.8 B    |    88.9    | || -------------------------------------------------------------------------------- ||  || Weights obtained from the official TensorFlow repository found at || https:\/\/github.com\/tensorflow\/models\/tree\/master\/research\/slim\/nets\/nasnet ||  || # References ||  ||  - [Learning Transferable Architectures for Scalable Image Recognition] ||     (https:\/\/arxiv.org\/abs\/1707.07012) ||  || This model is based on the following implementations: ||  ||  - [TF Slim Implementation] ||    (https:\/\/github.com\/tensorflow\/models\/blob\/master\/research\/slim\/nets\/nasnet\/nasnet.py) ||  - [TensorNets implementation] ||    (https:\/\/github.com\/taehoonlee\/tensornets\/blob\/master\/tensornets\/nasnets.py) || \"\"\"
aahjgbhdc,ryfeus/gcf-packs,tensorflow2.0/source/keras_preprocessing/image.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed if we want to do something like:
aahjgbhja,ryfeus/gcf-packs,tensorflow2.0/source/keras_preprocessing/image/iterator.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed if we want to do something like:
aahjgbhjf,ryfeus/gcf-packs,tensorflow2.0/source/keras_preprocessing/image/iterator.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,self.filepaths is dynamic; is better to call it once outside the loop
aahjgbiga,ryfeus/gcf-packs,tensorflow2.0/source/markdown/blockparser.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: remove this later
aahjgcbcd,ryfeus/gcf-packs,tensorflow2.0/source/markdown/extensions/footnotes.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Blank line: _maybe_ we are done.
aahjgcbdh,ryfeus/gcf-packs,tensorflow2.0/source/markdown/extensions/footnotes.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,additional links if needed.
aahjgcbhf,ryfeus/gcf-packs,tensorflow2.0/source/markdown/extensions/smarty.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: remove this later
aahjgcbib,ryfeus/gcf-packs,tensorflow2.0/source/markdown/extensions/tables.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Get alignment of columns
aahjgcbif,ryfeus/gcf-packs,tensorflow2.0/source/markdown/extensions/tables.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,contains the same number of columns.
aahjgccdf,ryfeus/gcf-packs,tensorflow2.0/source/markdown/extensions/toc.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Remove the data-toc-label attribute as it is no longer needed
aahjgcchb,ryfeus/gcf-packs,tensorflow2.0/source/markdown/inlinepatterns.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: remove this later
aahjgcdbc,ryfeus/gcf-packs,tensorflow2.0/source/markdown/pep562.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Assemble major; minor; micro version and append `pre`; `post`; or `dev` if needed..
aahjgcddh,ryfeus/gcf-packs,tensorflow2.0/source/markdown/preprocessors.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,keep checking conditions below and maybe just append
aahjgcebb,ryfeus/gcf-packs,tensorflow2.0/source/markdown/test_tools.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: If\/when actual output ends with a newline; then use:
aahjgceca,ryfeus/gcf-packs,tensorflow2.0/source/markdown/test_tools.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: If\/when py2 support is dropped change to:
aahjgcecf,ryfeus/gcf-packs,tensorflow2.0/source/markdown/treeprocessors.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: remove this later
aahjgcehd,ryfeus/gcf-packs,tensorflow2.0/source/markdown/util.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: remove this later
aahjgcfbd,ryfeus/gcf-packs,tensorflow2.0/source/numpy/_globals.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,needed for python 2 to preserve identity through a pickle
aahjgcfbf,ryfeus/gcf-packs,tensorflow2.0/source/numpy/_pytesttester.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,FIXME This is no longer needed? Assume it was for use in tests.
aahjgcfeg,ryfeus/gcf-packs,tensorflow2.0/source/numpy/conftest.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,FIXME when yield tests are gone.
aahjgcffa,ryfeus/gcf-packs,tensorflow2.0/source/numpy/core/__init__.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,convention for storing \/ loading the DLL from
aahjgcgji,ryfeus/gcf-packs,tensorflow2.0/source/numpy/core/_dtype.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,hack to obtain the native and swapped byte order characters
aahjgcgjj,ryfeus/gcf-packs,tensorflow2.0/source/numpy/core/_dtype.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: this path can never be reached
aahjgchaa,ryfeus/gcf-packs,tensorflow2.0/source/numpy/core/_dtype.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: this duplicates the C append_metastr_to_string
aahjgchca,ryfeus/gcf-packs,tensorflow2.0/source/numpy/core/_dtype_ctypes.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\" || Conversion from ctypes to dtype. ||  || In an ideal world; we could acheive this through the PEP3118 buffer protocol; || something like:: ||  ||     def dtype_from_ctypes_type(t): ||         # needed to ensure that the shape of `t` is within memoryview.format ||         class DummyStruct(ctypes.Structure): ||             _fields_ = [('a'; t)] ||  ||         # empty to avoid memory allocation ||         ctype_0 = (DummyStruct * 0)() ||         mv = memoryview(ctype_0) ||  ||         # convert the struct; and slice back out the field ||         return _dtype_from_pep3118(mv.format)['a'] ||  || Unfortunately; this fails because: ||  || * ctypes cannot handle length-0 arrays with PEP3118 (bpo-32782) || * PEP3118 cannot represent unions; but both numpy and ctypes can || * ctypes cannot handle big-endian structs with PEP3118 (bpo-32780) || \"\"\"
aahjgchdb,ryfeus/gcf-packs,tensorflow2.0/source/numpy/core/_internal.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,This calls itself recursively but should eventually hit
aahjgchea,ryfeus/gcf-packs,tensorflow2.0/source/numpy/core/_internal.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,astr is a string (perhaps comma separated)
aahjgciej,ryfeus/gcf-packs,tensorflow2.0/source/numpy/core/arrayprint.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Note: Both scalartypes.c.src and arrayprint.py implement strs for numpy
aahjgcijg,ryfeus/gcf-packs,tensorflow2.0/source/numpy/core/arrayprint.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,pad_right is only needed for nan length calculation
aahjgcjcj,ryfeus/gcf-packs,tensorflow2.0/source/numpy/core/arrayprint.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,needed if __array_function__ is disabled
aahjgcjjc,ryfeus/gcf-packs,tensorflow2.0/source/numpy/core/einsumfunc.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Now propagate as many unused contractions as possible to next iteration
aahjgdaag,ryfeus/gcf-packs,tensorflow2.0/source/numpy/core/einsumfunc.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,DDOT without aligned data (better to use einsum)
aahjgdagf,ryfeus/gcf-packs,tensorflow2.0/source/numpy/core/einsumfunc.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Build a new view if needed
aahjgdaia,ryfeus/gcf-packs,tensorflow2.0/source/numpy/core/fromnumeric.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,This branch is needed for reductions like any which don't
aahjgdbaj,ryfeus/gcf-packs,tensorflow2.0/source/numpy/core/generate_numpy_api.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,FIXME: ordered_funcs_api is unused
aahjgdbee,ryfeus/gcf-packs,tensorflow2.0/source/numpy/core/machar.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\" || Machine arithmetics - determine the parameters of the || floating-point arithmetic system ||  || Author: Pearu Peterson; September 2003 ||  || \"\"\"
aahjgdbia,ryfeus/gcf-packs,tensorflow2.0/source/numpy/core/numeric.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,needed instead of a 0 to get same result as zeros for for string dtypes
aahjgdbib,ryfeus/gcf-packs,tensorflow2.0/source/numpy/core/numeric.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: this works around .astype(bool) not working properly (gh-9847)
aahjgdbja,ryfeus/gcf-packs,tensorflow2.0/source/numpy/core/numeric.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,fix hack in scipy which imports this function
aahjgdbjc,ryfeus/gcf-packs,tensorflow2.0/source/numpy/core/numeric.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Move working axis to the end of the shape
aahjgdccb,ryfeus/gcf-packs,tensorflow2.0/source/numpy/core/numeric.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to treat masked arrays correctly. = True would not work.
aahjgdcge,ryfeus/gcf-packs,tensorflow2.0/source/numpy/core/overrides.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: remove this when we drop Python 2 support (functools.wraps
aahjgdcgg,ryfeus/gcf-packs,tensorflow2.0/source/numpy/core/records.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\" || Record Arrays || ============= || Record arrays expose the fields of structured arrays as properties. ||  || Most commonly; ndarrays contain elements of a single type; e.g. floats; || integers; bools etc.  However; it is possible for elements to be combinations || of these using structured types; such as:: ||  ||   >>> a = np.array([(1; 2.0); (1; 2.0)]; dtype=[('x'; int); ('y'; float)]) ||   >>> a ||   array([(1; 2.0); (1; 2.0)]; ||         dtype=[('x'; '<i4'); ('y'; '<f8')]) ||  || Here; each element consists of two fields: x (and int); and y (a float). || This is known as a structured array.  The different fields are analogous || to columns in a spread-sheet.  The different fields can be accessed as || one would a dictionary:: ||  ||   >>> a['x'] ||   array([1; 1]) ||  ||   >>> a['y'] ||   array([ 2.;  2.]) ||  || Record arrays allow us to access fields as properties:: ||  ||   >>> ar = np.rec.array(a) ||  ||   >>> ar.x ||   array([1; 1]) ||  ||   >>> ar.y ||   array([ 2.;  2.]) ||  || \"\"\"
aahjgdded,ryfeus/gcf-packs,tensorflow2.0/source/numpy/core/setup.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX: ugly; we use a class to avoid calling twice some expensive functions in
aahjgddfc,ryfeus/gcf-packs,tensorflow2.0/source/numpy/core/setup.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Perhaps a fancier check is in order here.
aahjgddfi,ryfeus/gcf-packs,tensorflow2.0/source/numpy/core/setup.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Distutils hack on AMD64 on windows
aahjgddhd,ryfeus/gcf-packs,tensorflow2.0/source/numpy/core/setup.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,os.uname not available on all platforms. blanket except ugly but safe
aahjgdebc,ryfeus/gcf-packs,tensorflow2.0/source/numpy/core/setup.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Ugly: this can be called within a library and not an extension;
aahjgdebe,ryfeus/gcf-packs,tensorflow2.0/source/numpy/core/setup.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,needed).
aahjgdedg,ryfeus/gcf-packs,tensorflow2.0/source/numpy/core/setup.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Another ugly hack: the mathlib info is known once build_src is run;
aahjgdeij,ryfeus/gcf-packs,tensorflow2.0/source/numpy/core/setup_common.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,recompile of extension modules is needed.
aahjgdfcj,ryfeus/gcf-packs,tensorflow2.0/source/numpy/core/setup_common.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,MMX only needed for icc; but some clangs don't have it
aahjgdgah,ryfeus/gcf-packs,tensorflow2.0/source/numpy/core/shape_base.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,algorithm is implemented which would likely be faster than the python
aahjgdgjh,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/ccompiler.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,FIXME:RELATIVE_IMPORT
aahjgdhdj,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/ccompiler.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,NOTE:  I removed this fix in revision 4481 (see ticket #619); but it appears
aahjgdhfa,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/ccompiler.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,white-space. XXX: send a patch to distutils
aahjgdhfj,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/command/build_clib.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Fix Python distutils bug sf #1718574:
aahjgdhgh,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/command/build_clib.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,this hack works around the msvc compiler attributes
aahjgdhgi,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/command/build_clib.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,problem; msvc uses its own convention :(
aahjgdhhg,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/command/build_clib.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,fix library dependencies
aahjgdhjg,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/command/build_ext.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Determine if C++\/Fortran 77\/Fortran 90 compilers are needed.
aahjgdiah,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/command/build_ext.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,FIXME: In the case where there are more than two packages;
aahjgdibc,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/command/build_ext.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to compile kiva.agg._agg extension.
aahjgdibd,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/command/build_ext.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,this hack works around the msvc compiler attributes
aahjgdibe,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/command/build_ext.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,problem; msvc uses its own convention :(
aahjgdidd,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/command/build_py.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX: Fix find_source_files for item in py_modules such that item is 3-tuple
aahjgdifa,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/command/build_src.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX: another ugly workaround to circumvent distutils brain damage. We
aahjgdigi,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/command/build_src.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX TODO: --inplace support for sdist command
aahjgdiid,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/command/config.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX: hack to circumvent a python 2.6 bug with msvc9compiler:
aahjgdjce,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/command/config_compiler.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX: Linker flags
aahjgdjfh,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/command/install_headers.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Kind of a hack; but I don't know where else to change this...
aahjgdjfj,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/conv_template.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\" || takes templated file .xxx.src and produces .xxx file  where .xxx is || .i or .c or .h; using the following template rules ||  || \/**begin repeat  -- on a line by itself marks the start of a repeated code ||                     segment || \/**end repeat**\/ -- on a line by itself marks it's end ||  || After the \/**begin repeat and before the *\/; all the named templates are placed || these should all have the same number of replacements ||  || Repeat blocks can be nested; with each nested block labeled with its depth; || i.e. || \/**begin repeat1 ||  *.... ||  *\/ || \/**end repeat1**\/ ||  || When using nested loops; you can optionally exclude particular || combinations of the variables using (inside the comment portion of the inner loop): ||  ||  :exclude: var1=value1; var2=value2; ... ||  || This will exclude the pattern where var1 is value1 and var2 is value2 when || the result is being generated. ||  ||  || In the main body each replace will use one entry from the list of named replacements ||  ||  Note that all #..# forms in a block must have the same number of ||    comma-separated entries. ||  || Example: ||  ||     An input file containing ||  ||         \/**begin repeat ||          * #a = 1;2;3# ||          * #b = 1;2;3# ||          *\/ ||  ||         \/**begin repeat1 ||          * #c = ted; jim# ||          *\/ ||         @a@; @b@; @c@ ||         \/**end repeat1**\/ ||  ||         \/**end repeat**\/ ||  ||     produces ||  ||         line 1 \"template.c.src\" ||  ||         \/* ||          ********************************************************************* ||          **       This file was autogenerated from a template  DO NOT EDIT!!** ||          **       Changes should be made to the original source (.src) file ** ||          ********************************************************************* ||          *\/ ||  ||         #line 9 ||         1; 1; ted ||  ||         #line 9 ||         1; 1; jim ||  ||         #line 9 ||         2; 2; ted ||  ||         #line 9 ||         2; 2; jim ||  ||         #line 9 ||         3; 3; ted ||  ||         #line 9 ||         3; 3; jim ||  || \"\"\"
aahjgdjgi,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/conv_template.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,replaces occurrences of xxx*3 with xxx; xxx; xxx
aahjgdjih,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/core.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX Hack to get numpy installable with easy_install.
aahjgeaag,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/cpuinfo.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX
aahjgeaai,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/cpuinfo.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX: what does the value of
aahjgeabb,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/cpuinfo.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX: Bad style to use so long `try:...except:...`. Fix it!
aahjgeabj,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/cpuinfo.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX: other OS's. Eg. use _winreg on Win32. Or os.uname on unices.
aahjgebbf,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/fcompiler/__init__.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX Assuming that free format is default for f90 compiler.
aahjgebcb,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/fcompiler/__init__.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,should perhaps eventually be more throughly tested and more
aahjgebcg,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/fcompiler/__init__.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX: Do we need LDSHARED->SOSHARED; LDFLAGS->SOFLAGS
aahjgebdf,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/fcompiler/__init__.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: implement get_f90flags and use it in _compile similarly to get_f77flags
aahjgebfa,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/fcompiler/absoft.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,of Absoft though I don't think versions earlier than 9 can
aahjgebgi,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/fcompiler/gnu.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX: handle cross compilation
aahjgecbc,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/fcompiler/gnu.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: could use -Xlinker here; if it's supported
aahjgecdb,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/fcompiler/hpux.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX status==256 may indicate 'unrecognized option' or
aahjgeceb,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/fcompiler/intel.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: could use -Xlinker here; if it's supported
aahjgecfd,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/fcompiler/intel.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX this is a wild guess
aahjgecgj,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/from_template.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\" ||  || process_file(filename) ||  ||   takes templated file .xxx.src and produces .xxx file where .xxx ||   is .pyf .f90 or .f using the following template rules: ||  ||   '<..>' denotes a template. ||  ||   All function and subroutine blocks in a source file with names that ||   contain '<..>' will be replicated according to the rules in '<..>'. ||  ||   The number of comma-separated words in '<..>' will determine the number of ||   replicates. ||  ||   '<..>' may have two different forms; named and short. For example; ||  ||   named: ||    <p=d;s;z;c> where anywhere inside a block '<p>' will be replaced with ||    'd'; 's'; 'z'; and 'c' for each replicate of the block. ||  ||    <_c>  is already defined: <_c=s;d;c;z> ||    <_t>  is already defined: <_t=real;double precision;complex;double complex> ||  ||   short: ||    <s;d;c;z>; a short form of the named; useful when no <p> appears inside ||    a block. ||  ||   In general; '<..>' contains a comma separated list of arbitrary ||   expressions. If these expression must contain a comma|leftarrow|rightarrow; ||   then prepend the comma|leftarrow|rightarrow with a backslash. ||  ||   If an expression matches '\\\\<index>' then it will be replaced ||   by <index>-th expression. ||  ||   Note that all '<..>' forms in a block must have the same number of ||   comma-separated entries. ||  ||  Predefined named template rules: ||   <prefix=s;d;c;z> ||   <ftype=real;double precision;complex;double complex> ||   <ftypereal=real;double precision;\\\\0;\\\\1> ||   <ctype=float;double;complex_float;complex_double> ||   <ctypereal=float;double;\\\\0;\\\\1> ||  || \"\"\"
aahjgechf,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/from_template.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,newnames are constructed as needed
aahjgecif,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/log.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,likely a good reason why we're running at this level.
aahjgedbg,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/mingw32ccompiler.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,kind of bad consequences; like using Py_ModuleInit4 instead of
aahjgedcf,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/mingw32ccompiler.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,no additional libraries needed
aahjgedii,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/mingw32ccompiler.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX: ideally; we should use exactly the same version as used by python. I
aahjgeeef,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/misc_util.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX need support for .C that is also C++
aahjgeeeg,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/misc_util.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,break  # XXX can we assume that there is one module per file?
aahjgeeej,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/misc_util.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX: *.hpp files??
aahjgeefb,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/misc_util.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX scan sources for include statements
aahjgeehb,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/misc_util.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,## XXX Implement add_py_modules
aahjgeeie,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/misc_util.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX: import here for bootstrapping reasons
aahjgefbc,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/npy_pkg_config.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,FIXME: document this. If pkgname is defined in the variables section; and
aahjgefbf,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/npy_pkg_config.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Trivial cache to cache LibraryInfo instances creation. To be really
aahjgefbg,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/npy_pkg_config.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,efficient; the cache should be handled in read_config; since a same file can
aahjgefbj,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/npy_pkg_config.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO:
aahjgefcb,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/npy_pkg_config.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,pkg-config simple emulator - useful for debugging; and maybe later to query
aahjgefcd,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/numpy_distribution.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX: Handle setuptools ?
aahjgefdd,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/system_info.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,good to keep its class name. Use of RawConfigParser is needed in
aahjgefhe,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/system_info.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX: disabled by default; may disappear in
aahjgefji,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/system_info.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,FIXME: lapack_atlas is unused
aahjgegci,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/system_info.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,The check for windows is needed because has_cblas uses the
aahjgegdc,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/system_info.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX: is it generally true?
aahjgegfb,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/system_info.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX: should we check here actual existence of source files?
aahjgegfi,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/system_info.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,FIXME: r not used
aahjgeggg,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/unixccompiler.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,all headers needed by a c file as a side effect of compilation (-MMD)
aahjgeghc,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/unixccompiler.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,think the only major Unix that does.  Maybe we need some
aahjgeghe,ryfeus/gcf-packs,tensorflow2.0/source/numpy/distutils/unixccompiler.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,needed -- or maybe Python's configure script took care of
aahjgeghg,ryfeus/gcf-packs,tensorflow2.0/source/numpy/doc/basics.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\" || ============ || Array basics || ============ ||  || Array types and conversions between types || ========================================= ||  || NumPy supports a much greater variety of numerical types than Python does. || This section shows which are available; and how to modify an array's data-type. ||  || The primitive types supported are tied closely to those in C: ||  || .. list-table:: ||     :header-rows: 1 ||  ||     * - Numpy type ||       - C type ||       - Description ||  ||     * - `np.bool` ||       - ``bool`` ||       - Boolean (True or False) stored as a byte ||  ||     * - `np.byte` ||       - ``signed char`` ||       - Platform-defined ||  ||     * - `np.ubyte` ||       - ``unsigned char`` ||       - Platform-defined ||  ||     * - `np.short` ||       - ``short`` ||       - Platform-defined ||  ||     * - `np.ushort` ||       - ``unsigned short`` ||       - Platform-defined ||  ||     * - `np.intc` ||       - ``int`` ||       - Platform-defined ||  ||     * - `np.uintc` ||       - ``unsigned int`` ||       - Platform-defined ||  ||     * - `np.int_` ||       - ``long`` ||       - Platform-defined ||  ||     * - `np.uint` ||       - ``unsigned long`` ||       - Platform-defined ||  ||     * - `np.longlong` ||       - ``long long`` ||       - Platform-defined ||  ||     * - `np.ulonglong` ||       - ``unsigned long long`` ||       - Platform-defined ||  ||     * - `np.half` \/ `np.float16` ||       - ||       - Half precision float: ||         sign bit; 5 bits exponent; 10 bits mantissa ||  ||     * - `np.single` ||       - ``float`` ||       - Platform-defined single precision float: ||         typically sign bit; 8 bits exponent; 23 bits mantissa ||  ||     * - `np.double` ||       - ``double`` ||       - Platform-defined double precision float: ||         typically sign bit; 11 bits exponent; 52 bits mantissa. ||  ||     * - `np.longdouble` ||       - ``long double`` ||       - Platform-defined extended-precision float ||  ||     * - `np.csingle` ||       - ``float complex`` ||       - Complex number; represented by two single-precision floats (real and imaginary components) ||  ||     * - `np.cdouble` ||       - ``double complex`` ||       - Complex number; represented by two double-precision floats (real and imaginary components). ||  ||     * - `np.clongdouble` ||       - ``long double complex`` ||       - Complex number; represented by two extended-precision floats (real and imaginary components). ||  ||  || Since many of these have platform-dependent definitions; a set of fixed-size || aliases are provided: ||  || .. list-table:: ||     :header-rows: 1 ||  ||     * - Numpy type ||       - C type ||       - Description ||  ||     * - `np.int8` ||       - ``int8_t`` ||       - Byte (-128 to 127) ||  ||     * - `np.int16` ||       - ``int16_t`` ||       - Integer (-32768 to 32767) ||  ||     * - `np.int32` ||       - ``int32_t`` ||       - Integer (-2147483648 to 2147483647) ||  ||     * - `np.int64` ||       - ``int64_t`` ||       - Integer (-9223372036854775808 to 9223372036854775807) ||  ||     * - `np.uint8` ||       - ``uint8_t`` ||       - Unsigned integer (0 to 255) ||  ||     * - `np.uint16` ||       - ``uint16_t`` ||       - Unsigned integer (0 to 65535) ||  ||     * - `np.uint32` ||       - ``uint32_t`` ||       - Unsigned integer (0 to 4294967295) ||  ||     * - `np.uint64` ||       - ``uint64_t`` ||       - Unsigned integer (0 to 18446744073709551615) ||  ||     * - `np.intp` ||       - ``intptr_t`` ||       - Integer used for indexing; typically the same as ``ssize_t`` ||  ||     * - `np.uintp` ||       - ``uintptr_t`` ||       - Integer large enough to hold a pointer ||  ||     * - `np.float32` ||       - ``float`` ||       - ||  ||     * - `np.float64` \/ `np.float_` ||       - ``double`` ||       - Note that this matches the precision of the builtin python `float`. ||  ||     * - `np.complex64` ||       - ``float complex`` ||       - Complex number; represented by two 32-bit floats (real and imaginary components) ||  ||     * - `np.complex128` \/ `np.complex_` ||       - ``double complex`` ||       - Note that this matches the precision of the builtin python `complex`. ||  ||  || NumPy numerical types are instances of ``dtype`` (data-type) objects; each || having unique characteristics.  Once you have imported NumPy using ||  ||   :: ||  ||     >>> import numpy as np ||  || the dtypes are available as ``np.bool_``; ``np.float32``; etc. ||  || Advanced types; not listed in the table above; are explored in || section :ref:`structured_arrays`. ||  || There are 5 basic numerical types representing booleans (bool); integers (int); || unsigned integers (uint) floating point (float) and complex. Those with numbers || in their name indicate the bitsize of the type (i.e. how many bits are needed || to represent a single value in memory).  Some types; such as ``int`` and || ``intp``; have differing bitsizes; dependent on the platforms (e.g. 32-bit || vs. 64-bit machines).  This should be taken into account when interfacing || with low-level code (such as C or Fortran) where the raw memory is addressed. ||  || Data-types can be used as functions to convert python numbers to array scalars || (see the array scalar section for an explanation); python sequences of numbers || to arrays of that type; or as arguments to the dtype keyword that many numpy || functions or methods accept. Some examples:: ||  ||     >>> import numpy as np ||     >>> x = np.float32(1.0) ||     >>> x ||     1.0 ||     >>> y = np.int_([1;2;4]) ||     >>> y ||     array([1; 2; 4]) ||     >>> z = np.arange(3; dtype=np.uint8) ||     >>> z ||     array([0; 1; 2]; dtype=uint8) ||  || Array types can also be referred to by character codes; mostly to retain || backward compatibility with older packages such as Numeric.  Some || documentation may still refer to these; for example:: ||  ||   >>> np.array([1; 2; 3]; dtype='f') ||   array([ 1.;  2.;  3.]; dtype=float32) ||  || We recommend using dtype objects instead. ||  || To convert the type of an array; use the .astype() method (preferred) or || the type itself as a function. For example: :: ||  ||     >>> z.astype(float)                 #doctest: +NORMALIZE_WHITESPACE ||     array([  0.;  1.;  2.]) ||     >>> np.int8(z) ||     array([0; 1; 2]; dtype=int8) ||  || Note that; above; we use the *Python* float object as a dtype.  NumPy knows || that ``int`` refers to ``np.int_``; ``bool`` means ``np.bool_``; || that ``float`` is ``np.float_`` and ``complex`` is ``np.complex_``. || The other data-types do not have Python equivalents. ||  || To determine the type of an array; look at the dtype attribute:: ||  ||     >>> z.dtype ||     dtype('uint8') ||  || dtype objects also contain information about the type; such as its bit-width || and its byte-order.  The data type can also be used indirectly to query || properties of the type; such as whether it is an integer:: ||  ||     >>> d = np.dtype(int) ||     >>> d ||     dtype('int32') ||  ||     >>> np.issubdtype(d; np.integer) ||     True ||  ||     >>> np.issubdtype(d; np.floating) ||     False ||  ||  || Array Scalars || ============= ||  || NumPy generally returns elements of arrays as array scalars (a scalar || with an associated dtype).  Array scalars differ from Python scalars; but || for the most part they can be used interchangeably (the primary || exception is for versions of Python older than v2.x; where integer array || scalars cannot act as indices for lists and tuples).  There are some || exceptions; such as when code requires very specific attributes of a scalar || or when it checks specifically whether a value is a Python scalar. Generally; || problems are easily fixed by explicitly converting array scalars || to Python scalars; using the corresponding Python type function || (e.g.; ``int``; ``float``; ``complex``; ``str``; ``unicode``). ||  || The primary advantage of using array scalars is that || they preserve the array type (Python may not have a matching scalar type || available; e.g. ``int16``).  Therefore; the use of array scalars ensures || identical behaviour between arrays and scalars; irrespective of whether the || value is inside an array or not.  NumPy scalars also have many of the same || methods arrays do. ||  || Extended Precision || ================== ||  || Python's floating-point numbers are usually 64-bit floating-point numbers; || nearly equivalent to ``np.float64``. In some unusual situations it may be || useful to use floating-point numbers with more precision. Whether this || is possible in numpy depends on the hardware and on the development || environment: specifically; x86 machines provide hardware floating-point || with 80-bit precision; and while most C compilers provide this as their || ``long double`` type; MSVC (standard for Windows builds) makes || ``long double`` identical to ``double`` (64 bits). NumPy makes the || compiler's ``long double`` available as ``np.longdouble`` (and || ``np.clongdouble`` for the complex numbers). You can find out what your || numpy provides with ``np.finfo(np.longdouble)``. ||  || NumPy does not provide a dtype with more precision than C || ``long double``\\\\s; in particular; the 128-bit IEEE quad precision || data type (FORTRAN's ``REAL*16``\\\\) is not available. ||  || For efficient memory alignment; ``np.longdouble`` is usually stored || padded with zero bits; either to 96 or 128 bits. Which is more efficient || depends on hardware and development environment; typically on 32-bit || systems they are padded to 96 bits; while on 64-bit systems they are || typically padded to 128 bits. ``np.longdouble`` is padded to the system || default; ``np.float96`` and ``np.float128`` are provided for users who || want specific padding. In spite of the names; ``np.float96`` and || ``np.float128`` provide only as much precision as ``np.longdouble``; || that is; 80 bits on most x86 machines and 64 bits in standard || Windows builds. ||  || Be warned that even if ``np.longdouble`` offers more precision than || python ``float``; it is easy to lose that extra precision; since || python often forces values to pass through ``float``. For example; || the ``%`` formatting operator requires its arguments to be converted || to standard python types; and it is therefore impossible to preserve || extended precision even if many decimal places are requested. It can || be useful to test your code with the value || ``1 + np.finfo(np.longdouble).eps``. ||  || \"\"\"
aahjgeghh,ryfeus/gcf-packs,tensorflow2.0/source/numpy/doc/broadcasting.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\" || ======================== || Broadcasting over arrays || ======================== ||  || .. note:: ||     See `this article ||     <https:\/\/numpy.org\/devdocs\/user\/theory.broadcasting.html>`_ ||     for illustrations of broadcasting concepts. ||  ||  || The term broadcasting describes how numpy treats arrays with different || shapes during arithmetic operations. Subject to certain constraints; || the smaller array is \"broadcast\" across the larger array so that they || have compatible shapes. Broadcasting provides a means of vectorizing || array operations so that looping occurs in C instead of Python. It does || this without making needless copies of data and usually leads to || efficient algorithm implementations. There are; however; cases where || broadcasting is a bad idea because it leads to inefficient use of memory || that slows computation. ||  || NumPy operations are usually done on pairs of arrays on an || element-by-element basis.  In the simplest case; the two arrays must || have exactly the same shape; as in the following example: ||  ||   >>> a = np.array([1.0; 2.0; 3.0]) ||   >>> b = np.array([2.0; 2.0; 2.0]) ||   >>> a * b ||   array([ 2.;  4.;  6.]) ||  || NumPy's broadcasting rule relaxes this constraint when the arrays' || shapes meet certain constraints. The simplest broadcasting example occurs || when an array and a scalar value are combined in an operation: ||  || >>> a = np.array([1.0; 2.0; 3.0]) || >>> b = 2.0 || >>> a * b || array([ 2.;  4.;  6.]) ||  || The result is equivalent to the previous example where ``b`` was an array. || We can think of the scalar ``b`` being *stretched* during the arithmetic || operation into an array with the same shape as ``a``. The new elements in || ``b`` are simply copies of the original scalar. The stretching analogy is || only conceptual.  NumPy is smart enough to use the original scalar value || without actually making copies; so that broadcasting operations are as || memory and computationally efficient as possible. ||  || The code in the second example is more efficient than that in the first || because broadcasting moves less memory around during the multiplication || (``b`` is a scalar rather than an array). ||  || General Broadcasting Rules || ========================== || When operating on two arrays; NumPy compares their shapes element-wise. || It starts with the trailing dimensions; and works its way forward.  Two || dimensions are compatible when ||  || 1) they are equal; or || 2) one of them is 1 ||  || If these conditions are not met; a || ``ValueError: operands could not be broadcast together`` exception is  || thrown; indicating that the arrays have incompatible shapes. The size of  || the resulting array is the maximum size along each dimension of the input  || arrays. ||  || Arrays do not need to have the same *number* of dimensions.  For example; || if you have a ``256x256x3`` array of RGB values; and you want to scale || each color in the image by a different value; you can multiply the image || by a one-dimensional array with 3 values. Lining up the sizes of the || trailing axes of these arrays according to the broadcast rules; shows that || they are compatible:: ||  ||   Image  (3d array): 256 x 256 x 3 ||   Scale  (1d array):             3 ||   Result (3d array): 256 x 256 x 3 ||  || When either of the dimensions compared is one; the other is || used.  In other words; dimensions with size 1 are stretched or \"copied\" || to match the other. ||  || In the following example; both the ``A`` and ``B`` arrays have axes with || length one that are expanded to a larger size during the broadcast || operation:: ||  ||   A      (4d array):  8 x 1 x 6 x 1 ||   B      (3d array):      7 x 1 x 5 ||   Result (4d array):  8 x 7 x 6 x 5 ||  || Here are some more examples:: ||  ||   A      (2d array):  5 x 4 ||   B      (1d array):      1 ||   Result (2d array):  5 x 4 ||  ||   A      (2d array):  5 x 4 ||   B      (1d array):      4 ||   Result (2d array):  5 x 4 ||  ||   A      (3d array):  15 x 3 x 5 ||   B      (3d array):  15 x 1 x 5 ||   Result (3d array):  15 x 3 x 5 ||  ||   A      (3d array):  15 x 3 x 5 ||   B      (2d array):       3 x 5 ||   Result (3d array):  15 x 3 x 5 ||  ||   A      (3d array):  15 x 3 x 5 ||   B      (2d array):       3 x 1 ||   Result (3d array):  15 x 3 x 5 ||  || Here are examples of shapes that do not broadcast:: ||  ||   A      (1d array):  3 ||   B      (1d array):  4 # trailing dimensions do not match ||  ||   A      (2d array):      2 x 1 ||   B      (3d array):  8 x 4 x 3 # second from last dimensions mismatched ||  || An example of broadcasting in practice:: ||  ||  >>> x = np.arange(4) ||  >>> xx = x.reshape(4;1) ||  >>> y = np.ones(5) ||  >>> z = np.ones((3;4)) ||  ||  >>> x.shape ||  (4;) ||  ||  >>> y.shape ||  (5;) ||  ||  >>> x + y ||  ValueError: operands could not be broadcast together with shapes (4;) (5;) ||  ||  >>> xx.shape ||  (4; 1) ||  ||  >>> y.shape ||  (5;) ||  ||  >>> (xx + y).shape ||  (4; 5) ||  ||  >>> xx + y ||  array([[ 1.;  1.;  1.;  1.;  1.]; ||         [ 2.;  2.;  2.;  2.;  2.]; ||         [ 3.;  3.;  3.;  3.;  3.]; ||         [ 4.;  4.;  4.;  4.;  4.]]) ||  ||  >>> x.shape ||  (4;) ||  ||  >>> z.shape ||  (3; 4) ||  ||  >>> (x + z).shape ||  (3; 4) ||  ||  >>> x + z ||  array([[ 1.;  2.;  3.;  4.]; ||         [ 1.;  2.;  3.;  4.]; ||         [ 1.;  2.;  3.;  4.]]) ||  || Broadcasting provides a convenient way of taking the outer product (or || any other outer operation) of two arrays. The following example shows an || outer addition operation of two 1-d arrays:: ||  ||   >>> a = np.array([0.0; 10.0; 20.0; 30.0]) ||   >>> b = np.array([1.0; 2.0; 3.0]) ||   >>> a[:; np.newaxis] + b ||   array([[  1.;   2.;   3.]; ||          [ 11.;  12.;  13.]; ||          [ 21.;  22.;  23.]; ||          [ 31.;  32.;  33.]]) ||  || Here the ``newaxis`` index operator inserts a new axis into ``a``; || making it a two-dimensional ``4x1`` array.  Combining the ``4x1`` array || with ``b``; which has shape ``(3;)``; yields a ``4x3`` array. ||  || \"\"\"
aahjgegii,ryfeus/gcf-packs,tensorflow2.0/source/numpy/doc/glossary.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\" || ======== || Glossary || ======== ||  || .. glossary:: ||  ||    along an axis ||        Axes are defined for arrays with more than one dimension.  A ||        2-dimensional array has two corresponding axes: the first running ||        vertically downwards across rows (axis 0); and the second running ||        horizontally across columns (axis 1). ||  ||        Many operations can take place along one of these axes.  For example; ||        we can sum each row of an array; in which case we operate along ||        columns; or axis 1:: ||  ||          >>> x = np.arange(12).reshape((3;4)) ||  ||          >>> x ||          array([[ 0;  1;  2;  3]; ||                 [ 4;  5;  6;  7]; ||                 [ 8;  9; 10; 11]]) ||  ||          >>> x.sum(axis=1) ||          array([ 6; 22; 38]) ||  ||    array ||        A homogeneous container of numerical elements.  Each element in the ||        array occupies a fixed amount of memory (hence homogeneous); and ||        can be a numerical element of a single type (such as float; int ||        or complex) or a combination (such as ``(float; int; float)``).  Each ||        array has an associated data-type (or ``dtype``); which describes ||        the numerical type of its elements:: ||  ||          >>> x = np.array([1; 2; 3]; float) ||  ||          >>> x ||          array([ 1.;  2.;  3.]) ||  ||          >>> x.dtype # floating point number; 64 bits of memory per element ||          dtype('float64') ||  ||  ||          # More complicated data type: each array element is a combination of ||          # and integer and a floating point number ||          >>> np.array([(1; 2.0); (3; 4.0)]; dtype=[('x'; int); ('y'; float)]) ||          array([(1; 2.0); (3; 4.0)]; ||                dtype=[('x'; '<i4'); ('y'; '<f8')]) ||  ||        Fast element-wise operations; called a :term:`ufunc`; operate on arrays. ||  ||    array_like ||        Any sequence that can be interpreted as an ndarray.  This includes ||        nested lists; tuples; scalars and existing arrays. ||  ||    attribute ||        A property of an object that can be accessed using ``obj.attribute``; ||        e.g.; ``shape`` is an attribute of an array:: ||  ||          >>> x = np.array([1; 2; 3]) ||          >>> x.shape ||          (3;) ||  ||    big-endian ||        When storing a multi-byte value in memory as a sequence of bytes; the ||        sequence addresses\/sends\/stores the most significant byte first (lowest ||        address) and the least significant byte last (highest address). Common in ||        micro-processors and used for transmission of data over network protocols. ||  ||    BLAS ||        `Basic Linear Algebra Subprograms <https:\/\/en.wikipedia.org\/wiki\/Basic_Linear_Algebra_Subprograms>`_ ||  ||    broadcast ||        NumPy can do operations on arrays whose shapes are mismatched:: ||  ||          >>> x = np.array([1; 2]) ||          >>> y = np.array([[3]; [4]]) ||  ||          >>> x ||          array([1; 2]) ||  ||          >>> y ||          array([[3]; ||                 [4]]) ||  ||          >>> x + y ||          array([[4; 5]; ||                 [5; 6]]) ||  ||        See `numpy.doc.broadcasting` for more information. ||  ||    C order ||        See `row-major` ||  ||    column-major ||        A way to represent items in a N-dimensional array in the 1-dimensional ||        computer memory. In column-major order; the leftmost index \"varies the ||        fastest\": for example the array:: ||  ||             [[1; 2; 3]; ||              [4; 5; 6]] ||  ||        is represented in the column-major order as:: ||  ||            [1; 4; 2; 5; 3; 6] ||  ||        Column-major order is also known as the Fortran order; as the Fortran ||        programming language uses it. ||  ||    decorator ||        An operator that transforms a function.  For example; a ``log`` ||        decorator may be defined to print debugging information upon ||        function execution:: ||  ||          >>> def log(f): ||          ...     def new_logging_func(*args; **kwargs): ||          ...         print(\"Logging call with parameters:\"; args; kwargs) ||          ...         return f(*args; **kwargs) ||          ... ||          ...     return new_logging_func ||  ||        Now; when we define a function; we can \"decorate\" it using ``log``:: ||  ||          >>> @log ||          ... def add(a; b): ||          ...     return a + b ||  ||        Calling ``add`` then yields: ||  ||        >>> add(1; 2) ||        Logging call with parameters: (1; 2) {} ||        3 ||  ||    dictionary ||        Resembling a language dictionary; which provides a mapping between ||        words and descriptions thereof; a Python dictionary is a mapping ||        between two objects:: ||  ||          >>> x = {1: 'one'; 'two': [1; 2]} ||  ||        Here; `x` is a dictionary mapping keys to values; in this case ||        the integer 1 to the string \"one\"; and the string \"two\" to ||        the list ``[1; 2]``.  The values may be accessed using their ||        corresponding keys:: ||  ||          >>> x[1] ||          'one' ||  ||          >>> x['two'] ||          [1; 2] ||  ||        Note that dictionaries are not stored in any specific order.  Also; ||        most mutable (see *immutable* below) objects; such as lists; may not ||        be used as keys. ||  ||        For more information on dictionaries; read the ||        `Python tutorial <https:\/\/docs.python.org\/tutorial\/>`_. ||  ||    field ||        In a :term:`structured data type`; each sub-type is called a `field`. ||        The `field` has a name (a string); a type (any valid :term:`dtype`; and ||        an optional `title`. See :ref:`arrays.dtypes` ||  ||    Fortran order ||        See `column-major` ||  ||    flattened ||        Collapsed to a one-dimensional array. See `numpy.ndarray.flatten` ||        for details. ||  ||    homogenous ||        Describes a block of memory comprised of blocks; each block comprised of  ||        items and of the same size; and blocks are interpreted in exactly the ||        same way. In the simplest case each block contains a single item; for ||        instance int32 or float64. ||  ||    immutable ||        An object that cannot be modified after execution is called ||        immutable.  Two common examples are strings and tuples. ||  ||    instance ||        A class definition gives the blueprint for constructing an object:: ||  ||          >>> class House(object): ||          ...     wall_colour = 'white' ||  ||        Yet; we have to *build* a house before it exists:: ||  ||          >>> h = House() # build a house ||  ||        Now; ``h`` is called a ``House`` instance.  An instance is therefore ||        a specific realisation of a class. ||  ||    iterable ||        A sequence that allows \"walking\" (iterating) over items; typically ||        using a loop such as:: ||  ||          >>> x = [1; 2; 3] ||          >>> [item**2 for item in x] ||          [1; 4; 9] ||  ||        It is often used in combination with ``enumerate``:: ||          >>> keys = ['a';'b';'c'] ||          >>> for n; k in enumerate(keys): ||          ...     print(\"Key %d: %s\" % (n; k)) ||          ... ||          Key 0: a ||          Key 1: b ||          Key 2: c ||  ||    list ||        A Python container that can hold any number of objects or items. ||        The items do not have to be of the same type; and can even be ||        lists themselves:: ||  ||          >>> x = [2; 2.0; \"two\"; [2; 2.0]] ||  ||        The list `x` contains 4 items; each which can be accessed individually:: ||  ||          >>> x[2] # the string 'two' ||          'two' ||  ||          >>> x[3] # a list; containing an integer 2 and a float 2.0 ||          [2; 2.0] ||  ||        It is also possible to select more than one item at a time; ||        using *slicing*:: ||  ||          >>> x[0:2] # or; equivalently; x[:2] ||          [2; 2.0] ||  ||        In code; arrays are often conveniently expressed as nested lists:: ||  ||  ||          >>> np.array([[1; 2]; [3; 4]]) ||          array([[1; 2]; ||                 [3; 4]]) ||  ||        For more information; read the section on lists in the `Python ||        tutorial <https:\/\/docs.python.org\/tutorial\/>`_.  For a mapping ||        type (key-value); see *dictionary*. ||  ||    little-endian ||        When storing a multi-byte value in memory as a sequence of bytes; the ||        sequence addresses\/sends\/stores the least significant byte first (lowest ||        address) and the most significant byte last (highest address). Common in ||        x86 processors. ||  ||    mask ||        A boolean array; used to select only certain elements for an operation:: ||  ||          >>> x = np.arange(5) ||          >>> x ||          array([0; 1; 2; 3; 4]) ||  ||          >>> mask = (x > 2) ||          >>> mask ||          array([False; False; False; True;  True]) ||  ||          >>> x[mask] = -1 ||          >>> x ||          array([ 0;  1;  2;  -1; -1]) ||  ||    masked array ||        Array that suppressed values indicated by a mask:: ||  ||          >>> x = np.ma.masked_array([np.nan; 2; np.nan]; [True; False; True]) ||          >>> x ||          masked_array(data = [-- 2.0 --]; ||                       mask = [ True False  True]; ||                 fill_value = 1e+20) ||          <BLANKLINE> ||  ||          >>> x + [1; 2; 3] ||          masked_array(data = [-- 4.0 --]; ||                       mask = [ True False  True]; ||                 fill_value = 1e+20) ||          <BLANKLINE> ||  ||  ||        Masked arrays are often used when operating on arrays containing ||        missing or invalid entries. ||  ||    matrix ||        A 2-dimensional ndarray that preserves its two-dimensional nature ||        throughout operations.  It has certain special operations; such as ``*`` ||        (matrix multiplication) and ``**`` (matrix power); defined:: ||  ||          >>> x = np.mat([[1; 2]; [3; 4]]) ||          >>> x ||          matrix([[1; 2]; ||                  [3; 4]]) ||  ||          >>> x**2 ||          matrix([[ 7; 10]; ||                [15; 22]]) ||  ||    method ||        A function associated with an object.  For example; each ndarray has a ||        method called ``repeat``:: ||  ||          >>> x = np.array([1; 2; 3]) ||          >>> x.repeat(2) ||          array([1; 1; 2; 2; 3; 3]) ||  ||    ndarray ||        See *array*. ||  ||    record array ||        An :term:`ndarray` with :term:`structured data type` which has been ||        subclassed as ``np.recarray`` and whose dtype is of type ``np.record``; ||        making the fields of its data type to be accessible by attribute. ||  ||    reference ||        If ``a`` is a reference to ``b``; then ``(a is b) == True``.  Therefore; ||        ``a`` and ``b`` are different names for the same Python object. ||  ||    row-major ||        A way to represent items in a N-dimensional array in the 1-dimensional ||        computer memory. In row-major order; the rightmost index \"varies ||        the fastest\": for example the array:: ||  ||             [[1; 2; 3]; ||              [4; 5; 6]] ||  ||        is represented in the row-major order as:: ||  ||            [1; 2; 3; 4; 5; 6] ||  ||        Row-major order is also known as the C order; as the C programming ||        language uses it. New NumPy arrays are by default in row-major order. ||  ||    self ||        Often seen in method signatures; ``self`` refers to the instance ||        of the associated class.  For example: ||  ||          >>> class Paintbrush(object): ||          ...     color = 'blue' ||          ... ||          ...     def paint(self): ||          ...         print(\"Painting the city %s!\" % self.color) ||          ... ||          >>> p = Paintbrush() ||          >>> p.color = 'red' ||          >>> p.paint() # self refers to 'p' ||          Painting the city red! ||  ||    slice ||        Used to select only certain elements from a sequence:: ||  ||          >>> x = range(5) ||          >>> x ||          [0; 1; 2; 3; 4] ||  ||          >>> x[1:3] # slice from 1 to 3 (excluding 3 itself) ||          [1; 2] ||  ||          >>> x[1:5:2] # slice from 1 to 5; but skipping every second element ||          [1; 3] ||  ||          >>> x[::-1] # slice a sequence in reverse ||          [4; 3; 2; 1; 0] ||  ||        Arrays may have more than one dimension; each which can be sliced ||        individually:: ||  ||          >>> x = np.array([[1; 2]; [3; 4]]) ||          >>> x ||          array([[1; 2]; ||                 [3; 4]]) ||  ||          >>> x[:; 1] ||          array([2; 4]) ||  ||    structure ||        See :term:`structured data type` ||  ||    structured data type ||        A data type composed of other datatypes ||  ||    tuple ||        A sequence that may contain a variable number of types of any ||        kind.  A tuple is immutable; i.e.; once constructed it cannot be ||        changed.  Similar to a list; it can be indexed and sliced:: ||  ||          >>> x = (1; 'one'; [1; 2]) ||          >>> x ||          (1; 'one'; [1; 2]) ||  ||          >>> x[0] ||          1 ||  ||          >>> x[:2] ||          (1; 'one') ||  ||        A useful concept is \"tuple unpacking\"; which allows variables to ||        be assigned to the contents of a tuple:: ||  ||          >>> x; y = (1; 2) ||          >>> x; y = 1; 2 ||  ||        This is often used when a function returns multiple values: ||  ||          >>> def return_many(): ||          ...     return 1; 'alpha'; None ||  ||          >>> a; b; c = return_many() ||          >>> a; b; c ||          (1; 'alpha'; None) ||  ||          >>> a ||          1 ||          >>> b ||          'alpha' ||  ||    ufunc ||        Universal function.  A fast element-wise array operation.  Examples include ||        ``add``; ``sin`` and ``logical_or``. ||  ||    view ||        An array that does not own its data; but refers to another array's ||        data instead.  For example; we may create a view that only shows ||        every second element of another array:: ||  ||          >>> x = np.arange(5) ||          >>> x ||          array([0; 1; 2; 3; 4]) ||  ||          >>> y = x[::2] ||          >>> y ||          array([0; 2; 4]) ||  ||          >>> x[0] = 3 # changing x changes y as well; since y is a view on x ||          >>> y ||          array([3; 2; 4]) ||  ||    wrapper ||        Python is a high-level (highly abstracted; or English-like) language. ||        This abstraction comes at a price in execution speed; and sometimes ||        it becomes necessary to use lower level languages to do fast ||        computations.  A wrapper is code that provides a bridge between ||        high and the low level languages; allowing; e.g.; Python to execute ||        code written in C or Fortran. ||  ||        Examples include ctypes; SWIG and Cython (which wraps C and C++) ||        and f2py (which wraps Fortran). ||  || \"\"\"
aahjgegij,ryfeus/gcf-packs,tensorflow2.0/source/numpy/doc/indexing.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\"============== || Array indexing || ============== ||  || Array indexing refers to any use of the square brackets ([]) to index || array values. There are many options to indexing; which give numpy || indexing great power; but with power comes some complexity and the || potential for confusion. This section is just an overview of the || various options and issues related to indexing. Aside from single || element indexing; the details on most of these options are to be || found in related sections. ||  || Assignment vs referencing || ========================= ||  || Most of the following examples show the use of indexing when || referencing data in an array. The examples work just as well || when assigning to an array. See the section at the end for || specific examples and explanations on how assignments work. ||  || Single element indexing || ======================= ||  || Single element indexing for a 1-D array is what one expects. It work || exactly like that for other standard Python sequences. It is 0-based; || and accepts negative indices for indexing from the end of the array. :: ||  ||     >>> x = np.arange(10) ||     >>> x[2] ||     2 ||     >>> x[-2] ||     8 ||  || Unlike lists and tuples; numpy arrays support multidimensional indexing || for multidimensional arrays. That means that it is not necessary to || separate each dimension's index into its own set of square brackets. :: ||  ||     >>> x.shape = (2;5) # now x is 2-dimensional ||     >>> x[1;3] ||     8 ||     >>> x[1;-1] ||     9 ||  || Note that if one indexes a multidimensional array with fewer indices || than dimensions; one gets a subdimensional array. For example: :: ||  ||     >>> x[0] ||     array([0; 1; 2; 3; 4]) ||  || That is; each index specified selects the array corresponding to the || rest of the dimensions selected. In the above example; choosing 0 || means that the remaining dimension of length 5 is being left unspecified; || and that what is returned is an array of that dimensionality and size. || It must be noted that the returned array is not a copy of the original; || but points to the same values in memory as does the original array. || In  this case; the 1-D array at the first position (0) is returned. || So using a single index on the returned array; results in a single || element being returned. That is: :: ||  ||     >>> x[0][2] ||     2 ||  || So note that ``x[0;2] = x[0][2]`` though the second case is more || inefficient as a new temporary array is created after the first index || that is subsequently indexed by 2. ||  || Note to those used to IDL or Fortran memory order as it relates to || indexing.  NumPy uses C-order indexing. That means that the last || index usually represents the most rapidly changing memory location; || unlike Fortran or IDL; where the first index represents the most || rapidly changing location in memory. This difference represents a || great potential for confusion. ||  || Other indexing options || ====================== ||  || It is possible to slice and stride arrays to extract arrays of the || same number of dimensions; but of different sizes than the original. || The slicing and striding works exactly the same way it does for lists || and tuples except that they can be applied to multiple dimensions as || well. A few examples illustrates best: :: ||  ||  >>> x = np.arange(10) ||  >>> x[2:5] ||  array([2; 3; 4]) ||  >>> x[:-7] ||  array([0; 1; 2]) ||  >>> x[1:7:2] ||  array([1; 3; 5]) ||  >>> y = np.arange(35).reshape(5;7) ||  >>> y[1:5:2;::3] ||  array([[ 7; 10; 13]; ||         [21; 24; 27]]) ||  || Note that slices of arrays do not copy the internal array data but || only produce new views of the original data. ||  || It is possible to index arrays with other arrays for the purposes of || selecting lists of values out of arrays into new arrays. There are || two different ways of accomplishing this. One uses one or more arrays || of index values. The other involves giving a boolean array of the proper || shape to indicate the values to be selected. Index arrays are a very || powerful tool that allow one to avoid looping over individual elements in || arrays and thus greatly improve performance. ||  || It is possible to use special features to effectively increase the || number of dimensions in an array through indexing so the resulting || array aquires the shape needed for use in an expression or with a || specific function. ||  || Index arrays || ============ ||  || NumPy arrays may be indexed with other arrays (or any other sequence- || like object that can be converted to an array; such as lists; with the || exception of tuples; see the end of this document for why this is). The || use of index arrays ranges from simple; straightforward cases to || complex; hard-to-understand cases. For all cases of index arrays; what || is returned is a copy of the original data; not a view as one gets for || slices. ||  || Index arrays must be of integer type. Each value in the array indicates || which value in the array to use in place of the index. To illustrate: :: ||  ||  >>> x = np.arange(10;1;-1) ||  >>> x ||  array([10;  9;  8;  7;  6;  5;  4;  3;  2]) ||  >>> x[np.array([3; 3; 1; 8])] ||  array([7; 7; 9; 2]) ||  ||  || The index array consisting of the values 3; 3; 1 and 8 correspondingly || create an array of length 4 (same as the index array) where each index || is replaced by the value the index array has in the array being indexed. ||  || Negative values are permitted and work as they do with single indices || or slices: :: ||  ||  >>> x[np.array([3;3;-3;8])] ||  array([7; 7; 4; 2]) ||  || It is an error to have index values out of bounds: :: ||  ||  >>> x[np.array([3; 3; 20; 8])] ||  <type 'exceptions.IndexError'>: index 20 out of bounds 0<=index<9 ||  || Generally speaking; what is returned when index arrays are used is || an array with the same shape as the index array; but with the type || and values of the array being indexed. As an example; we can use a || multidimensional index array instead: :: ||  ||  >>> x[np.array([[1;1];[2;3]])] ||  array([[9; 9]; ||         [8; 7]]) ||  || Indexing Multi-dimensional arrays || ================================= ||  || Things become more complex when multidimensional arrays are indexed; || particularly with multidimensional index arrays. These tend to be || more unusual uses; but they are permitted; and they are useful for some || problems. We'll  start with the simplest multidimensional case (using || the array y from the previous examples): :: ||  ||  >>> y[np.array([0;2;4]); np.array([0;1;2])] ||  array([ 0; 15; 30]) ||  || In this case; if the index arrays have a matching shape; and there is || an index array for each dimension of the array being indexed; the || resultant array has the same shape as the index arrays; and the values || correspond to the index set for each position in the index arrays. In || this example; the first index value is 0 for both index arrays; and || thus the first value of the resultant array is y[0;0]. The next value || is y[2;1]; and the last is y[4;2]. ||  || If the index arrays do not have the same shape; there is an attempt to || broadcast them to the same shape.  If they cannot be broadcast to the || same shape; an exception is raised: :: ||  ||  >>> y[np.array([0;2;4]); np.array([0;1])] ||  <type 'exceptions.ValueError'>: shape mismatch: objects cannot be ||  broadcast to a single shape ||  || The broadcasting mechanism permits index arrays to be combined with || scalars for other indices. The effect is that the scalar value is used || for all the corresponding values of the index arrays: :: ||  ||  >>> y[np.array([0;2;4]); 1] ||  array([ 1; 15; 29]) ||  || Jumping to the next level of complexity; it is possible to only || partially index an array with index arrays. It takes a bit of thought || to understand what happens in such cases. For example if we just use || one index array with y: :: ||  ||  >>> y[np.array([0;2;4])] ||  array([[ 0;  1;  2;  3;  4;  5;  6]; ||         [14; 15; 16; 17; 18; 19; 20]; ||         [28; 29; 30; 31; 32; 33; 34]]) ||  || What results is the construction of a new array where each value of || the index array selects one row from the array being indexed and the || resultant array has the resulting shape (number of index elements; || size of row). ||  || An example of where this may be useful is for a color lookup table || where we want to map the values of an image into RGB triples for || display. The lookup table could have a shape (nlookup; 3). Indexing || such an array with an image with shape (ny; nx) with dtype=np.uint8 || (or any integer type so long as values are with the bounds of the || lookup table) will result in an array of shape (ny; nx; 3) where a || triple of RGB values is associated with each pixel location. ||  || In general; the shape of the resultant array will be the concatenation || of the shape of the index array (or the shape that all the index arrays || were broadcast to) with the shape of any unused dimensions (those not || indexed) in the array being indexed. ||  || Boolean or \"mask\" index arrays || ============================== ||  || Boolean arrays used as indices are treated in a different manner || entirely than index arrays. Boolean arrays must be of the same shape || as the initial dimensions of the array being indexed. In the || most straightforward case; the boolean array has the same shape: :: ||  ||  >>> b = y>20 ||  >>> y[b] ||  array([21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34]) ||  || Unlike in the case of integer index arrays; in the boolean case; the || result is a 1-D array containing all the elements in the indexed array || corresponding to all the true elements in the boolean array. The || elements in the indexed array are always iterated and returned in || :term:`row-major` (C-style) order. The result is also identical to || ``y[np.nonzero(b)]``. As with index arrays; what is returned is a copy || of the data; not a view as one gets with slices. ||  || The result will be multidimensional if y has more dimensions than b. || For example: :: ||  ||  >>> b[:;5] # use a 1-D boolean whose first dim agrees with the first dim of y ||  array([False; False; False;  True;  True]) ||  >>> y[b[:;5]] ||  array([[21; 22; 23; 24; 25; 26; 27]; ||         [28; 29; 30; 31; 32; 33; 34]]) ||  || Here the 4th and 5th rows are selected from the indexed array and || combined to make a 2-D array. ||  || In general; when the boolean array has fewer dimensions than the array || being indexed; this is equivalent to y[b; ...]; which means || y is indexed by b followed by as many : as are needed to fill || out the rank of y. || Thus the shape of the result is one dimension containing the number || of True elements of the boolean array; followed by the remaining || dimensions of the array being indexed. ||  || For example; using a 2-D boolean array of shape (2;3) || with four True elements to select rows from a 3-D array of shape || (2;3;5) results in a 2-D result of shape (4;5): :: ||  ||  >>> x = np.arange(30).reshape(2;3;5) ||  >>> x ||  array([[[ 0;  1;  2;  3;  4]; ||          [ 5;  6;  7;  8;  9]; ||          [10; 11; 12; 13; 14]]; ||         [[15; 16; 17; 18; 19]; ||          [20; 21; 22; 23; 24]; ||          [25; 26; 27; 28; 29]]]) ||  >>> b = np.array([[True; True; False]; [False; True; True]]) ||  >>> x[b] ||  array([[ 0;  1;  2;  3;  4]; ||         [ 5;  6;  7;  8;  9]; ||         [20; 21; 22; 23; 24]; ||         [25; 26; 27; 28; 29]]) ||  || For further details; consult the numpy reference documentation on array indexing. ||  || Combining index arrays with slices || ================================== ||  || Index arrays may be combined with slices. For example: :: ||  ||  >>> y[np.array([0;2;4]);1:3] ||  array([[ 1;  2]; ||         [15; 16]; ||         [29; 30]]) ||  || In effect; the slice is converted to an index array || np.array([[1;2]]) (shape (1;2)) that is broadcast with the index array || to produce a resultant array of shape (3;2). ||  || Likewise; slicing can be combined with broadcasted boolean indices: :: ||  ||  >>> y[b[:;5];1:3] ||  array([[22; 23]; ||         [29; 30]]) ||  || Structural indexing tools || ========================= ||  || To facilitate easy matching of array shapes with expressions and in || assignments; the np.newaxis object can be used within array indices || to add new dimensions with a size of 1. For example: :: ||  ||  >>> y.shape ||  (5; 7) ||  >>> y[:;np.newaxis;:].shape ||  (5; 1; 7) ||  || Note that there are no new elements in the array; just that the || dimensionality is increased. This can be handy to combine two || arrays in a way that otherwise would require explicitly reshaping || operations. For example: :: ||  ||  >>> x = np.arange(5) ||  >>> x[:;np.newaxis] + x[np.newaxis;:] ||  array([[0; 1; 2; 3; 4]; ||         [1; 2; 3; 4; 5]; ||         [2; 3; 4; 5; 6]; ||         [3; 4; 5; 6; 7]; ||         [4; 5; 6; 7; 8]]) ||  || The ellipsis syntax maybe used to indicate selecting in full any || remaining unspecified dimensions. For example: :: ||  ||  >>> z = np.arange(81).reshape(3;3;3;3) ||  >>> z[1;...;2] ||  array([[29; 32; 35]; ||         [38; 41; 44]; ||         [47; 50; 53]]) ||  || This is equivalent to: :: ||  ||  >>> z[1;:;:;2] ||  array([[29; 32; 35]; ||         [38; 41; 44]; ||         [47; 50; 53]]) ||  || Assigning values to indexed arrays || ================================== ||  || As mentioned; one can select a subset of an array to assign to using || a single index; slices; and index and mask arrays. The value being || assigned to the indexed array must be shape consistent (the same shape || or broadcastable to the shape the index produces). For example; it is || permitted to assign a constant to a slice: :: ||  ||  >>> x = np.arange(10) ||  >>> x[2:7] = 1 ||  || or an array of the right size: :: ||  ||  >>> x[2:7] = np.arange(5) ||  || Note that assignments may result in changes if assigning || higher types to lower types (like floats to ints) or even || exceptions (assigning complex to floats or ints): :: ||  ||  >>> x[1] = 1.2 ||  >>> x[1] ||  1 ||  >>> x[1] = 1.2j ||  <type 'exceptions.TypeError'>: can't convert complex to long; use ||  long(abs(z)) ||  ||  || Unlike some of the references (such as array and mask indices) || assignments are always made to the original data in the array || (indeed; nothing else would make sense!). Note though; that some || actions may not work as one may naively expect. This particular || example is often surprising to people: :: ||  ||  >>> x = np.arange(0; 50; 10) ||  >>> x ||  array([ 0; 10; 20; 30; 40]) ||  >>> x[np.array([1; 1; 3; 1])] += 1 ||  >>> x ||  array([ 0; 11; 20; 31; 40]) ||  || Where people expect that the 1st location will be incremented by 3. || In fact; it will only be incremented by 1. The reason is because || a new array is extracted from the original (as a temporary) containing || the values at 1; 1; 3; 1; then the value 1 is added to the temporary; || and then the temporary is assigned back to the original array. Thus || the value of the array at x[1]+1 is assigned to x[1] three times; || rather than being incremented 3 times. ||  || Dealing with variable numbers of indices within programs || ======================================================== ||  || The index syntax is very powerful but limiting when dealing with || a variable number of indices. For example; if you want to write || a function that can handle arguments with various numbers of || dimensions without having to write special case code for each || number of possible dimensions; how can that be done? If one || supplies to the index a tuple; the tuple will be interpreted || as a list of indices. For example (using the previous definition || for the array z): :: ||  ||  >>> indices = (1;1;1;1) ||  >>> z[indices] ||  40 ||  || So one can use code to construct tuples of any number of indices || and then use these within an index. ||  || Slices can be specified within programs by using the slice() function || in Python. For example: :: ||  ||  >>> indices = (1;1;1;slice(0;2)) # same as [1;1;1;0:2] ||  >>> z[indices] ||  array([39; 40]) ||  || Likewise; ellipsis can be specified by code by using the Ellipsis || object: :: ||  ||  >>> indices = (1; Ellipsis; 1) # same as [1;...;1] ||  >>> z[indices] ||  array([[28; 31; 34]; ||         [37; 40; 43]; ||         [46; 49; 52]]) ||  || For this reason it is possible to use the output from the np.nonzero() || function directly as an index since it always returns a tuple of index || arrays. ||  || Because the special treatment of tuples; they are not automatically || converted to an array as a list would be. As an example: :: ||  ||  >>> z[[1;1;1;1]] # produces a large array ||  array([[[[27; 28; 29]; ||           [30; 31; 32]; ... ||  >>> z[(1;1;1;1)] # returns a single value ||  40 ||  || \"\"\"
aahjgegja,ryfeus/gcf-packs,tensorflow2.0/source/numpy/doc/internals.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\" || =============== || Array Internals || =============== ||  || Internal organization of numpy arrays || ===================================== ||  || It helps to understand a bit about how numpy arrays are handled under the covers to help understand numpy better. This section will not go into great detail. Those wishing to understand the full details are referred to Travis Oliphant's book \"Guide to NumPy\". ||  || NumPy arrays consist of two major components; the raw array data (from now on; || referred to as the data buffer); and the information about the raw array data. || The data buffer is typically what people think of as arrays in C or Fortran; || a contiguous (and fixed) block of memory containing fixed sized data items. || NumPy also contains a significant set of data that describes how to interpret || the data in the data buffer. This extra information contains (among other things): ||  ||  1) The basic data element's size in bytes ||  2) The start of the data within the data buffer (an offset relative to the ||     beginning of the data buffer). ||  3) The number of dimensions and the size of each dimension ||  4) The separation between elements for each dimension (the 'stride'). This ||     does not have to be a multiple of the element size ||  5) The byte order of the data (which may not be the native byte order) ||  6) Whether the buffer is read-only ||  7) Information (via the dtype object) about the interpretation of the basic ||     data element. The basic data element may be as simple as a int or a float; ||     or it may be a compound object (e.g.; struct-like); a fixed character field; ||     or Python object pointers. ||  8) Whether the array is to interpreted as C-order or Fortran-order. ||  || This arrangement allow for very flexible use of arrays. One thing that it allows || is simple changes of the metadata to change the interpretation of the array buffer. || Changing the byteorder of the array is a simple change involving no rearrangement || of the data. The shape of the array can be changed very easily without changing || anything in the data buffer or any data copying at all ||  || Among other things that are made possible is one can create a new array metadata || object that uses the same data buffer || to create a new view of that data buffer that has a different interpretation || of the buffer (e.g.; different shape; offset; byte order; strides; etc) but || shares the same data bytes. Many operations in numpy do just this such as || slices. Other operations; such as transpose; don't move data elements || around in the array; but rather change the information about the shape and strides so that the indexing of the array changes; but the data in the doesn't move. ||  || Typically these new versions of the array metadata but the same data buffer are || new 'views' into the data buffer. There is a different ndarray object; but it || uses the same data buffer. This is why it is necessary to force copies through || use of the .copy() method if one really wants to make a new and independent || copy of the data buffer. ||  || New views into arrays mean the object reference counts for the data buffer || increase. Simply doing away with the original array object will not remove the || data buffer if other views of it still exist. ||  || Multidimensional Array Indexing Order Issues || ============================================ ||  || What is the right way to index || multi-dimensional arrays? Before you jump to conclusions about the one and || true way to index multi-dimensional arrays; it pays to understand why this is || a confusing issue. This section will try to explain in detail how numpy || indexing works and why we adopt the convention we do for images; and when it || may be appropriate to adopt other conventions. ||  || The first thing to understand is || that there are two conflicting conventions for indexing 2-dimensional arrays. || Matrix notation uses the first index to indicate which row is being selected and || the second index to indicate which column is selected. This is opposite the || geometrically oriented-convention for images where people generally think the || first index represents x position (i.e.; column) and the second represents y || position (i.e.; row). This alone is the source of much confusion; || matrix-oriented users and image-oriented users expect two different things with || regard to indexing. ||  || The second issue to understand is how indices correspond || to the order the array is stored in memory. In Fortran the first index is the || most rapidly varying index when moving through the elements of a two || dimensional array as it is stored in memory. If you adopt the matrix || convention for indexing; then this means the matrix is stored one column at a || time (since the first index moves to the next row as it changes). Thus Fortran || is considered a Column-major language. C has just the opposite convention. In || C; the last index changes most rapidly as one moves through the array as || stored in memory. Thus C is a Row-major language. The matrix is stored by || rows. Note that in both cases it presumes that the matrix convention for || indexing is being used; i.e.; for both Fortran and C; the first index is the || row. Note this convention implies that the indexing convention is invariant || and that the data order changes to keep that so. ||  || But that's not the only way || to look at it. Suppose one has large two-dimensional arrays (images or || matrices) stored in data files. Suppose the data are stored by rows rather than || by columns. If we are to preserve our index convention (whether matrix or || image) that means that depending on the language we use; we may be forced to || reorder the data if it is read into memory to preserve our indexing || convention. For example if we read row-ordered data into memory without || reordering; it will match the matrix indexing convention for C; but not for || Fortran. Conversely; it will match the image indexing convention for Fortran; || but not for C. For C; if one is using data stored in row order; and one wants || to preserve the image index convention; the data must be reordered when || reading into memory. ||  || In the end; which you do for Fortran or C depends on || which is more important; not reordering data or preserving the indexing || convention. For large images; reordering data is potentially expensive; and || often the indexing convention is inverted to avoid that. ||  || The situation with || numpy makes this issue yet more complicated. The internal machinery of numpy || arrays is flexible enough to accept any ordering of indices. One can simply || reorder indices by manipulating the internal stride information for arrays || without reordering the data at all. NumPy will know how to map the new index || order to the data without moving the data. ||  || So if this is true; why not choose || the index order that matches what you most expect? In particular; why not define || row-ordered images to use the image convention? (This is sometimes referred || to as the Fortran convention vs the C convention; thus the 'C' and 'FORTRAN' || order options for array ordering in numpy.) The drawback of doing this is || potential performance penalties. It's common to access the data sequentially; || either implicitly in array operations or explicitly by looping over rows of an || image. When that is done; then the data will be accessed in non-optimal order. || As the first index is incremented; what is actually happening is that elements || spaced far apart in memory are being sequentially accessed; with usually poor || memory access speeds. For example; for a two dimensional image 'im' defined so || that im[0; 10] represents the value at x=0; y=10. To be consistent with usual || Python behavior then im[0] would represent a column at x=0. Yet that data || would be spread over the whole array since the data are stored in row order. || Despite the flexibility of numpy's indexing; it can't really paper over the fact || basic operations are rendered inefficient because of data order or that getting || contiguous subarrays is still awkward (e.g.; im[:;0] for the first row; vs || im[0]); thus one can't use an idiom such as for row in im; for col in im does || work; but doesn't yield contiguous column data. ||  || As it turns out; numpy is || smart enough when dealing with ufuncs to determine which index is the most || rapidly varying one in memory and uses that for the innermost loop. Thus for || ufuncs there is no large intrinsic advantage to either approach in most cases. || On the other hand; use of .flat with an FORTRAN ordered array will lead to || non-optimal memory access as adjacent elements in the flattened array (iterator; || actually) are not contiguous in memory. ||  || Indeed; the fact is that Python || indexing on lists and other sequences naturally leads to an outside-to inside || ordering (the first index gets the largest grouping; the next the next largest; || and the last gets the smallest element). Since image data are normally stored || by rows; this corresponds to position within rows being the last item indexed. ||  || If you do want to use Fortran ordering realize that || there are two approaches to consider: 1) accept that the first index is just not || the most rapidly changing in memory and have all your I\/O routines reorder || your data when going from memory to disk or visa versa; or use numpy's || mechanism for mapping the first index to the most rapidly varying data. We || recommend the former if possible. The disadvantage of the latter is that many || of numpy's functions will yield arrays without Fortran ordering unless you are || careful to use the 'order' keyword. Doing this would be highly inconvenient. ||  || Otherwise we recommend simply learning to reverse the usual order of indices || when accessing elements of an array. Granted; it goes against the grain; but || it is more in line with Python semantics and the natural order of the data. ||  || \"\"\"
aahjgegjb,ryfeus/gcf-packs,tensorflow2.0/source/numpy/doc/misc.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\" || ============= || Miscellaneous || ============= ||  || IEEE 754 Floating Point Special Values || -------------------------------------- ||  || Special values defined in numpy: nan; inf; ||  || NaNs can be used as a poor-man's mask (if you don't care what the || original value was) ||  || Note: cannot use equality to test NaNs. E.g.: :: ||  ||  >>> myarr = np.array([1.; 0.; np.nan; 3.]) ||  >>> np.nonzero(myarr == np.nan) ||  (array([]; dtype=int64);) ||  >>> np.nan == np.nan  # is always False! Use special numpy functions instead. ||  False ||  >>> myarr[myarr == np.nan] = 0. # doesn't work ||  >>> myarr ||  array([  1.;   0.;  NaN;   3.]) ||  >>> myarr[np.isnan(myarr)] = 0. # use this instead find ||  >>> myarr ||  array([ 1.;  0.;  0.;  3.]) ||  || Other related special value functions: :: ||  ||  isinf():    True if value is inf ||  isfinite(): True if not nan or inf ||  nan_to_num(): Map nan to 0; inf to max float; -inf to min float ||  || The following corresponds to the usual functions except that nans are excluded || from the results: :: ||  ||  nansum() ||  nanmax() ||  nanmin() ||  nanargmax() ||  nanargmin() ||  ||  >>> x = np.arange(10.) ||  >>> x[3] = np.nan ||  >>> x.sum() ||  nan ||  >>> np.nansum(x) ||  42.0 ||  || How numpy handles numerical exceptions || -------------------------------------- ||  || The default is to ``'warn'`` for ``invalid``; ``divide``; and ``overflow`` || and ``'ignore'`` for ``underflow``.  But this can be changed; and it can be || set individually for different kinds of exceptions. The different behaviors || are: ||  ||  - 'ignore' : Take no action when the exception occurs. ||  - 'warn'   : Print a `RuntimeWarning` (via the Python `warnings` module). ||  - 'raise'  : Raise a `FloatingPointError`. ||  - 'call'   : Call a function specified using the `seterrcall` function. ||  - 'print'  : Print a warning directly to ``stdout``. ||  - 'log'    : Record error in a Log object specified by `seterrcall`. ||  || These behaviors can be set for all kinds of errors or specific ones: ||  ||  - all       : apply to all numeric exceptions ||  - invalid   : when NaNs are generated ||  - divide    : divide by zero (for integers as well!) ||  - overflow  : floating point overflows ||  - underflow : floating point underflows ||  || Note that integer divide-by-zero is handled by the same machinery. || These behaviors are set on a per-thread basis. ||  || Examples || -------- ||  || :: ||  ||  >>> oldsettings = np.seterr(all='warn') ||  >>> np.zeros(5;dtype=np.float32)\/0. ||  invalid value encountered in divide ||  >>> j = np.seterr(under='ignore') ||  >>> np.array([1.e-100])**10 ||  >>> j = np.seterr(invalid='raise') ||  >>> np.sqrt(np.array([-1.])) ||  FloatingPointError: invalid value encountered in sqrt ||  >>> def errorhandler(errstr; errflag): ||  ...      print(\"saw stupid error!\") ||  >>> np.seterrcall(errorhandler) ||  <function err_handler at 0x...> ||  >>> j = np.seterr(all='call') ||  >>> np.zeros(5; dtype=np.int32)\/0 ||  FloatingPointError: invalid value encountered in divide ||  saw stupid error! ||  >>> j = np.seterr(**oldsettings) # restore previous ||  ...                              # error-handling settings ||  || Interfacing to C || ---------------- || Only a survey of the choices. Little detail on how each works. ||  || 1) Bare metal; wrap your own C-code manually. ||  ||  - Plusses: ||  ||    - Efficient ||    - No dependencies on other tools ||  ||  - Minuses: ||  ||    - Lots of learning overhead: ||  ||      - need to learn basics of Python C API ||      - need to learn basics of numpy C API ||      - need to learn how to handle reference counting and love it. ||  ||    - Reference counting often difficult to get right. ||  ||      - getting it wrong leads to memory leaks; and worse; segfaults ||  ||    - API will change for Python 3.0! ||  || 2) Cython ||  ||  - Plusses: ||  ||    - avoid learning C API's ||    - no dealing with reference counting ||    - can code in pseudo python and generate C code ||    - can also interface to existing C code ||    - should shield you from changes to Python C api ||    - has become the de-facto standard within the scientific Python community ||    - fast indexing support for arrays ||  ||  - Minuses: ||  ||    - Can write code in non-standard form which may become obsolete ||    - Not as flexible as manual wrapping ||  || 3) ctypes ||  ||  - Plusses: ||  ||    - part of Python standard library ||    - good for interfacing to existing sharable libraries; particularly ||      Windows DLLs ||    - avoids API\/reference counting issues ||    - good numpy support: arrays have all these in their ctypes ||      attribute: :: ||  ||        a.ctypes.data              a.ctypes.get_strides ||        a.ctypes.data_as           a.ctypes.shape ||        a.ctypes.get_as_parameter  a.ctypes.shape_as ||        a.ctypes.get_data          a.ctypes.strides ||        a.ctypes.get_shape         a.ctypes.strides_as ||  ||  - Minuses: ||  ||    - can't use for writing code to be turned into C extensions; only a wrapper ||      tool. ||  || 4) SWIG (automatic wrapper generator) ||  ||  - Plusses: ||  ||    - around a long time ||    - multiple scripting language support ||    - C++ support ||    - Good for wrapping large (many functions) existing C libraries ||  ||  - Minuses: ||  ||    - generates lots of code between Python and the C code ||    - can cause performance problems that are nearly impossible to optimize ||      out ||    - interface files can be hard to write ||    - doesn't necessarily avoid reference counting issues or needing to know ||      API's ||  || 5) scipy.weave ||  ||  - Plusses: ||  ||    - can turn many numpy expressions into C code ||    - dynamic compiling and loading of generated C code ||    - can embed pure C code in Python module and have weave extract; generate ||      interfaces and compile; etc. ||  ||  - Minuses: ||  ||    - Future very uncertain: it's the only part of Scipy not ported to Python 3 ||      and is effectively deprecated in favor of Cython. ||  || 6) Psyco ||  ||  - Plusses: ||  ||    - Turns pure python into efficient machine code through jit-like ||      optimizations ||    - very fast when it optimizes well ||  ||  - Minuses: ||  ||    - Only on intel (windows?) ||    - Doesn't do much for numpy? ||  || Interfacing to Fortran: || ----------------------- || The clear choice to wrap Fortran code is || `f2py <https:\/\/docs.scipy.org\/doc\/numpy\/f2py\/>`_. ||  || Pyfort is an older alternative; but not supported any longer. || Fwrap is a newer project that looked promising but isn't being developed any || longer. ||  || Interfacing to C++: || ------------------- ||  1) Cython ||  2) CXX ||  3) Boost.python ||  4) SWIG ||  5) SIP (used mainly in PyQT) ||  || \"\"\"
aahjgegjc,ryfeus/gcf-packs,tensorflow2.0/source/numpy/doc/structured_arrays.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\" || ================= || Structured Arrays || ================= ||  || Introduction || ============ ||  || Structured arrays are ndarrays whose datatype is a composition of simpler || datatypes organized as a sequence of named :term:`fields <field>`. For example; || :: ||  ||  >>> x = np.array([('Rex'; 9; 81.0); ('Fido'; 3; 27.0)]; ||  ...              dtype=[('name'; 'U10'); ('age'; 'i4'); ('weight'; 'f4')]) ||  >>> x ||  array([('Rex'; 9; 81.0); ('Fido'; 3; 27.0)]; ||        dtype=[('name'; 'S10'); ('age'; '<i4'); ('weight'; '<f4')]) ||  || Here ``x`` is a one-dimensional array of length two whose datatype is a || structure with three fields: 1. A string of length 10 or less named 'name'; 2. || a 32-bit integer named 'age'; and 3. a 32-bit float named 'weight'. ||  || If you index ``x`` at position 1 you get a structure:: ||  ||  >>> x[1] ||  ('Fido'; 3; 27.0) ||  || You can access and modify individual fields of a structured array by indexing || with the field name:: ||  ||  >>> x['age'] ||  array([9; 3]; dtype=int32) ||  >>> x['age'] = 5 ||  >>> x ||  array([('Rex'; 5; 81.0); ('Fido'; 5; 27.0)]; ||        dtype=[('name'; 'S10'); ('age'; '<i4'); ('weight'; '<f4')]) ||  || Structured datatypes are designed to be able to mimic 'structs' in the C || language; and share a similar memory layout. They are meant for interfacing with || C code and for low-level manipulation of structured buffers; for example for || interpreting binary blobs. For these purposes they support specialized features || such as subarrays; nested datatypes; and unions; and allow control over the || memory layout of the structure. ||  || Users looking to manipulate tabular data; such as stored in csv files; may find || other pydata projects more suitable; such as xarray; pandas; or DataArray. || These provide a high-level interface for tabular data analysis and are better || optimized for that use. For instance; the C-struct-like memory layout of || structured arrays in numpy can lead to poor cache behavior in comparison. ||  || .. _defining-structured-types: ||  || Structured Datatypes || ==================== ||  || A structured datatype can be thought of as a sequence of bytes of a certain || length (the structure's :term:`itemsize`) which is interpreted as a collection || of fields. Each field has a name; a datatype; and a byte offset within the || structure. The datatype of a field may be any numpy datatype including other || structured datatypes; and it may also be a :term:`sub-array` which behaves like || an ndarray of a specified shape. The offsets of the fields are arbitrary; and || fields may even overlap. These offsets are usually determined automatically by || numpy; but can also be specified. ||  || Structured Datatype Creation || ---------------------------- ||  || Structured datatypes may be created using the function :func:`numpy.dtype`. || There are 4 alternative forms of specification which vary in flexibility and || conciseness. These are further documented in the || :ref:`Data Type Objects <arrays.dtypes.constructing>` reference page; and in || summary they are: ||  || 1.   A list of tuples; one tuple per field ||  ||      Each tuple has the form ``(fieldname; datatype; shape)`` where shape is ||      optional. ``fieldname`` is a string (or tuple if titles are used; see ||      :ref:`Field Titles <titles>` below); ``datatype`` may be any object ||      convertible to a datatype; and ``shape`` is a tuple of integers specifying ||      subarray shape. ||  ||       >>> np.dtype([('x'; 'f4'); ('y'; np.float32); ('z'; 'f4'; (2;2))]) ||       dtype=[('x'; '<f4'); ('y'; '<f4'); ('z'; '<f4'; (2; 2))]) ||  ||      If ``fieldname`` is the empty string ``''``; the field will be given a ||      default name of the form ``f#``; where ``#`` is the integer index of the ||      field; counting from 0 from the left:: ||  ||       >>> np.dtype([('x'; 'f4');(''; 'i4');('z'; 'i8')]) ||       dtype([('x'; '<f4'); ('f1'; '<i4'); ('z'; '<i8')]) ||  ||      The byte offsets of the fields within the structure and the total ||      structure itemsize are determined automatically. ||  || 2.   A string of comma-separated dtype specifications ||  ||      In this shorthand notation any of the :ref:`string dtype specifications ||      <arrays.dtypes.constructing>` may be used in a string and separated by ||      commas. The itemsize and byte offsets of the fields are determined ||      automatically; and the field names are given the default names ``f0``; ||      ``f1``; etc. :: ||  ||       >>> np.dtype('i8;f4;S3') ||       dtype([('f0'; '<i8'); ('f1'; '<f4'); ('f2'; 'S3')]) ||       >>> np.dtype('3int8; float32; (2;3)float64') ||       dtype([('f0'; 'i1'; 3); ('f1'; '<f4'); ('f2'; '<f8'; (2; 3))]) ||  || 3.   A dictionary of field parameter arrays ||  ||      This is the most flexible form of specification since it allows control ||      over the byte-offsets of the fields and the itemsize of the structure. ||  ||      The dictionary has two required keys; 'names' and 'formats'; and four ||      optional keys; 'offsets'; 'itemsize'; 'aligned' and 'titles'. The values ||      for 'names' and 'formats' should respectively be a list of field names and ||      a list of dtype specifications; of the same length. The optional 'offsets' ||      value should be a list of integer byte-offsets; one for each field within ||      the structure. If 'offsets' is not given the offsets are determined ||      automatically. The optional 'itemsize' value should be an integer ||      describing the total size in bytes of the dtype; which must be large ||      enough to contain all the fields. ||      :: ||  ||       >>> np.dtype({'names': ['col1'; 'col2']; 'formats': ['i4';'f4']}) ||       dtype([('col1'; '<i4'); ('col2'; '<f4')]) ||       >>> np.dtype({'names': ['col1'; 'col2']; ||       ...           'formats': ['i4';'f4']; ||       ...           'offsets': [0; 4]; ||       ...           'itemsize': 12}) ||       dtype({'names':['col1';'col2']; 'formats':['<i4';'<f4']; 'offsets':[0;4]; 'itemsize':12}) ||  ||      Offsets may be chosen such that the fields overlap; though this will mean ||      that assigning to one field may clobber any overlapping field's data. As ||      an exception; fields of :class:`numpy.object` type cannot overlap with ||      other fields; because of the risk of clobbering the internal object ||      pointer and then dereferencing it. ||  ||      The optional 'aligned' value can be set to ``True`` to make the automatic ||      offset computation use aligned offsets (see :ref:`offsets-and-alignment`); ||      as if the 'align' keyword argument of :func:`numpy.dtype` had been set to ||      True. ||  ||      The optional 'titles' value should be a list of titles of the same length ||      as 'names'; see :ref:`Field Titles <titles>` below. ||  || 4.   A dictionary of field names ||  ||      The use of this form of specification is discouraged; but documented here ||      because older numpy code may use it. The keys of the dictionary are the ||      field names and the values are tuples specifying type and offset:: ||  ||       >>> np.dtype=({'col1': ('i1';0); 'col2': ('f4';1)}) ||       dtype([(('col1'); 'i1'); (('col2'); '>f4')]) ||  ||      This form is discouraged because Python dictionaries do not preserve order ||      in Python versions before Python 3.6; and the order of the fields in a ||      structured dtype has meaning. :ref:`Field Titles <titles>` may be ||      specified by using a 3-tuple; see below. ||  || Manipulating and Displaying Structured Datatypes || ------------------------------------------------ ||  || The list of field names of a structured datatype can be found in the ``names`` || attribute of the dtype object:: ||  ||  >>> d = np.dtype([('x'; 'i8'); ('y'; 'f4')]) ||  >>> d.names ||  ('x'; 'y') ||  || The field names may be modified by assigning to the ``names`` attribute using a || sequence of strings of the same length. ||  || The dtype object also has a dictionary-like attribute; ``fields``; whose keys || are the field names (and :ref:`Field Titles <titles>`; see below) and whose || values are tuples containing the dtype and byte offset of each field. :: ||  ||  >>> d.fields ||  mappingproxy({'x': (dtype('int64'); 0); 'y': (dtype('float32'); 8)}) ||  || Both the ``names`` and ``fields`` attributes will equal ``None`` for || unstructured arrays. The recommended way to test if a dtype is structured is || with `if dt.names is not None` rather than `if dt.names`; to account for dtypes || with 0 fields. ||  || The string representation of a structured datatype is shown in the \"list of || tuples\" form if possible; otherwise numpy falls back to using the more general || dictionary form. ||  || .. _offsets-and-alignment: ||  || Automatic Byte Offsets and Alignment || ------------------------------------ ||  || Numpy uses one of two methods to automatically determine the field byte offsets || and the overall itemsize of a structured datatype; depending on whether || ``align=True`` was specified as a keyword argument to :func:`numpy.dtype`. ||  || By default (``align=False``); numpy will pack the fields together such that || each field starts at the byte offset the previous field ended; and the fields || are contiguous in memory. :: ||  ||  >>> def print_offsets(d): ||  ...     print(\"offsets:\"; [d.fields[name][1] for name in d.names]) ||  ...     print(\"itemsize:\"; d.itemsize) ||  >>> print_offsets(np.dtype('u1;u1;i4;u1;i8;u2')) ||  offsets: [0; 1; 2; 6; 7; 15] ||  itemsize: 17 ||  || If ``align=True`` is set; numpy will pad the structure in the same way many C || compilers would pad a C-struct. Aligned structures can give a performance || improvement in some cases; at the cost of increased datatype size. Padding || bytes are inserted between fields such that each field's byte offset will be a || multiple of that field's alignment; which is usually equal to the field's size || in bytes for simple datatypes; see :c:member:`PyArray_Descr.alignment`.  The || structure will also have trailing padding added so that its itemsize is a || multiple of the largest field's alignment. :: ||  ||  >>> print_offsets(np.dtype('u1;u1;i4;u1;i8;u2'; align=True)) ||  offsets: [0; 1; 4; 8; 16; 24] ||  itemsize: 32 ||  || Note that although almost all modern C compilers pad in this way by default; || padding in C structs is C-implementation-dependent so this memory layout is not || guaranteed to exactly match that of a corresponding struct in a C program. Some || work may be needed; either on the numpy side or the C side; to obtain exact || correspondence. ||  || If offsets were specified using the optional ``offsets`` key in the || dictionary-based dtype specification; setting ``align=True`` will check that || each field's offset is a multiple of its size and that the itemsize is a || multiple of the largest field size; and raise an exception if not. ||  || If the offsets of the fields and itemsize of a structured array satisfy the || alignment conditions; the array will have the ``ALIGNED`` :ref:`flag || <numpy.ndarray.flags>` set. ||  || A convenience function :func:`numpy.lib.recfunctions.repack_fields` converts an || aligned dtype or array to a packed one and vice versa. It takes either a dtype || or structured ndarray as an argument; and returns a copy with fields re-packed; || with or without padding bytes. ||  || .. _titles: ||  || Field Titles || ------------ ||  || In addition to field names; fields may also have an associated :term:`title`; || an alternate name; which is sometimes used as an additional description or || alias for the field. The title may be used to index an array; just like a || field name. ||  || To add titles when using the list-of-tuples form of dtype specification; the || field name may be specified as a tuple of two strings instead of a single || string; which will be the field's title and field name respectively. For || example:: ||  ||  >>> np.dtype([(('my title'; 'name'); 'f4')]) ||  || When using the first form of dictionary-based specification; the titles may be || supplied as an extra ``'titles'`` key as described above. When using the second || (discouraged) dictionary-based specification; the title can be supplied by || providing a 3-element tuple ``(datatype; offset; title)`` instead of the usual || 2-element tuple:: ||  ||  >>> np.dtype({'name': ('i4'; 0; 'my title')}) ||  || The ``dtype.fields`` dictionary will contain :term:`titles` as keys; if any || titles are used.  This means effectively that a field with a title will be || represented twice in the fields dictionary. The tuple values for these fields || will also have a third element; the field title. Because of this; and because || the ``names`` attribute preserves the field order while the ``fields`` || attribute may not; it is recommended to iterate through the fields of a dtype || using the ``names`` attribute of the dtype; which will not list titles; as || in:: ||  ||  >>> for name in d.names: ||  ...     print(d.fields[name][:2]) ||  || Union types || ----------- ||  || Structured datatypes are implemented in numpy to have base type || :class:`numpy.void` by default; but it is possible to interpret other numpy || types as structured types using the ``(base_dtype; dtype)`` form of dtype || specification described in || :ref:`Data Type Objects <arrays.dtypes.constructing>`.  Here; ``base_dtype`` is || the desired underlying dtype; and fields and flags will be copied from || ``dtype``. This dtype is similar to a 'union' in C. ||  || Indexing and Assignment to Structured arrays || ============================================ ||  || Assigning data to a Structured Array || ------------------------------------ ||  || There are a number of ways to assign values to a structured array: Using python || tuples; using scalar values; or using other structured arrays. ||  || Assignment from Python Native Types (Tuples) || ```````````````````````````````````````````` ||  || The simplest way to assign values to a structured array is using python tuples. || Each assigned value should be a tuple of length equal to the number of fields || in the array; and not a list or array as these will trigger numpy's || broadcasting rules. The tuple's elements are assigned to the successive fields || of the array; from left to right:: ||  ||  >>> x = np.array([(1;2;3);(4;5;6)]; dtype='i8;f4;f8') ||  >>> x[1] = (7;8;9) ||  >>> x ||  array([(1; 2.; 3.); (7; 8.; 9.)]; ||       dtype=[('f0'; '<i8'); ('f1'; '<f4'); ('f2'; '<f8')]) ||  || Assignment from Scalars || ``````````````````````` ||  || A scalar assigned to a structured element will be assigned to all fields. This || happens when a scalar is assigned to a structured array; or when an || unstructured array is assigned to a structured array:: ||  ||  >>> x = np.zeros(2; dtype='i8;f4;?;S1') ||  >>> x[:] = 3 ||  >>> x ||  array([(3; 3.0; True; b'3'); (3; 3.0; True; b'3')]; ||        dtype=[('f0'; '<i8'); ('f1'; '<f4'); ('f2'; '?'); ('f3'; 'S1')]) ||  >>> x[:] = np.arange(2) ||  >>> x ||  array([(0; 0.0; False; b'0'); (1; 1.0; True; b'1')]; ||        dtype=[('f0'; '<i8'); ('f1'; '<f4'); ('f2'; '?'); ('f3'; 'S1')]) ||  || Structured arrays can also be assigned to unstructured arrays; but only if the || structured datatype has just a single field:: ||  ||  >>> twofield = np.zeros(2; dtype=[('A'; 'i4'); ('B'; 'i4')]) ||  >>> onefield = np.zeros(2; dtype=[('A'; 'i4')]) ||  >>> nostruct = np.zeros(2; dtype='i4') ||  >>> nostruct[:] = twofield ||  ValueError: Can't cast from structure to non-structure; except if the structure only has a single field. ||  >>> nostruct[:] = onefield ||  >>> nostruct ||  array([0; 0]; dtype=int32) ||  || Assignment from other Structured Arrays || ``````````````````````````````````````` ||  || Assignment between two structured arrays occurs as if the source elements had || been converted to tuples and then assigned to the destination elements. That || is; the first field of the source array is assigned to the first field of the || destination array; and the second field likewise; and so on; regardless of || field names. Structured arrays with a different number of fields cannot be || assigned to each other. Bytes of the destination structure which are not || included in any of the fields are unaffected. :: ||  ||  >>> a = np.zeros(3; dtype=[('a'; 'i8'); ('b'; 'f4'); ('c'; 'S3')]) ||  >>> b = np.ones(3; dtype=[('x'; 'f4'); ('y'; 'S3'); ('z'; 'O')]) ||  >>> b[:] = a ||  >>> b ||  array([(0.0; b'0.0'; b''); (0.0; b'0.0'; b''); (0.0; b'0.0'; b'')]; ||        dtype=[('x'; '<f4'); ('y'; 'S3'); ('z'; 'O')]) ||  ||  || Assignment involving subarrays || `````````````````````````````` ||  || When assigning to fields which are subarrays; the assigned value will first be || broadcast to the shape of the subarray. ||  || Indexing Structured Arrays || -------------------------- ||  || Accessing Individual Fields || ``````````````````````````` ||  || Individual fields of a structured array may be accessed and modified by indexing || the array with the field name. :: ||  ||  >>> x = np.array([(1;2);(3;4)]; dtype=[('foo'; 'i8'); ('bar'; 'f4')]) ||  >>> x['foo'] ||  array([1; 3]) ||  >>> x['foo'] = 10 ||  >>> x ||  array([(10; 2.); (10; 4.)]; ||        dtype=[('foo'; '<i8'); ('bar'; '<f4')]) ||  || The resulting array is a view into the original array. It shares the same || memory locations and writing to the view will modify the original array. :: ||  ||  >>> y = x['bar'] ||  >>> y[:] = 10 ||  >>> x ||  array([(10; 5.); (10; 5.)]; ||        dtype=[('foo'; '<i8'); ('bar'; '<f4')]) ||  || This view has the same dtype and itemsize as the indexed field; so it is || typically a non-structured array; except in the case of nested structures. ||  ||  >>> y.dtype; y.shape; y.strides ||  (dtype('float32'); (2;); (12;)) ||  || If the accessed field is a subarray; the dimensions of the subarray || are appended to the shape of the result:: ||  ||    >>> x = np.zeros((2;2); dtype=[('a'; np.int32); ('b'; np.float64; (3;3))]) ||    >>> x['a'].shape ||    (2; 2) ||    >>> x['b'].shape ||    (2; 2; 3; 3) ||  || Accessing Multiple Fields || ``````````````````````````` ||  || One can index and assign to a structured array with a multi-field index; where || the index is a list of field names. ||  || .. warning:: ||     The behavior of multi-field indexes changed from Numpy 1.15 to Numpy 1.16. ||  || The result of indexing with a multi-field index is a view into the original || array; as follows:: ||  ||  >>> a = np.zeros(3; dtype=[('a'; 'i4'); ('b'; 'i4'); ('c'; 'f4')]) ||  >>> a[['a'; 'c']] ||  array([(0; 0.); (0; 0.); (0; 0.)]; ||       dtype={'names':['a';'c']; 'formats':['<i4';'<f4']; 'offsets':[0;8]; 'itemsize':12}) ||  || Assignment to the view modifies the original array. The view's fields will be || in the order they were indexed. Note that unlike for single-field indexing; the || view's dtype has the same itemsize as the original array; and has fields at the || same offsets as in the original array; and unindexed fields are merely missing. ||  || .. warning:: ||     In Numpy 1.15; indexing an array with a multi-field index returned a copy of ||     the result above; but with fields packed together in memory as if ||     passed through :func:`numpy.lib.recfunctions.repack_fields`. ||  ||     The new behavior as of Numpy 1.16 leads to extra \"padding\" bytes at the ||     location of unindexed fields compared to 1.15. You will need to update any ||     code which depends on the data having a \"packed\" layout. For instance code ||     such as:: ||  ||      >>> a = np.zeros(3; dtype=[('a'; 'i4'); ('b'; 'i4'); ('c'; 'f4')]) ||      >>> a[['a';'c']].view('i8')  # Fails in Numpy 1.16 ||      ValueError: When changing to a smaller dtype; its size must be a divisor of the size of original dtype ||  ||     will need to be changed. This code has raised a ``FutureWarning`` since ||     Numpy 1.12; and similar code has raised ``FutureWarning`` since 1.7. ||  ||     In 1.16 a number of functions have been introduced in the ||     :module:`numpy.lib.recfunctions` module to help users account for this ||     change. These are ||     :func:`numpy.lib.recfunctions.repack_fields`. ||     :func:`numpy.lib.recfunctions.structured_to_unstructured`; ||     :func:`numpy.lib.recfunctions.unstructured_to_structured`; ||     :func:`numpy.lib.recfunctions.apply_along_fields`; ||     :func:`numpy.lib.recfunctions.assign_fields_by_name`;  and ||     :func:`numpy.lib.recfunctions.require_fields`. ||  ||     The function :func:`numpy.lib.recfunctions.repack_fields` can always be ||     used to reproduce the old behavior; as it will return a packed copy of the ||     structured array. The code above; for example; can be replaced with: ||  ||      >>> repack_fields(a[['a';'c']]).view('i8')  # supported in 1.16 ||      array([0; 0; 0]) ||  ||     Furthermore; numpy now provides a new function ||     :func:`numpy.lib.recfunctions.structured_to_unstructured` which is a safer ||     and more efficient alternative for users who wish to convert structured ||     arrays to unstructured arrays; as the view above is often indeded to do. ||     This function allows safe conversion to an unstructured type taking into ||     account padding; often avoids a copy; and also casts the datatypes ||     as needed; unlike the view. Code such as: ||  ||      >>> a = np.zeros(3; dtype=[('x'; 'f4'); ('y'; 'f4'); ('z'; 'f4')]) ||      >>> a[['x'; 'z']].view('f4') ||  ||     can be made safer by replacing with: ||  ||      >>> structured_to_unstructured(a[['x'; 'z']]) ||      array([0; 0; 0]) ||  ||  || Assignment to an array with a multi-field index modifies the original array:: ||  ||  >>> a[['a'; 'c']] = (2; 3) ||  >>> a ||  array([(2; 0; 3.0); (2; 0; 3.0); (2; 0; 3.0)]; ||        dtype=[('a'; '<i8'); ('b'; '<i4'); ('c'; '<f8')]) ||  || This obeys the structured array assignment rules described above. For example; || this means that one can swap the values of two fields using appropriate || multi-field indexes:: ||  ||  >>> a[['a'; 'c']] = a[['c'; 'a']] ||  || Indexing with an Integer to get a Structured Scalar || ``````````````````````````````````````````````````` ||  || Indexing a single element of a structured array (with an integer index) returns || a structured scalar:: ||  ||  >>> x = np.array([(1; 2.; 3.)]; dtype='i;f;f') ||  >>> scalar = x[0] ||  >>> scalar ||  (1; 2.; 3.) ||  >>> type(scalar) ||  numpy.void ||  || Unlike other numpy scalars; structured scalars are mutable and act like views || into the original array; such that modifying the scalar will modify the || original array. Structured scalars also support access and assignment by field || name:: ||  ||  >>> x = np.array([(1;2);(3;4)]; dtype=[('foo'; 'i8'); ('bar'; 'f4')]) ||  >>> s = x[0] ||  >>> s['bar'] = 100 ||  >>> x ||  array([(1; 100.); (3; 4.)]; ||        dtype=[('foo'; '<i8'); ('bar'; '<f4')]) ||  || Similarly to tuples; structured scalars can also be indexed with an integer:: ||  ||  >>> scalar = np.array([(1; 2.; 3.)]; dtype='i;f;f')[0] ||  >>> scalar[0] ||  1 ||  >>> scalar[1] = 4 ||  || Thus; tuples might be thought of as the native Python equivalent to numpy's || structured types; much like native python integers are the equivalent to || numpy's integer types. Structured scalars may be converted to a tuple by || calling :func:`ndarray.item`:: ||  ||  >>> scalar.item(); type(scalar.item()) ||  ((1; 2.0; 3.0); tuple) ||  || Viewing Structured Arrays Containing Objects || -------------------------------------------- ||  || In order to prevent clobbering object pointers in fields of || :class:`numpy.object` type; numpy currently does not allow views of structured || arrays containing objects. ||  || Structure Comparison || -------------------- ||  || If the dtypes of two void structured arrays are equal; testing the equality of || the arrays will result in a boolean array with the dimensions of the original || arrays; with elements set to ``True`` where all fields of the corresponding || structures are equal. Structured dtypes are equal if the field names; || dtypes and titles are the same; ignoring endianness; and the fields are in || the same order:: ||  ||  >>> a = np.zeros(2; dtype=[('a'; 'i4'); ('b'; 'i4')]) ||  >>> b = np.ones(2; dtype=[('a'; 'i4'); ('b'; 'i4')]) ||  >>> a == b ||  array([False; False]) ||  || Currently; if the dtypes of two void structured arrays are not equivalent the || comparison fails; returning the scalar value ``False``. This behavior is || deprecated as of numpy 1.10 and will raise an error or perform elementwise || comparison in the future. ||  || The ``<`` and ``>`` operators always return ``False`` when comparing void || structured arrays; and arithmetic and bitwise operations are not supported. ||  || Record Arrays || ============= ||  || As an optional convenience numpy provides an ndarray subclass; || :class:`numpy.recarray`; and associated helper functions in the || :mod:`numpy.rec` submodule; that allows access to fields of structured arrays || by attribute instead of only by index. Record arrays also use a special || datatype; :class:`numpy.record`; that allows field access by attribute on the || structured scalars obtained from the array. ||  || The simplest way to create a record array is with :func:`numpy.rec.array`:: ||  ||  >>> recordarr = np.rec.array([(1;2.;'Hello');(2;3.;\"World\")]; ||  ...                    dtype=[('foo'; 'i4');('bar'; 'f4'); ('baz'; 'S10')]) ||  >>> recordarr.bar ||  array([ 2.;  3.]; dtype=float32) ||  >>> recordarr[1:2] ||  rec.array([(2; 3.0; 'World')]; ||        dtype=[('foo'; '<i4'); ('bar'; '<f4'); ('baz'; 'S10')]) ||  >>> recordarr[1:2].foo ||  array([2]; dtype=int32) ||  >>> recordarr.foo[1:2] ||  array([2]; dtype=int32) ||  >>> recordarr[1].baz ||  'World' ||  || :func:`numpy.rec.array` can convert a wide variety of arguments into record || arrays; including structured arrays:: ||  ||  >>> arr = array([(1;2.;'Hello');(2;3.;\"World\")]; ||  ...             dtype=[('foo'; 'i4'); ('bar'; 'f4'); ('baz'; 'S10')]) ||  >>> recordarr = np.rec.array(arr) ||  || The :mod:`numpy.rec` module provides a number of other convenience functions for || creating record arrays; see :ref:`record array creation routines || <routines.array-creation.rec>`. ||  || A record array representation of a structured array can be obtained using the || appropriate :ref:`view`:: ||  ||  >>> arr = np.array([(1;2.;'Hello');(2;3.;\"World\")]; ||  ...                dtype=[('foo'; 'i4');('bar'; 'f4'); ('baz'; 'a10')]) ||  >>> recordarr = arr.view(dtype=dtype((np.record; arr.dtype)); ||  ...                      type=np.recarray) ||  || For convenience; viewing an ndarray as type :class:`np.recarray` will || automatically convert to :class:`np.record` datatype; so the dtype can be left || out of the view:: ||  ||  >>> recordarr = arr.view(np.recarray) ||  >>> recordarr.dtype ||  dtype((numpy.record; [('foo'; '<i4'); ('bar'; '<f4'); ('baz'; 'S10')])) ||  || To get back to a plain ndarray both the dtype and type must be reset. The || following view does so; taking into account the unusual case that the || recordarr was not a structured type:: ||  ||  >>> arr2 = recordarr.view(recordarr.dtype.fields or recordarr.dtype; np.ndarray) ||  || Record array fields accessed by index or by attribute are returned as a record || array if the field has a structured type but as a plain ndarray otherwise. :: ||  ||  >>> recordarr = np.rec.array([('Hello'; (1;2));(\"World\"; (3;4))]; ||  ...                 dtype=[('foo'; 'S6');('bar'; [('A'; int); ('B'; int)])]) ||  >>> type(recordarr.foo) ||  <type 'numpy.ndarray'> ||  >>> type(recordarr.bar) ||  <class 'numpy.core.records.recarray'> ||  || Note that if a field has the same name as an ndarray attribute; the ndarray || attribute takes precedence. Such fields will be inaccessible by attribute but || will still be accessible by index. ||  || \"\"\"
aahjgegjd,ryfeus/gcf-packs,tensorflow2.0/source/numpy/doc/subclassing.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\"============================= || Subclassing ndarray in python || ============================= ||  || Introduction || ------------ ||  || Subclassing ndarray is relatively simple; but it has some complications || compared to other Python objects.  On this page we explain the machinery || that allows you to subclass ndarray; and the implications for || implementing a subclass. ||  || ndarrays and object creation || ============================ ||  || Subclassing ndarray is complicated by the fact that new instances of || ndarray classes can come about in three different ways.  These are: ||  || #. Explicit constructor call - as in ``MySubClass(params)``.  This is ||    the usual route to Python instance creation. || #. View casting - casting an existing ndarray as a given subclass || #. New from template - creating a new instance from a template ||    instance. Examples include returning slices from a subclassed array; ||    creating return types from ufuncs; and copying arrays.  See ||    :ref:`new-from-template` for more details ||  || The last two are characteristics of ndarrays - in order to support || things like array slicing.  The complications of subclassing ndarray are || due to the mechanisms numpy has to support these latter two routes of || instance creation. ||  || .. _view-casting: ||  || View casting || ------------ ||  || *View casting* is the standard ndarray mechanism by which you take an || ndarray of any subclass; and return a view of the array as another || (specified) subclass: ||  || >>> import numpy as np || >>> # create a completely useless ndarray subclass || >>> class C(np.ndarray): pass || >>> # create a standard ndarray || >>> arr = np.zeros((3;)) || >>> # take a view of it; as our useless subclass || >>> c_arr = arr.view(C) || >>> type(c_arr) || <class 'C'> ||  || .. _new-from-template: ||  || Creating new from template || -------------------------- ||  || New instances of an ndarray subclass can also come about by a very || similar mechanism to :ref:`view-casting`; when numpy finds it needs to || create a new instance from a template instance.  The most obvious place || this has to happen is when you are taking slices of subclassed arrays. || For example: ||  || >>> v = c_arr[1:] || >>> type(v) # the view is of type 'C' || <class 'C'> || >>> v is c_arr # but it's a new instance || False ||  || The slice is a *view* onto the original ``c_arr`` data.  So; when we || take a view from the ndarray; we return a new ndarray; of the same || class; that points to the data in the original. ||  || There are other points in the use of ndarrays where we need such views; || such as copying arrays (``c_arr.copy()``); creating ufunc output arrays || (see also :ref:`array-wrap`); and reducing methods (like || ``c_arr.mean()``. ||  || Relationship of view casting and new-from-template || -------------------------------------------------- ||  || These paths both use the same machinery.  We make the distinction here; || because they result in different input to your methods.  Specifically; || :ref:`view-casting` means you have created a new instance of your array || type from any potential subclass of ndarray.  :ref:`new-from-template` || means you have created a new instance of your class from a pre-existing || instance; allowing you - for example - to copy across attributes that || are particular to your subclass. ||  || Implications for subclassing || ---------------------------- ||  || If we subclass ndarray; we need to deal not only with explicit || construction of our array type; but also :ref:`view-casting` or || :ref:`new-from-template`.  NumPy has the machinery to do this; and this || machinery that makes subclassing slightly non-standard. ||  || There are two aspects to the machinery that ndarray uses to support || views and new-from-template in subclasses. ||  || The first is the use of the ``ndarray.__new__`` method for the main work || of object initialization; rather then the more usual ``__init__`` || method.  The second is the use of the ``__array_finalize__`` method to || allow subclasses to clean up after the creation of views and new || instances from templates. ||  || A brief Python primer on ``__new__`` and ``__init__`` || ===================================================== ||  || ``__new__`` is a standard Python method; and; if present; is called || before ``__init__`` when we create a class instance. See the `python || __new__ documentation || <https:\/\/docs.python.org\/reference\/datamodel.html#object.__new__>`_ for more detail. ||  || For example; consider the following Python code: ||  || .. testcode:: ||  ||   class C(object): ||       def __new__(cls; *args): ||           print('Cls in __new__:'; cls) ||           print('Args in __new__:'; args) ||           return object.__new__(cls; *args) ||  ||       def __init__(self; *args): ||           print('type(self) in __init__:'; type(self)) ||           print('Args in __init__:'; args) ||  || meaning that we get: ||  || >>> c = C('hello') || Cls in __new__: <class 'C'> || Args in __new__: ('hello';) || type(self) in __init__: <class 'C'> || Args in __init__: ('hello';) ||  || When we call ``C('hello')``; the ``__new__`` method gets its own class || as first argument; and the passed argument; which is the string || ``'hello'``.  After python calls ``__new__``; it usually (see below) || calls our ``__init__`` method; with the output of ``__new__`` as the || first argument (now a class instance); and the passed arguments || following. ||  || As you can see; the object can be initialized in the ``__new__`` || method or the ``__init__`` method; or both; and in fact ndarray does || not have an ``__init__`` method; because all the initialization is || done in the ``__new__`` method. ||  || Why use ``__new__`` rather than just the usual ``__init__``?  Because || in some cases; as for ndarray; we want to be able to return an object || of some other class.  Consider the following: ||  || .. testcode:: ||  ||   class D(C): ||       def __new__(cls; *args): ||           print('D cls is:'; cls) ||           print('D args in __new__:'; args) ||           return C.__new__(C; *args) ||  ||       def __init__(self; *args): ||           # we never get here ||           print('In D __init__') ||  || meaning that: ||  || >>> obj = D('hello') || D cls is: <class 'D'> || D args in __new__: ('hello';) || Cls in __new__: <class 'C'> || Args in __new__: ('hello';) || >>> type(obj) || <class 'C'> ||  || The definition of ``C`` is the same as before; but for ``D``; the || ``__new__`` method returns an instance of class ``C`` rather than || ``D``.  Note that the ``__init__`` method of ``D`` does not get || called.  In general; when the ``__new__`` method returns an object of || class other than the class in which it is defined; the ``__init__`` || method of that class is not called. ||  || This is how subclasses of the ndarray class are able to return views || that preserve the class type.  When taking a view; the standard || ndarray machinery creates the new ndarray object with something || like:: ||  ||   obj = ndarray.__new__(subtype; shape; ... ||  || where ``subdtype`` is the subclass.  Thus the returned view is of the || same class as the subclass; rather than being of class ``ndarray``. ||  || That solves the problem of returning views of the same type; but now || we have a new problem.  The machinery of ndarray can set the class || this way; in its standard methods for taking views; but the ndarray || ``__new__`` method knows nothing of what we have done in our own || ``__new__`` method in order to set attributes; and so on.  (Aside - || why not call ``obj = subdtype.__new__(...`` then?  Because we may not || have a ``__new__`` method with the same call signature). ||  || The role of ``__array_finalize__`` || ================================== ||  || ``__array_finalize__`` is the mechanism that numpy provides to allow || subclasses to handle the various ways that new instances get created. ||  || Remember that subclass instances can come about in these three ways: ||  || #. explicit constructor call (``obj = MySubClass(params)``).  This will ||    call the usual sequence of ``MySubClass.__new__`` then (if it exists) ||    ``MySubClass.__init__``. || #. :ref:`view-casting` || #. :ref:`new-from-template` ||  || Our ``MySubClass.__new__`` method only gets called in the case of the || explicit constructor call; so we can't rely on ``MySubClass.__new__`` or || ``MySubClass.__init__`` to deal with the view casting and || new-from-template.  It turns out that ``MySubClass.__array_finalize__`` || *does* get called for all three methods of object creation; so this is || where our object creation housekeeping usually goes. ||  || * For the explicit constructor call; our subclass will need to create a ||   new ndarray instance of its own class.  In practice this means that ||   we; the authors of the code; will need to make a call to ||   ``ndarray.__new__(MySubClass;...)``; a class-hierarchy prepared call to ||   ``super(MySubClass; cls).__new__(cls; ...)``; or do view casting of an ||   existing array (see below) || * For view casting and new-from-template; the equivalent of ||   ``ndarray.__new__(MySubClass;...`` is called; at the C level. ||  || The arguments that ``__array_finalize__`` receives differ for the three || methods of instance creation above. ||  || The following code allows us to look at the call sequences and arguments: ||  || .. testcode:: ||  ||    import numpy as np ||  ||    class C(np.ndarray): ||        def __new__(cls; *args; **kwargs): ||            print('In __new__ with class %s' % cls) ||            return super(C; cls).__new__(cls; *args; **kwargs) ||  ||        def __init__(self; *args; **kwargs): ||            # in practice you probably will not need or want an __init__ ||            # method for your subclass ||            print('In __init__ with class %s' % self.__class__) ||  ||        def __array_finalize__(self; obj): ||            print('In array_finalize:') ||            print('   self type is %s' % type(self)) ||            print('   obj type is %s' % type(obj)) ||  ||  || Now: ||  || >>> # Explicit constructor || >>> c = C((10;)) || In __new__ with class <class 'C'> || In array_finalize: ||    self type is <class 'C'> ||    obj type is <type 'NoneType'> || In __init__ with class <class 'C'> || >>> # View casting || >>> a = np.arange(10) || >>> cast_a = a.view(C) || In array_finalize: ||    self type is <class 'C'> ||    obj type is <type 'numpy.ndarray'> || >>> # Slicing (example of new-from-template) || >>> cv = c[:1] || In array_finalize: ||    self type is <class 'C'> ||    obj type is <class 'C'> ||  || The signature of ``__array_finalize__`` is:: ||  ||     def __array_finalize__(self; obj): ||  || One sees that the ``super`` call; which goes to || ``ndarray.__new__``; passes ``__array_finalize__`` the new object; of our || own class (``self``) as well as the object from which the view has been || taken (``obj``).  As you can see from the output above; the ``self`` is || always a newly created instance of our subclass; and the type of ``obj`` || differs for the three instance creation methods: ||  || * When called from the explicit constructor; ``obj`` is ``None`` || * When called from view casting; ``obj`` can be an instance of any ||   subclass of ndarray; including our own. || * When called in new-from-template; ``obj`` is another instance of our ||   own subclass; that we might use to update the new ``self`` instance. ||  || Because ``__array_finalize__`` is the only method that always sees new || instances being created; it is the sensible place to fill in instance || defaults for new object attributes; among other tasks. ||  || This may be clearer with an example. ||  || Simple example - adding an extra attribute to ndarray || ----------------------------------------------------- ||  || .. testcode:: ||  ||   import numpy as np ||  ||   class InfoArray(np.ndarray): ||  ||       def __new__(subtype; shape; dtype=float; buffer=None; offset=0; ||                   strides=None; order=None; info=None): ||           # Create the ndarray instance of our type; given the usual ||           # ndarray input arguments.  This will call the standard ||           # ndarray constructor; but return an object of our type. ||           # It also triggers a call to InfoArray.__array_finalize__ ||           obj = super(InfoArray; subtype).__new__(subtype; shape; dtype; ||                                                   buffer; offset; strides; ||                                                   order) ||           # set the new 'info' attribute to the value passed ||           obj.info = info ||           # Finally; we must return the newly created object: ||           return obj ||  ||       def __array_finalize__(self; obj): ||           # ``self`` is a new object resulting from ||           # ndarray.__new__(InfoArray; ...); therefore it only has ||           # attributes that the ndarray.__new__ constructor gave it - ||           # i.e. those of a standard ndarray. ||           # ||           # We could have got to the ndarray.__new__ call in 3 ways: ||           # From an explicit constructor - e.g. InfoArray(): ||           #    obj is None ||           #    (we're in the middle of the InfoArray.__new__ ||           #    constructor; and self.info will be set when we return to ||           #    InfoArray.__new__) ||           if obj is None: return ||           # From view casting - e.g arr.view(InfoArray): ||           #    obj is arr ||           #    (type(obj) can be InfoArray) ||           # From new-from-template - e.g infoarr[:3] ||           #    type(obj) is InfoArray ||           # ||           # Note that it is here; rather than in the __new__ method; ||           # that we set the default value for 'info'; because this ||           # method sees all creation of default objects - with the ||           # InfoArray.__new__ constructor; but also with ||           # arr.view(InfoArray). ||           self.info = getattr(obj; 'info'; None) ||           # We do not need to return anything ||  ||  || Using the object looks like this: ||  ||   >>> obj = InfoArray(shape=(3;)) # explicit constructor ||   >>> type(obj) ||   <class 'InfoArray'> ||   >>> obj.info is None ||   True ||   >>> obj = InfoArray(shape=(3;); info='information') ||   >>> obj.info ||   'information' ||   >>> v = obj[1:] # new-from-template - here - slicing ||   >>> type(v) ||   <class 'InfoArray'> ||   >>> v.info ||   'information' ||   >>> arr = np.arange(10) ||   >>> cast_arr = arr.view(InfoArray) # view casting ||   >>> type(cast_arr) ||   <class 'InfoArray'> ||   >>> cast_arr.info is None ||   True ||  || This class isn't very useful; because it has the same constructor as the || bare ndarray object; including passing in buffers and shapes and so on. || We would probably prefer the constructor to be able to take an already || formed ndarray from the usual numpy calls to ``np.array`` and return an || object. ||  || Slightly more realistic example - attribute added to existing array || ------------------------------------------------------------------- ||  || Here is a class that takes a standard ndarray that already exists; casts || as our type; and adds an extra attribute. ||  || .. testcode:: ||  ||   import numpy as np ||  ||   class RealisticInfoArray(np.ndarray): ||  ||       def __new__(cls; input_array; info=None): ||           # Input array is an already formed ndarray instance ||           # We first cast to be our class type ||           obj = np.asarray(input_array).view(cls) ||           # add the new attribute to the created instance ||           obj.info = info ||           # Finally; we must return the newly created object: ||           return obj ||  ||       def __array_finalize__(self; obj): ||           # see InfoArray.__array_finalize__ for comments ||           if obj is None: return ||           self.info = getattr(obj; 'info'; None) ||  ||  || So: ||  ||   >>> arr = np.arange(5) ||   >>> obj = RealisticInfoArray(arr; info='information') ||   >>> type(obj) ||   <class 'RealisticInfoArray'> ||   >>> obj.info ||   'information' ||   >>> v = obj[1:] ||   >>> type(v) ||   <class 'RealisticInfoArray'> ||   >>> v.info ||   'information' ||  || .. _array-ufunc: ||  || ``__array_ufunc__`` for ufuncs || ------------------------------ ||  ||   .. versionadded:: 1.13 ||  || A subclass can override what happens when executing numpy ufuncs on it by || overriding the default ``ndarray.__array_ufunc__`` method. This method is || executed *instead* of the ufunc and should return either the result of the || operation; or :obj:`NotImplemented` if the operation requested is not || implemented. ||  || The signature of ``__array_ufunc__`` is:: ||  ||     def __array_ufunc__(ufunc; method; *inputs; **kwargs): ||  ||     - *ufunc* is the ufunc object that was called. ||     - *method* is a string indicating how the Ufunc was called; either ||       ``\"__call__\"`` to indicate it was called directly; or one of its ||       :ref:`methods<ufuncs.methods>`: ``\"reduce\"``; ``\"accumulate\"``; ||       ``\"reduceat\"``; ``\"outer\"``; or ``\"at\"``. ||     - *inputs* is a tuple of the input arguments to the ``ufunc`` ||     - *kwargs* contains any optional or keyword arguments passed to the ||       function. This includes any ``out`` arguments; which are always ||       contained in a tuple. ||  || A typical implementation would convert any inputs or outputs that are || instances of one's own class; pass everything on to a superclass using || ``super()``; and finally return the results after possible || back-conversion. An example; taken from the test case || ``test_ufunc_override_with_super`` in ``core\/tests\/test_umath.py``; is the || following. ||  || .. testcode:: ||  ||     input numpy as np ||  ||     class A(np.ndarray): ||         def __array_ufunc__(self; ufunc; method; *inputs; **kwargs): ||             args = [] ||             in_no = [] ||             for i; input_ in enumerate(inputs): ||                 if isinstance(input_; A): ||                     in_no.append(i) ||                     args.append(input_.view(np.ndarray)) ||                 else: ||                     args.append(input_) ||  ||             outputs = kwargs.pop('out'; None) ||             out_no = [] ||             if outputs: ||                 out_args = [] ||                 for j; output in enumerate(outputs): ||                     if isinstance(output; A): ||                         out_no.append(j) ||                         out_args.append(output.view(np.ndarray)) ||                     else: ||                         out_args.append(output) ||                 kwargs['out'] = tuple(out_args) ||             else: ||                 outputs = (None;) * ufunc.nout ||  ||             info = {} ||             if in_no: ||                 info['inputs'] = in_no ||             if out_no: ||                 info['outputs'] = out_no ||  ||             results = super(A; self).__array_ufunc__(ufunc; method; ||                                                      *args; **kwargs) ||             if results is NotImplemented: ||                 return NotImplemented ||  ||             if method == 'at': ||                 if isinstance(inputs[0]; A): ||                     inputs[0].info = info ||                 return ||  ||             if ufunc.nout == 1: ||                 results = (results;) ||  ||             results = tuple((np.asarray(result).view(A) ||                              if output is None else output) ||                             for result; output in zip(results; outputs)) ||             if results and isinstance(results[0]; A): ||                 results[0].info = info ||  ||             return results[0] if len(results) == 1 else results ||  || So; this class does not actually do anything interesting: it just || converts any instances of its own to regular ndarray (otherwise; we'd || get infinite recursion!); and adds an ``info`` dictionary that tells || which inputs and outputs it converted. Hence; e.g.; ||  || >>> a = np.arange(5.).view(A) || >>> b = np.sin(a) || >>> b.info || {'inputs': [0]} || >>> b = np.sin(np.arange(5.); out=(a;)) || >>> b.info || {'outputs': [0]} || >>> a = np.arange(5.).view(A) || >>> b = np.ones(1).view(A) || >>> c = a + b || >>> c.info || {'inputs': [0; 1]} || >>> a += b || >>> a.info || {'inputs': [0; 1]; 'outputs': [0]} ||  || Note that another approach would be to to use ``getattr(ufunc; || methods)(*inputs; **kwargs)`` instead of the ``super`` call. For this example; || the result would be identical; but there is a difference if another operand || also defines ``__array_ufunc__``. E.g.; lets assume that we evalulate || ``np.add(a; b)``; where ``b`` is an instance of another class ``B`` that has || an override.  If you use ``super`` as in the example; || ``ndarray.__array_ufunc__`` will notice that ``b`` has an override; which || means it cannot evaluate the result itself. Thus; it will return || `NotImplemented` and so will our class ``A``. Then; control will be passed || over to ``b``; which either knows how to deal with us and produces a result; || or does not and returns `NotImplemented`; raising a ``TypeError``. ||  || If instead; we replace our ``super`` call with ``getattr(ufunc; method)``; we || effectively do ``np.add(a.view(np.ndarray); b)``. Again; ``B.__array_ufunc__`` || will be called; but now it sees an ``ndarray`` as the other argument. Likely; || it will know how to handle this; and return a new instance of the ``B`` class || to us. Our example class is not set up to handle this; but it might well be || the best approach if; e.g.; one were to re-implement ``MaskedArray`` using || ``__array_ufunc__``. ||  || As a final note: if the ``super`` route is suited to a given class; an || advantage of using it is that it helps in constructing class hierarchies. || E.g.; suppose that our other class ``B`` also used the ``super`` in its || ``__array_ufunc__`` implementation; and we created a class ``C`` that depended || on both; i.e.; ``class C(A; B)`` (with; for simplicity; not another || ``__array_ufunc__`` override). Then any ufunc on an instance of ``C`` would || pass on to ``A.__array_ufunc__``; the ``super`` call in ``A`` would go to || ``B.__array_ufunc__``; and the ``super`` call in ``B`` would go to || ``ndarray.__array_ufunc__``; thus allowing ``A`` and ``B`` to collaborate. ||  || .. _array-wrap: ||  || ``__array_wrap__`` for ufuncs and other functions || ------------------------------------------------- ||  || Prior to numpy 1.13; the behaviour of ufuncs could only be tuned using || ``__array_wrap__`` and ``__array_prepare__``. These two allowed one to || change the output type of a ufunc; but; in contrast to || ``__array_ufunc__``; did not allow one to make any changes to the inputs. || It is hoped to eventually deprecate these; but ``__array_wrap__`` is also || used by other numpy functions and methods; such as ``squeeze``; so at the || present time is still needed for full functionality. ||  || Conceptually; ``__array_wrap__`` \"wraps up the action\" in the sense of || allowing a subclass to set the type of the return value and update || attributes and metadata.  Let's show how this works with an example.  First || we return to the simpler example subclass; but with a different name and || some print statements: ||  || .. testcode:: ||  ||   import numpy as np ||  ||   class MySubClass(np.ndarray): ||  ||       def __new__(cls; input_array; info=None): ||           obj = np.asarray(input_array).view(cls) ||           obj.info = info ||           return obj ||  ||       def __array_finalize__(self; obj): ||           print('In __array_finalize__:') ||           print('   self is %s' % repr(self)) ||           print('   obj is %s' % repr(obj)) ||           if obj is None: return ||           self.info = getattr(obj; 'info'; None) ||  ||       def __array_wrap__(self; out_arr; context=None): ||           print('In __array_wrap__:') ||           print('   self is %s' % repr(self)) ||           print('   arr is %s' % repr(out_arr)) ||           # then just call the parent ||           return super(MySubClass; self).__array_wrap__(self; out_arr; context) ||  || We run a ufunc on an instance of our new array: ||  || >>> obj = MySubClass(np.arange(5); info='spam') || In __array_finalize__: ||    self is MySubClass([0; 1; 2; 3; 4]) ||    obj is array([0; 1; 2; 3; 4]) || >>> arr2 = np.arange(5)+1 || >>> ret = np.add(arr2; obj) || In __array_wrap__: ||    self is MySubClass([0; 1; 2; 3; 4]) ||    arr is array([1; 3; 5; 7; 9]) || In __array_finalize__: ||    self is MySubClass([1; 3; 5; 7; 9]) ||    obj is MySubClass([0; 1; 2; 3; 4]) || >>> ret || MySubClass([1; 3; 5; 7; 9]) || >>> ret.info || 'spam' ||  || Note that the ufunc (``np.add``) has called the ``__array_wrap__`` method || with arguments ``self`` as ``obj``; and ``out_arr`` as the (ndarray) result || of the addition.  In turn; the default ``__array_wrap__`` || (``ndarray.__array_wrap__``) has cast the result to class ``MySubClass``; || and called ``__array_finalize__`` - hence the copying of the ``info`` || attribute.  This has all happened at the C level. ||  || But; we could do anything we wanted: ||  || .. testcode:: ||  ||   class SillySubClass(np.ndarray): ||  ||       def __array_wrap__(self; arr; context=None): ||           return 'I lost your data' ||  || >>> arr1 = np.arange(5) || >>> obj = arr1.view(SillySubClass) || >>> arr2 = np.arange(5) || >>> ret = np.multiply(obj; arr2) || >>> ret || 'I lost your data' ||  || So; by defining a specific ``__array_wrap__`` method for our subclass; || we can tweak the output from ufuncs. The ``__array_wrap__`` method || requires ``self``; then an argument - which is the result of the ufunc - || and an optional parameter *context*. This parameter is returned by || ufuncs as a 3-element tuple: (name of the ufunc; arguments of the ufunc; || domain of the ufunc); but is not set by other numpy functions. Though; || as seen above; it is possible to do otherwise; ``__array_wrap__`` should || return an instance of its containing class.  See the masked array || subclass for an implementation. ||  || In addition to ``__array_wrap__``; which is called on the way out of the || ufunc; there is also an ``__array_prepare__`` method which is called on || the way into the ufunc; after the output arrays are created but before any || computation has been performed. The default implementation does nothing || but pass through the array. ``__array_prepare__`` should not attempt to || access the array data or resize the array; it is intended for setting the || output array type; updating attributes and metadata; and performing any || checks based on the input that may be desired before computation begins. || Like ``__array_wrap__``; ``__array_prepare__`` must return an ndarray or || subclass thereof or raise an error. ||  || Extra gotchas - custom ``__del__`` methods and ndarray.base || ----------------------------------------------------------- ||  || One of the problems that ndarray solves is keeping track of memory || ownership of ndarrays and their views.  Consider the case where we have || created an ndarray; ``arr`` and have taken a slice with ``v = arr[1:]``. || The two objects are looking at the same memory.  NumPy keeps track of || where the data came from for a particular array or view; with the || ``base`` attribute: ||  || >>> # A normal ndarray; that owns its own data || >>> arr = np.zeros((4;)) || >>> # In this case; base is None || >>> arr.base is None || True || >>> # We take a view || >>> v1 = arr[1:] || >>> # base now points to the array that it derived from || >>> v1.base is arr || True || >>> # Take a view of a view || >>> v2 = v1[1:] || >>> # base points to the view it derived from || >>> v2.base is v1 || True ||  || In general; if the array owns its own memory; as for ``arr`` in this || case; then ``arr.base`` will be None - there are some exceptions to this || - see the numpy book for more details. ||  || The ``base`` attribute is useful in being able to tell whether we have || a view or the original array.  This in turn can be useful if we need || to know whether or not to do some specific cleanup when the subclassed || array is deleted.  For example; we may only want to do the cleanup if || the original array is deleted; but not the views.  For an example of || how this can work; have a look at the ``memmap`` class in || ``numpy.core``. ||  || Subclassing and Downstream Compatibility || ---------------------------------------- ||  || When sub-classing ``ndarray`` or creating duck-types that mimic the ``ndarray`` || interface; it is your responsibility to decide how aligned your APIs will be || with those of numpy. For convenience; many numpy functions that have a corresponding || ``ndarray`` method (e.g.; ``sum``; ``mean``; ``take``; ``reshape``) work by checking || if the first argument to a function has a method of the same name. If it exists; the || method is called instead of coercing the arguments to a numpy array. ||  || For example; if you want your sub-class or duck-type to be compatible with || numpy's ``sum`` function; the method signature for this object's ``sum`` method || should be the following: ||  || .. testcode:: ||  ||     def sum(self; axis=None; dtype=None; out=None; keepdims=False): ||     ... ||  || This is the exact same method signature for ``np.sum``; so now if a user calls || ``np.sum`` on this object; numpy will call the object's own ``sum`` method and || pass in these arguments enumerated above in the signature; and no errors will || be raised because the signatures are completely compatible with each other. ||  || If; however; you decide to deviate from this signature and do something like this: ||  || .. testcode:: ||  ||    def sum(self; axis=None; dtype=None): ||    ... ||  || This object is no longer compatible with ``np.sum`` because if you call ``np.sum``; || it will pass in unexpected arguments ``out`` and ``keepdims``; causing a TypeError || to be raised. ||  || If you wish to maintain compatibility with numpy and its subsequent versions (which || might add new keyword arguments) but do not want to surface all of numpy's arguments; || your function's signature should accept ``**kwargs``. For example: ||  || .. testcode:: ||  ||    def sum(self; axis=None; dtype=None; **unused_kwargs): ||    ... ||  || This object is now compatible with ``np.sum`` again because any extraneous arguments || (i.e. keywords that are not ``axis`` or ``dtype``) will be hidden away in the || ``**unused_kwargs`` parameter. ||  || \"\"\"
aahjgehbc,ryfeus/gcf-packs,tensorflow2.0/source/numpy/f2py/capi_maps.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,The eviroment provided by auxfuncs.py is needed for some calls to eval.
aahjgehbd,ryfeus/gcf-packs,tensorflow2.0/source/numpy/f2py/capi_maps.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,As the needed functions cannot be determined by static inspection of the
aahjgehei,ryfeus/gcf-packs,tensorflow2.0/source/numpy/f2py/capi_maps.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX: Evaluate intent_flags here.
aahjgehjb,ryfeus/gcf-packs,tensorflow2.0/source/numpy/f2py/cfuncs.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX: Note that CNUMFROMARROBJ is identical with NUMFROMARROBJ
aahjgeicc,ryfeus/gcf-packs,tensorflow2.0/source/numpy/f2py/crackfortran.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,The eviroment provided by auxfuncs.py is needed for some calls to eval.
aahjgeicd,ryfeus/gcf-packs,tensorflow2.0/source/numpy/f2py/crackfortran.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,As the needed functions cannot be determined by static inspection of the
aahjgeifi,ryfeus/gcf-packs,tensorflow2.0/source/numpy/f2py/crackfortran.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX: non-zero reset values need testing
aahjgeihd,ryfeus/gcf-packs,tensorflow2.0/source/numpy/f2py/crackfortran.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX: subsequent init expressions may get wrong values.
aahjgeihj,ryfeus/gcf-packs,tensorflow2.0/source/numpy/f2py/crackfortran.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX: apply mapping
aahjgeiic,ryfeus/gcf-packs,tensorflow2.0/source/numpy/f2py/crackfortran.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX: How to catch dependence cycles correctly?
aahjgeiig,ryfeus/gcf-packs,tensorflow2.0/source/numpy/f2py/crackfortran.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX: return something sensible.
aahjgeija,ryfeus/gcf-packs,tensorflow2.0/source/numpy/f2py/crackfortran.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: test .eq.; .neq.; etc replacements.
aahjgejaf,ryfeus/gcf-packs,tensorflow2.0/source/numpy/f2py/crackfortran.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,FIXME complex numbers may also have exponents
aahjgejag,ryfeus/gcf-packs,tensorflow2.0/source/numpy/f2py/crackfortran.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,FIXME; unused l looks like potential bug
aahjgejce,ryfeus/gcf-packs,tensorflow2.0/source/numpy/f2py/f2py2e.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX
aahjgejdb,ryfeus/gcf-packs,tensorflow2.0/source/numpy/f2py/f90mod_rules.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,The eviroment provided by auxfuncs.py is needed for some calls to eval.
aahjgejdc,ryfeus/gcf-packs,tensorflow2.0/source/numpy/f2py/f90mod_rules.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,As the needed functions cannot be determined by static inspection of the
aahjgfadf,ryfeus/gcf-packs,tensorflow2.0/source/numpy/f2py/rules.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Workaround for Python 2.6; 2.6.1 bug: https:\/\/bugs.python.org\/issue4720
aahjgfagh,ryfeus/gcf-packs,tensorflow2.0/source/numpy/fft/info.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\" || Discrete Fourier Transform (:mod:`numpy.fft`) || ============================================= ||  || .. currentmodule:: numpy.fft ||  || Standard FFTs || ------------- ||  || .. autosummary:: ||    :toctree: generated\/ ||  ||    fft       Discrete Fourier transform. ||    ifft      Inverse discrete Fourier transform. ||    fft2      Discrete Fourier transform in two dimensions. ||    ifft2     Inverse discrete Fourier transform in two dimensions. ||    fftn      Discrete Fourier transform in N-dimensions. ||    ifftn     Inverse discrete Fourier transform in N dimensions. ||  || Real FFTs || --------- ||  || .. autosummary:: ||    :toctree: generated\/ ||  ||    rfft      Real discrete Fourier transform. ||    irfft     Inverse real discrete Fourier transform. ||    rfft2     Real discrete Fourier transform in two dimensions. ||    irfft2    Inverse real discrete Fourier transform in two dimensions. ||    rfftn     Real discrete Fourier transform in N dimensions. ||    irfftn    Inverse real discrete Fourier transform in N dimensions. ||  || Hermitian FFTs || -------------- ||  || .. autosummary:: ||    :toctree: generated\/ ||  ||    hfft      Hermitian discrete Fourier transform. ||    ihfft     Inverse Hermitian discrete Fourier transform. ||  || Helper routines || --------------- ||  || .. autosummary:: ||    :toctree: generated\/ ||  ||    fftfreq   Discrete Fourier Transform sample frequencies. ||    rfftfreq  DFT sample frequencies (for usage with rfft; irfft). ||    fftshift  Shift zero-frequency component to center of spectrum. ||    ifftshift Inverse of fftshift. ||  ||  || Background information || ---------------------- ||  || Fourier analysis is fundamentally a method for expressing a function as a || sum of periodic components; and for recovering the function from those || components.  When both the function and its Fourier transform are || replaced with discretized counterparts; it is called the discrete Fourier || transform (DFT).  The DFT has become a mainstay of numerical computing in || part because of a very fast algorithm for computing it; called the Fast || Fourier Transform (FFT); which was known to Gauss (1805) and was brought || to light in its current form by Cooley and Tukey [CT]_.  Press et al. [NR]_ || provide an accessible introduction to Fourier analysis and its || applications. ||  || Because the discrete Fourier transform separates its input into || components that contribute at discrete frequencies; it has a great number || of applications in digital signal processing; e.g.; for filtering; and in || this context the discretized input to the transform is customarily || referred to as a *signal*; which exists in the *time domain*.  The output || is called a *spectrum* or *transform* and exists in the *frequency || domain*. ||  || Implementation details || ---------------------- ||  || There are many ways to define the DFT; varying in the sign of the || exponent; normalization; etc.  In this implementation; the DFT is defined || as ||  || .. math:: ||    A_k =  \\\\sum_{m=0}^{n-1} a_m \\\\exp\\\\left\\\\{-2\\\\pi i{mk \\\\over n}\\\\right\\\\} ||    \\\\qquad k = 0;\\\\ldots;n-1. ||  || The DFT is in general defined for complex inputs and outputs; and a || single-frequency component at linear frequency :math:`f` is || represented by a complex exponential || :math:`a_m = \\\\exp\\\\{2\\\\pi i\\\\;f m\\\\Delta t\\\\}`; where :math:`\\\\Delta t` || is the sampling interval. ||  || The values in the result follow so-called \"standard\" order: If ``A = || fft(a; n)``; then ``A[0]`` contains the zero-frequency term (the sum of || the signal); which is always purely real for real inputs. Then ``A[1:n\/2]`` || contains the positive-frequency terms; and ``A[n\/2+1:]`` contains the || negative-frequency terms; in order of decreasingly negative frequency. || For an even number of input points; ``A[n\/2]`` represents both positive and || negative Nyquist frequency; and is also purely real for real input.  For || an odd number of input points; ``A[(n-1)\/2]`` contains the largest positive || frequency; while ``A[(n+1)\/2]`` contains the largest negative frequency. || The routine ``np.fft.fftfreq(n)`` returns an array giving the frequencies || of corresponding elements in the output.  The routine || ``np.fft.fftshift(A)`` shifts transforms and their frequencies to put the || zero-frequency components in the middle; and ``np.fft.ifftshift(A)`` undoes || that shift. ||  || When the input `a` is a time-domain signal and ``A = fft(a)``; ``np.abs(A)`` || is its amplitude spectrum and ``np.abs(A)**2`` is its power spectrum. || The phase spectrum is obtained by ``np.angle(A)``. ||  || The inverse DFT is defined as ||  || .. math:: ||    a_m = \\\\frac{1}{n}\\\\sum_{k=0}^{n-1}A_k\\\\exp\\\\left\\\\{2\\\\pi i{mk\\\\over n}\\\\right\\\\} ||    \\\\qquad m = 0;\\\\ldots;n-1. ||  || It differs from the forward transform by the sign of the exponential || argument and the default normalization by :math:`1\/n`. ||  || Normalization || ------------- || The default normalization has the direct transforms unscaled and the inverse || transforms are scaled by :math:`1\/n`. It is possible to obtain unitary || transforms by setting the keyword argument ``norm`` to ``\"ortho\"`` (default is || `None`) so that both direct and inverse transforms will be scaled by || :math:`1\/\\\\sqrt{n}`. ||  || Real and Hermitian transforms || ----------------------------- ||  || When the input is purely real; its transform is Hermitian; i.e.; the || component at frequency :math:`f_k` is the complex conjugate of the || component at frequency :math:`-f_k`; which means that for real || inputs there is no information in the negative frequency components that || is not already available from the positive frequency components. || The family of `rfft` functions is || designed to operate on real inputs; and exploits this symmetry by || computing only the positive frequency components; up to and including the || Nyquist frequency.  Thus; ``n`` input points produce ``n\/2+1`` complex || output points.  The inverses of this family assumes the same symmetry of || its input; and for an output of ``n`` points uses ``n\/2+1`` input points. ||  || Correspondingly; when the spectrum is purely real; the signal is || Hermitian.  The `hfft` family of functions exploits this symmetry by || using ``n\/2+1`` complex points in the input (time) domain for ``n`` real || points in the frequency domain. ||  || In higher dimensions; FFTs are used; e.g.; for image analysis and || filtering.  The computational efficiency of the FFT means that it can || also be a faster way to compute large convolutions; using the property || that a convolution in the time domain is equivalent to a point-by-point || multiplication in the frequency domain. ||  || Higher dimensions || ----------------- ||  || In two dimensions; the DFT is defined as ||  || .. math:: ||    A_{kl} =  \\\\sum_{m=0}^{M-1} \\\\sum_{n=0}^{N-1} ||    a_{mn}\\\\exp\\\\left\\\\{-2\\\\pi i \\\\left({mk\\\\over M}+{nl\\\\over N}\\\\right)\\\\right\\\\} ||    \\\\qquad k = 0; \\\\ldots; M-1;\\\\quad l = 0; \\\\ldots; N-1; ||  || which extends in the obvious way to higher dimensions; and the inverses || in higher dimensions also extend in the same way. ||  || References || ---------- ||  || .. [CT] Cooley; James W.; and John W. Tukey; 1965; \"An algorithm for the ||         machine calculation of complex Fourier series;\" *Math. Comput.* ||         19: 297-301. ||  || .. [NR] Press; W.; Teukolsky; S.; Vetterline; W.T.; and Flannery; B.P.; ||         2007; *Numerical Recipes: The Art of Scientific Computing*; ch. ||         12-13.  Cambridge Univ. Press; Cambridge; UK. ||  || Examples || -------- ||  || For examples; see the various functions. ||  || \"\"\"
aahjgfahc,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/_datasource.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,gzip is lacking read1 needed for TextIOWrapper
aahjgfahf,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/_datasource.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,deferring the import of lzma; bz2 and gzip until needed
aahjgfahg,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/_datasource.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: .zip support; .tar support?
aahjgfaid,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/_datasource.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,BUG : URLs require a scheme string ('http:\/\/') to be used.
aahjgfajb,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/_datasource.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: Doesn't handle compressed files!
aahjgfaji,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/_datasource.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO:  This should be more robust.  Handles case where path includes
aahjgfbbe,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/_datasource.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: Add a ``subdir`` parameter for specifying the subdirectory
aahjgfbie,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/_iotools.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Structured dtype: just validate the names as needed
aahjgfcif,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/arraypad.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Converting the array with `tolist` seems to improve performance
aahjgfdbd,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/arraysetops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\" || Set operations for arrays based on sorting. ||  || :Contains: ||   unique; ||   isin; ||   ediff1d; ||   intersect1d; ||   setxor1d; ||   in1d; ||   union1d; ||   setdiff1d ||  || :Notes: ||  || For floating point arrays; inaccurate results may appear due to usual round-off || and floating point comparison issues. ||  || Speed could be gained in some operations by an implementation of || sort(); that can provide directly the permutation vectors; avoiding || thus calls to argsort(). ||  || To do: Optionally return indices analogously to unique for all functions. ||  || :Author: Robert Cimrman ||  || \"\"\"
aahjgfddf,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/arrayterator.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Fix index; handling ellipsis and incomplete slices.
aahjgfdhg,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/format.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\" || Binary serialization ||  || NPY format || ========== ||  || A simple format for saving numpy arrays to disk with the full || information about them. ||  || The ``.npy`` format is the standard binary file format in NumPy for || persisting a *single* arbitrary NumPy array on disk. The format stores all || of the shape and dtype information necessary to reconstruct the array || correctly even on another machine with a different architecture. || The format is designed to be as simple as possible while achieving || its limited goals. ||  || The ``.npz`` format is the standard format for persisting *multiple* NumPy || arrays on disk. A ``.npz`` file is a zip file containing multiple ``.npy`` || files; one for each array. ||  || Capabilities || ------------ ||  || - Can represent all NumPy arrays including nested record arrays and ||   object arrays. ||  || - Represents the data in its native binary form. ||  || - Supports Fortran-contiguous arrays directly. ||  || - Stores all of the necessary information to reconstruct the array ||   including shape and dtype on a machine of a different ||   architecture.  Both little-endian and big-endian arrays are ||   supported; and a file with little-endian numbers will yield ||   a little-endian array on any machine reading the file. The ||   types are described in terms of their actual sizes. For example; ||   if a machine with a 64-bit C \"long int\" writes out an array with ||   \"long ints\"; a reading machine with 32-bit C \"long ints\" will yield ||   an array with 64-bit integers. ||  || - Is straightforward to reverse engineer. Datasets often live longer than ||   the programs that created them. A competent developer should be ||   able to create a solution in their preferred programming language to ||   read most ``.npy`` files that he has been given without much ||   documentation. ||  || - Allows memory-mapping of the data. See `open_memmep`. ||  || - Can be read from a filelike stream object instead of an actual file. ||  || - Stores object arrays; i.e. arrays containing elements that are arbitrary ||   Python objects. Files with object arrays are not to be mmapable; but ||   can be read and written to disk. ||  || Limitations || ----------- ||  || - Arbitrary subclasses of numpy.ndarray are not completely preserved. ||   Subclasses will be accepted for writing; but only the array data will ||   be written out. A regular numpy.ndarray object will be created ||   upon reading the file. ||  || .. warning:: ||  ||   Due to limitations in the interpretation of structured dtypes; dtypes ||   with fields with empty names will have the names replaced by 'f0'; 'f1'; ||   etc. Such arrays will not round-trip through the format entirely ||   accurately. The data is intact; only the field names will differ. We are ||   working on a fix for this. This fix will not require a change in the ||   file format. The arrays with such structures can still be saved and ||   restored; and the correct dtype may be restored by using the ||   ``loadedarray.view(correct_dtype)`` method. ||  || File extensions || --------------- ||  || We recommend using the ``.npy`` and ``.npz`` extensions for files saved || in this format. This is by no means a requirement; applications may wish || to use these file formats but use an extension specific to the || application. In the absence of an obvious alternative; however; || we suggest using ``.npy`` and ``.npz``. ||  || Version numbering || ----------------- ||  || The version numbering of these formats is independent of NumPy version || numbering. If the format is upgraded; the code in `numpy.io` will still || be able to read and write Version 1.0 files. ||  || Format Version 1.0 || ------------------ ||  || The first 6 bytes are a magic string: exactly ``\\\\x93NUMPY``. ||  || The next 1 byte is an unsigned byte: the major version number of the file || format; e.g. ``\\\\x01``. ||  || The next 1 byte is an unsigned byte: the minor version number of the file || format; e.g. ``\\\\x00``. Note: the version of the file format is not tied || to the version of the numpy package. ||  || The next 2 bytes form a little-endian unsigned short int: the length of || the header data HEADER_LEN. ||  || The next HEADER_LEN bytes form the header data describing the array's || format. It is an ASCII string which contains a Python literal expression || of a dictionary. It is terminated by a newline (``\\\ || ``) and padded with || spaces (``\\\\x20``) to make the total of || ``len(magic string) + 2 + len(length) + HEADER_LEN`` be evenly divisible || by 64 for alignment purposes. ||  || The dictionary contains three keys: ||  ||     \"descr\" : dtype.descr ||       An object that can be passed as an argument to the `numpy.dtype` ||       constructor to create the array's dtype. ||     \"fortran_order\" : bool ||       Whether the array data is Fortran-contiguous or not. Since ||       Fortran-contiguous arrays are a common form of non-C-contiguity; ||       we allow them to be written directly to disk for efficiency. ||     \"shape\" : tuple of int ||       The shape of the array. ||  || For repeatability and readability; the dictionary keys are sorted in || alphabetic order. This is for convenience only. A writer SHOULD implement || this if possible. A reader MUST NOT depend on this. ||  || Following the header comes the array data. If the dtype contains Python || objects (i.e. ``dtype.hasobject is True``); then the data is a Python || pickle of the array. Otherwise the data is the contiguous (either C- || or Fortran-; depending on ``fortran_order``) bytes of the array. || Consumers can figure out the number of bytes by multiplying the number || of elements given by the shape (noting that ``shape=()`` means there is || 1 element) by ``dtype.itemsize``. ||  || Format Version 2.0 || ------------------ ||  || The version 1.0 format only allowed the array header to have a total size of || 65535 bytes.  This can be exceeded by structured arrays with a large number of || columns.  The version 2.0 format extends the header size to 4 GiB. || `numpy.save` will automatically save in 2.0 format if the data requires it; || else it will always use the more compatible 1.0 format. ||  || The description of the fourth element of the header therefore has become: || \"The next 4 bytes form a little-endian unsigned int: the length of the header || data HEADER_LEN.\" ||  || Notes || ----- || The ``.npy`` format; including motivation for creating it and a comparison of || alternatives; is described in the `\"npy-format\" NEP  || <https:\/\/www.numpy.org\/neps\/nep-0001-npy-format.html>`_; however details have || evolved with time and this document is more current. ||  || \"\"\"
aahjgfdib,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/format.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,This is a record array. The .descr is fine.  XXX: parts of the
aahjgfdif,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/format.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,No padding removal needed
aahjgfdih,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/format.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Once support for blank names is removed; only \"if name == ''\" needed)
aahjgfeaa,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/format.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,adding newline as python 2.7.5 workaround
aahjgfeab,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/format.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,removing newline (see above) as python 2.7.5 workaround
aahjgfeeg,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/function_base.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,needed in this module for compatibility
aahjgfefj,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/function_base.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,as the shape is needed for the result. Doing it separately optimizes
aahjgfejh,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/function_base.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Caching to improve default performance
aahjgfgce,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/histograms.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,truncate the range if needed
aahjgfhai,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/index_tricks.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,This is needed to don't have tall matrix have the diagonal wrap.
aahjgfhbe,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/info.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\" || Basic functions used by several sub-packages and || useful to have in the main name-space. ||  || Type Handling || ------------- || ================ =================== || iscomplexobj     Test for complex object; scalar result || isrealobj        Test for real object; scalar result || iscomplex        Test for complex elements; array result || isreal           Test for real elements; array result || imag             Imaginary part || real             Real part || real_if_close    Turns complex number with tiny imaginary part to real || isneginf         Tests for negative infinity; array result || isposinf         Tests for positive infinity; array result || isnan            Tests for nans; array result || isinf            Tests for infinity; array result || isfinite         Tests for finite numbers; array result || isscalar         True if argument is a scalar || nan_to_num       Replaces NaN's with 0 and infinities with large numbers || cast             Dictionary of functions to force cast to each type || common_type      Determine the minimum common type code for a group ||                  of arrays || mintypecode      Return minimal allowed common typecode. || ================ =================== ||  || Index Tricks || ------------ || ================ =================== || mgrid            Method which allows easy construction of N-d ||                  'mesh-grids' || ``r_``           Append and construct arrays: turns slice objects into ||                  ranges and concatenates them; for 2d arrays appends rows. || index_exp        Konrad Hinsen's index_expression class instance which ||                  can be useful for building complicated slicing syntax. || ================ =================== ||  || Useful Functions || ---------------- || ================ =================== || select           Extension of where to multiple conditions and choices || extract          Extract 1d array from flattened array according to mask || insert           Insert 1d array of values into Nd array according to mask || linspace         Evenly spaced samples in linear space || logspace         Evenly spaced samples in logarithmic space || fix              Round x to nearest integer towards zero || mod              Modulo mod(x;y) = x % y except keeps sign of y || amax             Array maximum along axis || amin             Array minimum along axis || ptp              Array max-min along axis || cumsum           Cumulative sum along axis || prod             Product of elements along axis || cumprod          Cumluative product along axis || diff             Discrete differences along axis || angle            Returns angle of complex argument || unwrap           Unwrap phase along given axis (1-d algorithm) || sort_complex     Sort a complex-array (based on real; then imaginary) || trim_zeros       Trim the leading and trailing zeros from 1D array. || vectorize        A class that wraps a Python function taking scalar ||                  arguments into a generalized function which can handle ||                  arrays of arguments using the broadcast rules of ||                  numerix Python. || ================ =================== ||  || Shape Manipulation || ------------------ || ================ =================== || squeeze          Return a with length-one dimensions removed. || atleast_1d       Force arrays to be >= 1D || atleast_2d       Force arrays to be >= 2D || atleast_3d       Force arrays to be >= 3D || vstack           Stack arrays vertically (row on row) || hstack           Stack arrays horizontally (column on column) || column_stack     Stack 1D arrays as columns into 2D array || dstack           Stack arrays depthwise (along third dimension) || stack            Stack arrays along a new axis || split            Divide array into a list of sub-arrays || hsplit           Split into columns || vsplit           Split into rows || dsplit           Split along third dimension || ================ =================== ||  || Matrix (2D Array) Manipulations || ------------------------------- || ================ =================== || fliplr           2D array with columns flipped || flipud           2D array with rows flipped || rot90            Rotate a 2D array a multiple of 90 degrees || eye              Return a 2D array with ones down a given diagonal || diag             Construct a 2D array from a vector; or return a given ||                  diagonal from a 2D array. || mat              Construct a Matrix || bmat             Build a Matrix from blocks || ================ =================== ||  || Polynomials || ----------- || ================ =================== || poly1d           A one-dimensional polynomial class || poly             Return polynomial coefficients from roots || roots            Find roots of polynomial given coefficients || polyint          Integrate polynomial || polyder          Differentiate polynomial || polyadd          Add polynomials || polysub          Subtract polynomials || polymul          Multiply polynomials || polydiv          Divide polynomials || polyval          Evaluate polynomial at given argument || ================ =================== ||  || Iterators || --------- || ================ =================== || Arrayterator     A buffered iterator for big arrays. || ================ =================== ||  || Import Tricks || ------------- || ================ =================== || ppimport         Postpone module import until trying to use it || ppimport_attr    Postpone module import until trying to use its attribute || ppresolve        Import postponed module and return it. || ================ =================== ||  || Machine Arithmetics || ------------------- || ================ =================== || machar_single    Single precision floating point arithmetic parameters || machar_double    Double precision floating point arithmetic parameters || ================ =================== ||  || Threading Tricks || ---------------- || ================ =================== || ParallelExec     Execute commands in parallel thread. || ================ =================== ||  || Array Set Operations || ----------------------- || Set operations for numeric arrays based on sort() function. ||  || ================ =================== || unique           Unique elements of an array. || isin             Test whether each element of an ND array is present  ||                  anywhere within a second array. || ediff1d          Array difference (auxiliary function). || intersect1d      Intersection of 1D arrays with unique elements. || setxor1d         Set exclusive-or of 1D arrays with unique elements. || in1d             Test whether elements in a 1D array are also present in ||                  another array. || union1d          Union of 1D arrays with unique elements. || setdiff1d        Set difference of 1D arrays with unique elements. || ================ =================== ||  || \"\"\"
aahjgfhcd,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/mixins.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: handle the optional third argument for __pow__?
aahjgfhdc,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/nanfunctions.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,which do not implement isnan (gh-9009); or fmin correctly (gh-8975)
aahjgfhdg,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/nanfunctions.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,which do not implement isnan (gh-9009); or fmax correctly (gh-8975)
aahjgfhea,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/nanfunctions.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,action is needed to handle bad results.
aahjgfhfb,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/nanfunctions.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,convention.
aahjgfhgj,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/npyio.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Implement the Mapping ABC
aahjgfhha,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/npyio.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,FIXME: This seems like it will copy strings around
aahjgfhhf,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/npyio.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,It would be better if the zipfile could read
aahjgfhhj,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/npyio.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,python 3. We forgot to implement itervalues() at all in earlier
aahjgfhjf,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/npyio.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: Use contextlib.ExitStack once we drop Python 2
aahjgfica,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/npyio.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO emit portability warning?
aahjgfidb,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/npyio.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,it to estimate the number of columns; N.
aahjgfihe,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/npyio.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Check the columns to use: make sure `usecols` is a list
aahjgfihf,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/npyio.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Check the names and overwrite the dtype.names if needed
aahjgfiij,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/npyio.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Redefine the key as needed if it's a column number
aahjgfjbj,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/npyio.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Fixme: possible error as following variable never used.
aahjgfjch,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/npyio.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Select only the columns we need
aahjgfjcj,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/npyio.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Upgrade the converters (if needed)
aahjgfjee,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/npyio.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Find the columns with strings...
aahjgfjei,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/npyio.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Overwrite the initial dtype names if needed
aahjgfjgb,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/npyio.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,We have only one field: drop the name if not needed.
aahjggaae,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/recfunctions.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Minimal processing needed: just make sure everythng's a-ok
aahjggaai,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/recfunctions.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Get the dtype of the output (flattening if needed)
aahjggabc,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/recfunctions.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Get the filling value (if needed)
aahjggaed,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/recfunctions.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,If masked data must be ignored; set the flag to false where needed
aahjggaej,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/recfunctions.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Fixme: nb2 below is never used. Commenting out for pyflakes.
aahjggbca,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/stride_tricks.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,never really has writebackifcopy semantics
aahjggbdc,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/stride_tricks.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: consider making the results of broadcast_arrays readonly to match
aahjggbdi,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/type_check.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,convert to scalar if needed
aahjggbjd,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/utils.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX: maybe using a real stemming search engine would be better?
aahjggbjg,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/utils.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX: this is full Harrison-Stetson heuristics now;
aahjggbjh,ryfeus/gcf-packs,tensorflow2.0/source/numpy/lib/utils.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX: it probably could be improved
aahjggcfc,ryfeus/gcf-packs,tensorflow2.0/source/numpy/linalg/linalg.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,increasing powers of 2; and multiply into the result as needed.
aahjggcha,ryfeus/gcf-packs,tensorflow2.0/source/numpy/linalg/linalg.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Convention is to return scalars instead of 0d arrays
aahjggchf,ryfeus/gcf-packs,tensorflow2.0/source/numpy/linalg/linalg.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,FIXME: real_t is unused
aahjggdaf,ryfeus/gcf-packs,tensorflow2.0/source/numpy/linalg/linalg.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,m[i;j]: min number of scalar multiplications needed to compute A_{i..j}
aahjggdcd,ryfeus/gcf-packs,tensorflow2.0/source/numpy/ma/core.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\" || numpy.ma : a package to handle missing or invalid values. ||  || This package was initially written for numarray by Paul F. Dubois || at Lawrence Livermore National Laboratory. || In 2006; the package was completely rewritten by Pierre Gerard-Marchant || (University of Georgia) to make the MaskedArray class a subclass of ndarray; || and to improve support of structured arrays. ||  ||  || Copyright 1999; 2000; 2001 Regents of the University of California. || Released for unlimited redistribution. ||  || * Adapted for numpy_core 2005 by Travis Oliphant and (mainly) Paul Dubois. || * Subclassing of the base `ndarray` 2006 by Pierre Gerard-Marchant ||   (pgmdevlist_AT_gmail_DOT_com) || * Improvements suggested by Reggie Dugard (reggie_AT_merfinllc_DOT_com) ||  || .. moduleauthor:: Pierre Gerard-Marchant ||  || \"\"\"
aahjggdcf,ryfeus/gcf-packs,tensorflow2.0/source/numpy/ma/core.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,no warning needed - but switch to -1 anyway; to avoid surprising
aahjggdcg,ryfeus/gcf-packs,tensorflow2.0/source/numpy/ma/core.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,subclasses; which are more likely to implement scalar axes.
aahjggedb,ryfeus/gcf-packs,tensorflow2.0/source/numpy/ma/core.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,FIXME _sharedmask is never used.
aahjggeeb,ryfeus/gcf-packs,tensorflow2.0/source/numpy/ma/core.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Force shrinking of the mask if needed (and possible)
aahjggehj,ryfeus/gcf-packs,tensorflow2.0/source/numpy/ma/core.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,that. This is also horribly broken but somewhat less so. Maybe.
aahjggeia,ryfeus/gcf-packs,tensorflow2.0/source/numpy/ma/core.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XX: This looks like a bug -- shouldn't it check self.dtype
aahjggejd,ryfeus/gcf-packs,tensorflow2.0/source/numpy/ma/core.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,needed and use astype instead of copy.
aahjggfbd,ryfeus/gcf-packs,tensorflow2.0/source/numpy/ma/core.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Make sure to reset the _fill_value if needed
aahjggffa,ryfeus/gcf-packs,tensorflow2.0/source/numpy/ma/core.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Update the mask if needed
aahjggfie,ryfeus/gcf-packs,tensorflow2.0/source/numpy/ma/core.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Reshape if needed
aahjggfih,ryfeus/gcf-packs,tensorflow2.0/source/numpy/ma/core.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Temporary workaround to account for the fact that str and bytes
aahjgggci,ryfeus/gcf-packs,tensorflow2.0/source/numpy/ma/core.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Cast fill value to bool_ if needed. If it cannot be cast; the
aahjgggef,ryfeus/gcf-packs,tensorflow2.0/source/numpy/ma/core.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,!!!: implement out + test!
aahjgggfg,ryfeus/gcf-packs,tensorflow2.0/source/numpy/ma/core.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Set the mask if needed
aahjggheg,ryfeus/gcf-packs,tensorflow2.0/source/numpy/ma/core.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Fix the invalid parts
aahjgghhc,ryfeus/gcf-packs,tensorflow2.0/source/numpy/ma/core.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed by dot; so move here from extras.py. It will still be exported
aahjgghig,ryfeus/gcf-packs,tensorflow2.0/source/numpy/ma/core.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,workaround for #8666; to preserve identity. Ideally the bottom line
aahjggiia,ryfeus/gcf-packs,tensorflow2.0/source/numpy/ma/mrecords.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,or whatever restricted keywords.  An idea would be to no bother in the
aahjggjcb,ryfeus/gcf-packs,tensorflow2.0/source/numpy/ma/mrecords.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Grab the initial _fieldmask; if needed:
aahjggjcf,ryfeus/gcf-packs,tensorflow2.0/source/numpy/ma/mrecords.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Set the fill_value if needed
aahjggjfg,ryfeus/gcf-packs,tensorflow2.0/source/numpy/ma/timer_comparison.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Fixme: this does not look right.
aahjggjji,ryfeus/gcf-packs,tensorflow2.0/source/numpy/polynomial/_polybase.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\" || Abstract base class for the various polynomial Classes. ||  || The ABCPolyBase class provides the methods needed to implement the common API || for the various polynomial classes. It operates as a mixin; but uses the || abc module from the stdlib; hence it is only available for Python >= 2.6. ||  || \"\"\"
aahjghaad,ryfeus/gcf-packs,tensorflow2.0/source/numpy/polynomial/_polybase.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: we're stuck with disabling math formatting until we handle
aahjghaeg,ryfeus/gcf-packs,tensorflow2.0/source/numpy/polynomial/chebyshev.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,This can be made more efficient by using powers of two
aahjghaei,ryfeus/gcf-packs,tensorflow2.0/source/numpy/polynomial/chebyshev.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,FIXME: t never used
aahjghaff,ryfeus/gcf-packs,tensorflow2.0/source/numpy/polynomial/chebyshev.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Determine the norms of the design matrix columns.
aahjghaie,ryfeus/gcf-packs,tensorflow2.0/source/numpy/polynomial/hermite.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,This can be made more efficient by using powers of two
aahjghajb,ryfeus/gcf-packs,tensorflow2.0/source/numpy/polynomial/hermite.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Determine the norms of the design matrix columns.
aahjghaji,ryfeus/gcf-packs,tensorflow2.0/source/numpy/polynomial/hermite.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,matrix is symmetric in this case in order to obtain better zeros.
aahjghbcg,ryfeus/gcf-packs,tensorflow2.0/source/numpy/polynomial/hermite_e.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,This can be made more efficient by using powers of two
aahjghbdd,ryfeus/gcf-packs,tensorflow2.0/source/numpy/polynomial/hermite_e.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Determine the norms of the design matrix columns.
aahjghbea,ryfeus/gcf-packs,tensorflow2.0/source/numpy/polynomial/hermite_e.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,matrix is symmetric in this case in order to obtain better zeros.
aahjghbgi,ryfeus/gcf-packs,tensorflow2.0/source/numpy/polynomial/laguerre.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,This can be made more efficient by using powers of two
aahjghbhf,ryfeus/gcf-packs,tensorflow2.0/source/numpy/polynomial/laguerre.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Determine the norms of the design matrix columns.
aahjghbic,ryfeus/gcf-packs,tensorflow2.0/source/numpy/polynomial/laguerre.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,matrix is symmetric in this case in order to obtain better zeros.
aahjghcaj,ryfeus/gcf-packs,tensorflow2.0/source/numpy/polynomial/legendre.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,This can be made more efficient by using powers of two
aahjghcbc,ryfeus/gcf-packs,tensorflow2.0/source/numpy/polynomial/legendre.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,as reverse recursion in this application but it is more efficient.
aahjghcbi,ryfeus/gcf-packs,tensorflow2.0/source/numpy/polynomial/legendre.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Determine the norms of the design matrix columns.
aahjghccf,ryfeus/gcf-packs,tensorflow2.0/source/numpy/polynomial/legendre.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,matrix is symmetric in this case in order to obtain better zeros.
aahjghcff,ryfeus/gcf-packs,tensorflow2.0/source/numpy/polynomial/polynomial.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,This can be made more efficient by using powers of two
aahjghcgj,ryfeus/gcf-packs,tensorflow2.0/source/numpy/polynomial/polynomial.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Determine the norms of the design matrix columns.
aahjghcjc,ryfeus/gcf-packs,tensorflow2.0/source/numpy/random/setup.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,We need the mingw workaround for _ftime if the msvc runtime version is
aahjghdbh,ryfeus/gcf-packs,tensorflow2.0/source/numpy/testing/_private/noseclasses.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,These classes implement a doctest runner plugin for nose; a \"known failure\"
aahjghdei,ryfeus/gcf-packs,tensorflow2.0/source/numpy/testing/_private/noseclasses.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Subclass nose.plugins.doctests.DocTestCase to work around a bug in
aahjghdjh,ryfeus/gcf-packs,tensorflow2.0/source/numpy/testing/_private/nosetester.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,numpy. Those semantics were fine for testing numpy; but not so
aahjghebe,ryfeus/gcf-packs,tensorflow2.0/source/numpy/testing/_private/nosetester.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,our way of doing coverage
aahjghebh,ryfeus/gcf-packs,tensorflow2.0/source/numpy/testing/_private/nosetester.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Relies on pkg_resources; not a hard dependency
aahjghejb,ryfeus/gcf-packs,tensorflow2.0/source/numpy/testing/_private/utils.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,precision argument is only needed if the objects are ndarrays
aahjghfdg,ryfeus/gcf-packs,tensorflow2.0/source/numpy/testing/_private/utils.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,The below comparison is a hack to ensure that fully masked
aahjghhgh,ryfeus/gcf-packs,tensorflow2.0/source/pasta/base/annotate.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Workaround for ast.Module which does not have a lineno
aahjghhid,ryfeus/gcf-packs,tensorflow2.0/source/pasta/base/annotate.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: Check default formatting for different values of \"simple\"
aahjghicb,ryfeus/gcf-packs,tensorflow2.0/source/pasta/base/annotate.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,unused
aahjghjef,ryfeus/gcf-packs,tensorflow2.0/source/pasta/base/scope.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: Support relative imports
aahjghjfg,ryfeus/gcf-packs,tensorflow2.0/source/pasta/base/scope.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: else? relative imports
aahjghjfh,ryfeus/gcf-packs,tensorflow2.0/source/pasta/base/scope.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: Support referencing methods by \"self\" where possible
aahjghjhi,ryfeus/gcf-packs,tensorflow2.0/source/pasta/base/scope_test.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: Test references to C.aaa; C.bbb once supported
aahjgiafb,ryfeus/gcf-packs,tensorflow2.0/source/pkg_resources/__init__.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX backward compat
aahjgiagb,ryfeus/gcf-packs,tensorflow2.0/source/pkg_resources/__init__.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX Linux and other platforms' special cases should go here
aahjgiagj,ryfeus/gcf-packs,tensorflow2.0/source/pkg_resources/__init__.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX add more info
aahjgiaha,ryfeus/gcf-packs,tensorflow2.0/source/pkg_resources/__init__.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,workaround a cache issue
aahjgiaid,ryfeus/gcf-packs,tensorflow2.0/source/pkg_resources/__init__.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Oops; the \"best\" so far conflicts with a dependency
aahjgiaif,ryfeus/gcf-packs,tensorflow2.0/source/pkg_resources/__init__.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Register the new requirements needed by req
aahjgiaje,ryfeus/gcf-packs,tensorflow2.0/source/pkg_resources/__init__.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX backward compatibility
aahjgiajj,ryfeus/gcf-packs,tensorflow2.0/source/pkg_resources/__init__.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX
aahjgibcj,ryfeus/gcf-packs,tensorflow2.0/source/pkg_resources/__init__.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Python 2 on Windows needs to be handled this way :(
aahjgibeg,ryfeus/gcf-packs,tensorflow2.0/source/pkg_resources/__init__.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,metadata until\/unless it's actually needed.  (i.e.; some distributions
aahjgibhe,ryfeus/gcf-packs,tensorflow2.0/source/pkg_resources/__init__.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,this method. In the future this should be smarter and follow PEP 440
aahjgicff,ryfeus/gcf-packs,tensorflow2.0/source/pkg_resources/_vendor/packaging/requirements.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: Can we test whether something is contained within a requirement?
aahjgicid,ryfeus/gcf-packs,tensorflow2.0/source/pkg_resources/_vendor/packaging/specifiers.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,implement this in terms of the other specifiers instead of
aahjgidjj,ryfeus/gcf-packs,tensorflow2.0/source/pkg_resources/_vendor/packaging/version.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,which uses the defacto standard originally implemented by setuptools;
aahjgiefh,ryfeus/gcf-packs,tensorflow2.0/source/pkg_resources/_vendor/pyparsing.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\"(Deprecated) Predefined expression of 1 or more printable words or quoted strings; separated by commas.\r ||    This expression is deprecated in favor of L{pyparsing_common.comma_separated_list}.\"\"\"
aahjgigej,ryfeus/gcf-packs,tensorflow2.0/source/pkg_resources/_vendor/six.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,This is a bit ugly; but it avoids running this again by
aahjgigfi,ryfeus/gcf-packs,tensorflow2.0/source/pkg_resources/_vendor/six.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Workaround for standalone backslash
aahjgighh,ryfeus/gcf-packs,tensorflow2.0/source/pkg_resources/extern/__init__.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,mysterious hack:
aahjgigjh,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/__init__.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Make sure we have any requirements needed to interpret 'attrs'.
aahjgihdd,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/_vendor/packaging/requirements.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: Can we test whether something is contained within a requirement?
aahjgihgb,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/_vendor/packaging/specifiers.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,implement this in terms of the other specifiers instead of
aahjgiihh,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/_vendor/packaging/version.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,which uses the defacto standard originally implemented by setuptools;
aahjgijdf,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/_vendor/pyparsing.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\"(Deprecated) Predefined expression of 1 or more printable words or quoted strings; separated by commas.\r ||    This expression is deprecated in favor of L{pyparsing_common.comma_separated_list}.\"\"\"
aahjgjbch,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/_vendor/six.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,This is a bit ugly; but it avoids running this again by
aahjgjbdg,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/_vendor/six.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Workaround for standalone backslash
aahjgjbge,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/archive_util.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX Ugh
aahjgjbgg,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/build_meta.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\"A PEP 517 interface to setuptools ||  || Previously; when a user or a command line tool (let's call it a \"frontend\") || needed to make a request of setuptools to take a certain action; for || example; generating a list of installation requirements; the frontend would || would call \"setup.py egg_info\" or \"setup.py bdist_wheel\" on the command line. ||  || PEP 517 defines a different method of interfacing with setuptools. Rather || than calling \"setup.py\" directly; the frontend should: ||  ||   1. Set the current directory to the directory with a setup.py file ||   2. Import this module into a safe python interpreter (one in which ||      setuptools can potentially set global variables or crash hard). ||   3. Call one of the functions defined in PEP 517. ||  || What each function does is defined in PEP 517. However; here is a \"casual\" || definition of the functions (this definition should not be relied on for || bug reports or API stability): ||  ||   - `build_wheel`: build a wheel in the folder and return the basename ||   - `get_requires_for_build_wheel`: get the `setup_requires` to build ||   - `prepare_metadata_for_build_wheel`: get the `install_requires` ||   - `build_sdist`: build an sdist in the folder and return the basename ||   - `get_requires_for_build_sdist`: get the `setup_requires` to build ||  || Again; this is not a formal definition! Just a \"taste\" of the module. || \"\"\"
aahjgjbii,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/command/bdist_egg.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Hack for packages that install data to install's --install-lib
aahjgjbij,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/command/bdist_egg.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX else: raise ???
aahjgjcda,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/command/build_ext.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX Help!  I don't have any idea whether these are right...
aahjgjcdh,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/command/build_ext.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX what to do with conflicts?
aahjgjcdj,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/command/build_ext.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,hack so distutils' build_extension() builds a library instead
aahjgjcea,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/command/build_ext.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX this should check to ensure the lib is actually being built
aahjgjceb,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/command/build_ext.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX as dynamic; and not just using a locally-found version or a
aahjgjcec,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/command/build_ext.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX static-compiled version
aahjgjcej,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/command/build_ext.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX we need to either disallow these attrs on Library instances;
aahjgjcib,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/command/develop.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX
aahjgjcij,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/command/develop.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX should also check for entry point scripts!
aahjgjddb,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/command/easy_install.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,cast to str as workaround for #709 and #710 and #712
aahjgjddf,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/command/easy_install.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Installation is also needed if file in tmpdir or is not an egg
aahjgjdea,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/command/easy_install.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX this is not the distribution we were looking for
aahjgjdee,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/command/easy_install.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,if the only thing there is a directory; move it instead
aahjgjdfh,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/command/easy_install.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX
aahjgjdgh,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/command/easy_install.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: self.report_extras(req; dist)
aahjgjdhf,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/command/easy_install.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX should this check for latest version?
aahjgjebb,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/command/easy_install.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,exact loader instance used when importing them. Python 3.4 docs state
aahjgjech,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/command/easy_install.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,this is really needed.
aahjgjeea,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/command/easy_install.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,and have PyPy repopulate it as needed. The downside is that if there are any
aahjgjeei,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/command/easy_install.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,spelling; but that seems like a way too invasive move (this cache
aahjgjfcb,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/command/egg_info.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Fix older sdists built on Windows
aahjgjfef,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/command/install.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX Python 3.1 doesn't see _nc if this is inside the class
aahjgjffb,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/command/install_lib.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: is it necessary to short-circuit here? i.e. what's the cost
aahjgjfhf,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/command/sdist.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,only needed on Python prior to 3.6.
aahjgjfhg,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/command/sdist.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,This grody hack closes the template file (MANIFEST.in) if an
aahjgjggj,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/dist.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Some people apparently take \"version number\" too literally :)
aahjgjgid,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/dist.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX may convert to set here when we can rely on set being builtin
aahjgjgjg,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/dist.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,workaround for #334.
aahjgjhaa,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/extension.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,The *args is needed for compatibility as calls may use positional
aahjgjhad,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/extern/__init__.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,mysterious hack:
aahjgjhgf,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/monkey.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\" || Everything is private. Contact the project team || if you think you need this functionality. || \"\"\"
aahjgjhgh,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/monkey.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,fix findall bug in distutils (http:\/\/bugs.python.org\/issue12885)
aahjgjhjf,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/msvc.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,For VC++ 9.0; if IA64 support is needed; redirect user
aahjgjidf,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/package_index.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX Yuck
aahjgjifh,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/package_index.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,this line is here to fix emacs' cruddy broken syntax highlighting
aahjgjjch,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/pep425tags.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX remove distutils dependency
aahjgjjgj,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/py27compat.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\"Workaround for http:\/\/bugs.python.org\/issue24672\"\"\"
aahjhaabd,ryfeus/gcf-packs,tensorflow2.0/source/setuptools/ssl_support.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX according to RFC 2818; the most specific Common Name
aahjhaagd,ryfeus/gcf-packs,tensorflow2.0/source/six.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,This is a bit ugly; but it avoids running this again by
aahjhaahc,ryfeus/gcf-packs,tensorflow2.0/source/six.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Workaround for standalone backslash
aahjhabec,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/bleach/encoding.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,encoding; errors); so that if s is a SafeBytes; it ends up being
aahjhabfd,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/bleach/sanitizer.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: Make sure this does what it's meant to - I *think* it wants to
aahjhabji,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/filters/lint.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX: what to do with token[\"data\"] ?
aahjhacad,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/filters/optionaltags.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX: we also omit the start tag if the head element is empty
aahjhacaj,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/filters/optionaltags.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX: we do not look at the preceding event; so we never omit
aahjhacbg,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/filters/optionaltags.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX: we do not look at the preceding event; so instead we never
aahjhacgb,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/filters/optionaltags.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX: we also look for an immediately following colgroup
aahjhacjb,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/html5parser.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX The idea is to make errorcode mandatory.
aahjhacji,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/html5parser.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX Need a check here to see if the first start tag token emitted is
aahjhadad,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/html5parser.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Encoding it as UTF-8 here is a hack; as really we should pass
aahjhadai,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/html5parser.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX If we implement a parser for which scripting is disabled we need to
aahjhadaj,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/html5parser.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,implement this phase.
aahjhadcc,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/html5parser.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX Need tests that trigger the following
aahjhadcf,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/html5parser.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX Localization ...
aahjhadde,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/html5parser.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX Better parseError messages appreciated.
aahjhadhi,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/html5parser.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,can move in step 9.7
aahjhaeaj,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/html5parser.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX associate with form
aahjhaebg,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/html5parser.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX Have to duplicate logic here to find out if the tag is ignored
aahjhaecg,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/html5parser.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX AT Any ideas on how to share this with endTagTable?
aahjhaeda,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/html5parser.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,helper methods (XXX unify this with other table helper methods)
aahjhaeeh,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/html5parser.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX this isn't in the spec but it seems necessary
aahjhaeej,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/html5parser.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,This is needed because data is to be appended to the <html> element
aahjhaefh,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/html5parser.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX \"inHeadNoscript\": InHeadNoScriptPhase;
aahjhaefi,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/html5parser.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX after after frameset
aahjhaehb,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/inputstream.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Work around Python bug #20007: read(0) closes the connection.
aahjhaehh,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/inputstream.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,number of columns in the last line of the previous chunk
aahjhafdc,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/inputstream.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX: This is ugly; but I can't see a nicer way to fix this.
aahjhafic,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/serializer/__init__.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX: Should we cache this?
aahjhafjd,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/serializer/htmlserializer.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: Add namespace support here
aahjhafje,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/serializer/htmlserializer.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX The idea is to make data mandatory.
aahjhagaf,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/tokenizer.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Should speed up this check somehow (e.g. move the set to a constant)
aahjhagag,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/tokenizer.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Try\/except needed as UCS-2 Python builds' unichar only works
aahjhagei,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/tokenizer.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX data can be _'_...
aahjhagfb,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/tokenizer.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX If we emit here the attributes are converted to a dict
aahjhagfh,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/tokenizer.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX Fix for above XXX
aahjhaggi,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/tokenizer.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX EMIT
aahjhagha,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/treebuilders/__init__.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\"A collection of modules for building different kinds of tree from || HTML documents. ||  || To create a treebuilder for a new type of tree; you need to do || implement several things: ||  || 1) A set of classes for various types of elements: Document; Doctype; || Comment; Element. These must implement the interface of || _base.treebuilders.Node (although comment nodes have a different || signature for their constructor; see treebuilders.etree.Comment) || Textual content may also be implemented as another node type; or not; as || your tree implementation requires. ||  || 2) A treebuilder object (called TreeBuilder by convention) that || inherits from treebuilders._base.TreeBuilder. This has 4 required attributes: || documentClass - the class to use for the bottommost node of a document || elementClass - the class to use for HTML Elements || commentClass - the class to use for comments || doctypeClass - the class to use for doctypes || It also has one required method: || getDocument - Returns the root node of the complete document tree ||  || 3) If you wish to run the unit tests; you must also create a || testSerializer method on your treebuilder which accepts a node and || returns a string containing Node and its children serialized according || to the format used in the unittests || \"\"\"
aahjhaghh,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/treebuilders/_base.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX - should this method be made more general?
aahjhagid,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/treebuilders/_base.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX - rename these to headElement; formElement
aahjhahaj,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/treebuilders/_base.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX - this is really inelegant
aahjhahba,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/treebuilders/_base.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX - we should really check that this parent is actually a
aahjhahbc,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/treebuilders/_base.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX td; th and tr are not actually needed
aahjhahbd,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/treebuilders/_base.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX This is not entirely what the specification says. We should
aahjhahbg,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/treebuilders/dom.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,HACK: allow text nodes as children of the document node
aahjhahbj,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/treebuilders/etree.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX - there may be a better way to do this...
aahjhahcf,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/treebuilders/etree_lxml.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\"Module for supporting the lxml.etree library. The idea here is to use as much || of the native library as possible; without using fragile hacks like custom element || names that break between releases. The downside of this is that we cannot represent || all possible trees; specifically the following are known to cause problems: ||  || Text or comments as siblings of the root element || Docypes with no name ||  || When any of these things occur; we emit a DataLossWarning || \"\"\"
aahjhahea,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/treewalkers/__init__.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\"A collection of modules for iterating through different kinds of || tree; generating tokens identical to those produced by the tokenizer || module. ||  || To create a tree walker for a new type of tree; you need to do || implement a tree walker object (called TreeWalker by convention) that || implements a 'serialize' method taking a tree as sole argument and || returning an iterator generating tokens. || \"\"\"
aahjhaheb,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/treewalkers/__init__.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX: NEVER cache here; caching is done in the etree submodule
aahjhahfd,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/html5lib/treewalkers/lxmletree.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX: we cannot use a \"bool(node) and node[0] or None\" construct here
aahjhajac,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/_vendor/tensorflow_serving/apis/prediction_service_pb2.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,## @@AUTOGENERATED SECTION ENDS HERE@@
aahjhbaeb,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/backend/event_processing/db_import_multiplexer.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Defer event sink creation until needed; this ensures it will only exist in
aahjhbafc,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/backend/event_processing/db_import_multiplexer.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: reject file version < 2 (at loader level)
aahjhbafd,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/backend/event_processing/db_import_multiplexer.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: implement purging via sqlite writer truncation method
aahjhbafe,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/backend/event_processing/db_import_multiplexer.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: support graphs
aahjhbaff,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/backend/event_processing/db_import_multiplexer.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: support run metadata
aahjhbbii,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/backend/event_processing/event_file_inspector.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: Consider changing this to only check for out-of-order
aahjhbcfg,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/backend/event_processing/io_wrapper.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,literal prefix. This should improve efficiency in cases where a single
aahjhbeac,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/backend/event_processing/sqlite_writer.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: track computed time from run start times
aahjhbead,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/backend/event_processing/sqlite_writer.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: track actual run start times
aahjhbeae,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/backend/event_processing/sqlite_writer.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: for huge numbers of tags (e.g. 1000+); this is slower than just
aahjhbehh,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/compat/__init__.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\"Compatibility interfaces for TensorBoard. ||  || This module provides logic for importing variations on the TensorFlow APIs; as || lazily loaded imports to help avoid circular dependency issues and defer the || search and loading of the module until necessary. || \"\"\"
aahjhbjaj,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/compat/tensorflow_stub/dtypes.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(#1677): _np_bfloat16 is defined as 0. This causes `as_dtype` to
aahjhbjch,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/compat/tensorflow_stub/error_codes.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,''' || Some resource has been exhausted; perhaps a per-user quota; or || perhaps the entire file system is out of space. || '''
aahjhcbaa,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/compat/tensorflow_stub/io/gfile.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: Remove and instead handle in GetLogdirSubdirectories.
aahjhcbaj,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/compat/tensorflow_stub/io/gfile.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Only methods needed for TensorBoard are implemented.
aahjhcbdd,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/compat/tensorflow_stub/pywrap_tensorflow.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: Handle gzip and zlib compressed files
aahjhcbfj,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/compat/tensorflow_stub/tensor_shape.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,This is needed for Windows.
aahjhcbgi,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/compat/tensorflow_stub/tensor_shape.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(mrry): Handle these maybe.
aahjhcbhc,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/compat/tensorflow_stub/tensor_shape.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,and perhaps we should do that.
aahjhcbhd,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/compat/tensorflow_stub/tensor_shape.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(mrry): Handle this better; as it will be useful for handling
aahjhcceh,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/lazy.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,make future lookups efficient (only failed lookups call __getattr__).
aahjhccej,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/lazy.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,load_once() the first time it's needed. The class is nested so we can close
aahjhceff,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/plugins/audio/summary_v2.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Workaround for map_fn returning float dtype for an empty elems input.
aahjhcejh,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/plugins/beholder/beholder.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: hack to clear the info. Should be cleaner.
aahjhcgag,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/plugins/beholder/video_writing.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,unused
aahjhcgjc,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/plugins/custom_scalar/custom_scalars_plugin.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: return HTTP status code for malformed requests
aahjhcifb,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/plugins/debugger/debugger_plugin.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,specific types of exceptions; instead of the broad catching here.
aahjhcjce,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/plugins/debugger/debugger_server_lib.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,watch name because it is not distinguishing: every health pill entry ends
aahjhcjhf,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/plugins/debugger/events_writer_manager.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,The events file does not exist. Perhaps the user had manually
aahjhdaed,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/plugins/debugger/interactive_debugger_plugin.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Unused.
aahjhdafd,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/plugins/debugger/interactive_debugger_plugin.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,self._debugger_data_server). This is how the HTTP long polling ends.
aahjhdeha,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/plugins/hparams/backend_context.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,This could likely be more efficiently implemented with a trie
aahjhdfij,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/plugins/hparams/list_session_groups.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,need to iterate on these columns in reverse order (thus the primary key
aahjhdgjf,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/plugins/image/images_plugin.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Unused.
aahjhdhfj,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/plugins/image/summary_v2.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Workaround for map_fn returning float dtype for an empty elems input.
aahjhdhig,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/plugins/interactive_inference/interactive_inference_plugin.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(jameswex): Maybe enable if config flags were specified?
aahjheadf,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/plugins/profile/profile_plugin.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Workaround the grpc's 4MB message limitation.
aahjhebjj,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/plugins/scalar/scalars_plugin.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: return HTTP status code for malformed requests
aahjhedhj,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/program.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,times. This seems to work fine; and looking at the superclass
aahjhedif,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/program.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,probably by switching composing around a Werkzeug server
aahjhedjc,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/program.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,unused
aahjhedjd,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/program.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Kludge to override a SocketServer.py method so we can get rid of noisy
aahjhegcg,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/util/util.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(stephanwlee): Move this to program.py
aahjhegdg,ryfeus/gcf-packs,tensorflow2.0/source/tensorboard/util/util.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,We're counting columns in the terminal; not bytes. So we don't
aahjhfdcg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/compiler/tf2tensorrt/ops/gen_trt_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aahjhgebf,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/lite/python/op_hint.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"stuff\"; \"foo\"; \"bar\"; -1 (where -1 is unused). So you would set this
aahjhggdj,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/__init__.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Import boosted trees ops to make sure the ops are registered (but unused).
aahjhgjcb,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/autograph/converters/directives.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,those into AST references; we simply remove them. By convention; directives
aahjhgjjh,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/autograph/converters/lists.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,In general; a better mechanism could look like:
aahjhhbdi,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/autograph/converters/side_effect_guards.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(mdan): Move these names into config.
aahjhhbef,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/autograph/converters/side_effect_guards.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Or maybe we should just raise an \"unsafe assign\" error?
aahjhhbeh,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/autograph/converters/side_effect_guards.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(mdan): This is brutal. It will even rename modules - any fix?
aahjhhbie,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/autograph/core/converter.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\"Converter construction support. ||  || This module contains a base class for all converters; as well as supporting || structures. These structures are referred to as contexts. ||  || The class hierarchy is as follows: ||  ||     <your converter> ||       [extends] converter.Base ||         [extends] transformer.Base ||             [extends] gast.nodeTransformer ||           [uses] transfomer.SourceInfo ||         [uses] converter.EntityContext ||           [uses] converter.ProgramContext ||           [uses] transfomer.SourceInfo ||  || converter.Base is a specialization of transformer.Base for AutoGraph. It's a || very lightweight subclass that adds a `ctx` attribute holding the corresponding || EntityContext object (see below). Note that converters are not reusable; and || `visit` will raise an error if called more than once. ||  || converter.EntityContext contains mutable state associated with an entity that || the converter processes. ||  || converter.ProgramContext contains mutable state across related entities. For || example; when converting several functions that call one another; the || ProgramContext should be shared across these entities. ||  || Below is the overall flow at conversion: ||  ||     program_ctx = ProgramContext(<entities to convert>; <global settings>; ...) ||     while <program_ctx has more entities to convert>: ||       entity; source_info = <get next entity from program_ctx> ||       entity_ctx = EntityContext(program_ctx; source_info) ||       for <each ConverterClass>: ||         converter = ConverterClass(entity_ctx) ||  ||         # May update entity_ctx and program_ctx ||         entity = converter.visit(entity) ||  ||       <add entity's dependencies to program_ctx> ||  || Note that pyct contains a small number of transformers used for static analysis. || These implement transformer.Base; rather than converter.Base; to avoid a || dependency on AutoGraph. || \"\"\"
aahjhhcdi,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/autograph/core/converter_testing.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(mdan): Move this into self.prepare()
aahjhhcea,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/autograph/core/converter_testing.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,This is a bit of a hack; but works for things like tf.int32
aahjhhdhd,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/autograph/impl/api.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(mdan): Move this entire block inside to_graph.
aahjhheaf,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/autograph/impl/api.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,needed so long as the function doesn't return any executable code.
aahjhhebb,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/autograph/impl/api.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Remove self to avoid circular references. This will probably only work
aahjhhedh,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/autograph/impl/conversion.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(b\/120224672): Fix this.
aahjhheih,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/autograph/impl/conversion.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,it uses regex matching to adjust the exact location around the line number
aahjhhfic,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/autograph/operators/control_flow.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Workaround for Datset.reduce not allowing empty state tensors - create
aahjhhfid,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/autograph/operators/control_flow.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,a dummy state variable that remains unused.
aahjhhgif,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/autograph/operators/py_builtins.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,which is probably never what you want.
aahjhhiac,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/autograph/pyct/cfg.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,They should rather be called 'block statements'; because they include
aahjhhibc,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/autograph/pyct/cfg.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(mdan): Perhaps detail this in a markdown doc.
aahjhhidi,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/autograph/pyct/cfg.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Subsequent splits move back to the split point; and memorize the
aahjhhijd,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/autograph/pyct/common_transformers/anf.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,and explicitly name the intermediate values needed to compute the value of
aahjhhjab,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/autograph/pyct/common_transformers/anf.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Any additional assignments needed to name all the intermediate values in the
aahjhiaag,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/autograph/pyct/inspect_utils.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(mdan): Figure out why six.get_unbound_function fails in some cases.
aahjhiaaj,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/autograph/pyct/inspect_utils.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Python 3 class methods only work this way it seems :S
aahjhibij,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/autograph/pyct/static_analysis/activity.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,be strictly needed.
aahjhicfa,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/autograph/pyct/static_analysis/live_values.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Maybe just leave as-is?
aahjhicha,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/autograph/pyct/static_analysis/liveness.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,activity analysis should have both so that wouldn't be needed.
aahjhiche,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/autograph/pyct/static_analysis/liveness.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(mdan): Move this to the superclass?
aahjhicie,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/autograph/pyct/static_analysis/liveness.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,probably a case of undefined assignment; like this:
aahjhicjc,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/autograph/pyct/static_analysis/liveness.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Note: additional nodes may be added as needed.
aahjhidbi,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/autograph/pyct/static_analysis/reaching_definitions.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(mdan): Move this to the superclass?
aahjhiedf,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/autograph/pyct/transformer.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(mdan): We can modify node here too; if ever needed.
aahjhihdf,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/client/session.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,These movers are no longer needed when _do_run() completes; and
aahjhihdg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/client/session.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,are deleted when `movers` goes out of scope when this _run() ends.
aahjhihhd,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/client/session.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Transfer a tensor to the right device if needed.
aahjhiiaa,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/client/session.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,- Grow GPU memory as needed at the cost of fragmentation.
aahjhijja,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/compiler/tensorrt/trt_convert.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,other user-defined collections. A better way would be to use
aahjhijjd,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/compiler/tensorrt/trt_convert.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(laigd): maybe add back AssetFileDef.
aahjhjadg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/compiler/tensorrt/trt_convert.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(laigd): a better way would be to use self._calibration_sess to list
aahjhjegg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/data/experimental/ops/iterator_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,As a temporary fix we enter the following implicit contract between this
aahjhjjfc,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/data/kernel_tests/test_base.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,tensors when needed.
aahjiaafi,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/data/ops/dataset_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,implement `element_structure`.
aahjiacbi,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/data/ops/iterator_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(b\/77291417): Fix
aahjiachg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/data/ops/multi_device_iterator_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(b\/121378567): Get rid of this shared_name hack.
aahjiadgh,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/data/util/nest.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\"## Functions for working with arbitrarily nested sequences of elements. ||  || NOTE(mrry): This fork of the `tensorflow.python.util.nest` module || makes two changes: ||  || 1. It removes support for lists as a level of nesting in nested structures. || 2. It adds support for `SparseTensorValue` as an atomic element. ||  || The motivation for this change is twofold: ||  || 1. It seems more natural for lists to be treated (e.g. in Dataset constructors) ||    as tensors; rather than lists of (lists of...) tensors. || 2. This is needed because `SparseTensorValue` is implemented as a `namedtuple` ||    that would normally be flattened and we want to be able to create sparse ||    tensor from `SparseTensorValue's similarly to creating tensors from numpy ||    arrays. || \"\"\"
aahjiafad,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/data/util/traverse.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(b\/120873778): Make this more efficient.
aahjiafed,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/debug/cli/analyzer_cli.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(cais): Implement list_nodes.
aahjiaffh,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/debug/cli/analyzer_cli.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(cais): Implement filter by lambda on tensor value.
aahjiafge,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/debug/cli/analyzer_cli.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,term \"partition graph\" means and how it is related to TF graph objects
aahjiafgg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/debug/cli/analyzer_cli.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"A tensorflow graph defined in Python is stripped of unused ops
aahjiagdd,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/debug/cli/base_ui.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Currently unused.
aahjiahbb,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/debug/cli/command_parser.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Strip any brackets at the two ends.
aahjibaah,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/debug/cli/profile_analyzer_cli.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,add margin between columns
aahjibabd,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/debug/cli/profile_analyzer_cli.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Currently unused.
aahjibahf,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/debug/cli/stepper_cli.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Currently unused.
aahjibahh,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/debug/cli/stepper_cli.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(cais): Implement list_inputs
aahjibahi,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/debug/cli/stepper_cli.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(cais): Implement list_outputs
aahjibahj,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/debug/cli/stepper_cli.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(cais): Implement node_info
aahjibccg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/debug/lib/debug_data.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Datum metadata is unused in this predicate.
aahjibchb,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/debug/lib/debug_gradients.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(cais): Implement value_stack.
aahjibejb,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/debug/lib/session_debug_testlib.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Next; set the two times stamps to be the same; which should be fine.
aahjibfai,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/debug/lib/session_debug_testlib.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,graph. Let the debugger watch the unused slot 1.
aahjibhbd,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/debug/wrappers/framework.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\"Framework of debug wrapper sessions. ||  || A debug wrapper session is a wrapper around a TensorFlow Python Session. || The wrapper preserves the Session interface; most importantly the run() method; || while providing abilities to: || a) Intercept a run() call to a wrapped session and insert debug tensor watches ||    according to externally-specified debug URLs. ||  || b) Release control to an external (i.e.; non-Session) object before and after ||    the run() call; so that the external object can perform actions such as ||    launching a UI to let users inspect the intermediate tensors and partition ||    graphs from the run() call. ||  || c) (To be implemented) Intercept a run() call and give control to DebugStepper ||    to let it perform stepping \/ continuing-to actions on the graph. ||  || b) (To be implemented in a future CL) Enter an instruction loop to let an ||    external object (e.g.; remote client) launch run() and cont() calls ||    remotely. ||  || *** The lifetime of a debug wrapper session: *** ||  || 1) The wrapper session is created by calling the constructor with a ||    wrapped (normal) session as the argument: ||      wrapper = FooDebugWrapperSession(sess) ||    wherein FooDebugWrapperSession is a concrete subclass implementing the ||    abstract BaseDebugWrapperSession class below. ||  || 2) Near the end of the constructor call; the on_session_init() callback is ||    invoked; with a OnSessionInitRequest object as the argument. The object ||    carries the wrapped (normal) session object. ||  || 3) The callback handles the request and returns a OnSessionInitResponse ||    object with an action field; directing the wrapper session what to do next. ||  || If the action field in the OnSessionInitResponse is PROCEED; the constuctor || returns. Control is released back to the caller of the constructor; which can || invoke run() method of wrapper session with the same syntax as a non-wrapped || session; e.g.;: ||   wrapper.run(fetches; feed_dict=feeds; options=run_options) ||  || Below; A1 - A2 is the lifetime of a wrapper run() call if the action is || PROCEED: ||  || A1) Right at the start of each run() call; the on_run_start() callback is ||     invoked; with an OnRunStartRequest object carrying information such as ||     the fetches; the feed dict; the run options and run metadata used in ||     this run call; along with a count of how many run calls has occurred ||     on this wrapper session. The callback then returns an OnRunStartResponse ||     object; of which the action field directs what the wrapper session ||     actually will do of the run() call. ||  ||     If the action is DEBUG_RUN; a debugged (tensor-watched) run will ensue; ||     with the debug URLs supplied in the debug_urls field of the response. ||     These can be file:\/\/ or grpc:\/\/ URLs; for example. ||  ||     If the action is NON_DEBUG_RUN; a non-debug (normal) run will ensue. ||  ||     If the action is INVOKE_STEPPER; no run() call will be issued to the ||     wrapped session. But instead; a DebugStepper (i.e.; \"continuation ||     debugger\") will be used to perform stepping \/ continue-to actions on ||     the graph. ||  || TODO(cais): The event loop for the DebugStepper will request additional ||    callbacks including on_cont_start() and on_cont_end(). Add those. ||  || A2) Right before the run() returns; the on_run_end() callback is invoked; ||     with an OnRunEndRequest object as the argument; which carries information ||     including the actual action performed in the warpper run() call and the ||     run_metadata from the run() call. ||  || However; if the action field in OnSessionInitResponse is || REMOTE_INSTR_LOOP; the constructor will automatically invoke an instruction loop || that gives the control to a remote caller. ||  || In the remote instruction loop; the following steps will happen: ||  || B1) Callback on_instr_start() is invoked. The callback will return an ||     OnInstrStartResponse object with an action field which can order one of ||     the following actions: ||         i) a run() call with fetches; feeds and debug_urls specified. ||        ii) a DebugStepper cont() call with target specified. ||       iii) value overrides in the cached tensors from the DebugStepper. ||        iv) exit the instruction loop. ||  || B2) The wrapper session carries out the action specified above. ||  || B3) If still in the instruction loop; the wrapper session invokes the ||     on_instr_end() callback. After the on_instr_end() callback returns; jump ||     back to B1. ||  || TODO(cais): Implemented the instruction loop in B1 - B3. ||  || \"\"\"
aahjibhfd,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/debug/wrappers/framework.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(cais): Implement REMOTE_INSTR_LOOP
aahjibhjd,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/debug/wrappers/grpc_wrapper.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Unused.
aahjibici,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/debug/wrappers/hooks.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Unused.
aahjibjaj,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/debug/wrappers/local_cli_wrapper.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(cais): Perhaps some users will want the effect of the interactive
aahjibjej,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/distribute/all_reduce.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,i.e. a different reduction algorithm would be better;
aahjicaaf,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/distribute/cluster_resolver/cluster_resolver.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Unused
aahjicagi,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/distribute/cluster_resolver/slurm_cluster_resolver.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Unused; since this is set in __init__ manually.
aahjiccaj,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/distribute/collective_all_reduce_strategy.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Collective group leader is needed for collective ops to coordinate
aahjiccgh,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/distribute/cross_device_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(yuefengz): move this logic to array_ops.size.
aahjiccih,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/distribute/cross_device_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,an allgather under the hood but not an efficient one.
aahjiccjb,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/distribute/cross_device_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(yuefengz): when tensor sharding is not needed; static shapes are not
aahjicdfc,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/distribute/cross_device_utils.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(tucker): maybe support non-default subdiv spec
aahjicejg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/distribute/distribute_lib.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Internal context managers used to implement the DistributionStrategy
aahjicfgf,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/distribute/distribute_lib.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,thread-local; so not needed with multiple threads
aahjicfha,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/distribute/distribute_lib.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(josh11b): Implement `start_all_reduce(method; t)` for efficient
aahjicfhd,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/distribute/distribute_lib.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,needed the reduce result; allowing an efficient implementation:
aahjicfhf,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/distribute/distribute_lib.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,in the background; not blocking until the result was needed.
aahjicfhh,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/distribute/distribute_lib.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,to that point that the first result is needed. Most likely this can be
aahjicgbh,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/distribute/distribution_strategy_context.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,load it lazily to workaround this.
aahjicgci,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/distribute/distribution_strategy_context.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,We create them lazily in a function so that we can workaround the circular
aahjicgje,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/distribute/estimator_training.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(yuefengz): maybe merge the following two functions?
aahjiciad,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/distribute/mirrored_strategy.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed for per-thread device; etc. contexts in graph mode.
aahjicida,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/distribute/mirrored_strategy.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Ignore user-specified caching device; not needed for mirrored variables.
aahjicidb,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/distribute/mirrored_strategy.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(josh11b;apassos): It would be better if variable initialization
aahjicidc,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/distribute/mirrored_strategy.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,was never recorded on the tape instead of having to do this manually
aahjicjab,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/distribute/mirrored_strategy.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,State needed to run and return the results of `fn`.
aahjicjac,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/distribute/mirrored_strategy.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,State needed to run the next merge_call() (if any) requested via
aahjidadg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/distribute/parameter_server_strategy.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(yuefengz): maybe cache variables on local CPU.
aahjidafc,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/distribute/parameter_server_strategy.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,The `_parameter_devices` is needed for the `parameter_devices` property
aahjidbfe,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/distribute/tpu_strategy.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Ignore user-specified caching device; not needed for mirrored variables.
aahjidbff,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/distribute/tpu_strategy.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(josh11b;apassos): It would be better if variable initialization
aahjidbfg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/distribute/tpu_strategy.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,was never recorded on the tape instead of having to do this manually
aahjidbgh,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/distribute/tpu_strategy.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Workaround for `tpu.replicate` behaviour when single `Tensor` returned.
aahjidcdg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/distribute/tpu_strategy.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,when using device_assignment. This is a temporary workaround to support
aahjiddei,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/distribute/values.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed for GradientTape
aahjiddhj,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/distribute/values.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to pass ResourceVariable checks.
aahjideha,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/eager/backprop.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(b\/119775953): fix the circular dependencies.
aahjidgcb,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/eager/def_function.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,better than the alternative; tracing the initialization graph but giving
aahjidhce,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/eager/execute.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(josh11b): Could we do a better job if we also passed in the
aahjidhjg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/eager/function.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,It might be worth creating a convenient way to re-use status.
aahjidhji,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/eager/function.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,signature; but also in general it's nice not to depend on it.
aahjidici,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/eager/function.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,method's functionality better. Remove register_gradient_functions argument
aahjidige,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/eager/function.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(nareshmodi): Skip ndarray conversion to tensor altogether; perhaps
aahjidjgc,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/eager/function.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(mdan): For better consistency; use the wrapper's call().
aahjieadb,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/eager/lift_to_graph.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,ends in the initializer. We copy those to the outermost graph and
aahjieggc,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/feature_column/feature_column.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\"This API defines FeatureColumn abstraction. ||  || FeatureColumns provide a high level abstraction for ingesting and representing || features. FeatureColumns are also the primary way of encoding features for || canned `tf.estimator.Estimator`s. ||  || When using FeatureColumns with `Estimators`; the type of feature column you || should choose depends on (1) the feature type and (2) the model type. ||  || 1. Feature type: ||  ||   * Continuous features can be represented by `numeric_column`. ||   * Categorical features can be represented by any `categorical_column_with_*` ||   column: ||     - `categorical_column_with_vocabulary_list` ||     - `categorical_column_with_vocabulary_file` ||     - `categorical_column_with_hash_bucket` ||     - `categorical_column_with_identity` ||     - `weighted_categorical_column` ||  || 2. Model type: ||  ||   * Deep neural network models (`DNNClassifier`; `DNNRegressor`). ||  ||     Continuous features can be directly fed into deep neural network models. ||  ||       age_column = numeric_column(\"age\") ||  ||     To feed sparse features into DNN models; wrap the column with ||     `embedding_column` or `indicator_column`. `indicator_column` is recommended ||     for features with only a few possible values. For features with many ||     possible values; to reduce the size of your model; `embedding_column` is ||     recommended. ||  ||       embedded_dept_column = embedding_column( ||           categorical_column_with_vocabulary_list( ||               \"department\"; [\"math\"; \"philosophy\"; ...]); dimension=10) ||  ||   * Wide (aka linear) models (`LinearClassifier`; `LinearRegressor`). ||  ||     Sparse features can be fed directly into linear models. They behave like an ||     indicator column but with an efficient implementation. ||  ||       dept_column = categorical_column_with_vocabulary_list(\"department\"; ||           [\"math\"; \"philosophy\"; \"english\"]) ||  ||     It is recommended that continuous features be bucketized before being ||     fed into linear models. ||  ||       bucketized_age_column = bucketized_column( ||           source_column=age_column; ||           boundaries=[18; 25; 30; 35; 40; 45; 50; 55; 60; 65]) ||  ||     Sparse features can be crossed (also known as conjuncted or combined) in ||     order to form non-linearities; and then fed into linear models. ||  ||       cross_dept_age_column = crossed_column( ||           columns=[\"department\"; bucketized_age_column]; ||           hash_bucket_size=1000) ||  || Example of building canned `Estimator`s using FeatureColumns: ||  ||   ```python ||   # Define features and transformations ||   deep_feature_columns = [age_column; embedded_dept_column] ||   wide_feature_columns = [dept_column; bucketized_age_column; ||       cross_dept_age_column] ||  ||   # Build deep model ||   estimator = DNNClassifier( ||       feature_columns=deep_feature_columns; ||       hidden_units=[500; 250; 50]) ||   estimator.train(...) ||  ||   # Or build a wide model ||   estimator = LinearClassifier( ||       feature_columns=wide_feature_columns) ||   estimator.train(...) ||  ||   # Or build a wide and deep model! ||   estimator = DNNLinearCombinedClassifier( ||       linear_feature_columns=wide_feature_columns; ||       dnn_feature_columns=deep_feature_columns; ||       dnn_hidden_units=[500; 250; 50]) ||   estimator.train(...) ||   ``` ||  ||  || FeatureColumns can also be transformed into a generic input layer for || custom models using `input_layer`. ||  || Example of building model using FeatureColumns; this can be used in a || `model_fn` which is given to the {tf.estimator.Estimator}: ||  ||   ```python ||   # Building model via layers ||  ||   deep_feature_columns = [age_column; embedded_dept_column] ||   columns_to_tensor = parse_feature_columns_from_examples( ||       serialized=my_data; ||       feature_columns=deep_feature_columns) ||   first_layer = input_layer( ||       features=columns_to_tensor; ||       feature_columns=deep_feature_columns) ||   second_layer = fully_connected(first_layer; ...) ||   ``` ||  || NOTE: Functions prefixed with \"_\" indicate experimental or private parts of || the API subject to change; and should not be relied upon! ||  || NOTE: The new feature columns are being developed in feature_column_v2.py and || are a somewhat duplicate of the code here. Please make sure to update logic || in both places. || \"\"\"
aahjiegih,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/feature_column/feature_column.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,should implement the logic in input_layer using Layer's build-and-call
aahjiegjj,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/feature_column/feature_column.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Having the fully expressed variable scope name ends up doubly
aahjiehfb,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/feature_column/feature_column.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,that the ops for different columns have distinct names.
aahjieidc,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/feature_column/feature_column_v2.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\"This API defines FeatureColumn abstraction. ||  || FeatureColumns provide a high level abstraction for ingesting and representing || features. FeatureColumns are also the primary way of encoding features for || canned `tf.estimator.Estimator`s. ||  || When using FeatureColumns with `Estimators`; the type of feature column you || should choose depends on (1) the feature type and (2) the model type. ||  || 1. Feature type: ||  ||   * Continuous features can be represented by `numeric_column`. ||   * Categorical features can be represented by any `categorical_column_with_*` ||   column: ||     - `categorical_column_with_vocabulary_list` ||     - `categorical_column_with_vocabulary_file` ||     - `categorical_column_with_hash_bucket` ||     - `categorical_column_with_identity` ||     - `weighted_categorical_column` ||  || 2. Model type: ||  ||   * Deep neural network models (`DNNClassifier`; `DNNRegressor`). ||  ||     Continuous features can be directly fed into deep neural network models. ||  ||       age_column = numeric_column(\"age\") ||  ||     To feed sparse features into DNN models; wrap the column with ||     `embedding_column` or `indicator_column`. `indicator_column` is recommended ||     for features with only a few possible values. For features with many ||     possible values; to reduce the size of your model; `embedding_column` is ||     recommended. ||  ||       embedded_dept_column = embedding_column( ||           categorical_column_with_vocabulary_list( ||               \"department\"; [\"math\"; \"philosophy\"; ...]); dimension=10) ||  ||   * Wide (aka linear) models (`LinearClassifier`; `LinearRegressor`). ||  ||     Sparse features can be fed directly into linear models. They behave like an ||     indicator column but with an efficient implementation. ||  ||       dept_column = categorical_column_with_vocabulary_list(\"department\"; ||           [\"math\"; \"philosophy\"; \"english\"]) ||  ||     It is recommended that continuous features be bucketized before being ||     fed into linear models. ||  ||       bucketized_age_column = bucketized_column( ||           source_column=age_column; ||           boundaries=[18; 25; 30; 35; 40; 45; 50; 55; 60; 65]) ||  ||     Sparse features can be crossed (also known as conjuncted or combined) in ||     order to form non-linearities; and then fed into linear models. ||  ||       cross_dept_age_column = crossed_column( ||           columns=[\"department\"; bucketized_age_column]; ||           hash_bucket_size=1000) ||  || Example of building canned `Estimator`s using FeatureColumns: ||  ||   ```python ||   # Define features and transformations ||   deep_feature_columns = [age_column; embedded_dept_column] ||   wide_feature_columns = [dept_column; bucketized_age_column; ||       cross_dept_age_column] ||  ||   # Build deep model ||   estimator = DNNClassifier( ||       feature_columns=deep_feature_columns; ||       hidden_units=[500; 250; 50]) ||   estimator.train(...) ||  ||   # Or build a wide model ||   estimator = LinearClassifier( ||       feature_columns=wide_feature_columns) ||   estimator.train(...) ||  ||   # Or build a wide and deep model! ||   estimator = DNNLinearCombinedClassifier( ||       linear_feature_columns=wide_feature_columns; ||       dnn_feature_columns=deep_feature_columns; ||       dnn_hidden_units=[500; 250; 50]) ||   estimator.train(...) ||   ``` ||  ||  || FeatureColumns can also be transformed into a generic input layer for || custom models using `input_layer`. ||  || Example of building model using FeatureColumns; this can be used in a || `model_fn` which is given to the {tf.estimator.Estimator}: ||  ||   ```python ||   # Building model via layers ||  ||   deep_feature_columns = [age_column; embedded_dept_column] ||   columns_to_tensor = parse_feature_columns_from_examples( ||       serialized=my_data; ||       feature_columns=deep_feature_columns) ||   first_layer = input_layer( ||       features=columns_to_tensor; ||       feature_columns=deep_feature_columns) ||   second_layer = fully_connected(first_layer; ...) ||   ``` ||  || NOTE: Functions prefixed with \"_\" indicate experimental or private parts of || the API subject to change; and should not be relied upon! || \"\"\"
aahjieija,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/feature_column/feature_column_v2.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(ptucker): Move to third_party\/tensorflow\/python\/ops\/sparse_ops.py
aahjiejdb,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/feature_column/feature_column_v2.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,that the ops for different columns have distinct names.
aahjifabj,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/feature_column/feature_column_v2.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,If the name already exists; re-use the column from columns_by_name;
aahjifaca,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/feature_column/feature_column_v2.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,(new_instance remains unused).
aahjifaee,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/feature_column/sequence_feature_column.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(b\/119409767): Implement parents; _{get;from}_config.
aahjifaeg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/framework/auto_control_deps.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\"AutomaticControlDependencies and related functionality.\"\"\"
aahjifbad,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/framework/auto_control_deps.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,probably other things as well).
aahjifbfg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/framework/auto_control_deps.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,in op getting a control dependency on itself.
aahjifdca,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/framework/constant_op.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(josh11b): Implement shape -> eager tensor conversion.
aahjifdcc,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/framework/constant_op.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Fill on CPU and then copy to GPU if needed.
aahjiffee,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/framework/framework_lib.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed when you defined a new Op in C++.
aahjiffef,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/framework/framework_lib.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed when interfacing tensorflow to new array libraries
aahjifffb,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/framework/func_graph.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\"FuncGraph and related functionality.\"\"\"
aahjiffih,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/framework/func_graph.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,inheriting this stack from the default graph even in eager mode. Maybe
aahjifgci,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/framework/func_graph.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,converted even though they would meet autograph's whitelisting
aahjifgdd,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/framework/func_graph.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Wrapping around a decorator allows checks like tf_inspect.getargspec
aahjifhei,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/framework/function.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(skyewm): is this needed?
aahjifhjd,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/framework/function.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,The Python callable is only needed to create a FunctionDef. Since we have
aahjifjeb,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/framework/graph_util_impl.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,This graph only includes the nodes needed to evaluate the output nodes; and
aahjigaha,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/framework/load_library.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,that are no longer needed.
aahjigcfi,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/framework/op_def_library.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Update the maps with the default; if needed.
aahjigdcc,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/framework/ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,more efficient `isinstance` checks later.
aahjigfgf,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/framework/ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,If a names ends with a '\/' it is a \"name scope\" and we use it as-is;
aahjiggaf,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/framework/ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Ideally; it would be nice to split it up; but we
aahjighcb,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/framework/ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,apply to inner graph mode code. Fix that.
aahjighda,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/framework/ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(mrry): Perhaps log that the default graph is being used; or set
aahjighff,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/framework/ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,execution doesn't implement device stacks and in particular it
aahjigiei,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/framework/ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,However; that would require a circular import dependency.
aahjigifb,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/framework/ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,happen soon; perhaps this hack to work around the circular
aahjigjdc,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/framework/sparse_tensor.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Assert number of columns in indices matches the number of elements in
aahjigjgg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/framework/subscribe.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Check that the tensor name matches the convention in place for identity ops
aahjigjjc,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/framework/tensor_shape.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,This is needed for Windows.
aahjigjjj,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/framework/tensor_shape.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(mrry): Handle these maybe.
aahjihaad,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/framework/tensor_shape.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,and perhaps we should do that.
aahjihaae,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/framework/tensor_shape.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(mrry): Handle this better; as it will be useful for handling
aahjihaed,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/framework/tensor_util.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO: Remove the conversion if cython supports np.float16_t
aahjihbcc,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/framework/test_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aahjiiibb,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/framework/test_util.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(allenl): Figure out why this debug flag reset doesn't work. It would
aahjiiibc,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/framework/test_util.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,be nice to be able to decorate arbitrary tests in a large test suite and
aahjiiibh,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/framework/test_util.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,better isolation.
aahjiiiei,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/framework/test_util.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,f1 == f2 is needed here as we might have: f1; f2 = inf; inf
aahjiiihi,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/framework/test_util.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Fix Python 3 compatibility issues
aahjijggc,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/keras/backend.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Allows to give unique autogenerated names to layers; in a graph-specific way.
aahjijhbj,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/keras/backend.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Fix shape representation
aahjijhgb,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/keras/backend.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Temporary workaround due to `convert_to_tensor` not casting floats.
aahjijhii,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/keras/backend.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,maybe set shape.
aahjijiei,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/keras/backend.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(priyag; yuefengz): Remove this workaround when Distribute
aahjjabcd,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/keras/datasets/imdb.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,by convention; use 2 as OOV word
aahjjabfh,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/keras/datasets/reuters.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,by convention; use 2 as OOV word
aahjjacga,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/keras/engine/base_layer.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,implement `compute_output_shape` themselves).
aahjjacje,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/keras/engine/base_layer.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,would enable us to run the underlying graph if needed.
aahjjaebf,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/keras/engine/base_layer.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,This is a hack so that the is_layer (within
aahjjaehj,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/keras/engine/base_layer_utils.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Import of `base_layer` needed in order to create `TensorFlowOpLayer`.
aahjjafaf,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/keras/engine/base_layer_utils.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Ignore ReadVariableOps as they are not needed to be run separately.
aahjjafga,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/keras/engine/distributed_training_utils.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,no longer needed.
aahjjagbg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/keras/engine/distributed_training_utils.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,needed. This method does initialization or waiting for initialization
aahjjahdd,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/keras/engine/network.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Private attributes to implement compatibility with Layer.
aahjjahjh,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/keras/engine/network.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,This is a super ugly; confusing check necessary to
aahjjaigg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/keras/engine/network.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,and building the layer if needed.
aahjjajcf,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/keras/engine/network.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,To provide a better error msg.
aahjjbbfg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/keras/engine/training.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,since TPUStrategy does not implement replica local variables.
aahjjbbfh,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/keras/engine/training.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Remove this hack once we support TPUReplicaLocalVariables.
aahjjbdgc,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/keras/engine/training_distributed.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,workaround until new metrics are in place.
aahjjbdig,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/keras/engine/training_distributed.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(priyag): This is a workaround as we do not know the batch dimension
aahjjbfdi,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/keras/engine/training_utils.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Deserialize loss configuration; if needed.
aahjjbfed,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/keras/engine/training_utils.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(rohanj): This is a hack to get around not depending on feature_column and
aahjjbfee,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/keras/engine/training_utils.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,create a cyclical dependency. Figure out a cleaner solution
aahjjbffb,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/keras/engine/training_utils.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,We fix the placeholder shape except the batch size.
aahjjbgdb,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/keras/keras_parameterized.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,it. Or perhaps make 'subclass' always use a custom build method.
aahjjbhid,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/keras/layers/core.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Subclasses of `Dropout` may implement `_get_noise_shape(self; inputs)`;
aahjjbihe,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/keras/layers/kernelized.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\"Keras layers that implement explicit (approximate) kernel feature maps.\"\"\"
aahjjcaeg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/keras/layers/normalization.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,used during evaluation; it is more efficient to just update in one
aahjjcafe,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/keras/layers/normalization.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(reedwm): Maybe do math in float32 if given float16 inputs; if doing
aahjjccaf,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/keras/layers/recurrent.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,CuDNN uses following setting by default and not configurable.
aahjjcddh,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/keras/layers/wrappers.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Applies the same workaround as in `RNN.__call__`
aahjjcejj,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/keras/mixed_precision/experimental/policy.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(reedwm): Implement get_config\/from_config.
aahjjcgjh,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/keras/optimizer_v2/gradient_descent.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,This method is only needed for momentum optimization.
aahjjchca,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/keras/optimizer_v2/learning_rate_schedule.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,The default isn't needed here because our conditions are mutually
aahjjchhb,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/keras/optimizer_v2/optimizer_v2.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to avoid infinite recursion with __setattr__.
aahjjchhf,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/keras/optimizer_v2/optimizer_v2.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(tanzheny): Maybe share this logic with base_layer.
aahjjdbbb,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/keras/saving/saved_model.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,one save is needed once the weights can be copied from the model to clone.
aahjjdcjb,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/keras/utils/generic_utils.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,in this case by convention `config` holds
aahjjdddi,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/keras/utils/layer_utils.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\"Utilities related to layer\/model functionality. || \"\"\"
aahjjdehf,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/keras/utils/multi_gpu_utils.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Relocate the model definition under CPU device scope if needed
aahjjdfeg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/keras/utils/vis_utils.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,pydot-ng is a fork of pydot that is better maintained.
aahjjdhjj,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/lib/io/file_io.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,We needed to make offset a keyword argument for backwards-compatibility.
aahjjdjci,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/array_grad.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Some more reshaping is needed to assemble this tensor with the
aahjjdjgh,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/array_grad.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Fix shape inference
aahjjeahd,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/array_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(keveman): Implement a standalone type and shape checker.
aahjjebbg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/array_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Prepare reshape by inserting dimensions with size 1 where needed
aahjjebbi,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/array_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(nolivia): improve performance with a broadcast
aahjjebgd,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/array_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,subtle race conditions. TODO(apassos) implement axis != 0 on sparse_read
aahjjebhc,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/array_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Move params[axis] up to params[batch_dims].
aahjjebhd,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/array_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Move the result dimensions corresponding to params[batch_dims:axis]
aahjjebia,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/array_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,We want to expose tf.quantize instead of tf.quantize_v2; we can deprecate
aahjjecic,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/check_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Unused.
aahjjedgb,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/clustering_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(agarwal): This colocation seems to run into problems. Fix it.
aahjjeebi,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/cond_v2.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\"cond_v2 and gradient. ||  || This is a version of cond that emits a single If op; as well as the gradient || function for If ops produced by cond_v2. This will eventually replace the || current tf.cond implementation once it reaches feature and performance parity. || \"\"\"
aahjjeefa,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/cond_v2.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Modify 'op' to output the intermediates needed by the grad functions. Note
aahjjeefb,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/cond_v2.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,that all needed intermediates are wrapped in optionals. Each optional
aahjjehai,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/control_flow_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Information needed by backprop.
aahjjehei,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/control_flow_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Only the shape of value is needed for backprop.
aahjjehhc,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/control_flow_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,We are in a cond context. Use a switch to create zeros only when needed.
aahjjehjg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/control_flow_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Use the real value if it comes from outer context. This is needed in
aahjjeidg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/control_flow_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,We needed to make true_fn\/false_fn keyword arguments for
aahjjejcb,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/control_flow_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,for the zeros is only needed for the base case when the loop exits
aahjjfabb,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/control_flow_util.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(skyewm): do something better than hasattr without messing up imports.
aahjjfafb,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/control_flow_util.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,forward pass context. This case is needed for the gradients of while
aahjjfafd,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/control_flow_util.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,input_op is in the gradient context of op's context. This case is needed
aahjjfafh,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/control_flow_util.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,needed when the gradient of a while loop gradient is requested (this
aahjjfehf,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/distributions/bijector_impl.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,We want the default convention to be snake_case rather than CamelCase
aahjjfgdi,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/distributions/dirichlet_multinomial.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,* The broadcasting convention is to prepend dimensions of size [1]; and
aahjjfgjg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/distributions/distribution.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Maybe expand_dims.
aahjjfjac,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/distributions/special_math.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Best wishes for good luck with your projects!
aahjjfjeg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/distributions/special_math.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,conservative; meaning we use the approximation earlier than needed.
aahjjgacj,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/distributions/transformed_distribution.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,tf.select even though we use tf.select to implement it.
aahjjgbdg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/distributions/util.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,following the TF convention. Typically; you might expect to see
aahjjgdhh,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/functional_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(zhfiengc): Needs some math expert to say the comment above better.
aahjjgdjh,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/functional_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,It should be possible and probably better to write a XLA C++ kernel
aahjjgece,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gen_array_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aaiaaaeha,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gen_audio_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aaiaaafjd,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gen_bitwise_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aaiaaaicd,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gen_boosted_trees_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aaiaabeeg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gen_candidate_sampling_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aaiaabihj,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gen_checkpoint_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aaiaabjgj,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gen_clustering_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aaiaacadi,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gen_collective_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aaiaaccdi,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gen_control_flow_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aaiaacfgc,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gen_ctc_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aaiaachab,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gen_cudnn_rnn_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aaiaadidb,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gen_data_flow_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aaiaahedj,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gen_dataset_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aaiaajdah,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gen_experimental_dataset_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aaiabahhe,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gen_functional_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aaiabbbie,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gen_image_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aaiabcjae,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gen_io_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aaiabdhgc,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gen_linalg_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aaiabegjc,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gen_list_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aaiabfcdf,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gen_logging_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aaiabfgcd,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gen_lookup_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aaiabgeaa,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gen_manip_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aaiabgeeh,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gen_math_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aaiacbjbi,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gen_nccl_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aaiaccaee,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gen_nn_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aaiacibdb,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gen_parsing_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aaiaciihf,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gen_ragged_array_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aaiacijcj,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gen_ragged_conversion_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aaiacijgh,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gen_ragged_math_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aaiacjaba,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gen_random_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aaiacjfjc,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gen_resource_variable_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aaiadabjd,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gen_script_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aaiadachi,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gen_sdca_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aaiadaffi,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gen_set_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aaiadahia,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gen_sparse_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aaiadcchj,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gen_spectral_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aaiadcgeb,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gen_state_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aaiaddgfg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gen_stateful_random_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aaiaddhcg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gen_stateless_random_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aaiadeach,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gen_string_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aaiadehec,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gen_summary_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aaiadfahd,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gen_tensor_forest_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aaiadfbjb,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gen_tpu_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aaiadhfge,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gen_user_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aaiadhhhd,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gradients_util.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(skyewm): plumbing xs through everywhere is ugly; consider making
aaiadhhja,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gradients_util.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,been collected.  Then aggregate these gradients if needed; call the op's
aaiadhiee,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gradients_util.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,For an unused exit; if it has trainable outputs; backprop
aaiadhieg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gradients_util.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,All exits are \"unused\" so use None as gradient.
aaiadhigg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/gradients_util.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,reduce performance; but it can improve memory because the
aaiadhjhi,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/image_ops_impl.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Maybe crop if needed.
aaiadhjhj,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/image_ops_impl.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Maybe pad if needed.
aaiadibdg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/init_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,randomly zeroing out some columns
aaiadidfc,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/linalg/linalg_impl.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,reshaping the batch makes the where statements work better
aaiadieaa,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/linalg/linear_operator.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Override if a more efficient implementation is available.
aaiadiedi,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/linalg/linear_operator_addition.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,operators with an efficient .add_to_tensor() method.
aaiadieef,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/linalg/linear_operator_addition.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,(ii) produce the \"most efficient\" final operator.
aaiadigbe,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/linalg/linear_operator_circulant.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,the following line is NaN * [] = []; as needed.
aaiadigfh,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/linalg/linear_operator_composition.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(langmore) Implement solve using solve_ls if some intermediate
aaiadihjf,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/linalg/linear_operator_kronecker.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,where vec stacks all the columns of the matrix under each other. In our
aaiadijih,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/linalg/linear_operator_test_util.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(langmore) Test tf.float16 once tf.matrix_solve works in 16bit.
aaiadjabg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/linalg/linear_operator_test_util.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Totally random matrix.  Has no nice properties.
aaiadjaca,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/linalg/linear_operator_test_util.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Shape of the matrix with only n - 1 columns that we will embed in higher
aaiadjagd,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/linalg/linear_operator_util.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,This could be handled in the future; but seems less common.
aaiadjbjf,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/linalg_grad.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(rmlarsen): The implementation could be more efficient:
aaiadjbji,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/linalg_grad.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,b) Implement a symmetric rank-k update op instead of computing
aaiadjfdi,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/losses/util.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,ends; holding on to a loss when executing eagerly is indistingishable from
aaiadjgbg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/math_grad.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(rmlarsen): Implement gradient.
aaiadjgid,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/math_grad.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(ebrevdo): Perhaps add the derivative w.r.t. a; b
aaiadjhbd,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/math_grad.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO This fails when x contains 0 and should be fixed
aaiadjhgc,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/math_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,__invert__ corresponds to the ~ operator.  Here we follow the numpy convention
aaiadjhhe,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/math_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(aselle): Deprecate this once all internal functionality uses
aaiadjhje,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/math_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(apassos) remove _shape_tuple here when it is not needed.
aaiadjiaf,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/math_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,casting to bfloat16 also matches non-sparse matmul behavior better.
aaiadjibf,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/math_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,cast needed for SparseTensor reductions
aaiadjihj,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/metrics_impl.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,This special handling is needed because sometimes the metric is created
aaiadjiia,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/metrics_impl.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,inside a while_loop (and perhaps a TPU rewrite context). But we don't
aaiadjjba,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/metrics_impl.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(ptucker): Make this more efficient; maybe add a sparse version of
aaiadjjja,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/nn.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Bring more nn-associated functionality into this package.
aaiaeaaca,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/nn_grad.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(apassos) add an efficient way to detect eager zeros here.
aaiaeabac,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/nn_impl.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,sufficient statistics. As a workaround we simply perform the operations
aaiaeabdd,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/nn_impl.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,a matrix.  The gradient of _sum_rows(x) is more efficient than
aaiaeaccf,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/nn_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,convention as conv2d.
aaiaeaceh,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/nn_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Give a better error message if EXPLICIT is passed.
aaiaeachg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/nn_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,following the same convention as conv2d().
aaiaeadbe,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/nn_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,[0; num_classes). Note: This could break users who call this with bad
aaiaeaegh,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/parallel_for/pfor.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(agarwal): implement a strategy for converting Variable reads\/writes. The
aaiaeafdb,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/parallel_for/pfor.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,employ a hack here. We output a dummy invalid value with an incorrect
aaiaeafgf,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/parallel_for/pfor.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(agarwal): avoid this stacking. See TODO earlier in
aaiaeafhi,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/parallel_for/pfor.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Note that we use a list as a hack since we need the nested function body
aaiaeafja,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/parallel_for/pfor.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,partition the inputs if needed.
aaiaeagdf,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/parallel_for/pfor.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Move S dimension next to C dimension.
aaiaeaggd,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/parallel_for/pfor.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(agarwal): Implement this efficiently.
aaiaeagic,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/parallel_for/pfor.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(agarwal): There may be a more efficient way to do this instead of
aaiaeahfg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/parallel_for/pfor.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,unstacked. So the check is only needed in this branch.
aaiaeaicf,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/parallel_for/pfor.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,We use an ugly logic to find whether values in Stack data structure are
aaiaeaidc,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/parallel_for/pfor.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(agarwal): move _stack_cache inside pfor?
aaiaeajaa,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/parsing_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,efficient especially for large inputs.
aaiaeajbf,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/partitioned_variables.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\"Helper functions for creating partitioned variables. ||  || This is a convenient abstraction to partition a large variable across || multiple smaller variables that can be assigned to different devices. ||  || The full variable can be reconstructed by concatenating the smaller variables. || Using partitioned variables instead of a single variable is mostly a || performance choice.  It however also has an impact on: ||  || 1. Random initialization; as the random number generator is called once per ||    slice || 2. Updates; as they happen in parallel across slices ||  || A key design goal is to allow a different graph to repartition a variable || with the same name but different slicings; including possibly no partitions. ||  || TODO(touts): If an initializer provides a seed; the seed must be changed || deterministically for each slice; maybe by adding one to it; otherwise each || slice will use the same values.  Maybe this can be done by passing the || slice offsets to the initializer functions. ||  || Typical usage: ||  || ```python || # Create a list of partitioned variables with: || vs = create_partitioned_variables( ||     <shape>; <slicing>; <initializer>; name=<optional-name>) ||  || # Pass the list as inputs to embedding_lookup for sharded; parallel lookup: || y = embedding_lookup(vs; ids; partition_strategy=\"div\") ||  || # Or fetch the variables in parallel to speed up large matmuls: || z = matmul(x; concat(slice_dim; vs)) || ``` || \"\"\"
aaiaebdec,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/ragged/ragged_getitem.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Slicing a range of columns in a ragged inner dimension.  We use a
aaiaebefa,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/ragged/ragged_math_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,efficient; and only works for associative ops.  (In particular; it
aaiaebefc,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/ragged/ragged_math_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,once will probably require a nontrivial c++ op.
aaiaebfia,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/ragged/ragged_tensor.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,mask to clear out the unused data in the passed tensor.
aaiaebhdg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/ragged/ragged_util.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,when axis is not specified.  Rather than implement that special behavior; we
aaiaebidg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/random_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(shlens): Implement edge case to guarantee output size dimensions.
aaiaebjeg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/resource_variable_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,atomic if there's a way to do so without a performance cost for those who
aaiaeccca,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/script_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(akshayka): Implement higher-order derivatives.
aaiaeceid,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/signal/reconstruction_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Now; reduce over the columns; to achieve the desired sum.
aaiaecgbe,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/sort_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Unused.
aaiaechag,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/sparse_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,This is needed to make documentation without fully qualified module paths
aaiaechec,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/special_math_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,This is consistent with a convention that the sum over the empty set 0; and
aaiaecjcj,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/stateful_random_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(wangpeng): implement other distributions (`uniform`;
aaiaecjdb,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/stateful_random_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(wangpeng): implement `make_seeds`
aaiaecjdc,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/stateful_random_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(wangpeng): implement `make_generators`
aaiaedcgc,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/tensor_array_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Unused.
aaiaeddie,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/variable_scope.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,functions to create variables) so we take more than needed in the
aaiaedegi,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/variable_scope.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Hack to reenter
aaiaedfgh,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/variables.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(b\/70206927): Fix handling of ResourceVariables.
aaiaedgbc,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/while_v2.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\"while_v2 and gradient. ||  || This is a version of while_loop that emits a single While op; as well as the || gradient function for While ops produced by while_loop. This will eventually || replace the current tf.while_loop implementation once it reaches feature and || performance parity. || \"\"\"
aaiaedgdb,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/while_v2.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,to them and then pass those in as data inputs. This should probably be
aaiaedgdi,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/while_v2.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Add loop counter needed for computing gradients.
aaiaedgja,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/ops/while_v2.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Modify 'op' to output the intermediate accumulators needed by the grad
aaiaeecba,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/profiler/internal/flops_registry.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,graph and node are unused
aaiaeegbg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/saved_model/function_deserialization.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,these would be good; maybe by adding TensorSpec names to cache keys so renamed
aaiaeeida,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/saved_model/main_op_impl.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(sukritiramesh): Integrate with Saver for complete restore functionality.
aaiaeejch,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/saved_model/nested_structure_coder.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\"Module that encodes (decodes) nested structures into (from) protos. ||  || The intended use is to serialize everything needed to restore a `Function` that || was saved into a SavedModel. This may include concrete function inputs and || outputs; signatures; function specs; etc. ||  || Example use: || coder = nested_structure_coder.StructureCoder() || # Encode into proto. || signature_proto = coder.encode_structure(function.input_signature) || # Decode into a Python object. || restored_signature = coder.decode_proto(signature_proto) || \"\"\"
aaiaeffec,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/tools/freeze_graph.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Unused by updated loading code.
aaiaeffhb,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/tools/import_pb_to_tensorboard.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(aaroey): ideally we should import everything from contrib; but currently
aaiaeffhd,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/tools/import_pb_to_tensorboard.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,tensorflow\/contrib\/__init__.py. Fix it.
aaiaeffjc,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/tools/optimize_for_inference.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,r\"\"\"Removes parts of a graph that are only needed for training. ||  || There are several common transformations that can be applied to GraphDefs || created to train a model; that help reduce the amount of computation needed when || the network is used only for inference. These include: ||  ||  - Removing training-only operations like checkpoint saving. ||  ||  - Stripping out parts of the graph that are never reached. ||  ||  - Removing debug operations like CheckNumerics. ||  ||  - Folding batch normalization ops into the pre-calculated weights. ||  ||  - Fusing common operations into unified versions. ||  || This script takes either a frozen binary GraphDef file (where the weight || variables have been converted into constants by the freeze_graph script); or a || text GraphDef proto file (the weight variables are stored in a separate || checkpoint file); and outputs a new GraphDef with the optimizations applied. ||  || If the input graph is a text graph file; make sure to include the node that || restores the variable weights in output_names. That node is usually named || \"restore_all\". ||  || An example of command-line usage is: ||  || bazel build tensorflow\/python\/tools:optimize_for_inference && \\ || bazel-bin\/tensorflow\/python\/tools\/optimize_for_inference \\ || --input=frozen_inception_graph.pb \\ || --output=optimized_inception_graph.pb \\ || --frozen_graph=True \\ || --input_names=Mul \\ || --output_names=softmax ||  ||  || \"\"\"
aaiaefgai,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/tools/optimize_for_inference_lib.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,r\"\"\"Removes parts of a graph that are only needed for training. ||  || There are several common transformations that can be applied to GraphDefs || created to train a model; that help reduce the amount of computation needed when || the network is used only for inference. These include: ||  ||  - Removing training-only operations like checkpoint saving. ||  ||  - Stripping out parts of the graph that are never reached. ||  ||  - Removing debug operations like CheckNumerics. ||  ||  - Folding batch normalization ops into the pre-calculated weights. ||  ||  - Fusing common operations into unified versions. ||  || This script takes a frozen GraphDef file (where the weight variables have been || converted into constants by the freeze_graph script) and outputs a new GraphDef || with the optimizations applied. ||  || An example of command-line usage is: ||  || bazel build tensorflow\/python\/tools:optimize_for_inference && \\ || bazel-bin\/tensorflow\/python\/tools\/optimize_for_inference \\ || --input_graph=some_graph_def.pb \\ || --output_graph=\/tmp\/optimized_graph.pb \\ || --input_names=Mul \\ || --output_names=softmax ||  || \"\"\"
aaiaefghg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/tools/saved_model_cli.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,This is needed for freshly started worker; or if the job
aaiaefhdi,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/tools/selective_registration_header_lib.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,corresponding kernel; nodes without a corresponding kernel (perhaps due to
aaiaefheg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/tools/strip_unused.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,r\"\"\"Removes unneeded nodes from a GraphDef file. ||  || This script is designed to help streamline models; by taking the input and || output nodes that will be used by an application and figuring out the smallest || set of operations that are required to run for those arguments. The resulting || minimal graph is then saved out. ||  || The advantages of running this script are: ||  - You may be able to shrink the file size. ||  - Operations that are unsupported on your platform but still present can be ||    safely removed. || The resulting graph may not be as flexible as the original though; since any || input nodes that weren't explicitly mentioned may not be accessible any more. ||  || An example of command-line usage is: || bazel build tensorflow\/python\/tools:strip_unused && \\ || bazel-bin\/tensorflow\/python\/tools\/strip_unused \\ || --input_graph=some_graph_def.pb \\ || --output_graph=\/tmp\/stripped_graph.pb \\ || --input_node_names=input0 || --output_node_names=softmax ||  || You can also look at strip_unused_test.py for an example of how to use it. ||  || \"\"\"
aaiaefhhi,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/tools/strip_unused_lib.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,placeholders so that any unused nodes that are inputs to them are
aaiaefibg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/tpu/_tpu_estimator_embedding.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,or TPU mode. So allow non-TPU embedding columns also.
aaiaefifh,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/tpu/bfloat16.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,This if statement is needed to guard the cast; because batch norm
aaiaefjfc,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/tpu/feature_column.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,initializer differently. See shared_embedding_columns for details.
aaiaegced,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/tpu/tpu.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,that puts any op clustered with root_cluster.<xxx> in
aaiaegcee,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/tpu/tpu.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,root_cluster.gradient_uid; even if xxx was initial_gradient_uid.
aaiaegdah,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/tpu/tpu.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Add identity ops so even unused inputs are \"consumed\" by the
aaiaegdaj,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/tpu/tpu.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(phawkins): consider instead pruning unused TPUReplicatedInput
aaiaegdbd,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/tpu/tpu.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,encapsulate TPU computation pass if unused. However we don't remove
aaiaegdea,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/tpu/tpu.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,supported flat outputs and thus for consistency it was nice to convert even
aaiaegfbd,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/tpu/tpu_embedding.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(shizhiw): move `optimization_parameters` into `_optimizer_handler`
aaiaegfgf,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/tpu/tpu_estimator.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Ideally _USE_TPU_KEY should be reserved as well. However there are already
aaiaegggc,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/tpu/tpu_estimator.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,unused; required in function signature.
aaiaehadc,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/tpu/training_loop.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Add a dummy output; if needed.
aaiaehdaf,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/training/checkpoint_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Offset is unused for cols (no partitioning).
aaiaehfbd,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/training/gen_training_ops.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed to trigger the call to _set_call_cpp_shape_fn.
aaiafbgea,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/training/monitored_session.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,hack to make it easy to find the saver.  Is there a better way?
aaiafbhfe,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/training/optimizer.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,since they do most of their dependency management themselves (slot
aaiafbhhd,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/training/optimizer.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(jhseu): Figure out why GradientTape's gradients don't require loss
aaiafbhid,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/training/optimizer.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,as needed.
aaiafbicf,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/training/optimizer.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,No slots needed by default
aaiafcaab,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/training/saver.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Before .save() finishes; they will be (hopefully; atomically) renamed to
aaiafcbfc,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/training/saving/saveable_object.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(allenl): Consider another way to gather device information. Lower
aaiafcfbd,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/training/tracking/base.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,added or deleted. Stores unused attributes so an exception can be
aaiafcfdc,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/training/tracking/base.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,eventually depend. Maps local name -> CheckpointPosition list. Optimizers
aaiafcfec,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/training/tracking/base.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,restore-on-create when executing eagerly; and so is unused when graph
aaiafchbi,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/training/tracking/graph_view.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(b\/110718070): Fix Keras imports.
aaiafchdj,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/training/tracking/layer_utils.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\"Utilities related to layer\/model functionality.\"\"\"
aaiafchff,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/training/tracking/layer_utils.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,once __init__ files no longer require all of tf.keras to be imported together.
aaiafcihi,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/training/tracking/util.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(allenl): If this ends up in a public API; consider adding LINT.IfChange
aaiafcjea,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/training/tracking/util.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,when graph building. Creating it earlier would lead to double
aaiafdabc,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/training/training_util.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,this run. This is needed for example Estimator makes all model_fn build
aaiafdadh,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/training/warm_starting_util.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(eddz): Fix functionality for rank-1 Variables (like FC biases).
aaiafdaej,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/training/warm_starting_util.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,exception if any are unused by the end of the loop.  It is easy to misname
aaiafddci,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/util/lazy_loader.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,LazyLoader; lookups are efficient (__getattr__ is only called on lookups
aaiafdecf,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/python/util/protobuf/compare.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Some Python versions would perform regular diff instead of multi-line
aaiafdghh,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/tools/compatibility/ast_edits.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,ast.Call's constructor is really picky about how many arguments it
aaiafdhci,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/tools/compatibility/ipynb.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,the smallest -> here is another hack
aaiafdhda,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/tools/compatibility/ipynb.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,that's the hack for this
aaiafdidi,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/tools/compatibility/tf_upgrade_v2.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Lots of unused arguments below; since these are called in a standard manner.
aaiafdiff,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow/tools/compatibility/tf_upgrade_v2.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Maybe it was a positional arg
aaiafebbb,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/contrib/estimator/python/estimator/dnn_with_layer_annotations.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Annotate feature columns.
aaiafebbc,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/contrib/estimator/python/estimator/dnn_with_layer_annotations.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(cyfoo): Find a better way to serialize and deserialize
aaiafecad,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/contrib/estimator/python/estimator/head.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Unused for this head.
aaiafedbf,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/contrib/estimator/python/estimator/replicate_model_fn.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,If needed; alert that TowerOptimizer needs to be used with model_fn.
aaiafedfg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/contrib/estimator/python/estimator/rnn.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Unused. Part of the rnn_cell_fn interface since user specified functions
aaiafedgh,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/contrib/estimator/python/estimator/rnn_v2.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,\"\"\"Recurrent Neural Network estimators. ||  || NOTE: This API is under development; is subject to change; and should not be || relied upon! || This version will eventually be moved to TensorFlow core and the original one be || deprecated. || \"\"\"
aaiafedif,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/contrib/estimator/python/estimator/rnn_v2.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Unused. Part of the rnn_cell_fn interface since user specified functions
aaiafeeia,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/python/estimator/canned/baseline.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Unused.
aaiafefbi,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/python/estimator/canned/boosted_trees.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,columns.
aaiafefce,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/python/estimator/canned/boosted_trees.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,An ugly hack - for categorical features; in order to have lookup tables
aaiafefcg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/python/estimator/canned/boosted_trees.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,cat columns separately (it is not as expensive as bucketizing) and then
aaiafefch,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/python/estimator/canned/boosted_trees.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,merge these processed features with other columns in cond branches.
aaiafefgf,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/python/estimator/canned/boosted_trees.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Split columns into categorical and other columns.
aaiafefgj,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/python/estimator/canned/boosted_trees.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Variable that determines whether bias centering is needed.
aaiafefhd,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/python/estimator/canned/boosted_trees.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(youngheek): perhaps storage could be optimized by storing stats
aaiafejbd,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/python/estimator/canned/head.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,a local convention without any special meaning.
aaiafejif,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/python/estimator/canned/kmeans.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,in clustering_ops is unused; and some of the values below are lists of a
aaiaffadg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/python/estimator/canned/linear.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Iterate over all feature columns and create appropriate lists for dense
aaiaffaeb,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/python/estimator/canned/linear.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,For real valued columns; the variables list contains exactly one
aaiaffbif,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/python/estimator/canned/linear_optimizer/python/utils/sharded_mutable_dense_hashtable.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,shared_name attribute value (but is better than the alternative of
aaiaffbjc,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/python/estimator/canned/linear_optimizer/python/utils/sharded_mutable_dense_hashtable.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,unused
aaiaffbje,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/python/estimator/canned/linear_optimizer/python/utils/sharded_mutable_dense_hashtable.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(rohanj): This should subclass Checkpointable and implement
aaiaffccg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/python/estimator/canned/linear_testing_utils.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,unused; required by Fn signature.
aaiafffgj,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/python/estimator/canned/saved_model_estimator.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,If the key ends with the value suffix; and there is a corresponding
aaiaffgad,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/python/estimator/canned/timeseries/ar_model.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(allenl; agarwal): Consider better ways of warm-starting predictions.
aaiaffgbc,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/python/estimator/canned/timeseries/ar_model.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Transpose to move batch dimension outside.
aaiaffgdd,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/python/estimator/canned/timeseries/ar_model.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,unused
aaiaffghf,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/python/estimator/canned/timeseries/estimators.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,endogenous features can be passed in as FeatureColumns. Move ARModel's loss
aaiaffhbi,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/python/estimator/canned/timeseries/head.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,needed for custom metrics.
aaiaffhdd,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/python/estimator/canned/timeseries/head.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,times. Even though we're predicting eventually; we need values for
aaiaffijd,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/python/estimator/canned/timeseries/model.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,input_from_feature_columns does not support completely unknown
aaiaffije,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/python/estimator/canned/timeseries/model.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,feature shapes; so we save on a bit of logic and provide a better
aaiaffijh,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/python/estimator/canned/timeseries/model.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,with only batch and window dimensions); input_from_feature_columns
aaiaffjgd,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/python/estimator/canned/timeseries/state_management.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,unused
aaiafgbjd,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/python/estimator/canned/v1/linear_testing_utils_v1.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,unused; required by Fn signature.
aaiafgejg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/python/estimator/estimator.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(sourabhbajaj): Remove this hack once we migrate the other strategies
aaiafgfcj,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/python/estimator/estimator.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO(priyag): Fix eval step hook to account for steps_per_run.
aaiafghcb,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/python/estimator/head/base_head.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,a local convention without any special meaning.
aaiafghia,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/python/estimator/head/binary_class_head.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Unused for this head.
aaiafgida,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/python/estimator/head/multi_class_head.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Unused for this head.
aaiafgjac,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/python/estimator/head/multi_label_head.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Unused for this head.
aaiafgjcj,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/python/estimator/head/regression_head.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Unused for this head.
aaiafgjfg,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/python/estimator/head/sequential_head.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Verify and set feature columns (to be flattened).
aaiafhdef,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/python/estimator/training.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,unused; required by signature.
aaiafhdgd,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/python/estimator/training.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,the training. When the training ends; a new checkpoint is generated; which
aaiafhdhf,ryfeus/gcf-packs,tensorflow2.0/source/tensorflow_estimator/python/estimator/training.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Start in-process TensorFlow server if needed. It's important to start the
aaiafheeg,ryfeus/gcf-packs,tensorflow2.0/source/werkzeug/__init__.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,once; even if unused.
aaiafheig,ryfeus/gcf-packs,tensorflow2.0/source/werkzeug/_reloader.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Flask works around previous behavior by putting
aaiafheii,ryfeus/gcf-packs,tensorflow2.0/source/werkzeug/_reloader.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,TODO remove this once Flask no longer misbehaves
aaiafhfcc,ryfeus/gcf-packs,tensorflow2.0/source/werkzeug/contrib/cache.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX unicode review
aaiafhfhg,ryfeus/gcf-packs,tensorflow2.0/source/werkzeug/contrib/wrappers.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,: You may however want to set this to utf-8 to better support
aaiafhfjb,ryfeus/gcf-packs,tensorflow2.0/source/werkzeug/datastructures.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,better quality or same quality but more specific => better match
aaiafhgbi,ryfeus/gcf-packs,tensorflow2.0/source/werkzeug/datastructures.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,SpooledTemporaryFile doesn't implement IOBase; get the
aaiafhghh,ryfeus/gcf-packs,tensorflow2.0/source/werkzeug/debug/repr.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,if the repr looks like a standard string; add subclass info if needed
aaiafhhde,ryfeus/gcf-packs,tensorflow2.0/source/werkzeug/formparser.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,If the line ends with windows CRLF we write everything except
aaiafhhfe,ryfeus/gcf-packs,tensorflow2.0/source/werkzeug/http.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,unused
aaiafhhfj,ryfeus/gcf-packs,tensorflow2.0/source/werkzeug/http.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX: utf8 fallback?
aaiafhhgj,ryfeus/gcf-packs,tensorflow2.0/source/werkzeug/http.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX: validate
aaiafhhhf,ryfeus/gcf-packs,tensorflow2.0/source/werkzeug/http.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,continuation ends.
aaiafhiab,ryfeus/gcf-packs,tensorflow2.0/source/werkzeug/http.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX: In theory all of these parameters that are not marked with `None`
aaiafhida,ryfeus/gcf-packs,tensorflow2.0/source/werkzeug/middleware/proxy_fix.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,todo: remove deprecated keys
aaiafhigc,ryfeus/gcf-packs,tensorflow2.0/source/werkzeug/routing.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,this seems like a silly case to ever come up but:
aaiafhjfg,ryfeus/gcf-packs,tensorflow2.0/source/werkzeug/serving.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,ways to implement this.
aaiafhjjc,ryfeus/gcf-packs,tensorflow2.0/source/werkzeug/test.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX: py2v3 review
aaiafiadi,ryfeus/gcf-packs,tensorflow2.0/source/werkzeug/urls.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,we want to fix this up manually:
aaiafibcd,ryfeus/gcf-packs,tensorflow2.0/source/werkzeug/wrappers/base_request.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,: implement similar validation in the proxy (if application is being run
aaiafibec,ryfeus/gcf-packs,tensorflow2.0/source/werkzeug/wrappers/base_request.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,XXX: this should eventually be deprecated.
aaiaficbf,ryfeus/gcf-packs,tensorflow2.0/source/werkzeug/wrappers/json.py,44d36f9e5045fd859ee188d74dc31f3921ad4796,STILL_EXISTS,Needed for Python < 3.6
aaiafichc,cschoeller/pycandle,layers/conv.py,2a4897d80ee3cdb38214ba5ca5d611af05541462,STILL_EXISTS,TODO be aware of contiguous (problematic?)
aaiafidad,cschoeller/pycandle,torch/callbacks.py,2a4897d80ee3cdb38214ba5ca5d611af05541462,661117b8d4739a75df4829ba863205dc32354a6f,#TODO stop training
aaiafidgg,cschoeller/pycandle,torch/model_trainer.py,23948f3da1bd057477cee8bcedbe8d298b4e9d60,STILL_EXISTS,move to GPU
aaiafidje,cschoeller/pycandle,torch/model_trainer.py,d161299b86d1467b77d8a3c3f49f2fabd3d4a6d2,69a15c48ae50a19e80dd8d8492bdf97f07bd04f8,batch_x; batch_y = self._recursive_to_cuda(batch_x); self._recursive_to_cuda(batch_y) # move to GPU
aaiafiebf,cschoeller/pycandle,training/callbacks.py,28c1f772edfe442fdd4854303d33b81218421788,STILL_EXISTS,TODO print per param group?
aaiafieii,karolzak/keras-unet,keras_unet/__init__.py,70978047d409773d45cbd06a1571d4ac38d281d6,STILL_EXISTS,TODO add __all__
aaiafifgb,yxonic/fret,fret/common.py,988d429bd1a98046d4ea4d1f1ae3df93b1a12ce3,4dfffb8514aa19f0559d2e0ce86c7dbc0777f685,TODO: change default value to None if config not in kwargs
aaiafifgc,yxonic/fret,fret/common.py,988d429bd1a98046d4ea4d1f1ae3df93b1a12ce3,STILL_EXISTS,TODO: add arg style (java\/gnu)
aaiafifgj,yxonic/fret,fret/common.py,919fc3f9fd2d95c4c0d2fdf9d3c4e09728712eb7,b01553a08b738a23718fe07eb339d4d023e6e6c6,TODO: make an util class for this
aaiafifid,yxonic/fret,fret/app.py,b01553a08b738a23718fe07eb339d4d023e6e6c6,523404976814bee571a6fb8458fa614285dae6e3,TODO: make an util class for this
aaiafifie,yxonic/fret,fret/app.py,b01553a08b738a23718fe07eb339d4d023e6e6c6,STILL_EXISTS,TODO: add arg style (java\/gnu); better logic
aaiafiheh,yxonic/fret,docs/source/conf.py,db9862b26cdd564efc09b355d2d636db1ff4eae5,STILL_EXISTS,-- Options for todo extension ----------------------------------------------
aaiafihei,yxonic/fret,docs/source/conf.py,db9862b26cdd564efc09b355d2d636db1ff4eae5,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaiafihii,yxonic/fret,fret/common.py,ff675c48bd873ba39020087e32e476e2f9559a70,91e8a3596e2cc3a388a16b9ff0840abf6edd3a4f,TODO: accurate name search
aaiafiifi,yxonic/fret,fret/workspace.py,91e8a3596e2cc3a388a16b9ff0840abf6edd3a4f,STILL_EXISTS,TODO: accurate name search
aaiafijgf,keithjjones/malgazer,library/files.py,c4f135a1ca6ee8639a2f7b3aefc4c4f324cc75e2,STILL_EXISTS,TODO: Above may be section.SizeOfRawData - test this.
aaiafijhh,keithjjones/malgazer,library/files.py,a2195888c71455f07290978f9bb5abe35209256d,STILL_EXISTS,TODO: Above may be section.SizeOfRawData - test this.
aaiafjaab,keithjjones/malgazer,batch_statistics.py,4b5a9d9e2e6f0e4add0ff5162603a78d080b8be9,STILL_EXISTS,Create the directory if needed...
aaiafjaeg,keithjjones/malgazer,batch_anomaly.py,1b3bbbf561823f8d6a2ac0c6f46daf49576b36c0,STILL_EXISTS,Create the directory if needed...
aaiafjafi,keithjjones/malgazer,old/batch_anomaly.py,57eebe3fe647cdba63539dc78826391abb7dea97,STILL_EXISTS,Create the directory if needed...
aaiafjaif,keithjjones/malgazer,batch_entropy.py,27c158e5bd223860b01c4b82103680075602c23f,73b32190c5d4b1e0951ddae1f4b2a1a4cf4ae0d0,# Create the directory if needed...
aaiafjcgc,keithjjones/malgazer,batch_entropy.py,73b32190c5d4b1e0951ddae1f4b2a1a4cf4ae0d0,STILL_EXISTS,# Create the DB directory if needed...
aaiafjcgg,keithjjones/malgazer,batch_entropy.py,73b32190c5d4b1e0951ddae1f4b2a1a4cf4ae0d0,STILL_EXISTS,Create the directory if needed...
aaiafjgab,keithjjones/malgazer,batch_entropy.py,a24ef93e1d97b11f6201f372a6bc8bb75298a651,4c63d2d84f922f2e23e402e7e4a89c3af16ffd42,TODO: May add this back in later...
aaiafjgad,keithjjones/malgazer,library/files.py,7375b9b341119af23d807572fd25ba5310d96c55,46b7f4dd8105247df38ab334402382c363321c18,# TODO: Fix this up later...
aaiafjgch,keithjjones/malgazer,library/files.py,7375b9b341119af23d807572fd25ba5310d96c55,STILL_EXISTS,TODO: Fix this up later...
aaiafjhcf,keithjjones/malgazer,library/files.py,2758264b2d1412554691dd8f9c7f9e6a7428e7a8,8c6244d17d07a04c1bc6dff672820c282de3e473,TODO: Above may be section.Misc_VirtualSize - test this.
aaiafjiae,keithjjones/malgazer,library/utils.py,32417c6a69b69a5c6bf6e78636748007e2c9d81a,8c6244d17d07a04c1bc6dff672820c282de3e473,Create the directory if needed...
aaiagabfe,keithjjones/malgazer,library/utils.py,a97351ae49174da3381a8a75c55c9af742768f77,8c6244d17d07a04c1bc6dff672820c282de3e473,Create the directory if needed...
aaiagadfa,keithjjones/malgazer,library/ml.py,c3fd1e3dbd6ca2a9696aa9d1204390128e8a0d81,bdef354704c5aa46c22ea0109944b9b6a52bb41b,Maybe max instead?
aaiagagdd,keithjjones/malgazer,build_classifications_from_vt_data.py,efe1034f6f4127eb682081485c080f3defb1f2c5,STILL_EXISTS,hashes.columns = ['index']
aaiagagdi,keithjjones/malgazer,build_classifications_from_vt_data.py,efe1034f6f4127eb682081485c080f3defb1f2c5,STILL_EXISTS,worm_hashes.columns = ['sha256']
aaiagaged,keithjjones/malgazer,build_classifications_from_vt_data.py,efe1034f6f4127eb682081485c080f3defb1f2c5,STILL_EXISTS,trojan_hashes.columns = ['sha256']
aaiagagei,keithjjones/malgazer,build_classifications_from_vt_data.py,efe1034f6f4127eb682081485c080f3defb1f2c5,STILL_EXISTS,backdoor_hashes.columns = ['sha256']
aaiagagfd,keithjjones/malgazer,build_classifications_from_vt_data.py,efe1034f6f4127eb682081485c080f3defb1f2c5,STILL_EXISTS,virus_hashes.columns = ['sha256']
aaiagagfj,keithjjones/malgazer,build_classifications_from_vt_data.py,efe1034f6f4127eb682081485c080f3defb1f2c5,STILL_EXISTS,pua_hashes.columns = ['sha256']
aaiagaggf,keithjjones/malgazer,build_classifications_from_vt_data.py,efe1034f6f4127eb682081485c080f3defb1f2c5,STILL_EXISTS,ransom_hashes.columns = ['sha256']
aaiagajbf,yubin-park/bonsai-dt,bonsai/core/_utils.py,b1ca25aef72162b278f34d9d8ac6d537308b4fae,STILL_EXISTS,output tree row - 8 columns:
aaiagajee,yubin-park/bonsai-dt,research/preprocessing.py,b1ca25aef72162b278f34d9d8ac6d537308b4fae,STILL_EXISTS,0. divide columns to 1) categorical and 2) numeric features
aaiagbacf,yubin-park/bonsai-dt,bonsai/ensemble/paloforest.py,33337d36fe76b3ea0ce6c91800a5ca4a6781143d,STILL_EXISTS,subsample rate for the columns
aaiagbagb,yubin-park/bonsai-dt,bonsai/core/bonsaic.py,87da5b768b50b7cb40d0c2c5c1b028a0db9294a1,STILL_EXISTS,TODO: smart guidance on \"n_jobs\"
aaiagbahi,yubin-park/bonsai-dt,research/utils.py,9c52999229f91e4b103123fd1803e5ca944ade25,STILL_EXISTS,0. divide columns to 1) categorical and 2) numeric features
aaiagbaji,funkey/gunpowder,gunpowder/elastic_augmentation.py,2d87c4d8dd8a1a6eb9046e5c6d097692f762b318,STILL_EXISTS,get bounding box of needed data (plus save padding of 1)
aaiagbbbj,funkey/gunpowder,gunpowder/train.py,0136940ff337fc4cf197690983f1e50779062766,2ab62e0d25c53b69291cc382f614bfda340eade1,TODO: are the following two lines needed here?
aaiagbbgi,funkey/gunpowder,gunpowder/train.py,a992fe8bae14393d2bfcec82fca59ace504c9e4b,STILL_EXISTS,TODO: add prediction; gradient; loss
aaiagbbgj,funkey/gunpowder,gunpowder/train.py,a992fe8bae14393d2bfcec82fca59ace504c9e4b,STILL_EXISTS,Why don't we update gt_affinities in the same way?
aaiagbbha,funkey/gunpowder,gunpowder/train.py,a992fe8bae14393d2bfcec82fca59ace504c9e4b,STILL_EXISTS,-> not needed
aaiagbcgi,funkey/gunpowder,examples/segem.py,b0cddfd863e3d62ac62bb6d12f4077fbc3483bd1,STILL_EXISTS,Needed for real training:
aaiagbcgj,funkey/gunpowder,examples/segem.py,b0cddfd863e3d62ac62bb6d12f4077fbc3483bd1,STILL_EXISTS,Needed for real training (get it from gunpowder.caffe):
aaiagbdbc,funkey/gunpowder,gunpowder/nodes/elastic_augmentation.py,ff70859b1e5428ec9f41b392b1367726d5c185bc,e55dba87407c09035a747e452d813cc32dff91a8,TODO: why copy?
aaiagbdca,funkey/gunpowder,gunpowder/nodes/padding.py,ff70859b1e5428ec9f41b392b1367726d5c185bc,STILL_EXISTS,TODO: this should be moved to super class; this should hold for any
aaiagbdej,funkey/gunpowder,examples/autocontext/train.py,54e8e2823be78a26f3902934ef462fcce541cd6e,STILL_EXISTS,TODO: implement same_process
aaiagbdfa,funkey/gunpowder,examples/autocontext/train.py,54e8e2823be78a26f3902934ef462fcce541cd6e,STILL_EXISTS,TODO: implement get_net()
aaiagbdfb,funkey/gunpowder,examples/autocontext/train.py,54e8e2823be78a26f3902934ef462fcce541cd6e,STILL_EXISTS,TODO: Predict needs to change size of RAW; and add PRED_AFFINITIES if
aaiagbeda,funkey/gunpowder,gunpowder/nodes/hdf5_source.py,f1b23401c100581647eafeccb7bc044e1c35974f,1379f40c08b51d12f827926089cc4df65bb3544e,TODO: so far assumed that all points have resolution of raw volume
aaiagbege,funkey/gunpowder,gunpowder/nodes/hdf5_source.py,af35ca3357fde1b2bafad2aeb71778ede12a1f15,1379f40c08b51d12f827926089cc4df65bb3544e,TODO: different volumes can have different offsets; needs to be added.
aaiagbfja,funkey/gunpowder,docs/build/conf.py,05c8c9989b3b29228c07f63f0e844c7dab58018c,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaiagbhai,funkey/gunpowder,gunpowder/nodes/hdf5_source.py,24b692b743ce322fbcdba98564ab8bdff3cfb495,664e63d0c73de6286a68a40091b247a2af3d040a,TODO: different volumes can have different offsets; needs to be added.
aaiagbhbd,funkey/gunpowder,gunpowder/nodes/hdf5_source.py,24b692b743ce322fbcdba98564ab8bdff3cfb495,664e63d0c73de6286a68a40091b247a2af3d040a,TODO: so far assumed that all points have resolution of raw volume
aaiagbhda,funkey/gunpowder,gunpowder/nodes/hdf5_source.py,3be8c5bb8b5b15178c383004bde10d7c96dddf93,STILL_EXISTS,TODO: different volumes can have different offsets; needs to be added.
aaiagbhde,funkey/gunpowder,gunpowder/nodes/hdf5_source.py,3be8c5bb8b5b15178c383004bde10d7c96dddf93,STILL_EXISTS,TODO: so far assumed that all points have resolution of raw volume
aaiagbhjc,funkey/gunpowder,gunpowder/nodes/elastic_augment.py,599fd43f367a03ea4201eced4a6812b7544ad388,STILL_EXISTS,TODO: How to avoid having to allocate a new volume each time (rather reuse; but difficult since shape is alternating)
aaiagbhjf,funkey/gunpowder,gunpowder/nodes/elastic_augment.py,599fd43f367a03ea4201eced4a6812b7544ad388,STILL_EXISTS,TODO: This is partially a duplicate of add_gt_binary_map_points:get_binary_map; refactor!
aaiagbhjg,funkey/gunpowder,gunpowder/nodes/elastic_augment.py,599fd43f367a03ea4201eced4a6812b7544ad388,STILL_EXISTS,TODO: Consider problem of negative or 0 id; maybe relabel point id list.
aaiagbhjh,funkey/gunpowder,gunpowder/nodes/elastic_augment.py,599fd43f367a03ea4201eced4a6812b7544ad388,STILL_EXISTS,TODO: does not consider problem of having the same location for multiple point ids. Currently; the id is overwritten.
aaiagbidj,funkey/gunpowder,examples/fib25/mknet.py,f4c8471fdad878a9b3f0bbb34be746226c6cb262,STILL_EXISTS,Silence the not needed data and label integer values
aaiagbifj,funkey/gunpowder,examples/fib25/train.py,f4c8471fdad878a9b3f0bbb34be746226c6cb262,STILL_EXISTS,input and output shapes of the network; needed to formulate matching batch
aaiagcadc,funkey/gunpowder,examples/fib25/process.py,ccd1a24fcd191d3a21f64da62de1b78ea9f5b6a4,STILL_EXISTS,input and output sizes of the network (needed to formulate Chunk requests
aaiagccec,funkey/gunpowder,examples/heartbeat/train.py,bcea6a67f2f70c37ddd67400ae23aa4151656ea7,STILL_EXISTS,input and output shapes of the network; needed to formulate matching batch
aaiagccgj,funkey/gunpowder,gunpowder/nodes/prepare_malis.py,da7cb3de651780781a4cdb52d0a9b004378a96b8,STILL_EXISTS,Why don't we update gt_affinities in the same way?
aaiagccha,funkey/gunpowder,gunpowder/nodes/prepare_malis.py,da7cb3de651780781a4cdb52d0a9b004378a96b8,STILL_EXISTS,-> not needed
aaiagcgci,funkey/gunpowder,gunpowder/tensorflow/nodes/train.py,ed2d8c9480475d12ce178e35e343693921dac076,41e016889422025893e997980d1699441107855b,TODO: use gpu
aaiagcgda,funkey/gunpowder,gunpowder/tensorflow/nodes/train.py,ed2d8c9480475d12ce178e35e343693921dac076,f056e3654f7d25c8fb049b18fa04e9db40df4093,TODO: how to get gradients from tf?
aaiagcgdh,funkey/gunpowder,gunpowder/tensorflow/nodes/train.py,ed2d8c9480475d12ce178e35e343693921dac076,STILL_EXISTS,TODO: get iteration
aaiagcghj,funkey/gunpowder,tests/cases/crop.py,a786f7c90288096d23be9adad13ed7d95460af68,21faae3ce71f928a4f0fd33b37af32f943de5cc1,move request to provided crop rois
aaiagcihi,funkey/gunpowder,gunpowder/nodes/defect_augment.py,852bd900359861443bedc4978aa36c483d391c8c,STILL_EXISTS,in case we needed to change the ROI due to a deformation augment;
aaiagdbah,funkey/gunpowder,gunpowder/nodes/add_long_range_affinities.py,2b2820952180dce88506eec643976f74d86e131c,STILL_EXISTS,grow labels ROI to accomodate padding TODO: vol 2
aaiagdddd,funkey/gunpowder,gunpowder/nodes/rasterize_points.py,d414d58b78e4d69d09fc39937f0b5a5876cd203b,STILL_EXISTS,TODO: optionally add stay_inside_arraytype to request
aaiagdeed,funkey/gunpowder,gunpowder/contrib/nodes/dvid_points_source.py,0accf36f1b26c767e61104a54146970a56ad27ea,STILL_EXISTS,TODO: This seems broken. There is code involving a voxel size; but points
aaiagdhbf,funkey/gunpowder,gunpowder/nodes/rasterize_points.py,19f92418ca888d84610a4a59427a50577342bebb,STILL_EXISTS,fix bg\/fg labelling if requested
aaiageice,funkey/gunpowder,gunpowder/nodes/rasterize_points.py,a9e90a921a37df572f29b7da8f5be1f3f9599748,STILL_EXISTS,use more efficient bitwise operation when possible
aaiagfafb,funkey/gunpowder,gunpowder/tensorflow/nodes/predict.py,59cc8014b2a3e05d9c25a5ca90742e2abe1507cd,10f6abae0d145cc3ba167198f7cdb31bcc5cc54a,TODO: is the server still needed?
aaiagfagi,funkey/gunpowder,gunpowder/tensorflow/nodes/predict.py,5b86810f499e9e14c43fa06592a3fb4211beef52,STILL_EXISTS,TODO: is the server still needed?
aaiagfahb,funkey/gunpowder,gunpowder/nodes/jitter.py,e430647897340b9b30d51d3ce45cdfcbbc86fab9,STILL_EXISTS,TODO: consider voxel size
aaiagfahc,funkey/gunpowder,gunpowder/nodes/jitter.py,e430647897340b9b30d51d3ce45cdfcbbc86fab9,7b9a4103b29da96a53a6709d9b7230af9b18d82c,TODO: is it possible that different parts of the request could have different ndim?
aaiagfehe,funkey/gunpowder,tests/cases/specified_location.py,017d80093ff80608d2e325a2c3ec18ced5f6d4da,STILL_EXISTS,Unclear what result should be; so no errors means passing
aaiagffad,funkey/gunpowder,gunpowder/array.py,06a64031dd1bcb22437d4a6a3594c0bf3f9a4bee,STILL_EXISTS,part of self have to be replaced; a copy is needed
aaiagffci,funkey/gunpowder,gunpowder/nodes/pad.py,b4a910da9c6524232162e7b612c5dad818349a74,STILL_EXISTS,TODO: remove this?
aaiagffde,funkey/gunpowder,gunpowder/nodes/noise_augment.py,a748dfd6d7c5f31d974f76fb2d2f6c1c24cea26b,0c30d333fda9e9af5803686de6742e61a215cc94,TODO: move all randomness into the prepare method
aaiagfffc,funkey/gunpowder,gunpowder/batch.py,8b823b04437bb808ac41ffb895ab1f84e97cf81a,STILL_EXISTS,TODO: What is the goal of `val.spec.roi is None`? Why should that
aaiagfffe,funkey/gunpowder,gunpowder/graph.py,ba9c87dcaab25831401c5814b2316059ef5da7c2,cdeda456932c3cddd591cc86e8a5fa75c4f0b216,''' || from .points_base import PointsBase; PointBase; PointsKey; PointsKeys || from .points_spec import PointsSpec || from .roi import Roi || from .coordinate import Coordinate ||  || from copy import deepcopy || from typing import Dict; List; Tuple; Optional; Set || import logging || import numpy as np || import networkx as nx ||  ||  || logger = logging.getLogger(__name__) ||  ||  || class SpatialGraph(nx.DiGraph): ||     \"\"\" ||     An extension of a DiGraph that assumes each point has a spatial coordinate. ||     Adds utility functions such as cropping to an ROI; shifting all points by ||     an offset; and relabelling connected components. ||     \"\"\" ||  ||     def crop(self; roi: Roi; copy: bool = False; relabel_nodes=False): ||         \"\"\" ||         Remove all nodes not in this roi. ||         \"\"\" ||  ||         # Copy self if needed ||         if copy: ||             cropped = deepcopy(self) ||         else: ||             cropped = self ||  ||         if len(cropped.nodes) == 0: ||             return cropped ||  ||         # Group nodes based on location ||         all_nodes = set(cropped.nodes.keys()) ||         to_keep = set( ||             key ||             for key in cropped.nodes.keys() ||             if roi.contains(cropped.nodes[key][\"location\"]) ||         ) ||         to_remove = all_nodes - to_keep ||  ||         # Get new boundary nodes and edges ||         new_nodes; new_edges = self._handle_boundaries( ||             to_keep; roi; next_node_id=max(all_nodes) + 1 ||         ) ||  ||         # Handle node and edge changes ||         for node in to_remove: ||             cropped.remove_node(node) ||         for node_id; attrs in new_nodes.items(): ||             cropped.add_node(node_id; **attrs) ||         for u; v in new_edges: ||             if u not in cropped.nodes or v not in cropped.nodes: ||                 raise Exception(\"Trying to add an edge between non-existant points!\") ||             cropped.add_edge(u; v) ||  ||         if relabel_nodes: ||             cropped = nx.convert_node_labels_to_integers(cropped) ||  ||         return cropped ||  ||     def crop_out(self; roi: Roi; copy: bool = False; relabel_nodes=False): ||         \"\"\" ||         Remove all nodes in this roi. ||         \"\"\" ||  ||         # Copy self if needed ||         if copy: ||             cropped = deepcopy(self) ||         else: ||             cropped = self ||  ||         if len(cropped.nodes) == 0: ||             return cropped ||  ||         # Group nodes based on location ||         all_nodes = set(cropped.nodes.keys()) ||         to_remove = set( ||             key ||             for key in cropped.nodes.keys() ||             if roi.contains(cropped.nodes[key][\"location\"]) ||         ) ||         to_keep = all_nodes - to_remove ||  ||         # Get new boundary nodes and edges ||         new_nodes; new_edges = self._handle_boundaries( ||             to_keep; roi; next_node_id=max(all_nodes) + 1 ||         ) ||  ||         # Handle node and edge changes ||         for node in to_remove: ||             cropped.remove_node(node) ||         for node_id; attrs in new_nodes.items(): ||             cropped.add_node(node_id; **attrs) ||         for u; v in new_edges: ||             if u not in cropped.nodes or v not in cropped.nodes: ||                 raise Exception(\"Trying to add an edge between non-existant points!\") ||             cropped.add_edge(u; v) ||  ||         if relabel_nodes: ||             cropped = nx.convert_node_labels_to_integers(cropped) ||  ||         return cropped ||  ||     def merge(self; other; combine_overlapping=True): ||         \"\"\" ||         Merge this graph with another graph. ||         Each PointBase will recieve a new id in range ||         0; ...; len(self.nodes) + len(other.nodes) ||  ||         if combine_overlapping = True; nodes that ||         share the same location will be merged; keeping ||         edges from both. ||         \"\"\" ||         combined = nx.disjoint_union(self; other) ||  ||         if combine_overlapping: ||             combined.merge_overlapping_points() ||  ||         return combined ||  ||      ||  ||     def merge_overlapping_points(self): ||         # TODO: this could probably be improved by using scipy.spatial.cKDTree. ||  ||         \"\"\" ||         it is not enough to just check location of a single node for overlap; ||         multiple neurons that get close to each other might get merged. ||         Instead; check that the neighbors overlap as well. for smaller crops; ||         some neighbors may be cut off. So check that for any two points; ||         they are the same if their locations match; but also that the union ||         of the sets of neighbor locations for both nodes; is no longer than ||         the length of the larger set of neighbor locations. i.e. they contain ||         only neighbors that overlap; or are unseen by the potential match. ||         \"\"\" ||         locations = {} ||         replacements = {} ||         for node_id; node_attrs in self.nodes.items(): ||             loc = node_attrs[\"location\"] ||             # convert to int to get hashable value ||             # multiply by 1000 to get higher precision than rounding ||             loc = tuple(int(x * 1000) for x in loc) ||             if loc not in locations: ||                 locations[loc] = {node_id: set()} ||                 for pred in self.pred[node_id]: ||                     pred_loc = self.nodes[pred][\"location\"] ||                     pred_loc = tuple(int(x * 1000) for x in pred_loc) ||                     locations[loc][node_id].add(pred_loc) ||                 for succ in self.succ[node_id]: ||                     succ_loc = self.nodes[succ][\"location\"] ||                     succ_loc = tuple(int(x * 1000) for x in succ_loc) ||                     locations[loc][node_id].add(succ_loc) ||             else: ||                 neighbor_locations = set() ||                 for pred in self.pred[node_id]: ||                     pred_loc = self.nodes[pred][\"location\"] ||                     pred_loc = tuple(int(x * 1000) for x in pred_loc) ||                     neighbor_locations.add(pred_loc) ||                 for succ in self.succ[node_id]: ||                     succ_loc = self.nodes[succ][\"location\"] ||                     succ_loc = tuple(int(x * 1000) for x in succ_loc) ||                     neighbor_locations.add(succ_loc) ||                 matched = False ||                 for potential_match; neighbor_locs in locations[loc].items(): ||                     if len(neighbor_locs | neighbor_locations) <= max( ||                         len(neighbor_locs); len(neighbor_locations) ||                     ): ||                         replacements[node_id] = potential_match ||                         locations[loc][potential_match] = ( ||                             neighbor_locs | neighbor_locations ||                         ) ||                         matched = True ||                 if not matched: ||                     locations[loc][node_id] = neighbor_locations ||         nx.relabel_nodes(self; replacements; copy=False) ||         self.remove_edges_from(nx.selfloop_edges(self)) ||  ||     def shift(self; offset: Coordinate): ||         \"\"\" ||         Shift every node's location by some offset ||         \"\"\" ||         for point_attrs in self.nodes.values(): ||             point_attrs[\"location\"] += offset ||  ||     def _relabel_connected_components(self): ||         \"\"\" ||         Assign a label to each connected component ||         \"\"\" ||         for i; connected_component in enumerate(nx.weakly_connected_components(self)): ||             for point in connected_component: ||                 self.nodes[point][\"component\"] = i ||         return self ||  ||     def _handle_boundaries(self; contained: Set[int]; roi: Roi; next_node_id: int): ||         new_points = {} ||         new_edges = [] ||         for u; v in self.edges: ||             u_in; v_in = u in contained; v in contained ||             if u_in != v_in: ||                 in_id; out_id = (u; v) if u_in else (v; u) ||                 in_attrs; out_attrs = (self.nodes[in_id]; self.nodes[out_id]) ||                 new_location = self._roi_intercept( ||                     in_attrs[\"location\"]; out_attrs[\"location\"]; roi ||                 ) ||                 if not all(np.isclose(new_location; in_attrs[\"location\"])): ||                     new_attrs = deepcopy(out_attrs) ||                     new_attrs[\"location\"] = new_location ||                     new_points[next_node_id] = new_attrs ||                     new_edges.append( ||                         (in_id; next_node_id) if u_in else (next_node_id; in_id) ||                     ) ||                     next_node_id += 1 ||         return new_points; new_edges ||  ||     def _roi_intercept( ||         self; inside: np.ndarray; outside: np.ndarray; bb: Roi ||     ) -> np.ndarray: ||         \"\"\" ||         Given two points; one inside a bounding box and one outside; ||         get the intercept between the line and the bounding box. ||         \"\"\" ||  ||         offset = outside - inside ||         distance = np.linalg.norm(offset) ||         assert not np.isclose(distance; 0); \"Offset cannot be zero\" ||         direction = offset \/ distance ||  ||         # `offset` can be 0 on some but not all axes leaving a 0 in the denominator. ||         # `inside` can be on the bounding box; leaving a 0 in the numerator. ||         # `x\/0` throws a division warning; `0\/0` throws an invalid warning (both are fine here) ||         with np.errstate(divide=\"ignore\"; invalid=\"ignore\"): ||             bb_x = np.asarray( ||                 [ ||                     (np.asarray(bb.get_begin()) - inside) \/ offset; ||                     (np.asarray(bb.get_end()) - inside) \/ offset; ||                 ]; ||                 dtype=float; ||             ) ||  ||         with np.errstate(invalid=\"ignore\"): ||             s = np.min(bb_x[np.logical_and((bb_x >= 0); (bb_x <= 1))]) ||  ||         # TODO: Fix this; it seems unsound ||         # subtract a small amount from distance to round towards \"inside\" rather ||         # than attempting to round down if too high and up if too low. ||         new_location = np.floor(inside + s * distance * direction) ||         new_location = np.clip( ||             new_location; bb.get_begin(); bb.get_end() - Coordinate([1; 1; 1]) ||         ) ||         if not bb.contains(new_location): ||             raise Exception( ||                 ( ||                     \"Roi {} does not contain point {}!\ || \" ||                     + \"inside {}; outside: {}; distance: {}; direction: {}; s: {}\" ||                 ).format(bb; new_location; inside; outside; distance; direction; s) ||             ) ||         return new_location ||  ||  || class Point(PointBase): ||     \"\"\"An extension of ``PointBase`` that allows arbitrary attributes ||     to be defined on each point in a networkx friendly way. ||  ||     Args: ||  ||         location (array-like of ``float``): ||  ||             The location of this point. ||     \"\"\" ||  ||     def __init__(self; location; **kwargs): ||         super().__init__(location) ||         self.thaw() ||         self.kwargs = kwargs ||         self.freeze() ||  ||     @property ||     def attrs(self): ||         attrs = {} ||         attrs.update(self.kwargs) ||         attrs[\"location\"] = self.location ||         return attrs ||  ||     def __repr__(self): ||         return str(self.location) ||  ||     def copy(self): ||         return Point(self.location; **self.kwargs) ||  ||  || class Points(PointsBase): ||     \"\"\"A subclass of points that supports edges between points. ||     Uses a networkx DiGraph to store points and cropping the graph ||     generates nodes at the intersection of the boundary box and edges ||     in the graph. ||  ||     Differences between PointsGraph and PointsBase: ||     - crop(): ||         will create new nodes along edges crossing the roi. ||         Given adjacent nodes `a`;`b` with `a` in the `roi`; and `b` out of the `roi`; ||         a new node `c` will be created intersecting the `roi` between ||         `a` and `b`. `c` will not use the same id as `b` to avoid problems ||         encountered when two `inside` nodes are adjacent to one `outside` node ||  ||     Args: ||  ||         data (``dict``; ``int`` -> :class:`PointBase`): ||  ||             A dictionary of IDs mapping to :class:`PointsBase<PointBase>`. ||  ||         edges (``list``; ``tuple``; ``int``): ||  ||             A list of ID pairs (a;b) denoting edges from a to b. ||  ||         spec (:class:`PointsSpec`): ||  ||             A spec describing the data. ||     \"\"\" ||  ||     def __init__( ||         self; ||         data: Dict[int; PointBase]; ||         spec: PointsSpec; ||         edges: Optional[List[Tuple[int; int]]] = None; ||     ): ||         self.spec = spec ||         self._graph = self._initialize_graph(data; edges) ||         self.freeze() ||  ||     @property ||     def graph(self) -> SpatialGraph: ||         return self._graph ||  ||     @property ||     def data(self) -> Dict[int; PointBase]: ||         return self._graph_to_points() ||  ||     def _initialize_graph( ||         self; ||         data: Optional[Dict[int; PointBase]] = None; ||         edges: Optional[List[Tuple[int; int]]] = None; ||     ): ||         self._graph = SpatialGraph() ||         if data is not None: ||             self._add_points(data) ||         if edges is not None: ||             self._add_edges(edges) ||         return self._graph ||  ||     def crop(self; roi: Roi; copy: bool = False): ||         \"\"\" ||         Crop this point set to the given ROI. ||         \"\"\" ||  ||         if copy: ||             cropped = deepcopy(self) ||         else: ||             cropped = self ||  ||         # Crop the graph representation of the point set ||         cropped._graph.crop(roi) ||  ||         # Override the current roi with the original crop roi ||         cropped.spec.roi = roi ||         return cropped ||  ||     def loose_merge(self; points; copy_from_self=False; copy=False): ||         \"\"\" ||         Merge two sets of points together roi's which may or may not overlap ||         partially or fully. ||         The Roi of self will grow to contain all points; and any points in self ||         that have the same id as points in points will be overwritten. ||         \"\"\" ||         if self is points or self._graph is points._graph: ||             return self ||  ||         self.spec.roi = self.spec.roi.union(points.spec.roi) ||         merged_graph = self._graph.merge(points._graph) ||  ||         # replace points ||         if copy: ||             merged = deepcopy(self) ||             merged._graph = deepcopy(merged_graph) ||         else: ||             merged = self ||             merged._graph = merged_graph ||  ||         return merged ||  ||     def merge(self; points; copy_from_self=False; copy=False): ||         \"\"\"Merge these points with another set of points. The resulting points ||         will have the ROI of the larger one. ||  ||         This only works if one of the two point sets is contained in the other. ||         In this case; ``points`` will overwrite points with the same ID in ||         ``self`` (unless ``copy_from_self`` is set to ``True``). ||  ||         A copy will only be made if necessary or ``copy`` is set to ``True``. ||         \"\"\" ||         if self is points or self._graph is points._graph: ||             return self ||  ||         self_roi = self.spec.roi ||         points_roi = points.spec.roi ||  ||         assert self_roi.contains(points_roi) or points_roi.contains( ||             self_roi ||         ); \"Can not merge point sets that are not contained in each other.\" ||  ||         # make sure self contains points ||         if not self_roi.contains(points_roi): ||             return points.merge(self; not copy_from_self; copy) ||  ||         # crop out points in roi from self; replace them with new points ||         self._graph.crop_out(points_roi) ||         merged_graph = self._graph.merge(points._graph) ||  ||         # replace points ||         if copy: ||             merged = deepcopy(self) ||             merged._graph = deepcopy(merged_graph) ||         else: ||             merged = self ||             merged._graph = merged_graph ||  ||         return merged ||  ||     def remove(self; point_id: int): ||         \"\"\" ||         Removes a node with id `point_id` if it exists. ||         for every pair of different points a; b that exists s.t. ||         there exists edges (a; point_id) and (point_id; b) ||         will result in an edge (a; b) being added. ||         \"\"\" ||         if point_id in self._graph.nodes: ||             preds = self._graph.pred[point_id] ||             succs = self._graph.succ[point_id] ||             for pred in preds: ||                 for succ in succs: ||                     if pred != succ: ||                         self._graph.add_edge(pred; succ) ||             self._graph.remove_node(point_id) ||  ||     def disjoint_merge(self; other): ||         \"\"\" ||         Merge two graphs from different sources. i.e. none of the nodes ||         should overwrite each other ||         \"\"\" ||         g1; g2 = self._graph; other._graph ||         g = g1.merge(g2) ||         return Points._from_graph(g) ||  ||     def _add_points(self; data: Dict[int; PointBase]): ||         \"\"\" ||         Add points from the old data format: Dict from node id to PointBase; ||         into the new graph. ||         \"\"\" ||         for point_id; point in data.items(): ||             loc = point.location ||             if isinstance(point; Point) and point.kwargs: ||                 self._graph.add_node( ||                     point_id; location=deepcopy(loc); **deepcopy(point.kwargs) ||                 ) ||             else: ||                 self._graph.add_node(point_id; location=deepcopy(loc)) ||  ||     def _add_edges(self; edges: List[Tuple[int; int]]): ||         \"\"\" ||         Add list of edges in id pair tuples into the graph. ||         \"\"\" ||         for u; v in edges: ||             if u not in self._graph.nodes or v not in self._graph.nodes: ||                 logging.warning( ||                     ( ||                         \"{} is{} in the graph; {} is{} in the graph; \" ||                         + \"thus an edge cannot be added between them\" ||                     ).format( ||                         u; ||                         \"\" if u in self._graph.nodes else \" not\"; ||                         v; ||                         \"\" if v in self._graph.nodes else \" not\"; ||                     ) ||                 ) ||                 raise Exception( ||                     \"This graph does not contain a point with id {}! The edge {} is invalid\".format( ||                         v if u in self._graph.nodes else u ||                     ) ||                 ) ||             else: ||                 self._graph.add_edge(u; v) ||  ||     def _graph_to_points(self) -> Dict[int; Point]: ||         \"\"\" ||         Turn a graph into the old data format of Dict mapping from ||         node_id to PointBase class ||         \"\"\" ||         point_data = {} ||         for point_id; point_attrs in self._graph.nodes.items(): ||             # do not deep copy location here. Modifying an attribute on the ||             # point needs to modify that attribute on the graph ||             attrs = deepcopy(point_attrs) ||             loc = attrs.pop(\"location\") ||             point_data[point_id] = Point(location=loc; **attrs) ||         return point_data ||  ||     def _update_graph(self; points: Dict[int; PointBase]): ||         \"\"\" ||         Add a Dict mapping node_ids to PointsBase to the graph; ||         if they are not already contained. ||         \"\"\" ||         for point_id; point in points.items(): ||             if point_id not in self._graph.nodes: ||                 continue ||             if isinstance(point; Point): ||                 self._graph.nodes[point_id].update(point.attrs) ||             elif isinstance(point; PointBase): ||                 self._graph.nodes[point_id][\"location\"] = point.location ||  ||     @classmethod ||     def _from_graph(cls; graph: nx.Graph; spec: PointsSpec): ||         \"\"\" ||         Create a new Points datastructure from a networkx graph. ||         \"\"\" ||         graph = graph.to_directed() ||         x = cls({}; spec) ||         graph.__class__ = SpatialGraph ||         x._graph = graph ||         return x || '''
aaiagfgfi,funkey/gunpowder,gunpowder/nodes/intensity_augment.py,7e74c1d95f0c7f44c87cb098ba60d1cf649cf7eb,bc5f5030c42caf9e2623ba0bfb93ba55ee351afc,TODO: move all randomness into the prepare method
aaiagfgfj,funkey/gunpowder,gunpowder/nodes/intensity_augment.py,7e74c1d95f0c7f44c87cb098ba60d1cf649cf7eb,bc5f5030c42caf9e2623ba0bfb93ba55ee351afc,TODO: write a test for this node
aaiagfhai,funkey/gunpowder,gunpowder/nodes/intensity_augment.py,343358efac96d3db6bc8246ca010faadc1c49ae6,STILL_EXISTS,TODO: move all randomness into the prepare method
aaiagfhaj,funkey/gunpowder,gunpowder/nodes/intensity_augment.py,343358efac96d3db6bc8246ca010faadc1c49ae6,STILL_EXISTS,TODO: write a test for this node
aaiagfjdh,lRomul/argus,argus/metrics/metric.py,04b7a5535882ce6330a03337ccf9d10a7979d686,STILL_EXISTS,better == 'max':
aaiaggahf,nicklhy/DLInfBench,inference_template.py,0770a9f45ca22641a7b0903939084ee35d7bbe63,STILL_EXISTS,transform the data from numpy.ndarrays to the needed format(i.e. torch.Tensor)
aaiaggajb,nicklhy/DLInfBench,inference_tensorflow.py,6aa08cdceb5874d18b39b69d5be726be597f41b7,STILL_EXISTS,transform the data from numpy.ndarrays to the needed format(i.e. torch.Tensor)
aaiaggeae,nicklhy/DLInfBench,models/tensorflow/resnet_v2.py,6aa08cdceb5874d18b39b69d5be726be597f41b7,STILL_EXISTS,This is needed because the pre-activation variant does not have batch
aaiaghceb,jefkine/zeta-learn,docs/conf.py,453298c085f5bfcb44110f725dd9af9d45820360,STILL_EXISTS,-- Options for todo extension ----------------------------------------------
aaiaghcec,jefkine/zeta-learn,docs/conf.py,453298c085f5bfcb44110f725dd9af9d45820360,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaiaghdff,jefkine/zeta-learn,zeta/dl/layers/convolutional.py,453298c085f5bfcb44110f725dd9af9d45820360,STILL_EXISTS,convert to columns
aaiaghfje,jefkine/zeta-learn,ztlearn/dl/layers/convolutional.py,51f1940c4df0a7573e55717ee05057e18096195c,STILL_EXISTS,TODO: weights need to be rearraged in a way to have a matrix
aaiagiacf,jefkine/zeta-learn,ztlearn/utils/data_utils.py,c9289bd0ac73af5a346a4e7ff066c4be97544a22,STILL_EXISTS,@@TODO: write a more robust method for this
aaiagibdj,jefkine/zeta-learn,ztlearn/optimizers.py,9f62b9c3deb0dc4397899d8aab00ec6c84139057,bd537c4303f89077bfb7a90b1ceb258d8b438dbc,@@TODO: this should come from the trainer class as the current epoch number
aaiagibid,jefkine/zeta-learn,ztlearn/utils/data_utils.py,b70f3ec614f831b250af418e4aa82036c39bd568,STILL_EXISTS,@@TODO: split the sample_folds and label_folds into sizes K-1 and 1 for test and training sets
aaiagjddf,volotat/GANLib,GANLib/DiscoGAN.py,eb2b06198b3cccde8bd7b7f1625850dc90cf6842,STILL_EXISTS,Discriminator works in a bit different way that it described in the paper
aaiagjddg,volotat/GANLib,GANLib/DiscoGAN.py,eb2b06198b3cccde8bd7b7f1625850dc90cf6842,STILL_EXISTS,simply because it shows better performance in my experiments.
aaiagjdfj,volotat/GANLib,GANLib/AAE.py,35d9d23250f101b9fb3902b14297f36c3809d309,STILL_EXISTS,Make it work properly
aaiagjdhe,volotat/GANLib,GANLib/AAE.py,a4839920a56b689ad8c6e7864b0ec00c30f7b6b2,STILL_EXISTS,Make it work properly
aaiagjeei,volotat/GANLib,GANLib/ProgGAN.py,1e4345c1b6d4b0f4f984943c1ca51651bfeeb601,05307d588ecc1341b14bb796df6737c8bdb32c78,Add He's initialization to trainable layers and improved Wasserstein loss to models
aaiagjeia,volotat/GANLib,GANLib/ProgGAN.py,a1a7b1cb77616f1364d7ff9bf4051bbc0440cb47,05307d588ecc1341b14bb796df6737c8bdb32c78,Add He's initialization to trainable layers and improved Wasserstein loss to models
aaiagjfee,volotat/GANLib,tests/ProgGAN_test.py,05307d588ecc1341b14bb796df6737c8bdb32c78,STILL_EXISTS,He's normal dynamic weight scaler. Used in paper. Completely not working for me. Probably I'm doing something horribly wrong.
aaiagjhhe,volotat/GANLib,tests/CramerGAN_test.py,c0a5e0bf3f213d33ad53b07a8911210ee5e8bd7e,STILL_EXISTS,more outputs is better
aaiagjjcd,volotat/GANLib,tests/ImpWGAN_test.py,b59dbc8b6254fd2a436e0511c07a5da3afcab0e5,STILL_EXISTS,more outputs is better
aaiahabig,volotat/GANLib,GANLib/utils.py,5daa0753c5cb9e694c6c081695f820415b3b6257,STILL_EXISTS,''' || def Gravity(x; boundaries = [0;1]; pressure = 0.5): ||     min = boundaries[0] ||     max = boundaries[1] ||     dist = max - min ||     res = min + (1. - np.abs(1. - (x - min) \/ dist) ** (1. - pressure)) * dist ||     return res ||  ||      || #tensor values manipulation || class tensor_value(): ||     def __init__(self; x): ||         self.tensor = K.variable(value = x) ||          ||     def set(self; x): ||         K.set_value(self.tensor; x) ||          ||     def get(self): ||         return K.eval(self.tensor) ||  ||          || def gradient_control(x; a): ||     return K.stop_gradient(x)* (1 - a * 0.99) +  x * (0.01 + a * 0.99)   ||      ||      ||      || # --------------- || #  Layers || # --------------- ||  || #Pixelwise feature vector normalization layer from \"Progressive Growing of GANs\" paper || class PixelNorm(Layer): #It will work only if channels are last in order! I have to do something with it. ||     def __init__(self; epsilon = 1e-8; **kwargs): ||         self.epsilon = epsilon ||         super(PixelNorm; self).__init__(**kwargs) ||  ||     def build(self; input_shape): ||         super(PixelNorm; self).build(input_shape) ||  ||     def call(self; x; *args; **kwargs): ||         return x \/ K.sqrt(K.mean(K.square(x); axis=-1; keepdims=True) + self.epsilon) ||  ||     def compute_output_shape(self; input_shape): ||         return input_shape ||          ||          || #MiniBatchStddev layer from \"Progressive Growing of GANs\" paper         || class MiniBatchStddev(Layer): #again position of channels matter! ||     def __init__(self; group_size=4; **kwargs): ||         self.group_size = group_size ||         super(MiniBatchStddev; self).__init__(**kwargs) ||  ||     def build(self; input_shape): ||         super(MiniBatchStddev; self).build(input_shape) ||  ||     def call(self; x; *args; **kwargs): ||         group_size = tf.minimum(self.group_size; tf.shape(x)[0])# Minibatch must be divisible by (or smaller than) group_size. ||         s = x.shape                                             # [NCHW]  Input shape. ||         y = tf.reshape(x; [group_size; -1; s[1]; s[2]; s[3]])   # [GMCHW] Split minibatch into M groups of size G. ||         y = tf.cast(y; tf.float32)                              # [GMCHW] Cast to FP32. ||         y -= tf.reduce_mean(y; axis=0; keepdims=True)           # [GMCHW] Subtract mean over group. ||         y = tf.reduce_mean(tf.square(y); axis=0)                # [MCHW]  Calc variance over group. ||         y = tf.sqrt(y + 1e-8)                                   # [MCHW]  Calc stddev over group. ||         y = tf.reduce_mean(y; axis=[1;2;3]; keepdims=True)      # [M111]  Take average over fmaps and pixels. ||         y = tf.cast(y; x.dtype)                                 # [M111]  Cast back to original data type. ||         y = tf.tile(y; [group_size; s[1]; s[2]; 1])             # [N1HW]  Replicate over group and pixels. ||         return tf.concat([x; y]; axis=-1)     ||          ||     def compute_output_shape(self; input_shape): ||         return (*input_shape[:3]; input_shape[3]+1)         || '''
aaiahaeff,volotat/GANLib,GANLib/utils.py,a139ffac86b06bccfcf875e36e0b635741603f41,STILL_EXISTS,''' || def Gravity(x; boundaries = [0;1]; pressure = 0.5): ||     min = boundaries[0] ||     max = boundaries[1] ||     dist = max - min ||     res = min + (1. - np.abs(1. - (x - min) \/ dist) ** (1. - pressure)) * dist ||     return res ||  ||      || #tensor values manipulation || class tensor_value(): ||     def __init__(self; x): ||         self.tensor = K.variable(value = x) ||          ||     def set(self; x): ||         K.set_value(self.tensor; x) ||          ||     def get(self): ||         return K.eval(self.tensor) ||  ||          || def gradient_control(x; a): ||     return K.stop_gradient(x)* (1 - a * 0.99) +  x * (0.01 + a * 0.99)   ||      ||      ||      || # --------------- || #  Layers || # --------------- ||  || #Pixelwise feature vector normalization layer from \"Progressive Growing of GANs\" paper || class PixelNorm(Layer): #It will work only if channels are last in order! I have to do something with it. ||     def __init__(self; epsilon = 1e-8; **kwargs): ||         self.epsilon = epsilon ||         super(PixelNorm; self).__init__(**kwargs) ||  ||     def build(self; input_shape): ||         super(PixelNorm; self).build(input_shape) ||  ||     def call(self; x; *args; **kwargs): ||         return x \/ K.sqrt(K.mean(K.square(x); axis=-1; keepdims=True) + self.epsilon) ||  ||     def compute_output_shape(self; input_shape): ||         return input_shape ||          ||          || #MiniBatchStddev layer from \"Progressive Growing of GANs\" paper         || class MiniBatchStddev(Layer): #again position of channels matter! ||     def __init__(self; group_size=4; **kwargs): ||         self.group_size = group_size ||         super(MiniBatchStddev; self).__init__(**kwargs) ||  ||     def build(self; input_shape): ||         super(MiniBatchStddev; self).build(input_shape) ||  ||     def call(self; x; *args; **kwargs): ||         group_size = tf.minimum(self.group_size; tf.shape(x)[0])# Minibatch must be divisible by (or smaller than) group_size. ||         s = x.shape                                             # [NCHW]  Input shape. ||         y = tf.reshape(x; [group_size; -1; s[1]; s[2]; s[3]])   # [GMCHW] Split minibatch into M groups of size G. ||         y = tf.cast(y; tf.float32)                              # [GMCHW] Cast to FP32. ||         y -= tf.reduce_mean(y; axis=0; keepdims=True)           # [GMCHW] Subtract mean over group. ||         y = tf.reduce_mean(tf.square(y); axis=0)                # [MCHW]  Calc variance over group. ||         y = tf.sqrt(y + 1e-8)                                   # [MCHW]  Calc stddev over group. ||         y = tf.reduce_mean(y; axis=[1;2;3]; keepdims=True)      # [M111]  Take average over fmaps and pixels. ||         y = tf.cast(y; x.dtype)                                 # [M111]  Cast back to original data type. ||         y = tf.tile(y; [group_size; s[1]; s[2]; 1])             # [N1HW]  Replicate over group and pixels. ||         return tf.concat([x; y]; axis=-1)     ||          ||     def compute_output_shape(self; input_shape): ||         return (*input_shape[:3]; input_shape[3]+1)         || '''
aaiahagbd,serengil/chefboost,training/Training.py,448eb3a38ed8d28e526440970a659353464f4445,STILL_EXISTS,print(instances;\" rows; \";columns;\" columns\")
aaiahagci,serengil/chefboost,training/Training.py,448eb3a38ed8d28e526440970a659353464f4445,STILL_EXISTS,find winner index; this cannot be returned by find decision because columns dropped in previous steps
aaiahagdh,serengil/chefboost,training/Training.py,448eb3a38ed8d28e526440970a659353464f4445,STILL_EXISTS,if decision cannot be made even though all columns dropped
aaiahaghj,serengil/chefboost,tuning/randomforest.py,448eb3a38ed8d28e526440970a659353464f4445,a7ac5dbb7b0090c0c6233e1a188a169482dced00,TODO: pooling expects to run in main. this will be fixed later.
aaiahaigf,serengil/chefboost,chefboost/training/Training.py,20fb15d4eea5db5db5950968d7463bbc80a478fe,STILL_EXISTS,TO-DO: This block could be paralellised
aaiahaiib,serengil/chefboost,chefboost/training/Training.py,0bda5524f4ab0a1ef28a65103e1930d945f03db9,STILL_EXISTS,TODO: elif checks might be above than if statements in parallel
aaiahaiid,serengil/chefboost,chefboost/training/Training.py,0bda5524f4ab0a1ef28a65103e1930d945f03db9,STILL_EXISTS,find winner index; this cannot be returned by find decision because columns dropped in previous steps
aaiahaiig,serengil/chefboost,chefboost/training/Training.py,0bda5524f4ab0a1ef28a65103e1930d945f03db9,STILL_EXISTS,TO-DO: you should specify the number of cores in config
aaiahaijf,serengil/chefboost,chefboost/training/Training.py,0bda5524f4ab0a1ef28a65103e1930d945f03db9,STILL_EXISTS,TO-DO: if random forest trees are handled in parallel; this would be a problem. You cannot know the related tree of a rule. You should store a global tree id in a rule.
aaiahajac,serengil/chefboost,chefboost/training/Preprocess.py,0f0f119d1071c259d7498f2de7c4b951eff712e7,STILL_EXISTS,TO-DO: this causes very long running when unique numbers are high. Find a workaround for this.
aaiahbaed,serengil/chefboost,chefboost/training/Training.py,81d4ee5098f9106707140cce63c3875a3a19b66c,224a9d8129de5da1c50433c0e99c09fe6beb5720,TODO: just apply parallelism for the 1st level?
aaiahbagb,serengil/chefboost,chefboost/tuning/randomforest.py,224a9d8129de5da1c50433c0e99c09fe6beb5720,STILL_EXISTS,TODO: is this logical for 48x2 cores?
aaiahbagf,serengil/chefboost,chefboost/training/Training.py,374a6d4dd045e5722b4058133472f9201df52cf0,STILL_EXISTS,TODO: this control might be modified based on num of cores.
aaiahbahd,serengil/chefboost,chefboost/tuning/randomforest.py,374a6d4dd045e5722b4058133472f9201df52cf0,STILL_EXISTS,TODO: reconstruct for parallel run is problematic. you should reconstruct based on tree id.
aaiahbbag,serengil/chefboost,chefboost/training/Training-Draft.py,f42220860f040cbafd2d2648bc4a250ef03f6639,STILL_EXISTS,print(instances;\" rows; \";columns;\" columns\")
aaiahbbci,serengil/chefboost,chefboost/training/Training-Draft.py,f42220860f040cbafd2d2648bc4a250ef03f6639,STILL_EXISTS,if decision cannot be made even though all columns dropped
aaiahbbde,serengil/chefboost,chefboost/training/Training-Draft.py,f42220860f040cbafd2d2648bc4a250ef03f6639,STILL_EXISTS,TODO: elif checks might be above than if statements in parallel
aaiahbbee,serengil/chefboost,chefboost/training/Training-Draft.py,f42220860f040cbafd2d2648bc4a250ef03f6639,STILL_EXISTS,find winner index; this cannot be returned by find decision because columns dropped in previous steps
aaiahbbgg,serengil/chefboost,chefboost/training/Training-Draft.py,f42220860f040cbafd2d2648bc4a250ef03f6639,STILL_EXISTS,workaround for hang problem. set num_cores and active threads same.
aaiahbcac,serengil/chefboost,chefboost/training/Training.py,f42220860f040cbafd2d2648bc4a250ef03f6639,STILL_EXISTS,TO-DO: if random forest trees are handled in parallel; this would be a problem. You cannot know the related tree of a rule. You should store a global tree id in a rule.
aaiahbcci,serengil/chefboost,chefboost/training/Training.py,45f4d55442a3ce3465bb4d85d120d6435e409170,STILL_EXISTS,workaround for hang problem. set num_cores and active threads same.
aaiahbcdj,serengil/chefboost,chefboost/training/Training.py,45f4d55442a3ce3465bb4d85d120d6435e409170,STILL_EXISTS,TO-DO: if random forest trees are handled in parallel; this would be a problem. You cannot know the related tree of a rule. You should store a global tree id in a rule.
aaiahbcfh,serengil/chefboost,chefboost/training/Training.py,5398873fc68dcabd544a0cd880d789af9cc95f9a,STILL_EXISTS,column_name = df.columns[i]; column_type = df[column_name].dtypes #numeric field already transformed to object. you cannot check it with df itself; you should check df_copy
aaiahbcic,sfischer13/python-arpa,arpa/arpa.py,006da63e2fc1cdcad8a76f9b4c756a031140dc98,STILL_EXISTS,TODO: obj.write(fp)
aaiahbdfa,sfischer13/python-arpa,docs/conf.py,f861141ac9c68629ee4a46391140afa3a8afbc6d,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaiahbeje,nielstron/quantulum3,docs/conf.py,3c2b3af86e9a5a32cc52cda548d2a7b2694c3900,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaiahbied,nielstron/quantulum3,quantulum3/parser.py,99c79737e7bf5febac3425da6df62ca897bb48bc,STILL_EXISTS,TODO this should be uncertainty
aaiahbihf,nielstron/quantulum3,quantulum3/parser.py,d26b046d7ff355cc483e7b6c764ef9655a78bcf4,d8abfa5f65612b7fdddfc1a759fce2fb2925a5b9,TODO make sure that this is tested at some point
aaiahbihh,nielstron/quantulum3,quantulum3/regex.py,d26b046d7ff355cc483e7b6c764ef9655a78bcf4,STILL_EXISTS,TODO do not allow spaces in between unit symbols
aaiahbjaf,nielstron/quantulum3,quantulum3/parser.py,b81807b783183ee66d8d9bf046d305f89a693465,c0e214233c37a3dfa07bc86e4abc459441ecf936,TODO use classifier to decide if 3K is 3 thousand or 3 Kelvin
aaiahbjbe,nielstron/quantulum3,quantulum3/parser.py,ca2bca27c9fce0270e104f21e683d23b8766b281,d8abfa5f65612b7fdddfc1a759fce2fb2925a5b9,TODO make sure that this is tested at some point
aaiahbjbg,nielstron/quantulum3,quantulum3/regex.py,ca2bca27c9fce0270e104f21e683d23b8766b281,508df21c6a0a938146775bec4f3f798924cb8215,TODO do not allow spaces in between unit symbols => currently handled inside parser.py
aaiahbjdf,nielstron/quantulum3,quantulum3/parser.py,2b0aeb5f2f3ca18093e2b42fe7b5cf85fda32b6d,STILL_EXISTS,TODO
aaiahbjdg,nielstron/quantulum3,quantulum3/regex.py,2b0aeb5f2f3ca18093e2b42fe7b5cf85fda32b6d,2085482f02aa350ad3fbce67e26903325ac76f49,TODO do not allow spaces in between unit symbols => currently handled inside parser.py
aaiahbjge,nielstron/quantulum3,quantulum3/load.py,a0f2eabdc60812c6053531a1ce5187c82ee8b018,2a6ea97ee043d791a0cc8b2bb05981b97c46a431,TODO don't do this comparison at every start up; use a build script
aaiahbjhd,nielstron/quantulum3,quantulum3/tests.py,2a6ea97ee043d791a0cc8b2bb05981b97c46a431,STILL_EXISTS,TODO don't do this comparison at every start up; use a build script
aaiahbjhj,nielstron/quantulum3,scripts/build.py,2a6ea97ee043d791a0cc8b2bb05981b97c46a431,aab9937760ed38748f41f819f41a41933cdfcc58,TODO don't do this comparison at every start up; use a build script
aaiahcadb,nielstron/quantulum3,quantulum3/parser.py,e8835ca3265d1619889ed394e95e44c4cb252e3b,c0e214233c37a3dfa07bc86e4abc459441ecf936,TODO rerun if change occurred
aaiahcbei,nielstron/quantulum3,quantulum3/tests/test_setup.py,6447f3ef533f12dc87696e0806c2dc50738cd54b,STILL_EXISTS,TODO be aware that there may never be two identical units
aaiahccbi,nielstron/quantulum3,quantulum3/lang/en_US/parser.py,5466f4515fa3c710797ead951ed828572e17896b,STILL_EXISTS,TODO rerun if change occurred
aaiahcccc,nielstron/quantulum3,quantulum3/lang/en_US/parser.py,5466f4515fa3c710797ead951ed828572e17896b,STILL_EXISTS,TODO use classifier to decide if 3K is 3 thousand or 3 Kelvin
aaiahccji,nielstron/quantulum3,quantulum3/_lang/en_US/parser.py,c0e214233c37a3dfa07bc86e4abc459441ecf936,STILL_EXISTS,TODO rerun if change occurred
aaiahcdac,nielstron/quantulum3,quantulum3/_lang/en_US/parser.py,c0e214233c37a3dfa07bc86e4abc459441ecf936,STILL_EXISTS,TODO use classifier to decide if 3K is 3 thousand or 3 Kelvin
aaiahcddd,nielstron/quantulum3,quantulum3/load.py,c0e214233c37a3dfa07bc86e4abc459441ecf936,668b7d1171c8b02e77a6284e3d0b52d1471f4d80,TODO simplify
aaiahcdhc,nielstron/quantulum3,quantulum3/load.py,668b7d1171c8b02e77a6284e3d0b52d1471f4d80,3217d8296755cd13c79bda4559e90ada06336774,TODO currently overrides unit; does not extend
aaiahcebe,nielstron/quantulum3,quantulum3/_lang/en_US/load.py,dfbf873cd525b39ce2c13a80b6fdbad2c989bd49,STILL_EXISTS,TODO remove this; as soon as the correct plural branch is merges into inflect
aaiahcgdd,nielstron/quantulum3,scripts/download_wiki.py,883c4e52f5cc709b02b02952d027d4ac0a8c6128,STILL_EXISTS,TODO lang support
aaiahcgde,nielstron/quantulum3,scripts/extract_vere.py,a70c147c7bd757d7f2395e9a07adc048a9f38cee,STILL_EXISTS,TODO language support
aaiahcgdi,nielstron/quantulum3,quantulum3/_lang/en_US/load.py,07b1b0db933847ef61a56b970aa0c4816c3fc2f4,8800b73686e515d6fde738f15c51f56cb8dcd800,TODO remove this; as soon as the correct plural branch is merges into
aaiahcgji,nielstron/quantulum3,quantulum3/_lang/en_US/parser.py,7acb40c412e0efcba6fe69bac5053c07a6c300d5,STILL_EXISTS,TODO quick hack that works; do remove and eventually merge with common word removal
aaiahchfe,crcollins/molml,molml/features.py,a9f078097b19eabe1c40970e8bcc51387bbb3350,b005d81724931dba182b15359f3e620ef508006f,This is to make the loop simpler when handling both ends of the
aaiahchfh,crcollins/molml,molml/features.py,a9f078097b19eabe1c40970e8bcc51387bbb3350,b005d81724931dba182b15359f3e620ef508006f,Case where order reversal is needed
aaiahcidd,crcollins/molml,tests/test_atom.py,6795193da5747f5a9abcfbf05e82470718e37765,STILL_EXISTS,This should not raise an error; becaues no fitting is needed
aaiahcigb,crcollins/molml,molml/molecule.py,b005d81724931dba182b15359f3e620ef508006f,STILL_EXISTS,This is to make the loop simpler when handling both ends of the
aaiahcige,crcollins/molml,molml/molecule.py,b005d81724931dba182b15359f3e620ef508006f,07682e9f075fa11a35c61fcdbb474197e2596280,Case where order reversal is needed
aaiahcjbh,crcollins/molml,molml/utils.py,07682e9f075fa11a35c61fcdbb474197e2596280,STILL_EXISTS,Case where order reversal is needed
aaiahdagc,crcollins/molml,docs/conf.py,eccfc2291951a57f672d4f9ddf4b8f853e8d7726,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaiahdbaa,crcollins/molml,docs/conf.py,eccfc2291951a57f672d4f9ddf4b8f853e8d7726,STILL_EXISTS,Fix issue with autosummary
aaiahdbcd,crcollins/molml,molml/utils.py,6f0627b0d3391621dcc88d034358d300643c5fdb,b8a8e74bae2c6471c124c00e5d1dde50e1ad2ea0,Fix assumptions about inputs here
aaiahdbce,crcollins/molml,molml/utils.py,6f0627b0d3391621dcc88d034358d300643c5fdb,4c64025dc55fe9da66fa17af82e02c9bc30ce5e9,TODO: Add in geoms
aaiahdbgc,crcollins/molml,tests/test_utils.py,ecb4567827487eaa6378707ddc0b07a53905ce9b,STILL_EXISTS,We will hack on connectivity a sub transformer on its `depth` param.
aaiahdbje,crcollins/molml,examples/missing_constants.py,cbd8521891c97c1655a3b89863e1a2170d9edc6b,STILL_EXISTS,Maybe at some point; molml will include more constants; but it seems outside
aaiahdcbh,crcollins/molml,molml/fragment.py,d2cdbb6ca22a8b4a662da1e8459fd28e95627d2e,STILL_EXISTS,Hack to get length of features
aaiahdcch,crcollins/molml,molml/molecule.py,b1bf2961b9ef7113cd12e0e4699751fbfd49c2b6,STILL_EXISTS,Add a hack to label the parents of root node separately
aaiahdcdj,crcollins/molml,molml/molecule.py,6bd2cec58e99319d281a309abf43184a39bd9990,e28594f7df87c27194e4dab1b982b0c16ba54a0d,We sort this to fix consistency in 3.6 dicts.
aaiahdcee,crcollins/molml,tests/test_utils.py,006db2c8db14b9f816df60ccfe7339b6c01a1ed3,STILL_EXISTS,We will hack on connectivity a sub transformer on its `depth` param.
aaiahdcha,jfilter/text-classification-keras,keras_text/embeddings.py,c85c9d7113ef91b72abc21918544fbe4df4b8aa0,STILL_EXISTS,Add more types here as needed.
aaiahddaa,jfilter/text-classification-keras,keras_text/processing.py,c85c9d7113ef91b72abc21918544fbe4df4b8aa0,STILL_EXISTS,Infer max lengths if needed.
aaiahdddb,jfilter/text-classification-keras,keras_text/models/sentence_model.py,9a87ef45465bc9ef262b0d3f46fc2d2165d6e4d7,STILL_EXISTS,TODO: Get rid of this restriction when https:\/\/github.com\/fchollet\/keras\/issues\/6917 resolves.
aaiahdded,jfilter/text-classification-keras,keras_text/processing.py,c81a4ab60b6b0f1785c2c885555675d4f0d5759c,STILL_EXISTS,TODO: better variable names (see below)
aaiahddee,jfilter/text-classification-keras,keras_text/processing.py,c81a4ab60b6b0f1785c2c885555675d4f0d5759c,STILL_EXISTS,TODO: better names (see below)
aaiahddgg,jfilter/text-classification-keras,keras_text/embeddings.py,58afb5f54d49f52d452bfd393de91df4b1f92d66,STILL_EXISTS,some hack for fasttext vectors where the first line is (num_token; dimensions)
aaiahddje,jfilter/text-classification-keras,keras_text/preprocessing/utils.py,19128cea518e3000068634af41219c8275e80b55,STILL_EXISTS,TODO: better variable names (see below)
aaiahddjf,jfilter/text-classification-keras,keras_text/preprocessing/utils.py,19128cea518e3000068634af41219c8275e80b55,STILL_EXISTS,TODO: better names (see below)
aaiahddjg,jfilter/text-classification-keras,keras_text/preprocessing/utils.py,19128cea518e3000068634af41219c8275e80b55,STILL_EXISTS,Infer max lengths if needed.
aaiahdebb,jfilter/text-classification-keras,keras_text/libs/twokenize/twokenize.py,cf4c15bace30c9d5837f53446564ecff6dbdb54e,STILL_EXISTS,BTO 2012-06: everyone thinks the daringfireball regex should be better; but they're wrong.
aaiahdebe,jfilter/text-classification-keras,keras_text/libs/twokenize/twokenize.py,cf4c15bace30c9d5837f53446564ecff6dbdb54e,STILL_EXISTS,TODO: remove obscure country domains?
aaiahdedg,jfilter/text-classification-keras,keras_text/libs/twokenize/twokenize.py,cf4c15bace30c9d5837f53446564ecff6dbdb54e,STILL_EXISTS,TODO should try a big precompiled lexicon from Wikipedia; Dan Ramage told me (BTO) he does this
aaiahdedi,jfilter/text-classification-keras,keras_text/libs/twokenize/twokenize.py,cf4c15bace30c9d5837f53446564ecff6dbdb54e,STILL_EXISTS,between this and the Java version. One little hack won't hurt...
aaiahdegd,jfilter/text-classification-keras,keras_text/libs/twokenize/twokenize.py,cf4c15bace30c9d5837f53446564ecff6dbdb54e,STILL_EXISTS,I remember it causing lots of trouble in the past as well.  Would be good to revisit or eliminate.
aaiahdfgh,jfilter/text-classification-keras,texcla/embeddings.py,592c6f7f8a315878289ae58a4434cf796ecac8fb,STILL_EXISTS,is ignoring errors a good idea? \uD83E\uDD14
aaiahdfha,jfilter/text-classification-keras,texcla/embeddings.py,7cd1438571a6183b590829bc09935f2a95e5d140,STILL_EXISTS,some hack for fasttext vectors where the first line is (num_token; dimensions)
aaiahdfjh,jfilter/text-classification-keras,examples/fasttext_imdb.py,6f02a924513dc4277660302228b41adb2d0cf791,STILL_EXISTS,FIXME: NOT WORKING. PLEASE FIX ME. There seem to be something wrong with the n-gram features.
aaiahdgea,jfilter/text-classification-keras,texcla/preprocessing/tokenizer.py,6f02a924513dc4277660302228b41adb2d0cf791,STILL_EXISTS,TODO: Counts?
aaiahdgij,nlgranger/SeqTools,docs/conf.py,e8dc7b2a634317b851450e0522d7f670b97d4175,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaiahdhcj,nlgranger/SeqTools,lproc/utils.py,e8dc7b2a634317b851450e0522d7f670b97d4175,05e59b3ef095193fcb4364fa37a7bba84c817752,TODO: add support for fixed seed
aaiahdjcb,nlgranger/SeqTools,lproc/indexing.py,a06a95b37420ffce72012824410648e1595442aa,5a2baf3a65fd36b523ad432ba8bea1b9de45159e,todo: optimize with array
aaiaheahb,nlgranger/SeqTools,seqtools/evaluation.py,c62b37ed8937ad1141a196fc243d6d11eb791bd2,090d55d352ced0029cd56c57eafcaaf744915ab1,TODO: manage slices
aaiaheaji,nlgranger/SeqTools,seqtools/__init__.py,6474807e4b468df7096ec154af8ef1383f1a8855,STILL_EXISTS,\"\"\" || The seqtools package contains functions to manipulate sequences || (anything that supports indexing such as lists or arrays). || Its objective is to simplify the execution of pipelines transformations. ||  || Unless otherwise specified; all functions feature on-demand evaluation || which means operations on an item or sequence are only executed when || needed which is convenient for rapid prototyping. || Most function apply 'transparently' over sequence and return objects || that support integer indexing; iteration; slicing; item assignment; || slice based assignment... ||  || The library also feature a robust multihreading\/multiprocessing || prefetch routine which hides away the difficulties of concurrent || processing into a simple sequence wrapper. || \"\"\"
aaiahebeh,nlgranger/SeqTools,seqtools/buffer.py,b9082c24bdbfd691a897c8a981aacb1b617ffbfe,STILL_EXISTS,TODO: save this value
aaiahebfd,nlgranger/SeqTools,seqtools/serialization.py,b9082c24bdbfd691a897c8a981aacb1b617ffbfe,STILL_EXISTS,\"\"\" || Hackish workaround to help serialize a pipeline inclusing its functions. || \"\"\"
aaiahefgi,SekouD/mlconjug,setup.py,76495ce4b6d7f99d4bd131b1cf77384d751439d8,768b32288e47ebf773658f5a56d35cd0c6771995,TODO: put package requirements here
aaiahefha,SekouD/mlconjug,setup.py,76495ce4b6d7f99d4bd131b1cf77384d751439d8,768b32288e47ebf773658f5a56d35cd0c6771995,TODO: put package test requirements here
aaiahefid,SekouD/mlconjug,travis_pypi_setup.py,76495ce4b6d7f99d4bd131b1cf77384d751439d8,STILL_EXISTS,workaround for https:\/\/github.com\/travis-ci\/travis-api\/issues\/196
aaiahegab,SekouD/mlconjug,mlconjug/PyVerbiste.py,5d2ef43caad553252216fb0d8bdd0465e1bfb381,STILL_EXISTS,TODO: add pronouns for languages other than french.
aaiahehei,SekouD/mlconjug,utils/cooljugator_scraper.py,1262224ab1da03a7b679adb82740fc67c171e512,eb08a7bc0c6cd6e78ffe8aa228ac80ff32f25a11,TODO: Skip saved languagesSekouD <sekoud.pythonail.com>
aaiahfaij,SekouD/mlconjug,mlconjug/PyVerbiste.py,f22fedee8adb16a163078f102372edd82a630756,cbe7269c92fbcd9face8917906379b7b497859c7,TODO: Check why english regular verbs are not properly populated
aaiahfbaa,stevewyl/nlp_toolkit,nlp_toolkit/data.py,cac6a930d7a05294b0bba3d7e01ae92dca579c35,STILL_EXISTS,TODO
aaiahfbed,stevewyl/nlp_toolkit,nlp_toolkit/classifier.py,7d5a7999b4b3427f77a305f0d8fd573758f32880,STILL_EXISTS,TODO
aaiahfbei,stevewyl/nlp_toolkit,nlp_toolkit/model_zoo.py,7d5a7999b4b3427f77a305f0d8fd573758f32880,STILL_EXISTS,\"\"\" || Model Zoos. || TODO add shape size for each layer || \"\"\"
aaiahfbgj,stevewyl/nlp_toolkit,nlp_toolkit/layer.py,b6ff3a1432021d337e4eea44cef3010b2945b686,STILL_EXISTS,TODO to handle all kinds of embedding layer variants
aaiahfcge,stevewyl/nlp_toolkit,nlp_toolkit/data.py,35f3c0b40c5376f76ad3f7789262878e7ee9b447,STILL_EXISTS,TODO
aaiahfche,stevewyl/nlp_toolkit,nlp_toolkit/sequence.py,40ca96917f4fa2eb34d614023469affdc85d7ed5,STILL_EXISTS,TODO
aaiahfchg,stevewyl/nlp_toolkit,nlp_toolkit/trainer.py,40ca96917f4fa2eb34d614023469affdc85d7ed5,STILL_EXISTS,TODO \u81EA\u9002\u5E94\u7684\u5B66\u4E60\u7387
aaiahfcjc,rodrigobressan/entity_embeddings_categorical,entity_embeddings/Pipeline.py,cb0097e48b0500a12d0033cf639518644075c449,STILL_EXISTS,TODO precheck target; df..
aaiahfdbi,rodrigobressan/entity_embeddings_categorical,entity_embeddings/config.py,156c73c8cb206dc4c0498ed78a411a9a94b21a12,STILL_EXISTS,TODO check processor and assembler
aaiahfdcc,rodrigobressan/entity_embeddings_categorical,tests/test_assembler.py,156c73c8cb206dc4c0498ed78a411a9a94b21a12,STILL_EXISTS,TODO move those things to layer_utils class
aaiahfdhf,rodrigobressan/entity_embeddings_categorical,docs/conf.py,f841f77cbf1656db903038f7672c147bdde1cdb4,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaiahffed,golsun/SpaceFusion,v1/dataset.py,abc1555c96e33634086f03db6b6632b1725e5d46,STILL_EXISTS,out ends with EOS
aaiahfija,chrislit/abydos,abydos/tests/test_clustering.py,8d270c21424352b490e12be798bbcc50dca52eb2,857d1f89acea9dfc178d30a5f5b94ec3dcd1fffb,TODO: add non-trivial tests
aaiahfjhd,chrislit/abydos,abydos/tests/test_distance.py,58124166d0296db8e97d377fc5aeb9750fdd6b5b,STILL_EXISTS,TODO: Add bias test(s) and unequal alpha & beta tests
aaiahfjhe,chrislit/abydos,abydos/tests/test_distance.py,58124166d0296db8e97d377fc5aeb9750fdd6b5b,STILL_EXISTS,TODO: find non-trivial strcmp95 tests or manufacture some
aaiahgbgd,chrislit/abydos,abydos/tests/test_distance.py,02325f737a34587aa03617689cc0509227ecef37,STILL_EXISTS,TODO: Add bias test(s) and unequal alpha & beta tests
aaiahgbge,chrislit/abydos,abydos/tests/test_distance.py,02325f737a34587aa03617689cc0509227ecef37,STILL_EXISTS,TODO: find non-trivial strcmp95 tests or manufacture some
aaiahgcaj,chrislit/abydos,abydos/phonetic.py,b29d7a1e5309476dbcd46d8f670ef8738cb76bd3,STILL_EXISTS,(needed by 'check_rules')
aaiahgfba,chrislit/abydos,abydos/util.py,80f1243d543745ff1c5a52b534f92c5f9c3be23e,24d1dac3fc07fdeaeb4c325e791a2a60e1de519e,gives me the minimum number of bits needed to resolve
aaiahggjb,chrislit/abydos,abydos/distance.py,6da092970e952341c60ab024a4e35c36a820f239,d733d53ba4038fe843b1a320e9cf3a541370c970,TODO: finish implementation
aaiahggjc,chrislit/abydos,abydos/phones.py,6da092970e952341c60ab024a4e35c36a820f239,STILL_EXISTS,TODO: finish implementation\/testing\/tuning
aaiahihbf,chrislit/abydos,abydos/bm/gen_rules_portuguese.py,8ffbc8ad70fab7b9b53c9198bdb40190d942f6d6,STILL_EXISTS,maybe to add a rule for m\/n before a consonant that disappears [preceeding vowel becomes nasalized]
aaiahjagi,chrislit/abydos,abydos/bm/sep_rules_portuguese.py,8ffbc8ad70fab7b9b53c9198bdb40190d942f6d6,STILL_EXISTS,maybe to add a rule for m\/n before a consonant that disappears [preceeding vowel becomes nasalized]
aaiaiacgd,chrislit/abydos,abydos/bm.py,2bbb95ac2461b82c994bb70eab96ad33aae586c2,712ae43eba2abfaf031d3c194e9fcae4d8e4a373,FIXME:
aaiaiachh,chrislit/abydos,abydos/bm.py,2bbb95ac2461b82c994bb70eab96ad33aae586c2,STILL_EXISTS,TODO: check this. since it's likely to represent bugs
aaiaiagfg,chrislit/abydos,abydos/distance.py,f18cfe06ed5a9a1ad882ab84adecda7e684e38ec,STILL_EXISTS,TODO: confirm the correctness of this when the docs are different
aaiaiaggh,chrislit/abydos,abydos/compression.py,24d1dac3fc07fdeaeb4c325e791a2a60e1de519e,STILL_EXISTS,gives me the minimum number of bits needed to resolve
aaiaiahbb,chrislit/abydos,abydos/stemmer.py,b6ee28f72a78aa3fcf5526bd4bd4816b922cd930,STILL_EXISTS,TODO: verify restriction
aaiaiajbh,chrislit/abydos,docs/conf.py,2185e8256ca8f75ed523fac6e135021f9f51aa17,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaiaibagg,chrislit/abydos,docs/conf.py,2185e8256ca8f75ed523fac6e135021f9f51aa17,c389f7f2d0e1d3487b748167826aea0bf7bc12a6,Fix unsupported image types using the Pillow.
aaiaibfha,chrislit/abydos,helpers/bm_php2py.py,60a07eaadb75aa1fd68f91aa97064879a4908f85,STILL_EXISTS,fix upstream
aaiaibihi,chrislit/abydos,abydos/stemmer.py,f042c7fd402730a6332d0962877d179a96c2457e,STILL_EXISTS,TODO: This looks very incomplete.
aaiaicaai,chrislit/abydos,abydos/phonetic.py,c8af4db33f468b91d4ab0766f20ca28f8f48a364,bcb2390173692a3936e5b765aeeff1678413eda8,TODO: decide how best to return an error
aaiaicafa,chrislit/abydos,abydos/phonetic.py,d30f8e98fe3a59dffcce165643782f052619c6ae,1f86573fe7a24029fa8976e2e396ea9bf73c2220,TODO: implement w\/o re?
aaiaicccj,chrislit/abydos,tests/test_phonetic.py,86ac104c8b4b2112e9514a165834d402118e8b0e,STILL_EXISTS,manually checked against algorithm
aaiaiccef,chrislit/abydos,abydos/clustering.py,a5517c3e9872da72caef361fc0706e6eea0c2792,612c7679667cb1acd409358e012067949c732c11,TODO: Dump all these to a data file.
aaiaicdii,chrislit/abydos,abydos/distance.py,f3a89258c65031326b1331beed951b0b1cae819e,fa1ac43e3617ecc7740e7a1e0ae0f16adf8f53ac,do elder\/younger move
aaiaicead,chrislit/abydos,abydos/clustering.py,fa1ac43e3617ecc7740e7a1e0ae0f16adf8f53ac,612c7679667cb1acd409358e012067949c732c11,do elder\/younger move
aaiaicegj,chrislit/abydos,abydos/fingerprint.py,612c7679667cb1acd409358e012067949c732c11,bcb2390173692a3936e5b765aeeff1678413eda8,TODO: Dump all these to a data file.
aaiaiceih,chrislit/abydos,abydos/fingerprint.py,612c7679667cb1acd409358e012067949c732c11,STILL_EXISTS,do elder\/younger move
aaiaicjdd,chrislit/abydos,abydos/qgram.py,3ba9e049ba84b6c98ef1c6bb12bf66ec29acb639,STILL_EXISTS,TODO: Raise exception?
aaiaicjec,chrislit/abydos,abydos/phonetic.py,ebc9fdef69f1d6049e3c2309964988cbb57e376c,STILL_EXISTS,Rule Ia seems to be covered entirely in II
aaiaicjii,chrislit/abydos,tests/test_phonetic.py,903ae1f77aba1ff9bd9aea83de75ceca2d49a6eb,STILL_EXISTS,Checked against Cunningham; et al. (1969) pp. 127--136; as needed.
aaiaidaif,chrislit/abydos,helpers/call_and_write_log.py,f371f4e5a64849ecc78a11e2bcd014e8175c55b3,STILL_EXISTS,\"\"\"call_and_write_log.py ||  || This helper script takes one argument; a call to pylint; pycodestyle; || or flake8. It captures stdout and writes it to a log file. ||  || The reason for this script to exist is as a workaround for tox || not supporting writing to files; even though I want it to do that || to maintain logs & create badges. || \"\"\"
aaiaidijg,chrislit/abydos,abydos/compression/arithmetic.py,aaeaf65978c50c8dddc528a7a335f7e636bd6c15,STILL_EXISTS,gives me the minimum number of bits needed to resolve
aaiaieegd,chrislit/abydos,abydos/fingerprint/synoname.py,acd3ea25074dddb2819162450e8325785968233f,STILL_EXISTS,do elder\/younger move
aaiaijgic,chrislit/abydos,abydos/phonetic/alpha_sis.py,8a7bbac520fdef72a7788fe1dbe419b3ab4622f6,STILL_EXISTS,\"\"\"abydos.phonetic.alpha_sis. ||  || The phonetic.alpha_sis module implements IBM's Alpha Search Inquiry System || coding. || \"\"\"
aaiajaadd,chrislit/abydos,abydos/phonetic/fr.py,8a7bbac520fdef72a7788fe1dbe419b3ab4622f6,STILL_EXISTS,Rule Ia seems to be covered entirely in II
aaiajacha,chrislit/abydos,abydos/phonetic/phonet.py,8a7bbac520fdef72a7788fe1dbe419b3ab4622f6,STILL_EXISTS,needed by 'check_rules'
aaiajaebb,chrislit/abydos,abydos/phonetic/soundex.py,8a7bbac520fdef72a7788fe1dbe419b3ab4622f6,STILL_EXISTS,TODO: Should these prefixes be supplemented? (VANDE; DELA; VON)
aaiajcehg,chrislit/abydos,tests/test_phonetic_dolby.py,2120dd1da45ea908fd81e3311147ac01882a4d81,STILL_EXISTS,Checked against Cunningham; et al. (1969) pp. 127--136; as needed.
aaiajcffi,chrislit/abydos,tests/test_phonetic_eudex.py,2120dd1da45ea908fd81e3311147ac01882a4d81,STILL_EXISTS,manually checked against algorithm
aaibbacdh,chrislit/abydos,abydos/phones/_phones.py,0186998d684753e070edf1fdc124aaa56133db27,1977546f059207e942d2fe98818a7cf17716a9bf,TODO: finish implementation\/testing\/tuning
aaibbaiaj,chrislit/abydos,abydos/distance/_compression.py,81370ac704e54f8052efe132684ba1d372af7dfa,60c582f15bc240bfd93e01b62bf0e143eca7d7a4,TODO: 15 should be 10
aaibbbcjj,chrislit/abydos,abydos/distance/_MLIPNS.py,1d6e1bb737180f482552ee24e8b192f4e652c02e,139c55abfa59bffb42578a19ba81dd3e16d2255f,TODO: should Hamming.dist be available? Hamming.dist_abs?
aaibbbdaa,chrislit/abydos,abydos/distance/_MLIPNS.py,1d6e1bb737180f482552ee24e8b192f4e652c02e,139c55abfa59bffb42578a19ba81dd3e16d2255f,TODO: should we just incorporate a Hamming object?
aaibbdfhb,chrislit/abydos,abydos/phonetic/_HenryEarly.py,85ea41984116dbea8ad49c361904d22288dec768,STILL_EXISTS,Rule Ia seems to be covered entirely in II
aaibbdjdf,chrislit/abydos,abydos/phonetic/_Soundex.py,85ea41984116dbea8ad49c361904d22288dec768,STILL_EXISTS,TODO: Should these prefixes be supplemented? (VANDE; DELA; VON)
aaibfaaed,chrislit/abydos,abydos/distance/_rees_levenshtein.py,1fec72bde48987861344ffe1b76563f55d0f0cf9,STILL_EXISTS,populate remaining columns
aaibfbbcc,explosion/spaCy,spacy/cli/conll17_ud_eval.py,3478ea76d162f5b69c2051820d9c23638fb1fd52,STILL_EXISTS,- [25 Jan 2017] Version 0.9.1: Fix bug in LCS alignment computation
aaibfbbji,explosion/spaCy,spacy/cli/conll17_ud_eval.py,3478ea76d162f5b69c2051820d9c23638fb1fd52,STILL_EXISTS,10 columns of the CoNLL-U file: ID; FORM; LEMMA;...
aaibfbcic,explosion/spaCy,examples/vectors_tensorboard.py,eef9430f0710562b4582c11d0692d7c56c5fd1da,STILL_EXISTS,Define columns in the first row
aaibfbedj,explosion/spaCy,examples/vectors_tensorboard_standalone.py,4eeb1788562a31af4e910a3288b5abcd55e4c870,STILL_EXISTS,Define columns in the first row
aaibfcdae,explosion/spaCy,spacy/language.py,95a96152213a0c3e4827c00ab396357d3abf02d2,4f82a02b70664a660736d629d5571122766fe5c6,TODO: Replace this once we handle vectors consistently as static
aaibfcdfd,explosion/spaCy,spacy/cli/ud_run_test.py,723b328062f02f0833234900314418abc7d372eb,STILL_EXISTS,TODO: This shuldn't be necessary? Should be handled in merge
aaibfchjh,explosion/spaCy,spacy/util.py,e7b075565d770c91e0b709c4a09a1d5c94252afb,e0caf3ae8c5a1d7fe79bae7e7d04ec2b31fc7561,hack to handle broken jsonl
aaibfciac,explosion/spaCy,spacy/util.py,3c30d1763ceff735a44761d7553dbb6a672abb91,8809dc45141fdf3e5cd54a0f16de00d64baa5ed8,hack to handle broken jsonl
aaibfcigf,explosion/spaCy,spacy/tests/regression/test_issue1501-2000.py,75f3234404c08d90ee3e8bae72ebabe72a8c3e45,STILL_EXISTS,TODO: Currently segfaulting; due to l_edge and r_edge misalignment
aaibfcihf,explosion/spaCy,spacy/tests/regression/test_issue2001-2500.py,75f3234404c08d90ee3e8bae72ebabe72a8c3e45,STILL_EXISTS,fix bug in labels with a 'b' character
aaibfcijd,explosion/spaCy,examples/pipeline/custom_sentence_segmentation.py,f762d52b2419e1388229d64753f40fc5351ced39,STILL_EXISTS,'''Example of adding a pipeline component to prohibit sentence boundaries || before certain tokens. ||  || What we do is write to the token.is_sent_start attribute; which || takes values in {True; False; None}. The default value None allows the parser || to predict sentence segments. The value False prohibits the parser from inserting || a sentence boundary before that token. Note that fixing the sentence segmentation || should also improve the parse quality. ||  || The specific example here is drawn from https:\/\/github.com\/explosion\/spaCy\/issues\/2627 || Other versions of the model may not make the original mistake; so the specific || example might not be apt for future versions. || '''
aaibfcijj,explosion/spaCy,spacy/__init__.py,1c0614ecd20c9a960fb821de18f00c9a94994dc1,19f5046934cc384a4be5dda44d2ade95c3264b82,Ideally we would somehow specify the warning to suppress?
aaibfcjai,explosion/spaCy,spacy/tests/parser/test_add_label.py,6e749d3c709512d7e0b065caeb12a66c4d3e0cb4,444a3abfe5e69bf5e5f0aeda665408f27c5eb63e,TODO: This now seems to be implicated in segfaults. Not sure what's up!
aaibfcjbe,explosion/spaCy,spacy/tests/conftest.py,26849874adcad723e7ed7883948df34098426086,b6e991440c6e2f5421a13893291d1a68e24edd82,TODO: re-implement generic tokenizer tests
aaibfcjde,explosion/spaCy,spacy/tests/regression/test_issue2772.py,6cd920e088d0a755644e380807db61a472a03eae,STILL_EXISTS,'''Test that deprojectivization doesn't mess up sentence boundaries.'''
aaibfcjed,explosion/spaCy,examples/keras_parikh_entailment/__main__.py,9faea3ff10c6e5159f6c7605e246f52087636308,STILL_EXISTS,workaround for keras\/tensorflow bug
aaibfdaai,explosion/spaCy,spacy/cli/pretrain.py,8fdb9bc278b950436d6a6d28cb27ef093fca9560,822ea4ef619072a94ce565bf78add9f9ea9d2866,The simplest way to implement this would be to vstack the
aaibfdabf,explosion/spaCy,spacy/util.py,3e7b214e571e0b9b7d41d93f5a0583007b22b831,f37863093a8c329d9e6e318f36fe7d0ca1cefdf6,hack to handle broken jsonl
aaibfdcbh,explosion/spaCy,spacy/cli/debug_data.py,37c7c85a86bb6472d6918dd87c99ea6a2170037f,STILL_EXISTS,Make sure all files and paths exists if they are needed
aaibfdccb,explosion/spaCy,spacy/cli/debug_data.py,37c7c85a86bb6472d6918dd87c99ea6a2170037f,9ee1c54f40e901533ef16cd148556cbf83cca6a7,TODO: update once the new format is ready
aaibfdccd,explosion/spaCy,spacy/cli/debug_data.py,37c7c85a86bb6472d6918dd87c99ea6a2170037f,STILL_EXISTS,TODO: validate_json(train_data; schema)
aaibfdcce,explosion/spaCy,spacy/cli/debug_data.py,37c7c85a86bb6472d6918dd87c99ea6a2170037f,STILL_EXISTS,TODO: validate_json(dev_data; schema)
aaibfdccf,explosion/spaCy,spacy/cli/debug_data.py,37c7c85a86bb6472d6918dd87c99ea6a2170037f,STILL_EXISTS,Create the gold corpus to be able to better analyze data
aaibfdcdj,explosion/spaCy,spacy/cli/schemas/__init__.py,37c7c85a86bb6472d6918dd87c99ea6a2170037f,STILL_EXISTS,TODO: replace with (stable) Draft6Validator; if available
aaibfdcea,explosion/spaCy,spacy/cli/train.py,37c7c85a86bb6472d6918dd87c99ea6a2170037f,STILL_EXISTS,Make sure all files and paths exists if they are needed
aaibfdcgg,explosion/spaCy,spacy/cli/ud/ud_run_test.py,37c7c85a86bb6472d6918dd87c99ea6a2170037f,STILL_EXISTS,TODO: This shuldn't be necessary? Should be handled in merge
aaibfdehc,explosion/spaCy,setup.py,3f16af123e66537cc98eb827bc916090032d2dc0,fa617997de379587ee6e5c3efa2777e0149afab1,Try to fix OSX 10.7 problem. Running blind here too.
aaibfdeij,explosion/spaCy,setup.py,33fca8672f0a9bbcab78999d8c384120cd119f99,f37863093a8c329d9e6e318f36fe7d0ca1cefdf6,Try to fix OSX 10.7 problem. Running blind here too.
aaibfdfad,explosion/spaCy,spacy/cli/train.py,b2bfd1e1c8fb1fe038f2da1fc3b188f42bdc1041,STILL_EXISTS,Make sure all files and paths exists if they are needed
aaibfdfag,explosion/spaCy,setup.py,7dd21b66d59c28abf588e6a98918acbb6c687a6f,ffdd5e964f0f41a3140a6df5e967bfd277f57f2a,Try to fix OSX 10.7 problem. Running blind here too.
aaibfdfbe,explosion/spaCy,spacy/cli/train.py,24f2e9bc0714e29893e6980a8c8b5e23a1e0e4b4,STILL_EXISTS,Make sure all files and paths exists if they are needed
aaibfdfja,explosion/spaCy,spacy/tests/conftest.py,2e84ec151320866efaa13b4f48351865cf481330,9745b0d5233b97698f6c2b8832f6e6687d640bc0,TODO: re-implement generic tokenizer tests
aaibfdgdf,explosion/spaCy,setup.py,021d04069a9c49bc29c6f4415e3d2b4b7a02012e,f407954b27c0108965db8042642d9ad089def4d0,Try to fix OSX 10.7 problem. Running blind here too.
aaibfdhbi,explosion/spaCy,spacy/tests/conftest.py,b41d64825af40c0bff5e9daff14d94f4d4618ec9,e2d93e4852a2284f355839d80f111683615f2d69,TODO: re-implement generic tokenizer tests
aaibfdhfc,explosion/spaCy,spacy/tests/conftest.py,1448ad100cfa3642904ca5e23426f37fca13905e,9e652afa4b1031bcc018a21515651d5a75fc3464,TODO: re-implement generic tokenizer tests
aaibfdiaj,explosion/spaCy,spacy/pipeline/functions.py,a9f8d1763298c406e79ac8534a4d1a780b171312,5651a0d052bcfd160b187828aa3d8d90652929fe,TODO: replace doc.merge with doc.retokenize
aaibfdjfg,explosion/spaCy,spacy/tests/conftest.py,f1c3108d52e3306a8f2c91b1f71d9d69b88ac672,76ce8b2662173a5a20e1c3c43373bf5d507cbe71,TODO: re-implement generic tokenizer tests
aaibfegfh,explosion/spaCy,spacy/cli/convert.py,76764fcf59dc661c0641515db78e63b4f9d9537e,e92df281cef2de3c0c95f73c79a032eb2194b104,TODO: support msgpack via stdout in srsly?
aaibfegjc,explosion/spaCy,spacy/util.py,7ba3a5d95cbfbb4381dc925da56aa063e4e50235,STILL_EXISTS,TODO: user warning?
aaibfehai,explosion/spaCy,spacy/tests/regression/test_issue2001-2500.py,8f45ff3dc2e3e4ac51abe72dfe67dc7b1aac9e7c,STILL_EXISTS,Problem: The dot is now properly split off; but the prefix\/suffix rules
aaibfehed,explosion/spaCy,spacy/language.py,852e1f105c9d0146a3489c842854154e28fadcd1,d0c763ba447282d53ac7d25354afde468f0e4a73,TODO: document
aaibffghh,explosion/spaCy,spacy/language.py,d849eb2455a168203a8394f260a43e521bd98255,ae15c9de7971679df9bb60034d007530957205ad,TODO: document
aaibffjei,explosion/spaCy,examples/pipeline/wikidata_entity_linking.py,6e997be4b4b364583c2e148992756992cd195b43,STILL_EXISTS,TODO remove hardcoded path
aaibffjfa,explosion/spaCy,examples/pipeline/wikidata_entity_linking.py,b31a390a9aaedccbdc4dc4c7ce62197ef2e9e533,STILL_EXISTS,TODO: filter on rank:  preferred; normal or deprecated
aaibffjfc,explosion/spaCy,examples/pipeline/wikidata_entity_linking.py,3163331b1ee4238265e9584247fc36965fb9da13,STILL_EXISTS,TODO remove hardcoded path
aaibffjhf,explosion/spaCy,examples/pipeline/wikidata_entity_linking.py,6763e025e1f351b5c3f133d5a334217f172867b9,STILL_EXISTS,TODO: compile the regular expressions
aaibffjhg,explosion/spaCy,examples/pipeline/wikidata_entity_linking.py,6763e025e1f351b5c3f133d5a334217f172867b9,STILL_EXISTS,TODO: this doesn't work yet
aaibffjja,explosion/spaCy,examples/pipeline/wikidata_entity_linking.py,10ee8dfea240ffe7e2b4d644df12b5179b6f01b6,STILL_EXISTS,TODO: remove hardcoded paths
aaibfgaac,explosion/spaCy,examples/pipeline/wikidata_entity_linking.py,9f308eb5dc8fab4dc3a625480abf567f6841d144,STILL_EXISTS,TODO: read wikipedia texts for entity context
aaibfgabb,explosion/spaCy,examples/pipeline/wikidata_entity_linking.py,004e5e7d1c76bd507e83aab6321177ce5d27f39b,6e3223f23494a8c3361290a748de39f5768438d4,TODO
aaibfgaga,explosion/spaCy,examples/pipeline/wikidata_entity_linking.py,581dc9742d2a7dc790bab7fe59993de8b1279b3b,STILL_EXISTS,TODO: this doesn't work yet
aaibfgahi,explosion/spaCy,examples/pipeline/wikidata_entity_linking.py,581dc9742d2a7dc790bab7fe59993de8b1279b3b,STILL_EXISTS,TODO
aaibfgaid,explosion/spaCy,examples/pipeline/wikidata_entity_linking.py,581dc9742d2a7dc790bab7fe59993de8b1279b3b,STILL_EXISTS,TODO coreference resolution
aaibfgaie,explosion/spaCy,examples/pipeline/wikidata_entity_linking.py,cba9680d13cd2cc1b5a2af9d82acf378dce8fede,STILL_EXISTS,TODO: investigate these cases
aaibfgbaj,explosion/spaCy,examples/pipeline/wikidata_entity_linking.py,4e929600e53f9650f254c7beb17292fca7a20df5,STILL_EXISTS,TODO: take care of these conflicts ! Currently they are being removed from the dataset
aaibfgbbb,explosion/spaCy,examples/pipeline/wikidata_entity_linking.py,f5190267e7ba24d395b9933d9fd2ab63c8e5e866,STILL_EXISTS,TODO: full dataset
aaibfgbcf,explosion/spaCy,examples/pipeline/wiki_entity_linking/run_el.py,69612155782d586c26532dce0f3816d8befcf41a,STILL_EXISTS,TODO: this doesn't work yet
aaibfgbci,explosion/spaCy,examples/pipeline/wiki_entity_linking/run_el.py,69612155782d586c26532dce0f3816d8befcf41a,STILL_EXISTS,TODO: expand to non-persons
aaibfgbcj,explosion/spaCy,examples/pipeline/wiki_entity_linking/run_el.py,69612155782d586c26532dce0f3816d8befcf41a,STILL_EXISTS,TODO: investigate these cases
aaibfgbdd,explosion/spaCy,examples/pipeline/wiki_entity_linking/training_set_creator.py,69612155782d586c26532dce0f3816d8befcf41a,STILL_EXISTS,TODO: full dataset
aaibfgbfd,explosion/spaCy,examples/pipeline/wiki_entity_linking/training_set_creator.py,69612155782d586c26532dce0f3816d8befcf41a,STILL_EXISTS,TODO: take care of these conflicts ! Currently they are being removed from the dataset
aaibfgbjc,explosion/spaCy,examples/pipeline/wiki_entity_linking/wiki_nel_pipeline.py,69612155782d586c26532dce0f3816d8befcf41a,STILL_EXISTS,TODO coreference resolution
aaibfgbjf,explosion/spaCy,examples/pipeline/wiki_entity_linking/wikidata_processor.py,69612155782d586c26532dce0f3816d8befcf41a,STILL_EXISTS,TODO: remove hardcoded paths
aaibfgcad,explosion/spaCy,examples/pipeline/wiki_entity_linking/wikipedia_processor.py,69612155782d586c26532dce0f3816d8befcf41a,STILL_EXISTS,TODO: remove hardcoded paths
aaibfgcca,explosion/spaCy,examples/pipeline/wiki_entity_linking/run_el.py,7e348d7f7ff2d79beec90f8f9862fc52cad8b654,STILL_EXISTS,TODO: expand to other types
aaibfgcci,explosion/spaCy,examples/pipeline/wiki_entity_linking/train_el.py,9f33732b96310dc482097e1a6661415a08acc57a,fb37cdb2d30a6ac3a66df9cddb39951c4bcc93e8,\"\"\" TODO: this code needs to be implemented in pipes.pyx\"\"\"
aaibfgcdh,explosion/spaCy,examples/pipeline/wiki_entity_linking/train_el.py,c6ca8649d7ab67af88af1682fa93a63fc635481c,STILL_EXISTS,TODO ? link_vectors_to_models(self.vocab)
aaibfgcea,explosion/spaCy,examples/pipeline/wiki_entity_linking/train_el.py,c6ca8649d7ab67af88af1682fa93a63fc635481c,2713abc651dc9f601d98e5f9b402852798e22b79,TODO: proper backpropagation taking ranking of elements into account ?
aaibfgceb,explosion/spaCy,examples/pipeline/wiki_entity_linking/train_el.py,c6ca8649d7ab67af88af1682fa93a63fc635481c,STILL_EXISTS,TODO backpropagation also for negative examples
aaibfgcec,explosion/spaCy,examples/pipeline/wiki_entity_linking/train_el.py,c6ca8649d7ab67af88af1682fa93a63fc635481c,STILL_EXISTS,TODO delete ?
aaibfgceg,explosion/spaCy,examples/pipeline/wiki_entity_linking/train_el.py,c6ca8649d7ab67af88af1682fa93a63fc635481c,STILL_EXISTS,TODO: local vectors
aaibfgceh,explosion/spaCy,examples/pipeline/wiki_entity_linking/train_el.py,9d089c0410c8f71cdf80b0b5d613d8c2983fb454,4142e8dd1b05e396c6e24efb7550a86837359118,TODO
aaibfgcjc,explosion/spaCy,examples/pipeline/wiki_entity_linking/train_el.py,b6d788064afdd5871e3d15303d6f622b91a59cc0,STILL_EXISTS,TODO: expand to more than 2 vectors
aaibfgcji,explosion/spaCy,spacy/tokens/underscore.py,8baff1c7c0418b26bf690756e1cac81ecf655816,STILL_EXISTS,Hack to enable autocomplete on custom extensions
aaibfgcjj,explosion/spaCy,spacy/tokens/underscore.py,8baff1c7c0418b26bf690756e1cac81ecf655816,STILL_EXISTS,Hack to port over docstrings of the original function
aaibfgdab,explosion/spaCy,examples/pipeline/wiki_entity_linking/train_el.py,3b81b009547b5c48dea7660e8081f050014f8609,d51bffe63b9e92b3f6c2b4cfb09d2039e6e55a5f,TODO: use lowest_mse and combine with prior probability
aaibfgdad,explosion/spaCy,examples/pipeline/wiki_entity_linking/train_el.py,4142e8dd1b05e396c6e24efb7550a86837359118,STILL_EXISTS,TODO: one call only to begin_update ?
aaibfgdbc,explosion/spaCy,examples/pipeline/wiki_entity_linking/train_el.py,2713abc651dc9f601d98e5f9b402852798e22b79,d51bffe63b9e92b3f6c2b4cfb09d2039e6e55a5f,TODO: batch per doc
aaibfgdbe,explosion/spaCy,examples/pipeline/wiki_entity_linking/train_el.py,2713abc651dc9f601d98e5f9b402852798e22b79,STILL_EXISTS,TODO: FIX
aaibfgdia,explosion/spaCy,examples/pipeline/wiki_entity_linking/train_el.py,b5470f3d753dd3bac3423121a44c0862a67b607c,d51bffe63b9e92b3f6c2b4cfb09d2039e6e55a5f,TODO: use lowest_mse and combine with prior probability
aaibfgdib,explosion/spaCy,examples/pipeline/wiki_entity_linking/train_el.py,b5470f3d753dd3bac3423121a44c0862a67b607c,STILL_EXISTS,TODO list
aaibfgdjh,explosion/spaCy,examples/pipeline/wiki_entity_linking/train_el.py,b5470f3d753dd3bac3423121a44c0862a67b607c,d51bffe63b9e92b3f6c2b4cfb09d2039e6e55a5f,TODO ? link_vectors_to_models(self.vocab) depr?
aaibfgeaa,explosion/spaCy,examples/pipeline/wiki_entity_linking/train_el.py,b5470f3d753dd3bac3423121a44c0862a67b607c,d51bffe63b9e92b3f6c2b4cfb09d2039e6e55a5f,TODO: deprecated ?
aaibfgedj,explosion/spaCy,examples/pipeline/wiki_entity_linking/train_el.py,b5470f3d753dd3bac3423121a44c0862a67b607c,d51bffe63b9e92b3f6c2b4cfb09d2039e6e55a5f,TODO: delete ? try again ?
aaibfgfhb,explosion/spaCy,examples/pipeline/wiki_entity_linking/train_el.py,400b19353de9768805b6a4bcc7bcd72ba57bd001,97241a3ed78d7fa41aaea3de30843ca49b0ae6d0,TODO: proper batches. Currently 1 article at the time
aaibfgfhg,explosion/spaCy,examples/pipeline/wiki_entity_linking/train_el.py,400b19353de9768805b6a4bcc7bcd72ba57bd001,STILL_EXISTS,TODO: ReLu instead of LN(Maxout)  ?
aaibfgfig,explosion/spaCy,examples/pipeline/wiki_entity_linking/train_el.py,dd691d00530eed432d6cf60b39d99206e5830f69,STILL_EXISTS,TODO remove
aaibfggaf,explosion/spaCy,examples/pipeline/wiki_entity_linking/train_el.py,0a15ee4541b2b46db716990830eb0d67d71fa45a,97241a3ed78d7fa41aaea3de30843ca49b0ae6d0,TODO shuffle data (currently positive is always followed by several negatives)
aaibfggbh,explosion/spaCy,examples/pipeline/wiki_entity_linking/train_el.py,2fa3fac8512c1ed102a64017123246ca156cfef5,97241a3ed78d7fa41aaea3de30843ca49b0ae6d0,TODO: multiple docs\/articles
aaibfggbj,explosion/spaCy,examples/pipeline/wiki_entity_linking/train_el.py,2fa3fac8512c1ed102a64017123246ca156cfef5,STILL_EXISTS,concat = entity + doc; but doc is the same within this function (TODO: multiple docs\/articles)
aaibfggfe,explosion/spaCy,examples/pipeline/wiki_entity_linking/train_el.py,4392c01b7bfb22e435249128ac15c196c5b50bd1,STILL_EXISTS,TODO: fix this - doesn't look like all entities are found
aaibfggjd,explosion/spaCy,examples/pipeline/wiki_entity_linking/train_el.py,992fa92b6630ec8eb78ad378602a5774d7327de3,STILL_EXISTS,TODO: more appropriate loss for the whole cluster (currently only pos entities)
aaibfggje,explosion/spaCy,examples/pipeline/wiki_entity_linking/train_el.py,992fa92b6630ec8eb78ad378602a5774d7327de3,a761929fa50365663c8e897c8e5664a22438b3bd,TODO: speed up
aaibfghhh,explosion/spaCy,spacy/_ml.py,fb37cdb2d30a6ac3a66df9cddb39951c4bcc93e8,STILL_EXISTS,TODO: ReLu or LN(Maxout)  ?
aaibfghia,explosion/spaCy,examples/pipeline/wiki_entity_linking/train_descriptions.py,9abbd0899fe2fb64601f02bca206dcad1431365c,STILL_EXISTS,TODO: apply and write to file afterwards !
aaibfghic,explosion/spaCy,examples/pipeline/wiki_entity_linking/train_descriptions.py,9abbd0899fe2fb64601f02bca206dcad1431365c,STILL_EXISTS,TODO: when loss gets too low; a 'mean of empty slice' warning is thrown by numpy
aaibfghid,explosion/spaCy,examples/pipeline/wiki_entity_linking/train_descriptions.py,9abbd0899fe2fb64601f02bca206dcad1431365c,STILL_EXISTS,TODO: min? max?
aaibfghig,explosion/spaCy,examples/pipeline/wiki_entity_linking/wiki_nel_pipeline.py,9abbd0899fe2fb64601f02bca206dcad1431365c,STILL_EXISTS,TODO: write back to file
aaibfghih,explosion/spaCy,examples/pipeline/wiki_entity_linking/kb_creator.py,5c723c32c3e5e639f99005130c050afcf8230346,STILL_EXISTS,TODO: entity vectors !
aaibfghii,explosion/spaCy,examples/pipeline/wiki_entity_linking/kb_creator.py,5c723c32c3e5e639f99005130c050afcf8230346,STILL_EXISTS,TODO: vector_list !
aaibfghij,explosion/spaCy,examples/pipeline/wiki_entity_linking/wiki_nel_pipeline.py,5c723c32c3e5e639f99005130c050afcf8230346,STILL_EXISTS,TODO entity vectors
aaibfghjg,explosion/spaCy,examples/pipeline/wiki_entity_linking/kb_creator.py,d8b435ceffcf9143a78678d7c87a8e4e4216dcc5,STILL_EXISTS,TODO: should we remove entities from the KB where there is no description ?
aaibfgicc,explosion/spaCy,examples/pipeline/wiki_entity_linking/training_set_creator.py,a5c061f50633831ce49e4cc6660d177569bb9767,b312f2d0e79b886d0d824f9294ccc2f1f24b725a,TODO: fix this - doesn't look like all entities are found
aaibfgihi,explosion/spaCy,examples/pipeline/wiki_entity_linking/wiki_nel_pipeline.py,b312f2d0e79b886d0d824f9294ccc2f1f24b725a,81731907ba0c3589c28367c5ec08f8a8f3eaeeae,TODO: entity_defs should also contain entities not in the KB
aaibfgjhf,explosion/spaCy,spacy/_ml.py,68a0662019760a20bbc740be43b2ec58aa5a816e,6e809e9b8b3c961bc4c7c4565edef699717c0919,TODO proper error
aaibfhdai,explosion/spaCy,spacy/_ml.py,09dc01a4266b0f6b3a557918a4af44eac685f1bb,569cc9898200772cc894b1663eb03e9fd017c1c9,This is a hack to avoid the problem in #3853. Maybe we should
aaibfhhjg,explosion/spaCy,spacy/cli/download.py,6bec24cdd09c8168d2ce8667e376bb1f0e320c07,a84025d70b276df3a84b31b024525c51c04f53cf,Maybe it's possible to remove this \u2013 mostly worried about cross-
aaibfhiec,explosion/spaCy,examples/training/pretrain_kb.py,0ba1b5eebcf8d7d8a5c1e2859948622481df70d7,STILL_EXISTS,\"\"\"Example of defining and (pre)training spaCy's knowledge base; || which is needed to implement entity linking functionality. ||  || For more details; see the documentation: || * Knowledge base: https:\/\/spacy.io\/api\/kb || * Entity Linking: https:\/\/spacy.io\/usage\/linguistic-features#entity-linking ||  || Compatible with: spaCy vX.X || Last tested with: vX.X || \"\"\"
aaibfhjaa,explosion/spaCy,spacy/cli/debug_data.py,a58cb023d7312d218f45c566f02bf59e708ec4fb,STILL_EXISTS,Minimum number of expected occurrences of dependency labels
aaibfhjab,explosion/spaCy,spacy/cli/debug_data.py,a58cb023d7312d218f45c566f02bf59e708ec4fb,b7111da1d782b24a80ad269c096424271fb4b6fc,TODO: move validation to GoldCorpus in order to be able to load from dir
aaibfibga,explosion/spaCy,bin/wiki_entity_linking/train_descriptions.py,bcd08f20afb1cb96d5431a9eb36a341f15b4ee8a,STILL_EXISTS,TODO: when loss gets too low; a 'mean of empty slice' warning is thrown by numpy
aaibfigfb,explosion/spaCy,spacy/util.py,5ca7dd0f9471967a06b989ff8ee331e6954ad17e,3e8f136ba7e400dc046e4a4571ffd3def948daf0,TODO: move to spacy.errors
aaibfijhg,explosion/spaCy,bin/ud/ud_train.py,482c7cd1b94d9fab299635bc9ee12d8b31b8706a,12158c1e3a96d4d17cf4ecfe36b899924200f3de,temp fix to avoid import issues cf https:\/\/github.com\/explosion\/spaCy\/issues\/4200
aaibfijhh,explosion/spaCy,examples/training/conllu.py,482c7cd1b94d9fab299635bc9ee12d8b31b8706a,12158c1e3a96d4d17cf4ecfe36b899924200f3de,temp fix to avoid import issues cf https:\/\/github.com\/explosion\/spaCy\/issues\/4200
aaibfijia,explosion/spaCy,examples/vectors_tensorboard.py,482c7cd1b94d9fab299635bc9ee12d8b31b8706a,12158c1e3a96d4d17cf4ecfe36b899924200f3de,temp fix to avoid import issues cf https:\/\/github.com\/explosion\/spaCy\/issues\/4200
aaibfijif,explosion/spaCy,spacy/cli/profile.py,482c7cd1b94d9fab299635bc9ee12d8b31b8706a,12158c1e3a96d4d17cf4ecfe36b899924200f3de,temp fix to avoid import issues cf https:\/\/github.com\/explosion\/spaCy\/issues\/4200
aaibfjaff,explosion/spaCy,spacy/lookups.py,7d8df6915865b76d6ad44f6c8bbd7e6ddcd1fa06,8c43cfc75421ca7dbc21dce81cb2195410d40dfb,TODO: serialize bloom too. For now just reconstruct it.
aaibfjdca,explosion/spaCy,bin/ud/ud_run_test.py,06d8c3a20f8fd198239052efc3bc314f03f45c0f,33ba9ff464f52994b4eda37983426556cb33752b,TODO: This shouldn't be necessary? Should be handled in merge
aaibfjddc,explosion/spaCy,spacy/cli/download.py,06d8c3a20f8fd198239052efc3bc314f03f45c0f,9cd6ca3e4d9074f373c3cac7f96b4bd725bc2730,Maybe it's possible to remove this \u2013 mostly worried about cross-
aaibfjeec,explosion/spaCy,examples/streamlit_spacy.py,867e93aae2503abe08c10dd577b7dc5d592823f7,STILL_EXISTS,Double newlines seem to mess with the rendering
aaibfjeed,explosion/spaCy,examples/streamlit_spacy.py,867e93aae2503abe08c10dd577b7dc5d592823f7,STILL_EXISTS,Newlines seem to mess with the rendering
aaibfjfbj,explosion/spaCy,spacy/tests/matcher/test_matcher_logic.py,5efae495f18f37316bd641a05ca26e62cb78e242,STILL_EXISTS,removing once should work
aaibfjfdf,explosion/spaCy,bin/wiki_entity_linking/wiki_io.py,2d249a9502bfc5d3d2111165672d964b1eebe35e,STILL_EXISTS,min() needed to prevent error on windows; cf https:\/\/stackoverflow.com\/questions\/52404416\/
aaibfjffa,explosion/spaCy,bin/wiki_entity_linking/wiki_namespaces.py,2d249a9502bfc5d3d2111165672d964b1eebe35e,STILL_EXISTS,TODO: add more cases from non-English WP's
aaibfjfjc,explosion/spaCy,bin/wiki_entity_linking/wikipedia_processor.py,2d249a9502bfc5d3d2111165672d964b1eebe35e,cafe94ee045fe9937cadbdc0e1c96d6eabde5dec,TODO: check that alias == doc.text[start:end]
aaibfjgbh,explosion/spaCy,spacy/cli/debug_data.py,135e3de5310497f96cc8f8e877557391f7ff75ab,STILL_EXISTS,TODO: validate_json
aaibfjhih,explosion/spaCy,spacy/ml/tok2vec.py,406eb95a4748c0c88f188787aa755711d9f33769,STILL_EXISTS,This is a pretty ugly hack. Not sure what the best solution should be.
aaibfjidf,explosion/spaCy,spacy/ml/tok2vec.py,9e210fa7fdb8e376655e7a7ab7debd3ffd718a63,06f0a8daa0b919edbafa966db42fc74dce5cab02,is a bit ugly here.
aaibfjiec,explosion/spaCy,spacy/ml/_legacy_tok2vec.py,e82306937eff71914b847b380286e122f81a7b15,STILL_EXISTS,Work around thinc API limitations :(. TODO: Revise in Thinc 7
aaibfjjgj,explosion/spaCy,spacy/errors.py,e48a09df4eb580bd4ffb2270d9034dc69e081742,732142bf2825be61824d453009cc0cea130c3b4b,TODO: fix numbering after merging develop into master
aaibfjjha,explosion/spaCy,spacy/language.py,e48a09df4eb580bd4ffb2270d9034dc69e081742,ae15c9de7971679df9bb60034d007530957205ad,TODO: document
aaibgaace,explosion/spaCy,spacy/language.py,392c4880d9b52c045990d9916a7ed8081573b80b,ae15c9de7971679df9bb60034d007530957205ad,TODO: document
aaibgaahg,explosion/spaCy,spacy/errors.py,158b98a3ef643f93e1e13e11b8119b28f5164b19,732142bf2825be61824d453009cc0cea130c3b4b,TODO: fix numbering after merging develop into master
aaibgabbd,explosion/spaCy,spacy/errors.py,de33b6d56630241ad383b0b2a6af2d0fbc6f811d,84d5b7ad0a6a99f8f06a4bd1cd712ce37da80561,TODO: fix numbering after merging develop into master
aaibgaedg,explosion/spaCy,spacy/language.py,e1b493ae8521af36fd1f0dfeafe7d9eb0408fe75,ae15c9de7971679df9bb60034d007530957205ad,TODO: document
aaibgaehh,explosion/spaCy,bin/wiki_entity_linking/train_descriptions.py,569cc9898200772cc894b1663eb03e9fd017c1c9,STILL_EXISTS,TODO: removed the zero_init here - is oK?
aaibgaeib,explosion/spaCy,examples/training/pretrain_textcat.py,569cc9898200772cc894b1663eb03e9fd017c1c9,04ba37b667764c5b18825a5ee8ce513962e73bcd,TODO FIX
aaibgafca,explosion/spaCy,spacy/cli/pretrain.py,569cc9898200772cc894b1663eb03e9fd017c1c9,257246017572433af7825d561de573dae73828f0,TODO: this code originally didn't normalize; but shouldn't normalize=True ?
aaibgafdi,explosion/spaCy,spacy/language.py,569cc9898200772cc894b1663eb03e9fd017c1c9,ae15c9de7971679df9bb60034d007530957205ad,TODO: document
aaibgafdj,explosion/spaCy,spacy/language.py,569cc9898200772cc894b1663eb03e9fd017c1c9,cabd60fa1e87e95398b60a2a2246a45711a7ffee,TODO: shouldn't this be as_example=as_example ?
aaibgafif,explosion/spaCy,spacy/ml/component_models.py,569cc9898200772cc894b1663eb03e9fd017c1c9,STILL_EXISTS,TODO: experiment with init_w=zero_init
aaibgafig,explosion/spaCy,spacy/ml/component_models.py,569cc9898200772cc894b1663eb03e9fd017c1c9,STILL_EXISTS,TODO: glorot_uniform_init seems to work a bit better than zero_init here?!
aaibgafih,explosion/spaCy,spacy/ml/component_models.py,569cc9898200772cc894b1663eb03e9fd017c1c9,STILL_EXISTS,Work around thinc API limitations :(. TODO: Revise in Thinc 7
aaibgafjb,explosion/spaCy,spacy/ml/tok2vec.py,569cc9898200772cc894b1663eb03e9fd017c1c9,06f0a8daa0b919edbafa966db42fc74dce5cab02,TODO: actual vectors instead of name
aaibgafjc,explosion/spaCy,spacy/ml/tok2vec.py,569cc9898200772cc894b1663eb03e9fd017c1c9,STILL_EXISTS,TODO FIX
aaibgafjd,explosion/spaCy,spacy/ml/tok2vec.py,569cc9898200772cc894b1663eb03e9fd017c1c9,06f0a8daa0b919edbafa966db42fc74dce5cab02,TODO: update
aaibgafje,explosion/spaCy,spacy/pipeline/tok2vec.py,569cc9898200772cc894b1663eb03e9fd017c1c9,STILL_EXISTS,TODO: use examples instead ?
aaibgagjb,explosion/spaCy,spacy/cli/pretrain.py,de11ea753a507724c73c22905f9bd86d7a743561,89967f3701549cdd889c6f78c5b5e05898d0b1f1,TODO: this code originally didn't normalize; but shouldn't normalize=True ?
aaibgagji,explosion/spaCy,spacy/language.py,de11ea753a507724c73c22905f9bd86d7a743561,ae15c9de7971679df9bb60034d007530957205ad,TODO: document
aaibgahcb,explosion/spaCy,spacy/cli/train_from_config.py,e3f40a6a0f590088d16dbdbc252d9304cf482cfc,06f0a8daa0b919edbafa966db42fc74dce5cab02,TODO: relative imports?
aaibgaiaf,explosion/spaCy,spacy/cli/pretrain.py,5d21d3e8b900cbf46c14515bd07bbf8edfcd5586,ffe0451d0972ec209556dc7aad356deca1cbe0a7,TODO: this code originally didn't normalize; but shouldn't normalize=True ?
aaibgaidj,explosion/spaCy,spacy/language.py,5d21d3e8b900cbf46c14515bd07bbf8edfcd5586,ae15c9de7971679df9bb60034d007530957205ad,TODO: document
aaibgbacf,explosion/spaCy,examples/training/pretrain_textcat.py,06f0a8daa0b919edbafa966db42fc74dce5cab02,STILL_EXISTS,TODO: replace with config version
aaibgbacg,explosion/spaCy,spacy/cli/pretrain.py,06f0a8daa0b919edbafa966db42fc74dce5cab02,STILL_EXISTS,TODO: replace with config
aaibgbadh,explosion/spaCy,spacy/errors.py,06f0a8daa0b919edbafa966db42fc74dce5cab02,84d5b7ad0a6a99f8f06a4bd1cd712ce37da80561,TODO: fix numbering after merging develop into master
aaibgbaec,explosion/spaCy,spacy/language.py,06f0a8daa0b919edbafa966db42fc74dce5cab02,ae15c9de7971679df9bb60034d007530957205ad,TODO: document
aaibgbaee,explosion/spaCy,spacy/ml/models/multi_task.py,06f0a8daa0b919edbafa966db42fc74dce5cab02,ffe0451d0972ec209556dc7aad356deca1cbe0a7,TODO cf https:\/\/github.com\/explosion\/spaCy\/blob\/2c107f02a4d60bda2440db0aad1a88cbbf4fb52d\/spacy\/_ml.py#L828
aaibgbaeg,explosion/spaCy,spacy/ml/models/tagger.py,06f0a8daa0b919edbafa966db42fc74dce5cab02,STILL_EXISTS,TODO: glorot_uniform_init seems to work a bit better than zero_init here?!
aaibgbaeh,explosion/spaCy,spacy/ml/models/textcat.py,06f0a8daa0b919edbafa966db42fc74dce5cab02,311133e579158a26f34379e44054762dac8d93fc,TODO: experiment with init_w=zero_init
aaibgbafi,explosion/spaCy,spacy/ml/models/tok2vec.py,06f0a8daa0b919edbafa966db42fc74dce5cab02,5847be6022e615cdea55ca5a7856d203254e7ddf,is a bit ugly here.
aaibgbafj,explosion/spaCy,spacy/ml/models/tok2vec.py,06f0a8daa0b919edbafa966db42fc74dce5cab02,123f8b832d7c00e5479e3d814bbaadb54ba54966,TODO FIX
aaibgbaga,explosion/spaCy,spacy/ml/models/tok2vec.py,06f0a8daa0b919edbafa966db42fc74dce5cab02,5847be6022e615cdea55ca5a7856d203254e7ddf,TODO: update
aaibgbahb,explosion/spaCy,spacy/tests/parser/test_ner.py,06f0a8daa0b919edbafa966db42fc74dce5cab02,343a44abe9dd9f505eb1d7d3ba95e3415864f66f,if this goes wrong; the initialization of the parser's upper layer is probably broken
aaibgbaic,explosion/spaCy,spacy/tests/test_tok2vec.py,06f0a8daa0b919edbafa966db42fc74dce5cab02,c9da9605f74e7d2aff308c50001fbd5043d50bce,TODO: fix tok2vec arguments
aaibgbbdc,explosion/spaCy,spacy/_ml.py,d307e9ca58c84dc24e6717fccafe7b55c604ee7c,24f72c669c38935c50007d7036e764192cbe253f,This is a hack to avoid the problem in #3853.
aaibgbbdf,explosion/spaCy,spacy/_ml.py,d307e9ca58c84dc24e6717fccafe7b55c604ee7c,24f72c669c38935c50007d7036e764192cbe253f,TODO Make concatenate support lists
aaibgbbic,explosion/spaCy,spacy/language.py,3adc511cb055eec44d9520f923f0da9fc0a1b5e5,ae15c9de7971679df9bb60034d007530957205ad,TODO: document
aaibgbcch,explosion/spaCy,spacy/language.py,5847be6022e615cdea55ca5a7856d203254e7ddf,ae15c9de7971679df9bb60034d007530957205ad,TODO: document
aaibgbcej,explosion/spaCy,spacy/cli/train.py,353f8486f52c46e0f051c488f4d0c20aaf2e6d48,828acffc12d6e57f48c345196e79ffa1fb917419,Bit of a hack after the refactor to get the vectors into a default config
aaibgbcig,explosion/spaCy,spacy/language.py,fba219f73765725afb7468c3c1b114df3e1a27f4,ae15c9de7971679df9bb60034d007530957205ad,TODO: document
aaibgbdcg,explosion/spaCy,spacy/cli/train.py,828acffc12d6e57f48c345196e79ffa1fb917419,8952effcc43b1694a4c0377904667a45b4ed1318,Bit of a hack after the refactor to get the vectors into a default config
aaibgbdfd,explosion/spaCy,spacy/language.py,46568f40a7fa985ed3a9e9f9a839e2e757c77523,ae15c9de7971679df9bb60034d007530957205ad,TODO: document
aaibgbegc,explosion/spaCy,spacy/tests/lang/hy/test_tokenizer.py,beef184e53f5fed4721a69190958a6b0b4cf6a89,STILL_EXISTS,TODO add test cases with valid punctuation signs.
aaibgbejh,explosion/spaCy,spacy/util.py,3d2c308906e2bde7ca57d2e8213252530b944502,6918d99b6c631b5256aa24302050b085af841cc8,TODO: replace with (stable) Draft6Validator; if available
aaibgbfed,explosion/spaCy,spacy/util.py,84e06f9fb767910011ffeff69d5895ac6eeebf23,eb117e2fce9d1029670f52690d30d17e6edbd24e,TODO: replace with (stable) Draft6Validator; if available
aaibgbgjj,explosion/spaCy,spacy/util.py,792c8af8cfda7468a89106f1dc951ba931c132ab,333b1a308b8edd91e06ce914e49b10834e2de3ce,TODO: replace with (stable) Draft6Validator; if available
aaibgbhce,explosion/spaCy,spacy/language.py,e0fda2bd81bd7e7b1a9006c403d52c470954701b,a5cd2032843b26fbff9d6e0b53637e9477af3f7f,TODO: de-uglify (incorporating into component decorator didn't work because of circular imports)
aaibgbhcj,explosion/spaCy,spacy/language.py,e0fda2bd81bd7e7b1a9006c403d52c470954701b,ae15c9de7971679df9bb60034d007530957205ad,TODO: document
aaibgbhdf,explosion/spaCy,spacy/ml/_biluo.py,333b1a308b8edd91e06ce914e49b10834e2de3ce,STILL_EXISTS,TODO: Fix error
aaibgbhga,explosion/spaCy,spacy/ml/_iob.py,333b1a308b8edd91e06ce914e49b10834e2de3ce,STILL_EXISTS,TODO: Fix error
aaibgbidb,explosion/spaCy,spacy/cli/train.py,a5cd2032843b26fbff9d6e0b53637e9477af3f7f,84d5b7ad0a6a99f8f06a4bd1cd712ce37da80561,Bit of a hack after the refactor to get the vectors into a default config
aaibgbigi,explosion/spaCy,spacy/language.py,f00de445dd04b61bc55a0fe010c9cd3862d38aef,ae15c9de7971679df9bb60034d007530957205ad,TODO: document
aaibgbiha,explosion/spaCy,spacy/tests/parser/test_ner.py,f00de445dd04b61bc55a0fe010c9cd3862d38aef,343a44abe9dd9f505eb1d7d3ba95e3415864f66f,if this goes wrong; the initialization of the parser's upper layer is probably broken
aaibgbjbh,explosion/spaCy,spacy/util.py,d8f3190c0a265033ca367097e00cbf085b34615a,24f72c669c38935c50007d7036e764192cbe253f,TODO: replace with (stable) Draft6Validator; if available
aaibgcafa,explosion/spaCy,spacy/language.py,24f72c669c38935c50007d7036e764192cbe253f,ae15c9de7971679df9bb60034d007530957205ad,TODO: document
aaibgceah,explosion/spaCy,spacy/cli/train.py,84d5b7ad0a6a99f8f06a4bd1cd712ce37da80561,291483157dacfc80ecd6ba2f7e097fbe98a4395a,Bit of a hack after the refactor to get the vectors into a default config
aaibgcebc,explosion/spaCy,spacy/errors.py,84d5b7ad0a6a99f8f06a4bd1cd712ce37da80561,d79964bcb182d00ae1a590e99f724be19db9c317,TODO: fix numbering after merging develop into master
aaibgcefa,explosion/spaCy,spacy/util.py,84d5b7ad0a6a99f8f06a4bd1cd712ce37da80561,STILL_EXISTS,TODO: replace with (stable) Draft6Validator; if available
aaibgcejj,explosion/spaCy,spacy/_ml.py,dc94052d6ee35f70cd8d5bbce399e4c52e8bd19e,STILL_EXISTS,This is a hack to avoid the problem in #3853.
aaibgcfac,explosion/spaCy,spacy/_ml.py,dc94052d6ee35f70cd8d5bbce399e4c52e8bd19e,STILL_EXISTS,TODO Make concatenate support lists
aaibgchad,explosion/spaCy,spacy/language.py,6e6db6afb62a0377bcd5f0c64220ad05f512c073,ae15c9de7971679df9bb60034d007530957205ad,TODO: document
aaibgchej,explosion/spaCy,spacy/language.py,58750b06f8011dd854b22a926803add047fbe0b7,ae15c9de7971679df9bb60034d007530957205ad,TODO: document
aaibgchjj,explosion/spaCy,spacy/util.py,bbc1836581932d24818df064da8d64c7ec03ca23,STILL_EXISTS,TODO: replace with (stable) Draft6Validator; if available
aaibgcibh,explosion/spaCy,spacy/util.py,5b350a6c9998ccb53439f2721159ab92ca61003f,e9711c2f17f9404d0a799b41881912d8a884a6a2,Handle cases where exact version is provided as constraint
aaibgcjef,explosion/spaCy,spacy/tests/parser/test_ner.py,56a9d1b78c38eedc89a17ec1cd1e38cdaa543bef,343a44abe9dd9f505eb1d7d3ba95e3415864f66f,if this goes wrong; the initialization of the parser's upper layer is probably broken
aaibgcjii,explosion/spaCy,spacy/util.py,d79964bcb182d00ae1a590e99f724be19db9c317,STILL_EXISTS,TODO: replace with (stable) Draft6Validator; if available
aaibgcjjh,explosion/spaCy,spacy/cli/pretrain.py,4ed6278663c9482e14b549b2079f02cc186bc078,07886a3de35f6a9188c6a2963c45e4fbda138004,TODO
aaibgcjji,explosion/spaCy,spacy/cli/pretrain.py,4ed6278663c9482e14b549b2079f02cc186bc078,STILL_EXISTS,Load in pretrained weights - TODO test
aaibgdabb,explosion/spaCy,spacy/tests/lang/ja/test_tokenizer.py,410fb7ee437b649c8bd291da84db5dc7cd65db45,656574a01a2e2d12b26cfa63dea4076e3f93dc93,XXX This isn't working? Always passes
aaibgdace,explosion/spaCy,spacy/util.py,d93cbeb14fe71e8e17445224241e0d26f0223dbd,e9711c2f17f9404d0a799b41881912d8a884a6a2,Handle cases where exact version is provided as constraint
aaibgdafa,explosion/spaCy,spacy/util.py,d93cbeb14fe71e8e17445224241e0d26f0223dbd,STILL_EXISTS,TODO: replace with (stable) Draft6Validator; if available
aaibgdaga,explosion/spaCy,spacy/cli/train_from_config.py,c0f4a1e43b5210ea631809ea590b486bca066d25,STILL_EXISTS,Make sure all files and paths exists if they are needed
aaibgdbcf,explosion/spaCy,spacy/util.py,e9711c2f17f9404d0a799b41881912d8a884a6a2,STILL_EXISTS,TODO: replace with (stable) Draft6Validator; if available
aaibgdbfh,explosion/spaCy,spacy/tests/parser/test_ner.py,8283df80e91d7fba385b12c42eb976ab30ca1e2a,343a44abe9dd9f505eb1d7d3ba95e3415864f66f,if this goes wrong; the initialization of the parser's upper layer is probably broken
aaibgdchj,explosion/spaCy,spacy/language.py,52728d8fa37fe21fc599626f04d615809fc42dab,ae15c9de7971679df9bb60034d007530957205ad,TODO: document
aaibgdgcd,explosion/spaCy,spacy/cli/train_from_config.py,c12713a8be09b4c9c5bd7c02ccf2f853d8698881,f77e0bc028c9c6fbad6801aee97d479dde37f113,from ..schemas import ConfigSchema  # TODO: include?
aaibgdgcf,explosion/spaCy,spacy/schemas.py,c12713a8be09b4c9c5bd7c02ccf2f853d8698881,7adffc5361b921e36a8c60a8072c9126650311c4,TODO: write
aaibgdgfb,explosion/spaCy,spacy/cli/project.py,5ba1df5e78de64ae123b7c3fb8bf401c906e4637,1e5b4d85249ebdec6819df21663215fc6d04e4c0,TODO: check for \"dvc\" command instead of Python library?
aaibgdgfe,explosion/spaCy,spacy/cli/project.py,5ba1df5e78de64ae123b7c3fb8bf401c906e4637,STILL_EXISTS,TODO: support loading from GitHub URLs? Automatically convert to raw?
aaibgdhae,explosion/spaCy,spacy/cli/train_from_config.py,83b4aa05c973d046b37c413b640766d768bf5889,STILL_EXISTS,from ..schemas import ConfigSchema  # TODO: include?
aaibgdhee,explosion/spaCy,spacy/util.py,6fe6e761de836550aa71105e1dfd75335612fa82,STILL_EXISTS,TODO: replace with (stable) Draft6Validator; if available
aaibgdhfg,explosion/spaCy,spacy/cli/project.py,82a03ee18e8f2ca3982646c1076c1edf02ce0698,STILL_EXISTS,TODO: is this needed \/ a good idea?
aaibgdhfh,explosion/spaCy,spacy/cli/_app.py,01c394eb230144b41df673f8ce54f39ed162fb8e,STILL_EXISTS,keep the names short; but not needed at the moment.
aaibgdhgd,explosion/spaCy,bin/ud/ud_train.py,8c29268749fc7ffc47ed662d5fb65dc8c57157f9,343a44abe9dd9f505eb1d7d3ba95e3415864f66f,TODO: doc-level format
aaibgdhge,explosion/spaCy,examples/training/rehearsal.py,8c29268749fc7ffc47ed662d5fb65dc8c57157f9,STILL_EXISTS,TODO: further fix & test this script for v.3 ? (read_gold_data is never called)
aaibgdhgg,explosion/spaCy,spacy/cli/convert.py,8c29268749fc7ffc47ed662d5fb65dc8c57157f9,abad56db7d0032d6e75e08c8cb1cf763bef5129d,\"conllubio\": conllu2docs; TODO
aaibgdhgh,explosion/spaCy,spacy/cli/convert.py,8c29268749fc7ffc47ed662d5fb65dc8c57157f9,abad56db7d0032d6e75e08c8cb1cf763bef5129d,\"conll\": conllu2docs; TODO
aaibgdhgj,explosion/spaCy,spacy/cli/convert.py,8c29268749fc7ffc47ed662d5fb65dc8c57157f9,e92df281cef2de3c0c95f73c79a032eb2194b104,TODO: support msgpack via stdout in srsly?
aaibgdhhc,explosion/spaCy,spacy/cli/train.py,8c29268749fc7ffc47ed662d5fb65dc8c57157f9,3e78e82a834b42ff165fbb2614cb6f42206ce390,from ..schemas import ConfigSchema  # TODO: include?
aaibgdhif,explosion/spaCy,spacy/gold/augment.py,8c29268749fc7ffc47ed662d5fb65dc8c57157f9,STILL_EXISTS,TODO: naming
aaibgdiaf,explosion/spaCy,spacy/gold/converters/__init__.py,8c29268749fc7ffc47ed662d5fb65dc8c57157f9,abad56db7d0032d6e75e08c8cb1cf763bef5129d,TODO: Update this one
aaibgdibc,explosion/spaCy,spacy/gold/iob_utils.py,8c29268749fc7ffc47ed662d5fb65dc8c57157f9,STILL_EXISTS,TODO: We shouldn't be getting these malformed inputs. Fix this.
aaibgdigf,explosion/spaCy,spacy/tests/regression/test_issue4665.py,8c29268749fc7ffc47ed662d5fb65dc8c57157f9,343a44abe9dd9f505eb1d7d3ba95e3415864f66f,TODO
aaibgdigi,explosion/spaCy,spacy/tests/test_cli.py,8c29268749fc7ffc47ed662d5fb65dc8c57157f9,abad56db7d0032d6e75e08c8cb1cf763bef5129d,TODO
aaibgdijb,explosion/spaCy,spacy/tests/test_new_example.py,8c29268749fc7ffc47ed662d5fb65dc8c57157f9,8128e5eb354d534692d13c29c47fce3a764cde84,TODO: shouldn't this throw some sort of warning ?
aaibgdijg,explosion/spaCy,spacy/util.py,8c29268749fc7ffc47ed662d5fb65dc8c57157f9,STILL_EXISTS,Handle cases where exact version is provided as constraint
aaibgdjbj,explosion/spaCy,spacy/util.py,8c29268749fc7ffc47ed662d5fb65dc8c57157f9,STILL_EXISTS,TODO: replace with (stable) Draft6Validator; if available
aaibgdjcc,explosion/spaCy,spacy/cli/project.py,8b305253d3746e94f2b0d0f70c94dbfd53e5d194,ed46951842e2428f74e1ec9a5b2a0b09281ad156,TODO: check that .dvc exists in other commands?
aaibgdjjj,explosion/spaCy,spacy/cli/project.py,c96b4a37b69d81b298da810a56dd52664698a0a1,STILL_EXISTS,TODO: find a better solution for this?
aaibgeaab,explosion/spaCy,spacy/cli/project.py,c96b4a37b69d81b298da810a56dd52664698a0a1,STILL_EXISTS,TODO: add support for chaches
aaibgeaad,explosion/spaCy,spacy/cli/project.py,c96b4a37b69d81b298da810a56dd52664698a0a1,d6aa4cb478e9ba9c85f0c20de30f4b991a2b5432,TODO: Can we read out Weak ETags error?
aaibgeaae,explosion/spaCy,spacy/cli/project.py,c96b4a37b69d81b298da810a56dd52664698a0a1,569376e34ea331f6530dd32d30fe9b9321af098f,TODO: replace curl
aaibgeceb,explosion/spaCy,spacy/util.py,894b8e7ff652a26c71ebbe6fa3d75a25104991c8,STILL_EXISTS,Handle cases where exact version is provided as constraint
aaibgeded,explosion/spaCy,spacy/cli/project.py,1d2c646e579f0e018e2eed363cc0a0240dde8d47,7e4cbda89ac86cb2e45701c8335f7bfb9f8f4e33,Remove unused and confusing plot templates from .dvc directory
aaibgedee,explosion/spaCy,spacy/cli/project.py,1d2c646e579f0e018e2eed363cc0a0240dde8d47,7e4cbda89ac86cb2e45701c8335f7bfb9f8f4e33,TODO: maybe we shouldn't do this; but it's otherwise super confusing
aaibgedfb,explosion/spaCy,spacy/cli/project.py,b311ce982f097d0275236e24861f1de19ebc122c,STILL_EXISTS,Remove unused and confusing plot templates from .dvc directory
aaibgedfc,explosion/spaCy,spacy/cli/project.py,b311ce982f097d0275236e24861f1de19ebc122c,STILL_EXISTS,TODO: maybe we shouldn't do this; but it's otherwise super confusing
aaibgedgf,explosion/spaCy,spacy/util.py,60f97bc519ecf607f7711c3ae0402f89d939989b,STILL_EXISTS,Handle cases where exact version is provided as constraint
aaibgedii,explosion/spaCy,spacy/util.py,60f97bc519ecf607f7711c3ae0402f89d939989b,STILL_EXISTS,TODO: replace with (stable) Draft6Validator; if available
aaibgeeff,explosion/spaCy,spacy/cli/pretrain.py,2bd1bf81f1a64f6dbbbd4971fba3af39cc8da175,3e78e82a834b42ff165fbb2614cb6f42206ce390,TODO
aaibgefbj,explosion/spaCy,examples/training/rehearsal.py,fcbf899b086c785261abe9ddb776af9615fd1f58,STILL_EXISTS,TODO: further fix & test this script for v.3 ? (read_gold_data is never called)
aaibgefdb,explosion/spaCy,spacy/cli/train.py,fcbf899b086c785261abe9ddb776af9615fd1f58,27a1cd3c630055802513a20c1e75d0b37943cc39,from ..schemas import ConfigSchema  # TODO: include?
aaibgefhe,explosion/spaCy,spacy/language.py,fcbf899b086c785261abe9ddb776af9615fd1f58,ae15c9de7971679df9bb60034d007530957205ad,TODO: document
aaibgefig,explosion/spaCy,spacy/cli/pretrain.py,412dbb1f3885ccc3e26db67a2799cbc8a9e7f54f,19dc42776af33ee209c256c739303f27aa458144,TODO
aaibgejbj,explosion/spaCy,spacy/cli/project.py,8cb7f9ccff5da3a5eaeb3c3ebe99214f6673d084,STILL_EXISTS,Writing them like this for readability but maybe replace with regex?
aaibgejdd,explosion/spaCy,spacy/cli/project/assets.py,8f9552d9e722d6e14e47304c0fc40ec5b4177677,aeebc6678dfa6612fe2a59a50e221324d92e27d9,TODO: find a solution for caches
aaibgejed,explosion/spaCy,spacy/cli/project/assets.py,8f9552d9e722d6e14e47304c0fc40ec5b4177677,aeebc6678dfa6612fe2a59a50e221324d92e27d9,TODO: add support for caches
aaibgejge,explosion/spaCy,spacy/cli/project/run.py,8f9552d9e722d6e14e47304c0fc40ec5b4177677,STILL_EXISTS,Not sure if this is needed or a good idea. Motivation: users may often
aaibgfchi,explosion/spaCy,spacy/cli/project/util.py,a60562f208b4565f8cafc07cfcadcc719f889baa,STILL_EXISTS,TODO: this is currently pretty slow
aaibgfchj,explosion/spaCy,spacy/cli/train.py,240e0a62cae2994bb55d4a1e57256330d23377c0,92d7832a86b9bb525cfc02f97378712ff4770cfb,TODO: separate checks from loading
aaibgfcii,explosion/spaCy,spacy/schemas.py,240e0a62cae2994bb55d4a1e57256330d23377c0,STILL_EXISTS,TODO: add config schema \/ types for components so we can fill and validate
aaibgfcjc,explosion/spaCy,spacy/cli/_util.py,73332ddb67b22949a9cb2600fbaa0c475f6967c2,STILL_EXISTS,keep the names short; but not needed at the moment.
aaibgfdab,explosion/spaCy,spacy/cli/_util.py,73332ddb67b22949a9cb2600fbaa0c475f6967c2,STILL_EXISTS,TODO: this is currently pretty slow
aaibgfdad,explosion/spaCy,spacy/cli/_util.py,3583ea84d8fb6979d068c23816c0994dab5f81a4,STILL_EXISTS,TODO: improve logic to handle simple types like list of strings?
aaibgfdbd,explosion/spaCy,spacy/cli/train.py,de6a32315c7c0305cea96ce92d7d28d37aedc041,27a1cd3c630055802513a20c1e75d0b37943cc39,from ..schemas import ConfigSchema  # TODO: include?
aaibgfdbg,explosion/spaCy,spacy/cli/train.py,defe1e7213ea3a974fd9131dd89f99739cb99d89,92d7832a86b9bb525cfc02f97378712ff4770cfb,TODO: separate checks from loading
aaibgfdbh,explosion/spaCy,spacy/cli/train.py,defe1e7213ea3a974fd9131dd89f99739cb99d89,27a1cd3c630055802513a20c1e75d0b37943cc39,from ..schemas import ConfigSchema  # TODO: include?
aaibgfddg,explosion/spaCy,spacy/schemas.py,b7111da1d782b24a80ad269c096424271fb4b6fc,f98a04434a737ff64c35e5d9d8dc6797d0ce9817,TODO: use a more detailed schema for this?
aaibgfdej,explosion/spaCy,spacy/schemas.py,d1d7fd5f5db427029138ac5422966c5794341a24,STILL_EXISTS,TODO: add config schema \/ types for components so we can fill and validate
aaibgfdfc,explosion/spaCy,spacy/schemas.py,d1d7fd5f5db427029138ac5422966c5794341a24,f98a04434a737ff64c35e5d9d8dc6797d0ce9817,TODO: use a more detailed schema for this?
aaibgfedd,explosion/spaCy,spacy/cli/pretrain.py,872938ec761ecc44694e1a31d498a864b2f53995,19dc42776af33ee209c256c739303f27aa458144,TODO
aaibgfefh,explosion/spaCy,spacy/cli/train.py,872938ec761ecc44694e1a31d498a864b2f53995,92d7832a86b9bb525cfc02f97378712ff4770cfb,TODO: separate checks from loading
aaibgffab,explosion/spaCy,spacy/cli/debug_data.py,597bcc629e173dfd87422188dc76a2f1053a9bba,9ee1c54f40e901533ef16cd148556cbf83cca6a7,TODO: Validate data format using the JSON schema
aaibgffac,explosion/spaCy,spacy/cli/debug_data.py,597bcc629e173dfd87422188dc76a2f1053a9bba,9ee1c54f40e901533ef16cd148556cbf83cca6a7,TODO: update once the new format is ready
aaibgffad,explosion/spaCy,spacy/cli/debug_data.py,597bcc629e173dfd87422188dc76a2f1053a9bba,9ee1c54f40e901533ef16cd148556cbf83cca6a7,TODO: move validation to GoldCorpus in order to be able to load from dir
aaibgffhi,explosion/spaCy,spacy/cli/train.py,9ee1c54f40e901533ef16cd148556cbf83cca6a7,92d7832a86b9bb525cfc02f97378712ff4770cfb,TODO: separate checks from loading
aaibggdei,explosion/spaCy,spacy/tests/test_tok2vec.py,644074b9548274bbf190834130a37c8d0e0cbed8,c7d1ece3ebf5e4fb45d14faf106a0aba7b179ee2,TODO: fix tok2vec arguments
aaibggdfh,explosion/spaCy,spacy/cli/debug_data.py,43b960c01b0c64e56859ad5eb304a5422af46516,STILL_EXISTS,TODO: handle base model
aaibggdfj,explosion/spaCy,spacy/cli/debug_model.py,43b960c01b0c64e56859ad5eb304a5422af46516,60f22e18005414ebe63dee0da1890739eef23bf8,the dim inference doesn't always work 100%; we need this hack like we have it in pipe.pyx
aaibggdga,explosion/spaCy,spacy/cli/pretrain.py,43b960c01b0c64e56859ad5eb304a5422af46516,19dc42776af33ee209c256c739303f27aa458144,TODO: validate that [pretraining] block exists
aaibggdgc,explosion/spaCy,spacy/cli/train.py,43b960c01b0c64e56859ad5eb304a5422af46516,ecb3c4e8f4a2360202e33ef7f008d9528cf8c3c6,TODO: do something to check base_nlp against regular nlp described in config?
aaibggdgf,explosion/spaCy,spacy/cli/train.py,43b960c01b0c64e56859ad5eb304a5422af46516,92d7832a86b9bb525cfc02f97378712ff4770cfb,TODO: separate checks from loading
aaibggdgg,explosion/spaCy,spacy/cli/train.py,43b960c01b0c64e56859ad5eb304a5422af46516,92d7832a86b9bb525cfc02f97378712ff4770cfb,TODO: maybe we should validate based on the actual components; the list
aaibggdie,explosion/spaCy,spacy/language.py,43b960c01b0c64e56859ad5eb304a5422af46516,3589a64d44efad29a340b13b505cc47a7fe2c797,Component meta and configs are only needed on the instance
aaibggebe,explosion/spaCy,spacy/language.py,43b960c01b0c64e56859ad5eb304a5422af46516,STILL_EXISTS,TODO: customize validation to make it more readable \/ relate it to
aaibggeee,explosion/spaCy,spacy/pipeline/entity_linker.py,43b960c01b0c64e56859ad5eb304a5422af46516,82347110f5ce5905b5acbde851c4b712b79130c1,TODO - what kind of default makes sense here?
aaibggehb,explosion/spaCy,spacy/pipeline/functions.py,43b960c01b0c64e56859ad5eb304a5422af46516,STILL_EXISTS,TODO: make stateful component with \"label\" config
aaibggehc,explosion/spaCy,spacy/pipeline/hooks.py,43b960c01b0c64e56859ad5eb304a5422af46516,STILL_EXISTS,TODO: do we want to keep these?
aaibggehg,explosion/spaCy,spacy/pipeline/textcat.py,43b960c01b0c64e56859ad5eb304a5422af46516,60f22e18005414ebe63dee0da1890739eef23bf8,This functionality was available previously; but was broken.
aaibggeif,explosion/spaCy,spacy/pipeline/textcat.py,43b960c01b0c64e56859ad5eb304a5422af46516,STILL_EXISTS,TODO: begin_training is not guaranteed to see all data \/ labels ?
aaibggejj,explosion/spaCy,spacy/tests/pipeline/test_pipe_factories.py,43b960c01b0c64e56859ad5eb304a5422af46516,STILL_EXISTS,TODO: fix this
aaibggfcb,explosion/spaCy,spacy/tests/test_language.py,43b960c01b0c64e56859ad5eb304a5422af46516,823e533dc182eadce00695da3f8b0798de7ed4f9,TODO: add more tests
aaibggfdd,explosion/spaCy,spacy/lookups.py,945f795a3e4ebb0bab6e4c0420ec1dc590437422,STILL_EXISTS,TODO: import spacy_lookups_data instead of going via entry points here?
aaibggfgj,explosion/spaCy,spacy/language.py,38f6ea7a78c5d2d11af31b7bc9a53f6d8bf9d25b,8d9d28eb8b1fb8484631ba4c690a9a1aacadb08d,TODO: where does the vocab data come in?
aaibgggci,explosion/spaCy,spacy/util.py,ae4d8a6ffd6230e55ac60cc7bd65bd0629d1b9ac,37814b608d57fc9982d8dc84812a8688a01360c1,TODO: Do we still want to allow env_opt?
aaibgggda,explosion/spaCy,spacy/language.py,274824921725b24bd271fc689eff9b8c20c8d01d,3589a64d44efad29a340b13b505cc47a7fe2c797,TODO: Adding this back to prevent breaking people's code etc.; but
aaibgggdg,explosion/spaCy,spacy/language.py,e5d9eaf79c5c935b4553c5ede31de383571fe0dc,30a76fcf6f662a3ef2d63648beba9f7a82e02150,TODO: integrate pipeline analyis
aaibgggdh,explosion/spaCy,spacy/language.py,e5d9eaf79c5c935b4553c5ede31de383571fe0dc,e962784531478841f902494f0f336104ba8a9a18,TODO: Will be replaced when the lemmatizer becomes a pipeline component
aaibgggea,explosion/spaCy,spacy/language.py,0cddb0dbe9b9af85bb017393ec017a16f2acdaec,ae15c9de7971679df9bb60034d007530957205ad,TODO: document
aaibggged,explosion/spaCy,spacy/language.py,0cddb0dbe9b9af85bb017393ec017a16f2acdaec,30a76fcf6f662a3ef2d63648beba9f7a82e02150,TODO: integrate pipeline analyis
aaibgggee,explosion/spaCy,spacy/language.py,0cddb0dbe9b9af85bb017393ec017a16f2acdaec,e962784531478841f902494f0f336104ba8a9a18,TODO: Will be replaced when the lemmatizer becomes a pipeline component
aaibgggfd,explosion/spaCy,spacy/language.py,e257e66ab908e289b64976558f843956376923d5,30a76fcf6f662a3ef2d63648beba9f7a82e02150,TODO: integrate pipeline analyis
aaibgggfe,explosion/spaCy,spacy/language.py,e257e66ab908e289b64976558f843956376923d5,e962784531478841f902494f0f336104ba8a9a18,TODO: Will be replaced when the lemmatizer becomes a pipeline component
aaibgggff,explosion/spaCy,spacy/language.py,e257e66ab908e289b64976558f843956376923d5,ae15c9de7971679df9bb60034d007530957205ad,TODO: document
aaibgghab,explosion/spaCy,spacy/cli/_util.py,4c055f0aa703974ff3d14fb4ea5966c226013a1d,ca3c9970623f86a1638e9e3b38b3958659f0baaa,TODO: This is kinda hacky and we should probably provide a better
aaibgghbb,explosion/spaCy,spacy/cli/pretrain.py,4c055f0aa703974ff3d14fb4ea5966c226013a1d,STILL_EXISTS,TODO: validate that [pretraining] block exists
aaibgghbe,explosion/spaCy,spacy/cli/pretrain.py,4c055f0aa703974ff3d14fb4ea5966c226013a1d,822ea4ef619072a94ce565bf78add9f9ea9d2866,TODO
aaibgghid,explosion/spaCy,spacy/cli/debug_data.py,b795f02fbdd095d7c326dab0a823c94815a17484,4cbb954281ad47148667de130e5c4eb23e579edf,TODO: make this feedback more fine-grained and report on updated
aaibgghii,explosion/spaCy,spacy/cli/train.py,b795f02fbdd095d7c326dab0a823c94815a17484,92d7832a86b9bb525cfc02f97378712ff4770cfb,TODO: refactor this so we don't have to run it separately in here
aaibgghij,explosion/spaCy,spacy/language.py,b795f02fbdd095d7c326dab0a823c94815a17484,3589a64d44efad29a340b13b505cc47a7fe2c797,TODO: handle errors and mismatches (vectors etc.)
aaibggjid,explosion/spaCy,spacy/cli/init_config.py,88b0a96801ca9abd7421c9ac70aa43fe87a9c653,STILL_EXISTS,TODO: base_path: Optional[Path] = Opt(None; \"--base\"; \"-b\"; help=\"Optional base config to fill\"; exists=True; dir_okay=False);
aaibggjif,explosion/spaCy,spacy/cli/init_config.py,88b0a96801ca9abd7421c9ac70aa43fe87a9c653,STILL_EXISTS,TODO: check this somehow
aaibghbbj,explosion/spaCy,spacy/cli/_util.py,2771e4f2b3bfcc19f6c11a6801d95f4bb595c029,STILL_EXISTS,This ends up being RIDICULOUS. omg.
aaibghbcc,explosion/spaCy,spacy/cli/_util.py,2771e4f2b3bfcc19f6c11a6801d95f4bb595c029,STILL_EXISTS,turns out to be completely broken. The only way to specify a \"path\" is..
aaibghcdf,explosion/spaCy,spacy/language.py,3ce5be4b76da3435e58dd6fd5d2f30bf6d575789,0687d7148e5ba47a7374d8ff31b249fc5fcff083,TODO: Should we make this available via  a user-facing property? (The
aaibghcdi,explosion/spaCy,spacy/language.py,3ce5be4b76da3435e58dd6fd5d2f30bf6d575789,STILL_EXISTS,TODO: should we raise if pipe is already disabled?
aaibghcdj,explosion/spaCy,spacy/language.py,3ce5be4b76da3435e58dd6fd5d2f30bf6d575789,STILL_EXISTS,TODO: should we raise if pipe is already enabled?
aaibghcea,explosion/spaCy,spacy/language.py,3ce5be4b76da3435e58dd6fd5d2f30bf6d575789,6a999c9303c8bba83ea4625cd27ef08385ea9370,TODO: why did we add this?
aaibghced,explosion/spaCy,spacy/language.py,3ce5be4b76da3435e58dd6fd5d2f30bf6d575789,10da74382f052b731de715a442e09fcb0efb79c4,TODO: maybe add some more checks \/ catch errors that may occur if
aaibghcfj,explosion/spaCy,spacy/language.py,1e0363290e71a3383c5fbdce3d1fecea5bf107df,0687d7148e5ba47a7374d8ff31b249fc5fcff083,TODO: Should we make this available via  a user-facing property? (The
aaibghcgd,explosion/spaCy,spacy/language.py,1e0363290e71a3383c5fbdce3d1fecea5bf107df,6a999c9303c8bba83ea4625cd27ef08385ea9370,TODO: why did we add this?
aaibghcgg,explosion/spaCy,spacy/language.py,1e0363290e71a3383c5fbdce3d1fecea5bf107df,10da74382f052b731de715a442e09fcb0efb79c4,TODO: maybe add some more checks \/ catch errors that may occur if
aaibghcja,explosion/spaCy,spacy/pipeline/tok2vec.py,58f19421b1a1630099c65cc0e651c0fa21a9f052,STILL_EXISTS,This is pretty grim; but it's hard to do better :(.
aaibghdgh,explosion/spaCy,spacy/tests/test_tok2vec.py,ec14744ee44e5dfb42f27f9c4edd02910420bdca,STILL_EXISTS,TODO: should this warn or error?
aaibghehe,explosion/spaCy,spacy/cli/train.py,122cb020010a3f4e34e696726206eb92ea9974e8,822ea4ef619072a94ce565bf78add9f9ea9d2866,TODO: refactor this so we don't have to run it separately in here
aaibghehh,explosion/spaCy,spacy/cli/train.py,122cb020010a3f4e34e696726206eb92ea9974e8,ed2aff2db346d7be9d94e73e0e2e2921cf966ccf,TODO: separate checks from loading
aaibghehi,explosion/spaCy,spacy/cli/train.py,122cb020010a3f4e34e696726206eb92ea9974e8,3f0d61232dbc8b45845463b27d766cdbb813af5e,TODO: maybe we should validate based on the actual components; the list
aaibghfbg,explosion/spaCy,spacy/cli/init_model.py,b5a0657fd6a104ff61c7c18a0fbdd1c251df5d31,39b178999c67fc8512b93a8c83ca90676351d7c9,TODO: Is this correct? Does this matter?
aaibgiiia,explosion/spaCy,spacy/tests/pipeline/test_morphologizer.py,60f22e18005414ebe63dee0da1890739eef23bf8,STILL_EXISTS,you shouldn't really call this more than once; but for testing it should be fine
aaibgiiib,explosion/spaCy,spacy/tests/pipeline/test_senter.py,60f22e18005414ebe63dee0da1890739eef23bf8,STILL_EXISTS,you shouldn't really call this more than once; but for testing it should be fine
aaibgiiid,explosion/spaCy,spacy/tests/pipeline/test_simple_ner.py,60f22e18005414ebe63dee0da1890739eef23bf8,STILL_EXISTS,you shouldn't really call this more than once; but for testing it should be fine
aaibgiiif,explosion/spaCy,spacy/tests/pipeline/test_tagger.py,60f22e18005414ebe63dee0da1890739eef23bf8,STILL_EXISTS,you shouldn't really call this more than once; but for testing it should be fine
aaibgiiih,explosion/spaCy,spacy/tests/pipeline/test_textcat.py,60f22e18005414ebe63dee0da1890739eef23bf8,343a44abe9dd9f505eb1d7d3ba95e3415864f66f,you shouldn't really call this more than once; but for testing it should be fine
aaibgiiij,explosion/spaCy,spacy/cli/_util.py,3e83a509bb21d2d315aa1f54f4615d196cb19ea2,08a831ce83c87733369c50c75c82bedb00027ec0,TODO: this seems kinda brittle?
aaibgiijb,explosion/spaCy,spacy/cli/_util.py,5acd4fbcd820599201a7a39cfbf50404c0159974,115147804a21bf7144f69072e271215d6f51f012,TODO: this seems kinda brittle?
aaibgiijh,explosion/spaCy,spacy/cli/_util.py,a75cfe0da6095c0d15d4558dc4322ab68a12b3d8,a5633b205f287775ea92b617b052662f4f4a1081,TODO: this seems kinda brittle?
aaibgijch,explosion/spaCy,spacy/language.py,4a573d18b3a818d3f9de3115d5376bf564337ba5,3589a64d44efad29a340b13b505cc47a7fe2c797,I had though; \"Why do we do this inside the Language object? Shouldn't
aaibgijeg,explosion/spaCy,spacy/scorer.py,bbdb5f62b70e9e12c6d4a8d9581e064ce846d19c,a4b32b955200c48a17bdca46fbf36402603b2f49,TODO
aaibgijeh,explosion/spaCy,spacy/scorer.py,bbdb5f62b70e9e12c6d4a8d9581e064ce846d19c,a4b32b955200c48a17bdca46fbf36402603b2f49,This is a temporary hack to work around the problem that the scorer
aaibgijgj,explosion/spaCy,spacy/cli/debug_model.py,73ff52b9ec9e61ae2d7faeacfef1b7bee53ea10e,39872de1f6e49c4b59ed747a2f15ca448a52f7db,ugly hack to deal with Tok2Vec listeners
aaibgijhf,explosion/spaCy,spacy/cli/debug_model.py,6db1d5dc0dff848dded3d2990543f749707afc45,13fbf6556a973c7f15dca20525cf0c02702b936b,ugly hack to deal with Tok2Vec listeners
aaibgijhg,explosion/spaCy,spacy/cli/debug_model.py,6db1d5dc0dff848dded3d2990543f749707afc45,13fbf6556a973c7f15dca20525cf0c02702b936b,the dim inference doesn't always work 100%; we need this hack like we have it in pipe.pyx
aaibgijig,explosion/spaCy,spacy/cli/debug_model.py,eb9b447960acbf1cf5902429b1fe9255dd81eef0,13fbf6556a973c7f15dca20525cf0c02702b936b,ugly hack to deal with Tok2Vec listeners
aaibgijih,explosion/spaCy,spacy/cli/debug_model.py,eb9b447960acbf1cf5902429b1fe9255dd81eef0,13fbf6556a973c7f15dca20525cf0c02702b936b,the dim inference doesn't always work 100%; we need this hack like we have it in pipe.pyx
aaibgjadb,explosion/spaCy,spacy/cli/debug_model.py,447b3e5787dec59f2ed4b8a96c4b2ceb808d182f,STILL_EXISTS,ugly hack to deal with Tok2Vec listeners
aaibgjade,explosion/spaCy,spacy/cli/debug_model.py,447b3e5787dec59f2ed4b8a96c4b2ceb808d182f,45b29c4a5b926c8f85b0a2ed4a9b8be13c5bf7eb,the dim inference doesn't always work 100%; we need this hack like we have it in pipe.pyx
aaibgjbgh,explosion/spaCy,spacy/ml/models/parser.py,6c85fab3167a468953b23b25d4a25a7fbdb478cd,76bbed3466519d384834715f48f240140c43e02e,TODO error
aaibgjbhb,explosion/spaCy,spacy/ml/models/parser.py,dd2292793f3bbd7cdfd2cf42bad205ec7428016a,916050bf2fd2d4bf6b73dff11f02a75da8ec32cf,TODO error
aaibgjbic,explosion/spaCy,spacy/cli/project/pull.py,17a6b0a1731321380914d3638e7e3bc25fd23a28,STILL_EXISTS,TODO: We don't have tests for this :(. It would take a bit of mockery to
aaibgjddj,explosion/spaCy,spacy/cli/train.py,a3e1791c9cdafa2459d8f81e4f8c0c8a5771bfbe,3a0a3b8db684eb4cc67551814e7f8f8be1675362,TODO: This might not be called corpora
aaibgjdee,explosion/spaCy,spacy/training/initialize.py,13b1605ee6fddc527f703ed86715ef4f4cb24a50,d5155376fd7d913734507f0647ddd4d33c625bbe,TODO: It might not be 'corpora'
aaibgjdfh,explosion/spaCy,spacy/training/initialize.py,13b1605ee6fddc527f703ed86715ef4f4cb24a50,d5155376fd7d913734507f0647ddd4d33c625bbe,TODO: Is this correct? Does this matter?
aaibgjdig,explosion/spaCy,spacy/cli/_util.py,553bfea6418e76c28b8786de35df7a3df0e0b56a,e44a7519cdac903a64b0dec5e98b8b828952d4b9,TODO: separate checks from loading
aaibgjdih,explosion/spaCy,spacy/cli/init_pipeline.py,553bfea6418e76c28b8786de35df7a3df0e0b56a,c22ecc66bbed5a98242d0b8b45c145f6abc5598f,TODO: add more instructions
aaibgjdii,explosion/spaCy,spacy/cli/init_pipeline.py,553bfea6418e76c28b8786de35df7a3df0e0b56a,822ea4ef619072a94ce565bf78add9f9ea9d2866,TODO: this should be handled better?
aaibgjdij,explosion/spaCy,spacy/cli/init_pipeline.py,553bfea6418e76c28b8786de35df7a3df0e0b56a,822ea4ef619072a94ce565bf78add9f9ea9d2866,TODO: maybe we should validate based on the actual components; the list
aaibgjdjf,explosion/spaCy,spacy/cli/train.py,553bfea6418e76c28b8786de35df7a3df0e0b56a,STILL_EXISTS,TODO: add warnings \/ --initialize (?) argument
aaibgjdjg,explosion/spaCy,spacy/cli/train.py,553bfea6418e76c28b8786de35df7a3df0e0b56a,2fdb7285a02be4148610aaadd77861a2170dcbd5,TODO: random seed; GPU allocator
aaibgjeaa,explosion/spaCy,spacy/cli/init_pipeline.py,d5155376fd7d913734507f0647ddd4d33c625bbe,e44a7519cdac903a64b0dec5e98b8b828952d4b9,TODO: move lookups to [initialize]; add vocab data
aaibgjecg,explosion/spaCy,spacy/cli/train.py,e44a7519cdac903a64b0dec5e98b8b828952d4b9,822ea4ef619072a94ce565bf78add9f9ea9d2866,TODO: this is currently imported by the ray extension and not used otherwise
aaibgjech,explosion/spaCy,spacy/cli/train.py,e44a7519cdac903a64b0dec5e98b8b828952d4b9,a5f2cc05090a3fde472b7a61958cc08c86099a8f,TODO: separate checks from loading
aaibgjeda,explosion/spaCy,spacy/cli/train.py,a5f2cc05090a3fde472b7a61958cc08c86099a8f,822ea4ef619072a94ce565bf78add9f9ea9d2866,TODO: do this better and more fine-grained
aaibgjedj,explosion/spaCy,spacy/cli/debug_data.py,822ea4ef619072a94ce565bf78add9f9ea9d2866,4cbb954281ad47148667de130e5c4eb23e579edf,TODO: does this need to be updated?
aaibgjejb,explosion/spaCy,spacy/training/initialize.py,822ea4ef619072a94ce565bf78add9f9ea9d2866,44160cd52fd054dea9829313fdf75876e11890b5,TODO: do this better and more fine-grained
aaibgjejf,explosion/spaCy,spacy/training/initialize.py,822ea4ef619072a94ce565bf78add9f9ea9d2866,dd542ec6a4d3784f20f44c726893a4a80c67baac,TODO: maybe we should validate based on the actual components; the list
aaibgjfaf,explosion/spaCy,spacy/training/loop.py,822ea4ef619072a94ce565bf78add9f9ea9d2866,STILL_EXISTS,TODO: refactor this so we don't have to run it separately in here
aaibgjfbe,explosion/spaCy,spacy/training/pretrain.py,822ea4ef619072a94ce565bf78add9f9ea9d2866,STILL_EXISTS,TODO: move this to logger function?
aaibgjfbf,explosion/spaCy,spacy/training/pretrain.py,822ea4ef619072a94ce565bf78add9f9ea9d2866,STILL_EXISTS,TODO: I think we probably want this to look more like the
aaibgjfcc,explosion/spaCy,spacy/training/pretrain.py,822ea4ef619072a94ce565bf78add9f9ea9d2866,f98a04434a737ff64c35e5d9d8dc6797d0ce9817,The simplest way to implement this would be to vstack the
aaibgjfcg,explosion/spaCy,spacy/training/pretrain.py,822ea4ef619072a94ce565bf78add9f9ea9d2866,f98a04434a737ff64c35e5d9d8dc6797d0ce9817,TODO
aaibgjfch,explosion/spaCy,spacy/util.py,822ea4ef619072a94ce565bf78add9f9ea9d2866,c1c841940cd600046e4e58e12b7ed78f761ae232,TODO: include schema?
aaibgjfci,explosion/spaCy,spacy/util.py,822ea4ef619072a94ce565bf78add9f9ea9d2866,c1c841940cd600046e4e58e12b7ed78f761ae232,TODO: clean this up and avoid duplication
aaibgjfdd,explosion/spaCy,spacy/training/initialize.py,4925ad760a87d84b7cc4bb2fb48b45845a2e0c30,STILL_EXISTS,TODO: Is this correct? Does this matter?
aaibgjfdj,explosion/spaCy,spacy/cli/_util.py,c1c841940cd600046e4e58e12b7ed78f761ae232,78021089f908a3dcc8adb1be1cf8303bb17ad759,TODO: This is kinda hacky and we should probably provide a better
aaibgjfeg,explosion/spaCy,spacy/cli/pretrain.py,c1c841940cd600046e4e58e12b7ed78f761ae232,78021089f908a3dcc8adb1be1cf8303bb17ad759,TODO: I think we probably want this to look more like the
aaibgjffd,explosion/spaCy,spacy/cli/pretrain.py,c1c841940cd600046e4e58e12b7ed78f761ae232,78021089f908a3dcc8adb1be1cf8303bb17ad759,The simplest way to implement this would be to vstack the
aaibgjffg,explosion/spaCy,spacy/cli/pretrain.py,c1c841940cd600046e4e58e12b7ed78f761ae232,78021089f908a3dcc8adb1be1cf8303bb17ad759,TODO
aaibgjfhd,explosion/spaCy,spacy/cli/train.py,c1c841940cd600046e4e58e12b7ed78f761ae232,3f0d61232dbc8b45845463b27d766cdbb813af5e,TODO: refactor this so we don't have to run it separately in here
aaibgjfhg,explosion/spaCy,spacy/cli/train.py,c1c841940cd600046e4e58e12b7ed78f761ae232,3f0d61232dbc8b45845463b27d766cdbb813af5e,TODO: separate checks from loading
aaibgjfhh,explosion/spaCy,spacy/cli/train.py,c1c841940cd600046e4e58e12b7ed78f761ae232,3f0d61232dbc8b45845463b27d766cdbb813af5e,TODO: maybe we should validate based on the actual components; the list
aaibgjgie,explosion/spaCy,spacy/pipeline/textcat.py,58c8d4b414e61ecd612d44521216ff3e8fa9affa,d093d6343b3cd8ab4814037e5e75bbff3177690b,TODO: This really shouldn't be here. I had a look and I added it when
aaibgjhch,explosion/spaCy,spacy/util.py,4ad26f4a2f5ea73eff3b179c3a234d4713c1da6c,acc391c2a841936b44e91a243f39ae864d661400,TODO: include schema?
aaibgjhci,explosion/spaCy,spacy/util.py,4ad26f4a2f5ea73eff3b179c3a234d4713c1da6c,2be80379ec644c253af7d312c0a5bcad4fd3515d,TODO: clean this up and avoid duplication
aaibgjjcb,explosion/spaCy,spacy/cli/debug_data.py,4cbb954281ad47148667de130e5c4eb23e579edf,94a5a9814f820eff3ddf5716a7000fb0f3da9d9e,TODO: Validate data format using the JSON schema
aaibgjjcc,explosion/spaCy,spacy/cli/debug_data.py,4cbb954281ad47148667de130e5c4eb23e579edf,94a5a9814f820eff3ddf5716a7000fb0f3da9d9e,TODO: update once the new format is ready
aaibgjjcd,explosion/spaCy,spacy/cli/debug_data.py,4cbb954281ad47148667de130e5c4eb23e579edf,94a5a9814f820eff3ddf5716a7000fb0f3da9d9e,TODO: move validation to GoldCorpus in order to be able to load from dir
aaibhaajb,explosion/spaCy,spacy/util.py,02247cccaf4df6524343271bc6abd8464da52dff,STILL_EXISTS,TODO: include schema?
aaibhabaa,explosion/spaCy,spacy/language.py,3589a64d44efad29a340b13b505cc47a7fe2c797,ae15c9de7971679df9bb60034d007530957205ad,This is messy; but it's the minimal working fix to Issue #639.
aaibhabae,explosion/spaCy,spacy/language.py,3589a64d44efad29a340b13b505cc47a7fe2c797,ae15c9de7971679df9bb60034d007530957205ad,TODO: document
aaibhabbf,explosion/spaCy,spacy/language.py,3589a64d44efad29a340b13b505cc47a7fe2c797,ae15c9de7971679df9bb60034d007530957205ad,TODO: Replace this once we handle vectors consistently as static
aaibhacci,explosion/spaCy,spacy/language.py,ae15c9de7971679df9bb60034d007530957205ad,STILL_EXISTS,Component meta and configs are only needed on the instance
aaibhacdc,explosion/spaCy,spacy/language.py,ae15c9de7971679df9bb60034d007530957205ad,STILL_EXISTS,TODO: Adding this back to prevent breaking people's code etc.; but
aaibhachi,explosion/spaCy,spacy/language.py,ae15c9de7971679df9bb60034d007530957205ad,STILL_EXISTS,TODO: handle errors and mismatches (vectors etc.)
aaibhacji,explosion/spaCy,spacy/language.py,ae15c9de7971679df9bb60034d007530957205ad,STILL_EXISTS,I had though; \"Why do we do this inside the Language object? Shouldn't
aaibhbigb,explosion/spaCy,spacy/tests/regression/test_issue5918.py,549758f67dea544ec64271fe88513dbc4117fed8,STILL_EXISTS,TODO: test for logging here
aaibhbihd,explosion/spaCy,spacy/tests/test_models.py,8ec79ad3fadd97f39b220c874e0df46921646fd0,STILL_EXISTS,Check they look at different columns.
aaibhbijj,explosion/spaCy,spacy/util.py,9db670b996416bb1551a1f16c6aece0594f29ecc,STILL_EXISTS,TODO: replace with (stable) Draft6Validator; if available
aaibhbjac,explosion/spaCy,spacy/util.py,9db670b996416bb1551a1f16c6aece0594f29ecc,STILL_EXISTS,TODO: user warning?
aaibhbjej,explosion/spaCy,spacy/ml/tok2vec.py,2998131416a57ff27ca050e44a2722a336a1c088,STILL_EXISTS,is a bit ugly here.
aaibhcacd,explosion/spaCy,spacy/util.py,d093d6343b3cd8ab4814037e5e75bbff3177690b,STILL_EXISTS,TODO: include schema?
aaibhcafb,explosion/spaCy,spacy/util.py,d093d6343b3cd8ab4814037e5e75bbff3177690b,STILL_EXISTS,TODO: replace with (stable) Draft6Validator; if available
aaibhcafe,explosion/spaCy,spacy/util.py,d093d6343b3cd8ab4814037e5e75bbff3177690b,STILL_EXISTS,TODO: user warning?
aaibhdicd,explosion/spaCy,spacy/tests/regression/test_issue5501-6000.py,b1d568a4dffca6828824953d0cfa0e56ae3cfbfc,STILL_EXISTS,TODO: test for logging here
aaibhdidh,explosion/spaCy,spacy/ml/models/textcat.py,75a202ce6506177d5de97b47bfd96fd3c7909503,a203e3dbb8ae3c202f4f24d2bcceed7ed7568b15,TODO: move to legacy
aaibhdjgd,explosion/spaCy,spacy/tests/package/test_requirements.py,724831b0667a26507349a9f8c8980c5000c57947,STILL_EXISTS,TODO: correct checks for numpy rather than ignoring
aaibheabd,explosion/spaCy,spacy/ml/models/multi_task.py,f98a04434a737ff64c35e5d9d8dc6797d0ce9817,STILL_EXISTS,The simplest way to implement this would be to vstack the
aaibheahi,explosion/spaCy,spacy/util.py,d25b1606d65fa47636222a2c48d7229698fe5cf9,STILL_EXISTS,TODO: include schema?
aaibhebdd,explosion/spaCy,spacy/cli/debug_data.py,94a5a9814f820eff3ddf5716a7000fb0f3da9d9e,343a44abe9dd9f505eb1d7d3ba95e3415864f66f,TODO: make this feedback more fine-grained and report on updated
aaibhebdf,explosion/spaCy,spacy/cli/debug_data.py,94a5a9814f820eff3ddf5716a7000fb0f3da9d9e,343a44abe9dd9f505eb1d7d3ba95e3415864f66f,TODO: does this need to be updated?
aaibhebeh,explosion/spaCy,spacy/cli/pretrain.py,94a5a9814f820eff3ddf5716a7000fb0f3da9d9e,STILL_EXISTS,TODO: What's the solution here? How do we handle optional blocks?
aaibhecbd,explosion/spaCy,spacy/util.py,0afb54ac93e4a0e292b2d408e9ce7daeb524bcec,STILL_EXISTS,TODO: replace with (stable) Draft6Validator; if available
aaibhecbg,explosion/spaCy,spacy/util.py,0afb54ac93e4a0e292b2d408e9ce7daeb524bcec,STILL_EXISTS,TODO: user warning?
aaibhecic,explosion/spaCy,spacy/util.py,05a2812ae0eceb3e9101a7cf104b0f63965fe171,STILL_EXISTS,TODO: include schema?
aaibhedbb,explosion/spaCy,spacy/util.py,05a2812ae0eceb3e9101a7cf104b0f63965fe171,STILL_EXISTS,TODO: replace with (stable) Draft6Validator; if available
aaibhedbe,explosion/spaCy,spacy/util.py,05a2812ae0eceb3e9101a7cf104b0f63965fe171,STILL_EXISTS,TODO: user warning?
aaibhedcc,explosion/spaCy,spacy/language.py,19802032292c7597e2dac97cf0abe77fb419f759,STILL_EXISTS,Component meta and configs are only needed on the instance
aaibhedcg,explosion/spaCy,spacy/language.py,19802032292c7597e2dac97cf0abe77fb419f759,STILL_EXISTS,TODO: Adding this back to prevent breaking people's code etc.; but
aaibhedhc,explosion/spaCy,spacy/language.py,19802032292c7597e2dac97cf0abe77fb419f759,STILL_EXISTS,TODO: handle errors and mismatches (vectors etc.)
aaibhedjg,explosion/spaCy,spacy/language.py,19802032292c7597e2dac97cf0abe77fb419f759,STILL_EXISTS,I had though; \"Why do we do this inside the Language object? Shouldn't
aaibheehf,explosion/spaCy,spacy/cli/debug_data.py,20e18cc2461c09af2d4364c127e97f4121d5a1e4,343a44abe9dd9f505eb1d7d3ba95e3415864f66f,TODO: make this feedback more fine-grained and report on updated
aaibheehh,explosion/spaCy,spacy/cli/debug_data.py,20e18cc2461c09af2d4364c127e97f4121d5a1e4,343a44abe9dd9f505eb1d7d3ba95e3415864f66f,TODO: does this need to be updated?
aaibhefbb,explosion/spaCy,spacy/util.py,cbb8c66da328b1b000ec433234909d3f3fcda4da,STILL_EXISTS,TODO: include schema?
aaibhefea,explosion/spaCy,spacy/util.py,cbb8c66da328b1b000ec433234909d3f3fcda4da,STILL_EXISTS,TODO: replace with (stable) Draft6Validator; if available
aaibhefed,explosion/spaCy,spacy/util.py,cbb8c66da328b1b000ec433234909d3f3fcda4da,STILL_EXISTS,TODO: user warning?
aaibhegdh,explosion/spaCy,spacy/cli/convert.py,343a44abe9dd9f505eb1d7d3ba95e3415864f66f,47c1ec678ba945dd8d868c68fe3139e3c93aa466,TODO: support msgpack via stdout in srsly?
aaibhegfb,explosion/spaCy,spacy/cli/debug_data.py,343a44abe9dd9f505eb1d7d3ba95e3415864f66f,3f90bffa270f5939e12dc299d621e430008e4933,TODO: Validate data format using the JSON schema
aaibhegfc,explosion/spaCy,spacy/cli/debug_data.py,343a44abe9dd9f505eb1d7d3ba95e3415864f66f,3f90bffa270f5939e12dc299d621e430008e4933,TODO: update once the new format is ready
aaibhegfd,explosion/spaCy,spacy/cli/debug_data.py,343a44abe9dd9f505eb1d7d3ba95e3415864f66f,3f90bffa270f5939e12dc299d621e430008e4933,TODO: move validation to GoldCorpus in order to be able to load from dir
aaibhfcjg,explosion/spaCy,spacy/util.py,343a44abe9dd9f505eb1d7d3ba95e3415864f66f,STILL_EXISTS,TODO: replace with (stable) Draft6Validator; if available
aaibhfcjj,explosion/spaCy,spacy/util.py,343a44abe9dd9f505eb1d7d3ba95e3415864f66f,STILL_EXISTS,TODO: user warning?
aaibhfdce,explosion/spaCy,spacy/util.py,343a44abe9dd9f505eb1d7d3ba95e3415864f66f,STILL_EXISTS,TODO: include schema?
aaibhfdgi,explosion/spaCy,spacy/util.py,546af3966aed857de2f5649fad3bfe3f463902fd,STILL_EXISTS,TODO: include schema?
aaibhfdjh,explosion/spaCy,spacy/util.py,546af3966aed857de2f5649fad3bfe3f463902fd,STILL_EXISTS,TODO: replace with (stable) Draft6Validator; if available
aaibhfeaa,explosion/spaCy,spacy/util.py,546af3966aed857de2f5649fad3bfe3f463902fd,STILL_EXISTS,TODO: user warning?
aaibhfeag,explosion/spaCy,spacy/cli/debug_data.py,3f90bffa270f5939e12dc299d621e430008e4933,STILL_EXISTS,TODO: make this feedback more fine-grained and report on updated
aaibhfeai,explosion/spaCy,spacy/cli/debug_data.py,3f90bffa270f5939e12dc299d621e430008e4933,STILL_EXISTS,TODO: does this need to be updated?
aaibhfegi,explosion/spaCy,spacy/cli/pretrain.py,47c1ec678ba945dd8d868c68fe3139e3c93aa466,STILL_EXISTS,TODO: What's the solution here? How do we handle optional blocks?
aaibhffdd,explosion/spaCy,spacy/language.py,47c1ec678ba945dd8d868c68fe3139e3c93aa466,STILL_EXISTS,Component meta and configs are only needed on the instance
aaibhffdh,explosion/spaCy,spacy/language.py,47c1ec678ba945dd8d868c68fe3139e3c93aa466,STILL_EXISTS,TODO: Adding this back to prevent breaking people's code etc.; but
aaibhffid,explosion/spaCy,spacy/language.py,47c1ec678ba945dd8d868c68fe3139e3c93aa466,STILL_EXISTS,TODO: handle errors and mismatches (vectors etc.)
aaibhfgah,explosion/spaCy,spacy/language.py,47c1ec678ba945dd8d868c68fe3139e3c93aa466,STILL_EXISTS,I had though; \"Why do we do this inside the Language object? Shouldn't
aaibhfhhd,explosion/spaCy,spacy/tests/parser/test_ner.py,47c1ec678ba945dd8d868c68fe3139e3c93aa466,STILL_EXISTS,if this goes wrong; the initialization of the parser's upper layer is probably broken
aaibhfhid,explosion/spaCy,spacy/tests/pipeline/test_textcat.py,47c1ec678ba945dd8d868c68fe3139e3c93aa466,STILL_EXISTS,you shouldn't really call this more than once; but for testing it should be fine
aaibhficg,explosion/spaCy,spacy/tests/pipeline/test_tagger.py,0a923a7915a98c7786accbd33b87a8db8c43e826,STILL_EXISTS,you shouldn't really call this more than once; but for testing it should be fine
aaibhfijf,explosion/spaCy,spacy/tests/regression/test_issue4001-4500.py,402dbc5baecb36db1c5cbe898626b1913facf547,6b68ad027b4fe42369842c9cd8433b11e3396f57,TODO: not sure if we want this to be necessary...
aaibhfjfj,explosion/spaCy,spacy/tokens/_dict_proxies.py,f277bfdf0f0c61b233f9eab311784af21947c9ac,STILL_EXISTS,for performance reasons. The UserDict is slower by better behaved.
aaibhfjjh,explosion/spaCy,spacy/util.py,a203e3dbb8ae3c202f4f24d2bcceed7ed7568b15,28256522c8a6311e6a20d66927ef2bd230755464,specific error messages and implement a fallback to spacy-legacy.
aaibhgabc,explosion/spaCy,spacy/util.py,28256522c8a6311e6a20d66927ef2bd230755464,STILL_EXISTS,TODO: replace with (stable) Draft6Validator; if available
aaibhgabf,explosion/spaCy,spacy/util.py,28256522c8a6311e6a20d66927ef2bd230755464,STILL_EXISTS,TODO: user warning?
aaibhgaeh,explosion/spaCy,spacy/pipeline/functions.py,bf0cdae8d41f2cc458739c2512c8bdb625757770,STILL_EXISTS,TODO: make stateful component with \"label\" config
aaibhgaib,explosion/spaCy,spacy/util.py,f4d547b73c31e7578dcc676b9276dab3dce851ad,STILL_EXISTS,specific error messages and implement a fallback to spacy-legacy.
aaibhgaif,explosion/spaCy,spacy/util.py,f4d547b73c31e7578dcc676b9276dab3dce851ad,STILL_EXISTS,TODO: include schema?
aaibhgbbf,explosion/spaCy,spacy/util.py,f4d547b73c31e7578dcc676b9276dab3dce851ad,STILL_EXISTS,TODO: replace with (stable) Draft6Validator; if available
aaibhgbbi,explosion/spaCy,spacy/util.py,f4d547b73c31e7578dcc676b9276dab3dce851ad,STILL_EXISTS,TODO: user warning?
aaibhhjfe,explosion/spaCy,spacy/language.py,911dfcccfcdfe1e9effc38b013f0266dbbd79afb,bbb94b37c61d090e307da16519390839513dfc52,TODO: likely bug in spaCy if this happens
aaibhjhhf,scikit-learn/scikit-learn,scikits/learn/datasets/oldfaithful/data.py,fbb3c512ecbffc97825eaaaf925c9af580608fa8,STILL_EXISTS,* Neither the author nor the names of any contributors may be used
aaibhjiag,scikit-learn/scikit-learn,setup.py,fbb3c512ecbffc97825eaaaf925c9af580608fa8,9e79310f854ec24696d02381242a97d45665dd21,XXX: once in SVN; should add svn version...
aaibhjicd,scikit-learn/scikit-learn,setup.py,fbb3c512ecbffc97825eaaaf925c9af580608fa8,4315b69b141e36b16fe8f1e47f5d70cf9567159a,FIXME url; download_url; ext_modules
aaibhjieg,scikit-learn/scikit-learn,scikits/learn/datasets/iris/data.py,def073bedb0cd71fbdc1e06d5fed1acf07960388,STILL_EXISTS,* Neither the author nor the names of any contributors may be used
aaibhjijb,scikit-learn/scikit-learn,scikits/learn/datasets/pendigits/data.py,2cc9f2be7bf7fbd36484bec76b4446704971bbbc,STILL_EXISTS,* Neither the author nor the names of any contributors may be used
aaibhjjej,scikit-learn/scikit-learn,scikits/learn/datasets/heart/data.py,7288b734c286166820257ff64b2ef8464240882a,STILL_EXISTS,* Neither the author nor the names of any contributors may be used
aaibhjjjc,scikit-learn/scikit-learn,scikits/learn/datasets/german/data.py,763528655833562e0785f0ecb259934efd95fba5,STILL_EXISTS,* Neither the author nor the names of any contributors may be used
aaibiabae,scikit-learn/scikit-learn,scikits/learn/utils/preprocessing.py,2e66160132f8b2d1c688cd367384a0d725d0c50b,4b049f1102ff2646aa0c5de6f546d5e6e5abe82f,XXX: handling Nan is important here.
aaibiabib,scikit-learn/scikit-learn,scikits/learn/ann/mlp.py,9cd20400130152479dc8c3c0dccb9e1e9f4930f7,STILL_EXISTS,TODO: check for overflow here and...
aaibiabic,scikit-learn/scikit-learn,scikits/learn/ann/mlp.py,9cd20400130152479dc8c3c0dccb9e1e9f4930f7,STILL_EXISTS,TODO: here...
aaibiacbi,scikit-learn/scikit-learn,scikits/learn/ann/mlp.py,394dd1f2276766c7d86f3fe2fbdb62606e57dc97,STILL_EXISTS,THIS IS AN AWFUL-LOOKING HACK; BUT I HAVEN'T FOUND A BETTER
aaibiaceb,scikit-learn/scikit-learn,scikits/learn/ann/srn.py,394dd1f2276766c7d86f3fe2fbdb62606e57dc97,STILL_EXISTS,TODO: check for overflow here...
aaibiacec,scikit-learn/scikit-learn,scikits/learn/ann/srn.py,394dd1f2276766c7d86f3fe2fbdb62606e57dc97,STILL_EXISTS,TODO: and here...
aaibiaced,scikit-learn/scikit-learn,scikits/learn/ann/srn.py,a1645015326ed7b6fe9390cdac9aafe918fbe27c,STILL_EXISTS,hopefully eventually RTRL and EKF
aaibiacfe,scikit-learn/scikit-learn,scikits/learn/ann/srn.py,a1645015326ed7b6fe9390cdac9aafe918fbe27c,STILL_EXISTS,I'll implement this next week.
aaibiacii,scikit-learn/scikit-learn,scikits/learn/ann/srn.py,633fbccdc6b26836d64792083f3127dd51975d7a,STILL_EXISTS,hopefully eventually RTRL and EKF
aaibiadcc,scikit-learn/scikit-learn,scikits/learn/ann/rbf.py,7bcd7fc32c55804886e11526cfebc97eee644899,STILL_EXISTS,TODO: check for overflow here...
aaibiadcd,scikit-learn/scikit-learn,scikits/learn/ann/rbf.py,7bcd7fc32c55804886e11526cfebc97eee644899,STILL_EXISTS,TODO: and here...
aaibiaeab,scikit-learn/scikit-learn,scikits/learn/ga/ga_gnm.py,3de66ffb26eaa00bad25cfc9d02186362528b870,STILL_EXISTS,\"\"\" ||  || This module adds gradient optimization capabilities to the  ||  || standard genomes.  Basically this means that any problem set up ||  || for a GA is automatically able to be gradient optimized... ||  ||  ||  || For purity sake; the grad and genome ||  || modules have been left totally separate.  It might have ||  || been just as easy to derive genomes directly from  ||  || grad.grad - and maybe that will happen in the future. ||  ||  ||  || Caveats: ||  || \tThis has only be set up for list_genomes made up of  ||  || \tfloating point genes.  The tree_genomes just need to ||  || \tbe recoded here translating the pick_numbers functions ||  || \tfrom tree_opt.   ||  || \t ||  || \tgenomes of discrete variable genes should be able to work also. ||  || \"\"\"
aaibiaeji,scikit-learn/scikit-learn,scikits/learn/ga/selection.py,3de66ffb26eaa00bad25cfc9d02186362528b870,STILL_EXISTS,binary search would be faster
aaibiafbj,scikit-learn/scikit-learn,scikits/learn/ga/tree.py,3de66ffb26eaa00bad25cfc9d02186362528b870,STILL_EXISTS,I needed this to output string chromosomes for antennas
aaibiafda,scikit-learn/scikit-learn,scikits/learn/ga/tree_opt.py,3de66ffb26eaa00bad25cfc9d02186362528b870,STILL_EXISTS,\"\"\" ||     some basic language objects ||     if you don't specify a node_type for these; it is usually a good idea to  ||     not allow crossovers at these nodes.  That would allow two (maybe different) ||     list_range nodes to exchange places. || \"\"\"
aaibiafgh,scikit-learn/scikit-learn,scikits/learn/ga/selection.py,c017b1f34da310ee64fc3098c7d6a16f1c5baa24,STILL_EXISTS,binary search would be faster
aaibiafji,scikit-learn/scikit-learn,scikits/learn/pyem/densities.py,e3ed0fdcca8555a3e5bd54e7359fc6a6b99c5a73,STILL_EXISTS,TODO: Get a confidence interval using the Chi2 distribution
aaibiaggj,scikit-learn/scikit-learn,scikits/learn/pyem/gmm_em.py,e3ed0fdcca8555a3e5bd54e7359fc6a6b99c5a73,bd1e6b281a436e856eafbd999aa4d19c6cdfd07c,TODO:
aaibiaghc,scikit-learn/scikit-learn,scikits/learn/pyem/gmm_em.py,e3ed0fdcca8555a3e5bd54e7359fc6a6b99c5a73,STILL_EXISTS,( general EM should be outside this module. This one should be GMM specific; maybe)
aaibiaghj,scikit-learn/scikit-learn,scikits/learn/pyem/gmm_em.py,e3ed0fdcca8555a3e5bd54e7359fc6a6b99c5a73,STILL_EXISTS,TODO Check args here
aaibiagia,scikit-learn/scikit-learn,scikits/learn/pyem/gmm_em.py,e3ed0fdcca8555a3e5bd54e7359fc6a6b99c5a73,STILL_EXISTS,TODO: check each value of inverse distribution is
aaibiagic,scikit-learn/scikit-learn,scikits/learn/pyem/gmm_em.py,e3ed0fdcca8555a3e5bd54e7359fc6a6b99c5a73,STILL_EXISTS,This one should be a bit faster
aaibiahaf,scikit-learn/scikit-learn,scikits/learn/pyem/gmm_em.py,e3ed0fdcca8555a3e5bd54e7359fc6a6b99c5a73,STILL_EXISTS,initialization. This is ugly; and we should have a class to model a GMM
aaibiahbi,scikit-learn/scikit-learn,scikits/learn/pyem/gmm_em.py,e3ed0fdcca8555a3e5bd54e7359fc6a6b99c5a73,STILL_EXISTS,TODO: adjustable level (to do in gauss_ell).
aaibiahce,scikit-learn/scikit-learn,scikits/learn/pyem/gmm_em.py,e3ed0fdcca8555a3e5bd54e7359fc6a6b99c5a73,STILL_EXISTS,No attempt to be efficient has been made...
aaibiaiai,scikit-learn/scikit-learn,scikits/learn/pyem/pyem/gmm_em.py,3d83d80756d805319dbadaeeced80c7ec541a9a3,STILL_EXISTS,initialization. This is ugly; and we should have a class to model a GMM
aaibiaige,scikit-learn/scikit-learn,scikits/learn/pyem/pyem/kmean.py,3d83d80756d805319dbadaeeced80c7ec541a9a3,STILL_EXISTS,No attempt to be efficient has been made...
aaibiaihi,scikit-learn/scikit-learn,scikits/learn/pyem/pyem/gauss_mix.py,d2fbad45d98c5c0ed0eda18cdb2d4c2985385282,STILL_EXISTS,the parameters afterward. There should be a better way ?
aaibiaiih,scikit-learn/scikit-learn,scikits/learn/pyem/pyem/gauss_mix.py,d2fbad45d98c5c0ed0eda18cdb2d4c2985385282,5b70fa2975f0409e51f03adc636200584bb3f0fc,TODO: adjustable level (to do in gauss_ell).
aaibiaijc,scikit-learn/scikit-learn,scikits/learn/pyem/pyem/gauss_mix.py,d2fbad45d98c5c0ed0eda18cdb2d4c2985385282,STILL_EXISTS,TODO Check args here
aaibiaijd,scikit-learn/scikit-learn,scikits/learn/pyem/pyem/gauss_mix.py,d2fbad45d98c5c0ed0eda18cdb2d4c2985385282,STILL_EXISTS,TODO: check each value of inverse distribution is
aaibiaijf,scikit-learn/scikit-learn,scikits/learn/pyem/pyem/gauss_mix.py,d2fbad45d98c5c0ed0eda18cdb2d4c2985385282,STILL_EXISTS,This one should be a bit faster
aaibiajbd,scikit-learn/scikit-learn,scikits/learn/pyem/pyem/gmm_em.py,d2fbad45d98c5c0ed0eda18cdb2d4c2985385282,STILL_EXISTS,TODO:
aaibiajfa,scikit-learn/scikit-learn,scikits/learn/pyem/pyem/gmm_em.py,d2fbad45d98c5c0ed0eda18cdb2d4c2985385282,STILL_EXISTS,initialization. This is ugly; and we should have a class to model a GMM
aaibibabb,scikit-learn/scikit-learn,scikits/learn/pyem/setup.py,18e7b0eceb22d2fed6651d7b26006fa4b4620903,STILL_EXISTS,TODO:
aaibibaeh,scikit-learn/scikit-learn,scikits/learn/pyem/pyem/_c_densities.py,a2dd902b05e2f96c2e71c76cdde31f766e37c249,STILL_EXISTS,TODO:
aaibibahc,scikit-learn/scikit-learn,scikits/learn/pyem/pyem/gauss_mix.py,a2dd902b05e2f96c2e71c76cdde31f766e37c249,STILL_EXISTS,TODO:
aaibibaia,scikit-learn/scikit-learn,scikits/learn/pyem/pyem/gmm_em.py,a2dd902b05e2f96c2e71c76cdde31f766e37c249,STILL_EXISTS,TODO:
aaibibbgc,scikit-learn/scikit-learn,scikits/learn/pyem/pyem/gauss_mix.py,fb652557b3a5f962c58b3da77ddf251963090357,STILL_EXISTS,modify the result plot (maybe returns a dic with global pdf; component pdf and
aaibibcdd,scikit-learn/scikit-learn,scikits/learn/pyem/pyem/kmean.py,2d0a200a4faa6b583891e69d5a999cf92361c96b,STILL_EXISTS,TODO:
aaibibeig,scikit-learn/scikit-learn,scikits/learn/pyem/pyem/setup.py,8ccb80733fb00b2b37f7203f5c2919510767832c,STILL_EXISTS,TODO:
aaibibgab,scikit-learn/scikit-learn,scikits/learn/pyem/online_em.py,b6015fb9f3f9bd810b6c143c0fdb732229d8d55c,STILL_EXISTS,TODO:
aaibibgag,scikit-learn/scikit-learn,scikits/learn/pyem/online_em.py,b6015fb9f3f9bd810b6c143c0fdb732229d8d55c,STILL_EXISTS,big problem; but it would be better to really return them as the name implied.
aaibibhah,scikit-learn/scikit-learn,scikits/learn/pyem/tests/test_densities.py,b6015fb9f3f9bd810b6c143c0fdb732229d8d55c,STILL_EXISTS,TODO:
aaibibjbh,scikit-learn/scikit-learn,scikits/learn/pyem/basic_example2.py,b0ecea1badf6c62b38bc0f56f4eb87dc0505c738,STILL_EXISTS,To reproduce results; fix the random seed
aaibiccda,scikit-learn/scikit-learn,scikits/learn/pyem/gauss_mix.py,672d1e71c8552c3dc07d87cdd636a11df80b6da3,bd1e6b281a436e856eafbd999aa4d19c6cdfd07c,XXX separete the computation from the plotting
aaibiccfg,scikit-learn/scikit-learn,scikits/learn/pyem/data/oldfaithful/data.py,179dde0bc97870e12c4d54a7f51d75991cffcd10,STILL_EXISTS,* Neither the author nor the names of any contributors may be used
aaibiccib,scikit-learn/scikit-learn,scikits/learn/pyem/gauss_mix.py,b0f6ea4431607404dda4e52046e84f86ee5a59fa,c311be44e87b977161faea65a2863717d3503792,XXX: have a public function to compute the pdf at given points
aaibiccja,scikit-learn/scikit-learn,scikits/learn/pyem/gauss_mix.py,b0f6ea4431607404dda4e52046e84f86ee5a59fa,c311be44e87b977161faea65a2863717d3503792,XXX: we need log pdf; not the pdf... this can save some computing
aaibiccjb,scikit-learn/scikit-learn,scikits/learn/pyem/gauss_mix.py,b0f6ea4431607404dda4e52046e84f86ee5a59fa,c311be44e87b977161faea65a2863717d3503792,XXX refactor computing pdf
aaibiccjc,scikit-learn/scikit-learn,scikits/learn/pyem/gmm_em.py,b0f6ea4431607404dda4e52046e84f86ee5a59fa,STILL_EXISTS,XXX: This is bogus: should do better (in kmean or here; do not know yet)
aaibiceai,scikit-learn/scikit-learn,scikits/learn/pyem/examples/utils.py,542b00d0b8da2374b3bb31a0ef5010589734a4c2,9d537106c1c1236ce193414435431ccad16a8371,XXX: Bouah; hackish... Will go away once scipydata found its way
aaibicefh,scikit-learn/scikit-learn,scikits/learn/pyem/gauss_mix.py,b4b152793569d8f7d4e4bd47015d2aefb1738219,STILL_EXISTS,\"\"\"Module implementing GM; a class which represents Gaussian mixtures. ||  || GM instances can be used to create; sample mixtures. They also provide || different plotting facilities; such as isodensity contour for multi dimensional || models; ellipses of confidence.\"\"\"
aaibicege,scikit-learn/scikit-learn,scikits/learn/pyem/gauss_mix.py,b4b152793569d8f7d4e4bd47015d2aefb1738219,STILL_EXISTS,XXX: how to find \"good\" values for level ?
aaibicghi,scikit-learn/scikit-learn,scikits/learn/pyem/gauss_mix.py,b32b13287789e09afe88861a4828969e87dd6829,STILL_EXISTS,XXX: when fromvalues is called; parameters are called twice...
aaibicjdi,scikit-learn/scikit-learn,scikits/learn/pyem/gmm_em.py,c14e5e085bb3a4604df119aa92ee4e20a0c3a30a,STILL_EXISTS,TODO: do it in log domain instead
aaibicjgd,scikit-learn/scikit-learn,scikits/learn/pyem/data/pendigits/data.py,f72cefa7ada9eac1ae5b7ef3c3254a6ab230234e,STILL_EXISTS,* Neither the author nor the names of any contributors may be used
aaibicjii,scikit-learn/scikit-learn,scikits/learn/pyem/gmm_em.py,9fe7ff776888b2adac5775878a7bb10e30ef81ff,STILL_EXISTS,TODO: separate regularizer from EM class ?
aaibidacd,scikit-learn/scikit-learn,scikits/learn/pyem/data/iris/data.py,c4dd6214148973a6e7f30550a9c2e08c87da5192,STILL_EXISTS,* Neither the author nor the names of any contributors may be used
aaibidbfi,scikit-learn/scikit-learn,scikits/learn/pyem/gmm_em.py,bd1e6b281a436e856eafbd999aa4d19c6cdfd07c,STILL_EXISTS,XXX: Is this really needed ?
aaibidbjg,scikit-learn/scikit-learn,scikits/learn/pyem/densities.py,11e3e1b1432c1393b4c3c5a2b922e7bc15c8419b,STILL_EXISTS,XXX optimize log case as non log case above
aaibidcbh,scikit-learn/scikit-learn,scikits/learn/svm/classification.py,d0552f585ee9098ad21e5b49a39c5bddd61ffea7,STILL_EXISTS,XXX classification models need weights
aaibidcfe,scikit-learn/scikit-learn,scikits/learn/svm/model.py,d0552f585ee9098ad21e5b49a39c5bddd61ffea7,STILL_EXISTS,XXX find better way to keep x and y references
aaibidcff,scikit-learn/scikit-learn,scikits/learn/svm/model.py,d0552f585ee9098ad21e5b49a39c5bddd61ffea7,5cc746e9ae21237b4611281628a7bf575fdee888,XXX find better way to keep svm_data reference
aaibidcfg,scikit-learn/scikit-learn,scikits/learn/svm/model.py,d0552f585ee9098ad21e5b49a39c5bddd61ffea7,5cc746e9ae21237b4611281628a7bf575fdee888,XXX nr_weight; weight_label; weight
aaibidcfh,scikit-learn/scikit-learn,scikits/learn/svm/model.py,d0552f585ee9098ad21e5b49a39c5bddd61ffea7,5cc746e9ae21237b4611281628a7bf575fdee888,XXX setting these to None zeros svm_type
aaibidcga,scikit-learn/scikit-learn,scikits/learn/svm/tests/test_classification.py,d0552f585ee9098ad21e5b49a39c5bddd61ffea7,1537b28a39510843ec4b7aa468a0c405fd21098c,XXX remove this
aaibidcgb,scikit-learn/scikit-learn,scikits/learn/svm/tests/test_libsvm.py,d0552f585ee9098ad21e5b49a39c5bddd61ffea7,1537b28a39510843ec4b7aa468a0c405fd21098c,XXX remove this
aaibidcgc,scikit-learn/scikit-learn,scikits/learn/svm/tests/test_oneclass.py,d0552f585ee9098ad21e5b49a39c5bddd61ffea7,1537b28a39510843ec4b7aa468a0c405fd21098c,XXX remove this
aaibidcgd,scikit-learn/scikit-learn,scikits/learn/svm/tests/test_regression.py,d0552f585ee9098ad21e5b49a39c5bddd61ffea7,5cc746e9ae21237b4611281628a7bf575fdee888,XXX remove this
aaibidcge,scikit-learn/scikit-learn,scikits/learn/svm/tests/test_utils.py,d0552f585ee9098ad21e5b49a39c5bddd61ffea7,STILL_EXISTS,XXX remove this
aaibiddac,scikit-learn/scikit-learn,scikits/learn/svm/classification.py,1537b28a39510843ec4b7aa468a0c405fd21098c,b871fa4449b57ca12ba839f5c8e62ec2727dfa0d,XXX this is probably suboptimal when training many models
aaibiddbd,scikit-learn/scikit-learn,scikits/learn/svm/model.py,1537b28a39510843ec4b7aa468a0c405fd21098c,STILL_EXISTS,XXX because libsvm only does a shallow copy of the
aaibiddbg,scikit-learn/scikit-learn,scikits/learn/svm/oneclass.py,1537b28a39510843ec4b7aa468a0c405fd21098c,b04f33a769d38a5768f9d5dd2473d7e3f7922f2a,XXX maybe we want to cast return value to int
aaibiddbh,scikit-learn/scikit-learn,scikits/learn/svm/oneclass.py,1537b28a39510843ec4b7aa468a0c405fd21098c,b04f33a769d38a5768f9d5dd2473d7e3f7922f2a,XXX predict_values might also be useful
aaibiddbi,scikit-learn/scikit-learn,scikits/learn/svm/regression.py,1537b28a39510843ec4b7aa468a0c405fd21098c,STILL_EXISTS,XXX document why get_svr_probability could be useful
aaibiddch,scikit-learn/scikit-learn,scikits/learn/svm/model.py,4f8d73deb4034c4ec61c270ffce0654017499e29,STILL_EXISTS,XXX because libsvm only does a shallow copy of the
aaibidddb,scikit-learn/scikit-learn,scikits/learn/svm/tests/test_classification.py,4f8d73deb4034c4ec61c270ffce0654017499e29,STILL_EXISTS,XXX decimal=4 to suppress slight differences in values
aaibiddde,scikit-learn/scikit-learn,scikits/learn/svm/model.py,1bbaba78d77af0b2254123f8242b57231267c10c,STILL_EXISTS,XXX possible ctypes bug: setting these to None instead of
aaibiddeb,scikit-learn/scikit-learn,scikits/learn/svm/model.py,f6ee8eed62b015b92a9761ca6f93c48413269cc3,3ec80d479c32efa4bb609add7be10f898637f5d4,XXX workaround for bug in ctypes 0.9.9.6
aaibiddeg,scikit-learn/scikit-learn,scikits/learn/svm/model.py,8143c8bfd18f2f42bca44870f42207cc6d9e7f38,eb68f215855cc91ae92f02bbce540a34efb8186a,XXX keep references to y and x inside problem; if ctypes allows
aaibiddfa,scikit-learn/scikit-learn,scikits/learn/svm/tests/test_classification.py,8143c8bfd18f2f42bca44870f42207cc6d9e7f38,STILL_EXISTS,XXX check cross-validation with and without probability
aaibiddgi,scikit-learn/scikit-learn,scikits/learn/svm/dataset.py,ccbf12530c7959b53dad34efbf52a7d2ad6e3ed7,STILL_EXISTS,XXX look at using a list of vectors instead of a matrix when
aaibiddhc,scikit-learn/scikit-learn,scikits/learn/svm/dataset.py,ccbf12530c7959b53dad34efbf52a7d2ad6e3ed7,STILL_EXISTS,extra columns for id and end of record marker
aaibiddif,scikit-learn/scikit-learn,scikits/learn/svm/dataset.py,3ec80d479c32efa4bb609add7be10f898637f5d4,b505041b4eea028d4a3f0f1773568f1c1d0ce491,XXX possible numpy bug
aaibiddja,scikit-learn/scikit-learn,scikits/learn/svm/dataset.py,3ec80d479c32efa4bb609add7be10f898637f5d4,STILL_EXISTS,XXX N.resize is our friend here
aaibidebc,scikit-learn/scikit-learn,scikits/learn/svm/dataset.py,6d2ebc9e1a8a1d52dab6d3e8cd9a4ec15814f57c,STILL_EXISTS,XXX we can handle gamma=None here
aaibidebe,scikit-learn/scikit-learn,scikits/learn/svm/model.py,6d2ebc9e1a8a1d52dab6d3e8cd9a4ec15814f57c,b871fa4449b57ca12ba839f5c8e62ec2727dfa0d,XXX we can hide an id in the end of record marker so that we
aaibidecc,scikit-learn/scikit-learn,scikits/learn/svm/tests/test_precomputed.py,6d2ebc9e1a8a1d52dab6d3e8cd9a4ec15814f57c,414eb0f654888c64b13ea29527dd38859105aece,XXX sigmas don't match yet. need to find out why.
aaibideeb,scikit-learn/scikit-learn,scikits/learn/svm/predict.py,414eb0f654888c64b13ea29527dd38859105aece,95ade90c6931420dd0240217f5586c839675b5a2,fix support vector ids in precomputed data
aaibideec,scikit-learn/scikit-learn,scikits/learn/svm/predict.py,414eb0f654888c64b13ea29527dd38859105aece,STILL_EXISTS,XXX need to map back from these support vector pointers to
aaibideeh,scikit-learn/scikit-learn,scikits/learn/svm/predict.py,414eb0f654888c64b13ea29527dd38859105aece,9179e3b55bd6df90143b2939d84a7a9982b23959,XXX possible optimization: izip
aaibidegg,scikit-learn/scikit-learn,scikits/learn/svm/predict.py,55bb7423db322e0ffec9f225934be8e4a60e0339,9179e3b55bd6df90143b2939d84a7a9982b23959,XXX possible optimization: izip
aaibidegh,scikit-learn/scikit-learn,scikits/learn/svm/predict.py,55bb7423db322e0ffec9f225934be8e4a60e0339,STILL_EXISTS,XXX need to map back from these support vector pointers to
aaibidfaj,scikit-learn/scikit-learn,scikits/learn/svm/tests/test_classification.py,7c5cd773571a8bcffafd1f5c32b4a4076119bfcc,STILL_EXISTS,XXX there are slight differences between
aaibidfhi,scikit-learn/scikit-learn,scikits/learn/svm/tests/test_classification.py,3aa08282f37a3c6b78d5e788860281993e072457,STILL_EXISTS,XXX this test fails
aaibidfia,scikit-learn/scikit-learn,scikits/learn/svm/tests/test_classification.py,3aa08282f37a3c6b78d5e788860281993e072457,STILL_EXISTS,XXX these tests fail
aaibidfid,scikit-learn/scikit-learn,scikits/learn/svm/tests/test_speed.py,3aa08282f37a3c6b78d5e788860281993e072457,STILL_EXISTS,XXX shows bug where we can't get any support vectors
aaibidfjj,scikit-learn/scikit-learn,tester.py,88dd6f335977c303caf68b26fd31ec75cfab6084,85c4c9b3f210594b093c22bec00f48844d06c178,XXX: does this guarantee that the package is the one in the dev trunk; and
aaibidgad,scikit-learn/scikit-learn,scikits/learn/utils/__init__.py,70f6234bb09204711546385b6ba436a6893da515,18bd1e02da0455dde30215d53bebb516de4f0b60,XXX: this is to avoid recursive call to itself. This is an horrible hack;
aaibidgae,scikit-learn/scikit-learn,scikits/learn/utils/__init__.py,70f6234bb09204711546385b6ba436a6893da515,18bd1e02da0455dde30215d53bebb516de4f0b60,I have no idea why infinite recursion happens otherwise.
aaibidgaf,scikit-learn/scikit-learn,scikits/learn/machine/pyem/__init__.py,136683609930562997379d78ecf9f3465adf527e,STILL_EXISTS,XXX: this is to avoid recursive call to itself. This is an horrible hack;
aaibidgba,scikit-learn/scikit-learn,scikits/learn/machine/svm/__init__.py,167872381e16090b1b47184a1a80bbe948d5fd91,85c4c9b3f210594b093c22bec00f48844d06c178,XXX: this is to avoid recursive call to itself. This is an horrible hack;
aaibidgbb,scikit-learn/scikit-learn,scikits/learn/machine/svm/__init__.py,167872381e16090b1b47184a1a80bbe948d5fd91,85c4c9b3f210594b093c22bec00f48844d06c178,I have no idea why infinite recursion happens otherwise.
aaibidgff,scikit-learn/scikit-learn,scikits/learn/datasets/pendigits/testing/data.py,bc1f4b5b54475d0ce2fd59de41350ff5d285a2fa,STILL_EXISTS,* Neither the author nor the names of any contributors may be used
aaibidgjj,scikit-learn/scikit-learn,scikits/learn/datasets/pendigits/training/data.py,bc1f4b5b54475d0ce2fd59de41350ff5d285a2fa,STILL_EXISTS,* Neither the author nor the names of any contributors may be used
aaibidhde,scikit-learn/scikit-learn,scikits/learn/utils/arffread.py,d5b59358212f470ac4eb1b0d68092157ca9f7611,e846b095d1e023440b59641a9833396bc27539df,TODO:
aaibidhif,scikit-learn/scikit-learn,scikits/learn/utils/arffread.py,9065f500e4e52b96c8e55042f120effdc632470b,STILL_EXISTS,TODO: this is where we are spending times. I think things could be
aaibididc,scikit-learn/scikit-learn,scikits/learn/utils/arffread.py,cf7d2b6509bba851bd0fc546287445c4689d3ee6,STILL_EXISTS,XXX The above code is ugly
aaibidijj,scikit-learn/scikit-learn,scikits/learn/common/myfunctools.py,b56690d046021e036b5b15c484d86c92f3519600,STILL_EXISTS,Implement partial application (should only be used if functools is not
aaibidjfh,scikit-learn/scikit-learn,scikits/learn/utils/arffread.py,a05ec042e5871aa7e4b6707f773855f8f384238e,e846b095d1e023440b59641a9833396bc27539df,No error should happen here: it is a bug otherwise
aaibieaaf,scikit-learn/scikit-learn,scikits/learn/utils/arffread.py,fe9bbcc5d88a3dc29e7b6d4e659797c0c9b29223,e846b095d1e023440b59641a9833396bc27539df,XXX: may be better to implement an ordered dictionary
aaibieada,scikit-learn/scikit-learn,scikits/learn/datasets/nwis/data.py,a82765a3b13d3f2e48b9ab9ae8e8187362c921ed,STILL_EXISTS,* Neither the author nor the names of any contributors may be used
aaibiebia,scikit-learn/scikit-learn,scikits/learn/machine/manifold_learning/regression/neighboors/neighboors.py,46c619eee71f54065dc4edc80073ffdd9861f65d,STILL_EXISTS,needed for saving the state
aaibiedea,scikit-learn/scikit-learn,scikits/learn/machine/manifold_learning/regression/neighbors/neighboors.py,2b5d2b1657c16ec2507ebb3115a1f8714462ea0c,STILL_EXISTS,needed for saving the state
aaibiedfd,scikit-learn/scikit-learn,scikits/learn/machine/manifold_learning/regression/neighbors/neighbors.py,16f7762fea5c7d2a3cee0f6ed928df4b75042e57,STILL_EXISTS,needed for saving the state
aaibiedhf,scikit-learn/scikit-learn,scikits/learn/machine/em2/gm.py,3a89497dab2377426749ff9d28c3338add863cda,STILL_EXISTS,\"\"\"Module implementing GM; a class which represents Gaussian mixtures. ||  || GM instances can be used to create; sample mixtures. They also provide || different plotting facilities; such as isodensity contour for multi dimensional || models; ellipses of confidence; etc...\"\"\"
aaibiedhi,scikit-learn/scikit-learn,scikits/learn/machine/em2/gm.py,3a89497dab2377426749ff9d28c3338add863cda,STILL_EXISTS,TODO:
aaibiedid,scikit-learn/scikit-learn,scikits/learn/machine/em2/gm.py,3a89497dab2377426749ff9d28c3338add863cda,STILL_EXISTS,should be a sensible way to modify the result plot (maybe returns a dic
aaibiedjf,scikit-learn/scikit-learn,scikits/learn/machine/em2/gm.py,3a89497dab2377426749ff9d28c3338add863cda,STILL_EXISTS,TODO Check args here
aaibiedjg,scikit-learn/scikit-learn,scikits/learn/machine/em2/gm.py,3a89497dab2377426749ff9d28c3338add863cda,STILL_EXISTS,TODO: check each value of inverse distribution is different
aaibiedjh,scikit-learn/scikit-learn,scikits/learn/machine/em2/gm.py,3a89497dab2377426749ff9d28c3338add863cda,STILL_EXISTS,This one should be a bit faster
aaibieeaf,scikit-learn/scikit-learn,scikits/learn/machine/em2/gm.py,77016cb67eb77b7c40be48642ec6a71f2ce5d8ff,STILL_EXISTS,XXX: make this faster ?
aaibieeaj,scikit-learn/scikit-learn,scikits/learn/machine/em2/yop.py,c9c862a0bee2a1b314862d0ff5f6ed63de167c35,STILL_EXISTS,XXX: This is bogus initialization should do better (in kmean with CV)
aaibieebb,scikit-learn/scikit-learn,scikits/learn/machine/em2/gmm.py,9ff9132539ca3680a8f89a78aac8974b04b2851d,STILL_EXISTS,XXX: we should use clip here before exp; to remove too small values
aaibieebg,scikit-learn/scikit-learn,scikits/learn/machine/em2/gmm.py,421c06aa63bed65ffd03785e1e379ec3f0bc1c5f,STILL_EXISTS,XXX: This is bogus initialization should do better (in kmean with CV)
aaibieffc,scikit-learn/scikit-learn,scikits/learn/machine/svm/tests/test_speed.py,85c4c9b3f210594b093c22bec00f48844d06c178,9165acf55a74a99647eeb42fa6d974c9330a203b,#         # XXX shows bug where we can't get any support vectors
aaibiefji,scikit-learn/scikit-learn,scikits/learn/datasets/narr/data.py,f2c190a968300574674988118593d9ca1c63ae96,STILL_EXISTS,\"\"\" || XXX: currently disabled || \"\"\"
aaibiefjj,scikit-learn/scikit-learn,scikits/learn/datasets/narr/data.py,f2c190a968300574674988118593d9ca1c63ae96,STILL_EXISTS,narr = load(1979; 1;1) XXX: currently disabled
aaibiegeb,scikit-learn/scikit-learn,web/conf.py,cdf45a1150ea61ca1a7ccf48c629d618de30e64c,STILL_EXISTS,unused_docs = []
aaibiehgg,scikit-learn/scikit-learn,doc/conf.py,362fe46563b32820d594b7b7f6c9e039ab2790a6,6b130f15cf44b449dc4bb8394644072ec5ec5187,unused_docs = []
aaibieiig,scikit-learn/scikit-learn,scikits/learn/machine/svm/tests/test_speed.py,9165acf55a74a99647eeb42fa6d974c9330a203b,STILL_EXISTS,XXX shows bug where we can't get any support vectors
aaibifabi,scikit-learn/scikit-learn,scikits/learn/neighbors/neighbors.py,1fc649fe40bb16bc7a82d004d848e6a52b1546c3,STILL_EXISTS,\"\"\" || k-Nearest Neighbor Algorithm. ||  || Uses BallTree algorithm; which is an efficient way to perform fast || neighbor searches in high dimensionality. || \"\"\"
aaibifabj,scikit-learn/scikit-learn,scikits/learn/neighbors/neighbors.py,1fc649fe40bb16bc7a82d004d848e6a52b1546c3,fcc11d22e5017857a0235220140d1278de6578a0,needed for saving the state
aaibifaia,scikit-learn/scikit-learn,scikits/learn/glm/lasso_cd.py,7ab28cd01a47b64dda6c165d1be3fc72e54a1144,STILL_EXISTS,Compute norms of the columns of X
aaibifbec,scikit-learn/scikit-learn,scikits/learn/glm/enet_cd.py,fae9cab918a6164156edf32219a8773f61d4a8ad,STILL_EXISTS,Compute norms of the columns of X
aaibifbhf,scikit-learn/scikit-learn,scikits/learn/feature_select/tests/test_feature_select.py,60ddea6aed7b3e9abbac32a9a7a8eddc8c57645e,STILL_EXISTS,\"\"\" || Todo: cross-check the F-value with stats model || \"\"\"
aaibifcbe,scikit-learn/scikit-learn,scikits/learn/glm/cd.py,50da323cb1c64b25597bf9bc6eda8d0a0dda522d,STILL_EXISTS,TODO: check the last time stamp to avoid computing the stats too often
aaibifche,scikit-learn/scikit-learn,examples/plot_svm.py,35e6a87c48a753033722f4a40204d510cc994cda,STILL_EXISTS,avoid this ugly slicing by using a two-dim dataset
aaibifdcf,scikit-learn/scikit-learn,examples/plot_neighbors.py,96ed447d9e1445abbc9a42a4785cf23fbf69c098,STILL_EXISTS,avoid this ugly slicing by using a two-dim dataset
aaibifdhb,scikit-learn/scikit-learn,scikits/learn/feature_selection/univ_selection.py,f77d9b746018d09cfe96ba0f394b8ad25b7360de,STILL_EXISTS,FIXME : support estimate is done again in predict too in
aaibifebd,scikit-learn/scikit-learn,scikits/learn/setup.py,ad7a81f0997f4db10bddbfac77487ac44b63c72c,0c969d12779df7311f255dba2c32ccee9dcf6fea,Workaround to enforce building cython extensions while
aaibifebh,scikit-learn/scikit-learn,setup.py,ad7a81f0997f4db10bddbfac77487ac44b63c72c,0c969d12779df7311f255dba2c32ccee9dcf6fea,Workaround to enforce building cython extensions while
aaibifffa,scikit-learn/scikit-learn,examples/em/basic_example2.py,e5c769de44809523e207847b45d277ac3a6bfe2d,STILL_EXISTS,To reproduce results; fix the random seed
aaibifgie,scikit-learn/scikit-learn,doc/sphinxext/gen_rst.py,1896240830dc1e1fb886627972d93b8e0b3999f5,f27f234d44099ff25c3fb31823c7d7342b30e49a,better than nested.
aaibifgii,scikit-learn/scikit-learn,examples/svm/plot_svm.py,03c7d926f670905a0ac54ae8ca988cc34001be3c,STILL_EXISTS,avoid this ugly slicing by using a two-dim dataset
aaibifijd,scikit-learn/scikit-learn,scikits/learn/glm/tests/test_coordinate_descent.py,6557471fe544058aae9593054ea5b3e62ca648aa,ec364fe93246d2f79ac19ae5c6f8943e9e74b967,TODO: not sure of these results.
aaibifije,scikit-learn/scikit-learn,scikits/learn/glm/tests/test_coordinate_descent.py,6557471fe544058aae9593054ea5b3e62ca648aa,ec364fe93246d2f79ac19ae5c6f8943e9e74b967,dual_gap seems a bit too high
aaibigcee,scikit-learn/scikit-learn,scikits/learn/glm/coordinate_descent.py,5c82f77c7574e06aec44eb77580c137721b85a0e,ddbb8755c963789797e35f60cd76284f51f63898,fine tune at optimal alpha on complete data set
aaibigcfb,scikit-learn/scikit-learn,scikits/learn/glm/coordinate_descent.py,4576ff62c1b711d9fd47be84fe44418eb5c987ac,STILL_EXISTS,XXX: store a path of Lasso instances instead?
aaibigcfi,scikit-learn/scikit-learn,scikits/learn/glm/coordinate_descent.py,ddbb8755c963789797e35f60cd76284f51f63898,STILL_EXISTS,XXX: store a path of Lasso instances instead?
aaibigcib,scikit-learn/scikit-learn,scikits/learn/glm/coordinate_descent.py,e783e38e0af630ded0f856e315883a9420626797,STILL_EXISTS,XXX: store a path of Lasso instances instead?
aaibigcic,scikit-learn/scikit-learn,scikits/learn/glm/coordinate_descent.py,e783e38e0af630ded0f856e315883a9420626797,07fa138e58c9f054c362a3dba2d09bb4ead900bc,fine tune at optimal alpha on complete data set
aaibigdbb,scikit-learn/scikit-learn,scikits/learn/glm/coordinate_descent.py,3dab5a165eff7c0a3d0feb3507a2e466f708ec13,STILL_EXISTS,fine tune at optimal alpha on complete data set
aaibigdii,scikit-learn/scikit-learn,scikits/learn/glm/coordinate_descent.py,5c7200eee909d07fbd763db25328a1f0f67b3638,STILL_EXISTS,FIXME : should not early stop
aaibigdjc,scikit-learn/scikit-learn,scikits/learn/svm.py,f642719d2ebc62de473f55085eeb754a77829019,STILL_EXISTS,TODO: put keyword copy to copy on demand
aaibigeag,scikit-learn/scikit-learn,examples/svm/plot_custom_kernel.py,8143abbadf8fbbf3a67ccd31de9155fbfa624514,STILL_EXISTS,avoid this ugly slicing by using a two-dim dataset
aaibigfeh,scikit-learn/scikit-learn,scikits/learn/tests/test_svm.py,2f4844ca54934b842fd76a5fe38b2c4de090b666,8725a895669049833564feb15f5121c2fdb2179a,\"\"\" || Testing for Support Vector Machine ||  || TODO: remove hardcoding numerical results when possible || \"\"\"
aaibigfei,scikit-learn/scikit-learn,scikits/learn/tests/test_svm.py,2f4844ca54934b842fd76a5fe38b2c4de090b666,STILL_EXISTS,TODO: why are we getting all the dataset as support vectors
aaibigffi,scikit-learn/scikit-learn,scikits/learn/tests/test_svm.py,8725a895669049833564feb15f5121c2fdb2179a,STILL_EXISTS,TODO: why are we getting all the dataset as support vectors
aaibigfjf,scikit-learn/scikit-learn,scikits/learn/tests/test_svm.py,1d3754b5a9e9c354b258dac0e94fdfde5f47f3c1,STILL_EXISTS,\"\"\" || Testing for Support Vector Machine ||  || TODO: remove hardcoding numerical results when possible || \"\"\"
aaibigfjg,scikit-learn/scikit-learn,scikits/learn/tests/test_svm.py,1d3754b5a9e9c354b258dac0e94fdfde5f47f3c1,STILL_EXISTS,TODO: why are we getting all the dataset as support vectors
aaibiggaf,scikit-learn/scikit-learn,scikits/learn/glm/coordinate_descent.py,c77a7a7ca1a4402f8bfefca3027dec009f2ad547,STILL_EXISTS,TODO: why not define a method rsquared that computes this ?
aaibiggcj,scikit-learn/scikit-learn,scikits/learn/gmm.py,3d6e8355452cb2b08ecf6922ca1074789ceb48ee,STILL_EXISTS,FIXME: this representation of covars is going to lose for caching
aaibigibg,scikit-learn/scikit-learn,scikits/learn/glm.py,b3b1fee67c7cba9a3880537062324d8840821948,STILL_EXISTS,TODO: why not define a method rsquared that computes this ?
aaibigidg,scikit-learn/scikit-learn,scikits/learn/glm.py,28c57f78494045191719c158343fa8615cf53705,d9f5c76ada1d9d7e5297889d1b237a0c13b7ff28,TODO: use numpy.linalg instead
aaibigidi,scikit-learn/scikit-learn,scikits/learn/glm.py,28c57f78494045191719c158343fa8615cf53705,STILL_EXISTS,## TODO: intercept for all models
aaibigieg,scikit-learn/scikit-learn,scikits/learn/glm.py,28c57f78494045191719c158343fa8615cf53705,9c0408ac95e7d9fcaac4ae468a6e75c9e0e3a9fc,# TODO: it's a bit strange that the above method returns a value
aaibigiei,scikit-learn/scikit-learn,scikits/learn/glm.py,28c57f78494045191719c158343fa8615cf53705,STILL_EXISTS,todo; shouldn't most of these have trailing underscores ?
aaibigiej,scikit-learn/scikit-learn,scikits/learn/glm.py,28c57f78494045191719c158343fa8615cf53705,STILL_EXISTS,TODO: add intercept
aaibigihi,scikit-learn/scikit-learn,scikits/learn/glm.py,9cbd1cd6fab0d6a0f00313bca6227218df87c564,STILL_EXISTS,needed by LeastAngleRegression
aaibigjcg,scikit-learn/scikit-learn,scikits/learn/shortcuts.py,46f3fafe53c02811f5f6cd5373364223b3e8558b,STILL_EXISTS,todo: not general!
aaibigjdc,scikit-learn/scikit-learn,examples/plot_gmm.py,7ada25ae8c847192a5c91a317de9fa8aaaed2fad,STILL_EXISTS,\"\"\" || Simple Gaussian Mixture model plotting example ||  || TODO: use the faithful dataset || \"\"\"
aaibihaag,scikit-learn/scikit-learn,scikits/learn/tests/test_glm.py,e3a75ec58e26f66f0989debfdcc7ce797535cd24,3a59f749ac9c33a4d0b8cdd93de3d500deabf220,XXX: there seems to be somthing borked in the shape reconstruction of the
aaibihacc,scikit-learn/scikit-learn,scikits/learn/glm.py,27d3900dceac69273f9208e983752a5338574a58,a178da50b50481ea25e0c16d6833efbf25fa0e5a,TODO: I believe this is not really much used
aaibihacd,scikit-learn/scikit-learn,scikits/learn/glm.py,27d3900dceac69273f9208e983752a5338574a58,STILL_EXISTS,# TODO: this should have a tests.
aaibihace,scikit-learn/scikit-learn,scikits/learn/glm.py,27d3900dceac69273f9208e983752a5338574a58,STILL_EXISTS,# TODO: resize (not create) arrays; check shape;
aaibihadh,scikit-learn/scikit-learn,scikits/learn/tests/test_glm.py,2bac216ca128cccb560b65092437d08d9bbc800e,3a59f749ac9c33a4d0b8cdd93de3d500deabf220,XXX: there seems to be somthing borked in the shape reconstruction of the
aaibihahc,scikit-learn/scikit-learn,scikits/learn/glm.py,f9a8a9524d5cc5d1ee10274d41ba1c6ff8c0f895,STILL_EXISTS,will only normalize non-zero columns
aaibihaid,scikit-learn/scikit-learn,scikits/learn/tests/test_glm.py,87223524bcd8cb57c1b183e6ca766f16cdf03413,3eb4f5a730184ebfc5d41733721904886c3856b1,XXX: there seems to be somthing borked in the shape reconstruction of the
aaibihbdg,scikit-learn/scikit-learn,scikits/learn/glm.py,42cec216b44ac2e509772c7355529db49f4a4cd7,STILL_EXISTS,will only normalize non-zero columns
aaibihbdh,scikit-learn/scikit-learn,scikits/learn/__init__.py,c2145fbd81f96deec9b3bf707b7b58cef16190c5,STILL_EXISTS,\"\"\" || Machine Learning module in python || ================================= ||  || scikits.learn is a Python module integrating classique machine || learning algorithms in the tightly-nit world of scientific Python || packages (numpy; scipy; matplotlib). ||  || It aims to provide simple and efficient solutions to learning problems || that are accessible to everybody and reusable in various contexts: || machine-learning as a versatile tool for science and engineering. ||  || See http:\/\/scikit-learn.sourceforge.net for complete documentation. || \"\"\"
aaibihcih,scikit-learn/scikit-learn,scikits/learn/features/text.py,3e7af84136ffb9bb04468439f028c2e795e2b7a3,3b9b8b469f3e2ee57b6ee9b99089f0db926fd3c2,TODO: implement me using the murmurhash that might be faster: but profile
aaibihcjc,scikit-learn/scikit-learn,scikits/learn/features/text.py,3e7af84136ffb9bb04468439f028c2e795e2b7a3,STILL_EXISTS,TODO add support for cooccurence tokens in a sentence
aaibihcjf,scikit-learn/scikit-learn,scikits/learn/features/text.py,3e7af84136ffb9bb04468439f028c2e795e2b7a3,STILL_EXISTS,TODO: write unittests instead!
aaibihdai,scikit-learn/scikit-learn,scikits/learn/datasets/mlcomp.py,7759f8b0a58e8bc7ceffa38577d64984a74cc5ef,STILL_EXISTS,TODO: implement the remaining domain formats
aaibihdbf,scikit-learn/scikit-learn,examples/mlcomp_document_classification.py,434eed65b4b2eb5f0f8814467576cd808742ccfd,STILL_EXISTS,\"\"\" || ================================ || Classification of text documents || ================================ ||  || This is an example showing how the scikit-learn can be used to classify || documents by topics using a bag-of-words approach. ||  || The dataset used in this example is the 20 newsgroups dataset and should be || downloaded from the http:\/\/mlcomp.org (free registration required): ||  ||   http:\/\/mlcomp.org\/datasets\/379 ||  || Once downloaded unzip the arhive somewhere on your filesystem. For instance in:: ||  ||   % mkdir -p ~\/data\/mlcomp ||   % cd  ~\/data\/mlcomp ||   % unzip \/path\/to\/dataset-379-20news-18828_XXXXX.zip ||  || You should get a folder ``~\/data\/mlcomp\/379`` with a file named ``metadata`` and || subfolders ``raw``; ``train`` and ``test`` holding the text documents organized by || newsgroups. ||  || Then set the ``MLCOMP_DATASETS_HOME`` environment variable pointing to || the root folder holding the uncompressed archive:: ||  ||   % export MLCOMP_DATASETS_HOME=\"~\/data\/mlcomp\" ||  || Then you are ready to run this example using your favorite python shell:: ||  ||   % ipython examples\/mlcomp_document_classification.py ||  || \"\"\"
aaibihdcd,scikit-learn/scikit-learn,examples/mlcomp_document_classification.py,434eed65b4b2eb5f0f8814467576cd808742ccfd,STILL_EXISTS,representation of a text dataset. Efficient handling of sparse data
aaibihdcg,scikit-learn/scikit-learn,scikits/learn/datasets/mlcomp.py,434eed65b4b2eb5f0f8814467576cd808742ccfd,41d3f72b7930f87bcccc9e3478c752006cdc2316,TODO: make it possible to plug a several pass system to filter-out tokens
aaibihdci,scikit-learn/scikit-learn,scikits/learn/datasets/mlcomp.py,434eed65b4b2eb5f0f8814467576cd808742ccfd,41d3f72b7930f87bcccc9e3478c752006cdc2316,TODO: use joblib.Parallel or multiprocessing to parallelize the following
aaibihdda,scikit-learn/scikit-learn,scikits/learn/features/text.py,434eed65b4b2eb5f0f8814467576cd808742ccfd,41b0562e7942a1e1b9d1fb057ad62c5b3ce7cea4,TODO: make it possible to pass stop words list here
aaibihddb,scikit-learn/scikit-learn,scikits/learn/features/text.py,434eed65b4b2eb5f0f8814467576cd808742ccfd,3b9b8b469f3e2ee57b6ee9b99089f0db926fd3c2,TODO: make it possible to select between the current dense representation
aaibihhae,scikit-learn/scikit-learn,scikits/learn/base_estimator.py,da3a328e41016aa801eae0081961e3b68412af7d,e0872efe6a180cee301c7c2f317fea3524f00703,XXX: This is work in progress and will probably need refactoring
aaibihiha,scikit-learn/scikit-learn,scikits/learn/base_estimator.py,39788832dc775acf95cbd7685c2fa3c506f8526e,be82ca3ecc0a9cf05253340765e68c2313935047,TODO:
aaibiiggc,scikit-learn/scikit-learn,scikits/learn/svm.py,1132797f4aefd992b9487c95f20556782aed5d6b,STILL_EXISTS,TODO: put keyword copy to copy on demand
aaibiihab,scikit-learn/scikit-learn,doc/sphinxext/gen_rst.py,51543b1e74453ae23b578da58b98c90019b06e68,c14be56680f2f647fb082a7d81565bd92cfc83e2,better than nested.
aaibiihjb,scikit-learn/scikit-learn,doc/sphinxext/gen_rst.py,b5fd8aee4ee300c463c38f8c7a97113b877e00cf,STILL_EXISTS,better than nested.
aaibiiibe,scikit-learn/scikit-learn,scikits/learn/grid_search.py,73d46859dc654ccd65a978b2a23c562d672b622e,8f1c3fcf4ce58c03f525cf01e80be6ba1ce89623,XXX: cross_val_factory should have a default
aaibiijac,scikit-learn/scikit-learn,scikits/learn/sparse/svm.py,dd92df672e9822fa6cccb6b189769989128f589d,STILL_EXISTS,TODO: explicitly specify size
aaibiijad,scikit-learn/scikit-learn,scikits/learn/sparse/svm.py,dd92df672e9822fa6cccb6b189769989128f589d,STILL_EXISTS,TODO: is this always a 1-d array ?
aaibiijah,scikit-learn/scikit-learn,scikits/learn/svm.py,dd92df672e9822fa6cccb6b189769989128f589d,cd73b6a877b5dfb3b3cb92e0c2db0d256cb886bc,TODO: some cleanup: is nSV_ really needed ?
aaibiijai,scikit-learn/scikit-learn,scikits/learn/cross_val.py,8f1c3fcf4ce58c03f525cf01e80be6ba1ce89623,cd73b6a877b5dfb3b3cb92e0c2db0d256cb886bc,XXX: Should maybe have an argument to raise when
aaibiijch,scikit-learn/scikit-learn,scikits/learn/grid_search.py,cd73b6a877b5dfb3b3cb92e0c2db0d256cb886bc,0ea8827e8eafb1b9b99d1e5518864a4747f6529e,XXX: cross_val_factory should have a default
aaibijaba,scikit-learn/scikit-learn,scikits/learn/svm.py,c3e7b530680a6af0e64a383c848e2cee95b71837,STILL_EXISTS,TODO: some cleanup: is nSV_ really needed ?
aaibijabb,scikit-learn/scikit-learn,scikits/learn/cross_val.py,0ea8827e8eafb1b9b99d1e5518864a4747f6529e,bf83c352bc9ebb42430f5994938501d8c8143e14,XXX: Should maybe have an argument to raise when
aaibijadf,scikit-learn/scikit-learn,examples/plot_lda_qda.py,2e10705428cd44fa8377cb102eb9823d888370a2,STILL_EXISTS,QDA #FIXME
aaibijafc,scikit-learn/scikit-learn,scikits/learn/cross_val.py,e08e595db4b3887a93172f7df825c5324f7a3af4,c77024dee3112c214b501c7bc6ef7307a5b25349,XXX: should have a n_jobs to be able to do this in parallel.
aaibijajd,scikit-learn/scikit-learn,scikits/learn/cross_val.py,b2f3f6151de5067307021038c8e1db567bd07136,a19a4320fdcf43194636ed7575ffc87d6947a5b7,XXX: should have a n_jobs to be able to do this in parallel.
aaibijaje,scikit-learn/scikit-learn,examples/svm/plot_svm_anova.py,faeb984b89d0c5123a9ab68bada82ad574521dd9,STILL_EXISTS,\"\"\" || ================================================= || SVM-Anova: SVM with univariate feature selection || ================================================= ||  || This example shows how to perform univariate feature before running a SVC || (support vector classifier) to improve the classification scores. || \"\"\"
aaibijcfg,scikit-learn/scikit-learn,scikits/learn/externals/joblib/format_stack.py,a3bd41971720ae466b4e1e99f8b1484501c8954e,STILL_EXISTS,Hack to avoid printing the interals of IPython
aaibijchb,scikit-learn/scikit-learn,scikits/learn/externals/joblib/func_inspect.py,a3bd41971720ae466b4e1e99f8b1484501c8954e,STILL_EXISTS,Hack to detect functions not defined at the module-level
aaibijchc,scikit-learn/scikit-learn,scikits/learn/externals/joblib/func_inspect.py,a3bd41971720ae466b4e1e99f8b1484501c8954e,STILL_EXISTS,TODO: Maybe add a warning here?
aaibijcia,scikit-learn/scikit-learn,scikits/learn/externals/joblib/func_inspect.py,a3bd41971720ae466b4e1e99f8b1484501c8954e,STILL_EXISTS,XXX: Maybe I need an inspect.isbuiltin to detect C-level methods; such
aaibijcif,scikit-learn/scikit-learn,scikits/learn/externals/joblib/func_inspect.py,a3bd41971720ae466b4e1e99f8b1484501c8954e,STILL_EXISTS,XXX: Return a sorted list of pairs?
aaibijcjf,scikit-learn/scikit-learn,scikits/learn/externals/joblib/hashing.py,a3bd41971720ae466b4e1e99f8b1484501c8954e,STILL_EXISTS,XXX: There might be a more efficient way of doing this
aaibijdbc,scikit-learn/scikit-learn,scikits/learn/externals/joblib/logger.py,a3bd41971720ae466b4e1e99f8b1484501c8954e,STILL_EXISTS,XXX: This conflicts with the debug flag used in children class
aaibijdbg,scikit-learn/scikit-learn,scikits/learn/externals/joblib/logger.py,a3bd41971720ae466b4e1e99f8b1484501c8954e,STILL_EXISTS,XXX: Need argument docstring
aaibijdcc,scikit-learn/scikit-learn,scikits/learn/externals/joblib/logger.py,a3bd41971720ae466b4e1e99f8b1484501c8954e,STILL_EXISTS,FIXME: Too much logic duplicated
aaibijddc,scikit-learn/scikit-learn,scikits/learn/externals/joblib/memory.py,a3bd41971720ae466b4e1e99f8b1484501c8954e,STILL_EXISTS,TODO: The following object should have a data store object as a sub
aaibijddj,scikit-learn/scikit-learn,scikits/learn/externals/joblib/memory.py,a3bd41971720ae466b4e1e99f8b1484501c8954e,STILL_EXISTS,implement HDF5 pickling.
aaibijdea,scikit-learn/scikit-learn,scikits/learn/externals/joblib/memory.py,a3bd41971720ae466b4e1e99f8b1484501c8954e,STILL_EXISTS,TODO: Same remark for the logger; and probably use the Python logging
aaibijdec,scikit-learn/scikit-learn,scikits/learn/externals/joblib/memory.py,a3bd41971720ae466b4e1e99f8b1484501c8954e,c88b5bcca53140f558249e8972c847fad76172d1,TODO: Track history as objects are called; to be able to garbage
aaibijdfd,scikit-learn/scikit-learn,scikits/learn/externals/joblib/memory.py,a3bd41971720ae466b4e1e99f8b1484501c8954e,STILL_EXISTS,FIXME: The statements below should be try\/excepted
aaibijdfe,scikit-learn/scikit-learn,scikits/learn/externals/joblib/memory.py,a3bd41971720ae466b4e1e99f8b1484501c8954e,STILL_EXISTS,XXX: Should use an exception logger
aaibijdfi,scikit-learn/scikit-learn,scikits/learn/externals/joblib/memory.py,a3bd41971720ae466b4e1e99f8b1484501c8954e,STILL_EXISTS,XXX: Ugly
aaibijdha,scikit-learn/scikit-learn,scikits/learn/externals/joblib/memory.py,a3bd41971720ae466b4e1e99f8b1484501c8954e,STILL_EXISTS,XXX: Should be using warnings; and giving stacklevel
aaibijdhb,scikit-learn/scikit-learn,scikits/learn/externals/joblib/memory.py,a3bd41971720ae466b4e1e99f8b1484501c8954e,STILL_EXISTS,XXX: Not using logging framework
aaibijdhd,scikit-learn/scikit-learn,scikits/learn/externals/joblib/memory.py,a3bd41971720ae466b4e1e99f8b1484501c8954e,STILL_EXISTS,XXX: This should be moved out to a function
aaibijdhe,scikit-learn/scikit-learn,scikits/learn/externals/joblib/memory.py,a3bd41971720ae466b4e1e99f8b1484501c8954e,STILL_EXISTS,XXX: Should this use inspect.formatargvalues\/formatargspec?
aaibijdhg,scikit-learn/scikit-learn,scikits/learn/externals/joblib/memory.py,a3bd41971720ae466b4e1e99f8b1484501c8954e,STILL_EXISTS,XXX: Need a method to check if results are available.
aaibijdig,scikit-learn/scikit-learn,scikits/learn/externals/joblib/memory.py,a3bd41971720ae466b4e1e99f8b1484501c8954e,STILL_EXISTS,XXX: Bad explaination of the None value of cachedir
aaibijeac,scikit-learn/scikit-learn,scikits/learn/externals/joblib/numpy_pickle.py,a3bd41971720ae466b4e1e99f8b1484501c8954e,STILL_EXISTS,XXX: We should have a logging mechanism
aaibijebd,scikit-learn/scikit-learn,scikits/learn/externals/joblib/parallel.py,a3bd41971720ae466b4e1e99f8b1484501c8954e,STILL_EXISTS,XXX: Not using the logger framework: need to
aaibijebe,scikit-learn/scikit-learn,scikits/learn/externals/joblib/parallel.py,a3bd41971720ae466b4e1e99f8b1484501c8954e,STILL_EXISTS,learn to use logger better.
aaibijfbd,scikit-learn/scikit-learn,scikits/learn/externals/joblib/test/test_memory.py,a3bd41971720ae466b4e1e99f8b1484501c8954e,STILL_EXISTS,FIXME: Need to test that memmapping does not force recomputing.
aaibjadea,scikit-learn/scikit-learn,scikits/learn/features/text.py,4efdd7798242b911b41f5adf78529e1c36754b0b,STILL_EXISTS,TODO: implement me using the murmurhash that might be faster: but profile
aaibjadec,scikit-learn/scikit-learn,scikits/learn/features/text.py,4efdd7798242b911b41f5adf78529e1c36754b0b,d53a6686c27c1a7c8289e13b0307cd41e0dcae0c,TODO: make it possible to select between the current dense representation
aaibjadei,scikit-learn/scikit-learn,scikits/learn/features/text.py,4efdd7798242b911b41f5adf78529e1c36754b0b,STILL_EXISTS,TODO add support for cooccurence tokens in a sentence
aaibjadgf,scikit-learn/scikit-learn,examples/mlcomp_sparse_document_classification.py,8c61c972e2278f9a4f6eb919521e198bb6e20834,STILL_EXISTS,\"\"\" || ====================================================== || Classification of text documents using sparse features || ====================================================== ||  || This is an example showing how the scikit-learn can be used to classify || documents by topics using a bag-of-words approach. This example uses || a scipy.sparse matrix to store the features instead of standard numpy arrays. ||  || The dataset used in this example is the 20 newsgroups dataset and should be || downloaded from the http:\/\/mlcomp.org (free registration required): ||  ||   http:\/\/mlcomp.org\/datasets\/379 ||  || Once downloaded unzip the arhive somewhere on your filesystem. For instance in:: ||  ||   % mkdir -p ~\/data\/mlcomp ||   % cd  ~\/data\/mlcomp ||   % unzip \/path\/to\/dataset-379-20news-18828_XXXXX.zip ||  || You should get a folder ``~\/data\/mlcomp\/379`` with a file named ``metadata`` and || subfolders ``raw``; ``train`` and ``test`` holding the text documents organized by || newsgroups. ||  || Then set the ``MLCOMP_DATASETS_HOME`` environment variable pointing to || the root folder holding the uncompressed archive:: ||  ||   % export MLCOMP_DATASETS_HOME=\"~\/data\/mlcomp\" ||  || Then you are ready to run this example using your favorite python shell:: ||  ||   % ipython examples\/mlcomp_sparse_document_classification.py ||  || \"\"\"
aaibjadig,scikit-learn/scikit-learn,scikits/learn/glm.py,619821a115e9f10faddc939e0719ff179db594a0,a2fefd4b90c92b72db6c51d72428cfdb48d311d4,XXX: Should use numpy.logspace
aaibjadja,scikit-learn/scikit-learn,scikits/learn/glm.py,d5452dacb50a334acf01a636964879ac32f3a883,a2fefd4b90c92b72db6c51d72428cfdb48d311d4,XXX: Maybe should reorder the models when outputing them; so
aaibjaeag,scikit-learn/scikit-learn,scikits/learn/qda.py,497f1fddf4027362c1152040c8ed3d6b8cf37d18,STILL_EXISTS,FIXME :
aaibjaedh,scikit-learn/scikit-learn,scikits/learn/glm.py,64d4a193ba89cf88fd131d8c99f0796370e2a600,1a9cbeb0e9465837201721164e41dc55bbfdb215,XXX: Should use numpy.logspace
aaibjaedi,scikit-learn/scikit-learn,scikits/learn/glm.py,64d4a193ba89cf88fd131d8c99f0796370e2a600,8eae7e948ce5fec89dc2a00982f0ebe5849a755b,XXX: Maybe should reorder the models when outputing them; so
aaibjaehg,scikit-learn/scikit-learn,examples/plot_lda_qda.py,4451bba5c02fee4ce32fa2cb01ac04cc59f60a11,STILL_EXISTS,QDA #FIXME
aaibjafcd,scikit-learn/scikit-learn,scikits/learn/glm.py,97690909ccd2a64e67cec4ece1de396d0a375b9f,STILL_EXISTS,XXX: Maybe should reorder the models when outputing them; so
aaibjajba,scikit-learn/scikit-learn,scikits/learn/svm.py,263a4544ddb43c67e330d03a7a38d55e7b90d131,de451f7f44e79cce2d6b91f40a93983751f90018,how can this be; logisitic *does* implement this
aaibjajef,scikit-learn/scikit-learn,scikits/learn/svm.py,2c57f5598e1c49fc8d90e66cf5bdcf9ed4ac24eb,STILL_EXISTS,how can this be; logisitic *does* implement this
aaibjajij,scikit-learn/scikit-learn,examples/svm/plot_iris.py,b6e2e13c7e31527eb44a90c9485ccce565dc0229,STILL_EXISTS,avoid this ugly slicing by using a two-dim dataset
aaibjbaca,scikit-learn/scikit-learn,scikits/learn/tests/test_svm.py,570787dec65f767e9681ef267443d0d0a3875e36,STILL_EXISTS,TODO: why are we getting all the dataset as support vectors
aaibjbahb,scikit-learn/scikit-learn,scikits/learn/tests/test_svm.py,0f3081a993e0529844328ea77c710bb1dd5618a0,STILL_EXISTS,TODO: why are we getting all the dataset as support vectors
aaibjbbbd,scikit-learn/scikit-learn,scikits/learn/hmm.py,c716c75641f08340da8ad6e1a512d120aa81b2bf,STILL_EXISTS,FIXME: replace  with GaussianHMMMAPTrainer code?
aaibjbcje,scikit-learn/scikit-learn,scikits/learn/features/image.py,fad03fb7fb58fd97886c294233ab7e25a00ba84a,daea67cda0b8299e7c72af628e3fdf98e8d51a38,XXX: Notes: what it the difference between the normed and the non
aaibjbcjg,scikit-learn/scikit-learn,scikits/learn/features/image.py,fad03fb7fb58fd97886c294233ab7e25a00ba84a,STILL_EXISTS,XXX: Why mask the image after computing the weights?
aaibjbdfe,scikit-learn/scikit-learn,examples/plot_lena_segmentation.py,8f008f3fdffe26026abc5efa7b66d379a5b200b7,STILL_EXISTS,\"\"\" || =========================================== || Segmenting the picture of Lena in regions || =========================================== ||  || This example uses spectral clustering on a graph created from || voxel-to-voxel difference on an image to break this image into multiple || partly-homogenous regions. ||  || This procedure (spectral clustering on an image) is an efficient || approximate solution for finding normalized graph cuts. || \"\"\"
aaibjbdgd,scikit-learn/scikit-learn,examples/plot_lena_segmentation.py,8f008f3fdffe26026abc5efa7b66d379a5b200b7,STILL_EXISTS,Apply spectral clustering (this step goes much faster if you have pyamg
aaibjbegb,scikit-learn/scikit-learn,scikits/learn/clustering/spectral.py,c0a72abc457cd062ddd24725e31c04f1801ff349,STILL_EXISTS,XXX: Should we check that the matrices given is symmetric
aaibjbibe,scikit-learn/scikit-learn,scikits/learn/utils/extmath.py,1d645ea9b3538f03cbdab031840ecdba5d95e40b,STILL_EXISTS,XXX: Should be renamed to slogdet
aaibjbibg,scikit-learn/scikit-learn,scikits/learn/utils/extmath.py,d1a9d8e508d7fe7ebee3aa3171db2fdcb1a1a59a,STILL_EXISTS,XXX: Should be implemented as in numpy; using ATLAS
aaibjbide,scikit-learn/scikit-learn,scikits/learn/glm/base.py,df8c593ce694869e11fd459fc310d0ce0a55788e,STILL_EXISTS,## TODO: intercept for all models
aaibjbieb,scikit-learn/scikit-learn,scikits/learn/glm/base.py,df8c593ce694869e11fd459fc310d0ce0a55788e,d8d7e5d4ab2cb3f2a8e4aac3836d2a2c67da6aa7,# TODO: this should have a tests.
aaibjbief,scikit-learn/scikit-learn,scikits/learn/glm/bayes.py,df8c593ce694869e11fd459fc310d0ce0a55788e,STILL_EXISTS,todo; shouldn't most of these have trailing underscores ?
aaibjbieh,scikit-learn/scikit-learn,scikits/learn/glm/bayes.py,df8c593ce694869e11fd459fc310d0ce0a55788e,STILL_EXISTS,TODO: add intercept
aaibjbihi,scikit-learn/scikit-learn,scikits/learn/glm/coordinate_descent.py,df8c593ce694869e11fd459fc310d0ce0a55788e,STILL_EXISTS,XXX: Maybe should reorder the models when outputing them; so
aaibjbije,scikit-learn/scikit-learn,scikits/learn/glm/coordinate_descent.py,df8c593ce694869e11fd459fc310d0ce0a55788e,STILL_EXISTS,TODO: why not define a method rsquared that computes this ?
aaibjbjbc,scikit-learn/scikit-learn,scikits/learn/glm/lars.py,df8c593ce694869e11fd459fc310d0ce0a55788e,STILL_EXISTS,needed by LeastAngleRegression
aaibjbjbe,scikit-learn/scikit-learn,scikits/learn/glm/lars.py,df8c593ce694869e11fd459fc310d0ce0a55788e,f5bb8ab5a8cd1e9ef2274cda361841381135dea1,product. Maybe we should implement in C our own masked_dot that does
aaibjbjcf,scikit-learn/scikit-learn,scikits/learn/glm/lars.py,df8c593ce694869e11fd459fc310d0ce0a55788e,STILL_EXISTS,should have a better name
aaibjbjch,scikit-learn/scikit-learn,scikits/learn/glm/lars.py,df8c593ce694869e11fd459fc310d0ce0a55788e,STILL_EXISTS,there are better ways
aaibjbjec,scikit-learn/scikit-learn,scikits/learn/glm/lars.py,df8c593ce694869e11fd459fc310d0ce0a55788e,STILL_EXISTS,equation 2.13; there's probably a simpler way
aaibjbjfe,scikit-learn/scikit-learn,scikits/learn/glm/lars.py,df8c593ce694869e11fd459fc310d0ce0a55788e,5c170584a7050417e1429f912aeba893ec34917f,# TODO: resize (not create) arrays; check shape;
aaibjbjfj,scikit-learn/scikit-learn,examples/glm/plot_lar.py,f2f0d8863f52e9d2f90709002c9c252784ad3104,6e982be900b9c26f28d984b70ff3ed7b05129189,XXX : wrong label
aaibjbjgb,scikit-learn/scikit-learn,examples/glm/plot_lasso_lars.py,f2f0d8863f52e9d2f90709002c9c252784ad3104,6e982be900b9c26f28d984b70ff3ed7b05129189,XXX : wrong label
aaibjbjjd,scikit-learn/scikit-learn,scikits/learn/glm/tests/test_lars.py,e401e05be0eb890ee21e40c51bb932e0c1789ebb,5c170584a7050417e1429f912aeba893ec34917f,TODO: check that Lasso with coordinate descent finds the same
aaibjcaef,scikit-learn/scikit-learn,scikits/learn/glm/bayes.py,7993c24d1de96cc9ae6e5f39c02972948a5c1c34,STILL_EXISTS,todo; shouldn't most of these have trailing underscores ?
aaibjcaeh,scikit-learn/scikit-learn,scikits/learn/glm/bayes.py,7993c24d1de96cc9ae6e5f39c02972948a5c1c34,STILL_EXISTS,TODO: add intercept
aaibjcbac,scikit-learn/scikit-learn,scikits/learn/glm/bayes.py,ebf568f10ccf614d7292852599cf5fb92c364961,STILL_EXISTS,TODO: add intercept
aaibjccij,scikit-learn/scikit-learn,scikits/learn/fastica.py,c21d0a603a2456a5861d4607365f6d0f85e018a7,STILL_EXISTS,XXX: these should be optimized; as they can be a bottleneck.
aaibjccja,scikit-learn/scikit-learn,scikits/learn/fastica.py,c21d0a603a2456a5861d4607365f6d0f85e018a7,STILL_EXISTS,Centering the columns (ie the variables)
aaibjceja,scikit-learn/scikit-learn,scikits/learn/glm/sparse/coordinate_descent.py,b21fbbb143dee35343d01af7fe5068c32b951548,STILL_EXISTS,TODO: add support for non centered data
aaibjcejd,scikit-learn/scikit-learn,scikits/learn/glm/sparse/coordinate_descent.py,b21fbbb143dee35343d01af7fe5068c32b951548,STILL_EXISTS,TODO: implement me!
aaibjcejf,scikit-learn/scikit-learn,scikits/learn/glm/sparse/coordinate_descent.py,b21fbbb143dee35343d01af7fe5068c32b951548,STILL_EXISTS,TODO: implement intercept_ fitting
aaibjcgbi,scikit-learn/scikit-learn,examples/plot_ica_vs_pca.py,d2914081ea9fe0c80dacef13efd864d49b489787,STILL_EXISTS,\"\"\" || ========================== || FastICA on 2D point clouds || ========================== ||  || XXX: Still buggy !!! ||  || \"\"\"
aaibjcifj,scikit-learn/scikit-learn,scikits/learn/glm/bayes.py,83df9996ef297398132cebb92ccb7cdffcca317b,STILL_EXISTS,TODO: add intercept
aaibjdaac,scikit-learn/scikit-learn,scikits/learn/pipeline.py,b20abddaac29f0109128f747a3c4d40467c49530,1c590544056d235e0680265f0f743db943479da5,XXX: should be done
aaibjdbci,scikit-learn/scikit-learn,scikits/learn/glm/bayes.py,da285ea94645d48f163ee47c10dd2aa8debee7bf,STILL_EXISTS,TODO: add intercept
aaibjdbib,scikit-learn/scikit-learn,scikits/learn/glm/lars.py,06cbe38aa42080ee8aecfdb63554ef6b5bbf0510,5c170584a7050417e1429f912aeba893ec34917f,XXX : should use self._set_intercept
aaibjdeef,scikit-learn/scikit-learn,scikits/learn/base.py,d52c5c8b54ba773c5a4c43ace887a272138f7a18,STILL_EXISTS,XXX: This is going to fail if the init is a staticmethod; but
aaibjdfgf,scikit-learn/scikit-learn,scikits/learn/glm/lars.py,fd315bc38db8d1fff92ddd42c5e077bc112cebc9,5c170584a7050417e1429f912aeba893ec34917f,TODO: precompute : empty for now
aaibjdfgh,scikit-learn/scikit-learn,scikits/learn/glm/lars.py,fd315bc38db8d1fff92ddd42c5e077bc112cebc9,STILL_EXISTS,TODO: detect stationary points.
aaibjdfig,scikit-learn/scikit-learn,scikits/learn/hmm.py,54e8b7707c3b9e85573cc353c7cee0cfa4b96289,STILL_EXISTS,XXX: Hotfit for n_mix that is incompatible with the scikit's
aaibjdgea,scikit-learn/scikit-learn,examples/glm/lasso_dense_vs_sparse_data.py,bb2e0663febb46ed9d7e8857212be3b13e3f2f8c,STILL_EXISTS,\"\"\" || ============================== || Lasso on dense and sparse data || ============================== ||  || We show that glm.Lasso and glm.sparse.Lasso || provide the same results. ||  || XXX : At the end of the day it should also lead to a speed improvement ||  || \"\"\"
aaibjdgei,scikit-learn/scikit-learn,examples/glm/lasso_dense_vs_sparse_data.py,39e42084a57f16bcbd3833c8c4e5edfa111a1cd5,STILL_EXISTS,\"\"\" || ============================== || Lasso on dense and sparse data || ============================== ||  || We show that glm.Lasso and glm.sparse.Lasso || provide the same results. ||  || XXX : At the end of the day it should also lead to a speed improvement ||  || \"\"\"
aaibjdhec,scikit-learn/scikit-learn,scikits/learn/glm/lars.py,6053027f7b509541e686fef839797b7fe021cb19,STILL_EXISTS,there are better ways
aaibjdhef,scikit-learn/scikit-learn,scikits/learn/glm/lars.py,6053027f7b509541e686fef839797b7fe021cb19,STILL_EXISTS,np.delete (Cov; imax) # very ugly; has to be fixed
aaibjdhjd,scikit-learn/scikit-learn,scikits/learn/features/text.py,0f79a867cf9166ea301ff0526bf17da7c988bd87,3dbe9f3855d770e89a9abf666b1088253c102383,To avoid aving to do two passes on the dataset; which would require
aaibjdhji,scikit-learn/scikit-learn,scikits/learn/glm/lars.py,ca9c5690296b6a7464b981c08bd4d16378164d68,STILL_EXISTS,XXX : should handle also unnormalized datasets
aaibjdjdc,scikit-learn/scikit-learn,scikits/learn/base.py,452f72396f442842e994708e204d567dec3092c4,STILL_EXISTS,XXX: Temporary solution to figure out if an estimator is a classifier
aaibjdjdj,scikit-learn/scikit-learn,scikits/learn/base.py,a4ed78e23f83a0723e06157b4777cd3b4b382ac9,STILL_EXISTS,XXX: not handling dictionnaries
aaibjeabi,scikit-learn/scikit-learn,scikits/learn/glm/lars.py,7b12364f992fc0258ebca104f609da2e057d810f,4b7167568261317ee708564173bd04f909754f99,TODO: precompute : empty for now
aaibjeacb,scikit-learn/scikit-learn,scikits/learn/glm/lars.py,7b12364f992fc0258ebca104f609da2e057d810f,4b7167568261317ee708564173bd04f909754f99,# TODO: resize (not create) arrays; check shape;
aaibjeacd,scikit-learn/scikit-learn,scikits/learn/glm/lars.py,7b12364f992fc0258ebca104f609da2e057d810f,4b7167568261317ee708564173bd04f909754f99,will only normalize non-zero columns
aaibjeace,scikit-learn/scikit-learn,scikits/learn/glm/lars.py,7b12364f992fc0258ebca104f609da2e057d810f,4b7167568261317ee708564173bd04f909754f99,XXX : should use self._set_intercept
aaibjeacj,scikit-learn/scikit-learn,scikits/learn/glm/tests/test_lars.py,7b12364f992fc0258ebca104f609da2e057d810f,4b7167568261317ee708564173bd04f909754f99,TODO: check that Lasso with coordinate descent finds the same
aaibjebfa,scikit-learn/scikit-learn,scikits/learn/features/text.py,c9aaaaa6babf75230fac8f42762c5ed4eb557e23,STILL_EXISTS,To avoid aving to do two passes on the dataset; which would require
aaibjebja,scikit-learn/scikit-learn,scikits/learn/svm/base.py,0f37104fe99479183dc4b1b0815191e8bb8308e8,893a44761038ac726d01548f3d74dd5142bd12b1,TODO: some cleanup: is n_support_ really needed ?
aaibjebjh,scikit-learn/scikit-learn,scikits/learn/svm/base.py,0f37104fe99479183dc4b1b0815191e8bb8308e8,STILL_EXISTS,TODO: put keyword copy to copy on demand
aaibjecag,scikit-learn/scikit-learn,scikits/learn/svm/base.py,0f37104fe99479183dc4b1b0815191e8bb8308e8,STILL_EXISTS,how can this be; logisitic *does* implement this
aaibjecfc,scikit-learn/scikit-learn,scikits/learn/svm/sparse/base.py,ef26ea4ef199455cea54845268fc6c241c0c461b,STILL_EXISTS,TODO: explicitly specify size
aaibjecfd,scikit-learn/scikit-learn,scikits/learn/svm/sparse/base.py,ef26ea4ef199455cea54845268fc6c241c0c461b,STILL_EXISTS,TODO: is this always a 1-d array ?
aaibjedgi,scikit-learn/scikit-learn,scikits/learn/gmm.py,edf4fd4d0dc2421bcc23edef34e63a03903f97ad,STILL_EXISTS,TODO: this lacks a docstring
aaibjedgj,scikit-learn/scikit-learn,scikits/learn/glm/lars.py,612e9e4c0a493f8a2f7d1fef7e982724449745d6,STILL_EXISTS,: make sure it works with non-normalized columns of X
aaibjefid,scikit-learn/scikit-learn,scikits/learn/utils/graph.py,4b025a04a3b503a4aabf1f2b3ef0566165b9d7d1,STILL_EXISTS,versions of scipy prior to 0.7 do not implement .tolil()
aaibjefij,scikit-learn/scikit-learn,scikits/learn/datasets/mlcomp.py,26bdc720b71c530560bad0b0826130bede6c7d40,50902c445ed182895be0aea4d0f5ed05b011dfdd,TODO: make it possible to plug a several pass system to filter-out tokens
aaibjefjb,scikit-learn/scikit-learn,scikits/learn/datasets/mlcomp.py,26bdc720b71c530560bad0b0826130bede6c7d40,50902c445ed182895be0aea4d0f5ed05b011dfdd,TODO: use joblib.Parallel or multiprocessing to parallelize the following
aaibjehgd,scikit-learn/scikit-learn,scikits/learn/tests/test_metrics.py,b610b3eef0833d706eb14f3d3147fb355f477a72,STILL_EXISTS,FIXME: precision and recall aren't symmetric either
aaibjehih,scikit-learn/scikit-learn,scikits/learn/sgd/base.py,58a432c874ad148e62ef70649d14031e592923bf,STILL_EXISTS,Author: Peter Prettenhofer <peter.prettenhofer@gmail.com>
aaibjehja,scikit-learn/scikit-learn,scikits/learn/sgd/base.py,58a432c874ad148e62ef70649d14031e592923bf,2847e3d3e897758aa6193fe9b3d5b54a2d1af50b,FIXME what if prediction is 0 - break randomly?
aaibjehjb,scikit-learn/scikit-learn,scikits/learn/sgd/base.py,58a432c874ad148e62ef70649d14031e592923bf,2847e3d3e897758aa6193fe9b3d5b54a2d1af50b,how can this be; logisitic *does* implement this
aaibjehjg,scikit-learn/scikit-learn,scikits/learn/sgd/sparse/sgd.py,58a432c874ad148e62ef70649d14031e592923bf,STILL_EXISTS,Author: Peter Prettenhofer <peter.prettenhofer@gmail.com>
aaibjeici,scikit-learn/scikit-learn,scikits/learn/datasets/mlcomp.py,b7a1afb3933e09eb4fe2985a4ce184d3b3c8ed7b,d5c62cf83082264ea395e0575631c9a8fad76bf9,TODO: make it possible to plug a several pass system to filter-out tokens
aaibjeida,scikit-learn/scikit-learn,scikits/learn/datasets/mlcomp.py,b7a1afb3933e09eb4fe2985a4ce184d3b3c8ed7b,d5c62cf83082264ea395e0575631c9a8fad76bf9,TODO: use joblib.Parallel or multiprocessing to parallelize the following
aaibjeiea,scikit-learn/scikit-learn,scikits/learn/svm/tests/test_svm.py,11465983d158e3e42e114b137ccd45f88d665caf,fccc2ec1a92b913ff4cc485c538f62965eb3c220,TODO: distance should be sqrt(2)\/2; but libsvm returns 1.
aaibjejcg,scikit-learn/scikit-learn,scikits/learn/feature_extraction/sparse/text.py,d5c62cf83082264ea395e0575631c9a8fad76bf9,STILL_EXISTS,TODO add support for cooccurence tokens in a sentence
aaibjejej,scikit-learn/scikit-learn,scikits/learn/preprocessing/sparse/__init__.py,626c7e8154e50426a15977fee9c47b839fc193f1,STILL_EXISTS,FIXME: sparsity structure changed
aaibjejfa,scikit-learn/scikit-learn,scikits/learn/preprocessing/sparse/__init__.py,626c7e8154e50426a15977fee9c47b839fc193f1,STILL_EXISTS,FIXME: if enough values became 0; it may be worth changing
aaibjejga,scikit-learn/scikit-learn,scikits/learn/svm/tests/test_svm.py,192e7557ec667ac7aea7d6de820d70262686eb6d,665b2a5930924cb176345102dc4effaa1eb06761,TODO: distance should be sqrt(2)\/2; but libsvm returns 1.
aaibjfacf,scikit-learn/scikit-learn,scikits/learn/feature_extraction/text.py,75ac75eccf74ddbf9be725c67bad1f1dbfba8dc5,131927934dbab14a69af28f39452098f1c960822,TODO: write docstring!
aaibjfacg,scikit-learn/scikit-learn,scikits/learn/feature_extraction/text.py,75ac75eccf74ddbf9be725c67bad1f1dbfba8dc5,STILL_EXISTS,TODO: refactor the HashingVectorizer implementation to reuse the
aaibjfaci,scikit-learn/scikit-learn,scikits/learn/feature_extraction/sparse/text.py,131927934dbab14a69af28f39452098f1c960822,STILL_EXISTS,TODO: refactor the HashingVectorizer implementation to reuse the
aaibjfaeb,scikit-learn/scikit-learn,scikits/learn/sgd/sparse/sgd.py,2847e3d3e897758aa6193fe9b3d5b54a2d1af50b,STILL_EXISTS,FIXME what if sign == 0? break randomly?
aaibjfaec,scikit-learn/scikit-learn,scikits/learn/sgd/sparse/sgd.py,2847e3d3e897758aa6193fe9b3d5b54a2d1af50b,STILL_EXISTS,how can this be; logisitic *does* implement this
aaibjfaid,scikit-learn/scikit-learn,examples/sgd/mlcomp_sparse_document_classification_sgd.py,658002360d5bc3394ab199665498c8c262916e4f,STILL_EXISTS,\"\"\" || ====================================================== || Classification of text documents using sparse features || ====================================================== ||  || This is an example showing how the scikit-learn can be used to classify || documents by topics using a bag-of-words approach. This example uses || a scipy.sparse matrix to store the features instead of standard numpy arrays. ||  || The dataset used in this example is the 20 newsgroups dataset and should be || downloaded from the http:\/\/mlcomp.org (free registration required): ||  ||   http:\/\/mlcomp.org\/datasets\/379 ||  || Once downloaded unzip the arhive somewhere on your filesystem. For instance in:: ||  ||   % mkdir -p ~\/data\/mlcomp ||   % cd  ~\/data\/mlcomp ||   % unzip \/path\/to\/dataset-379-20news-18828_XXXXX.zip ||  || You should get a folder ``~\/data\/mlcomp\/379`` with a file named ``metadata`` and || subfolders ``raw``; ``train`` and ``test`` holding the text documents organized by || newsgroups. ||  || Then set the ``MLCOMP_DATASETS_HOME`` environment variable pointing to || the root folder holding the uncompressed archive:: ||  ||   % export MLCOMP_DATASETS_HOME=\"~\/data\/mlcomp\" ||  || Then you are ready to run this example using your favorite python shell:: ||  ||   % ipython examples\/mlcomp_sparse_document_classification.py ||  || \"\"\"
aaibjfaie,scikit-learn/scikit-learn,examples/sgd/mlcomp_sparse_document_classification_sgd.py,658002360d5bc3394ab199665498c8c262916e4f,STILL_EXISTS,Author: Peter Prettenhofer <peter.prettenhofer@gmail.com>
aaibjfbhf,scikit-learn/scikit-learn,scikits/learn/sgd/sparse/sgd.py,280eaec479cc4e471ec786d91f60bb7877a0895c,dad8deb08e17aa7577792d0e8781ac030d620c86,2 # XXX : shouldn't verbose be a instance param
aaibjfbjh,scikit-learn/scikit-learn,scikits/learn/datasets/mlcomp.py,314c6870f10f46434ab3cde4776ca95b2a6787c8,21e5c2b2f0a94b0bef33518bf117be2d89081938,TODO: make it possible to plug a several pass system to filter-out tokens
aaibjfbjj,scikit-learn/scikit-learn,scikits/learn/datasets/mlcomp.py,314c6870f10f46434ab3cde4776ca95b2a6787c8,21e5c2b2f0a94b0bef33518bf117be2d89081938,TODO: use joblib.Parallel or multiprocessing to parallelize the following
aaibjfdhi,scikit-learn/scikit-learn,scikits/learn/sgd/base.py,6ae9bcd100b8fa83b3c53efbdd008333d933b0d7,843fe8b29208dd6d1cba55354689bfdc07546fd6,FIXME what if prediction is 0 - break randomly?
aaibjfdhj,scikit-learn/scikit-learn,scikits/learn/sgd/base.py,6ae9bcd100b8fa83b3c53efbdd008333d933b0d7,843fe8b29208dd6d1cba55354689bfdc07546fd6,how can this be; logisitic *does* implement this
aaibjfech,scikit-learn/scikit-learn,scikits/learn/sgd/sparse/sgd.py,6ae9bcd100b8fa83b3c53efbdd008333d933b0d7,dad8deb08e17aa7577792d0e8781ac030d620c86,2 # XXX : shouldn't verbose be a instance param
aaibjfedb,scikit-learn/scikit-learn,scikits/learn/sgd/sparse/sgd.py,6ae9bcd100b8fa83b3c53efbdd008333d933b0d7,STILL_EXISTS,how can this be; logisitic *does* implement this
aaibjfegc,scikit-learn/scikit-learn,scikits/learn/sgd/sparse/sgd.py,843fe8b29208dd6d1cba55354689bfdc07546fd6,STILL_EXISTS,FIXME what if sign == 0? break randomly?
aaibjfegd,scikit-learn/scikit-learn,scikits/learn/sgd/sparse/sgd.py,843fe8b29208dd6d1cba55354689bfdc07546fd6,STILL_EXISTS,how can this be; logisitic *does* implement this
aaibjffde,scikit-learn/scikit-learn,scikits/learn/sgd/sparse/sgd.py,e84b4ea479e8d802f3683b8cdfb5eb2c9eacd871,dad8deb08e17aa7577792d0e8781ac030d620c86,2 # XXX : shouldn't verbose be a instance param
aaibjfiac,scikit-learn/scikit-learn,scikits/learn/sgd/sparse/sgd.py,02b56e71cd981b98f803310352527b358fe05fc1,STILL_EXISTS,TODO change if multi class
aaibjfidb,scikit-learn/scikit-learn,scikits/learn/tests/test_metrics.py,766c244a2a42527db07178dd921965a2a8d25932,e1163178bb59f5cc9a8946e8954273d38275ce6e,temporary disabling multilabel support to implement proper multiclass
aaibjfidf,scikit-learn/scikit-learn,scikits/learn/tests/test_metrics.py,ddbc0432924cbd27f2667b7bcca238f722db5cda,STILL_EXISTS,XXX: do we really want a special API for the binary case?
aaibjfjcc,scikit-learn/scikit-learn,examples/gmm/gmm_classifier.py,678ae5e69fc11fdc3901477e149025bfb9161a5e,STILL_EXISTS,\"\"\" || ================== || GMM classification || ================== ||  || Demonstration of Gaussian Mixture Models for classification. ||  || Plots predicted labels on both training and held out test data using a || variety of GMM classifiers on the iris dataset. ||  || Compares GMMs with spherical; diagonal; full; and tied covariance || matrices in increasing order of performance.  Although one would || expect full covariance to perform best in general; it is prone to || overfitting on small datasets and does not generalize well to held out || test data. || \"\"\"
aaibjfjhf,scikit-learn/scikit-learn,scikits/learn/datasets/mlcomp.py,d0828deba072cc9d343f24c91bdd23c75d27ceda,1e3df61fec7ce85bac012c26f0ccabaa3968eea4,TODO: make it possible to plug a several pass system to filter-out tokens
aaibjfjhh,scikit-learn/scikit-learn,scikits/learn/datasets/mlcomp.py,d0828deba072cc9d343f24c91bdd23c75d27ceda,1e3df61fec7ce85bac012c26f0ccabaa3968eea4,TODO: use joblib.Parallel or multiprocessing to parallelize the following
aaibjgacg,scikit-learn/scikit-learn,scikits/learn/sgd/sparse/sgd.py,2a76cbeab6c26d43540382b8282d8bf72d4fb656,dad8deb08e17aa7577792d0e8781ac030d620c86,2 # XXX : shouldn't verbose be a instance param
aaibjgdgc,scikit-learn/scikit-learn,scikits/learn/sgd/sparse/sgd.py,dad8deb08e17aa7577792d0e8781ac030d620c86,de4caee868b1ad1f62b58b1a8e97937a3a4c04fd,TODO: parallel training using joblib.
aaibjgdgh,scikit-learn/scikit-learn,examples/cluster/kmeans_digits.py,76f5b546cf5629c4ee81ae1217b004713b130ac7,STILL_EXISTS,\"\"\" || =========================================================== || A demo of K-Means clustering on the handwritten digits data || =========================================================== ||  || Comparing various initialization strategies in terms of runtime and quality of || the results. ||  || TODO: explode the ouput of the cluster labeling and digits.target groundtruth || as categorical boolean arrays of shape (n_sample; n_unique_labels) and measure || the Pearson correlation as an additional measure of the clustering quality. || \"\"\"
aaibjgdic,scikit-learn/scikit-learn,scikits/learn/sgd/sgd.py,13e059fadc4ae7c4a81ae5da806dbd229ffd2aa9,STILL_EXISTS,Author: Peter Prettenhofer <peter.prettenhofer@gmail.com>
aaibjgedf,scikit-learn/scikit-learn,examples/sgd/covertype_dense_sgd.py,ee8d60e0bbd09dd5b0d68855539d055fcc8f2ec7,STILL_EXISTS,Author: Peter Prettenhoer <peter.prettenhofer@gmail.com>
aaibjgehf,scikit-learn/scikit-learn,examples/sgd/plot_iris.py,2ebd6326dc6b2f9a8fbce571b4f17f980814b4e4,STILL_EXISTS,avoid this ugly slicing by using a two-dim dataset
aaibjhaia,scikit-learn/scikit-learn,examples/document_classification_20newsgroups.py,a21319d9426ab1644a6b201f3d13631cc0a9757d,STILL_EXISTS,Author: Peter Prettenhofer <peter.prettenhofer@gmail.com>
aaibjhbdi,scikit-learn/scikit-learn,scikits/learn/glm/lars.py,ce9f68e3cfb72d60a4187113f125b5e98ffa9fff,STILL_EXISTS,and allows to easily swap columns
aaibjhbfa,scikit-learn/scikit-learn,scikits/learn/glm/lars.py,ce9f68e3cfb72d60a4187113f125b5e98ffa9fff,STILL_EXISTS,Cov_n = Cov_j + x_j * X + increment(betas) TODO:
aaibjhbfc,scikit-learn/scikit-learn,scikits/learn/glm/lars.py,ce9f68e3cfb72d60a4187113f125b5e98ffa9fff,STILL_EXISTS,recompute covariance. Probably could be done better
aaibjhbfh,scikit-learn/scikit-learn,scikits/learn/glm/tests/test_lars.py,ce9f68e3cfb72d60a4187113f125b5e98ffa9fff,STILL_EXISTS,TODO: use another dataset that has multiple drops
aaibjhfch,scikit-learn/scikit-learn,examples/plot_species_distribution_modeling.py,12855394791791fe6eaa5733eeaa85caf333f413,STILL_EXISTS,Author: Peter Prettenhofer <peter.prettenhofer@gmail.com>
aaibjhfdd,scikit-learn/scikit-learn,scikits/learn/qda.py,428d457779fde199c18eae337876bafccfcd5ab0,STILL_EXISTS,maybe in BaseEstimator ?
aaibjhfgg,scikit-learn/scikit-learn,scikits/learn/lda.py,7cc2577761011b94a34cdfebad86b5c925ced098,d2ec8a36d85d7fd821a6f770210fcc0785db4f12,XXX : can do better to avoid precision overflows
aaibjhfgh,scikit-learn/scikit-learn,scikits/learn/qda.py,7cc2577761011b94a34cdfebad86b5c925ced098,d2ec8a36d85d7fd821a6f770210fcc0785db4f12,XXX : can do better to avoid precision overflows
aaibjhgbb,scikit-learn/scikit-learn,examples/svm/svm_gui.py,da3f3faeaa5688000da56cc87f3fe7e305d1fdb1,STILL_EXISTS,Author: Peter Prettenhoer <peter.prettenhofer@gmail.com>
aaibjhgia,scikit-learn/scikit-learn,examples/plot_face_recognition.py,6b5f857777de299153f6ef730b40521c29ad3a9b,STILL_EXISTS,TODO: factorize this out as a utility function in scikit-learn
aaibjhgjc,scikit-learn/scikit-learn,examples/plot_face_recognition.py,6b5f857777de299153f6ef730b40521c29ad3a9b,STILL_EXISTS,TODO: find a way to hide the x and y axis
aaibjhgjd,scikit-learn/scikit-learn,examples/plot_face_recognition.py,6b5f857777de299153f6ef730b40521c29ad3a9b,STILL_EXISTS,TODO: plot the top eigenfaces and the singular values absolute values
aaibjhjaa,scikit-learn/scikit-learn,scikits/learn/grid_search.py,2877985de5ad85c8def46fad2dd6c835e44cba11,STILL_EXISTS,XXX: the name is too specific; it shouldn't have
aaibjiija,scikit-learn/scikit-learn,scikits/learn/utils/tests/test_svd.py,6c1c6c312b5815dd7371529671640f9033522ce3,1d2a66b2b470765a0881417399ed8991f5c37a58,XXX: some vectors are not equal; while others perfectly match?
aaibjijbf,scikit-learn/scikit-learn,scikits/learn/neighbors.py,c0b4aa41e36ba78185f78020682282e52c080484,STILL_EXISTS,XXX : the next loop could be done in parallel
aaibjijea,scikit-learn/scikit-learn,examples/gaussian_process/plot_gp_diabetes_dataset.py,f068df4f62aae7febd0d9fc2319c100af4dd17df,849937980f07070f9cdefc66d0d85513476f5786,XXX : why doing this below?
aaibjijeb,scikit-learn/scikit-learn,examples/gaussian_process/plot_gp_diabetes_dataset.py,f068df4f62aae7febd0d9fc2319c100af4dd17df,849937980f07070f9cdefc66d0d85513476f5786,XXX : I'm lost. Why y_pred = y + ... ?
aaibjijed,scikit-learn/scikit-learn,examples/gaussian_process/plot_gp_probabilistic_classification_after_regression.py,f068df4f62aae7febd0d9fc2319c100af4dd17df,849937980f07070f9cdefc66d0d85513476f5786,XXX : is it really useful to define those lambda if it's just
aaibjijef,scikit-learn/scikit-learn,scikits/learn/gaussian_process/correlation_models.py,f068df4f62aae7febd0d9fc2319c100af4dd17df,cb3326cb641b629f68cfcfa26886e33bac786c3b,XXX : computation do not match docstring
aaibjijeh,scikit-learn/scikit-learn,scikits/learn/gaussian_process/gaussian_process.py,f068df4f62aae7febd0d9fc2319c100af4dd17df,849937980f07070f9cdefc66d0d85513476f5786,XXX : why not storing explicitely X_mean; X_std; y_mean; y_std
aaibjijej,scikit-learn/scikit-learn,scikits/learn/gaussian_process/gaussian_process.py,f068df4f62aae7febd0d9fc2319c100af4dd17df,849937980f07070f9cdefc66d0d85513476f5786,XXX : code duplication. You should create a separate
aaibjijfd,scikit-learn/scikit-learn,scikits/learn/gaussian_process/gaussian_process.py,f068df4f62aae7febd0d9fc2319c100af4dd17df,849937980f07070f9cdefc66d0d85513476f5786,XXX : avoid lambda functions. It won't pickle hence not
aaibjijff,scikit-learn/scikit-learn,scikits/learn/gaussian_process/gaussian_process.py,f068df4f62aae7febd0d9fc2319c100af4dd17df,STILL_EXISTS,XXX : same thing about lambda functions
aaibjijgj,scikit-learn/scikit-learn,scikits/learn/sgd/tests/test_sgd.py,dd3ba58acbdf3c3be299349aeb942a6f30b60cc4,STILL_EXISTS,XXX: alpha = 0.1 seems to cause convergence problems
aaibjijie,scikit-learn/scikit-learn,scikits/learn/nmf.py,1d69a72305b2672d6b3cc50401510ad325f8955a,STILL_EXISTS,TODO: self.copy
aaibjijih,scikit-learn/scikit-learn,scikits/learn/nmf.py,1d69a72305b2672d6b3cc50401510ad325f8955a,0f51919df9c7aeeb3d022cda28012d8952492f5c,unused parameters? max_iter was
aaibjijjb,scikit-learn/scikit-learn,scikits/learn/nmf.py,1d69a72305b2672d6b3cc50401510ad325f8955a,STILL_EXISTS,of around 1 was expected. Maybe it's something to do
aaibjijjd,scikit-learn/scikit-learn,scikits/learn/nmf.py,1d69a72305b2672d6b3cc50401510ad325f8955a,0f51919df9c7aeeb3d022cda28012d8952492f5c,initt = time(); time limit should probably be removed?
aaibjjaab,scikit-learn/scikit-learn,scikits/learn/utils/extmath.py,cdeee0d4484249a540d348cc76c23c1cbdc6d23b,STILL_EXISTS,transpose back the results according to the input convention
aaibjjaba,scikit-learn/scikit-learn,scikits/learn/nmf.py,0f51919df9c7aeeb3d022cda28012d8952492f5c,e05b7958bf3644f0ce92fffe8b84ccabeb632fc3,Apparently this sometimes happens. I think this is
aaibjjabc,scikit-learn/scikit-learn,scikits/learn/nmf.py,0f51919df9c7aeeb3d022cda28012d8952492f5c,e05b7958bf3644f0ce92fffe8b84ccabeb632fc3,I probably messed something up.
aaibjjaca,scikit-learn/scikit-learn,scikits/learn/lda.py,fdc0aa01eda7c063a127ab79feacc770481e5302,cc921d8f06b4484c719e1f61c39e2ecad16ed4b8,XXX : can do better to avoid precision overflows
aaibjjacb,scikit-learn/scikit-learn,scikits/learn/qda.py,fdc0aa01eda7c063a127ab79feacc770481e5302,STILL_EXISTS,XXX : can do better to avoid precision overflows
aaibjjaeb,scikit-learn/scikit-learn,scikits/learn/linear_model/base.py,a99078063aaa8db9c3be8f5074e2ffcc7ad33cd3,2df3f2a271b4797b7881d507c2d517a50d6c980e,# TODO add sample_weights to signature (see LibSVM)
aaibjjaec,scikit-learn/scikit-learn,scikits/learn/linear_model/base.py,a99078063aaa8db9c3be8f5074e2ffcc7ad33cd3,2df3f2a271b4797b7881d507c2d517a50d6c980e,# TODO adher to svm signature (return values of score etc.)
aaibjjaef,scikit-learn/scikit-learn,scikits/learn/linear_model/sparse/stochastic_gradient.py,a99078063aaa8db9c3be8f5074e2ffcc7ad33cd3,STILL_EXISTS,# TODO add flag for intercept learning rate heuristic
aaibjjahi,scikit-learn/scikit-learn,examples/wikipedia_principal_eigenvector.py,dbacdf8169c2db94bde0e6e5d1ccf74164e3325e,STILL_EXISTS,disabling joblib as the pickling of large dicts seems much too slow
aaibjjbaa,scikit-learn/scikit-learn,scikits/learn/linear_model/coordinate_descent.py,494dd3fbc3ce12425364e0719f289b5bb95fd5b9,4cffab4e8377a2f5d6c3748a0c0eb9bd91b3c2ba,XXX
aaibjjbdi,scikit-learn/scikit-learn,scikits/learn/nmf.py,c6822e3e1adcc4ebe909adc7e26469a0e8c4e2a2,STILL_EXISTS,!!!!! apparently not!
aaibjjbha,scikit-learn/scikit-learn,scikits/learn/svm/base.py,87ace904afa4e3b746514017be83dc86b5fc8c88,STILL_EXISTS,libsvm has the convention of returning negative values for
aaibjjcic,scikit-learn/scikit-learn,scikits/learn/linear_model/least_angle.py,c09ecff6c034218eb74436ae4998b29dde1d8325,STILL_EXISTS,is this really needed ?
aaibjjhaf,scikit-learn/scikit-learn,examples/plot_ward_feature_agglomeration.py,9ab9d49e472f1ed89c16e427c22c3ea4503810f0,STILL_EXISTS,\"\"\" || =========================================================== || A demo of feature agglomeration - structured ward || =========================================================== ||  || Author : Vincent Michel; 2010 ||  || \"\"\"
aaibjjhia,scikit-learn/scikit-learn,scikits/learn/cluster/k_means_.py,5b9605ee290595e2b7d5b95950b68adfa144a677,c2e17ac1f8635106c764172217048a515eeba4f7,TODO: offer an argument to allow doing this inplace
aaibjjicd,scikit-learn/scikit-learn,scikits/learn/metrics/pairwise.py,e7929e61e8844b476943d4aacb50200f523d43b3,583dfcc1ec3362d36e7da6340ef6c68bde4397bf,TODO:
aaibjjifb,scikit-learn/scikit-learn,scikits/learn/linear_model/sparse/ridge.py,2f6bf57c2e960f7c2e235cda24f1910b451294ff,STILL_EXISTS,FIXME: handle fit_intercept
aaibjjigd,scikit-learn/scikit-learn,scikits/learn/linear_model/tests/test_ridge.py,5ec3d6f14e4e8e35ff7fab9511121aec78a844cc,STILL_EXISTS,efficient LOO
aaibjjigg,scikit-learn/scikit-learn,scikits/learn/linear_model/tests/test_ridge.py,4fa80b60f15a0b3ba04b867b506abb469c39f005,STILL_EXISTS,check that efficient and brute-force LOO give same results
aaibjjihd,scikit-learn/scikit-learn,scikits/learn/tests/test_neighbors.py,ca2e2101bf74358322fd1993947f3d563ee566db,STILL_EXISTS,TODO: result should be compared
aaibjjihi,scikit-learn/scikit-learn,scikits/learn/cluster/tests/test_feature_agglomeration.py,5359bbb998c979fcd97591d7fa2b010c7b46f75d,STILL_EXISTS,XXX
aaibjjjbc,scikit-learn/scikit-learn,scikits/learn/linear_model/ridge.py,a56ec44b79ef0f365871de65bcaea762f18dc34d,STILL_EXISTS,FIXME: need to implement sample_weight in Ridge
aaibjjjgg,scikit-learn/scikit-learn,scikits/learn/pipeline.py,06ac5f9890c08da8e64cfaa3b87531e0872c9f86,eb60275ed4c601014f5be722891e2a9e015dfe91,XXX don't like that
aaicaaaig,scikit-learn/scikit-learn,scikits/learn/tests/test_pca.py,b1714bae967c61fe235d7941459cac990e24c023,5f49bdfd24d612ddb48dff615f75144f1bbf6644,XXX: this does not seam to work as expected:
aaicaabch,scikit-learn/scikit-learn,scikits/learn/cluster/hierarchical.py,a81df0a104f46e55c204cecb6b070aad2a0aba25,STILL_EXISTS,return as numpy array for efficient caching
aaicaacig,scikit-learn/scikit-learn,scikits/learn/linear_model/least_angle.py,269ab1af31d362a7bb2955936646c7478602a68b,STILL_EXISTS,TODO: better names for these variables: z
aaicaadcj,scikit-learn/scikit-learn,scikits/learn/tests/test_pca.py,1b38e732280402e45d2162245cb371969b5b6055,STILL_EXISTS,XXX: this does not seam to work as expected:
aaicaaecj,scikit-learn/scikit-learn,benchmarks/bench_plot_ward.py,bfb819eb58e28d15d55d6d4def6137e75ad0db18,STILL_EXISTS,\"\"\" || Bench the scikit's ward implement compared to scipy's || \"\"\"
aaicaaedd,scikit-learn/scikit-learn,scikits/learn/linear_model/ridge.py,fedd5499e161c731402cd1ba0bed01c4df6bbef4,STILL_EXISTS,FIXME: sample_weight must be split into training\/validation data
aaicaafec,scikit-learn/scikit-learn,examples/applications/plot_face_verification.py,6609703cb38d56261b20ae09f2b7b98fcf1239d2,STILL_EXISTS,TODO
aaicaahhe,scikit-learn/scikit-learn,scikits/learn/cluster/hierarchical.py,9fdbbcb54eb0b71cbeeda7b40c0ba33d22bdc2aa,458c2b281595cf849164fa47d65e03ae1841d6c8,XXX: Should create A and coord_row; coord_row here
aaicaaiia,scikit-learn/scikit-learn,scikits/learn/mixture.py,bbb60337c26403b946fb41ca3ecd804ccd916855,STILL_EXISTS,for each component; generate all needed samples
aaicabajc,scikit-learn/scikit-learn,examples/applications/plot_face_recognition.py,170af3f74910c72e89982c4b80531c6392e860de,STILL_EXISTS,TODO: plot the top eigenfaces and the singular values absolute values
aaicabbeh,scikit-learn/scikit-learn,data/languages/fetch_data.py,63177aa0041d0f33016362e40c80f256c2c5de50,STILL_EXISTS,FIXME: whitespace tokenizing does not work on chinese and japanese
aaicabcah,scikit-learn/scikit-learn,tutorial/conf.py,63177aa0041d0f33016362e40c80f256c2c5de50,STILL_EXISTS,unused_docs = []
aaicabcji,scikit-learn/scikit-learn,scikits/learn/cluster/k_means_.py,9c3bbb01d54dc631c7b9bfb77bd70d0a7ca7750b,STILL_EXISTS,Permanently add best center candidate found in local tries
aaicabdbd,scikit-learn/scikit-learn,skeletons/exercise_01_language_train_model.py,d859f402eb8b1eb0a03b961280e50b50144e15e1,STILL_EXISTS,TODO: define variables 'filenames_train' and 'filenames_test'
aaicabdbe,scikit-learn/scikit-learn,skeletons/exercise_01_language_train_model.py,d859f402eb8b1eb0a03b961280e50b50144e15e1,STILL_EXISTS,TODO: define variables 'y_train' and 'y_test'
aaicabdbh,scikit-learn/scikit-learn,skeletons/exercise_01_language_train_model.py,d859f402eb8b1eb0a03b961280e50b50144e15e1,STILL_EXISTS,TODO: define a variable named analyzer
aaicabdcb,scikit-learn/scikit-learn,skeletons/exercise_01_language_train_model.py,d859f402eb8b1eb0a03b961280e50b50144e15e1,STILL_EXISTS,TODO: define a variable named 'vectorizer'
aaicabdcc,scikit-learn/scikit-learn,skeletons/exercise_01_language_train_model.py,d859f402eb8b1eb0a03b961280e50b50144e15e1,STILL_EXISTS,TODO: define a variable named 'X_train'
aaicabdcd,scikit-learn/scikit-learn,skeletons/exercise_01_language_train_model.py,d859f402eb8b1eb0a03b961280e50b50144e15e1,STILL_EXISTS,TODO: define a variable named 'X_test'
aaicabdce,scikit-learn/scikit-learn,skeletons/exercise_01_language_train_model.py,d859f402eb8b1eb0a03b961280e50b50144e15e1,STILL_EXISTS,XXX: Don't forget to read the content of the text files before feeding it to
aaicabdch,scikit-learn/scikit-learn,skeletons/exercise_01_language_train_model.py,d859f402eb8b1eb0a03b961280e50b50144e15e1,STILL_EXISTS,TODO: define a variable named 'clf'
aaicabdcj,scikit-learn/scikit-learn,skeletons/exercise_01_language_train_model.py,d859f402eb8b1eb0a03b961280e50b50144e15e1,STILL_EXISTS,TODO: define a variable named 'y_predicted'
aaicabdia,scikit-learn/scikit-learn,skeletons/exercise_04_face_recognition.py,40beda3eb74bb5a4a89b2304d160c2785c4595dc,STILL_EXISTS,\"\"\"Face recognition using PCA (eigenfaces) and SVM ||  ||  || This exercises has a lot of boilerplate code to extrat the most represented || faces: ||  || TODO: once the LFW dataset loader is stable enough in scikit-learn 0.8; use it || here instead. || \"\"\"
aaicabdjh,scikit-learn/scikit-learn,skeletons/exercise_04_face_recognition.py,40beda3eb74bb5a4a89b2304d160c2785c4595dc,ae7ab21dfddd51b648369df26392c0eb59ffe111,TODO: define variables X_train; X_test; y_train; y_test by splitting the data
aaicabeae,scikit-learn/scikit-learn,skeletons/exercise_04_face_recognition.py,40beda3eb74bb5a4a89b2304d160c2785c4595dc,STILL_EXISTS,TODO: implement a grid search for the best SVM with gaussian kernel
aaicabeag,scikit-learn/scikit-learn,skeletons/exercise_04_face_recognition.py,40beda3eb74bb5a4a89b2304d160c2785c4595dc,ae7ab21dfddd51b648369df26392c0eb59ffe111,TODO store the best model in a variable named 'clf'
aaicabegh,scikit-learn/scikit-learn,examples/applications/plot_face_recognition.py,4d31f558b2546cccf2ddee3ba569172ed61cd166,STILL_EXISTS,TODO: plot the top eigenfaces and the singular values absolute values
aaicabgjh,scikit-learn/scikit-learn,solutions/exercise_02_language_train_model.py,7101ddd737263a1b8e3f4f47b58569d95c3a3061,b179503d73ec204f51c1d01e305984f04e5cb0ea,TODO: the pipeline instance must be named 'clf'
aaicabgjj,scikit-learn/scikit-learn,solutions/exercise_02_language_train_model.py,7101ddd737263a1b8e3f4f47b58569d95c3a3061,b179503d73ec204f51c1d01e305984f04e5cb0ea,TODO: the predicted outcome must be named 'y_predicted'
aaicabhaa,scikit-learn/scikit-learn,solutions/exercise_02_language_train_model.py,7101ddd737263a1b8e3f4f47b58569d95c3a3061,b179503d73ec204f51c1d01e305984f04e5cb0ea,TODO: uncomment the following once all of the above is implemented
aaicabhbi,scikit-learn/scikit-learn,skeletons/exercise_02_language_train_model.py,b179503d73ec204f51c1d01e305984f04e5cb0ea,STILL_EXISTS,TODO: the pipeline instance must be named 'clf'
aaicabhca,scikit-learn/scikit-learn,skeletons/exercise_02_language_train_model.py,b179503d73ec204f51c1d01e305984f04e5cb0ea,STILL_EXISTS,TODO: the predicted outcome must be named 'y_predicted'
aaicabhcb,scikit-learn/scikit-learn,skeletons/exercise_02_language_train_model.py,b179503d73ec204f51c1d01e305984f04e5cb0ea,STILL_EXISTS,TODO: uncomment the following once all of the above is implemented
aaicabhgj,scikit-learn/scikit-learn,solutions/exercise_01_sentiment.py,1556aafabcb3db8378e2fd77204d7579739755fe,STILL_EXISTS,Refit the best parameter set on the complete training set
aaicabjdj,scikit-learn/scikit-learn,scikits/learn/neighbors.py,7fe51315e6ba671bb713edf09a78833fb8438a96,17c4a3c8d93b5e49df1647f726b75fbe2fe7a184,XXX: should be implemented with a partial sort
aaicacadi,scikit-learn/scikit-learn,scikits/learn/cluster/k_means_.py,491ba872dec64ad7f63bbf8f2d4fe24c8c2cff73,STILL_EXISTS,Permanently add best center candidate found in local tries
aaicacejd,scikit-learn/scikit-learn,scikits/learn/svm/tests/test_svm.py,e1ecda69b0b0d46aa66067587062b108366582b9,STILL_EXISTS,TODO: check on NuSVR; OneClass; etc.
aaicacfei,scikit-learn/scikit-learn,scikits/learn/externals/joblib/parallel.py,160c9529d57fdc3eaacb5eef7192a77c0902ad04,STILL_EXISTS,XXX: Not using the logger framework: need to
aaicacfej,scikit-learn/scikit-learn,scikits/learn/externals/joblib/parallel.py,160c9529d57fdc3eaacb5eef7192a77c0902ad04,STILL_EXISTS,learn to use logger better.
aaicacffe,scikit-learn/scikit-learn,scikits/learn/externals/joblib/parallel.py,160c9529d57fdc3eaacb5eef7192a77c0902ad04,STILL_EXISTS,XXX: possible race condition shuffling the order of
aaicacfif,scikit-learn/scikit-learn,scikits/learn/grid_search.py,0f103f5e707bb35e3dc6647768fa0823554b2e5a,9ec0824056b82e134691d7c66b4ed1410ab4dec7,XXX: Need to make use of Parallel's new pre_dispatch
aaicadahd,scikit-learn/scikit-learn,scikits/learn/datasets/mldata.py,744f25ade0af609a36b5e230e24458a0a83d3903,STILL_EXISTS,TODO: test: it download the first time; it loads it the second
aaicadahe,scikit-learn/scikit-learn,scikits/learn/datasets/mldata.py,744f25ade0af609a36b5e230e24458a0a83d3903,STILL_EXISTS,TODO: function to search dataset name and return best match
aaicadahf,scikit-learn/scikit-learn,scikits/learn/datasets/mldata.py,744f25ade0af609a36b5e230e24458a0a83d3903,STILL_EXISTS,TODO: error checking!
aaicadaib,scikit-learn/scikit-learn,scikits/learn/datasets/mldata.py,b5d5a8951727ea7d05a450b7e8e8eb95e39579b7,STILL_EXISTS,XXX: what is 'mldata_descr_ordering'?
aaicadbcc,scikit-learn/scikit-learn,scikits/learn/lda.py,33cd3f19c5d842e20fa8360b0caa64ea802c377f,STILL_EXISTS,XXX : can do better to avoid precision overflows
aaicadfhj,scikit-learn/scikit-learn,scikits/learn/pca.py,ab506f1141de4e50a78e6a0afc75162d55c23da6,STILL_EXISTS,XXX: Doesn't the below get computed twice if calling fit_transform?
aaicadgff,scikit-learn/scikit-learn,scikits/learn/mixture/dpgmm.py,2eff92e86dfd90f0cb35cd6ea8d7023eeefa8020,0f4aa9e57c3fa49cf195ed4883558c69a67eb802,distribution. This class allows for easy and efficient inference
aaicaebif,scikit-learn/scikit-learn,scikits/learn/decomposition/pca.py,53c92b036c96e9ac4f0c1b5515007554f4f5999d,42a891e3e0424963636336e6090966aea09f0ccb,XXX: ugly; see why it didn't work w\/o .T
aaicaecgg,scikit-learn/scikit-learn,examples/cluster/batchkmeans_digits.py,8747939cd9944fd5825a26a846fe3cdebcefc64b,STILL_EXISTS,\"\"\" || =========================================================== || A demo of K-Means clustering on the handwritten digits data || =========================================================== ||  || Comparing various initialization strategies in terms of runtime and quality of || the results. ||  || TODO: explode the ouput of the cluster labeling and digits.target groundtruth || as categorical boolean arrays of shape (n_sample; n_unique_labels) and measure || the Pearson correlation as an additional measure of the clustering quality. || \"\"\"
aaicaecgj,scikit-learn/scikit-learn,scikits/learn/cluster/k_means_.py,8747939cd9944fd5825a26a846fe3cdebcefc64b,914d3b169d7d56fcb61e6217ae3b7f5870a37d77,FIXME code deduplication with the k_means method
aaicaechd,scikit-learn/scikit-learn,scikits/learn/cluster/k_means_.py,8747939cd9944fd5825a26a846fe3cdebcefc64b,914d3b169d7d56fcb61e6217ae3b7f5870a37d77,FIXME code deduplication with kmeans fit method
aaicaechi,scikit-learn/scikit-learn,scikits/learn/boosting/adaboost.py,5a71b609e12655f5d331084e19916f466a3abd00,STILL_EXISTS,TODO request that classifiers return classification of training sets when fitting
aaicaeebb,scikit-learn/scikit-learn,examples/tree_model/plot_decision_tree_iris.py,c68ad194e92b72e05302e5a2f476a46672a2f4b8,STILL_EXISTS,TODO: Add tree diagram
aaicaeecf,scikit-learn/scikit-learn,examples/tree_model/plot_random_forest_iris.py,7868f38f58c09501199ea559e641d25bad865ae3,STILL_EXISTS,avoid this ugly slicing by using a two-dim dataset
aaicaeede,scikit-learn/scikit-learn,examples/tree_model/plot_random_forest_iris.py,7868f38f58c09501199ea559e641d25bad865ae3,STILL_EXISTS,TODO: Add tree diagram
aaicaeegd,scikit-learn/scikit-learn,scikits/learn/svm/__init__.py,850644fcdf122f7d62321cf46c25cf11f40c36b3,STILL_EXISTS,Author: Fabian Pedregosa <fabian.pedregosa@inria.fr> with help from
aaicaeegi,scikit-learn/scikit-learn,scikits/learn/svm/sparse/__init__.py,850644fcdf122f7d62321cf46c25cf11f40c36b3,STILL_EXISTS,Author: Fabian Pedregosa <fabian.pedregosa@inria.fr> with help from
aaicaefae,scikit-learn/scikit-learn,scikits/learn/cluster/k_means_.py,f57b26ea9d391023847485f8db54f44f31493e19,7bae140d84480a3bb0878ae7c05aa12bd9cf0548,FIXME
aaicaeige,scikit-learn/scikit-learn,examples/mixture/plot_gmm.py,f2fc81e4fba200caf9ef7ba3b6fa4acff3d1bf9f,STILL_EXISTS,the dirichlet process model will only use as many are needed to
aaicafbgi,scikit-learn/scikit-learn,scikits/learn/decomposition/sparsepca.py,6c3402cf19acdac287ec2dcc8a8f9bb7ac5cdbf1,STILL_EXISTS,XXX: should be in joblib
aaicafcfi,scikit-learn/scikit-learn,scikits/learn/cluster/k_means_.py,ef42c40c56323dd149d17014f77c8519d1466168,4f0158b6faa863a7334621fed7a05d70f663ed4c,FIXME
aaicafece,scikit-learn/scikit-learn,benchmarks/bench_sgd_regression.py,db8fb49395036acd29807326bdc0cc49c577d4b9,STILL_EXISTS,Author: Peter Prettenhofer <peter.prettenhofer@gmail.com>
aaicaffad,scikit-learn/scikit-learn,scikits/learn/cluster/k_means_.py,52c4cef7839a1399e2b2afd4908d295a2930b6ee,4f0158b6faa863a7334621fed7a05d70f663ed4c,FIXME
aaicafife,scikit-learn/scikit-learn,scikits/learn/cluster/k_means_.py,dfefb0168af491fabd1874fe42dc6ff73e2b8876,5bb104f59f3022794cec707e8a6c63e5aa210651,FIXME
aaicafifj,scikit-learn/scikit-learn,scikits/learn/metrics/cluster.py,55e386706d2a8951228c422b9b6be8b9496888cb,STILL_EXISTS,\"\"\"Utilities to evaluate the clustering performance of models ||  || Functions named as *_score return a scalar value to maximize: the higher the || better || \"\"\"
aaicagebd,scikit-learn/scikit-learn,examples/manifold/plot_swissroll.py,71819eb8aec9a4f139d01db0d208c23963482831,STILL_EXISTS,This import is needed to modify the way figure behaves
aaicagfij,scikit-learn/scikit-learn,scikits/learn/manifold/MLLE.py,84961ce4d96cd877ef32beefe10e89459e3cf5c2,STILL_EXISTS,some variables to hold needed values
aaicagfje,scikit-learn/scikit-learn,scikits/learn/manifold/MLLE.py,84961ce4d96cd877ef32beefe10e89459e3cf5c2,STILL_EXISTS,we want V[i] to be a [k x k] matrix; where the columns
aaicagjbf,scikit-learn/scikit-learn,scikits/learn/datasets/mldata.py,84525f6fb18081b6b7e484900ed59e6e3f8cffc0,STILL_EXISTS,a) copy all columns in the bunch; using their column name
aaicagjcc,scikit-learn/scikit-learn,scikits/learn/datasets/tests/test_mldata.py,946ff5158e45b96fc978c1ad3db339ed15e5d41d,STILL_EXISTS,\"\"\"Test functionality of mldata fetching utilities.\"\"\"
aaicagjcf,scikit-learn/scikit-learn,scikits/learn/datasets/tests/test_mldata.py,946ff5158e45b96fc978c1ad3db339ed15e5d41d,c9860970cf8f947a74d804a89408ca0874f6597a,non-standard columns
aaicahabe,scikit-learn/scikit-learn,scikits/learn/grid_search.py,6dd017e1cf3167b114acc81ef29b7c0d5a81addf,a8bf9e5be96729a9a668fbb92428e836558e9d09,XXX: Need to make use of Parallel's new pre_dispatch
aaicahgdb,scikit-learn/scikit-learn,scikits/learn/datasets/tests/test_mldata.py,c9860970cf8f947a74d804a89408ca0874f6597a,33c4d15aec402cbc64079c0fe5510cf8f1a7240e,data sets with non-standard columns
aaicahjfj,scikit-learn/scikit-learn,scikits/learn/naive_bayes.py,5f87e43cb462d4df4b982254746b4ce3dc79a1b4,71cffc441b3fa42bbad0cec0155d69be8b58a143,XXX: In the dense case; we could vectorize this loop. It might be
aaicaiaii,scikit-learn/scikit-learn,scikits/learn/preprocessing/__init__.py,f9156e418d89d13756874d7abf93938e8b347df3,76b8a9e63775e27174e0b5b75345bfa180c0b25c,FIXME: if enough values became 0; it may be worth changing
aaicaibeg,scikit-learn/scikit-learn,scikits/learn/grid_search.py,76b8a9e63775e27174e0b5b75345bfa180c0b25c,c9a73457979ee326401ecbf255bbd22668b120da,XXX: Need to make use of Parallel's new pre_dispatch
aaicaibjh,scikit-learn/scikit-learn,scikits/learn/preprocessing/__init__.py,4504663348f94e500c460c1370a0736101542cc9,STILL_EXISTS,FIXME: if enough values became 0; it may be worth changing
aaicaicbj,scikit-learn/scikit-learn,scikits/learn/naive_bayes.py,77817a86c9a78ea9793fb8bda21c6f37f34acf73,daf64539adfd3a0cfb1c3347480755dab1cf61dd,XXX: In the dense case; we could vectorize this loop. It might be
aaicaidcg,scikit-learn/scikit-learn,scikits/learn/naive_bayes.py,657b41d4eb1fecabd8c37ef8395505a3c94d46ba,cdcc8eae2584b5f08091c44e1bb9ed0edc043342,XXX: In the dense case; we could vectorize this loop. It might be
aaicaidde,scikit-learn/scikit-learn,scikits/learn/metrics/metrics.py,d53e3f996e8fc073dfb4822c7cee08f3610bb869,STILL_EXISTS,TODO: multi-class hinge-loss
aaicaidea,scikit-learn/scikit-learn,examples/document_clustering.py,50e9f18d7defd9f7c65f6086b990450b7fd45f49,STILL_EXISTS,Author: Peter Prettenhofer <peter.prettenhofer@gmail.com>
aaicaifbd,scikit-learn/scikit-learn,scikits/learn/naive_bayes.py,f035f4e85c8a17a460013932c098084bde877c1b,STILL_EXISTS,XXX: In the dense case; we could vectorize this loop. It might be
aaicaiffg,scikit-learn/scikit-learn,scikits/learn/tests/test_naive_bayes.py,ea11518e0d60c5b25bf7bac793fd696ae7849da2,dee7edde01dd0bb627749a686f935c82d9084e9d,FIXME: write a test to show this.
aaicaiffi,scikit-learn/scikit-learn,scikits/learn/decomposition/sparse_pca.py,025ce377d5018e369546945db0bda0081d760741,8fdd8275d5186c15973106837f6255236a9edb25,XXX: where should this be?
aaicaigfg,scikit-learn/scikit-learn,examples/decomposition/plot_sparse_pca.py,bd6ac1ad1de0b4b9e85a4dc3efc64b55d166da0b,STILL_EXISTS,todo: use preprocessors
aaicaiggd,scikit-learn/scikit-learn,scikits/learn/decomposition/sparse_pca.py,bd6ac1ad1de0b4b9e85a4dc3efc64b55d166da0b,STILL_EXISTS,TODO: Ridge
aaicaiggi,scikit-learn/scikit-learn,scikits/learn/tests/test_naive_bayes.py,841a295ffc876604d849123c6c2f5a1682a89138,STILL_EXISTS,FIXME: write a test to show this.
aaicaighi,scikit-learn/scikit-learn,scikits/learn/manifold/locally_linear.py,236243ceef7336c8a0cafb9048336249370611ad,26cf261015b2426d2601467e6198b4040cc5f957,where the columns are eigenvectors
aaicaiibb,scikit-learn/scikit-learn,scikits/learn/manifold/scipy_future.py,236243ceef7336c8a0cafb9048336249370611ad,STILL_EXISTS,real;imaginary in consecutive columns
aaicaiibj,scikit-learn/scikit-learn,scikits/learn/manifold/scipy_future.py,236243ceef7336c8a0cafb9048336249370611ad,STILL_EXISTS,in consecutive columns
aaicajaai,scikit-learn/scikit-learn,scikits/learn/manifold/locally_linear.py,491129f415de9ce6ec758a2e0e40226451581b7b,c3591b34693c1090ec5a5098dc4cf638f028ceb0,where the columns are eigenvectors
aaicajggh,scikit-learn/scikit-learn,scikits/learn/feature_extraction/image.py,6ee443a1f1811f0abe0a73b28558207b845ecfa1,b8ed9cd8369a8077a7c6a8b278ad367f5dea612d,XXX: make it work with colour images too!
aaicajghd,scikit-learn/scikit-learn,scikits/learn/feature_extraction/text.py,5bdc3ab5e67b51769a0edb2dfc22ce47454cd699,STILL_EXISTS,TODO: parallelize the following loop with joblib?
aaicajghe,scikit-learn/scikit-learn,scikits/learn/feature_extraction/text.py,5bdc3ab5e67b51769a0edb2dfc22ce47454cd699,STILL_EXISTS,(see XXX up ahead)
aaicajghf,scikit-learn/scikit-learn,scikits/learn/feature_extraction/text.py,5bdc3ab5e67b51769a0edb2dfc22ce47454cd699,STILL_EXISTS,XXX @larsmans tried to parallelize the following loop with joblib.
aaicajhje,scikit-learn/scikit-learn,scikits/learn/decomposition/sparse_pca.py,38235cf950ddc6a15cb710c1e50b46e2d1b1a640,1a745d3d0d1c446470d0efde6a93794cc0e27c5d,XXX: Can the residuals be of any use?
aaicajhjf,scikit-learn/scikit-learn,scikits/learn/decomposition/sparse_pca.py,38235cf950ddc6a15cb710c1e50b46e2d1b1a640,1a745d3d0d1c446470d0efde6a93794cc0e27c5d,Maybe we need a stopping criteria based on the amount of
aaicajigc,scikit-learn/scikit-learn,scikits/learn/feature_extraction/image.py,afd860a8b1cc2bb82734378b55ae70f06a693b3e,STILL_EXISTS,XXX: make it work with colour images too!
aaicajihc,scikit-learn/scikit-learn,scikits/learn/decomposition/sparse_pca.py,1598c4025d52d3730a20684e809de27a0133eecb,e9b834733076cc9e5c6610f4fcaf73cc50f23d3d,XXX: Can the residuals be of any use?
aaicajihd,scikit-learn/scikit-learn,scikits/learn/decomposition/sparse_pca.py,1598c4025d52d3730a20684e809de27a0133eecb,e9b834733076cc9e5c6610f4fcaf73cc50f23d3d,Maybe we need a stopping criteria based on the amount of
aaicajihf,scikit-learn/scikit-learn,examples/decomposition/plot_dict_learning.py,43acd7fd2eee5c8ae6054723b3abb327bd96ef3d,STILL_EXISTS,\"\"\" || ==================================== || Dictionary learning on image patches || ==================================== ||  || XXX: explain ||  || \"\"\"
aaicajije,scikit-learn/scikit-learn,scikits/learn/decomposition/dict_learning.py,7af69d76b7f155939a3671629cea451f302db940,ff671f55537e00a436d0f2d03540ec7f69671893,XXX: parameters should be made explicit so we can have defaults
aaicajjad,scikit-learn/scikit-learn,scikits/learn/decomposition/sparsepca.py,5cc047396880bd7918fdbe4cfbea74f3485657ec,STILL_EXISTS,XXX: should be in joblib
aaicajjhc,scikit-learn/scikit-learn,scikits/learn/decomposition/sparse_pca.py,36aa5b8f0454a1a623d0299353c6c15921440bc7,8fdd8275d5186c15973106837f6255236a9edb25,XXX: where should this be?
aaicajjib,scikit-learn/scikit-learn,examples/decomposition/plot_sparse_pca.py,246f95e428a1bb1db1c158edb5c574e4ea454f50,STILL_EXISTS,todo: use preprocessors
aaicajjii,scikit-learn/scikit-learn,scikits/learn/decomposition/sparse_pca.py,246f95e428a1bb1db1c158edb5c574e4ea454f50,STILL_EXISTS,TODO: Ridge
aaicbaaca,scikit-learn/scikit-learn,scikits/learn/decomposition/sparse_pca.py,e19a440618560f8488f7f19c4d5e9bea54b2a601,e9b834733076cc9e5c6610f4fcaf73cc50f23d3d,XXX: Can the residuals be of any use?
aaicbaacb,scikit-learn/scikit-learn,scikits/learn/decomposition/sparse_pca.py,e19a440618560f8488f7f19c4d5e9bea54b2a601,e9b834733076cc9e5c6610f4fcaf73cc50f23d3d,Maybe we need a stopping criteria based on the amount of
aaicbabfd,scikit-learn/scikit-learn,scikits/learn/feature_extraction/image.py,a9ea5454e237d11c2d4486718f499a168bebf653,7d1200da3c7bed43c809df5dd699ebc40fef689a,XXX : would be better to avoid a realloc for each image
aaicbabfe,scikit-learn/scikit-learn,scikits/learn/decomposition/dict_learning.py,98f592d01a98a01e4dd29440e7a5491f8dba7ea4,STILL_EXISTS,XXX : kwargs is not documented
aaicbabhf,scikit-learn/scikit-learn,scikits/learn/decomposition/sparse_pca.py,1a51660d3654a0e0d2683095c065364aa670c0da,7f6b1ffc5b329c7f1eb9ba156c09d67b32522ea3,XXX: should be in joblib
aaicbabie,scikit-learn/scikit-learn,scikits/learn/decomposition/sparse_pca.py,1b76e967fda3d31991c0982b06acea685fbb2345,b45a5ff3e6a30444d4f30f665ccf0c94705d5d67,XXX: Can the residuals be of any use?
aaicbabif,scikit-learn/scikit-learn,scikits/learn/decomposition/sparse_pca.py,1b76e967fda3d31991c0982b06acea685fbb2345,b45a5ff3e6a30444d4f30f665ccf0c94705d5d67,Maybe we need a stopping criteria based on the amount of
aaicbacah,scikit-learn/scikit-learn,scikits/learn/decomposition/sparse_pca.py,f68b50aa892e7d06d5382ff1a459aff2e13554d7,993fbe7dc3d92b0a60f8881775e8967fa50da0b7,XXX: Can the residuals be of any use?
aaicbacai,scikit-learn/scikit-learn,scikits/learn/decomposition/sparse_pca.py,f68b50aa892e7d06d5382ff1a459aff2e13554d7,993fbe7dc3d92b0a60f8881775e8967fa50da0b7,Maybe we need a stopping criteria based on the amount of
aaicbacea,scikit-learn/scikit-learn,scikits/learn/decomposition/sparse_pca.py,b99397a65d2211f7dded6a7e55c1e127b849803b,7b7f32f6157d26f95fa499566590a9f5041b4a87,XXX: Can the residuals be of any use?
aaicbaceb,scikit-learn/scikit-learn,scikits/learn/decomposition/sparse_pca.py,b99397a65d2211f7dded6a7e55c1e127b849803b,7b7f32f6157d26f95fa499566590a9f5041b4a87,Maybe we need a stopping criteria based on the amount of
aaicbafac,scikit-learn/scikit-learn,scikits/learn/manifold/locally_linear.py,c1316965784ad8bbcc4c60bba6267f4decfbf465,STILL_EXISTS,where the columns are eigenvectors
aaicbafga,scikit-learn/scikit-learn,scikits/learn/decomposition/dict_learning.py,2f741ac9ffcd74a2d1e16762fa5510e01c579e21,STILL_EXISTS,XXX : kwargs is not documented
aaicbafgb,scikit-learn/scikit-learn,scikits/learn/decomposition/dict_learning.py,2f741ac9ffcd74a2d1e16762fa5510e01c579e21,ff671f55537e00a436d0f2d03540ec7f69671893,XXX: parameters should be made explicit so we can have defaults
aaicbafhc,scikit-learn/scikit-learn,scikits/learn/decomposition/sparse_pca.py,e3ef711e74cdcab384058c835a4a9658ae8e336a,3cad001254f40806de664d13cbad0c6ec113a796,XXX: Can the residuals be of any use?
aaicbafhd,scikit-learn/scikit-learn,scikits/learn/decomposition/sparse_pca.py,e3ef711e74cdcab384058c835a4a9658ae8e336a,3cad001254f40806de664d13cbad0c6ec113a796,Maybe we need a stopping criteria based on the amount of
aaicbafhf,scikit-learn/scikit-learn,scikits/learn/decomposition/dict_learning.py,db08066bb9055dd6c75be55e1d54cab01d9b7f28,STILL_EXISTS,feature vector is split into a positive and negative side
aaicbbcda,scikit-learn/scikit-learn,scikits/learn/manifold/tests/test_locally_linear.py,24ab71e11182e81ed13be5c201ddcfd3a1f50f47,STILL_EXISTS,FIXME: ARPACK fails this test ...
aaicbbebi,scikit-learn/scikit-learn,scikits/learn/decomposition/sparse_pca.py,c154810e188ac11beb196d15d9d2a4b79136d789,9bc1a90543994e07c9367d964d317f5151e23097,XXX: Can the residuals be of any use?
aaicbbebj,scikit-learn/scikit-learn,scikits/learn/decomposition/sparse_pca.py,c154810e188ac11beb196d15d9d2a4b79136d789,9bc1a90543994e07c9367d964d317f5151e23097,Maybe we need a stopping criteria based on the amount of
aaicbbecd,scikit-learn/scikit-learn,scikits/learn/hmm.py,5bcbb3c203cf07db01d8d9a8c6c7f30182c5d79e,ec13d954f452154f4b2e45227f95edb38321dbdf,XXX Replacing this with logaddexp.reduce; as in mixture.py; causes a test to
aaicbbeed,scikit-learn/scikit-learn,scikits/learn/decomposition/sparse_pca.py,80b98544798ff7292aef3a40828717f1de5c5cf6,34b3276c91574e4f3af70c4306096358d418ba12,XXX: Can the residuals be of any use?
aaicbbeee,scikit-learn/scikit-learn,scikits/learn/decomposition/sparse_pca.py,80b98544798ff7292aef3a40828717f1de5c5cf6,34b3276c91574e4f3af70c4306096358d418ba12,Maybe we need a stopping criteria based on the amount of
aaicbbegd,scikit-learn/scikit-learn,scikits/learn/decomposition/sparse_pca.py,e95d364902e2a66c12ba008c103a3d2005445659,b730d3dbeb9d31aa9bd718530d219cc690bbd077,XXX: Can the residuals be of any use?
aaicbbege,scikit-learn/scikit-learn,scikits/learn/decomposition/sparse_pca.py,e95d364902e2a66c12ba008c103a3d2005445659,b730d3dbeb9d31aa9bd718530d219cc690bbd077,Maybe we need a stopping criteria based on the amount of
aaicbbeha,scikit-learn/scikit-learn,examples/linear_model/logistic_l1_l2_sparsity.py,298259070ecc08f30c695bb5c6bb32e2c8d398a1,STILL_EXISTS,FIXME: the iris dataset has only 4 features!
aaicbbeic,scikit-learn/scikit-learn,scikits/learn/feature_extraction/image.py,61cb91238fe2bf565529abd25dc2c1169b3ecd63,94eb7c03ef135f8c7b8e63a67c97fe769f560296,XXX: is this the most efficient way? memory-wise yes; cpu wise?
aaicbbeid,scikit-learn/scikit-learn,scikits/learn/feature_extraction/image.py,61cb91238fe2bf565529abd25dc2c1169b3ecd63,767de181faac929880fd86377cbe0781158a60ad,XXX : would be better to avoid a realloc for each image
aaicbbfeh,scikit-learn/scikit-learn,scikits/learn/decomposition/sparse_pca.py,7ec38ba34dc061305d652766c98e969c876375ee,2ec3f203530fb9e35b53d82f0ffb5234841593fa,XXX: Can the residuals be of any use?
aaicbbfei,scikit-learn/scikit-learn,scikits/learn/decomposition/sparse_pca.py,7ec38ba34dc061305d652766c98e969c876375ee,0a5dfb1d58d610b9701f4488e1cb7fd8b1baa528,Maybe we need a stopping criteria based on the amount of
aaicbbfje,scikit-learn/scikit-learn,scikits/learn/decomposition/sparse_pca.py,0a5dfb1d58d610b9701f4488e1cb7fd8b1baa528,d439d8d95807eb1cbc0df16001f3139453d4a193,XXX: should we always do this?
aaicbbgac,scikit-learn/scikit-learn,scikits/learn/decomposition/sparse_pca.py,0a5dfb1d58d610b9701f4488e1cb7fd8b1baa528,2ec3f203530fb9e35b53d82f0ffb5234841593fa,XXX: Can the residuals be of any use?
aaicbbgfa,scikit-learn/scikit-learn,scikits/learn/decomposition/sparse_pca.py,99a64dfb6224643df5b954c715417a4e388e8f43,2ec3f203530fb9e35b53d82f0ffb5234841593fa,XXX: Can the residuals be of any use?
aaicbbgfb,scikit-learn/scikit-learn,scikits/learn/decomposition/sparse_pca.py,99a64dfb6224643df5b954c715417a4e388e8f43,2ec3f203530fb9e35b53d82f0ffb5234841593fa,Maybe we need a stopping criteria based on the amount of
aaicbbgfj,scikit-learn/scikit-learn,scikits/learn/feature_extraction/image.py,99a64dfb6224643df5b954c715417a4e388e8f43,4ca0cce7b5ea07688f23b1f97a3069712c840ab3,XXX: is this the most efficient way? memory-wise yes; cpu wise?
aaicbbhbd,scikit-learn/scikit-learn,scikits/learn/decomposition/sparse_pca.py,2fcbe5b5b436e0f8437c52157690faee5f71fef9,12392169f479a0330bc37dcdc264e17f95ef33ed,XXX: should we always do this?
aaicbbhcf,scikit-learn/scikit-learn,scikits/learn/decomposition/sparse_pca.py,c7f4b361dd24cb34bfd3c6cf84c1a33d85dc79c7,75b98959ec8cf30842de8b48d1a53a319c6b0573,XXX: should we always do this?
aaicbcfaj,scikit-learn/scikit-learn,scikits/learn/utils/__init__.py,32629255baab65f04b5768d6be356cdc3ff55b34,STILL_EXISTS,O(n) time; O(1) solution. XXX: will fail if the sum over X is
aaicbcgdh,scikit-learn/scikit-learn,scikits/learn/decomposition/sparse_pca.py,a7fc975186d74152baf83be6686457ed9d98146d,6dfd938aff87f80f6a4a9225bf76939ad66811a6,XXX: Can the residuals be of any use?
aaicbcgdi,scikit-learn/scikit-learn,scikits/learn/decomposition/sparse_pca.py,a7fc975186d74152baf83be6686457ed9d98146d,6dfd938aff87f80f6a4a9225bf76939ad66811a6,Maybe we need a stopping criteria based on the amount of
aaicbcifb,scikit-learn/scikit-learn,scikits/learn/feature_selection/univariate_selection.py,ae43df218a05685958520be98c8debd721a082ff,STILL_EXISTS,XXX: we might want to do some of the following in logspace instead for
aaicbcjac,scikit-learn/scikit-learn,examples/cluster/plot_kmeans_vq.py,b95d7191ee1ceb678375ee30e321abbf1ec30f04,STILL_EXISTS,Features (0; 3) is better; but this graph looks better as an example of VQ.
aaicbcjdj,scikit-learn/scikit-learn,scikits/learn/manifold/isomap.py,29e828dc9fcfe33cb121dd12f26cee8ec6d8e325,8d0e9575a1ddde48ada79314619b7af31f8736b2,note: there's probably a more efficient way to do this
aaicbdaaj,scikit-learn/scikit-learn,scikits/learn/utils/__init__.py,7df22e086f0ac6e30c20b92aeb74cabc06d74eac,54cc23e510aceaf0e76d317d452729a5b7790544,FIXME: we should probably reset __new__ for full generality
aaicbdjbb,scikit-learn/scikit-learn,scikits/learn/linear_model/tests/test_omp.py,32d78bdbf7e9d9ba27606bcef7d48d42ac4bb523,STILL_EXISTS,XXX: use signal generator
aaicbdjei,scikit-learn/scikit-learn,scikits/learn/tree_model/tests/test_tree.py,24348af2242e2acbcb92895b4bfb8e8311d50b67,STILL_EXISTS,@TODO Find a way of passing in a pseudo-random generator
aaicbdjhe,scikit-learn/scikit-learn,scikits/learn/tree_model/tree.py,37805033924ad35bfe0a3a37f1864c4a0bafe944,STILL_EXISTS,\"\"\"     || @TODO Print the tree to a .dot file using graphViz || \"\"\"
aaicbecib,scikit-learn/scikit-learn,scikits/learn/linear_model/tests/test_omp.py,f7ab53d7f560aecabf1b36a28449e53389504330,ed0859297a384bdf27622992c7344c5abf7c062b,XXX: change samples_generator to the transpose problem; makes more sense
aaicbfcec,scikit-learn/scikit-learn,scikits/learn/utils/__init__.py,1e75e9ae18461dd8641232753a4e9daa0a828279,STILL_EXISTS,FIXME: we should probably reset __new__ for full generality
aaicbfhbf,scikit-learn/scikit-learn,scikits/learn/metrics/pairwise.py,1798eeae0e03b057927b220c5f69723df03624c9,STILL_EXISTS,TODO:
aaicbfhfd,scikit-learn/scikit-learn,scikits/learn/metrics/pairwise.py,11fd404f9337388742f380bef6d635a8aaed15b5,STILL_EXISTS,TODO:
aaicbfihi,scikit-learn/scikit-learn,scikits/learn/manifold/isomap.py,f0ff849c6126fdf3dd4664d1195d6c93959327d9,470562438830fb4329c193bee08e5692372ed391,good way to do this without duplicating kneighbors_graph code.
aaicbfijj,scikit-learn/scikit-learn,examples/label_propagation/plot_label_propagation_structure.py,e66443453be10f936d3dd0ceb24f2566d875b0f8,49e6f5e63a790ba6cb49336f7910575bf19d55d1,TODO ALL OF THIS
aaicbfjac,scikit-learn/scikit-learn,examples/label_propagation/plot_label_propagation_versus_svm_iris.py,e66443453be10f936d3dd0ceb24f2566d875b0f8,49e6f5e63a790ba6cb49336f7910575bf19d55d1,THE REST IS TODO !!!
aaicbggdf,scikit-learn/scikit-learn,scikits/learn/decomposition/sparse_pca.py,d2cbaaa29be4a165558549a458723fa9da327307,9a5c0b92fe00509f687978909747a98ff672b42a,XXX: Can the residuals be of any use?
aaicbggdg,scikit-learn/scikit-learn,scikits/learn/decomposition/sparse_pca.py,d2cbaaa29be4a165558549a458723fa9da327307,9a5c0b92fe00509f687978909747a98ff672b42a,Maybe we need a stopping criteria based on the amount of
aaicbggee,scikit-learn/scikit-learn,scikits/learn/feature_extraction/image.py,d2cbaaa29be4a165558549a458723fa9da327307,STILL_EXISTS,XXX: is this the most efficient way? memory-wise yes; cpu wise?
aaicbggge,scikit-learn/scikit-learn,scikits/learn/feature_extraction/image.py,755fac53d0b1f694668bc07c38587ca3586982be,STILL_EXISTS,XXX: make it work with colour images too!
aaicbgggg,scikit-learn/scikit-learn,scikits/learn/feature_extraction/image.py,755fac53d0b1f694668bc07c38587ca3586982be,STILL_EXISTS,XXX: is this the most efficient way? memory-wise yes; cpu wise?
aaicbgggj,scikit-learn/scikit-learn,scikits/learn/feature_extraction/image.py,2fd16ada3e4dc4f08666382f00c3518520786533,a1fa061e922b32dab821011375779d19c944a646,XXX : would be better to avoid a realloc for each image
aaicbhdif,scikit-learn/scikit-learn,scikits/learn/tree/tree.py,f49eec5c50d592ab2c79fdd484e5ae8055a2f1d0,165125f324c2b4125daba3c9a6778220afc8fd7b,This ugly construction is required because np.random
aaicbhjgb,scikit-learn/scikit-learn,scikits/learn/manifold/locally_linear.py,207d25a1e0c58c9191e8800a0350a8c4aeca56e3,STILL_EXISTS,choose the most efficient way to find the eigenvectors
aaicbiife,scikit-learn/scikit-learn,scikits/learn/metrics/__init__.py,041651e72b136609d3b6e5ae5712def4c269f21d,1fcb4f81dd2e49c274aa3c0213c4e683fc749352,FIXME: the distance module doesn't support sparse matrices!
aaicbjcfj,scikit-learn/scikit-learn,scikits/learn/metrics/pairwise.py,1fcb4f81dd2e49c274aa3c0213c4e683fc749352,STILL_EXISTS,FIXME: the distance module doesn't support sparse matrices!
aaicbjhhf,scikit-learn/scikit-learn,scikits/learn/grid_search.py,34aee3387b13a212c2e98fca565cc9826830566f,STILL_EXISTS,FIXME we should be doing a clone here
aaicbjjgc,scikit-learn/scikit-learn,scikits/learn/meta/multiclass.py,913c883896ea0a9f068d2b1892f64d449517526e,STILL_EXISTS,FIXME: there are more elaborate methods than generating the codebook
aaicbjjgh,scikit-learn/scikit-learn,scikits/learn/meta/tests/test_multiclass.py,913c883896ea0a9f068d2b1892f64d449517526e,8deeaf47f54e8a82bf4890fa610d40ffa9807d99,FIXME: doesn't work... why?
aaicbjjja,scikit-learn/scikit-learn,scikits/learn/metrics/metrics.py,fffe8292ed6ab0e3130a4966adc8be899e38f08e,9dcbc929960cb2e784cbb8e90b869b18944c5c10,FIXME: the distance module doesn't support sparse matrices!
aaicbjjje,scikit-learn/scikit-learn,scikits/learn/metrics/pairwise.py,9dcbc929960cb2e784cbb8e90b869b18944c5c10,STILL_EXISTS,FIXME: the distance module doesn't support sparse matrices!
aaiccaaja,scikit-learn/scikit-learn,scikits/learn/decomposition/dict_learning.py,9a5c0b92fe00509f687978909747a98ff672b42a,STILL_EXISTS,XXX: Can the residuals be of any use?
aaiccaajb,scikit-learn/scikit-learn,scikits/learn/decomposition/dict_learning.py,9a5c0b92fe00509f687978909747a98ff672b42a,STILL_EXISTS,Maybe we need a stopping criteria based on the amount of
aaiccacef,scikit-learn/scikit-learn,scikits/learn/decomposition/sparse_pca.py,378ad2268d3c928e22e68c120f006c567bf959d3,STILL_EXISTS,XXX: Can the residuals be of any use?
aaiccaceg,scikit-learn/scikit-learn,scikits/learn/decomposition/sparse_pca.py,378ad2268d3c928e22e68c120f006c567bf959d3,STILL_EXISTS,Maybe we need a stopping criteria based on the amount of
aaiccacjj,scikit-learn/scikit-learn,examples/applications/topics_extraction_with_nmf.py,74dd2d8f6a53d39b608d8151441b4b7245c78968,STILL_EXISTS,\"\"\" || ======================================================== || Topics extraction with Non-Negative Matrix Factorization || ======================================================== ||  || This is a proof of concept application of Non Negative Matrix || Factorization of the term frequency matrix of a corpus of documents so || as to extract an additive model of the topic structure of the corpus. ||  || The default parameters (n_samples \/ n_features \/ n_topics) should make || the example runnable in a couple of tens of seconds. You can try to || increase the dimensions of the problem be ware than the time complexity || is polynomial. ||  || Here are some sample extracted topics that look quite good: ||  || Topic #0: || god people bible israel jesus christian true moral think christians || believe don say human israeli church life children jewish ||  || Topic #1: || drive windows card drivers video scsi software pc thanks vga || graphics help disk uni dos file ide controller work ||  || Topic #2: || game team nhl games ca hockey players buffalo edu cc year play || university teams baseball columbia league player toronto ||  || Topic #3: || window manager application mit motif size display widget program || xlib windows user color event information use events x11r5 values ||  || Topic #4: || pitt gordon banks cs science pittsburgh univ computer soon disease || edu reply pain health david article medical medicine 16 || \"\"\"
aaiccajae,scikit-learn/scikit-learn,sklearn/metrics/pairwise.py,fd0d3b879d419015dbd8423d8b22f3fb288bf7d6,STILL_EXISTS,FIXME: the distance module doesn't support sparse matrices!
aaiccbhhg,scikit-learn/scikit-learn,scikits/learn/neighbors.py,2f116839b317f7c2cd760d69c49082af34e294a7,17c4a3c8d93b5e49df1647f726b75fbe2fe7a184,XXX: should be implemented with a partial sort
aaiccdhac,scikit-learn/scikit-learn,sklearn/utils/fixes.py,732f18a74a070c5dd0c0b8cf66c511b510ec8e4d,a95af4d8d837a3ed2e6d9d8dff923fc592aca131,pure python workaround:
aaiccdhch,scikit-learn/scikit-learn,sklearn/metrics/pairwise.py,e60977551c92dc4e074596af9167102ef98c90a6,STILL_EXISTS,FIXME: the distance module doesn't support sparse matrices!
aaiccdhfi,scikit-learn/scikit-learn,sklearn/neighbors/base.py,1583dbbbb4b0bd6dd7b0c07665b15cef0eede9fe,STILL_EXISTS,XXX: should be implemented with a partial sort
aaiccdiaf,scikit-learn/scikit-learn,sklearn/neighbors/tests/test_neighbors.py,1583dbbbb4b0bd6dd7b0c07665b15cef0eede9fe,7a6bad9e7a24f597b98abbab9c5e7e95a42e8cdd,check that columns sum to one
aaiccdihi,scikit-learn/scikit-learn,sklearn/manifold/tests/test_locally_linear.py,7a6bad9e7a24f597b98abbab9c5e7e95a42e8cdd,STILL_EXISTS,check that columns sum to one
aaiccdijd,scikit-learn/scikit-learn,sklearn/metrics/pairwise.py,0c47a521107bf5988e4d5fd5f480cb94cb4eda53,STILL_EXISTS,FIXME: the distance module doesn't support sparse matrices!
aaicceigb,scikit-learn/scikit-learn,sklearn/metrics/pairwise.py,9997b8156f60793ebed6d76ab82b052cb67fd111,STILL_EXISTS,FIXME: the distance module doesn't support sparse matrices!
aaiccejde,scikit-learn/scikit-learn,sklearn/neighbors/base.py,bc02942bc322b04221679fa9ddb0dd64f3004a80,STILL_EXISTS,FIXME: include float parameter p for using different distance metrics.
aaiccejdj,scikit-learn/scikit-learn,examples/cluster/plot_adjustment_for_chance.py,ddbf08c2fc7a77a9933549832ce42f48c8e287ae,STILL_EXISTS,\"\"\" || ========================================================== || Adjustment for chance in clustering performance evaluation || ========================================================== ||  || The following plots demonstrate the impact of the number of cluster and || number of samples on various clustering performance evaluation metrics. ||  || Non-adjusted measures such as the V-Measure show a dependency between || the number of clusters and the number of samples: the mean V-Measure || of random labeling increases signicantly as the number of clusters is || closer to the total number of samples used to compute the measure. ||  || Adjusted for chance measure such as ARI display some random variations || centered around a mean score of 0.0 for any number of samples and || clusters. ||  || Only adjusted measures can hence safely be used as a consensus index || to evaluate the average stability of clustering algorithms for a given || value of k on various overlapping sub-samples of the dataset. ||  || \"\"\"
aaiccfaae,scikit-learn/scikit-learn,sklearn/manifold/tests/test_locally_linear.py,1be34bcdccc861216b0de028efea5e22bdce0a78,STILL_EXISTS,check that columns sum to one
aaiccfjcj,scikit-learn/scikit-learn,sklearn/manifold/tests/test_locally_linear.py,54af74d18c15265d463c22657b9d0d2245bf43fa,STILL_EXISTS,check that columns sum to one
aaiccgaeg,scikit-learn/scikit-learn,sklearn/tests/test_label_propagation.py,ff4a3f318105c73798386522bf2d702505bbc296,1a06a4619bea62c563badd2b73dab2dde037ea44,TODO: test LabelSpreading as well
aaiccgaej,scikit-learn/scikit-learn,sklearn/tests/test_label_propagation.py,ff4a3f318105c73798386522bf2d702505bbc296,1a06a4619bea62c563badd2b73dab2dde037ea44,FIXME: this does not look correct
aaiccgagg,scikit-learn/scikit-learn,sklearn/utils/fixes.py,1a06a4619bea62c563badd2b73dab2dde037ea44,a95af4d8d837a3ed2e6d9d8dff923fc592aca131,pure python workaround:
aaiccgagi,scikit-learn/scikit-learn,sklearn/tree/tree.py,1187db68797f8831d3ab5742995494cef3e5e54d,STILL_EXISTS,compute split over all X -> needed for shapes to match
aaiccjfaa,scikit-learn/scikit-learn,sklearn/neighbors/tests/test_neighbors.py,5d1d437ba88f851fc2f2a31d3e51f6ce29b6ae03,STILL_EXISTS,hack to fix this: we'll override the warning call with a new message
aaiccjfab,scikit-learn/scikit-learn,sklearn/neighbors/tests/test_neighbors.py,5d1d437ba88f851fc2f2a31d3e51f6ce29b6ae03,STILL_EXISTS,end hack
aaiccjicd,scikit-learn/scikit-learn,sklearn/utils/fixes.py,2f7e997eef4d1a8f316b65acbc197366ed5eb06e,c68f4f540dba0eea7f713a36363aacba7111e74e,pure python workaround:
aaiccjicf,scikit-learn/scikit-learn,sklearn/label_propagation.py,5e7d3c965feb473e0de39bd4df548a43c6952582,24ac2f518d489356837c6469d417e02c9d923b86,TODO DO SPARSE STUFF
aaiccjidf,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,9aabbdb211c7e7bbab520ca1819ad9f06cbc7552,942b354f5a679a21d7efc0eb621abd2701ca5d87,TODO: initialize the counts after random assignement here
aaiccjidg,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,9aabbdb211c7e7bbab520ca1819ad9f06cbc7552,6d1dda8a1d0dd3da1b03702c5eb56323a2fcd807,TODO: cython variance implementation for CSR matrix to normalize
aaicdabec,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,6168d9c21c47b88a913c7283e8dc3717c030a27d,28f947d86c6ce216b9f4bcf2a721e91f38ce0a9b,TODO: implement a cython version to avoid the memory copy of the
aaicdabee,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,6168d9c21c47b88a913c7283e8dc3717c030a27d,942b354f5a679a21d7efc0eb621abd2701ca5d87,TODO: add support for CSR input
aaicdabef,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,6168d9c21c47b88a913c7283e8dc3717c030a27d,942b354f5a679a21d7efc0eb621abd2701ca5d87,TODO: explicit dtype handling
aaicdafih,scikit-learn/scikit-learn,sklearn/ensemble/gradient_boosting.py,39da308b4ac41881f005e32b47c3ebb47c238ed5,STILL_EXISTS,FIXME - most of the time is spend on the stmt below.
aaicdagab,scikit-learn/scikit-learn,sklearn/tree/tree.py,7ce51631f10bc07e5ad850ac70e616cb5da67081,71d524396c11274b1187871b15bebacf6af2781e,FIXME compute error for leaf
aaicdagad,scikit-learn/scikit-learn,sklearn/ensemble/gradient_boosting.py,8a57c4e21d5a7dda3e3d8573cefdf03712a61895,9d4998250b3807aee19aa7ef3e5cb0a5cca77549,TODO:
aaicdagcj,scikit-learn/scikit-learn,sklearn/ensemble/gradient_boosting.py,191fffb4ff8e5e5f9ad8663c94022eb62c5fa06b,STILL_EXISTS,FIXME free mem - maybe we should use index arrays instead of a mask
aaicdahcd,scikit-learn/scikit-learn,sklearn/ensemble/gradient_boosting.py,4ab62e7807fd0493ab74f137e2113da7c1cfa5f8,d63f39c669177747b665711ced742ef40efe602b,FIXME free mem - rename `sample_mask` since its actually an index arr
aaicdahcf,scikit-learn/scikit-learn,sklearn/ensemble/gradient_boosting.py,4ab62e7807fd0493ab74f137e2113da7c1cfa5f8,55c20cb84450c5f607cb4ce20f3c760c003354e7,y[y == 0] = -1  ## FIXME
aaicdaiab,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,c68a368b45d5435de3f4843f8b3bd0c6372a5ce0,28f947d86c6ce216b9f4bcf2a721e91f38ce0a9b,TODO: implement a cython version to avoid the memory copy of the
aaicdaiad,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,c68a368b45d5435de3f4843f8b3bd0c6372a5ce0,942b354f5a679a21d7efc0eb621abd2701ca5d87,TODO: add support for CSR input
aaicdaiae,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,c68a368b45d5435de3f4843f8b3bd0c6372a5ce0,942b354f5a679a21d7efc0eb621abd2701ca5d87,TODO: explicit dtype handling
aaicdaiag,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,c68a368b45d5435de3f4843f8b3bd0c6372a5ce0,942b354f5a679a21d7efc0eb621abd2701ca5d87,TODO: initialize the counts after random assignement here
aaicdajbi,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,744072e67adc0846c41c51da65d25016cb9d5b85,28f947d86c6ce216b9f4bcf2a721e91f38ce0a9b,TODO: implement a cython version to avoid the memory copy of the
aaicdajca,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,744072e67adc0846c41c51da65d25016cb9d5b85,95d497f5508f61d01958b42c070f037b0fa62ae8,TODO: add support for CSR input
aaicdajcb,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,744072e67adc0846c41c51da65d25016cb9d5b85,95d497f5508f61d01958b42c070f037b0fa62ae8,TODO: explicit dtype handling
aaicdajce,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,744072e67adc0846c41c51da65d25016cb9d5b85,95d497f5508f61d01958b42c070f037b0fa62ae8,TODO: compute the variance of the data both for sparse and
aaicdajcg,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,744072e67adc0846c41c51da65d25016cb9d5b85,95d497f5508f61d01958b42c070f037b0fa62ae8,TODO: initialize the counts after random assignement here
aaicdajdg,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,95d497f5508f61d01958b42c070f037b0fa62ae8,28f947d86c6ce216b9f4bcf2a721e91f38ce0a9b,TODO: implement a cython version to avoid the memory copy of the
aaicdajhd,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,76e6197fb159cea9cd7bee68e1a01614620fd62b,28f947d86c6ce216b9f4bcf2a721e91f38ce0a9b,TODO: implement a cython version to avoid the memory copy of the
aaicdajhf,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,76e6197fb159cea9cd7bee68e1a01614620fd62b,a786bdcb746dfe851fb0a98b6ec43e8aa1ec88c8,TODO: add support for CSR input
aaicdajhg,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,76e6197fb159cea9cd7bee68e1a01614620fd62b,a786bdcb746dfe851fb0a98b6ec43e8aa1ec88c8,TODO: explicit dtype handling
aaicdajhj,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,76e6197fb159cea9cd7bee68e1a01614620fd62b,STILL_EXISTS,TODO: compute the variance of the data both for sparse and
aaicdajib,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,76e6197fb159cea9cd7bee68e1a01614620fd62b,a786bdcb746dfe851fb0a98b6ec43e8aa1ec88c8,TODO: initialize the counts after random assignement here
aaicdajjh,scikit-learn/scikit-learn,sklearn/cluster/tests/test_k_means.py,7813be096edc938acd72de0daebc98b477572571,STILL_EXISTS,TODO: make MiniBatchKMeans more stables using either restarts or
aaicdbaid,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,d17fba0de7865c929cd15873d03bd9517fb3ff14,28f947d86c6ce216b9f4bcf2a721e91f38ce0a9b,TODO: implement a cython version to avoid the memory copy of the
aaicdbaif,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,d17fba0de7865c929cd15873d03bd9517fb3ff14,32fb601eb247fc65323628b118fd04c0e88a4a94,TODO: add support for CSR input
aaicdbaig,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,d17fba0de7865c929cd15873d03bd9517fb3ff14,32fb601eb247fc65323628b118fd04c0e88a4a94,TODO: explicit dtype handling
aaicdbaij,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,d17fba0de7865c929cd15873d03bd9517fb3ff14,32fb601eb247fc65323628b118fd04c0e88a4a94,dense variant in mostly numpy (not as memory efficient though)
aaicdbajc,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,d17fba0de7865c929cd15873d03bd9517fb3ff14,32fb601eb247fc65323628b118fd04c0e88a4a94,TODO: implement efficient variance for CSR input
aaicdbajd,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,d17fba0de7865c929cd15873d03bd9517fb3ff14,STILL_EXISTS,TODO: initialize the counts after random assignement here
aaicdbcde,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,161430cf66fe18305b0eef3f37fe795c37a4eb00,28f947d86c6ce216b9f4bcf2a721e91f38ce0a9b,TODO: implement a cython version to avoid the memory copy of the
aaicdbcdi,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,161430cf66fe18305b0eef3f37fe795c37a4eb00,433a3811f31b0314c81ee277cc3f9b6df9b10d0a,TODO: add support for CSR input
aaicdbcdj,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,161430cf66fe18305b0eef3f37fe795c37a4eb00,433a3811f31b0314c81ee277cc3f9b6df9b10d0a,TODO: explicit dtype handling
aaicdbced,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,161430cf66fe18305b0eef3f37fe795c37a4eb00,433a3811f31b0314c81ee277cc3f9b6df9b10d0a,dense variant in mostly numpy (not as memory efficient though)
aaicdbceg,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,161430cf66fe18305b0eef3f37fe795c37a4eb00,433a3811f31b0314c81ee277cc3f9b6df9b10d0a,TODO: implement efficient variance for CSR input
aaicdbcfb,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,161430cf66fe18305b0eef3f37fe795c37a4eb00,STILL_EXISTS,TODO: initialize the counts after random assignement here
aaicdbcie,scikit-learn/scikit-learn,sklearn/cluster/tests/test_k_means.py,161430cf66fe18305b0eef3f37fe795c37a4eb00,STILL_EXISTS,TODO: make MiniBatchKMeans more stables using either restarts or
aaicdbdab,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,433a3811f31b0314c81ee277cc3f9b6df9b10d0a,28f947d86c6ce216b9f4bcf2a721e91f38ce0a9b,TODO: implement a cython version to avoid the memory copy of the
aaicdbdbh,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,433a3811f31b0314c81ee277cc3f9b6df9b10d0a,STILL_EXISTS,TODO: initialize the counts after random assignement here
aaicdbddf,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,113d3945def6c9ce26daeea2461bb78e66864f49,28f947d86c6ce216b9f4bcf2a721e91f38ce0a9b,TODO: implement a cython version to avoid the memory copy of the
aaicdbddj,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,113d3945def6c9ce26daeea2461bb78e66864f49,033e657d3c080cb6d35836002a189a32f5bbba72,TODO: add support for CSR input
aaicdbdea,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,113d3945def6c9ce26daeea2461bb78e66864f49,033e657d3c080cb6d35836002a189a32f5bbba72,TODO: explicit dtype handling
aaicdbdee,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,113d3945def6c9ce26daeea2461bb78e66864f49,033e657d3c080cb6d35836002a189a32f5bbba72,dense variant in mostly numpy (not as memory efficient though)
aaicdbdeh,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,113d3945def6c9ce26daeea2461bb78e66864f49,033e657d3c080cb6d35836002a189a32f5bbba72,TODO: implement efficient variance for CSR input
aaicdbdfc,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,113d3945def6c9ce26daeea2461bb78e66864f49,STILL_EXISTS,TODO: initialize the counts after random assignement here
aaicdbdhh,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,033e657d3c080cb6d35836002a189a32f5bbba72,28f947d86c6ce216b9f4bcf2a721e91f38ce0a9b,TODO: implement a cython version to avoid the memory copy of the
aaicdbdjd,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,033e657d3c080cb6d35836002a189a32f5bbba72,STILL_EXISTS,TODO: initialize the counts after random assignement here
aaicdbecj,scikit-learn/scikit-learn,sklearn/cluster/tests/test_k_means.py,033e657d3c080cb6d35836002a189a32f5bbba72,STILL_EXISTS,TODO: make MiniBatchKMeans more stables using either restarts or
aaicdbeea,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,37df7968539ee617364e6ce49ef13bb8692814dd,28f947d86c6ce216b9f4bcf2a721e91f38ce0a9b,TODO: implement a cython version to avoid the memory copy of the
aaicdbeee,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,37df7968539ee617364e6ce49ef13bb8692814dd,9f6ca7fe7b22a33e257eb0cf07f0d9fe0a457fab,TODO: add support for CSR input
aaicdbeef,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,37df7968539ee617364e6ce49ef13bb8692814dd,9f6ca7fe7b22a33e257eb0cf07f0d9fe0a457fab,TODO: explicit dtype handling
aaicdbeej,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,37df7968539ee617364e6ce49ef13bb8692814dd,9f6ca7fe7b22a33e257eb0cf07f0d9fe0a457fab,dense variant in mostly numpy (not as memory efficient though)
aaicdbefc,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,37df7968539ee617364e6ce49ef13bb8692814dd,9f6ca7fe7b22a33e257eb0cf07f0d9fe0a457fab,TODO: implement efficient variance for CSR input
aaicdbfad,scikit-learn/scikit-learn,sklearn/cluster/tests/test_k_means.py,8858a8010913671abde588b3e2e43a2890866b25,STILL_EXISTS,TODO: make MiniBatchKMeans more stables using either restarts or
aaicdbfbg,scikit-learn/scikit-learn,sklearn/cluster/tests/test_k_means.py,9fbe55913978461cde9fa457534d3b4db42b4895,STILL_EXISTS,TODO: make MiniBatchKMeans more stables using either restarts or
aaicdbhbb,scikit-learn/scikit-learn,sklearn/tree/tree.py,b6f4de56fbdc841aa406e8bc29bb99c4195ba674,STILL_EXISTS,double old capacity  FIXME this will break if too large
aaicdbhgf,scikit-learn/scikit-learn,sklearn/covariance/g_lasso_.py,f2697cb0083e7fd8e01186faf6e9515048f411e2,65fa358920b126c0c0819fadde84dd0478f5edff,XXX: need to be able to give an initial guess
aaicdbhhc,scikit-learn/scikit-learn,sklearn/covariance/g_lasso_.py,f2697cb0083e7fd8e01186faf6e9515048f411e2,b9e6d54cfc6c2b25162f8356cdd490151f5be7dd,XXX: verbosity
aaicdbiag,scikit-learn/scikit-learn,sklearn/covariance/g_lasso_.py,71d76ebf58f06e6446c83da8f1c96f0c3a59f57c,STILL_EXISTS,XXX: need to store the scores in a name consistent with the
aaicdbifc,scikit-learn/scikit-learn,examples/applications/plot_stock_market.py,da97008a464ee866e93ce8659313858505666647,STILL_EXISTS,is more efficient for structure recovery
aaicdbijb,scikit-learn/scikit-learn,sklearn/svm/tests/test_svm.py,6def64c3e68efeddb3d0b9bd22194f505ce4b205,5504034f4a38c7d496a65321dc8603b763d89c11,svm.NuSVC(tol=1e-6; kernel='linear');  # XXX : why no C ?
aaicdcfdc,scikit-learn/scikit-learn,examples/ensemble/plot_gradient_boosting_regression.py,0d54dfa6896de520ef5cdb26cd9ffa63ac7f0e5f,STILL_EXISTS,Author: Peter Prettenhofer <peter.prettenhofer@gmail.com>
aaicddbif,scikit-learn/scikit-learn,examples/tutorial/plot_cv_diabetes.py,75672f1e05483c6e94810c838d7603a8a48d7a71,STILL_EXISTS,Bonus: how much can you trust the selection of alpha?
aaicdddic,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,50af0c326429983aed156b6bce5f67305e0346a3,28f947d86c6ce216b9f4bcf2a721e91f38ce0a9b,TODO: implement a cython version to avoid the memory copy of the
aaicdddig,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,50af0c326429983aed156b6bce5f67305e0346a3,28f947d86c6ce216b9f4bcf2a721e91f38ce0a9b,TODO: add support for CSR input
aaicdddih,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,50af0c326429983aed156b6bce5f67305e0346a3,28f947d86c6ce216b9f4bcf2a721e91f38ce0a9b,TODO: explicit dtype handling
aaicdddjb,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,50af0c326429983aed156b6bce5f67305e0346a3,28f947d86c6ce216b9f4bcf2a721e91f38ce0a9b,dense variant in mostly numpy (not as memory efficient though)
aaicddeaf,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,50af0c326429983aed156b6bce5f67305e0346a3,STILL_EXISTS,TODO: implement efficient variance for CSR input
aaicddfje,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,38d8444345596c4fc8d870c0bebc75cd494335ea,STILL_EXISTS,TODO: once the `k_means` function works with sparse input we
aaicddiai,scikit-learn/scikit-learn,sklearn/datasets/samples_generator.py,1648c2163b556f8a3278f893b24edadfa1383e19,738891562cf65b26639f5d2902b92f8fd02fe955,FIXME: use a distribution
aaicddjbb,scikit-learn/scikit-learn,sklearn/tests/test_multiclass.py,117be7e25b8b9b7fbc99dd35c14dc49a3cee2d29,d33634d0ce7a88829c69418547398bd63ef64524,FIXME: - should use sets
aaicddjbc,scikit-learn/scikit-learn,sklearn/tests/test_multiclass.py,117be7e25b8b9b7fbc99dd35c14dc49a3cee2d29,d33634d0ce7a88829c69418547398bd63ef64524,- should move to metrics module
aaicdehec,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,86f909d0d335b3285e379ace90d8a71c217a287f,24af95a211086cf032d263e0f2ed57b63e06c2f4,TODO: implement a cython version to avoid the memory copy of the
aaicdeheg,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,86f909d0d335b3285e379ace90d8a71c217a287f,dfb31f54d9a43db115052773bb98c09daba8e6dd,TODO: add support for CSR input
aaicdeheh,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,86f909d0d335b3285e379ace90d8a71c217a287f,dfb31f54d9a43db115052773bb98c09daba8e6dd,TODO: explicit dtype handling
aaicdehfa,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,86f909d0d335b3285e379ace90d8a71c217a287f,STILL_EXISTS,dense variant in mostly numpy (not as memory efficient though)
aaicdehgj,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,86f909d0d335b3285e379ace90d8a71c217a287f,STILL_EXISTS,TODO: once the `k_means` function works with sparse input we
aaicdfdae,scikit-learn/scikit-learn,sklearn/datasets/samples_generator.py,ad53ecd6577cff3b71fce7439c95ef91fc7e7308,df4351d4bec2507a4c9d61160f5cfcbe5c5d4972,FIXME: use a distribution
aaicdfibh,scikit-learn/scikit-learn,doc/sphinxext/gen_rst.py,cad0ee07f8ee182303de1eca5d9ad63e3bf38683,STILL_EXISTS,Sphinx hack: sphinx copies generated images to the build directory
aaicdficg,scikit-learn/scikit-learn,doc/sphinxext/gen_rst.py,cad0ee07f8ee182303de1eca5d9ad63e3bf38683,STILL_EXISTS,The following is a hack that prevents this behavior by clearing the
aaicdficj,scikit-learn/scikit-learn,doc/sphinxext/gen_rst.py,cad0ee07f8ee182303de1eca5d9ad63e3bf38683,STILL_EXISTS,it should probably not cause a crash).  Tested successfully
aaicdfief,scikit-learn/scikit-learn,sklearn/tests/test_multiclass.py,f41352e960fdb8567380594af92da84c11d856c3,STILL_EXISTS,Toy dataset where features correspond directly to labels.
aaicdfihd,scikit-learn/scikit-learn,sklearn/mixture/gmm.py,38f78a0b0fd338de037ced79a70ee0f69ee0cc43,2826ac7b72ead019e1f30bd5f7a2d0465b9a2100,FIXME: why return the weights ?
aaicdfijg,scikit-learn/scikit-learn,sklearn/mixture/gmm.py,9e2115697644f4ee4822cad1e37f2353d9d5dff0,STILL_EXISTS,if the results is better; keep it
aaicdfjab,scikit-learn/scikit-learn,sklearn/pipeline.py,5be18b4eff779c1d10d17ea51b7694cb2a45eb54,0597c23e859b003512cf6ddca4452f2657fa7ee4,is needed in the Attributes section so as not to upset sphinx.
aaicdfjbj,scikit-learn/scikit-learn,sklearn/mixture/gmm.py,e087daf585639ea46712ae01241f749ec3e6ed90,5c6ea0c1e228dabdee1d7e380c00e0a774251471,FIXME this lacks a proper docstring
aaicdgafb,scikit-learn/scikit-learn,examples/decomposition/plot_sparse_coding.py,0cf16dc7bedbcf35274c246f3499a9267c8774ce,STILL_EXISTS,\"\"\" || =========================================== || Sparse coding with a precomputed dictionary || =========================================== ||  || Transform a signal as a sparse combination of Ricker wavelets. This example || visually compares different sparse coding methods using the || :class:`sklearn.decomposition.SparseCoder` estimator. The Ricker (also known || as mexican hat or the second derivative of a gaussian) is not a particularily || good kernel to represent piecewise constant signals like this one. It can || therefore be seen how much adding different widths of atoms matters and it || therefore motivates learning the dictionary to best fit your type of signals. ||  || The richer dictionary on the right is not larger in size; heavier subsampling || is performed in order to stay on the same order of magnitude. || \"\"\"
aaicdgcdb,scikit-learn/scikit-learn,sklearn/mixture/gmm.py,5c6ea0c1e228dabdee1d7e380c00e0a774251471,STILL_EXISTS,if the results is better; keep it
aaicdgcfh,scikit-learn/scikit-learn,sklearn/mixture/gmm.py,0ebf87bd2ff8e930a3206b842118c2747f3cc9d0,74b95bb865dd9b2aa9567cfad13bd379399ce2dc,FIXME this lacks a proper docstring
aaicdgche,scikit-learn/scikit-learn,sklearn/mixture/gmm.py,0ebf87bd2ff8e930a3206b842118c2747f3cc9d0,STILL_EXISTS,if the results is better; keep it
aaicdgdcd,scikit-learn/scikit-learn,sklearn/mixture/gmm.py,74b95bb865dd9b2aa9567cfad13bd379399ce2dc,STILL_EXISTS,if the results is better; keep it
aaicdggab,scikit-learn/scikit-learn,sklearn/mixture/gmm.py,ab23e22fd0c508027c3f85ec3e3186f2d3da5d89,0c926b57ac8635ac881adf587044031470a2aac3,FIXME this lacks a proper docstring
aaicdggjc,scikit-learn/scikit-learn,sklearn/mixture/gmm.py,0c926b57ac8635ac881adf587044031470a2aac3,STILL_EXISTS,if the results is better; keep it
aaicdhaba,scikit-learn/scikit-learn,sklearn/mixture/gmm.py,84a4d39c5f587adb2b057a36e3805bd050a56449,ad3de7f77da5e727a0370dc989af8b6836f07f94,FIXME this lacks a proper docstring
aaicdhahc,scikit-learn/scikit-learn,sklearn/mixture/gmm.py,ad3de7f77da5e727a0370dc989af8b6836f07f94,STILL_EXISTS,if the results is better; keep it
aaicdhbad,scikit-learn/scikit-learn,sklearn/mixture/gmm.py,4b60c026cee336dda5726e25750fb6a36a70e125,e2861258be06f85bca23b99ac6ae7e40943148bd,FIXME this lacks a proper docstring
aaicdhbcb,scikit-learn/scikit-learn,sklearn/mixture/gmm.py,4b60c026cee336dda5726e25750fb6a36a70e125,STILL_EXISTS,if the results is better; keep it
aaicdhejf,scikit-learn/scikit-learn,sklearn/mixture/gmm.py,e2861258be06f85bca23b99ac6ae7e40943148bd,STILL_EXISTS,if the results is better; keep it
aaicdhijj,scikit-learn/scikit-learn,sklearn/mixture/gmm.py,4a70636701d71549fc2f5474ab6d521509912f6c,06c48ff65dda3b8ec26222d33eed154d37b17430,FIXME this lacks a proper docstring
aaicdhjbh,scikit-learn/scikit-learn,sklearn/mixture/gmm.py,4a70636701d71549fc2f5474ab6d521509912f6c,STILL_EXISTS,if the results is better; keep it
aaicdiaaa,scikit-learn/scikit-learn,sklearn/externals/joblib/numpy_pickle.py,54fcfc2da3ca7a8a4e5f5fe6de60147eaa3ddf2c,fdfb53177d5d16bc5db0aceab76341760bd20eb0,Efficient compressed storage:
aaicdiaaf,scikit-learn/scikit-learn,sklearn/externals/joblib/numpy_pickle.py,54fcfc2da3ca7a8a4e5f5fe6de60147eaa3ddf2c,fdfb53177d5d16bc5db0aceab76341760bd20eb0,disk; it is more efficient to use standard pickling
aaicdiabd,scikit-learn/scikit-learn,sklearn/externals/joblib/parallel.py,54fcfc2da3ca7a8a4e5f5fe6de60147eaa3ddf2c,STILL_EXISTS,XXX: Not using the logger framework: need to
aaicdiabe,scikit-learn/scikit-learn,sklearn/externals/joblib/parallel.py,54fcfc2da3ca7a8a4e5f5fe6de60147eaa3ddf2c,STILL_EXISTS,learn to use logger better.
aaicdiaje,scikit-learn/scikit-learn,sklearn/mixture/dpgmm.py,06c48ff65dda3b8ec26222d33eed154d37b17430,STILL_EXISTS,fixme: should not require self.means_ to be defined
aaicdiajf,scikit-learn/scikit-learn,sklearn/mixture/dpgmm.py,06c48ff65dda3b8ec26222d33eed154d37b17430,STILL_EXISTS,could be more memory efficient ?
aaicdiajg,scikit-learn/scikit-learn,sklearn/mixture/gmm.py,06c48ff65dda3b8ec26222d33eed154d37b17430,STILL_EXISTS,if the results is better; keep it
aaicdibbj,scikit-learn/scikit-learn,sklearn/linear_model/coordinate_descent.py,f97734698902f8240d5b10bb20f1b1cd09664ce0,fdfb53177d5d16bc5db0aceab76341760bd20eb0,FIXME: it could be updated from precompute and X_mean
aaicdibeh,scikit-learn/scikit-learn,sklearn/mixture/dpgmm.py,6a9a449a0b2db098879c13b6a736fbcbba40f790,8db0338bfda93ef582ae99fcf11c6573597ea952,fixme
aaicdiiaj,scikit-learn/scikit-learn,sklearn/externals/joblib/parallel.py,fdfb53177d5d16bc5db0aceab76341760bd20eb0,STILL_EXISTS,XXX: Not using the logger framework: need to
aaicdiiba,scikit-learn/scikit-learn,sklearn/externals/joblib/parallel.py,fdfb53177d5d16bc5db0aceab76341760bd20eb0,STILL_EXISTS,learn to use logger better.
aaicdiige,scikit-learn/scikit-learn,sklearn/mixture/gmm.py,fdfb53177d5d16bc5db0aceab76341760bd20eb0,2729f063292691d6d1871fc05b3beb199aeec9b8,FIXME this lacks a proper docstring
aaicdijgf,scikit-learn/scikit-learn,sklearn/externals/joblib/numpy_pickle.py,af0feb953cad00f78fc24d9e556d6d2b287b90cc,STILL_EXISTS,Efficient compressed storage:
aaicdijha,scikit-learn/scikit-learn,sklearn/externals/joblib/numpy_pickle.py,af0feb953cad00f78fc24d9e556d6d2b287b90cc,STILL_EXISTS,disk; it is more efficient to use standard pickling
aaicdjaba,scikit-learn/scikit-learn,sklearn/linear_model/coordinate_descent.py,af0feb953cad00f78fc24d9e556d6d2b287b90cc,48687a77a438ce7caaa4645c3e1f1a6d6ce31229,FIXME: it could be updated from precompute and X_mean
aaicdjacc,scikit-learn/scikit-learn,sklearn/mixture/gmm.py,2729f063292691d6d1871fc05b3beb199aeec9b8,STILL_EXISTS,if the results is better; keep it
aaicdjafc,scikit-learn/scikit-learn,sklearn/tests/test_multiclass.py,f748b230143a9c99955908106baf9e5763ff5c2f,STILL_EXISTS,lambda is needed because we don't want coef_ to be evaluated right away
aaicdjbbd,scikit-learn/scikit-learn,sklearn/linear_model/stochastic_gradient.py,0d48f49bbfd2cf51c62d8b9c6ed0fd589393f35a,f95e5c9b74394ece252137dc7dbd8828d4689fa2,Authors: Peter Prettenhofer <peter.prettenhofer@gmail.com> (main author)
aaicdjbea,scikit-learn/scikit-learn,sklearn/linear_model/stochastic_gradient.py,f95e5c9b74394ece252137dc7dbd8828d4689fa2,3228a534c8b2e74968a3d203b9b8c976521cba38,Author: Peter Prettenhofer <peter.prettenhofer@gmail.com>
aaicdjbhf,scikit-learn/scikit-learn,sklearn/svm/base.py,658897497399147a78fad5f7001fc62dd1e487ed,263d6c02f781fb533c26bbc5c8e4e488e73437b0,XXX we could do preallocation of coef but
aaicdjbii,scikit-learn/scikit-learn,sklearn/linear_model/stochastic_gradient.py,3228a534c8b2e74968a3d203b9b8c976521cba38,STILL_EXISTS,Authors: Peter Prettenhofer <peter.prettenhofer@gmail.com> (main author)
aaicdjceh,scikit-learn/scikit-learn,sklearn/svm/base.py,263d6c02f781fb533c26bbc5c8e4e488e73437b0,STILL_EXISTS,libsvm has the convention of returning negative values for
aaicdjdbj,scikit-learn/scikit-learn,sklearn/naive_bayes.py,de16cb49a2beac8504ffefdca8d8f4c9cedec94e,STILL_EXISTS,XXX The following is a stopgap measure; we need to set the dimensions
aaicdjdic,scikit-learn/scikit-learn,sklearn/preprocessing/preprocessing.py,ebb42e78e6a16933541459bf6246e835b24bc5ec,STILL_EXISTS,FIXME: if enough values became 0; it may be worth changing
aaicdjeae,scikit-learn/scikit-learn,sklearn/utils/fixes.py,213f0a21c25a92661d143ebbc5882d3bcfa6a8fb,c68f4f540dba0eea7f713a36363aacba7111e74e,pure python workaround:
aaicdjeaj,scikit-learn/scikit-learn,sklearn/preprocessing.py,0d54938e58c26924cb1ced1aa154791450c9d080,4ebd382f8c06a141b6076384beb3866cf271b315,FIXME: if enough values became 0; it may be worth changing
aaicdjedb,scikit-learn/scikit-learn,sklearn/metrics/pairwise.py,3b8f54e58667be80af6c099bfda40c7fe6579712,STILL_EXISTS,FIXME: can use n_jobs here too
aaicdjhhf,scikit-learn/scikit-learn,sklearn/mixture/gmm.py,c68f4f540dba0eea7f713a36363aacba7111e74e,3242c6a04168b99c91ad67dd1e6d71ff5385ec7f,FIXME this lacks a proper docstring
aaicdjiie,scikit-learn/scikit-learn,sklearn/svm/base.py,e786ac53cc32122c15199fe015d6007ea6836ec0,60cdaa777aaa94cb310f724e4f2ae636410b8be8,XXX we could do preallocation of coef but
aaicdjjai,scikit-learn/scikit-learn,sklearn/svm/base.py,60cdaa777aaa94cb310f724e4f2ae636410b8be8,STILL_EXISTS,libsvm has the convention of returning negative values for
aaicdjjdc,scikit-learn/scikit-learn,sklearn/svm/base.py,a6fdecd1f032bb01870fdff7dc5d48ae09fe9289,STILL_EXISTS,XXX we could do preallocation of coef but
aaicdjjeb,scikit-learn/scikit-learn,sklearn/svm/base.py,a6fdecd1f032bb01870fdff7dc5d48ae09fe9289,STILL_EXISTS,libsvm has the convention of returning negative values for
aaicdjjha,scikit-learn/scikit-learn,sklearn/linear_model/randomized_lasso.py,b0c119ca52c1ff0ef2ad8726f34d27511db0c1ae,STILL_EXISTS,XXX: must implement this in a more generic way so that it can be
aaicdjjhc,scikit-learn/scikit-learn,sklearn/linear_model/randomized_lasso.py,b0c119ca52c1ff0ef2ad8726f34d27511db0c1ae,3746eb68904e7fabf9456ddd4659efdd59168d18,XXX: must transform and use OLS to fit
aaicdjjhd,scikit-learn/scikit-learn,sklearn/linear_model/randomized_lasso.py,b0c119ca52c1ff0ef2ad8726f34d27511db0c1ae,3746eb68904e7fabf9456ddd4659efdd59168d18,XXX: challenge: it is going to interfere with the
aaicdjjhg,scikit-learn/scikit-learn,sklearn/linear_model/randomized_lasso.py,b0c119ca52c1ff0ef2ad8726f34d27511db0c1ae,STILL_EXISTS,XXX: the two function below are copy\/pasted from feature_selection;
aaicdjjib,scikit-learn/scikit-learn,sklearn/linear_model/base.py,e4919e61bd7aeac4573f440e9ccc0ff25d815a56,STILL_EXISTS,## TODO: bayesian_ridge_regression and bayesian_regression_ard
aaicdjjie,scikit-learn/scikit-learn,sklearn/linear_model/randomized_lasso.py,e4919e61bd7aeac4573f440e9ccc0ff25d815a56,STILL_EXISTS,XXX: should we refit the intercept?
aaiceaaca,scikit-learn/scikit-learn,sklearn/linear_model/randomized_l1.py,b1ab2ce133a9f251638ef1d3b03e7c9592667757,6fccfef007b971bccdb6f5bff83b47b1db47145e,XXX
aaiceaacb,scikit-learn/scikit-learn,sklearn/linear_model/randomized_l1.py,b1ab2ce133a9f251638ef1d3b03e7c9592667757,STILL_EXISTS,XXX : would be great to do it with a warm_start ...
aaiceaegf,scikit-learn/scikit-learn,sklearn/utils/fixes.py,b36beb4e7b3bd24d3103d9121d74c3b814fb44dd,fb9b7ce79ebb95974bbb9b171322e5a415bc380e,pure python workaround:
aaiceaehb,scikit-learn/scikit-learn,sklearn/mixture/gmm.py,3242c6a04168b99c91ad67dd1e6d71ff5385ec7f,STILL_EXISTS,if the results is better; keep it
aaicebbib,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,b26f03f46470a15936a52092d994ddfe8418c289,STILL_EXISTS,For a single thread; less memory is needed if we just store one set
aaicebhff,scikit-learn/scikit-learn,examples/tutorial/plot_iris_dataset_3class.py,e23908dfe954c1935c2f139b638a617d81e6c925,STILL_EXISTS,\"\"\" || ========================================================= || The Iris Dataset || ========================================================= || This data sets consists of 3 different types of irises' || (Setosa; Versicolour; and Virginica) petal and sepal || length; stored in a 150x4 numpy.ndarray ||  || The rows being the samples and the columns being: || Sepal Length; Sepal Width; Petal Length\tand Petal Width. ||  || The below plot uses the first two features. || See  || `here <http:\/\/en.wikipedia.org\/wiki\/Iris_flower_data_set>`_ || for more information on this dataset. || \"\"\"
aaicebifb,scikit-learn/scikit-learn,sklearn/linear_model/coordinate_descent.py,580afa808f589f6930f929f2d6838ab51f7a79cf,STILL_EXISTS,XXX: is this really useful: we are fitting models that we won't
aaicebifd,scikit-learn/scikit-learn,sklearn/linear_model/coordinate_descent.py,580afa808f589f6930f929f2d6838ab51f7a79cf,e9eb1bbf77c0639f8d3f502aa833af0472a2fc1b,XXX: need to store the whole path later
aaicebjca,scikit-learn/scikit-learn,skeletons/exercise_02_sentiment.py,ae7ab21dfddd51b648369df26392c0eb59ffe111,STILL_EXISTS,\"\"\"Build a sentiment analysis \/ polarity model ||  || Sentiment analysis can be casted as a binary text classification problem; || that is fitting a linear classifier on features extracted from the text || of the user messages so as to guess wether the opinion of the author is || positive or negative. ||  || In this examples we will use a movie review dataset. ||  || \"\"\"
aaicebjcd,scikit-learn/scikit-learn,skeletons/exercise_02_sentiment.py,ae7ab21dfddd51b648369df26392c0eb59ffe111,STILL_EXISTS,Refit the best parameter set on the complete training set
aaicebjgj,scikit-learn/scikit-learn,solutions/exercise_02_sentiment.py,ae7ab21dfddd51b648369df26392c0eb59ffe111,STILL_EXISTS,\"\"\"Build a sentiment analysis \/ polarity model ||  || Sentiment analysis can be casted as a binary text classification problem; || that is fitting a linear classifier on features extracted from the text || of the user messages so as to guess wether the opinion of the author is || positive or negative. ||  || In this examples we will use a movie review dataset. ||  || \"\"\"
aaicecaee,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_ridge.py,1021a8275224e3236b45438cf45bb65a77322737,f23e92ed4cdc5a952331e597023bd2c9922e6f9d,generalized cross-validation (efficient leave-one-out;
aaicecaeg,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_ridge.py,1021a8275224e3236b45438cf45bb65a77322737,f23e92ed4cdc5a952331e597023bd2c9922e6f9d,check that efficient and SVD efficient LOO give same results
aaicecaej,scikit-learn/scikit-learn,sklearn/linear_model/ridge.py,f5ddde0049e70972789340fd3e7dae6f8236e4a3,6bf9501a9a61af63df7579a3de5d38b1dddd295e,FIXME non-uniform sample weights not yet supported
aaicecbdd,scikit-learn/scikit-learn,sklearn/cluster/spectral.py,b55d62c6a6f919bab3a36a43f41e27aec1b94e5a,7f5b64231c574bf7ccfd2fbb16dd23975d09fceb,I - L has eigenvalues between -1 and 1.  ARPACK is most efficient
aaiceccgh,scikit-learn/scikit-learn,sklearn/linear_model/ridge.py,9061131d7f60285d8cd8cbe2e5c50c5133730eb6,STILL_EXISTS,FIXME non-uniform sample weights not yet supported
aaiceccgj,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_ridge.py,9061131d7f60285d8cd8cbe2e5c50c5133730eb6,f23e92ed4cdc5a952331e597023bd2c9922e6f9d,generalized cross-validation (efficient leave-one-out;
aaicecchb,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_ridge.py,9061131d7f60285d8cd8cbe2e5c50c5133730eb6,f23e92ed4cdc5a952331e597023bd2c9922e6f9d,check that efficient and SVD efficient LOO give same results
aaicecedc,scikit-learn/scikit-learn,sklearn/feature_extraction/text.py,1f542c29d2fd29fa5a33ba2150ccc9538c2ff7fe,53f76d1a24ef42eb8c620fc1116c53db11dd07d9,unfortunately python functools package does not have an efficient
aaiceceji,scikit-learn/scikit-learn,sklearn/feature_extraction/tests/test_text.py,864e4fb155e30c4a4512de92f79ec1c6ba734425,STILL_EXISTS,XXX: broken
aaicecfcf,scikit-learn/scikit-learn,examples/ensemble/plot_gradient_boosting_regularization.py,24a1b3aa025c546d80f5414cea6e0917bec56d33,STILL_EXISTS,Author: Peter Prettenhofer <peter.prettenhofer@gmail.com>
aaicecgha,scikit-learn/scikit-learn,sklearn/feature_extraction/text.py,c68623210a6196687dab1d507d9b2cea6baa6458,53f76d1a24ef42eb8c620fc1116c53db11dd07d9,unfortunately python functools package does not have an efficient
aaicechgg,scikit-learn/scikit-learn,sklearn/feature_extraction/text.py,0d498c6fefb78fa221201126bc87dcb682082696,53f76d1a24ef42eb8c620fc1116c53db11dd07d9,unfortunately python functools package does not have an efficient
aaicedbac,scikit-learn/scikit-learn,sklearn/feature_extraction/text.py,c0541fa03bc9af9d3616cee9920b161adcd01008,53f76d1a24ef42eb8c620fc1116c53db11dd07d9,unfortunately python functools package does not have an efficient
aaicedbjb,scikit-learn/scikit-learn,sklearn/feature_extraction/text.py,f59495ffce047b0a426156b22d8faca1955a48ec,53f76d1a24ef42eb8c620fc1116c53db11dd07d9,unfortunately python functools package does not have an efficient
aaicedcbh,scikit-learn/scikit-learn,sklearn/feature_extraction/text.py,5d0ea6cf6de330b3ab4f9eb0779c4bf3808fd53d,53f76d1a24ef42eb8c620fc1116c53db11dd07d9,unfortunately python functools package does not have an efficient
aaicedejf,scikit-learn/scikit-learn,sklearn/ensemble/gradient_boosting.py,775588e9ab7d639c2e4cea6775b019aa11b3048f,STILL_EXISTS,# FIXME store classes in ``tree``?
aaicedgad,scikit-learn/scikit-learn,sklearn/ensemble/gradient_boosting.py,03f088758c59acbdc39eba63deac8a55633ff5a8,STILL_EXISTS,# FIXME store classes in ``tree``?
aaicedgbe,scikit-learn/scikit-learn,benchmarks/bench_gbm.py,e6a5713e02748094ed4cae21c89e00a5380a438d,STILL_EXISTS,# FIXME this will kill my laptop due to memory consumption
aaicedgcd,scikit-learn/scikit-learn,sklearn/ensemble/gradient_boosting.py,8920d482ca49798f26863290975b462d82f262e2,251fa607fdd110ede10088ed2b7a1f245ab3281c,# FIXME
aaicedgch,scikit-learn/scikit-learn,sklearn/ensemble/gradient_boosting.py,251fa607fdd110ede10088ed2b7a1f245ab3281c,STILL_EXISTS,FIXME ``tree.predict`` is faster than taking `tree.terminal_region``
aaicedhgg,scikit-learn/scikit-learn,sklearn/linear_model/nearest_centroid.py,bc5deea740c1471851f9f6d59d2a2804a1508a2d,STILL_EXISTS,TODO: Apply shrink here
aaicedjjg,scikit-learn/scikit-learn,sklearn/ensemble/gradient_boosting.py,9ef2a0f6ee49fd59a0b003865246804b313abfe9,STILL_EXISTS,TODO replace with ``np.choice`` if possible.
aaiceeffb,scikit-learn/scikit-learn,examples/neighbors/plot_nearest_centroid.py,fd30ddc561c49c62c7123b06d36540055276055b,STILL_EXISTS,avoid this ugly slicing by using a two-dim dataset
aaiceeggh,scikit-learn/scikit-learn,sklearn/tests/test_hmm.py,81c222f26522b1b2af5014e07e76b0b6cebba50f,bbc272c1b52822a9c3ba6778756e88380d66493b,XXX: This test is bugged and creates weird errors -- skipped
aaiceegha,scikit-learn/scikit-learn,sklearn/tests/test_hmm.py,81c222f26522b1b2af5014e07e76b0b6cebba50f,STILL_EXISTS,XXX: Why such a large tolerance?
aaiceeghc,scikit-learn/scikit-learn,sklearn/tests/test_hmm.py,81c222f26522b1b2af5014e07e76b0b6cebba50f,STILL_EXISTS,XXX: this test appears to check that training log likelihood should
aaiceeghi,scikit-learn/scikit-learn,sklearn/datasets/svmlight_format.py,6d6c9a8937eac9461e758bcae97e404c0d570421,7b4842d1dbc2b964faa62a4d7cb3b0739280a10f,TODO this might not properly close the files on other Python
aaiceegjd,scikit-learn/scikit-learn,sklearn/grid_search.py,31546a1d0ca9dfd3f5972433987170969c3f5784,77e69edcec4d4b8195dd53b0e83d56610a4eec4e,TODO around 0.13: remove this property; make it an attribute
aaiceegjj,scikit-learn/scikit-learn,sklearn/svm/tests/test_sparse.py,89d73c7b1f790b5b23198d6a5f29d657a19fcd20,STILL_EXISTS,b.decision_function(X_sp)  # XXX : should be supported
aaiceehbe,scikit-learn/scikit-learn,sklearn/manifold/tests/test_isomap.py,e7c4acc5a41e50b6d58f59fcd6dd64b0eedb0dbc,STILL_EXISTS,TODO check that it actually does something useful
aaiceehbg,scikit-learn/scikit-learn,sklearn/manifold/tests/test_locally_linear.py,e7c4acc5a41e50b6d58f59fcd6dd64b0eedb0dbc,STILL_EXISTS,TODO check that it actually does something useful
aaiceehbh,scikit-learn/scikit-learn,sklearn/utils/arpack.py,072cfbbee678c530deeace5c7a63ae693dc4142e,STILL_EXISTS,check if backport is actually needed:
aaiceehcd,scikit-learn/scikit-learn,examples/manifold/plot_compare_methods.py,dcc1c7431c270f81e37e3e43d4e368decf0aa17f,STILL_EXISTS,Next line to silence pyflakes. This import is needed.
aaiceehch,scikit-learn/scikit-learn,sklearn/feature_selection/univariate_selection.py,258f19383c8dfb0790e996065c4472028d2a8b99,185beb3e78d1460bb170ed9ab3ab088c69d98ac6,XXX This should be refactored; we're getting an array of indices
aaiceeiif,scikit-learn/scikit-learn,sklearn/manifold/nmds.py,25bdb539e364eeecfcd825652c0421370bf761ce,STILL_EXISTS,FIXME ties ?
aaiceeiig,scikit-learn/scikit-learn,sklearn/manifold/nmds.py,25bdb539e364eeecfcd825652c0421370bf761ce,STILL_EXISTS,FIXME: deal with floating points errors. Else; it stays stuck in the
aaiceeiij,scikit-learn/scikit-learn,sklearn/manifold/nmds.py,4dbe1a05c7aa12e0c8d43cc10831547a030a1f12,STILL_EXISTS,XXX FIXME: should we use proximities or similarities ??
aaiceeija,scikit-learn/scikit-learn,sklearn/manifold/nmds.py,4dbe1a05c7aa12e0c8d43cc10831547a030a1f12,STILL_EXISTS,FIXME this isn't working
aaiceejaj,scikit-learn/scikit-learn,sklearn/manifold/mds.py,6366a8e0bad9d970311ae6e8fa13bfdf08a952e2,fe5ac323b2e391152d8ced5a11f36e55ddbd1b41,TODO
aaiceejea,scikit-learn/scikit-learn,sklearn/datasets/svmlight_format.py,4eedded8db4a48b5a1160b0be7a2ae9f69bbb10d,e0e738760625129be71839fac2dad9325fb90225,XXX remove closing when Python 2.7+\/3.1+ required
aaicefafb,scikit-learn/scikit-learn,sklearn/neighbors/classification.py,b15401f37747d8b143ed49b8ba7e9ab6b801c8cf,STILL_EXISTS,This may not be needed provided classes labels are guaranteed to be
aaicefbcb,scikit-learn/scikit-learn,sklearn/svm/base.py,802ed82737783b6dec5b2b3dedd67a0c3e30ffc8,STILL_EXISTS,TODO: add keyword copy to copy on demand
aaicefbgi,scikit-learn/scikit-learn,sklearn/tests/test_common.py,4264acdbd3ce14edd3811a5211fe34c2e92aba52,718f6ad19f90794e62f56d980c6e730a854e56d7,TODO also test these!
aaicefbgj,scikit-learn/scikit-learn,sklearn/tests/test_common.py,4264acdbd3ce14edd3811a5211fe34c2e92aba52,STILL_EXISTS,TODO: libsvm decision functions; input validation
aaicefbha,scikit-learn/scikit-learn,sklearn/tests/test_common.py,6aa167c5c83948539773295cbc1749e65a0e9dcf,718f6ad19f90794e62f56d980c6e730a854e56d7,TODO: test with intercept
aaicefbhb,scikit-learn/scikit-learn,sklearn/tests/test_common.py,6aa167c5c83948539773295cbc1749e65a0e9dcf,718f6ad19f90794e62f56d980c6e730a854e56d7,TODO: test with multiple responses
aaicefbhc,scikit-learn/scikit-learn,sklearn/tests/test_common.py,6aa167c5c83948539773295cbc1749e65a0e9dcf,STILL_EXISTS,TODO this is not a regressor!
aaicefbhi,scikit-learn/scikit-learn,sklearn/tests/test_common.py,a93e0afc160639149e76a154ebb9bc0282c92d54,STILL_EXISTS,TODO: make work with next line :)
aaicefbjc,scikit-learn/scikit-learn,sklearn/tests/test_common.py,bd94b49a2d75efed045a3ca2c68c0474e0350caf,718f6ad19f90794e62f56d980c6e730a854e56d7,TODO also test these!
aaicefegh,scikit-learn/scikit-learn,sklearn/grid_search.py,6e9c2716973f0a42c0abadeb3cd8d6ce02ef0970,8b3e4d06c05ac82130176161404f0434b74fe2c7,they should be indices so conversion is done if needed.
aaicegbfi,scikit-learn/scikit-learn,sklearn/tests/test_multiclass.py,a4845351afe906e74c8f316f12655b7fdc36deb4,STILL_EXISTS,Toy dataset where features correspond directly to labels.
aaicegbhh,scikit-learn/scikit-learn,sklearn/datasets/tests/test_svmlight_format.py,35b24cac4ce8e9187d5f0aa66f401bbb55d0355c,STILL_EXISTS,XXX we have to update this to support Python 3.x
aaicegfjg,scikit-learn/scikit-learn,sklearn/utils/extmath.py,46ebdd41393986fcc66d120a37a86f314ed52e01,00177825aa66d6f4f16ac743ed5ecf5d620447c0,XXX: use lapack\/blas routines for dot
aaicegfjh,scikit-learn/scikit-learn,sklearn/utils/extmath.py,46ebdd41393986fcc66d120a37a86f314ed52e01,d9171498afc1b4139ec221de229c07ee38a5366e,XXX: above comment is from scipy; but I (@vene)'ll take a look
aaiceggib,scikit-learn/scikit-learn,sklearn/tests/test_common.py,54076d5bf2c02c4f05408e7749a31152a89831bd,f54087596461f12c2f7398537a634798fe34fc6a,TODO: fix ridge decision threshold
aaiceghgg,scikit-learn/scikit-learn,sklearn/tests/test_common.py,e0c6c117b14bc6c3b7d0eb0a6165df4c67f19ad0,STILL_EXISTS,TODO: find out why
aaicegjac,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_coordinate_descent.py,2c9ede570d63d6186e8f348b3a98d11a69b5331d,51d02d395e248a48dffce61e0370e8a960fd90f2,XXX: There is a bug when precompute is not None!
aaicegjai,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_sparse_coordinate_descent.py,48687a77a438ce7caaa4645c3e1f1a6d6ce31229,STILL_EXISTS,XXX: There is a bug when precompute is not None!
aaicegjbc,scikit-learn/scikit-learn,sklearn/linear_model/coordinate_descent.py,36cdf1c408a63655a1e0cd36fc73a30f7dd783a3,STILL_EXISTS,XXX : in the sparse case the data will be centered
aaicegjcg,scikit-learn/scikit-learn,sklearn/linear_model/least_angle.py,eac812bca3deace7868cc11e61e9b028ca55362b,STILL_EXISTS,better ideas?
aaicegjdb,scikit-learn/scikit-learn,sklearn/decomposition/dict_learning.py,28cecfeb6d86172f54888dac608ac3ffe8be6641,STILL_EXISTS,XXX : could be moved to the linear_model module
aaicegjfg,scikit-learn/scikit-learn,sklearn/ensemble/gradient_boosting.py,d7fc7862731fae1a34f9fb67a26730c73a1c030e,edee5cacc98e890ad65a57864ab7c9b97b73869c,verbose syntax needed to avoid recursive __setattr__ invokation
aaicegjjd,scikit-learn/scikit-learn,sklearn/utils/extmath.py,4a5a1d6d848d2bc81004bb5dc270f64c0db7da5c,776b060098e9d4fa4aedb60086069cf2896e7d35,XXX: use lapack\/blas routines for dot
aaicegjje,scikit-learn/scikit-learn,sklearn/utils/extmath.py,4a5a1d6d848d2bc81004bb5dc270f64c0db7da5c,776b060098e9d4fa4aedb60086069cf2896e7d35,XXX: above comment is from scipy; but I (@vene)'ll take a look
aaicehade,scikit-learn/scikit-learn,sklearn/linear_model/isotonic_regression_.py,f28d0887fe204ae808d8b35519dac1180b334d13,c1abbf221464e3528f868387aabccdf53f1bfd1f,FIXME X should be a 1D array
aaicehafc,scikit-learn/scikit-learn,examples/datasets/plot_iris_dataset.py,1be816da970c4648c75c1418b02893cfcf90c9eb,STILL_EXISTS,To getter a better understanding of interaction of the dimensions
aaicehaha,scikit-learn/scikit-learn,sklearn/linear_model/base.py,dc943722de01ba9ab7c3955759950ed755257fc8,STILL_EXISTS,XXX Should this derive from LinearModel? It should be a mixin; not an ABC.
aaicehahb,scikit-learn/scikit-learn,sklearn/linear_model/base.py,dc943722de01ba9ab7c3955759950ed755257fc8,STILL_EXISTS,Maybe the n_features checking can be moved to LinearModel.
aaicehahc,scikit-learn/scikit-learn,sklearn/feature_selection/univariate_selection.py,66c8a706d25c0da074c7c8ad77a5682deb43d74d,0480a6681412c674b573f884c08dfcc67acbb380,XXX refactor the indices -> mask -> indices -> mask thing
aaicehbch,scikit-learn/scikit-learn,sklearn/tests/test_common.py,531dcb009028d784ac5d0ad8fb5f596aa93fbd92,d27e8fc97a74201af09d0798f6d74c2f05944c37,flat classes array: XXX inconsistent
aaicehbda,scikit-learn/scikit-learn,sklearn/feature_extraction/dict_vectorizer.py,749ea70370cac5adca1c71401f3262b2a274af1e,7354359bca58c080b36bf4459448d0ea0d2e5338,best thing: typecode \"i\" (int). However; if that gives larger or
aaicehbdc,scikit-learn/scikit-learn,sklearn/feature_extraction/dict_vectorizer.py,749ea70370cac5adca1c71401f3262b2a274af1e,7354359bca58c080b36bf4459448d0ea0d2e5338,XXX we could change values to an array.array as well; but it
aaicehbde,scikit-learn/scikit-learn,examples/plot_feature_stacker.py,7b78abb24be01d2bde7c4b6346fe6f0853789342,STILL_EXISTS,this dataset is way to high-dimensional. Better do PCA:
aaicehbdf,scikit-learn/scikit-learn,examples/plot_feature_stacker.py,7b78abb24be01d2bde7c4b6346fe6f0853789342,STILL_EXISTS,but maybe some original features where good?
aaicehbej,scikit-learn/scikit-learn,examples/feature_stacker.py,098cfbcae7045f6bfacb2923da25a13074ab9bfe,STILL_EXISTS,\"\"\" || ================================================= || Concatenating multiple feature extraction methods || ================================================= ||  || In many real-world examples; there are many ways to extract features from a || dataset. Often it is benefitial to combine several methods to obtain good || performance. This example shows how to use ``FeatureStacker`` to combine || features obtained by PCA and univariate selection. ||  || Combining features using this transformer has the benefit that it allows || cross validation and grid searches over the whole process. ||  || The combination used in this example is not particularly helpful on this || dataset and is only used to illustrate the usage of FeatureStacker. || \"\"\"
aaicehbfd,scikit-learn/scikit-learn,examples/feature_stacker.py,098cfbcae7045f6bfacb2923da25a13074ab9bfe,STILL_EXISTS,This dataset is way to high-dimensional. Better do PCA:
aaicehbfe,scikit-learn/scikit-learn,examples/feature_stacker.py,098cfbcae7045f6bfacb2923da25a13074ab9bfe,STILL_EXISTS,Maybe some original features where good; too?
aaicehbga,scikit-learn/scikit-learn,sklearn/tests/test_common.py,244e8a68fa611937c735d0e1eff1a11f71286ad3,b31696c8ca8abb84f05f8a6b45cebe495d9c7492,flat classes array: XXX inconsistent
aaicehcac,scikit-learn/scikit-learn,sklearn/base.py,f18295c8aeea9361d56f200de2586083a6f8ef7f,156a1f37a9d29a4db2d50d7e62ba596da73e4657,XXX: should we rather test if instance of estimator?
aaicehcbf,scikit-learn/scikit-learn,sklearn/neighbors/classification.py,f26f33c8828699871aad8e769b9714484be79cee,STILL_EXISTS,This may not be needed provided classes labels are guaranteed to be
aaicehcif,scikit-learn/scikit-learn,sklearn/feature_selection/univariate_selection.py,b77804dcb4b2e417f071cd888fdf9aec586723d3,STILL_EXISTS,XXX where should this function be called? fit? scoring functions
aaicehdhh,scikit-learn/scikit-learn,sklearn/cluster/spectral.py,6ac7fbfc97fc8ba92d0d097ec60939ad5ba50abc,7f5b64231c574bf7ccfd2fbb16dd23975d09fceb,XXX: Should we check that the matrices given is symmetric
aaicehfef,scikit-learn/scikit-learn,sklearn/ensemble/gradient_boosting.py,021e9f9a683fe8371c9a32979d8022a00ea3a73b,56d285f4dd88ab6ac3bd305d04a0ef800f47877d,FIXME
aaicehgcj,scikit-learn/scikit-learn,sklearn/metrics/pairwise.py,0dd841f6ee1a747324124485e902d88fdba1eaf0,STILL_EXISTS,TODO: a faster Cython implementation would do the clipping of negative
aaicehgge,scikit-learn/scikit-learn,sklearn/cluster/spectral.py,d4a6958908cbea118517fb841f34a42e4ee49472,7f5b64231c574bf7ccfd2fbb16dd23975d09fceb,XXX: Should we check that the matrices given is symmetric
aaicehhba,scikit-learn/scikit-learn,examples/cluster/plot_lena_segmentation_spectral_comparison.py,64bfe41c3402aec02af01be5741ae1b419efd967,STILL_EXISTS,Apply spectral clustering (this step goes much faster if you have pyamg
aaicehhdd,scikit-learn/scikit-learn,examples/plot_classifier_comparison.py,6f3ccc18555175a9920b1b1dc4839506d79f051d,STILL_EXISTS,\"\"\" || ====================== || Classifiers Comparison || ====================== || A comparison of a several classifiers in scikit-learn on synthetic datasets. || The point of this example is to illustrate the nature of decision boundaries || of different classifiers. || This should be taken with a grain of salt; as the intuition conveyed by || these examples does not necessarily carry over to real datasets. ||  || In high dimensional spaces; data can more easily be separated linearly and || the simplicity of classifiers such as naive Bayes and linear SVMs might lead || to better generalization. ||  || \"\"\"
aaicehieh,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,917f309559d1b441dc28a5aca1cda9f2681a9da0,STILL_EXISTS,XXX This skips _check_test_data; which may change the dtype;
aaicehjge,scikit-learn/scikit-learn,sklearn/tests/test_hmm.py,bf87bf5893c18b1e7e4966b40043104f392608c1,d3dfbec478d3b4b5ebbec3bc12e697a129132757,XXX: This test is bugged and creates weird errors -- skipped
aaiceibha,scikit-learn/scikit-learn,examples/ensemble/plot_random_forest_hasher.py,ae8a7a5aca68b8f557765798bb1970456986f011,STILL_EXISTS,\"\"\" || =================================================== || Hashing feature transformation using Random Forests || =================================================== ||  || RandomForestHasher provide a way to map data to a || very high-dimensional; sparse representation; which might || be beneficial for classification. || The mapping is completely unsupervised and very efficient. ||  || This example visualizes the partitionings given by several || trees and shows how the transformation can also be used for || non-linear dimensionality reduction or manifold learning. ||  || Points that are neighboring often share the same leaf of a tree will share || large parts of their hashed representation. This allows to || separate two circles simply based on the principal components of the || transformed data. ||  || In the high-dimensional space; a simple classifier if often || enough for a good fit. For sparse binary data; BernoulliNB || is particularly well-suited. The bottom row compares the || decision boundary obtained by BernoulliNB in the transformed || space with an ExtraTreesClassifier forests learned on the || original data. || \"\"\"
aaiceibjb,scikit-learn/scikit-learn,sklearn/metrics/tests/test_metrics.py,c4e978b383ca44e0277f9247e895d47cfe4a2807,1f6d7e09ec5fd3d94017b76a368b0ede5096465d,could be swapped around; creating an imperfect sorting. This
aaiceicaa,scikit-learn/scikit-learn,sklearn/metrics/metrics.py,e31376a3c578054510690a10c1b477fd7809a6dd,STILL_EXISTS,concatenate values onto the ends of the curve.
aaiceifca,scikit-learn/scikit-learn,examples/ensemble/plot_random_forest_embedding.py,1644a7c41183b682d5608fc0ca6fa5c1de224a82,STILL_EXISTS,\"\"\" || =================================================== || Hashing feature transformation using Random Forests || =================================================== ||  || RandomForestEmbedding provides a way to map data to a || very high-dimensional; sparse representation; which might || be beneficial for classification. || The mapping is completely unsupervised and very efficient. ||  || This example visualizes the partitions given by several || trees and shows how the transformation can also be used for || non-linear dimensionality reduction or non-linear classification. ||  || Points that are neighboring often share the same leaf of a tree and therefore || share large parts of their hashed representation. This allows to || separate two concentric circles simply based on the principal components of the || transformed data. ||  || In high-dimensional spaces; linear classifiers often achieve || excellent accuracy. For sparse binary data; BernoulliNB || is particularly well-suited. The bottom row compares the || decision boundary obtained by BernoulliNB in the transformed || space with an ExtraTreesClassifier forests learned on the || original data. || \"\"\"
aaiceifdb,scikit-learn/scikit-learn,examples/ensemble/plot_random_forest_embedding.py,1644a7c41183b682d5608fc0ca6fa5c1de224a82,STILL_EXISTS,\"\"\" || ========================================================= || Hashing feature transformation using Totally Random Trees || ========================================================= ||  || RandomTreesEmbedding provides a way to map data to a || very high-dimensional; sparse representation; which might || be beneficial for classification. || The mapping is completely unsupervised and very efficient. ||  || This example visualizes the partitions given by several || trees and shows how the transformation can also be used for || non-linear dimensionality reduction or non-linear classification. ||  || Points that are neighboring often share the same leaf of a tree and therefore || share large parts of their hashed representation. This allows to || separate two concentric circles simply based on the principal components of the || transformed data. ||  || In high-dimensional spaces; linear classifiers often achieve || excellent accuracy. For sparse binary data; BernoulliNB || is particularly well-suited. The bottom row compares the || decision boundary obtained by BernoulliNB in the transformed || space with an ExtraTreesClassifier forests learned on the || original data. || \"\"\"
aaiceific,scikit-learn/scikit-learn,sklearn/metrics/tests/test_metrics.py,1644a7c41183b682d5608fc0ca6fa5c1de224a82,STILL_EXISTS,could be swapped around; creating an imperfect sorting. This
aaiceigeb,scikit-learn/scikit-learn,setup.py,0f247204ba34358701744cbd2296b91781ce0589,1967a0b3236ab65e6a18d402753d344860d85b9c,Ugly hack to make pip work with Python 3; see
aaiceigfe,scikit-learn/scikit-learn,sklearn/preprocessing.py,aab8162ecfaa53a15cdca8aa551f77e2df8596ba,a7ebd1b72c4b86575d0ab09a667a1590cf8a1ac5,Shift columns to the right.
aaiceihif,scikit-learn/scikit-learn,sklearn/externals/joblib/test/test_memory.py,b2dcfdd0a837781923dab3ea96cc860e66babdc4,STILL_EXISTS,we capture it; as it is ugly
aaiceiiee,scikit-learn/scikit-learn,sklearn/preprocessing.py,cfd8b7913e4bf08e87fefb13d1a102e236576df6,STILL_EXISTS,Shift columns to the right.
aaicejbhg,scikit-learn/scikit-learn,sklearn/linear_model/least_angle.py,41a03a5093205fd5cc667eddac8836ea05b3f382,STILL_EXISTS,XXX: need to figure a 'drop for good' way
aaicejfic,scikit-learn/scikit-learn,sklearn/decomposition/spectra_embedding.py,9a79b02b565872fb245a7f9f6e2d01db9e07b30d,STILL_EXISTS,XXX: Should we check that the matrices given is symmetric
aaicejfjf,scikit-learn/scikit-learn,sklearn/decomposition/spectra_embedding.py,9a79b02b565872fb245a7f9f6e2d01db9e07b30d,STILL_EXISTS,eigenvalues between -1 and 1.  ARPACK is most efficient when
aaicejgdi,scikit-learn/scikit-learn,sklearn/decomposition/spectra_embedding.py,7faaf08f9373f0173ac9fa72d2decc283a832e70,STILL_EXISTS,XXX: Should we check that the matrices given is symmetric
aaicejgfb,scikit-learn/scikit-learn,sklearn/decomposition/spectra_embedding.py,7faaf08f9373f0173ac9fa72d2decc283a832e70,STILL_EXISTS,eigenvalues between -1 and 1.  ARPACK is most efficient when
aaicejgjg,scikit-learn/scikit-learn,sklearn/manifold/spectral_embedding.py,daae623c90dfcb55e5224155cb7b78a224d5f815,STILL_EXISTS,XXX: Should we check that the matrices given is symmetric
aaicfabbg,scikit-learn/scikit-learn,sklearn/kernel_approximation.py,f3e8fb840e7c9730ed6a8af074e3b1e31b69f444,STILL_EXISTS,XXX should we just bail?
aaicfabid,scikit-learn/scikit-learn,sklearn/random_projection.py,e9d69d82f97355dc719c1ad38aee6d70c29c99d2,STILL_EXISTS,\"\"\"Random Projection transformers ||  || Random Projections are an efficient way to reduce the dimensionality of || the data by trading a controlled amout of accuracy (as aditional variance) || for faster processing times and smaller model sizes. || \"\"\"
aaicfabja,scikit-learn/scikit-learn,sklearn/random_projection.py,e9d69d82f97355dc719c1ad38aee6d70c29c99d2,fc1e9a6cde466f562a2e55c88c55b49d88d753f7,TODO: check
aaicfabjd,scikit-learn/scikit-learn,sklearn/tests/test_random_projection.py,e9d69d82f97355dc719c1ad38aee6d70c29c99d2,fc1e9a6cde466f562a2e55c88c55b49d88d753f7,TODO: check the Johnson Lindenstrauss embedding
aaicfacdf,scikit-learn/scikit-learn,examples/plot_johnson_lindenstraus_bound.py,8840fb9b1e132326f5eb74e15c23be9ada588302,STILL_EXISTS,Todo plot here eps vs n_components for a fixed value of n_samples
aaicfacdj,scikit-learn/scikit-learn,examples/plot_johnson_lindenstraus_bound.py,8840fb9b1e132326f5eb74e15c23be9ada588302,STILL_EXISTS,TODO: compute the expected value of eps and add them to the previous plot
aaicfaced,scikit-learn/scikit-learn,sklearn/random_projection.py,2feb067d358050cd7a18e6589a70b6c890645429,add00fa8c9986664637eb5bee235ce9877699c35,TODO: avoid conversion of sparse matrices to CSR by adding support for
aaicfacef,scikit-learn/scikit-learn,sklearn/random_projection.py,2feb067d358050cd7a18e6589a70b6c890645429,STILL_EXISTS,TODO: handle sparse input
aaicfaceg,scikit-learn/scikit-learn,sklearn/tests/test_random_projection.py,2feb067d358050cd7a18e6589a70b6c890645429,82cc48ffa3a9cfccecaef9951bd4865398e96f5d,TODO: fix me with some tolerance
aaicfacfg,scikit-learn/scikit-learn,sklearn/random_projection.py,a850892b216b97e28008777b5b2598ff2fee5936,add00fa8c9986664637eb5bee235ce9877699c35,TODO: avoid conversion of sparse matrices to CSR by adding support for
aaicfacgc,scikit-learn/scikit-learn,sklearn/random_projection.py,2b329b906a19cd14e4d885cb82ff8c3fa603de59,add00fa8c9986664637eb5bee235ce9877699c35,TODO: rewrite the following nested for loops in cython
aaicfacgd,scikit-learn/scikit-learn,sklearn/random_projection.py,2b329b906a19cd14e4d885cb82ff8c3fa603de59,STILL_EXISTS,TODO: precompute the list hashed component indices out of the sample
aaicfacib,scikit-learn/scikit-learn,sklearn/random_projection.py,3621693afdbb94eb5cee91bd413c2e04260c5a86,add00fa8c9986664637eb5bee235ce9877699c35,TODO: implement support for CSC here
aaicfadfh,scikit-learn/scikit-learn,benchmarks/bench_random_projections.py,743f4162163d90726bf358d2bf444eb952966677,STILL_EXISTS,\"\"\" || =========================== || Random projection benchmark || =========================== ||  || Transformer      |  dense-fit   | dense-transf |  sparse-fit  | sparse-transf || -----------------|--------------|--------------|--------------|-------------- || Bernouilli       |   6.9329s    |   13.7461s   |   7.0625s    |   0.3768s || Gaussian         |   9.4521s    |  148.2662s   |   9.9962s    |   1.5707s ||  || Still TODO: ||     - add args to select either sparse or dense problem. ||  || \"\"\"
aaicfaebj,scikit-learn/scikit-learn,sklearn/random_projection/tests/test_random_projection.py,0d20c6cf32be1725ae6483d75d57548dac2d785c,STILL_EXISTS,with zero mean and unit norm for each columns
aaicfafji,scikit-learn/scikit-learn,sklearn/tests/test_common.py,c75decd577756fb30339dad8eeb6752682e12ad2,8c41be39e1fb775fd81ff33b71a8cfad9c6848cc,FIXME!
aaicfagaa,scikit-learn/scikit-learn,sklearn/tests/test_common.py,2d5034ded6b8eded98e913e4876cfe97168e4b69,8c41be39e1fb775fd81ff33b71a8cfad9c6848cc,FIXME!
aaicfaged,scikit-learn/scikit-learn,sklearn/metrics/metrics.py,eb5b586f4a125858f6ffa6293870168ef8ff5556,c91a4af5d288c2f220c5a5a36deaba741573c672,TODO: multi-class hinge-loss
aaicfagja,scikit-learn/scikit-learn,sklearn/preprocessing.py,9fe2e93550dda51b2c2826c5644ad019a7258469,0cf30e27fbd31e6e1569fad95ee0a9adbcf3369d,FIXME: if enough values became 0; it may be worth changing
aaicfaici,scikit-learn/scikit-learn,examples/ensemble/plot_adaboost_hastie_10_2.py,b6d1fa8800a71a9cd75c7a48f4f35f7cf6bb74bd,STILL_EXISTS,\"\"\"Figure 10.2 from Elements of Statistical Learning; Ed. 2. ||  || .. author:: Peter Prettenhofer <peter.prettenhofer@gmail.com> || \"\"\"
aaicfaiej,scikit-learn/scikit-learn,sklearn/ensemble/weight_boosting.py,f45afdcf71d06bbe7c428adba4311afa243851a3,71ff28c87d9626b2d39b492f83b08edc4f5d5db1,if you have a better idea of how to handle negative
aaicfaiic,scikit-learn/scikit-learn,sklearn/metrics/metrics.py,901d933077c7e89ae81657196053ef7fba5bce60,b2a3f972c471ec34606b178ba57dfb35ec992448,XXX: Consider using  ``scipy.integrate`` instead; or moving to
aaicfaiii,scikit-learn/scikit-learn,sklearn/metrics/metrics.py,9be24cb710b8fdff867f59c392881331c89be9d1,cb701b0267480318a0baf1893df57c26e649e96f,XXX: Consider using  ``scipy.integrate`` instead; or moving to
aaicfajch,scikit-learn/scikit-learn,examples/ensemble/plot_adaboost_hastie_10_2.py,6ef53e8c9a3543d8e2e03816d869d02120f004c7,STILL_EXISTS,\"\"\" || ============================= || Discrete versus Real AdaBoost || ============================= ||  || This example is based on Figure 10.2 from Hastie et al 2009 [1] and illustrates || the difference in performance between the discrete SAMME [2] boosting || algorithm and real SAMME.R boosting algorithm. ||  || Discrete SAMME AdaBoost adapts based on errors in predicted class labels || whereas real SAMME.R uses the predicted class probabilities. ||  || .. [1] T. Hastie; R. Tibshirani and J. Friedman; \"Elements of Statistical ||     Learning Ed. 2\"; Springer; 2009. ||  || .. [2] J. Zhu; H. Zou; S. Rosset; T. Hastie; \"Multi-class AdaBoost\"; 2009. ||  || \"\"\"
aaicfajci,scikit-learn/scikit-learn,examples/ensemble/plot_adaboost_hastie_10_2.py,6ef53e8c9a3543d8e2e03816d869d02120f004c7,STILL_EXISTS,Author: Peter Prettenhofer <peter.prettenhofer@gmail.com>;
aaicfajdf,scikit-learn/scikit-learn,examples/ensemble/plot_adaboost_multiclass.py,e93bd6e8898d092b7d747fab3fb1032030c6d76d,STILL_EXISTS,\"\"\" || ===================================== || Multi-Class AdaBoosted Decision Trees || ===================================== ||  || This example reproduces Figure 1 of Zhu et al [1] and shows how boosting can || improve prediction accuracy on a multi-class problem. The classification || dataset is constructed by taking a ten-dimensional standard normal distribution || and defining three classes separated by nested concentric ten-dimensional || spheres such that roughly equal numbers of samples are in each class (quantiles || of the :math:`\\Chi^2` distribution). ||  || The performance of the SAMME and SAMME.R [1] algorithms are compared. || The error of each algorithm on the test set after each boosting iteration is || shown on the left; the classification error on the test set of each tree is || shown in the middle; and the boost weight of each tree is shown on the right. ||  || .. [1] J. Zhu; H. Zou; S. Rosset; T. Hastie; \"Multi-class AdaBoost\"; 2009. ||  || \"\"\"
aaicfajhd,scikit-learn/scikit-learn,sklearn/datasets/svmlight_format.py,c59af393374a2cf39544beea57d8be175746dc28,STILL_EXISTS,TODO We can do this cheaper; sorted_indices copies the whole matrix.
aaicfbajg,scikit-learn/scikit-learn,sklearn/decomposition/nmf.py,f7e002345fb0470f7168e24174c062a47e599d50,STILL_EXISTS,XXX: must be a cleaner way
aaicfbbjb,scikit-learn/scikit-learn,examples/symbolic_features/gender_classifier.py,837093df7f4ab270ed391ce509fe23cc6a8d750b,STILL_EXISTS,TODO scale\/center X
aaicfbcbj,scikit-learn/scikit-learn,sklearn/grid_search.py,6eb2c1bb45b8708bb0500814846bbfa743e16c84,801e265cf276d83b74802fe23748853b0de532bf,FIXME BaseString
aaicfbccc,scikit-learn/scikit-learn,sklearn/grid_search.py,6eb2c1bb45b8708bb0500814846bbfa743e16c84,b4ef84cd1c9986156b207cea85387c6fd7d33fb7,XXX we could get rid of this by renaming the new score_func.
aaicfbccf,scikit-learn/scikit-learn,sklearn/metrics/score_objects.py,6eb2c1bb45b8708bb0500814846bbfa743e16c84,e8fa4c38581eafa03d3c9bf07ac3c283bc2a3c38,TODO
aaicfbcei,scikit-learn/scikit-learn,sklearn/tests/test_cross_validation.py,50542dc4d4f58d8c08ebe2b1bc6e69dd8fafdf04,STILL_EXISTS,also test deprecated old way
aaicfbdbc,scikit-learn/scikit-learn,sklearn/externals/six.py,92be92302e9b3004ba7c3a48ce57986eb866934f,STILL_EXISTS,This is a bit ugly; but it avoids running this again.
aaicfbdbf,scikit-learn/scikit-learn,sklearn/feature_selection/univariate_selection.py,19ed9b01651c9bd8735c117d79b652132dbb4fcd,STILL_EXISTS,TODO this class should fit on either p-values or scores;
aaicfbffg,scikit-learn/scikit-learn,sklearn/tests/test_common.py,a3b55f4eae874c4822d9fc75d7cc472f886fb72d,669a4ea853114dbba3621cb96c72b35560471646,FIXME!
aaicfbffi,scikit-learn/scikit-learn,sklearn/tests/test_common.py,a3b55f4eae874c4822d9fc75d7cc472f886fb72d,718f6ad19f90794e62f56d980c6e730a854e56d7,FIXME
aaicfbjab,scikit-learn/scikit-learn,sklearn/tests/test_common.py,5e3230cc507df59307f81a629e9a6f13d4ee49e8,718f6ad19f90794e62f56d980c6e730a854e56d7,TODO also test these!
aaicfbjac,scikit-learn/scikit-learn,sklearn/tests/test_common.py,5e3230cc507df59307f81a629e9a6f13d4ee49e8,2c3861fc1f740447ea4d968e9716f0f944920d01,TODO not so easy because of multi-output
aaicfbjbe,scikit-learn/scikit-learn,sklearn/tests/test_common.py,60374eeb3baefdea513b49610a73555e6af2b162,718f6ad19f90794e62f56d980c6e730a854e56d7,TODO some complication with -1 label
aaicfbjbf,scikit-learn/scikit-learn,sklearn/tests/test_common.py,60374eeb3baefdea513b49610a73555e6af2b162,2c3861fc1f740447ea4d968e9716f0f944920d01,TODO not so easy because of multi-output
aaicfbjcb,scikit-learn/scikit-learn,sklearn/tests/test_common.py,60374eeb3baefdea513b49610a73555e6af2b162,718f6ad19f90794e62f56d980c6e730a854e56d7,TODO: test with intercept
aaicfbjcc,scikit-learn/scikit-learn,sklearn/tests/test_common.py,60374eeb3baefdea513b49610a73555e6af2b162,718f6ad19f90794e62f56d980c6e730a854e56d7,TODO: test with multiple responses
aaicfbjda,scikit-learn/scikit-learn,sklearn/tests/test_common.py,2ba21ecebef1260528b030a2843d8e33a98f19a5,718f6ad19f90794e62f56d980c6e730a854e56d7,TODO also test these!
aaicfbjgg,scikit-learn/scikit-learn,sklearn/feature_extraction/text.py,8453dff8af6eba3a9627d60bc5203de14d06d1e0,STILL_EXISTS,XXX max_df; min_df and max_features have no effect
aaicfcagi,scikit-learn/scikit-learn,sklearn/linear_model/stochastic_gradient.py,29ed16883124c3235b242ad495876140bd7c9789,07560e45c234fe209f13f8689a6a6f36490bd21c,XXX use a LabelBinarizer?
aaicfcajd,scikit-learn/scikit-learn,sklearn/cross_validation.py,d96a3c2c9895dc2891f4562b0aa45eb6e17df154,0d756f9ca6585885533ca03cf09d66ebb56176cb,also a copy; XXX do we need this?
aaicfcaje,scikit-learn/scikit-learn,sklearn/grid_search.py,44d31cb8909542109c8431ff8e218196531be9fd,d428606ce768974ec0b059349a59456472b55ba6,XXX Why? The behavior when passing a list is undocumented;
aaicfcbbi,scikit-learn/scikit-learn,sklearn/tests/test_common.py,ec0fc91d97bb97d5135c591e54beeb63f2cd7ec3,718f6ad19f90794e62f56d980c6e730a854e56d7,TODO also test these!
aaicfcbcf,scikit-learn/scikit-learn,sklearn/tests/test_common.py,ec0fc91d97bb97d5135c591e54beeb63f2cd7ec3,718f6ad19f90794e62f56d980c6e730a854e56d7,TODO: test with intercept
aaicfcbcg,scikit-learn/scikit-learn,sklearn/tests/test_common.py,ec0fc91d97bb97d5135c591e54beeb63f2cd7ec3,718f6ad19f90794e62f56d980c6e730a854e56d7,TODO: test with multiple responses
aaicfcbdj,scikit-learn/scikit-learn,sklearn/decomposition/tests/test_kernel_pca.py,ae700081a61994c0ad18b06aadb4eaa0ef9b963b,STILL_EXISTS,XXX use a least-squares approximation?
aaicfcbhe,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_sgd.py,01801886b23579b87345b2f6fae94958c79e1407,STILL_EXISTS,XXX the sparse test gets a different X2 (?)
aaicfcbhi,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_sgd.py,01801886b23579b87345b2f6fae94958c79e1407,STILL_EXISTS,XXX not true in sparse test case (why?)
aaicfcchj,scikit-learn/scikit-learn,sklearn/neighbors/base.py,b72e9449d0348704e5c33b222795b05eaee31a93,dacfd8bd5d943cb899ed8cd423aaf11b4f27c186,needed for ckdtree
aaicfccia,scikit-learn/scikit-learn,sklearn/neighbors/base.py,b72e9449d0348704e5c33b222795b05eaee31a93,dacfd8bd5d943cb899ed8cd423aaf11b4f27c186,A tree approach is better for small number of neighbors;
aaicfcehb,scikit-learn/scikit-learn,doc/themes/scikit-learn/static/pyparsing.py,707887d85841f987f1ce82028906a3490456d365,STILL_EXISTS,.setName(\"columns\")
aaicfcfae,scikit-learn/scikit-learn,sklearn/ensemble/tests/test_weight_boosting.py,10ee464177be4b931197e46ba735a52488edbfad,STILL_EXISTS,XXX doesn't work with y_class because RF doesn't support classes_
aaicfcfag,scikit-learn/scikit-learn,sklearn/svm/base.py,719ca98118ba35b9a69540af61eaf87c6e7bb29f,STILL_EXISTS,XXX this is ugly.
aaicfcfdh,scikit-learn/scikit-learn,sklearn/neighbors/base.py,dacfd8bd5d943cb899ed8cd423aaf11b4f27c186,STILL_EXISTS,FIXME: include float parameter p for using different distance metrics.
aaicfcfgc,scikit-learn/scikit-learn,sklearn/feature_extraction/dict_vectorizer.py,e163f8bfef86424dc2bcf10bcf8b03a62e23957f,7354359bca58c080b36bf4459448d0ea0d2e5338,workaround for bug in older NumPy:
aaicfcgah,scikit-learn/scikit-learn,sklearn/feature_selection/univariate_selection.py,313193fb0f02b99ccc0ce9001be6d8905c24f7d7,84b2326486116eb946f4ae384a212b304ec0626f,XXX refactor the indices -> mask -> indices -> mask thing
aaicfcgba,scikit-learn/scikit-learn,sklearn/feature_selection/tests/test_feature_select.py,4fd975ce805a2ed414c5d364ecb9cb4663754d17,84b2326486116eb946f4ae384a212b304ec0626f,Check other columns are empty
aaicfcgcc,scikit-learn/scikit-learn,sklearn/feature_selection/tests/test_feature_select.py,2b57e4e52e0442a43ab0786335ddaa494d88024b,STILL_EXISTS,Check other columns are empty
aaicfcihc,scikit-learn/scikit-learn,doc/tutorial/machine_learning_map/pyparsing.py,962e2e1d9946523785715e17474dae9d62d3b1dc,STILL_EXISTS,.setName(\"columns\")
aaicfciii,scikit-learn/scikit-learn,doc/tutorial/text_analytics/data/languages/fetch_data.py,7c16dadc12ae75008d477739b6b9ed6a53950b53,STILL_EXISTS,FIXME: whitespace tokenizing does not work on chinese and japanese
aaicfcjbf,scikit-learn/scikit-learn,doc/tutorial/text_analytics/skeletons/exercise_02_sentiment.py,7c16dadc12ae75008d477739b6b9ed6a53950b53,STILL_EXISTS,\"\"\"Build a sentiment analysis \/ polarity model ||  || Sentiment analysis can be casted as a binary text classification problem; || that is fitting a linear classifier on features extracted from the text || of the user messages so as to guess wether the opinion of the author is || positive or negative. ||  || In this examples we will use a movie review dataset. ||  || \"\"\"
aaicfcjdd,scikit-learn/scikit-learn,doc/tutorial/text_analytics/skeletons/exercise_02_sentiment.py,7c16dadc12ae75008d477739b6b9ed6a53950b53,STILL_EXISTS,Refit the best parameter set on the complete training set
aaicfcjja,scikit-learn/scikit-learn,doc/tutorial/text_analytics/solutions/exercise_02_sentiment.py,7c16dadc12ae75008d477739b6b9ed6a53950b53,STILL_EXISTS,\"\"\"Build a sentiment analysis \/ polarity model ||  || Sentiment analysis can be casted as a binary text classification problem; || that is fitting a linear classifier on features extracted from the text || of the user messages so as to guess wether the opinion of the author is || positive or negative. ||  || In this examples we will use a movie review dataset. ||  || \"\"\"
aaicfdaai,scikit-learn/scikit-learn,doc/tutorial/text_analytics/solutions/exercise_02_sentiment.py,7c16dadc12ae75008d477739b6b9ed6a53950b53,STILL_EXISTS,Refit the best parameter set on the complete training set
aaicfdahd,scikit-learn/scikit-learn,doc/tutorial/text_analytics/tutorial/conf.py,7c16dadc12ae75008d477739b6b9ed6a53950b53,STILL_EXISTS,unused_docs = []
aaicfegac,scikit-learn/scikit-learn,sklearn/tests/test_common.py,fe0335748252cb75a6b1eb2f7d08843edb28216f,718f6ad19f90794e62f56d980c6e730a854e56d7,TODO also test these!
aaicfegaj,scikit-learn/scikit-learn,sklearn/tests/test_common.py,fe0335748252cb75a6b1eb2f7d08843edb28216f,718f6ad19f90794e62f56d980c6e730a854e56d7,TODO: test with intercept
aaicfegba,scikit-learn/scikit-learn,sklearn/tests/test_common.py,fe0335748252cb75a6b1eb2f7d08843edb28216f,718f6ad19f90794e62f56d980c6e730a854e56d7,TODO: test with multiple responses
aaicfegeg,scikit-learn/scikit-learn,sklearn/utils/multiclass.py,fd3bd2d200027f4823a71e6f07fed3fe277f124a,be5826d6b9a3708f00cddd16bed6c4d2a586ab7d,XXX: is there a way to duck-type this condition?
aaicfegff,scikit-learn/scikit-learn,examples/randomized_search.py,5d7aaaec50391506a9b1ffd2d7e26b9a4d96015f,STILL_EXISTS,\"\"\" || ========================================================================= || Comparing randomized search and grid search for hyperparameter estimation || ========================================================================= ||  || Compare randomized search and grid search for optimizing hyperparameters of a || random forest. || All parameters that influence the learning are searched simultaneously || (except for the number of estimators; which poses a time \/ quality tradeoff). ||  || The randomized search and the grid search explore exactly the same space of || parameters. The result in parameter settings is quite similar; while the run || time for randomized search is drastically lower. ||  || The performance is slightly worse for the randomized search; though this || is most likely a noise effect and would not carry over to a held-out test set. ||  || Note that in practice; one would not search over this many different parameters || simultaneously using grid search; but pick only the ones deemed most important. || \"\"\"
aaicfeggf,scikit-learn/scikit-learn,sklearn/grid_search.py,5d7aaaec50391506a9b1ffd2d7e26b9a4d96015f,STILL_EXISTS,A raw namedtuple is very memory efficient as it packs the attributes
aaicfeghd,scikit-learn/scikit-learn,sklearn/grid_search.py,5d7aaaec50391506a9b1ffd2d7e26b9a4d96015f,STILL_EXISTS,TODO: shall we also store the test_fold_sizes?
aaicfeghg,scikit-learn/scikit-learn,sklearn/tests/test_grid_search.py,5d7aaaec50391506a9b1ffd2d7e26b9a4d96015f,STILL_EXISTS,Make a dataset with a lot of noise to get various kind of prediction
aaicfeghi,scikit-learn/scikit-learn,sklearn/tests/test_grid_search.py,5d7aaaec50391506a9b1ffd2d7e26b9a4d96015f,STILL_EXISTS,XXX: as of today (scipy 0.12) it's not possible to set the random seed
aaicfehag,scikit-learn/scikit-learn,sklearn/cross_validation.py,721be7f8a06a22eb2a38062d80f7381c90329074,8b761b8689267d2c36077018e5d2d03bf8c68610,Since subclasses must implement either iter_test_masks or
aaicfehca,scikit-learn/scikit-learn,sklearn/decomposition/tests/test_truncated_svd.py,cacb14ef0a5605e10e4c476061774c706aa1ad63,STILL_EXISTS,XXX newer versions of SciPy have scipy.sparse.rand for this.
aaicfehce,scikit-learn/scikit-learn,sklearn/decomposition/tests/test_truncated_svd.py,cacb14ef0a5605e10e4c476061774c706aa1ad63,STILL_EXISTS,equal\" in all positions. XXX Test means or sums instead?
aaicfejih,scikit-learn/scikit-learn,sklearn/svm/base.py,bba422515cde24a0ec108d17c379144594a26bad,STILL_EXISTS,XXX this is ugly.
aaicfejjg,scikit-learn/scikit-learn,examples/applications/plot_out_of_core_classification.py,63df99d7e2f59cf605c9143e351218b400a6f5ee,STILL_EXISTS,Hack to detect whether we are running by the sphinx builder
aaicffbed,scikit-learn/scikit-learn,sklearn/neighbors/base.py,7f9b7834686c7498c6977c7363b87c9cc4ea64a1,STILL_EXISTS,For minkowski distance; use more efficient methods where available
aaicffbee,scikit-learn/scikit-learn,sklearn/neighbors/base.py,7f9b7834686c7498c6977c7363b87c9cc4ea64a1,STILL_EXISTS,A tree approach is better for small number of neighbors;
aaicffbeh,scikit-learn/scikit-learn,sklearn/neighbors/base.py,7f9b7834686c7498c6977c7363b87c9cc4ea64a1,STILL_EXISTS,FIXME: include float parameter p for using different distance metrics.
aaicffbhb,scikit-learn/scikit-learn,sklearn/neighbors/base.py,8861833ec9ee67656670af66605991c44207ff81,STILL_EXISTS,FIXME: include float parameter p for using different distance metrics.
aaicffbhj,scikit-learn/scikit-learn,sklearn/neighbors/base.py,8861833ec9ee67656670af66605991c44207ff81,STILL_EXISTS,For minkowski distance; use more efficient methods where available
aaicffbia,scikit-learn/scikit-learn,sklearn/neighbors/base.py,8861833ec9ee67656670af66605991c44207ff81,STILL_EXISTS,A tree approach is better for small number of neighbors;
aaicffbih,scikit-learn/scikit-learn,sklearn/neighbors/kde.py,df98f79ba24506513f4d10324f8f6d170bb58415,STILL_EXISTS,TODO: implement a brute force version for testing purposes
aaicffbii,scikit-learn/scikit-learn,sklearn/neighbors/kde.py,df98f79ba24506513f4d10324f8f6d170bb58415,STILL_EXISTS,TODO: bandwidth estimation
aaicffbij,scikit-learn/scikit-learn,sklearn/neighbors/kde.py,df98f79ba24506513f4d10324f8f6d170bb58415,STILL_EXISTS,TODO: create a density estimation base class?
aaicffbjj,scikit-learn/scikit-learn,sklearn/neighbors/kde.py,df98f79ba24506513f4d10324f8f6d170bb58415,STILL_EXISTS,TODO: implement sampling for other valid kernel shapes
aaicffhfh,scikit-learn/scikit-learn,sklearn/decomposition/tests/test_kernel_pca.py,eaac7aa19f8e3188b8eccc0be593ac2ae1c87a9f,STILL_EXISTS,XXX use a least-squares approximation?
aaicffiei,scikit-learn/scikit-learn,sklearn/neighbors/base.py,eaac7aa19f8e3188b8eccc0be593ac2ae1c87a9f,STILL_EXISTS,For minkowski distance; use more efficient methods where available
aaicffiej,scikit-learn/scikit-learn,sklearn/neighbors/base.py,eaac7aa19f8e3188b8eccc0be593ac2ae1c87a9f,STILL_EXISTS,A tree approach is better for small number of neighbors;
aaicffifc,scikit-learn/scikit-learn,sklearn/neighbors/base.py,eaac7aa19f8e3188b8eccc0be593ac2ae1c87a9f,STILL_EXISTS,FIXME: include float parameter p for using different distance metrics.
aaicffjee,scikit-learn/scikit-learn,sklearn/utils/tests/test_multiclass.py,e40a7f56f0970cb06c3574a379452b9951542c9e,91e4b9a91f84eb8e164fc5c0dd501fb99ebef473,XXX : not considered as multilabel-indicator at the moment
aaicfgbga,scikit-learn/scikit-learn,sklearn/tree/tree.py,d27763bed39594efa1a4d8f8df00a48ade4fbc26,STILL_EXISTS,Author: Gilles Louppe; Peter Prettenhofer; Brian Holt; Noel Dawe; Satrajit Gosh
aaicfgdah,scikit-learn/scikit-learn,sklearn/tests/test_multiclass.py,cbd65449c0ab1eed32df2fd3879f26fbb5302a3a,STILL_EXISTS,There may be a better (probably is) test for this
aaicfgdba,scikit-learn/scikit-learn,sklearn/tests/test_multiclass.py,5088875a0c9d8ee2492b6c2f070186ccf2c01a95,STILL_EXISTS,There may be a better (probably is) test for this
aaicfhafc,scikit-learn/scikit-learn,sklearn/neighbors/tests/test_kde.py,e48870484981643bb2e03eefa25186436719262f,STILL_EXISTS,FIXME
aaicfhajh,scikit-learn/scikit-learn,examples/ensemble/plot_gradient_boosting_oob.py,eecca4b06f94eee9a969f56e921b2585f0abe3ee,STILL_EXISTS,Author: Peter Prettenhofer <peter.prettenhofer@gmail.com>
aaicfhbej,scikit-learn/scikit-learn,sklearn/metrics/metrics.py,913e815a2ef985cafc7cb41d1f5f189a1561835c,c91a4af5d288c2f220c5a5a36deaba741573c672,We can't have more than one value on y_type => The set is no more needed
aaicfhbfi,scikit-learn/scikit-learn,examples/rbm_logistic_classification.py,2f17015f40de576c35f31e31b199d42b15b17fd0,STILL_EXISTS,\"\"\" || ========================================================= || Pipelining: chaining a RBM and a logistic regression || ========================================================= ||  || The RestrictedBolzmannMachine does unsupervised feature extraction; || while the logistic regression does the prediction. ||  || We use a GridSearchCV to set the number of hidden units and the learning rate || of the RestrictedBolzmannMachine. ||  || We also train a simple logistic regression for comparison. The example shows || that the features extracted by the RestrictedBolzmannMachine help improve || the classification accuracy. ||  || Note || ---- ||  || Much better performance can be achieved by using larger n_components and n_iter || for the RestrictedBolzmannMachine. ||  || \"\"\"
aaicfhbic,scikit-learn/scikit-learn,examples/plot_rbm_logistic_classification.py,2ed3192e8551b9f46f1b04d04325df11a15f517b,STILL_EXISTS,More component tend to give better prediction performance; but larger
aaicfhdae,scikit-learn/scikit-learn,sklearn/naive_bayes.py,7362e283cc9c185dc927230117f41e30a4ef6879,STILL_EXISTS,XXX: OPTIM: we could introduce a public finalization method to
aaicfhdbj,scikit-learn/scikit-learn,sklearn/tests/test_naive_bayes.py,ba69ce46b8efdd7f13bb7b5d293ec8cfbbb3dbe9,0abe21eaacc147c7be33c73bce80444bb017d48e,Passing class_prior as a fit param should raise a deprecation
aaicfhddd,scikit-learn/scikit-learn,sklearn/tests/test_naive_bayes.py,b00f12087b9b935ba97aa0f0f27579f68eea261b,STILL_EXISTS,Bernoulli NB (maybe not appropriate for this dataset...)
aaicfhgah,scikit-learn/scikit-learn,examples/imputation.py,b86071b403b2793b7443441e3705fcd66ddc0d17,STILL_EXISTS,\"\"\" || ====================================================== || Imputing missing values before building an estimator || ====================================================== ||  || This example shows that imputing the missing values can give better results || than discarding the samples containing any missing value. ||  || Missing value can be replaced by the mean; the median or the most frequent || value using the `strategy` hyper-parameter. ||  || Script output: ||  ||   Score with the entire dataset = 0.556914670698 ||   Score without the samples containing missing values = 0.520166053514 ||   Score after imputation of the missing values = 0.532282005499 ||  || \"\"\"
aaicfhgch,scikit-learn/scikit-learn,sklearn/preprocessing.py,b86071b403b2793b7443441e3705fcd66ddc0d17,STILL_EXISTS,Ignore the error; columns with a np.nan statistics_
aaicfhgci,scikit-learn/scikit-learn,sklearn/preprocessing.py,b86071b403b2793b7443441e3705fcd66ddc0d17,STILL_EXISTS,are not an error at this point. These columns will
aaicfhged,scikit-learn/scikit-learn,sklearn/preprocessing.py,b86071b403b2793b7443441e3705fcd66ddc0d17,STILL_EXISTS,To be able access the elements by columns
aaicfhgfb,scikit-learn/scikit-learn,sklearn/preprocessing.py,b86071b403b2793b7443441e3705fcd66ddc0d17,STILL_EXISTS,Delete the invalid rows\/columns
aaicfhgfh,scikit-learn/scikit-learn,sklearn/tests/test_preprocessing.py,b86071b403b2793b7443441e3705fcd66ddc0d17,STILL_EXISTS,Create a matrix X with columns
aaicfhggc,scikit-learn/scikit-learn,sklearn/tests/test_preprocessing.py,b86071b403b2793b7443441e3705fcd66ddc0d17,STILL_EXISTS,Create the columns
aaicfhgge,scikit-learn/scikit-learn,sklearn/tests/test_preprocessing.py,b86071b403b2793b7443441e3705fcd66ddc0d17,STILL_EXISTS,Mean doesn't support columns containing NaNs; median does
aaicfhgic,scikit-learn/scikit-learn,sklearn/metrics/tests/test_metrics.py,f1ac2e0d6071ecda6cdf4c324577d53cb8fad899,STILL_EXISTS,Ugly; but handle case with a pos_label
aaicfhgjf,scikit-learn/scikit-learn,scikits/learn/utils/hungarian.py,19033bd9e5f32e81367f0a46827ee522ffe676a3,STILL_EXISTS,Look for the starred columns
aaicfhhie,scikit-learn/scikit-learn,scikits/learn/utils/hungarian.py,4157f20f2910bd0ce424ec227f274fe2935bfdda,STILL_EXISTS,Make sure the array is 2D with 2 columns.
aaicfhhif,scikit-learn/scikit-learn,scikits/learn/utils/hungarian.py,4157f20f2910bd0ce424ec227f274fe2935bfdda,STILL_EXISTS,This is needed when dealing with an empty list
aaicfhhih,scikit-learn/scikit-learn,scikits/learn/utils/hungarian.py,bd53545156abd8d9a1f5dce2c7b200b6e6ad4359,STILL_EXISTS,If there are more rows than columns; then the algorithm
aaicfhhij,scikit-learn/scikit-learn,scikits/learn/utils/hungarian.py,bd53545156abd8d9a1f5dce2c7b200b6e6ad4359,STILL_EXISTS,transpose the cost function when needed. Just have to
aaicfhhja,scikit-learn/scikit-learn,scikits/learn/utils/hungarian.py,bd53545156abd8d9a1f5dce2c7b200b6e6ad4359,STILL_EXISTS,remember to swap the result columns later in this function.
aaicfhhjb,scikit-learn/scikit-learn,scikits/learn/utils/hungarian.py,bd53545156abd8d9a1f5dce2c7b200b6e6ad4359,STILL_EXISTS,We need to swap the columns because we originally
aaicfhicj,scikit-learn/scikit-learn,sklearn/cluster/bicluster/tests/test_spectral.py,9c20a9e2abb5a7ba3b2f4e80fbd2b2155adaa200,5ad0dfb69b22aecc0a3ea2c52fd16a2c17293805,TODO: replace with hungarian algorithm; when it is implemented
aaicfhiic,scikit-learn/scikit-learn,sklearn/linear_model/omp.py,bad053852a079b801c1d7a84cbcb8eddbe8d82d8,4ce4863fc7e634daa19bd97a131a9f007af498ae,XXX : rename precompute_gram to precompute for consistency
aaicfhjdd,scikit-learn/scikit-learn,sklearn/preprocessing/data.py,ec6057a384fd95a510220c7a62b8a9ac73ec2964,STILL_EXISTS,Shift columns to the right.
aaicfhjfh,scikit-learn/scikit-learn,sklearn/preprocessing/imputation.py,ec6057a384fd95a510220c7a62b8a9ac73ec2964,STILL_EXISTS,Ignore the error; columns with a np.nan statistics_
aaicfhjfi,scikit-learn/scikit-learn,sklearn/preprocessing/imputation.py,ec6057a384fd95a510220c7a62b8a9ac73ec2964,STILL_EXISTS,are not an error at this point. These columns will
aaicfhjhd,scikit-learn/scikit-learn,sklearn/preprocessing/imputation.py,ec6057a384fd95a510220c7a62b8a9ac73ec2964,STILL_EXISTS,To be able access the elements by columns
aaicfhjhh,scikit-learn/scikit-learn,sklearn/preprocessing/imputation.py,ec6057a384fd95a510220c7a62b8a9ac73ec2964,STILL_EXISTS,Delete the invalid rows\/columns
aaicfiadd,scikit-learn/scikit-learn,sklearn/preprocessing/tests/test_imputation.py,b8201b452f7b689b20739f384b10b79e792fc043,STILL_EXISTS,Create a matrix X with columns
aaicfiadi,scikit-learn/scikit-learn,sklearn/preprocessing/tests/test_imputation.py,b8201b452f7b689b20739f384b10b79e792fc043,STILL_EXISTS,Create the columns
aaicfiaea,scikit-learn/scikit-learn,sklearn/preprocessing/tests/test_imputation.py,b8201b452f7b689b20739f384b10b79e792fc043,STILL_EXISTS,Mean doesn't support columns containing NaNs; median does
aaicfibca,scikit-learn/scikit-learn,sklearn/externals/joblib/func_inspect.py,107a0c8b374c1370c1af37d02ae50ecb1d6f2226,STILL_EXISTS,will change with every new kernel instance. This hack
aaicfibcd,scikit-learn/scikit-learn,sklearn/tests/test_common.py,1c133e8eb5d07a7f56f1592ec9c8dd8fc184e9da,718f6ad19f90794e62f56d980c6e730a854e56d7,TODO some complication with -1 label
aaicfibfg,scikit-learn/scikit-learn,sklearn/utils/testing.py,cdfaa825f574399b5af72238c4f26834e4a6cffc,e5bf61eee1c62dbb5a2103e68cf73936e4ac3e58,XXX: once we may depend on python >= 2.6; this can be replaced by the
aaicfibhb,scikit-learn/scikit-learn,sklearn/ensemble/tests/test_gradient_boosting.py,12d1b66e7b80ccbdf89c49f63569298bd0d4090c,STILL_EXISTS,FIXME: the following snippet does not yield the same results on 32 bits
aaicfibhf,scikit-learn/scikit-learn,sklearn/tests/test_naive_bayes.py,b9cc3a72342dcac515bce8f49e02c23db3daf1be,a9a51e2c75d78f4dd7aac03bb20eb1a329c25dd8,Partial fit on very small mini-batches can cause some columns
aaicficai,scikit-learn/scikit-learn,sklearn/decomposition/pca.py,09da6b5e4ce6ffe6fc39ed995c9de5965a73a1de,94dad56f34ab89f56809ffd3d094839c23c553fc,XXX remove scipy.sparse support here in 0.16
aaicficeb,scikit-learn/scikit-learn,sklearn/utils/fixes.py,b236030015a131784e3c8582e290d2cd006e9eba,a6f91b395524a7a45cf0fae10c9d16536c9d15f7,fix tanh for old numpy
aaicfjbha,scikit-learn/scikit-learn,examples/applications/plot_out_of_core_classification.py,af549f2b06c6fac656a39543500b0f0bd537dafd,STILL_EXISTS,Hack to detect whether we are running by the sphinx builder
aaicfjbhg,scikit-learn/scikit-learn,sklearn/utils/linear_assignment_.py,1627a9231187877394bcc28743263f96c45516f4,STILL_EXISTS,Make sure the array is 2D with 2 columns.
aaicfjbhh,scikit-learn/scikit-learn,sklearn/utils/linear_assignment_.py,1627a9231187877394bcc28743263f96c45516f4,STILL_EXISTS,This is needed when dealing with an empty list
aaicfjbic,scikit-learn/scikit-learn,sklearn/utils/tests/test_linear_assignment.py,1627a9231187877394bcc28743263f96c45516f4,STILL_EXISTS,XXX we should be testing the public API here
aaicfjbid,scikit-learn/scikit-learn,sklearn/cluster/mean_shift_.py,130729b848a0a4b3d0679f07af9742317c360921,cb896792ea8e70fa3b270ed54ead099ff04eca68,FIXME: can use pairwise_distances_argmin when ready.
aaicfjbie,scikit-learn/scikit-learn,sklearn/cluster/affinity_propagation_.py,ce1fdf27a22384854198cef7d44cdddb12609230,cb896792ea8e70fa3b270ed54ead099ff04eca68,FIXME: can use pairwise_distances_argmin when ready.
aaicfjcdj,scikit-learn/scikit-learn,sklearn/cluster/hierarchical.py,d5c3933628810e2763d3302e584bf6af16d14992,49954dc5b4ced7b28273814fd1b42d49bed5e5af,XXX: should silence the SparseEfficiencyWarning
aaicfjcea,scikit-learn/scikit-learn,sklearn/cluster/hierarchical.py,d5c3933628810e2763d3302e584bf6af16d14992,49954dc5b4ced7b28273814fd1b42d49bed5e5af,XXX: should retrieve the fixed indices; to complete the
aaicfjcfg,scikit-learn/scikit-learn,sklearn/cluster/hierarchical.py,d5c3933628810e2763d3302e584bf6af16d14992,49954dc5b4ced7b28273814fd1b42d49bed5e5af,return numpy array for efficient caching
aaicfjcfi,scikit-learn/scikit-learn,sklearn/cluster/hierarchical.py,d393f48d738a8bf07b7f0ea1fc929410602dda90,49954dc5b4ced7b28273814fd1b42d49bed5e5af,XXX: we probably don't need a LIL anymore; but a CSR would do
aaicfjcif,scikit-learn/scikit-learn,sklearn/cluster/hierarchical.py,6bae11f1313c31cbaea7fe70cf2135e5c18ca726,STILL_EXISTS,XXX: pruning doesn't happen right
aaicfjcjb,scikit-learn/scikit-learn,sklearn/cluster/hierarchical.py,3e54eab851b599b0344da19c84798e0e22d223f3,49954dc5b4ced7b28273814fd1b42d49bed5e5af,XXX: should silence the SparseEfficiencyWarning
aaicfjcjc,scikit-learn/scikit-learn,sklearn/cluster/hierarchical.py,3e54eab851b599b0344da19c84798e0e22d223f3,49954dc5b4ced7b28273814fd1b42d49bed5e5af,XXX: should retrieve the fixed indices; to complete the
aaicfjcjf,scikit-learn/scikit-learn,sklearn/cluster/hierarchical.py,3e54eab851b599b0344da19c84798e0e22d223f3,49954dc5b4ced7b28273814fd1b42d49bed5e5af,XXX: we probably don't need a LIL anymore; but a CSR would do
aaicfjdab,scikit-learn/scikit-learn,sklearn/cluster/hierarchical.py,3e54eab851b599b0344da19c84798e0e22d223f3,STILL_EXISTS,XXX: would be faster to maitain a table of heads
aaicfjdaf,scikit-learn/scikit-learn,sklearn/cluster/hierarchical.py,3e54eab851b599b0344da19c84798e0e22d223f3,49954dc5b4ced7b28273814fd1b42d49bed5e5af,return numpy array for efficient caching
aaicfjdag,scikit-learn/scikit-learn,sklearn/cluster/hierarchical.py,35bc01a6230d1af25e8f0c053d0a520f617046e0,49954dc5b4ced7b28273814fd1b42d49bed5e5af,XXX: which MST algorithm is this?? Kruskal; I believe
aaicfjdfg,scikit-learn/scikit-learn,examples/cluster/plot_hierarchical_clustering.py,5a5d944b998431bdb0f14359f10db0064d798345,STILL_EXISTS,XXX: random_state=0 triggers a bug...
aaicfjdga,scikit-learn/scikit-learn,sklearn/cluster/hierarchical.py,4fdd3ee484d35cf6177ef72a18d8d99acca73dd5,4333ec7762ee83f3e9d7c30ea7347095236e5425,XXX: fishy stuff with COO\/LIL
aaicfjdgc,scikit-learn/scikit-learn,sklearn/cluster/hierarchical.py,4fdd3ee484d35cf6177ef72a18d8d99acca73dd5,1e61f4dc8147c064af416d61255cc0e12bf44499,XXX: the above line does a copy anyhow; so we need to remove the
aaicfjdgg,scikit-learn/scikit-learn,sklearn/cluster/hierarchical.py,4fdd3ee484d35cf6177ef72a18d8d99acca73dd5,STILL_EXISTS,XXX: Can we do without this above?
aaicfjdgh,scikit-learn/scikit-learn,sklearn/cluster/hierarchical.py,4fdd3ee484d35cf6177ef72a18d8d99acca73dd5,STILL_EXISTS,XXX: distance should be plugable
aaicfjdij,scikit-learn/scikit-learn,sklearn/cluster/hierarchical.py,4109e34ed82a78b27f5d5ac6b2e695dc041172b9,99e32456580c6476004b92788386cd1413e78cf9,FIXME We compute all the distances; while we could have only computed
aaicfjdjb,scikit-learn/scikit-learn,sklearn/cluster/hierarchical.py,4109e34ed82a78b27f5d5ac6b2e695dc041172b9,99e32456580c6476004b92788386cd1413e78cf9,FIXME check affinity exists.
aaicfjebb,scikit-learn/scikit-learn,sklearn/cluster/hierarchical.py,a8ee6f9cb0adfd7c7d6287a8a1142b7a35a0cfe8,3aa9158a27f50c4152775329ec357e254c1eceb1,XXX: should silence the SparseEfficiencyWarning
aaicfjebc,scikit-learn/scikit-learn,sklearn/cluster/hierarchical.py,a8ee6f9cb0adfd7c7d6287a8a1142b7a35a0cfe8,3aa9158a27f50c4152775329ec357e254c1eceb1,XXX: should retrieve the fixed indices; to complete the
aaicfjeca,scikit-learn/scikit-learn,sklearn/cluster/hierarchical.py,511748feecef021212293c08e0ce8762f089f48c,3aa9158a27f50c4152775329ec357e254c1eceb1,XXX: we probably don't need a LIL anymore; but a CSR would do
aaicfjefd,scikit-learn/scikit-learn,sklearn/cluster/hierarchical.py,3f24dc3b7c364fa681d7b6fb136a5118ff084f9f,STILL_EXISTS,XXX: pruning doesn't happen right
aaicfjefj,scikit-learn/scikit-learn,sklearn/cluster/hierarchical.py,2eb7e553ec468f1919c246b71a90bdea26313c23,3aa9158a27f50c4152775329ec357e254c1eceb1,XXX: should silence the SparseEfficiencyWarning
aaicfjega,scikit-learn/scikit-learn,sklearn/cluster/hierarchical.py,2eb7e553ec468f1919c246b71a90bdea26313c23,3aa9158a27f50c4152775329ec357e254c1eceb1,XXX: should retrieve the fixed indices; to complete the
aaicfjegd,scikit-learn/scikit-learn,sklearn/cluster/hierarchical.py,2eb7e553ec468f1919c246b71a90bdea26313c23,3aa9158a27f50c4152775329ec357e254c1eceb1,XXX: we probably don't need a LIL anymore; but a CSR would do
aaicfjegj,scikit-learn/scikit-learn,sklearn/cluster/hierarchical.py,2eb7e553ec468f1919c246b71a90bdea26313c23,STILL_EXISTS,XXX: would be faster to maitain a table of heads
aaicfjehd,scikit-learn/scikit-learn,sklearn/cluster/hierarchical.py,2eb7e553ec468f1919c246b71a90bdea26313c23,3aa9158a27f50c4152775329ec357e254c1eceb1,return numpy array for efficient caching
aaicfjehe,scikit-learn/scikit-learn,sklearn/cluster/hierarchical.py,a1c95831a28963edd83b5ef32605434855fb1ce2,3aa9158a27f50c4152775329ec357e254c1eceb1,XXX: which MST algorithm is this?? Kruskal; I believe
aaicfjfce,scikit-learn/scikit-learn,examples/cluster/plot_hierarchical_clustering.py,20777793db1cabca4a634cca27326bc644c29605,STILL_EXISTS,XXX: random_state=0 triggers a bug...
aaicfjfdg,scikit-learn/scikit-learn,sklearn/cluster/hierarchical.py,7aa627b81496c633974d539bf3666c61e879d91c,f95502df456fd2510687463d68de3d5a1d073d50,XXX: fishy stuff with COO\/LIL
aaicfjfdi,scikit-learn/scikit-learn,sklearn/cluster/hierarchical.py,7aa627b81496c633974d539bf3666c61e879d91c,057bc275724fe4d2701132ca662fbb5cb1660d70,XXX: the above line does a copy anyhow; so we need to remove the
aaicfjfec,scikit-learn/scikit-learn,sklearn/cluster/hierarchical.py,7aa627b81496c633974d539bf3666c61e879d91c,STILL_EXISTS,XXX: Can we do without this above?
aaicfjfed,scikit-learn/scikit-learn,sklearn/cluster/hierarchical.py,7aa627b81496c633974d539bf3666c61e879d91c,STILL_EXISTS,XXX: distance should be plugable
aaicfjfid,scikit-learn/scikit-learn,sklearn/cluster/hierarchical.py,61e1fdc1ea86e0f3425282adabc5b66fe5b065ce,99e32456580c6476004b92788386cd1413e78cf9,FIXME We compute all the distances; while we could have only computed
aaicfjfif,scikit-learn/scikit-learn,sklearn/cluster/hierarchical.py,61e1fdc1ea86e0f3425282adabc5b66fe5b065ce,99e32456580c6476004b92788386cd1413e78cf9,FIXME check affinity exists.
aaicfjgbc,scikit-learn/scikit-learn,sklearn/cluster/hierarchical.py,1a4a167686f2777cf8c1bf6afd37037c4e0d014d,STILL_EXISTS,XXX: if affinity is precomputed or callable; the following will
aaicfjgeh,scikit-learn/scikit-learn,sklearn/metrics/pairwise.py,fe768182ef45893bbcdde7663ffb860b3ac3ffa0,STILL_EXISTS,FIXME: np.zeros can be replaced by np.empty
aaicfjgfc,scikit-learn/scikit-learn,sklearn/metrics/pairwise.py,5a16ed78794ff858edd659f49a409bfe935fdb60,85b241c9da0dd4363d1cd1579a16216335cc80bb,FIXME: chunk_x_num -> n_batches_x
aaicfjgif,scikit-learn/scikit-learn,sklearn/metrics/pairwise.py,6a885cca75f27d53967f3fde87f998cb00926dbf,STILL_EXISTS,FIXME: np.zeros can be replaced by np.empty
aaicfjhdc,scikit-learn/scikit-learn,sklearn/decomposition/tests/test_pca.py,bf1cf77e4ddfa781fea5da1f63612961b6487c81,88b05ff90889a9854505beae793ff80e1c78aef9,XXX : Don't test as homoscedastic=False is buggy
aaicfjhjg,scikit-learn/scikit-learn,examples/ensemble/plot_bias_variance.py,74d395215bbd2009dc922967b41c43f5469942ce,STILL_EXISTS,\"\"\" || ============================================================ || Single estimator versus bagging: bias-variance decomposition || ============================================================ ||  || This example illustrates and compares the bias-variance decomposition of the || expected mean squared error of a single estimator against a bagging ensemble. ||  || In regression; the expected mean squared error of an estimator can be || decomposed in terms of bias; variance and noise. On average over datasets of || the regression problem; the bias term measures the average amount by which the || predictions of the estimator differ from the predictions of the best possible || estimator for the problem (i.e.; the Bayes model). The variance term measures || the variability of the predictions of the estimator when fit over different || instances LS of the problem. Finally; the noise measures the irreducible part || of the error which is due the variability in the data. ||  || The upper left figure illustrates the predictions (in dark red) of a single || decision tree trained over a random dataset LS (the blue dots) of a toy 1d || regression problem. It also illustrates the predictions (in light red) of other || single decision trees trained over other (and different) randomly drawn || instances LS of the problem. Intuitively; the variance term here corresponds to || the width of the beam of predictions (in light red) of the individual || estimators. The larger the variance; the more sensitive are the predictions for || `x` to small changes in the training set. The bias term corresponds to the || difference between the average prediction of the estimator (in cyan) and the || best possible model (in dark blue). On this problem; we can thus observe that || the bias is quite low (both the cyan and the blue curves are close to each || other) while the variance is large (the red beam is rather wide). ||  || The lower left figure plots the pointwise decomposition of the expected mean || squared error of a single decision tree. It confirms that the bias term (in || blue) is low while the variance is large (in green). It also illustrates the || noise part of the error which; as expected; appears to be constant and around || `0.01`. ||  || The right figures correspond to the same plots but using instead a bagging || ensemble of decision trees. In both figures; we can observe that the bias term || is larger than in the previous case. In the upper right figure; the difference || between the average prediction (in cyan) and the best possible model is larger || (e.g.; notice the offset around `x=2`). In the lower right figure; the bias || curve is also slightly higher than in the lower left figure. In terms of || variance however; the beam of predictions is narrower; which suggests that the || variance is lower. Indeed; as the lower right figure confirms; the variance || term (in green) is lower than for single decision trees. Overall; the bias- || variance decomposition is therefore no longer the same. The tradeoff is better || for bagging: averaging several decision trees fit on bootstrap copies of the || dataset slightly increases the bias term but allows for a larger reduction of || the variance; which results in a lower overall mean squared error (compare the || red curves int the lower figures). The script output also confirms this || intuition. The total error of the bagging ensemble is lower than the total || error of a single decision tree; and this difference indeed mainly stems from a || reduced variance. ||  || For further details on bias-variance decomposition; see section 7.3 of [1]_. ||  || References || ---------- ||  || .. [1] T. Hastie; R. Tibshirani and J. Friedman; ||        \"Elements of Statistical Learning\"; Springer; 2009. ||  || \"\"\"
aaicgaaga,scikit-learn/scikit-learn,sklearn/tests/test_cross_validation.py,cf9788cb07abcedee68fff808ef6132ab3b92553,38f703c80f1f0a43f50192d69c3bc4b4bd303953,The digits samples are dependent as they are apparently grouped
aaicgabde,scikit-learn/scikit-learn,sklearn/tests/test_cross_validation.py,e3002c5efc2a34defe65e017e2a0cc3b6f250c1a,STILL_EXISTS,The digits samples are dependent: they are apparently grouped by authors
aaicgabeb,scikit-learn/scikit-learn,sklearn/tests/test_cross_validation.py,e3002c5efc2a34defe65e017e2a0cc3b6f250c1a,STILL_EXISTS,Shuffling the data artificially breaks the dependency and hides the
aaicgabgd,scikit-learn/scikit-learn,sklearn/decomposition/nmf.py,a2a4257bc6e793faf6867cfe781cdfad7e5a7b41,5c4b1bb23192a137ac22ced229c50d6b69859ac6,fix up negative zeros
aaicgabhg,scikit-learn/scikit-learn,doc/sphinxext/gen_rst.py,bb2d0071192fdd19f9c52a7becb92fd957d52b8f,STILL_EXISTS,auto examples gallery to the _build folder. This works fine as is; but it would be cleaner to
aaicgabih,scikit-learn/scikit-learn,examples/applications/plot_prediction_latency.py,8160b8a444e9edf131dc72bdbf3bc675d84ba49e,STILL_EXISTS,Hack to detect whether we are running by the sphinx builder
aaicgacbi,scikit-learn/scikit-learn,sklearn/feature_selection/univariate_selection.py,9e10ec017b15f7549ba462dcd5c2bf089bc318e4,729be7470877cfccbcd052b9f85c20062c0b4a8f,XXX could use corr \/= row_norms(X.T) here; but the test doesn't pass
aaicgaiga,scikit-learn/scikit-learn,sklearn/preprocessing/data.py,568c43d6f383fde88a80fa7ec6528b2b1c023781,f372e126c0ed4a897d9b1887c022c3398aaf7817,TODO: make this work with sparse data
aaicgaige,scikit-learn/scikit-learn,examples/applications/plot_model_complexity_influence.py,dd84e10d3a1ea48dbae5b4fbaec58f6418c33355,STILL_EXISTS,\"\"\" || ========================== || Model Complexity Influence || ========================== ||  || Demonstrate how model complexity influences both prediction accuracy and || computational performance. ||  || The dataset is the boston housing dataset. ||  || \"\"\"
aaicgbega,scikit-learn/scikit-learn,sklearn/metrics/tests/test_metrics.py,f9cd5990ebaaf44564d5cb90be3db3ba0a464c58,STILL_EXISTS,XXX cruel hack to work with partial functions
aaicgbfih,scikit-learn/scikit-learn,sklearn/metrics/tests/test_score_objects.py,3df6e8a1eedfcd95795e54cecf9456bbe4e1f996,STILL_EXISTS,TODO Is there any yet?
aaicgbgbb,scikit-learn/scikit-learn,sklearn/metrics/tests/test_metrics.py,a9aa616a325f1ca004a9223fd5b7dc055948fe6d,STILL_EXISTS,In order to improve the overall metric testing; it is a good idea to write
aaicgbgbf,scikit-learn/scikit-learn,sklearn/metrics/tests/test_metrics.py,a9aa616a325f1ca004a9223fd5b7dc055948fe6d,STILL_EXISTS,Two type of datastructures are used in order to implement this system:
aaicgbied,scikit-learn/scikit-learn,sklearn/pipeline.py,7a22b77de2207cdaeb4df45a127f5a44894b3710,STILL_EXISTS,XXX it would be nice to have a keyword-only n_jobs argument to this function;
aaicgbihd,scikit-learn/scikit-learn,sklearn/externals/joblib/pool.py,8615684f511a39ed50f088f752aee094db11277a,STILL_EXISTS,\"\"\"Custom implementation of multiprocessing.Pool with custom pickler ||  || This module provides efficient ways of working with data stored in || shared memory with numpy.memmap arrays without inducing any memory || copy between the parent and child processes. ||  || This module should not be imported if multiprocessing is not || available as it implements subclasses of multiprocessing Pool || that uses a custom alternative to SimpleQueue. ||  || \"\"\"
aaicgbiih,scikit-learn/scikit-learn,sklearn/externals/joblib/pool.py,8615684f511a39ed50f088f752aee094db11277a,40e6c43cb49d6c1eb56faa46ca0ffa40ab6228b8,Support for efficient transient pickling of numpy data structures
aaicgbiii,scikit-learn/scikit-learn,sklearn/externals/joblib/pool.py,8615684f511a39ed50f088f752aee094db11277a,40e6c43cb49d6c1eb56faa46ca0ffa40ab6228b8,TODO: check scipy sparse datastructure if scipy is installed
aaicgbjbb,scikit-learn/scikit-learn,sklearn/externals/joblib/pool.py,8615684f511a39ed50f088f752aee094db11277a,40e6c43cb49d6c1eb56faa46ca0ffa40ab6228b8,XXX: implement an explicit reference counting scheme to make it
aaicgbjij,scikit-learn/scikit-learn,sklearn/externals/joblib/test/test_pool.py,8615684f511a39ed50f088f752aee094db11277a,STILL_EXISTS,Memory has been copied; the pool filesystem folder is unused
aaicgcabe,scikit-learn/scikit-learn,sklearn/ensemble/forest.py,c681e5da194d718154269cbf2ae314c2a44af10a,STILL_EXISTS,threading always more efficient than multiprocessing in that case.
aaicgchae,scikit-learn/scikit-learn,sklearn/learning_curve.py,a876682444cd2e7053e944616a93750f85713f7d,STILL_EXISTS,HACK as long as boolean indices are allowed in cv generators
aaicgchbb,scikit-learn/scikit-learn,sklearn/tests/test_learning_curve.py,a876682444cd2e7053e944616a93750f85713f7d,STILL_EXISTS,training score becomes worse (2 -> 1); test error better (0 -> 1)
aaicgchga,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_theilsen.py,d4519d2c800a86fd71902ca4d126b2dc7259b906,STILL_EXISTS,Check fix point
aaicgdaca,scikit-learn/scikit-learn,sklearn/cross_validation.py,5d8b429f17e0aab6742417a672501b59300e8d23,1d4d3f815352423c61bfc20aac8523fb89061f8e,TODO replace with grid_search.fit_grid_point()
aaicgdacb,scikit-learn/scikit-learn,sklearn/cross_validation.py,5d8b429f17e0aab6742417a672501b59300e8d23,2bc3d49cfbed606fa2e708e723386294e1488c3a,TODO why is this necessary?
aaicgdahb,scikit-learn/scikit-learn,sklearn/linear_model/coordinate_descent.py,5ea9aad7ecb3d907ee2dbaeb14f25cda3768e698,STILL_EXISTS,Workaround to find alpha_max for sparse matrices.
aaicgdgie,scikit-learn/scikit-learn,sklearn/cluster/hierarchical.py,c5af7cb3980e42b9b5e1ba258f60492579429064,STILL_EXISTS,XXX: Can we do without completing the matrix?
aaicgdgii,scikit-learn/scikit-learn,sklearn/cluster/hierarchical.py,c5af7cb3980e42b9b5e1ba258f60492579429064,5ed6b26a0a16c2893884e5237aa4354067736262,XXX: if affinity is a callable; the following will not work
aaicgdgjf,scikit-learn/scikit-learn,sklearn/cluster/hierarchical.py,c5af7cb3980e42b9b5e1ba258f60492579429064,STILL_EXISTS,FIXME We compute all the distances; while we could have only computed
aaicgdgji,scikit-learn/scikit-learn,sklearn/cluster/hierarchical.py,c5af7cb3980e42b9b5e1ba258f60492579429064,STILL_EXISTS,XXX: can we avoid switching to lil
aaicgdhbc,scikit-learn/scikit-learn,sklearn/cluster/hierarchical.py,c5af7cb3980e42b9b5e1ba258f60492579429064,STILL_EXISTS,return numpy array for efficient caching
aaicgdhbe,scikit-learn/scikit-learn,sklearn/cluster/hierarchical.py,c5af7cb3980e42b9b5e1ba258f60492579429064,e757a67c4918d2a38a4574f006fef010764715b6,FIXME check affinity exists.
aaicgdhgc,scikit-learn/scikit-learn,examples/cluster/plot_agglomerative_clustering.py,fa3edaac33872e40967023d71c0b7deae151479d,STILL_EXISTS,\"\"\" || Agglomerative clustering with and without structure || =================================================== ||  || This example shows the effect of imposing a connectivity graph to capture || local structure in the data. The graph is simply the graph of 20 nearest || neighbors. ||  || Two consequences of imposing a connectivity can be seen. First clustering || with a connectivity matrix is much faster. ||  || Second; when using a connectivity matrix; average and complete linkage are || unstable and tend to create a few clusters that grow very quickly. Indeed; || average and complete linkage fight this percolation behavior by considering all || the distances between two clusters when merging them. The connectivity || graph breaks this mechanism. This effect is more pronounced for very || sparse graphs (try decreasing the number of neighbors in || kneighbors_graph) and with complete linkage. In particular; having a very || small number of neighbors in the graph; imposes a geometry that is || close to that of single linkage; which is well known to have this || percolation instability. || \"\"\"
aaicgdica,scikit-learn/scikit-learn,sklearn/svm/base.py,0f5685b8eed8e71c91611c5d48e2acabec09261f,dd335cb02cf6ab923167f601d511b99c350cf24c,Hacky way of getting predict_proba to raise an AttributeError when
aaicgdicj,scikit-learn/scikit-learn,sklearn/svm/base.py,f73ce69c91615e26e5b2c7963f20b134d055e6f9,566237dbab75ac26d380dc63775455c2c5884058,Hacky way of getting predict_proba to raise an AttributeError when
aaicgdifb,scikit-learn/scikit-learn,sklearn/svm/base.py,88ecfa0339b1d26b8758cd309b663cb4cc91da7e,STILL_EXISTS,Hacky way of getting predict_proba to raise an AttributeError when
aaicgfahc,scikit-learn/scikit-learn,sklearn/linear_model/stochastic_gradient.py,85922d3a33509aa50c6d26d579dfb0c57ffd11f2,STILL_EXISTS,XXX should have random_state_!
aaicgfbcj,scikit-learn/scikit-learn,sklearn/linear_model/base.py,a786e12ace3ef0c7e77cea93edd5e316bed6f93c,STILL_EXISTS,XXX: currently scaled to variance=n_samples to match center_data
aaicgfbda,scikit-learn/scikit-learn,sklearn/linear_model/base.py,a786e12ace3ef0c7e77cea93edd5e316bed6f93c,eec7649236d5216380d05916bb7f6aa3b2fc5508,XXX: currently scaled to variance=n_samples
aaicgfbdc,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_base.py,a786e12ace3ef0c7e77cea93edd5e316bed6f93c,STILL_EXISTS,XXX: if normalize=True; should we expect a weighted standard deviation?
aaicgfbdj,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_least_angle.py,3445813f78b5308a1ad4e5c12e59606eed48e7ab,58ffba9e9f8b41fd5ae307fc507093a764dfafcc,TODO: raise a specific warning class when drop for good kicks in and
aaicgfbgg,scikit-learn/scikit-learn,sklearn/covariance/robust_covariance.py,05807fd5bcd0d63af380ff32042cd53e904eaaa6,STILL_EXISTS,XXX Is this really a public function? It's not listed in the docs or
aaicgfcaf,scikit-learn/scikit-learn,doc/sphinxext/numpy_ext/numpydoc.py,2e03618a0f593c41567c84dc6069367e5200fb59,STILL_EXISTS,Only needed to check Python version
aaicgfeac,scikit-learn/scikit-learn,sklearn/externals/joblib/func_inspect.py,181ffb87ff87ed9098b7f711041dd9f5643e9a31,STILL_EXISTS,XXX: Should this use inspect.formatargvalues\/formatargspec?
aaicgfead,scikit-learn/scikit-learn,sklearn/externals/joblib/func_inspect.py,181ffb87ff87ed9098b7f711041dd9f5643e9a31,STILL_EXISTS,XXX: Not using logging framework
aaicgfech,scikit-learn/scikit-learn,sklearn/externals/joblib/memory.py,181ffb87ff87ed9098b7f711041dd9f5643e9a31,STILL_EXISTS,objects; as it ends up being too fragile
aaicgfeec,scikit-learn/scikit-learn,sklearn/externals/joblib/memory.py,181ffb87ff87ed9098b7f711041dd9f5643e9a31,8935df616eabeef9e10c42bec53d02ce05eb10c7,matter of replacing repr() above by something smarter.
aaicgfeic,scikit-learn/scikit-learn,examples/manifold/plot_tsne_iris.py,da43e4c230f4cb964ea6ca1af3e6c451a5842c78,STILL_EXISTS,\"\"\" || =================================== || t-SNE Visualization of Iris Dataset || =================================== ||  || TODO || \"\"\"
aaicgffaa,scikit-learn/scikit-learn,sklearn/manifold/t_sne.py,da43e4c230f4cb964ea6ca1af3e6c451a5842c78,53e015c9613e68e2c38c7b8e8c647836c76231e1,\"A less obvious way to improve the optimization; which we call
aaicgffaj,scikit-learn/scikit-learn,sklearn/manifold/t_sne.py,da43e4c230f4cb964ea6ca1af3e6c451a5842c78,53e015c9613e68e2c38c7b8e8c647836c76231e1,makes it much easier for the clusters to move around relative to
aaicgffbc,scikit-learn/scikit-learn,examples/manifold/plot_tsne_digits.py,7a1f84eb693766cef4704e0d5e13cb54ddb20105,STILL_EXISTS,\"\"\" || ===================================== || t-SNE Visualization of Digits Dataset || ===================================== ||  || TODO || \"\"\"
aaicgffeh,scikit-learn/scikit-learn,sklearn/manifold/tests/test_t_sne.py,58717d34f323650f7383588b2939eb944003fb92,405bf17b438b24e0e1c1f538b7ae789a1f8cfcc5,TODO tests with verbose = 1 or 2
aaicgfggi,scikit-learn/scikit-learn,sklearn/preprocessing/label.py,18c399e20ae34222cd91be0d2a6c9af5a217d1ad,STILL_EXISTS,sort classes and reorder columns
aaicgfhbi,scikit-learn/scikit-learn,doc/sphinxext/gen_rst.py,6f6a537d5f832b4d06a690cf1dad8f131235e2a2,8935df616eabeef9e10c42bec53d02ce05eb10c7,HACK: Stop nosetests running setup() above
aaicgfiai,scikit-learn/scikit-learn,doc/sphinxext/gen_rst.py,aaefdbd3def81a0318b07990dadc907bf63123ce,8935df616eabeef9e10c42bec53d02ce05eb10c7,HACK: Stop nosetests running setup() above
aaicgfigf,scikit-learn/scikit-learn,sklearn/linear_model/stochastic_gradient.py,c29ef67cc0ddb590ec761b13591e9e1e4a3fe003,STILL_EXISTS,XXX should have random_state_!
aaicgfjai,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_omp.py,1f32603b860c4c0230ee50f7858196e8ec37319f,211f204767e248d4c45923c60768c3729de63249,FIXME: This test is unstable on Travis; see issue #3190 for more detail.
aaicgfjbb,scikit-learn/scikit-learn,sklearn/tests/test_common.py,25e11813b7a19cd336cc6697ac94106920531446,8935df616eabeef9e10c42bec53d02ce05eb10c7,FIXME: This test is unstable on Travis; see issue #3190.
aaicgfjbh,scikit-learn/scikit-learn,sklearn/tests/test_common.py,59b0f61224fd88d67cd4ec22e6bab1ef271ef18c,8935df616eabeef9e10c42bec53d02ce05eb10c7,FIXME: try to isolate a minimalistic reproduction case only depending
aaicgfjch,scikit-learn/scikit-learn,sklearn/tests/test_common.py,14d03f60ed366df942be09ee4bc394a69958e09c,8935df616eabeef9e10c42bec53d02ce05eb10c7,FIXME: try to isolate a minimalistic reproduction case only depending
aaicgfjfh,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,894c5302e172a0533cd23b22ff96c1bf94029afc,8935df616eabeef9e10c42bec53d02ce05eb10c7,to avoid instant reassignment. This is a pretty dirty hack as it
aaicgfjgh,scikit-learn/scikit-learn,sklearn/tests/test_common.py,a9ad5a21422b3e5fdb99546f962dcbb0318de4bf,8935df616eabeef9e10c42bec53d02ce05eb10c7,FIXME: try to isolate a minimalistic reproduction case only depending
aaicggaah,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_coordinate_descent.py,e65150a24a23e1cd4fc0be3cbb18c4d65821e812,STILL_EXISTS,the problem as a better starting point. This should also converge
aaicggabd,scikit-learn/scikit-learn,sklearn/feature_selection/tests/test_rfe.py,9248948344a878edbcafce6640202e420799e241,STILL_EXISTS,Test fix on grid_scores
aaicggaeh,scikit-learn/scikit-learn,examples/text/mlcomp_sparse_document_classification.py,5b247f90e4b518ef7ce470bafa17f281de3a1c01,STILL_EXISTS,\"\"\" || ======================================================== || Classification of text documents: using a MLComp dataset || ======================================================== ||  || This is an example showing how the scikit-learn can be used to classify || documents by topics using a bag-of-words approach. This example uses || a scipy.sparse matrix to store the features instead of standard numpy arrays. ||  || The dataset used in this example is the 20 newsgroups dataset and should be || downloaded from the http:\/\/mlcomp.org (free registration required): ||  ||   http:\/\/mlcomp.org\/datasets\/379 ||  || Once downloaded unzip the archive somewhere on your filesystem. || For instance in:: ||  ||   % mkdir -p ~\/data\/mlcomp ||   % cd  ~\/data\/mlcomp ||   % unzip \/path\/to\/dataset-379-20news-18828_XXXXX.zip ||  || You should get a folder ``~\/data\/mlcomp\/379`` with a file named ``metadata`` || and subfolders ``raw``; ``train`` and ``test`` holding the text documents || organized by newsgroups. ||  || Then set the ``MLCOMP_DATASETS_HOME`` environment variable pointing to || the root folder holding the uncompressed archive:: ||  ||   % export MLCOMP_DATASETS_HOME=\"~\/data\/mlcomp\" ||  || Then you are ready to run this example using your favorite python shell:: ||  ||   % ipython examples\/mlcomp_sparse_document_classification.py ||  || \"\"\"
aaicggbag,scikit-learn/scikit-learn,sklearn/tests/test_common.py,e42ecd1466a78bac98964eee904cab945e7b8340,8935df616eabeef9e10c42bec53d02ce05eb10c7,XXX: some transformers are transforming the input
aaicggbbe,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_omp.py,ce29c70cae50d46b851effa40deb5e7e44b3976a,211f204767e248d4c45923c60768c3729de63249,FIXME: This test is unstable on Travis; see issue #3190 for more detail.
aaicggcfh,scikit-learn/scikit-learn,sklearn/externals/joblib/memory.py,8935df616eabeef9e10c42bec53d02ce05eb10c7,STILL_EXISTS,XXX: Not using logging framework
aaicggcfj,scikit-learn/scikit-learn,sklearn/externals/joblib/memory.py,8935df616eabeef9e10c42bec53d02ce05eb10c7,STILL_EXISTS,XXX: This should be moved out to a function
aaicggcga,scikit-learn/scikit-learn,sklearn/externals/joblib/memory.py,8935df616eabeef9e10c42bec53d02ce05eb10c7,STILL_EXISTS,XXX: Should this use inspect.formatargvalues\/formatargspec?
aaicggdag,scikit-learn/scikit-learn,sklearn/feature_selection/univariate_selection.py,8935df616eabeef9e10c42bec53d02ce05eb10c7,4b6768f368b14566fe8b6e5da469593f07ba199e,XXX This should be refactored; we're getting an array of indices
aaicggdaj,scikit-learn/scikit-learn,sklearn/grid_search.py,8935df616eabeef9e10c42bec53d02ce05eb10c7,b12d1ac48739e44dde67b6afd1fee8edb8ad6288,XXX Why? The behavior when passing a list is undocumented;
aaicggegh,scikit-learn/scikit-learn,sklearn/tests/test_common.py,e7d096cc2e5bc0942eddbbad9ed64db6893b02a0,718f6ad19f90794e62f56d980c6e730a854e56d7,FIXME: try to isolate a minimalistic reproduction case only depending
aaicggeid,scikit-learn/scikit-learn,sklearn/tests/test_common.py,e7d096cc2e5bc0942eddbbad9ed64db6893b02a0,718f6ad19f90794e62f56d980c6e730a854e56d7,XXX: some transformers are transforming the input
aaicggfbd,scikit-learn/scikit-learn,doc/sphinxext/gen_rst.py,4b6768f368b14566fe8b6e5da469593f07ba199e,STILL_EXISTS,HACK: Stop nosetests running setup() above
aaicggffd,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,4b6768f368b14566fe8b6e5da469593f07ba199e,STILL_EXISTS,to avoid instant reassignment. This is a pretty dirty hack as it
aaicgggch,scikit-learn/scikit-learn,sklearn/externals/joblib/memory.py,4b6768f368b14566fe8b6e5da469593f07ba199e,STILL_EXISTS,objects; as it ends up being too fragile
aaicgggec,scikit-learn/scikit-learn,sklearn/externals/joblib/memory.py,4b6768f368b14566fe8b6e5da469593f07ba199e,STILL_EXISTS,matter of replacing repr() above by something smarter.
aaicgggee,scikit-learn/scikit-learn,sklearn/externals/joblib/memory.py,4b6768f368b14566fe8b6e5da469593f07ba199e,STILL_EXISTS,XXX: Not using logging framework
aaicgggeg,scikit-learn/scikit-learn,sklearn/externals/joblib/memory.py,4b6768f368b14566fe8b6e5da469593f07ba199e,STILL_EXISTS,XXX: This should be moved out to a function
aaicgggeh,scikit-learn/scikit-learn,sklearn/externals/joblib/memory.py,4b6768f368b14566fe8b6e5da469593f07ba199e,STILL_EXISTS,XXX: Should this use inspect.formatargvalues\/formatargspec?
aaicggida,scikit-learn/scikit-learn,sklearn/preprocessing/label.py,b080d93b5ea15111ee1a97f142e020dee661dbb6,STILL_EXISTS,XXX Workaround that will be removed when list of list format is dropped
aaicggiea,scikit-learn/scikit-learn,sklearn/utils/mocking.py,b080d93b5ea15111ee1a97f142e020dee661dbb6,STILL_EXISTS,ugly hack to make iloc work.
aaicghadj,scikit-learn/scikit-learn,sklearn/utils/estimator_checks.py,718f6ad19f90794e62f56d980c6e730a854e56d7,STILL_EXISTS,FIXME: try to isolate a minimalistic reproduction case only depending
aaicghbai,scikit-learn/scikit-learn,sklearn/utils/estimator_checks.py,718f6ad19f90794e62f56d980c6e730a854e56d7,STILL_EXISTS,TODO some complication with -1 label
aaicghbci,scikit-learn/scikit-learn,sklearn/utils/estimator_checks.py,718f6ad19f90794e62f56d980c6e730a854e56d7,STILL_EXISTS,TODO: find out why PLS and CCA fail. RANSAC is random
aaicghbdi,scikit-learn/scikit-learn,sklearn/utils/estimator_checks.py,718f6ad19f90794e62f56d980c6e730a854e56d7,8cf714cc9d14a0050f6bb2e6f998ee50a4134975,FIXME
aaicghdgf,scikit-learn/scikit-learn,sklearn/metrics/classification.py,6d881d3fb1e69938db4d184f2e79978cb12ca6a0,STILL_EXISTS,\"\"\"Utilities to evaluate the predictive performance of models ||  || Functions named as ``*_score`` return a scalar value to maximize: the higher || the better ||  || Function named as ``*_error`` or ``*_loss`` return a scalar value to minimize: || the lower the better || \"\"\"
aaicghdhf,scikit-learn/scikit-learn,sklearn/metrics/classification.py,6d881d3fb1e69938db4d184f2e79978cb12ca6a0,STILL_EXISTS,We can't have more than one value on y_type => The set is no more needed
aaicgheba,scikit-learn/scikit-learn,sklearn/metrics/ranking.py,6d881d3fb1e69938db4d184f2e79978cb12ca6a0,STILL_EXISTS,\"\"\"Utilities to evaluate the predictive performance of models ||  || Functions named as ``*_score`` return a scalar value to maximize: the higher || the better ||  || Function named as ``*_error`` or ``*_loss`` return a scalar value to minimize: || the lower the better || \"\"\"
aaicghecc,scikit-learn/scikit-learn,sklearn/metrics/ranking.py,6d881d3fb1e69938db4d184f2e79978cb12ca6a0,85c9c84bc7cc0a981962f4a6136ac6a8bea03cbe,TODO: multi-class hinge-loss
aaicgheeh,scikit-learn/scikit-learn,sklearn/metrics/regression.py,6d881d3fb1e69938db4d184f2e79978cb12ca6a0,STILL_EXISTS,\"\"\"Utilities to evaluate the predictive performance of models in regression ||  || Functions named as ``*_score`` return a scalar value to maximize: the higher || the better ||  || Function named as ``*_error`` or ``*_loss`` return a scalar value to minimize: || the lower the better || \"\"\"
aaicghegj,scikit-learn/scikit-learn,sklearn/metrics/tests/test_classification.py,6d881d3fb1e69938db4d184f2e79978cb12ca6a0,STILL_EXISTS,XXX: do we really want a special API for the binary case?
aaicgheij,scikit-learn/scikit-learn,sklearn/metrics/tests/test_classification.py,6d881d3fb1e69938db4d184f2e79978cb12ca6a0,STILL_EXISTS,could be swapped around; creating an imperfect sorting. This
aaicghfgg,scikit-learn/scikit-learn,sklearn/metrics/tests/test_common.py,6d881d3fb1e69938db4d184f2e79978cb12ca6a0,STILL_EXISTS,TODO Curve are currently not coverd by invariance test
aaicghfhi,scikit-learn/scikit-learn,sklearn/metrics/tests/test_common.py,6d881d3fb1e69938db4d184f2e79978cb12ca6a0,STILL_EXISTS,In order to improve the overall metric testing; it is a good idea to write
aaicghfic,scikit-learn/scikit-learn,sklearn/metrics/tests/test_common.py,6d881d3fb1e69938db4d184f2e79978cb12ca6a0,STILL_EXISTS,Two types of datastructures are used in order to implement this system:
aaicghgah,scikit-learn/scikit-learn,sklearn/metrics/tests/test_common.py,6d881d3fb1e69938db4d184f2e79978cb12ca6a0,STILL_EXISTS,When you add a new metric or functionality; check if a general test
aaicghgde,scikit-learn/scikit-learn,sklearn/metrics/tests/test_common.py,6d881d3fb1e69938db4d184f2e79978cb12ca6a0,STILL_EXISTS,TODO those metrics doesn't support string label yet
aaicghgeg,scikit-learn/scikit-learn,sklearn/metrics/tests/test_common.py,6d881d3fb1e69938db4d184f2e79978cb12ca6a0,STILL_EXISTS,XXX cruel hack to work with partial functions
aaicghgih,scikit-learn/scikit-learn,sklearn/metrics/tests/test_ranking.py,6d881d3fb1e69938db4d184f2e79978cb12ca6a0,STILL_EXISTS,XXX: do we really want a special API for the binary case?
aaicghhhc,scikit-learn/scikit-learn,sklearn/metrics/tests/test_score_objects.py,6598ae89ddecc6c2664a580285d76f8d89f48d09,STILL_EXISTS,scores really should be unequal.
aaicgigij,scikit-learn/scikit-learn,sklearn/metrics/metrics.py,c03d4bdc721334305bf54e9b53d484f749fc0011,2fcba229c4c97a689919f894c01814a47c01d841,\"\"\"Utilities to evaluate the predictive performance of models ||  || Functions named as ``*_score`` return a scalar value to maximize: the higher || the better ||  || Function named as ``*_error`` or ``*_loss`` return a scalar value to minimize: || the lower the better || \"\"\"
aaicgihad,scikit-learn/scikit-learn,sklearn/metrics/metrics.py,c03d4bdc721334305bf54e9b53d484f749fc0011,2fcba229c4c97a689919f894c01814a47c01d841,We can't have more than one value on y_type => The set is no more needed
aaicgihba,scikit-learn/scikit-learn,sklearn/metrics/metrics.py,c03d4bdc721334305bf54e9b53d484f749fc0011,2fcba229c4c97a689919f894c01814a47c01d841,TODO: multi-class hinge-loss
aaicgihjj,scikit-learn/scikit-learn,sklearn/ensemble/forest.py,6e2a83b4e184f0e51aead75d5c82fc0284fa6233,feceab6f620a6314f5206f97e2e598ff91f82ecb,for 1d. FIXME make this consistent in the future.
aaichaaea,scikit-learn/scikit-learn,sklearn/metrics/classification.py,85c9c84bc7cc0a981962f4a6136ac6a8bea03cbe,STILL_EXISTS,TODO: multi-class hinge-loss
aaichabcd,scikit-learn/scikit-learn,sklearn/linear_model/logistic.py,6e4f02c4273b2d4f11fec5e3de460ffe45b1c82e,bb74497c67e00354ba5dcf63be5d7711b7b591bb,XXX: how to do this with sparse matrices?
aaichabcj,scikit-learn/scikit-learn,sklearn/linear_model/logistic.py,6e4f02c4273b2d4f11fec5e3de460ffe45b1c82e,STILL_EXISTS,XXX: I am not sure that this last line of the Hessian is right
aaichabeb,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_logistic.py,6e4f02c4273b2d4f11fec5e3de460ffe45b1c82e,7c935a32358839588b9509d6b71efda466ffae88,XXX: we should check a few simple properties of our problem; such
aaichabgi,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_logistic.py,91ed4bb376275ff403e5f4d9e84643d422e666fa,48a862886eee3d20628b96c2e9cf77a22a2d3428,TODO: do something
aaichabic,scikit-learn/scikit-learn,sklearn/linear_model/logistic.py,137d40bad609cdfdb8f8d7daf731997936b54134,2e3b92f25d6affc47fcd356be1b0129ba123f3db,XXX: Is this right?
aaichacjc,scikit-learn/scikit-learn,sklearn/linear_model/stochastic_gradient.py,9e875bf0a69169cb041b1bd26d670bdcb3d574a5,STILL_EXISTS,TODO: pass in average array for second argument
aaichacjd,scikit-learn/scikit-learn,sklearn/linear_model/stochastic_gradient.py,9e875bf0a69169cb041b1bd26d670bdcb3d574a5,2f4ea66dd8cb6124a8b1f8de227d4e159f679bb4,TODO: add implementation for asgd
aaichadhb,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_sgd.py,6a0e7f7f68eb02d6e724af4e359e915acc123c06,07560e45c234fe209f13f8689a6a6f36490bd21c,TODO: remove label encoding when class bug is fixed
aaichadhc,scikit-learn/scikit-learn,sklearn/tests/test_dummy.py,bdd5cb162e9e2a37b84b2f06fb31fed1c825a1a4,4052878c2c7c59dea09b20c95ba8cc578904228c,XXX a test of the cahnge to the fit function only
aaichadhe,scikit-learn/scikit-learn,sklearn/tests/test_dummy.py,bdd5cb162e9e2a37b84b2f06fb31fed1c825a1a4,4052878c2c7c59dea09b20c95ba8cc578904228c,XXX Remove ^^^
aaichadic,scikit-learn/scikit-learn,sklearn/dummy.py,c0981d24e6223e44ec64ef3e3d3c3a88f65cb982,STILL_EXISTS,XXX how does this work?
aaichadid,scikit-learn/scikit-learn,sklearn/dummy.py,c0981d24e6223e44ec64ef3e3d3c3a88f65cb982,STILL_EXISTS,XXX remove zeros
aaichadjf,scikit-learn/scikit-learn,sklearn/dummy.py,5c5ef1ad7c284ac5a9736f829eed28479bb866d0,05a8dd7cd06d3d117d6b127dcb9eb2521936be85,XXX select non zeros
aaichadji,scikit-learn/scikit-learn,sklearn/utils/sparsefuncs.py,3d692a96615ad2311dfa8e016746574907e8a606,STILL_EXISTS,XXX Corner case: size 0
aaichadjj,scikit-learn/scikit-learn,sklearn/utils/sparsefuncs.py,3d692a96615ad2311dfa8e016746574907e8a606,STILL_EXISTS,XXX Validate: p and a have the same length
aaichaeaa,scikit-learn/scikit-learn,sklearn/utils/sparsefuncs.py,3d692a96615ad2311dfa8e016746574907e8a606,STILL_EXISTS,XXX maybe round
aaichaeaj,scikit-learn/scikit-learn,sklearn/utils/sparsefuncs.py,91a5b9288253ec1a502db298c57300ea99cc5490,6e9d166337e19e7279fac9675ba5a426a021321e,XXX np.asarray()?
aaichagdh,scikit-learn/scikit-learn,sklearn/linear_model/stochastic_gradient.py,0943c922e13b560e71ce828b4066286d4d812e7c,8dbec4f76c1659591e8ed5c7ee42d5f5d4f0e80c,XXX use a LabelBinarizer?
aaichaged,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_sgd.py,0943c922e13b560e71ce828b4066286d4d812e7c,8dbec4f76c1659591e8ed5c7ee42d5f5d4f0e80c,TODO: remove label encoding when class bug is fixed
aaichahff,scikit-learn/scikit-learn,sklearn/linear_model/logistic.py,6b5b9e3eed72a2b3eaa1bedc58c6aa8f7e47f033,STILL_EXISTS,hack to iterate only once for multinomial case.
aaichaicc,scikit-learn/scikit-learn,sklearn/linear_model/logistic.py,a7ccbb8096368159e7c171cf572af69ae138a155,STILL_EXISTS,Hack so that we iterate only once for the multinomial case.
aaichaide,scikit-learn/scikit-learn,sklearn/linear_model/stochastic_gradient.py,14eb82bfeb297c92461ab9294b0c699fe89db57f,8dbec4f76c1659591e8ed5c7ee42d5f5d4f0e80c,seen will be unused
aaichaidi,scikit-learn/scikit-learn,sklearn/__init__.py,b4e41cb614a7039fd429a77d7cffa929f1fc37da,STILL_EXISTS,avoid flakes unused variable error
aaichbcig,scikit-learn/scikit-learn,sklearn/tests/test_cross_validation.py,8dbec4f76c1659591e8ed5c7ee42d5f5d4f0e80c,STILL_EXISTS,also test deprecated old way
aaichbddd,scikit-learn/scikit-learn,sklearn/linear_model/stochastic_gradient.py,149d97f78e6919919d05879a0c841dfc5592a7b9,e95a7f0e8e4da79b3380c756d2ac988bea6585a5,seen will be unused
aaichbdde,scikit-learn/scikit-learn,sklearn/linear_model/stochastic_gradient.py,149d97f78e6919919d05879a0c841dfc5592a7b9,8081745013cdbca011852a06aaa3e8277550ba75,XXX use a LabelBinarizer?
aaichbddj,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_sgd.py,149d97f78e6919919d05879a0c841dfc5592a7b9,STILL_EXISTS,TODO: remove label encoding when class bug is fixed
aaichbefj,scikit-learn/scikit-learn,sklearn/linear_model/stochastic_gradient.py,39c87018b2812315e3e65482cb29a805dafb506f,b0ab7146994fee2e2d05bfcc5123f6690ee2d913,unused
aaichbegf,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_sgd.py,39c87018b2812315e3e65482cb29a805dafb506f,STILL_EXISTS,TODO: remove label encoding when class bug is fixed
aaichbehb,scikit-learn/scikit-learn,sklearn/feature_extraction/dict_vectorizer.py,324e3c9419a84ca245bfc9e69719674a2d9935cb,7354359bca58c080b36bf4459448d0ea0d2e5338,best thing: typecode \"i\" (int). However; if that gives larger or
aaichbehe,scikit-learn/scikit-learn,sklearn/feature_extraction/dict_vectorizer.py,324e3c9419a84ca245bfc9e69719674a2d9935cb,7354359bca58c080b36bf4459448d0ea0d2e5338,XXX we could change values to an array.array as well; but it
aaichbehh,scikit-learn/scikit-learn,sklearn/feature_extraction/dict_vectorizer.py,324e3c9419a84ca245bfc9e69719674a2d9935cb,7354359bca58c080b36bf4459448d0ea0d2e5338,workaround for bug in older NumPy:
aaichbffa,scikit-learn/scikit-learn,sklearn/utils/extmath.py,b2d6a30aeb155948fbee1c3a9f0ce9cb5bfb278a,STILL_EXISTS,columns of u; rows of v
aaichbffb,scikit-learn/scikit-learn,sklearn/utils/extmath.py,b2d6a30aeb155948fbee1c3a9f0ce9cb5bfb278a,STILL_EXISTS,rows of v; columns of u
aaichbhgj,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_sgd.py,b0ab7146994fee2e2d05bfcc5123f6690ee2d913,STILL_EXISTS,TODO: remove label encoding when class bug is fixed
aaichbhjh,scikit-learn/scikit-learn,sklearn/linear_model/stochastic_gradient.py,5fe405bd6545cd1ebf8ed7aeb3fa94be087c7449,STILL_EXISTS,unused
aaichbjgg,scikit-learn/scikit-learn,sklearn/neighbors/nearest_centroid.py,6f41bd1c882b5f6064fc3db2a830bde5bf3885bd,STILL_EXISTS,XXX: Update other averaging methods according to the metrics.
aaichcacc,scikit-learn/scikit-learn,sklearn/metrics/tests/test_common.py,8eee4bc7e1b26c6b5d04970940855e083befc095,STILL_EXISTS,XXX: Handle multi_class metrics that has a labels argument as well as a
aaichcbad,scikit-learn/scikit-learn,examples/hetero_feature_union.py,cf96bba944ca38333d275f32a243e1076286e12c,STILL_EXISTS,\"\"\" || ============================================= || Feature Union with Heterogeneous Data Sources || ============================================= ||  || Datasets can often contain components of that require different feature || extraction and processing pipelines.  This scenario might occur when: ||  || 1. Your dataset consists of heterogeneous data types (e.g. raster images and ||    text captions) || 2. Your dataset is stored in a Pandas DataFrame and different columns ||    require different processing pipelines. ||  || This example demonstrates how to use || :class:`sklearn.feature_extraction.FeatureUnion` on a dataset containing || different types of features.  We use the 20-newsgroups dataset and compute || standard bag-of-words features for the subject line and body in separate || pipelines as well as ad hoc features on the body. We combine them (with || weights) using a FeatureUnion and finally train a classifier on the combined || set of features. ||  || The choice of features is not particularly helpful; but serves to illustrate || the technique. || \"\"\"
aaichcbff,scikit-learn/scikit-learn,sklearn/utils/fixes.py,54f483aafa9a5ac7358adafcca355c8dc2149ce1,2bd87f6ed8d36f89cab73552665e4e59e892262b,Backport fix for scikit-learn\/scikit-learn#2986 \/ scipy\/scipy#4142
aaichcdgc,scikit-learn/scikit-learn,sklearn/externals/joblib/pool.py,f5e7cb14df045b09db1d712d9007e30480b7d492,STILL_EXISTS,Let us use a closure to workaround this limitation.
aaichceij,scikit-learn/scikit-learn,sklearn/metrics/tests/test_common.py,081a5546988e4514afecf00250901f66e29b7543,STILL_EXISTS,These are needed to test averaging
aaichcgcc,scikit-learn/scikit-learn,sklearn/feature_selection/rfe.py,ad7e77242217c291e0845cb2264d2dc388d8797a,c165327940cd1d16038995e20dfd2ecad69fb2a2,but perhaps reordered
aaichcgdf,scikit-learn/scikit-learn,sklearn/tests/test_naive_bayes.py,c81c679033fd767001306d04ceefc39feeb0cada,STILL_EXISTS,FIXME Remove this test once the more general partial_fit tests are merged
aaichcgig,scikit-learn/scikit-learn,sklearn/lda.py,4e1c63baac8565473ae54b0b0b439676fe2be8e4,STILL_EXISTS,TODO: support equal priors (should probably be the default)
aaichcgij,scikit-learn/scikit-learn,examples/classification/plot_simple_lda.py,7d812994b7bf599552827a05b9a2e487dcbfd721,STILL_EXISTS,\"\"\" || Created on Wed Sep 24 10:18:50 2014 ||  || @author: clemens || \"\"\"
aaichcije,scikit-learn/scikit-learn,sklearn/neighbors/approximate.py,7de8e3fc35c0bb9e67d8861478c161a7170bcece,STILL_EXISTS,XXX: perhaps non-copying operation better
aaichdbed,scikit-learn/scikit-learn,sklearn/neighbors/approximate.py,e736d76f524ee0ec456365b266755a1af4a80048,91c2386e89a98022e4aa3de3a665b974c25bd29d,XXX: would be great to vectorize or parallelise this:
aaichdbgc,scikit-learn/scikit-learn,sklearn/isotonic.py,d255866c667b74801b68e828b6fb5a769c531662,5a3b9e93ceb20d7681c1bd15ef28d808a84a701c,XXX: Address scipy issue 4304 by adding a tiny value to
aaichdbge,scikit-learn/scikit-learn,sklearn/neighbors/approximate.py,a39465464a105b182c3bb1407c93984cbe7016bd,STILL_EXISTS,needed since _fit_X[np.array([])] doesn't work if _fit_X sparse
aaichdbgg,scikit-learn/scikit-learn,sklearn/neighbors/approximate.py,a39465464a105b182c3bb1407c93984cbe7016bd,STILL_EXISTS,XXX: not sure whether this is being calculated correctly wrt
aaichdfej,scikit-learn/scikit-learn,sklearn/tests/test_common.py,16662744acda542b45e997289d051621ac02b04d,e3e0827243b93b5a51893f39215d7284d5d319c6,FIXME these should be done also for non-mixin estimators!
aaichdffb,scikit-learn/scikit-learn,sklearn/tests/test_common.py,16662744acda542b45e997289d051621ac02b04d,669a4ea853114dbba3621cb96c72b35560471646,FIXME!
aaichdfid,scikit-learn/scikit-learn,sklearn/tests/test_common.py,0fce60d285e6c9154422b0b57d143a587d914dd5,e3e0827243b93b5a51893f39215d7284d5d319c6,FIXME these should be done also for non-mixin estimators!
aaichdfja,scikit-learn/scikit-learn,benchmarks/bench_mnist.py,fa0ceee4dc943fee1f7550282363968069c40d09,STILL_EXISTS,\"\"\" || ======================= || MNIST dataset benchmark || ======================= ||  || Benchmark on the MNIST dataset.  The dataset comprises 70;000 samples || and 784 features. Here; we consider the task of predicting || 10 classes -  digits from 0 to 9 from their raw images. By contrast to the || covertype dataset; the feature space is homogenous. ||  ||     [..] ||     Classification performance: ||     =========================== ||     Classifier       train-time test-time error-rate ||     -------------------------------------------- ||     Nystroem-SVM     118.4512s   0.8624s     0.0231 ||     ExtraTrees        47.5039s   0.5075s     0.0288 ||     RandomForest      46.2317s   0.4342s     0.0304 ||     SampledRBF-SVM   137.0131s   0.8688s     0.0488 ||     CART              21.0593s   0.0134s     0.1214 ||  ||  ||  || \"\"\"
aaichdgbf,scikit-learn/scikit-learn,examples/svm/plot_rbf_parameters.py,6e0b95fd6dfc85b4d2c5912eab96ac537a8ddcdf,STILL_EXISTS,Utility function to move the midpoint of a colormap to be around
aaichdgeh,scikit-learn/scikit-learn,sklearn/tests/test_common.py,c1f3080e1d38bf147c05bbbcba0603da1262e89f,e3e0827243b93b5a51893f39215d7284d5d319c6,FIXME these should be done also for non-mixin estimators!
aaichdgej,scikit-learn/scikit-learn,sklearn/tests/test_common.py,c1f3080e1d38bf147c05bbbcba0603da1262e89f,669a4ea853114dbba3621cb96c72b35560471646,FIXME!
aaichdggh,scikit-learn/scikit-learn,sklearn/cluster/mean_shift_.py,b3fbccca3824e58f10ff48ed619ad5e07577bba3,4c4b1f7067eb02f1bf52de21fe1704c3d353ee8f,FIXME To be removed in 0.18
aaichdggj,scikit-learn/scikit-learn,sklearn/cluster/dbscan_.py,494b8e574337e510bcb6fd0c941e390371ef1879,STILL_EXISTS,fix for scipy sparse indexing issue
aaichdgia,scikit-learn/scikit-learn,examples/cluster/plot_kmeans_silhouette_analysis.py,b5b1c5993f438c84fdeae9fc53bc145f447cea9d,STILL_EXISTS,Create a subplot with 1 row and 2 columns
aaichdhbb,scikit-learn/scikit-learn,sklearn/metrics/pairwise.py,0c9377ece993980da90b6c67019a10b096ab4f44,STILL_EXISTS,TODO: in some cases; backend='threading' may be appropriate
aaichdhea,scikit-learn/scikit-learn,sklearn/kernel_ridge.py,d3eb3113045cf52c001bbaf80e80bce1698bf3b9,eaaf84d730e27d598f9743d7a5c7bd2ba0964f0f,XXX: Check if overwrite_a=True is ok when copy is False
aaichdhfb,scikit-learn/scikit-learn,sklearn/linear_model/ridge.py,eaaf84d730e27d598f9743d7a5c7bd2ba0964f0f,STILL_EXISTS,XXX: Check if overwrite_a=True is ok when copy is False
aaichdhfh,scikit-learn/scikit-learn,sklearn/tests/test_kernel_ridge.py,ff1ec8d963f27a3b71237105b00e2b6f1ee40a15,a2c8d1214a99ef36fcb81107c420dd7b24fe6c11,XXX: Other solvers than lsqr give different results for Ridge
aaichdhfi,scikit-learn/scikit-learn,sklearn/tests/test_kernel_ridge.py,ff1ec8d963f27a3b71237105b00e2b6f1ee40a15,a2c8d1214a99ef36fcb81107c420dd7b24fe6c11,XXX: we are only equal within the first two decimals
aaichdhge,scikit-learn/scikit-learn,sklearn/preprocessing/tests/test_data.py,22f718dcceab10e36583779a365fe8a109cdbaa7,ed47b5b74450a885fab8ab56f4af9cf39fb1f9ad,TODO: for some reason assert_raise doesn't test this correctly
aaichdidh,scikit-learn/scikit-learn,sklearn/cluster/dbscan_.py,21e63aaf01404f4eb3e7ae54ef9f71e8bb905f6b,27d4376dcf1fc0084526966b42ca3219433eb90c,The tolist() is needed for NumPy 1.6.
aaichdiha,scikit-learn/scikit-learn,sklearn/neighbors/base.py,ec245c8bc3b60d172ff97cca31888263af3e7e01,STILL_EXISTS,if you want to understand why this is initialized this way.
aaicheabc,scikit-learn/scikit-learn,sklearn/utils/estimator_checks.py,e3e0827243b93b5a51893f39215d7284d5d319c6,8cf714cc9d14a0050f6bb2e6f998ee50a4134975,FIXME
aaicheabe,scikit-learn/scikit-learn,sklearn/utils/testing.py,e3e0827243b93b5a51893f39215d7284d5d319c6,ab2f539a32b8099a941cefc598c9625e830ecfe4,GradientBoosting base estimators; maybe should
aaicheafd,scikit-learn/scikit-learn,sklearn/calibration.py,ecfc93ddf601c5facee9c826f1ed6f3d4b7504a9,c1fa16f5be5f507ce334e8a0c24513e83c272902,XXX: isotonic regression cannot deal correctly with
aaicheafj,scikit-learn/scikit-learn,sklearn/calibration.py,ecfc93ddf601c5facee9c826f1ed6f3d4b7504a9,STILL_EXISTS,XXX : for some reason all probas can be 0
aaicheaic,scikit-learn/scikit-learn,sklearn/tests/test_calibration.py,6ec7a0efbbe2d2265e5e17bf86126bee7d748657,STILL_EXISTS,but should improve in both cases
aaichebbf,scikit-learn/scikit-learn,examples/calibration/plot_calibration_curve.py,9cc7018497b4dc50d65a0e3211dbdf49dba59190,STILL_EXISTS,\"\"\" || ============================== || Probability Calibration curves || ============================== ||  || When performing classification one often wants to predict not only the class || label; but also the associated probability. This probability gives some || kind of confidence on the prediction. This example demonstrates how to display || how well calibrated the predicted probabilities are and how to calibrate an || uncalibrated classifier. ||  || The experiment is performed on an artificial dataset for binary classification || with 100.000 samples (1.000 of them are used for model fitting) with 20 || features. Of the 20 features; only 2 are informative and 10 are redundant. The || first figure shows the estimated probabilities obtained with logistic || regression; Gaussian naive Bayes; and Gaussian naive Bayes with both isotonic || calibration and sigmoid calibration. The calibration performance is evaluated || with Brier score; reported in the legend (the smaller the better). One can || observe here that logistic regression is well calibrated while raw Gaussian || naive Bayes performs very badly. This is because of the redundant features || which violate the assumption of feature-independence and result in an overly || confident classifier; which is indicated by the typical transposed-sigmoid || curve. ||  || Calibration of the probabilities of Gaussian naive Bayes with isotonic || regression can fix this issue as can be seen from the nearly diagonal || calibration curve. Sigmoid calibration also improves the brier score slightly; || albeit not as strongly as the non-parametric isotonic regression. This can be || attributed to the fact that we have plenty of calibration data such that the || greater flexibility of the non-parametric model can be exploited. ||  || The second figure shows the calibration curve of a linear support-vector || classifier (LinearSVC). LinearSVC shows the opposite behavior as Gaussian || naive Bayes: the calibration curve has a sigmoid curve; which is typical for || an under-confident classifier. In the case of LinearSVC; this is caused by the || margin property of the hinge loss; which lets the model focus on hard samples || that are close to the decision boundary (the support vectors). ||  || Both kinds of calibration can fix this issue and yield nearly identical || results. This shows that sigmoid calibration can deal with situations where || the calibration curve of the base classifier is sigmoid (e.g.; for LinearSVC) || but not where it is transposed-sigmoid (e.g.; Gaussian naive Bayes). || \"\"\"
aaichebee,scikit-learn/scikit-learn,examples/calibration/plot_compare_calibration.py,9cc7018497b4dc50d65a0e3211dbdf49dba59190,STILL_EXISTS,\"\"\" || ======================================= || Comparison of Calibration of Classifiers || ======================================= ||  || Well calibrated classifiers are probabilistic classifiers for which the output || of the predict_proba method can be directly interpreted as a confidence level. || For instance a well calibrated (binary) classifier should classify the samples || such that among the samples to which it gave a predict_proba value close to || 0.8; approx. 80% actually belong to the positive class. ||  || LogisticRegression returns well calibrated predictions as it directly || optimizes log-loss. In contrast; the other methods return biased probilities; || with different biases per method: ||  || * GaussianNaiveBayes tends to push probabilties to 0 or 1 (note the counts in ||   the histograms). This is mainly because it makes the assumption that features ||   are conditionally independent given the class; which is not the case in this ||   dataset which contains 2 redundant features. ||  || * RandomForestClassifier shows the opposite behavior: the histograms show ||   peaks at approx. 0.2 and 0.9 probability; while probabilities close to 0 or 1 ||   are very rare. An explanation for this is given by Niculescu-Mizil and Caruana ||   [1]: \"Methods such as bagging and random forests that average predictions from ||   a base set of models can have difficulty making predictions near 0 and 1 ||   because variance in the underlying base models will bias predictions that ||   should be near zero or one away from these values. Because predictions are ||   restricted to the interval [0;1]; errors caused by variance tend to be one- ||   sided near zero and one. For example; if a model should predict p = 0 for a ||   case; the only way bagging can achieve this is if all bagged trees predict ||   zero. If we add noise to the trees that bagging is averaging over; this noise ||   will cause some trees to predict values larger than 0 for this case; thus ||   moving the average prediction of the bagged ensemble away from 0. We observe ||   this effect most strongly with random forests because the base-level trees ||   trained with random forests have relatively high variance due to feature ||   subseting.\" As a result; the calibration curve shows a characteristic sigmoid ||   shape; indicating that the classifier could trust its \"intuition\" more and ||   return probabilties closer to 0 or 1 typically. ||  || * Support Vector Classification (SVC) shows an even more sigmoid curve as ||   the  RandomForestClassifier; which is typical for maximum-margin methods ||   (compare Niculescu-Mizil and Caruana [1]); which focus on hard samples ||   that are close to the decision boundary (the support vectors). ||  || .. topic:: References: ||  ||     .. [1] Predicting Good Probabilities with Supervised Learning; ||           A. Niculescu-Mizil & R. Caruana; ICML 2005 || \"\"\"
aaichecbg,scikit-learn/scikit-learn,sklearn/svm/base.py,9df01a8a4e78d0c9f6569047139bdfad3042694a,fffa98ad1f2636ca734a2901193685449521cc6a,FIXME loss.lower() --> loss in 0.18
aaichecbi,scikit-learn/scikit-learn,sklearn/svm/base.py,9df01a8a4e78d0c9f6569047139bdfad3042694a,fffa98ad1f2636ca734a2901193685449521cc6a,FIXME Remove case insensitivity in 0.18 ---------------------
aaichecca,scikit-learn/scikit-learn,sklearn/svm/base.py,9df01a8a4e78d0c9f6569047139bdfad3042694a,fffa98ad1f2636ca734a2901193685449521cc6a,FIXME loss_l --> loss in 0.18
aaicheccc,scikit-learn/scikit-learn,sklearn/svm/classes.py,354c35cd5ba32105c9785b560acb20973d599e73,2a9492b38f66596d5cc464120dc23a36cc014c06,FIXME change loss_l --> self.loss after 0.18
aaichecch,scikit-learn/scikit-learn,sklearn/svm/tests/test_svm.py,6d222fde1010c09cd76891981417d07140e129e2,STILL_EXISTS,FIXME remove in 1.0
aaichecde,scikit-learn/scikit-learn,sklearn/svm/tests/test_svm.py,6d222fde1010c09cd76891981417d07140e129e2,fffa98ad1f2636ca734a2901193685449521cc6a,FIXME remove in 0.18
aaichecfi,scikit-learn/scikit-learn,sklearn/utils/tests/test_validation.py,69827c4b58c3778f99e9bb8909a3971c2964d209,STILL_EXISTS,XXX: We should have a test with a string; but what is correct behaviour?
aaichecig,scikit-learn/scikit-learn,sklearn/utils/estimator_checks.py,8820701fddc1150f362fba4b37cb6da3b3cbbaf0,8820701fddc1150f362fba4b37cb6da3b3cbbaf0,FIXME: This test is unstable on Travis; see issue #3190.
aaichedab,scikit-learn/scikit-learn,sklearn/utils/estimator_checks.py,ed5519fe8adf70437433a5b17487cc0c1decfe38,ed5519fe8adf70437433a5b17487cc0c1decfe38,FIXME: This test is unstable on Travis; see issue #3190.
aaichedae,scikit-learn/scikit-learn,sklearn/utils/estimator_checks.py,ed5519fe8adf70437433a5b17487cc0c1decfe38,211f204767e248d4c45923c60768c3729de63249,FIXME: This test is unstable on Travis; see issue #3190.
aaichedja,scikit-learn/scikit-learn,sklearn/utils/estimator_checks.py,26964ab4df6157d665a55d4444cccbcdfe381fff,STILL_EXISTS,FIXME: try to isolate a minimalistic reproduction case only depending
aaicheifa,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_ridge.py,cd2ee7e454f9bedceb04ba8613a4e52d1d9a76bf,STILL_EXISTS,TODO: for this test to be robust; we should use a dataset instead
aaicheiff,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_ridge.py,cd2ee7e454f9bedceb04ba8613a4e52d1d9a76bf,STILL_EXISTS,TODO: test also n_samples > n_features
aaichfdej,scikit-learn/scikit-learn,sklearn/preprocessing/data.py,04078be7da9cbb658ef9c93941f43c9e475ed86d,STILL_EXISTS,concerned feature is efficient; for instance by its mean or
aaichfecc,scikit-learn/scikit-learn,sklearn/cross_validation.py,34c85ed995907f018279decb36be41fe2d29fba6,STILL_EXISTS,todo this won't work correctly when test_size > train_size
aaichfecd,scikit-learn/scikit-learn,sklearn/cross_validation.py,34c85ed995907f018279decb36be41fe2d29fba6,6cd1cae83044b86ad1c6fb5052e4eb7bb90519bc,todo shuffle parameter of StratifiedKFold not exposed
aaichfeeg,scikit-learn/scikit-learn,sklearn/cross_decomposition/pls_.py,5035b18410bd505e86a2715946631fac946d858c,STILL_EXISTS,FIXME what's with the if?
aaichffcc,scikit-learn/scikit-learn,sklearn/utils/estimator_checks.py,d89c2150133b82a28cd8416a270414ea901cdef6,edf60c0ee2e6926dfd233295bfe31ded0a650d0d,FIXME CCA; PLS is not robust to rank 1 effects
aaichfgad,scikit-learn/scikit-learn,sklearn/utils/estimator_checks.py,932726fe9d8a24063436400ff758edee323d8e5c,4ddd07967382c1aaea163b0ea8fd27682d3a5f67,FIXME CCA; PLS is not robust to rank 1 effects
aaichfgib,scikit-learn/scikit-learn,sklearn/covariance/tests/test_graph_lasso.py,06b78a07213100a3312499f2527c401d6e041272,STILL_EXISTS,Hard-coded solution from R glasso package for alpha=1.0
aaichfgig,scikit-learn/scikit-learn,sklearn/covariance/tests/test_graph_lasso.py,06b78a07213100a3312499f2527c401d6e041272,STILL_EXISTS,Hard-coded solution from R glasso package for alpha=0.01
aaichfhee,scikit-learn/scikit-learn,sklearn/utils/estimator_checks.py,669a4ea853114dbba3621cb96c72b35560471646,eec7649236d5216380d05916bb7f6aa3b2fc5508,FIXME!
aaichfhej,scikit-learn/scikit-learn,sklearn/utils/estimator_checks.py,669a4ea853114dbba3621cb96c72b35560471646,STILL_EXISTS,TODO some complication with -1 label
aaichfhfe,scikit-learn/scikit-learn,sklearn/utils/estimator_checks.py,669a4ea853114dbba3621cb96c72b35560471646,STILL_EXISTS,TODO: test with intercept
aaichfhff,scikit-learn/scikit-learn,sklearn/utils/estimator_checks.py,669a4ea853114dbba3621cb96c72b35560471646,STILL_EXISTS,TODO: test with multiple responses
aaichfhha,scikit-learn/scikit-learn,sklearn/utils/estimator_checks.py,669a4ea853114dbba3621cb96c72b35560471646,1c1f273d1015b385c7dc1892bfa8b4628e9ef4e0,FIXME SOON!
aaichfjeh,scikit-learn/scikit-learn,sklearn/metrics/regression.py,1b1e10c3251bda9240f115123f6c17c8fde50e35,08b72539228cec5dd31a22600eaeb20590dff042,@FIXME change in 0.18
aaichgajd,scikit-learn/scikit-learn,sklearn/utils/estimator_checks.py,175f50ebc8a374b6f9158897c00976ef69ccb722,28cd9c8e0f0ad84769b48048f8cf59c6563d1031,FIXME
aaichgbbc,scikit-learn/scikit-learn,sklearn/preprocessing/tests/test_data.py,4ff47d9eface2aab6772d0c8269a02d88d35fd4d,d86a629ac71a4d6781e2094ee9a6932ec42aa950,TODO: for some reason assert_raise doesn't test this correctly
aaichgbhb,scikit-learn/scikit-learn,sklearn/preprocessing/data.py,89c1018c6413bde20b0e74dfedf740870d808676,STILL_EXISTS,concerned feature is efficient; for instance by its mean or
aaichgcbc,scikit-learn/scikit-learn,sklearn/linear_model/logistic.py,c51f0676522a31ddb913567614bef3a9fc3de1fe,STILL_EXISTS,`loss` is unused. Refactoring to avoid computing it does not
aaichgchg,scikit-learn/scikit-learn,sklearn/preprocessing/data.py,0b07536990c2c30de7b6d372186836081268dafc,STILL_EXISTS,concerned feature is efficient; for instance by its mean or
aaichgdbi,scikit-learn/scikit-learn,sklearn/cluster/mean_shift_.py,7041ed283d513d8b1c75612ebb55c2ff2d50ba04,4c4b1f7067eb02f1bf52de21fe1704c3d353ee8f,FIXME To be removed in 0.18
aaichgdgc,scikit-learn/scikit-learn,sklearn/cluster/mean_shift_.py,0650d5502e01e6b4245ce99729fc8e7a71aacff3,5db2adf93ce1625bc112767c44c0e2385e91524a,FIXME To be removed in 0.18
aaichgdjj,scikit-learn/scikit-learn,sklearn/svm/base.py,41788c353bfb25cb6c76d85fe22935868ca66f17,fffa98ad1f2636ca734a2901193685449521cc6a,FIXME Remove gamma=0.0 support in 0.18
aaichgeaa,scikit-learn/scikit-learn,sklearn/svm/base.py,41788c353bfb25cb6c76d85fe22935868ca66f17,fffa98ad1f2636ca734a2901193685449521cc6a,FIXME remove (self.gamma == 0) in 0.18
aaichgedc,scikit-learn/scikit-learn,sklearn/metrics/tests/test_classification.py,019fc9b8e70211cb430efb2d44a614c0062ce6fe,STILL_EXISTS,(although it would only affect performance if average='macro'\/None)
aaichgedf,scikit-learn/scikit-learn,sklearn/linear_model/ridge.py,2f69574393d822326a68d3238536d60b50a9e2fe,bdd3cb756e7f22e5978ebc8a39a98932e45bb1c9,FIXME: sample_weight must be split into training\/validation data
aaichgehh,scikit-learn/scikit-learn,sklearn/cluster/mean_shift_.py,3d097ae7df78000885caf44ebaeeec04c1f373d8,5db2adf93ce1625bc112767c44c0e2385e91524a,FIXME To be removed in 0.18
aaichgfad,scikit-learn/scikit-learn,sklearn/grid_search.py,febefb0894438505071fc6062d97e9199c9e847f,STILL_EXISTS,efficient.
aaichgfae,scikit-learn/scikit-learn,sklearn/grid_search.py,febefb0894438505071fc6062d97e9199c9e847f,STILL_EXISTS,XXX: could memoize information used here
aaichgfdc,scikit-learn/scikit-learn,sklearn/externals/joblib/hashing.py,4d1c1d5d31bd0b689ba4332e9df94dcab1b63355,STILL_EXISTS,workaround is to view the array as bytes before
aaichgffb,scikit-learn/scikit-learn,sklearn/externals/joblib/numpy_pickle.py,4d1c1d5d31bd0b689ba4332e9df94dcab1b63355,e5d3cbce0da945bfd084beaec9dd47e77d7bcf01,protocol < 3 ends up creating a unicode string which is very
aaichgfif,scikit-learn/scikit-learn,sklearn/externals/joblib/parallel.py,4d1c1d5d31bd0b689ba4332e9df94dcab1b63355,e0d501f3109e60f9fec12d4d4b4f6c74a0ff75ef,batches. Better reduce the batch size a bit to limit the
aaichgich,scikit-learn/scikit-learn,sklearn/linear_model/least_angle.py,27f14d0edc09e0bb3d22275192bf51ddfb5ab86e,99eff75e8d0deae3da06c09a3fa03a0f1214ff3b,NON-NEGATIVE MODIFICATION \/\/ FIXME : can we ultimately remove this?
aaichgifj,scikit-learn/scikit-learn,examples/preprocessing/plot_callable_transformer.py,cb0916c44068874b5518238fba1a740c4e1a04f6,STILL_EXISTS,\"\"\" || ========================================================= || Using CallableTransformer to select columns || ========================================================= ||  || Shows how to use a callable transformer in a pipeline. If you know your || dataset's first principle component is irrelevant for a classification task; || you can use the CallableTransformer to select all but the first column of the || PCA transformed data. || \"\"\"
aaichhbfi,scikit-learn/scikit-learn,examples/ensemble/plot_feature_transformation.py,89ec72ea2fca97d2e5f7921fdf5e345bd308819f,STILL_EXISTS,\"\"\" || =============================================== || Feature transformations with ensembles of trees || =============================================== ||  || Transform your features into a higher dimensional; sparse space. Then || train a linear model on these features. ||  || First fit an ensemble of trees (e.g. gradient boosted trees or a || random forest) on the training set. Then each leaf of each tree in the || ensemble is assigned a fixed arbitrary feature index in a new feature || space. These leaf indices are then encoded in a one-hot fashion. ||  || Each sample goes through the decisions of each tree of the ensemble || and ends up in one leaf per tree. The sample is encoded by setting || feature values for these leaves to 1 and the other feature values to 0. ||  || The resulting transformer has then learned a supervised; sparse; || high-dimensional categorical embedding of the data. || \"\"\"
aaichhbij,scikit-learn/scikit-learn,sklearn/linear_model/least_angle.py,8e3b22ccdc3c7a497c22839d2bc44a550c769a67,e50aaba7dc06cbeec11cc6237111556577780b5d,NON-NEGATIVE MODIFICATION \/\/ FIXME : can we ultimately remove this?
aaichjggb,scikit-learn/scikit-learn,sklearn/metrics/pairwise.py,3aa4fc260a8db758fa2c56429b2bfb6d6acd23d6,STILL_EXISTS,HACK: precomputed is always allowed; never called
aaichjgig,scikit-learn/scikit-learn,sklearn/neighbors/tests/test_neighbors.py,ab8f155641af856d974bbe4c0b631d290323b485,STILL_EXISTS,TODO: also test radius_neighbors; but requires different assertion
aaichjhaf,scikit-learn/scikit-learn,sklearn/cluster/mean_shift_.py,55c32ef25c72448516fdc79df5b32d189e198ba6,5db2adf93ce1625bc112767c44c0e2385e91524a,FIXME To be removed in 0.18
aaichjhfc,scikit-learn/scikit-learn,sklearn/cluster/dbscan_.py,343f9b4dcb31878110e18ed5957dd3c7aeb67211,e52e9c8d7536b6315da655164951060642a52707,XXX: modifies X's internals in-place
aaichjiei,scikit-learn/scikit-learn,sklearn/linear_model/sag.py,4ceffe05a1bd5f63433f02cfd02af036f6026806,94eb61960a13f84a8be0dd527012f929172a0d60,check which type of Sequential Dataset is needed
aaichjiej,scikit-learn/scikit-learn,sklearn/linear_model/sag.py,4ceffe05a1bd5f63433f02cfd02af036f6026806,94eb61960a13f84a8be0dd527012f929172a0d60,set the eta0 if needed; 'auto' is 1 \/ 4L where L is the max sum of
aaichjijg,scikit-learn/scikit-learn,sklearn/linear_model/ridge.py,94eb61960a13f84a8be0dd527012f929172a0d60,0dac63f48c5b3cd30e0a17dff5f5bbbc94bb4c39,SAG needs X and y columns to be C-contiguous and np.float64
aaichjjei,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_sag.py,94eb61960a13f84a8be0dd527012f929172a0d60,STILL_EXISTS,TODO: uncomment when sparse Ridge with intercept will be fixed (#4710)
aaichjjjg,scikit-learn/scikit-learn,sklearn/discriminant_analysis.py,679936f6bcb66b1b734e4f3609bdb203e41d8460,STILL_EXISTS,XXX : can do better to avoid precision overflows
aaiciaaja,scikit-learn/scikit-learn,sklearn/decomposition/online_lda.py,62ad838be046b9624068ba0882f100e536fcd303,STILL_EXISTS,TODO: make Parallel._effective_n_jobs public instead?
aaiciadjg,scikit-learn/scikit-learn,sklearn/ensemble/bagging.py,1a15fdc4f537b8ab87f17695abc4699446454abb,80e22b3ddf51d36fb9021110b0d3287d460c3bcb,TODO: Remove this check when liblinear is patched to support
aaiciafhf,scikit-learn/scikit-learn,sklearn/externals/odict.py,6966881328c256bbef37752cce23bbac8e23fb27,STILL_EXISTS,The circular doubly linked list starts and ends with a sentinel element.
aaiciaicd,scikit-learn/scikit-learn,sklearn/utils/linear_assignment_.py,8fcd0632fa807f5f1e34e2af1a626519717c6ba1,STILL_EXISTS,TODO: a version of this algorithm has been incorporated in SciPy; use that
aaiciaida,scikit-learn/scikit-learn,sklearn/decomposition/dict_learning.py,46c0e85a85d88e87a3a17e22a2e83cb15204cb1d,STILL_EXISTS,TODO: Make verbosity argument for Lasso?
aaiciaidf,scikit-learn/scikit-learn,sklearn/decomposition/dict_learning.py,46c0e85a85d88e87a3a17e22a2e83cb15204cb1d,b271e205701cbe1b6d220b51619e381cc7d0c805,TODO: Should verbose argument be passed to this?
aaiciajce,scikit-learn/scikit-learn,sklearn/ensemble/tests/test_forest.py,5a0db1717a66ce39bae9b7c6f27e2e6e3f6c7647,5c4b1bb23192a137ac22ced229c50d6b69859ac6,XXX: Remove this test in 0.19 after transform support to estimators
aaiciajcg,scikit-learn/scikit-learn,sklearn/ensemble/tests/test_gradient_boosting.py,5a0db1717a66ce39bae9b7c6f27e2e6e3f6c7647,5c4b1bb23192a137ac22ced229c50d6b69859ac6,XXX: Remove this test in 0.19 after transform support to estimators
aaiciajib,scikit-learn/scikit-learn,sklearn/preprocessing/data.py,6a5a2f79603350781ec32b583f7969bda072ddce,STILL_EXISTS,This is needed for the incremental computation of the var
aaicibjab,scikit-learn/scikit-learn,sklearn/gaussian_process/kernels.py,ca89b34fe2d1adc9fa4e80fbd9c9ccff25788bbc,a5dc3491ad4fa052ef7db5b600abd213f3a3872c,XXX: enforce element-wise comparison to None
aaicibjad,scikit-learn/scikit-learn,sklearn/gaussian_process/kernels.py,ca89b34fe2d1adc9fa4e80fbd9c9ccff25788bbc,STILL_EXISTS,XXX: Deal with situations in which only some of the hyperparameter
aaicibjcc,scikit-learn/scikit-learn,sklearn/gaussian_process/gpr.py,ca03d52bb7ab9151cf1180cc62d82dbab5c6db21,2feb6e02cfe68628bb6bc0cddb494c512056bcc9,XXX: Avoid deepcopy
aaicibjdh,scikit-learn/scikit-learn,sklearn/gaussian_process/gpc.py,b76ca723fcd6a68389899975b48030b7a6475085,STILL_EXISTS,XXX: Assert that y is binary and labels are {0; 1}
aaicibjgb,scikit-learn/scikit-learn,sklearn/gaussian_process/gpc.py,b76ca723fcd6a68389899975b48030b7a6475085,2feb6e02cfe68628bb6bc0cddb494c512056bcc9,XXX
aaicibjhf,scikit-learn/scikit-learn,sklearn/gaussian_process/gpc.py,b76ca723fcd6a68389899975b48030b7a6475085,STILL_EXISTS,XXX: avoid creating square matrix?
aaicibjie,scikit-learn/scikit-learn,sklearn/gaussian_process/gpc.py,b76ca723fcd6a68389899975b48030b7a6475085,STILL_EXISTS,XXX: more complex convergence criterion
aaicibjjb,scikit-learn/scikit-learn,sklearn/gaussian_process/tests/test_gpr.py,804c1d9042accefa92c16be9806035652ad71d93,STILL_EXISTS,XXX: quite hacky; works only for current kernels
aaicicadj,scikit-learn/scikit-learn,sklearn/gaussian_process/kernels.py,5eaa8cf5945b361cf8367e9fe71d99b57bcc1d91,STILL_EXISTS,XXX: avoid python for-loops
aaicicaeb,scikit-learn/scikit-learn,sklearn/gaussian_process/kernels.py,4dcd70678f4f5f8dc03c0053a1c3c81eebc5a5d7,STILL_EXISTS,XXX: avoid python for-loops
aaicicaec,scikit-learn/scikit-learn,sklearn/gaussian_process/gpc.py,e05daaec520ccb47760dabadb2a8b42d98aadea9,STILL_EXISTS,XXX: Get rid of the np.diag() in the next line
aaicicaff,scikit-learn/scikit-learn,examples/gaussian_process/new/plot_gpr_co2.py,f534a972df9a80b96d471738ae163efc0e489bc3,STILL_EXISTS,XXX: Upload data on mldata
aaicicaia,scikit-learn/scikit-learn,sklearn/gaussian_process/tests/test_gpr.py,cb7c4243e8010827831526ee9757d88b44ff15ef,555bb11554158c04a7c0956fc88e23235e280e29,XXX: Check why only 2 digits
aaicicaif,scikit-learn/scikit-learn,sklearn/gaussian_process/gpc.py,d0bc5dc35cfb168ef56401f8aea5f61322b61fec,2feb6e02cfe68628bb6bc0cddb494c512056bcc9,XXX
aaicicaih,scikit-learn/scikit-learn,sklearn/gaussian_process/gpr.py,d0bc5dc35cfb168ef56401f8aea5f61322b61fec,2feb6e02cfe68628bb6bc0cddb494c512056bcc9,XXX
aaicicbjg,scikit-learn/scikit-learn,sklearn/gaussian_process/gpr.py,84b49f9c7ab86e648f86d8053a4cd21badbdcd14,2839f66933b5056d216f17ae90f683462189f951,XXX: std per dim?
aaiciccej,scikit-learn/scikit-learn,examples/gaussian_process/plot_gpc_iris.py,f6d8686eba556295dca7208c5fce651239a7f858,2dd1f285b95c681d2dd6aa41d7c625ad53e235fa,avoid this ugly slicing by using a two-dim dataset
aaiciccib,scikit-learn/scikit-learn,sklearn/gaussian_process/kernels.py,eb54b496d2d846365703395d7f874e97ce3d5634,STILL_EXISTS,A raw namedtuple is very memory efficient as it packs the attributes
aaicicihf,scikit-learn/scikit-learn,sklearn/utils/deprecation.py,61ae8d56c4af2495aa685518bcab357191a52f19,STILL_EXISTS,FIXME: we should probably reset __new__ for full generality
aaicicihg,scikit-learn/scikit-learn,sklearn/utils/validation.py,857cb0925434b7708fc309a9df646c0583bb25b4,5c4b1bb23192a137ac22ced229c50d6b69859ac6,FIXME NotFittedError_ --> NotFittedError in 0.19
aaicicijb,scikit-learn/scikit-learn,sklearn/utils/estimator_checks.py,92068ef7de92752ab14a1e601e021cda4a51149d,7745319e337213fa9c419b280697e0fbfe9375c3,TODO: Test with Multitask later
aaicicjai,scikit-learn/scikit-learn,sklearn/svm/base.py,52a6acc15ce4952d602d053f194e0f316bfd3df8,65aee0c46f1fdc0a32b4e82161dbfd8e1e197140,FIXME Remove gamma=0.0 support in 0.18
aaicicjaj,scikit-learn/scikit-learn,sklearn/svm/base.py,52a6acc15ce4952d602d053f194e0f316bfd3df8,65aee0c46f1fdc0a32b4e82161dbfd8e1e197140,FIXME remove (self.gamma == 0) in 0.18
aaicicjbb,scikit-learn/scikit-learn,sklearn/svm/base.py,52a6acc15ce4952d602d053f194e0f316bfd3df8,65aee0c46f1fdc0a32b4e82161dbfd8e1e197140,FIXME loss.lower() --> loss in 0.18
aaicicjbd,scikit-learn/scikit-learn,sklearn/svm/base.py,52a6acc15ce4952d602d053f194e0f316bfd3df8,65aee0c46f1fdc0a32b4e82161dbfd8e1e197140,FIXME Remove case insensitivity in 0.18 ---------------------
aaicicjbf,scikit-learn/scikit-learn,sklearn/svm/base.py,52a6acc15ce4952d602d053f194e0f316bfd3df8,65aee0c46f1fdc0a32b4e82161dbfd8e1e197140,FIXME loss_l --> loss in 0.18
aaicicjcg,scikit-learn/scikit-learn,benchmarks/bench_plot_randomized_svd.py,b18f2951e4be3ce293a9d0bb61d4ca3be19496ca,STILL_EXISTS,\"\"\" || Benchmarks on the power iterations phase in randomized SVD. ||  || We test on various synthetic and real datasets the effect of increasing || the number of power iterations in terms of quality of approximation || and running time. A number greater than 0 should help with noisy matrices; || which are characterized by a slow spectral decay. ||  || We test several policy for normalizing the power iterations. Normalization || is crucial to avoid numerical issues. ||  || The quality of the approximation is measured by the spectral norm discrepancy || between the original input matrix and the reconstructed one (by multiplying || the randomized_svd's outputs). The spectral norm is always equivalent to the || largest singular value of a matrix. (3) justifies this choice. However; one can || notice in these experiments that Frobenius and spectral norms behave || very similarly in a qualitative sense. Therefore; we suggest to run these || benchmarks with `enable_spectral_norm = False`; as Frobenius' is MUCH faster to || compute. ||  || The benchmarks follow. ||  || (a) plot: time vs norm; varying number of power iterations ||     data: many datasets ||     goal: compare normalization policies and study how the number of power ||     iterations affect time and norm ||  || (b) plot: n_iter vs norm; varying rank of data and number of components for ||     randomized_SVD ||     data: low-rank matrices on which we control the rank ||     goal: study whether the rank of the matrix and the number of components ||     extracted by randomized SVD affect \"the optimal\" number of power iterations ||  || (c) plot: time vs norm; varing datasets ||     data: many datasets ||     goal: compare default configurations ||  || We compare the following algorithms: || -   randomized_svd(...; power_iteration_normalizer='none') || -   randomized_svd(...; power_iteration_normalizer='LU') || -   randomized_svd(...; power_iteration_normalizer='QR') || -   randomized_svd(...; power_iteration_normalizer='auto') || -   fbpca.pca() from https:\/\/github.com\/facebook\/fbpca (if installed) ||  || Conclusion || ---------- || - n_iter=2 appears to be a good default value || - power_iteration_normalizer='none' is OK if n_iter is small; otherwise LU ||   gives similar errors to QR but is cheaper. That's what 'auto' implements. ||  || References || ---------- || (1) Finding structure with randomness: Stochastic algorithms for constructing ||     approximate matrix decompositions ||     Halko; et al.; 2009 http:\/\/arxiv.org\/abs\/arXiv:0909.4061 ||  || (2) A randomized algorithm for the decomposition of matrices ||     Per-Gunnar Martinsson; Vladimir Rokhlin and Mark Tygert ||  || (3) An implementation of a randomized algorithm for principal component ||     analysis ||     A. Szlam et al. 2014 || \"\"\"
aaicicjcj,scikit-learn/scikit-learn,benchmarks/bench_plot_randomized_svd.py,b18f2951e4be3ce293a9d0bb61d4ca3be19496ca,STILL_EXISTS,TODO: compute approximate spectral norms with the power method as in
aaicicjea,scikit-learn/scikit-learn,benchmarks/bench_plot_randomized_svd.py,b18f2951e4be3ce293a9d0bb61d4ca3be19496ca,STILL_EXISTS,There is a different convention for l here
aaicidadj,scikit-learn/scikit-learn,examples/neural_networks/plot_mlp_alpha.py,587315e3a9a750e45d4ea17becaa97ef2a3bdb6c,STILL_EXISTS,\"\"\" || ================================================ || Varying regularization in Multi-layer Perceptron || ================================================ ||  || A comparison of different regularization term 'alpha' values on synthetic || datasets. The plot shows that different alphas yield different decision || functions. ||  || Alpha is a regularization term; or also known as penalty term; that combats || overfitting by constraining the weights' size. Increasing alpha may fix high || variance (a sign of overfitting) by encouraging smaller weights; resulting || in a decision function plot that may appear with lesser curvatures. || Similarly; decreasing alpha may fix high bias (a sign of underfitting) by || encouraging larger weights; potentially resulting in more curvatures in the || decision function plot. ||  || \"\"\"
aaicidbge,scikit-learn/scikit-learn,examples/neural_networks/plot_mnist_filters.py,3891af8d99b290894861b61b0d985faa03053174,STILL_EXISTS,\"\"\" || ===================================== || Visualization of MLP weights on MNIST || ===================================== ||  || Sometimes looking at the learned coefficients of a neural network can provide || inside into the learning behavior. For example if weights look unstructured; || maybe a weight was not used at all; or if very large coefficients exist; maybe || regularization was too low or the learning rate too high. ||  || This example shows how to plot some of the first layer weights in a || MLPClassifier trained on the MNIST dataset. ||  || The input data consists of 28x28 pixel handwritten digits; leading to 784 || features in the dataset. Therefore the first layer weight have the shape (784; || hidden_layer_sizes[0]).  We can therefore visualize a single column of the || weight matrix as a 28x28 pixel image. ||  || To make the example run faster; we use very few hidden units; and train only || for a very short time. Training longer would result in much smoother weights. || \"\"\"
aaicidbhb,scikit-learn/scikit-learn,sklearn/neural_network/multilayer_perceptron.py,3891af8d99b290894861b61b0d985faa03053174,STILL_EXISTS,He et al instead maybe?
aaicidcig,scikit-learn/scikit-learn,sklearn/cross_decomposition/tests/test_pls.py,1c7168d3982af969400c59acc44b97b536320137,STILL_EXISTS,x_weights_sign_flip holds columns of 1 or -1; depending on sign flip
aaiciddfc,scikit-learn/scikit-learn,sklearn/model_selection/_search.py,3f8743f47b61a269e8bfff2322cb544170976574,STILL_EXISTS,efficient.
aaiciddfd,scikit-learn/scikit-learn,sklearn/model_selection/_search.py,3f8743f47b61a269e8bfff2322cb544170976574,STILL_EXISTS,XXX: could memoize information used here
aaiciddga,scikit-learn/scikit-learn,sklearn/model_selection/_search.py,3f8743f47b61a269e8bfff2322cb544170976574,e0e738760625129be71839fac2dad9325fb90225,A raw namedtuple is very memory efficient as it packs the attributes
aaiciddgj,scikit-learn/scikit-learn,sklearn/model_selection/_search.py,3f8743f47b61a269e8bfff2322cb544170976574,STILL_EXISTS,TODO: shall we also store the test_fold_sizes?
aaiciddib,scikit-learn/scikit-learn,sklearn/model_selection/_split.py,3f8743f47b61a269e8bfff2322cb544170976574,STILL_EXISTS,Since subclasses must implement either _iter_test_masks or
aaicidebe,scikit-learn/scikit-learn,sklearn/model_selection/_split.py,3f8743f47b61a269e8bfff2322cb544170976574,STILL_EXISTS,XXX This is copied from BaseEstimator's get_params
aaicideee,scikit-learn/scikit-learn,sklearn/model_selection/tests/test_search.py,3f8743f47b61a269e8bfff2322cb544170976574,dc42bde3e9588ca7ed0232ff70dc8048a1411869,TODO Import from sklearn.exceptions once merged.
aaicidfbc,scikit-learn/scikit-learn,sklearn/model_selection/tests/test_search.py,3f8743f47b61a269e8bfff2322cb544170976574,STILL_EXISTS,Make a dataset with a lot of noise to get various kind of prediction
aaicidfbe,scikit-learn/scikit-learn,sklearn/model_selection/tests/test_search.py,3f8743f47b61a269e8bfff2322cb544170976574,STILL_EXISTS,XXX: as of today (scipy 0.12) it's not possible to set the random seed
aaicidfjf,scikit-learn/scikit-learn,sklearn/model_selection/tests/test_split.py,3f8743f47b61a269e8bfff2322cb544170976574,STILL_EXISTS,The digits samples are dependent: they are apparently grouped by authors
aaicidgac,scikit-learn/scikit-learn,sklearn/model_selection/tests/test_split.py,3f8743f47b61a269e8bfff2322cb544170976574,STILL_EXISTS,Shuffling the data artificially breaks the dependency and hides the
aaicidgfd,scikit-learn/scikit-learn,sklearn/model_selection/tests/test_split.py,3f8743f47b61a269e8bfff2322cb544170976574,eec7649236d5216380d05916bb7f6aa3b2fc5508,Check if split works correctly
aaicidghb,scikit-learn/scikit-learn,sklearn/model_selection/tests/test_validation.py,3f8743f47b61a269e8bfff2322cb544170976574,STILL_EXISTS,training score becomes worse (2 -> 1); test error better (0 -> 1)
aaicidghc,scikit-learn/scikit-learn,sklearn/model_selection/tests/test_validation.py,3f8743f47b61a269e8bfff2322cb544170976574,STILL_EXISTS,XXX: use 2D array; since 1D X is being detected as a single sample in
aaicieajc,scikit-learn/scikit-learn,sklearn/datasets/kddcup99.py,a75eb631948e13eda0fb7783f6f15c97bd8fe6c2,STILL_EXISTS,XXX bug when compress!=0:
aaiciebag,scikit-learn/scikit-learn,sklearn/ensemble/iforest.py,a75eb631948e13eda0fb7783f6f15c97bd8fe6c2,STILL_EXISTS,minus as bigger is better (here less abnormal):
aaiciechj,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_ridge.py,e232ad60d3251340a01fdd3c7b391354e61e5137,9cbceb32c100703d8f2e374b571974a523e42d8a,temporary fix
aaiciedbe,scikit-learn/scikit-learn,setup.py,986fb60947f91306d9dcf8d09a321ba52f4417af,6a2d8d5bbc261309d59ac30bf650e982f1477dd7,Cythonize if needed
aaiciedbi,scikit-learn/scikit-learn,sklearn/_build_utils/cythonize.py,986fb60947f91306d9dcf8d09a321ba52f4417af,STILL_EXISTS,\"\"\" cythonize ||  || Cythonize pyx files into C files as needed. ||  || Usage: cythonize [root_dir] ||  || Default [root_dir] is 'sklearn'. ||  || Checks pyx files to see if they have been changed relative to their || corresponding C files.  If they have; then runs cython on these files to || recreate the C files. ||  || The script detects changes in the pyx files using checksums [or hashes] stored || in a database file ||  || Simple script to invoke Cython on all .pyx || files; while waiting for a proper build system. Uses file hashes to || figure out if rebuild is needed. ||  || It is called by .\/setup.py sdist so that sdist package can be installed without || cython ||  || Originally written by Dag Sverre Seljebotn; and adapted from statsmodel 0.6.1 || (Modified BSD 3-clause) ||  || We copied it for scikit-learn. ||  || Note: this script does not check any of the dependent C libraries; it only || operates on the Cython .pyx files. || \"\"\"
aaiciedfd,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_base.py,1d16ec4718f5dfeaca4db0dcff0d60efc071efe8,STILL_EXISTS,TODO: loop over sparse data as well
aaiciedfh,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_ridge.py,1d16ec4718f5dfeaca4db0dcff0d60efc071efe8,STILL_EXISTS,TODO: loop over sparse data as well
aaiciefcj,scikit-learn/scikit-learn,examples/ensemble/plot_partial_dependence.py,3daab8a99862674514735a3caadd45cc542b43c1,STILL_EXISTS,Needed on Windows because plot_partial_dependence uses multiprocessing
aaicieffe,scikit-learn/scikit-learn,sklearn/linear_model/ransac.py,761b1f7118558b6d22944672539a83328ca8a081,eec7649236d5216380d05916bb7f6aa3b2fc5508,XXX: Deprecation: Remove this if block in 0.20
aaiciefff,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_ransac.py,761b1f7118558b6d22944672539a83328ca8a081,eec7649236d5216380d05916bb7f6aa3b2fc5508,XXX: Remove in 0.20
aaiciefjj,scikit-learn/scikit-learn,sklearn/externals/joblib/my_exceptions.py,6f6d878feadc1604b9e919b99dc487cf882a8ba3,STILL_EXISTS,The next line set the .args correctly. This is needed to
aaiciegbi,scikit-learn/scikit-learn,examples/linear_model/plot_ridge_coeffs.py,d3175db3627d739d17ab36bf8752b44fcaf6f638,STILL_EXISTS,\"\"\" || ============================================================== || Plot Ridge coefficients as a function of the L2 regularization || ============================================================== ||  || .. currentmodule:: sklearn.linear_model ||  || :class:`Ridge` Regression is the estimator used in this example. || Each color in the left plot represents one different dimension of the || coefficient vector; and this is displayed as a function of the || regularization parameter. The right plot shows how exact the solution || is. This example illustrates how a well defined solution is || found by Ridge regression and how regularization affects the || coefficients and their values. The plot on the right shows how || the difference of the coefficients from the estimator changes || as a function of regularization. ||  || In this example the dependent variable Y is set as a function || of the input features: y = X*w + c. The coefficient vector w is || randomly sampled from a normal distribution; whereas the bias term c is || set to a constant. ||  || As alpha tends toward zero the coefficients found by Ridge || regression stabilize towards the randomly sampled vector w. || For big alpha (strong regularisation) the coefficients || are smaller (eventually converging at 0) leading to a || simpler and biased solution. || These dependencies can be observed on the left plot. ||  || The right plot shows the mean squared error between the || coefficients found by the model and the chosen vector w. || Less regularised models retrieve the exact || coefficients (error is equal to 0); stronger regularised || models increase the error. ||  || Please note that in this example the data is non-noisy; hence || it is possible to extract the exact coefficients. || \"\"\"
aaicieghg,scikit-learn/scikit-learn,examples/feature_selection/plot_f_test_vs_mi.py,689ed0dbba7f2abc6c076b235958f09f7b4953e5,STILL_EXISTS,\"\"\" || =========================================== || Comparison of F-test and mutual information || =========================================== ||  || This example illustrates the differences between univariate F-test statistics || and mutual information. ||  || We consider 3 features x_1; x_2; x_3 distributed uniformly over [0; 1]; the || target depends on them as follows: ||  || y = x_1 + sin(6 * pi * x_2) + 0.1 * N(0; 1); that is the third features is completely irrelevant. ||  || The code below plots the dependency of y against individual x_i and normalized || values of univariate F-tests statistics and mutual information. ||  || As F-test captures only linear dependency; it rates x_1 as the most || discriminative feature. On the other hand; mutual information can capture any || kind of dependency between variables and it rates x_2 as the most || discriminative feature; which probably agrees better with our intuitive || perception for this example. Both methods correctly marks x_3 as irrelevant. || \"\"\"
aaicjbdje,scikit-learn/scikit-learn,sklearn/manifold/tests/test_spectral_embedding.py,19024a82e67d4c5b6899d023dfa54ec3d4592b02,STILL_EXISTS,We should retrieve the same component mask by starting by both ends
aaicjbebg,scikit-learn/scikit-learn,continuous_integration/circle/check_build_doc.py,fcfaa5fc3708fece56105c2a7fbc1b815951f9c8,STILL_EXISTS,as apparently there is way to reliably get the target of a PR with circle
aaicjbffb,scikit-learn/scikit-learn,sklearn/tests/test_isotonic.py,b5286c35fbd9b809cba71df617cf67397b8321fb,STILL_EXISTS,work-around for pearson divide warnings in scipy <= 0.17.0
aaicjbffc,scikit-learn/scikit-learn,sklearn/tests/test_isotonic.py,9bff7d6817b014924e55515dcf0ac3f4a86572b7,STILL_EXISTS,work-around for pearson divide warnings in scipy <= 0.17.0
aaicjbfff,scikit-learn/scikit-learn,sklearn/linear_model/base.py,140a5acda8e44384f8e072e2d50a1d28a798cded,eec7649236d5216380d05916bb7f6aa3b2fc5508,XXX: currently scaled to variance=n_samples
aaicjbffg,scikit-learn/scikit-learn,sklearn/linear_model/base.py,140a5acda8e44384f8e072e2d50a1d28a798cded,STILL_EXISTS,TODO: f_normalize could be used here as well but the function
aaicjbfga,scikit-learn/scikit-learn,sklearn/linear_model/base.py,140a5acda8e44384f8e072e2d50a1d28a798cded,STILL_EXISTS,TODO: _rescale_data should be factored into _preprocess_data.
aaicjbhec,scikit-learn/scikit-learn,sklearn/multioutput.py,07cede74ca0cb142da3fdd5be3d2f8d46a73fd9e,4953ec36a41694d38f8d92fd48c7049cdd82e0ed,XXX remove in 0.19 when r2_score default for multioutput changes
aaicjbhhi,scikit-learn/scikit-learn,sklearn/utils/arpack.py,328ebfa865207f689dcf555d218a00b1a6e6a3e9,bd0fc236e00c57b9a5212e8802c325c99af7b1b4,CHECK IF BACKPORT IS ACTUALLY NEEDED
aaicjbiab,scikit-learn/scikit-learn,sklearn/utils/arpack.py,328ebfa865207f689dcf555d218a00b1a6e6a3e9,bd0fc236e00c57b9a5212e8802c325c99af7b1b4,XXX This can be slow!
aaicjccdb,scikit-learn/scikit-learn,sklearn/model_selection/_search.py,afd5d18b0cfe1512a4a8f99835c693b3724cf6ed,e0e738760625129be71839fac2dad9325fb90225,XXX Remove in 0.20
aaicjcceb,scikit-learn/scikit-learn,sklearn/model_selection/tests/test_search.py,afd5d18b0cfe1512a4a8f99835c693b3724cf6ed,STILL_EXISTS,TODO Remove in 0.20
aaicjcdec,scikit-learn/scikit-learn,sklearn/externals/joblib/_parallel_backends.py,e0d501f3109e60f9fec12d4d4b4f6c74a0ff75ef,STILL_EXISTS,batches. Better reduce the batch size a bit to limit the
aaicjcdha,scikit-learn/scikit-learn,sklearn/externals/joblib/hashing.py,e0d501f3109e60f9fec12d4d4b4f6c74a0ff75ef,STILL_EXISTS,workaround is to view the array as bytes before
aaicjcehi,scikit-learn/scikit-learn,sklearn/externals/joblib/numpy_pickle_utils.py,e0d501f3109e60f9fec12d4d4b4f6c74a0ff75ef,STILL_EXISTS,BZ2File doesn't implement the file object context manager in python 2
aaicjcfjb,scikit-learn/scikit-learn,sklearn/externals/joblib/pool.py,e0d501f3109e60f9fec12d4d4b4f6c74a0ff75ef,STILL_EXISTS,Workaround  occasional \"[Error 5] Access is denied\" issue
aaicjcgai,scikit-learn/scikit-learn,sklearn/feature_selection/tests/test_chi2.py,9539c0c00ebac2d9f99633d7080931deaa046386,STILL_EXISTS,Unused feature should evaluate to NaN
aaicjcggi,scikit-learn/scikit-learn,doc/sphinxext/sphinx_gallery/docs_resolv.py,ca4e8023e4244e4638210b7ee6a2ead70430ad76,STILL_EXISTS,XXX: also at the time of writing this fixes make html-noplot
aaicjcgha,scikit-learn/scikit-learn,doc/sphinxext/sphinx_gallery/docs_resolv.py,ca4e8023e4244e4638210b7ee6a2ead70430ad76,STILL_EXISTS,XXX: Whitelist of builders for which it makes sense to embed
aaicjcghj,scikit-learn/scikit-learn,doc/sphinxext/sphinx_gallery/gen_gallery.py,ca4e8023e4244e4638210b7ee6a2ead70430ad76,STILL_EXISTS,Sphinx hack: sphinx copies generated images to the build directory
aaicjcgii,scikit-learn/scikit-learn,doc/sphinxext/sphinx_gallery/gen_gallery.py,ca4e8023e4244e4638210b7ee6a2ead70430ad76,STILL_EXISTS,The following is a hack that prevents this behavior by clearing the
aaicjcgjb,scikit-learn/scikit-learn,doc/sphinxext/sphinx_gallery/gen_gallery.py,ca4e8023e4244e4638210b7ee6a2ead70430ad76,STILL_EXISTS,it should probably not cause a crash).  Tested successfully
aaicjcgjg,scikit-learn/scikit-learn,doc/sphinxext/sphinx_gallery/gen_gallery.py,ca4e8023e4244e4638210b7ee6a2ead70430ad76,STILL_EXISTS,better than nested.
aaicjcgjh,scikit-learn/scikit-learn,doc/sphinxext/sphinx_gallery/gen_gallery.py,ca4e8023e4244e4638210b7ee6a2ead70430ad76,STILL_EXISTS,HACK: Stop nosetests running setup() above
aaicjdafb,scikit-learn/scikit-learn,sklearn/gaussian_process/tests/test_kernels.py,c31ad7ada3d46f8b8119685b91d1562f8e710b09,STILL_EXISTS,XXX: Should this be fixed?
aaicjdbae,scikit-learn/scikit-learn,sklearn/linear_model/coordinate_descent.py,084ef97f16342e1c5ac630ac46476c08da3bea62,STILL_EXISTS,workaround since _set_intercept will cast self.coef_ into float64
aaicjdbhc,scikit-learn/scikit-learn,sklearn/tests/test_pipeline.py,5b20d484add50aec64a1bda5c52ed2ceb7557f36,STILL_EXISTS,we validate in construction (despite scikit-learn convention)
aaicjdbje,scikit-learn/scikit-learn,sklearn/neural_network/tests/test_mlp.py,6a2b4f7e7b46785ef9b18dcc9410a338ae916b47,STILL_EXISTS,Non linear models perform much better than linear bottleneck:
aaicjdcgi,scikit-learn/scikit-learn,sklearn/metrics/tests/test_classification.py,18055e84ecaf053a67843980cb74a5fd1dc157d8,STILL_EXISTS,Check warning that pos_label unused when set to non-default value
aaicjdchf,scikit-learn/scikit-learn,sklearn/metrics/scorer.py,011472a72e87e6539697e48f0e2e10221ab928d6,eec7649236d5216380d05916bb7f6aa3b2fc5508,XXX After removing the deprecated scorers (v0.20) remove the
aaicjdchg,scikit-learn/scikit-learn,sklearn/metrics/scorer.py,011472a72e87e6539697e48f0e2e10221ab928d6,STILL_EXISTS,XXX deprecation_msg property again and make __call__ abstract again
aaicjdeih,scikit-learn/scikit-learn,doc/sphinxext/sphinx_gallery/gen_rst.py,1054e072da3235d6befcbd0d3cd2625f1b4e6473,STILL_EXISTS,XXX This check can break during testing e.g. if you uncomment the
aaicjdfca,scikit-learn/scikit-learn,sklearn/metrics/cluster/tests/test_unsupervised.py,3e7a7ca7fa6620afc68f2454c373c069be57dbf3,STILL_EXISTS,Cluster 0: 1 sample -> score of 0 by Rousseeuw's convention
aaicjdfif,scikit-learn/scikit-learn,sklearn/linear_model/ridge.py,b32897f1c32ad184cc84634567d87eab83c1e1eb,STILL_EXISTS,detect constant columns
aaicjdfii,scikit-learn/scikit-learn,sklearn/linear_model/ridge.py,b32897f1c32ad184cc84634567d87eab83c1e1eb,STILL_EXISTS,Note that by centering; the other columns are orthogonal to that one
aaicjdfij,scikit-learn/scikit-learn,sklearn/linear_model/ridge.py,b32897f1c32ad184cc84634567d87eab83c1e1eb,STILL_EXISTS,detect columns colinear to ones
aaicjdgag,scikit-learn/scikit-learn,sklearn/utils/fixes.py,868a58b2e0ff23427fbfb625e4fc7f997c734480,75d3f29e724c9a2db7bcb5afb2d7e8c08ddbd80b,This fix is needed to make our model_selection.GridSearchCV
aaicjdhai,scikit-learn/scikit-learn,sklearn/neighbors/lof.py,788a458bba353c2cf3cfa5a15d6f68315149ef9e,STILL_EXISTS,as bigger is better:
aaicjdhef,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,061803c4ff46fd5a34bb5220b572b8df4dcb2638,STILL_EXISTS,TODO: Once PR #7383 is merged use check_inputs=False in metric_kwargs.
aaicjdjej,scikit-learn/scikit-learn,examples/text/document_classification_20newsgroups.py,61ec187ea81f395de56baf1ee1564799a5075708,STILL_EXISTS,work-around for Jupyter notebook and IPython console
aaicjdjfa,scikit-learn/scikit-learn,examples/text/document_clustering.py,61ec187ea81f395de56baf1ee1564799a5075708,STILL_EXISTS,work-around for Jupyter notebook and IPython console
aaicjeacj,scikit-learn/scikit-learn,sklearn/decomposition/nmf.py,ae4f7104d65cecef11c26208bb15266b43ddc0a2,STILL_EXISTS,fast and memory efficient computation of np.sum(np.dot(W; H))
aaicjeade,scikit-learn/scikit-learn,sklearn/decomposition/nmf.py,ae4f7104d65cecef11c26208bb15266b43ddc0a2,STILL_EXISTS,slow loop; but memory efficient computation of :
aaicjebea,scikit-learn/scikit-learn,benchmarks/bench_plot_nmf.py,3f0af165f9f557b205a0d6aac2f38792e3a2a4d4,STILL_EXISTS,values justified in the paper (alpha is renamed gamma)
aaicjebej,scikit-learn/scikit-learn,benchmarks/bench_plot_nmf.py,3f0af165f9f557b205a0d6aac2f38792e3a2a4d4,STILL_EXISTS,fix up negative zeros
aaicjecbj,scikit-learn/scikit-learn,doc/sphinxext/sphinx_gallery/backreferences.py,bddda7bc69f86470d2b046d2b116fba444360e97,STILL_EXISTS,XXX This figure:: uses a forward slash even on Windows; but the op.join's
aaicjeddd,scikit-learn/scikit-learn,examples/preprocessing/plot_scaling_importance.py,eb9fe80e50f32fb164e9c0f840b45b25b7b31580,STILL_EXISTS,\"\"\" || ========================================================= || Importance of Feature Scaling || ========================================================= ||  || Feature scaling though standardization (or Z-score normalization) || can be an important preprocessing step for many machine learning || algorithms. Standardization involves rescaling the features such || that they have the properties of a standard normal distribution || with a mean of zero and a standard deviation of one. ||  || While many algorithms (such as SVM; K-nearest neighbors; and logistic || regression) require features to be normalized; intuitively we can || think of Principle Component Analysis (PCA) as being a prime example || of when normalization is important. In PCA we are interested in the || components that maximize the variance. If one component (e.g. human || height) varies less than another (e.g. weight) because of their || respective scales (meters vs. kilos); PCA might determine that the || direction of maximal variance more closely corresponds with the || 'weight' axis; if those features are not scaled. As a change in || height of one meter can be considered much more important than the || change in weight of one kilogram; this is clearly incorrect. ||  || To illustrate this; PCA is performed comparing the use of data with || :class:`StandardScaler <sklearn.preprocessing.StandardScaler>` applied; || to unscaled data. The results are visualized and a clear difference noted. || The 1st principal component in the unscaled set can be seen. It can be seen || that feature #13 dominates the direction; being a whole two orders of || magnitude above the other features. This is contrasted when observing || the principal component for the scaled version of the data. In the scaled || version; the orders of magnitude are roughly the same across all the features. ||  || The dataset used is the Wine Dataset available at UCI. This dataset || has continuous features that are heterogeneous in scale due to differing || properties that they measure (i.e alcohol content; and malic acid). ||  || The transformed data is then used to train a naive Bayes classifier; and a || clear difference in prediction accuracies is observed wherein the dataset || which is scaled before PCA vastly outperforms the unscaled version. ||  || \"\"\"
aaicjedff,scikit-learn/scikit-learn,sklearn/utils/tests/test_class_weight.py,a0db45db0e86243757b53294db2483228c917ce0,STILL_EXISTS,Fix exception in error message formatting when missing label is a string
aaicjeeeh,scikit-learn/scikit-learn,examples/linear_model/plot_sparse_logistic_regression_20newsgroups.py,5147fd09c6a063188efde444f47bd006fa5f95f0,STILL_EXISTS,\"\"\" || ===================================================== || Multiclass sparse logisitic regression on newgroups20 || ===================================================== ||  || Comparison of multinomial logistic L1 vs one-versus-rest L1 logistic regression || to classify documents from the newgroups20 dataset. Multinomial logistic || regression yields more accurate results and is faster to train on the larger || scale dataset. ||  || Here we use the l1 sparsity that trims the weights of not informative || features to zero. This is good if the goal is to extract the strongly || discriminative vocabulary of each class. If the goal is to get the best || predictive accuracy; it is better to use the non sparsity-inducing l2 penalty || instead. ||  || A more traditional (and possibly better) way to predict on a sparse subset of || input features would be to use univariate feature selection followed by a || traditional (l2-penalised) logistic regression model. || \"\"\"
aaicjefad,scikit-learn/scikit-learn,sklearn/pipeline.py,fb5a498d0bd00fc2b42fbd19b6ef18e1dfeee47e,STILL_EXISTS,Use Bunch object to improve autocomplete
aaicjeiec,scikit-learn/scikit-learn,doc/sphinxext/sphinx_gallery/gen_gallery.py,7a23b2493fbe094db2528114d75edc2e9739da17,STILL_EXISTS,TODO: Test this behavior.
aaicjejce,scikit-learn/scikit-learn,sklearn/covariance/tests/test_robust_covariance.py,b429868d07f874b81cafd1228a38f9990fb6ef91,STILL_EXISTS,i.e. one of the rows and columns are all zeros
aaicjfcbg,scikit-learn/scikit-learn,sklearn/preprocessing/tests/test_data.py,26a1027a830b7b658782229d936ffb84a340caad,55da79f5fd93708e1d5a5c54be30cb6dd438f1ad,TODO: rng should be seeded once we drop support for older versions of
aaicjfccb,scikit-learn/scikit-learn,sklearn/preprocessing/tests/test_data.py,26a1027a830b7b658782229d936ffb84a340caad,STILL_EXISTS,of a constant feature and binary feature; the bounds are properly mapped.
aaicjfcda,scikit-learn/scikit-learn,sklearn/linear_model/least_angle.py,32b88d8e2dff3b2ebd93c8cdc7511e72a33d71bf,STILL_EXISTS,XXX : we don't use super(LarsCV; self).__init__
aaicjfceh,scikit-learn/scikit-learn,sklearn/model_selection/tests/test_split.py,93563b0ac1d96403eff9988dfcb680d42c6304aa,STILL_EXISTS,fix for issue 9037
aaicjfcgb,scikit-learn/scikit-learn,sklearn/utils/estimator_checks.py,f0c5568fcd9a6c9ace37082b573fc28bfdc17cec,STILL_EXISTS,XXX: Generally can use 0.89 here. On Windows; LinearSVC gets
aaicjfgeb,scikit-learn/scikit-learn,sklearn/tests/test_docstring_parameters.py,b6f8865b0ac04700750e8d67e93146625ef40640,72b2ed9ee1bfa91f18273788233a28d3395903f4,TODO Uncomment all modules and fix doc inconsistencies everywhere
aaicjfgff,scikit-learn/scikit-learn,sklearn/tests/test_docstring_parameters.py,b6f8865b0ac04700750e8d67e93146625ef40640,STILL_EXISTS,user probably should have run \"make clean\"
aaicjfgjf,scikit-learn/scikit-learn,benchmarks/bench_tsne_mnist.py,cb1b6c4734b2989ad0492b56c326858d030f3fa2,STILL_EXISTS,TODO find a way to report the number of iterations
aaicjfhgj,scikit-learn/scikit-learn,sklearn/model_selection/tests/test_split.py,75d6005feacfedad33df8bed31e34f7bec51f62d,STILL_EXISTS,fix for issue 9037
aaicjfhja,scikit-learn/scikit-learn,doc/conf.py,71408e09e4bf01a72567f9167a1e33fb9620d786,STILL_EXISTS,this is needed for some reason...
aaicjfhje,scikit-learn/scikit-learn,doc/sphinxext/sphinx_gallery/gen_gallery.py,71408e09e4bf01a72567f9167a1e33fb9620d786,STILL_EXISTS,TODO: Test this behavior.
aaicjfiae,scikit-learn/scikit-learn,sklearn/neighbors/tests/test_nearest_centroid.py,90607f16675cd8706c8de5fb3e2d33f440f2b59c,STILL_EXISTS,which is implemented by the author of the original paper.
aaicjfihe,scikit-learn/scikit-learn,sklearn/utils/__init__.py,682e85fb1afcd050d9559b4d156593b2c32280df,STILL_EXISTS,Work-around for indexing with read-only indices in pandas
aaicjfije,scikit-learn/scikit-learn,sklearn/ensemble/gradient_boosting.py,312b1dfc74aeb6666a869fdc59971bd348dd097b,STILL_EXISTS,Require validation_score to be better (less) than at least
aaicjfjbc,scikit-learn/scikit-learn,sklearn/utils/_unittest_backport.py,2b9405f977b175a96d0bef6d68e1a74d6b90e60f,STILL_EXISTS,\"\"\" || This is a backport of assertRaises() and assertRaisesRegex from Python 3.5.4 ||  || The original copyright message is as follows ||  || Python unit testing framework; based on Erich Gamma's JUnit and Kent Beck's || Smalltalk testing framework (used with permission). ||  || This module contains the core framework classes that form the basis of || specific test cases and suites (TestCase; TestSuite etc.); and also a || text-based utility class for running the tests and reporting the results ||  (TextTestRunner). ||  || Simple usage: ||  ||     import unittest ||  ||     class IntegerArithmeticTestCase(unittest.TestCase): ||         def testAdd(self):  # test method names begin with 'test' ||             self.assertEqual((1 + 2); 3) ||             self.assertEqual(0 + 1; 1) ||         def testMultiply(self): ||             self.assertEqual((0 * 10); 0) ||             self.assertEqual((5 * 8); 40) ||  ||     if __name__ == '__main__': ||         unittest.main() ||  || Further information is available in the bundled documentation; and from ||  ||   http:\/\/docs.python.org\/library\/unittest.html ||  || Copyright (c) 1999-2003 Steve Purcell || Copyright (c) 2003-2010 Python Software Foundation || This module is free software; and you may redistribute it and\/or modify || it under the same terms as Python itself; so long as this copyright message || and disclaimer are retained in their original form. ||  || IN NO EVENT SHALL THE AUTHOR BE LIABLE TO ANY PARTY FOR DIRECT; INDIRECT; || SPECIAL; INCIDENTAL; OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OF || THIS CODE; EVEN IF THE AUTHOR HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH || DAMAGE. ||  || THE AUTHOR SPECIFICALLY DISCLAIMS ANY WARRANTIES; INCLUDING; BUT NOT || LIMITED TO; THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A || PARTICULAR PURPOSE.  THE CODE PROVIDED HEREUNDER IS ON AN \"AS IS\" BASIS; || AND THERE IS NO OBLIGATION WHATSOEVER TO PROVIDE MAINTENANCE; || SUPPORT; UPDATES; ENHANCEMENTS; OR MODIFICATIONS. || \"\"\"
aaicjgaii,scikit-learn/scikit-learn,sklearn/utils/validation.py,ecc96be8c5e831fd0a12f3274ed4a31dabcbffe6,STILL_EXISTS,to an error. This is needed because specifying a non complex
aaicjgbfe,scikit-learn/scikit-learn,sklearn/pipeline.py,a65a9e53af4873729ee98de76f0e8afa6f39d8e5,STILL_EXISTS,shallow copy of steps - this should really be steps_
aaicjgbhc,scikit-learn/scikit-learn,sklearn/ensemble/gradient_boosting.py,45dc891c96eebdb3b81bf14c2737d8f6540fabfe,STILL_EXISTS,Require validation_score to be better (less) than at least
aaicjgbhe,scikit-learn/scikit-learn,sklearn/utils/estimator_checks.py,45dc891c96eebdb3b81bf14c2737d8f6540fabfe,eec7649236d5216380d05916bb7f6aa3b2fc5508,FIXME
aaicjgbhf,scikit-learn/scikit-learn,sklearn/utils/estimator_checks.py,45dc891c96eebdb3b81bf14c2737d8f6540fabfe,eec7649236d5216380d05916bb7f6aa3b2fc5508,XXX GaussianProcess deprecated in 0.20
aaicjgcec,scikit-learn/scikit-learn,sklearn/utils/deprecation.py,688950eb5a075f398830e8b4b9c825274c6975e1,fb7be879a3dd08c2a8209639d7624fb67d98b46e,dict does not implement it like this; hence it needs to be overridden
aaicjgced,scikit-learn/scikit-learn,sklearn/model_selection/_search.py,766ba9320878ecc854edfa37988aa09096ad4adb,fb7be879a3dd08c2a8209639d7624fb67d98b46e,TODO: replace by a dict in 0.21
aaicjgcef,scikit-learn/scikit-learn,sklearn/model_selection/_validation.py,766ba9320878ecc854edfa37988aa09096ad4adb,fb7be879a3dd08c2a8209639d7624fb67d98b46e,TODO: replace by a dict in 0.21
aaicjgdbd,scikit-learn/scikit-learn,examples/plot_anomaly_comparison.py,8e599c68fb06967313d0e76a053062158cd90312,STILL_EXISTS,LOF does not implement predict
aaicjgdii,scikit-learn/scikit-learn,sklearn/preprocessing/tests/test_data.py,a2ebb8cfd2d126ad8e6fb36e0bdadba7de8fcd9f,007aa710bd45cd0df8c56ad581a4e59886128771,multiple columns
aaicjgfgf,scikit-learn/scikit-learn,sklearn/preprocessing/tests/test_data.py,62e9bb8deff5bb96606d766a17ca88331cb0756a,STILL_EXISTS,Exceptions should be raised for arrays with different num_columns
aaicjggeg,scikit-learn/scikit-learn,examples/preprocessing/plot_transformed_target.py,4f710cdd088aa8851e8b049e4faafa03767fda10,STILL_EXISTS,targets; allowing better prediction even with a similar linear model as
aaicjgggg,scikit-learn/scikit-learn,sklearn/preprocessing/_target.py,4f710cdd088aa8851e8b049e4faafa03767fda10,STILL_EXISTS,XXX: sample_weight is not currently passed to the
aaicjgghe,scikit-learn/scikit-learn,sklearn/preprocessing/_target.py,4f710cdd088aa8851e8b049e4faafa03767fda10,STILL_EXISTS,transform y and convert back to 1d array if needed
aaicjgghf,scikit-learn/scikit-learn,sklearn/preprocessing/_target.py,4f710cdd088aa8851e8b049e4faafa03767fda10,STILL_EXISTS,FIXME: a FunctionTransformer can return a 1D array even when validate
aaicjghcj,scikit-learn/scikit-learn,sklearn/linear_model/logistic.py,4dafa52eb06a40391bbc78b79887b47b19bcd100,STILL_EXISTS,Workaround for multi_class=\"multinomial\" and binary outcomes
aaicjgheh,scikit-learn/scikit-learn,sklearn/utils/estimator_checks.py,4a9034a97c9a303326e1cc4ae40eb06accea8657,1c1f273d1015b385c7dc1892bfa8b4628e9ef4e0,TODO remove cases when corrected
aaicjghhi,scikit-learn/scikit-learn,sklearn/externals/_pilutil.py,afe540c7f2cbad259dd333e6744b088213180bee,STILL_EXISTS,Workaround for crash in PIL. When im is 1-bit; the call array(im)
aaicjghie,scikit-learn/scikit-learn,sklearn/externals/_pilutil.py,afe540c7f2cbad259dd333e6744b088213180bee,STILL_EXISTS,columns show up first
aaicjgide,scikit-learn/scikit-learn,doc/tutorial/machine_learning_map/pyparsing.py,00d44d1ba6259dead0f4e4e5858cb05fbc8293f8,STILL_EXISTS,\"\"\"(Deprecated) Predefined expression of 1 or more printable words or quoted strings; separated by commas.\r ||    This expression is deprecated in favor of L{pyparsing_common.comma_separated_list}.\"\"\"
aaicjgjai,scikit-learn/scikit-learn,sklearn/manifold/t_sne.py,6af51e64a3c4e91e672b860f20a51697a817979b,STILL_EXISTS,only compute the error when needed
aaicjgjbj,scikit-learn/scikit-learn,sklearn/tests/test_naive_bayes.py,011c0b3de5b8f9ea8e466cf16010fa8120803130,STILL_EXISTS,Test feature probabilities uses pseudo-counts (alpha)
aaicjgjhd,scikit-learn/scikit-learn,sklearn/impute.py,71b29acbfb2f19de5f5244be47f93260b3846e96,STILL_EXISTS,Ignore the error; columns with a np.nan statistics_
aaicjgjhe,scikit-learn/scikit-learn,sklearn/impute.py,71b29acbfb2f19de5f5244be47f93260b3846e96,STILL_EXISTS,are not an error at this point. These columns will
aaicjgjjd,scikit-learn/scikit-learn,sklearn/impute.py,71b29acbfb2f19de5f5244be47f93260b3846e96,STILL_EXISTS,To be able access the elements by columns
aaicjgjjh,scikit-learn/scikit-learn,sklearn/impute.py,71b29acbfb2f19de5f5244be47f93260b3846e96,STILL_EXISTS,Delete the invalid rows\/columns
aaicjhabc,scikit-learn/scikit-learn,sklearn/tests/test_impute.py,71b29acbfb2f19de5f5244be47f93260b3846e96,STILL_EXISTS,Create a matrix X with columns
aaicjhabh,scikit-learn/scikit-learn,sklearn/tests/test_impute.py,71b29acbfb2f19de5f5244be47f93260b3846e96,STILL_EXISTS,Create the columns
aaicjhabj,scikit-learn/scikit-learn,sklearn/tests/test_impute.py,71b29acbfb2f19de5f5244be47f93260b3846e96,STILL_EXISTS,Mean doesn't support columns containing NaNs; median does
aaicjhajc,scikit-learn/scikit-learn,sklearn/covariance/tests/test_graphical_lasso.py,63a2f0a02bf155b161704da57a961c348f29fd7b,STILL_EXISTS,Hard-coded solution from R glasso package for alpha=1.0
aaicjhajh,scikit-learn/scikit-learn,sklearn/covariance/tests/test_graphical_lasso.py,63a2f0a02bf155b161704da57a961c348f29fd7b,STILL_EXISTS,Hard-coded solution from R glasso package for alpha=0.01
aaicjhbgf,scikit-learn/scikit-learn,sklearn/svm/base.py,eed83790b024adf1bdd956aca7f7d0c75712d904,28480f4cb2573b9dfd13acedacad04b02d60e40f,NOTE: when deprecation ends we need to remove explicitly
aaicjhcfc,scikit-learn/scikit-learn,examples/plot_missing_values.py,b97eda5d2ad146a4efdc332a67403908f45a69d1,STILL_EXISTS,\"\"\" || ==================================================== || Imputing missing values before building an estimator || ==================================================== ||  || Missing values can be replaced by the mean; the median or the most frequent || value using the basic ``SimpleImputer``. || The median is a more robust estimator for data with high magnitude variables || which could dominate results (otherwise known as a 'long tail'). ||  || Another option is the MICE imputer. This uses round-robin linear regression; || treating every variable as an output in turn. The version implemented assumes || Gaussian (output) variables. If your features are obviously non-Normal; || consider transforming them to look more Normal so as to improve performance. || \"\"\"
aaicjhchf,scikit-learn/scikit-learn,sklearn/impute.py,b97eda5d2ad146a4efdc332a67403908f45a69d1,f819704880b5d6affb49996f832f1aa5c8799571,note this is probably too slow for large feature data (d > 100000)
aaicjhchg,scikit-learn/scikit-learn,sklearn/impute.py,b97eda5d2ad146a4efdc332a67403908f45a69d1,f819704880b5d6affb49996f832f1aa5c8799571,and a better way would be good.
aaicjhdbb,scikit-learn/scikit-learn,sklearn/preprocessing/tests/test_common.py,c3548a8e295a793c5400b54431d3f65864850b6e,STILL_EXISTS,FIXME: we can introduce equal_nan=True in recent version of numpy.
aaicjhdbj,scikit-learn/scikit-learn,sklearn/utils/validation.py,9ea723f07a9d50ce96cddc3d8da24f9a12109e09,STILL_EXISTS,store reference to original array to check if copy is needed when
aaicjhdci,scikit-learn/scikit-learn,sklearn/manifold/tests/test_t_sne.py,4aaf45baf1cddbd368ab39801f4de40a6caaeece,91e019df5e86bb3b1db8c283c3bdc4a7ef1ce74b,FIXME: Remove this test in v0.23
aaicjhdga,scikit-learn/scikit-learn,sklearn/metrics/pairwise.py,ef8d22af7c4e1df2b872722b4a4f2f58630b2c45,575064b8e17c7243ff958e762b042d484283cd55,XXX: we use int(_num_samples...) because sometimes _num_samples
aaicjhdih,scikit-learn/scikit-learn,examples/column_transformer.py,0b6308c2708fe03071cdbf24997eb967403f5965,STILL_EXISTS,\"\"\" || ================================================== || Column Transformer with Heterogeneous Data Sources || ================================================== ||  || Datasets can often contain components of that require different feature || extraction and processing pipelines.  This scenario might occur when: ||  || 1. Your dataset consists of heterogeneous data types (e.g. raster images and ||    text captions) || 2. Your dataset is stored in a Pandas DataFrame and different columns ||    require different processing pipelines. ||  || This example demonstrates how to use || :class:`sklearn.compose.ColumnTransformer` on a dataset containing || different types of features.  We use the 20-newsgroups dataset and compute || standard bag-of-words features for the subject line and body in separate || pipelines as well as ad hoc features on the body. We combine them (with || weights) using a ColumnTransformer and finally train a classifier on the || combined set of features. ||  || The choice of features is not particularly helpful; but serves to illustrate || the technique. || \"\"\"
aaicjhdjb,scikit-learn/scikit-learn,examples/column_transformer.py,0b6308c2708fe03071cdbf24997eb967403f5965,STILL_EXISTS,construct object dtype array with two columns
aaicjhead,scikit-learn/scikit-learn,sklearn/compose/_column_transformer.py,0b6308c2708fe03071cdbf24997eb967403f5965,STILL_EXISTS,\"\"\" || The :mod:`sklearn.compose._column_transformer` module implements utilities || to work with heterogeneous data and to apply different transformers to || different columns. || \"\"\"
aaicjhebb,scikit-learn/scikit-learn,sklearn/compose/_column_transformer.py,0b6308c2708fe03071cdbf24997eb967403f5965,895dfd3f5c605fa6c151c9045e9174e77a6b64f0,empty list -> no need to select passthrough columns
aaicjhebc,scikit-learn/scikit-learn,sklearn/compose/_column_transformer.py,0b6308c2708fe03071cdbf24997eb967403f5965,STILL_EXISTS,Use Bunch object to improve autocomplete
aaicjheed,scikit-learn/scikit-learn,sklearn/compose/tests/test_column_transformer.py,0b6308c2708fe03071cdbf24997eb967403f5965,STILL_EXISTS,test multiple columns
aaicjhegd,scikit-learn/scikit-learn,sklearn/compose/tests/test_column_transformer.py,0b6308c2708fe03071cdbf24997eb967403f5965,STILL_EXISTS,specify to drop remaining columns
aaicjjadf,scikit-learn/scikit-learn,sklearn/preprocessing/data.py,a6028fc059982f04cb12256016702387e11964f5,007aa710bd45cd0df8c56ad581a4e59886128771,The columns of X which are not transformed need
aaicjjahb,scikit-learn/scikit-learn,examples/compose/column_transformer_mixed_types.py,bd19a848578ebabe120313ac15db86efa9b133c4,STILL_EXISTS,\"\"\" || =================================== || Column Transformer with Mixed Types || =================================== ||  || This example illustrates how to apply different preprocessing and || feature extraction pipelines to different subsets of features; || using :class:`sklearn.compose.ColumnTransformer`. || This is particularly handy for the case of datasets that contain || heterogeneous data types; since we may want to scale the || numeric features and one-hot encode the categorical ones. ||  || In this example; the numeric data is standard-scaled after || mean-imputation; while the categorical data is one-hot || encoded after imputing missing values with a new category || (``'missing'``). ||  || Finally; the preprocessing pipeline is integrated in a || full prediction pipeline using :class:`sklearn.pipeline.Pipeline`; || together with a simple classification model. || \"\"\"
aaicjjbje,scikit-learn/scikit-learn,sklearn/utils/__init__.py,0d8a04bd17e3393df271845e663698e2f48354bf,575064b8e17c7243ff958e762b042d484283cd55,Redondant np.floating is needed because numbers can't match np.float32
aaicjjbjj,scikit-learn/scikit-learn,sklearn/utils/fixes.py,0d8a04bd17e3393df271845e663698e2f48354bf,42706ebf2d4cac551dc8348dd0d049461cf2c3f8,instead of equality. This fix returns the mask of NaNs in an array of
aaicjjcai,scikit-learn/scikit-learn,sklearn/preprocessing/_encoders.py,007aa710bd45cd0df8c56ad581a4e59886128771,bb719e188c549b699bd14f189fbd6068c4e53edd,The columns of X which are not transformed need
aaicjjdfe,scikit-learn/scikit-learn,sklearn/preprocessing/tests/test_encoders.py,007aa710bd45cd0df8c56ad581a4e59886128771,STILL_EXISTS,multiple columns
aaicjjefh,scikit-learn/scikit-learn,sklearn/compose/tests/test_column_transformer.py,895dfd3f5c605fa6c151c9045e9174e77a6b64f0,STILL_EXISTS,second and third columns are doubled when remainder = DoubleTrans
aaicjjefi,scikit-learn/scikit-learn,sklearn/compose/tests/test_column_transformer.py,895dfd3f5c605fa6c151c9045e9174e77a6b64f0,STILL_EXISTS,columns are doubled when remainder = DoubleTrans
aaicjjeji,scikit-learn/scikit-learn,examples/linear_model/plot_sgd_early_stopping.py,0fc7ce6bb8bb6b5b98c66ad2c0a009753def945a,STILL_EXISTS,\"\"\" || ============================================= || Early stopping of Stochastic Gradient Descent || ============================================= ||  || Stochastic Gradient Descent is an optimization technique which minimizes a loss || function in a stochastic fashion; performing a gradient descent step sample by || sample. In particular; it is a very efficient method to fit linear models. ||  || As a stochastic method; the loss function is not necessarily decreasing at each || iteration; and convergence is only guaranteed in expectation. For this reason; || monitoring the convergence on the loss function can be difficult. ||  || Another approach is to monitor convergence on a validation score. In this case; || the input data is split into a training set and a validation set. The model is || then fitted on the training set and the stopping criterion is based on the || prediction score computed on the validation set. This enables us to find the || least number of iterations which is sufficient to build a model that || generalizes well to unseen data and reduces the chance of over-fitting the || training data. ||  || This early stopping strategy is activated if ``early_stopping=True``; otherwise || the stopping criterion only uses the training loss on the entire input data. To || better control the early stopping strategy; we can specify a parameter || ``validation_fraction`` which set the fraction of the input dataset that we || keep aside to compute the validation score. The optimization will continue || until the validation score did not improve by at least ``tol`` during the last || ``n_iter_no_change`` iterations. The actual number of iterations is available || at the attribute ``n_iter_``. ||  || This example illustrates how the early stopping can used in the || :class:`sklearn.linear_model.SGDClassifier` model to achieve almost the same || accuracy as compared to a model built without early stopping. This can || significantly reduce training time. Note that scores differ between the || stopping criteria even from early iterations because some of the training data || is held out with the validation stopping criterion. || \"\"\"
aaicjjfdj,scikit-learn/scikit-learn,sklearn/utils/estimator_checks.py,f8adfa27ae8256f708a575f75927bd6f6fff6cd7,1c1f273d1015b385c7dc1892bfa8b4628e9ef4e0,TODO remove cases when corrected
aaicjjfic,scikit-learn/scikit-learn,sklearn/utils/estimator_checks.py,f8adfa27ae8256f708a575f75927bd6f6fff6cd7,STILL_EXISTS,FIXME!
aaicjjfii,scikit-learn/scikit-learn,sklearn/utils/estimator_checks.py,f8adfa27ae8256f708a575f75927bd6f6fff6cd7,STILL_EXISTS,Workaround: https:\/\/github.com\/scikit-learn\/scikit-learn\/issues\/10562
aaicjjfij,scikit-learn/scikit-learn,sklearn/utils/estimator_checks.py,f8adfa27ae8256f708a575f75927bd6f6fff6cd7,STILL_EXISTS,TODO some complication with -1 label
aaicjjgcd,scikit-learn/scikit-learn,sklearn/linear_model/logistic.py,40d1f00c82a93cd6fabef63797e709fe2a6528fa,STILL_EXISTS,Workaround for multi_class=\"multinomial\" and binary outcomes
aaicjjige,scikit-learn/scikit-learn,sklearn/preprocessing/base.py,bb719e188c549b699bd14f189fbd6068c4e53edd,STILL_EXISTS,The columns of X which are not transformed need
aaicjjjac,scikit-learn/scikit-learn,sklearn/preprocessing/_function_transformer.py,a2b56453e2817cc496f7bcf23a2cd883c82cb30d,76ce7c5b6376951e607f7b2d5290be4dd9516321,FIXME: Future warning to be removed in 0.22
aaicjjjad,scikit-learn/scikit-learn,sklearn/preprocessing/tests/test_function_transformer.py,a2b56453e2817cc496f7bcf23a2cd883c82cb30d,76ce7c5b6376951e607f7b2d5290be4dd9516321,FIXME: to be removed in 0.22
aaidaaafg,scikit-learn/scikit-learn,sklearn/utils/fixes.py,c09352c2419217588c5f49c9258fd9722722f12b,2bd87f6ed8d36f89cab73552665e4e59e892262b,To be removed once this fix is included in six
aaidaacac,scikit-learn/scikit-learn,sklearn/utils/__init__.py,14e7c328df9968ca403e47168314ae28390c07d5,575064b8e17c7243ff958e762b042d484283cd55,Redondant np.floating is needed because numbers can't match np.float32
aaidaaccj,scikit-learn/scikit-learn,sklearn/ensemble/tests/test_forest.py,2242c59fc890455bd121e4a03375c5632f31ef93,8a8e21b2a395aea950f878c572771541d097ac16,FIXME: to be removed 0.22
aaidaacdc,scikit-learn/scikit-learn,sklearn/utils/estimator_checks.py,2242c59fc890455bd121e4a03375c5632f31ef93,8a8e21b2a395aea950f878c572771541d097ac16,FIXME: The default number of trees was changed and is set to 'warn'
aaidaadfe,scikit-learn/scikit-learn,sklearn/model_selection/tests/test_search.py,8e2c2aa35d234ba5d14f6c2492e2e5ca57de8af6,STILL_EXISTS,TODO Remove test in 0.20
aaidaadfj,scikit-learn/scikit-learn,sklearn/model_selection/tests/test_validation.py,8e2c2aa35d234ba5d14f6c2492e2e5ca57de8af6,STILL_EXISTS,FIXME issue in error_score parameter
aaidaadgd,scikit-learn/scikit-learn,sklearn/model_selection/tests/test_validation.py,8e2c2aa35d234ba5d14f6c2492e2e5ca57de8af6,STILL_EXISTS,FIXME this is an error in the error_score change!
aaidaafhd,scikit-learn/scikit-learn,sklearn/preprocessing/data.py,5592a2eda9c5367ddf8fbbc0965f7bd93982c2f3,STILL_EXISTS,Needed for backported inspect.signature compatibility with PyPy
aaidaaggj,scikit-learn/scikit-learn,sklearn/utils/fixes.py,6d15840432c08b2c628babe62d7bef6d6a01fcf6,42706ebf2d4cac551dc8348dd0d049461cf2c3f8,instead of equality. This fix returns the mask of NaNs in an array of
aaidaagic,scikit-learn/scikit-learn,sklearn/ensemble/iforest.py,53622e856c2747c7b559d3d29410cd37ca0addd2,778b11904e8ec0286e977582d37e7ca495947ee5,threshold_ attribute is implicitly 0 and is not needed anymore:
aaidaagie,scikit-learn/scikit-learn,sklearn/utils/estimator_checks.py,53622e856c2747c7b559d3d29410cd37ca0addd2,778b11904e8ec0286e977582d37e7ca495947ee5,XXX to be removed in 0.22.
aaidaaiai,scikit-learn/scikit-learn,sklearn/externals/joblib/_memmapping_reducer.py,40e6c43cb49d6c1eb56faa46ca0ffa40ab6228b8,STILL_EXISTS,Support for efficient transient pickling of numpy data structures
aaidaaiaj,scikit-learn/scikit-learn,sklearn/externals/joblib/_memmapping_reducer.py,40e6c43cb49d6c1eb56faa46ca0ffa40ab6228b8,STILL_EXISTS,TODO: check scipy sparse datastructure if scipy is installed
aaidaaiei,scikit-learn/scikit-learn,sklearn/externals/joblib/_memmapping_reducer.py,40e6c43cb49d6c1eb56faa46ca0ffa40ab6228b8,STILL_EXISTS,XXX: implement an explicit reference counting scheme to make it
aaidaajfj,scikit-learn/scikit-learn,sklearn/externals/joblib/externals/cloudpickle/cloudpickle.py,40e6c43cb49d6c1eb56faa46ca0ffa40ab6228b8,STILL_EXISTS,\"\"\"Constructors for 3rd party libraries || Note: These can never be renamed due to client compatibility issues\"\"\"
aaidaajgh,scikit-learn/scikit-learn,sklearn/externals/joblib/externals/cloudpickle/cloudpickle.py,40e6c43cb49d6c1eb56faa46ca0ffa40ab6228b8,STILL_EXISTS,is what gives us the ``nonlocal`` behavior in a Python 2 compatible way.
aaidabaac,scikit-learn/scikit-learn,sklearn/externals/joblib/externals/cloudpickle/cloudpickle.py,40e6c43cb49d6c1eb56faa46ca0ffa40ab6228b8,STILL_EXISTS,essentially save_reduce; but workaround needed to avoid recursion
aaidabadg,scikit-learn/scikit-learn,sklearn/externals/joblib/externals/cloudpickle/cloudpickle.py,40e6c43cb49d6c1eb56faa46ca0ffa40ab6228b8,STILL_EXISTS,save the rest of the func data needed by _fill_function
aaidabaei,scikit-learn/scikit-learn,sklearn/externals/joblib/externals/cloudpickle/cloudpickle.py,40e6c43cb49d6c1eb56faa46ca0ffa40ab6228b8,STILL_EXISTS,XXX Assert it's a sequence
aaidabafg,scikit-learn/scikit-learn,sklearn/externals/joblib/externals/cloudpickle/cloudpickle.py,40e6c43cb49d6c1eb56faa46ca0ffa40ab6228b8,STILL_EXISTS,hack for __import__ not working as desired
aaidabbia,scikit-learn/scikit-learn,sklearn/externals/joblib/externals/loky/backend/fork_exec.py,40e6c43cb49d6c1eb56faa46ca0ffa40ab6228b8,STILL_EXISTS,Launch a subprocess using forkexec and make sure only the needed fd are
aaidabdeg,scikit-learn/scikit-learn,sklearn/externals/joblib/externals/loky/backend/semaphore_tracker.py,40e6c43cb49d6c1eb56faa46ca0ffa40ab6228b8,STILL_EXISTS,python\" would probably leave unlinked semaphores.
aaidabdie,scikit-learn/scikit-learn,sklearn/externals/joblib/externals/loky/backend/semlock.py,40e6c43cb49d6c1eb56faa46ca0ffa40ab6228b8,STILL_EXISTS,needs to be cast to proper python value. Unix failure convention is to
aaidabebf,scikit-learn/scikit-learn,sklearn/externals/joblib/externals/loky/backend/spawn.py,40e6c43cb49d6c1eb56faa46ca0ffa40ab6228b8,STILL_EXISTS,Multiprocessing module helpers to fix up the main module in
aaidabffd,scikit-learn/scikit-learn,sklearn/externals/joblib/externals/loky/process_executor.py,40e6c43cb49d6c1eb56faa46ca0ffa40ab6228b8,8cbb13161dab31780dd18a1d42072e8cd618e1d1,TODO: Add option to deactivate; as it increases pickling time.
aaidabfge,scikit-learn/scikit-learn,sklearn/externals/joblib/externals/loky/process_executor.py,40e6c43cb49d6c1eb56faa46ca0ffa40ab6228b8,STILL_EXISTS,open files or shared memory that is not needed anymore
aaidabfgg,scikit-learn/scikit-learn,sklearn/externals/joblib/externals/loky/process_executor.py,40e6c43cb49d6c1eb56faa46ca0ffa40ab6228b8,STILL_EXISTS,Memory usage stays within bounds: everything is fine.
aaidabifh,scikit-learn/scikit-learn,sklearn/model_selection/tests/test_search.py,477c921c0f4a9182c2a7f2afe2c740b5bab0a15f,STILL_EXISTS,XXX: results['params'] is a list :|
aaidabiha,scikit-learn/scikit-learn,doc/conf.py,c564df963456a9928e7ae892b056331f7b791d2d,STILL_EXISTS,this is needed for some reason...
aaidabiii,scikit-learn/scikit-learn,sklearn/datasets/openml.py,ab82f5739f98a7ea18e8f8220506638a240ebce4,STILL_EXISTS,XXX: unnecessary decompression on first access
aaidabije,scikit-learn/scikit-learn,sklearn/datasets/openml.py,ab82f5739f98a7ea18e8f8220506638a240ebce4,STILL_EXISTS,TODO: improve for efficiency
aaidabjab,scikit-learn/scikit-learn,sklearn/datasets/openml.py,ab82f5739f98a7ea18e8f8220506638a240ebce4,STILL_EXISTS,TODO: feature request OpenML.
aaidabjcd,scikit-learn/scikit-learn,sklearn/datasets/openml.py,ab82f5739f98a7ea18e8f8220506638a240ebce4,STILL_EXISTS,prepare which columns and data types should be returned for the X and y
aaidabjce,scikit-learn/scikit-learn,sklearn/datasets/openml.py,ab82f5739f98a7ea18e8f8220506638a240ebce4,STILL_EXISTS,XXX: col_slice_y should be all nominal or all numeric
aaidabjcj,scikit-learn/scikit-learn,sklearn/datasets/openml.py,ab82f5739f98a7ea18e8f8220506638a240ebce4,STILL_EXISTS,to None if there are not target columns
aaidabjdc,scikit-learn/scikit-learn,sklearn/datasets/tests/test_openml.py,ab82f5739f98a7ea18e8f8220506638a240ebce4,STILL_EXISTS,XXX Test is intended to verify\/ensure correct decoding behavior
aaidabjdd,scikit-learn/scikit-learn,sklearn/datasets/tests/test_openml.py,ab82f5739f98a7ea18e8f8220506638a240ebce4,STILL_EXISTS,Not usable with sparse data or datasets that have columns marked as
aaidabjdf,scikit-learn/scikit-learn,sklearn/datasets/tests/test_openml.py,ab82f5739f98a7ea18e8f8220506638a240ebce4,STILL_EXISTS,XXX: This would be faster with np.take; although it does not
aaidabjdj,scikit-learn/scikit-learn,sklearn/datasets/tests/test_openml.py,ab82f5739f98a7ea18e8f8220506638a240ebce4,STILL_EXISTS,XXX: Test per column; as this makes it easier to avoid problems with
aaidabjfa,scikit-learn/scikit-learn,sklearn/datasets/tests/test_openml.py,ab82f5739f98a7ea18e8f8220506638a240ebce4,STILL_EXISTS,TODO: pass in a list of expected nominal features
aaidabjfi,scikit-learn/scikit-learn,sklearn/datasets/tests/test_openml.py,ab82f5739f98a7ea18e8f8220506638a240ebce4,STILL_EXISTS,XXX: Global variable
aaidabjgf,scikit-learn/scikit-learn,sklearn/datasets/tests/test_openml.py,ab82f5739f98a7ea18e8f8220506638a240ebce4,STILL_EXISTS,regression dataset with numeric and categorical columns
aaidacace,scikit-learn/scikit-learn,sklearn/externals/_arff.py,ab82f5739f98a7ea18e8f8220506638a240ebce4,STILL_EXISTS,It does not ensure that the line starts with '{' or ends with '}'.
aaidacaef,scikit-learn/scikit-learn,sklearn/externals/_arff.py,ab82f5739f98a7ea18e8f8220506638a240ebce4,STILL_EXISTS,XXX: int 0 is used for implicit values; not '0'
aaidacbaj,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_logistic.py,b42a5152af2cf51d9d7c30c40abc56bf0fe327f5,STILL_EXISTS,FIXME
aaidacccd,scikit-learn/scikit-learn,sklearn/cluster/tests/test_optics.py,1c84b818bb581484069e2bd347e9a777ed4737da,STILL_EXISTS,FIXME: known failure in 32bit Linux; numerical imprecision results in
aaidacchf,scikit-learn/scikit-learn,sklearn/externals/joblib/memory.py,5e101a2a07ea3586fe663598495cdc3893cb7665,STILL_EXISTS,FIXME: The statements below should be try\/excepted
aaidacchh,scikit-learn/scikit-learn,sklearn/externals/joblib/memory.py,5e101a2a07ea3586fe663598495cdc3893cb7665,STILL_EXISTS,XXX: Should use an exception logger
aaidacdaf,scikit-learn/scikit-learn,build_tools/generate_authors_table.py,1906c959a9350ba388bcd349969ad1555fa4e2f9,STILL_EXISTS,fix missing names
aaidacdbf,scikit-learn/scikit-learn,examples/compose/plot_digits_pipe.py,fc7d6e698668b983cee2867b1bf3c65f1384e4cf,STILL_EXISTS,Define a pipeline to search for the best combination of PCA truncation
aaidacdch,scikit-learn/scikit-learn,sklearn/compose/tests/test_column_transformer.py,f15ebb953b7b8971093962514187f447df4c716c,8d815af7326506e78a115f51e97670b3ea00e938,TODO: this warning is not very useful in this case; would be good
aaidacdda,scikit-learn/scikit-learn,sklearn/compose/tests/test_column_transformer.py,e58f366e034bcb284efe15ab04117a0ed67af80b,STILL_EXISTS,a given transformer doesn't have any columns to work on
aaidacdjb,scikit-learn/scikit-learn,sklearn/cluster/tests/test_bicluster.py,e0e738760625129be71839fac2dad9325fb90225,STILL_EXISTS,XXX test always skipped
aaidacdjd,scikit-learn/scikit-learn,sklearn/decomposition/tests/test_sparse_pca.py,e0e738760625129be71839fac2dad9325fb90225,STILL_EXISTS,XXX: test always skipped
aaidaceaf,scikit-learn/scikit-learn,sklearn/compose/_column_transformer.py,58228cb6d35b069c701a5baf1e25510f76fd5ef3,STILL_EXISTS,since all columns should be numeric before stacking them
aaidacecg,scikit-learn/scikit-learn,sklearn/datasets/openml.py,03c3af5bdec29903567fa3d63f2e6776a2b3041b,STILL_EXISTS,logic for determining on which columns can be learned. Note that from the
aaidacech,scikit-learn/scikit-learn,sklearn/datasets/openml.py,03c3af5bdec29903567fa3d63f2e6776a2b3041b,STILL_EXISTS,OpenML guide follows that columns that have the `is_row_identifier` or
aaidaceci,scikit-learn/scikit-learn,sklearn/datasets/openml.py,03c3af5bdec29903567fa3d63f2e6776a2b3041b,STILL_EXISTS,`is_ignore` flag; these can not be learned on. Also target columns are
aaidacfgd,scikit-learn/scikit-learn,sklearn/utils/tests/test_sparsefuncs.py,afe0a9b70d5ac53836b38b9af3a95659307574fc,STILL_EXISTS,XXX: test fails on Appveyor (python2.7 32bit)
aaidacfhc,scikit-learn/scikit-learn,sklearn/preprocessing/tests/test_data.py,a5fa7d3e6f4094da336108c9b8082887043c2fdd,2bd87f6ed8d36f89cab73552665e4e59e892262b,TODO: remove once scipy < 0.17 is no longer supported and just use
aaidached,scikit-learn/scikit-learn,sklearn/externals/joblib/externals/loky/backend/reduction.py,c81e25586988e5d021673fe7413991191876b144,STILL_EXISTS,Let us use a closure to workaround this limitation.
aaidachhi,scikit-learn/scikit-learn,sklearn/ensemble/iforest.py,664ff3474f939db61387d7b9872ce9eafb407797,STILL_EXISTS,ExtraTreeRegressor releases the GIL; so it's more efficient to use
aaidachif,scikit-learn/scikit-learn,sklearn/cluster/tests/test_bicluster.py,362cb3bcabad2ec5d9d0c29ea1ec69558a64d76b,STILL_EXISTS,XXX test always skipped
aaidacicc,scikit-learn/scikit-learn,sklearn/preprocessing/_encoders.py,2afee939df90e92e7dcde7d738c5be5f5611dae0,9ee164baa3c0de6678ff0ca61afe75a7e7d586ff,Set categories_ to empty list if no categorical columns exist
aaidacifi,scikit-learn/scikit-learn,sklearn/utils/__init__.py,d25da1be2054170034548b63ce87bd459ed8bee0,STILL_EXISTS,needed to tune `scikit-learn` behavior and have different effect if called
aaidacijg,scikit-learn/scikit-learn,sklearn/compose/_column_transformer.py,a816af7d1d89a9a62f523b81be24d5058928ddd8,7fdac52c19d63dad5f790ed2befa1669b2c1403e,XXX Remove in v0.22
aaidacijh,scikit-learn/scikit-learn,sklearn/compose/tests/test_column_transformer.py,a816af7d1d89a9a62f523b81be24d5058928ddd8,7fdac52c19d63dad5f790ed2befa1669b2c1403e,XXX remove in v0.22
aaidacjdh,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_logistic.py,c1f58745be7c4923cf0a666f1c6bf052042f131e,STILL_EXISTS,better results than just l1 or just l2.
aaidacjie,scikit-learn/scikit-learn,sklearn/decomposition/tests/test_sparse_pca.py,fa98a72dcca91920e5e807fdc1ce5b54486ad652,STILL_EXISTS,XXX: test always skipped
aaidadaaf,scikit-learn/scikit-learn,sklearn/preprocessing/tests/test_data.py,fa98a72dcca91920e5e807fdc1ce5b54486ad652,2bd87f6ed8d36f89cab73552665e4e59e892262b,TODO: remove once scipy < 0.17 is no longer supported and just use
aaidadada,scikit-learn/scikit-learn,sklearn/compose/tests/test_column_transformer.py,aae4e337d2c93d9005f5d13c62e80209740dce6a,7fdac52c19d63dad5f790ed2befa1669b2c1403e,XXX remove in v0.22
aaidadaec,scikit-learn/scikit-learn,sklearn/discriminant_analysis.py,6b1d8e5e1404853eece7bf56bde00d3683840a29,42e17b34876101bec79786b42206d4f2e946c278,FIXME: Future warning to be removed in 0.23
aaidadaee,scikit-learn/scikit-learn,sklearn/tests/test_discriminant_analysis.py,6b1d8e5e1404853eece7bf56bde00d3683840a29,42e17b34876101bec79786b42206d4f2e946c278,FIXME: Future warning to be removed in 0.23
aaidadbhc,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_logistic.py,55a98ab7e3b10966f6d00c3562f3a99896797964,STILL_EXISTS,better results than just l1 or just l2.
aaidadcdi,scikit-learn/scikit-learn,sklearn/utils/_pprint.py,a50c03f975c4bbf306c5af1826f46ba5dc5230a0,STILL_EXISTS,NO REPRESENTATIONS OR WARRANTIES; EXPRESS OR IMPLIED. BY WAY OF EXAMPLE; BUT
aaidadcga,scikit-learn/scikit-learn,sklearn/utils/_pprint.py,a50c03f975c4bbf306c5af1826f46ba5dc5230a0,STILL_EXISTS,needed for _dispatch[tuple.__repr__] not to be overridden
aaidaddga,scikit-learn/scikit-learn,sklearn/preprocessing/tests/test_encoders.py,fce73db57a602eec89583dd1582e69781244fec5,9ee164baa3c0de6678ff0ca61afe75a7e7d586ff,GH12881 bug in combination of categorical_features with ignore
aaidaddhf,scikit-learn/scikit-learn,examples/plot_multiple_imputation.py,486f8fc5438d4625ec05d22bb24ca5afb3c396fd,STILL_EXISTS,\"\"\" || ================================================= || Imputing missing values using multiple imputation || ================================================= ||  || By default; the IterativeImputer performs single imputation: a method where || every missing value is replaced with one imputed value. The chained character || of the method and the possiblity to draw imputation values from the posterior || distribution of a Bayesian imputation model allows for the finding of unbiased || statistical estimates. However; the disadvantage is that every imputed value is || treated as if the value was observed; leading to an imputed dataset that does || not reflect the uncertainty that occurs due to the presence of missing values. || This makes it hard to find valid statistical inferences because the variance || (and standard error) of statistical estimates become too small. ||  || An alternative is using the IterativeImputer to perform multiple imputation: a || method where every missing value is imputed multiple times. The procedure || results in multiple datasets where the observed data is similar in every || dataset; but the imputed data is different. All desired steps after imputation || are performed on every dataset; such as standardization and other feature || engineering steps. The estimation model is also fitted on each of the datasets. ||  || One final model is obtained by combining the estimates of each model with || Rubin's pooling rules. These rules assume that the parameters of interest are || normally distributed which is the case with; for example; estimates of the mean || and regression coefficients. Other parameters; such as correlation || coefficients need transformation to suit the assumption of normality. || If it is not possible to approximate a normal distribution; it is better to use || robust summary measures such as medians or ranges instead of using Rubin's || pooling rules. This applies to an estimate like explained variance. ||  || In sum; Rubin's pooling rules are as follows. The overall point estimate after || multiple imputation (denoted by Qbar) is the average of all the m point || estimates. The variance of the overall point estimate is a combination of || so-called within imputation variance (Ubar) and between imputation || variance (B). Ubar is the average of the m variances of the m point estimates. || Both Qbar and Ubar are corrected with a factor 1 \/ m to account for sampling || variance. The between imputation variance (B) is the sum of the squared || difference between Qbar and the m point estimates; corrected with a factor || 1 \/ (m \u2013 1). Then; the total variance (T) of the MI overall point estimate is || Ubar + B + B\/m. ||  || In this document we will show how to use the IterativeImputer to perform || multiple imputation. In example 1 we show the effect of Rubin\u2019s pooling || rules on the variance of regression estimates. Due to the between imputation || variance; the standard errors of all regression coefficients are larger with || multiple imputation than with single imputation. This allows for valid || statistical inference making. ||  || In example 2 we show how to set up a prediction model using multiple || imputation. We compare two approaches. In one approach; we make predictions for || each of the m datasets and combine the m evaluation error metrics into one || overall value. In the other approach; we combine the predictions and calculate || one evaluation error metric over the averaged predictions. A short simulation || study shows that the second approach results in the smallest Mean Squared || Error. || \"\"\"
aaidadgai,scikit-learn/scikit-learn,sklearn/impute.py,b8d1226d69bc4930d73c83394d2fee22dfe7ce2c,STILL_EXISTS,note this is probably too slow for large feature data (d > 100000)
aaidadgaj,scikit-learn/scikit-learn,sklearn/impute.py,b8d1226d69bc4930d73c83394d2fee22dfe7ce2c,STILL_EXISTS,and a better way would be good.
aaidadhbb,scikit-learn/scikit-learn,sklearn/impute.py,ab2f539a32b8099a941cefc598c9625e830ecfe4,STILL_EXISTS,note this is probably too slow for large feature data (d > 100000)
aaidadhbc,scikit-learn/scikit-learn,sklearn/impute.py,ab2f539a32b8099a941cefc598c9625e830ecfe4,STILL_EXISTS,and a better way would be good.
aaidadhcj,scikit-learn/scikit-learn,sklearn/multiclass.py,ab2f539a32b8099a941cefc598c9625e830ecfe4,df767ebc84c311d9ca1681cc7dabe9c3875f722f,FIXME Remove once #10440 is merged
aaidadhdg,scikit-learn/scikit-learn,sklearn/tests/test_common.py,ab2f539a32b8099a941cefc598c9625e830ecfe4,530be8f442a1a946f5a1e610d4dd5d8a5b96d791,FIXME _skip_test should be used here (if we could)
aaidadhdh,scikit-learn/scikit-learn,sklearn/tests/test_common.py,ab2f539a32b8099a941cefc598c9625e830ecfe4,STILL_EXISTS,FIXME
aaidadheb,scikit-learn/scikit-learn,sklearn/utils/estimator_checks.py,ab2f539a32b8099a941cefc598c9625e830ecfe4,STILL_EXISTS,This is ugly :-\/
aaidadhhd,scikit-learn/scikit-learn,sklearn/preprocessing/_encoders.py,350cd4aefa449badc67440315fdcc069c29ea667,STILL_EXISTS,If we have both dropped columns and ignored unknown
aaidadieh,scikit-learn/scikit-learn,sklearn/__init__.py,ffd27c8594fa58b8b0559ae0cf461116bf09e381,STILL_EXISTS,take care of potential over-subcription performance issues; in sections of
aaidadijc,scikit-learn/scikit-learn,examples/neighbors/plot_nca_classification.py,d31b67f23d3d785b17261ada293710380683bc82,STILL_EXISTS,we only take two features. We could avoid this ugly
aaidadjdf,scikit-learn/scikit-learn,sklearn/neighbors/nca.py,d31b67f23d3d785b17261ada293710380683bc82,STILL_EXISTS,needed
aaidadjgi,scikit-learn/scikit-learn,sklearn/neighbors/tests/test_nca.py,d31b67f23d3d785b17261ada293710380683bc82,STILL_EXISTS,Initialize a fake NCA and variables needed to compute the loss:
aaidaeabd,scikit-learn/scikit-learn,sklearn/neighbors/tests/test_nca.py,d31b67f23d3d785b17261ada293710380683bc82,STILL_EXISTS,Initialize a fake NCA and variables needed to call the loss
aaidaeaeh,scikit-learn/scikit-learn,sklearn/ensemble/gradient_boosting.py,ea63c56638aa6e0b987afe742c77d1e18007b768,STILL_EXISTS,FIXME: 0.23
aaidaebac,scikit-learn/scikit-learn,sklearn/utils/fixes.py,cf9a74059851c91b3b7c5b354b85e1e87ff628bf,STILL_EXISTS,TODO: replace by copy=False; when only scipy > 1.1 is supported.
aaidaebcf,scikit-learn/scikit-learn,sklearn/cluster/optics_.py,40441e86f587e8d7066618b328c43d22f8554f96,STILL_EXISTS,TODO: handle working_memory somehow?
aaidaecbj,scikit-learn/scikit-learn,sklearn/linear_model/least_angle.py,fec7670ad07d4e639995ad005796021928344805,STILL_EXISTS,and allows to easily swap columns
aaidaecdd,scikit-learn/scikit-learn,sklearn/tests/test_discriminant_analysis.py,4140657700cc55830347c871134c8e982d29fab5,STILL_EXISTS,implement the method to compute the probability given in The Elements
aaidaecfi,scikit-learn/scikit-learn,sklearn/base.py,73e2ecf7ce199998d5f934c4f86d5ab511b1d5ea,4256542b10bcc6bd10ddf31dfb356ecc084f2e7d,XXX: Remove the check in 0.23
aaidaechg,scikit-learn/scikit-learn,sklearn/multioutput.py,73e2ecf7ce199998d5f934c4f86d5ab511b1d5ea,4953ec36a41694d38f8d92fd48c7049cdd82e0ed,XXX Remove this method in 0.23
aaidaechi,scikit-learn/scikit-learn,sklearn/tests/test_base.py,73e2ecf7ce199998d5f934c4f86d5ab511b1d5ea,4256542b10bcc6bd10ddf31dfb356ecc084f2e7d,XXX: Remove in 0.23
aaidaecjb,scikit-learn/scikit-learn,sklearn/ensemble/iforest.py,c22b8712d88133ddb9f3572944c111c93b9af300,STILL_EXISTS,the data needed to compute the scores -- the returned scores
aaidaeddh,scikit-learn/scikit-learn,sklearn/decomposition/kernel_pca.py,2f5bb34b6d6093efe0ab1c10857ef07fd8aa503b,STILL_EXISTS,But choose to perform that LATER when needed; in `fit()` and in
aaidaedhh,scikit-learn/scikit-learn,sklearn/ensemble/gradient_boosting.py,d6b368e81892115d4be0697dde0666dca38ee89a,STILL_EXISTS,XXX clean this once we have a support_sample_weight tag
aaidaeeaj,scikit-learn/scikit-learn,sklearn/model_selection/_validation.py,24df999c10e0099284beca33ee3c045a0aea2e40,STILL_EXISTS,If classification methods produce multiple columns of output;
aaidaeecf,scikit-learn/scikit-learn,sklearn/model_selection/tests/test_validation.py,24df999c10e0099284beca33ee3c045a0aea2e40,STILL_EXISTS,binary indicator columns. The output of predict_proba
aaidaeegf,scikit-learn/scikit-learn,sklearn/tests/test_impute.py,50834f7c330ef226e37dfa72943ea84873d433d4,STILL_EXISTS,that's why we're looking at the last 4 columns only.
aaidaefbe,scikit-learn/scikit-learn,sklearn/utils/tests/test_linear_assignment.py,cfaf352caf0f5c80094b7389d5ca69b50050b472,STILL_EXISTS,TODO #0.23: Remove this test module as the methods being tested
aaidaegdb,scikit-learn/scikit-learn,sklearn/base.py,1fe00b58949a6b9bce45e9e15eb8b9c138bd6a2e,STILL_EXISTS,apprx number of chars to keep on both ends
aaidaehba,scikit-learn/scikit-learn,examples/inspection/plot_partial_dependence.py,6b7dc9bc30e3a84b173a79635b891111807c9e4d,STILL_EXISTS,\"\"\" || ======================== || Partial Dependence Plots || ======================== ||  || Partial dependence plots show the dependence between the target function [2]_ || and a set of 'target' features; marginalizing over the || values of all other features (the complement features). Due to the limits || of human perception the size of the target feature set must be small (usually; || one or two) thus the target features are usually chosen among the most || important features. ||  || This example shows how to obtain partial dependence plots from a || :class:`~sklearn.neural_network.MLPRegressor` and a || :class:`~sklearn.ensemble.GradientBoostingRegressor` trained on the || California housing dataset. The example is taken from [1]_. ||  || The plots show four 1-way and two 1-way partial dependence plots (ommitted for || :class:`~sklearn.neural_network.MLPRegressor` due to computation time). || The target variables for the one-way PDP are: median income (`MedInc`); || average occupants per household (`AvgOccup`); median house age (`HouseAge`); || and average rooms per household (`AveRooms`). ||  || We can clearly see that the median house price shows a linear relationship || with the median income (top left) and that the house price drops when the || average occupants per household increases (top middle). || The top right plot shows that the house age in a district does not have || a strong influence on the (median) house price; so does the average rooms || per household. || The tick marks on the x-axis represent the deciles of the feature values || in the training data. ||  || We also observe that :class:`~sklearn.neural_network.MLPRegressor` has much || smoother predictions than || :class:`~sklearn.ensemble.GradientBoostingRegressor`. For the plots to be || comparable; it is necessary to subtract the average value of the target || ``y``: The 'recursion' method; used by default for || :class:`~sklearn.ensemble.GradientBoostingRegressor`; does not account for || the initial predictor (in our case the average target). Setting the target || average to 0 avoids this bias. ||  || Partial dependence plots with two target features enable us to visualize || interactions among them. The two-way partial dependence plot shows the || dependence of median house price on joint values of house age and average || occupants per household. We can clearly see an interaction between the || two features: for an average occupancy greater than two; the house price is || nearly independent of the house age; whereas for values less than two there || is a strong dependence on age. ||  || On a third figure; we have plotted the same partial dependence plot; this time || in 3 dimensions. ||  || .. [1] T. Hastie; R. Tibshirani and J. Friedman; ||     \"Elements of Statistical Learning Ed. 2\"; Springer; 2009. ||  || .. [2] For classification you can think of it as the regression score before ||        the link function. || \"\"\"
aaidaehbi,scikit-learn/scikit-learn,examples/inspection/plot_partial_dependence.py,6b7dc9bc30e3a84b173a79635b891111807c9e4d,STILL_EXISTS,Needed on Windows because plot_partial_dependence uses multiprocessing
aaidaeibg,scikit-learn/scikit-learn,sklearn/inspection/tests/test_partial_dependence.py,6b7dc9bc30e3a84b173a79635b891111807c9e4d,STILL_EXISTS,If the target y only depends on one feature in an obvious way (linear or
aaidaeica,scikit-learn/scikit-learn,sklearn/inspection/tests/test_partial_dependence.py,6b7dc9bc30e3a84b173a79635b891111807c9e4d,STILL_EXISTS,needed) and compute r_squared to check that the partial dependence
aaidaeicc,scikit-learn/scikit-learn,sklearn/inspection/tests/test_partial_dependence.py,6b7dc9bc30e3a84b173a79635b891111807c9e4d,STILL_EXISTS,add polynomial features if needed
aaidaejdd,scikit-learn/scikit-learn,sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py,9f68c992aede232d539339ef10fa1aadda532d2a,STILL_EXISTS,create validation data if needed
aaidaejff,scikit-learn/scikit-learn,sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py,9f68c992aede232d539339ef10fa1aadda532d2a,STILL_EXISTS,needed for training anyway) for evaluating the training
aaidaejge,scikit-learn/scikit-learn,sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py,9f68c992aede232d539339ef10fa1aadda532d2a,db2342f416bbf2d264b053b1830ce27dc16dfbbe,TODO: not critical but stratify using resample()
aaidaejhc,scikit-learn/scikit-learn,sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py,9f68c992aede232d539339ef10fa1aadda532d2a,STILL_EXISTS,maybe we could also early stop if all the trees are stumps?
aaidaejic,scikit-learn/scikit-learn,sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py,9f68c992aede232d539339ef10fa1aadda532d2a,STILL_EXISTS,TODO: This could be done in parallel
aaidafaaf,scikit-learn/scikit-learn,sklearn/ensemble/_hist_gradient_boosting/grower.py,9f68c992aede232d539339ef10fa1aadda532d2a,STILL_EXISTS,(if needed)
aaidafade,scikit-learn/scikit-learn,sklearn/ensemble/_hist_gradient_boosting/loss.py,9f68c992aede232d539339ef10fa1aadda532d2a,STILL_EXISTS,TODO: This could be done in parallel
aaidafbbj,scikit-learn/scikit-learn,sklearn/ensemble/_hist_gradient_boosting/tests/test_grower.py,9f68c992aede232d539339ef10fa1aadda532d2a,STILL_EXISTS,Calling split next applies the next split and computes the best split
aaidafchf,scikit-learn/scikit-learn,sklearn/metrics/pairwise.py,f16227ba44ea5c2c95228b09474d3f7e8bc22210,dc1ea2751e8f4e18f61c7e6d767cf42c6e636256,If norms are passed as float32; they are unused. If arrays are passed as
aaidafdfi,scikit-learn/scikit-learn,sklearn/linear_model/ridge.py,f23e92ed4cdc5a952331e597023bd2c9922e6f9d,STILL_EXISTS,if X has more rows than columns; use decomposition of X^T.X;
aaidafefj,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_ridge.py,ff2f923d3a2d556967065f04d36bb53484b19031,STILL_EXISTS,XXX: Sparse CG seems to be far less numerically stable than the
aaidaffad,scikit-learn/scikit-learn,sklearn/impute/_base.py,1675e77e3e41ca287a451bc65e37a2eba5ca1e39,STILL_EXISTS,To be able access the elements by columns
aaidaffah,scikit-learn/scikit-learn,sklearn/impute/_base.py,1675e77e3e41ca287a451bc65e37a2eba5ca1e39,STILL_EXISTS,Delete the invalid columns if strategy is not constant
aaidaffch,scikit-learn/scikit-learn,sklearn/impute/_iterative.py,1675e77e3e41ca287a451bc65e37a2eba5ca1e39,STILL_EXISTS,note this is probably too slow for large feature data (d > 100000)
aaidaffci,scikit-learn/scikit-learn,sklearn/impute/_iterative.py,1675e77e3e41ca287a451bc65e37a2eba5ca1e39,STILL_EXISTS,and a better way would be good.
aaidaffhg,scikit-learn/scikit-learn,sklearn/model_selection/tests/test_search.py,69eb4d4678604147a8ed144b08b837a352fe7801,ac8cbb3799bdfa31360c87bf1097410326dba0bd,FIXME: remove in 0.24
aaidafgbc,scikit-learn/scikit-learn,setup.py,2a1e9686eeb203f5fddf44fd06414db8ab6a554a,STILL_EXISTS,Python 2 compat: just to be able to declare that Python >=3.5 is needed.
aaidafgcj,scikit-learn/scikit-learn,sklearn/metrics/pairwise.py,61de4021dae4a2edfa42b59357a7b628e634ac14,STILL_EXISTS,TODO: do it also for other norms.
aaidafhfb,scikit-learn/scikit-learn,sklearn/cross_decomposition/pls_.py,c41876126582577461cead2c9ce8b0335f401d81,STILL_EXISTS,Replace columns that are all close to zero with zeros
aaidafhfg,scikit-learn/scikit-learn,sklearn/model_selection/_split.py,f3a6a1a6c87a47a1da561013206346ac39ee6cf0,STILL_EXISTS,(Needed for external libraries that may use nose.)
aaidafhih,scikit-learn/scikit-learn,sklearn/decomposition/sparse_pca.py,a98db9a4d6d2a751b155ebaf4177a93704c0d456,STILL_EXISTS,FIXME: remove in 0.24
aaidagaej,scikit-learn/scikit-learn,sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py,da96da96f0dca3d23ee53722a617ee0cb5992286,STILL_EXISTS,This is needed in order to have the same split when using
aaidagagd,scikit-learn/scikit-learn,sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py,da96da96f0dca3d23ee53722a617ee0cb5992286,db2342f416bbf2d264b053b1830ce27dc16dfbbe,TODO: not critical but stratify using resample()
aaidagbhe,scikit-learn/scikit-learn,sklearn/externals/_lobpcg.py,b8ab2149679aa4bde0affe93406f2e038ca8cb83,STILL_EXISTS,\"\"\" || scikit-learn copy of scipy\/sparse\/linalg\/eigen\/lobpcg\/lobpcg.py v1.3.0 || to be deleted after scipy 1.3.0 becomes a dependency in scikit-lean || ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ || Locally Optimal Block Preconditioned Conjugate Gradient Method (LOBPCG). ||  || References || ---------- || .. [1] A. V. Knyazev (2001); ||        Toward the Optimal Preconditioned Eigensolver: Locally Optimal ||        Block Preconditioned Conjugate Gradient Method. ||        SIAM Journal on Scientific Computing 23; no. 2; ||        pp. 517-541. http:\/\/dx.doi.org\/10.1137\/S1064827500366124 ||  || .. [2] A. V. Knyazev; I. Lashuk; M. E. Argentati; and E. Ovchinnikov (2007); ||        Block Locally Optimal Preconditioned Eigenvalue Xolvers (BLOPEX) ||        in hypre and PETSc.  https:\/\/arxiv.org\/abs\/0705.2626 ||  || .. [3] A. V. Knyazev's C and MATLAB implementations: ||        https:\/\/bitbucket.org\/joseroman\/blopex || \"\"\"
aaidagcbj,scikit-learn/scikit-learn,sklearn/cluster/tests/test_bicluster.py,19c068a2ec95b18bea41e717406d333ff2b6e94c,STILL_EXISTS,XXX test always skipped
aaidagcij,scikit-learn/scikit-learn,sklearn/model_selection/tests/test_search.py,19c068a2ec95b18bea41e717406d333ff2b6e94c,ac8cbb3799bdfa31360c87bf1097410326dba0bd,FIXME: remove in 0.24
aaidagegi,scikit-learn/scikit-learn,sklearn/inspection/tests/test_partial_dependence.py,0eaaeafd1b43f2e9c657cb9036d3fad29474ad17,STILL_EXISTS,TODO: extend to HistGradientBoosting once sample_weight is supported
aaidagehf,scikit-learn/scikit-learn,sklearn/datasets/base.py,da6611148fa2b7a73277cde20b7251da6a551bf0,STILL_EXISTS,TODO: REMOVE in v0.23
aaidagehg,scikit-learn/scikit-learn,sklearn/datasets/california_housing.py,da6611148fa2b7a73277cde20b7251da6a551bf0,STILL_EXISTS,TODO: Revert to the following line in v0.23
aaidagehi,scikit-learn/scikit-learn,sklearn/datasets/covtype.py,da6611148fa2b7a73277cde20b7251da6a551bf0,STILL_EXISTS,TODO: Revert to the following two lines in v0.23
aaidageib,scikit-learn/scikit-learn,sklearn/datasets/kddcup99.py,da6611148fa2b7a73277cde20b7251da6a551bf0,STILL_EXISTS,TODO: Revert to the following two lines in v0.23
aaidageie,scikit-learn/scikit-learn,sklearn/datasets/olivetti_faces.py,da6611148fa2b7a73277cde20b7251da6a551bf0,STILL_EXISTS,TODO: Revert to the following line in v0.23
aaidagejc,scikit-learn/scikit-learn,sklearn/datasets/species_distributions.py,da6611148fa2b7a73277cde20b7251da6a551bf0,STILL_EXISTS,TODO: Revert to the following line in v0.23
aaidagfbf,scikit-learn/scikit-learn,sklearn/datasets/tests/test_openml.py,cf3e303be60f10f1869ce5808c6e41aca5457fd6,STILL_EXISTS,regression dataset with numeric and categorical columns
aaidagfce,scikit-learn/scikit-learn,sklearn/datasets/tests/test_openml.py,cf3e303be60f10f1869ce5808c6e41aca5457fd6,STILL_EXISTS,classification dataset with numeric and categorical columns
aaidagfee,scikit-learn/scikit-learn,sklearn/svm/classes.py,b0fdc4c433bb0dfdb5993d10f306c38eea415b21,STILL_EXISTS,FIXME Remove l1\/l2 support in 0.23 ----------------------------------
aaidaghag,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,67130a583206f0ccf2f5a224129bdf90b2c5ece7,STILL_EXISTS,XXX: numerical imprecision can result in a candidate_id out of range
aaidaghch,scikit-learn/scikit-learn,sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py,dd78658d25672f8ca56e61c8d0797808bc360be3,STILL_EXISTS,This is not strictly True; but it's needed since
aaidaghhb,scikit-learn/scikit-learn,sklearn/compose/_column_transformer.py,9115ab0ed3fcee50d750585b670003d3957a4897,8965abb264aaf70d11d9f56d2947bcc0b5ddaf75,Make it possible to check for reordered named columns on transform
aaidaghhe,scikit-learn/scikit-learn,sklearn/compose/tests/test_column_transformer.py,9115ab0ed3fcee50d750585b670003d3957a4897,STILL_EXISTS,Should accept added columns
aaidaghhf,scikit-learn/scikit-learn,sklearn/compose/tests/test_column_transformer.py,9115ab0ed3fcee50d750585b670003d3957a4897,STILL_EXISTS,No error for added columns if ordering is identical
aaidaghhh,scikit-learn/scikit-learn,sklearn/compose/tests/test_column_transformer.py,9115ab0ed3fcee50d750585b670003d3957a4897,STILL_EXISTS,No 'columns' AttributeError when transform input is a numpy array
aaidagida,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_bayes.py,42659232bd42cd38846ffbf3e579f72604772b96,STILL_EXISTS,Expect an accuracy of better than 1E-4 in most cases -
aaidagife,scikit-learn/scikit-learn,sklearn/ensemble/tests/test_voting.py,5925fb9e4fb372f254b5401c6011215132fa61ee,STILL_EXISTS,FIXME: to be removed when meta-estimators can be specified themselves
aaidagigd,scikit-learn/scikit-learn,sklearn/utils/estimator_checks.py,5925fb9e4fb372f254b5401c6011215132fa61ee,STILL_EXISTS,FIXME: The default number of trees was changed and is set to 'warn'
aaidagigg,scikit-learn/scikit-learn,sklearn/utils/estimator_checks.py,5925fb9e4fb372f254b5401c6011215132fa61ee,STILL_EXISTS,XXX to be removed in 0.22.
aaidagijb,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_logistic.py,583e18fc46369ca67b8eff70fa3677335c2a1939,STILL_EXISTS,FIXME
aaidagijc,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_logistic.py,583e18fc46369ca67b8eff70fa3677335c2a1939,STILL_EXISTS,FIXME: SAGA on sparse data fits the intercept inaccurately with the
aaidagjfj,scikit-learn/scikit-learn,sklearn/model_selection/tests/test_search.py,38fd24ac1e9c7048a32c673a7827afd4501f0050,ac8cbb3799bdfa31360c87bf1097410326dba0bd,FIXME: remove in 0.24
aaidahadj,scikit-learn/scikit-learn,sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py,2df846281f74f398431220b504ab7eac7072f70e,STILL_EXISTS,This is needed in order to have the same split when using
aaidahagb,scikit-learn/scikit-learn,sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py,2df846281f74f398431220b504ab7eac7072f70e,STILL_EXISTS,This is not strictly True; but it's needed since
aaidahagi,scikit-learn/scikit-learn,sklearn/compose/_column_transformer.py,da6614fe4211c0bb85200b65b01a2953cf1154b0,STILL_EXISTS,TODO: this should be `feature_names_in_` when we start having it
aaidahagj,scikit-learn/scikit-learn,sklearn/compose/_column_transformer.py,da6614fe4211c0bb85200b65b01a2953cf1154b0,313568be79a91c583425251d130e89a3f34091f6,TODO: remove this mechanism in 0.24; once we enforce strict column
aaidahahb,scikit-learn/scikit-learn,sklearn/compose/_column_transformer.py,da6614fe4211c0bb85200b65b01a2953cf1154b0,313568be79a91c583425251d130e89a3f34091f6,TODO: remove in v0.24
aaidahahi,scikit-learn/scikit-learn,sklearn/feature_extraction/tests/test_text.py,deec2baf00a0b34db48d08bdecb3f211b0872ab7,ac8cbb3799bdfa31360c87bf1097410326dba0bd,FIXME Remove copy parameter support in 0.24
aaidahahj,scikit-learn/scikit-learn,sklearn/feature_extraction/text.py,deec2baf00a0b34db48d08bdecb3f211b0872ab7,ac8cbb3799bdfa31360c87bf1097410326dba0bd,FIXME Remove copy parameter support in 0.24
aaidahbbc,scikit-learn/scikit-learn,sklearn/metrics/pairwise.py,796620723231dfa753e64d3a6ef26fe3aa19ea42,dc1ea2751e8f4e18f61c7e6d767cf42c6e636256,If norms are passed as float32; they are unused. If arrays are passed as
aaidahbda,scikit-learn/scikit-learn,sklearn/metrics/pairwise.py,796620723231dfa753e64d3a6ef26fe3aa19ea42,STILL_EXISTS,TODO: do it also for other norms.
aaidahbgf,scikit-learn/scikit-learn,sklearn/svm/classes.py,02be9949268d601e0afde3b8b8c29c63176ecf9f,STILL_EXISTS,FIXME Remove l1\/l2 support in 0.23 ----------------------------------
aaidahcdb,scikit-learn/scikit-learn,examples/tree/plot_cost_complexity_pruning.py,67c94c7e2a24bf5848ff8dc068849b3d83e3c96a,STILL_EXISTS,of the tree is pruned; thus creating a decision tree that generalizes better.
aaidahcfd,scikit-learn/scikit-learn,sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py,4b6273b87442a4437d8b3873ea3022ae163f4fdf,STILL_EXISTS,convention is that n_bins == max_bins + 1
aaidahdaj,scikit-learn/scikit-learn,sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py,4b6273b87442a4437d8b3873ea3022ae163f4fdf,STILL_EXISTS,First feature has missing values completely at random:
aaidahehb,scikit-learn/scikit-learn,sklearn/ensemble/tests/test_iforest.py,bcaf38134e8a000fe5e3a896745c6ef4266387be,STILL_EXISTS,2-d array where columns contain the same value across rows
aaidahfic,scikit-learn/scikit-learn,sklearn/tests/test_common.py,d9a12aa0a98140490676dac27094f9f6569b8c1a,4f5384b249c488ada8dd012bdc029600ac912917,TODO: meta-estimators like GridSearchCV has required parameters
aaidahggg,scikit-learn/scikit-learn,examples/inspection/plot_partial_dependence.py,9d62e2bb412653f24e883893737cf903906c795e,STILL_EXISTS,models is also orders of magnitude faster making it cheap to compute partial
aaidahghj,scikit-learn/scikit-learn,sklearn/impute/_knn.py,2d1b9e3513253d014a9cb83cfad16590545594dd,STILL_EXISTS,Removes columns where the training data is all nan
aaidahgif,scikit-learn/scikit-learn,sklearn/impute/_knn.py,2d1b9e3513253d014a9cb83cfad16590545594dd,STILL_EXISTS,distances for samples that needed imputation for column
aaidahhbh,scikit-learn/scikit-learn,sklearn/impute/tests/test_knn.py,2d1b9e3513253d014a9cb83cfad16590545594dd,STILL_EXISTS,Samples with needed feature has nan distance
aaidahhdg,scikit-learn/scikit-learn,sklearn/utils/__init__.py,ae5f55888b433f2d07bbd8988ae2be2518b0a243,STILL_EXISTS,FIXME: Remove the check for NumPy when using >= 1.12
aaidahhfg,scikit-learn/scikit-learn,sklearn/inspection/tests/test_partial_dependence.py,03ea20db0f9585fa0d44f4d3cae4b4c4a7c7f235,STILL_EXISTS,TODO: extend to HistGradientBoosting once sample_weight is supported
aaidahiag,scikit-learn/scikit-learn,sklearn/ensemble/_hist_gradient_boosting/loss.py,5cf88db24491112d2b8672f75df22f65a140d167,STILL_EXISTS,TODO: ideally this should be computed in parallel over the leaves
aaidahidd,scikit-learn/scikit-learn,sklearn/tree/tests/test_tree.py,99d3d34d615a7d7b541b24d264b1108238c1953e,ac8cbb3799bdfa31360c87bf1097410326dba0bd,TODO: remove in v0.24
aaidahjaj,scikit-learn/scikit-learn,sklearn/neural_network/multilayer_perceptron.py,2cf26db61e142da48d96aed34355f1f313e4398f,STILL_EXISTS,TODO: remove entire file in 0.24
aaidahjgj,scikit-learn/scikit-learn,sklearn/neural_network/rbm.py,2cf26db61e142da48d96aed34355f1f313e4398f,STILL_EXISTS,TODO: remove entire file in 0.24
aaidahjif,scikit-learn/scikit-learn,sklearn/tests/test_import_deprecations.py,2cf26db61e142da48d96aed34355f1f313e4398f,STILL_EXISTS,TODO: remove in 0.24
aaidahjjc,scikit-learn/scikit-learn,sklearn/utils/deprecation.py,2cf26db61e142da48d96aed34355f1f313e4398f,7494da6f520b70db490fbe0d602b05ed4078bcbc,TODO: remove in 0.24 since this shouldn't be needed anymore.
aaidaiaac,scikit-learn/scikit-learn,sklearn/utils/estimator_checks.py,66b0f5fed84d15b63943571e7b124d02763ef3ca,1931e142edf2adb324b641a76d7ae5615fd9435c,TODO: remove in 0.24
aaidaiadg,scikit-learn/scikit-learn,sklearn/utils/estimator_checks.py,dd6e5f8a0ce6b498f2704adb2f9989769903d462,1931e142edf2adb324b641a76d7ae5615fd9435c,TODO: remove in 0.24
aaidaiafh,scikit-learn/scikit-learn,sklearn/cluster/k_means_.py,0b07938e77dfeae9ce809c9f471bf617c72843c6,STILL_EXISTS,TODO: Once PR #7383 is merged use check_inputs=False in metric_kwargs.
aaidaicfd,scikit-learn/scikit-learn,sklearn/ensemble/tests/test_stacking.py,bab5926024e6ce43b6211ab71ffa3d2872be60ca,STILL_EXISTS,checking that fixing the random state of the CV will lead to the same
aaidaicgi,scikit-learn/scikit-learn,sklearn/metrics/scorer.py,d369a5f294bed106135216325bbc12a85f161a3d,STILL_EXISTS,XXX After removing the deprecated scorers (v0.24) remove the
aaidaicgj,scikit-learn/scikit-learn,sklearn/metrics/scorer.py,d369a5f294bed106135216325bbc12a85f161a3d,STILL_EXISTS,XXX deprecation_msg property again and remove __call__'s body again
aaidaichj,scikit-learn/scikit-learn,sklearn/__init__.py,125a54d9440567afe77ed45f57be0fa7edaa74c3,STILL_EXISTS,Workaround issue discovered in intel-openmp 2019.5:
aaidaidbe,scikit-learn/scikit-learn,examples/plot_partial_dependence_visualization_api.py,97185ecd3ec0bc8409781f3259b2436ba2a075bb,STILL_EXISTS,computed information needed to recreate the partial dependence curves. This
aaidaidci,scikit-learn/scikit-learn,examples/plot_partial_dependence_visualization_api.py,97185ecd3ec0bc8409781f3259b2436ba2a075bb,STILL_EXISTS,we create a figure with one row and two columns. The axes are passed into the
aaidaidib,scikit-learn/scikit-learn,sklearn/utils/estimator_checks.py,70f9e9f76f9fc1c23fb841661e81b298f511e7de,1931e142edf2adb324b641a76d7ae5615fd9435c,TODO: remove in 0.24
aaidaidic,scikit-learn/scikit-learn,sklearn/model_selection/tests/test_search.py,186629b60aa208d125a21132b58bd8d6b7565264,ac8cbb3799bdfa31360c87bf1097410326dba0bd,TODO: remove in v0.24; the deprecation goes away then.
aaidaidid,scikit-learn/scikit-learn,sklearn/tree/tree.py,186629b60aa208d125a21132b58bd8d6b7565264,STILL_EXISTS,TODO: tree should't need this in this case
aaidaidie,scikit-learn/scikit-learn,sklearn/tree/tree.py,186629b60aa208d125a21132b58bd8d6b7565264,STILL_EXISTS,TODO: the tree shouldn't need this param
aaidaiegd,scikit-learn/scikit-learn,sklearn/_build_utils/deprecated_modules.py,dba753a200fadcd25cb2d6550c8b33cf45e8d421,STILL_EXISTS,TODO: Remove in 0.24
aaidaiege,scikit-learn/scikit-learn,sklearn/utils/__init__.py,96c988cddeb1b8014d7e676e4abbfde62c05f939,1931e142edf2adb324b641a76d7ae5615fd9435c,TODO: remove in 0.24
aaidaiegf,scikit-learn/scikit-learn,sklearn/utils/tests/test_deprecated_utils.py,96c988cddeb1b8014d7e676e4abbfde62c05f939,1931e142edf2adb324b641a76d7ae5615fd9435c,TODO: remove in 0.24
aaidaifab,scikit-learn/scikit-learn,sklearn/pipeline.py,16f4ac90f0732988e3b7efe0c937eaff70e99692,ac8cbb3799bdfa31360c87bf1097410326dba0bd,TODO: Remove in 0.24 when None is removed
aaidaifac,scikit-learn/scikit-learn,sklearn/tests/test_pipeline.py,16f4ac90f0732988e3b7efe0c937eaff70e99692,ac8cbb3799bdfa31360c87bf1097410326dba0bd,TODO: Remove parametrization in 0.24 when None is removed for FeatureUnion
aaidaifad,scikit-learn/scikit-learn,sklearn/tests/test_pipeline.py,16f4ac90f0732988e3b7efe0c937eaff70e99692,ac8cbb3799bdfa31360c87bf1097410326dba0bd,TODO: Remove in 0.24 when None is removed
aaidaifid,scikit-learn/scikit-learn,sklearn/_build_utils/deprecated_modules.py,14295f9ceb2f1a9ea281ae19c06f3169be8a674f,STILL_EXISTS,TODO: Remove the whole file in 0.24
aaidaigab,scikit-learn/scikit-learn,conftest.py,56edce84caf6f3c8ef96376e1b684c45311130f3,7494da6f520b70db490fbe0d602b05ed4078bcbc,TODO: Remove when modules are deprecated in 0.24
aaidaigbf,scikit-learn/scikit-learn,sklearn/ensemble/_base.py,939fa3cccefe708db7a81c5248db32a1d600bf8d,ac8cbb3799bdfa31360c87bf1097410326dba0bd,FIXME: deprecate the usage of None to drop an estimator from the
aaidaigbh,scikit-learn/scikit-learn,sklearn/ensemble/tests/test_voting.py,939fa3cccefe708db7a81c5248db32a1d600bf8d,STILL_EXISTS,TODO: Remove parametrization in 0.24 when None is removed in Voting*
aaidaigbi,scikit-learn/scikit-learn,sklearn/ensemble/tests/test_voting.py,939fa3cccefe708db7a81c5248db32a1d600bf8d,ac8cbb3799bdfa31360c87bf1097410326dba0bd,TODO: Remove drop=None in 0.24 when None is removed in Voting*
aaidaigcb,scikit-learn/scikit-learn,sklearn/ensemble/tests/test_voting.py,939fa3cccefe708db7a81c5248db32a1d600bf8d,ac8cbb3799bdfa31360c87bf1097410326dba0bd,TODO: Remove in 0.24 when None is removed in Voting*
aaidaigcd,scikit-learn/scikit-learn,sklearn/model_selection/_split.py,7effc37174ff82e2252a7f4ff442a2bc7562ea0b,ac8cbb3799bdfa31360c87bf1097410326dba0bd,TODO 0.24: raise a ValueError instead of a warning
aaidaigcf,scikit-learn/scikit-learn,sklearn/model_selection/tests/test_split.py,7effc37174ff82e2252a7f4ff442a2bc7562ea0b,ac8cbb3799bdfa31360c87bf1097410326dba0bd,TODO 0.24: raise a ValueError instead of a warning
aaidaigfh,scikit-learn/scikit-learn,sklearn/feature_extraction/tests/test_image.py,bb3b3f9261bd092ed010d80de35fe031123c1687,ac8cbb3799bdfa31360c87bf1097410326dba0bd,TODO: Remove in 0.24
aaidaigfi,scikit-learn/scikit-learn,sklearn/feature_extraction/tests/test_text.py,bb3b3f9261bd092ed010d80de35fe031123c1687,ac8cbb3799bdfa31360c87bf1097410326dba0bd,TODO: Remove in 0.24
aaidaigja,scikit-learn/scikit-learn,sklearn/utils/_testing.py,b92455a6b25d5843c3ee9825fed5f268bf2f209f,1931e142edf2adb324b641a76d7ae5615fd9435c,TODO: Remove in 0.24. This class is now in utils.__init__.
aaidaigjc,scikit-learn/scikit-learn,sklearn/ensemble/tests/test_voting.py,7c47337f7b15a5368c922ed1781a267bf66c7367,STILL_EXISTS,TODO: Remove drop parametrize in 0.24 when None is removed in Voting*
aaidaihda,scikit-learn/scikit-learn,sklearn/inspection/_partial_dependence.py,7ea78614829a144f2f82e94fca515722a0fc2ef3,STILL_EXISTS,TODO: to be removed if\/when pipeline get a `steps_` attributes
aaidaihdd,scikit-learn/scikit-learn,sklearn/inspection/_partial_dependence.py,7ea78614829a144f2f82e94fca515722a0fc2ef3,STILL_EXISTS,FIXME: remove the None option when it will be deprecated
aaidaihee,scikit-learn/scikit-learn,sklearn/inspection/tests/test_partial_dependence.py,7ea78614829a144f2f82e94fca515722a0fc2ef3,STILL_EXISTS,dependence of the right columns
aaidaiiae,scikit-learn/scikit-learn,maint_tools/test_docstrings.py,1ede443b7a004f36618b106b8d982e64cf865cdd,STILL_EXISTS,TODO: this detection can be improved. Currently we assume that we have
aaidaiibb,scikit-learn/scikit-learn,sklearn/tests/test_docstring_parameters.py,f447dbcc1f1db386cd1a29b0b43033a5101021af,STILL_EXISTS,TODO: Remove when minimum python version is 3.7
aaidaiibf,scikit-learn/scikit-learn,sklearn/multioutput.py,12c2b40761d263ce0724e9a152d70b1131cb58ec,ac8cbb3799bdfa31360c87bf1097410326dba0bd,TODO: remove in 0.24
aaidaiica,scikit-learn/scikit-learn,sklearn/tests/test_multioutput.py,12c2b40761d263ce0724e9a152d70b1131cb58ec,ac8cbb3799bdfa31360c87bf1097410326dba0bd,TODO: remove in 0.24
aaidaiicb,scikit-learn/scikit-learn,sklearn/tests/test_naive_bayes.py,12c2b40761d263ce0724e9a152d70b1131cb58ec,ac8cbb3799bdfa31360c87bf1097410326dba0bd,TODO: remove in 0.24
aaidaiicc,scikit-learn/scikit-learn,sklearn/tests/test_random_projection.py,12c2b40761d263ce0724e9a152d70b1131cb58ec,ac8cbb3799bdfa31360c87bf1097410326dba0bd,TODO remove in 0.24
aaidaiief,scikit-learn/scikit-learn,sklearn/dummy.py,ee5b34d2f1bb6460972d74d440c86e5c349e1920,ac8cbb3799bdfa31360c87bf1097410326dba0bd,TODO: Remove in 0.24
aaidaiieg,scikit-learn/scikit-learn,sklearn/ensemble/tests/test_bagging.py,ee5b34d2f1bb6460972d74d440c86e5c349e1920,ac8cbb3799bdfa31360c87bf1097410326dba0bd,TODO: Remove in 0.24 when DummyClassifier's `strategy` default updates
aaidaiieh,scikit-learn/scikit-learn,sklearn/ensemble/tests/test_gradient_boosting.py,ee5b34d2f1bb6460972d74d440c86e5c349e1920,ac8cbb3799bdfa31360c87bf1097410326dba0bd,TODO: Remove in 0.24 when DummyClassifier's `strategy` default updates
aaidaiiei,scikit-learn/scikit-learn,sklearn/ensemble/tests/test_weight_boosting.py,ee5b34d2f1bb6460972d74d440c86e5c349e1920,ac8cbb3799bdfa31360c87bf1097410326dba0bd,TODO: Remove in 0.24 when DummyClassifier's `strategy` default changes
aaidaiifb,scikit-learn/scikit-learn,sklearn/tests/test_dummy.py,ee5b34d2f1bb6460972d74d440c86e5c349e1920,ac8cbb3799bdfa31360c87bf1097410326dba0bd,TODO: Remove in 0.24 when DummyClassifier's `strategy` default updates
aaidaijgh,scikit-learn/scikit-learn,sklearn/_build_utils/__init__.py,9876f748b04c2c862f1b5440cb7da8a582bd4431,STILL_EXISTS,to actually build the compiled extensions with OpenMP flags if needed.
aaidajafc,scikit-learn/scikit-learn,sklearn/ensemble/tests/test_partial_dependence.py,e254b30b6ea2fbdc3f05abeee71b4fc9a4b2e713,STILL_EXISTS,TODO: Remove when https:\/\/github.com\/numpy\/numpy\/issues\/14397 is resolved
aaidajafd,scikit-learn/scikit-learn,sklearn/inspection/tests/test_plot_partial_dependence.py,e254b30b6ea2fbdc3f05abeee71b4fc9a4b2e713,STILL_EXISTS,TODO: Remove when https:\/\/github.com\/numpy\/numpy\/issues\/14397 is resolved
aaidajafe,scikit-learn/scikit-learn,sklearn/metrics/_plot/tests/test_plot_confusion_matrix.py,e254b30b6ea2fbdc3f05abeee71b4fc9a4b2e713,STILL_EXISTS,TODO: Remove when https:\/\/github.com\/numpy\/numpy\/issues\/14397 is resolved
aaidajaff,scikit-learn/scikit-learn,sklearn/metrics/_plot/tests/test_plot_precision_recall.py,e254b30b6ea2fbdc3f05abeee71b4fc9a4b2e713,STILL_EXISTS,TODO: Remove when https:\/\/github.com\/numpy\/numpy\/issues\/14397 is resolved
aaidajafg,scikit-learn/scikit-learn,sklearn/metrics/_plot/tests/test_plot_roc_curve.py,e254b30b6ea2fbdc3f05abeee71b4fc9a4b2e713,STILL_EXISTS,TODO: Remove when https:\/\/github.com\/numpy\/numpy\/issues\/14397 is resolved
aaidajbde,scikit-learn/scikit-learn,sklearn/utils/__init__.py,4ce39dbc699cd9cd33ab2b67ca029698713aec3e,STILL_EXISTS,TODO: Remove when FeatureHasher is implemented in PYPY
aaidajbdi,scikit-learn/scikit-learn,sklearn/tree/tests/test_export.py,9c62eee695cdcd75c6fd23d02334b8d0c241ddde,STILL_EXISTS,FIXME: to be removed in 0.25
aaidajccb,scikit-learn/scikit-learn,sklearn/cluster/tests/test_spectral.py,2d687bfd0206cd5f3758ea8a6d24f8dffc8ef3f2,STILL_EXISTS,TODO: Remove when pyamg does replaces sp.rand call with np.random.rand
aaidajcih,scikit-learn/scikit-learn,sklearn/inspection/_permutation_importance.py,a68ba97309512f691feb25e6a07af7561fc56e3b,STILL_EXISTS,writable data-structure whose columns can be shuffled inplace.
aaidajfbd,scikit-learn/scikit-learn,sklearn/svm/tests/test_svm.py,8ea05cbd4190c2a6fbc65a8026b40301ba62db3f,STILL_EXISTS,TODO: Remove in 0.25 when probA_ and probB_ are deprecated
aaidajfeg,scikit-learn/scikit-learn,sklearn/svm/_base.py,84bc8d341e5a0a0d0b20b8acda58a33d72a2c23a,STILL_EXISTS,unused but needs to be a float for cython code that ignores
aaidajgdf,scikit-learn/scikit-learn,sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py,98b3c7c8719e2158a61891dd78e7982929c1cea0,STILL_EXISTS,would be 4 prior to bug fix
aaidajgee,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_passive_aggressive.py,4913037f8f81808b58967fa002faa19946c86f63,STILL_EXISTS,TODO: remove in 0.25
aaidajgef,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_sgd.py,4913037f8f81808b58967fa002faa19946c86f63,STILL_EXISTS,TODO: remove in 0.25
aaidajgfg,scikit-learn/scikit-learn,sklearn/model_selection/_search.py,c2e742c48c3a8dd1b963276a7a48102595447e79,STILL_EXISTS,FIXME Remove fit_grid_point in 0.25
aaidajgfh,scikit-learn/scikit-learn,sklearn/model_selection/tests/test_search.py,c2e742c48c3a8dd1b963276a7a48102595447e79,STILL_EXISTS,FIXME remove test_fit_grid_point as the function will be removed on 0.25
aaidajgfi,scikit-learn/scikit-learn,sklearn/model_selection/tests/test_search.py,c2e742c48c3a8dd1b963276a7a48102595447e79,STILL_EXISTS,FIXME remove test_fit_grid_point_deprecated as
aaidajggb,scikit-learn/scikit-learn,sklearn/gaussian_process/tests/test_kernels.py,9b39c4c4d20eef7a2b0b8420945f09d3731e1b67,STILL_EXISTS,XXX unreached code as of v0.22
aaidajggc,scikit-learn/scikit-learn,sklearn/impute/tests/test_impute.py,9b39c4c4d20eef7a2b0b8420945f09d3731e1b67,STILL_EXISTS,XXX unreached code as of v0.22
aaidajgge,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_sgd.py,9b39c4c4d20eef7a2b0b8420945f09d3731e1b67,STILL_EXISTS,XXX untested as of v0.22
aaidajggg,scikit-learn/scikit-learn,sklearn/neighbors/tests/test_kde.py,9b39c4c4d20eef7a2b0b8420945f09d3731e1b67,STILL_EXISTS,XXX Duplicated in test_neighbors_tree; test_kde
aaidajghb,scikit-learn/scikit-learn,sklearn/tests/test_common.py,9b39c4c4d20eef7a2b0b8420945f09d3731e1b67,STILL_EXISTS,XXX unreached code as of v0.22
aaidajghg,scikit-learn/scikit-learn,sklearn/utils/tests/test_validation.py,9b39c4c4d20eef7a2b0b8420945f09d3731e1b67,STILL_EXISTS,XXX unreached code as of v0.22
aaidajiea,scikit-learn/scikit-learn,sklearn/cluster/tests/test_bicluster.py,769794249c2e361c15ae15f4de8adcc02976ae74,STILL_EXISTS,FIXME: remove in 0.25
aaidajigb,scikit-learn/scikit-learn,sklearn/cluster/tests/test_k_means.py,769794249c2e361c15ae15f4de8adcc02976ae74,STILL_EXISTS,FIXME: remove in 0.25
aaidajijf,scikit-learn/scikit-learn,examples/compose/plot_column_transformer_mixed_types.py,68197ff6a4c95a0314d8ca0501a4da5dfc0ffea1,STILL_EXISTS,First; let's only select a subset of columns to simplify our
aaidajjaa,scikit-learn/scikit-learn,examples/compose/plot_column_transformer_mixed_types.py,68197ff6a4c95a0314d8ca0501a4da5dfc0ffea1,STILL_EXISTS,We can observe that the `embarked` and `sex` columns were tagged as
aaidajjab,scikit-learn/scikit-learn,examples/compose/plot_column_transformer_mixed_types.py,68197ff6a4c95a0314d8ca0501a4da5dfc0ffea1,STILL_EXISTS,`category` columns when loading the data with ``fetch_openml``. Therefore; we
aaidajjac,scikit-learn/scikit-learn,examples/compose/plot_column_transformer_mixed_types.py,68197ff6a4c95a0314d8ca0501a4da5dfc0ffea1,STILL_EXISTS,can use this information to dispatch the categorical columns to the
aaidajjad,scikit-learn/scikit-learn,examples/compose/plot_column_transformer_mixed_types.py,68197ff6a4c95a0314d8ca0501a4da5dfc0ffea1,STILL_EXISTS,``categorical_transformer`` and the remaining columns to the
aaidajjah,scikit-learn/scikit-learn,examples/compose/plot_column_transformer_mixed_types.py,68197ff6a4c95a0314d8ca0501a4da5dfc0ffea1,STILL_EXISTS,If you want some columns to be considered as `category`; you will have to
aaidajjai,scikit-learn/scikit-learn,examples/compose/plot_column_transformer_mixed_types.py,68197ff6a4c95a0314d8ca0501a4da5dfc0ffea1,STILL_EXISTS,convert them into categorical columns. If you are using pandas; you can
aaidajjej,scikit-learn/scikit-learn,sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py,e24998f19c428f0dc8e770b70bd03a037a839d6b,STILL_EXISTS,TODO: remove when PDP suports sample weights
aaidajjfa,scikit-learn/scikit-learn,sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py,e24998f19c428f0dc8e770b70bd03a037a839d6b,STILL_EXISTS,TODO: incorporate sample_weight in sampling here; as well as
aaidajjff,scikit-learn/scikit-learn,sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py,e24998f19c428f0dc8e770b70bd03a037a839d6b,STILL_EXISTS,TODO: incorporate sample_weights here in `resample`
aaidajjjc,scikit-learn/scikit-learn,sklearn/inspection/tests/test_partial_dependence.py,e24998f19c428f0dc8e770b70bd03a037a839d6b,STILL_EXISTS,TODO: remove\/fix when PDP supports HGBT with sample weights
aaidbaaca,scikit-learn/scikit-learn,sklearn/compose/_column_transformer.py,d205638475ca542dc46862652e3bb0be663a8eac,313568be79a91c583425251d130e89a3f34091f6,TODO: also call _check_n_features(reset=False) in 0.24
aaidbaadc,scikit-learn/scikit-learn,sklearn/decomposition/_lda.py,d205638475ca542dc46862652e3bb0be663a8eac,548a4524b43f2e63d8eeb1ea75c052d4d4bf6dd8,TODO: set reset=first_time when addressing reset in
aaidbaadi,scikit-learn/scikit-learn,sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py,d205638475ca542dc46862652e3bb0be663a8eac,STILL_EXISTS,TODO: remove when PDP suports sample weights
aaidbaadj,scikit-learn/scikit-learn,sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py,d205638475ca542dc46862652e3bb0be663a8eac,STILL_EXISTS,TODO: incorporate sample_weight in sampling here; as well as
aaidbaaed,scikit-learn/scikit-learn,sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py,d205638475ca542dc46862652e3bb0be663a8eac,STILL_EXISTS,TODO: incorporate sample_weights here in `resample`
aaidbaagg,scikit-learn/scikit-learn,sklearn/preprocessing/_data.py,d205638475ca542dc46862652e3bb0be663a8eac,d933c20befea779f9bfd35b4d85adaed9c30d684,TODO: set reset=in_fit when addressing reset in
aaidbabfi,scikit-learn/scikit-learn,examples/linear_model/plot_poisson_regression_non_normal_loss.py,69ea066412abc0c4a4c59c03e532ffe26b96c9c1,STILL_EXISTS,time. Using the full dataset would lead to similar conclusions.
aaidbabgb,scikit-learn/scikit-learn,examples/linear_model/plot_poisson_regression_non_normal_loss.py,69ea066412abc0c4a4c59c03e532ffe26b96c9c1,STILL_EXISTS,The remaining columns can be used to predict the frequency of claim events.
aaidbabgc,scikit-learn/scikit-learn,examples/linear_model/plot_poisson_regression_non_normal_loss.py,69ea066412abc0c4a4c59c03e532ffe26b96c9c1,STILL_EXISTS,Those columns are very heterogeneous with a mix of categorical and numeric
aaidbabhg,scikit-learn/scikit-learn,examples/linear_model/plot_poisson_regression_non_normal_loss.py,69ea066412abc0c4a4c59c03e532ffe26b96c9c1,STILL_EXISTS,were to convert this problem into a binary classification task; it would be
aaidbacaf,scikit-learn/scikit-learn,examples/linear_model/plot_poisson_regression_non_normal_loss.py,69ea066412abc0c4a4c59c03e532ffe26b96c9c1,STILL_EXISTS,a desired behavior. However this effect is limited for deep enough trees
aaidbacbh,scikit-learn/scikit-learn,examples/linear_model/plot_poisson_regression_non_normal_loss.py,69ea066412abc0c4a4c59c03e532ffe26b96c9c1,STILL_EXISTS,cross-validated performance metrics would lead to similar conclusions.
aaidbacda,scikit-learn/scikit-learn,examples/linear_model/plot_poisson_regression_non_normal_loss.py,69ea066412abc0c4a4c59c03e532ffe26b96c9c1,STILL_EXISTS,Thus; among the considered estimators; ``PoissonRegressor`` is better suited
aaidbacga,scikit-learn/scikit-learn,examples/linear_model/plot_poisson_regression_non_normal_loss.py,69ea066412abc0c4a4c59c03e532ffe26b96c9c1,STILL_EXISTS,This plot reveals that the random forest model is slightly better at ranking
aaidbacha,scikit-learn/scikit-learn,examples/linear_model/plot_poisson_regression_non_normal_loss.py,69ea066412abc0c4a4c59c03e532ffe26b96c9c1,STILL_EXISTS,captured in the columns of the dataset or that are indeed random.
aaidbadee,scikit-learn/scikit-learn,examples/linear_model/plot_tweedie_regression_insurance_claims.py,69ea066412abc0c4a4c59c03e532ffe26b96c9c1,STILL_EXISTS,predict the average claim amount per policy in general.
aaidbaeej,scikit-learn/scikit-learn,sklearn/linear_model/_glm/glm.py,69ea066412abc0c4a4c59c03e532ffe26b96c9c1,STILL_EXISTS,TODO: if alpha=0 check that X is not rank deficient
aaidbaeha,scikit-learn/scikit-learn,sklearn/linear_model/_glm/glm.py,69ea066412abc0c4a4c59c03e532ffe26b96c9c1,STILL_EXISTS,TODO: make the returned object immutable
aaidbafch,scikit-learn/scikit-learn,sklearn/decomposition/_pca.py,ea3181879c43f7423c32007111285d1f725a578b,STILL_EXISTS,TODO: this line is never executed because _infer_dimension's
aaidbafcj,scikit-learn/scikit-learn,sklearn/decomposition/_pca.py,ea3181879c43f7423c32007111285d1f725a578b,STILL_EXISTS,TODO: this line is never executed
aaidbafji,scikit-learn/scikit-learn,examples/inspection/plot_linear_model_coefficient_interpretation.py,e9340e3f4b1d7c285dc02062cd594ab457a5c268,STILL_EXISTS,As seen previously; the dataset contains columns with different data types
aaidbagag,scikit-learn/scikit-learn,examples/inspection/plot_linear_model_coefficient_interpretation.py,e9340e3f4b1d7c285dc02062cd594ab457a5c268,STILL_EXISTS,columns;
aaidbagcd,scikit-learn/scikit-learn,examples/inspection/plot_linear_model_coefficient_interpretation.py,e9340e3f4b1d7c285dc02062cd594ab457a5c268,STILL_EXISTS,this is obvious when looking at the plot above; where good predictions
aaidbahdg,scikit-learn/scikit-learn,examples/inspection/plot_linear_model_coefficient_interpretation.py,e9340e3f4b1d7c285dc02062cd594ab457a5c268,STILL_EXISTS,For the coefficient analysis; scaling is not needed this time.
aaidbahgf,scikit-learn/scikit-learn,examples/inspection/plot_linear_model_coefficient_interpretation.py,e9340e3f4b1d7c285dc02062cd594ab457a5c268,STILL_EXISTS,predictive variables; so neither alone would be very strongly weighted.
aaidbbaea,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_base.py,fa1ea2ab6f16bcd3ec46dd3c5220c36b6d249e2a,STILL_EXISTS,Warning is raised only when some of the columns is sparse
aaidbbaeb,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_base.py,fa1ea2ab6f16bcd3ec46dd3c5220c36b6d249e2a,STILL_EXISTS,all columns but the first column is sparse
aaidbbaeh,scikit-learn/scikit-learn,sklearn/utils/validation.py,fa1ea2ab6f16bcd3ec46dd3c5220c36b6d249e2a,STILL_EXISTS,When all dataframe columns are sparse; convert to a sparse array
aaidbbafi,scikit-learn/scikit-learn,sklearn/ensemble/__init__.py,5a4340834d23c4bdcd813ccda24a690ae174c168,STILL_EXISTS,TODO: remove this check once the estimator is no longer experimental.
aaidbbagf,scikit-learn/scikit-learn,sklearn/impute/__init__.py,5a4340834d23c4bdcd813ccda24a690ae174c168,STILL_EXISTS,TODO: remove this check once the estimator is no longer experimental.
aaidbbcad,scikit-learn/scikit-learn,sklearn/ensemble/_hist_gradient_boosting/loss.py,a93b15f19bf7db8027e04705603161c336fb1454,STILL_EXISTS,TODO: For speed; we could remove the constant xlogy(y_true; y_true)
aaidbbcba,scikit-learn/scikit-learn,sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py,a93b15f19bf7db8027e04705603161c336fb1454,STILL_EXISTS,For Poisson distributed target; Poisson loss should give better results
aaidbbcja,scikit-learn/scikit-learn,sklearn/datasets/_openml.py,a14953aca9cb0c1b0e33b2c31ec5666919b95871,STILL_EXISTS,Prepare which columns and data types should be returned for the X and y
aaidbbcjb,scikit-learn/scikit-learn,sklearn/datasets/_openml.py,a14953aca9cb0c1b0e33b2c31ec5666919b95871,STILL_EXISTS,XXX: col_slice_y should be all nominal or all numeric
aaidbbcjh,scikit-learn/scikit-learn,sklearn/datasets/_openml.py,a14953aca9cb0c1b0e33b2c31ec5666919b95871,STILL_EXISTS,back to None if there are not target columns
aaidbbdai,scikit-learn/scikit-learn,sklearn/metrics/tests/test_pairwise.py,5b2c931a994c9f4e39d202efd3b8a3de44309728,STILL_EXISTS,TODO: Remove warn_checker in 0.25
aaidbbdba,scikit-learn/scikit-learn,sklearn/tests/test_common.py,fc0041546a6e186bbacdf96e200fc863d620c44d,4f5384b249c488ada8dd012bdc029600ac912917,TODO: remove test in 0.24
aaidbbdbc,scikit-learn/scikit-learn,sklearn/tests/test_common.py,fc0041546a6e186bbacdf96e200fc863d620c44d,4f5384b249c488ada8dd012bdc029600ac912917,TODO in 0.24: remove checks on passing a class
aaidbbdbd,scikit-learn/scikit-learn,sklearn/tests/test_common.py,fc0041546a6e186bbacdf96e200fc863d620c44d,4f5384b249c488ada8dd012bdc029600ac912917,TODO: remove in 0.24
aaidbbdbh,scikit-learn/scikit-learn,sklearn/utils/estimator_checks.py,fc0041546a6e186bbacdf96e200fc863d620c44d,4f5384b249c488ada8dd012bdc029600ac912917,TODO: probably not needed anymore in 0.24 since _generate_class_checks should
aaidbbdbj,scikit-learn/scikit-learn,sklearn/utils/estimator_checks.py,fc0041546a6e186bbacdf96e200fc863d620c44d,4f5384b249c488ada8dd012bdc029600ac912917,TODO: remove this in 0.24
aaidbbdca,scikit-learn/scikit-learn,sklearn/utils/estimator_checks.py,fc0041546a6e186bbacdf96e200fc863d620c44d,4f5384b249c488ada8dd012bdc029600ac912917,TODO: remove this if block in 0.24 since passing instances isn't
aaidbbdcf,scikit-learn/scikit-learn,sklearn/utils/estimator_checks.py,fc0041546a6e186bbacdf96e200fc863d620c44d,4f5384b249c488ada8dd012bdc029600ac912917,TODO: Always convert to class in 0.24; because check_estimator() will
aaidbbdcj,scikit-learn/scikit-learn,sklearn/utils/tests/test_estimator_checks.py,fc0041546a6e186bbacdf96e200fc863d620c44d,4f5384b249c488ada8dd012bdc029600ac912917,TODO: remove whole test in 0.24 since passes classes to check_estimator()
aaidbbdig,scikit-learn/scikit-learn,examples/impute/plot_missing_values.py,fb76de72e7560aaa739872e94bee761777e54c0a,STILL_EXISTS,The version implemented assumes Gaussian (output) variables. If your features
aaidbbdii,scikit-learn/scikit-learn,examples/impute/plot_missing_values.py,fb76de72e7560aaa739872e94bee761777e54c0a,STILL_EXISTS,to potentially improve performance.
aaidbbehc,scikit-learn/scikit-learn,examples/linear_model/plot_poisson_regression_non_normal_loss.py,2dd12af9d687f4349a37ee0df90e61c9f992b092,STILL_EXISTS,trees would still perform relatively well and in particular better than
aaidbbggg,scikit-learn/scikit-learn,sklearn/cluster/tests/test_affinity_propagation.py,f72d9711f5242cd73f91551b75399dcfb88a5861,STILL_EXISTS,FIXME: to be removed in 0.25
aaidbbhcf,scikit-learn/scikit-learn,sklearn/ensemble/_hist_gradient_boosting/tests/test_loss.py,a6a20f29d7baf51960172aa64b981ffb240d3ebd,STILL_EXISTS,has its minimum at zero; which is needed for the newton method.
aaidbbhfb,scikit-learn/scikit-learn,sklearn/utils/tests/test_fixes.py,cbe9f08e356f494b39e0bcf7c5390bf4c839fd05,STILL_EXISTS,TODO: remove in 0.25
aaidbbhfj,scikit-learn/scikit-learn,sklearn/utils/tests/test_pprint.py,5f9555b3ea98e4ce8bb62b1fc6521772418d9144,STILL_EXISTS,Estimator with a kwargs argument. These need to hack around
aaidbbhgi,scikit-learn/scikit-learn,examples/compose/plot_transformed_target.py,78a213b42c7437d806a6289372ad5411d2ee37ad,STILL_EXISTS,Remove columns with NaN or Inf values
aaidbbhic,scikit-learn/scikit-learn,examples/compose/plot_column_transformer_mixed_types.py,9d677b56f4244eb08f1c7e2061a1e86fd0aee748,STILL_EXISTS,pipeline becase the dtype-based selector treats the ``pclass`` columns as
aaidbbihc,scikit-learn/scikit-learn,examples/applications/plot_model_complexity_influence.py,1986c89a12203a2df02f65e0764acea2bcd027cc,STILL_EXISTS,performance and complexities to see how those changes affect the estimator.
aaidbcaac,scikit-learn/scikit-learn,examples/covariance/plot_mahalanobis_distances.py,b4db36d337a4ff83f1bcb37c5a8c615d3134d372,STILL_EXISTS,To better visualize the difference; we plot contours of the
aaidbcaae,scikit-learn/scikit-learn,examples/covariance/plot_mahalanobis_distances.py,b4db36d337a4ff83f1bcb37c5a8c615d3134d372,STILL_EXISTS,MCD based Mahalanobis distances fit the inlier black points much better;
aaidbcacd,scikit-learn/scikit-learn,sklearn/inspection/_plot/tests/test_plot_partial_dependence.py,049d71b070188e4252b3a811bb618b5bbe0e1d4b,STILL_EXISTS,Use columns 0 & 2 as 1 is not quantitative (sex)
aaidbcebj,scikit-learn/scikit-learn,examples/inspection/plot_partial_dependence.py,45a6ef7b722811ff1dfdc76652a144190c9e0ef8,STILL_EXISTS,on an efficient way using the `'recursion'` method.
aaidbcedd,scikit-learn/scikit-learn,sklearn/inspection/_partial_dependence.py,45a6ef7b722811ff1dfdc76652a144190c9e0ef8,STILL_EXISTS,TODO 0.26: Remove kind == 'legacy' section
aaidbcedg,scikit-learn/scikit-learn,sklearn/inspection/tests/test_partial_dependence.py,45a6ef7b722811ff1dfdc76652a144190c9e0ef8,STILL_EXISTS,FIXME: to be removed in 0.24
aaidbcefc,scikit-learn/scikit-learn,sklearn/preprocessing/tests/test_discretization.py,846d51771dfb108abd191b525584663d9df4dcbb,STILL_EXISTS,TODO this check is redundant with common checks and can be removed
aaidbcefg,scikit-learn/scikit-learn,sklearn/tree/_classes.py,e4ebcbcafd758652545550aae8e71caadab42250,STILL_EXISTS,TODO: Remove in v0.26
aaidbcefh,scikit-learn/scikit-learn,sklearn/tree/tests/test_tree.py,e4ebcbcafd758652545550aae8e71caadab42250,STILL_EXISTS,TODO: Remove in v0.26
aaidbcege,scikit-learn/scikit-learn,sklearn/ensemble/tests/test_gradient_boosting.py,5a33360ebb84c5f89035417c5aee042dab235127,STILL_EXISTS,FIXME: We temporarily bypass this test. This is due to the fact
aaidbceie,scikit-learn/scikit-learn,sklearn/utils/tests/test_estimator_checks.py,c29092d6994a43ea19a82e91c0331e8b7d6e7d36,STILL_EXISTS,TODO: remove whole test in 0.24 since passes classes to check_estimator()
aaidbcfch,scikit-learn/scikit-learn,sklearn/decomposition/tests/test_dict_learning.py,b0c03d128007bd264e0e2b82e14c123a51f22308,STILL_EXISTS,TODO: remove in 0.26
aaidbcfjg,scikit-learn/scikit-learn,sklearn/svm/setup.py,c4c0fe2ce4e61ba5bf8f6b3f03ac6ea8f99cf688,STILL_EXISTS,Use C++11 random number generator fix
aaidbcgij,scikit-learn/scikit-learn,sklearn/covariance/_graph_lasso.py,233f2ce052624b9bf28d190776f2bc05acc517cf,STILL_EXISTS,TODO: Remove in 0.26 when grid_scores_ is deprecated
aaidbcgjd,scikit-learn/scikit-learn,sklearn/covariance/_graph_lasso.py,233f2ce052624b9bf28d190776f2bc05acc517cf,STILL_EXISTS,TODO: Remove in 0.26 when cv_alphas_ is deprecated
aaidbcgjg,scikit-learn/scikit-learn,sklearn/covariance/tests/test_graphical_lasso.py,233f2ce052624b9bf28d190776f2bc05acc517cf,STILL_EXISTS,TODO: Remove in 0.26 when grid_scores_ is deprecated
aaidbchbc,scikit-learn/scikit-learn,sklearn/cluster/_kmeans.py,7836a98cc01bac053bd6446eacf9f1249385a3a1,STILL_EXISTS,FIXME: init_size_ will be deprecated and this line will be removed
aaidbchea,scikit-learn/scikit-learn,sklearn/manifold/_t_sne.py,89e49b6641871d7106d5e196be69707d81c8d30d,STILL_EXISTS,TODO Revisit deprecation of square_distances for 0.26-0.28 (#12401)
aaidbchej,scikit-learn/scikit-learn,sklearn/manifold/tests/test_t_sne.py,89e49b6641871d7106d5e196be69707d81c8d30d,STILL_EXISTS,FIXME remove test when square_distances=True becomes the default in 0.26
aaidbchfg,scikit-learn/scikit-learn,sklearn/cluster/_kmeans.py,5d2107e9998805a64112b51b26ac13020dc5d2bc,STILL_EXISTS,FIXME: init_size_ will be deprecated and this line will be removed
aaidbchfi,scikit-learn/scikit-learn,sklearn/cluster/tests/test_k_means.py,5d2107e9998805a64112b51b26ac13020dc5d2bc,STILL_EXISTS,FIXME: remove in 0.26
aaidbcifi,scikit-learn/scikit-learn,sklearn/cluster/tests/test_affinity_propagation.py,3cb3d4109e7acc497ad1e306013547e5f72ee5f4,STILL_EXISTS,Test to fix incorrect clusters due to dtype change
aaidbcjcj,scikit-learn/scikit-learn,examples/feature_selection/plot_permutation_test_for_classification.py,e087f8a395f9db97145654c2429b7bdd107fd440,STILL_EXISTS,on the original data. The score is much better than those obtained by
aaidbdade,scikit-learn/scikit-learn,sklearn/cluster/_kmeans.py,1dedc7e6a8f3e96e6e7772b90bf6ec803f442567,STILL_EXISTS,FIXME: init_size_ will be deprecated and this line will be removed
aaidbdafg,scikit-learn/scikit-learn,sklearn/utils/estimator_checks.py,1dedc7e6a8f3e96e6e7772b90bf6ec803f442567,STILL_EXISTS,Mark (estimator; check) pairs as XFAIL if needed (see conditions in
aaidbdafj,scikit-learn/scikit-learn,sklearn/utils/estimator_checks.py,1dedc7e6a8f3e96e6e7772b90bf6ec803f442567,STILL_EXISTS,Wrap a check so that it's skipped if needed (see conditions in
aaidbdaib,scikit-learn/scikit-learn,sklearn/utils/estimator_checks.py,1dedc7e6a8f3e96e6e7772b90bf6ec803f442567,STILL_EXISTS,TODO: probably not needed anymore in 0.24 since _generate_class_checks should
aaidbdaif,scikit-learn/scikit-learn,sklearn/utils/estimator_checks.py,1dedc7e6a8f3e96e6e7772b90bf6ec803f442567,STILL_EXISTS,TODO: remove this if block in 0.24 since passing instances isn't
aaidbdajf,scikit-learn/scikit-learn,sklearn/utils/estimator_checks.py,1dedc7e6a8f3e96e6e7772b90bf6ec803f442567,STILL_EXISTS,TODO: Always convert to class in 0.24; because check_estimator() will
aaidbdbfa,scikit-learn/scikit-learn,sklearn/utils/tests/test_estimator_checks.py,86101d7ac8a42c96b205658a23db728e811a93ee,STILL_EXISTS,TODO: remove whole test in 0.24 since passes classes to check_estimator()
aaidbdcag,scikit-learn/scikit-learn,sklearn/ensemble/_gb.py,989613e5c33df49f99796e0ba17effbd28d8b831,STILL_EXISTS,FIXME: to be removed in 0.26
aaidbddaf,scikit-learn/scikit-learn,examples/plot_scalable_poly_kernels.py,daebcac781f4563f60017937a10e12eee08e9f40,STILL_EXISTS,showing that these kernel feature approximations improve the accuracy
aaidbddfb,scikit-learn/scikit-learn,sklearn/compose/tests/test_column_transformer.py,2b655efaf26f7802e4e41f2e64e1b9abdcaa6cd2,STILL_EXISTS,Remainder shows the columns after fitting
aaidbdeaa,scikit-learn/scikit-learn,sklearn/model_selection/tests/test_search.py,28c1ed473f0738e28a6fd61c105da8ada4ab880d,STILL_EXISTS,TODO: remove in v0.24; the deprecation goes away then.
aaidbdfdj,scikit-learn/scikit-learn,sklearn/ensemble/tests/test_common.py,8ba49f628092fe3fb1deea101910006aa6c76d49,STILL_EXISTS,FIXME: we should move this test in `estimator_checks` once we are able
aaidbdfed,scikit-learn/scikit-learn,sklearn/tests/test_multiclass.py,8ba49f628092fe3fb1deea101910006aa6c76d49,STILL_EXISTS,FIXME: we should move this test in `estimator_checks` once we are able
aaidbdfei,scikit-learn/scikit-learn,sklearn/tests/test_multioutput.py,8ba49f628092fe3fb1deea101910006aa6c76d49,STILL_EXISTS,FIXME: we should move this test in `estimator_checks` once we are able
aaidbdgcf,scikit-learn/scikit-learn,sklearn/cross_decomposition/_pls.py,8061aace11e36c9f5384e0bde723e0a98b81a1bd,STILL_EXISTS,exactly implement the Wold algorithm since it does not normalize
aaidbdggi,scikit-learn/scikit-learn,sklearn/cross_decomposition/tests/test_pls.py,8061aace11e36c9f5384e0bde723e0a98b81a1bd,STILL_EXISTS,TODO: remove attributes and test in 0.26
aaidbdhad,scikit-learn/scikit-learn,sklearn/impute/_knn.py,c50597926f69286fce5c416d23271a302b2da962,STILL_EXISTS,Removes columns where the training data is all nan
aaidbdhbi,scikit-learn/scikit-learn,sklearn/metrics/tests/test_common.py,a54cb47c74e54345001fbe2f9b5ed34e0fe9f9f8,96b86b619f57e26d9d83e245726116b8fdfa9a7e,FIXME: `brier_score_loss` does not follow this convention.
aaidbdhcd,scikit-learn/scikit-learn,sklearn/ensemble/_hist_gradient_boosting/grower.py,e325f16d6a3b6e3b7810038bdc579477ea354cb3,STILL_EXISTS,Release memory used by histograms as they are no longer needed
aaidbdhcf,scikit-learn/scikit-learn,sklearn/ensemble/_hist_gradient_boosting/grower.py,e325f16d6a3b6e3b7810038bdc579477ea354cb3,STILL_EXISTS,Release memory used by histograms as they are no longer needed for
aaidbdhdb,scikit-learn/scikit-learn,sklearn/ensemble/tests/test_gradient_boosting.py,4de0f97aa3b28e99f9bb378b1c4b459421d97893,STILL_EXISTS,TODO: Update in 0.26 to check for the error raised
aaidbdhge,scikit-learn/scikit-learn,examples/model_selection/plot_successive_halving_iterations.py,0a5af0d2a11c64d59381110f3967acbe7d88a031,STILL_EXISTS,\"\"\" || Successive Halving Iterations || ============================= ||  || This example illustrates how a successive halving search ( || :class:`~sklearn.model_selection.HalvingGridSearchCV` and || :class:`~sklearn.model_selection.HalvingRandomSearchCV`) iteratively chooses || the best parameter combination out of multiple candidates. ||  || \"\"\"
aaidbdhjg,scikit-learn/scikit-learn,sklearn/model_selection/__init__.py,0a5af0d2a11c64d59381110f3967acbe7d88a031,STILL_EXISTS,TODO: remove this check once the estimator is no longer experimental.
aaidbdibc,scikit-learn/scikit-learn,sklearn/model_selection/_search_successive_halving.py,0a5af0d2a11c64d59381110f3967acbe7d88a031,STILL_EXISTS,n_required_iterations is the number of iterations needed so that the
aaidbdicf,scikit-learn/scikit-learn,sklearn/model_selection/_search_successive_halving.py,0a5af0d2a11c64d59381110f3967acbe7d88a031,STILL_EXISTS,iterations as needed (while candidates are being
aaidbdich,scikit-learn/scikit-learn,sklearn/model_selection/_search_successive_halving.py,0a5af0d2a11c64d59381110f3967acbe7d88a031,STILL_EXISTS,guard; probably not needed
aaidbdigg,scikit-learn/scikit-learn,sklearn/model_selection/tests/test_successive_halving.py,0a5af0d2a11c64d59381110f3967acbe7d88a031,STILL_EXISTS,generate exactly as much as needed
aaidbeahc,scikit-learn/scikit-learn,sklearn/cluster/_affinity_propagation.py,02fa8f1adb2ac5e7b0e1c0fea3bd95f249c94816,STILL_EXISTS,TODO: Remove in 0.26
aaidbeahf,scikit-learn/scikit-learn,sklearn/cluster/_spectral.py,02fa8f1adb2ac5e7b0e1c0fea3bd95f249c94816,STILL_EXISTS,TODO: Remove in 0.26
aaidbeahi,scikit-learn/scikit-learn,sklearn/cluster/tests/test_affinity_propagation.py,02fa8f1adb2ac5e7b0e1c0fea3bd95f249c94816,STILL_EXISTS,TODO: Remove in 0.26
aaidbeahj,scikit-learn/scikit-learn,sklearn/cluster/tests/test_spectral.py,02fa8f1adb2ac5e7b0e1c0fea3bd95f249c94816,STILL_EXISTS,TODO: Remove in 0.26
aaidbeaia,scikit-learn/scikit-learn,sklearn/decomposition/_kernel_pca.py,02fa8f1adb2ac5e7b0e1c0fea3bd95f249c94816,STILL_EXISTS,TODO: Remove in 0.26
aaidbeaid,scikit-learn/scikit-learn,sklearn/decomposition/tests/test_kernel_pca.py,02fa8f1adb2ac5e7b0e1c0fea3bd95f249c94816,STILL_EXISTS,TODO: Remove in 0.26
aaidbeaie,scikit-learn/scikit-learn,sklearn/kernel_ridge.py,02fa8f1adb2ac5e7b0e1c0fea3bd95f249c94816,STILL_EXISTS,TODO: Remove in 0.26
aaidbeaih,scikit-learn/scikit-learn,sklearn/manifold/_mds.py,02fa8f1adb2ac5e7b0e1c0fea3bd95f249c94816,STILL_EXISTS,TODO: Remove in 0.26
aaidbeaja,scikit-learn/scikit-learn,sklearn/manifold/_spectral_embedding.py,02fa8f1adb2ac5e7b0e1c0fea3bd95f249c94816,STILL_EXISTS,TODO: Remove in 0.26
aaidbeajf,scikit-learn/scikit-learn,sklearn/manifold/tests/test_spectral_embedding.py,02fa8f1adb2ac5e7b0e1c0fea3bd95f249c94816,STILL_EXISTS,TODO: Remove in 0.26
aaidbeajg,scikit-learn/scikit-learn,sklearn/model_selection/_search.py,02fa8f1adb2ac5e7b0e1c0fea3bd95f249c94816,STILL_EXISTS,TODO: Remove in 0.26
aaidbebaa,scikit-learn/scikit-learn,sklearn/model_selection/tests/test_search.py,02fa8f1adb2ac5e7b0e1c0fea3bd95f249c94816,STILL_EXISTS,TODO: Remove in 0.26
aaidbebab,scikit-learn/scikit-learn,sklearn/model_selection/tests/test_validation.py,02fa8f1adb2ac5e7b0e1c0fea3bd95f249c94816,STILL_EXISTS,TODO: Remove in 0.26 when the _pairwise attribute is removed
aaidbebbd,scikit-learn/scikit-learn,sklearn/neighbors/_base.py,02fa8f1adb2ac5e7b0e1c0fea3bd95f249c94816,STILL_EXISTS,TODO: Remove in 0.26
aaidbebbh,scikit-learn/scikit-learn,sklearn/neighbors/_regression.py,02fa8f1adb2ac5e7b0e1c0fea3bd95f249c94816,STILL_EXISTS,TODO: Remove in 0.26
aaidbebcb,scikit-learn/scikit-learn,sklearn/neighbors/tests/test_neighbors.py,02fa8f1adb2ac5e7b0e1c0fea3bd95f249c94816,STILL_EXISTS,TODO: Remove in 0.26
aaidbebcc,scikit-learn/scikit-learn,sklearn/pipeline.py,02fa8f1adb2ac5e7b0e1c0fea3bd95f249c94816,STILL_EXISTS,TODO: Remove in 0.26
aaidbebcg,scikit-learn/scikit-learn,sklearn/preprocessing/_data.py,02fa8f1adb2ac5e7b0e1c0fea3bd95f249c94816,STILL_EXISTS,TODO: Remove in 0.26
aaidbebdb,scikit-learn/scikit-learn,sklearn/svm/_base.py,02fa8f1adb2ac5e7b0e1c0fea3bd95f249c94816,STILL_EXISTS,TODO: Remove in 0.26
aaidbebdf,scikit-learn/scikit-learn,sklearn/tests/test_base.py,02fa8f1adb2ac5e7b0e1c0fea3bd95f249c94816,STILL_EXISTS,TODO: Remove in 0.26 when the _pairwise attribute is removed
aaidbebed,scikit-learn/scikit-learn,sklearn/tests/test_kernel_ridge.py,02fa8f1adb2ac5e7b0e1c0fea3bd95f249c94816,STILL_EXISTS,TODO: Remove in 0.26
aaidbebee,scikit-learn/scikit-learn,sklearn/tests/test_multiclass.py,02fa8f1adb2ac5e7b0e1c0fea3bd95f249c94816,STILL_EXISTS,TODO: Remove in 0.26
aaidbecfh,scikit-learn/scikit-learn,conftest.py,1ea9ae2245046a6194dfc3f7125c1140804e18a2,STILL_EXISTS,TODO: Remove when modules are deprecated in 0.24
aaidbechd,scikit-learn/scikit-learn,sklearn/tests/test_common.py,fe654ba93224b73413a7a7b68b3e5ba4e6c80ebf,ad3c28818220a4ec9a40b3b674adcdb676a419ea,FIXME : should be removed in 0.26
aaidbechf,scikit-learn/scikit-learn,sklearn/utils/estimator_checks.py,fe654ba93224b73413a7a7b68b3e5ba4e6c80ebf,STILL_EXISTS,FIXME : init should be removed in 0.26
aaidbecja,scikit-learn/scikit-learn,sklearn/preprocessing/_data.py,d933c20befea779f9bfd35b4d85adaed9c30d684,STILL_EXISTS,TODO: This should be refactored because binarize also calls
aaidbedih,scikit-learn/scikit-learn,doc/conf.py,7da9153e066626e5a1250ee1a0817050900b1486,STILL_EXISTS,Hack to get kwargs to appear in docstring #18434
aaidbedii,scikit-learn/scikit-learn,doc/conf.py,7da9153e066626e5a1250ee1a0817050900b1486,STILL_EXISTS,TODO: Remove when https:\/\/github.com\/sphinx-doc\/sphinx\/pull\/8234 gets
aaidbedjd,scikit-learn/scikit-learn,sklearn/calibration.py,9358a94aa7c8bb887bd64ae156e0d0a7ee637d1c,STILL_EXISTS,TODO: Remove in 0.26
aaidbedjg,scikit-learn/scikit-learn,sklearn/tests/test_calibration.py,9358a94aa7c8bb887bd64ae156e0d0a7ee637d1c,STILL_EXISTS,FIXME: remove in 0.26
aaidbeebf,scikit-learn/scikit-learn,sklearn/tree/tests/test_tree.py,de59efe7cbe9e33245e7573cb9d0d98478b05f98,STILL_EXISTS,For a Poisson distributed target; Poisson loss should give better results
aaidbeebj,scikit-learn/scikit-learn,sklearn/tree/tests/test_tree.py,de59efe7cbe9e33245e7573cb9d0d98478b05f98,STILL_EXISTS,Note: Some fine tuning was needed to have metric_poi < metric_dummy on
aaidbeedi,scikit-learn/scikit-learn,examples/model_selection/plot_grid_search_stats.py,5e85a65ab66a42995a0572aebad0d46d9a1756e6,STILL_EXISTS,\"\"\" || ================================================== || Statistical comparison of models using grid search || ================================================== ||  || This example illustrates how to statistically compare the performance of models || trained and evaluated using :class:`~sklearn.model_selection.GridSearchCV`. || \"\"\"
aaidbeegc,scikit-learn/scikit-learn,examples/model_selection/plot_grid_search_stats.py,5e85a65ab66a42995a0572aebad0d46d9a1756e6,STILL_EXISTS,Usually; the analysis just ends here; but half the story is missing. The
aaidbefcc,scikit-learn/scikit-learn,examples/model_selection/plot_grid_search_stats.py,5e85a65ab66a42995a0572aebad0d46d9a1756e6,STILL_EXISTS,:math:`x` is the difference in performance of the models;
aaidbefci,scikit-learn/scikit-learn,examples/model_selection/plot_grid_search_stats.py,5e85a65ab66a42995a0572aebad0d46d9a1756e6,STILL_EXISTS,Let's implement a corrected right-tailed paired t-test to evaluate if the
aaidbegbc,scikit-learn/scikit-learn,examples/model_selection/plot_grid_search_stats.py,5e85a65ab66a42995a0572aebad0d46d9a1756e6,STILL_EXISTS,way. A naive approach [4]_ would be to define estimators as practically
aaidbegeb,scikit-learn/scikit-learn,examples/model_selection/plot_grid_search_stats.py,5e85a65ab66a42995a0572aebad0d46d9a1756e6,STILL_EXISTS,probability that the true (mean) difference of performance between models is
aaidbegfe,scikit-learn/scikit-learn,examples/model_selection/plot_grid_search_stats.py,5e85a65ab66a42995a0572aebad0d46d9a1756e6,STILL_EXISTS,We could also be interested in comparing the performance of all our models
aaidbeggh,scikit-learn/scikit-learn,examples/model_selection/plot_grid_search_stats.py,5e85a65ab66a42995a0572aebad0d46d9a1756e6,STILL_EXISTS,implement Bonferroni correction
aaidbegig,scikit-learn/scikit-learn,examples/model_selection/plot_grid_search_stats.py,5e85a65ab66a42995a0572aebad0d46d9a1756e6,STILL_EXISTS,performs better; worse or practically equivalent to another.
aaidbegjg,scikit-learn/scikit-learn,examples/model_selection/plot_grid_search_stats.py,5e85a65ab66a42995a0572aebad0d46d9a1756e6,STILL_EXISTS,models have a 100% probability of being better than `'2_poly'`; and none have
aaidbehba,scikit-learn/scikit-learn,examples/model_selection/plot_grid_search_stats.py,5e85a65ab66a42995a0572aebad0d46d9a1756e6,STILL_EXISTS,tell us if the performance of one model is better than another with a
aaidbehbh,scikit-learn/scikit-learn,examples/model_selection/plot_grid_search_stats.py,5e85a65ab66a42995a0572aebad0d46d9a1756e6,STILL_EXISTS,correction is needed when using the frequentist approach.
aaidbeicj,scikit-learn/scikit-learn,sklearn/cross_decomposition/tests/test_pls.py,0e6d415c517aa65528cfa0c189745de50e4c6565,STILL_EXISTS,TODO: Remove test in 0.26
aaidbeiec,scikit-learn/scikit-learn,sklearn/cluster/tests/test_k_means.py,11f97429929568e3061997e9cd878086edccb27f,STILL_EXISTS,better score
aaidbeihh,scikit-learn/scikit-learn,sklearn/cluster/tests/test_k_means.py,11f97429929568e3061997e9cd878086edccb27f,STILL_EXISTS,FIXME: remove in 0.26
aaidbfaeh,scikit-learn/scikit-learn,examples/ensemble/plot_gradient_boosting_categorical.py,b4453f126f34447967f52996039d11b0d2fa0090,STILL_EXISTS,to recover an equivalent split that could be obtained in one single split
aaidbfahd,scikit-learn/scikit-learn,sklearn/ensemble/_hist_gradient_boosting/binning.py,b4453f126f34447967f52996039d11b0d2fa0090,STILL_EXISTS,TODO: complexity is O(n_categorical_features * 255). Maybe this is
aaidbfcfa,scikit-learn/scikit-learn,sklearn/multiclass.py,8ce329174e31b1eecd5f92ca81273c233b35de1c,STILL_EXISTS,TODO: Remove coef_ attribute in 0.26
aaidbfcfd,scikit-learn/scikit-learn,sklearn/multiclass.py,8ce329174e31b1eecd5f92ca81273c233b35de1c,STILL_EXISTS,TODO: Remove intercept_ attribute in 0.26
aaidbfcga,scikit-learn/scikit-learn,sklearn/tests/test_multiclass.py,8ce329174e31b1eecd5f92ca81273c233b35de1c,STILL_EXISTS,TODO: Remove this test in version 0.26 when
aaidbfcgd,scikit-learn/scikit-learn,sklearn/utils/tests/test_validation.py,b197cc04e55fc2890a0199f46040d3d7cb3c4a12,STILL_EXISTS,TODO: remove in 0.26
aaidbfcge,scikit-learn/scikit-learn,sklearn/utils/tests/test_validation.py,b197cc04e55fc2890a0199f46040d3d7cb3c4a12,STILL_EXISTS,TODO: remove test in 0.26 once this behavior is deprecated
aaidbfdjd,scikit-learn/scikit-learn,sklearn/utils/tests/test_arpack.py,da562b4fa58bdce4a7f3470f733f33d728747a66,STILL_EXISTS,where we can fix the random state
aaidbfehg,scikit-learn/scikit-learn,examples/release_highlights/plot_release_highlights_0_24_0.py,76e82ba00a3418b19b90a59ec1c7c7f39c2dc26f,STILL_EXISTS,models; but uses much less memory than
aaidbffge,scikit-learn/scikit-learn,sklearn/model_selection/tests/test_search.py,255718b4ad9a3490bc99c992d467f85737bd1291,STILL_EXISTS,FIXME: Replace this test with a full `check_estimator` once we have API only
aaidbffhg,scikit-learn/scikit-learn,sklearn/tests/test_pipeline.py,255718b4ad9a3490bc99c992d467f85737bd1291,STILL_EXISTS,FIXME: Replace this test with a full `check_estimator` once we have API only
aaidbffif,scikit-learn/scikit-learn,sklearn/utils/tests/test_estimator_checks.py,255718b4ad9a3490bc99c992d467f85737bd1291,STILL_EXISTS,FIXME: this test should be uncommented when the checks will be granular
aaidbffij,scikit-learn/scikit-learn,sklearn/utils/tests/test_estimator_checks.py,255718b4ad9a3490bc99c992d467f85737bd1291,STILL_EXISTS,FIXME
aaidbfgab,scikit-learn/scikit-learn,sklearn/cluster/tests/test_k_means.py,2218ec46227c92301ac6837c4a8ae9b8dc5d3960,STILL_EXISTS,FIXME: remove in 1.0
aaidbfgac,scikit-learn/scikit-learn,sklearn/cluster/tests/test_k_means.py,2218ec46227c92301ac6837c4a8ae9b8dc5d3960,STILL_EXISTS,FIXME: remove in 1.1
aaidbfgae,scikit-learn/scikit-learn,sklearn/cross_decomposition/_pls.py,2218ec46227c92301ac6837c4a8ae9b8dc5d3960,STILL_EXISTS,TODO: raise error in 1.1 instead
aaidbfgag,scikit-learn/scikit-learn,sklearn/cross_decomposition/_pls.py,2218ec46227c92301ac6837c4a8ae9b8dc5d3960,STILL_EXISTS,TODO: remove in 1.1
aaidbfgba,scikit-learn/scikit-learn,sklearn/decomposition/tests/test_dict_learning.py,2218ec46227c92301ac6837c4a8ae9b8dc5d3960,STILL_EXISTS,TODO: remove in 1.1
aaidbfgcg,scikit-learn/scikit-learn,sklearn/manifold/tests/test_mds.py,2218ec46227c92301ac6837c4a8ae9b8dc5d3960,STILL_EXISTS,TODO: Remove in 1.1
aaidbfgch,scikit-learn/scikit-learn,sklearn/manifold/tests/test_mds.py,2218ec46227c92301ac6837c4a8ae9b8dc5d3960,STILL_EXISTS,TODO: Remove in 0.26
aaidbfgdc,scikit-learn/scikit-learn,sklearn/tests/test_docstring_parameters.py,2218ec46227c92301ac6837c4a8ae9b8dc5d3960,STILL_EXISTS,FIXME: TO BE REMOVED for 1.0 (avoid FutureWarning)
aaidbfgdd,scikit-learn/scikit-learn,sklearn/tests/test_docstring_parameters.py,2218ec46227c92301ac6837c4a8ae9b8dc5d3960,STILL_EXISTS,For PLS; TODO remove in 1.1
aaidbfghe,scikit-learn/scikit-learn,sklearn/tests/test_naive_bayes.py,dfc5e16066b3a3bbf34238cc0f67639d0965f1a8,STILL_EXISTS,TODO remove in 1.2 once sigma_ attribute is removed (GH #18842)
aaidbfhbc,scikit-learn/scikit-learn,sklearn/linear_model/_base.py,306826f7b6bf5fd61af74062c0ba8f0f21aa3dae,STILL_EXISTS,FIXME in 1.2: parameter 'normalize' should be removed from linear models
aaidbfhbf,scikit-learn/scikit-learn,sklearn/linear_model/_base.py,306826f7b6bf5fd61af74062c0ba8f0f21aa3dae,STILL_EXISTS,FIXME: 'normalize' to be removed in 1.2
aaidbfhbi,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_base.py,306826f7b6bf5fd61af74062c0ba8f0f21aa3dae,STILL_EXISTS,FIXME update test in 1.2 for new versions
aaidbfhcd,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_base.py,306826f7b6bf5fd61af74062c0ba8f0f21aa3dae,STILL_EXISTS,FIXME remove test in 1.4
aaidbfhcg,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_base.py,306826f7b6bf5fd61af74062c0ba8f0f21aa3dae,STILL_EXISTS,FIXME: 'normalize' to be removed in 1.2 in LinearRegression
aaidbfhdi,scikit-learn/scikit-learn,sklearn/metrics/_plot/tests/test_confusion_matrix_display.py,8c6a045e46abe94e43a971d4f8042728addfd6a7,STILL_EXISTS,TODO: Remove when https:\/\/github.com\/numpy\/numpy\/issues\/14397 is resolved
aaidbfhfc,scikit-learn/scikit-learn,sklearn/metrics/_plot/tests/test_plot_confusion_matrix.py,8c6a045e46abe94e43a971d4f8042728addfd6a7,STILL_EXISTS,TODO: remove this file when plot_confusion_matrix will be deprecated in 1.2
aaidbfhff,scikit-learn/scikit-learn,examples/linear_model/plot_polynomial_interpolation.py,27f1c737f2cfc6e589c292776df6f78c08a4da91,STILL_EXISTS,\"\"\" || =================================== || Polynomial and Spline interpolation || =================================== ||  || This example demonstrates how to approximate a function with polynomials up to || degree ``degree`` by using ridge regression. We show two different ways given || ``n_samples`` of 1d points ``x_i``: ||  || - :class:`~sklearn.preprocessing.PolynomialFeatures` generates all monomials ||   up to ``degree``. This gives us the so called Vandermonde matrix with ||   ``n_samples`` rows and ``degree + 1`` columns:: ||  ||     [[1; x_0; x_0 ** 2; x_0 ** 3; ...; x_0 ** degree]; ||      [1; x_1; x_1 ** 2; x_1 ** 3; ...; x_1 ** degree]; ||      ...] ||  ||   Intuitively; this matrix can be interpreted as a matrix of pseudo features ||   (the points raised to some power). The matrix is akin to (but different from) ||   the matrix induced by a polynomial kernel. ||  || - :class:`~sklearn.preprocessing.SplineTransformer` generates B-spline basis ||   functions. A basis function of a B-spline is a piece-wise polynomial function ||   of degree ``degree`` that is non-zero only between ``degree+1`` consecutive ||   knots. Given ``n_knots`` number of knots; this results in matrix of ||   ``n_samples`` rows and ``n_knots + degree - 1`` columns:: ||  ||     [[basis_1(x_0); basis_2(x_0); ...]; ||      [basis_1(x_1); basis_2(x_1); ...]; ||      ...] ||  || This example shows that these two transformers are well suited to model || non-linear effects with a linear model; using a pipeline to add non-linear || features. Kernel methods extend this idea and can induce very high (even || infinite) dimensional feature spaces. || \"\"\"
aaidbfhhi,scikit-learn/scikit-learn,examples/linear_model/plot_polynomial_interpolation.py,27f1c737f2cfc6e589c292776df6f78c08a4da91,STILL_EXISTS,constant. Note that most often; you would rather increase the number of knots
aaidbfhic,scikit-learn/scikit-learn,examples/linear_model/plot_polynomial_interpolation.py,27f1c737f2cfc6e589c292776df6f78c08a4da91,STILL_EXISTS,columns of both transformers separately.
aaidbfhja,scikit-learn/scikit-learn,examples/linear_model/plot_polynomial_interpolation.py,27f1c737f2cfc6e589c292776df6f78c08a4da91,STILL_EXISTS,there for technical reasons; so we refrain from showing them. Every basis
aaidbfiaa,scikit-learn/scikit-learn,sklearn/preprocessing/_polynomial.py,27f1c737f2cfc6e589c292776df6f78c08a4da91,STILL_EXISTS,TODO:
aaidbfieh,scikit-learn/scikit-learn,sklearn/preprocessing/tests/test_polynomial.py,27f1c737f2cfc6e589c292776df6f78c08a4da91,STILL_EXISTS,TODO: add PolynomialFeatures if it moves to _polynomial.py
aaidbfigj,scikit-learn/scikit-learn,sklearn/ensemble/_forest.py,35b2bbf851c6967ba08f1eea07a37c35dc0f4b18,STILL_EXISTS,FIXME: we could consider to support multiclass-multioutput if
aaidbfjec,scikit-learn/scikit-learn,sklearn/cluster/tests/test_birch.py,aa1e69a38f1b8bc3aaa5aa34f49868684b61a7e6,STILL_EXISTS,TODO: Remove in 1.2
aaidbfjfg,scikit-learn/scikit-learn,sklearn/cluster/tests/test_spectral.py,71f7085dae11a0a7091a3c07df4bc5bc8b119fe6,STILL_EXISTS,TODO: Remove when pyamg removes the use of np.float
aaidbfjgb,scikit-learn/scikit-learn,examples/ensemble/plot_feature_transformation.py,69d6378666dc8a4c5b370c05ddc78359149170cd,STILL_EXISTS,\"\"\" || =============================================== || Feature transformations with ensembles of trees || =============================================== ||  || Transform your features into a higher dimensional; sparse space. Then train a || linear model on these features. ||  || First fit an ensemble of trees (totally random trees; a random forest; or || gradient boosted trees) on the training set. Then each leaf of each tree in the || ensemble is assigned a fixed arbitrary feature index in a new feature space. || These leaf indices are then encoded in a one-hot fashion. ||  || Each sample goes through the decisions of each tree of the ensemble and ends up || in one leaf per tree. The sample is encoded by setting feature values for these || leaves to 1 and the other feature values to 0. ||  || The resulting transformer has then learned a supervised; sparse; || high-dimensional categorical embedding of the data. || \"\"\"
aaidbgaaj,scikit-learn/scikit-learn,sklearn/decomposition/_dict_learning.py,66560d61b5361e1f2d892423a6dd495ffff68329,STILL_EXISTS,TODO change to self.alpha in 1.2
aaidbgaeg,scikit-learn/scikit-learn,examples/ensemble/plot_forest_importances.py,920f2257bb0adc0344b398849d2653ef6e86f1d2,STILL_EXISTS,will correspond to the three first columns of X. In addition; we will split
aaidbgcci,scikit-learn/scikit-learn,examples/ensemble/plot_gradient_boosting_quantile.py,6a6217f15f654284d227c27c9179bb02a793e811,STILL_EXISTS,We observe that the search procedure identifies that deeper trees are needed
aaidbgcfj,scikit-learn/scikit-learn,examples/ensemble/plot_gradient_boosting_quantile.py,6a6217f15f654284d227c27c9179bb02a793e811,STILL_EXISTS,better assess the variability of those estimates.
aaidbgdae,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_base.py,dbd68b2846905efb3682db46c798298b1fd3d6c2,STILL_EXISTS,shifts the mean value for each columns in X further away from
aaidbgdah,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_base.py,dbd68b2846905efb3682db46c798298b1fd3d6c2,STILL_EXISTS,better check the impact of feature scaling.
aaidbgdcj,scikit-learn/scikit-learn,sklearn/ensemble/_bagging.py,6852e31da88a94262c8a6a82a6ad00a34ddc385b,STILL_EXISTS,TODO: Remove in 1.2
aaidbgddc,scikit-learn/scikit-learn,sklearn/ensemble/_forest.py,6852e31da88a94262c8a6a82a6ad00a34ddc385b,STILL_EXISTS,TODO: Remove in 1.2
aaidbgddf,scikit-learn/scikit-learn,sklearn/ensemble/_gb.py,6852e31da88a94262c8a6a82a6ad00a34ddc385b,STILL_EXISTS,TODO: Remove in 1.2
aaidbgddj,scikit-learn/scikit-learn,sklearn/ensemble/tests/test_bagging.py,6852e31da88a94262c8a6a82a6ad00a34ddc385b,STILL_EXISTS,FIXME: remove in 1.2
aaidbgdec,scikit-learn/scikit-learn,sklearn/ensemble/tests/test_forest.py,6852e31da88a94262c8a6a82a6ad00a34ddc385b,STILL_EXISTS,FIXME: remove in 1.2
aaidbgdef,scikit-learn/scikit-learn,sklearn/ensemble/tests/test_gradient_boosting.py,6852e31da88a94262c8a6a82a6ad00a34ddc385b,STILL_EXISTS,FIXME: remove in 1.2
aaidbgdei,scikit-learn/scikit-learn,sklearn/ensemble/tests/test_iforest.py,6852e31da88a94262c8a6a82a6ad00a34ddc385b,STILL_EXISTS,FIXME: remove in 1.2
aaidbgdfc,scikit-learn/scikit-learn,sklearn/naive_bayes.py,5c246225ddf130f1eee398e889e4c2a19b5f1791,STILL_EXISTS,TODO: Remove in 1.2
aaidbgdff,scikit-learn/scikit-learn,sklearn/tests/test_naive_bayes.py,5c246225ddf130f1eee398e889e4c2a19b5f1791,STILL_EXISTS,FIXME: remove in 1.2
aaidbgeac,scikit-learn/scikit-learn,sklearn/tests/test_discriminant_analysis.py,28ee486b44f8e7e6440f3439e7315ba1e6d35e43,STILL_EXISTS,XXX: RuntimeWarning is also raised at predict time because of divisions
aaidbgeae,scikit-learn/scikit-learn,sklearn/tests/test_discriminant_analysis.py,28ee486b44f8e7e6440f3439e7315ba1e6d35e43,STILL_EXISTS,regularization: should this be considered a bug? Either by the fit-time
aaidbgeed,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_coordinate_descent.py,fcf4740b4538657997b0f4b8015728d64e2d563e,STILL_EXISTS,XXX: this test does not pass for weaker regularization (lower values of
aaidbgega,scikit-learn/scikit-learn,examples/linear_model/plot_polynomial_interpolation.py,95b7c680ab027fcd23bcbf47ebae58ee3e130ec9,STILL_EXISTS,knot. In the following case we show how periodic splines provide a better fit
aaidbgehi,scikit-learn/scikit-learn,sklearn/preprocessing/_polynomial.py,95b7c680ab027fcd23bcbf47ebae58ee3e130ec9,STILL_EXISTS,would have inferior behaviour at boundaries if combined with
aaidbgfeg,scikit-learn/scikit-learn,sklearn/ensemble/_base.py,b9d6db81ec2e75ec40404db49f97999a08f00c55,STILL_EXISTS,TODO: Remove in v1.2
aaidbgfej,scikit-learn/scikit-learn,sklearn/ensemble/_forest.py,b9d6db81ec2e75ec40404db49f97999a08f00c55,STILL_EXISTS,TODO: Remove in v1.2
aaidbgffa,scikit-learn/scikit-learn,sklearn/ensemble/_gb.py,b9d6db81ec2e75ec40404db49f97999a08f00c55,STILL_EXISTS,TODO: Remove in v1.2. By then it should raise an error.
aaidbgffb,scikit-learn/scikit-learn,sklearn/ensemble/_gb.py,b9d6db81ec2e75ec40404db49f97999a08f00c55,STILL_EXISTS,TODO: remove \"ls\" in verion 1.2
aaidbgffc,scikit-learn/scikit-learn,sklearn/ensemble/_gb_losses.py,b9d6db81ec2e75ec40404db49f97999a08f00c55,STILL_EXISTS,TODO: Remove entry 'ls' in version 1.2.
aaidbgffd,scikit-learn/scikit-learn,sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py,b9d6db81ec2e75ec40404db49f97999a08f00c55,STILL_EXISTS,TODO: Remove in v1.2
aaidbgffj,scikit-learn/scikit-learn,sklearn/ensemble/_hist_gradient_boosting/tests/test_loss.py,b9d6db81ec2e75ec40404db49f97999a08f00c55,STILL_EXISTS,has its minimum at zero; which is needed for the newton method.
aaidbgfgc,scikit-learn/scikit-learn,sklearn/ensemble/tests/test_forest.py,b9d6db81ec2e75ec40404db49f97999a08f00c55,STILL_EXISTS,TODO: Remove in v1.2
aaidbgfgg,scikit-learn/scikit-learn,sklearn/linear_model/_ransac.py,b9d6db81ec2e75ec40404db49f97999a08f00c55,STILL_EXISTS,TODO: Remove squared_loss in v1.2.
aaidbgfgj,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_ransac.py,b9d6db81ec2e75ec40404db49f97999a08f00c55,STILL_EXISTS,TODO: Remove in v1.2
aaidbgfha,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_sgd.py,b9d6db81ec2e75ec40404db49f97999a08f00c55,STILL_EXISTS,TODO: Remove filterwarnings in v1.2.
aaidbgfhb,scikit-learn/scikit-learn,sklearn/linear_model/tests/test_sgd.py,b9d6db81ec2e75ec40404db49f97999a08f00c55,STILL_EXISTS,TODO: Remove in v1.2
aaidbgfhe,scikit-learn/scikit-learn,sklearn/tree/_classes.py,b9d6db81ec2e75ec40404db49f97999a08f00c55,STILL_EXISTS,TODO: Remove \"mse\" in version 1.2.
aaidbgfhf,scikit-learn/scikit-learn,sklearn/tree/_classes.py,b9d6db81ec2e75ec40404db49f97999a08f00c55,STILL_EXISTS,TODO: Remove in v1.2
aaidbgfhh,scikit-learn/scikit-learn,sklearn/tree/tests/test_tree.py,b9d6db81ec2e75ec40404db49f97999a08f00c55,STILL_EXISTS,TODO: Remove in v1.2
aaidbgfih,scikit-learn/scikit-learn,examples/model_selection/plot_cv_indices.py,0892a98fc9972b1da88ca25282e70105bd463608,STILL_EXISTS,To better demonstrate the difference; we will assign samples to groups
aaidbggcb,scikit-learn/scikit-learn,sklearn/model_selection/tests/test_split.py,0892a98fc9972b1da88ca25282e70105bd463608,STILL_EXISTS,produces better stratified folds than regular GroupKFold
aaidbggcd,scikit-learn/scikit-learn,benchmarks/bench_online_ocsvm.py,c854b83c91dd8c1bf9282112dba10d50e43b59a4,STILL_EXISTS,\"\"\" || ===================================== || SGDOneClassSVM benchmark || ===================================== || This benchmark compares the :class:`SGDOneClassSVM` with :class:`OneClassSVM`. || The former is an online One-Class SVM implemented with a Stochastic Gradient || Descent (SGD). The latter is based on the LibSVM implementation. The || complexity of :class:`SGDOneClassSVM` is linear in the number of samples || whereas the one of :class:`OneClassSVM` is at best quadratic in the number of || samples. We here compare the performance in terms of AUC and training time on || classical anomaly detection datasets. ||  || The :class:`OneClassSVM` is applied with a Gaussian kernel and we therefore || use a kernel approximation prior to the application of :class:`SGDOneClassSVM`. || \"\"\"
aaidbghic,weizhenzhao/rasa_nlu,docs/conf.py,e58a5ffcdbffbf198b5a131375ce7e2cd04a3eee,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaidbhadg,weizhenzhao/rasa_nlu,rasa_nlu/classifiers/sklearn_intent_classifier.py,e58a5ffcdbffbf198b5a131375ce7e2cd04a3eee,STILL_EXISTS,dirty str fix because sklearn is expecting
aaidbhdbi,weizhenzhao/rasa_nlu,rasa_nlu/project.py,e58a5ffcdbffbf198b5a131375ce7e2cd04a3eee,STILL_EXISTS,NOTE: for better parse performance; currently although
aaidbhdcd,weizhenzhao/rasa_nlu,rasa_nlu/project.py,e58a5ffcdbffbf198b5a131375ce7e2cd04a3eee,STILL_EXISTS,then model will be cached; this is a kind of workaround to
aaidbhdcf,weizhenzhao/rasa_nlu,rasa_nlu/project.py,e58a5ffcdbffbf198b5a131375ce7e2cd04a3eee,STILL_EXISTS,BTW if refresh function is wanted; maybe add implement code to
aaidbhdec,weizhenzhao/rasa_nlu,rasa_nlu/project.py,e58a5ffcdbffbf198b5a131375ce7e2cd04a3eee,STILL_EXISTS,download model from cloud storage if needed and possible
aaidbhdhc,weizhenzhao/rasa_nlu,rasa_nlu/test.py,e58a5ffcdbffbf198b5a131375ce7e2cd04a3eee,STILL_EXISTS,todo: make the two different modes two subparsers
aaidbhdji,weizhenzhao/rasa_nlu,rasa_nlu/test.py,e58a5ffcdbffbf198b5a131375ce7e2cd04a3eee,STILL_EXISTS,TODO: move parsing into sub parser
aaidbhfbf,weizhenzhao/rasa_nlu,tests/base/test_featurizers.py,e58a5ffcdbffbf198b5a131375ce7e2cd04a3eee,STILL_EXISTS,if this starts failing for the default model; we should think about
aaidbhhhc,adines/DeepClas4Bio,setup.py,5dbfc530a40604433a083594e564551f1d0b0095,STILL_EXISTS,You can just specify package directories manually here if your project is
aaidbhidd,Jopepato/SMiLE,SMiLE/bin/activate_this.py,7b7a61dc924950d5c1d221e3f0edf88fde3c9e53,STILL_EXISTS,Move the added items to the front of the path:
aaidbhidf,Jopepato/SMiLE,SMILE/smile_.py,90347f47f8f4d6a100c90f56fc323e4f49f0864d,STILL_EXISTS,TODO Ensure the input format
aaidbhjig,Kashu7100/Qualia2.0,qualia2/functions/conv.py,1770997806f02c7ba0db81024cfd3f9908960bd5,f7714baa88202e41147171b1fd27276caaab503f,TODO
aaidbiabi,Kashu7100/Qualia2.0,qualia2/functions/conv.py,fa7ade7b5e561db46199c8d19d7ee5d1baf0b14c,f7714baa88202e41147171b1fd27276caaab503f,TODO
aaidbiach,Kashu7100/Qualia2.0,qualia2/functions/recurrent.py,2fa3d2353b21f774dd3fe1f9bdedee49fe559c4c,STILL_EXISTS,TODO
aaidbibbc,Kashu7100/Qualia2.0,examples/supervised_learning/openpose/util.py,279ee17cae36ee8b544831d002dea8eda80ebdbc,STILL_EXISTS,It is better to use 0.1 as threshold when evaluation; but 0.3 for demo purpose.
aaidbibfh,Kashu7100/Qualia2.0,examples/supervised_learning/openpose/util.py,279ee17cae36ee8b544831d002dea8eda80ebdbc,STILL_EXISTS,(eg: a right forearm ends in a right wrist)
aaidbiche,Kashu7100/Qualia2.0,qualia2/functions/recurrent.py,3ea53d3cf16e294bfafc2e85755e854393c16c75,STILL_EXISTS,TODO: fix backward pass
aaidbidbg,lai-bluejay/diego,diego/classifier.py,33ea32953c6219d750942a0ebae7a81ec7247b62,STILL_EXISTS,Map test values to actual values - TODO: copy to all kinds of
aaidbidgc,lai-bluejay/diego,diego/preprocessor/auto_binning.py,33ea32953c6219d750942a0ebae7a81ec7247b62,STILL_EXISTS,TODO add hyperparameter to gbdt binning
aaidbidia,lai-bluejay/diego,diego/preprocessor/base_binning.py,33ea32953c6219d750942a0ebae7a81ec7247b62,STILL_EXISTS,TODO numpy to numba
aaidbiedb,lai-bluejay/diego,diego/preprocessor/base_binning.py,33ea32953c6219d750942a0ebae7a81ec7247b62,STILL_EXISTS,TODO  min_sum_hessian_in_leaf
aaidbiehf,lai-bluejay/diego,diego/preprocessor/local_uncertainty_sampling.py,33ea32953c6219d750942a0ebae7a81ec7247b62,STILL_EXISTS,TODO add hyperparameter to gbdt binning
aaidbifdf,lai-bluejay/diego,diego/ensemble_net/base.py,651d423c536b3244ffaf49d1f9ada2ed775d695f,STILL_EXISTS,TODO \u5F53\u524D\u90FD\u662F\u57FA\u4E8Esklearn API\u5199\u7684\uFF0C\u5BF9XGB\u7684\u539F\u751FAPI\u8FD8\u6CA1\u505A\u652F\u6301\u3002
aaidbifdj,lai-bluejay/diego,diego/ensemble_net/base.py,651d423c536b3244ffaf49d1f9ada2ed775d695f,STILL_EXISTS,TODO dynamic selection
aaidbiffe,lai-bluejay/diego,diego/preprocessor/__init__.py,651d423c536b3244ffaf49d1f9ada2ed775d695f,STILL_EXISTS,TODO remove autosklearn extends
aaidbigaf,lai-bluejay/diego,diego/metrics/classification.py,86797e02d8e55d293b5df61920fd9362a4d7dff8,STILL_EXISTS,We sum the contributions of the columns.
aaidbigda,lai-bluejay/diego,diego/metrics/metrics.py,86797e02d8e55d293b5df61920fd9362a4d7dff8,2b7e42e2eaee18f9be84242c8068cef8bcef62a9,TODO what about mathews correlation coefficient etc?
aaidbigdc,lai-bluejay/diego,diego/metrics/metrics.py,86797e02d8e55d293b5df61920fd9362a4d7dff8,2b7e42e2eaee18f9be84242c8068cef8bcef62a9,TODO maybe annotate metrics to define which cases they can
aaidbigfi,lai-bluejay/diego,diego/study.py,86797e02d8e55d293b5df61920fd9362a4d7dff8,STILL_EXISTS,Values are dataframe columns such as ('trial_id'; '') and ('params'; 'n_layers').
aaidbiggh,lai-bluejay/diego,diego/study.py,86797e02d8e55d293b5df61920fd9362a4d7dff8,STILL_EXISTS,TODO multi clf
aaidbihgd,lai-bluejay/diego,diego/classifier.py,cac3aa228b7559029be6ebdfae9f5154909b26df,STILL_EXISTS,TODO Rewrite Ensemble methods
aaidbiida,lai-bluejay/diego,diego/study.py,2bd3871d8c0cdfe521fb5092079da000a15a500e,STILL_EXISTS,TODO Preprocess Trial
aaidbiidg,lai-bluejay/diego,diego/study.py,8971f9957412aaccd8f43cdfb4fabd5119c597f1,STILL_EXISTS,TODO decorator; add trials to pipeline.
aaidbiieb,lai-bluejay/diego,diego/classifier.py,12e33a920ac8d0cb28d2717fe03d26d42dcf74b8,STILL_EXISTS,TODO add hyperparameter to gbdt binning
aaidbiifb,lai-bluejay/diego,diego/study.py,12e33a920ac8d0cb28d2717fe03d26d42dcf74b8,STILL_EXISTS,TODO Preprocess Trial
aaidbiigd,lai-bluejay/diego,diego/study.py,12e33a920ac8d0cb28d2717fe03d26d42dcf74b8,STILL_EXISTS,TODO decorator; add trials to pipeline.
aaidbiijb,lai-bluejay/diego,diego/study.py,dc88d8b59c4a0530f521fdd792ebb89402328966,STILL_EXISTS,TODO decorator; add trials to pipeline.
aaidbijde,lai-bluejay/diego,diego/classifier/logistic_regression.py,3cf932b8ce784661425d5b08cbd338f8e0b9da16,STILL_EXISTS,TODO remove svrg; performance no good with same parameter space with sgd
aaidbijgf,lai-bluejay/diego,diego_benchmark/diego_titanic.py,3cf932b8ce784661425d5b08cbd338f8e0b9da16,STILL_EXISTS,As we saw before; the two missing values for embarked columns can be replaced by 'C' (Cherbourg)
aaidbijgg,lai-bluejay/diego,diego_benchmark/diego_titanic.py,3cf932b8ce784661425d5b08cbd338f8e0b9da16,STILL_EXISTS,We replace missing ages by the mean age of passengers who belong to the same group of class\/sex\/family
aaidbijgi,lai-bluejay/diego,diego_benchmark/diego_titanic.py,3cf932b8ce784661425d5b08cbd338f8e0b9da16,STILL_EXISTS,######### Feature engineering on columns ##########
aaidbijha,lai-bluejay/diego,diego_benchmark/diego_titanic.py,3cf932b8ce784661425d5b08cbd338f8e0b9da16,STILL_EXISTS,Create a Family Size; Is Alone; Child and Mother columns
aaidbijia,lai-bluejay/diego,diego_benchmark/diego_titanic.py,3cf932b8ce784661425d5b08cbd338f8e0b9da16,STILL_EXISTS,Note : this implementation is ugly and unefficient; if sombody found a way to do it easily with pandas (it must be a way); please comment the kernel with your solution !
aaidbijib,lai-bluejay/diego,diego_benchmark/diego_titanic.py,3cf932b8ce784661425d5b08cbd338f8e0b9da16,STILL_EXISTS,These two columns are not useful anymore
aaidbjbcf,vdorobantu/lyapy,lyapy/learning/util.py,75e267bd9d1550909aecee48d22310df01f22d88,35bc15d644950d61babdff6394c2c88c1abac934,generate all data needed to train neural network
aaidbjbfb,vdorobantu/lyapy,lyapy/learning/util.py,32cd3e28fd5ed541040d1f95dc53abc8a9da5e53,4f3c50494d1bb39998c31f67b1179923ae2159d9,generate all data needed to train neural network
aaidbjbhc,vdorobantu/lyapy,lyapy/learning/util.py,9c6dd2404217661a64ee3a54a11a9197d2777fc9,4f3c50494d1bb39998c31f67b1179923ae2159d9,the method used below to calculate dV_r_hats is a temporary fix
aaidbjcab,vdorobantu/lyapy,lyapy/examples/segway.py,a0487b24488f19b278221dd9733d1c9da5dadbeb,725acddaff47583b25f9064159aa84b22f28a1a1,TODO: Fix this; it wrong
aaidbjehd,vdorobantu/lyapy,lyapy/examples/segway.py,e7d16385d5f8534042cfcc1cf1b7b01fb0657be3,d4102d0b0ac55b849fdc47c0d9dc8cfb4721a3ce,TODO: Remove this weighted thing
aaidbjehf,vdorobantu/lyapy,lyapy/examples/segway.py,6c7d95e10653a37c3bf85bdef9fc2692d88a4dbe,d4102d0b0ac55b849fdc47c0d9dc8cfb4721a3ce,TODO: FIX THIS
aaidbjehh,vdorobantu/lyapy,lyapy/examples/segway.py,6c7d95e10653a37c3bf85bdef9fc2692d88a4dbe,d4102d0b0ac55b849fdc47c0d9dc8cfb4721a3ce,TODO: FIX THIS TOO
aaidcacjg,Wason1797/Nn-Learn-Lib,nn_samples/NeuralNetworks/Neuron.py,ac45fe309bcd77ed40c295d098d58735089916d2,STILL_EXISTS,will be needed as coordinates for position with pygame
aaidcadaa,Wason1797/Nn-Learn-Lib,nn_samples/NeuralNetworksAnimation/Neuron.py,ac45fe309bcd77ed40c295d098d58735089916d2,STILL_EXISTS,will be needed as coordinates for position with pygame
aaidcaibc,cod3licious/nlputils,vptree.py,7532b6707aa8363e69766812c0cb71dd939a6468,STILL_EXISTS,(would probably be faster with np boolean indexing)
aaidcbfaf,jmaggio14/imagepypelines,util/Tester.py,1c80e85de829b11e6bdca5cf90622d5e276af117,STILL_EXISTS,Unused
aaidcbfdh,jmaggio14/imagepypelines,io/Emailer.py,9c9471ea9c9888a66c3665fec5e2d0af38ee680a,STILL_EXISTS,TODO verfify that recipients are valid here
aaidcbfed,jmaggio14/imagepypelines,io/Emailer.py,9c9471ea9c9888a66c3665fec5e2d0af38ee680a,STILL_EXISTS,# TODO: error check text
aaidcbffj,jmaggio14/imagepypelines,io/Emailer.py,11ba4c836502df2ce6bf64136fbd2f146446e2a8,88fe7f7f73e09802ff055c8bf8675165545e2770,# TODO: automatically retrieve a free port instead of hardcoding it
aaidcfeeh,jmaggio14/imagepypelines,imsciutils/io/Emailer.py,e8562a8854211fbe5e7471acb6e8c3e58dfc59d0,16af246793b6eb315d6f221368471bd00262c2b8,simplicity trumps functionality here; we mostly want it to be easy to use
aaidcfehf,jmaggio14/imagepypelines,imsciutils/io/Emailer.py,e8562a8854211fbe5e7471acb6e8c3e58dfc59d0,16af246793b6eb315d6f221368471bd00262c2b8,# TODO verfify that recipients are valid here
aaidcfgjg,jmaggio14/imagepypelines,docs/source/conf.py,3e51f980727edb25f686e7bf8f01caaa868d75d5,STILL_EXISTS,Fix navigation bar to top of page?
aaidcfhed,jmaggio14/imagepypelines,imsciutils/util/timing.py,85c44dd72137b913333b39487f201ff84273448a,STILL_EXISTS,TODO: JM: remove relative imports inside these functions and move to top of file
aaidcgacf,jmaggio14/imagepypelines,imsciutils/core/Pipeline.py,c1ac03fc9d1ec151609d23f073f9c242b522a3ec,f80d4a657e8bbce5bd38c72af66bdeef45ec6fd0,JM: TODO: make special exception for this purpose
aaidcgacg,jmaggio14/imagepypelines,imsciutils/core/Pipeline.py,c1ac03fc9d1ec151609d23f073f9c242b522a3ec,f80d4a657e8bbce5bd38c72af66bdeef45ec6fd0,JM: TODO: add auto batching and intermediate data retrieval
aaidcgafj,jmaggio14/imagepypelines,imsciutils/io/Emailer.py,16af246793b6eb315d6f221368471bd00262c2b8,STILL_EXISTS,TODO verify that recipients are valid here
aaidcggbi,jmaggio14/imagepypelines,imsciutils/core/Pipeline.py,ab1d4c8bf90a947d62267a3cdec533d8f50d230a,dbef2cb641d3fb14093a77fdeee05fbf8c457332,JM: TODO: make special exception for this purpose
aaidcggca,jmaggio14/imagepypelines,imsciutils/core/Pipeline.py,ab1d4c8bf90a947d62267a3cdec533d8f50d230a,STILL_EXISTS,JM: TODO: add auto batching and intermediate data retrieval
aaidcgjbb,jmaggio14/imagepypelines,imsciutils/core/builtin_blocks/Orb.py,7598d5b33e24690601bba9f84b65688d154c7f0b,STILL_EXISTS,AS A TEMPORARY FIX; I am cutting out descriptors past the maximum
aaidchbge,jmaggio14/imagepypelines,imsciutils/core/BaseBlock.py,ab0ca49f24f6b16ac09636dd229ede21bf511f69,STILL_EXISTS,so no more comparisons are needed for this element
aaidchbie,jmaggio14/imagepypelines,imsciutils/core/BaseBlock.py,0ae3b3ce59338b1e4fb09105bdad8c07e934cdbb,STILL_EXISTS,so no more comparisons are needed for this element
aaidchcbh,jmaggio14/imagepypelines,imsciutils/core/BaseBlock.py,f9112f509f72e5ad2be8a92ba1a34e0595b8f074,STILL_EXISTS,JM: this is stupid hack to get keyword only arguments in python2
aaidchfda,jmaggio14/imagepypelines,imsciutils/ml/DatasetManager.py,89ae59311274fdb7f5d2799e9452950d25133da5,STILL_EXISTS,JM: this is stupid hack to get keyword only arguments in python2
aaidciidi,jmaggio14/imagepypelines,imagepypelines_gui/QtInterp.py,c29127cceef564e7d95d12fb4b165480624f884b,STILL_EXISTS,TODO write out history
aaidciieh,jmaggio14/imagepypelines,imagepypelines_gui/QtInterp.py,c29127cceef564e7d95d12fb4b165480624f884b,STILL_EXISTS,TODO make it non clickable
aaidciiei,jmaggio14/imagepypelines,imagepypelines_gui/QtInterp.py,c29127cceef564e7d95d12fb4b165480624f884b,STILL_EXISTS,TODO fix history
aaidciiej,jmaggio14/imagepypelines,imagepypelines_gui/QtInterp.py,c29127cceef564e7d95d12fb4b165480624f884b,STILL_EXISTS,TODO make this robust
aaidcjaje,jmaggio14/imagepypelines,imagepypelines_gui/diagram_items.py,13da9ad6653083f55307da743d9baa207ef688bd,STILL_EXISTS,This is only needed for Python v2 but is harmless for Python v3.
aaidcjbde,jmaggio14/imagepypelines,imagepypelines_gui/diagramscene.py,13da9ad6653083f55307da743d9baa207ef688bd,STILL_EXISTS,This is only needed for Python v2 but is harmless for Python v3.
aaidcjbdf,jmaggio14/imagepypelines,imagepypelines_gui/diagramscene.py,13da9ad6653083f55307da743d9baa207ef688bd,068c3cb50ebac06bd1884386b7a7a37759583b2d,TODO center \/ add other info
aaidcjcbi,jmaggio14/imagepypelines,imagepypelines_gui/QtInterp.py,bebccc86824d95ebf47830d444a2564877193e12,STILL_EXISTS,ND TODO: implement rich text option (i.e. html)
aaidcjcbj,jmaggio14/imagepypelines,imagepypelines_gui/QtInterp.py,bebccc86824d95ebf47830d444a2564877193e12,STILL_EXISTS,ND TODO: implement non-blocking runsource call
aaidcjcci,jmaggio14/imagepypelines,imagepypelines_gui/diagram_items.py,bebccc86824d95ebf47830d444a2564877193e12,068c3cb50ebac06bd1884386b7a7a37759583b2d,TODO center the text
aaidcjdfi,jmaggio14/imagepypelines,imagepypelines_gui/MainWindow.py,068c3cb50ebac06bd1884386b7a7a37759583b2d,STILL_EXISTS,This is only needed for Python v2 but is harmless for Python v3.
aaidcjdfj,jmaggio14/imagepypelines,imagepypelines_gui/MainWindow.py,068c3cb50ebac06bd1884386b7a7a37759583b2d,STILL_EXISTS,ND FIXME: Hack for testing
aaidcjdga,jmaggio14/imagepypelines,imagepypelines_gui/MainWindow.py,068c3cb50ebac06bd1884386b7a7a37759583b2d,STILL_EXISTS,TODO consider .ui file for most of this class
aaidcjdgb,jmaggio14/imagepypelines,imagepypelines_gui/MainWindow.py,068c3cb50ebac06bd1884386b7a7a37759583b2d,STILL_EXISTS,ND TODO: decide on programmable interface \/ items to pass here
aaidcjdgc,jmaggio14/imagepypelines,imagepypelines_gui/MainWindow.py,068c3cb50ebac06bd1884386b7a7a37759583b2d,STILL_EXISTS,TODO: change this title
aaidcjdgd,jmaggio14/imagepypelines,imagepypelines_gui/MainWindow.py,068c3cb50ebac06bd1884386b7a7a37759583b2d,STILL_EXISTS,TODO: add logo
aaidcjdge,jmaggio14/imagepypelines,imagepypelines_gui/MainWindow.py,068c3cb50ebac06bd1884386b7a7a37759583b2d,STILL_EXISTS,TODO add builtin widgets to the tool box
aaidcjdgf,jmaggio14/imagepypelines,imagepypelines_gui/MainWindow.py,068c3cb50ebac06bd1884386b7a7a37759583b2d,STILL_EXISTS,TODO add builtin pipelines to the tool box
aaidcjdgg,jmaggio14/imagepypelines,imagepypelines_gui/MainWindow.py,068c3cb50ebac06bd1884386b7a7a37759583b2d,STILL_EXISTS,TODO add user-defined widgets to the tool box
aaidcjdgh,jmaggio14/imagepypelines,imagepypelines_gui/MainWindow.py,068c3cb50ebac06bd1884386b7a7a37759583b2d,STILL_EXISTS,TODO add user-defined pipelines to the tool box
aaidcjdgi,jmaggio14/imagepypelines,imagepypelines_gui/MainWindow.py,068c3cb50ebac06bd1884386b7a7a37759583b2d,STILL_EXISTS,TODO add \"run pipeline\" action
aaidcjdgj,jmaggio14/imagepypelines,imagepypelines_gui/MainWindow.py,068c3cb50ebac06bd1884386b7a7a37759583b2d,STILL_EXISTS,TODO add \"save pipeline\" action
aaidcjdha,jmaggio14/imagepypelines,imagepypelines_gui/MainWindow.py,068c3cb50ebac06bd1884386b7a7a37759583b2d,STILL_EXISTS,TODO add \"load pipeline\" action
aaidcjdhb,jmaggio14/imagepypelines,imagepypelines_gui/MainWindow.py,068c3cb50ebac06bd1884386b7a7a37759583b2d,STILL_EXISTS,TODO add \"switch pipeline\" action
aaidcjdhc,jmaggio14/imagepypelines,imagepypelines_gui/MainWindow.py,068c3cb50ebac06bd1884386b7a7a37759583b2d,STILL_EXISTS,TODO add \"debug pipeline\" aation
aaidcjdhd,jmaggio14/imagepypelines,imagepypelines_gui/MainWindow.py,068c3cb50ebac06bd1884386b7a7a37759583b2d,STILL_EXISTS,TODO add \"new pipeline\" action
aaidcjdhe,jmaggio14/imagepypelines,imagepypelines_gui/MainWindow.py,068c3cb50ebac06bd1884386b7a7a37759583b2d,STILL_EXISTS,TODO add actions to menus
aaidcjdhf,jmaggio14/imagepypelines,imagepypelines_gui/MainWindow.py,068c3cb50ebac06bd1884386b7a7a37759583b2d,STILL_EXISTS,TODO add actions to toolbars
aaidcjdhi,jmaggio14/imagepypelines,imagepypelines_gui/QtBlock.py,068c3cb50ebac06bd1884386b7a7a37759583b2d,STILL_EXISTS,This is only needed for Python v2 but is harmless for Python v3.
aaidcjdhj,jmaggio14/imagepypelines,imagepypelines_gui/QtBlock.py,068c3cb50ebac06bd1884386b7a7a37759583b2d,STILL_EXISTS,TODO center the text (use boundingRect on self and on the textItem)
aaidcjdia,jmaggio14/imagepypelines,imagepypelines_gui/QtBlock.py,068c3cb50ebac06bd1884386b7a7a37759583b2d,STILL_EXISTS,TODO add other info
aaidcjdib,jmaggio14/imagepypelines,imagepypelines_gui/QtInterp.py,068c3cb50ebac06bd1884386b7a7a37759583b2d,STILL_EXISTS,TODO disallow deleting \/ backspacing the prompt
aaidcjgjj,jmaggio14/imagepypelines,test/automated_tests/test_core.py,1225eb49ae23a0adf69ab5f4a0ed56688d1f9543,STILL_EXISTS,JM: TODO: add dtype checking
aaidcjiid,jmaggio14/imagepypelines,imagepypelines_gui/MainWindow.py,b34984b5e325d27ca2682636339d3b2748304397,STILL_EXISTS,TODO figure out if slots\/signals need to be connected here
aaiddaaad,jmaggio14/imagepypelines,imagepypelines_gui/QtInterpreter.py,b34984b5e325d27ca2682636339d3b2748304397,STILL_EXISTS,ND TODO: implement rich text option (i.e. html)
aaiddaaae,jmaggio14/imagepypelines,imagepypelines_gui/QtInterpreter.py,b34984b5e325d27ca2682636339d3b2748304397,STILL_EXISTS,ND TODO: implement non-blocking runsource call
aaiddaaag,jmaggio14/imagepypelines,imagepypelines_gui/QtInterpreter.py,b34984b5e325d27ca2682636339d3b2748304397,STILL_EXISTS,TODO: write out history
aaiddaacc,jmaggio14/imagepypelines,imagepypelines_gui/QtInterpreter.py,b34984b5e325d27ca2682636339d3b2748304397,STILL_EXISTS,TODO disallow deleting \/ backspacing the prompt
aaiddaacd,jmaggio14/imagepypelines,imagepypelines_gui/QtInterpreter.py,b34984b5e325d27ca2682636339d3b2748304397,STILL_EXISTS,TODO make it non clickable
aaiddaace,jmaggio14/imagepypelines,imagepypelines_gui/QtInterpreter.py,b34984b5e325d27ca2682636339d3b2748304397,STILL_EXISTS,TODO fix history
aaiddaacf,jmaggio14/imagepypelines,imagepypelines_gui/QtInterpreter.py,b34984b5e325d27ca2682636339d3b2748304397,STILL_EXISTS,TODO make this robust
aaiddaaej,jmaggio14/imagepypelines,imagepypelines_gui/QtPipeline.py,8805f4d9682fb46542a86321c8df6f0dd3614253,STILL_EXISTS,TODO make this robust
aaiddaafb,jmaggio14/imagepypelines,imagepypelines_gui/main.py,8805f4d9682fb46542a86321c8df6f0dd3614253,STILL_EXISTS,TODO figure this out. Should not need to be set here
aaiddaafc,jmaggio14/imagepypelines,imagepypelines_gui/main.py,8805f4d9682fb46542a86321c8df6f0dd3614253,STILL_EXISTS,TODO: add logo properly
aaiddaafh,jmaggio14/imagepypelines,imagepypelines_gui/MainWindow.py,183d70cc5c5d9c49482ef26232f88cfd70003471,STILL_EXISTS,TODO add user cache directory settings
aaiddaafi,jmaggio14/imagepypelines,imagepypelines_gui/MainWindow.py,183d70cc5c5d9c49482ef26232f88cfd70003471,STILL_EXISTS,TODO add user interpreter setup script location setting
aaiddaafj,jmaggio14/imagepypelines,imagepypelines_gui/MainWindow.py,183d70cc5c5d9c49482ef26232f88cfd70003471,STILL_EXISTS,TODO add
aaiddabjg,jmaggio14/imagepypelines,imagepypelines_gui/MainWindow.py,2be3dc3eaab103dac52605dd74edf99d54c7f91e,STILL_EXISTS,TODO need a \"delete arrow\" button thing
aaiddabjh,jmaggio14/imagepypelines,imagepypelines_gui/MainWindow.py,2be3dc3eaab103dac52605dd74edf99d54c7f91e,STILL_EXISTS,TODO: need some sort of module creator function to wrap this ability
aaiddabjj,jmaggio14/imagepypelines,imagepypelines_gui/MainWindow.py,2be3dc3eaab103dac52605dd74edf99d54c7f91e,STILL_EXISTS,TODO will need programmtic access to the pipeline \/ blocks in it
aaiddacab,jmaggio14/imagepypelines,imagepypelines_gui/MainWindow.py,2be3dc3eaab103dac52605dd74edf99d54c7f91e,STILL_EXISTS,TODO figure out the opened directory and remember it
aaiddacac,jmaggio14/imagepypelines,imagepypelines_gui/MainWindow.py,2be3dc3eaab103dac52605dd74edf99d54c7f91e,STILL_EXISTS,TODO make platform invariant (windows for now)
aaiddacad,jmaggio14/imagepypelines,imagepypelines_gui/MainWindow.py,2be3dc3eaab103dac52605dd74edf99d54c7f91e,STILL_EXISTS,TODO set opening directory to something useful \/ make it a setting
aaiddacae,jmaggio14/imagepypelines,imagepypelines_gui/MainWindow.py,2be3dc3eaab103dac52605dd74edf99d54c7f91e,STILL_EXISTS,TODO add
aaiddacah,jmaggio14/imagepypelines,imagepypelines_gui/QtPipeline.py,2be3dc3eaab103dac52605dd74edf99d54c7f91e,STILL_EXISTS,TODO decide this scaling.
aaiddacba,jmaggio14/imagepypelines,imagepypelines_gui/QtPipeline.py,2be3dc3eaab103dac52605dd74edf99d54c7f91e,STILL_EXISTS,turn blocks red if they need an input; maybe highlight a field?
aaiddacbe,jmaggio14/imagepypelines,imagepypelines_gui/diagram_items.py,2be3dc3eaab103dac52605dd74edf99d54c7f91e,STILL_EXISTS,TODO Ryan make up a design for these items?
aaiddaeeg,jmaggio14/imagepypelines,tests/test_core.py,fa4055fdaef890073ebfe6370c4f53b60f0ed82f,STILL_EXISTS,TODO - JM
aaidddedc,jmaggio14/imagepypelines,tests/test_core.py,94dd6763a7029fa67b713484ac8ca8f84fcd6c40,STILL_EXISTS,TODO - JM
aaiddedfi,jmaggio14/imagepypelines,tests/test_core.py,132babb00dffcfa1bfd6c94993bc1f1be73a98b6,STILL_EXISTS,TODO - JM
aaiddedgg,jmaggio14/imagepypelines,imagepypelines/core/string_parse.py,f04a14388f89b959e7f2ac729f5e747783e49861,ffe8b8036adb67e06f4654a6e5a2e20a01d018ea,TODO; add function support
aaiddefce,jmaggio14/imagepypelines,imagepypelines/core/string_parse.py,ffe8b8036adb67e06f4654a6e5a2e20a01d018ea,STILL_EXISTS,# TODO; add function support
aaiddfajg,jmaggio14/imagepypelines,imagepypelines/core/blockio.py,0222a2b81ef6026a498a63a1ae74770ee15ca521,98edb93ddd73495c53eea62e9aa669f193d23812,\"\"\" ||  || goal is to enable this functionality ||  || 1) specify an axis length by equation ||     ArrayType(['N';'M']) : ArrayType(['N*M';'1']) ||  || 2) be able to apply a rule to an unknown number of axis ||     ArrayType(all_axis=\"5*N\") ||  || 3) be able to pass in your own function to generate an output ||     FuncType(func) where func takes in input_type ||  || 4) add dimensional flexability ||     ArrayType(['<expr>']; error='+\/-1') ||  || 5) ||  || TODO: ||     [ ] string compiler ||     [ ] new IoMap structure ||  || \"\"\"
aaiddfbag,jmaggio14/imagepypelines,imagepypelines/core/blockio.py,0222a2b81ef6026a498a63a1ae74770ee15ca521,0f980ac31b0edde0fbd8c169cae7f34fcadba2b0,TODO: generate string parse here
aaiddfbhh,jmaggio14/imagepypelines,imagepypelines/core/blockio.py,98edb93ddd73495c53eea62e9aa669f193d23812,6508821fb001eb33449809424f9ca0505ff15e1c,TODO:
aaiddfede,jmaggio14/imagepypelines,imagepypelines/core/blockio.py,6508821fb001eb33449809424f9ca0505ff15e1c,94cc49705893655895794880b719710ff170c31d,\"\"\" ||  || goal is to enable this functionality ||  || 1) specify an axis length by equation ||     ArrayType(['N';'M']) : ArrayType(['N*M';'1']) ||  || 2) be able to apply a rule to an unknown number of axis ||     ArrayType(all_axis=\"5*N\") ||  || 3) be able to pass in your own function to generate an output ||     FuncType(func) where func takes in input_type ||  || 4) add dimensional flexability ||     ArrayType(['<expr>']; error='+\/-1') ||  || 5) ||  || TODO: ||     [ ] string compiler ||     [ ] new IoMap structure ||  || \"\"\"
aaiddfhdh,jmaggio14/imagepypelines,imagepypelines/core/blockio.py,94cc49705893655895794880b719710ff170c31d,30f8ea9e1c8b4a3c4151d9640cd40719ddf7ee23,TODO:
aaiddgbec,jmaggio14/imagepypelines,imagepypelines/core/blockio.py,30f8ea9e1c8b4a3c4151d9640cd40719ddf7ee23,STILL_EXISTS,\"\"\" ||  || goal is to enable this functionality ||  || 1) specify an axis length by equation ||     ArrayType(['N';'M']) : ArrayType(['N*M';'1']) ||  || 2) be able to apply a rule to an unknown number of axis ||     ArrayType(all_axis=\"5*N\") ||  || 3) be able to pass in your own function to generate an output ||     FuncType(func) where func takes in input_type ||  || 4) add dimensional flexability ||     ArrayType(['<expr>']; error='+\/-1') ||  || 5) ||  || TODO: ||     [ ] string compiler ||     [ ] new IoMap structure ||  || \"\"\"
aaiddgfed,jmaggio14/imagepypelines,imagepypelines/core/Pipeline.py,20b24df18e5092a4990d4392dc425a0c3ce0fc02,1389455a43eda9c56ed08f98e79f248c92e6fd07,NOTE - it may be better practice to check in a list instead of
aaiddgjic,jmaggio14/imagepypelines,imagepypelines/core/BaseBlock.py,1389455a43eda9c56ed08f98e79f248c92e6fd07,f6d3395e9e661e83d2cd0ce9caa82c8657d8c4e6,so no more comparisons are needed for this element
aaiddibgj,jmaggio14/imagepypelines,imagepypelines/core/io_tools.py,06e610246087c9b06c361dfbf4511bc9c2e43454,STILL_EXISTS,TODO verify that recipients are valid here
aaiddibjj,jmaggio14/imagepypelines,imagepypelines/core/Pipeline2.py,412bc6356a28b9ace9efa77efa9a31cba3b66ab5,STILL_EXISTS,TODO:
aaiddicid,jmaggio14/imagepypelines,imagepypelines/core/Pipeline2.py,d04fe0f0412e79ae3eddc7021a6eb3bc2f9fa65e,STILL_EXISTS,# move the data into a wavelet plane
aaiddihgh,jmaggio14/imagepypelines,imagepypelines/core/Pipeline.py,fd4109066a78e2782780e6c078142e4621b4d090,e566933174db04f8f70415d4476c49b41c9169d8,\"\"\"TODO: Placeholder function for @Ryan to create\"\"\"
aaiddihhi,jmaggio14/imagepypelines,imagepypelines/core/Pipeline.py,fd4109066a78e2782780e6c078142e4621b4d090,e566933174db04f8f70415d4476c49b41c9169d8,\"\"\"TODO: Sets up a priority\/order of operations list for operations
aaiddjcfh,jmaggio14/imagepypelines,imagepypelines/core/Pipeline.py,d00763d90965f0696448d2d612bca2b9584b67a1,c0332e34cf1fc6598d5bde7157f3f8e5bbb0f145,# move the data into a wavelet plane
aaiddjibi,jmaggio14/imagepypelines,imagepypelines/core/Pipeline.py,2d57849cf5dc28e433ac2d8189d957c90bd70872,0de4ad7230a5399e21fb2d7408096f3773750403,maybe this should be called a 'task' instead?????)
aaiddjigf,jmaggio14/imagepypelines,graphing_test.py,846fe9b2d49fb986b99fff63a6bd56b23cb5627b,29d4cf38ed976a459b2d67c0af7ade4bdd338ece,# move the data into a wavelet plane
aaideaead,jmaggio14/imagepypelines,imagepypelines/core/BaseBlock.py,f6d3395e9e661e83d2cd0ce9caa82c8657d8c4e6,c0332e34cf1fc6598d5bde7157f3f8e5bbb0f145,# so no more comparisons are needed for this element
aaideafcb,jmaggio14/imagepypelines,imagepypelines/core/Pipeline.py,f6d3395e9e661e83d2cd0ce9caa82c8657d8c4e6,STILL_EXISTS,edge['data'] = task_a._pipeline_process() # no data needed
aaidebigd,jmaggio14/imagepypelines,imagepypelines/core/Pipeline.py,8b25eaca8e2fa5cf67057b3a19ddc24d26eb916c,7897d00b383899736871f0114d10ae9d5973112e,no inputs or outputs - currently unused
aaidebjcb,jmaggio14/imagepypelines,imagepypelines/core/pipeline_tools.py,7897d00b383899736871f0114d10ae9d5973112e,204b4dc77af22a9d35807249e5de2f62c4141a40,no inputs or outputs - currently unused
aaidecdef,jmaggio14/imagepypelines,imagepypelines/core/block_subclasses.py,7db1bc16526503b55a3cc050d40dd8073f0619cb,STILL_EXISTS,JM: this is an ugly hack to make FuncBlock's serializable - by
aaidedcea,jmaggio14/imagepypelines,imagepypelines/core/arg_checking.py,afc8d14bf4fd565d657f63e78e275edda213f316,5d32b6f040436ebdbf759c8c2dd0fe9fa445d1e3,NOTE: maybe do a check to ensure shape is a list of lists?
aaidedhji,jmaggio14/imagepypelines,graphing_test.py,a12896b8a31810a583d3b4fd175817364348af17,STILL_EXISTS,TODO:
aaidefidi,jmaggio14/imagepypelines,tests/graphing_test.py,f3443014fc879d92f3a66a2c69dd477a756fb5cc,STILL_EXISTS,TODO:
aaidegfhh,jxx123/fireTS,fireTS/core.py,b4d35285d2c33fa8454f6efc9537d571695be666,9625da6366d34a2452b85ccba4572de74bce2f83,TODO: support multi-step ahead prediction
aaidegggi,jxx123/fireTS,docs/source/conf.py,3bbd8b0f9d849e7464420670df612651224ec7d9,STILL_EXISTS,-- Options for todo extension ----------------------------------------------
aaidegggj,jxx123/fireTS,docs/source/conf.py,3bbd8b0f9d849e7464420670df612651224ec7d9,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaidegghe,jxx123/fireTS,fireTS/models.py,146fe5697da791bc1fb2f99771b661ad3ad2b237,70f261b27f00bf447a7db8b3b8ebc295f4632cc3,TODO: add forecast method
aaideghaj,jxx123/fireTS,fireTS/utils.py,7cbcebab010e6a2ac71193bfb09d2a88b2061375,STILL_EXISTS,TODO: this is not memory efficient; need to do this in a
aaideghba,jxx123/fireTS,fireTS/utils.py,7cbcebab010e6a2ac71193bfb09d2a88b2061375,STILL_EXISTS,better way in the future
aaideghcj,lzfelix/minuet,utils/loader.py,32baded1baeb8517f6610d3a2f5232eeacce6071,9965a447a4fc3117c68974d43a5261832e3bf1ca,TODO: improve number parsing
aaideghdd,lzfelix/minuet,utils/preprocessing.py,9965a447a4fc3117c68974d43a5261832e3bf1ca,STILL_EXISTS,TODO: improve number parsing
aaideghgi,lzfelix/minuet,minuet/minuet.py,98b90c10f7b2b5d2ccbd6bce3fd2eaaec2213ff1,STILL_EXISTS,TODO: fix hyperparameters reloading
aaideghia,lzfelix/minuet,examples/server.py,57aea9a002f3db40b88df41caabb41277caf6513,STILL_EXISTS,\"\"\" ||     Minimal example of how to expose a Minuet model for prediction on a server. ||     Simply send a POST request in the following format to localhost:8090\/: ||     { || \t    \"sentences\": [\"apple inc is selling apples in the bay area\"] ||     } || \"\"\"
aaidegidf,plstcharles/thelper,src/thelper/data.py,cf0c723a1d905370aa598c4d6eb6f60bda557e7c,STILL_EXISTS,todo : check compatibility between predicted types? (thru key map?)
aaidegieb,plstcharles/thelper,src/thelper/data.py,cf0c723a1d905370aa598c4d6eb6f60bda557e7c,STILL_EXISTS,todo: add method to reset sampler shuffling when epoch complete?
aaidegife,plstcharles/thelper,src/thelper/transforms.py,cf0c723a1d905370aa598c4d6eb6f60bda557e7c,STILL_EXISTS,todo
aaidegiga,plstcharles/thelper,src/thelper/utils.py,cf0c723a1d905370aa598c4d6eb6f60bda557e7c,STILL_EXISTS,XXX: there seems to be no way to detect static method call - it will
aaidegigh,plstcharles/thelper,src/thelper/utils.py,cf0c723a1d905370aa598c4d6eb6f60bda557e7c,STILL_EXISTS,copy needed here due to ocv 3.3 bug
aaidegigi,plstcharles/thelper,src/thelper/utils.py,cf0c723a1d905370aa598c4d6eb6f60bda557e7c,STILL_EXISTS,normalize & pass mask to draw func also? todo
aaidegiha,plstcharles/thelper,src/thelper/train.py,bb617076ae2090245d66c8375d378e969bfe8baa,5cbbca12e05c641d1e485181cfb9848a1a2565ba,not saving final eval results anywhere...? todo
aaidegihb,plstcharles/thelper,src/thelper/train.py,bb617076ae2090245d66c8375d378e969bfe8baa,STILL_EXISTS,valid device still ignored; todo
aaidegjbf,plstcharles/thelper,src/thelper/optim.py,54cb8b9e1b6dbe7f9c3afbbb344b28aca2ce2782,STILL_EXISTS,override and return false if reset not needed
aaidegjhd,plstcharles/thelper,src/thelper/train.py,aac5d8b089b18e5ae70d53795017364934a3dd79,04e8e613380eca76f94c92653e5fdc0a340d519d,todo: run eval in parallel (i.e. at the same time as training?)
aaidegjhf,plstcharles/thelper,src/thelper/optim.py,50027aade38436071b7509ea1b0a9ed64c5c28a3,STILL_EXISTS,needed if outputting tbx txt
aaidegjid,plstcharles/thelper,src/thelper/data.py,877591c9723491eff290553bc92134a5fd842fec,STILL_EXISTS,parse config for oversampling here? todo
aaidehaaa,plstcharles/thelper,src/thelper/data.py,5aa7ccf4783e0358befd19cc5b2b0a05ff528c01,STILL_EXISTS,could add checks to see if the sample already behaves like a dict? todo
aaidehacj,plstcharles/thelper,src/thelper/transforms.py,ab0071b5fa923f7c1cbb2c44cf01912e8afce85e,STILL_EXISTS,no transform needed; channel reordering done at end
aaidehafi,plstcharles/thelper,src/thelper/train.py,3ef1a3a41b1d20b8074cdd2b24c89d3599ee9fce,STILL_EXISTS,todo: dig into members and check only critical ones
aaidehahi,plstcharles/thelper,src/thelper/modules/coordconv.py,39b7e5aaa3ad094424fd0da9dc003fbdc02e3025,STILL_EXISTS,todo @@@@ fixup to device stuff here
aaidehbce,plstcharles/thelper,src/thelper/data.py,0f5599a3e53e8d589f6c9808a22d79cec07dbe50,STILL_EXISTS,for seed generation below (if needed); will be reseeded afterwards
aaidehbcj,plstcharles/thelper,src/thelper/utils.py,b7c7d634619e78f4f54a78d5309c35de659a18a0,STILL_EXISTS,TODO
aaidehbdc,plstcharles/thelper,src/thelper/transforms.py,7a3e59fc4bff2d0e3359971b889dca3dbf7e66e3,STILL_EXISTS,TODO
aaidehbdf,plstcharles/thelper,src/thelper/optim.py,1eb7f9fd813da0c143e042e6054641a255f15c6b,STILL_EXISTS,TODO @@@
aaidehbeb,plstcharles/thelper,src/thelper/transforms.py,ac12ce94119c87d754f6c4b288d75bbc501e458b,317536b2d8b5ff60993f4fa01501424f98766740,todo: add support for torch.Tensor i\/o here?
aaidehbee,plstcharles/thelper,src/thelper/transforms.py,ac12ce94119c87d754f6c4b288d75bbc501e458b,317536b2d8b5ff60993f4fa01501424f98766740,reseed with const value generated outside loop; but only if processing a list? TODO
aaidehbfa,plstcharles/thelper,src/thelper/gui.py,266844763a93267902ce5cb8be58b13c5f4ef1cc,STILL_EXISTS,todo: update to use meta keys?
aaidehbhe,plstcharles/thelper,src/thelper/tasks.py,a428869712d76df581192285ae9670022727b106,STILL_EXISTS,REALLY DIRTY HACK FOR CHECKPOINT BACKWARD COMPAT HERE; TO BE REMOVED ASAP @@@@@@
aaidehbhi,plstcharles/thelper,src/thelper/tasks.py,a428869712d76df581192285ae9670022727b106,STILL_EXISTS,todo: add new task types (objdetecton; segmentation; regression; superres; ...)
aaidehbih,plstcharles/thelper,src/thelper/data.py,cee0f87f4d439cdab0715c3bf0a7c1679d9cd54c,STILL_EXISTS,extra name added for unlabeled samples (if needed!)
aaidehcae,plstcharles/thelper,thelper/modules/mobilenet.py,2fa3019861e829bedcb4b57e094475ce6b479980,STILL_EXISTS,default count; will be updated if needed
aaidehcah,plstcharles/thelper,thelper/train.py,d7c27afeb75650b4783a0f9a7c886422a9affa13,STILL_EXISTS,todo: add flag to toggle loss comp in validation?
aaidehcai,plstcharles/thelper,thelper/train.py,d7c27afeb75650b4783a0f9a7c886422a9affa13,STILL_EXISTS,todo: use validation loss instead? more stable?
aaidehdcf,plstcharles/thelper,thelper/data/parsers.py,f58b1844718d24294e41a1ae9ab1d57d9aaf4992,fc9e7c6f013e23437c56fbf9f9b59ed156f97cce,could add checks to see if the sample already behaves like a dict? todo
aaidehdch,plstcharles/thelper,thelper/data/utils.py,f58b1844718d24294e41a1ae9ab1d57d9aaf4992,STILL_EXISTS,todo: 'data_config' field is deprecated; might be removed later
aaidehdij,plstcharles/thelper,thelper/tasks/classif.py,eb9b7df68bd6dcabf7fb48f3c6070232ad4f54c9,465f90f2425836f06e41467fc6b9573c287b282b,REALLY DIRTY HACK FOR CHECKPOINT BACKWARD COMPAT HERE; TO BE REMOVED ASAP @@@@@@
aaidehdjd,plstcharles/thelper,thelper/tasks/classif.py,eb9b7df68bd6dcabf7fb48f3c6070232ad4f54c9,0b568a2cf32f303f0dfed9715142c83af2c520b5,todo: add new task types (objdetecton; segmentation; regression; superres; ...)
aaidehfdi,plstcharles/thelper,thelper/tasks/segm.py,465f90f2425836f06e41467fc6b9573c287b282b,0b568a2cf32f303f0dfed9715142c83af2c520b5,todo: add new task types (objdetecton; segmentation; regression; superres; ...)
aaidehfeb,plstcharles/thelper,thelper/utils.py,03c4733efab44a55cf89d98cc03e4c1ea468e38e,3cf83ed85c2c6bb7e48309de1c10b445fc22e138,copy needed here due to ocv 3.3 bug
aaidehffa,plstcharles/thelper,thelper/utils.py,e252eacd45e5cf41ca3883684add63045d2278a6,STILL_EXISTS,todo: display predictions if available? (currently skipped)
aaidehffe,plstcharles/thelper,thelper/cli.py,05d4f3f0c69d4923e156934226f857dea84ca635,809043a70269c18ad85a0b9eb8ce643c99b14c18,todo: move all 'viz' miniconfig stuff to its own section in the config file?
aaidehfga,plstcharles/thelper,thelper/data/pascalvoc.py,177312c06dad31d8e89cb26ee73330ecff6800fd,c2a00ac84ea327ebf7adcd4ac3175b1dfa5a7676,@@@ CHANGE TODO
aaidehfgg,plstcharles/thelper,thelper/data/pascalvoc.py,177312c06dad31d8e89cb26ee73330ecff6800fd,54d28a041b765069e386b2356187af8a226acc4a,TODO: update w\/ task-compat structure
aaidehfgh,plstcharles/thelper,thelper/data/pascalvoc.py,177312c06dad31d8e89cb26ee73330ecff6800fd,STILL_EXISTS,TODO: loss might not like uint8; check for useless convs later
aaidehfje,plstcharles/thelper,thelper/train/segm.py,c5f4dd8d8036b2c07cef99824ae39a237f05c6ab,STILL_EXISTS,todo: find a more efficient way to compute loss w\/ byte vals directly?
aaidehfjf,plstcharles/thelper,thelper/data/pascalvoc.py,5a5b1934e8578b059aa72001082a61e2dadd8d05,1e7b8de990cf1cebe820525196dbb71b2b6bd066,TODO : gt maps are not currently transformed! (need refact w\/ dict keys)
aaidehfjg,plstcharles/thelper,thelper/train/base.py,ea2c0b0e64b4a5210d04a0b7128f3d51f2f28bb7,STILL_EXISTS,todo: also support reweighting w\/ other compatible tasks? (e.g. semantic segmentation)
aaidehgbi,plstcharles/thelper,thelper/train/regr.py,dd49a5b63533d37c3794fb22d5fa4eae9abdca72,STILL_EXISTS,todo: add support to fraction samples that are too big for a single iteration
aaidehgdd,plstcharles/thelper,thelper/optim/utils.py,06c4873f82272ec58c4e9ccc800c7527614b2747,STILL_EXISTS,todo: add flag to toggle loss comp in validation? (add to trainer config maybe?)
aaidehgdg,plstcharles/thelper,thelper/optim/utils.py,06c4873f82272ec58c4e9ccc800c7527614b2747,cc6f717ac3ed5f80d8085ddffb649ae6515fd4a7,loss_params[weight_param_name] = self._upload_tensor(torch.FloatTensor(weight_list); dev=self.devices) # might actually be needed?
aaidehgfa,plstcharles/thelper,thelper/data/loaders.py,56ee37af169a2ec618de7acf45065b310001bd5f,STILL_EXISTS,for seed generation below (if needed); will be reseeded afterwards
aaidehgfg,plstcharles/thelper,thelper/data/loaders.py,56ee37af169a2ec618de7acf45065b310001bd5f,STILL_EXISTS,extra name added for unlabeled samples (if needed!)
aaidehgha,plstcharles/thelper,thelper/utils.py,b8b55f214ac3209f3acba6d59a95f505c3c05281,3cf83ed85c2c6bb7e48309de1c10b445fc22e138,copy needed here due to ocv 3.3 bug
aaidehgic,plstcharles/thelper,thelper/utils.py,adbf2985b5b2e102f94c8d204d19546c576a53f1,STILL_EXISTS,todo: add more global opencv flags setups here
aaidehgid,plstcharles/thelper,thelper/data/loaders.py,594669d02644858a9000ffb2737f37d04834641d,STILL_EXISTS,todo: investigate need to copy at all?
aaidehhbf,plstcharles/thelper,thelper/utils.py,992b211c0c4a2635a9c64efac2047fac7c5bb2a7,0ed9d637b664cdc897695cbfbba1fa58a5b68160,todo: display both preds and targets below? (still only using one of the two)
aaidehhdb,plstcharles/thelper,thelper/data/pascalvoc.py,b23c877e805b5a23c957f21dc0fa00c83d594cf8,STILL_EXISTS,fix unsupported negative strides in PyTorch <= 0.4.0
aaidehhde,plstcharles/thelper,thelper/utils.py,3094992f27bc34d6df27e924b1ad3a9559c3292b,f02b86566df119924c05f279b57c0a7881452516,@@@@ TODO: UPDATE TO EXPECT RAW PREDICTS (BxC) INSTEAD OF DIRECT CLASS LABELS
aaidehidg,plstcharles/thelper,thelper/utils.py,e3f56bf4868f0dd2a876a2e843a247e4cc2e3809,3cf83ed85c2c6bb7e48309de1c10b445fc22e138,copy needed here due to ocv 3.3 bug
aaidehjfc,plstcharles/thelper,thelper/transforms/wrappers.py,934d47e5b136966571850611a6213f36a313a740,STILL_EXISTS,todo: add list unwrapping\/interlacing support like in other wrappers?
aaideiaac,plstcharles/thelper,thelper/utils.py,4ef032374dd9c1a9500f36ae1af4afc3d77ce313,STILL_EXISTS,TODO: create 'scheduler' field to restore previous state? (not so important for early versions)
aaideiabe,plstcharles/thelper,thelper/utils.py,4ef032374dd9c1a9500f36ae1af4afc3d77ce313,STILL_EXISTS,move 'loss' section to 'optimization' section
aaideiaca,plstcharles/thelper,thelper/utils.py,4ef032374dd9c1a9500f36ae1af4afc3d77ce313,STILL_EXISTS,TODO: add scheduler 0-based step fix here? (unlikely to cause serious issues)
aaideibai,plstcharles/thelper,thelper/data/parsers.py,b94984fc8a3537b4ae006b46b3ca822fabb827ff,12086313f5feb0409bc1c035eeb826c88435ed39,reassemble string if needed
aaideibbd,plstcharles/thelper,thelper/nn/resnet.py,2c9a8deb64e34458d6e8d3e73d8d6b4e5c770822,326c22a9de4ef2c1b6cd604f429e7c9e155bd1be,TODO: add pretrained param to toggle loading weights from imagenet before applying task?
aaideibcf,plstcharles/thelper,thelper/utils.py,638744e6accb9197beee3cfcaf9efb8ee8c9d445,STILL_EXISTS,load model trace if needed (we do it here since we can locate the neighboring file)
aaideibfb,plstcharles/thelper,thelper/utils.py,319e489c6c92d19a582acda9c841ae98a2ae6f83,3cf83ed85c2c6bb7e48309de1c10b445fc22e138,@@@ todo: cleanup swap_channels above via flag in superres task?
aaideicaa,plstcharles/thelper,thelper/train/regr.py,7e9cf6120dcae53d1fb74dd05ebb6fbd87dcce36,STILL_EXISTS,@@@@@ todo: use target_min\/target_max and other props from task?
aaideicac,plstcharles/thelper,thelper/nn/utils.py,cef68b5115fc2dc28e12278a2643f6a7deca79ac,STILL_EXISTS,@@@@@@ TODO
aaideiegc,plstcharles/thelper,thelper/train/utils.py,3854062c5bb330cf4f1bc48f5f3d35513aa87f64,STILL_EXISTS,TODO
aaideieih,plstcharles/thelper,thelper/train/utils.py,3854062c5bb330cf4f1bc48f5f3d35513aa87f64,STILL_EXISTS,return None if rendering fails (probably due to matplotlib on displayless server)
aaideifce,plstcharles/thelper,thelper/train/detect.py,e6042ebaee708c7d45eaebb7b31b00e2a844f844,STILL_EXISTS,user should probably implement their own trainer
aaideifdg,plstcharles/thelper,thelper/train/base.py,1d3f188c3c6aa075c9c4c9cb6567babe573484f1,STILL_EXISTS,TODO
aaideifff,plstcharles/thelper,thelper/optim/eval.py,c385713914240d65b51d168723e2df3f0c41224b,STILL_EXISTS,(note: we could do some combinatorial optim w\/ hungarian method to solve ideally instead)
aaideifid,plstcharles/thelper,thelper/optim/metrics.py,5755d3521816fd1c7defe4f44404c6c1ca4c1c12,STILL_EXISTS,maybe need to concat?
aaideigdf,plstcharles/thelper,thelper/nn/resnet.py,808e40487254553e3d0c5176faaf458e6c1d91ed,326c22a9de4ef2c1b6cd604f429e7c9e155bd1be,TODO: add pretrained param to toggle loading weights from imagenet before applying task?
aaideigdh,plstcharles/thelper,thelper/train/base.py,852d1e05b1a4a8564895f2132456bcc11943a789,7c5701210ff0e6f559c7c82b27e59169b7d782b9,will be instantiated only when needed based on above path
aaideihea,plstcharles/thelper,thelper/train/utils.py,c1561f6c120f1a0d264ad4bf99302035bc5177d9,STILL_EXISTS,TODO
aaideiibc,plstcharles/thelper,thelper/train/utils.py,e0aef0c7135b916350b7175ed89d6304b50bfe61,64b25f8ded5c6a9a21fe353b3059b5ca7f5d60cc,FIXME: we should still accumulate predictions on missing targets for evalution-only logging
aaideiibf,plstcharles/thelper,thelper/train/utils.py,e0aef0c7135b916350b7175ed89d6304b50bfe61,STILL_EXISTS,TODO
aaideiicj,plstcharles/thelper,thelper/train/utils.py,ed87c0dafcecfc4410b0bd2b0c80f38b2227f1c4,STILL_EXISTS,FIXME:
aaideiidg,plstcharles/thelper,thelper/typedefs.py,ed87c0dafcecfc4410b0bd2b0c80f38b2227f1c4,880b5184eb88106188a78d7e445d28fca8788ad3,FIXME: experimental
aaideiijf,plstcharles/thelper,thelper/train/utils.py,8ceaac629ead4f2fd3efca589933ee1b3e4c96b4,64b25f8ded5c6a9a21fe353b3059b5ca7f5d60cc,FIXME: we should still accumulate predictions on missing targets for evalution-only logging
aaideijab,plstcharles/thelper,thelper/ifaces.py,1ccecd2eec8f7abf06d880f592ffef28b47300d7,STILL_EXISTS,\"\"\"Common object interfaces module. ||  || The interfaces defined here are fairly generic and used to eliminate || issues related to circular module importation. || \"\"\"
aaidejbcg,plstcharles/thelper,thelper/draw.py,3cf83ed85c2c6bb7e48309de1c10b445fc22e138,STILL_EXISTS,@@@ todo: cleanup swap_channels above via flag in superres task?
aaidejcdj,plstcharles/thelper,thelper/utils.py,db20299b5a43846182dc2984cd2ec69ef9beba4e,STILL_EXISTS,TODO make config dict comparison smarter...?
aaidejcei,plstcharles/thelper,thelper/train/base.py,954e3dd86de710e3b54e56ce503951a31dad23f7,STILL_EXISTS,TODO: rewrite so that lists of callbacks can be supported @@@@
aaidejcha,plstcharles/thelper,thelper/data/geo/utils.py,3760256a3bbff7a65eb8dbc11dc325bfc35cb722,STILL_EXISTS,FIXME: no specific mapping of inputs; each is different
aaidejchb,plstcharles/thelper,thelper/data/geo/utils.py,3760256a3bbff7a65eb8dbc11dc325bfc35cb722,STILL_EXISTS,add dirty hack for geojsons used in testbed15-d104
aaidejcia,plstcharles/thelper,thelper/data/geo/parsers.py,1a7b540d219fc4d1d8b585d2a041734b3bca7784,STILL_EXISTS,note: we use a flag here instead of removing bad features so that end-users can still use them if needed
aaidejcjc,plstcharles/thelper,thelper/data/geo/ogc.py,555691ac7b1080b0232e29e1a8a9dbe2da93daf8,STILL_EXISTS,note: we use a flag here instead of removing bad features so that end-users can still use them if needed
aaidejcjf,plstcharles/thelper,thelper/data/geo/ogc.py,555691ac7b1080b0232e29e1a8a9dbe2da93daf8,STILL_EXISTS,TODO @@@@
aaidejcjg,plstcharles/thelper,thelper/data/geo/ogc.py,555691ac7b1080b0232e29e1a8a9dbe2da93daf8,STILL_EXISTS,TODO: MERGE CROP + MASK(DTRANSF) @@@@
aaidejddj,plstcharles/thelper,thelper/data/geo/utils.py,9fce29d5f7a4512d6d69f245e0c44850a708e37d,STILL_EXISTS,FIXME: no specific mapping of inputs; each is different
aaidejdea,plstcharles/thelper,thelper/data/geo/utils.py,9fce29d5f7a4512d6d69f245e0c44850a708e37d,STILL_EXISTS,add dirty hack for geojsons used in testbed15-d104
aaidejdej,plstcharles/thelper,thelper/data/utils.py,2c3a6ff3e22a14b7dde22ef7c02dcbb5c7c42ba3,cc4b296e04d08bc167593150a3551ae284d9567c,@@@@ TODO: add util to truncate size of string in each member of samples below?
aaidejdfa,plstcharles/thelper,thelper/gui/annotators.py,2c3a6ff3e22a14b7dde22ef7c02dcbb5c7c42ba3,STILL_EXISTS,@@@@ TODO: add util to truncate size of string in each member of samples below?
aaidejdfd,plstcharles/thelper,thelper/data/geo/gdl.py,74d4b8c58b4d60596f8cc61c5796b74e013470d2,STILL_EXISTS,TODO: add more
aaidejdga,plstcharles/thelper,thelper/nn/coordconv.py,7bfd08cf06b8ce5d6b5f6f3e6f8bae429f611526,STILL_EXISTS,note: this is a pretty 'dumb' way to add coord maps to a model; as it will add them everywhere; even
aaidejegh,plstcharles/thelper,thelper/transforms/utils.py,ce8be0c937c87c35aba0b14fce6710cbe20acaa6,STILL_EXISTS,NOTE DISTINCTION BETWEEN OPERATORS AND TRANSFORMERS? @@@@@ TODO
aaidejegi,plstcharles/thelper,thelper/transforms/utils.py,ce8be0c937c87c35aba0b14fce6710cbe20acaa6,STILL_EXISTS,huge skip; user probably provided ops pipeline as function pointers
aaidejffj,plstcharles/thelper,tests/optim/test_metrics.py,beb9b322c572e7bf47c44d270bee0a9aca0c5b26,STILL_EXISTS,@@@@ TODO
aaidejfhh,plstcharles/thelper,thelper/train/base.py,a4c877a84f01ba302da38dd46c2868aae5d6c042,7c5701210ff0e6f559c7c82b27e59169b7d782b9,todo: replace w\/ pytorch's internal tbx @@@@@
aaidejgif,plstcharles/thelper,thelper/session/base.py,7c5701210ff0e6f559c7c82b27e59169b7d782b9,4ec7aadacc450125dcff2d1c1a59225abbc36bc7,todo: replace w\/ pytorch's internal tbx @@@@@
aaidejgig,plstcharles/thelper,thelper/session/base.py,7c5701210ff0e6f559c7c82b27e59169b7d782b9,STILL_EXISTS,will be instantiated only when needed based on above path
aaidfachb,plstcharles/thelper,thelper/utils.py,8c31d00f71356d6d3988000e0b4ac641b45759c7,4f7db400937d515f7716d351a09eb5b501b5ce0c,check with re-parsed version after fixing release dash
aaidfache,plstcharles/thelper,thelper/tasks/segm.py,3fbea64a637ac2ed386add2005e49e8e14a78f77,STILL_EXISTS,TODO Check the validity of this assert
aaidfachf,plstcharles/thelper,thelper/tasks/segm.py,3fbea64a637ac2ed386add2005e49e8e14a78f77,STILL_EXISTS,assert self.dontcare == task.dontcare; \"dontcare value mismatch; cannot create compatible task\"
aaidfachi,plstcharles/thelper,thelper/utils.py,804d8901b2e75ba9d07d43b9337944a9e4cf085f,STILL_EXISTS,check with re-parsed version after fixing release dash
aaidfaeaa,plstcharles/thelper,thelper/utils.py,12086313f5feb0409bc1c035eeb826c88435ed39,c9c336829869880287d4469aee992b072970e1f1,reassemble string if needed
aaidfaeai,plstcharles/thelper,thelper/data/geo/bigearthnet.py,478fb0dc2bbd0f0654ebf5709160db5d65529084,STILL_EXISTS,@@@@ TODO: CONVERT TO PROPER TEST
aaidfaedh,plstcharles/thelper,thelper/data/loaders.py,225d4e2bb64ec635a5452a821dc0159dae18ae0b,STILL_EXISTS,idx-label pairs below are passed through to the sampler for label-specific indexing (if needed)
aaidfaeid,plstcharles/thelper,thelper/data/geo/agrivis.py,aa1d56a767165251dd55953d268411bcc58c62ed,STILL_EXISTS,\"\"\" || Agricultural Semantic Segentation Challenge Dataset Interface ||  || Original author: David Landry (david.landry@crim.ca) || Updated by Pierre-Luc St-Charles (April 2020) || \"\"\"
aaidfaejd,plstcharles/thelper,thelper/data/geo/agrivis.py,aa1d56a767165251dd55953d268411bcc58c62ed,STILL_EXISTS,@@@@ TODO: CONVERT TO PROPER TEST
aaidfafah,plstcharles/thelper,thelper/nn/resnet.py,233cb25887a03737cda87b14b1e827930fa21887,STILL_EXISTS,todo: support more prebuilt\/custom layer types here; if needed...
aaidfafgi,plstcharles/thelper,thelper/infer/base.py,de9d850215026e29e30e2cf5515a4284f2c67c2f,STILL_EXISTS,default epoch 0 if omitted as they are not actually needed for single pass inference
aaidfagdd,serengil/neural-networks-py,service/FeatureNormalization.py,a3515853c745596526c0d59631ce6161666e8b2e,STILL_EXISTS,columns
aaidfahab,serengil/neural-networks-py,Vectorization.py,6f52618a7bb001a9e8b05afcb3561bcd6c3e845f,STILL_EXISTS,weights = weights * np.sqrt(6\/(rows + columns)) #normalized initializtion
aaidfahgh,eliorc/denzel,denzel/app/__init__.py,dce1ef3f1a094066304fbf7753621070f8003264,b3086880370e21f33c995b2e97181a55b029b758,TODO - Fill in \"Usage\" section in app\/info.txt
aaidfahgi,eliorc/denzel,denzel/app/__init__.py,dce1ef3f1a094066304fbf7753621070f8003264,b3086880370e21f33c995b2e97181a55b029b758,TODO - Support for batching
aaidfahhc,eliorc/denzel,denzel/app/__init__.py,dce1ef3f1a094066304fbf7753621070f8003264,b3086880370e21f33c995b2e97181a55b029b758,TODO: DSG - Rewrite this to describe what is the expected input and output of the API
aaidfahih,eliorc/denzel,denzel/app/logic.py,dce1ef3f1a094066304fbf7753621070f8003264,STILL_EXISTS,TODO - DSG - verify the input data format
aaidfahii,eliorc/denzel,denzel/app/logic.py,dce1ef3f1a094066304fbf7753621070f8003264,STILL_EXISTS,TODO - DSG - transform data into model ready data. Must return list; where each element is an example.
aaidfahij,eliorc/denzel,denzel/app/logic.py,dce1ef3f1a094066304fbf7753621070f8003264,STILL_EXISTS,TODO - DSG - Perform prediction. Must return dictionary of {<id>: <result>}
aaidfahjf,eliorc/denzel,denzel/app/logic.py,18a5b259ff524265d0a2df35ceb6e9aeece27594,STILL_EXISTS,TODO - DSG - load model from disk to memory
aaidfaiab,eliorc/denzel,denzel/app/logic.py,5520ca2ebb037649043d7f536d94fb8e7211e007,STILL_EXISTS,TODO - DSG - transform data into model ready data. Must return list; where each element is an example.
aaidfaiga,eliorc/denzel,denzel/app/logic/pipeline.py,b3086880370e21f33c995b2e97181a55b029b758,STILL_EXISTS,callback_uri is needed to sent the responses to
aaidfajjj,eliorc/denzel,build/lib/denzel/app/logic/pipeline.py,d4723b28c3f15d64eada014e300d1f6d3ace15d7,STILL_EXISTS,callback_uri is needed to sent the responses to
aaidfbadh,eliorc/denzel,src/denzel/app/tasks.py,2107d8bea361343104e6fd241bafa8f5aaa39e18,STILL_EXISTS,Needed because concurrent workers will retry to remove
aaidfbagb,eliorc/denzel,tests/assets/pipeline.py,2107d8bea361343104e6fd241bafa8f5aaa39e18,STILL_EXISTS,callback_uri is needed to sent the responses to
aaidfbagf,eliorc/denzel,tests/assets/pipeline.py,2107d8bea361343104e6fd241bafa8f5aaa39e18,STILL_EXISTS,Verify all features needed were sent
aaidfbbch,fabianmax/ML-Automation,load_data.py,2221f2a49968531cd34340421e77ff2acb97c401,STILL_EXISTS,Columns
aaidfbbci,fabianmax/ML-Automation,load_data.py,2221f2a49968531cd34340421e77ff2acb97c401,STILL_EXISTS,Subset relevant columns in data
aaidfbbda,fabianmax/ML-Automation,auto_ml.py,be2dafa188049a8163c1eac567e41dc079c76064,STILL_EXISTS,TODO - Add custom return for each backend
aaidfbbeh,fabianmax/ML-Automation,dummy_code.py,be2dafa188049a8163c1eac567e41dc079c76064,STILL_EXISTS,Columns
aaidfbbei,fabianmax/ML-Automation,dummy_code.py,be2dafa188049a8163c1eac567e41dc079c76064,STILL_EXISTS,Subset relevant columns in data
aaidfbcdj,fabianmax/ML-Automation,results.py,d82112fcf600ffbcc2f7e3ea9efc32024c3af4ff,STILL_EXISTS,Number of times h2o is better
aaidfbcif,familyld/DeepCF,Dataset.py,f7329c8e01f61e6e7640938921d8b44001fcb286,STILL_EXISTS,''' || Created on Aug 8; 2016 || Processing datasets.  || @author: Xiangnan He (xiangnanhe@gmail.com) || '''
aaidfbdaj,familyld/DeepCF,evaluate.py,f7329c8e01f61e6e7640938921d8b44001fcb286,STILL_EXISTS,''' || Created on Apr 15; 2016 || Evaluate the performance of Top-K recommendation: ||     Protocol: leave-1-out evaluation ||     Measures: Hit Ratio and NDCG ||     (more details are in: Xiangnan He; et al. Fast Matrix Factorization for Online Recommendation with Implicit Feedback. SIGIR'16) || @author: hexiangnan || '''
aaidfbeac,sraashis/deepdyn,preprocess/common/image_utils.py,b80f3703da2103b62aba28fe0fcd644eef228662,2b1899e77fd4768fa432009a3019972fe4f23535,todo
aaidfbfbd,sraashis/deepdyn,preprocess/algorithms/mst.py,1e33346dbcac5fdd034edc512134bc12f4e37c9a,2da7817fa692c685e20542476d0b08bd991c7b2f,implement once UnionFind exists; and second; because the only slow
aaidfbjih,sraashis/deepdyn,script_03_drive_masks.py,6126e1b97a79ac181122156bf6a98b867c32563b,STILL_EXISTS,Fix masks
aaidfcbbe,sraashis/deepdyn,commons/segmentation.py,f8b3b8295f5e16ce1dd15d019fe19813e908a9e6,STILL_EXISTS,Todo implement logic to disable previous conected component in _run() method:
aaidfcbbf,sraashis/deepdyn,commons/segmentation.py,f8b3b8295f5e16ce1dd15d019fe19813e908a9e6,2880cf8a80c94b12a2b4c5b8a4984d6128b65e3c,Todo load mask and ground truth
aaidfcbbg,sraashis/deepdyn,commons/segmentation.py,f8b3b8295f5e16ce1dd15d019fe19813e908a9e6,STILL_EXISTS,todo something with previous accumulator.img_obj.graph to disable the connectivity
aaidfcebc,sraashis/deepdyn,testing/CONV_PyTORCH.py,4f9ffa04e0478d598bda9bb5ca393c4f1beefda8,STILL_EXISTS,## Define folders. Create if needed.
aaidfcffg,sraashis/deepdyn,neuralnet/unet/model/unet_parts.py,6d49423965a514bb62e5fe5283fbd54586ec296a,STILL_EXISTS,would be a nice idea if the upsampling could be learned too;
aaidfciab,sraashis/deepdyn,testing/UNET_AV-WIDE.py,637cefed283a7e4265b00f191fb83b477421fb81,STILL_EXISTS,## Define folders. Create if needed.
aaidfciie,sraashis/deepdyn,testing/UNET_DRIVE.py,637cefed283a7e4265b00f191fb83b477421fb81,STILL_EXISTS,img_obj.working_arr = None  # todo
aaidfdaee,sraashis/deepdyn,testing/unet_runner.py,ef855a102042c6fb062ed7f338f6cc65781b1737,STILL_EXISTS,img_obj.working_arr = None  # todo
aaidfebci,sraashis/deepdyn,neuralnet/thrnet/thrnet_dataloader.py,0ad2030997656c695529199d8f251abfd6c426ff,STILL_EXISTS,todo
aaidfebdg,sraashis/deepdyn,neuralnet/mapnet/runs.py,9fb6dc4bab681c18a4c8bae92c64cfad5dd87f31,d7139225d8739847d2c5a3f6d4c2fa20a274b72e,# TODO after we verify for DRIVE
aaidfedcg,sraashis/deepdyn,nnbee/utils/img_utils.py,d3c09dc00b31257ad0f4407191aee19879d8b892,STILL_EXISTS,\"\"\" || Very useful utilities for working with images || ### author: Aashis Khanal || ### sraashis@gmail.com || ### date: 9\/10\/2018 || \"\"\"
aaidfefhg,sraashis/deepdyn,nbee/torchbee.py,6f7a844c0ec1cf1e8ed08fdb653fa4821c5c84bc,STILL_EXISTS,Todo score accumulation to check if this model is better than the saved one
aaidffeai,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/ANN_simulation.py,60c75c54a9d47050fa774adc5a70de6dc4a5e266,b47290a7b56a8fedaf72517e4678a3d0e78a3560,fix node_num
aaidffedf,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/ANN_simulation.py,60c75c54a9d47050fa774adc5a70de6dc4a5e266,STILL_EXISTS,TODO
aaidffeia,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/previous_runs/snapshot_2015_08_14_12_43_14(unbiased_simulation_with_water)/generate_coordinates.py,b4bd06a86036d35ff5dbba73be488ff604add4b1,STILL_EXISTS,the output consists of 21 columns
aaidffejh,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/previous_runs/snapshot_2015_08_16_14_06_19(biased_simulation_with_water)/generate_coordinates.py,b4bd06a86036d35ff5dbba73be488ff604add4b1,STILL_EXISTS,the output consists of 21 columns
aaidfffbe,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/previous_runs/snapshot_2015_08_16_14_25_12(biased_simulation_with_water)/generate_coordinates.py,b4bd06a86036d35ff5dbba73be488ff604add4b1,STILL_EXISTS,the output consists of 21 columns
aaidfffdb,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/previous_runs/snapshot_2015_08_16_14_41_02(biased_simulation_with_water)/generate_coordinates.py,b4bd06a86036d35ff5dbba73be488ff604add4b1,STILL_EXISTS,the output consists of 21 columns
aaidfffei,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/previous_runs/snapshot_2015_08_16_15_21_21(biased_simulation_with_water)/generate_coordinates.py,b4bd06a86036d35ff5dbba73be488ff604add4b1,STILL_EXISTS,the output consists of 21 columns
aaidfffgf,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/previous_runs/snapshot_2015_08_16_15_41_58(biased_simulation_with_water)/generate_coordinates.py,b4bd06a86036d35ff5dbba73be488ff604add4b1,STILL_EXISTS,the output consists of 21 columns
aaidfffic,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/previous_runs/snapshot_2015_08_18_19_01_30(biased_simulation_with_water)/generate_coordinates.py,b4bd06a86036d35ff5dbba73be488ff604add4b1,STILL_EXISTS,the output consists of 21 columns
aaidfffjj,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/previous_runs/snapshot_2015_08_18_19_43_26(biased_simulation_with_water)/generate_coordinates.py,b4bd06a86036d35ff5dbba73be488ff604add4b1,STILL_EXISTS,the output consists of 21 columns
aaidffgbg,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/previous_runs/snapshot_2015_08_18_21_15_02(biased_simulation_with_water)/generate_coordinates.py,b4bd06a86036d35ff5dbba73be488ff604add4b1,STILL_EXISTS,the output consists of 21 columns
aaidffgdd,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/previous_runs/snapshot_2015_09_06_15_42_46(biased_water_good_result_with_coeff_500)/generate_coordinates.py,b4bd06a86036d35ff5dbba73be488ff604add4b1,STILL_EXISTS,the output consists of 21 columns
aaidffgfa,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/previous_runs/snapshot_2015_09_06_16_47_19(biased_water_with_coeff_500)/generate_coordinates.py,b4bd06a86036d35ff5dbba73be488ff604add4b1,STILL_EXISTS,the output consists of 21 columns
aaidffggh,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/previous_runs/snapshot_2015_09_06_17_19_47(biased_water_with_coeff_500)/generate_coordinates.py,b4bd06a86036d35ff5dbba73be488ff604add4b1,STILL_EXISTS,the output consists of 21 columns
aaidffgie,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/previous_runs/snapshot_2015_09_06_18_45_27(biased_water_with_coeff_1000)/generate_coordinates.py,b4bd06a86036d35ff5dbba73be488ff604add4b1,STILL_EXISTS,the output consists of 21 columns
aaidffhab,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/previous_runs/snapshot_2015_09_06_18_56_24(biased_water_with_coeff_5000)/generate_coordinates.py,b4bd06a86036d35ff5dbba73be488ff604add4b1,STILL_EXISTS,the output consists of 21 columns
aaidffhbi,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/previous_runs/snapshot_2015_09_06_19_30_14(biased_water_with_coeff_10000)/generate_coordinates.py,b4bd06a86036d35ff5dbba73be488ff604add4b1,STILL_EXISTS,the output consists of 21 columns
aaidffhdf,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/previous_runs/snapshot_2015_09_06_21_20_34(biased_water_with_coeff_30000)/generate_coordinates.py,b4bd06a86036d35ff5dbba73be488ff604add4b1,STILL_EXISTS,the output consists of 21 columns
aaidffhff,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/previous_runs/snapshot_2015_09_28_18_34_59/src/generate_coordinates.py,b4bd06a86036d35ff5dbba73be488ff604add4b1,STILL_EXISTS,the output consists of 21 columns
aaidffhhj,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/previous_runs/snapshot_2015_09_29_19_27_06(first round expanding simulation region)/src/generate_coordinates.py,b4bd06a86036d35ff5dbba73be488ff604add4b1,STILL_EXISTS,the output consists of 21 columns
aaidffiaj,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/previous_runs/snapshot_2015_10_26_21_11_33(manually do 4 iterations)/src/generate_coordinates.py,b4bd06a86036d35ff5dbba73be488ff604add4b1,STILL_EXISTS,the output consists of 21 columns
aaidffidf,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/previous_runs/snapshot_2015_11_04_21_02_56(automatically_9_iterations)/src/ANN_simulation.py,b4bd06a86036d35ff5dbba73be488ff604add4b1,STILL_EXISTS,fix node_num
aaidffied,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/previous_runs/snapshot_2015_11_04_21_02_56(automatically_9_iterations)/src/ANN_simulation.py,b4bd06a86036d35ff5dbba73be488ff604add4b1,STILL_EXISTS,is it needed?
aaidffifd,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/previous_runs/snapshot_2015_11_04_21_02_56(automatically_9_iterations)/src/ANN_simulation.py,b4bd06a86036d35ff5dbba73be488ff604add4b1,STILL_EXISTS,TODO
aaidffjac,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/previous_runs/snapshot_2015_11_28_14_48_30(finish_27_iterations_and_WHAM)/src/ANN_simulation.py,b4bd06a86036d35ff5dbba73be488ff604add4b1,STILL_EXISTS,fix node_num
aaidffjcd,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/previous_runs/snapshot_2015_11_28_14_48_30(finish_27_iterations_and_WHAM)/src/ANN_simulation.py,b4bd06a86036d35ff5dbba73be488ff604add4b1,STILL_EXISTS,TODO
aaidffjhe,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/previous_runs/snapshot_2015_11_29_19_36_54(another_series_of_11_iterations)/src/ANN_simulation.py,b4bd06a86036d35ff5dbba73be488ff604add4b1,STILL_EXISTS,fix node_num
aaidfgaab,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/previous_runs/snapshot_2015_11_29_19_36_54(another_series_of_11_iterations)/src/ANN_simulation.py,b4bd06a86036d35ff5dbba73be488ff604add4b1,STILL_EXISTS,TODO
aaidfgafe,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/test_cases/report_issue_of_evaluation_order/case_1/generate_coordinates.py,b4bd06a86036d35ff5dbba73be488ff604add4b1,STILL_EXISTS,the output consists of 21 columns
aaidfgahe,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/test_cases/report_issue_of_evaluation_order/case_2/generate_coordinates.py,b4bd06a86036d35ff5dbba73be488ff604add4b1,STILL_EXISTS,the output consists of 21 columns
aaidfgaje,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/test_cases/report_issue_of_evaluation_order/case_3/generate_coordinates.py,b4bd06a86036d35ff5dbba73be488ff604add4b1,STILL_EXISTS,the output consists of 21 columns
aaidfgbcd,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/test_cases/test_circular_layer_in_pybrain/test_circular.py,b4bd06a86036d35ff5dbba73be488ff604add4b1,STILL_EXISTS,fix node_num
aaidfgbfb,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/test_cases/test_circular_layer_in_pybrain/test_circular.py,b4bd06a86036d35ff5dbba73be488ff604add4b1,STILL_EXISTS,TODO
aaidfgcbh,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/ANN_simulation.py,24790db129d49cfb7fa6971a2b3ca551cc22f20a,23532598b1428213a9a8d03da6a58ee338edd7de,TODO: get coordinates data
aaidfgcgj,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/ANN_simulation.py,ed5ce144516d2b96381d2abfdf0c7d8f8dea6e23,489e715563ee09bb2682fab79f919a187502c051,TODO: delete this function?
aaidfgche,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/ANN_simulation.py,5bddef3bc89bdfdeba67b72f34f814ce2d277a8f,489e715563ee09bb2682fab79f919a187502c051,FIXME: _PCs should be declared
aaidfgdeg,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/previous_runs/snapshot_2016_02_05_04_20_49/src/ANN_simulation.py,a174caf4b90907a335a3bb681e3c567a1717297a,STILL_EXISTS,fix node_num
aaidfgecd,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/ANN_simulation.py,d3a039e6f49d925ca4429d0d4b3ea1025b49b41a,STILL_EXISTS,TODO: get expression for network with circular nodes
aaidfgece,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/ANN_simulation.py,d3a039e6f49d925ca4429d0d4b3ea1025b49b41a,84730650afceb5c2c32430bb625ffb8147305184,TODO: plotting for circular layer network
aaidfgeda,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/ANN_simulation.py,d3a039e6f49d925ca4429d0d4b3ea1025b49b41a,STILL_EXISTS,TODO:
aaidfgehh,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/previous_runs/snapshot_2016_02_20_15_26_09/src/ANN_simulation.py,944d9bfe27ae99b696c44b5b6e7a22706b26a727,STILL_EXISTS,TODO: get expression for network with circular nodes
aaidfgeic,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/previous_runs/snapshot_2016_02_20_15_26_09/src/ANN_simulation.py,944d9bfe27ae99b696c44b5b6e7a22706b26a727,STILL_EXISTS,fix node_num
aaidfgejf,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/previous_runs/snapshot_2016_02_20_15_26_09/src/ANN_simulation.py,944d9bfe27ae99b696c44b5b6e7a22706b26a727,STILL_EXISTS,TODO: plotting for circular layer network
aaidfgfac,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/previous_runs/snapshot_2016_02_20_15_26_09/src/ANN_simulation.py,944d9bfe27ae99b696c44b5b6e7a22706b26a727,STILL_EXISTS,TODO:
aaidfgihe,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/ANN_simulation.py,256644edcb36bab94a18a6f923223d9c77c31b42,STILL_EXISTS,FIXME: use better implementation later
aaidfgjbe,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/previous_runs/snapshot_2016_02_25_21_09_14/src/ANN_simulation.py,26e2813307226ef310e35e02a2ce1eadade18faa,STILL_EXISTS,fix node_num
aaidfgjch,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/previous_runs/snapshot_2016_02_25_21_09_14/src/ANN_simulation.py,26e2813307226ef310e35e02a2ce1eadade18faa,STILL_EXISTS,TODO: plotting for circular layer network
aaidfgjcj,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/previous_runs/snapshot_2016_02_25_21_09_14/src/ANN_simulation.py,26e2813307226ef310e35e02a2ce1eadade18faa,STILL_EXISTS,FIXME: use better implementation later
aaidfgjdf,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/previous_runs/snapshot_2016_02_25_21_09_14/src/ANN_simulation.py,26e2813307226ef310e35e02a2ce1eadade18faa,STILL_EXISTS,TODO:
aaidfhcjf,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/ANN_simulation.py,3a2c79bfd770ce1b531005d76cde94e925319bc7,30090f676519ea0c4adfe8f32c205f31bcbc7455,FIXME: this one does not work quite well for circular layer case; need further processing
aaidfhddj,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/previous_runs/snapshot_2016_03_03_00_33_17/src/ANN_simulation.py,a43ca05ebd85f448ce09c6bce0418d8b7ebb4278,STILL_EXISTS,fix node_num
aaidfhdei,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/previous_runs/snapshot_2016_03_03_00_33_17/src/ANN_simulation.py,a43ca05ebd85f448ce09c6bce0418d8b7ebb4278,STILL_EXISTS,FIXME: this one does not work quite well for circular layer case; need further processing
aaidfhdfd,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/previous_runs/snapshot_2016_03_03_00_33_17/src/ANN_simulation.py,a43ca05ebd85f448ce09c6bce0418d8b7ebb4278,STILL_EXISTS,TODO: plotting for circular layer network
aaidfhdff,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/previous_runs/snapshot_2016_03_03_00_33_17/src/ANN_simulation.py,a43ca05ebd85f448ce09c6bce0418d8b7ebb4278,STILL_EXISTS,FIXME: use better implementation later
aaidfhdgb,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/previous_runs/snapshot_2016_03_03_00_33_17/src/ANN_simulation.py,a43ca05ebd85f448ce09c6bce0418d8b7ebb4278,STILL_EXISTS,TODO:
aaidfhhde,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/ANN_simulation.py,f77310874a92734fa65c2eb190764bb92b69d1a1,STILL_EXISTS,TODO: assertion
aaidfhhdf,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/ANN_simulation.py,f77310874a92734fa65c2eb190764bb92b69d1a1,STILL_EXISTS,TODO: find file
aaidfhhji,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_trp_cage/current_work/src/ANN_simulation.py,a7302867c4dd1af740752faf5cb80483aaef66cb,STILL_EXISTS,fix node_num
aaidfhiah,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_trp_cage/current_work/src/ANN_simulation.py,a7302867c4dd1af740752faf5cb80483aaef66cb,STILL_EXISTS,FIXME: this one does not work quite well for circular layer case; need further processing
aaidfhibc,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_trp_cage/current_work/src/ANN_simulation.py,a7302867c4dd1af740752faf5cb80483aaef66cb,STILL_EXISTS,TODO: plotting for circular layer network
aaidfhibe,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_trp_cage/current_work/src/ANN_simulation.py,a7302867c4dd1af740752faf5cb80483aaef66cb,STILL_EXISTS,with open(\"temp_command_file_%d.txt\" % (self._mynetwork._index); 'a') as temp_command_f:  # FIXME: use better implementation later
aaidfhicb,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_trp_cage/current_work/src/ANN_simulation.py,a7302867c4dd1af740752faf5cb80483aaef66cb,STILL_EXISTS,TODO:
aaidfhieh,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_trp_cage/current_work/src/ANN_simulation.py,a7302867c4dd1af740752faf5cb80483aaef66cb,STILL_EXISTS,ends with exception
aaidfhiej,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_trp_cage/current_work/src/ANN_simulation.py,a7302867c4dd1af740752faf5cb80483aaef66cb,STILL_EXISTS,TODO: handle this case
aaidfhjdg,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_trp_cage/current_work/src/ANN_simulation_trp_cage.py,f8631f290d3f2158597f714f1a50876b47a0f205,STILL_EXISTS,FIXME: why in some cases it is not close to 1??
aaidfhjdj,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_trp_cage/current_work/src/ANN_simulation_trp_cage.py,5dd74579b240cf3f54d85ec6b47fea9336cf6f49,STILL_EXISTS,FIXME: modify this later
aaidfhjee,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_trp_cage/current_work/src/ANN_simulation_trp_cage.py,17e30302667386004e7574c6f8b99d0ffc87580a,STILL_EXISTS,TODO: to be tested
aaidfhjij,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_trp_cage/current_work/src/biased_simulation_trp_cage.py,3fc7a9b8daf9e0bc7321b01d3da6a678614d68af,STILL_EXISTS,TODO: parse coef_file
aaidfiaae,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_trp_cage/current_work/src/biased_simulation_trp_cage.py,b014e612cdcd5bd6a12bae8ad6d9d5e56bdfb0a0,c6037291624b1a850104dfedf76e0b95aebdebb9,FIXME
aaidfiacg,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_trp_cage/current_work/src/cluster_management.py,d85e90c380a7905803c0c71fbcc6d590d45dfba1,STILL_EXISTS,ends with exception
aaidfiaci,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_trp_cage/current_work/src/cluster_management.py,d85e90c380a7905803c0c71fbcc6d590d45dfba1,STILL_EXISTS,TODO: handle this case
aaidfiadb,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_trp_cage/current_work/src/ANN_simulation_trp_cage.py,c8a6db05daccb1d045b1f5ef7222092ac32ae84e,STILL_EXISTS,TODO
aaidfiaej,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/ANN_simulation.py,e04a40e5698fba0ad62e5c3b77b3dac81264f5f2,135bc55984e9ca39ad79d0c3ad857c32b4ae1d81,TODO: currently they are not run in parallel; fix this later
aaidfiajc,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/biased_simulation.py,e04a40e5698fba0ad62e5c3b77b3dac81264f5f2,STILL_EXISTS,TODO: parse coef_file
aaidfibad,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/cluster_management.py,e04a40e5698fba0ad62e5c3b77b3dac81264f5f2,248550bf485c00e8a6af6a2fd08c47a5648bc3bf,ends with exception
aaidfibaf,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/cluster_management.py,e04a40e5698fba0ad62e5c3b77b3dac81264f5f2,STILL_EXISTS,TODO: handle this case
aaidficbj,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/ANN_simulation.py,72225f136f92b2ab04e3cd1e278e07eef4092d8e,557945e2d7a51c93d3baf8e4e78efa7c8fdc90d7,FIXME: the expression no longer works; since I made input list for input layer of autoencoder consistent
aaidficfb,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_trp_cage/current_work/src/ANN_simulation_trp_cage.py,165aeaf90c8653a63a8f6c77b3025efe2af05f2e,STILL_EXISTS,TODO: fix this for non-circular case
aaidficfc,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_trp_cage/current_work/src/workqueue.py,165aeaf90c8653a63a8f6c77b3025efe2af05f2e,STILL_EXISTS,TODO: create a file that contains finished jobs.
aaidficfd,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/ANN_simulation.py,b79cc356802ee3c09507a8c47ef5294a84f96335,STILL_EXISTS,TODO: add this for non-circular case
aaidficgd,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/biased_simulation.py,dfd065ee71acc00fb6e24da27d7d3e7cd4522b40,16705ca31899a10b1ea3d6d6db374e61557b29b4,FIXME: following expression is out-of-date due to the change in API for higher-dimensional cases
aaidficii,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_trp_cage/current_work/src/ANN_simulation_trp_cage.py,75356e72965b2c491cf1975353905a150442055f,STILL_EXISTS,TODO: code not concise and general enough; fix this later
aaidfidag,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/ANN_simulation_trp_cage.py,0f7dc1afffb031619f0c6f3ed6355a5648e48f8f,STILL_EXISTS,FIXME: why in some cases it is not close to 1??
aaidfidah,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/ANN_simulation_trp_cage.py,0f7dc1afffb031619f0c6f3ed6355a5648e48f8f,STILL_EXISTS,FIXME: how to write unit test for this function?
aaidfidai,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/ANN_simulation_trp_cage.py,0f7dc1afffb031619f0c6f3ed6355a5648e48f8f,STILL_EXISTS,TODO: to be tested
aaidfidbj,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/ANN_simulation_trp_cage.py,0f7dc1afffb031619f0c6f3ed6355a5648e48f8f,STILL_EXISTS,TODO: code not concise and general enough; fix this later
aaidfideb,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/ANN_simulation_trp_cage.py,0f7dc1afffb031619f0c6f3ed6355a5648e48f8f,STILL_EXISTS,fix node_num
aaidfidfd,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/ANN_simulation_trp_cage.py,0f7dc1afffb031619f0c6f3ed6355a5648e48f8f,STILL_EXISTS,FIXME: this one does not work quite well for circular layer case; need further processing
aaidfidfi,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/ANN_simulation_trp_cage.py,0f7dc1afffb031619f0c6f3ed6355a5648e48f8f,STILL_EXISTS,TODO: plotting for circular layer network
aaidfidie,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/ANN_simulation_trp_cage.py,0f7dc1afffb031619f0c6f3ed6355a5648e48f8f,STILL_EXISTS,TODO
aaidfidig,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/ANN_simulation_trp_cage.py,0f7dc1afffb031619f0c6f3ed6355a5648e48f8f,STILL_EXISTS,TODO: currently they are not run in parallel; fix this later
aaidfiecc,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/biased_simulation_Trp_cage.py,0f7dc1afffb031619f0c6f3ed6355a5648e48f8f,0afc0b4d8c9adb8d456f759a5d12760741f2138e,TODO: parse coef_file
aaidfiecg,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/workqueue.py,0f7dc1afffb031619f0c6f3ed6355a5648e48f8f,STILL_EXISTS,TODO: create a file that contains finished jobs.
aaidfiefb,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/molecule_spec_sutils.py,39215c64324741916cee01bc723799e7b31881c0,STILL_EXISTS,TODO: code not concise and general enough; fix this later
aaidfiegh,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/molecule_spec_sutils.py,39215c64324741916cee01bc723799e7b31881c0,STILL_EXISTS,FIXME: why in some cases it is not close to 1??
aaidfiegi,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/molecule_spec_sutils.py,39215c64324741916cee01bc723799e7b31881c0,16705ca31899a10b1ea3d6d6db374e61557b29b4,FIXME: how to write unit test for this function?
aaidfiegj,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/molecule_spec_sutils.py,39215c64324741916cee01bc723799e7b31881c0,16705ca31899a10b1ea3d6d6db374e61557b29b4,TODO: to be tested
aaidfigid,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/ANN_simulation.py,be935c802b1e9090b6bb5e555d2c485d1b24a382,STILL_EXISTS,TODO: move the last two parameters into somewhere else (not hard-coded)
aaidfihac,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/ANN_simulation.py,fd58debd73281d1e8915b596d41ed64e571c5b3a,STILL_EXISTS,TODO: find better coordinates
aaidfihad,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/ANN_simulation.py,fd58debd73281d1e8915b596d41ed64e571c5b3a,STILL_EXISTS,TODO: use better way instead of global variable
aaidfihae,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/ANN_simulation.py,fd58debd73281d1e8915b596d41ed64e571c5b3a,STILL_EXISTS,TODO: better naming
aaidfihdc,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/config.py,7722416e669fc21d6f550f39dba6a32047755963,84b385e73ce9cf321a2e97fcf8b2d072c9e7466e,FIXME: modify this later
aaidfihig,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/biased_simulation_Trp_cage.py,5579110353c9a84d693f1f626394c6d4488cdd44,993fd4b2c82ad531648087f017e47739758df6a7,FIXME: there is seg fault when loading checkpoint?
aaidfihjb,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/ANN_simulation.py,8828914920f5f44bad5ce1d56adc42da0f2fa8dd,b47290a7b56a8fedaf72517e4678a3d0e78a3560,FIXME: here we only calculate the first half of the network; need to be fixed later
aaidfihjf,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/ANN_simulation.py,a1a928872934465dc869f9f775afc0e3f8479543,b78c34aa50382e1c8f16f115e9e5a542543fec35,TODO: this function only works for non-hierarchical cases; for hierarchical cases it is not implemented
aaidfiigj,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/ANN_simulation.py,21e32af1cf673a3e0784df62664abafc6e3bc094,b47290a7b56a8fedaf72517e4678a3d0e78a3560,TODO: this function only works for non-hierarchical cases; for hierarchical cases it is not implemented
aaidfiiha,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/ANN_simulation.py,21e32af1cf673a3e0784df62664abafc6e3bc094,b47290a7b56a8fedaf72517e4678a3d0e78a3560,FIXME: here we only calculate the first half of the network; need to be fixed later
aaidfijdc,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/autoencoders.py,557945e2d7a51c93d3baf8e4e78efa7c8fdc90d7,STILL_EXISTS,FIXME: the expression no longer works; since I made input list for input layer of autoencoder consistent
aaidfjaac,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/autoencoders.py,56a162f27b385f2b2b6f393cf262666cd6940dd0,1178dafbba240e0a8f7801e6d63857d5c40deff6,TODO: update _connection_between_layers_coeffs
aaidfjbhi,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/autoencoders.py,be284e840b631f14c1cb4bd1ac46294e5ee844d2,STILL_EXISTS,this backup is required to get the correct results; no idea why
aaidfjbhj,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/tests/ANN_simulation_test.py,e6324a33e3e02c08c5377ba887d405806633db38,ec06db344ac4fd1c8f65457b2d12a4ad8da4a5ff,TODO: add testing for values; currently only tests basic functionality
aaidfjbji,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/ANN_simulation.py,9c78eceefa5833d88cfb42c0e76f5e0eeee08bd8,STILL_EXISTS,TODO: fix following for data augmentation
aaidfjbjj,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/ANN_simulation.py,9c78eceefa5833d88cfb42c0e76f5e0eeee08bd8,c8c2241057cdad40c96ac3c62c365b19e0e84a0d,TODO: may need to add arbitrary rotation to each frame
aaidfjcad,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/ANN_simulation.py,703c39849c6667888e9b40e486f1edcba1a7e270,e4ad6319fe8dfb480ab6453ed5b59d6df8a68d54,TODO: use better way than hardcoding it
aaidfjcda,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/cluster_management.py,248550bf485c00e8a6af6a2fd08c47a5648bc3bf,STILL_EXISTS,TODO: handle this case
aaidfjcdi,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/autoencoders.py,34c180d9a9133b6fad5b0a1a7a78373cdc34ed0f,52bf9a4002517790a3d107f8cbb6e88fcf15d4dc,TODO
aaidfjcig,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/biased_simulation.py,213c4685a6b4a30df28b89de036dac8d52bcdb34,STILL_EXISTS,TODO: this is temporary version
aaidfjcjb,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/biased_simulation_general.py,e7e2fa503a85242c9081817fa524515bbb66f7c9,a4db83fbaf5c77b8180fd30646f1cb564259c82d,TODO
aaidfjdff,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/ANN_simulation.py,48bbcb9fa86995ab6ed2cd866cfeb97e6b077285,135bc55984e9ca39ad79d0c3ad857c32b4ae1d81,TODO: add GPU support
aaidfjdgg,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/biased_simulation.py,63fc1bb490d4a5fe397fe5546b3720b6d4ee007b,STILL_EXISTS,TODO: this is temporary version
aaidfjdgi,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/biased_simulation_general.py,ab149b94cdb8006fb5e5382b266174427a58dcf7,STILL_EXISTS,TODO: this is temporary version
aaidfjdij,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/config.py,bf2ab13cd67763eb082cf597723ad216f7915507,5ba1d938eb67889af599d7dd4cd1858fc79c71ea,TODO: temp
aaidfjdjj,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/molecule_spec_sutils.py,eff262a4819be687f59c988d0c86b0e5c798a71c,16705ca31899a10b1ea3d6d6db374e61557b29b4,TODO
aaidfjeab,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/config.py,c21bb30ea16d07247594028569f91c01c07f5d15,dbb34f0b7f76283d810459b97281d1a513d9e7f7,TODO: is it good to combine these two?
aaidfjebd,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/train_network_and_save_for_iter.py,793f824103bf9e4b05b5743ac3a40d5018416aff,d9802b6a88d3b1bdeb0db5722505fb0ff169d23a,TODO: custom folder list?
aaidfjebf,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/ANN_simulation.py,25e6572ce08e4ed5b2a3cf9ff52800610e6237f5,STILL_EXISTS,TODO: does it work by adding quotation marks to everything
aaidfjece,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/string_method.py,f1feb3a296f71c5e93e9b3682c5a81f2333eb1a5,STILL_EXISTS,TODO: order is not preserved; fix this later
aaidfjedj,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/string_method.py,54bc6a7342d36d267f8f4b3fd3e2bbdfc240a5f1,STILL_EXISTS,# item_positions \/= 10.0 # TODO: does it work?
aaidfjega,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/string_method.py,f2944277c3342164e0759cede74aa6ce18156846,STILL_EXISTS,TODO: is it good to use backbone?
aaidfjegb,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/string_method.py,f2944277c3342164e0759cede74aa6ce18156846,STILL_EXISTS,TODO: refactor following into a function later and include remove water
aaidfjfgg,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/train_network_and_save_for_iter.py,0bbd8242b26ba64443f5f76efdc1b7cfc54cc5e1,STILL_EXISTS,TODO: may modify this relative weight later
aaidfjfhc,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/train_network_and_save_for_iter.py,f2ad417971384c47024e13283407b4819a9de2c8,05db2c47b314516f9686b75c7f15f08b3435eb6d,TODO: may need better scaling factor?
aaidfjfig,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/autoencoders.py,52654de4b7a946002c6d2d8ad95a4729bff17065,18fa83ec2265f8cb42399f0b270c0ce122294e7c,_1 = coordinates_data_files_list(['..\/target\/BetaHairpin'])  # TODO: temp version
aaidfjgaa,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/autoencoders.py,18fa83ec2265f8cb42399f0b270c0ce122294e7c,STILL_EXISTS,TODO: temp version
aaidfjgfe,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/autoencoders.py,c47ae9e893f9cafbb81b566d5f638ce8bfd3e920,STILL_EXISTS,TODO: other way to check shapes of weights..
aaidfjgff,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/autoencoders.py,7abe282a37e1dc559a9bcf974dfb3addeb196bd2,STILL_EXISTS,TODO
aaidfjghb,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/train_network_and_save_for_iter.py,949d915a97e6e1b040263977d89a618f831e6b1f,STILL_EXISTS,TODO: refactor this part later
aaidfjghc,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/train_network_and_save_for_iter.py,949d915a97e6e1b040263977d89a618f831e6b1f,05db2c47b314516f9686b75c7f15f08b3435eb6d,TODO: may need better scaling factor?
aaidfjghd,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/autoencoders.py,18d19bd3cc3be5fb9d178654ad6e21bdb7698d03,STILL_EXISTS,TODO: only implemented for 2-CV case
aaidfjgih,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/autoencoders.py,a5bba1181f313f90415ede0bc584764fbf0d35f0,STILL_EXISTS,not needed; but do not want to see endless warning...
aaidfjhba,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/train_network_and_save_for_iter.py,ba0c008311b8b2720dbbcabfc79d2b50f241354b,STILL_EXISTS,TODO: refactor this part later
aaidfjhbd,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/train_network_and_save_for_iter.py,ba0c008311b8b2720dbbcabfc79d2b50f241354b,STILL_EXISTS,TODO: may modify this relative weight later
aaidfjhbe,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/train_network_and_save_for_iter.py,ba0c008311b8b2720dbbcabfc79d2b50f241354b,05db2c47b314516f9686b75c7f15f08b3435eb6d,TODO: may need better scaling factor?
aaidfjhbi,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/molecule_spec_sutils.py,5115aea8a98c0feaac88d7fe1d412fe900a077b3,d6d077e61f55bfcc73fbac6adfe8e0ef9f052cc3,fix based on this format: https:\/\/www.cgl.ucsf.edu\/chimera\/docs\/UsersGuide\/tutorials\/pdbintro.html
aaidfjhcd,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/autoencoders.py,7c97852a5371c2c1f023bc6eb7101e519631344e,STILL_EXISTS,return K.mean(K.variable(weight_for_MSE) * K.square(y_pred - y_true); axis=-1)  # TODO: do this later
aaidfjhci,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/helper_func.py,64fe53efeebecae66e5784f0bf6306fed1619382,STILL_EXISTS,TODO: test if this function is correct
aaidfjhfa,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/autoencoders.py,e656064522a6ee11d2448cebc1e22986924286af,16705ca31899a10b1ea3d6d6db374e61557b29b4,FIXME: 2. this does not support multi-hidden layer cases
aaidfjhfb,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/biased_simulation_general.py,e656064522a6ee11d2448cebc1e22986924286af,STILL_EXISTS,TODO: need to fix following for multi-hidden layer cases
aaidfjhfc,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/train_network_and_save_for_iter.py,e656064522a6ee11d2448cebc1e22986924286af,5e30734c7aecf76bc027b6b6938164b8f315c9a1,FIXME: fix for multi-hidden layer cases
aaidfjhgh,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/helper_func.py,890e4ad84dce9a78910969468625b039e4d0dab3,cf0cbd1abeb9cf942083c72881e0c31018175403,TODO: generalize to multi-shell cases
aaidfjicd,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/train_network_and_save_for_iter.py,a35eec923a6d54734d25785396f8a6f328f6764d,STILL_EXISTS,TODO: what is this line used for?  I do not remember
aaidfjide,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/autoencoders.py,21b7319b8c2e47a674f3d04417842da218ad0101,STILL_EXISTS,TODO: is it good?
aaidfjieh,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/biased_simulation_general.py,f072308a4ac44af439025547c3b1f4057d11beb3,10d5b7f8ca6f60eaca342f17b7e65155a6ac5a11,TODO: default setting is to use all pairs; may modify this later
aaidfjiei,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/biased_simulation.py,c108d151801ac6c12ae0eab6f4f95ca420d0b06c,10d5b7f8ca6f60eaca342f17b7e65155a6ac5a11,TODO: default setting is to use all pairs; may modify this later
aaidfjifa,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/biased_simulation.py,c108d151801ac6c12ae0eab6f4f95ca420d0b06c,STILL_EXISTS,TODO: need to fix following for multi-hidden layer cases
aaidfjife,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/sample_with_classifier.py,269e660823e8eb7d0854af43a21109a9e7867a07,a471c9091f5d7e8a88db5c2edc275faf6445cd79,first find two states closest to two ends respectively
aaidfjifi,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/sample_with_classifier.py,269e660823e8eb7d0854af43a21109a9e7867a07,97c7a618c17acd70bc9a450c4b149bcaf9242f38,TODO: modify this later
aaidfjjbc,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/autoencoders.py,523501a7ed516da30d95c98472d1db202c091212,STILL_EXISTS,TODO: do this later
aaidfjjff,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/autoencoders.py,6dc84c6a20ee056fbda30dcd9b4f6bd108c0c891,098ded5c1eae0ae8537ca84d9e6e692dbe3fb814,TODO: currently only for first 2 Dense layers
aaidfjjgf,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/autoencoders.py,e05b150a6489543ba58b9fcda9f926011cd6fe4e,STILL_EXISTS,TODO: consider hierarchical case
aaidfjjgj,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/autoencoders.py,fe79afd646476d99c62f982d91edfdd85f85d787,b147e676a72d92deab2b748f0c8a43ce7c3e1c54,TODO: make this part consistent with else branch
aaidfjjif,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/autoencoders.py,77d63fbc95f2db520e1aecec56faa57b873e96a9,098ded5c1eae0ae8537ca84d9e6e692dbe3fb814,TODO: 1. use better training parameters. 2. use consistant activation functions; 3. consider how to do this for hierarchical case
aaidgaaga,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/autoencoders.py,4937c53ae4ecdfb43b24140a7f396fcf90183f6d,098ded5c1eae0ae8537ca84d9e6e692dbe3fb814,TODO: 4. make activation function consistent with neural network
aaidgaagh,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/autoencoders.py,23a6f60c3f10a06debdaa90736968de89c462136,a25db019f8d4b1b678b50f57b94bf56b4d086c82,TODO: lag_time is obsolete
aaidgaajh,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/autoencoders.py,78ebb049acd1996c65387a98ff9fbc39aa9e6872,STILL_EXISTS,TODO: fix this; merge mean_penalty and component_penalty in the future
aaidgabii,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/autoencoders.py,d65bf48b05ffca96a05fa71f5afb628d817428fe,STILL_EXISTS,It is implemented this way to handle hierarchical case;
aaidgabjb,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/biased_simulation.py,67db16f75b087586de5ed016aecab78d8da4755a,STILL_EXISTS,TODO: check unit scaling
aaidgabjc,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/biased_simulation_general.py,67db16f75b087586de5ed016aecab78d8da4755a,STILL_EXISTS,TODO: check unit scaling
aaidgabjd,weiHelloWorld/accelerated_sampling_with_autoencoder,MD_simulation_on_alanine_dipeptide/current_work/src/molecule_spec_sutils.py,67db16f75b087586de5ed016aecab78d8da4755a,16f6b03656bf61fd8fd4eb5076e9d9f1df959e61,TODO: may need to fix factor for scaling factor
aaidgadeg,akoury/ml-helper,helper.py,cd42f56d832b7f801d57d711a61f55451620fcca,e924b921961145350cf3da0df4087310f1278b0a,all_scores  = pd.DataFrame(columns = ['Model'; 'Function'; 'CV Score'; 'Difference'; 'Outcome'; 'Time'])
aaidgafdj,andriyadi/Maix-LogoClassifier,training/mobilenet.py,f030d4ed1988cb52001782f00bcf1886b582f082,STILL_EXISTS,\"\"\"MobileNet v1 models for Keras. ||  || MobileNet is a general architecture and can be used for multiple use cases. || Depending on the use case; it can use different input layer size and || different width factors. This allows different width models to reduce || the number of multiply-adds and thereby || reduce inference cost on mobile devices. ||  || MobileNets support any input size greater than 32 x 32; with larger image sizes || offering better performance. || The number of parameters and number of multiply-adds || can be modified by using the `alpha` parameter; || which increases\/decreases the number of filters in each layer. || By altering the image size and `alpha` parameter; || all 16 models from the paper can be built; with ImageNet weights provided. ||  || The paper demonstrates the performance of MobileNets using `alpha` values of || 1.0 (also called 100 % MobileNet); 0.75; 0.5 and 0.25. || For each of these `alpha` values; weights for 4 different input image sizes || are provided (224; 192; 160; 128). ||  || The following table describes the size and accuracy of the 100% MobileNet || on size 224 x 224: || ---------------------------------------------------------------------------- || Width Multiplier (alpha) | ImageNet Acc |  Multiply-Adds (M) |  Params (M) || ---------------------------------------------------------------------------- || |   1.0 MobileNet-224    |    70.6 %     |        529        |     4.2     | || |   0.75 MobileNet-224   |    68.4 %     |        325        |     2.6     | || |   0.50 MobileNet-224   |    63.7 %     |        149        |     1.3     | || |   0.25 MobileNet-224   |    50.6 %     |        41         |     0.5     | || ---------------------------------------------------------------------------- ||  || The following table describes the performance of || the 100 % MobileNet on various input sizes: || ------------------------------------------------------------------------ ||       Resolution      | ImageNet Acc | Multiply-Adds (M) | Params (M) || ------------------------------------------------------------------------ || |  1.0 MobileNet-224  |    70.6 %    |        529        |     4.2     | || |  1.0 MobileNet-192  |    69.1 %    |        529        |     4.2     | || |  1.0 MobileNet-160  |    67.2 %    |        529        |     4.2     | || |  1.0 MobileNet-128  |    64.4 %    |        529        |     4.2     | || ------------------------------------------------------------------------ ||  || The weights for all 16 models are obtained and translated || from TensorFlow checkpoints found at || https:\/\/github.com\/tensorflow\/models\/blob\/master\/research\/slim\/nets\/mobilenet_v1.md ||  || # Reference ||  || - [MobileNets: Efficient Convolutional Neural Networks for ||    Mobile Vision Applications](https:\/\/arxiv.org\/pdf\/1704.04861.pdf)) || \"\"\"
aaidgafeg,andriyadi/Maix-LogoClassifier,mobilenet/mobilenet.py,7e2f372b8dd8d5f9baf8bd977190f0bb76984fa4,STILL_EXISTS,\"\"\"MobileNet v1 models for Keras. ||  || MobileNet is a general architecture and can be used for multiple use cases. || Depending on the use case; it can use different input layer size and || different width factors. This allows different width models to reduce || the number of multiply-adds and thereby || reduce inference cost on mobile devices. ||  || MobileNets support any input size greater than 32 x 32; with larger image sizes || offering better performance. || The number of parameters and number of multiply-adds || can be modified by using the `alpha` parameter; || which increases\/decreases the number of filters in each layer. || By altering the image size and `alpha` parameter; || all 16 models from the paper can be built; with ImageNet weights provided. ||  || The paper demonstrates the performance of MobileNets using `alpha` values of || 1.0 (also called 100 % MobileNet); 0.75; 0.5 and 0.25. || For each of these `alpha` values; weights for 4 different input image sizes || are provided (224; 192; 160; 128). ||  || The following table describes the size and accuracy of the 100% MobileNet || on size 224 x 224: || ---------------------------------------------------------------------------- || Width Multiplier (alpha) | ImageNet Acc |  Multiply-Adds (M) |  Params (M) || ---------------------------------------------------------------------------- || |   1.0 MobileNet-224    |    70.6 %     |        529        |     4.2     | || |   0.75 MobileNet-224   |    68.4 %     |        325        |     2.6     | || |   0.50 MobileNet-224   |    63.7 %     |        149        |     1.3     | || |   0.25 MobileNet-224   |    50.6 %     |        41         |     0.5     | || ---------------------------------------------------------------------------- ||  || The following table describes the performance of || the 100 % MobileNet on various input sizes: || ------------------------------------------------------------------------ ||       Resolution      | ImageNet Acc | Multiply-Adds (M) | Params (M) || ------------------------------------------------------------------------ || |  1.0 MobileNet-224  |    70.6 %    |        529        |     4.2     | || |  1.0 MobileNet-192  |    69.1 %    |        529        |     4.2     | || |  1.0 MobileNet-160  |    67.2 %    |        529        |     4.2     | || |  1.0 MobileNet-128  |    64.4 %    |        529        |     4.2     | || ------------------------------------------------------------------------ ||  || The weights for all 16 models are obtained and translated || from TensorFlow checkpoints found at || https:\/\/github.com\/tensorflow\/models\/blob\/master\/research\/slim\/nets\/mobilenet_v1.md ||  || # Reference ||  || - [MobileNets: Efficient Convolutional Neural Networks for ||    Mobile Vision Applications](https:\/\/arxiv.org\/pdf\/1704.04861.pdf)) || \"\"\"
aaidgafii,delzac/cntkx,layers/tests/qrnn.py,c1149b113fbcd060ca87515a838e92910478932a,7196e48697d3cc08df00934749553b3e04e7ad41,TODO: pad sequence check google
aaidgagbc,delzac/cntkx,layers/layers.py,f0dfc2fb49d08f834fe99c012bb282032c8ecd1b,81fa5059a3dd1eb0fdf8731c5b36de5c33c4b84f,TODO: create an auto-regressive decoder
aaidgagbd,delzac/cntkx,ops/__init__.py,f0dfc2fb49d08f834fe99c012bb282032c8ecd1b,0f62404d448593cffb859c2fee6a1e63338f25bf,TODO: does times_transpose behave correctly for unpacked sequence
aaidgagbf,delzac/cntkx,layers/models/attention.py,81fa5059a3dd1eb0fdf8731c5b36de5c33c4b84f,30c4f869bdbf936c7c6886ecac0a5c6fa7ca1967,TODO: create an auto-regressive decoder
aaidgagcg,delzac/cntkx,layers/models/vision.py,7d14f0e70c1d4470e992dd88ded5f1dea9e2c594,STILL_EXISTS,TODO: allow for depth to be varied
aaidgagdb,delzac/cntkx,layers/tests/models_test.py,7d14f0e70c1d4470e992dd88ded5f1dea9e2c594,STILL_EXISTS,TODO: assert the shape with no padding
aaidgahbb,delzac/cntkx,layers/blocks.py,95c6daf524ce97b60370c3a08c892bc6d16a6159,STILL_EXISTS,otherwise we'd need to fix slicing and Param initializers
aaidgahbc,delzac/cntkx,layers/blocks.py,95c6daf524ce97b60370c3a08c892bc6d16a6159,STILL_EXISTS,for efficient computation; we stack multiple variables (along the fastest-changing one; to match BS)
aaidgahec,delzac/cntkx,layers/blocks.py,95c6daf524ce97b60370c3a08c892bc6d16a6159,STILL_EXISTS,TODO: should both activations be replaced?
aaidgahgh,delzac/cntkx,layers/blocks.py,95c6daf524ce97b60370c3a08c892bc6d16a6159,STILL_EXISTS,h(t) = (1 - i(t) .* h'(t)) + i(t) .* h(t-1)                     --TODO: need to confirm bracketing with NVIDIA
aaidgahib,delzac/cntkx,layers/layers.py,daaf0a5db1517510253220cadb53fe9250dac600,STILL_EXISTS,TODO: if this gets usage then offer a Softplus version like Stabilizer() for stability?
aaidgahig,delzac/cntkx,layers/models/attention.py,daaf0a5db1517510253220cadb53fe9250dac600,STILL_EXISTS,TODO: make efficient by multiplying in one chunk and slice attention it
aaidgahih,delzac/cntkx,layers/models/attention.py,daaf0a5db1517510253220cadb53fe9250dac600,STILL_EXISTS,TODO: use regex probably more straightforward
aaidgahjb,delzac/cntkx,layers/layers.py,9893565cc3d0a468f20f43aae2a27eb1e37ecba2,STILL_EXISTS,TODO: To be removed once the main cntk line accepts the pull request to fix initialisation bug
aaidgaiaj,delzac/cntkx,layers/layers.py,9893565cc3d0a468f20f43aae2a27eb1e37ecba2,STILL_EXISTS,TODO: if this gets usage then offer a Softplus version like Stabilizer() for stability?
aaidgaicf,delzac/cntkx,layers/tests/attention_tests.py,9893565cc3d0a468f20f43aae2a27eb1e37ecba2,STILL_EXISTS,alias is used to work around bug when arguments in block funcion are the same
aaidgaiej,delzac/cntkx,layers/layers.py,4d4ab58c3b22a3ebd898d2e52fa68ba7e68712ee,STILL_EXISTS,to work around bug
aaidgaiid,delzac/cntkx,layers/sequence.py,b855270550480d17b1c68cd98cf54a2c6354f959,STILL_EXISTS,TODO: if initial_state is a CNTK Function rather than an initializer; then require to pass it multiple times; otherwise broadcast to all
aaidgajcb,delzac/cntkx,layers/layers.py,ebb1c872a4f302f5212496a514c057e819079e72,STILL_EXISTS,TODO: work on groups; understand how reduction==0 and init=np might affect group which doesn't have inferred
aaidgajig,delzac/cntkx,layers/sequence.py,ebb1c872a4f302f5212496a514c057e819079e72,STILL_EXISTS,TODO: Include RecurrenceFrom in here
aaidgbadh,delzac/cntkx,layers/blocks.py,fd0a374042fec8b79ab8d56ed866993b51531c6e,STILL_EXISTS,TODO: should both activations be replaced?
aaidgbaec,delzac/cntkx,layers/blocks.py,fd0a374042fec8b79ab8d56ed866993b51531c6e,STILL_EXISTS,TODO: change activation default to relu and implement weight constraint
aaidgbbda,delzac/cntkx,layers/layers.py,6494c4ba204c464b891635ad1d3d682dda790641,2502222f94564a6ff63efca61e693a70f09bd103,TODO: include dimension in arguments as that inner can be made into block function
aaidgbbdi,delzac/cntkx,layers/layers.py,6494c4ba204c464b891635ad1d3d682dda790641,STILL_EXISTS,TODO: work on groups; understand how reduction==0 and init=np might affect group which doesn't have inferred
aaidgbbhj,delzac/cntkx,layers/layers.py,52f717151b1b2b26dba09199d810c726a0677b69,STILL_EXISTS,TODO: potential speed up by splice into one big matrix to times
aaidgbbie,delzac/cntkx,layers/layers.py,52f717151b1b2b26dba09199d810c726a0677b69,STILL_EXISTS,shape = (-1; -1)  # TODO: input_shape input_rank
aaidgbccb,delzac/cntkx,layers/models/language.py,dbd6190a96b8de78eaa7d973fa497b65889abe22,6cf1d84ef7fb0935f417570ac66c87097a48a8ed,TODO: Check if weight is tied to encoding embedding
aaidgbdeh,delzac/cntkx,layers/layers.py,a9db0137e9a72120f0a4f079dc5a258849f8eac1,STILL_EXISTS,TODO: if this gets usage then offer a Softplus version like Stabilizer() for stability?
aaidgbehf,DreamingRaven/nemesyst,Misc/movieLenseDataCombiner.py,49d9c582aa380b18974684d56507345f53142d49,STILL_EXISTS,ratings = ratings.reindex(columns = newColumns)#ratings.columns.tolist() + ['ello']) #+ uniqueTags.tolist())
aaidgbejb,DreamingRaven/nemesyst,Misc/movieLenseDataCombiner.py,776585c3fb37bb3b0fe2523df2c1c9786f48b343,STILL_EXISTS,make a specific row the columns
aaidgbfag,DreamingRaven/nemesyst,Misc/movieLenseDataCombiner.py,8ba06e8e2792f5d1be45b06c16877f22e1c4f148,STILL_EXISTS,reformedMetaData.columns = specificColumnNames
aaidgbfbh,DreamingRaven/nemesyst,Misc/movieLenseDataCombiner.py,524d06a1d4521c578c9b4ee679492080f3701b58,STILL_EXISTS,TODO: prob's not necessary if other changes below
aaidgbfbi,DreamingRaven/nemesyst,Misc/movieLenseDataCombiner.py,524d06a1d4521c578c9b4ee679492080f3701b58,STILL_EXISTS,combine column headers into one list #TODO: this is probably not neccessary as lists are combined at modelling
aaidgbfcd,DreamingRaven/nemesyst,Misc/movieLenseDataCombiner.py,524d06a1d4521c578c9b4ee679492080f3701b58,STILL_EXISTS,get columns for new dataFrame (here to reduce computation)
aaidgbfcg,DreamingRaven/nemesyst,Misc/movieLenseDataCombiner.py,524d06a1d4521c578c9b4ee679492080f3701b58,STILL_EXISTS,yes yes I know so why even load it? TODO: don't load ratings since unused but keep rename \/ change name convention
aaidgbgaf,DreamingRaven/nemesyst,Misc/knn.py,d57fa96a5bf50b63a5642919d94b0f1dace50d85,b87ed9a3d0fb3fbf1fd1f6919ae6d3a6eb4137bf,test_noLabel = test[test.columns.difference(['rating'])]
aaidgbgga,DreamingRaven/nemesyst,Misc/svd.py,a5a4952341825992877f54bf754a349bd3be3bab,4ea46d661a49618f07ae40307f4152d80ef6f207,why is U changing shape ??
aaidgbhcf,DreamingRaven/nemesyst,Misc/addMongoDocument.py,82c4209d569e60cf7283ff24ada26a840cdf6d69,STILL_EXISTS,unfortunately loop needed since small adjustments must be made
aaidgbhhd,DreamingRaven/nemesyst,src/helpers.py,3f54daed71ece76946e5778b7f14c9da210e5234,STILL_EXISTS,neat trick to always ensure path ends in seperator '\/' by appending empty
aaidgbhji,DreamingRaven/nemesyst,ravenRecSyst.py,880ad91018999fbaaa4d2dfcfc5effaaf5c8bd48,STILL_EXISTS,TODO: devise a method to make erros in nested try; catch
aaidgbiaa,DreamingRaven/nemesyst,ravenRecSyst.py,880ad91018999fbaaa4d2dfcfc5effaaf5c8bd48,STILL_EXISTS,raise ValueError('A very specific bad thing happened.')
aaidgbicc,DreamingRaven/nemesyst,ravenRecSyst.py,f9c2ba78e6210d7a4b2bcec131732504d3f61c46,STILL_EXISTS,test #TODO: implement complementary testing to training set selection
aaidgbiid,DreamingRaven/nemesyst,src/importer.py,1677fe529a8bb479a9ddc85e0563203f05da4283,STILL_EXISTS,TODO: current assumption is that each document is already less than
aaidgbiif,DreamingRaven/nemesyst,src/importer.py,1677fe529a8bb479a9ddc85e0563203f05da4283,STILL_EXISTS,this should probably be additional functionality
aaidgbjaf,DreamingRaven/nemesyst,src/helpers.py,39be22af43a72784b5f5d85767ea7605a176d5b5,c46b0b2af176abc58342f8b3885b87209642daec,TODO: add an arg for this
aaidgbjbb,DreamingRaven/nemesyst,src/helpers.py,d5f32444d414b139e1d2e0b345f05e070c06bff1,c46b0b2af176abc58342f8b3885b87209642daec,TODO: this is very awkward because it might be called where conf is None
aaidgbjcf,DreamingRaven/nemesyst,src/log.py,bcce598cde605b4e0bf989dd1543261a8fa2108b,STILL_EXISTS,TODO implement level specific formating
aaidgbjdh,DreamingRaven/nemesyst,src/neuralNetwork.py,80e2bcb62d43d5e63a09c849284e98358291c397,b83577b8d08c6df6601141b833cd8c64162bc8c0,for unused in range(args[\"layers\"]-1):
aaidgbjge,DreamingRaven/nemesyst,src/arg.py,c46b0b2af176abc58342f8b3885b87209642daec,8f5c8c0da980ecaa20be985f65200ff930f625a2,TODO: add an arg for this
aaidgbjgi,DreamingRaven/nemesyst,src/arg.py,c46b0b2af176abc58342f8b3885b87209642daec,STILL_EXISTS,TODO: this is very awkward because it might be called where conf is None
aaidgbjig,DreamingRaven/nemesyst,src/neuralNetwork.py,5ff909d83e319eed6f54d4eb92f0a16de67a00cc,STILL_EXISTS,TODO: this should be in mongodb class itself
aaidgcadj,DreamingRaven/nemesyst,src/neuralNetwork.py,eece98912460069ed02e6ca018cf222cdf2d691d,95d29536598cefddb6d33b49263cd33040b54303,TODO: off by one error please for the love of god george
aaidgcaea,DreamingRaven/nemesyst,src/neuralNetwork.py,eece98912460069ed02e6ca018cf222cdf2d691d,STILL_EXISTS,TODO this is fine if both are pushed lists
aaidgcaeb,DreamingRaven/nemesyst,src/neuralNetwork.py,eece98912460069ed02e6ca018cf222cdf2d691d,STILL_EXISTS,TODO this needs to be generalised
aaidgcaed,DreamingRaven/nemesyst,src/neuralNetwork.py,eece98912460069ed02e6ca018cf222cdf2d691d,95d29536598cefddb6d33b49263cd33040b54303,TODO: off by one ... you fool george; sort this out
aaidgcbce,DreamingRaven/nemesyst,src/neuralNetwork.py,c174440a1134dbf8ff614b0e4cffdcdeb2cb2f00,95d29536598cefddb6d33b49263cd33040b54303,TODO: off by one ... you fool george; sort this out
aaidgcbfe,DreamingRaven/nemesyst,src/neuralNetwork.py,4e3741c7f69cd19d433898364d869c1a1a722029,105aaeef8cc6ee2f3e26c1082f14590b1307c414,for unused in range(self.args[\"layers\"]-1):
aaidgccii,DreamingRaven/nemesyst,src/neuralNetwork.py,7cfeec9d89ade8568534daf3f64adef5ed6c3015,STILL_EXISTS,TODO seems like this is also called in normal testing but the message suggest recursiveness may want to reword
aaidgchhg,DreamingRaven/nemesyst,examples/gan.py,71cf9f535174f0345344082e1a2e72281065e439,STILL_EXISTS,TODO: make sure we arent falling for any https:\/\/medium.com\/@utk.is.here\/keep-calm-and-train-a-gan-pitfalls-and-tips-on-training-generative-adversarial-networks-edd529764aa9
aaidgchhj,DreamingRaven/nemesyst,examples/gan.py,71cf9f535174f0345344082e1a2e72281065e439,STILL_EXISTS,TODO: dimensionality is actually 5 as target is rolled in; this needs to
aaidgcidd,DreamingRaven/nemesyst,nemesyst.py,269597c8e032c626c592d9708f3fa6db015ee170,7170b73cdf7ab5a70b11c0acb90bcad597317257,passing the 3 needed args to argument handler and main with minimal
aaidgcjag,DreamingRaven/nemesyst,docs/nemesyst.py,2894f971f5e6d50be0bce4e2619e66a7cce59734,7415192cf4d53766865bd43a63be3ddd13e3015c,passing the 3 needed args to argument handler and main with minimal
aaidgdabd,DreamingRaven/nemesyst,nemesyst.py,0332f05f399c4d17cf790882b5bf588271304222,7170b73cdf7ab5a70b11c0acb90bcad597317257,deleting password as it is no longer needed
aaidgddgf,DreamingRaven/nemesyst,nemesyst_core/mongo.py,d334b8c351fc7e917d4815f8fc71562dc9fa6fca,ed157d2d5dce9870d71cb22abbe1c059c1e87c02,TODO: OVERIDE THESE
aaidgddih,DreamingRaven/nemesyst,nemesyst_core/mongo.py,d334b8c351fc7e917d4815f8fc71562dc9fa6fca,STILL_EXISTS,TODO add these in next if user has them seperate
aaidgdedh,DreamingRaven/nemesyst,examples/cleaners/mnist_cleaner.py,79e5dc40492af98ac65424f35b58ba80e2bae33f,1bd1a4642e472496bed694f155653b26ead3152b,TODO: vectorize on outermost dimension (rowise not elementwise)
aaidgdjad,manuwhs/Trapyng,Examples/00.BookExamples/2.1. main_MAs.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: Why is this one not shown ?
aaidgdjgb,manuwhs/Trapyng,Examples/00.BookExamples/2.2. main_Ranges.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: I think grid conmutates if nothing specified and it is False as showing
aaidgeafe,manuwhs/Trapyng,Examples/00.BookExamples/2.2. main_volatility.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: Divide 3-1
aaidgedhd,manuwhs/Trapyng,Examples/1. TimeData Examples/1.1. main_Basic.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: call data filling functions
aaidgedii,manuwhs/Trapyng,Examples/1. TimeData Examples/1.1. main_Basic.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,complex stuff but it is very underdeveloped and it is better to use the graph library
aaidgedjb,manuwhs/Trapyng,Examples/1. TimeData Examples/1.1. main_Basic.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: Allow to specify extra info to the subplots like to erate the separation between graphs so that they share shit.
aaidgedjh,manuwhs/Trapyng,Examples/1. TimeData Examples/1.1. main_Basic.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: Plot reconstruction to show that it is not the same.
aaidgeeaf,manuwhs/Trapyng,Examples/1. TimeData Examples/1.1. main_Basic.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# TODO: We have to fix the fact of TimeZone; the fact that on
aaidgeeah,manuwhs/Trapyng,Examples/1. TimeData Examples/1.1. main_Basic.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# Maybe displaze date and join stuff.
aaidgeebd,manuwhs/Trapyng,Examples/1. TimeData Examples/1.1. main_Basic.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,I could not find a fucking way to modify it inline TODO
aaidgeedb,manuwhs/Trapyng,Examples/1. TimeData Examples/1.2. main_TradingPlatforms.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# Example on how to construct complex graphs showing better graphs
aaidgeeib,manuwhs/Trapyng,Examples/1. TimeData Examples/1.3. main_visualizations.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# Example on how to construct complex graphs showing better graphs
aaidgegba,manuwhs/Trapyng,Examples/1. TimeData Examples/4.1. main_VARIMA.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: Obtener las mejores componented de una serie; hacemos PCA y detransformamos ?
aaidgegbe,manuwhs/Trapyng,Examples/1. TimeData Examples/4.1. main_VARIMA.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: Use this df[\"date\"] = pd.to_datetime(df.index)
aaidgegcf,manuwhs/Trapyng,Examples/1. TimeData Examples/4.1. main_VARIMA.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: Event detection; like huge drops and so on; with the Drawdown ?\uFFFF
aaidgegje,manuwhs/Trapyng,Examples/1. TimeData Examples/4.2. main_Kalman.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,Maybe same as the measurement noise would be appropiate.
aaidgehed,manuwhs/Trapyng,Examples/1. TimeData Examples/4.2. main_Kalman.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: Obtener las mejores componented de una serie; hacemos PCA y detransformamos ?
aaidgeheh,manuwhs/Trapyng,Examples/1. TimeData Examples/4.2. main_Kalman.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: Use this df[\"date\"] = pd.to_datetime(df.index)
aaidgehfi,manuwhs/Trapyng,Examples/1. TimeData Examples/4.2. main_Kalman.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: Event detection; like huge drops and so on; with the Drawdown ?\uFFFF
aaidgeich,manuwhs/Trapyng,Examples/1. TimeData Examples/4.2. main_Kalman2.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: Obtener las mejores componented de una serie; hacemos PCA y detransformamos ?
aaidgeidb,manuwhs/Trapyng,Examples/1. TimeData Examples/4.2. main_Kalman2.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: Use this df[\"date\"] = pd.to_datetime(df.index)
aaidgeiec,manuwhs/Trapyng,Examples/1. TimeData Examples/4.2. main_Kalman2.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: Event detection; like huge drops and so on; with the Drawdown ?
aaidgeied,manuwhs/Trapyng,Examples/1. TimeData Examples/4.2. main_Kalman2.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: Ideas:
aaidgfbaf,manuwhs/Trapyng,Examples/1. TimeData Examples/X2.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# We have to fix the fact of TimeZone; the fact that on
aaidgfbah,manuwhs/Trapyng,Examples/1. TimeData Examples/X2.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# Maybe displaze date and join stuff.
aaidgfbaj,manuwhs/Trapyng,Examples/1. TimeData Examples/X2.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,I could not find a fucking way to modify it inline TODO
aaidgfbeb,manuwhs/Trapyng,Examples/1. TimeData Examples/X2.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# Example on how to construct complex graphs showing better graphs
aaidgfcch,manuwhs/Trapyng,Examples/2. Symbol Examples/Symbol_main.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: Be able to automatize the shareX thing
aaidgfcfj,manuwhs/Trapyng,Examples/2. Symbol Examples/intraday_plotting.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: Be able to automatize the shareX thing
aaidgfchh,manuwhs/Trapyng,Examples/3. Porfolio Examples/1. main_Basic.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: I think I have to do properly the loading of the info into the mdoel
aaidgfcif,manuwhs/Trapyng,Examples/3. Porfolio Examples/1. main_Basic.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,Download if needed.
aaidgfdaf,manuwhs/Trapyng,Examples/3. Porfolio Examples/1. main_Basic.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: Be able to automatize the shareX thing
aaidgfdcd,manuwhs/Trapyng,Examples/3. Porfolio Examples/2. main_Indicators.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: I think I have to do properly the loading of the info into the mdoel
aaidgfddb,manuwhs/Trapyng,Examples/3. Porfolio Examples/2. main_Indicators.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,Download if needed.
aaidgfdjf,manuwhs/Trapyng,Examples/3.1. Trading Signals/SimpleTradeSigal.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,Download if needed.
aaidgfecj,manuwhs/Trapyng,Examples/4. CAPM Examples/1.main_basic.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: Define what are the dates of a portfolio; or well; it depends on the period
aaidgfeda,manuwhs/Trapyng,Examples/4. CAPM Examples/1.main_basic.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# Get some efficient frontier and simulate the portfolio
aaidgfedb,manuwhs/Trapyng,Examples/4. CAPM Examples/1.main_basic.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# IDEA !! Maybe use the portfolio in the frontier that maximizes
aaidgfede,manuwhs/Trapyng,Examples/4. CAPM Examples/1.main_basic.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# Alpha and beta say: Does out portolio perform better than the market ?
aaidgfeee,manuwhs/Trapyng,Examples/4. CAPM Examples/1.main_basic.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,WARNING !! The calculation of the efficient frontier this way could be
aaidgfefa,manuwhs/Trapyng,Examples/4. CAPM Examples/1.main_basic.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,Get the efficient frontier where we cannot borrow or lend money
aaidgfefc,manuwhs/Trapyng,Examples/4. CAPM Examples/1.main_basic.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,Other way for finding efficient frontier
aaidgffdd,manuwhs/Trapyng,Examples/5. ML Systems Examples/1. main_features_experiments.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: Define better the get_timeSeries and dates for Cartera
aaidgfffc,manuwhs/Trapyng,Examples/5. ML Systems Examples/1. main_features_experiments.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# TODO: Put somewhere else
aaidgffgb,manuwhs/Trapyng,Examples/5. ML Systems Examples/1. main_features_experiments.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# TODO: idea; a high variance; indicates consinuity in the parameters
aaidgffhg,manuwhs/Trapyng,Examples/5. ML Systems Examples/1. main_features_experiments.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# Graoca bonita !! TODO
aaidgffjc,manuwhs/Trapyng,Examples/5. ML Systems Examples/2. main_LinearModels.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: Just to put it in the sahpe as it was before writing it to disk
aaidgfgga,manuwhs/Trapyng,Examples/6. Trading Systems Examples/1. main_features_experiments.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,Download if needed.
aaidgfhbj,manuwhs/Trapyng,Examples/6. Trading Systems Examples/1. main_features_experiments.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# Graoca bonita !! TODO
aaidgfhdi,manuwhs/Trapyng,Examples/6. Trading Systems Examples/1. main_features_experiments.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,#### TODO ##################3
aaidgfiaj,manuwhs/Trapyng,Examples/6. Trading Systems Examples/2.Coliseum_rmain.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,Download if needed.
aaidgfiea,manuwhs/Trapyng,Examples/6. Trading Systems Examples/2.Strategy_Pool_Example.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,Download if needed.
aaidgfihb,manuwhs/Trapyng,Examples/6. Trading Systems Examples/2.TrailingStop.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,Download if needed.
aaidgfjac,manuwhs/Trapyng,Examples/6. Trading Systems Examples/2.XingMA_Example.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,Download if needed.
aaidgggfc,manuwhs/Trapyng,Examples/XXX. Advanced Examples/1.0 main_GP_example.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,\"\"\" || I guess this would show the probability of up or down given that you know the value of || the real signal at that point. More noise will make it less sure with same overall shape. || If we add correlation noise; we should actually get way better estimates... and we do. || Do they depend only on the previous ? Or also in all the previous ? I guess it is just the substaction of both || so the common terms dissapear nicely :) ||  || \"\"\"
aaidggjdh,manuwhs/Trapyng,InformationClasses/CPortfolio/Portfolio_indicators.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,### FOR FUTURE ADDING MAYBE!!!
aaidggjja,manuwhs/Trapyng,InformationClasses/CSymbol/CSymbol.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# TODO define the variables that state the properties of the symbol
aaidghaje,manuwhs/Trapyng,InformationClasses/CSymbol/Symbol_graph.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO make it possible to plot dates in the X axis
aaidghbbi,manuwhs/Trapyng,InformationClasses/CSymbol/Symbol_indicators.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,### FOR FUTURE ADDING MAYBE!!!
aaidghbja,manuwhs/Trapyng,InformationClasses/CTimeData/TiemData_pandasIndicators.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,### FOR FUTURE ADDING MAYBE!!!
aaidghcdb,manuwhs/Trapyng,InformationClasses/CTimeData/TimeData_DDBB.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: Maybe some processing on the \"Date part\"
aaidghcde,manuwhs/Trapyng,InformationClasses/CTimeData/TimeData_DDBB.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,Loads a CSV from the folder with the naming convention for the period.
aaidghcei,manuwhs/Trapyng,InformationClasses/CTimeData/TimeData_DDBB.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO
aaidghcga,manuwhs/Trapyng,InformationClasses/CTimeData/TimeData_DDBB.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# TODO
aaidghdei,manuwhs/Trapyng,InformationClasses/CTimeData/TimeData_core.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO. Make it work for series Names not in the dataset.
aaidghecb,manuwhs/Trapyng,InformationClasses/CTimeData/TimeData_indicators.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO
aaidghedi,manuwhs/Trapyng,InformationClasses/CTradingSignal/CTradingSignal.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,We have to add this new input (maybe delete some old one as well)
aaidghefi,manuwhs/Trapyng,MarketModels/CAPM/CAPM_BL.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,This could be calculated using for example Portfolio Theory; efficient frontier.
aaidghfdd,manuwhs/Trapyng,MarketModels/CAPM/CAPM_IFE.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# With monthly data; calculate the Efficient frontier
aaidghfed,manuwhs/Trapyng,MarketModels/CAPM/CAPM_IFE.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# The efficient frontier is not going to change.
aaidghffe,manuwhs/Trapyng,MarketModels/CAPM/CAPM_IFE.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,Then; using also the last year ( test); recalculate the portfolio needed
aaidghgaa,manuwhs/Trapyng,MarketModels/CAPM/CAPM_IFE.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,### Crazy idea !! Lets plot where the fucking efficient frontier went
aaidghggb,manuwhs/Trapyng,MarketModels/CAPM/CAPM_IFE.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# IDEA !! Maybe use the portfolio in the frontier that maximizes
aaidghgge,manuwhs/Trapyng,MarketModels/CAPM/CAPM_IFE.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# Alpha and beta say: Does out portolio perform better than the market ?
aaidghheg,manuwhs/Trapyng,MarketModels/CAPM/CAPM_ab.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,We calculate it in a gaussian way
aaidghhgj,manuwhs/Trapyng,MarketModels/CAPM/CAPM_ab.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# has a nice gaussian distribution.
aaidghiig,manuwhs/Trapyng,MarketModels/CAPM/CAPM_eff.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# Calculates the efficient frontier using convex optimization
aaidghijd,manuwhs/Trapyng,MarketModels/CAPM/CAPM_eff.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,N is the number of points of the efficient frontier we calculate.
aaidghijg,manuwhs/Trapyng,MarketModels/CAPM/CAPM_eff.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,Only the real efficient frontier !
aaidghjcd,manuwhs/Trapyng,MarketModels/CAPM/CAPM_eff.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,Calculate efficient frontier weights using quadratic programming
aaidghjee,manuwhs/Trapyng,MarketModels/CAPM/CAPM_eff.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# TODO: This is not useful
aaidghjia,manuwhs/Trapyng,MarketModels/CAPM/CAPM_eff.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,nstocks = returns.columns.size
aaidgiacg,manuwhs/Trapyng,Other/codigos python/functions.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,subtract the mean (along columns)
aaidgiaih,manuwhs/Trapyng,Other/libs (copy)/DDBB/get_data_lib.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,print df.columns
aaidgibeg,manuwhs/Trapyng,Other/libs (copy)/ML/GaussianProcess.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: Estimate likelihood of jghj datasetjgj; given the model
aaidgicaj,manuwhs/Trapyng,Other/libs (copy)/ML/KalmanFilter.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,Maybe same as the measurement noise would be appropiate.
aaidgichc,manuwhs/Trapyng,Other/libs (copy)/graph/GUI/graph_GUI.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# Shirnk the main axes. TODO; move this to another general func
aaidgicji,manuwhs/Trapyng,Other/libs (copy)/graph/GUI/graph_GUI.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO me estas puto diciendo que solo port seleccionar esto me jodes ?
aaidgidac,manuwhs/Trapyng,Other/libs (copy)/graph/GUI/graph_GUI.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,We also need to resize \"where\" vector if needed.
aaidgidfg,manuwhs/Trapyng,Other/libs (copy)/graph/GUI/graph_GUI.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# TODO. Second case where NcY = NcX !!
aaidgifej,manuwhs/Trapyng,Other/libs (copy)/graph/graph_3D.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# TODO make it work with no subplots; apparently it does not work anymore.
aaidgifge,manuwhs/Trapyng,Other/libs (copy)/graph/graph_3D.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: make this a parameters
aaidgigcd,manuwhs/Trapyng,Other/libs (copy)/graph/graph_advanced.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: maybe in the future differentiate between sigma_eps and dy
aaidgigce,manuwhs/Trapyng,Other/libs (copy)/graph/graph_advanced.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: what is this     ec='None'
aaidgigjf,manuwhs/Trapyng,Other/libs (copy)/graph/graph_basic.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO If the position is the same; then;
aaidgihec,manuwhs/Trapyng,Other/libs (copy)/graph/graph_basic.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# Function to change position of axes !! TODO
aaidgihfa,manuwhs/Trapyng,Other/libs (copy)/graph/graph_basic.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,nc is the numbel of columns
aaidgiiag,manuwhs/Trapyng,Other/libs (copy)/graph/graph_basic.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: the -2 is only if the last element has shared axes; we should detect that
aaidgijgj,manuwhs/Trapyng,Other/libs (copy)/graph/graph_plots.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,pre post mid ## TODO; part of the step. How thw shit is done
aaidgjcbi,manuwhs/Trapyng,Other/libs (copy)/graph/specific/trading_graphs.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# TODO; it used to be this all the way:
aaidgjded,manuwhs/Trapyng,Other/libs (copy)/math/basicMathlib.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,of the original signal is needed.
aaidgjdid,manuwhs/Trapyng,Other/libs (copy)/tradersInfo/indicators_cython.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,more efficient. (In this example this doesn't matter though.)
aaidgjebf,manuwhs/Trapyng,Other/libs (copy)/tradersInfo/indicators_lib.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,######### Efficient Moving Averages ###################
aaidgjedd,manuwhs/Trapyng,Other/libs (copy)/tradersInfo/indicators_lib.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO  : Nada puto funciona !!!!!!!!!!
aaidgjeeh,manuwhs/Trapyng,Other/libs (copy)/tradersInfo/indicators_pandas.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,### FOR FUTURE ADDING MAYBE!!!
aaidgjega,manuwhs/Trapyng,Other/libs (copy)/tradersInfo/indicators_pandas.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# Different types of BB bands ? TODO
aaidgjegi,manuwhs/Trapyng,Other/libs (copy)/tradersInfo/indicators_pandas.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: This could be inf
aaidgjfhd,manuwhs/Trapyng,Other/libs (copy)/utilities/Intraday_lib.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# TODO !! Improve this shit !!
aaidgjgaj,manuwhs/Trapyng,Other/libs (copy)/utilities/Intraday_lib.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,WHAT WE WANT TO DO IS ELIMINATE THE DATES WHERE THESE IS NEVER TRADING !!
aaidgjgcc,manuwhs/Trapyng,Other/libs (copy)/utilities/Intraday_lib.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# TODO; learn really the different timeData types
aaidgjgeb,manuwhs/Trapyng,Other/libs (copy)/utilities/Intraday_lib.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,## TODO
aaidgjgfg,manuwhs/Trapyng,Other/libs (copy)/utilities/Intraday_lib.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# the rest of the days as columns
aaidgjhie,manuwhs/Trapyng,Other/libs (copy)/utilities/tasks_lib.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,rt.stop() # better in a try\/finally block to make sure the program ends!
aaidgjjbh,manuwhs/Trapyng,Other/libs (copy)/utilities/utilities_lib.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,this should probably be centralized someplace
aaidhaace,manuwhs/Trapyng,Other/rubish/Intraday_google.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,Maybe if they start higher the usually go down (unless we are in a possitive trend maybe)
aaidhabje,manuwhs/Trapyng,TradingClasses/Coliseum/CTrailingStop.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: Preprocess datetome so that it fits in a given interval
aaidhabjj,manuwhs/Trapyng,TradingClasses/Coliseum/CTrailingStop.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: Set interval for the original date ?
aaidhacde,manuwhs/Trapyng,TradingClasses/Coliseum/Coliseum_core.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,Npos_col = len(position_col.columns.tolis())
aaidhaceg,manuwhs/Trapyng,TradingClasses/Coliseum/Coliseum_core.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# TODO info about the price
aaidhaceh,manuwhs/Trapyng,TradingClasses/Coliseum/Coliseum_core.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# TODO add the commision
aaidhachi,manuwhs/Trapyng,TradingClasses/StrategyPool/CFilter/CFilter.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: Apply histeresis to the filters so that they not fluctuate as much ?
aaidhacib,manuwhs/Trapyng,TradingClasses/StrategyPool/CFilter/CFilter.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO
aaidhacig,manuwhs/Trapyng,TradingClasses/StrategyPool/CFilter/CFilter.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: We cannot do it like this if the original has Nans.
aaidhadcg,manuwhs/Trapyng,TradingClasses/StrategyPool/CStrategy_KNNPrice.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# TODO -> Closser patterns or patterns in the same periodic state (check periodicity) hace preference
aaidhadci,manuwhs/Trapyng,TradingClasses/StrategyPool/CStrategy_KNNPrice.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO -> Incorporate metrics for Volume also.
aaidhadcj,manuwhs/Trapyng,TradingClasses/StrategyPool/CStrategy_KNNPrice.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO -> Try doinf this with open; close; high; low or other shit instead
aaidhadfj,manuwhs/Trapyng,TradingClasses/StrategyPool/CStrategy_TurboTrend.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# TODO -> Closser patterns or patterns in the same periodic state (check periodicity) hace preference
aaidhadgb,manuwhs/Trapyng,TradingClasses/StrategyPool/CStrategy_TurboTrend.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO -> Incorporate metrics for Volume also.
aaidhadgc,manuwhs/Trapyng,TradingClasses/StrategyPool/CStrategy_TurboTrend.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO -> Try doinf this with open; close; high; low or other shit instead
aaidhaeah,manuwhs/Trapyng,caca.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,\"\"\" || Created on Tue Sep 12 10:14:47 2017 ||  || @author: montoya || \"\"\"
aaidhaefd,manuwhs/Trapyng,libs/DDBB/get_data_lib.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,print df.columns
aaidhafcg,manuwhs/Trapyng,libs/ML/GaussianProcess.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: Estimate likelihood of jghj datasetjgj; given the model
aaidhafij,manuwhs/Trapyng,libs/ML/KalmanFilter.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,Maybe same as the measurement noise would be appropiate.
aaidhagfd,manuwhs/Trapyng,libs/graph/GUI/graph_GUI.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# Shirnk the main axes. TODO; move this to another general func
aaidhaghj,manuwhs/Trapyng,libs/graph/GUI/graph_GUI.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO me estas puto diciendo que solo port seleccionar esto me jodes ?
aaidhagid,manuwhs/Trapyng,libs/graph/GUI/graph_GUI.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,We also need to resize \"where\" vector if needed.
aaidhahdh,manuwhs/Trapyng,libs/graph/GUI/graph_GUI.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# TODO. Second case where NcY = NcX !!
aaidhajfe,manuwhs/Trapyng,libs/graph/graph_3D.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: make this a parameters
aaidhajic,manuwhs/Trapyng,libs/graph/graph_3D.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# TODO make it work with no subplots; apparently it does not work anymore.
aaidhbahh,manuwhs/Trapyng,libs/graph/graph_advanced.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: maybe in the future differentiate between sigma_eps and dy
aaidhbahi,manuwhs/Trapyng,libs/graph/graph_advanced.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: what is this     ec='None'
aaidhbbah,manuwhs/Trapyng,libs/graph/graph_axes.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO If the position is the same; then;
aaidhbbej,manuwhs/Trapyng,libs/graph/graph_axes.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# Function to change position of axes !! TODO
aaidhbcdf,manuwhs/Trapyng,libs/graph/graph_axis.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: the -2 is only if the last element has shared axes; we should detect that
aaidhbdgj,manuwhs/Trapyng,libs/graph/graph_plots.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,Specify if we are gonna format somehow the data.
aaidhbdhh,manuwhs/Trapyng,libs/graph/graph_plots.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,Filling if needed
aaidhbecj,manuwhs/Trapyng,libs/graph/graph_plots.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: might be wrong to use Npx due to subselection
aaidhbegd,manuwhs/Trapyng,libs/graph/graph_plots.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,pre post mid ## TODO; part of the step. How thw shit is done
aaidhbehd,manuwhs/Trapyng,libs/graph/graph_plots.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# TODO. Second case where NcY = NcX !!
aaidhbgab,manuwhs/Trapyng,libs/graph/graph_setting (copy).py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO If the position is the same; then;
aaidhbgei,manuwhs/Trapyng,libs/graph/graph_setting (copy).py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# Function to change position of axes !! TODO
aaidhbgfg,manuwhs/Trapyng,libs/graph/graph_setting (copy).py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,nc is the numbel of columns
aaidhbhbc,manuwhs/Trapyng,libs/graph/graph_setting (copy).py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: the -2 is only if the last element has shared axes; we should detect that
aaidhbiab,manuwhs/Trapyng,libs/graph/graph_setting.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,Number of columns of the legend
aaidhbiff,manuwhs/Trapyng,libs/graph/graph_setting.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,This funciton will store the data needed to later use the widgets
aaidhbifh,manuwhs/Trapyng,libs/graph/graph_setting.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# TODO. Second case where NcY = NcX !!
aaidhbigi,manuwhs/Trapyng,libs/graph/graph_subplots.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,nc is the numbel of columns
aaidhbijf,manuwhs/Trapyng,libs/graph/graph_subplots.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: the -2 is only if the last element has shared axes; we should detect that
aaidhbijh,manuwhs/Trapyng,libs/graph/graph_subplots.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO; should it be if nf == 1
aaidhbjjd,manuwhs/Trapyng,libs/graph/specific/trading_graphs.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# TODO; it used to be this all the way:
aaidhcaji,manuwhs/Trapyng,libs/math/basicMathlib.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,of the original signal is needed.
aaidhcbia,manuwhs/Trapyng,libs/tradersInfo/indicators_cython.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,more efficient. (In this example this doesn't matter though.)
aaidhccbc,manuwhs/Trapyng,libs/tradersInfo/indicators_lib.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,######### Efficient Moving Averages ###################
aaidhccda,manuwhs/Trapyng,libs/tradersInfo/indicators_pandas.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,### FOR FUTURE ADDING MAYBE!!!
aaidhccei,manuwhs/Trapyng,libs/tradersInfo/indicators_pandas.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: do this properly
aaidhccej,manuwhs/Trapyng,libs/tradersInfo/indicators_pandas.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# Different types of BB bands ? TODO
aaidhccgd,manuwhs/Trapyng,libs/tradersInfo/indicators_pandas.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: This could be inf
aaidhccid,manuwhs/Trapyng,libs/tradersInfo/indicators_pandas.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: I dont get this
aaidhcdif,manuwhs/Trapyng,libs/utilities/Intraday_lib.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# TODO !! Improve this shit !!
aaidhcecb,manuwhs/Trapyng,libs/utilities/Intraday_lib.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,WHAT WE WANT TO DO IS ELIMINATE THE DATES WHERE THESE IS NEVER TRADING !!
aaidhcede,manuwhs/Trapyng,libs/utilities/Intraday_lib.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# TODO; learn really the different timeData types
aaidhcefd,manuwhs/Trapyng,libs/utilities/Intraday_lib.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,## TODO
aaidhcegi,manuwhs/Trapyng,libs/utilities/Intraday_lib.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,# the rest of the days as columns
aaidhcgfh,manuwhs/Trapyng,libs/utilities/tasks_lib.py,df8c673a0b2963d29f4402c83912b5c8320475db,51cd064a4727e108c81935f0ed69956e4113853d,rt.stop() # better in a try\/finally block to make sure the program ends!
aaidhcibb,manuwhs/Trapyng,libs/utilities/utilities_lib.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,TODO: Could be done faster with np.where. Also perform the tolerance thing.
aaidhcici,manuwhs/Trapyng,libs/utilities/utilities_lib.py,df8c673a0b2963d29f4402c83912b5c8320475db,STILL_EXISTS,this should probably be centralized someplace
aaidhcigb,manuwhs/Trapyng,libs/graph/graph_plots.py,8e155023c9265815ba1195b4200e407256e6c621,STILL_EXISTS,TODO: marker = marker[0]; markersize = marker[1]; markerfacecolor = marker[2]
aaidhcjag,manuwhs/Trapyng,Examples/10.EM_algorithm/1.main_visualization.py,20f60bf19da6f0e06e8d386e198e27eedd5c549c,STILL_EXISTS,# TODO: How are we filling the data ?
aaidhcjhg,manuwhs/Trapyng,Examples/10.EM_algorithm/2.main_CV_K.py,20f60bf19da6f0e06e8d386e198e27eedd5c549c,STILL_EXISTS,# TODO: How are we filling the data ?
aaidhdahh,manuwhs/Trapyng,libs/Distributions/Gaussian/Gaussian_distribution.py,20f60bf19da6f0e06e8d386e198e27eedd5c549c,STILL_EXISTS,TODO: Play with norm constant
aaidhdaja,manuwhs/Trapyng,libs/Distributions/Gaussian/Gaussian_distribution.py,20f60bf19da6f0e06e8d386e198e27eedd5c549c,STILL_EXISTS,# Efficient computation of the likelihood for many samples
aaidhdbbg,manuwhs/Trapyng,libs/Distributions/Gaussian/Gaussian_distribution.py,20f60bf19da6f0e06e8d386e198e27eedd5c549c,STILL_EXISTS,# TODO: Not used now
aaidhdbec,manuwhs/Trapyng,libs/Distributions/Gaussian/Gaussian_estimators.py,20f60bf19da6f0e06e8d386e198e27eedd5c549c,STILL_EXISTS,# TODO: More efficient
aaidhdbfg,manuwhs/Trapyng,libs/Distributions/Watson/Watson_distribution.py,20f60bf19da6f0e06e8d386e198e27eedd5c549c,STILL_EXISTS,# Obtain the coefficients if needed
aaidhdcbj,manuwhs/Trapyng,libs/Distributions/Watson/Watson_distribution.py,20f60bf19da6f0e06e8d386e198e27eedd5c549c,STILL_EXISTS,f_log = kummer_own_log(a;b;x)  # TODO: We cannot do negative x; the functions is in log
aaidhdccd,manuwhs/Trapyng,libs/Distributions/Watson/Watson_distribution.py,20f60bf19da6f0e06e8d386e198e27eedd5c549c,STILL_EXISTS,Default tolerance is tol = 1e-10.  Feel free to change this as needed.
aaidhdcff,manuwhs/Trapyng,libs/Distributions/Watson/Watson_estimators.py,20f60bf19da6f0e06e8d386e198e27eedd5c549c,83dbeb328b71058ba87a1078900379c88e5f47ec,# TODO: better manage this
aaidhdcgf,manuwhs/Trapyng,libs/Distributions/Watson/Watson_estimators.py,20f60bf19da6f0e06e8d386e198e27eedd5c549c,83dbeb328b71058ba87a1078900379c88e5f47ec,print \"TODO: Warninig we do not try negative Kappa coz explained variance is too low !\"
aaidhdcig,manuwhs/Trapyng,libs/Distributions/Watson/Watson_estimators.py,20f60bf19da6f0e06e8d386e198e27eedd5c549c,STILL_EXISTS,Maybe rk is very small and this fails
aaidhdcij,manuwhs/Trapyng,libs/Distributions/Watson/Watson_estimators.py,20f60bf19da6f0e06e8d386e198e27eedd5c549c,STILL_EXISTS,TODO: Check what causes the imaginarity
aaidhddih,manuwhs/Trapyng,libs/Distributions/Watson/Watson_sampling.py,20f60bf19da6f0e06e8d386e198e27eedd5c549c,STILL_EXISTS,# TODO: It is a 2 right ?
aaidhdeae,manuwhs/Trapyng,libs/Distributions/Watson/Watson_sampling.py,20f60bf19da6f0e06e8d386e198e27eedd5c549c,STILL_EXISTS,TODO: Obtain a lot of samples first from np.random.uniform() snd
aaidhdebf,manuwhs/Trapyng,libs/Distributions/Watson/Watson_sampling.py,20f60bf19da6f0e06e8d386e198e27eedd5c549c,STILL_EXISTS,# TODO: Here is the shit to avoid the problem of that the maximum does not happen
aaidhdfgd,manuwhs/Trapyng,libs/Distributions/vonMisesFisher/vonMisesFisher_distribution.py,20f60bf19da6f0e06e8d386e198e27eedd5c549c,STILL_EXISTS,TODO: Play with norm constant
aaidhdgag,manuwhs/Trapyng,libs/Distributions/vonMisesFisher/vonMisesFisher_estimators.py,20f60bf19da6f0e06e8d386e198e27eedd5c549c,STILL_EXISTS,# TODO: better manage this
aaidhdggb,manuwhs/Trapyng,libs/EM/EM POO/CDistribution.py,20f60bf19da6f0e06e8d386e198e27eedd5c549c,STILL_EXISTS,# TODO: Variational inference ! Change in this !
aaidhdghd,manuwhs/Trapyng,libs/EM/EM POO/CDistribution.py,20f60bf19da6f0e06e8d386e198e27eedd5c549c,STILL_EXISTS,TODO: Move only the following line inside the function below
aaidhdihh,manuwhs/Trapyng,libs/EM/HMM_lib.py,20f60bf19da6f0e06e8d386e198e27eedd5c549c,STILL_EXISTS,Otherwise we reuse it since we needed to compute the ll for the previous iteration
aaidhdijh,manuwhs/Trapyng,libs/EM/HMM_lib.py,20f60bf19da6f0e06e8d386e198e27eedd5c549c,STILL_EXISTS,Maybe removing the cluster or reininitializing the cluster randomly
aaidhdjba,manuwhs/Trapyng,libs/EM/HMM_lib.py,20f60bf19da6f0e06e8d386e198e27eedd5c549c,STILL_EXISTS,Maybe abs
aaidhdjbg,manuwhs/Trapyng,libs/EM/HMM_libfunc.py,20f60bf19da6f0e06e8d386e198e27eedd5c549c,STILL_EXISTS,distribution.pdf_log_K(data[n][[0];:].T;theta;  [cp_logs[i]]);  # Maybe need to transpose
aaidhdjgd,manuwhs/Trapyng,libs/EM/HMM_libfunc.py,20f60bf19da6f0e06e8d386e198e27eedd5c549c,STILL_EXISTS,way; the a priori probability of going from a state i to a state j is
aaidheaah,manuwhs/Trapyng,libs/EM/HMM_libfunc.py,20f60bf19da6f0e06e8d386e198e27eedd5c549c,STILL_EXISTS,TODO Check
aaidheaei,manuwhs/Trapyng,libs/EM/decoder_lib.py,20f60bf19da6f0e06e8d386e198e27eedd5c549c,STILL_EXISTS,TODO: fix
aaidheafc,manuwhs/Trapyng,libs/EM/decoder_lib.py,20f60bf19da6f0e06e8d386e198e27eedd5c549c,STILL_EXISTS,TODO: Maybe we do not need them ? Maybe we should use the gammas instead ??
aaidheafh,manuwhs/Trapyng,libs/EM/decoder_lib.py,20f60bf19da6f0e06e8d386e198e27eedd5c549c,STILL_EXISTS,Journeys(t;i) contains the survival path at time index t; that ends at
aaidheaga,manuwhs/Trapyng,libs/EM/decoder_lib.py,20f60bf19da6f0e06e8d386e198e27eedd5c549c,STILL_EXISTS,that ends atstate number i
aaidheahc,manuwhs/Trapyng,libs/EM/decoder_lib.py,20f60bf19da6f0e06e8d386e198e27eedd5c549c,STILL_EXISTS,Generate the new journey that ends in state i at time t as the
aaidheahe,manuwhs/Trapyng,libs/EM/decoder_lib.py,20f60bf19da6f0e06e8d386e198e27eedd5c549c,STILL_EXISTS,TODO: Maybe the index of the following is wrong !!
aaidheajb,manuwhs/Trapyng,libs/EM/decoder_lib.py,20f60bf19da6f0e06e8d386e198e27eedd5c549c,STILL_EXISTS,It is the one that ends with the i; that is: Survival_paths(:;best_S)
aaidhebfd,manuwhs/Trapyng,libs/EM/utils/data_preprocessing.py,20f60bf19da6f0e06e8d386e198e27eedd5c549c,STILL_EXISTS,TODO: change this because different number of classes might have different number of elements
aaidhebjf,manuwhs/Trapyng,libs/EM/utils/data_preprocessing.py,20f60bf19da6f0e06e8d386e198e27eedd5c549c,STILL_EXISTS,TODO no entiendo por que pelotas tengo que transformar aqui
aaidhecie,manuwhs/Trapyng,libs/EM/utils/general_func.py,20f60bf19da6f0e06e8d386e198e27eedd5c549c,STILL_EXISTS,TODO: probably move from here
aaidhecif,manuwhs/Trapyng,libs/EM/utils/general_func.py,20f60bf19da6f0e06e8d386e198e27eedd5c549c,STILL_EXISTS,TODO: Not normalized anymore
aaidheeci,manuwhs/Trapyng,libs/EM/utils/system_modules.py,20f60bf19da6f0e06e8d386e198e27eedd5c549c,STILL_EXISTS,# TODO: Idea... project different PCA for classes TO
aaidhehcc,manuwhs/Trapyng,libs/utilities/utilities_libXXX look at this.py,20f60bf19da6f0e06e8d386e198e27eedd5c549c,STILL_EXISTS,this should probably be centralized someplace
aaidhehef,manuwhs/Trapyng,Examples/4.1 BBBLSTM/1. main_data_gen.py,020f88a525e1718a5cfbbccde8410c321ff08a4b,STILL_EXISTS,\"\"\" || I guess this would show the probability of up or down given that you know the value of || the real signal at that point. More noise will make it less sure with same overall shape. || If we add correlation noise; we should actually get way better estimates... and we do. || Do they depend only on the previous ? Or also in all the previous ? I guess it is just the substaction of both || so the common terms dissapear nicely :) ||  || \"\"\"
aaidheiih,manuwhs/Trapyng,Examples/4.1 BBBLSTM/main_BBB_LSTM.py,020f88a525e1718a5cfbbccde8410c321ff08a4b,STILL_EXISTS,TODO: Is the supervisor the one saving the state every X time ?
aaidheijc,manuwhs/Trapyng,libs/BBBLSTM/BBB_LSTM_Model.py,020f88a525e1718a5cfbbccde8410c321ff08a4b,STILL_EXISTS,TODO: Make a study of when the prediction of the network is the better possible (which time-step).
aaidheijd,manuwhs/Trapyng,libs/BBBLSTM/BBB_LSTM_Model.py,020f88a525e1718a5cfbbccde8410c321ff08a4b,STILL_EXISTS,Probably at the beggining; it should not have any idea about it; predicting the trend maybe ?
aaidheije,manuwhs/Trapyng,libs/BBBLSTM/BBB_LSTM_Model.py,020f88a525e1718a5cfbbccde8410c321ff08a4b,STILL_EXISTS,And then; the more samples it has; the better the prediction of the future is.
aaidhejbc,manuwhs/Trapyng,libs/BBBLSTM/BBB_LSTM_Model.py,020f88a525e1718a5cfbbccde8410c321ff08a4b,STILL_EXISTS,TODO: This is the structure we just saw...
aaidhejbg,manuwhs/Trapyng,libs/BBBLSTM/BBB_LSTM_Model.py,020f88a525e1718a5cfbbccde8410c321ff08a4b,STILL_EXISTS,TODO: For now we set it the same as the hidden_size. Probably for matrix concatenation purposes ?
aaidhejee,manuwhs/Trapyng,libs/BBBLSTM/BBB_LSTM_Model.py,020f88a525e1718a5cfbbccde8410c321ff08a4b,STILL_EXISTS,TODO: maybe due to the initial embedding that has to be done; all inputs are given when defining the model;
aaidhejff,manuwhs/Trapyng,libs/BBBLSTM/BBB_LSTM_Model.py,020f88a525e1718a5cfbbccde8410c321ff08a4b,STILL_EXISTS,TODO: We need to provide info about the Batch size ? That is the number of chains
aaidhejhb,manuwhs/Trapyng,libs/BBBLSTM/BBB_LSTM_Model.py,020f88a525e1718a5cfbbccde8410c321ff08a4b,STILL_EXISTS,TODO: This is probably why we want the chains to have the same length. Also maybe to not having to worry later to weight the
aaidhejhf,manuwhs/Trapyng,libs/BBBLSTM/BBB_LSTM_Model.py,020f88a525e1718a5cfbbccde8410c321ff08a4b,STILL_EXISTS,Maybe this is so that we do not create the LSTMS a lot of times in the TensorBoard ?
aaidhejja,manuwhs/Trapyng,libs/BBBLSTM/BBB_LSTM_Model.py,020f88a525e1718a5cfbbccde8410c321ff08a4b,STILL_EXISTS,TODO: Not really a TODO; but the important part here is that we changed size vy config.hidden_size
aaidhejjh,manuwhs/Trapyng,libs/BBBLSTM/BBB_LSTM_Model.py,020f88a525e1718a5cfbbccde8410c321ff08a4b,STILL_EXISTS,TODO: maybe do not execute this line in the training model to save computation ? Maybe it wouldnt be executed anyway ?
aaidhfaaj,manuwhs/Trapyng,libs/BBBLSTM/BBB_LSTM_Model.py,020f88a525e1718a5cfbbccde8410c321ff08a4b,STILL_EXISTS,TODO: Remove increased by 2 the cost so that the total cost is more influenced
aaidhfacc,manuwhs/Trapyng,libs/BBBLSTM/BBB_LSTM_Model.py,020f88a525e1718a5cfbbccde8410c321ff08a4b,STILL_EXISTS,TODO
aaidhfadb,manuwhs/Trapyng,libs/BBBLSTM/BBB_LSTM_Model.py,020f88a525e1718a5cfbbccde8410c321ff08a4b,STILL_EXISTS,TODO: Each batch is going to reuse the state of the previous batch ?
aaidhfaei,manuwhs/Trapyng,libs/BBBLSTM/BBB_LSTM_configs.py,020f88a525e1718a5cfbbccde8410c321ff08a4b,STILL_EXISTS,TODO: Make a network that accepts chains of different length
aaidhfafa,manuwhs/Trapyng,libs/BBBLSTM/BBB_LSTM_configs.py,020f88a525e1718a5cfbbccde8410c321ff08a4b,STILL_EXISTS,Probably way harder to program and less efficient in parallel computation
aaidhfaga,manuwhs/Trapyng,libs/BBBLSTM/BBB_LSTM_configs.py,020f88a525e1718a5cfbbccde8410c321ff08a4b,STILL_EXISTS,TODO No idea
aaidhfahe,manuwhs/Trapyng,libs/BBBLSTM/BBB_LSTM_configs.py,020f88a525e1718a5cfbbccde8410c321ff08a4b,STILL_EXISTS,TODO: We need to set it to at least one.
aaidhfbce,manuwhs/Trapyng,libs/BBBLSTM/Variational_inferences_lib.py,020f88a525e1718a5cfbbccde8410c321ff08a4b,STILL_EXISTS,## TODO: I have no idea why we multiply by 1
aaidhfcdj,manuwhs/Trapyng,Examples/4.0 TensorFlow/1.2 Linear_Regression.py,90b81cedf475d2bd101cb60e352aeb7955882449,STILL_EXISTS,\"\"\" || Finally we need to initialize the variables ?? || TODO: why we need to initialize it ? || \"\"\"
aaidhfdaa,manuwhs/Trapyng,Examples/4.1 BBB_LSTM/1. main_BBB_LSTM.py,90b81cedf475d2bd101cb60e352aeb7955882449,STILL_EXISTS,TODO: Is the supervisor the one saving the state every X time ?
aaidhfdfc,manuwhs/Trapyng,Examples/4.0 TensorFlow/1.2 Linear_Regression_TensorBoard.py,1ff50fdcfd2a002504ef00455a2ec4f179b41b00,STILL_EXISTS,\"\"\" || Finally we need to initialize the variables ?? || TODO: why we need to initialize it ? || \"\"\"
aaidhfdji,manuwhs/Trapyng,Examples/4.0 TensorFlow/1.3 Linear_Regression2_TensorBoard.py,8643838cc922bdc1d0ac2f9a7929dd947f66ef67,STILL_EXISTS,\"\"\" || 1. INITIALIZE VARIABLES || Finally we need to initialize the variables ?? || TODO: why we need to initialize it ? || \"\"\"
aaidhfdjj,manuwhs/Trapyng,Examples/4.0 TensorFlow/1.3 Linear_Regression2_TensorBoard.py,8643838cc922bdc1d0ac2f9a7929dd947f66ef67,STILL_EXISTS,\"\"\" || 2. TENSORBOARD || Finally we need to initialize the variables ?? || TODO: why we need to initialize it ? || \"\"\"
aaidhfeeh,manuwhs/Trapyng,Examples/4.0 TensorFlow/1.3 Linear_Regression2_TensorBoard.py,8643838cc922bdc1d0ac2f9a7929dd947f66ef67,STILL_EXISTS,We use two summary writers. This is a hack that allows us to write
aaidhfehj,manuwhs/Trapyng,Examples/4.0 TensorFlow/1.4 Conditionals.py,8643838cc922bdc1d0ac2f9a7929dd947f66ef67,STILL_EXISTS,\"\"\" || In this document we will perform a basic Linear Regression including TensorBoard. || It is a very simple manual program with no proper naming of the variables. || We will: ||     - Use the conditional in the huber loss. ||     - We can also save the model parameters. || \"\"\"
aaidhffbh,manuwhs/Trapyng,Examples/4.0 TensorFlow/1.4 Conditionals.py,8643838cc922bdc1d0ac2f9a7929dd947f66ef67,STILL_EXISTS,We use two summary writers. This is a hack that allows us to write
aaidhffdh,manuwhs/Trapyng,Examples/4.0 TensorFlow/1.5 Gradients and Optimizers.py,8643838cc922bdc1d0ac2f9a7929dd947f66ef67,STILL_EXISTS,\"\"\" || In this document we will perform a basic Linear Regression including TensorBoard. || It is a very simple manual program with no proper naming of the variables. || We will: ||     - Use the conditional in the huber loss. ||     - We can also save the model parameters. || \"\"\"
aaidhffhf,manuwhs/Trapyng,Examples/4.0 TensorFlow/1.5 Gradients and Optimizers.py,8643838cc922bdc1d0ac2f9a7929dd947f66ef67,STILL_EXISTS,We use two summary writers. This is a hack that allows us to write
aaidhffjf,manuwhs/Trapyng,Examples/4.0 TensorFlow/2.1 main_full_model.py,8643838cc922bdc1d0ac2f9a7929dd947f66ef67,STILL_EXISTS,\"\"\" || In this document we will perform a basic Linear Regression including TensorBoard. || It is a very simple manual program with no proper naming of the variables. || We will: ||     - Use the conditional in the huber loss. ||     - We can also save the model parameters. || \"\"\"
aaidhfgdd,manuwhs/Trapyng,Examples/4.0 TensorFlow/2.1 main_full_model.py,8643838cc922bdc1d0ac2f9a7929dd947f66ef67,STILL_EXISTS,We use two summary writers. This is a hack that allows us to write
aaidhfgfd,manuwhs/Trapyng,libs/BBBLSTM/tf_general_model1.py,8643838cc922bdc1d0ac2f9a7929dd947f66ef67,STILL_EXISTS,\"\"\" || In this document we will perform a basic Linear Regression including TensorBoard. || It is a very simple manual program with no proper naming of the variables. || We will: ||     - Use the conditional in the huber loss. ||     - We can also save the model parameters. || \"\"\"
aaidhfgid,manuwhs/Trapyng,Examples/3.3 EURUSD example/1. main_data_exploration.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,Load all the directories needed for the code to be executed both from
aaidhfgif,manuwhs/Trapyng,Examples/3.3 EURUSD example/1. main_data_exploration.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,Ideally this code should be executed using Spyder with working directory
aaidhfhfa,manuwhs/Trapyng,Examples/3.3 EURUSD example/2. main_feature_extraction.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,Load all the directories needed for the code to be executed both from
aaidhfhfc,manuwhs/Trapyng,Examples/3.3 EURUSD example/2. main_feature_extraction.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,Ideally this code should be executed using Spyder with working directory
aaidhfidh,manuwhs/Trapyng,Examples/3.3 EURUSD example/3. main_feature_selection.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,Load all the directories needed for the code to be executed both from
aaidhfidj,manuwhs/Trapyng,Examples/3.3 EURUSD example/3. main_feature_selection.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,Ideally this code should be executed using Spyder with working directory
aaidhfjaf,manuwhs/Trapyng,Examples/3.3 EURUSD example/3. main_feature_selection.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,ax1.set_xticks(data_df_train.columns) # ; rotation='vertical'
aaidhfjbg,manuwhs/Trapyng,Examples/3.3 EURUSD example/4. main_classifier_systems.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,Load all the directories needed for the code to be executed both from
aaidhfjbi,manuwhs/Trapyng,Examples/3.3 EURUSD example/4. main_classifier_systems.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,Ideally this code should be executed using Spyder with working directory
aaidhfjij,manuwhs/Trapyng,Examples/3.3 EURUSD example/5. final_model.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,Load all the directories needed for the code to be executed both from
aaidhfjjb,manuwhs/Trapyng,Examples/3.3 EURUSD example/5. final_model.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,Ideally this code should be executed using Spyder with working directory
aaidhgahb,manuwhs/Trapyng,Examples/3.3 EURUSD example/old version/3. main_TimeSeries.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,to move the price up; and how many to move it down. This is hard to know from daily data but we can get a better representation from 15M
aaidhgbii,manuwhs/Trapyng,Examples/4.0 TensorFlow/2.2 main_full_model2.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,\"\"\" || In this document we will perform a basic Linear Regression including TensorBoard. || It is a very simple manual program with no proper naming of the variables. || We will: ||     - Use the conditional in the huber loss. ||     - We can also save the model parameters. || \"\"\"
aaidhgddh,manuwhs/Trapyng,Examples/5.0 PyTorch/1. main_DataGeneration.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,\"\"\" || I guess this would show the probability of up or down given that you know the value of || the real signal at that point. More noise will make it less sure with same overall shape. || If we add correlation noise; we should actually get way better estimates... and we do. || Do they depend only on the previous ? Or also in all the previous ? I guess it is just the substaction of both || so the common terms dissapear nicely :) ||  || \"\"\"
aaidhgjja,manuwhs/Trapyng,Examples/5.1 AllenNLP/0. main DataSet example.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,## TODO: Create a Vocab from files or previously created or set of Tokens directly.
aaidhgjjc,manuwhs/Trapyng,Examples/5.1 AllenNLP/0. main DataSet example.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,TODO: It looks like it does show all of the namespaces ?
aaidhhafd,manuwhs/Trapyng,Examples/5.1 AllenNLP/2.main_example_name_country.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,\"\"\" ||   Thoughts on DataReader: ||            To allow lazy mode we should add the lazy flag in the initialization and the _read() function should not return a list of instances but ||            rather yield them within a loop. ||            When calling read() of the DataReader; if it is not lazy mode it will read the Iterator (Yielder) until it has all samples and will join them into a list and return them. ||            If we have lazy mode then we only call read() once as well; and then every time we iterate over the return element; it will fetch the data from _read() and return the instance. ||            instances = reader.read('my_instances.txt') ||            for epoch in range(10): ||                for instance in instances: ||                    process(instance) ||  ||             Then each epoch's for instance in instances results in a new call to MyDatasetReader._read(); and your instances will be read from disk 10 times. ||  ||         AllenNLP uses a DataIterator abstraction to iterate over datasets using configurable batching; shuffling; and so on. ||         (the padding is taken care of by the Batching; but we could order the sequences in our dataset do have batches with same lenghts; this is what iterators can do). ||  ||         The included dataset readers all work with lazy datasets; but they have extra options that you might want to specify in that case. ||  ||        How do you know when the yield iterator is over ? because it returns None ? for the non lazy loading it is fine; we have full control. ||  ||         _max_instances_in_memory _instances_per_epoch ||  ||          BucketIterator; which can order your instances by e.g. sentence length. ||  ||       --- ||       In the seq2seq entropy loss we used the mask as the vector of weights for the elements in the chains. ||  ||       -- ||       For using the Iterator; once the iterator object is initialized; we just call __call__ with the iterable from the data reader and it will ||       output another iterable with the batches; already converted to tensors of indexes using the vocabulary. ||       We could also use the function: get_num_batches(self; instances: Iterable[Instance]) but how is this going to work for lazy mode ? do we have to load everything once to test it ?  ||       Ok; for lazy mode it wount work unless we specify the number of samples in each epoch. for non lazy it will but it also will ahve do reload everything; so not worth it. ||           Shold we check at each iteration if the batch result is None and the call the iterator again ?  ||  || \"\"\"
aaidhhajg,manuwhs/Trapyng,Examples/5.1 AllenNLP/3.main_BiDAF_Experiments.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,\"\"\" || ################ LOAD PRETRAINED MODEL ############### || We load the pretrained model and we can see what parts it has and maybe reused if needed. || \"\"\"
aaidhhbcd,manuwhs/Trapyng,Examples/5.1 AllenNLP/3.main_BiDAF_Experiments.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,# Parameters needed for the next layer
aaidhhchf,manuwhs/Trapyng,Examples/5.1 AllenNLP/3.main_BiDAF_eval_results_model_comparison.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,# Fix backwards compatibility
aaidhhchi,manuwhs/Trapyng,Examples/5.1 AllenNLP/3.main_BiDAF_eval_results_model_comparison.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,TODO: Bayesian weights are moved to device by default; we need to change that.
aaidhhdcb,manuwhs/Trapyng,Examples/5.1 AllenNLP/3.main_BiDAF_train.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,\"\"\" || This file trains the ELMO + BiDAF architecture. ||     - All the information needed to characterize the hyperparameters of the model is in the loaded ||     - Parameters can be overwritten when calling the program using: parameter_name=value ||       The parser will try to convert the number to int or float if necessary by checking the default value type ||       zmart hum ? ||     - If the folders where to place the results have not been created; they will || \"\"\"
aaidhhdee,manuwhs/Trapyng,Examples/5.1 AllenNLP/3.main_BiDAF_train.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,# Move the tensor to cuda
aaidhhdhe,manuwhs/Trapyng,Examples/5.1 AllenNLP/3.main_CV_analysis.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,MAnual fix for Dropout
aaidhheba,manuwhs/Trapyng,Examples/5.1 AllenNLP/3.main_CV_analysis_Bayesian.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,MAnual fix for Dropout
aaidhhegc,manuwhs/Trapyng,Examples/5.1 AllenNLP/3.main_QA_ensemble.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,# Fix backwards compatibility
aaidhhfba,manuwhs/Trapyng,Examples/5.1 AllenNLP/3.main_pretrained_BiDAF.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,\"\"\" || ################ LOAD PRETRAINED MODEL ############### || We load the pretrained model and we can see what parts it has and maybe reused if needed. || \"\"\"
aaidhhfea,manuwhs/Trapyng,Examples/IoTubes/1. main.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,rt.stop() # better in a try\/finally block to make sure the program ends!
aaidhhfic,manuwhs/Trapyng,Examples/IoTubes/CUpdateable_Chart.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,Not really needed in the end because a Timer() only has a Thread.
aaidhhgad,manuwhs/Trapyng,Examples/IoTubes/CUpdateable_Chart.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,I think we needed to keep them in memory of they would die
aaidhhgic,manuwhs/Trapyng,Examples/IoTubes/DOCX_lib.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,# Hardcoded param:
aaidhhhad,manuwhs/Trapyng,Examples/IoTubes/DOCX_lib.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,Otherwise we would have different time labels and the SQL dabase would need a table per sensor to be efficient;.
aaidhhhaf,manuwhs/Trapyng,Examples/IoTubes/DOCX_lib.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,The data should be provided externally; probably with a DDBB call.
aaidhhhba,manuwhs/Trapyng,Examples/IoTubes/DOCX_lib.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,Monitors is a list of Monitor objects; the \"ID\" of the object would the columns of data
aaidhhhbb,manuwhs/Trapyng,Examples/IoTubes/DOCX_lib.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,TODO
aaidhhhbg,manuwhs/Trapyng,Examples/IoTubes/DOCX_lib.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,# TODO: Big task... manage different windows in gl library
aaidhhhcb,manuwhs/Trapyng,Examples/IoTubes/DOCX_lib.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,TODO: I need to add it somethow.
aaidhhhgd,manuwhs/Trapyng,Examples/IoTubes/FTP_lib.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,might be needed for a different port FTP.connect(host[; port[; timeout]]) and login
aaidhhhja,manuwhs/Trapyng,Examples/IoTubes/SQL_lib.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,TODO: Change to look over the possible columns
aaidhhigc,manuwhs/Trapyng,Examples/IoTubes/utilities_project.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,# HACK
aaidhhiid,manuwhs/Trapyng,Examples/IoTubes/utilities_project.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,rt.stop() # better in a try\/finally block to make sure the program ends!
aaidhhjcb,manuwhs/Trapyng,libs/AllenNLP_lib/Name_country_utils.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,Apparently AllenNLP needs the vocabulary
aaidhhjcc,manuwhs/Trapyng,libs/AllenNLP_lib/Name_country_utils.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,# TODO: What is the mask ? For the final output.
aaidhhjff,manuwhs/Trapyng,libs/AllenNLP_lib/bidaf_model.py,51cd064a4727e108c81935f0ed69956e4113853d,83dbeb328b71058ba87a1078900379c88e5f47ec,TODO: Only implemented for ELMO
aaidhhjgh,manuwhs/Trapyng,libs/AllenNLP_lib/bidaf_model.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,### TODO: Learning rate scheduler ####
aaidhiaac,manuwhs/Trapyng,libs/AllenNLP_lib/bidaf_model.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,# Move the tensor to cuda
aaidhiacc,manuwhs/Trapyng,libs/AllenNLP_lib/bidaf_model.py,51cd064a4727e108c81935f0ed69956e4113853d,83dbeb328b71058ba87a1078900379c88e5f47ec,tensor_dict = pytut.move_to_device(tensor_dict; device) ## Move the tensor to cuda
aaidhiaii,manuwhs/Trapyng,libs/AllenNLP_lib/bidaf_utils.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,# Move the tensor to cuda
aaidhiccg,manuwhs/Trapyng,libs/BBBLSTM/tf_checkpoints_utils.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,HACK : global step is not restored for some unknown reason
aaidhiceh,manuwhs/Trapyng,libs/BBBLSTM/tf_general_model2.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,\"\"\" || In this document we will perform a basic Linear Regression including TensorBoard. || It is a very simple manual program with no proper naming of the variables. || We will: ||     - Use the conditional in the huber loss. ||     - We can also save the model parameters. || \"\"\"
aaidhicie,manuwhs/Trapyng,libs/BBBLSTM/tf_optimizers.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,\"\"\" || In this document we will perform a basic Linear Regression including TensorBoard. || It is a very simple manual program with no proper naming of the variables. || We will: ||     - Use the conditional in the huber loss. ||     - We can also save the model parameters. || \"\"\"
aaidhiddb,manuwhs/Trapyng,libs/InformationClasses/CTimeData/TimeData_DDBB.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,TODO: Maybe some processing on the \"Date part\"
aaidhiddf,manuwhs/Trapyng,libs/InformationClasses/CTimeData/TimeData_DDBB.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,Otherwise it uses the naming convention to find it from the root folder:
aaidhidej,manuwhs/Trapyng,libs/InformationClasses/CTimeData/TimeData_DDBB.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,TODO
aaidhidgb,manuwhs/Trapyng,libs/InformationClasses/CTimeData/TimeData_DDBB.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,# TODO
aaidhiedc,manuwhs/Trapyng,libs/InformationClasses/CTimeData/TimeData_core.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,# TODO: We have to fix the fact of TimeZone; the fact that on
aaidhiede,manuwhs/Trapyng,libs/InformationClasses/CTimeData/TimeData_core.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,# Maybe displaze date and join stuff.
aaidhieec,manuwhs/Trapyng,libs/InformationClasses/CTimeData/TimeData_core.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,Not needed if the sequence was already ordered
aaidhiefb,manuwhs/Trapyng,libs/InformationClasses/CTimeData/TimeData_core.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,TODO. Make it work for series Names not in the dataset.
aaidhihbd,manuwhs/Trapyng,libs/Pytorch/RNNVB.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,NB: This is a temporary hack while we still don't have Tensor
aaidhihcg,manuwhs/Trapyng,libs/Pytorch/Variational_inferences_lib.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,TODO: REMOVE THE NEED OF CALLING IT HERE
aaidhiijj,manuwhs/Trapyng,libs/Pytorch/plotting_func.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,\"\"\" || This is a nice small library to plot things related to the training of the algorithms !! || \"\"\"
aaidhjafb,manuwhs/Trapyng,libs/TradingClasses/Coliseum/CTrailingStop.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,TODO: Preprocess datetome so that it fits in a given interval
aaidhjafg,manuwhs/Trapyng,libs/TradingClasses/Coliseum/CTrailingStop.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,TODO: Set interval for the original date ?
aaidhjahc,manuwhs/Trapyng,libs/graph/graph_advanced.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,TODO: Handle the legend better
aaidhjahd,manuwhs/Trapyng,libs/graph/graph_axes.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,# TODO: Change to update axes ?
aaidhjbbj,manuwhs/Trapyng,libs/tradersInfo/indicators_pandas.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,to move the price up; and how many to move it down. This is hard to know from daily data but we can get a better representation from 15M
aaidhjbca,manuwhs/Trapyng,libs/tradersInfo/indicators_pandas.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,# TODO: Redesign this operator
aaidhjbcc,manuwhs/Trapyng,libs/utilities/DOCX_lib.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,# Hardcoded param:
aaidhjbed,manuwhs/Trapyng,libs/utilities/DOCX_lib.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,Otherwise we would have different time labels and the SQL dabase would need a table per sensor to be efficient;.
aaidhjbef,manuwhs/Trapyng,libs/utilities/DOCX_lib.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,The data should be provided externally; probably with a DDBB call.
aaidhjbfa,manuwhs/Trapyng,libs/utilities/DOCX_lib.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,Monitors is a list of Monitor objects; the \"ID\" of the object would the columns of data
aaidhjbfb,manuwhs/Trapyng,libs/utilities/DOCX_lib.py,51cd064a4727e108c81935f0ed69956e4113853d,83dbeb328b71058ba87a1078900379c88e5f47ec,TODO
aaidhjbfg,manuwhs/Trapyng,libs/utilities/DOCX_lib.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,# TODO: Big task... manage different windows in gl library
aaidhjbgb,manuwhs/Trapyng,libs/utilities/DOCX_lib.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,TODO: I need to add it somethow.
aaidhjcad,manuwhs/Trapyng,libs/utilities/FTP_lib.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,might be needed for a different port FTP.connect(host[; port[; timeout]]) and login
aaidhjcda,manuwhs/Trapyng,libs/utilities/SQL_lib.py,51cd064a4727e108c81935f0ed69956e4113853d,STILL_EXISTS,TODO: Change to look over the possible columns
aaidhjeca,manuwhs/Trapyng,Examples/1.1 Trading Information Classes/1.1 main_CtimeData.py,6b25ff82f383b3dac092795657538789ae7160c8,STILL_EXISTS,Not needed but implemented
aaidhjecb,manuwhs/Trapyng,Examples/1.1 Trading Information Classes/1.1 main_CtimeData.py,6b25ff82f383b3dac092795657538789ae7160c8,STILL_EXISTS,Information needed for proper plotting.
aaidhjecg,manuwhs/Trapyng,Examples/1.1 Trading Information Classes/1.1 main_CtimeData.py,6b25ff82f383b3dac092795657538789ae7160c8,STILL_EXISTS,TODO: call data filling functions
aaidhjeee,manuwhs/Trapyng,Examples/1.1 Trading Information Classes/1.1 main_CtimeData.py,6b25ff82f383b3dac092795657538789ae7160c8,STILL_EXISTS,I could not find a fucking way to modify it inline TODO
aaidhjeei,manuwhs/Trapyng,Examples/1.1 Trading Information Classes/1.1 main_CtimeData.py,6b25ff82f383b3dac092795657538789ae7160c8,STILL_EXISTS,TODO: Plot reconstruction to show that it is not the same.
aaidhjejf,manuwhs/Trapyng,Examples/1.1 Trading Information Classes/1.3 main_visualizations.py,6b25ff82f383b3dac092795657538789ae7160c8,STILL_EXISTS,# Example on how to construct complex graphs showing better graphs
aaidhjfgh,manuwhs/Trapyng,Examples/1.1 Trading Information Classes/2.1 main_CSymbol.py,6b25ff82f383b3dac092795657538789ae7160c8,STILL_EXISTS,TODO: Align the transformation of the daily prices to
aaidhjfij,manuwhs/Trapyng,Examples/1.1 Trading Information Classes/3.1 main_CPortfolio.py,6b25ff82f383b3dac092795657538789ae7160c8,STILL_EXISTS,Download if needed.
aaidhjgaj,manuwhs/Trapyng,Examples/1.1 Trading Information Classes/3.1 main_CPortfolio.py,6b25ff82f383b3dac092795657538789ae7160c8,STILL_EXISTS,TODO: Be able to automatize the shareX thing
aaidhjgbj,manuwhs/Trapyng,Examples/3.3 EURUSD_example/4. main_GAN.py,6b25ff82f383b3dac092795657538789ae7160c8,STILL_EXISTS,Load all the directories needed for the code to be executed both from
aaidhjgcb,manuwhs/Trapyng,Examples/3.3 EURUSD_example/4. main_GAN.py,6b25ff82f383b3dac092795657538789ae7160c8,STILL_EXISTS,Ideally this code should be executed using Spyder with working directory
aaidhjghf,manuwhs/Trapyng,Examples/3.3 EURUSD_example/4. main_VAE.py,6b25ff82f383b3dac092795657538789ae7160c8,STILL_EXISTS,Load all the directories needed for the code to be executed both from
aaidhjghh,manuwhs/Trapyng,Examples/3.3 EURUSD_example/4. main_VAE.py,6b25ff82f383b3dac092795657538789ae7160c8,STILL_EXISTS,Ideally this code should be executed using Spyder with working directory
aaidhjhed,manuwhs/Trapyng,Examples/3.3 EURUSD_example/partidos_politicos.py,6b25ff82f383b3dac092795657538789ae7160c8,STILL_EXISTS,\"\"\" || CONCLUSION: DADO UN THRESHOLD; EL NUMERO DE VOTOS DEL ULTIMO ESCANHO DADO; LA MEJOR CONFIGURACION POSIBLE ES TENERLO TODO REPARTIDO. ||  || OTRA INFORMACION: EL THRESHOLD FINAL CAMBIA EN FUNCION DE LA COMPETENCIA.  ||  || PREGUNTA: ES POSIBLE QUE SEA MEJOR TENER UNA DISTRIBUCION MAS REPARTIDA DEL VOTO PARA QUE EL THRESHOLD SE SITUE UN POCO MAYOR  ||  ||  || \"\"\"
aaidhjjec,manuwhs/Trapyng,Examples/5.2 HBM/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers-master/Chapter6_Priorities/other_strats.py,6b25ff82f383b3dac092795657538789ae7160c8,STILL_EXISTS,TODO: UBC strat; epsilon-greedy
aaidhjjjd,manuwhs/Trapyng,Examples/5.2 HBM/pymc3-master/benchmarks/benchmarks/benchmarks.py,6b25ff82f383b3dac092795657538789ae7160c8,STILL_EXISTS,Initialization can be poorly specified; this is a hack to make it work
aaidiaagf,manuwhs/Trapyng,Examples/5.2 HBM/pymc3-master/docs/source/conf.py,6b25ff82f383b3dac092795657538789ae7160c8,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaidiacah,manuwhs/Trapyng,Examples/5.2 HBM/pymc3-master/pymc3/backends/ndarray.py,6b25ff82f383b3dac092795657538789ae7160c8,STILL_EXISTS,support stop value in slice (which is needed by
aaidiacga,manuwhs/Trapyng,Examples/5.2 HBM/pymc3-master/pymc3/diagnostics.py,6b25ff82f383b3dac092795657538789ae7160c8,STILL_EXISTS,needed. We could use np.squeeze here; but we don't want to squeeze
aaidiachh,manuwhs/Trapyng,Examples/5.2 HBM/pymc3-master/pymc3/distributions/continuous.py,6b25ff82f383b3dac092795657538789ae7160c8,STILL_EXISTS,XXX these two are redundant. Please; check.
aaidiadce,manuwhs/Trapyng,Examples/5.2 HBM/pymc3-master/pymc3/distributions/distribution.py,6b25ff82f383b3dac092795657538789ae7160c8,STILL_EXISTS,param was drawn in related contexts
aaidiagdc,manuwhs/Trapyng,Examples/5.2 HBM/pymc3-master/pymc3/examples/lasso_missing.py,6b25ff82f383b3dac092795657538789ae7160c8,STILL_EXISTS,mother with HS education or better; hearing loss identified by 3 months
aaidiagfi,manuwhs/Trapyng,Examples/5.2 HBM/pymc3-master/pymc3/glm/families.py,6b25ff82f383b3dac092795657538789ae7160c8,STILL_EXISTS,Hack as assigning a function in the class definition automatically binds
aaidiagja,manuwhs/Trapyng,Examples/5.2 HBM/pymc3-master/pymc3/glm/utils.py,6b25ff82f383b3dac092795657538789ae7160c8,STILL_EXISTS,maybe it was a tuple ot whatever
aaidiahee,manuwhs/Trapyng,Examples/5.2 HBM/pymc3-master/pymc3/model.py,6b25ff82f383b3dac092795657538789ae7160c8,STILL_EXISTS,needed for `add_random_variable` method
aaidiaied,manuwhs/Trapyng,Examples/5.2 HBM/pymc3-master/pymc3/sampling.py,6b25ff82f383b3dac092795657538789ae7160c8,STILL_EXISTS,TODO sample_posterior_predictive_w is currently only work for model with
aaidiaiih,manuwhs/Trapyng,Examples/5.2 HBM/pymc3-master/pymc3/stats.py,6b25ff82f383b3dac092795657538789ae7160c8,STILL_EXISTS,Replace ends of qlist with 'lo' and 'hi'
aaidiajbb,manuwhs/Trapyng,Examples/5.2 HBM/pymc3-master/pymc3/step_methods/gibbs.py,6b25ff82f383b3dac092795657538789ae7160c8,STILL_EXISTS,TODO: It would be great to come up with a way to make
aaidiajjf,manuwhs/Trapyng,Examples/5.2 HBM/pymc3-master/pymc3/tests/conftest.py,6b25ff82f383b3dac092795657538789ae7160c8,STILL_EXISTS,TODO: use this instead of SeededTest
aaidibcdg,manuwhs/Trapyng,Examples/5.2 HBM/pymc3-master/pymc3/tests/test_model_helpers.py,6b25ff82f383b3dac092795657538789ae7160c8,STILL_EXISTS,Create a generator object. Apparently the generator object needs to
aaidibcgi,manuwhs/Trapyng,Examples/5.2 HBM/pymc3-master/pymc3/tests/test_modelcontext.py,6b25ff82f383b3dac092795657538789ae7160c8,STILL_EXISTS,takes B from the stack; such that b ends up in model A
aaidibdcj,manuwhs/Trapyng,Examples/5.2 HBM/pymc3-master/pymc3/tests/test_transforms.py,6b25ff82f383b3dac092795657538789ae7160c8,STILL_EXISTS,Hack to get relative tolerance
aaidibdfb,manuwhs/Trapyng,Examples/5.2 HBM/pymc3-master/pymc3/tests/test_variational_inference.py,6b25ff82f383b3dac092795657538789ae7160c8,STILL_EXISTS,TODO: theano_config
aaidibeib,manuwhs/Trapyng,Examples/5.2 HBM/pymc3-master/pymc3/variational/inference.py,6b25ff82f383b3dac092795657538789ae7160c8,STILL_EXISTS,hack to allow pm.fit() access to loss hist
aaidibeii,manuwhs/Trapyng,Examples/5.2 HBM/pymc3-master/pymc3/variational/opvi.py,6b25ff82f383b3dac092795657538789ae7160c8,STILL_EXISTS,R\"\"\" || Variational inference is a great approach for doing really complex; || often intractable Bayesian inference in approximate form. Common methods || (e.g. ADVI) lack from complexity so that approximate posterior does not || reveal the true nature of underlying problem. In some applications it can || yield unreliable decisions. ||  || Recently on NIPS 2017 `OPVI  <https:\/\/arxiv.org\/abs\/1610.09033\/>`_ framework || was presented. It generalizes variational inference so that the problem is || build with blocks. The first and essential block is Model itself. Second is || Approximation; in some cases :math:`log Q(D)` is not really needed. Necessity || depends on the third and fourth part of that black box; Operator and || Test Function respectively. ||  || Operator is like an approach we use; it constructs loss from given Model; || Approximation and Test Function. The last one is not needed if we minimize || KL Divergence from Q to posterior. As a drawback we need to compute :math:`loq Q(D)`. || Sometimes approximation family is intractable and :math:`loq Q(D)` is not available; || here comes LS(Langevin Stein) Operator with a set of test functions. ||  || Test Function has more unintuitive meaning. It is usually used with LS operator || and represents all we want from our approximate distribution. For any given vector || based function of :math:`z` LS operator yields zero mean function under posterior. || :math:`loq Q(D)` is no more needed. That opens a door to rich approximation || families as neural networks. ||  || References || ---------- || -   Rajesh Ranganath; Jaan Altosaar; Dustin Tran; David M. Blei ||     Operator Variational Inference ||     https:\/\/arxiv.org\/abs\/1610.09033 (2016) || \"\"\"
aaidibejj,manuwhs/Trapyng,Examples/5.2 HBM/pymc3-master/pymc3/variational/opvi.py,6b25ff82f383b3dac092795657538789ae7160c8,STILL_EXISTS,better error message
aaidibhag,manuwhs/Trapyng,libs/Pytorch/VAE_plotting_func.py,6b25ff82f383b3dac092795657538789ae7160c8,STILL_EXISTS,\"\"\" || This is a nice small library to plot things related to the training of the algorithms !! || \"\"\"
aaidibibd,manuwhs/Trapyng,libs/utilities/sockets_lib.py,6b25ff82f383b3dac092795657538789ae7160c8,STILL_EXISTS,TODO: Make polict of read all based on time or size of the message.
aaidicegd,xinjli/pytensor,network/base/parameter.py,a184fd5c26c2406c1eb030665ccf538cbeb818dc,STILL_EXISTS,this is for efficient update
aaidicihf,electronick1/stepist,stepist/flow/workers/adapters/celery_queue.py,acf7f3bf4e07cf1fd833ef3ff95382f9f84c239d,c2923c0857aa018aefc645b7b2c2e1f9cccacbb9,TODO:
aaidicjia,hav4ik/Hydra,src/trainers/gradnorm.py,d912f3dd430636961ffcc5928b612797f75b368b,STILL_EXISTS,wtf_loss = wtf_loss + grad_norm[index][task_idx]
aaidicjic,hav4ik/Hydra,src/trainers/gradnorm.py,d912f3dd430636961ffcc5928b612797f75b368b,STILL_EXISTS,wtf_loss.backward(retain_graph=True; create_graph=True)
aaidicjid,hav4ik/Hydra,src/trainers/gradnorm.py,d912f3dd430636961ffcc5928b612797f75b368b,STILL_EXISTS,print('WTF 1'; self.coeffs.grad.shape)
aaidicjif,hav4ik/Hydra,src/trainers/gradnorm.py,d912f3dd430636961ffcc5928b612797f75b368b,STILL_EXISTS,wtf_loss = wtf_loss + grad_norm[2].mean() + relative_inverse.mean() + self.alpha
aaidicjig,hav4ik/Hydra,src/trainers/gradnorm.py,d912f3dd430636961ffcc5928b612797f75b368b,STILL_EXISTS,wtf_loss.backward()
aaididaah,hav4ik/Hydra,src/applications/measures/feature_similarity.py,452a05d6374e1d268ffb4c2a25bef2929234c3de,STILL_EXISTS,TODO: make this prettier somehow
aaididaai,hav4ik/Hydra,src/applications/measures/feature_similarity.py,452a05d6374e1d268ffb4c2a25bef2929234c3de,STILL_EXISTS,PHEW; THIS UGLY STUFF IS OVER...
aaididaaj,hav4ik/Hydra,src/applications/measures/feature_similarity.py,452a05d6374e1d268ffb4c2a25bef2929234c3de,STILL_EXISTS,TODO: but seriously; we need to fix it!
aaididabh,hav4ik/Hydra,src/applications/measures/stress_points.py,c05753a4a63f21fe529c0e7cb0f70fb4b8edebf7,STILL_EXISTS,TODO: make this thing less ugly. We don't want to track
aaididaii,proycon/foliapy,input/timbl.py,c93e6ee473e7d9bae9a5ef65e4e520be35f8f64b,STILL_EXISTS,TODO: Completely redesign
aaididbag,proycon/foliapy,input/timbl.py,522ffb69be55d7a5f391714fdb697d56a49e0951,STILL_EXISTS,numbers; ignore the specified FEATURE columns: first column is 1
aaididbda,proycon/foliapy,input/sonar.py,57227a30394d5a1bb0eb1d3b1ec71924919640b1,STILL_EXISTS,TODO: can we do this with xpath instead?
aaididbdb,proycon/foliapy,input/sonar.py,57227a30394d5a1bb0eb1d3b1ec71924919640b1,STILL_EXISTS,TODO: download XSD from web
aaididbde,proycon/foliapy,input/sonar.py,7c8f1aa03e9218ec18d544d1e3c56221205ec6eb,STILL_EXISTS,TODO
aaididbfa,proycon/foliapy,input/taggerdata.py,df57ca12ea6d9cef6a2e6da538c533a1678f679a,STILL_EXISTS,TODO: write index
aaididbjd,proycon/foliapy,docs/conf.py,3d7fc8b45bf6e0734266437ef374f1a92e058371,STILL_EXISTS,unused_docs = []
aaididcjd,proycon/foliapy,formats/folia.py,e3d970b3ecb9ee785552336bc79c4e266df90e6c,STILL_EXISTS,TODO: Inherit set from parent
aaididdbc,proycon/foliapy,formats/folia.py,e8bc5140e3b1fce9b7b6490d8167c6a98ce960ac,STILL_EXISTS,TODO: ADD IMDI
aaididdcc,proycon/foliapy,formats/folia.py,f70beb8c3b59a1e5bdcbff8b76844c8d3bca63a4,d4d0a05da0881d5806de5acd431fe6a9439c77d6,TODO
aaididdda,proycon/foliapy,formats/folia.py,f2f4717f5936712d7536bb55d2e75168b6b0ca59,9925f0c687c3002aba69c33adf0974387e97d680,TODO
aaididddi,proycon/foliapy,formats/folia.py,c4872d24a4441313e56f9e01829e2a1a2f6b0d37,9925f0c687c3002aba69c33adf0974387e97d680,TODO
aaididded,proycon/foliapy,formats/folia.py,338fa4294e0971a593a25b3e97275347e2e6f87b,STILL_EXISTS,TODO: Parse CMDI
aaididdff,proycon/foliapy,formats/folia.py,33bfe4270f401626474c869d9923c42d25151b88,STILL_EXISTS,TODO: node or filename
aaididdfg,proycon/foliapy,formats/folia.py,327bcf50b86273cf778ca415e66621f632c7c8f6,STILL_EXISTS,TODO: sanity check; there may be no other child within the same set
aaididdfi,proycon/foliapy,formats/folia.py,19a95b1c2eb24a0b115443c8be52e14d0429661e,67b8916a8e0179e18163dc0c0505ac54b2b4ac04,TODO: IMPLEMENT
aaididdfj,proycon/foliapy,formats/folia.py,cb26eb0d45a6007503e23bdfd1cb95cd86e6eb2d,STILL_EXISTS,TODO: add XMLATTRIB
aaididdgb,proycon/foliapy,formats/folia.py,019b755045c1e1a955bdeb3ea369605971d0c143,2be908c34c5a47917d6cb95a34983a9e9b34b5df,TODO: replace other child within the same set
aaididdjj,proycon/foliapy,formats/folia.py,265a1f38fac38750063bd65c04ef5b6a016119b3,2efe2e087e62d8ecb6fe3357fdc2df13ecc8837e,TODO: implement
aaididedi,proycon/foliapy,formats/folia.py,41c5413c89ff255c737a062b324ba744996e8d95,2be908c34c5a47917d6cb95a34983a9e9b34b5df,TODO: ID trouble?
aaididffh,proycon/foliapy,tests/folia.py,e3dfbf6718a851d41bd06ac65489acf22a95fe28,891aacb1494adc568f924fce08b900d14726719d,TODO: THIS TEST FAILS; SHOULD BE FIXED; TEST MAY CHANGE
aaididffi,proycon/foliapy,tests/folia.py,e3dfbf6718a851d41bd06ac65489acf22a95fe28,8c494b780abfcebffbe56bedbab9b0a8ad9c2aa3,TODO: THIS TEST FAILS NOW; BUT SHOULDN'T
aaididffj,proycon/foliapy,formats/folia.py,19390249f4af518b0f35977351507b83eb4d41fb,d254dd8bcf0e560c89e433fa215c103f306ae06a,TODO
aaididfhh,proycon/foliapy,formats/folia.py,0e050bf8abe2785dcb5e17a668bd9b9d28232467,d254dd8bcf0e560c89e433fa215c103f306ae06a,TODO: make obsolete
aaididfii,proycon/foliapy,formats/folia.py,0e050bf8abe2785dcb5e17a668bd9b9d28232467,cf9c9b714ea3e1f7c56971c457c9dd429752f5cd,MAYBE TODO: corrected attribute?
aaididfij,proycon/foliapy,formats/folia.py,0e050bf8abe2785dcb5e17a668bd9b9d28232467,66f22a8a8e90953a30937c47535022c9f0c2af58,TODO: Implement
aaididgad,proycon/foliapy,formats/folia.py,d254dd8bcf0e560c89e433fa215c103f306ae06a,67b8916a8e0179e18163dc0c0505ac54b2b4ac04,TODO: IMPLEMENT
aaididgdj,proycon/foliapy,formats/folia.py,a4c0fe9a7c970779305d3c8532baf3b39cd2ff58,STILL_EXISTS,TODO: Implement handling of INLINE corrected textcontent
aaididhdc,proycon/foliapy,formats/folia.py,62263742ba3931a85f6bfa3f0ebfa57be168d78b,39581e173e03da377384bd2e3a231fb889956384,TODO: Refactor
aaididhge,proycon/foliapy,formats/folia.py,d747beb3cef867c28a3fb834cbd876938d889502,cf9c9b714ea3e1f7c56971c457c9dd429752f5cd,MAYBE TODO: corrected attribute?
aaididhhb,proycon/foliapy,formats/folia.py,6df1d9463d99cb7076796ba1e6645ed5824beded,9ebb1aa99ccaad7cfe4e74c586bd0d9e79b24ac7,TODO: relaxNG
aaididhhc,proycon/foliapy,formats/folia.py,317553e6f9b6184bcfd42ea6b5ad73730bb81f1e,103247e0ac46e498e26d56f43d6eeb4069f409c2,TODO parse datetime string
aaididhhd,proycon/foliapy,formats/folia.py,317553e6f9b6184bcfd42ea6b5ad73730bb81f1e,b11feeb87b441ec826fd24b55b757758abc0da4a,TODO: format string
aaididida,proycon/foliapy,formats/folia.py,205f500bf1f48021d9147e99028fc83d256c6f08,7a7cd3360ded65aa0e7684b17b65efa9a7730aed,ugly patch to get rid of namespace prefix
aaididjaa,proycon/foliapy,formats/folia.py,a8f73f9eabbf2bda2378a4b9d6763dd5f9933974,STILL_EXISTS,TODO
aaididjag,proycon/foliapy,formats/folia.py,7119e7ae89caf6b860c7949e63bfa7490a9f4982,STILL_EXISTS,TODO
aaididjbd,proycon/foliapy,formats/folia.py,8b38080641fc04cc5436c352e2cda70b7a64de99,9ebb1aa99ccaad7cfe4e74c586bd0d9e79b24ac7,TODO!
aaididjbf,proycon/foliapy,formats/folia.py,ab47946d5c73c12a0fbc447a1f17d04651d349c2,9ebb1aa99ccaad7cfe4e74c586bd0d9e79b24ac7,TODO: relaxNG
aaididjcb,proycon/foliapy,formats/folia.py,ab47946d5c73c12a0fbc447a1f17d04651d349c2,9ebb1aa99ccaad7cfe4e74c586bd0d9e79b24ac7,TODO!
aaididjda,proycon/foliapy,formats/folia.py,48f8bb5a14bd0349b9c7eb6e9cecc1cd788fe09d,STILL_EXISTS,XML Tree is now obsolete (only needed when partially loaded for xpath queries)
aaididjgg,proycon/foliapy,formats/folia.py,0965bdddfbe18a000670aa47fb7f69ca808fcc9e,7eac5458e174ee9dda2c04c3805fd4347a8dd108,TODO: Implement
aaididjhh,proycon/foliapy,formats/folia.py,2824fd2188b660e7e09e31c030222de0142f13e9,STILL_EXISTS,workaround for xml:id problem
aaidieach,proycon/foliapy,formats/folia.py,e96e6ab74a42542df2aa1036b5a65dc971844952,STILL_EXISTS,XML Tree is now obsolete (only needed when partially loaded for xpath queries)
aaidieaea,proycon/foliapy,formats/folia.py,3a62632a2149e72f968adca65906ae7e8099d1ae,STILL_EXISTS,TODO: Parse xlink:href
aaidieaei,proycon/foliapy,formats/folia.py,4bc0a9e826b053d92e283f7598f8dfb8f6d55ae3,STILL_EXISTS,TODO: Parse xlink:href
aaidieaic,proycon/foliapy,formats/folia.py,4a8cf3eee8244370bed9522e592ec0b7ef1dad98,STILL_EXISTS,if a quote ends in a sentence; we don't want any delimiter
aaidiebbh,proycon/foliapy,formats/folia.py,347703fe920cdcede504d52a85eb2837f6899d62,STILL_EXISTS,TODO: add phoneme when it becomes available
aaidiebff,proycon/foliapy,formats/folia.py,cb5bde641b856a0fcfabe7d8215e3f9e7b734695,STILL_EXISTS,XML Tree is now obsolete (only needed when partially loaded for xpath queries); free memory
aaidiebie,proycon/foliapy,formats/folia.py,dc61d5f61b00c0e85d552ac1f76f0ad7c1ca9b9c,9c7edccedf7e65957b687cb11718ca5e0cdc9556,ignorelist = [ globals()[c] if not inspect.isclass(c) else c for c in ignorelist ]  #TODO: make more efficient; resolving the same default ignorelist every time is slow; isclass is costly
aaidiebij,proycon/foliapy,formats/folia.py,1a57cc8008831f536a0d244f3565277cd02a1e0a,8d457591a7ca8d0c789cbe720241844423c6252d,There is a leak in lxml :( ; specialise file handler to replace xml:id to id; ugly hack (especially for Python2)
aaidiecdj,proycon/foliapy,formats/folia.py,28b985691cbde356b5d06deb7bc51c806f06a4d3,STILL_EXISTS,TODO
aaidiecgh,proycon/foliapy,formats/folia.py,a04cfda3933a0d22fd2ec476975e1ba529c157f0,e2e893c56a5ff8c83b3db518582de5decb579f1d,TODO: won't work in text <x\/> text scenarios
aaidiechc,proycon/foliapy,formats/folia.py,c9ba76ccc0a301a5e9b60de8a4ea8c48375861b2,7eac5458e174ee9dda2c04c3805fd4347a8dd108,TODO: Implement
aaidieddj,proycon/foliapy,formats/folia.py,c47fb294f4488fd23f5a3514cd5c55b4af1e3ce2,7a7cd3360ded65aa0e7684b17b65efa9a7730aed,TODO: check validity of elements under subdoc\/text with respect to self.parent
aaidieded,proycon/foliapy,formats/folia.py,c47fb294f4488fd23f5a3514cd5c55b4af1e3ce2,7a7cd3360ded65aa0e7684b17b65efa9a7730aed,will hold all subdocs (sourcestring => document) ; needed so the index can resolve IDs in subdocs
aaidiedef,proycon/foliapy,formats/folia.py,c47fb294f4488fd23f5a3514cd5c55b4af1e3ce2,7a7cd3360ded65aa0e7684b17b65efa9a7730aed,perhaps the key is in one of our subdocs?
aaidiedfa,proycon/foliapy,formats/folia.py,87d32793457e1fbd216128f17fb54bd27d66330c,7a7cd3360ded65aa0e7684b17b65efa9a7730aed,ugly patch to get rid of namespace prefix
aaidieeah,proycon/foliapy,formats/folia.py,ca45fc26124ed8a9361beceab41bc5eacb8e10aa,STILL_EXISTS,TODO: check validity of elements under subdoc\/text with respect to self.parent
aaidieebd,proycon/foliapy,formats/folia.py,ca45fc26124ed8a9361beceab41bc5eacb8e10aa,STILL_EXISTS,will hold all subdocs (sourcestring => document) ; needed so the index can resolve IDs in subdocs
aaidieebf,proycon/foliapy,formats/folia.py,ca45fc26124ed8a9361beceab41bc5eacb8e10aa,STILL_EXISTS,perhaps the key is in one of our subdocs?
aaidieeca,proycon/foliapy,formats/folia.py,ca45fc26124ed8a9361beceab41bc5eacb8e10aa,STILL_EXISTS,ugly patch to get rid of namespace prefix
aaidieehe,proycon/foliapy,formats/fql.py,0d071cf15c1dd0406432afaf081224125ab2f90a,9ce37c587a7ddb753fcfcc3ea68949d2e8e418f9,self.form = #TODO
aaidieeih,proycon/foliapy,formats/fql.py,3280e80f2eb44a03b0050048348594cd726ae5e2,3479c519c79a7d4b795f3ae136e1d780793988e9,TODO
aaidieejb,proycon/foliapy,formats/fql.py,61326a304253ddedd53aa39c37b14ee1cd5b6dfe,3479c519c79a7d4b795f3ae136e1d780793988e9,TODO
aaidiefag,proycon/foliapy,formats/fql.py,f95e2faa3b92dea6a14f33c659776c6a98bade55,82bd8654e94ac6a531dd64d2314a497bad581ceb,TODO
aaidiefef,proycon/foliapy,formats/fql.py,81922f5fc232dfd0ceb56058d61ac805703f1a69,6c6c4381a2cef809a97be110a1de4f165ecde71f,TODO: process modifier
aaidiefha,proycon/foliapy,formats/fql.py,0d81393c1ddcaeb5a4e0413a9e24b1a207ce637c,b18bf05763d05f176213b57d75fe08085fe929f0,TODO: efficiency
aaidiefjb,proycon/foliapy,formats/fql.py,5eb4d32bfddfc9b343540d90c65c35631a75080f,fa2b943c5e09bed5c7b66a9aefb0bcefea5d39d8,TODO: span annotation?
aaidiegjg,proycon/foliapy,formats/fql.py,94b5256e1df1f22e425486480aa710534f29dc27,STILL_EXISTS,current only needed for structure annotation
aaidieiae,proycon/foliapy,formats/fql.py,0849e1828531db920d3d7bcf571b82a231177a16,STILL_EXISTS,TODO: implement
aaidiejcb,proycon/foliapy,formats/folia.py,1453680abb749dd374f178c5299ac7781a96c856,STILL_EXISTS,if a sentence ends in a linebreak; we don't want any delimiter
aaidifaji,proycon/foliapy,formats/folia.py,4c8af5cecfb8f5ea6a59fcab64a160cfcad7ee38,STILL_EXISTS,(twice; better safe than sorry)
aaidifbaf,proycon/foliapy,formats/folia.py,f11a449c71dced885d4c8fd85fd1933e3bc24e68,STILL_EXISTS,OPTIONAL_ATTRIBS = (Attrib.ID; Attrib.SETONLY;) #TODO: handle SETONLY in new scheme
aaidifbag,proycon/foliapy,formats/folia.py,f11a449c71dced885d4c8fd85fd1933e3bc24e68,46e0b8bd36da4193f60bd976e65b37b13b61cf31,REQUIRED_ATTRIBS = (Attrib.ID;)  #TODO: See if this is handled correctly in new scheme
aaidifbbc,proycon/foliapy,formats/folia.py,f11a449c71dced885d4c8fd85fd1933e3bc24e68,STILL_EXISTS,(twice; better safe than sorry)
aaidifdhe,proycon/foliapy,formats/folia.py,818817a50b15c0ff69bc534df8bfec20e7c05271,STILL_EXISTS,nasty backward-compatibility hack to allow deprecated listitem element (actually called item)
aaidifeab,proycon/foliapy,formats/folia.py,0df2cfce4d1debb80f1c912523d38f167be3b2d5,STILL_EXISTS,definitions needed for ForeignData (allow any content) - see http:\/\/www.microhowto.info\/howto\/match_arbitrary_content_using_relax_ng.html
aaidifefa,proycon/foliapy,formats/folia.py,13471e19e9a0da8a4a9414b65c84e5214ad96658,STILL_EXISTS,TODO: span roles don't take classes; derived off spanannotation allows too much
aaidifejh,proycon/foliapy,formats/foliaset.py,7eac5458e174ee9dda2c04c3805fd4347a8dd108,STILL_EXISTS,TODO
aaidiffaj,proycon/foliapy,formats/foliaset.py,7eac5458e174ee9dda2c04c3805fd4347a8dd108,4878cf1189b7ee91e344ad0d26e865479588d1f3,TODO: serialise to json
aaidiffdj,proycon/foliapy,formats/folia.py,fe5390ce765b864a4788b8ff34e80f97fb800969,STILL_EXISTS,TODO: Implement
aaidifggj,proycon/foliapy,pynlpl/formats/folia.py,ee54da08688ff0e52dbeb2173cfd31f920d13b73,STILL_EXISTS,AbstractSpanAnnotation is needed when requesting text() on nested span annotations
aaidifhed,proycon/foliapy,pynlpl/formats/folia.py,600aec25bb799cdedba79a04642aa442e51469fe,STILL_EXISTS,well; we couldn't find our textclass in any correction; just fall back to current and let text validation fail if needed
aaidifhfe,proycon/foliapy,pynlpl/formats/folia.py,3e7bd40897ee1688c872cbae021fe023dd69d5a8,STILL_EXISTS,TODO: what about corrections?
aaidifiba,proycon/foliapy,folia/main.py,2b5cea1c6c2ebd16699ab98087f4fb70ea84ab00,STILL_EXISTS,TODO: Generate documentation INSIDE the RelaxNG (I did something similar for CLAM XML) (relates to #43)
aaidifibb,proycon/foliapy,folia/main.py,2b5cea1c6c2ebd16699ab98087f4fb70ea84ab00,STILL_EXISTS,TODO: Add data types #27
aaidifice,proycon/foliapy,folia/tests/maintest.py,af866328006b5f488eb8f55e25d37f3649e7b82b,STILL_EXISTS,basically the same thing as above; but resolved to Processor instances:
aaidifjfd,proycon/foliapy,folia/tests/maintest.py,04a6b294b35fec937e952a7b9c25231d2943c285,STILL_EXISTS,signal we are done editing; needed to invoke postprocessing
aaidigaeh,proycon/foliapy,folia/tests/maintest.py,0bee8cf1394ca6fa0d8eadcb263d466077eb1ca9,STILL_EXISTS,basically the same thing as above; but resolved to Processor instances:
aaidigbbb,proycon/foliapy,folia/tests/maintest.py,20fbe24298415c1bcea6b752c733f4874f85b907,STILL_EXISTS,xxx -> replace with a number at some point when there are more new tests
aaidigbbj,proycon/foliapy,folia/tests/maintest.py,66c8cf2e416e2a49dcd2d7d2d625e60808ad348f,STILL_EXISTS,xxx -> replace with a number at some point when there are more new tests
aaidigcge,proycon/foliapy,folia/foliaset.py,3124e52a3c0f284c71bdf3d52ea7006b57ffe700,STILL_EXISTS,TODO
aaidigfha,arguiot/Neuron,neuron_ml/script/TensorFlow/retrain.py,96883f37793860c1f1323fa51a95b7b5cf099a0b,STILL_EXISTS,Needed to make sure the logging output is visible.
aaidigfjd,arguiot/Neuron,neuron_ml/script/TensorFlow/retrain.py,96883f37793860c1f1323fa51a95b7b5cf099a0b,STILL_EXISTS,TODO: Make this use an eval graph; to avoid quantization
aaidighde,arthurpaulino/miraiml,util.py,c262c5640a47d481990aad50945e646c3da5caef,STILL_EXISTS,Better parameters and features have higher chances of being chosen.
aaidighdj,arthurpaulino/miraiml,mirai_ml.py,20c7521cde91099d6fed9132f7bc1017b7939300,STILL_EXISTS,Better parameters and features have higher chances of being chosen.
aaidigich,arthurpaulino/miraiml,miraiml/__init__.py,0e1d3a1100e8feb22053ef2f8450ba74efd15b02,STILL_EXISTS,\"\"\" || MiraiML is an asynchronous engine for autonomous & continuous machine learning; || built for real-time usage. ||  || MiraiML attempts to improve the chosen metric by searching good hyperparameters || and sets of features to feed statistical models. || \"\"\"
aaidigidi,arthurpaulino/miraiml,docs/conf.py,0f92f60c076fd3766e5e19ba8bdd040a51762dcd,STILL_EXISTS,-- Options for todo extension ----------------------------------------------
aaidigidj,arthurpaulino/miraiml,docs/conf.py,0f92f60c076fd3766e5e19ba8bdd040a51762dcd,9cbee6027b15d9c77d29ec0d6e5f73f4015db825,If true; `todo` and `todoList` produce output; else they produce nothing.
aaidihafd,arthurpaulino/miraiml,examples/hyperparameter_constraining.py,63f3fdf9d850e80af011fe2daf384a13c46ae2e3,STILL_EXISTS,which receives a dictionary of parameters and changes them if needed.
aaidihahh,arthurpaulino/miraiml,examples/lightgbm_wrapper.py,adfd852bd76acf7c1032fd2d7aa904fe0bee94b9,50b5652eb7246019fdb035b432cbfc1e4e002d6d,TODO
aaidihaie,arthurpaulino/miraiml,examples/lightgbm_wrapper.py,50b5652eb7246019fdb035b432cbfc1e4e002d6d,STILL_EXISTS,returning a 2-columns numpy.ndarray
aaidihbbh,arthurpaulino/miraiml,examples/stacking.py,87b5635e407281a70ce65fdc75547b1b164c4867,STILL_EXISTS,returning a 2-columns numpy.ndarray
aaidihbjd,johnolafenwa/TorchFusion-Utils,tfutils/fp16/fp16.py,bfc7abac0a67f8fa3b6678e2197727b12c57e24c,STILL_EXISTS,More efficient version that can be used if .sum() returns a Python scalar
aaidihcag,johnolafenwa/TorchFusion-Utils,tfutils/fp16/fp16.py,bfc7abac0a67f8fa3b6678e2197727b12c57e24c,STILL_EXISTS,TODO:  Update overflow check + downscale to use Carl's fused kernel.
aaidihcah,johnolafenwa/TorchFusion-Utils,tfutils/fp16/fp16.py,bfc7abac0a67f8fa3b6678e2197727b12c57e24c,STILL_EXISTS,init_state_dict sets up an alternative way to cast per-param state tensors.
aaidihcbd,johnolafenwa/TorchFusion-Utils,tfutils/fp16/fp16.py,bfc7abac0a67f8fa3b6678e2197727b12c57e24c,STILL_EXISTS,alternative way to cast per-param state tensors:
aaidihcfj,johnolafenwa/TorchFusion-Utils,tfutils/fp16/fp16.py,bfc7abac0a67f8fa3b6678e2197727b12c57e24c,STILL_EXISTS,discarding the iteration;  but probably wouldn't improve overall efficiency.
aaidiheah,Roulbac/GanSeg,datasets/__init__.py,0441a43230c576bce5c461323ab9c3463877fbc8,STILL_EXISTS,TODO: Am i going to use this?
aaidihedj,Roulbac/GanSeg,models/base_model.py,0441a43230c576bce5c461323ab9c3463877fbc8,STILL_EXISTS,net.print_info() #TODO
aaidihgcb,iamDecode/sklearn-pmml-model,base.py,0e8d8b704567f497a5a2c95a2647d29726388050,STILL_EXISTS,TODO: date; time; dateTime; dateDaysSince[0\/1960\/1970\/1980]; timeSeconds; dateTimeSecondsSince[0\/1960\/1970\/1980]
aaidihgcd,iamDecode/sklearn-pmml-model,base.py,0e8d8b704567f497a5a2c95a2647d29726388050,STILL_EXISTS,TODO: isCyclic
aaidihgdf,iamDecode/sklearn-pmml-model,base.py,cfd47a7b92e3978626a6e0c320bb5f7983a57740,STILL_EXISTS,TODO: isCyclic
aaidihgii,iamDecode/sklearn-pmml-model,sklearn_pmml_model/tree/__init__.py,b702c40eb3fb8a7010f88a65442f98e5dd81f137,47158af40265875a63d10c823b5756ac6164defb,TODO: impurity is not really required for anything; but would be nice to have
aaidihgij,iamDecode/sklearn-pmml-model,sklearn_pmml_model/tree/__init__.py,b702c40eb3fb8a7010f88a65442f98e5dd81f137,STILL_EXISTS,TODO: not really necessary; but do it anyway
aaidihhcd,iamDecode/sklearn-pmml-model,setup.py,83378e9890ed356637d216bbe1b7425cb001dcc7,STILL_EXISTS,Customize these as needed.
aaidihhfj,iamDecode/sklearn-pmml-model,setup.py,83378e9890ed356637d216bbe1b7425cb001dcc7,STILL_EXISTS,Note that my_ext_modules is just a list of Extension objects. We could add any C sources (not coming from Cython modules) here if needed.
aaidihhgh,iamDecode/sklearn-pmml-model,setup.py,83378e9890ed356637d216bbe1b7425cb001dcc7,STILL_EXISTS,FIXME: force sdist; but sdist only; to keep the .pyx files (this puts them also in the bdist)
aaidihhhd,iamDecode/sklearn-pmml-model,sklearn_pmml_model/tree/tree.py,3a2d194fba55f77e8d8cc86f6f96b38c40f47c43,1dd73f5c629fae23af321b780ca4c11f817357bc,TODO: support <Extension> as well
aaidihhhi,iamDecode/sklearn-pmml-model,sklearn_pmml_model/tree/tree.py,be96ede9f1fe2c878af356870367f186368242fb,370f7c0533c4d29d632508eb93cff1f9b7852629,TODO: support <Extension> as well
aaidihiad,iamDecode/sklearn-pmml-model,tests/linear_model/test_base.py,d16bcfb0661ce5ccac055e31305a8a098d8bff32,2355637ce04993f9e6845c494cd2bc503be56c8d,TODO: invalidity tests required for 100% coverage
aaidihjac,jmwoloso/pychattr,pychattr/channel_attribution.py,1892bf85ca963496bcf129917bf8c82fb920751f,c5febc03006534df6597a264537920f6be133be2,TODO: parameterize \"channel_name\"
aaidihjda,jmwoloso/pychattr,pychattr/channel_attribution.py,c5febc03006534df6597a264537920f6be133be2,STILL_EXISTS,TODO: parameterize \"channel_name\"?
aaidihjgi,jmwoloso/pychattr,dev/channel_attribution.py,bb7f15686f2d50cd3a770d63887f82ae04acb322,STILL_EXISTS,TODO: parameterize \"channel_name\"?
aaidiifah,thu-ml/zhusuan,examples/deconv.py,a1dec00bdc6018e0f9024cd244335e67b127469b,STILL_EXISTS,\"\"\"A quick hack to try deconv out.\"\"\"
aaidiifcc,thu-ml/zhusuan,examples/deconv.py,a1dec00bdc6018e0f9024cd244335e67b127469b,STILL_EXISTS,Compute number of columns in the output; based on the padding.
aaidiigbi,thu-ml/zhusuan,zhusuan/layers.py,a0df3dfd56f7077c02f1b94eef994a0ed1f830ae,9fef86b298f7bc62ffcebf1594b303333ee5ebe9,suggestions.append('%s (perhaps you meant %s)'
aaidiigcc,thu-ml/zhusuan,zhusuan/layers.py,a0df3dfd56f7077c02f1b94eef994a0ed1f830ae,9fef86b298f7bc62ffcebf1594b303333ee5ebe9,warn(\"get_output() was called with unused kwargs:\ || \\t%s\"
aaidiigha,thu-ml/zhusuan,tests/test_layers.py,b4da9b7c008f2bbd68398686aca1dbce708a1cc9,STILL_EXISTS,TODO: test values
aaidiihcj,thu-ml/zhusuan,zhusuan/mcmc/candidate.py,4a7fa151ae21b885c1cf8c94cc0d144e1d6bb785,STILL_EXISTS,TODO I think this is incorrect; but NUTS paper and Stan use this...
aaidiiibg,thu-ml/zhusuan,tests/test_evaluation.py,12cafb24fd3be0b34772e0a930691226b1127323,STILL_EXISTS,TODO: is_loglikelihood test
aaidiiibh,thu-ml/zhusuan,tests/test_variational.py,12cafb24fd3be0b34772e0a930691226b1127323,STILL_EXISTS,TODO: advi test
aaidiiicg,thu-ml/zhusuan,zhusuan/variational.py,12cafb24fd3be0b34772e0a930691226b1127323,b03c5e49862902deda03c6403a7683368b2a51d2,TODO: assert n_samples == 1 for discrete layers (run time)
aaidijbci,thu-ml/zhusuan,tests/test_layers.py,b03c5e49862902deda03c6403a7683368b2a51d2,STILL_EXISTS,TODO: test values
aaidijbia,thu-ml/zhusuan,zhusuan/variational.py,d8fd50fe3a61d657500e7e7df6d4d19c76cab425,8b2db549b8383b88a92e46d43bae86dffbc0b077,TODO: assert n_samples == 1 for discrete layers (run time)
aaidijded,thu-ml/zhusuan,tests/test_layers.py,6db1ce0d902e0d5ca50eaadb8d1a502c42b82848,STILL_EXISTS,TODO: test values
aaidijejh,thu-ml/zhusuan,tests/model/test_base.py,2a84fba6df541f3bb80556403acf48d73c1b4319,776b06718a639c957d0138b65d4caa64005c7416,TODO: add control flow test for StochasticGraph.get_output
aaidijfbc,thu-ml/zhusuan,tests/model/test_base.py,2a84fba6df541f3bb80556403acf48d73c1b4319,776b06718a639c957d0138b65d4caa64005c7416,TODO: deal with name_scope conflicts when copying batch_norm
aaidijfef,thu-ml/zhusuan,zhusuan/distributions.py,2a84fba6df541f3bb80556403acf48d73c1b4319,STILL_EXISTS,TODO: as_list() fails when static_shape unknown
aaidijfgc,thu-ml/zhusuan,zhusuan/model/base.py,2a84fba6df541f3bb80556403acf48d73c1b4319,776b06718a639c957d0138b65d4caa64005c7416,fix for control flow (Copy _control_flow_context)
aaidijged,thu-ml/zhusuan,docs/conf.py,776b06718a639c957d0138b65d4caa64005c7416,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaidijiaa,thu-ml/zhusuan,examples/variational_dropout.py,776b06718a639c957d0138b65d4caa64005c7416,STILL_EXISTS,TODO: there seems to be a bug because h are using relu already
aaidijiab,thu-ml/zhusuan,examples/variational_dropout.py,776b06718a639c957d0138b65d4caa64005c7416,STILL_EXISTS,TODO: why sampling here?
aaidijicj,thu-ml/zhusuan,tests/distributions/test_multivariate.py,776b06718a639c957d0138b65d4caa64005c7416,STILL_EXISTS,TODO: This failed with a bug in Tensorflow; waiting fix.
aaidijidd,thu-ml/zhusuan,tests/distributions/test_multivariate.py,776b06718a639c957d0138b65d4caa64005c7416,STILL_EXISTS,TODO: fix for case when alpha=1; given=0
aaidijidg,thu-ml/zhusuan,tests/distributions/test_univariate.py,776b06718a639c957d0138b65d4caa64005c7416,STILL_EXISTS,TODO: test sample value
aaidijidh,thu-ml/zhusuan,tests/distributions/test_univariate.py,776b06718a639c957d0138b65d4caa64005c7416,STILL_EXISTS,TODO: test sample type
aaidijiha,thu-ml/zhusuan,tests/model/test_base.py,776b06718a639c957d0138b65d4caa64005c7416,STILL_EXISTS,TODO: test all operators
aaidijjij,thu-ml/zhusuan,zhusuan/distributions/multivariate.py,776b06718a639c957d0138b65d4caa64005c7416,STILL_EXISTS,fix of no static shape inference for tf.lbeta
aaidijjjc,thu-ml/zhusuan,zhusuan/distributions/univariate.py,776b06718a639c957d0138b65d4caa64005c7416,STILL_EXISTS,TODO: add type argument for distributions
aaidijjje,thu-ml/zhusuan,zhusuan/distributions/univariate.py,776b06718a639c957d0138b65d4caa64005c7416,STILL_EXISTS,TODO: not right when given=0 or 1
aaidjaaaa,thu-ml/zhusuan,zhusuan/distributions_old.py,776b06718a639c957d0138b65d4caa64005c7416,STILL_EXISTS,TODO: check maxval > minval
aaidjaaab,thu-ml/zhusuan,zhusuan/distributions_old.py,776b06718a639c957d0138b65d4caa64005c7416,STILL_EXISTS,TODO: as_list() fails when static_shape unknown
aaidjaacj,thu-ml/zhusuan,zhusuan/hmc.py,776b06718a639c957d0138b65d4caa64005c7416,00caa44ce19cb2bc0ed43ba0b957b50092b7ee9c,TODO make sure adapt_step_size is a placeholder
aaidjaadb,thu-ml/zhusuan,zhusuan/hmc.py,776b06718a639c957d0138b65d4caa64005c7416,STILL_EXISTS,TODO incorrect shape?
aaidjacea,thu-ml/zhusuan,zhusuan/distributions/univariate.py,0c3c4e7122e480b482027cbcfbdffb783d11573f,STILL_EXISTS,TODO: not right when given=0 or 1
aaidjacgc,thu-ml/zhusuan,zhusuan/distributions/utils.py,0c3c4e7122e480b482027cbcfbdffb783d11573f,STILL_EXISTS,Below code seems to induce a BUG when this function is
aaidjacja,thu-ml/zhusuan,zhusuan/variational.py,c8c3d961c00dbc736be694cb45f771594e60e163,STILL_EXISTS,TODO: check ndim of sample axis should be larger than 1; else raise.
aaidjacjh,thu-ml/zhusuan,examples/dlgm_nf.py,8c15e8057ef454e76aadbbf9263e0953e507d451,STILL_EXISTS,TODO: add tests for repeated calls of flows
aaidjacjj,thu-ml/zhusuan,zhusuan/transform.py,8c15e8057ef454e76aadbbf9263e0953e507d451,STILL_EXISTS,TODO: check input shape (N>=2) of samples; hidden and log_probs.
aaidjadab,thu-ml/zhusuan,zhusuan/transform.py,8c15e8057ef454e76aadbbf9263e0953e507d451,STILL_EXISTS,TODO: properly deal with hidden.
aaidjadac,thu-ml/zhusuan,zhusuan/variational.py,18753ac0791729dd8ecb9cc92e3627dec225594b,STILL_EXISTS,TODO: extend to non-scalar
aaidjadid,thu-ml/zhusuan,setup.py,8f50c1db66f75b0c0b1cfc511f3335f56eac3542,STILL_EXISTS,You can just specify the packages manually here if your project is
aaidjafje,thu-ml/zhusuan,tests/distributions/test_multivariate.py,c3642b98b1cb166f5d29031b555c527ad019c2ca,STILL_EXISTS,TODO: This failed with a bug in Tensorflow; waiting fix.
aaidjagbi,thu-ml/zhusuan,tests/distributions/utils.py,c3642b98b1cb166f5d29031b555c527ad019c2ca,STILL_EXISTS,TODO: This failed with a bug in Tensorflow in Dirichlet.
aaidjbbei,thu-ml/zhusuan,zhusuan/evaluation.py,69352436e7c1a3ffd9340a1f70aa08595179885c,b9bd12205823d01c4f3ca17e1a7fc7ed86d64c3e,TODO: remove dependencies on scipy
aaidjbbgg,thu-ml/zhusuan,zhusuan/distributions/multivariate.py,ce7c0c03205e66a47d85b1ad2016ea481543c7e3,STILL_EXISTS,TODO: Add Gumbel distribution
aaidjbbgh,thu-ml/zhusuan,zhusuan/distributions/univariate.py,ce7c0c03205e66a47d85b1ad2016ea481543c7e3,STILL_EXISTS,TODO: add Logistic distribution
aaidjbcbb,thu-ml/zhusuan,zhusuan/variational/base.py,0ffd15a58a542761309594a7d71eecb73a5ce8d2,STILL_EXISTS,TODO: Add input name check by matching them
aaidjbcbg,thu-ml/zhusuan,zhusuan/variational/exclusive_kl.py,0ffd15a58a542761309594a7d71eecb73a5ce8d2,STILL_EXISTS,TODO: extend to non-scalar; add variance_reduction option which
aaidjbcfg,thu-ml/zhusuan,tests/variational/test_monte_carlo.py,60cc4ec7399c2b8869db96c34c6be03a09db4598,85f3e861a52605394d11062e3e6c6f980f8f49bd,TODO: test k=1 equal to elbo
aaidjbcfh,thu-ml/zhusuan,tests/variational/test_monte_carlo.py,60cc4ec7399c2b8869db96c34c6be03a09db4598,85f3e861a52605394d11062e3e6c6f980f8f49bd,TODO: test with k increase; increase
aaidjbcfi,thu-ml/zhusuan,tests/variational/test_monte_carlo.py,60cc4ec7399c2b8869db96c34c6be03a09db4598,85f3e861a52605394d11062e3e6c6f980f8f49bd,TODO: test when k=1; gradients equal to elbo grads
aaidjbcfj,thu-ml/zhusuan,tests/variational/test_monte_carlo.py,60cc4ec7399c2b8869db96c34c6be03a09db4598,85f3e861a52605394d11062e3e6c6f980f8f49bd,TODO: test grads with variance reduction equal to grads without it.
aaidjbcgg,thu-ml/zhusuan,tests/variational/test_exclusive_kl.py,4ff74906830c3666330d1ed1c4a535dabbde589b,STILL_EXISTS,TODO: Check grads when use variance reduction and baseline
aaidjbcgh,thu-ml/zhusuan,zhusuan/variational/exclusive_kl.py,4ff74906830c3666330d1ed1c4a535dabbde589b,STILL_EXISTS,TODO: fix get variable failure for repeated calls.
aaidjbhhb,thu-ml/zhusuan,zhusuan/framework/bn.py,c4cde59a41903468b27a5f6fea7a4340d96ad8af,STILL_EXISTS,TODO: __str__; __repr__ for StochasticTensor
aaidjbhhe,thu-ml/zhusuan,zhusuan/framework/bn.py,c4cde59a41903468b27a5f6fea7a4340d96ad8af,bd6701b2fc218437b5ed713d4e915604af1cd4d8,TODO: check whether `self` is BayesianNet or _BayesianNet
aaidjbhhh,thu-ml/zhusuan,zhusuan/framework/meta_bn.py,c4cde59a41903468b27a5f6fea7a4340d96ad8af,5701578a4ee79560125fc324b6914891bf0984ec,TODO: Whether to copy?
aaidjbhhi,thu-ml/zhusuan,zhusuan/framework/meta_bn.py,c4cde59a41903468b27a5f6fea7a4340d96ad8af,5701578a4ee79560125fc324b6914891bf0984ec,TODO: make args and kwargs changeable after construction.
aaidjbhhj,thu-ml/zhusuan,zhusuan/variational/base.py,c4cde59a41903468b27a5f6fea7a4340d96ad8af,7d128e3d9f47aa45d8ada4f0b2a1204138c181ad,TODO: raise warning
aaidjbhib,thu-ml/zhusuan,zhusuan/variational/base.py,c4cde59a41903468b27a5f6fea7a4340d96ad8af,7d128e3d9f47aa45d8ada4f0b2a1204138c181ad,TODO: remove v_log_probs
aaidjbhic,thu-ml/zhusuan,zhusuan/variational/base.py,c4cde59a41903468b27a5f6fea7a4340d96ad8af,7d128e3d9f47aa45d8ada4f0b2a1204138c181ad,TODO: Whether to copy?
aaidjbiaa,thu-ml/zhusuan,zhusuan/variational/base.py,f86abdce252e773079767cdbdb388deffdd857c0,STILL_EXISTS,TODO: cache bn for the same `meta_model` and `variational`.
aaidjbiab,thu-ml/zhusuan,zhusuan/framework/utils.py,beac7f30d51243818106ae84c206c72663b560e7,8eb78607808dcc81b7207c90379d0bfa347ef451,TODO: raise warning
aaidjbiac,thu-ml/zhusuan,zhusuan/variational/inclusive_kl.py,beac7f30d51243818106ae84c206c72663b560e7,8eb78607808dcc81b7207c90379d0bfa347ef451,TODO: raise warning
aaidjbijc,thu-ml/zhusuan,zhusuan/sgmcmc.py,3fb4e7502eee3c0bcefdd067422903c5863c6726,STILL_EXISTS,TODO: 1. enable matrix parameter 2. enable mass setting\uFFFF
aaidjbjdf,thu-ml/zhusuan,examples/variational_autoencoders/vae_conv.py,89d6b0021a91b3e3206d095ab59416740c77c6b3,87bffe223e413ec1c10e9d8faa9f06a8e1f2d8a3,TODO: fix n for generation
aaidjbjhh,thu-ml/zhusuan,examples/normalizing_flows/vae_nf.py,794c8fc3458437a3e8a15203ebb1c385d5746c7c,STILL_EXISTS,TODO: add tests for repeated calls of flows
aaidjbjje,thu-ml/zhusuan,examples/semi_supervised_vae/vae_ssl.py,bd13d81a8558c03b78bbeb7f8fbfbbbe483c29cb,STILL_EXISTS,TODO: n not match.
aaidjcafg,thu-ml/zhusuan,zhusuan/hmc.py,789053db0b3dde3047227648164b88ec8be11d8f,STILL_EXISTS,TODO: raise warning
aaidjcafj,thu-ml/zhusuan,zhusuan/sgmcmc.py,dd8b56dea6e10db69f2664acd9d4d97697a7f53d,STILL_EXISTS,TODO: raise warning
aaidjcagc,thu-ml/zhusuan,zhusuan/framework/bn.py,6ac9eeaffe2db442d2ae8d6a5fdfcc70ff0a17a3,STILL_EXISTS,TODO: Deprecate deterministic?
aaidjcajb,thu-ml/zhusuan,zhusuan/sgmcmc.py,162a6098c2995f7678606ed603e97bdf1a1c15f7,STILL_EXISTS,TODO: remove direct; use tf.greater
aaidjcbbb,thu-ml/zhusuan,zhusuan/framework/bn.py,1d1877a14876d7b6fbbaa56106b37da6227ab1c7,4e9230af8cb389d86fdae4d5c62a39da9aeb37da,TODO: Deprecate and prefer unbounded input as function arguments
aaidjcbcj,thu-ml/zhusuan,zhusuan/framework/bn.py,7dbfc0de0b5dd12916cb859d984a75115534df60,88f036fa2d525fcf77f109ce4a2df9cc52c15f48,TODO: the cache only works when no further nodes is added to the bn;
aaidjccdc,thu-ml/zhusuan,zhusuan/framework/bn.py,7fd4bb9686f5b5a1b8352d5a16ccfd3157232317,88f036fa2d525fcf77f109ce4a2df9cc52c15f48,TODO: check whether `self` is BayesianNet or _BayesianNet
aaidjccgc,thu-ml/zhusuan,zhusuan/variational/base.py,016028298f17ec6626807f02787292cf5f55effb,7d128e3d9f47aa45d8ada4f0b2a1204138c181ad,TODO: remove v_log_probs
aaidjcgbc,pytorch/text,torchtext/datasets/snli.py,360699caeac546575818b7082f8478b54d1319f8,dd01cd4dd9a6a6839a40cc62492836b15b153d86,TODO
aaidjcgea,pytorch/text,torchtext/datasets/sstb.py,24fc3908edacaa24e1c9cc7a9ee9e60695be85fb,cc77efd945c76ed0f4846f3a7d7daf554d7ce9a3,perhaps interleave keys is not required here?
aaidjchag,pytorch/text,torchtext/datasets/sstb.py,91110d73446f1ffa98e73b22d8acfd3a7fd9b7c8,96380308644cef8c5d725b0420ec7a5e5a5e65de,perhaps interleave keys is not required here?
aaidjcjfb,pytorch/text,docs/source/conf.py,80f3eb099eb1eec667d103372cabdf5e12edf6a6,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaidjcjjb,pytorch/text,docs/source/conf.py,80f3eb099eb1eec667d103372cabdf5e12edf6a6,STILL_EXISTS,`kw` catches `env=None` needed for newer sphinx while maintaining
aaidjdaac,pytorch/text,test/sequence_tagging.py,7a2e4420ecdad958e352ff629d0576f7c9bb8048,STILL_EXISTS,We can also define more than two columns.
aaidjdbca,pytorch/text,torchtext/datasets/sequence_tagging.py,7ab2ee56c3590ec112da81cf09dcc057c0563b6b,STILL_EXISTS,HACK: Saving the sort key function as the split() call removes it
aaidjdbce,pytorch/text,torchtext/datasets/sequence_tagging.py,7ab2ee56c3590ec112da81cf09dcc057c0563b6b,STILL_EXISTS,HACK: Set the sort key
aaidjdbhj,pytorch/text,torchtext/vocab.py,9902e793a0c5f5f0a8fccee09684c5cd4a86afea,STILL_EXISTS,TODO (@mttk): Populate classs with default values of special symbols
aaidjdbjd,pytorch/text,test/data/test_builtin_datasets.py,844f4038b4ab86835322863e3df1266ec8bc7ebb,STILL_EXISTS,smoke test to ensure ag_news dataset works properly
aaidjdbjh,pytorch/text,torchtext/datasets/text_classification.py,b743ea9b19fcfd5230a0715957454095b90ad060,25fe0b2d9206788cccb626fbd9d0f7e87547c270,TODO: Replicate below
aaidjdcac,pytorch/text,torchtext/datasets/text_classification.py,b743ea9b19fcfd5230a0715957454095b90ad060,11b030dadd33dc13f21e214d7bab6e0c9434f9e0,TODO: Clean up and use Vocab object
aaidjdcaf,pytorch/text,test/data/test_utils.py,944d98cd09c493806b7f24553e4b0c20466c7118,00e793f42c6bd230082b0c5c136c36fc9c87be26,TODO: Remove this once issue was been resolved.
aaidjdcbf,pytorch/text,torchtext/data/utils.py,038515ccfb6df094e22b9f14fcf9d8c391bc4eaa,STILL_EXISTS,TODO: Write more tests!
aaidjdcdi,pytorch/text,test/data/test_utils.py,4b1941bbed4e68bd8b5977086004db56cd4e0d71,00e793f42c6bd230082b0c5c136c36fc9c87be26,TODO# Add nltk data back in build_tools\/travis\/install.sh.
aaidjdddi,pytorch/text,torchtext/data/metrics.py,f765e517d6cc456b876715f1cb9c843bec8b14ed,STILL_EXISTS,TODO: no need to loop through the whole counter
aaidjddjd,pytorch/text,test/data/test_functional.py,9bdfb046c37b473accb7749ecc7c8e0e4ed7860f,STILL_EXISTS,So as workaround we copy the asset data to temporary directory and load it from there.
aaidjdgdj,pytorch/text,torchtext/experimental/datasets/raw/translation.py,902287160cfb83a81d3f6e22e878877bfcf3de71,STILL_EXISTS,TODO: Fix utf-8 next line mark
aaidjdgij,pytorch/text,test/experimental/test_vectors.py,f4d40ac43da37b253ff24ee5c1b2b4ece23105fe,STILL_EXISTS,TODO (Nayef211): use self.assertRaisesRegex() to check
aaidjeahe,pytorch/text,examples/BERT/metrics.py,6521e7b0fd4240c19e9fff2240a87270daf36324,STILL_EXISTS,white space fix
aaidjebaa,pytorch/text,test/experimental/test_vectors.py,3f433ba1610eadf1688b69cbc65964de45bf2a11,8d0f7cb1babdbcedf7669f2dfddc88cd4c030167,TODO: reenable test once the GloVe dataset url starts working
aaidjebff,pytorch/text,benchmark/benchmark_pytext_vocab.py,8b13166bbbd14106a64ea1354b94dfcee964764a,STILL_EXISTS,this is needed because we want to add 'torchtext\/examples\/data_pipeline' directory to the
aaidjecic,pytorch/text,test/experimental/test_vectors.py,b6af5927b9d2318f0474adfa237f81fd9aa251d8,ad854116e4d8c3f88c08cb2de9b10b6d54e0acbb,TODO: reenable test once the GloVe dataset url starts working
aaidjedjb,pytorch/text,test/experimental/test_vocab.py,ee177fefcfc038e7148497018cec1c9a8c944383,STILL_EXISTS,[TODO]
aaidjeeih,pytorch/text,test/experimental/test_vectors.py,5542685816b24b682b877d9dd4cf26b2206c9739,STILL_EXISTS,TODO: use self.assertRaisesRegex() to check
aaidjefgc,pytorch/text,torchtext/experimental/datasets/raw/common.py,9053d951d2900a087cf33ea455143f2d14c1aad5,STILL_EXISTS,values is needed to support that behavior for new_fn as well.
aaidjefhc,pytorch/text,torchtext/experimental/datasets/raw/common.py,651c1f70ee6e75705aa1c5e4d4cf86ff69b6cbed,STILL_EXISTS,values is needed to support that behavior for new_fn as well.
aaidjehcd,pytorch/text,torchtext/experimental/datasets/raw/iwslt.py,2f3780976a9489d382c04f82e9e1d85ef011912d,STILL_EXISTS,TODO: Fix utf-8 next line mark
aaidjehdc,pytorch/text,torchtext/experimental/datasets/raw/wmt14.py,2f3780976a9489d382c04f82e9e1d85ef011912d,34338f9975eb9bcce1a72ba1cf0ba4597ca3dda2,TODO: Fix utf-8 next line mark
aaidjehef,pytorch/text,torchtext/datasets/iwslt2016.py,57a178ff42dcf3c55fbc9698b536e3c3146903d6,34338f9975eb9bcce1a72ba1cf0ba4597ca3dda2,TODO: Fix utf-8 next line mark
aaidjehfd,pytorch/text,torchtext/datasets/iwslt2017.py,57a178ff42dcf3c55fbc9698b536e3c3146903d6,34338f9975eb9bcce1a72ba1cf0ba4597ca3dda2,TODO: Fix utf-8 next line mark
aaidjehgh,pytorch/text,torchtext/experimental/datasets/raw/wmt14.py,036df73d5ae17cbcd71a9a2d8efb3dd034d79c75,34338f9975eb9bcce1a72ba1cf0ba4597ca3dda2,TODO: Fix utf-8 next line mark
aaidjehhc,pytorch/text,torchtext/data/datasets_utils.py,34338f9975eb9bcce1a72ba1cf0ba4597ca3dda2,STILL_EXISTS,TODO: Fix utf-8 next line mark
aaidjeihc,biolab/orange3,setup.py,06d29c889fd7c916c2d7baf1db95536af7c865f2,6426d029f7eb96450b1baee64b2ead7ca9eb722c,no further work needed
aaidjeihj,biolab/orange3,setup.py,06d29c889fd7c916c2d7baf1db95536af7c865f2,6426d029f7eb96450b1baee64b2ead7ca9eb722c,A Hack to unlink liborange.so -> orange.so if it already exists;
aaidjejaa,biolab/orange3,Orange/__init__.py,1c65f8552ef4a590cffa50cc43e5777708cc2509,5cf6b70c5115524f48b8f3eb9a2ce2632ebe4933,Definitely ugly; but I see no other workaround.
aaidjejbe,biolab/orange3,Orange/data/io.py,1c65f8552ef4a590cffa50cc43e5777708cc2509,771c291ba16d1db00de18b3980490fcc2308813d,sparse data format; begin with '{'; ends with '}'
aaidjejdg,biolab/orange3,Orange/data/io.py,1c65f8552ef4a590cffa50cc43e5777708cc2509,STILL_EXISTS,max 5MB sample TODO: What if this is not enough. Try with a bigger sample
aaidjejeh,biolab/orange3,Orange/data/io.py,1c65f8552ef4a590cffa50cc43e5777708cc2509,STILL_EXISTS,TODO: Other unknown strings?
aaidjejej,biolab/orange3,Orange/data/io.py,1c65f8552ef4a590cffa50cc43e5777708cc2509,771c291ba16d1db00de18b3980490fcc2308813d,TODO escape spaces
aaidjejff,biolab/orange3,Orange/data/io.py,1c65f8552ef4a590cffa50cc43e5777708cc2509,STILL_EXISTS,TODO: windows drive letters.
aaidjejfh,biolab/orange3,Orange/data/variable.py,1c65f8552ef4a590cffa50cc43e5777708cc2509,f0304f160ddb7afac211f327df6c1b7e4a260938,\"\"\" || ======================== || Variables (``variable``) || ======================== ||  || Data instances in Orange can contain several types of variables: || :ref:`discrete <discrete>`; :ref:`continuous <continuous>`; || :ref:`strings <string>`; and :ref:`Python <Python>` and types derived from it. || The latter represent arbitrary Python objects. || The names; types; values (where applicable); functions for computing the || variable value from values of other variables; and other properties of the || variables are stored in descriptor classes defined in this module. ||  || Variable descriptors || -------------------- ||  || Variable descriptors can be constructed either directly; using  || constructors and passing attributes as parameters; or by a  || factory function :func:`Orange.data.variable.make`; which either  || retrieves an existing descriptor or constructs a new one. ||  || .. class:: Variable ||  ||     An abstract base class for variable descriptors. ||  ||     .. attribute:: name ||  ||         The name of the variable. Variable names do not need to be unique since two ||         variables are considered the same only if they have the same descriptor ||         (e.g. even multiple variables in the same table can have the same name). ||         This should; however; be avoided since it may result in unpredictable ||         behavior. ||      ||     .. attribute:: var_type ||         ||         Variable type; it can be Orange.data.Type.Discrete; ||         Orange.data.Type.Continuous; Orange.data.Type.String or ||         Orange.data.Type.Other.   ||  ||     .. attribute:: get_value_from ||  ||         A function (an instance of :obj:`Orange.classification.Classifier`) which computes ||         a value of the variable from values of one or more other variables. This ||         is used; for instance; in discretization where the variables describing ||         the discretized variable are computed from the original variable.  ||  ||     .. attribute:: ordered ||      ||         A flag telling whether the values of a discrete variable are ordered. At ||         the moment; no built-in method treats ordinal variables differently than ||         nominal ones. ||      ||     .. attribute:: distributed ||      ||         A flag telling whether the values of the variables are distributions. ||         As for the flag ordered; no methods treat such variables in any special ||         manner. ||      ||     .. attribute:: random_generator ||      ||         A local random number generator used by method ||         :obj:`Variable.random_value`. ||      ||     .. attribute:: default_meta_id ||      ||         A proposed (but not guaranteed) meta id to be used for that variable. ||         This is used; for instance; by the data loader for tab-delimited file ||         format instead of assigning an arbitrary new value; or by ||         :obj:`Orange.data.new_meta_id` if the variable is passed as an argument.  ||          ||     .. attribute:: attributes ||          ||         A dictionary which allows the user to store additional information ||         about the variable. All values should be strings. See the section  ||         about :ref:`storing additional information <attributes>`. ||  ||     .. method:: __call__(obj) ||      ||            Convert a string; number; or other suitable object into a variable ||            value. ||             ||            :param obj: An object to be converted into a variable value ||            :type o: any suitable ||            :rtype: :class:`Orange.data.Value` ||         ||     .. method:: randomvalue() ||  ||            Return a random value for the variable. ||         ||            :rtype: :class:`Orange.data.Value` ||         ||     .. method:: compute_value(inst) ||  ||            Compute the value of the variable given the instance by calling ||            obj:`~Variable.get_value_from` through a mechanism that prevents deadlocks by ||            circular calls. ||  ||            :rtype: :class:`Orange.data.Value` ||  || .. _discrete: || .. class:: Discrete ||  ||     Bases: :class:`Variable` ||     ||     Descriptor for discrete variables. ||      ||     .. attribute:: values ||      ||         A list with symbolic names for variables' values. Values are stored as ||         indices referring to this list. Therefore; modifying this list  ||         instantly changes the (symbolic) names of values as they are printed out or ||         referred to by user. ||      ||         .. note:: ||          ||             The size of the list is also used to indicate the number of ||             possible values for this variable. Changing the size - especially ||             shrinking the list - can have disastrous effects and is therefore not ||             really recommended. Also; do not add values to the list by ||             calling its append or extend method: call the :obj:`add_value` ||             method instead. ||  ||             It is also assumed that this attribute is always defined (but can ||             be empty); so never set it to None. ||      ||     .. attribute:: base_value ||  ||             Stores the base value for the variable as an index in `values`. ||             This can be; for instance; a \"normal\" value; such as \"no ||             complications\" as opposed to abnormal \"low blood pressure\". The ||             base value is used by certain statistics; continuization etc. ||             potentially; learning algorithms. The default is -1 which means that ||             there is no base value. ||      ||     .. method:: add_value ||      ||             Add a value to values. Always call this function instead of ||             appending to values. ||  || .. _continuous: || .. class:: Continuous ||  ||     Bases: :class:`Variable` ||  ||     Descriptor for continuous variables. ||      ||     .. attribute:: number_of_decimals ||      ||         The number of decimals used when the value is printed out; converted to ||         a string or saved to a file. ||      ||     .. attribute:: scientific_format ||      ||         If ``True``; the value is printed in scientific format whenever it ||         would have more than 5 digits. In this case; :obj:`number_of_decimals` is ||         ignored. ||  ||     .. attribute:: adjust_decimals ||      ||         Tells Orange to monitor the number of decimals when the value is ||         converted from a string (when the values are read from a file or ||         converted by; e.g. ``inst[0]=\"3.14\"``):  ||         0: the number of decimals is not adjusted automatically; ||         1: the number of decimals is (and has already) been adjusted; ||         2: automatic adjustment is enabled; but no values have been converted yet. ||  ||         By default; adjustment of the number of decimals goes as follows: ||      ||         If the variable was constructed when data was read from a file; it will  ||         be printed with the same number of decimals as the largest number of  ||         decimals encountered in the file. If scientific notation occurs in the  ||         file; :obj:`scientific_format` will be set to ``True`` and scientific format  ||         will be used for values too large or too small.  ||      ||         If the variable is created in a script; it will have; by default; three ||         decimal places. This can be changed either by setting the value ||         from a string (e.g. ``inst[0]=\"3.14\"``; but not ``inst[0]=3.14``) or by ||         manually setting the :obj:`number_of_decimals`. ||  ||     .. attribute:: start_value; end_value; step_value ||      ||         The range used for :obj:`randomvalue`. ||  || .. _String: || .. class:: String ||  ||     Bases: :class:`Variable` ||  ||     Descriptor for variables that contain strings. No method can use them for  ||     learning; some will complain and others will silently ignore them when they  ||     encounter them. They can be; however; useful for meta-attributes; if  ||     instances in a dataset have unique IDs; the most efficient way to store them  ||     is to read them as meta-attributes. In general; never use discrete  ||     attributes with many (say; more than 50) values. Such attributes are  ||     probably not of any use for learning and should be stored as string ||     attributes. ||  ||     When converting strings into values and back; empty strings are treated  ||     differently than usual. For other types; an empty string can be used to ||     denote undefined values; while :obj:`String` will take empty strings ||     as empty strings -- except when loading or saving into file. ||     Empty strings in files are interpreted as undefined; to specify an empty ||     string; enclose the string in double quotes; these are removed when the ||     string is loaded. ||  || .. _Python: || .. class:: Python ||  ||     Bases: :class:`Variable` ||  ||     Base class for descriptors defined in Python. It is fully functional ||     and can be used as a descriptor for attributes that contain arbitrary Python ||     values. Since this is an advanced topic; PythonVariables are described on a  ||     separate page. !!TODO!! ||      ||      || Variables computed from other variables || --------------------------------------- ||  || Values of variables are often computed from other variables; such as in || discretization. The mechanism described below usually functions behind the scenes; || so understanding it is required only for implementing specific transformations. ||  || Monk 1 is a well-known dataset with target concept ``y := a==b or e==1``. || It can help the learning algorithm if the four-valued attribute ``e`` is || replaced with a binary attribute having values `\"1\"` and `\"not 1\"`. The || new variable will be computed from the old one on the fly.  ||  || .. literalinclude:: code\/variable-get_value_from.py ||     :lines: 7-17 ||      || The new variable is named ``e2``; we define it with a descriptor of type  || :obj:`Discrete`; with appropriate name and values ``\"not 1\"`` and ``1`` (we  || chose this order so that the ``not 1``'s index is ``0``; which can be; if  || needed; interpreted as ``False``). Finally; we tell e2 to use  || ``checkE`` to compute its value when needed; by assigning ``checkE`` to  || ``e2.get_value_from``.  ||  || ``checkE`` is a function that is passed an instance and another argument we  || do not care about here. If the instance's ``e`` equals ``1``; the function  || returns value ``1``; otherwise it returns ``not 1``. Both are returned as  || values; not plain strings. ||  || In most circumstances the value of ``e2`` can be computed on the fly - we can  || pretend that the variable exists in the data; although it does not (but  || can be computed from it). For instance; we can compute the information gain of || variable ``e2`` or its distribution without actually constructing data containing || the new variable. ||  || .. literalinclude:: code\/variable-get_value_from.py ||     :lines: 19-22 ||  || There are methods which cannot compute values on the fly because it would be || too complex or time consuming. In such cases; the data need to be converted || to a new :obj:`Orange.data.Table`:: ||  ||     new_domain = Orange.data.Domain([data.domain[\"a\"]; data.domain[\"b\"]; e2; data.domain.class_var]) ||     new_data = Orange.data.Table(new_domain; data)  ||  || Automatic computation is useful when the data is split into training and  || testing examples. Training instances can be modified by adding; removing  || and transforming variables (in a typical setup; continuous variables  || are discretized prior to learning; therefore the original variables are  || replaced by new ones). Test instances; on the other hand; are left as they  || are. When they are classified; the classifier automatically converts the  || testing instances into the new domain; which includes recomputation of  || transformed variables.  ||  || .. literalinclude:: code\/variable-get_value_from.py ||     :lines: 24- ||  || .. _attributes: ||  || Storing additional variables || ----------------------------- ||  || All variables have a field :obj:`~Variable.attributes`; a dictionary || which can contain strings. Although the current implementation allows all || types of value we strongly advise to use only strings. An example: ||  || .. literalinclude:: code\/attributes.py ||  || These attributes can only be saved to a .tab file. They are listed in the || third line in <name>=<value> format; after other attribute specifications || (such as \"meta\" or \"class\"); and are separated by spaces.  ||  || .. _variable_descriptor_reuse: ||  || Reuse of descriptors || -------------------- ||  || There are situations when variable descriptors need to be reused. Typically; the  || user loads some training examples; trains a classifier; and then loads a separate || test set. For the classifier to recognize the variables in the second data set; || the descriptors; not just the names; need to be the same.  ||  || When constructing new descriptors for data read from a file or during unpickling; || Orange checks whether an appropriate descriptor (with the same name and; in case || of discrete variables; also values) already exists and reuses it. When new || descriptors are constructed by explicitly calling the above constructors; this || always creates new descriptors and thus new variables; although a variable with || the same name may already exist. ||  || The search for an existing variable is based on four attributes: the variable's name; || type; ordered values; and unordered values. As for the latter two; the values can  || be explicitly ordered by the user; e.g. in the second line of the tab-delimited  || file. For instance; sizes can be ordered as small; medium; or big. ||  || The search for existing variables can end with one of the following statuses. ||  || .. data:: Orange.data.variable.MakeStatus.NotFound (4) ||  ||     The variable with that name and type does not exist.  ||  || .. data:: Orange.data.variable.MakeStatus.Incompatible (3) ||  ||     There are variables with matching name and type; but their ||     values are incompatible with the prescribed ordered values. For example; ||     if the existing variable already has values [\"a\"; \"b\"] and the new one ||     wants [\"b\"; \"a\"]; the old variable cannot be reused. The existing list can; ||     however be appended with the new values; so searching for [\"a\"; \"b\"; \"c\"] would ||     succeed. Likewise a search for [\"a\"] would be successful; since the extra existing value ||     does not matter. The formal rule is thus that the values are compatible iff ``existing_values[:len(ordered_values)] == ordered_values[:len(existing_values)]``. ||  || .. data:: Orange.data.variable.MakeStatus.NoRecognizedValues (2) ||  ||     There is a matching variable; yet it has none of the values that the new ||     variable will have (this is obviously possible only if the new variable has ||     no prescribed ordered values). For instance; we search for a variable ||     \"sex\" with values \"male\" and \"female\"; while there is a variable of the same  ||     name with values \"M\" and \"F\" (or; well; \"no\" and \"yes\" :). Reuse of this  ||     variable is possible; though this should probably be a new variable since it  ||     obviously comes from a different data set. If we do decide to reuse the variable; the  ||     old variable will get some unneeded new values and the new one will inherit  ||     some from the old. ||  || .. data:: Orange.data.variable.MakeStatus.MissingValues (1) ||  ||     There is a matching variable with some of the values that the new one  ||     requires; but some values are missing. This situation is neither uncommon  ||     nor suspicious: in case of separate training and testing data sets there may ||     be values which occur in one set but not in the other. ||  || .. data:: Orange.data.variable.MakeStatus.OK (0) ||  ||     There is a perfect match which contains all the prescribed values in the ||     correct order. The existing variable may have some extra values; though. ||  || Continuous variables can obviously have only two statuses;  || :obj:`~Orange.data.variable.MakeStatus.NotFound` or :obj:`~Orange.data.variable.MakeStatus.OK`. ||  || When loading the data using :obj:`Orange.data.Table`; Orange takes the safest  || approach and; by default; reuses everything that is compatible up to  || and including :obj:`~Orange.data.variable.MakeStatus.NoRecognizedValues`. Unintended reuse would be obvious from the || variable having too many values; which the user can notice and fix. More on that  || in the page on `loading data`. !!TODO!! ||  || There are two functions for reusing the variables instead of creating new ones. ||  || .. function:: Orange.data.variable.make(name; type; ordered_values; unordered_values[; create_new_on]) ||  ||     Find and return an existing variable or create a new one if none of the existing ||     variables matches the given name; type and values. ||      ||     The optional `create_new_on` specifies the status at which a new variable is ||     created. The status must be at most :obj:`~Orange.data.variable.MakeStatus.Incompatible` since incompatible (or ||     non-existing) variables cannot be reused. If it is set lower; for instance  ||     to :obj:`~Orange.data.variable.MakeStatus.MissingValues`; a new variable is created even if there exists ||     a variable which is only missing the same values. If set to :obj:`~Orange.data.variable.MakeStatus.OK`; the function ||     always creates a new variable. ||      ||     The function returns a tuple containing a variable descriptor and the ||     status of the best matching variable. So; if ``create_new_on`` is set to ||     :obj:`~Orange.data.variable.MakeStatus.MissingValues`; and there exists a variable whose status is; say; ||     :obj:`~Orange.data.variable.MakeStatus.NoRecognizedValues`; a variable would be created; while the second  ||     element of the tuple would contain :obj:`~Orange.data.variable.MakeStatus.NoRecognizedValues`. If; on the other ||     hand; there exists a variable which is perfectly OK; its descriptor is  ||     returned and the returned status is :obj:`~Orange.data.variable.MakeStatus.OK`. The function returns no  ||     indicator whether the returned variable is reused or not. This can be; ||     however; read from the status code: if it is smaller than the specified ||     ``create_new_on``; the variable is reused; otherwise a new descriptor has been constructed. ||  ||     The exception to the rule is when ``create_new_on`` is OK. In this case; the  ||     function does not search through the existing variables and cannot know the  ||     status; so the returned status in this case is always :obj:`~Orange.data.variable.MakeStatus.OK`. ||  ||     :param name: Variable name ||     :param type: Variable type ||     :type type: Orange.data.variable.Type ||     :param ordered_values: a list of ordered values ||     :param unordered_values: a list of values; for which the order does not ||         matter ||     :param create_new_on: gives the condition for constructing a new variable instead ||         of using the new one ||      ||     :return_type: a tuple (:class:`Orange.data.variable.Variable`; int) ||      || .. function:: Orange.data.variable.retrieve(name; type; ordered_values; onordered_values[; create_new_on]) ||  ||     Find and return an existing variable; or :obj:`None` if no match is found. ||      ||     :param name: variable name. ||     :param type: variable type. ||     :type type: Orange.data.variable.Type ||     :param ordered_values: a list of ordered values ||     :param unordered_values: a list of values; for which the order does not ||         matter ||     :param create_new_on: gives the condition for constructing a new variable instead ||         of using the new one ||  ||     :return_type: :class:`Orange.data.variable.Variable` ||      || These following examples (from :download:`variable-reuse.py <code\/variable-reuse.py>`) give the shown results if || executed only once (in a Python session) and in this order. ||  || :func:`Orange.data.variable.make` can be used for the construction of new variables. :: ||      ||     >>> v1; s = Orange.data.variable.make(\"a\"; Orange.data.Type.Discrete; [\"a\"; \"b\"]) ||     >>> print s; v1.values ||     4 <a; b> ||  || No surprises here: a new variable is created and the status is :obj:`~Orange.data.variable.MakeStatus.NotFound`. :: ||  ||     >>> v2; s = Orange.data.variable.make(\"a\"; Orange.data.Type.Discrete; [\"a\"]; [\"c\"]) ||     >>> print s; v2 is v1; v1.values ||     1 True <a; b; c> ||  || The status is 1 (:obj:`~Orange.data.variable.MakeStatus.MissingValues`); yet the variable is reused (``v2 is v1``). || ``v1`` gets a new value; ``\"c\"``; which was given as an unordered value. It does || not matter that the new variable does not need the value ``b``. :: ||  ||     >>> v3; s = Orange.data.variable.make(\"a\"; Orange.data.Type.Discrete; [\"a\"; \"b\"; \"c\"; \"d\"]) ||     >>> print s; v3 is v1; v1.values ||     1 True <a; b; c; d> ||  || This is like before; except that the new value; ``d`` is not among the || ordered values. :: ||  ||     >>> v4; s = Orange.data.variable.make(\"a\"; Orange.data.Type.Discrete; [\"b\"]) ||     >>> print s; v4 is v1; v1.values; v4.values ||     3; False; <b>; <a; b; c; d> ||  || The new variable needs to have ``b`` as the first value; so it is incompatible  || with the existing variables. The status is thus 3 (:obj:`~Orange.data.variable.MakeStatus.Incompatible`); the two  || variables are not equal and have different lists of values. :: ||  ||     >>> v5; s = Orange.data.variable.make(\"a\"; Orange.data.Type.Discrete; None; [\"c\"; \"a\"]) ||     >>> print s; v5 is v1; v1.values; v5.values ||     0 True <a; b; c; d> <a; b; c; d> ||  || The new variable has values ``c`` and ``a``; but the order is not important;  || so the existing attribute is :obj:`~Orange.data.variable.MakeStatus.OK`. :: ||  ||     >>> v6; s = Orange.data.variable.make(\"a\"; Orange.data.Type.Discrete; None; [\"e\"]) \"a\"]) ||     >>> print s; v6 is v1; v1.values; v6.values ||     2 True <a; b; c; d; e> <a; b; c; d; e> ||  || The new variable has different values than the existing variable (status is 2; || :obj:`~Orange.data.variable.MakeStatus.NoRecognizedValues`); but the existing one is nonetheless reused. Note that we || gave ``e`` in the list of unordered values. If it was among the ordered; the || reuse would fail. :: ||  ||     >>> v7; s = Orange.data.variable.make(\"a\"; Orange.data.Type.Discrete; None; ||             [\"f\"]; Orange.data.variable.MakeStatus.NoRecognizedValues))) ||     >>> print s; v7 is v1; v1.values; v7.values ||     2 False <a; b; c; d; e> <f> ||  || This is the same as before; except that we prohibited reuse when there are no || recognized values. Hence a new variable is created; though the returned status is  || the same as before:: ||  ||     >>> v8; s = Orange.data.variable.make(\"a\"; Orange.data.Type.Discrete; ||             [\"a\"; \"b\"; \"c\"; \"d\"; \"e\"]; None; Orange.data.variable.MakeStatus.OK) ||     >>> print s; v8 is v1; v1.values; v8.values ||     0 False <a; b; c; d; e> <a; b; c; d; e> ||  || Finally; this is a perfect match; but any reuse is prohibited; so a new  || variable is created. ||  || \"\"\"
aaidjejih,biolab/orange3,setup.py,c404cba8e3f1f2f1bb329899b622c5e776b85469,STILL_EXISTS,TODO: Doc datasets and files should be installed using data_files.
aaidjejjc,biolab/orange3,setup.py,70a76272b2d8f45495479df682895787e63af5ab,STILL_EXISTS,TODO: Doc datasets and files should be installed using data_files.
aaidjejjj,biolab/orange3,setup.py,743e9ff8af5e75ef180fb429b87b3326f6d2d944,STILL_EXISTS,not conform to StrictVersion needed by bdist_msi).
aaidjfacg,biolab/orange3,Orange/__init__.py,aa2f2bd28a5dfc0b9b039f92abfe21269c8028ba,5cf6b70c5115524f48b8f3eb9a2ce2632ebe4933,TODO hide utils from the user
aaidjfadd,biolab/orange3,Orange/misc/__init__.py,2fece659e06d63d8c7e2af365e1af79b96b6a287,STILL_EXISTS,\"\"\" || .. index:: misc ||  || .. index: CostMatrix ||  || ----------------------- || CostMatrix || ----------------------- ||  || CostMatrix is an object that stores costs of (mis)classifications. Costs can be either negative or positive. ||  || .. class:: CostMatrix ||  ||     .. attribute:: class_var  ||          ||         The (class) attribute to which the matrix applies. This can ||         also be None. ||          ||     .. attribute:: dimension (read only) ||      ||         Matrix dimension; ie. number of classes. ||          ||     .. method:: CostMatrix(dimension[; default cost]) ||      ||         Constructs a matrix of the given size and initializes it with ||         the default cost (1; if not given). All elements of the matrix ||         are assigned the given cost; except for the diagonal that have ||         the default cost of 0.  (Diagonal elements represent correct ||         classifications and these usually have no price; you can; ||         however; change this.) ||          ||         .. literalinclude:: code\/CostMatrix.py ||             :lines: 1-8 ||          ||         This initializes the matrix and print it out: ||          ||         .. literalinclude:: code\/CostMatrix.res ||             :lines: 1-3 ||      ||     .. method:: CostMatrix(class descriptor[; default cost]) ||      ||         Similar as above; except that classVar is also set to the given descriptor. ||         The number of values of the given attribute (which must be discrete) is used ||         for dimension. ||          ||         .. literalinclude:: code\/CostMatrix.py ||             :lines: 10-11 ||              ||         This constructs a matrix similar to the one above (the class attribute in iris ||         domain is three-valued) except that the matrix contains 2s instead of 1s. ||          ||     .. method:: CostMatrix([attribute descriptor; ]matrix) ||      ||         Initializes the matrix with the elements given as a sequence of sequences (you ||         can mix lists and tuples if you find it funny). Each subsequence represents a row. ||          ||         .. literalinclude:: code\/CostMatrix.py ||             :lines: 13 ||  ||         If you print this matrix out; will it look like this: ||          ||         .. literalinclude:: code\/CostMatrix.res ||             :lines: 5-7 ||              ||     .. method:: setcost(predicted; correct; cost) ||      ||         Set the misclassification cost. The matrix above could be ||         constructed by first initializing it with 2s and then changing ||         the prices for virginica's into 1s. ||          ||         .. literalinclude:: code\/CostMatrix.py ||             :lines: 15-17 ||              ||     .. method:: getcost(predicted; correct) ||      ||         Returns the cost of prediction. Values must be integer ||         indices; if class_var is set; you can also use symbolic values ||         (strings). Note that there's no way to change the size of the ||         matrix. Size is set at construction and does not change.  For ||         the final example; we shall compute the profits of knowing ||         attribute values in the dataset lenses with the same ||         cost-matrix as printed above. ||          ||         .. literalinclude:: code\/CostMatrix.py ||             :lines: 19-23 ||              ||         As the script shows; you don't have to (and usually won't) call the constructor ||         explicitly. Instead; you will set the corresponding field (in our case meas.cost) ||         to a matrix and let Orange convert it to CostMatrix automatically. Funny as it ||         might look; but since Orange uses constructor to perform such conversion; even ||         the above statement is correct (although the cost matrix is rather dull; ||         with 0s on the diagonal and 1s around):             ||              ||         .. literalinclude:: code\/CostMatrix.py ||             :lines: 25 ||                  || .. index: SymMatrix ||  || ----------------------- || SymMatrix || ----------------------- ||  || :obj:`SymMatrix` implements symmetric matrices of size fixed at  || construction time (and stored in :obj:`SymMatrix.dim`). ||  || .. class:: SymMatrix ||  ||     .. attribute:: dim || \t ||         Matrix dimension. ||              ||     .. attribute:: matrix_type  ||  ||         Can be ``SymMatrix.Lower`` (0); ``SymMatrix.Upper`` (1);  ||         ``SymMatrix.Symmetric`` (2; default); ``SymMatrix.LowerFilled`` (3) or ||         ``SymMatrix.Upper_Filled`` (4).  ||  ||         If the matrix type is ``Lower`` or ``Upper``; indexing  ||         above or below the diagonal; respectively; will fail.  ||         With ``LowerFilled`` and ``Upper_Filled``; ||         the elements upper or lower; respectively; still  ||         exist and are set to zero; but they cannot be modified. The  ||         default matrix type is ``Symmetric``; but can be changed  ||         at any time. ||  ||         If matrix type is ``Upper``; it is printed as: ||  ||         >>> import Orange ||         >>> m = Orange.misc.SymMatrix( ||         ...     [[1];  ||         ...      [2; 4];  ||         ...      [3; 6; 9];  ||         ...      [4; 8; 12; 16]]) ||         >>> m.matrix_type = m.Upper ||         >>> print m ||         (( 1.000;  2.000;  3.000;  4.000); ||          (         4.000;  6.000;  8.000); ||          (                 9.000; 12.000); ||          (                        16.000)) ||  ||         Changing the type to ``LowerFilled`` changes the printout to ||  ||         >>> m.matrix_type = m.LowerFilled ||         >>> print m ||         (( 1.000;  0.000;  0.000;  0.000); ||          ( 2.000;  4.000;  0.000;  0.000); ||          ( 3.000;  6.000;  9.000;  0.000); ||          ( 4.000;  8.000; 12.000; 16.000)) || \t ||     .. method:: __init__(dim[; value]) ||  ||         Construct a symmetric matrix of the given dimension. ||  ||         :param dim: matrix dimension ||         :type dim: int ||  ||         :param value: default value (0 by default) ||         :type value: double ||          ||          ||     .. method:: __init__(data) ||  ||         Construct a new symmetric matrix containing the given data.  ||         These can be given as Python list containing lists or tuples. ||          ||         The following example fills a matrix created above with ||         data in a list:: ||  ||             import Orange ||             m = [[]; ||                  [ 3]; ||                  [ 2; 4]; ||                  [17; 5; 4]; ||                  [ 2; 8; 3; 8]; ||                  [ 7; 5; 10; 11; 2]; ||                  [ 8; 4; 1; 5; 11; 13]; ||                  [ 4; 7; 12; 8; 10; 1; 5]; ||                  [13; 9; 14; 15; 7; 8; 4; 6]; ||                  [12; 10; 11; 15; 2; 5; 7; 3; 1]] ||                      ||             matrix = Orange.data.SymMatrix(m) ||  ||         SymMatrix also stores diagonal elements. They are set ||         to zero; if they are not specified. The missing elements ||         (shorter lists) are set to zero as well. If a list ||         spreads over the diagonal; the constructor checks ||         for asymmetries. For instance; the matrix ||  ||         :: ||  ||             m = [[]; ||                  [ 3;  0; f]; ||                  [ 2;  4]] ||      ||         is only OK if f equals 2. Finally; no row can be longer  ||         than matrix size.   ||  ||     .. method:: get_values() ||      ||         Return all matrix values in a Python list. ||  ||     .. method:: get_KNN(i; k) ||      ||         Return k columns with the lowest value in the i-th row.  ||          ||         :param i: i-th row ||         :type i: int ||          ||         :param k: number of neighbors ||         :type k: int ||          ||     .. method:: avg_linkage(clusters) ||      ||         Return a symmetric matrix with average distances between given clusters.   ||        ||         :param clusters: list of clusters ||         :type clusters: list of lists ||          ||     .. method:: invert(type) ||      ||         Invert values in the symmetric matrix. ||          ||         :param type: 0 (-X); 1 (1 - X); 2 (max - X); 3 (1 \/ X) ||         :type type: int ||  ||     .. method:: normalize(type) ||      ||         Normalize values in the symmetric matrix. ||          ||         :param type: 0 (normalize to [0; 1] interval); 1 (Sigmoid) ||         :type type: int ||          ||          ||  || Indexing || .......... ||  || For symmetric matrices the order of indices is not important:  || if ``m`` is a SymMatrix; then ``m[2; 4]`` addresses the same element as ``m[4; 2]``. ||  || .. ||     .. literalinclude:: code\/symmatrix.py ||         :lines: 1-6 ||  || >>> import Orange || >>> m = Orange.misc.SymMatrix(4) || >>> for i in range(4): || ...    for j in range(i+1): || ...        m[i; j] = (i+1)*(j+1) ||  ||  || Although only the lower left half of the matrix was set explicitely;  || the whole matrix is constructed. ||  || >>> print m || (( 1.000;  2.000;  3.000;  4.000); ||  ( 2.000;  4.000;  6.000;  8.000); ||  ( 3.000;  6.000;  9.000; 12.000); ||  ( 4.000;  8.000; 12.000; 16.000)) ||   || Entire rows are indexed with a single index. They can be iterated || over in a for loop or sliced (with; for example; ``m[:3]``): ||  || >>> print m[1] || (2.0; 4.0; 6.0; 8.0) || >>> m.matrix_type = m.Lower || >>> for row in m: || ...     print row || (1.0;) || (2.0; 4.0) || (3.0; 6.0; 9.0) || (4.0; 8.0; 12.0; 16.0) ||  || .. index: Random number generator ||  || ----------------------- || Random number generator || ----------------------- ||  || :obj:`Random` uses the  || `Mersenne twister <http:\/\/en.wikipedia.org\/wiki\/Mersenne_twister>`_ algorithm || to generate random numbers. ||  || :: ||  ||     >>> import Orange ||     >>> rg = Orange.misc.Random(42) ||     >>> rg(10) ||     4 ||     >>> rg(10) ||     7 ||     >>> rg.uses  # We called rg two times. ||     2 ||     >>> rg.reset() ||     >>> rg(10) ||     4 ||     >>> rg(10) ||     7 ||     >>> rg.uses ||     2 ||  ||  || .. class:: Random(seed) ||  ||     :param initseed: Seed used for initializing the random generator. ||     :type initseed: int ||  ||     .. method:: __call__(n) ||  ||         Return a random integer R such that 0 <= R < n. ||  ||         :type n: int ||  ||     .. method:: reset([seed]) ||  ||         Reinitialize the random generator with `initseed`. If `initseed` ||         is not given use the existing value of attribute `initseed`. ||  ||     .. attribute:: uses ||          ||         The number of times the generator was called after ||         initialization\/reset. ||      ||     .. attribute:: initseed ||  ||         Random seed. ||  || Two examples or random number generator uses found in the documentation || are :obj:`Orange.evaluation.testing` and :obj:`Orange.data.Table`. ||  ||  || ---------------- || Other submodules || ---------------- ||  || .. automodule:: Orange.misc.selection ||  || \"\"\"
aaidjfafa,biolab/orange3,setup.py,b37b7836ac0a3dbfcedbd1875c5a864dec8726ae,6426d029f7eb96450b1baee64b2ead7ca9eb722c,Fix extension modules so they can be linked
aaidjfaje,biolab/orange3,setup.py,740a63a9e2a14c424d25346b56b515ab6122e973,6426d029f7eb96450b1baee64b2ead7ca9eb722c,TODO: Simply replace with include_package_data = True and configure missed files in MANIFEST.in?
aaidjfajf,biolab/orange3,setup.py,740a63a9e2a14c424d25346b56b515ab6122e973,f18ff0630b5ea6e2d4da9f894caf9e1c27417063,TODO: Use entry points for automatic script creation
aaidjfajh,biolab/orange3,setup.py,740a63a9e2a14c424d25346b56b515ab6122e973,6426d029f7eb96450b1baee64b2ead7ca9eb722c,TODO: Should migrate those for distribute (using entry points)
aaidjfajj,biolab/orange3,setup.py,533417a5138dcf30fa313b668e3f9cac2b2c3fed,STILL_EXISTS,A hack to make setuptools in distutils work together on Ubuntu
aaidjfbbh,biolab/orange3,Orange/data/io.py,771c291ba16d1db00de18b3980490fcc2308813d,b10b1331a973995cdd6998f965c2f939c22c4367,TODO how many columns?!
aaidjfbii,biolab/orange3,Orange/data/table.py,771c291ba16d1db00de18b3980490fcc2308813d,STILL_EXISTS,single row; multiple columns
aaidjfbja,biolab/orange3,Orange/data/table.py,771c291ba16d1db00de18b3980490fcc2308813d,81b54283ec2eff5bc89b20bc55132a5e748c667e,TODO implement metas
aaidjfbje,biolab/orange3,Orange/data/table.py,771c291ba16d1db00de18b3980490fcc2308813d,a0003073a0c8cc02429ec46cf981d926a45bd473,multiple rows; multiple columns;
aaidjfbjf,biolab/orange3,Orange/data/table.py,771c291ba16d1db00de18b3980490fcc2308813d,a0003073a0c8cc02429ec46cf981d926a45bd473,return a new table; create new domain if needed
aaidjfbjg,biolab/orange3,Orange/data/table.py,771c291ba16d1db00de18b3980490fcc2308813d,STILL_EXISTS,TODO domain conversion
aaidjfcaa,biolab/orange3,Orange/data/table.py,771c291ba16d1db00de18b3980490fcc2308813d,STILL_EXISTS,multiple rows; multiple columns
aaidjfcab,biolab/orange3,Orange/data/table.py,771c291ba16d1db00de18b3980490fcc2308813d,1cf2af38365e412a0455b05a7dffc739d889189a,TODO: check whether .data builds a new buffer; try avoiding it
aaidjfcac,biolab/orange3,Orange/data/table.py,771c291ba16d1db00de18b3980490fcc2308813d,a0003073a0c8cc02429ec46cf981d926a45bd473,TODO: is there a better way to do it?!
aaidjfcba,biolab/orange3,Orange/__init__.py,5cf6b70c5115524f48b8f3eb9a2ce2632ebe4933,f18ff0630b5ea6e2d4da9f894caf9e1c27417063,\"\"\" || _import(\"data.sample\") || _import(\"data.outliers\") || _import(\"data.preprocess\") || _import(\"data.preprocess.scaling\") || _import(\"data.utils\") || _import(\"data.discretization\") || _import(\"data.continuization\") || _import(\"data.filter\") || _import(\"data.imputation\") ||  || _import(\"feature\") || _import(\"feature.construction\") || _import(\"feature.construction.functionDecomposition\") || _import(\"feature.construction.univariate\") || _import(\"feature.discretization\") || _import(\"feature.imputation\") || _import(\"feature.scoring\") || _import(\"feature.selection\") ||  || _import(\"network\") ||  || _import(\"stat\") ||  || _import(\"statistics\") || _import(\"statistics.estimate\") || _import(\"statistics.contingency\") || _import(\"statistics.distribution\") || _import(\"statistics.basic\") || _import(\"statistics.evd\") ||  || _import(\"classification\") || _import(\"classification.tree\") ||  || _import(\"classification.rules\") ||  || _import(\"classification.lookup\") || _import(\"classification.bayes\") || _import(\"classification.svm\") || _import(\"classification.logreg\") || _import(\"classification.knn\") || _import(\"classification.majority\") ||  || _import(\"tuning\") ||  || _import(\"projection\") || _import(\"projection.linear\") || _import(\"projection.mds\") || _import(\"projection.som\") ||  || _import(\"ensemble\") || _import(\"ensemble.bagging\") || _import(\"ensemble.boosting\") || _import(\"ensemble.forest\") || _import(\"ensemble.stacking\") ||  || _import(\"regression\") || _import(\"regression.base\") || _import(\"regression.earth\") || _import(\"regression.lasso\") || _import(\"regression.linear\") || _import(\"regression.mean\") || _import(\"regression.pls\") || _import(\"regression.tree\") ||  || _import(\"multitarget\") || _import(\"multitarget.tree\") ||  || _import(\"multilabel\") || _import(\"multilabel.multibase\") || _import(\"multilabel.br\") || _import(\"multilabel.lp\") || _import(\"multilabel.mlknn\") || _import(\"multilabel.brknn\") || _import(\"multilabel.mulan\") ||  || _import(\"associate\") ||  || _import(\"distance\") ||  || _import(\"wrappers\") ||  || _import(\"featureConstruction\") || _import(\"featureConstruction.univariate\") || _import(\"featureConstruction.functionDecomposition\") ||  || _import(\"evaluation\") || _import(\"evaluation.scoring\") || _import(\"evaluation.testing\") ||  || _import(\"clustering\") || _import(\"clustering.kmeans\") || _import(\"clustering.hierarchical\") || _import(\"clustering.consensus\") ||  || _import(\"misc\") ||  || _import(\"utils\") #TODO hide utils from the user || _import(\"utils.environ\") || _import(\"utils.counters\") || _import(\"utils.addons\") || _import(\"utils.render\") || _import(\"utils.serverfiles\") ||  || _import_addons() || \"\"\"
aaidjfcbi,biolab/orange3,Orange/data/table.py,81b54283ec2eff5bc89b20bc55132a5e748c667e,STILL_EXISTS,TODO: write a function in Cython that would do this in place
aaidjfccc,biolab/orange3,Orange/data/instance.py,c0900ae883e9632397c95c5f459e88ebff314c0e,fdb96724cc8d6024ffe7d7b8780b6684a3afb706,TODO: rewrite to Cython
aaidjfccf,biolab/orange3,Orange/data/domain.py,a5828c0b7ceb0aa3d95b67943e2b0ee6e7145c64,71acdcc18dd5c6406aa19c886e80f8acfc70eceb,We will have to fix this when we implement an SQL proxy table
aaidjfcda,biolab/orange3,Orange/data/table.py,71acdcc18dd5c6406aa19c886e80f8acfc70eceb,62e93ce51932239f1fd4ef957718844f3cb4311b,TODO Does not work
aaidjfcdc,biolab/orange3,Orange/data/table.py,66abc8fb46c77b971b07fe8d3e0d8431464343af,STILL_EXISTS,TODO implement checking by columns
aaidjfceg,biolab/orange3,Orange/data/instance.py,35edb91438522895683666015a256251b3dd0f49,STILL_EXISTS,TODO Should we return an instance of `object` if we have a meta attribute
aaidjfcfc,biolab/orange3,Orange/data/table.py,ae6a9021ea67630dfc31442a0f856cc38a7cc43e,5cf6dc81c5fd608021f5b655e1773f51ad3f25ba,TODO implement
aaidjfcfd,biolab/orange3,Orange/data/domain.py,a8a9ab50093d34816b5e1ac5a9544106a2be7bdb,STILL_EXISTS,TODO use source if provided!
aaidjfcfh,biolab/orange3,Orange/data/table.py,2f8acb9438eb28a4990a0ca1c8b331e4591f7ee1,d25f362e69e754445994cd0c1ac433df0dec7ffe,TODO: handle get_value_from
aaidjfcid,biolab/orange3,Orange/tests/test_table.py,61e4eddd037f51cc5a39484387b8981d862b33d3,f18ff0630b5ea6e2d4da9f894caf9e1c27417063,TODO reenable tests with metas when they are implemented
aaidjfcja,biolab/orange3,Orange/tests/test_table.py,61e4eddd037f51cc5a39484387b8981d862b33d3,STILL_EXISTS,TODO Test conjunctions and disjunctions of conditions
aaidjfdad,biolab/orange3,Orange/tests/test_table.py,d84ead845dbd6bd9866655141dfc41265ae7388f,STILL_EXISTS,TODO: indexing with [[0;1]; [0;1]] produces weird results
aaidjfdae,biolab/orange3,Orange/tests/test_table.py,d84ead845dbd6bd9866655141dfc41265ae7388f,STILL_EXISTS,TODO: what should be the results of table[1; :]
aaidjfdaf,biolab/orange3,Orange/classification/naive_bayes.py,07abc1829d3d470af69f4430f7ec62d2911cefd9,2a805e974b66146b0a3d103b8551f027130a602c,expand probability predictions with zero columns for class values which are not present
aaidjfedc,biolab/orange3,Orange/tests/test_classification.py,f7ed411bd39efdd93ebbc2189dae6eb7d3829844,43d8b8f9b89fbe29ea64c21bcae616c41a9273b2,TODO
aaidjfefa,biolab/orange3,Orange/tests/test_variable.py,ef1a53d4acf57c0f6b106af4a0a0925b839130dd,STILL_EXISTS,TODO: with self.assertRaises(ValueError): var.to_val(2)
aaidjfefe,biolab/orange3,Orange/data/domain.py,ecfe841a95e5a0b89ef4a76d32c9332b7f4df6cd,791f52b1914f0b84c6311e1d4387e96afccbfad8,TODO ... or have we decided that the arguments should be
aaidjfehj,biolab/orange3,Orange/data/variable.py,ff2d295fc00964085acbf6d25e0b8c97a3e9db20,7e86b673843a2b76b1687560636e875d86180a96,TODO: do we need locking? Don't we expect the code to be reentrant?
aaidjfeia,biolab/orange3,Orange/data/variable.py,ff2d295fc00964085acbf6d25e0b8c97a3e9db20,7e86b673843a2b76b1687560636e875d86180a96,TODO: the original intention was to prevent deadlocks. Locks block;
aaidjfeih,biolab/orange3,Orange/data/variable.py,ff2d295fc00964085acbf6d25e0b8c97a3e9db20,61b75509caf668963db9cd60fe2b102712d7d74e,TODO: This may significantly slow down file reading.
aaidjfhbg,biolab/orange3,Orange/data/table.py,f78a57fe124bb314749a2bfaa9ac0f486a327e1c,e3d5b729e5fb902b976d72c02f04ef70a7079447,TODO: this is unnecessary; let's remove it
aaidjfhbh,biolab/orange3,Orange/data/table.py,f78a57fe124bb314749a2bfaa9ac0f486a327e1c,STILL_EXISTS,Why; and should we fix it or remove it?
aaidjfhca,biolab/orange3,Orange/data/table.py,8d4d199c35f5163ed21ae705a16ce3c8548d60c6,STILL_EXISTS,TODO Patch for sparse data or add another class
aaidjfhdg,biolab/orange3,Orange/data/filter.py,ac892eddd865681c9746efeda9f1f094f26cc32f,24c20eb16939951983f8be968443da162dedf655,TODO Implement __call__ methods for filters below
aaidjfhfb,biolab/orange3,Orange/statistics/distribution.py,3c563027303920288e3b2b75674bbd462f1d7844,cd1ea25c61112778778ad23ac314c8e592fe96fa,TODO: Handle sparse data -- at least by get_distributions
aaidjfhgc,biolab/orange3,Orange/data/table.py,64cb7c5764587596e7821b53964e06480e403003,be8b246e7884428d44bbd9b56964e0abffa5b41b,TODO: unit tests do not pass because sort does not sort by \"header row\"; but rather sorts each row separately
aaidjfhgi,biolab/orange3,Orange/data/table.py,3a6c06335a2be5d4609e6f89bbebe860ec883ec2,e7ae5f5d085d157a765ef24e78b852e31c7d987f,TODO Add sparse data like in distributions
aaidjfhjc,biolab/orange3,Orange/canvas/application/aboutdialog.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,TODO: Also list all known add-on versions.
aaidjfhjf,biolab/orange3,Orange/canvas/application/canvasmain.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,TODO: Orange Version in the base link
aaidjfibf,biolab/orange3,Orange/canvas/application/canvasmain.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,TODO: This is bad (should be moved here).
aaidjfidf,biolab/orange3,Orange/canvas/application/canvasmain.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,TODO: should the default name be platform specific
aaidjfieh,biolab/orange3,Orange/canvas/application/canvasmain.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,TODO: Use a dialog instance and use 'addSidebarUrls' to
aaidjfifa,biolab/orange3,Orange/canvas/application/canvasmain.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,TODO: Search for a temp backup scheme with per process
aaidjfifd,biolab/orange3,Orange/canvas/application/canvasmain.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,TODO: should handle permission errors with a
aaidjfjdb,biolab/orange3,Orange/canvas/canvas/editlinksdialog.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,TODO: specifiy if the geometry should be set relative to the
aaidjfjdf,biolab/orange3,Orange/canvas/canvas/editlinksdialog.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,TODO: More sensible size hints.
aaidjfjfa,biolab/orange3,Orange/canvas/canvas/items/controlpoints.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,Return the rect normalized. During the control point move the
aaidjfjfg,biolab/orange3,Orange/canvas/canvas/items/controlpoints.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,TODO: keyboard modifiers and constraints.
aaidjfjia,biolab/orange3,Orange/canvas/canvas/items/linkitem.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,scene event filter when not needed
aaidjfjic,biolab/orange3,Orange/canvas/canvas/items/nodeitem.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,TODO: The body item should allow the setting of arbitrary painter
aaidjfjii,biolab/orange3,Orange/canvas/canvas/items/nodeitem.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,TODO: The selected and focus states should be set using the
aaidjgaag,biolab/orange3,Orange/canvas/canvas/items/nodeitem.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,TODO: Important because of this any time the child
aaidjgahi,biolab/orange3,Orange/canvas/document/interactions.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,Emitted when the interaction ends (canceled or finished)
aaidjgajf,biolab/orange3,Orange/canvas/document/interactions.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,TODO: How to indicate that the connection is not possible?
aaidjgbbj,biolab/orange3,Orange/canvas/document/interactions.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,TODO: map to scene if item has a parent.
aaidjgbcb,biolab/orange3,Orange/canvas/document/interactions.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,item move as a part of a selection group.
aaidjgbcf,biolab/orange3,Orange/canvas/document/quickmenu.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,TODO: A uniform size box layout.
aaidjgbdb,biolab/orange3,Orange/canvas/document/quickmenu.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,TODO: get the background.
aaidjgbdi,biolab/orange3,Orange/canvas/document/quickmenu.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,TODO: right to left locale
aaidjgbef,biolab/orange3,Orange/canvas/document/quickmenu.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,TODO: move by columns
aaidjgbeg,biolab/orange3,Orange/canvas/document/quickmenu.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,TODO: Does this work
aaidjgbei,biolab/orange3,Orange/canvas/document/schemeedit.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,TODO: Should this be moved to CanvasScene?
aaidjgbjg,biolab/orange3,Orange/canvas/gui/dock.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,A workaround for forcing the QDockWidget layout to disregard the
aaidjgcaf,biolab/orange3,Orange/canvas/gui/dropshadow.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,TODO: Use QPainter.drawPixmapFragments on Qt 4.7
aaidjgcfi,biolab/orange3,Orange/canvas/gui/toolbox.py,cae097749e098191b6789e0bc4ca68b85851c749,0b8d1f55642f47016f42c90dc2c6b9480ca7c0b2,TODO: add shift for pressed as set by the style (PM_ButtonShift...)
aaidjgcfj,biolab/orange3,Orange/canvas/gui/toolbox.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,Background  (TODO: Should the tab button have native
aaidjgcja,biolab/orange3,Orange/canvas/gui/utils.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,TODO: wayland??
aaidjgddi,biolab/orange3,Orange/canvas/orngSignalManager.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,TODO: This should be retrieved from orngRegistry.WidgetDescription
aaidjgdje,biolab/orange3,Orange/canvas/preview/previewmodel.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,TODO: how to set the icon from the svg contents string
aaidjgdji,biolab/orange3,Orange/canvas/preview/scanner.py,cae097749e098191b6789e0bc4ca68b85851c749,cfd94695889a2c3070b7fc06545b67058e1ab0cf,workaround for bugs.python.org\/issue11159
aaidjgeae,biolab/orange3,Orange/canvas/registry/__init__.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,TODO: lock
aaidjgece,biolab/orange3,Orange/canvas/registry/description.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,TODO: Should also check that the name is real.
aaidjgefd,biolab/orange3,Orange/canvas/registry/discovery.py,cae097749e098191b6789e0bc4ca68b85851c749,e14715a78752cf1365d60930d2f0a32250fc2dfc,TODO: zipped modules
aaidjgejd,biolab/orange3,Orange/canvas/scheme/readwrite.py,cae097749e098191b6789e0bc4ca68b85851c749,ff062da91ca42f483a3a7c739835de3825c7898d,TODO: handle errors.
aaidjgfah,biolab/orange3,Orange/canvas/scheme/scheme.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,TODO: Speedup - keep index of links by nodes and channels
aaidjgfbc,biolab/orange3,Orange/canvas/scheme/scheme.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,TODO: should we clear the scheme and load it.
aaidjgfdb,biolab/orange3,Orange/canvas/scheme/widgetsscheme.py,cae097749e098191b6789e0bc4ca68b85851c749,ff062da91ca42f483a3a7c739835de3825c7898d,TODO: Change how the signal is emitted in signal manager (should
aaidjgfeg,biolab/orange3,Orange/canvas/utils/settings.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,TODO: Could check if QSettings.Format stores type info.
aaidjggbj,biolab/orange3,Orange/widgets/OWBaseWidget.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,this definitions are needed only to define ExampleTable as subclass of ExampleTableWithClass
aaidjggdc,biolab/orange3,Orange/widgets/OWBaseWidget.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,directories are better defined this way; otherwise .ini files get written in many places
aaidjggdf,biolab/orange3,Orange/widgets/OWBaseWidget.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,needed by signalWrapper to know when everything was sent
aaidjgggb,biolab/orange3,Orange/widgets/OWBaseWidget.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,call this function if needed in __init__ of the widget
aaidjggge,biolab/orange3,Orange/widgets/OWBaseWidget.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,Move the widget to the center of available space if it is currently outside it
aaidjghdh,biolab/orange3,Orange/widgets/OWBaseWidget.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,implement this in your widget if you want to process something only after you received multiple signals
aaidjghgf,biolab/orange3,Orange/widgets/OWBaseWidget.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,# TODO: make this thread safe
aaidjghij,biolab/orange3,Orange/widgets/OWContexts.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,TODO: shouldn't we check the attribute type here; too? or should we change self.saveLow for these field types then?
aaidjgiae,biolab/orange3,Orange/widgets/OWGUI.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,I (JD) commented this out. This is ugly and dangerous.
aaidjgigc,biolab/orange3,Orange/widgets/OWGUI.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,TODO: optimize this code. use the fact that the selectedItemIndices is a sorted list
aaidjgjch,biolab/orange3,Orange/widgets/OWGUI.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,TODO: Style dependent margins?
aaidjgjci,biolab/orange3,Orange/widgets/OWGUI.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,TODO: Check ForegroundRole.
aaidjgjdd,biolab/orange3,Orange/widgets/OWGUI.py,cae097749e098191b6789e0bc4ca68b85851c749,STILL_EXISTS,TODO: add other styles (Maybe load corrections from .cfg file?)
aaidjgjjf,biolab/orange3,Orange/canvas/utils/environ.py,0c967b4e61ad6c7528580bb1f5e623a9ae417d42,9d91c30d193804d9533ae9498065b6074ddb4288,TODO: This are not needed if using orange without the GUI
aaidjhada,biolab/orange3,Orange/canvas/__init__.py,3b07648e60c26c463f7fa749e05cce40e08c05a4,36a814e26e1e7742d1ff20f735610863f9c60306,By settings __loader__ to None; we workaround the pkg_resources bug.
aaidjjebj,biolab/orange3,Orange/tests/test_sparse_reader.py,330bdff350d56e594c5df18ce39533ccadc6a047,STILL_EXISTS,TODO checks for quotes; escapes; error checking
aaidjjecf,biolab/orange3,Orange/canvas/help/manager.py,08f5ef320b94059a3ac811050bd59372b6b2d1f0,STILL_EXISTS,TODO: Ensure initialization of the provider
aaidjjeeg,biolab/orange3,Orange/widgets/widget.py,a7ebbb89c969002141c77bb978f609a63e15d078,STILL_EXISTS,TODO add!
aaidjjeei,biolab/orange3,Orange/widgets/data/owfile.py,c48f645a8d58ec3f9ed5b3e3e2454a9ab4f04c4d,bcc0a375ba25b475b564491b21a0be93da2a9a38,TODO What is this?!
aaidjjefd,biolab/orange3,Orange/widgets/data/owfile.py,c48f645a8d58ec3f9ed5b3e3e2454a9ab4f04c4d,STILL_EXISTS,TODO handle self.new_variables
aaidjjefg,biolab/orange3,Orange/statistics/basic_stats.py,a9801cf581b6f7a843988e329eeaf1410e4901fd,1bfc00fa651e0f13e5bbb27953dcacc7288108bf,TODO: this is all just a quick hack; do it properly -- call data storage etc.
aaidjjefh,biolab/orange3,Orange/statistics/basic_stats.py,a9801cf581b6f7a843988e329eeaf1410e4901fd,1bfc00fa651e0f13e5bbb27953dcacc7288108bf,TODO sparse data
aaidjjfcg,biolab/orange3,Orange/widgets/utils/colorpalette.py,a2bad3b9fa8b9ed9b8da9d08dd3097369319abce,e177f2635385731455d8ac23326a77e83d13a5d2,TODO is this needed in Python 3?!
aaidjjfci,biolab/orange3,Orange/widgets/utils/colorpalette.py,a2bad3b9fa8b9ed9b8da9d08dd3097369319abce,e1bb0274bbe8ca3ab110763da3eef39ba6177f49,TODO is this needed in Python 3?! Does it work at all?
aaidjjfed,biolab/orange3,Orange/widgets/data/owtable.py,dbef470b7aa568c1c9d6693ee17ef1fa951463f5,STILL_EXISTS,TODO: html\/rtf table
aaidjjffg,biolab/orange3,Orange/widgets/data/owtable.py,dbef470b7aa568c1c9d6693ee17ef1fa951463f5,4a43891765d19602ccf03a8bd805976325f3a78a,TODO Implement
aaidjjfgg,biolab/orange3,Orange/widgets/data/owtable.py,dbef470b7aa568c1c9d6693ee17ef1fa951463f5,25b6b819ac90fa7b4db577d9ea4cb234950ba32f,TODO implement
aaidjjfhg,biolab/orange3,Orange/widgets/data/owtable.py,daf8cba59feb17e2a2bc7428c4c72ed1c23c6576,4a43891765d19602ccf03a8bd805976325f3a78a,check whether we have a sparse columns;
aaidjjfih,biolab/orange3,Orange/canvas/canvas/items/linkitem.py,9a2d415d4e4534ca8b9502fe2d0b0abe7b705b35,2d6dd725e238ec351f28e7db352b74ce9ad581ab,TODO: Ales; help!
aaidjjfjb,biolab/orange3,Orange/widgets/basewidget.py,afb61879c10b6c096f6dfc98f0574859ca5c3dba,5c7a238e7200f0f3ea8d4adc7aa7585a536235df,TODO Reimplement this
aaidjjfjd,biolab/orange3,Orange/widgets/basewidget.py,afb61879c10b6c096f6dfc98f0574859ca5c3dba,STILL_EXISTS,TODO Remove this when all widgets are migrated to 3.0
aaidjjgfd,biolab/orange3,Orange/widgets/basewidget.py,5c7a238e7200f0f3ea8d4adc7aa7585a536235df,STILL_EXISTS,TODO: Puzzled. setattr calls obj.__setattr__. If obj is self;
aaidjjgha,biolab/orange3,Orange/widgets/settings.py,5c7a238e7200f0f3ea8d4adc7aa7585a536235df,STILL_EXISTS,TODO: this is here only since __del__ is not properly called
aaidjjghb,biolab/orange3,Orange/widgets/settings.py,5c7a238e7200f0f3ea8d4adc7aa7585a536235df,4a674b7c99982a1a5b963733ceb54bdc8c80af9e,TODO this method has misleading name (method 'initialize' does what
aaidjjghd,biolab/orange3,Orange/widgets/settings.py,5c7a238e7200f0f3ea8d4adc7aa7585a536235df,bbb7b1a72a7e69f6ffc80502aedb75dc590555d0,called by this class but only by ContextHandlers. Perhaps it should
aaidjjghf,biolab/orange3,Orange/widgets/settings.py,5c7a238e7200f0f3ea8d4adc7aa7585a536235df,STILL_EXISTS,TODO similar to settingsToWidget; update_class_defaults does this for
aaidjjghh,biolab/orange3,Orange/widgets/settings.py,5c7a238e7200f0f3ea8d4adc7aa7585a536235df,c431c5ae5d9276a34c09b071fe01cbf6d93ab6a4,TODO would we like this method to store the changed settings back to
aaidjjhad,biolab/orange3,Orange/widgets/data/owselectattributes.py,25f68c5cedefa11cce8411c5f8b2537134a8fec1,STILL_EXISTS,TODO do we still need this?
aaidjjhbd,biolab/orange3,Orange/widgets/utils/itemmodels.py,25f68c5cedefa11cce8411c5f8b2537134a8fec1,aa0f4f3f09078da744231e93d8ab6c68908be132,TODO: group indexes into ranges
aaidjjhcf,biolab/orange3,Orange/canvas/orngSignalManager.py,e14715a78752cf1365d60930d2f0a32250fc2dfc,STILL_EXISTS,TODO This does not work in Python 3.0
aaidjjhch,biolab/orange3,Orange/canvas/registry/description.py,e14715a78752cf1365d60930d2f0a32250fc2dfc,STILL_EXISTS,TODO: Should also check that the name is real.
aaidjjhgj,biolab/orange3,Orange/canvas/orngSignalManager.py,4f9acebe583d7e654ffbb0f3abf3e6ccf01fb582,STILL_EXISTS,TODO inputSignal is sometimes (after updating widget and rereading
aaidjjhig,biolab/orange3,Orange/widgets/widget.py,20fcb75695f136abf5076307c7ab899890030488,STILL_EXISTS,TODO add!
aaidjjiaa,biolab/orange3,Orange/widgets/widget.py,20fcb75695f136abf5076307c7ab899890030488,5d425371d54db37bfa6898b2141c5532e07ca440,TODO Missing \"__\"?
aaidjjijc,biolab/orange3,Orange/widgets/plot/owaxis.py,dce61557b849ef135d55bd702833f3738f88a510,STILL_EXISTS,\"\"\" ||     The Axis class display an axis on a graph ||      ||     The axis contains a line with configurable style; possible arrows; and a title ||      ||     .. attribute:: line_style ||         The LineStyle with which the axis line is drawn ||          ||     .. attribute:: title ||         The string to be displayed alongside the axis ||          ||     .. attribute:: title_above ||         A boolean which specifies whether the title should be placed above or below the axis ||         Normally the title would be above for top and left axes.  ||          ||     .. attribute:: title_location ||         can either be AxisStart; AxisEnd or AxisMiddle. The default is AxisMiddle ||          ||     .. attribute:: arrows ||         A bitfield containing AxisEnd if an arrow should be drawn at the line's end (line.p2())  ||         and AxisStart if there should be an arrows at the first point.  ||          ||         By default; there's an arrow at the end of the line ||          ||     .. attribute:: zoomable ||         If this is set to True; the axis line will zoom together with the rest of the graph.  ||         Otherwise; the line will remain in place and only tick marks will zoom.  ||                  ||     .. method:: make_title ||         Makes a pretty title; with the quantity title in italics and the unit in normal text ||                  ||     .. method:: label_pos ||         Controls where the axis title and tick marks are placed relative to the axis || \"\"\"
aaidjjijd,biolab/orange3,Orange/widgets/plot/owaxis.py,dce61557b849ef135d55bd702833f3738f88a510,STILL_EXISTS,# TODO: Move it according to self.label_pos
aaidjjjab,biolab/orange3,Orange/widgets/plot/owopenglrenderer.py,dce61557b849ef135d55bd702833f3738f88a510,STILL_EXISTS,TODO: sizeof(type)
aaidjjjac,biolab/orange3,Orange/widgets/plot/owopenglrenderer.py,dce61557b849ef135d55bd702833f3738f88a510,STILL_EXISTS,TODO
aaidjjjbf,biolab/orange3,Orange/widgets/plot/owplot.py,dce61557b849ef135d55bd702833f3738f88a510,STILL_EXISTS,# TODO
aaidjjjbh,biolab/orange3,Orange/widgets/plot/owplot.py,dce61557b849ef135d55bd702833f3738f88a510,STILL_EXISTS,# TODO: Check if the title is too big
aaidjjjca,biolab/orange3,Orange/widgets/plot/owplot.py,dce61557b849ef135d55bd702833f3738f88a510,STILL_EXISTS,# If the legend hasn't been moved it; we set it outside; in the top right corner
aaidjjjcb,biolab/orange3,Orange/widgets/plot/owplot.py,dce61557b849ef135d55bd702833f3738f88a510,STILL_EXISTS,# TODO: Implement this in Curve.cpp
aaidjjjcd,biolab/orange3,Orange/widgets/plot/owplot.py,dce61557b849ef135d55bd702833f3738f88a510,STILL_EXISTS,# We implement a workaround here; because sometimes mouseMoveEvents are not fast enough
aaidjjjcg,biolab/orange3,Orange/widgets/plot/owplot.py,dce61557b849ef135d55bd702833f3738f88a510,STILL_EXISTS,# TODO: Perhaps change this from a fixed percentage to always round to a round number
aaidjjjed,biolab/orange3,Orange/widgets/plot/owplot3d.py,dce61557b849ef135d55bd702833f3738f88a510,STILL_EXISTS,TODO: check hardware for OpenGL 3.x+ support
aaidjjjfg,biolab/orange3,Orange/widgets/plot/owplot3d.py,dce61557b849ef135d55bd702833f3738f88a510,STILL_EXISTS,TODO: colors is list of QColor
aaidjjjie,biolab/orange3,Orange/widgets/plot/primitives/owprimitives3d.py,dce61557b849ef135d55bd702833f3738f88a510,STILL_EXISTS,Cache: contains triangles + vertices normals for each needed symbol.
aaieaabeg,biolab/orange3,Orange/canvas/scheme/widgetsscheme.py,ff062da91ca42f483a3a7c739835de3825c7898d,STILL_EXISTS,Remove node->widgets backup mapping no longer needed.
aaieaabfd,biolab/orange3,Orange/canvas/scheme/widgetsscheme.py,ff062da91ca42f483a3a7c739835de3825c7898d,STILL_EXISTS,TODO: Does any widget actually use it; or could it be
aaieaabff,biolab/orange3,Orange/canvas/scheme/widgetsscheme.py,ff062da91ca42f483a3a7c739835de3825c7898d,STILL_EXISTS,TODO: Test if async processing works; then remove this
aaieaadia,biolab/orange3,Orange/widgets/visualize/owboxplot.py,e46d0e86c19b3c976437862e4c312ce827442d50,STILL_EXISTS,TODO: add another box for the entire data set
aaieaadih,biolab/orange3,Orange/widgets/visualize/owboxplot.py,398bcc6b3332753cedba2b9bdeb4393ce9341b9c,8f5842fc7489ecf39c751d184c3b24a60351ac78,TODO remove anything that is printed out
aaieaadij,biolab/orange3,Orange/widgets/visualize/owboxplot.py,398bcc6b3332753cedba2b9bdeb4393ce9341b9c,STILL_EXISTS,TODO: add another box for the entire data set
aaieaafej,biolab/orange3,Orange/widgets/visualize/owboxplot.py,a3df1693398d37ad61229a1a52602b584048e007,STILL_EXISTS,TODO check!!!
aaieaafha,biolab/orange3,Orange/canvas/canvas/scene.py,1c6b09f8e6fa24bf0f9304b34df0ab067fdd189e,7d98b04ab27d9180bdacf6124cf78faf8ef1ab1e,TODO: change the base class of Node\/LinkItem to QGraphicsWidget.
aaieaafjd,biolab/orange3,Orange/statistics/tests.py,d37ef7074821764a8f0fe5c1cf603e2d957b62f3,STILL_EXISTS,TODO Check this function!!!
aaieaafjh,biolab/orange3,Orange/widgets/visualize/owboxplot.py,d37ef7074821764a8f0fe5c1cf603e2d957b62f3,STILL_EXISTS,TODO: Check this function
aaieaagbj,biolab/orange3,Orange/widgets/data/owselectdata.py,fd06749442a93dd2c01292cb77eceeafdbe7a26d,b68bcd1309ef35c15ac4a9c84efa2c71fd8cc4dd,\"\"\" || class Operator: ||     operatorsD = staticmethod([\"equals\";\"in\"]) ||     operatorsC = staticmethod([\"=\";\"<\";\"<=\";\">\";\">=\";\"between\";\"outside\"]) ||     operatorsS = staticmethod([\"=\";\"<\";\"<=\";\">\";\">=\";\"contains\";\"begins with\";\"ends with\";\"between\";\"outside\"]) ||     operatorDef = staticmethod(\"is defined\") ||     getOperators = staticmethod(lambda: Operator.operatorsD + Operator.operatorsS + [Operator.operatorDef]) ||  ||     negations = {\"equals\": \"does not equal\"; \"in\": \"is not in\"; ||                  \"between\": \"not between\"; \"outside\": \"not outside\"; ||                  \"contains\": \"does not contain\"; \"begins with\": \"does not begin with\"; \"ends with\": \"does not end with\"; ||                  \"is defined\": \"is undefined\"} ||  || #\"Equal\"; \"NotEqual\"; \"Less\"; \"LessEqual\"; \"Greater\"; \"GreaterEqual\"; || #     \"Between\"; \"Outside\" ||  ||     _operFilter = {\"=\":orange.Filter_values.Equal; ||                    \"<\":orange.Filter_values.Less; ||                    \"<=\":orange.Filter_values.LessEqual; ||                    \">\":orange.Filter_values.Greater; ||                    \">=\":orange.Filter_values.GreaterEqual; ||                    \"between\":orange.Filter_values.Between; ||                    \"outside\":orange.Filter_values.Outside; ||                    \"contains\":orange.Filter_values.Contains; ||                    \"begins with\":orange.Filter_values.BeginsWith; ||                    \"ends with\":orange.Filter_values.EndsWith} ||  ||     def __init__(self; operator; varType): ||         \"\"Members: operator; varType; isInterval. ||         \"\" ||         assert operator in Operator.getOperators(); \"Unknown operator: %s\" % str(operator) ||         self.operator = operator ||         self.varType = varType ||         self.isInterval = False ||         if operator in Operator.operatorsC and Operator.operatorsC.index(operator) > 4 \\ ||            or operator in Operator.operatorsD and Operator.operatorsD.index(operator) > 0 \\ ||            or operator in Operator.operatorsS and Operator.operatorsS.index(operator) > 7: ||             self.isInterval = True ||  ||     def __eq__(self; other): ||         assert other in Operator.getOperators() ||         return  self.operator == other ||  ||     def __ne__(self; other): ||         assert other in Operator.getOperators() ||         return self.operator != other ||  ||     def __repr__(self): ||         return str(self.operator) ||  ||     def __strr__(self): ||         return str(self.operator) ||  ||     def getFilter(self; domain; variable; value1; value2; negate; caseSensitive): ||         \"\"Returns orange filter. ||         \"\" ||         if self.operator == Operator.operatorDef: ||             try: ||                 id = domain.index(variable) ||             except: ||                 error(\"Error: unknown attribute (%s).\" % variable) ||  ||             if id >= 0: ||                 f = orange.Filter_isDefined(domain=domain) ||                 for v in domain.variables: ||                     f.check[v] = 0 ||                 f.check[variable] = 1 ||             else: # variable is a meta ||                     f = orange.Filter_hasMeta(id = domain.index(variable)) ||         elif self.operator in Operator.operatorsD: ||             f = orange.Filter_values(domain=domain) ||             f[variable] = value1 ||         else: ||             f = orange.Filter_values(domain=domain) ||             if value2: ||                 f[variable] = (Operator._operFilter[str(self.operator)]; value1; value2) ||             else: ||                 f[variable] = (Operator._operFilter[str(self.operator)]; value1) ||             if self.varType == orange.VarTypes.String: ||                 f[variable].caseSensitive = caseSensitive ||         f.negate = negate ||         return f || \"\"\"
aaieaagcc,biolab/orange3,Orange/widgets/data/owselectdata.py,fd06749442a93dd2c01292cb77eceeafdbe7a26d,b68bcd1309ef35c15ac4a9c84efa2c71fd8cc4dd,fix: trash
aaieaagcd,biolab/orange3,Orange/widgets/data/owselectdata.py,fd06749442a93dd2c01292cb77eceeafdbe7a26d,b68bcd1309ef35c15ac4a9c84efa2c71fd8cc4dd,fix!
aaieaageg,biolab/orange3,Orange/widgets/data/owselectdata.py,fd06749442a93dd2c01292cb77eceeafdbe7a26d,b68bcd1309ef35c15ac4a9c84efa2c71fd8cc4dd,This is a fix for Qt bug (4.3). When Qt is fixed; the setChecked above should suffice
aaieaagii,biolab/orange3,Orange/widgets/data/owselectdata.py,b68bcd1309ef35c15ac4a9c84efa2c71fd8cc4dd,STILL_EXISTS,tr = QtGui.QIcon() # fix: trash
aaieaahea,biolab/orange3,Orange/widgets/data/owdatasampler.py,6202ed46e5f6471d9e8d5afe26d9e773311eb27a,STILL_EXISTS,Util methods that really shouldn't be here and should be moved outside of this module
aaieaahec,biolab/orange3,Orange/widgets/data/owdatasampler.py,833832b403af9a63c20f2e06b6ff8c5a872155ec,STILL_EXISTS,TODO: Util methods that really shouldn't be here and should be moved outside of this module
aaieaahff,biolab/orange3,Orange/widgets/data/owdatasampler.py,833832b403af9a63c20f2e06b6ff8c5a872155ec,STILL_EXISTS,Util methods that really shouldn't be here and should be moved outside of this module
aaieaahhf,biolab/orange3,Orange/canvas/application/canvasmain.py,4ff3dd7e518b29d9f39b1381ddb0bf0b2ed02c6e,STILL_EXISTS,Try fixing some common problems.
aaieaahjj,biolab/orange3,Orange/canvas/application/canvastooldock.py,4ff3dd7e518b29d9f39b1381ddb0bf0b2ed02c6e,STILL_EXISTS,TODO: Should animate (accept) hide.
aaieaaiag,biolab/orange3,Orange/canvas/application/canvastooldock.py,4ff3dd7e518b29d9f39b1381ddb0bf0b2ed02c6e,STILL_EXISTS,TODO: right to left locale
aaieaaibd,biolab/orange3,Orange/canvas/canvas/items/annotationitem.py,4ff3dd7e518b29d9f39b1381ddb0bf0b2ed02c6e,STILL_EXISTS,topLeft can move changing the local coordinates.
aaieaaice,biolab/orange3,Orange/canvas/canvas/items/nodeitem.py,4ff3dd7e518b29d9f39b1381ddb0bf0b2ed02c6e,STILL_EXISTS,TODO: Remove the set[Widget|Category]Description. The user should
aaieaaicg,biolab/orange3,Orange/canvas/canvas/items/nodeitem.py,4ff3dd7e518b29d9f39b1381ddb0bf0b2ed02c6e,7d98b04ab27d9180bdacf6124cf78faf8ef1ab1e,TODO: The palette should override the `setColor`
aaieaaied,biolab/orange3,Orange/canvas/canvas/scene.py,4ff3dd7e518b29d9f39b1381ddb0bf0b2ed02c6e,STILL_EXISTS,TODO: Remove\/Deprecate. Is used only to get the category\/background
aaieaaijh,biolab/orange3,Orange/canvas/document/schemeedit.py,4ff3dd7e518b29d9f39b1381ddb0bf0b2ed02c6e,STILL_EXISTS,TODO: The the current layout implementation is BAD (fix is urgent).
aaieaaiji,biolab/orange3,Orange/canvas/document/schemeedit.py,4ff3dd7e518b29d9f39b1381ddb0bf0b2ed02c6e,STILL_EXISTS,Uncheck the text annotation action if needed.
aaieaajac,biolab/orange3,Orange/canvas/document/schemeedit.py,4ff3dd7e518b29d9f39b1381ddb0bf0b2ed02c6e,STILL_EXISTS,Uncheck the arrow annotation if needed.
aaieaajce,biolab/orange3,Orange/canvas/registry/description.py,4ff3dd7e518b29d9f39b1381ddb0bf0b2ed02c6e,STILL_EXISTS,TODO: Should also check that the name is real.
aaieaajch,biolab/orange3,Orange/canvas/registry/description.py,4ff3dd7e518b29d9f39b1381ddb0bf0b2ed02c6e,b35aa24941cc8d1a268c9be8450d4b1ba2a4baf7,should be able to handle missing types better).
aaieaajci,biolab/orange3,Orange/canvas/registry/discovery.py,4ff3dd7e518b29d9f39b1381ddb0bf0b2ed02c6e,b35aa24941cc8d1a268c9be8450d4b1ba2a4baf7,TODO: zipped modules
aaieabbab,biolab/orange3,Orange/canvas/application/canvasmain.py,eef94931eca0c0913d0edca5637e994d6d280c96,e9861a2cb91458ada9f2b45fabff128aec27f8b9,TODO: Ask for overwrite confirmation instead
aaieabbgg,biolab/orange3,Orange/statistics/distribution.py,aac39d3cfb522ad6967e885ab01a5344cb3a02bc,8e6529f1ad9ab0e7651ea8ce8c71f3c59126a21e,TODO implement __getitem__ that will return a normal array; not Continuous
aaieabbgj,biolab/orange3,Orange/data/table.py,83b06a6f0636818ee50bd68185d519e92e525111,f112e1305c0516a69b2e211e6aa225dad5364d06,TODO Basket column.
aaieabbjg,biolab/orange3,Orange/data/table.py,586d50fad4a33d28b91a6478faa2ffa812b37ac2,21b946eec0ae475aab18fcf5f8a0d46fcc2da38c,TODO Basket column.
aaieabchh,biolab/orange3,Orange/widgets/widget.py,829f2807fda8c3ed7a3f816e19e6c6889ad3951b,40e3718a7f1ba31a50c778c83d54b8a80d715815,needed by signalWrapper to know when everything was sent
aaieabdcf,biolab/orange3,Orange/widgets/data/owpaintdata.py,4ad016e3490f2049fe62c4e7ccc8069377af7351,STILL_EXISTS,(\"Magnet\"; \"Move (drag) multiple instances\"; None; icon_magnet);
aaieabebc,biolab/orange3,Orange/data/sql/table.py,f69cfd265ce1167eb09eb0e7c9aff518c5c1640d,STILL_EXISTS,multiple rows OR single row but multiple columns:
aaieabeib,biolab/orange3,Orange/widgets/data/owsave.py,4a384e00a0a4931117371fb4bdee938492b213f8,c554ec068c26b135ac954d86562a7ea121310f5f,The preceding lines should work as per API; but do not; it's probably a PyQt bug as per March 2010.
aaieabeic,biolab/orange3,Orange/widgets/data/owsave.py,4a384e00a0a4931117371fb4bdee938492b213f8,c554ec068c26b135ac954d86562a7ea121310f5f,The following is a workaround.
aaieabeih,biolab/orange3,Orange/widgets/gui.py,4b803479322219967f7daa836e9db02dce0b65d1,0dec7e559532036d9353039ee29cb9f1d74a8f5d,TODO: can we avoid the valueType by keeping the type the same?
aaieabfai,biolab/orange3,Orange/widgets/gui.py,4b803479322219967f7daa836e9db02dce0b65d1,3c020e32d3908d37b2e5c6e2d9c0ac13c1b68a0a,TODO comboBox looks overly complicated:
aaieabfaj,biolab/orange3,Orange/widgets/gui.py,4b803479322219967f7daa836e9db02dce0b65d1,8b3afb25c2fc9e5c5bb0490cc81840bdd8d1047b,- is the argument control2attributeDict needed? doesn't emptyString do the
aaieabfbe,biolab/orange3,Orange/widgets/gui.py,4b803479322219967f7daa836e9db02dce0b65d1,e69f71ce7fbd1d5f9c0e9283bf17d26612d7dace,fix to setAttributes. This doesn't break anything; but maybe fixes an
aaieabfca,biolab/orange3,Orange/widgets/gui.py,4b803479322219967f7daa836e9db02dce0b65d1,STILL_EXISTS,A workaround for a bug in Qt
aaieabfcc,biolab/orange3,Orange/widgets/gui.py,4b803479322219967f7daa836e9db02dce0b65d1,9794b07a37e56a1b7ebbbb1779251c84d465e61e,TODO: SmallWidgetButton is used only in OWkNNOptimization.py. (Re)Move.
aaieabfce,biolab/orange3,Orange/widgets/gui.py,4b803479322219967f7daa836e9db02dce0b65d1,8347551f460bb89a44b5c649fcb326cb93b6249b,TODO Class SearchLineEdit: it doesn't seem to be used anywhere
aaieabfcg,biolab/orange3,Orange/widgets/gui.py,4b803479322219967f7daa836e9db02dce0b65d1,8347551f460bb89a44b5c649fcb326cb93b6249b,TODO Class Searcher: it doesn't seem to be used anywhere
aaieabfci,biolab/orange3,Orange/widgets/gui.py,4b803479322219967f7daa836e9db02dce0b65d1,9794b07a37e56a1b7ebbbb1779251c84d465e61e,TODO collapsableWidgetBox is used only in OWMosaicDisplay.py; (re)move
aaieabfcj,biolab/orange3,Orange/widgets/gui.py,4b803479322219967f7daa836e9db02dce0b65d1,9794b07a37e56a1b7ebbbb1779251c84d465e61e,TODO Class widgetHider doesn't seem to be used anywhere; remove?
aaieabfdb,biolab/orange3,Orange/widgets/gui.py,4b803479322219967f7daa836e9db02dce0b65d1,STILL_EXISTS,TODO ControllgedList.item2name is probably never used
aaieabfhh,biolab/orange3,Orange/widgets/data/owcontinuize.py,4b3eef72bb2d4707f95e13cc55d6e280f1b5c079,STILL_EXISTS,TODO
aaieabfjb,biolab/orange3,Orange/widgets/data/owsave.py,4b3eef72bb2d4707f95e13cc55d6e280f1b5c079,9471f1c41ce0d97404a1b2a5aff74440a3028c78,The preceding lines should work as per API; but do not; it's probably a PyQt bug as per March 2010.
aaieabfjc,biolab/orange3,Orange/widgets/data/owsave.py,4b3eef72bb2d4707f95e13cc55d6e280f1b5c079,9471f1c41ce0d97404a1b2a5aff74440a3028c78,The following is a workaround.
aaieabghg,biolab/orange3,Orange/data/sql/table.py,264fcdaecf3a27681c7a45edcbd743687b5d83c5,ff83586c4ba4789247bf66fe02eaf7121f6b7d80,TODO: parse schema
aaieabgjb,biolab/orange3,Orange/data/sql/table.py,25fcf17392c88c2d5fda62706083dba526f3e27e,fd7e9380db6ca294f228b936d5da09a13245c78f,TODO: parse schema
aaieabgjc,biolab/orange3,Orange/data/sql/table.py,25fcf17392c88c2d5fda62706083dba526f3e27e,STILL_EXISTS,TODO if row_idx specify multiple rows; one of the following must
aaieabgji,biolab/orange3,Orange/data/sql/table.py,25fcf17392c88c2d5fda62706083dba526f3e27e,STILL_EXISTS,TODO: this returns all rows between min(rows) and max(rows): fix!
aaieabhab,biolab/orange3,Orange/canvas/canvas/items/nodeitem.py,988ca547322b7e67240865fa4c631cd292c89db5,STILL_EXISTS,TODO: Group messages by message_id not by severity
aaieabhdh,biolab/orange3,Orange/widgets/settings.py,e41efe651c1f7847be3e6c7ae086a381771d2bdd,fb97878f8d752c806ad9721c847c0531a46f47dc,FIXME: Who was supposed to fill the settings list?
aaieabjfh,biolab/orange3,Orange/widgets/settings.py,bbb7b1a72a7e69f6ffc80502aedb75dc590555d0,4a674b7c99982a1a5b963733ceb54bdc8c80af9e,TODO this method has misleading name (method 'initialize' does what
aaieabjfj,biolab/orange3,Orange/widgets/settings.py,bbb7b1a72a7e69f6ffc80502aedb75dc590555d0,4a674b7c99982a1a5b963733ceb54bdc8c80af9e,TODO similar to settings_to_widget; update_class_defaults does this for
aaieabjgf,biolab/orange3,Orange/widgets/settings.py,fb97878f8d752c806ad9721c847c0531a46f47dc,9bd352ed35c522c5e6ca5a28191c4a4d0a1cc08a,TODO: should we only use tuple syntax?
aaieabjjj,biolab/orange3,Orange/widgets/utils/scaling.py,460a1f71598a1e68ea77a1e2cea366675ab3a471,a0ba1f4696a51c3fc67bd7e5dc0428cf4e2538f1,fix values below zero
aaieaccde,biolab/orange3,Orange/widgets/visualize/owparallelcoordinates.py,e5d66e51e2018c2df5f4bf2f171afd39e40ba856,b42dfe6f02912f2303578eaf57eb6dc9aaefb7d8,FIXME: self.zoomSelectToolbar = OWToolbars.ZoomSelectToolbar(self; self.GeneralTab; self.graph; self.autoSendSelection;
aaieaccea,biolab/orange3,Orange/widgets/visualize/owparallelcoordinates.py,e5d66e51e2018c2df5f4bf2f171afd39e40ba856,b42dfe6f02912f2303578eaf57eb6dc9aaefb7d8,FIXME:
aaieaccib,biolab/orange3,Orange/widgets/visualize/owparallelcoordinates.py,17e91cb1d896ae8c5b1b2c6a026c50c69f9cd7b6,713f76dfadf06d7552a77c3ce44569e27c8dda56,FIXME: ; [
aaieaccie,biolab/orange3,Orange/widgets/visualize/owparallelgraph.py,e1df0918de7795060650ebaa340540f46233cd20,a0ea0e2a8288b7799f5d67427fa64062f3afc7a7,FIXME:
aaieaccih,biolab/orange3,Orange/widgets/visualize/owparallelgraph.py,577b4d8c7d952ed848de131c4aa106bf27f3af68,a0ea0e2a8288b7799f5d67427fa64062f3afc7a7,FIXME:
aaieacgfa,biolab/orange3,Orange/widgets/utils/concurrent.py,d028a052ebbe01bfcc8fd8a942749e305e685b99,cfaaeb10fe87b6b9795559c116e0b2a4cb0f0c00,TODO: If the task encounters an critical error it might not emit
aaieacgfj,biolab/orange3,Orange/widgets/utils/concurrent.py,d028a052ebbe01bfcc8fd8a942749e305e685b99,1d2abe8b26f19667dd5d2f02f0a392b3512fa316,DEPRECATED (not to mention extremely ugly)
aaieacgjh,biolab/orange3,Orange/widgets/widget.py,2439b6cde412518e813a390b4527c2d0d947a3eb,40e3718a7f1ba31a50c778c83d54b8a80d715815,Definitions needed to define Table as subclass of TableWithClass
aaieacifc,biolab/orange3,Orange/widgets/visualize/owheatmap.py,d9536dc3bf3e7edccad535ad86687e13ff844079,25c017f28bad9ebdf9a2949eba95589bd5e9fa9d,grid[0; :] is the x-axis ( columns have widths )
aaieadeba,biolab/orange3,Orange/widgets/visualize/owmosaic.py,f1dedfc03355d29f1491095d16800d9c25199a4a,a3d3968b964b437b73fc8cd3b5ee4c8da2afa465,self.information(2; \"Unused attribute values were removed.\")
aaieadebj,biolab/orange3,Orange/widgets/visualize/owmosaic.py,f1dedfc03355d29f1491095d16800d9c25199a4a,784f80900daa2ce7451d108790d963d3c489f570,TODO: preveri kaj je stem
aaieadedi,biolab/orange3,Orange/widgets/visualize/owmosaic.py,f1dedfc03355d29f1491095d16800d9c25199a4a,STILL_EXISTS,we remove the space needed for separating different attr. values
aaieadeie,biolab/orange3,Orange/widgets/visualize/owmosaic.py,f1dedfc03355d29f1491095d16800d9c25199a4a,36d3bc7a114c2ed445b6654f0ef94efeae8efb0a,TODO: poglej kaj je s tem
aaieadhff,biolab/orange3,Orange/widgets/visualize/owsieve.py,261ee8298f594f1f5deeca1f887e0e205c02367a,b4cd5da428b6bf562326d7130c0eb7518846e294,self.information(1; \"Unused attribute values were removed.\")
aaieafbfe,biolab/orange3,Orange/widgets/visualize/owmosaic.py,b26ead1fcefd993228429b74ab23ceed71eabf8f,STILL_EXISTS,zgornja vrstica je diskretizirala tabelo in odstranila unused values
aaieafbja,biolab/orange3,Orange/widgets/data/owpurgedomain.py,7a330d379d92905c8df962d014dbdb8a6234f87f,0afa82c9a4080f266eb769bea63c5bea0b1e904a,TODO: Extend the reporting for multi-class domains
aaieafffd,biolab/orange3,Orange/statistics/distribution.py,6e8ec0c01727663d251dffcc4fa98d32cfa09b0b,STILL_EXISTS,Normailze the columns to int indices
aaieafieb,biolab/orange3,Orange/widgets/visualize/owmosaic.py,535cda9ea90eaad5ec2209c5becc45ab3317c799,STILL_EXISTS,zgornja vrstica je diskretizirala tabelo in odstranila unused values
aaieagghj,biolab/orange3,Orange/widgets/data/owimpute.py,975e89a7488f3e5d37937ab7f92ff24ad2992018,4441bf373e1dcbb3ce5cae034f0f3a85480bfb06,Columns to filter unknowns by dropping rows.
aaieaghdi,biolab/orange3,Orange/widgets/visualize/owscatterplot.py,f1e43c50c47784192d7c7f515bf4e15807bf2def,67ae053940975c9de0df4fdb79c4d8a0e0d5de0e,#TODO tukaj mas testni graf!
aaieaghea,biolab/orange3,Orange/widgets/visualize/owscatterplot.py,f1e43c50c47784192d7c7f515bf4e15807bf2def,67ae053940975c9de0df4fdb79c4d8a0e0d5de0e,#TODO pazi
aaieaghed,biolab/orange3,Orange/widgets/visualize/owscatterplot.py,f1e43c50c47784192d7c7f515bf4e15807bf2def,67ae053940975c9de0df4fdb79c4d8a0e0d5de0e,# TODO spodaj je se en POZOR; kjer nastavis palette
aaieaghfg,biolab/orange3,Orange/widgets/visualize/owscatterplot.py,f1e43c50c47784192d7c7f515bf4e15807bf2def,67ae053940975c9de0df4fdb79c4d8a0e0d5de0e,# TODO: jitter size slider ima samo interger values -> ali lahko slajda po self.jitterSizeNums
aaieaghga,biolab/orange3,Orange/widgets/visualize/owscatterplot.py,f1e43c50c47784192d7c7f515bf4e15807bf2def,67ae053940975c9de0df4fdb79c4d8a0e0d5de0e,#TODO POZOR!
aaieaghgd,biolab/orange3,Orange/widgets/visualize/owscatterplot.py,f1e43c50c47784192d7c7f515bf4e15807bf2def,67ae053940975c9de0df4fdb79c4d8a0e0d5de0e,# TODO tole je zdej minimum size --> najdi drug nacin za resize
aaieagjjd,biolab/orange3,Orange/widgets/visualize/owscatterplotgraph.py,f1e43c50c47784192d7c7f515bf4e15807bf2def,STILL_EXISTS,TODO plot se ne animira vec?
aaieahaeg,biolab/orange3,Orange/widgets/visualize/owscatterplotgraph.py,f1e43c50c47784192d7c7f515bf4e15807bf2def,STILL_EXISTS,# TODO: legendi se prekrijeta; ce se malo potrudis
aaieahagi,biolab/orange3,Orange/widgets/visualize/owscatterplotgraph.py,f1e43c50c47784192d7c7f515bf4e15807bf2def,67ae053940975c9de0df4fdb79c4d8a0e0d5de0e,#TODO kaj je to? spodi je metoda z istim imenom
aaieahahd,biolab/orange3,Orange/widgets/visualize/owscatterplotgraph.py,f1e43c50c47784192d7c7f515bf4e15807bf2def,STILL_EXISTS,TODO plot se animira  -> naredi na koncu
aaieahbcc,biolab/orange3,Orange/widgets/visualize/owscatterplotgraph.py,f1e43c50c47784192d7c7f515bf4e15807bf2def,STILL_EXISTS,self.pgPlotWidget.replot()     #TODO \u0161e zmeri ne vem kaj je tale replot()
aaieahbgf,biolab/orange3,Orange/widgets/visualize/owscatterplotgraph.py,f1e43c50c47784192d7c7f515bf4e15807bf2def,67ae053940975c9de0df4fdb79c4d8a0e0d5de0e,TODO: dinami\u010Dno dolo\u010Di \u0161irino glede na vsebino
aaieahceg,biolab/orange3,Orange/widgets/visualize/owmosaic.py,548d4c7e79c0b119ba911c477101ea2fa4293225,STILL_EXISTS,zgornja vrstica je diskretizirala tabelo in odstranila unused values
aaieahica,biolab/orange3,Orange/widgets/visualize/owmosaic.py,ab619029c5b8b20bb59d79f77f320eb77bc63955,STILL_EXISTS,# TODO: tale if za sqlTable je \u010Dist brezvezen; ker se itak naredi v poizvedba v sql\/u
aaieahicc,biolab/orange3,Orange/widgets/visualize/owmosaic.py,ab619029c5b8b20bb59d79f77f320eb77bc63955,7fc6858ae670e7f07d9c52bcbe2928d986d204bd,#TODO: problem se pojavi; ko so bili atributi diskretizirani (npr. iris)
aaieahicf,biolab/orange3,Orange/widgets/visualize/owmosaic.py,ab619029c5b8b20bb59d79f77f320eb77bc63955,bcf7e2b4f9c2852d066304640b5fbebdd8971bd7,# TODO: hm; tale self sem dodal tako na hitro
aaieahida,biolab/orange3,Orange/widgets/visualize/owheatmap.py,52e34668f466a5569e40f2676f3f9999e664250f,5787fc5a54593e18c93787c7dabbf6740ee3e553,#TODO: remove the lines that \"repair\" the tables
aaieahidb,biolab/orange3,Orange/widgets/visualize/owmosaic.py,7fc6858ae670e7f07d9c52bcbe2928d986d204bd,784f80900daa2ce7451d108790d963d3c489f570,# TODO: this function is used both in owmosaic and owsieve --> where to put it?
aaieahihf,biolab/orange3,Orange/widgets/visualize/owscatterplot.py,56d5dcd562f0f8a68959e75cff81d1934c20539d,STILL_EXISTS,TODO: NE delaj tega!
aaieahijd,biolab/orange3,Orange/widgets/data/owdatasampler.py,e421fde9ab2b740d2390625981d687e5fe77606b,2cb7eec32803492cb91d6115f9c15de5e42200b8,XXX: StratifiedKFold does not support random_state
aaieahjce,biolab/orange3,Orange/data/sql/parser.py,f8b12258bd679f561f3d571f8ef59f64b1664e9c,STILL_EXISTS,TODO: replace xxx with sth autogenerated
aaieahjdd,biolab/orange3,Orange/feature/discretization.py,f66d5ef4c6fd35b1a7d099e0c93cbdfdbdbc7902,STILL_EXISTS,TODO: why is the whole distribution computed instead of just min\/max
aaieahjgc,biolab/orange3,Orange/widgets/evaluate/owtestlearners.py,2da78f3224624173933b47eaa5634a36d00bbae6,STILL_EXISTS,TODO: Test each learner individually
aaieahjgd,biolab/orange3,Orange/widgets/visualize/owscatterplot.py,714f5f02e38b21f9f4bcfeb216245a4dbecdd600,67ae053940975c9de0df4fdb79c4d8a0e0d5de0e,TODO: Add support for visualizing ExperimentResults or remove code for it
aaieahjid,biolab/orange3,Orange/widgets/data/owdiscretize.py,b89eb6930e5a51578f5a0c17e970a323cf2ee36a,STILL_EXISTS,Need a better way to distinguish discretization states
aaieaiabj,biolab/orange3,Orange/widgets/data/owdiscretize.py,b89eb6930e5a51578f5a0c17e970a323cf2ee36a,850b12f1cdbf7c73611e4ae0f39faaa9cbf1cb3b,Information gain of the best split
aaieaiach,biolab/orange3,Orange/widgets/evaluate/owconfusionmatrix.py,f9764fa416f4cd36cba5882391417a7b28c3c4f5,STILL_EXISTS,TODO: Store learners or their names in the results.
aaieaiage,biolab/orange3,Orange/widgets/visualize/owdistributions.py,b5653c32c30b3eec53441a0eed141185e341ad2a,52f5db96e0cd2f45daec2af8d4ebc90d80de20f2,# XXX: sum the individual curves and not the distributions.
aaieaiahj,biolab/orange3,Orange/widgets/visualize/owdistributions.py,b5653c32c30b3eec53441a0eed141185e341ad2a,fda56d91b7e442ef5963cabdf131ceead23d02a5,XXX: what to do with unknowns
aaieaiaic,biolab/orange3,Orange/widgets/visualize/owdistributions.py,b5653c32c30b3eec53441a0eed141185e341ad2a,STILL_EXISTS,XXX: This is only a part of Silverman's rule.
aaieaibje,biolab/orange3,Orange/widgets/visualize/owscatterplotgraph.py,67ae053940975c9de0df4fdb79c4d8a0e0d5de0e,e1bb0274bbe8ca3ab110763da3eef39ba6177f49,for example in self.raw_data]  # FIX!
aaieaiced,biolab/orange3,Orange/widgets/visualize/owscatterplotgraph.py,67ae053940975c9de0df4fdb79c4d8a0e0d5de0e,a26b113ca00938141e21165973a8b226b9d518ef,self.scatterplot_item.setSize(size=self.point_width)  # TODO: FIX
aaieaicee,biolab/orange3,Orange/widgets/visualize/owscatterplotgraph.py,67ae053940975c9de0df4fdb79c4d8a0e0d5de0e,871e3ad37e49df56b8c715acef3ece0489095d48,self.plot_widget.plotItem.setAlpha(self.alpha_value)  # TODO: FIX
aaieaidje,biolab/orange3,Orange/widgets/visualize/owscatterplotgraph.py,241004fdd129560bcb7130d22dc8cfa258af433b,e1bb0274bbe8ca3ab110763da3eef39ba6177f49,for example in self.raw_data]  # FIX!
aaieaiebb,biolab/orange3,Orange/widgets/visualize/owscatterplotgraph.py,391df88bf4e6d44a3ff1b0e99ef680f130b8f3bf,e1bb0274bbe8ca3ab110763da3eef39ba6177f49,for example in self.raw_data]  # FIX!
aaieaiebe,biolab/orange3,Orange/widgets/visualize/owscatterplotgraph.py,391df88bf4e6d44a3ff1b0e99ef680f130b8f3bf,871e3ad37e49df56b8c715acef3ece0489095d48,TODO This could be slow! Can it be somehow changed to vector
aaieaiebg,biolab/orange3,Orange/widgets/visualize/owscatterplotgraph.py,391df88bf4e6d44a3ff1b0e99ef680f130b8f3bf,871e3ad37e49df56b8c715acef3ece0489095d48,self.plot_widget.plotItem.setAlpha(self.alpha_value)  # TODO: FIX
aaieaiffc,biolab/orange3,Orange/widgets/evaluate/owrocanalysis.py,f35668e92c8166d6cc9498b60485bf24d138a421,STILL_EXISTS,Fix points exactly on the right boundary.
aaieaifhf,biolab/orange3,Orange/widgets/evaluate/owrocanalysis.py,1e607cf4287cb12557f0c8b28b2dd722feed8f61,STILL_EXISTS,TODO: Check for no FP or no TP
aaieaifjc,biolab/orange3,Orange/widgets/evaluate/owrocanalysis.py,aa585db4c6cce1f4cc813b0eed29dcb767b153c4,STILL_EXISTS,Fix points exactly on the right boundary.
aaieaigch,biolab/orange3,Orange/widgets/visualize/owscatterplotgraph.py,e1bb0274bbe8ca3ab110763da3eef39ba6177f49,STILL_EXISTS,TODO: move this to ScatterplotGraph -- send value_rect as argument;
aaieaigci,biolab/orange3,Orange/widgets/visualize/owscatterplotgraph.py,e1bb0274bbe8ca3ab110763da3eef39ba6177f49,55a2ca35242875078f3e6316943dbda015e30733,TODO: rename ScatterViewBox to something general
aaieaigdg,biolab/orange3,Orange/widgets/visualize/owscatterplotgraph.py,55a2ca35242875078f3e6316943dbda015e30733,0e53317bae485fbe2394930b17f32672db71e07d,TODO Move utility classes to another module; so they can be used elsewhere
aaieaigdh,biolab/orange3,Orange/widgets/visualize/owscatterplotgraph.py,55a2ca35242875078f3e6316943dbda015e30733,STILL_EXISTS,TODO: move this to ScatterplotGraph -- send value_rect as argument;
aaieaigfg,biolab/orange3,Orange/widgets/data/oweditdomain.py,9733ccb5b404396f687f7634c05129c4190db88d,STILL_EXISTS,Implement a proper model with in-place editing.
aaieaigfh,biolab/orange3,Orange/widgets/data/oweditdomain.py,9733ccb5b404396f687f7634c05129c4190db88d,STILL_EXISTS,(Maybe it should be a TableModel with 2 columns)
aaieaigga,biolab/orange3,Orange/widgets/data/oweditdomain.py,9733ccb5b404396f687f7634c05129c4190db88d,6b2269c89dd91d9731409ab53d7917413e32568e,TODO: enable editing of number_of_decimals; scientific format ...
aaieaigig,biolab/orange3,Orange/widgets/data/owimageviewer.py,d276f901d454015c6a77e7f18d56a903089f40c1,STILL_EXISTS,XXX Maybe convert the error into standard
aaieaihbh,biolab/orange3,Orange/feature/discretization.py,850b12f1cdbf7c73611e4ae0f39faaa9cbf1cb3b,STILL_EXISTS,Information gain of the best split
aaieaihgg,biolab/orange3,Orange/data/io.py,21b946eec0ae475aab18fcf5f8a0d46fcc2da38c,4d8d2835248666c8d880236e2ce78a3c82a86038,TODO Basket column.
aaieaihjg,biolab/orange3,Orange/data/table.py,4d8d2835248666c8d880236e2ce78a3c82a86038,476cf4140fd502e208d67187cda8d3b487f797ad,TODO Basket column.
aaieaiiab,biolab/orange3,Orange/data/io.py,476cf4140fd502e208d67187cda8d3b487f797ad,b10b1331a973995cdd6998f965c2f939c22c4367,TODO Basket column.
aaieaiibe,biolab/orange3,Orange/widgets/data/owselectcolumns.py,7f6d2b6153b3319d96d21ef7fa4f15697b028077,STILL_EXISTS,TODO do we still need this?
aaieaiife,biolab/orange3,Orange/data/table.py,285c800be4b58dbe69eed4693bd2bb87412243b7,267253b5e97cb2003d80589c69b126760f5cbebd,TODO Basket column.
aaieaiiib,biolab/orange3,Orange/widgets/visualize/owheatmap.py,25c017f28bad9ebdf9a2949eba95589bd5e9fa9d,STILL_EXISTS,XXX: Non linear intensity scaling
aaieaijgb,biolab/orange3,Orange/widgets/visualize/owscatterplot.py,3fdca1c1cefc017a9839cd3230adade99d0d0268,453222b587dd5dcdb75386c7d528b6f52b8c32f0,TODO: Implement selection for sql data
aaieajaae,biolab/orange3,Orange/data/sql/table.py,cf5c74413ed70e05241668610166b2ab5eb5bca8,23c6b6187430b12030c80f5b83243be4b26791ab,TODO: make sure that int values are OK
aaieajadb,biolab/orange3,Orange/classification/random_forest.py,9bfec46b55821439835846fb9575b9ac6efee816,bda9bc3574b14ead6f51e1fb0f6864e07ccefd88,TODO: implement sending a single decision tree
aaieajagj,biolab/orange3,Orange/data/sql/table.py,a28a55decb983dbdbaef98d8e7568c1057f5a75b,721ade397fc1ff2cb0a0a0b7a2131fd910cc0f82,TODO: faster check for size limit
aaieajahf,biolab/orange3,Orange/data/sql/table.py,a9f46af255f911bb0cbf38e48276938687c2a483,c9ec1bed466bfc527c61c41a65f4b74bc0cf60ad,TODO: faster check for size limit
aaieajahj,biolab/orange3,Orange/widgets/visualize/owparallelgraph.py,ce90e2e65a47e96e944aab9b6f05ceecf836a189,STILL_EXISTS,TODO: Implement using BasicStats
aaieajdha,biolab/orange3,Orange/widgets/classify/owclassificationtreegraph.py,33f365a80604236ebac8f08043f1cbb4073b73af,9c39d3a009cec58e6efde636553655fe6abee5ac,fix = lambda str: str.replace(\">\"; \"&gt;\").replace(\"<\"; \"&lt;\")
aaieajdjd,biolab/orange3,Orange/widgets/classify/owclassificationtreegraph.py,9c39d3a009cec58e6efde636553655fe6abee5ac,STILL_EXISTS,TODO: this is easily extended to Classification Rules-compatible form
aaieajdje,biolab/orange3,Orange/widgets/classify/owclassificationtreegraph.py,9c39d3a009cec58e6efde636553655fe6abee5ac,887ee40cf11e939de737903a1d867eb41f49acb4,TODO
aaieajeia,biolab/orange3,Orange/widgets/classify/owclassificationtreegraph.py,10ee286df8b5d9cc0885931bc27f5d22cbbe0953,STILL_EXISTS,TODO: this is easily extended to Classification Rules-compatible form
aaieajjie,biolab/orange3,Orange/widgets/classify/owclassificationtreegraph.py,0a0fee72f071e13eccb592ecd0fa0399d0614a11,STILL_EXISTS,TODO This context handler is too strict: we only need the same class
aaiebaada,biolab/orange3,Orange/widgets/unsupervised/owmds.py,2ac2b0b3bd39fa084383acbdc88797a98f4009c3,0088176376f87417437131c0e2fe911213351bd3,TODO: This should already be a part of palette
aaiebabia,biolab/orange3,Orange/widgets/visualize/owvenndiagram.py,e448ddc6224230c4e4f1e38366816ee5894b3aee,05e02399afbe7b49ae3d53113d10125b992cdbbc,: Use identifier columns for instance matching
aaiebabjd,biolab/orange3,Orange/widgets/visualize/owvenndiagram.py,e448ddc6224230c4e4f1e38366816ee5894b3aee,STILL_EXISTS,TODO: Allow setting more them 5 inputs and let the user
aaiebacag,biolab/orange3,Orange/widgets/visualize/owvenndiagram.py,e448ddc6224230c4e4f1e38366816ee5894b3aee,STILL_EXISTS,add columns with source table id and set id
aaiebacbc,biolab/orange3,Orange/widgets/visualize/owvenndiagram.py,e448ddc6224230c4e4f1e38366816ee5894b3aee,3ece2a0594b91f1c52e239dc2d06c2d123a670bf,TODO: What about metas.
aaiebacbe,biolab/orange3,Orange/widgets/visualize/owvenndiagram.py,e448ddc6224230c4e4f1e38366816ee5894b3aee,STILL_EXISTS,XXX: comparing NaN with different payload
aaiebaccf,biolab/orange3,Orange/widgets/visualize/owvenndiagram.py,e448ddc6224230c4e4f1e38366816ee5894b3aee,STILL_EXISTS,general does not look nice in a super script so we use
aaiebacch,biolab/orange3,Orange/widgets/visualize/owvenndiagram.py,e448ddc6224230c4e4f1e38366816ee5894b3aee,STILL_EXISTS,TODO: Use palette's selected\/highligted text \/ background colors to
aaiebacgd,biolab/orange3,Orange/widgets/visualize/owvenndiagram.py,e448ddc6224230c4e4f1e38366816ee5894b3aee,STILL_EXISTS,TODO: Should include anchors for text layout (both inside and outside).
aaiebadeh,biolab/orange3,Orange/widgets/unsupervised/owdistancemap.py,6d6c5642defeb7082f0a1f962166c67ef0a76993,STILL_EXISTS,TODO: Fix extended selection.
aaiebadfc,biolab/orange3,Orange/data/variable.py,06b422317171c58cb46b2d787ec830e5239b1bea,61b75509caf668963db9cd60fe2b102712d7d74e,TODO: This may significantly slow down file reading.
aaiebadga,biolab/orange3,Orange/widgets/data/owpaintdata.py,7ffe2f4a8e2454c47b9cf22a6ceb755bbb78686d,STILL_EXISTS,TODO: union of indices; ...
aaiebadgf,biolab/orange3,Orange/data/variable.py,a63c441151b049f0bfca10586d3e1eac01ddeeb3,7e86b673843a2b76b1687560636e875d86180a96,TODO: do we need locking? Don't we expect the code to be reentrant?
aaiebadgg,biolab/orange3,Orange/data/variable.py,a63c441151b049f0bfca10586d3e1eac01ddeeb3,7e86b673843a2b76b1687560636e875d86180a96,TODO: the original intention was to prevent deadlocks. Locks block;
aaiebadgj,biolab/orange3,Orange/data/variable.py,a63c441151b049f0bfca10586d3e1eac01ddeeb3,61b75509caf668963db9cd60fe2b102712d7d74e,TODO: This may significantly slow down file reading.
aaiebadjc,biolab/orange3,Orange/widgets/data/owdatasampler.py,8dc019fd27d59c346f2ed50d841187a1877003d6,67376e7b0f23f02f3cfb1cbb422783d5b243053b,one example is needed; not the whole (100%) set
aaiebaebc,biolab/orange3,Orange/widgets/utils/itemmodels.py,bc2e6b06204df6bce204da20aa2c448a7268255d,ec0df5ed096aef07c76a40e3685d59e25b38ad96,domain roles for all table columns
aaiebaeeh,biolab/orange3,Orange/widgets/data/owtable.py,b4034649ec375f856fa7313c630f2697d4bacd2e,STILL_EXISTS,check whether we have a sparse columns;
aaiebaefd,biolab/orange3,Orange/widgets/data/owtable.py,b4034649ec375f856fa7313c630f2697d4bacd2e,ec614c1b87d39e7c1c1f2e0cbeae401cdfe92412,TODO Implement
aaiebaehd,biolab/orange3,Orange/widgets/utils/itemmodels.py,c448e4b04f800e735fd7222e56f56c1aac683616,ec0df5ed096aef07c76a40e3685d59e25b38ad96,first needed.
aaiebafab,biolab/orange3,Orange/widgets/data/owtable.py,106a55487847c9d097213f3380fd65faab712e42,STILL_EXISTS,check whether we have a sparse columns;
aaiebafaj,biolab/orange3,Orange/widgets/data/owtable.py,106a55487847c9d097213f3380fd65faab712e42,bdc5546e5642b559ca2dcaf394f484d8ea4634fd,TODO Implement
aaiebafcg,biolab/orange3,Orange/widgets/data/owtable.py,f7a7a99e23a72edf8ff86cd193e613de42f9f96b,b5e6150dd130ddd5489be836dbb3abc5fb9e90d2,TODO: Are any other signals emitted during a sort?
aaiebafdb,biolab/orange3,Orange/widgets/data/owtable.py,0e2a708d0be994377403a4a34a6dcd5f91323aed,STILL_EXISTS,check whether we have a sparse columns;
aaiebafdi,biolab/orange3,Orange/widgets/data/owtable.py,0e2a708d0be994377403a4a34a6dcd5f91323aed,STILL_EXISTS,TODO Implement
aaiebagaa,biolab/orange3,Orange/classification/base.py,d1b8ab8ad5cf5e9a9017d64dd0b38b1a416b9105,d57daba27b90427e91404a16757e2ac8de568f9c,TODO: prettify the name (pull the class up if it is imported at
aaiebagae,biolab/orange3,Orange/classification/base.py,4e6e613b5bc36f523656926c0235b625a00b97dd,d7196733d31f6d5d4c9b3717319a0c68fe1611af,TODO: prettify the name (pull the class up if it is imported at
aaiebahbg,biolab/orange3,Orange/data/io.py,6f8211c512d1c71ed817f54ee9e3d8d7a8598f00,b10b1331a973995cdd6998f965c2f939c22c4367,TODO how many columns?!
aaiebahga,biolab/orange3,Orange/evaluation/scoring.py,584b776492b66aca21e5ce2b9516f9a032cfb8a3,STILL_EXISTS,calculate height needed height of an image
aaiebahjd,biolab/orange3,Orange/misc/wrapper_meta.py,d7196733d31f6d5d4c9b3717319a0c68fe1611af,STILL_EXISTS,TODO: prettify the name (pull the class up if it is imported at
aaiebahjg,biolab/orange3,Orange/tests/test_svm.py,6993ef8bcf62f1e9c33a1a7b25b835e6406b0725,16cc8757f02295b058128befbbdcd63dceed3635,TODO: improve the test - what does it check?
aaiebaiab,biolab/orange3,Orange/classification/base.py,541702b1c663f45e46af2f1536faa28479dd7ad9,145e988208f263190ef1443cca896c539c906fc9,TODO: prettify the name (pull the class up if it is imported at
aaiebajfh,biolab/orange3,Orange/widgets/utils/itemmodels.py,f0db54bffc49c3e5d75504101e31fa70b21deafa,STILL_EXISTS,domain roles for all table columns
aaiebajfj,biolab/orange3,Orange/widgets/utils/itemmodels.py,f0db54bffc49c3e5d75504101e31fa70b21deafa,STILL_EXISTS,first needed.
aaiebajhg,biolab/orange3,Orange/widgets/data/owtable.py,d5dbe0fef25ebc922086a99db954e54bd30447b9,b5e6150dd130ddd5489be836dbb3abc5fb9e90d2,TODO: change when QTBUG-44143 is fixed
aaiebajia,biolab/orange3,Orange/widgets/data/owtable.py,d5dbe0fef25ebc922086a99db954e54bd30447b9,STILL_EXISTS,As this is an ugly hack; do everything in
aaiebajif,biolab/orange3,Orange/widgets/data/owtable.py,d5dbe0fef25ebc922086a99db954e54bd30447b9,STILL_EXISTS,check whether we have a sparse columns;
aaiebajja,biolab/orange3,Orange/widgets/data/owtable.py,d5dbe0fef25ebc922086a99db954e54bd30447b9,STILL_EXISTS,TODO Implement
aaiebbaae,biolab/orange3,Orange/widgets/utils/itemmodels.py,11381600debbe3067448f074a19890048681a9b2,STILL_EXISTS,domain roles for all table columns
aaiebbaii,biolab/orange3,Orange/widgets/data/owpreprocess.py,2533f3b5aee7b84166f61fa93e6601b0dad8f8bf,7b2b0edf4272742b05ed09e3048870ef87f14da7,TODO: implement top percentile selection
aaiebbaij,biolab/orange3,Orange/widgets/data/owpreprocess.py,2533f3b5aee7b84166f61fa93e6601b0dad8f8bf,STILL_EXISTS,TODO: Model based FS (random forest variable importance; ...); RFE
aaiebbajd,biolab/orange3,Orange/widgets/data/owpreprocess.py,2533f3b5aee7b84166f61fa93e6601b0dad8f8bf,eef7cae6970c1a4b2976726ddd22903447e3f75d,TODO: Remember if we have seen enter with the proper data
aaiebbbah,biolab/orange3,Orange/widgets/data/owpreprocess.py,2533f3b5aee7b84166f61fa93e6601b0dad8f8bf,eef7cae6970c1a4b2976726ddd22903447e3f75d,TODO: Check if widget is already inserted.
aaiebbbbf,biolab/orange3,Orange/widgets/data/owpreprocess.py,2533f3b5aee7b84166f61fa93e6601b0dad8f8bf,eef7cae6970c1a4b2976726ddd22903447e3f75d,TODO: Get the proper title bar geometry.
aaiebbbbi,biolab/orange3,Orange/widgets/data/owpreprocess.py,2533f3b5aee7b84166f61fa93e6601b0dad8f8bf,STILL_EXISTS,TODO: Fix this (subclass even if just to pass a function
aaiebbbfb,biolab/orange3,Orange/widgets/data/owpreprocess.py,bb7d1f39ba08ed9448484fde6b6b18beda0f62c0,STILL_EXISTS,functionality is desired (for instance in Orange v2.* Rank widget
aaiebbbfd,biolab/orange3,Orange/widgets/data/owpreprocess.py,bb7d1f39ba08ed9448484fde6b6b18beda0f62c0,STILL_EXISTS,TODO: Extend with entry points here
aaiebbbic,biolab/orange3,Orange/widgets/data/owtable.py,e0d2b20b77f060cf72584e09d6cac6d669dc45c2,STILL_EXISTS,only a subset of the columns is selected
aaiebbecb,biolab/orange3,Orange/widgets/data/owtable.py,a60d9b221c3a89452f60da2a113f742637e587d2,STILL_EXISTS,(workaround for QTBUG-18490 \/ QTBUG-28631)
aaiebbeef,biolab/orange3,Orange/widgets/unsupervised/owhierarchicalclustering.py,b9d419aa6cbe43f77a530c8f76bf086db0815498,STILL_EXISTS,Select columns
aaiebbefe,biolab/orange3,Orange/widgets/visualize/owlinearprojection.py,48a476c35829943cb777e7f7c605aae069966463,STILL_EXISTS,TODO: Allow for axes to be supplied from an external source.
aaiebbfdc,biolab/orange3,Orange/widgets/evaluate/owpredictions.py,19042d3fa30bac04ef6b6ba61c12d47d78e074c8,683df404847c5afe5c2186093d054349ee04b65f,enforce fixed size but provide a sensible minimum width constraint.
aaiebbffa,biolab/orange3,Orange/widgets/unsupervised/owmds.py,1ba12aa9e6c7d1f88f0b9b42c59fb570aeb8b70a,683df404847c5afe5c2186093d054349ee04b65f,TODO: decrease the max iteration count by the already
aaiebbfig,biolab/orange3,Orange/widgets/evaluate/owpredictions.py,9924c73e392d42f58d3378517cde26d68e57c4f2,163b18f61f4ef2ef9cddd191813346ac8c12c85b,enforce fixed size but provide a sensible minimum width constraint.
aaiebbgaf,biolab/orange3,Orange/widgets/unsupervised/owmds.py,6c3cdadac4727dd6e347bbbf20166054ed90ebe2,STILL_EXISTS,TODO: decrease the max iteration count by the already
aaiebbgfd,biolab/orange3,Orange/widgets/visualize/owheatmap.py,a01ee70df34b4200bbc0ca306946dadb58905ac2,f56c634bff1175af3c6b66ca5d027ec4a8c0a838,TODO: improve O(N ** 2)
aaiebbgfg,biolab/orange3,Orange/widgets/visualize/owheatmap.py,a01ee70df34b4200bbc0ca306946dadb58905ac2,917bb41dde86b109d3738e19457b11211d4d977c,TODO:
aaiebbggd,biolab/orange3,Orange/widgets/visualize/owheatmap.py,a01ee70df34b4200bbc0ca306946dadb58905ac2,b42197cc67ff455bfbd189a417433b8ff897ab7b,Type of sorting to apply on columns
aaiebbgha,biolab/orange3,Orange/widgets/visualize/owheatmap.py,a01ee70df34b4200bbc0ca306946dadb58905ac2,917bb41dde86b109d3738e19457b11211d4d977c,TODO: Add 'Manage\/Add\/Remove' action.
aaiebbghe,biolab/orange3,Orange/widgets/visualize/owheatmap.py,a01ee70df34b4200bbc0ca306946dadb58905ac2,STILL_EXISTS,Restore\/update the row\/columns items descriptions from cache if
aaiebbhaf,biolab/orange3,Orange/widgets/visualize/owheatmap.py,a01ee70df34b4200bbc0ca306946dadb58905ac2,917bb41dde86b109d3738e19457b11211d4d977c,TODO: Move\/delegate to (Scene) helpEvent
aaiebcadg,biolab/orange3,Orange/canvas/scheme/widgetsscheme.py,b54ea4c652459ad050c33a2c11d766a1526046cd,STILL_EXISTS,TODO: Update\/sync whenever the widget settings change.
aaiebcajj,biolab/orange3,Orange/widgets/evaluate/owtestlearners.py,18f3a0dfea1f27f44072a69509b323ae34fc256b,STILL_EXISTS,remove possible extra columns from the model.
aaiebcbac,biolab/orange3,Orange/widgets/evaluate/owtestlearners.py,18f3a0dfea1f27f44072a69509b323ae34fc256b,STILL_EXISTS,computed as needed in this method.
aaiebcbcb,biolab/orange3,Orange/widgets/visualize/owdistributions.py,138c2f5f3adb67a55d8442665c24add12b26efb7,fda56d91b7e442ef5963cabdf131ceead23d02a5,FIXME here it would be better to just get values
aaiebcbhb,biolab/orange3,Orange/tests/test_distances.py,4665312f102ed28910e0e793b40df72e4ebf8797,STILL_EXISTS,TODO: introduce Table.__eq__() ???
aaiebcdbj,biolab/orange3,Orange/widgets/unsupervised/owmds.py,197bd3b390e74750b95a772018f6a50e83df604a,11ea523390dd9c76c4a37e81f7eaad9f89c88151,TODO: enable drawing similar pairs within reasonable time
aaiebcddj,biolab/orange3,Orange/widgets/unsupervised/owmds.py,2dbbedb3bf28fc39c35a3f549dc4972e73d15375,2998b4c75bc031fdb2b178890c7be1ebb0f907db,TODO: enable drawing similar pairs within reasonable time
aaiebceab,biolab/orange3,Orange/widgets/gui.py,90ddbe52bdd70a6486fbac88e02d7738848660be,STILL_EXISTS,TODO Replace hardcoded alignment
aaiebcebh,biolab/orange3,Orange/widgets/gui.py,3996080ce8163f763c79966195058753051d5ad0,STILL_EXISTS,TODO Replace hardcoded alignment
aaiebceii,biolab/orange3,Orange/data/io.py,b10b1331a973995cdd6998f965c2f939c22c4367,0606101ef6421dac1ef6513903b2b2224102dc9f,Iterate through the columns
aaiebcfcg,biolab/orange3,Orange/data/io.py,9cd8487bbc9df6454ab56a8412c4f5cb20a751e2,0606101ef6421dac1ef6513903b2b2224102dc9f,better
aaiebcgei,biolab/orange3,Orange/widgets/visualize/owheatmap.py,5df5efb6981ff7313915c4d9eec1fbcf9a5c2d9f,b42197cc67ff455bfbd189a417433b8ff897ab7b,For columns
aaiebcgej,biolab/orange3,Orange/widgets/visualize/owheatmap.py,d2237046b686c1be19bbe602178a0fc265dabaf1,287278a7c08311a55b54caa0ce7c4f01a5252b88,Fix points exactly on the right boundary.
aaiebcggf,biolab/orange3,Orange/data/io.py,6feb97079f61fce5cfc01226539843c0b4bdc1f5,0aeda0934ab19f290580e35b576cef9ca8b82d53,Sort order in OWSave widget combo box; lower is better
aaiebcggh,biolab/orange3,Orange/data/io.py,a0da0101bf54715f7e3934bfe442c6c6146b8aee,STILL_EXISTS,Sort order in OWSave widget combo box; lower is better
aaiebcghc,biolab/orange3,Orange/evaluation/testing.py,1673efe81350a85308844a7d11fedfecec064b1d,STILL_EXISTS,append new columns to meta attributes
aaiebchcc,biolab/orange3,Orange/preprocess/preprocess.py,22c6c29011af5647abedaea987ab8e9d08adb64b,a580281571e5fd8f7148bae2d84ebaf0d99ee018,(i.e. all NaN columns). `sklearn.preprocessing.Imputer` already
aaiebchdf,biolab/orange3,Orange/canvas/report/owreport.py,f11300e089ddc0d62cf86edee0a8c6100a66f037,2dcfbc69466198929f81fcdb4c5ca06ace7b1d2e,TODO - ko kliknes na webview; oznaci item
aaiebchia,biolab/orange3,Orange/widgets/widget.py,84dfffb0fed9f1ee3b6f5bdbca63a98bd3125a24,STILL_EXISTS,TODO add!
aaiebchie,biolab/orange3,Orange/widgets/visualize/owscatterplot.py,7fdca426a97f6d9017b15dafe6569c0d67756104,STILL_EXISTS,TODO: adapt scatter plot to work on SqlTables (avoid use of X and Y)
aaiebcjac,biolab/orange3,Orange/widgets/unsupervised/owpca.py,45a9c47634088b71df3f428bd71fedd087576cd4,012f34f160233c4c22240e7707b0bcdc1d253a9d,TODO move the following normalization outside
aaiebdaag,biolab/orange3,Orange/widgets/visualize/owscatterplot.py,bbd56978ebc13f39312b874bc6d9f8a90f88effe,STILL_EXISTS,TODO: adapt scatter plot to work on SqlTables (avoid use of X and Y)
aaiebdabf,biolab/orange3,Orange/widgets/unsupervised/owdistancemap.py,8dce5e337879d52e219a39d2ee6d7d709c4b0542,STILL_EXISTS,: type of ordering to apply to matrix rows\/columns
aaiebdach,biolab/orange3,Orange/preprocess/preprocess.py,2cdb22356d979fbe02579f037381e9318b563957,STILL_EXISTS,(i.e. all NaN columns). `sklearn.preprocessing.Imputer` already
aaiebdaie,biolab/orange3,Orange/data/table.py,90e8bd1f75ab5c1b6a7a4b61881b71777f966180,a0cd8fdaf39d1aef45037407883d881585f47984,delete=False is a workaround for https:\/\/bugs.python.org\/issue14243
aaiebdece,biolab/orange3,doc/scripting-tutorial/source/conf.py,144d88b1c9d4abd42a1a292d8d4b0948f552102e,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaiebdjif,biolab/orange3,doc/data-mining-library/source/conf.py,71684ab154bdc81057f5af1d01862b6605ae2766,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaiebebde,biolab/orange3,doc/data-mining-library/source/conf.py,71684ab154bdc81057f5af1d01862b6605ae2766,STILL_EXISTS,Fix unsupported image types using the Pillow.
aaiebecai,biolab/orange3,doc/visual-programming/source/conf.py,447da1216a19f9c5b7e52f591441e182ff140b84,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaiebedfh,biolab/orange3,doc/visual-programming/source/conf.py,447da1216a19f9c5b7e52f591441e182ff140b84,STILL_EXISTS,Fix unsupported image types using the Pillow.
aaiebedic,biolab/orange3,doc/development/source/conf.py,af0d680865eece3e2127c8b634e02e8fc5d0a81e,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaiebeecc,biolab/orange3,doc/development/source/conf.py,af0d680865eece3e2127c8b634e02e8fc5d0a81e,STILL_EXISTS,Fix unsupported image types using the Pillow.
aaiebeefa,biolab/orange3,Orange/canvas/report/owreport.py,d200da19899d7a53c96b3a0b4c0a9e5c2de052cb,0fa9c2ab21ebaeb91466f146a43259e8b5b0cd05,TODO - ko kliknes na webview; oznaci item
aaiebefbg,biolab/orange3,Orange/widgets/data/owdatasampler.py,990f9586e2f72f4ab1bd4723247633aa35ca270f,STILL_EXISTS,not needed for the code below; just for the user
aaiebefbh,biolab/orange3,Orange/preprocess/score.py,84fa9e0217334ffcf87c8113916ea23fd890586d,3e235f104ef477b48a2ab3a27415ee99d8621c5e,TODO: cache
aaiebefdj,biolab/orange3,Orange/canvas/__main__.py,91ee341706ce2b5ed2c9ee08a484d9d88dbf5a4c,7f0ab13fa30a2af965aa6b56b6e0c398c7238573,Fix fonts on Os X (QTBUG 47206; 40833; 32789)
aaiebefef,biolab/orange3,Orange/canvas/__main__.py,ea502a0b54df33cc623251695b0626309639931d,b28e912049aae12636ffa86064666a13d7d2cdfd,Fix fonts on Os X (QTBUG 47206; 40833; 32789)
aaiebeffg,biolab/orange3,Orange/widgets/evaluate/owpredictions.py,163b18f61f4ef2ef9cddd191813346ac8c12c85b,STILL_EXISTS,: Display the full input dataset or only the target variable columns (if
aaiebefif,biolab/orange3,Orange/canvas/application/canvasmain.py,169e721bb5b269457a7f8c17908973cde8fad21e,511182206c7ea33f4dc9b7cd3745273716e2c927,HACK: Preventing calls to QDockWidget.paintEvent() seems to fix the
aaiebegbb,biolab/orange3,Orange/widgets/data/owcolor.py,338af03b4094d762e3538661b2a8c872abc3dcf6,ceb74bf9380bc6f7be25142162bed8dc17998daa,with support for pre-standard css (needed at least for Qt 4.8)
aaiebegbh,biolab/orange3,Orange/tests/test_table.py,5d1c6e4c144e900c5067d16025ba08ff743f2f72,209908b58733c12f5416c726dee3931f132e1dba,similar as np.testing.assert_array_equal but with better handling of
aaiebegcg,biolab/orange3,Orange/widgets/data/owfile.py,42d066415c1cd8235fd0f1b0495a1a3bdf24b018,560d36c72172ad37da3475bad19f885026ffcf2e,TODO Remove!
aaiebegee,biolab/orange3,Orange/widgets/unsupervised/owdistancematrix.py,717506ff4103921f908924cd9d6abdb48bab0f2f,991411dd86242ee8bd2c477db177d8fb4aa487dc,TODO keep selection when changing annotation from None to ... whatever
aaiebegef,biolab/orange3,Orange/widgets/unsupervised/owdistancematrix.py,717506ff4103921f908924cd9d6abdb48bab0f2f,STILL_EXISTS,TODO this should be contex setting
aaiebegeg,biolab/orange3,Orange/widgets/unsupervised/owdistancematrix.py,717506ff4103921f908924cd9d6abdb48bab0f2f,7aace8f6930daa30a8e2a120fff28dc0aa331fa6,TODO save selection as setting
aaiebegfi,biolab/orange3,Orange/tests/test_classification.py,0b436531113b11c536012ee554cf21871953c3ad,STILL_EXISTS,TODO: Errors raised from various data checks should be made consistent
aaiebeiii,biolab/orange3,Orange/widgets/visualize/owsieve.py,f58e30620bb86f1e2efed49280766cd905405d58,STILL_EXISTS,TODO: This signal has to disable or hide the combo boxes
aaiebeijg,biolab/orange3,Orange/widgets/visualize/owsieve.py,878343328e3be5cc69853079346ebeecb3fa4d38,98fe508f962372c8b05bfe52be84507f83a17193,Ugly! This is needed since
aaiebfade,biolab/orange3,Orange/tests/test_logistic_regression.py,621bb6b1e084bc9562add6fab1d143eca117e8a1,STILL_EXISTS,check that normalization produces better results
aaiebfahj,biolab/orange3,Orange/tests/test_variable.py,b78cb1ed10c9e8aa5652574a6477c97c182ed8b8,91386e42e5383d398a4060c6fd1d668fd472520d,HACK: Prevent closing of streams
aaiebfajd,biolab/orange3,Orange/tests/test_variable.py,353aeb151c6a0326c58efb2d1360b9b3041ed2d6,STILL_EXISTS,HACK: Prevent closing of streams
aaiebfbcf,biolab/orange3,Orange/widgets/visualize/owmosaic.py,784f80900daa2ce7451d108790d963d3c489f570,STILL_EXISTS,TODO: check this
aaiebfbch,biolab/orange3,Orange/widgets/visualize/owmosaic.py,784f80900daa2ce7451d108790d963d3c489f570,STILL_EXISTS,# TODO: this function is also used in owsieve --> where to put it?
aaiebfcdh,biolab/orange3,Orange/widgets/visualize/owmosaic.py,d94a98707943e552bd34861bee018193be1b295a,STILL_EXISTS,TODO: check this
aaiebfcgi,biolab/orange3,Orange/widgets/utils/owlearnerwidget.py,8b6d7fa537e2fb13c6e6eee6d40ebb05da078257,STILL_EXISTS,Ensure all needed attributes are present
aaiebfebd,biolab/orange3,Orange/widgets/highcharts.py,0e67d3dfd3c5a499c4a9419a4eab22213c2433dd,212b2903656288cb3c5397bd9d8ebaff1e015e32,Why do we need this async? I don't know. But performance of
aaiebfebe,biolab/orange3,Orange/widgets/highcharts.py,0e67d3dfd3c5a499c4a9419a4eab22213c2433dd,212b2903656288cb3c5397bd9d8ebaff1e015e32,loading\/evaluating any JS code is greatly improved this way.
aaiebfedh,biolab/orange3,doc/development/source/code/owScatterplot.py,a18ea4d3a86f586103baba1c98ed1841b6628f71,STILL_EXISTS,with x and y values in the two columns ...
aaiebfeee,biolab/orange3,doc/development/source/code/owScatterplot.py,a18ea4d3a86f586103baba1c98ed1841b6628f71,STILL_EXISTS,value. This is one way of doing it. If you know of a better one;
aaiebffhj,biolab/orange3,Orange/widgets/classify/owclassificationtreegraph.py,52721701fa2413d51b17a60d3902c89a65b68625,STILL_EXISTS,This is not needed yet because it is imported from owtreeviewer2d :(
aaiebffjh,biolab/orange3,Orange/canvas/config.py,7f11254cf39f126ab0b6b2682579b1cc97acb8ab,4b11cc24761b24b7b8c601837c9b065dd882b1eb,Hack to make sure the correct plugin search path is added. (Github issue #1143)
aaiebfgeg,biolab/orange3,Orange/data/io.py,a0cd8fdaf39d1aef45037407883d881585f47984,STILL_EXISTS,delete=False is a workaround for https:\/\/bugs.python.org\/issue14243
aaiebfghi,biolab/orange3,Orange/widgets/settings.py,a4f5d97a6aa9e93a6e61ea386d664722835e0ba6,8be2120ed6bf125fdf683b26793e08af1a70f652,Otherwise check if a better match is available in global_contexts
aaiebfhac,biolab/orange3,Orange/canvas/application/addons.py,4b11cc24761b24b7b8c601837c9b065dd882b1eb,240fea85e4a87cb37cae1d5c987c33e643480620,perhaps aren't featured on PyPI
aaiebfhbb,biolab/orange3,Orange/canvas/report/report.py,a81a92d3941dfa1a5fe02791a6926e42ccfbbf13,68473986c3f49176998beb82dc1b46d9006d2d46,iterate only over visible columns of QTableView
aaiebfhbc,biolab/orange3,Orange/canvas/report/report.py,a81a92d3941dfa1a5fe02791a6926e42ccfbbf13,68473986c3f49176998beb82dc1b46d9006d2d46,If all columns are hidden
aaiebfhbe,biolab/orange3,Orange/widgets/evaluate/owpredictions.py,a81a92d3941dfa1a5fe02791a6926e42ccfbbf13,STILL_EXISTS,iterate only over visible columns of data's QTableView
aaiebfhdh,biolab/orange3,Orange/canvas/application/addons.py,c62b1bc55fc805c1903d795e166a240954ededf2,8023c50f03ba243b941053e679557112f06d688f,Workaround for PyPI bug of search not returning the latest versions
aaiebfhef,biolab/orange3,Orange/canvas/report/report.py,fe59e44b4440e83eee5947ef3391f36359e5a54e,10cf72aef0a0c57ff2f03e1340b6cd55a3290a7a,iterate only over visible columns of QTableView
aaiebfheg,biolab/orange3,Orange/canvas/report/report.py,fe59e44b4440e83eee5947ef3391f36359e5a54e,10cf72aef0a0c57ff2f03e1340b6cd55a3290a7a,If all columns are hidden
aaiebfheh,biolab/orange3,Orange/canvas/config.py,59faf2936ead553a14117b4f374f5132bf180e35,6a70f4bc8d4f1e5a28dfbcbf96e129617707e647,Hack to make sure the correct plugin search path is added. (Github issue #1143)
aaiebfhia,biolab/orange3,Orange/widgets/data/owfile.py,300de684eeb4b4bf475a39e4d31e86ec14c760e1,a697d7ed57333cc101bcaf06d3bdcd8d85acbebc,This assumes that no columns were skipped in the original file!
aaiebfhid,biolab/orange3,Orange/widgets/utils/domaineditor.py,300de684eeb4b4bf475a39e4d31e86ec14c760e1,STILL_EXISTS,This ugly hack closes the combo when the user selects an item
aaiebfhie,biolab/orange3,Orange/widgets/utils/domaineditor.py,300de684eeb4b4bf475a39e4d31e86ec14c760e1,STILL_EXISTS,setItemDelegate(ForColumn) apparently does not take ownership
aaiebfhih,biolab/orange3,Orange/widgets/data/owdiscretize.py,724acb23c79d9a2dccb0499704827664c39ff88a,STILL_EXISTS,This condition is ugly; but it keeps the same order of
aaiebfief,biolab/orange3,Orange/evaluation/testing.py,6c9ac48b173902136b0d46a742d12ea8e6d6dde3,cf88cdf6719620e1418c7e65a0c3bc8417419461,Workaround for NumPy locking on Macintosh.
aaiebfjfd,biolab/orange3,Orange/evaluation/testing.py,19ce603a87e15ab69239fe93a596b13e602ba0ab,STILL_EXISTS,Workaround for NumPy locking on Macintosh.
aaiebgaaf,biolab/orange3,Orange/widgets/visualize/owpythagorastree.py,28a22ee0456305e89584fb5730dbee00adc42c23,6432cffdcb6057b34c14c0b4787f0ae625fabe81,We need to know what kind of tree we have in order to properly
aaiebgabc,biolab/orange3,Orange/widgets/visualize/owpythagorastree.py,28a22ee0456305e89584fb5730dbee00adc42c23,STILL_EXISTS,TODO this messes up the viewport in pythagoras tree viewer
aaiebgagb,biolab/orange3,Orange/widgets/visualize/pythagorastreeviewer.py,28a22ee0456305e89584fb5730dbee00adc42c23,STILL_EXISTS,z index range; increase if needed
aaiebgagg,biolab/orange3,Orange/widgets/visualize/pythagorastreeviewer.py,28a22ee0456305e89584fb5730dbee00adc42c23,6432cffdcb6057b34c14c0b4787f0ae625fabe81,Provide a nice green default in case no color function is provided
aaiebgbbb,biolab/orange3,Orange/widgets/visualize/widgetutils/common/owgrid.py,28a22ee0456305e89584fb5730dbee00adc42c23,STILL_EXISTS,This is needed because the bounding rect is caluclated with the size
aaiebgbbd,biolab/orange3,Orange/widgets/visualize/widgetutils/common/owgrid.py,28a22ee0456305e89584fb5730dbee00adc42c23,STILL_EXISTS,We store the offsets from the top left corner to move widget properly
aaiebgbbe,biolab/orange3,Orange/widgets/visualize/widgetutils/common/owgrid.py,28a22ee0456305e89584fb5730dbee00adc42c23,STILL_EXISTS,Move the widget back to origin then perfom transformations
aaiebgbbf,biolab/orange3,Orange/widgets/visualize/widgetutils/common/owgrid.py,28a22ee0456305e89584fb5730dbee00adc42c23,STILL_EXISTS,Move the tranform origin to top left; so it stays in place when
aaiebgbcd,biolab/orange3,Orange/widgets/visualize/widgetutils/common/owgrid.py,28a22ee0456305e89584fb5730dbee00adc42c23,STILL_EXISTS,The number of columns is already optimal
aaiebgbdb,biolab/orange3,Orange/widgets/visualize/widgetutils/common/owlegend.py,28a22ee0456305e89584fb5730dbee00adc42c23,STILL_EXISTS,This check is needed because simply resizing the window will cause
aaiebgbie,biolab/orange3,Orange/widgets/visualize/widgetutils/tree/skltreeadapter.py,28a22ee0456305e89584fb5730dbee00adc42c23,STILL_EXISTS,TODO this kind of cache can lead to all sorts of problems; but numpy
aaiebgcah,biolab/orange3,Orange/widgets/visualize/widgetutils/common/scene.py,7ef0d70540c76b62b62e3da27d5fea06794792c3,STILL_EXISTS,\"\"\"Common QGraphicsScene components that can be composed when needed.\"\"\"
aaiebgcai,biolab/orange3,Orange/widgets/visualize/widgetutils/common/view.py,7ef0d70540c76b62b62e3da27d5fea06794792c3,STILL_EXISTS,\"\"\"Common useful `QGraphicsView` classes that can be composed to achieve || desired functionality.\"\"\"
aaiebgcbc,biolab/orange3,Orange/widgets/visualize/widgetutils/tree/treeadapter.py,7ef0d70540c76b62b62e3da27d5fea06794792c3,STILL_EXISTS,\"\"\"Base tree adapter class with common methods needed for visualisations.\"\"\"
aaiebgcjd,biolab/orange3,Orange/widgets/regression/tests/test_owknnregression.py,2abe947227e95a44b2fd3e7029909352b7ef0fb1,48135a303a8a3f189732539826f18322907bd327,FIXME: widget crashes when Mahalanobis metric is chosen.
aaiebgifa,biolab/orange3,Orange/widgets/utils/messages.py,cf3abcef7140efa9f195b11e63543e08b2c3580f,134873b63640589b14d80a7e04f849f22844b58e,TODO: fix tooltip background color - it is not white
aaiebgjda,biolab/orange3,Orange/data/io.py,1f44512d983276bebacd9030b983f78807a01e3e,STILL_EXISTS,TODO: Name can be set unconditionally when\/if
aaiebgjfa,biolab/orange3,Orange/widgets/evaluate/owpredictions.py,68b48aa07c558d8fa389636ca685062b43fa9b03,STILL_EXISTS,This is probably not needed in Qt5?
aaiebgjgj,biolab/orange3,Orange/clustering/hierarchical.py,2ef7f74b5d3d3f630ecb7930a2d528fcf338cbc6,967de58ab3db425c0dd3e8b9041382e052f87f97,Does scipy implement a O(n**2) NN chain algorithm?
aaiebgjhg,biolab/orange3,Orange/classification/rules.py,1c8ca2c1ba928e9bb59ae45e7abb29fd7b5c2f84,STILL_EXISTS,TODO: component based
aaiebhdih,biolab/orange3,Orange/widgets/settings.py,ad7b92f8d14c6a854b008c17b15039b139c2d8d9,f4203a9b7e2321e34e66d680f39406c0912e4a69,TODO: exclude_metas should be disabled by default
aaiebhhje,biolab/orange3,Orange/widgets/visualize/owboxplot.py,faaa9683764e14d67a483b6948a96577a045f4f2,6f8a941ef480082971959802f8add1092397616a,TODO: Fix context settings
aaiebhibc,biolab/orange3,Orange/widgets/visualize/utils/__init__.py,42f47c5a4466e00a38931b8c707aad9d56e71381,STILL_EXISTS,necessarily a good idea since we can make a mess of multiple
aaiebhibe,biolab/orange3,Orange/widgets/visualize/utils/__init__.py,42f47c5a4466e00a38931b8c707aad9d56e71381,STILL_EXISTS,per-class patching would be better than per-instance; but we don't
aaiebhjch,biolab/orange3,Orange/preprocess/preprocess.py,9b43694b5eed0f38729e6e6904bb22f05ff15c33,STILL_EXISTS,normalization is not really needed.
aaiebhjig,biolab/orange3,Orange/widgets/classify/owclassificationtree.py,a1c9e902822e0dd6c33ac0a1b633de8737b1bf26,1317bbf379ab5dc6fd8ec40f5ceab2f529dab871,TODO: Common base class for tree learners
aaiebiabb,biolab/orange3,Orange/classification/tree.py,dc328b1fba945ca9be54ec2f65b3b3dd29fe4696,1317bbf379ab5dc6fd8ec40f5ceab2f529dab871,anything) by not reserving space for unused threshold space
aaiebiagc,biolab/orange3,Orange/tree.py,1317bbf379ab5dc6fd8ec40f5ceab2f529dab871,STILL_EXISTS,anything) by not reserving space for unused threshold space
aaiebidgi,biolab/orange3,Orange/widgets/tests/base.py,69ceb538919215ebc369a737bbb8215f6a7750ff,e34b931368050dd86f992337839476aea084d2cc,only needed in TestOWMDS
aaiebiejb,biolab/orange3,Orange/widgets/utils/webview.py,dea6932c9e94b4fe98fd71561644eb8e6305a551,afd7e849d9d6ff312e58d7af2fb3feb3391f26e4,TODO: Use QWebFrame.javaScriptWindowObjectCleared
aaiebifaa,biolab/orange3,Orange/widgets/utils/webview.py,afd7e849d9d6ff312e58d7af2fb3feb3391f26e4,dacd72c02b73e2031b9062b08754182c338ae0ab,TODO REMOVE
aaiebifae,biolab/orange3,Orange/widgets/utils/webview.py,afd7e849d9d6ff312e58d7af2fb3feb3391f26e4,796b0a3591edfd092b32de0dd6010bffe8cb33f5,FIXME: There might be a race condition between here and in JS
aaiebifbd,biolab/orange3,Orange/widgets/utils/webview.py,afd7e849d9d6ff312e58d7af2fb3feb3391f26e4,dacd72c02b73e2031b9062b08754182c338ae0ab,This is needed to stall any evalJS() calls which may expect
aaiebigab,biolab/orange3,Orange/data/sql/backend/mssql.py,b72336bc1047d18e8b2bedda078b6a9a9aaa49bc,d14d18b0b397f3f96760f14923fb2243cd313e18,TODO: Figure out how to do count estimates on mssql
aaiebigcf,biolab/orange3,Orange/widgets/visualize/tests/test_owpythagorastree.py,4784b4dfcf90d8b0963d9238bd779376c91c15e9,4d745ae6619fe436dbd76afdf8f3eef9b3182250,Apparently `currentIndexChanged` just isn't good enough...
aaiebihca,biolab/orange3,Orange/widgets/visualize/owmosaic.py,293e43832024abc85cd00cb9abb927b8faed5dfa,883ec090b6e31029b8663fdd13154202950d4f97,TODO: this doesn't reset combos to (None)
aaiebihhe,biolab/orange3,Orange/widgets/visualize/tests/test_owpythagoreanforest.py,8f9ae15b1a1773dd2932fbca53bd865468632f44,STILL_EXISTS,Apparently `currentIndexChanged` just isn't good enough...
aaiebiiib,biolab/orange3,Orange/widgets/data/owpythonscript.py,b535452389d79b579dd499374bab66b4ecc87f1f,STILL_EXISTS,TODO: use `tokenize.detect_encoding`
aaiebiiic,biolab/orange3,Orange/preprocess/remove.py,95885b2d1941bc9bb54179b22de4cfd7ceb0f42b,STILL_EXISTS,TODO this check is suboptimal for sparse since get_column_view
aaiebiiih,biolab/orange3,Orange/canvas/canvas/items/linkitem.py,9b9bd1218080f1543e51fe8ebb3cf82b0efde3c1,STILL_EXISTS,between the two ends
aaiebjaac,biolab/orange3,Orange/widgets/data/utils/preprocess.py,eef7cae6970c1a4b2976726ddd22903447e3f75d,STILL_EXISTS,TODO: Remember if we have seen enter with the proper data
aaiebjabi,biolab/orange3,Orange/widgets/data/utils/preprocess.py,eef7cae6970c1a4b2976726ddd22903447e3f75d,STILL_EXISTS,TODO: Check if widget is already inserted.
aaiebjacg,biolab/orange3,Orange/widgets/data/utils/preprocess.py,eef7cae6970c1a4b2976726ddd22903447e3f75d,STILL_EXISTS,TODO: Get the proper title bar geometry.
aaiebjbbe,biolab/orange3,Orange/__init__.py,bb5bf2223d46c7326f1a81dd2e1fe6e3675ead7c,95b62d2be13082e04d11e066843650ea0181eb42,Needed because the pure-Python Unpickler that dill uses can also fail
aaiebjbeh,biolab/orange3,Orange/base.py,08182939070fddfe3315eb6582e327d1ff766f9f,f532a1bee81b594fde5ca8a5584dba5d86eb2fa1,Constants to indicate what kind of problem we're dealing with
aaiebjbfb,biolab/orange3,Orange/misc/wrapper_meta.py,08182939070fddfe3315eb6582e327d1ff766f9f,83da5f3d5cffe0d1ad31c91c5805113fbe155bc2,TODO This is not okay
aaiebjbge,biolab/orange3,Orange/modelling/fitter.py,f532a1bee81b594fde5ca8a5584dba5d86eb2fa1,STILL_EXISTS,Constants to indicate what kind of problem we're dealing with
aaiebjcff,biolab/orange3,Orange/widgets/data/owcreateclass.py,eff12708f168c42cc9e844c61ff456d5acbb421d,STILL_EXISTS,TODO: Resizing upon changing the number of rules does not work
aaiebjcfg,biolab/orange3,Orange/widgets/data/owcreateclass.py,eff12708f168c42cc9e844c61ff456d5acbb421d,STILL_EXISTS,TODO: Indicator that changes need to be applied
aaiebjcje,biolab/orange3,Orange/base.py,234aa3407010e04a0b2de0378505fcb112f4c82b,fbc17a12056190c3cde7cb4d9ed9f55217661382,TODO: Implement __eq__ on preprocessors to avoid comparing reprs
aaiebjdba,biolab/orange3,Orange/preprocess/util.py,5b0631093b17355088f5ab05dc4b2349264f9b63,STILL_EXISTS,TODO: drop this as soon as possible
aaiebjehd,biolab/orange3,Orange/widgets/visualize/owtreeviewer.py,346658e4ff4ee62a7a8275693c4dc0a6008d970e,8bfbfb8496992c733537a34e53381c1c9697517b,TODO This is not yet good; description is  >3 for each node
aaiebjehf,biolab/orange3,Orange/widgets/visualize/owtreeviewer.py,9f2a852840c0e5e94fd99155c2a0b938ff41e18d,e554d416697b9ff2f32a6580d150db77eb39a2d1,TODO calculate variance in tree adapter
aaiebjfaj,biolab/orange3,Orange/widgets/data/owselectcolumns.py,30a0cedceb42d86caa1d0a0a121b229ce6427305,STILL_EXISTS,TODO do we still need this?
aaiebjgcb,biolab/orange3,Orange/widgets/unsupervised/owmds.py,1764414c719d20b0b3f143d8442bcba0297779fa,0088176376f87417437131c0e2fe911213351bd3,maybe 0?
aaiebjgea,biolab/orange3,Orange/widgets/data/owselectcolumns.py,0d1d7de1daf61d76a6bdef05275aa7e0ca93a1c8,STILL_EXISTS,TODO do we still need this?
aaiebjhic,biolab/orange3,Orange/widgets/model/tests/test_owsgd.py,78f51648eecdbe91df6a6c9b80c46228f9abeaf4,STILL_EXISTS,TODO Fix the base tests to support parameter delegation with
aaiebjicf,biolab/orange3,Orange/widgets/regression/tests/test_owadaboostregression.py,f7218c34f385af9539c182caa7c91ea1d5af41f3,STILL_EXISTS,Needed because the fitter type needs to be set to regression in order
aaiebjich,biolab/orange3,Orange/widgets/model/tests/test_owadaboost.py,f74547687080f419319fec87d1f9b931fae6f71a,fe4f0c09f9b6ef38349a413b5c38643b2c7fa55a,TODO Due to the way params are tested on the learner and the fact
aaiecadii,biolab/orange3,Orange/widgets/unsupervised/owkmeans.py,efdb51f5358f2411e65c00c4f628e270d749e517,bd6b4d97fe05b9aea2dc35ee780dc58d88dbcf92,TODO: Show columns with all metrics
aaiecadjd,biolab/orange3,Orange/widgets/unsupervised/owkmeans.py,efdb51f5358f2411e65c00c4f628e270d749e517,144baa82cbf7b0571b69b5afa191f4d4932c39be,TODO: add (n) if a column with this name is already in domain
aaiecaehc,biolab/orange3,Orange/widgets/data/owmergedata.py,a05213a263ee41aae5de1c8ebde8e7da7366de55,STILL_EXISTS,local function due to better performance
aaiecaehh,biolab/orange3,Orange/base.py,8ee65c0f5905cf6c9bd7297d5dd256a274a8d303,STILL_EXISTS,TODO: Implement __eq__ on preprocessors to avoid comparing reprs
aaiecaejh,biolab/orange3,Orange/widgets/utils/webview.py,6a5a8bc606be9b3e6723e3a6fa4060d077365efe,dacd72c02b73e2031b9062b08754182c338ae0ab,TODO: remove once anaconda will provide PyQt without this bug.
aaiecafei,biolab/orange3,Orange/widgets/visualize/owtreeviewer2d.py,d9f557b9ff2d56d45cbb605edbe5f01098d4bc2e,STILL_EXISTS,Implement edges as an ordered dict to get the nice speed benefits as
aaiecagcg,biolab/orange3,Orange/widgets/data/owmergedata.py,e91b7238fe717cc0883bfe52ba5cd431065def30,STILL_EXISTS,Don't match nans. This is needed since numpy supports using nan as
aaiecahch,biolab/orange3,Orange/widgets/visualize/owpythagorastree.py,44635bded3bf9554e193ac0c40d182a93d9916bd,STILL_EXISTS,TODO There is still something wrong with this
aaiecahhf,biolab/orange3,Orange/statistics/util.py,c0808ba7b6cb26a0fe9d4b7e8997eeb2d9890751,STILL_EXISTS,TODO check & transform to correct format
aaiecahjc,biolab/orange3,Orange/widgets/utils/domaineditor.py,53bd6f7c3532b12e74df28fe9086c3e2684cd717,STILL_EXISTS,merge columns for X; Y and metas
aaiecahjd,biolab/orange3,Orange/widgets/visualize/owscatterplot.py,cceeee36b0b35955c80c3f2a2851b877f66bb7ca,cbba90c24a018418893da2c0a9c34476f64a5b1f,TODO: remove once we make sure Y is always dense.
aaiecaied,biolab/orange3,Orange/widgets/evaluate/owtestlearners.py,074b61ef29847c1f41eb3c944b283b53797a4485,STILL_EXISTS,TODO: Change\/replace the current implementation of ThreadExecutor
aaiecaifb,biolab/orange3,Orange/widgets/evaluate/owtestlearners.py,074b61ef29847c1f41eb3c944b283b53797a4485,17f12c0caa93fedfe243ba9844696b8e7ccaddd9,TODO: clear the results view?
aaiecaigh,biolab/orange3,Orange/base.py,cc7aa9d32f9c335befbe52515894c39ebd7cc9ad,STILL_EXISTS,TODO: Disallow (or mirror) __setattr__ for keys in params?
aaiecaiic,biolab/orange3,Orange/projection/cur.py,88904827dfe48b21bd36b4b02f50fdbbf09fbd35,STILL_EXISTS,TODO: FIX - do not retain state and ref for this instance
aaiecbaeb,biolab/orange3,doc/development/source/orange-demo/orangedemo/OWLearningCurveC.py,e28e9fc0af85e97eab5cfb7b1611abdd36128a1e,STILL_EXISTS,(in a thread safe manner) and to implement cooperative cancellation.
aaiecbahd,biolab/orange3,Orange/widgets/utils/concurrent.py,5632c7ea30ea050b473ac4750d2a27241c81223a,STILL_EXISTS,TODO: Rename the module to something that does not conflict with stdlib
aaiecbcjg,biolab/orange3,Orange/preprocess/score.py,9d4e8af6e3aeedd97660b0211e66eb546c05c112,1dfb3166471d4f04c919fe580b4d32ebfcabf3ab,TODO: cache
aaiecbdfd,biolab/orange3,Orange/canvas/canvas/items/annotationitem.py,93bfdbdd35be263204152fdd106e53fe0f1fa772,STILL_EXISTS,TODO: Separate the editor from the view.
aaiecbdge,biolab/orange3,Orange/canvas/canvas/items/annotationitem.py,8a61be376840fe253371b26d3b684ad0bb659abe,STILL_EXISTS,: Signal emitted when editing operation ends (the item loses edit focus)
aaiecbdii,biolab/orange3,Orange/widgets/visualize/ownomogram.py,a622f7aeffd2cbeed6f43b531fd7bd65d62e2002,STILL_EXISTS,XXX: This prevents some tests failing
aaiecbeaf,biolab/orange3,Orange/statistics/util.py,2de58f5de8fe72d2faf7cc06016eae56cf72e172,STILL_EXISTS,Orange mainly deals with `csr_matrix`; but `lil_matrix` is more efficient
aaiecbeda,biolab/orange3,Orange/statistics/util.py,c314f2315fda062e66d9640501776af4efef5ab4,STILL_EXISTS,TODO Remove reshaping logic when support for numpy==1.9 is dropped
aaiecbeej,biolab/orange3,Orange/widgets/settings.py,05e0e396cac5473fba8c8670d0fbf7dc8cc798ee,0f370877591496c3d49fd2aae9d0c1cb462540c7,FIXME: Sadly; schema-only settings aren't cleared when non-contextual
aaiecbfae,biolab/orange3,Orange/widgets/data/tests/test_owimpute.py,5ad90627db589b3894e7b697a3ee37fbc235dcbd,STILL_EXISTS,only meta columns
aaiecbfdg,biolab/orange3,Orange/widgets/utils/messagewidget.py,060888176275a30ae56d86748ea943168349768e,STILL_EXISTS,TODO: update popup if open
aaiecbfgg,biolab/orange3,Orange/distance/__init__.py,561bcd28262a3c81acd6023786c3dafa72caf73b,STILL_EXISTS,Ugly; but needed for backwards compatibility hack below; to allow
aaiecbfjb,biolab/orange3,Orange/distance/__init__.py,ef9df3a489c0e13c2d96cdc623e7d54cf45cc6ed,62a5072aa038fa13e711db8d1f3ca607b90a8024,TODO this *private* function is called from several widgets to prepare
aaiecbfje,biolab/orange3,Orange/distance/__init__.py,ef9df3a489c0e13c2d96cdc623e7d54cf45cc6ed,62a5072aa038fa13e711db8d1f3ca607b90a8024,Backward compatibility fix
aaiecbfjg,biolab/orange3,Orange/distance/__init__.py,ef9df3a489c0e13c2d96cdc623e7d54cf45cc6ed,STILL_EXISTS,To be removed as the corresponding functionality is implemented above
aaiecbgae,biolab/orange3,Orange/distance/__init__.py,689b4068b9a4ceec32c2d1cd626a74de8f9e6648,62a5072aa038fa13e711db8d1f3ca607b90a8024,TODO I have put this function here as a substitute the above `_preprocess`.
aaiecbgba,biolab/orange3,Orange/distance/__init__.py,689b4068b9a4ceec32c2d1cd626a74de8f9e6648,62a5072aa038fa13e711db8d1f3ca607b90a8024,TODO: Appears to have been used only in the Distances widget (where it had
aaiecbgcd,biolab/orange3,Orange/distance/__init__.py,415be180b13e088d2efb47cb0dc7285c989ab293,62a5072aa038fa13e711db8d1f3ca607b90a8024,TODO: When we upgrade to numpy 1.13; change use argument copy=False in
aaiecbgfh,biolab/orange3,Orange/distance/base.py,62a5072aa038fa13e711db8d1f3ca607b90a8024,STILL_EXISTS,TODO: When we upgrade to numpy 1.13; change use argument copy=False in
aaiecbgfj,biolab/orange3,Orange/distance/base.py,62a5072aa038fa13e711db8d1f3ca607b90a8024,STILL_EXISTS,TODO this *private* function is called from several widgets to prepare
aaiecbggc,biolab/orange3,Orange/distance/base.py,62a5072aa038fa13e711db8d1f3ca607b90a8024,STILL_EXISTS,TODO I have put this function here as a substitute the above `_preprocess`.
aaiecbggf,biolab/orange3,Orange/distance/base.py,62a5072aa038fa13e711db8d1f3ca607b90a8024,STILL_EXISTS,Ugly; but needed to allow allow setting subclass-specific parameters
aaiecbghd,biolab/orange3,Orange/distance/base.py,62a5072aa038fa13e711db8d1f3ca607b90a8024,STILL_EXISTS,Backward compatibility fix
aaiecbghh,biolab/orange3,Orange/distance/base.py,62a5072aa038fa13e711db8d1f3ca607b90a8024,STILL_EXISTS,To be removed as the corresponding functionality is implemented properly
aaiecbgif,biolab/orange3,Orange/distance/distance.py,62a5072aa038fa13e711db8d1f3ca607b90a8024,STILL_EXISTS,TODO: Appears to have been used only in the Distances widget (where it had
aaiecbgjj,biolab/orange3,Orange/tests/test_distances.py,890c5e1f0981c29463699105bcdbc0e31d9983fe,STILL_EXISTS,Because it looks better
aaiecbjaa,biolab/orange3,Orange/tests/test_preprocess.py,0a73871b6f0039cbe33bda27337dc89981a9e855,99c19e9781bcd367fcdbf46669359fdc719c0883,Do not mix data between columns
aaiecbjgg,biolab/orange3,Orange/tests/test_randomize.py,99c19e9781bcd367fcdbf46669359fdc719c0883,STILL_EXISTS,Do not mix data between columns
aaiecbjih,biolab/orange3,Orange/projection/freeviz.py,4f6f205acee0b3a00fd8e541cd64a853a227dc21,STILL_EXISTS,Scale the changes (the largest anchor move is alpha * radius)
aaiecbjjb,biolab/orange3,Orange/projection/freeviz.py,daeb537764fc3cc47282ddd3e8079d0cbaa38cd9,b4fc3721c74ad0e6af775f4e49667ccf9444bdab,TODO: center=False and scale=False  (widget calls with these two parameters!)
aaieccaeh,biolab/orange3,Orange/widgets/data/owdatasets.py,e4387638b9bfbf264ea21341b864421a3fd12f78,STILL_EXISTS,TODO: There are possible pending __progress_advance queued
aaieccbci,biolab/orange3,Orange/widgets/settings.py,3c7eb43bf063397d88708cd29c2933ee0f41901f,a0b2ad5ab6282a7d96d68e42c450697f046b3fbb,TODO: exclude_metas should be disabled by default
aaieccefd,biolab/orange3,Orange/widgets/visualize/tests/test_owscatterplot.py,a2f95cc7425ad471f0df19620b67dea775585cc1,e5f5128b39e664cc88712e249b9a2202fd997b2d,This is a workaround
aaieccehb,biolab/orange3,Orange/widgets/visualize/tests/test_owscatterplot.py,cfecca8286eaa2b08e06339aaa2d25b1690c98d8,STILL_EXISTS,This is a workaround
aaieccfgi,biolab/orange3,Orange/data/table.py,7fa9f05bfd37861fd4b1c58b1a9eda33c777649c,ea250b810192e86c540067a6988c922b2b762f94,initialize final array & set `match_density` for columns
aaieccgfd,biolab/orange3,Orange/data/table.py,ed53796265cfd0e5b9a0d20fcfcecbb3cbc50376,STILL_EXISTS,initialize final array & set `match_density` for columns
aaieccghf,biolab/orange3,Orange/widgets/gui.py,fb0fd3fde882361c83337fcf09e418949774218b,STILL_EXISTS,FIXME: irrespective of PyListModel check; this might\/should always
aaiecchfa,biolab/orange3,Orange/data/pandas_compat.py,e602be23e097d42bc50181b623bfd560f2140558,a5b0f92333dc4a01aef1454a358efdb989f754a1,Iter over columns
aaiecchgi,biolab/orange3,Orange/widgets/visualize/tests/test_owradviz.py,3210b3f9d7175388ba90f3e6f953b069dce38cca,453222b587dd5dcdb75386c7d528b6f52b8c32f0,TODO: fix this
aaieccigd,biolab/orange3,Orange/data/table.py,520cad780732f7898c3c0acb4900fcab05ce87a0,STILL_EXISTS,domain equality; fix manually.
aaiecdafa,biolab/orange3,Orange/widgets/unsupervised/owkmeans.py,3526ea61ea715634b3315c0be089d9d12f71bb67,STILL_EXISTS,the better alternative
aaiecdbbf,biolab/orange3,Orange/widgets/visualize/tests/test_owheatmap.py,d55d65254e37e651a0314a70d5543454f3926cc8,STILL_EXISTS,Pearson distance used for clustering of columns does not
aaiecdbbg,biolab/orange3,Orange/widgets/visualize/tests/test_owheatmap.py,d55d65254e37e651a0314a70d5543454f3926cc8,STILL_EXISTS,handle all zero columns well
aaiecdbec,biolab/orange3,Orange/widgets/utils/SVGExporter.py,292dcd7b05a09559335e52afd150a2f795b19b64,bca1f96266383eb16535c7d5c94cff134ee7865b,# 1) Qt SVG does not implement clipping paths. This is absurd.
aaiecdcae,biolab/orange3,Orange/widgets/utils/SVGExporter.py,292dcd7b05a09559335e52afd150a2f795b19b64,bca1f96266383eb16535c7d5c94cff134ee7865b,TODO: correct gradient coordinates inside defs
aaiecdccc,biolab/orange3,Orange/widgets/utils/SVGExporter.py,292dcd7b05a09559335e52afd150a2f795b19b64,bca1f96266383eb16535c7d5c94cff134ee7865b,# correct line widths if needed
aaiecdefd,biolab/orange3,Orange/widgets/data/owdatasets.py,d71f0f8e2b7ae5c3aefb91997ce08ed5b71b8b1b,STILL_EXISTS,override HEADER_SCHEMA to define new columns
aaiecdefg,biolab/orange3,Orange/data/io.py,2c0ecb4ba8df03b16220385fbf5f2ece9f70bc98,0606101ef6421dac1ef6513903b2b2224102dc9f,Iterate through the columns
aaiecdefi,biolab/orange3,Orange/data/io.py,1a0de98df04d25340bbda3955533b5b6e43a6c24,0606101ef6421dac1ef6513903b2b2224102dc9f,Map discrete data to 'ints' (or at least what passes as int around
aaiecdehf,biolab/orange3,Orange/widgets/model/owneuralnetwork.py,5bb4d008873803df70c8ff1ea0937c5a06509842,STILL_EXISTS,can still be quoued (perhaps change)
aaiecdfed,biolab/orange3,Orange/canvas/application/canvasmain.py,ff96b9610b28649c4a407158e8989651005496f4,STILL_EXISTS,TODO: Help view and manager to separate singleton instance.
aaiecdfee,biolab/orange3,Orange/canvas/application/canvasmain.py,ff96b9610b28649c4a407158e8989651005496f4,STILL_EXISTS,TODO: Log view to separate singleton instance.
aaiecdfef,biolab/orange3,Orange/canvas/application/canvasmain.py,ff96b9610b28649c4a407158e8989651005496f4,STILL_EXISTS,TODO: Read\/store in QSettings; record\/sync as soon as added.
aaiecdfeg,biolab/orange3,Orange/canvas/application/canvasmain.py,ff96b9610b28649c4a407158e8989651005496f4,STILL_EXISTS,TODO: sync between CanvasMainWindow instances?.
aaiecdffb,biolab/orange3,Orange/canvas/application/canvasmain.py,ff96b9610b28649c4a407158e8989651005496f4,STILL_EXISTS,TODO: report is per main window \/ workflow
aaiecdffc,biolab/orange3,Orange/canvas/application/canvasmain.py,ff96b9610b28649c4a407158e8989651005496f4,STILL_EXISTS,TODO: Notify all instances
aaiecdfij,biolab/orange3,Orange/widgets/report/report.py,83691ba15fcfe660e14d74be3ed2fd38d8369d94,c45922c03b7ed31c90c2047da8c1b697d044bb22,Should really have a signal `report_ready` or similar to decouple
aaiecdgab,biolab/orange3,Orange/widgets/data/owdatasets.py,ad01ede0f8b52b7accae08dd0f663704c52d6c6f,STILL_EXISTS,NOTE: All columns must have size hinting delegates.
aaiecdgac,biolab/orange3,Orange/widgets/data/owdatasets.py,ad01ede0f8b52b7accae08dd0f663704c52d6c6f,STILL_EXISTS,QTreeView queries only the columns displayed in the viewport so
aaiecdgce,biolab/orange3,Orange/widgets/gui.py,857ff5b1c60f7e76b839afffb1a34dadd6e009b6,9794b07a37e56a1b7ebbbb1779251c84d465e61e,workaround for QTBUG-67583
aaiecdhbb,biolab/orange3,Orange/__init__.py,fbe0cd5e4eb9291a0ccb1272d82f029182387de6,STILL_EXISTS,A hack that prevents segmentation fault with Nvidia drives on Linux if Qt's browser window
aaiecdhbe,biolab/orange3,Orange/widgets/unsupervised/tests/test_owkmeans.py,aff45be1a56d7f5c65b9aa453b979366393b1f14,STILL_EXISTS,due to a bug where wrong init was passed to _compute_clustering
aaiecdhid,biolab/orange3,Orange/canvas/document/interactions.py,77ccb3c56f806a59ea8856ac145199667d43be77,STILL_EXISTS,TODO; using suggestinos
aaiecdhji,biolab/orange3,Orange/canvas/document/schemeedit.py,2836e20dcef7723299d3e9c6697f794dd7b2c5e8,97f5a0c0062098932a586b0b23cde03ddf2ce8bf,view (maybe move that shortcut here)
aaiecdiah,biolab/orange3,Orange/canvas/document/schemeedit.py,c967390c5b22c1bcd33cea159391b99fc6226bbe,63eb06f16ab68d9d24004d23879f0782697a952a,TODO: This should be moved to WidgetManager
aaiecdida,biolab/orange3,Orange/canvas/scheme/widgetsscheme.py,63eb06f16ab68d9d24004d23879f0782697a952a,STILL_EXISTS,avoid creating widgets if not needed
aaiecdidd,biolab/orange3,Orange/canvas/scheme/widgetsscheme.py,63eb06f16ab68d9d24004d23879f0782697a952a,STILL_EXISTS,also create it if needed
aaiececci,biolab/orange3,Orange/canvas/document/schemeedit.py,1012e20d37adf37e6d9a9645cac4d713e1817a3b,STILL_EXISTS,view (maybe move that shortcut here)
aaieceche,biolab/orange3,Orange/widgets/utils/matplotlib_export.py,2eb3a3a7d228f757d84826cec448fc10f8084508,9be2afbef1f91cfa8e4eb1f1c4497e0188665dde,TODO labels are missing
aaiecechg,biolab/orange3,Orange/widgets/utils/matplotlib_export.py,2eb3a3a7d228f757d84826cec448fc10f8084508,842d3b61a9cb0dcd84e73365d252414aea64af73,indices are unused
aaiecegcf,biolab/orange3,Orange/widgets/data/oweditdomain.py,6b2269c89dd91d9731409ab53d7917413e32568e,STILL_EXISTS,mark as changed (maybe also change color; add text; ...)
aaiecegjg,biolab/orange3,Orange/widgets/visualize/owscatterplotgraph.py,e90f984063b77b360b04e827e6b17f9ca111694f,e3eaa690ada689d6b2c95c7a440775fa2f64a02b,TODO: Add this into pull request:
aaiecehaa,biolab/orange3,Orange/widgets/visualize/owscatterplotgraph.py,e90f984063b77b360b04e827e6b17f9ca111694f,e3eaa690ada689d6b2c95c7a440775fa2f64a02b,data that contained only the needed attributes. I think that widget should
aaiecehba,biolab/orange3,Orange/widgets/visualize/owscatterplotgraph.py,e90f984063b77b360b04e827e6b17f9ca111694f,STILL_EXISTS,TODO: do we need this?!
aaiecehbe,biolab/orange3,Orange/widgets/visualize/owscatterplotgraph.py,e90f984063b77b360b04e827e6b17f9ca111694f,STILL_EXISTS,TODO -- just guessing; fix this!
aaiecehbg,biolab/orange3,Orange/widgets/visualize/owscatterplotgraph.py,e90f984063b77b360b04e827e6b17f9ca111694f,STILL_EXISTS,backward compatibility (needed?!)
aaiecehbi,biolab/orange3,Orange/widgets/visualize/owscatterplotgraph.py,e90f984063b77b360b04e827e6b17f9ca111694f,STILL_EXISTS,TODO: is this needed? Simplify above code (use return instead of else)
aaiecehca,biolab/orange3,Orange/widgets/visualize/owscatterplotgraph.py,e90f984063b77b360b04e827e6b17f9ca111694f,STILL_EXISTS,TODO This is stupid -- needed for FreeViz; basically. Should be fixed
aaieceheg,biolab/orange3,Orange/widgets/visualize/owscatterplot.py,9807b212a5545a871210471f89657e1e303e4b78,e3eaa690ada689d6b2c95c7a440775fa2f64a02b,TODO: migration of settings from graph to the widget
aaiecehgf,biolab/orange3,Orange/widgets/visualize/owscatterplotgraph.py,9807b212a5545a871210471f89657e1e303e4b78,STILL_EXISTS,Todo: doesn't work: try MDS with density on
aaiecehhc,biolab/orange3,Orange/widgets/visualize/owscatterplotgraph.py,9807b212a5545a871210471f89657e1e303e4b78,e3eaa690ada689d6b2c95c7a440775fa2f64a02b,Todo: Bug: color; shape and size do not update when changed from something
aaiecehhg,biolab/orange3,Orange/widgets/visualize/owscatterplotgraph.py,9807b212a5545a871210471f89657e1e303e4b78,STILL_EXISTS,TODO: do we need this?!
aaiecehib,biolab/orange3,Orange/widgets/visualize/owscatterplotgraph.py,9807b212a5545a871210471f89657e1e303e4b78,STILL_EXISTS,TODO: is this needed? Simplify above code (use return instead of else)
aaiecehid,biolab/orange3,Orange/widgets/visualize/owscatterplotgraph.py,9807b212a5545a871210471f89657e1e303e4b78,STILL_EXISTS,TODO This is stupid -- needed for FreeViz; basically. Should be fixed
aaieceihd,biolab/orange3,Orange/widgets/visualize/owscatterplotgraph_obsolete.py,99d366eabb69751a57b92c6dc398cc814b881839,STILL_EXISTS,fix values outside [0; 1]
aaiecejba,biolab/orange3,Orange/widgets/visualize/owscatterplotgraph.py,cba5cc5c16742049caa09dd40a0a89c1ee3dc749,STILL_EXISTS,TODO: Try avoiding this when we move imputation to the widget
aaiecejcc,biolab/orange3,Orange/widgets/visualize/owradviz.py,5f06367882cb5a889abbaae750d81c0b9a552fd3,453222b587dd5dcdb75386c7d528b6f52b8c32f0,Todo: this button introduces some margin at the bottom?!
aaiecfebj,biolab/orange3,Orange/widgets/visualize/owscatterplotgraph.py,453222b587dd5dcdb75386c7d528b6f52b8c32f0,STILL_EXISTS,TODO: Rename to remove_plot_items
aaiecfeca,biolab/orange3,Orange/widgets/visualize/owscatterplotgraph.py,453222b587dd5dcdb75386c7d528b6f52b8c32f0,STILL_EXISTS,TODO: I hate `keep_something` and `reset_something` arguments
aaiecfecg,biolab/orange3,Orange/widgets/visualize/owscatterplotgraph.py,453222b587dd5dcdb75386c7d528b6f52b8c32f0,STILL_EXISTS,TODO: It could be nice if this method was run on entire data; not just
aaiecfeci,biolab/orange3,Orange/widgets/visualize/owscatterplotgraph.py,453222b587dd5dcdb75386c7d528b6f52b8c32f0,STILL_EXISTS,`get_coordinates` before sampling (very ugly) or call
aaiecfecj,biolab/orange3,Orange/widgets/visualize/owscatterplotgraph.py,453222b587dd5dcdb75386c7d528b6f52b8c32f0,9045f49ce259b184fec3b23fe284bc13065b089f,`self.master.get_coordinates_data` (beyond ugly) or the widget would
aaiecfeda,biolab/orange3,Orange/widgets/visualize/owscatterplotgraph.py,453222b587dd5dcdb75386c7d528b6f52b8c32f0,STILL_EXISTS,have to store the ranges of unsampled data (ugly).
aaiecfedb,biolab/orange3,Orange/widgets/visualize/owscatterplotgraph.py,453222b587dd5dcdb75386c7d528b6f52b8c32f0,STILL_EXISTS,Maybe we leave it as it is.
aaiecfedf,biolab/orange3,Orange/widgets/visualize/owscatterplotgraph.py,453222b587dd5dcdb75386c7d528b6f52b8c32f0,STILL_EXISTS,a \"sample size\" slider); points would move around when the sample
aaiecfedi,biolab/orange3,Orange/widgets/visualize/owscatterplotgraph.py,453222b587dd5dcdb75386c7d528b6f52b8c32f0,STILL_EXISTS,TODO: Is this really needed?
aaiecfeei,biolab/orange3,Orange/widgets/visualize/tests/test_owprojectionwidget.py,453222b587dd5dcdb75386c7d528b6f52b8c32f0,STILL_EXISTS,Test that get_columns modify a copy of the data and not the data
aaiecfehf,biolab/orange3,Orange/widgets/visualize/utils/widget.py,453222b587dd5dcdb75386c7d528b6f52b8c32f0,01c3a71dc8b2582f30756ec1484eaebcf071c2fe,Needed for overlapping
aaiecfjbg,biolab/orange3,Orange/widgets/visualize/owradviz.py,9045f49ce259b184fec3b23fe284bc13065b089f,STILL_EXISTS,Todo: this button introduces some margin at the bottom?!
aaiecfjcg,biolab/orange3,Orange/widgets/visualize/owscatterplot.py,9045f49ce259b184fec3b23fe284bc13065b089f,STILL_EXISTS,TODO: Implement selection for sql data
aaiecfjdg,biolab/orange3,Orange/widgets/visualize/owscatterplotgraph.py,9045f49ce259b184fec3b23fe284bc13065b089f,STILL_EXISTS,TODO -- just guessing; fix this!
aaiecgadg,biolab/orange3,Orange/widgets/visualize/tests/test_owradviz.py,9045f49ce259b184fec3b23fe284bc13065b089f,STILL_EXISTS,TODO: fix this
aaiecgcbb,biolab/orange3,Orange/widgets/data/owfeaturestatistics.py,77fde44aa229d9b7c16092b5b82bd43b22b085e2,STILL_EXISTS,Return string labels for the returned matrix columns e.g. 'Mean';
aaiecgcbj,biolab/orange3,Orange/widgets/data/owfeaturestatistics.py,77fde44aa229d9b7c16092b5b82bd43b22b085e2,STILL_EXISTS,Prepare vartype indices so ready when needed
aaiecgcfi,biolab/orange3,Orange/widgets/data/owfeaturestatistics.py,77fde44aa229d9b7c16092b5b82bd43b22b085e2,STILL_EXISTS,TODO: This shifts the scrollarea a bit down when opening widget
aaiecgcgb,biolab/orange3,Orange/widgets/data/owfeaturestatistics.py,77fde44aa229d9b7c16092b5b82bd43b22b085e2,STILL_EXISTS,TODO: This does not work properly
aaiecgcha,biolab/orange3,Orange/widgets/data/owfeaturestatistics.py,77fde44aa229d9b7c16092b5b82bd43b22b085e2,STILL_EXISTS,TODO: Implement filtering on the model
aaiecgcie,biolab/orange3,Orange/widgets/data/owfeaturestatistics.py,77fde44aa229d9b7c16092b5b82bd43b22b085e2,STILL_EXISTS,Send a table with only selected columns to output
aaiecgdac,biolab/orange3,Orange/widgets/data/utils/histogram.py,77fde44aa229d9b7c16092b5b82bd43b22b085e2,STILL_EXISTS,bins; it is better to assign each their own bin. We will require
aaiecgdbc,biolab/orange3,Orange/widgets/data/utils/histogram.py,77fde44aa229d9b7c16092b5b82bd43b22b085e2,STILL_EXISTS,TODO This probably also isn't the best handling of sparse data...
aaiecgdbh,biolab/orange3,Orange/widgets/data/utils/histogram.py,77fde44aa229d9b7c16092b5b82bd43b22b085e2,eb0f00529c9674bf16d0822bca41d68a62aa3602,will produce too few columns. This causes problems; so we need to
aaiecgdcc,biolab/orange3,Orange/widgets/data/utils/histogram.py,77fde44aa229d9b7c16092b5b82bd43b22b085e2,STILL_EXISTS,TODO It probably isn't a very good idea to convert a sparse row
aaiecgdec,biolab/orange3,Orange/widgets/data/owfeaturestatistics.py,eb4c6f6a214eea350bb42f50cd6ea31fce7a61fa,880f3748b07f3f690f406b83c62b85f418a39eb8,This is much better than overengineering various formatters...
aaiecghhf,biolab/orange3,Orange/widgets/tests/base.py,47381b10c892d45c1b8011cefd52c6004a02b2d0,STILL_EXISTS,simulate manual move
aaiecgich,biolab/orange3,Orange/widgets/visualize/owmosaic.py,486e95cc2b4a5d9452f124f02bfcef5cb539bc61,0d3dc6613578b0bf96fbbff9d6a1ab98c31e337e,TODO: This is not setting
aaiecgjic,biolab/orange3,Orange/canvas/document/editlinksdialog.py,40413f956688a34218e66236c3e453a639c2d519,STILL_EXISTS,'Unused' imports are used in docstrings
aaiechagi,biolab/orange3,Orange/widgets/widget.py,85aad4191f45978b43a984df6eb023499d8e30f8,8be2120ed6bf125fdf683b26793e08af1a70f652,TODO: This needs a better check.
aaiechbag,biolab/orange3,Orange/widgets/visualize/owscatterplotgraph.py,96919417b97021258f94972f3c4883bdc31df49d,cdfa54f4397af4e36215b766c25fc463de0f9529,implement it with threads
aaiechbbg,biolab/orange3,Orange/widgets/visualize/owscatterplotgraph.py,1602d56711ca229f543e5e6d1a7aff9206281e8a,STILL_EXISTS,implement it with threads
aaiechbif,biolab/orange3,Orange/projection/manifold.py,5bf1fb94562cacaa54a6e0d5b317b916813933ef,STILL_EXISTS,Build up the affinity matrix; using multiscale if needed
aaiechdba,biolab/orange3,Orange/widgets/data/owfeaturestatistics.py,a8f4a11f963a4cc495f20dca22a1f88be48a76e0,STILL_EXISTS,Since scipy apparently can't do mode on sparse matrices; cast it to
aaiechibi,biolab/orange3,Orange/data/table.py,4683cef2f314781be19d0989ddd88c5dc250936b,STILL_EXISTS,TODO: Add attributes = {} to __init__
aaieciaaf,biolab/orange3,Orange/tests/test_naive_bayes.py,9f3ce8ff7592992dd92b813d2d23cc7885232f6e,STILL_EXISTS,Test prediction by directly calling predict. This is needed to test
aaieciadf,biolab/orange3,Orange/widgets/unsupervised/tests/test_owtsne.py,86ed4798aef942b6ed64ad80ceff27e338f80f6b,STILL_EXISTS,Normalization shouldn't work on sparse data
aaieciaff,biolab/orange3,Orange/tests/test_naive_bayes.py,5e0fa06f195aab47aef1a8877636557faf83d7f2,STILL_EXISTS,Test prediction by directly calling predict. This is needed to test
aaiecibee,biolab/orange3,Orange/widgets/data/owcorrelations.py,39bcd3d4445a128beadf0448f135324b03e130b8,STILL_EXISTS,This flag is needed because data in the model could be
aaiecibia,biolab/orange3,Orange/widgets/data/owsave.py,98a7415b612fd6a014b64a119e21a2062b3fa74a,f482387fd8b17c161722c1f076e5927bdbdfc3cc,The only known workaround with native dialog is to use suffix *.*.
aaiecibie,biolab/orange3,Orange/widgets/data/owsave.py,98a7415b612fd6a014b64a119e21a2062b3fa74a,d165d9eb2f5ca6d90daa6a88112e0cd29ee76469,dialog; but I see no better solution.
aaiecibif,biolab/orange3,Orange/widgets/data/owsave.py,98a7415b612fd6a014b64a119e21a2062b3fa74a,STILL_EXISTS,TODO: This is not tested!!!
aaiecicgc,biolab/orange3,Orange/widgets/data/owpreprocess.py,ebfb039c4c5593e3126dcfd560cc05e4edea498d,8ce2b02e9ecfbdc2d9b3cd70d619b153477d9e11,TODO: implement top percentile selection
aaiecicjd,biolab/orange3,Orange/widgets/data/owimpute.py,1150bea8aa0436bd401d357b926ae9d978d883ed,STILL_EXISTS,Method is well documented; splitting it is not needed for readability;
aaiecidgd,biolab/orange3,Orange/widgets/unsupervised/owmds.py,8ce2b02e9ecfbdc2d9b3cd70d619b153477d9e11,STILL_EXISTS,TODO: decrease the max iteration count by the already
aaiecidjj,biolab/orange3,Orange/widgets/unsupervised/owmds.py,b6a88e3b3220826e302e47b4a0cc677d20c9c3f8,STILL_EXISTS,TODO: decrease the max iteration count by the already
aaieciege,biolab/orange3,Orange/widgets/evaluate/owtestlearners.py,bd1f21232ac4e19661dfdca04d6012a0bc1b221f,1e1ebf4a3fc61ea40f4365df8a9433315a85663d,updates warnings for displayed columns
aaiecieha,biolab/orange3,Orange/widgets/tests/base.py,20e69954034053c66c5ea970a94c2872daed1638,STILL_EXISTS,FIXME high-dpi screens
aaieciehh,biolab/orange3,Orange/canvas/canvas/items/nodeitem.py,093af5e6f4f678679e86ef09680e48814385fcaf,STILL_EXISTS,Logitech's Smart Move).
aaieciffi,biolab/orange3,Orange/widgets/visualize/owlinearprojection.py,f7f380c106dc6015e4aaab322fba7f6f5c303e48,STILL_EXISTS,Ugly; but the alternative is to have yet another signal to which
aaiecifga,biolab/orange3,Orange/widgets/visualize/owradviz.py,f7f380c106dc6015e4aaab322fba7f6f5c303e48,STILL_EXISTS,Ugly; but the alternative is to have yet another signal to which
aaieciged,biolab/orange3,Orange/widgets/unsupervised/owtsne.py,2e3b863c601c7486b844cebd18e5741f8cbda52a,STILL_EXISTS,Only execute jobs if needed
aaiecighd,biolab/orange3,Orange/widgets/unsupervised/tests/test_owtsne.py,2e3b863c601c7486b844cebd18e5741f8cbda52a,STILL_EXISTS,TODO: The base widget immediately calls `setup_plot` and `commit`
aaiecighe,biolab/orange3,Orange/widgets/unsupervised/tests/test_owtsne.py,2e3b863c601c7486b844cebd18e5741f8cbda52a,STILL_EXISTS,even though there's nothing to show yet. Unfortunately; fixing this
aaiecighf,biolab/orange3,Orange/widgets/unsupervised/tests/test_owtsne.py,2e3b863c601c7486b844cebd18e5741f8cbda52a,STILL_EXISTS,would require changing `OWDataProjectionWidget` in some strange way;
aaiecighg,biolab/orange3,Orange/widgets/unsupervised/tests/test_owtsne.py,2e3b863c601c7486b844cebd18e5741f8cbda52a,STILL_EXISTS,so as a temporary fix; we reset the mocks; so they reflect the calls
aaiecigie,biolab/orange3,Orange/widgets/unsupervised/owtsne.py,93095a8b784547b28f39464cbb0242b86002a920,STILL_EXISTS,current phase (iterations_needed)
aaiecihah,biolab/orange3,Orange/widgets/unsupervised/owtsne.py,f7dbb03f1e83f6e160ac702c98b82d7fee821114,STILL_EXISTS,won't call `run` if needed. `run` also relies on the state of
aaiecihhi,biolab/orange3,Orange/widgets/data/owpivot.py,327f1c53c58b804ecc1fe4458425c023276e0954,STILL_EXISTS,TODO - optimize - after this line is executed;
aaiecihif,biolab/orange3,Orange/widgets/data/owpivot.py,327f1c53c58b804ecc1fe4458425c023276e0954,STILL_EXISTS,number of leading columns
aaiecihig,biolab/orange3,Orange/widgets/data/owpivot.py,327f1c53c58b804ecc1fe4458425c023276e0954,STILL_EXISTS,TODO - inconsistent for different variable types
aaiecjcda,biolab/orange3,Orange/widgets/settings.py,d7c1a0ee4c0095e30add2b39cc3a6ffa2badcf31,37e7b0232760ec39b8206a578f63449fdd004615,Otherwise check if a better match is available in global_contexts
aaiecjdcc,biolab/orange3,Orange/widgets/widget.py,d7c1a0ee4c0095e30add2b39cc3a6ffa2badcf31,5f78d1ecdf7c0d62555beff7afbfe7595c70f1a4,TODO: This needs a better check.
aaiecjeij,biolab/orange3,Orange/widgets/visualize/utils/__init__.py,ea5e89b547bbee5c6fc7e1e95aa8641248d3142d,STILL_EXISTS,apparently this has to be called to reset position
aaiedacch,biolab/orange3,Orange/widgets/data/owcsvimport.py,30c4b437b1c622a93260e267e6df479f5113609f,STILL_EXISTS,TODO: Help button
aaiedachh,biolab/orange3,Orange/widgets/data/owcsvimport.py,30c4b437b1c622a93260e267e6df479f5113609f,ce83e8142a82bc3936b6c2312f61ffed2509af56,Is it better to fail then to lose a item slot?
aaiedacih,biolab/orange3,Orange/widgets/data/owcsvimport.py,30c4b437b1c622a93260e267e6df479f5113609f,STILL_EXISTS,TODO: use 'usecols' parameter in `read_csv` call to
aaiedacii,biolab/orange3,Orange/widgets/data/owcsvimport.py,30c4b437b1c622a93260e267e6df479f5113609f,STILL_EXISTS,avoid loading\/parsing the columns
aaiedacij,biolab/orange3,Orange/widgets/data/owcsvimport.py,30c4b437b1c622a93260e267e6df479f5113609f,STILL_EXISTS,TODO: Is this enough to allow immediate gc of the stack?
aaiedacjb,biolab/orange3,Orange/widgets/data/owcsvimport.py,30c4b437b1c622a93260e267e6df479f5113609f,STILL_EXISTS,Maybe just return None.
aaiedaddf,biolab/orange3,Orange/widgets/utils/textimport.py,30c4b437b1c622a93260e267e6df479f5113609f,STILL_EXISTS,TODO: Consider a wizard-like interface:
aaiedadie,biolab/orange3,Orange/widgets/utils/textimport.py,30c4b437b1c622a93260e267e6df479f5113609f,STILL_EXISTS,: Signal emitted when the preview content parsing ends with an error.
aaiedadjb,biolab/orange3,Orange/widgets/utils/textimport.py,30c4b437b1c622a93260e267e6df479f5113609f,STILL_EXISTS,TODO: Treat all space (THIN SPACE; NO-BREAK SPACE; ...) the same?
aaiedadjd,biolab/orange3,Orange/widgets/utils/textimport.py,30c4b437b1c622a93260e267e6df479f5113609f,STILL_EXISTS,Maybe just use unicodedata.normalize('NFKC'; ...) as a converter?
aaiedaebd,biolab/orange3,Orange/widgets/utils/textimport.py,30c4b437b1c622a93260e267e6df479f5113609f,STILL_EXISTS,Might be better to always use buffer? (compressed streams are
aaiedaebf,biolab/orange3,Orange/widgets/utils/textimport.py,30c4b437b1c622a93260e267e6df479f5113609f,STILL_EXISTS,TODO: Fetch until the same number of rows as at method entry?
aaiedaeji,biolab/orange3,Orange/evaluation/testing.py,f98aaea9b3c456dc74ae804b082a380488690f6b,772db721384442a2058bff61d7c38745026c742f,__init__ methods have all kinds of unused arguments to match signatures
aaiedbaeb,biolab/orange3,Orange/evaluation/testing.py,95b8d5f845861b33aff4469dc123236699a4354b,772db721384442a2058bff61d7c38745026c742f,TODO: Remove arguments that go to call and swallow them in **kwargs?
aaiedbafd,biolab/orange3,Orange/evaluation/testing.py,bc61f182f155de198f43de018d576afb88265a54,772db721384442a2058bff61d7c38745026c742f,TODO: Remove arguments that go to call and swallow them in **kwargs?
aaiedbafe,biolab/orange3,Orange/evaluation/testing.py,bc61f182f155de198f43de018d576afb88265a54,STILL_EXISTS,TODO: list `warning` contains just repetitions of the same message
aaiedbafj,biolab/orange3,Orange/canvas/__main__.py,a14819082102e9b42b573eb8dbd285680c20a5e6,4b6fbcfad2af783c77506cbbfd3fc79e9012e4ba,Fix fonts on Os X (QTBUG 47206; 40833; 32789)
aaiedbcde,biolab/orange3,Orange/widgets/gui.py,2fa175022b0b23f28dfacea9fa90b6a71aabeba0,37e7b0232760ec39b8206a578f63449fdd004615,workaround for QTBUG-67583
aaiedbcdi,biolab/orange3,Orange/widgets/gui.py,2fa175022b0b23f28dfacea9fa90b6a71aabeba0,37e7b0232760ec39b8206a578f63449fdd004615,TODO: SmallWidgetButton is used only in OWkNNOptimization.py. (Re)Move.
aaiedbcee,biolab/orange3,Orange/widgets/gui.py,2fa175022b0b23f28dfacea9fa90b6a71aabeba0,37e7b0232760ec39b8206a578f63449fdd004615,TODO collapsableWidgetBox is used only in OWMosaicDisplay.py; (re)move
aaiedbceg,biolab/orange3,Orange/widgets/gui.py,2fa175022b0b23f28dfacea9fa90b6a71aabeba0,37e7b0232760ec39b8206a578f63449fdd004615,TODO Class widgetHider doesn't seem to be used anywhere; remove?
aaiedbchj,biolab/orange3,Orange/widgets/gui.py,2fa175022b0b23f28dfacea9fa90b6a71aabeba0,37e7b0232760ec39b8206a578f63449fdd004615,TODO: Style dependent margins?
aaiedbcia,biolab/orange3,Orange/widgets/gui.py,2fa175022b0b23f28dfacea9fa90b6a71aabeba0,37e7b0232760ec39b8206a578f63449fdd004615,TODO: Check ForegroundRole.
aaiedbcig,biolab/orange3,Orange/widgets/gui.py,2fa175022b0b23f28dfacea9fa90b6a71aabeba0,37e7b0232760ec39b8206a578f63449fdd004615,TODO: add other styles (Maybe load corrections from .cfg file?)
aaiedbdci,biolab/orange3,Orange/classification/calibration.py,1cfbeece2ec7c853683b9286643257f3ce9a55a0,STILL_EXISTS,we fix nanx below
aaiedcfgf,biolab/orange3,Orange/widgets/visualize/owdistributions.py,dc0824e015524afddc1e9735d6208324ea8220d3,STILL_EXISTS,TODO: Don't clear selection when replotting; rename _replot to
aaiedcfha,biolab/orange3,Orange/widgets/visualize/owdistributions.py,1fa69155822a31e67e6b105a10dd58fcf4d49d95,004455e4616a448a88a4d303670c35f4bcc8c17c,TODO: do something if all are nan; perhaps in on_var_changed
aaiedcfjh,biolab/orange3,Orange/widgets/visualize/tests/test_owdistributions.py,8ebcad92f49968537e512db2b9142a6e39217d65,STILL_EXISTS,Code for fitter; stacked_columns and show_probs is independent; so
aaiedcgfc,biolab/orange3,Orange/widgets/data/owmergedata.py,c12ba34d5e3fe16a0237b41d89710e13f9487d62,STILL_EXISTS,TODO: Context migration
aaiedcggg,biolab/orange3,Orange/widgets/data/owmergedata.py,1572677f5a18edc9bd77b869aca376d2c2800a9c,81b8db014819f9edd223cbc090b0987ee6fd2ea9,TODO: This doesn't work; but should after #3925 (remove make)
aaiedcida,biolab/orange3,Orange/widgets/unsupervised/owsom.py,2b383aa169cfd4681a6133846e100f21e855f926,76a9593cf988465a9f5aeb09ab6d4fe631012fd5,This is apparently needed to advance the bar
aaiedcidi,biolab/orange3,Orange/widgets/unsupervised/owsom.py,c04a7e549fd6194c7d0a00c03799a5d35277acc2,619f06ea6d86d1c5468a2401e8f54ec22c8bc1d0,TODO: Use intelligent binning from #3896; when it's merged
aaieddadh,biolab/orange3,Orange/base.py,e01abf9bd58a42a20305084455050b8ee9f5166f,STILL_EXISTS,Also; backmap probs and values if needed
aaieddaeb,biolab/orange3,Orange/base.py,8a6b895fb0d7e3c594a1970fbf2eedeb6f138758,STILL_EXISTS,For multitarget; recursive call by columns
aaieddchb,biolab/orange3,Orange/widgets/data/tests/test_owmergedata.py,98060f2d55b9f7eb2e3a1545d2439392025415ef,STILL_EXISTS,Table has additional rows; keep all columns
aaieddcic,biolab/orange3,Orange/widgets/data/tests/test_owsave.py,7678a007d0945f7233b59dbaaf10a19f0f4f2325,d165d9eb2f5ca6d90daa6a88112e0cd29ee76469,Proper tests of OWSaveBase would require too much mocking; so we test most
aaiedddch,biolab/orange3,Orange/widgets/utils/save/owsavebase.py,d165d9eb2f5ca6d90daa6a88112e0cd29ee76469,STILL_EXISTS,dialog; but I see no better solution.
aaieddddg,biolab/orange3,Orange/widgets/utils/save/tests/test_owsavebase.py,d165d9eb2f5ca6d90daa6a88112e0cd29ee76469,STILL_EXISTS,Proper tests of OWSaveBase would require too much mocking; so we test most
aaiedddef,biolab/orange3,Orange/widgets/unsupervised/tests/test_owsavedistances.py,2e97da5d800218a0c8abd12c78189962e26f5d92,STILL_EXISTS,switch from columns to rows for the second test
aaieddeed,biolab/orange3,Orange/widgets/visualize/owvenndiagram.py,d5b8e49e6cfe3fd471cfb332717ae29ef41db211,STILL_EXISTS,columns are different; copy all; rename them
aaieddejh,biolab/orange3,Orange/widgets/data/owmergedata.py,7c2254f55cbaaa1a95c75dcb9b869206ff7cb603,7062eec3953b66e11c60dc054d4e99e9d2fd9afd,migrating non-context settings to context settings would be a mess
aaieddfbd,biolab/orange3,Orange/widgets/unsupervised/tests/test_owdistances.py,06e9639ea3e214870c8bf5412d7aa6a3cffdb641,STILL_EXISTS,between columns; normalized
aaieddfbe,biolab/orange3,Orange/widgets/unsupervised/tests/test_owdistances.py,06e9639ea3e214870c8bf5412d7aa6a3cffdb641,STILL_EXISTS,between columns; not normalized
aaieddffi,biolab/orange3,Orange/widgets/data/oweditdomain.py,3c05722eaa996f18d74cae97e2e959c5ad6f4e99,STILL_EXISTS,maybe sorted(set(data.data[...]))
aaieddfgg,biolab/orange3,Orange/widgets/data/oweditdomain.py,3c05722eaa996f18d74cae97e2e959c5ad6f4e99,STILL_EXISTS,Explicitly skip VariableEditor's __init__; this is ugly but we have
aaieddfjf,biolab/orange3,Orange/widgets/data/oweditdomain.py,3c05722eaa996f18d74cae97e2e959c5ad6f4e99,STILL_EXISTS,Building (and applying) concrete type transformations on Table columns
aaieddgaf,biolab/orange3,Orange/widgets/unsupervised/owdistances.py,660a503726632341041701db660eef6874b41984,STILL_EXISTS,when computing distances by columns; we want < 100 rows
aaieddgah,biolab/orange3,Orange/widgets/unsupervised/tests/test_owdistances.py,660a503726632341041701db660eef6874b41984,STILL_EXISTS,by columns -- cannot handle too many rows
aaieddhga,biolab/orange3,Orange/data/io_base.py,0606101ef6421dac1ef6513903b2b2224102dc9f,45871186d5cb08ae466db18399cf2f2e07825457,Iterate through the columns
aaieddhgc,biolab/orange3,Orange/data/io_base.py,0606101ef6421dac1ef6513903b2b2224102dc9f,STILL_EXISTS,might view `data` (string columns)
aaieddhid,biolab/orange3,Orange/data/io_util.py,0606101ef6421dac1ef6513903b2b2224102dc9f,STILL_EXISTS,better
aaieddhjd,biolab/orange3,Orange/data/io_util.py,0606101ef6421dac1ef6513903b2b2224102dc9f,STILL_EXISTS,Map discrete data to 'ints' (or at least what passes as int around
aaieddica,biolab/orange3,Orange/widgets/unsupervised/owhierarchicalclustering.py,090d61da26cec026cff8623d32d75373fcf5272a,6f5d4b9e35a48dac0a1954efe204cc970abaf2e0,Use better better precision then double provides.
aaieddicc,biolab/orange3,Orange/widgets/unsupervised/owhierarchicalclustering.py,090d61da26cec026cff8623d32d75373fcf5272a,6f5d4b9e35a48dac0a1954efe204cc970abaf2e0,scaled by this factor to better condition the geometry
aaieddieg,biolab/orange3,Orange/data/io.py,0eaca003b56477484b50b37f91593ccd2195c3fd,dbf762d5b968408361fa4a3a3c8b2a3f13793a88,better
aaieddifg,biolab/orange3,Orange/data/io.py,0eaca003b56477484b50b37f91593ccd2195c3fd,dbf762d5b968408361fa4a3a3c8b2a3f13793a88,Map discrete data to 'ints' (or at least what passes as int around
aaieddiji,biolab/orange3,Orange/data/io.py,0eaca003b56477484b50b37f91593ccd2195c3fd,dbf762d5b968408361fa4a3a3c8b2a3f13793a88,Iterate through the columns
aaieddjaa,biolab/orange3,Orange/data/io.py,0eaca003b56477484b50b37f91593ccd2195c3fd,dbf762d5b968408361fa4a3a3c8b2a3f13793a88,might view `data` (string columns)
aaiedecdb,biolab/orange3,Orange/widgets/visualize/owvenndiagram.py,8784364dfbecfd5dd8fb85f82245e87e0f06f98c,STILL_EXISTS,TODO: warning because of a weird clash?
aaiedecdc,biolab/orange3,Orange/widgets/visualize/owvenndiagram.py,8784364dfbecfd5dd8fb85f82245e87e0f06f98c,5badf04cb137c8e77a8daad799ddba450b8e982d,for columns
aaiedecde,biolab/orange3,Orange/widgets/visualize/owvenndiagram.py,8784364dfbecfd5dd8fb85f82245e87e0f06f98c,STILL_EXISTS,inflates columns with nans
aaiededab,biolab/orange3,Orange/widgets/visualize/owvenndiagram.py,f96e5669835a39e4e1f59990c9539aeb72d2130d,STILL_EXISTS,columns are different; copy all; rename them
aaiededaf,biolab/orange3,Orange/widgets/visualize/owvenndiagram.py,f96e5669835a39e4e1f59990c9539aeb72d2130d,STILL_EXISTS,add columns with source table id and set id
aaiededbi,biolab/orange3,Orange/widgets/visualize/owvenndiagram.py,f96e5669835a39e4e1f59990c9539aeb72d2130d,STILL_EXISTS,TODO: warning because of a weird clash?
aaiededca,biolab/orange3,Orange/widgets/visualize/owvenndiagram.py,f96e5669835a39e4e1f59990c9539aeb72d2130d,STILL_EXISTS,inflates columns with nans
aaiededce,biolab/orange3,Orange/widgets/visualize/owvenndiagram.py,97a485678fe359e80a8db06f863567e3cb7a38d5,5badf04cb137c8e77a8daad799ddba450b8e982d,for columns
aaiededcg,biolab/orange3,Orange/widgets/visualize/owvenndiagram.py,97a485678fe359e80a8db06f863567e3cb7a38d5,6262599a0f1882021076d5888bb5fee74dd499ee,TODO: rename function
aaiededch,biolab/orange3,Orange/widgets/visualize/owvenndiagram.py,97a485678fe359e80a8db06f863567e3cb7a38d5,6262599a0f1882021076d5888bb5fee74dd499ee,TODO : pohendlaj manjako\u010De atr_type & columns
aaiededda,biolab/orange3,Orange/widgets/visualize/owvenndiagram.py,97a485678fe359e80a8db06f863567e3cb7a38d5,STILL_EXISTS,inflates columns with nans
aaiededde,biolab/orange3,Orange/widgets/visualize/owvenndiagram.py,97a485678fe359e80a8db06f863567e3cb7a38d5,STILL_EXISTS,add columns with source table id and set id
aaiededid,biolab/orange3,Orange/widgets/visualize/owvenndiagram.py,eb00fd2b32c3b6e71d3aecbf643c5c11018c71c6,STILL_EXISTS,change to `if not \"test_rows\" to test columns
aaiedeeab,biolab/orange3,Orange/data/table.py,6aacd42264a7b687f7762c66f1804ba6eca61673,STILL_EXISTS,would probably slow down the process - conversion coo to csr
aaiedefdj,biolab/orange3,Orange/widgets/visualize/utils/heatmap.py,917bb41dde86b109d3738e19457b11211d4d977c,STILL_EXISTS,: define the splits of data over columns; and define dendrogram and\/or
aaiedefij,biolab/orange3,Orange/widgets/utils/graphicstextlist.py,ca7916603274e40b1e91b1080936c686db0e13b3,STILL_EXISTS,return start - fix
aaiedefje,biolab/orange3,Orange/widgets/utils/graphicstextlist.py,c0a7c558cbc65cc4df315d834fd06ca25586a3a3,STILL_EXISTS,return start - fix
aaiedeghc,biolab/orange3,Orange/widgets/utils/dendrogram.py,6f5d4b9e35a48dac0a1954efe204cc970abaf2e0,STILL_EXISTS,Use better better precision then double provides.
aaiedegig,biolab/orange3,Orange/widgets/utils/dendrogram.py,6f5d4b9e35a48dac0a1954efe204cc970abaf2e0,STILL_EXISTS,scaled by this factor to better condition the geometry
aaiedehbf,biolab/orange3,Orange/widgets/visualize/utils/heatmap.py,0cb47350765f6d498868d56db5ea89c95b9d1364,STILL_EXISTS,: (0.05; 0.95) squeezes the effective range by 5% from both ends
aaiedfddi,biolab/orange3,Orange/widgets/data/owpivot.py,7e19481817f2811105adf43eaad63eb9e2774dc6,STILL_EXISTS,rename leading vars (seems the easiest) if needed
aaiedfdgh,biolab/orange3,Orange/tests/test_classification.py,98b0b521a2901a50b86e46f7bf208a7cf7a595fc,ec21f138b15a452f3175a2f7ef46f96780908ea8,TODO: Softmax Regression will be fixed as a separate PR
aaiedfdii,biolab/orange3,Orange/widgets/data/owfeaturestatistics.py,05e8a65ba5fe9ada1a21f8dc5667cd2ae7b5ffca,STILL_EXISTS,Send a table with only selected columns to output
aaiedfeec,biolab/orange3,Orange/widgets/visualize/owheatmap.py,4e75223574860e5ec9733ae8c9b52c19d21f6310,cb05816014f9228c4393e2f30591062fd54efacd,Split heatmap columns by 'key' (horizontal)
aaiedfefb,biolab/orange3,Orange/widgets/visualize/owheatmap.py,4e75223574860e5ec9733ae8c9b52c19d21f6310,63d5ad9c0e81ee853f06be50359553bac30ee68f,if self.split_columns_key not in self.col_split_model:
aaiedfefc,biolab/orange3,Orange/widgets/visualize/owheatmap.py,4e75223574860e5ec9733ae8c9b52c19d21f6310,63d5ad9c0e81ee853f06be50359553bac30ee68f,self.split_columns_key = None
aaiedfejj,biolab/orange3,Orange/widgets/visualize/owheatmap.py,e35913bc877c1339909a0c8766a25cf281a760c7,STILL_EXISTS,Split heatmap columns by 'key' (horizontal)
aaiedffdd,biolab/orange3,Orange/widgets/visualize/owmosaic.py,893a14f78d2d126147be4a2a1572a6622c97826c,cf6417a19db6f1894db86ca846a6e83252d3c7c4,TODO: rename here
aaiedffig,biolab/orange3,Orange/widgets/data/owfeaturestatistics.py,0cc522f8e3000d6e6ae7b626596f41c76f39c6e4,STILL_EXISTS,Send a table with only selected columns to output
aaiedffjg,biolab/orange3,Orange/clustering/louvain.py,3f4b331a6842af202dd37592c27f19ee683c0980,STILL_EXISTS,Euclidean distance; so we use the latter; which is more efficient
aaiedfgch,biolab/orange3,Orange/widgets/visualize/utils/tests/test_plotutils.py,12b39c44599821489820b6e9a9b2b3ac46391433,STILL_EXISTS,_updateMaxTextSize are no longer needed.
aaiedfhch,biolab/orange3,Orange/widgets/data/oweditdomain.py,cbca2eb60c3941a42d1ac72b680a92c835c2ed55,STILL_EXISTS,this could be better
aaiedfhgi,biolab/orange3,Orange/widgets/data/owcsvimport.py,0d47f473c54c9456f4fe4bc30f5ce0735b665221,STILL_EXISTS,is set. XXX: Always use menu; disable Import relative... action?
aaiedfhji,biolab/orange3,Orange/widgets/data/owcsvimport.py,16f4671b283638eadda06d4aed96ca4f069d3600,STILL_EXISTS,Is it better to fail then to lose a item slot?
aaiedfica,biolab/orange3,Orange/widgets/unsupervised/owcorrespondence.py,94d7e30afd5a69e4b1c4a1c25bbab908a47ac1a6,STILL_EXISTS,uninformative and would better be silenced.
aaiedfice,biolab/orange3,Orange/widgets/data/owpaintdata.py,6e227abd328666f95d709dc720fc8f082e7b2487,STILL_EXISTS,data could be a np.array. This would raise an error in the future.
aaiedfihh,biolab/orange3,Orange/widgets/data/owcsvimport.py,39c7ce8683e2bd6becc71b4b83f44dbb5aa75679,STILL_EXISTS,is set. XXX: Always use menu; disable Import relative... action?
aaiedfjic,biolab/orange3,Orange/widgets/data/owsimulator.py,87fff89ee2bbbbc7e7aba82132fdafb473a229e6,STILL_EXISTS,TODO: check ordering (proxy)
aaiedfjij,biolab/orange3,Orange/widgets/data/owcreateinstance.py,b7fab5f88a265c8ed23cb02a2f59d738fae24ad8,STILL_EXISTS,FIXME: after setting focus proxy to the spin; the text is highlighted
aaiedgaai,biolab/orange3,Orange/widgets/data/owcreateinstance.py,01597780dbe7214e20ec7f675fc514f72b8f763e,STILL_EXISTS,provide keyboard functionality; i.e.: pressing ESC after changing
aaiedgaic,biolab/orange3,Orange/data/sql/backend/mssql.py,bcdb5875b307ababc24b5bf8c7ed6c11448a5dec,STILL_EXISTS,workaround for collations that are not case sensitive and
aaiedgaji,biolab/orange3,Orange/widgets/data/tests/test_owselectcolumns.py,fd7972b3c53e511162c143818033dd87b978a6cc,STILL_EXISTS,features columns - domain equal original
aaiedgbfi,biolab/orange3,Orange/widgets/utils/headerview.py,8da3fb06e48058a1254ffbe7bdcba7c2be45fbbd,STILL_EXISTS,Workaround for QTBUG-89910
aaiedgbje,biolab/orange3,Orange/widgets/utils/itemselectionmodel.py,84bb0dc531ddf6b85bce92709726342be6737cb8,STILL_EXISTS,extend selection ranges in `selection` to span all row\/columns
aaiedgcag,biolab/orange3,Orange/widgets/utils/itemselectionmodel.py,33dac33fa721a144075d8d0ef898e152d98e48c1,STILL_EXISTS,row\/columns.
aaiedgcah,biolab/orange3,Orange/widgets/utils/itemselectionmodel.py,33dac33fa721a144075d8d0ef898e152d98e48c1,STILL_EXISTS,extend ranges in sym_ranges to span all current rows\/columns
aaiedgcbc,biolab/orange3,Orange/widgets/data/owtable.py,c25f1855c61f67914c356468031a480a1c8020e0,5236e3b9a2d529dc68a50052ed50d8cf582cfc63,extend selection ranges in `selection` to span all row\/columns
aaiedgdgg,biolab/orange3,Orange/widgets/utils/tableview.py,f54e128da2bd6af7df0ad56733ea84d1b2b75ddf,STILL_EXISTS,TODO: use style item delegate displayText?
aaiedgdhf,biolab/orange3,Orange/widgets/visualize/tests/test_owlineplot.py,03f2412cdbfc0ef4ed4beac0e134e3c63896d2f5,774c90b754ed97ebcb57822b22488f3f6d9f6f1e,- remove test_selection_line workaround for 0.11.0
aaiedgech,biolab/orange3,Orange/widgets/visualize/owviolinplot.py,bea6430a3dbc2bdc65c39113355bb3ad97f12afa,STILL_EXISTS,TODO
aaiedgfed,biolab/orange3,Orange/data/pandas_compat.py,a5b0f92333dc4a01aef1454a358efdb989f754a1,STILL_EXISTS,a hack to keep Orange df _metadata in sparse->dense conversion
aaiedgfeh,biolab/orange3,Orange/data/pandas_compat.py,a5b0f92333dc4a01aef1454a358efdb989f754a1,STILL_EXISTS,for this all columns must be of the same role
aaiedgfej,biolab/orange3,Orange/data/pandas_compat.py,a5b0f92333dc4a01aef1454a358efdb989f754a1,STILL_EXISTS,TODO eliminate expr (preprocessing for pandas -> table)
aaiedghab,pytorch/ignite,tests/ignite/trainer/test_trainer.py,78beb636670bf810134bc43feece960bdc6dffa7,STILL_EXISTS,TODO add test to assure history is written to from trainer
aaiedgiba,pytorch/ignite,docs/source/conf.py,ee400ab711ec111e68f6cae10e6f0a5eea60a055,STILL_EXISTS,-- Options for todo extension ----------------------------------------------
aaiedgibb,pytorch/ignite,docs/source/conf.py,ee400ab711ec111e68f6cae10e6f0a5eea60a055,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaiedgicc,pytorch/ignite,tests/__init__.py,75bc3951035a528fe07ec68412c93f02e5c335ba,STILL_EXISTS,Needed to collect coverage data
aaiedgicd,pytorch/ignite,tests/ignite/__init__.py,75bc3951035a528fe07ec68412c93f02e5c335ba,STILL_EXISTS,Needed to collect coverage data
aaiedgice,pytorch/ignite,tests/ignite/engine/__init__.py,75bc3951035a528fe07ec68412c93f02e5c335ba,67eb7b85f50a16db92d08a1c781d92209f1f0d1e,Needed to collect coverage data
aaiedgicf,pytorch/ignite,tests/ignite/handlers/__init__.py,75bc3951035a528fe07ec68412c93f02e5c335ba,STILL_EXISTS,Needed to collect coverage data
aaiedgicg,pytorch/ignite,tests/ignite/metrics/__init__.py,75bc3951035a528fe07ec68412c93f02e5c335ba,STILL_EXISTS,Needed to collect coverage data
aaiedgjcc,pytorch/ignite,ignite/metrics/running_average.py,747bf4f295bff1e8a8a076b1ad7435f359f129f8,STILL_EXISTS,Implement abstract method
aaiedgjid,pytorch/ignite,ignite/metrics/accuracy.py,8558a8fc3be0b01af86da6f2e2dbd9464624950c,1a8ead8b851c5d0462ed4ca8098b15229c1f92f8,TODO: Uncomment the following after 0.1.2 release
aaiedgjji,pytorch/ignite,tests/ignite/metrics/test_accuracy.py,8558a8fc3be0b01af86da6f2e2dbd9464624950c,STILL_EXISTS,TODO: Uncomment the following after 0.1.2 release
aaiedhaea,pytorch/ignite,tests/ignite/metrics/test_precision.py,8558a8fc3be0b01af86da6f2e2dbd9464624950c,STILL_EXISTS,TODO: Uncomment the following after 0.1.2 release
aaiedhahi,pytorch/ignite,tests/ignite/metrics/test_recall.py,8558a8fc3be0b01af86da6f2e2dbd9464624950c,STILL_EXISTS,TODO: Uncomment the following after 0.1.2 release
aaiedhbhc,pytorch/ignite,tests/ignite/metrics/test_accuracy.py,1a8ead8b851c5d0462ed4ca8098b15229c1f92f8,STILL_EXISTS,TODO: Uncomment the following after 0.1.2 release
aaiedhcah,pytorch/ignite,tests/ignite/metrics/test_precision.py,1a8ead8b851c5d0462ed4ca8098b15229c1f92f8,STILL_EXISTS,TODO: Uncomment the following after 0.1.2 release
aaiedhcbh,pytorch/ignite,tests/ignite/metrics/test_precision.py,1a8ead8b851c5d0462ed4ca8098b15229c1f92f8,1a8ead8b851c5d0462ed4ca8098b15229c1f92f8,TODO: y_pred should be binary after 0.1.2 release
aaiedhccd,pytorch/ignite,tests/ignite/metrics/test_precision.py,1a8ead8b851c5d0462ed4ca8098b15229c1f92f8,STILL_EXISTS,TODO: y_pred should be binary after 0.1.2 release
aaiedhcea,pytorch/ignite,tests/ignite/metrics/test_recall.py,1a8ead8b851c5d0462ed4ca8098b15229c1f92f8,STILL_EXISTS,TODO: Uncomment the following after 0.1.2 release
aaiedhcfa,pytorch/ignite,tests/ignite/metrics/test_recall.py,1a8ead8b851c5d0462ed4ca8098b15229c1f92f8,1a8ead8b851c5d0462ed4ca8098b15229c1f92f8,TODO: y_pred should be binary after 0.1.2 release
aaiedhcfg,pytorch/ignite,tests/ignite/metrics/test_recall.py,1a8ead8b851c5d0462ed4ca8098b15229c1f92f8,STILL_EXISTS,TODO: y_pred should be binary after 0.1.2 release
aaiedhhfd,pytorch/ignite,ignite/engine/engine.py,1313d16008f425581edf186758151cffda140fce,STILL_EXISTS,TODO: remove refs on batch to avoid high mem consumption ? -> need verification
aaiedhjdf,pytorch/ignite,examples/contrib/mnist/mnist_with_neptune_logger.py,400c26a281506284866be8c9cd496ae605d3f8d3,STILL_EXISTS,Move model before creating optimizer
aaiedhjdg,pytorch/ignite,examples/contrib/mnist/mnist_with_tensorboard_logger.py,400c26a281506284866be8c9cd496ae605d3f8d3,STILL_EXISTS,Move model before creating optimizer
aaiedhjdh,pytorch/ignite,examples/contrib/mnist/mnist_with_tqdm_logger.py,400c26a281506284866be8c9cd496ae605d3f8d3,STILL_EXISTS,Move model before creating optimizer
aaiedhjdi,pytorch/ignite,examples/contrib/mnist/mnist_with_visdom_logger.py,400c26a281506284866be8c9cd496ae605d3f8d3,STILL_EXISTS,Move model before creating optimizer
aaiedhjdj,pytorch/ignite,examples/mnist/mnist.py,400c26a281506284866be8c9cd496ae605d3f8d3,STILL_EXISTS,Move model before creating optimizer
aaiedhjea,pytorch/ignite,examples/mnist/mnist_save_resume_engine.py,400c26a281506284866be8c9cd496ae605d3f8d3,STILL_EXISTS,Move model before creating optimizer
aaiedhjeb,pytorch/ignite,examples/mnist/mnist_with_tensorboard.py,400c26a281506284866be8c9cd496ae605d3f8d3,STILL_EXISTS,Move model before creating optimizer
aaiedhjec,pytorch/ignite,examples/mnist/mnist_with_visdom.py,400c26a281506284866be8c9cd496ae605d3f8d3,STILL_EXISTS,Move model before creating optimizer
aaiedhjed,pytorch/ignite,tests/ignite/contrib/engines/test_tbptt.py,400c26a281506284866be8c9cd496ae605d3f8d3,STILL_EXISTS,Move model before creating optimizer
aaiedhjgg,pytorch/ignite,examples/mnist/mnist_with_tensorboard_on_tpu.py,1fa40f4d05c6d9382b29a02f27c4da3773ea7583,STILL_EXISTS,Move model before creating optimizer
aaiediaii,pytorch/ignite,examples/contrib/mnist/mnist_with_trains_logger.py,15eeb8791a2e0c2f55265e1f6b91f91dc35286c5,STILL_EXISTS,Move model before creating optimizer
aaiedibda,pytorch/ignite,ignite/contrib/handlers/trains_logger.py,dff59967081aab08fc4ec40259db4c62f211e79c,2d0f30adac705bb5c82c4f600b2ffbce1cdf8c68,Maybe wont work with XLA
aaiedibdc,pytorch/ignite,ignite/distributed/comp_models/base.py,dff59967081aab08fc4ec40259db4c62f211e79c,592f5b71c61c11eb76bb2a1a1a05ab0fffb783ab,use fix padded size
aaiedibeh,pytorch/ignite,ignite/distributed/utils.py,dff59967081aab08fc4ec40259db4c62f211e79c,STILL_EXISTS,maybe warn about this
aaiedibfi,pytorch/ignite,tests/ignite/contrib/handlers/test_trains_logger.py,dff59967081aab08fc4ec40259db4c62f211e79c,STILL_EXISTS,hack to have all proc properly sync:
aaiedibhc,pytorch/ignite,tests/ignite/handlers/test_checkpoint.py,dff59967081aab08fc4ec40259db4c62f211e79c,STILL_EXISTS,hack to have all proc properly sync:
aaiedibhf,pytorch/ignite,tests/ignite/distributed/comp_models/test_xla.py,f9645c0f07c3d07f77251f3d6c92e68bc8cc6ac3,STILL_EXISTS,Move model before creating optimizer
aaiedibje,pytorch/ignite,tests/ignite/test_utils.py,00ef5710b10191f412d33b32baa1e01148b985ff,STILL_EXISTS,Needed by windows to release FileHandler in the loggers
aaiediccj,pytorch/ignite,ignite/distributed/auto.py,b4b2335430fa9f33d86e74cc6274f01d98be4463,STILL_EXISTS,TODO: How about XLA GPU ?
aaiedifcd,pytorch/ignite,tests/ignite/contrib/handlers/test_trains_logger.py,1fe84b0164555f13682cf2275e6add32d82f56b5,STILL_EXISTS,Probably related to https:\/\/github.com\/pytorch\/xla\/issues\/2576
aaiedifcg,pytorch/ignite,tests/ignite/handlers/test_checkpoint.py,1fe84b0164555f13682cf2275e6add32d82f56b5,STILL_EXISTS,Probably related to https:\/\/github.com\/pytorch\/xla\/issues\/2576
aaiedifea,pytorch/ignite,ignite/metrics/frequency.py,9b57459c61cdf12d6c3a0fbd52c437f009d9d530,STILL_EXISTS,TODO: see issue https:\/\/github.com\/pytorch\/ignite\/issues\/1405
aaiedifgj,pytorch/ignite,ignite/contrib/metrics/gpu_info.py,a5396471550ad6641dd54f7ae689759f7c370d1f,STILL_EXISTS,TODO: see issue https:\/\/github.com\/pytorch\/ignite\/issues\/1405
aaiedjaeb,pytorch/ignite,examples/mnist/mnist_patch.py,14bd8a4640440365a447111b8fd9d8b629ea4136,STILL_EXISTS,\"\"\"Patch to fix MNIST download issue as described here: || - https:\/\/github.com\/pytorch\/ignite\/issues\/1737 || - https:\/\/github.com\/pytorch\/vision\/issues\/3500 || \"\"\"
aaiedjddb,stanfordnlp/stanfordnlp,setup.py,6755b5ff223cc750bc697cb0cde2ef309ea27c10,STILL_EXISTS,You can just specify the packages manually here if your project is
aaiedjdjj,stanfordnlp/stanfordnlp,corenlp/pipeline.py,bb2acb2bdd8bc6e0ef2e7d537812f4f3b28e930d,STILL_EXISTS,Fill in the text attribute if needed.
aaiedjfii,stanfordnlp/stanfordnlp,tests/happyfuntokenizer.py,f313df9a59fdd1223c548c5909b53988637896fd,STILL_EXISTS,\"\"\" || This code implements a basic; Twitter-aware tokenizer. ||  || A tokenizer is a function that splits a string of text into words. In || Python terms; we map string and unicode objects into lists of unicode || objects. ||  || There is not a single right way to do tokenizing. The best method || depends on the application.  This tokenizer is designed to be flexible || and this easy to adapt to new domains and tasks.  The basic logic is || this: ||  || 1. The tuple regex_strings defines a list of regular expression ||    strings. ||  || 2. The regex_strings strings are put; in order; into a compiled ||    regular expression object called word_re. ||  || 3. The tokenization is done by word_re.findall(s); where s is the ||    user-supplied string; inside the tokenize() method of the class ||    Tokenizer. ||  || 4. When instantiating Tokenizer objects; there is a single option: ||    preserve_case.  By default; it is set to True. If it is set to ||    False; then the tokenizer will downcase everything except for ||    emoticons. ||  || The __main__ method illustrates by tokenizing a few examples. ||  || I've also included a Tokenizer method tokenize_random_tweet(). If the || twitter library is installed (http:\/\/code.google.com\/p\/python-twitter\/) || and Twitter is cooperating; then it should tokenize a random || English-language tweet. || \"\"\"
aaiedjgbe,stanfordnlp/stanfordnlp,tests/happyfuntokenizer.py,f313df9a59fdd1223c548c5909b53988637896fd,STILL_EXISTS,The emoticon string gets its own regex so that we can preserve case for them as needed:
aaiedjgbi,stanfordnlp/stanfordnlp,tests/happyfuntokenizer.py,f313df9a59fdd1223c548c5909b53988637896fd,STILL_EXISTS,Fix HTML character entitites:
aaiedjicj,stanfordnlp/stanfordnlp,docs/source/conf.py,733d364487fe54d81646e6381b52640e55214d79,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaiedjihj,stanfordnlp/stanfordnlp,corenlp/client.py,55ff618a0445da34eaef0f030dc020218990752b,STILL_EXISTS,HACK: For some stupid reason; CoreNLPServer will timeout if we
aaiedjjah,stanfordnlp/stanfordnlp,utils/conll18_ud_eval.py,166c8ee9d67775e019f3adefb1603c0073aa7e37,STILL_EXISTS,- [19 Apr 2018] Version 1.0: Fix bug in MLAS (duplicate entries in functional_children).
aaiedjjih,stanfordnlp/stanfordnlp,utils/conll18_ud_eval.py,166c8ee9d67775e019f3adefb1603c0073aa7e37,STILL_EXISTS,10 columns of the CoNLL-U file: ID; FORM; LEMMA;...
aaieeacig,stanfordnlp/stanfordnlp,utils/post_insert_mwt.py,5e030faaea848df026dfcea55580e6f7b41cf9e0,STILL_EXISTS,fix up multi-word token
aaieeacji,stanfordnlp/stanfordnlp,models/tokenize/utils.py,764fa90a0e64dadca741068a5f344cf98f36f9bd,STILL_EXISTS,hack la_ittb
aaieeadfb,stanfordnlp/stanfordnlp,models/pos/data.py,bcfd551fd32956889e30ba37db5f36df83a63394,STILL_EXISTS,TODO: make this line language-specific
aaieeadic,stanfordnlp/stanfordnlp,models/pos/data.py,797725fe61e9017ce27109263a0594d2cd472378,9f30d5026c82a07a7dacde78dffba41c11c16b18,TODO: deal with UPOS; XPOS; and UFeats
aaieeafgh,stanfordnlp/stanfordnlp,models/pos/data.py,0be670e74d4ea4a1f71e1c41d93b1b12e545f0c2,b69f24631c479d980017708fa3171f4065ab6c4d,TODO: fix the BaseVocab interface
aaieeafhi,stanfordnlp/stanfordnlp,models/common/pretrain.py,b69f24631c479d980017708fa3171f4065ab6c4d,8776554ef09b87ab188c510fff680635aa01b180,TODO: fix the BaseVocab interface
aaieeagca,stanfordnlp/stanfordnlp,models/pos/build_xpos_vocab_factory.py,dfa94f2815dd3b6ab13908c929263c1da5d1125c,STILL_EXISTS,the number of total classes needed to predict by all tagger classifiers. This is
aaieeajdi,stanfordnlp/stanfordnlp,stanfordnlp/models/pos/build_xpos_vocab_factory.py,19867de86746cfe95b8ad67df88e0a4d379fc4f6,STILL_EXISTS,the number of total classes needed to predict by all tagger classifiers. This is
aaieeajji,stanfordnlp/stanfordnlp,stanfordnlp/models/tokenize/utils.py,19867de86746cfe95b8ad67df88e0a4d379fc4f6,STILL_EXISTS,hack la_ittb
aaieebaji,stanfordnlp/stanfordnlp,stanfordnlp/pipeline/tokenize_processor.py,19867de86746cfe95b8ad67df88e0a4d379fc4f6,0f6d84b2b85df950429483e84521cb225496deab,hack la_ittb
aaieebbeg,stanfordnlp/stanfordnlp,stanfordnlp/server/client.py,19867de86746cfe95b8ad67df88e0a4d379fc4f6,STILL_EXISTS,HACK: For some stupid reason; CoreNLPServer will timeout if we
aaieebbha,stanfordnlp/stanfordnlp,stanfordnlp/utils/conll18_ud_eval.py,19867de86746cfe95b8ad67df88e0a4d379fc4f6,STILL_EXISTS,- [19 Apr 2018] Version 1.0: Fix bug in MLAS (duplicate entries in functional_children).
aaieebcfd,stanfordnlp/stanfordnlp,stanfordnlp/utils/conll18_ud_eval.py,19867de86746cfe95b8ad67df88e0a4d379fc4f6,STILL_EXISTS,10 columns of the CoNLL-U file: ID; FORM; LEMMA;...
aaieebcjh,stanfordnlp/stanfordnlp,stanfordnlp/utils/post_insert_mwt.py,19867de86746cfe95b8ad67df88e0a4d379fc4f6,STILL_EXISTS,fix up multi-word token
aaieeeggj,Tencent/PocketFlow,learners/channel_pruning/learner.py,82244d9db61f0cf6f28c3d2e31f94b057ce6e623,STILL_EXISTS,record the best combination of pruning ratios
aaieeeijc,Tencent/PocketFlow,learners/nonuniform_quantization/bit_optimizer.py,82244d9db61f0cf6f28c3d2e31f94b057ce6e623,STILL_EXISTS,TODO: the results are somehow not good yet.
aaieeejff,Tencent/PocketFlow,learners/nonuniform_quantization/learner.py,82244d9db61f0cf6f28c3d2e31f94b057ce6e623,STILL_EXISTS,TODO: add layerwise finetuning. working not very weill
aaieeejie,Tencent/PocketFlow,learners/nonuniform_quantization/rl_helper.py,82244d9db61f0cf6f28c3d2e31f94b057ce6e623,STILL_EXISTS,TODO: the design of states is experimental
aaieefaaf,Tencent/PocketFlow,learners/nonuniform_quantization/utils.py,82244d9db61f0cf6f28c3d2e31f94b057ce6e623,STILL_EXISTS,TODO: add more types of activations and mul
aaieefaai,Tencent/PocketFlow,learners/nonuniform_quantization/utils.py,82244d9db61f0cf6f28c3d2e31f94b057ce6e623,STILL_EXISTS,TODO: wrap it into a function; as also used above.
aaieefaga,Tencent/PocketFlow,learners/uniform_quantization/bit_optimizer.py,82244d9db61f0cf6f28c3d2e31f94b057ce6e623,STILL_EXISTS,TODO: Layerwise FT can only be done by one card currently.
aaieefage,Tencent/PocketFlow,learners/uniform_quantization/bit_optimizer.py,82244d9db61f0cf6f28c3d2e31f94b057ce6e623,STILL_EXISTS,TODO: the results are somehow not good yet.
aaieefahc,Tencent/PocketFlow,learners/uniform_quantization/learner.py,82244d9db61f0cf6f28c3d2e31f94b057ce6e623,STILL_EXISTS,\"\"\" ||   Uniform Quantization Learner. ||   Without buckets; min\/max is calculated per layer; otherwise per bucket_size. ||   Actually with bucket; better performance could be achieved in most time. || \"\"\"
aaieefbci,Tencent/PocketFlow,learners/uniform_quantization/learner.py,82244d9db61f0cf6f28c3d2e31f94b057ce6e623,STILL_EXISTS,add layerwise finetuning. TODO: working not very well
aaieefbfi,Tencent/PocketFlow,learners/uniform_quantization/rl_helper.py,82244d9db61f0cf6f28c3d2e31f94b057ce6e623,STILL_EXISTS,TODO: the design of states is experimental
aaieefbib,Tencent/PocketFlow,learners/uniform_quantization/utils.py,82244d9db61f0cf6f28c3d2e31f94b057ce6e623,STILL_EXISTS,TODO: add more types of activations and matmuls
aaieefbif,Tencent/PocketFlow,learners/uniform_quantization/utils.py,82244d9db61f0cf6f28c3d2e31f94b057ce6e623,STILL_EXISTS,TODO: wrap it into a function; as also used
aaieefdhc,Tencent/PocketFlow,learners/weight_sparsification/pr_optimizer.py,82244d9db61f0cf6f28c3d2e31f94b057ce6e623,STILL_EXISTS,record the best combination of pruning ratios
aaieefiec,Tencent/PocketFlow,tools/conversion/export_pb_tflite_models.py,82244d9db61f0cf6f28c3d2e31f94b057ce6e623,STILL_EXISTS,skip un-initialized variables; which is not needed in the final *.pb file
aaieegdce,Tencent/PocketFlow,nets/mobilenet_at_ilsvrc12.py,8033dd4e93443faa0ab1b60da7f9682648cb9a02,STILL_EXISTS,which is better; 0.98 OR (0.98 ** epoch_step)?
aaieegdej,Tencent/PocketFlow,examples/convnet_at_fmnist.py,6795bf4e550a1ed4f55b590ff5caa6233506b047,STILL_EXISTS,tranpose the image tensor if needed
aaieegeed,Tencent/PocketFlow,learners/weight_sparsification/learner.py,ddc8af16297d5cfaca65511871e583b99a92af76,STILL_EXISTS,needed whatever the execution mode is
aaieegfcg,Tencent/PocketFlow,utils/external/SSD.TensorFlow/eval_ssd.py,c4ed35709f0a4ccafdd2fde1e95538d9ec4ef33f,STILL_EXISTS,couldn't find better way to pass params from input_fn to model_fn
aaieeghcb,Tencent/PocketFlow,utils/external/SSD.TensorFlow/train_ssd.py,c4ed35709f0a4ccafdd2fde1e95538d9ec4ef33f,STILL_EXISTS,couldn't find better way to pass params from input_fn to model_fn
aaieegiec,Tencent/PocketFlow,utils/external/SSD.TensorFlow/utility/anchor_manipulator.py,c4ed35709f0a4ccafdd2fde1e95538d9ec4ef33f,STILL_EXISTS,the labels here maybe chaos at those non-positive positions
aaieehaga,Tencent/PocketFlow,nets/vgg_at_pascalvoc.py,bc7793a992c9c3c3d10ce82b5fe2c3f6e8ca68a4,c794f963beddf110fdadac66ff7615e1c65ce864,TODO use tf.map_fn
aaieehajc,Tencent/PocketFlow,nets/vgg_at_pascalvoc.py,c8e5c2c508ac050193ee5ad3a5f2f785ef376d0f,STILL_EXISTS,FIXME cannot build
aaieeieja,Tencent/PocketFlow,tools/conversion/export_pb_tflite_models.py,e120202b1891c3b23e25e0487164e5a87e7a6bda,STILL_EXISTS,skip un-initialized variables; which is not needed in the final *.pb file
aaieeijdd,Tencent/PocketFlow,learners/channel_pruning/learner.py,405c5dc172285682b19c34cbd91899a3b16f1a1d,STILL_EXISTS,record the best combination of pruning ratios
aaiefaahg,skorch-dev/skorch,inferno/callbacks.py,3cca3b439fddae5aab7e4946147d04a5f5ab20d2,STILL_EXISTS,TODO: remove loop when net.history supports tuples
aaiefaaia,skorch-dev/skorch,inferno/net.py,3cca3b439fddae5aab7e4946147d04a5f5ab20d2,STILL_EXISTS,TODO: more useful message
aaiefabjf,skorch-dev/skorch,examples/word_language_model/model.py,d97f48a24ebca57951141051b6fd99fd0356bc09,STILL_EXISTS,\"Using the Output Embedding to Improve Language Models\" (Press & Wolf 2016)
aaiefacac,skorch-dev/skorch,examples/word_language_model/train.py,d97f48a24ebca57951141051b6fd99fd0356bc09,f48b0685db6918dc0cf8a93fc044fc325428de3f,FIXME
aaiefacba,skorch-dev/skorch,examples/word_language_model/train.py,e82c5a5ad0873d0ac8bf824b603f163ee5ac18ef,f554f53e1c399a90a7ad2ab4e7bc466806ea820d,TODO
aaiefacbb,skorch-dev/skorch,examples/word_language_model/train.py,e82c5a5ad0873d0ac8bf824b603f163ee5ac18ef,8ed9789fd2a25e72737e85f88ec306650c46bccc,TODO: we cannot use predict() directly as the y supplied by GridSearchCV
aaiefacbg,skorch-dev/skorch,examples/word_language_model/train.py,e82c5a5ad0873d0ac8bf824b603f163ee5ac18ef,8da89d9f1984d285074f700a388adef0c65ff191,TODO: lr annealing:
aaiefacbh,skorch-dev/skorch,examples/word_language_model/train.py,e82c5a5ad0873d0ac8bf824b603f163ee5ac18ef,8ed9789fd2a25e72737e85f88ec306650c46bccc,FIXME: this is kind of stupid; we supply TensorDatasets to the loader
aaiefacde,skorch-dev/skorch,examples/word_language_model/data.py,8ed9789fd2a25e72737e85f88ec306650c46bccc,f7cc0822895b4864784fd1589aed747b91fe28e4,FIXME: this is kind of stupid; we supply TensorDatasets to the loader
aaiefacfb,skorch-dev/skorch,examples/word_language_model/trainer.py,8ed9789fd2a25e72737e85f88ec306650c46bccc,STILL_EXISTS,TODO: we cannot use predict() directly as the y supplied by GridSearchCV
aaiefacfg,skorch-dev/skorch,examples/word_language_model/generate.py,de2c3a4006c11c986d9c2499cf572fc6c9bbbb34,b11abe44d9c30b5dd4d53f62f5e6f37b3ad7b319,TODO: set seed
aaiefacfh,skorch-dev/skorch,examples/word_language_model/train.py,c91c5e170fee25a8bac629f8e4fb921f0145240a,STILL_EXISTS,TODO: set seed
aaiefadde,skorch-dev/skorch,inferno/dataset.py,82bcbd65ffdf51b185465863f6fa6c1af1f5d87e,0eaf05d3ed8317e326f22afb97d45c5def73c86f,TODO: Find a better solution for this mess
aaiefaddf,skorch-dev/skorch,inferno/dataset.py,82bcbd65ffdf51b185465863f6fa6c1af1f5d87e,0eaf05d3ed8317e326f22afb97d45c5def73c86f,TODO
aaiefadeh,skorch-dev/skorch,inferno/dataset.py,135806f549a0211d4df458f05bff95490526861e,5ece88e8664a2da1cf0816d787f45113627c793a,TODO: Find a better solution for this mess
aaiefadfb,skorch-dev/skorch,inferno/dataset.py,135806f549a0211d4df458f05bff95490526861e,5ece88e8664a2da1cf0816d787f45113627c793a,TODO
aaiefadfd,skorch-dev/skorch,examples/word_language_model/learner.py,13af4cd8c54674b907ca97616bc5c1ba822ad788,STILL_EXISTS,TODO: resetting the hidden layer must be done BEFORE
aaiefadfg,skorch-dev/skorch,examples/word_language_model/learner.py,13af4cd8c54674b907ca97616bc5c1ba822ad788,STILL_EXISTS,TODO: decide if predict should be stateful or not.
aaiefadgb,skorch-dev/skorch,examples/word_language_model/train.py,47227980c2b0e3c3c8dc4ae0dfc0e9e1fc457344,STILL_EXISTS,FIXME: iterator_test does not use corpus.valid as dataset
aaiefadif,skorch-dev/skorch,inferno/dataset.py,ce26b29bb9bad5847c1fc1b48a45ce6b5003b792,STILL_EXISTS,TODO: Find a better solution for this mess
aaiefadii,skorch-dev/skorch,inferno/dataset.py,ce26b29bb9bad5847c1fc1b48a45ce6b5003b792,b54b1ef852fef919c5bf83f2e534bb74605b8612,TODO
aaiefadjg,skorch-dev/skorch,inferno/tests/test_dataset.py,b54b1ef852fef919c5bf83f2e534bb74605b8612,8a61e3a15d2824e40289cd18ace3e39782d0d9b0,TODO: test regression with dummy y as well
aaiefaeaj,skorch-dev/skorch,inferno/tests/test_dataset.py,a1f30103525c50445567565d8ff0e6f730b3a0c0,STILL_EXISTS,TODO: test regression with dummy y as well
aaiefaefj,skorch-dev/skorch,examples/word_language_model/learner.py,f4969e2ec63686911b6697099e9cf55bfbe1d441,STILL_EXISTS,TODO: discuss this as the iterator is executed twice
aaiefaegc,skorch-dev/skorch,examples/word_language_model/train.py,f4969e2ec63686911b6697099e9cf55bfbe1d441,STILL_EXISTS,FIXME: but we need validation data during training (LR annealing)
aaiefaegd,skorch-dev/skorch,examples/word_language_model/train.py,f4969e2ec63686911b6697099e9cf55bfbe1d441,STILL_EXISTS,FIXME: currently we have iterators for training and validation. Both of those
aaiefaegh,skorch-dev/skorch,examples/word_language_model/train.py,f4969e2ec63686911b6697099e9cf55bfbe1d441,STILL_EXISTS,TODO: easy way to write own score() that accesses the validation data only.
aaiefaehi,skorch-dev/skorch,examples/word_language_model/predict.py,7b5a114c16815e499c05a6e497b9b94c1a696176,STILL_EXISTS,TODO: set seed
aaiefafbd,skorch-dev/skorch,inferno/callbacks.py,4af0b6781e23f585f0757bb132c19c26fa5cc1c9,315577500118d62e4ef5553be0e6d3c88b0578d3,TODO: make py2.7 compatible
aaiefagcc,skorch-dev/skorch,inferno/net.py,77cdb78f301703d42368cd7e36298f2440b3526d,a335673f6830b0e822c6b9cd00a6bab5055e2d0c,This is an ugly work-around (relating to #56); but
aaiefagha,skorch-dev/skorch,inferno/net.py,fc1ec46711cce04ab43bed9ffc7b11ced092d9f5,STILL_EXISTS,This is an ugly work-around (relating to #56); but
aaiefaghh,skorch-dev/skorch,inferno/net.py,6d7968199193ee237e707058658b10004451e4a4,0f234d7e930d64daf68f8107b8c6cc5b2f085b68,be obsolete once pytorch scalars arrive.
aaiefagig,skorch-dev/skorch,inferno/net.py,ae5f977f87751b30dd7b4b863a294de69927b634,STILL_EXISTS,This is a temporary; ugly work-around (relating to #56); but
aaiefagja,skorch-dev/skorch,inferno/net.py,ae5f977f87751b30dd7b4b863a294de69927b634,STILL_EXISTS,be obsolete once pytorch scalars arrive.
aaiefahhi,skorch-dev/skorch,setup.py,e1170c2c337099c0883a4355347c8d33b7680d54,618e9be6dc0a92986b6ca5058c347be314e4cde3,TODO: read from requirements.txt
aaiefaief,skorch-dev/skorch,docs/conf.py,a16a8a7f0f6a6e8cf506f68d8505551a01d757da,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaiefbbbg,skorch-dev/skorch,skorch/tests/test_net.py,af17fd3145c4af495baaa1ac7f775895ceea4152,STILL_EXISTS,FIXME: When dataset is initialized; X and y do not matter
aaiefbccb,skorch-dev/skorch,examples/word_language_model/train.py,d64d5070fd1bd86dadec5925e7e47d270928cfac,5184dd3a62751f35a46be3eac71713aba5570184,TODO: remove dummy y values once #112 is fixed.
aaiefbeaa,skorch-dev/skorch,skorch/tests/test_net.py,66f52b5df2f8e28b908026ac476b06afe82f8234,STILL_EXISTS,remove callbacks to have better control over side_effect
aaiefbebd,skorch-dev/skorch,skorch/net.py,6cace378716da35b4761804c02a84e6d29a9f0b1,STILL_EXISTS,XXX remove once deprecation for grad norm clipping is phased out
aaiefbfbd,skorch-dev/skorch,skorch/utils.py,408f7db33368978cc99b978b00e37faf29007fd4,8d4ea748b87eb8e83b611dc867bf8aea463f38fb,TODO: change import to pytorch
aaiefbfci,skorch-dev/skorch,skorch/dataset.py,218716e0631ab33c820562a8b18c60380ba0f6ff,1c1aa4e46bce0f838d1f8c6cb02f048546cc314c,FIXME:  BatchScoring and EpochScoring with caching will get
aaiefbfcj,skorch-dev/skorch,skorch/dataset.py,218716e0631ab33c820562a8b18c60380ba0f6ff,1c1aa4e46bce0f838d1f8c6cb02f048546cc314c,FIXME:: this placeholder value as `y` since they are operating
aaiefbfda,skorch-dev/skorch,skorch/dataset.py,218716e0631ab33c820562a8b18c60380ba0f6ff,1c1aa4e46bce0f838d1f8c6cb02f048546cc314c,FIXME:: on batch level. This might be easy to trip over; esp.
aaiefbfdb,skorch-dev/skorch,skorch/dataset.py,218716e0631ab33c820562a8b18c60380ba0f6ff,1c1aa4e46bce0f838d1f8c6cb02f048546cc314c,FIXME:: since this value may look meaningful.
aaiefbgaj,skorch-dev/skorch,skorch/utils.py,ae78e3df5833fe9803fc5ecd346d97e5bdbfdc3a,787ccea34e276d4672408b7d135f9c66e9d5984f,ugly work-around - torch constructor does not accept np scalars
aaiefbgbc,skorch-dev/skorch,skorch/tests/test_net.py,64795c5f4588b218e9573b8648d258db3dd1a6dd,4103b64bc06c1246a17b43fe518bd460f5bf5235,TODO: check error message more precisely; depending on what
aaiefbgbe,skorch-dev/skorch,skorch/tests/test_callbacks.py,4b22f2133b8e0b064f01702d8d60226693e39442,4103b64bc06c1246a17b43fe518bd460f5bf5235,TODO: check error message more precisely; depending on what
aaiefbhff,skorch-dev/skorch,skorch/tests/test_net.py,5136ce786cbe56cbbe72a332daeac5c8bae74263,STILL_EXISTS,TODO: check error message more precisely; depending on what
aaiefbhfh,skorch-dev/skorch,skorch/tests/test_callbacks.py,a3e5b5880feca93947e70c71857298401b02e55f,STILL_EXISTS,TODO: check error message more precisely; depending on what
aaiefbich,skorch-dev/skorch,skorch/dataset.py,787ccea34e276d4672408b7d135f9c66e9d5984f,45e42df2ab73e80c3698e16d9f3f50ed25da7872,TODO: Remove warning in a future release
aaiefbici,skorch-dev/skorch,skorch/dataset.py,787ccea34e276d4672408b7d135f9c66e9d5984f,45e42df2ab73e80c3698e16d9f3f50ed25da7872,FIXME:  BatchScoring and EpochScoring with caching will get
aaiefbicj,skorch-dev/skorch,skorch/dataset.py,787ccea34e276d4672408b7d135f9c66e9d5984f,45e42df2ab73e80c3698e16d9f3f50ed25da7872,FIXME:: this placeholder value as `y` since they are operating
aaiefbida,skorch-dev/skorch,skorch/dataset.py,787ccea34e276d4672408b7d135f9c66e9d5984f,45e42df2ab73e80c3698e16d9f3f50ed25da7872,FIXME:: on batch level. This might be easy to trip over; esp.
aaiefbidb,skorch-dev/skorch,skorch/dataset.py,787ccea34e276d4672408b7d135f9c66e9d5984f,45e42df2ab73e80c3698e16d9f3f50ed25da7872,FIXME:: since this value may look meaningful.
aaiefbiec,skorch-dev/skorch,skorch/utils.py,45e42df2ab73e80c3698e16d9f3f50ed25da7872,STILL_EXISTS,ugly work-around - torch constructor does not accept np scalars
aaiefbiee,skorch-dev/skorch,skorch/dataset.py,75817283a66d907af9f2fd4caed0d89ba2ce9abb,STILL_EXISTS,TODO: Remove warning in a future release
aaiefbifh,skorch-dev/skorch,skorch/utils.py,88ed9a79250cfc0e4390bfdbf597dc803e725a58,STILL_EXISTS,TODO: Is there a better way than just to try to index? This
aaiefbige,skorch-dev/skorch,skorch/utils.py,88ed9a79250cfc0e4390bfdbf597dc803e725a58,STILL_EXISTS,ugly work-around - torch constructor does not accept np scalars
aaiefbjcd,skorch-dev/skorch,skorch/tests/callbacks/test_all.py,f8cdf3378efd1722154914c155c85f9f4a7580fe,STILL_EXISTS,TODO: check error message more precisely; depending on what
aaiefcahj,skorch-dev/skorch,skorch/callbacks/lr_scheduler.py,54232bfaf23517843898a7aafa803dad5efe35fd,4097e90b96d85b1d8d3d558da3f1e034ea4bab42,TODO: Remove warning in a future release
aaiefcbbd,skorch-dev/skorch,skorch/tests/callbacks/test_training.py,e38d5d92c6ec69efbe03034fe659c88d2815e7cc,4d680861167e19f06963852cb89a8c21fdbfa7ab,TODO: remove this test when the target argument is removed
aaiefcbfh,skorch-dev/skorch,examples/benchmarks/freezing.py,350dfc4003609d2d034f327debd831c819fdeb9e,STILL_EXISTS,\"\"\"Benchmark to test runtime and memory performance of || different freezing approaches. ||  || Test A is done by setting `requires_grad=False` manually || while filtering these parameters from the optimizer using || ``skorch.helper.filtered_optimizer``. ||  || Test B uses the ``Freezer`` via ``ParamMapper`` without || explicitly removing the parameters from the optimizer. ||  || In theory there should be no difference in memory || consumption and runtime. || \"\"\"
aaiefcbib,skorch-dev/skorch,examples/benchmarks/history.py,c50f3bb29571a2cfb2445815dae5c015fb5a708e,STILL_EXISTS,\"\"\"Benchmark to test time and memory performance of History. ||  || Before #312; the timing would be roughly 5 sec and memory usage would || triple. After #312; the timing would be roughly 2 sec and memory usage || roughly constant. ||  || For the reasons; see #306. ||  || \"\"\"
aaiefcbjg,skorch-dev/skorch,skorch/history.py,c50f3bb29571a2cfb2445815dae5c015fb5a708e,4d680861167e19f06963852cb89a8c21fdbfa7ab,which should really be
aaiefcccf,skorch-dev/skorch,skorch/tests/test_history.py,c50f3bb29571a2cfb2445815dae5c015fb5a708e,4d680861167e19f06963852cb89a8c21fdbfa7ab,TODO: remove once argument 3 as string is removed; since won't
aaiefcccj,skorch-dev/skorch,skorch/tests/test_history.py,c50f3bb29571a2cfb2445815dae5c015fb5a708e,4d680861167e19f06963852cb89a8c21fdbfa7ab,TODO: remove once argument 3 as string is removed
aaiefcdbg,skorch-dev/skorch,skorch/callbacks/training.py,830f713d2899f23e8c6ceda4bb35223a5711947a,4d680861167e19f06963852cb89a8c21fdbfa7ab,TODO: Remove warning in release 0.5.0
aaiefcdgi,skorch-dev/skorch,skorch/net.py,95c82c9268dbd620dd02e5fee802f175d5c1709b,4d680861167e19f06963852cb89a8c21fdbfa7ab,TODO: remove this with the next release
aaiefcdhi,skorch-dev/skorch,skorch/net.py,581f57e574421b47ffd09b63aa895fad0864cb49,4d680861167e19f06963852cb89a8c21fdbfa7ab,workaround for cuda_dependent_attributes_ being misused as storage
aaiefcecf,skorch-dev/skorch,skorch/callbacks/lr_scheduler.py,af04b559445a76da33a2750ff630dee2749402d3,4097e90b96d85b1d8d3d558da3f1e034ea4bab42,TODO: Remove class in 0.7
aaiefcggi,skorch-dev/skorch,skorch/dataset.py,043db4d334d832e4f6a6f567a80ef1c8ed28c923,STILL_EXISTS,TODO: raise a ValueError instead of a warning
aaiefchad,skorch-dev/skorch,skorch/tests/test_net.py,6a9cf4f96515687ad0386d23cfbe84b51576d845,STILL_EXISTS,attributes whose name ends in underscore should not be
aaiefchbb,skorch-dev/skorch,skorch/callbacks/lr_scheduler.py,5ddae2ab486a42e436e2924f3a08c2e94b8f89f8,ddf6247c39f0bc20365262813941cf07ed88f944,TODO: Remove this warning on 0.10 release
aaiefchbj,skorch-dev/skorch,skorch/__init__.py,02ebefec622b37af0cf3927080c69357425136ae,STILL_EXISTS,TODO: remove in skorch 0.10.0
aaiefcifa,skorch-dev/skorch,skorch/callbacks/lr_scheduler.py,a86bc85aecd973faa854c2aafe3f23253af03d5c,ddf6247c39f0bc20365262813941cf07ed88f944,TODO: Remove this warning on 0.10 release
aaiefcihc,skorch-dev/skorch,skorch/net.py,afa697ed1b16b0c0e6b661ed0018d4a332da90fb,STILL_EXISTS,\"\"\"Neural net base class ||  || This is the most flexible class; not making assumptions on the kind of || task being peformed. Subclass this to create more specialized and || sklearn-conforming classes like NeuralNetClassifier. ||  || \"\"\"
aaiefcihd,skorch-dev/skorch,skorch/net.py,afa697ed1b16b0c0e6b661ed0018d4a332da90fb,STILL_EXISTS,TODO: remove after some deprecation period; e.g. skorch 0.12
aaiefcihi,skorch-dev/skorch,skorch/tests/callbacks/test_base.py,afa697ed1b16b0c0e6b661ed0018d4a332da90fb,STILL_EXISTS,TODO: remove after some deprecation period; e.g. skorch 0.12
aaiefcjge,RasaHQ/rasa_core,docs/conf.py,ecb764bf75f9e44caf5504049f36a8d827248d04,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaiefdajj,RasaHQ/rasa_core,rasa_core/actions/factories.py,ecb764bf75f9e44caf5504049f36a8d827248d04,STILL_EXISTS,TODO: double check that action names are unique?
aaiefdbdb,RasaHQ/rasa_core,rasa_core/dispatcher.py,ecb764bf75f9e44caf5504049f36a8d827248d04,9b2430a1dc5e4dfbe3cc785e885709177cf47511,TODO: Decide if this should do more
aaiefdcbc,RasaHQ/rasa_core,rasa_core/policies/online_policy_trainer.py,ecb764bf75f9e44caf5504049f36a8d827248d04,STILL_EXISTS,intent wrong and maybe action wrong
aaiefdcih,RasaHQ/rasa_core,rasa_core/slots.py,ecb764bf75f9e44caf5504049f36a8d827248d04,STILL_EXISTS,TODO: this needs to be implemented - slots are not reset yet
aaiefdcja,RasaHQ/rasa_core,rasa_core/slots.py,ecb764bf75f9e44caf5504049f36a8d827248d04,daaa7cd762c8a58b7a6752e739925ff70fe62a19,probably hit something strange
aaiefdehc,RasaHQ/rasa_core,rasa_core/visualize.py,ecb764bf75f9e44caf5504049f36a8d827248d04,STILL_EXISTS,this is optional; only needed if the `_greet` type of
aaiefdgbb,RasaHQ/rasa_core,docs/conf.py,999a1bbb3d964bb592226501e4f410a387d21a28,df7298dc1110982987fabc7099b48ae6f35e5ac1,patch agent handle to fix indefinite running during doctests
aaiefdgde,RasaHQ/rasa_core,rasa_core/training_utils/story_graph.py,873a7d398d368faf9820734d5cf08d46350aa166,STILL_EXISTS,TODO: we can't use tracker filter here to filter for
aaiefdihd,RasaHQ/rasa_core,rasa_core/training/structures.py,f164fb77eb5262977bf725c39e17e90d9e76ad90,77a32b697ff73552230fb57470796769970d7a6b,TODO: we can't use tracker filter here to filter for
aaiefface,RasaHQ/rasa_core,rasa_core/training/generator.py,4a48b27ede50471bda2397ba57cebcfdf874de00,STILL_EXISTS,TODO: this does not work if a step is the conversational start
aaieffdfa,RasaHQ/rasa_core,rasa_core/featurizers.py,1689c64e6c8a6b0eb7f48329517dcf4321a41459,STILL_EXISTS,TODO need to align X then
aaieffdfc,RasaHQ/rasa_core,rasa_core/policies/keras_policy.py,1689c64e6c8a6b0eb7f48329517dcf4321a41459,3b351bd09ad42e567e4b39509587c859e11c769c,TODO actually shuffle?
aaieffdga,RasaHQ/rasa_core,rasa_core/training/generator.py,1689c64e6c8a6b0eb7f48329517dcf4321a41459,3b351bd09ad42e567e4b39509587c859e11c769c,TODO don't like that: encoding after each event
aaieffdgb,RasaHQ/rasa_core,rasa_core/training/generator.py,1689c64e6c8a6b0eb7f48329517dcf4321a41459,3b351bd09ad42e567e4b39509587c859e11c769c,TODO might not work with label_featurizer encoding;
aaieffdgc,RasaHQ/rasa_core,rasa_core/training/generator.py,1689c64e6c8a6b0eb7f48329517dcf4321a41459,3b351bd09ad42e567e4b39509587c859e11c769c,TODO because we save only intents and slots
aaieffdgj,RasaHQ/rasa_core,rasa_core/featurizers.py,3b351bd09ad42e567e4b39509587c859e11c769c,STILL_EXISTS,TODO questinable thing: to delete begining of stories
aaieffdhf,RasaHQ/rasa_core,rasa_core/featurizers.py,3b351bd09ad42e567e4b39509587c859e11c769c,STILL_EXISTS,TODO need to align X then
aaieffeca,RasaHQ/rasa_core,rasa_core/policies/policy.py,46b6ca77bb6ef51a6e07115cb0de57c6076c0f8b,bf3eaf1400fb4de1fe86f35c23efed9426ecfef5,TODO due to the new system; change below
aaieffedd,RasaHQ/rasa_core,rasa_core/training/data.py,42eff5dad8c9c0b6363d1f7b10f66c4bdee150c8,9b2430a1dc5e4dfbe3cc785e885709177cf47511,TODO is it correct?
aaiefffeg,RasaHQ/rasa_core,rasa_core/policies/ensemble.py,f35f4dea11cadedddef690f38ede6938e92465f4,STILL_EXISTS,TODO change path so that we could have several
aaiefffeh,RasaHQ/rasa_core,rasa_core/policies/ensemble.py,f35f4dea11cadedddef690f38ede6938e92465f4,STILL_EXISTS,TODO keras policies with different featurizers
aaiefffei,RasaHQ/rasa_core,rasa_core/policies/keras_policy.py,f35f4dea11cadedddef690f38ede6938e92465f4,27381017344c50886d1837328b5476669ca8bf7f,TODO pass trackers
aaieffffb,RasaHQ/rasa_core,rasa_core/policies/memoization.py,f35f4dea11cadedddef690f38ede6938e92465f4,27381017344c50886d1837328b5476669ca8bf7f,TODO pass trackers?
aaiefffjh,RasaHQ/rasa_core,rasa_core/policies/trainer.py,2ee6c4ee65ec55b535454f744e5054fdab1cd34b,STILL_EXISTS,TODO pass extract_story_graph as a function or
aaiefffji,RasaHQ/rasa_core,rasa_core/policies/trainer.py,2ee6c4ee65ec55b535454f744e5054fdab1cd34b,STILL_EXISTS,TODO pass graph to agent
aaiefffjj,RasaHQ/rasa_core,rasa_core/policies/trainer.py,2ee6c4ee65ec55b535454f744e5054fdab1cd34b,STILL_EXISTS,TODO to support code creating stories
aaieffgbh,RasaHQ/rasa_core,rasa_core/policies/ensemble.py,fa12e8c8ee65fd93e49bf7e8b824bbaf802c209e,85305a04d70c837d287b350af2a97aad9aa67e35,TODO is known_slot_events is the same as action_fingerprints?
aaieffgbi,RasaHQ/rasa_core,rasa_core/policies/ensemble.py,fa12e8c8ee65fd93e49bf7e8b824bbaf802c209e,85305a04d70c837d287b350af2a97aad9aa67e35,TODO if so remove this init; else here is an error
aaieffgdi,RasaHQ/rasa_core,rasa_core/policies/online_policy_trainer.py,fa12e8c8ee65fd93e49bf7e8b824bbaf802c209e,STILL_EXISTS,TODO update tracker with last action
aaieffgef,RasaHQ/rasa_core,rasa_core/policies/online_policy_trainer.py,27381017344c50886d1837328b5476669ca8bf7f,ef35cf2b346ab8e8218ac6fee9ac66fe6561b886,TODO change it?
aaieffgid,RasaHQ/rasa_core,tests/test_domain.py,97980c7277924880e75def490624aa813efc1886,6b908071f0c4dc51034583f9847eaba52779626e,TODO creation of training data changed
aaieffgii,RasaHQ/rasa_core,tests/test_featurizer.py,97980c7277924880e75def490624aa813efc1886,STILL_EXISTS,TODO featurizers changed quite a lot this is more like for testing FeaturizeMechanism
aaieffgij,RasaHQ/rasa_core,tests/test_policies.py,97980c7277924880e75def490624aa813efc1886,6b908071f0c4dc51034583f9847eaba52779626e,TODO new tests due to new system
aaieffgje,RasaHQ/rasa_core,tests/test_dsl.py,6b908071f0c4dc51034583f9847eaba52779626e,STILL_EXISTS,TODO do we want to assert if trackers are the same?
aaieffgjg,RasaHQ/rasa_core,tests/test_dsl.py,6b908071f0c4dc51034583f9847eaba52779626e,STILL_EXISTS,TODO creation of training data changed
aaieffhac,RasaHQ/rasa_core,tests/test_dsl.py,031df4c36b48fc26e9bc2765c1394ad38ff2c50f,0a4ad8bfbf79be4e7584f88851930086e3ed9fa2,TODO max_history was controlling augmentation
aaieffhah,RasaHQ/rasa_core,rasa_core/policies/memoization.py,8adb3a165e8b22ee79a3a05d65959d601194e6fc,f04f9243d7a466e189f27c48d40f05414b39e355,TODO deal with setting self.featurizer directly
aaieffhbg,RasaHQ/rasa_core,rasa_core/domain.py,bbf0accc7c02f0368fe0d4293c0c02e2dfe5e3e3,191158f81f1bdbf80bc9e6038d30e58d238282fc,TODO that were not actually set
aaieffhbj,RasaHQ/rasa_core,rasa_core/domain.py,bbf0accc7c02f0368fe0d4293c0c02e2dfe5e3e3,45c2b404bb725414eab1572bd42798d19442e1cc,TODO doing too much work here: generate all prior trackers
aaieffhca,RasaHQ/rasa_core,rasa_core/domain.py,bbf0accc7c02f0368fe0d4293c0c02e2dfe5e3e3,45c2b404bb725414eab1572bd42798d19442e1cc,TODO then create states only for latest massage
aaieffhfj,RasaHQ/rasa_core,rasa_core/featurizers.py,76869bb90be0bf4df99ab16363dae818521099ed,4d20799e7de5a8b91e57ed1c1b5777bd29a59072,TODO unite with padding
aaiefficj,RasaHQ/rasa_core,rasa_core/policies/embedding_policy.py,ab0b996854cf5f1cfb1a2f3d822bf51c2e9a65d7,STILL_EXISTS,TODO do we need hidden layers for slots?
aaieffiec,RasaHQ/rasa_core,rasa_core/policies/embedding_policy.py,ab0b996854cf5f1cfb1a2f3d822bf51c2e9a65d7,STILL_EXISTS,TODO
aaieffigj,RasaHQ/rasa_core,rasa_core/policies/embedding_policy.py,e5c266edef1d4948b918c28a7ee37d3d22da748d,STILL_EXISTS,TODO we reload params again in train do we need it here?
aaieffiid,RasaHQ/rasa_core,rasa_core/policies/embedding_policy.py,e5c266edef1d4948b918c28a7ee37d3d22da748d,STILL_EXISTS,TODO
aaieffijd,RasaHQ/rasa_core,rasa_core/policies/embedding_policy.py,45c2b404bb725414eab1572bd42798d19442e1cc,d57eeff89192c4566627a1b92e4b91ca5af7b128,TODO do we want to make it work with max_history_featurizer?
aaieffjcb,RasaHQ/rasa_core,tests/test_dsl.py,ff13f8cab71c0c90036e292ccba0d50b6192ac6f,STILL_EXISTS,TODO for some reason sometimes it creates 1 or 2 trackers
aaieffjcc,RasaHQ/rasa_core,tests/test_training.py,ff13f8cab71c0c90036e292ccba0d50b6192ac6f,0a4ad8bfbf79be4e7584f88851930086e3ed9fa2,TODO I output the graph; and it looks correct
aaieffjfd,RasaHQ/rasa_core,rasa_core/domain.py,85305a04d70c837d287b350af2a97aad9aa67e35,9b2430a1dc5e4dfbe3cc785e885709177cf47511,TODO backwards incompatible change. needs to be added to migrations and changelog
aaieffjff,RasaHQ/rasa_core,rasa_core/domain.py,85305a04d70c837d287b350af2a97aad9aa67e35,9b2430a1dc5e4dfbe3cc785e885709177cf47511,TODO During loading this should be checked
aaieffjfg,RasaHQ/rasa_core,rasa_core/domain.py,85305a04d70c837d287b350af2a97aad9aa67e35,9b2430a1dc5e4dfbe3cc785e885709177cf47511,TODO (comparing the persisted version with the current version)
aaieffjfj,RasaHQ/rasa_core,rasa_core/featurizers.py,85305a04d70c837d287b350af2a97aad9aa67e35,STILL_EXISTS,only one story; so no padding is needed
aaiefgbfe,RasaHQ/rasa_core,rasa_core/policies/embedding_policy.py,d57eeff89192c4566627a1b92e4b91ca5af7b128,STILL_EXISTS,TODO do we need hidden layers for slots?
aaiefgbfi,RasaHQ/rasa_core,rasa_core/policies/embedding_policy.py,d57eeff89192c4566627a1b92e4b91ca5af7b128,STILL_EXISTS,TODO inner similarity is not bounded by 1; need normalization?
aaiefgbhh,RasaHQ/rasa_core,rasa_core/training/generator.py,ef35cf2b346ab8e8218ac6fee9ac66fe6561b886,STILL_EXISTS,TODO find a number or pass it as an argument
aaiefgcjd,RasaHQ/rasa_core,rasa_core/training/__init__.py,85d2c9dafbb148f8a16801c79ae8492fb8e4e436,f034edb6035c751d91051a690b7314c4f0532bc8,TODO pass extract_story_graph as a function or
aaiefgcje,RasaHQ/rasa_core,rasa_core/training/__init__.py,85d2c9dafbb148f8a16801c79ae8492fb8e4e436,f034edb6035c751d91051a690b7314c4f0532bc8,TODO pass graph to agent
aaiefgcjf,RasaHQ/rasa_core,rasa_core/training/__init__.py,85d2c9dafbb148f8a16801c79ae8492fb8e4e436,f034edb6035c751d91051a690b7314c4f0532bc8,TODO to support code creating stories
aaiefgdda,RasaHQ/rasa_core,tests/test_agent.py,f034edb6035c751d91051a690b7314c4f0532bc8,STILL_EXISTS,TODO: deprecated - remove in version 0.10
aaiefgdif,RasaHQ/rasa_core,rasa_core/training/generator.py,460c980cc5843665537530b6e17be1cbfa4dd2b6,STILL_EXISTS,TODO find a number or pass it as an argument
aaiefgffi,RasaHQ/rasa_core,rasa_core/policies/embedding_policy.py,6dc8124eb32f9f47e3cd783589dbe67a4f138397,dc04d08391971c20970cca4499d4d1fcd369d02e,TODO delete it!
aaiefgiji,RasaHQ/rasa_core,rasa_core/policies/embedding_policy.py,af529947c8d8ed03b00d44b484eb375657bef43a,STILL_EXISTS,TODO should it be max?
aaiefgjei,RasaHQ/rasa_core,rasa_core/policies/embedding_policy.py,d32cc982d7de3eb2cbbd725d3302e297a1d9cb30,STILL_EXISTS,TODO should it be max?
aaiefgjid,RasaHQ/rasa_core,rasa_core/utils.py,fc4453e0eceb1638a74167123465252165e517f7,c519c5328cd25a5b34e5a9e2c3d13d382a903a30,It would be better at this point to properly shutdown every
aaiefhafj,RasaHQ/rasa_core,rasa_core/dispatcher.py,663fb4c09ba979f57e80ab523a5c1901638c82d0,289df11b13a1f25699283f48b8edc900e462a1f4,TODO: deprecate this function
aaiefhjhf,RasaHQ/rasa_core,rasa_core/utils.py,53f1cad56e5daee067d5ab4bdb8e43598af3d44e,30aba2a2138751d4b80351de48ca3c907fa61708,It would be better at this point to properly shutdown every
aaieficeb,RasaHQ/rasa_core,rasa_core/utils.py,b5f5bb9d80ea0ed26373a041040db999f5c74361,8acca07316fb2fe032c6a031dc7f825a13462d50,It would be better at this point to properly shutdown every
aaiefieda,RasaHQ/rasa_core,rasa_core/policies/policy.py,90c79b51b91582d03c4dd424a941a9e1dd50c7dc,0f5e92b151d87ded092793c954dd3382220c46b8,TODO change that
aaiefifdi,RasaHQ/rasa_core,rasa_core/policies/policy.py,9fe2efec5ebada64d8e16130b2cf318f849edb27,STILL_EXISTS,TODO change that
aaiefifid,RasaHQ/rasa_core,rasa_core/evaluate.py,44254923e8c55b4a57c896a55a438047c4749ee2,1fe4cf38ef25359ec225fade5a3dcce19790d43d,# TODO: check this doesn't mess anything up
aaiefifie,RasaHQ/rasa_core,rasa_core/evaluate_paper.py,44254923e8c55b4a57c896a55a438047c4749ee2,1fe4cf38ef25359ec225fade5a3dcce19790d43d,# TODO: check if dir exists
aaiefigac,RasaHQ/rasa_core,rasa_core/evaluate.py,4a0490cdcba829071be154ecd772b058a6390886,a4c3e968d293fe4ce363b20e6d05d3d8544bb3c3,# TODO: check this doesn't mess anything up
aaiefigad,RasaHQ/rasa_core,rasa_core/evaluate_paper.py,4a0490cdcba829071be154ecd772b058a6390886,a4c3e968d293fe4ce363b20e6d05d3d8544bb3c3,# TODO: check if dir exists
aaiefigbc,RasaHQ/rasa_core,rasa_core/events/__init__.py,21adf7da9885d94cf07f0a2a017729d614c47820,STILL_EXISTS,TODO: DEPRECATED - remove in version 0.10.0
aaiefigbj,RasaHQ/rasa_core,rasa_core/training/generator.py,2d010d9004e7eaeb0f1dd1ff72154de54e88c2f6,STILL_EXISTS,unused_checkpoints = set()  # type: Set[Text]
aaiefigca,RasaHQ/rasa_core,rasa_core/training/generator.py,2d010d9004e7eaeb0f1dd1ff72154de54e88c2f6,STILL_EXISTS,TODO remove permutation; just for test
aaiefigcg,RasaHQ/rasa_core,rasa_core/training/generator.py,2d010d9004e7eaeb0f1dd1ff72154de54e88c2f6,STILL_EXISTS,unused_checkpoints -= used_checkpoints
aaiefigdd,RasaHQ/rasa_core,rasa_core/training/generator.py,bf02a5f414cb5ba4993259179ba9fb6012ffce86,STILL_EXISTS,track unused checkpoints for this phase
aaiefigdh,RasaHQ/rasa_core,rasa_core/training/generator.py,bf02a5f414cb5ba4993259179ba9fb6012ffce86,ad6adc98262745f49c1cdb8ba771107391da3f2f,add end checkpoint as unused
aaiefigej,RasaHQ/rasa_core,rasa_core/training/generator.py,bda69e85fc0ab81de09bb4087ef5f02e3174f08b,ad6adc98262745f49c1cdb8ba771107391da3f2f,TODO move it to config and make it configurable
aaiefigfa,RasaHQ/rasa_core,rasa_core/training/generator.py,bda69e85fc0ab81de09bb4087ef5f02e3174f08b,STILL_EXISTS,TODO this is harsh: uncontrollably loose information
aaiefigfg,RasaHQ/rasa_core,rasa_core/training/generator.py,730ca481288016b8df3228cb1348e54675bb39e2,ad6adc98262745f49c1cdb8ba771107391da3f2f,process trackers ended with unused checkpoints further
aaiefigfj,RasaHQ/rasa_core,rasa_core/training/generator.py,730ca481288016b8df3228cb1348e54675bb39e2,8a42f59f0259f786b91117788d78d40524fd6160,after we processed all unused checkpoints
aaiefigge,RasaHQ/rasa_core,rasa_core/training/generator.py,9bc791425029e389e0957f05179c4d552bb1561a,ad6adc98262745f49c1cdb8ba771107391da3f2f,TODO move it to config and make it configurable
aaiefiggi,RasaHQ/rasa_core,rasa_core/training/generator.py,9bc791425029e389e0957f05179c4d552bb1561a,STILL_EXISTS,track unused checkpoints for this phase
aaiefigha,RasaHQ/rasa_core,rasa_core/training/generator.py,9bc791425029e389e0957f05179c4d552bb1561a,ad6adc98262745f49c1cdb8ba771107391da3f2f,TODO remove permutation; just for testing
aaiefighh,RasaHQ/rasa_core,rasa_core/training/generator.py,9bc791425029e389e0957f05179c4d552bb1561a,ad6adc98262745f49c1cdb8ba771107391da3f2f,add end checkpoint as unused
aaiefigih,RasaHQ/rasa_core,rasa_core/training/generator.py,9bc791425029e389e0957f05179c4d552bb1561a,ad6adc98262745f49c1cdb8ba771107391da3f2f,process trackers ended with unused checkpoints further
aaiefigjd,RasaHQ/rasa_core,rasa_core/training/structures.py,9bc791425029e389e0957f05179c4d552bb1561a,STILL_EXISTS,we will remove unused start ones
aaiefihaa,RasaHQ/rasa_core,rasa_core/training/structures.py,9bc791425029e389e0957f05179c4d552bb1561a,STILL_EXISTS,the process above may generate unused start checkpoints
aaiefihac,RasaHQ/rasa_core,rasa_core/training/structures.py,9bc791425029e389e0957f05179c4d552bb1561a,STILL_EXISTS,also there might be generated unused end checkpoints
aaiefihad,RasaHQ/rasa_core,rasa_core/training/structures.py,9bc791425029e389e0957f05179c4d552bb1561a,STILL_EXISTS,remove generated unused end checkpoints
aaiefihbi,RasaHQ/rasa_core,rasa_core/training/generator.py,a7500e21d661741f67a3db709484c17516d3455f,8a42f59f0259f786b91117788d78d40524fd6160,after we processed all unused checkpoints
aaiefihda,RasaHQ/rasa_core,rasa_core/training/generator.py,a7500e21d661741f67a3db709484c17516d3455f,8a42f59f0259f786b91117788d78d40524fd6160,TODO: TB - ask vova what this is needed for
aaiefihge,RasaHQ/rasa_core,rasa_core/training/generator.py,8a42f59f0259f786b91117788d78d40524fd6160,ad6adc98262745f49c1cdb8ba771107391da3f2f,process trackers ended with unused checkpoints further
aaiefiiag,RasaHQ/rasa_core,rasa_core/training/generator.py,36cbf7692e34e443566722904bb999a66ca4004e,52ed6f0558e5188502564bd47a5da4a373e46bbd,TODO: TB - ask vova what this is needed for
aaiefiifb,RasaHQ/rasa_core,rasa_core/training/generator.py,fc5b3a49f8c638989db3be44f56df5af1f418e67,ad6adc98262745f49c1cdb8ba771107391da3f2f,TODO move it to config and make it configurable
aaiefiiff,RasaHQ/rasa_core,rasa_core/training/generator.py,fc5b3a49f8c638989db3be44f56df5af1f418e67,STILL_EXISTS,track unused checkpoints for this phase
aaiefiifh,RasaHQ/rasa_core,rasa_core/training/generator.py,fc5b3a49f8c638989db3be44f56df5af1f418e67,ad6adc98262745f49c1cdb8ba771107391da3f2f,TODO remove permutation; just for testing
aaiefiihc,RasaHQ/rasa_core,rasa_core/training/generator.py,fc5b3a49f8c638989db3be44f56df5af1f418e67,ad6adc98262745f49c1cdb8ba771107391da3f2f,add end checkpoint as unused
aaiefiiic,RasaHQ/rasa_core,rasa_core/training/generator.py,fc5b3a49f8c638989db3be44f56df5af1f418e67,ad6adc98262745f49c1cdb8ba771107391da3f2f,process trackers ended with unused checkpoints further
aaiefiiii,RasaHQ/rasa_core,rasa_core/training/generator.py,fc5b3a49f8c638989db3be44f56df5af1f418e67,STILL_EXISTS,deduplication of finished trackers is needed;
aaiefijaa,RasaHQ/rasa_core,rasa_core/training/structures.py,fc5b3a49f8c638989db3be44f56df5af1f418e67,b8fa8edfb7e9ba23202f38236680548f31440a74,TODO do not do dict1 == dict2
aaiefijca,RasaHQ/rasa_core,rasa_core/training/generator.py,ad6adc98262745f49c1cdb8ba771107391da3f2f,STILL_EXISTS,track unused checkpoints for this phase
aaiefijfd,RasaHQ/rasa_core,rasa_core/training/generator.py,ad6adc98262745f49c1cdb8ba771107391da3f2f,STILL_EXISTS,deduplication of finished trackers is needed;
aaiefijgh,RasaHQ/rasa_core,rasa_core/channels/teams.py,12c0db314c6e9968a28cae85621780dde47c21f9,STILL_EXISTS,TODO : Not ask a new token if token still valid. Expiration check
aaiefijhd,RasaHQ/rasa_core,rasa_core/training/generator.py,85222a1bf349d07e439955b311abeaaaa73d00a6,69c021e67ca624c1f60deefda9c02c96d9658242,TODO move it to config and make it configurable
aaiefijhi,RasaHQ/rasa_core,rasa_core/training/generator.py,85222a1bf349d07e439955b311abeaaaa73d00a6,STILL_EXISTS,track unused checkpoints for this phase
aaiefijid,RasaHQ/rasa_core,rasa_core/training/generator.py,85222a1bf349d07e439955b311abeaaaa73d00a6,69c021e67ca624c1f60deefda9c02c96d9658242,add end checkpoint as unused
aaiefijje,RasaHQ/rasa_core,rasa_core/training/generator.py,85222a1bf349d07e439955b311abeaaaa73d00a6,69c021e67ca624c1f60deefda9c02c96d9658242,process trackers ended with unused checkpoints further
aaiefjaaa,RasaHQ/rasa_core,rasa_core/training/generator.py,85222a1bf349d07e439955b311abeaaaa73d00a6,STILL_EXISTS,deduplication of finished trackers is needed;
aaiefjada,RasaHQ/rasa_core,rasa_core/channels/channel.py,62c1c41fa28264fae0d50de6c9e42020b1635b18,f373fd241db1c3c0c1c92b9a5f660613957b89ac,TODO: TB - ensure backward compatibility with old webhooks
aaiefjadg,RasaHQ/rasa_core,rasa_core/channels/file.py,62c1c41fa28264fae0d50de6c9e42020b1635b18,d803fa5e0e1c072fb65c64f10523561565ee47e4,TODO: TB - rethink; should read from file and post to simple web channel
aaiefjaia,RasaHQ/rasa_core,rasa_core/channels/rest.py,62c1c41fa28264fae0d50de6c9e42020b1635b18,2f978ec53fe6606ccfb4e4ae4db452b1b6f7811a,TODO: TB - think about making this removal backwards compatible
aaiefjaij,RasaHQ/rasa_core,rasa_core/dispatcher.py,62c1c41fa28264fae0d50de6c9e42020b1635b18,f373fd241db1c3c0c1c92b9a5f660613957b89ac,TODO: deprecate this function
aaiefjbab,RasaHQ/rasa_core,rasa_core/nlg/generator.py,62c1c41fa28264fae0d50de6c9e42020b1635b18,06b392276b304fb7b5ec97dafa2adb2e7ef7c1cd,TODO: TB - check if we should really do it this way
aaiefjbac,RasaHQ/rasa_core,rasa_core/nlg/generator.py,62c1c41fa28264fae0d50de6c9e42020b1635b18,STILL_EXISTS,TODO: default generator
aaiefjbad,RasaHQ/rasa_core,rasa_core/policies/online_trainer.py,62c1c41fa28264fae0d50de6c9e42020b1635b18,STILL_EXISTS,TODO: TB - handle channel is gone!
aaiefjbbc,RasaHQ/rasa_core,rasa_core/remote.py,62c1c41fa28264fae0d50de6c9e42020b1635b18,d803fa5e0e1c072fb65c64f10523561565ee47e4,TODO: TB - figure out how NLG works with remote core
aaiefjbbf,RasaHQ/rasa_core,rasa_core/run.py,62c1c41fa28264fae0d50de6c9e42020b1635b18,d803fa5e0e1c072fb65c64f10523561565ee47e4,TODO: TB - find a way to exit the commandline
aaiefjbdg,RasaHQ/rasa_core,rasa_core/train.py,62c1c41fa28264fae0d50de6c9e42020b1635b18,22778fd8bca495c293dc37f399d64e520035864b,TODO: TB - start server instead and then run cmd to communicate with
aaiefjbdi,RasaHQ/rasa_core,rasa_core/train.py,62c1c41fa28264fae0d50de6c9e42020b1635b18,22778fd8bca495c293dc37f399d64e520035864b,FIX!
aaiefjbid,RasaHQ/rasa_core,rasa_core/training/generator.py,69c021e67ca624c1f60deefda9c02c96d9658242,STILL_EXISTS,track unused checkpoints for this phase
aaiefjcae,RasaHQ/rasa_core,rasa_core/training/generator.py,69c021e67ca624c1f60deefda9c02c96d9658242,STILL_EXISTS,deduplication of finished trackers is needed;
aaiefjcbc,RasaHQ/rasa_core,rasa_core/training/generator.py,3e6100521bb53aaaecda4b3b0febc25b05f5414d,STILL_EXISTS,TODO move it to config and make it configurable
aaiefjcbh,RasaHQ/rasa_core,rasa_core/training/generator.py,3e6100521bb53aaaecda4b3b0febc25b05f5414d,STILL_EXISTS,track unused checkpoints for this phase
aaiefjccc,RasaHQ/rasa_core,rasa_core/training/generator.py,3e6100521bb53aaaecda4b3b0febc25b05f5414d,STILL_EXISTS,add end checkpoint as unused
aaiefjcdd,RasaHQ/rasa_core,rasa_core/training/generator.py,3e6100521bb53aaaecda4b3b0febc25b05f5414d,STILL_EXISTS,process trackers ended with unused checkpoints further
aaiefjcdi,RasaHQ/rasa_core,rasa_core/training/generator.py,3e6100521bb53aaaecda4b3b0febc25b05f5414d,52ed6f0558e5188502564bd47a5da4a373e46bbd,TODO: TB - ask vova what this is needed for
aaiefjcfc,RasaHQ/rasa_core,rasa_core/training/generator.py,3e6100521bb53aaaecda4b3b0febc25b05f5414d,STILL_EXISTS,deduplication of finished trackers is needed;
aaiefjdai,RasaHQ/rasa_core,rasa_core/dispatcher.py,d143d757000c196268cd883ac73afed7ec599127,f373fd241db1c3c0c1c92b9a5f660613957b89ac,TODO: this should really be done at the channel level
aaiefjdaj,RasaHQ/rasa_core,rasa_core/dispatcher.py,d143d757000c196268cd883ac73afed7ec599127,f373fd241db1c3c0c1c92b9a5f660613957b89ac,TODO: deprecate this function
aaiefjdch,RasaHQ/rasa_core,rasa_core/training/generator.py,9df0720a3977821a2a9232b59c2b95c1fe9bf8de,048e4a2237d1d83c90d79177aef984bfdb8e3d01,TODO: TB - ask vova what this is needed for
aaiefjeae,RasaHQ/rasa_core,rasa_core/training/generator.py,22778fd8bca495c293dc37f399d64e520035864b,75fa7a162337f6dd163993b32085b4e1303768c8,TODO: DEPRECATED - remove in version 0.10
aaiefjgad,RasaHQ/rasa_core,rasa_core/train.py,981aaa05ee1c605b1b19520621e4a3342611cd2e,de0af7b640059b8348ac0027c07563945730ae92,TODO: TB - start server instead and then run cmd to communicate with
aaiefjgaf,RasaHQ/rasa_core,rasa_core/train.py,981aaa05ee1c605b1b19520621e4a3342611cd2e,de0af7b640059b8348ac0027c07563945730ae92,FIX!
aaiefjgbb,RasaHQ/rasa_core,rasa_core/policies/memoization.py,206e51deb863f17e8be1fb51fccfb5b795c449fb,dbfb325c69f3171940944e4913a6816465a87cc9,TODO: DEPRECATED - remove in version 0.10
aaiefjjda,RasaHQ/rasa_core,rasa_core/policies/augmented_memoization.py,1b99cbbd8e7767d881e65d57fc7d3a85ddc981f0,STILL_EXISTS,TODO: DEPRECATED - remove in version 0.10
aaiefjjdf,RasaHQ/rasa_core,rasa_core/policies/memoization.py,6ee9c714fc9929b30b63435077fe2c5847411cab,dbfb325c69f3171940944e4913a6816465a87cc9,TODO: DEPRECATED - remove in version 0.10
aaiefjjef,RasaHQ/rasa_core,rasa_core/policies/memoization.py,a10fba5e9d178bdc4f1f133d7910c48c6ead90a4,97a5ba4d0e830be274b09c2a7b08beafe0205734,TODO: DEPRECATED - remove in version 0.10
aaiefjjib,RasaHQ/rasa_core,rasa_core/actions/action.py,d803fa5e0e1c072fb65c64f10523561565ee47e4,49bd56865fd06faf591c484364433dba71d09c49,TODO: TB - make sure the json is valid
aaiefjjic,RasaHQ/rasa_core,rasa_core/actions/action.py,d803fa5e0e1c072fb65c64f10523561565ee47e4,49bd56865fd06faf591c484364433dba71d09c49,TODO: TB - make sure no invalid events are logged
aaiegaadb,RasaHQ/rasa_core,rasa_core/dispatcher.py,d803fa5e0e1c072fb65c64f10523561565ee47e4,f373fd241db1c3c0c1c92b9a5f660613957b89ac,TODO: deprecate this function
aaiegaaei,RasaHQ/rasa_core,rasa_core/nlg/template.py,d803fa5e0e1c072fb65c64f10523561565ee47e4,STILL_EXISTS,TODO: TB - make use of the additional information about the channel
aaiegaaih,RasaHQ/rasa_core,rasa_sdk/executor.py,d803fa5e0e1c072fb65c64f10523561565ee47e4,STILL_EXISTS,TODO: TB - deprecate this function?
aaiegaajd,RasaHQ/rasa_core,rasa_sdk/forms.py,d803fa5e0e1c072fb65c64f10523561565ee47e4,STILL_EXISTS,this slot is used to store information needed
aaiegacfe,RasaHQ/rasa_core,rasa_core/dispatcher.py,2f978ec53fe6606ccfb4e4ae4db452b1b6f7811a,f373fd241db1c3c0c1c92b9a5f660613957b89ac,TODO: this should really be done at the channel level
aaiegacff,RasaHQ/rasa_core,rasa_core/dispatcher.py,2f978ec53fe6606ccfb4e4ae4db452b1b6f7811a,f373fd241db1c3c0c1c92b9a5f660613957b89ac,TODO: deprecate this function
aaiegacib,RasaHQ/rasa_core,rasa_core/policies/augmented_memoization.py,2f978ec53fe6606ccfb4e4ae4db452b1b6f7811a,STILL_EXISTS,TODO: DEPRECATED - remove in version 0.10
aaiegafgb,RasaHQ/rasa_core,rasa_core/trackers.py,06b392276b304fb7b5ec97dafa2adb2e7ef7c1cd,8a5bd3f47c2856069da5d8bdc0b9c39446377656,TODO: TB - figure out if this condition is a BUG
aaiegafha,RasaHQ/rasa_core,rasa_core/actions/action.py,f44d9f1b2774fca8e981a84799ca374b1b6c64b8,5dcf26144653f0d88cf753c602eed62d95d3521c,TODO: TB - make sure the json is valid
aaiegafhb,RasaHQ/rasa_core,rasa_core/actions/action.py,f44d9f1b2774fca8e981a84799ca374b1b6c64b8,5dcf26144653f0d88cf753c602eed62d95d3521c,TODO: TB - make sure no invalid events are logged
aaiegafhd,RasaHQ/rasa_core,rasa_core/actions/action.py,f44d9f1b2774fca8e981a84799ca374b1b6c64b8,5dcf26144653f0d88cf753c602eed62d95d3521c,TODO: TB - add link to endpoint docs
aaiegafhe,RasaHQ/rasa_core,rasa_core/actions/action.py,f44d9f1b2774fca8e981a84799ca374b1b6c64b8,5dcf26144653f0d88cf753c602eed62d95d3521c,TODO: TB - properly handling failing status codes
aaiegbbae,RasaHQ/rasa_core,rasa_core/policies/embedding_policy.py,5f31a5a6a4fe2019ca3f99e379c5f0dd16c1f551,STILL_EXISTS,is needed to calculate train accuracy
aaiegbbbh,RasaHQ/rasa_core,rasa_core/actions/action.py,27a7d8440521ad92ab87bb1ea22062d2d34ed148,f373fd241db1c3c0c1c92b9a5f660613957b89ac,TODO: TB - make sure the json is valid
aaiegbbbi,RasaHQ/rasa_core,rasa_core/actions/action.py,27a7d8440521ad92ab87bb1ea22062d2d34ed148,f373fd241db1c3c0c1c92b9a5f660613957b89ac,TODO: TB - make sure no invalid events are logged
aaiegbbca,RasaHQ/rasa_core,rasa_core/actions/action.py,27a7d8440521ad92ab87bb1ea22062d2d34ed148,f373fd241db1c3c0c1c92b9a5f660613957b89ac,TODO: TB - add link to endpoint docs
aaiegbbcb,RasaHQ/rasa_core,rasa_core/actions/action.py,27a7d8440521ad92ab87bb1ea22062d2d34ed148,de9dd6acd2de28979b8407373b2374f2cf733bad,TODO: TB - properly handling failing status codes
aaiegbbej,RasaHQ/rasa_core,rasa_core/channels/online.py,27a7d8440521ad92ab87bb1ea22062d2d34ed148,STILL_EXISTS,TODO: TB - implement
aaiegbbgc,RasaHQ/rasa_core,rasa_core/channels/online.py,27a7d8440521ad92ab87bb1ea22062d2d34ed148,STILL_EXISTS,intent wrong and maybe action wrong
aaiegbbgf,RasaHQ/rasa_core,rasa_core/dispatcher.py,27a7d8440521ad92ab87bb1ea22062d2d34ed148,f373fd241db1c3c0c1c92b9a5f660613957b89ac,TODO: deprecate this function
aaiegbbjf,RasaHQ/rasa_core,rasa_core/trackers.py,27a7d8440521ad92ab87bb1ea22062d2d34ed148,STILL_EXISTS,TODO: TB - figure out if this condition is a BUG
aaiegbbjg,RasaHQ/rasa_core,rasa_core/train.py,27a7d8440521ad92ab87bb1ea22062d2d34ed148,de9dd6acd2de28979b8407373b2374f2cf733bad,TODO: TB - start server instead and then run cmd to communicate with
aaiegbbji,RasaHQ/rasa_core,rasa_core/train.py,27a7d8440521ad92ab87bb1ea22062d2d34ed148,de9dd6acd2de28979b8407373b2374f2cf733bad,FIX!
aaiegbdbe,RasaHQ/rasa_core,rasa_core/server.py,de9dd6acd2de28979b8407373b2374f2cf733bad,STILL_EXISTS,TODO: TB - implement properly for agent \/ bot
aaiegbdga,RasaHQ/rasa_core,rasa_core/agent.py,f373fd241db1c3c0c1c92b9a5f660613957b89ac,9f420c0027847ea98be0cfc78a9e64a02acdbc15,TODO: DEPRECATED - remove in version 0.10
aaiegbfbd,RasaHQ/rasa_core,rasa_core/dispatcher.py,57c93870ab8e1e8f4d1efc68dabb51fa4ce9c4e9,58fb41d29bb83f49ac3981b436cf86f0dfb0d082,TODO: deprecate this function
aaiegbgfg,RasaHQ/rasa_core,rasa_core/policies/embedding_policy.py,08beb39a8779cbe206b5d113032736671421b118,STILL_EXISTS,num_paths=2;  # TODO = 1
aaiegbghe,RasaHQ/rasa_core,rasa_core/training/structures.py,4cbd486b41ad8d7cbb31de179ebf39379cbb4e2d,STILL_EXISTS,we will remove unused start ones
aaiegbgih,RasaHQ/rasa_core,rasa_core/domain.py,f7379cd6daa6b64acde126a693067ae474f9097c,48c9adc28944e9386676728ea308238a48da0b41,TODO: check last event was a useruttered
aaiegbhaj,RasaHQ/rasa_core,rasa_core/dispatcher.py,5e4bdde86cb8435a4f398f2fac309c37d59906b5,8bcb96c9598f74f38a45e2f1c6eb09781a7d0629,TODO: deprecate this function
aaiegbhhe,RasaHQ/rasa_core,rasa_core/domain.py,109e674621553d23c0e298eb975f20c436531fa9,48c9adc28944e9386676728ea308238a48da0b41,TODO: check last event was a useruttered
aaiegbjhh,RasaHQ/rasa_core,rasa_core/training/structures.py,c780395e4419594822061e759670a836db20b940,STILL_EXISTS,we will remove unused start ones
aaiegcbjj,RasaHQ/rasa_core,rasa_core/actions/action.py,9f420c0027847ea98be0cfc78a9e64a02acdbc15,96136d5695c57c444846b66ea247030005c6afa4,TODO: TB - make sure the json is valid
aaiegccaa,RasaHQ/rasa_core,rasa_core/actions/action.py,9f420c0027847ea98be0cfc78a9e64a02acdbc15,96136d5695c57c444846b66ea247030005c6afa4,TODO: TB - make sure no invalid events are logged
aaiegccac,RasaHQ/rasa_core,rasa_core/actions/action.py,9f420c0027847ea98be0cfc78a9e64a02acdbc15,96136d5695c57c444846b66ea247030005c6afa4,TODO: TB - add link to endpoint docs
aaiegcceg,RasaHQ/rasa_core,rasa_core/channels/channel.py,9f420c0027847ea98be0cfc78a9e64a02acdbc15,6b9c0503c107a050da1856e363676d749f3d021d,TODO: TB - ensure backward compatibility with old webhooks
aaiegccfh,RasaHQ/rasa_core,rasa_core/dispatcher.py,9f420c0027847ea98be0cfc78a9e64a02acdbc15,STILL_EXISTS,TODO: deprecate this function
aaiegccga,RasaHQ/rasa_core,rasa_core/policies/online_trainer.py,9f420c0027847ea98be0cfc78a9e64a02acdbc15,STILL_EXISTS,TODO: TB - handle channel is gone!
aaiegcdaf,RasaHQ/rasa_core,rasa_core/server.py,9f420c0027847ea98be0cfc78a9e64a02acdbc15,STILL_EXISTS,TODO: TB - implement properly for agent \/ bot
aaiegcdch,RasaHQ/rasa_core,rasa_core/server.py,3db74d73fc24505a8eea0bc3e842ab156e21e7e5,STILL_EXISTS,TODO: TB - implement properly for agent \/ bot
aaiegcdif,RasaHQ/rasa_core,rasa_core/agent.py,bba07d45692d388113234662a8ab8d852acc9e09,765fad3a814d95041e36b9a3de8ffcead7039b5e,TODO: DEPRECATED - remove in version 0.10
aaiegceia,RasaHQ/rasa_core,rasa_core/server.py,765fad3a814d95041e36b9a3de8ffcead7039b5e,STILL_EXISTS,TODO: TB - implement properly for agent \/ bot
aaiegcfaf,RasaHQ/rasa_core,rasa_core/dispatcher.py,934d24a5c86f17d26da5b2d98218a6a4ce7b3aad,STILL_EXISTS,TODO: Decide if this should do more
aaiegcfcb,RasaHQ/rasa_core,rasa_core/dispatcher.py,934d24a5c86f17d26da5b2d98218a6a4ce7b3aad,STILL_EXISTS,TODO: deprecate this function
aaiegcfeg,RasaHQ/rasa_core,rasa_core/actions/action.py,d27b865996c091c080681d4bcf1a9743097dc652,96136d5695c57c444846b66ea247030005c6afa4,TODO: TB - make sure the json is valid
aaiegcfeh,RasaHQ/rasa_core,rasa_core/actions/action.py,d27b865996c091c080681d4bcf1a9743097dc652,96136d5695c57c444846b66ea247030005c6afa4,TODO: TB - make sure no invalid events are logged
aaiegcfej,RasaHQ/rasa_core,rasa_core/actions/action.py,d27b865996c091c080681d4bcf1a9743097dc652,96136d5695c57c444846b66ea247030005c6afa4,TODO: TB - add link to endpoint docs
aaiegcffh,RasaHQ/rasa_core,rasa_core/channels/channel.py,d27b865996c091c080681d4bcf1a9743097dc652,c783d6b7987c4b2d66672905a3dae3dc40bf630a,TODO: TB - ensure backward compatibility with old webhooks
aaiegcfic,RasaHQ/rasa_core,rasa_core/dispatcher.py,d27b865996c091c080681d4bcf1a9743097dc652,STILL_EXISTS,TODO: deprecate this function
aaiegcfjg,RasaHQ/rasa_core,rasa_core/dispatcher.py,d27b865996c091c080681d4bcf1a9743097dc652,STILL_EXISTS,TODO: Decide if this should do more
aaiegcggj,RasaHQ/rasa_core,rasa_core/dispatcher.py,c783d6b7987c4b2d66672905a3dae3dc40bf630a,STILL_EXISTS,TODO: Decide if this should do more
aaiegchaa,RasaHQ/rasa_core,rasa_core/policies/embedding_policy.py,2ee9b54a85495f98c470a5b632c102d88025a78e,STILL_EXISTS,is needed to calculate train accuracy
aaiegdafe,RasaHQ/rasa_core,tests/test_agent.py,16fc5eadc99bc3d61d94a88f883203ea14a4f1cc,STILL_EXISTS,TODO: DEPRECATED - remove in version 0.10
aaiegdahi,RasaHQ/rasa_core,rasa_core/server.py,2af89ffbe0e5b85365ff158371e3b5f18125eb87,STILL_EXISTS,TODO: TB - implement properly for agent \/ bot
aaiegdcfd,RasaHQ/rasa_core,rasa_core/actions/action.py,b396adafb03b840225f418dfcfba4c0e77282134,STILL_EXISTS,TODO: TB - make sure the json is valid
aaiegdcfe,RasaHQ/rasa_core,rasa_core/actions/action.py,b396adafb03b840225f418dfcfba4c0e77282134,STILL_EXISTS,TODO: TB - make sure no invalid events are logged
aaiegdcfg,RasaHQ/rasa_core,rasa_core/actions/action.py,b396adafb03b840225f418dfcfba4c0e77282134,STILL_EXISTS,TODO: TB - add link to endpoint docs
aaiegddgb,RasaHQ/rasa_core,tests/test_agent.py,b396adafb03b840225f418dfcfba4c0e77282134,STILL_EXISTS,TODO: DEPRECATED - remove in version 0.10
aaiegddhi,RasaHQ/rasa_core,tests/test_channels.py,8433baa606856fd7e70731a2ca297afb8733e916,fd5e19731f74ac81213c5409f0967fbce143f546,this is needed so that the tests included as code examples look better
aaiegdehb,RasaHQ/rasa_core,rasa_core/actions/action.py,369797e0e33a75a954d48c7e7652c41fe4e1019a,STILL_EXISTS,TODO: TB - make sure the json is valid
aaiegdehc,RasaHQ/rasa_core,rasa_core/actions/action.py,369797e0e33a75a954d48c7e7652c41fe4e1019a,STILL_EXISTS,TODO: TB - make sure no invalid events are logged
aaiegdehe,RasaHQ/rasa_core,rasa_core/actions/action.py,369797e0e33a75a954d48c7e7652c41fe4e1019a,STILL_EXISTS,TODO: TB - add link to endpoint docs
aaiegdgaa,RasaHQ/rasa_core,tests/test_channels.py,e14a3cae885b8e30a466b2413538a180fc3b82f5,b82f9609382cb4699d25097b4ed7aca9eab2b4a5,this is needed so that the tests included as code examples look better
aaiegdghj,RasaHQ/rasa_core,rasa_core/dispatcher.py,5c635e7664f84174c4e32796cf55579846486ce6,STILL_EXISTS,TODO: deprecate this function
aaiegdhfh,RasaHQ/rasa_core,rasa_core/actions/action.py,1cf4afd6e771f476582ac77ed786218a78d7a110,STILL_EXISTS,this logic is a bit reversed; you'd think that we should remove
aaiegdjbf,RasaHQ/rasa_core,rasa_core/dispatcher.py,31feb99dc26036e254a3f519622b922d65ed5dfc,STILL_EXISTS,TODO: deprecate this function
aaiegeade,RasaHQ/rasa_core,tests/test_channels.py,31feb99dc26036e254a3f519622b922d65ed5dfc,STILL_EXISTS,this is needed so that the tests included as code examples look better
aaiegedfh,RasaHQ/rasa_core,rasa_core/policies/embedding_policy.py,aee30ae549c5ec1d0e94ba83ed517caede637791,03e0cf6a4a63ac40a00145a294b987d5cb74cc95,Step 8: Maybe copy output and cell state from history
aaiegefgc,RasaHQ/rasa_core,rasa_core/policies/tf_utils.py,03e0cf6a4a63ac40a00145a294b987d5cb74cc95,STILL_EXISTS,Step 8: Maybe copy output and cell state from history
aaieggajf,RasaHQ/rasa_core,rasa_core/training/online.py,8359f643ca8e8b0710035f31970cd1e3fbadcf4d,STILL_EXISTS,TODO: write to temp file; just to be save
aaieggdci,RasaHQ/rasa_core,rasa_core/policies/ensemble.py,02d4785a9a4f96ee5a93a0426a4fcfeba22ae8de,88fadb5cb8495b4f55740f90a5ad8c24df87df27,AugmentedMemoizationPolicy also ends with MemoizationPolicy
aaieggfhi,RasaHQ/rasa_core,rasa_core/domain.py,a31ed8b241e0ff1a96f93c65b91652038f14694a,e0a0945a98a6de2296fda091f47b65de947bb5e4,TODO: this should really only be done when there's a FormPolicy present
aaieghbji,RasaHQ/rasa_core,examples/formbot/actions.py,56ba7a27c715c0d51ef1854ab046f0139ff7c7b4,d696d486b9d5e08e6a819bf44a69c53118a9c35a,no validation needed
aaieghfbg,RasaHQ/rasa_core,docs/conf.py,83d8adb11d9f754a029a84b28176c99c8e0963fc,acbabfda7d7d74b3ae58ca5303d2571341ca0149,TODO: remove when https:\/\/github.com\/sphinx-doc\/sphinx\/issues\/5480 fixed
aaiegiejj,RasaHQ/rasa_core,rasa_core/training/interactive.py,42716d65890c757d50ff2bbaf1ab9a7deea1af8c,42d0b40615339649f8eae8c955aa6e1c3f948012,TODO add policy and confidence back in once #1013 merged
aaiegifbd,RasaHQ/rasa_core,rasa_core/training/interactive.py,2c805de028735eda18a62c0089d15e4312c1dea7,19341b574db7cce7ceae8630dba08e43a8fffb4c,TODO add policy and confidence back in once #1013 merged
aaiegjaec,RasaHQ/rasa_core,rasa_core/training/interactive.py,abbfc42b1b935c0e4cf7d5b5d617eef0cbed8874,f049f3b643a04aba280456103fec77440a3cd5dc,TODO add policy and confidence back in once #1013 merged
aaiegjcib,RasaHQ/rasa_core,rasa_core/evaluate.py,e2d5a4dece7c7176c75848a371860d42d3dd02b5,1de785fa7cbdbbbc097636e8dde13b7327a230a3,TODO: return num_stories somewhere
aaiehaica,RasaHQ/rasa_core,rasa_core/channels/console.py,390792a0008237995a47da2f7b1cfb430d08788b,STILL_EXISTS,this builtin is needed so we can overwrite in test
aaiehbgif,RasaHQ/rasa_core,rasa_core/training/generator.py,a6926fa14c4f1952ed4146bce873f38947b73073,STILL_EXISTS,add trackers with unused checkpoints
aaiehbgjf,RasaHQ/rasa_core,tests/test_dsl.py,a6926fa14c4f1952ed4146bce873f38947b73073,STILL_EXISTS,2 with unused end checkpoints -> training_trackers
aaiehbgjg,RasaHQ/rasa_core,tests/test_dsl.py,a6926fa14c4f1952ed4146bce873f38947b73073,STILL_EXISTS,1 with unused start checkpoints -> ignored
aaiehchee,RasaHQ/rasa_core,rasa_core/policies/two_stage_fallback.py,d2e54bb8fc745690f6b4ed61319ee245b086f66b,55ff3ee6ed0d3375fb5b747e6599a353dd252852,TODO: Revert old events here?
aaiehdaec,RasaHQ/rasa_core,rasa_core/agent.py,8185e4bd0495e30a4d5fd6a4f5f3f0ae421dcfa9,53b7e40158dcc9c7ec856055df616f6a2ba9fbc5,TODO AS adapt to use asyncio loop
aaiehdaed,RasaHQ/rasa_core,rasa_core/agent.py,8185e4bd0495e30a4d5fd6a4f5f3f0ae421dcfa9,87826c4ccb971911752adbc4bdac8d3e7c28989e,todo follow
aaiehdagg,RasaHQ/rasa_core,rasa_core/channels/facebook.py,8185e4bd0495e30a4d5fd6a4f5f3f0ae421dcfa9,53b7e40158dcc9c7ec856055df616f6a2ba9fbc5,TODO AS this needs fixing
aaiehdaii,RasaHQ/rasa_core,rasa_core/processor.py,8185e4bd0495e30a4d5fd6a4f5f3f0ae421dcfa9,53b7e40158dcc9c7ec856055df616f6a2ba9fbc5,todo: follow
aaiehdaij,RasaHQ/rasa_core,rasa_core/processor.py,8185e4bd0495e30a4d5fd6a4f5f3f0ae421dcfa9,53b7e40158dcc9c7ec856055df616f6a2ba9fbc5,TODO: follow
aaiehdaja,RasaHQ/rasa_core,rasa_core/processor.py,8185e4bd0495e30a4d5fd6a4f5f3f0ae421dcfa9,53b7e40158dcc9c7ec856055df616f6a2ba9fbc5,todo: AS handle reminder is async
aaiehdajb,RasaHQ/rasa_core,rasa_core/training/interactive.py,8185e4bd0495e30a4d5fd6a4f5f3f0ae421dcfa9,72af2f80ee5f666476eabedf0099a6d100233a66,TODO AS adapt to asyncio loop
aaiehdajc,RasaHQ/rasa_core,tests/test_channels.py,8185e4bd0495e30a4d5fd6a4f5f3f0ae421dcfa9,890943957cbe8dd27122615845f71f615fc71d5c,TODO check by sending a request
aaiehdajf,RasaHQ/rasa_core,rasa_core/training/interactive.py,be35d78d5931e4dc530e1f816beb26ffb3e7466e,089cf5bcf9b116a634bb1ddc4a68e49f042de6c1,TODO AS adapt to asyncio loop
aaiehdbbb,RasaHQ/rasa_core,rasa_core/training/interactive.py,1d2cd3feef936b7db621a738e2ef40018994fd53,53b7e40158dcc9c7ec856055df616f6a2ba9fbc5,TODO AS adapt to asyncio loop
aaiehdbca,RasaHQ/rasa_core,rasa_core/channels/slack.py,50083f8e331d2948e4b28985a1bc71a541168f27,STILL_EXISTS,can be adjusted to taste later on if needed; but is a good first approximation
aaiehdbgb,RasaHQ/rasa_core,rasa_core/channels/slack.py,f67de53926d4349ee67949846d53b7071515ba98,53b7e40158dcc9c7ec856055df616f6a2ba9fbc5,a bit arbitrary but probably OK
aaiehdbgi,RasaHQ/rasa_core,tests/test_channels.py,f67de53926d4349ee67949846d53b7071515ba98,890943957cbe8dd27122615845f71f615fc71d5c,TODO check by sending a request
aaiehdcgh,RasaHQ/rasa_core,rasa_core/channels/slack.py,2f1bf99269d8d841b2fa3782dda96a166d2b4ebc,STILL_EXISTS,can be adjusted to taste later if needed;
aaiehdcgj,RasaHQ/rasa_core,rasa_core/channels/slack.py,2f1bf99269d8d841b2fa3782dda96a166d2b4ebc,STILL_EXISTS,a bit arbitrary but probably OK
aaiehdcif,RasaHQ/rasa_core,rasa_core/agent.py,0cec1a010695d9e54d2fd7e7b4668a8c8cd246ef,87826c4ccb971911752adbc4bdac8d3e7c28989e,TODO AS adapt to use asyncio loop
aaiehdcig,RasaHQ/rasa_core,rasa_core/agent.py,0cec1a010695d9e54d2fd7e7b4668a8c8cd246ef,87826c4ccb971911752adbc4bdac8d3e7c28989e,todo follow
aaiehdcjb,RasaHQ/rasa_core,tests/test_channels.py,0cec1a010695d9e54d2fd7e7b4668a8c8cd246ef,890943957cbe8dd27122615845f71f615fc71d5c,TODO check by sending a request
aaiehddbh,RasaHQ/rasa_core,rasa_core/channels/console.py,87826c4ccb971911752adbc4bdac8d3e7c28989e,c2893111f8b50d6ed8523942dc9e59ddf6d94c26,TODO: check if this properly receives UTF-8 data
aaiehdddc,RasaHQ/rasa_core,rasa_core/channels/console.py,6779d33019c7bf99a95241e6b9ec0d1d851dd877,c2893111f8b50d6ed8523942dc9e59ddf6d94c26,needed for python 3.5 compatibility
aaiehdebd,RasaHQ/rasa_core,rasa_core/agent.py,8acca07316fb2fe032c6a031dc7f825a13462d50,bce6541337104f41d93b42080eb519bd9d11fe9d,TODO async check if it is really a good idea to not await this task
aaiehdeca,RasaHQ/rasa_core,rasa_core/channels/console.py,8acca07316fb2fe032c6a031dc7f825a13462d50,b0a289be9d9d3097f9a938fdb1fdc9322698ac9d,needed for python 3.5 compatibility
aaiehdecb,RasaHQ/rasa_core,rasa_core/channels/console.py,8acca07316fb2fe032c6a031dc7f825a13462d50,b0a289be9d9d3097f9a938fdb1fdc9322698ac9d,TODO: check if this properly receives UTF-8 data
aaiehdecd,RasaHQ/rasa_core,rasa_core/processor.py,8acca07316fb2fe032c6a031dc7f825a13462d50,d53f0e78b4b7a13df9470093edfccb93cf1f0887,TODO async we need to fix this; most likely this is using
aaiehdecg,RasaHQ/rasa_core,rasa_core/training/dsl.py,8acca07316fb2fe032c6a031dc7f825a13462d50,15c48a0e4978b0d251b8f694868c2de1546970dd,TODO async make sure this is tested
aaiehdfaf,RasaHQ/rasa_core,rasa_core/processor.py,94d5f728636e4fa3b931db14fb3cc4a0264bc214,8af9ea43265dd99be23f8b2aef264ce285609c27,TODO async we need to fix this; most likely this is using
aaiehdfai,RasaHQ/rasa_core,tests/test_channels.py,94d5f728636e4fa3b931db14fb3cc4a0264bc214,STILL_EXISTS,TODO check by sending a request
aaiehdfbj,RasaHQ/rasa_core,tests/test_channels.py,d1901acbe79abcbd2f2892541ea9133ce796dd24,STILL_EXISTS,TODO check by sending a request
aaiehdfie,RasaHQ/rasa_core,rasa_core/channels/console.py,16c96a62e7fe13ab6bd9debbee577baf5f97c2ca,STILL_EXISTS,needed for python 3.5 compatibility
aaiehdfif,RasaHQ/rasa_core,rasa_core/channels/console.py,16c96a62e7fe13ab6bd9debbee577baf5f97c2ca,STILL_EXISTS,TODO: check if this properly receives UTF-8 data
aaiehdfjb,RasaHQ/rasa_core,tests/test_channels.py,16c96a62e7fe13ab6bd9debbee577baf5f97c2ca,STILL_EXISTS,TODO check by sending a request
aaiehdgcg,RasaHQ/rasa_core,rasa_core/training/dsl.py,a75820dd72bf9bcc24df894ec87c45a559f0138f,15c48a0e4978b0d251b8f694868c2de1546970dd,TODO async make sure this is tested
aaiehdgej,RasaHQ/rasa_core,rasa_core/utils.py,464c70832e821ed8d1fbd0c154597ad3eb88c938,aebf66ecc4991162ed5a044f149936c4d517eb8f,It would be better at this point to properly shutdown every
aaiehdidd,RasaHQ/rasa_core,rasa/cli/show.py,471a1f9aed0acd8620dcc3434361a5b3f9a6a47b,5d5d28c4f0ca08b5d8cc3fb9a4bb65333a2baa42,TODO: Fix
aaiehdidg,RasaHQ/rasa_core,rasa/cli/test.py,471a1f9aed0acd8620dcc3434361a5b3f9a6a47b,5d5d28c4f0ca08b5d8cc3fb9a4bb65333a2baa42,TODO: Fix
aaiehdiee,RasaHQ/rasa_core,rasa_core/policies/fallback.py,3a484a4c8e65769330ac3e1e6b6f189ce44ce62b,aae58c70ee29e70c8ed44d1ab449e548777ed87c,TODO: check in on priorities with nlu vs core fallback
aaiehdief,RasaHQ/rasa_core,rasa_core/policies/fallback.py,3a484a4c8e65769330ac3e1e6b6f189ce44ce62b,ba9ad365c11b26fb1f38da668365bf253a04d1b1,TODO: check into handling this case
aaiehdiei,RasaHQ/rasa_core,rasa_core/policies/ensemble.py,56678cfd632690c86087184af716fde72bc0fbea,098dfde576e7df2f2ddaf570abbeb340797aef2f,TODO: make sure priority persists
aaiehdifd,RasaHQ/rasa_core,rasa_core/policies/ensemble.py,56678cfd632690c86087184af716fde72bc0fbea,098dfde576e7df2f2ddaf570abbeb340797aef2f,TODO: Update priority here
aaiehdiff,RasaHQ/rasa_core,rasa_core/policies/policy.py,a1bac40325ae19fa2d5b765e4e376e75ed1360e5,098dfde576e7df2f2ddaf570abbeb340797aef2f,TODO: Raise no priority error here
aaiehdjeb,RasaHQ/rasa_core,rasa_core/policies/ensemble.py,60528977bac3d8755db844cc12b55e15fa9f0157,aae58c70ee29e70c8ed44d1ab449e548777ed87c,TODO: make sure priority persists
aaieheacb,RasaHQ/rasa_core,rasa_core/utils.py,d5badc6ed17daeff458b75815eaa24a1f32675bb,a005a6aa4506bcbacc7fa8cd93e85ef08370c89a,It would be better at this point to properly shutdown every
aaieheafg,RasaHQ/rasa_core,rasa_core/agent.py,c5a389e8cf1cb6b4d809eeeba36eaeca5c1d8496,de46e3ba7be00a257263e388a3ef97b7edbca95f,TODO: find a better way to check this
aaieheaga,RasaHQ/rasa_core,rasa_core/interpreter.py,c5a389e8cf1cb6b4d809eeeba36eaeca5c1d8496,de46e3ba7be00a257263e388a3ef97b7edbca95f,TODO: hotfix to append attributes that NLU is adding as a server
aaieheagd,RasaHQ/rasa_core,rasa/cli/up.py,4f600a14bc014d80e3e9d8d8009dbe46aa82431c,STILL_EXISTS,TODO: LOCAL make endpoints more configurable; esp ports
aaieheage,RasaHQ/rasa_core,rasa/cli/up.py,4f600a14bc014d80e3e9d8d8009dbe46aa82431c,STILL_EXISTS,TODO: LOCAL configure auth token
aaieheagh,RasaHQ/rasa_core,rasa_core/nlg/template.py,50ccaf0f3b86c65bfcd857ebf4c64f64b9b7c4a0,STILL_EXISTS,blacklist characters that probably should not be
aaieheajd,RasaHQ/rasa_core,rasa_core/training/interactive.py,8e78687c64ad4ee24770cb355624324f16956fce,38359d05bbd6d40a59dc8a78ad03869d8e87af18,TODO: add saving of tracker up to now in stories here?
aaiehebjb,RasaHQ/rasa_core,rasa_core/utils.py,0931c1d6b046b24ad6d2a670dcdd6eeae3411f84,7aad97549452d8c092c39779d113f5665e29eb16,It would be better at this point to properly shutdown every
aaieheccg,RasaHQ/rasa_core,rasa_core/server.py,5c969d88a45f2ff872a3c5a37a50819dadbafd31,4260d1712039d148df7c918d311ae2b3891d4f1c,TODO LOCAL: validate namespace json in request
aaiehecch,RasaHQ/rasa_core,rasa_core/server.py,5c969d88a45f2ff872a3c5a37a50819dadbafd31,4260d1712039d148df7c918d311ae2b3891d4f1c,TODO local: except specific training exception that can occur
aaiehecdc,RasaHQ/rasa_core,rasa_core/agent.py,6c76d3503d57bf649ecf8af5e7493102ac5c1a83,d21373c9e5fb51e0641a1c1ae8f33968b6685080,TODO: find a better way to check this
aaiehecde,RasaHQ/rasa_core,rasa_core/interpreter.py,6c76d3503d57bf649ecf8af5e7493102ac5c1a83,d21373c9e5fb51e0641a1c1ae8f33968b6685080,TODO: hotfix to append attributes that NLU is adding as a server
aaieheche,RasaHQ/rasa_core,rasa_core/agent.py,5124609e65844f3733fb0c04815833b75cc9520a,453d55d3c770768a4d210c72ef185f3cd8cd6844,TODO: find a better way to check this
aaiehechg,RasaHQ/rasa_core,rasa_core/interpreter.py,5124609e65844f3733fb0c04815833b75cc9520a,453d55d3c770768a4d210c72ef185f3cd8cd6844,TODO: hotfix to append attributes that NLU is adding as a server
aaiehechi,RasaHQ/rasa_core,rasa_core/server.py,5124609e65844f3733fb0c04815833b75cc9520a,453d55d3c770768a4d210c72ef185f3cd8cd6844,TODO LOCAL: validate namespace json in request
aaiehechj,RasaHQ/rasa_core,rasa_core/server.py,5124609e65844f3733fb0c04815833b75cc9520a,453d55d3c770768a4d210c72ef185f3cd8cd6844,TODO local: except specific training exception that can occur
aaieheeif,RasaHQ/rasa_core,rasa_core/interpreter.py,809e200efd1d7ce8f7f544f2fbee6bab9fd6405f,STILL_EXISTS,TODO: hotfix to append attributes that NLU is adding as a server
aaieheeii,RasaHQ/rasa_core,rasa_core/server.py,bca29e139ba895206050ad6eb146d645c363f9c8,bff3e030ab3ed95be90b1743e976b82d85d2d995,TODO LOCAL: validate namespace json in request
aaieheeij,RasaHQ/rasa_core,rasa_core/server.py,bca29e139ba895206050ad6eb146d645c363f9c8,bff3e030ab3ed95be90b1743e976b82d85d2d995,TODO local: except specific training exception that can occur
aaieheffi,RasaHQ/rasa_core,rasa_core/training/dsl.py,7aad97549452d8c092c39779d113f5665e29eb16,2d2b205d34503012fcec1a79511fd92a941530ea,TODO async make sure this is tested
aaiehefia,RasaHQ/rasa_core,rasa_core/interpreter.py,daccb0423b5757b34027026730a03398405962e2,2d2b205d34503012fcec1a79511fd92a941530ea,TODO: I doubt we should allow the NLU interpreter here!
aaiehegih,RasaHQ/rasa_core,rasa_core/utils.py,2d2b205d34503012fcec1a79511fd92a941530ea,280b5f50efb6c06c2c017a9799c77405bbfbd8e1,It would be better at this point to properly shutdown every
aaiehehdh,RasaHQ/rasa_core,rasa/jupyter.py,54d077fb1dfff9e1990b948e92d0869259fbba88,STILL_EXISTS,HACK: this skips loading the interpreter and directly sets it afterwards
aaieheibg,RasaHQ/rasa_core,docs/conf.py,8e497ccf03645b7b657eb69c113fcc93af07aec9,60a1ee7e318b26a8c5c76e909abb7d3396b62f5c,needed to properly handle async functions in docs
aaiehfabc,RasaHQ/rasa_core,docs/conf.py,6ad9a5167c9e54554c2eaa6a251b515fe6e68c98,10e6cba82dcd10d9f4fe668683d4e99dd181ffc3,needed to properly handle async functions in docs
aaiehfhbi,RasaHQ/rasa_core,rasa_core/channels/console.py,396766285ab9ee9f89b537268362bdf34cfad01e,STILL_EXISTS,needed for python 3.5 compatibility
aaiehfhbj,RasaHQ/rasa_core,rasa_core/channels/console.py,396766285ab9ee9f89b537268362bdf34cfad01e,STILL_EXISTS,TODO: check if this properly receives UTF-8 data
aaiehfjeh,RasaHQ/rasa_core,docs/conf.py,8cd4d86faf0e11644b5ed6ef572972c560a66d1b,STILL_EXISTS,needed to properly handle async functions in docs
aaiehggjd,dmlc/dgl,mx.py,62a39e5b37144c06eb11c5282b7844c71baf8dd3,STILL_EXISTS,TODO: make representation numpy\/tensor from pytorch
aaiehggje,dmlc/dgl,mx.py,62a39e5b37144c06eb11c5282b7844c71baf8dd3,STILL_EXISTS,TODO: make message\/update functions pytorch functions
aaiehggjf,dmlc/dgl,mx.py,62a39e5b37144c06eb11c5282b7844c71baf8dd3,STILL_EXISTS,TODO: loss functions and training
aaiehggjg,dmlc/dgl,mx.py,62a39e5b37144c06eb11c5282b7844c71baf8dd3,STILL_EXISTS,TODO: per node message function
aaiehggjh,dmlc/dgl,mx.py,62a39e5b37144c06eb11c5282b7844c71baf8dd3,STILL_EXISTS,TODO: 'sum' should be a separate function
aaiehggji,dmlc/dgl,mx.py,62a39e5b37144c06eb11c5282b7844c71baf8dd3,STILL_EXISTS,TODO: per node update function
aaiehggjj,dmlc/dgl,mx.py,62a39e5b37144c06eb11c5282b7844c71baf8dd3,STILL_EXISTS,TODO: this asks v to pull from nodes other than
aaiehghaa,dmlc/dgl,mx.py,62a39e5b37144c06eb11c5282b7844c71baf8dd3,STILL_EXISTS,TODO: u; is this a good thing?
aaiehghag,dmlc/dgl,mx_scalar.py,05547b37117ef164d2226e8fff136b018c2eb38d,STILL_EXISTS,TODO: make representation numpy\/tensor from pytorch
aaiehghah,dmlc/dgl,mx_scalar.py,05547b37117ef164d2226e8fff136b018c2eb38d,STILL_EXISTS,TODO: make message\/update functions pytorch functions
aaiehghai,dmlc/dgl,mx_scalar.py,05547b37117ef164d2226e8fff136b018c2eb38d,STILL_EXISTS,TODO: loss functions and training
aaiehghaj,dmlc/dgl,mx_scalar.py,05547b37117ef164d2226e8fff136b018c2eb38d,STILL_EXISTS,TODO: per node message function
aaiehghba,dmlc/dgl,mx_scalar.py,05547b37117ef164d2226e8fff136b018c2eb38d,STILL_EXISTS,TODO: 'sum' should be a separate function
aaiehghbb,dmlc/dgl,mx_scalar.py,05547b37117ef164d2226e8fff136b018c2eb38d,STILL_EXISTS,TODO: per node update function
aaiehghbc,dmlc/dgl,mx_scalar.py,05547b37117ef164d2226e8fff136b018c2eb38d,STILL_EXISTS,TODO: this asks v to pull from nodes other than
aaiehghbd,dmlc/dgl,mx_scalar.py,05547b37117ef164d2226e8fff136b018c2eb38d,STILL_EXISTS,TODO: u; is this a good thing?
aaiehghbf,dmlc/dgl,mx.py,7a50be64b75a82ee4380284b4a74496d18106b0a,STILL_EXISTS,TODO: ugly hack
aaiehghbh,dmlc/dgl,graph.py,10ec6e8aad15744e1877fb3001c9972cde739fd8,2ed3989cba9df9ba63ade79155a87051d81facb5,FIXME: need to optimize since we are repeatedly stacking and
aaiehghca,dmlc/dgl,graph.py,10ec6e8aad15744e1877fb3001c9972cde739fd8,2ed3989cba9df9ba63ade79155a87051d81facb5,TODO: does it make sense to batch update the nodes?
aaiehghhj,dmlc/dgl,model.py,51391012c1c524bf9a776ed819b23aad5e1d0f3c,STILL_EXISTS,FIXME: really using self.x is a bad design here
aaiehghia,dmlc/dgl,model.py,51391012c1c524bf9a776ed819b23aad5e1d0f3c,STILL_EXISTS,TODO: add an attribute (g) to h
aaiehgied,dmlc/dgl,graph.py,2ed3989cba9df9ba63ade79155a87051d81facb5,STILL_EXISTS,TODO: simplify the logics
aaiehgiga,dmlc/dgl,python/dgl/graph.py,15a2c22c98416304689e90601230843ac4bece6e,STILL_EXISTS,TODO Call pre-defined routine to unroll the computation.
aaiehgijf,dmlc/dgl,examples/pytorch/topdown.py,5b5baee1653663c4b61037a290caaaa5f5969739,STILL_EXISTS,TODO: the following two lines is needed for single object
aaiehgijg,dmlc/dgl,examples/pytorch/topdown.py,5b5baee1653663c4b61037a290caaaa5f5969739,STILL_EXISTS,TODO: but not useful or wrong for multi-obj
aaiehgjha,dmlc/dgl,examples/pytorch/topdown/topdown.py,2455934ac41647d24b743871a6dc531253fa8f63,STILL_EXISTS,TODO: the following two lines is needed for single object
aaiehgjhb,dmlc/dgl,examples/pytorch/topdown/topdown.py,2455934ac41647d24b743871a6dc531253fa8f63,STILL_EXISTS,TODO: but not useful or wrong for multi-obj
aaiehgjhc,dmlc/dgl,examples/pytorch/topdown/topdown.py,2455934ac41647d24b743871a6dc531253fa8f63,STILL_EXISTS,XXX(minjie): could replace the following loop with propagate call.
aaiehhabb,dmlc/dgl,examples/pytorch/mgcn.py,174c1d55163e4c13ab59db41eaa1e823fd311da2,STILL_EXISTS,TODO: we don't need this one anymore
aaiehhabe,dmlc/dgl,python/dgl/graph.py,174c1d55163e4c13ab59db41eaa1e823fd311da2,9219349a8f805e2aadb5a6fb7cbd53fc13e2947b,TODO(zz): this is a hack
aaiehhadc,dmlc/dgl,examples/pytorch/gcn.py,1248bd24bbfbfb191df944139af21290dc8605f7,STILL_EXISTS,TODO: (lingfan) use gather to speed up
aaiehhaec,dmlc/dgl,examples/pytorch/gat.py,9f0d9e42466b3e07b2bbe0a25f41bbd9bd898e94,STILL_EXISTS,FIXME: does pytorch has something similar to tf.add_n which sum over a list?
aaiehhafd,dmlc/dgl,examples/pytorch/gat.py,9f0d9e42466b3e07b2bbe0a25f41bbd9bd898e94,STILL_EXISTS,TODO: (lingfan) use gather to speed up
aaiehhbag,dmlc/dgl,examples/pytorch/line_graph/gnn.py,b9073209f1f97c6074b3325dc9f2a39d907c7652,f52fc3fcdf6cf22fd6a7c4db789f1a530a01db19,TODO self-loop?
aaiehhbah,dmlc/dgl,examples/pytorch/line_graph/gnn.py,b9073209f1f97c6074b3325dc9f2a39d907c7652,2b09281178b6bec23b1f95cc650eca0ad53cfa28,TODO in-place edit of node_reprs\/edge_reprs in message_func\/update_func?
aaiehhbai,dmlc/dgl,examples/pytorch/line_graph/gnn.py,b9073209f1f97c6074b3325dc9f2a39d907c7652,2b09281178b6bec23b1f95cc650eca0ad53cfa28,TODO batch-norm
aaiehhbaj,dmlc/dgl,examples/pytorch/line_graph/gnn.py,b9073209f1f97c6074b3325dc9f2a39d907c7652,2b09281178b6bec23b1f95cc650eca0ad53cfa28,TODO context manager
aaiehhbbd,dmlc/dgl,examples/pytorch/line_graph/sbm.py,b9073209f1f97c6074b3325dc9f2a39d907c7652,STILL_EXISTS,TODO: dedup edges
aaiehhbcf,dmlc/dgl,examples/pytorch/line_graph/test.py,b9073209f1f97c6074b3325dc9f2a39d907c7652,STILL_EXISTS,TODO SBM
aaiehhbed,dmlc/dgl,examples/pytorch/gat.py,68fb5f7e51a8a735f372cb0893dbeed0bc0fb623,STILL_EXISTS,TODO (lingfan): use batched dropout once we have better api
aaiehhbeg,dmlc/dgl,examples/pytorch/gcn.py,68fb5f7e51a8a735f372cb0893dbeed0bc0fb623,STILL_EXISTS,TODO (lingfan): use batched dropout once we have better api
aaiehhbgi,dmlc/dgl,examples/pytorch/ginipath.py,ef18dab2a1cc413a5647d10a6e7c2aa14719b054,STILL_EXISTS,TODO (lingfan): use batched dropout once we have better api
aaiehhcec,dmlc/dgl,_backup/array.py,11e42d1094e9c99ed45d6167e823ab93423d18be,STILL_EXISTS,TODO: device
aaiehhcii,dmlc/dgl,python/dgl/cached_graph.py,11e42d1094e9c99ed45d6167e823ab93423d18be,STILL_EXISTS,\"\"\"High-performance graph structure query component. ||  || TODO: Currently implemented by igraph. Should replace with more efficient || solution later. || \"\"\"
aaiehhcjf,dmlc/dgl,python/dgl/cached_graph.py,11e42d1094e9c99ed45d6167e823ab93423d18be,2c489fadec58fd6be7f555897b222cdef31d98b5,TODO: tensorize the loop
aaiehhdbe,dmlc/dgl,python/dgl/graph.py,11e42d1094e9c99ed45d6167e823ab93423d18be,STILL_EXISTS,TODO: clear partial messages
aaiehhddi,dmlc/dgl,tests/pytorch/_test_specialization.py,11e42d1094e9c99ed45d6167e823ab93423d18be,STILL_EXISTS,TODO: use internal interface to set data.
aaiehhffb,dmlc/dgl,examples/pytorch/generative_graph/model.py,96179b0c96a5dfed18f961cdd0635ae6a8ce84b8,STILL_EXISTS,if Tensor; move directly
aaiehhfgg,dmlc/dgl,examples/pytorch/generative_graph/model.py,96179b0c96a5dfed18f961cdd0635ae6a8ce84b8,STILL_EXISTS,FIXME: what's the initial grpah repr for empty graph?
aaiehhgag,dmlc/dgl,python/dgl/batch.py,96179b0c96a5dfed18f961cdd0635ae6a8ce84b8,STILL_EXISTS,FIXME (lingfan): Do we really need the batch API?
aaiehhgcg,dmlc/dgl,python/dgl/graph.py,3721822e9e3ef2e06b1587c85f0101c7175045c9,STILL_EXISTS,TODO
aaiehhgdb,dmlc/dgl,python/dgl/graph.py,3721822e9e3ef2e06b1587c85f0101c7175045c9,STILL_EXISTS,FIXME: add callback
aaiehhgfb,dmlc/dgl,tests/pytorch/test_frame.py,3721822e9e3ef2e06b1587c85f0101c7175045c9,STILL_EXISTS,delete all the columns
aaiehhghj,dmlc/dgl,python/dgl/frame.py,6105e441426f97f31d96c54d6f35830028c2b3f6,21255b65284a85440abafad8ee95b220d5698433,the rhs of the spmv is the concatenation of all the frame columns
aaiehhhag,dmlc/dgl,examples/pytorch/generative_graph/model.py,c42eac718fe09aae40b7f8dfd8c3466b08e0f0be,STILL_EXISTS,generate dataset if needed
aaiehhhah,dmlc/dgl,python/dgl/graph.py,5e75f5dbfcc30d9170dc1a2999ef19efc10246d7,STILL_EXISTS,FIXME: this will only trigger if reduced_msgs is empty.  Remove?
aaiehhhgg,dmlc/dgl,python/dgl/graph.py,b7eb16595faac0e62d653d52df931412bed50fd4,STILL_EXISTS,FIXME: this will only trigger if reduced_msgs is empty.  Remove?
aaiehhiif,dmlc/dgl,python/dgl/_ffi/__init__.py,2694b12725923d66d73b6f3f7ffa05a4db0cb10a,STILL_EXISTS,\"\"\"C interfacing code. ||  || This namespace contains everything that interacts with C code. || Most C related object are ctypes compatible; which means || they contains a handle field that is ctypes.c_void_p and can || be used via ctypes function calls. ||  || Some performance critical functions are implemented by cython || and have a ctypes fallback implementation. || \"\"\"
aaiehhijd,dmlc/dgl,python/dgl/_ffi/_ctypes/function.py,2694b12725923d66d73b6f3f7ffa05a4db0cb10a,STILL_EXISTS,TVM_FREE_PYOBJ will be called after it is no longer needed.
aaiehhjba,dmlc/dgl,python/dgl/_ffi/base.py,2694b12725923d66d73b6f3f7ffa05a4db0cb10a,STILL_EXISTS,this function is needed for python3
aaiehhjei,dmlc/dgl,python/dgl/backend/pytorch.py,842d37682b3ed80a584eda27f077b667045d1f0f,758cb16e674a3d10ff864f80f2947b4748abb3e5,TODO: dtype
aaiehhjej,dmlc/dgl,python/dgl/backend/pytorch.py,842d37682b3ed80a584eda27f077b667045d1f0f,758cb16e674a3d10ff864f80f2947b4748abb3e5,TODO: ctx
aaiehhjfh,dmlc/dgl,python/dgl/cgraph.py,a81d27dc67a9c14435b3bd9bf7122fa0871bf76e,STILL_EXISTS,TODO: convert from graph data
aaiehhjgc,dmlc/dgl,python/dgl/cgraph.py,a81d27dc67a9c14435b3bd9bf7122fa0871bf76e,STILL_EXISTS,TODO
aaiehhjgh,dmlc/dgl,python/dgl/function/message.py,9b0a01db41abaab4437649deedf42a1c45e6146c,STILL_EXISTS,TODO: support this with g-spmv
aaiehhjhb,dmlc/dgl,python/dgl/function/reducer.py,9b0a01db41abaab4437649deedf42a1c45e6146c,STILL_EXISTS,TODO: support max
aaiehiaag,dmlc/dgl,python/dgl/graph.py,44db98c4e0988c095ee388aa98a93cf482f6bfe9,fde4f581d219e0d25148ffb0f8c44d6848032b53,TODO: keyword attr
aaiehiaba,dmlc/dgl,python/dgl/graph.py,44db98c4e0988c095ee388aa98a93cf482f6bfe9,882e2a7be09e08153108d4da7b4746956df6f744,TODO: attributes
aaiehiacg,dmlc/dgl,python/dgl/graph.py,79f62400fe05cfe6145b748e2272174198761896,882e2a7be09e08153108d4da7b4746956df6f744,TODO: attributes
aaiehiaci,dmlc/dgl,python/dgl/batch.py,314a75f367a0d41e158edfc2edbb54eb0a1ae235,STILL_EXISTS,NOTE: following code will materialize the columns of the input graphs.
aaiehiaej,dmlc/dgl,python/dgl/batch.py,2be55fb50ab08c1f0a3bbb40df8f9265d73b4d2d,fde4f581d219e0d25148ffb0f8c44d6848032b53,TODO: device context
aaiehiagb,dmlc/dgl,python/dgl/graph.py,cc372e3774bbdfcaaa35d7daae3a08a173208cbf,STILL_EXISTS,TODO: keyword attr
aaiehiagd,dmlc/dgl,python/dgl/graph.py,cc372e3774bbdfcaaa35d7daae3a08a173208cbf,26dcfb5bc2398e67e84eecba50a3076595134ab0,TODO(minjie): better way to figure out `batchable` flag
aaiehiaha,dmlc/dgl,python/dgl/batch.py,74888fee5cf4b83b1dcd84d401364643fca892eb,STILL_EXISTS,TODO
aaiehiahc,dmlc/dgl,python/dgl/batch.py,74888fee5cf4b83b1dcd84d401364643fca892eb,STILL_EXISTS,TODO: device context
aaiehidfg,dmlc/dgl,python/dgl/graph.py,8a6d25ae69ac0a559a1f6e36ae02fa5fe01a1eea,STILL_EXISTS,TODO: keyword attr
aaiehifee,dmlc/dgl,python/dgl/graph.py,596ca471a511004d0fbc2ce0faee6b8150066d3c,STILL_EXISTS,TODO: keyword attr
aaiehific,dmlc/dgl,examples/pytorch/jtnn/jtnn/jtnn_dec.py,ec4216dda8ee8d2a7d85a52ed7ea75d6a5adb598,ac932c665b0a06ea9e222136351f52b5d6772ed0,TODO: context
aaiehifie,dmlc/dgl,examples/pytorch/jtnn/jtnn/jtnn_dec.py,ec4216dda8ee8d2a7d85a52ed7ea75d6a5adb598,STILL_EXISTS,Only newly generated nodes are needed for label prediction
aaiehifjd,dmlc/dgl,examples/pytorch/jtnn/jtnn/jtnn_enc.py,ec4216dda8ee8d2a7d85a52ed7ea75d6a5adb598,STILL_EXISTS,TODO: context?
aaiehihai,dmlc/dgl,python/dgl/backend/mxnet.py,5567f4a4d91687ca6dd869ff01ab8615b4c1d65f,STILL_EXISTS,TODO this isn't the best way of running unique.
aaiehihba,dmlc/dgl,python/dgl/backend/mxnet.py,5567f4a4d91687ca6dd869ff01ab8615b4c1d65f,STILL_EXISTS,TODO this isn't an ideal implementation.
aaiehihbh,dmlc/dgl,tests/mxnet/test_basics.py,5567f4a4d91687ca6dd869ff01ab8615b4c1d65f,STILL_EXISTS,TODO we need to enable the test later.
aaiehihdi,dmlc/dgl,tests/mxnet/test_basics.py,5567f4a4d91687ca6dd869ff01ab8615b4c1d65f,4af3f8bcc859276d2e40d2dad69fdec6bf373f72,TODO we need to enable it after index_copy is implemented.
aaiehjcci,dmlc/dgl,tutorial/capsule/codes/capsule_layer.py,9276aa9334436af4f156ce34e8b23e585d2fa3c0,STILL_EXISTS,Implement equation 2 in the paper.
aaiehjdjg,dmlc/dgl,python/dgl/scheduler.py,750e50372aee684d9693373f91c8896d2b35dc76,STILL_EXISTS,TODO: convert directly from ndarary to python list?
aaiehjdjj,dmlc/dgl,python/dgl/scheduler.py,750e50372aee684d9693373f91c8896d2b35dc76,STILL_EXISTS,FIXME: refactorize in scheduler\/executor redesign
aaiehjeac,dmlc/dgl,python/dgl/scheduler.py,750e50372aee684d9693373f91c8896d2b35dc76,STILL_EXISTS,FIXME (lingfan): handle zero-degree case
aaiehjeae,dmlc/dgl,python/dgl/scheduler.py,750e50372aee684d9693373f91c8896d2b35dc76,STILL_EXISTS,FIXME: handle builtin spmv executor case
aaiehjeah,dmlc/dgl,python/dgl/graph.py,bc3f852dab30cb2b920fddd7455a623d82ca3a6a,STILL_EXISTS,TODO: replace with the new DegreeBucketingScheduler
aaiehjeaj,dmlc/dgl,python/dgl/scheduler.py,bc3f852dab30cb2b920fddd7455a623d82ca3a6a,STILL_EXISTS,FIXME: also take into account
aaiehjedh,dmlc/dgl,python/dgl/graph.py,66261aee03870bcd5770dc147a9d918792b34aff,STILL_EXISTS,TODO(minjie): Fix these codes in next PR.
aaiehjegb,dmlc/dgl,tests/pytorch/test_frame.py,b9631912dbe8e10198e909f15d13370afca789ce,STILL_EXISTS,append dict of different length columns should fail
aaieiacjg,dmlc/dgl,tutorials/first.py,797b20792f67d4817e34bf259f008ca127e77b89,STILL_EXISTS,\"\"\" || Your first example in DGL || ========================= ||  || TODO: either a pagerank or SSSP example || \"\"\"
aaieiadac,dmlc/dgl,tutorials/graph.py,797b20792f67d4817e34bf259f008ca127e77b89,STILL_EXISTS,\"\"\" || Use DGLGraph || ============ ||  || In this tutorial; we introduce how to use our graph class -- ``DGLGraph``. || The ``DGLGraph`` is the very core data structure in our library. It provides the basic || interfaces to manipulate graph structure; set\/get node\/edge features and convert || from\/to many other graph formats. You can also perform computation on the graph || using our message passing APIs. (TODO: give a link here to the message passing doc) || \"\"\"
aaieiagca,dmlc/dgl,tutorials/1_first.py,2046980231c37935b8c405db1d7b1abb80530c30,87ed21ec42c4d471c5916e28b39eda7f51cb0a7a,\"\"\" || .. _tutorial-first: ||  || Your first example in DGL || ========================= ||  || TODO: either a pagerank or SSSP example || \"\"\"
aaieiaidf,dmlc/dgl,tutorials/3_mp.py,2046980231c37935b8c405db1d7b1abb80530c30,STILL_EXISTS,allows DGL to use more efficient implementation automatically. To see this;
aaieibgfa,dmlc/dgl,tutorials/capsule.py,4fd359cbff9cd9c2db78ef030537e47d35e30c35,STILL_EXISTS,\"\"\" || Capsule Network || ================ || **Author**: `Jinjing Zhou` ||   || This tutorial explains how to use DGL library and its language to implement the `capsule network <http:\/\/arxiv.org\/abs\/1710.09829>`__ proposed by Geoffrey Hinton and his team. The algorithm aims to provide a better alternative to current neural network structures. By using DGL library; users can implement the algorithm in a more intuitive way. || \"\"\"
aaieibjdi,dmlc/dgl,tutorials/models/capsule.py,9fd94b53ac5d3e7ba1585bc3acef289531eff21d,STILL_EXISTS,\"\"\" || Capsule Network || ================ ||  || **Author**: `Jinjing Zhou` ||   || This tutorial explains how to use DGL library and its language to implement the || `capsule network <http:\/\/arxiv.org\/abs\/1710.09829>`__ proposed by Geoffrey Hinton and his team. || The algorithm aims to provide a better alternative to current neural network structures. || By using DGL library; users can implement the algorithm in a more intuitive way. || \"\"\"
aaieicafb,dmlc/dgl,examples/mxnet/sse/sse_batch.py,9e9a9488f1fb421c92f89da518f30518b57e057e,bea07b41b3429f841ed1599ed25bd40ea86ac9da,TODO should we use concat?
aaieicafc,dmlc/dgl,examples/mxnet/sse/sse_batch.py,9e9a9488f1fb421c92f89da518f30518b57e057e,bea07b41b3429f841ed1599ed25bd40ea86ac9da,TODO what is the dimension here?
aaieicafe,dmlc/dgl,examples/mxnet/sse/sse_batch.py,9e9a9488f1fb421c92f89da518f30518b57e057e,bea07b41b3429f841ed1599ed25bd40ea86ac9da,TODO here we apply dropout on all vertex representation.
aaieicagi,dmlc/dgl,python/dgl/backend/mxnet_immutable_graph_index.py,9e9a9488f1fb421c92f89da518f30518b57e057e,STILL_EXISTS,TODO we can't generate a csr_matrix with np.int64 directly.
aaieicaib,dmlc/dgl,examples/pytorch/gcn/gcn.py,2758c249559cbd6cb7c582018a453d324913df5f,STILL_EXISTS,Todo: adjacency normalization
aaieicaic,dmlc/dgl,examples/pytorch/gcn/gcn_spmv.py,2758c249559cbd6cb7c582018a453d324913df5f,STILL_EXISTS,Todo: adjacency normalization
aaieicbfg,dmlc/dgl,tutorials/2_graph.py,68ec624782bb4e4fb9f1adf1088cf39c1826533e,STILL_EXISTS,It's more efficient to add many edges with a pair of list; or better still; with a pair of tensors.
aaieicccd,dmlc/dgl,tutorials/models/gcnTutorialNew.py,68ec624782bb4e4fb9f1adf1088cf39c1826533e,STILL_EXISTS,TODO: build a star graph (reuse the one in 2_graph.py); and use pull (or push)
aaieicccf,dmlc/dgl,tutorials/models/gcnTutorialNew.py,68ec624782bb4e4fb9f1adf1088cf39c1826533e,STILL_EXISTS,TODO: build a much bigger graph; explain with spMV and the use of ``update_all``
aaieiccdc,dmlc/dgl,tutorials/models/gcnTutorialNew.py,68ec624782bb4e4fb9f1adf1088cf39c1826533e,STILL_EXISTS,This is a simple implementation of Kipf & Welling's Semi-Supervised Classificaton with Graph Convolutional Networks in ICLR 2017; which propose a simple yet efficient model that extends convolutional neual network from the grid structured data we all familiar and like to graphs; like social network and knowledge graph. It starts from the framework of spectral graph convolutions and makes reasonable simplifications to achieve both faster training and higher prediction accuracy. It also achieves start-of-the-art classification results on a number of graph datasets like CORA; etc. \/TODO: elaborate.
aaieicced,dmlc/dgl,tutorials/models/gcnTutorialNew.py,68ec624782bb4e4fb9f1adf1088cf39c1826533e,STILL_EXISTS,The shape of the input :math:`H^{(0)}` is :math:`N \\times D`; where :math:`N` is the number of nodes and :math:`D` is the number of input features. We can chain up multiple layers as such to produce a node-level representation output with shape :math:`N \\times F`; where :math:`F` is the dimension of the output node feature vector.
aaieiccjg,dmlc/dgl,tutorials/models/gcnTutorialNew.py,68ec624782bb4e4fb9f1adf1088cf39c1826533e,STILL_EXISTS,It could be both tedious and inefficient for user to call ``send()`` and ``recv()`` respectively. DGL comes into aid by providing a series of higher level APIs which also increase the performance by operator fusion in the backend ``\/TODO(gaiyu) verify this statement please``.
aaieicdcc,dmlc/dgl,tutorials/models/gcnTutorialNew.py,68ec624782bb4e4fb9f1adf1088cf39c1826533e,STILL_EXISTS,In many cases; user would like to perform message passing on all the edges simoutaneously; such as in the case of adjacency matrix multiplication in GCN. DGL also provides ``update_all()`` method to achieve this; also optimizing the performance under the hood.
aaieiceeg,dmlc/dgl,tutorials/models/gcn_gat.py,68ec624782bb4e4fb9f1adf1088cf39c1826533e,STILL_EXISTS,A better structured implementation would wrap the update procedure as a
aaieicehf,dmlc/dgl,python/dgl/backend/backend.py,7241a9c0086167fbad1f41068f8d8640c6502980,STILL_EXISTS,These functions are performance critical; so it's better to have efficient
aaieiceib,dmlc/dgl,python/dgl/backend/backend.py,7241a9c0086167fbad1f41068f8d8640c6502980,STILL_EXISTS,numpy operators if currently missing in the framework. Ideally in the future;
aaieicejf,dmlc/dgl,python/dgl/backend/mxnet/tensor.py,7241a9c0086167fbad1f41068f8d8640c6502980,STILL_EXISTS,TODO: this isn't an ideal implementation.
aaieicgcb,dmlc/dgl,tutorials/3_pagerank.py,87ed21ec42c4d471c5916e28b39eda7f51cb0a7a,STILL_EXISTS,\"\"\" || PageRank with DGL Message Passing || ================================= ||  || **Author**: Minjie Wang; Quan Gan; Yu Gai; Zheng Zhang ||  || In this section we illustrate the usage of different levels of message || passing API with PageRank on a small graph. In DGL; the message passing and || feature transformations are all **User-Defined Functions** (UDFs). ||  || The goal of this tutorial: to implement PageRank using DGL message passing || interface. || \"\"\"
aaieichah,dmlc/dgl,tutorials/3_pagerank.py,87ed21ec42c4d471c5916e28b39eda7f51cb0a7a,STILL_EXISTS,for more details. (TODO: a link to the document).
aaieichdf,dmlc/dgl,tutorials/3_pagerank.py,87ed21ec42c4d471c5916e28b39eda7f51cb0a7a,STILL_EXISTS,please read their API reference documents. (TODO: a link here).
aaieichei,dmlc/dgl,tutorials/3_pagerank.py,87ed21ec42c4d471c5916e28b39eda7f51cb0a7a,STILL_EXISTS,thus allows more efficient implementation for you. For example; in the case
aaieichfj,dmlc/dgl,tutorials/3_pagerank.py,87ed21ec42c4d471c5916e28b39eda7f51cb0a7a,STILL_EXISTS,Computing this equation is quite efficient because there exists efficient
aaieichha,dmlc/dgl,tutorials/models/1_gcn.py,79ceccef434569eeff7cc3fb3cc0d80059e23a88,STILL_EXISTS,\"\"\" || .. _model-gcn: ||  || Graph Convolutional Network || ==================================== ||  || **Author:** Qi Huang; `Minjie Wang  <https:\/\/jermainewang.github.io\/>`_; || Yu Gai; Quan Gan; Zheng Zhang ||  || This is a gentle introduction of using DGL to implement Graph Convolutional || Networks (Kipf & Welling et al.; `Semi-Supervised Classificaton with Graph || Convolutional Networks <https:\/\/arxiv.org\/pdf\/1609.02907.pdf>`_). We build upon || the :doc:`earlier tutorial <..\/3_pagerank>` on DGLGraph and demonstrate || how DGL combines graph with deep neural network and learn structural representations. || \"\"\"
aaieicidf,dmlc/dgl,tutorials/models/2_capsule.py,79ceccef434569eeff7cc3fb3cc0d80059e23a88,7cb50072a4c3eec6619295d64bf5faf93cfb1f58,\"\"\" || .. _model-capsule: ||  || Capsule Network || ================ ||  || **Author**: `Jinjing Zhou` ||   || This tutorial explains how to use DGL library and its language to implement the || `capsule network <http:\/\/arxiv.org\/abs\/1710.09829>`__ proposed by Geoffrey || Hinton and his team.  The algorithm aims to provide a better alternative to || current neural network structures.  By using DGL library; users can implement || the algorithm in a more intuitive way. || \"\"\"
aaieicjcg,dmlc/dgl,tutorials/models/2_capsule.py,79ceccef434569eeff7cc3fb3cc0d80059e23a88,7cb50072a4c3eec6619295d64bf5faf93cfb1f58,Message function is to define which attributes are needed in further computations
aaieicjdd,dmlc/dgl,tutorials/models/2_capsule.py,79ceccef434569eeff7cc3fb3cc0d80059e23a88,7cb50072a4c3eec6619295d64bf5faf93cfb1f58,Other Workaround
aaieidadc,dmlc/dgl,tutorials/models/2_capsule.py,7cb50072a4c3eec6619295d64bf5faf93cfb1f58,STILL_EXISTS,\"\"\" || .. _model-capsule: ||  || Capsule Network Tutorial || =========================== ||  || **Author**: `Jinjing Zhou`; `Zheng Zhang` ||  || It is perhaps a little surprising that some of the more classical models can also be described in terms of graphs; || offering a different perspective. || This tutorial describes how this is done for the `capsule network <http:\/\/arxiv.org\/abs\/1710.09829>`__. || \"\"\"
aaieidaef,dmlc/dgl,tutorials/models/2_capsule.py,7cb50072a4c3eec6619295d64bf5faf93cfb1f58,STILL_EXISTS,way to construct higher level feature from its low levels. Consider a
aaieidfab,dmlc/dgl,python/dgl/frame.py,79a510252becbf265fe3ec17c8a6e67923c75eb8,33b9c38389a37086b69397872e75d9fdbc58ee09,FIXME:
aaieidfac,dmlc/dgl,python/dgl/frame.py,79a510252becbf265fe3ec17c8a6e67923c75eb8,STILL_EXISTS,Python 3.5.2 is unable to pickle torch dtypes; this is a workaround.
aaieidfcg,dmlc/dgl,tutorials/1_first.py,cab1fdf2ec8bb5b281db804dc8f5d282b653d5f8,7156c7163b046686064d7c9de445041870e672bc,and merging on graphs. It is implemented by using a message passing interface
aaieidfei,dmlc/dgl,examples/mxnet/sse/sse_batch.py,5f89cd662efe826393d45971e6259da8c77332f6,bea07b41b3429f841ed1599ed25bd40ea86ac9da,TODO here we apply dropout on all vertex representation.
aaieidfga,dmlc/dgl,tests/mxnet/test_specialization.py,440aecee8f114625a56be81cd4efec86e51d164b,STILL_EXISTS,TODO for some reason; this test doesn't pass in MXNet.
aaieidfhg,dmlc/dgl,python/dgl/frame.py,0ec1a492b302561d7417f3087e1fce09cdd38706,8c8c5b1dc1b70212799c7443695820aada74dccd,directly updating columns.
aaieidhbh,dmlc/dgl,python/dgl/runtime/degree_bucketing.py,deb653f8dc9b396ba99d5c66e0658fb056f37a38,STILL_EXISTS,XXX: convert directly from ndarary to python list?
aaieidhej,dmlc/dgl,python/dgl/runtime/scheduler.py,deb653f8dc9b396ba99d5c66e0658fb056f37a38,STILL_EXISTS,TODO: handle duplicate messages
aaieidiab,dmlc/dgl,python/dgl/runtime/scheduler.py,deb653f8dc9b396ba99d5c66e0658fb056f37a38,STILL_EXISTS,FIXME: for now; use send_and_recv to implement push
aaieidiac,dmlc/dgl,python/dgl/runtime/scheduler.py,deb653f8dc9b396ba99d5c66e0658fb056f37a38,STILL_EXISTS,FIXME: for now; use send_and_recv to implement pull
aaieidiah,dmlc/dgl,python/dgl/runtime/spmv.py,deb653f8dc9b396ba99d5c66e0658fb056f37a38,STILL_EXISTS,FIXME: should pre-compile a look up table
aaieidibd,dmlc/dgl,python/dgl/runtime/spmv.py,deb653f8dc9b396ba99d5c66e0658fb056f37a38,653428bdc7880ebc45b759e675df09ae6eb146f8,FIXME(minjie): data type
aaieidich,dmlc/dgl,tests/mxnet/test_basics.py,deb653f8dc9b396ba99d5c66e0658fb056f37a38,STILL_EXISTS,is no edge to node#0 so the send_and_recv is skipped. Fix this
aaieidigf,dmlc/dgl,python/dgl/frame.py,b1eeb934494ef1eee20cf2d35b718790cb9cb550,22167f7203a8ba1845873c5006b59353af148a21,TODO(minjie): hack; cannot rely on keys as the _initializers
aaieidigg,dmlc/dgl,python/dgl/frame.py,b1eeb934494ef1eee20cf2d35b718790cb9cb550,22167f7203a8ba1845873c5006b59353af148a21,now supports non-exist columns.
aaieidihc,dmlc/dgl,python/dgl/runtime/ir/executor.py,b1eeb934494ef1eee20cf2d35b718790cb9cb550,STILL_EXISTS,FIXME: the last argument here can only be idx; range
aaieieaif,dmlc/dgl,tutorials/models/5_dgmg.py,e02acc4c01759866a003d29d9c8c841c4aeb4ede,STILL_EXISTS,) as an exercise to implement a graph generative model using DGL; primarily
aaieiedhe,dmlc/dgl,tutorials/models/5_dgmg.py,e02acc4c01759866a003d29d9c8c841c4aeb4ede,STILL_EXISTS,only within one graph. For a batch of graphs; a for loop is then needed:
aaieieegc,dmlc/dgl,python/dgl/contrib/data/knowledge_graph.py,0f121eeb76a8584b349a50217b63e6a861863a6d,STILL_EXISTS,FIXME (lingfan): not sure about python 2 and 3 str compatibility
aaieieegd,dmlc/dgl,tutorials/models/4_rgcn.py,0f121eeb76a8584b349a50217b63e6a861863a6d,STILL_EXISTS,\"\"\" || .. _model-rgcn: ||  || Relational Graph Convolutional Network Tutorial || ================================================ ||  || **Author:** Lingfan Yu; Mufei Li; Zheng Zhang ||  || The vanilla Graph Convolutional Network (GCN) || (`paper <https:\/\/arxiv.org\/pdf\/1609.02907.pdf>`_; || `DGL tutorial <http:\/\/doc.dgl.ai\/tutorials\/index.html>`_) exploits || structural information of the dataset (i.e. the graph connectivity) to || improve the extraction of node representations. Graph edges are left as || untyped. ||  || A knowledge graph is made up by a collection of triples of the form || (subject; relation; object). Edges thus encode important information and || have their own embeddings to be learned. Furthermore; there may exist || multiple edges among any given pair. ||  || A recent model Relational-GCN (R-GCN) from the paper || `Modeling Relational Data with Graph Convolutional || Networks <https:\/\/arxiv.org\/pdf\/1703.06103.pdf>`_ is one effort to || generalize GCN to handle different relations between entities in knowledge || base. This tutorial shows how to implement R-GCN with DGL. || \"\"\"
aaieiefeb,dmlc/dgl,tutorials/models/4_rgcn.py,0f121eeb76a8584b349a50217b63e6a861863a6d,STILL_EXISTS,Implement R-GCN in DGL
aaieiegdf,dmlc/dgl,tutorials/1_first.py,7156c7163b046686064d7c9de445041870e672bc,STILL_EXISTS,\"\"\" || .. currentmodule:: dgl ||  || DGL at a Glance || ========================= ||  || **Author**: `Minjie Wang <https:\/\/jermainewang.github.io\/>`_; Quan Gan; `Jake || Zhao <https:\/\/cs.nyu.edu\/~jakezhao\/>`_; Zheng Zhang ||  || The goal of this tutorial: ||  || - Understand how DGL builds a graph and performs computation on graph from a ||   high level. || - Train a simple graph neural network in DGL to classify nodes in a graph. ||  || At the end of this tutorial; we hope you get a brief feeling of how DGL works. || \"\"\"
aaieiegih,dmlc/dgl,tutorials/1_first.py,7156c7163b046686064d7c9de445041870e672bc,STILL_EXISTS,The GCN layer can be easily implemented in DGL using the message passing
aaieiehjf,dmlc/dgl,examples/pytorch/jtnn/jtnn/datautils.py,ac932c665b0a06ea9e222136351f52b5d6772ed0,STILL_EXISTS,TODO: either support pickling or get around ctypes pointers using scipy
aaieifadg,dmlc/dgl,examples/pytorch/rgcn/utils.py,b98dc92c59911cf72e27f87df05c74ef1894bc43,STILL_EXISTS,TODO (lingfan): implement filtered metrics
aaieifbdd,dmlc/dgl,tutorials/models/6_line_graph.py,b1e8d95e993cc6f0720af81bb2bcfba23c291bd4,STILL_EXISTS,illustrate how DGL can implement an advanced graph algorithm by flexibly
aaieifdcf,dmlc/dgl,tutorials/models/6_line_graph.py,b1e8d95e993cc6f0720af81bb2bcfba23c291bd4,STILL_EXISTS,Implement LGNN in DGL
aaieifeda,dmlc/dgl,tutorials/models/6_line_graph.py,b1e8d95e993cc6f0720af81bb2bcfba23c291bd4,STILL_EXISTS,and implement :math:`\\text{fuse}` as a sparse matrix multiplication.
aaieifehc,dmlc/dgl,tutorials/models/6_line_graph.py,b1e8d95e993cc6f0720af81bb2bcfba23c291bd4,STILL_EXISTS,To implement:
aaieiffff,dmlc/dgl,tutorials/models/6_line_graph.py,b1e8d95e993cc6f0720af81bb2bcfba23c291bd4,STILL_EXISTS,Here; we implement :math:`\\{Pm; Pd\\}` as scipy coo sparse matrix in the datset;
aaieifhaf,dmlc/dgl,python/dgl/backend/mxnet/tensor.py,eafcb7e7f55d385099a9289b275e8371897edb9f,STILL_EXISTS,FIXME: cannot use int64
aaieifhba,dmlc/dgl,python/dgl/immutable_graph_index.py,eafcb7e7f55d385099a9289b275e8371897edb9f,STILL_EXISTS,FIXME(minjie): calculate the shuffle index
aaieifhjc,dmlc/dgl,tutorials/models/1_gnn/8_sse_mx.py,cd907cddfa79bfc1a1d09bf1561d16092ac7a87a,STILL_EXISTS,In this tutorial we implement in DGL with MXNet
aaieififj,dmlc/dgl,tutorials/models/1_gnn/8_sse_mx.py,cd907cddfa79bfc1a1d09bf1561d16092ac7a87a,STILL_EXISTS,We can easily implement flood-fill in DGL:
aaieifjgi,dmlc/dgl,tutorials/models/1_gnn/8_sse_mx.py,cd907cddfa79bfc1a1d09bf1561d16092ac7a87a,STILL_EXISTS,the computation to more efficient sparse operators in the backend
aaieifjhd,dmlc/dgl,tutorials/models/1_gnn/8_sse_mx.py,cd907cddfa79bfc1a1d09bf1561d16092ac7a87a,STILL_EXISTS,Efficient semi-supervised learning on graph
aaieigagf,dmlc/dgl,tutorials/models/1_gnn/8_sse_mx.py,cd907cddfa79bfc1a1d09bf1561d16092ac7a87a,STILL_EXISTS,sampled subgraphs; which is perhaps not surprising if you know
aaieigaic,dmlc/dgl,tutorials/models/1_gnn/8_sse_mx.py,cd907cddfa79bfc1a1d09bf1561d16092ac7a87a,STILL_EXISTS,DGL supports very efficient subgraph sampling natively to help users
aaieigbff,dmlc/dgl,python/dgl/immutable_graph_index.py,c0d1f8e43b73bdca0fa8637472f4f001b350d457,STILL_EXISTS,TODO we can't generate a csr_matrix with np.int64 directly.
aaieigcba,dmlc/dgl,examples/pytorch/transformer/modules/viz.py,9f32554296613f5448b14d0814fc5bfff77f0448,STILL_EXISTS,Note: Waiting for someone to implement arrow to intersection with
aaieigdif,dmlc/dgl,tutorials/models/4_old_wines/7_transformer.py,9f32554296613f5448b14d0814fc5bfff77f0448,STILL_EXISTS,Once we defined the graph structure; we can move on to defining the
aaieigjeg,dmlc/dgl,tutorials/models/4_old_wines/7_transformer.py,9f32554296613f5448b14d0814fc5bfff77f0448,STILL_EXISTS,In DGL; the algorithm is easy to implement; we just need to call
aaieihbjh,dmlc/dgl,python/dgl/frame.py,2664ed2dff9d0e9e45d2349c531b460b31cec215,STILL_EXISTS,pad columns that are not provided in the other frame with initial values
aaieihceb,dmlc/dgl,examples/mxnet/tree_lstm/train.py,1e50cd2edae5eb62a56ad24abcb7d46d6a820f3a,STILL_EXISTS,TODO begin_states function?
aaieihcfg,dmlc/dgl,python/dgl/backend/mxnet/immutable_graph_index.py,4bd4d6e3489bcc9c95f63eff71c0ab9aa5e1e829,STILL_EXISTS,TODO we can't generate a csr_matrix with np.int64 directly.
aaieihchh,dmlc/dgl,python/dgl/immutable_graph_index.py,4bd4d6e3489bcc9c95f63eff71c0ab9aa5e1e829,STILL_EXISTS,TODO we can't generate a csr_matrix with np.int64 directly.
aaieihcje,dmlc/dgl,tests/graph_index/test_basics.py,4bd4d6e3489bcc9c95f63eff71c0ab9aa5e1e829,STILL_EXISTS,TODO: disabled due to torch support
aaieihdaf,dmlc/dgl,tests/pytorch/test_graph.py,4bd4d6e3489bcc9c95f63eff71c0ab9aa5e1e829,STILL_EXISTS,XXX: not enabled for pytorch
aaieihdbj,dmlc/dgl,tutorials/basics/3_pagerank.py,6f9ae8d61eca4a2841bb06b47a993c523de6f43c,STILL_EXISTS,It is time to move on to some real models in DGL.
aaieihddc,dmlc/dgl,python/dgl/backend/mxnet/tensor.py,e19cd62ecdd14097926e707bf854360b8ee9862e,STILL_EXISTS,This is a workaround.
aaieihddd,dmlc/dgl,python/dgl/backend/mxnet/tensor.py,e19cd62ecdd14097926e707bf854360b8ee9862e,STILL_EXISTS,MXNet workaround for empty row index
aaieihded,dmlc/dgl,tests/backend/backend_unittest.py,e19cd62ecdd14097926e707bf854360b8ee9862e,STILL_EXISTS,These functions are performance critical; so it's better to have efficient
aaieihdej,dmlc/dgl,tests/backend/backend_unittest.py,e19cd62ecdd14097926e707bf854360b8ee9862e,STILL_EXISTS,numpy operators if currently missing in the framework. Ideally in the future;
aaieihdgf,dmlc/dgl,tests/compute/test_frame.py,e19cd62ecdd14097926e707bf854360b8ee9862e,STILL_EXISTS,delete all the columns
aaieihdha,dmlc/dgl,tests/compute/test_frame.py,e19cd62ecdd14097926e707bf854360b8ee9862e,STILL_EXISTS,append dict of different length columns should fail
aaieiheef,dmlc/dgl,python/dgl/backend/pytorch/tensor.py,efae0f97d8b18867398981d226423472a3069844,STILL_EXISTS,should adopt a workaround using custom op.
aaieihega,dmlc/dgl,python/dgl/graph_index.py,929742b58888fc16ad9bde96076109476da1a38b,STILL_EXISTS,FIXME(zhengda): we should construct a C graph index before constructing GraphIndex.
aaieihegi,dmlc/dgl,tutorials/basics/4_batch.py,3cbfc25a32b3f62e116de91563fa5f3d6cc468ca,STILL_EXISTS,\"\"\" || .. currentmodule:: dgl ||  || Batched Graph Classification with DGL || ===================================== ||  || **Author**: `Mufei Li <https:\/\/github.com\/mufeili>`_; || `Minjie Wang <https:\/\/jermainewang.github.io\/>`_; || `Zheng Zhang <https:\/\/shanghai.nyu.edu\/academics\/faculty\/directory\/zheng-zhang>`_. ||  || Graph classification is an important problem || with applications across many fields -- bioinformatics; chemoinformatics; social || network analysis; urban computing and cyber-security. Applying graph neural || networks to this problem has been a popular approach recently ( || `Ying et al.; 2018 <https:\/\/arxiv.org\/pdf\/1806.08804.pdf>`_; || `Cangea et al.; 2018 <https:\/\/arxiv.org\/pdf\/1811.01287.pdf>`_; || `Knyazev et al.; 2018 <https:\/\/arxiv.org\/pdf\/1811.09595.pdf>`_; || `Bianchi et al.; 2019 <https:\/\/arxiv.org\/pdf\/1901.01343.pdf>`_; || `Liao et al.; 2019 <https:\/\/arxiv.org\/pdf\/1901.01484.pdf>`_; || `Gao et al.; 2019 <https:\/\/openreview.net\/pdf?id=HJePRoAct7>`_). ||  || This tutorial demonstrates: ||  * batching multiple graphs of variable size and shape with DGL ||  * training a graph neural network for a simple graph classification task || \"\"\"
aaieihehi,dmlc/dgl,tutorials/basics/4_batch.py,3cbfc25a32b3f62e116de91563fa5f3d6cc468ca,STILL_EXISTS,We implement a synthetic dataset :class:`data.MiniGCDataset` in DGL. The dataset has 8
aaieihfdc,dmlc/dgl,tutorials/basics/4_batch.py,3cbfc25a32b3f62e116de91563fa5f3d6cc468ca,STILL_EXISTS,degrees; which gives a better performance for this experiment.
aaieihfjd,dmlc/dgl,tutorials/basics/4_batch.py,3cbfc25a32b3f62e116de91563fa5f3d6cc468ca,STILL_EXISTS,our node features. Meanwhile; the graph features are way better separated.
aaieihgbg,dmlc/dgl,python/dgl/runtime/degree_bucketing.py,5b9147c4645c3eba15e228d468b578f030e1873d,STILL_EXISTS,XXX: convert directly from ndarary to python list?
aaieihgbj,dmlc/dgl,python/dgl/runtime/scheduler.py,5b9147c4645c3eba15e228d468b578f030e1873d,653428bdc7880ebc45b759e675df09ae6eb146f8,TODO (lingfan): check if apply_func is a DGL builtin
aaieihgcf,dmlc/dgl,examples/pytorch/transformer/dataset/__init__.py,29dd22e66605e04a8a69d75faa49f73326325173,STILL_EXISTS,XXX: partition then shuffle may not be equivalent to shuffle then
aaieihhec,dmlc/dgl,tutorials/models/1_gnn/9_gat.py,98ff82e52770053b890cb71f150ad62ddd66ee35,STILL_EXISTS,\"\"\" || .. _model-gat: ||  || Understand Graph Attention Network || ================================== ||  || **Authors:** `Hao Zhang <https:\/\/github.com\/sufeidechabei\/>`_; `Mufei Li || <https:\/\/github.com\/mufeili>`_; `Minjie Wang || <https:\/\/jermainewang.github.io\/>`_  `Zheng Zhang || <https:\/\/shanghai.nyu.edu\/academics\/faculty\/directory\/zheng-zhang>`_ ||  || From `Graph Convolutional Network (GCN) <https:\/\/arxiv.org\/abs\/1609.02907>`_; || we learned that combining local graph structure and node-level features yields || good performance on node classification task. However; the way GCN aggregates || is structure-dependent; which may hurt its generalizability. ||  || One workaround is to simply average over all neighbor node features as in || `GraphSAGE || <https:\/\/www-cs-faculty.stanford.edu\/people\/jure\/pubs\/graphsage-nips17.pdf>`_. || `Graph Attention Network <https:\/\/arxiv.org\/abs\/1710.10903>`_ proposes an || alternative way by weighting neighbor features with feature dependent and || structure free normalization; in the style of attention. ||  || The goal of this tutorial: ||  || * Explain what is Graph Attention Network. || * Demonstrate how it can be implemented in DGL. || * Understand the attentions learnt. || * Introduce to inductive learning. || \"\"\"
aaieihjie,dmlc/dgl,tutorials/models/1_gnn/9_gat.py,98ff82e52770053b890cb71f150ad62ddd66ee35,STILL_EXISTS,explains why the performance of GAT is close to that of GCN on Cora
aaieiiaie,dmlc/dgl,tutorials/models/1_gnn/9_gat.py,98ff82e52770053b890cb71f150ad62ddd66ee35,STILL_EXISTS,lies perhaps more in its ability to handle a graph with more complex
aaieiiaja,dmlc/dgl,tutorials/models/1_gnn/9_gat.py,98ff82e52770053b890cb71f150ad62ddd66ee35,STILL_EXISTS,So far; we demonstrated how to use DGL to implement GAT. There are some
aaieiidch,dmlc/dgl,python/dgl/nodeflow.py,8651be54c27acbec3b4b8d84a4435a548aebb39b,STILL_EXISTS,# FIXME(minjie): data type
aaieiiddd,dmlc/dgl,python/dgl/nodeflow.py,8651be54c27acbec3b4b8d84a4435a548aebb39b,fc7775a20f8c4b9f58ee9cbf76516b2b7e823b67,TODO does it work for bipartite graph?
aaieiiddh,dmlc/dgl,python/dgl/runtime/scheduler.py,8651be54c27acbec3b4b8d84a4435a548aebb39b,STILL_EXISTS,TODO is the eid here correct?
aaieiidef,dmlc/dgl,python/dgl/runtime/scheduler.py,8651be54c27acbec3b4b8d84a4435a548aebb39b,STILL_EXISTS,TODO get all edges in the block.
aaieiideh,dmlc/dgl,python/dgl/runtime/spmv.py,8651be54c27acbec3b4b8d84a4435a548aebb39b,653428bdc7880ebc45b759e675df09ae6eb146f8,TODO why is this constructed twice?
aaieiidei,dmlc/dgl,python/dgl/runtime/spmv.py,8651be54c27acbec3b4b8d84a4435a548aebb39b,653428bdc7880ebc45b759e675df09ae6eb146f8,TODO Why invoking adjacency_matrix twice?
aaieiidej,dmlc/dgl,python/dgl/runtime/spmv.py,8651be54c27acbec3b4b8d84a4435a548aebb39b,653428bdc7880ebc45b759e675df09ae6eb146f8,inc mat will not use data tensor so conversion index is not needed
aaieiidjb,dmlc/dgl,examples/pytorch/recommendation/rec/datasets/movielens.py,6066fee935b637cea327f0bac24e478eb2dd7a0d,e705118bcca5d39532b9cac3d89b708b6e7a7988,TODO: replace with more efficient PPR estimation
aaieiiehf,dmlc/dgl,examples/pytorch/gin/dataloader.py,a3febc061b867a30ffa80cbc7e394cb0208e7346,9a0511c8e91a7f633c9c3292fccbcbad5281d1f5,TODO torch.Tensor is not recommended
aaieiifce,dmlc/dgl,python/dgl/data/gindt.py,a3febc061b867a30ffa80cbc7e394cb0208e7346,f05bd4971cee7b9957a5c748c1d1d18fb373eb69,TODO move to dgl host _get_dgl_url
aaieiifjg,dmlc/dgl,examples/mxnet/sampling/gcn_cv_sc.py,3a1392e6417ce966318b0ee1033249a2cfe06665,STILL_EXISTS,TODO we could use DGLGraph.pull to implement this; but the current
aaieiifjh,dmlc/dgl,examples/mxnet/sampling/gcn_cv_sc.py,3a1392e6417ce966318b0ee1033249a2cfe06665,STILL_EXISTS,implementation of pull is very slow. Let's manually do it for now.
aaieiifji,dmlc/dgl,examples/mxnet/sampling/graphsage_cv.py,3a1392e6417ce966318b0ee1033249a2cfe06665,STILL_EXISTS,TODO we could use DGLGraph.pull to implement this; but the current
aaieiifjj,dmlc/dgl,examples/mxnet/sampling/graphsage_cv.py,3a1392e6417ce966318b0ee1033249a2cfe06665,STILL_EXISTS,implementation of pull is very slow. Let's manually do it for now.
aaieiigag,dmlc/dgl,python/dgl/contrib/graph_store.py,3a1392e6417ce966318b0ee1033249a2cfe06665,STILL_EXISTS,Here I manually implement multi-processing barrier with RPC.
aaieiigfj,dmlc/dgl,tutorials/models/5_giant_graph/sampling_mx.py,edf3951c1855452b425b5b964fdaed6ec45fa220,STILL_EXISTS,In DGL; we implement GCN on the full graph with ``update_all`` in ``DGLGraph``.
aaieiiggj,dmlc/dgl,tutorials/models/5_giant_graph/sampling_mx.py,edf3951c1855452b425b5b964fdaed6ec45fa220,STILL_EXISTS,full graph would no longer be efficient or even feasible.
aaieiihbi,dmlc/dgl,tutorials/models/5_giant_graph/sampling_mx.py,edf3951c1855452b425b5b964fdaed6ec45fa220,STILL_EXISTS,moderately deep (e.g.\u00A03 layers) GCN would often depend on input features
aaieiihef,dmlc/dgl,tutorials/models/5_giant_graph/sampling_mx.py,edf3951c1855452b425b5b964fdaed6ec45fa220,STILL_EXISTS,We then implement *neighbor smapling* by ``NodeFlow``:
aaieiiijj,dmlc/dgl,examples/mxnet/sampling/dis_sampling/dis_gcn_cv_sc.py,28379f927f6f1dfb5d627e56df49c25ba9b3f35c,STILL_EXISTS,TODO we could use DGLGraph.pull to implement this; but the current
aaieiijaa,dmlc/dgl,examples/mxnet/sampling/dis_sampling/dis_gcn_cv_sc.py,28379f927f6f1dfb5d627e56df49c25ba9b3f35c,STILL_EXISTS,implementation of pull is very slow. Let's manually do it for now.
aaieiijdd,dmlc/dgl,examples/mxnet/sampling/dis_sampling/dis_graphsage_cv.py,28379f927f6f1dfb5d627e56df49c25ba9b3f35c,STILL_EXISTS,TODO we could use DGLGraph.pull to implement this; but the current
aaieiijde,dmlc/dgl,examples/mxnet/sampling/dis_sampling/dis_graphsage_cv.py,28379f927f6f1dfb5d627e56df49c25ba9b3f35c,STILL_EXISTS,implementation of pull is very slow. Let's manually do it for now.
aaieijdhb,dmlc/dgl,python/dgl/graph_index.py,01a4cc5b7b10da8a8bec669280f70188c1f0766c,67cb7a43a064d2d2017d793660e426ac9b37013e,TODO(minjie): better behavior in the future
aaieijdhg,dmlc/dgl,tests/compute/test_nodeflow.py,84702916d36ad0b35a508cbce77d0ee36e0fedb6,STILL_EXISTS,todo need fix for \"both\"
aaieijeef,dmlc/dgl,python/dgl/runtime/scheduler.py,653428bdc7880ebc45b759e675df09ae6eb146f8,STILL_EXISTS,TODO: should replace this with an IR call to make the program
aaieijgdd,dmlc/dgl,python/dgl/_ffi/_ctypes/object.py,90e78c5854665aca1b0507daff81d52f1be8a678,STILL_EXISTS,This allows child class to implement their own __init__
aaieijgej,dmlc/dgl,python/dgl/_ffi/object_generic.py,90e78c5854665aca1b0507daff81d52f1be8a678,STILL_EXISTS,\"\"\"Common implementation of Object generic related logic\"\"\"
aaieijgfe,dmlc/dgl,python/dgl/graph_index.py,b0d9e7aa43ccb71ea678057e44c212f7422430a9,STILL_EXISTS,TODO(minjie): very ugly code; should fix this
aaieijggi,dmlc/dgl,python/dgl/graph_index.py,7ad663c3a968260055ce698b95a5a0f15b435534,52d4535b61cd82bc82a1ebb79d8dc66c5c50856e,FIXME(minjie): data type
aaieijgjh,dmlc/dgl,python/dgl/batched_graph.py,5d3f470b721db2a23dcc1a3880c1e31216e06233,STILL_EXISTS,a more efficient way is to use segment max; we need to implement it in
aaieijhaa,dmlc/dgl,python/dgl/batched_graph.py,5d3f470b721db2a23dcc1a3880c1e31216e06233,STILL_EXISTS,a more efficient way is to use segment sum\/max; we need to implement
aaieijhcd,dmlc/dgl,tests/compute/test_readout.py,5d3f470b721db2a23dcc1a3880c1e31216e06233,44089c8b4d4db4ca71e816e0de50dca972dbabdb,TODO(zihao): fix -inf issue
aaieijiaj,dmlc/dgl,examples/pytorch/model_zoo/chem/property_prediction/utils.py,cd9fb7baa075b8108d96dcd90c4f0d8034dcf27d,189c2c0907cdff54f733299a93360ddfcff4ccb7,Todo: Allow different evaluation metrics
aaieijiba,dmlc/dgl,examples/pytorch/model_zoo/chem/property_prediction/utils.py,cd9fb7baa075b8108d96dcd90c4f0d8034dcf27d,STILL_EXISTS,Todo: support categorical classes
aaieijibc,dmlc/dgl,examples/pytorch/model_zoo/chem/property_prediction/utils.py,cd9fb7baa075b8108d96dcd90c4f0d8034dcf27d,189c2c0907cdff54f733299a93360ddfcff4ccb7,Todo: this is not true for all metrics.
aaieijihg,dmlc/dgl,python/dgl/heterograph.py,52d4535b61cd82bc82a1ebb79d8dc66c5c50856e,9b4d60799a25409506a73ef4fbaa640a9c4e9ee2,TODO: replace this after implementing frame
aaieijiih,dmlc/dgl,python/dgl/heterograph_index.py,52d4535b61cd82bc82a1ebb79d8dc66c5c50856e,STILL_EXISTS,FIXME(minjie): data type
aaiejaddc,dmlc/dgl,examples/pytorch/model_zoo/chem/generative_models/jtnn/jtnn/datautils.py,3192beb42dc9a31bdfd1d5947484c49224072914,STILL_EXISTS,TODO: either support pickling or get around ctypes pointers using scipy
aaiejadjg,dmlc/dgl,python/dgl/model_zoo/chem/jtnn/jtnn_dec.py,3192beb42dc9a31bdfd1d5947484c49224072914,STILL_EXISTS,Only newly generated nodes are needed for label prediction
aaiejaebg,dmlc/dgl,python/dgl/model_zoo/chem/jtnn/jtnn_enc.py,3192beb42dc9a31bdfd1d5947484c49224072914,STILL_EXISTS,TODO: context?
aaiejaeea,dmlc/dgl,python/dgl/model_zoo/chem/jtnn/mpn.py,3192beb42dc9a31bdfd1d5947484c49224072914,STILL_EXISTS,TODO: replace with unbatch or readout
aaiejafbi,dmlc/dgl,examples/pytorch/metapath2vec/reading_data.py,ddeb86f91c2a7a538ef58cf70320ce46e5368c14,STILL_EXISTS,TODO check equality with target
aaiejafig,dmlc/dgl,python/dgl/contrib/dis_kvstore.py,7782276962eb5d7a1b42588a3b04426c1acc6808,STILL_EXISTS,we hack the msg format here:
aaiejafji,dmlc/dgl,python/dgl/contrib/dis_kvstore.py,7782276962eb5d7a1b42588a3b04426c1acc6808,cccde032f42f9351bc7b7cb4e36928c212f5c7ce,We hack the msg format here
aaiejahhb,dmlc/dgl,python/dgl/convert.py,9b4d60799a25409506a73ef4fbaa640a9c4e9ee2,STILL_EXISTS,TODO(minjie): with_edge_id is only reasonable for csr matrix. How to fix?
aaiejahib,dmlc/dgl,python/dgl/convert.py,9b4d60799a25409506a73ef4fbaa640a9c4e9ee2,STILL_EXISTS,XXX: assuming networkx iteration order is deterministic
aaiejahid,dmlc/dgl,python/dgl/convert.py,9b4d60799a25409506a73ef4fbaa640a9c4e9ee2,STILL_EXISTS,TODO attributes
aaiejaifi,dmlc/dgl,python/dgl/heterograph.py,9b4d60799a25409506a73ef4fbaa640a9c4e9ee2,STILL_EXISTS,find common columns and check if their schemes match
aaiejaifj,dmlc/dgl,python/dgl/heterograph.py,9b4d60799a25409506a73ef4fbaa640a9c4e9ee2,STILL_EXISTS,concatenate the columns
aaiejbbhe,dmlc/dgl,examples/pytorch/han/model_hetero.py,c03046a08f1407bce914905509acbeb343e75db8,STILL_EXISTS,\"\"\"This model shows an example of using dgl.metapath_reachable_graph on the original heterogeneous || graph. ||  || Because the original HAN implementation only gives the preprocessed homogeneous graph; this model || could not reproduce the result in HAN as they did not provide the preprocessing code; and we || constructed another dataset from ACM with a different set of papers; connections; features and || labels. || \"\"\"
aaiejbcbi,dmlc/dgl,examples/pytorch/cluster_gcn/utils.py,51a73504fe2ac31b47e405e92b273f69f30e6dc3,9bcce7beade62b12c23b1a7b7a169f5c06159ee2,hack to dodge the potential bugs of to_networkx
aaiejbchg,dmlc/dgl,apps/kg/dataloader/KGDataset.py,15b951d4c4d4a75cc30442d5dd72fb72ed110e09,STILL_EXISTS,TODO: to deal with contries dataset.
aaiejbcia,dmlc/dgl,apps/kg/dataloader/sampler.py,15b951d4c4d4a75cc30442d5dd72fb72ed110e09,ffe5898317c4f971483f649a1ffb53d81a29705a,TODO: weight to be added
aaiejbcjj,dmlc/dgl,apps/kg/models/general_models.py,15b951d4c4d4a75cc30442d5dd72fb72ed110e09,STILL_EXISTS,TODO: add subsampling to new sampler
aaiejbdac,dmlc/dgl,apps/kg/models/general_models.py,15b951d4c4d4a75cc30442d5dd72fb72ed110e09,STILL_EXISTS,TODO: only reg ent&rel embeddings. other params to be added.
aaiejbdad,dmlc/dgl,apps/kg/models/mxnet/score_fun.py,15b951d4c4d4a75cc30442d5dd72fb72ed110e09,STILL_EXISTS,TODO: check if there exists minus sign and if gamma should be used here(jin)
aaiejbdae,dmlc/dgl,apps/kg/models/mxnet/tensor_models.py,15b951d4c4d4a75cc30442d5dd72fb72ed110e09,STILL_EXISTS,TODO(zhengda) fix this later
aaiejbdbb,dmlc/dgl,apps/kg/models/pytorch/score_fun.py,15b951d4c4d4a75cc30442d5dd72fb72ed110e09,STILL_EXISTS,TODO: check if use self.gamma
aaiejbdch,dmlc/dgl,apps/kg/train_mxnet.py,15b951d4c4d4a75cc30442d5dd72fb72ed110e09,bb6a64763e9a8e64f27bf5f380b62672665d692d,TODO: loading model emb only work for genernal Embedding; not for ExternalEmbedding
aaiejbdda,dmlc/dgl,apps/kg/train_pytorch.py,15b951d4c4d4a75cc30442d5dd72fb72ed110e09,bb6a64763e9a8e64f27bf5f380b62672665d692d,TODO: loading model emb only work for genernal Embedding; not for ExternalEmbedding
aaiejbffd,dmlc/dgl,tutorials/hetero/1_basics.py,3ee7e11268eb96c44cad3e57d4c8aa870ed2fa02,STILL_EXISTS,which we implement in MXNet
aaiejbfgj,dmlc/dgl,apps/kg/models/mxnet/score_fun.py,bde75256be4d76bf785a0f157d3cd3ef72004bc6,STILL_EXISTS,TODO: check if there exists minus sign and if gamma should be used here(jin)
aaiejbfha,dmlc/dgl,apps/kg/models/mxnet/score_fun.py,02fb05817bd6864f10074c2a4cd7d6af1ece3e99,STILL_EXISTS,TODO: check if use self.gamma
aaiejbgca,dmlc/dgl,apps/kg/tests/test_score.py,7f65199a867fef67d0d747344beaa809fabea7f3,STILL_EXISTS,init score_func specific data if needed
aaiejbheb,dmlc/dgl,examples/pytorch/model_zoo/geometric/coarsening.py,9a0511c8e91a7f633c9c3292fccbcbad5281d1f5,STILL_EXISTS,Permute the rows and the columns.
aaiejbihd,dmlc/dgl,tutorials/basics/3_pagerank.py,409183bc56836488910732983fa9fd83328f6aac,STILL_EXISTS,more efficient message passing.
aaiejbjgf,dmlc/dgl,tutorials/models/1_gnn/6_line_graph.py,22d4de77a02a60b6c67e85d8cbbec06e40878b68,STILL_EXISTS,it helps to understand the following information before you implement the LGNN.
aaiejbjgi,dmlc/dgl,tutorials/models/1_gnn/6_line_graph.py,22d4de77a02a60b6c67e85d8cbbec06e40878b68,STILL_EXISTS,Here; you implement :math:`\\{Pm; Pd\\}` as a SciPy COO sparse matrix in the dataset;
aaiejbjjh,dmlc/dgl,tutorials/models/3_generative_model/5_dgmg.py,5fc7eb6d97a16e7dea8cf3517aa2b05bf6755184,STILL_EXISTS,essential building block. The tutorial ends with a simple optimization that
aaiejccah,dmlc/dgl,python/dgl/backend/tensorflow/tensor.py,d30a69bf277effd9a013ddd81fa33661f2e31a01,STILL_EXISTS,TODO: TF has unsorted_segment_max; which can accelerate _max_on on batched graph
aaiejccch,dmlc/dgl,python/dgl/backend/tensorflow/tensor.py,d30a69bf277effd9a013ddd81fa33661f2e31a01,STILL_EXISTS,TODO: ???
aaiejccdh,dmlc/dgl,tests/backend/pytorch/__init__.py,d30a69bf277effd9a013ddd81fa33661f2e31a01,cadcc1c2a61c614a624019f2392bb52963932922,Fix for torch 1.3.1
aaiejcgaj,dmlc/dgl,python/dgl/nn/pytorch/softmax.py,e4948c5cbce13a054d639c5b4f0be20ba166d68e,f25b1a064d82e159b24d5f0a6965cb6d4401b913,TODO(BarclayII): this is a temporary fix of memory leakage in PyTorch
aaiejcgeh,dmlc/dgl,apps/life_sci/dgllife/model/model_zoo/acnn.py,828a5e5bc6ffaa5716b02283874ec830e1b786fc,STILL_EXISTS,Hack the case when we are working with a single graph.
aaiejchcf,dmlc/dgl,apps/life_sci/dgllife/model/model_zoo/jtnn/jtnn_dec.py,828a5e5bc6ffaa5716b02283874ec830e1b786fc,STILL_EXISTS,Only newly generated nodes are needed for label prediction
aaiejchef,dmlc/dgl,apps/life_sci/dgllife/model/model_zoo/jtnn/jtnn_enc.py,828a5e5bc6ffaa5716b02283874ec830e1b786fc,STILL_EXISTS,TODO: context?
aaiejchgi,dmlc/dgl,apps/life_sci/dgllife/model/model_zoo/jtnn/mpn.py,828a5e5bc6ffaa5716b02283874ec830e1b786fc,STILL_EXISTS,TODO: replace with unbatch or readout
aaiejcjjg,dmlc/dgl,apps/life_sci/examples/generative_models/jtnn/jtnn/datautils.py,828a5e5bc6ffaa5716b02283874ec830e1b786fc,STILL_EXISTS,TODO: either support pickling or get around ctypes pointers using scipy
aaiejdcee,dmlc/dgl,python/dgl/runtime/scheduler.py,37d992ecf69680e2367eac1a9dcba3de528710d2,STILL_EXISTS,XXX(minjie): a temporary hack to detect Nodeflow object
aaiejdcgg,dmlc/dgl,apps/kg/kvclient.py,00ba409440d098cce8ad1dd32de7cde38b4a47fe,STILL_EXISTS,Fix the number of threads to 1 on kvclient
aaiejdchd,dmlc/dgl,apps/kg/kvserver.py,00ba409440d098cce8ad1dd32de7cde38b4a47fe,STILL_EXISTS,Fix the number of threads to 1 on kvstore
aaiejddab,dmlc/dgl,examples/mxnet/scenegraph/data/prepare_visualgenome.py,cbee4278390f2b009e8807022fff37ff9934a17c,STILL_EXISTS,move all images into folder `VG_100K`
aaiejddha,dmlc/dgl,examples/mxnet/scenegraph/train_faster_rcnn.py,cbee4278390f2b009e8807022fff37ff9934a17c,STILL_EXISTS,input format is differnet than training; thus rehybridization is needed.
aaiejdecg,dmlc/dgl,python/dgl/graph.py,93ac29ce3446ff5d82f9ec91eb597ca236cbd8fb,STILL_EXISTS,NOTE: following code will materialize the columns of the input graphs.
aaiejdede,dmlc/dgl,python/dgl/readout.py,93ac29ce3446ff5d82f9ec91eb597ca236cbd8fb,44089c8b4d4db4ca71e816e0de50dca972dbabdb,a more efficient way is to use segment max; we need to implement it in
aaiejdedh,dmlc/dgl,python/dgl/readout.py,93ac29ce3446ff5d82f9ec91eb597ca236cbd8fb,44089c8b4d4db4ca71e816e0de50dca972dbabdb,a more efficient way is to use segment sum\/max; we need to implement
aaiejdegd,dmlc/dgl,examples/pytorch/graphsage/graphsage_sampling.py,a9520f71ce9699aefb4ca83dc1cec87ddc826fb2,STILL_EXISTS,TODO: can we standardize this?
aaiejdegi,dmlc/dgl,examples/pytorch/graphsage/graphsage_sampling.py,a9520f71ce9699aefb4ca83dc1cec87ddc826fb2,STILL_EXISTS,TODO: confirm if this is necessary for MXNet and Tensorflow.  If so; we need
aaiejdeij,dmlc/dgl,tools/partition.py,0e153c4baf3394ca4043896b0195737c81ed8623,2190c39d674f76c65db9ee8da7b43d3021f19c29,TODO I duplicate some node features.
aaiejdfda,dmlc/dgl,python/dgl/nn/mxnet/conv/gatconv.py,988c8b20a41d613243f152a28f6c32dec9174589,STILL_EXISTS,Our implementation is much efficient because we do not need to
aaiejdfea,dmlc/dgl,python/dgl/nn/pytorch/conv/gatconv.py,988c8b20a41d613243f152a28f6c32dec9174589,STILL_EXISTS,Our implementation is much efficient because we do not need to
aaiejdffa,dmlc/dgl,python/dgl/nn/tensorflow/conv/gatconv.py,988c8b20a41d613243f152a28f6c32dec9174589,STILL_EXISTS,Our implementation is much efficient because we do not need to
aaiejebag,dmlc/dgl,tutorials/basics/2_basics.py,2cdc4d3c1d17e1ac8be1c44ca54d772084c76f18,STILL_EXISTS,Although it is not as efficient as the above constructors; it is suitable
aaiejecdc,dmlc/dgl,python/dgl/backend/tensorflow/tensor.py,e9440acb06e1ab7a9d2f6f888a092b115da3155c,STILL_EXISTS,alignment and make a copy if needed. The functionality is better in TF's main repo.
aaiejeejh,dmlc/dgl,examples/pytorch/graphsage/train_cv.py,97bb85d505880e13bb70879cac7c5effe05af4b1,STILL_EXISTS,TODO: can we standardize this?
aaiejefci,dmlc/dgl,examples/pytorch/graphsage/train_cv_multi_gpu.py,97bb85d505880e13bb70879cac7c5effe05af4b1,STILL_EXISTS,TODO: can we standardize this?
aaiejefdg,dmlc/dgl,examples/pytorch/graphsage/train_cv_multi_gpu.py,97bb85d505880e13bb70879cac7c5effe05af4b1,STILL_EXISTS,TODO: confirm if this is necessary for MXNet and Tensorflow.  If so; we need
aaiejefie,dmlc/dgl,examples/pytorch/graphsage/train_sampling_unsupervised.py,dc0432e7378638e877778ca59b86bd29d2c53b85,STILL_EXISTS,TODO: can we standardize this?
aaiejegdi,dmlc/dgl,python/dgl/distributed/dist_graph.py,2190c39d674f76c65db9ee8da7b43d3021f19c29,50962c433fd2bc55eedb667c99ea5f6717f7debc,TODO we need to fix this. We should be able to init ndata even when there is no node data.
aaiejegdj,dmlc/dgl,python/dgl/distributed/dist_graph.py,2190c39d674f76c65db9ee8da7b43d3021f19c29,50962c433fd2bc55eedb667c99ea5f6717f7debc,TODO we need to fix this. We should be able to init ndata even when there is no edge data.
aaiejegeg,dmlc/dgl,python/dgl/distributed/partition.py,2190c39d674f76c65db9ee8da7b43d3021f19c29,STILL_EXISTS,TODO we need to fix this. DGL backend doesn't support boolean or byte.
aaiejehia,dmlc/dgl,examples/pytorch/ogb/ogbn-products/graphsage/main.py,ed60f401233c2e8ff8362c5e1eda028ac6ef4336,STILL_EXISTS,TODO: can we standardize this?
aaiejeiij,dmlc/dgl,python/dgl/graph_index.py,20ec7bb0d93c34c1c5f33cf70efb5722bc23a232,STILL_EXISTS,TODO: we should store a storage version number in later releases.
aaiejeijc,dmlc/dgl,python/dgl/heterograph.py,20ec7bb0d93c34c1c5f33cf70efb5722bc23a232,STILL_EXISTS,TODO: version the storage
aaiejeijg,dmlc/dgl,python/dgl/heterograph_index.py,20ec7bb0d93c34c1c5f33cf70efb5722bc23a232,STILL_EXISTS,TODO: we should store a storage version number in later releases.
aaiejejab,dmlc/dgl,python/dgl/utils.py,20ec7bb0d93c34c1c5f33cf70efb5722bc23a232,STILL_EXISTS,TODO: we should store a storage version number in later releases.
aaiejfbae,dmlc/dgl,examples/pytorch/graphsage/utils.py,901e0c24d699217a6e442f1ef5001b70a65649e5,STILL_EXISTS,TODO: confirm if this is necessary for MXNet and Tensorflow.  If so; we need
aaiejfbbd,dmlc/dgl,examples/pytorch/sign/sign.py,e979685ad916dc03b2a3513bcb48593c4eed5873,STILL_EXISTS,move to device
aaiejfdia,dmlc/dgl,examples/pytorch/ogb/ogbn-products/gat/main.py,8192b10b08343ade918da4ca4aaab62e4bfd3c16,STILL_EXISTS,TODO: can we standardize this?
aaiejfgeb,dmlc/dgl,python/dgl/backend/pytorch/tensor.py,cadcc1c2a61c614a624019f2392bb52963932922,STILL_EXISTS,Fix for torch 1.3.1
aaiejfhhg,dmlc/dgl,python/dgl/transform.py,200340ab3812cc5aa031dc21a9171983b5245192,STILL_EXISTS,TODO(0.5 release; xiangsx) need to handle BLOCK
aaiejfihj,dmlc/dgl,python/dgl/transform.py,ea420c0abd5f068cbe881d27fa970cfe6e5b274a,a089b91fdfc1d3b4df029df2bc74bdf20176b488,g is no longer needed. Free memory.
aaiejfjae,dmlc/dgl,examples/pytorch/graphsage/experimental/train_dist.py,562871e76b2d5adbcd907bcf62ca28c0611e50e1,STILL_EXISTS,TODO: can we standardize this?
aaiejfjbh,dmlc/dgl,python/dgl/partition.py,33abd2757b29c3c98cbecdb8113d338a277d8125,STILL_EXISTS,g is no longer needed. Free memory.
aaiejgaig,dmlc/dgl,python/dgl/heterograph.py,44089c8b4d4db4ca71e816e0de50dca972dbabdb,STILL_EXISTS,TODO(minjie): temporary hack
aaiejgbbi,dmlc/dgl,python/dgl/segment.py,44089c8b4d4db4ca71e816e0de50dca972dbabdb,STILL_EXISTS,TODO(minjie): a more efficient implementation is to create a graph
aaiejgbdj,dmlc/dgl,tests/compute/test_basics.py,44089c8b4d4db4ca71e816e0de50dca972dbabdb,STILL_EXISTS,TODO: tmp hack
aaiejgcah,dmlc/dgl,tests/compute/test_heterograph.py,44089c8b4d4db4ca71e816e0de50dca972dbabdb,STILL_EXISTS,TODO: rewrite this test case to accept different graphs so we
aaiejgdfh,dmlc/dgl,tests/pytorch/test_nn.py,44089c8b4d4db4ca71e816e0de50dca972dbabdb,STILL_EXISTS,TODO: add test for blocks
aaiejgdgf,dmlc/dgl,python/dgl/heterograph.py,40950629b2b8f639d46a7efea0f40557108fbdcf,STILL_EXISTS,TODO: Formats should not be specified; just saving all the materialized formats
aaiejgdgj,dmlc/dgl,tests/compute/test_shared_mem.py,40950629b2b8f639d46a7efea0f40557108fbdcf,STILL_EXISTS,TODO: Test calling shared_memory with Blocks (a subclass of HeteroGraph)
aaiejgeeg,dmlc/dgl,python/dgl/sparse.py,a28bfa9f5f7b254c9b928fa494c44fca1cf53de8,STILL_EXISTS,The workaround is proposed by Jinjing; and we still need to investigate
aaiejgfai,dmlc/dgl,examples/pytorch/graphsage/experimental/train_dist_unsupervised.py,56bbf9cb6dab8d34fc7a8d98d66d240eb955213e,STILL_EXISTS,TODO: can we standardize this?
aaiejgfdg,dmlc/dgl,examples/pytorch/ogb/cluster-sage/main.py,4f7bd0d0fb256b13248cb838029d7f8ecf49cb62,STILL_EXISTS,TODO: can we standardize this?
aaiejgfih,dmlc/dgl,python/dgl/distributed/dist_dataloader.py,4f499c7ffb5f6746c4ccd87b93b0ef09c32cf424,cd204a4ac9960aada8e01fce84792e63798b1ee5,probably need better solution in the future
aaiejgfji,dmlc/dgl,tests/distributed/test_mp_dataloader.py,4f499c7ffb5f6746c4ccd87b93b0ef09c32cf424,STILL_EXISTS,this is needed since there's two test here in one process
aaiejggba,dmlc/dgl,python/dgl/_deprecate/frame.py,22167f7203a8ba1845873c5006b59353af148a21,STILL_EXISTS,Pickling torch dtypes could be problemetic; this is a workaround.
aaiejggcj,dmlc/dgl,python/dgl/_deprecate/frame.py,22167f7203a8ba1845873c5006b59353af148a21,STILL_EXISTS,directly updating columns.
aaiejggda,dmlc/dgl,python/dgl/_deprecate/frame.py,22167f7203a8ba1845873c5006b59353af148a21,STILL_EXISTS,pad columns that are not provided in the other frame with initial values
aaiejggeg,dmlc/dgl,python/dgl/_deprecate/frame.py,22167f7203a8ba1845873c5006b59353af148a21,STILL_EXISTS,TODO(minjie): hack; cannot rely on keys as the _initializers
aaiejggeh,dmlc/dgl,python/dgl/_deprecate/frame.py,22167f7203a8ba1845873c5006b59353af148a21,STILL_EXISTS,now supports non-exist columns.
aaiejggei,dmlc/dgl,python/dgl/backend/mxnet/sparse.py,22167f7203a8ba1845873c5006b59353af148a21,STILL_EXISTS,XXX(minjie): There is a bug in MXNet's autograd system when one of the inputs
aaiejggfb,dmlc/dgl,python/dgl/backend/mxnet/sparse.py,22167f7203a8ba1845873c5006b59353af148a21,STILL_EXISTS,input gradients to be zero. Fix this by enforcing all the inputs to require
aaiejggfh,dmlc/dgl,python/dgl/backend/tensorflow/tensor.py,22167f7203a8ba1845873c5006b59353af148a21,STILL_EXISTS,XXX(minjie): Normally; the copy_to here is unnecessary. However; TF has this
aaiejgheh,dmlc/dgl,tests/compute/test_basics.py,22167f7203a8ba1845873c5006b59353af148a21,STILL_EXISTS,TODO: tmp hack
aaiejgidi,dmlc/dgl,python/dgl/distributed/dist_dataloader.py,bab32d5ba1585b46f8353e4fbdcc5badcc614b24,cd204a4ac9960aada8e01fce84792e63798b1ee5,probably need better solution in the future
aaiejgihb,dmlc/dgl,tests/distributed/test_dist_graph_store.py,5454471fc8333297143bb95ebe23b5b7d3e991b3,STILL_EXISTS,TODO(zhengda) this is a temporary fix. We need to make initialize work
aaiejgjhj,dmlc/dgl,examples/pytorch/graph_matching/ged.py,4097fa21737d108c92e54017a9ca6b93e1282ccc,STILL_EXISTS,TODO: Create a node that processes multi node insertion deletion in one search node;
aaiejgjjg,dmlc/dgl,tests/distributed/test_mp_dataloader.py,ee30b2aadd49af86696c4bab8b7b15153a55c9c7,STILL_EXISTS,this is needed since there's two test here in one process
aaiejhacb,dmlc/dgl,examples/pytorch/graphsage/experimental/train_dist_unsupervised.py,d340ea3aa861a936430b9f37caafa2788bfae185,STILL_EXISTS,TODO: can we standardize this?
aaiejhaia,dmlc/dgl,python/dgl/utils/internal.py,f5eb80d221fec8690e8cfb087256671545bb9a5a,STILL_EXISTS,TODO: this is faster (37s -> 21s per epoch compared to searchsorted in GCMC) but takes
aaiejhbdg,dmlc/dgl,tests/distributed/test_mp_dataloader.py,1ad46fd0157639f8d8a8e641f6b4b17e29890b38,STILL_EXISTS,this is needed since there's two test here in one process
aaiejhbie,dmlc/dgl,python/dgl/convert.py,be444e52d99ef94052c9b5a574c4c41ac41f8868,STILL_EXISTS,XXX: assuming networkx iteration order is deterministic
aaiejhbih,dmlc/dgl,python/dgl/heterograph.py,be444e52d99ef94052c9b5a574c4c41ac41f8868,STILL_EXISTS,Todo (Mufei) Replace the syntax g.nodes[...].ndata[...] with g.nodes[...][...]
aaiejhbjf,dmlc/dgl,python/dgl/utils/data.py,be444e52d99ef94052c9b5a574c4c41ac41f8868,STILL_EXISTS,TODO(minjie): move these checks to C for faster graph construction.
aaiejhcdc,dmlc/dgl,python/dgl/sampling/neighbor.py,edfbee2ca8d24aef8600a440d7ab3fd70c61e1f8,STILL_EXISTS,(TODO) (BarclayII) DGL distributed fails with bus error; freezes; or other
aaiejheba,dmlc/dgl,python/dgl/dataloading/pytorch/__init__.py,ac5c79cdb853d0d7e13b8431934ce67746a17843,STILL_EXISTS,The following code is a fix to the PyTorch-specific issue in
aaiejhebj,dmlc/dgl,python/dgl/dataloading/pytorch/__init__.py,ac5c79cdb853d0d7e13b8431934ce67746a17843,STILL_EXISTS,to the main process is costly in PyTorch as it uses shared memory.  We work around
aaiejheib,dmlc/dgl,python/dgl/dataloading/dataloader.py,76d66fd35d0d401068f9f9da935b072edf7049ad,STILL_EXISTS,this function does not work.  I'm again skipping this step as a workaround.
aaiejhfaj,dmlc/dgl,examples/pytorch/ogb/ogbn-arxiv/models.py,47bb05939d4241c2c8d980bc1dbbb2aace1693fc,STILL_EXISTS,Our implementation is much efficient because we do not need to
aaiejhfie,dmlc/dgl,examples/pytorch/ogb/sign/sign.py,b8cc26e34ad0ce83ac8ae987c0f21365a24ce6b8,STILL_EXISTS,move to device
aaiejhhja,dmlc/dgl,tests/compute/test_batched_graph.py,f960468f50b732c0b8684cc69879232e094f1814,STILL_EXISTS,NOTE(Zihao): this implementation is not efficient and we can replace it with
aaiejhidj,dmlc/dgl,python/dgl/dataloading/dataloader.py,bdb88df576aa88610fc9793478e25ca10348f4a9,STILL_EXISTS,After idtype and device get properly implemented; we should remove these two
aaiejhiej,dmlc/dgl,tests/pytorch/test_dataloader.py,bdb88df576aa88610fc9793478e25ca10348f4a9,d93a9759972249596a1325951cf3885ebb1b4415,TODO: check if items match output nodes\/edges
aaiejiaih,dmlc/dgl,new-tutorial/2_dglgraph.py,e01893972901e45f0cae3da6c90bb1fce6947568,STILL_EXISTS,If you have an undirected graph; it is better to convert it
aaiejibbg,dmlc/dgl,new-tutorial/3_message_passing.py,e01893972901e45f0cae3da6c90bb1fce6947568,STILL_EXISTS,\"\"\" || Write your own GNN module || ========================= ||  || Sometimes; your model goes beyond simply stacking existing GNN modules. || For example; you would like to invent a new way of aggregating neighbor || information by considering node importance or edge weights. ||  || By the end of this tutorial you will be able to ||  || -  Understand DGL\u2019s message passing APIs. || -  Implement GraphSAGE convolution module by your own. ||  || This tutorial assumes that you already know :doc:`the basics of training a || GNN for node classification <1_introduction>`. ||  || (Time estimate: 10 minutes) ||  || \"\"\"
aaiejibgi,dmlc/dgl,new-tutorial/3_message_passing.py,e01893972901e45f0cae3da6c90bb1fce6947568,STILL_EXISTS,here is how you can implement GraphSAGE convolution in DGL by your own.
aaiejichd,dmlc/dgl,new-tutorial/3_message_passing.py,e01893972901e45f0cae3da6c90bb1fce6947568,STILL_EXISTS,-  :ref:`Writing Efficient Message Passing
aaiejifcj,dmlc/dgl,new-tutorial/6_load_data.py,e01893972901e45f0cae3da6c90bb1fce6947568,STILL_EXISTS,normalizing the feature would work better. This tutorial directly
aaiejifej,dmlc/dgl,new-tutorial/6_load_data.py,e01893972901e45f0cae3da6c90bb1fce6947568,STILL_EXISTS,-  ``graph_edges.csv``: containing three columns:
aaiejifff,dmlc/dgl,new-tutorial/6_load_data.py,e01893972901e45f0cae3da6c90bb1fce6947568,STILL_EXISTS,-  ``graph_properties.csv``: containing three columns:
aaiejigac,dmlc/dgl,benchmarks/benchmarks/model_acc/bench_sage_ns.py,71283997cef2234c3978cabedc5d9f1dc284b8f9,STILL_EXISTS,TODO: can we standardize this?
aaiejihhj,dmlc/dgl,examples/pytorch/hgp_sl/layers.py,b36b6c268efb59b59046a74976067050141b1d6e,STILL_EXISTS,maybe there's more efficient way?
aaiejiifb,dmlc/dgl,python/dgl/distributed/graph_partition_book.py,25ac33440334fd2fdfe3fe4e4dd3dd1428601a47,STILL_EXISTS,The key is the node type; the value is a NumPy matrix with two columns; in which
aaiejjadb,dmlc/dgl,benchmarks/benchmarks/utils.py,362f72cbe92d63e9f106f397ba5ddb81638714c7,STILL_EXISTS,fix random seed
aaiejjcbf,dmlc/dgl,examples/pytorch/NGCF/NGCF/utility/metrics.py,2f71bc507ace6f197718b14c27cba8336588e68a,STILL_EXISTS,''' || Created on Oct 10; 2018 || Tensorflow Implementation of Neural Graph Collaborative Filtering (NGCF) model in: || Wang Xiang et al. Neural Graph Collaborative Filtering. In SIGIR 2019. || @author: Xiang Wang (xiangwang@u.nus.edu) || '''
aaiejjdbf,dmlc/dgl,new-tutorial/L0_neighbor_sampling_overview.py,98ac391ed9081ba28b2e8085d1dc05ba2d75fd67,STILL_EXISTS,\"\"\" || Introduction of Neighbor Sampling for GNN Training || ================================================== ||  || In :doc:`previous tutorials <1_introduction>` you have learned how to || train GNNs by computing the representations of all nodes on a graph. || However; sometimes your graph is too large to fit the computation of all || nodes in a single GPU. ||  || By the end of this tutorial; you will be able to ||  || -  Understand the pipeline of stochastic GNN training. || -  Understand what is neighbor sampling and why it yields a bipartite ||    graph for each GNN layer. || \"\"\"
aaiejjdha,dmlc/dgl,new-tutorial/L0_neighbor_sampling_overview.py,98ac391ed9081ba28b2e8085d1dc05ba2d75fd67,STILL_EXISTS,aggregation is often too costly since the nodes needed for input
aaiejjdic,dmlc/dgl,new-tutorial/L0_neighbor_sampling_overview.py,98ac391ed9081ba28b2e8085d1dc05ba2d75fd67,STILL_EXISTS,You can see that this method uses much fewer nodes needed in message
aaiejjeia,dmlc/dgl,new-tutorial/L1_large_node_classification.py,98ac391ed9081ba28b2e8085d1dc05ba2d75fd67,STILL_EXISTS,are needed on the first GNN layer for this minibatch.
aaiejjggc,dmlc/dgl,new-tutorial/L2_large_link_prediction.py,98ac391ed9081ba28b2e8085d1dc05ba2d75fd67,STILL_EXISTS,whose input features are needed on the first GNN layer for this minibatch.
aaiejjhee,dmlc/dgl,new-tutorial/L4_message_passing.py,98ac391ed9081ba28b2e8085d1dc05ba2d75fd67,STILL_EXISTS,\"\"\" || Writing GNN Modules for Stochastic GNN Training || =============================================== ||  || All GNN modules DGL provides support stochastic GNN training. This || tutorial teaches you how to write your own graph neural network module || for stochastic GNN training. It assumes that ||  || 1. You know :doc:`how to write GNN modules for full graph ||    training <3_message_passing>`. || 2. You know :doc:`how stochastic GNN training pipeline ||    works <L1_large_node_classification>`. ||  || \"\"\"
aaiejjiac,dmlc/dgl,new-tutorial/L4_message_passing.py,98ac391ed9081ba28b2e8085d1dc05ba2d75fd67,STILL_EXISTS,Putting them together; you can implement a GraphSAGE convolution for
aaiejjjei,dmlc/dgl,tools/convert_partition.py,e4ff48449421b023c5af8a989154ef67cee7daee,STILL_EXISTS,Get the first two columns which is the node ID and node type.
aaifaaacj,dmlc/dgl,tutorials/models/1_gnn/1_gcn.py,9e04a52abd34f4482cdd0348b3b2b9b509498829,STILL_EXISTS,This is showing how to implement a GCN from scratch.  DGL provides a more
aaifaaada,dmlc/dgl,tutorials/models/1_gnn/1_gcn.py,9e04a52abd34f4482cdd0348b3b2b9b509498829,STILL_EXISTS,efficient :class:`builtin GCN layer module <dgl.nn.pytorch.conv.GraphConv>`.
aaifaaade,dmlc/dgl,tutorials/models/1_gnn/4_rgcn.py,9e04a52abd34f4482cdd0348b3b2b9b509498829,STILL_EXISTS,This is showing how to implement an R-GCN from scratch.  DGL provides a more
aaifaaadf,dmlc/dgl,tutorials/models/1_gnn/4_rgcn.py,9e04a52abd34f4482cdd0348b3b2b9b509498829,STILL_EXISTS,efficient :class:`builtin R-GCN layer module <dgl.nn.pytorch.conv.RelGraphConv>`.
aaifaaaea,dmlc/dgl,tutorials/models/1_gnn/9_gat.py,9e04a52abd34f4482cdd0348b3b2b9b509498829,STILL_EXISTS,This is showing how to implement a GAT from scratch.  DGL provides a more
aaifaaaeb,dmlc/dgl,tutorials/models/1_gnn/9_gat.py,9e04a52abd34f4482cdd0348b3b2b9b509498829,STILL_EXISTS,efficient :class:`builtin GAT layer module <dgl.nn.pytorch.conv.GATConv>`.
aaifaabea,dmlc/dgl,examples/pytorch/vgae/preprocess.py,f7938649d10ea9f871af770f0b5edbb42e809c9c,STILL_EXISTS,TODO: Clean up.
aaifaaceh,dmlc/dgl,python/dgl/geometry/capi.py,c88fca5055b0e7dfac2c36a306956ce345fbfd47,STILL_EXISTS,TODO: actually we can add `return_inverse` option for `unique`
aaifaaeac,dmlc/dgl,examples/pytorch/gnn_explainer/gengraph.py,9aac93ff21114c715767224483883b05c7bc95b8,STILL_EXISTS,\"\"\"gengraph.py\r || \r ||    Generating and manipulaton the synthetic graphs needed for the paper's experiments.\r || \"\"\"
aaifaafde,dmlc/dgl,examples/pytorch/tgn/modules.py,d04d59eef9744e7133f5b84938523d20a7321f54,STILL_EXISTS,Implement skip connection
aaifaafei,dmlc/dgl,examples/pytorch/tgn/train.py,d04d59eef9744e7133f5b84938523d20a7321f54,STILL_EXISTS,Implement Logging mechanism
aaifaagig,dmlc/gluon-cv,docs/conf.py,3cb6f57ecc2771a6632425833b33bc29fff0e77e,STILL_EXISTS,-- Options for todo extension ----------------------------------------------
aaifaagih,dmlc/gluon-cv,docs/conf.py,3cb6f57ecc2771a6632425833b33bc29fff0e77e,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaifaaiei,dmlc/gluon-cv,gluonvision/model_zoo/features.py,3cb6f57ecc2771a6632425833b33bc29fff0e77e,STILL_EXISTS,\"\"\"Feature extraction blocks. || Feature or Multi-Feature extraction is a key component in object detection. || Class predictor\/Box predictor are usually applied on feature layer(s). || A good feature extraction mechanism is critical to performance. || \"\"\"
aaifaaihd,dmlc/gluon-cv,gluonvision/model_zoo/ssd/ssd.py,3cb6f57ecc2771a6632425833b33bc29fff0e77e,STILL_EXISTS,TODO(zhreshold): fix pattern
aaifaajcd,dmlc/gluon-cv,gluonvision/utils/metrics/voc_detection.py,3cb6f57ecc2771a6632425833b33bc29fff0e77e,STILL_EXISTS,append sentinel values at both ends
aaifabeac,dmlc/gluon-cv,scripts/datasets/imagenet.py,f33b6869d0fff4420623829042ce454c1b280232,STILL_EXISTS,move images to proper subfolders
aaifabeje,dmlc/gluon-cv,scripts/detection/train_ssd.py,5767d1d1dee332faef3057a8416aa48179006edb,STILL_EXISTS,\"\"\" || Train SSD on Pascal VOC dataset || =============================== ||  || This article walk you through the components GluonVision provided to you || that are very useful to start an object detection project. By going || through this tutorial; we show how stacking the existing modules can || produce a SOTA Single Shot Multibox Detection [1] model. ||  || **Feel free to skip this tutorial because the training script is || self-complete and only requires a single command line to launch.** ||  || Dataset || ------- ||  || We hope you already read this `article <http:\/\/gluon-vision.mxnet.io.s3-website-us-west-2.amazonaws.com\/examples\/datasets\/setup_pascal_voc.html>`__ so Pascal VOC dataset is || well sitting on your disk. If so we are ready to load some training and || validation images. ||  || .. code:: python ||  ||     from gluonvision.data import VOCDetection ||     # typically we use 2007+2012 trainval splits as training data ||     train_dataset = VOCDetection(splits=[(2007; 'trainval'); (2012; 'trainval')]) ||     # use 2007 test as validation ||     val_dataset = VOCDetection(splits=[(2007; 'test')]) ||  ||     print('Training images:'; len(train_dataset)) ||     print('Validation images:'; len(val_dataset)) ||  ||  || .. parsed-literal:: ||  ||     Training images: 16551 ||     Validation images: 4952 ||  ||  || Data Transform || -------------- ||  || We can read a image and label pair from training dataset: ||  || .. code:: python ||  ||     train_image; train_label = train_dataset[0] ||     bboxes = train_label[:; :4] ||     cids = train_label[:; 4:5] ||     print('image:'; train_image.shape) ||     print('bboxes:'; bboxes.shape; 'class ids:'; cids.shape) ||  ||  || .. parsed-literal:: ||  ||     image: (375; 500; 3) ||     bboxes: (5; 4) class ids: (5; 1) ||  ||  || We could illustrate the image; together with the bounding box labels. ||  || .. code:: python ||  ||     from matplotlib import pyplot as plt ||     %matplotlib inline ||     from gluonvision.utils import viz ||  ||     ax = viz.plot_bbox(train_image.asnumpy(); bboxes; labels=cids; class_names=train_dataset.classes) ||     plt.show() ||  ||  ||  || .. image:: https:\/\/github.com\/zhreshold\/gluonvision-tutorials\/blob\/master\/detection\/ssd_train_voc\/output_6_0.png?raw=true ||  ||  || At this point; validation images are quite similar. ||  || .. code:: python ||  ||     val_image; val_label = val_dataset[0] ||     bboxes = val_label[:; :4] ||     cids = val_label[:; 4:5] ||     ax = viz.plot_bbox(val_image.asnumpy(); bboxes; labels=cids; class_names=train_dataset.classes) ||     plt.show() ||  ||  ||  || .. image:: https:\/\/github.com\/zhreshold\/gluonvision-tutorials\/blob\/master\/detection\/ssd_train_voc\/output_8_0.png?raw=true ||  ||  || For SSD networks; it is critical to apply data augmentation (see || explanations in paper [1]). We provide tons of image and bounding box || transform functions to supply that. It is very convenient to use as || well. ||  || .. code:: python ||  ||     from gluonvision.data.transforms import presets ||     from gluonvision import utils ||     from mxnet import nd ||  ||     width; height = 512; 512  # suppose we use 512 as base training size ||     train_transform = presets.ssd.SSDDefaultTrainTransform(width; height) ||     val_transform = presets.ssd.SSDDefaultValTransform(width; height) ||  ||     utils.random.seed(233)  # fix seed in this tutorial ||  ||     # apply transforms to train image ||     train_image2; train_label2 = train_transform(train_image; train_label) ||     print('tensor shape:'; train_image2.shape) ||  ||  || .. parsed-literal:: ||  ||     tensor shape: (3; 512; 512) ||  ||  || Images directly from tensor is distorted because they no longer sit in || (0; 255) range. Let's convert it back so we can see it clearly. ||  || .. code:: python ||  ||     train_image2 = train_image2.transpose((1; 2; 0)) * nd.array((0.229; 0.224; 0.225)) + nd.array((0.485; 0.456; 0.406)) ||     train_image2 = (train_image2 * 255).clip(0; 255) ||     ax = viz.plot_bbox(train_image2.asnumpy(); train_label2[:; :4]; ||                        labels=train_label2[:; 4:5]; class_names=train_dataset.classes) ||     plt.show() ||  ||     # apply transforms to validation image ||     val_image2; val_label2 = val_transform(val_image; val_label) ||     val_image2 = val_image2.transpose((1; 2; 0)) * nd.array((0.229; 0.224; 0.225)) + nd.array((0.485; 0.456; 0.406)) ||     val_image2 = (val_image2 * 255).clip(0; 255) ||     ax = viz.plot_bbox(val_image2.clip(0; 255).asnumpy(); val_label2[:; :4]; ||                        labels=val_label2[:; 4:5]; class_names=train_dataset.classes) ||     plt.show() ||  ||  ||  || .. image:: https:\/\/github.com\/zhreshold\/gluonvision-tutorials\/blob\/master\/detection\/ssd_train_voc\/output_12_0.png?raw=true ||  ||  ||  || .. image:: https:\/\/github.com\/zhreshold\/gluonvision-tutorials\/blob\/master\/detection\/ssd_train_voc\/output_12_1.png?raw=true ||  ||  || Transforms used in training include random expanding; random cropping; || color distortion; random flipping; etc. In comparison; validation || transforms are conservative; where only resizing and color normalization || is used. ||  || DataLoader || ---------- ||  || We want iterate through the entire dataset many times during training. || Keep in mind that raw images have to be transformed into tensors(mxnet || use BCHW format) before they are fed into neural networks. Besides; to || be able to run in mini-batches; images must be resized to same shape. ||  || A handy DataLoader would be very convenient for us to apply different || transforms and aggregate data into mini-batches. ||  || Because number of objects varys a lot in different images; we have || fluctuating label sizes. As a result; we need to pad those labels to the || same size. In response; we have DetectionDataLoader ready for you which || handles it automatically. ||  || .. code:: python ||  ||     from gluonvision.data import DetectionDataLoader ||  ||     batch_size = 4  # for tutorial; we use smaller batch-size ||     num_workers = 4  # multi processing worker to accelerate data processing ||  ||     train_loader = DetectionDataLoader(train_dataset.transform(train_transform); batch_size; shuffle=True; ||                                        last_batch='rollover'; num_workers=num_workers) ||     val_loader = DetectionDataLoader(val_dataset.transform(val_transform); batch_size; shuffle=False; ||                                      last_batch='keep'; num_workers=num_workers) ||  ||     for ib; batch in enumerate(train_loader): ||         if ib > 5: ||             break ||         print('data:'; batch[0].shape; 'label:'; batch[1].shape) ||  ||  || .. parsed-literal:: ||  ||     data: (4; 3; 512; 512) label: (4; 1; 6) ||     data: (4; 3; 512; 512) label: (4; 1; 6) ||     data: (4; 3; 512; 512) label: (4; 1; 6) ||     data: (4; 3; 512; 512) label: (4; 6; 6) ||     data: (4; 3; 512; 512) label: (4; 2; 6) ||     data: (4; 3; 512; 512) label: (4; 5; 6) ||  ||  || SSD network || ----------- ||  || SSD network is a composite Gluon HybridBlock(which means it can be || exported to symbol to run in C++; Scala and other language bindings; but || we will cover it future tutorials). In terms of structure; SSD networks || are composed of feature extraction base network; anchor generators; || class predictors and bounding box offsets predictors. If you have read || our introductory || `tutorial <http:\/\/gluon.mxnet.io\/chapter08_computer-vision\/object-detection.html>`__ || of SSD; you may have better idea how it works. You can also refer to || original paper and entry level tutorials for idea that support SSD. ||  || GluonVision has a model zoo which has a lot of built-in SSD networks. || Therefore you can simply load them from model\\_zoo module like this: ||  || .. code:: python ||  ||     from gluonvision import model_zoo ||     net = model_zoo.get_model('ssd_300_vgg16_atrous_voc'; pretrained_base=False) ||     print(net) ||  ||  || .. parsed-literal:: ||  ||     SSD( ||       (class_predictors): HybridSequential( ||         (0): ConvPredictor( ||           (predictor): Conv2D(None -> 84; kernel_size=(3; 3); stride=(1; 1); padding=(1; 1)) ||         ) ||         (1): ConvPredictor( ||           (predictor): Conv2D(None -> 126; kernel_size=(3; 3); stride=(1; 1); padding=(1; 1)) ||         ) ||         (2): ConvPredictor( ||           (predictor): Conv2D(None -> 126; kernel_size=(3; 3); stride=(1; 1); padding=(1; 1)) ||         ) ||         (3): ConvPredictor( ||           (predictor): Conv2D(None -> 126; kernel_size=(3; 3); stride=(1; 1); padding=(1; 1)) ||         ) ||         (4): ConvPredictor( ||           (predictor): Conv2D(None -> 84; kernel_size=(3; 3); stride=(1; 1); padding=(1; 1)) ||         ) ||         (5): ConvPredictor( ||           (predictor): Conv2D(None -> 84; kernel_size=(3; 3); stride=(1; 1); padding=(1; 1)) ||         ) ||       ) ||       (box_predictors): HybridSequential( ||         (0): ConvPredictor( ||           (predictor): Conv2D(None -> 16; kernel_size=(3; 3); stride=(1; 1); padding=(1; 1)) ||         ) ||         (1): ConvPredictor( ||           (predictor): Conv2D(None -> 24; kernel_size=(3; 3); stride=(1; 1); padding=(1; 1)) ||         ) ||         (2): ConvPredictor( ||           (predictor): Conv2D(None -> 24; kernel_size=(3; 3); stride=(1; 1); padding=(1; 1)) ||         ) ||         (3): ConvPredictor( ||           (predictor): Conv2D(None -> 24; kernel_size=(3; 3); stride=(1; 1); padding=(1; 1)) ||         ) ||         (4): ConvPredictor( ||           (predictor): Conv2D(None -> 16; kernel_size=(3; 3); stride=(1; 1); padding=(1; 1)) ||         ) ||         (5): ConvPredictor( ||           (predictor): Conv2D(None -> 16; kernel_size=(3; 3); stride=(1; 1); padding=(1; 1)) ||         ) ||       ) ||       (cls_decoder): MultiPerClassDecoder( ||  ||       ) ||       (features): VGGAtrousExtractor( ||         (norm4): Normalize( ||  ||         ) ||         (extras): HybridSequential( ||           (0): HybridSequential( ||             (0): Conv2D(None -> 256; kernel_size=(1; 1); stride=(1; 1)) ||             (1): Activation(relu) ||             (2): Conv2D(None -> 512; kernel_size=(3; 3); stride=(2; 2); padding=(1; 1)) ||             (3): Activation(relu) ||           ) ||           (1): HybridSequential( ||             (0): Conv2D(None -> 128; kernel_size=(1; 1); stride=(1; 1)) ||             (1): Activation(relu) ||             (2): Conv2D(None -> 256; kernel_size=(3; 3); stride=(2; 2); padding=(1; 1)) ||             (3): Activation(relu) ||           ) ||           (2): HybridSequential( ||             (0): Conv2D(None -> 128; kernel_size=(1; 1); stride=(1; 1)) ||             (1): Activation(relu) ||             (2): Conv2D(None -> 256; kernel_size=(3; 3); stride=(1; 1)) ||             (3): Activation(relu) ||           ) ||           (3): HybridSequential( ||             (0): Conv2D(None -> 128; kernel_size=(1; 1); stride=(1; 1)) ||             (1): Activation(relu) ||             (2): Conv2D(None -> 256; kernel_size=(3; 3); stride=(1; 1)) ||             (3): Activation(relu) ||           ) ||         ) ||         (stages): HybridSequential( ||           (0): HybridSequential( ||             (0): Conv2D(None -> 64; kernel_size=(3; 3); stride=(1; 1); padding=(1; 1)) ||             (1): Activation(relu) ||             (2): Conv2D(None -> 64; kernel_size=(3; 3); stride=(1; 1); padding=(1; 1)) ||             (3): Activation(relu) ||           ) ||           (1): HybridSequential( ||             (0): Conv2D(None -> 128; kernel_size=(3; 3); stride=(1; 1); padding=(1; 1)) ||             (1): Activation(relu) ||             (2): Conv2D(None -> 128; kernel_size=(3; 3); stride=(1; 1); padding=(1; 1)) ||             (3): Activation(relu) ||           ) ||           (2): HybridSequential( ||             (0): Conv2D(None -> 256; kernel_size=(3; 3); stride=(1; 1); padding=(1; 1)) ||             (1): Activation(relu) ||             (2): Conv2D(None -> 256; kernel_size=(3; 3); stride=(1; 1); padding=(1; 1)) ||             (3): Activation(relu) ||             (4): Conv2D(None -> 256; kernel_size=(3; 3); stride=(1; 1); padding=(1; 1)) ||             (5): Activation(relu) ||           ) ||           (3): HybridSequential( ||             (0): Conv2D(None -> 512; kernel_size=(3; 3); stride=(1; 1); padding=(1; 1)) ||             (1): Activation(relu) ||             (2): Conv2D(None -> 512; kernel_size=(3; 3); stride=(1; 1); padding=(1; 1)) ||             (3): Activation(relu) ||             (4): Conv2D(None -> 512; kernel_size=(3; 3); stride=(1; 1); padding=(1; 1)) ||             (5): Activation(relu) ||           ) ||           (4): HybridSequential( ||             (0): Conv2D(None -> 512; kernel_size=(3; 3); stride=(1; 1); padding=(1; 1)) ||             (1): Activation(relu) ||             (2): Conv2D(None -> 512; kernel_size=(3; 3); stride=(1; 1); padding=(1; 1)) ||             (3): Activation(relu) ||             (4): Conv2D(None -> 512; kernel_size=(3; 3); stride=(1; 1); padding=(1; 1)) ||             (5): Activation(relu) ||           ) ||           (5): HybridSequential( ||             (0): Conv2D(None -> 1024; kernel_size=(3; 3); stride=(1; 1); padding=(6; 6); dilation=(6; 6)) ||             (1): Activation(relu) ||             (2): Conv2D(None -> 1024; kernel_size=(1; 1); stride=(1; 1)) ||             (3): Activation(relu) ||           ) ||         ) ||       ) ||       (anchor_generators): HybridSequential( ||         (0): SSDAnchorGenerator( ||  ||         ) ||         (1): SSDAnchorGenerator( ||  ||         ) ||         (2): SSDAnchorGenerator( ||  ||         ) ||         (3): SSDAnchorGenerator( ||  ||         ) ||         (4): SSDAnchorGenerator( ||  ||         ) ||         (5): SSDAnchorGenerator( ||  ||         ) ||       ) ||       (bbox_decoder): NormalizedBoxCenterDecoder( ||  ||       ) ||     ) ||  ||  || SSD network is a HybridBlock as mentioned before. So you can call it || with an input as simple as: ||  || .. code:: python ||  ||     import mxnet as mx ||     x = mx.nd.zeros(shape=(1; 3; 300; 300)) ||     net.initialize() ||     cids; scores; bboxes = net(x) ||  || where ``cids`` is the class labels; ``scores`` are confidences of each || predictions; ``bboxes`` are corresponding bounding boxes' absolute || coordinates. ||  || Training targets || ---------------- ||  || Unlike a single ``SoftmaxCrossEntropyLoss`` used in image || classification; the losses used in SSD is more complicated. Don't worry || though; because we have these modules available out of box. ||  || Checkout the ``target_generator`` in SSD networks. ||  || .. code:: python ||  ||     print(net.target_generator) ||  ||  || .. parsed-literal:: ||  ||     SSDTargetGenerator( ||       (_sampler): OHEMSampler( ||  ||       ) ||       (_center_to_corner): BBoxCenterToCorner( ||  ||       ) ||       (_box_encoder): NormalizedBoxCenterEncoder( ||         (corner_to_center): BBoxCornerToCenter( ||  ||         ) ||       ) ||       (_cls_encoder): MultiClassEncoder( ||  ||       ) ||       (_matcher): CompositeMatcher( ||  ||       ) ||     ) ||  ||  || You can see there are bounding boxes encoder which transfers raw || coordinates to bbox prediction targets; a class encoder which generates || class labels for each anchor box. Matcher and samplers included are used || to apply various advanced strategies described in paper. ||  || References || ---------- ||  || [1] Wei Liu; Dragomir Anguelov; Dumitru Erhan; Christian Szegedy; Scott || Reed; Cheng-Yang Fu; Alexander C. Berg. SSD: Single Shot MultiBox || Detector. ECCV 2016. ||  || Dive deep into training script || ------------------------------ ||  || We include an training script for reproducing SOTA models. ||  || Example: ||  || :: ||  ||     python train_ssd.py --network vgg16_atrous --data-shape 300 --batch-size 32 --gpus 0;1;2;3 -j 32 --log-interval 20 ||  || \"\"\"
aaifabfaf,dmlc/gluon-cv,scripts/classification/cifar/demo.py,e7389b15ea6509268a5c54d239992df43603d50b,STILL_EXISTS,\"\"\"Training Your First Classification Model on CIFAR10 || =================================================== ||  || ```CIFAR10`` <https:\/\/www.cs.toronto.edu\/~kriz\/cifar.html>`__ is a || labeled dataset of tiny (32x32) images; collected by Alex Krizhevsky; || Vinod Nair; and Geoffrey Hinton. It is widely used as a benchmark in || conputer vision research. ||  || In this tutorial; we will demonstrate how to use ``Gluon`` to train a || model from scratch and reproduce the performance from papers. || Specifically; we offer a script to prepare the ``CIFAR10`` dataset and || train a ``ResNet`` model at || `scripts\/classification\/cifar\/train.py <https:\/\/github.com\/dmlc\/gluon-vision\/blob\/master\/scripts\/classification\/cifar\/train.py>`__. ||  || In the following content; we will demonstrate how to ||  || -  How well can our model predict || -  train a model || -  plot the training history ||  || Demo and Benchmark || ------------------ ||  || Before busying with training and parameter tuning; you may want to get || an idea of what the result may look like. ||  || Here we provide you a script; || ```demo.py`` <https:\/\/github.com\/hetong007\/gluon-vision\/blob\/master\/scripts\/classification\/cifar\/demo.py>`__; || to load a pre-trained model from us and predict on any image on your || disk. ||  || This is an airplane: ||  || |image0| ||  || We can make prediction by ||  || :: ||  ||     python demo.py --model cifar_resnet110_v1 --input-pic bird.jpg ||  || And the model thinks that ||  || :: ||  ||     The input picture is classified to be [airplane]; with probability 0.517. ||  || Feel free to feed in your own image to see how well it does the job. || Keep in mind; that ``CIFAR10`` is relatively small and has only 10 || classes. Models trained on ``CIFAR10`` only recognize objects from those || 10 classes; therefore it may surprise you: ||  || :: ||  ||     python demo.py --model cifar_resnet110_v1 --input-pic surprise.jpg ||  || The result is: ||  || :: ||  ||     bird! ||  || To experience a more real world friendly demo; please checkout models || trained on `ImageNet <>`__. ||  || Train Your First Model || ---------------------- ||  || In the demo; we have used a pretrained model. So how did we train it? ||  || We trained the models with || ```train.py`` <https:\/\/github.com\/hetong007\/gluon-vision\/blob\/master\/scripts\/classification\/cifar\/train.py>`__. || It takes a lot of parameters to control the model training process. To || start; you can try the following command: ||  || :: ||  ||     python train.py --num-epochs 240 --mode hybrid --num-gpus 2 -j 32 --batch-size 64\\ ||         --wd 0.0001 --lr 0.1 --lr-decay 0.1 --lr-decay-epoch 80;160 --model cifar_resnet20_v2 ||  || This command trains a ``ResNet20_V2`` model for 240 epochs on two GPUs. || The batch size for each GPU is 64; thus the total batch size is 128. We || decay the learning rate by a factor of 10 at the 80-th and 160-th epoch. || The script prints information for each epoch so that we can have a sense || of the progress and watchout for any unexpected issues. ||  || :: ||  ||     INFO:root:[Epoch 0] train=0.229176 val=0.422300 loss=101760.375931 time: 21.937484 ||     INFO:root:[Epoch 1] train=0.218320 val=0.524200 loss=93098.808853 time: 21.637539 ||     INFO:root:[Epoch 2] train=0.211201 val=0.559400 loss=89708.618820 time: 21.596765 ||     INFO:root:[Epoch 3] train=0.205876 val=0.609300 loss=87576.185600 time: 20.972680 ||     INFO:root:[Epoch 4] train=0.202815 val=0.614800 loss=85852.031380 time: 21.104631 ||     INFO:root:[Epoch 5] train=0.200063 val=0.659800 loss=83747.976288 time: 21.788607 ||     ... ||  || The dataset and the model are relatively small; thus it won\u2019t take you || too long to train the model. Of course it depends on how powerful your || machine is; the result on our side is: ||  || -  20 seconds with two V100 GPUs per epoch; and 32 CPU threads. || -  100 seconds with a GTX1060 GPU per epoch; and 4 CPU threads. ||  || With limited computational power; it is good in practice to firstly test || a few epochs to ensure everything works; then leave it running for a || night; and wake up to see the result :) ||  || After the training; the accuracy is expect to be around 91%. To get a || better accuracy; we can train a ``ResNet110_V2`` model instead by || ``--model cifar_resnet110_v2``; at the cost of around 4 times of the || training time. With ``ResNet110_V2``; we expect the accuracy to be || around 94%. ||  || Don\u2019t Overfit || ------------- ||  || The training of a deep learning model is usually a trial-and-error || process. A good way to review the result is to have a plot: ||  || This is a plot generated from the following command: ||  || :: ||  || We see that the issue could be not enough epochs. We then change to ||  || :: ||  || and observe that ||  || Model Zoo || --------- ||  || We train various models and store them on cloud as a \u201Czoo of the || models\u201D. Users can pick the model with regards to the accuracy and model || complexity. ||  || Here\u2019s what we have for ``CIFAR10`` so far: ||  || +---------------------------+----------+ || | Model                     | Accuracy | || +===========================+==========+ || | ``CIFAR_ResNet20_v1``     | 0.9160   | || +---------------------------+----------+ || | ``CIFAR_ResNet56_v1``     | 0.9387   | || +---------------------------+----------+ || | ``CIFAR_ResNet110_v1``    | 0.9471   | || +---------------------------+----------+ || | ``CIFAR_ResNet20_v2``     | 0.9130   | || +---------------------------+----------+ || | ``CIFAR_ResNet56_v2``     | 0.9413   | || +---------------------------+----------+ || | ``CIFAR_ResNet110_v2``    | 0.9464   | || +---------------------------+----------+ || | ``CIFAR_WideResNet16_10`` | 0.9614   | || +---------------------------+----------+ || | ``CIFAR_WideResNet28_10`` | 0.9667   | || +---------------------------+----------+ || | ``CIFAR_WideResNet40_8``  | 0.9673   | || +---------------------------+----------+ ||  || Most of them are more accurate than the claims in the original papers. || The reason is that we incorporate a technique called || ```Mix-Up`` <https:\/\/arxiv.org\/abs\/1710.09412>`__ to improve the || performance without changing the network structure. ||  || Specifically; we train the ``cifar_resnet`` models with: ||  || :: ||  ||     python train_mixup.py --num-epochs 450 --mode hybrid --num-gpus 2 -j 32\\ ||         --batch-size 64 --wd 0.0001 --lr 0.1 --lr-decay 0.1 --lr-decay-epoch 150;250\\ ||         --model cifar_resnet20_v1 ||  || and the ``cifar_wideresnet`` models with: ||  || :: ||  ||     python train_mixup.py --num-epochs 500 --mode hybrid --num-gpus 2 -j 32\\ ||         --batch-size 64 --wd 0.0001 --lr 0.1 --lr-decay 0.1 --lr-decay-epoch 100;200;300\\ ||         --model cifar_wideresnet16_10 ||  || Next Step || --------- ||  || Congratulations! You\u2019ve just finished reading our first tutorial. We || have a lot more others to help you learn more and get familiar with || ``gluonvision``. ||  || If you would like to dig deeper in the topic of ``CIFAR10`` training; || feel free to read `the next tutorial on ``CIFAR10`` <>`__. ||  || Or; if you would like to try a more powerful demo; i.e. models trained || on ImageNet; please read `xxx <>`__. ||  || .. |image0| image:: plane-draw.jpeg ||  || \"\"\"
aaifabfaj,dmlc/gluon-cv,scripts/classification/cifar/train.py,e7389b15ea6509268a5c54d239992df43603d50b,STILL_EXISTS,\"\"\"Dive Deep Into CIFAR10 || ====================== ||  || Hope you enjoyed playing with our demo script. One question; as may || naturally arise in your mind: how exactly did we train the model? ||  || In this tutorial; we will focus on answering this question. || Specifically; the following discusses ||  || -  Model Structure || -  Data Augmentation and Data Loader || -  Optimizer; Loss and Metric || -  Validation || -  Training || -  Model save and load ||  || Prerequisites || ------------- ||  || We assume readers have a basic understanding of ``Gluon``. If you would || like to know more about it; we suggest `Crash || Course <http:\/\/gluon-crash-course.mxnet.io\/index.html>`__ as a good || place to start. ||  || As we all know; deep learning training is way faster on GPU than on CPU. || In our demo; we only used CPU by default since that script only performs || minimum conputation. However; since we are about to train a model; it is || strongly recommended to have a platform with GPU(s). ||  || Training usually takes several hours. While you are reading the || tutorial; it is a good idea to start the training script with ||  || :: ||  ||     python train.py --num-epochs 240 --mode hybrid --num-gpus 2 -j 32 --batch-size 64\\ ||         --wd 0.0001 --lr 0.1 --lr-decay 0.1 --lr-decay-epoch 80;160 --model cifar_resnet20_v1 ||  || and remember to replace ``--num-gpus`` to the number of GPUs you have; || and ``-j`` to a number not larger than your CPU threads. ||  || Let's load the necessary libraries first. ||  || .. code:: ipython2 ||  ||     from __future__ import division ||  ||     import matplotlib ||     matplotlib.use('Agg') ||  ||     import argparse; time; logging; random; math ||  ||     import numpy as np ||     import mxnet as mx ||  ||     from mxnet import gluon; nd ||     from mxnet import autograd as ag ||     from mxnet.gluon import nn ||     from mxnet.gluon.data.vision import transforms ||  ||     from gluonvision.model_zoo import get_model ||     from gluonvision.utils import makedirs; TrainingHistory ||  || Network Structure || ----------------- ||  || There are numerous structures for convolutional neural networks. The || structure mainly affects ||  || -  The upperbound of the accuracy. || -  The cost of resources; in terms of training time and memory. ||  || Here we pick a simple yet good structure; ``cifar_resnet20_v1``; for the || tutorial. ||  || .. code:: ipython2 ||  ||     # GPUs to use ||     ctx = [mx.gpu(0); mx.gpu(1)] ||  ||     # Get the model CIFAR_ResNet20_v1; with 10 output classes ||     net = get_model('cifar_resnet20_v1'; classes=10) ||     net.initialize(mx.init.Xavier(); ctx = ctx) ||  || Data Augmentation and Data Loader || --------------------------------- ||  || Data augmentation is a common technique used in model training. It is || proposed base on this assumption: given an object; photos with different || composition; lighting condition; or different color may still be || classified as the same object. ||  || Here are photos taken by different people; at different time. We can all || tell that they are the photo for the same thing. ||  || .. figure:: ||    :alt: ||  || We would like to teach the model to learn about it; by playing \"tricks\" || on the input picture. The trick is to transform the picture with || resizing; cropping and flipping before sending to the model. ||  || In ``Gluon``; we can compose our transform function as following: ||  || .. code:: ipython2 ||  ||     transform_train = transforms.Compose([ ||         # Resize the short edge of the input to 32 pixels ||         transforms.Resize(32); ||         # Randomly crop an area; and then resize it to be 32x32 ||         transforms.RandomResizedCrop(32); ||         # Randomly flip the picture horizontally ||         transforms.RandomFlipLeftRight(); ||         # Randomly manipulate the brightness; contrast and saturation of the picture ||         transforms.RandomColorJitter(brightness=0.1; contrast=0.1; saturation=0.1); ||         # Randomly adding noise to the picture ||         transforms.RandomLighting(0.1); ||         # Transpose the data from Height*Width*Channel to Channel*Height*Width ||         # and map values from [0; 255] to [0;1] ||         transforms.ToTensor(); ||         # Normalize the image ||         transforms.Normalize([0.4914; 0.4822; 0.4465]; [0.2023; 0.1994; 0.2010]) ||     ]) ||  || You may notice that most of the operations are at random. This largely || increase the number of pictures the model can see during the training || process. The more data we have; the better our model can generalize on || unseen pictures. ||  || On the other hand; when making prediction; we would like to remove all || random operations because we want a deterministic result. The transform || function for prediction is: ||  || .. code:: ipython2 ||  ||     transform_test = transforms.Compose([ ||         transforms.Resize(32); ||         transforms.ToTensor(); ||         transforms.Normalize([0.4914; 0.4822; 0.4465]; [0.2023; 0.1994; 0.2010]) ||     ]) ||  || Notice that it is important to keep the normalization step; since the || model only works well on input with the same distribution. ||  || With the transform functions; we can define data loaders for our || training and validation datasets. ||  || .. code:: ipython2 ||  ||     # Batch Size for Each GPU ||     per_device_batch_size = 64 ||     # Number of data loader workers ||     num_workers = 32 ||     # Calculate effective total batch size ||     batch_size = per_device_batch_size * 2 ||  ||     # Set train=True for training data ||     # Set shuffle=True to shuffle the training data ||     train_data = gluon.data.DataLoader( ||         gluon.data.vision.CIFAR10(train=True).transform_first(transform_train); ||         batch_size=batch_size; shuffle=True; last_batch='discard'; num_workers=num_workers) ||  ||     # Set train=False for validation data ||     val_data = gluon.data.DataLoader( ||         gluon.data.vision.CIFAR10(train=False).transform_first(transform_test); ||         batch_size=batch_size; shuffle=False; num_workers=num_workers) ||  ||  || Now the data is ready. Let's move on to the optimizer. ||  || Optimizer; Loss and Metric || -------------------------- ||  || Optimizer is what improves the model during training. We use the popular || Nesterov accelerated gradient descent algorithm. ||  || .. code:: ipython2 ||  ||     # Learning rate decay factor ||     lr_decay = 0.1 ||     # Epochs where learning rate decays ||     lr_decay_epoch = [80; 160; np.inf] ||  ||     # Nesterov accelerated gradient descent ||     optimizer = 'nag' ||     # Set parameters ||     optimizer_params = {'learning_rate': 0.1; 'wd': 0.0001; 'momentum': 0.9} ||  ||     # Define our trainer for net ||     trainer = gluon.Trainer(net.collect_params(); optimizer; optimizer_params) ||  || In the above code; ``lr_decay`` and ``lr_decay_epoch`` are not directly || used in ``trainer``. One important idea in model training is to decrease || the learning rate in a later stage. This means the model takes larger || steps at the beginning; and smaller steps after a while. ||  || Our plan is to have the learning rate as 0.1 at the beginning; then || divide it by 10 at the 80-th epoch; then again at the 160-th epoch. || Later we'll show how to implement it. ||  || In order to let the optimizer work; we need a loss function. In plain || words; the loss function measures how good our model performs; and pass || the \"difference\" to the model. For the Nesterov algorithm we are using; || the difference is the gradient of the loss function. With the || difference; the optimizer knows towards which direction to improve the || model parameters. ||  || For classification tasks; we usually use softmax cross entropy as the || loss function. ||  || .. code:: ipython2 ||  ||     loss_fn = gluon.loss.SoftmaxCrossEntropyLoss() ||  || Metric is somehow similar to loss function; but they are essentially || different. ||  || -  Metric is how we evaluate the model performance. It is related to the ||    specific task; but independent from the model training process. || -  For classification; we usually only use one loss function to train ||    our model; but we can have multiplt metrics to evaluate the ||    performance. || -  Loss function can be used as a metric; but sometimes it is hard to ||    interpretate its value. For instance; the concept \"accuracy\" is ||    easier to understand than \"softmax cross entropy\" ||  || For simplicity; we use accuracy as the metric to monitor our training || process. Besides; we record the metric values; and will print it in the || end of the training. ||  || .. code:: ipython2 ||  ||     train_metric = mx.metric.Accuracy() ||     train_history = TrainingHistory(['training-error'; 'validation-error']) ||  || Validation || ---------- ||  || The existance of the validation dataset provides us a way to monitor the || training process. We have the labels on validation data; but just don't || use it to train. Therefore we can predict on the validation with the || model; and evaluate the performance at anytime. ||  || .. code:: ipython2 ||  ||     def test(ctx; val_data): ||         metric = mx.metric.Accuracy() ||         for i; batch in enumerate(val_data): ||             data = gluon.utils.split_and_load(batch[0]; ctx_list=ctx; batch_axis=0) ||             label = gluon.utils.split_and_load(batch[1]; ctx_list=ctx; batch_axis=0) ||             outputs = [net(X) for X in data] ||             metric.update(label; outputs) ||         return metric.get() ||  || In order to evaluate the performance; we need a metric. Then we loop || through the validation data and predict with our model. We'll plug it || into the end of each training epoch to show the improvement. ||  || Training || -------- ||  || After all these preparation; we can finally start our training process! || Following is the script. ||  || Notice: in order to speed up the training process; we only train the || model for 5 epochs. ||  || .. code:: ipython2 ||  ||     epochs = 5 ||     lr_decay_count = 0 ||  ||     for epoch in range(epochs): ||         tic = time.time() ||         train_metric.reset() ||         train_loss = 0 ||  ||         # Learning rate decay ||         if epoch == lr_decay_epoch[lr_decay_count]: ||             trainer.set_learning_rate(trainer.learning_rate*lr_decay) ||             lr_decay_count += 1 ||  ||         # Loop through each batch of training data ||         for i; batch in enumerate(train_data): ||             # Extract data and label ||             data = gluon.utils.split_and_load(batch[0]; ctx_list=ctx; batch_axis=0) ||             label = gluon.utils.split_and_load(batch[1]; ctx_list=ctx; batch_axis=0) ||  ||             # AutoGrad ||             with ag.record(): ||                 output = [net(X) for X in data] ||                 loss = [loss_fn(yhat; y) for yhat; y in zip(output; label)] ||  ||             # Backpropagation ||             for l in loss: ||                 l.backward() ||  ||             # Optimize ||             trainer.step(batch_size) ||  ||             # Update metrics ||             train_loss += sum([l.sum().asscalar() for l in loss]) ||             train_metric.update(label; output) ||  ||         name; acc = train_metric.get() ||         # Evaluate on Validation data ||         name; val_acc = test(ctx; val_data) ||  ||         # Update history and print metrics ||         train_history.update({'training-error': 1-acc; 'validation-error': 1-val_acc}) ||         print('[Epoch %d] train=%f val=%f loss=%f time: %f' % ||             (epoch; acc; val_acc; train_loss; time.time()-tic)) ||  ||  || .. parsed-literal:: ||  ||     [Epoch 0] train=0.362941 val=0.426600 loss=86895.413788 time: 25.992320 ||     [Epoch 1] train=0.496014 val=0.584200 loss=69834.481152 time: 25.585870 ||     [Epoch 2] train=0.562099 val=0.646200 loss=61467.327183 time: 25.127841 ||     [Epoch 3] train=0.599479 val=0.658300 loss=56717.487179 time: 25.180641 ||     [Epoch 4] train=0.624119 val=0.670500 loss=52825.070946 time: 25.824175 ||  ||  || We can plot the metric scores with: ||  || .. code:: ipython2 ||  ||     train_history.plot(items=['training-error'; 'validation-error']) ||  || This is just a plot for 5 epochs. Instead; if you change to || ``epochs=240``; the plot may look like: ||  || Model Save and Load || ------------------- ||  || Since the model is here; we may want to save it for later use; for || example; to predict the class from an arbitrary picture. ||  || It's simple! We can do it by: ||  || .. code:: ipython2 ||  ||     net.save_params('dive_deep_cifar10_resnet20_v2.params') ||  || Next time if you need to use it; just ||  || .. code:: ipython2 ||  ||     net.load_params('dive_deep_cifar10_resnet20_v2.params'; ctx=ctx) ||  || Next Step || --------- ||  || This is the end of our adventure with ``CIFAR10``; but there are still a || lot more we can do! ||  || Following is a script extending our tutorial with command line arguments. || Please train a model with your own configurations. ||  || If you would like to know how to train a model on a much larger dataset || than ``CIFAR10``; e.g. ImageNet; please read `xxx <>`__. ||  || Or; if you want like to know what can be done with the model you just || trained; please read `finetune <>`__. ||  || \"\"\"
aaifabfbb,dmlc/gluon-cv,scripts/classification/imagenet/demo.py,e7389b15ea6509268a5c54d239992df43603d50b,STILL_EXISTS,\"\"\"Training Your First Classification Model on ImageNet || =================================================== ||  || ```ImageNet`` <http:\/\/www.image-net.org\/>`__ is a || large labeled dataset of real-world images. It is the most || well-known dataset for computer vision tasks. ||  || In this tutorial; we will demonstrate how to use ``Gluon`` to train a || model from scratch and reproduce the performance from papers. || Specifically; we offer a script to prepare the ``CIFAR10`` dataset and || train a ``ResNet`` model at || `scripts\/classification\/cifar\/train.py <https:\/\/github.com\/dmlc\/gluon-vision\/blob\/master\/scripts\/classification\/cifar\/train.py>`__. ||  || In the following content; we will demonstrate how to ||  || -  How well can our model predict || -  train a model || -  plot the training history ||  || Demo and Benchmark || ------------------ ||  || Before busying with training and parameter tuning; you may want to get || an idea of what the result may look like. ||  || Here we provide you a script; || ```demo.py`` <https:\/\/github.com\/hetong007\/gluon-vision\/blob\/master\/scripts\/classification\/cifar\/demo.py>`__; || to load a pre-trained model from us and predict on any image on your || disk. ||  || This is an airplane: ||  || |image0| ||  || We can make prediction by ||  || :: ||  ||     python demo.py --model cifar_resnet110_v1 --input-pic bird.jpg ||  || And the model thinks that ||  || :: ||  ||     The input picture is classified to be [airplane]; with probability 0.517. ||  || Feel free to feed in your own image to see how well it does the job. || Keep in mind; that ``CIFAR10`` is relatively small and has only 10 || classes. Models trained on ``CIFAR10`` only recognize objects from those || 10 classes; therefore it may surprise you: ||  || :: ||  ||     python demo.py --model cifar_resnet110_v1 --input-pic surprise.jpg ||  || The result is: ||  || :: ||  ||     bird! ||  || To experience a more real world friendly demo; please checkout models || trained on `ImageNet <>`__. ||  || Train Your First Model || ---------------------- ||  || In the demo; we have used a pretrained model. So how did we train it? ||  || We trained the models with || ```train.py`` <https:\/\/github.com\/hetong007\/gluon-vision\/blob\/master\/scripts\/classification\/cifar\/train.py>`__. || It takes a lot of parameters to control the model training process. To || start; you can try the following command: ||  || :: ||  ||     python train.py --num-epochs 240 --mode hybrid --num-gpus 2 -j 32 --batch-size 64\\ ||         --wd 0.0001 --lr 0.1 --lr-decay 0.1 --lr-decay-epoch 80;160 --model cifar_resnet20_v2 ||  || This command trains a ``ResNet20_V2`` model for 240 epochs on two GPUs. || The batch size for each GPU is 64; thus the total batch size is 128. We || decay the learning rate by a factor of 10 at the 80-th and 160-th epoch. || The script prints information for each epoch so that we can have a sense || of the progress and watchout for any unexpected issues. ||  || :: ||  ||     INFO:root:[Epoch 0] train=0.229176 val=0.422300 loss=101760.375931 time: 21.937484 ||     INFO:root:[Epoch 1] train=0.218320 val=0.524200 loss=93098.808853 time: 21.637539 ||     INFO:root:[Epoch 2] train=0.211201 val=0.559400 loss=89708.618820 time: 21.596765 ||     INFO:root:[Epoch 3] train=0.205876 val=0.609300 loss=87576.185600 time: 20.972680 ||     INFO:root:[Epoch 4] train=0.202815 val=0.614800 loss=85852.031380 time: 21.104631 ||     INFO:root:[Epoch 5] train=0.200063 val=0.659800 loss=83747.976288 time: 21.788607 ||     ... ||  || The dataset and the model are relatively small; thus it won\u2019t take you || too long to train the model. Of course it depends on how powerful your || machine is; the result on our side is: ||  || -  20 seconds with two V100 GPUs per epoch; and 32 CPU threads. || -  100 seconds with a GTX1060 GPU per epoch; and 4 CPU threads. ||  || With limited computational power; it is good in practice to firstly test || a few epochs to ensure everything works; then leave it running for a || night; and wake up to see the result :) ||  || After the training; the accuracy is expect to be around 91%. To get a || better accuracy; we can train a ``ResNet110_V2`` model instead by || ``--model cifar_resnet110_v2``; at the cost of around 4 times of the || training time. With ``ResNet110_V2``; we expect the accuracy to be || around 94%. ||  || Don\u2019t Overfit || ------------- ||  || The training of a deep learning model is usually a trial-and-error || process. A good way to review the result is to have a plot: ||  || This is a plot generated from the following command: ||  || :: ||  || We see that the issue could be not enough epochs. We then change to ||  || :: ||  || and observe that ||  || Model Zoo || --------- ||  || We train various models and store them on cloud as a \u201Czoo of the || models\u201D. Users can pick the model with regards to the accuracy and model || complexity. ||  || Here\u2019s what we have for ``CIFAR10`` so far: ||  || +---------------------------+----------+ || | Model                     | Accuracy | || +===========================+==========+ || | ``CIFAR_ResNet20_v1``     | 0.9160   | || +---------------------------+----------+ || | ``CIFAR_ResNet56_v1``     | 0.9387   | || +---------------------------+----------+ || | ``CIFAR_ResNet110_v1``    | 0.9471   | || +---------------------------+----------+ || | ``CIFAR_ResNet20_v2``     | 0.9130   | || +---------------------------+----------+ || | ``CIFAR_ResNet56_v2``     | 0.9413   | || +---------------------------+----------+ || | ``CIFAR_ResNet110_v2``    | 0.9464   | || +---------------------------+----------+ || | ``CIFAR_WideResNet16_10`` | 0.9614   | || +---------------------------+----------+ || | ``CIFAR_WideResNet28_10`` | 0.9667   | || +---------------------------+----------+ || | ``CIFAR_WideResNet40_8``  | 0.9673   | || +---------------------------+----------+ ||  || Most of them are more accurate than the claims in the original papers. || The reason is that we incorporate a technique called || ```Mix-Up`` <https:\/\/arxiv.org\/abs\/1710.09412>`__ to improve the || performance without changing the network structure. ||  || Specifically; we train the ``cifar_resnet`` models with: ||  || :: ||  ||     python train_mixup.py --num-epochs 450 --mode hybrid --num-gpus 2 -j 32\\ ||         --batch-size 64 --wd 0.0001 --lr 0.1 --lr-decay 0.1 --lr-decay-epoch 150;250\\ ||         --model cifar_resnet20_v1 ||  || and the ``cifar_wideresnet`` models with: ||  || :: ||  ||     python train_mixup.py --num-epochs 500 --mode hybrid --num-gpus 2 -j 32\\ ||         --batch-size 64 --wd 0.0001 --lr 0.1 --lr-decay 0.1 --lr-decay-epoch 100;200;300\\ ||         --model cifar_wideresnet16_10 ||  || Next Step || --------- ||  || Congratulations! You\u2019ve just finished reading our first tutorial. We || have a lot more others to help you learn more and get familiar with || ``gluonvision``. ||  || If you would like to dig deeper in the topic of ``CIFAR10`` training; || feel free to read `the next tutorial on ``CIFAR10`` <>`__. ||  || Or; if you would like to try a more powerful demo; i.e. models trained || on ImageNet; please read `xxx <>`__. ||  || .. |image0| image:: mt_baker.jpeg ||  || \"\"\"
aaifabffg,dmlc/gluon-cv,scripts/segmentation/train.py,f1fd98531a1706cf1b6afa0872f5990b937087de,STILL_EXISTS,\"\"\"Train FCN on Pascal VOC Dataset || =============================== ||  || This is a semantic segmentation tutorial using Gluon Vison; a step-by-step example. || The readers should have basic knowledge of deep learning and should be familiar with Gluon API. || New users may first go through Gluon tutorials || `Deep Learning - The Straight Dope <http:\/\/gluon.mxnet.io\/>`_. ||  || Fully Convolutional Network || --------------------------- ||  || .. image:: https:\/\/cdn-images-1.medium.com\/max\/800\/1*wRkj6lsQ5ckExB5BoYkrZg.png ||     :width: 70% ||     :align: center ||  || (figure redit to `Long et al. <https:\/\/arxiv.org\/pdf\/1411.4038.pdf>`_ ) ||  || State-of-the-art approaches of semantic segmentation are typically based on || Fully Convolutional Network (FCN) [Long15]_ . || The key idea of a fully convolutional network is that it is \"fully convolutional\"; || which means it does have any fully connected layers. Therefore; the network can || accept arbitrary input size and make dense per-pixel predictions. || Base\/Encoder network is typically pre-trained on ImageNet; because the features || learned from diverse set of images contain rich contextual information; which || can be beneficial for semantic segmentation. ||  ||  || Model Dilation || ~~~~~~~~~~~~~~ ||  || The adaption of base network pre-trained on ImageNet leads to loss spatial resolution; || because these networks are originally designed for classification task. || Following recent works in semantic segmentation; we apply dilation strategy to the || stage 3 and stage 4 of the pre-trained networks; which produces stride of 8 || featuremaps (models are provided in :class:`gluonvision.model_zoo.Dilated_ResNetV2`). || Visualization of dilated\/atrous convoution: ||  || .. image:: https:\/\/raw.githubusercontent.com\/vdumoulin\/conv_arithmetic\/master\/gif\/dilation.gif ||     :width: 40% ||     :align: center ||  || (figure credit to `conv_arithmetic <https:\/\/github.com\/vdumoulin\/conv_arithmetic>`_ ) ||  || For example; loading a dilated ResNet50 is simply:: ||  ||     pretrained_net = gluonvision.model_zoo.dilated_resnet50(pretrained=True) ||  || For convenience; we provide a base model for semantic segmentation; which automatically || load the pre-trained dilated ResNet :class:`gluonvision.model_zoo.SegBaseModel`; which can || be easily inherited and used. ||  || FCN Block || ~~~~~~~~~ ||  || We build a fully convolutional \"head\" on top of the basenetwork (FCN model is provided || in :class:`gluonvision.model_zoo.FCN`):: ||  ||     class _FCNHead(HybridBlock): ||         def __init__(self; nclass; norm_layer): ||             super(_FCNHead; self).__init__() ||             with self.name_scope(): ||                 self.block = nn.HybridSequential(prefix='') ||                 self.block.add(norm_layer(in_channels=2048)) ||                 self.block.add(nn.Activation('relu')) ||                 self.block.add(nn.Conv2D(in_channels=2048; channels=512; ||                                          kernel_size=3; padding=1)) ||                 self.block.add(norm_layer(in_channels=512)) ||                 self.block.add(nn.Activation('relu')) ||                 self.block.add(nn.Dropout(0.1)) ||                 self.block.add(nn.Conv2D(in_channels=512; channels=nclass; ||                                          kernel_size=1)) ||  ||         def hybrid_forward(self; F; x): ||             return self.block(x) ||  ||     class FCN(SegBaseModel): ||         def __init__(self; nclass; backbone='resnet50'; norm_layer=nn.BatchNorm): ||             super(FCN; self).__init__(backbone; norm_layer) ||             self._prefix = '' ||             with self.name_scope(): ||                 self.head = _FCNHead(nclass; norm_layer=norm_layer) ||             self.head.initialize(init=init.Xavier()) ||  ||         def forward(self; x): ||             _; _; H; W = x.shape ||             x = self.pretrained(x) ||             x = self.head(x) ||             x = F.contrib.BilinearResize2D(x; height=H; width=W) ||             return x ||  || Dataset and Data Augmentation || ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ||  || We provide semantic segmentation datasets in :class:`gluonvision.data`. || For example; we can easily get the Pascal VOC 2012 dataset:: ||  ||     train_set = gluonvision.data.VOCSegmentationDataset(root) ||  || We follow the standard data augmentation routine to transform the input image || and the ground truth label map synchronously. (Note that \"nearest\" || mode upsample are applied to the label maps to avoid messing up the boundaries.) || We first randomly scale the input image from 0.5 to 2.0 times; then rotate || the image from -10 to 10 degrees; and crop the image with padding if needed. ||  || Benchmarks and Training || ~~~~~~~~~~~~~~~~~~~~~~~ ||  || - Training command example:: ||  ||     # First training on augmented set ||     CUDA_VISIBLE_DEVICES=0;1;2;3 python main.py --dataset pascal_aug --model fcn --backbone resnet50 --lr 0.001 --checkname mycheckpoint ||     # Finetuning on original set ||     CUDA_VISIBLE_DEVICES=0;1;2;3 python main.py --dataset pascal_voc --model fcn --backbone resnet50 --lr 0.0001 --checkname mycheckpoint --resume runs\/pascal_aug\/fcn\/mycheckpoint\/checkpoint.params ||  ||   For more training commands; please see the ``Commands`` in the pre-trained Table_. ||  || - Detail training options:: ||      ||     -h; --help            show this help message and exit ||     --model MODEL         model name (default: fcn) ||     --backbone BACKBONE   backbone name (default: resnet50) ||     --dataset DATASET     dataset name (default: pascal) ||     --nclass NCLASS       nclass for pre-trained model (default: None) ||     --workers N           dataloader threads ||     --data-folder         training dataset folder (default: $(HOME)\/data\/) ||     --epochs N            number of epochs to train (default: 50) ||     --start_epoch N       start epochs (default:0) ||     --batch-size N        input batch size for training (default: 16) ||     --test-batch-size N   input batch size for testing (default: 32) ||     --lr LR               learning rate (default: 1e-3) ||     --momentum M          momentum (default: 0.9) ||     --weight-decay M      w-decay (default: 1e-4) ||     --kvstore KVSTORE     kvstore to use for trainer\/module. ||     --no-cuda             disables CUDA training ||     --ngpus NGPUS         number of GPUs (default: 4) ||     --seed S              random seed (default: 1) ||     --resume RESUME       put the path to resuming file if needed ||     --checkname           set the checkpoint name ||     --eval                evaluating mIoU ||     --test                test a set of images and save the prediction ||     --syncbn              using Synchronized Cross-GPU BatchNorm ||  ||  || - Table of pre-trained models and its performance (models :math:`^\\ast` denotes pre-trained on COCO): ||  || .. role:: raw-html(raw) ||    :format: html ||  || .. _Table: ||  ||     +------------------------+------------+-----------+-----------+-----------+-----------+----------------------------------------------------------------------------------------------+ ||     | Method                 | Backbone   | Dataset   | Note      | pixAcc    | mIoU      | Training Scripts                                                                             | ||     +========================+============+===========+===========+===========+===========+==============================================================================================+ ||     | FCN                    | ResNet50   | PASCAL12  | stride 8  | N\/A       | 70.9_     | :raw-html:`<a href=\"javascript:toggleblock('cmd_fcn_50')\" class=\"toggleblock\">cmd<\/a>`       | ||     +------------------------+------------+-----------+-----------+-----------+-----------+----------------------------------------------------------------------------------------------+ ||     | FCN                    | ResNet101  | PASCAL12  | stride 8  | N\/A       |           | :raw-html:`<a href=\"javascript:toggleblock('cmd_fcn_101')\" class=\"toggleblock\">cmd<\/a>`      | ||     +------------------------+------------+-----------+-----------+-----------+-----------+----------------------------------------------------------------------------------------------+ ||  ||     .. _70.9:  http:\/\/host.robots.ox.ac.uk:8080\/anonymous\/FR9APO.html ||  || .. raw:: html ||  ||     <code xml:space=\"preserve\" id=\"cmd_fcn_50\" style=\"display: none; text-align: left; white-space: pre-wrap\"> ||     # First training on augmented set ||     CUDA_VISIBLE_DEVICES=0;1;2;3 python main.py --dataset pascal_aug --model fcn --backbone resnet50 --lr 0.001 --syncbn --checkname mycheckpoint ||     # Finetuning on original set ||     CUDA_VISIBLE_DEVICES=0;1;2;3 python main.py --dataset pascal_voc --model fcn --backbone resnet50 --lr 0.0001 --syncbn --checkname mycheckpoint --resume runs\/pascal_aug\/fcn\/mycheckpoint\/checkpoint.params ||     <\/code> ||  ||     <code xml:space=\"preserve\" id=\"cmd_fcn_101\" style=\"display: none; text-align: left; white-space: pre-wrap\"> ||     # First training on augmented set ||     CUDA_VISIBLE_DEVICES=0;1;2;3 python main.py --dataset pascal_aug --model fcn --backbone resnet101 --lr 0.001 --syncbn --checkname mycheckpoint ||     # Finetuning on original set ||     CUDA_VISIBLE_DEVICES=0;1;2;3 python main.py --dataset pascal_voc --model fcn --backbone resnet101 --lr 0.0001 --syncbn --checkname mycheckpoint --resume runs\/pascal_aug\/fcn\/mycheckpoint\/checkpoint.params ||     <\/code> ||  ||     <code xml:space=\"preserve\" id=\"cmd_psp_50\" style=\"display: none; text-align: left; white-space: pre-wrap\"> ||     # First training on augmented set ||     CUDA_VISIBLE_DEVICES=0;1;2;3 python main.py --dataset pascal_aug --model pspnet --backbone resnet50 --lr 0.001 --syncbn --checkname mycheckpoint ||     # Finetuning on original set ||     CUDA_VISIBLE_DEVICES=0;1;2;3 python main.py --dataset pascal_voc --model pspnet --backbone resnet50 --lr 0.0001 --syncbn --checkname mycheckpoint --resume runs\/pascal_aug\/fcn\/mycheckpoint\/checkpoint.params ||     <\/code> ||  ||     <code xml:space=\"preserve\" id=\"cmd_psp_101\" style=\"display: none; text-align: left; white-space: pre-wrap\"> ||     # First training on augmented set ||     CUDA_VISIBLE_DEVICES=0;1;2;3 python main.py --dataset pascal_aug --model pspnet --backbone resnet101 --lr 0.001 --syncbn --checkname mycheckpoint ||     # Finetuning on original set ||     CUDA_VISIBLE_DEVICES=0;1;2;3 python main.py --dataset pascal_voc --model pspnet --backbone resnet101 --lr 0.0001 --syncbn --checkname mycheckpoint --resume runs\/pascal_aug\/fcn\/mycheckpoint\/checkpoint.params ||     <\/code> ||  ||     <code xml:space=\"preserve\" id=\"cmd_psp_101_coco\" style=\"display: none; text-align: left; white-space: pre-wrap\"> ||     # Pre-training on COCO dataset ||     CUDA_VISIBLE_DEVICES=0;1;2;3 python main.py --dataset mscoco --model pspnet --backbone resnet101 --lr 0.01 --syncbn --checkname mycheckpoint ||     # Training on augmented set ||     CUDA_VISIBLE_DEVICES=0;1;2;3 python main.py --dataset pascal_aug --model pspnet --backbone resnet101 --lr 0.001 --syncbn --checkname mycheckpoint ||     # Finetuning on original set ||     CUDA_VISIBLE_DEVICES=0;1;2;3 python main.py --dataset pascal_voc --model pspnet --backbone resnet101 --lr 0.0001 --syncbn --checkname mycheckpoint --resume runs\/pascal_aug\/fcn\/mycheckpoint\/checkpoint.params ||     <\/code> ||  || References || ---------- ||  || .. [Long15] Long; Jonathan; Evan Shelhamer; and Trevor Darrell. \\ ||     \"Fully convolutional networks for semantic segmentation.\" \\ ||     Proceedings of the IEEE conference on computer vision and pattern recognition. 2015. ||  || Dive Deep into the code || ----------------------- ||  || \"\"\"
aaifabfhg,dmlc/gluon-cv,scripts/datasets/pascal_voc.py,d4d14fb7b36e8557e956ff4f9093babba160be4f,STILL_EXISTS,Download and extract the xxx into ``path``
aaifabgcj,dmlc/gluon-cv,scripts/datasets/ade20k.py,97bb60b6f4e5e66fa4fdc257bc724f2b29cbfd54,STILL_EXISTS,\"\"\"Prepare ADE20K datasets. || ======================== ||  || This script download and prepare the `ADE20K || <http:\/\/sceneparsing.csail.mit.edu\/>`_ dataset for scene parsing.  It contains || more than 20 thousands scene-centric images annotated with 150 object || categories. ||  || .. image:: http:\/\/groups.csail.mit.edu\/vision\/datasets\/ADE20K\/assets\/images\/examples.png ||    :width: 600 px ||  || Prepare the dataset || ------------------- ||  || The easiest way is simply running this script; which will automatically download || and extract the data into ``~\/.mxnet\/datasets\/ade``. ||  || .. code-block:: bash ||  ||    python scripts\/datasets\/ade20k.py ||  || .. note:: ||  ||    You need 2.3 GB disk space to download and extract this dataset. SSD is ||    preferred over HDD because of its better performance. ||  || .. note:: ||  ||    The total time to prepare the dataset depends on your Internet speed and disk ||    performance. For example; it may take 15 min on AWS EC2 with EBS. ||  ||  || If you have already downloaded the following required files; whose URLs can be || obtained from the source codes at the end of this tutorial; ||  || ===========================  ====== || Filename                     Size || ===========================  ====== || ADEChallengeData2016.zip     923 MB || release_test.zip             202 MB || ===========================  ====== ||  || then you can specify the folder name through ``--download-dir`` to avoid || download them again. For example ||  || .. code-block:: python ||  ||    python scripts\/datasets\/ade20k.py --download-dir ~\/ade_downloads ||  || How to load the dataset || ----------------------- ||  || TODO. ||  || Dive deep into source code || -------------------------- ||  || The implementation of ade20k.py is straightforward. It simply downloads and || extract the data. || \"\"\"
aaifabhgj,dmlc/gluon-cv,docs/tutorials/detection/train_ssd_voc.py,d7115de54d0fc3d064e9b83e5d6044f4aa5a6871,STILL_EXISTS,fix seed in this tutorial
aaifabhig,dmlc/gluon-cv,docs/tutorials/detection/train_ssd_voc.py,d7115de54d0fc3d064e9b83e5d6044f4aa5a6871,STILL_EXISTS,A handy DataLoader would be very convenient for us to apply different transforms and aggregate data into mini-batches.
aaifabhjg,dmlc/gluon-cv,docs/tutorials/detection/train_ssd_voc.py,d7115de54d0fc3d064e9b83e5d6044f4aa5a6871,STILL_EXISTS,If you have read our introductory [tutorial](http:\/\/gluon.mxnet.io\/chapter08_computer-vision\/object-detection.html) of SSD; you may have better idea how it works.
aaifabifa,dmlc/gluon-cv,docs/tutorials/detection/demo_ssd.py,d15a42d0f6c0e4771d99ef7709ea8b68e7519f18,ce3634e6c7fa81ffdccdacf8570618e6019eeda6,the name convention for ssd models is: 'ssd_size_basename_dataset'
aaifabjgj,dmlc/gluon-cv,docs/tutorials/segmentation/train_fcn.py,706e6ddeb7569ad77cc0d3ea431cd8f3ed656043,STILL_EXISTS,the image from -10 to 10 degrees; and crop the image with padding if needed.
aaifacbdd,dmlc/gluon-cv,docs/tutorials/classification/demo_imagenet.py,419afe55b6ab663d1f5e7c2f0b8e06bbe0a3ea70,STILL_EXISTS,\"\"\"Getting Started with Pre-trained Models on ImageNet || =================================================== ||  || `ImageNet <http:\/\/www.image-net.org\/>`__ is a || large labeled dataset of real-world images. It is the most || well-known dataset for computer vision tasks. ||  || |imagenet| ||  || In this tutorial; we will demonstrate how a well-trained model || classifies real life images. ||  || Specifically; we offer a script to load a pretrained ``ResNet50_v2`` model. || For a list of models we have; please visit our `Model Zoo <..\/model_zoo\/index.html>`__. ||  || Demo || ------------------ ||  || A model trained on ImageNet can classify images into 1000 classes; this makes it || much more powerful than the one we showed in `CIFAR10 demo tutorial <demo_cifar10.html>`__. ||  || :download:`Download Python Script demo_imagenet.py<..\/..\/..\/scripts\/classification\/imagenet\/demo_imagenet.py>` ||  || With this script; you can load a pre-trained model and predict on any image you have. ||  || Let's take the photo of Mt. Baker again as the first photo. ||  || |image0| ||  || We can make prediction by ||  || :: ||  ||     python demo_imagenet.py --model ResNet50_v2 --input-pic mt_baker.jpg ||  || And the model thinks that ||  || :: ||  ||     The input picture is classified to be ||     \t[volcano]; with probability 0.558. ||     \t[alp]; with probability 0.398. ||     \t[valley]; with probability 0.018. ||     \t[lakeside]; with probability 0.006. ||     \t[mountain_tent]; with probability 0.006. ||  || This time it does a perfect job. Note that we have listed the top five || possible classes; because with 1000 classes the model may not always rate the || correct answer with the highest rank. Besides the top-1 accuracy; we also || consider top-5 accuracy as a measurement of how well a model can predict. ||  || Next Step || --------- ||  || If you would like to dig deeper in the topic of ``ImageNet`` training; || feel free to read `the next tutorial on ``ImageNet`` <>`__. ||  || Or; if you would like to know how to train a powerful model on your own image data; || please go ahead and read the tutorial about `Transfer learning <transfer_learning_minc.html>`__. ||  || .. |imagenet| image:: https:\/\/raw.githubusercontent.com\/dmlc\/web-data\/master\/gluonvision\/datasets\/imagenet_mosaic.jpg || .. |image0| image:: https:\/\/raw.githubusercontent.com\/dmlc\/web-data\/master\/gluonvision\/classification\/mt_baker.jpg ||  || \"\"\"
aaifaccbh,dmlc/gluon-cv,docs/tutorials/classification/dive_deep_cifar10.py,419afe55b6ab663d1f5e7c2f0b8e06bbe0a3ea70,STILL_EXISTS,Later we'll show how to implement it.
aaifaccdc,dmlc/gluon-cv,docs/tutorials/classification/dive_deep_cifar10.py,419afe55b6ab663d1f5e7c2f0b8e06bbe0a3ea70,STILL_EXISTS,-  Metric is how we evaluate the model performance. It is related to the
aaifacdbb,dmlc/gluon-cv,docs/tutorials/classification/dive_deep_cifar10.py,419afe55b6ab663d1f5e7c2f0b8e06bbe0a3ea70,STILL_EXISTS,If you would like to know how to train a model on a much larger dataset
aaifacdde,dmlc/gluon-cv,docs/tutorials/datasets/imagenet.py,2bff181a52a12c64a5450637808b9cbe3201af24,STILL_EXISTS,TODO; print several images
aaifacddg,dmlc/gluon-cv,docs/tutorials/detection/train_ssd_advanced.py,45295ca1816cdd7044d8af83df1407482952f605,STILL_EXISTS,\"\"\"Dive deep into SSD training: 3 tips to boost performance || ============================================================ ||  || In the previous tutorial :ref:`sphx_glr_build_examples_detection_train_ssd_voc.py`; we briefly went through || the fundamental APIs that help building the training pipeline of SSD. ||  || In this article; we will dive deep into the details and introduce something critical || to reproduce SOTA that you may never know by reading the paper and tech reports. ||  || .. contents:: :local: ||  || \"\"\"
aaifacdef,dmlc/gluon-cv,docs/tutorials/detection/train_ssd_advanced.py,45295ca1816cdd7044d8af83df1407482952f605,STILL_EXISTS,But the question is; what is the proper way to calculate ``N``? Should we sum up
aaifacdie,dmlc/gluon-cv,docs/tutorials/detection/train_ssd_advanced.py,45295ca1816cdd7044d8af83df1407482952f605,STILL_EXISTS,Which one is better?
aaifacdjc,dmlc/gluon-cv,docs/tutorials/detection/train_ssd_advanced.py,45295ca1816cdd7044d8af83df1407482952f605,STILL_EXISTS,In our experiments; batch-wise norm is always better on Pascal VOC dataset;
aaifacefe,dmlc/gluon-cv,docs/tutorials/classification/dive_deep_imagenet.py,037379107e3b7960a07987e8519d955d1f1fa494,STILL_EXISTS,\"\"\"Train Your Own Model on ImageNet || ====================== ||  || ``ImageNet`` is the most well-known dataset for image classification task. || Since it's been published; there has been plenty of works pushing the performance || of classification accuracy. ||  || Although there are a lot of available models; it is still a non-trivial job to || train a well-performing model on ``ImageNet`` from scratch. In this tutorial; we will || try to pull necessary code together to walk you through the process of || training a model on ``ImageNet``. ||  || .. note:: ||  ||     :download:`Download Python Script train_imagenet.py<..\/..\/..\/scripts\/classification\/imagenet\/train_imagenet.py>` ||  ||     Before dive into the details of the training; one can take a look at the complete ||     training script. The commands to reproduce papers are given in our model zoo. ||  ||     Since the training is extremely resource consuming; we don't actually ||     execute code blocks in this tutorial. ||  || Prerequisites || ------------- ||  || **Knowledge** ||  || We assume readers have a basic understanding of ``Gluon``. If you would || like to know more about it; we suggest the `Gluon Crash || Course <http:\/\/gluon-crash-course.mxnet.io\/index.html>`__ as a good || place to start. ||  || Also; it is suggested that readers have read our tutorials on || `CIFAR10 Training <dive_deep_cifar10.html>`_ || and `ImageNet Demo <demo_imagenet.html>`_ || to gain some experience of model training. ||  || **Data Preparation** ||  || Unlike ``CIFAR10``; we need to prepare the data manually. || If you haven't done so; please go through our tutorial on || `Prepare ImageNet Data <..\/examples_datasets\/imagenet.html>`_ and || prepare the data. ||  || **Hardware** ||  || Training a deep learning model on a dataset of over 100GB is resource demanding. || Two main bottlenecks are training computation and data IO. ||  || For training computation; it is required to have a GPU; preferably a high-end || GPU. Training on CPU is almost mission impossible; and training with a low-end GPU || may still largely limit your performance. It is the best to have multiple || strong GPUs to work together. ||  || For data IO; we recommend a strong CPU and a SSD disk. Data loading can largely benefit || from multiple CPU threads; and a fast SSD disk. Note that in total the compressed || and extracted ``ImageNet`` data could occupy around 300GB disk space; thus a SSD with || at least 300GB is necessary. ||  || Network structure || ----------------- ||  || If everything's prepared; Let's get started! ||  || First; load the necessary libraries into python. ||  || .. code-block:: python ||  ||     import argparse; time; ||  ||     import numpy as np ||     import mxnet as mx ||  ||     from mxnet import gluon; nd ||     from mxnet import autograd as ag ||     from mxnet.gluon import nn ||     from mxnet.gluon.data.vision import transforms ||  ||     from gluonvision.model_zoo import get_model ||     from gluonvision.utils import makedirs; TrainingHistory ||  || ``ResNet50_v2`` is a network with balanced prediction accuracy and computational cost. || Here we use this model to demonstrate the training process. ||  || .. code-block:: python ||  ||     # number of GPUs to use ||     num_gpus = 4 ||     ctx = [mx.gpu(i) for i in range(num_gpus)] ||  ||     # Get the model ResNet50_v2; with 10 output classes ||     net = get_model('ResNet50_v2'; classes=1000) ||     net.initialize(mx.init.Xavier(magnitude=2); ctx = ctx) ||  ||  || Note that the ResNet model we use here for ``ImageNet`` is different in structure from || the one we used to train ``CIFAR10``. Please refer to the original paper or || our implementations for details. ||  || Data Augmentation and Data Loader || --------------------------------- ||  || Data augmentation is essential for a good result. It is similar to what we have || in the ``CIFAR10`` training tutorial; just different in the parameters. ||  || We compose our transform functions as following: ||  || .. code-block:: python ||  ||     normalize = transforms.Normalize([0.485; 0.456; 0.406]; [0.229; 0.224; 0.225]) ||     jitter_param = 0.4 ||     lighting_param = 0.1 ||  ||     transform_train = transforms.Compose([ ||         transforms.Resize(480); ||         transforms.RandomResizedCrop(224); ||         transforms.RandomFlipLeftRight(); ||         transforms.RandomColorJitter(brightness=jitter_param; contrast=jitter_param; ||                                      saturation=jitter_param); ||         transforms.RandomLighting(lighting_param); ||         transforms.ToTensor(); ||         normalize ||     ]) ||  ||  || Since ``ImageNet`` contains images with much higher resolution and quality than || ``CIFAR10``; thus we can crop a larger image (224x224) as the input to the model. ||  || For prediction; we still need a deterministic result. The transform function is: ||  || .. code-block:: python ||  ||     transform_test = transforms.Compose([ ||         transforms.Resize(256); ||         transforms.CenterCrop(224); ||         transforms.ToTensor(); ||         normalize ||     ]) ||  ||  || Notice that it is important to keep the normalization consistent; since the || model only works well on input with the same distribution. ||  || With the transform functions; we can define data loaders for our || training and validation datasets. ||  || .. code-block:: python ||  ||     # Batch Size for Each GPU ||     per_device_batch_size = 64 ||     # Number of data loader workers ||     num_workers = 32 ||     # Calculate effective total batch size ||     batch_size = per_device_batch_size * num_gpus ||  ||     data_path = '~\/.mxnet\/datasets\/imagenet' ||  ||     # Set train=True for training data ||     # Set shuffle=True to shuffle the training data ||     train_data = gluon.data.DataLoader( ||         imagenet.classification.ImageNet(data_path; train=True).transform_first(transform_train); ||         batch_size=batch_size; shuffle=True; last_batch='discard'; num_workers=num_workers) ||  ||     # Set train=False for validation data ||     val_data = gluon.data.DataLoader( ||         imagenet.classification.ImageNet(data_path; train=False).transform_first(transform_test); ||         batch_size=batch_size; shuffle=False; num_workers=num_workers) ||  || Note that we set ``per_device_batch_size=64``; which may not suit GPUs with || Memory smaller than 12GB. Please tune the value according to your specific configuration. ||  || The path ``'~\/.mxnet\/datasets\/imagenet'`` is the default path if you || prepare the data `with our script <..\/examples_datasets\/imagenet.html>`_. ||  || Optimizer; Loss and Metric || -------------------------- ||  || Optimizer is what improves the model during training. We use the popular || Nesterov accelerated gradient descent algorithm. ||  || .. code-block:: python ||  ||     # Learning rate decay factor ||     lr_decay = 0.1 ||     # Epochs where learning rate decays ||     lr_decay_epoch = [30; 60; 90; np.inf] ||  ||     # Nesterov accelerated gradient descent ||     optimizer = 'nag' ||     # Set parameters ||     optimizer_params = {'learning_rate': 0.1; 'wd': 0.0001; 'momentum': 0.9} ||  ||     # Define our trainer for net ||     trainer = gluon.Trainer(net.collect_params(); optimizer; optimizer_params) ||  ||  || For classification tasks; we usually use softmax cross entropy as the || loss function. ||  || .. code-block:: python ||  ||     loss_fn = gluon.loss.SoftmaxCrossEntropyLoss() ||  ||  || With 1000 classes the model may not always rate the correct answer with the highest || rank. Besides the top-1 accuracy; we also consider top-5 accuracy as a measurement || of how well a model can predict. ||  || At the end of every epoch; we record and print the metric scores. ||  || .. code-block:: python ||  ||     acc_top1 = mx.metric.Accuracy() ||     acc_top5 = mx.metric.TopKAccuracy(5) ||     train_history = TrainingHistory(['training-top1-err'; 'training-top5-err'; ||                                      'validation-top1-err'; 'validation-top5-err']) ||  ||  || Validation || ---------- ||  || At the end of every training epoch; we evaluate it on the validation data set; || and report the top-1 and top-5 error rate. ||  || .. code-block:: python ||  ||     def test(ctx; val_data): ||         acc_top1_val = mx.metric.Accuracy() ||         acc_top5_val = mx.metric.TopKAccuracy(5) ||         for i; batch in enumerate(val_data): ||             data = gluon.utils.split_and_load(batch[0]; ctx_list=ctx; batch_axis=0) ||             label = gluon.utils.split_and_load(batch[1]; ctx_list=ctx; batch_axis=0) ||             outputs = [net(X) for X in data] ||             acc_top1_val.update(label; outputs) ||             acc_top5_val.update(label; outputs) ||  ||         _; top1 = acc_top1_val.get() ||         _; top5 = acc_top5_val.get() ||         return (1 - top1; 1 - top5) ||  || Training || -------- ||  || After all these preparation; we can finally start our training process! || Following is the main training loop ||  || .. code-block:: python ||  ||     epochs = 120 ||     lr_decay_count = 0 ||     log_interval = 50 ||     num_batch = len(train_data) ||  ||     for epoch in range(epochs): ||         tic = time.time() ||         btic = time.time() ||         acc_top1.reset() ||         acc_top5.reset() ||         train_loss = 0 ||  ||         if lr_decay_period == 0 and epoch == lr_decay_epoch[lr_decay_count]: ||             trainer.set_learning_rate(trainer.learning_rate*lr_decay) ||             lr_decay_count += 1 ||  ||         for i; batch in enumerate(train_data): ||             data = gluon.utils.split_and_load(batch[0]; ctx_list=ctx; batch_axis=0) ||             label = gluon.utils.split_and_load(batch[1]; ctx_list=ctx; batch_axis=0) ||             with ag.record(): ||                 outputs = [net(X) for X in data] ||                 loss = [L(yhat; y) for yhat; y in zip(outputs; label)] ||             for l in loss: ||                 l.backward() ||             trainer.step(batch_size) ||             acc_top1.update(label; outputs) ||             acc_top5.update(label; outputs) ||             train_loss += sum([l.sum().asscalar() for l in loss]) ||             if log_interval and not log_interval: ||                 _; top1 = acc_top1.get() ||                 _; top5 = acc_top5.get() ||                 err_top1; err_top5 = (1-top1; 1-top5) ||                 print('Epoch[%d] Batch [%d]\\tSpeed: %f samples\/sec\\ttop1-err=%f\\ttop5-err=%f'%( ||                              epoch; i; batch_size*opt.log_interval\/(time.time()-btic); err_top1; err_top5)) ||                 btic = time.time() ||  ||         _; top1 = acc_top1.get() ||         _; top5 = acc_top5.get() ||         err_top1; err_top5 = (1-top1; 1-top5) ||         train_loss \/= num_batch * batch_size ||  ||         err_top1_val; err_top5_val = test(ctx; val_data) ||         train_history.update([err_top1; err_top5; err_top1_val; err_top5_val]) ||  ||         print('[Epoch %d] training: err-top1=%f err-top5=%f loss=%f'%(epoch; err_top1; err_top5; train_loss)) ||         print('[Epoch %d] time cost: %f'%(epoch; time.time()-tic)) ||         print('[Epoch %d] validation: err-top1=%f err-top5=%f'%(epoch; err_top1_val; err_top5_val)) ||  ||  || We can plot the top-1 error rates with: ||  || .. code-block:: python ||  ||     train_history.plot(['training-top1-err'; 'validation-top1-err']) ||  || If you train the model with ``epochs=120``; the plot may look like: ||  || |image-imagenet-curve| ||  || Next Step || --------- ||  || This is our script to help you to train a model on ``ImageNet``. ||  || If you want like to know what can be done with the model you just || trained; please read the tutorial about `Transfer learning <transfer_learning_minc.html>`__. ||  || Besides classification; deep learning models nowadays can do other exciting jobs || like `object detection <..\/examples_detection\/index.html>`_; || `semantic segmentation <..\/examples_segmentation\/index.html>`_. Check out their tutorials! ||  || .. |image-imagenet-curve| image:: https:\/\/raw.githubusercontent.com\/dmlc\/web-data\/master\/gluonvision\/classification\/imagenet_resnet50_v2.png || \"\"\"
aaifacfhe,dmlc/gluon-cv,scripts/segmentation/train.py,dfac51fcd0fbceb3f394a1d8f1ab7c8652e9cf78,STILL_EXISTS,resume checkpoint if needed
aaifacfhi,dmlc/gluon-cv,tests/unittests/test_segmentation.py,dfac51fcd0fbceb3f394a1d8f1ab7c8652e9cf78,STILL_EXISTS,TODO FIXME: change it to ADE20K dataset and pretrained model
aaifacigi,dmlc/gluon-cv,docs/tutorials/datasets/pascal_voc.py,ab9c08bb5f9096f2bf2d1a3de7dac298d2127d4b,STILL_EXISTS,Take bounding boxes by slice columns from 0 to 4
aaifacihe,dmlc/gluon-cv,tests/unittests/test_data_datasets.py,c6d58621819af733a59b20bb24acd92331074dc8,STILL_EXISTS,use valid only; loading training split is very slow
aaifacihj,dmlc/gluon-cv,gluoncv/data/recordio/detection.py,c4e3bca25a4926820512bf31b8f9857451f7b157,STILL_EXISTS,swap columns; gluon-cv requires [xmin-ymin-xmax-ymax-id-extra0-extra1-xxx]
aaifacjai,dmlc/gluon-cv,tests/unittests/test_data_datasets.py,4999f9795af7a21ca69adf5f72301b79f719498e,STILL_EXISTS,use valid only; loading training split is very slow
aaifadaji,dmlc/gluon-cv,scripts/detection/faster_rcnn/train_faster_rcnn.py,4b8716fd4bd6f462a9db9eee958f2508576ec019,STILL_EXISTS,fix batchnorm; fix first stage; etc...
aaifadbfg,dmlc/gluon-cv,docs/tutorials/detection/train_faster_rcnn_voc.py,1d4d3384b6254e6a7907cace951e8521aac99030,STILL_EXISTS,\"\"\"5. Train Faster-RCNN end-to-end on PASCAL VOC || ================================================ ||  || This tutorial goes through the basic steps of training a Faster-RCNN object detection model || provided by GluonCV. || Specifically; we show how to build a state-of-the-art Faster-RCNN model by stacking GluonCV components. ||  ||  || .. hint:: ||  ||     You can skip the rest of this tutorial and start training your SSD model ||     right away by downloading this script: ||  ||     :download:`Download train_faster_rcnn.py<..\/..\/..\/scripts\/detection\/faster_rcnn\/train_faster_rcnn.py>` ||  ||     Example usage: ||  ||     Train a default resnet50_v2a model with Pascal VOC on GPU 0: ||  ||     .. code-block:: bash ||  ||         python train_faster_rcnn.py --gpus 0 ||  ||     Train a resnet50_v2a model on GPU 0;1;2;3: ||  ||     .. code-block:: bash ||  ||         python train_faster_rcnn.py --gpus 0;1;2;3 --network resnet50_v2a ||  ||     Check the supported arguments: ||  ||     .. code-block:: bash ||  ||         python train_faster_rcnn.py --help ||  ||  || .. hint:: ||  ||     Since lots of contents in this tutorial is very similar to :doc:`.\/train_ssd_voc`; you can skip any part ||     if you feel comfortable. ||  || \"\"\"
aaifadbib,dmlc/gluon-cv,docs/tutorials/detection/train_faster_rcnn_voc.py,1d4d3384b6254e6a7907cace951e8521aac99030,STILL_EXISTS,fix seed in this tutorial
aaifadbjh,dmlc/gluon-cv,docs/tutorials/detection/train_faster_rcnn_voc.py,1d4d3384b6254e6a7907cace951e8521aac99030,STILL_EXISTS,A handy DataLoader would be very convenient for us to apply different transforms and aggregate data into mini-batches.
aaifaddhc,dmlc/gluon-cv,docs/tutorials/segmentation/train_psp.py,5921740d9f69d853fe543c52811b590a398031f5,STILL_EXISTS,the image from -10 to 10 degrees; and crop the image with padding if needed.
aaifadfhc,dmlc/gluon-cv,gluoncv/model_zoo/yolo/yolo_target.py,0a699a5ccc21310c7ce41d4737f0de9f54fbf45a,STILL_EXISTS,should not be a problem right now. Switch to better solution is needed.
aaifadgfb,dmlc/gluon-cv,gluoncv/model_zoo/rpn/rpn_target.py,1c0886a3d5217af5e33ea570604997cad9e6e907,STILL_EXISTS,row maybe sampled by 2 columns but still only matches to most overlapping gt
aaifadhbg,dmlc/gluon-cv,docs/tutorials/detection/train_yolo_v3.py,684355e48101eb3ca035425fbac733a400a70a5f,STILL_EXISTS,\"\"\"7. Train YOLOv3 on PASCAL VOC || ================================ ||  || This tutorial goes through the basic steps of training a YOLOv3 object detection model || provided by GluonCV. ||  || Specifically; we show how to build a state-of-the-art YOLOv3 model by stacking GluonCV components. ||  ||  || .. hint:: ||  ||     You can skip the rest of this tutorial and start training your YOLOv3 model ||     right away by downloading this script: ||  ||     :download:`Download train_yolo3.py<..\/..\/..\/scripts\/detection\/yolo\/train_yolo3.py>` ||     or a random shape training script: ||     :download:`Download train_yolo3_rand_size.py<..\/..\/..\/scripts\/detection\/yolo\/train_yolo3_rand_size.py>` ||     Random shape training requires more GPU memory but generates better results. ||  ||     Example usage: ||  ||     Train a default darknet53 model with Pascal VOC on GPU 0: ||  ||     .. code-block:: bash ||  ||         python train_yolo3(_rand_size).py --gpus 0 ||  ||     Train a darknet53 model on GPU 0;1;2;3 with synchronize BatchNorm: ||  ||     .. code-block:: bash ||  ||         python train_yolo3(_rand_size).py --gpus 0;1;2;3 --network darknet53 --syncbn ||  ||     Check the supported arguments: ||  ||     .. code-block:: bash ||  ||         python train_yolo3(_rand_size).py --help ||  ||  || .. hint:: ||  ||     Since lots of contents in this tutorial is very similar to :doc:`.\/train_ssd_voc`; you can skip any part ||     if you feel comfortable. ||  || \"\"\"
aaifadhea,dmlc/gluon-cv,docs/tutorials/detection/train_yolo_v3.py,684355e48101eb3ca035425fbac733a400a70a5f,STILL_EXISTS,fix seed in this tutorial
aaifadhfh,dmlc/gluon-cv,docs/tutorials/detection/train_yolo_v3.py,684355e48101eb3ca035425fbac733a400a70a5f,STILL_EXISTS,A handy DataLoader would be very convenient for us to apply different transforms and aggregate data into mini-batches.
aaifadjfd,dmlc/gluon-cv,scripts/instance/mask_rcnn/train_mask_rcnn.py,3bf8f9132ceb34a6bbd74c3d013bb6cb26617f4a,STILL_EXISTS,fix batchnorm; fix first stage; etc...
aaifadjhf,dmlc/gluon-cv,tests/unittests/test_data_datasets.py,3bf8f9132ceb34a6bbd74c3d013bb6cb26617f4a,STILL_EXISTS,use valid only; loading training split is very slow
aaifadjhg,dmlc/gluon-cv,tests/unittests/test_data_transforms.py,3bf8f9132ceb34a6bbd74c3d013bb6cb26617f4a,STILL_EXISTS,use valid only; loading training split is very slow
aaifaebha,dmlc/gluon-cv,docs/tutorials/datasets/detection_custom.py,fdaa43e88795e636fb67765d126321a2864ab5b0,STILL_EXISTS,Following the convention used in MXNet; we recommand a LST file which is a plain text list file to store labels.
aaifaedhd,dmlc/gluon-cv,docs/tutorials/instance/train_mask_rcnn_coco.py,336d14c384d3b56247ba2c1cdd3ce3fbce8c0d85,STILL_EXISTS,\"\"\"2. Train Mask RCNN end-to-end on MS COCO || =========================================== ||  || This tutorial goes through the steps for training a Mask R-CNN [He17]_ instance segmentation model || provided by GluonCV. ||  || Mask R-CNN is an extension to the Faster R-CNN [Ren15]_ object detection model. || As such; this tutorial is also an extension to :doc:`..\/examples_detection\/train_faster_rcnn_voc`. || We will focus on the extra work on top of Faster R-CNN to show how to use GluonCV components || to construct a Mask R-CNN model. ||  || It is highly recommended to read the original papers [Girshick14]_; [Girshick15]_; [Ren15]_; [He17]_ || to learn more about the ideas behind Mask R-CNN. || Appendix from [He16]_ and experiment detail from [Lin17]_ may also be useful reference. ||  || .. hint:: ||  ||     Please first go through this :ref:`sphx_glr_build_examples_datasets_mscoco.py` tutorial to ||     setup MSCOCO dataset on your disk. ||  || .. hint:: ||  ||     You can skip the rest of this tutorial and start training your Mask RCNN model ||     right away by downloading this script: ||  ||     :download:`Download train_mask_rcnn.py<..\/..\/..\/scripts\/instance\/mask_rcnn\/train_mask_rcnn.py>` ||  ||     Example usage: ||  ||     Train a default resnet50_v1b model with COCO dataset on GPU 0: ||  ||     .. code-block:: bash ||  ||         python train_mask_rcnn.py --gpus 0 ||  ||     Train on GPU 0;1;2;3: ||  ||     .. code-block:: bash ||  ||         python train_mask_rcnn.py --gpus 0;1;2;3 ||  ||     Check the supported arguments: ||  ||     .. code-block:: bash ||  ||         python train_mask_rcnn.py --help ||  || \"\"\"
aaifaeeaf,dmlc/gluon-cv,docs/tutorials/instance/train_mask_rcnn_coco.py,336d14c384d3b56247ba2c1cdd3ce3fbce8c0d85,STILL_EXISTS,fix seed in this tutorial
aaifaefbi,dmlc/gluon-cv,scripts/gan/wgan/train_wgan.py,c748e84cf46cb9e140900f5eee21a554a97ec52f,STILL_EXISTS,random.randint(1; 10000) # fix seed
aaifaefcb,dmlc/gluon-cv,scripts/gan/wgan/train_wgan.py,c748e84cf46cb9e140900f5eee21a554a97ec52f,STILL_EXISTS,load checkpoint if needed
aaifaefdg,dmlc/gluon-cv,docs/tutorials/deployment/export_network.py,bf9b035e431144711241f656c8e6a17d80cf5899,STILL_EXISTS,\"\"\"1. Export trained GluonCV network to JSON || ============================================ ||  || It is awesome if you are enjoy using GluonCV in Python for training and testing. || At some point; you might ask: \"Is it possible to deploy the existing models to somewhere out of Python environments?\" ||  || The answer is \"Absolutely!\"; and it's super easy actually. ||  || This article will show you how to export networks\/models to be used somewhere other than Python. ||  || \"\"\"
aaifaehfg,dmlc/gluon-cv,docs/tutorials/segmentation/voc_sota.py,0ed0fafff859bf01e882acc5a2c65597d219627c,STILL_EXISTS,fine-tune again on Pascal VOC dataset to get the best performance.
aaifaeiaj,dmlc/gluon-cv,tests/unittests/test_data_datasets.py,aa4ab27d7e140b78046bea09c3d720ef59dcf124,STILL_EXISTS,use valid only; loading training split is very slow
aaifaeicb,dmlc/gluon-cv,docs/tutorials/detection/finetune_detection.py,1868c55b73bccc22602d87e3f2dc65699d9efa14,STILL_EXISTS,\"\"\"8. Finetune a pretrained detection model || ============================================ ||  || Fine-tuning is commonly used approach to transfer previously trained model to a new dataset. || It is especially useful if the targeting new dataset is relatively small. ||  || Finetuning from pre-trained models can help reduce the risk of overfitting. || Finetuned model may also generalizes better if the previously used dataset is in the similar domain of the new dataset. ||  || This tutorial opens up a good approach for fine-tuning object detection models || provided by GluonCV. || More Specifically; we show how to use a customized Pikachu dataset and illustrate the finetuning fundamentals step by step. || You will be familiarize the steps and modify it to fit your own object detection projects. ||  || \"\"\"
aaifaeicf,dmlc/gluon-cv,docs/tutorials/detection/finetune_detection.py,1868c55b73bccc22602d87e3f2dc65699d9efa14,STILL_EXISTS,First we will start with a nice Pikachu dataset generated by rendering 3D models on random real-world scenes.
aaifaeifa,dmlc/gluon-cv,docs/tutorials/detection/finetune_detection.py,1868c55b73bccc22602d87e3f2dc65699d9efa14,STILL_EXISTS,Therefore finetuning may converge significantly faster and better in some situations.
aaifaeiif,dmlc/gluon-cv,docs/tutorials/detection/demo_webcam.py,7a4863aef069f8c3d5b3d146df987d0fe673e2d7,STILL_EXISTS,\"\"\"9. Run an object detection model on your webcam || ================================================== ||  || This article will shows how to play with pre-trained object detection models by running || them directly on your webcam video stream. ||  || .. note:: ||  ||     - This tutorial has only been tested in a MacOS environment ||     - Python packages required: cv2; matplotlib ||     - You need a webcam :) ||     - Python compatible with matplotlib rendering; installed as a framework in MacOS see guide `here <https:\/\/matplotlib.org\/faq\/osx_framework.html>`__ ||  ||  || Loading the model and webcam || ---------------------------- || Finished preparation? Let's get started! || First; import the necessary libraries into python. ||  || .. code-block:: python ||  ||     import time ||  ||     import cv2 ||     import gluoncv as gcv ||     import matplotlib.pyplot as plt ||     import mxnet as mx ||  ||  || In this tutorial we use ``ssd_512_mobilenet1.0_voc``; a snappy network with good accuracy that should be || well above 1 frame per second on most laptops. Feel free to try a different model from || the `Gluon Model Zoo <..\/..\/model_zoo\/detection.html>`__ ! ||  || .. code-block:: python ||  ||     # Load the model ||     net = gcv.model_zoo.get_model('ssd_512_mobilenet1.0_voc'; pretrained=True) ||  ||  || We create the webcam handler in opencv to be able to acquire the frames: ||  || .. code-block:: python ||  ||     # Load the webcam handler ||     cap = cv2.VideoCapture(0) ||     time.sleep(1) ### letting the camera autofocus ||  ||  || Detection loop || -------------- ||  || The detection loop consists of four phases: ||  || * loading the webcam frame ||  || * pre-processing the image ||  || * running the image through the network ||  || * updating the output with the resulting predictions ||  ||  || .. code-block:: python ||  ||     axes = None ||     NUM_FRAMES = 200 # you can change this ||     for i in range(NUM_FRAMES): ||         # Load frame from the camera ||         ret; frame = cap.read() ||  ||         # Image pre-processing ||         frame = mx.nd.array(cv2.cvtColor(frame; cv2.COLOR_BGR2RGB)).astype('uint8') ||         rgb_nd; frame = gcv.data.transforms.presets.ssd.transform_test(frame; short=512; max_size=700) ||  ||         # Run frame through network ||         class_IDs; scores; bounding_boxes = net(rgb_nd) ||  ||         # Display the result ||         plt.cla() ||         axes = gcv.utils.viz.plot_bbox(frame; bounding_boxes[0]; scores[0]; class_IDs[0]; class_names=net.classes; ax=axes) ||         plt.draw() ||         plt.pause(0.001) ||  ||  || We release the webcam before exiting the script ||  || .. code-block:: python ||  ||     cap.release() ||  || Results || --------- ||  || Download the script to run the demo: ||  || :download:`Download demo_webcam_run.py<..\/..\/..\/scripts\/detection\/demo_webcam_run.py>` ||  ||  || Run the script using `pythonw` on MacOS: ||  || .. code-block:: bash ||  ||     pythonw demo_webcam_run.py --num-frames 200 ||  ||  || .. note:: ||  ||     On MacOS; to enable matplotlib rendering you need python installed as a framework; ||     see guide `here <https:\/\/matplotlib.org\/faq\/osx_framework.html>`__ ||  ||  || If all goes well you should be able to detect objects from the available || classes of the VOC dataset. That includes persons; chairs and TV Screens! ||  || .. image:: https:\/\/media.giphy.com\/media\/9JvoKeUeCt4bdRf3Cv\/giphy.gif ||  ||  || \"\"\"
aaifaejfc,dmlc/gluon-cv,scripts/gan/cycle_gan/train_cgan.py,a48a6d1b0819fb585d2f7c48fe39ccc45ec991b9,STILL_EXISTS,random.randint(1; 10000) # fix seed
aaifafbeg,dmlc/gluon-cv,scripts/gan/srgan/train_srgan.py,47c13d863297dfe3435411f71fc5bdce07ffaea5,STILL_EXISTS,random.randint(1; 10000) # fix seed
aaifafcha,dmlc/gluon-cv,docs/tutorials/pose/dive_deep_simple_pose.py,d587979a0b383d9bc27c797215dabb66f38a9af7,STILL_EXISTS,Since similar behavior is observed; we implement a ``BatchNormCudnnOff`` layer as a temporary solution.
aaifafdjj,dmlc/gluon-cv,gluoncv/model_zoo/quantized/quantized.py,960a107ad419d2690e352972950e76495deb1ad6,STILL_EXISTS,so we hack it by load and export once to a temporary params file
aaifafegb,dmlc/gluon-cv,docs/tutorials/deployment/int8_inference.py,a83ef8c935dd40907da35925dfc2bbbb675cb1b6,STILL_EXISTS,\"\"\"3. Inference with Quantized Models || ===================================== ||  || This is a tutorial which illustrates how to use quantized GluonCV || models for inference on Intel Xeon Processors to gain higher performance. ||  || The following example requires ``GluonCV>=0.4`` and ``MXNet-mkl>=1.5.0b20190314``. Please follow `our installation guide <..\/..\/index.html#installation>`__ to install or upgrade GluonCV and nightly build of MXNet if necessary. ||  || Introduction || ------------ ||  || GluonCV delivered some quantized models to improve the performance and reduce the deployment costs for the computer vision inference tasks. In real production; there are two main benefits of lower precision (INT8). First; the computation can be accelerated by the low precision instruction; like Intel Vector Neural Network Instruction (VNNI). Second; lower precision data type would save the memory bandwidth and allow for better cache locality and save the power. The new feature can get up to 2X performance speedup in the current AWS EC2 CPU instances and will reach 4X under the `Intel Deep Learning Boost (VNNI) <https:\/\/www.intel.ai\/intel-deep-learning-boost\/#gs.0ngn54>`_ enabled hardware with less than 0.5% accuracy drop. ||  || Please checkout `verify_pretrained.py <https:\/\/raw.githubusercontent.com\/dmlc\/gluon-cv\/master\/scripts\/classification\/imagenet\/verify_pretrained.py>`_ for imagenet inference || and `eval_ssd.py <https:\/\/raw.githubusercontent.com\/dmlc\/gluon-cv\/master\/scripts\/detection\/ssd\/eval_ssd.py>`_ for SSD inference. ||  || Performance || ----------- ||  || GluonCV supports some quantized classification models and detection models. || For the throughput; the target is to achieve the maximum machine efficiency to combine the inference requests together and get the results by one iteration. From the bar-chart; it is clearly that the quantization approach improved the throughput from 1.46X to 2.71X for selected models. || Below CPU performance is from AWS EC2 C5.18xlarge with 18 cores. ||  || .. figure:: https:\/\/user-images.githubusercontent.com\/17897736\/54540947-dc08c480-49d3-11e9-9a0d-a97d44f9792c.png ||    :alt: Gluon Quantization Performance ||  ||    Gluon Quantization Performance ||  || +-----------------------+----------+------------+------------------+------------------+---------+-----------------+-----------------+ || |  Model                | Dataset  | Batch Size | C5.18xlarge FP32 | C5.18xlarge INT8 | Speedup | FP32 Accuracy   | INT8 Accuracy   | || +=======================+==========+============+==================+==================+=========+=================+=================+ || | ResNet50 V1           | ImageNet | 128        | 122.02           | 276.72           | 2.27    | 77.21%\/93.55%   | 76.86%\/93.46%   | || +-----------------------+----------+------------+------------------+------------------+---------+-----------------+-----------------+ || | MobileNet 1.0         | ImageNet | 128        | 375.33           | 1016.39          | 2.71    | 73.28%\/91.22%   | 72.85%\/90.99%   | || +-----------------------+----------+------------+------------------+------------------+---------+-----------------+-----------------+ || | SSD-VGG 300*          | VOC      | 224        | 21.55            | 31.47            | 1.46    | 77.4            | 77.46           | || +-----------------------+----------+------------+------------------+------------------+---------+-----------------+-----------------+ || | SSD-VGG 512*          | VOC      | 224        | 7.63             | 11.69            | 1.53    | 78.41           | 78.39           | || +-----------------------+----------+------------+------------------+------------------+---------+-----------------+-----------------+ || | SSD-resnet50_v1 512*  | VOC      | 224        | 17.81            | 34.55            | 1.94    | 80.21           | 80.16           | || +-----------------------+----------+------------+------------------+------------------+---------+-----------------+-----------------+ || | SSD-mobilenet1.0 512* | VOC      | 224        | 31.13            | 48.72            | 1.57    | 75.42           | 75.04           | || +-----------------------+----------+------------+------------------+------------------+---------+-----------------+-----------------+ ||  || Quantized SSD models are evaluated with ``nms_thresh=0.45``; ``nms_topk=200``. ||  || Demo usage for SSD || ------------------ ||  || .. code:: bash ||  ||    # with Pascal VOC validation dataset saved on disk ||    python eval_ssd.py --network=vgg16_atrous --quantized --data-shape=300 --batch-size=224 --dataset=voc ||  || Usage: ||  || :: ||  ||    SYNOPSIS ||             python eval_ssd.py [-h] [--network NETWORK] [--quantized] ||                                [--data-shape DATA_SHAPE] [--batch-size BATCH_SIZE] ||                                [--dataset DATASET] [--num-workers NUM_WORKERS] ||                                [--num-gpus NUM_GPUS] [--pretrained PRETRAINED] ||                                [--save-prefix SAVE_PREFIX] ||  ||    OPTIONS ||             -h; --help            show this help message and exit ||             --network NETWORK     Base network name ||             --quantized           use int8 pretrained model ||             --data-shape DATA_SHAPE ||                                     Input data shape ||             --batch-size BATCH_SIZE ||                                     eval mini-batch size ||             --dataset DATASET     eval dataset. ||             --num-workers NUM_WORKERS; -j NUM_WORKERS ||                                     Number of data workers ||             --num-gpus NUM_GPUS   number of gpus to use. ||             --pretrained PRETRAINED ||                                     Load weights from previously saved parameters. ||             --save-prefix SAVE_PREFIX ||                                     Saving parameter prefix || \"\"\"
aaifafffe,dmlc/gluon-cv,scripts/detection/ssd/train_ssd.py,87111d536f743850b1a03dacd5e69b385209664b,STILL_EXISTS,needed for net to be first gpu when using AMP
aaifafgab,dmlc/gluon-cv,gluoncv/data/transforms/pose.py,9e951df82dece17a34a57bb43253c2100aef2c6b,STILL_EXISTS,Move to center
aaifafiad,dmlc/gluon-cv,scripts/detection/faster_rcnn/train_faster_rcnn.py,eca6c013d603eb96df520c537418bc7717f1ebd8,STILL_EXISTS,fix batchnorm; fix first stage; etc...
aaifafjej,dmlc/gluon-cv,scripts/instance/mask_rcnn/train_mask_rcnn.py,123af8cf9f15a879c16a5c7d12f01ce1471d85b6,STILL_EXISTS,fix batchnorm; fix first stage; etc...
aaifagacd,dmlc/gluon-cv,docs/tutorials/datasets/kinetics400.py,fdd55d36c05c747643fc98430a7f9b787b6cec16,STILL_EXISTS,\"\"\"Prepare the Kinetics400 dataset || ============================ ||  || `Kinetics400 <https:\/\/deepmind.com\/research\/open-source\/kinetics>`_  is an action recognition dataset || of realistic action videos; collected from YouTube. With 306;245 short trimmed videos || from 400 action categories; it is one of the largest and most widely used dataset in the research || community for benchmarking state-of-the-art video action recognition models. This tutorial || will go through the steps of preparing this dataset for GluonCV. ||  ||  || Download || -------- ||  || Please refer to the `official website <https:\/\/github.com\/activitynet\/ActivityNet\/tree\/master\/Crawler\/Kinetics>`_ on how to download the videos. || Note that the downloaded videos will consume about 450G disk space; make sure there is enough space before downloading. The crawling process can take several days. ||  || Once download is complete; please rename the folder names (since class names have white space) for ease of processing. Suppose the videos are || downloaded to ``~\/.mxnet\/datasets\/kinetics400``; there will be three folders in it: ``annotations``; ``train`` and ``val``. You can use the following command to || rename the folder names: ||  || .. code-block:: bash ||  ||    # sudo apt-get install detox ||    detox -r train\/ ||    detox -r val\/ ||  || Decode into frames || ------------------ ||  || The easiest way to prepare the videos in frames format is to download helper script || :download:`kinetics400.py<..\/..\/..\/scripts\/datasets\/kinetics400.py>` and run the following command: ||  || .. code-block:: bash ||  ||    python kinetics400.py --src_dir ~\/.mxnet\/datasets\/kinetics400\/train --out_dir ~\/.mxnet\/datasets\/kinetics400\/rawframes_train --decode_video --new_width 450 --new_height 340 ||    python kinetics400.py --src_dir ~\/.mxnet\/datasets\/kinetics400\/val --out_dir ~\/.mxnet\/datasets\/kinetics400\/rawframes_val --decode_video --new_width 450 --new_height 340 ||  || This script will help you decode the videos to raw frames. We specify the width and height of frames for resizing because this will save lots of disk space without losing much accuracy. || All the resized frames will consume 2.9T disk space. If we don't specify the dimension; the original decoded frames will consume 6.8T disk space. || The data preparation process may take a while. The total time to prepare the dataset depends on your machine. For example; it takes about 8 hours on an AWS EC2 instance with EBS and using 56 workers. ||  ||  || Generate the training files || --------------------------- ||  || The last step is to generate training files for standard data loading. You can run the helper script as: ||  || .. code-block:: bash ||  ||    python kinetics.py --build_file_list --frame_path ~\/.mxnet\/datasets\/kinetics400\/rawframes_train --subset train --shuffle ||    python kinetics.py --build_file_list --frame_path  ~\/.mxnet\/datasets\/kinetics400\/rawframes_val --subset val --shuffle ||  || Now you can start training your action recognition models on Kinetics400 dataset. ||  || .. note:: ||  ||    You need at least 4T disk space to download and extract the dataset. SSD ||    (Solid-state disks) is preferred over HDD because of faster speed. ||  ||    You may need to install ``Cython`` and ``mmcv`` by ``pip install Cython mmcv``. || \"\"\"
aaifagcfb,dmlc/gluon-cv,tests/unittests/test_utils_export.py,bd6abd23feba4f7955a6f240412e7be06c5b6ca3,STILL_EXISTS,semantic segmentation models require fixed data shape
aaifagdda,dmlc/gluon-cv,scripts/detection/center_net/train_center_net.py,7d9d6030bd44e3a3a937d565c32468944fc0b2e9,STILL_EXISTS,needed for net to be first gpu when using AMP
aaifagdfd,dmlc/gluon-cv,docs/tutorials/detection/demo_center_net.py,c94f8338937461d51b129db08313855b83ceb82e,STILL_EXISTS,feed an arbitrarily sized image; however; models may perform better with certain
aaifagedh,dmlc/gluon-cv,docs/tutorials/datasets/kinetics400.py,6294b14467a96e24d5a1d45848ceebf58f640b64,STILL_EXISTS,\"\"\"Prepare the Kinetics400 dataset || ================================== ||  || `Kinetics400 <https:\/\/deepmind.com\/research\/open-source\/kinetics>`_  is an action recognition dataset || of realistic action videos; collected from YouTube. With 306;245 short trimmed videos || from 400 action categories; it is one of the largest and most widely used dataset in the research || community for benchmarking state-of-the-art video action recognition models. This tutorial || will go through the steps of preparing this dataset for GluonCV. ||  ||  || Download || -------- ||  || Please refer to the `official website <https:\/\/github.com\/activitynet\/ActivityNet\/tree\/master\/Crawler\/Kinetics>`_ on how to download the videos. || Note that the downloaded videos will consume about 450G disk space; make sure there is enough space before downloading. The crawling process can take several days. ||  || Once download is complete; please rename the folder names (since class names have white space and parenthese) for ease of processing. Suppose the videos are || downloaded to ``~\/.mxnet\/datasets\/kinetics400``; there will be three folders in it: ``annotations``; ``train`` and ``val``. You can use the following command to || rename the folder names: ||  || .. code-block:: bash ||  ||    # sudo apt-get install detox ||    detox -r train\/ ||    detox -r val\/ ||  || Decode into frames || ------------------ ||  || If you decide to train your model using video data directly; you can skip this section. ||  || The easiest way to prepare the videos in frames format is to download helper script || :download:`kinetics400.py<..\/..\/..\/scripts\/datasets\/kinetics400.py>` and run the following command: ||  || .. code-block:: bash ||  ||    python kinetics400.py --src_dir ~\/.mxnet\/datasets\/kinetics400\/train --out_dir ~\/.mxnet\/datasets\/kinetics400\/rawframes_train --decode_video --new_width 450 --new_height 340 ||    python kinetics400.py --src_dir ~\/.mxnet\/datasets\/kinetics400\/val --out_dir ~\/.mxnet\/datasets\/kinetics400\/rawframes_val --decode_video --new_width 450 --new_height 340 ||  || This script will help you decode the videos to raw frames. We specify the width and height of frames for resizing because this will save lots of disk space without losing much accuracy. || All the resized frames will consume 2.9T disk space. If we don't specify the dimension; the original decoded frames will consume 6.8T disk space. || The data preparation process may take a while. The total time to prepare the dataset depends on your machine. For example; it takes about 8 hours on an AWS EC2 instance with EBS and using 56 workers. ||  ||  || Generate the training files || --------------------------- ||  || The last step is to generate training files for standard data loading. You can run the helper script as: ||  || .. code-block:: bash ||  ||    python kinetics400.py --build_file_list --frame_path ~\/.mxnet\/datasets\/kinetics400\/rawframes_train --subset train --shuffle ||    python kinetics400.py --build_file_list --frame_path  ~\/.mxnet\/datasets\/kinetics400\/rawframes_val --subset val --shuffle ||  || Now you can start training your action recognition models on Kinetics400 dataset. ||  || .. note:: ||  ||    You need at least 4T disk space to download and extract the dataset. SSD ||    (Solid-state disks) is preferred over HDD because of faster speed. ||  ||    You may need to install ``Cython`` and ``mmcv`` by ``pip install Cython mmcv``. || \"\"\"
aaifagfii,dmlc/gluon-cv,docs/tutorials/action_recognition/dive_deep_i3d_kinetics400.py,87f9cd4d22fa73a232f2eab1b3f8bf65e7fd6f88,STILL_EXISTS,I3D is proposed to improve C3D model by inflating from 2D models.
aaifagihc,dmlc/gluon-cv,docs/tutorials/action_recognition/feat_i3d_custom.py,225163630e2f5b2172c2e1daa48f54dda78c4e45,STILL_EXISTS,\"\"\"8. Extracting features from I3D models || ========================================= ||  || This is a video action recognition tutorial using Gluon CV toolkit; a step-by-step example. || The readers should have basic knowledge of deep learning and should be familiar with Gluon API. || New users may first go through `A 60-minute Gluon Crash Course <http:\/\/gluon-crash-course.mxnet.io\/>`_. || You can `Start Training Now`_ or `Dive into Deep`_. ||  || Feature extraction is a very useful tool to have when you don't have large annotated dataset or don't have the || computing resources to train a model for your use cases. In this tutorial; we provide a simple solution. || The only thing you need to prepare is a text file containing the video paths; we will take care of the rest. || You can extract all kinds of features from many popular pre-trained models using a single command line. ||  || .. note:: ||  ||     Feel free to skip the tutorial because the feature extraction script is self-complete and ready to launch. ||  ||     :download:`Download Full Python Script: feat_extract.py<..\/..\/..\/scripts\/action-recognition\/feat_extract.py>` ||  ||     For more command options; please run ``python feat_extract.py -h`` ||     Please checkout the `model_zoo <..\/model_zoo\/index.html#action_recognition>`_ to select your preferred pretrained model. ||  ||  || \"\"\"
aaifahaca,dmlc/gluon-cv,docs/tutorials/action_recognition/decord_loader.py,42ff79b3e17341e7e43ffe96a3a8545f94dfe0b0,STILL_EXISTS,\"\"\"10. Introducing Decord: an efficient video reader || ==================================================== ||  || Training deep neural networks on videos is very time consuming. For example; training a state-of-the-art SlowFast network || on Kinetics400 dataset using a server with 8 V100 GPUs takes more than 10 days. Slow training causes long research cycles || and is not friendly for new comers and students to work on video related problems. There are several reasons causing the slowness; || big batch of data; inefficiency of video reader and huge model computation. ||  || In this tutotial; we start to solve this problem by introduing a new video reader; `Decord <https:\/\/github.com\/zhreshold\/decord>`_. || Decord is efficient and flexible. It provides convenient video slicing methods based on a wrapper on top of hardware accelerated video decoders; || e.g. FFMPEG\/LibAV and Nvidia Codecs. It is designed to handle awkward video shuffling experience in order to provide smooth experiences || similar to random image loader for deep learning. In additin; it works cross-platform; e.g.; Linux; Windows and Mac OS. ||  || \"\"\"
aaifahaga,dmlc/gluon-cv,docs/tutorials/action_recognition/decord_loader.py,42ff79b3e17341e7e43ffe96a3a8545f94dfe0b0,STILL_EXISTS,In conclusion; Decord is an efficient and flexible video reader. It supports get_batch; GPU loading; fast random access; etc; which is
aaifahaic,dmlc/gluon-cv,docs/tutorials/distributed/distributed_slowfast.py,11f152a88e05767b7f35974aa4b5f906f2891fe9,STILL_EXISTS,\"\"\"1. Distributed training of deep video models || ================================================ ||  || Training deep neural networks on videos is very time consuming. For example; training a state-of-the-art SlowFast network [Feichtenhofer18]_ || on Kinetics400 dataset using a server with 8 V100 GPUs takes more than 10 days. Slow training causes long research cycles || and is not friendly for new comers and students to work on video related problems. ||  || Using distributed training is a natural choice. Spreading the huge computation over multiple machines can speed up training || a lot. However; only a few open sourced Github repositories on video understanding support distributed training; || and they often lack documentation for this feature. || Besides; there is not much information\/tutorial online on how to perform distributed training for deep video models. ||  || Hence; we provide a simple tutorial here to demonstrate how to use our code to perform distributed training of SlowFast models || on Kinetics400 dataset. ||  || \"\"\"
aaifahajc,dmlc/gluon-cv,docs/tutorials/distributed/distributed_slowfast.py,11f152a88e05767b7f35974aa4b5f906f2891fe9,STILL_EXISTS,In this tutorial; we describe how to train a model with devices distributed across machines in a data parallel way.
aaifahbfb,dmlc/gluon-cv,docs/tutorials/distributed/distributed_slowfast.py,11f152a88e05767b7f35974aa4b5f906f2891fe9,STILL_EXISTS,Before kickstarting the actual distributed training; it is better to perform some sanity checks to make sure the communication is good.
aaifahcdg,dmlc/gluon-cv,docs/tutorials/action_recognition/dive_deep_tsn_ucf101.py,9e08a724be2f2df00337d3199eedf67367283517,STILL_EXISTS,If you would like to use a bigger 3D model (e.g.; I3D) on a larger dataset (e.g.; Kinetics400);
aaifahcef,dmlc/gluon-cv,docs/tutorials/action_recognition/finetune_custom.py,c030232c22906f839926ae8fc4687b30d5da53b2,STILL_EXISTS,Try fine-tuning other SOTA video models on your own dataset and see how it goes.
aaifahcij,dmlc/gluon-cv,gluoncv/utils/filesystem.py,6c92fbd2628872e8f9a1f21f4bca88b9cad6c1a8,STILL_EXISTS,fix for pip 19.3
aaifahgfh,dmlc/gluon-cv,gluoncv/model_zoo/hrnet.py,bdfb0e9efbcd37a2a98d8fda45393fbe6b69be24,STILL_EXISTS,TODO: Now; We use OrigHRBottleneck to match with the origial implementation. You
aaifahjeh,dmlc/gluon-cv,scripts/depth/trainer.py,e071f8b59d8a8f4e68d3b946081347783ca3ba05,STILL_EXISTS,resume checkpoint if needed
aaifaiacc,dmlc/gluon-cv,docs/tutorials/depth/demo_monodepth2.py,1c41c89c414cc9e447bbc9d2f96f3b6ac519c07c,4c60fefd33e50744e729dc7f453beb45f6875b3e,\"\"\"01. Predict depth from a single image with pre-trained Monodepth2 models || =========================================================================== ||  || TODO || \"\"\"
aaifaiacd,dmlc/gluon-cv,docs/tutorials/depth/train_monodepth2.py,1c41c89c414cc9e447bbc9d2f96f3b6ac519c07c,4c60fefd33e50744e729dc7f453beb45f6875b3e,\"\"\"02. Monodepth2 training on KITTI dataset || ================================================== ||  || TODO\"\"\"
aaifajbbh,dmlc/gluon-cv,gluoncv/torch/data/gluoncv_motion_dataset/io/video_io.py,213c65ab69bc65155503d860520be28b15ec345a,STILL_EXISTS,muxing queue size bug workaround:
aaifajbhf,dmlc/gluon-cv,gluoncv/torch/data/video_cls/dataset_classification.py,213c65ab69bc65155503d860520be28b15ec345a,STILL_EXISTS,TODO: multiscale corner cropping
aaifajcaa,dmlc/gluon-cv,gluoncv/torch/model_zoo/action_recognition/non_local.py,213c65ab69bc65155503d860520be28b15ec345a,STILL_EXISTS,TODO: need to add zero initialized BN; also the conv output
aaifajdbf,dmlc/gluon-cv,docs/conf.py,679acab57a8e6b4117beb75913cf49acd7d9d68e,STILL_EXISTS,Disable tutorial if needed
aaifbahed,dmlc/gluon-cv,gluoncv/auto/tasks/object_detection.py,3fa96132262d2c50ecb92ad23a1f07e6ea710ad5,STILL_EXISTS,fix estimator-transfer relationship
aaifbahhe,dmlc/gluon-cv,docs/tutorials_torch/action_recognition/extract_feat.py,dce2f28661f8d1e1cb5f68c6e699f0a0177c5b27,STILL_EXISTS,\"\"\"3. Extracting video features from pre-trained models || ======================================================= ||  || Feature extraction is a very useful tool when you don't have large annotated dataset or don't have the || computing resources to train a model from scratch for your use case. It's also useful to visualize what the model have learned. || In this tutorial; we provide a simple unified solution. || The only thing you need to prepare is a text file containing the information of your videos (e.g.; the path to your videos); || we will take care of the rest. || You can extract strong video features from many popular pre-trained models in the GluonCV video model zoo using a single command line. ||  || .. note:: ||  ||     Feel free to skip the tutorial because the feature extraction script is self-complete and ready to launch. ||  ||     :download:`Download Full Python Script: feat_extract_pytorch.py<..\/..\/..\/scripts\/action-recognition\/feat_extract_pytorch.py>` ||  ||     Please checkout the `model_zoo <..\/model_zoo\/index.html#action_recognition>`_ to select your preferred pretrained model. ||  ||     ``python feat_extract_pytorch.py --config-file CONFIG`` ||  ||  || \"\"\"
aaifbaifj,dmlc/gluon-cv,docs/tutorials_torch/action_recognition/finetune_custom.py,dce2f28661f8d1e1cb5f68c6e699f0a0177c5b27,STILL_EXISTS,Try fine-tuning these SOTA video models on your own dataset and see how it goes.
aaifbaihb,dmlc/gluon-cv,docs/tutorials_torch/action_recognition/speed.py,849411ed56632cd854850b07142087d599f97dcb,STILL_EXISTS,\"\"\"4. Computing FLOPS; latency and fps of a model || ======================================================= ||  || It is important to have an idea of how to measure a video model's speed; so that you can choose the model that suits best for your use case. || In this tutorial; we provide two simple scripts to help you compute (1) FLOPS; (2) number of parameters; (3) fps and (4) latency. || These four numbers will help you evaluate the speed of this model. || To be specific; FLOPS means floating point operations per second; and fps means frame per second. || In terms of comparison; (1) FLOPS; the lower the better; || (2) number of parameters; the lower the better; || (3) fps; the higher the better; || (4) latency; the lower the better. ||  ||  || In terms of input; we use the setting in each model's training config. || For example; I3D models will use 32 frames with stride 2 in crop size 224; but R2+1D models will use 16 frames with stride 2 in crop size 112. || This will make sure that the speed performance here correlates well with the reported accuracy number. || We list these four numbers and the models' accuracy on Kinetics400 dataset in the table below. ||  ||  || +----------------------------------------+----------------+------------+---------------------+-----------------+-----------------+ || |  Model                                 | FLOPS          | # params   | fps                 | Latency         | Top-1 Accuracy  | || +========================================+================+============+=====================+=================+=================+ || | resnet18_v1b_kinetics400               | 1.819          | 11.382     | 264.01              | 0.0038          | 66.73           | || +----------------------------------------+----------------+------------+---------------------+-----------------+-----------------+ || | resnet34_v1b_kinetics400               | 3.671          | 21.49      | 151.96              | 0.0066          | 69.85           | || +----------------------------------------+----------------+------------+---------------------+-----------------+-----------------+ || | resnet50_v1b_kinetics400               | 4.110          | 24.328     | 114.05              | 0.0088          | 70.88           | || +----------------------------------------+----------------+------------+---------------------+-----------------+-----------------+ || | resnet101_v1b_kinetics400              | 7.833          | 43.320     | 59.56               | 0.0167          | 72.25           | || +----------------------------------------+----------------+------------+---------------------+-----------------+-----------------+ || | resnet152_v1b_kinetics400              | 11.558         | 58.963     | 36.93               | 0.0271          | 72.45           | || +----------------------------------------+----------------+------------+---------------------+-----------------+-----------------+ || | i3d_resnet50_v1_kinetics400            | 33.275         | 28.863     | 1719.50             | 0.0372          | 74.87           | || +----------------------------------------+----------------+------------+---------------------+-----------------+-----------------+ || | i3d_resnet101_v1_kinetics400           | 51.864         | 52.574     | 1137.74             | 0.0563          | 75.10           | || +----------------------------------------+----------------+------------+---------------------+-----------------+-----------------+ || | i3d_nl5_resnet50_v1_kinetics400        | 47.737         | 38.069     | 1403.16             | 0.0456          | 75.17           | || +----------------------------------------+----------------+------------+---------------------+-----------------+-----------------+ || | i3d_nl10_resnet50_v1_kinetics400       | 62.199         | 42.275     | 1200.69             | 0.0533          | 75.93           | || +----------------------------------------+----------------+------------+---------------------+-----------------+-----------------+ || | i3d_nl5_resnet101_v1_kinetics400       | 66.326         | 61.780     | 999.94              | 0.0640          | 75.81           | || +----------------------------------------+----------------+------------+---------------------+-----------------+-----------------+ || | i3d_nl10_resnet101_v1_kinetics400      | 80.788         | 70.985     | 890.33              | 0.0719          | 75.93           | || +----------------------------------------+----------------+------------+---------------------+-----------------+-----------------+ || | i3d_slow_resnet50_f8s8_kinetics400     | 41.919         | 32.454     | 1702.60             | 0.0376          | 74.41           | || +----------------------------------------+----------------+------------+---------------------+-----------------+-----------------+ || | i3d_slow_resnet50_f16s4_kinetics400    | 83.838         | 32.454     | 1406.00             | 0.0455          | 76.36           | || +----------------------------------------+----------------+------------+---------------------+-----------------+-----------------+ || | i3d_slow_resnet50_f32s2_kinetics400    | 167.675        | 32.454     | 860.74              | 0.0744          | 77.89           | || +----------------------------------------+----------------+------------+---------------------+-----------------+-----------------+ || | i3d_slow_resnet101_f8s8_kinetics400    | 85.675         | 60.359     | 1114.22             | 0.0574          | 76.15           | || +----------------------------------------+----------------+------------+---------------------+-----------------+-----------------+ || | i3d_slow_resnet101_f16s4_kinetics400   | 171.348        | 60.359     | 876.20              | 0.0730          | 77.11           | || +----------------------------------------+----------------+------------+---------------------+-----------------+-----------------+ || | i3d_slow_resnet101_f32s2_kinetics400   | 342.696        | 60.359     | 541.16              | 0.1183          | 78.57           | || +----------------------------------------+----------------+------------+---------------------+-----------------+-----------------+ || | r2plus1d_v1_resnet18_kinetics400       | 40.645         | 31.505     | 804.31              | 0.0398          | 71.72           | || +----------------------------------------+----------------+------------+---------------------+-----------------+-----------------+ || | r2plus1d_v1_resnet34_kinetics400       | 75.400         | 61.832     | 503.17              | 0.0636          | 72.63           | || +----------------------------------------+----------------+------------+---------------------+-----------------+-----------------+ || | r2plus1d_v1_resnet50_kinetics400       | 65.543         | 53.950     | 667.06              | 0.0480          | 74.92           | || +----------------------------------------+----------------+------------+---------------------+-----------------+-----------------+ || | r2plus1d_v2_resnet152_kinetics400      | 252.900        | 118.227    | 546.19              | 0.1172          | 81.34           | || +----------------------------------------+----------------+------------+---------------------+-----------------+-----------------+ || | ircsn_v2_resnet152_f32s2_kinetics400   | 74.758         | 29.704     | 435.77              | 0.1469          | 83.18           | || +----------------------------------------+----------------+------------+---------------------+-----------------+-----------------+ || | slowfast_4x16_resnet50_kinetics400     | 27.820         | 34.480     | 1396.45             | 0.0458          | 75.25           | || +----------------------------------------+----------------+------------+---------------------+-----------------+-----------------+ || | slowfast_8x8_resnet50_kinetics400      | 50.583         | 34.566     | 1297.24             | 0.0493          | 76.66           | || +----------------------------------------+----------------+------------+---------------------+-----------------+-----------------+ || | slowfast_8x8_resnet101_kinetics400     | 96.794         | 62.827     | 889.62              | 0.0719          | 76.95           | || +----------------------------------------+----------------+------------+---------------------+-----------------+-----------------+ || | tpn_resnet50_f8s8_kinetics400          | 50.457         | 71.800     | 1350.39             | 0.0474          | 77.04           | || +----------------------------------------+----------------+------------+---------------------+-----------------+-----------------+ || | tpn_resnet50_f16s4_kinetics400         | 99.929         | 71.800     | 1128.39             | 0.0567          | 77.33           | || +----------------------------------------+----------------+------------+---------------------+-----------------+-----------------+ || | tpn_resnet50_f32s2_kinetics400         | 198.874        | 71.800     | 716.89              | 0.0893          | 78.90           | || +----------------------------------------+----------------+------------+---------------------+-----------------+-----------------+ || | tpn_resnet101_f8s8_kinetics400         | 94.366         | 99.705     | 942.61              | 0.0679          | 78.10           | || +----------------------------------------+----------------+------------+---------------------+-----------------+-----------------+ || | tpn_resnet101_f16s4_kinetics400        | 187.594        | 99.705     | 754.00              | 0.0849          | 79.39           | || +----------------------------------------+----------------+------------+---------------------+-----------------+-----------------+ || | tpn_resnet101_f32s2_kinetics400        | 374.048        | 99.705     | 479.77              | 0.1334          | 79.70           | || +----------------------------------------+----------------+------------+---------------------+-----------------+-----------------+ ||  ||  || .. note:: ||  ||     Feel free to skip the tutorial because the speed computation scripts are self-complete and ready to launch. ||  ||     :download:`Download Full Python Script: get_flops.py<..\/..\/..\/scripts\/action-recognition\/get_flops.py>` ||  ||     :download:`Download Full Python Script: get_fps.py<..\/..\/..\/scripts\/action-recognition\/get_fps.py>` ||  ||     You can reproduce the numbers in the above table by ||  ||     ``python get_flops.py --config-file CONFIG`` and ``python get_fps.py --config-file CONFIG`` ||  ||     If you encouter missing dependecy issue of ``thop``; please install the package first. ||  ||     ``pip install thop`` ||  || \"\"\"
aaifbaiig,dmlc/gluon-cv,scripts/detection/faster_rcnn/train_faster_rcnn.py,158e40e03671a75673e7e04b5395869ebc90aaf3,STILL_EXISTS,input format is differnet than training; thus rehybridization is needed.
aaifbbbii,dmlc/gluon-cv,docs/tutorials_torch/action_recognition/ddp_pytorch.py,18913750053cd40b6277e8e821ff35f5fec34cce,STILL_EXISTS,\"\"\"5. DistributedDataParallel (DDP) Framework || ======================================================= ||  || Training deep neural networks on videos is very time consuming. || For example; training a state-of-the-art SlowFast network on Kinetics400 dataset (with 240K 10-seconds short videos) || using a server with 8 V100 GPUs takes more than 10 days. || Slow training causes long research cycles and is not friendly for new comers and students to work on video related problems. || Using distributed training is a natural choice. || Spreading the huge computation over multiple machines can speed up training a lot. || However; only a few open sourced Github repositories on video understanding support distributed training; || and they often lack documentation for this feature. || Besides; there is not much information\/tutorial online on how to perform distributed training for deep video models. ||  || Hence; we provide a simple tutorial here to demonstrate how to use our DistributedDataParallel (DDP) framework to perform || efficient distributed training. Note that; even in a single instance with multiple GPUs; || DDP should be used and is much more efficient that vanilla dataparallel. ||  ||  || \"\"\"
aaifbbbji,dmlc/gluon-cv,docs/tutorials_torch/action_recognition/ddp_pytorch.py,18913750053cd40b6277e8e821ff35f5fec34cce,STILL_EXISTS,In this tutorial; we describe how to train a model with devices distributed across machines in a data parallel way.
aaifbbccj,dmlc/gluon-cv,docs/tutorials_torch/action_recognition/ddp_pytorch.py,18913750053cd40b6277e8e821ff35f5fec34cce,STILL_EXISTS,Before moving on to next step; it is better to perform some sanity checks to make sure the communication is good.
aaifbbcgg,dmlc/gluon-cv,docs/tutorials_torch/action_recognition/ddp_pytorch.py,18913750053cd40b6277e8e821ff35f5fec34cce,STILL_EXISTS,python train_ddp_pytorch.py --config-file XXX.yaml
aaifbbcih,dmlc/gluon-cv,docs/tutorials/tracking/demo_smot.py,0f0b226e71519493e0744017f2af3b71ad77eb1c,STILL_EXISTS,\"\"\"03. Multiple object tracking with pre-trained SMOT models || ============================================================= ||  || In this tutorial; we present a method; || called `Single-Shot Multi Object Tracking (SMOT) <https:\/\/arxiv.org\/abs\/2010.16031>`_; to perform multi-object tracking. || SMOT is a new tracking framework that converts any single-shot detector (SSD) model into an online multiple object tracker; || which emphasizes simultaneously detecting and tracking of the object paths. || As an example below; we directly use the SSD-Mobilenet object detector pretrained on COCO from :ref:`gluoncv-model-zoo` || and perform multiple object tracking on an arbitrary video. || We want to point out that; SMOT is very efficient; its runtime is close to the runtime of the chosen detector. ||  || \"\"\"
aaifbbdid,dmlc/gluon-cv,gluoncv/model_zoo/smot/object_detector.py,0f0b226e71519493e0744017f2af3b71ad77eb1c,STILL_EXISTS,set anchors if needed
aaifbbegh,dmlc/gluon-cv,gluoncv/torch/model_zoo/video_language/coot_model.py,134ede8a3884277ebb9ebef94dba9b3c0c46eb80,STILL_EXISTS,TODO: convert the return to an object class or maybe dictionary
aaifbbfac,dmlc/gluon-cv,scripts/vision-language/video-language/coot/train_pytorch.py,134ede8a3884277ebb9ebef94dba9b3c0c46eb80,STILL_EXISTS,check if model did not improve for too long
aaifbbiff,dmlc/gluon-cv,docs/tutorials/auto_module/demo_auto_data.py,8477bf4d9a81176ee6c80134c4613c4bc6507c0e,STILL_EXISTS,The dataset supports random split as well
aaifbbihe,dmlc/gluon-cv,docs/tutorials/auto_module/demo_auto_detection.py,8477bf4d9a81176ee6c80134c4613c4bc6507c0e,STILL_EXISTS,\"\"\"03. Train classifier or detector with HPO using GluonCV Auto task || ==================================================================== ||  || The previous image classification example shows the basic usages to train\/evaluate\/predict || using estimators provided by `gluoncv.auto.estimators`. Similarly; you can train object detectors || using  `SSDEstimator`; `YOLOv3Estimator`; `CenterNetEstimator`; `FasterRCNNEstimator`. ||  || In this tutorial; we will move forward a little bit; into the hyper-parameter tunning space! || We will show you how to offload an experiment to the backend HPO searcher; to offer better result if || computational cost is abundant. || \"\"\"
aaifbbijc,dmlc/gluon-cv,docs/tutorials/auto_module/demo_auto_detection.py,8477bf4d9a81176ee6c80134c4613c4bc6507c0e,STILL_EXISTS,in order to achieve better results
aaifbbjjf,dmlc/gluon-cv,gluoncv/model_zoo/smot/general_detector.py,10e17a1fb58a88834976a1186631d3d83dfd37bd,STILL_EXISTS,set anchors if needed
aaifbcdge,snipsco/snips-nlu,custom-intent-parser/dataset.py,b7b9c1367934c42f56e9b19982069eedcc3d4a17,STILL_EXISTS,TODO: @ClemDoum maybe not the best place to ensure consistency
aaifbcdia,snipsco/snips-nlu,custom_intent_parser/dataset.py,0b88869b53444d0173cebecd9028a341c5341ba4,STILL_EXISTS,TODO: find a better way to ensure intent_name validity
aaifbcfab,snipsco/snips-nlu,custom_intent_parser/entity_extractor/regex_entity_extractor.py,72bc84e1f2afaa43b2cfa3d862d5736e3b292017,STILL_EXISTS,TODO: handle role
aaifbcfac,snipsco/snips-nlu,custom_intent_parser/entity_extractor/regex_entity_extractor.py,72bc84e1f2afaa43b2cfa3d862d5736e3b292017,STILL_EXISTS,TODO: modify the pattern extract to have entity patterns by intent
aaifbcfaj,snipsco/snips-nlu,custom_intent_parser/entity_extractor/regex_entity_extractor.py,55930f33c1a7d6fba14a9aa2accc8867395e0d4c,STILL_EXISTS,TODO: handle use_learning = True
aaifbchfa,snipsco/snips-nlu,snips_nlu/nlu_engine/nlu_engine.py,85c3e95ced6b2e9e849e9d1a9e6e9f406cb32e84,bdb3bd5bd812fdc9860d876d48489c89675491aa,TODO: update engine with builtin parsers using builtin_dir_path
aaifbchfb,snipsco/snips-nlu,snips_nlu/nlu_engine/nlu_engine.py,85c3e95ced6b2e9e849e9d1a9e6e9f406cb32e84,STILL_EXISTS,TODO: update engine with builtin parsers using builtin_byte_array
aaifbchhc,snipsco/snips-nlu,snips_nlu/slot_filler/crf_slot_filler.py,4cbb2f5a715a0a0525194defd5871f62372ca4af,11f5421a67f7d64f1d374697380cb905b79eeac2,TODO: dump as json + use PyCRFSuite serialization
aaifbchhg,snipsco/snips-nlu,snips_nlu/slot_filler/feature_functions.py,8f5707dd696b66d9d1149dc76b2f068456e36d41,09fb53048b54924127585ddf88131d8ba251ec71,TODO: if possible avoid looping on the tokens for better efficiency
aaifbdejh,snipsco/snips-nlu,snips_nlu/slot_filler/features_utils.py,09fb53048b54924127585ddf88131d8ba251ec71,86ac1f9d1c2606c08d5cde6e8533bb1854d2e538,TODO: if possible avoid looping on the tokens for better efficiency
aaifbdhbj,snipsco/snips-nlu,snips_nlu/nlu_engine_utils.py,017f1ec43e85b0e9ed66b2efd45b29ffb5bc0169,STILL_EXISTS,TODO: Find a way to avoid tagging multiple times
aaifbdheg,snipsco/snips-nlu,snips_nlu/nlu_engine.py,02752a02c7d22c7ede76d39fa6cf61608b1e0216,01a428ad63b24bd89400c1e58ab0ec5fefaa9e35,TODO: Find a way to avoid tagging multiple times
aaifbdibd,snipsco/snips-nlu,snips_nlu/nlu_engine.py,6b2d05e866872e4a702abed158f1cdd5f52ece48,8d56e3f8a6ef34b11d76dde769bd9e7e5107f620,TODO handle case properly but can be tricky with the synonyms mapping
aaifbdieb,snipsco/snips-nlu,snips_nlu/nlu_engine.py,f83dd115acb0d119fa466fad51b473a30749f684,04dd02daf338383355cc47f5c5745f70ec836557,TODO: Find a way to avoid tagging multiple times
aaifbeahg,snipsco/snips-nlu,snips_nlu/intent_parser/probabilistic_intent_parser.py,480f2ff1b33f585ae01300c2e1cfb05ad37e04e1,0ae5d6d43d879e0693574965d525c41a39e59c5f,TODO: put it in a capitalization config in the probabilistic parser
aaifbedab,snipsco/snips-nlu,snips_nlu/slot_filler/features_utils.py,adfc6426e1e2ed40b6c7ad4bdecfffed5a45c9aa,51319c85a70e8de00fe976ce08d4aca92383f77d,Hack for Rust parallelism (we could use istitle but it does not exists)
aaifbeega,snipsco/snips-nlu,snips_nlu/dataset.py,1f750836b3f7193756829e925cbdef01ea4dbd8d,STILL_EXISTS,Validate format and filter out unused data
aaifbeehg,snipsco/snips-nlu,snips_nlu/intent_parser/regex_intent_parser.py,8d56e3f8a6ef34b11d76dde769bd9e7e5107f620,0a853d0ebf1e4cc6ec522f7b80aabbd52a623e07,This is a hack; here we don't care what language is actually used to
aaifbegbe,snipsco/snips-nlu,snips_nlu/slot_filler/default/default_features_functions.py,0ae5d6d43d879e0693574965d525c41a39e59c5f,8d6b5995f575d72b703da5d26b59d9fb61b5259f,TODO: implement some more complex logic here like getting the number
aaifbgcgc,snipsco/snips-nlu,docs/source/conf.py,60ed9852d6fef59e19416ba5eddb07fb05de8225,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaifbhhbd,snipsco/snips-nlu,snips_nlu/intent_parser/probabilistic_intent_parser.py,9bac26ac7424ed066d4923066e472511bf0bad8b,615011a0cf53cda2d42660612a6edb46e1ac9484,Only needed to improve testability
aaifbhjae,snipsco/snips-nlu,snips_nlu/intent_parser/probabilistic_intent_parser.py,e8ab2c6a2e467bfc91ae774b3290e2e0a724d8e1,8fbce77a1b0042bec00b1d05e559cb97313c5e98,Only needed to improve testability
aaifbiagc,snipsco/snips-nlu,snips_nlu/dataset.py,4f53f76929756eff69a3c33f1207726cfc96c128,STILL_EXISTS,TODO: this is here temporarily; only to allow backward compatibility
aaifbibaa,snipsco/snips-nlu,snips_nlu/tests/test_dataset.py,4f53f76929756eff69a3c33f1207726cfc96c128,STILL_EXISTS,TODO: This test is temporary; and must be removed once the backward
aaifbidjh,snipsco/snips-nlu,snips_nlu/tests/test_dataset.py,8bb4db23851421a241170d765da24be41771c051,STILL_EXISTS,TODO: This test is temporary; and must be removed once the backward
aaifbjdhe,snipsco/snips-nlu,snips_nlu/intent_classifier/featurizer.py,eefe2bc323cb98c573a4228a9c4251bfdb00f1ef,0ea00b38f98d5c05e84ca8683c58c51b14398e72,TODO: missing docstring
aaifbjeaf,snipsco/snips-nlu,snips_nlu/pipeline/configs/intent_classifier.py,929ee9498495e50d2882089ad8f57c655e9d3529,0ea00b38f98d5c05e84ca8683c58c51b14398e72,TODO: update docstring
aaifbjeag,snipsco/snips-nlu,snips_nlu/pipeline/configs/intent_classifier.py,929ee9498495e50d2882089ad8f57c655e9d3529,0ea00b38f98d5c05e84ca8683c58c51b14398e72,TODO: finish docstring
aaifbjeeh,snipsco/snips-nlu,snips_nlu/intent_classifier/featurizer.py,0df03347b9e26ef6153d1a1bed49991b51b09a9d,bf92dbf0239645123fa74a7dfc8b1b460a056401,TODO: raise a custom error here
aaifbjeei,snipsco/snips-nlu,snips_nlu/intent_classifier/featurizer.py,0df03347b9e26ef6153d1a1bed49991b51b09a9d,c2ba7d8819e131075805c5d1a50067871542d4f5,TODO: fix top k
aaifbjefe,snipsco/snips-nlu,snips_nlu/intent_classifier/featurizer.py,0df03347b9e26ef6153d1a1bed49991b51b09a9d,STILL_EXISTS,Remove the unknownword strings if needed
aaifcaegg,iterative/dvc,neatlynx/cmd_repro.py,306ac694a91f553aa427dcffec6eace059851436,STILL_EXISTS,HACK!!!
aaifcahce,iterative/dvc,dvc/command/repro.py,b5dd8c1d717000b54523ba2e06610e61ee81c0f6,STILL_EXISTS,return self.commit_if_needed('DVC repro: {}'.format(' '.join(target)))
aaifcaibh,iterative/dvc,dvc/command/data_sync.py,a01ef3197cd245f2b9b0573f07340f84a2b8e5ce,df6abc7528627453c0592a0ac25d8fc4bf451e0a,FIXME add cmdline argument and config option for POOL_SIZE
aaifcaija,iterative/dvc,dvc/command/base.py,ee34fdfa31d543a827e7179174ff40892b2a3622,STILL_EXISTS,NOTE: this name is really confusing. It should really be called \"command\" or smth;
aaifcajcb,iterative/dvc,dvc/data_cloud.py,2eae3d1af8f3606840431fb8dbdb7c6671fe09c1,5c062aadafb44a11d7fffe9b950b2a0a694e7f12,FIX: It won't work in Windows.
aaifcajeg,iterative/dvc,dvc/cloud/credentials_aws.py,5c062aadafb44a11d7fffe9b950b2a0a694e7f12,STILL_EXISTS,FIX: It won't work in Windows.
aaifcajfe,iterative/dvc,dvc/data_cloud.py,a2343119809b71beb9a52cd3649c82f0e7b0ac62,ee0e012f86eecd5b2d22d51955ea2baaf187ea62,FIX: It won't work in Windows.
aaifcbaib,iterative/dvc,dvc/data_cloud.py,4befe3f7c6846239458cdd1dd9b1b805833bee48,9d9a91d55d48dcf6f3f6967e020fccc3bbba1695,FIX: It won't work in Windows.
aaifcbaji,iterative/dvc,dvc/data_cloud.py,42d86148379dc3676884d8e853c26e26252c7219,487eae3f0719d5ce46081e7555a0d836dca49891,Python3 workaround for ResumableDownloadHandler.
aaifcbbgd,iterative/dvc,dvc/cli.py,22fa9d50f167c72b212a70fafd18058a9227af06,6fec8b16cd6fe64c33185cead5b776cc0736d68a,NOTE: Workaround for bug in Python 3
aaifcbcag,iterative/dvc,dvc/command/run.py,7620e14c8256d59bc95cc8f040ef91bc5871af6d,be159e779809c2d0584ffaf100d74f681a911ff7,FIXME OLOLO don't forget to overwrite with cmdline args
aaifcbcid,iterative/dvc,dvc/data_cloud.py,2b6d997fd381c25da0f799e06cfc079405db5e32,96f9c50de8b6959e4524561dd1d7fa35c12333b3,FIX: It won't work in Windows.
aaifcbdai,iterative/dvc,dvc/data_cloud.py,1ce2633704ef97a926cacd14522630eb2ea95d29,STILL_EXISTS,Python3 workaround for ResumableDownloadHandler.
aaifcbdbi,iterative/dvc,dvc/cloud/aws.py,a130a25e39994b9e0d838a20c851a94770a8d9b1,0f8f682a6b843089ab403999ec01aefc38eb02f2,Python3 workaround for ResumableDownloadHandler.
aaifcbdhj,iterative/dvc,tests/test_data_cloud.py,a05eddd746317f3274763a8eb3f30c2fecca0754,0068e90074653c7dc5c7e481674ab76eeddaa07d,FIXME enable this test for windows
aaifcbdia,iterative/dvc,tests/test_data_cloud.py,a05eddd746317f3274763a8eb3f30c2fecca0754,STILL_EXISTS,FIXME check status output
aaifcbdic,iterative/dvc,tests/test_config.py,d87e8e47af6b981bb60ff1171100f13f89c4b28e,STILL_EXISTS,FIXME check stdout\/err
aaifcbhgf,iterative/dvc,dvc/system.py,7094ec01519f678cb2c9ffb7e33be81eff3206f5,STILL_EXISTS,FIXME: current implementation from ntfsutils doesn't support dirs;
aaifcbhha,iterative/dvc,tests/test_checkout.py,440768b056b807d8a19e6095b18ce61ed74ef94a,b728d942c7e0dd5a388b3932cfac2318a5e0e2ee,FIXME debug
aaifcbhhh,iterative/dvc,dvc/output.py,71fef4d8576a7dd33724084934e9e37c0f6f9653,STILL_EXISTS,FIXME better msgs
aaifcbiah,iterative/dvc,dvc/cloud/ssh.py,bd6688ff57388f3db7b2754cf7f730c1d8bcea7f,6dc9fbd08eb386fdf30ac99b456550fd1ea9da90,FIXME actually compare md5
aaifcbiai,iterative/dvc,tests/test_data_cloud.py,36d924c4c24640d4911379c93803647d3c7e874a,STILL_EXISTS,FIXME: enable on windows
aaifcbiaj,iterative/dvc,tests/test_data_cloud.py,36d924c4c24640d4911379c93803647d3c7e874a,828f00bf860fcceaf9b63fbc46a7a6aca6b49ca1,FIXME: this is ugly
aaifcbidf,iterative/dvc,tests/test_metrics.py,1e7d548ca4d2e65c858fdda78b0dadbb4b04db34,bd382c87d90826113b0166e9cb5e2e7ec9a6d7ba,FIXME check output
aaifcbidj,iterative/dvc,tests/test_metrics.py,5985fd3c96f04ffb921894029dff34a070be1784,834f9502dd9786b5875cb5d4b710acca8626a4a9,FIXME enable on windows
aaifcbifd,iterative/dvc,dvc/dependency/__init__.py,21bf52ee9df46a20af1080d197024e9861664927,2bbefc91ef9296c49e55f07313dac57e9bd33f89,FIXME better msgs
aaifcbifh,iterative/dvc,dvc/dependency/base.py,2bbefc91ef9296c49e55f07313dac57e9bd33f89,7cd24e0c1fda50e9cb79cd74f5433122f9751ee8,FIXME better msgs
aaifcbiga,iterative/dvc,tests/test_repro.py,ac3d37e58cdcb0a1b57bfa1703bfae75b6774b84,fb4fc726427b8b22f5b6aa367fbfd691511da59c,FIXME enable
aaifcbigd,iterative/dvc,dvc/cache.py,88397ef43025ec90d6a82a624ab525aabc43e54e,7a7c9e77132895731229df1b0473643e48160cd3,FIXME
aaifcbihh,iterative/dvc,dvc/dependency/__init__.py,88397ef43025ec90d6a82a624ab525aabc43e54e,7a7c9e77132895731229df1b0473643e48160cd3,FIXME PARAM_MD5; PARAM_ETAG
aaifcbiia,iterative/dvc,dvc/dependency/local.py,88397ef43025ec90d6a82a624ab525aabc43e54e,7a7c9e77132895731229df1b0473643e48160cd3,FIXME
aaifcbiib,iterative/dvc,dvc/output/local.py,88397ef43025ec90d6a82a624ab525aabc43e54e,7a7c9e77132895731229df1b0473643e48160cd3,FIXME
aaifcbiic,iterative/dvc,dvc/remote/gs.py,88397ef43025ec90d6a82a624ab525aabc43e54e,7a7c9e77132895731229df1b0473643e48160cd3,FIXME
aaifcbiid,iterative/dvc,dvc/remote/local.py,88397ef43025ec90d6a82a624ab525aabc43e54e,7a7c9e77132895731229df1b0473643e48160cd3,FIXME
aaifcbiig,iterative/dvc,dvc/remote/s3.py,88397ef43025ec90d6a82a624ab525aabc43e54e,7a7c9e77132895731229df1b0473643e48160cd3,FIXME
aaifcbijc,iterative/dvc,dvc/remote/base.py,1ef21039c48821d5b61638cc8b5963bebe49bb07,7a7c9e77132895731229df1b0473643e48160cd3,FIXME
aaifcbijd,iterative/dvc,dvc/remote/local.py,1ef21039c48821d5b61638cc8b5963bebe49bb07,7a7c9e77132895731229df1b0473643e48160cd3,FIXME
aaifcbjjc,iterative/dvc,dvc/cli.py,594a99ba9df23b65f1819bc6f0c0cd04db15ff89,6fec8b16cd6fe64c33185cead5b776cc0736d68a,NOTE: Workaround for bug in Python 3
aaifcbjjj,iterative/dvc,dvc/system.py,11f7eb0c4ea01166ca3e54da7e8aa7409eab3a34,a6ea2f3db98d73ac9a17f3cbd384bc50e33b064e,FIXME
aaifccacg,iterative/dvc,dvc/cli.py,38aed70995aa6b2cec5edb6c97b4ae51717c6eaf,6fec8b16cd6fe64c33185cead5b776cc0736d68a,Move
aaifccafe,iterative/dvc,dvc/state.py,f471f7714bb235bc8cd682b3b4863685c57d02a4,0cea36a2dd508709c78d73e7ab5469a78cd504b9,FIXME: we could've used `md5 = self.update(path; dump=False)` here;
aaifccaij,iterative/dvc,dvc/remote/azure.py,6882a781a4219559cff94a0d1dc6eb6144fb0a05,STILL_EXISTS,TODO: why does S3 fetch all blobs first?
aaifccbag,iterative/dvc,dvc/project.py,6d6653840ea94c1972819e34501a6d94ea114818,cb142f9f0e515b3db667caa1049d899646fca2d2,find pipeline ends aka \"output stages\"
aaifccbjb,iterative/dvc,tests/test_metrics.py,236305716554f4a25c1a63aa56abada45a0d330f,bd382c87d90826113b0166e9cb5e2e7ec9a6d7ba,FIXME check output
aaifccbjc,iterative/dvc,dvc/remote/local.py,0cea36a2dd508709c78d73e7ab5469a78cd504b9,STILL_EXISTS,FIXME: we could've used `md5 = state.update(path; dump=False)`
aaifcccaj,iterative/dvc,dvc/utils.py,77b2e45a64b2fb3e9eb0ecd385c862201ff16439,STILL_EXISTS,NOTE: Fix env variables modified by PyInstaller
aaifccdch,iterative/dvc,dvc/data_cloud.py,78a20a3895162095a41b3d34286050bf040ff5bf,14c4a9c52f8176bc5c659f5c64f8b5636d2badbd,NOTE: check if the class itself has everything needed for operation.
aaifccedd,iterative/dvc,dvc/remote/azure.py,8ad317e9067c6543d24b37b7ef753522f252a2c5,STILL_EXISTS,FIXME: currently Azure doesn't support prefixes
aaifcceeg,iterative/dvc,tests/test_metrics.py,a67988a80ef5c8c3db16ca01526fc129319586c9,bd382c87d90826113b0166e9cb5e2e7ec9a6d7ba,FIXME check output
aaifccefj,iterative/dvc,dvc/project.py,df044c60ac78a2bfbcaf4130ac83e795c401fe93,b7e473fc2030ab6a46f09113fc9344045df201e6,find pipeline ends aka \"output stages\"
aaifccegd,iterative/dvc,dvc/command/repro.py,e8a081030fad7af950a8516a3ccc07df60c6b111,aec8d657912b7fd2b4a0a13afc220d9496a1b999,Dirty hack so the for loop below can at least enter once
aaifccehj,iterative/dvc,tests/test_run.py,7ecb9e87aeab1872ed62d64160ff88dc35615058,STILL_EXISTS,NOTE: using sleep() is a workaround  for filesystems
aaifccfib,iterative/dvc,dvc/remote/base.py,ecb80d98dd9148d997ac829c33f1b692d426e86b,4029116b85b7e91002bfe87d8b081957b21d384b,so we need to use such an ugly logic.
aaifccgaj,iterative/dvc,dvc/output/base.py,7cd24e0c1fda50e9cb79cd74f5433122f9751ee8,26c9fc1ba450eaee3b7db20fb3c60e6e6d84537c,FIXME better msgs
aaifccjae,iterative/dvc,dvc/repo/metrics/show.py,bd382c87d90826113b0166e9cb5e2e7ec9a6d7ba,45944ddde7e1ad147823f11b1efbd7f07b300edb,order to fix this too broad except clause.
aaifccjhj,iterative/dvc,dvc/repo/__init__.py,9e6c52079b0cb0bfcab61c5b3372f3f557b98359,STILL_EXISTS,needed to look at all the files (project root_dir)
aaifccjjb,iterative/dvc,dvc/scm/git/tree.py,9e6c52079b0cb0bfcab61c5b3372f3f557b98359,STILL_EXISTS,GitPython's obj.data_stream is a fragile thing; it is better to
aaifccjjh,iterative/dvc,dvc/scm/git/tree.py,9e6c52079b0cb0bfcab61c5b3372f3f557b98359,STILL_EXISTS,`return (i in tree)` doesn't work so here is a workaround:
aaifcdaab,iterative/dvc,dvc/stage.py,9e6c52079b0cb0bfcab61c5b3372f3f557b98359,71b15d259a0c0baddf14547c870f62d75010f5e6,not needed to decode the path from py2's str
aaifcdbjj,iterative/dvc,tests/test_checkout.py,e505c38f203e7de972b52272b49d3317f4090ecb,STILL_EXISTS,move instead of remove; to lock inode assigned to stage_files[0].path
aaifcdcdb,iterative/dvc,tests/conftest.py,0c867399bbd665bf7354b73e62bb21a0b618318f,a4b55a00ecc9d9b75b120f86b5eca71b79bba020,would fix itself once class-based fixtures are removed
aaifcdebg,iterative/dvc,tests/func/test_api.py,ca93dbcf1323836657d212faa45fbe640ceee0f7,caa67c725e1e351ed122bdad17db0f29a8e73c39,NOTE: staticmethod is only needed in Python 2
aaifcdfbi,iterative/dvc,dvc/remote/local/__init__.py,5d25d5ce965720b6339845c84802801eca14b995,b5464cbc59740d546f9089fe1b492542191d68a3,TODO handle existing link
aaifcdfib,iterative/dvc,dvc/output/local.py,d80293c44d49713ed84c395a914b13d44688ee1b,STILL_EXISTS,FIXME: if we have Windows path containig \/ or posix one with \\
aaifcdgaf,iterative/dvc,dvc/remote/local/__init__.py,d80293c44d49713ed84c395a914b13d44688ee1b,STILL_EXISTS,A clunky way to detect cache dir
aaifcdhce,iterative/dvc,dvc/pkg.py,14dc0c0115cfd9ffb5c236b9cea1107ba8312ae8,STILL_EXISTS,we won't be able to use move properly.
aaifcdhdd,iterative/dvc,dvc/external_repo.py,1ba8e0171d11edc27e1139f4e858675dbf0c8c16,STILL_EXISTS,we won't be able to use move properly.
aaifcdhii,iterative/dvc,tests/__init__.py,1cb70fc63130d1fb9c4bca09d4534bd3237124d4,STILL_EXISTS,Workaround for two bugs:
aaifcdiii,iterative/dvc,dvc/remote/hdfs.py,68bebe79441c078641e85a1db7f14c16521fe294,STILL_EXISTS,NOTE: this is how `hadoop fs -cp` works too: it copies through
aaifcdjhb,iterative/dvc,dvc/progress.py,1511fd6dcc522c2cfbb33ff5daf08dff7b7a4617,70ee31329f16564cbbf07982393971d23313766f,TODO: up to 10 nested bars
aaifcdjhc,iterative/dvc,dvc/progress.py,1511fd6dcc522c2cfbb33ff5daf08dff7b7a4617,d6f816bc48e5131df7cd04ff256ed9526fd598d8,TODO: We have to write a msg about finished target
aaifcdjhd,iterative/dvc,dvc/progress.py,1511fd6dcc522c2cfbb33ff5daf08dff7b7a4617,STILL_EXISTS,TODO: is this meant to be a thin wrapper for multi-progress; or just one bar?
aaifcdjhe,iterative/dvc,dvc/progress.py,0ebce9c1e1fc05bab73304c3d12ce0c640cd7dfd,STILL_EXISTS,TODO: True?
aaifcdjia,iterative/dvc,dvc/remote/http.py,62cbeed8b38bc52bf72a3b42b77c67e6fae1a5a7,STILL_EXISTS,TODO: persistent progress only for \"large\" files?
aaifcdjje,iterative/dvc,tests/func/test_checkout.py,ee4248df92a81d0442057f69048fdf5548549f92,534d6fdb0cb4e1f36e57f206734272d9211ceed4,but maybe no intermediate ones
aaifceaec,iterative/dvc,dvc/remote/ssh/__init__.py,110da0c366b133a9981e89f221fe6dcd1ca166f2,f4563bdd85cd598284de5bd896d2f0864f51cc53,XXX: Friendly reminder to add \"reflink\" to the default types
aaifceaee,iterative/dvc,dvc/remote/ssh/__init__.py,110da0c366b133a9981e89f221fe6dcd1ca166f2,c9342754ed2b090f71bc7d97e5f307527f47992d,XXX: We can remove the `exists` call; since `makedirs`
aaifceaeh,iterative/dvc,dvc/remote/ssh/__init__.py,110da0c366b133a9981e89f221fe6dcd1ca166f2,c9342754ed2b090f71bc7d97e5f307527f47992d,XXX: We are testing if file exists rather than if file is a link
aaifceagj,iterative/dvc,dvc/remote/base.py,745b2e13a04756bf1a30d4adca3d57ee884319ba,edcd1724b4e591e8ee6f550844ee39983a2379ae,XXX: This is a fallback until the following methods are
aaifceaja,iterative/dvc,dvc/remote/base.py,745b2e13a04756bf1a30d4adca3d57ee884319ba,edcd1724b4e591e8ee6f550844ee39983a2379ae,That's why we simply create an empty file rather than a link.
aaifcebbe,iterative/dvc,dvc/remote/base.py,9c9079753bd61ed5fb1fa4ddeb76f315210d4f7c,c9342754ed2b090f71bc7d97e5f307527f47992d,XXX: This is a fallback until the following methods are
aaifcebdf,iterative/dvc,dvc/remote/base.py,9c9079753bd61ed5fb1fa4ddeb76f315210d4f7c,c9342754ed2b090f71bc7d97e5f307527f47992d,That's why we simply create an empty file rather than a link.
aaifcebia,iterative/dvc,dvc/remote/base.py,40c61b3aaa5a29fa69842fa3a8fe2e73734a4cae,88574f300e8efb788e68261d62f8180dc01e0ff8,XXX: We are testing if file exists rather than if file is a link
aaifceceg,iterative/dvc,dvc/remote/local/__init__.py,42168d34f30eeed13f5733a97b3062315825cf2c,STILL_EXISTS,That's why we simply create an empty file rather than a link.
aaifcecfa,iterative/dvc,dvc/scm/git/__init__.py,60d8e8fbecaa78b6430c7a755b50e1b891a45ceb,515286663434569f7b5b2e48ec8ed5654ce5d776,needed before we can fix it in __init__
aaifcefaj,iterative/dvc,tests/func/test_version.py,803ffaec5700971a7742b2b1483b1ade54ed994e,STILL_EXISTS,adding a file so that dvc creates `.dvc\/cache`; that is needed for proper
aaifcefbf,iterative/dvc,dvc/progress.py,8eb520f5169284c0b3c6b186f2b627fcb5a24aaa,STILL_EXISTS,work-around for zero-width desc
aaifcefbg,iterative/dvc,dvc/lock.py,566d7b48d033a3379c86e5611f4a48aa19c074f4,STILL_EXISTS,Workaround for slow and obsoleted gethostbyaddr used in
aaifceffa,iterative/dvc,dvc/remote/s3.py,27b63e26747553d43a9b01bd9a2f64658cd7c583,STILL_EXISTS,We need to make sure that the path ends with a forward slash;
aaifcegba,iterative/dvc,dvc/main.py,a0b6c38fa5a0800f43598bbc6d611c3cef0bd59e,STILL_EXISTS,Workaround for CPython bug. See [1] and [2] for more info.
aaifceggb,iterative/dvc,dvc/remote/local.py,8681cc3cd1b50c13ea6c90f7eca5c7f9788d78c7,461a79e1e8989e2984c868caad290d6e8dd6585a,A clunky way to detect cache dir
aaifcehbc,iterative/dvc,tests/func/remotes.py,caa67c725e1e351ed122bdad17db0f29a8e73c39,STILL_EXISTS,NOTE: staticmethod is only needed in Python 2
aaifcehed,iterative/dvc,tests/remotes.py,42991d3b75c7551649736a4a48504831b53d6457,STILL_EXISTS,FIXME: enable on windows
aaifcehfd,iterative/dvc,tests/func/test_api.py,30848306e86d97e4baf7486c045d5f2fe6e26877,f4f836e17cdd7ddcc124a25114ce90d510916a0d,NOTE: staticmethod is only needed in Python 2
aaifceicd,iterative/dvc,tests/func/test_api.py,fcebad821e9102eefa0e4ab27c1fd7510a3fbd5f,8041d70552f04137a232e09227be8ff76ffc6c51,FIXME: this test doesn't use scm ;D
aaifceice,iterative/dvc,tests/func/test_api.py,fcebad821e9102eefa0e4ab27c1fd7510a3fbd5f,STILL_EXISTS,TODO: simplify; we shouldn't need run. This also duplicates the previous one.
aaifceich,iterative/dvc,tests/dir_helpers.py,0385810662049abf4ccd04dedf2880e951ad252f,833e82a7eaf95f9410e0599e4850ea4eab113d1f,Not needed in Python 3.6+
aaifceidf,iterative/dvc,dvc/utils/compat.py,02eaa9a14fa2994aa04936ae0d4c692c4d9067be,7e8bdbd6035221c4ebae2058b21011f9b7ad6e7a,implement `__fspath__`
aaifcejhb,iterative/dvc,tests/dir_helpers.py,659eb04705b2788693c8723f144e461b9b13a281,c6c67ace20b2a8978b5379c61444deb0196f2ce6,\"\"\" || The goal of this module is making dvc functional tests setup a breeze. This || includes a temporary dir; initializing git and dvc repos and bootstraping some || file structure. ||  || The cornerstone of these fixtures is `tmp_dir`; which creates a temporary dir || and changes path to it; it might be combined with `scm` and `dvc` to initialize || empty git and dvc repos. `tmp_dir` returns a Path instance; which should save || you from using `open()`; `os` and `os.path` utils many times: ||  ||     (tmp_dir \/ \"some_file\").write_text(\"some text\") ||     # ... ||     assert \"some text\" == (tmp_dir \/ \"some_file\").read_text() ||     assert (tmp_dir \/ \"some_file\").exists() ||  || Additionally it provides `.gen()`; `.scm_gen()` and `.dvc_gen()` methoda to || bootstrap a required file structure in a single call: ||  ||     # Generate a dir with files ||     tmp_dir.gen({\"dir\": {\"file\": \"file text\"; \"second_file\": \"...\"}}) ||  ||     # Generate a single file; dirs will be created along the way ||     tmp_dir.gen(\"dir\/file\"; \"file text\") ||  ||     # Generate + git add ||     tmp_dir.scm_gen({\"file1\": \"...\"; ...}) ||  ||     # Generate + git add + git commit ||     tmp_dir.scm_gen({\"file1\": \"...\"; ...}; commit=\"add files\") ||  ||     # Generate + dvc add ||     tmp_dir.dvc_gen({\"file1\": \"...\"; ...}) ||  ||     # Generate + dvc add + git commit -am \"...\" ||     # This commits stages to git not the generated files. ||     tmp_dir.dvc_gen({\"file1\": \"...\"; ...}; commit=\"add files\") ||  || Making it easier to bootstrap things has a supergoal of incentivizing a move || from global repo template to creating everything inplace; which: ||  ||     - makes all path references local to test; enhancing readability ||     - allows using telling filenames; e.g. \"git_tracked_file\" instead of \"foo\" ||     - does not create whatever is not needed || \"\"\"
aaifcejjd,iterative/dvc,tests/dir_helpers.py,0cd6106189e373b54efa2d8f5b917f230dfabda1,STILL_EXISTS,\"\"\" || The goal of this module is making dvc functional tests setup a breeze. This || includes a temporary dir; initializing git and dvc repos and bootstraping some || file structure. ||  || The cornerstone of these fixtures is `tmp_dir`; which creates a temporary dir || and changes path to it; it might be combined with `scm` and `dvc` to initialize || empty git and dvc repos. `tmp_dir` returns a Path instance; which should save || you from using `open()`; `os` and `os.path` utils many times: ||  ||     (tmp_dir \/ \"some_file\").write_text(\"some text\") ||     # ... ||     assert \"some text\" == (tmp_dir \/ \"some_file\").read_text() ||     assert (tmp_dir \/ \"some_file\").exists() ||  || Additionally it provides `.gen()`; `.scm_gen()` and `.dvc_gen()` methods to || bootstrap a required file structure in a single call: ||  ||     # Generate a dir with files ||     tmp_dir.gen({\"dir\": {\"file\": \"file text\"; \"second_file\": \"...\"}}) ||  ||     # Generate a single file; dirs will be created along the way ||     tmp_dir.gen(\"dir\/file\"; \"file text\") ||  ||     # Generate + git add ||     tmp_dir.scm_gen({\"file1\": \"...\"; ...}) ||  ||     # Generate + git add + git commit ||     tmp_dir.scm_gen({\"file1\": \"...\"; ...}; commit=\"add files\") ||  ||     # Generate + dvc add ||     tmp_dir.dvc_gen({\"file1\": \"...\"; ...}) ||  ||     # Generate + dvc add + git commit -am \"...\" ||     # This commits stages to git not the generated files. ||     tmp_dir.dvc_gen({\"file1\": \"...\"; ...}; commit=\"add files\") ||  || Making it easier to bootstrap things has a supergoal of incentivizing a move || from global repo template to creating everything inplace; which: ||  ||     - makes all path references local to test; enhancing readability ||     - allows using telling filenames; e.g. \"git_tracked_file\" instead of \"foo\" ||     - does not create whatever is not needed || \"\"\"
aaifcejji,iterative/dvc,tests/dir_helpers.py,237704ef2bdf45f4c5f32ef8d423728c20d8a12a,STILL_EXISTS,\"\"\" || The goal of this module is making dvc functional tests setup a breeze. This || includes a temporary dir; initializing git and dvc repos and bootstrapping some || file structure. ||  || The cornerstone of these fixtures is `tmp_dir`; which creates a temporary dir || and changes path to it; it might be combined with `scm` and `dvc` to initialize || empty git and dvc repos. `tmp_dir` returns a Path instance; which should save || you from using `open()`; `os` and `os.path` utils many times: ||  ||     (tmp_dir \/ \"some_file\").write_text(\"some text\") ||     # ... ||     assert \"some text\" == (tmp_dir \/ \"some_file\").read_text() ||     assert (tmp_dir \/ \"some_file\").exists() ||  || Additionally it provides `.gen()`; `.scm_gen()` and `.dvc_gen()` methods to || bootstrap a required file structure in a single call: ||  ||     # Generate a dir with files ||     tmp_dir.gen({\"dir\": {\"file\": \"file text\"; \"second_file\": \"...\"}}) ||  ||     # Generate a single file; dirs will be created along the way ||     tmp_dir.gen(\"dir\/file\"; \"file text\") ||  ||     # Generate + git add ||     tmp_dir.scm_gen({\"file1\": \"...\"; ...}) ||  ||     # Generate + git add + git commit ||     tmp_dir.scm_gen({\"file1\": \"...\"; ...}; commit=\"add files\") ||  ||     # Generate + dvc add ||     tmp_dir.dvc_gen({\"file1\": \"...\"; ...}) ||  ||     # Generate + dvc add + git commit -am \"...\" ||     # This commits stages to git not the generated files. ||     tmp_dir.dvc_gen({\"file1\": \"...\"; ...}; commit=\"add files\") ||  || Making it easier to bootstrap things has a supergoal of incentivizing a move || from global repo template to creating everything inplace; which: ||  ||     - makes all path references local to test; enhancing readability ||     - allows using telling filenames; e.g. \"git_tracked_file\" instead of \"foo\" ||     - does not create unnecessary files || \"\"\"
aaifcfaac,iterative/dvc,dvc/repo/get.py,9607e5f873e0495872dd289aa40ea81b348bbdc1,033ef88a4eab29a85869b8170b3f03d4c3030392,This might happen when pull haven't really pulled all the files
aaifcfaed,iterative/dvc,dvc/api.py,3d133186d4ec0095f103dc2a9fdcf0ad7881800b,b921730f37497ce08628867489724220dfa3272e,ugly af; don't use exec \/ global; pls
aaifcfaef,iterative/dvc,tests/func/test_api.py,789db9169ac697fad73df75d3ae0569e2e63050d,1c22cd5af4a307058fe138920bda08ed883f34fa,XXX: Chdir because `stage.create` defaults to `wdir = os.curdir`
aaifcfaeg,iterative/dvc,dvc/api.py,ee72280e603eefa1de74e1137082134a2d661097,afd29e5dad42d2ad64fc9f97064baae47207783b,XXX: Some issues with this approach:
aaifcfafc,iterative/dvc,dvc/api.py,7dd340aeeb4382fd4c7542448080ef27047e1421,d45f07cbecf357575e8a0eedb89afab203f8e684,XXX: Optional
aaifcfafd,iterative/dvc,dvc/api.py,7dd340aeeb4382fd4c7542448080ef27047e1421,STILL_EXISTS,XXX: No such YAML file with path: '<path>'
aaifcfafe,iterative/dvc,dvc/api.py,7dd340aeeb4382fd4c7542448080ef27047e1421,1e65c76ef1d454fd88891bf3f1ad90424fcc0049,XXX: Failed to parse YAML correctly
aaifcfaff,iterative/dvc,dvc/api.py,7dd340aeeb4382fd4c7542448080ef27047e1421,d45f07cbecf357575e8a0eedb89afab203f8e684,XXX: YAML file doesn't include the \"objects\" keyword
aaifcfafg,iterative/dvc,dvc/api.py,1c22cd5af4a307058fe138920bda08ed883f34fa,STILL_EXISTS,TODO: Write a meaningful docstring about `summon`
aaifcfagd,iterative/dvc,dvc/api.py,0658622ca2b211d2b816b45aa82c975e4d5bd7e6,afd29e5dad42d2ad64fc9f97064baae47207783b,XXX: sys.path manipulation is \"theoretically\" not needed
aaifcfahb,iterative/dvc,dvc/utils/fs.py,d8c0ae6909a32d37c9ab59df2f5b27db99e13a0f,e225601031c98f3fd21a22dcc4731811b225c94f,FIXME: There is no use case of get_mtime_and_size without tree; we should
aaifcfahc,iterative/dvc,dvc/utils/fs.py,d8c0ae6909a32d37c9ab59df2f5b27db99e13a0f,e225601031c98f3fd21a22dcc4731811b225c94f,move it to tree
aaifcfbjd,iterative/dvc,tests/func/test_api.py,b194bfa1ffd51ccac662b9b549ace34fdf0df691,8041d70552f04137a232e09227be8ff76ffc6c51,FIXME: this test doesn't use scm ;D
aaifcfbje,iterative/dvc,tests/func/test_api.py,b194bfa1ffd51ccac662b9b549ace34fdf0df691,8041d70552f04137a232e09227be8ff76ffc6c51,TODO: simplify; we shouldn't need run.
aaifcfcbc,iterative/dvc,dvc/stage.py,ee0a8fa92d1bb005aafd1c57bae33a1c7bcbe692,049367e4751238363ee110d6c867d5b74de67009,not needed to decode the path from py2's str
aaifcfcfd,iterative/dvc,dvc/repo/get.py,0dd5647d3c3f8fafdfa5bed524d9f0ef3e1842b5,7873abcadeb3e992b1f68470fa021acf84eb713b,Dummy exception raised to signal a plain file copy is needed
aaifcfcgd,iterative/dvc,dvc/api.py,d0de9d0ed0a7df3284aabe6ec24665e1c6187cd3,614515e6317621946e84df87668415e2f9d36891,XXX: Some issues with this approach:
aaifcfcgf,iterative/dvc,dvc/api.py,d0de9d0ed0a7df3284aabe6ec24665e1c6187cd3,STILL_EXISTS,* sys.path manipulation is \"theoretically\" not needed;
aaifcfdch,iterative/dvc,dvc/external_repo.py,033ef88a4eab29a85869b8170b3f03d4c3030392,57d02195b571789f67c616aed959e8c2a828e859,This might happen when pull haven't really pulled all the files
aaifcfddi,iterative/dvc,dvc/external_repo.py,eb97e0b3939ad40f9a66c6cddea729f9263dcd67,STILL_EXISTS,Only pull unless all needed cache is present
aaifcffbg,iterative/dvc,dvc/config.py,6ccae711fe8d2080d10b7cf7538b6681ee284479,c04cdaf70fd3402a7d3ff14d7f1eefbb6e0e74eb,Windows absolute paths should really have scheme == \"\" (local)
aaifcfgdb,iterative/dvc,dvc/dependency/__init__.py,65d22c7dbdb0f4051f90c1d4d8931fd8657b4e5b,8f546f253ba255c4b2a850b7a197ff25b83c6b23,TODO: Make support for `eropo=` as well ?
aaifcfged,iterative/dvc,dvc/repo/__init__.py,a4094f0a7f5cab688139d79e6a429efda9788723,STILL_EXISTS,so we provide this undocumented hack to skip it. See [1] for
aaifcfgee,iterative/dvc,dvc/repo/__init__.py,a4094f0a7f5cab688139d79e6a429efda9788723,STILL_EXISTS,more details. The hack can be used as:
aaifcfgfg,iterative/dvc,tests/unit/repo/test_repo.py,a4094f0a7f5cab688139d79e6a429efda9788723,STILL_EXISTS,check that our hack can be enabled
aaifcfgfh,iterative/dvc,tests/unit/repo/test_repo.py,a4094f0a7f5cab688139d79e6a429efda9788723,STILL_EXISTS,check that our hack can be disabled
aaifcfhad,iterative/dvc,dvc/remote/base.py,ee0066fae4257d7447e2f82c174569df5400aa70,STILL_EXISTS,NOTE: this ends up re-fetching checksums that were already
aaifcfhib,iterative/dvc,tests/func/test_dvcfile.py,128fa7f887923d792d872ac2be349627f1babbdc,63c466d821213764bc1162af23edc5bcc9c66381,TODO: Better exception
aaifcfhic,iterative/dvc,tests/func/test_repro.py,128fa7f887923d792d872ac2be349627f1babbdc,1937527249e286d122692109596b238f2f12679d,TODO: Test ^ for multistage
aaifcfhif,iterative/dvc,tests/func/test_repro_multistage.py,128fa7f887923d792d872ac2be349627f1babbdc,1937527249e286d122692109596b238f2f12679d,TODO: TestReproWorkingDirectoryAsOutput
aaifcfifb,iterative/dvc,dvc/repo/tree.py,45944ddde7e1ad147823f11b1efbd7f07b300edb,75e795c491b7b6a300488601343ad4f6e114b976,temporary hack to make cache use WorkingTree and not GitTree; because
aaifcfjbd,iterative/dvc,tests/func/test_lockfile.py,22c60dd6083a53b9cccb8abb569fc3f155b15247,STILL_EXISTS,XXX: temporary workaround due to lack of params support in build cache
aaifcfjci,iterative/dvc,dvc/stage/cache.py,7b4466c98212d95f4a36ff32541c492780343aaa,STILL_EXISTS,stage and to workaround `commit\/checkout` not working for uncached
aaifcfjjc,iterative/dvc,dvc/repo/tree.py,afbb3a0add741a823397c4eca93f145a755d5197,STILL_EXISTS,will pull dir cache if needed
aaifcgabg,iterative/dvc,dvc/repo/tree.py,75e795c491b7b6a300488601343ad4f6e114b976,STILL_EXISTS,pull dir cache if needed
aaifcgabi,iterative/dvc,dvc/repo/tree.py,75e795c491b7b6a300488601343ad4f6e114b976,STILL_EXISTS,will pull dir cache if needed
aaifcgbca,iterative/dvc,dvc/remote/hdfs.py,2c0116ccf974fdef5018e2bb0888bd7eb142333f,STILL_EXISTS,NOTE: this is how `hadoop fs -cp` works too: it copies through
aaifcgbhc,iterative/dvc,dvc/remote/base.py,939f78948afe2c1fd7c73fe8e93754b7180770b0,76228a21183e996b613fa01944d2d953702335e5,turn very ugly; as the user might think that he has
aaifcgdaf,iterative/dvc,tests/remotes/gdrive.py,51a8c782aeac0b758adef9a161ae97b17503cb01,STILL_EXISTS,NOTE: temporary workaround
aaifcgdah,iterative/dvc,tests/remotes/ssh.py,51a8c782aeac0b758adef9a161ae97b17503cb01,4c9245a3b3ec6aaecbf8911b5978386079564445,FIXME: enable on windows
aaifcgehf,iterative/dvc,dvc/cli.py,8964967af06b7861537d9ad5bfd5001114e8e032,STILL_EXISTS,help message instead of generic `dvc` usage.
aaifcghdg,iterative/dvc,dvc/tree/base.py,76228a21183e996b613fa01944d2d953702335e5,STILL_EXISTS,turn very ugly; as the user might think that he has
aaifcghei,iterative/dvc,dvc/tree/base.py,76228a21183e996b613fa01944d2d953702335e5,f0ad1b4f93515c628cc7b48ad958196e567502b8,NOTE: this ends up re-fetching hashes that were already
aaifcghgf,iterative/dvc,dvc/tree/local.py,76228a21183e996b613fa01944d2d953702335e5,STILL_EXISTS,That's why we simply create an empty file rather than a link.
aaifcgjbe,iterative/dvc,dvc/daemon.py,0b111d2a1052cdffcd3ddbc117097ae7148519f7,STILL_EXISTS,workaround for MacOS bug
aaifcgjbj,iterative/dvc,setup.py,9ca3c05f6dc8d53d11af80c1f2b965e93fb28cdf,99ffa7c6fd9dbb63c04e950e557ca0816529fe6b,FIXME: pylint complaining for pytest.mark.* on v6.0
aaifcgjcj,iterative/dvc,dvc/repo/experiments/executor.py,c8d2f4f1fbff944615c7ce526520f98723ba1616,STILL_EXISTS,TODO: come up with better way to stash repro arguments
aaifcgjif,iterative/dvc,dvc/tree/webdav.py,2f48bb4089c25c2144fa3c9d9d8d0ba69c4c3e51,STILL_EXISTS,Webdav client move
aaifchafh,iterative/dvc,dvc/repo/experiments/__init__.py,958aa265e70670c49520e393d599fcdce3c122ac,176a23e29180461f3504dda1a569687490be8a92,TODO: parallelize this; currently --jobs for repro applies to
aaifchahf,iterative/dvc,dvc/tree/dvc.py,41e6c22e7d05fdd5b6fc5fa883acd0b49b97e9a1,STILL_EXISTS,pull dir cache if needed
aaifchahg,iterative/dvc,dvc/tree/dvc.py,41e6c22e7d05fdd5b6fc5fa883acd0b49b97e9a1,969a85efb6d4b758109778e8ee5d99d2f2ce0fd9,pull dir contents if needed
aaifchbdi,iterative/dvc,dvc/tree/git.py,bc7d952e88553eda33bb377a333ab018a1e854ef,3b23dfebaed5fc245dfaf15a3f5952d485d3ebf9,`return (i in tree)` doesn't work so here is a workaround:
aaifchcdb,iterative/dvc,tests/unit/test_info.py,8b16af965c563dfa60596c4d198db9c1cfc48c9f,STILL_EXISTS,Create `.dvc\/cache`; that is needed to check supported link types.
aaifchcdh,iterative/dvc,dvc/repo/experiments/__init__.py,6a3c516b7177bc7439bb71bbf99495329386204e,379ed8e6fc97d9a8fc17923826657f584f815fb9,TODO: patching changes into an extended branch will require
aaifchcgc,iterative/dvc,tests/__init__.py,fc6999ad17dc219d1f0db6da6d1650b3c2f918a0,b2c2929869fc27aaf6ccafb56ffa0af6048c402d,FIXME: Search and replace these from the tests if pyarrow wheel is available
aaifchche,iterative/dvc,dvc/command/experiments.py,f0a729df4d6ae259f3b40be32ea2f9f6be0aa3db,aec8d657912b7fd2b4a0a13afc220d9496a1b999,Dirty hack so the for loop below can at least enter once
aaifchchj,iterative/dvc,dvc/repo/experiments/__init__.py,f0a729df4d6ae259f3b40be32ea2f9f6be0aa3db,STILL_EXISTS,checkpoint run was interrupted via SIGINT; so we need this hack
aaifchdaj,iterative/dvc,dvc/parsing/context.py,6a79111047a838b2e7daee0861570ffe01c38d02,b0ae5e2251112f8b3d19426c3f99cdea8394ff5b,FIXME: after implementing of reading of \"params\".
aaifchdcf,iterative/dvc,dvc/repo/experiments/__init__.py,10a2eeb2b807b881f9fcd8ee61a55728b14b07b5,STILL_EXISTS,Create final checkpoint commit if needed
aaifchded,iterative/dvc,tests/unit/test_context.py,b0ae5e2251112f8b3d19426c3f99cdea8394ff5b,STILL_EXISTS,FIXME: either support tracking list values in ParamsDependency
aaifchdga,iterative/dvc,tests/unit/test_stage_resolver.py,6a9ab9cdfbf8ddd5ccb647b072cc36955a69a0e1,STILL_EXISTS,FIXME: using for convenience; figure out better way to do it
aaifcheac,iterative/dvc,dvc/cache/base.py,f0ad1b4f93515c628cc7b48ad958196e567502b8,STILL_EXISTS,NOTE: this ends up re-fetching hashes that were already
aaifchfeg,iterative/dvc,dvc/parsing/__init__.py,7f66db7039256474a41ca3f3b2f0e4963f70b0f9,STILL_EXISTS,FIXME: cannot reraise because of how we log \"cause\" of the exception
aaifchfei,iterative/dvc,dvc/parsing/__init__.py,7f66db7039256474a41ca3f3b2f0e4963f70b0f9,STILL_EXISTS,FIXME: Should `vars` be templatized?
aaifchffe,iterative/dvc,dvc/parsing/__init__.py,c8f37fdb8829d2d2ecb54b11959cfa59997c7353,STILL_EXISTS,Fix if\/when those assumptions are no longer valid.
aaifchfff,iterative/dvc,dvc/parsing/__init__.py,c8f37fdb8829d2d2ecb54b11959cfa59997c7353,1e1a0999f6b3cde830d9b8810009d0451bcc953d,FIXME: Decide if we should track them or not (it does right now)
aaifchhde,iterative/dvc,dvc/scm/git/backend/gitpython.py,515286663434569f7b5b2e48ec8ed5654ce5d776,STILL_EXISTS,needed before we can fix it in __init__
aaifchhhb,iterative/dvc,dvc/scm/git/backend/gitpython.py,3b23dfebaed5fc245dfaf15a3f5952d485d3ebf9,STILL_EXISTS,GitPython's obj.data_stream is a fragile thing; it is better to
aaifchicc,iterative/dvc,dvc/parsing/__init__.py,620d082b0b882c0e5d54bb8e950a17b79eb71dc6,STILL_EXISTS,so; we better warn them if they have them already in the context
aaifchjhh,iterative/dvc,dvc/config_schema.py,c04cdaf70fd3402a7d3ff14d7f1eefbb6e0e74eb,STILL_EXISTS,Windows absolute paths should really have scheme == \"\" (local)
aaifchjja,iterative/dvc,dvc/repo/diff.py,6b05cc5e24788975a3357504bc2b3ab9f3a7a941,STILL_EXISTS,It is needed to be dict of lists to cover cases
aaifciaje,iterative/dvc,dvc/utils/fs.py,cbd7472079cd8af190557a8a0b7cf81f0c33d6cc,STILL_EXISTS,xxx\/newdir\/. exists if xxx\/newdir exists
aaifcibbe,iterative/dvc,dvc/tree/base.py,a5e19ecd6aee5ed92fd5a39704dd9fe4ca080d10,STILL_EXISTS,Needed for some providers; and http open()
aaifcibde,iterative/dvc,dvc/utils/fs.py,a5e19ecd6aee5ed92fd5a39704dd9fe4ca080d10,STILL_EXISTS,xxx\/newdir\/. exists if xxx\/newdir exists
aaifciech,iterative/dvc,dvc/fs/s3.py,06aad3e2b3229a47f8d8a80711fac46dad25c2b9,STILL_EXISTS,temporary workaround; will be removed when migrating to s3fs
aaifciegd,iterative/dvc,dvc/repo/experiments/executor/base.py,d365e6d5b4881cf6a9ce8d08232c7289d9d1b786,STILL_EXISTS,with --force as needed
aaifciegi,iterative/dvc,dvc/fs/fsspec_wrapper.py,11b0581536a0b2062bdac19fa8d399cc277dcc55,STILL_EXISTS,file where it's name ends with a forward slash
aaifcieha,iterative/dvc,tests/func/test_import_url.py,11b0581536a0b2062bdac19fa8d399cc277dcc55,STILL_EXISTS,of directories. So instead we create an empty file that ends with a
aaifciehi,iterative/dvc,scripts/hooks/hook-googleapiclient.model.py,4ed55d00511ea3d9115b76c463e1a466408b11ef,STILL_EXISTS,\"\"\"Temporary fix for https:\/\/github.com\/iterative\/dvc\/issues\/5618.\"\"\"
aaifciejg,iterative/dvc,scripts/fpm/build.py,19cdef16758dbf6d787f7441f2f7995aa8e57219,STILL_EXISTS,needed for gitpython
aaifcihii,microsoft/nni,examples/trials/ga_squad/graph_to_tf.py,252f36f8092abeb86d13fb5bfda186b32004b875,STILL_EXISTS,Second part; apply the cosine to even columns and sin to odds.
aaifcijag,microsoft/nni,examples/trials/mnist-keras/mnist-keras.py,252f36f8092abeb86d13fb5bfda186b32004b875,STILL_EXISTS,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and associated
aaifcijba,microsoft/nni,examples/trials/mnist-keras/mnist-keras.py,252f36f8092abeb86d13fb5bfda186b32004b875,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
aaifcijgb,microsoft/nni,examples/tuners/ga_customer_tuner/__main__.py,252f36f8092abeb86d13fb5bfda186b32004b875,STILL_EXISTS,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and associated
aaifcijgf,microsoft/nni,examples/tuners/ga_customer_tuner/__main__.py,252f36f8092abeb86d13fb5bfda186b32004b875,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
aaifcijhh,microsoft/nni,examples/tuners/ga_customer_tuner/customer_tuner.py,252f36f8092abeb86d13fb5bfda186b32004b875,STILL_EXISTS,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and associated
aaifcijib,microsoft/nni,examples/tuners/ga_customer_tuner/customer_tuner.py,252f36f8092abeb86d13fb5bfda186b32004b875,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
aaifcjaac,microsoft/nni,src/nni_manager/core/test/assessor.py,252f36f8092abeb86d13fb5bfda186b32004b875,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and associated
aaifcjaag,microsoft/nni,src/nni_manager/core/test/assessor.py,252f36f8092abeb86d13fb5bfda186b32004b875,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
aaifcjabi,microsoft/nni,src/nni_manager/core/test/dummy_assessor.py,252f36f8092abeb86d13fb5bfda186b32004b875,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and associated
aaifcjacc,microsoft/nni,src/nni_manager/core/test/dummy_assessor.py,252f36f8092abeb86d13fb5bfda186b32004b875,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
aaifcjade,microsoft/nni,src/nni_manager/core/test/hyperopt.py,252f36f8092abeb86d13fb5bfda186b32004b875,STILL_EXISTS,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and associated
aaifcjadi,microsoft/nni,src/nni_manager/core/test/hyperopt.py,252f36f8092abeb86d13fb5bfda186b32004b875,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
aaifcjafb,microsoft/nni,src/nni_manager/scripts/gpu_metrics_collector.py,252f36f8092abeb86d13fb5bfda186b32004b875,STILL_EXISTS,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and associated
aaifcjaff,microsoft/nni,src/nni_manager/scripts/gpu_metrics_collector.py,252f36f8092abeb86d13fb5bfda186b32004b875,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
aaifcjagd,microsoft/nni,src/nni_manager/scripts/gpu_metrics_collector.py,252f36f8092abeb86d13fb5bfda186b32004b875,STILL_EXISTS,TODO: change to sleep time configurable via arguments
aaifcjaha,microsoft/nni,src/nni_manager/scripts/metrics_reader.py,252f36f8092abeb86d13fb5bfda186b32004b875,STILL_EXISTS,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and associated
aaifcjahe,microsoft/nni,src/nni_manager/scripts/metrics_reader.py,252f36f8092abeb86d13fb5bfda186b32004b875,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
aaifcjajd,microsoft/nni,src/nni_manager/scripts/metrics_reader.py,252f36f8092abeb86d13fb5bfda186b32004b875,STILL_EXISTS,TODO error logging to file
aaifcjajj,microsoft/nni,src/sdk/pynni/nni/__init__.py,252f36f8092abeb86d13fb5bfda186b32004b875,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifcjbaf,microsoft/nni,src/sdk/pynni/nni/__init__.py,252f36f8092abeb86d13fb5bfda186b32004b875,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or
aaifcjbbg,microsoft/nni,src/sdk/pynni/nni/assessor.py,252f36f8092abeb86d13fb5bfda186b32004b875,8314d6eec52aaa6c994e393cc231cf1d3d8753a4,'''trial_job_id of all ended trials. || We need this because NNI manager may send metrics after reporting a trial ended. || TODO: move this logic to NNI manager || '''
aaifcjbcb,microsoft/nni,src/sdk/pynni/nni/assessor.py,252f36f8092abeb86d13fb5bfda186b32004b875,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifcjbch,microsoft/nni,src/sdk/pynni/nni/assessor.py,252f36f8092abeb86d13fb5bfda186b32004b875,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or
aaifcjbed,microsoft/nni,src/sdk/pynni/nni/common.py,252f36f8092abeb86d13fb5bfda186b32004b875,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifcjbej,microsoft/nni,src/sdk/pynni/nni/common.py,252f36f8092abeb86d13fb5bfda186b32004b875,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or
aaifcjcce,microsoft/nni,src/sdk/pynni/nni/darkopt_assessor/test.py,252f36f8092abeb86d13fb5bfda186b32004b875,STILL_EXISTS,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and associated
aaifcjcci,microsoft/nni,src/sdk/pynni/nni/darkopt_assessor/test.py,252f36f8092abeb86d13fb5bfda186b32004b875,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
aaifcjdeg,microsoft/nni,src/sdk/pynni/nni/hyperopt_tuner/hyperopt_tuner.py,252f36f8092abeb86d13fb5bfda186b32004b875,STILL_EXISTS,TODO - catch exact Exception
aaifcjdff,microsoft/nni,src/sdk/pynni/nni/medianstop_assessor/__main__.py,252f36f8092abeb86d13fb5bfda186b32004b875,STILL_EXISTS,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and associated
aaifcjdfj,microsoft/nni,src/sdk/pynni/nni/medianstop_assessor/__main__.py,252f36f8092abeb86d13fb5bfda186b32004b875,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
aaifcjdhb,microsoft/nni,src/sdk/pynni/nni/medianstop_assessor/medianstop_assessor.py,252f36f8092abeb86d13fb5bfda186b32004b875,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and associated
aaifcjdhf,microsoft/nni,src/sdk/pynni/nni/medianstop_assessor/medianstop_assessor.py,252f36f8092abeb86d13fb5bfda186b32004b875,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
aaifcjdih,microsoft/nni,src/sdk/pynni/nni/medianstop_assessor/test.py,252f36f8092abeb86d13fb5bfda186b32004b875,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and associated
aaifcjdjb,microsoft/nni,src/sdk/pynni/nni/medianstop_assessor/test.py,252f36f8092abeb86d13fb5bfda186b32004b875,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
aaifcjeai,microsoft/nni,src/sdk/pynni/nni/platform/__init__.py,252f36f8092abeb86d13fb5bfda186b32004b875,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifcjebe,microsoft/nni,src/sdk/pynni/nni/platform/__init__.py,252f36f8092abeb86d13fb5bfda186b32004b875,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or
aaifcjeci,microsoft/nni,src/sdk/pynni/nni/platform/local.py,252f36f8092abeb86d13fb5bfda186b32004b875,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifcjede,microsoft/nni,src/sdk/pynni/nni/platform/local.py,252f36f8092abeb86d13fb5bfda186b32004b875,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or
aaifcjeeh,microsoft/nni,src/sdk/pynni/nni/platform/standalone.py,252f36f8092abeb86d13fb5bfda186b32004b875,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifcjefd,microsoft/nni,src/sdk/pynni/nni/platform/standalone.py,252f36f8092abeb86d13fb5bfda186b32004b875,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or
aaifcjegg,microsoft/nni,src/sdk/pynni/nni/platform/test.py,252f36f8092abeb86d13fb5bfda186b32004b875,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifcjehc,microsoft/nni,src/sdk/pynni/nni/platform/test.py,252f36f8092abeb86d13fb5bfda186b32004b875,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or
aaifcjeig,microsoft/nni,src/sdk/pynni/nni/protocol.py,252f36f8092abeb86d13fb5bfda186b32004b875,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifcjejc,microsoft/nni,src/sdk/pynni/nni/protocol.py,252f36f8092abeb86d13fb5bfda186b32004b875,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or
aaifcjfah,microsoft/nni,src/sdk/pynni/nni/smartparam.py,252f36f8092abeb86d13fb5bfda186b32004b875,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifcjfbd,microsoft/nni,src/sdk/pynni/nni/smartparam.py,252f36f8092abeb86d13fb5bfda186b32004b875,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or
aaifcjfdd,microsoft/nni,src/sdk/pynni/nni/trial.py,252f36f8092abeb86d13fb5bfda186b32004b875,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifcjfdj,microsoft/nni,src/sdk/pynni/nni/trial.py,252f36f8092abeb86d13fb5bfda186b32004b875,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or
aaifcjfei,microsoft/nni,src/sdk/pynni/nni/trial.py,252f36f8092abeb86d13fb5bfda186b32004b875,7c4b8c0d3d7d14c362892e44a1d256732d13a258,TODO: may be unnecessary
aaifcjffe,microsoft/nni,src/sdk/pynni/nni/tuner.py,252f36f8092abeb86d13fb5bfda186b32004b875,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifcjfga,microsoft/nni,src/sdk/pynni/nni/tuner.py,252f36f8092abeb86d13fb5bfda186b32004b875,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or
aaifcjfhj,microsoft/nni,src/sdk/pynni/setup.py,252f36f8092abeb86d13fb5bfda186b32004b875,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifcjfif,microsoft/nni,src/sdk/pynni/setup.py,252f36f8092abeb86d13fb5bfda186b32004b875,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or
aaifcjfji,microsoft/nni,src/sdk/pynni/tests/__init__.py,252f36f8092abeb86d13fb5bfda186b32004b875,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifcjgae,microsoft/nni,src/sdk/pynni/tests/__init__.py,252f36f8092abeb86d13fb5bfda186b32004b875,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or
aaifcjgbh,microsoft/nni,src/sdk/pynni/tests/test_assessor.py,252f36f8092abeb86d13fb5bfda186b32004b875,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifcjgcd,microsoft/nni,src/sdk/pynni/tests/test_assessor.py,252f36f8092abeb86d13fb5bfda186b32004b875,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or
aaifcjgdg,microsoft/nni,src/sdk/pynni/tests/test_protocol.py,252f36f8092abeb86d13fb5bfda186b32004b875,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifcjgec,microsoft/nni,src/sdk/pynni/tests/test_protocol.py,252f36f8092abeb86d13fb5bfda186b32004b875,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or
aaifcjgff,microsoft/nni,src/sdk/pynni/tests/test_smartparam.py,252f36f8092abeb86d13fb5bfda186b32004b875,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifcjggb,microsoft/nni,src/sdk/pynni/tests/test_smartparam.py,252f36f8092abeb86d13fb5bfda186b32004b875,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or
aaifcjghg,microsoft/nni,src/sdk/pynni/tests/test_trial.py,252f36f8092abeb86d13fb5bfda186b32004b875,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifcjgic,microsoft/nni,src/sdk/pynni/tests/test_trial.py,252f36f8092abeb86d13fb5bfda186b32004b875,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or
aaifcjgjf,microsoft/nni,src/sdk/pynni/tests/test_tuner.py,252f36f8092abeb86d13fb5bfda186b32004b875,STILL_EXISTS,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifcjhab,microsoft/nni,src/sdk/pynni/tests/test_tuner.py,252f36f8092abeb86d13fb5bfda186b32004b875,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or
aaifcjhbj,microsoft/nni,test/naive/run.py,252f36f8092abeb86d13fb5bfda186b32004b875,STILL_EXISTS,TODO: check the output of rest server
aaifcjhce,microsoft/nni,tools/annotation/__init__.py,252f36f8092abeb86d13fb5bfda186b32004b875,STILL_EXISTS,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifcjhda,microsoft/nni,tools/annotation/__init__.py,252f36f8092abeb86d13fb5bfda186b32004b875,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or
aaifcjheg,microsoft/nni,tools/annotation/code_generator.py,252f36f8092abeb86d13fb5bfda186b32004b875,STILL_EXISTS,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifcjhfc,microsoft/nni,tools/annotation/code_generator.py,252f36f8092abeb86d13fb5bfda186b32004b875,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or
aaifcjhhj,microsoft/nni,tools/annotation/examples/mnist_with_annotation.py,252f36f8092abeb86d13fb5bfda186b32004b875,STILL_EXISTS,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and associated
aaifcjhid,microsoft/nni,tools/annotation/examples/mnist_with_annotation.py,252f36f8092abeb86d13fb5bfda186b32004b875,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
aaifcjibc,microsoft/nni,tools/annotation/examples/mnist_without_annotation.py,252f36f8092abeb86d13fb5bfda186b32004b875,STILL_EXISTS,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and associated
aaifcjibg,microsoft/nni,tools/annotation/examples/mnist_without_annotation.py,252f36f8092abeb86d13fb5bfda186b32004b875,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
aaifcjief,microsoft/nni,tools/annotation/search_space_generator.py,252f36f8092abeb86d13fb5bfda186b32004b875,STILL_EXISTS,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifcjifb,microsoft/nni,tools/annotation/search_space_generator.py,252f36f8092abeb86d13fb5bfda186b32004b875,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or
aaifcjihh,microsoft/nni,tools/annotation/test_annotation.py,252f36f8092abeb86d13fb5bfda186b32004b875,STILL_EXISTS,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifcjiid,microsoft/nni,tools/annotation/test_annotation.py,252f36f8092abeb86d13fb5bfda186b32004b875,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or
aaifcjijd,microsoft/nni,tools/annotation/testcase/annotated/handwrite.py,252f36f8092abeb86d13fb5bfda186b32004b875,STILL_EXISTS,FIXME: search space not generated
aaifdaadg,microsoft/nni,tools/nnicmd/launcher_utils.py,252f36f8092abeb86d13fb5bfda186b32004b875,STILL_EXISTS,TODO: use elegent way to detect keys
aaifdabji,microsoft/nni,examples/trials/pytorch_cifar10/models/mobilenet.py,8314d6eec52aaa6c994e393cc231cf1d3d8753a4,STILL_EXISTS,'''MobileNet in PyTorch. ||  || See the paper \"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications\" || for more details. || '''
aaifdacde,microsoft/nni,examples/trials/pytorch_cifar10/models/shufflenet.py,8314d6eec52aaa6c994e393cc231cf1d3d8753a4,STILL_EXISTS,'''ShuffleNet in PyTorch. ||  || See the paper \"ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices\" for more details. || '''
aaifdacef,microsoft/nni,setup.py,8314d6eec52aaa6c994e393cc231cf1d3d8753a4,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifdacfb,microsoft/nni,setup.py,8314d6eec52aaa6c994e393cc231cf1d3d8753a4,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or
aaifdacgf,microsoft/nni,src/nni_manager/core/test/dummy_tuner.py,8314d6eec52aaa6c994e393cc231cf1d3d8753a4,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and associated
aaifdacgj,microsoft/nni,src/nni_manager/core/test/dummy_tuner.py,8314d6eec52aaa6c994e393cc231cf1d3d8753a4,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
aaifdadaa,microsoft/nni,src/sdk/pynni/nni/msg_dispatcher.py,8314d6eec52aaa6c994e393cc231cf1d3d8753a4,STILL_EXISTS,'''trial_job_id of all ended trials. || We need this because NNI manager may send metrics after reporting a trial ended. || TODO: move this logic to NNI manager || '''
aaifdadag,microsoft/nni,src/sdk/pynni/nni/msg_dispatcher.py,8314d6eec52aaa6c994e393cc231cf1d3d8753a4,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifdadbc,microsoft/nni,src/sdk/pynni/nni/msg_dispatcher.py,8314d6eec52aaa6c994e393cc231cf1d3d8753a4,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or
aaifdaddb,microsoft/nni,src/sdk/pynni/nni/msg_dispatcher_base.py,8314d6eec52aaa6c994e393cc231cf1d3d8753a4,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifdaddh,microsoft/nni,src/sdk/pynni/nni/msg_dispatcher_base.py,8314d6eec52aaa6c994e393cc231cf1d3d8753a4,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or
aaifdadfd,microsoft/nni,src/sdk/pynni/nni/recoverable.py,8314d6eec52aaa6c994e393cc231cf1d3d8753a4,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifdadfj,microsoft/nni,src/sdk/pynni/nni/recoverable.py,8314d6eec52aaa6c994e393cc231cf1d3d8753a4,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or
aaifdadhi,microsoft/nni,tools/nni_annotation/__init__.py,8314d6eec52aaa6c994e393cc231cf1d3d8753a4,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifdadie,microsoft/nni,tools/nni_annotation/__init__.py,8314d6eec52aaa6c994e393cc231cf1d3d8753a4,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or
aaifdaeaa,microsoft/nni,tools/nni_annotation/code_generator.py,8314d6eec52aaa6c994e393cc231cf1d3d8753a4,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifdaeag,microsoft/nni,tools/nni_annotation/code_generator.py,8314d6eec52aaa6c994e393cc231cf1d3d8753a4,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or
aaifdaedd,microsoft/nni,tools/nni_annotation/examples/mnist_with_annotation.py,8314d6eec52aaa6c994e393cc231cf1d3d8753a4,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and associated
aaifdaedh,microsoft/nni,tools/nni_annotation/examples/mnist_with_annotation.py,8314d6eec52aaa6c994e393cc231cf1d3d8753a4,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
aaifdaegg,microsoft/nni,tools/nni_annotation/examples/mnist_without_annotation.py,8314d6eec52aaa6c994e393cc231cf1d3d8753a4,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and associated
aaifdaeha,microsoft/nni,tools/nni_annotation/examples/mnist_without_annotation.py,8314d6eec52aaa6c994e393cc231cf1d3d8753a4,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
aaifdaejj,microsoft/nni,tools/nni_annotation/search_space_generator.py,8314d6eec52aaa6c994e393cc231cf1d3d8753a4,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifdafaf,microsoft/nni,tools/nni_annotation/search_space_generator.py,8314d6eec52aaa6c994e393cc231cf1d3d8753a4,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or
aaifdafdb,microsoft/nni,tools/nni_annotation/test_annotation.py,8314d6eec52aaa6c994e393cc231cf1d3d8753a4,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifdafdh,microsoft/nni,tools/nni_annotation/test_annotation.py,8314d6eec52aaa6c994e393cc231cf1d3d8753a4,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or
aaifdafjf,microsoft/nni,examples/trials/auto-gbdt/main.py,5d711ccf013d16733ecdaaf2a0fe29bad601c181,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
aaifdaggb,microsoft/nni,examples/trials/mnist-batch-tune-keras/mnist-keras.py,71688b860684a55c86d4aaf10140918ffaf13b45,STILL_EXISTS,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and associated
aaifdaggf,microsoft/nni,examples/trials/mnist-batch-tune-keras/mnist-keras.py,71688b860684a55c86d4aaf10140918ffaf13b45,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
aaifdahbj,microsoft/nni,src/sdk/pynni/nni/smac_tuner/convert_ss_to_scenario.py,ff390b4d5457cd5dec9e5c1c3a98892e5f2aed2a,485245a00699a367708a778617975c5258958292,TODO: support lower bound in randint
aaifdaibb,microsoft/nni,tools/trial_tool/metrics_reader.py,d3506e3420a06f076acb8d419beac81b1d8e98ac,STILL_EXISTS,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and associated
aaifdaibf,microsoft/nni,tools/trial_tool/metrics_reader.py,d3506e3420a06f076acb8d419beac81b1d8e98ac,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
aaifdaicf,microsoft/nni,tools/trial_tool/metrics_reader.py,d3506e3420a06f076acb8d419beac81b1d8e98ac,STILL_EXISTS,TODO error logging to file
aaifdaihc,microsoft/nni,tools/trial_tool/trial_keeper.py,d3506e3420a06f076acb8d419beac81b1d8e98ac,STILL_EXISTS,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and associated
aaifdaihg,microsoft/nni,tools/trial_tool/trial_keeper.py,d3506e3420a06f076acb8d419beac81b1d8e98ac,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
aaifdajbd,microsoft/nni,examples/trials/sklearn/classification/main.py,a0820dd5561e19b67cc93c19c4ef987a406ee14e,STILL_EXISTS,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and associated
aaifdajbh,microsoft/nni,examples/trials/sklearn/classification/main.py,a0820dd5561e19b67cc93c19c4ef987a406ee14e,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
aaifdajda,microsoft/nni,examples/trials/sklearn/regression/main.py,a0820dd5561e19b67cc93c19c4ef987a406ee14e,STILL_EXISTS,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and associated
aaifdajde,microsoft/nni,examples/trials/sklearn/regression/main.py,a0820dd5561e19b67cc93c19c4ef987a406ee14e,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
aaifdajfh,microsoft/nni,src/sdk/pynni/nni/multi_phase/multi_phase_dispatcher.py,39085789cb7dcdd75e3ac2dbb9daadab02909269,STILL_EXISTS,'''trial_job_id of all ended trials. || We need this because NNI manager may send metrics after reporting a trial ended. || TODO: move this logic to NNI manager || '''
aaifdajgd,microsoft/nni,src/sdk/pynni/nni/multi_phase/multi_phase_dispatcher.py,39085789cb7dcdd75e3ac2dbb9daadab02909269,STILL_EXISTS,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifdajgj,microsoft/nni,src/sdk/pynni/nni/multi_phase/multi_phase_dispatcher.py,39085789cb7dcdd75e3ac2dbb9daadab02909269,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or
aaifdajii,microsoft/nni,src/sdk/pynni/nni/multi_phase/multi_phase_tuner.py,39085789cb7dcdd75e3ac2dbb9daadab02909269,STILL_EXISTS,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifdajje,microsoft/nni,src/sdk/pynni/nni/multi_phase/multi_phase_tuner.py,39085789cb7dcdd75e3ac2dbb9daadab02909269,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or
aaifdbaca,microsoft/nni,tools/nnicmd/nnictl_utils.py,16dcfebbb1266e423b045de21d0c5ff1dc746dc3,ebbadfeba6884dca56e8bc7cc43d47ef4914945b,4.If the id ends with *; nnictl will match all ids matchs the regular
aaifdbada,microsoft/nni,test/naive_test.py,8d866b5b9dbf7d9b2ed4ea85f9133ccf1fec62bc,STILL_EXISTS,TODO: check the output of rest server
aaifdbhaf,microsoft/nni,examples/trials/network_morphism/FashionMNIST/FashionMNIST_keras.py,e31839cc4a212311cac677dfebd4526ea343e9be,STILL_EXISTS,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and associated
aaifdbhaj,microsoft/nni,examples/trials/network_morphism/FashionMNIST/FashionMNIST_keras.py,e31839cc4a212311cac677dfebd4526ea343e9be,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
aaifdbhdd,microsoft/nni,examples/trials/network_morphism/FashionMNIST/FashionMNIST_pytorch.py,e31839cc4a212311cac677dfebd4526ea343e9be,STILL_EXISTS,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and associated
aaifdbhdh,microsoft/nni,examples/trials/network_morphism/FashionMNIST/FashionMNIST_pytorch.py,e31839cc4a212311cac677dfebd4526ea343e9be,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
aaifdbhfh,microsoft/nni,examples/trials/network_morphism/cifar10/cifar10_keras.py,e31839cc4a212311cac677dfebd4526ea343e9be,STILL_EXISTS,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and associated
aaifdbhgb,microsoft/nni,examples/trials/network_morphism/cifar10/cifar10_keras.py,e31839cc4a212311cac677dfebd4526ea343e9be,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
aaifdbhif,microsoft/nni,examples/trials/network_morphism/cifar10/cifar10_pytorch.py,e31839cc4a212311cac677dfebd4526ea343e9be,STILL_EXISTS,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and associated
aaifdbhij,microsoft/nni,examples/trials/network_morphism/cifar10/cifar10_pytorch.py,e31839cc4a212311cac677dfebd4526ea343e9be,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
aaifdbiba,microsoft/nni,src/sdk/pynni/nni/networkmorphism_tuner/bayesian.py,e31839cc4a212311cac677dfebd4526ea343e9be,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifdbibg,microsoft/nni,src/sdk/pynni/nni/networkmorphism_tuner/bayesian.py,e31839cc4a212311cac677dfebd4526ea343e9be,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or
aaifdbied,microsoft/nni,src/sdk/pynni/nni/networkmorphism_tuner/graph.py,e31839cc4a212311cac677dfebd4526ea343e9be,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifdbiej,microsoft/nni,src/sdk/pynni/nni/networkmorphism_tuner/graph.py,e31839cc4a212311cac677dfebd4526ea343e9be,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or
aaifdbihh,microsoft/nni,src/sdk/pynni/nni/networkmorphism_tuner/graph_transformer.py,e31839cc4a212311cac677dfebd4526ea343e9be,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifdbiid,microsoft/nni,src/sdk/pynni/nni/networkmorphism_tuner/graph_transformer.py,e31839cc4a212311cac677dfebd4526ea343e9be,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or
aaifdbijj,microsoft/nni,src/sdk/pynni/nni/networkmorphism_tuner/layer_transformer.py,e31839cc4a212311cac677dfebd4526ea343e9be,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifdbjaf,microsoft/nni,src/sdk/pynni/nni/networkmorphism_tuner/layer_transformer.py,e31839cc4a212311cac677dfebd4526ea343e9be,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or
aaifdbjca,microsoft/nni,src/sdk/pynni/nni/networkmorphism_tuner/layers.py,e31839cc4a212311cac677dfebd4526ea343e9be,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifdbjcg,microsoft/nni,src/sdk/pynni/nni/networkmorphism_tuner/layers.py,e31839cc4a212311cac677dfebd4526ea343e9be,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or
aaifdbjeb,microsoft/nni,src/sdk/pynni/nni/networkmorphism_tuner/networkmorphism_tuner.py,e31839cc4a212311cac677dfebd4526ea343e9be,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifdbjeh,microsoft/nni,src/sdk/pynni/nni/networkmorphism_tuner/networkmorphism_tuner.py,e31839cc4a212311cac677dfebd4526ea343e9be,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or
aaifdbjgi,microsoft/nni,src/sdk/pynni/nni/networkmorphism_tuner/nn.py,e31839cc4a212311cac677dfebd4526ea343e9be,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifdbjhe,microsoft/nni,src/sdk/pynni/nni/networkmorphism_tuner/nn.py,e31839cc4a212311cac677dfebd4526ea343e9be,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or
aaifdbjih,microsoft/nni,src/sdk/pynni/nni/networkmorphism_tuner/utils.py,e31839cc4a212311cac677dfebd4526ea343e9be,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifdbjjd,microsoft/nni,src/sdk/pynni/nni/networkmorphism_tuner/utils.py,e31839cc4a212311cac677dfebd4526ea343e9be,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or
aaifdcaag,microsoft/nni,src/sdk/pynni/tests/test_multi_phase_tuner.py,e31839cc4a212311cac677dfebd4526ea343e9be,STILL_EXISTS,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifdcabc,microsoft/nni,src/sdk/pynni/tests/test_multi_phase_tuner.py,e31839cc4a212311cac677dfebd4526ea343e9be,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or
aaifdcacf,microsoft/nni,src/sdk/pynni/tests/test_networkmorphism_tuner.py,e31839cc4a212311cac677dfebd4526ea343e9be,STILL_EXISTS,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifdcadb,microsoft/nni,src/sdk/pynni/tests/test_networkmorphism_tuner.py,e31839cc4a212311cac677dfebd4526ea343e9be,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or
aaifdcagh,microsoft/nni,src/sdk/pynni/nni/curvefitting_assessor/curvefitting_assessor.py,102faea12e690450746f02cc9279450d3f1c702f,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and associated
aaifdcahb,microsoft/nni,src/sdk/pynni/nni/curvefitting_assessor/curvefitting_assessor.py,102faea12e690450746f02cc9279450d3f1c702f,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
aaifdcaic,microsoft/nni,src/sdk/pynni/nni/curvefitting_assessor/curvefitting_assessor.py,102faea12e690450746f02cc9279450d3f1c702f,STILL_EXISTS,Record the best performance
aaifdcaii,microsoft/nni,src/sdk/pynni/nni/curvefitting_assessor/curvefunctions.py,102faea12e690450746f02cc9279450d3f1c702f,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and associated
aaifdcajc,microsoft/nni,src/sdk/pynni/nni/curvefitting_assessor/curvefunctions.py,102faea12e690450746f02cc9279450d3f1c702f,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
aaifdcbaf,microsoft/nni,src/sdk/pynni/nni/curvefitting_assessor/model_factory.py,102faea12e690450746f02cc9279450d3f1c702f,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and associated
aaifdcbaj,microsoft/nni,src/sdk/pynni/nni/curvefitting_assessor/model_factory.py,102faea12e690450746f02cc9279450d3f1c702f,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
aaifdcbdd,microsoft/nni,src/sdk/pynni/nni/curvefitting_assessor/test.py,102faea12e690450746f02cc9279450d3f1c702f,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and associated
aaifdcbdh,microsoft/nni,src/sdk/pynni/nni/curvefitting_assessor/test.py,102faea12e690450746f02cc9279450d3f1c702f,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
aaifdcebd,microsoft/nni,examples/trials/network_morphism/utils.py,0a3cc459d497ed838455c4954472dbdde32939b0,STILL_EXISTS,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and associated
aaifdcebg,microsoft/nni,examples/trials/network_morphism/utils.py,0a3cc459d497ed838455c4954472dbdde32939b0,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
aaifddahf,microsoft/nni,examples/trials/weight_sharing/ga_squad/graph_to_tf.py,358efb26b6d7576bc8179a37c190e5f916773b5e,STILL_EXISTS,Second part; apply the cosine to even columns and sin to odds.
aaifddbhj,microsoft/nni,examples/tuners/weight_sharing/ga_customer_tuner/customer_tuner.py,358efb26b6d7576bc8179a37c190e5f916773b5e,STILL_EXISTS,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and associated
aaifddbid,microsoft/nni,examples/tuners/weight_sharing/ga_customer_tuner/customer_tuner.py,358efb26b6d7576bc8179a37c190e5f916773b5e,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
aaifdfahf,microsoft/nni,deployment/pypi/setup.py,b475b125ce6c632b142aeea5e351eb23e1f1f056,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifdfaib,microsoft/nni,deployment/pypi/setup.py,b475b125ce6c632b142aeea5e351eb23e1f1f056,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or
aaifdfbad,microsoft/nni,test/config_test.py,5f8ffcd51e88287d8d29454c73710af8c9bd80dd,STILL_EXISTS,hack for kubeflow trial config
aaifdfcdg,microsoft/nni,src/sdk/pynni/nni/utils.py,64e8b686f600907f5fba0c1e385f48ee7a0bcab6,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifdfcec,microsoft/nni,src/sdk/pynni/nni/utils.py,64e8b686f600907f5fba0c1e385f48ee7a0bcab6,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or
aaifdffgd,microsoft/nni,src/sdk/pynni/nni/env_vars.py,62a7982859192f4c9828739f37f2ee810c7880e6,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifdffgj,microsoft/nni,src/sdk/pynni/nni/env_vars.py,62a7982859192f4c9828739f37f2ee810c7880e6,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or
aaifdffid,microsoft/nni,tools/nni_gpu_tool/gpu_metrics_collector.py,cfda0dae8cd5df5ef96c53ce4d488579322deebb,STILL_EXISTS,TODO check process in windows
aaifdfghi,microsoft/nni,src/sdk/pynni/tests/test_utils.py,5af01545fce906a682f44741b4f6b6bd2b1d2585,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifdfgie,microsoft/nni,src/sdk/pynni/tests/test_utils.py,5af01545fce906a682f44741b4f6b6bd2b1d2585,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or
aaifdfjff,microsoft/nni,tools/nni_annotation/specific_code_generator.py,cae707299565e8fc6e0787171c3497bbc960e249,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifdfjgb,microsoft/nni,tools/nni_annotation/specific_code_generator.py,cae707299565e8fc6e0787171c3497bbc960e249,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or
aaifdgcge,microsoft/nni,src/sdk/pynni/nni/gp_tuner/target_space.py,21725f96d7a0cb869c3e5a5ee3bf569d67c3f042,17be7967d6905c3d370c5e42eff58e8aad1e8ad9,TODO: support randint; quniform
aaifdgcha,microsoft/nni,src/sdk/pynni/nni/gp_tuner/util.py,21725f96d7a0cb869c3e5a5ee3bf569d67c3f042,STILL_EXISTS,Store it if better than previous minimum(maximum).
aaifdgeag,microsoft/nni,src/sdk/pynni/nni/gp_tuner/util.py,17be7967d6905c3d370c5e42eff58e8aad1e8ad9,STILL_EXISTS,TODO why int here ?
aaifdgeaj,microsoft/nni,src/sdk/pynni/nni/gp_tuner/util.py,3906b34131005e38910e8522f92ce2958c618d9d,STILL_EXISTS,TODO why int here ?
aaifdgeba,microsoft/nni,src/sdk/pynni/nni/gp_tuner/util.py,37f4b126f757aa5ef81ec6f2a220e0cd0fa6fa97,c5f3da0f089a8dbcc20593ec91d17838ad2b8769,TODO: sample_y ??
aaifdgebg,microsoft/nni,tools/nni_cmd/nnictl_utils.py,8329d30f8affa35827e665c915fbbb1f5eb62da1,STILL_EXISTS,TODO: support all platforms
aaifdgecd,microsoft/nni,src/sdk/pynni/nni/nas_utils.py,2aa825b4e1dba822624b497735c3bf0c0ffafd01,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifdgecj,microsoft/nni,src/sdk/pynni/nni/nas_utils.py,2aa825b4e1dba822624b497735c3bf0c0ffafd01,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or
aaifdggib,microsoft/nni,examples/trials/toy/main.py,99dfd656feb62d009f22ef698150a870f499aa3c,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
aaifdhbfj,microsoft/nni,src/sdk/pycli/nnicli/__init__.py,c181116276552cd45d8bbe2be5f7d88e12bff481,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifdhbgf,microsoft/nni,src/sdk/pycli/nnicli/__init__.py,c181116276552cd45d8bbe2be5f7d88e12bff481,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or
aaifdhbhj,microsoft/nni,src/sdk/pycli/nnicli/nni_client.py,c181116276552cd45d8bbe2be5f7d88e12bff481,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifdhbif,microsoft/nni,src/sdk/pycli/nnicli/nni_client.py,c181116276552cd45d8bbe2be5f7d88e12bff481,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or
aaifdheid,microsoft/nni,src/sdk/pynni/nni/nas_utils.py,875a0da84c4a7e02e49bc863665e486baf520510,STILL_EXISTS,TODO: Calculate loss
aaifdhgee,microsoft/nni,src/sdk/pynni/nni/ppo_tuner/model.py,55f48d27eedae9bb7f88c928cb979433f4592196,STILL_EXISTS,Entropy is used to improve exploration by limiting the premature convergence to suboptimal policy.
aaifdhheb,microsoft/nni,src/sdk/pynni/nni/ppo_tuner/ppo_tuner.py,55f48d27eedae9bb7f88c928cb979433f4592196,STILL_EXISTS,ugly
aaifdhhji,microsoft/nni,examples/tuners/mnist_keras_customized_advisor/dummy_advisor.py,168d74e448d19aad13ad7cde670787248c827867,STILL_EXISTS,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and associated
aaifdhiac,microsoft/nni,examples/tuners/mnist_keras_customized_advisor/dummy_advisor.py,168d74e448d19aad13ad7cde670787248c827867,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
aaifdhibf,microsoft/nni,examples/tuners/mnist_keras_customized_advisor/mnist_keras.py,168d74e448d19aad13ad7cde670787248c827867,STILL_EXISTS,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and associated
aaifdhibj,microsoft/nni,examples/tuners/mnist_keras_customized_advisor/mnist_keras.py,168d74e448d19aad13ad7cde670787248c827867,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
aaifdhifa,microsoft/nni,src/sdk/pynni/nni/compression/torch/builtin_quantizers.py,e93d2c25e9301c00bb62c749f815f0258517a218,STILL_EXISTS,TODO: user layer
aaifdhifb,microsoft/nni,src/sdk/pynni/nni/compression/torch/compressor.py,e93d2c25e9301c00bb62c749f815f0258517a218,358bdb1837d92126362a23c6414e7bf5f07d338b,TODO: support multiple weight tensors
aaifdieca,microsoft/nni,examples/trials/systems/rocksdb-fillrandom/main.py,bc800581cdecb5fbe0521c37fcb388e902c96279,STILL_EXISTS,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and associated
aaifdiece,microsoft/nni,examples/trials/systems/rocksdb-fillrandom/main.py,bc800581cdecb5fbe0521c37fcb388e902c96279,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
aaifdieif,microsoft/nni,src/sdk/pynni/nni/smac_tuner/smac_tuner.py,223168007359257de9cb83351a6ebddf441379d3,STILL_EXISTS,TODO: this is ugly; we put all the initialization work in this method; because initialization relies
aaifdifab,microsoft/nni,src/sdk/pynni/nni/hyperopt_tuner/hyperopt_tuner.py,7c4b8c0d3d7d14c362892e44a1d256732d13a258,STILL_EXISTS,note: this is not written by original author; feel free to modify if you think it's incorrect
aaifdifah,microsoft/nni,src/sdk/pynni/nni/networkmorphism_tuner/layers.py,7c4b8c0d3d7d14c362892e44a1d256732d13a258,STILL_EXISTS,note: this is not written by original author; feel free to modify if you think it's incorrect
aaifdifbb,microsoft/nni,src/sdk/pynni/nni/tuner.py,7c4b8c0d3d7d14c362892e44a1d256732d13a258,STILL_EXISTS,FIXME: because tuner is designed as interface; this API should not be here
aaifdifea,microsoft/nni,src/sdk/pynni/nni/nas/pytorch/darts/trainer.py,2116189f0e144a605b1f688c8b6bba0a3ff92625,bb797e10e460c086a7de192dce2dae6681bbfcf0,TODO: should use model instead of self.model
aaifdifei,microsoft/nni,src/sdk/pynni/nni/tuner.py,0521a0c2fa6e64b63d46e1840f9fdbaec989704e,STILL_EXISTS,FIXME: some tuners raise NoMoreTrialError when they are waiting for more trial results
aaifdiffa,microsoft/nni,src/sdk/pynni/nni/tuner.py,0521a0c2fa6e64b63d46e1840f9fdbaec989704e,STILL_EXISTS,FIXME: because Tuner is designed as interface; this API should not be here
aaifdifha,microsoft/nni,src/sdk/pynni/tests/test_msg_dispatcher.py,1f9b76173c1a60f31f29aedb2d5c771dde44c2bf,587dd3af02e191906d9e267e206a0d8a86cbf962,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifdifhg,microsoft/nni,src/sdk/pynni/tests/test_msg_dispatcher.py,1f9b76173c1a60f31f29aedb2d5c771dde44c2bf,587dd3af02e191906d9e267e206a0d8a86cbf962,The above copyright notice and this permission notice shall be included in all copies or
aaifdifjb,microsoft/nni,src/sdk/pynni/tests/test_tuner.py,1f9b76173c1a60f31f29aedb2d5c771dde44c2bf,STILL_EXISTS,TODO: not strict
aaifdigbg,microsoft/nni,src/sdk/pynni/nni/compression/torch/compressor.py,86f8c2abfc66269f3259b5c682aa3bcf14cde44c,STILL_EXISTS,input info needed
aaifdiigb,microsoft/nni,src/sdk/pynni/nni/nas/pytorch/enas/mutator.py,1cada380ff768d6e59aa4089734cbed74014b9bb,STILL_EXISTS,NOTE(yuge): We might implement an interface later. Judging by key now.
aaifdijai,microsoft/nni,examples/model_compress/L1_filter_pruner_torch_vgg16.py,dbf987145cb697fdbb36d2ea920625373d6ea119,STILL_EXISTS,Pruning Configuration; in paper 'PRUNING FILTERS FOR EFFICIENT CONVNETS';
aaifdijcc,microsoft/nni,examples/model_compress/slim_pruner_torch_vgg19.py,dbf987145cb697fdbb36d2ea920625373d6ea119,STILL_EXISTS,Pruning Configuration; in paper 'Learning efficient convolutional networks through network slimming';
aaifdijdd,microsoft/nni,src/sdk/pynni/nni/common.py,a63f2ed352ef609270b194ad6c2b1e7cb4f98586,STILL_EXISTS,`logging.getLogger('xxx')` in all examples.
aaifdjagf,microsoft/nni,examples/feature_engineering/gbdt_selector/gbdt_selector_test.py,8ac61b774419766dfd194fa7f1aba8b934763ac9,STILL_EXISTS,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and associated
aaifdjagj,microsoft/nni,examples/feature_engineering/gbdt_selector/gbdt_selector_test.py,8ac61b774419766dfd194fa7f1aba8b934763ac9,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
aaifdjaib,microsoft/nni,examples/feature_engineering/gradient_feature_selector/sklearn_test.py,8ac61b774419766dfd194fa7f1aba8b934763ac9,STILL_EXISTS,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and associated
aaifdjaif,microsoft/nni,examples/feature_engineering/gradient_feature_selector/sklearn_test.py,8ac61b774419766dfd194fa7f1aba8b934763ac9,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
aaifdjajg,microsoft/nni,src/sdk/pynni/nni/feature_engineering/feature_selector.py,8ac61b774419766dfd194fa7f1aba8b934763ac9,STILL_EXISTS,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifdjbac,microsoft/nni,src/sdk/pynni/nni/feature_engineering/feature_selector.py,8ac61b774419766dfd194fa7f1aba8b934763ac9,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or
aaifdjbbg,microsoft/nni,src/sdk/pynni/nni/feature_engineering/gbdt_selector/gbdt_selector.py,8ac61b774419766dfd194fa7f1aba8b934763ac9,STILL_EXISTS,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifdjbcc,microsoft/nni,src/sdk/pynni/nni/feature_engineering/gbdt_selector/gbdt_selector.py,8ac61b774419766dfd194fa7f1aba8b934763ac9,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or
aaifdjbdg,microsoft/nni,src/sdk/pynni/nni/feature_engineering/gradient_selector/constants.py,8ac61b774419766dfd194fa7f1aba8b934763ac9,STILL_EXISTS,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifdjbec,microsoft/nni,src/sdk/pynni/nni/feature_engineering/gradient_selector/constants.py,8ac61b774419766dfd194fa7f1aba8b934763ac9,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or
aaifdjbff,microsoft/nni,src/sdk/pynni/nni/feature_engineering/gradient_selector/fginitialize.py,8ac61b774419766dfd194fa7f1aba8b934763ac9,STILL_EXISTS,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifdjbgb,microsoft/nni,src/sdk/pynni/nni/feature_engineering/gradient_selector/fginitialize.py,8ac61b774419766dfd194fa7f1aba8b934763ac9,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or
aaifdjbhf,microsoft/nni,src/sdk/pynni/nni/feature_engineering/gradient_selector/fginitialize.py,8ac61b774419766dfd194fa7f1aba8b934763ac9,STILL_EXISTS,TODO: use a passed-in RNG here
aaifdjbib,microsoft/nni,src/sdk/pynni/nni/feature_engineering/gradient_selector/fginitialize.py,8ac61b774419766dfd194fa7f1aba8b934763ac9,STILL_EXISTS,TODO: set these; for each class?
aaifdjbjc,microsoft/nni,src/sdk/pynni/nni/feature_engineering/gradient_selector/fgtrain.py,8ac61b774419766dfd194fa7f1aba8b934763ac9,STILL_EXISTS,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifdjbji,microsoft/nni,src/sdk/pynni/nni/feature_engineering/gradient_selector/fgtrain.py,8ac61b774419766dfd194fa7f1aba8b934763ac9,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or
aaifdjcbc,microsoft/nni,src/sdk/pynni/nni/feature_engineering/gradient_selector/gradient_selector.py,8ac61b774419766dfd194fa7f1aba8b934763ac9,STILL_EXISTS,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifdjcbi,microsoft/nni,src/sdk/pynni/nni/feature_engineering/gradient_selector/gradient_selector.py,8ac61b774419766dfd194fa7f1aba8b934763ac9,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or
aaifdjcgh,microsoft/nni,src/sdk/pynni/nni/feature_engineering/gradient_selector/learnability.py,8ac61b774419766dfd194fa7f1aba8b934763ac9,STILL_EXISTS,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifdjchd,microsoft/nni,src/sdk/pynni/nni/feature_engineering/gradient_selector/learnability.py,8ac61b774419766dfd194fa7f1aba8b934763ac9,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or
aaifdjcjb,microsoft/nni,src/sdk/pynni/nni/feature_engineering/gradient_selector/learnability.py,8ac61b774419766dfd194fa7f1aba8b934763ac9,STILL_EXISTS,TODO: consider other functions here
aaifdjcjd,microsoft/nni,src/sdk/pynni/nni/feature_engineering/gradient_selector/learnability.py,8ac61b774419766dfd194fa7f1aba8b934763ac9,STILL_EXISTS,TODO: could make this a user given parameter
aaifdjcji,microsoft/nni,src/sdk/pynni/nni/feature_engineering/gradient_selector/learnability.py,8ac61b774419766dfd194fa7f1aba8b934763ac9,STILL_EXISTS,should be the average of the gradients of the columns
aaifdjdbd,microsoft/nni,src/sdk/pynni/nni/feature_engineering/gradient_selector/syssettings.py,8ac61b774419766dfd194fa7f1aba8b934763ac9,STILL_EXISTS,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifdjdbj,microsoft/nni,src/sdk/pynni/nni/feature_engineering/gradient_selector/syssettings.py,8ac61b774419766dfd194fa7f1aba8b934763ac9,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or
aaifdjdde,microsoft/nni,src/sdk/pynni/nni/feature_engineering/gradient_selector/utils.py,8ac61b774419766dfd194fa7f1aba8b934763ac9,STILL_EXISTS,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and
aaifdjdea,microsoft/nni,src/sdk/pynni/nni/feature_engineering/gradient_selector/utils.py,8ac61b774419766dfd194fa7f1aba8b934763ac9,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or
aaifdjehe,microsoft/nni,src/sdk/pynni/nni/compression/torch/builtin_quantizers.py,06a98372aaaaff2bc4f8caf6a115a348bc250191,STILL_EXISTS,TODO simulate folded weight
aaifebejc,microsoft/nni,src/sdk/pynni/nni/ppo_tuner/ppo_tuner.py,aa51e79cdbcbedbedeef68bcef646b2d43993753,STILL_EXISTS,FIXME: risk; candidates might also have None
aaifebejj,microsoft/nni,examples/feature_engineering/gradient_feature_selector/benchmark_test.py,13d6b15e7eef7c140ee047d202bf102cea1297d6,STILL_EXISTS,Permission is hereby granted; free of charge; to any person obtaining a copy of this software and associated
aaifebfad,microsoft/nni,examples/feature_engineering/gradient_feature_selector/benchmark_test.py,13d6b15e7eef7c140ee047d202bf102cea1297d6,STILL_EXISTS,The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
aaifeceic,microsoft/nni,src/sdk/pynni/nni/compression/torch/compressor.py,4f3ee9cb3f2d39dc3a105c928cef62d9520073af,STILL_EXISTS,the reason why weight is buffer instead of parameter is because in pytorch parameter is used as leaf
aaifecfaj,microsoft/nni,examples/nas/spos/tester.py,6f256c781a5c2cc841060435d3b732daa6adb108,STILL_EXISTS,use a fixed set of image will improve the performance
aaifecfcj,microsoft/nni,examples/model_compress/APoZ_torch_cifar10.py,b0c0eb7b1f2ac9a983c550ee971cea275463d8fc,STILL_EXISTS,Pruning Configuration; in paper 'PRUNING FILTERS FOR EFFICIENT CONVNETS';
aaifecfeb,microsoft/nni,examples/model_compress/MeanActivation_torch_cifar10.py,b0c0eb7b1f2ac9a983c550ee971cea275463d8fc,STILL_EXISTS,Pruning Configuration; in paper 'PRUNING FILTERS FOR EFFICIENT CONVNETS';
aaifedcje,microsoft/nni,src/sdk/pynni/nni/compression/torch/pruners.py,d452a1665f802bfbe75372b6c942272b252c70a2,7425098787ae8dce175c3e78b30eddffdd7d4b28,TODO: directly use weight_mask is not good
aaifeddfg,microsoft/nni,src/sdk/pynni/nni/compression/torch/pruners.py,4e21e721a65d0ac7c8465c6b7842dd39338bb3d0,STILL_EXISTS,TODO: directly use weight_mask is not good
aaifedeci,microsoft/nni,src/sdk/pynni/nni/compression/speedup/torch/compressor.py,eab0da154b4c8cf68f95ef294844649c9e17ee60,5c861676fc0ce336b89ba5db40de05a81d8e987b,TODO: scope name could be empty
aaifedeec,microsoft/nni,src/sdk/pynni/nni/compression/speedup/torch/compressor.py,eab0da154b4c8cf68f95ef294844649c9e17ee60,5c861676fc0ce336b89ba5db40de05a81d8e987b,TODO: one input could be the input of multiple nodes
aaifedege,microsoft/nni,src/sdk/pynni/nni/compression/speedup/torch/infer_shape.py,eab0da154b4c8cf68f95ef294844649c9e17ee60,STILL_EXISTS,TODO: double check this assert; is it possible that a module is passed twice
aaifedegi,microsoft/nni,src/sdk/pynni/nni/compression/speedup/torch/infer_shape.py,eab0da154b4c8cf68f95ef294844649c9e17ee60,STILL_EXISTS,TODO: fine grained mask speedup
aaifedeid,microsoft/nni,examples/nas/proxylessnas/main.py,affb21187f7aaaed8c164c7f4b9c77e212ae3c5d,STILL_EXISTS,move network to GPU if available
aaifedejd,microsoft/nni,examples/nas/proxylessnas/ops.py,affb21187f7aaaed8c164c7f4b9c77e212ae3c5d,STILL_EXISTS,TODO: unify idx format
aaifedfdb,microsoft/nni,src/sdk/pynni/nni/nas/pytorch/proxylessnas/mutator.py,affb21187f7aaaed8c164c7f4b9c77e212ae3c5d,STILL_EXISTS,FIXME: return mask; to be consistent with other algorithms
aaifedfec,microsoft/nni,src/sdk/pynni/nni/nas/pytorch/proxylessnas/trainer.py,affb21187f7aaaed8c164c7f4b9c77e212ae3c5d,STILL_EXISTS,remove unused modules to save memory
aaifedffb,microsoft/nni,src/sdk/pynni/nni/nas/pytorch/proxylessnas/trainer.py,affb21187f7aaaed8c164c7f4b9c77e212ae3c5d,STILL_EXISTS,remove unused module for speedup
aaifedffe,microsoft/nni,src/sdk/pynni/nni/nas/pytorch/proxylessnas/trainer.py,affb21187f7aaaed8c164c7f4b9c77e212ae3c5d,STILL_EXISTS,unused modules back
aaifedfhc,microsoft/nni,src/sdk/pynni/nni/compression/torch/compressor.py,fb5ef932ca3b31f26d21e242b69235b36f0830d8,STILL_EXISTS,move newly registered buffers to the same device of weight
aaifedfjf,microsoft/nni,src/sdk/pynni/nni/compression/speedup/torch/compressor.py,ebcd602480a1490a4e9b2d0de9f12d2598fbed64,6e6299084b5626b15bda09f882f30033baaa5ebc,must be in pattern `xxx[xxx]`
aaifeeaag,microsoft/nni,src/sdk/pynni/nni/nas/pytorch/_graph_utils.py,9987014e6aa90e5f974c546633412340d458f945,STILL_EXISTS,TODO: move outside of torch.onnx?
aaifeeacg,microsoft/nni,tools/nni_cmd/nnictl_utils.py,9987014e6aa90e5f974c546633412340d458f945,39f211c1060656702a0fb83bfe1d4a439ad621fe,TODO: find file path on installing with pypi
aaifeeach,microsoft/nni,tools/nni_cmd/nnictl_utils.py,9987014e6aa90e5f974c546633412340d458f945,39f211c1060656702a0fb83bfe1d4a439ad621fe,TODO: use correct node on win32
aaifeebbi,microsoft/nni,test/nni_test/nnitest/run_tests.py,c9720e95eb846b547288e2526beb8a88b9e9c8dd,STILL_EXISTS,hack for kubeflow trial config
aaifeebca,microsoft/nni,test/nni_test/nnitest/run_tests.py,c9720e95eb846b547288e2526beb8a88b9e9c8dd,STILL_EXISTS,hack for windows
aaifeebcd,microsoft/nni,test/nni_test/nnitest/run_tests.py,c9720e95eb846b547288e2526beb8a88b9e9c8dd,STILL_EXISTS,the hack for kubeflow should be applied at last step
aaifeebci,microsoft/nni,test/nni_test/nnitest/run_tests.py,c9720e95eb846b547288e2526beb8a88b9e9c8dd,STILL_EXISTS,hack for windows; not limited to local training service
aaifeecec,microsoft/nni,src/sdk/pynni/nni/pbt_tuner/pbt_tuner.py,a82b4a3bf7c64e18172cc3a93de567ae6e607a76,d2c57770967fc14fe9d6172606b8c2e1903d4e3b,TODO think about different type of hyperparameters for 1.perturbation 2.within search space
aaifeedfa,microsoft/nni,src/sdk/pynni/nni/_graph_utils.py,5c861676fc0ce336b89ba5db40de05a81d8e987b,STILL_EXISTS,TODO: scope name could be empty
aaifeedjc,microsoft/nni,src/sdk/pynni/tests/test_model_speedup.py,5c861676fc0ce336b89ba5db40de05a81d8e987b,STILL_EXISTS,TODO support resnet
aaifeeefc,microsoft/nni,src/sdk/pynni/nni/nas/pytorch/fixed.py,f42e53d2ec3bad45cbbbeee681273cb450e394da,STILL_EXISTS,remove unused parameters
aaifeeege,microsoft/nni,examples/nas/enas-tf/search.py,76c819c00acb2952436da611c574030aa0beedd4,STILL_EXISTS,TODO: argparse
aaifeeeib,microsoft/nni,src/sdk/pynni/nni/nas/tensorflow/enas/mutator.py,76c819c00acb2952436da611c574030aa0beedd4,STILL_EXISTS,workaround it by omitting `reset_states` for first run
aaifeeeja,microsoft/nni,src/sdk/pynni/nni/nas/tensorflow/mutables.py,76c819c00acb2952436da611c574030aa0beedd4,e7fccfb4a01285795c7a68ea233af119e0fd5a3c,FIXME: not tested
aaifeeejd,microsoft/nni,src/sdk/pynni/nni/nas/tensorflow/mutator.py,76c819c00acb2952436da611c574030aa0beedd4,STILL_EXISTS,TODO: status
aaifeeeje,microsoft/nni,src/sdk/pynni/nni/nas/tensorflow/mutator.py,76c819c00acb2952436da611c574030aa0beedd4,STILL_EXISTS,TODO: graph
aaifeegca,microsoft/nni,src/sdk/pynni/nni/compression/torch/pruning/lottery_ticket.py,89fa23cb77746d36b4f1ff6cd4fcefbab18c7d70,STILL_EXISTS,TODO: directly use weight_mask is not good
aaifeegfi,microsoft/nni,src/sdk/pynni/nni/compression/torch/utils/sensitivity_analysis.py,c3d7b4c5799a6c18e1271fb0516a7ddbcc7d3bfe,STILL_EXISTS,TODO In current L1\/L2 Filter Pruner; the 'op_types' is still necessary
aaifeegig,microsoft/nni,src/sdk/pynni/tests/test_compression_utils.py,c3d7b4c5799a6c18e1271fb0516a7ddbcc7d3bfe,STILL_EXISTS,Fix the mask conflict
aaifeegii,microsoft/nni,src/sdk/pynni/tests/test_compression_utils.py,c3d7b4c5799a6c18e1271fb0516a7ddbcc7d3bfe,STILL_EXISTS,fix the mask conflict successfully
aaifeeibe,microsoft/nni,examples/nas/classic_nas-tf/train.py,9a1fb17bd46c30110ebeadecda7d2adf9c26b289,STILL_EXISTS,training=training is needed only if there are layers with different
aaifeeibg,microsoft/nni,examples/nas/classic_nas-tf/train.py,9a1fb17bd46c30110ebeadecda7d2adf9c26b289,STILL_EXISTS,training=False is needed only if there are layers with different
aaifeeigc,microsoft/nni,examples/trials/systems/opevo/src/compiler_auto_tune_stable.py,6de15707c1f9ec7409432aea231e149c91b79626,STILL_EXISTS,# TODO: optimize c-mcpu metric; early-stop handler; fp16\/int8; Kill pyRPC;
aaifeejbj,microsoft/nni,tools/nni_trial_tool/trial_runner.py,0b9d6ce6d1cf6515c5b553a61f78cd333bf4700f,STILL_EXISTS,try best to send latest errors to server
aaifeejgf,microsoft/nni,src/sdk/pynni/nni/compression/torch/pruning/auto_compress_pruner.py,f5caa19316a2337eeec6ed2dd304b86506ce22a4,STILL_EXISTS,input info needed
aaifeejjj,microsoft/nni,src/sdk/pynni/nni/compression/torch/pruning/simulated_annealing_pruner.py,f5caa19316a2337eeec6ed2dd304b86506ce22a4,STILL_EXISTS,init current performance & best performance
aaifefaba,microsoft/nni,src/sdk/pynni/nni/compression/torch/pruning/simulated_annealing_pruner.py,f5caa19316a2337eeec6ed2dd304b86506ce22a4,STILL_EXISTS,if better evaluation result; then accept the perturbation
aaifefabb,microsoft/nni,src/sdk/pynni/nni/compression/torch/pruning/simulated_annealing_pruner.py,f5caa19316a2337eeec6ed2dd304b86506ce22a4,STILL_EXISTS,save best performance and best params
aaifefbgj,microsoft/nni,src/sdk/pynni/nni/compression/tensorflow/compressor.py,5623dbf32952bd7842d3d9c5cf12a8e97e2b1fab,STILL_EXISTS,TODO: maybe it should be able to calc on weight-granularity; beside from layer-granularity
aaifefbha,microsoft/nni,src/sdk/pynni/nni/compression/tensorflow/compressor.py,5623dbf32952bd7842d3d9c5cf12a8e97e2b1fab,STILL_EXISTS,TODO: designed to replace `patch_optimizer`
aaifefbii,microsoft/nni,src/sdk/pynni/nni/compression/tensorflow/pruning/one_shot.py,5623dbf32952bd7842d3d9c5cf12a8e97e2b1fab,STILL_EXISTS,TODO
aaifefdce,microsoft/nni,src/sdk/pynni/nni/compression/torch/pruning/amc/channel_pruning_env.py,e9f3cddf95e58ca17641db793c420a1b6e5424c0,STILL_EXISTS,TODO replace this speedup implementation with nni.compression.torch.ModelSpeedup
aaifefdda,microsoft/nni,src/sdk/pynni/nni/compression/torch/pruning/amc/channel_pruning_env.py,e9f3cddf95e58ca17641db793c420a1b6e5424c0,STILL_EXISTS,TODO: add other member in the set
aaifefdeg,microsoft/nni,src/sdk/pynni/nni/compression/torch/pruning/amc/channel_pruning_env.py,e9f3cddf95e58ca17641db793c420a1b6e5424c0,STILL_EXISTS,TODO: to be tested! Share index for residual connection
aaifefdfc,microsoft/nni,src/sdk/pynni/nni/compression/torch/pruning/amc/channel_pruning_env.py,e9f3cddf95e58ca17641db793c420a1b6e5424c0,STILL_EXISTS,TODO replace this flops counter with nni.compression.torch.utils.counter.count_flops_params
aaifefgcd,microsoft/nni,nni/compression/pytorch/utils/sensitivity_analysis.py,e21a6984cc66df1c66b47107e81c0ed1c7ba58d5,STILL_EXISTS,FIXME: I don't know where \"utils\" should be
aaifefgcg,microsoft/nni,nni/runtime/common.py,e21a6984cc66df1c66b47107e81c0ed1c7ba58d5,765206cbd7ec459a2b78bec72bab4b3298c099c9,fixme: launching logic needs refactor
aaifefgdi,microsoft/nni,setup_ts.py,e21a6984cc66df1c66b47107e81c0ed1c7ba58d5,STILL_EXISTS,todo: I don't think these should be here
aaifefgeb,microsoft/nni,nni/runtime/common.py,3b90b9d9b74d2fdc38a4f95674cba7604fd4b522,765206cbd7ec459a2b78bec72bab4b3298c099c9,FIXME
aaifefged,microsoft/nni,nni/runtime/common.py,3b90b9d9b74d2fdc38a4f95674cba7604fd4b522,765206cbd7ec459a2b78bec72bab4b3298c099c9,This behavior depends on underlying implementation of `nnictl` and is likely to break in future.
aaifefgee,microsoft/nni,nni/runtime/common.py,80b6cb3b934d3c2965a7412128645e0eefdefaf2,765206cbd7ec459a2b78bec72bab4b3298c099c9,fixme: launching logic needs refactor
aaifefghd,microsoft/nni,test/ut/__init__.py,bc0f8f338ba8a7e42e29cbbf47a0edca8244cfcd,990364b73dd1ee05858684c86cc72bd5f6f45ff1,\"\"\" || Unit test of NNI Python modules. ||  || Test cases of each module should be placed at same path of their source files. || For example if `nni\/tool\/annotation` has one test case; it should be placed at `test\/ut\/tool\/annotation.py`; || if it has multiple test cases; they should be placed in `test\/ut\/tool\/annotation\/` directory. ||  || \"Legacy\" test cases carried from NNI v1.x might not follow above convention: ||  ||   + Directory `sdk` contains old test cases previously in `src\/sdk\/pynni\/tests`. ||   + Directory `tools\/cmd` contains old test cases previously in `tools\/cmd\/tests`. ||   + Directory `tools\/annotation` contains old test cases previously in `tools\/nni_annotation`. ||   + Directory `tools\/trial_tool` contains old test cases previously in `tools\/nni_trial_tool\/test`. || \"\"\"
aaifefghe,microsoft/nni,test/ut/sdk/test_model_speedup.py,bc0f8f338ba8a7e42e29cbbf47a0edca8244cfcd,be652aa4375b84549dd0a462aee23b80556fe532,FIXME:
aaifefghj,microsoft/nni,test/ut/tools/nnictl/test_config_utils.py,bc0f8f338ba8a7e42e29cbbf47a0edca8244cfcd,e6629a767b52e1bd4ae63b2ed9fe79ab94f302bf,FIXME:
aaifefgia,microsoft/nni,test/ut/tools/nnictl/test_config_utils.py,bc0f8f338ba8a7e42e29cbbf47a0edca8244cfcd,e6629a767b52e1bd4ae63b2ed9fe79ab94f302bf,`experiment.get_all_experiments()` returns empty dict. No idea why.
aaifefgif,microsoft/nni,test/ut/tools/nnictl/test_nnictl_utils.py,bc0f8f338ba8a7e42e29cbbf47a0edca8244cfcd,STILL_EXISTS,FIXME: debug it later
aaifefhha,microsoft/nni,nni/retiarii/graph.py,19726d4d8111f0ba299d456a081cc93e3a223dd8,STILL_EXISTS,TODO
aaifefhia,microsoft/nni,test/ut/retiarii/test_mutator.py,19726d4d8111f0ba299d456a081cc93e3a223dd8,STILL_EXISTS,FIXME
aaifefhjc,microsoft/nni,nni/retiarii/codegen/pytorch.py,002af91f0bedc3100638c8b9568ba7f22a8ccea1,efa4e31c4b1da4a4a987c0280e1fc3b8adf5c004,FIXME: topological sort is needed here
aaifefhjf,microsoft/nni,nni/retiarii/codegen/pytorch.py,002af91f0bedc3100638c8b9568ba7f22a8ccea1,STILL_EXISTS,TODO: handle imports
aaifefhjg,microsoft/nni,nni/retiarii/codegen/tensorflow.py,002af91f0bedc3100638c8b9568ba7f22a8ccea1,STILL_EXISTS,\"\"\" || FIXME || This file is inherited from last version. ||  || I expect it can work with a few modifications to incorporate with the latest API; but it hasn't || been tested and I'm not sure. || \"\"\"
aaifefiaf,microsoft/nni,nni/retiarii/execution/interface.py,002af91f0bedc3100638c8b9568ba7f22a8ccea1,STILL_EXISTS,\"\"\" || To be designed.  Discussion needed. ||  || This describes the properties of a worker machine. (e.g. memory size) || \"\"\"
aaifefibf,microsoft/nni,nni/retiarii/trainer/pytorch.py,002af91f0bedc3100638c8b9568ba7f22a8ccea1,STILL_EXISTS,TODO: we will need at least two (maybe three) data loaders in future.
aaifefida,microsoft/nni,nni/retiarii/codegen/pytorch.py,8af731463df46f6c73c933bdc45ed9a4ecd4e422,efa4e31c4b1da4a4a987c0280e1fc3b8adf5c004,TODO: fix: inputs is a list; how to deal with single element list and single element
aaifefidb,microsoft/nni,nni/retiarii/codegen/pytorch.py,8af731463df46f6c73c933bdc45ed9a4ecd4e422,STILL_EXISTS,TODO: set correct PATH for the packages (after launch refactor)
aaifefidc,microsoft/nni,nni/retiarii/codegen/pytorch.py,8af731463df46f6c73c933bdc45ed9a4ecd4e422,efa4e31c4b1da4a4a987c0280e1fc3b8adf5c004,TODO: remove _convert_names (after merging input_names and input_node)
aaifefidd,microsoft/nni,nni/retiarii/codegen/pytorch.py,8af731463df46f6c73c933bdc45ed9a4ecd4e422,efa4e31c4b1da4a4a987c0280e1fc3b8adf5c004,TODO: refactor graph output_node
aaifefiee,microsoft/nni,nni/retiarii/converter/graph_gen.py,8af731463df46f6c73c933bdc45ed9a4ecd4e422,STILL_EXISTS,TODO: add scope name
aaifefieh,microsoft/nni,nni/retiarii/converter/graph_gen.py,8af731463df46f6c73c933bdc45ed9a4ecd4e422,STILL_EXISTS,TODO: support constant expression
aaifefifa,microsoft/nni,nni/retiarii/converter/graph_gen.py,8af731463df46f6c73c933bdc45ed9a4ecd4e422,STILL_EXISTS,TODO: try not-connected placeholder in TorchScript
aaifefifb,microsoft/nni,nni/retiarii/converter/graph_gen.py,8af731463df46f6c73c933bdc45ed9a4ecd4e422,STILL_EXISTS,TODO: match subgraph with maintained graphs
aaifefifh,microsoft/nni,nni/retiarii/converter/graph_gen.py,8af731463df46f6c73c933bdc45ed9a4ecd4e422,STILL_EXISTS,TODO: how about calling a function twice? two constant nodes or one?
aaifefifi,microsoft/nni,nni/retiarii/converter/graph_gen.py,8af731463df46f6c73c933bdc45ed9a4ecd4e422,STILL_EXISTS,handle aten::XXX
aaifefifj,microsoft/nni,nni/retiarii/converter/graph_gen.py,8af731463df46f6c73c933bdc45ed9a4ecd4e422,STILL_EXISTS,TODO: remove positional args
aaifefiha,microsoft/nni,nni/retiarii/graph.py,8af731463df46f6c73c933bdc45ed9a4ecd4e422,STILL_EXISTS,TODO: why not merge the names into input_node and output_node???
aaifefihf,microsoft/nni,nni/retiarii/model_apis/nn.py,8af731463df46f6c73c933bdc45ed9a4ecd4e422,STILL_EXISTS,TODO: users have to pass init's arguments to super init's arguments
aaifefihg,microsoft/nni,nni/retiarii/model_apis/nn.py,8af731463df46f6c73c933bdc45ed9a4ecd4e422,STILL_EXISTS,TODO: change tuple to dict
aaifefiid,microsoft/nni,nni/retiarii/operation.py,8af731463df46f6c73c933bdc45ed9a4ecd4e422,STILL_EXISTS,TODO: ugly; think about how to refactor this part
aaifefijg,microsoft/nni,test/convert_test/base_mnasnet.py,8af731463df46f6c73c933bdc45ed9a4ecd4e422,STILL_EXISTS,TODO: restrict that \"choose\" can only be used within mutator
aaifefjdc,microsoft/nni,test/convert_test/mutator.py,8af731463df46f6c73c933bdc45ed9a4ecd4e422,STILL_EXISTS,fix possible shape mismatch
aaifefjdd,microsoft/nni,test/convert_test/mutator.py,8af731463df46f6c73c933bdc45ed9a4ecd4e422,STILL_EXISTS,TODO: use formal method function to update parameters
aaifefjdj,microsoft/nni,test/convert_test/test.py,8af731463df46f6c73c933bdc45ed9a4ecd4e422,STILL_EXISTS,TODO: new interface
aaifefjei,microsoft/nni,nni/retiarii/graph.py,efa4e31c4b1da4a4a987c0280e1fc3b8adf5c004,STILL_EXISTS,TODO: use node copy instead
aaifefjej,microsoft/nni,nni/retiarii/graph.py,efa4e31c4b1da4a4a987c0280e1fc3b8adf5c004,STILL_EXISTS,TODO: why not merge the names into input_node and output_node???
aaifegahj,microsoft/nni,examples/nas/cream/lib/models/builders/build_supernet.py,604f8431d1670418312cecab213038b17988c92b,STILL_EXISTS,FIXME this is a hack to work around mismatch in origin impl input
aaifegbcg,microsoft/nni,examples/nas/cream/lib/utils/builder_util.py,604f8431d1670418312cecab213038b17988c92b,STILL_EXISTS,FIXME hack to deal with in_chs issue in TPU def
aaifegbie,microsoft/nni,examples/nas/cream/retrain.py,604f8431d1670418312cecab213038b17988c92b,STILL_EXISTS,fix random seeds
aaifegcca,microsoft/nni,examples/nas/cream/train.py,604f8431d1670418312cecab213038b17988c92b,STILL_EXISTS,fix random seeds
aaifegidd,microsoft/nni,test/ut/__init__.py,990364b73dd1ee05858684c86cc72bd5f6f45ff1,STILL_EXISTS,\"\"\" || Unit test of NNI Python modules. ||  || Test cases of each module should be placed at same path of their source files. || For example if `nni\/tool\/annotation` has one test case; it should be placed at `test\/ut\/tool\/test_annotation.py`; || if it has multiple test cases; they should be placed in `test\/ut\/tool\/annotation\/` directory. ||  || \"Legacy\" test cases carried from NNI v1.x might not follow above convention: ||  ||   + Directory `sdk` contains old test cases previously in `src\/sdk\/pynni\/tests`. ||   + Directory `tools\/nnictl` contains old test cases previously in `tools\/nni_cmd\/tests`. ||   + Directory `tools\/annotation` contains old test cases previously in `tools\/nni_annotation`. ||   + Directory `tools\/trial_tool` contains old test cases previously in `tools\/nni_trial_tool\/test`. || \"\"\"
aaifegihe,microsoft/nni,nni/compression/pytorch/speedup/compress_modules.py,3af2ad9654784325c96aa3f7157ec53bb61db62f,STILL_EXISTS,TODO bug here; the groups is directly get from conv.groups; if the whole group is removed;
aaifegiie,microsoft/nni,nni/compression/pytorch/speedup/infer_shape.py,3af2ad9654784325c96aa3f7157ec53bb61db62f,STILL_EXISTS,TODO support the Convtranspose2d Pruning for the L1FilterPruner
aaifegijj,microsoft/nni,examples/trials/mnist-tfv2/launch.py,765206cbd7ec459a2b78bec72bab4b3298c099c9,STILL_EXISTS,FIXME: For demonstration only. It should not be here
aaifegjca,microsoft/nni,nni/experiment/config/base.py,765206cbd7ec459a2b78bec72bab4b3298c099c9,d165905d0ba24cfba414b8e0c20fa8d7c8ab6a6e,check type (TODO)
aaifegjci,microsoft/nni,nni/experiment/config/common.py,765206cbd7ec459a2b78bec72bab4b3298c099c9,STILL_EXISTS,TODO: verify algorithm installation and class args
aaifegjef,microsoft/nni,nni/experiment/experiment.py,765206cbd7ec459a2b78bec72bab4b3298c099c9,3ec26b40afd88b8255ebc74b46f475b77aa4d19b,TODO: register experiment management metadata
aaifegjeg,microsoft/nni,nni/experiment/management.py,765206cbd7ec459a2b78bec72bab4b3298c099c9,STILL_EXISTS,TODO: port shangning's work here; and use it in Experiment.start()\/.stop()
aaifegjgf,microsoft/nni,nni/runtime/log.py,765206cbd7ec459a2b78bec72bab4b3298c099c9,6330df2f68f28eb2ea828a854f79131eae7111a3,`logging.getLogger('xxx').info()` in all examples.
aaifegjig,microsoft/nni,nni/retiarii/codegen/pytorch.py,7d1acfbd939f8e4f5aa9fceb2a47771157b09420,192a807b39bc029c273c2d1349fb73850ed9484c,FIXME: topological sort is needed here
aaifehaba,microsoft/nni,nni/retiarii/execution/logical_optimizer/logical_plan.py,7d1acfbd939f8e4f5aa9fceb2a47771157b09420,STILL_EXISTS,FIXME: allow user to specify
aaifehacf,microsoft/nni,nni/retiarii/execution/logical_optimizer/logical_plan.py,7d1acfbd939f8e4f5aa9fceb2a47771157b09420,STILL_EXISTS,TODO: currently; only support single input slot and output slot.
aaifehaci,microsoft/nni,nni/retiarii/execution/logical_optimizer/logical_plan.py,7d1acfbd939f8e4f5aa9fceb2a47771157b09420,STILL_EXISTS,TODO: it does not have duplicated edges if only supporting dedup input
aaifehafg,microsoft/nni,nni/experiment/config/base.py,d165905d0ba24cfba414b8e0c20fa8d7c8ab6a6e,3ec26b40afd88b8255ebc74b46f475b77aa4d19b,TODO: check type
aaifehagc,microsoft/nni,nni/experiment/config/base.py,d165905d0ba24cfba414b8e0c20fa8d7c8ab6a6e,3ec26b40afd88b8255ebc74b46f475b77aa4d19b,FIXME: expose this field to users
aaifehaih,microsoft/nni,nni/experiment/experiment.py,d165905d0ba24cfba414b8e0c20fa8d7c8ab6a6e,3ec26b40afd88b8255ebc74b46f475b77aa4d19b,FIXME: remove this argument
aaifehajg,microsoft/nni,nni/experiment/launcher.py,d165905d0ba24cfba414b8e0c20fa8d7c8ab6a6e,3ec26b40afd88b8255ebc74b46f475b77aa4d19b,todo: kill on fail
aaifehbbe,microsoft/nni,nni/retiarii/converter/graph_gen.py,d165905d0ba24cfba414b8e0c20fa8d7c8ab6a6e,STILL_EXISTS,%25 : __torch__.xxx = prim::GetAttr[name=\"input_switch\"](%self)
aaifehbca,microsoft/nni,nni/retiarii/converter/graph_gen.py,d165905d0ba24cfba414b8e0c20fa8d7c8ab6a6e,STILL_EXISTS,FIXME: exchange
aaifehbcf,microsoft/nni,nni/retiarii/execution/base.py,d165905d0ba24cfba414b8e0c20fa8d7c8ab6a6e,192a807b39bc029c273c2d1349fb73850ed9484c,FIXME: update this part to dump code to a correct path!!!
aaifehbcj,microsoft/nni,nni/retiarii/nn/pytorch/nn.py,d165905d0ba24cfba414b8e0c20fa8d7c8ab6a6e,192a807b39bc029c273c2d1349fb73850ed9484c,TODO: users have to pass init's arguments to super init's arguments
aaifehcaa,microsoft/nni,nni/experiment/config/base.py,3ec26b40afd88b8255ebc74b46f475b77aa4d19b,STILL_EXISTS,check type (TODO)
aaifehche,microsoft/nni,nni/retiarii/converter/graph_gen.py,192a807b39bc029c273c2d1349fb73850ed9484c,STILL_EXISTS,when slice is for one dimension list; there are only 4 inputs; thus merge is not needed
aaifehchf,microsoft/nni,nni/retiarii/converter/graph_gen.py,192a807b39bc029c273c2d1349fb73850ed9484c,ae50ed146684240adb3e9fc41f051fc586f953cf,TODO: if we parse this module; it means we will create a graph (module class)
aaifehcic,microsoft/nni,nni/retiarii/experiment.py,192a807b39bc029c273c2d1349fb73850ed9484c,STILL_EXISTS,TODO: remove
aaifehcif,microsoft/nni,nni/retiarii/experiment.py,192a807b39bc029c273c2d1349fb73850ed9484c,08af7771d1f1149f3da5a98bcdbf7e65abd409ba,FIXME:
aaifehcij,microsoft/nni,nni/retiarii/experiment.py,192a807b39bc029c273c2d1349fb73850ed9484c,6330df2f68f28eb2ea828a854f79131eae7111a3,TODO: register experiment management metadata
aaifehcja,microsoft/nni,nni/retiarii/experiment.py,192a807b39bc029c273c2d1349fb73850ed9484c,08af7771d1f1149f3da5a98bcdbf7e65abd409ba,TODO: double check the status
aaifehcjc,microsoft/nni,nni/retiarii/nn/pytorch/nn.py,192a807b39bc029c273c2d1349fb73850ed9484c,STILL_EXISTS,TODO: 'ModuleDict'; 'ParameterList'; 'ParameterDict';
aaifehcjg,microsoft/nni,nni/retiarii/nn/pytorch/nn.py,192a807b39bc029c273c2d1349fb73850ed9484c,STILL_EXISTS,TODO: support multiple chosen inputs
aaifehcji,microsoft/nni,nni/retiarii/nn/pytorch/nn.py,192a807b39bc029c273c2d1349fb73850ed9484c,STILL_EXISTS,TODO: support different versions of pytorch
aaifehdca,microsoft/nni,nni/retiarii/utils.py,192a807b39bc029c273c2d1349fb73850ed9484c,STILL_EXISTS,TODO: support both pytorch and tensorflow
aaifehdfa,microsoft/nni,nni/retiarii/graph.py,b3cdee850cd61539834659ad96b35abd94ae1d72,STILL_EXISTS,TODO: the operation is likely to be considered editable by end-user and it will be hard to debug
aaifehdfb,microsoft/nni,nni/retiarii/graph.py,b3cdee850cd61539834659ad96b35abd94ae1d72,STILL_EXISTS,maybe we should copy it here or make Operation class immutable; in next release
aaifehdhb,microsoft/nni,test/ut/retiarii/test_cgo_engine.py,0f0c6288c35a456707057b0aab7754fcb5ac4ffd,d5a551c85d90f9f6890884d26402ebc40177a8bc,TODO: fix ut
aaifeiabc,microsoft/nni,nni/retiarii/utils.py,08af7771d1f1149f3da5a98bcdbf7e65abd409ba,STILL_EXISTS,TODO: refactor later
aaifeiagc,microsoft/nni,test/ut/retiarii/test_convert.py,0f88b86b4ec6760bf8107fc8bdd2f6219953d6cd,STILL_EXISTS,For deterministic testing TODO: change by fixing seed in checkTrace?
aaifeiagf,microsoft/nni,test/ut/retiarii/test_convert.py,0f88b86b4ec6760bf8107fc8bdd2f6219953d6cd,STILL_EXISTS,TODO: add future as input with default val
aaifeiagh,microsoft/nni,test/ut/retiarii/test_convert.py,0f88b86b4ec6760bf8107fc8bdd2f6219953d6cd,STILL_EXISTS,TODO: chunk call should appear as the for loop iterable
aaifeiaie,microsoft/nni,examples/trials/mnist-tfv1/launch_hybrid.py,b177bdc8ea5cf3b627f05f9af325b010817c1561,STILL_EXISTS,FIXME: For demonstration only. It should not be here
aaifeiajb,microsoft/nni,test/ut/sdk/test_model_speedup.py,c66b74720e355a4252e58cd586269fabe376cf89,STILL_EXISTS,Note: hack trick; may be updated in the future
aaifeicfh,microsoft/nni,nni/retiarii/nn/pytorch/api.py,8c8db3746c9ef6b526497c973277bab4b458d4ed,STILL_EXISTS,TODO: docstring
aaifeichc,microsoft/nni,examples/model_compress/pruning/basic_pruners_torch.py,a9dcc00670c611f8d610213d12ca2e54099a3913,STILL_EXISTS,Reproduced result in paper 'PRUNING FILTERS FOR EFFICIENT CONVNETS';
aaifeidde,microsoft/nni,nni/retiarii/experiment/pytorch.py,445e7e0b74495326ef6f90f238d16513a437d797,STILL_EXISTS,TODO: The current design of init interface of Retiarii experiment needs to be reviewed.
aaifeidfb,microsoft/nni,nni/retiarii/converter/graph_gen.py,58d5c2faf0303751e432a4f99af19ac25e3065fb,STILL_EXISTS,TODO: serialize complex data type; and output proper error message
aaifeidfj,microsoft/nni,nni/retiarii/converter/graph_gen.py,58d5c2faf0303751e432a4f99af19ac25e3065fb,STILL_EXISTS,TODO: support non member functions
aaifeidgg,microsoft/nni,nni/retiarii/converter/graph_gen.py,58d5c2faf0303751e432a4f99af19ac25e3065fb,STILL_EXISTS,TODO: check whether there could be multiple output nodes???
aaifeidgi,microsoft/nni,nni/retiarii/converter/graph_gen.py,58d5c2faf0303751e432a4f99af19ac25e3065fb,STILL_EXISTS,handle aten::XXX
aaifeidij,microsoft/nni,nni/retiarii/operation_def/torch_op_def.py,58d5c2faf0303751e432a4f99af19ac25e3065fb,STILL_EXISTS,TODO: refactor this part; maybe we can remove the code gen of prim::Constant
aaifeidja,microsoft/nni,nni/retiarii/operation_def/torch_op_def.py,58d5c2faf0303751e432a4f99af19ac25e3065fb,STILL_EXISTS,TODO: deal with all the types
aaifeidjh,microsoft/nni,nni/retiarii/operation_def/torch_op_def.py,58d5c2faf0303751e432a4f99af19ac25e3065fb,STILL_EXISTS,TODO: deal with this argument
aaifeieae,microsoft/nni,nni/retiarii/operation_def/torch_op_def.py,58d5c2faf0303751e432a4f99af19ac25e3065fb,STILL_EXISTS,only process 'torch.XXX'
aaifeiebb,microsoft/nni,nni/retiarii/operation_def/torch_op_def.py,58d5c2faf0303751e432a4f99af19ac25e3065fb,STILL_EXISTS,TODO: deal with conditional ops
aaifeiefb,microsoft/nni,test/ut/retiarii/test_convert_basic.py,58d5c2faf0303751e432a4f99af19ac25e3065fb,STILL_EXISTS,TODO: topological sort should be improved
aaifeiefi,microsoft/nni,test/ut/retiarii/test_convert_basic.py,58d5c2faf0303751e432a4f99af19ac25e3065fb,STILL_EXISTS,TODO: add back this line
aaifeifcc,microsoft/nni,nni/retiarii/graph.py,fddc8adc872cea6c2662e178ca24254e92c22642,STILL_EXISTS,TODO this may be a problem when training config is large
aaifeiffj,microsoft/nni,nni/retiarii/serializer.py,cae4308fcec14df40d22adfabd962b7f7b26eeb8,STILL_EXISTS,FIXME: raise error
aaifeigbb,microsoft/nni,nni/retiarii/execution/base.py,e457047cac527d3f4d524ec77d6ef127eadd55c1,STILL_EXISTS,FIXME: should be a warning message here
aaifeigha,microsoft/nni,nni/compression/pytorch/compressor.py,f51d985bf70de3468f3dd7fbe55f56c56a410b31,STILL_EXISTS,input info needed
aaifeigia,OpenMined/PySyft,syft/nn/convnet.py,1902f104093f0db910b3f0484e9b0ebe6963aab4,STILL_EXISTS,TODO: build this
aaifeigib,OpenMined/PySyft,syft/nn/rnn.py,1902f104093f0db910b3f0484e9b0ebe6963aab4,STILL_EXISTS,TODO: build this
aaifeigid,OpenMined/PySyft,syft/nn/rl.py,dc6e85df56e47739c9308ed20b6c8e9c367c901b,STILL_EXISTS,TODO: build this
aaifeigie,OpenMined/PySyft,syft/nn/fasttext.py,fb4f1387c0cfcb28778c0cd0f6ad8993b93c6935,STILL_EXISTS,TODO: build this
aaifeijda,OpenMined/PySyft,bin/syft_cmd.py,a00b420dad441276ff984fc158c7d0b1d648b070,STILL_EXISTS,TODO: don't read whole input dataset every time
aaifejbfe,OpenMined/PySyft,syft/he/paillier/FixedPoint.py,b1f5fdf3178d7fbac1419f813583b3077f9c945e,STILL_EXISTS,\"\"\" || The Simple Python Fixed-Point Module (SPFPM) provides objects of types || FXnum and FXfamily which implement basic mathematical operations || on fixed-point binary numbers (i.e. having a fixed number of || fractional binary digits; with the number of integer digits being either || arbitrary or subject to a user-defined limit). ||  || FXnum objects exist within a user-controllable collection of families || managed by the FXfamily class; which sets the number of fractional || & integer digits for each family. This can be used; for example; || to ensure that a set of 8-bit quantities can be manipulated consistently || and kept separate from a set of 200-bit quantities in the same program. || Conversion between FXnum objects in different families is supported; || but solely through an explicit cast. ||  || >>> x = FXnum(2.1)                  # default FXfamily; with 64-bits || >>> print(x) || 2.100000000000000088817 || >>> x = FXnum(21) \/ 10              # fractional error ~1\/2^64 or ~5e-20 || >>> print(x) || 2.099999999999999999967 || >>> rx = x.sqrt()                   # rx created in same family as x || >>> print(rx) || 1.449137674618943857354 || >>> v = x + 2 * rx || >>> print(v) || 4.998275349237887714675 ||  || >>> y = FXnum(3.2; FXfamily(12))    # lower-precision 12-bit number || >>> ly = y.log()                    # ly created in same family as y || >>> print(ly)                       # fractional error ~1\/2^12 or ~2e-4 || 1.1628 || >>> print(ly.exp()) || 3.1987 || >>> fy = float(y) || >>> print(fy) || 3.199951171875 ||  || >>> # a = x + y                     # throws exception - different families || >>> a = x + FXnum(y; _defaultFamily) || >>> print(a) || 5.300073242187499999967 || >>> b = rx + x                      # ok - same families || >>> # c = rx + ly                   # throws exception - different families || >>> d = ly + y                      # ok - same families ||  || >>> a = FXnum(1.4; FXfamily(12; 4)) # limit magnitude to 2^(4-1) || >>> print(a) || 1.3999 || >>> print(a * 5; a * -5) || 6.9995 -6.9995 || >>> #print(a * 6; a * -6)           # throws exception indicating overflow ||  || >>> fam = FXfamily(200) || >>> print(fam.pi) || 3.141592653589793238462643383279502884197169399375105820974944478108 ||  || Note: ||     Be careful not to assume that a large number of fractional bits within ||     a number will necessarily mean large accuracy. For example; computations ||     involving exponentiation and logarithms are intrinsically vulnerable to ||     magnifying mere rounding errors in their inputs into significant errors ||     in their outputs. This is a fact of life with any approximation to ||     real arithmetic using finite-precision quantities. ||  || SPFPM is provided as-is; with no warranty of any form. || \"\"\"
aaifejbgc,OpenMined/PySyft,syft/he/paillier/FixedPoint.py,b1f5fdf3178d7fbac1419f813583b3077f9c945e,STILL_EXISTS,However; the author welcomes *constructive* feedback
aaifejeig,OpenMined/PySyft,build/lib/syft/syft.py,8442437dc3a51e002b1498d9a9d92a37c3ca3f03,032777631443af85ac108759a59289f358921399,TODO: this fails with x = x + x if x is a FloatTensor
aaifejejc,OpenMined/PySyft,syft/syft.py,8442437dc3a51e002b1498d9a9d92a37c3ca3f03,b78e9f22fdc0d0b0551dc51823e7ce23c01c6d8c,TODO: this fails with x = x + x if x is a FloatTensor
aaifejfbf,OpenMined/PySyft,build/lib/syft/syft.py,d11528b610304238d3768bbf65359a43b832ef30,STILL_EXISTS,TODO: this fails with x = x + x if x is a FloatTensor
aaifejfcb,OpenMined/PySyft,syft/syft.py,d11528b610304238d3768bbf65359a43b832ef30,04e8dcf888e6a2cedfe5dc1ad7c1f83ea4253895,TODO: this fails with x = x + x if x is a FloatTensor
aaifejgib,OpenMined/PySyft,syft/nn.py,6c378d93cd9eb4411b5b58f6f845ca0860785174,71cf8d81c5067bdc292b7cd6c2c062e5a3a766b9,TODO backward() to be implemented: grad = target - prediction
aaifejgic,OpenMined/PySyft,syft/nn.py,6c378d93cd9eb4411b5b58f6f845ca0860785174,71cf8d81c5067bdc292b7cd6c2c062e5a3a766b9,TODO backward(): until IntegerTensor is available assume a one-hot vector is passed in.
aaifejgih,OpenMined/PySyft,syft/nn.py,2b0efc395f5b1e08e359f619939aaadb05e3a5fc,31b4ec36b53aeb41e932a01245fce872312307d6,TODO backward() to be implemented: grad = target - prediction
aaifejgii,OpenMined/PySyft,syft/nn.py,2b0efc395f5b1e08e359f619939aaadb05e3a5fc,31b4ec36b53aeb41e932a01245fce872312307d6,TODO backward(): until IntegerTensor is available assume a one-hot vector is passed in.
aaifejgjb,OpenMined/PySyft,syft/nn.py,26cc1849d259f5f99082f960656f5c6dd38bff3d,STILL_EXISTS,TODO backward() to be implemented: grad = target - prediction
aaifejgjc,OpenMined/PySyft,syft/nn.py,26cc1849d259f5f99082f960656f5c6dd38bff3d,STILL_EXISTS,TODO backward(): until IntegerTensor is available assume a one-hot vector is passed in.
aaiffabac,OpenMined/PySyft,syft/core/torch_/hook.py,3dfa5e2d2e373d0b887082c9507ebf8de06d8e33,STILL_EXISTS,TODO: May want to handle the ones in \"exclude\" manually at
aaiffabae,OpenMined/PySyft,syft/core/torch_/hook.py,3dfa5e2d2e373d0b887082c9507ebf8de06d8e33,STILL_EXISTS,This one wasn't in dir(Variable) -- probably a C++ thing
aaiffabah,OpenMined/PySyft,syft/core/torch_/hook.py,3dfa5e2d2e373d0b887082c9507ebf8de06d8e33,STILL_EXISTS,TODO: Assign default id more intelligently (low priority)
aaiffabbh,OpenMined/PySyft,syft/core/torch_/hook.py,3dfa5e2d2e373d0b887082c9507ebf8de06d8e33,STILL_EXISTS,TODO: extend to iterables of tensor pointers
aaiffabbi,OpenMined/PySyft,syft/core/torch_/hook.py,3dfa5e2d2e373d0b887082c9507ebf8de06d8e33,STILL_EXISTS,TODO: Extend to responses that are iterables.
aaiffabch,OpenMined/PySyft,syft/core/torch_/hook.py,3dfa5e2d2e373d0b887082c9507ebf8de06d8e33,STILL_EXISTS,TODO: fully generalize this to multiple workers; consider
aaiffabeh,OpenMined/PySyft,syft/core/torch_/hook.py,3dfa5e2d2e373d0b887082c9507ebf8de06d8e33,STILL_EXISTS,TODO: extend to iterables of pointers
aaiffabhh,OpenMined/PySyft,syft/core/worker.py,f3b7ec5cf3237606da89ccb2606f33d1e1cf8944,STILL_EXISTS,TODO: Implement get_owners and refactor to make it prettier
aaiffacfg,OpenMined/PySyft,docs/conf.py,0ce3c933086b21f128e20c78a57a5b5ddf600072,572b36d8403ea2697350ffabb41fdf7045eddc35,If true; `todo` and `todoList` produce output; else they produce nothing.
aaiffagei,OpenMined/PySyft,syft/core/hooks.py,cb9f7abd9947d50107a814055a1011fdb73320dc,STILL_EXISTS,TODO: May want to handle the ones in \"exclude\" manually at
aaiffagfa,OpenMined/PySyft,syft/core/hooks.py,cb9f7abd9947d50107a814055a1011fdb73320dc,STILL_EXISTS,This one wasn't in dir(Variable) -- probably a C++ thing
aaiffaggb,OpenMined/PySyft,syft/core/hooks.py,cb9f7abd9947d50107a814055a1011fdb73320dc,a58b8fa399dd10f1f8e2040ce5f48e4cc339caa2,TODO: extend to iterables of pointers
aaiffaghe,OpenMined/PySyft,syft/core/hooks.py,cb9f7abd9947d50107a814055a1011fdb73320dc,62ab7bc352c9452cf268dcee5a658818013cc158,# TODO: Assign default id more intelligently (low priority)
aaiffahdh,OpenMined/PySyft,syft/core/hooks.py,cb9f7abd9947d50107a814055a1011fdb73320dc,STILL_EXISTS,# TODO: extend to iterables of tensor pointers
aaiffahfi,OpenMined/PySyft,syft/core/hooks.py,cb9f7abd9947d50107a814055a1011fdb73320dc,e9c0fd05ddcea6f0c2f92e175e9333918ccb9803,# TODO: Extend to responses that are iterables.
aaiffaicg,OpenMined/PySyft,syft/core/hooks.py,cb9f7abd9947d50107a814055a1011fdb73320dc,STILL_EXISTS,# TODO: fully generalize this to multiple workers; consider
aaiffbebb,OpenMined/PySyft,syft/core/hooks.py,d3d7fa25932be4a20bcd83dd1a5f4dea7769fc92,STILL_EXISTS,TODO: fully generalize this to multiple workers; consider
aaiffbgeh,OpenMined/PySyft,syft/core/workers.py,b9dea67cd9e0c012a39afdff3cd9d1e838a4ab95,STILL_EXISTS,TODO: Assign default id more intelligently (low priority)
aaiffbggd,OpenMined/PySyft,syft/core/hooks.py,e9c0fd05ddcea6f0c2f92e175e9333918ccb9803,STILL_EXISTS,TODO: make this a non-inline version
aaiffbheg,OpenMined/PySyft,syft/core/workers.py,e9c0fd05ddcea6f0c2f92e175e9333918ccb9803,STILL_EXISTS,TODO: Extend to responses that are iterables.
aaiffbheh,OpenMined/PySyft,syft/core/workers.py,e9c0fd05ddcea6f0c2f92e175e9333918ccb9803,STILL_EXISTS,TODO: Fix the case when response contains only a numeric
aaiffcadj,OpenMined/PySyft,syft/core/hooks.py,8bbf6306bd74ee3b5b924657dc188174e8eebbec,STILL_EXISTS,because x.grad.id does not exist. As a result; we've needed to register objects temporarily which seems to
aaiffcaeb,OpenMined/PySyft,syft/core/hooks.py,8bbf6306bd74ee3b5b924657dc188174e8eebbec,STILL_EXISTS,workaround seems to work well for now. Anyway; we don't need the temporary objects past this point.
aaiffcaed,OpenMined/PySyft,syft/core/hooks.py,8bbf6306bd74ee3b5b924657dc188174e8eebbec,STILL_EXISTS,this returns grad because garbage collection seems to do something really strange
aaiffcaee,OpenMined/PySyft,syft/core/hooks.py,8bbf6306bd74ee3b5b924657dc188174e8eebbec,STILL_EXISTS,if grad isn't returned here. It re-initializes the gradient somehow but in a way
aaiffcafe,OpenMined/PySyft,syft/core/workers.py,8bbf6306bd74ee3b5b924657dc188174e8eebbec,STILL_EXISTS,x.grad.id to fail because it does not exist. As a result; we've needed to
aaiffcaff,OpenMined/PySyft,syft/core/workers.py,8bbf6306bd74ee3b5b924657dc188174e8eebbec,STILL_EXISTS,store objects temporarily in self._tmpobjects which seems to fix it. Super strange bug which took
aaiffcafg,OpenMined/PySyft,syft/core/workers.py,8bbf6306bd74ee3b5b924657dc188174e8eebbec,STILL_EXISTS,multiple days to figure out. The true cause is still unknown but this workaround seems to work
aaiffcaji,OpenMined/PySyft,syft/core/workers.py,7ab041f6f691a94b3f919da58565191b20efcff4,STILL_EXISTS,TODO: Implement get_owners and refactor to make it prettier
aaiffcbba,OpenMined/PySyft,syft/core/workers.py,86f974085ef31f81dc1b1049da0b04319607d078,STILL_EXISTS,TODO: pass in just the id instead of the entire obj_msg.
aaiffcedj,OpenMined/PySyft,syft/mpc/spdz.py,a91a292c562fc06cdb13f8ad5e2d3952139fbc34,STILL_EXISTS,TODO set these intelligently
aaiffcega,OpenMined/PySyft,syft/core/workers.py,6dc6c66697498720474eb33868eb5ec366a3150c,878002110a9fd656dde51b1df7cb607379e68c72,is experimental functionality but seems to solve the symptoms we
aaiffceha,OpenMined/PySyft,test/torch_test.py,f6b233b9f8e1bff47eb97214706d90d60f721993,92c5da6a19163efaaa10d653e2622e61bcf0871a,previously; newly created Variable objects would lose their OpenMined given
aaiffcfcc,OpenMined/PySyft,syft/core/workers.py,7a451731809aa464e7a061955a9eac20a71f4828,STILL_EXISTS,is experimental functionality but seems to solve the symptoms we
aaiffcfjg,OpenMined/PySyft,syft/core/hooks.py,5616b191da16a1e01c1dfa7d9f2bfbdf3821c19e,STILL_EXISTS,are created before their parents? TODO: fix
aaiffcgjc,OpenMined/PySyft,syft/core/workers/base.py,0a734fb356dfd3e8a6e3b93d6210c4b7b8ed141c,STILL_EXISTS,TODO: Extend to responses that are iterables.
aaiffcgjd,OpenMined/PySyft,syft/core/workers/base.py,0a734fb356dfd3e8a6e3b93d6210c4b7b8ed141c,STILL_EXISTS,TODO: Fix the case when response contains only a numeric
aaiffcgje,OpenMined/PySyft,syft/core/workers/base.py,0a734fb356dfd3e8a6e3b93d6210c4b7b8ed141c,04fd92d7876b41d893d592fbad958841e58bdfd2,TODO: Assign default id more intelligently (low priority)
aaiffchae,OpenMined/PySyft,syft/core/workers/base.py,0a734fb356dfd3e8a6e3b93d6210c4b7b8ed141c,dfb66eafd551d5c92d190a157e1311fec68fa601,is experimental functionality but seems to solve the symptoms we
aaiffchcc,OpenMined/PySyft,syft/core/workers/base.py,0a734fb356dfd3e8a6e3b93d6210c4b7b8ed141c,d69231a360ab70fa6d1b24a7be80bf39f9025728,TODO: Implement get_owners and refactor to make it prettier
aaiffchcj,OpenMined/PySyft,syft/core/workers/base.py,0a734fb356dfd3e8a6e3b93d6210c4b7b8ed141c,709f1c1c2c30a121ea7ef3eaac3c75749e260f51,TODO: pass in just the id instead of the entire obj_msg.
aaiffchdi,OpenMined/PySyft,syft/core/workers/base.py,0a734fb356dfd3e8a6e3b93d6210c4b7b8ed141c,dfb66eafd551d5c92d190a157e1311fec68fa601,it does not exist. As a result; we've needed to store objects temporarily
aaiffchdj,OpenMined/PySyft,syft/core/workers/base.py,0a734fb356dfd3e8a6e3b93d6210c4b7b8ed141c,dfb66eafd551d5c92d190a157e1311fec68fa601,in self._tmpobjects which seems to fix it. Super strange bug which took
aaiffcheb,OpenMined/PySyft,syft/core/workers/base.py,0a734fb356dfd3e8a6e3b93d6210c4b7b8ed141c,dfb66eafd551d5c92d190a157e1311fec68fa601,workaround seems to work well for now. Anyway; so we need to return a cleanup
aaiffdfie,OpenMined/PySyft,syft/core/hooks/torch/hook.py,835df7c336857fd9dbeb10c6e06a261a346db4ce,c367df37408926beeac8b5b962ef1a513f568291,TODO: implementing _execute_remote_fixed_precision_call for remote fixed precision tensors
aaiffdhcd,OpenMined/PySyft,test/torch_test.py,92c5da6a19163efaaa10d653e2622e61bcf0871a,9431d62aa74f92f5ebe47fccb19e395811d3f4c6,# previously; newly created Variable objects would lose their OpenMined given
aaiffdjag,OpenMined/PySyft,syft/core/hooks/torch/hook.py,9431d62aa74f92f5ebe47fccb19e395811d3f4c6,735034ffab5ba206bdaf5827588b13be9a5fb48c,TODO: implementing _execute_remote_fixed_precision_call for remote fixed precision tensors
aaiffdjbd,OpenMined/PySyft,test/torch_test.py,9431d62aa74f92f5ebe47fccb19e395811d3f4c6,c03fa17bc9b31600e4e8e26114830135253ba603,previously; newly created Variable objects would lose their OpenMined given
aaiffebjb,OpenMined/PySyft,syft/core/frameworks/torch/hook.py,ce57a0983eb46e1221b1721b549c022b2bf946af,STILL_EXISTS,TODO: May want to handle the ones in \"exclude\" manually at
aaiffecbe,OpenMined/PySyft,syft/core/frameworks/torch/hook.py,ce57a0983eb46e1221b1721b549c022b2bf946af,9bd1b82cdc977595c442135d5c59fe1e7d6cb776,# makes singleton; if needed
aaiffeded,OpenMined/PySyft,test/torch_test.py,c03fa17bc9b31600e4e8e26114830135253ba603,52978481587642eadcb04faca4f960e5fb5564e5,# previously; newly created Variable objects would lose their OpenMined given
aaiffegff,OpenMined/PySyft,test/torch_test.py,52978481587642eadcb04faca4f960e5fb5564e5,0242180c5e53290b6ce54f2fe770281160375a5f,previously; newly created Variable objects would lose their OpenMined given
aaifffdea,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,9bd1b82cdc977595c442135d5c59fe1e7d6cb776,STILL_EXISTS,TOOD: figure out how to avoid this performance waste.
aaifffgbi,OpenMined/PySyft,test/torch_test.py,0242180c5e53290b6ce54f2fe770281160375a5f,69377ed47c0f1692845cccaad135d71715a60979,# previously; newly created Variable objects would lose their OpenMined given
aaiffgajj,OpenMined/PySyft,test/torch_test.py,69377ed47c0f1692845cccaad135d71715a60979,b68846d23a098d1e734ad349eac600b09c69be20,previously; newly created Variable objects would lose their OpenMined given
aaiffgicd,OpenMined/PySyft,syft/core/workers/base.py,c22840b82447b4cdc62ecc9677e677678439e706,04fd92d7876b41d893d592fbad958841e58bdfd2,TODO =False ??
aaiffhaij,OpenMined/PySyft,test/torch_test.py,b68846d23a098d1e734ad349eac600b09c69be20,6f1e36889183d6f336a8c83b76cbdee47dfdbc5f,# previously; newly created Variable objects would lose their OpenMined given
aaiffhfab,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,53f1751679f39475d692c70dfc36c187e16dcbb6,04fd92d7876b41d893d592fbad958841e58bdfd2,TODO .id_at_location
aaiffhfac,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,53f1751679f39475d692c70dfc36c187e16dcbb6,04fd92d7876b41d893d592fbad958841e58bdfd2,args = args[1:] # TODO compare to master
aaiffhfbf,OpenMined/PySyft,syft/core/utils.py,53f1751679f39475d692c70dfc36c187e16dcbb6,bcf49b06c05d3b0eda2e8a529a8d014c8e0eb698,TODO Will break with PyTorch >= 0.4
aaiffhfbg,OpenMined/PySyft,syft/core/workers/base.py,53f1751679f39475d692c70dfc36c187e16dcbb6,e4886274ebd18269c1cf492a91f632392f2a102a,TODO Guard (self._command_guard(command_msg['command']; self.hook.tensorvar_methods))
aaiffhfbi,OpenMined/PySyft,syft/core/workers/base.py,55a8b30721322a453b1915bdf7ed189b61eccbaa,04fd92d7876b41d893d592fbad958841e58bdfd2,TODO Guard (self._command_guard(command_msg['command']; self.hook.tensorvar_methods))
aaiffhfha,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,e3c65387775b3d0fd8b5fea03f9816555624bd9d,STILL_EXISTS,TODO: add pointer on attr grad: create .grad as a property
aaiffhfhd,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,e3c65387775b3d0fd8b5fea03f9816555624bd9d,STILL_EXISTS,TODO: get grad if any
aaiffhfij,OpenMined/PySyft,syft/core/frameworks/torch/hook.py,12c53e0e75baa516af5fe15f1e3913b05d6dbd6e,STILL_EXISTS,TODO: check and validate how the registration is done
aaiffhfja,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,12c53e0e75baa516af5fe15f1e3913b05d6dbd6e,b51c9844a2ac0f25929caffedcbe6800d4299486,TODO: 'type' is compulsory to pass the guard; but is not very appropriate
aaiffhfji,OpenMined/PySyft,syft/core/workers/base.py,12c53e0e75baa516af5fe15f1e3913b05d6dbd6e,04fd92d7876b41d893d592fbad958841e58bdfd2,TODO Guard (command = self._command_guard(command_msg['command']; self.hook.torch_funcs))
aaiffhgdd,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,d4c143eb7506b57dbbec6cd9dd7205349f3e40a8,STILL_EXISTS,TODO: want to define a particular id ? cant use new_id 2 or 3 times
aaiffhgeh,OpenMined/PySyft,syft/core/workers/base.py,22734d293a4128758676e3c031470f7e7712c3a1,1c9c3892503bffdf5359d849dbfb7215e0262b1a,# TODO: Implement get_owners and refactor to make it prettier
aaiffhghh,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,1c9c3892503bffdf5359d849dbfb7215e0262b1a,b51c9844a2ac0f25929caffedcbe6800d4299486,TODO: 'type' is compulsory to pass the guard; but is not very appropriate
aaiffhgja,OpenMined/PySyft,syft/core/workers/base.py,1c9c3892503bffdf5359d849dbfb7215e0262b1a,04fd92d7876b41d893d592fbad958841e58bdfd2,TODO Guard (command = self._command_guard(command_msg['command']; self.hook.torch_funcs))
aaiffhhdf,OpenMined/PySyft,syft/core/frameworks/torch/hook.py,b51c9844a2ac0f25929caffedcbe6800d4299486,04fd92d7876b41d893d592fbad958841e58bdfd2,We just extract what is needed to make a pointer on this _SyftTensor;
aaiffhhdi,OpenMined/PySyft,syft/core/frameworks/torch/hook.py,b51c9844a2ac0f25929caffedcbe6800d4299486,6a708363545c1c48d9577461881e5524fbd345eb,TODO: put this in an appropriate place
aaiffhjbd,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,fdab0c9841cb4103f01d149e1c0be77da0a56c8a,760ca925021b4a1e60b0fdcaa047703ec7f012c7,TODO: 'type' is compulsory to pass the guard; but is not very appropriate
aaiffhjbf,OpenMined/PySyft,syft/core/workers/base.py,caaa9b0359b11db284d18511570f348914fe45d4,STILL_EXISTS,TODO: Extend to responses that are iterables.
aaiffhjgj,OpenMined/PySyft,syft/core/workers/base.py,359d14e143eaf7dcce48d4a371df3d6cbc4f95e2,STILL_EXISTS,TODO: Fix the case when response contains only a numeric
aaiffhjhb,OpenMined/PySyft,syft/core/workers/base.py,359d14e143eaf7dcce48d4a371df3d6cbc4f95e2,04fd92d7876b41d893d592fbad958841e58bdfd2,TODO =False ??
aaiffhjhc,OpenMined/PySyft,syft/core/workers/base.py,359d14e143eaf7dcce48d4a371df3d6cbc4f95e2,5364ea11d5fb920b187c70215268a6ea49ef81f1,Probably a LocalTensor (TODO and if not ? or if list ?)
aaiffiaaj,OpenMined/PySyft,syft/core/workers/base.py,5364ea11d5fb920b187c70215268a6ea49ef81f1,STILL_EXISTS,TODO: Extend to responses that are iterables.
aaiffiaba,OpenMined/PySyft,syft/core/workers/base.py,5364ea11d5fb920b187c70215268a6ea49ef81f1,STILL_EXISTS,TODO: Fix the case when response contains only a numeric
aaiffiaed,OpenMined/PySyft,syft/core/frameworks/torch/hook.py,04fd92d7876b41d893d592fbad958841e58bdfd2,c7a9c8e9563fba5594e03c34ae2254e277af6646,TODO Guard
aaiffiafa,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,04fd92d7876b41d893d592fbad958841e58bdfd2,STILL_EXISTS,TODO do it in a smart (and dual?) way
aaiffiahe,OpenMined/PySyft,syft/core/utils.py,04fd92d7876b41d893d592fbad958841e58bdfd2,4b592ede2af40f8b9bf6f745c0382f79d2810d47,TODO: Find a smart way to skip register and not leaking the info to the local worker
aaiffiajd,OpenMined/PySyft,syft/core/workers/base.py,04fd92d7876b41d893d592fbad958841e58bdfd2,STILL_EXISTS,TODO: handle this conversion again if needed
aaiffiajf,OpenMined/PySyft,syft/core/workers/base.py,04fd92d7876b41d893d592fbad958841e58bdfd2,STILL_EXISTS,if(is_binary): # Todo fix type handling
aaiffiaji,OpenMined/PySyft,syft/core/workers/base.py,04fd92d7876b41d893d592fbad958841e58bdfd2,58ce89a5cebe01469f7f1581fd202721a7925416,TODO: Extend to response which is iterable.
aaiffiajj,OpenMined/PySyft,syft/core/workers/base.py,04fd92d7876b41d893d592fbad958841e58bdfd2,STILL_EXISTS,TODO: Extend to responses Variable.
aaiffibbc,OpenMined/PySyft,syft/core/workers/base.py,04fd92d7876b41d893d592fbad958841e58bdfd2,STILL_EXISTS,TODO: Extend to responses that are iterables.
aaiffibgg,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,acd97194c7c74f9567490953610f87ac2a60f3e9,STILL_EXISTS,TOOD: figure out how to avoid this performance waste.
aaiffibhe,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,acd97194c7c74f9567490953610f87ac2a60f3e9,7a003bea94f26db7fe1e0bc922e659950e47954a,ok... now we can deregister it. This is a little bit of a hack but it works
aaiffibhf,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,acd97194c7c74f9567490953610f87ac2a60f3e9,7a003bea94f26db7fe1e0bc922e659950e47954a,TODO: perhaps there's a better strategy for getting access to the local_worker
aaiffibhj,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,acd97194c7c74f9567490953610f87ac2a60f3e9,STILL_EXISTS,TODO .id_at_location
aaiffibia,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,acd97194c7c74f9567490953610f87ac2a60f3e9,d4845c0c0c86cb539c6143674fa069d61f30b416,args = args[1:] # TODO compare to master
aaifficci,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,d4845c0c0c86cb539c6143674fa069d61f30b416,STILL_EXISTS,TODO do it in a smart (and dual?) way
aaifficdf,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,d4845c0c0c86cb539c6143674fa069d61f30b416,STILL_EXISTS,TOOD: figure out how to avoid this performance waste.
aaifficea,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,d4845c0c0c86cb539c6143674fa069d61f30b416,STILL_EXISTS,TODO .id_at_location
aaiffidaf,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,e931b0b7f62744ef7481a5aff30cf9d7a8455fe6,STILL_EXISTS,TODO: Maybe we x.get() should change its id with the id of the remote (but then beware that the registration is properly done); but maybe not.
aaiffidah,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,e931b0b7f62744ef7481a5aff30cf9d7a8455fe6,STILL_EXISTS,TODO: modifying self is maybe not a good practice; but it's the only way I found
aaiffifif,OpenMined/PySyft,syft/core/workers/base.py,48eb8dcdc3e088a3595e6a1a0aa1ce5dd3fb1981,STILL_EXISTS,TODO: Extend to responses that are iterables.
aaiffifig,OpenMined/PySyft,syft/core/workers/base.py,48eb8dcdc3e088a3595e6a1a0aa1ce5dd3fb1981,f5b24fb55dde495c749a08d7f27c69ff1949ce00,TODO: Assign default id more intelligently (low priority)
aaiffigad,OpenMined/PySyft,syft/core/workers/base.py,48eb8dcdc3e088a3595e6a1a0aa1ce5dd3fb1981,d69231a360ab70fa6d1b24a7be80bf39f9025728,TODO: Implement get_owners and refactor to make it prettier
aaiffigaj,OpenMined/PySyft,syft/core/workers/base.py,48eb8dcdc3e088a3595e6a1a0aa1ce5dd3fb1981,f5b24fb55dde495c749a08d7f27c69ff1949ce00,TODO: pass in just the id instead of the entire obj_msg.
aaiffigbh,OpenMined/PySyft,syft/core/frameworks/torch/hook.py,f5b24fb55dde495c749a08d7f27c69ff1949ce00,7a003bea94f26db7fe1e0bc922e659950e47954a,Todo there is a pb with decode because it acquire a Variable in any case (it has a child);
aaiffigdf,OpenMined/PySyft,syft/core/utils.py,f5b24fb55dde495c749a08d7f27c69ff1949ce00,004e40c654be7dbe6c139d02591ea2042f7b3c35,TODO Guard
aaiffigdg,OpenMined/PySyft,syft/core/workers/base.py,f5b24fb55dde495c749a08d7f27c69ff1949ce00,STILL_EXISTS,TODO: handle this conversion again if needed
aaiffigdi,OpenMined/PySyft,syft/core/workers/base.py,f5b24fb55dde495c749a08d7f27c69ff1949ce00,STILL_EXISTS,if(is_binary): # Todo fix type handling
aaiffigec,OpenMined/PySyft,syft/core/workers/base.py,f5b24fb55dde495c749a08d7f27c69ff1949ce00,58ce89a5cebe01469f7f1581fd202721a7925416,TODO: Extend to response which is iterable.
aaiffigge,OpenMined/PySyft,syft/core/workers/base.py,f5b24fb55dde495c749a08d7f27c69ff1949ce00,d69231a360ab70fa6d1b24a7be80bf39f9025728,TODO: Implement get_owners and refactor to make it prettier
aaiffigjd,OpenMined/PySyft,syft/core/workers/base.py,2c82c08c0286e8a347b1c3e6b014f765271e7d34,STILL_EXISTS,TODO: Extend to responses that are iterables.
aaiffigje,OpenMined/PySyft,syft/core/workers/base.py,2c82c08c0286e8a347b1c3e6b014f765271e7d34,004e40c654be7dbe6c139d02591ea2042f7b3c35,TODO: Assign default id more intelligently (low priority)
aaiffihbb,OpenMined/PySyft,syft/core/workers/base.py,2c82c08c0286e8a347b1c3e6b014f765271e7d34,d69231a360ab70fa6d1b24a7be80bf39f9025728,TODO: Implement get_owners and refactor to make it prettier
aaiffihbh,OpenMined/PySyft,syft/core/workers/base.py,2c82c08c0286e8a347b1c3e6b014f765271e7d34,004e40c654be7dbe6c139d02591ea2042f7b3c35,TODO: pass in just the id instead of the entire obj_msg.
aaiffihdh,OpenMined/PySyft,syft/core/workers/base.py,004e40c654be7dbe6c139d02591ea2042f7b3c35,STILL_EXISTS,TODO: handle this conversion again if needed
aaiffihdj,OpenMined/PySyft,syft/core/workers/base.py,004e40c654be7dbe6c139d02591ea2042f7b3c35,STILL_EXISTS,if(is_binary): # Todo fix type handling
aaiffiheg,OpenMined/PySyft,syft/core/workers/base.py,004e40c654be7dbe6c139d02591ea2042f7b3c35,58ce89a5cebe01469f7f1581fd202721a7925416,TODO: Extend to response which is iterable.
aaiffihgi,OpenMined/PySyft,syft/core/workers/base.py,004e40c654be7dbe6c139d02591ea2042f7b3c35,d69231a360ab70fa6d1b24a7be80bf39f9025728,TODO: Implement get_owners and refactor to make it prettier
aaiffihib,OpenMined/PySyft,syft/core/workers/base.py,6f1e36889183d6f336a8c83b76cbdee47dfdbc5f,STILL_EXISTS,TODO: Extend to responses that are iterables.
aaiffihic,OpenMined/PySyft,syft/core/workers/base.py,6f1e36889183d6f336a8c83b76cbdee47dfdbc5f,d69231a360ab70fa6d1b24a7be80bf39f9025728,TODO: Assign default id more intelligently (low priority)
aaiffihjh,OpenMined/PySyft,syft/core/workers/base.py,6f1e36889183d6f336a8c83b76cbdee47dfdbc5f,d69231a360ab70fa6d1b24a7be80bf39f9025728,TODO: Implement get_owners and refactor to make it prettier
aaiffiiad,OpenMined/PySyft,syft/core/workers/base.py,6f1e36889183d6f336a8c83b76cbdee47dfdbc5f,d69231a360ab70fa6d1b24a7be80bf39f9025728,TODO: pass in just the id instead of the entire obj_msg.
aaiffiibb,OpenMined/PySyft,syft/core/workers/base.py,6f1e36889183d6f336a8c83b76cbdee47dfdbc5f,STILL_EXISTS,TODO: handle this conversion again if needed
aaiffiibd,OpenMined/PySyft,syft/core/workers/base.py,6f1e36889183d6f336a8c83b76cbdee47dfdbc5f,STILL_EXISTS,if(is_binary): # Todo fix type handling
aaiffiicj,OpenMined/PySyft,test/torch_test.py,6f1e36889183d6f336a8c83b76cbdee47dfdbc5f,d69231a360ab70fa6d1b24a7be80bf39f9025728,previously; newly created Variable objects would lose their OpenMined given
aaiffjdgb,OpenMined/PySyft,syft/core/utils.py,d69231a360ab70fa6d1b24a7be80bf39f9025728,bcf49b06c05d3b0eda2e8a529a8d014c8e0eb698,TODO It would be good to have a standardized place to put the 'mode' argument
aaiffjdhc,OpenMined/PySyft,syft/core/workers/base.py,d69231a360ab70fa6d1b24a7be80bf39f9025728,58ce89a5cebe01469f7f1581fd202721a7925416,TODO: Extend to response which is iterable.
aaiffjfec,OpenMined/PySyft,test/torch_test.py,d69231a360ab70fa6d1b24a7be80bf39f9025728,STILL_EXISTS,# previously; newly created Variable objects would lose their OpenMined given
aaifgaacd,OpenMined/PySyft,syft/core/utils.py,7a003bea94f26db7fe1e0bc922e659950e47954a,STILL_EXISTS,TODO: Stop with this prior that data should be a dict; which require the following lines to fix with real cases
aaifgabeh,OpenMined/PySyft,syft/core/workers/base.py,88ece7d5d804a4631311f0e88b874e1d00d1d779,STILL_EXISTS,TODO Guard
aaifgabhd,OpenMined/PySyft,syft/core/frameworks/torch/hook.py,58ce89a5cebe01469f7f1581fd202721a7925416,STILL_EXISTS,TODO: This is added because of the following contradiction: instanciate x = FloatTensor(...)
aaifgabia,OpenMined/PySyft,test/torch_test.py,58ce89a5cebe01469f7f1581fd202721a7925416,STILL_EXISTS,TODO: To pass this test; y and x should be connected. What happens right now this that x.abs_()
aaifgadee,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,c7a9c8e9563fba5594e03c34ae2254e277af6646,STILL_EXISTS,TODO: and is an inplace mthod
aaifgadef,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,c7a9c8e9563fba5594e03c34ae2254e277af6646,STILL_EXISTS,TODO specify owner
aaifgadeg,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,c7a9c8e9563fba5594e03c34ae2254e277af6646,6a708363545c1c48d9577461881e5524fbd345eb,TODO: register
aaifgadei,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,c7a9c8e9563fba5594e03c34ae2254e277af6646,STILL_EXISTS,TODO: remove duplicate on base.py
aaifgadfc,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,c7a9c8e9563fba5594e03c34ae2254e277af6646,STILL_EXISTS,TODO : controled registration process
aaifgadfh,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,c7a9c8e9563fba5594e03c34ae2254e277af6646,STILL_EXISTS,and todo is in-place
aaifgadfi,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,c7a9c8e9563fba5594e03c34ae2254e277af6646,STILL_EXISTS,TODO: use the wrapper
aaifgadgg,OpenMined/PySyft,syft/core/workers/base.py,c7a9c8e9563fba5594e03c34ae2254e277af6646,20395214c8f4a7db8d96ba0b43cb17123fa93b09,TODO  self;?
aaifgadgj,OpenMined/PySyft,syft/core/workers/base.py,c7a9c8e9563fba5594e03c34ae2254e277af6646,20395214c8f4a7db8d96ba0b43cb17123fa93b09,ToDO: For LOCAL
aaifgadha,OpenMined/PySyft,syft/core/workers/base.py,c7a9c8e9563fba5594e03c34ae2254e277af6646,20395214c8f4a7db8d96ba0b43cb17123fa93b09,TODO keep it ?
aaifgadjc,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,20395214c8f4a7db8d96ba0b43cb17123fa93b09,b328c45740e181fae25bfe1798d3ec241b39698e,command = eval(command)  # TODO Guard
aaifgaeda,OpenMined/PySyft,syft/core/utils.py,b328c45740e181fae25bfe1798d3ec241b39698e,STILL_EXISTS,TODO
aaifgaegb,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,7f7fc3b1e84eb13119773a4b3cee65052cbde46c,STILL_EXISTS,Ensure that the loop is made; if needed
aaifgaeje,OpenMined/PySyft,syft/core/utils.py,35d08718c032aede0e990718f790f46039c0f180,ecc1157c63db7e18dd45fc36cc243c6d5c6c1dfa,TODO: Stop with this prior that data should be a dict;
aaifgafac,OpenMined/PySyft,syft/core/workers/base.py,35d08718c032aede0e990718f790f46039c0f180,STILL_EXISTS,TODO: instead of looping on the objects;
aaifgafdg,OpenMined/PySyft,syft/core/utils.py,d4f6113fcb00e9db33c166760a8079c84ea671ad,bcf49b06c05d3b0eda2e8a529a8d014c8e0eb698,TODO: Stop with this prior that data should be a dict;
aaifgafgh,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,75cbd988ebd021d7b714d2233dbbc37d2f293b4a,STILL_EXISTS,If we have the original object (TODO: add checks) return it
aaifgafjf,OpenMined/PySyft,syft/core/frameworks/torch/hook.py,bb84497c290bed558fc9a76e7910cb7757f2dbd3,STILL_EXISTS,TODO: May want to handle the ones in \"exclude\" manually at
aaifgaghj,OpenMined/PySyft,syft/core/frameworks/torch/hook.py,ff93adbbe536cd4fb0e5120520b9c745182d8bc3,STILL_EXISTS,retrieve the var to fix
aaifgahad,OpenMined/PySyft,test/torch_test.py,ff93adbbe536cd4fb0e5120520b9c745182d8bc3,STILL_EXISTS,then this is a TODO!
aaifgaibd,OpenMined/PySyft,test/torch_test.py,bb57ff8964dcb5b2388ea592b8f41ad652bbaa69,STILL_EXISTS,then this is a TODO!
aaifgajbh,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,cba5eabb1bdf3b403305b59c6c25a0bb4865d7c0,STILL_EXISTS,TODO: Find a smart way to skip register and not leaking the info to the local worker
aaifgajfg,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,6a708363545c1c48d9577461881e5524fbd345eb,STILL_EXISTS,TODO: same for grad ?
aaifgajfh,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,6a708363545c1c48d9577461881e5524fbd345eb,94f5d501187311a5fb8642892784f09a197804bf,TODO : control registration process
aaifgajjc,OpenMined/PySyft,syft/core/frameworks/torch/encode.py,bcf49b06c05d3b0eda2e8a529a8d014c8e0eb698,STILL_EXISTS,TODO It would be good to have a standardized place to put the 'mode' argument
aaifgajjf,OpenMined/PySyft,syft/core/frameworks/torch/encode.py,bcf49b06c05d3b0eda2e8a529a8d014c8e0eb698,STILL_EXISTS,TODO: Stop with this prior that data should be a dict;
aaifgbaae,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,bcf49b06c05d3b0eda2e8a529a8d014c8e0eb698,STILL_EXISTS,Init new remote ids if needed
aaifgbacg,OpenMined/PySyft,syft/core/frameworks/torch/utils.py,bcf49b06c05d3b0eda2e8a529a8d014c8e0eb698,STILL_EXISTS,TODO Will break with PyTorch >= 0.4
aaifgbbgi,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,cc2b2fe176483eb1c1ef6d89af82395a577a3aa3,STILL_EXISTS,If we have retrieved an already existing object (TODO: add checks) then return it
aaifgbbgj,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,cc2b2fe176483eb1c1ef6d89af82395a577a3aa3,STILL_EXISTS,TODO: Find a smart way to skip register and not leaking the info to the local worker
aaifgbbhb,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,cc2b2fe176483eb1c1ef6d89af82395a577a3aa3,STILL_EXISTS,Ensure that the loop is made; if needed
aaifgbbjg,OpenMined/PySyft,syft/core/workers/base.py,49102dabe6a080d80dd2487de25247d574897b4f,STILL_EXISTS,TODO: Extend to responses that are iterables.
aaifgbbjh,OpenMined/PySyft,syft/core/workers/base.py,49102dabe6a080d80dd2487de25247d574897b4f,STILL_EXISTS,TODO: Fix the case when response contains only a numeric
aaifgbbji,OpenMined/PySyft,syft/core/workers/base.py,49102dabe6a080d80dd2487de25247d574897b4f,89f308370013dd1f4d0074b26dd0d55bbc2a198f,TODO: Assign default id more intelligently (low priority)
aaifgbcbd,OpenMined/PySyft,syft/core/workers/base.py,49102dabe6a080d80dd2487de25247d574897b4f,b054679fb210604994b3ade333549d3a167cae15,TODO: Implement get_owners and refactor to make it prettier
aaifgbcbi,OpenMined/PySyft,syft/core/workers/base.py,49102dabe6a080d80dd2487de25247d574897b4f,STILL_EXISTS,TODO: pass in just the id instead of the entire obj_msg.
aaifgbdcc,OpenMined/PySyft,test/core/utils_test.py,61480901508950581c895fddaa91fa013d1e8915,48059601bd4b0eccc9c73db94cf759cd807233fe,{'__tuple__':}; TODO
aaifgbdcd,OpenMined/PySyft,test/core/utils_test.py,61480901508950581c895fddaa91fa013d1e8915,48059601bd4b0eccc9c73db94cf759cd807233fe,{'__set__':}; TODO
aaifgbdcf,OpenMined/PySyft,test/core/utils_test.py,61480901508950581c895fddaa91fa013d1e8915,48059601bd4b0eccc9c73db94cf759cd807233fe,{'__range__': }; TODO
aaifgbdcg,OpenMined/PySyft,test/core/utils_test.py,61480901508950581c895fddaa91fa013d1e8915,48059601bd4b0eccc9c73db94cf759cd807233fe,{'__slice__': }  TODO
aaifgbdeb,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,89f308370013dd1f4d0074b26dd0d55bbc2a198f,7dd58bf2f47864c2b5b1b32bd49d7f63ff6c441e,Add the remote location worker key if needed
aaifgbdig,OpenMined/PySyft,test/torch_test.py,8c2c70f756b8c32c0125d8933958a390c40527a8,STILL_EXISTS,then this is a TODO!
aaifgbedj,OpenMined/PySyft,syft/core/frameworks/torch/utils.py,8cad60095841c6994aeb98bd480a8da362651bbd,STILL_EXISTS,TODO: What are the risks due to this assimilation? (see usage @ torch\/utils.py l.74)
aaifgbeea,OpenMined/PySyft,syft/core/frameworks/torch/utils.py,8cad60095841c6994aeb98bd480a8da362651bbd,STILL_EXISTS,TODO: should allow to mix Variable and Parameter in next_child_types
aaifgbgff,OpenMined/PySyft,syft/core/workers/base.py,182991f9ca939ed04451aafc11a1b2c868401700,b054679fb210604994b3ade333549d3a167cae15,TODO: Implement get_owners and refactor to make it prettier
aaifgccjg,OpenMined/PySyft,syft/core/workers/base.py,67b850bf7646100852d7898fb27c3b241183e9c3,STILL_EXISTS,is experimental functionality but seems to solve the symptoms we
aaifgcdbi,OpenMined/PySyft,syft/core/workers/base.py,67b850bf7646100852d7898fb27c3b241183e9c3,STILL_EXISTS,it does not exist. As a result; we've needed to store objects temporarily
aaifgcdbj,OpenMined/PySyft,syft/core/workers/base.py,67b850bf7646100852d7898fb27c3b241183e9c3,STILL_EXISTS,in self._tmpobjects which seems to fix it. Super strange bug which took
aaifgcdcb,OpenMined/PySyft,syft/core/workers/base.py,67b850bf7646100852d7898fb27c3b241183e9c3,STILL_EXISTS,workaround seems to work well for now. Anyway; so we need to return a cleanup
aaifgcehb,OpenMined/PySyft,test/core/utils_test.py,48059601bd4b0eccc9c73db94cf759cd807233fe,STILL_EXISTS,#{'__tuple__':}; TODO
aaifgcehc,OpenMined/PySyft,test/core/utils_test.py,48059601bd4b0eccc9c73db94cf759cd807233fe,STILL_EXISTS,#{'__set__':}; TODO
aaifgcehe,OpenMined/PySyft,test/core/utils_test.py,48059601bd4b0eccc9c73db94cf759cd807233fe,STILL_EXISTS,#{'__range__': }; TODO
aaifgcehf,OpenMined/PySyft,test/core/utils_test.py,48059601bd4b0eccc9c73db94cf759cd807233fe,STILL_EXISTS,#{'__slice__': }  TODO
aaifgcicc,OpenMined/PySyft,syft/core/frameworks/encode.py,beb0adfda2ba0f7d9a93a16f64fcddd5dc3cc969,STILL_EXISTS,TODO: Plan C should never be called - but is used extensively in PySyft's PyTorch integratio
aaifgcieb,OpenMined/PySyft,syft/core/frameworks/numpy/ndarray.py,592f46861e188e90d44a556b2939d9149b83449b,STILL_EXISTS,Remove this pointer - TODO: call deregister function instead of doing it by hand
aaifgcigd,OpenMined/PySyft,syft/core/frameworks/torch/utils.py,e77252e5f2221a638a2d43a336e39082dfe81ce6,STILL_EXISTS,TODO: this seems hard coded - likely breaks when using
aaifgcigg,OpenMined/PySyft,syft/core/frameworks/torch/utils.py,e77252e5f2221a638a2d43a336e39082dfe81ce6,27630ba76a986c3db3f07cc4488508a9d495cccd,Torch is circular this creates an infinite recursion. TODO: fix after Torch 1.0
aaifgcjac,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,8d82fbf05553df57d7e14c70e9d4e1689738710a,dd832bc7736ebc756926b3d58ced9da41ac75fb3,TODO: @trask @theo could you take a look at this if you have better ideas on how to get these parameters
aaifgcjdg,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,dc52872630ec3c7b8284948e74e19f5002d220f4,dd832bc7736ebc756926b3d58ced9da41ac75fb3,TODO: @trask @theo could you take a look at this if you have better ideas on how to get these parameters
aaifgcjdh,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,dc52872630ec3c7b8284948e74e19f5002d220f4,STILL_EXISTS,Todo: Add a generic child dependign on a torch_type
aaifgcjea,OpenMined/PySyft,syft/core/frameworks/torch/utils.py,341c5304064797a6a071bcd1f762b5f8d2bd2013,STILL_EXISTS,TODO: See Issue #1480
aaifgcjef,OpenMined/PySyft,syft/core/frameworks/torch/mpc_tensor.py,9d020f37a4e3b8e7f5830819800e832b8726063a,dae4a309df998c852c333c493fa4c4a3e111d2ad,Fixme: remove the share on init; declaring a MPCTensor should autmatically create a _GeneralizedPointerTensor
aaifgcjei,OpenMined/PySyft,test/torch_test.py,0a506107ff57dc2caff0cdfd20880d25446584eb,5b4e03b10e5305761f517c35da4c327ae0e43f8c,self.mpc_sum(3; -5) TODO: Future work: how to handle gracefully minus numbers
aaifgcjej,OpenMined/PySyft,test/torch_test.py,0a506107ff57dc2caff0cdfd20880d25446584eb,5b4e03b10e5305761f517c35da4c327ae0e43f8c,self.mpc_mul(5; -5) TODO: Future work: how to handle gracefully minus numbers
aaifgcjhh,OpenMined/PySyft,syft/core/frameworks/torch/utils.py,049a38e185e2154a1c4acf4bf0b975a5ec926403,27630ba76a986c3db3f07cc4488508a9d495cccd,Torch is circular this creates an infinite recursion. TODO: fix after Torch 1.0
aaifgcjia,OpenMined/PySyft,syft/core/frameworks/torch/utils.py,049a38e185e2154a1c4acf4bf0b975a5ec926403,STILL_EXISTS,TODO: See Issue #1480
aaifgdajg,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,acc59e0abae60e79d6f4c1db016f2506239e850e,STILL_EXISTS,TODO: @trask @theo could you take a look at this if you have better ideas on how to get these parameters
aaifgdajh,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,acc59e0abae60e79d6f4c1db016f2506239e850e,STILL_EXISTS,Fixme: Add a generic child depending on a torch_type
aaifgdaji,OpenMined/PySyft,syft/core/frameworks/torch/utils.py,acc59e0abae60e79d6f4c1db016f2506239e850e,STILL_EXISTS,TODO: See Issue #1480
aaifgdbad,OpenMined/PySyft,syft/core/frameworks/torch/generalized_pointer.py,dae4a309df998c852c333c493fa4c4a3e111d2ad,STILL_EXISTS,TODO: @trask @theo could you take a look at this if you have better ideas on how to get these parameters
aaifgdbae,OpenMined/PySyft,syft/core/frameworks/torch/generalized_pointer.py,dae4a309df998c852c333c493fa4c4a3e111d2ad,STILL_EXISTS,Fixme: Add a generic child depending on a torch_type
aaifgdbbb,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,dae4a309df998c852c333c493fa4c4a3e111d2ad,STILL_EXISTS,Fixme: remove the share on init; declaring a MPCTensor should autmatically create a _GeneralizedPointerTensor
aaifgdbbh,OpenMined/PySyft,syft/core/frameworks/torch/tensor/generalized_pointer_tensor.py,c832fe8fd8cbb0dfe4858b92f81210c17512ef49,STILL_EXISTS,TODO: @trask @theo could you take a look at this if you have better ideas on how to get these parameters
aaifgdbbi,OpenMined/PySyft,syft/core/frameworks/torch/tensor/generalized_pointer_tensor.py,c832fe8fd8cbb0dfe4858b92f81210c17512ef49,STILL_EXISTS,Fixme: Add a generic child depending on a torch_type
aaifgdbbj,OpenMined/PySyft,syft/core/frameworks/torch/tensor/local_tensor.py,c832fe8fd8cbb0dfe4858b92f81210c17512ef49,STILL_EXISTS,TODO : control registration process
aaifgdbdc,OpenMined/PySyft,syft/core/frameworks/torch/tensor/mpc_tensor.py,c832fe8fd8cbb0dfe4858b92f81210c17512ef49,STILL_EXISTS,Fixme: remove the share on init; declaring a MPCTensor should autmatically create a _GeneralizedPointerTensor
aaifgdbed,OpenMined/PySyft,syft/core/frameworks/torch/tensor/pointer_tensor.py,c832fe8fd8cbb0dfe4858b92f81210c17512ef49,STILL_EXISTS,Add the remote location worker key if needed
aaifgdbfd,OpenMined/PySyft,syft/core/frameworks/torch/tensor/pointer_tensor.py,c832fe8fd8cbb0dfe4858b92f81210c17512ef49,STILL_EXISTS,Remove this pointer - TODO: call deregister function instead of doing it by hand
aaifgdbid,OpenMined/PySyft,syft/core/frameworks/torch/tensor/syft_tensor.py,c832fe8fd8cbb0dfe4858b92f81210c17512ef49,STILL_EXISTS,TODO: same for grad ?
aaifgdbie,OpenMined/PySyft,syft/core/frameworks/torch/tensor/torch_tensor.py,c832fe8fd8cbb0dfe4858b92f81210c17512ef49,STILL_EXISTS,If we have retrieved an already existing object (TODO: add checks) then return it
aaifgdbif,OpenMined/PySyft,syft/core/frameworks/torch/tensor/torch_tensor.py,c832fe8fd8cbb0dfe4858b92f81210c17512ef49,STILL_EXISTS,TODO: Find a smart way to skip register and not leaking the info to the local worker
aaifgdbih,OpenMined/PySyft,syft/core/frameworks/torch/tensor/torch_tensor.py,c832fe8fd8cbb0dfe4858b92f81210c17512ef49,STILL_EXISTS,Ensure that the loop is made; if needed
aaifgdcag,OpenMined/PySyft,syft/core/frameworks/torch/tensor/torch_variable.py,c832fe8fd8cbb0dfe4858b92f81210c17512ef49,STILL_EXISTS,Init new remote ids if needed
aaifgdccf,OpenMined/PySyft,syft/core/frameworks/torch/tensor/torch_variable.py,c832fe8fd8cbb0dfe4858b92f81210c17512ef49,STILL_EXISTS,TODO: Find a smart way to skip register and not leaking the info to the local worker
aaifgdjbf,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,7f5da7d3cdea24232b342119426b8745ed5645b3,STILL_EXISTS,TODO: deregister_ptr doesn't work
aaifgdjdc,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,60d785138f16996474580842adedff0f1b39e19b,e3004c3769544edb66df240284753364fc43081a,TODO: value per value
aaifgdjjj,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,27afe9aa213210cea97d6b73077041aa00a61f60,ca2c06af18a984cf898b35014194d4a9b11d92b3,TODO: value per value
aaifgecfh,OpenMined/PySyft,test/torch_test.py,ae4edd3b7503599384125b7bf6f37243a59bd5f6,STILL_EXISTS,TODO: figure out why some machines prefer one of these options
aaifgecgg,OpenMined/PySyft,syft/mpc/spdz.py,cbb4d383d2641aee8be3689d603d809149ca9b0b,STILL_EXISTS,TODO: explore this
aaifgedai,OpenMined/PySyft,syft/differential_privacy/pate.py,d0a712ac4398eb29552ae0bdb4f722c6140003de,STILL_EXISTS,Maybe download data and retrieve local storage urls
aaifgedfg,OpenMined/PySyft,syft/differential_privacy/pate.py,d0a712ac4398eb29552ae0bdb4f722c6140003de,STILL_EXISTS,Store unused part of test set for use as a test set after student training
aaifgedjj,OpenMined/PySyft,syft/differential_privacy/pate.py,424be2ba1fd77a5ffe6ac749fa2d2eaf690a67f0,4258676cd8c0144e7b2b99d1421851b86deb1766,def create_dir_if_needed(dest_directory):
aaifgeebh,OpenMined/PySyft,syft/differential_privacy/pate.py,424be2ba1fd77a5ffe6ac749fa2d2eaf690a67f0,4258676cd8c0144e7b2b99d1421851b86deb1766,assert create_dir_if_needed(directory)
aaifgfcai,OpenMined/PySyft,syft/core/workers/base.py,0fccded9a5c47eff56332e9c2687f1bc3ede322e,STILL_EXISTS,TODO: send a \"successful\" or \"not successful\" response?
aaifgfcdh,OpenMined/PySyft,syft/mpc/securenn.py,e0648308b26d4850f8a5765738f8f27b4cfd3067,2b07a4b1bd9a25b6de4d3fb873ac6570ca49cc3c,67 in original # TODO: extend to ops over multiple rings
aaifgfcdi,OpenMined/PySyft,syft/mpc/securenn.py,e0648308b26d4850f8a5765738f8f27b4cfd3067,ba47d3c55f0f15e8b7114d7310a5291839b2cdba,TODO: recombine c properly here
aaifgfcec,OpenMined/PySyft,syft/mpc/securenn.py,e0648308b26d4850f8a5765738f8f27b4cfd3067,6c9d75425f4ef0b908595ed0584a410d6545e13b,FIXME: implement share convert protocol
aaifgfced,OpenMined/PySyft,syft/mpc/securenn.py,e0648308b26d4850f8a5765738f8f27b4cfd3067,6c9d75425f4ef0b908595ed0584a410d6545e13b,FIXME: the conditional on the right should include a plaintext add of x and y;
aaifgfcef,OpenMined/PySyft,syft/mpc/securenn.py,e0648308b26d4850f8a5765738f8f27b4cfd3067,STILL_EXISTS,needed to complete share_convert
aaifgfcej,OpenMined/PySyft,syft/mpc/securenn.py,e0648308b26d4850f8a5765738f8f27b4cfd3067,64bc289c919472539e2bdaa0952b2f451fbd5d3a,FIXME: double check if keepdim should be True\/False here
aaifgfddc,OpenMined/PySyft,syft/mpc/securenn.py,407fde281b43c8d069c97780c73937e57bed0776,2b07a4b1bd9a25b6de4d3fb873ac6570ca49cc3c,FIXME: generate_zero_shares_communication should be updated with new pysyft API
aaifgfdef,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,1655e6ecb4fbfa3bfe69b88ab58605265ea35572,STILL_EXISTS,TODO: Should fix_me be an inplace op?
aaifgfdgb,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,2d9032adbdca61eae89e532b09d39128578e4892,STILL_EXISTS,FIXME or sy.Variable
aaifgfdhh,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,7e4c48eb73f8573e4955d58576ae183d97417fe0,STILL_EXISTS,TODO: Doublon with the new functionality send(*worker)
aaifgfdjc,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,225b179caf48de0eb93f78624c574ccdf9d519fd,STILL_EXISTS,TODO: Add this thing for negative values
aaifgfeag,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,fd836005d89d3204fe299e96846ab0beff1383c8,STILL_EXISTS,TODO: and the grad ?
aaifgfeah,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,fd836005d89d3204fe299e96846ab0beff1383c8,STILL_EXISTS,FIXME: is it necessary?
aaifgfebg,OpenMined/PySyft,syft/mpc/securenn.py,ba47d3c55f0f15e8b7114d7310a5291839b2cdba,6c9d75425f4ef0b908595ed0584a410d6545e13b,FIXME: implement share convert protocol
aaifgfebj,OpenMined/PySyft,syft/mpc/securenn.py,c171f57a56004c2a1385b04d93e01af9ec6c5f09,6c9d75425f4ef0b908595ed0584a410d6545e13b,TODO: remove this line when public-private addition works
aaifgfecc,OpenMined/PySyft,syft/core/frameworks/pandas/utils.py,98e23c5b2e11b79dd3ca1da52c24ae5b53e04410,STILL_EXISTS,TODO rangeindex; multiindex; CategoricalIndex; IntervalIndex on return function
aaifgfegc,OpenMined/PySyft,syft/mpc/securenn.py,a451e5f4eb57b2479003f66b90570daea6dfff21,STILL_EXISTS,TODO: make this secure by exchanging shares remotely
aaifgfehd,OpenMined/PySyft,syft/mpc/securenn.py,a451e5f4eb57b2479003f66b90570daea6dfff21,6c9d75425f4ef0b908595ed0584a410d6545e13b,so coerces the shares to an odd ring if needed (FIXME)
aaifgffbj,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,e8a49357334abefcf9c6ce52b718be69fa9c7727,5e0f7507919c9c2a2cdee8bdd8aa71b9fa5852c0,FIXME:
aaifgfffc,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,547e472bfde9e68c5c0b7e7e813c65d710e406a1,STILL_EXISTS,TODO: and the grad ?
aaifgffid,OpenMined/PySyft,syft/mpc/securenn.py,c3b36ea0146bc5274a6bf3771bba10b776ca4400,7f672ef9b5cfa9cd84a955bc75a1af8beb69acc6,TODO: recombine c properly here
aaifgffih,OpenMined/PySyft,syft/mpc/securenn.py,c3b36ea0146bc5274a6bf3771bba10b776ca4400,7f672ef9b5cfa9cd84a955bc75a1af8beb69acc6,FIXME: implement share convert protocol
aaifgffii,OpenMined/PySyft,syft/mpc/securenn.py,c3b36ea0146bc5274a6bf3771bba10b776ca4400,7f672ef9b5cfa9cd84a955bc75a1af8beb69acc6,FIXME: the conditional on the right should include a plaintext add of x and y;
aaifgffja,OpenMined/PySyft,syft/mpc/securenn.py,c3b36ea0146bc5274a6bf3771bba10b776ca4400,7f672ef9b5cfa9cd84a955bc75a1af8beb69acc6,needed for share_convert
aaifgfgcd,OpenMined/PySyft,syft/mpc/securenn.py,7f672ef9b5cfa9cd84a955bc75a1af8beb69acc6,STILL_EXISTS,.send(bob; alice) #TODO: make this secure by exchanging shares remotely
aaifgfgeb,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,5e0f7507919c9c2a2cdee8bdd8aa71b9fa5852c0,STILL_EXISTS,TODO: Should fix_me be an inplace op?
aaifgfiea,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,3144835388806ca5db23d31c88c526fcd74c71f1,b2619e6aa102877155ef1f88a97c5bfa74e6fc5d,FIXME:
aaifgfiee,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,3144835388806ca5db23d31c88c526fcd74c71f1,STILL_EXISTS,TODO: and the grad ?
aaifgfifj,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,b2619e6aa102877155ef1f88a97c5bfa74e6fc5d,STILL_EXISTS,TODO: Should fix_me be an inplace op?
aaifgfiic,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,94850790110fce86a96cf0ab317b0f4718ff18a5,STILL_EXISTS,getting the dimension of the tensor which prod will be applied to. (needed for fixing
aaifgfijj,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,6f11059face276befc01f5fd30b6780901eae015,4e5728450f08e0fb10b41882b6d0ddc80ce97a89,FIXME:
aaifgfjac,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,6f11059face276befc01f5fd30b6780901eae015,STILL_EXISTS,TODO: and the grad ?
aaifgfjdg,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,e0d922056a4dd9c063e58e718393e835c1b9e8c0,STILL_EXISTS,getting the dimension of the tensor which prod will be applied to. (needed for fixing
aaifgfjhj,OpenMined/PySyft,syft/core/workers/base.py,fb60c1ad733f1de4ef587a90b2adb7c1dce64c8f,STILL_EXISTS,TODO: fix this properly: don't wrap the same way if syft or Variable
aaifggbff,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,64f93b5f573e31223d4f64baffe14e420699609d,STILL_EXISTS,TODO: calculating the inverse every time is stupid slow - but i need to keep moving
aaifggehf,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,92901387678318831f0159cc8ec6a629d6c1658d,24b051c8eaddf64c59632a6b8e00ad4dc7942194,time to fix. TODO: optimize the crap out of this
aaifggeij,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,f425c51ae4bcadefdcc5782103bbb443cb7bd5c5,24b051c8eaddf64c59632a6b8e00ad4dc7942194,time to fix. TODO: optimize the crap out of this
aaifggfaf,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,10954ca34a8b11d6b57dd293a3d1615400fa5ede,STILL_EXISTS,time to fix. TODO: optimize the crap out of this
aaifggfde,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,8a1d25f9fc04e795a7cd0db2da6c7933cb9e13c3,7dd58bf2f47864c2b5b1b32bd49d7f63ff6c441e,Add the remote location worker key if needed
aaifggfee,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,8a1d25f9fc04e795a7cd0db2da6c7933cb9e13c3,STILL_EXISTS,time to fix. TODO: optimize the crap out of this
aaifggfgi,OpenMined/PySyft,syft/core/frameworks/torch/hook.py,7fcbd0bf4ef46cc526f6e8529d711afbd3427e72,bc7c355674c5390c60702d21b7bd7af5eef60a6f,FIXME: this doesn't work since putting owner='...' => RuntimeError: torch.FloatTensor constructor doesn't accept any keyword arguments
aaifggfhb,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,7fcbd0bf4ef46cc526f6e8529d711afbd3427e72,STILL_EXISTS,time to fix. TODO: optimize the crap out of this
aaifgggag,OpenMined/PySyft,syft/core/frameworks/torch/hook.py,1921efeeda2c7b0bf93f17a33ddf59f8020fa653,a385028bcc91d0185f27e360b6a36b7f7944f359,FIXME: this doesn't work since putting owner='...' => RuntimeError: torch.FloatTensor constructor doesn't accept any keyword arguments
aaifgggbd,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,1921efeeda2c7b0bf93f17a33ddf59f8020fa653,STILL_EXISTS,time to fix. TODO: optimize the crap out of this
aaifgggbe,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,4036adcac0ea20d702d5f9eee314a188b2e297d2,STILL_EXISTS,TODO: and the grad ?
aaifgggcb,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,f280663868db7afb23840d94e8ee346cf2f75798,STILL_EXISTS,TODO: and the grad ?
aaifgggcd,OpenMined/PySyft,syft/__init__.py,6502c6ff888a60a792dedaed533fe7828cced786,11be8ddb485a2ec7fce07bfca665a6fba64d08f7,TODO: figure out how to let this be hooked here so that it happens
aaifgggda,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,a5f5c76b76885ad41d21ebfbf291b798868562a7,c12758d29ee281fb6607befd9b5ca113e8116dbe,todo: fix this in link_var_chain_to_data_and_grad_chains
aaifgggee,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,ba75d3563d32add3dc992989c22a3ed6ce2e5090,c12758d29ee281fb6607befd9b5ca113e8116dbe,torch_utils.fix_chain_ends(self)
aaifgggeh,OpenMined/PySyft,syft/core/frameworks/torch/utils.py,ba75d3563d32add3dc992989c22a3ed6ce2e5090,fa4d9762d965cfff70171f788d2732d77279703b,Fix parents
aaifgggfc,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,a403da924e69ac5aad7d6e89bdcc456dc9efa871,STILL_EXISTS,todo: fix this in link_var_chain_to_data_and_grad_chains
aaifgggfe,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,a403da924e69ac5aad7d6e89bdcc456dc9efa871,4818f5b791363e1332e67660ab25e478a416438f,Fix chain ends and links between chains
aaifggghb,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,4818f5b791363e1332e67660ab25e478a416438f,STILL_EXISTS,Ensure that the loop is made; if needed
aaifggghj,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,8d078978bb2afc7b5abcd3691568878d2b947842,STILL_EXISTS,todo: fix this in link_var_chain_to_data_and_grad_chains
aaifgggib,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,8d078978bb2afc7b5abcd3691568878d2b947842,STILL_EXISTS,Fix chain ends and links between chains
aaifgggie,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,c820514e445db2bc06b5593b0a808c2066157101,STILL_EXISTS,TODO: calculating the inverse every time is stupid slow - but i need to keep moving
aaifgggif,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,c820514e445db2bc06b5593b0a808c2066157101,STILL_EXISTS,Ensure that the loop is made; if needed
aaifgggij,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,c820514e445db2bc06b5593b0a808c2066157101,STILL_EXISTS,todo: fix this in link_var_chain_to_data_and_grad_chains
aaifgggjb,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,c820514e445db2bc06b5593b0a808c2066157101,STILL_EXISTS,Fix chain ends and links between chains
aaifgggji,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,56f3b70a180b1af84a118512bc16837b1fc6fc94,STILL_EXISTS,TODO: calculating the inverse every time is stupid slow - but i need to keep moving
aaifgghai,OpenMined/PySyft,syft/core/frameworks/torch/hook.py,fa4d9762d965cfff70171f788d2732d77279703b,8876fa363b891866a6c5f1eec5e8d208146f979d,FIXME: this doesn't work since putting owner='...' => RuntimeError: torch.FloatTensor constructor doesn't accept any keyword arguments
aaifgghbc,OpenMined/PySyft,syft/core/frameworks/torch/utils.py,fa4d9762d965cfff70171f788d2732d77279703b,STILL_EXISTS,Torch is circular this creates an infinite recursion. TODO: fix after Torch 1.0
aaifgghde,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,8876fa363b891866a6c5f1eec5e8d208146f979d,STILL_EXISTS,TODO: calculating the inverse every time is stupid slow - but i need to keep moving
aaifgghdf,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,8876fa363b891866a6c5f1eec5e8d208146f979d,STILL_EXISTS,todo: fix this in link_var_chain_to_data_and_grad_chains
aaifgghdh,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,8876fa363b891866a6c5f1eec5e8d208146f979d,STILL_EXISTS,Fix chain ends and links between chains
aaifgghei,OpenMined/PySyft,syft/core/frameworks/torch/utils.py,8876fa363b891866a6c5f1eec5e8d208146f979d,STILL_EXISTS,Fix parents
aaifgghfb,OpenMined/PySyft,syft/core/frameworks/torch/utils.py,8876fa363b891866a6c5f1eec5e8d208146f979d,STILL_EXISTS,Torch is circular this creates an infinite recursion. TODO: fix after Torch 1.0
aaifgghgb,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,d05b5ff48500c0968e762019ee918dcd289ae4c5,STILL_EXISTS,Ensure that the loop is made; if needed
aaifgghge,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,0565e09789fc789a3419d8f2b7d7e04113616d9a,STILL_EXISTS,TODO: calculating the inverse every time is stupid slow - but i need to keep moving
aaifgghgf,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,0565e09789fc789a3419d8f2b7d7e04113616d9a,STILL_EXISTS,todo: fix this in link_var_chain_to_data_and_grad_chains
aaifgghgh,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,0565e09789fc789a3419d8f2b7d7e04113616d9a,STILL_EXISTS,Fix chain ends and links between chains
aaifgghhb,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,0565e09789fc789a3419d8f2b7d7e04113616d9a,STILL_EXISTS,Ensure that the loop is made; if needed
aaifgghhe,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,6df46955f2ad68c0bc6200e7f1e84eb285f34d27,STILL_EXISTS,TODO: calculating the inverse every time is stupid slow - but i need to keep moving
aaifgghia,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,a0c8d7b4caf46f6cea08876a7180d194c0370729,STILL_EXISTS,not needed: torch_utils.is_syft_tensor(child):
aaifggiag,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,2cf27636e8b7d786a4ae3aba600eff764eb7ec4d,STILL_EXISTS,FIXME: this check is only here because when you bring back a GenPtrTensor from remote;
aaifggibh,OpenMined/PySyft,syft/core/frameworks/torch/utils.py,1189107b83a28569e9b76a0c32584c2e203a38c6,STILL_EXISTS,FIXME: This shouldn't be here
aaifggibi,OpenMined/PySyft,syft/core/workers/base.py,1189107b83a28569e9b76a0c32584c2e203a38c6,STILL_EXISTS,TODO: rm .data; .grad; .grad.data if any
aaifggibj,OpenMined/PySyft,syft/core/workers/base.py,1189107b83a28569e9b76a0c32584c2e203a38c6,STILL_EXISTS,TODO: rm .grad; .grad.data if any
aaifggidb,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,94f5d501187311a5fb8642892784f09a197804bf,STILL_EXISTS,TODO: calculating the inverse every time is stupid slow - but i need to keep moving
aaifggidc,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,94f5d501187311a5fb8642892784f09a197804bf,STILL_EXISTS,If we have retrieved an already existing object (TODO: add checks) then return it
aaifggieh,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,94f5d501187311a5fb8642892784f09a197804bf,STILL_EXISTS,Fix chain ends and links between chains
aaifggifg,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,171a63a4a74996644586823a108b924ebc05c515,STILL_EXISTS,not needed: torch_utils.is_syft_tensor(child):
aaifggiga,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,171a63a4a74996644586823a108b924ebc05c515,19d6c91f58f7e2cd53504f7eac4ffce6163a464d,TODO : control registration process
aaifggige,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,171a63a4a74996644586823a108b924ebc05c515,STILL_EXISTS,FIXME: this check is only here because when you bring back a GenPtrTensor from remote;
aaifggijd,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,19d6c91f58f7e2cd53504f7eac4ffce6163a464d,STILL_EXISTS,TODO: calculating the inverse every time is stupid slow - but i need to keep moving
aaifggije,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,19d6c91f58f7e2cd53504f7eac4ffce6163a464d,STILL_EXISTS,If we have retrieved an already existing object (TODO: add checks) then return it
aaifggjaj,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,19d6c91f58f7e2cd53504f7eac4ffce6163a464d,STILL_EXISTS,Fix chain ends and links between chains
aaifggjba,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,19d6c91f58f7e2cd53504f7eac4ffce6163a464d,STILL_EXISTS,not needed: torch_utils.is_syft_tensor(child):
aaifggjbi,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,19d6c91f58f7e2cd53504f7eac4ffce6163a464d,STILL_EXISTS,FIXME: this check is only here because when you bring back a GenPtrTensor from remote;
aaifggjfh,OpenMined/PySyft,syft/core/frameworks/torch/utils.py,6e36407a24c51a59ac8837d096e878615f74aa7b,STILL_EXISTS,FIXME: This shouldn't be here
aaifggjfi,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,9dc971cbac045cf88fc157dbf0185093a51d1a67,STILL_EXISTS,not needed: torch_utils.is_syft_tensor(child):
aaifggjge,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,9dc971cbac045cf88fc157dbf0185093a51d1a67,STILL_EXISTS,FIXME: this check is only here because when you bring back a GenPtrTensor from remote;
aaifghabc,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,b919457cc825e4db5c8194805ca30148009672db,b9f2aca80ed6c36eb55e4d9157401e25cd336909,Fixme: remove the share on init;
aaifghccc,OpenMined/PySyft,syft/serde.py,dc7356580670427ca23ebaef3189c087ec439517,STILL_EXISTS,\"\"\" || This file exists to provide one common place for all serialization to occur || regardless of framework. As msgpack only supports basic types and binary formats || every type must be first be converted to one of these types. Thus; we've split our || functionality into two sections. || \"\"\"
aaifghcdj,OpenMined/PySyft,syft/serde.py,8634f5fc255f4487456ba95a16335302e7b3db92,STILL_EXISTS,\"\"\" || This file exists to provide one common place for all serialization to occur || regardless of framework. As msgpack only supports basic types and binary formats || every type must be first be converted to one of these types. Thus; we've split our || functionality into two sections. || \"\"\"
aaifghehf,OpenMined/PySyft,syft/serde.py,533caf481ae4af83a0b519bca0210db3ab1da5d5,STILL_EXISTS,needed as otherwise we will get: module 'lz4' has no attribute 'frame'
aaifghfig,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,b9f2aca80ed6c36eb55e4d9157401e25cd336909,STILL_EXISTS,FIXME: this check is only here because when you bring back a GenPtrTensor from remote;
aaifghgag,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,7b3e5eb7d803aeaf3f4130ef349300bf21748091,STILL_EXISTS,Fixme: remove the share on init;
aaifghgba,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,7b3e5eb7d803aeaf3f4130ef349300bf21748091,STILL_EXISTS,FIXME: this check is only here because when you bring back a GenPtrTensor from remote;
aaifghgbg,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,157e09843f84ef0a622754b180a3880a3c61cc75,STILL_EXISTS,FIXME: this check is only here because when you bring back a GenPtrTensor from remote;
aaifghgci,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,157e09843f84ef0a622754b180a3880a3c61cc75,STILL_EXISTS,Fixme: remove the share on init;
aaifghgdi,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,cce7ef0ff81d71630b96ff8f8fd5fab6198f9355,STILL_EXISTS,Fixme: remove the share on init;
aaifghgee,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,cce7ef0ff81d71630b96ff8f8fd5fab6198f9355,STILL_EXISTS,FIXME: this check is only here because when you bring back a GenPtrTensor from remote;
aaifghgfc,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,ae37f9d0b1f6169e206c6bb64bbfc029271af8ad,STILL_EXISTS,FIXME: this check is only here because when you bring back a GenPtrTensor from remote;
aaifghgge,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,ae37f9d0b1f6169e206c6bb64bbfc029271af8ad,STILL_EXISTS,Fixme: remove the share on init;
aaifghgje,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,01e041b0bfe8f298bb7543c284ac34603f6ac269,STILL_EXISTS,Fixme: remove the share on init;
aaifghhaa,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,01e041b0bfe8f298bb7543c284ac34603f6ac269,STILL_EXISTS,FIXME: this check is only here because when you bring back a GenPtrTensor from remote;
aaifghhba,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,d38cd0672af2762730d259a937e8e7015a46496c,STILL_EXISTS,FIXME: this check is only here because when you bring back a GenPtrTensor from remote;
aaifghhcc,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,d38cd0672af2762730d259a937e8e7015a46496c,STILL_EXISTS,Fixme: remove the share on init;
aaifghhde,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,a1dbebc151691e1ab13a356652c58645d6943e5f,STILL_EXISTS,Fixme: remove the share on init;
aaifghhea,OpenMined/PySyft,syft/core/frameworks/torch/tensor.py,a1dbebc151691e1ab13a356652c58645d6943e5f,STILL_EXISTS,FIXME: this check is only here because when you bring back a GenPtrTensor from remote;
aaifghjbh,OpenMined/PySyft,syft/frameworks/torch/hook.py,004a97d558b581f79d12bf1482666a8dbf6503c8,f5e7ee11eccb7a39aac24267fd70725c86dbb434,TODO: isn't it redundant iwth the upper one ?
aaifgigcg,OpenMined/PySyft,syft/frameworks/torch/hook.py,cc0a75f525780fcfc8a9300e9036cc0628643bf6,b8031dfb009d75523c4653bb547c1421c61c214a,TODO: finish - note that this functionality should not cause
aaifgihaj,OpenMined/PySyft,syft/workers/base.py,354fc6c2a99a33a8ee6c0d04ba81f5c4245998a9,7b66a5f6422990b415e18744577e6a0df4262e2d,TODO: Not useful for the moment
aaifgijfh,OpenMined/PySyft,syft/frameworks/torch/tensors/pointer.py,bfe77c6fa84d63fe2121ee551ccb8967705c24e2,ff680fd2c4ecfe470c449bac2218c0bad5b68680,TODO: finish AbstractTensor which should handle register\/parent\/id stuff
aaifgijje,OpenMined/PySyft,syft/workers/base.py,982826e4798125d87cba6f2f9a9d786ec3c5591e,b9ff0a2a1606c3dce29bf98ce9a0c8b44beb87f8,TODO: Not useful for the moment
aaifgjcbd,OpenMined/PySyft,test/test_serde.py,d04dda2c729d7406ed7c3fc0950462faf0ef3537,STILL_EXISTS,hook (TODO: look into why this is needed here)
aaifgjead,OpenMined/PySyft,syft/serde.py,9cdcb1920462a61d65b90a97ba3849f096be6288,eb7b626b7dec735ebab86a9fd3d48f1b2e8d22b3,TODO: fix comment for this and simplifier
aaifgjfbd,OpenMined/PySyft,syft/frameworks/torch/tensors/pointer.py,de71ad864434320fb2309a6759111066ca289987,09575fb877fd3cb5ba4e06c2d879d71cc154ddaf,TODO: finish AbstractTensor which should handle register\/parent\/id stuff
aaifgjfbg,OpenMined/PySyft,test/test_serde.py,f3c3f74361cf0db0603373fe1baa8467a5a512cf,STILL_EXISTS,hook (TODO: look into why this is needed here)
aaifgjfdj,OpenMined/PySyft,syft/workers/base.py,1b2f178e4e49ce6a2142ed9ff1d610e8faaca2b2,bd95bc7546dd35c5c62ad10c2962fe063227c879,FIXME: should be added automatically
aaifgjfeb,OpenMined/PySyft,syft/workers/base.py,1b2f178e4e49ce6a2142ed9ff1d610e8faaca2b2,STILL_EXISTS,TODO: case for functions (self_ is None)
aaifgjfjc,OpenMined/PySyft,syft/frameworks/torch/hook.py,7aed4209745fabff8d8b6914939dd25f21cc3850,fa92b2e87dbad23aedfedf7ad3bb00d4d53ef6ea,TODO Replace with syft.torch.torch_modules when hooking 'torch' will not break msgpack
aaifgjgaa,OpenMined/PySyft,syft/frameworks/torch/hook.py,7aed4209745fabff8d8b6914939dd25f21cc3850,STILL_EXISTS,4. Move the native function
aaifgjgij,OpenMined/PySyft,syft/frameworks/torch/hook.py,a45e8d27436b8f0ef20272702343bbb3fafb257d,d7551c8fb51e7e3bbf4ed1ff6d0b73c7cad46c90,Not needed in the headless setting
aaifgjhab,OpenMined/PySyft,syft/frameworks/torch/hook.py,a45e8d27436b8f0ef20272702343bbb3fafb257d,d7551c8fb51e7e3bbf4ed1ff6d0b73c7cad46c90,Not needed at the moment
aaifhaabi,OpenMined/PySyft,syft/frameworks/torch/hook.py,ed951ed8bc795be9344dc1a34869f065f624ef86,25e4f48dae542377f55405162d875e917618b454,TODO: change if statement to \"if has_pointer_child\"
aaifhaahh,OpenMined/PySyft,syft/frameworks/torch/hook_args.py,25e4f48dae542377f55405162d875e917618b454,STILL_EXISTS,TODO: analyse exactly the role of adding the type of self in the id
aaifhaaie,OpenMined/PySyft,syft/frameworks/torch/hook_args.py,25e4f48dae542377f55405162d875e917618b454,2bae31cbe5ca1eb6fdaf5add35a10e02909261fa,TODO: Why do we need to cast it in a tuple? this is a (small) time waste
aaifhabah,OpenMined/PySyft,syft/frameworks/torch/tensors/native.py,25e4f48dae542377f55405162d875e917618b454,80993cded394618539c5685a03c5a06cfa5a51e5,TODO: add kwargs
aaifhabbe,OpenMined/PySyft,syft/frameworks/torch/tensors/native.py,25e4f48dae542377f55405162d875e917618b454,80993cded394618539c5685a03c5a06cfa5a51e5,Put back the wrappers where needed
aaifhabbf,OpenMined/PySyft,syft/frameworks/torch/tensors/pointer.py,25e4f48dae542377f55405162d875e917618b454,80993cded394618539c5685a03c5a06cfa5a51e5,TODO: add kwargs
aaifhabbi,OpenMined/PySyft,syft/workers/base.py,25e4f48dae542377f55405162d875e917618b454,STILL_EXISTS,TODO add kwargs
aaifhabdb,OpenMined/PySyft,syft/frameworks/torch/tensors/log.py,fe5ba9077adf15b999a3c6770212484c37656636,da7d0f4a44b38c79e2521e616b9c01788cb372c4,TODO: add kwargs in command
aaifhabdd,OpenMined/PySyft,syft/frameworks/torch/tensors/log.py,fe5ba9077adf15b999a3c6770212484c37656636,da7d0f4a44b38c79e2521e616b9c01788cb372c4,TODO: I can't manage the import issue; can you?
aaifhabdi,OpenMined/PySyft,syft/serde.py,fe5ba9077adf15b999a3c6770212484c37656636,eb7b626b7dec735ebab86a9fd3d48f1b2e8d22b3,TODO: fix comment for this and simplifier
aaifhabdj,OpenMined/PySyft,syft/frameworks/torch/hook.py,a8e983237229f95205c7caabdc77b0d1f8cca4bf,ce5715efd389c1c435cab72d645693d89a9c8a21,TODO add kwargs
aaifhabgc,OpenMined/PySyft,syft/frameworks/torch/hook_args.py,1e27b3b57cb26f3ae3fbbe09e62f5c3998dbb9d2,STILL_EXISTS,TODO rename registry or use another one than for methods
aaifhabhi,OpenMined/PySyft,syft/frameworks/torch/tensors/native.py,1e27b3b57cb26f3ae3fbbe09e62f5c3998dbb9d2,80993cded394618539c5685a03c5a06cfa5a51e5,TODO: add kwargs
aaifhabid,OpenMined/PySyft,syft/frameworks/torch/tensors/native.py,1e27b3b57cb26f3ae3fbbe09e62f5c3998dbb9d2,80993cded394618539c5685a03c5a06cfa5a51e5,Put back the wrappers where needed
aaifhabif,OpenMined/PySyft,syft/frameworks/torch/tensors/native.py,1e27b3b57cb26f3ae3fbbe09e62f5c3998dbb9d2,STILL_EXISTS,TODO: clean this line
aaifhabih,OpenMined/PySyft,syft/frameworks/torch/tensors/native.py,1e27b3b57cb26f3ae3fbbe09e62f5c3998dbb9d2,STILL_EXISTS,TODO: guard
aaifhacaa,OpenMined/PySyft,syft/frameworks/torch/tensors/log.py,6aaf06689b6f756b6a67f3cb2168f1f46afdf94b,da7d0f4a44b38c79e2521e616b9c01788cb372c4,TODO: add kwargs in command
aaifhacac,OpenMined/PySyft,syft/frameworks/torch/tensors/log.py,6aaf06689b6f756b6a67f3cb2168f1f46afdf94b,da7d0f4a44b38c79e2521e616b9c01788cb372c4,TODO: I can't manage the import issue; can you?
aaifhacfj,OpenMined/PySyft,syft/frameworks/torch/hook.py,80993cded394618539c5685a03c5a06cfa5a51e5,STILL_EXISTS,TODO: I can't manage the import issue; can you?
aaifhacgi,OpenMined/PySyft,syft/frameworks/torch/hook.py,80993cded394618539c5685a03c5a06cfa5a51e5,STILL_EXISTS,Put back the wrappers where needed
aaifhaeje,OpenMined/PySyft,syft/workers/base.py,eb16e65c5546f4d1adcd9b86be8e21e13410d308,1331fc3aa8b1f5aee1e4ecbbf48a1aade2b9f4b0,TODO: not sure if someone needed this - if this comment
aaifhafgb,OpenMined/PySyft,syft/frameworks/torch/hook.py,b4827c7abc2c9090919cebd188a9f1d47e7ec39c,STILL_EXISTS,TODO: confusion between inplace and not inplace method to disambiguate
aaifhafhh,OpenMined/PySyft,syft/frameworks/torch/hook.py,5559feac7ac6c399dfdb63d0e9f66d249f06d348,STILL_EXISTS,Hook __repr__ to handle chain repr when needed
aaifhafhi,OpenMined/PySyft,syft/frameworks/torch/hook.py,5559feac7ac6c399dfdb63d0e9f66d249f06d348,STILL_EXISTS,Hook .data to handle chain assignment when needed
aaifhagbj,OpenMined/PySyft,syft/frameworks/torch/tensors/pointer.py,0cb37fc594502088b99800dad840bdabeecd0a6e,STILL_EXISTS,TODO: remove these 3 lines
aaifhagcb,OpenMined/PySyft,syft/frameworks/torch/tensors/pointer.py,0cb37fc594502088b99800dad840bdabeecd0a6e,STILL_EXISTS,something else is probably broken
aaifhagdg,OpenMined/PySyft,syft/frameworks/torch/hook.py,4b648f81528e18c3d028c784352d578a4a34610c,fa92b2e87dbad23aedfedf7ad3bb00d4d53ef6ea,TODO Replace with syft.torch.torch_modules when hooking 'torch' will not break msgpack
aaifhahaa,OpenMined/PySyft,syft/frameworks/torch/optim/sgd.py,165a90d5faf06426f8ea266fcdba45bdba2eb523,STILL_EXISTS,doing this as a list isn't memory efficient
aaifhahab,OpenMined/PySyft,syft/frameworks/torch/optim/sgd.py,165a90d5faf06426f8ea266fcdba45bdba2eb523,STILL_EXISTS,TODO: all all the SGD features from PyTorch's SGD
aaifhaibg,OpenMined/PySyft,test/conftest.py,11257e2037aab4b4e34fe6cc901b262a233a829c,STILL_EXISTS,TODO: should one set this boolean to true?
aaifhbaif,OpenMined/PySyft,syft/frameworks/torch/tensors/precision.py,52d3fe67af520c7e29187740a2da49a072a00fe0,STILL_EXISTS,TODO: add kwargs in command
aaifhbaih,OpenMined/PySyft,syft/frameworks/torch/tensors/precision.py,52d3fe67af520c7e29187740a2da49a072a00fe0,STILL_EXISTS,TODO: I can't manage the import issue; can you?
aaifhbbdh,OpenMined/PySyft,syft/frameworks/torch/tensors/add.py,6c00de7fd03260df5eb857d97c505b484919cc30,46c13999f8940a93dfa78f3839cfe5356549d1a7,# TODO: add kwargs in command
aaifhbbed,OpenMined/PySyft,syft/frameworks/torch/tensors/add.py,6c00de7fd03260df5eb857d97c505b484919cc30,46c13999f8940a93dfa78f3839cfe5356549d1a7,# TODO: I can't manage the import issue; can you?
aaifhdhja,OpenMined/PySyft,syft/frameworks/torch/hook.py,221c2c247559315fe29a2012b2a1ad352a3cf933,11fe2982b600ad1a05ad6ef0a61178e0b7f9e444,TODO and add special functions to include \/ avoid
aaifhdhjh,OpenMined/PySyft,syft/serde.py,221c2c247559315fe29a2012b2a1ad352a3cf933,STILL_EXISTS,TODO fix pointer bug
aaifhdibj,OpenMined/PySyft,syft/frameworks/torch/hook.py,d695273e32c0b08a564afce0e0e8e45c3fc313b8,adc8b3fc5241469a3b90d0ce3ba435ad77567e15,TODO and add special functions to include \/ avoid
aaifhdifb,OpenMined/PySyft,syft/frameworks/torch/hook_args.py,d6c378dc7d678c5bb30d7385b873fbde8ed8375f,STILL_EXISTS,Could add more checks but not sure it is needed so far.
aaifhdifh,OpenMined/PySyft,syft/frameworks/torch/hook.py,80e976bea3efd7e404ec887a2c3407cd951d531c,STILL_EXISTS,Hook .grad to handle chain assignment when needed
aaifhdigf,OpenMined/PySyft,syft/frameworks/torch/hook.py,cb4033c1af7f2bfebe300e2a9dce4737e1cdc3eb,STILL_EXISTS,TODO: add .data and .grad to syft tensors
aaifhdiii,OpenMined/PySyft,syft/frameworks/torch/hook.py,bfb6c6cfb3b3ac0365f9a16e7aeb1546f8bc3554,STILL_EXISTS,# TODO: confusion between inplace and not inplace method to disambiguate
aaifhdijf,OpenMined/PySyft,syft/frameworks/torch/hook.py,cde623fbbd0da889cbe4b4aa03bbc905911030a9,STILL_EXISTS,Hook .grad to handle chain assignment when needed
aaifhdjag,OpenMined/PySyft,syft/frameworks/torch/hook.py,cde623fbbd0da889cbe4b4aa03bbc905911030a9,STILL_EXISTS,Hard fix for PyTorch versions < 1.0.2
aaifhdjbf,OpenMined/PySyft,syft/frameworks/torch/hook.py,bd88035d94611a0c023d0b1938e49462721e71e9,STILL_EXISTS,TODO: add .data and .grad to syft tensors
aaifhdjdh,OpenMined/PySyft,syft/frameworks/torch/hook.py,6c180ff95ba44ef7340465650aa2b9b804510582,STILL_EXISTS,# TODO: confusion between inplace and not inplace method to disambiguate
aaifhdjdj,OpenMined/PySyft,syft/frameworks/torch/hook.py,6c180ff95ba44ef7340465650aa2b9b804510582,STILL_EXISTS,TODO: add .data and .grad to syft tensors
aaifhdjja,OpenMined/PySyft,syft/frameworks/torch/hook.py,34022eaa96a831db384f09a1b5fc4fd9ec1e828a,STILL_EXISTS,TODO: add .data and .grad to syft tensors
aaifhdjji,OpenMined/PySyft,syft/frameworks/torch/hook.py,34022eaa96a831db384f09a1b5fc4fd9ec1e828a,STILL_EXISTS,# TODO: confusion between inplace and not inplace method to disambiguate
aaifheaab,OpenMined/PySyft,syft/frameworks/torch/federated/dataset.py,b8f9a5dfe945c040a199db47714f5e93d44c1fcc,91a65d1852a1fbecd8b2d1d971d6ad5e545ebe6f,Convert in the Federated format if needed
aaifheaag,OpenMined/PySyft,test/workers/test_worker.py,10069456d6d92f1e2a2028d3f5ee3e4edff683d4,STILL_EXISTS,Ensure adding data on signup functionality works as expected
aaifheaai,OpenMined/PySyft,syft/frameworks/torch/federated/dataset.py,91a65d1852a1fbecd8b2d1d971d6ad5e545ebe6f,241859029a180eedd74c626ed0429448adcc7bf0,Fix for old versions of torchvision
aaifheabe,OpenMined/PySyft,syft/serde.py,6ee7789f436b78157ec04d08d62c6f4bfa8039bc,0902097d4bc2e29d2ce95d9ef6593d3a43e3bc02,1; 2) Decompress the binary if needed
aaifheaef,OpenMined/PySyft,syft/serde.py,7c95bbb02930a7be84b52f6cc8a76d629449fdb2,49960d4d0c3f95bef2721064e1de058c0063cf6f,1) Decompress the binary if needed
aaifheafc,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/autograd.py,cf7abbf4d38fd7e1f4458029662fb5ea8ec5746d,641e3fe14ff5b3d94b88929079fc5a3b8f2df8d1,TODO: add kwargs in command
aaifheaff,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/autograd.py,cf7abbf4d38fd7e1f4458029662fb5ea8ec5746d,39394c00aadee0d4d8b18aa84b6bf33911ce8ec1,TODO: I can't manage the import issue; can you?
aaifheaga,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/build_gradients.py,cf7abbf4d38fd7e1f4458029662fb5ea8ec5746d,STILL_EXISTS,constructed GradFunc classes to gradients.py. It could probably be optimized a bit.
aaifheaij,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/build_gradients.py,39c8355501fefdc35ed290ef35b9cb38587c7609,STILL_EXISTS,There is probably a better way to do this
aaifhebca,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/native.py,5db088399b59ead3c4adaf91f66d2cd6d49d082b,421bb785ba3de9f9db7074a1a77cbbf07fde866b,TODO: add kwargs
aaifhebcb,OpenMined/PySyft,syft/serde.py,5db088399b59ead3c4adaf91f66d2cd6d49d082b,712e3029626ed9b37c975348dfdd428e39b98a74,1) Decompress the binary if needed
aaifhebhb,OpenMined/PySyft,syft/workers/utils.py,587231331fd55cf6ce3ad19dcc2be1cb7dfe955d,d99e6c142b98c5489ea79524335d98ff579b5bca,Send the local plan to the remote_plan worker (TODO: here just a copy)
aaifhebhc,OpenMined/PySyft,syft/workers/utils.py,587231331fd55cf6ce3ad19dcc2be1cb7dfe955d,d99e6c142b98c5489ea79524335d98ff579b5bca,Update plan with current args for every argument (TODO: remotely)
aaifhehae,OpenMined/PySyft,syft/serde.py,78e41debd4f567e011380deb9cc0b83a566de5b3,02f6315bb32408c161d993ae58abb7f75f8d304f,1) Decompress the binary if needed
aaifhehba,OpenMined/PySyft,syft/serde.py,78e41debd4f567e011380deb9cc0b83a566de5b3,02f6315bb32408c161d993ae58abb7f75f8d304f,TODO: fix comment for this and simplifier
aaifheijc,OpenMined/PySyft,test/conftest.py,429367af7b4426f2ce9d1138f661da32c60b4e7d,STILL_EXISTS,TODO: should one set this boolean to true?
aaifheije,OpenMined/PySyft,syft/workers/websocket.py,23231b0b734277ebadcfb2e10e2e12821df3ab4a,STILL_EXISTS,TODO get angry when we have no connection params
aaifheijf,OpenMined/PySyft,syft/workers/websocket.py,23231b0b734277ebadcfb2e10e2e12821df3ab4a,STILL_EXISTS,TODO figure out how to get response from other worker efficiently
aaifheijh,OpenMined/PySyft,syft/workers/websocket.py,23231b0b734277ebadcfb2e10e2e12821df3ab4a,STILL_EXISTS,TODO investigate using a binary flag for local to save cycles
aaifheiji,OpenMined/PySyft,syft/serde.py,5907e4f13d784cb17961bdaa27c643e77116cdaa,c0baf24baf35c100a80238759f14c9a4d5f933d2,1) Decompress the binary if needed
aaifhejae,OpenMined/PySyft,syft/serde.py,5907e4f13d784cb17961bdaa27c643e77116cdaa,c0baf24baf35c100a80238759f14c9a4d5f933d2,TODO: fix comment for this and simplifier
aaifhejbc,OpenMined/PySyft,syft/workers/base.py,5907e4f13d784cb17961bdaa27c643e77116cdaa,3a0de19f843579ea28f9f3fbf04849fc4c30d11b,TODO: not sure if someone needed this - if this comment
aaifhejfa,OpenMined/PySyft,syft/workers/websocket.py,3a0de19f843579ea28f9f3fbf04849fc4c30d11b,STILL_EXISTS,TODO get angry when we have no connection params
aaifhfajg,OpenMined/PySyft,test/conftest.py,c0baf24baf35c100a80238759f14c9a4d5f933d2,STILL_EXISTS,TODO: should one set this boolean to true?
aaifhfcfc,OpenMined/PySyft,syft/workers/websocket_client.py,6917846cf981485ca83f61142e073c9c343eba4c,83c9aad9293e492c8f195cdd4b9792e5be2fe708,TODO get angry when we have no connection params
aaifhfcgd,OpenMined/PySyft,syft/workers/websocket_server.py,6917846cf981485ca83f61142e073c9c343eba4c,STILL_EXISTS,(this is needed for the websocket library)
aaifhfefe,OpenMined/PySyft,syft/frameworks/torch/hook_args.py,bd95bc7546dd35c5c62ad10c2962fe063227c879,2bae31cbe5ca1eb6fdaf5add35a10e02909261fa,TODO: Why do we need to cast it in a tuple? this is a (small) time waste
aaifhfegh,OpenMined/PySyft,syft/frameworks/torch/hook_args.py,bd95bc7546dd35c5c62ad10c2962fe063227c879,2bae31cbe5ca1eb6fdaf5add35a10e02909261fa,FIXME: should be added automatically
aaifhfejj,OpenMined/PySyft,syft/workers/base.py,04935249d9fdc260188c7f012ad3542da12ca62e,0450c7ec6ab3de141f3a28612e87b90dc478f104,FIXME: should be added automatically
aaifhffaa,OpenMined/PySyft,syft/workers/base.py,04935249d9fdc260188c7f012ad3542da12ca62e,0450c7ec6ab3de141f3a28612e87b90dc478f104,TODO: Handle when the response is not simply a tensor
aaifhfgce,OpenMined/PySyft,syft/workers/base.py,d1b40c2b538b0a526006546dd310469e26e910ad,55402144e5a970276f3008cf6637866b82b70d24,FIXME: should be added automatically
aaifhfgcf,OpenMined/PySyft,syft/workers/base.py,d1b40c2b538b0a526006546dd310469e26e910ad,55402144e5a970276f3008cf6637866b82b70d24,TODO: Handle when the response is not simply a tensor
aaifhfihh,OpenMined/PySyft,syft/workers/base.py,2bae31cbe5ca1eb6fdaf5add35a10e02909261fa,380597e76d0a1a32e4198474054004bbe361085e,FIXME: should be added automatically
aaifhfihi,OpenMined/PySyft,syft/workers/base.py,2bae31cbe5ca1eb6fdaf5add35a10e02909261fa,380597e76d0a1a32e4198474054004bbe361085e,TODO: Handle when the response is not simply a tensor
aaifhfjbi,OpenMined/PySyft,syft/frameworks/torch/hook_args.py,380597e76d0a1a32e4198474054004bbe361085e,STILL_EXISTS,TODO: Why do we need to cast it in a tuple? this is a (small) time waste
aaifhfjdb,OpenMined/PySyft,syft/frameworks/torch/hook_args.py,380597e76d0a1a32e4198474054004bbe361085e,eeac3dca38ff56771b0b034e5d1f109690b6f27a,FIXME: should be added automatically
aaifhgcaa,OpenMined/PySyft,syft/serde.py,91bf2bbcc065ad0a3d5c8606bb7bf77dcc8a7fbb,ff3249fe85631d3e676e95cd373356c5d931dbc3,1) Decompress the binary if needed
aaifhgcag,OpenMined/PySyft,syft/serde.py,91bf2bbcc065ad0a3d5c8606bb7bf77dcc8a7fbb,ff3249fe85631d3e676e95cd373356c5d931dbc3,TODO: fix comment for this and simplifier
aaifhgcei,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/additive_shared.py,7904249b46cad13ed702b9ffac6c6a497a3529ee,ff3249fe85631d3e676e95cd373356c5d931dbc3,TODO: Handle public mul more efficiently
aaifhgcff,OpenMined/PySyft,syft/frameworks/torch/hook_args.py,327d860360edd06b56383808c56643feeb764a18,d66c11b9ffd42510354997decebd174484cba249,FIXME: should be added automatically
aaifhgeig,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/additive_shared.py,3a7b2bab5eef93267d316acae5391b13b2e14fd4,761a91a663d94214f103bd1935612c5256ff990a,TODO: Handle public mul more efficiently
aaifhgfei,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/additive_shared.py,2456c630e4fd429d5ee7db7698aa2df6c1b6f91c,24ff99eaaf78ec245056e0860d4f9398dec76c94,TODO: Handle public mul more efficiently
aaifhgffa,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/autograd.py,6f1648a620da66fafbac3f0d3b1aa15ba3b9b4a5,STILL_EXISTS,so doing this to work my way down the chain to the base tensor. TODO
aaifhgfge,OpenMined/PySyft,syft/serde.py,d66c11b9ffd42510354997decebd174484cba249,STILL_EXISTS,1) Decompress the binary if needed
aaifhgfgj,OpenMined/PySyft,syft/serde.py,d66c11b9ffd42510354997decebd174484cba249,0391661c90156ffe84fbbf91e4c1e324b6b55c29,TODO: fix comment for this and simplifier
aaifhggbd,OpenMined/PySyft,syft/serde.py,0391661c90156ffe84fbbf91e4c1e324b6b55c29,STILL_EXISTS,1) Decompress the binary if needed
aaifhggcb,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/additive_shared.py,fd3ec18951147f4558c7c5065315e16b2a0b958d,bb2030deb29094b5beff2be6adac5426b8ce7907,TODO: Handle public mul more efficiently
aaifhggcd,OpenMined/PySyft,syft/serde.py,78701062a7db70b8cdc0fd405d68aeb4f35ee6ca,STILL_EXISTS,1) Decompress the binary if needed
aaifhggcj,OpenMined/PySyft,syft/serde.py,78701062a7db70b8cdc0fd405d68aeb4f35ee6ca,74ba400ccd473c9e2165aef34d38c049369fcd9f,TODO: fix comment for this and simplifier
aaifhghej,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/abstract.py,0dca0e8e9e22676000ddaf672989d168b3b0fada,276fbcb873687c09a1fa716156f49f65d5bca23c,TODO: I can't manage the import issue; can you?
aaifhghga,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/utils.py,a9811886201c8ada268cf339763797ac483cfe75,STILL_EXISTS,TODO have a better way
aaifhgibj,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/abstract.py,3807b7456b412afb5c64bbcd7e505a1735283286,b69e517a453afae4b778e9011ab5eadedd894c29,TODO: I can't manage the import issue; can you?
aaifhgjdd,OpenMined/PySyft,syft/frameworks/torch/overload_torch.py,51876ffa7ca855f1c7a6cd78045434ece4ba4c4a,STILL_EXISTS,TODO have a better way
aaifhgjdj,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/abstract.py,51876ffa7ca855f1c7a6cd78045434ece4ba4c4a,3a55b41d7460e445e83e318a7436b62032256c3c,TODO: I can't manage the import issue; can you?
aaifhgjfh,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/abstract.py,2de33387c6866950715e3d5010193adf991bfa5b,61192e39e17c0f7423f20056c387b750b9c71dec,TODO: I can't manage the import issue; can you?
aaifhhagj,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/abstract.py,4ce08d2d81bca6cecf20e427e524ab36bf310a1d,f00a4bf9db5a9b2973801e4b44ab6d475b91f3c8,TODO: I can't manage the import issue; can you?
aaifhhbae,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/abstract.py,a5131bfc7baeee6b3d24ef9b1aeae9ab2310ae51,2260f1270f19b1a0a5a2c29acb9abca96ffc976c,TODO: I can't manage the import issue; can you?
aaifhhbca,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/abstract.py,ad850c32fe47c7bc3dce8c88fef12b371dceddfa,STILL_EXISTS,TODO: I can't manage the import issue; can you?
aaifhhcdf,OpenMined/PySyft,syft/frameworks/torch/hook.py,68331736a11da536c930dd121dc8baddc9fd4144,3df139554132fa32813b30233286378a23302094,TODO add kwargs
aaifhhcdg,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/autograd.py,68331736a11da536c930dd121dc8baddc9fd4144,9c59bfd07be1a5fb728896c2a1c0f4edb6de09f1,TODO: figure out how to fix this so grad_fns use Syft Tensors
aaifhhcea,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/native.py,68331736a11da536c930dd121dc8baddc9fd4144,3df139554132fa32813b30233286378a23302094,TODO: add kwargs
aaifhhfed,OpenMined/PySyft,syft/workers/plan.py,0bc718da6cc3a475b9339decaa8631e263c6bccf,a085a4d48d433e35724e8e4e3efbcb85e4a53033,FIXME
aaifhhfhd,OpenMined/PySyft,syft/workers/base.py,11d168e42a4b00302aa939f5fafc091f5d3cfb50,27562d607be4960f2736de9ea508264dc6c51829,TODO Andrew thinks this is gross; please fix. Instead need to properly deserialize strings
aaifhhfia,OpenMined/PySyft,syft/workers/base.py,5452c9c8a6050aa6602f808d143994987a968bc3,27562d607be4960f2736de9ea508264dc6c51829,TODO Andrew thinks this is gross; please fix. Instead need to properly deserialize strings
aaifhhgba,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/multi_pointer.py,ee2dd66ee57951dc3772e35b5f21f498877a0d97,2b8caf5cb0d375d6311545c622ecb18e7ddbaccd,FIXME
aaifhhgbd,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/multi_pointer.py,ee2dd66ee57951dc3772e35b5f21f498877a0d97,STILL_EXISTS,TODO: I can't manage the import issue; can you?
aaifhhgbi,OpenMined/PySyft,syft/frameworks/torch/hook_args.py,167c31e19718e17973b03209c2954b48400f1039,STILL_EXISTS,FIXME This is for additiveShareTensor.child; it can be confusing and AST.child
aaifhhgbj,OpenMined/PySyft,syft/frameworks/torch/hook_args.py,167c31e19718e17973b03209c2954b48400f1039,STILL_EXISTS,should perhaps be of type ShareDict extending dict or something like this
aaifhhgch,OpenMined/PySyft,syft/frameworks/torch/hook.py,adc8b3fc5241469a3b90d0ce3ba435ad77567e15,STILL_EXISTS,\"__eq__\"; # FIXME it now overwritten in native.py to use torch.eq; because of pb between == & __eq__
aaifhhgdb,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/precision.py,946dd9b193155ff5de21977a1231465de41ddc2e,06d050a1eaee508ab0889b566afa99adec6da352,FIXME
aaifhhgfd,OpenMined/PySyft,syft/frameworks/torch/crypto/securenn.py,e92b3443c56d6b9c9de7ae55741ae85880601072,5beb16b1ec70c57d3c1aa1a1d22866fbf6488727,FIXME: unused
aaifhhgfi,OpenMined/PySyft,syft/frameworks/torch/crypto/securenn.py,e92b3443c56d6b9c9de7ae55741ae85880601072,STILL_EXISTS,FIXME: Add 14): permutation and sending to crypto provider
aaifhhgfj,OpenMined/PySyft,syft/frameworks/torch/crypto/securenn.py,e92b3443c56d6b9c9de7ae55741ae85880601072,STILL_EXISTS,FIXME: Reconst remotely and get back beta prime only
aaifhhggj,OpenMined/PySyft,syft/frameworks/torch/crypto/securenn.py,e92b3443c56d6b9c9de7ae55741ae85880601072,STILL_EXISTS,.send(bob; alice) #TODO: make this secure by exchanging shares remotely
aaifhhgif,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/additive_shared.py,a4b962887adeab26b99b3f743d67c12bd69965ad,0280b7cbe755cff3cd433f2e7d558864a45c9d7b,TODO: I can't manage the import issue; can you?
aaifhhgjg,OpenMined/PySyft,syft/frameworks/torch/crypto/securenn.py,ccc56083d1c705da9be652e1dced55558d94a29a,d565e7247b4c3e6316210bf3600f7b08ab38a9a9,# TODO: sum shares remotely
aaifhhhgd,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/additive_shared.py,06d050a1eaee508ab0889b566afa99adec6da352,2b8caf5cb0d375d6311545c622ecb18e7ddbaccd,FIXME
aaifhhhgg,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/additive_shared.py,06d050a1eaee508ab0889b566afa99adec6da352,STILL_EXISTS,TODO: I can't manage the import issue; can you?
aaifhiegi,OpenMined/PySyft,syft/frameworks/torch/federated/dataloader.py,19045a1c1d6476a3a714cd0238224ee0f0e3ddd7,STILL_EXISTS,TODO: implement a length function. It should return the number of elements of the federated dataset that are
aaifhieia,OpenMined/PySyft,examples/tutorials/run_websocket_client.py,c474ae6eb6e71d72cd80be1415662637938c6718,STILL_EXISTS,TODO momentum is not supported at the moment
aaifhiejf,OpenMined/PySyft,examples/tutorials/Advanced/Websockets - Federated training/run_websocket_client.py,9ab483e908ddf8f54e32cc61785b5b6c6b655f52,STILL_EXISTS,TODO momentum is not supported at the moment
aaifhifad,OpenMined/PySyft,syft/frameworks/torch/hook.py,9e8e2dbd527540b7b24a6b887c732577b5928122,STILL_EXISTS,TODO: add .data and .grad to syft tensors
aaifhifag,OpenMined/PySyft,test/torch/tensors/test_precision.py,1e883e3a794933d1549cd9e7e976fd39ee60eb0d,0f4deaa6106c84ae9a228ea7b74548503bb91b7c,mul with non standard fix precision
aaifhifdf,OpenMined/PySyft,syft/workers/plan.py,9d7bf25825f54a91a747dad00807062197f5dbad,9c5b8b305de51c6fad03ff768a58ae08fc2227e9,We build the plan only if needed
aaifhigce,OpenMined/PySyft,syft/serde.py,386272bde356504f6311c30becf5f7383b0347eb,e5acb56adfcf620b2f3bcf11963c54e62c7ce8b4,TODO: fix comment for this and simplifier
aaifhigci,OpenMined/PySyft,syft/frameworks/torch/crypto/securenn.py,81a26e9e725d2fdd7390308cc6d07a8f8acb19be,c3bfd3d461b67d0f576f6f5988be31fec1a72037,TODO: send to P2 (or is it the local worker?)
aaifhigcj,OpenMined/PySyft,syft/frameworks/torch/crypto/securenn.py,81a26e9e725d2fdd7390308cc6d07a8f8acb19be,c3bfd3d461b67d0f576f6f5988be31fec1a72037,TODO: should not be needed
aaifhighb,OpenMined/PySyft,syft/frameworks/torch/crypto/securenn.py,06e113467ad90e361e3c09a53eb2b06700dca3f9,STILL_EXISTS,FIXME security
aaifhigjb,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/additive_shared.py,64472dfc09f8f3da582d6fc8e5f915081ae1300b,STILL_EXISTS,FIXME make it cleaner and robust for more options
aaifhigjh,OpenMined/PySyft,syft/frameworks/torch/crypto/securenn.py,b9bb23a62f42ad3ffb35d308b777cb7863475f75,STILL_EXISTS,it is a small field for efficient additive sharing
aaifhihbg,OpenMined/PySyft,syft/serde.py,a21b941930c9c564663caba0189aaa824fe3c546,3db28e9a662256fb2651d43dbcbc26c1e025c4e2,TODO: fix comment for this and simplifier
aaifhihcd,OpenMined/PySyft,test/torch/tensors/test_precision.py,a21b941930c9c564663caba0189aaa824fe3c546,STILL_EXISTS,mul with non standard fix precision
aaifhjhbc,OpenMined/PySyft,syft/workers/websocket_client.py,a7a8e53f3eeaa5ec8a0581ebe3a2a5c26e691568,STILL_EXISTS,Daniele Gadler: Fix timeout issues on the server-side
aaifhjhbe,OpenMined/PySyft,syft/workers/websocket_server.py,a7a8e53f3eeaa5ec8a0581ebe3a2a5c26e691568,STILL_EXISTS,Daniele Gadler: fix the timeout issues at the client side
aaifhjheh,OpenMined/PySyft,docs/conf.py,77cf8a133497bb635c4f257be5b4c99414035c73,572b36d8403ea2697350ffabb41fdf7045eddc35,-- Options for todo extension ----------------------------------------------
aaifhjhei,OpenMined/PySyft,docs/conf.py,77cf8a133497bb635c4f257be5b4c99414035c73,572b36d8403ea2697350ffabb41fdf7045eddc35,If true; `todo` and `todoList` produce output; else they produce nothing.
aaifhjjdb,OpenMined/PySyft,syft/workers/base.py,7a9d7cd1a6721d8ff7ecc164e13fa7c68eb0c860,27562d607be4960f2736de9ea508264dc6c51829,TODO Andrew thinks this is gross; please fix. Instead need to properly deserialize strings
aaifhjjhe,OpenMined/PySyft,syft/workers/base.py,c79603040809c7be7e7694f8f1789a69acd93bbc,27562d607be4960f2736de9ea508264dc6c51829,TODO Andrew thinks this is gross; please fix. Instead need to properly deserialize strings
aaifiacdj,OpenMined/PySyft,syft/workers/base.py,a8c2faec149c9b3bd9ab4bcc7e199351258de8cc,a49c10648ad18190840949ae13636bce7c56de3c,TODO Andrew thinks this is gross; please fix. Instead need to properly deserialize strings
aaifiaced,OpenMined/PySyft,syft/workers/base.py,641e3fe14ff5b3d94b88929079fc5a3b8f2df8d1,a49c10648ad18190840949ae13636bce7c56de3c,TODO Andrew thinks this is gross; please fix. Instead need to properly deserialize strings
aaifiadba,OpenMined/PySyft,test/federated/test_plan.py,f9f23774a646db713d3063effeae8746e8458f65,6397e0bc20eeff8f09575870c6348e9198a21523,TODO: remove this line when issue #2062 is fixed
aaifiadfg,OpenMined/PySyft,test/federated/test_plan.py,c2d2326a840474110ed53a434da4b992949ea9f0,6397e0bc20eeff8f09575870c6348e9198a21523,TODO: remove this line when issue #2062 is fixed
aaifibdbb,OpenMined/PySyft,test/torch/test_hook.py,c0e03c19c7bf72b00f7f0da5e57625812c86ffdf,STILL_EXISTS,so we better check it beforehand
aaifibdbc,OpenMined/PySyft,test/workers/test_websocket_worker.py,f56ab8b8bb6ea4c729794a518030a7d125861e27,STILL_EXISTS,Search multiple times should still work
aaifibdcg,OpenMined/PySyft,syft/workers/base.py,ec411cef9f1114d3970fa234aba9008edc64d38a,a49c10648ad18190840949ae13636bce7c56de3c,TODO Andrew thinks this is gross; please fix. Instead need to properly deserialize strings
aaifibfbb,OpenMined/PySyft,syft/generic/id_provider.py,46b8a3c1e9b0ae81330c3422578171dce60f7832,STILL_EXISTS,TODO: use sorted container to quickly check whether a value is contained
aaifibfbg,OpenMined/PySyft,syft/workers/base.py,fa8ac98fcac931ce75dd26bbb1c9c4cefd803e16,68a52dcd9353572f391c771ffc7816003d5779e2,TODO Andrew thinks this is gross; please fix. Instead need to properly deserialize strings
aaifibfda,OpenMined/PySyft,syft/workers/base.py,810fb2491a0b92afe7d34b1c7ac105084ea66109,d7e1efc6f96c7fd281f12717470c442f7dfd69c5,TODO Andrew thinks this is gross; please fix. Instead need to properly deserialize strings
aaifibgbi,OpenMined/PySyft,syft/workers/base.py,ddea5682d269f59b56418e32456885be235fa7c7,STILL_EXISTS,TODO Andrew thinks this is gross; please fix. Instead need to properly deserialize strings
aaifibidh,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/large_precision_tensor.py,3e815bdf202dd05cb43a8cc08316204c243ba1c4,STILL_EXISTS,TODO Start with a single dim
aaifibidi,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/large_precision_tensor.py,3e815bdf202dd05cb43a8cc08316204c243ba1c4,197185278440d256056eea213ea658842365f989,TODO Having issues with the hook
aaifibiea,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/large_precision_tensor.py,3e815bdf202dd05cb43a8cc08316204c243ba1c4,STILL_EXISTS,TODO Check types; precision
aaificdai,OpenMined/PySyft,syft/keras/hook/hook.py,40ed025b9d9957c207ab61259949f7170118b40e,STILL_EXISTS,TODO
aaificdcd,OpenMined/PySyft,syft/keras/__init__.py,ec729b0939023d0d50c736a1fb4b1cfeda8c423a,STILL_EXISTS,TODO[jason]: this is currently very ugly; sync on this with Andrew & Theo
aaificdde,OpenMined/PySyft,syft/frameworks/keras/model/sequential.py,973592d1bd78575502a847aaf234ec44f5411297,STILL_EXISTS,TODO: rethink how we warn the user about this; maybe codegen a list of
aaificddg,OpenMined/PySyft,syft/frameworks/keras/model/sequential.py,973592d1bd78575502a847aaf234ec44f5411297,STILL_EXISTS,TODO[jason]: find a better way
aaificdfg,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/additive_shared.py,d0c249f3a541562f822e8babf201d3eda1d5a5fb,3a2835e816009d2bee514a21c39c518889800744,The code is the same for cat and stack; maybe we could factorize
aaificdgc,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/additive_shared.py,d0c249f3a541562f822e8babf201d3eda1d5a5fb,3a2835e816009d2bee514a21c39c518889800744,Add a bias if needed
aaificiie,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/precision.py,7b9dcf301c4aef04bb423886a5fef724188a2a4d,1b0faa3ede31d481c8b535998dc92b229439f02e,Add a bias if needed
aaifidbeb,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/additive_shared.py,4ad5b2a03b90e0227b11ce2e9d718c217c810046,STILL_EXISTS,The code is the same for cat and stack; maybe we could factorize
aaifidbfd,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/additive_shared.py,4ad5b2a03b90e0227b11ce2e9d718c217c810046,409bdec438e82b20cd646c48f009cba2e7e3ce22,Add a bias if needed
aaifijeah,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/large_precision_tensor.py,8d05acbe4c85f582074869977e77be5aaef535fe,STILL_EXISTS,TODO refs_ok might not be needed as we are passing now torch.tensors here
aaifijeaj,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/large_precision_tensor.py,8d05acbe4c85f582074869977e77be5aaef535fe,STILL_EXISTS,TODO Is Vectorization possible here?
aaifijecc,OpenMined/PySyft,syft/serde/serde.py,01250723041c1e906d183791089f6394a27ee050,2ae23cccf2006ec0c478219aa914d84195f1650e,needed as otherwise we will get: module 'lz4' has no attribute 'frame'
aaifijeea,OpenMined/PySyft,syft/serde/serde.py,01250723041c1e906d183791089f6394a27ee050,475fd8d46a24d8f8ad81c7edaf0af29cb45b3426,1) Decompress the binary if needed
aaifijfcg,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/additive_shared.py,9111d38d6e775536c22abcb94c360c068d9e4e32,7ec4573dcd8a506dbbe533d29faa3466126d3946,Add a bias if needed
aaifijfjh,OpenMined/PySyft,test/federated/test_train_config.py,2626ecabc02c837d67fb6e8a6f4b0fcfd25ede03,72589f6578da2fa514b6d7d1a9f4e62760f642ce,TODO: force forward build
aaifijgad,OpenMined/PySyft,test/federated/test_train_config.py,2626ecabc02c837d67fb6e8a6f4b0fcfd25ede03,0d40252b97b2fe2d2f612f1ff446f78bae4aebeb,TODO: uncomment this line
aaifijgbb,OpenMined/PySyft,syft/federated/federated_client.py,0a062b1e942edb4e509e0f3902e2f3ae42c1d627,db96478b2e99776158d2dfda04473f8611ef97d7,TODO: how to get the actual model?
aaifijgbd,OpenMined/PySyft,syft/federated/federated_client.py,0a062b1e942edb4e509e0f3902e2f3ae42c1d627,db96478b2e99776158d2dfda04473f8611ef97d7,TODO: how to create\/set a dataset?
aaifijgbe,OpenMined/PySyft,syft/federated/federated_client.py,0a062b1e942edb4e509e0f3902e2f3ae42c1d627,db96478b2e99776158d2dfda04473f8611ef97d7,TODO: check if multiple returns are supported.
aaifijgcc,OpenMined/PySyft,syft/frameworks/torch/hook/hook_args.py,1cd95c06a209ee7074949a21463b4afe9902276c,0d40252b97b2fe2d2f612f1ff446f78bae4aebeb,TODO: uncomment these lines.
aaifijgdg,OpenMined/PySyft,examples/experimental/Federated Learning with TrainConfig/run_training_train_config.py,85d4fd776e2debe74e73da8bb35231abad3b5b61,96682efa04ece9acd9b54cadb7b2675af191cee4,TODO: maybe by default is_client_worker could be False?
aaifijgei,OpenMined/PySyft,examples/experimental/Federated Learning with TrainConfig/run_training_train_config.py,85d4fd776e2debe74e73da8bb35231abad3b5b61,96682efa04ece9acd9b54cadb7b2675af191cee4,TODO: Returns a tensor when it should actually return a Pointer to the result Tensor.
aaifijgej,OpenMined/PySyft,syft/federated/federated_client.py,85d4fd776e2debe74e73da8bb35231abad3b5b61,0d40252b97b2fe2d2f612f1ff446f78bae4aebeb,TODO: how to get the actual model?
aaifijjda,OpenMined/PySyft,syft/frameworks/torch/pointers/pointer_tensor.py,7ec4573dcd8a506dbbe533d29faa3466126d3946,43989c86fe1c4a17a3930a74e5001ba71364fc5d,TODO: fix comment for this and simplifier
aaifjabad,OpenMined/PySyft,syft/federated/plan.py,26b5c511a04e580e407afeadff92dc87bad770dc,414288693d0c6098788fba49da8d1e107d37b441,TODO: raise instead of assert
aaifjabcd,OpenMined/PySyft,test/federated/test_plan.py,26b5c511a04e580e407afeadff92dc87bad770dc,STILL_EXISTS,TODO: better support to method execution
aaifjabeb,OpenMined/PySyft,test/federated/test_plan.py,26b5c511a04e580e407afeadff92dc87bad770dc,STILL_EXISTS,TODO: better support for method execution
aaifjabif,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/additive_shared.py,f7f3f0d727ddd136e2a8c778c80269fadd54fbb4,9c074cde14426b258974d35312b9b6bad0729ffd,TODO i - max_index doesn't work
aaifjacci,OpenMined/PySyft,test/federated/test_plan.py,414288693d0c6098788fba49da8d1e107d37b441,STILL_EXISTS,TODO: better support to method execution
aaifjacef,OpenMined/PySyft,test/federated/test_plan.py,414288693d0c6098788fba49da8d1e107d37b441,STILL_EXISTS,TODO: better support for method execution
aaifjaddj,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/additive_shared.py,47a601cc047fc5f52b962d9f831d34966d70c712,STILL_EXISTS,The code is the same for cat and stack; maybe we could factorize
aaifjadej,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/additive_shared.py,47a601cc047fc5f52b962d9f831d34966d70c712,fe217de400f1adf11a6b9ece17d46a4be29c9501,Add a bias if needed
aaifjadic,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/additive_shared.py,fe217de400f1adf11a6b9ece17d46a4be29c9501,STILL_EXISTS,The code is the same for cat and stack; maybe we could factorize
aaifjadid,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/additive_shared.py,fe217de400f1adf11a6b9ece17d46a4be29c9501,9c074cde14426b258974d35312b9b6bad0729ffd,TODO i - max_index doesn't work
aaifjaecf,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/additive_shared.py,1e9179face4518b1de7d866a9eaa66b8d1441caf,dfe782d4a2214778b390827f7ee5d7045abb3e81,Add a bias if needed
aaifjaeda,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/additive_shared.py,1e9179face4518b1de7d866a9eaa66b8d1441caf,9c074cde14426b258974d35312b9b6bad0729ffd,TODO i - max_index doesn't work
aaifjaegi,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/additive_shared.py,dfe782d4a2214778b390827f7ee5d7045abb3e81,9c074cde14426b258974d35312b9b6bad0729ffd,TODO i - max_index doesn't work
aaifjafdf,OpenMined/PySyft,syft/frameworks/torch/pointers/pointer_tensor.py,a3f363e2957c6f9c86fabda5312b4fa25790c12a,868e6e87455e72165eae116aa69dfec99b38ff9b,TODO: fix comment for this and simplifier
aaifjaggg,OpenMined/PySyft,syft/frameworks/torch/pointers/pointer_tensor.py,887e558fe094f7245421a23c9da65370fde2f121,ca7f7e116f06cf027e4f71bb8bc57e7e9605747d,TODO: fix comment for this and simplifier
aaifjbcbc,OpenMined/PySyft,syft/frameworks/torch/pointers/pointer_tensor.py,786c2fc51d13bdf26836e9d9faf1eed7e1a530a7,1b2b62eb72e96f59b854927247c02eaeb3fc07b4,TODO: fix comment for this and simplifier
aaifjbead,OpenMined/PySyft,syft/frameworks/torch/hook/hook.py,6397e0bc20eeff8f09575870c6348e9198a21523,611eecb5107d234154efbc8f65e2bfb6041765ce,Hard fix for PyTorch versions < 1.0.2
aaifjbfif,OpenMined/PySyft,syft/federated/plan.py,44163b022964e690e13d0b1524113bcbf9140009,STILL_EXISTS,TODO: A more complex strategy could be used
aaifjbjch,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/large_precision.py,6e7b401f67fdc4c8e8df22a82838f8d507f05b50,STILL_EXISTS,TODO This should be done by the backward_func.
aaifjbjci,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/large_precision.py,6e7b401f67fdc4c8e8df22a82838f8d507f05b50,STILL_EXISTS,self_ + other returns a np.array which is probably why it's not going through hook_args.backward_func
aaifjbjdf,OpenMined/PySyft,syft/frameworks/torch/crypto/securenn.py,e04088babfab7a484cd403f86e99c19a2a7b650f,0996feb1ef8389288f53df1a7d82470e36b645e8,TODO put this in mul at some point
aaifjcagj,OpenMined/PySyft,syft/frameworks/torch/hook/hook.py,24b4d6de8b542aa3be8590aa3d447cd703032683,dac50591098abac22b4456bee9c671ffb3a4a392,Hard fix for PyTorch versions < 1.0.2
aaifjcbag,OpenMined/PySyft,test/workers/test_websocket_worker.py,39517b00f603b63344c60485a891e8ea10d6d24e,STILL_EXISTS,unused args
aaifjcbfj,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/large_precision.py,91850f4674de9551452e2cbaa91d29dffe194a31,STILL_EXISTS,self_ + other returns a np.array which is probably why it's not going through hook_args.backward_func
aaifjcefi,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/additive_shared.py,4d02b1056f24a17ec71fea81b2d6d3943ea602a7,ce70b777e83216f357e21d49ff0b8b4cd46910ca,TODO: truediv in Zq
aaifjcfhe,OpenMined/PySyft,syft/frameworks/torch/pointers/pointer_tensor.py,21249e182768d6277307913292fcb564437ac180,STILL_EXISTS,TODO: fix comment for this and simplifier
aaifjchhj,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/gradients_core.py,3ff6d08afa7feabc5d815699ea35cc553874eba8,STILL_EXISTS,Initialize the dimension if needed
aaifjchia,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/gradients_core.py,3ff6d08afa7feabc5d815699ea35cc553874eba8,STILL_EXISTS,Inverse x and y if needed to satisfy len(x_shape) <= len(y_shape)
aaifjchie,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/gradients_core.py,3ff6d08afa7feabc5d815699ea35cc553874eba8,STILL_EXISTS,Flip self and other if needed
aaifjchij,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/additive_shared.py,a604bbaa8ca187ffbb221d02d8fa2d9ea2c0a5c4,593299634c2810fe2d6481b1bf5dd89b6af50d5e,TODO: truediv in Zq
aaifjcjae,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/additive_shared.py,0d889a9135b0633489a5f947930ae4e2cf59997a,ef384bd99305a6f5e3c220f3da25b8dde30fc5ed,TODO: how to correctly handle division in Zq?
aaifjdaga,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/additive_shared.py,75a03f82fbba5bf8cef756bb2b7262f2764f91be,0c485111af3972cfe9dcca568f825a0933cb4f0d,TODO: how to correctly handle division in Zq?
aaifjdajd,OpenMined/PySyft,test/torch/tensors/test_chinese_remainder.py,fae0432879bad6adae9028a6561a94c8d81f8ccd,STILL_EXISTS,TODO see why I have to do that
aaifjdehj,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/additive_shared.py,f1d0b16241def428a7d9b2ede610f519f9994829,0c485111af3972cfe9dcca568f825a0933cb4f0d,TODO: how to correctly handle division in Zq?
aaifjdffg,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/chinese_remainder_theorem.py,a1a3a4103ec95b717db584d2330940782d458567,STILL_EXISTS,TODO why % mod is needed?
aaifjdfgc,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/chinese_remainder_theorem.py,37cc8d061f1f28762e85dc274a66a9fd1c61a594,STILL_EXISTS,This is a bit ugly; I want the result to be stored in a CRT tensor with same modulos
aaifjdfha,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/additive_shared.py,217e70c90916aed65725f2e6f6aa5d1cf4cf3662,57cd91e3302ecc55d06095e4a7e9917aea291758,TODO: how to correctly handle division in Zq?
aaifjdfic,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/chinese_remainder_theorem.py,726ed3d64eef39932918a7c7eae652e6a4aa4753,STILL_EXISTS,FIXME need to cast to tuple otherwise tensor_solve_system_coeffs is an unashable list
aaifjdghb,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/additive_shared.py,9d7cccfd62d891b989263fce2e1a6f1813288cd9,STILL_EXISTS,TODO: how to correctly handle division in Zq?
aaifjdgja,OpenMined/PySyft,test/torch/tensors/test_chinese_remainder.py,929ec3d415e106087e72ad9f88e16359cd0c3c90,d02b4eb8744c003b107f3ec1bb81ce0af955f7c4,TODO add tests for values > Q\/2 and also wrapping
aaifjdhbe,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/additive_shared.py,aff49fe813dc27aa7750f9ee41413c5763cec53e,STILL_EXISTS,TODO: how to correctly handle division in Zq?
aaifjdief,OpenMined/PySyft,test/workers/test_websocket_worker.py,14c6591f583d664398fd2486f7a98348a751abae,c752cea1cc298c6ad63644006f0cf9c0bb9b7b1e,TODO: check why unit test sometimes fails when WebsocketServerWorker is started from the unit test. Fails when run after test_federated_client.py
aaifjecid,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/precision.py,a15f66e88f612420146353d89105ba9b49379620,STILL_EXISTS,TODO how to divide by AST?
aaifjeddf,OpenMined/PySyft,syft/workers/base.py,7a078d89f38ac49e3afc11f4b78db04c8c29e643,STILL_EXISTS,TODO[jvmancuso]: avoid branching here if possible; maybe by changing code in
aaifjedhc,OpenMined/PySyft,syft/frameworks/torch/nn/rnn.py,4eccf777cf15a6333c15289b396712ae6aa090a7,STILL_EXISTS,TODO: implement a nn.Dropout class for PySyft
aaifjedhi,OpenMined/PySyft,syft/frameworks/torch/nn/rnn.py,4eccf777cf15a6333c15289b396712ae6aa090a7,STILL_EXISTS,Initiate states if needed
aaifjedif,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/precision.py,4eccf777cf15a6333c15289b396712ae6aa090a7,STILL_EXISTS,TODO: change to max_degree == degrees[-1] once MPC computations with high exponentials
aaifjeeei,OpenMined/PySyft,syft/frameworks/torch/linalg/operations.py,cb6ac27b7c803ca78496efc196d01dd2be6e063b,STILL_EXISTS,Needed to compute inv of a number in MPC
aaifjeefa,OpenMined/PySyft,syft/frameworks/torch/linalg/operations.py,cb6ac27b7c803ca78496efc196d01dd2be6e063b,6795f969d85af931c9f59a1e3bce45452ddce224,TODO
aaifjeegf,OpenMined/PySyft,syft/frameworks/hook.py,e6a107c14eec6dde40bc3c73c4e2b54dae3996df,STILL_EXISTS,TODO: I can't manage the import issue; can you?
aaifjeehh,OpenMined/PySyft,syft/frameworks/hook.py,e6a107c14eec6dde40bc3c73c4e2b54dae3996df,STILL_EXISTS,Put back the wrappers where needed
aaifjefei,OpenMined/PySyft,syft/frameworks/torch/crypto/securenn.py,e8a2da8d899198c1095701c1b02721bea8d26540,1944167f25943494d472f2e8a2edb9e2afda50f7,TODO: use array instead of a sharing zeros
aaifjeffa,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/precision.py,cd0a134288ab041884537b04eb65358c4d53a471,1b0faa3ede31d481c8b535998dc92b229439f02e,So this needs to be done manually
aaifjeffc,OpenMined/PySyft,test/torch/tensors/test_additive_shared.py,cd0a134288ab041884537b04eb65358c4d53a471,STILL_EXISTS,TODO: uncomment maxpool2d operations
aaifjeffg,OpenMined/PySyft,syft/frameworks/torch/crypto/securenn.py,adc2ece9c24ccf4b4004e84a48bb9ebe37ce2a14,9d882b43a1f2c72cf0299a54f8e82f158fb6bb88,TODO: support dilation.
aaifjeffi,OpenMined/PySyft,syft/frameworks/torch/crypto/securenn.py,f804eaffa5c0dabe7b7488b7c63a6a93177bb738,9d882b43a1f2c72cf0299a54f8e82f158fb6bb88,TODO: make this operation more efficient in order to be used with cnn modules.
aaifjefjh,OpenMined/PySyft,syft/messaging/message.py,02f4861e920560c509f8b5f12f48c7d9eb731ce1,39394c00aadee0d4d8b18aa84b6bf33911ce8ec1,TODO: add more efficient detailer and simplifier custom for this type
aaifjehee,OpenMined/PySyft,syft/generic/frameworks/hook/hook_args.py,39394c00aadee0d4d8b18aa84b6bf33911ce8ec1,STILL_EXISTS,FIXME This is for additiveShareTensor.child; it can be confusing and AST.child
aaifjehef,OpenMined/PySyft,syft/generic/frameworks/hook/hook_args.py,39394c00aadee0d4d8b18aa84b6bf33911ce8ec1,STILL_EXISTS,should perhaps be of type ShareDict extending dict or something like this
aaifjehga,OpenMined/PySyft,syft/generic/frameworks/hook/hook_args.py,39394c00aadee0d4d8b18aa84b6bf33911ce8ec1,STILL_EXISTS,TODO rename registry or use another one than for methods
aaifjehjb,OpenMined/PySyft,syft/generic/frameworks/hook/hook_args.py,39394c00aadee0d4d8b18aa84b6bf33911ce8ec1,STILL_EXISTS,return the same obj with an identity fct with a type check if needed
aaifjeicc,OpenMined/PySyft,syft/generic/frameworks/hook/hook_args.py,39394c00aadee0d4d8b18aa84b6bf33911ce8ec1,STILL_EXISTS,Could add more checks but not sure it is needed so far.
aaifjejij,OpenMined/PySyft,test/torch/tensors/test_large_precision.py,7e14632b65f9bb95b73ef62b0678858704fc6b4c,STILL_EXISTS,# TODO
aaifjfabf,OpenMined/PySyft,syft/messaging/plan.py,8211c8382dfefacae0b878da951012bac853f3af,STILL_EXISTS,TODO: we shouldn't use the same state_ids since we want to avoid
aaifjfadj,OpenMined/PySyft,test/message/test_plan.py,8211c8382dfefacae0b878da951012bac853f3af,STILL_EXISTS,TODO: this test is not working properly with remote workers.
aaifjfajd,OpenMined/PySyft,syft/generic/pointers/pointer_plan.py,d88ef33f92bd26c39e156881f22cc146a29092a7,STILL_EXISTS,TODO: fix comment for this and simplifier
aaifjfaji,OpenMined/PySyft,syft/generic/pointers/pointer_protocol.py,d88ef33f92bd26c39e156881f22cc146a29092a7,STILL_EXISTS,TODO: fix comment for this and simplifier
aaifjfbdd,OpenMined/PySyft,syft/frameworks/torch/federated/dataset.py,b8147210ff12298355e711ffd5c50266eb9a0c15,STILL_EXISTS,Fix for old versions of torchvision
aaifjgbcg,OpenMined/PySyft,syft/generic/frameworks/hook/hook.py,048a991d76564e7c031079d8b49004a546059650,STILL_EXISTS,4. Move the native function
aaifjgbci,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/native.py,e6ef7c205c7898e4401287791672a41947cf846b,STILL_EXISTS,Fix handle_command_function to correct this. #2637
aaifjgbdb,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/native.py,e6ef7c205c7898e4401287791672a41947cf846b,STILL_EXISTS,TODO change to inplace
aaifjgbej,OpenMined/PySyft,syft/messaging/plan/plan.py,e6ef7c205c7898e4401287791672a41947cf846b,STILL_EXISTS,TODO: A more complex strategy could be used
aaifjgbgg,OpenMined/PySyft,syft/messaging/plan/plan.py,e6ef7c205c7898e4401287791672a41947cf846b,STILL_EXISTS,We build the plan only if needed
aaifjgbhd,OpenMined/PySyft,syft/messaging/plan/state.py,e6ef7c205c7898e4401287791672a41947cf846b,STILL_EXISTS,TODO Make it only valid for AST
aaifjgiaf,OpenMined/PySyft,syft/generic/pointers/object_pointer.py,b62ed45c1d63de8d643941e09ce190ba6f2b376d,STILL_EXISTS,TODO: fix comment for this and simplifier
aaifjgica,OpenMined/PySyft,test/federated/test_train_config.py,da3afec066c1c58f9116b0e44bf493233db03b5c,STILL_EXISTS,TODO: I'm not sure this is valid torch JIT anyway
aaifjgigd,OpenMined/PySyft,syft/workers/base.py,6b233928a324ba2e1ec7b38d3d9927d5604adb73,d6da2d6d85cf444fbfaa081e5a0ea4f25518ddd7,FIXME: there is no ObjectDeleteMessage
aaifjgihf,OpenMined/PySyft,syft/frameworks/torch/hook/hook.py,beefe6403e1969c9224f2082fa5ebfd0cef68605,1212963eb907df24eff46aa658b64298bc35aca7,Hook .data to handle chain assignment when needed
aaifjgjbi,OpenMined/PySyft,syft/workers/base.py,d23c27ab79e3a883399e7ed824e2b45cb94f3d41,STILL_EXISTS,TODO[jvmancuso]: avoid branching here if possible; maybe by changing code in
aaifjhdih,OpenMined/PySyft,syft/frameworks/torch/mpc/securenn.py,f692ac551d41ebb4546274001ad192434b85e522,STILL_EXISTS,TODO: support dilation.
aaifjhdjc,OpenMined/PySyft,syft/frameworks/torch/mpc/securenn.py,f692ac551d41ebb4546274001ad192434b85e522,STILL_EXISTS,TODO: make this operation more efficient in order to be used with cnn modules.
aaifjheci,OpenMined/PySyft,syft/messaging/plan/plan.py,21ae9bbed39677358ecdfd7110ad6d908997b35f,dacb0b12aab6851b20af4812b7ea9391873a8f35,but a better syntax is being worked on
aaifjhedh,OpenMined/PySyft,syft/messaging/promise.py,21ae9bbed39677358ecdfd7110ad6d908997b35f,STILL_EXISTS,FIXME Ugly fix because I had id_to_add != self.owner.get_obj(id_to_add).id...
aaifjheei,OpenMined/PySyft,syft/messaging/promise.py,21ae9bbed39677358ecdfd7110ad6d908997b35f,STILL_EXISTS,some functionality like this might be necessary.
aaifjhefa,OpenMined/PySyft,syft/messaging/promise.py,21ae9bbed39677358ecdfd7110ad6d908997b35f,STILL_EXISTS,TODO: probably need to register the Promise
aaifjhefb,OpenMined/PySyft,syft/messaging/protocol.py,21ae9bbed39677358ecdfd7110ad6d908997b35f,dacb0b12aab6851b20af4812b7ea9391873a8f35,TODO if self.location is not None:
aaifjheff,OpenMined/PySyft,syft/messaging/protocol.py,21ae9bbed39677358ecdfd7110ad6d908997b35f,dacb0b12aab6851b20af4812b7ea9391873a8f35,TODO see if a better solution exists
aaifjhefi,OpenMined/PySyft,syft/messaging/protocol.py,21ae9bbed39677358ecdfd7110ad6d908997b35f,STILL_EXISTS,If the \"fake\" ids manually set by the user when writing the protocol exactly match the ids
aaifjheic,OpenMined/PySyft,test/torch/tensors/test_promise.py,21ae9bbed39677358ecdfd7110ad6d908997b35f,STILL_EXISTS,TODO fix need to wrap
aaifjhfce,OpenMined/PySyft,syft/generic/frameworks/hook/hook.py,242085f9ce05529315f49176091dbe36512026c0,STILL_EXISTS,if there's a type mismatch; try to fix it!
aaifjhfcj,OpenMined/PySyft,syft/generic/frameworks/hook/hook.py,242085f9ce05529315f49176091dbe36512026c0,STILL_EXISTS,TODO: add check to make sure this isn't getting around a security class
aaifjhfdj,OpenMined/PySyft,syft/generic/frameworks/hook/hook.py,242085f9ce05529315f49176091dbe36512026c0,STILL_EXISTS,Put back the wrappers where needed
aaifjhfgj,OpenMined/PySyft,test/torch/tensors/test_private.py,242085f9ce05529315f49176091dbe36512026c0,STILL_EXISTS,mul with non standard fix precision
aaifjibca,OpenMined/PySyft,syft/serde/compression.py,2ae23cccf2006ec0c478219aa914d84195f1650e,STILL_EXISTS,needed as otherwise we will get: module 'lz4' has no attribute 'frame'
aaifjibif,OpenMined/PySyft,syft/serde/msgpack/serde.py,475fd8d46a24d8f8ad81c7edaf0af29cb45b3426,STILL_EXISTS,1) Decompress the binary if needed
aaifjidgb,OpenMined/PySyft,syft/generic/pointers/pointer_plan.py,8847d8176fa1b26222664d2ce9d57db247700caf,STILL_EXISTS,TODO implement this feature using MultiPointerTensor
aaifjidig,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/precision.py,a35542ab518ea1b014e37d44060a5b3a8c19a211,STILL_EXISTS,TODO: should we add non-approximate version if self.child is a pure tensor?
aaifjieia,OpenMined/PySyft,syft/serde/protobuf/serde.py,f37eb9408e0a7917686536894e7d8a12ae766a4e,STILL_EXISTS,1) Decompress the binary if needed
aaifjifie,OpenMined/PySyft,syft/serde/serde.py,c4f33aab94e6bfbc704d8856b4e570c7d3069059,e44610bcea374571fbac244134e61a58fcf15808,needed as otherwise we will get: module 'lz4' has no attribute 'frame'
aaifjigch,OpenMined/PySyft,syft/serde/serde.py,c4f33aab94e6bfbc704d8856b4e570c7d3069059,e44610bcea374571fbac244134e61a58fcf15808,1) Decompress the binary if needed
aaifjihji,OpenMined/PySyft,syft/messaging/plan/plan.py,dacb0b12aab6851b20af4812b7ea9391873a8f35,STILL_EXISTS,TODO: can we reuse result_ids?
aaifjihjj,OpenMined/PySyft,syft/messaging/plan/plan.py,dacb0b12aab6851b20af4812b7ea9391873a8f35,STILL_EXISTS,TODO is only works up to 9 return values; because comparison is done on str and '7' > '16'
aaifjiidf,OpenMined/PySyft,test/message/test_plan.py,dacb0b12aab6851b20af4812b7ea9391873a8f35,STILL_EXISTS,TODO: Re-enable these once the rest of the Plan rework is completed
aaifjiihi,OpenMined/PySyft,test/serde/serde_helpers.py,dacb0b12aab6851b20af4812b7ea9391873a8f35,987126d487f51004d28b954523c115e4f5fc6351,TODO
aaifjiiji,OpenMined/PySyft,syft/messaging/plan/plan.py,987126d487f51004d28b954523c115e4f5fc6351,STILL_EXISTS,TODO is only works up to 9 return values; because comparison is done on str and '7' > '16'
aaifjijdh,OpenMined/PySyft,test/notebooks/test_notebooks.py,b899b2adda4fe7f0e01c19c44f845d871c16155d,ce8026e15f394bcbe61711e9478fdcccd2546b8a,new note: now that travis has been replaced with github actions; someone should test this to see if this is still needed or removed.
aaifjijgi,OpenMined/PySyft,syft/messaging/plan/plan.py,bfc22297658d92bfc818c86d69fe548483797d7c,STILL_EXISTS,TODO is only works up to 9 return values; because comparison is done on str and '7' > '16'
aaifjijjb,OpenMined/PySyft,syft/execution/plan.py,7dc983865a8db7e51918b44bff44ac0ba82b120a,STILL_EXISTS,TODO is only works up to 9 return values; because comparison is done on str and '7' > '16'
aaifjjcag,OpenMined/PySyft,syft/generic/object.py,a72f2715eeb329eab0bdeca28d928e21d232dc2e,368630afaba985ee9c26cc0aadd1c56c42bb38a6,NOTE: this is a fix to correct faulty registration that can sometimes happen
aaifjjccf,OpenMined/PySyft,syft/messaging/message.py,c9b84f4dbc347398450e60c6d6ea504cdd8c3da2,STILL_EXISTS,TODO: add more efficient detailer and simplifier custom for this type
aaifjjcci,OpenMined/PySyft,syft/workers/base.py,c9b84f4dbc347398450e60c6d6ea504cdd8c3da2,1eb369ae3a1865789f5809bec59f066ac1cbe58d,TODO [midokura-silvia]: send the tensor directly
aaifjjccj,OpenMined/PySyft,syft/workers/base.py,c9b84f4dbc347398450e60c6d6ea504cdd8c3da2,1eb369ae3a1865789f5809bec59f066ac1cbe58d,TODO this code is currently necessary for the async_fit method in websocket_client.py
aaifjjcgf,OpenMined/PySyft,syft/workers/base.py,a3f6a1c3ce6beef35dccf73954f213ccf739ecfb,STILL_EXISTS,Create the pointer if needed
aaifjjcie,OpenMined/PySyft,syft/generic/pointers/pointer_tensor.py,63dab685d71efac0bb0401165be46f7094aa0157,STILL_EXISTS,move to local target is equivalent to doing .get()
aaifjjcjc,OpenMined/PySyft,syft/serde/msgpack/torch_serde.py,63dab685d71efac0bb0401165be46f7094aa0157,STILL_EXISTS,TODO: fix serialisation of parameters (check in particular .child & .data) See #3214
aaifjjcjg,OpenMined/PySyft,syft/workers/base.py,63dab685d71efac0bb0401165be46f7094aa0157,STILL_EXISTS,Create the pointer if needed
aaifjjdah,OpenMined/PySyft,syft/workers/base.py,63dab685d71efac0bb0401165be46f7094aa0157,17909136b1918435a718c990bb428370dd7da9e5,TODO: needs more discussion
aaifjjddg,OpenMined/PySyft,syft/execution/plan.py,10979350cee471114e141fff45c5e2da5b22ea94,STILL_EXISTS,NOTE Maybe state shouldn't contain instanciated placeholders but values directly?
aaifjjdia,OpenMined/PySyft,syft/frameworks/torch/nn/conv.py,1b0faa3ede31d481c8b535998dc92b229439f02e,STILL_EXISTS,# because my particular experiment does not demand full functionality of
aaifjjdib,OpenMined/PySyft,syft/frameworks/torch/nn/conv.py,1b0faa3ede31d481c8b535998dc92b229439f02e,STILL_EXISTS,# a convolutional layer; I will only implement the basic functionality.
aaifjjehb,OpenMined/PySyft,syft/frameworks/torch/nn/functional.py,1b0faa3ede31d481c8b535998dc92b229439f02e,STILL_EXISTS,Add a bias if needed
aaifjjffa,OpenMined/PySyft,syft/frameworks/torch/rnn.py,6114c9332cb9b6582aa39dbcda6deb2610ad86e3,STILL_EXISTS,TODO Workaround to cyclic import
aaifjjffb,OpenMined/PySyft,syft/frameworks/torch/rnn.py,6114c9332cb9b6582aa39dbcda6deb2610ad86e3,STILL_EXISTS,HELP NEEDED HERE
aaifjjfgg,OpenMined/PySyft,test/torch/nn/test_nn.py,6114c9332cb9b6582aa39dbcda6deb2610ad86e3,STILL_EXISTS,TODO: uncomment maxpool2d operations
aaifjjgaf,OpenMined/PySyft,syft/execution/placeholder.py,c2544d1f7e48cedf9508f7cce75ac57d9452507f,STILL_EXISTS,TODO: A more complex strategy could be used
aaifjjgag,OpenMined/PySyft,syft/execution/plan.py,c2544d1f7e48cedf9508f7cce75ac57d9452507f,b3eadffc7033be6a563a3e4ccd437975d9677bc0,TODO is it necessary to maintain this?
aaifjjgeb,OpenMined/PySyft,syft/execution/role.py,c2544d1f7e48cedf9508f7cce75ac57d9452507f,67cc188d6d01acf54ce9352cd3ea88ee37b54eb2,TODO torch shouldn't be used here
aaifjjgei,OpenMined/PySyft,syft/execution/role.py,c2544d1f7e48cedf9508f7cce75ac57d9452507f,STILL_EXISTS,TODO Should we be able to rebuild?
aaifjjgfb,OpenMined/PySyft,syft/execution/role.py,c2544d1f7e48cedf9508f7cce75ac57d9452507f,STILL_EXISTS,TODO isn't it weird that state placeholders are both in state and plan?
aaifjjgfc,OpenMined/PySyft,syft/execution/role.py,c2544d1f7e48cedf9508f7cce75ac57d9452507f,STILL_EXISTS,TODO not the cleanest method ever
aaifjjjhb,OpenMined/PySyft,test/execution/test_state.py,20670fe2c481d42f279fdaeb3fa81647b3eb1c14,STILL_EXISTS,TODO: this test is not working properly with remote workers.
aaigaaaae,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/gradients.py,e4b5cab232910968036f8bac1d499b19356de41c,STILL_EXISTS,grad_self_ = grad \/ (2 * self.result) TODO: Broken as of Garbage Collection for `AutoGradTensor` (#3387)
aaigaaaag,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/gradients_core.py,e4b5cab232910968036f8bac1d499b19356de41c,STILL_EXISTS,self.result = None TODO: Broken as of Garbage Collection for `AutoGradTensor` (#3387)
aaigaaacb,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/additive_shared.py,9c074cde14426b258974d35312b9b6bad0729ffd,STILL_EXISTS,The code is the same for cat and stack; maybe we could factorize
aaigaaaef,OpenMined/PySyft,syft/execution/state.py,26e5aea90bfcac5558ee01679504e12e4e662687,f07392e16ce56fe699bea00b5662774b7c43354e,TODO clean this function
aaigaaagf,OpenMined/PySyft,syft/execution/communication.py,53cdb0e311a91661beedae409c3fe36e8a21d1d6,b457124961a800a5c6c2735ac7d096d876be2ccd,the pointer_tensor method # @TODO: reactor to enum
aaigaaagi,OpenMined/PySyft,syft/execution/communication.py,53cdb0e311a91661beedae409c3fe36e8a21d1d6,b457124961a800a5c6c2735ac7d096d876be2ccd,key word args needed for the pointer tensor method == self.name
aaigaabdd,OpenMined/PySyft,syft/frameworks/torch/tensors/crypten/syft_crypten.py,8aeba9521d2dddfae55d1d40ba7b2e124bcea33c,STILL_EXISTS,TODO: what we should return specific for this one?
aaigaabhi,OpenMined/PySyft,syft/frameworks/torch/mpc/fss.py,63af4f2a487d5e96d3a85d256dff13ecd8cbdbb5,STILL_EXISTS,TODO case >2 workers ?
aaigaacgb,OpenMined/PySyft,syft/frameworks/crypten/context.py,52226d1ed06f2d7c441672a5744ff69366483b05,STILL_EXISTS,This is needed because at building we use a set of methods defined in syft (ex: load)
aaigaaddd,OpenMined/PySyft,syft/execution/protocol.py,5cc94122a1ab58f6640e8cf9a9e79cc09ff485ac,STILL_EXISTS,TODO: can we reuse result_ids?
aaigaaddg,OpenMined/PySyft,syft/execution/protocol.py,5cc94122a1ab58f6640e8cf9a9e79cc09ff485ac,STILL_EXISTS,NOTE Maybe state shouldn't contain instanciated placeholders but values directly?
aaigaadha,OpenMined/PySyft,test/serde/serde_helpers.py,5cc94122a1ab58f6640e8cf9a9e79cc09ff485ac,STILL_EXISTS,TODO temporary trick to tell during the protocol building to whom belongs the tensors
aaigaaeag,OpenMined/PySyft,test/serde/serde_helpers.py,7a5420bbf95df4e113c6f9f512e61892b8c56e88,STILL_EXISTS,TODO temporary trick to tell during the protocol building to whom belongs the tensors
aaigaaebf,OpenMined/PySyft,syft/frameworks/torch/nn/rnn.py,952e2c6a655f59901d2bdd2cb4aecc51bede3e2a,STILL_EXISTS,Build RNN backward layers; if needed
aaigaaeec,OpenMined/PySyft,syft/execution/action.py,b457124961a800a5c6c2735ac7d096d876be2ccd,STILL_EXISTS,implementations; but must implement their own conversions to the appropriate classes.
aaigaaefg,OpenMined/PySyft,syft/frameworks/torch/mpc/fss.py,b457124961a800a5c6c2735ac7d096d876be2ccd,STILL_EXISTS,TODO case >2 workers ?
aaigaafbg,OpenMined/PySyft,syft/execution/protocol.py,d2b62ed9c9f88ca90110850171da0a2e3cf5e561,5180765e1cd99fa199fd59a2357504d5b85627c0,TODO add a reset method on Role
aaigaafbh,OpenMined/PySyft,syft/execution/role.py,d2b62ed9c9f88ca90110850171da0a2e3cf5e561,STILL_EXISTS,TODO mock for now; fetch will use worker's store in a future work
aaigaafcd,OpenMined/PySyft,syft/frameworks/torch/mpc/fss.py,0519ff024992c87c346d5c2fdf27d917d1b6b962,STILL_EXISTS,TODO case >2 workers ?
aaigaaggh,OpenMined/PySyft,test/serde/serde_helpers.py,e44610bcea374571fbac244134e61a58fcf15808,STILL_EXISTS,TODO: Add proper testing for paillier tensor
aaigaahcc,OpenMined/PySyft,syft/generic/object_storage.py,368630afaba985ee9c26cc0aadd1c56c42bb38a6,STILL_EXISTS,NOTE: this is a fix to correct faulty registration that can sometimes happen
aaigaaibc,OpenMined/PySyft,syft/frameworks/torch/mpc/fss.py,4662f435da30215920ea4e0e9b11e52cb849bf92,STILL_EXISTS,TODO case >2 workers ?
aaigaajga,OpenMined/PySyft,syft/grid/grid_client.py,dde02fdb7b0a3bcf4e15bb660f9efe7c37bb7eba,STILL_EXISTS,TODO
aaigaajgc,OpenMined/PySyft,syft/frameworks/torch/hook/hook.py,bb7730ba6371b3c744390cd8fbee65f79d51ddd4,STILL_EXISTS,TODO: add .data and .grad to syft tensors
aaigaajgd,OpenMined/PySyft,syft/frameworks/torch/mpc/fss.py,bb7730ba6371b3c744390cd8fbee65f79d51ddd4,STILL_EXISTS,TODO case >2 workers ?
aaigabdgc,OpenMined/PySyft,syft/frameworks/torch/hook/hook.py,753add230ab7c522ec164d15cbe314eacf1b6e9d,STILL_EXISTS,TODO: add .data and .grad to syft tensors
aaigabgja,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/autograd.py,682bfd2a8f3276e1c079e60eeebb32f96d9bcf13,STILL_EXISTS,TODO why is that? Normally .data is detached from autograd
aaigabibj,OpenMined/PySyft,syft/frameworks/torch/he/fv/util/rns_tool.py,a3a47bdf8f219fca179c5a8d2dc84bbb5caec433,STILL_EXISTS,No correction needed
aaigabich,OpenMined/PySyft,syft/messaging/message.py,f63bd7148bc4c3bb885a1049e2dc519f1abe9538,STILL_EXISTS,TODO: uncomment this when solving the WorkerCommandMessage issue.
aaigabida,OpenMined/PySyft,syft/messaging/message.py,f63bd7148bc4c3bb885a1049e2dc519f1abe9538,STILL_EXISTS,TODO: when testing is fixed; uncomment this to enable worker command message support.
aaigabiea,OpenMined/PySyft,test/serde/protobuf/test_protobuf_serde_full.py,f63bd7148bc4c3bb885a1049e2dc519f1abe9538,STILL_EXISTS,TODO: this should be fixed in a future PR.
aaigabijh,OpenMined/PySyft,syft/workers/message_handler.py,1eb369ae3a1865789f5809bec59f066ac1cbe58d,STILL_EXISTS,TODO Andrew thinks this is gross; please fix. Instead need to
aaigabjcd,OpenMined/PySyft,syft/workers/message_handler.py,1eb369ae3a1865789f5809bec59f066ac1cbe58d,STILL_EXISTS,TODO [midokura-silvia]: send the tensor directly
aaigabjce,OpenMined/PySyft,syft/workers/message_handler.py,1eb369ae3a1865789f5809bec59f066ac1cbe58d,STILL_EXISTS,TODO this code is currently necessary for the async_fit method in websocket_client.py
aaigacbff,OpenMined/PySyft,syft/frameworks/torch/mpc/fss.py,7658159fada48a26e546f9a68ff40bd32c19c08d,STILL_EXISTS,TODO case >2 workers ?
aaigacfgb,OpenMined/PySyft,syft/frameworks/crypten/context.py,18f204925d978bac8c3ae52b12116162a084f94c,STILL_EXISTS,TODO: run ttp in a specified worker
aaigacfhf,OpenMined/PySyft,syft/frameworks/torch/hook/hook.py,18f204925d978bac8c3ae52b12116162a084f94c,STILL_EXISTS,TODO: add .data and .grad to syft tensors
aaigacgbc,OpenMined/PySyft,syft/frameworks/torch/hook/hook.py,646db8eaa88a329cb1e66299183f1a400afc67be,STILL_EXISTS,TODO: add .data and .grad to syft tensors
aaigaciei,OpenMined/PySyft,syft/frameworks/torch/mpc/fss.py,279f3722ff0ca0606b62e1091370267d658cea3f,STILL_EXISTS,TODO case >2 workers ?
aaigadchb,OpenMined/PySyft,docs/conf.py,572b36d8403ea2697350ffabb41fdf7045eddc35,STILL_EXISTS,This hack is necessary since RTD does not issue `sphinx-apidoc` before running
aaigadchg,OpenMined/PySyft,docs/conf.py,572b36d8403ea2697350ffabb41fdf7045eddc35,STILL_EXISTS,Additionally it helps us to avoid running apidoc manually
aaigadeef,OpenMined/PySyft,src/syft/skeleton.py,572b36d8403ea2697350ffabb41fdf7045eddc35,STILL_EXISTS,\"\"\" || This is a skeleton file that can serve as a starting point for a Python || console script. To run this script uncomment the following lines in the || [options.entry_points] section in setup.cfg: ||  ||     console_scripts = ||          fibonacci = syft.skeleton:run ||  || Then run `python setup.py install` which will install the command `fibonacci` || inside your current environment. || Besides console scripts; the header (i.e. until _logger...) of this file can || also be used as template for Python modules. ||  || Note: This skeleton file can be safely removed if not needed! || \"\"\"
aaigadegb,OpenMined/PySyft,src/syft/ast/klass.py,a0e6322c3d5dfc7a9e7a7f52fc5b9bb1afaf4a78,STILL_EXISTS,TODO: lookup actual return type instead of just guessing that it's identical
aaigadegh,OpenMined/PySyft,src/syft/ast/util.py,a0e6322c3d5dfc7a9e7a7f52fc5b9bb1afaf4a78,STILL_EXISTS,\"\"\" || This is a skeleton file that can serve as a starting point for a Python || console script. To run this script uncomment the following lines in the || [options.entry_points] section in setup.cfg: ||  ||     console_scripts = ||          fibonacci = syft.skeleton:run ||  || Then run `python setup.py install` which will install the command `fibonacci` || inside your current environment. || Besides console scripts; the header (i.e. until _logger...) of this file can || also be used as template for Python modules. ||  || Note: This skeleton file can be safely removed if not needed! || \"\"\"
aaigadehf,OpenMined/PySyft,syft/frameworks/crypten/message_handler.py,35837814d2a9962b0be1cb3bbeed20df1f0187c1,STILL_EXISTS,TODO Change this; we need a way to handle multiple plan definitions
aaigadfah,OpenMined/PySyft,src/syft/__init__.py,670a6fe79cc38987a2fe16ad15bce879b8377cf2,STILL_EXISTS,\"\"\" || Welcome to the syft package! This package is the primary package for PySyft. || This package has two kinds of attributes: submodules; and convenience functions. || Submodules are configured in the standard way; but the convenience || functions exist to allow for a convenient `import syft as sy` to then expose || the most-used functionalities directly on syft. Note that this way of importing || PySyft is the strict convention in this codebase. Do NOT simply call || `import syft` and then directly use `syft.<method>` everywhere. Previous || versions of the codebase mix and matched `sy` and `syft` references and it was || very annoying. ||  || Now; it's important to recognize that the syft module is split into two || distinct groups of functionality which we casually refer to as syft \"core\" || and syft \"python\". \"core\" functionality is functionality which is designed || to be universal across all Syft languages (javascript; kotlin; swift; etc.). || Syft \"python\" includes all functionality which by its very nature cannot be || truly polyglot. Syft \"core\" functionality includes the following modules: ||  || * :py:mod:`syft.ast` - code generates external library abstract syntax tree using a white\\ || list of methods || * :py:mod:`syft.worker` - APIs for interacting with remote machines you do not directly control. || * :py:mod:`syft.message` - APIs for serializing messages sent between Client and Worker classes. || * :py:mod:`syft.pointer` - Client side API for referring to objects on a Worker ||  ||  || This is a skeleton file that can serve as a starting point for a Python || console script. To run this script uncomment the following lines in the || [options.entry_points] section in setup.cfg: ||  ||     console_scripts = ||          fibonacci = syft.skeleton:run ||  || Then run `python setup.py install` which will install the command `fibonacci` || inside your current environment. || Besides console scripts; the header (i.e. until _logger...) of this file can || also be used as template for Python modules. ||  || Note: This skeleton file can be safely removed if not needed! || \"\"\"
aaigaebia,OpenMined/PySyft,tests/syft/worker/virtual/virtual_client_test.py,fd4644785826c16ca7ad951a3dc1af99dd91693a,STILL_EXISTS,TODO (gmuraru) Add tests after we finish the VirtualClient
aaigaebib,OpenMined/PySyft,tests/syft/worker/virtual/virtual_worker_test.py,fd4644785826c16ca7ad951a3dc1af99dd91693a,STILL_EXISTS,TODO (gmuraru) Add tests after we finish the VirtualWorker
aaigaecag,OpenMined/PySyft,syft/frameworks/crypten/hook/hook.py,91b1bf8aa7ea2f28dd045366639885cdc134c4c8,STILL_EXISTS,\"\"\" || Methods which we overwrite when building the plan || This list might become bigger as we should support more operations ||  || Why is needed? || When the local party builds the plan it needs to trace the operations that || are done on a CrypTensor and CrypTenModule. It does not know about || what data\/model are involved in the computation. || Because of this; when building the plan; the local party can work with || only some \"shells\" of specific operations ||  || Workflow for local party -- can be seen in syft\/frameworks\/crypten\/context.py: || 1. replace real functions\/methods with shell like functions\/methods (only for some) || 2. Call \"crypten_init\" (to be able to perform some of the CrypTen computation on the ||   shell CryptenTensors\/CryptenModules) ||    Eg: cryptensor + cryptensor ||   If this is not done CrypTen will throw an exception because there is tried || to run crypten specific computation in a not crypten environment || 3. build the plan (register the set of actions that should be performed) || 4. Call \"crypten_uninit\" || 5. Undo the operations done at step 1 ||  || Q: Why get_plain_text appears only in the crypten_to_auto_overload; but not || in crypten_plan_hook? || A: 1. The method is added to the PlaceHolder class such that when the plan is || built it would be traced. ||    2. The local party will build the plan by being in a CrypTen context (there || is called \"crypten.init()\" before building the plan) and calling \"get_plain_text\" || on the \"shell\" CrypTensor (in our case is a CrypTensor that has only values of 0) || will not require to overwrite another function || \"\"\"
aaigaecda,OpenMined/PySyft,syft/execution/role.py,bbeb960e9709814932bd001d4f16cd36620a3b2e,STILL_EXISTS,Remove unused placeholders; except inputs
aaigaecde,OpenMined/PySyft,test/execution/test_plan.py,bbeb960e9709814932bd001d4f16cd36620a3b2e,STILL_EXISTS,unused actions
aaigaechb,OpenMined/PySyft,syft/execution/translation/threepio.py,8d61947dfe3d45eacb000aae6077c8d9ea4b6fcf,STILL_EXISTS,Create action informat needed for role's register_action method
aaigaecjf,OpenMined/PySyft,src/syft/core/worker/__init__.py,9d6106b7a8ba4475a580ba122864298aca52252a,c76363f5011f7796d8b3490b229ad0d6a67d70d3,\"\"\" || Welcome to the :py:mod:`syft.core.worker` module! This is a good place to begin your education of || what Syft is and how it works. ||  || At it's core; Syft is a set of libraries which allows you to perform data processing on data you || cannot see. We have two core \"personas\" within the Syft ecosystem; the \"data owner\" and the \"data scientist\" || (who is sometimes referred to as the \"model owner\"). The data owner has data to protect; and the data scientist || wants to answer a question using data owned by one or more data owners. ||  || Note that a data owner could be a consumer who has data on their phone; a hospital with medical records; or || even a Raspberry PI floating in the middle of the Pacific Ocean! It just represents a collection of data || within a Data Owner's domain of ownership. As such; there are three core abstractions you should know about: ||  || * :py:mod:`syft.core.worker.domain` - this API is the interface to a collection of datasets owned by a single entity. || * :py:mod:`syft.core.worker.worker` - this API is the interface to a remote machine within a data owner's domain. || * :py:mod:`syft.core.worker.client` - This API is the interface a data scientist uses to interact with a worker within\\ ||  a domain ||  || So; a domain would be something like \"Big Fancy Hospital\" and a worker would be a single machine within that hospital || which a Data Scientist can use to process some data. A domain will have many workers; each of which could be serving || a different Data Scientist. Alternatively; one Data Scientist could be using multiple workers (such as if they have || very computationally expensive programs to run and they want to run them in parallel). ||  || \"\"\"
aaigaecjj,OpenMined/PySyft,src/syft/core/worker/__init__.py,c76363f5011f7796d8b3490b229ad0d6a67d70d3,STILL_EXISTS,\"\"\" || Welcome to the :py:mod:`syft.core.worker` module! This is a good place to begin your education of || what Syft is and how it works. ||  || At it's core; Syft is a set of libraries which allows you to perform data processing on data you || cannot see. We have two core \"personas\" within the Syft ecosystem; the \"data owner\" and the \"data scientist\" || (who is sometimes referred to as the \"model owner\"). The data owner has data to protect; and the data scientist || wants to answer a question using data owned by one or more data owners. ||  || Note that a data owner could be a consumer who has data on their phone; a hospital with medical records; or || even a Raspberry PI floating in the middle of the Pacific Ocean! It just represents a collection of data || within a Data Owner's domain of ownership. As such; there are three core abstractions you should know about: ||  || * :py:mod:`syft.core.worker.domain.Domain` - this API is the interface to a collection of datasets owned by a single \\ || entity. || * :py:mod:`syft.core.worker.worker.Worker` - this API is the interface to a remote machine within a data owner's \\ || domain. || * :py:mod:`syft.core.worker.client.Client` - This API is the interface a data scientist uses to interact with a worker \\ || within a domain ||  || So; a domain would be something like \"Big Fancy Hospital\" and a worker would be a single machine within that hospital || which a Data Scientist can use to process some data. A domain will have many workers; each of which could be serving || a different Data Scientist. Alternatively; one Data Scientist could be using multiple workers (such as if they have || very computationally expensive programs to run and they want to run them in parallel). ||  || However; a Data Scientist wouldn't interact with these remote Domain and Worker APIs directly. Instead; they use their || :py:mod:`syft.core.worker.client.Client` api which has lots of convenience functions for interacting with remote || machines held within remote domains. ||  || The :py:mod:`syft.core.worker.client.Client` API will send :py:mod:`syft.core.message.message.Message` objects to the || :py:mod:`syft.core.worker.domain.Domain` API which will handle some messages itself and; if appropriate; forward other || messages to the appropriate :py:mod:`syft.core.worker.worker.Worker` for execution on real objects. ||  || However; you will find that Domain; Worker; and Client all have missing functionality; namely how to send a message! || This might seem like a critical oversight; but it's actually an important abstraction. These classes are merely || interfaces for the API language that all Domain; Worker; and Client objects should use. This language is universal || regardless of whether two phones; two hospitals; or two satellites are talking to each other! However; the exact || transport protocol for _how_ messages are sent we leave up to the _specific instantiation_ of a worker. ||  || The primary instantiation of Domain\/Worker\/Client that we use for development; testing; and learning is the || VirtualDomain\/VirtualWorker\/VirtualClient instantiation. More on that in a moment... ||  || If you're learning Syft; the next step you should take is to jump into the :py:mod:`syft.core.worker.client.Client` || class... ||  ||  || \"\"\"
aaigaedii,OpenMined/PySyft,syft/frameworks/crypten/hook/hook.py,2bec35ffb2c579421edfc3ff2d01187a6a6fa01d,STILL_EXISTS,4. Move the native function
aaigaeefd,OpenMined/PySyft,src/syft/core/worker/__init__.py,77c09a85b96e995d599cefc289aa04c8935a39ba,STILL_EXISTS,\"\"\" || Welcome to the :py:mod:`syft.core.worker` module! This is a good place to begin your education of || what Syft is and how it works. ||  || At it's core; Syft is a set of libraries which allows you to perform data processing on data you || cannot see. We have two core \"personas\" within the Syft ecosystem; the \"data owner\" and the \"data scientist\" || (who is sometimes referred to as the \"model owner\"). The data owner has data to protect; and the data scientist || wants to answer a question using data owned by one or more data owners. ||  || Note that a data owner could be a consumer who has data on their phone; a hospital with medical records; or || even a Raspberry PI floating in the middle of the Pacific Ocean! It just represents a collection of data || within a Data Owner's domain of ownership. As such; there are three core abstractions you should know about: ||  || * :py:mod:`syft.core.worker.domain.Domain` - this API is the interface to a collection of datasets owned by a single \\ || entity. || * :py:mod:`syft.core.worker.worker.Worker` - this API is the interface to a remote machine within a data owner's \\ || domain. || * :py:mod:`syft.core.worker.client.Client` - This API is the interface a data scientist uses to interact with a worker \\ || within a domain ||  || So; a domain would be something like \"Big Fancy Hospital\" and a worker would be a single machine within that hospital || which a Data Scientist can use to process some data. A domain will have many workers; each of which could be serving || a different Data Scientist. Alternatively; one Data Scientist could be using multiple workers (such as if they have || very computationally expensive programs to run and they want to run them in parallel). ||  || However; a Data Scientist wouldn't interact with these remote Domain and Worker APIs directly. Instead; they use their || :py:mod:`syft.core.worker.client.Client` api which has lots of convenience functions for interacting with remote || machines held within remote domains. ||  || The :py:mod:`syft.core.worker.client.Client` API will send :py:mod:`syft.core.message.message.Message` objects to the || :py:mod:`syft.core.worker.domain.Domain` API which will handle some messages itself and; if appropriate; forward other || messages to the appropriate :py:mod:`syft.core.worker.worker.Worker` for execution on real objects. ||  || However; you will find that Domain; Worker; and Client all have missing functionality; namely how to send a message! || This might seem like a critical oversight; but it's actually an important abstraction. These classes are merely || interfaces for the API language that all Domain; Worker; and Client objects should use. This language is universal || regardless of whether two phones; two hospitals; or two satellites are talking to each other! However; the exact || transport protocol for _how_ messages are sent we leave up to the _specific instance of a worker. ||  || The primary instance of Domain\/Worker\/Client that we use for development; testing; and learning is the || VirtualDomain\/VirtualWorker\/VirtualClient instance. More on that in a moment... ||  || If you're learning Syft; the next step you should take is to jump into the :py:mod:`syft.core.worker.domain.Domain` || class... ||  ||  || \"\"\"
aaigaeegg,OpenMined/PySyft,src/syft/core/worker/service/get_object_service.py,a5627b56c2af7d690565f6fb51a1e8e0d6dd57fb,STILL_EXISTS,TODO: return StoreableObject
aaigaeeii,OpenMined/PySyft,src/syft/core/io/virtual.py,4ce3735bc20d5be2d7090ddb16bd6995e426868b,STILL_EXISTS,\"\"\"In this class; we support the functionality necessary to support || virtual network connections between nodes in the Syft ecosystem. || Replacing this object with an actual network connection object || (such as one powered by P2P tech; web sockets; or HTTP) should || execute the exact same functionality but do so over a network\"\"\"
aaigaeejh,OpenMined/PySyft,src/syft/core/nodes/vm/client.py,4ce3735bc20d5be2d7090ddb16bd6995e426868b,6294f99de7c626268a27c5652a58c5752f83998b,TODO: remove this - security risk
aaigaefba,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/additive_shared.py,aaf373985b79030a3676c4021373fa0aab23b1a6,STILL_EXISTS,FIXME make it cleaner and robust for more options
aaigaefcb,OpenMined/PySyft,src/syft/core/nodes/device/device.py,361e2250497d67695709132bdef20976e4f7f0fc,cfd8321521716748db80fe397342189cb80a68c1,TODO: populate list of services
aaigaefcc,OpenMined/PySyft,src/syft/core/nodes/abstract/worker.py,a0a496d1c4fec3c7e2ad054ee3a1b873243b44a0,STILL_EXISTS,TODO: change services type  to List[WorkerService] when typechecker allows subclassing
aaigaefed,OpenMined/PySyft,syft/frameworks/torch/nn/functional.py,1ee500768073b2fd7f106357fbf7c6b48e575faa,STILL_EXISTS,Compute the max as a binary tree: 2 steps are needed for 4 values
aaigaefgi,OpenMined/PySyft,src/syft/common/id.py,8d0979d9faf3d70abaeeecd324b2069b94b5fd29,67892c8bca63ccc72e02c678164903b4b1ead91b,TODO find out what type is smaller for protobuf msgs.
aaigaefgj,OpenMined/PySyft,src/syft/core/nodes/domain/domain.py,2f2cee5b9a7217b03e0e35c7df7059cacfd93c81,STILL_EXISTS,TODO: add available compute types
aaigaefha,OpenMined/PySyft,src/syft/core/nodes/domain/domain.py,2f2cee5b9a7217b03e0e35c7df7059cacfd93c81,STILL_EXISTS,TODO: add default compute type
aaigaegcf,OpenMined/PySyft,src/syft/core/nodes/device/client.py,cfd8321521716748db80fe397342189cb80a68c1,835cd36ceb62c21d1a17adb73a09fa439d5cf50d,particular kind of context to know how to use it. We should be
aaigaehde,OpenMined/PySyft,src/syft/core/nodes/abstract/worker.py,e7710b2973957dde862dcea376eb90311ceb7140,STILL_EXISTS,TODO: change services type  to List[WorkerService] when typechecker allows subclassing
aaigaehfa,OpenMined/PySyft,src/syft/core/nodes/abstract/remote_nodes.py,31cd35b3eca592dd9018f1f04a74e880c0c351f6,STILL_EXISTS,we probably shouldn't allow very large keys.
aaigaeibg,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/additive_shared.py,4ddcd8980d56fbb7ede710962e5126286906e965,STILL_EXISTS,FIXME make it cleaner and robust for more options
aaigaeifd,OpenMined/PySyft,src/syft/core/nodes/abstract/node.py,4547f365dcac757ab10144727f5a4b828c01dec0,b4f274703d9e46e74d992b1c3c96480bd401c230,TODO: change services type  to List[NodeService] when typechecker allows subclassing
aaigaeiga,OpenMined/PySyft,src/syft/grid/device.py,79e3c6d8382d57614eff8eedd0813aff97c0b681,STILL_EXISTS,\"\"\"The purpose of this application is to allow us to dev and test PySyft || functionality on an actual local network. This is NOT meant to be run in || production (that's the *actual* grid's job).\"\"\"
aaigaeigb,OpenMined/PySyft,src/syft/grid/domain.py,79e3c6d8382d57614eff8eedd0813aff97c0b681,STILL_EXISTS,\"\"\"The purpose of this application is to allow us to dev and test PySyft || functionality on an actual local network. This is NOT meant to be run in || production (that's the *actual* grid's job).\"\"\"
aaigaeigc,OpenMined/PySyft,src/syft/grid/network.py,79e3c6d8382d57614eff8eedd0813aff97c0b681,71db6b2d0bb3d9aa1f0170eecad6cea8abd9b839,\"\"\"The purpose of this application is to allow us to dev and test PySyft || functionality on an actual local network. This is NOT meant to be run in || production (that's the *actual* grid's job).\"\"\"
aaigaeigd,OpenMined/PySyft,src/syft/grid/pks.py,79e3c6d8382d57614eff8eedd0813aff97c0b681,STILL_EXISTS,\"\"\"The purpose of this application is to allow us to dev and test PySyft || functionality on an actual local network. This is NOT meant to be run in || production (that's the *actual* grid's job).\"\"\"
aaigaejjj,OpenMined/PySyft,src/syft/core/serialization/serde.py,5e7c25e3fae7ad984d570fd8668bec7be7059d89,STILL_EXISTS,TODO create
aaigafaaa,OpenMined/PySyft,src/syft/core/serialization/serde.py,5e7c25e3fae7ad984d570fd8668bec7be7059d89,STILL_EXISTS,TODO: compression step
aaigafbcj,OpenMined/PySyft,src/syft/core/io/route.py,835cd36ceb62c21d1a17adb73a09fa439d5cf50d,052ef45de3220b4318526fdc25d712dc9d8ad8e6,TODO: instead of passing in a list; come up with an abstractoin for
aaigafbdd,OpenMined/PySyft,src/syft/core/message/syft_message.py,835cd36ceb62c21d1a17adb73a09fa439d5cf50d,STILL_EXISTS,TODO: I think this is really cool but we don't have the notion of a signature yet.
aaigafccb,OpenMined/PySyft,src/syft/core/nodes/device/client.py,835cd36ceb62c21d1a17adb73a09fa439d5cf50d,STILL_EXISTS,# particular kind of context to know how to use it. We should be
aaigafdhe,OpenMined/PySyft,src/syft/core/nodes/abstract/service/heritage_update_service.py,3e5c4ba3e34132ab54be7975d9c009ac04971f6e,STILL_EXISTS,TODO: change all message names in syft to have \"WithReply\" or \"WithoutReply\" at teh end of the name
aaigafdhf,OpenMined/PySyft,src/syft/core/nodes/abstract/service/heritage_update_service.py,3e5c4ba3e34132ab54be7975d9c009ac04971f6e,STILL_EXISTS,TODO: solve this with node group address?
aaigafhjh,OpenMined/PySyft,syft/frameworks/torch/he/fv/context.py,786bb4aec8413b95340b4befab9a85b789823af4,STILL_EXISTS,TODO: Check if the parameters are secure according to HomomorphicEncryption.org standards
aaigafiaj,OpenMined/PySyft,src/syft/core/io/route.py,052ef45de3220b4318526fdc25d712dc9d8ad8e6,STILL_EXISTS,\"\"\"Route is an abstraction between Connection and Client. || A Client represents an API for interfacing with a Node of || a particular type at a specific location. We assume that || a Client may have multiple ways of interacting with a node || (such as multiple protocols; http; websockets; broadcast server || ; etc.). We assume that a Client also has knowledge of the || full variety of messages and message types which can be sent || to a Node. It is the highest level of abstraction. ||  || A connection is the lowest level of abstraction; representing || a real network interface to a single destination. Importantly; || this destination may not even be the ultimate destination || for a message. In this sense; a connection is just a single \"hop\" || in a potentially long series of hops between a Client and || a Node. ||  || However; it's important to note that a Client doesn't need to || have direct access to the node it communicates with because || Nodes have a variety of services for forwarding messages || around. For example; if I have two clients; one pointing || to a Domain node and another pointing to a Device within || the Domain; both might use the same connection object || because all messages to a device have to go through a domain. || This is because; for permissions reasons; the domain represents || an intentional information bottleneck. ||  || This might beg the question; what is the point of Route if || the right combination of Clients and Connnections can always || get a message to where it needs to go? ||  || The answer is that neither a Connection; Client; or Node are || appropriate for the nature of four combined constraints. ||  || 1) the existence of multiple connections from client -> node || with varying tradeoffs (websocket; http; webRTC; gRPC; etc.) || 2) the existence of multiple clients which share the same || connection because of intentional information bottlenecks. || 3) the existence of 1->many connection types (pub-sub) || 4) the varying cost of different networks (edge cellular; and || cloud networking costs being the biggest) ||  || We need the ability for an arbitrary client (which represents || an abstract; indirect connection to a specific remote node of || a specific type) to be able to consider a message; it's address || ; and other metadata; and make a logical decision on which connection || should be used. (For example; some messages block for a reply) ||  || We need the connection decision to take into account the latency; || connection health; and overall cost of each connection option as || measured by fixed constants as well as previous uses of the || connection. ||  || This necessitates an abstraction which lets us store connection || information both about hops we know about and about hops we don't || know about. ||  || This abstraction is called a \"Route\" and each hop is called a \"Hop\" || where any collection of hops within the route is called a  || \"RouteSegment\". Note that a Route need only be initialized with  || it's source and destination(s).  ||  || When a route is used by a client; it is used to decide which route || would be best to take for a particular message. ||  || When a route is attached to a message; it implies the expected || journey the message is to take to the address. A node may or may || not actually respect this journey. ||  || Routes can be created locally; but they are best created by asking || the network for a good route to a remote worker which allows for || things like network speed to be interrogated in the creation of || a route. ||  || While in theory this data-structure could allow for very sophisticated || network analysis; in the beginning it will mostly exist to choose || between Pub-sub; Request-Reponse; and Streaming options. In the || long run it will allow us to design routes which explicitly || avoid the most costly network bottlenecks. For example; if Grid is || advertising a new Federated Learning job; instead of sending the || model directly to 10;000 clients (lots of bandwidth costs on the || cloud node); it could send it to one cellphone with WiFi which || would have instructions for how to forward to a binary tree of other || nodes; propagating the model to all nodes which asked for it. || \"\"\"
aaigafidg,OpenMined/PySyft,src/syft/core/nodes/common/action/get_object_action.py,32932e33a2015a9b579375273d56ae61ee559c30,STILL_EXISTS,TODO: send EventualActionWithoutReply to delete the object at the node's
aaigagcdh,OpenMined/PySyft,src/syft/core/nodes/common/action/garbage_collect_object_action.py,063b6b635ee9adb646ea968f2f1236c3bc725ca9,STILL_EXISTS,TODO: make lazy
aaigagchh,OpenMined/PySyft,syft/frameworks/torch/tensors/interpreters/additive_shared.py,8ae51803bfa87fe2334bf4f7d3fe27f67f463ab4,STILL_EXISTS,FIXME make it cleaner and robust for more options
aaigagdij,OpenMined/PySyft,src/syft/core/common/message.py,b42fd51a4f0383efb52db5d64acf3a59a0d52e3f,STILL_EXISTS,TODO: I think this is really cool but we don't have the notion of a signature yet.
aaigageee,OpenMined/PySyft,src/syft/util.py,ea4a23f04f11699a7c4f180097b3f01f1ee078d9,STILL_EXISTS,breaking convention here because index_globals needs
aaigagfhe,OpenMined/PySyft,src/syft/grid/domain.py,370aeb5c7fc2898bd40161944dd67e7e8f6d105f,STILL_EXISTS,nosec # TODO make less insecure
aaigaggfb,OpenMined/PySyft,tests/syft/core/common/object_test.py,3b48fdd730009a69559249fe9d9ceaba541defa2,08a23081fde63d7e0dddc268671491406196b2b7,Put the other way around - blocking people from modifying
aaigagggi,OpenMined/PySyft,tests/syft/core/common/object_test.py,08a23081fde63d7e0dddc268671491406196b2b7,STILL_EXISTS,TODO: filter on this error to only include errors
aaigagigi,OpenMined/PySyft,src/syft/core/common/serializable.py,8d501345378b445a28e2c5e0d18b4d817a6b4ec8,STILL_EXISTS,\"\"\" ||  || *************************************************** || Tutorial: Serialization and Deserialization in Syft || *************************************************** ||  || In this file; we have the main Serializable class which orchestrates || the serialization of objects within the Syft ecosystem. Users and || developers of Syft need to serialize objects for a myriad of reasons; || but the most common 3 are: ||  || - to save an object into a database which requires serialization (such as key-value dbs like Redis) || - to send an object over the network (any protocol). || - to save an object to disk. ||  || All serialization in Syft uses a library called 'protobuf'. || This is a technology developed at Google for fast; secure || serialization of objects (https:\/\/developers.google.com\/protocol-buffers). || We use an existing library like Protobuf for multiple reasons: ||  || - Protobuf creates cross-language serialization abilities || - Protobuf is secure || - Protobuf is fast. ||  || In short; lots of projects need serialization and so a lot of time || and effort has gone into creating great serialization libraries. Thus; || we want to inherit the work of others instead of having to reinvest the time || to build these things ourselves. ||  || .. note:: DO NOT attempt to create your own serialization by ||     creating strings out of objects yourself. Not only will this almost ||     certainly be slower than using a technology like protobuf; ||     but you will require everyone to re-implement your serialization ||     techniques in every language which needs to support your object ||     AND you run the risk of introducing dangerous SECURITY FLAWS. ||     This is a place where we definitely want to use a robust library ||     like protobuf. ||  ||  || Serializing and Deserializing Syft Objects: || ########################################### ||  || If you want to serialize an object in the syft ecosystem; the easiest way || to do so is to just call one of the serialization methods which we install || on the object for your convenience:: ||  ||     import syft as sy ||  ||     # ObjectWithID is the simplest object in the Syft ecosystem ||     # It's the parent class of many other classes. As the name ||     # implies; it's literally just an object with an ID. ||     # We'll use it to show serialization; but you can use the ||     # same approach with any serializable object in the Syft ||     # ecosystem. ||     from syft.core.common.object import ObjectWithID ||  ||     # this creates an object which has an id ||     my_object = ObjectWithID() ||  ||     print(my_object) ||     # >>> <ObjectWithID:fb1bb067-5bb7-4c49-bece-e700ab0a1514> ||  ||     # by default; .serialize() will serialize it to a protobuf Message object ||     proto_obj = my_object.serialize() ||  ||     print(proto_obj) ||     # >>> obj_type: \"syft.core.common.object.ObjectWithID\" ||     # >>> id { ||     # >>>   obj_type: \"syft.core.common.uid.UID\" ||     # >>>   value: \"23hi23hgo2ih23ih2;o3igh;2oih;iagapwihpag\" ||     # >>> } ||  ||     # deserialization also assumes you are accepting a protobuf object ||     my_object_again = sy.deserialize(blob=proto_obj) ||  ||     print(my_object_again) ||     # >>> <ObjectWithID:fb1bb067-5bb7-4c49-bece-e700ab0a1514> ||  || However; perhaps the best thing about protobuf is that it can easily || be turned into a wide variety of very portable representations. We have || convenience functions for 4 popular represenations: protobuf; json; binary; || and hex. Setup:: ||  ||     import syft as sy ||     from syft.core.common.object import ObjectWithID ||  ||     obj = ObjectWithId() ||  || Protobuf || ******** ||  || .. code:: ||  ||     proto_obj = obj.serialize(to_proto=True) ||     proto_obj = obj.to_proto() ||     proto_obj = obj.proto() ||  ||     print(proto_obj) ||     # >>> obj_type: \"syft.core.common.object.ObjectWithID\" ||     # >>> id { ||     # >>>   obj_type: \"syft.core.common.uid.UID\" ||     # >>>   value: \"23hi23hgo2ih23ih2;o3igh;2oih;iagapwihpag\" ||     # >>> } ||  ||     obj_again = sy.deserialize(blob=proto_obj; from_proto=True) ||  || JSON || **** ||  || .. code:: ||  ||     json_obj = obj.serialize(to_json=True) ||     json_obj = obj.to_json() ||     json_obj = obj.json() ||  ||     print(json_obj) ||     # >>> { ||     # >>>   \"objType\": \"syft.core.common.object.ObjectWithID\"; ||     # >>>   \"id\": { ||     # >>>     \"objType\": \"syft.core.common.uid.UID\"; ||     # >>>     \"value\": \"+xuwZ1u3TEm+zucAqwoVFA==\" ||     # >>>   } ||     # >>> } ||  ||     obj_again = sy.deserialize(blob=proto_obj; from_json=True) ||  || Binary || ****** ||  || .. code:: ||  ||     binary_obj = obj.serialize(to_binary=True) ||     binary_obj = obj.to_binary() ||     binary_obj = obj.binary() ||  ||     # print(binary_obj) ||     # >>> b'{  \"objType\": \"syft.core.common.object.ObjectWithID\"; ||     # >>> \"id\": {    \"objType\": \"syft.core.common.uid.UID\"; ||     # >>> \"value\": \"+xuwZ1u3TEm+zucAqwoVFA==\"  }}' ||  ||     obj_again = sy.deserialize(blob=proto_obj; from_binary=True) ||  || Now we can continue with the class definition for the Serializable class; which || is the parent class for all serializable objects within Syft. ||  || If you'd like to see a simple example of a class which can be serialized; please read || the source code of :py:mod:`syft.core.common.object.ObjectWithID`. || \"\"\"
aaigagihg,OpenMined/PySyft,src/syft/core/common/serde/__init__.py,7d95fca83c0a94d66cc07b1ad79d30e9356f595c,STILL_EXISTS,\"\"\" ||  || *************************************************** || Tutorial: Serialization and Deserialization in Syft || *************************************************** ||  || In this file; we have the main Serializable class which orchestrates || the serialization of objects within the Syft ecosystem. Users and || developers of Syft need to serialize objects for a myriad of reasons; || but the most common 3 are: ||  || - to save an object into a database which requires serialization (such as key-value dbs like Redis) || - to send an object over the network (any protocol). || - to save an object to disk. ||  || All serialization in Syft uses a library called 'protobuf'. || This is a technology developed at Google for fast; secure || serialization of objects (https:\/\/developers.google.com\/protocol-buffers). || We use an existing library like Protobuf for multiple reasons: ||  || - Protobuf creates cross-language serialization abilities || - Protobuf is secure || - Protobuf is fast. ||  || In short; lots of projects need serialization and so a lot of time || and effort has gone into creating great serialization libraries. Thus; || we want to inherit the work of others instead of having to reinvest the time || to build these things ourselves. ||  || .. note:: DO NOT attempt to create your own serialization by ||     creating strings out of objects yourself. Not only will this almost ||     certainly be slower than using a technology like protobuf; ||     but you will require everyone to re-implement your serialization ||     techniques in every language which needs to support your object ||     AND you run the risk of introducing dangerous SECURITY FLAWS. ||     This is a place where we definitely want to use a robust library ||     like protobuf. ||  ||  || Serializing and Deserializing Syft Objects: || ########################################### ||  || If you want to serialize an object in the syft ecosystem; the easiest way || to do so is to just call one of the serialization methods which we install || on the object for your convenience:: ||  ||     import syft as sy ||  ||     # ObjectWithID is the simplest object in the Syft ecosystem ||     # It's the parent class of many other classes. As the name ||     # implies; it's literally just an object with an ID. ||     # We'll use it to show serialization; but you can use the ||     # same approach with any serializable object in the Syft ||     # ecosystem. ||     from syft.core.common.object import ObjectWithID ||  ||     # this creates an object which has an id ||     my_object = ObjectWithID() ||  ||     print(my_object) ||     # >>> <ObjectWithID:fb1bb067-5bb7-4c49-bece-e700ab0a1514> ||  ||     # by default; .serialize() will serialize it to a protobuf Message object ||     proto_obj = my_object.serialize() ||  ||     print(proto_obj) ||     # >>> obj_type: \"syft.core.common.object.ObjectWithID\" ||     # >>> id { ||     # >>>   obj_type: \"syft.core.common.uid.UID\" ||     # >>>   value: \"23hi23hgo2ih23ih2;o3igh;2oih;iagapwihpag\" ||     # >>> } ||  ||     # deserialization also assumes you are accepting a protobuf object ||     my_object_again = sy.deserialize(blob=proto_obj) ||  ||     print(my_object_again) ||     # >>> <ObjectWithID:fb1bb067-5bb7-4c49-bece-e700ab0a1514> ||  || However; perhaps the best thing about protobuf is that it can easily || be turned into a wide variety of very portable representations. We have || convenience functions for 4 popular represenations: protobuf; json; binary; || and hex. Setup:: ||  ||     import syft as sy ||     from syft.core.common.object import ObjectWithID ||  ||     obj = ObjectWithId() ||  || Protobuf || ******** ||  || .. code:: ||  ||     proto_obj = obj.serialize(to_proto=True) ||     proto_obj = obj.to_proto() ||     proto_obj = obj.proto() ||  ||     print(proto_obj) ||     # >>> obj_type: \"syft.core.common.object.ObjectWithID\" ||     # >>> id { ||     # >>>   obj_type: \"syft.core.common.uid.UID\" ||     # >>>   value: \"23hi23hgo2ih23ih2;o3igh;2oih;iagapwihpag\" ||     # >>> } ||  ||     obj_again = sy.deserialize(blob=proto_obj; from_proto=True) ||  || JSON || **** ||  || .. code:: ||  ||     json_obj = obj.serialize(to_json=True) ||     json_obj = obj.to_json() ||     json_obj = obj.json() ||  ||     print(json_obj) ||     # >>> { ||     # >>>   \"objType\": \"syft.core.common.object.ObjectWithID\"; ||     # >>>   \"id\": { ||     # >>>     \"objType\": \"syft.core.common.uid.UID\"; ||     # >>>     \"value\": \"+xuwZ1u3TEm+zucAqwoVFA==\" ||     # >>>   } ||     # >>> } ||  ||     obj_again = sy.deserialize(blob=proto_obj; from_json=True) ||  || Binary || ****** ||  || .. code:: ||  ||     binary_obj = obj.serialize(to_binary=True) ||     binary_obj = obj.to_binary() ||     binary_obj = obj.binary() ||  ||     # print(binary_obj) ||     # >>> b'{  \"objType\": \"syft.core.common.object.ObjectWithID\"; ||     # >>> \"id\": {    \"objType\": \"syft.core.common.uid.UID\"; ||     # >>> \"value\": \"+xuwZ1u3TEm+zucAqwoVFA==\"  }}' ||  ||     obj_again = sy.deserialize(blob=proto_obj; from_binary=True) ||  || Now we can continue with the class definition for the Serializable class; which || is the parent class for all serializable objects within Syft. ||  || If you'd like to see a simple example of a class which can be serialized; please read || the source code of :py:mod:`syft.core.common.object.ObjectWithID`. || \"\"\"
aaigagjij,OpenMined/PySyft,src/syft/core/node/common/node.py,51d8c253ce7abf8f17f246d03e200bcad7ad0367,7dc9466be22644f267dcd707ec539e284e5583b6,TODO: Support ImmediateNodeServiceWithoutReply Parent Class
aaigahaec,OpenMined/PySyft,src/syft/core/node/common/node.py,bffb0081305a0a5d6ff167b2c0ca843c1555cda1,STILL_EXISTS,TODO: Support ImmediateNodeServiceWithoutReply Parent Class
aaigahagf,OpenMined/PySyft,src/syft/core/common/serde/serializable.py,e45fef601195a9cb850e29703015d2a062aa9ea9,0f48a11b1d7fdb97be9fd9f09930d553d5908156,this is a check to see if the must implement methods were implemented;
aaigahcaj,OpenMined/PySyft,src/syft/core/node/common/client.py,db443082f3aed4e01adaa509219b58300a28ce01,b2cc6d7d3291fcf739415a65ac34026afac2a877,TODO: Fix AbstractNode and LocationAwareObject being incompatible
aaigahcbb,OpenMined/PySyft,src/syft/core/node/common/node.py,db443082f3aed4e01adaa509219b58300a28ce01,STILL_EXISTS,TODO: Fix AbstractNode and LocationAwareObject being incompatible
aaigahdah,OpenMined/PySyft,src/syft/core/store/storeable_object.py,5c22445b1dd6b87145b47cc7704880b6205ecbe8,ef2783a1df2dff540fde52a37a1e35d467041fb4,TODO: remove this flag if commenting it out doesn't break anything
aaigahdfi,OpenMined/PySyft,src/syft/grid/duet/duet.py,5c22445b1dd6b87145b47cc7704880b6205ecbe8,658f477c38d6ba0ceb08d13fec8337437a620208,return pickle.loads(binary)  # nosec # TODO make less insecure
aaigahdhi,OpenMined/PySyft,src/syft/grid/duet/duet.py,5c22445b1dd6b87145b47cc7704880b6205ecbe8,STILL_EXISTS,client_metadata = pickle.loads(binary)  # nosec # TODO make less insecure
aaigaheaf,OpenMined/PySyft,src/syft/grid/duet/duet.py,5c22445b1dd6b87145b47cc7704880b6205ecbe8,STILL_EXISTS,)  # nosec # TODO make less insecure
aaigahegc,OpenMined/PySyft,src/syft/lib/generic.py,5c22445b1dd6b87145b47cc7704880b6205ecbe8,STILL_EXISTS,TODO: ensure that constructor has been installed!!!
aaigahgjg,OpenMined/PySyft,src/syft/ast/klass.py,658f477c38d6ba0ceb08d13fec8337437a620208,STILL_EXISTS,TODO: these uses of the word \"location\" shoudl change to \"address\"
aaigahhcf,OpenMined/PySyft,src/syft/core/node/common/action/function_or_constructor_action.py,658f477c38d6ba0ceb08d13fec8337437a620208,c2a8735b98a90c996a508128b11c204659d2f1aa,TODO: eliminate this explicit parameter and just set the object
aaigahhdb,OpenMined/PySyft,src/syft/core/node/common/service/heritage_update_service.py,658f477c38d6ba0ceb08d13fec8337437a620208,STILL_EXISTS,TODO: Client (and possibly Node) should subclass from StorableObject
aaigahhdc,OpenMined/PySyft,src/syft/core/node/common/service/msg_forwarding_service.py,658f477c38d6ba0ceb08d13fec8337437a620208,2f1f9866566d7d629401008e96fc07d12de956ba,TODO: don't return .data - instead have storableObject's parameters actually
aaigahhgi,OpenMined/PySyft,src/syft/core/store/store_memory.py,658f477c38d6ba0ceb08d13fec8337437a620208,STILL_EXISTS,TODO: obj should be just \"object\" and the attributes
aaigahhjf,OpenMined/PySyft,syft/frameworks/torch/he/fv/encryption_params.py,092717cb4af439c4e0d7fd6faeeb0d67b9d7623d,b82e4d079c2ceb2a52ca581e33a6aff884c61454,TODO: use hash function here.
aaigahiab,OpenMined/PySyft,src/syft/grid/duet/duet.py,7855edc2b322d93b672b1d374635c67b959d5387,b2cc6d7d3291fcf739415a65ac34026afac2a877,TODO: remove this
aaigahiac,OpenMined/PySyft,src/syft/grid/duet/duet.py,7855edc2b322d93b672b1d374635c67b959d5387,b2cc6d7d3291fcf739415a65ac34026afac2a877,TODO: when removed - make sure it's added back ot the list of security
aaigahjfh,OpenMined/PySyft,src/syft/core/node/common/action/run_class_method_action.py,fbd904f244259ce794850c6487346afba019185c,STILL_EXISTS,TODO: overload all methods to incorporate this automatically
aaigahjib,OpenMined/PySyft,tests/syft/lib/torch/tensor_test.py,fbd904f244259ce794850c6487346afba019185c,4799011c72a144199bdd2ba80b887bca23134c40,TODO: put thought into garbage collection and then
aaigaiaad,OpenMined/PySyft,src/syft/core/node/common/node.py,5544ba595403117b1042904d9022c3f37acc26d8,STILL_EXISTS,TODO: Support ImmediateNodeServiceWithoutReply Parent Class
aaigaiaae,OpenMined/PySyft,src/syft/core/node/common/node.py,5544ba595403117b1042904d9022c3f37acc26d8,STILL_EXISTS,TODO: Support ImmediateNodeServiceWithReply Parent Class
aaigaibjh,OpenMined/PySyft,tests/syft/lib/torch/tensor_test.py,046104f5be4b600bd143536090df482561f263c8,STILL_EXISTS,TODO: put thought into garbage collection and then
aaigaifei,OpenMined/PySyft,tests/syft/lib/torch/parameter_test.py,38cc1a6200c7937abce00310e2fc377de7a32a6c,1f8120ca9511b4d024d8da2742e4cabfa984fece,TODO: put thought into garbage collection and then
aaigaigci,OpenMined/PySyft,src/syft/core/common/message.py,2f1f9866566d7d629401008e96fc07d12de956ba,STILL_EXISTS,TODO: horrible temp hack; need to rethink address on SignedMessage
aaigaighi,OpenMined/PySyft,src/syft/core/node/common/action/save_object_action.py,2f1f9866566d7d629401008e96fc07d12de956ba,STILL_EXISTS,better way. But we want to support other frameworks so - gotta do it.
aaigaigid,OpenMined/PySyft,src/syft/core/node/common/client.py,2f1f9866566d7d629401008e96fc07d12de956ba,STILL_EXISTS,WARNING: Gross hack
aaigaigjc,OpenMined/PySyft,src/syft/core/node/common/node.py,2f1f9866566d7d629401008e96fc07d12de956ba,STILL_EXISTS,TODO: CHANGE
aaigaigji,OpenMined/PySyft,src/syft/core/node/common/node.py,2f1f9866566d7d629401008e96fc07d12de956ba,0d66931e6da93fda1c07388e6a2ed16d9a129040,TODO: Make this work again
aaigaihad,OpenMined/PySyft,src/syft/core/node/common/node.py,2f1f9866566d7d629401008e96fc07d12de956ba,STILL_EXISTS,TODO: Add SignedEventualSyftMessageWithoutReply and others
aaigaihbi,OpenMined/PySyft,src/syft/core/node/common/service/child_node_lifecycle_service.py,2f1f9866566d7d629401008e96fc07d12de956ba,STILL_EXISTS,TODO: Fix; see above
aaigaihda,OpenMined/PySyft,src/syft/core/node/common/service/heritage_update_service.py,2f1f9866566d7d629401008e96fc07d12de956ba,STILL_EXISTS,TODO: Client (and possibly Node) should subclass from StorableObject
aaigaihdi,OpenMined/PySyft,src/syft/core/node/common/service/msg_forwarding_service.py,2f1f9866566d7d629401008e96fc07d12de956ba,STILL_EXISTS,TODO: Add verify_key?
aaigaiibh,OpenMined/PySyft,tests/syft/core/node/common/service/child_node_lifecycle_service_test.py,2f1f9866566d7d629401008e96fc07d12de956ba,STILL_EXISTS,TODO: not sure if this is needed anymore
aaigaiiea,OpenMined/PySyft,tests/syft/core/node/node_test.py,2f1f9866566d7d629401008e96fc07d12de956ba,STILL_EXISTS,TODO: prevent device being set when Authorization fails
aaigajdee,OpenMined/PySyft,src/syft/core/node/common/client.py,777da5e578e95ff306c83d66c59c4d71a8d458be,STILL_EXISTS,serialize a client object. TODO: Fix limitation in Pointer so that we don't need this.
aaigajdgd,OpenMined/PySyft,src/syft/core/node/domain/domain.py,777da5e578e95ff306c83d66c59c4d71a8d458be,01a8518cc2e2f58f88612efad844b6085c149d51,TODO: implement RequestStatus.Accepted
aaigajdig,OpenMined/PySyft,src/syft/core/pointer/pointer.py,777da5e578e95ff306c83d66c59c4d71a8d458be,STILL_EXISTS,TODO: we need _proto2object to include a reference to the node doing the
aaigajdje,OpenMined/PySyft,src/syft/core/store/storeable_object.py,777da5e578e95ff306c83d66c59c4d71a8d458be,STILL_EXISTS,TODO: FIX THIS SECURITY BUG!!! WE CANNOT USE
aaigajedj,OpenMined/PySyft,src/syft/core/node/domain/domain.py,01a8518cc2e2f58f88612efad844b6085c149d51,STILL_EXISTS,TODO remove brute search of all store objects
aaigajefc,OpenMined/PySyft,src/syft/core/store/storeable_object.py,01a8518cc2e2f58f88612efad844b6085c149d51,STILL_EXISTS,TODO: FIX THIS SECURITY BUG!!! WE CANNOT USE
aaigajhai,OpenMined/PySyft,src/syft/ast/attribute.py,99f74b5df7ba50caa874d8e626f50aeab6a2e0af,STILL_EXISTS,TODO: Fix circular import for Client interface
aaigajhbc,OpenMined/PySyft,src/syft/ast/klass.py,99f74b5df7ba50caa874d8e626f50aeab6a2e0af,STILL_EXISTS,TODO: Fix circular import for Client interface
aaigajhcf,OpenMined/PySyft,src/syft/core/node/common/client.py,99f74b5df7ba50caa874d8e626f50aeab6a2e0af,STILL_EXISTS,serialize a client object. TODO: Fix limitation in Pointer so that we don't need this.
aaigajhcg,OpenMined/PySyft,src/syft/core/pointer/pointer.py,99f74b5df7ba50caa874d8e626f50aeab6a2e0af,STILL_EXISTS,TODO: Fix circular import for Client interface
aaigajhci,OpenMined/PySyft,src/syft/core/pointer/pointer.py,99f74b5df7ba50caa874d8e626f50aeab6a2e0af,STILL_EXISTS,TODO: Fix the Client; Address; Location confusion
aaigajhcj,OpenMined/PySyft,src/syft/core/pointer/pointer.py,99f74b5df7ba50caa874d8e626f50aeab6a2e0af,STILL_EXISTS,TODO: we need _proto2object to include a reference to the node doing the
aaigbabfj,OpenMined/PySyft,src/syft/ast/klass.py,4b9463865d264b1607cd0e982024f255c7aba143,STILL_EXISTS,TODO: tighten to return Callable
aaigbafgj,OpenMined/PySyft,tests/syft/lib/torch/tensor/tensor_remote_method_api_suite_test.py,603a9d19f8a87250cbe087ad257ac9b2e96c7c37,STILL_EXISTS,BASIC_METHOD_ARGS.append(\"0\") #TODO: add ints as remote argument types (can't call .serialize() on int)
aaigbafha,OpenMined/PySyft,tests/syft/lib/torch/tensor/tensor_remote_method_api_suite_test.py,603a9d19f8a87250cbe087ad257ac9b2e96c7c37,STILL_EXISTS,BASIC_METHOD_ARGS.append(\"1\") #TODO: add ints as remote argument types (can't call .serialize() on int)
aaigbafhb,OpenMined/PySyft,tests/syft/lib/torch/tensor/tensor_remote_method_api_suite_test.py,603a9d19f8a87250cbe087ad257ac9b2e96c7c37,4cf7f921f08d59ad28bd41c058a5ae2dd39a2b98,BASIC_METHOD_ARGS.append(\"True\") #TODO: add ints as remote argument types (can't call .serialize() on bool)
aaigbafhc,OpenMined/PySyft,tests/syft/lib/torch/tensor/tensor_remote_method_api_suite_test.py,603a9d19f8a87250cbe087ad257ac9b2e96c7c37,4cf7f921f08d59ad28bd41c058a5ae2dd39a2b98,BASIC_METHOD_ARGS.append(\"False\") #TODO: add ints as remote argument types (can't call .serialize() on bool)
aaigbafjg,OpenMined/PySyft,tests/syft/lib/torch/tensor/tensor_remote_method_api_suite_test.py,603a9d19f8a87250cbe087ad257ac9b2e96c7c37,STILL_EXISTS,TODO: put thought into garbage collection and then
aaigbaggb,OpenMined/PySyft,src/syft/core/node/common/action/get_object_action.py,46989791954f09511ae6cfb833c99bbc44032dff,STILL_EXISTS,TODO: Fix this hack
aaigbaggi,OpenMined/PySyft,src/syft/core/node/common/action/run_class_method_action.py,46989791954f09511ae6cfb833c99bbc44032dff,STILL_EXISTS,TODO: overload all methods to incorporate this automatically
aaigbaghb,OpenMined/PySyft,src/syft/core/node/common/action/run_class_method_action.py,46989791954f09511ae6cfb833c99bbc44032dff,c2a8735b98a90c996a508128b11c204659d2f1aa,# TODO: Add tests; this could happen if the isprimitive fails due to an
aaigbahje,OpenMined/PySyft,tests/syft/lib/torch/tensor/tensor_remote_method_api_suite_test.py,46989791954f09511ae6cfb833c99bbc44032dff,4cf7f921f08d59ad28bd41c058a5ae2dd39a2b98,TODO: This needs to be enforced to something more specific
aaigbahjg,OpenMined/PySyft,tests/syft/lib/torch/tensor/tensor_remote_method_api_suite_test.py,46989791954f09511ae6cfb833c99bbc44032dff,4cf7f921f08d59ad28bd41c058a5ae2dd39a2b98,TODO: We should detect tensor vs primitive in a more reliable way
aaigbbgic,OpenMined/PySyft,src/syft/core/node/common/action/function_or_constructor_action.py,5a8c69273c7b08ada41ee0dd440cc4eb1e11a1a0,STILL_EXISTS,FIXME duplicated in run_class_method_action.py
aaigbbgij,OpenMined/PySyft,src/syft/lib/torch/allowlist.py,5a8c69273c7b08ada41ee0dd440cc4eb1e11a1a0,STILL_EXISTS,TODO here for testing purpose
aaigbbhjh,OpenMined/PySyft,src/syft/core/node/common/action/function_or_constructor_action.py,1c0e6de0e0878744e8cb3a53cf03f88a60aa63c6,4cf7f921f08d59ad28bd41c058a5ae2dd39a2b98,TODO reintroduce new primitive logic
aaigbbiih,OpenMined/PySyft,src/syft/lib/python/complex.py,1c0e6de0e0878744e8cb3a53cf03f88a60aa63c6,STILL_EXISTS,TODO - actually make all of this work
aaigbbjih,OpenMined/PySyft,tests/syft/lib/python/bool_test.py,1c0e6de0e0878744e8cb3a53cf03f88a60aa63c6,STILL_EXISTS,TODO: this should work but they are not
aaigbbjjj,OpenMined/PySyft,tests/syft/lib/python/bool_test.py,1c0e6de0e0878744e8cb3a53cf03f88a60aa63c6,STILL_EXISTS,TODO this sould work
aaigbcabj,OpenMined/PySyft,tests/syft/lib/python/bool_test.py,1c0e6de0e0878744e8cb3a53cf03f88a60aa63c6,STILL_EXISTS,TODO: this should work
aaigbcada,OpenMined/PySyft,tests/syft/lib/python/bool_test.py,1c0e6de0e0878744e8cb3a53cf03f88a60aa63c6,STILL_EXISTS,TODO; this might be a good lead on how to make Bool feel more real; make Bool subclass int
aaigbcadd,OpenMined/PySyft,tests/syft/lib/python/bool_test.py,1c0e6de0e0878744e8cb3a53cf03f88a60aa63c6,STILL_EXISTS,TODO this should work
aaigbcedd,OpenMined/PySyft,tests/syft/lib/python/int_test.py,1c0e6de0e0878744e8cb3a53cf03f88a60aa63c6,STILL_EXISTS,TODO: These are not going to work if we use is; brainstorm for a workaround
aaigbcedf,OpenMined/PySyft,tests/syft/lib/python/int_test.py,1c0e6de0e0878744e8cb3a53cf03f88a60aa63c6,STILL_EXISTS,TODO these should fail
aaigbcffd,OpenMined/PySyft,tests/syft/lib/torch/tensor/tensor_remote_method_api_suite_test.py,1c0e6de0e0878744e8cb3a53cf03f88a60aa63c6,4cf7f921f08d59ad28bd41c058a5ae2dd39a2b98,TODO: adapt to the new primitive
aaigbchbj,OpenMined/PySyft,tests/syft/lib/torch/nn/parameter/parameter_remote_method_api_suite_test.py,40953e644e858cc3a4fefca1895694b455ed6bd0,4cf7f921f08d59ad28bd41c058a5ae2dd39a2b98,BASIC_METHOD_ARGS.append(\"True\") #TODO: can't call .serialize() on bool
aaigbchca,OpenMined/PySyft,tests/syft/lib/torch/nn/parameter/parameter_remote_method_api_suite_test.py,40953e644e858cc3a4fefca1895694b455ed6bd0,4cf7f921f08d59ad28bd41c058a5ae2dd39a2b98,BASIC_METHOD_ARGS.append(\"False\") #TODO: can't call .serialize() on bool
aaigbchde,OpenMined/PySyft,tests/syft/lib/torch/nn/parameter/parameter_remote_method_api_suite_test.py,40953e644e858cc3a4fefca1895694b455ed6bd0,4cf7f921f08d59ad28bd41c058a5ae2dd39a2b98,TODO: This needs to be enforced to something more specific
aaigbchfb,OpenMined/PySyft,tests/syft/lib/torch/nn/parameter/parameter_remote_method_api_suite_test.py,40953e644e858cc3a4fefca1895694b455ed6bd0,4cf7f921f08d59ad28bd41c058a5ae2dd39a2b98,TODO: We should detect tensor vs primitive in a more reliable way
aaigbchhc,OpenMined/PySyft,tests/syft/lib/torch/nn/parameter/parameter_remote_method_api_suite_test.py,40953e644e858cc3a4fefca1895694b455ed6bd0,STILL_EXISTS,TODO: put thought into garbage collection and then
aaigbciii,OpenMined/PySyft,tests/syft/lib/torch/nn/parameter/parameter_remote_method_api_suite_test.py,0898958655fcacaea6b272be1997a375a644c6fd,STILL_EXISTS,TODO why?
aaigbdadi,OpenMined/PySyft,tests/syft/lib/python/string/string_utils_test.py,eb6091be07123f3d6f663073b9b5b4c645fb5b7c,STILL_EXISTS,TODO after adding the list
aaigbdbcj,OpenMined/PySyft,tests/syft/lib/python/string/string_utils_test.py,eb6091be07123f3d6f663073b9b5b4c645fb5b7c,STILL_EXISTS,XXX: on a 64-bit system; this doesn't raise an overflow error;
aaigbdbdf,OpenMined/PySyft,tests/syft/lib/python/string/string_utils_test.py,eb6091be07123f3d6f663073b9b5b4c645fb5b7c,STILL_EXISTS,TODO Uncomment this
aaigbdcbd,OpenMined/PySyft,tests/syft/lib/python/complex/complex_test.py,51b658ea058670fea68155253b102b1e6af2dc6f,STILL_EXISTS,FIXME improve Complex.__new__() to have the same TypeError as complex
aaigbddfh,OpenMined/PySyft,tests/syft/core/node/common/action/function_or_constructor_action_test.py,e9903b81627dca360250c86223cfbab7933ab6a1,STILL_EXISTS,TODO test execution
aaigbddfi,OpenMined/PySyft,tests/syft/core/node/common/action/function_or_constructor_action_test.py,e9903b81627dca360250c86223cfbab7933ab6a1,STILL_EXISTS,TODO test permissions
aaigbddfj,OpenMined/PySyft,tests/syft/core/node/common/action/function_or_constructor_action_test.py,e9903b81627dca360250c86223cfbab7933ab6a1,STILL_EXISTS,FIXME this cannot be checked before we fix the Pointer serde problem (see _proto2object in Pointer)
aaigbddgf,OpenMined/PySyft,src/syft/lib/python/float.py,f1eb3ed0d049d9baf8cbdbed5f97d66b4d2c02c6,STILL_EXISTS,TODO add this
aaigbdebi,OpenMined/PySyft,tests/syft/lib/python/float/id_test.py,f1eb3ed0d049d9baf8cbdbed5f97d66b4d2c02c6,STILL_EXISTS,TODO finish this when we have bytes support
aaigbdecc,OpenMined/PySyft,src/syft/core/node/abstract/node.py,71db6b2d0bb3d9aa1f0170eecad6cea8abd9b839,STILL_EXISTS,TODO: remove hacky signaling_msgs when SyftMessages become Storable.
aaigbdecd,OpenMined/PySyft,src/syft/core/node/common/node.py,71db6b2d0bb3d9aa1f0170eecad6cea8abd9b839,STILL_EXISTS,TODO: remove hacky signaling_msgs when SyftMessages become Storable.
aaigbdgeb,OpenMined/PySyft,src/syft/grid/example_nodes/pks.py,71db6b2d0bb3d9aa1f0170eecad6cea8abd9b839,STILL_EXISTS,\"\"\"The purpose of this application is to allow us to dev and test PySyft || functionality on an actual local network. This is NOT meant to be run in || production (that's the *actual* grid's job).\"\"\"
aaigbdgfg,OpenMined/PySyft,src/syft/grid/webrtc_duet.py,71db6b2d0bb3d9aa1f0170eecad6cea8abd9b839,STILL_EXISTS,\"\"\" || PySyft Duet (WebRTC) ||  || This class aims to implement the PySyft Duet concept by using WebRTC protocol as a || connection channel in order to allow two different users to establish a direct || connection with high-quality Real-time Communication using private addresses. ||  || The most common example showing how it can be used is the notebook demo example: ||  || Two different jupyter \/ collab notebooks in different machines using private addresses || behind routers; proxies and firewalls can connect to each other using a full-duplex || channel in order to perform machine learning and data science tasks; working as a client || and server at the same time. ||  || PS 1: You need a signaling server running somewhere. || If you don't know any public address running this service; or want to set up your own || signaling network you can use PyGrid's network app. ||  || For local development you can run: || $ python src\/syft\/grid\/example_nodes\/network.py ||  || PS 2: The PyGrid's dev \/ main branches are still supporting PySyft 0.2.x; || To use this feature you must use the pygrid_0.3.0 branch. || (https:\/\/github.com\/OpenMined\/PyGrid\/tree\/pygrid_0.3.0) ||  || You can get more details about all this process; in the syft\/grid\/connections\/webrtc.py || source code. || \"\"\"
aaigbdhfa,OpenMined/PySyft,src/syft/grid/webrtc_duet.py,71db6b2d0bb3d9aa1f0170eecad6cea8abd9b839,STILL_EXISTS,\"\"\" || PySyft Duet (WebRTC) ||  ||     This class aims to implement the PySyft Duet || concept by using WebRTC protocol as a connection || channel in order to allow two different users to || estabilish a direct connection with high-quality || Real-time Communication using private addresses. ||  ||     The most common example showing how does it || can be used is the notebook demo example: ||  ||     Two different jupyter\/collab notebooks in || different machines using private addresses behind || routers; proxies and firewalls can connect to || each other using a full-duplex channel in order || to perform machine learning and data science tasks; || working as a client and server at the same time. ||  || PS 1: You'll need a signaling server running somewhere. || If you don't know any public address running this service; or || want to set up your own signaling network you can use PyGrid's network app. ||  || PS 2: The PyGrid's dev\/master branches are still supporting PySyft 0.2.x; || To use this feature you must use the pygrid_0.3.0 branch. || (https:\/\/github.com\/OpenMined\/PyGrid\/tree\/pygrid_0.3.0) ||  || You can get more details about all this process; || in the syft\/grid\/connections\/webrtc.py source code. || \"\"\"
aaigbeddb,OpenMined/PySyft,src/syft/ast/klass.py,48adfbe88f37f8d1e18eb7f93fe476c61db2934f,STILL_EXISTS,a bunch of initialized data needed to work. The second step is that when manually
aaigbedgg,OpenMined/PySyft,src/syft/lib/python/list.py,48adfbe88f37f8d1e18eb7f93fe476c61db2934f,STILL_EXISTS,TODO: remove temp hack
aaigbedgh,OpenMined/PySyft,src/syft/lib/python/list.py,48adfbe88f37f8d1e18eb7f93fe476c61db2934f,STILL_EXISTS,hack for working around generators
aaigbeehe,OpenMined/PySyft,src/syft/util.py,48adfbe88f37f8d1e18eb7f93fe476c61db2934f,STILL_EXISTS,TODO: Improve \/ or remove with better solution
aaigbeeif,OpenMined/PySyft,tests/syft/lib/torch/parameter_test.py,48adfbe88f37f8d1e18eb7f93fe476c61db2934f,STILL_EXISTS,TODO: fix backward is failing
aaigbehch,OpenMined/PySyft,src/syft/core/node/common/node.py,25604f6c3237ae5a9b28130a84cc19b17f7f3c84,STILL_EXISTS,TODO: Move but right now import loop prevents importing from the RequestMessage
aaigbehej,OpenMined/PySyft,src/syft/core/node/common/node.py,25604f6c3237ae5a9b28130a84cc19b17f7f3c84,STILL_EXISTS,TODO: finish code to send ExceptionMessage back
aaigbejdd,OpenMined/PySyft,tests/syft/lib/torch/tensor/tensor_remote_and_serde_test.py,26ab28931cc80d50ccb019ae40a612442110a2cf,STILL_EXISTS,TODO: Fix this from deleting the object in the store due to the variable
aaigbejgi,OpenMined/PySyft,syft/generic/frameworks/hook/hook.py,182627db2369d6f93aa0667f5ea2abee5b878d58,STILL_EXISTS,TODO: add check to make sure this isn't getting around
aaigbfaij,OpenMined/PySyft,examples/pytorch/fast_neural_style/original/download_saved_models.py,e7b5527d29ca8e4d67d338035a6b8366af163110,STILL_EXISTS,TODO: If you remove support PyTorch 1.0 or older;
aaigbfcdd,OpenMined/PySyft,examples/pytorch/word_language_model/original/main.py,e7b5527d29ca8e4d67d338035a6b8366af163110,STILL_EXISTS,Starting from sequential data; batchify arranges the dataset into columns.
aaigbfceb,OpenMined/PySyft,examples/pytorch/word_language_model/original/main.py,e7b5527d29ca8e4d67d338035a6b8366af163110,STILL_EXISTS,These columns are treated as independent by the model; which means that the
aaigbfcec,OpenMined/PySyft,examples/pytorch/word_language_model/original/main.py,e7b5527d29ca8e4d67d338035a6b8366af163110,STILL_EXISTS,dependence of e. g. 'g' on 'f' can not be learned; but allows more efficient
aaigbfcia,OpenMined/PySyft,examples/pytorch/word_language_model/original/model.py,e7b5527d29ca8e4d67d338035a6b8366af163110,STILL_EXISTS,\"Using the Output Embedding to Improve Language Models\" (Press & Wolf 2016)
aaigbffbj,OpenMined/PySyft,tests/syft/lib/allowlist_test.py,2fd955c62224e74db32bf4e648d68d51c2bd968f,STILL_EXISTS,and using them as both Tensors and inputs covers a lot of the functionality.
aaigbfgfj,OpenMined/PySyft,tests/syft/lib/allowlist_test.py,2fd955c62224e74db32bf4e648d68d51c2bd968f,STILL_EXISTS,actually needed
aaigbfggi,OpenMined/PySyft,tests/syft/lib/allowlist_test.py,2fd955c62224e74db32bf4e648d68d51c2bd968f,STILL_EXISTS,TODO: Fix this workaround for types that sometimes return Tensor tuples
aaigbfghc,OpenMined/PySyft,tests/syft/lib/allowlist_test.py,2fd955c62224e74db32bf4e648d68d51c2bd968f,STILL_EXISTS,TODO: Fix this when we find one
aaigbfgih,OpenMined/PySyft,tests/syft/lib/torch/tensor/tensor_remote_and_serde_test.py,3744888d12be03a9964b124abf36cf7a374fe600,STILL_EXISTS,TODO: Fix this from deleting the object in the store due to the variable
aaigbgaga,OpenMined/PySyft,tests/syft/lib/python/string/string_utils_test.py,0d601f9bcb41dd6aaed16cf39dd75f6f0fb63930,STILL_EXISTS,XXX: on a 64-bit system; this doesn't raise an overflow error;
aaigbibgh,OpenMined/PySyft,tests/syft/lib/allowlist_test.py,726d4b350386610dcc6a38d9f1b6219931498cc5,STILL_EXISTS,TODO: Fix this workaround for types that sometimes return Tensor tuples
aaigbibha,OpenMined/PySyft,tests/syft/lib/allowlist_test.py,726d4b350386610dcc6a38d9f1b6219931498cc5,STILL_EXISTS,TODO: Fix this when we find one
aaigbibja,OpenMined/PySyft,src/syft/core/common/module.py,5c66e3d20241bdcb2d885c683dc721ca884eaf37,STILL_EXISTS,TODO: make work nested with a count and add tests
aaigbicaj,OpenMined/PySyft,src/syft/core/common/module.py,5c66e3d20241bdcb2d885c683dc721ca884eaf37,STILL_EXISTS,TODO: support torch.nn.modules.module._IncompatibleKeys
aaigbicdc,OpenMined/PySyft,src/syft/core/store/store_disk.py,5c66e3d20241bdcb2d885c683dc721ca884eaf37,STILL_EXISTS,TODO: this wont fly long term
aaigcbgib,OpenMined/PySyft,src/syft/grid/example_nodes/domain.py,db02d49361b9274dd5e9208731d102903d813031,STILL_EXISTS,nosec # TODO make less insecure
aaigcgbjj,OpenMined/PySyft,src/syft/core/store/storeable_object.py,4e29dd199fbc5cd4bc52e21d2f0f646b095a1199,STILL_EXISTS,TODO: FIX THIS SECURITY BUG!!! WE CANNOT USE
aaigcgdjj,OpenMined/PySyft,src/syft/lib/torch/allowlist.py,39c49921eba8f1d13fb602ac0e2b3033f0d706e4,STILL_EXISTS,allowlist[\"torch.Tensor.__torch_function__\"] = \"unknown\" # 1.7.0 # probably wont work
aaigcgecj,OpenMined/PySyft,src/syft/lib/python/dict.py,e984378da3f9bd882afe82a4836340c93cb1fcb0,STILL_EXISTS,keep this workaround
aaigcghic,OpenMined/PySyft,tests/syft/grid/connections/webrtc_test.py,8a1ba1118dc7e11ecd675bca4ec58662235cf49d,STILL_EXISTS,FIXME: Nahua is not happy with this test because it \"indirectly\" triggered exception
aaigcgija,OpenMined/PySyft,src/syft/ast/klass.py,b84d1671122fb91bff1f1c2d390020630f1b97ec,c2a8735b98a90c996a508128b11c204659d2f1aa,TODO: rethink this and perhaps dunder __sy prefix all our attached methods
aaigchbbh,OpenMined/PySyft,src/syft/core/node/common/action/run_class_method_action.py,b7aa3526779ee6bd335d0af0bfc9bdffe61c5e18,c2a8735b98a90c996a508128b11c204659d2f1aa,# TODO: Add tests; this could happen if the isprimitive fails due to an
aaigchbeg,OpenMined/PySyft,src/syft/util.py,b7aa3526779ee6bd335d0af0bfc9bdffe61c5e18,c2a8735b98a90c996a508128b11c204659d2f1aa,TODO maybe return AnyPointer?
aaigchbjg,OpenMined/PySyft,src/syft/core/node/common/action/run_class_method_action.py,7a2fe47edc033dfa2232a58edc710b0c339db061,STILL_EXISTS,TODO: Remove this Opacus workaround
aaigchgcd,OpenMined/PySyft,src/syft/core/node/common/action/function_or_constructor_action.py,953318be37b7d3d990ce99fae1f6c794008919c3,c2a8735b98a90c996a508128b11c204659d2f1aa,TODO: eliminate this explicit parameter and just set the object
aaigchgcf,OpenMined/PySyft,src/syft/core/node/common/action/function_or_constructor_action.py,953318be37b7d3d990ce99fae1f6c794008919c3,c2a8735b98a90c996a508128b11c204659d2f1aa,TODO: replace with proper tuple support
aaigchgch,OpenMined/PySyft,src/syft/core/node/common/action/function_or_constructor_action.py,953318be37b7d3d990ce99fae1f6c794008919c3,c2a8735b98a90c996a508128b11c204659d2f1aa,TODO: overload all methods to incorporate this automatically
aaigchgcj,OpenMined/PySyft,src/syft/core/node/common/action/function_or_constructor_action.py,953318be37b7d3d990ce99fae1f6c794008919c3,c2a8735b98a90c996a508128b11c204659d2f1aa,TODO: Solve this problem where its an issue
aaigchgfc,OpenMined/PySyft,src/syft/ast/__init__.py,c2a8735b98a90c996a508128b11c204659d2f1aa,STILL_EXISTS,\"\"\" ||  The AST part of syft is responsible for the overall remote call execution. Basically; the AST is a ||  tree that maps function call to their exact path and know what to do with that node in the tree. ||  ||  Example: We want to append an object to a List; meaning ||  that we know where should we find it; so we need to know the following chain: ||  ||                 globals  <- the global scope or the entry point of execution (hidden) ||                    | ||                   syft   <- the syft module ||                    | ||                   lib   <- a submodule of syft ||                    | ||                   List <- the class we were looking for ||                    | ||                 append <- the method we were looking for ||  || This path has to be solved when doing remote execution as well; this is where the AST comes handy. || The ast is responsible for: ||  || A. Remote execution. || B. Local execution. ||  || A. Remote execution. || Remote execution can be performed only when the AST has been constructed with a client. Check || syft\/core\/node to be familiar with the roles of clients and nodes. Each valid action on the ast || triggers an Action (GetSetStaticAttributeAction; GetSetPropertyAction; etc. This kind of actions || require: the path on solving the required node (on the above example; the path is || syft.lib.List.append); the object on which to perform it (given by the __self attribute and from || id_at_location if executed on a pointer) and the attributes if needed. ||  || B Local execution. || After a call has been made; the remote execution starts; but we need a local handler for the result || of the remote execution through a Pointer. The AST is responsible to generate all the || permitted methods and attributes on a Pointer and the return type of the performed action. ||  ||  || The existing types of nodes are: || * a Globals; which is the entrypoint of execution; from here you can only access Modules. ||  || * a Callable; which can be a node for: a method; a static method; a function; a constructor. This || node can no longer have any attributes. ||  || * a Class; which is a node that represents a python class. This node can contain in it: methods - Callable; || static methods - Callable; class method - Callable; slot attributes - StaticAttribute; properties - Property; || enum attributes - EnumAttribute. ||  || * a Module; which represents a python file\/module. This node can have as an attribute: global || variables - StaticAttribute; global functions - Callable and classes - Klass. ||  || * a StaticAttribute - represents attribute of a Class or of a Module. This node can no longer have || any more attributes. This can be remotely get and set. ||  || * a Property - represents a @property object of a class. This node can no longer have any more || attributes - a leaf. This can remotely get and set. ||  || * a EnumAttribute - represents the fields generated by an Enum. This node can no longer have any || more attributes - a leaf. This can be remotely get. || \"\"\"
aaigdacbb,OpenMined/PySyft,src/syft/lib/tensor/tensorbase.py,1c42b0af26c9d3e5ace14549622f7e2220644e12,STILL_EXISTS,todo: make sure that operations return the correct tensor type
aaigddcfc,chainer/chainer,chainer/variable.py,fa7b5fdc8fced15cecfa1ce3e23273fb59743b5e,866d6d822b2a65e25d326e0b5efae5295a57c360,TODO(sonots): Implement for ChainerX
aaigddcfe,chainer/chainer,chainer/variable.py,fa7b5fdc8fced15cecfa1ce3e23273fb59743b5e,ec00f9cf02038e9b6f3e8f1366557217aaef2652,Fix F812 (Python 2)
aaigdedih,chainer/chainer,chainer/variable.py,016deb77488e9e5726f712118f77b4bc1abefec6,ec00f9cf02038e9b6f3e8f1366557217aaef2652,Fix F812 (Python 2)
aaigdfefi,chainer/chainer,chainer/functions/array/resize_images.py,fce583e9c13ee4814f6c96d552705bb4fe917932,a4adddb65a3cd78379a1dc63a6d072f8545c60b5,in order to better utilize CPU cache memory.
aaigdidbb,chainer/chainer,chainer/links/normalization/batch_normalization.py,d518feb1ee563c9ec5dc22d6fb5a1481fe3561b1,6e9f5b10033813443e4097d0c8fa441c180dc050,(TODO: mkusumoto) Test finetuning in recomputation.
aaigebfhh,chainer/chainer,chainer/__init__.py,afaf2a3564d1ce24b3bbc28fcb5ffb858d859567,f798cf3c965ed30141d45f7fd97e50e8390beec2,TODO(hvy): Move this function to backend?
aaigebhce,chainer/chainer,chainer/functions/loss/softmax_cross_entropy.py,afaf2a3564d1ce24b3bbc28fcb5ffb858d859567,baf3f98a6e707b98c592eddaa6fd0d90fec65813,TODO(beam2d): implement mean
aaigebhcf,chainer/chainer,chainer/functions/loss/softmax_cross_entropy.py,afaf2a3564d1ce24b3bbc28fcb5ffb858d859567,STILL_EXISTS,TODO(niboshi): Remove this workaround after ChainerX supports
aaigebhdh,chainer/chainer,chainer/functions/math/basic_math.py,afaf2a3564d1ce24b3bbc28fcb5ffb858d859567,STILL_EXISTS,TODO(hvy): Implement multi-add with chainerx.ndarrays.
aaigebiab,chainer/chainer,chainer/training/extensions/evaluator.py,afaf2a3564d1ce24b3bbc28fcb5ffb858d859567,bea8f5921843858597ba0313aa7e153e50fbe761,TODO(niboshi): This is a temporary workaround to keep backward
aaigebiag,chainer/chainer,chainer/training/updaters/standard_updater.py,afaf2a3564d1ce24b3bbc28fcb5ffb858d859567,2808806d41ab90a8c6d0eb825d5915ff155c03e9,TODO(niboshi): This is a temporary workaround to keep backward
aaigecbfd,chainer/chainer,examples/imagenet/train_imagenet.py,afaf2a3564d1ce24b3bbc28fcb5ffb858d859567,STILL_EXISTS,TODO(sonots): Temporarily disabled for chainerx. Fix it.
aaigecbff,chainer/chainer,examples/mnist/train_mnist.py,afaf2a3564d1ce24b3bbc28fcb5ffb858d859567,STILL_EXISTS,TODO(niboshi): Temporarily disabled for chainerx. Fix it.
aaigedccf,chainer/chainer,chainer/variable.py,e69f6451d58c2a43b70217a5fa759732b1e9159d,bd0490d301b2146998257707542829e29113b65f,FIXME: avoid `isinstance`
aaigefjih,chainer/chainer,chainer/gradient_check.py,758f4fad5504b495b5d69a48289bc515b6cfdd1b,a607f7abb56d842060c612033bb1305766c7d86b,Copy is needed to avoid being updated during backprop; which
aaigehifi,chainer/chainer,chainer/variable.py,6a7413f261a658db8b6157a175e6dcb8e428a330,9935917216bbe6d1646ad8b2cf0f175d1f28cab2,FIXME: avoid `isinstance`
aaigehijb,chainer/chainer,chainer/gradient_check.py,87f497b23441c7392224bf842576f5871df66e08,744b6eee43858713ec241c3bc8ae5a16d20582c2,Copy is needed to avoid being updated during backprop; which
aaigejejd,chainer/chainer,chainer/gradient_check.py,9bab9bdf5df93619ca25fd376cb90f6cf4279c12,1862daae5b59d17807bc76e7af6e3481cc3efae6,Copy is needed to avoid being updated during backprop; which
aaigejfai,chainer/chainer,chainer/variable.py,9bab9bdf5df93619ca25fd376cb90f6cf4279c12,9935917216bbe6d1646ad8b2cf0f175d1f28cab2,FIXME: avoid `isinstance`
aaigfaibg,chainer/chainer,chainerx/_fallback_workarounds.py,86782b7dcf2fb09f03dfed47fa06ffa5dbd8c484,STILL_EXISTS,This file defines workaround implementation for
aaigfaibj,chainer/chainer,chainerx/_fallback_workarounds.py,86782b7dcf2fb09f03dfed47fa06ffa5dbd8c484,STILL_EXISTS,The workaround does not support backprop; and also requires external
aaigfajgh,chainer/chainer,chainer/variable.py,d78a9cb3dc7ebd5f9ba4453a82c5179e0b7a5a99,f798cf3c965ed30141d45f7fd97e50e8390beec2,TODO(sonots): Implement for ChainerX
aaigfajgj,chainer/chainer,chainer/variable.py,d78a9cb3dc7ebd5f9ba4453a82c5179e0b7a5a99,ec00f9cf02038e9b6f3e8f1366557217aaef2652,Fix F812 (Python 2)
aaigfcfdh,chainer/chainer,chainer/variable.py,1cbbba843ad531c92c1c2e9a7beec4011df13e62,f798cf3c965ed30141d45f7fd97e50e8390beec2,TODO(sonots): Implement for ChainerX
aaigfcfdj,chainer/chainer,chainer/variable.py,1cbbba843ad531c92c1c2e9a7beec4011df13e62,b8fba24babb31065ca16628776646fb7e236303e,Fix F812 (Python 2)
aaigfehgi,chainer/chainer,chainer/variable.py,b8fba24babb31065ca16628776646fb7e236303e,f798cf3c965ed30141d45f7fd97e50e8390beec2,TODO(sonots): Implement for ChainerX
aaigfgdgb,chainer/chainer,chainer/gradient_check.py,e302f64f6344605b4e48109b04aa8a9ed9b73fea,f798cf3c965ed30141d45f7fd97e50e8390beec2,Copy is needed to avoid being updated during backprop; which
aaigfgdhi,chainer/chainer,chainer/variable.py,e302f64f6344605b4e48109b04aa8a9ed9b73fea,9935917216bbe6d1646ad8b2cf0f175d1f28cab2,Fix F812 (Python 2)
aaigfgeda,chainer/chainer,chainer/functions/normalization/local_response_normalization.py,eb81d98074bf1db8b3a525854b4bbe1c73f73d94,STILL_EXISTS,No trivial way to implement double-backward for this function.
aaigfggfc,chainer/chainer,chainer/functions/activation/relu.py,40f5a633d4b4656f13716fc91ae746f3f11f7d93,994949239bf0747719666cee845601930f74bf37,Workaround for ChainerX
aaiggeffh,chainer/chainer,chainer/functions/pooling/roi_pooling_2d.py,f3147bb0e7a79fb1dc9bd4ae8da61e852eca92c3,STILL_EXISTS,No trivial way to implement double-backward for this function.
aaigggbbg,chainer/chainer,chainer/backends/cuda.py,83bb1d03f5b6ad02e1e41dddeedce85ae0c04b1d,b8ce29428cfbf080cfe63f8bdb73f26a1557ae42,with the CUDA backend. This is needed in order to share the GPU memory
aaigggbjf,chainer/chainer,chainer/variable.py,b8ce29428cfbf080cfe63f8bdb73f26a1557ae42,9935917216bbe6d1646ad8b2cf0f175d1f28cab2,FIXME: avoid `isinstance`
aaigggcfd,chainer/chainer,chainer/variable.py,b8ce29428cfbf080cfe63f8bdb73f26a1557ae42,9935917216bbe6d1646ad8b2cf0f175d1f28cab2,Fix F812 (Python 2)
aaigggcig,chainer/chainer,chainer/functions/loss/softmax_cross_entropy.py,88d0d1ae50d7fc2592672c19d9f639406873c42f,baf3f98a6e707b98c592eddaa6fd0d90fec65813,TODO(beam2d): implement mean
aaigggcii,chainer/chainer,chainer/functions/loss/softmax_cross_entropy.py,88d0d1ae50d7fc2592672c19d9f639406873c42f,STILL_EXISTS,TODO(niboshi): Remove this workaround after ChainerX supports
aaigggdjj,chainer/chainer,chainer/functions/loss/softmax_cross_entropy.py,1d5130aed2bdf36b2600ace32ef367bdf4d58dfb,baf3f98a6e707b98c592eddaa6fd0d90fec65813,TODO(beam2d): implement mean
aaigggeab,chainer/chainer,chainer/functions/loss/softmax_cross_entropy.py,1d5130aed2bdf36b2600ace32ef367bdf4d58dfb,STILL_EXISTS,TODO(niboshi): Remove this workaround after ChainerX supports
aaigggfgh,chainer/chainer,chainer/functions/loss/softmax_cross_entropy.py,36963522f70fec070e254880bba271f07bdd4e56,baf3f98a6e707b98c592eddaa6fd0d90fec65813,TODO(beam2d): implement mean
aaigggfgj,chainer/chainer,chainer/functions/loss/softmax_cross_entropy.py,36963522f70fec070e254880bba271f07bdd4e56,STILL_EXISTS,TODO(niboshi): Remove this workaround after ChainerX supports
aaiggjaah,chainer/chainer,chainer/variable.py,9935917216bbe6d1646ad8b2cf0f175d1f28cab2,f798cf3c965ed30141d45f7fd97e50e8390beec2,TODO(sonots): Implement for ChainerX
aaiggjafj,chainer/chainer,chainerx_cc/examples/mnist_py/train_mnist.py,5748d6b62355dc93f9fff8abcaed2315504c919b,050053582e4e2451b5ce8e01a30f96b3cdf0954d,x.flags.writeable = True  # TODO(beam2d): remove this workaround
aaiggjccg,chainer/chainer,chainer/functions/connection/convolution_nd.py,f798cf3c965ed30141d45f7fd97e50e8390beec2,STILL_EXISTS,Work-around for NumPy's bug?
aaiggjfcj,chainer/chainer,chainer/graph_optimizations/static_graph.py,f798cf3c965ed30141d45f7fd97e50e8390beec2,STILL_EXISTS,casuses some needed variables to be set to None
aaiggjhff,chainer/chainer,chainer/graph_optimizations/static_graph.py,f798cf3c965ed30141d45f7fd97e50e8390beec2,STILL_EXISTS,causes some needed variables to be set to None
aaighaajd,chainer/chainer,chainermn/functions/batch_normalization.py,f798cf3c965ed30141d45f7fd97e50e8390beec2,36a31fa4ae8e13baf05837f324957ac7253c4879,ChainerMN diff (1\/2) ends
aaighaajh,chainer/chainer,chainermn/functions/batch_normalization.py,f798cf3c965ed30141d45f7fd97e50e8390beec2,36a31fa4ae8e13baf05837f324957ac7253c4879,ChainerMN diff (2\/2) ends
aaighagfh,chainer/chainer,chainer/__init__.py,36a31fa4ae8e13baf05837f324957ac7253c4879,STILL_EXISTS,TODO(hvy): Move this function to backend?
aaighaiig,chainer/chainer,chainer/functions/connection/convolution_nd.py,36a31fa4ae8e13baf05837f324957ac7253c4879,STILL_EXISTS,Work-around for NumPy's bug?
aaighajcj,chainer/chainer,chainer/functions/math/basic_math.py,36a31fa4ae8e13baf05837f324957ac7253c4879,STILL_EXISTS,TODO(hvy): Implement multi-add with chainerx.ndarrays.
aaighbbeg,chainer/chainer,chainer/gradient_check.py,36a31fa4ae8e13baf05837f324957ac7253c4879,STILL_EXISTS,Copy is needed to avoid being updated during backprop; which
aaighbcai,chainer/chainer,chainer/link.py,36a31fa4ae8e13baf05837f324957ac7253c4879,17806d636316064cc0f2645b23001a7d586f3df1,`skip_between_cupy_devices` argument is a workaround
aaighbeei,chainer/chainer,chainer/variable.py,36a31fa4ae8e13baf05837f324957ac7253c4879,STILL_EXISTS,FIXME: avoid `isinstance`
aaighbfbe,chainer/chainer,chainer/variable.py,36a31fa4ae8e13baf05837f324957ac7253c4879,a8d08cb7a1fac6cacc647f51c4bbdcae7db0246c,Fix F812 (Python 2)
aaighbiff,chainer/chainer,chainerx_cc/examples/mnist_py/train_mnist.py,36a31fa4ae8e13baf05837f324957ac7253c4879,050053582e4e2451b5ce8e01a30f96b3cdf0954d,x.flags.writeable = True  # TODO(beam2d): remove this workaround
aaighbifh,chainer/chainer,chainerx_cc/examples/mnist_py/train_mnist.py,36a31fa4ae8e13baf05837f324957ac7253c4879,STILL_EXISTS,y.flags.writeable = True  # TODO(beam2d): remove this workaround
aaighbigc,chainer/chainer,examples/imagenet/train_imagenet.py,36a31fa4ae8e13baf05837f324957ac7253c4879,STILL_EXISTS,TODO(sonots): Temporarily disabled for chainerx. Fix it.
aaighcjdb,chainer/chainer,chainerx/_cuda/__init__.py,4ad475c7e4d58d714540105a7bcccb5555b61a7b,STILL_EXISTS,the CUDA backend. This is needed in order to share the GPU memory
aaighegcf,chainer/chainer,chainer/variable.py,12516b5e0111868e282623a04593e5fd7fc66a1b,4080d29351edfaeffc72ae6d739d284203b200ba,self._device as much as possible because it is really costly.
aaighfaea,chainer/chainer,tests/chainerx_tests/cuda_utils.py,d03f9c96f603364bac6b9d7c08e4a8d674162609,STILL_EXISTS,TODO(niboshi): Better remove dependency to cupy
aaighfahb,chainer/chainer,chainer/variable.py,be70c259b09dc75becc72b4910a2bb239c929f42,eb673963199aea018b7b9eb8d28dcf79956271b8,self._device as much as possible because it is really costly.
aaighffjh,chainer/chainer,chainer/testing/function.py,8e81fb31c6941464051452f2c7c33b2bba191e92,fd62b128604abbddbdb898ce49b248cfcf39921c,This is a dirty workaround to avoid writing the skip logic in every
aaighfgee,chainer/chainer,chainer/functions/normalization/decorrelated_batch_normalization.py,21760e89e3a2e2836d26664e5775d2a8c0cf7a49,87640e9cadeac6f1ef406bdcf83060c3e63b7c73,TODO(tommi): Implement group whitening
aaighfjef,chainer/chainer,chainer/functions/normalization/decorrelated_batch_normalization.py,95213ab0e100b42aa74a88a041fbc33219b95fa5,STILL_EXISTS,TODO(crcrpar): Implement this.
aaighfjgb,chainer/chainer,chainerx_cc/examples/mnist_py/train_mnist.py,e2d041bb5176385189e41169cbb8448589584c6f,050053582e4e2451b5ce8e01a30f96b3cdf0954d,x.flags.writeable = True  # TODO(beam2d): remove this workaround
aaighgbdb,chainer/chainer,chainer/functions/normalization/decorrelated_batch_normalization.py,530d2ef98cf33dde4fe9121faa88c9afe457a720,STILL_EXISTS,TODO(crcrpar): Implement this.
aaighgcgf,chainer/chainer,chainer/variable.py,d7de72ca773cf5f1002ece952cd4382a7dd2ecb2,24ce10c4d34c1dcd954788fafd65e0c7928b74e6,self._device as much as possible because it is really costly.
aaighgdja,chainer/chainer,chainer/variable.py,11a9cd0e7f35a9b52ac3186335c8abec0eea85bb,a8d08cb7a1fac6cacc647f51c4bbdcae7db0246c,fix py2 memory leak
aaighgdjc,chainer/chainer,chainer/variable.py,679fd27709443d5a7752ee95594e1a0e82a06fc4,843bc6f1e89f2995cbc319abfb2d2fcacc108bde,self._device as much as possible because it is really costly.
aaighgdje,chainer/chainer,chainer/variable.py,679fd27709443d5a7752ee95594e1a0e82a06fc4,a8d08cb7a1fac6cacc647f51c4bbdcae7db0246c,fix py2 memory leak
aaighgeab,chainer/chainer,chainer/variable.py,843bc6f1e89f2995cbc319abfb2d2fcacc108bde,a8d08cb7a1fac6cacc647f51c4bbdcae7db0246c,fix py2 memory leak
aaighgebc,chainer/chainer,chainer/__init__.py,e515b2bafe5ea9e529ba1d2de4e8a2633e7cd3f1,STILL_EXISTS,TODO(hvy): Move this function to backend?
aaighgebj,chainer/chainer,chainer/variable.py,6323a18043120591e92bc83712de90d484e58027,STILL_EXISTS,FIXME: avoid `isinstance`
aaighgeif,chainer/chainer,chainer/variable.py,6323a18043120591e92bc83712de90d484e58027,a8d08cb7a1fac6cacc647f51c4bbdcae7db0246c,Fix F812 (Python 2)
aaighgfaf,chainer/chainer,chainer/variable.py,54d0b84dd34203c7e0347ba39d96318cb2d3012e,STILL_EXISTS,FIXME: avoid `isinstance`
aaighgfbb,chainer/chainer,chainer/variable.py,54d0b84dd34203c7e0347ba39d96318cb2d3012e,fa9e7bdb058ad3391f054e808c89c5591cde461a,self._device as much as possible because it is really costly.
aaighgfhf,chainer/chainer,chainer/variable.py,54d0b84dd34203c7e0347ba39d96318cb2d3012e,a8d08cb7a1fac6cacc647f51c4bbdcae7db0246c,Fix F812 (Python 2)
aaighgfjc,chainer/chainer,chainer/variable.py,54d0b84dd34203c7e0347ba39d96318cb2d3012e,a8d08cb7a1fac6cacc647f51c4bbdcae7db0246c,fix py2 memory leak
aaighggaa,chainer/chainer,chainer/__init__.py,fa9e7bdb058ad3391f054e808c89c5591cde461a,STILL_EXISTS,TODO(hvy): Move this function to backend?
aaighgidi,chainer/chainer,chainer/variable.py,fa9e7bdb058ad3391f054e808c89c5591cde461a,a8d08cb7a1fac6cacc647f51c4bbdcae7db0246c,fix py2 memory leak
aaighibji,chainer/chainer,chainer/variable.py,53db677ce1ba63e41f322022aabb69dc7cb2e07e,4b0c0242ed1cb792eb3a1250a6809ebe6469e279,self._device as much as possible because it is really costly.
aaighicbj,chainer/chainer,tests/chainerx_tests/unit_tests/test_backward.py,9fb61255d78833ec197c8cd3174a1dcd39bcc577,7ba7d6e2a400b55486f03a8e3fd3eac9f45dcc05,TODO(hvy): Fix exception.
aaighiccd,chainer/chainer,tests/chainer_tests/test_backprop.py,6454e26feaea1b140d19769d50514c5b1025a180,891487dd587a8694ceabfc81639330d31e4ae006,TODO(niboshi): Fix ChainerX for None inputs
aaighifac,chainer/chainer,chainer/testing/function.py,8bdab17bbdd4c966370919ed39fc0be074ba73dc,ca48f1b8efbf980423631accb8264bd8bdfa8ccd,This is a dirty workaround to avoid writing the skip logic in every
aaighifha,chainer/chainer,chainer/variable.py,7bf54f809c52705874fb578fc6d13eb83c0c31b1,STILL_EXISTS,self._device as much as possible because it is really costly.
aaighigjb,chainer/chainer,chainer/variable.py,b2cbda108760d68fc65a94ab91c087632b91029c,STILL_EXISTS,FIXME: avoid `isinstance`
aaighigjh,chainer/chainer,chainer/variable.py,b2cbda108760d68fc65a94ab91c087632b91029c,STILL_EXISTS,self._device as much as possible because it is really costly.
aaighihgd,chainer/chainer,chainer/variable.py,b2cbda108760d68fc65a94ab91c087632b91029c,a8d08cb7a1fac6cacc647f51c4bbdcae7db0246c,Fix F812 (Python 2)
aaighihjf,chainer/chainer,chainer/iterators/multiprocess_iterator.py,af17fdde944f46233615d42d40ba1e2047496873,6ddb92a59f852b6e1aa14497a1885573f9f7062b,As there is no way to ensure correct finalization; we simply skip
aaighjbag,chainer/chainer,chainer/variable.py,6ddb92a59f852b6e1aa14497a1885573f9f7062b,STILL_EXISTS,FIXME: avoid `isinstance`
aaighjbbc,chainer/chainer,chainer/variable.py,6ddb92a59f852b6e1aa14497a1885573f9f7062b,STILL_EXISTS,self._device as much as possible because it is really costly.
aaighjbhi,chainer/chainer,chainer/variable.py,6ddb92a59f852b6e1aa14497a1885573f9f7062b,a8d08cb7a1fac6cacc647f51c4bbdcae7db0246c,Fix F812 (Python 2)
aaighjhdi,chainer/chainer,chainer/iterators/multiprocess_iterator.py,a18c8977ad9dbc715fae002fd147e055b6485fca,0e75cc5df26dfac0649589bfabb6581c36f63b51,As there is no way to ensure correct finalization; we simply skip
aaighjiih,chainer/chainer,chainer/variable.py,62397082f35b6026c3c958f08109368e5efbd12c,STILL_EXISTS,self._device as much as possible because it is really costly.
aaighjjfc,chainer/chainer,chainer/variable.py,62397082f35b6026c3c958f08109368e5efbd12c,a8d08cb7a1fac6cacc647f51c4bbdcae7db0246c,Fix F812 (Python 2)
aaighjjjd,chainer/chainer,chainer/__init__.py,c75404aaa1fbf35e7bc9d248599e360e5a9bbd40,STILL_EXISTS,TODO(hvy): Move this function to backend?
aaigiaegb,chainer/chainer,tests/chainerx_tests/unit_tests/routines_tests/test_math.py,f42ce4954fbb72262c4005f3a93157111ce3f662,53aea9d5bdbf9f08497ef2cfd5c3d4fb24907848,TODO(beam2d): Fix after supporting correct dtype promotion.
aaigiaege,chainer/chainer,tests/chainerx_tests/unit_tests/routines_tests/test_math.py,f42ce4954fbb72262c4005f3a93157111ce3f662,53aea9d5bdbf9f08497ef2cfd5c3d4fb24907848,TODO(hvy): Fix after supporting correct dtype promotion.
aaigiaehg,chainer/chainer,chainer/variable.py,48ab387b155b2e223294371faf72e4ca2c1f6780,STILL_EXISTS,self._device as much as possible because it is really costly.
aaigiafeb,chainer/chainer,chainer/variable.py,48ab387b155b2e223294371faf72e4ca2c1f6780,a8d08cb7a1fac6cacc647f51c4bbdcae7db0246c,Fix F812 (Python 2)
aaigiagbg,chainer/chainer,chainer/variable.py,13b7c473cb1d26217f11254fc028d763a4c46ad4,b55ffab5930c8be88b4a9acbe4ff7ebf39d1a093,TODO(sonots): Implement for ChainerX
aaigiagcd,chainer/chainer,chainer/variable.py,355c84b3752ef84d88f95b5618ebd5688a6737d7,b55ffab5930c8be88b4a9acbe4ff7ebf39d1a093,TODO(sonots): Implement for ChainerX
aaigiahgg,chainer/chainer,chainer/variable.py,e3d957421a80c9d3723ba7cedc64741225239f53,STILL_EXISTS,self._device as much as possible because it is really costly.
aaigiaidc,chainer/chainer,chainer/variable.py,e3d957421a80c9d3723ba7cedc64741225239f53,a8d08cb7a1fac6cacc647f51c4bbdcae7db0246c,Fix F812 (Python 2)
aaigibabd,chainer/chainer,chainer/functions/array/resize_images.py,625d40b5590ccdd6d691342fe8a38f5533591008,STILL_EXISTS,in order to better utilize CPU cache memory.
aaigibcbh,chainer/chainer,chainer/_link.py,17806d636316064cc0f2645b23001a7d586f3df1,STILL_EXISTS,`entry_method_info` is for backward compatibility workaround for
aaigibccd,chainer/chainer,chainer/_link.py,17806d636316064cc0f2645b23001a7d586f3df1,STILL_EXISTS,Backward compatibility workaround for overridden methods
aaigibdfh,chainer/chainer,chainer/link.py,62807a5f803d39b9d15bf1eae12ca3eaf3e09ab8,09fafc3f94afffa020d8296ee4877d3619a02252,`skip_between_cupy_devices` argument is a workaround
aaigibdhc,chainer/chainer,chainer/sequential.py,62807a5f803d39b9d15bf1eae12ca3eaf3e09ab8,78aae8080238e5a678aeee2582348c1f5ee6d62f,Explore better representation by if-block.
aaigibeeg,chainer/chainer,chainer/variable.py,78aae8080238e5a678aeee2582348c1f5ee6d62f,73ed9c4e9e744f33a77820c09498d645a332d934,TODO(sonots): Implement for ChainerX
aaigibhae,chainer/chainer,chainer/variable.py,36c537222cb21139780ea65e61d76896da9c8148,73ed9c4e9e744f33a77820c09498d645a332d934,TODO(sonots): Implement for ChainerX
aaigibicf,chainer/chainer,chainer/link.py,d4aa952bcd82805000ac0090662ab7639dd143ab,53e28855e118241be6c344a70953a18df2ed1216,`skip_between_cupy_devices` argument is a workaround
aaigiccjj,chainer/chainer,tests/chainer_tests/test_link.py,88923f42a9dec8289d08ef32a401d911ad284dcc,d7a2b00529b5701a6dbed6f7b7c7e74de61d23c2,TODO(niboshi): Deprecate overriding these methods in a future release
aaigiceae,chainer/chainer,chainer/gradient_check.py,3023c93f4206d5707a5b4a0664b4fc9e57a916ea,STILL_EXISTS,It allows passing `params` as ndarrays instead of `Parameter`s and thus
aaigicefj,chainer/chainer,tests/chainer_tests/links_tests/normalization_tests/test_batch_normalization.py,3023c93f4206d5707a5b4a0664b4fc9e57a916ea,STILL_EXISTS,cast. This is needed when forward is called in order to compute
aaigicege,chainer/chainer,tests/chainer_tests/links_tests/normalization_tests/test_batch_normalization.py,3023c93f4206d5707a5b4a0664b4fc9e57a916ea,STILL_EXISTS,TODO(hvy): Remove this relaxation. It is currently needed as the
aaigicejc,chainer/chainer,chainer/sequential.py,ecdb60966f83d5b7394580db9be1cbf2b73c10dc,STILL_EXISTS,Explore better representation by if-block.
aaigicicf,chainer/chainer,chainer/variable.py,4862e2b822d2fc1f2067198316e4f48ef5c2bc0e,73ed9c4e9e744f33a77820c09498d645a332d934,TODO(sonots): Implement for ChainerX
aaigidbdb,chainer/chainer,tests/chainerx_tests/unit_tests/routines_tests/test_manipulation.py,82876590c8ec87c926e45acd28721904e36e5ad3,STILL_EXISTS,TODO(niboshi): Fix strides for 0-size inputs
aaigidbde,chainer/chainer,tests/chainerx_tests/unit_tests/routines_tests/test_manipulation.py,82876590c8ec87c926e45acd28721904e36e5ad3,6e2b4115824af1baa7c6c187892d22aafdbdb83d,sorted order. Fix this.
aaigiddib,chainer/chainer,examples/imagenet/train_imagenet.py,bb54288ef77572cdaa24e808b449496e8274dbbf,STILL_EXISTS,TODO(sonots): Temporarily disabled for chainerx. Fix it.
aaigidfhc,chainer/chainer,chainer/variable.py,53e28855e118241be6c344a70953a18df2ed1216,73ed9c4e9e744f33a77820c09498d645a332d934,TODO(sonots): Implement for ChainerX
aaigidfih,chainer/chainer,tests/chainer_tests/test_link.py,53e28855e118241be6c344a70953a18df2ed1216,57dc60b85af7da7db99e6622556ad1d54fe403e6,TODO(niboshi): Deprecate overriding these methods in a future release
aaigidgid,chainer/chainer,chainer/link.py,f8520d9a261e20333471013d04690befc3e74807,51b97651753faef72f665135497744c389ceb217,`skip_between_cupy_devices` argument is a workaround
aaigiebdg,chainer/chainer,chainer/optimizers/adam.py,6a01e112dd410bd1b93bb6322c58394bf7ad566c,0603a5dc91822fd42b2e7966623a97673316d1eb,Workaround to reflect changing `alpha` in `final_lr`.
aaigieecg,chainer/chainer,tests/chainer_tests/test_link.py,51b97651753faef72f665135497744c389ceb217,82aa6dd0fcc08a14328fb7b33bc5871e786997e8,TODO(niboshi): Deprecate overriding these methods in a future release
aaigiegaa,chainer/chainer,chainer/functions/array/resize_images.py,7db6ee6a930b94b4afd297572d1b2aee1978b06e,STILL_EXISTS,in order to better utilize CPU cache memory.
aaigiejdf,chainer/chainer,tests/chainer_tests/test_link.py,7db6ee6a930b94b4afd297572d1b2aee1978b06e,72edc924faa03a6f009c13c45d369c309f7e6310,TODO(niboshi): Deprecate overriding these methods in a future release
aaigifcdc,chainer/chainer,chainer/optimizers/adam.py,42e658aa162cacc1751b965c68858df13c298367,5c29beab703bc5c097f70f8c6e747b6d9e665aae,Workaround to reflect changing `alpha` in `final_lr`.
aaigigbae,chainer/chainer,examples/vae/train_vae.py,b4d372f70c3e2e078698bedf0725764057856013,5c29beab703bc5c097f70f8c6e747b6d9e665aae,TODO(niboshi): Temporarily disabled for chainerx. Fix it.
aaigiihcc,chainer/chainer,chainer/optimizers/adam.py,2f05180c8a02b5651ff5d9b51324d6400be7bd04,a5604e64cc99b7e810832f03b4a53c5204f5721a,Workaround to reflect changing `alpha` in `final_lr`.
aaigijccj,chainer/chainer,examples/wavenet/generate.py,8a626a25ae836a24ef7191a218b6018c3fdf39ec,22c1640710b637163196a8e5b3a4babc357a0b46,A workaround for ChainerX; which does not have random.choice.
aaigijcda,chainer/chainer,examples/wavenet/generate.py,8a626a25ae836a24ef7191a218b6018c3fdf39ec,22c1640710b637163196a8e5b3a4babc357a0b46,TODO(niboshi): Implement it in ChainerX
aaigijcdb,chainer/chainer,examples/wavenet/train.py,8a626a25ae836a24ef7191a218b6018c3fdf39ec,STILL_EXISTS,TODO(niboshi): Temporarily disabled for chainerx. Fix it.
aaigijfii,chainer/chainer,tests/chainer_tests/link_hooks_tests/test_spectral_normalization.py,3c724431f3e36b5feae9db04d3ee4d0318d1febb,STILL_EXISTS,Test loading is nice.
aaigijgca,chainer/chainer,examples/vae/train_vae.py,b2501e00a07e0d8a7be70d490c1b590fb3042c6e,STILL_EXISTS,TODO(niboshi): Temporarily disabled for chainerx. Fix it.
aaigijhdh,chainer/chainer,tests/chainerx_tests/unit_tests/routines_tests/test_logic.py,cda3b8525dbc4dc2fbb26705d46f580f609c253e,751743afcb659c7b800b29a92f384a8b6f84c56c,Hack for Issue #6778
aaigijhgj,chainer/chainer,tests/chainer_tests/functions_tests/array_tests/test_repeat.py,6931f3eddf561dd00fff9ef0fb83716914ca090d,STILL_EXISTS,Workaround NumPy 1.9 issue.
aaigijhhd,chainer/chainer,examples/wavenet/generate.py,ef51c2671ec914928f61651302dfcf0113c544e9,STILL_EXISTS,A workaround for ChainerX; which does not have random.choice.
aaigijhhe,chainer/chainer,examples/wavenet/generate.py,ef51c2671ec914928f61651302dfcf0113c544e9,STILL_EXISTS,TODO(niboshi): Implement it in ChainerX
aaigjaehj,chainer/chainer,tests/chainerx_tests/unit_tests/routines_tests/test_logic.py,04bf87ac3b2e702ae0481d6ba031780331bc2f31,31dc3be95e06aa2dd69a9a99c5f1c7dded8b8d04,Hack for #6778
aaigjbbgc,chainer/chainer,chainer/device_resident.py,1d70566c4dba04559275640b1ff0d0850056e4b4,670284356f425e00cb6ac6afa1f0ff28d014cbcc,workaround for overridden methods.
aaigjbbgd,chainer/chainer,chainer/device_resident.py,1d70566c4dba04559275640b1ff0d0850056e4b4,670284356f425e00cb6ac6afa1f0ff28d014cbcc,It is a DeviceResident if to_xxx methods were initially called
aaigjbbgf,chainer/chainer,chainer/device_resident.py,1d70566c4dba04559275640b1ff0d0850056e4b4,670284356f425e00cb6ac6afa1f0ff28d014cbcc,that would occur by calling to_xxx methods.
aaigjbcca,chainer/chainer,chainer/optimizers/adam.py,e50c2227979f971cb4a8932b1416ab9df7a5f2c2,STILL_EXISTS,Workaround to reflect changing `alpha` in `final_lr`.
aaigjbhce,chainer/chainer,chainer/variable.py,8b7cde403cd213a5186e6cd61e71a3e3fbd41ee4,a012e084f3a9b16f372c14243be6f2fdfdb8f5fc,TODO(kataoka): Implement chainerx.backward(output; grad_outputs)
aaigjbhcf,chainer/chainer,chainer/variable.py,8b7cde403cd213a5186e6cd61e71a3e3fbd41ee4,a012e084f3a9b16f372c14243be6f2fdfdb8f5fc,and remove this workaround.
aaigjbhch,chainer/chainer,tests/chainerx_tests/unit_tests/routines_tests/test_manipulation.py,6de9d0fb24e5b2df1f629ce06eb93b2a87caac03,fa7f0c0c3fffed52c6199ddf9cdebb8595630049,FIXME
aaigjbhha,chainer/chainer,chainer/device_resident.py,a54a340d180aa6d70fd6b4ecae9c9f54e168393b,STILL_EXISTS,workaround for overridden methods.
aaigjbhhb,chainer/chainer,chainer/device_resident.py,a54a340d180aa6d70fd6b4ecae9c9f54e168393b,STILL_EXISTS,It is a DeviceResident if to_xxx methods were initially called
aaigjbhhd,chainer/chainer,chainer/device_resident.py,a54a340d180aa6d70fd6b4ecae9c9f54e168393b,STILL_EXISTS,that would occur by calling to_xxx methods.
aaigjbiei,chainer/chainer,chainer/training/extensions/evaluator.py,3bea970cac7256babbd661e4a1edfdc2cf12bdc7,6fbb2b7a2c427b77f0fa2065a8efddb370d0af5e,TODO(niboshi): This is a temporary workaround to keep backward
aaigjbjdh,chainer/chainer,chainer/training/extensions/evaluator.py,111b2b93b10c3cff556fa276aa03a4af8cac9bbb,72ed38e8b5f18cf38553c15035c961e43c8f2b91,TODO(niboshi): This is a temporary workaround to keep backward
aaigjeebb,chainer/chainer,chainer/variable.py,152f7d51b8a56cb7ae4bcf8ea622ce7077a691ff,044f2f171726401961c5d7848b9ff6b357409409,TODO(kataoka): Implement chainerx.backward(output; grad_outputs)
aaigjeebc,chainer/chainer,chainer/variable.py,152f7d51b8a56cb7ae4bcf8ea622ce7077a691ff,044f2f171726401961c5d7848b9ff6b357409409,and remove this workaround.
aaigjeebe,chainer/chainer,chainer/variable.py,152f7d51b8a56cb7ae4bcf8ea622ce7077a691ff,044f2f171726401961c5d7848b9ff6b357409409,Move the check to elsewhere and remove this workaround.
aaigjegjg,chainer/chainer,chainer/variable.py,d24dd57005cb575635538d8b477352266031252f,STILL_EXISTS,TODO(kataoka): Implement chainerx.backward(output; grad_outputs)
aaigjegjh,chainer/chainer,chainer/variable.py,d24dd57005cb575635538d8b477352266031252f,STILL_EXISTS,and remove this workaround.
aaigjegjj,chainer/chainer,chainer/variable.py,d24dd57005cb575635538d8b477352266031252f,STILL_EXISTS,Move the check to elsewhere and remove this workaround.
aaigjfcji,chainer/chainer,chainer/variable.py,ee12dfbb871013999f22a45689c457d824201c75,a8d08cb7a1fac6cacc647f51c4bbdcae7db0246c,fix py2 memory leak
aaigjfdab,chainer/chainer,chainer/variable.py,ee12dfbb871013999f22a45689c457d824201c75,a8d08cb7a1fac6cacc647f51c4bbdcae7db0246c,Fix F812 (Python 2)
aaigjgegh,chainer/chainer,chainer/variable.py,136396bc68a78934753272870a6de7e9cb22f89d,5dfde2f9c6e64925ce75d4dbf86fdab557abb5ab,TODO(sonots): Implement for ChainerX
aaigjgeig,chainer/chainer,chainer/variable.py,2da21778c4dafe4c9c9cda9989655f785f67326a,5dfde2f9c6e64925ce75d4dbf86fdab557abb5ab,TODO(sonots): Implement for ChainerX
aaigjheha,chainer/chainer,examples/mnist/train_mnist_data_parallel.py,6b9a4aef4941fb0c59c2e7130c1486956403a4d8,41021abd7686f089e3602e7b8f6c264ff48559d3,TODO(niboshi): Temporarily disabled for chainerx. Fix it.
aaigjhiah,chainer/chainer,tests/chainerx_tests/unit_tests/routines_tests/test_reduction.py,5baa2e484606e4d51df871831a958fc5ca7ad2d6,STILL_EXISTS,((); 0); # TODO(sonots): Fix compatibility
aaigjhibg,chainer/chainer,tests/chainerx_tests/unit_tests/routines_tests/test_math.py,3555e1304175e4ff42be818b20b2d6cfa78eeac8,400a249aad12fb9f5bec7e9fb209d42e1839abdb,((); 0); # TODO(sonots): Fix compatibility
aaigjhiee,chainer/chainer,examples/mnist/train_mnist_data_parallel.py,e2103a5cd7daf90acc182514650866196adcdad7,a8d08cb7a1fac6cacc647f51c4bbdcae7db0246c,TODO(niboshi): Temporarily disabled for chainerx. Fix it.
aaigjhija,chainer/chainer,tests/chainerx_tests/unit_tests/routines_tests/test_math.py,c98c942143c5c5e85ddb1ced78e9c77ee011e4bc,ee29d4df9dfec078b71ab8db991aa43e849d7f24,((); 0); # TODO(sonots): Fix compatibility
aaigjicch,chainer/chainer,tests/chainerx_tests/unit_tests/routines_tests/test_math.py,a8d08cb7a1fac6cacc647f51c4bbdcae7db0246c,e47f03538a0ab8cc154a915d065397aa9c0445fd,((); 0); # TODO(sonots): Fix compatibility
aaigjidjd,chainer/chainer,chainer/variable.py,32d3c44c6c29c0b0dd0e02b41bfe5f19bb6cfa7f,a1220d9db6d1bc31529fd47e579337adbc90a690,fix py2 memory leak
aaigjidjg,chainer/chainer,chainer/variable.py,32d3c44c6c29c0b0dd0e02b41bfe5f19bb6cfa7f,a1220d9db6d1bc31529fd47e579337adbc90a690,Fix F812 (Python 2)
aaigjiebc,chainer/chainer,examples/mnist/train_mnist_data_parallel.py,32d3c44c6c29c0b0dd0e02b41bfe5f19bb6cfa7f,STILL_EXISTS,TODO(niboshi): Temporarily disabled for chainerx. Fix it.
aaihaaddc,chainer/chainer,chainer/variable.py,274e84c3fb4612bd0cd72964202e823159f6d004,1c4808666a1fc6c68948473608fc9f6fb614fd88,TODO(sonots): Implement for ChainerX
aaihaaeaj,chainer/chainer,tests/chainerx_tests/unit_tests/routines_tests/test_math.py,274e84c3fb4612bd0cd72964202e823159f6d004,d2b2b29f9f3437d614c45cc280f06d915dc17fca,((); 0); # TODO(sonots): Fix compatibility
aaihaaeii,chainer/chainer,chainer/variable.py,da2d7799181c0f57b292fd464b5906004ecc311f,1c4808666a1fc6c68948473608fc9f6fb614fd88,TODO(sonots): Implement for ChainerX
aaihaahdc,chainer/chainer,examples/static_graph_optimizations/mnist/train_mnist.py,d4245f9234df37540b7ab80fc814311a3d4ff2af,18c31dfc332208194f445a3f598a61bc69be852a,TODO(hvy): Temporarily disabled for chainerx. Fix it.
aaihabcag,chainer/chainer,chainer/variable.py,18c31dfc332208194f445a3f598a61bc69be852a,1c4808666a1fc6c68948473608fc9f6fb614fd88,TODO(sonots): Implement for ChainerX
aaihacjcc,chainer/chainer,chainer/variable.py,d04b799f0c1a6dec8a312caf728016784f96d405,155f9c079e2a2f8c6c5ee4607953ac8124da4af7,fix py2 memory leak
aaihacjcf,chainer/chainer,chainer/variable.py,d04b799f0c1a6dec8a312caf728016784f96d405,155f9c079e2a2f8c6c5ee4607953ac8124da4af7,Fix F812 (Python 2)
aaihadhgf,chainer/chainer,chainer/variable.py,1c4808666a1fc6c68948473608fc9f6fb614fd88,9e093f5ca1acb3ce62815fbcffc67a4d38aaedc4,fix py2 memory leak
aaihaecig,chainer/chainer,examples/static_graph_optimizations/mnist/train_mnist.py,9960b4c53a9b77d3e3a80432d4f9bb8053ae1ce8,STILL_EXISTS,TODO(hvy): Temporarily disabled for chainerx. Fix it.
aaihaehch,chainer/chainer,examples/imagenet/.testdata/replacements/train_imagenet.py,9bc7eda5f8537b4800d287d1b993a5eacb97cdc3,STILL_EXISTS,TODO(sonots): Temporarily disabled for chainerx. Fix it.
aaihaejhb,chainer/chainer,chainer/variable.py,97ed0868c40b21d1421d0f6ac70826e25b2fb404,9e093f5ca1acb3ce62815fbcffc67a4d38aaedc4,fix py2 memory leak
aaihaejhe,chainer/chainer,chainer/variable.py,97ed0868c40b21d1421d0f6ac70826e25b2fb404,9e093f5ca1acb3ce62815fbcffc67a4d38aaedc4,Fix F812 (Python 2)
aaihafbgg,chainer/chainer,examples/mnist/train_mnist_model_parallel.py,362722831b5ab8050adf58d22f6e32801543b768,6e2b4115824af1baa7c6c187892d22aafdbdb83d,TODO(niboshi): Temporarily disabled for chainerx. Fix it.
aaihafdha,chainer/chainer,examples/mnist/train_mnist_data_parallel.py,7361920cc1b5832b5606dbe4a53658816d0e8274,STILL_EXISTS,TODO(niboshi): Temporarily disabled for chainerx. Fix it.
aaihafdia,chainer/chainer,examples/static_graph_optimizations/mnist/train_mnist.py,7361920cc1b5832b5606dbe4a53658816d0e8274,STILL_EXISTS,TODO(hvy): Temporarily disabled for chainerx. Fix it.
aaihahdhg,chainer/chainer,tests/chainer_tests/iterators_tests/test_multiprocess_iterator.py,5288f4df6a8e91e01cbfd5c32d2728e38f82e50c,15226a99d1eb38c04ba85ed98ecce6c3950e88cb,TODO (emcastill) : pickle fails when using order_sampler as lambda function
aaihaidid,chainer/chainer,chainer/iterators/multiprocess_iterator.py,4fe088c6f0d7ad24b6f301b8a977be80e99596f1,STILL_EXISTS,TODO: when pickling in one process and restoring in other
aaihaidjd,chainer/chainer,tests/chainer_tests/iterators_tests/test_multiprocess_iterator.py,4fe088c6f0d7ad24b6f301b8a977be80e99596f1,STILL_EXISTS,work is needed to wrap samplers in classes instead of
aaihaijcj,chainer/chainer,tests/chainer_tests/test_optimizer.py,b31aa81d5f875626d287a81ce3d60a5b1c026f5e,5a26c2bc989e0c6cfd9c45f9a159d880db935441,TODO(niboshi): Fix it
aaihajhaf,chainer/chainer,tests/chainer_tests/test_optimizer.py,83898d5bf353de0472d5bf4f74fbde4b96f30e4b,4a0653817821d23fcc1420c0cf7bfb33dad58a48,TODO(niboshi): Fix it
aaihbaddc,chainer/chainer,tests/chainerx_tests/unit_tests/routines_tests/test_manipulation.py,4a0653817821d23fcc1420c0cf7bfb33dad58a48,6e2b4115824af1baa7c6c187892d22aafdbdb83d,sorted order. Fix this.
aaihbafca,chainer/chainer,tests/chainer_tests/test_optimizer.py,e0075dd9144201ca345b1e2149820d0806901e99,62030984c829831d86f71d92792f71395f3adaa3,TODO(niboshi): Fix it
aaihbbbhb,chainer/chainer,chainer/optimizer_hooks/gradient_lars.py,5717635b591142273834f5eef6d49dc35247228d,STILL_EXISTS,TODO norm in chainerx
aaihbbcdg,chainer/chainer,tests/chainer_tests/test_optimizer.py,8b511ba2f8f4f237159de2f40a0878a2280d2557,62030984c829831d86f71d92792f71395f3adaa3,TODO(niboshi): Fix it
aaihbbchh,chainer/chainer,chainer/datasets/pickle_dataset.py,09252fb964a7159156b3975033c46835717841ae,6e2b4115824af1baa7c6c187892d22aafdbdb83d,TODO: Avoid using undocumented feature
aaihbbddb,chainer/chainer,tests/chainer_tests/test_optimizer.py,0e7e9617db1caec6556eca6f4222072fc841db08,62030984c829831d86f71d92792f71395f3adaa3,TODO(niboshi): Fix it
aaihbbdjj,chainer/chainer,chainer/optimizer_hooks/gradient_hard_clipping.py,1145bafbbc7101f19523b491503c9d1758a84626,f28641d2a134684fdaa6882d8db5983da6561dd9,TODO(kshitij12345): Fix when chainerx.clip
aaihbcccb,chainer/chainer,chainer/optimizer_hooks/gradient_hard_clipping.py,b642b6b3e4cf1378a5860e52c5f126868ff09fc6,6e2b4115824af1baa7c6c187892d22aafdbdb83d,TODO(kshitij12345): Fix when chainerx.clip
aaihbccjg,chainer/chainer,tests/chainer_tests/datasets_tests/test_pickle_dataset.py,c05a1fbe9ee5307aec6453913ef4c6ce9d4ea34d,STILL_EXISTS,Touch to suppress \"unused variable' warning
aaihbcddf,chainer/chainer,tests/chainer_tests/test_optimizer.py,b21a06feec0f4160d650cb924508f27563e8d6da,d2e2911a4213835fba33a59f94332d5f85b200bb,TODO(niboshi): Fix it
aaihbcgah,chainer/chainer,chainer/variable.py,ccc62934d5e9611aca88d51d8f4a16738638b95c,STILL_EXISTS,Fix F812 (Python 2)
aaihbcihb,chainer/chainer,tests/chainer_tests/test_optimizer.py,ccc62934d5e9611aca88d51d8f4a16738638b95c,d2e2911a4213835fba33a59f94332d5f85b200bb,TODO(niboshi): Fix it
aaihbdehf,chainer/chainer,chainer/variable.py,59a54e6835690adfb8a63db6af884587afcb6bf5,STILL_EXISTS,Fix F812 (Python 2)
aaihbdfec,chainer/chainer,tests/chainer_tests/test_optimizer.py,59a54e6835690adfb8a63db6af884587afcb6bf5,d2e2911a4213835fba33a59f94332d5f85b200bb,TODO(niboshi): Fix it
aaihbdhda,chainer/chainer,tests/chainerx_tests/unit_tests/routines_tests/test_manipulation.py,9249ee6742e3aeffe786749e94aeb8101b685c0c,6e2b4115824af1baa7c6c187892d22aafdbdb83d,sorted order. Fix this.
aaihbiaag,chainer/chainer,tests/chainerx_tests/unit_tests/routines_tests/test_manipulation.py,edab17c22c5047f7f92043bc7ea7918f5812c367,49e9ee4e7374152f89d2fef3c255acb1cf23eba7,sorted order. Fix this.
aaihbibcd,chainer/chainer,chainer/datasets/pickle_dataset.py,cd8291bd7fe6bcac16167a4fe0d128932d46175d,STILL_EXISTS,TODO: Avoid using undocumented feature
aaihbibjg,chainer/chainer,chainer/optimizer_hooks/gradient_hard_clipping.py,cd8291bd7fe6bcac16167a4fe0d128932d46175d,STILL_EXISTS,TODO(kshitij12345): Fix when chainerx.clip
aaihbicfa,chainer/chainer,examples/mnist/train_mnist_model_parallel.py,cd8291bd7fe6bcac16167a4fe0d128932d46175d,STILL_EXISTS,TODO(niboshi): Temporarily disabled for chainerx. Fix it.
aaihbjcij,chainer/chainer,tests/chainer_tests/functions_tests/connection_tests/test_convolution_nd.py,8771f9eb18bee1321d4fab466c98cc61008bd8a7,STILL_EXISTS,support autotune so this hack is necessary
aaihbjdaf,chainer/chainer,examples/static_graph_optimizations/mnist/train_mnist.py,2fc91103254ab7df3d641a0572b44dfe6ae5dda7,STILL_EXISTS,TODO(hvy): Temporarily disabled for chainerx. Fix it.
aaihbjdii,chainer/chainer,tests/chainer_tests/test_link.py,48865038c1f53e54dd5fb0c92cf86dcb726b3377,STILL_EXISTS,untouched; but maybe uninitialized state should be restored? (#7916)
aaihcadfe,chainer/chainer,examples/mnist/train_mnist_data_parallel.py,49852c4af358e62ae6a22a4336b7829838934750,STILL_EXISTS,TODO(niboshi): Temporarily disabled for chainerx. Fix it.
aaihcaeca,chainer/chainer,chainer/functions/pooling/max_pooling_nd.py,915652640543a56e3e9de106cc4afd35954d2fb3,becf72c292110471ac0f3fed529e39ec2558b7a9,FIXME
aaihcaedb,chainer/chainer,chainer/functions/pooling/max_pooling_2d.py,becf72c292110471ac0f3fed529e39ec2558b7a9,0eb5c3fd282ced0848a7b6a53d9a9812be265a58,FIXME
aaihcafaf,chainer/chainer,chainer/functions/pooling/max_pooling_nd.py,0eb5c3fd282ced0848a7b6a53d9a9812be265a58,07fb9ffb55a4ae811a9cd118f0b8cf24c8284b76,FIXME
aaihcafei,chainer/chainer,chainer/functions/pooling/max_pooling_2d.py,07fb9ffb55a4ae811a9cd118f0b8cf24c8284b76,56a49ddf770b63edaeca268a4407709d4847bd23,FIXME
aaihcagbh,chainer/chainer,chainer/functions/pooling/max_pooling_nd.py,56a49ddf770b63edaeca268a4407709d4847bd23,STILL_EXISTS,FIXME
aaihcahgd,chainer/chainer,examples/static_graph_optimizations/mnist/train_mnist.py,9457b0031abc0a79bce6cbc5223b8e9e9f6a731d,STILL_EXISTS,TODO(hvy): Temporarily disabled for chainerx. Fix it.
aaihcbfib,chainer/chainer,examples/static_graph_optimizations/mnist/train_mnist.py,232aa399ef047114c311bcc713d0b99ec6c96c7c,STILL_EXISTS,TODO(hvy): Temporarily disabled for chainerx. Fix it.
aaihccafh,chainer/chainer,tests/chainer_tests/test_link.py,94c8945ad5960b0beb5099cb89f7b28a302221e2,72edc924faa03a6f009c13c45d369c309f7e6310,TODO(niboshi): Deprecate overriding these methods in a future release
aaihccedh,chainer/chainer,examples/mnist/train_mnist_data_parallel.py,2de869c798296de406e8f43caa8f38a4b423ce84,STILL_EXISTS,TODO(niboshi): Temporarily disabled for chainerx. Fix it.
aaihccfjd,chainer/chainer,tests/chainer_tests/test_link.py,2de869c798296de406e8f43caa8f38a4b423ce84,9336d1529a13c244b8bfcc828b9ec524578a01ee,TODO(niboshi): Deprecate overriding these methods in a future release
aaihcdhif,chainer/chainer,tests/onnx_chainer_tests/functions_tests/test_arrays.py,8382d89d82cfdb2ce4957ea8e16163528b578459,STILL_EXISTS,FIXME(syoyo): Currently the test will fail due to the different
aaihcdide,chainer/chainer,tests/onnx_chainer_tests/test_inout.py,8382d89d82cfdb2ce4957ea8e16163528b578459,STILL_EXISTS,Unused for some reason; then params are not initialized.
aaihceejf,chainer/chainer,examples/mnist/.testdata/replacements/train_mnist.py,ffc12c4808913c4f08f12c9c093643b3d72bd21d,STILL_EXISTS,TODO(niboshi): Temporarily disabled for chainerx. Fix it.
aaihcefci,chainer/chainer,tests/chainerx_tests/unit_tests/routines_tests/test_manipulation.py,21226b361a5da1bf8edea7522e470a9d75bb2841,49e9ee4e7374152f89d2fef3c255acb1cf23eba7,sorted order. Fix this.
aaihcgcec,chainer/chainer,chainer/functions/loss/softmax_cross_entropy.py,a81227f6f1f23d3108ca30a6251fd754423c8b20,295190a0b4e457666a4ba49a857a2c9d5e35286b,TODO(beam2d): implement mean
aaihdefdd,chainer/chainer,chainer/memory_layouts.py,a65143c2019e6403b35ea12ec1bbf7ea73bc32e2,STILL_EXISTS,TODO(niboshi): memory layouts and conversions should better be implemented
aaihdehag,chainer/chainer,tests/chainer_tests/links_tests/rnn_tests/test_link_n_step_rnn.py,65e6d3265d5aaf269297fdb154844a5da4775998,e125f807797c53ad3c8de16f9f25124671a0a0f6,FIXME (himkt) workaround
aaihdejbi,chainer/chainer,tests/onnx_chainer_tests/functions_tests/test_rnn.py,7f77a763df96d2de5a10d601e5dde56729c42085,STILL_EXISTS,XXX
aaihdejbj,chainer/chainer,tests/onnx_chainer_tests/functions_tests/test_rnn.py,7f77a763df96d2de5a10d601e5dde56729c42085,STILL_EXISTS,XXX: Replace Permutate converter for avoiding error like:
aaihfagce,allenai/allennlp,allennlp/data/dataset_readers/language_modeling.py,51c26a933996e9415d4c5e059c196b2ca2e46db0,614c507d88e9797fb86216e41ef6fa83e33b30f7,TODO(matt): this isn't quite right; because you really want to split on sentences;
aaihfagha,allenai/allennlp,allennlp/data/fields/text_field.py,51c26a933996e9415d4c5e059c196b2ca2e46db0,STILL_EXISTS,\"\"\" || A ``TextField`` represents a string of text; the kind that you might want to represent with || standard word vectors; or pass through an LSTM. || \"\"\"
aaihfbdaj,allenai/allennlp,allennlp/nlp_api.py,3dd6e6d4e3b3cb7393626ecb12bd05c0e6d25596,STILL_EXISTS,TODO(mattg): actually implement this.
aaihfbdbc,allenai/allennlp,allennlp/service/server.py,88d3803e9d1933b9be0303775b410bf026978361,STILL_EXISTS,TODO: replace with actual types
aaihfbdbg,allenai/allennlp,allennlp/service/server.py,88d3803e9d1933b9be0303775b410bf026978361,STILL_EXISTS,TODO: error handling
aaihfbdbi,allenai/allennlp,allennlp/service/server.py,88d3803e9d1933b9be0303775b410bf026978361,STILL_EXISTS,TODO: replace with actual models
aaihfbefj,allenai/allennlp,allennlp/service/models/simple_tagger.py,6fada919dbf086079ed4d9bd9df77886b9848fca,STILL_EXISTS,this is a bad hack to get the same data as the test case
aaihfbeha,allenai/allennlp,tests/service/server_flask_test.py,6fada919dbf086079ed4d9bd9df77886b9848fca,d9e2565f8a8ba055cd80ca50fddc73f28cf7dbd1,TODO(joelgrus): write a better test
aaihfbeij,allenai/allennlp,allennlp/experiments/registry.py,17361796c4e0cb7bfab5a24b60737645e4cd064d,STILL_EXISTS,Throughout this class; in the `get_*` methods; we have unused import statements.  That is
aaihfbfhg,allenai/allennlp,allennlp/data/dataset_readers/semantic_role_labelling.py,42139016cd7b54ee84b55411db2049bd61c37b25,STILL_EXISTS,Conll format data begins and ends with lines containing a hash;
aaihfbhhh,allenai/allennlp,allennlp/service/servable/servable.py,d657b0ed22ff20720bfcaf6854d59b22d42e5666,STILL_EXISTS,TODO(joelgrus): add a predict_tensor method maybe?
aaihfbhid,allenai/allennlp,allennlp/service/servable/servable.py,d657b0ed22ff20720bfcaf6854d59b22d42e5666,STILL_EXISTS,TODO(joelgrus) implement this
aaihfbhie,allenai/allennlp,allennlp/service/server_flask.py,d657b0ed22ff20720bfcaf6854d59b22d42e5666,STILL_EXISTS,TODO(joelgrus): make this configurable
aaihfbhig,allenai/allennlp,allennlp/service/server_sanic.py,d657b0ed22ff20720bfcaf6854d59b22d42e5666,a87b6034357320f95af0f8f141905b6f2befbb0e,TODO(joelgrus): make this configurable
aaihfbjad,allenai/allennlp,allennlp/data/data_iterator.py,6f415a2d96dfabb911b566eaa8076c9a7cc71081,59e1cb2f04355344e608e6de5f73fc83ec215974,TODO(joelgrus): implement `from_params` methods on subclasses + change
aaihfbjag,allenai/allennlp,allennlp/training/initializer.py,6f415a2d96dfabb911b566eaa8076c9a7cc71081,STILL_EXISTS,There are no classes to decorate; so we hack these into Registrable._registry
aaihfbjei,allenai/allennlp,allennlp/training/trainer.py,5cbfd3239f673203cc11c5cfc77ce90e9a6ce426,STILL_EXISTS,TODO(Mark): Add user specified metrics here; maybe a \"metrics\" key?
aaihfcacf,allenai/allennlp,tests/common/tensor_test.py,971100f9204a869a28bf57e4ec7c780376d88e9e,STILL_EXISTS,Best path would normally be [3; 2; 3] but we add a
aaihfcacg,allenai/allennlp,tests/common/tensor_test.py,971100f9204a869a28bf57e4ec7c780376d88e9e,STILL_EXISTS,potential from 2 -> 1; making [3; 2; 1] the best path.
aaihfcagf,allenai/allennlp,tests/models/bidaf_test.py,e4c9540147b3bf140df587380b09815ef21b455f,58a813dc5ad727afd7ea0ec69912a31a7751271f,characters.  We need to fix that somehow.
aaihfcbbi,allenai/allennlp,allennlp/nn/activations.py,3bf6f6d44248c9f3ba8fc943fff648c1fccadd6b,STILL_EXISTS,There are no classes to decorate; so we hack these into Registrable._registry
aaihfcbcb,allenai/allennlp,allennlp/nn/initializers.py,3bf6f6d44248c9f3ba8fc943fff648c1fccadd6b,8e3c612815cb03dfaf4f17f9ce79fcfe03088e30,There are no classes to decorate; so we hack these into Registrable._registry
aaihfcbdh,allenai/allennlp,allennlp/service/server_sanic.py,d4253ecd138763d0577a5ad821342e11e27606e5,9a837027e0a8b105dd524995c24e8a1bc23bcfd3,If someone really wants them; they can move them back
aaihfccgb,allenai/allennlp,allennlp/nn/util.py,a19f102c65b943a668616df5f3b46cfb4376e04c,ed63b7ee9e68ba120893aaaaca0d4525a56ed484,This is ugly; but required - we are creating a new variable at runtime; so we
aaihfcdci,allenai/allennlp,allennlp/service/servable/servable.py,050504f0f1ee8b96adb4fddce468bd309ac8b746,STILL_EXISTS,TODO: get rid of this
aaihfcffg,allenai/allennlp,doc/conf.py,3318f134cb1cfabcc8562d32b05a75b30eaf404c,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaihfcgfe,allenai/allennlp,doc/source/conf.py,3318f134cb1cfabcc8562d32b05a75b30eaf404c,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaihfchfi,allenai/allennlp,allennlp/training/trainer.py,14f78745a7cef0f1e5b4314069d566394f860bd3,ed37843d528b0902d73e4d5dde931812fd65835d,epochs is better than current value?
aaihfcijc,allenai/allennlp,allennlp/training/trainer.py,a9d04a6ea811918545e3e3d4069c438df695fb8b,b71ef434b310e9b1ba400b42c7334c794996b40e,Grim hack to determine whether the validation metric we are recording
aaihfcjga,allenai/allennlp,allennlp/commands/evaluate.py,360455ea056561992445613b5ac6f17718623a4f,STILL_EXISTS,\"\"\" || The ``evaluate`` subcommand can be used to || evaluate a trained model against a dataset || and report any metrics calculated by the model. ||  || .. code-block:: bash ||  ||     $ python -m allennlp.run evaluate --help ||     usage: run [command] evaluate [-h] --archive_file ARCHIVE_FILE ||                                 --evaluation_data_file EVALUATION_DATA_FILE ||                                 [--cuda_device CUDA_DEVICE] ||  ||     Evaluate the specified model + dataset ||  ||     optional arguments: ||     -h; --help            show this help message and exit ||     --archive_file ARCHIVE_FILE ||                             path to an archived trained model ||     --evaluation_data_file EVALUATION_DATA_FILE ||                             path to the file containing the evaluation data ||     --cuda_device CUDA_DEVICE ||                             id of GPU to use (if any) || \"\"\"
aaihfcjgd,allenai/allennlp,allennlp/commands/serve.py,360455ea056561992445613b5ac6f17718623a4f,STILL_EXISTS,\"\"\" || The ``serve`` subcommand launches a server || that exposes trained models via a REST API; || and that includes a web interface for exploring || their predictions. ||  || .. code-block:: bash ||  ||     $ python -m allennlp.run serve --help ||     usage: run [command] serve [-h] [--port PORT] [--workers WORKERS] ||                             [--config-file CONFIG_FILE] ||  ||     Run the web service; which provides an HTTP API as well as a web demo. ||  ||     optional arguments: ||     -h; --help            show this help message and exit ||     --port PORT ||     --workers WORKERS ||     --config-file CONFIG_FILE ||                             path to a JSON file specifying the configuration for ||                             the models || \"\"\"
aaihfdaeb,allenai/allennlp,tests/models/bidaf_test.py,614c507d88e9797fb86216e41ef6fa83e33b30f7,STILL_EXISTS,When we were using exlcusive span ends; this was an edge case of the dynamic program.
aaihfdafi,allenai/allennlp,allennlp/data/fields/__init__.py,c034eeefc69a928cc9b58c45624fc85553476887,STILL_EXISTS,\"\"\" || A :class:`~allennlp.data.fields.field.Field` is some piece of data instance || that ends up as an array in a model. || \"\"\"
aaihfdcag,allenai/allennlp,allennlp/models/model.py,58a813dc5ad727afd7ea0ec69912a31a7751271f,dc6510d98c6e235f531c47c6244c2c02884496f4,Hack to see what cuda device the model is on; so we know where to put these inputs.  For
aaihfdcai,allenai/allennlp,allennlp/models/model.py,58a813dc5ad727afd7ea0ec69912a31a7751271f,dc6510d98c6e235f531c47c6244c2c02884496f4,a way to actually query what device a tensor \/ parameter is on.
aaihfdcgj,allenai/allennlp,allennlp/data/tokenizers/word_splitter.py,2c4a6e537126f4123de7c97f30587310d3712c06,3c761ba9d857677efb60ae5c2dbbe321f0b3b702,In order to avoid loading spacy models a whole bunch of times; we'll save references to them;
aaihfdffe,allenai/allennlp,allennlp/models/encoder_decoders/encoder_decoder.py,7a5b58fede93821b8eb4f8d01cb6209ad659373a,STILL_EXISTS,attention with ``DotProductSimilarity``; this is needed.
aaihfdffh,allenai/allennlp,allennlp/models/encoder_decoders/encoder_decoder.py,7a5b58fede93821b8eb4f8d01cb6209ad659373a,STILL_EXISTS,TODO (pradeep): Do not hardcode decoder cell type.
aaihfdfge,allenai/allennlp,allennlp/models/encoder_decoders/encoder_decoder.py,7a5b58fede93821b8eb4f8d01cb6209ad659373a,STILL_EXISTS,TODO (pradeep): Define a DecoderState class?
aaihfdfhc,allenai/allennlp,allennlp/models/encoder_decoders/encoder_decoder.py,7a5b58fede93821b8eb4f8d01cb6209ad659373a,STILL_EXISTS,TODO: Define metrics
aaihfdgcf,allenai/allennlp,allennlp/nn/util.py,3592970d4b680404d91f8a1605550631caeecadb,STILL_EXISTS,We'll special-case a few settings here; where there are efficient (but poorly-named)
aaihfdhaj,allenai/allennlp,allennlp/modules/conditional_random_field.py,e8aba3fe479293388b722ff1da46baea7bb49466,d1f6748120abc8b1b0bfccd2e6ccc4142ba127d0,Is (sequence_length; batch_size); but all the columns are the same; so take the first.
aaihfdhih,allenai/allennlp,tests/models/reading_comprehension/bidaf_test.py,e2a649354e67584eff90bf9fc5b405b0c6e0d6ce,STILL_EXISTS,Some recent efficiency changes (using bmm for `weighted_sum`; the more efficient
aaihfdjef,allenai/allennlp,allennlp/data/dataset_readers/coreference_resolution/conll.py,3c761ba9d857677efb60ae5c2dbbe321f0b3b702,85b2c2d2b9fa8bbaa3335d889c610e46d253e6cc,The span begins and ends at this word (single word span).
aaihfdjie,allenai/allennlp,allennlp/models/coreference_resolution/coref.py,3c761ba9d857677efb60ae5c2dbbe321f0b3b702,STILL_EXISTS,the multiple calls to util.batched_index_select below more efficient.
aaihfedaf,allenai/allennlp,allennlp/modules/encoder_base.py,5acb5a785b9ed60743e6f687a96bd92dd4e88578,STILL_EXISTS,which way around is best?
aaihfedbe,allenai/allennlp,allennlp/modules/encoder_base.py,5acb5a785b9ed60743e6f687a96bd92dd4e88578,STILL_EXISTS,Masks for the unused states of shape (1; new_batch_size; 1)
aaihfeddb,allenai/allennlp,allennlp/modules/encoder_base.py,5acb5a785b9ed60743e6f687a96bd92dd4e88578,STILL_EXISTS,that there are some unused elements (zero-length) for the RNN computation.
aaihfedff,allenai/allennlp,scripts/check_docs.py,5acb5a785b9ed60743e6f687a96bd92dd4e88578,STILL_EXISTS,Private base class; no docs needed.
aaihfedie,allenai/allennlp,tests/modules/seq2seq_encoders/pytorch_seq2seq_wrapper_test.py,5acb5a785b9ed60743e6f687a96bd92dd4e88578,STILL_EXISTS,To test the stateful functionality we need to call the encoder multiple times.
aaihfeecj,allenai/allennlp,allennlp/modules/elmo_lstm.py,10469ac6dc47a5474b9e6830fb5f6b05000241db,STILL_EXISTS,handle the different gate order convention
aaihfefdf,allenai/allennlp,allennlp/data/dataset_readers/dataset_utils/ontonotes.py,c2203c414b0b88b378ec2b07100b985df2f66512,STILL_EXISTS,The span begins and ends at this word (single word span).
aaihfefgd,allenai/allennlp,allennlp/modules/elmo.py,64c5a5b1f415bda92a4205e91799ecf1438712e4,STILL_EXISTS,reshape the input if needed
aaihfegae,allenai/allennlp,tests/models/archival_test.py,1793768b3c3be52c144b43266a83e87be52f7c2f,STILL_EXISTS,(which we don't know; but we know what it ends with).
aaihfehbd,allenai/allennlp,allennlp/service/server_simple.py,c5e1cf8c4c4aa6592e2175fc9ad2d961a8aa75c2,STILL_EXISTS,to do this (except maybe to test changes to the stock HTML); but this shows
aaihfejgc,allenai/allennlp,allennlp/commands/evaluate.py,3c428f9cbe3fa149e30bc85ebfcb6e567cabb0a7,7edcdf5a90a8ca067badb8903ad30bf50c18e0ec,Import any additional modules needed (to register custom classes)
aaihfejgf,allenai/allennlp,allennlp/commands/predict.py,3c428f9cbe3fa149e30bc85ebfcb6e567cabb0a7,7edcdf5a90a8ca067badb8903ad30bf50c18e0ec,Import any additional modules needed (to register custom classes)
aaihfejgg,allenai/allennlp,allennlp/commands/train.py,3c428f9cbe3fa149e30bc85ebfcb6e567cabb0a7,7edcdf5a90a8ca067badb8903ad30bf50c18e0ec,Import any additional modules needed (to register custom classes)
aaihffadi,allenai/allennlp,allennlp/common/tqdm.py,2a90ed83ac62a3ab77bb8eb9d4c1543b73df729c,STILL_EXISTS,\"\"\" || :class:`~allennlp.common.tqdm.Tqdm` wraps tqdm so we can add configurable || global defaults for certain tqdm parameters. || \"\"\"
aaihffagd,allenai/allennlp,allennlp/models/coreference_resolution/coref_v2.py,2a86dc32eef9c69ed1d6a096d12308d2fa1495ca,STILL_EXISTS,the multiple calls to util.batched_index_select below more efficient.
aaihffcbf,allenai/allennlp,allennlp/commands/evaluate.py,924e56f301ee01533fb6bb07fb4d1b5ae24589a9,9121eed62a717baa0c5dcbcd10c60a52d3b6d285,Import any additional modules needed (to register custom classes)
aaihffcbi,allenai/allennlp,allennlp/commands/predict.py,924e56f301ee01533fb6bb07fb4d1b5ae24589a9,9121eed62a717baa0c5dcbcd10c60a52d3b6d285,Import any additional modules needed (to register custom classes)
aaihffcbj,allenai/allennlp,allennlp/commands/train.py,924e56f301ee01533fb6bb07fb4d1b5ae24589a9,9121eed62a717baa0c5dcbcd10c60a52d3b6d285,Import any additional modules needed (to register custom classes)
aaihffcha,allenai/allennlp,allennlp/modules/span_extractors/self_attentive_span_extractor.py,0b3ca77cf33aa688f298fae06f3c221af7c7161d,2e811b2796f77e9e796fc3a91f6618fdac68973b,These span widths are off by 1; because the span ends are `inclusive`.
aaihffcid,allenai/allennlp,allennlp/modules/span_extractors/self_attentive_span_extractor.py,0b3ca77cf33aa688f298fae06f3c221af7c7161d,2e811b2796f77e9e796fc3a91f6618fdac68973b,We're using <= here (and for the mask below) because the span ends are
aaihffdfd,allenai/allennlp,allennlp/data/iterators/basic_iterator.py,c0cb3275d7e2b25e4965763f7f83e7a89ef7c0e4,fa34344baffe2f8e2a50d5353a6e05f1ef668b8f,of instances each epoch; and we didn't specify how to many instances to load
aaihffdhc,allenai/allennlp,tests/data/iterators/basic_iterator_test.py,c0cb3275d7e2b25e4965763f7f83e7a89ef7c0e4,STILL_EXISTS,With 100 instances; shuffling better change the order.
aaihffdig,allenai/allennlp,allennlp/data/dataset_readers/penn_tree_bank.py,7d2b63cda3968bd818c2773e8b3fbfc937f06815,STILL_EXISTS,NLTK is so performance orientated (ha ha) that they have lazy imports. Why? Who knows.
aaihffece,allenai/allennlp,allennlp/training/trainer.py,ab4aeb28f1b81747c985a188b6c4c569c02aaa75,e08ade81ff6684869883ce3da66941b9d3a37503,Only the 'loss' is needed.
aaihffedc,allenai/allennlp,allennlp/training/trainer.py,ab4aeb28f1b81747c985a188b6c4c569c02aaa75,236e1fd01ca30409cd736625901292609009f5c4,Save model if needed.
aaihfffff,allenai/allennlp,allennlp/training/optimizers.py,e3c54d2cc75da8c027629eca44b393e0ce895314,STILL_EXISTS,check for unused regex
aaihffgfi,allenai/allennlp,allennlp/modules/span_extractors/bidirectional_endpoint_span_extractor.py,2d9bf0507696fe553eafeeafd164dbb9359ad135,STILL_EXISTS,We want `exclusive` span ends for the backward direction
aaihffgga,allenai/allennlp,allennlp/modules/span_extractors/bidirectional_endpoint_span_extractor.py,2d9bf0507696fe553eafeeafd164dbb9359ad135,STILL_EXISTS,we add 1 to the span ends as the AllenNLP ``SpanField`` is inclusive.
aaihffgge,allenai/allennlp,allennlp/modules/span_extractors/bidirectional_endpoint_span_extractor.py,2d9bf0507696fe553eafeeafd164dbb9359ad135,STILL_EXISTS,As we added 1 to the span_ends to make them exclusive; which might have caused indices
aaihffghb,allenai/allennlp,allennlp/modules/span_extractors/bidirectional_endpoint_span_extractor.py,2d9bf0507696fe553eafeeafd164dbb9359ad135,STILL_EXISTS,Forward Direction: end indices are inclusive; so we can just use span_ends.
aaihffiad,allenai/allennlp,allennlp/commands/__init__.py,dc2f80cd721e1e6890669165e741f5cd193ffb03,e28415ca6a93d76006a8544b7fc71f2179b2ea42,`model_overrides`; and maybe the whole `serve` command as a public API (we only need that
aaihffibc,allenai/allennlp,allennlp/commands/fine_tune.py,dc2f80cd721e1e6890669165e741f5cd193ffb03,9121eed62a717baa0c5dcbcd10c60a52d3b6d285,Import any additional modules needed (to register custom classes)
aaihffibf,allenai/allennlp,allennlp/commands/fine_tune.py,dc2f80cd721e1e6890669165e741f5cd193ffb03,STILL_EXISTS,TODO(mattg): pull this block out into a separate function (maybe just add this to
aaihfficb,allenai/allennlp,allennlp/commands/train.py,dc2f80cd721e1e6890669165e741f5cd193ffb03,STILL_EXISTS,TODO(mattg): pull this block out into a separate function (maybe just add this to
aaihffiih,allenai/allennlp,allennlp/data/dataset_readers/penn_tree_bank.py,0cb2f60609b9d15e156a523ab3fe71acc1eb5f3a,STILL_EXISTS,This is un-needed and clutters the label space.
aaihffjbg,allenai/allennlp,allennlp/commands/__init__.py,9121eed62a717baa0c5dcbcd10c60a52d3b6d285,STILL_EXISTS,Import any additional modules needed (to register custom classes).
aaihffjgj,allenai/allennlp,allennlp/training/trainer.py,40d337ea80c56ed9f9771fa605afd4aa0add2c6c,236e1fd01ca30409cd736625901292609009f5c4,Save model if needed.
aaihfgaci,allenai/allennlp,allennlp/commands/__init__.py,f403207f3ba207256f83d7e8da75da44819d52a4,STILL_EXISTS,Import any additional modules needed (to register custom classes).
aaihfgbfd,allenai/allennlp,allennlp/common/testing/model_test_case.py,0a918aa7bb3ca41647ea9070420be60dda6b41bc,STILL_EXISTS,\"non_padded_namespaces\"; \"min_count\" etc. can be set if needed.
aaihfgbif,allenai/allennlp,allennlp/nn/decoding/decoder_state.py,1655f229e8ce5ab2b705549435ab06e1541394f8,STILL_EXISTS,lists of a `DecoderState` subclass and output structures with the subclass.  Really ugly that we
aaihfgcaa,allenai/allennlp,allennlp/nn/decoding/decoder_trainers/maximum_marginal_likelihood.py,1655f229e8ce5ab2b705549435ab06e1541394f8,e1fdad6ed97d1af40bca9d2112a257ab323ad27a,We group together all current states to get more efficient (batched) computation.
aaihfgcbi,allenai/allennlp,tests/nn/decoding/beam_search_test.py,1655f229e8ce5ab2b705549435ab06e1541394f8,STILL_EXISTS,Instance with batch index 2 needed too many steps to finish; and batch index 3 had no
aaihfgcij,allenai/allennlp,allennlp/semparse/action_space_walker.py,2e6f2dd5c6b86ca7c35604dfb78a71ddc9c5a60b,STILL_EXISTS,TODO (pradeep): Sort the indices and do intersections in order; so that we can return the
aaihfgcjf,allenai/allennlp,allennlp/semparse/contexts/table_question_knowledge_graph.py,2e6f2dd5c6b86ca7c35604dfb78a71ddc9c5a60b,STILL_EXISTS,Following Sempre's convention for naming columns.  Sempre gives columns unique names when
aaihfgcjg,allenai/allennlp,allennlp/semparse/contexts/table_question_knowledge_graph.py,2e6f2dd5c6b86ca7c35604dfb78a71ddc9c5a60b,STILL_EXISTS,columns normalize to a collision; so we keep track of these.  We do not give cell text
aaihfgdaa,allenai/allennlp,allennlp/semparse/contexts/table_question_knowledge_graph.py,2e6f2dd5c6b86ca7c35604dfb78a71ddc9c5a60b,STILL_EXISTS,Following Sempre's convention for naming cells.
aaihfgdaf,allenai/allennlp,allennlp/semparse/contexts/table_question_knowledge_graph.py,2e6f2dd5c6b86ca7c35604dfb78a71ddc9c5a60b,STILL_EXISTS,need for this function somehow?  It's causing a whole lot of headaches.
aaihfgdbb,allenai/allennlp,allennlp/semparse/contexts/table_question_knowledge_graph.py,2e6f2dd5c6b86ca7c35604dfb78a71ddc9c5a60b,STILL_EXISTS,way we're doing this could lead to false positives for something like '1e2'; but
aaihfgdbg,allenai/allennlp,allennlp/semparse/contexts/table_question_knowledge_graph.py,2e6f2dd5c6b86ca7c35604dfb78a71ddc9c5a60b,STILL_EXISTS,linking score won't have any way to differentiate them...  We should figure
aaihfgdbh,allenai/allennlp,allennlp/semparse/contexts/table_question_knowledge_graph.py,2e6f2dd5c6b86ca7c35604dfb78a71ddc9c5a60b,STILL_EXISTS,out a better way to handle this.
aaihfgdca,allenai/allennlp,allennlp/semparse/type_declarations/nlvr_type_declaration.py,2e6f2dd5c6b86ca7c35604dfb78a71ddc9c5a60b,STILL_EXISTS,appear in the logical forms; we have a way of specifying ``constant_type_prefixes`` and passing
aaihfgddc,allenai/allennlp,allennlp/semparse/type_declarations/type_declaration.py,2e6f2dd5c6b86ca7c35604dfb78a71ddc9c5a60b,STILL_EXISTS,\"\"\" || This module defines some classes that are generally useful for defining a type system for a new || domain. We inherit the type logic in ``nltk.sem.logic`` and add some functionality on top of it || here. There are two main improvements: || 1) Firstly; we allow defining multiple basic types with their own names (see ``NamedBasicType``). || 2) Secondly; we allow defining function types that have placeholders in them (see || ``PlaceholderType``). || We also extend NLTK's ``LogicParser`` to define a ``DynamicTypeLogicParser`` that knows how to deal || with the two improvements above. || \"\"\"
aaihfgddd,allenai/allennlp,allennlp/semparse/type_declarations/type_declaration.py,2e6f2dd5c6b86ca7c35604dfb78a71ddc9c5a60b,STILL_EXISTS,TODO (pradeep): This limits the number of basic types we can have to 26. We may want to
aaihfgdeb,allenai/allennlp,allennlp/semparse/type_declarations/type_declaration.py,2e6f2dd5c6b86ca7c35604dfb78a71ddc9c5a60b,STILL_EXISTS,TODO(pradeep): Assuming the mapping of \"var\" function is \"V\". Do something better.
aaihfgdgb,allenai/allennlp,allennlp/semparse/type_declarations/type_declaration.py,2e6f2dd5c6b86ca7c35604dfb78a71ddc9c5a60b,STILL_EXISTS,this; it's just something that works for now; that we can fix later if \/ when it's needed.
aaihfgecb,allenai/allennlp,allennlp/semparse/worlds/nlvr_world.py,2e6f2dd5c6b86ca7c35604dfb78a71ddc9c5a60b,STILL_EXISTS,TODO (pradeep): Rules for \"member_*\" productions (\"tower\" or \"box\" followed by a color;
aaihfgefh,allenai/allennlp,allennlp/semparse/worlds/nlvr_world.py,2e6f2dd5c6b86ca7c35604dfb78a71ddc9c5a60b,53b166ec49b3ba6d621915406f21cb61e4937d48,TODO (pradeep): This is messy. Fix the type declaration so that we don't have to deal
aaihfgehc,allenai/allennlp,allennlp/semparse/worlds/wikitables_world.py,2e6f2dd5c6b86ca7c35604dfb78a71ddc9c5a60b,STILL_EXISTS,cells and columns and a few simple numbers; so we can get them as valid actions in the
aaihfgejj,allenai/allennlp,allennlp/semparse/worlds/world.py,2e6f2dd5c6b86ca7c35604dfb78a71ddc9c5a60b,STILL_EXISTS,NLTK has a naming convention for variable types. If the world has predicate or entity names beyond
aaihfggfi,allenai/allennlp,tests/semparse/worlds/wikitables_world_test.py,2e6f2dd5c6b86ca7c35604dfb78a71ddc9c5a60b,STILL_EXISTS,Most of these are instance-specific production rules.  These are the columns in the
aaihfgggj,allenai/allennlp,tests/semparse/worlds/wikitables_world_test.py,2e6f2dd5c6b86ca7c35604dfb78a71ddc9c5a60b,STILL_EXISTS,We add columns to the name mapping in sorted order; so \"league\" and \"year\" end up as C2
aaihfghbg,allenai/allennlp,allennlp/data/fields/knowledge_graph_field.py,f7924bc07effd7ad20cd97a6056df0ca28352dee,STILL_EXISTS,above; some of these functions have unused arguments.
aaihfghce,allenai/allennlp,allennlp/data/fields/production_rule_field.py,f7924bc07effd7ad20cd97a6056df0ca28352dee,STILL_EXISTS,be in the bounds of DataArray; but ProductionRuleArray definitely isn't.  TODO(mattg): maybe we
aaihfghcf,allenai/allennlp,allennlp/data/fields/production_rule_field.py,f7924bc07effd7ad20cd97a6056df0ca28352dee,STILL_EXISTS,should find a better way to loosen those bounds; or let people extend them.  E.g.; we could have
aaihfghji,allenai/allennlp,allennlp/data/dataset_readers/nlvr.py,cbe58971a15aaf6a80d3cc53b1e7c4a9ba4e089c,4c99f8e82f7fd70c86652109bfca5282d470e981,needed if we want to define some sort of a hinge-loss based trainer. Deal with
aaihfgica,allenai/allennlp,scripts/nlvr/get_nlvr_logical_forms.py,cbe58971a15aaf6a80d3cc53b1e7c4a9ba4e089c,STILL_EXISTS,TODO (pradeep): Assuming all worlds give the same agenda.
aaihfgija,allenai/allennlp,allennlp/models/semantic_parsing/nlvr/nlvr_coverage_semantic_parser.py,7dfc34247cedfe16c9d35a557855977fbd3df15c,STILL_EXISTS,We look at the epoch number and adjust the checklist cost weight if needed here.
aaihfgijb,allenai/allennlp,allennlp/models/semantic_parsing/nlvr/nlvr_coverage_semantic_parser.py,7dfc34247cedfe16c9d35a557855977fbd3df15c,STILL_EXISTS,TODO (pradeep): Assuming all worlds give the same set of valid actions.
aaihfgjaj,allenai/allennlp,allennlp/models/semantic_parsing/nlvr/nlvr_coverage_semantic_parser.py,7dfc34247cedfe16c9d35a557855977fbd3df15c,STILL_EXISTS,TODO (pradeep): The denotation based cost below is strict. May be define a cost based on
aaihfgjbc,allenai/allennlp,allennlp/models/semantic_parsing/nlvr/nlvr_coverage_semantic_parser.py,7dfc34247cedfe16c9d35a557855977fbd3df15c,STILL_EXISTS,TODO (pradeep): Make this cleaner.
aaihfgjdd,allenai/allennlp,allennlp/models/semantic_parsing/nlvr/nlvr_decoder_step.py,7dfc34247cedfe16c9d35a557855977fbd3df15c,STILL_EXISTS,TODO (pradeep): Make the distinction between the two kinds of trainers in the way they
aaihfhaae,allenai/allennlp,allennlp/models/semantic_parsing/nlvr/nlvr_direct_semantic_parser.py,7dfc34247cedfe16c9d35a557855977fbd3df15c,STILL_EXISTS,TODO (pradeep): Assuming all worlds give the same set of valid actions.
aaihfhabi,allenai/allennlp,allennlp/models/semantic_parsing/nlvr/nlvr_semantic_parser.py,7dfc34247cedfe16c9d35a557855977fbd3df15c,STILL_EXISTS,TODO (pradeep): Use an unindexed field for labels?
aaihfhced,allenai/allennlp,allennlp/models/semantic_parsing/wikitables/wikitables_semantic_parser.py,0df8978073fe1ebe1af18af5cab48ce70eecca81,STILL_EXISTS,No entities of this type; move along...
aaihfhcfg,allenai/allennlp,allennlp/models/semantic_parsing/wikitables/wikitables_semantic_parser.py,0df8978073fe1ebe1af18af5cab48ce70eecca81,STILL_EXISTS,TODO(mattg): this could probably be moved into a FullSequenceMatch metric; or something.
aaihfhdgh,allenai/allennlp,allennlp/service/predictors/wikitables_parser.py,960f913dc92f35fdf7840174b4968599916630a6,5352a198e1831e63c76ee8e51694c1bf6b63b1b1,TODO(mattg): We should merge how this works with how the `WikiTablesAccuracy` metric works; maybe
aaihfheab,allenai/allennlp,allennlp/models/semantic_parsing/nlvr/nlvr_coverage_semantic_parser.py,812dfcf24e336e063e5e37ac3b1682f2cc49af61,STILL_EXISTS,sequence currently. Maybe define top-k metrics?
aaihfheja,allenai/allennlp,allennlp/common/tqdm.py,af283d162778b9d41b92b7a24e6474f385fdb72f,STILL_EXISTS,TODO(Mark): Remove this once tqdm cleans up after itself properly.
aaihfhfbd,allenai/allennlp,allennlp/models/semantic_parsing/wikitables/wikitables_erm_semantic_parser.py,7142962d330ca5a95cade114c26a361c78f2042e,abfb32dd9303358c670a7567df445de02f560868,TODO(pradeep): Can move most of this block to super class.
aaihfhfcd,allenai/allennlp,allennlp/models/semantic_parsing/wikitables/wikitables_mml_semantic_parser.py,7142962d330ca5a95cade114c26a361c78f2042e,abfb32dd9303358c670a7567df445de02f560868,TODO(pradeep): Most of the functionality in this black can be moved to the super
aaihfhfef,allenai/allennlp,allennlp/nn/decoding/grammar_state.py,7142962d330ca5a95cade114c26a361c78f2042e,STILL_EXISTS,Looking for lambda productions; but not for cells or columns with the word \"lambda\" in
aaihfhgci,allenai/allennlp,allennlp/nn/decoding/decoder_trainers/expected_risk_minimization.py,b5af3e477a0a64178d2a1065c1c3c4df5e932932,STILL_EXISTS,TODO(pradeep): Move this method to nn.decoding.util
aaihfhiji,allenai/allennlp,allennlp/predictors/wikitables_parser.py,5352a198e1831e63c76ee8e51694c1bf6b63b1b1,0fbd1ca8982cb3b0cdbf94f7c03b0420687adc3d,TODO(mattg): We should merge how this works with how the `WikiTablesAccuracy` metric works; maybe
aaihfiacd,allenai/allennlp,scripts/write_srl_predictions_to_conll_format.py,cebf719c4645bd6660e14a499c48e23fbe272e1c,STILL_EXISTS,Hack to allow evaluation on different domains than the
aaihfiaeh,allenai/allennlp,allennlp/common/configuration.py,03d6fadbc4fcb61caa5980ab61d1ca673fc0922b,STILL_EXISTS,Hack in our Optimizer class to the trainer
aaihfiafd,allenai/allennlp,allennlp/common/configuration.py,03d6fadbc4fcb61caa5980ab61d1ca673fc0922b,STILL_EXISTS,Anything with a from_params method is itself configurable
aaihfiahb,allenai/allennlp,allennlp/common/configuration.py,0c2d5c77f3ee7a48693af822fa74b2dfe3eaa184,STILL_EXISTS,Hack for RNNs
aaihfiahf,allenai/allennlp,allennlp/service/config_explorer.py,0c2d5c77f3ee7a48693af822fa74b2dfe3eaa184,STILL_EXISTS,\"\"\" || This is a tiny webapp for generating configuration stubs for your models. || It's very hacky and very experimental; so don't rely on it for anything important. || \"\"\"
aaihfiaic,allenai/allennlp,allennlp/service/config_explorer.py,0c2d5c77f3ee7a48693af822fa74b2dfe3eaa184,d844a9ac592f4396e4f0b991d5891b5dd6c32964,TODO(joelgrus): better error handling
aaihfibej,allenai/allennlp,allennlp/common/configuration.py,d844a9ac592f4396e4f0b991d5891b5dd6c32964,STILL_EXISTS,Hack because e.g. typing.Union isn't a type.
aaihfibfc,allenai/allennlp,allennlp/common/configuration.py,d844a9ac592f4396e4f0b991d5891b5dd6c32964,STILL_EXISTS,Ugly check that default value is actually serializable
aaihfibga,allenai/allennlp,allennlp/common/configuration.py,d844a9ac592f4396e4f0b991d5891b5dd6c32964,STILL_EXISTS,Hack in embedding num_embeddings as optional (it can be inferred from the pretrained file)
aaihficaj,allenai/allennlp,allennlp/tests/models/sniff_test.py,3dff9c7a5f06c23977041e15218c7bf50f953af4,421f9a4639cd2a8abd8e94f044dae84cee7cba61,figure out where it really belongs
aaihfidee,allenai/allennlp,allennlp/models/model.py,43fc89ee59944dca0de01ca5ab2843c9926323d6,STILL_EXISTS,NOTE(markn): This is a hack because 0-dim pytorch tensors are not iterable.
aaihfieac,allenai/allennlp,allennlp/data/iterators/data_iterator.py,fa34344baffe2f8e2a50d5353a6e05f1ef668b8f,STILL_EXISTS,of instances each epoch; and we didn't specify how to many instances to load
aaihfifaf,allenai/allennlp,allennlp/data/vocabulary.py,580dc8b0e2c6491d4d75b54c3b15b34b462e0c67,ae69b9ea7807e25c1cab23c4d148a15f1eaaa180,There's enough logic here to require a custom from_params.
aaihfigcb,allenai/allennlp,allennlp/data/fields/sequence_label_field.py,7df8275e7f70013185f1afeaa2779c2abce4492d,STILL_EXISTS,The empty_list here is needed for mypy
aaihfighg,allenai/allennlp,allennlp/models/biaffine_dependency_parser.py,de0d3f7329c9c4f0204d87e76b6a956b0930ef3e,STILL_EXISTS,This next statement is quite a complex piece of indexing; which you really
aaihfiibc,allenai/allennlp,allennlp/predictors/biaffine_dependency_parser.py,09c2cc5616a8aec897f8fe06fda13179955bec69,STILL_EXISTS,TODO(Mark) Make the language configurable and based on a model attribute.
aaihfiigh,allenai/allennlp,allennlp/modules/openai_transformer.py,15e36458b1f6a7452492bd107cd631fede3617a8,STILL_EXISTS,\"\"\" || An implementation of the OpenAI Transformer Language Model. ||  || Mostly just a slightly modified version of || https:\/\/github.com\/huggingface\/pytorch-openai-transformer-lm || so thanks to them! ||  || Some of these modules duplicate code elsewhere in AllenNLP; || but the serialized weights depend on the exact parameter setup || here; so it's easiest to just reimplement them. || \"\"\"
aaihfiiih,allenai/allennlp,allennlp/modules/openai_transformer.py,15e36458b1f6a7452492bd107cd631fede3617a8,STILL_EXISTS,numpy can't read from a tarfile directly; so we need a workaround
aaihfjbbd,allenai/allennlp,allennlp/tests/common/file_utils_test.py,659bf2562b3fc8524477368bd0f72bbfd0832cbc,26ac34991cb9139d3b99bd051e89f657e5c27ce4,Good request; should work.
aaihfjccf,allenai/allennlp,allennlp/tests/models/sniff_test.py,681a9cfee26b6a0bccb4d4671e64e5fb9f84475e,362060ac513c7bbb82d075a9304637a9bbbebbc5,TODO(Mark) - Fix this test once a new dependency parser is ready.
aaihfjcdb,allenai/allennlp,allennlp/training/learning_rate_schedulers.py,301f2d68b100683c04693669c7b1157fd8a16d6b,STILL_EXISTS,HACK: We need to check if this is the first time ``self.get_lr()`` was called;
aaihfjcje,allenai/allennlp,allennlp/models/semantic_parsing/wikitables/basic_transition_function.py,abfb32dd9303358c670a7567df445de02f560868,STILL_EXISTS,them later; as that's a really easy operation.
aaihfjdcg,allenai/allennlp,allennlp/models/semantic_parsing/wikitables/basic_transition_function.py,abfb32dd9303358c670a7567df445de02f560868,STILL_EXISTS,regroup them later; as that's a really easy operation.
aaihfjgab,allenai/allennlp,allennlp/data/dataset_readers/dataset_utils/span_utils.py,335d8996a0ab6a2a3ea6ce323f57d2e76d9ddf1a,STILL_EXISTS,bmes_tag == 'M'; move to next.
aaihfjgac,allenai/allennlp,allennlp/data/dataset_readers/dataset_utils/span_utils.py,335d8996a0ab6a2a3ea6ce323f57d2e76d9ddf1a,STILL_EXISTS,Move to next span.
aaihfjgie,allenai/allennlp,allennlp/state_machines/states/__init__.py,a61aa67e363aef99bd4ae43af92bc8a2b3378de2,STILL_EXISTS,\"\"\" || This module contains the ``State`` abstraction for defining state-machine-based decoders; and some || pre-built concrete ``State`` classes for various kinds of decoding (e.g.; a ``GrammarBasedState`` || for doing grammar-based decoding; where the output is a sequence of production rules from a || grammar). ||  || The module also has some ``Statelet`` classes to help represent the ``State`` by grouping together || related pieces; including ``RnnStatelet``; which you can use to keep track of a decoder RNN's || internal state; ``GrammarStatelet``; which keeps track of what actions are allowed at each timestep || of decoding (if your outputs are production rules from a grammar); and ``ChecklistStatelet`` that || keeps track of coverage information if you are training a coverage-based parser. || \"\"\"
aaihfjhcb,allenai/allennlp,allennlp/data/dataset_readers/dataset_utils/text2sql_utils.py,72c9e98c2a56094f713c6090805fd10c677c0b95,4c99f8e82f7fd70c86652109bfca5282d470e981,would make it really hard to compare to previous work. Sad times.
aaihfjieb,allenai/allennlp,allennlp/data/dataset_readers/semantic_parsing/nlvr.py,4c99f8e82f7fd70c86652109bfca5282d470e981,STILL_EXISTS,needed if we want to define some sort of a hinge-loss based trainer. Deal with
aaihfjjba,allenai/allennlp,allennlp/data/dataset_readers/dataset_utils/span_utils.py,4674b0182187ef10c54d0578d97f4ba9769a2863,STILL_EXISTS,bmes_tag == 'M'; move to next.
aaihfjjcb,allenai/allennlp,allennlp/semparse/type_declarations/wikitables_variable_free.py,1d81d8bc97a971b8ec2d8a6e3124ca75ff7099e4,STILL_EXISTS,between them. Also note that columns are basic types in this grammar.
aaihfjjcc,allenai/allennlp,allennlp/semparse/type_declarations/wikitables_variable_free.py,1d81d8bc97a971b8ec2d8a6e3124ca75ff7099e4,STILL_EXISTS,TODO(pradeep): Add different column types for string; number; and date columns; and may be a
aaihfjjic,allenai/allennlp,allennlp/tests/semparse/worlds/wikitables_variable_free_world_test.py,1d81d8bc97a971b8ec2d8a6e3124ca75ff7099e4,STILL_EXISTS,These are the columns in table; and are instance specific.
aaihgaaee,allenai/allennlp,scripts/check_requirements_and_setup.py,64f253faa5aeb42edff1cb367b8e1fbb96da2931,STILL_EXISTS,Parse packages only needed for testing.
aaihgabbf,allenai/allennlp,allennlp/models/event2mind.py,8759ea393bf0c27380c4d33e0f15938f68f4ce35,b529f6df91ac13992171f3e73d6eb232e8e6110c,search. Whether this problem would manifest otherwise
aaihgacaa,allenai/allennlp,tutorials/tagger/basic_allennlp.py,63dbdf1f8e16186b10274012281c769215182a2f,STILL_EXISTS,### Our first order of business is to implement our <code>DatasetReader<\/code> subclass.
aaihgacae,allenai/allennlp,tutorials/tagger/basic_allennlp.py,63dbdf1f8e16186b10274012281c769215182a2f,STILL_EXISTS,### The other class you'll basically always have to implement is <code>Model<\/code>; which is a subclass of <code>torch.nn.Module<\/code>. How it works is largely up to you; it mostly just needs a <code>forward<\/code> method that takes tensor inputs and produces a dict of tensor outputs that includes the loss you'll use to train the model. As mentioned above; our model will consist of an embedding layer; a sequence encoder; and a feedforward network.
aaihgacdb,allenai/allennlp,tutorials/tagger/basic_allennlp.py,63dbdf1f8e16186b10274012281c769215182a2f,STILL_EXISTS,### As in the original PyTorch tutorial; we'd like to look at the predictions our model generates. AllenNLP contains a <code>Predictor<\/code> abstraction that takes inputs; converts them to instances; feeds them through your model; and returns JSON-serializable results. Often you'd need to implement your own Predictor; but AllenNLP already has a <code>SentenceTaggerPredictor<\/code> that works perfectly here; so we can use it. It requires our model (for making predictions) and a dataset reader (for creating instances).
aaihgacij,allenai/allennlp,scripts/examine_sql_coverage.py,6039ac001945458ebb9bbd5aa984be2a4817007f,STILL_EXISTS,still TODO:
aaihgacje,allenai/allennlp,scripts/examine_sql_coverage.py,6039ac001945458ebb9bbd5aa984be2a4817007f,STILL_EXISTS,NOTE: DATA hack alert - the geography dataset doesn't alias derived tables consistently;
aaihgadce,allenai/allennlp,allennlp/models/semantic_parsing/atis/atis_semantic_parser.py,0459261c388bec72937e0268c4316b85279d0bac,STILL_EXISTS,TODO(kevin): get this in a more principled way somehow?
aaihgadda,allenai/allennlp,allennlp/models/semantic_parsing/atis/atis_semantic_parser.py,0459261c388bec72937e0268c4316b85279d0bac,STILL_EXISTS,TODO(kevin) Move some of this functionality to a separate method for computing validation outputs.
aaihgadfg,allenai/allennlp,allennlp/models/semantic_parsing/atis/atis_semantic_parser.py,0459261c388bec72937e0268c4316b85279d0bac,STILL_EXISTS,TODO(mattg): this could probably be moved into a FullSequenceMatch metric; or something.
aaihgadgc,allenai/allennlp,allennlp/models/semantic_parsing/atis/atis_semantic_parser.py,0459261c388bec72937e0268c4316b85279d0bac,53bba3db6a0796a908417e05ba8e9e413ac096c1,TODO(kevin): move the SQL execution to an executor.
aaihgafba,allenai/allennlp,allennlp/semparse/executors/nlvr_executor.py,53b166ec49b3ba6d621915406f21cb61e4937d48,STILL_EXISTS,TODO (pradeep): This is messy. Fix the type declaration so that we don't have to deal
aaihgafej,allenai/allennlp,allennlp/semparse/executors/wikitables_variable_free_executor.py,53b166ec49b3ba6d621915406f21cb61e4937d48,STILL_EXISTS,TODO (pradeep): This will be unnecessary when we have column types identified.
aaihgagbh,allenai/allennlp,allennlp/semparse/worlds/wikitables_variable_free_world.py,53b166ec49b3ba6d621915406f21cb61e4937d48,STILL_EXISTS,TODO (pradeep): Define an executor in this world when we have a new context class.
aaihgagji,allenai/allennlp,allennlp/tests/semparse/executors/wikitables_variable_free_executor_test.py,53b166ec49b3ba6d621915406f21cb61e4937d48,STILL_EXISTS,in \"league\" columns.
aaihgahae,allenai/allennlp,allennlp/tests/semparse/executors/wikitables_variable_free_executor_test.py,53b166ec49b3ba6d621915406f21cb61e4937d48,STILL_EXISTS,TODO (pradeep): Figure out whether this is expected behavior by looking at data.
aaihgahdf,allenai/allennlp,allennlp/data/dataset_readers/semantic_parsing/grammar_based_text2sql.py,99308f64ef7ecd5abf5bb7edae0911f75f35423a,ffab3201c14bf0f4ce04a4c6b1e77b10c20020fd,TODO(Mark): Horrible hack; remove
aaihgahff,allenai/allennlp,allennlp/models/semantic_parsing/wikitables/wikitables_semantic_parser.py,d8b13e01e4d85742d3c3e34aaa598e48a21b446a,STILL_EXISTS,TODO(mattg): Move the \"valid_actions\" construction to another method.
aaihgahhb,allenai/allennlp,allennlp/state_machines/states/lambda_grammar_statelet.py,d8b13e01e4d85742d3c3e34aaa598e48a21b446a,STILL_EXISTS,Looking for lambda productions; but not for cells or columns with the word \"lambda\" in
aaihgaiaj,allenai/allennlp,allennlp/semparse/contexts/table_question_context.py,c7289513f9fc93b37cecc28ce1e8d4ece7f84956,STILL_EXISTS,way we're doing this could lead to false positives for something like '1e2'; but
aaihgaibe,allenai/allennlp,allennlp/semparse/contexts/table_question_context.py,c7289513f9fc93b37cecc28ce1e8d4ece7f84956,STILL_EXISTS,linking score won't have any way to differentiate them...  We should figure
aaihgaibf,allenai/allennlp,allennlp/semparse/contexts/table_question_context.py,c7289513f9fc93b37cecc28ce1e8d4ece7f84956,STILL_EXISTS,out a better way to handle this.
aaihgaicb,allenai/allennlp,allennlp/semparse/contexts/table_question_context.py,c7289513f9fc93b37cecc28ce1e8d4ece7f84956,STILL_EXISTS,need for this function somehow?  It's causing a whole lot of headaches.
aaihgaicj,allenai/allennlp,allennlp/semparse/type_declarations/wikitables_variable_free.py,c7289513f9fc93b37cecc28ce1e8d4ece7f84956,STILL_EXISTS,takes any column; \"argmax\" and \"argmin\" take only comparable columns (i.e. dates or numbers).
aaihgaidb,allenai/allennlp,allennlp/semparse/worlds/wikitables_variable_free_world.py,c7289513f9fc93b37cecc28ce1e8d4ece7f84956,STILL_EXISTS,TODO (pradeep): Do we need constant type prefixes?
aaihgaidf,allenai/allennlp,allennlp/semparse/worlds/wikitables_variable_free_world.py,c7289513f9fc93b37cecc28ce1e8d4ece7f84956,STILL_EXISTS,TODO (pradeep): Update agenda definition and rethink this field.
aaihgaifj,allenai/allennlp,allennlp/data/fields/adjacency_field.py,c635bc4747b5ee5f14d7a31602010d4892e6e27e,STILL_EXISTS,The empty_list here is needed for mypy
aaihgaiii,allenai/allennlp,allennlp/data/dataset_readers/semantic_parsing/quarel.py,8ff832471364b09278d5863f31c47fa32662dd08,STILL_EXISTS,TODO: Clarify this into an appropriate parameter
aaihgaijj,allenai/allennlp,allennlp/data/dataset_readers/semantic_parsing/quarel.py,8ff832471364b09278d5863f31c47fa32662dd08,STILL_EXISTS,TODO: Keep token level information here?
aaihgajce,allenai/allennlp,allennlp/models/semantic_parsing/quarel/quarel_semantic_parser.py,8ff832471364b09278d5863f31c47fa32662dd08,STILL_EXISTS,TODO(mattg): get this in a more principled way somehow?
aaihgajcj,allenai/allennlp,allennlp/models/semantic_parsing/quarel/quarel_semantic_parser.py,8ff832471364b09278d5863f31c47fa32662dd08,STILL_EXISTS,Rest of init not needed for denotation only where no decoding to actions needed
aaihgajia,allenai/allennlp,allennlp/models/semantic_parsing/quarel/quarel_semantic_parser.py,8ff832471364b09278d5863f31c47fa32662dd08,STILL_EXISTS,No entities of this type; move along...
aaihgajjd,allenai/allennlp,allennlp/models/semantic_parsing/quarel/quarel_semantic_parser.py,8ff832471364b09278d5863f31c47fa32662dd08,STILL_EXISTS,TODO(mattg): this could probably be moved into a FullSequenceMatch metric; or something.
aaihgbaag,allenai/allennlp,allennlp/predictors/quarel_parser.py,8ff832471364b09278d5863f31c47fa32662dd08,STILL_EXISTS,TODO: Fix protected access usage
aaihgbacj,allenai/allennlp,allennlp/semparse/contexts/quarel_utils.py,8ff832471364b09278d5863f31c47fa32662dd08,STILL_EXISTS,TODO: Fix protected access
aaihgbaea,allenai/allennlp,allennlp/semparse/type_declarations/quarel_type_declaration.py,8ff832471364b09278d5863f31c47fa32662dd08,STILL_EXISTS,TODO: Remove this?
aaihgbaeg,allenai/allennlp,allennlp/semparse/type_declarations/quarel_type_declaration.py,8ff832471364b09278d5863f31c47fa32662dd08,STILL_EXISTS,Hack to expose types
aaihgbafb,allenai/allennlp,allennlp/semparse/worlds/quarel_world.py,8ff832471364b09278d5863f31c47fa32662dd08,STILL_EXISTS,First entry is by convention (above in __init__) the friction subset
aaihgbafh,allenai/allennlp,allennlp/semparse/worlds/quarel_world.py,8ff832471364b09278d5863f31c47fa32662dd08,STILL_EXISTS,Remove \"a:\" prefixes from attributes (hack)
aaihgbbcd,allenai/allennlp,allennlp/semparse/action_space_walker.py,1691cb3a61740fbbc8305d52d4e830c79302a666,STILL_EXISTS,an columns of type \"date\"; then this condition would be triggered. We should
aaihgbbch,allenai/allennlp,allennlp/semparse/contexts/table_question_context.py,1691cb3a61740fbbc8305d52d4e830c79302a666,STILL_EXISTS,Mapping from cell values to the types of the columns they are under.
aaihgbbdg,allenai/allennlp,allennlp/semparse/contexts/table_question_context.py,1691cb3a61740fbbc8305d52d4e830c79302a666,STILL_EXISTS,the candidate matched a cell value that occurs under columns with multiple types. This is
aaihgbbff,allenai/allennlp,allennlp/semparse/worlds/wikitables_variable_free_world.py,1691cb3a61740fbbc8305d52d4e830c79302a666,STILL_EXISTS,TODO (pradeep): Update agenda definition and rethink this field.
aaihgbcda,allenai/allennlp,scripts/wikitables/search_for_logical_forms.py,1691cb3a61740fbbc8305d52d4e830c79302a666,c6fb86d26cba3781a8937a87641a1236794c3146,TODO (pradeep): Use a DatasetReader (when available) instead of directly reading from the examples
aaihgbcdf,allenai/allennlp,scripts/wikitables/search_for_logical_forms.py,1691cb3a61740fbbc8305d52d4e830c79302a666,c6fb86d26cba3781a8937a87641a1236794c3146,TODO (pradeep): We may eventually want to produce gzipped files like DPD output instead of
aaihgbdad,allenai/allennlp,allennlp/data/dataset_readers/event2mind.py,a94a23ebfd7425ee94fdb5a4c2352e45e7b84baa,STILL_EXISTS,Generate instances where each token of input appears once.
aaihgbdef,allenai/allennlp,allennlp/models/bidirectional_lm.py,0e47d1605fa9b8ffb8285825a365e71963a80aa4,088f0bb685231a17320bf916c091dfa8e60e3ce5,this is stored to compute perplexity if needed
aaihgbebf,allenai/allennlp,allennlp/commands/find_learning_rate.py,9fcc79566cc148cce9f967a7962ac03bc300f011,STILL_EXISTS,\"\"\" || The ``find-lr`` subcommand can be used to find a good learning rate for a model. || It requires a configuration file and a directory in || which to write the results. ||  || .. code-block:: bash ||  ||    $ allennlp find-lr --help ||    usage: allennlp train [-h] -s SERIALIZATION_DIR [-o OVERRIDES] ||                          [--start-lr START_LR] [--end-lr END_LR] ||                          [--num-batches NUM_BATCHES] [--linear] ||                          param_path ||  ||    Train the specified model on the specified dataset. ||  ||    positional arguments: ||    param_path            path to parameter file describing the model to be ||                            trained ||  ||    optional arguments: ||    -h; --help              show this help message and exit ||    -s SERIALIZATION_DIR; --serialization-dir SERIALIZATION_DIR ||                            directory in which to save Learning rate vs loss ||    -o OVERRIDES; --overrides OVERRIDES ||                            a JSON structure used to override the experiment ||                            configuration ||    --start-lr START_LR ||                            Learning rate to start the search. ||    --end-lr END_LR ||                            Learning rate up to which search is done. ||    --num-batches NUM_BATCHES ||                            Number of mini-batches to run Learning rate finder ||    --linear                Increase learning rate linearly instead of exponential increase ||  || \"\"\"
aaihgbega,allenai/allennlp,allennlp/models/semantic_parsing/text2sql_parser.py,360c3e10d40a1e9e64085be571eeed4725c53095,STILL_EXISTS,TODO(mattg): this could probably be moved into a FullSequenceMatch metric; or something.
aaihgbehc,allenai/allennlp,allennlp/semparse/contexts/text2sql_table_context.py,360c3e10d40a1e9e64085be571eeed4725c53095,STILL_EXISTS,TODO(MARK): Massive hack; remove and modify the grammar accordingly
aaihgbejd,allenai/allennlp,allennlp/models/encoder_decoders/simple_seq2seq.py,b529f6df91ac13992171f3e73d6eb232e8e6110c,2648d952c5f74acb728f50f8d9fc0b447a949a49,TODO: Run beam search whenever self.training is False so that we can get
aaihgbgif,allenai/allennlp,allennlp/data/instance.py,947bd16521dd31bc79f7499ec17a07acf15a89a1,STILL_EXISTS,mutable; we don't implement ``MutableMapping`` because we want
aaihgbgjg,allenai/allennlp,allennlp/semparse/contexts/table_question_context.py,c6fb86d26cba3781a8937a87641a1236794c3146,STILL_EXISTS,now; and later add number and string entities as needed.
aaihgbgji,allenai/allennlp,allennlp/semparse/contexts/table_question_context.py,c6fb86d26cba3781a8937a87641a1236794c3146,STILL_EXISTS,For all numbers (except -1); we add all number and date columns as their neighbors.
aaihgbgjj,allenai/allennlp,allennlp/semparse/contexts/table_question_context.py,c6fb86d26cba3781a8937a87641a1236794c3146,STILL_EXISTS,Add \"-1\" as an entity only if we have date columns in the table because we will need
aaihgbhaa,allenai/allennlp,allennlp/semparse/contexts/table_question_context.py,c6fb86d26cba3781a8937a87641a1236794c3146,STILL_EXISTS,it as a wild-card in dates. The neighbors are the date columns.
aaihgbhaf,allenai/allennlp,allennlp/semparse/type_declarations/wikitables_variable_free.py,c6fb86d26cba3781a8937a87641a1236794c3146,STILL_EXISTS,because we will add those to the final name mapping only if needed; based on the table content.
aaihgbhbf,allenai/allennlp,allennlp/semparse/worlds/wikitables_variable_free_world.py,c6fb86d26cba3781a8937a87641a1236794c3146,STILL_EXISTS,columns as the neighbors only if any date columns exist in the table.
aaihgbhcc,allenai/allennlp,allennlp/tests/semparse/contexts/table_question_context_test.py,c6fb86d26cba3781a8937a87641a1236794c3146,STILL_EXISTS,-1 is not in entities because there are no date columns in the table.
aaihgbhcd,allenai/allennlp,allennlp/tests/semparse/contexts/table_question_context_test.py,c6fb86d26cba3781a8937a87641a1236794c3146,STILL_EXISTS,Each number extracted from the question will have all number and date columns as
aaihgbhcg,allenai/allennlp,allennlp/tests/semparse/contexts/table_question_context_test.py,c6fb86d26cba3781a8937a87641a1236794c3146,STILL_EXISTS,'5000' is neighbors with number and date columns. '-1' is in entities because there is a
aaihgbhda,allenai/allennlp,allennlp/tests/semparse/worlds/wikitables_variable_free_world_test.py,c6fb86d26cba3781a8937a87641a1236794c3146,STILL_EXISTS,The table does not have date or number columns.
aaihgbhdj,allenai/allennlp,allennlp/semparse/worlds/wikitables_variable_free_world.py,19d106e7d0a0b8a5111294d66743515f7f9c0626,STILL_EXISTS,and the table does not have number columns; we should not add \"<r;<f;n>> -> sum\" to
aaihgbicb,allenai/allennlp,allennlp/modules/sampled_softmax_loss.py,86da88098edba318b8e6f19f33e3670967dee24f,STILL_EXISTS,TODO(joelgrus): implement tie_embeddings (maybe)
aaihgbiic,allenai/allennlp,allennlp/tests/modules/sampled_softmax_loss_test.py,86da88098edba318b8e6f19f33e3670967dee24f,STILL_EXISTS,Should be really close
aaihgbjdd,allenai/allennlp,allennlp/tests/modules/seq2seq_encoders/bidirectional_transformer_encoder_test.py,6b16222b72596ebe7b809dea26cc253195e02590,STILL_EXISTS,columns = positions used for attention
aaihgbjee,allenai/allennlp,allennlp/data/token_indexers/wordpiece_indexer.py,582d0e4fda34ab44d8a8ae739fbce09bddb3e9f5,STILL_EXISTS,TODO(joelgrus): figure out a better way to handle this
aaihgcadd,allenai/allennlp,allennlp/common/configuration.py,ca7e3cf37025bb1320c2e938e17db2b7c7381779,STILL_EXISTS,TODO: figure out a better way to deal with them
aaihgcadf,allenai/allennlp,allennlp/common/configuration.py,ca7e3cf37025bb1320c2e938e17db2b7c7381779,STILL_EXISTS,Anything with a from_params method is itself configurable.
aaihgcaej,allenai/allennlp,allennlp/data/token_indexers/wordpiece_indexer.py,7dbd7d34a2f1390d1ff01f2e9ed6f8bdaaef77eb,STILL_EXISTS,TODO(joelgrus): figure out a better way to handle this
aaihgcagi,allenai/allennlp,allennlp/models/archival.py,89cef2f08c97510c6f098e9381fdc22116ad5b1a,c7b7c0085d946bdfec010c614f3545d6bab7a35c,Postpone cleanup until exit in case the unarchived contents are needed outside
aaihgcbha,allenai/allennlp,allennlp/modules/token_embedders/bidirectional_language_model_token_embedder.py,ce060badd12d3047e3af81cf97d0b62805e397e5,088f0bb685231a17320bf916c091dfa8e60e3ce5,TODO(brendanr): Find a way to remove this hack. The issue fundamentally is that the
aaihgcbhg,allenai/allennlp,allennlp/modules/token_embedders/bidirectional_language_model_token_embedder.py,ce060badd12d3047e3af81cf97d0b62805e397e5,088f0bb685231a17320bf916c091dfa8e60e3ce5,For the equivalent hack in the ELMo embedder see:
aaihgccfd,allenai/allennlp,allennlp/models/language_model.py,088f0bb685231a17320bf916c091dfa8e60e3ce5,STILL_EXISTS,TODO(joelgrus): implement tie_embeddings (maybe)
aaihgccga,allenai/allennlp,allennlp/models/language_model.py,088f0bb685231a17320bf916c091dfa8e60e3ce5,STILL_EXISTS,TODO(joelgrus): more sampled softmax configuration options; as needed.
aaihgccja,allenai/allennlp,allennlp/models/language_model.py,088f0bb685231a17320bf916c091dfa8e60e3ce5,STILL_EXISTS,this is stored to compute perplexity if needed
aaihgcddf,allenai/allennlp,allennlp/modules/token_embedders/language_model_token_embedder.py,088f0bb685231a17320bf916c091dfa8e60e3ce5,STILL_EXISTS,TODO(brendanr): Find a way to remove this hack. The issue fundamentally is that the
aaihgcdeb,allenai/allennlp,allennlp/modules/token_embedders/language_model_token_embedder.py,088f0bb685231a17320bf916c091dfa8e60e3ce5,STILL_EXISTS,For the equivalent hack in the ELMo embedder see:
aaihgcgee,allenai/allennlp,allennlp/models/encoder_decoders/copynet_seq2seq.py,b0e2956677e3191ffd51eef25373f0bf4504c05b,STILL_EXISTS,Update copy_probs needed for getting the `selective_weights` at the next timestep.
aaihgchjb,allenai/allennlp,allennlp/semparse/domain_languages/domain_language.py,0409371e7cd21777ac976296a891d3868f101f25,STILL_EXISTS,object; instead of calling it (maybe as an argument to a higher-order function).  In
aaihgciaj,allenai/allennlp,allennlp/semparse/domain_languages/nlvr_language.py,0409371e7cd21777ac976296a891d3868f101f25,STILL_EXISTS,tries to guess what production rules should be needed in the logical form.
aaihgcibf,allenai/allennlp,allennlp/semparse/domain_languages/nlvr_language.py,0409371e7cd21777ac976296a891d3868f101f25,STILL_EXISTS,TODO (pradeep): Rules for \"member_*\" productions (\"tower\" or \"box\" followed by a color;
aaihgcjae,allenai/allennlp,allennlp/tests/training/multi_task_trainer_test.py,e08ade81ff6684869883ce3da66941b9d3a37503,STILL_EXISTS,\"\"\" || This isn't testing a real class; it's a proof-of-concept || for how multi-task training could work. This is certainly || not the only way to do multi-task training using AllenNLP. ||  || Note that you could almost fit this whole setup into || the \"SingleTaskTrainer\" paradigm; if you just wrote like a || ``MinglingDatasetReader`` that wrapped multiple dataset readers. || The main problem is that the ``SingleTaskTrainer`` expects || a single ``train_path``. (Even that you could fudge by passing || in a Dict[str; str] serialized as JSON; but that's really hacky.) || \"\"\"
aaihgcjga,allenai/allennlp,allennlp/training/tensorboard_writer.py,e08ade81ff6684869883ce3da66941b9d3a37503,a0edfae9ca571ed7d43749974bb842167201c2da,And maybe log to console
aaihgdadc,allenai/allennlp,allennlp/training/util.py,e08ade81ff6684869883ce3da66941b9d3a37503,ca453c8b1186b4f346e70428c889a139ca21f794,Only the 'loss' is needed.
aaihgdafc,allenai/allennlp,allennlp/semparse/domain_languages/wikitables_language.py,ae63a2ae2d564eb59af09e96cb550c475ae1af4c,STILL_EXISTS,Unfortunately; mypy doesn't like this very much; so we have to \"type: ignore\" a bunch of things.
aaihgdaia,allenai/allennlp,allennlp/semparse/domain_languages/wikitables_language.py,ae63a2ae2d564eb59af09e96cb550c475ae1af4c,STILL_EXISTS,columns as the neighbors only if any date columns exist in the table.
aaihgdaie,allenai/allennlp,allennlp/semparse/domain_languages/wikitables_language.py,ae63a2ae2d564eb59af09e96cb550c475ae1af4c,STILL_EXISTS,type in the hierarchy going from its concrete class to the base Column.  String columns
aaihgdaif,allenai/allennlp,allennlp/semparse/domain_languages/wikitables_language.py,ae63a2ae2d564eb59af09e96cb550c475ae1af4c,STILL_EXISTS,get added as StringColumn and Column; and date and number columns get added as DateColumn
aaihgdajj,allenai/allennlp,allennlp/semparse/domain_languages/wikitables_language.py,ae63a2ae2d564eb59af09e96cb550c475ae1af4c,STILL_EXISTS,and the table does not have number columns; we should not add \"<r;<f;n>> -> sum\" to
aaihgdbbg,allenai/allennlp,allennlp/semparse/domain_languages/wikitables_language.py,ae63a2ae2d564eb59af09e96cb550c475ae1af4c,STILL_EXISTS,column-type-specific predicates; which only get added if we see columns of particular types
aaihgdcce,allenai/allennlp,allennlp/tests/semparse/domain_languages/wikitables_language_test.py,ae63a2ae2d564eb59af09e96cb550c475ae1af4c,STILL_EXISTS,in \"league\" columns.
aaihgdcda,allenai/allennlp,allennlp/tests/semparse/domain_languages/wikitables_language_test.py,ae63a2ae2d564eb59af09e96cb550c475ae1af4c,f8d2ec5b8fa22ff051ad83623327c1bccae653b9,TODO (pradeep): Figure out whether this is expected behavior by looking at data.
aaihgdcdh,allenai/allennlp,allennlp/tests/semparse/domain_languages/wikitables_language_test.py,ae63a2ae2d564eb59af09e96cb550c475ae1af4c,STILL_EXISTS,These are the columns in table; and are instance specific.
aaihgdcec,allenai/allennlp,allennlp/tests/semparse/domain_languages/wikitables_language_test.py,ae63a2ae2d564eb59af09e96cb550c475ae1af4c,STILL_EXISTS,The table does not have date or number columns.
aaihgddfc,allenai/allennlp,tutorials/tagger/basic_allennlp.py,55b9bd089edb7f97b6dba7c16dd1e7f358acb416,STILL_EXISTS,\"\"\" || <p> ||     Although this tutorial has been an explanation of how AllenNLP works; ||     in practice you probably wouldn't write your code this way. Most AllenNLP objects ||     (Models; DatasetReaders; and so on) can be constructed declaratively ||     from JSON-like objects.  || <\/p> || <p> ||     So a more typical use case would involve implementing the Model and ||     DatasetReader as above; but creating a <a href = \"https:\/\/jsonnet.org\/\">Jsonnet<\/a> ||     file indicating how you want to instantiate and train them; ||     and then simply using the command line tool <code>allennlp train<\/code>  ||     (which would automatically save an archive of the trained model). || <\/p> || <p> ||     For more details on how this would work in this example; please read ||     <a href = \"https:\/\/github.com\/allenai\/allennlp\/blob\/master\/tutorials\/tagger\/README.md#using-config-files\">Using Config Files<\/a>. || <\/p> || \"\"\"
aaihgdebb,allenai/allennlp,tutorials/tagger/basic_allennlp.py,55b9bd089edb7f97b6dba7c16dd1e7f358acb416,STILL_EXISTS,### Next we need to implement <code>forward<\/code>; which is where
aaihgdege,allenai/allennlp,tutorials/tagger/basic_allennlp.py,55b9bd089edb7f97b6dba7c16dd1e7f358acb416,STILL_EXISTS,### and returns JSON-serializable results. Often you'd need to implement your own Predictor;
aaihgdfeg,allenai/allennlp,allennlp/tests/training/trainer_test.py,00c877b9b8737d547f845bac5d5c69a86d6177e7,STILL_EXISTS,Because of using -loss; 2nd epoch would be better than 1st. So best val metrics should not be same.
aaihgdfej,allenai/allennlp,allennlp/tests/training/trainer_test.py,00c877b9b8737d547f845bac5d5c69a86d6177e7,STILL_EXISTS,Because of using +loss; 2nd epoch won't be better than 1st. So best val metrics should be same.
aaihgdghi,allenai/allennlp,allennlp/models/reading_comprehension/util.py,08a8c5e56d8728c17eb96de3ae603ce6799a5b2f,STILL_EXISTS,the span ends before it starts.
aaihgdhah,allenai/allennlp,allennlp/tests/models/reading_comprehension/util_test.py,08a8c5e56d8728c17eb96de3ae603ce6799a5b2f,STILL_EXISTS,When we were using exclusive span ends; this was an edge case of the dynamic program.
aaihgdhgf,allenai/allennlp,allennlp/tests/commands/fine_tune_test.py,23ea5908117c379b44ef04e94695d5f4371c26bd,4b4d8be6b2f1cc818eaaf25513749350f762db84,TEST 1: Passing correct embedding_sources_mapping should work when pretrained_file attribute
aaihgdhgh,allenai/allennlp,allennlp/tests/commands/fine_tune_test.py,23ea5908117c379b44ef04e94695d5f4371c26bd,4b4d8be6b2f1cc818eaaf25513749350f762db84,TEST 2: Passing correct embedding_sources_mapping should work when pretrained_file
aaihgdhgj,allenai/allennlp,allennlp/tests/commands/fine_tune_test.py,23ea5908117c379b44ef04e94695d5f4371c26bd,4b4d8be6b2f1cc818eaaf25513749350f762db84,TEST 3: Passing no embedding_sources_mapping should work; if available pretrained_file
aaihgdicb,allenai/allennlp,allennlp/tests/commands/train_test.py,39413f220f2e682001299f8975a38f2221fac840,ea715d4d9fd1bb6a5b52b13c6ad0a8e450ff76a3,(2) model dumped in such a way is loadable.
aaihgdigh,allenai/allennlp,allennlp/data/dataset_readers/reading_comprehension/drop.py,bbb67e9f5f2e804dc5f093f1f6e30ef3bbd96030,STILL_EXISTS,hack to guarantee minimal length of padded number
aaihgdiif,allenai/allennlp,allennlp/data/dataset_readers/reading_comprehension/drop.py,bbb67e9f5f2e804dc5f093f1f6e30ef3bbd96030,STILL_EXISTS,TODO: Try smaller numbers?
aaihgdjde,allenai/allennlp,allennlp/models/reading_comprehension/naqanet.py,c54fcc62936392cff23a7174a0309165c749df90,STILL_EXISTS,TODO: the joint probability would be very small.
aaihgebbd,allenai/allennlp,allennlp/tests/modules/pruner_test.py,f19c0ee2a57b731442c14c5014099ab1e5f8f401,STILL_EXISTS,Really simple scorer - sum up the embedding_dim.
aaihgebdg,allenai/allennlp,allennlp/common/util.py,0bbd359f0840637675a4e6cb9f593871390bb87b,831daeb5c96957d8bf94a716fd240aa7cfedecac,NOTE(mattg): The following four lines are a workaround suggested by Ines for spacy
aaihgebea,allenai/allennlp,allennlp/common/util.py,0bbd359f0840637675a4e6cb9f593871390bb87b,STILL_EXISTS,at some point in the future; once spacy has figured out a better way to handle this.
aaihgebid,allenai/allennlp,allennlp/models/language_model.py,e2d153f89c40e331a2bb072866f12f6d482ecf72,STILL_EXISTS,This buffer is now unused and exists only for backwards compatibility reasons.
aaihgecgc,allenai/allennlp,allennlp/common/from_params.py,7e2e592df5e3928924bde38af1bcba32df97554e,STILL_EXISTS,register pytorch modules directly).  This is a bit of a hack to make those work;
aaihgechd,allenai/allennlp,allennlp/tests/common/from_params_test.py,7e2e592df5e3928924bde38af1bcba32df97554e,STILL_EXISTS,This is a contrived; ugly example (why would you want to duplicate names in a nested
aaihgedac,allenai/allennlp,allennlp/tests/data/dataset_readers/dataset_reader_test.py,b90722188a1ff670f5b1cb8a8f2bdb57c42efe1b,STILL_EXISTS,because of a bug in `_CachedLazyInstance` that's hard to detect; we'll read the
aaihgedbg,allenai/allennlp,allennlp/training/util.py,b90722188a1ff670f5b1cb8a8f2bdb57c42efe1b,8cb07b261c4825e56ec0925f05412fca70d19a57,Laziness doesn't affect how the data is computed; so it shouldn't affect the hash.
aaihgedbi,allenai/allennlp,allennlp/semparse/contexts/table_question_context.py,f8d2ec5b8fa22ff051ad83623327c1bccae653b9,STILL_EXISTS,Mapping from strings to the columns they are under.
aaihgedce,allenai/allennlp,allennlp/semparse/contexts/table_question_context.py,f8d2ec5b8fa22ff051ad83623327c1bccae653b9,STILL_EXISTS,columns of multiple types. So we just keep track of the first column type. Hence; the
aaihgedcf,allenai/allennlp,allennlp/semparse/contexts/table_question_context.py,f8d2ec5b8fa22ff051ad83623327c1bccae653b9,STILL_EXISTS,``token_columns[0]``.
aaihgedja,allenai/allennlp,allennlp/common/from_params.py,3c226042ae3982d50c91886982043c25892bc416,STILL_EXISTS,register pytorch modules directly).  This is a bit of a hack to make those work;
aaihgedjb,allenai/allennlp,allennlp/common/from_params.py,3c226042ae3982d50c91886982043c25892bc416,STILL_EXISTS,instead of adding a `from_params` method for them somehow. Then the extras
aaihgeeba,allenai/allennlp,allennlp/modules/seq2seq_encoders/stacked_self_attention.py,6acfc6ca7f34c50fc0c1d9de22a7f351e09b2760,STILL_EXISTS,Note: Please use `ModuleList` in new code. It provides better
aaihgeedc,allenai/allennlp,allennlp/nn/util.py,24f90a82928e0ff6e8f2d187148b6f5156f7083b,STILL_EXISTS,This is the best way to detect a NamedTuple; it turns out.
aaihgeggg,allenai/allennlp,allennlp/tests/training/callback_trainer_test.py,9e6e0af620ecb44f78488fae7102b50e4ce52750,STILL_EXISTS,Because of using -loss; 2nd epoch would be better than 1st. So best val metrics should not be same.
aaihgeghb,allenai/allennlp,allennlp/tests/training/callback_trainer_test.py,9e6e0af620ecb44f78488fae7102b50e4ce52750,STILL_EXISTS,Because of using +loss; 2nd epoch won't be better than 1st. So best val metrics should be same.
aaihgehae,allenai/allennlp,allennlp/training/callback_trainer.py,9e6e0af620ecb44f78488fae7102b50e4ce52750,STILL_EXISTS,Save model if needed.
aaihgehea,allenai/allennlp,allennlp/training/callbacks/log_to_tensorboard.py,9e6e0af620ecb44f78488fae7102b50e4ce52750,STILL_EXISTS,This is an ugly hack to get the tensorboard instance to know about the trainer; because
aaihgehec,allenai/allennlp,allennlp/training/callbacks/log_to_tensorboard.py,9e6e0af620ecb44f78488fae7102b50e4ce52750,STILL_EXISTS,TODO: figure out a better way to handle this.
aaihgehhh,allenai/allennlp,allennlp/training/callbacks/train_supervised.py,9e6e0af620ecb44f78488fae7102b50e4ce52750,STILL_EXISTS,TODO: gradient accumulation
aaihgeidi,allenai/allennlp,allennlp/data/dataset_readers/semantic_parsing/wikitables/wikitables.py,0fbd1ca8982cb3b0cdbf94f7c03b0420687adc3d,9e52e0f68816a2cf3ead375e3233e72fe74db6d3,TODO(pradeep): We'll need a better way to input CoreNLP processed lines.
aaihgeiia,allenai/allennlp,allennlp/semparse/contexts/table_question_context.py,0fbd1ca8982cb3b0cdbf94f7c03b0420687adc3d,STILL_EXISTS,We assume that the lines are just the table data; with rows being newline separated; and columns
aaihgeiid,allenai/allennlp,allennlp/semparse/domain_languages/wikitables_language.py,0fbd1ca8982cb3b0cdbf94f7c03b0420687adc3d,9e52e0f68816a2cf3ead375e3233e72fe74db6d3,TODO (pradeep): We do not want the start types to be a static set. We want it to depend on the table
aaihgeiie,allenai/allennlp,allennlp/semparse/domain_languages/wikitables_language.py,0fbd1ca8982cb3b0cdbf94f7c03b0420687adc3d,9e52e0f68816a2cf3ead375e3233e72fe74db6d3,context instead; and include start types only when columns of a given type are present in the table.
aaihgejfj,allenai/allennlp,allennlp/semparse/action_space_walker.py,0a2673971ea54a77d374f797762a1012d0c05abf,STILL_EXISTS,TODO (pradeep): Sort the indices and do intersections in order; so that we can return the
aaihgfbbb,allenai/allennlp,allennlp/models/semantic_parsing/wikitables/wikitables_erm_semantic_parser.py,7728b12f7af4177c7d150be328b905f0d11df70e,STILL_EXISTS,be able to use interactive beam search (a functionality that's only implemented in the ``BeamSearch``
aaihgfbjb,allenai/allennlp,allennlp/interpret/saliency_interpreters/smooth_gradient.py,9166c18439eb30cea4f668d113066e2ed6cb86eb,STILL_EXISTS,TODO (@Eric-Wallace); SmoothGrad is not using times input normalization.
aaihgfbjc,allenai/allennlp,allennlp/interpret/saliency_interpreters/smooth_gradient.py,9166c18439eb30cea4f668d113066e2ed6cb86eb,STILL_EXISTS,Fine for now; but should fix for consistency.
aaihgfdbh,allenai/allennlp,allennlp/models/semantic_role_labeler.py,adad1bc34f4b250942a1a058c73b42db3168c15d,STILL_EXISTS,TODO (nfliu): This is kind of a hack; consider splitting out part
aaihgfdcf,allenai/allennlp,allennlp/models/srl_bert.py,0f6b3b891f8110d874687e4e039cae6e7aaa7b33,STILL_EXISTS,TODO (nfliu): This is kind of a hack; consider splitting out part
aaihgfded,allenai/allennlp,allennlp/models/crf_tagger.py,23efadd9a394d9d69e10e8539a7332665b118df0,256d4dac824cdd858ce8967febd36967207b5cd7,It's not clear why; but pylint seems to think `log_likelihood` is tuple
aaihgfeda,allenai/allennlp,allennlp/models/masked_language_model.py,d78ac70a27ced5e00ff2e51bfda424afca9e846b,STILL_EXISTS,A bit of a hack to get the right targets out of the TextField output...
aaihgfedg,allenai/allennlp,allennlp/models/masked_language_model.py,d78ac70a27ced5e00ff2e51bfda424afca9e846b,62e3d1dbdc88af9ed8afb37f7e8ecc011f5486a3,Using the namespace here is a hack...
aaihgfeec,allenai/allennlp,allennlp/models/next_token_lm.py,d78ac70a27ced5e00ff2e51bfda424afca9e846b,62e3d1dbdc88af9ed8afb37f7e8ecc011f5486a3,Using the namespace here is a hack...
aaihgfeed,allenai/allennlp,allennlp/models/next_token_lm.py,d78ac70a27ced5e00ff2e51bfda424afca9e846b,62e3d1dbdc88af9ed8afb37f7e8ecc011f5486a3,Hack - we're assuming you only have one target indexer.
aaihgfehd,allenai/allennlp,allennlp/interpret/attackers/hotflip.py,78ee3d8532b2ec5522cce205c7221be15810ba4a,STILL_EXISTS,A bit of a hack; this will only work with some dataset readers; but it'll do for now.
aaihgfejg,allenai/allennlp,allennlp/interpret/attackers/hotflip.py,78ee3d8532b2ec5522cce205c7221be15810ba4a,STILL_EXISTS,TODO(mattg): perhaps there's a way to get `predictions_to_labeled_instances` to
aaihgffac,allenai/allennlp,allennlp/interpret/attackers/hotflip.py,78ee3d8532b2ec5522cce205c7221be15810ba4a,STILL_EXISTS,TODO(mattg): This is quite a bit of a hack; both for gpt2 and for getting the
aaihgffad,allenai/allennlp,allennlp/interpret/attackers/hotflip.py,78ee3d8532b2ec5522cce205c7221be15810ba4a,STILL_EXISTS,vocab id in general...  I don't have better ideas at the moment; though.
aaihgffdg,allenai/allennlp,allennlp/interpret/attackers/utils.py,78ee3d8532b2ec5522cce205c7221be15810ba4a,STILL_EXISTS,Coref needs a special case here; apparently.  I (mattg) am not sure why the check below
aaihgffdh,allenai/allennlp,allennlp/interpret/attackers/utils.py,78ee3d8532b2ec5522cce205c7221be15810ba4a,STILL_EXISTS,doesn't catch this case; TODO: look into this.
aaihgffhc,allenai/allennlp,allennlp/data/iterators/multiprocess_iterator.py,bbaf1fc0dff3574a4cf6f8bdb5ad0713dd4f84b9,STILL_EXISTS,this process ends. Otherwise we'll crash. See
aaihgffhe,allenai/allennlp,allennlp/data/iterators/multiprocess_iterator.py,bbaf1fc0dff3574a4cf6f8bdb5ad0713dd4f84b9,STILL_EXISTS,issue specifically with tensors; perhaps due to the refcounting involved
aaihgfgcb,allenai/allennlp,scripts/benchmark_iter.py,bbaf1fc0dff3574a4cf6f8bdb5ad0713dd4f84b9,STILL_EXISTS,Presumably this could be worked around with
aaihgfggi,allenai/allennlp,allennlp/nn/util.py,76d248f814967541b8e69959ac68c6979a1d1a30,STILL_EXISTS,We'll have a check for single Embedding cases; because we can be more efficient
aaihgfhae,allenai/allennlp,allennlp/models/encoder_decoders/composed_seq2seq.py,3b2201120b7fd8ef8065fafe301ba8f99c6e1428,STILL_EXISTS,A bit of a ugly hack to tie embeddings.
aaihgfhfg,allenai/allennlp,allennlp/modules/seq2seq_decoders/auto_regressive_seq_decoder.py,3b2201120b7fd8ef8065fafe301ba8f99c6e1428,STILL_EXISTS,and use that instead of running beam search again; if performance in validation is taking a hit
aaihgficb,allenai/allennlp,allennlp/interpret/attackers/utils.py,ff0d44a5e21d5e6256c73b5b9f216a87c5743f91,STILL_EXISTS,reading comprehension models; and the interpret code can't really know about the internals of
aaihgfiee,allenai/allennlp,scripts/verify.py,e85ddef63bb5bb10b72cebce28e4797ccb848eb6,STILL_EXISTS,TODO: tighten up our type annotations and remove this
aaihghafj,allenai/allennlp,allennlp/interpret/saliency_interpreters/simple_gradient.py,135db83a151179715e32e9093cde0ac9bd17ec20,4e33a87b602d9236443d50b56e2cac291eda42eb,TODO; hardcoded position
aaihghbeh,allenai/allennlp,scripts/ai2_internal/resume_daemon.py,3c38347a728a4a961fd780c009d77055ca02032c,STILL_EXISTS,TODO(brendanr): Is there a better way to do this?
aaihghbhj,allenai/allennlp,scripts/ai2_internal/resume_daemon.py,d6f5a000bf5ff84b45185b2875cea604d0a9cf80,STILL_EXISTS,functionality to their service directly; which will obsolete this tool.
aaihghbia,allenai/allennlp,scripts/ai2_internal/resume_daemon.py,d6f5a000bf5ff84b45185b2875cea604d0a9cf80,STILL_EXISTS,Create the DB if needed.
aaihghbib,allenai/allennlp,scripts/ai2_internal/resume_daemon.py,d6f5a000bf5ff84b45185b2875cea604d0a9cf80,STILL_EXISTS,Modify the crontab if needed.
aaihghcag,allenai/allennlp,allennlp/tests/nn/util_test.py,f3083c8fb9150f07e3ca98bb3ea9368a081df028,STILL_EXISTS,Best path would normally be [3; 2; 3] but we add a
aaihghcah,allenai/allennlp,allennlp/tests/nn/util_test.py,f3083c8fb9150f07e3ca98bb3ea9368a081df028,STILL_EXISTS,potential from 2 -> 1; making [3; 2; 1] the best path.
aaihghcbi,allenai/allennlp,allennlp/data/dataset_readers/masked_language_modeling.py,bb1b1c6e27b273a66135d02a04232835d5bfc3ca,STILL_EXISTS,temporary hack to not to add special tokens
aaihghccc,allenai/allennlp,allennlp/data/dataset_readers/masked_language_modeling.py,bb1b1c6e27b273a66135d02a04232835d5bfc3ca,STILL_EXISTS,to be indexed with PretrainedTokenIndexer. It also requeires hack to avoid adding special tokens...
aaihghcce,allenai/allennlp,allennlp/data/dataset_readers/next_token_lm.py,bb1b1c6e27b273a66135d02a04232835d5bfc3ca,STILL_EXISTS,TODO: if we index word that was not split into wordpieces with
aaihghcch,allenai/allennlp,allennlp/data/dataset_readers/next_token_lm.py,bb1b1c6e27b273a66135d02a04232835d5bfc3ca,STILL_EXISTS,to be indexed with PretrainedTokenIndexer. It also requeires hack to avoid adding special tokens...
aaihghdee,allenai/allennlp,allennlp/tests/data/dataset_readers/reading_comprehension/util_test.py,50759d78142c6c6d62f2782a372b9bb00b762152,STILL_EXISTS,scenario 1: negative start character span (this should really never happen)
aaihghdeg,allenai/allennlp,scripts/check_docs.py,71a57133be5749851f807ea0ab44a15011848fa6,STILL_EXISTS,Moved to another repo. Docs replaced by link to same.
aaihghfdg,allenai/allennlp,allennlp/tests/commands/train_test.py,4b4d8be6b2f1cc818eaaf25513749350f762db84,ea715d4d9fd1bb6a5b52b13c6ad0a8e450ff76a3,TEST 1: Passing correct embedding_sources_mapping should work when pretrained_file attribute
aaihghfdi,allenai/allennlp,allennlp/tests/commands/train_test.py,4b4d8be6b2f1cc818eaaf25513749350f762db84,ea715d4d9fd1bb6a5b52b13c6ad0a8e450ff76a3,TEST 2: Passing correct embedding_sources_mapping should work when pretrained_file
aaihghfea,allenai/allennlp,allennlp/tests/commands/train_test.py,4b4d8be6b2f1cc818eaaf25513749350f762db84,ea715d4d9fd1bb6a5b52b13c6ad0a8e450ff76a3,TEST 3: Passing no embedding_sources_mapping should work; if available pretrained_file
aaihghgha,allenai/allennlp,allennlp/nn/util.py,2e811b2796f77e9e796fc3a91f6618fdac68973b,STILL_EXISTS,These span widths are off by 1; because the span ends are `inclusive`.
aaihghgic,allenai/allennlp,allennlp/nn/util.py,2e811b2796f77e9e796fc3a91f6618fdac68973b,STILL_EXISTS,We're using <= here (and for the mask below) because the span ends are
aaihghhbf,allenai/allennlp,allennlp/tests/data/token_indexers/pretrained_transformer_mismatched_indexer_test.py,7960a7c84fc17150b87dc5735c5026219c4d1a35,STILL_EXISTS,Hardcoding a few things because we know how BERT tokenization works
aaihghjhe,allenai/allennlp,allennlp/common/from_params.py,700abc65fd2172a2c6809dd9b72cf50fc2407772,be62ec35c160dd893b4524255a7d4b75076143c6,pick the wrong superclass.  We'll worry about how to fix that when we run into an actual
aaihgiaca,allenai/allennlp,allennlp/tests/training/learning_rate_schedulers/slanted_triangular_test.py,59b70108ec2bab36837563d9c31a44616a1d9c90,STILL_EXISTS,really want to.  Not sure why you would ever want to in this case; this is just testing
aaihgibcd,allenai/allennlp,allennlp/tests/commands/train_test.py,73bc922ad7a1d2265f84e4f15e15d9fc3345ecee,STILL_EXISTS,TODO: This is somewhat brittle. Make these constants in trainer.py.
aaihgibej,allenai/allennlp,allennlp/modules/token_embedders/embedding.py,1e923fd19c8eda2719c150e9716eeb1374a049bb,STILL_EXISTS,TODO: having to pass tokens here is SUPER gross; but otherwise this breaks the
aaihgiefj,allenai/allennlp,allennlp/modules/softmax_loss.py,61ec73b3ad9d8c4524789814bfa687888a71b996,STILL_EXISTS,TODO(joelgrus): implement tie_embeddings (maybe)
aaihgifea,allenai/allennlp,scripts/py2md.py,63f1dd6aefc338879111d33364cbf91a57532fc7,STILL_EXISTS,Two blank lines ends a section.
aaihgiffd,allenai/allennlp,allennlp/common/testing/test_case.py,6ea6c5941162c347b81872b048a186254ab87b95,c7b7c0085d946bdfec010c614f3545d6bab7a35c,be able to remove this work-around.
aaihgigcd,allenai/allennlp,allennlp/nn/util.py,2544e598bcea3f30cd99047a49af301b02073d69,b7cec515448f46daec9a76551d0a3342ef51b27b,especially as we can't really call it here in a type-safe way; anyway; as we're
aaihgigcg,allenai/allennlp,allennlp/models/model.py,e56992bc5bb085cf7e2ddf76360ff10faf161555,STILL_EXISTS,If you're using from_archive to specify your model (e.g.; for fine tuning); then you
aaihgihej,allenai/allennlp,allennlp/data/tokenizers/pretrained_transformer_tokenizer.py,6574823c20a09c2028ad39fd422223b34137bcc2,STILL_EXISTS,For most transformer models; \"a\" and \"b\" work just fine as dummy tokens.  For a few;
aaihgijgh,allenai/allennlp,allennlp/data/dataset_readers/sharded_dataset_reader.py,c75a1ebdc305c359ad0f1244c67b636895a7c0fd,STILL_EXISTS,We have to check that the base reader doesn't implement manual distributed
aaihgjabb,allenai/allennlp,allennlp/training/optimizers.py,18a4eb3495903a31d4fd7991b922854889d6c37f,STILL_EXISTS,warn about any other unused options in that group.
aaihgjccf,allenai/allennlp,tests/data/dataset_readers/dataset_reader_test.py,2c54cf8b67f0d70f6c0f0374838a2ea6c1e6b41a,STILL_EXISTS,because of a bug that's hard to detect; we'll read the
aaihgjcjf,allenai/allennlp,allennlp/common/plugins.py,bf3206a28cd504d91ccd4a8fdbd07cbf549e2f2f,STILL_EXISTS,Workaround for a presumed Python issue where spawned processes can't find modules in the current directory.
aaihgjdhd,allenai/allennlp,tests/common/registrable_test.py,97db5387e846350285c25bf7e39be7b64102baa7,STILL_EXISTS,TODO(mattg): maybe move all of these into tests for the base class?
aaihgjdhh,allenai/allennlp,allennlp/commands/find_learning_rate.py,c3b5ed74eabbed9ce92e56d907781a387c40739f,STILL_EXISTS,NOTE: this is actually more efficient than calling `self.optimizer.zero_grad()`
aaihgjdia,allenai/allennlp,allennlp/predictors/predictor.py,c3b5ed74eabbed9ce92e56d907781a387c40739f,STILL_EXISTS,NOTE: this is actually more efficient than calling `self._model.zero_grad()`
aaihgjgbi,allenai/allennlp,allennlp/predictors/predictor.py,b7cec515448f46daec9a76551d0a3342ef51b27b,STILL_EXISTS,I know of); so we'll ignore this corner case until it's needed.
aaihgjhca,allenai/allennlp,tests/data/dataset_readers/dataset_reader_test.py,f27ef38b3074ec89a964d9b2d8de110ed75e7cd1,STILL_EXISTS,because of a bug that's hard to detect; we'll read the
aaihgjica,allenai/allennlp,allennlp/common/task_card.py,d1f032d82bb3a3cde21db1d9efe19a3367024f38,STILL_EXISTS,\"\"\" || A specification for defining task cards (derived from model cards). || Motivation: A model's capabilities and limitations are dependent on || the task definition. Thus; it is helpful to separate the information || in the model card that comes from specifically the task itself. || \"\"\"
aaihhafae,allenai/allennlp,allennlp/modules/token_embedders/pretrained_transformer_embedder.py,9267ce7cefed6456d7e0431aa235582c00996851,STILL_EXISTS,Can't resize for transformers models that don't implement base_model.get_input_embeddings()
aaihhagab,allenai/allennlp,allennlp/common/testing/model_test_case.py,5b57be292c7d193419d7c8436441aff907068d5e,STILL_EXISTS,TODO: can this be done using pytest.skipif?
aaihhagdb,allenai/allennlp,allennlp/training/callbacks/log_writer.py,c5c9df584f98d71b34fb68cd46fa3c2257c8a7b5,STILL_EXISTS,W&B gives us much better system metrics
aaihhaihg,clips/pattern,pattern/en/inflect/__init__.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,Guess common cases where the base form ends in -e:
aaihhajde,clips/pattern,pattern/en/inflect/quantify.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,Converting the integral part to a long ensures a better accuracy during the recursion.
aaihhbaff,clips/pattern,pattern/en/parser/__init__.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,Rule 2: convert any type to adverb if it ends in \"ly\".
aaihhbafg,clips/pattern,pattern/en/parser/__init__.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,Rule 3: if a word has been categorized as a common noun and it ends with \"s\";
aaihhbafj,clips/pattern,pattern/en/parser/__init__.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,Rule 5: convert a common noun (NN or NNS) to a adjective if it ends with \"al\"; \"ient\"; \"ish\"; \"less\"
aaihhbagb,clips/pattern,pattern/en/parser/__init__.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,Rule 6: convert a noun to a past participle if word ends with \"ed\".
aaihhbagj,clips/pattern,pattern/en/parser/__init__.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,In this case; if the NN-word ends with an \"s\"; it is tagged as NNS.
aaihhbbbg,clips/pattern,pattern/en/parser/__init__.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,python -m pattern.en.parser xml -s \"Hello; my name is Dr. Sbaitso. Nice to meet you.\" -OTCLI
aaihhbbcb,clips/pattern,pattern/en/parser/__init__.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,- explicit: define what to parse manually.
aaihhbcbc,clips/pattern,pattern/en/parser/modality.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,wish => feel => believe => seem => think => know => prove + THAT
aaihhbcbd,clips/pattern,pattern/en/parser/modality.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,unlikely => supposedly => maybe => probably => usually => clearly => definitely
aaihhbcdf,clips/pattern,pattern/en/parser/modality.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,of course; objectively; personally; really; roughly; seriously; simply; sincerely;
aaihhbdfe,clips/pattern,pattern/en/parser/tree.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,Improve 3rd person singular \"'s\" lemma to \"be\"; e.g. as in \"he's fine\".
aaihhbdhe,clips/pattern,pattern/en/parser/tree.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,since it starts with the PP and ends with the chunk head (and is meaningless without these).
aaihhbfgi,clips/pattern,pattern/en/wordnet/pywordnet/wntools.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,workaround for lack of nested closures in Python < 2.1
aaihhbfja,clips/pattern,pattern/en/wordnet/pywordnet/wordnet.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,work around a Windows Python bug
aaihhbgih,clips/pattern,pattern/en/wordnet/pywordnet/wordnet.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,Ideally; we'd use a weak dict; but there aren't any.  A strong dict
aaihhbiaa,clips/pattern,pattern/examples/01-web/06-feed.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,An better way to do this is to use a DOM parser and select the HTML elements we want.
aaihhbigg,clips/pattern,pattern/examples/02-table/01-table.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,A list of columns; where each column is a list of values.
aaihhbigh,clips/pattern,pattern/examples/02-table/01-table.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,Columns can be manipulated directly like any other Python list.
aaihhbihf,clips/pattern,pattern/examples/02-table/02-group.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,A copy with only the type and id columns.
aaihhbiig,clips/pattern,pattern/examples/02-table/03-date.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,with extra functionality to make it easy to parse or print it as a string.
aaihhcafb,clips/pattern,pattern/examples/04-search/04-taxonomy.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,A better approach is to use the taxonomy:
aaihhcbfg,clips/pattern,pattern/examples/05-vector/02-corpus.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,A better measure is term frequency - inverse document frequency (TF-IDF).
aaihhccbb,clips/pattern,pattern/examples/06-graph/03-template.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,Fix HTML source indentation:
aaihhcegh,clips/pattern,pattern/search.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,XXX - We can probably rewrite all of this using (faster) regular expressions.
aaihhcfde,clips/pattern,pattern/table.py,6a6bc557626b1a0fe8115f44b364139379b28905,4ac07dea6459b605e3a33f2f7bec9d559627222b,Number of columns per row; see Table.insert().
aaihhcfdg,clips/pattern,pattern/table.py,6a6bc557626b1a0fe8115f44b364139379b28905,4ac07dea6459b605e3a33f2f7bec9d559627222b,Table.columns property can't be set; except in special cases such as Table.columns += column.
aaihhcfea,clips/pattern,pattern/table.py,6a6bc557626b1a0fe8115f44b364139379b28905,4ac07dea6459b605e3a33f2f7bec9d559627222b,Creates a copy of the row (fast + safe for generators and Columns).
aaihhcfeb,clips/pattern,pattern/table.py,6a6bc557626b1a0fe8115f44b364139379b28905,4ac07dea6459b605e3a33f2f7bec9d559627222b,The given row might have more columns than the rows in the table.
aaihhcfeg,clips/pattern,pattern/table.py,6a6bc557626b1a0fe8115f44b364139379b28905,4ac07dea6459b605e3a33f2f7bec9d559627222b,applying the group function to the other columns.
aaihhcffc,clips/pattern,pattern/table.py,6a6bc557626b1a0fe8115f44b364139379b28905,4ac07dea6459b605e3a33f2f7bec9d559627222b,Table.columns[x].map(lambda s: date(s))
aaihhcffg,clips/pattern,pattern/table.py,6a6bc557626b1a0fe8115f44b364139379b28905,4ac07dea6459b605e3a33f2f7bec9d559627222b,--- COLUMNS -----------------------------------------------------------------------------------------
aaihhcffh,clips/pattern,pattern/table.py,6a6bc557626b1a0fe8115f44b364139379b28905,4ac07dea6459b605e3a33f2f7bec9d559627222b,Keep a reference to Column objects generated with Table.columns[j].
aaihhcffj,clips/pattern,pattern/table.py,6a6bc557626b1a0fe8115f44b364139379b28905,4ac07dea6459b605e3a33f2f7bec9d559627222b,Columns[-1]
aaihhcfgc,clips/pattern,pattern/table.py,6a6bc557626b1a0fe8115f44b364139379b28905,4ac07dea6459b605e3a33f2f7bec9d559627222b,At one point a Column object was created with Table.columns[j].
aaihhcfgg,clips/pattern,pattern/table.py,6a6bc557626b1a0fe8115f44b364139379b28905,4ac07dea6459b605e3a33f2f7bec9d559627222b,This makes most sense if the order in which columns should appear is supplied.
aaihhcgfi,clips/pattern,pattern/vector/__init__.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,# For array [1; 2; 3]; m=4; n=5; returns an array with 4 rows and 5 columns:
aaihhcghb,clips/pattern,pattern/vector/__init__.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,Filter words (columns) that have a low relevancy in all documents from the matrix.
aaihhchcc,clips/pattern,pattern/vector/stemmer.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,- If the word ends -at; -bl or -iz add -e (luxuriat => luxuriate).
aaihhchcd,clips/pattern,pattern/vector/stemmer.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,- If the word ends with a double remove the last letter (hopp => hop).
aaihhcibb,clips/pattern,pattern/web/__init__.py,6a6bc557626b1a0fe8115f44b364139379b28905,41c5ad9d6a20574f713779f7f1153e426f037d78,Ends with .com; .net; .org
aaihhcicb,clips/pattern,pattern/web/__init__.py,6a6bc557626b1a0fe8115f44b364139379b28905,da00fffe24926ac7dd0cd14f56dff78e0c38bac7,Fix: ASCII ends at 127; not 255
aaihhcjej,clips/pattern,pattern/web/__init__.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,The Document Object Model (DOM) is a cross-platform and language-independent convention
aaihhdafd,clips/pattern,pattern/web/feed/feedparser.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,this is really out of control and should be refactored
aaihhdagb,clips/pattern,pattern/web/feed/feedparser.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,Note: probably shouldn't simply recreate localname here; but
aaihhdaha,clips/pattern,pattern/web/feed/feedparser.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,special hack for better tracking of empty textinput\/image elements in illformed feeds
aaihhdajh,clips/pattern,pattern/web/feed/feedparser.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,probably a better way to do the following; but it passes all the tests
aaihhdbbd,clips/pattern,pattern/web/feed/feedparser.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,thanks to Kevin Marks for this breathtaking hack to deal with (valid) high-bit attribute values in UTF-8 feeds
aaihhdbee,clips/pattern,pattern/web/feed/feedparser.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,This evil genius hack has been brought to you by Aaron Swartz.
aaihhdbhd,clips/pattern,pattern/web/feed/feedparser.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,daylight savings is complex; but not needed for feedparser's purposes
aaihhdcgf,clips/pattern,pattern/web/feed/feedparser.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,work around bug in built-in SAX parser (doesn't recognize xml: namespace)
aaihhdddi,clips/pattern,pattern/web/feed/feedparser.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,2.7.4 - 1\/14\/2004 - MAP - added workaround for improperly formed <br\/> tags in
aaihhddec,clips/pattern,pattern/web/feed/feedparser.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,2.7.5 - 1\/15\/2004 - MAP - added workaround for malformed DOCTYPE (seen on many
aaihhddee,clips/pattern,pattern/web/feed/feedparser.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,2.7.6 - 1\/16\/2004 - MAP - fixed bug with StringIO importing
aaihhddei,clips/pattern,pattern/web/feed/feedparser.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,support for cloud which got dropped somehow; added support for expirationDate
aaihhddhc,clips/pattern,pattern/web/feed/feedparser.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,in copyright; better sanitizing of dangerous HTML elements with end tags
aaihhddhe,clips/pattern,pattern/web/feed/feedparser.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,3.0b13 - 2\/8\/2004 - MAP - better handling of empty HTML tags (br; hr; img;
aaihhddhj,clips/pattern,pattern/web/feed/feedparser.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,fixed bug capturing author and contributor URL; fixed bug resolving relative
aaihhddia,clips/pattern,pattern/web/feed/feedparser.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,links in author and contributor URL; fixed bug resolvin relative links in
aaihhddii,clips/pattern,pattern/web/feed/feedparser.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,3.0b19 - 3\/15\/2004 - MAP - fixed bug exploding author information when author
aaihhddja,clips/pattern,pattern/web/feed/feedparser.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,workaround crash in PyXML\/expat when encountering invalid entities
aaihhddjg,clips/pattern,pattern/web/feed/feedparser.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,as well as results[key]; work around embedded illformed HTML with half
aaihhdebd,clips/pattern,pattern/web/feed/feedparser.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,better textinput and image tracking in illformed RSS 1.0 feeds
aaihhdefd,clips/pattern,pattern/web/feed/feedparser.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,3.3 - 7\/15\/2004 - MAP - optimize EBCDIC to ASCII conversion; fix obscure
aaihhdegc,clips/pattern,pattern/web/feed/feedparser.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,better than dictionary-like objects; added NonXMLContentType exception;
aaihhdegg,clips/pattern,pattern/web/feed/feedparser.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,generator dict is now FeedParserDict; better tracking of xml:lang;
aaihhdfea,clips/pattern,pattern/web/soup/BeautifulSoup.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,\"\"\"Beautiful Soup || Elixir and Tonic || \"The Screen-Scraper's Friend\" || http:\/\/www.crummy.com\/software\/BeautifulSoup\/ ||  || Beautiful Soup parses a (possibly invalid) XML or HTML document into a || tree representation. It provides methods and Pythonic idioms that make || it easy to navigate; search; and modify the tree. ||  || A well-formed XML\/HTML document yields a well-formed data || structure. An ill-formed XML\/HTML document yields a correspondingly || ill-formed data structure. If your document is only locally || well-formed; you can use this library to find and process the || well-formed part of it. ||  || Beautiful Soup works with Python 2.2 and up. It has no external || dependencies; but you'll have more success at converting data to UTF-8 || if you also install these three packages: ||  || * chardet; for auto-detecting character encodings ||   http:\/\/chardet.feedparser.org\/ || * cjkcodecs and iconv_codec; which add more encodings to the ones supported ||   by stock Python. ||   http:\/\/cjkpython.i18n.org\/ ||  || Beautiful Soup defines classes for two main parsing strategies: ||  ||  * BeautifulStoneSoup; for parsing XML; SGML; or your domain-specific ||    language that kind of looks like XML. ||  ||  * BeautifulSoup; for parsing run-of-the-mill HTML code; be it valid ||    or invalid. This class has web browser-like heuristics for ||    obtaining a sensible parse tree in the face of common HTML errors. ||  || Beautiful Soup also defines a class (UnicodeDammit) for autodetecting || the encoding of an HTML or XML document; and converting it to || Unicode. Much of this code is taken from Mark Pilgrim's Universal Feed Parser. ||  || For more than you ever wanted to know about Beautiful Soup; see the || documentation: || http:\/\/www.crummy.com\/software\/BeautifulSoup\/documentation.html ||  || Here; have some legalese: ||  || Copyright (c) 2004-2010; Leonard Richardson ||  || All rights reserved. ||  || Redistribution and use in source and binary forms; with or without || modification; are permitted provided that the following conditions are || met: ||  ||   * Redistributions of source code must retain the above copyright ||     notice; this list of conditions and the following disclaimer. ||  ||   * Redistributions in binary form must reproduce the above ||     copyright notice; this list of conditions and the following ||     disclaimer in the documentation and\/or other materials provided ||     with the distribution. ||  ||   * Neither the name of the the Beautiful Soup Consortium and All ||     Night Kosher Bakery nor the names of its contributors may be ||     used to endorse or promote products derived from this software ||     without specific prior written permission. ||  || THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS || \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES; INCLUDING; BUT NOT || LIMITED TO; THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR || A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR || CONTRIBUTORS BE LIABLE FOR ANY DIRECT; INDIRECT; INCIDENTAL; SPECIAL; || EXEMPLARY; OR CONSEQUENTIAL DAMAGES (INCLUDING; BUT NOT LIMITED TO; || PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE; DATA; OR || PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF || LIABILITY; WHETHER IN CONTRACT; STRICT LIABILITY; OR TORT (INCLUDING || NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS || SOFTWARE; EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE; DAMMIT. ||  || \"\"\"
aaihhdfjd,clips/pattern,pattern/web/soup/BeautifulSoup.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,TODO: replace with apos when
aaihhdgcd,clips/pattern,pattern/web/soup/BeautifulSoup.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,TODO: This only exists for backwards-compatibility
aaihhdgdf,clips/pattern,pattern/web/soup/BeautifulSoup.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,ASCII ends at 127; not 255
aaihhdgdh,clips/pattern,pattern/web/soup/BeautifulSoup.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,TODO: We get rid of markupMassage so that the
aaihhdgga,clips/pattern,pattern/web/soup/BeautifulSoup.py,6a6bc557626b1a0fe8115f44b364139379b28905,STILL_EXISTS,TODO: We've got a problem here. We're told this is
aaihhdiid,clips/pattern,pattern/web/json/encoder.py,0929f9b2ccd34f56437b3fe41e1b2070d63281b3,STILL_EXISTS,# HACK: hand-optimized bytecode; turn globals into locals
aaihhdjif,clips/pattern,pattern/metrics.py,e215e074334836d33c4d3c036c66333545bf3ecd,STILL_EXISTS,- precision: how good it is at filtering out irrelevant results (e.g.; bad web search results).
aaihheagf,clips/pattern,en/parser/brill.py,ae0d060025202effeb4b45bfe44c7cde0644f407,STILL_EXISTS,In this case; if the NN-word ends with an \"s\"; it is tagged as NNS.
aaihhecie,clips/pattern,vector/__init__.py,f6a055a0eb8302a6f7652fdb0bd50976e9727470,STILL_EXISTS,Pairs of nearest clusters are merged as we move up the hierarchy:
aaihhedag,clips/pattern,vector/__init__.py,db1ea8ae54f57608356b8b5bd5f4a35205e74812,STILL_EXISTS,columns = numpy.max(matrix; axis=0)
aaihhedah,clips/pattern,vector/__init__.py,db1ea8ae54f57608356b8b5bd5f4a35205e74812,STILL_EXISTS,columns = [i for i; v in enumerate(columns) if v <= min] # Indices of removed columns.
aaihhedai,clips/pattern,vector/__init__.py,db1ea8ae54f57608356b8b5bd5f4a35205e74812,STILL_EXISTS,matrix = numpy.delete(matrix; columns; axis=1)
aaihhedaj,clips/pattern,vector/__init__.py,db1ea8ae54f57608356b8b5bd5f4a35205e74812,STILL_EXISTS,return matrix; columns
aaihheeab,clips/pattern,examples/05-vector/04-KNN.py,a6d792c495226aea481b2ae121af452be1c48890,STILL_EXISTS,The only way to really know if you're classifier is working correctly
aaihheegf,clips/pattern,pattern/vector/__init__.py,ff4fb4cbf4d566d0796187971dfa11a753756107,STILL_EXISTS,XXX term count is lost.
aaihheejd,clips/pattern,examples/05-vector/02-corpus.py,d82416c52c9a26ca66c505089a7c77e62313b4f2,STILL_EXISTS,and words as columns. Each document row is called a \"vector\";
aaihheeje,clips/pattern,examples/05-vector/02-corpus.py,d82416c52c9a26ca66c505089a7c77e62313b4f2,STILL_EXISTS,containing the TF-IDF scores for word columns.
aaihhefee,clips/pattern,pattern/web/__init__.py,5f997ed6802badf11ec692bd72e53a19adc67d07,STILL_EXISTS,The output will be ugly: it may be useful for mining but probably not for displaying.
aaihhehhc,clips/pattern,pattern/web/pdf/layout.py,5f997ed6802badf11ec692bd72e53a19adc67d07,STILL_EXISTS,XXX this still takes O(n^2)  :(
aaihhejab,clips/pattern,pattern/web/pdf/pdfinterp.py,5f997ed6802badf11ec692bd72e53a19adc67d07,STILL_EXISTS,XXX
aaihhfaaa,clips/pattern,pattern/web/pdf/pdfparser.py,5f997ed6802badf11ec692bd72e53a19adc67d07,STILL_EXISTS,(maybe multiple times) at the end of the document.
aaihhface,clips/pattern,pattern/web/pdf/pdfparser.py,5f997ed6802badf11ec692bd72e53a19adc67d07,STILL_EXISTS,XXX may be AES
aaihhfaci,clips/pattern,pattern/web/pdf/pdfparser.py,5f997ed6802badf11ec692bd72e53a19adc67d07,STILL_EXISTS,#### hack around malformed pdf files
aaihhfada,clips/pattern,pattern/web/pdf/pdfparser.py,5f997ed6802badf11ec692bd72e53a19adc67d07,STILL_EXISTS,#### end hack around malformed pdf files
aaihhfaea,clips/pattern,pattern/web/pdf/pdfparser.py,5f997ed6802badf11ec692bd72e53a19adc67d07,STILL_EXISTS,XXX limit objlen not to exceed object boundary
aaihhfaij,clips/pattern,pattern/web/pdf/psparser.py,5f997ed6802badf11ec692bd72e53a19adc67d07,STILL_EXISTS,WTF; they said balanced parens need no special treatment.
aaihhfbec,clips/pattern,pattern/web/pdf/utils.py,5f997ed6802badf11ec692bd72e53a19adc67d07,STILL_EXISTS,XXX re-rasterize every line
aaihhfbjd,clips/pattern,pattern/db/__init__.py,854d853d9ffb84c234bed3bf4a39ee09571719a2,STILL_EXISTS,b.append(\"create %sindex if not exists `%s_%s` on `%s` (`%s`);\" % ( # XXX
aaihhfdgg,clips/pattern,pattern/db/__init__.py,117dac959a4e543f32679056a7a6cc08e7658a67,STILL_EXISTS,- or do Table.columns[x].map(lambda s: date(s))
aaihhfdgj,clips/pattern,pattern/db/__init__.py,117dac959a4e543f32679056a7a6cc08e7658a67,STILL_EXISTS,Number of columns per row; see Datasheet.insert().
aaihhfdhb,clips/pattern,pattern/db/__init__.py,117dac959a4e543f32679056a7a6cc08e7658a67,STILL_EXISTS,Datasheet.columns property can't be set; except in special case Datasheet.columns += column.
aaihhfdhf,clips/pattern,pattern/db/__init__.py,117dac959a4e543f32679056a7a6cc08e7658a67,STILL_EXISTS,Datasheet[i1:i2;j1:j2] => columns j1-j2 from rows i1-i2.
aaihhfdhh,clips/pattern,pattern/db/__init__.py,117dac959a4e543f32679056a7a6cc08e7658a67,STILL_EXISTS,Datasheet[i;j1:j2] => columns j1-j2 from row i.
aaihhfdid,clips/pattern,pattern/db/__init__.py,117dac959a4e543f32679056a7a6cc08e7658a67,STILL_EXISTS,The given row might have more columns than the rows in the matrix.
aaihhfdii,clips/pattern,pattern/db/__init__.py,117dac959a4e543f32679056a7a6cc08e7658a67,STILL_EXISTS,applying the group function to the other columns.
aaihhfdje,clips/pattern,pattern/db/__init__.py,117dac959a4e543f32679056a7a6cc08e7658a67,STILL_EXISTS,--- DATASHEET COLUMNS --------------------------------------------------------------------------------
aaihhfdjf,clips/pattern,pattern/db/__init__.py,117dac959a4e543f32679056a7a6cc08e7658a67,STILL_EXISTS,Keep a reference to DatasheetColumn objects generated with Datasheet.columns[j].
aaihhfeaa,clips/pattern,pattern/db/__init__.py,117dac959a4e543f32679056a7a6cc08e7658a67,STILL_EXISTS,At one point a DatasheetColumn object was created with Datasheet.columns[j].
aaihhfeae,clips/pattern,pattern/db/__init__.py,117dac959a4e543f32679056a7a6cc08e7658a67,STILL_EXISTS,This makes most sense if the order in which columns should appear is supplied.
aaihhffaa,clips/pattern,examples/02-db/01-database.py,4ac07dea6459b605e3a33f2f7bec9d559627222b,STILL_EXISTS,This is much more efficient than storing entire strings (e.g.; customer address).
aaihhffea,clips/pattern,pattern/db/__init__.py,4ac07dea6459b605e3a33f2f7bec9d559627222b,STILL_EXISTS,No warning is issued; seems a bad idea to document the method.
aaihhfgei,clips/pattern,examples/02-db/02-datasheet.py,444f1a7b0c4397ce7963637093cd8ca9a94dfb71,STILL_EXISTS,In this case; imported columns will automatically map values to the defined type.
aaihhfgjj,clips/pattern,pattern/en/parser/sentiment.py,37064e3f25853f53c99f17280c328642a30e8373,STILL_EXISTS,Implement dict methods to first call Lexicon._parse().
aaihhfhcj,clips/pattern,pattern/en/parser/sentiment.py,37064e3f25853f53c99f17280c328642a30e8373,STILL_EXISTS,print sentiment(\"A really bad; horrible book.\")
aaihhfhda,clips/pattern,pattern/en/parser/sentiment.py,37064e3f25853f53c99f17280c328642a30e8373,STILL_EXISTS,print sentiment(Text(parse(\"A bad book. Really horrible.\")))
aaihhfjch,clips/pattern,pattern/en/parser/__init__.py,3ae26ba4f6aa19964aa65da82aba989aad08e123,STILL_EXISTS,Rule 8: convert a noun to a verb if it ends with \"ate\"; \"ify\"; \"ise\"; \"ize\"; etc.
aaihhfjge,clips/pattern,test/test_en.py,9ccaaaeb52488850931a88e3234b1be00f9918d7,STILL_EXISTS,For example; in \"That very black cat must be really meowing really loud in the yard.\":
aaihhgabe,clips/pattern,pattern/en/parser/__init__.py,d8464a072f337d7594edf7b8367422c6326d5c67,STILL_EXISTS,\"Perhaps you\" (NP) => \"Perhaps\" (ADVP) + \"you\" (NP).
aaihhgadb,clips/pattern,test/test_en.py,c86d68f2b1a90c0281d9fad265152e3a0fbd3c4d,STILL_EXISTS,should <=> perhaps
aaihhgafj,clips/pattern,test/test_en.py,ea4e0829d79dd03362e7db4782afbcebe545c637,STILL_EXISTS,Assert \"nice\" => \"nicer\".
aaihhgagj,clips/pattern,test/test_en.py,ea4e0829d79dd03362e7db4782afbcebe545c637,STILL_EXISTS,- \"perhaps you\" (ADVP + NP)
aaihhgaha,clips/pattern,test/test_en.py,ea4e0829d79dd03362e7db4782afbcebe545c637,STILL_EXISTS,- \"very nice cats\" (NP)
aaihhgcid,clips/pattern,test/test_web.py,7bba151c8739ca2ccaabdb3b4e012d1314d68288,STILL_EXISTS,XXX should test <td colspan=\"x\"> more thoroughly.
aaihhgecg,clips/pattern,pattern/vector/__init__.py,b3165ada4f7789fd1e79e8d1ebc3ff5d6cd7326a,STILL_EXISTS,See the Vector class below = a dict with extra functionality (copy; norm).
aaihhgfif,clips/pattern,test/test_search.py,312892a32366063535d0084181014ff4b41bbbad,STILL_EXISTS,Assert messy syntax (fix brackets and whitespace; don't fix empty options).
aaihhghei,clips/pattern,test/test_db.py,e6a966ce4bcd3f06df034b829ff997bef9852229,STILL_EXISTS,Assert Schema (= table schema in a uniform way across database engines).
aaihhghjb,clips/pattern,test/test_db.py,3b84cdaec39ff6b95a5869468ad274db91e42be6,STILL_EXISTS,Assert default for new rows with missing columns.
aaihhghjc,clips/pattern,test/test_db.py,3b84cdaec39ff6b95a5869468ad274db91e42be6,STILL_EXISTS,Assert Datasheet.columns DatasheetColumns object.
aaihhghjd,clips/pattern,test/test_db.py,3b84cdaec39ff6b95a5869468ad274db91e42be6,STILL_EXISTS,Assert default for new columns with missing rows.
aaihhghjj,clips/pattern,test/test_db.py,3b84cdaec39ff6b95a5869468ad274db91e42be6,STILL_EXISTS,Assert new columns with header description.
aaihhgiii,clips/pattern,pattern/db/__init__.py,e3ff0287f2f726c0102470bec62ec1135b6ccf9a,STILL_EXISTS,--- DATASHEET COLUMNS -----------------------------------------------------------------------------
aaihhgjfj,clips/pattern,examples/05-vector/03-lsa.py,a13b708951dfd579aecc2de0f71134f38174625b,STILL_EXISTS,and hopefully decrease the time needed to run.
aaihhhabe,clips/pattern,examples/05-vector/05-bayes.py,a13b708951dfd579aecc2de0f71134f38174625b,STILL_EXISTS,Accuracy in itself is not very useful;
aaihhhafj,clips/pattern,pattern/graph/__init__.py,36e7ef6eccfb86b962fcd6f827e5da9f797c002c,STILL_EXISTS,Implement <canvas> draw().
aaihhhaig,clips/pattern,examples/01-web/12-spider.py,9cf2cce258910f25b48ffd42fb073b7499226b4c,STILL_EXISTS,A higher delay (in a real scenario; say 30 seconds) is better:
aaihhhcjg,clips/pattern,test/test_en.py,a3e50e0b595145fd6f7d6bffea88d87618f487a4,STILL_EXISTS,Note: simply training on more text will not improve accuracy.
aaihhheaj,clips/pattern,pattern/text/search.py,3bb8ae04dcbf27239aefc1b3eb088db14d26dd66,STILL_EXISTS,Pop latest group from stack if string ends with \"}\".
aaihhhhcf,clips/pattern,pattern/web/feed/feedparser.py,d64b5718b1ed186253cfc7cfa73777515a4e8db1,STILL_EXISTS,XXX - this is super ugly; properly fix this with issue 148
aaihhhhde,clips/pattern,pattern/web/feed/feedparser.py,d64b5718b1ed186253cfc7cfa73777515a4e8db1,STILL_EXISTS,not otherwise acceptable; perhaps it is MathML or SVG?
aaihhhhdi,clips/pattern,pattern/web/feed/feedparser.py,d64b5718b1ed186253cfc7cfa73777515a4e8db1,STILL_EXISTS,declare xlink namespace; if needed
aaihhhhii,clips/pattern,pattern/web/feed/feedparser.py,d64b5718b1ed186253cfc7cfa73777515a4e8db1,STILL_EXISTS,apparently MSIE and Firefox both do the following switch:
aaihhhijb,clips/pattern,pattern/text/de/inflect/__init__.py,1d45ffadc52e3a945156fa07587fd204dfabb8e1,STILL_EXISTS,Present tense 2sg gets -st; unless stem ends with -s or -z.
aaihhichg,clips/pattern,test/test_es.py,2f40656fc3c4ed13dda70da26e02a6f1f2d31c1f,STILL_EXISTS,XXX - shouldn't \"se\" be part of the verb phrase?
aaihhidfb,clips/pattern,pattern/text/es/inflect/__init__.py,fb35517f6e3b70ea3de6a3715df88a00752a8e71,STILL_EXISTS,Probably ends in -ir if preceding vowel in stem is -i.
aaihhidfc,clips/pattern,pattern/text/es/inflect/__init__.py,fb35517f6e3b70ea3de6a3715df88a00752a8e71,STILL_EXISTS,Probably infinitive if ends in -ar; -er or -ir.
aaihhihgd,clips/pattern,examples/01-web/13-wikia.py,0233d8a2bd8a45d219ff3bc030aa3d06bf3c86a9,STILL_EXISTS,a collection of thousands of wikis based on MediaWiki (i.e.; what Wikipedia uses too).
aaihhiihg,clips/pattern,pattern/text/fr/inflect/__init__.py,d8aedbdd8b2605df5383cde3fb711f599ca31ff9,STILL_EXISTS,If the stem ends in -g; use -ge before hard vowels -a and -o: manger => mangeons.
aaihhiihh,clips/pattern,pattern/text/fr/inflect/__init__.py,d8aedbdd8b2605df5383cde3fb711f599ca31ff9,STILL_EXISTS,If the stem ends in -c; use -\u00E7 before hard vowels -a and -o: lancer => lan\u00E7ons.
aaihhijdc,clips/pattern,pattern/metrics.py,053d93095f503fc7aef6573c4228cffd1ce19496,STILL_EXISTS,Deque is more efficient than list.pop(0).
aaihhjagc,clips/pattern,examples/05-vector/02-model.py,d1c1d897cde884dbbc18e4d2160aa1dfe53117e3,STILL_EXISTS,with features as columns and feature weights as rows.
aaihhjbhc,clips/pattern,pattern/vector/__init__.py,b5e47476309962c4f6da24f19070bde325defc9c,STILL_EXISTS,A Model is a matrix (or vector space) with features as columns and feature weights as rows;
aaihhjeag,clips/pattern,pattern/text/__init__.py,68fd41ef238d27a3ada846261460f63425884229,STILL_EXISTS,in an efficient way (usually at the expense of deeper configurational syntactic information).
aaihhjejf,clips/pattern,pattern/text/__init__.py,68fd41ef238d27a3ada846261460f63425884229,STILL_EXISTS,\"Very nice work\" (NP) <=> \"Perhaps\" (ADVP) + \"you\" (NP).
aaihhjfbi,clips/pattern,pattern/text/__init__.py,68fd41ef238d27a3ada846261460f63425884229,STILL_EXISTS,python -m pattern.en.parser xml -s \"Hello; my name is Dr. Sbaitso. Nice to meet you.\" -OTCLI
aaihhjfcd,clips/pattern,pattern/text/__init__.py,68fd41ef238d27a3ada846261460f63425884229,STILL_EXISTS,- explicit: define what to parse manually.
aaihhjhaa,clips/pattern,pattern/text/__init__.py,68fd41ef238d27a3ada846261460f63425884229,STILL_EXISTS,Known word preceded by a modifier (\"really good\").
aaihhjjhi,clips/pattern,pattern/text/en/inflect.py,68fd41ef238d27a3ada846261460f63425884229,STILL_EXISTS,Guess common cases where the base form ends in -e:
aaihiacbb,clips/pattern,pattern/text/fr/inflect.py,68fd41ef238d27a3ada846261460f63425884229,STILL_EXISTS,If the stem ends in -g; use -ge before hard vowels -a and -o: manger => mangeons.
aaihiacbc,clips/pattern,pattern/text/fr/inflect.py,68fd41ef238d27a3ada846261460f63425884229,STILL_EXISTS,If the stem ends in -c; use -\u00E7 before hard vowels -a and -o: lancer => lan\u00E7ons.
aaihiadfi,clips/pattern,pattern/text/xx/__init__.py,68fd41ef238d27a3ada846261460f63425884229,STILL_EXISTS,Subclass the base parser with the language-specific functionality:
aaihiaged,clips/pattern,examples/01-web/04-twitter.py,172918467b8f47f398bf3fcdd16e6cd63109a52d,STILL_EXISTS,A Datasheet is a table of rows and columns that can be exported as a CSV-file.
aaihiahec,clips/pattern,examples/01-web/09-wikia.py,172918467b8f47f398bf3fcdd16e6cd63109a52d,STILL_EXISTS,However; we may not know exactly what kind of articles exist;
aaihiahgh,clips/pattern,examples/01-web/11-facebook.py,172918467b8f47f398bf3fcdd16e6cd63109a52d,STILL_EXISTS,A Datasheet is a table of rows and columns that can be exported as a CSV-file.
aaihiaije,clips/pattern,pattern/web/__init__.py,f50d3e174cb987bf4730cb2adcf6ec2db8b1a319,STILL_EXISTS,XXX doesn't work with spaces.
aaihibcag,clips/pattern,pattern/text/it/inflect.py,cbda45a4c0bf06fbd89a3c34b89ba03b0dfd4d77,STILL_EXISTS,Probably infinitive if ends in -are; -ere; -ire or reflexive -rsi.
aaihibchi,clips/pattern,examples/01-web/13-crawler.py,c20f8e4a0835a726ef55c43a96de14bc796df59d,STILL_EXISTS,A higher delay (in a real-world scenario; say 30 seconds) is better:
aaihibeah,clips/pattern,pattern/text/__init__.py,46fe809f57d5913173fc68756cf37098ed402715,STILL_EXISTS,As more languages are implemented; this is becoming more problematic.
aaihibgch,clips/pattern,pattern/vector/__init__.py,326de056c57c830dc1afcd8b84ad64866862e37c,STILL_EXISTS,XXX Model.save() conflicts with Model.classifier.
aaihibggb,clips/pattern,pattern/web/docx/docx.py,4127a927e96a34cc37df7dbfaef2fb26521a4654,STILL_EXISTS,FIXME: rest of code below expects a single prefix
aaihibggg,clips/pattern,pattern/web/docx/docx.py,4127a927e96a34cc37df7dbfaef2fb26521a4654,STILL_EXISTS,Quick hack: it seems every element that has a 'w' nsprefix for
aaihibhga,clips/pattern,pattern/web/docx/docx.py,4127a927e96a34cc37df7dbfaef2fb26521a4654,STILL_EXISTS,We're creating the element from a string as a workaround...
aaihibhgc,clips/pattern,pattern/web/docx/docx.py,4127a927e96a34cc37df7dbfaef2fb26521a4654,STILL_EXISTS,FIXME: using string hack instead of making element
aaihibhgf,clips/pattern,pattern/web/docx/docx.py,4127a927e96a34cc37df7dbfaef2fb26521a4654,STILL_EXISTS,Move to the template data path
aaihicbeb,clips/pattern,examples/01-web/08-wiktionary.py,e1d1e356f2a8a922dbe8c9de1012d2e1225ebc28,STILL_EXISTS,We can of course improve the classifier by hand:
aaihicchh,clips/pattern,pattern/text/__init__.py,133eb202d89e85e45472ef580320c76cece7c6d4,STILL_EXISTS,\"Perhaps you\" => ADVP + NP
aaihicchi,clips/pattern,pattern/text/__init__.py,133eb202d89e85e45472ef580320c76cece7c6d4,STILL_EXISTS,\"Really nice work\" => NP
aaihicdji,clips/pattern,pattern/server/__init__.py,5e62f02ecc9dea0add2f81d041df889c67e49ec8,STILL_EXISTS,TODO: robots.txt; favicon.ico
aaihiceha,clips/pattern,pattern/server/cherrypy/__init__.py,5e62f02ecc9dea0add2f81d041df889c67e49ec8,STILL_EXISTS,Append\/remove trailing slash from path_info as needed
aaihicejh,clips/pattern,pattern/server/cherrypy/_cpchecker.py,5e62f02ecc9dea0add2f81d041df889c67e49ec8,STILL_EXISTS,Hack for 3.2's warning about body_params
aaihicffj,clips/pattern,pattern/server/cherrypy/_cpcompat_subprocess.py,5e62f02ecc9dea0add2f81d041df889c67e49ec8,STILL_EXISTS,r\"\"\"subprocess - Subprocesses with accessible I\/O streams\r || \r || This module allows you to spawn processes; connect to their\r || input\/output\/error pipes; and obtain their return codes.  This module\r || intends to replace several other; older modules and functions; like:\r || \r || os.system\r || os.spawn*\r || os.popen*\r || popen2.*\r || commands.*\r || \r || Information about how the subprocess module can be used to replace these\r || modules and functions can be found below.\r || \r || \r || \r || Using the subprocess module\r || ===========================\r || This module defines one class called Popen:\r || \r || class Popen(args; bufsize=0; executable=None;\r ||             stdin=None; stdout=None; stderr=None;\r ||             preexec_fn=None; close_fds=False; shell=False;\r ||             cwd=None; env=None; universal_newlines=False;\r ||             startupinfo=None; creationflags=0):\r || \r || \r || Arguments are:\r || \r || args should be a string; or a sequence of program arguments.  The\r || program to execute is normally the first item in the args sequence or\r || string; but can be explicitly set by using the executable argument.\r || \r || On UNIX; with shell=False (default): In this case; the Popen class\r || uses os.execvp() to execute the child program.  args should normally\r || be a sequence.  A string will be treated as a sequence with the string\r || as the only item (the program to execute).\r || \r || On UNIX; with shell=True: If args is a string; it specifies the\r || command string to execute through the shell.  If args is a sequence;\r || the first item specifies the command string; and any additional items\r || will be treated as additional shell arguments.\r || \r || On Windows: the Popen class uses CreateProcess() to execute the child\r || program; which operates on strings.  If args is a sequence; it will be\r || converted to a string using the list2cmdline method.  Please note that\r || not all MS Windows applications interpret the command line the same\r || way: The list2cmdline is designed for applications using the same\r || rules as the MS C runtime.\r || \r || bufsize; if given; has the same meaning as the corresponding argument\r || to the built-in open() function: 0 means unbuffered; 1 means line\r || buffered; any other positive value means use a buffer of\r || (approximately) that size.  A negative bufsize means to use the system\r || default; which usually means fully buffered.  The default value for\r || bufsize is 0 (unbuffered).\r || \r || stdin; stdout and stderr specify the executed programs' standard\r || input; standard output and standard error file handles; respectively.\r || Valid values are PIPE; an existing file descriptor (a positive\r || integer); an existing file object; and None.  PIPE indicates that a\r || new pipe to the child should be created.  With None; no redirection\r || will occur; the child's file handles will be inherited from the\r || parent.  Additionally; stderr can be STDOUT; which indicates that the\r || stderr data from the applications should be captured into the same\r || file handle as for stdout.\r || \r || If preexec_fn is set to a callable object; this object will be called\r || in the child process just before the child is executed.\r || \r || If close_fds is true; all file descriptors except 0; 1 and 2 will be\r || closed before the child process is executed.\r || \r || if shell is true; the specified command will be executed through the\r || shell.\r || \r || If cwd is not None; the current directory will be changed to cwd\r || before the child is executed.\r || \r || If env is not None; it defines the environment variables for the new\r || process.\r || \r || If universal_newlines is true; the file objects stdout and stderr are\r || opened as a text files; but lines may be terminated by any of '\ || ';\r || the Unix end-of-line convention; '\\r'; the Macintosh convention or\r || '\\r\ || '; the Windows convention.  All of these external representations\r || are seen as '\ || ' by the Python program.  Note: This feature is only\r || available if Python is built with universal newline support (the\r || default).  Also; the newlines attribute of the file objects stdout;\r || stdin and stderr are not updated by the communicate() method.\r || \r || The startupinfo and creationflags; if given; will be passed to the\r || underlying CreateProcess() function.  They can specify things such as\r || appearance of the main window and priority for the new process.\r || (Windows only)\r || \r || \r || This module also defines some shortcut functions:\r || \r || call(*popenargs; **kwargs):\r ||     Run command with arguments.  Wait for command to complete; then\r ||     return the returncode attribute.\r || \r ||     The arguments are the same as for the Popen constructor.  Example:\r || \r ||     retcode = call([\"ls\"; \"-l\"])\r || \r || check_call(*popenargs; **kwargs):\r ||     Run command with arguments.  Wait for command to complete.  If the\r ||     exit code was zero then return; otherwise raise\r ||     CalledProcessError.  The CalledProcessError object will have the\r ||     return code in the returncode attribute.\r || \r ||     The arguments are the same as for the Popen constructor.  Example:\r || \r ||     check_call([\"ls\"; \"-l\"])\r || \r || check_output(*popenargs; **kwargs):\r ||     Run command with arguments and return its output as a byte string.\r || \r ||     If the exit code was non-zero it raises a CalledProcessError.  The\r ||     CalledProcessError object will have the return code in the returncode\r ||     attribute and output in the output attribute.\r || \r ||     The arguments are the same as for the Popen constructor.  Example:\r || \r ||     output = check_output([\"ls\"; \"-l\"; \"\/dev\/null\"])\r || \r || \r || Exceptions\r || ----------\r || Exceptions raised in the child process; before the new program has\r || started to execute; will be re-raised in the parent.  Additionally;\r || the exception object will have one extra attribute called\r || 'child_traceback'; which is a string containing traceback information\r || from the childs point of view.\r || \r || The most common exception raised is OSError.  This occurs; for\r || example; when trying to execute a non-existent file.  Applications\r || should prepare for OSErrors.\r || \r || A ValueError will be raised if Popen is called with invalid arguments.\r || \r || check_call() and check_output() will raise CalledProcessError; if the\r || called process returns a non-zero return code.\r || \r || \r || Security\r || --------\r || Unlike some other popen functions; this implementation will never call\r || \/bin\/sh implicitly.  This means that all characters; including shell\r || metacharacters; can safely be passed to child processes.\r || \r || \r || Popen objects\r || =============\r || Instances of the Popen class have the following methods:\r || \r || poll()\r ||     Check if child process has terminated.  Returns returncode\r ||     attribute.\r || \r || wait()\r ||     Wait for child process to terminate.  Returns returncode attribute.\r || \r || communicate(input=None)\r ||     Interact with process: Send data to stdin.  Read data from stdout\r ||     and stderr; until end-of-file is reached.  Wait for process to\r ||     terminate.  The optional input argument should be a string to be\r ||     sent to the child process; or None; if no data should be sent to\r ||     the child.\r || \r ||     communicate() returns a tuple (stdout; stderr).\r || \r ||     Note: The data read is buffered in memory; so do not use this\r ||     method if the data size is large or unlimited.\r || \r || The following attributes are also available:\r || \r || stdin\r ||     If the stdin argument is PIPE; this attribute is a file object\r ||     that provides input to the child process.  Otherwise; it is None.\r || \r || stdout\r ||     If the stdout argument is PIPE; this attribute is a file object\r ||     that provides output from the child process.  Otherwise; it is\r ||     None.\r || \r || stderr\r ||     If the stderr argument is PIPE; this attribute is file object that\r ||     provides error output from the child process.  Otherwise; it is\r ||     None.\r || \r || pid\r ||     The process ID of the child process.\r || \r || returncode\r ||     The child return code.  A None value indicates that the process\r ||     hasn't terminated yet.  A negative value -N indicates that the\r ||     child was terminated by signal N (UNIX only).\r || \r || \r || Replacing older functions with the subprocess module\r || ====================================================\r || In this section; \"a ==> b\" means that b can be used as a replacement\r || for a.\r || \r || Note: All functions in this section fail (more or less) silently if\r || the executed program cannot be found; this module raises an OSError\r || exception.\r || \r || In the following examples; we assume that the subprocess module is\r || imported with \"from subprocess import *\".\r || \r || \r || Replacing \/bin\/sh shell backquote\r || ---------------------------------\r || output=`mycmd myarg`\r || ==>\r || output = Popen([\"mycmd\"; \"myarg\"]; stdout=PIPE).communicate()[0]\r || \r || \r || Replacing shell pipe line\r || -------------------------\r || output=`dmesg | grep hda`\r || ==>\r || p1 = Popen([\"dmesg\"]; stdout=PIPE)\r || p2 = Popen([\"grep\"; \"hda\"]; stdin=p1.stdout; stdout=PIPE)\r || output = p2.communicate()[0]\r || \r || \r || Replacing os.system()\r || ---------------------\r || sts = os.system(\"mycmd\" + \" myarg\")\r || ==>\r || p = Popen(\"mycmd\" + \" myarg\"; shell=True)\r || pid; sts = os.waitpid(p.pid; 0)\r || \r || Note:\r || \r || * Calling the program through the shell is usually not required.\r || \r || * It's easier to look at the returncode attribute than the\r ||   exitstatus.\r || \r || A more real-world example would look like this:\r || \r || try:\r ||     retcode = call(\"mycmd\" + \" myarg\"; shell=True)\r ||     if retcode < 0:\r ||         print >>sys.stderr; \"Child was terminated by signal\"; -retcode\r ||     else:\r ||         print >>sys.stderr; \"Child returned\"; retcode\r || except OSError; e:\r ||     print >>sys.stderr; \"Execution failed:\"; e\r || \r || \r || Replacing os.spawn*\r || -------------------\r || P_NOWAIT example:\r || \r || pid = os.spawnlp(os.P_NOWAIT; \"\/bin\/mycmd\"; \"mycmd\"; \"myarg\")\r || ==>\r || pid = Popen([\"\/bin\/mycmd\"; \"myarg\"]).pid\r || \r || \r || P_WAIT example:\r || \r || retcode = os.spawnlp(os.P_WAIT; \"\/bin\/mycmd\"; \"mycmd\"; \"myarg\")\r || ==>\r || retcode = call([\"\/bin\/mycmd\"; \"myarg\"])\r || \r || \r || Vector example:\r || \r || os.spawnvp(os.P_NOWAIT; path; args)\r || ==>\r || Popen([path] + args[1:])\r || \r || \r || Environment example:\r || \r || os.spawnlpe(os.P_NOWAIT; \"\/bin\/mycmd\"; \"mycmd\"; \"myarg\"; env)\r || ==>\r || Popen([\"\/bin\/mycmd\"; \"myarg\"]; env={\"PATH\": \"\/usr\/bin\"})\r || \r || \r || Replacing os.popen*\r || -------------------\r || pipe = os.popen(\"cmd\"; mode='r'; bufsize)\r || ==>\r || pipe = Popen(\"cmd\"; shell=True; bufsize=bufsize; stdout=PIPE).stdout\r || \r || pipe = os.popen(\"cmd\"; mode='w'; bufsize)\r || ==>\r || pipe = Popen(\"cmd\"; shell=True; bufsize=bufsize; stdin=PIPE).stdin\r || \r || \r || (child_stdin; child_stdout) = os.popen2(\"cmd\"; mode; bufsize)\r || ==>\r || p = Popen(\"cmd\"; shell=True; bufsize=bufsize;\r ||           stdin=PIPE; stdout=PIPE; close_fds=True)\r || (child_stdin; child_stdout) = (p.stdin; p.stdout)\r || \r || \r || (child_stdin;\r ||  child_stdout;\r ||  child_stderr) = os.popen3(\"cmd\"; mode; bufsize)\r || ==>\r || p = Popen(\"cmd\"; shell=True; bufsize=bufsize;\r ||           stdin=PIPE; stdout=PIPE; stderr=PIPE; close_fds=True)\r || (child_stdin;\r ||  child_stdout;\r ||  child_stderr) = (p.stdin; p.stdout; p.stderr)\r || \r || \r || (child_stdin; child_stdout_and_stderr) = os.popen4(\"cmd\"; mode;\r ||                                                    bufsize)\r || ==>\r || p = Popen(\"cmd\"; shell=True; bufsize=bufsize;\r ||           stdin=PIPE; stdout=PIPE; stderr=STDOUT; close_fds=True)\r || (child_stdin; child_stdout_and_stderr) = (p.stdin; p.stdout)\r || \r || On Unix; os.popen2; os.popen3 and os.popen4 also accept a sequence as\r || the command to execute; in which case arguments will be passed\r || directly to the program without shell intervention.  This usage can be\r || replaced as follows:\r || \r || (child_stdin; child_stdout) = os.popen2([\"\/bin\/ls\"; \"-l\"]; mode;\r ||                                         bufsize)\r || ==>\r || p = Popen([\"\/bin\/ls\"; \"-l\"]; bufsize=bufsize; stdin=PIPE; stdout=PIPE)\r || (child_stdin; child_stdout) = (p.stdin; p.stdout)\r || \r || Return code handling translates as follows:\r || \r || pipe = os.popen(\"cmd\"; 'w')\r || ...\r || rc = pipe.close()\r || if rc is not None and rc % 256:\r ||     print \"There were some errors\"\r || ==>\r || process = Popen(\"cmd\"; 'w'; shell=True; stdin=PIPE)\r || ...\r || process.stdin.close()\r || if process.wait() != 0:\r ||     print \"There were some errors\"\r || \r || \r || Replacing popen2.*\r || ------------------\r || (child_stdout; child_stdin) = popen2.popen2(\"somestring\"; bufsize; mode)\r || ==>\r || p = Popen([\"somestring\"]; shell=True; bufsize=bufsize\r ||           stdin=PIPE; stdout=PIPE; close_fds=True)\r || (child_stdout; child_stdin) = (p.stdout; p.stdin)\r || \r || On Unix; popen2 also accepts a sequence as the command to execute; in\r || which case arguments will be passed directly to the program without\r || shell intervention.  This usage can be replaced as follows:\r || \r || (child_stdout; child_stdin) = popen2.popen2([\"mycmd\"; \"myarg\"]; bufsize;\r ||                                             mode)\r || ==>\r || p = Popen([\"mycmd\"; \"myarg\"]; bufsize=bufsize;\r ||           stdin=PIPE; stdout=PIPE; close_fds=True)\r || (child_stdout; child_stdin) = (p.stdout; p.stdin)\r || \r || The popen2.Popen3 and popen2.Popen4 basically works as subprocess.Popen;\r || except that:\r || \r || * subprocess.Popen raises an exception if the execution fails\r || * the capturestderr argument is replaced with the stderr argument.\r || * stdin=PIPE and stdout=PIPE must be specified.\r || * popen2 closes all filedescriptors by default; but you have to specify\r ||   close_fds=True with subprocess.Popen.\r || \"\"\"
aaihicgce,clips/pattern,pattern/server/cherrypy/_cpcompat_subprocess.py,5e62f02ecc9dea0add2f81d041df889c67e49ec8,STILL_EXISTS,use at xxx\" and a hopeful warning about the
aaihicgda,clips/pattern,pattern/server/cherrypy/_cpcompat_subprocess.py,5e62f02ecc9dea0add2f81d041df889c67e49ec8,STILL_EXISTS,a subclass of OSError.  FIXME: We should really
aaihicggd,clips/pattern,pattern/server/cherrypy/_cpcompat_subprocess.py,5e62f02ecc9dea0add2f81d041df889c67e49ec8,STILL_EXISTS,Close parent's pipe ends
aaihicieb,clips/pattern,pattern/server/cherrypy/_cperror.py,5e62f02ecc9dea0add2f81d041df889c67e49ec8,STILL_EXISTS,than a given size. Fix this by returning a body over that size
aaihicigi,clips/pattern,pattern/server/cherrypy/_cpmodpy.py,5e62f02ecc9dea0add2f81d041df889c67e49ec8,STILL_EXISTS,\"\"\"Native adapter for serving CherryPy via mod_python ||  || Basic usage: ||  || ########################################## || # Application in a module called myapp.py || ########################################## ||  || import cherrypy ||  || class Root: ||     @cherrypy.expose ||     def index(self): ||         return 'Hi there; Ho there; Hey there' ||  ||  || # We will use this method from the mod_python configuration || # as the entry point to our application || def setup_server(): ||     cherrypy.tree.mount(Root()) ||     cherrypy.config.update({'environment': 'production'; ||                             'log.screen': False; ||                             'show_tracebacks': False}) ||  || ########################################## || # mod_python settings for apache2 || # This should reside in your httpd.conf || # or a file that will be loaded at || # apache startup || ########################################## ||  || # Start || DocumentRoot \"\/\" || Listen 8080 || LoadModule python_module \/usr\/lib\/apache2\/modules\/mod_python.so ||  || <Location \"\/\"> || \tPythonPath \"sys.path+['\/path\/to\/my\/application']\" || \tSetHandler python-program || \tPythonHandler cherrypy._cpmodpy::handler || \tPythonOption cherrypy.setup myapp::setup_server || \tPythonDebug On || <\/Location> || # End ||  || The actual path to your mod_python.so is dependent on your || environment. In this case we suppose a global mod_python || installation on a Linux distribution such as Ubuntu. ||  || We do set the PythonPath configuration setting so that || your application can be found by from the user running || the apache2 instance. Of course if your application || resides in the global site-package this won't be needed. ||  || Then restart apache2 and access http:\/\/127.0.0.1:8080 || \"\"\"
aaihicije,clips/pattern,pattern/server/cherrypy/_cpreqbody.py,5e62f02ecc9dea0add2f81d041df889c67e49ec8,STILL_EXISTS,\"\"\"Request body processing for CherryPy. ||  || .. versionadded:: 3.2 ||  || Application authors have complete control over the parsing of HTTP request || entities. In short; :attr:`cherrypy.request.body<cherrypy._cprequest.Request.body>` || is now always set to an instance of :class:`RequestBody<cherrypy._cpreqbody.RequestBody>`; || and *that* class is a subclass of :class:`Entity<cherrypy._cpreqbody.Entity>`. ||  || When an HTTP request includes an entity body; it is often desirable to || provide that information to applications in a form other than the raw bytes. || Different content types demand different approaches. Examples: ||  ||  * For a GIF file; we want the raw bytes in a stream. ||  * An HTML form is better parsed into its component fields; and each text field ||    decoded from bytes to unicode. ||  * A JSON body should be deserialized into a Python dict or list. ||  || When the request contains a Content-Type header; the media type is used as a || key to look up a value in the || :attr:`request.body.processors<cherrypy._cpreqbody.Entity.processors>` dict. || If the full media || type is not found; then the major type is tried; for example; if no processor || is found for the 'image\/jpeg' type; then we look for a processor for the 'image' || types altogether. If neither the full type nor the major type has a matching || processor; then a default processor is used || (:func:`default_proc<cherrypy._cpreqbody.Entity.default_proc>`). For most || types; this means no processing is done; and the body is left unread as a || raw byte stream. Processors are configurable in an 'on_start_resource' hook. ||  || Some processors; especially those for the 'text' types; attempt to decode bytes || to unicode. If the Content-Type request header includes a 'charset' parameter; || this is used to decode the entity. Otherwise; one or more default charsets may || be attempted; although this decision is up to each processor. If a processor || successfully decodes an Entity or Part; it should set the || :attr:`charset<cherrypy._cpreqbody.Entity.charset>` attribute || on the Entity or Part to the name of the successful charset; so that || applications can easily re-encode or transcode the value if they wish. ||  || If the Content-Type of the request entity is of major type 'multipart'; then || the above parsing process; and possibly a decoding process; is performed for || each part. ||  || For both the full entity and multipart parts; a Content-Disposition header may || be used to fill :attr:`name<cherrypy._cpreqbody.Entity.name>` and || :attr:`filename<cherrypy._cpreqbody.Entity.filename>` attributes on the || request.body or the Part. ||  || .. _custombodyprocessors: ||  || Custom Processors || ================= ||  || You can add your own processors for any specific or major MIME type. Simply add || it to the :attr:`processors<cherrypy._cprequest.Entity.processors>` dict in a || hook\/tool that runs at ``on_start_resource`` or ``before_request_body``. || Here's the built-in JSON tool for an example:: ||  ||     def json_in(force=True; debug=False): ||         request = cherrypy.serving.request ||         def json_processor(entity): ||             \\\"\"\"
aaihidaaf,clips/pattern,pattern/server/cherrypy/_cptools.py,5e62f02ecc9dea0add2f81d041df889c67e49ec8,STILL_EXISTS,\"\"\"CherryPy tools. A \"tool\" is any helper; adapted to CP. ||  || Tools are usually designed to be used in a variety of ways (although some || may only offer one if they choose): ||  ||     Library calls ||         All tools are callables that can be used wherever needed. ||         The arguments are straightforward and should be detailed within the ||         docstring. ||  ||     Function decorators ||         All tools; when called; may be used as decorators which configure ||         individual CherryPy page handlers (methods on the CherryPy tree). ||         That is; \"@tools.anytool()\" should \"turn on\" the tool via the ||         decorated function's _cp_config attribute. ||  ||     CherryPy config ||         If a tool exposes a \"_setup\" callable; it will be called ||         once per Request (if the feature is \"turned on\" via config). ||  || Tools may be implemented as any object with a namespace. The builtins || are generally either modules or instances of the tools.Tool class. || \"\"\"
aaihidabi,clips/pattern,pattern/server/cherrypy/_cptools.py,5e62f02ecc9dea0add2f81d041df889c67e49ec8,STILL_EXISTS,a huge amount of work to make it relocatable; but the only reason why
aaihidbib,clips/pattern,pattern/server/cherrypy/lib/cpstats.py,5e62f02ecc9dea0add2f81d041df889c67e49ec8,STILL_EXISTS,\"\"\"CPStats; a package for collecting and reporting on program statistics. ||  || Overview || ======== ||  || Statistics about program operation are an invaluable monitoring and debugging || tool. Unfortunately; the gathering and reporting of these critical values is || usually ad-hoc. This package aims to add a centralized place for gathering || statistical performance data; a structure for recording that data which || provides for extrapolation of that data into more useful information; || and a method of serving that data to both human investigators and || monitoring software. Let's examine each of those in more detail. ||  || Data Gathering || -------------- ||  || Just as Python's `logging` module provides a common importable for gathering || and sending messages; performance statistics would benefit from a similar || common mechanism; and one that does *not* require each package which wishes || to collect stats to import a third-party module. Therefore; we choose to || re-use the `logging` module by adding a `statistics` object to it. ||  || That `logging.statistics` object is a nested dict. It is not a custom class; || because that would 1) require libraries and applications to import a third- || party module in order to participate; 2) inhibit innovation in extrapolation || approaches and in reporting tools; and 3) be slow. There are; however; some || specifications regarding the structure of the dict. ||  ||     { ||    +----\"SQLAlchemy\": { ||    |        \"Inserts\": 4389745; ||    |        \"Inserts per Second\": ||    |            lambda s: s[\"Inserts\"] \/ (time() - s[\"Start\"]); ||    |  C +---\"Table Statistics\": { ||    |  o |        \"widgets\": {-----------+ ||  N |  l |            \"Rows\": 1.3M;      | Record ||  a |  l |            \"Inserts\": 400;    | ||  m |  e |        };---------------------+ ||  e |  c |        \"froobles\": { ||  s |  t |            \"Rows\": 7845; ||  p |  i |            \"Inserts\": 0; ||  a |  o |        }; ||  c |  n +---}; ||  e |        \"Slow Queries\": ||    |            [{\"Query\": \"SELECT * FROM widgets;\"; ||    |              \"Processing Time\": 47.840923343; ||    |              }; ||    |             ]; ||    +----}; ||     } ||  || The `logging.statistics` dict has four levels. The topmost level is nothing || more than a set of names to introduce modularity; usually along the lines of || package names. If the SQLAlchemy project wanted to participate; for example; || it might populate the item `logging.statistics['SQLAlchemy']`; whose value || would be a second-layer dict we call a \"namespace\". Namespaces help multiple || packages to avoid collisions over key names; and make reports easier to read; || to boot. The maintainers of SQLAlchemy should feel free to use more than one || namespace if needed (such as 'SQLAlchemy ORM'). Note that there are no case || or other syntax constraints on the namespace names; they should be chosen || to be maximally readable by humans (neither too short nor too long). ||  || Each namespace; then; is a dict of named statistical values; such as || 'Requests\/sec' or 'Uptime'. You should choose names which will look || good on a report: spaces and capitalization are just fine. ||  || In addition to scalars; values in a namespace MAY be a (third-layer) || dict; or a list; called a \"collection\". For example; the CherryPy StatsTool || keeps track of what each request is doing (or has most recently done) || in a 'Requests' collection; where each key is a thread ID; each || value in the subdict MUST be a fourth dict (whew!) of statistical data about || each thread. We call each subdict in the collection a \"record\". Similarly; || the StatsTool also keeps a list of slow queries; where each record contains || data about each slow query; in order. ||  || Values in a namespace or record may also be functions; which brings us to: ||  || Extrapolation || ------------- ||  || The collection of statistical data needs to be fast; as close to unnoticeable || as possible to the host program. That requires us to minimize I\/O; for example; || but in Python it also means we need to minimize function calls. So when you || are designing your namespace and record values; try to insert the most basic || scalar values you already have on hand. ||  || When it comes time to report on the gathered data; however; we usually have || much more freedom in what we can calculate. Therefore; whenever reporting || tools (like the provided StatsPage CherryPy class) fetch the contents of || `logging.statistics` for reporting; they first call `extrapolate_statistics` || (passing the whole `statistics` dict as the only argument). This makes a || deep copy of the statistics dict so that the reporting tool can both iterate || over it and even change it without harming the original. But it also expands || any functions in the dict by calling them. For example; you might have a || 'Current Time' entry in the namespace with the value \"lambda scope: time.time()\". || The \"scope\" parameter is the current namespace dict (or record; if we're || currently expanding one of those instead); allowing you access to existing || static entries. If you're truly evil; you can even modify more than one entry || at a time. ||  || However; don't try to calculate an entry and then use its value in further || extrapolations; the order in which the functions are called is not guaranteed. || This can lead to a certain amount of duplicated work (or a redesign of your || schema); but that's better than complicating the spec. ||  || After the whole thing has been extrapolated; it's time for: ||  || Reporting || --------- ||  || The StatsPage class grabs the `logging.statistics` dict; extrapolates it all; || and then transforms it to HTML for easy viewing. Each namespace gets its own || header and attribute table; plus an extra table for each collection. This is || NOT part of the statistics specification; other tools can format how they like. ||  || You can control which columns are output and how they are formatted by updating || StatsPage.formatting; which is a dict that mirrors the keys and nesting of || `logging.statistics`. The difference is that; instead of data values; it has || formatting values. Use None for a given key to indicate to the StatsPage that a || given column should not be output. Use a string with formatting (such as '%.3f') || to interpolate the value(s); or use a callable (such as lambda v: v.isoformat()) || for more advanced formatting. Any entry which is not mentioned in the formatting || dict is output unchanged. ||  || Monitoring || ---------- ||  || Although the HTML output takes pains to assign unique id's to each <td> with || statistical data; you're probably better off fetching \/cpstats\/data; which || outputs the whole (extrapolated) `logging.statistics` dict in JSON format. || That is probably easier to parse; and doesn't have any formatting controls; || so you get the \"original\" data in a consistently-serialized format. || Note: there's no treatment yet for datetime objects. Try time.time() instead || for now if you can. Nagios will probably thank you. ||  || Turning Collection Off || ---------------------- ||  || It is recommended each namespace have an \"Enabled\" item which; if False; || stops collection (but not reporting) of statistical data. Applications || SHOULD provide controls to pause and resume collection by setting these || entries to False or True; if present. ||  ||  || Usage || ===== ||  || To collect statistics on CherryPy applications: ||  ||     from cherrypy.lib import cpstats ||     appconfig['\/']['tools.cpstats.on'] = True ||  || To collect statistics on your own code: ||  ||     import logging ||     # Initialize the repository ||     if not hasattr(logging; 'statistics'): logging.statistics = {} ||     # Initialize my namespace ||     mystats = logging.statistics.setdefault('My Stuff'; {}) ||     # Initialize my namespace's scalars and collections ||     mystats.update({ ||         'Enabled': True; ||         'Start Time': time.time(); ||         'Important Events': 0; ||         'Events\/Second': lambda s: ( ||             (s['Important Events'] \/ (time.time() - s['Start Time']))); ||         }) ||     ... ||     for event in events: ||         ... ||         # Collect stats ||         if mystats.get('Enabled'; False): ||             mystats['Important Events'] += 1 ||  || To report statistics: ||  ||     root.cpstats = cpstats.StatsPage() ||  || To format statistics reports: ||  ||     See 'Reporting'; above. ||  || \"\"\"
aaihidcaf,clips/pattern,pattern/server/cherrypy/lib/cptools.py,5e62f02ecc9dea0add2f81d041df889c67e49ec8,STILL_EXISTS,add http:\/\/ or https:\/\/ if needed
aaihiddhb,clips/pattern,pattern/server/cherrypy/lib/httputil.py,5e62f02ecc9dea0add2f81d041df889c67e49ec8,STILL_EXISTS,Replace only if you really know what you're doing.
aaihidebd,clips/pattern,pattern/server/cherrypy/lib/sessions.py,5e62f02ecc9dea0add2f81d041df889c67e49ec8,STILL_EXISTS,\"\"\"Session implementation for CherryPy. ||  || You need to edit your config file to use sessions. Here's an example:: ||  ||     [\/] ||     tools.sessions.on = True ||     tools.sessions.storage_type = \"file\" ||     tools.sessions.storage_path = \"\/home\/site\/sessions\" ||     tools.sessions.timeout = 60 ||  || This sets the session to be stored in files in the directory \/home\/site\/sessions; || and the session timeout to 60 minutes. If you omit ``storage_type`` the sessions || will be saved in RAM.  ``tools.sessions.on`` is the only required line for || working sessions; the rest are optional. ||  || By default; the session ID is passed in a cookie; so the client's browser must || have cookies enabled for your site. ||  || To set data for the current session; use || ``cherrypy.session['fieldname'] = 'fieldvalue'``; || to get data use ``cherrypy.session.get('fieldname')``. ||  || ================ || Locking sessions || ================ ||  || By default; the ``'locking'`` mode of sessions is ``'implicit'``; which means || the session is locked early and unlocked late. If you want to control when the || session data is locked and unlocked; set ``tools.sessions.locking = 'explicit'``. || Then call ``cherrypy.session.acquire_lock()`` and ``cherrypy.session.release_lock()``. || Regardless of which mode you use; the session is guaranteed to be unlocked when || the request is complete. ||  || ================= || Expiring Sessions || ================= ||  || You can force a session to expire with :func:`cherrypy.lib.sessions.expire`. || Simply call that function at the point you want the session to expire; and it || will cause the session cookie to expire client-side. ||  || =========================== || Session Fixation Protection || =========================== ||  || If CherryPy receives; via a request cookie; a session id that it does not || recognize; it will reject that id and create a new one to return in the || response cookie. This `helps prevent session fixation attacks || <http:\/\/en.wikipedia.org\/wiki\/Session_fixation#Regenerate_SID_on_each_request>`_. || However; CherryPy \"recognizes\" a session id by looking up the saved session || data for that id. Therefore; if you never save any session data; || **you will get a new session id for every request**. ||  || ================ || Sharing Sessions || ================ ||  || If you run multiple instances of CherryPy (for example via mod_python behind || Apache prefork); you most likely cannot use the RAM session backend; since each || instance of CherryPy will have its own memory space. Use a different backend || instead; and verify that all instances are pointing at the same file or db || location. Alternately; you might try a load balancer which makes sessions || \"sticky\". Google is your friend; there. ||  || ================ || Expiration Dates || ================ ||  || The response cookie will possess an expiration date to inform the client at || which point to stop sending the cookie back in requests. If the server time || and client time differ; expect sessions to be unreliable. **Make sure the || system time of your server is accurate**. ||  || CherryPy defaults to a 60-minute session timeout; which also applies to the || cookie which is sent to the client. Unfortunately; some versions of Safari || (\"4 public beta\" on Windows XP at least) appear to have a bug in their parsing || of the GMT expiration date--they appear to interpret the date as one hour in || the past. Sixty minutes minus one hour is pretty close to zero; so you may || experience this bug as a new session id for every request; unless the requests || are less than one second apart. To fix; try increasing the session.timeout. ||  || On the other extreme; some users report Firefox sending cookies after their || expiration date; although this was on a system with an inaccurate system time. || Maybe FF doesn't trust system time. || \"\"\"
aaihidegi,clips/pattern,pattern/server/cherrypy/lib/static.py,5e62f02ecc9dea0add2f81d041df889c67e49ec8,STILL_EXISTS,If path is relative; users should fix it by making path absolute.
aaihidfcj,clips/pattern,pattern/server/cherrypy/process/plugins.py,5e62f02ecc9dea0add2f81d041df889c67e49ec8,STILL_EXISTS,For this workaround to have a decent probability of success; this module
aaihidfhd,clips/pattern,pattern/server/cherrypy/process/servers.py,5e62f02ecc9dea0add2f81d041df889c67e49ec8,STILL_EXISTS,adjust as needed
aaihidfig,clips/pattern,pattern/server/cherrypy/process/wspbus.py,5e62f02ecc9dea0add2f81d041df889c67e49ec8,STILL_EXISTS,will be the directory from which the startup script was run.  This is needed
aaihidgaa,clips/pattern,pattern/server/cherrypy/process/wspbus.py,5e62f02ecc9dea0add2f81d041df889c67e49ec8,STILL_EXISTS,This isn't strictly necessary; but it's better than seeing
aaihidgee,clips/pattern,pattern/server/cherrypy/test/benchmark.py,5e62f02ecc9dea0add2f81d041df889c67e49ec8,STILL_EXISTS,\"\"\"CherryPy Benchmark Tool ||  ||     Usage: ||         benchmark.py --null --notests --help --cpmodpy --modpython --ab=path --apache=path ||  ||     --null:        use a null Request object (to bench the HTTP server only) ||     --notests:     start the server but do not run the tests; this allows ||                    you to check the tested pages with a browser ||     --help:        show this help message ||     --cpmodpy:     run tests via apache on 54583 (with the builtin _cpmodpy) ||     --modpython:   run tests via apache on 54583 (with modpython_gateway) ||     --ab=path:     Use the ab script\/executable at 'path' (see below) ||     --apache=path: Use the apache script\/exe at 'path' (see below) ||  ||     To run the benchmarks; the Apache Benchmark tool \"ab\" must either be on ||     your system path; or specified via the --ab=path option. ||  ||     To run the modpython tests; the \"apache\" executable or script must be ||     on your system path; or provided via the --apache=path option. On some ||     platforms; \"apache\" may be called \"apachectl\" or \"apache2ctl\"--create ||     a symlink to them if needed. || \"\"\"
aaihidgha,clips/pattern,pattern/server/cherrypy/test/helper.py,5e62f02ecc9dea0add2f81d041df889c67e49ec8,STILL_EXISTS,This is perhaps the wrong place for this call but this is the only
aaihidhbe,clips/pattern,pattern/server/cherrypy/test/modfastcgi.py,5e62f02ecc9dea0add2f81d041df889c67e49ec8,STILL_EXISTS,\"\"\"Wrapper for mod_fastcgi; for use as a CherryPy HTTP server when testing. ||  || To autostart fastcgi; the \"apache\" executable or script must be || on your system path; or you must override the global APACHE_PATH. || On some platforms; \"apache\" may be called \"apachectl\"; \"apache2ctl\"; || or \"httpd\"--create a symlink to them if needed. ||  || You'll also need the WSGIServer from flup.servers. || See http:\/\/projects.amor.org\/misc\/wiki\/ModPythonGateway ||  ||  || KNOWN BUGS || ========== ||  || 1. Apache processes Range headers automatically; CherryPy's truncated ||     output is then truncated again by Apache. See test_core.testRanges. ||     This was worked around in http:\/\/www.cherrypy.org\/changeset\/1319. || 2. Apache does not allow custom HTTP methods like CONNECT as per the spec. ||     See test_core.testHTTPMethods. || 3. Max request header and body settings do not work with Apache. || 4. Apache replaces status \"reason phrases\" automatically. For example; ||     CherryPy may set \"304 Not modified\" but Apache will write out ||     \"304 Not Modified\" (capital \"M\"). || 5. Apache does not allow custom error codes as per the spec. || 6. Apache (or perhaps modpython; or modpython_gateway) unquotes %xx in the ||     Request-URI too early. || 7. mod_python will not read request bodies which use the \"chunked\" ||     transfer-coding (it passes REQUEST_CHUNKED_ERROR to ap_setup_client_block ||     instead of REQUEST_CHUNKED_DECHUNK; see Apache2's http_protocol.c and ||     mod_python's requestobject.c). || 8. Apache will output a \"Content-Length: 0\" response header even if there's ||     no response entity body. This isn't really a bug; it just differs from ||     the CherryPy default. || \"\"\"
aaihidhbi,clips/pattern,pattern/server/cherrypy/test/modfcgid.py,5e62f02ecc9dea0add2f81d041df889c67e49ec8,STILL_EXISTS,\"\"\"Wrapper for mod_fcgid; for use as a CherryPy HTTP server when testing. ||  || To autostart fcgid; the \"apache\" executable or script must be || on your system path; or you must override the global APACHE_PATH. || On some platforms; \"apache\" may be called \"apachectl\"; \"apache2ctl\"; || or \"httpd\"--create a symlink to them if needed. ||  || You'll also need the WSGIServer from flup.servers. || See http:\/\/projects.amor.org\/misc\/wiki\/ModPythonGateway ||  ||  || KNOWN BUGS || ========== ||  || 1. Apache processes Range headers automatically; CherryPy's truncated ||     output is then truncated again by Apache. See test_core.testRanges. ||     This was worked around in http:\/\/www.cherrypy.org\/changeset\/1319. || 2. Apache does not allow custom HTTP methods like CONNECT as per the spec. ||     See test_core.testHTTPMethods. || 3. Max request header and body settings do not work with Apache. || 4. Apache replaces status \"reason phrases\" automatically. For example; ||     CherryPy may set \"304 Not modified\" but Apache will write out ||     \"304 Not Modified\" (capital \"M\"). || 5. Apache does not allow custom error codes as per the spec. || 6. Apache (or perhaps modpython; or modpython_gateway) unquotes %xx in the ||     Request-URI too early. || 7. mod_python will not read request bodies which use the \"chunked\" ||     transfer-coding (it passes REQUEST_CHUNKED_ERROR to ap_setup_client_block ||     instead of REQUEST_CHUNKED_DECHUNK; see Apache2's http_protocol.c and ||     mod_python's requestobject.c). || 8. Apache will output a \"Content-Length: 0\" response header even if there's ||     no response entity body. This isn't really a bug; it just differs from ||     the CherryPy default. || \"\"\"
aaihidhcc,clips/pattern,pattern/server/cherrypy/test/modpy.py,5e62f02ecc9dea0add2f81d041df889c67e49ec8,STILL_EXISTS,\"\"\"Wrapper for mod_python; for use as a CherryPy HTTP server when testing. ||  || To autostart modpython; the \"apache\" executable or script must be || on your system path; or you must override the global APACHE_PATH. || On some platforms; \"apache\" may be called \"apachectl\" or \"apache2ctl\"-- || create a symlink to them if needed. ||  || If you wish to test the WSGI interface instead of our _cpmodpy interface; || you also need the 'modpython_gateway' module at: || http:\/\/projects.amor.org\/misc\/wiki\/ModPythonGateway ||  ||  || KNOWN BUGS || ========== ||  || 1. Apache processes Range headers automatically; CherryPy's truncated ||     output is then truncated again by Apache. See test_core.testRanges. ||     This was worked around in http:\/\/www.cherrypy.org\/changeset\/1319. || 2. Apache does not allow custom HTTP methods like CONNECT as per the spec. ||     See test_core.testHTTPMethods. || 3. Max request header and body settings do not work with Apache. || 4. Apache replaces status \"reason phrases\" automatically. For example; ||     CherryPy may set \"304 Not modified\" but Apache will write out ||     \"304 Not Modified\" (capital \"M\"). || 5. Apache does not allow custom error codes as per the spec. || 6. Apache (or perhaps modpython; or modpython_gateway) unquotes %xx in the ||     Request-URI too early. || 7. mod_python will not read request bodies which use the \"chunked\" ||     transfer-coding (it passes REQUEST_CHUNKED_ERROR to ap_setup_client_block ||     instead of REQUEST_CHUNKED_DECHUNK; see Apache2's http_protocol.c and ||     mod_python's requestobject.c). || 8. Apache will output a \"Content-Length: 0\" response header even if there's ||     no response entity body. This isn't really a bug; it just differs from ||     the CherryPy default. || \"\"\"
aaihidhcd,clips/pattern,pattern/server/cherrypy/test/modwsgi.py,5e62f02ecc9dea0add2f81d041df889c67e49ec8,STILL_EXISTS,\"\"\"Wrapper for mod_wsgi; for use as a CherryPy HTTP server. ||  || To autostart modwsgi; the \"apache\" executable or script must be || on your system path; or you must override the global APACHE_PATH. || On some platforms; \"apache\" may be called \"apachectl\" or \"apache2ctl\"-- || create a symlink to them if needed. ||  ||  || KNOWN BUGS || ========== ||  || ##1. Apache processes Range headers automatically; CherryPy's truncated || ##    output is then truncated again by Apache. See test_core.testRanges. || ##    This was worked around in http:\/\/www.cherrypy.org\/changeset\/1319. || 2. Apache does not allow custom HTTP methods like CONNECT as per the spec. ||     See test_core.testHTTPMethods. || 3. Max request header and body settings do not work with Apache. || ##4. Apache replaces status \"reason phrases\" automatically. For example; || ##    CherryPy may set \"304 Not modified\" but Apache will write out || ##    \"304 Not Modified\" (capital \"M\"). || ##5. Apache does not allow custom error codes as per the spec. || ##6. Apache (or perhaps modpython; or modpython_gateway) unquotes %xx in the || ##    Request-URI too early. || 7. mod_wsgi will not read request bodies which use the \"chunked\" ||     transfer-coding (it passes REQUEST_CHUNKED_ERROR to ap_setup_client_block ||     instead of REQUEST_CHUNKED_DECHUNK; see Apache2's http_protocol.c and ||     mod_python's requestobject.c). || 8. When responding with 204 No Content; mod_wsgi adds a Content-Length ||     header for you. || 9. When an error is raised; mod_wsgi has no facility for printing a ||     traceback as the response content (it's sent to the Apache log instead). || 10. Startup and shutdown of Apache when running mod_wsgi seems slow. || \"\"\"
aaihidhef,clips/pattern,pattern/server/cherrypy/test/test_bus.py,5e62f02ecc9dea0add2f81d041df889c67e49ec8,STILL_EXISTS,The start method MUST move the state to STARTED
aaihidhfa,clips/pattern,pattern/server/cherrypy/test/test_bus.py,5e62f02ecc9dea0add2f81d041df889c67e49ec8,STILL_EXISTS,The stop method MUST move the state to STOPPED
aaihidhfg,clips/pattern,pattern/server/cherrypy/test/test_bus.py,5e62f02ecc9dea0add2f81d041df889c67e49ec8,STILL_EXISTS,The exit method MUST move the state to EXITING
aaihiedih,clips/pattern,pattern/server/cherrypy/test/test_states.py,5e62f02ecc9dea0add2f81d041df889c67e49ec8,STILL_EXISTS,platform does not implement signals and sending SIGTERM
aaihiefcd,clips/pattern,pattern/server/cherrypy/test/webtest.py,5e62f02ecc9dea0add2f81d041df889c67e49ec8,STILL_EXISTS,Trying 10 times is simply in case of socket errors.
aaihiefga,clips/pattern,pattern/server/cherrypy/tutorial/tut01_helloworld.py,5e62f02ecc9dea0add2f81d041df889c67e49ec8,STILL_EXISTS,lesson number 01; we'll just send something really simple.
aaihiegac,clips/pattern,pattern/server/cherrypy/tutorial/tut04_complex_site.py,5e62f02ecc9dea0add2f81d041df889c67e49ec8,STILL_EXISTS,no reason why you shouldn't let your root object take care of
aaihiegai,clips/pattern,pattern/server/cherrypy/tutorial/tut05_derived_objects.py,5e62f02ecc9dea0add2f81d041df889c67e49ec8,STILL_EXISTS,\"\"\" || Tutorial - Object inheritance ||  || You are free to derive your request handler classes from any base || class you wish. In most real-world applications; you will probably || want to create a central base class used for all your pages; which takes || care of things like printing a common page header and footer. || \"\"\"
aaihiegcj,clips/pattern,pattern/server/cherrypy/tutorial/tut06_default_method.py,5e62f02ecc9dea0add2f81d041df889c67e49ec8,STILL_EXISTS,application; we would probably do some database lookups here
aaihieghh,clips/pattern,pattern/server/cherrypy/wsgiserver/ssl_builtin.py,5e62f02ecc9dea0add2f81d041df889c67e49ec8,STILL_EXISTS,TODO: fill this out more with mod ssl env
aaihiehba,clips/pattern,pattern/server/cherrypy/wsgiserver/wsgiserver2.py,5e62f02ecc9dea0add2f81d041df889c67e49ec8,STILL_EXISTS,TODO: what about TE and WWW-Authenticate?
aaihiehgh,clips/pattern,pattern/server/cherrypy/wsgiserver/wsgiserver2.py,5e62f02ecc9dea0add2f81d041df889c67e49ec8,STILL_EXISTS,\"Servers and gateways that implement HTTP 1.1 must provide
aaihiehib,clips/pattern,pattern/server/cherrypy/wsgiserver/wsgiserver2.py,5e62f02ecc9dea0add2f81d041df889c67e49ec8,STILL_EXISTS,We used to do 3; but are now doing 1. Maybe we'll do 2 someday;
aaihieiac,clips/pattern,pattern/server/cherrypy/wsgiserver/wsgiserver2.py,5e62f02ecc9dea0add2f81d041df889c67e49ec8,STILL_EXISTS,Both server and client are HTTP\/1.1 or better
aaihieijb,clips/pattern,pattern/server/cherrypy/wsgiserver/wsgiserver2.py,5e62f02ecc9dea0add2f81d041df889c67e49ec8,STILL_EXISTS,Someday; perhaps; we'll do the full lingering_close that
aaihiejba,clips/pattern,pattern/server/cherrypy/wsgiserver/wsgiserver2.py,5e62f02ecc9dea0add2f81d041df889c67e49ec8,STILL_EXISTS,allow AI_PASSIVE to work. Passing None instead
aaihiejdh,clips/pattern,pattern/server/cherrypy/wsgiserver/wsgiserver2.py,5e62f02ecc9dea0add2f81d041df889c67e49ec8,STILL_EXISTS,Apparently; the socket option is not available in
aaihiejjf,clips/pattern,pattern/server/cherrypy/wsgiserver/wsgiserver2.py,5e62f02ecc9dea0add2f81d041df889c67e49ec8,STILL_EXISTS,Fall back to latin 1 so apps can transcode if needed.
aaihifafe,clips/pattern,pattern/server/__init__.py,24a95f8d0e817435a19b6bebf2da7522f0528bd5,STILL_EXISTS,TODO: if you already have CherryPy installed; remove the one bundled in Pattern;
aaihifbbf,clips/pattern,examples/08-server/01-basic/basic.py,9dcfd6b59cd19da8cf03b1211d9f7516d13f5d6a,STILL_EXISTS,That's it. There is lot more you can do; but the general idea is:
aaihifbge,clips/pattern,examples/08-server/02-api/api.py,9dcfd6b59cd19da8cf03b1211d9f7516d13f5d6a,STILL_EXISTS,If you want to give it another name; use App(rate=\"xxx.db\").
aaihifbif,clips/pattern,examples/08-server/03-wiki/wiki.py,9dcfd6b59cd19da8cf03b1211d9f7516d13f5d6a,STILL_EXISTS,If the URL ends in \"?save\"; update the page content.
aaihifbig,clips/pattern,examples/08-server/03-wiki/wiki.py,9dcfd6b59cd19da8cf03b1211d9f7516d13f5d6a,STILL_EXISTS,If the URL ends in \"?edit\"; show the page editor.
aaihifcah,clips/pattern,examples/08-server/03-wiki/wiki.py,9dcfd6b59cd19da8cf03b1211d9f7516d13f5d6a,STILL_EXISTS,The edit() function is called when a URL ends in \"?edit\";
aaihifdbc,clips/pattern,pattern/server/__init__.py,9777daa85d36ce584c5b3899c74614bc03c89804,STILL_EXISTS,TODO: if you already have CherryPy installed; remove the one bundled in Pattern;
aaihifdfg,clips/pattern,examples/08-server/04-db/db.py,7f8baad0aa4cdf7af6d087282a0c3de58bcf79b3,STILL_EXISTS,Each table has fields (or columns) with a type (text; int; float; ...).
aaihifggb,clips/pattern,test/test_web.py,4a8159587bf33aee7374a536a05c55cb692b7666,STILL_EXISTS,Assert fix for common Unicode mistakes.
aaihifiac,clips/pattern,pattern/text/__init__.py,ebb1129b651ccfd24ac96c9711cf24116276284b,STILL_EXISTS,ooo; xxx; !!!; ...
aaihigbia,clips/pattern,pattern/text/en/wordnet/__init__.py,bf2885a6b9f275c0bf6373d9a7d21df87fb01a8a,STILL_EXISTS,- line  916: implement \"x in Dictionary\" instead of Dictionary.has_key(x)
aaihigced,clips/pattern,pattern/text/en/wordnet/__init__.py,ad3d72cb5ba5d15967efee666526f127b9393b5a,STILL_EXISTS,- line  916: implement \"x in Dictionary\" instead of Dictionary.has_key(x)
aaihigfff,clips/pattern,test/test_vector.py,8a45256899e832bfec84200be0ca99227096a410,60bf2970491e51e618eb629c5b340f77f5984703,probably there is a bug in IGTree Classifier
aaihiggia,ray-project/ray,test/runtest.py,2e23ec89853e688b61c5dbc156d85ea1b642b65a,b9d6cc8de4a32a7d0371bda050c21340632c4674,self.assertEqual(p.wait(); 0; \"argument was not received by the test program\") # todo: reactivate
aaihiggjf,ray-project/ray,lib/orchpy/orchpy/worker.py,f47bad382846a49eb56f8333bcf22c0f3e5dce79,08a7e4d4500276849f96fafc400b4d61bb7ae869,TODO: finish this
aaihighag,ray-project/ray,lib/orchpy/orchpy/worker.py,08a7e4d4500276849f96fafc400b4d61bb7ae869,b773003b70273b6cff72dec46e9f930cf8a9f125,TODO(rkn): Do we know that it is already there? Maybe we should call pull(arg; worker).
aaihighjd,ray-project/ray,test/runtest.py,bcc59e898d6236565086a8e81ed8428fbeda7232,5ecc2ab67df5de8abca6a140c74fc3995109dd0c,self.numpyTypeTest('int16') # TODO(pcm): implement this
aaihighje,ray-project/ray,test/runtest.py,bcc59e898d6236565086a8e81ed8428fbeda7232,5ecc2ab67df5de8abca6a140c74fc3995109dd0c,self.numpyTypeTest('int32') # TODO(pcm): implement this
aaihighjh,ray-project/ray,lib/orchpy/arrays/dist/core.py,2500cbaf729dad637a2da169a3391902cd352f29,STILL_EXISTS,TODO(rkn): fix this; this is just a placeholder that should work but is inefficient
aaihigieh,ray-project/ray,lib/orchpy/arrays/dist/core.py,37ac8faae5e6af040ae089d3a0bd31b988055aef,STILL_EXISTS,TODO(rkn): fix this; this is just a placeholder that should work but is inefficient
aaihigife,ray-project/ray,lib/orchpy/arrays/dist/linalg.py,37ac8faae5e6af040ae089d3a0bd31b988055aef,STILL_EXISTS,and has fewer rows than columns; this is a bit ugly so think about how to
aaihigjch,ray-project/ray,examples/imagenet/driver.py,acc51309e719ebbec68b97875c5367de038c1b0c,STILL_EXISTS,TODO(pcm): implement unicode serialization
aaihihcfc,ray-project/ray,doc/conf.py,2e2bc2c7409fbe8c284d720f433895b756ee749e,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaihihifb,ray-project/ray,scripts/ec2.py,8465df1146f6e47b1a9dd7460fc90aa5c7bd6809,STILL_EXISTS,It is needed to use group_id to make it work with VPC
aaihiibae,ray-project/ray,test/runtest.py,c27e6c076cf3fc8e5defc376d2be0ed732082139,072f442c1f17d73cf093e2e0243beea82aa40abe,This is a hack to make the test run.
aaihiibhj,ray-project/ray,test/runtest.py,362ffa1f3ce7be66d2977a9e8bc70ee95171247c,072f442c1f17d73cf093e2e0243beea82aa40abe,This is a hack to make the test run.
aaihiigcb,ray-project/ray,lib/python/photon.py,64c5e0880e9db4cca8f58954bc9d87c11e2f4ce2,STILL_EXISTS,TODO Extract the actual task. EXTRACT...(c_task)
aaihijigd,ray-project/ray,lib/python/ray/worker.py,90f88af902cdb04d8acd448c284f57b6728564eb,STILL_EXISTS,functions to run may set the Python path; which is needed to import a
aaihjaaig,ray-project/ray,src/common/redis_module/runtest.py,817f1e730c84b1cf25050866a3be2c0719bd7711,STILL_EXISTS,Check that it is fine if we add the same object ID multiple times with the
aaihjabha,ray-project/ray,lib/python/ray/worker.py,6cd02d71f8ee87c2ef9079d2a205082f91debfac,STILL_EXISTS,Set other fields needed for computing task IDs.
aaihjabih,ray-project/ray,scripts/start_ray.py,6cd02d71f8ee87c2ef9079d2a205082f91debfac,STILL_EXISTS,fix. We should actually redirect stdout and stderr to Redis in some way.
aaihjacdj,ray-project/ray,test/runtest.py,8d90c9f43216d3c3ba621fa8e433093529650b60,STILL_EXISTS,The functionality being tested here is really multi-node functionality;
aaihjaddh,ray-project/ray,lib/python/ray/worker.py,651aa6007a43cb8207b9e0c0f0a83ad0809ba1c0,STILL_EXISTS,TODO(rkn): This code currently takes around half a microsecond. Since we
aaihjaddj,ray-project/ray,lib/python/ray/worker.py,651aa6007a43cb8207b9e0c0f0a83ad0809ba1c0,STILL_EXISTS,logging code; perhaps in C.
aaihjadec,ray-project/ray,test/runtest.py,651aa6007a43cb8207b9e0c0f0a83ad0809ba1c0,STILL_EXISTS,This is a hack for getting the event log. It is not part of the API.
aaihjaebb,ray-project/ray,test/tensorflow_test.py,6fe69bec111d11b60f6b1ab137626ef25c459a8b,STILL_EXISTS,Return all of the data needed to use the network.
aaihjaeia,ray-project/ray,python/ray/worker.py,ab8c3432f7f887f90180f0f1a7df18ef6faf6b0b,STILL_EXISTS,The ID of the driver that this task belongs to. This is needed so that
aaihjafii,ray-project/ray,test/stress_tests.py,ca254b8689e49614346f977b634ac6251473caab,STILL_EXISTS,TODO(pcm): replace by 10 ** 5 once this is faster
aaihjafji,ray-project/ray,webui/backend/ray_ui.py,1fec94ef00e03c1228c4ad19b3d227fc366a723a,STILL_EXISTS,TODO(rkn): Maybe we should open a new web sockets for every request instead
aaihjahba,ray-project/ray,python/ray/actor.py,12a68e84d293c29150fc91f7218b7ae6eae7657e,STILL_EXISTS,The following is needed so we can still access self.actor_methods.
aaihjaheg,ray-project/ray,python/ray/worker.py,12a68e84d293c29150fc91f7218b7ae6eae7657e,88a5b4e77bb628b56514ec948044f7ee8ef61bdb,TODO(rkn): Maybe none of these counters are necessary? When executing a
aaihjahgh,ray-project/ray,test/actor_test.py,12a68e84d293c29150fc91f7218b7ae6eae7657e,STILL_EXISTS,# TODO(rkn): Implement this.
aaihjbbhd,ray-project/ray,examples/resnet/resnet_model.py,c66178bcd7a27dd9f5cf726cfe8293a49eb12587,31bf0e8da4188b228374e1893cba541d1d7b3d84,It is more memory efficient than very deep residual network and has
aaihjbbhj,ray-project/ray,examples/resnet/resnet_model.py,c66178bcd7a27dd9f5cf726cfe8293a49eb12587,STILL_EXISTS,elipson used to be 1e-5. Maybe 0.001 solves NaN problem in deeper net.
aaihjbccb,ray-project/ray,examples/a3c/runner.py,b463d9e5c7af4425c2c3f71c467776142487aebf,STILL_EXISTS,the timeout variable exists because apparently; if one worker dies; the other workers
aaihjbccj,ray-project/ray,examples/a3c/vectorized/monitoring.py,b463d9e5c7af4425c2c3f71c467776142487aebf,STILL_EXISTS,The real env is the first unwrapped env. Maybe we should
aaihjbcfa,ray-project/ray,examples/a3c/vectorized/multiprocessing_env.py,b463d9e5c7af4425c2c3f71c467776142487aebf,STILL_EXISTS,TODO: close envs?
aaihjbcfc,ray-project/ray,examples/a3c/vectorized/multiprocessing_env.py,b463d9e5c7af4425c2c3f71c467776142487aebf,STILL_EXISTS,TODO: better error handling: workers should die when we go away
aaihjbchb,ray-project/ray,examples/a3c/vectorized/wrappers.py,b463d9e5c7af4425c2c3f71c467776142487aebf,STILL_EXISTS,We'll maintain our own weakref; thank you very much.
aaihjbcjd,ray-project/ray,webui/backend/ray_ui.py,ced13ca5b120c52ecc0c41c9c8043c694a38810e,STILL_EXISTS,TODO(richard): Maybe also get rid of the coloring.
aaihjbeig,ray-project/ray,python/ray/plasma/plasma.py,ba02fc0eb08dd3914a8e060c08355be72d5ac667,STILL_EXISTS,better; but it requires some care because the slice may be backed by the
aaihjbfjc,ray-project/ray,python/ray/services.py,b6c4ae82c0153ff1bce08e3f7edbe7832bb05243,609c1b8acdb9696989757620b5efa34bc958cff8,128MB. This is a hack to make it less likely for pubsub messages to be
aaihjbgdi,ray-project/ray,python/ray/actor.py,0ac125e9b23daeeb854a0483a779b36ebdcbf7cc,STILL_EXISTS,Really we should encode this message as a flatbuffer object. However; we're
aaihjbibi,ray-project/ray,examples/evolution_strategies/policies.py,3c5375345f26f9f0d345608b9883baf10206a0c6,STILL_EXISTS,TODO: it would be nice to avoid pickle; but it's convenient to pass Python objects to _initialize
aaihjcabb,ray-project/ray,test/stress_tests.py,ee08c8274b5fe15d069f5d3d1eebc9a2fd60844c,STILL_EXISTS,TODO(pcm): replace by 10 ** 5 once this is faster.
aaihjcabj,ray-project/ray,python/ray/__init__.py,c440010cbd7eaef3a19bda9701695240ad98ceb1,fb6ac28b44947c74600d09ff2231bc0d1987537d,Fix this.
aaihjcbcf,ray-project/ray,python/setup.py,609b5c1a4cfc01e01ce3c3965d9e88c1b072e32d,STILL_EXISTS,been created; so we have to move the files manually.
aaihjcbeh,ray-project/ray,python/ray/scripts/scripts.py,1a682e28072e32c73a12a44defac40448a67fc34,STILL_EXISTS,fix. We should actually redirect stdout and stderr to Redis in some way.
aaihjcehc,ray-project/ray,python/ray/global_scheduler/test/test.py,e0867c88458dd846d88a815201bf5583de43491b,STILL_EXISTS,implement.
aaihjceih,ray-project/ray,python/ray/plasma/plasma.py,e0867c88458dd846d88a815201bf5583de43491b,c3b39b4d86960d3ebc14bf26e7f69379018e90c2,this better; but it requires some care because the slice may be
aaihjcggb,ray-project/ray,python/ray/rllib/models/preprocessor.py,420013774ca09875243a6e8b84d9dc3f269cc8df,STILL_EXISTS,TODO(ekl) implement common preprocessors
aaihjcggf,ray-project/ray,python/ray/rllib/policy_gradient/test/test.py,420013774ca09875243a6e8b84d9dc3f269cc8df,STILL_EXISTS,TODO(ekl): move to rllib\/models dir
aaihjcihi,ray-project/ray,python/setup.py,c3b39b4d86960d3ebc14bf26e7f69379018e90c2,03f2325780d3c67e1573aafee2b2f1d70c9a4e23,However; we moved it here because the previous approach seemed to fail in
aaihjciif,ray-project/ray,python/setup.py,c3b39b4d86960d3ebc14bf26e7f69379018e90c2,03f2325780d3c67e1573aafee2b2f1d70c9a4e23,location. An alternative fix would be to manually modify the easy-install.pth
aaihjcjcj,ray-project/ray,python/ray/utils.py,8c8258de20d238807ae3b777c15432c01decbaf0,96913be9396dafabae05088aacbef2a8c0b4352c,Really we should encode this message as a flatbuffer object. However;
aaihjcjdj,ray-project/ray,python/ray/worker.py,8c8258de20d238807ae3b777c15432c01decbaf0,STILL_EXISTS,The ID of the driver that this task belongs to. This is needed so
aaihjdafj,ray-project/ray,python/ray/experimental/ui.py,99badc7ae42cdcb3a93f5a8e29f8bae987b9d011,STILL_EXISTS,Feature received a callback; but it shouldn't be executed
aaihjdbjc,ray-project/ray,python/ray/worker.py,dbe3d9351cbd3dd98270d5d7297d2706c5fca743,STILL_EXISTS,be stored in Redis. Fix this.
aaihjdccc,ray-project/ray,python/setup.py,03f2325780d3c67e1573aafee2b2f1d70c9a4e23,STILL_EXISTS,TODO(rkn): Fix this.
aaihjdcjb,ray-project/ray,python/ray/rllib/evolution_strategies/policies.py,c81821b8562daecc9796d205c25d4ceeb72d1a31,STILL_EXISTS,TODO(ekl) the squeeze() is needed for Pong-v0; but we should fix
aaihjdegd,ray-project/ray,python/ray/worker.py,99c8b1f38cdb02390fafd8242196ce5d4e118488,668737f38309ec9d6de1800cee79b4a273eb249a,TODO(swang): This is a hack to prevent the object store from evicting
aaihjdehe,ray-project/ray,python/ray/worker.py,99c8b1f38cdb02390fafd8242196ce5d4e118488,3764f2f2e1447f4015d4e2bcd47700131e85bc80,numpy array as a hack to pin the object in the object store.
aaihjdfid,ray-project/ray,python/ray/worker.py,ad61af73337fc56c2585fa30fc8b47586c497ce8,STILL_EXISTS,TODO(rkn): This is a temporary workaround for
aaihjdfje,ray-project/ray,python/ray/rllib/models/preprocessors.py,cb6dea94bce34a3c0a3033a023815b5aff9db79d,STILL_EXISTS,Rescale needed for maintaining 1 channel
aaihjdgdg,ray-project/ray,python/ray/worker.py,0684258d2ebd338d7bb564299052cd8dd44746c6,97c6369b495e1e84b4a8511e92bfd7709745eec9,TODO: serializing Series without extra copy
aaihjdgee,ray-project/ray,python/ray/actor.py,3764f2f2e1447f4015d4e2bcd47700131e85bc80,STILL_EXISTS,a numpy array as a hack to pin the object in the object
aaihjdhfj,ray-project/ray,python/ray/actor.py,15486a14a0b168f2c6e95cf3c2b1e8a3aa7f1740,STILL_EXISTS,Once the actor executes a task; it counts as loaded.
aaihjeaej,ray-project/ray,python/ray/rllib/a3c/runner_thread.py,dc66a2d7d54e5e38055a600713535ccd40863b46,STILL_EXISTS,The timeout variable exists because apparently; if one worker
aaihjeagi,ray-project/ray,python/setup.py,fdf069bd1de0072e7acb08956c44a7efd09f6b1b,dd45664ab51d7e91ac6b33a583ed340fb79db43e,TODO(pcm): Fix this
aaihjebee,ray-project/ray,examples/cython/setup.py,4f0da6f81c3430d200e15db6427c810eef463c9a,STILL_EXISTS,TODO: Need scipy to run BrainIAK example; but don't want to add additional
aaihjecbe,ray-project/ray,python/ray/rllib/es/policies.py,0eae9177669aae82db948faf2e86653880642844,483dee2ff37bb43b8e4639521abddd761500b2e7,TODO(rkn): Move these filters out of PPO to somewhere common.
aaihjedcg,ray-project/ray,python/ray/rllib/utils/sampler.py,483dee2ff37bb43b8e4639521abddd761500b2e7,3304099cc4bbc4b243220261014e5c24c7e0ae93,copies everything back into original filter - needed
aaihjedci,ray-project/ray,python/ray/rllib/utils/sampler.py,483dee2ff37bb43b8e4639521abddd761500b2e7,STILL_EXISTS,The timeout variable exists because apparently; if one worker
aaihjedgj,ray-project/ray,python/ray/rllib/dqn/replay_evaluator.py,2d543b6e1911c7949154ad00ca8a0ad2086e282f,STILL_EXISTS,Create extra workers if needed
aaihjedhh,ray-project/ray,python/ray/rllib/optimizers/util.py,2d543b6e1911c7949154ad00ca8a0ad2086e282f,STILL_EXISTS,TODO(ekl) move this to a common location
aaihjedic,ray-project/ray,python/ray/worker.py,96c46d35ff7f6802070fecc74d9275aa0b1af130,decaa65cd6aa0b99317165e1fcd154cbf569b10d,used when passing around actor handles.
aaihjeedb,ray-project/ray,python/ray/autoscaler/autoscaler.py,f5ea44338eca392df3a868035df3901829cc2ca1,STILL_EXISTS,Launch a new node if needed
aaihjeefd,ray-project/ray,python/ray/autoscaler/node_provider.py,f5ea44338eca392df3a868035df3901829cc2ca1,37e4d29f8715330eb772f05513c696fd852ab565,TODO: support more node providers
aaihjeega,ray-project/ray,python/ray/autoscaler/tags.py,f5ea44338eca392df3a868035df3901829cc2ca1,STILL_EXISTS,Hash of the node runtime config; used to determine if updates are needed
aaihjefba,ray-project/ray,python/ray/dataframe/dataframe.py,a75a473d7f35acd6f9274073c4d13ffaa49303b0,STILL_EXISTS,TODO find a better way
aaihjefbd,ray-project/ray,python/ray/dataframe/dataframe.py,a75a473d7f35acd6f9274073c4d13ffaa49303b0,STILL_EXISTS,TODO stop reassigning df
aaihjefec,ray-project/ray,python/ray/rllib/a3c/base_evaluator.py,4bb5b6bd5b98e29c462475a2b671b1de7e889f4c,STILL_EXISTS,Technically not needed when not remote
aaihjegah,ray-project/ray,test/autoscaler_test.py,b6c42f96beab3ee00fe4b246e5e9d0479ad379ca,STILL_EXISTS,Scales down as nodes become unused
aaihjegcg,ray-project/ray,examples/carla/env.py,c60ccbad4629fa3d5b252ab8036a382f722a7c7b,STILL_EXISTS,TODO(ekl) this isn't really a proper gym spec
aaihjeghi,ray-project/ray,python/ray/rllib/utils/reshaper.py,37076a9ff8a8e1bc1b0a9451499895b9517bf1eb,STILL_EXISTS,FIXME (ev) This won't work for mixed action distributions like
aaihjegic,ray-project/ray,python/ray/experimental/state.py,a2b190e65bb00740f22387447502cd5a2b0933e9,0f0099fb909a862ed93ffc93c12fa411d5cc119b,TODO (hme): do something to correct slider here;
aaihjegjc,ray-project/ray,test/actor_test.py,74718efa73d3f416f0faeab478f0aac77bad3620,STILL_EXISTS,removed once join consistency is implemented.
aaihjehah,ray-project/ray,python/ray/rllib/dqn/dqn_evaluator.py,ee36effd8e34b56290d9873e80525939556e9764,STILL_EXISTS,truncate ends of the trajectory
aaihjehdf,ray-project/ray,python/ray/tune/web_server.py,a7d544424c40d600d6caa1407b6823087a07998d,5cf388f29debc1b0ebdbc70b51a3af6fd65846a0,TODO(rliaw): Better to specify address and port forward
aaihjehdg,ray-project/ray,python/ray/tune/web_server.py,a7d544424c40d600d6caa1407b6823087a07998d,5cf388f29debc1b0ebdbc70b51a3af6fd65846a0,TODO(rliaw): have better way of specifying a new trial
aaihjehff,ray-project/ray,python/ray/cloudpickle/cloudpickle.py,ab5d4a601024c6da1037004bc794c01cc050abd9,STILL_EXISTS,\"\"\"Constructors for 3rd party libraries || Note: These can never be renamed due to client compatibility issues\"\"\"
aaihjehgd,ray-project/ray,python/ray/cloudpickle/cloudpickle.py,ab5d4a601024c6da1037004bc794c01cc050abd9,STILL_EXISTS,is what gives us the ``nonlocal`` behavior in a Python 2 compatible way.
aaihjehji,ray-project/ray,python/ray/cloudpickle/cloudpickle.py,ab5d4a601024c6da1037004bc794c01cc050abd9,STILL_EXISTS,essentially save_reduce; but workaround needed to avoid recursion
aaihjeidc,ray-project/ray,python/ray/cloudpickle/cloudpickle.py,ab5d4a601024c6da1037004bc794c01cc050abd9,f0c3910d598e7964a28b4245bd6e4024114bdbd2,save the rest of the func data needed by _fill_function
aaihjeiee,ray-project/ray,python/ray/cloudpickle/cloudpickle.py,ab5d4a601024c6da1037004bc794c01cc050abd9,STILL_EXISTS,XXX Assert it's a sequence
aaihjeifc,ray-project/ray,python/ray/cloudpickle/cloudpickle.py,ab5d4a601024c6da1037004bc794c01cc050abd9,STILL_EXISTS,hack for __import__ not working as desired
aaihjejga,ray-project/ray,python/ray/tune/pbt.py,b948405532b4fc0c8d4cea80eea9d495cc3d2821,STILL_EXISTS,TODO(ekl) restarting the trial is expensive. We should implement a
aaihjfbdf,ray-project/ray,python/ray/rllib/a3c/a3c.py,7e998db65665f2f4b4d656c64a18870ec3a34eef,STILL_EXISTS,workaround for https:\/\/github.com\/ray-project\/ray\/issues\/1516
aaihjfbdg,ray-project/ray,python/ray/rllib/dqn/dqn.py,7e998db65665f2f4b4d656c64a18870ec3a34eef,STILL_EXISTS,workaround for https:\/\/github.com\/ray-project\/ray\/issues\/1516
aaihjfbdh,ray-project/ray,python/ray/rllib/es/es.py,7e998db65665f2f4b4d656c64a18870ec3a34eef,STILL_EXISTS,workaround for https:\/\/github.com\/ray-project\/ray\/issues\/1516
aaihjfbdj,ray-project/ray,python/ray/rllib/ppo/ppo.py,7e998db65665f2f4b4d656c64a18870ec3a34eef,STILL_EXISTS,workaround for https:\/\/github.com\/ray-project\/ray\/issues\/1516
aaihjfbed,ray-project/ray,test/tensorflow_test.py,f2b6a7b58ddb12d9e1d68affad76ecbf7fd52e81,STILL_EXISTS,Return all of the data needed to use the network.
aaihjfbge,ray-project/ray,python/ray/autoscaler/docker.py,1cd2703caccddd827e2b116be935a85dab40fa14,283f4d1060060d3a10a62ac5d74c9ca45c596791,needed because docker doesn't allow relative paths
aaihjfbgh,ray-project/ray,python/ray/dataframe/dataframe.py,de6fa02c85090ccf68c79bb35d48026b4df31175,STILL_EXISTS,TODO: Clean up later.
aaihjfbja,ray-project/ray,python/ray/dataframe/dataframe.py,529397b35eb8c3fa3de300f5619a92120fc9d62a,STILL_EXISTS,TODO: Clean up later.
aaihjfbjh,ray-project/ray,python/ray/dataframe/dataframe.py,1fa59f1887aac67868f58bd9f2d2bdf28dc22a03,STILL_EXISTS,to ndarray and maybe infer different dtype
aaihjfcbe,ray-project/ray,python/ray/dataframe/dataframe.py,d78a22f94c7413a093b0a2a20087131f1e6e85ad,STILL_EXISTS,Reassign the columns within partition to self.index.
aaihjfccd,ray-project/ray,python/ray/dataframe/io.py,d78a22f94c7413a093b0a2a20087131f1e6e85ad,STILL_EXISTS,Move after the next \ || 
aaihjfccf,ray-project/ray,python/ray/dataframe/dataframe.py,48bd7b147dcb968ca161329392ef46c039f2a80a,STILL_EXISTS,TODO: group series here into full df partitions to reduce
aaihjfcdc,ray-project/ray,python/ray/dataframe/indexing.py,4ab16d7fb32f63559b9b58ccb07e06df392f9ae0,825c227c2be27ab18094d57d9a7da427029bb512,do groupby. This might not be the most efficient method.
aaihjfdah,ray-project/ray,python/ray/rllib/optimizers/apex_optimizer.py,ecb811c26e4c04ce4a5f7168e638613e48fce847,STILL_EXISTS,Update weights if needed
aaihjfddd,ray-project/ray,python/ray/autoscaler/node_provider.py,162d063f0dd68c3c85f39bb9a8b9c275cc226f8c,37e4d29f8715330eb772f05513c696fd852ab565,TODO: support more node providers
aaihjfded,ray-project/ray,python/ray/dataframe/dataframe.py,2b747ba46cee4445c8ff1640a2a5d7c88e2d1723,STILL_EXISTS,TODO: return ray series instead of ray df
aaihjfdee,ray-project/ray,python/ray/dataframe/dataframe.py,2b747ba46cee4445c8ff1640a2a5d7c88e2d1723,STILL_EXISTS,TODO: Revist this to improve performance
aaihjfdef,ray-project/ray,python/ray/dataframe/test/test_dataframe.py,2b747ba46cee4445c8ff1640a2a5d7c88e2d1723,STILL_EXISTS,\"\"\" || TODO: Use this when Arrow issue resolves: || (https:\/\/issues.apache.org\/jira\/browse\/ARROW-2122) || @pytest.fixture || def test_fillna_datetime_columns(num_partitions=2): ||     # GH 7095 ||     df = pd.DataFrame({'A': [-1; -2; np.nan]; ||                        'B': date_range('20130101'; periods=3); ||                        'C': ['foo'; 'bar'; None]; ||                        'D': ['foo2'; 'bar2'; None]}; ||                       index=date_range('20130110'; periods=3)) ||     ray_df = rdf.from_pandas(df; num_partitions) ||     assert ray_df_equals_pandas( ||         ray_df.fillna('?'); ||         df.fillna('?') ||     ) ||  ||     df = pd.DataFrame({'A': [-1; -2; np.nan]; ||                        'B': [pd.Timestamp('2013-01-01'); ||                              pd.Timestamp('2013-01-02'); pd.NaT]; ||                        'C': ['foo'; 'bar'; None]; ||                        'D': ['foo2'; 'bar2'; None]}; ||                       index=date_range('20130110'; periods=3)) ||     ray_df = rdf.from_pandas(df; num_partitions) ||     assert ray_df_equals_pandas( ||         ray_df.fillna('?'); ||         df.fillna('?') ||     ) || \"\"\"
aaihjfdfb,ray-project/ray,python/ray/dataframe/test/test_dataframe.py,2b747ba46cee4445c8ff1640a2a5d7c88e2d1723,STILL_EXISTS,equivalence of the labels\/axis and index\/columns API's (GH12392)
aaihjfdih,ray-project/ray,python/ray/dataframe/test/test_dataframe.py,2b747ba46cee4445c8ff1640a2a5d7c88e2d1723,STILL_EXISTS,df2 may have different index and columns
aaihjfdii,ray-project/ray,python/ray/dataframe/test/test_dataframe.py,2b747ba46cee4445c8ff1640a2a5d7c88e2d1723,STILL_EXISTS,only those columns and indices which are shared get filled
aaihjfdjb,ray-project/ray,python/ray/dataframe/test/test_dataframe.py,2b747ba46cee4445c8ff1640a2a5d7c88e2d1723,STILL_EXISTS,TODO: Uncomment when iloc is implemented
aaihjfeca,ray-project/ray,python/ray/dataframe/test/test_dataframe.py,6455ec934b102353837e7d0537c25dc89f0cd79b,STILL_EXISTS,TODO: Uncomment when __setitem__ is implemented
aaihjfecf,ray-project/ray,python/ray/dataframe/test/test_dataframe.py,6455ec934b102353837e7d0537c25dc89f0cd79b,STILL_EXISTS,partial columns
aaihjfecg,ray-project/ray,python/ray/dataframe/test/test_dataframe.py,6455ec934b102353837e7d0537c25dc89f0cd79b,STILL_EXISTS,TODO: Uncomment when transpose works
aaihjfeef,ray-project/ray,python/ray/dataframe/test/test_dataframe.py,6455ec934b102353837e7d0537c25dc89f0cd79b,STILL_EXISTS,df.columns = ['2001-01-01']
aaihjfeei,ray-project/ray,python/ray/dataframe/test/test_dataframe.py,6455ec934b102353837e7d0537c25dc89f0cd79b,STILL_EXISTS,ray_df.columns = ['2001-01-01']
aaihjfgde,ray-project/ray,python/ray/tune/examples/pbt_tune_cifar10_with_keras.py,6b1e592d5c34acb9716cd625a2adfaac9a0cb63c,STILL_EXISTS,\"\"\"Train keras CNN on the CIFAR10 small images dataset. ||  || The model comes from: https:\/\/zhuanlan.zhihu.com\/p\/29214791; || and it gets to about 87% validation accuracy in 100 epochs. ||  || Note that the scipt requires a machine with 4 GPUs. You || can set {'gpu': 0} to use CPUs for training; although || it is less efficient. || \"\"\"
aaihjfhbj,ray-project/ray,test/runtest.py,5bde5e75e7bcbff278d07f5277b6ae699875dc9a,1f29a960f4fd21b7b43706aa2f75f8188cad22b5,could encounter this kind of issue as well.
aaihjfhcd,ray-project/ray,python/ray/dataframe/dataframe.py,0d9a7a3c1910fd4cd3b085b39e6187654f51feca,STILL_EXISTS,TODO convert _partition_pandas_dataframe to block partitioning.
aaihjfhcg,ray-project/ray,python/ray/dataframe/dataframe.py,0d9a7a3c1910fd4cd3b085b39e6187654f51feca,STILL_EXISTS,partitions to get the columns
aaihjfhde,ray-project/ray,python/ray/dataframe/dataframe.py,0d9a7a3c1910fd4cd3b085b39e6187654f51feca,STILL_EXISTS,transpose. This seems better.
aaihjfhdh,ray-project/ray,python/ray/dataframe/dataframe.py,0d9a7a3c1910fd4cd3b085b39e6187654f51feca,STILL_EXISTS,TODO: We don't support `level` right now
aaihjfheb,ray-project/ray,python/ray/dataframe/dataframe.py,0d9a7a3c1910fd4cd3b085b39e6187654f51feca,STILL_EXISTS,This exception is thrown when there are only non-numeric columns
aaihjfhec,ray-project/ray,python/ray/dataframe/dataframe.py,0d9a7a3c1910fd4cd3b085b39e6187654f51feca,STILL_EXISTS,Begin fixing index based on the columns inside.
aaihjfhef,ray-project/ray,python/ray/dataframe/dataframe.py,0d9a7a3c1910fd4cd3b085b39e6187654f51feca,STILL_EXISTS,TODO implement level
aaihjfhfc,ray-project/ray,python/ray/dataframe/dataframe.py,0d9a7a3c1910fd4cd3b085b39e6187654f51feca,STILL_EXISTS,TODO(kunalgosar): Implement Copartition and use to implement equals
aaihjfhgf,ray-project/ray,python/ray/dataframe/dataframe.py,0d9a7a3c1910fd4cd3b085b39e6187654f51feca,STILL_EXISTS,TODO Revisit for performance
aaihjfhic,ray-project/ray,python/ray/dataframe/dataframe.py,0d9a7a3c1910fd4cd3b085b39e6187654f51feca,STILL_EXISTS,columns. Putting single occurrences in a pd.DataFrame and transposing
aaihjfhie,ray-project/ray,python/ray/dataframe/dataframe.py,0d9a7a3c1910fd4cd3b085b39e6187654f51feca,STILL_EXISTS,Cast cols as pd.Series as duplicate columns mean result may be
aaihjfhih,ray-project/ray,python/ray/dataframe/dataframe.py,0d9a7a3c1910fd4cd3b085b39e6187654f51feca,STILL_EXISTS,Since we are replacing columns with RangeIndex inside the
aaihjfhjh,ray-project/ray,python/ray/dataframe/test/test_dataframe.py,0d9a7a3c1910fd4cd3b085b39e6187654f51feca,STILL_EXISTS,TODO Fix pandas so that the behavior is correct
aaihjfiaj,ray-project/ray,python/ray/dataframe/utils.py,0d9a7a3c1910fd4cd3b085b39e6187654f51feca,558942648447743616b3bf669438893b8aaebaaf,Columns and width
aaihjfibc,ray-project/ray,python/ray/dataframe/utils.py,0d9a7a3c1910fd4cd3b085b39e6187654f51feca,STILL_EXISTS,columns into rows. Fortunately numpy is efficent at this.
aaihjfibg,ray-project/ray,python/ray/dataframe/utils.py,0d9a7a3c1910fd4cd3b085b39e6187654f51feca,STILL_EXISTS,if not isinstance(df.columns; pd.RangeIndex):
aaihjfibh,ray-project/ray,python/ray/dataframe/utils.py,0d9a7a3c1910fd4cd3b085b39e6187654f51feca,STILL_EXISTS,df.columns = pd.RangeIndex(0; len(df.columns))
aaihjfibj,ray-project/ray,python/ray/dataframe/utils.py,0d9a7a3c1910fd4cd3b085b39e6187654f51feca,STILL_EXISTS,columns); this change is needed to ensure correctness.
aaihjfidc,ray-project/ray,python/ray/dataframe/concat.py,efeaacbedc6f6958070d0a485370eb75698c894a,1d1df7bbec1ddd13b561fd633877469430d3de65,(TODO) Group all the pandas dataframes
aaihjfidj,ray-project/ray,python/ray/dataframe/dataframe.py,521b549e4add3dbe8a15c712558958358d938786,STILL_EXISTS,TODO: This doesn't work if the expression is not an assignment
aaihjfiea,ray-project/ray,python/ray/dataframe/dataframe.py,521b549e4add3dbe8a15c712558958358d938786,STILL_EXISTS,may result in multiple columns?
aaihjfied,ray-project/ray,python/ray/dataframe/index_metadata.py,521b549e4add3dbe8a15c712558958358d938786,STILL_EXISTS,TODO: Find out what this does; and write a docstring
aaihjfieg,ray-project/ray,python/ray/dataframe/index_metadata.py,521b549e4add3dbe8a15c712558958358d938786,STILL_EXISTS,Since we are replacing columns with RangeIndex inside the
aaihjfifd,ray-project/ray,python/ray/dataframe/index_metadata.py,521b549e4add3dbe8a15c712558958358d938786,STILL_EXISTS,TODO: Stop-gap solution until we begin passing IndexMetadatas
aaihjfifg,ray-project/ray,python/ray/dataframe/index_metadata.py,521b549e4add3dbe8a15c712558958358d938786,STILL_EXISTS,TODO: Determine why self._coord_df{;_cache} are read-only
aaihjfifh,ray-project/ray,python/ray/dataframe/index_metadata.py,521b549e4add3dbe8a15c712558958358d938786,STILL_EXISTS,TODO: Determine if there's a better way to do a row-index insert in
aaihjfigf,ray-project/ray,python/ray/dataframe/index_metadata.py,521b549e4add3dbe8a15c712558958358d938786,STILL_EXISTS,TODO: Investigate \"modify view\/copy\" warning
aaihjfigj,ray-project/ray,python/ray/dataframe/dataframe.py,a3ddde398c75d5c49e70bf22c9bbb16e276638ab,STILL_EXISTS,Get first and last 10 columns if there are more than 20 columns
aaihjfihh,ray-project/ray,python/ray/dataframe/dataframe.py,a3ddde398c75d5c49e70bf22c9bbb16e276638ab,STILL_EXISTS,A column header is needed in the inf() output
aaihjfiia,ray-project/ray,python/ray/dataframe/dataframe.py,a3ddde398c75d5c49e70bf22c9bbb16e276638ab,STILL_EXISTS,replace the partition columns names with real column names
aaihjfjca,ray-project/ray,python/ray/rllib/ddpg/models.py,15a668dd126664bb507294f4301c6b6d08e16d22,47bc4c3009370d9a51e867d671099011c44e95e6,TODO: change action_bound to make more general
aaihjgaaf,ray-project/ray,python/ray/rllib/ddpg2/ddpg.py,c9a7744e5269a035eafceba35c7df3f52607b825,47bc4c3009370d9a51e867d671099011c44e95e6,workaround for https:\/\/github.com\/ray-project\/ray\/issues\/1516
aaihjgaea,ray-project/ray,python/ray/dataframe/dataframe.py,8f59546ef2fba5af2666c27e5480f6819389fedf,STILL_EXISTS,TODO Make pandas error
aaihjgafa,ray-project/ray,python/ray/dataframe/dataframe.py,8f59546ef2fba5af2666c27e5480f6819389fedf,STILL_EXISTS,TODO: This is very inefficient and needs fix
aaihjgahb,ray-project/ray,python/ray/dataframe/concat.py,1d1df7bbec1ddd13b561fd633877469430d3de65,STILL_EXISTS,Using concat on the columns and index is fast because they're empty;
aaihjgahc,ray-project/ray,python/ray/dataframe/concat.py,1d1df7bbec1ddd13b561fd633877469430d3de65,STILL_EXISTS,and it forces the error checking. It also puts the columns in the
aaihjgahf,ray-project/ray,python/ray/dataframe/concat.py,1d1df7bbec1ddd13b561fd633877469430d3de65,STILL_EXISTS,TODO just partition the DataFrames instead of building a new Ray DF.
aaihjgahg,ray-project/ray,python/ray/dataframe/concat.py,1d1df7bbec1ddd13b561fd633877469430d3de65,STILL_EXISTS,Here we reuse all_columns\/index so we don't have to materialize objects
aaihjgajf,ray-project/ray,python/ray/dataframe/dataframe.py,1d1df7bbec1ddd13b561fd633877469430d3de65,STILL_EXISTS,Joining the empty DataFrames with either index or columns is
aaihjgajh,ray-project/ray,python/ray/dataframe/dataframe.py,1d1df7bbec1ddd13b561fd633877469430d3de65,STILL_EXISTS,would otherwise require a lot more logic.
aaihjgbff,ray-project/ray,python/ray/rllib/ddpg/ddpg.py,47bc4c3009370d9a51e867d671099011c44e95e6,71eb558eb083084534668afb108ba9d2e796936f,workaround for https:\/\/github.com\/ray-project\/ray\/issues\/1516
aaihjgdaj,ray-project/ray,python/ray/rllib/ddpg2/models.py,47bc4c3009370d9a51e867d671099011c44e95e6,STILL_EXISTS,TODO: change action_bound to make more general
aaihjgddd,ray-project/ray,python/ray/dataframe/concat.py,0c477fbbca90e4945201e16dc066ca4f27aa42d4,STILL_EXISTS,Transposing the columns is necessary because the remote task treats
aaihjgddi,ray-project/ray,python/ray/dataframe/dataframe.py,0c477fbbca90e4945201e16dc066ca4f27aa42d4,STILL_EXISTS,TODO join the Index Metadata objects together for performance.
aaihjgdfe,ray-project/ray,python/ray/dataframe/dataframe.py,1231aa0582298e0e380b634f836cb9360fd424b9,STILL_EXISTS,The Empty Dataframe should have either columns or index specified
aaihjgdgh,ray-project/ray,python/ray/dataframe/dataframe.py,7c1d569a49baf0c78b91f6c7e961857fb60dbe39,STILL_EXISTS,the end; and the columns can be built asynchronously. This takes the
aaihjgdgi,ray-project/ray,python/ray/dataframe/dataframe.py,7c1d569a49baf0c78b91f6c7e961857fb60dbe39,STILL_EXISTS,columns defining off the critical path and speeds up the overall
aaihjgdjg,ray-project/ray,python/ray/dataframe/dataframe.py,558942648447743616b3bf669438893b8aaebaaf,STILL_EXISTS,TODO: write explicit tests for \"short and wide\"
aaihjgeaj,ray-project/ray,python/ray/dataframe/index_metadata.py,4badc04bb2da77fdaba9d37c17b53adfe8157fa8,STILL_EXISTS,cache to accept ObjectIDs and ray.get them when needed.
aaihjgebg,ray-project/ray,python/ray/dataframe/reshape.py,d85ee0bc04b8d8bec2dc1cb11a9d2f1ec698aed8,STILL_EXISTS,TODO(kunalgosar): Handle the case of duplicate columns here
aaihjgeca,ray-project/ray,python/ray/dataframe/dataframe.py,a1d7bb31a47576bf74732a78db3e339d67daedda,STILL_EXISTS,TODO: some checking on functions that return Series or Dataframe
aaihjgeda,ray-project/ray,python/ray/dataframe/test/test_dataframe.py,9f28529e2cb00b3b73d1fe4085b756f630e91d63,STILL_EXISTS,TODO Reolve once Pandas-20962 is resolved.
aaihjgedg,ray-project/ray,python/ray/dataframe/dataframe.py,ad1afeb268fcfe5cf1e9ce1a030445f9b324412b,STILL_EXISTS,aren't sorting over. We need the order of the columns\/rows and
aaihjgedj,ray-project/ray,python/ray/dataframe/dataframe.py,18487452237879688f3150e325e5b73bcf8b0bd7,STILL_EXISTS,check if there are any object columns
aaihjgeef,ray-project/ray,python/ray/dataframe/dataframe.py,18487452237879688f3150e325e5b73bcf8b0bd7,STILL_EXISTS,select only correct dtype columns
aaihjgeeh,ray-project/ray,python/ray/dataframe/dataframe.py,12da0217174f9e1b1a3461714845cf772e7fc7d1,STILL_EXISTS,TODO this is very inneficient; also see __array__
aaihjgeja,ray-project/ray,python/ray/dataframe/__init__.py,d2c193ed2cc6873847367c0d974b45da3adcfe66,4185aaed10ce4e416bdfe519b64e24c5eae21163,TODO: In the future `set_option` or similar needs to run on every node
aaihjgfaa,ray-project/ray,python/ray/dataframe/dataframe.py,72a3a6cb02565e408d87161a9f3add6a98c6adcd,STILL_EXISTS,We have to reset the index and columns here because we are coming
aaihjggaj,ray-project/ray,python/ray/dataframe/io.py,ae17ebd03289a35ae42c0eb893c6e301920f46f5,STILL_EXISTS,TODO: handle case where header is a list of lines
aaihjghaa,ray-project/ray,python/ray/monitor.py,6172f94c044fcd351819eb4779763140984a0e27,658c14282c384aa49a52b7d7bb4ac71c65cf8b86,implemented differently. TODO(rkn): Fix this.
aaihjghbd,ray-project/ray,python/ray/rllib/models/catalog.py,fd234e317108b5d5649a707dd6b3d6da0b4222f8,47d36d7bd6f011f144984f08e410e8367d607bbf,TODO(alok): fix to handle Discrete(n) state spaces
aaihjghda,ray-project/ray,python/ray/autoscaler/gcp/config.py,74dc14d1fc4da4773d317f8570402cbe69d84f51,STILL_EXISTS,TODO: make sure that we have usable subnet. Maybe call
aaihjghdi,ray-project/ray,python/ray/autoscaler/gcp/node_provider.py,74dc14d1fc4da4773d317f8570402cbe69d84f51,85294fb50348a66e1c8c3c3b727bac34ec282f0d,TODO: Is there a better and more reliable way to do this?
aaihjghfb,ray-project/ray,python/ray/dataframe/dataframe.py,b56c8ed8dce19c587da535e31e918cb3c3238978,STILL_EXISTS,Since we know that the index and columns match; we can just check the
aaihjghia,ray-project/ray,python/ray/monitor.py,100d8c207f3b899314e3c7e051e4967273ace9be,STILL_EXISTS,Similar functionality as local scheduler info channel
aaihjgidd,ray-project/ray,python/ray/rllib/dqn/dqn_policy_graph.py,71eb558eb083084534668afb108ba9d2e796936f,STILL_EXISTS,truncate ends of the trajectory
aaihjgife,ray-project/ray,python/ray/rllib/ppo/ppo_evaluator.py,71eb558eb083084534668afb108ba9d2e796936f,STILL_EXISTS,note: not needed since we don't truncate rollouts
aaihjgiga,ray-project/ray,python/ray/rllib/utils/tf_policy_graph.py,71eb558eb083084534668afb108ba9d2e796936f,b197c0c4044f66628c6672fe78581768a54d0e59,TODO(ekl) fix up handling of RNN inputs so that we can batch
aaihjhadd,ray-project/ray,python/ray/rllib/test/test_multi_agent_env.py,a9a26b756098dcbd108966c52e3e7e9c9bfaf7ee,STILL_EXISTS,TODO: support replay with non-DQN graphs. Currently this can't
aaihjhafg,ray-project/ray,python/ray/rllib/dqn/dqn_policy_graph.py,1251abf0d1878340e3628343e2ba4a09c95e3a91,STILL_EXISTS,truncate ends of the trajectory
aaihjhahi,ray-project/ray,python/ray/rllib/models/model.py,b197c0c4044f66628c6672fe78581768a54d0e59,0cecf6b79c1ba2d3761cfd3acb0bd71a83d983db,reshape needed for older tf versions
aaihjhbag,ray-project/ray,python/ray/rllib/tuned_examples/regression_tests/regression_test.py,3cc27d284010e9509fae73f69f5258f23bcc7d4a,STILL_EXISTS,We need to implement this in separate classes
aaihjhbeh,ray-project/ray,python/ray/rllib/agents/a3c/a3c.py,8aa56c12e60ba77325f6b3817ee5f0d8e1ed1a16,65dcafdc3f916579fbc495fef3de5bcec266cb1c,workaround for https:\/\/github.com\/ray-project\/ray\/issues\/1516
aaihjhbjj,ray-project/ray,python/ray/rllib/agents/ppo/ppo.py,8aa56c12e60ba77325f6b3817ee5f0d8e1ed1a16,65dcafdc3f916579fbc495fef3de5bcec266cb1c,workaround for https:\/\/github.com\/ray-project\/ray\/issues\/1516
aaihjhdgb,ray-project/ray,python/ray/profiling.py,8a3e180156c4d1fd2d611f464231f25ba7b3e08b,658c14282c384aa49a52b7d7bb4ac71c65cf8b86,TODO(rkn): This code currently takes around half a microsecond. Since
aaihjhdgd,ray-project/ray,python/ray/profiling.py,8a3e180156c4d1fd2d611f464231f25ba7b3e08b,658c14282c384aa49a52b7d7bb4ac71c65cf8b86,the logging code; perhaps in C.
aaihjhech,ray-project/ray,python/ray/worker.py,99d0d96aeff5a6c0756e63562b2d61606c75c536,STILL_EXISTS,TODO: clean up the SerializationContext once the job finished.
aaihjhedh,ray-project/ray,python/ray/rllib/examples/multiagent_two_trainers.py,68660453e4b0225a34b4ef0e051f3c04db51dc54,STILL_EXISTS,\"\"\"Example of using two different training methods at once in multi-agent. ||  || Here we create a number of CartPole agents; some of which are trained with || DQN; and some of which are trained with PPO. We periodically sync weights || between the two trainers (note that no such syncing is needed when using just || a single training method). ||  || For a simpler example; see also: multiagent_cartpole.py || \"\"\"
aaihjheej,ray-project/ray,python/ray/rllib/examples/multiagent_two_trainers.py,68660453e4b0225a34b4ef0e051f3c04db51dc54,STILL_EXISTS,improve the DQN policy
aaihjhefa,ray-project/ray,python/ray/rllib/examples/multiagent_two_trainers.py,68660453e4b0225a34b4ef0e051f3c04db51dc54,STILL_EXISTS,improve the PPO policy
aaihjheij,ray-project/ray,python/ray/rllib/agents/impala/impala.py,9ea57c2a9398ad52c5a91b2220ae61955ecf7754,65dcafdc3f916579fbc495fef3de5bcec266cb1c,workaround for https:\/\/github.com\/ray-project\/ray\/issues\/1516
aaihjhfcf,ray-project/ray,python/ray/rllib/agents/impala/vtrace_policy_graph.py,9ea57c2a9398ad52c5a91b2220ae61955ecf7754,STILL_EXISTS,boundaries. TODO(ekl) this is kind of a hack
aaihjhfdh,ray-project/ray,python/ray/rllib/optimizers/async_replay_optimizer.py,9ea57c2a9398ad52c5a91b2220ae61955ecf7754,STILL_EXISTS,Update weights if needed
aaihjhgfh,ray-project/ray,python/ray/rllib/agents/dqn/dqn.py,6670880f03bc6856342b6304cbf72fe4a8d78e4c,77689d1116c5d5eefc775740ae7a6a090d643186,Hack to workaround https:\/\/github.com\/ray-project\/ray\/issues\/2541
aaihjhgga,ray-project/ray,python/ray/services.py,06a58016d851bd54c4796591ab96fd5551d08021,STILL_EXISTS,Worker command for Java; not needed for Python.
aaihjhgja,ray-project/ray,python/ray/rllib/env/atari_wrappers.py,fbe6c59f72f9c467d02a46ab5aacd5b3aef16098,STILL_EXISTS,env = ScaledFloatFrame(env)  # TODO: use for dqn?
aaihjhgje,ray-project/ray,python/ray/tune/ray_trial_executor.py,38867eea4e011115515b77018c2310e7dd74ff55,72542c9016c19005807fad45fc0dccaf41052b33,TODO
aaihjhhfj,ray-project/ray,python/ray/rllib/agents/ars/ars.py,6201a6d1c7ce960b6c2c3e48c37d0ac4b9e6d384,STILL_EXISTS,workaround for https:\/\/github.com\/ray-project\/ray\/issues\/1516
aaihjhidb,ray-project/ray,python/ray/tune/automlboard/frontend/view.py,3813ae34b37e974295b617fa863838fb691d1226,STILL_EXISTS,TODO: support custom metrics here
aaihjhidf,ray-project/ray,python/ray/tune/automlboard/frontend/view.py,3813ae34b37e974295b617fa863838fb691d1226,STILL_EXISTS,TODO: sort_key should be customized here
aaihjhjfi,ray-project/ray,python/ray/tune/automl/genetic_searcher.py,f3c1194be33328d914f6f1f6cd88fee1b5c2d22b,STILL_EXISTS,TODO: implement an generic resource allocate algorithm.
aaihjhjgg,ray-project/ray,python/ray/tune/automl/search_space.py,f3c1194be33328d914f6f1f6cd88fee1b5c2d22b,STILL_EXISTS,TODO: logspace
aaihjiacb,ray-project/ray,python/ray/experimental/sgd/modified_allreduce.py,32676769949ae8f53bbdf36e4af693e79a0f2ba1,STILL_EXISTS,TODO(tucker): devise a way of better specifying the device
aaihjiafi,ray-project/ray,python/ray/experimental/sgd/tfbench/convnet_builder.py,32676769949ae8f53bbdf36e4af693e79a0f2ba1,STILL_EXISTS,Unused (for 'a trous' convolutions)
aaihjiafj,ray-project/ray,python/ray/experimental/sgd/tfbench/convnet_builder.py,32676769949ae8f53bbdf36e4af693e79a0f2ba1,STILL_EXISTS,HACK This may not always work
aaihjibbd,ray-project/ray,python/ray/experimental/sgd/tfbench/test_model.py,32676769949ae8f53bbdf36e4af693e79a0f2ba1,STILL_EXISTS,Minor hack to avoid H2D copy when using synthetic data
aaihjibca,ray-project/ray,python/ray/autoscaler/commands.py,1c9617bc1c076a2dd698a6f6ec150f9e3bafbe22,STILL_EXISTS,TODO: Consider providing named session functionality
aaihjibci,ray-project/ray,python/ray/rllib/agents/agent.py,65dcafdc3f916579fbc495fef3de5bcec266cb1c,37208216ae28687ff1c738a518642944592e5472,workaround for https:\/\/github.com\/ray-project\/ray\/issues\/1516
aaihjidhc,ray-project/ray,test/failure_test.py,faa31ae0185d9b67a2bb115ee28a22c0735ad987,STILL_EXISTS,infeasibility warning (even though the actor creation task itself
aaihjieee,ray-project/ray,python/ray/test/test_ray_init.py,a41bbc10ef5c3e0a8a3e8a36dbbe18171b556ada,f3efcd2342c42d541f328bb71ed711d643d10496,Workaround for https:\/\/github.com\/ray-project\/ray\/issues\/3045
aaihjiefc,ray-project/ray,python/ray/actor.py,2c52d9dfa043a168a00d0fe2083c73cef4b82228,08e4e3a1530e106d97f79a653ae51646a36cde19,created; there should be a better way to handle pickled
aaihjihgj,ray-project/ray,python/ray/experimental/sgd/tfbench/test_model.py,af0c1174cd35a65ec3ca3630b999577fd4614578,STILL_EXISTS,Implement model interface
aaihjjbfi,ray-project/ray,python/ray/rllib/examples/parametric_action_cartpole.py,f0df97db6f29d2e4e16135fde9156b7e756f6a63,STILL_EXISTS,\"\"\"Example of handling variable length and\/or parametric action spaces. ||  || This is a toy example of the action-embedding based approach for handling large || discrete action spaces (potentially infinite in size); similar to how || OpenAI Five works: ||  ||     https:\/\/neuro.cs.ut.ee\/the-use-of-embeddings-in-openai-five\/ ||  || This currently works with RLlib's policy gradient style algorithms || (e.g.; PG; PPO; IMPALA; A2C) and also DQN. ||  || Note that since the model outputs now include \"-inf\" tf.float32.min || values; not all algorithm options are supported at the moment. For example; || algorithms might crash if they don't properly ignore the -inf action scores. || Working configurations are given below. || \"\"\"
aaihjjcaj,ray-project/ray,python/ray/rllib/agents/dqn/dqn_policy_graph.py,d864f299d780405784722dcc0daf78a3f0d2f7b8,STILL_EXISTS,TODO: better Q stats for dist dqn
aaihjjdaa,ray-project/ray,python/ray/rllib/agents/agent.py,32473cf22e5a38714807051724541ca1f6110ddf,STILL_EXISTS,Specify how to evaluate the current policy. This only makes sense to set
aaihjjdbd,ray-project/ray,python/ray/rllib/agents/agent.py,32473cf22e5a38714807051724541ca1f6110ddf,37208216ae28687ff1c738a518642944592e5472,What sample batch columns to LZ4 compress in the output data.
aaihjjdbh,ray-project/ray,python/ray/rllib/agents/agent.py,32473cf22e5a38714807051724541ca1f6110ddf,STILL_EXISTS,TODO(ekl) implement this and multi-agent batch handling
aaihjjddd,ray-project/ray,python/ray/worker.py,0e00533ed4b9a572a2077cdf75794d5e451be44a,9dc671ae026db94b820ef177dc7c3b8bc3022ab3,Delay errors a little bit of time to attempt to suppress redundant
aaihjjddh,ray-project/ray,python/ray/experimental/sgd/model.py,5dcc33319919c9c4581b823a87452527b29c50a3,STILL_EXISTS,TODO support complex way of updating gradient;
aaihjjfgg,ray-project/ray,python/ray/rllib/optimizers/async_samples_optimizer.py,ffa6ee3ec8056aa1dbaef7b7569d802a5f0ec4b9,0d94f3eeefd1b31372458386052a72b540af63fd,ramp up slowly to better mix the input data
aaihjjfii,ray-project/ray,python/ray/actor.py,34bab6291c78ed064fe50917aa29ed26da4241df,08e4e3a1530e106d97f79a653ae51646a36cde19,created; there should be a better way to handle pickled
aaihjjfjd,ray-project/ray,python/ray/actor.py,34bab6291c78ed064fe50917aa29ed26da4241df,08e4e3a1530e106d97f79a653ae51646a36cde19,same actor is likely a performance bug. We should consider
aaihjjjhi,ray-project/ray,python/ray/rllib/agents/ppo/appo_policy_graph.py,16f7ca45e40364e76e5887a751baba38b64c9c3c,STILL_EXISTS,boundaries. TODO(ekl) this is kind of a hack
aaiiaabca,ray-project/ray,python/ray/services.py,9295ab8f6073302dd3219aff5bd80b6f19268c5c,fe4c6ab7784fa36b8a46660bcd2b42ac20516bde,fixed later. I had tried to fix it but failed because of heartbeat
aaiiaabdj,ray-project/ray,python/ray/autoscaler/gcp/config.py,976f018dab4bed46b7ca5d075b9dc44fbd297e33,STILL_EXISTS,roles; so only call setIamPolicy if needed.
aaiiaaefb,ray-project/ray,python/ray/rllib/evaluation/policy_evaluator.py,2dccf383dd0fbd50d01808cc8dcae06757d46eec,7501ee51db88e6aa642b068b329b813c0712f3d9,Do off-policy estimation if needed
aaiiaaehe,ray-project/ray,python/ray/worker.py,de17443dc26bc5ba8463b8df0741c5b2c741220e,07c4c6367af43380b4019202488f6370a4773df2,Note; the lock is needed because `serialization_context` isn't
aaiiaagah,ray-project/ray,python/ray/experimental/streaming/batched_queue.py,89ce4c56aadf1676f3a3dcbb3056a6326cb6ef49,STILL_EXISTS,TODO: doing the timer in Python land is a bit slow
aaiiaagdi,ray-project/ray,python/ray/experimental/streaming/communication.py,89ce4c56aadf1676f3a3dcbb3056a6326cb6ef49,STILL_EXISTS,TODO (john): In case of input skew; it might be better to pull from
aaiiaagea,ray-project/ray,python/ray/experimental/streaming/communication.py,89ce4c56aadf1676f3a3dcbb3056a6326cb6ef49,STILL_EXISTS,TODO (john): We should detect this earlier
aaiiaagfh,ray-project/ray,python/ray/experimental/streaming/communication.py,89ce4c56aadf1676f3a3dcbb3056a6326cb6ef49,STILL_EXISTS,TODO (john): Add support for other strategies
aaiiaahba,ray-project/ray,python/ray/experimental/streaming/examples/wordcount.py,89ce4c56aadf1676f3a3dcbb3056a6326cb6ef49,STILL_EXISTS,Custom data sources need to implement a get_next() method
aaiiaahbd,ray-project/ray,python/ray/experimental/streaming/examples/wordcount.py,89ce4c56aadf1676f3a3dcbb3056a6326cb6ef49,STILL_EXISTS,TODO (john): Handle possible exception here
aaiiaahgc,ray-project/ray,python/ray/experimental/streaming/operator_instance.py,89ce4c56aadf1676f3a3dcbb3056a6326cb6ef49,STILL_EXISTS,TODO (john): Specify the interface of state keepers
aaiiaahgi,ray-project/ray,python/ray/experimental/streaming/operator_instance.py,89ce4c56aadf1676f3a3dcbb3056a6326cb6ef49,STILL_EXISTS,TODO (john): Add more channel types here
aaiiaahhe,ray-project/ray,python/ray/experimental/streaming/operator_instance.py,89ce4c56aadf1676f3a3dcbb3056a6326cb6ef49,STILL_EXISTS,TODO (john): Handle possible exception here
aaiiaaiaa,ray-project/ray,python/ray/experimental/streaming/operator_instance.py,89ce4c56aadf1676f3a3dcbb3056a6326cb6ef49,STILL_EXISTS,TODO (john): Is there a way to update state with
aaiiaaicd,ray-project/ray,python/ray/experimental/streaming/streaming.py,89ce4c56aadf1676f3a3dcbb3056a6326cb6ef49,STILL_EXISTS,TODO (john): Actor placement information should be specified in
aaiiaaida,ray-project/ray,python/ray/experimental/streaming/streaming.py,89ce4c56aadf1676f3a3dcbb3056a6326cb6ef49,STILL_EXISTS,TODO (john): Add support for other types of operators
aaiiaaidj,ray-project/ray,python/ray/experimental/streaming/streaming.py,89ce4c56aadf1676f3a3dcbb3056a6326cb6ef49,STILL_EXISTS,TODO (john): Add support for other partitioning strategies
aaiiaaiej,ray-project/ray,python/ray/experimental/streaming/streaming.py,89ce4c56aadf1676f3a3dcbb3056a6326cb6ef49,STILL_EXISTS,TODO (john): There should be different types of sources; e.g. sources
aaiiaaife,ray-project/ray,python/ray/experimental/streaming/streaming.py,89ce4c56aadf1676f3a3dcbb3056a6326cb6ef49,STILL_EXISTS,TODO (john): There should be different types of sources;
aaiiaaifj,ray-project/ray,python/ray/experimental/streaming/streaming.py,89ce4c56aadf1676f3a3dcbb3056a6326cb6ef49,STILL_EXISTS,TODO (john): Check if dataflow has any 'logical inconsistencies'
aaiiaaijg,ray-project/ray,python/ray/experimental/streaming/streaming.py,89ce4c56aadf1676f3a3dcbb3056a6326cb6ef49,STILL_EXISTS,TODO (john): Currently; only forward (default); shuffle;
aaiiaajae,ray-project/ray,python/ray/experimental/streaming/streaming.py,89ce4c56aadf1676f3a3dcbb3056a6326cb6ef49,STILL_EXISTS,TODO (john): Expand set of supported operators.
aaiiaajaf,ray-project/ray,python/ray/experimental/streaming/streaming.py,89ce4c56aadf1676f3a3dcbb3056a6326cb6ef49,STILL_EXISTS,TODO (john): To support event-time windows we need a mechanism for
aaiiaajba,ray-project/ray,python/ray/experimental/streaming/streaming.py,89ce4c56aadf1676f3a3dcbb3056a6326cb6ef49,STILL_EXISTS,TODO (john): This should returned a KeyedDataStream
aaiiaajbf,ray-project/ray,python/ray/experimental/streaming/streaming.py,89ce4c56aadf1676f3a3dcbb3056a6326cb6ef49,STILL_EXISTS,TODO (john): This should return a WindowedDataStream
aaiiaajbh,ray-project/ray,python/ray/experimental/streaming/streaming.py,89ce4c56aadf1676f3a3dcbb3056a6326cb6ef49,STILL_EXISTS,TODO (john): Registers window join operator to the environment
aaiiaajca,ray-project/ray,python/ray/experimental/streaming/streaming.py,89ce4c56aadf1676f3a3dcbb3056a6326cb6ef49,STILL_EXISTS,TODO (john): A sink now just drops records but it should be able to
aaiiaajdg,ray-project/ray,python/ray/tests/test_logical_graph.py,89ce4c56aadf1676f3a3dcbb3056a6326cb6ef49,STILL_EXISTS,TODO (john): Currently each source has only one instance
aaiiaajfj,ray-project/ray,python/ray/tests/test_logical_graph.py,89ce4c56aadf1676f3a3dcbb3056a6326cb6ef49,STILL_EXISTS,TODO (john): Add simple wordcount test
aaiiaajjj,ray-project/ray,python/ray/tests/test_object_manager.py,6f1a29ad3f9b0fd6562a68519f0fcde9d4560e2d,d9027acaf25556dd9db0212f3bd2eb2dcff1aa16,better be put in Jenkins. However; it fails frequently in Jenkins; but
aaiiabaaa,ray-project/ray,python/ray/tests/test_object_manager.py,6f1a29ad3f9b0fd6562a68519f0fcde9d4560e2d,d9027acaf25556dd9db0212f3bd2eb2dcff1aa16,works well in Travis. We should consider moving it back to Jenkins once
aaiiabbih,ray-project/ray,python/ray/tune/commands.py,6630a35353c6bb722633180e728dd7ff3cd9d4d0,STILL_EXISTS,Drop all columns beyond terminal width
aaiiabbjd,ray-project/ray,python/ray/tune/tests/test_ray_trial_executor.py,6630a35353c6bb722633180e728dd7ff3cd9d4d0,STILL_EXISTS,Needed for flaky tests
aaiiabbjg,ray-project/ray,ci/long_running_tests/workloads/many_actor_tasks.py,f423909aecb49d4236587e872e62d3e2c3d4fd8e,11580fb7dc449c94db81feb08c47bdb5a0b84171,TODO (williamma12): Remove the num_cpus argument once
aaiiabbji,ray-project/ray,ci/long_running_tests/workloads/many_actor_tasks.py,f423909aecb49d4236587e872e62d3e2c3d4fd8e,11580fb7dc449c94db81feb08c47bdb5a0b84171,TODO (williamma12): Update the actors to each have only 0.1 of a cpu once
aaiiabdbj,ray-project/ray,python/ray/rllib/agents/agent.py,a45019d98c7067806295bea22c22a66f062fa50e,37208216ae28687ff1c738a518642944592e5472,Max number of times to retry a worker failure. We shouldn't try too many
aaiiabdid,ray-project/ray,python/ray/tune/tests/test_trial_runner.py,7c3274e65b75d4533b744639e8fb62fb05bf7f75,53641f1f74334d6ab259cdaf4c058fd6eb705e25,TODO: remove once the multi-threaded function runner is gone.
aaiiabfhj,ray-project/ray,python/ray/rllib/evaluation/metrics.py,09b29617507ee8ed3eeebb44bf4e77f23a7989c1,02583a8598cd3e0442188ad0978f154ccdcec742,By convention; metrics from optimizing the loss can be reported in the
aaiiabgci,ray-project/ray,python/ray/rllib/optimizers/aso_minibatch_buffer.py,0d94f3eeefd1b31372458386052a72b540af63fd,1337c98f025356580f7abc06e03fe4baf82217d8,ramp up slowly to better mix the input data
aaiiabgha,ray-project/ray,python/ray/tune/suggest/bayesopt.py,51dae23d5c8e81449bf381427fcb246c821a0196,STILL_EXISTS,Python 3 only -- needed for lint test.
aaiiabici,ray-project/ray,python/ray/rllib/agents/trainer.py,37208216ae28687ff1c738a518642944592e5472,STILL_EXISTS,Max number of times to retry a worker failure. We shouldn't try too many
aaiiabjda,ray-project/ray,python/ray/rllib/agents/trainer.py,37208216ae28687ff1c738a518642944592e5472,STILL_EXISTS,What sample batch columns to LZ4 compress in the output data.
aaiiabjfa,ray-project/ray,python/ray/rllib/agents/trainer.py,37208216ae28687ff1c738a518642944592e5472,7501ee51db88e6aa642b068b329b813c0712f3d9,workaround for https:\/\/github.com\/ray-project\/ray\/issues\/1516
aaiiabjha,ray-project/ray,python/ray/rllib/examples/multiagent_custom_policy.py,4f46d3e9bfc13ab9c60259b281f895f6e197f0e3,STILL_EXISTS,\"\"\"Example of running a custom hand-coded policy alongside trainable policies. ||  || This example has two policies: ||     (1) a simple PG policy ||     (2) a hand-coded policy that acts at random in the env (doesn't learn) ||  || In the console output; you can see the PG policy does much better than random: || Result for PG_multi_cartpole_0: ||   ... ||   policy_reward_mean: ||     pg_policy: 185.23 ||     random: 21.255 ||   ... || \"\"\"
aaiiabjhc,ray-project/ray,python/ray/rllib/examples/policy_evaluator_custom_workflow.py,4f46d3e9bfc13ab9c60259b281f895f6e197f0e3,STILL_EXISTS,\"\"\"Example of using policy evaluator classes directly to implement training. ||  || Instead of using the built-in Trainer classes provided by RLlib; here we define || a custom PolicyGraph class and manually coordinate distributed sample || collection and policy optimization. || \"\"\"
aaiiabjic,ray-project/ray,python/ray/rllib/examples/policy_evaluator_custom_workflow.py,4f46d3e9bfc13ab9c60259b281f895f6e197f0e3,STILL_EXISTS,Improve the policy using the T1 batch
aaiiacaff,ray-project/ray,python/ray/rllib/agents/trainer.py,06c768823c781244bf0be183cc7876641df0c290,STILL_EXISTS,TODO(kismuz): implement determ. actions and include relevant keys hints
aaiiacagf,ray-project/ray,python/ray/tests/test_basic.py,c99e3caaca3b46d4f02ae5830927c5662d872ae7,53641f1f74334d6ab259cdaf4c058fd6eb705e25,TODO: 5 retry attempts may be too little for Travis and we may need to
aaiiacbac,ray-project/ray,python/ray/rllib/agents/ddpg/ddpg.py,663e92ab3f010491ef7420480eb6be7f57a0485c,STILL_EXISTS,FIXME: what do magic constants mean? (0.4; 7)
aaiiacbbd,ray-project/ray,python/ray/rllib/agents/ddpg/ddpg_policy_graph.py,663e92ab3f010491ef7420480eb6be7f57a0485c,STILL_EXISTS,FIXME: what about turning off exploration? Isn't that a good
aaiiacdbe,ray-project/ray,python/ray/tune/track/__init__.py,1ef9c0729d104dd101b71323a6aec6c0ad502e03,67c01455fe313a65cc65640e52892ed91e08ea8b,TODO(ng): would be nice to stack crawl at creation time to report
aaiiacdca,ray-project/ray,python/ray/tune/track/session.py,1ef9c0729d104dd101b71323a6aec6c0ad502e03,67c01455fe313a65cc65640e52892ed91e08ea8b,TODO: Implement a batching mechanism for multiple calls to `log`
aaiiacdcc,ray-project/ray,python/ray/tune/track/session.py,1ef9c0729d104dd101b71323a6aec6c0ad502e03,67c01455fe313a65cc65640e52892ed91e08ea8b,TODO: Move Trainable autopopulation to a util function
aaiiacdfc,ray-project/ray,python/ray/rllib/evaluation/dynamic_tf_policy_graph.py,6cb5b90bd6b2ec5fbdc5926acd3e0efed3ed1e03,STILL_EXISTS,By convention; the loss inputs are followed by state inputs and then
aaiiacdje,ray-project/ray,python/ray/rllib/policy/policy.py,02583a8598cd3e0442188ad0978f154ccdcec742,STILL_EXISTS,By convention; metrics from optimizing the loss can be reported in the
aaiiacefa,ray-project/ray,python/ray/experimental/sgd/pytorch/pytorch_runner.py,c2ade075a3fc912a099b68851879f293cdbb63d8,e0e52f1871b529ba6a8d86f1547dc636c4c0f703,TODO: make num_workers configurable
aaiiacefb,ray-project/ray,python/ray/experimental/sgd/pytorch/pytorch_runner.py,c2ade075a3fc912a099b68851879f293cdbb63d8,STILL_EXISTS,TODO: restore timer stats
aaiiacefc,ray-project/ray,python/ray/experimental/sgd/pytorch/pytorch_trainer.py,c2ade075a3fc912a099b68851879f293cdbb63d8,94e2fcea2eabb7d9d47bc31ff7c161c337853dde,TODO: add support for mixed precision
aaiiacefd,ray-project/ray,python/ray/experimental/sgd/pytorch/pytorch_trainer.py,c2ade075a3fc912a099b68851879f293cdbb63d8,037aa2b961e82feaf563679e6074ed50ff9c60ff,TODO: add support for callbacks
aaiiaceid,ray-project/ray,python/ray/rllib/policy/tf_policy_template.py,665d081fe914eca4806413253aa02c679368785d,STILL_EXISTS,auto-add empty learner stats dict if needed
aaiiacfbi,ray-project/ray,python/ray/rllib/evaluation/rollout_worker.py,7501ee51db88e6aa642b068b329b813c0712f3d9,STILL_EXISTS,Do off-policy estimation if needed
aaiiacfde,ray-project/ray,python/ray/rllib/policy/dynamic_tf_policy.py,9e328fbe6f94a069b31cd98511244694efc96f92,f9043cc49abbfa6a3caf4a5b50d87c7a7b19c572,XXX experimental support for automatically eagerifying the loss.
aaiiacffb,ray-project/ray,python/ray/rllib/agents/dqn/apex.py,77689d1116c5d5eefc775740ae7a6a090d643186,STILL_EXISTS,Hack to workaround https:\/\/github.com\/ray-project\/ray\/issues\/2541
aaiiacfgg,ray-project/ray,python/ray/rllib/examples/saving_experiences.py,4f8e100fe0417da4fe1098defbfa478088502244,STILL_EXISTS,RLlib uses preprocessors to implement transforms such as one-hot encoding
aaiiacfhc,ray-project/ray,python/ray/experimental/sgd/pytorch/distributed_pytorch_runner.py,e0e52f1871b529ba6a8d86f1547dc636c4c0f703,8f6d73a93ab7ef9a38ca169404a4d3cccfa22d4f,TODO: make num_workers configurable
aaiiacfhd,ray-project/ray,python/ray/experimental/sgd/pytorch/distributed_pytorch_runner.py,e0e52f1871b529ba6a8d86f1547dc636c4c0f703,STILL_EXISTS,TODO: restore timer stats
aaiiacfjj,ray-project/ray,python/ray/tune/logger.py,9e0192bc0b4b041a410fff78de62cebdb42918f5,2965dc1b724010efcfb9a2709c01c650293f778a,TODO: change this because this is blocking. But failures
aaiiacgaa,ray-project/ray,python/ray/tune/logger.py,9e0192bc0b4b041a410fff78de62cebdb42918f5,2965dc1b724010efcfb9a2709c01c650293f778a,are rare; so maybe this is OK?
aaiiacgfd,ray-project/ray,python/ray/rllib/examples/custom_keras_model.py,34d054ff199727bd875300cefc962d1aafa86e08,STILL_EXISTS,Implement the core forward method
aaiiacghb,ray-project/ray,python/ray/rllib/models/tf/modelv1_compat.py,34d054ff199727bd875300cefc962d1aafa86e08,STILL_EXISTS,XXX: Try to guess the initial state size. Since the size of the
aaiiachee,ray-project/ray,python/ray/tune/ray_trial_executor.py,1530389822008216531075b6e71344ae63290aec,2965dc1b724010efcfb9a2709c01c650293f778a,TODO: Somehow; the call to get the current IP on the
aaiiachef,ray-project/ray,python/ray/tune/ray_trial_executor.py,1530389822008216531075b6e71344ae63290aec,2965dc1b724010efcfb9a2709c01c650293f778a,remote actor can be very slow - a better fix would
aaiiachfb,ray-project/ray,python/ray/tune/trial_runner.py,1530389822008216531075b6e71344ae63290aec,STILL_EXISTS,ideal since it just hides the issue - a better fix would
aaiiachgg,ray-project/ray,python/ray/tests/test_multi_node_2.py,3e0ad11ae0a71621978f26f5ace59dfcaf0eb0a8,f9f372c32707db3f2cba1f7c28c39aa9008a3199,TODO: Remove?
aaiiacjeb,ray-project/ray,python/ray/rllib/examples/rock_paper_scissors_multiagent.py,20450a4e82a9b9a83fc3b54018a540a13668a846,STILL_EXISTS,\"\"\"A simple multi-agent env with two agents playing rock paper scissors. ||  || This demonstrates running the following policies in competition: ||     (1) heuristic policy of repeating the same move ||     (2) heuristic policy of beating the last opponent move ||     (3) LSTM\/feedforward PG policies ||     (4) LSTM policy with custom safety loss || \"\"\"
aaiiacjii,ray-project/ray,python/ray/rllib/agents/sac/sac.py,13fb9fe3db81eacfeb03f2fe647e343bd0929d38,STILL_EXISTS,TODO(ekl) these are unused; remove them from sac config
aaiiadbic,ray-project/ray,rllib/examples/centralized_critic.py,592f3132106820f4613ab4fbd6ce5c221eaa78b6,bc6a95deb0bab8f4e0fad394f9feb53542a6eb15,hack: put in a noop VF so some of the inherited PPO code runs
aaiiadcbi,ray-project/ray,rllib/examples/autoregressive_action_dist.py,a1d2e1762325cd34e14dc411666d63bb15d6eaf0,658e002cdfc32527cb112b5fe00fea0664c27a10,TODO: implement this properly
aaiiadccf,ray-project/ray,rllib/examples/autoregressive_action_dist.py,a1d2e1762325cd34e14dc411666d63bb15d6eaf0,57544b1ff9f97d4da9f64d25c8ea5a3d8d247ffc,--note: typically you'd want to implement P(a2 | a1; obs) as follows:
aaiiadcib,ray-project/ray,python/ray/projects/scripts.py,341c6926e77b5a4dfec492dc49ef570c05e66b1e,STILL_EXISTS,HACK: Skip git clone if exists so the this command can be idempotent
aaiiaddig,ray-project/ray,rllib/models/catalog.py,b520f6141ecdd54496b0c26106f3df4442a5f91e,STILL_EXISTS,TODO: default LSTM v2 not implemented
aaiiaddjc,ray-project/ray,rllib/models/tf/visionnet_v2.py,b520f6141ecdd54496b0c26106f3df4442a5f91e,796a834c4886b5a97ec4be0477435fced22d7b7c,explicit cast to float32 needed in eager
aaiiaddjd,ray-project/ray,rllib/policy/dynamic_tf_policy.py,b520f6141ecdd54496b0c26106f3df4442a5f91e,STILL_EXISTS,model forward pass for the loss (needed after postprocess to
aaiiaddjg,ray-project/ray,rllib/policy/eager_tf_policy.py,b520f6141ecdd54496b0c26106f3df4442a5f91e,STILL_EXISTS,TODO: set seq len and state in properly
aaiiadeab,ray-project/ray,rllib/policy/eager_tf_policy.py,b520f6141ecdd54496b0c26106f3df4442a5f91e,STILL_EXISTS,model forward pass for the loss (needed after postprocess to
aaiiadfah,ray-project/ray,python/ray/experimental/sgd/examples/cifar_tf_example.py,1711e202a3b11691d39168534ad62026f1021fd8,STILL_EXISTS,Repeat is needed to avoid
aaiiadfjd,ray-project/ray,rllib/agents/ddpg/ddpg_policy.py,19bbf1eb4da1c486e2714a9a113ab1322f05e52f,STILL_EXISTS,FIXME: what about turning off exploration? Isn't that a good
aaiiadgci,ray-project/ray,python/ray/cloudpickle/cloudpickle.py,0010f543786d934879271a8d19789d9bb3f813cb,f6883bf725caca57bcebcc94aeb93f6a94542984,handling dotted names is not needed; so we simply define _getattribute as
aaiiadhbc,ray-project/ray,rllib/tests/test_lstm.py,0010f543786d934879271a8d19789d9bb3f813cb,796a834c4886b5a97ec4be0477435fced22d7b7c,This behavior is no longer the case; so we manually refresh
aaiiadhch,ray-project/ray,python/ray/__init__.py,07c4c6367af43380b4019202488f6370a4773df2,2116fd3bcaf9552b2273cbc10e90cc6d66e904a8,TODO(edoakes): Fix this.
aaiiadife,ray-project/ray,python/ray/worker.py,4c964c094173f994c9604222b1ac7e8c5c657939,2fb7d7846f18e7005560cce493e4dd13642a309a,Should implement zero-copy for PickleBuffer.
aaiiadjbj,ray-project/ray,python/ray/experimental/serve/utils.py,5f88823c499fdf19ebb9c0985fc1ec2248dceae9,STILL_EXISTS,TODO(simon): Make logging level configurable.
aaiiadjfi,ray-project/ray,rllib/policy/eager_tf_policy.py,8903bcd0c325f76f2642eb542140bdde5a94f7ac,STILL_EXISTS,TODO: remove python side effect to cull sources of bugs.
aaiiaeadg,ray-project/ray,doc/examples/plot_parameter_server.py,10f21fa313d8ee8cd636b1e945b1a02893d303f8,STILL_EXISTS,\"\"\" || Parameter Server || ================ ||  || The parameter server is a framework for distributed machine learning training. ||  || In the parameter server framework; a centralized server (or group of server || nodes) maintains global shared parameters of a machine-learning model || (e.g.; a neural network) while the data and computation of calculating || updates (i.e.; gradient descent updates) are distributed over worker nodes. ||  || .. image:: ..\/images\/param_actor.png ||     :align: center ||  || Parameter servers are a core part of many machine learning applications. This || document walks through how to implement simple synchronous and asynchronous || parameter servers using Ray actors. ||  || To run the application; first install some dependencies. ||  || .. code-block:: bash ||  ||   pip install torch torchvision filelock ||  || Let's first define some helper functions and import some dependencies. ||  || \"\"\"
aaiiaeagj,ray-project/ray,doc/examples/plot_parameter_server.py,10f21fa313d8ee8cd636b1e945b1a02893d303f8,STILL_EXISTS,When the epoch ends; start a new epoch.
aaiiaebaf,ray-project/ray,doc/examples/plot_parameter_server.py,10f21fa313d8ee8cd636b1e945b1a02893d303f8,STILL_EXISTS,This approach is powerful because it enables you to implement a parameter
aaiiaebbe,ray-project/ray,doc/examples/plot_pong_example.py,10f21fa313d8ee8cd636b1e945b1a02893d303f8,STILL_EXISTS,\"\"\" || Learning to Play Pong || ===================== ||  || In this example; we'll train a **very simple** neural network to play Pong using || the OpenAI Gym. ||  || At a high level; we will use multiple Ray actors to obtain simulation rollouts || and calculate gradient simultaneously. We will then centralize these || gradients and update the neural network. The updated neural network will || then be passed back to each Ray actor for more gradient calculation. ||  || This application is adapted; with minimal modifications; from || Andrej Karpathy's `source code`_ (see the accompanying `blog post`_). ||  || To run the application; first install some dependencies. ||  || .. code-block:: bash ||  ||   pip install gym[atari] ||  || At the moment; on a large machine with 64 physical cores; computing an update || with a batch of size 1 takes about 1 second; a batch of size 10 takes about 2.5 || seconds. A batch of size 60 takes about 3 seconds. On a cluster with 11 nodes; || each with 18 physical cores; a batch of size 300 takes about 10 seconds. If the || numbers you see differ from these by much; take a look at the || **Troubleshooting** section at the bottom of this page and consider `submitting || an issue`_. ||  || .. _`source code`: https:\/\/gist.github.com\/karpathy\/a4166c7fe253700972fcbc77e4ea32c5 || .. _`blog post`: http:\/\/karpathy.github.io\/2016\/05\/31\/rl\/ || .. _`submitting an issue`: https:\/\/github.com\/ray-project\/ray\/issues ||  || **Note** that these times depend on how long the rollouts take; which in turn || depends on how well the policy is doing. For example; a really bad policy will || lose very quickly. As the policy learns; we should expect these numbers to || increase. || \"\"\"
aaiiaebgg,ray-project/ray,doc/examples/plot_pong_example.py,10f21fa313d8ee8cd636b1e945b1a02893d303f8,STILL_EXISTS,To implement a neural network in NumPy; we need to provide helper functions
aaiiaecha,ray-project/ray,python/ray/autoscaler/updater.py,972dddd77669a866659a12d5f803e5cc4c58a622,STILL_EXISTS,running the actual command. This is a little ugly; but it should
aaiiaedcf,ray-project/ray,python/ray/tune/examples/pbt_memnn_example.py,b99cdf4e391ccb858ffa8c772e144dc32d2569c6,STILL_EXISTS,one regularization layer -- more would probably be needed.
aaiiaedhd,ray-project/ray,python/ray/experimental/serve/task_runner.py,9bb3633cd9db2db11a3e7ae85f4cfb7fefac114d,STILL_EXISTS,move on.
aaiiaeeii,ray-project/ray,python/ray/tune/progress_reporter.py,a851d7eb87e197ae55b73a922eb17245494afabc,STILL_EXISTS,Pre-process trials to figure out what columns to show.
aaiiaeeja,ray-project/ray,python/ray/tune/progress_reporter.py,a851d7eb87e197ae55b73a922eb17245494afabc,STILL_EXISTS,Parse columns.
aaiiaeejj,ray-project/ray,python/ray/autoscaler/autoscaler.py,6eaa8e31fa069f7106f3647cec4068d7c4821e1a,STILL_EXISTS,TODO(edoakes): Spawning these threads directly seems to cause
aaiiaefbb,ray-project/ray,python/ray/tune/tests/test_ray_trial_executor.py,52e5c9b22d93ec5b73567f1ac88b9b0152eb5fbf,STILL_EXISTS,TODO(rliaw): This behavior is probably undesirable; but right now
aaiiaegbj,ray-project/ray,python/ray/worker.py,3ac8592dcfc6e7fdc7838b6739590bab5c3cfe20,02931e08f39ed5995b3c19eacd57b773b98fd58e,TODO: Remove Worker.actor_id and just use CoreWorker.GetActorId.
aaiiaegfc,ray-project/ray,python/ray/signature.py,26a724c5e6fa82dcb166e14170731aee0dd51807,4d722bf0036deeec58899adebef68d1b809f82e6,\"\"\"This class is used to represent a function parameter in Ray. ||  || Note that this is different from the funcsigs.Parameter object because || we replace the funcsigs ParameterKind with an int. This is needed because || ParameterKind objects are currently non-serializable and the package is not || being updated. Replacement is done in `_scrub_parameters` and || `_restore_parameters`. ||  || Attributes: ||     name (str): The name of the parameter as a string. ||     kind (int): Describes how argument values are bound to the parameter. See ||         funcsigs.Parameter and `_convert_to_parameter_kind`. ||     default (object): The default value for the parameter if specified. If the ||         parameter has no default value; this attribute is not set. ||     annotation: The annotation for the parameter if specified.  If the ||         parameter has no annotation; this attribute is not set. ||     partial_kwarg (bool): True if the parameter is mapped ||         by 'functools.partial'. || \"\"\"
aaiiaehfa,ray-project/ray,python/ray/experimental/serve/api.py,9e2c5f821824e7785187685743c4519843e6d73c,STILL_EXISTS,Initialize ray if needed.
aaiiaehfc,ray-project/ray,python/ray/experimental/serve/api.py,9e2c5f821824e7785187685743c4519843e6d73c,STILL_EXISTS,Todo; move the db to session_dir
aaiiaeiih,ray-project/ray,python/ray/serialization.py,decaa65cd6aa0b99317165e1fcd154cbf569b10d,42cbf801e1922c65d1bc446e1b7513ce4db0b7a9,used when passing around actor handles.
aaiiafahf,ray-project/ray,rllib/agents/trainer.py,8fc2272f435a7e357949e60946b7001ae2ed14af,STILL_EXISTS,model inference batching; which can improve performance for inference
aaiiafbdb,ray-project/ray,rllib/agents/trainer.py,8fc2272f435a7e357949e60946b7001ae2ed14af,STILL_EXISTS,usually needed only if your env itself requires a GPU (i.e.; it is a
aaiiafgjg,ray-project/ray,python/ray/tests/test_advanced.py,53641f1f74334d6ab259cdaf4c058fd6eb705e25,304b4f0d3d672c0ece47da8aa9fded515c17c6f4,TODO: 5 retry attempts may be too little for Travis and we may need to
aaiiagcbd,ray-project/ray,python/ray/tests/test_stress_sharded.py,53641f1f74334d6ab259cdaf4c058fd6eb705e25,STILL_EXISTS,TODO(pcm): replace by 10 ** 5 once this is faster.
aaiiagcdd,ray-project/ray,python/ray/tune/tests/test_api.py,53641f1f74334d6ab259cdaf4c058fd6eb705e25,STILL_EXISTS,TODO: remove once the multi-threaded function runner is gone.
aaiiagefb,ray-project/ray,rllib/contrib/alpha_zero/core/alpha_zero_trainer.py,4e24c805eeffda096ae5f2485c9a2b4d8e143456,STILL_EXISTS,implement the ranked reward (r2) algorithm
aaiiagehd,ray-project/ray,rllib/contrib/alpha_zero/core/mcts.py,4e24c805eeffda096ae5f2485c9a2b4d8e143456,STILL_EXISTS,TODO (weak todo) add \"softmax\" version of the Q-value
aaiiaggfa,ray-project/ray,python/ray/tests/test_advanced_2.py,304b4f0d3d672c0ece47da8aa9fded515c17c6f4,STILL_EXISTS,TODO: 5 retry attempts may be too little for Travis and we may need to
aaiiagije,ray-project/ray,streaming/python/communication.py,6272907a57599f42a9effe685a423e1333c623dd,STILL_EXISTS,TODO (john): Add support for other strategies
aaiiagjae,ray-project/ray,streaming/python/communication.py,6272907a57599f42a9effe685a423e1333c623dd,STILL_EXISTS,TODO (john): Handle rescaling
aaiiagjbf,ray-project/ray,streaming/python/processor.py,6272907a57599f42a9effe685a423e1333c623dd,STILL_EXISTS,TODO (john): Handle possible exception here
aaiiagjde,ray-project/ray,streaming/python/processor.py,6272907a57599f42a9effe685a423e1333c623dd,STILL_EXISTS,TODO (john): Is there a way to update state with
aaiiagjef,ray-project/ray,streaming/python/runtime/transfer.py,6272907a57599f42a9effe685a423e1333c623dd,STILL_EXISTS,TODO set operator type
aaiiahaic,ray-project/ray,python/ray/async_compat.py,26ec500ef9637955b5af7181b3cc6154391f3a0b,a1e14380ce0a7708836ab0f273ede2f5820a947a,Here's the callback used to implement async get logic.
aaiiahbab,ray-project/ray,python/ray/async_compat.py,26ec500ef9637955b5af7181b3cc6154391f3a0b,a1e14380ce0a7708836ab0f273ede2f5820a947a,A hack to keep reference to the future so it doesn't get GC.
aaiiahbac,ray-project/ray,python/ray/async_compat.py,26ec500ef9637955b5af7181b3cc6154391f3a0b,a1e14380ce0a7708836ab0f273ede2f5820a947a,A hack to keep reference to inner_future so it doesn't get GC.
aaiiahbag,ray-project/ray,python/ray/tests/py3_test.py,26ec500ef9637955b5af7181b3cc6154391f3a0b,STILL_EXISTS,This is needed for async plasma
aaiiahbcj,ray-project/ray,python/ray/experimental/serve/queues.py,d78a1062db20c1eb306a768ca1fbb46d3b00de88,660eef6502f8ab37267fd791a4222576f59d0f75,different policies will implement different backend selection policies
aaiiahbff,ray-project/ray,python/ray/async_compat.py,41fa2e9604df1aed3d7e0982bd02ee5616c010b7,STILL_EXISTS,A hack to keep a reference to the object ID for ref counting.
aaiiahbge,ray-project/ray,python/ray/tests/test_reference_counting.py,e50aa99be1ec439236d697596e22a05c9175362c,cbf55d69a645208564ab8b17bda923913794e3e4,TODO: Clean up tmpfiles?
aaiiahbij,ray-project/ray,python/ray/tests/test_component_failures_3.py,d2bba596ab35cc771786fe26538ad7a25cf0e652,STILL_EXISTS,removing a node. This is needed because right now we don't
aaiiahcbj,ray-project/ray,python/ray/experimental/serve/api.py,a76fadb89945becefc9a8973182bfd2b7969f50d,STILL_EXISTS,save information about configurations needed to start the replicas
aaiiahcca,ray-project/ray,python/ray/experimental/serve/api.py,a76fadb89945becefc9a8973182bfd2b7969f50d,STILL_EXISTS,save the initial arguments needed by replicas
aaiiahccf,ray-project/ray,python/ray/experimental/serve/backend_config.py,a76fadb89945becefc9a8973182bfd2b7969f50d,STILL_EXISTS,configs not needed for actor creation when
aaiiahcgc,ray-project/ray,python/ray/experimental/serve/task_runner.py,a76fadb89945becefc9a8973182bfd2b7969f50d,STILL_EXISTS,unified context needed
aaiiahdgc,ray-project/ray,rllib/utils/numpy.py,8b16847c022ab33d8721dab503ee83d75d17c7e7,STILL_EXISTS,Some large int number. May be increased here; if needed.
aaiiahdii,ray-project/ray,python/ray/tests/test_metrics.py,65acb54553c201af5c6ff97e31cb4567af7cd5b3,8a0a30b5f01d8acb928c12e7fc545b86d9c24618,TODO: Currently there is a race condition of Dashboard subscription
aaiiahgda,ray-project/ray,rllib/utils/from_config.py,ae9a3a22376aa6621ce90f1b1234c8942831f899,STILL_EXISTS,`type_`: Indicator for the Configurable's constructor.
aaiiahghg,ray-project/ray,python/ray/tune/tests/test_cluster.py,1558307ac4e66d77d2ee92514b45311046fa93c6,STILL_EXISTS,TODO(ujvl): Fix test.
aaiiahgjc,ray-project/ray,rllib/examples/random_env.py,446cbdf2e0e3bcd0476adb9931af4cdacc00225d,STILL_EXISTS,Chance that an episode ends at any step.
aaiiahhab,ray-project/ray,python/ray/tune/tests/test_progress_reporter.py,ed9de8b2fa269d2866c83e1e04bcdb0019d7d625,STILL_EXISTS,Test list-initialized metric columns.
aaiiahhac,ray-project/ray,python/ray/tune/tests/test_progress_reporter.py,ed9de8b2fa269d2866c83e1e04bcdb0019d7d625,STILL_EXISTS,Test default-initialized (dict) metric columns.
aaiiahhba,ray-project/ray,rllib/optimizers/torch_distributed_data_parallel_optimizer.py,2fb53396ad1dc7a5b7bd0f6135b48c4f40c5adf6,STILL_EXISTS,add too much overhead and handles the case where the user manually
aaiiahhbe,ray-project/ray,rllib/optimizers/torch_distributed_data_parallel_optimizer.py,2fb53396ad1dc7a5b7bd0f6135b48c4f40c5adf6,STILL_EXISTS,needed unless the user is reading the local weights.
aaiiahhdb,ray-project/ray,python/ray/experimental/sgd/pytorch/distributed_pytorch_runner.py,037aa2b961e82feaf563679e6074ed50ff9c60ff,STILL_EXISTS,TODO: Temporarily removing since it causes hangs on MacOSX.
aaiiahhhc,ray-project/ray,python/ray/experimental/serve/queues.py,660eef6502f8ab37267fd791a4222576f59d0f75,STILL_EXISTS,NOTE: this method is needed because Query need to be serialized and
aaiiahhhe,ray-project/ray,python/ray/experimental/serve/queues.py,660eef6502f8ab37267fd791a4222576f59d0f75,STILL_EXISTS,replica worker the async_future is still needed to retrieve the final
aaiiahiad,ray-project/ray,python/ray/experimental/serve/queues.py,660eef6502f8ab37267fd791a4222576f59d0f75,STILL_EXISTS,TODO: consider await this with timeout; or use ray_kill
aaiiahiic,ray-project/ray,python/ray/experimental/sgd/tf/examples/tensorflow_train_example.py,64c8996a4320f3dd6e1534f5fd2e674fee6e059c,STILL_EXISTS,todo: batch size needs to scale with # of workers
aaiiahiif,ray-project/ray,python/ray/experimental/sgd/tf/examples/tensorflow_train_example.py,64c8996a4320f3dd6e1534f5fd2e674fee6e059c,STILL_EXISTS,model performance after training (should improve)
aaiiahijc,ray-project/ray,python/ray/experimental/sgd/tf/tf_runner.py,64c8996a4320f3dd6e1534f5fd2e674fee6e059c,STILL_EXISTS,every worker ends up with the exact same metrics and model
aaiiahijg,ray-project/ray,python/ray/experimental/sgd/tf/tf_trainer.py,64c8996a4320f3dd6e1534f5fd2e674fee6e059c,STILL_EXISTS,todo: are these resource quotas right?
aaiiahiji,ray-project/ray,python/ray/experimental/sgd/tf/tf_trainer.py,64c8996a4320f3dd6e1534f5fd2e674fee6e059c,STILL_EXISTS,todo: should we warn about using
aaiiahjgi,ray-project/ray,python/ray/cloudpickle/cloudpickle_fast.py,42cbf801e1922c65d1bc446e1b7513ce4db0b7a9,5ad5cb61ca7156428b34e2455b88ac7165dce6c0,in numpy C backend. This is a workaround for python3.5 pickling support.
aaiiaiccd,ray-project/ray,rllib/agents/dqn/dqn.py,6e1c3ea824e87c9ba9d6f137ffb41c3070ff480a,9a83908c460a8bdf38b665c290a003e492a87e16,TODO(sven): Move all PR-beta decays into Schedule components.
aaiiaicdc,ray-project/ray,rllib/agents/dqn/dqn_policy.py,6e1c3ea824e87c9ba9d6f137ffb41c3070ff480a,d537e9f0d8b84414a2aba7a7d0a68d59241f1490,TODO(sven): Move soft_q logic to different Exploration child-component.
aaiiaidcj,ray-project/ray,python/ray/autoscaler/commands.py,4139e02f0194925889f23e803002a8882fc97941,STILL_EXISTS,todo: technically could be reused in get_worker_node_ips
aaiiaihdi,ray-project/ray,rllib/tests/test_io.py,2e60f0d4d89069a6c46d0196ae32ff503d87d0c3,STILL_EXISTS,Magic numbers: 2: On travis; it seems to create only 2 files.
aaiiaiiie,ray-project/ray,python/ray/__init__.py,0aa9373d623fda00165fe518d96a93a444783cd5,e8941b1b7920a115273b391029f891699b013ec3,TODO(edoakes): Fix this.
aaiiajaib,ray-project/ray,rllib/utils/exploration/random.py,d537e9f0d8b84414a2aba7a7d0a68d59241f1490,STILL_EXISTS,TODO(sven): Move into (deterministic_)sample(logp=True|False)
aaiiajaje,ray-project/ray,rllib/utils/exploration/stochastic_sampling.py,d537e9f0d8b84414a2aba7a7d0a68d59241f1490,1775e89f262111791fabfd40f80a24f74738fe54,TODO(sven): Move into (deterministic_)sample(logp=True|False)
aaiiajbbe,ray-project/ray,python/ray/__init__.py,7bef7031c2ca5574cf6ca59bc0bdd186ee88a74c,b804d40c04e55b410d6aff56f8ef0a75f830eb87,TODO(edoakes): Fix this.
aaiiajcai,ray-project/ray,python/ray/serialization.py,007333b96006e090b14a415a8c5993d8b907f02a,STILL_EXISTS,TODO (kfstorm): exception serialization should be language
aaiiajdbc,ray-project/ray,streaming/python/context.py,8b6784de061a313da9d5be5fe8448862d048b3ef,STILL_EXISTS,TODO return a JobSubmissionResult future
aaiiajdbd,ray-project/ray,streaming/python/context.py,8b6784de061a313da9d5be5fe8448862d048b3ef,STILL_EXISTS,TODO support block to job finish
aaiiajdei,ray-project/ray,streaming/python/partition.py,8b6784de061a313da9d5be5fe8448862d048b3ef,STILL_EXISTS,TODO support key group
aaiiajeja,ray-project/ray,python/ray/util/iter.py,3c6b94f3f5b27ff66e60a15f4c6817468e09d505,a644060daa4c22aabedcfd9b891d43f9afa88f97,TODO(ekl) is this the best way to represent union() of metrics?
aaiiajgda,ray-project/ray,python/ray/util/sgd/pytorch/pytorch_trainer.py,48cdca843f2d06aeb356db27d48e01c1153eba22,STILL_EXISTS,TODO: Have an auto \"use_gpu\" option to detect and use GPUs.
aaiiajgij,ray-project/ray,python/ray/cloudpickle/cloudpickle.py,f6883bf725caca57bcebcc94aeb93f6a94542984,f0c3910d598e7964a28b4245bd6e4024114bdbd2,XXX: can this ever happen in Python 3? If so add a test.
aaiiajhci,ray-project/ray,python/ray/cloudpickle/cloudpickle_fast.py,f6883bf725caca57bcebcc94aeb93f6a94542984,STILL_EXISTS,TODO: not sure if we are going to implement it for python < 3.7.
aaiiajhed,ray-project/ray,python/ray/cloudpickle/cloudpickle.py,fb1c1e2d27348b139bf1b686a1e81b94049d5190,STILL_EXISTS,handling dotted names is not needed; so we simply define _getattribute as
aaiiajhha,ray-project/ray,python/ray/cloudpickle/cloudpickle.py,fb1c1e2d27348b139bf1b686a1e81b94049d5190,STILL_EXISTS,XXX Assert it's a sequence
aaiiajhif,ray-project/ray,python/ray/cloudpickle/cloudpickle.py,fb1c1e2d27348b139bf1b686a1e81b94049d5190,f0c3910d598e7964a28b4245bd6e4024114bdbd2,XXX: can this ever happen in Python 3? If so add a test.
aaiiajhjb,ray-project/ray,python/ray/cloudpickle/cloudpickle_fast.py,fb1c1e2d27348b139bf1b686a1e81b94049d5190,STILL_EXISTS,TODO: not sure if we are going to implement it for python < 3.7.
aaiiajiec,ray-project/ray,rllib/agents/sac/sac_policy.py,510c8506512b8e9873ffbab0abdc1a668b022022,STILL_EXISTS,Discrete case: \"Best\" means weighted by the policy (prob) outputs.
aaiiajiej,ray-project/ray,rllib/agents/sac/sac_policy.py,510c8506512b8e9873ffbab0abdc1a668b022022,STILL_EXISTS,loss terms (no expectations needed).
aaiiajigi,ray-project/ray,python/ray/services.py,a8bda9b55173e18335ed023531411473071e4d38,609c1b8acdb9696989757620b5efa34bc958cff8,HACK: Workaround for UNIX idiosyncrasy
aaiiajjcj,ray-project/ray,python/ray/tune/suggest/dragonfly.py,89ec4adb726aafd70026ac3ffdb21cadcde36278,STILL_EXISTS,Python 3 only -- needed for lint test.
aaiibaaga,ray-project/ray,rllib/policy/tests/test_compute_log_likelihoods.py,20ef4a860325d1735df038d4e4b8c7de5e013ed3,STILL_EXISTS,SAC cont uses a squashed normal distribution. Implement it's logp
aaiibabij,ray-project/ray,python/ray/tests/test_failure.py,53549314c58f33dac679616bca7eaaec2bf97a6b,ebdccde0305125c2d507ef459b710a6a5ea99760,NOTE: Needed to unset the config set by the lru_evict flag; for Travis.
aaiibacad,ray-project/ray,python/ray/autoscaler/azure/config.py,37e4d29f8715330eb772f05513c696fd852ab565,STILL_EXISTS,TODO: look at availability sets
aaiibacbj,ray-project/ray,python/ray/autoscaler/azure/node_provider.py,37e4d29f8715330eb772f05513c696fd852ab565,STILL_EXISTS,TODO: restart deallocated nodes if possible
aaiibaccd,ray-project/ray,python/ray/autoscaler/azure/node_provider.py,37e4d29f8715330eb772f05513c696fd852ab565,STILL_EXISTS,TODO: do we need to wait or fire and forget is fine?
aaiibadid,ray-project/ray,rllib/env/policy_client.py,9392cdbf749206279252894fe4e188d7e0867cd7,STILL_EXISTS,\"\"\"REST client to interact with a policy server. ||  || This client supports both local and remote policy inference modes. Local || inference is faster but causes more compute to be done on the client. || \"\"\"
aaiibadie,ray-project/ray,rllib/env/policy_client.py,9392cdbf749206279252894fe4e188d7e0867cd7,eb0038612f3cbb8def1a99e4464be41285bccb35,TODO(ekl) seems to be needed for cartpole_client.py
aaiibadjc,ray-project/ray,rllib/env/policy_server_input.py,9392cdbf749206279252894fe4e188d7e0867cd7,eb0038612f3cbb8def1a99e4464be41285bccb35,TODO(ekl) this is needed for cartpole_server.py
aaiibadje,ray-project/ray,rllib/env/policy_server_input.py,9392cdbf749206279252894fe4e188d7e0867cd7,STILL_EXISTS,worker. This is a bit of a hack since it is patching the get_metrics
aaiibaehh,ray-project/ray,rllib/contrib/bandits/agents/lin_ts.py,6ddf84b019be0b6dee52c9699f7c6ebe416dc3d3,9a83908c460a8bdf38b665c290a003e492a87e16,Uncomment if regret at each time step is needed
aaiibaeii,ray-project/ray,rllib/contrib/bandits/agents/lin_ucb.py,6ddf84b019be0b6dee52c9699f7c6ebe416dc3d3,9a83908c460a8bdf38b665c290a003e492a87e16,Uncomment if regret at each time step is needed
aaiibaejg,ray-project/ray,rllib/contrib/bandits/agents/policy.py,6ddf84b019be0b6dee52c9699f7c6ebe416dc3d3,STILL_EXISTS,TODO: Have a separate model catalogue for bandits
aaiibafcf,ray-project/ray,rllib/contrib/bandits/examples/tune_LinUCB_train_recommendation.py,6ddf84b019be0b6dee52c9699f7c6ebe416dc3d3,STILL_EXISTS,Temp fix to avoid OMP conflict
aaiibafcj,ray-project/ray,rllib/contrib/bandits/models/linear_regression.py,6ddf84b019be0b6dee52c9699f7c6ebe416dc3d3,STILL_EXISTS,TODO: Handle batch of data rather than individual points
aaiibafhg,ray-project/ray,python/ray/util/sgd/torch/distributed_torch_runner.py,7b27ce2b236e29d82e33c5e07645f845d433973c,STILL_EXISTS,TODO: we should make sure this NEVER dies.
aaiibaggb,ray-project/ray,python/ray/utils.py,fc23f79f8220bd89316e3b793323612d77459f56,STILL_EXISTS,TODO(mehrdadn): implement graceful process termination mechanism
aaiibagie,ray-project/ray,python/ray/util/sgd/torch/examples/cifar_pytorch_example.py,86cff17e7ef1b355e1ab4862ccd664ed4c856227,STILL_EXISTS,set the below if needed
aaiibagjj,ray-project/ray,python/ray/util/sgd/torch/torch_trainer.py,86cff17e7ef1b355e1ab4862ccd664ed4c856227,STILL_EXISTS,TODO: Implement autoscaling. If num_workers=-1; the trainer will use as
aaiibahad,ray-project/ray,python/ray/util/sgd/torch/torch_trainer.py,86cff17e7ef1b355e1ab4862ccd664ed4c856227,STILL_EXISTS,This is not the most efficient because you have to wait for
aaiibahhd,ray-project/ray,rllib/models/tf/__init__.py,66df8b8c3522dcb47ce163f1d8f9503c094459ce,bf25aee39228d25fe34b782ccfb02ded59f64f50,TODO(sven): Add once ModelV1 is deprecated and we no longer cause circular
aaiibahig,ray-project/ray,rllib/models/torch/__init__.py,66df8b8c3522dcb47ce163f1d8f9503c094459ce,28ab797cf51f35222d1ac0e8a4ca8a5f153a9326,TODO(sven): Add once ModelV1 is deprecated and we no longer cause circular
aaiibaijc,ray-project/ray,python/ray/util/sgd/torch/distributed_torch_runner.py,24bf6ad607b491fc28c83b930e7cec927599d73f,STILL_EXISTS,This is a pretty annoying workaround. To enable SyncBatchNorm;
aaiibajah,ray-project/ray,python/ray/util/sgd/torch/examples/cifar_pytorch_pbt.py,24bf6ad607b491fc28c83b930e7cec927599d73f,STILL_EXISTS,set the below if needed
aaiibajgg,ray-project/ray,rllib/agents/dqn/distributional_q_model.py,5537fe13b097097668f9c08a00051e8b7a2d1980,STILL_EXISTS,TODO(sven): Move `add_layer_norm` into ModelCatalog as
aaiibbahb,ray-project/ray,rllib/agents/dqn/dqn_torch_model.py,22ccc43670dac93eb7fe81520a84cf3979d05693,STILL_EXISTS,TODO(sven): Move `add_layer_norm` into ModelCatalog as
aaiibbcaj,ray-project/ray,python/ray/serve/global_state.py,d3c310f40877f55186bf43c1b5b164713d76ab1c,STILL_EXISTS,Save information about configurations needed to start the replicas.
aaiibbcba,ray-project/ray,python/ray/serve/global_state.py,d3c310f40877f55186bf43c1b5b164713d76ab1c,STILL_EXISTS,Save the initial arguments needed by replicas.
aaiibbehe,ray-project/ray,python/ray/serve/backend_worker.py,d8f5b522655f2b848ba20817dd18d6e4aefb8a42,7be7af11abe300945e2ca0106eaca1082e0b659c,TODO(edoakes): this is a hacky workaround because there is
aaiibbfaf,ray-project/ray,python/ray/serve/backend_worker.py,d8f5b522655f2b848ba20817dd18d6e4aefb8a42,55b6c19d98bab621f5e96fda5271b6e27f953f01,Check mixing of query context (unified context needed).
aaiibbffg,ray-project/ray,python/ray/serve/http_proxy.py,2cb9cfb2b6b1d2b5a14b543310761d64b76b8508,STILL_EXISTS,TODO(edoakes): this should probably be configurable.
aaiibbgbc,ray-project/ray,rllib/agents/sac/sac_torch_policy.py,428516056abe41f135133e732a8d44af6ce9a234,STILL_EXISTS,Discrete case: \"Best\" means weighted by the policy (prob) outputs.
aaiibbgce,ray-project/ray,rllib/agents/sac/sac_torch_policy.py,428516056abe41f135133e732a8d44af6ce9a234,STILL_EXISTS,loss terms (no expectations needed).
aaiibbhci,ray-project/ray,rllib/models/torch/fcnet.py,428516056abe41f135133e732a8d44af6ce9a234,STILL_EXISTS,TODO(sven): implement case: vf_shared_layers = False.
aaiibbhdf,ray-project/ray,rllib/models/torch/fcnet.py,428516056abe41f135133e732a8d44af6ce9a234,STILL_EXISTS,TODO(sven): Implement non-shared value branch.
aaiibbibb,ray-project/ray,rllib/agents/ddpg/ddpg_torch_policy.py,d0fab84e4dfdddc64cb4275ff98cf67f84ff86a0,STILL_EXISTS,one Model per policy. Note: Device placement is done automatically
aaiibbjdf,ray-project/ray,python/ray/util/sgd/torch/examples/image_models/train.py,d6f4e5b3e113b56424954ec4379467e4e45d895a,STILL_EXISTS,todo: verbose should depend on rank
aaiibbjhe,ray-project/ray,python/ray/util/sgd/torch/examples/transformers/utils.py,857e4dba2f4559c418cc142823f06992adf189a6,STILL_EXISTS,HACK(label indices are swapped in RoBERTa pretrained model)
aaiibbjhi,ray-project/ray,python/ray/util/sgd/torch/examples/transformers/utils.py,857e4dba2f4559c418cc142823f06992adf189a6,STILL_EXISTS,Create output directory if needed
aaiibcafc,ray-project/ray,rllib/models/tests/test_distributions.py,e9ee5c4e5f27c5fa547f1c5c504b9efebef21026,STILL_EXISTS,Fix categorical inputs (not needed for distribution itself; but
aaiibcafe,ray-project/ray,rllib/models/tests/test_distributions.py,e9ee5c4e5f27c5fa547f1c5c504b9efebef21026,STILL_EXISTS,Fix std inputs (shouldn't be too large for this test).
aaiibcajj,ray-project/ray,rllib/agents/impala/vtrace_torch_policy.py,499ad5fbe4d0326f8970f9dd10a88d8e96278f74,STILL_EXISTS,boundaries. TODO(ekl) this is kind of a hack
aaiibcbda,ray-project/ray,rllib/agents/dqn/tests/test_apex.py,2298f6fb40cb6d348b5b48593c93cdd58ddd1f29,STILL_EXISTS,TODO(ekl) fix iterator metrics bugs w\/multiple trainers.
aaiibcdad,ray-project/ray,python/ray/serve/master.py,7c0200c93b88898507dbafcb2d92314d0dbcd451,STILL_EXISTS,TODO(edoakes): we should probably have a timeout here.
aaiibcdbj,ray-project/ray,python/ray/serve/master.py,7c0200c93b88898507dbafcb2d92314d0dbcd451,STILL_EXISTS,TODO(edoakes): move this to client side.
aaiibceeb,ray-project/ray,rllib/tests/test_lstm.py,bf25aee39228d25fe34b782ccfb02ded59f64f50,796a834c4886b5a97ec4be0477435fced22d7b7c,This behavior is no longer the case; so we manually refresh
aaiibceee,ray-project/ray,python/setup.py,254b1ec370a07bbd7d60c502ce3b38f4c1fef567,STILL_EXISTS,Does not preserve file mode (needed to avoid read-only bit)
aaiibceef,ray-project/ray,python/setup.py,254b1ec370a07bbd7d60c502ce3b38f4c1fef567,STILL_EXISTS,Preserves file mode (needed to copy executable bit)
aaiibcefa,ray-project/ray,rllib/agents/ppo/ddppo.py,baadbdf8d43a5171d65f24a793640b9f9523f260,9d012626e550455ea739a28f97b57ea2915d0b8a,add too much overhead and handles the case where the user manually
aaiibcegb,ray-project/ray,rllib/agents/ppo/ddppo.py,baadbdf8d43a5171d65f24a793640b9f9523f260,STILL_EXISTS,needed unless the user is reading the local weights.
aaiibcfia,ray-project/ray,rllib/examples/env/random_env.py,42991d723fad1d1b698e895969908105ca22f72c,STILL_EXISTS,Chance that an episode ends at any step.
aaiibcgdf,ray-project/ray,rllib/examples/parametric_actions_cartpole.py,42991d723fad1d1b698e895969908105ca22f72c,STILL_EXISTS,\"\"\"Example of handling variable length and\/or parametric action spaces. ||  || This is a toy example of the action-embedding based approach for handling large || discrete action spaces (potentially infinite in size); similar to this: ||  ||     https:\/\/neuro.cs.ut.ee\/the-use-of-embeddings-in-openai-five\/ ||  || This currently works with RLlib's policy gradient style algorithms || (e.g.; PG; PPO; IMPALA; A2C) and also DQN. ||  || Note that since the model outputs now include \"-inf\" tf.float32.min || values; not all algorithm options are supported at the moment. For example; || algorithms might crash if they don't properly ignore the -inf action scores. || Working configurations are given below. || \"\"\"
aaiibchfd,ray-project/ray,python/setup.py,c5a5a5de8975985c54ea49718f6473c42605379b,STILL_EXISTS,Does not preserve file mode (needed to avoid read-only bit)
aaiibchfe,ray-project/ray,python/setup.py,c5a5a5de8975985c54ea49718f6473c42605379b,STILL_EXISTS,Preserves file mode (needed to copy executable bit)
aaiibcibf,ray-project/ray,rllib/examples/models/fast_model.py,5f278c6411eda9d0d5e69e5e14e3496a17daf298,STILL_EXISTS,Only needed to give some params to the optimizer (even though;
aaiibcifc,ray-project/ray,rllib/examples/policy/random_policy.py,5f278c6411eda9d0d5e69e5e14e3496a17daf298,STILL_EXISTS,Alternatively; a numpy array would work here as well.
aaiibcjgc,ray-project/ray,rllib/agents/ppo/appo.py,9d012626e550455ea739a28f97b57ea2915d0b8a,9a83908c460a8bdf38b665c290a003e492a87e16,TODO: impl update target.
aaiibdbjc,ray-project/ray,rllib/models/tf/fcnet_v2.py,7ce138a6dcc38f466222f87ea4927c3e93a6e247,796a834c4886b5a97ec4be0477435fced22d7b7c,Maybe generate free-floating bias variables for the second half of
aaiibdbjf,ray-project/ray,rllib/models/torch/fcnet.py,7ce138a6dcc38f466222f87ea4927c3e93a6e247,STILL_EXISTS,Maybe generate free-floating bias variables for the second half of
aaiibdcda,ray-project/ray,python/ray/ray_constants.py,00325eb2b2cb40bacaad07828ac79ffa1f46898a,STILL_EXISTS,Max 64 bit integer value; which is needed to ensure against overflow
aaiibdcdd,ray-project/ray,python/ray/serve/api.py,ef20564d8e4ba302a457b31929a6eba4af2eea42,a078a214378ca4d1c0cc317bc2f18730a91a9f14,serve.init() was run on. We should consider making this configurable
aaiibdchj,ray-project/ray,python/ray/tune/session.py,67c01455fe313a65cc65640e52892ed91e08ea8b,STILL_EXISTS,TODO(ng): would be nice to stack crawl at creation time to report
aaiibddab,ray-project/ray,python/ray/tune/tune.py,16f48078d923e0c9805e6d88dc48c7e6b68a3bf8,ae312af435a050c4ffb6891e78ccb71e1aae4740,XXX: force CI
aaiibddac,ray-project/ray,rllib/train.py,16f48078d923e0c9805e6d88dc48c7e6b68a3bf8,ae312af435a050c4ffb6891e78ccb71e1aae4740,XXX: force CI
aaiibddca,ray-project/ray,rllib/examples/models/rnn_spy_model.py,796a834c4886b5a97ec4be0477435fced22d7b7c,STILL_EXISTS,This behavior is no longer the case; so we manually refresh
aaiibddgg,ray-project/ray,rllib/models/tf/fcnet.py,796a834c4886b5a97ec4be0477435fced22d7b7c,STILL_EXISTS,Maybe generate free-floating bias variables for the second half of
aaiibdecd,ray-project/ray,rllib/models/tf/visionnet.py,796a834c4886b5a97ec4be0477435fced22d7b7c,STILL_EXISTS,explicit cast to float32 needed in eager
aaiibdehc,ray-project/ray,python/ray/util/iter.py,aa7a58e92fa23bf6bd1a1e860da66dfc7dd8a2ba,STILL_EXISTS,TOOD(ekl) how to best bound this?
aaiibdgaj,ray-project/ray,rllib/execution/replay_buffer.py,9a83908c460a8bdf38b665c290a003e492a87e16,STILL_EXISTS,TODO(ekl) move this class to common
aaiibdhab,ray-project/ray,python/ray/dashboard/memory.py,aa1cbe8abc845e1a6acf2f237b4b00aa7c3f295d,STILL_EXISTS,If the object id ends with =; that means it is base64 encoded.
aaiibdhgj,ray-project/ray,python/ray/tune/suggest/bayesopt.py,822de1b7f746736193cd6708654529a26f4a0479,STILL_EXISTS,Additionally; if somehow the error is True but
aaiibdiaj,ray-project/ray,rllib/tuned_examples/cleanup_experiment.py,baa053496a78bbb89279847277e49242f721af0e,STILL_EXISTS,Progress csv file -> Filter out some columns; cut; and write to
aaiibdibd,ray-project/ray,rllib/tuned_examples/cleanup_experiment.py,baa053496a78bbb89279847277e49242f721af0e,STILL_EXISTS,unwanted columns; then write to disk.
aaiibeaea,ray-project/ray,python/ray/cloudpickle/cloudpickle.py,ebea5c41110303523045f46a0f08bd03101b51d5,STILL_EXISTS,Workaround bug in old Python versions: prior to Python 3.7;
aaiibeaee,ray-project/ray,python/ray/cloudpickle/cloudpickle.py,ebea5c41110303523045f46a0f08bd03101b51d5,STILL_EXISTS,TypeVar instances and instead exhaustively lookup those instances in
aaiibeafa,ray-project/ray,python/ray/cloudpickle/cloudpickle.py,ebea5c41110303523045f46a0f08bd03101b51d5,STILL_EXISTS,This used to be needed for Python 2.7 support but is probably not
aaiibeafb,ray-project/ray,python/ray/cloudpickle/cloudpickle.py,ebea5c41110303523045f46a0f08bd03101b51d5,STILL_EXISTS,needed anymore. However we keep the __name__ introspection in case
aaiibeagj,ray-project/ray,python/ray/cloudpickle/cloudpickle.py,ebea5c41110303523045f46a0f08bd03101b51d5,f0c3910d598e7964a28b4245bd6e4024114bdbd2,XXX: can this ever happen in Python 3? If so add a test.
aaiibeahf,ray-project/ray,python/ray/cloudpickle/cloudpickle.py,ebea5c41110303523045f46a0f08bd03101b51d5,f0c3910d598e7964a28b4245bd6e4024114bdbd2,TODO: also support binary mode files with io.BytesIO
aaiibebce,ray-project/ray,python/ray/cloudpickle/cloudpickle.py,ebea5c41110303523045f46a0f08bd03101b51d5,STILL_EXISTS,XXX Assert it's a sequence
aaiibebdf,ray-project/ray,python/ray/cloudpickle/cloudpickle_fast.py,ebea5c41110303523045f46a0f08bd03101b51d5,f30ac73640c9ed4927150024cb0726055b576112,FIXME(suquark): The upstream cloudpickle cannot work in Ray
aaiibebdi,ray-project/ray,python/ray/cloudpickle/cloudpickle_fast.py,ebea5c41110303523045f46a0f08bd03101b51d5,f30ac73640c9ed4927150024cb0726055b576112,Here the workaround just set \"_abc_impl\" to None.
aaiibecij,ray-project/ray,python/ray/autoscaler/autoscaler.py,a24d117c689840ce1c67dadb78a7511a234a4bb2,STILL_EXISTS,Launch additional nodes of the default type; if still needed.
aaiibecja,ray-project/ray,python/ray/autoscaler/autoscaler.py,a24d117c689840ce1c67dadb78a7511a234a4bb2,STILL_EXISTS,Note: this is an (experimental) user-facing API; do not move.
aaiibeecj,ray-project/ray,python/ray/tests/test_actor_advanced.py,3388864768fa198f27496824407f9aee16256c2f,STILL_EXISTS,This shouldn't be broken because actor
aaiibegbb,ray-project/ray,rllib/agents/trainer.py,34bae27ac7d3c39335a2b8c7d4cd4efba56aa398,STILL_EXISTS,the policy to implement differentiable shared computations between
aaiibegcj,ray-project/ray,rllib/policy/sample_batch.py,34bae27ac7d3c39335a2b8c7d4cd4efba56aa398,c524f86785bbe9eced6ab629f4399905d7fdc282,TODO(ekl) reuse the other id def once we fix imports
aaiibehad,ray-project/ray,rllib/agents/dyna/dyna.py,14405b90d5457863d71168c613b4961d34f19cc5,STILL_EXISTS,TODO: (sven) Use random for testing purposes for now.
aaiibehba,ray-project/ray,rllib/agents/dyna/dyna.py,14405b90d5457863d71168c613b4961d34f19cc5,STILL_EXISTS,TODO: (sven) figure out API to query the latent space vector given
aaiibehbb,ray-project/ray,rllib/agents/dyna/dyna.py,14405b90d5457863d71168c613b4961d34f19cc5,STILL_EXISTS,some observation (not needed for MBMPO).
aaiibehbf,ray-project/ray,rllib/agents/dyna/dyna.py,14405b90d5457863d71168c613b4961d34f19cc5,STILL_EXISTS,TODO: loss function types: neg_log_llh; etc..?
aaiibehbh,ray-project/ray,rllib/agents/dyna/dyna.py,14405b90d5457863d71168c613b4961d34f19cc5,STILL_EXISTS,TODO: (sven) allow for having a default model config over many
aaiibehce,ray-project/ray,rllib/agents/dyna/dyna_torch_model.py,14405b90d5457863d71168c613b4961d34f19cc5,STILL_EXISTS,TODO: (sven) get rid of these restrictions on obs\/action spaces.
aaiibehci,ray-project/ray,rllib/agents/dyna/dyna_torch_policy.py,14405b90d5457863d71168c613b4961d34f19cc5,STILL_EXISTS,TODO: (sven) implement reward prediction.
aaiibeigc,ray-project/ray,ci/long_running_distributed_tests/workloads/pytorch_pbt_failure.py,f95ab4f50663a61dfd6e9ff7664275ac1b7a784f,STILL_EXISTS,set the below if needed
aaiibejdf,ray-project/ray,rllib/agents/maml/maml_tf_policy.py,cf0894d396113fdf87da4e8b9d28fc669fca2d24,STILL_EXISTS,@mluo: TODO for any network
aaiibfach,ray-project/ray,python/ray/utils.py,c152730e4a093f3e58f738a1275144286c4e27b2,f56d7c1a76f87953c53450ec9d4f7c445625f8ac,TODO (Alex): We should eventually be able to replace this with
aaiibfadf,ray-project/ray,python/ray/worker.py,c152730e4a093f3e58f738a1275144286c4e27b2,f56d7c1a76f87953c53450ec9d4f7c445625f8ac,TODO (Alex): Python seems to always flush when writing. If that is no
aaiibfadi,ray-project/ray,python/ray/worker.py,c152730e4a093f3e58f738a1275144286c4e27b2,f56d7c1a76f87953c53450ec9d4f7c445625f8ac,TODO (Alex): Flush the c\/c++ userspace buffers if necessary.
aaiibfafa,ray-project/ray,python/ray/worker.py,c152730e4a093f3e58f738a1275144286c4e27b2,c99c376d66810c13b00860de5227e894a74c934f,TODO (Alex): `current_logging_job` tracks the current job so that we know
aaiibfbfe,ray-project/ray,python/ray/compat.py,bc140b69ccf8fac240a0df8e4de62284ffb071ed,882f60012fca805cad30f0e9e5d281117e10eae4,Workaround: We ensure there is a space following each colon.
aaiibfbhg,ray-project/ray,python/ray/tune/examples/mnist_pytorch_lightning.py,e0b6984dce2220f0c447dbdfac696375f0c03d51,STILL_EXISTS,Workaround:
aaiibfcab,ray-project/ray,rllib/policy/torch_policy.py,0d37103f849287fc16b16ff5524c4e36c70274c8,e968b52cb7de4e6c2fcc6e7d5ccb98d984745715,TODO: (sven) support RNNs w\/ fast sampling.
aaiibfcca,ray-project/ray,rllib/policy/trajectory_view.py,0d37103f849287fc16b16ff5524c4e36c70274c8,STILL_EXISTS,TODO: (sven): Here; we actually do create a copy of the data (from a
aaiibfcid,ray-project/ray,rllib/agents/maml/maml_torch_policy.py,851d02463bfb98d7f7b6cbc472da552e948b19cf,59bc1e6c093fb6fa3b55c5830c5cf4063b2647e0,@mluo: TODO for any network
aaiibfdcf,ray-project/ray,rllib/policy/torch_policy.py,f43d934817ae08119b38b59fb5dbcb68d2c8120d,STILL_EXISTS,TODO: (sven) Unify hyperparam annealing procedures across RLlib (tf\/torch)
aaiibfddj,ray-project/ray,doc/source/tune/_tutorials/tune-sklearn.py,139d21e068585fc2eb4b79808c3477347eb3dafe,STILL_EXISTS,\"\"\" || Tune's Scikit Learn Adapters || ============================ ||  || Scikit-Learn is one of the most widely used tools in the ML community for working with data; offering dozens of easy-to-use machine learning algorithms. However; to achieve high performance for these algorithms; you often need to perform **model selection**. ||  ||  || .. image:: \/images\/tune-sklearn.png ||     :align: center ||     :width: 50% ||  || Scikit-Learn `has an existing module for model selection <https:\/\/scikit-learn.org\/stable\/modules\/grid_search.html>`_; but the algorithms offered (Grid Search\/``GridSearchCV`` and Random Search\/``RandomizedSearchCV``) are often considered inefficient. In this tutorial; we'll cover ``tune-sklearn``; a drop-in replacement for Scikit-Learn's model selection module with state-of-the-art optimization features such as early stopping and Bayesian Optimization. ||  || .. tip:: Check out the `tune-sklearn code`_ and :ref:`documentation <tune-sklearn-docs>`. ||  || .. _`tune-sklearn code`: https:\/\/github.com\/ray-project\/tune-sklearn ||  || Overview || -------- ||  || ``tune-sklearn`` is a module that integrates Ray Tune's hyperparameter tuning and scikit-learn's Classifier API. ``tune-sklearn`` has two APIs: :ref:`TuneSearchCV <tunesearchcv-docs>`; and :ref:`TuneGridSearchCV <tunegridsearchcv-docs>`. They are drop-in replacements for Scikit-learn's RandomizedSearchCV and GridSearchCV; so you only need to change less than 5 lines in a standard Scikit-Learn script to use the API. ||  || Ray Tune's Scikit-learn APIs allows you to easily leverage Bayesian Optimization; HyperBand; and other cutting edge tuning techniques by simply toggling a few parameters. It also supports and provides examples for many other frameworks with Scikit-Learn wrappers such as Skorch (Pytorch); KerasClassifiers (Keras); and XGBoostClassifiers (XGBoost). ||  || Run ``pip install ray[tune] tune-sklearn`` to get started. ||  || Walkthrough || ----------- ||  || Let's compare Tune's Scikit-Learn APIs to the standard scikit-learn GridSearchCV. For this example; we'll be using ``TuneGridSearchCV`` with a `SGDClassifier`_. ||  || .. _`digits dataset`: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.datasets.load_digits.html || .. _`SGDClassifier`: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.linear_model.SGDClassifier.html ||  || To start out; change the import statement to get tune-scikit-learn\u2019s grid search cross validation interface: ||  || \"\"\"
aaiibfdhi,ray-project/ray,doc/source/tune/_tutorials/tune-sklearn.py,139d21e068585fc2eb4b79808c3477347eb3dafe,STILL_EXISTS,As you can see; it\u2019s very simple to integrate tune-sklearn into existing code. Distributed execution is also easy - you can simply run ``ray.init(address=\"auto\")`` before
aaiibffcg,ray-project/ray,rllib/utils/framework.py,01125b8fcfce88472c2e20d116bc6662032088e0,244aafdcf89ae814975c8c4e3faf0bd4995c7878,TODO: (sven) Allow env var to force compat.v1 behavior even if tf2.x
aaiibfgaf,ray-project/ray,rllib/tests/test_eager_support.py,fcdf410ae1bb5071e7d92174eace7d79be2d4ef9,STILL_EXISTS,TODO: (sven) remove entire file in the future.
aaiibfgef,ray-project/ray,rllib/evaluation/trajectory.py,03ab86567fcee6a67b9341ddb0906f764893f5fc,STILL_EXISTS,Initialize all other columns.
aaiibfgej,ray-project/ray,rllib/evaluation/trajectory.py,03ab86567fcee6a67b9341ddb0906f764893f5fc,STILL_EXISTS,all other columns due to the additional obs returned by Env.reset()).
aaiibfggh,ray-project/ray,rllib/evaluation/trajectory.py,03ab86567fcee6a67b9341ddb0906f764893f5fc,STILL_EXISTS,Do an efficient memory swap: Move current trajectory simply to
aaiibfgha,ray-project/ray,rllib/utils/numpy.py,03ab86567fcee6a67b9341ddb0906f764893f5fc,STILL_EXISTS,TODO: (sven) this will replace `TorchPolicy._convert_to_non_torch_tensor()`.
aaiibfhgi,ray-project/ray,python/ray/serve/tests/test_router.py,d0398bf7e183f251730d2312a31b22af4487257a,ed3fdd2c0b5be7dcc01c4a9998c111abe3225224,serialization is done by Serve manually for performance.
aaiibfhjh,ray-project/ray,python/ray/autoscaler/aws/config.py,908c0c630a6c7c6e35b7bb0e172d41372bfb309d,STILL_EXISTS,todo: cli_logger should handle this assert properly
aaiibfhji,ray-project/ray,python/ray/autoscaler/aws/config.py,908c0c630a6c7c6e35b7bb0e172d41372bfb309d,STILL_EXISTS,this should probably also happens somewhere else
aaiibfhjj,ray-project/ray,python/ray/autoscaler/aws/config.py,908c0c630a6c7c6e35b7bb0e172d41372bfb309d,STILL_EXISTS,todo: this is technically fragile iff we ever use multiple configs
aaiibfiaa,ray-project/ray,python/ray/autoscaler/aws/config.py,908c0c630a6c7c6e35b7bb0e172d41372bfb309d,STILL_EXISTS,todo: handle plural vs singular?
aaiibfiad,ray-project/ray,python/ray/autoscaler/aws/config.py,908c0c630a6c7c6e35b7bb0e172d41372bfb309d,STILL_EXISTS,todo: verify schema beforehand?
aaiibfiba,ray-project/ray,python/ray/autoscaler/aws/config.py,908c0c630a6c7c6e35b7bb0e172d41372bfb309d,STILL_EXISTS,TODO: better error message
aaiibfibc,ray-project/ray,python/ray/autoscaler/aws/node_provider.py,908c0c630a6c7c6e35b7bb0e172d41372bfb309d,STILL_EXISTS,todo: handle plural vs singular?
aaiibfibf,ray-project/ray,python/ray/autoscaler/aws/node_provider.py,908c0c630a6c7c6e35b7bb0e172d41372bfb309d,STILL_EXISTS,todo: handle plurality?
aaiibfica,ray-project/ray,python/ray/autoscaler/aws/node_provider.py,908c0c630a6c7c6e35b7bb0e172d41372bfb309d,STILL_EXISTS,todo: show node names?
aaiibficd,ray-project/ray,python/ray/autoscaler/aws/utils.py,908c0c630a6c7c6e35b7bb0e172d41372bfb309d,STILL_EXISTS,todo: not sure if these exceptions always have response
aaiibfice,ray-project/ray,python/ray/autoscaler/aws/utils.py,908c0c630a6c7c6e35b7bb0e172d41372bfb309d,STILL_EXISTS,apparently
aaiibfide,ray-project/ray,python/ray/autoscaler/aws/utils.py,908c0c630a6c7c6e35b7bb0e172d41372bfb309d,STILL_EXISTS,fixme: replace with a Github URL that points
aaiibfidg,ray-project/ray,python/ray/autoscaler/aws/utils.py,908c0c630a6c7c6e35b7bb0e172d41372bfb309d,STILL_EXISTS,todo: any other errors that we should catch separately?
aaiibfidh,ray-project/ray,python/ray/autoscaler/aws/utils.py,908c0c630a6c7c6e35b7bb0e172d41372bfb309d,STILL_EXISTS,todo: implement timer
aaiibfidi,ray-project/ray,python/ray/autoscaler/cli_logger.py,908c0c630a6c7c6e35b7bb0e172d41372bfb309d,STILL_EXISTS,todo
aaiibfiea,ray-project/ray,python/ray/autoscaler/cli_logger.py,908c0c630a6c7c6e35b7bb0e172d41372bfb309d,STILL_EXISTS,todo:
aaiibfieb,ray-project/ray,python/ray/autoscaler/cli_logger.py,908c0c630a6c7c6e35b7bb0e172d41372bfb309d,STILL_EXISTS,todo: throw if given args\/kwargs?
aaiibfiec,ray-project/ray,python/ray/autoscaler/cli_logger.py,908c0c630a6c7c6e35b7bb0e172d41372bfb309d,STILL_EXISTS,fixme: this does not work :()
aaiibfied,ray-project/ray,python/ray/autoscaler/cli_logger.py,908c0c630a6c7c6e35b7bb0e172d41372bfb309d,STILL_EXISTS,todo: make sure we tell the user if they
aaiibfiej,ray-project/ray,python/ray/autoscaler/command_runner.py,908c0c630a6c7c6e35b7bb0e172d41372bfb309d,STILL_EXISTS,todo: add a flag for this; we might
aaiibfifb,ray-project/ray,python/ray/autoscaler/commands.py,908c0c630a6c7c6e35b7bb0e172d41372bfb309d,STILL_EXISTS,todo: disable by default when the command output handling PR makes it in
aaiibfifc,ray-project/ray,python/ray/autoscaler/commands.py,908c0c630a6c7c6e35b7bb0e172d41372bfb309d,STILL_EXISTS,todo: validate file_mounts; ssh keys; etc.
aaiibfifd,ray-project/ray,python/ray/autoscaler/commands.py,908c0c630a6c7c6e35b7bb0e172d41372bfb309d,STILL_EXISTS,disable the cli_logger here if needed
aaiibfiff,ray-project/ray,python/ray/autoscaler/commands.py,908c0c630a6c7c6e35b7bb0e172d41372bfb309d,STILL_EXISTS,todo: is it fine to re-resolve? afaik it should be.
aaiibfifj,ray-project/ray,python/ray/autoscaler/commands.py,908c0c630a6c7c6e35b7bb0e172d41372bfb309d,00fefba72c0b3d0322c8b60309f0f8e732a4c6e8,todo: better message
aaiibfiga,ray-project/ray,python/ray/autoscaler/commands.py,908c0c630a6c7c6e35b7bb0e172d41372bfb309d,STILL_EXISTS,todo: add a separate flag?
aaiibfigb,ray-project/ray,python/ray/autoscaler/commands.py,908c0c630a6c7c6e35b7bb0e172d41372bfb309d,STILL_EXISTS,todo: add better exception info
aaiibfigd,ray-project/ray,python/ray/autoscaler/commands.py,908c0c630a6c7c6e35b7bb0e172d41372bfb309d,STILL_EXISTS,todo: interval should be a variable
aaiibfige,ray-project/ray,python/ray/autoscaler/commands.py,908c0c630a6c7c6e35b7bb0e172d41372bfb309d,STILL_EXISTS,todo: msg
aaiibfihb,ray-project/ray,python/ray/autoscaler/updater.py,908c0c630a6c7c6e35b7bb0e172d41372bfb309d,STILL_EXISTS,todo: handle this better somehow?
aaiibfihc,ray-project/ray,python/ray/autoscaler/updater.py,908c0c630a6c7c6e35b7bb0e172d41372bfb309d,STILL_EXISTS,todo: print stderr here
aaiibfihd,ray-project/ray,python/ray/autoscaler/updater.py,908c0c630a6c7c6e35b7bb0e172d41372bfb309d,STILL_EXISTS,todo: why do we ignore this here
aaiibfihe,ray-project/ray,python/ray/autoscaler/updater.py,908c0c630a6c7c6e35b7bb0e172d41372bfb309d,STILL_EXISTS,todo: timed here?
aaiibfihf,ray-project/ray,python/ray/autoscaler/updater.py,908c0c630a6c7c6e35b7bb0e172d41372bfb309d,STILL_EXISTS,todo: we lie in the confirmation message since
aaiibfiic,ray-project/ray,rllib/policy/torch_policy_template.py,5acd3e66ddc1d7a1af6590567fcc3df95169d8a2,99ae7bae05b04908a392fa111e19f7cc72692bf4,Auto-add empty learner stats dict if needed.
aaiibfijd,ray-project/ray,ci/remote-watch.py,75b2fc4313eb3f9c71640dc411cbf378b1bc0d6f,STILL_EXISTS,This SIGTERM seems to be needed to prevent jobs from lingering.
aaiibfjdg,ray-project/ray,rllib/agents/dqn/dqn_torch_policy.py,5dc4b6686e5885df7d53c6162773ece336994feb,STILL_EXISTS,TODO: better Q stats for dist dqn
aaiibfjfg,ray-project/ray,python/ray/util/sgd/torch/func_trainable.py,f3fdb5c5db42324e53af7909d15922a71cbf1112,0c3b9ebeef167a9eeb6eed9fb18f328ecb5a3c6f,TODO: optimize if colocated
aaiibgajc,ray-project/ray,python/ray/prometheus_exporter.py,7e3ba289dc2c62ba8aef2eae891be5d48de5f44a,STILL_EXISTS,TODO: add start and end timestamp
aaiibgbdc,ray-project/ray,python/ray/import_thread.py,21af0ceb0c916bdafc1e8f82d13cf7c80b6d2b28,STILL_EXISTS,TODO (Alex): There's a race condition here if the worker is
aaiibgbde,ray-project/ray,python/ray/import_thread.py,21af0ceb0c916bdafc1e8f82d13cf7c80b6d2b28,STILL_EXISTS,worker's global worker is unset before shutdown and is needed
aaiibgbdh,ray-project/ray,python/ray/tests/test_advanced.py,21af0ceb0c916bdafc1e8f82d13cf7c80b6d2b28,STILL_EXISTS,TODO (Alex) :https:\/\/github.com\/ray-project\/ray\/pull\/9346
aaiibgbge,ray-project/ray,python/ray/tune/integration/torch.py,0c3b9ebeef167a9eeb6eed9fb18f328ecb5a3c6f,STILL_EXISTS,TODO: optimize if colocated
aaiibgcfb,ray-project/ray,ci/travis/bazel.py,a7b97b6f8a7890da7af788c82a14dd7837e535a3,STILL_EXISTS,Needed to remove read-only bit
aaiibgcgc,ray-project/ray,python/ray/autoscaler/updater.py,3506910c5da257215d38d02f424acc4f419ddbaf,STILL_EXISTS,todo: timed here?
aaiibgeif,ray-project/ray,python/ray/tune/examples/pbt_transformers/pbt_transformers.py,5af7d24f66b77b8d5c116f1c729043877cd3aa1f,STILL_EXISTS,Change these as needed.
aaiibgfcj,ray-project/ray,python/ray/serve/controller.py,38408574c4edee8e515fa4700609c4a5ac57413c,1cf95cb081f885f7cc3a954fcf35d119227a6dd2,TODO: remove old router stats when removing them.
aaiibgfda,ray-project/ray,rllib/policy/torch_policy_template.py,9b90f7db67c18f33f86e99f8e23ee592ad1a351d,99ae7bae05b04908a392fa111e19f7cc72692bf4,TODO: (sven) Replace \"fetches\" with \"process\".
aaiibgfde,ray-project/ray,rllib/agents/ppo/ppo_torch_policy.py,57690a3a9f19663428cb840dbaa8ab9f30e0555a,36bda8432b2ed23f623c8c3031b2cba148bec93b,Next obs are needed for PPO postprocessing.
aaiibgfdf,ray-project/ray,rllib/agents/ppo/ppo_torch_policy.py,57690a3a9f19663428cb840dbaa8ab9f30e0555a,36bda8432b2ed23f623c8c3031b2cba148bec93b,VF preds are needed for the loss.
aaiibggeg,ray-project/ray,doc/source/tune/_tutorials/tune-serve-integration-mnist.py,dee3322ab0a9b8ebbd8a5747120781f64325346d,STILL_EXISTS,\"\"\" || Model selection and serving with Ray Tune and Ray Serve || ======================================================= || This tutorial will show you an end-to-end example how to train a || model using Ray Tune on incrementally arriving data and deploy || the model using Ray Serve. ||  || A machine learning workflow can be quite simple: You decide on || the objective you're trying to solve; collect and annotate the || data; and build a model to hopefully solve your problem. But || usually the work is not over yet. First; you would likely continue || to do some hyperparameter optimization to obtain the best possible || model (called *model selection*). Second; your trained model || somehow has to be moved to production - in other words; users || or services should be enabled to use your model to actually make || predictions. This part is called *model serving*. ||  || Fortunately; Ray includes two libraries that help you with these || two steps: Ray Tune and Ray Serve. And even more; they compliment || each other nicely. Most notably; both are able to scale up your || workloads easily - so both your model training and serving benefit || from additional resources and can adapt to your environment. If you || need to train on more data or have more hyperparameters to tune; || Ray Tune can leverage your whole cluster for training. If you have || many users doing inference on your served models; Ray Serve can || automatically distribute the inference backends to multiple nodes. ||  || This tutorial will show you an end-to-end example how to train a MNIST || image classifier on incrementally arriving data and automatically || serve an updated model on a HTTP endpoint. ||  || By the end of this tutorial you will be able to ||  || 1. Do hyperparameter optimization on a simple MNIST classifier || 2. Continue to train this classifier from an existing model with ||    newly arriving data || 3. Automatically create and serve data backends with Ray Serve ||  || Roadmap and desired functionality || --------------------------------- || The general idea of this example is that we simulate newly arriving || data each day. So at day 0 we might have some initial data available || already; but at each day; new data arrives. ||  || Our approach here is that we offer two ways to train: From scratch and || from an existing model. Maybe you would like to train and select models || from scratch each week with all data available until then; e.g. each || Sunday; like this: ||  || .. code-block:: bash ||  ||     # Train with all data available at day 0 ||     python tune-serve-integration-mnist.py --from_scratch --day 0 ||  || During the other days you might want to improve your model; but || not train everything from scratch; saving some cluster resources. ||  || .. code-block:: bash ||  ||     # Train with data arriving between day 0 and day 1 ||     python tune-serve-integration-mnist.py --from_existing --day 1 ||     # Train with incremental data on the other days; too ||     python tune-serve-integration-mnist.py --from_existing --day 2 ||     python tune-serve-integration-mnist.py --from_existing --day 3 ||     python tune-serve-integration-mnist.py --from_existing --day 4 ||     python tune-serve-integration-mnist.py --from_existing --day 5 ||     python tune-serve-integration-mnist.py --from_existing --day 6 ||     # Retrain from scratch every 7th day: ||     python tune-serve-integration-mnist.py --from_scratch --day 7 ||  || This example will support both modes. After each model selection run; || we will tell Ray Serve to serve an updated model. We also include a || small utility to query our served model to see if it works as it should. ||  || .. code-block:: ||  ||     python tune-serve-integration-mnist.py --query 6 ||     Querying model with example #6. Label = 1; Response = 1; Correct = True ||  || Imports || ------- || Let's start with our dependencies. Most of these should be familiar || if you worked with PyTorch before. The most notable import for Ray || is the ``from ray import tune; serve`` import statement - which || includes almost all the things we need from the Ray side. || \"\"\"
aaiibggff,ray-project/ray,doc/source/tune/_tutorials/tune-serve-integration-mnist.py,dee3322ab0a9b8ebbd8a5747120781f64325346d,STILL_EXISTS,We also implement an incremental data method; so calling
aaiibgggh,ray-project/ray,doc/source/tune/_tutorials/tune-serve-integration-mnist.py,dee3322ab0a9b8ebbd8a5747120781f64325346d,STILL_EXISTS,We only introduced an additional neural network layer with a configurable
aaiibgggi,ray-project/ray,doc/source/tune/_tutorials/tune-serve-integration-mnist.py,dee3322ab0a9b8ebbd8a5747120781f64325346d,STILL_EXISTS,layer size. This is not strictly needed for learning good performance on
aaiibghcj,ray-project/ray,doc/source/tune/_tutorials/tune-serve-integration-mnist.py,dee3322ab0a9b8ebbd8a5747120781f64325346d,STILL_EXISTS,Here you could also implement an incremental rollout; where only
aaiibghgf,ray-project/ray,doc/source/tune/_tutorials/tune-serve-integration-mnist.py,dee3322ab0a9b8ebbd8a5747120781f64325346d,STILL_EXISTS,route only a certain percentage of users to the new model; maybe to
aaiibghjb,ray-project/ray,python/ray/autoscaler/command_runner.py,0392bb7a7205bb750e12c06f4f51199aac9cbc34,STILL_EXISTS,For now; if the output is needed we just skip the new logic.
aaiibghjd,ray-project/ray,python/ray/autoscaler/command_runner.py,0392bb7a7205bb750e12c06f4f51199aac9cbc34,STILL_EXISTS,capturing output; but it is probably not needed.
aaiibghjg,ray-project/ray,python/ray/autoscaler/subprocess_output_util.py,0392bb7a7205bb750e12c06f4f51199aac9cbc34,STILL_EXISTS,todo: check for other connection failures for better error messages?
aaiibgjfa,ray-project/ray,rllib/utils/exploration/curiosity.py,1826b29757dfb7b6ce0ddd6f6478d960dfe7efdc,2cbe29a7fa8bc63d57f97b03ba726676425e160b,TODO: (tanay) how to test if action space is discrete
aaiibgjfe,ray-project/ray,rllib/utils/exploration/curiosity.py,1826b29757dfb7b6ce0ddd6f6478d960dfe7efdc,2cbe29a7fa8bc63d57f97b03ba726676425e160b,TODO: what should this look like for multidimensional obs spaces
aaiibgjff,ray-project/ray,rllib/utils/exploration/curiosity.py,1826b29757dfb7b6ce0ddd6f6478d960dfe7efdc,2cbe29a7fa8bc63d57f97b03ba726676425e160b,TODO can we always assume 1
aaiibgjgi,ray-project/ray,rllib/utils/exploration/curiosity.py,1826b29757dfb7b6ce0ddd6f6478d960dfe7efdc,2cbe29a7fa8bc63d57f97b03ba726676425e160b,Equation (3) in paper. TODO discrete action space
aaiibgjic,ray-project/ray,python/ray/memory_monitor.py,0fe5722744318069da8c0a3b7e2cd7bac3954ef3,STILL_EXISTS,also be considered as unused memory
aaiibhaeh,ray-project/ray,rllib/evaluation/multi_agent_sample_collector.py,aeb5be77332606e97af7f569a34b8d211cc91558,STILL_EXISTS,TODO: (sven) make `num_agents` flexibly grow in size.
aaiibhafj,ray-project/ray,rllib/evaluation/multi_agent_sample_collector.py,aeb5be77332606e97af7f569a34b8d211cc91558,STILL_EXISTS,TODO: (sven) deprecate; use `self.total_env_steps`; instead.
aaiibhage,ray-project/ray,rllib/evaluation/multi_agent_sample_collector.py,aeb5be77332606e97af7f569a34b8d211cc91558,STILL_EXISTS,Add new columns' data to buffers.
aaiibhagg,ray-project/ray,rllib/evaluation/multi_agent_sample_collector.py,aeb5be77332606e97af7f569a34b8d211cc91558,STILL_EXISTS,TODO: (sven) do we really need this?
aaiibhajb,ray-project/ray,rllib/evaluation/per_policy_sample_collector.py,aeb5be77332606e97af7f569a34b8d211cc91558,STILL_EXISTS,Skip columns that will only get added through postprocessing
aaiibhbfi,ray-project/ray,python/ray/tests/test_resource_demand_scheduler.py,0b5d5ec17d907af70f52dba45b64868593c5180d,STILL_EXISTS,TODO (Alex): Autoscaler creates the node during one update then
aaiibhcbe,ray-project/ray,python/ray/tests/test_placement_group.py,263df6163c108c4aed7c4609cc2895bea9964966,STILL_EXISTS,# This is a hack to wait for placement group creation.
aaiibhcff,ray-project/ray,rllib/utils/exploration/curiosity.py,2cbe29a7fa8bc63d57f97b03ba726676425e160b,STILL_EXISTS,TODO: (sven) if sub_exploration is None; use Trainer's default
aaiibhdai,ray-project/ray,rllib/utils/exploration/tests/test_curiosity.py,2cbe29a7fa8bc63d57f97b03ba726676425e160b,STILL_EXISTS,A very large\u00A0frozen-lake that's hard for a random policy to solve
aaiibhdid,ray-project/ray,python/ray/tests/test_placement_group.py,224933b5e486d5be738f34bf8c52a84d44da0485,STILL_EXISTS,Currently; there's no way to prevent
aaiibheaa,ray-project/ray,rllib/agents/ppo/ppo_torch_policy.py,e968b52cb7de4e6c2fcc6e7d5ccb98d984745715,36bda8432b2ed23f623c8c3031b2cba148bec93b,Needed for postprocessing.
aaiibhecd,ray-project/ray,rllib/evaluation/sampler.py,e968b52cb7de4e6c2fcc6e7d5ccb98d984745715,STILL_EXISTS,TODO: (sven) add env infos to buffers as well.
aaiibhede,ray-project/ray,rllib/evaluation/sampler.py,e968b52cb7de4e6c2fcc6e7d5ccb98d984745715,36bda8432b2ed23f623c8c3031b2cba148bec93b,TODO: (sven) Case: rollout_fragment_length reached: Do not
aaiibheie,ray-project/ray,rllib/models/torch/recurrent_net.py,e968b52cb7de4e6c2fcc6e7d5ccb98d984745715,STILL_EXISTS,TODO: (sven) For now; only allow; iff time_major=True to not break
aaiibhfaj,ray-project/ray,rllib/policy/sample_batch.py,e968b52cb7de4e6c2fcc6e7d5ccb98d984745715,STILL_EXISTS,Keeps track of new columns added after initial ones.
aaiibhfbc,ray-project/ray,rllib/utils/typing.py,e968b52cb7de4e6c2fcc6e7d5ccb98d984745715,STILL_EXISTS,Represents an \"unroll\" (maybe across different sub-envs in a vector env).
aaiibhfbj,ray-project/ray,python/ray/autoscaler/aws/node_provider.py,245c0a9e43a6493611be48d949f2184acf86aea3,STILL_EXISTS,NOTE(maximsmol): This is needed for mocking
aaiibhfcd,ray-project/ray,python/ray/autoscaler/aws/node_provider.py,245c0a9e43a6493611be48d949f2184acf86aea3,STILL_EXISTS,to fix this (add <stateReason> to EC2_RUN_INSTANCES)
aaiibhfdd,ray-project/ray,python/ray/autoscaler/updater.py,245c0a9e43a6493611be48d949f2184acf86aea3,STILL_EXISTS,this is most probably a bug since the user has no control
aaiibhhea,ray-project/ray,python/ray/runtime_context.py,fe6daef85e1f3cd2aef12b39487124a6e2dcc2b9,STILL_EXISTS,TODO: this method should not be called in a normal task.
aaiibhjad,ray-project/ray,python/ray/cloudpickle/__init__.py,f0c3910d598e7964a28b4245bd6e4024114bdbd2,ddd62a177fb2cb82a3dc267705fa01ed6e7decc6,Conform to the convention used by python serialization libraries; which
aaiibiabh,ray-project/ray,python/ray/cloudpickle/cloudpickle_fast.py,f0c3910d598e7964a28b4245bd6e4024114bdbd2,ddd62a177fb2cb82a3dc267705fa01ed6e7decc6,TODO: decorrelate reducer_override (which is tied to CPython's
aaiibiada,ray-project/ray,python/ray/cloudpickle/cloudpickle_fast.py,f0c3910d598e7964a28b4245bd6e4024114bdbd2,ddd62a177fb2cb82a3dc267705fa01ed6e7decc6,When reducer_override is not available; hack the pure-Python
aaiibiafb,ray-project/ray,python/ray/cloudpickle/cloudpickle_fast.py,f0c3910d598e7964a28b4245bd6e4024114bdbd2,f30ac73640c9ed4927150024cb0726055b576112,FIXME(suquark): The upstream cloudpickle cannot work in Ray
aaiibiafe,ray-project/ray,python/ray/cloudpickle/cloudpickle_fast.py,f0c3910d598e7964a28b4245bd6e4024114bdbd2,f30ac73640c9ed4927150024cb0726055b576112,Here the workaround just set \"_abc_impl\" to None.
aaiibibdd,ray-project/ray,python/ray/tune/tests/test_trial_scheduler_pbt.py,afde3db4f0627cc4e9972d0d8d25983aef197745,STILL_EXISTS,Score gets better every iteration.
aaiibibdg,ray-project/ray,python/ray/tune/tests/ext_pytorch.py,efa1d51aeadeb23491d0159c588c31fd85e85eae,STILL_EXISTS,\"\"\" || Hyperparameter tuning with Ray Tune || =================================== ||  || Hyperparameter tuning can make the difference between an average model and a highly || accurate one. Often simple things like choosing a different learning rate or changing || a network layer size can have a dramatic impact on your model performance. ||  || Fortunately; there are tools that help with finding the best combination of parameters. || `Ray Tune <https:\/\/docs.ray.io\/en\/latest\/tune.html>`_ is an industry standard tool for || distributed hyperparameter tuning. Ray Tune includes the latest hyperparameter search || algorithms; integrates with TensorBoard and other analysis libraries; and natively || supports distributed training through `Ray's distributed machine learning engine || <https:\/\/ray.io\/>`_. ||  || In this tutorial; we will show you how to integrate Ray Tune into your PyTorch || training workflow. We will extend `this tutorial from the PyTorch documentation || <https:\/\/pytorch.org\/tutorials\/beginner\/blitz\/cifar10_tutorial.html>`_ for training || a CIFAR10 image classifier. ||  || As you will see; we only need to add some slight modifications. In particular; we || need to ||  || 1. wrap data loading and training in functions; || 2. make some network parameters configurable; || 3. add checkpointing (optional); || 4. and define the search space for the model tuning ||  || | ||  || To run this tutorial; please make sure the following packages are || installed: ||  || -  ``ray[tune]``: Distributed hyperparameter tuning library || -  ``torchvision``: For the data transformers ||  || Setup \/ Imports || --------------- || Let's start with the imports: || \"\"\"
aaiibibeg,ray-project/ray,python/ray/tune/tests/ext_pytorch.py,efa1d51aeadeb23491d0159c588c31fd85e85eae,STILL_EXISTS,Most of the imports are needed for building the PyTorch model. Only the last three
aaiibibfe,ray-project/ray,python/ray/tune/tests/ext_pytorch.py,efa1d51aeadeb23491d0159c588c31fd85e85eae,STILL_EXISTS,Configurable neural network
aaiibibfg,ray-project/ray,python/ray/tune/tests/ext_pytorch.py,efa1d51aeadeb23491d0159c588c31fd85e85eae,STILL_EXISTS,We can only tune those parameters that are configurable. In this example; we can specify
aaiibibhj,ray-project/ray,python/ray/tune/tests/ext_pytorch.py,efa1d51aeadeb23491d0159c588c31fd85e85eae,STILL_EXISTS,The learning rate of the optimizer is made configurable; too:
aaiibibih,ray-project/ray,python/ray/tune/tests/ext_pytorch.py,efa1d51aeadeb23491d0159c588c31fd85e85eae,STILL_EXISTS,with which we iterate through the training and test sets are configurable as well.
aaiibidii,ray-project/ray,python/ray/cloudpickle/cloudpickle.py,ddd62a177fb2cb82a3dc267705fa01ed6e7decc6,871cde989a45341129aa9bcd7b59115020ef13d2,XXX: can this ever happen in Python 3? If so add a test.
aaiibiebh,ray-project/ray,python/ray/cloudpickle/cloudpickle.py,ddd62a177fb2cb82a3dc267705fa01ed6e7decc6,871cde989a45341129aa9bcd7b59115020ef13d2,save the rest of the func data needed by _fill_function
aaiibieeg,ray-project/ray,python/ray/cloudpickle/cloudpickle.py,ddd62a177fb2cb82a3dc267705fa01ed6e7decc6,871cde989a45341129aa9bcd7b59115020ef13d2,TODO: also support binary mode files with io.BytesIO
aaiibiegi,ray-project/ray,python/ray/cloudpickle/cloudpickle_fast.py,ddd62a177fb2cb82a3dc267705fa01ed6e7decc6,f30ac73640c9ed4927150024cb0726055b576112,FIXME(suquark): The upstream cloudpickle cannot work in Ray
aaiibiehb,ray-project/ray,python/ray/cloudpickle/cloudpickle_fast.py,ddd62a177fb2cb82a3dc267705fa01ed6e7decc6,f30ac73640c9ed4927150024cb0726055b576112,Here the workaround just set \"_abc_impl\" to None.
aaiibiehg,ray-project/ray,python/ray/cloudpickle/cloudpickle_fast.py,ddd62a177fb2cb82a3dc267705fa01ed6e7decc6,5ad5cb61ca7156428b34e2455b88ac7165dce6c0,in numpy C backend. This is a workaround for python3.5 pickling support.
aaiibiffh,ray-project/ray,python/ray/tune/ray_trial_executor.py,09d4a3241ff872866e6c0573abe24305ab8deeab,STILL_EXISTS,TODO: Right now; we only support reuse if there has been
aaiibigbb,ray-project/ray,rllib/agents/ppo/appo_tf_policy.py,ef18893fb50c97c2c0d9d7b8b951455683f89635,STILL_EXISTS,TODO: (sven) deprecate this when trajectory view API gets activated.
aaiibigca,ray-project/ray,rllib/agents/ppo/appo_tf_policy.py,ef18893fb50c97c2c0d9d7b8b951455683f89635,STILL_EXISTS,TODO: (sven) remove this del once we have trajectory view API fully in
aaiibihdb,ray-project/ray,python/ray/tune/sample.py,06af62ba91f893e34e70c3aa1f272d758cd0c8bd,STILL_EXISTS,TODO (krfricke): Remove tune.function
aaiibihed,ray-project/ray,python/ray/tune/tune.py,06af62ba91f893e34e70c3aa1f272d758cd0c8bd,756a9ea6416b95ba16ebd06a901382b8a1778310,TODO (krfricke): Introduce metric\/mode as top level API
aaiibihgg,ray-project/ray,python/ray/tune/integration/horovod.py,43a7a64b300b22de991f1d9228ecc2ed89a3a3d7,STILL_EXISTS,TODO: optimize if colocated
aaiibiiia,ray-project/ray,python/ray/serve/api.py,786f12edfd3ba9850548d7752971cbe503bb64eb,STILL_EXISTS,Initialize ray if needed.
aaiibjaij,ray-project/ray,python/ray/tune/trial_runner.py,551c597312d0cf39da2244f05208a60b4d3d4866,STILL_EXISTS,TODO: Consider supporting ERRORED_ONLY+REMOTE?
aaiibjajb,ray-project/ray,python/ray/tune/utils/util.py,551c597312d0cf39da2244f05208a60b4d3d4866,STILL_EXISTS,TODO(rliaw): move into ray.constants
aaiibjajc,ray-project/ray,rllib/train.py,551c597312d0cf39da2244f05208a60b4d3d4866,6662efd74b28faa176208523ed0bddc39bf7ec67,TODO: this should be eventually removed as an arg
aaiibjajg,ray-project/ray,python/ray/autoscaler/cli_logger.py,f38dba09b2cd07a27d1e9659176c8a60972bfd44,STILL_EXISTS,todo: throw if given args\/kwargs?
aaiibjajh,ray-project/ray,python/ray/autoscaler/cli_logger.py,f38dba09b2cd07a27d1e9659176c8a60972bfd44,STILL_EXISTS,TODO: come up with a plan to unify logging.
aaiibjdbf,ray-project/ray,python/ray/util/sgd/torch/examples/raysgd_torch_signatures.py,415be78cc0d1275a29d0ceda550d0d7a7a5224ea,STILL_EXISTS,Setup all components needed for training here. This could include
aaiibjdei,ray-project/ray,python/ray/util/sgd/torch/training_operator.py,415be78cc0d1275a29d0ceda550d0d7a7a5224ea,STILL_EXISTS,TODO: Implement len for Dataset?
aaiibjdif,ray-project/ray,python/ray/tune/schedulers/async_hyperband.py,3501ea396c3b08c9fc2c220134bb1a68b423122a,STILL_EXISTS,TODO: fix up the output for this
aaiibjdjh,ray-project/ray,python/ray/autoscaler/updater.py,12261c2a19cd198c123158f4e45cd22c9a940913,STILL_EXISTS,defaults. Better to be defensive.
aaiibjdji,ray-project/ray,python/ray/autoscaler/updater.py,12261c2a19cd198c123158f4e45cd22c9a940913,STILL_EXISTS,TODO (Alex): This makes the assumption that $HOME on the head and
aaiibjegg,ray-project/ray,doc/examples/progress_bar.py,1a4023387d79712f8830c56778338d71560c0eac,STILL_EXISTS,Note that this is a Ray Task; but it could very well
aaiibjfba,ray-project/ray,python/ray/util/sgd/torch/torch_trainer.py,d5a7c53908018c72a663859990db016d86a4ac5e,STILL_EXISTS,Major hack. If we go from LocalDistributedRunner to a
aaiibjfcg,ray-project/ray,python/ray/util/sgd/torch/worker_group.py,d5a7c53908018c72a663859990db016d86a4ac5e,STILL_EXISTS,This is needed to handle calling ray.get on a dead actor.
aaiibjfdd,ray-project/ray,python/ray/util/sgd/torch/worker_group.py,d5a7c53908018c72a663859990db016d86a4ac5e,STILL_EXISTS,This is not the most efficient because you have to wait for
aaiibjffi,ray-project/ray,python/ray/utils.py,6f479d46974dfa30c97a8fc95ad1909f9c5dec28,STILL_EXISTS,TODO (Alex): Don't implement this logic oursleves.
aaiibjfgf,ray-project/ray,python/ray/utils.py,6f479d46974dfa30c97a8fc95ad1909f9c5dec28,STILL_EXISTS,TODO (Alex): We should probably add support for fractional cpus.
aaiibjgac,ray-project/ray,python/ray/autoscaler/updater.py,a159ae72b3089bbaf49a8b6145e92c40c03dc5a8,STILL_EXISTS,defaults. Better to be defensive.
aaiibjgad,ray-project/ray,python/ray/autoscaler/updater.py,a159ae72b3089bbaf49a8b6145e92c40c03dc5a8,STILL_EXISTS,TODO (Alex): This makes the assumption that $HOME on the head and
aaiibjgbi,ray-project/ray,python/ray/services.py,5cbc411e387469dbafd9a8046337ad87ae31bf26,609c1b8acdb9696989757620b5efa34bc958cff8,See `cluster_mode_test.cc` for why this workaround is currently needed
aaiibjgdh,ray-project/ray,python/ray/autoscaler/_private/updater.py,6a227ae50132639552b7d3601cc828c28143e2cb,STILL_EXISTS,defaults. Better to be defensive.
aaiibjgdi,ray-project/ray,python/ray/autoscaler/_private/updater.py,6a227ae50132639552b7d3601cc828c28143e2cb,STILL_EXISTS,TODO (Alex): This makes the assumption that $HOME on the head and
aaiibjhdj,ray-project/ray,ci/regression_test/stress_tests/placement_group/test_placement_group.py,e3b485022464109abee53cc2822070618dc3e327,STILL_EXISTS,failure. We need to fix it.
aaiibjhid,ray-project/ray,python/ray/services.py,390107b6cbe1550defdcd86d4dbf945cadd43372,609c1b8acdb9696989757620b5efa34bc958cff8,See `cluster_mode_test.cc` for why this workaround is currently needed
aaiibjiif,ray-project/ray,python/ray/_private/services.py,609c1b8acdb9696989757620b5efa34bc958cff8,STILL_EXISTS,HACK: Workaround for UNIX idiosyncrasy
aaiibjjgd,ray-project/ray,python/ray/_private/services.py,609c1b8acdb9696989757620b5efa34bc958cff8,STILL_EXISTS,128MB. This is a hack to make it less likely for pubsub messages to be
aaiibjjif,ray-project/ray,python/ray/_private/services.py,609c1b8acdb9696989757620b5efa34bc958cff8,361e5f0bef30cab6d2082767a59b04c6ab2431c7,See `cluster_mode_test.cc` for why this workaround is currently needed
aaiibjjjf,ray-project/ray,python/ray/services.py,609c1b8acdb9696989757620b5efa34bc958cff8,STILL_EXISTS,TODO(ekl) deprecate and move this to ray.util
aaiicabdi,ray-project/ray,rllib/execution/replay_buffer.py,8f79b4e45ee41565690c59769462c12208996c4a,STILL_EXISTS,Wrap around storage as a circular buffer once we hit maxsize.
aaiicacda,ray-project/ray,python/ray/tests/test_advanced_2.py,3504391fd2b65ef1777e3d361a23e126add107e5,STILL_EXISTS,Sleep a while to emulate a slow operation. This is needed to make
aaiicacgc,ray-project/ray,rllib/agents/a3c/a3c_torch_policy.py,36bda8432b2ed23f623c8c3031b2cba148bec93b,62c7ab518214286a4721dd7410978effd6d05471,Next obs are needed for PPO postprocessing; but not in loss.
aaiicacge,ray-project/ray,rllib/agents/a3c/a3c_torch_policy.py,36bda8432b2ed23f623c8c3031b2cba148bec93b,62c7ab518214286a4721dd7410978effd6d05471,Needed for PPO's loss function.
aaiicacia,ray-project/ray,rllib/evaluation/collectors/simple_list_collector.py,36bda8432b2ed23f623c8c3031b2cba148bec93b,99c81c6795ddd5c97958b2f8e1343f81960477d4,TODO: measure performance gains when using a UsageTrackingDict
aaiicacib,ray-project/ray,rllib/evaluation/collectors/simple_list_collector.py,36bda8432b2ed23f623c8c3031b2cba148bec93b,STILL_EXISTS,instead of a SampleBatch for postprocessing (this would eliminate
aaiicacic,ray-project/ray,rllib/evaluation/collectors/simple_list_collector.py,36bda8432b2ed23f623c8c3031b2cba148bec93b,99c81c6795ddd5c97958b2f8e1343f81960477d4,copies (for creating this SampleBatch) of many unused columns for
aaiicacif,ray-project/ray,rllib/evaluation/collectors/simple_list_collector.py,36bda8432b2ed23f623c8c3031b2cba148bec93b,STILL_EXISTS,Some columns don't exist yet (get created during postprocessing).
aaiicacjh,ray-project/ray,rllib/evaluation/collectors/simple_list_collector.py,36bda8432b2ed23f623c8c3031b2cba148bec93b,STILL_EXISTS,Skip columns that are not used for training.
aaiicadba,ray-project/ray,rllib/evaluation/collectors/simple_list_collector.py,36bda8432b2ed23f623c8c3031b2cba148bec93b,STILL_EXISTS,TODO: determine exact shift-before based on the view-req shifts.
aaiicadbf,ray-project/ray,rllib/evaluation/collectors/simple_list_collector.py,36bda8432b2ed23f623c8c3031b2cba148bec93b,STILL_EXISTS,TODO: (sven) Once we implement multi-agent communication channels;
aaiicadha,ray-project/ray,rllib/examples/models/rnn_model.py,36bda8432b2ed23f623c8c3031b2cba148bec93b,STILL_EXISTS,TODO: (sven): Get rid of `get_initial_state` once Trajectory
aaiicadhc,ray-project/ray,rllib/models/modelv2.py,36bda8432b2ed23f623c8c3031b2cba148bec93b,STILL_EXISTS,TODO: (sven): Get rid of `get_initial_state` once Trajectory
aaiicaebb,ray-project/ray,python/ray/util/sgd/torch/ptl_operator.py,874da9a25fd4f1d8aac44d29c365fa2143f7793e,STILL_EXISTS,This is needed for LightningDistributedDataParallel.
aaiicaebj,ray-project/ray,python/ray/util/sgd/torch/ptl_operator.py,874da9a25fd4f1d8aac44d29c365fa2143f7793e,STILL_EXISTS,TODO: Implement len for Dataset?
aaiicaeej,ray-project/ray,dashboard/memory_utils.py,9a6d01ebf9fad80cdca1810f447848fdd83d2f60,STILL_EXISTS,If the object ref ends with =; that means it is base64 encoded.
aaiicafhg,ray-project/ray,python/ray/util/sgd/torch/torch_runner.py,6325a973a224589dab99c70e99f6d458327ff902,STILL_EXISTS,Needed to make sure we don't loop forever if iterator is empty
aaiicagaj,ray-project/ray,rllib/env/wrappers/recsim_wrapper.py,56b56cf7a13b80d5b726a2e8ae81676d049c073a,STILL_EXISTS,\"\"\"Wrap Google's RecSim environment for RLlib ||  || RecSim is a configurable recommender systems simulation platform. || Source: https:\/\/github.com\/google-research\/recsim || \"\"\"
aaiicagba,ray-project/ray,python/ray/tests/test_global_state.py,dc7c2a70b85f7655ac498972e48aab30f7de96cc,STILL_EXISTS,Ideally we'd want to assert backlog_size == 8; but guaranteeing
aaiicagec,ray-project/ray,rllib/env/model_vector_env.py,ce96b03b07a4a4bdd851aa84493c616cd291aff2,e74947cc94f5b085510d8b95fb359e487cf70622,Note: Each actual env must implement one to output exact rewards.
aaiicahgh,ray-project/ray,python/ray/tune/ray_trial_executor.py,b450cb030a5d316f04d4f6322e152335853d1a88,6c23bef2a7306b0256a220f37201c091257fa7f8,TODO: Check preconditions - why is previous state needed?
aaiicahje,ray-project/ray,rllib/models/tests/test_convtranspose2d_stack.py,d3bc20b727b1e4c58644a5be303b8f9be0510549,STILL_EXISTS,Move to torch.
aaiicaiah,ray-project/ray,rllib/utils/framework.py,d3bc20b727b1e4c58644a5be303b8f9be0510549,STILL_EXISTS,TODO: (sven) move to models\/utils.py
aaiicaibg,ray-project/ray,python/ray/autoscaler/_private/resource_demand_scheduler.py,06fe690682b5fcc7a42078173476e1fb5885c429,STILL_EXISTS,TODO(ameer): Consider making frac configurable.
aaiicaijh,ray-project/ray,python/ray/autoscaler/_private/resource_demand_scheduler.py,7466ce82df32c47364e1a67e6aa29c68362eb801,STILL_EXISTS,TODO (Alex): We will hit this case every time a placement group
aaiicajde,ray-project/ray,rllib/evaluation/rollout_worker.py,414041c6dd3e2361a3cc9de1c646dd9114004fe8,STILL_EXISTS,No Env will be used in this particular worker (not needed).
aaiicajea,ray-project/ray,rllib/evaluation/worker_set.py,414041c6dd3e2361a3cc9de1c646dd9114004fe8,STILL_EXISTS,TODO: (sven) Allow for setting observation and action spaces to
aaiicbagc,ray-project/ray,python/ray/serve/tests/test_standalone.py,2c5cb95b42496dc418141082548bfcdb8d9f2a86,d7c95a4a9065cbad73901b4c1de087837e260316,HACK: using two different ip address so the placement constraint for
aaiicbcdh,ray-project/ray,python/ray/tune/schedulers/pb2.py,e7aafd7d24fdbac9a9440293ec2022ade066ebca,STILL_EXISTS,(TODO: convert to curve)
aaiicbdhg,ray-project/ray,rllib/utils/exploration/epsilon_greedy.py,26176ec5706cc7b3c153676554e6f51f071bf545,STILL_EXISTS,TODO: Support MultiActionDistr for tf.
aaiicbdhh,ray-project/ray,rllib/utils/exploration/epsilon_greedy.py,26176ec5706cc7b3c153676554e6f51f071bf545,STILL_EXISTS,TODO: (bcahlit) Mask out actions
aaiicbdid,ray-project/ray,python/ray/autoscaler/_private/constants.py,8d74a04a42921b2f94c8751d36b544e91a3a5803,STILL_EXISTS,demand scheduler bin packing algorithm takes a reasonable amount of time
aaiicbfde,ray-project/ray,rllib/examples/simulators/sumo/marlenvironment.py,e735add268ddd4290f335c84e860d9d572115ff0,STILL_EXISTS,hack to retrieve the subs
aaiicbgcg,ray-project/ray,rllib/agents/slateq/slateq.py,5af745c90d87a455bc2e3c389b6731311b580bb1,STILL_EXISTS,No training is needed for the RANDOM strategy.
aaiicbgdf,ray-project/ray,rllib/agents/slateq/slateq_torch_policy.py,5af745c90d87a455bc2e3c389b6731311b580bb1,STILL_EXISTS,real imact; and can be set arbitrarily. TODO: fix this.
aaiicbhdi,ray-project/ray,python/ray/serve/controller.py,c3074f559cda56cbf29168d1aaa27642b68a4ca6,4ada3e4c9952ffd6f573f9813a0231bafc6cf17c,TODO(edoakes): we should probably have a timeout here.
aaiicbhgb,ray-project/ray,rllib/policy/dynamic_tf_policy.py,5b788ccb1329a8a25d6fb2c1f4c7823dfbcf6236,STILL_EXISTS,State-outs (no placeholders needed).
aaiicbhgi,ray-project/ray,rllib/policy/policy.py,5b788ccb1329a8a25d6fb2c1f4c7823dfbcf6236,STILL_EXISTS,TODO: (sven) This hack will not work for attention net traj.
aaiicbhha,ray-project/ray,rllib/policy/policy.py,5b788ccb1329a8a25d6fb2c1f4c7823dfbcf6236,STILL_EXISTS,Add new columns automatically to view-reqs.
aaiicbhhb,ray-project/ray,rllib/policy/policy.py,5b788ccb1329a8a25d6fb2c1f4c7823dfbcf6236,STILL_EXISTS,Add those needed for postprocessing and training.
aaiicbhhc,ray-project/ray,rllib/policy/policy.py,5b788ccb1329a8a25d6fb2c1f4c7823dfbcf6236,STILL_EXISTS,Tag those only needed for post-processing.
aaiicbhhd,ray-project/ray,rllib/policy/policy.py,5b788ccb1329a8a25d6fb2c1f4c7823dfbcf6236,STILL_EXISTS,Remove those not needed at all (leave those that are needed
aaiicbibi,ray-project/ray,python/ray/tests/test_autoscaling_policy.py,53aac55739ba0a09d96814a44f7b22b7ae83b54b,STILL_EXISTS,TODO (Alex): Implement a more realistic scheduling algorithm.
aaiicbjif,ray-project/ray,python/ray/tune/utils/callback.py,603accf1c28fc08f8d0ab5351777bba288ab3df0,STILL_EXISTS,Track syncer obj\/index to move callback after loggers
aaiicbjje,ray-project/ray,python/ray/tune/utils/callback.py,603accf1c28fc08f8d0ab5351777bba288ab3df0,STILL_EXISTS,Todo(krfricke): Maybe check if syncer comes after all loggers
aaiiccabh,ray-project/ray,python/ray/cloudpickle/__init__.py,871cde989a45341129aa9bcd7b59115020ef13d2,STILL_EXISTS,Conform to the convention used by python serialization libraries; which
aaiiccbdj,ray-project/ray,python/ray/cloudpickle/cloudpickle_fast.py,871cde989a45341129aa9bcd7b59115020ef13d2,STILL_EXISTS,TODO: decorrelate reducer_override (which is tied to CPython's
aaiiccbfe,ray-project/ray,python/ray/cloudpickle/cloudpickle_fast.py,871cde989a45341129aa9bcd7b59115020ef13d2,STILL_EXISTS,When reducer_override is not available; hack the pure-Python
aaiicccff,ray-project/ray,python/ray/tune/integration/tensorflow.py,0c1bdaef595a9575b3049fea682f80c301e2701e,STILL_EXISTS,TODO: optimize if colocated
aaiicccgi,ray-project/ray,python/ray/tests/test_autoscaling_policy.py,8afd2acdc19c3f20bd4d6f79d186668b6befea1c,STILL_EXISTS,TODO (Alex): More accurate handling of non-STRICT_PACK groups.
aaiicccgj,ray-project/ray,python/ray/tests/test_autoscaling_policy.py,8afd2acdc19c3f20bd4d6f79d186668b6befea1c,STILL_EXISTS,TODO (Alex): Not clear what's actually worth asserting here.
aaiicccig,ray-project/ray,python/ray/tests/test_serialization.py,b8dda0e3d09c8214db04b45852b07367615560fe,STILL_EXISTS,bytes to be memory efficient.
aaiiccdgi,ray-project/ray,rllib/policy/dynamic_tf_policy.py,62c7ab518214286a4721dd7410978effd6d05471,STILL_EXISTS,Add new columns automatically to (loss) input_dict.
aaiiccdhb,ray-project/ray,rllib/policy/dynamic_tf_policy.py,62c7ab518214286a4721dd7410978effd6d05471,STILL_EXISTS,TODO: (sven) rename to simply stats_fn to match eager and torch.
aaiiccdhc,ray-project/ray,rllib/policy/dynamic_tf_policy.py,62c7ab518214286a4721dd7410978effd6d05471,STILL_EXISTS,Add new columns automatically to view-reqs.
aaiiccdhd,ray-project/ray,rllib/policy/dynamic_tf_policy.py,62c7ab518214286a4721dd7410978effd6d05471,STILL_EXISTS,Add those needed for postprocessing and training.
aaiiccdjd,ray-project/ray,rllib/policy/policy.py,62c7ab518214286a4721dd7410978effd6d05471,04bc0a9828e5d7460d808cf3cd9ae2b815409830,TODO: (sven) remove unsqueezing code here for non-traj.view API.
aaiiccdjf,ray-project/ray,rllib/policy/policy.py,62c7ab518214286a4721dd7410978effd6d05471,STILL_EXISTS,filtered out later again; if not needed for postprocessing or loss).
aaiicceah,ray-project/ray,rllib/policy/tf_policy_template.py,62c7ab518214286a4721dd7410978effd6d05471,04bc0a9828e5d7460d808cf3cd9ae2b815409830,TODO: (sven) deprecate once _use_trajectory_view_api is always True.
aaiiccebd,ray-project/ray,rllib/tests/test_io.py,62c7ab518214286a4721dd7410978effd6d05471,STILL_EXISTS,in the writeOutputs run (not needed in the
aaiicceec,ray-project/ray,python/ray/operator.py,ad639f12d85297690b1190837e55ee47ce554e60,STILL_EXISTS,TODO: Add support for user-specified redis port and password
aaiicceic,ray-project/ray,python/ray/experimental/client/server.py,272edcca94b063dfa9172c2dca7bf362806a779f,STILL_EXISTS,TODO(barakmich): Perhaps wrap ray init
aaiicceid,ray-project/ray,python/ray/experimental/client/util.py,272edcca94b063dfa9172c2dca7bf362806a779f,STILL_EXISTS,TODO(barakmich): This is a dirty hack that assumes the
aaiiccfcb,ray-project/ray,python/ray/serve/router.py,277558895df5b23505f2a3d2d8194d6bfca13123,d7c95a4a9065cbad73901b4c1de087837e260316,NOTE(simon): This is a just hack around the current data
aaiiccfhf,ray-project/ray,python/ray/tests/test_ray_init.py,d35de2272d31f0a8dcfb49c2ef3d54dc95b38a0b,STILL_EXISTS,Except; apparently sometimes redis-py raises a completely
aaiiccgij,ray-project/ray,python/ray/state.py,380df89069b88a1fa2199f9997dce97173488c94,STILL_EXISTS,redis connections from each worker until needed.
aaiiccgja,ray-project/ray,python/ray/autoscaler/_private/updater.py,23926f3e6e07c586d747e37d4c5e41140e16947e,STILL_EXISTS,todo: fix command numbering
aaiiccjbe,ray-project/ray,python/ray/_private/services.py,a74f1885db523572aca0b06bd4937ac7fe92e438,STILL_EXISTS,by checking that the contents look like we expect would probably be prone
aaiiccjch,ray-project/ray,python/ray/experimental/client/__init__.py,2fe1321c3f137ea62ef71e7ad5117cb3f6594499,STILL_EXISTS,Someday we might add methods in this module so that someone who
aaiiccjda,ray-project/ray,python/ray/experimental/client/__init__.py,2fe1321c3f137ea62ef71e7ad5117cb3f6594499,STILL_EXISTS,still gets expected functionality. This is the way the ray package
aaiiccjdf,ray-project/ray,python/ray/tests/test_mldataset.py,9481ecd180fb11db14394ff33bcbc8fe23ba1b51,STILL_EXISTS,without columns
aaiiccjdg,ray-project/ray,python/ray/tests/test_mldataset.py,9481ecd180fb11db14394ff33bcbc8fe23ba1b51,STILL_EXISTS,with columns one
aaiiccjdh,ray-project/ray,python/ray/tests/test_mldataset.py,9481ecd180fb11db14394ff33bcbc8fe23ba1b51,STILL_EXISTS,with columns two
aaiiccjfj,ray-project/ray,rllib/evaluation/collectors/simple_list_collector.py,dab241dcc6c10243f7427b64abe34a71865ca931,STILL_EXISTS,Pool of used\/unused PolicyCollectorGroups (attached to episodes for
aaiicdabj,ray-project/ray,python/ray/autoscaler/_private/constants.py,589555455548bca0aeaf434cdeb83f3bf4999446,STILL_EXISTS,demand scheduler bin packing algorithm takes a reasonable amount of time
aaiicdafb,ray-project/ray,rllib/evaluation/collectors/simple_list_collector.py,95175a822f66f0cdb9525ca4835381ed19f9b980,STILL_EXISTS,before all other data columns).
aaiicdaic,ray-project/ray,python/ray/tune/checkpoint_manager.py,323941c745204663a5f8d570d5628cd9fc902997,STILL_EXISTS,Apparently avoids memory leaks on k8s\/k3s\/pods
aaiicdbbg,ray-project/ray,rllib/evaluation/collectors/simple_list_collector.py,0df55a139c1c7d11760995ee8ea3c3f95742e30e,STILL_EXISTS,TODO: (sven) Once we have the additional
aaiicdbfd,ray-project/ray,rllib/agents/ddpg/ddpg_tf_policy.py,fb318addcb58548067f115b92e8e2265f8ff218c,STILL_EXISTS,TODO: (sven) make this function return both optimizers and
aaiicdbff,ray-project/ray,rllib/policy/eager_tf_policy.py,fb318addcb58548067f115b92e8e2265f8ff218c,STILL_EXISTS,TODO: (sven) Allow tf policy to have more than 1 optimizer.
aaiicdbfh,ray-project/ray,rllib/policy/tf_policy_template.py,fb318addcb58548067f115b92e8e2265f8ff218c,STILL_EXISTS,TODO: (sven) Allow tf-eager policy to have more than 1 optimizer.
aaiicdcdc,ray-project/ray,python/ray/serve/controller.py,e422ace0537098029b106ec8b2b3fda0fa6cf65b,b52cce6632dc0a91c72e2fae6312eac4e2ad4d0d,TODO(ilr) This is currently *unused* until the refactor of the serve
aaiicddej,ray-project/ray,rllib/models/tf/attention_net.py,19c8033df2bab4197d57b11fb95b840ef0d3ae1b,STILL_EXISTS,TODO: (sven) Deprecate this once trajectory view API has fully matured.
aaiicddfa,ray-project/ray,rllib/policy/dynamic_tf_policy.py,19c8033df2bab4197d57b11fb95b840ef0d3ae1b,04bc0a9828e5d7460d808cf3cd9ae2b815409830,TODO: (sven) deprecate once _use_trajectory_view_api is always True.
aaiicddgb,ray-project/ray,python/ray/util/check_serialize.py,a21523c709fd50538430070fdbe05992f575d5e6,STILL_EXISTS,TODO: we only differentiate between 'function' and 'object'
aaiicddgc,ray-project/ray,python/ray/util/check_serialize.py,a21523c709fd50538430070fdbe05992f575d5e6,STILL_EXISTS,but we should do a better job of diving into something
aaiicdeaj,ray-project/ray,rllib/tests/test_attention_net_learning.py,3f4bc16276d6d9b9ddc3dde441632273cf53c0f8,STILL_EXISTS,TODO: (sven) causes memory failures\/timeouts on Travis.
aaiicdedf,ray-project/ray,python/ray/autoscaler/_private/command_runner.py,f669830de6ae9f93ae4f81b921a667a33e53fc0d,36e46ed923ae809e50bfeaca1628e834028616e5,TODO (Dmitri): Think about how to use the node's HOME variable
aaiicdeid,ray-project/ray,rllib/agents/ppo/ppo_torch_policy.py,99c81c6795ddd5c97958b2f8e1343f81960477d4,04bc0a9828e5d7460d808cf3cd9ae2b815409830,TODO: (sven) Remove once trajectory view API is all-algo default.
aaiicdeji,ray-project/ray,rllib/models/modelv2.py,99c81c6795ddd5c97958b2f8e1343f81960477d4,STILL_EXISTS,TODO: (sven) Experimental method.
aaiicdfcd,ray-project/ray,python/ray/autoscaler/_private/command_runner.py,f61bc79a870bbad0cc754ee2b89f81f9de43b442,STILL_EXISTS,TODO (Dmitri): Think about how to use the node's HOME variable
aaiicdffi,ray-project/ray,python/ray/autoscaler/_private/autoscaler.py,a4dbb271bd4aa6b9a7ae7a7d8be3ca24df04d9f1,STILL_EXISTS,Don't terminate nodes needed by request_resources()
aaiicdffj,ray-project/ray,python/ray/autoscaler/_private/autoscaler.py,a4dbb271bd4aa6b9a7ae7a7d8be3ca24df04d9f1,STILL_EXISTS,or it is needed for request_resources().
aaiicdfhe,ray-project/ray,python/ray/autoscaler/_private/autoscaler.py,a4dbb271bd4aa6b9a7ae7a7d8be3ca24df04d9f1,STILL_EXISTS,No resources of the node were needed for request_resources().
aaiicdfjg,ray-project/ray,python/ray/tests/test_autoscaler.py,a4dbb271bd4aa6b9a7ae7a7d8be3ca24df04d9f1,STILL_EXISTS,2 nodes and not 1 because 1 is needed for min_worker and the other 1
aaiicdgdc,ray-project/ray,python/ray/experimental/client/__init__.py,dc4b5c7aa3ffadf71e2ba15b04c83c23b203e585,c4e273920f517b18c99fbabca49135dd6e30e683,The reason for _is_server is a hack around the above comment while running
aaiicdgee,ray-project/ray,python/ray/experimental/client/common.py,dc4b5c7aa3ffadf71e2ba15b04c83c23b203e585,5cfa1934e4a2157d07ef534e1033a3f0e5bc1082,TODO(barakmich): This ref might actually be better as a serialized
aaiicdgei,ray-project/ray,python/ray/experimental/client/server/core_ray_api.py,dc4b5c7aa3ffadf71e2ba15b04c83c23b203e585,5cfa1934e4a2157d07ef534e1033a3f0e5bc1082,Wrap single item into list if needed before calling server put.
aaiicdgej,ray-project/ray,python/ray/autoscaler/_private/load_metrics.py,bd7e26b768f0aca0ef2374e79bda58bd7a9d4feb,404161a3ffd2d5937be98ade718d8ea48ab4c5a3,TODO (Alex): Change this back to info after #12138.
aaiicdgfe,ray-project/ray,python/ray/autoscaler/_private/load_metrics.py,2f8e308444bbf552d6bf8d3167d694847e85ec63,404161a3ffd2d5937be98ade718d8ea48ab4c5a3,TODO (Alex): Change this back to info after #12138.
aaiicdhbg,ray-project/ray,python/ray/util/collective/collective.py,0b1fbc5e83c9321aaff0a74bb63656777b5744f1,STILL_EXISTS,TODO(Hao): implement a group auto-counter.
aaiicdhde,ray-project/ray,python/ray/util/collective/collective_group/nccl_collective_group.py,0b1fbc5e83c9321aaff0a74bb63656777b5744f1,STILL_EXISTS,TODO(Hao): implement a simple stream manager here
aaiicdhdh,ray-project/ray,python/ray/util/collective/collective_group/nccl_collective_group.py,0b1fbc5e83c9321aaff0a74bb63656777b5744f1,7e52351ae56a5068131d873252ad4f95d134c33d,TODO(Hao): implement a thin wrapper
aaiicdhdi,ray-project/ray,python/ray/util/collective/collective_group/nccl_collective_group.py,0b1fbc5e83c9321aaff0a74bb63656777b5744f1,STILL_EXISTS,TODO: implement a simple stream manager.
aaiicdiae,ray-project/ray,python/ray/operator/operator.py,11ce1dc743b24f5472d6773f997ded20ba001d50,STILL_EXISTS,TODO: Add support for user-specified redis port and password
aaiicdihb,ray-project/ray,python/ray/autoscaler/_private/command_runner.py,82f9c7014e2d0acd3e3869066f5dc3142ec9e7a7,STILL_EXISTS,TODO (Dmitri): Identify the exception we're trying to avoid.
aaiicdjfi,ray-project/ray,python/ray/experimental/client/server/server_pickler.py,5cfa1934e4a2157d07ef534e1033a3f0e5bc1082,STILL_EXISTS,We're passing back a reference; probably inside a reference.
aaiicdjga,ray-project/ray,python/ray/experimental/client/server/server_pickler.py,5cfa1934e4a2157d07ef534e1033a3f0e5bc1082,STILL_EXISTS,We're passing back a handle; probably inside a reference.
aaiicebbg,ray-project/ray,rllib/evaluation/collectors/simple_list_collector.py,b2bcab711d333442c282cf64c66a9fac2c93218f,STILL_EXISTS,Due to possible batch-repeats > 1; columns in the resulting batch
aaiicebeh,ray-project/ray,rllib/policy/policy.py,b2bcab711d333442c282cf64c66a9fac2c93218f,STILL_EXISTS,Due to different view requirements for the different columns;
aaiicebei,ray-project/ray,rllib/policy/policy.py,b2bcab711d333442c282cf64c66a9fac2c93218f,STILL_EXISTS,columns in the resulting batch may not all have the same batch size.
aaiicebfg,ray-project/ray,rllib/policy/sample_batch.py,b2bcab711d333442c282cf64c66a9fac2c93218f,STILL_EXISTS,Fix state_in_x data.
aaiicecgi,ray-project/ray,rllib/models/torch/attention_net.py,d5604eaba321c11c1b9616c283262c4ddea55049,STILL_EXISTS,Add the needed batch rank (tf Models' Input requires this).
aaiicechb,ray-project/ray,rllib/models/torch/attention_net.py,d5604eaba321c11c1b9616c283262c4ddea55049,STILL_EXISTS,Discard last output (not needed as a memory since it's the last
aaiicechc,ray-project/ray,rllib/models/torch/attention_net.py,d5604eaba321c11c1b9616c283262c4ddea55049,STILL_EXISTS,TODO: (sven) Deprecate this once trajectory view API has fully matured.
aaiicedfd,ray-project/ray,python/ray/autoscaler/_private/autoscaler.py,8df94e33e0c10d23dde4776e97fbd5f5bd828101,STILL_EXISTS,TODO (Alex): If a node's raylet has died; it shouldn't be marked
aaiicedff,ray-project/ray,python/ray/autoscaler/_private/autoscaler.py,8df94e33e0c10d23dde4776e97fbd5f5bd828101,534846a1d215605b1a61528eaf8e9c2210316a92,TODO (Alex): Failed nodes are now immediately killed; so
aaiicedge,ray-project/ray,python/ray/autoscaler/_private/load_metrics.py,8df94e33e0c10d23dde4776e97fbd5f5bd828101,STILL_EXISTS,demand scheduler bin packing algorithm takes a reasonable amount
aaiicedgi,ray-project/ray,python/ray/autoscaler/_private/load_metrics.py,8df94e33e0c10d23dde4776e97fbd5f5bd828101,STILL_EXISTS,TODO (Alex): Would there be a benefit to properly
aaiicedhf,ray-project/ray,python/ray/tests/test_resource_demand_scheduler.py,8df94e33e0c10d23dde4776e97fbd5f5bd828101,STILL_EXISTS,TODO (Alex): This set of nodes won't be very useful in practice
aaiicedhg,ray-project/ray,python/ray/tests/test_resource_demand_scheduler.py,8df94e33e0c10d23dde4776e97fbd5f5bd828101,STILL_EXISTS,because the node:xxx.xxx.xxx.xxx resources means that no 2 nodes
aaiicedie,ray-project/ray,rllib/policy/policy_template.py,99ae7bae05b04908a392fa111e19f7cc72692bf4,STILL_EXISTS,TODO: (sven) Unify this with `build_tf_policy` as well.
aaiicedif,ray-project/ray,rllib/policy/policy_template.py,99ae7bae05b04908a392fa111e19f7cc72692bf4,STILL_EXISTS,TODO: (sven) Replace \"fetches\" with \"process\".
aaiiceeae,ray-project/ray,rllib/policy/policy_template.py,99ae7bae05b04908a392fa111e19f7cc72692bf4,STILL_EXISTS,Auto-add empty learner stats dict if needed.
aaiiceehi,ray-project/ray,python/ray/util/collective/tests/conftest.py,18f5743416948f0f25e01945da463cfd71fa19fa,STILL_EXISTS,TODO (Hao): remove this clean_up function as it sometimes crashes Ray.
aaiicefbb,ray-project/ray,python/ray/autoscaler/_private/command_runner.py,7ad56826dbab8b718de47fbf2752ba0c81cbd843,STILL_EXISTS,NOTE(ilr) This rsync is needed because when starting from
aaiicefgi,ray-project/ray,rllib/agents/cql/cql_torch_policy.py,42cd414e5bf5a224703e8809881a035d5996f861,STILL_EXISTS,For best performance; turn deterministic off
aaiicefij,ray-project/ray,rllib/agents/trainer.py,391cdfae8c566c141c03832cfff3c3e0cb9e61eb,STILL_EXISTS,class to implement your own collection\/buffering\/retrieval logic.
aaiicegdf,ray-project/ray,rllib/models/catalog.py,9eba1871bb231591337eeb7ad37922042c691b49,STILL_EXISTS,TODO: Whether to feed a_{t-n:t-1} to GTrXL (one-hot encoded if discrete).
aaiicegjh,ray-project/ray,python/ray/serve/utils.py,d632b0f0f7b9430edca0f4a69882e298f39aa0bd,8111ff5c3fdc6cef448f2e11bd0347a185f754c5,Fix legacy behaviour in all memory objects
aaiiceici,ray-project/ray,python/ray/cloudpickle/cloudpickle_fast.py,dde49b8d48b0203d8230858b8c252b223628c160,f30ac73640c9ed4927150024cb0726055b576112,FIXME(suquark): The upstream cloudpickle cannot work in Ray
aaiiceidb,ray-project/ray,python/ray/cloudpickle/cloudpickle_fast.py,dde49b8d48b0203d8230858b8c252b223628c160,f30ac73640c9ed4927150024cb0726055b576112,Here the workaround just set \"_abc_impl\" to None.
aaiiceigb,ray-project/ray,rllib/evaluation/collectors/simple_list_collector.py,6f342a22214e2dc0c77abc8662600ef00ecdcbcc,d49c3fae0bc339c51468b91af773199848df95f5,TODO: For now; assume simple 1D data (B x x).
aaiiceiif,ray-project/ray,rllib/examples/lstm_auto_wrapping.py,6f342a22214e2dc0c77abc8662600ef00ecdcbcc,STILL_EXISTS,Implement your own forward logic; whose output will then be sent
aaiicfabg,ray-project/ray,rllib/models/modelv2.py,6f342a22214e2dc0c77abc8662600ef00ecdcbcc,STILL_EXISTS,Range needed.
aaiicfacc,ray-project/ray,rllib/policy/tf_policy.py,6f342a22214e2dc0c77abc8662600ef00ecdcbcc,STILL_EXISTS,Hardcoded old way: Build fixed fields; if provided.
aaiicfacd,ray-project/ray,rllib/policy/tf_policy.py,6f342a22214e2dc0c77abc8662600ef00ecdcbcc,STILL_EXISTS,TODO: (sven) This can be deprecated after trajectory view API flag is
aaiicfccf,ray-project/ray,release/long_running_tests/workloads/object_spilling_shuffle.py,0428537d0b9646a102f4c99f42abddc04c8fce2c,STILL_EXISTS,heap memory: O(partition size) -- TODO can be reduced too
aaiicfcdb,ray-project/ray,rllib/env/atari_wrappers.py,d49c3fae0bc339c51468b91af773199848df95f5,e74947cc94f5b085510d8b95fb359e487cf70622,TODO: (sven) Deprecated class. Remove once traj. view is the norm.
aaiicfcdf,ray-project/ray,rllib/env/atari_wrappers.py,d49c3fae0bc339c51468b91af773199848df95f5,e74947cc94f5b085510d8b95fb359e487cf70622,Old way (w\/o traj. view API) via model config key: `framestack=True`.
aaiicfceb,ray-project/ray,rllib/evaluation/rollout_worker.py,d49c3fae0bc339c51468b91af773199848df95f5,STILL_EXISTS,Deprecated way of framestacking is used.
aaiicfdhc,ray-project/ray,python/ray/util/client/runtime_context.py,84e110a949e83c4956cca14ed59fd5a92e34f3fe,STILL_EXISTS,Defer the import of RuntimeContext until needed to avoid cycles
aaiicfdhd,ray-project/ray,python/ray/tests/test_cli.py,0ec9ddabc1e8c52308355def9371df0c18264b87,STILL_EXISTS,TODO(maximsmol): this is a hack since stdout=sys.stdout
aaiicfdic,ray-project/ray,python/ray/log_monitor.py,8c8af2616e70ce66c9ebb5094b85c9cd869ef6c6,STILL_EXISTS,monitor logs are needed to report autoscaler events
aaiicfebj,ray-project/ray,python/ray/tune/tests/test_trial_runner_3.py,dc42abb2f5f7dd059cf6fac3eae2832931925573,4dd5c9e5411abdaa4fca4c1d692458ec0711b678,Todo: Re-enable when using buildkite
aaiicfedb,ray-project/ray,python/ray/tune/utils/placement_groups.py,dc42abb2f5f7dd059cf6fac3eae2832931925573,STILL_EXISTS,Sets of ready and unused placement groups by factory
aaiicfeef,ray-project/ray,python/ray/tune/utils/placement_groups.py,dc42abb2f5f7dd059cf6fac3eae2832931925573,STILL_EXISTS,Otherwise; return an unused ready placement group.
aaiicfeeh,ray-project/ray,python/ray/serve/router.py,6341f1fa2e3bb526d959da4dac79dd13ac6b107e,STILL_EXISTS,Directly passing args because it might contain an ObjectRef.
aaiicfgac,ray-project/ray,rllib/env/wrappers/atari_wrappers.py,e74947cc94f5b085510d8b95fb359e487cf70622,STILL_EXISTS,TODO: (sven) Deprecated class. Remove once traj. view is the norm.
aaiicfgag,ray-project/ray,rllib/env/wrappers/atari_wrappers.py,e74947cc94f5b085510d8b95fb359e487cf70622,STILL_EXISTS,env = ScaledFloatFrame(env)  # TODO: use for dqn?
aaiicfgba,ray-project/ray,rllib/env/wrappers/atari_wrappers.py,e74947cc94f5b085510d8b95fb359e487cf70622,STILL_EXISTS,Old way (w\/o traj. view API) via model config key: `framestack=True`.
aaiicfgdh,ray-project/ray,rllib/env/wrappers/model_vector_env.py,e74947cc94f5b085510d8b95fb359e487cf70622,STILL_EXISTS,Note: Each actual env must implement one to output exact rewards.
aaiicfhah,ray-project/ray,rllib/evaluation/postprocessing.py,2e3655e8a9b1e37fa6e29f11db02a6b53cfb5928,04bc0a9828e5d7460d808cf3cd9ae2b815409830,TODO: (sven) Remove once trajectory view API is all-algo default.
aaiicfhcd,ray-project/ray,release/xgboost_tests/workloads/distributed_api_test.py,8804758409a1a416ff8017843a3896b217d6f15c,STILL_EXISTS,\"\"\"Distributed XGBoost API test ||  || This test runs unit tests on a distributed cluster. This will confirm that || XGBoost API features like custom metrics\/objectives work with remote || trainables. ||  || Test owner: krfricke ||  || Acceptance criteria: Unit tests should pass (requires pytest). || \"\"\"
aaiicfhga,ray-project/ray,python/ray/serve/backend_state.py,68038741ac2e1892db2456fed71083996613c884,39755fdb20c294507035b061a3f75d03f18c092c,TODO(ilr): FIX
aaiicfigb,ray-project/ray,python/ray/util/lightning_accelerators/horovod_ray_accelerator.py,01d74af89d0c6b20277393e60e68044ead1e1615,STILL_EXISTS,TODO: Make changes in PTL to clean this up.
aaiicfigg,ray-project/ray,python/ray/util/lightning_accelerators/tests/test_horovod_ray_accelerator.py,01d74af89d0c6b20277393e60e68044ead1e1615,STILL_EXISTS,TODO: Upstream to PTL to not set this env var if using Ray.
aaiicfjbj,ray-project/ray,benchmarks/distributed/test_distributed.py,840987c7aff50bc246ca0d22eb94225d1f82293c,STILL_EXISTS,TODO (Alex): Dynamically set this based on number of cores
aaiicfjcf,ray-project/ray,python/ray/util/collective/collective.py,7a78f4e95960bf8560b0547802f171e2b40e4f6b,STILL_EXISTS,TODO (Dacheng): how do we recycle this name actor?
aaiicfjcg,ray-project/ray,python/ray/util/collective/collective.py,7a78f4e95960bf8560b0547802f171e2b40e4f6b,STILL_EXISTS,TODO (we need a declarative destroy() API here.)
aaiicfjda,ray-project/ray,python/ray/util/collective/collective_group/nccl_collective_group.py,7a78f4e95960bf8560b0547802f171e2b40e4f6b,STILL_EXISTS,TODO (Hao): we need a lock here...
aaiicfjea,ray-project/ray,python/ray/util/collective/collective_group/nccl_collective_group.py,7a78f4e95960bf8560b0547802f171e2b40e4f6b,STILL_EXISTS,FIXME: This behavior is different from nccl document. It seems like
aaiicgbee,ray-project/ray,python/ray/serve/backend_state.py,ab6a634a9492dd079278a30e9f8b0c2e960e8c16,a695c651ee360f5ea70596d9f3286bc3d0d052c6,TODO(ilr): FIX
aaiicgcha,ray-project/ray,release/data_processing_tests/workloads/streaming_shuffle.py,c21a79ae6e35bdc01b4b40d27f67502489d73390,STILL_EXISTS,heap memory: O(partition size) -- TODO can be reduced too
aaiicgcji,ray-project/ray,python/ray/node.py,d1ec787d9da6054765590169cd5ccaa34eef70cc,STILL_EXISTS,external storage is configurable.
aaiicgdgh,ray-project/ray,rllib/evaluation/rollout_worker.py,52c94b7ee970da69721ca64f291e38556310482e,STILL_EXISTS,Deprecated way of framestacking is used.
aaiicgdih,ray-project/ray,rllib/models/tf/complex_input_net.py,52c94b7ee970da69721ca64f291e38556310482e,STILL_EXISTS,TODO: (sven) Support Dicts as well.
aaiicgdjb,ray-project/ray,rllib/models/tf/complex_input_net.py,52c94b7ee970da69721ca64f291e38556310482e,STILL_EXISTS,TODO: (sven) Multidiscrete (see e.g. our auto-LSTM wrappers).
aaiicgebb,ray-project/ray,rllib/models/torch/complex_input_net.py,52c94b7ee970da69721ca64f291e38556310482e,STILL_EXISTS,TODO: (sven) Support Dicts as well.
aaiicgech,ray-project/ray,rllib/models/torch/complex_input_net.py,52c94b7ee970da69721ca64f291e38556310482e,STILL_EXISTS,TODO: (sven) Multidiscrete (see e.g. our auto-LSTM wrappers).
aaiicgeii,ray-project/ray,python/ray/ray_operator/operator.py,1187d1dd3eed65566b22876e6ad1091367d08679,STILL_EXISTS,TODO: This only detects errors in the parent process and thus doesn't
aaiicgeij,ray-project/ray,python/ray/ray_operator/operator.py,1187d1dd3eed65566b22876e6ad1091367d08679,STILL_EXISTS,catch cluster-specific autoscaling failures. Fix that (perhaps at
aaiicgejc,ray-project/ray,python/ray/ray_operator/operator_utils.py,1187d1dd3eed65566b22876e6ad1091367d08679,STILL_EXISTS,TODO: Add retry logic in case of 409 due to old resource version.
aaiicgfab,ray-project/ray,python/ray/experimental/shuffle.py,e79a380a7e8677b7bbf5fee424d8c1454f5d57d5,STILL_EXISTS,TODO(ekl) why doesn't TypeVar() deserialize properly in Ray?
aaiicgfaf,ray-project/ray,python/ray/experimental/shuffle.py,e79a380a7e8677b7bbf5fee424d8c1454f5d57d5,4357055305395ea309813851a539381bc7c32138,TODO(ekl) using ray.wait can be more efficient for pipelining.
aaiicggbi,ray-project/ray,python/ray/autoscaler/kubernetes/example_scripts/job_example.py,081f3e5f0776b1e196495c0880edf514d6f34959,STILL_EXISTS,\"\"\" This script is meant to be run from a pod in the same Kubernetes namespace || as your Ray cluster. ||  || Just below are the environment variables used to access Ray client via a || service targetting the Ray cluster's head node pod. || These environment variables are set by Kubernetes. || See https:\/\/kubernetes.io\/docs\/concepts\/services-networking\/service\/#environment-variables || In the documentation examples; the head service has || \"example-cluster-ray-head\" and the relevant port is named \"client\". || Modify the environment variables as needed to match the name of the service || and port. ||  || Note: The default head service set up by the Ray Kubernetes operator is named || <cluster-name>-ray-head; || where <cluster-name> is the metadata.name field you set in the RayCluster || custom resource. || \"\"\"
aaiicghcb,ray-project/ray,release/tune_tests/scalability_tests/workloads/test_result_throughput_cluster.py,1ef2a6790cca2b9bd334cc7491541bca80ebd2c3,STILL_EXISTS,\"\"\"Result throughput on a cluster ||  || In this run; we will start 1000 trials concurrently that report often || (10 results per second). We thus measure the amount of overhead incurred when || dealing with a large number of results from distributed trials. ||  || Cluster: cluster_16x64.yaml ||  || Test owner: krfricke ||  || Acceptance criteria: Should run faster than 120 seconds. ||  || Theoretical minimum time: 100 seconds || \"\"\"
aaiicghcf,ray-project/ray,release/tune_tests/scalability_tests/workloads/test_result_throughput_single_node.py,1ef2a6790cca2b9bd334cc7491541bca80ebd2c3,STILL_EXISTS,\"\"\"Result throughput on a single node ||  || In this run; we will start 96 trials concurrently that report very often || (500 results per second). We thus measure the amount of overhead incurred when || dealing with a large number of results. ||  || Cluster: cluster_1x96.yaml ||  || Test owner: krfricke ||  || Acceptance criteria: Should run faster than 120 seconds. ||  || Theoretical minimum time: 100 seconds || \"\"\"
aaiicghif,ray-project/ray,python/ray/worker.py,b45ae76765693a15faef2ecc1b8adf9a44d0b072,e457872fe124a9510eef7205f15e118b22d43d8b,Delay errors a little bit of time to attempt to suppress redundant
aaiicgici,ray-project/ray,python/ray/tune/trial.py,e434ffe06c021f4e8dc20c26145e09599f8cb4c9,STILL_EXISTS,of a hack.
aaiicgide,ray-project/ray,python/ray/util/client/dataclient.py,c43a64230ef4d8d05372e230669bb829e840af29,c30d5f445cda841cc81948365de453529125a48c,TODO: Make this nonblocking. There's a race here for named
aaiicgifg,ray-project/ray,python/ray/worker.py,3ce03a52bc88640e1f1c4a6ae2544207dedb65df,ee584e8328a276988afdeebb94c2995c23adbe3f,Delay errors a little bit of time to attempt to suppress redundant
aaiicgigc,ray-project/ray,python/ray/util/client/common.py,edf24580a62c4c9ba7ccb2abf35620c0093a13fa,STILL_EXISTS,TODO: Instead of just making the max message size large; the right thing to
aaiicgjec,ray-project/ray,python/ray/tune/suggest/hebo.py,58d7398246726a6e1752b9ad0486355efa839448,STILL_EXISTS,Python 3 only -- needed for lint test.
aaiicgjid,ray-project/ray,python/ray/worker.py,66f93a3d63625d98a8a8d859a13f663937e4f19c,cc156f7b3c554c5fccdb5465636e30777678c715,Delay errors a little bit of time to attempt to suppress redundant
aaiicgjij,ray-project/ray,rllib/evaluation/rollout_worker.py,775e6855311f69d4fedd4eb8f3dd1ade31ecf5de,STILL_EXISTS,Decompress SampleBatch; in case some columns are compressed.
aaiicgjjb,ray-project/ray,rllib/execution/train_ops.py,775e6855311f69d4fedd4eb8f3dd1ade31ecf5de,STILL_EXISTS,Decompress SampleBatch; in case some columns are compressed.
aaiichbgi,ray-project/ray,python/ray/autoscaler/_private/commands.py,fcd0dee581d524f35a8a8f9ca547c8d4b5bb329c,STILL_EXISTS,Inform the user what kind of logs are collected (before actually
aaiichbid,ray-project/ray,python/ray/autoscaler/_private/constants.py,fcd0dee581d524f35a8a8f9ca547c8d4b5bb329c,STILL_EXISTS,Python worker. TODO(mehrdadn): Fix for Windows
aaiichcbd,ray-project/ray,python/ray/tune/ray_trial_executor.py,757866ec01985b6c3477c6a7215b75f630b1c9af,STILL_EXISTS,Move PG into cache (disassociate from trial)
aaiichcei,ray-project/ray,python/ray/tune/tests/test_trial_runner_3.py,757866ec01985b6c3477c6a7215b75f630b1c9af,4dd5c9e5411abdaa4fca4c1d692458ec0711b678,Todo: Re-enable when using buildkite
aaiichcfg,ray-project/ray,python/ray/tune/tests/test_tune_restore.py,757866ec01985b6c3477c6a7215b75f630b1c9af,d921dca075456203727bff09329923e6b210cc3d,THIS IS A HACK -- should be removed once
aaiichegc,ray-project/ray,rllib/policy/dynamic_tf_policy.py,80002583333075c4b6095250b00a4d8cb93b4c98,STILL_EXISTS,TODO: Remove in future.
aaiichegh,ray-project/ray,rllib/policy/eager_tf_policy.py,80002583333075c4b6095250b00a4d8cb93b4c98,STILL_EXISTS,TODO: Remove in future.
aaiicheha,ray-project/ray,rllib/policy/rnn_sequencing.py,80002583333075c4b6095250b00a4d8cb93b4c98,STILL_EXISTS,TODO: This will not work with attention nets as their state_outs are
aaiichehd,ray-project/ray,rllib/policy/sample_batch.py,80002583333075c4b6095250b00a4d8cb93b4c98,STILL_EXISTS,Skip state in columns.
aaiicheig,ray-project/ray,rllib/policy/torch_policy.py,80002583333075c4b6095250b00a4d8cb93b4c98,STILL_EXISTS,TODO: Remove in future.
aaiichejh,ray-project/ray,rllib/agents/impala/impala.py,6cd0cd3bd99ec2df47bb571a18e0b70819134504,a2d2275ee13901240370e2039b400efd9a4e0198,Return PlacementGroupFactory containing all needed resources
aaiichfci,ray-project/ray,rllib/agents/trainer.py,6cd0cd3bd99ec2df47bb571a18e0b70819134504,a2d2275ee13901240370e2039b400efd9a4e0198,Return PlacementGroupFactory containing all needed resources
aaiichfea,ray-project/ray,rllib/tests/test_placement_groups.py,6cd0cd3bd99ec2df47bb571a18e0b70819134504,STILL_EXISTS,3 Trials: Can only run 2 at a time (num_cpus=6; needed: 3).
aaiichfed,ray-project/ray,rllib/tests/test_placement_groups.py,6cd0cd3bd99ec2df47bb571a18e0b70819134504,STILL_EXISTS,3 Trials: Can only run 1 at a time (num_cpus=6; needed: 5).
aaiichfjj,ray-project/ray,python/ray/util/dask/__init__.py,b844548b57ebd9db61cf4bbf8b333ad93645da0f,STILL_EXISTS,FIXME(Clark): Monkey patching is bad and we should try to avoid this.
aaiichged,ray-project/ray,python/ray/autoscaler/_private/aws/node_provider.py,4572c6cf0f6f64fff261b5c27d87f40274861e9c,STILL_EXISTS,TODO (Alex): We are leaking the tag cache here. Naively; we would
aaiichgei,ray-project/ray,python/ray/autoscaler/_private/cli_logger.py,4572c6cf0f6f64fff261b5c27d87f40274861e9c,STILL_EXISTS,We aren't using standard python logging convention; so we hardcode
aaiichhac,ray-project/ray,python/ray/tests/test_scheduling.py,5c6c9d5b91d37162f159bb3a3df1aa6864f56e2e,STILL_EXISTS,Needed because the above test sets this to False.
aaiichhcj,ray-project/ray,rllib/examples/serve_and_rllib.py,5637d89eccf29d19c197ff401caef28372d5f6a7,STILL_EXISTS,local mode -> local env inside Trainer not needed!
aaiichhgi,ray-project/ray,python/ray/util/collective/collective_group/nccl_collective_group.py,4135b0eb4a7873f682254c61fe2fd08cf245db4b,STILL_EXISTS,FIXME: This behavior is different from nccl document. It seems like
aaiichhih,ray-project/ray,python/ray/util/client/worker.py,2cf4c7253ce1fd01975fcf970e52434e44f6d71f,STILL_EXISTS,Implement non-blocking get with a short-polling loop. This allows
aaiichhje,ray-project/ray,python/ray/utils.py,6f151ad510e5f545a74475ab25e4229c72688c8d,6d5511cf8079f04d4f70ac724de8b62437adf0e7,also be considered as unused memory
aaiichiah,ray-project/ray,rllib/agents/impala/impala.py,ef944bc5f0d7764cd99d50500e470eac005a3d01,STILL_EXISTS,Return PlacementGroupFactory containing all needed resources
aaiichidh,ray-project/ray,rllib/agents/trainer.py,ef944bc5f0d7764cd99d50500e470eac005a3d01,STILL_EXISTS,Return PlacementGroupFactory containing all needed resources
aaiichjba,ray-project/ray,rllib/policy/dynamic_tf_policy.py,732197e23a937b7b6d196936519c16ec6317ea9f,STILL_EXISTS,TODO: (sven) hack; but works for `target_[q_]?model`.
aaiichjbb,ray-project/ray,rllib/policy/tf_policy_template.py,732197e23a937b7b6d196936519c16ec6317ea9f,STILL_EXISTS,TODO: (sven) in torch; extra_learn_fetches do not exist.
aaiichjih,ray-project/ray,rllib/examples/env/tests/test_coin_game_non_vectorized_env.py,9a7fbd3cdf73bb760ab631be05c0de80f1e32c3c,STILL_EXISTS,TODO add tests for grid_size != 3
aaiichjjd,ray-project/ray,rllib/examples/env/tests/test_coin_game_vectorized_env.py,9a7fbd3cdf73bb760ab631be05c0de80f1e32c3c,STILL_EXISTS,TODO add tests for grid_size != 3
aaiiciabi,ray-project/ray,python/ray/_private/runtime_env.py,ed8935406b0cd4f62ec9da536118fb46eadeaad7,STILL_EXISTS,TODO(yic): Fix this later to handle big directories in better way
aaiicibha,ray-project/ray,python/ray/experimental/packaging/load_package.py,4e8b53b3d03ff0b2372e77b9854d4c9427f3fa92,STILL_EXISTS,Only download the repo if needed.
aaiicieai,ray-project/ray,python/ray/autoscaler/_private/resource_demand_scheduler.py,c3d8ef1b4996a444b45392c33bba141dff9b0559,STILL_EXISTS,node needs to configure redis memory which is not needed
aaiiciebi,ray-project/ray,python/ray/_private/runtime_env.py,b240f5f0c9eeeb572a456c5d517b502e564dff22,STILL_EXISTS,TODO(ekl) we should have better schema validation here.
aaiiciecf,ray-project/ray,python/ray/worker.py,b240f5f0c9eeeb572a456c5d517b502e564dff22,STILL_EXISTS,TODO(ekl) get rid of the env var hack and get runtime env from the
aaiicieeh,ray-project/ray,rllib/models/modelv2.py,69202c6a7ded0d3f4e7def53e868f34cb8c97056,STILL_EXISTS,TODO: (sven) obsolete this method at some point (replace by
aaiicieej,ray-project/ray,rllib/policy/eager_tf_policy.py,69202c6a7ded0d3f4e7def53e868f34cb8c97056,STILL_EXISTS,TODO: (sven): Keep for a while to ensure backward compatibility.
aaiicieff,ray-project/ray,rllib/policy/sample_batch.py,69202c6a7ded0d3f4e7def53e868f34cb8c97056,STILL_EXISTS,TODO: deprecate
aaiiciege,ray-project/ray,rllib/policy/torch_policy.py,69202c6a7ded0d3f4e7def53e868f34cb8c97056,STILL_EXISTS,TODO: Remove in future.
aaiiciegf,ray-project/ray,rllib/policy/torch_policy.py,69202c6a7ded0d3f4e7def53e868f34cb8c97056,STILL_EXISTS,TODO: (sven): Keep for a while to ensure backward compatibility.
aaiiciejb,ray-project/ray,python/ray/tests/test_resource_demand_scheduler.py,534846a1d215605b1a61528eaf8e9c2210316a92,STILL_EXISTS,TODO (Alex): We should find a more robust way of simulating a node
aaiicifbe,ray-project/ray,python/ray/serve/controller.py,34b5781ae0d46b769e8bac400e0cbad6c60c8fcd,4061b72f2e0bdd6a739acbd727f54f37e5cc92ff,still ugly and should be removed once the old codepath
aaiicigfa,ray-project/ray,python/ray/serve/api.py,7e63090ca989a4771c90d1b9afa201e30d4702a1,STILL_EXISTS,BUT it's needed still because of the shared asyncio thread.
aaiicihcb,ray-project/ray,rllib/evaluation/sampler.py,f859ebb99f08e1d2bfb8c1dbce377613a5c3fc62,STILL_EXISTS,array) or take care of rendering itself (returning True).
aaiicihdb,ray-project/ray,rllib/examples/env_rendering_and_recording.py,f859ebb99f08e1d2bfb8c1dbce377613a5c3fc62,STILL_EXISTS,A simple fix for this is described here:
aaiicihdf,ray-project/ray,rllib/examples/env_rendering_and_recording.py,f859ebb99f08e1d2bfb8c1dbce377613a5c3fc62,STILL_EXISTS,mode=\"rgb_array\" is needed. RLlib will automatically produce a simple
aaiicjeia,ray-project/ray,python/ray/tests/test_failure_2.py,70f45af54110404f4f1d2c33ac736276575e81bc,STILL_EXISTS,infeasibility warning (even though the actor creation task itself
aaiiddhbf,OpenMined/PySyft,src/syft/lib/python/string.py,a0544c3434fc7c2902731b514d47a19e476d3cfc,STILL_EXISTS,TODO fix this
aaiiddhhh,OpenMined/PySyft,src/syft/grid/connections/webrtc.py,55cece34158493025b98ab8d75d5a85943d858b6,953db3f51a2b3d54adb1bf99d849fc4f2e0a161b,TODO: fix this mypy madness
aaiiddjfh,OpenMined/PySyft,src/syft/grid/connections/webrtc.py,3025582ff98507fbacbdfdc0a9d4da04b3cb295f,953db3f51a2b3d54adb1bf99d849fc4f2e0a161b,properly fix this!
aaiidfaec,OpenMined/PySyft,src/syft/lib/tenseal/context.py,66e43b72d1e7b2eb2aa8ef0a4fb166034765b40d,304f58f2b71db145f3aad8599913a23df93ae0b2,TODO: sending the secret key reduces the latency by a lot; but
aaiidfbei,OpenMined/PySyft,src/syft/core/node/common/action/run_class_method_action.py,576fc10d67d35d4a6b37a5f42f6333ae8d4cd243,STILL_EXISTS,insane hack
aaiidfbfa,OpenMined/PySyft,src/syft/core/node/common/action/run_class_method_action.py,3ef7b94aa8304b55322f5a3a37be660cd34cbfd9,5b3d3c58e40eac23f096604ead9a472745f8988e,TODO: replace with proper tuple support
aaiidfcge,OpenMined/PySyft,src/syft/util.py,ae6ced7fdebc0e80cbd1d844402960f4e61ad0ba,1bfd811a6f7fbbf45ee366ab6bc5846b4b9bdb7d,breaking convention here because index_globals needs
aaiidfdab,OpenMined/PySyft,src/syft/lib/pandas/frame.py,91c512d9ebc20699bfa2f366e7051a0b7ee8b07c,7a25c2a2d97cd21ce230bad737f7406f3b4a61fd,Filter out Pandas UserWarning about creating columns
aaiidfijc,OpenMined/PySyft,tests/syft/lib/python/dict/dict_test.py,b715244a72103bdd3bb1f3d0b3af092d64ca455d,d7cdc1821bb80f11796e7624098c4e6cb013eb52,TODO: investigate this
aaiidgade,OpenMined/PySyft,src/syft/core/node/common/action/run_class_method_action.py,1bfd811a6f7fbbf45ee366ab6bc5846b4b9bdb7d,STILL_EXISTS,TODO: overload all methods to incorporate this automatically
aaiidgadg,OpenMined/PySyft,src/syft/core/node/common/action/run_class_method_action.py,1bfd811a6f7fbbf45ee366ab6bc5846b4b9bdb7d,f03078ef8acc3f23cbf106a0814e794787865246,TODO: check if inherit_tags replaces this
aaiidgaei,OpenMined/PySyft,tests/syft/core/pointer/pointer_test.py,f03078ef8acc3f23cbf106a0814e794787865246,9e09f8673a4c848aa9d3e30200e7287f6b531036,TODO: Fix this test
aaiidgajb,OpenMined/PySyft,src/syft/core/node/common/action/run_class_method_action.py,9e09f8673a4c848aa9d3e30200e7287f6b531036,5b3d3c58e40eac23f096604ead9a472745f8988e,TODO: replace with proper tuple support
aaiidgajf,OpenMined/PySyft,src/syft/util.py,9e09f8673a4c848aa9d3e30200e7287f6b531036,5b3d3c58e40eac23f096604ead9a472745f8988e,breaking convention here because index_globals needs
aaiidgbfh,OpenMined/PySyft,src/syft/grid/connections/webrtc.py,5b3d3c58e40eac23f096604ead9a472745f8988e,STILL_EXISTS,TODO: fix this mypy madness
aaiidgbfj,OpenMined/PySyft,src/syft/grid/connections/webrtc.py,5b3d3c58e40eac23f096604ead9a472745f8988e,STILL_EXISTS,properly fix this!
aaiidgbib,OpenMined/PySyft,tests/syft/core/pointer/pointer_test.py,5b3d3c58e40eac23f096604ead9a472745f8988e,a6b1284613850cc74025bfb9917db79d377862fa,TODO: Fix this test
aaiidieie,OpenMined/PySyft,src/syft/core/node/common/action/run_class_method_action.py,d8f250e8d333ce01d68c80b76fbb113e878b4270,c931bb11d9f46d30aae33914bb0bc81d78349b6e,TODO: add numpy support https:\/\/github.com\/OpenMined\/PySyft\/issues\/5164
aaiidijja,OpenMined/PySyft,tests/syft/core/node/common/plan/test_plan.py,b7d198d425a08f86dc245f3ff380ff4b0f3c80e6,STILL_EXISTS,there is probably a better\/shorter way
aaiidjaac,OpenMined/PySyft,tests/syft/core/node/common/plan/plan_test.py,81e8fbda3dab13343915455b9a4d1d58ffc538e5,STILL_EXISTS,there is probably a better\/shorter way
aaiiebdad,OpenMined/PySyft,src/syft/core/node/common/action/run_class_method_action.py,6c794ae3cc2f0c90408ac33ee64030d1fd85752f,2c66190e13b27653fa724f535df87af77d1a34c6,Todo; fix this hacky isinstance. Right now it prevents circular dependency
aaiiebdij,OpenMined/PySyft,src/syft/lib/opacus/__init__.py,304f58f2b71db145f3aad8599913a23df93ae0b2,c3c1af490e3e8a58d5d4a11262ca15a367f2cc60,TODO: This is a Tuple
aaiiebegh,OpenMined/PySyft,tests/syft/lib/python/namedtuple/namedtuple_test.py,75e42a900c3f20603b36ed117b59e0212096cadc,5f3d80bb8fa88790a17e6b706422a714eb43dd90,horrible hack; we shouldnt be constructing these right now anyway
aaiiebhdi,OpenMined/PySyft,src/syft/core/node/common/action/run_class_method_action.py,b206b2cc793633f97a7e5234162997968e01ada0,b8307e3959c940f4125a91bf72f0b408ed9cd3b7,TODO: Remove this Opacus workaround
aaiiecjgj,OpenMined/PySyft,src/syft/lib/torch/module.py,f70d7fb57fb16772703276cdbaf64b6816960ea8,f68b9ab0daea686d6147b0339b3f25311532f302,TODO: this needs fixing but should be on by default for now
aaiiecjia,OpenMined/PySyft,src/syft/core/node/common/action/run_class_method_action.py,c3c1af490e3e8a58d5d4a11262ca15a367f2cc60,263a721fdc81a9453c93dbed39eb42d9831a1e72,TODO: add numpy support https:\/\/github.com\/OpenMined\/PySyft\/issues\/5164
aaiiedcdi,OpenMined/PySyft,src/syft/lib/opacus/__init__.py,f68b9ab0daea686d6147b0339b3f25311532f302,7a754b3ab25aa7c92b7d8a8597353d9a861d232d,TODO: This is a Tuple
aaiieedij,OpenMined/PySyft,src/syft/core/node/common/action/get_object_action.py,7a25c2a2d97cd21ce230bad737f7406f3b4a61fd,3d6859c0297bc71ac89e2c80ef6030b46df4287d,TODO: Make only for DataFrames etc
aaiieedjc,OpenMined/PySyft,src/syft/core/node/common/action/run_class_method_action.py,7a25c2a2d97cd21ce230bad737f7406f3b4a61fd,263a721fdc81a9453c93dbed39eb42d9831a1e72,TODO: add numpy support https:\/\/github.com\/OpenMined\/PySyft\/issues\/5164
aaiieeeff,OpenMined/PySyft,tests/syft/core/pointer/pointer_test.py,7a25c2a2d97cd21ce230bad737f7406f3b4a61fd,95e60308ced18f4c0af516ceee0b0ccf98113625,TODO: Make only for DataFrames etc
aaiieeeif,OpenMined/PySyft,src/syft/__init__.py,862ad84fc17683f9ce22b8eee401139b067cf13d,3d6859c0297bc71ac89e2c80ef6030b46df4287d,TODO: remove this requirement in pytorch lightning
aaiieefgi,OpenMined/PySyft,tests/syft/lib/pytorch_lightning/mnist_test.py,862ad84fc17683f9ce22b8eee401139b067cf13d,STILL_EXISTS,TODO: Automatically synthesize these Union types
aaiieegdc,OpenMined/PySyft,src/syft/core/node/common/action/run_class_method_action.py,e4b4163942e86ec9eb4deae0254415a0c59151c2,STILL_EXISTS,TODO: Remove this Opacus workaround
aaiieehca,OpenMined/PySyft,src/syft/lib/torch/module.py,bb479088adfe791090689bfdac662da1cdf86f2d,STILL_EXISTS,TODO: this needs fixing but should be on by default for now
aaiiefbfb,OpenMined/PySyft,src/syft/lib/misc/__init__.py,5b378086cb8368814f2e57cdb2cb71223d0770e6,66c67bb76de1d03b055265b3aa26fcb62ac1047e,TODO: a better way. Loot at https:\/\/github.com\/OpenMined\/PySyft\/issues\/5249
aaiiefbfc,OpenMined/PySyft,src/syft/lib/torch/return_types.py,5b378086cb8368814f2e57cdb2cb71223d0770e6,STILL_EXISTS,TODO: a better way. Loot at https:\/\/github.com\/OpenMined\/PySyft\/issues\/5249
aaiiefbgg,OpenMined/PySyft,tests/syft/lib/torch/returntypes_test.py,5b378086cb8368814f2e57cdb2cb71223d0770e6,STILL_EXISTS,TODO: Loot at https:\/\/github.com\/OpenMined\/PySyft\/issues\/5249
aaiiefiig,OpenMined/PySyft,tests/syft/core/node/common/plan/plan_test.py,c919fbd0228afc82b4dcbb34b535c983e57d4506,STILL_EXISTS,Todo: fix this
aaiiegaeb,OpenMined/PySyft,src/syft/lib/misc/__init__.py,e82585698e39b62e7054df2ba9e4643d45782c18,STILL_EXISTS,TODO: a better way. Loot at https:\/\/github.com\/OpenMined\/PySyft\/issues\/5249
aaiieggde,OpenMined/PySyft,src/syft/core/node/common/service/get_repr_service.py,1476f79e56c3c1e78f87d5b092942bbed8a3082d,STILL_EXISTS,TODO: Create a remote print interface for objects which displays them in a
aaiieggdf,OpenMined/PySyft,src/syft/core/node/common/service/get_repr_service.py,1476f79e56c3c1e78f87d5b092942bbed8a3082d,STILL_EXISTS,nice way; we could also even buffer this between chained ops until we
aaiieggdj,OpenMined/PySyft,src/syft/core/pointer/pointer.py,1476f79e56c3c1e78f87d5b092942bbed8a3082d,dcac32d07415be618a776dbff02445d47410846c,nice way; we could also even buffer this between chained ops until we return
aaiieggib,OpenMined/PySyft,src/syft/lib/sympc/__init__.py,457482da99ab0f3c04f42f8626689c87b70c8856,4795f2d8dca7ce8b59f4b0b364d472047ea8e3f4,FIXME: Can't we just return an int??
aaiiehaee,OpenMined/PySyft,tests/syft/core/pointer/pointer_test.py,02c2fc357a72bf6238546d4e8e233ef281b83786,4ea41c67a1eda1e149603b6ee07cb61a54488e4e,TODO: Fix this test
aaiiehcie,OpenMined/PySyft,tests/syft/core/plan/plan_test.py,cdfe5a3f11062da05194ccfcfe7a72e378f1fea6,STILL_EXISTS,TODO: https:\/\/github.com\/OpenMined\/PySyft\/issues\/5292
aaiiehdeb,OpenMined/PySyft,src/syft/lib/torchvision/allowlist.py,34a7f00deda5e11345503624b0b615302da4e687,STILL_EXISTS,This is an interesting case; for some versions p = 0.2 is needed; for others its not needed
aaiiehijg,OpenMined/PySyft,tests/syft/core/pointer/pointer_test.py,4795f2d8dca7ce8b59f4b0b364d472047ea8e3f4,81d0a032c9b171b63fe65d1b1f3c4828fb39b292,TODO: Fix this test
aaiiehjcc,OpenMined/PySyft,src/syft/lib/sympc/__init__.py,81d0a032c9b171b63fe65d1b1f3c4828fb39b292,STILL_EXISTS,FIXME: Can't we just return an int??
aaiiehjfe,OpenMined/PySyft,src/syft/ast/attribute.py,f8a79921d43f9cea9836a094505f553eb6d6441f,1d1008d2b1d15b15484c60446eb521e5ebe98986,TODO: fix hacky work around
aaiieicfj,OpenMined/PySyft,tests/syft/core/pointer/pointer_test.py,1d1008d2b1d15b15484c60446eb521e5ebe98986,05d8ebdb1da4a183881e9b498d19b9a061c45758,TODO: Make only for DataFrames etc
aaiieidag,OpenMined/PySyft,src/syft/__init__.py,b5897e7a8f3c9b1109db17e730c44b96badc2cf9,STILL_EXISTS,TODO: remove this requirement in pytorch lightning
aaiieidah,OpenMined/PySyft,src/syft/ast/attribute.py,b5897e7a8f3c9b1109db17e730c44b96badc2cf9,STILL_EXISTS,TODO: fix hacky work around
aaiieideb,OpenMined/PySyft,src/syft/ast/klass.py,05d8ebdb1da4a183881e9b498d19b9a061c45758,edbb758dd9d87de6499ffefb78628765e5175792,TODO: Allow Classes to opt out in the AST like Pandas where the properties
aaiieideg,OpenMined/PySyft,src/syft/ast/klass.py,05d8ebdb1da4a183881e9b498d19b9a061c45758,STILL_EXISTS,TODO: Make only for DataFrames etc
aaiieidfb,OpenMined/PySyft,src/syft/lib/pandas/__init__.py,05d8ebdb1da4a183881e9b498d19b9a061c45758,STILL_EXISTS,TODO: Pandas can't have tags and description because they break the dict
aaiieidff,OpenMined/PySyft,src/syft/lib/pandas/__init__.py,05d8ebdb1da4a183881e9b498d19b9a061c45758,STILL_EXISTS,TODO: add support for self referential unions using some kind of post update
aaiieiecc,OpenMined/PySyft,src/syft/lib/torchvision/allowlist.py,ad2c1c184dde068c6c5910b75ce5f759ce791fc2,0e9be469e4d532c64d8d42852b8f053370ac1f63,TODO: Fix when we have PIL support
aaiieieed,OpenMined/PySyft,src/syft/core/pointer/pointer.py,3dd8baafd5d38f5d8b24aa791f0943f8e212ee20,STILL_EXISTS,TODO: Create a remote print interface for objects which displays them in a
aaiieieee,OpenMined/PySyft,src/syft/core/pointer/pointer.py,3dd8baafd5d38f5d8b24aa791f0943f8e212ee20,STILL_EXISTS,nice way; we could also even buffer this between chained ops until we return
aaiieifhi,OpenMined/PySyft,src/syft/core/node/common/action/get_object_action.py,0a334f9dbf71c327163731d5772426da031cc2b5,STILL_EXISTS,TODO: Make only for DataFrames etc
aaiieigjj,OpenMined/PySyft,src/syft/core/common/environment.py,8a550d15e7ee4a50052a6ff797c299e317a5f4a9,STILL_EXISTS,google.colab check to fix issue #5315
aaiieihdh,OpenMined/PySyft,src/syft/lib/torchvision/allowlist.py,7d134a84c73a1908fa5f6f5e9cfa13bfc474d734,ddf0dfce2ea73e05f63b4f52d2baef4e9f9de6e2,TODO: Fix when we have PIL support
aaiieiihf,OpenMined/PySyft,src/syft/ast/klass.py,5e5181fa301264b87557cdb1b0e483464d136360,STILL_EXISTS,TODO: Allow Classes to opt out in the AST like Pandas where the properties
aaiiejgif,OpenMined/PySyft,tests/syft/lib/python/dict/dict_test.py,14360cd7773b7eb40b77f1cd39fb386e3118c19e,a7cafac2b30533643bf50680a9700af102925bdf,test fix for seg fault reported in bpo-27945 part 3.
aaiiejgig,OpenMined/PySyft,tests/syft/lib/python/dict/dict_test.py,14360cd7773b7eb40b77f1cd39fb386e3118c19e,a7cafac2b30533643bf50680a9700af102925bdf,test fix for seg fault reported in bpo-38588 part 1.
aaiiejhaf,OpenMined/PySyft,src/syft/lib/numpy/array.py,4a16035b006499eeb76f49019f07d4a7573f5207,STILL_EXISTS,TODO: support numpy.object_; numpy.str_ and numpy.unicode_
aaiifaadb,OpenMined/PySyft,src/syft/lib/misc/__init__.py,4daf013a596058c640337a2820ef0e38e4aeca13,STILL_EXISTS,TODO: Support dynamic properties for types in AST
aaiifaadj,OpenMined/PySyft,src/syft/lib/python/collections/ordered_dict.py,86b747a5b433c5e72f2dce612ba08132aa9f6c2d,04bd42c7ae71576f5896a9808346aa5fb533346d,keep this workaround
aaiifacdj,OpenMined/PySyft,tests/syft/lib/python/dict/dict_test.py,04bd42c7ae71576f5896a9808346aa5fb533346d,STILL_EXISTS,TODO: add support for custom objects
aaiifacec,OpenMined/PySyft,tests/syft/lib/python/dict/dict_test.py,04bd42c7ae71576f5896a9808346aa5fb533346d,a7cafac2b30533643bf50680a9700af102925bdf,test fix for seg fault reported in bpo-27945 part 3.
aaiifaced,OpenMined/PySyft,tests/syft/lib/python/dict/dict_test.py,04bd42c7ae71576f5896a9808346aa5fb533346d,a7cafac2b30533643bf50680a9700af102925bdf,test fix for seg fault reported in bpo-38588 part 1.
aaiifadjf,OpenMined/PySyft,src/syft/lib/python/collections/ordered_dict.py,4429eaf4e4d9b59598ae00bd3ee78865b26fbf59,STILL_EXISTS,keep this workaround
aaiifbcgd,OpenMined/PySyft,tests/syft/lib/python/complex/complex_test.py,104c47c4993996500359d79b10dc2921a5b2defd,STILL_EXISTS,FIXME improve Complex.__new__() to have the same TypeError as complex
aaiifbcgg,OpenMined/PySyft,tests/syft/lib/python/complex/complex_test.py,104c47c4993996500359d79b10dc2921a5b2defd,STILL_EXISTS,FIXME doesn't work
aaiifbdch,OpenMined/PySyft,tests/syft/lib/python/dict/dict_test.py,4de851e5f21bbebd109cec91a03bb386b7a5e85c,a7cafac2b30533643bf50680a9700af102925bdf,test fix for seg fault reported in bpo-27945 part 3.
aaiifbdci,OpenMined/PySyft,tests/syft/lib/python/dict/dict_test.py,4de851e5f21bbebd109cec91a03bb386b7a5e85c,a7cafac2b30533643bf50680a9700af102925bdf,test fix for seg fault reported in bpo-38588 part 1.
aaiifbddi,OpenMined/PySyft,tests/syft/lib/python/float/float_test.py,4de851e5f21bbebd109cec91a03bb386b7a5e85c,STILL_EXISTS,TODO this should work!!!
aaiifebeb,OpenMined/PySyft,tests/syft/lib/python/dict/dict_test.py,5dd8e23930f4f080ccb6e4ae60ea7ddc485b06be,STILL_EXISTS,test fix for seg fault reported in bpo-27945 part 3.
aaiifebec,OpenMined/PySyft,tests/syft/lib/python/dict/dict_test.py,5dd8e23930f4f080ccb6e4ae60ea7ddc485b06be,STILL_EXISTS,test fix for seg fault reported in bpo-38588 part 1.
aaiiffbfg,OpenMined/PySyft,tests/syft/core/fl/model-centric/mcfl_create_execute_plan_test.py,55c7a9865b0b65d7261f6f26d573c8690d896070,STILL_EXISTS,TODO migrate to syft-core protobufs
aaiiffjij,scikit-learn-contrib/imbalanced-learn,UnbalancedDataset.py,54338573a8e160dd7f7c1868215009a10733ab61,STILL_EXISTS,NN lines relate to original sample; columns to its nearest neighbours
aaiifgfgd,scikit-learn-contrib/imbalanced-learn,UnbalancedDataset.py,9a40fee409b814d8d94c733e1a565d7baedd80b6,STILL_EXISTS,FIX THIS SHIT!!!#
aaiifghcc,scikit-learn-contrib/imbalanced-learn,unbalanced_dataset/over_sampling.py,50484c24505c5c31dd7aba1d38155eab0c1adad7,STILL_EXISTS,FIX THIS SHIT!!!#
aaiifgiab,scikit-learn-contrib/imbalanced-learn,unbalanced_dataset/unbalanced_dataset.py,50484c24505c5c31dd7aba1d38155eab0c1adad7,STILL_EXISTS,\"\"\" || UnbalancedDataset || ================= ||  || UnbalancedDataset is a python module offering a number of re-sampling || techniques commonly used in datasets showing strong between-class || imbalance. ||  || Most classification algorithms will only perform optimally when the number of || samples of each class is roughly the same. Highly skewed datasets; where the || minority heavily outnumbered by one or more classes; haven proven to be a || challenge while at the same time becoming more and more common. ||  || One way of addresing this issue is by re-sampling the dataset as to offset this || imbalance with the hope of arriving and a more robust and fair decision || boundary than you would otherwise. ||  || Resampling techniques are divided in two categories: ||     1. Under-sampling the majority class(es). ||     2. Over-sampling the minority class. ||  || Bellow is a list of the methods currently implemented in this module. ||  || * Under-sampling ||     1. Random majority under-sampling with replacement ||     2. Extraction of majority-minority Tomek links ||     3. Under-sampling with Cluster Centroids ||     4. NearMiss-(1 & 2 & 3) ||     5. Condensend Nearest Neighbour ||     6. One-Sided Selection ||     7. Neighboorhood Cleaning Rule ||  || * Over-sampling ||     1. Random minority over-sampling with replacement ||     2. SMOTE - Synthetic Minority Over-sampling Technique ||     3. bSMOTE(1&2) - Borderline SMOTE of types 1 and 2 ||     4. SVM_SMOTE - Support Vectors SMOTE ||  || * Over-sampling follow by under-sampling ||     1. SMOTE + Tomek links ||     2. SMOTE + ENN ||  || * Ensemble sampling ||     1. EasyEnsemble ||     2. BalanceCascade ||  || This is a work in progress. Any comments; suggestions or corrections are || welcome. ||  || References: ||  || [1] SMOTE - \"SMOTE: synthetic minority over-sampling technique\" by Chawla; || N.V et al. ||  || [2] Borderline SMOTE -  \"Borderline-SMOTE: A New Over-Sampling Method in || Imbalanced Data Sets Learning; Hui Han; Wen-Yuan Wang; Bing-Huan Mao\" ||  || [3] SVM_SMOTE - \"Borderline Over-sampling for Imbalanced Data Classification; || Nguyen; Cooper; Kamei\" ||  || [4] NearMiss - \"kNN approach to unbalanced data distributions: A case study || involving information extraction\" by Zhang et al. ||  || [5] CNN - \"Addressing the Curse of Imbalanced Training Sets: One-Sided || Selection\" by Kubat et al. ||  || [6] One-Sided Selection - \"Addressing the Curse of Imbalanced Training Sets: || One-Sided Selection\" by Kubat et al. ||  || [7] NCL - \"Improving identification of difficult small classes by balancing || class distribution\" by Laurikkala et al. ||  || [8] SMOTE + Tomek - \"Balancing training data for automated annotation of || keywords: a case study\" by Batista et al. ||  || [9] SMOTE + ENN - \"A study of the behavior of several methods for balancing || machine learning training data\" by Batista et al. ||  || [10] EasyEnsemble & BalanceCascade - \"Exploratory Understanding for || Class-Imbalance Learning\" by Liu et al. ||  || TO DO LIST: || =========== || \"\"\"
aaiifgicg,scikit-learn-contrib/imbalanced-learn,unbalanced_dataset/unbalanced_dataset.py,50484c24505c5c31dd7aba1d38155eab0c1adad7,STILL_EXISTS,NN lines relate to original sample; columns to its
aaiifiejb,scikit-learn-contrib/imbalanced-learn,unbalanced_dataset/over_sampling/smote.py,e88d5edef8c368a91645263dc21a9870c69cfb43,STILL_EXISTS,NN lines relate to original sample; columns to its
aaiifihdb,scikit-learn-contrib/imbalanced-learn,doc/source/conf.py,4984230fbbe0e45d27a5cb99e9523a69d48a4fa4,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaiigbcdj,scikit-learn-contrib/imbalanced-learn,unbalanced_dataset/under_sampling/instance_hardness_threshold.py,249a1a53fe07a903d01d59fb69437c300b59461f,STILL_EXISTS,Compute the number of cluster needed
aaiigcdcg,scikit-learn-contrib/imbalanced-learn,imblearn/pipeline.py,f4e22a156ecb58683c57adc1b698124ebb61812e,e49c30ad1d1c9ecdf5f238c041986ee40cbf611a,XXX: Calling sample in pipeline it means that the
aaiigcdif,scikit-learn-contrib/imbalanced-learn,imblearn/under_sampling/nearmiss.py,d46a70a5d840e5667828a6c8a179eaba7076186c,STILL_EXISTS,Announce deprecation if needed
aaiigcefe,scikit-learn-contrib/imbalanced-learn,imblearn/metrics/classification.py,2731eaf98e875631f29e203ec17f4cef45304464,STILL_EXISTS,\"\"\"Metrics to assess performance on classification task given class prediction ||  || Functions named as ``*_score`` return a scalar value to maximize: the higher || the better ||  || Function named as ``*_error`` or ``*_loss`` return a scalar value to minimize: || the lower the better || \"\"\"
aaiigceij,scikit-learn-contrib/imbalanced-learn,imblearn/metrics/tests/test_classification.py,2731eaf98e875631f29e203ec17f4cef45304464,STILL_EXISTS,XXX: do we really want a special API for the binary case?
aaiigcffa,scikit-learn-contrib/imbalanced-learn,imblearn/metrics/classification.py,828a51fce6e7a4f99946ff68797b261f39dddd5c,STILL_EXISTS,We need to extract from kwargs only the one needed by the
aaiigchfc,scikit-learn-contrib/imbalanced-learn,imblearn/under_sampling/tests/test_nearmiss.py,ffa3fca1b8b0156f136e7fa2cd3ebc2b339ab169,STILL_EXISTS,FIXME remove at the end of the deprecation 0.4
aaiigcibb,scikit-learn-contrib/imbalanced-learn,imblearn/utils/estimator_checks.py,ffa3fca1b8b0156f136e7fa2cd3ebc2b339ab169,879da066eb83a11418c236a725a6af18ec721e11,FIXME already present in scikit-learn 0.19
aaiigdefe,scikit-learn-contrib/imbalanced-learn,imblearn/under_sampling/prototype_selection/edited_nearest_neighbours.py,b246d06fb7ea1de4557159935ac8c21720812d71,879da066eb83a11418c236a725a6af18ec721e11,FIXME: Deprecated in 0.2. To be removed in 0.4
aaiigdfbg,scikit-learn-contrib/imbalanced-learn,imblearn/under_sampling/prototype_selection/neighbourhood_cleaning_rule.py,b246d06fb7ea1de4557159935ac8c21720812d71,879da066eb83a11418c236a725a6af18ec721e11,FIXME: Deprecated from 0.2. To be removed in 0.4.
aaiigdgeb,scikit-learn-contrib/imbalanced-learn,imblearn/utils/validation.py,b246d06fb7ea1de4557159935ac8c21720812d71,STILL_EXISTS,FIXME: perfectly we should raise an error but the sklearn API does
aaiigdgfa,scikit-learn-contrib/imbalanced-learn,imblearn/datasets/imbalance.py,1e3fdfe5a5733aae38861eaacd406299b4b65c87,879da066eb83a11418c236a725a6af18ec721e11,FIXME: deprecated in 0.2 to be removed in 0.4
aaiigdgfh,scikit-learn-contrib/imbalanced-learn,imblearn/datasets/tests/test_imbalance.py,1e3fdfe5a5733aae38861eaacd406299b4b65c87,879da066eb83a11418c236a725a6af18ec721e11,FIXME: to be removed in 0.4 due to deprecation
aaiigdgij,scikit-learn-contrib/imbalanced-learn,examples/applications/plot_topic_classication.py,ca5452c70e91f4e5513c3fa8274eb95bff64a1de,STILL_EXISTS,To improve the prediction of the class \\#3; it could be interesting to apply
aaiigdgjj,scikit-learn-contrib/imbalanced-learn,examples/applications/plot_topic_classication.py,ca5452c70e91f4e5513c3fa8274eb95bff64a1de,STILL_EXISTS,implemented in imbalanced-learn to properly handle the samplers.
aaiigdhae,scikit-learn-contrib/imbalanced-learn,examples/applications/plot_topic_classication.py,ca5452c70e91f4e5513c3fa8274eb95bff64a1de,STILL_EXISTS,slightly better.
aaiigdifa,scikit-learn-contrib/imbalanced-learn,examples/under-sampling/plot_comparison_under_sampling.py,ca5452c70e91f4e5513c3fa8274eb95bff64a1de,STILL_EXISTS,``NearMiss`` algorithms implement some heuristic rules in order to select
aaiigdjej,scikit-learn-contrib/imbalanced-learn,examples/ensemble/plot_comparison_bagging_classifier.py,a4947ea04d8be912404cb4f3f859c36621dce22d,STILL_EXISTS,\"\"\" || ========================================================= || Comparison of balanced and imbalanced bagging classifiers || ========================================================= ||  || This example shows the benefit of balancing the training set when using a || bagging classifier. ``BalancedBaggingClassifier`` chains a || ``RandomUnderSampler`` and a given classifier while ``BaggingClassifier`` is || using directly the imbalanced data. ||  || Balancing the data set before training the classifier improve the || classification performance. In addition; it avoids the ensemble to focus on the || majority class which would be a known drawback of the decision tree || classifiers. ||  || \"\"\"
aaiigeaic,scikit-learn-contrib/imbalanced-learn,doc/conf.py,69a80c09d3bb52f265afab5464fc904321640fea,STILL_EXISTS,this is needed for some reason...
aaiigeaje,scikit-learn-contrib/imbalanced-learn,imblearn/ensemble/tests/test_classifier.py,e00e7f81e2b60238a4a3117dcaf9f0e82589f13f,STILL_EXISTS,FIXME: uncomment when #9723 is merged in scikit-learn
aaiigecjd,scikit-learn-contrib/imbalanced-learn,imblearn/combine/smote_enn.py,71ff0f665d748ef9022c8752e2a06a9e82b0ae97,6916fe9600053bd9a3eef64813e896425d29da50,FIXME: remove in 0.6
aaiigecje,scikit-learn-contrib/imbalanced-learn,imblearn/combine/smote_tomek.py,71ff0f665d748ef9022c8752e2a06a9e82b0ae97,6916fe9600053bd9a3eef64813e896425d29da50,FIXME: remove in 0.6
aaiigecjf,scikit-learn-contrib/imbalanced-learn,imblearn/datasets/imbalance.py,71ff0f665d748ef9022c8752e2a06a9e82b0ae97,STILL_EXISTS,FIXME remove ratio at 0.6
aaiigedae,scikit-learn-contrib/imbalanced-learn,imblearn/utils/estimator_checks.py,71ff0f665d748ef9022c8752e2a06a9e82b0ae97,c0a420808746bde68c536b0bea10c0ad8105df7b,FIXME remove in 0.6 -> ratio is deprecated
aaiigedaf,scikit-learn-contrib/imbalanced-learn,imblearn/utils/estimator_checks.py,71ff0f665d748ef9022c8752e2a06a9e82b0ae97,7032dcbcb23faa400f69dd701a9237eda4f683c9,FIXME remove in 0.6 -> ratio will be deprecated
aaiigedbb,scikit-learn-contrib/imbalanced-learn,imblearn/utils/validation.py,71ff0f665d748ef9022c8752e2a06a9e82b0ae97,STILL_EXISTS,FIXME: Turn into an error in 0.6
aaiigedcb,scikit-learn-contrib/imbalanced-learn,imblearn/over_sampling/smote.py,eafae67994476a0c239bf459d5ea5d875ef9f3f1,STILL_EXISTS,FIXME: remove in 0.6
aaiigedcc,scikit-learn-contrib/imbalanced-learn,imblearn/over_sampling/smote.py,eafae67994476a0c239bf459d5ea5d875ef9f3f1,STILL_EXISTS,FIXME: In 0.6; SMOTE should inherit only from BaseSMOTE.
aaiigedcf,scikit-learn-contrib/imbalanced-learn,imblearn/over_sampling/smote.py,eafae67994476a0c239bf459d5ea5d875ef9f3f1,STILL_EXISTS,FIXME: remove in 0.6 after deprecation cycle
aaiigedcg,scikit-learn-contrib/imbalanced-learn,imblearn/over_sampling/smote.py,eafae67994476a0c239bf459d5ea5d875ef9f3f1,STILL_EXISTS,FIXME: to be removed in 0.6
aaiigedch,scikit-learn-contrib/imbalanced-learn,imblearn/over_sampling/smote.py,eafae67994476a0c239bf459d5ea5d875ef9f3f1,STILL_EXISTS,FIXME: uncomment in version 0.6
aaiigedcj,scikit-learn-contrib/imbalanced-learn,examples/applications/porto_seguro_keras_under_sampling.py,7c48491b7ea62f02e5f6bb888fb806e489637e34,STILL_EXISTS,\"\"\" || ========================================================== || Porto Seguro: balancing samples in mini-batches with Keras || ========================================================== ||  || This example compares two strategies to train a neural-network on the Porto || Seguro Kaggle data set [1]_. The data set is imbalanced and we show that || balancing each mini-batch allows to improve performance and reduce the training || time. ||  || References || ---------- ||  || .. [1] https:\/\/www.kaggle.com\/c\/porto-seguro-safe-driver-prediction\/data ||  || \"\"\"
aaiigeeic,scikit-learn-contrib/imbalanced-learn,imblearn/keras/_generator.py,839df67494157eff4a908bce857c8f1f02e6732d,7032dcbcb23faa400f69dd701a9237eda4f683c9,FIXME: Remove in 0.6
aaiigeeii,scikit-learn-contrib/imbalanced-learn,imblearn/tensorflow/_generator.py,839df67494157eff4a908bce857c8f1f02e6732d,7032dcbcb23faa400f69dd701a9237eda4f683c9,FIXME: Remove in 0.6
aaiigeejb,scikit-learn-contrib/imbalanced-learn,imblearn/utils/estimator_checks.py,839df67494157eff4a908bce857c8f1f02e6732d,7032dcbcb23faa400f69dd701a9237eda4f683c9,FIXME: remove in 0.6
aaiigeejj,scikit-learn-contrib/imbalanced-learn,examples/ensemble/plot_comparison_ensemble_classifier.py,2f0a4b24da69a6717754acb3ad9add8b14c86659,STILL_EXISTS,\"\"\" || ============================================================== || Comparison of ensembling classifiers internally using sampling || ============================================================== ||  || Ensembling classifiers have shown to improve classification performance compare || to single learner. However; they will be affected by class imbalance. This || example shows the benefit of balancing the training set before to learn || learners. We are making the comparison with non-balanced ensemble methods. ||  || We make a comparison using the balanced accuracy and geometric mean which are || metrics widely used in the literature to evaluate models learned on imbalanced || set. ||  || \"\"\"
aaiigeffi,scikit-learn-contrib/imbalanced-learn,imblearn/ensemble/_forest.py,2f0a4b24da69a6717754acb3ad9add8b14c86659,STILL_EXISTS,making threading more efficient than multiprocessing in
aaiigeggd,scikit-learn-contrib/imbalanced-learn,doc/conf.py,59d9b4de8f933d3ecf7f3bc55872f7fdf4c9d7ee,STILL_EXISTS,this is needed for some reason...
aaiigehaa,scikit-learn-contrib/imbalanced-learn,imblearn/over_sampling/_random_over_sampler.py,ca7d3014899e25576bdaac4da424eea82733a5da,7032dcbcb23faa400f69dd701a9237eda4f683c9,TODO: remove the str tag once the following PR is merged:
aaiigehac,scikit-learn-contrib/imbalanced-learn,imblearn/under_sampling/_prototype_selection/_random_under_sampler.py,ca7d3014899e25576bdaac4da424eea82733a5da,7032dcbcb23faa400f69dd701a9237eda4f683c9,TODO: remove the str tag once the following PR is merged:
aaiigehcd,scikit-learn-contrib/imbalanced-learn,imblearn/over_sampling/_smote.py,aff41258c420ff6d0b63a3e68aed1e72185effb5,7032dcbcb23faa400f69dd701a9237eda4f683c9,FIXME: rename _sample -> _fit_resample in 0.6
aaiigehjd,scikit-learn-contrib/imbalanced-learn,doc/conf.py,cb45ad011bb20d20c8ac006dd589ed981859bdd6,STILL_EXISTS,Temporary work-around for spacing problem between parameter and parameter
aaiigeiah,scikit-learn-contrib/imbalanced-learn,doc/conf.py,2c7ed0f9c61cbe2f82defb3be8402aad90428525,STILL_EXISTS,Temporary work-around for spacing problem between parameter and parameter
aaiigeidc,scikit-learn-contrib/imbalanced-learn,maint_tools/test_docstring.py,dd287c33669f31352d21d7ce1dcc12676e74cf24,STILL_EXISTS,TODO: this detection can be improved. Currently we assume that we have
aaiigeieb,scikit-learn-contrib/imbalanced-learn,imblearn/over_sampling/_smote.py,158258ee583740b1fd142b04371bc4ab0712cdce,STILL_EXISTS,store the columns name to reconstruct a dataframe
aaiigeieh,scikit-learn-contrib/imbalanced-learn,examples/applications/plot_impact_imbalanced_classes.py,bbbf0e27f0e8d68e821ab35e54cdb2ac777ec2cb,STILL_EXISTS,\"\"\" || ======================================================================== || Model fitting on imbalanced dataset and comparison of methods to improve || its performance || ======================================================================== ||  || This example illustrates the problem induced by learning on datasets having || imbalanced classes. Subsequently; we compare different approaches alleviating || these negative effects. ||  || \"\"\"
aaiigeigb,scikit-learn-contrib/imbalanced-learn,examples/applications/plot_impact_imbalanced_classes.py,bbbf0e27f0e8d68e821ab35e54cdb2ac777ec2cb,STILL_EXISTS,This dataset is only slightly imbalanced. To better highlight the effect of
aaiigeijd,scikit-learn-contrib/imbalanced-learn,examples/applications/plot_impact_imbalanced_classes.py,bbbf0e27f0e8d68e821ab35e54cdb2ac777ec2cb,STILL_EXISTS,columns and standardized the numerical columns before to inject the data into
aaiigeijj,scikit-learn-contrib/imbalanced-learn,examples/applications/plot_impact_imbalanced_classes.py,bbbf0e27f0e8d68e821ab35e54cdb2ac777ec2cb,STILL_EXISTS,columns to the categorical pipeline and the numerical columns to the
aaiigejbf,scikit-learn-contrib/imbalanced-learn,examples/applications/plot_impact_imbalanced_classes.py,bbbf0e27f0e8d68e821ab35e54cdb2ac777ec2cb,STILL_EXISTS,to improve the performance of these 2 models.
aaiigejga,scikit-learn-contrib/imbalanced-learn,examples/applications/plot_impact_imbalanced_classes.py,bbbf0e27f0e8d68e821ab35e54cdb2ac777ec2cb,STILL_EXISTS,tree. However; instead of under-sampling once the dataset; one could
aaiigejgf,scikit-learn-contrib/imbalanced-learn,examples/applications/plot_impact_imbalanced_classes.py,bbbf0e27f0e8d68e821ab35e54cdb2ac777ec2cb,STILL_EXISTS,The performance with the `BalancedRandomForestClassifier` is better than
aaiigejib,scikit-learn-contrib/imbalanced-learn,examples/applications/plot_impact_imbalanced_classes.py,bbbf0e27f0e8d68e821ab35e54cdb2ac777ec2cb,STILL_EXISTS,`class_weight=\"balanced\"` does not improve the performance when using a
aaiigfade,scikit-learn-contrib/imbalanced-learn,imblearn/__init__.py,b9514b3a473e4ead16b6ad90385aa01893ad0ca6,STILL_EXISTS,# FIXME: When we get Python 3.7 as minimal version; we will need to switch to
aaiigfadj,scikit-learn-contrib/imbalanced-learn,imblearn/__init__.py,b9514b3a473e4ead16b6ad90385aa01893ad0ca6,STILL_EXISTS,LazyLoader; lookups are efficient (__getattr__ is only called on
aaiigfahb,scikit-learn-contrib/imbalanced-learn,doc/conf.py,b1d8d4fda81ce9e8632971e738b51113328c5313,STILL_EXISTS,Hack to get kwargs to appear in docstring #18434
aaiigfahc,scikit-learn-contrib/imbalanced-learn,doc/conf.py,b1d8d4fda81ce9e8632971e738b51113328c5313,STILL_EXISTS,TODO: Remove when https:\/\/github.com\/sphinx-doc\/sphinx\/pull\/8234 gets
aaiigfbgh,scikit-learn-contrib/imbalanced-learn,imblearn/metrics/tests/test_pairwise.py,ce4e1f7b231f01fc03505c2b0f7077b67caf09ca,STILL_EXISTS,described in \"Improved Heterogeneous Distance Functions\"; D.R. Wilson and
aaiigfbji,scikit-learn-contrib/imbalanced-learn,doc/conf.py,6155658185e911d96be1d852381da054735ab137,STILL_EXISTS,this is needed for some reason...
aaiigfdeg,scikit-learn-contrib/imbalanced-learn,imblearn/over_sampling/_smote/base.py,9f4173d8f6df93cb2d47d2177550057e15682aa3,STILL_EXISTS,change in sparsity structure more efficient with LIL than CSR
aaiigffch,scikit-learn-contrib/imbalanced-learn,examples/applications/plot_over_sampling_benchmark_lfw.py,71b1e873e70dca43cd12870d4c0f497015f41970,STILL_EXISTS,interpolation (i.e. ADASYN and SMOTE) perform better than random
aaiigfhfc,scikit-learn-contrib/imbalanced-learn,imblearn/utils/tests/test_testing.py,e39bf01166c8e4f4573374bdf586ff7ece9a90ee,STILL_EXISTS,TODO: remove in 0.9
aaiiggahb,NervanaSystems/neon,neon/backends/cc2.py,3f90773a6694aaf472b1ad84e02dece87cf1df48,STILL_EXISTS,fix the axis if we are dealing with a vector. This is a hack
aaiiggahf,NervanaSystems/neon,neon/backends/cc2.py,3f90773a6694aaf472b1ad84e02dece87cf1df48,STILL_EXISTS,the above is what we ought to do; but generates Exceptions due to
aaiiggaje,NervanaSystems/neon,neon/backends/cc2.py,3f90773a6694aaf472b1ad84e02dece87cf1df48,STILL_EXISTS,XXX: temporary fix.
aaiiggcbf,NervanaSystems/neon,neon/backends/tests/test_cc2.py,3f90773a6694aaf472b1ad84e02dece87cf1df48,STILL_EXISTS,TODO: cudanet doesn't currently support noaxis argmin
aaiiggcbg,NervanaSystems/neon,neon/backends/tests/test_cc2.py,3f90773a6694aaf472b1ad84e02dece87cf1df48,STILL_EXISTS,TODO: cudanet doesn't currently support noaxis argmax
aaiiggcei,NervanaSystems/neon,neon/backends/tests/test_cc2_tensor.py,3f90773a6694aaf472b1ad84e02dece87cf1df48,STILL_EXISTS,TODO: add >2 dimension support to cudanet
aaiiggcej,NervanaSystems/neon,neon/backends/tests/test_cc2_tensor.py,3f90773a6694aaf472b1ad84e02dece87cf1df48,STILL_EXISTS,TODO: add scalar assignment to self.gpt class
aaiiggcfa,NervanaSystems/neon,neon/backends/tests/test_cc2_tensor.py,3f90773a6694aaf472b1ad84e02dece87cf1df48,STILL_EXISTS,TODO: fix this for self.gpt
aaiiggecb,NervanaSystems/neon,neon/datasets/mnist.py,3f90773a6694aaf472b1ad84e02dece87cf1df48,STILL_EXISTS,TODO: try and download and read in directly?
aaiiggeeb,NervanaSystems/neon,neon/datasets/mobydick.py,3f90773a6694aaf472b1ad84e02dece87cf1df48,STILL_EXISTS,TODO: try and download and read in directly?
aaiiggegc,NervanaSystems/neon,neon/datasets/sparsenet.py,3f90773a6694aaf472b1ad84e02dece87cf1df48,STILL_EXISTS,TODO: try and download and read in directly?
aaiiggfai,NervanaSystems/neon,neon/datasets/tests/test_mnist.py,3f90773a6694aaf472b1ad84e02dece87cf1df48,STILL_EXISTS,TODO: make this work (numpy import errors at the moment)
aaiigghej,NervanaSystems/neon,neon/hyperopt/gen_config.py,3f90773a6694aaf472b1ad84e02dece87cf1df48,STILL_EXISTS,todo: Spearmint supports ENUM but we are not handling it yet.
aaiiggijg,NervanaSystems/neon,neon/layers/layer.py,3f90773a6694aaf472b1ad84e02dece87cf1df48,3497dcc8bb6200f2ce073b61bd6e5ad599f23141,TODO: Cool to reuse this here?
aaiiggjed,NervanaSystems/neon,neon/layers/recurrent.py,3f90773a6694aaf472b1ad84e02dece87cf1df48,3497dcc8bb6200f2ce073b61bd6e5ad599f23141,TODO: Get rid of output and pre_act. But they seem to be used in the
aaiiggjgd,NervanaSystems/neon,neon/layers/recurrent.py,3f90773a6694aaf472b1ad84e02dece87cf1df48,51d9c49c6711d272ddc577474c6dfbc6929fba81,Why only at tau > 0 vs. tau==0? why not weights vs weights_rec
aaiiggjhh,NervanaSystems/neon,neon/layers/recurrent.py,3f90773a6694aaf472b1ad84e02dece87cf1df48,3497dcc8bb6200f2ce073b61bd6e5ad599f23141,param: If this isn't initialized correctly; get NaNs pretty quickly.
aaiiggjjb,NervanaSystems/neon,neon/layers/recurrent.py,3f90773a6694aaf472b1ad84e02dece87cf1df48,3497dcc8bb6200f2ce073b61bd6e5ad599f23141,Hack to avoid creating a new copy for c_phip; just want assign vals
aaiighagh,NervanaSystems/neon,neon/metrics/metric.py,3f90773a6694aaf472b1ad84e02dece87cf1df48,STILL_EXISTS,TODO: better handle situation where metrics recorded differ from those
aaiighahb,NervanaSystems/neon,neon/metrics/metric.py,3f90773a6694aaf472b1ad84e02dece87cf1df48,STILL_EXISTS,no coloring needed
aaiighdec,NervanaSystems/neon,neon/models/rnn.py,3f90773a6694aaf472b1ad84e02dece87cf1df48,STILL_EXISTS,[TODO] why need deepcopy?
aaiighdej,NervanaSystems/neon,neon/models/rnn.py,3f90773a6694aaf472b1ad84e02dece87cf1df48,STILL_EXISTS,TODO: find some alternate way of re-assembling data that doesn't
aaiighdhb,NervanaSystems/neon,neon/models/tests/test_rbm.py,3f90773a6694aaf472b1ad84e02dece87cf1df48,STILL_EXISTS,TODO: remove randomness from expected target results
aaiighdhf,NervanaSystems/neon,neon/models/tests/test_rbm.py,3f90773a6694aaf472b1ad84e02dece87cf1df48,STILL_EXISTS,TODO: remove randomness
aaiigheia,NervanaSystems/neon,neon/params/tests/test_val_init.py,3f90773a6694aaf472b1ad84e02dece87cf1df48,STILL_EXISTS,TODO: test distribution of vals
aaiighfbf,NervanaSystems/neon,neon/tests/sanity_check.py,3f90773a6694aaf472b1ad84e02dece87cf1df48,c07814ded9420bfcf5d5d6dc6668629a7de56ff3,TODO: modelpar currently broken on synthetic-sanity_check.yaml
aaiighfbi,NervanaSystems/neon,neon/tests/sanity_check.py,3f90773a6694aaf472b1ad84e02dece87cf1df48,c07814ded9420bfcf5d5d6dc6668629a7de56ff3,temporary hack because we are not running via mpirun.
aaiighffb,NervanaSystems/neon,neon/tests/serialize_check.py,3f90773a6694aaf472b1ad84e02dece87cf1df48,STILL_EXISTS,todo: gpu -> cpu deserialization
aaiighfid,NervanaSystems/neon,neon/tests/speed_check.py,3f90773a6694aaf472b1ad84e02dece87cf1df48,c07814ded9420bfcf5d5d6dc6668629a7de56ff3,TODO: modelpar currently broken on synthetic-sanity_check.yaml
aaiighfif,NervanaSystems/neon,neon/tests/speed_check.py,3f90773a6694aaf472b1ad84e02dece87cf1df48,c07814ded9420bfcf5d5d6dc6668629a7de56ff3,temporary hack because we are not running via mpirun.
aaiighgef,NervanaSystems/neon,neon/transforms/batch_norm.py,3f90773a6694aaf472b1ad84e02dece87cf1df48,STILL_EXISTS,increment the global estimates (TODO: stop after an epoch)
aaiighjei,NervanaSystems/neon,neon/util/batch_writer.py,3f90773a6694aaf472b1ad84e02dece87cf1df48,STILL_EXISTS,global variable hack so that we can use multiprocess pool.map
aaiigigjf,NervanaSystems/neon,neon/tests/test_serialize.py,19ea9ac604ce4ce6edf1a0c765f5b723962027f4,STILL_EXISTS,may not be needed
aaiigihjc,NervanaSystems/neon,neon/tests/test_serialize.py,9478594fa736ac59a26c8c383a9cae7451ed45e5,STILL_EXISTS,may not be needed
aaiigijgc,NervanaSystems/neon,neon/tests/test_serialize.py,9dad921d87d3a2b65368408dba548295f9f6c709,STILL_EXISTS,may not be needed
aaiigjbig,NervanaSystems/neon,neon/tests/test_serialize.py,ca33357c13f67ecd8313c8012cc9803217721546,STILL_EXISTS,may not be needed
aaiigjjaa,NervanaSystems/neon,neon/backends/mgpu.py,19829290a7e7b4766723a9acaa94923224557769,STILL_EXISTS,TODO clean up this code to avoid indexing
aaiigjjad,NervanaSystems/neon,neon/backends/mgpu.py,19829290a7e7b4766723a9acaa94923224557769,STILL_EXISTS,TODO: set ptype to be fragment in this case ??
aaiihbaed,NervanaSystems/neon,neon/backends/autodiff.py,3497dcc8bb6200f2ce073b61bd6e5ad599f23141,STILL_EXISTS,TODO cleaner way to broadcast
aaiihbajd,NervanaSystems/neon,neon/backends/backend.py,3497dcc8bb6200f2ce073b61bd6e5ad599f23141,STILL_EXISTS,TODO: fix shape in smarter way
aaiihbbad,NervanaSystems/neon,neon/backends/backend.py,3497dcc8bb6200f2ce073b61bd6e5ad599f23141,STILL_EXISTS,TODO
aaiihbbgi,NervanaSystems/neon,neon/backends/float_ew.py,3497dcc8bb6200f2ce073b61bd6e5ad599f23141,STILL_EXISTS,TODO: potentially do this iteratively to find longest common set
aaiihbccj,NervanaSystems/neon,neon/backends/float_ew.py,3497dcc8bb6200f2ce073b61bd6e5ad599f23141,STILL_EXISTS,TODO: I can probably do much better than this code below;
aaiihbcdb,NervanaSystems/neon,neon/backends/float_ew.py,3497dcc8bb6200f2ce073b61bd6e5ad599f23141,STILL_EXISTS,TODO: build a program wide DAG and only call this once at startup per
aaiihbcea,NervanaSystems/neon,neon/backends/float_ew.py,3497dcc8bb6200f2ce073b61bd6e5ad599f23141,STILL_EXISTS,Each block gets a column and the threads work down the columns.
aaiihbced,NervanaSystems/neon,neon/backends/float_ew.py,3497dcc8bb6200f2ce073b61bd6e5ad599f23141,STILL_EXISTS,use more efficient 2d dimensions if this is a plain ew op.
aaiihbcga,NervanaSystems/neon,neon/backends/float_ew.py,3497dcc8bb6200f2ce073b61bd6e5ad599f23141,STILL_EXISTS,TODO: don't allow output tensor itself to be broadcastable.
aaiihbdbb,NervanaSystems/neon,neon/backends/layer_gpu.py,3497dcc8bb6200f2ce073b61bd6e5ad599f23141,STILL_EXISTS,\"\"\" || Definition of the GPU layers || These layers are mainly used for old benchmarking code; || but they also cache all the computed params for complex layers. || TODO: clean up merge with CPU layers || TODO: remove any non-param caching code; neon layers should replace benchmark code. || \"\"\"
aaiihbdcj,NervanaSystems/neon,neon/backends/layer_gpu.py,3497dcc8bb6200f2ce073b61bd6e5ad599f23141,STILL_EXISTS,remove unused dtype settings
aaiihbdfd,NervanaSystems/neon,neon/backends/layer_gpu.py,3497dcc8bb6200f2ce073b61bd6e5ad599f23141,STILL_EXISTS,TODO: detect other forms of gaps
aaiihbdga,NervanaSystems/neon,neon/backends/layer_gpu.py,3497dcc8bb6200f2ce073b61bd6e5ad599f23141,STILL_EXISTS,TODO: we need to accumulate the delta in the common output delta
aaiihbdgi,NervanaSystems/neon,neon/backends/layer_gpu.py,3497dcc8bb6200f2ce073b61bd6e5ad599f23141,STILL_EXISTS,3 is a special case that only ends up in the high bits
aaiihbdii,NervanaSystems/neon,neon/backends/make_kernels.py,3497dcc8bb6200f2ce073b61bd6e5ad599f23141,4e8060098b766775e0e3d0420beb11784e05377b,TODO require 52 for hpool and hconv kenels
aaiihbeag,NervanaSystems/neon,neon/backends/nervanacpu.py,3497dcc8bb6200f2ce073b61bd6e5ad599f23141,STILL_EXISTS,TODO: enable this flag to find numerical problems
aaiihbedj,NervanaSystems/neon,neon/backends/nervanacpu.py,3497dcc8bb6200f2ce073b61bd6e5ad599f23141,STILL_EXISTS,TODO add rand and onehot here
aaiihbehf,NervanaSystems/neon,neon/backends/nervanagpu.py,3497dcc8bb6200f2ce073b61bd6e5ad599f23141,STILL_EXISTS,TODO: add an is_binary_compat like function
aaiihbejh,NervanaSystems/neon,neon/backends/nervanagpu.py,3497dcc8bb6200f2ce073b61bd6e5ad599f23141,STILL_EXISTS,allocate buffer if needed
aaiihbfda,NervanaSystems/neon,neon/backends/nervanagpu.py,3497dcc8bb6200f2ce073b61bd6e5ad599f23141,STILL_EXISTS,TODO: to be removed; used in partial
aaiihbfdh,NervanaSystems/neon,neon/backends/nervanagpu.py,3497dcc8bb6200f2ce073b61bd6e5ad599f23141,STILL_EXISTS,and manually fine tune the selection for each layer.
aaiihbfdi,NervanaSystems/neon,neon/backends/nervanagpu.py,3497dcc8bb6200f2ce073b61bd6e5ad599f23141,STILL_EXISTS,TODO: Perhaps I'll add an autotuning mode.
aaiihbgai,NervanaSystems/neon,neon/backends/tests/test_autodiff.py,3497dcc8bb6200f2ce073b61bd6e5ad599f23141,STILL_EXISTS,TODO: stricter test to fix numerical issues
aaiihbgfi,NervanaSystems/neon,neon/backends/tests/test_autodiff_extra.py,3497dcc8bb6200f2ce073b61bd6e5ad599f23141,STILL_EXISTS,TODO Reduction only allowed along one axis per kernel.
aaiihbggf,NervanaSystems/neon,neon/backends/tests/test_autodiff_extra.py,3497dcc8bb6200f2ce073b61bd6e5ad599f23141,STILL_EXISTS,TODO potentially problematic
aaiihbjbb,NervanaSystems/neon,neon/callbacks/callbacks.py,3497dcc8bb6200f2ce073b61bd6e5ad599f23141,d947ec40e47e3bef0e8a71fa99eb64f94fbc2d99,TODO can serialize be handled by regular data callback or should it be separate?
aaiihbjcc,NervanaSystems/neon,neon/callbacks/callbacks.py,3497dcc8bb6200f2ce073b61bd6e5ad599f23141,STILL_EXISTS,state needed for the stop func
aaiihceja,NervanaSystems/neon,neon/visualizations/figure.py,3497dcc8bb6200f2ce073b61bd6e5ad599f23141,STILL_EXISTS,manually adjust pallette for better contrast
aaiihcfdc,NervanaSystems/neon,tests/gru_ref.py,3497dcc8bb6200f2ce073b61bd6e5ad599f23141,STILL_EXISTS,TODO: go back to 0.01 initialization
aaiihcfdd,NervanaSystems/neon,tests/gru_ref.py,3497dcc8bb6200f2ce073b61bd6e5ad599f23141,STILL_EXISTS,TODO: use glorot initialization?
aaiihcffa,NervanaSystems/neon,tests/gru_ref.py,3497dcc8bb6200f2ce073b61bd6e5ad599f23141,STILL_EXISTS,TODO: maybe we don't need to
aaiihcffi,NervanaSystems/neon,tests/gru_ref.py,3497dcc8bb6200f2ce073b61bd6e5ad599f23141,STILL_EXISTS,TODO: use an already existing sigmoid function
aaiihcfgc,NervanaSystems/neon,tests/gru_ref.py,3497dcc8bb6200f2ce073b61bd6e5ad599f23141,STILL_EXISTS,TODO: get rid of this
aaiihcfjj,NervanaSystems/neon,tests/lstm_ref.py,3497dcc8bb6200f2ce073b61bd6e5ad599f23141,STILL_EXISTS,needed
aaiihcgdf,NervanaSystems/neon,tests/lstm_ref.py,3497dcc8bb6200f2ce073b61bd6e5ad599f23141,STILL_EXISTS,loss = np.sum(H * wrand)  # weighted sum is a nice hash to use I think
aaiihddjg,NervanaSystems/neon,neon/models/model.py,d41031aca3a00b56268434004ef8e1e2be979191,15aee171d5e6c91e5a9375876cbcbf367c3eee2a,Keep track of how much of dataset we've seen
aaiihddjh,NervanaSystems/neon,neon/models/model.py,d41031aca3a00b56268434004ef8e1e2be979191,STILL_EXISTS,Move \"pointer\" back to beginning of dataset
aaiihdedi,NervanaSystems/neon,neon/models/model.py,dcc78a16c35ad1631d41377e0883b52af1dc36f6,ce6d47784bcc0ca9676d3b4844690b79002f0ce8,Keep track of how much of dataset we've seen
aaiihdfjb,NervanaSystems/neon,neon/layers/layer.py,4ef52334c587c36bc248616f80db42e0020327c2,c1235edc655e78fbecde61b9e68b7f0eb69f122f,TODO: bsum for linear is not implemented
aaiihdfjc,NervanaSystems/neon,neon/layers/layer.py,4ef52334c587c36bc248616f80db42e0020327c2,da492cd4d8c328694988358728841142f389e443,TODO: Set up self.batch_sum if self.bsum
aaiihdgfd,NervanaSystems/neon,neon/data/text.py,15b325b92d79b398fa8d16562c7758aff3566656,STILL_EXISTS,by convention; use 2 as OOV word
aaiihdgji,NervanaSystems/neon,neon/backends/nervanagpu.py,3199f8d3a51f91addfa493ee3d695a9635444c37,STILL_EXISTS,TODO: beta
aaiihdiba,NervanaSystems/neon,neon/layers/layer.py,f032d2ef7e9e1609614d67142316d335f0a72f30,fd5da9be0f92415500ed183af9e71bd0adca9e07,TODO: incorporate the alpha beta integration from backend ops
aaiihecjj,NervanaSystems/neon,neon/backends/test_pool.py,b9bb26d711ccdd8f8f65955d091b2711783be656,STILL_EXISTS,give gpu the input array without zero padding (not needed)
aaiihedae,NervanaSystems/neon,neon/backends/test_pool.py,b9bb26d711ccdd8f8f65955d091b2711783be656,STILL_EXISTS,There's probably a more elegant numpy way to do this..
aaiihedgc,NervanaSystems/neon,neon/callbacks/callbacks.py,66a7fb66d6a30646c371360dedee126360f902bb,6019ff0be14c424f24f5abe3df81a6e9059c6b1e,TODO: INSTEAD of just taking inputs... need to take the one that is specific to
aaiihedhb,NervanaSystems/neon,neon/visualizations/figure.py,66a7fb66d6a30646c371360dedee126360f902bb,6019ff0be14c424f24f5abe3df81a6e9059c6b1e,and somehow; that messed with some shape attribute.
aaiihedig,NervanaSystems/neon,neon/callbacks/callbacks.py,6019ff0be14c424f24f5abe3df81a6e9059c6b1e,6019ff0be14c424f24f5abe3df81a6e9059c6b1e,TODO: Can modify to get N max activations later
aaiiheeah,NervanaSystems/neon,neon/visualizations/data.py,6019ff0be14c424f24f5abe3df81a6e9059c6b1e,STILL_EXISTS,to avoid storing entire dataset; imgs are cached as needed; have to look up
aaiiheejc,NervanaSystems/neon,examples/babi/demo.py,79413823038fb86e2bb172e0475a70eec78b4654,STILL_EXISTS,\"\"\" || Interactive demo based on Facebook Q&A dataset: bAbI ||  || Reference: ||     \"Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks\" ||     http:\/\/arxiv.org\/abs\/1502.05698 ||  || Usage: ||     use -t to specify which bAbI task to run ||     python examples\/babi\/demo.py -t 1 --rlayer_type gru --model_weights babi.p || \"\"\"
aaiihefeg,NervanaSystems/neon,neon/data/loader/test/compare.py,68b16057440aee8b5e1085f07229a832452a8e64,STILL_EXISTS,\"\"\" || Verify that different ways of loading datasets lead to the same result. ||  || This test utility accepts the same command line parameters as neon. It || downloads the CIFAR-10 dataset and saves it as individual JPEG files. It then || proceeds to fit and evaluate a model using two different ways of loading the || data. Macrobatches are written to disk as needed. ||  || \"\"\"
aaiiheibe,NervanaSystems/neon,neon/backends/test_lrn.py,83ce4b1c5e1d2f8791694654f786fad6a6ce4a12,STILL_EXISTS,give gpu the input array without zero padding (not needed)
aaiiheijb,NervanaSystems/neon,neon/backends/float_ew.py,0316e7d4cec939e4036fe374b0fbce9e862d4d6b,aab86c41b1ef83fb52aca5795c66ccc6c1f55c94,TODO: The above code can lead to a race condition.
aaiiheijc,NervanaSystems/neon,neon/backends/float_ew.py,0316e7d4cec939e4036fe374b0fbce9e862d4d6b,aab86c41b1ef83fb52aca5795c66ccc6c1f55c94,Temporary workaround while investigating further:
aaiihejde,NervanaSystems/neon,neon/util/write_imagenet_macrobatches.py,159f64c4ba1fdd76b0fe4ef53384f016789c7716,STILL_EXISTS,TODO: could squish both these loops
aaiihfaeb,NervanaSystems/neon,neon/callbacks/callbacks.py,c10a2a5774e744efb91e3a62d095dfcef9e8c579,41af1e5ea3724f345ee6c468c77aac276bdcc5e1,TODO: generalize this for container layers
aaiihfaec,NervanaSystems/neon,neon/callbacks/callbacks.py,c10a2a5774e744efb91e3a62d095dfcef9e8c579,STILL_EXISTS,TODO: switch this to a general seralization op
aaiihfaed,NervanaSystems/neon,neon/callbacks/callbacks.py,c10a2a5774e744efb91e3a62d095dfcef9e8c579,STILL_EXISTS,TODO: generalize for more complex topologies
aaiihfbda,NervanaSystems/neon,neon/data/text_preprocessing.py,c10a2a5774e744efb91e3a62d095dfcef9e8c579,STILL_EXISTS,by convention; use 2 as OOV word
aaiihfcjd,NervanaSystems/neon,tests/serialization/inception.py,c10a2a5774e744efb91e3a62d095dfcef9e8c579,STILL_EXISTS,TODO put dropout back in
aaiihfedi,NervanaSystems/neon,neon/data/pascal_voc.py,edc89b09e264641f6424d552075548b59aec8700,STILL_EXISTS,Compute values needed for means and stds
aaiihfgcg,NervanaSystems/neon,neon/callbacks/callbacks.py,ccef2a4cb2f793b1b01e8d6ba50ffc55b51e6edf,STILL_EXISTS,get the list of all its columns that were nonzero
aaiihfgfb,NervanaSystems/neon,neon/data/ticker.py,ccef2a4cb2f793b1b01e8d6ba50ffc55b51e6edf,STILL_EXISTS,Set a mask over the unused part of the buffer
aaiihfghb,NervanaSystems/neon,neon/data/ticker.py,ccef2a4cb2f793b1b01e8d6ba50ffc55b51e6edf,STILL_EXISTS,TODO: should we normalize repeat count?
aaiihfgjb,NervanaSystems/neon,neon/data/ticker.py,ccef2a4cb2f793b1b01e8d6ba50ffc55b51e6edf,STILL_EXISTS,sort that set of columns by elt in the last row (the priority)
aaiihfgjc,NervanaSystems/neon,neon/data/ticker.py,ccef2a4cb2f793b1b01e8d6ba50ffc55b51e6edf,STILL_EXISTS,put those columns back into minibatch in the right places
aaiihfjbg,NervanaSystems/neon,examples/mnist_branch.py,d79e635ddff20b15dd51457f9bd17f4e986bbe7e,STILL_EXISTS,TODO: introduce Multicost metric support.  The line below currently failes
aaiihfjea,NervanaSystems/neon,neon/data/batch_writer.py,7ad499cdec32c51370000a947be3ee28be43141a,STILL_EXISTS,check that the needed csv files exist
aaiihfjje,NervanaSystems/neon,examples/imdb/prepare.py,cb92f0bc725bfa25684f51b8a5d7a53b0b1249cc,STILL_EXISTS,open the file; skip the headers if needed
aaiihfjjh,NervanaSystems/neon,examples/imdb/prepare.py,cb92f0bc725bfa25684f51b8a5d7a53b0b1249cc,STILL_EXISTS,update the vocab if needed
aaiihgaig,NervanaSystems/neon,neon/layers/layer.py,ba84954cc1786e6db05888a476d8b7b791751bb9,STILL_EXISTS,needed for serialization
aaiihgbcf,NervanaSystems/neon,neon/backends/convolution.py,6b5993b36f27c8155b969b2b6c6e803c6e35bcb6,STILL_EXISTS,3 is a special case that only ends up in the high bits
aaiihgbec,NervanaSystems/neon,neon/backends/layer_gpu.py,6b5993b36f27c8155b969b2b6c6e803c6e35bcb6,STILL_EXISTS,TODO small C bprop?
aaiihgbga,NervanaSystems/neon,neon/backends/layer_gpu.py,6b5993b36f27c8155b969b2b6c6e803c6e35bcb6,STILL_EXISTS,The overlap kernel can be much more efficient if we aren't doing superblocking
aaiihgchd,NervanaSystems/neon,neon/data/imageloader.py,daa1a122d2d17ec723eead2e2cdf2b6ce85175b3,de133b5a17cf02e485b6c12ae5268b42efb341f6,hack this up for now to get decent performnace on this op
aaiihgche,NervanaSystems/neon,neon/data/imageloader.py,daa1a122d2d17ec723eead2e2cdf2b6ce85175b3,de133b5a17cf02e485b6c12ae5268b42efb341f6,the real fix is 3d broadcast support in ew
aaiihgdcj,NervanaSystems/neon,neon/backends/winograd.py,b0593e2d22d3d65c2bb8297a11ab1706214aa94a,STILL_EXISTS,TODO remove loop logic here.
aaiihgdeb,NervanaSystems/neon,neon/backends/winograd.py,b0593e2d22d3d65c2bb8297a11ab1706214aa94a,STILL_EXISTS,add any zero padding if needed
aaiihgead,NervanaSystems/neon,neon/backends/winograd_conv.py,b0593e2d22d3d65c2bb8297a11ab1706214aa94a,STILL_EXISTS,perhaps tune this for smaller cache sizes
aaiihgfbg,NervanaSystems/neon,neon/backends/__init__.py,0c7ab59c46a8fc203d20c9d7a99729706a239637,bb338f7db4624334c9159cf69a9307ba42f21d4a,TODO add cache_dir to mgpu
aaiihggag,NervanaSystems/neon,neon/backends/winograd4.py,c5737f9e3c1423ccf29c6b71eb0eb6916b7f13e6,STILL_EXISTS,TODO remove loop logic here.
aaiihggfi,NervanaSystems/neon,neon/backends/winograd4.py,c5737f9e3c1423ccf29c6b71eb0eb6916b7f13e6,STILL_EXISTS,add any zero padding if needed
aaiihhadc,NervanaSystems/neon,neon/data/loader/test/compare.py,bfb79b0231e6ab6c84516d0e33bb13f1b5c35de7,a588ff6f3a29adc5e89dd0342b1609785307d37f,TODO: test macrobatch reading.
aaiihhagg,NervanaSystems/neon,neon/data/batch_writer.py,a588ff6f3a29adc5e89dd0342b1609785307d37f,STILL_EXISTS,TODO: fill in rest of metadata.
aaiihhggf,NervanaSystems/neon,neon/backends/kernel_specs.py,401f48875becf32329bddb9e5f40902d8494ab5c,STILL_EXISTS,TODO: generate these on the fly
aaiihhhhh,NervanaSystems/neon,neon/backends/winograd_conv.py,401f48875becf32329bddb9e5f40902d8494ab5c,STILL_EXISTS,TODO: explore more superblock shapes here.
aaiihhief,NervanaSystems/neon,neon/backends/convolution.py,32924648fe265f53255c8d1d1b3140d7fde61e59,STILL_EXISTS,TODO: allow hybrid types
aaiihhieg,NervanaSystems/neon,neon/backends/convolution.py,32924648fe265f53255c8d1d1b3140d7fde61e59,STILL_EXISTS,TODO: expose more compound operations
aaiihhjca,NervanaSystems/neon,neon/backends/make_kernels.py,32924648fe265f53255c8d1d1b3140d7fde61e59,STILL_EXISTS,This script no longer needed.
aaiihhjcc,NervanaSystems/neon,neon/backends/convolution.py,015625460a3f0171a06fe5358d8bae76c6a539e7,STILL_EXISTS,TODO: build 32x64 kernels
aaiihhjdb,NervanaSystems/neon,neon/backends/convolution.py,015625460a3f0171a06fe5358d8bae76c6a539e7,STILL_EXISTS,TODO: allow hybrid types
aaiihiaga,NervanaSystems/neon,neon/backends/winograd5.py,015625460a3f0171a06fe5358d8bae76c6a539e7,STILL_EXISTS,TODO remove loop logic here.
aaiihiaie,NervanaSystems/neon,neon/backends/winograd5.py,015625460a3f0171a06fe5358d8bae76c6a539e7,STILL_EXISTS,add any zero padding if needed
aaiihibcg,NervanaSystems/neon,neon/backends/winograd_conv.py,015625460a3f0171a06fe5358d8bae76c6a539e7,STILL_EXISTS,TODO: this needs more pruning; it takes too long for large HW
aaiihibhi,NervanaSystems/neon,neon/backends/convolution.py,fbb5033bbffb9fc3c916431f2715010f02f5b6e0,STILL_EXISTS,TODO: expose more compound operations
aaiihibig,NervanaSystems/neon,neon/backends/layer_cpu.py,fbb5033bbffb9fc3c916431f2715010f02f5b6e0,9d99c0aaddeae19433126bf09c38c71ee203a552,TODO remove loop logic here.
aaiihidfh,NervanaSystems/neon,neon/data/datasets.py,835f1093ef58a06937a6caae007ee06b77409c50,STILL_EXISTS,backport https limitation and workaround per http:\/\/python-future.org\/imports.html
aaiihidhj,NervanaSystems/neon,tests/run_examples.py,835f1093ef58a06937a6caae007ee06b77409c50,STILL_EXISTS,skip; implement subset pct to fit
aaiihidjj,NervanaSystems/neon,tests/test_mergebroadcast_layer.py,f0b8daf4795c6c00c3778db8c933e21441ceada6,c11415229feec19ed8df20e718f66ac80fdfee8e,TODO switch to pytest.skip() once verified to be expected failure
aaiihieaa,NervanaSystems/neon,neon/backends/winograd_conv.py,7fdc0bb82e700d35c071568d0ba7d92ea284d0be,STILL_EXISTS,TODO: explore more superblock shapes here.
aaiihifhd,NervanaSystems/neon,neon/data/media.py,079674c5d89a9df2d32fc5371d73f6b54e7c4554,STILL_EXISTS,FIXME: Do the packing of targets within the context of a
aaiihigfh,NervanaSystems/neon,examples/cifar10_msra.py,c11415229feec19ed8df20e718f66ac80fdfee8e,STILL_EXISTS,\"\"\" || Deep Residual Network on CIFAR10 data. ||  || Reference: ||  ||     Deep Residual Learning for Image Recognition `[He2015]`_ || ..  _[He2015]: http:\/\/arxiv.org\/abs\/1512.03385 ||  || This example has also been updated to use the \"preactivation\" structure || described in: He et. al.; \"Identity Mappings in Deep Residual Networks\"; || http:\/\/arxiv.org\/abs\/1603.05027 ||  || Usage: ||  ||     python examples\/cifar10_msra.py -r 0 -vv \\ ||         --log <logfile> \\ ||         --no_progress_bar \\ ||         --epochs 165 \\ ||         --depth 111 \\ ||         --save_path <save-path> \\ ||         --eval_freq 1 \\ ||         --backend gpu \\ ||         --batch_size 64 \\ ||  ||     This setting should get to ~4.84% top-1 error. (Could be as low as 4.7) ||  ||     NB:  It is good practice to set your data_dir where your batches are stored ||     to be local to your machine (to avoid accessing the macrobatches over network if; ||     for example; your data_dir is in an NFS mounted location) ||  || \"\"\"
aaiihihcc,NervanaSystems/neon,neon/layers/recurrent.py,36796720d43298705e070fd77e5da53a911affda,STILL_EXISTS,TODO: remember to change this to be devided by non-padded element
aaiihihdg,NervanaSystems/neon,neon/backends/abstract_backend.py,a3a7142430ddfa51718680cbe0d67d90f2a98b60,STILL_EXISTS,\"\"\" || Defines interface that any backend must implement || \"\"\"
aaiihijcb,NervanaSystems/neon,neon/__init__.py,2171b5e6c717c79124e43cba07ac7e74b41cf599,STILL_EXISTS,needed to keep yaml deserialization short
aaiihijhe,NervanaSystems/neon,neon/layers/container.py,8d1fb75b434a942f58291d11252c8064c0dbecfb,STILL_EXISTS,TODO: added this for resizing
aaiihijib,NervanaSystems/neon,neon/layers/container.py,8d1fb75b434a942f58291d11252c8064c0dbecfb,STILL_EXISTS,TODO: some sensible default behavior
aaiihijih,NervanaSystems/neon,neon/layers/container.py,8d1fb75b434a942f58291d11252c8064c0dbecfb,STILL_EXISTS,TODO: understand this better
aaiihijii,NervanaSystems/neon,neon/layers/container.py,8d1fb75b434a942f58291d11252c8064c0dbecfb,STILL_EXISTS,TODO: not relevant for Seq2Seq?
aaiihijje,NervanaSystems/neon,neon/layers/container.py,8d1fb75b434a942f58291d11252c8064c0dbecfb,STILL_EXISTS,TODO: look at impact of revert_tensors call inside encoder fprop
aaiihijjj,NervanaSystems/neon,neon/layers/container.py,8d1fb75b434a942f58291d11252c8064c0dbecfb,STILL_EXISTS,TODO: this assumes inputs will be given properly
aaiihjaaf,NervanaSystems/neon,neon/layers/container.py,8d1fb75b434a942f58291d11252c8064c0dbecfb,STILL_EXISTS,TODO: implement loopy training case
aaiihjaag,NervanaSystems/neon,neon/layers/container.py,8d1fb75b434a942f58291d11252c8064c0dbecfb,STILL_EXISTS,TODO: this just implements special case of one-one encoder-decoder recurrent layers
aaiihjacj,NervanaSystems/neon,neon/layers/container.py,8d1b86ceb6e34fde6819b6a7d36899cb57f61f2f,STILL_EXISTS,TODO: added this for resizing
aaiihjadc,NervanaSystems/neon,neon/layers/container.py,8d1b86ceb6e34fde6819b6a7d36899cb57f61f2f,STILL_EXISTS,TODO: some sensible default behavior
aaiihjadf,NervanaSystems/neon,neon/layers/container.py,8d1b86ceb6e34fde6819b6a7d36899cb57f61f2f,STILL_EXISTS,TODO: understand this better
aaiihjadg,NervanaSystems/neon,neon/layers/container.py,8d1b86ceb6e34fde6819b6a7d36899cb57f61f2f,STILL_EXISTS,TODO: not relevant for Seq2Seq?
aaiihjadj,NervanaSystems/neon,neon/layers/container.py,8d1b86ceb6e34fde6819b6a7d36899cb57f61f2f,STILL_EXISTS,TODO: look at impact of revert_tensors call inside encoder fprop
aaiihjaeb,NervanaSystems/neon,neon/layers/container.py,8d1b86ceb6e34fde6819b6a7d36899cb57f61f2f,STILL_EXISTS,TODO: this assumes inputs will be given properly
aaiihjaed,NervanaSystems/neon,neon/layers/container.py,8d1b86ceb6e34fde6819b6a7d36899cb57f61f2f,STILL_EXISTS,TODO: implement loopy training case
aaiihjaee,NervanaSystems/neon,neon/layers/container.py,8d1b86ceb6e34fde6819b6a7d36899cb57f61f2f,STILL_EXISTS,TODO: this just implements special case of one-one encoder-decoder recurrent layers
aaiihjbhi,NervanaSystems/neon,examples/rpn/objectlocalization.py,a79d8184efbbb2b791d0b5e94169b82d181daa3f,STILL_EXISTS,TODO: cache and remove import
aaiihjbie,NervanaSystems/neon,examples/rpn/objectlocalization.py,a79d8184efbbb2b791d0b5e94169b82d181daa3f,05933025fd836fc9a9ac9c2c23c520cc19824728,TODO:
aaiihjcfj,NervanaSystems/neon,examples/rpn/objectlocalization.py,a79d8184efbbb2b791d0b5e94169b82d181daa3f,STILL_EXISTS,subsample labels if needed
aaiihjcgh,NervanaSystems/neon,examples/rpn/objectlocalization.py,a79d8184efbbb2b791d0b5e94169b82d181daa3f,STILL_EXISTS,TODO: move these into the minibatch loop
aaiihjcgi,NervanaSystems/neon,examples/rpn/objectlocalization.py,a79d8184efbbb2b791d0b5e94169b82d181daa3f,STILL_EXISTS,TODO: remove redundant blobs
aaiihjfab,NervanaSystems/neon,examples/faster-rcnn/objectlocalization.py,05933025fd836fc9a9ac9c2c23c520cc19824728,ce992a7a916564cd00e88ea87286b039a5a9c180,TODO: cache and remove import
aaiihjfcg,NervanaSystems/neon,examples/faster-rcnn/objectlocalization.py,05933025fd836fc9a9ac9c2c23c520cc19824728,STILL_EXISTS,TODO: make this dataset specific
aaiihjfed,NervanaSystems/neon,examples/faster-rcnn/objectlocalization.py,05933025fd836fc9a9ac9c2c23c520cc19824728,ca28fd969df76ca42f842efd8a6a0ee18ab45a02,we also create some global buffers needed by the ProposalLayer
aaiihjfhf,NervanaSystems/neon,examples/faster-rcnn/objectlocalization.py,05933025fd836fc9a9ac9c2c23c520cc19824728,ca28fd969df76ca42f842efd8a6a0ee18ab45a02,subsample labels if needed
aaiihjfid,NervanaSystems/neon,examples/faster-rcnn/objectlocalization.py,05933025fd836fc9a9ac9c2c23c520cc19824728,ca28fd969df76ca42f842efd8a6a0ee18ab45a02,TODO: move these into the minibatch loop
aaiihjfie,NervanaSystems/neon,examples/faster-rcnn/objectlocalization.py,05933025fd836fc9a9ac9c2c23c520cc19824728,STILL_EXISTS,TODO: remove redundant blobs
aaiihjgdi,NervanaSystems/neon,examples/faster-rcnn/proposal_layer.py,05933025fd836fc9a9ac9c2c23c520cc19824728,STILL_EXISTS,-- TODO: move compute to device -- #
aaiihjgee,NervanaSystems/neon,examples/faster-rcnn/proposal_layer.py,05933025fd836fc9a9ac9c2c23c520cc19824728,STILL_EXISTS,-- END TODO -- #
aaiihjiej,NervanaSystems/neon,examples/faster-rcnn/objectlocalization.py,80d505f08447b960b240ca164df8578c97cb1d76,STILL_EXISTS,number of rois to train frcnn (needed to initialize global buffers)
aaiihjigb,NervanaSystems/neon,examples/faster-rcnn/objectlocalization.py,80d505f08447b960b240ca164df8578c97cb1d76,ca28fd969df76ca42f842efd8a6a0ee18ab45a02,Compute values needed for means and stds
aaiiiacba,NervanaSystems/neon,examples/faster-rcnn/objectlocalization.py,c271074dc6253eca2c7ed64685013967399a6597,ca28fd969df76ca42f842efd8a6a0ee18ab45a02,flip image if needed
aaiiiacie,NervanaSystems/neon,examples/faster-rcnn/faster_rcnn.py,c7c0f829af6daf59f7c410b88c15189d7b7e75e1,STILL_EXISTS,compute threshold needed to keep the top max_per_image
aaiiiacig,NervanaSystems/neon,examples/faster-rcnn/test.py,c7c0f829af6daf59f7c410b88c15189d7b7e75e1,STILL_EXISTS,normalize the model by the bbtarget mean and std if needed
aaiiiagjb,NervanaSystems/neon,examples/faster-rcnn/train.py,ce992a7a916564cd00e88ea87286b039a5a9c180,STILL_EXISTS,\"\"\" || Train a Faster-RCNN model to do object detection using PASCAL VOC dataset. || This training currently runs 1 image at a time. ||  || Reference: ||     \"Faster R-CNN\" ||     http:\/\/arxiv.org\/abs\/1506.01497 ||     https:\/\/github.com\/rbgirshick\/py-faster-rcnn ||  || Usage: ||     python examples\/faster-rcnn\/train.py -r0 -e 16 -s frcn_model.pkl -vv \\ ||     --epoch_step 5 --roi_branch_scale --lr_scale 1.0 -H 16 ||  || \"\"\"
aaiiiahbb,NervanaSystems/neon,examples/faster-rcnn/util.py,ce992a7a916564cd00e88ea87286b039a5a9c180,ca28fd969df76ca42f842efd8a6a0ee18ab45a02,compute threshold needed to keep the top max_per_image
aaiiiaidf,NervanaSystems/neon,examples/nmt/train.py,889c811eaaf39294300eb828249b7206bcabe4df,53f75560a9652337f81b89157935ee757bd1e151,TODO: deal with strings in Py 3. quick patch right now
aaiiiaifd,NervanaSystems/neon,neon/models/model.py,889c811eaaf39294300eb828249b7206bcabe4df,STILL_EXISTS,Move \"pointer\" back to beginning of dataset
aaiiiaihb,NervanaSystems/neon,neon/util/beamsearch.py,889c811eaaf39294300eb828249b7206bcabe4df,STILL_EXISTS,TODO: rename
aaiiiaiij,NervanaSystems/neon,neon/util/beamsearch.py,889c811eaaf39294300eb828249b7206bcabe4df,STILL_EXISTS,use index. TODO: Backend needs to catch these striding mismatches
aaiiiajag,NervanaSystems/neon,tests/test_beamsearch.py,889c811eaaf39294300eb828249b7206bcabe4df,STILL_EXISTS,create unused layers
aaiiiajei,NervanaSystems/neon,neon/backends/nervanagpu.py,fbde68388bf372e53dcb4dd2269e639108590d88,STILL_EXISTS,TODO: do the transpose as a DataLoaderTransformer instead of in host
aaiiibcfj,NervanaSystems/neon,neon/data/dataloader_transformers.py,640bf2c40cc7c43f8417964b1130419fed543bed,STILL_EXISTS,hack to convert single channel image to 3 channel for later processing
aaiiibdbi,NervanaSystems/neon,neon/data/hdf5iterator.py,169f2c04290b59e5c40bfd807c39830752be4380,STILL_EXISTS,needed for python3 to exit cleanly
aaiiibdcb,NervanaSystems/neon,neon/backends/convolution.py,3677654ee25f2fed1be454e6a7e3c89ec2313d8e,STILL_EXISTS,2x4  xxx
aaiiibdcj,NervanaSystems/neon,neon/backends/convolution.py,bd736327003a9368a5072bd7ae9b464f019a97a1,STILL_EXISTS,2x4  xxx(nn)
aaiiibdda,NervanaSystems/neon,neon/backends/convolution.py,bd736327003a9368a5072bd7ae9b464f019a97a1,STILL_EXISTS,4x4  xxx(xn)
aaiiibddb,NervanaSystems/neon,neon/backends/convolution.py,bd736327003a9368a5072bd7ae9b464f019a97a1,STILL_EXISTS,4x8  xxx(xx)
aaiiibedb,NervanaSystems/neon,examples/faster-rcnn/objectlocalization.py,ca28fd969df76ca42f842efd8a6a0ee18ab45a02,STILL_EXISTS,create placeholders for data needed by ProposalLayer
aaiiibedj,NervanaSystems/neon,examples/faster-rcnn/objectlocalization.py,ca28fd969df76ca42f842efd8a6a0ee18ab45a02,STILL_EXISTS,Data needed for training the RPN are provided by the DataLoader
aaiiibfge,NervanaSystems/neon,examples/faster-rcnn/proposal_layer.py,ca28fd969df76ca42f842efd8a6a0ee18ab45a02,STILL_EXISTS,get needed metadata from the dataloader
aaiiibhih,NervanaSystems/neon,examples/skip-thought/eval_sick.py,e7cb08fd5c8ffa9936554f71d28d1669ad88b08f,STILL_EXISTS,Get iterator from tokenized data. Second argument (train_text) not needed for evaluation
aaiiibjhc,NervanaSystems/neon,examples/skip-thought/data_loader.py,5f6c3ae7430e74fb9516499d6e73da1d887cbf51,STILL_EXISTS,create output dir if needed
aaiiibjij,NervanaSystems/neon,neon/layers/container.py,4204d913e2b2442abe17aa8ace57f1c0f9e88a8a,STILL_EXISTS,TODO: use sentinal class instead of None
aaiiibjjg,NervanaSystems/neon,neon/backends/layer_cpu.py,c8c1715764cbe63957f807284b272f42fe084393,25194ca047cb4c7b22aceff30f7d8c48ce236ec0,TODO: remove
aaiiiceca,NervanaSystems/neon,neon/backends/layer_mkl.py,1f3036c86ceb7bc4a61569d64c2d80c1527703c7,09ad9b8d6855ea6d9293198d583c9dce85b80e5b,hack for dealing with dilated conv
aaiiicecc,NervanaSystems/neon,neon/backends/layer_mkl.py,1f3036c86ceb7bc4a61569d64c2d80c1527703c7,56f1919c723c71ff9a9a024aa46fce7180897804,TODO; consider concact and beta
aaiiicecd,NervanaSystems/neon,neon/backends/layer_mkl.py,1f3036c86ceb7bc4a61569d64c2d80c1527703c7,d2b3e8f207187591d6aaccd1d2543a1d02e7d19b,TODO; support bias
aaiiiceee,NervanaSystems/neon,neon/backends/math_cpu.py,1f3036c86ceb7bc4a61569d64c2d80c1527703c7,STILL_EXISTS,TODO: cffi cast not compatible with f_contiguous array with py3.4
aaiiicegc,NervanaSystems/neon,neon/backends/nervanamkl.py,1f3036c86ceb7bc4a61569d64c2d80c1527703c7,STILL_EXISTS,TODO: enable this flag to find numerical problems
aaiiicfcf,NervanaSystems/neon,tests/test_backend_autodiff.py,fea87bc2574a3e11c2ef119d435dea951e9fb0ab,STILL_EXISTS,TODO: stricter test to fix numerical issues
aaiiicffa,NervanaSystems/neon,tests/test_backend_lrn.py,fea87bc2574a3e11c2ef119d435dea951e9fb0ab,STILL_EXISTS,give gpu the input array without zero padding (not needed)
aaiiichaf,NervanaSystems/neon,examples/ssd/datasets/ingest_pascalvoc.py,175bd3c94b9cea0f92424e5c4277206ce1a76a3d,STILL_EXISTS,\"emit_constraint_type\": \"center\"; TODO: enable when adds support for no gt boxes
aaiiichie,NervanaSystems/neon,examples/ssd/inference.py,175bd3c94b9cea0f92424e5c4277206ce1a76a3d,STILL_EXISTS,plot images if needed
aaiiiciab,NervanaSystems/neon,examples/ssd/layer.py,175bd3c94b9cea0f92424e5c4277206ce1a76a3d,STILL_EXISTS,these are needed to keep compute on the GPU
aaiiicice,NervanaSystems/neon,examples/ssd/layer.py,175bd3c94b9cea0f92424e5c4277206ce1a76a3d,STILL_EXISTS,move scores to host if needed
aaiiicicf,NervanaSystems/neon,examples/ssd/layer.py,175bd3c94b9cea0f92424e5c4277206ce1a76a3d,STILL_EXISTS,apply threshold if needed
aaiiicifb,NervanaSystems/neon,examples/ssd/mboxloss.py,175bd3c94b9cea0f92424e5c4277206ce1a76a3d,STILL_EXISTS,this is needed for the progress bar callback
aaiiicife,NervanaSystems/neon,examples/ssd/mboxloss.py,175bd3c94b9cea0f92424e5c4277206ce1a76a3d,STILL_EXISTS,just needed to interface with neon.models.model properly
aaiiicigc,NervanaSystems/neon,examples/ssd/mboxloss.py,175bd3c94b9cea0f92424e5c4277206ce1a76a3d,STILL_EXISTS,allocate buffers needed to store intermediate results
aaiiicigd,NervanaSystems/neon,examples/ssd/mboxloss.py,175bd3c94b9cea0f92424e5c4277206ce1a76a3d,STILL_EXISTS,transpose the results to y_conf_dev (needed for softmax)
aaiiicihc,NervanaSystems/neon,examples/ssd/mboxloss.py,175bd3c94b9cea0f92424e5c4277206ce1a76a3d,STILL_EXISTS,maybe here we need to move to host?
aaiiicjgh,NervanaSystems/neon,examples/ssd/ssd_container.py,175bd3c94b9cea0f92424e5c4277206ce1a76a3d,STILL_EXISTS,append conv8; conv9; conv10; etc. (if needed)
aaiiicjhh,NervanaSystems/neon,examples/ssd/ssd_container.py,175bd3c94b9cea0f92424e5c4277206ce1a76a3d,STILL_EXISTS,TODO: inference and no-inference return different outputs; can we normalize this somehow?
aaiiidjea,microsoft/MMdnn,mmdnn/conversion/caffe/mapper.py,7983748ad398b2df1c32359d6fd1311c1f6ec156,STILL_EXISTS,TODO: We need to identify whether this is 4D image data; otherwise we shouldn't change the dimension order
aaiiidjed,microsoft/MMdnn,mmdnn/conversion/caffe/mapper.py,7983748ad398b2df1c32359d6fd1311c1f6ec156,STILL_EXISTS,TODO: Axis
aaiiidjee,microsoft/MMdnn,mmdnn/conversion/caffe/mapper.py,7983748ad398b2df1c32359d6fd1311c1f6ec156,STILL_EXISTS,TODO: Unbiased
aaiiidjfj,microsoft/MMdnn,mmdnn/conversion/caffe/shape.py,7983748ad398b2df1c32359d6fd1311c1f6ec156,STILL_EXISTS,TODO: Find a better solution for this.
aaiiieaab,microsoft/MMdnn,mmdnn/conversion/caffe/writer.py,7983748ad398b2df1c32359d6fd1311c1f6ec156,STILL_EXISTS,FIXME:
aaiiieaji,microsoft/MMdnn,mmdnn/conversion/keras/keras2_graph.py,7983748ad398b2df1c32359d6fd1311c1f6ec156,STILL_EXISTS,Kit: TODO
aaiiiebba,microsoft/MMdnn,mmdnn/conversion/keras/keras2_parser.py,7983748ad398b2df1c32359d6fd1311c1f6ec156,STILL_EXISTS,Kit TODO: More activation functions
aaiiiebhf,microsoft/MMdnn,mmdnn/conversion/keras/keras2_parser.py,7983748ad398b2df1c32359d6fd1311c1f6ec156,STILL_EXISTS,Kit TODO : need to search the tf
aaiiieeji,microsoft/MMdnn,mmdnn/conversion/pytorch/pytorch_emitter.py,7983748ad398b2df1c32359d6fd1311c1f6ec156,STILL_EXISTS,Kit TODO: to handle padding
aaiiiefhd,microsoft/MMdnn,setup.py,7983748ad398b2df1c32359d6fd1311c1f6ec156,STILL_EXISTS,You can just specify the packages manually here if your project is
aaiiiegic,microsoft/MMdnn,mmdnn/conversion/common/utils.py,4c46b1fed3c2c08ea467a3c36bfabbfc784c3088,0d47a1f8b2a46d564f71f879a2ba1a5c9f1fe34f,\"\"\"\r || int64 effective_filter_size = (filter_size - 1) * dilation_rate + 1;\r ||   switch (padding_type) {\r ||     case Padding::SAME:\r ||       *output_size = (input_size + stride - 1) \/ stride;\r ||       const int64 padding_needed =\r ||           std::max(0LL; (*output_size - 1) * stride + effective_filter_size -\r ||                             input_size);\r ||       \/\/ For odd values of total padding; add more padding at the 'right'\r ||       \/\/ side of the given dimension.\r ||       *padding_before = padding_needed \/ 2;\r ||       *padding_after = padding_needed - *padding_before;\r ||       break;\r || \"\"\"
aaiiiehie,microsoft/MMdnn,mmdnn/conversion/examples/mxnet/extract_model.py,9b20c05bc078f2a512ff66aa81c3f446c9024391,STILL_EXISTS,Yuhao TODO: inference code
aaiiiehij,microsoft/MMdnn,mmdnn/conversion/examples/caffe/extract_model.py,dcf2f2fedd5fa72ab432583e41efb1a5f7b6b9d5,STILL_EXISTS,Yuhao TODO: inference code
aaiiieijj,microsoft/MMdnn,mmdnn/conversion/coreml/coreml_emitter.py,06b7a683a7b62eb8a3065eb2fee8c809f0b0cb8e,159662bc7939291287d56dcf360c4e9140816d4f,Kit TODO: set running mode
aaiiiejbc,microsoft/MMdnn,mmdnn/conversion/coreml/coreml_emitter.py,06b7a683a7b62eb8a3065eb2fee8c809f0b0cb8e,STILL_EXISTS,use this work-around
aaiiiejbj,microsoft/MMdnn,mmdnn/conversion/coreml/coreml_emitter.py,06b7a683a7b62eb8a3065eb2fee8c809f0b0cb8e,STILL_EXISTS,Kit TODO: set blob_order
aaiiifbjb,microsoft/MMdnn,mmdnn/conversion/coreml/coreml_utils.py,159662bc7939291287d56dcf360c4e9140816d4f,STILL_EXISTS,TODO - remove style transfer 1D hack
aaiiifccd,microsoft/MMdnn,mmdnn/conversion/coreml/coreml_emitter.py,648d79ec8262f75872281e5e5b1cf63e2223a9a8,7223ff319ffa36a7dabb5dd01704a23846529b3f,Kit TODO: set running mode
aaiiifdjf,microsoft/MMdnn,mmdnn/conversion/coreml/coreml_emitter.py,1b3efc8bf2e3aca2334091189af452fd1b0eea67,STILL_EXISTS,TODO: Deconv
aaiiigaaj,microsoft/MMdnn,mmdnn/conversion/caffe/mapper.py,a0e5dbeb927644467a908f6d2391e777ad9db8b4,STILL_EXISTS,TODO: The gamma parameter has to be set (in node.data?) and this should work.
aaiiigjae,microsoft/MMdnn,tests/test_conversion_imagenet.py,717583233367248995cacce5113e30eb0df0b4d3,STILL_EXISTS,TODO: PytorchEmit
aaiiigjef,microsoft/MMdnn,tests/test_conversion_imagenet.py,ad6ae75dceaa3ffaae89a12615ce96bf4c37ba07,d6ad676b7ebd3c10f51663141ef05d3d45b3f569,TODO: CntkEmit
aaiiigjfa,microsoft/MMdnn,tests/test_conversion_imagenet.py,0f452202619bec34bf241b0d4c27c52aa75c3150,STILL_EXISTS,TODO: PytorchEmit
aaiiihaig,microsoft/MMdnn,mmdnn/conversion/examples/tensorflow/models/nasnet_utils.py,564595db1d31af6fdc89d69b06e55607bcfb06f7,STILL_EXISTS,Check if a stride is needed; then use a strided 1x1 here
aaiiihajd,microsoft/MMdnn,tests/test_conversion_imagenet.py,564595db1d31af6fdc89d69b06e55607bcfb06f7,2e5a55f4589f7c1f18b0082fbf1a4d99497586c5,'nasnet-a_large' : [TensorflowEmit; KerasEmit; PytorchEmit]; # TODO
aaiiihffd,microsoft/MMdnn,tests/test_conversion_imagenet.py,b764afb56f8394ab149b6e7f7ca312eeb1104b3f,STILL_EXISTS,'inception_resnet_v2' : [CntkEmit; TensorflowEmit; KerasEmit; PytorchEmit]; # TODO
aaiiihffe,microsoft/MMdnn,tests/test_conversion_imagenet.py,b764afb56f8394ab149b6e7f7ca312eeb1104b3f,2e5a55f4589f7c1f18b0082fbf1a4d99497586c5,'nasnet-a_large' : [TensorflowEmit; KerasEmit; PytorchEmit]; # TODO
aaiiihfgd,microsoft/MMdnn,tests/test_conversion_imagenet.py,3a0aeb69eafb78f9a32b96e7c8ffc0dd074c190f,STILL_EXISTS,TODO: PytorchEmit
aaiiiiahd,microsoft/MMdnn,tests/test_conversion_imagenet.py,c73456824e7d68df894e994c0a8aba3370cd2114,STILL_EXISTS,'inception_v1'  : [CntkEmit; TensorflowEmit; KerasEmit; MXNetEmit]; # TODO: PytorchEmit
aaiiiiajf,microsoft/MMdnn,tests/test_conversion_imagenet.py,b9820eb3ec3c4f5a73aee30b761853bb35c0925e,35cd10a483b366f2cf53b697aa2b327a5791ea84,TODO
aaiiiiaji,microsoft/MMdnn,tests/test_conversion_imagenet.py,b9820eb3ec3c4f5a73aee30b761853bb35c0925e,STILL_EXISTS,'inception_v1'  : [CntkEmit; TensorflowEmit; KerasEmit; MXNetEmit]; # TODO: PytorchEmit
aaiiiibdg,microsoft/MMdnn,mmdnn/conversion/mxnet/mxnet_parser.py,7c9f2265b657bbc04f8395df13c03535d0537491,6e2b21d089ecb202555553482f435cb070988676,TODO: Need to re-implement
aaiiiibii,microsoft/MMdnn,tests/test_conversion_imagenet.py,6e2b21d089ecb202555553482f435cb070988676,STILL_EXISTS,TODO: MXNetEmit
aaiiiicbb,microsoft/MMdnn,tests/test_conversion_imagenet.py,317b1703039e9376947a7a9936bb346d72c4e0e8,35cd10a483b366f2cf53b697aa2b327a5791ea84,TODO
aaiiiicbi,microsoft/MMdnn,tests/test_conversion_imagenet.py,7d396c668fd74d159bc8e77d4fd0df27850221ad,cd7078c9e19c9db70cd765704f65da2f9471e687,TODO
aaiiiiccd,microsoft/MMdnn,tests/test_conversion_imagenet.py,3518d0114b200b9374ae94d0886207a9442ff816,STILL_EXISTS,TODO: Caffe
aaiiiicce,microsoft/MMdnn,tests/test_conversion_imagenet.py,3518d0114b200b9374ae94d0886207a9442ff816,STILL_EXISTS,TODO: KerasEmit
aaiiiiccf,microsoft/MMdnn,tests/test_conversion_imagenet.py,3518d0114b200b9374ae94d0886207a9442ff816,2e5a55f4589f7c1f18b0082fbf1a4d99497586c5,'nasnet-a_large' : [TensorflowEmit; KerasEmit; PytorchEmit]; # TODO
aaiiiicga,microsoft/MMdnn,mmdnn/conversion/cntk/cntk_parser.py,6324e7bb91692b7085731a830b4a1907c98ff2b5,STILL_EXISTS,TODO: Handle square
aaiiiicgg,microsoft/MMdnn,tests/test_conversion_imagenet.py,e620b7cd05169c80189486f74cf8d3260a4af0a8,cd7078c9e19c9db70cd765704f65da2f9471e687,TODO
aaiiiicha,microsoft/MMdnn,tests/test_conversion_imagenet.py,f5e77360cfc22b7e80b8410c09a47b02a7dd4954,STILL_EXISTS,TODO: Caffe
aaiiiiddf,microsoft/MMdnn,tests/test_conversion_imagenet.py,5c8c75a40af078d99faac621c235bbb623f10ef1,d6ad676b7ebd3c10f51663141ef05d3d45b3f569,TODO: CntkEmit
aaiiiided,microsoft/MMdnn,tests/test_conversion_imagenet.py,dbf13312909ffae03de58ea184e209d662145c46,STILL_EXISTS,'inception_v1' : [TensorflowEmit; KerasEmit; PytorchEmit; MXNetEmit]; # TODO: CntkEmit
aaiiiidef,microsoft/MMdnn,tests/test_conversion_imagenet.py,dbf13312909ffae03de58ea184e209d662145c46,STILL_EXISTS,'resnet_v1_50' : [TensorflowEmit; KerasEmit; PytorchEmit; MXNetEmit]; # TODO: CntkEmit
aaiiiideg,microsoft/MMdnn,tests/test_conversion_imagenet.py,dbf13312909ffae03de58ea184e209d662145c46,STILL_EXISTS,'resnet_v1_152' : [TensorflowEmit; KerasEmit; PytorchEmit; MXNetEmit]; # TODO: CntkEmit
aaiiiidfd,microsoft/MMdnn,tests/test_conversion_imagenet.py,6e70ce6e11a9e6bb5d2d7c2ea1685e14eab1449e,STILL_EXISTS,'resnet_v1_152' : [TensorflowEmit; KerasEmit; PytorchEmit; MXNetEmit]; # TODO: CntkEmit
aaiiiidgb,microsoft/MMdnn,mmdnn/conversion/torch/torch_graph.py,20310152595aa4e80b7526d4ac180e54a082a5c2,STILL_EXISTS,TODO
aaiiiiebd,microsoft/MMdnn,tests/test_conversion_imagenet.py,730bac427cf93ed47df181d4ada7c7931bdd6ff7,cd7078c9e19c9db70cd765704f65da2f9471e687,TODO
aaiiiiebh,microsoft/MMdnn,tests/test_conversion_imagenet.py,730bac427cf93ed47df181d4ada7c7931bdd6ff7,STILL_EXISTS,'inception_v3' : [CntkEmit; TensorflowEmit; KerasEmit; PytorchEmit; MXNetEmit]; # TODO: Caffe
aaiiiifjf,microsoft/MMdnn,tests/test_conversion_imagenet.py,37f95559809d815be7cc79bd1ed939c41b2ca652,STILL_EXISTS,'inception_v3' : [CntkEmit; TensorflowEmit; KerasEmit; PytorchEmit; MXNetEmit]; # TODO: Caffe
aaiiiighd,microsoft/MMdnn,tests/test_conversion_imagenet.py,cb6c47fbfce65be716e93594f31941994e2112f8,7f7a4feb77e87d579f88164e09d1dd246fc35b8b,'inception_v3' : [CoreMLEmit]; # TODO: Caffe
aaiiiiijg,microsoft/MMdnn,tests/test_conversion_imagenet.py,dbd1633bc3429c7ecfc9a73b50fe6ad6d7feef6b,d6ad676b7ebd3c10f51663141ef05d3d45b3f569,TODO: CntkEmit
aaiiijbab,microsoft/MMdnn,tests/test_conversion_imagenet.py,da8220917deb80924c474f0354c1e9da5314d1eb,73bdc37d757ed461bd07c17ed55eb9bb475fcc7c,TODO; same with caffe_Cntk_inception_v4
aaiiijbac,microsoft/MMdnn,tests/test_conversion_imagenet.py,da8220917deb80924c474f0354c1e9da5314d1eb,cd7078c9e19c9db70cd765704f65da2f9471e687,TODO
aaiiijbae,microsoft/MMdnn,tests/test_conversion_imagenet.py,da8220917deb80924c474f0354c1e9da5314d1eb,STILL_EXISTS,TODO: KerasEmit
aaiiijbaf,microsoft/MMdnn,tests/test_conversion_imagenet.py,da8220917deb80924c474f0354c1e9da5314d1eb,STILL_EXISTS,TODO MXNetEmit; CaffeEmit
aaiiijccc,microsoft/MMdnn,tests/test_conversion_imagenet.py,c661cf7b2f21e7e74bb23c062b4d98bf07125b95,cd7078c9e19c9db70cd765704f65da2f9471e687,TODO
aaiiijhje,microsoft/MMdnn,tests/test_conversion_imagenet.py,53e6a18dc9468a5404a2cb042b073d505d3e4312,6b03d0e4aa72c096ad4eb939f92be48b8b6b37d5,'inception_v3' : [CntkEmit; TensorflowEmit; KerasEmit; PytorchEmit; MXNetEmit; CoreMLEmit]; # TODO: Caffe
aaiiijidg,microsoft/MMdnn,mmdnn/conversion/keras/keras2_emitter.py,bd7ee442d7b15842d1619480a46d77c54c55a567,STILL_EXISTS,TODO: arguments won't be saved in keras export model
aaiiijied,microsoft/MMdnn,mmdnn/conversion/coreml/coreml_emitter.py,480efed7462f7719d779aad76e4235efbe10379e,STILL_EXISTS,TODO  global pooling modification
aaiiijieh,microsoft/MMdnn,mmdnn/conversion/coreml/coreml_emitter.py,480efed7462f7719d779aad76e4235efbe10379e,STILL_EXISTS,TODO remain to be modified!
aaiiijiji,microsoft/MMdnn,mmdnn/conversion/coreml/coreml_parser.py,855159a858cbbf10078eb4e8cb87b44c0f358d56,STILL_EXISTS,self.data_format ? TODO
aaiiijjae,microsoft/MMdnn,mmdnn/conversion/coreml/coreml_parser.py,855159a858cbbf10078eb4e8cb87b44c0f358d56,STILL_EXISTS,TODO dtype_map
aaiiijjcj,microsoft/MMdnn,mmdnn/conversion/coreml/coreml_parser.py,855159a858cbbf10078eb4e8cb87b44c0f358d56,STILL_EXISTS,TODO
aaiiijjed,microsoft/MMdnn,mmdnn/conversion/coreml/coreml_parser.py,855159a858cbbf10078eb4e8cb87b44c0f358d56,STILL_EXISTS,For concat axis TODO
aaiiijjeh,microsoft/MMdnn,mmdnn/conversion/coreml/coreml_parser.py,855159a858cbbf10078eb4e8cb87b44c0f358d56,STILL_EXISTS,Future Module TODO
aaiiijjgb,microsoft/MMdnn,mmdnn/conversion/coreml/coreml_parser.py,855159a858cbbf10078eb4e8cb87b44c0f358d56,STILL_EXISTS,axis TODO
aaiijaeaj,microsoft/MMdnn,mmdnn/conversion/examples/coreml/extractor.py,730c1fa12d695e8abd8c8611dd6682b9340c9eda,STILL_EXISTS,TODO: Multiple inputs
aaiijaeba,microsoft/MMdnn,mmdnn/conversion/examples/coreml/extractor.py,730c1fa12d695e8abd8c8611dd6682b9340c9eda,STILL_EXISTS,TODO: Multiple outputs
aaiijaece,microsoft/MMdnn,tests/test_conversion_imagenet.py,730c1fa12d695e8abd8c8611dd6682b9340c9eda,b486739631792057bd90b42f4e568e1937dc2490,'inception_v3' : [CntkEmit; TensorflowEmit; KerasEmit; PytorchEmit; MXNetEmit; CoreMLEmit]; # TODO: Caffe
aaiijaefb,microsoft/MMdnn,mmdnn/conversion/examples/keras/utils.py,453d11d07541e5f9b9159328f145b253ce2a14b7,STILL_EXISTS,TODO: It works with +1; don't know why.
aaiijaefj,microsoft/MMdnn,mmdnn/conversion/examples/keras/utils.py,453d11d07541e5f9b9159328f145b253ce2a14b7,STILL_EXISTS,TODO: use keras backend instead of tf.
aaiijbdgj,microsoft/MMdnn,tests/test_conversion_imagenet.py,7268df26db9695f84b95b8b14f2aa80011c6dd38,STILL_EXISTS,'imagenet1k-inception-bn'   : [CntkEmit; TensorflowEmit; KerasEmit; PytorchEmit; MXNetEmit]; # TODO: Caffe
aaiijbead,microsoft/MMdnn,tests/test_conversion_imagenet.py,8928d5c0373f084714604a949c7e46f44cc88613,cd7078c9e19c9db70cd765704f65da2f9471e687,TODO
aaiijbeae,microsoft/MMdnn,tests/test_conversion_imagenet.py,8928d5c0373f084714604a949c7e46f44cc88613,73bdc37d757ed461bd07c17ed55eb9bb475fcc7c,TODO; same with caffe_Cntk_inception_v4
aaiijbeai,microsoft/MMdnn,tests/test_conversion_imagenet.py,8928d5c0373f084714604a949c7e46f44cc88613,STILL_EXISTS,TODO: Caffe
aaiijbeba,microsoft/MMdnn,tests/test_conversion_imagenet.py,8928d5c0373f084714604a949c7e46f44cc88613,cd7078c9e19c9db70cd765704f65da2f9471e687,TODO: CaffeEmit; CntkEmit
aaiijbebb,microsoft/MMdnn,tests/test_conversion_imagenet.py,8928d5c0373f084714604a949c7e46f44cc88613,STILL_EXISTS,TODO: CaffeEmit
aaiijbebc,microsoft/MMdnn,tests/test_conversion_imagenet.py,8928d5c0373f084714604a949c7e46f44cc88613,STILL_EXISTS,TODO: CaffeEmit(Crash); PytorchEmit(DepthwiseConv)
aaiijbfdf,microsoft/MMdnn,tests/test_conversion_imagenet.py,7186de1848d1d3aa7b08c2d9d7f4133378ac5b6f,fd1112f2474aaf3e1f14796b019760c6d77eeed7,'inception_v3' : [CntkEmit; TensorflowEmit; KerasEmit; PytorchEmit; MXNetEmit; CoreMLEmit]; # TODO: Caffe
aaiijbfdj,microsoft/MMdnn,tests/test_conversion_imagenet.py,7186de1848d1d3aa7b08c2d9d7f4133378ac5b6f,fd1112f2474aaf3e1f14796b019760c6d77eeed7,'mobilenet'    : [TensorflowEmit; KerasEmit; CoreMLEmit]; # TODO: MXNetEmit
aaiijbfej,microsoft/MMdnn,tests/test_conversion_imagenet.py,7186de1848d1d3aa7b08c2d9d7f4133378ac5b6f,d6ad676b7ebd3c10f51663141ef05d3d45b3f569,TODO: CntkEmit
aaiijbfhc,microsoft/MMdnn,tests/test_conversion_imagenet.py,7186de1848d1d3aa7b08c2d9d7f4133378ac5b6f,cd7078c9e19c9db70cd765704f65da2f9471e687,TODO: CaffeEmit; CntkEmit
aaiijbfhd,microsoft/MMdnn,tests/test_conversion_imagenet.py,7186de1848d1d3aa7b08c2d9d7f4133378ac5b6f,STILL_EXISTS,TODO: CaffeEmit(Crash); PytorchEmit(DepthwiseConv)
aaiijbfii,microsoft/MMdnn,tests/test_conversion_imagenet.py,fd1112f2474aaf3e1f14796b019760c6d77eeed7,cd7078c9e19c9db70cd765704f65da2f9471e687,TODO
aaiijbfje,microsoft/MMdnn,tests/test_conversion_imagenet.py,fd1112f2474aaf3e1f14796b019760c6d77eeed7,STILL_EXISTS,TODO: MXNetEmit
aaiijbfjh,microsoft/MMdnn,tests/test_conversion_imagenet.py,fd1112f2474aaf3e1f14796b019760c6d77eeed7,cd7078c9e19c9db70cd765704f65da2f9471e687,TODO: CaffeEmit; CntkEmit
aaiijbfjj,microsoft/MMdnn,tests/test_conversion_imagenet.py,fd1112f2474aaf3e1f14796b019760c6d77eeed7,STILL_EXISTS,TODO: CaffeEmit(Crash); PytorchEmit(DepthwiseConv)
aaiijbhii,microsoft/MMdnn,tests/test_conversion_imagenet.py,cd7078c9e19c9db70cd765704f65da2f9471e687,d6ad676b7ebd3c10f51663141ef05d3d45b3f569,TODO: CntkEmit
aaiijbhji,microsoft/MMdnn,tests/test_conversion_imagenet.py,cd7078c9e19c9db70cd765704f65da2f9471e687,STILL_EXISTS,TODO: CaffeEmit
aaiijbiaa,microsoft/MMdnn,tests/test_conversion_imagenet.py,cd7078c9e19c9db70cd765704f65da2f9471e687,STILL_EXISTS,TODO: CaffeEmit(Crash); PytorchEmit(DepthwiseConv)
aaiijbiai,microsoft/MMdnn,tests/test_conversion_imagenet.py,c7dca97a40ffa645bdc1a84710fed5c7de90dfa2,044f755d769cff0aeff4dd368641e3577f01d6ff,TODO
aaiijbibg,microsoft/MMdnn,tests/test_conversion_imagenet.py,c7dca97a40ffa645bdc1a84710fed5c7de90dfa2,STILL_EXISTS,TODO: CaffeEmit
aaiijbibh,microsoft/MMdnn,tests/test_conversion_imagenet.py,c7dca97a40ffa645bdc1a84710fed5c7de90dfa2,4e9d575b1c9cae8461ddc0ccb8d2f2fc83e4359e,TODO: CaffeEmit(Crash)
aaiijbiif,microsoft/MMdnn,tests/test_conversion_imagenet.py,d9d34d1fe338b9c43d995ef854c6bf08aa5e4255,d9d34d1fe338b9c43d995ef854c6bf08aa5e4255,TODO
aaiijbiig,microsoft/MMdnn,tests/test_conversion_imagenet.py,d9d34d1fe338b9c43d995ef854c6bf08aa5e4255,STILL_EXISTS,TODO: KerasEmit
aaiijbiih,microsoft/MMdnn,tests/test_conversion_imagenet.py,d9d34d1fe338b9c43d995ef854c6bf08aa5e4255,STILL_EXISTS,TODO MXNetEmit; CaffeEmit
aaiijbjbe,microsoft/MMdnn,tests/test_conversion_imagenet.py,6b5aa146a73838e0e86e454793e3aefc098b6604,STILL_EXISTS,TODO: CaffeEmit
aaiijbjbh,microsoft/MMdnn,tests/test_conversion_imagenet.py,6b5aa146a73838e0e86e454793e3aefc098b6604,4e9d575b1c9cae8461ddc0ccb8d2f2fc83e4359e,TODO: CaffeEmit(Crash)
aaiijbjcj,microsoft/MMdnn,tests/test_conversion_imagenet.py,b146c9e655131012745e6ea76009528b75bbf306,STILL_EXISTS,TODO: KerasEmit
aaiijbjda,microsoft/MMdnn,tests/test_conversion_imagenet.py,b146c9e655131012745e6ea76009528b75bbf306,STILL_EXISTS,TODO MXNetEmit; CaffeEmit
aaiijbjfb,microsoft/MMdnn,tests/test_conversion_imagenet.py,59981839bec6089dd882870c5cdc5d09332bb220,73bdc37d757ed461bd07c17ed55eb9bb475fcc7c,TODO; same with caffe_Cntk_inception_v4
aaiijbjfe,microsoft/MMdnn,tests/test_conversion_imagenet.py,59981839bec6089dd882870c5cdc5d09332bb220,STILL_EXISTS,TODO: CaffeEmit; CntkEmit
aaiijbjfg,microsoft/MMdnn,tests/test_conversion_imagenet.py,59981839bec6089dd882870c5cdc5d09332bb220,4e9d575b1c9cae8461ddc0ccb8d2f2fc83e4359e,TODO: CaffeEmit(Crash)
aaiijbjfh,microsoft/MMdnn,tests/test_conversion_imagenet.py,59981839bec6089dd882870c5cdc5d09332bb220,2e5a55f4589f7c1f18b0082fbf1a4d99497586c5,'inception_resnet_v2' : [CntkEmit; TensorflowEmit; KerasEmit]; # TODO PytorchEmit
aaiijbjfi,microsoft/MMdnn,tests/test_conversion_imagenet.py,59981839bec6089dd882870c5cdc5d09332bb220,2e5a55f4589f7c1f18b0082fbf1a4d99497586c5,'nasnet-a_large' : [TensorflowEmit; KerasEmit; PytorchEmit]; # TODO
aaiijbjia,microsoft/MMdnn,tests/test_conversion_imagenet.py,732b6b9baa566aa384fbf1420970042ed08b9e6a,e957fa3d2c82325d30aea33b8e244d1a337d146b,'inception_v3' : [CntkEmit; TensorflowEmit; KerasEmit; PytorchEmit; MXNetEmit; CoreMLEmit]; # TODO: Caffe
aaiijbjie,microsoft/MMdnn,tests/test_conversion_imagenet.py,732b6b9baa566aa384fbf1420970042ed08b9e6a,e957fa3d2c82325d30aea33b8e244d1a337d146b,'mobilenet'    : [TensorflowEmit; KerasEmit; CoreMLEmit]; # TODO: MXNetEmit
aaiijcaae,microsoft/MMdnn,tests/test_conversion_imagenet.py,732b6b9baa566aa384fbf1420970042ed08b9e6a,STILL_EXISTS,TODO: CaffeEmit
aaiijcaaf,microsoft/MMdnn,tests/test_conversion_imagenet.py,732b6b9baa566aa384fbf1420970042ed08b9e6a,2e5a55f4589f7c1f18b0082fbf1a4d99497586c5,'inception_resnet_v2' : [CntkEmit; TensorflowEmit; KerasEmit]; # TODO PytorchEmit
aaiijcaag,microsoft/MMdnn,tests/test_conversion_imagenet.py,732b6b9baa566aa384fbf1420970042ed08b9e6a,2e5a55f4589f7c1f18b0082fbf1a4d99497586c5,'nasnet-a_large' : [TensorflowEmit; KerasEmit; PytorchEmit]; # TODO
aaiijcadh,microsoft/MMdnn,tests/test_conversion_imagenet.py,0d32ac2c72d776bde0bd50d2b95699ad605ce81d,73bdc37d757ed461bd07c17ed55eb9bb475fcc7c,TODO; same with caffe_Cntk_inception_v4
aaiijcadi,microsoft/MMdnn,tests/test_conversion_imagenet.py,0d32ac2c72d776bde0bd50d2b95699ad605ce81d,2e5a55f4589f7c1f18b0082fbf1a4d99497586c5,'nasnet-a_large' : [TensorflowEmit; KerasEmit; PytorchEmit]; # TODO
aaiijcaei,microsoft/MMdnn,tests/test_conversion_imagenet.py,5572a9ab40b80fec823897fb2418f49d034c3095,4e9d575b1c9cae8461ddc0ccb8d2f2fc83e4359e,TODO: CaffeEmit(Crash)
aaiijcaff,microsoft/MMdnn,tests/test_conversion_imagenet.py,99d01b1336fb70b72bddbab802e550e398ad5bcd,STILL_EXISTS,TODO: Caffe; Keras; and MXNet no constant layer
aaiijcafh,microsoft/MMdnn,tests/test_conversion_imagenet.py,99d01b1336fb70b72bddbab802e550e398ad5bcd,c7465374c2b45771139abaf77ad930381b14946c,'inception_v3' : [CoreMLEmit; CntkEmit; KerasEmit; MXNetEmit; PytorchEmit; TensorflowEmit]; # TODO: Caffe
aaiijcafi,microsoft/MMdnn,tests/test_conversion_imagenet.py,99d01b1336fb70b72bddbab802e550e398ad5bcd,c7465374c2b45771139abaf77ad930381b14946c,'mobilenet'    : [CoreMLEmit; KerasEmit; TensorflowEmit]; # TODO: MXNetEmit
aaiijcagg,microsoft/MMdnn,tests/test_conversion_imagenet.py,99d01b1336fb70b72bddbab802e550e398ad5bcd,c7465374c2b45771139abaf77ad930381b14946c,'inception_v1'      : [CoreMLEmit; KerasEmit; MXNetEmit; PytorchEmit; TensorflowEmit]; # TODO: CntkEmit
aaiijcagj,microsoft/MMdnn,tests/test_conversion_imagenet.py,99d01b1336fb70b72bddbab802e550e398ad5bcd,c7465374c2b45771139abaf77ad930381b14946c,'resnet_v1_50'      : [KerasEmit; MXNetEmit; PytorchEmit; TensorflowEmit]; # TODO: CntkEmit
aaiijcaha,microsoft/MMdnn,tests/test_conversion_imagenet.py,99d01b1336fb70b72bddbab802e550e398ad5bcd,c7465374c2b45771139abaf77ad930381b14946c,'resnet_v1_152'     : [KerasEmit; MXNetEmit; PytorchEmit; TensorflowEmit]; # TODO: CntkEmit
aaiijcahb,microsoft/MMdnn,tests/test_conversion_imagenet.py,99d01b1336fb70b72bddbab802e550e398ad5bcd,c7465374c2b45771139abaf77ad930381b14946c,'resnet_v2_50'      : [KerasEmit; MXNetEmit; PytorchEmit; TensorflowEmit]; # TODO: CntkEmit
aaiijcahc,microsoft/MMdnn,tests/test_conversion_imagenet.py,99d01b1336fb70b72bddbab802e550e398ad5bcd,c7465374c2b45771139abaf77ad930381b14946c,'resnet_v2_152'     : [KerasEmit; MXNetEmit; PytorchEmit; TensorflowEmit]; # TODO: CntkEmit
aaiijcahf,microsoft/MMdnn,tests/test_conversion_imagenet.py,99d01b1336fb70b72bddbab802e550e398ad5bcd,STILL_EXISTS,# 'inception_resnet_v2' : [CntkEmit; TensorflowEmit; KerasEmit]; # TODO PytorchEmit
aaiijcahg,microsoft/MMdnn,tests/test_conversion_imagenet.py,99d01b1336fb70b72bddbab802e550e398ad5bcd,STILL_EXISTS,# 'nasnet-a_large' : [TensorflowEmit; KerasEmit; PytorchEmit]; # TODO
aaiijcahh,microsoft/MMdnn,tests/test_conversion_imagenet.py,99d01b1336fb70b72bddbab802e550e398ad5bcd,d6ad676b7ebd3c10f51663141ef05d3d45b3f569,TODO: CntkEmit
aaiijcbaj,microsoft/MMdnn,tests/test_conversion_imagenet.py,99d01b1336fb70b72bddbab802e550e398ad5bcd,4e9d575b1c9cae8461ddc0ccb8d2f2fc83e4359e,TODO: CaffeEmit(Crash)
aaiijcbbf,microsoft/MMdnn,tests/test_conversion_imagenet.py,c7465374c2b45771139abaf77ad930381b14946c,4e9d575b1c9cae8461ddc0ccb8d2f2fc83e4359e,TODO: CaffeEmit(Crash)
aaiijcbbg,microsoft/MMdnn,tests/test_conversion_imagenet.py,c7465374c2b45771139abaf77ad930381b14946c,2e5a55f4589f7c1f18b0082fbf1a4d99497586c5,'inception_resnet_v2' : [CntkEmit; TensorflowEmit; KerasEmit]; # TODO PytorchEmit
aaiijcbbh,microsoft/MMdnn,tests/test_conversion_imagenet.py,c7465374c2b45771139abaf77ad930381b14946c,2e5a55f4589f7c1f18b0082fbf1a4d99497586c5,'nasnet-a_large' : [TensorflowEmit; KerasEmit; PytorchEmit]; # TODO
aaiijcbec,microsoft/MMdnn,tests/test_conversion_imagenet.py,c7465374c2b45771139abaf77ad930381b14946c,STILL_EXISTS,# 'inception_resnet_v2' : [CntkEmit; TensorflowEmit; KerasEmit]; # TODO PytorchEmit
aaiijcbed,microsoft/MMdnn,tests/test_conversion_imagenet.py,c7465374c2b45771139abaf77ad930381b14946c,STILL_EXISTS,# 'nasnet-a_large' : [TensorflowEmit; KerasEmit; PytorchEmit]; # TODO
aaiijcbhc,microsoft/MMdnn,mmdnn/conversion/keras/keras2_emitter.py,51fb740943bc41ecb9dad93af102faac98a59c46,STILL_EXISTS,TODO
aaiijcbig,microsoft/MMdnn,tests/test_conversion_imagenet.py,51fb740943bc41ecb9dad93af102faac98a59c46,4e9d575b1c9cae8461ddc0ccb8d2f2fc83e4359e,TODO: CaffeEmit(Crash)
aaiijcbij,microsoft/MMdnn,tests/test_conversion_imagenet.py,a630dfed0a97d9bfd01f43dae738ca0933343538,4e9d575b1c9cae8461ddc0ccb8d2f2fc83e4359e,TODO: CaffeEmit(Crash)
aaiijcbje,microsoft/MMdnn,tests/test_conversion_imagenet.py,a630dfed0a97d9bfd01f43dae738ca0933343538,d6ad676b7ebd3c10f51663141ef05d3d45b3f569,TODO: CntkEmit
aaiijccbd,microsoft/MMdnn,mmdnn/conversion/coreml/coreml_utils.py,2e5a55f4589f7c1f18b0082fbf1a4d99497586c5,STILL_EXISTS,!Alert! TODO
aaiijccbe,microsoft/MMdnn,mmdnn/conversion/coreml/coreml_utils.py,2e5a55f4589f7c1f18b0082fbf1a4d99497586c5,STILL_EXISTS,Future implement can be changed to the last two layer
aaiijccbf,microsoft/MMdnn,tests/test_conversion_imagenet.py,2e5a55f4589f7c1f18b0082fbf1a4d99497586c5,STILL_EXISTS,TODO PytorchEmit
aaiijccbg,microsoft/MMdnn,tests/test_conversion_imagenet.py,2e5a55f4589f7c1f18b0082fbf1a4d99497586c5,2df74e81f8ece9413ddf51d9530862032157c854,TODO
aaiijccci,microsoft/MMdnn,tests/test_conversion_imagenet.py,2df74e81f8ece9413ddf51d9530862032157c854,STILL_EXISTS,'nasnet-a_large' : [TensorflowEmit; KerasEmit; PytorchEmit]; # TODO
aaiijccdi,microsoft/MMdnn,mmdnn/conversion/tensorflow/tensorflow_parser.py,06b72115066ab5ccdee570ddd647f2ff9fd8fb4a,STILL_EXISTS,TODO: Current it is only for slice
aaiijcceb,microsoft/MMdnn,tests/test_conversion_imagenet.py,06b72115066ab5ccdee570ddd647f2ff9fd8fb4a,STILL_EXISTS,TODO: KerasEmit(Slice Layer: https:\/\/blog.csdn.net\/lujiandong1\/article\/details\/54936185)
aaiijccee,microsoft/MMdnn,tests/test_conversion_imagenet.py,c8355b7d4d69bac2724fb64920a90ec5ea245344,STILL_EXISTS,'nasnet-a_large' : [TensorflowEmit; KerasEmit; PytorchEmit]; # TODO
aaiijccei,microsoft/MMdnn,tests/test_conversion_imagenet.py,fcd6cb55cb82acaea23331394450c3fed7f3282c,STILL_EXISTS,TODO: KerasEmit(Slice Layer: https:\/\/blog.csdn.net\/lujiandong1\/article\/details\/54936185)
aaiijccgc,microsoft/MMdnn,mmdnn/conversion/tensorflow/tensorflow_emitter.py,b8adc23d04a06da1b9ee6d932ded545f3f56d279,STILL_EXISTS,flatten is needed
aaiijcchg,microsoft/MMdnn,tests/test_conversion_imagenet.py,d6ee9591f26d286126590901142d0894bcebc653,8db0a51c7734e246c1152108f11ba86e99caabda,TODO: CaffeEmit(Crash) CntkEmit
aaiijcfgj,microsoft/MMdnn,tests/test_conversion_imagenet.py,1727a38506f1f2997cd30522939c8ab90e165110,STILL_EXISTS,TODO: CaffeEmit(Crash) CntkEmit
aaiijcfhc,microsoft/MMdnn,mmdnn/conversion/coreml/coreml_parser.py,bc80d9bd84a03c87998452affbf331b3d8263d4b,STILL_EXISTS,TODO
aaiijcibd,microsoft/MMdnn,mmdnn/conversion/pytorch/pytorch_graph.py,e9b2a690b0599adf9d7d4e606d46b596fc5cd679,c9e18b8fa1caaab021aefc29587966b67eb09883,TODO
aaiijdbed,microsoft/MMdnn,mmdnn/conversion/tensorflow/tensorflow_parser.py,eb297065d1548cc7712a9fd16142846e1cc3fc34,STILL_EXISTS,TODO: Current it is only for slice
aaiijdbfd,microsoft/MMdnn,mmdnn/conversion/mxnet/mxnet_emitter.py,7e533e494bf9365689063dd25a3f80e73bf40a2a,70465e16463f08367b03f50d06896005614049ca,ends = IR_node.get_attr('ends')
aaiijdbfe,microsoft/MMdnn,mmdnn/conversion/mxnet/mxnet_emitter.py,7e533e494bf9365689063dd25a3f80e73bf40a2a,70465e16463f08367b03f50d06896005614049ca,ends = [ends[0]; ends[-1]] + ends[1:-1]
aaiijdbff,microsoft/MMdnn,mmdnn/conversion/mxnet/mxnet_emitter.py,7e533e494bf9365689063dd25a3f80e73bf40a2a,70465e16463f08367b03f50d06896005614049ca,ends = [i if i else None for i in ends]
aaiijdbgd,microsoft/MMdnn,mmdnn/conversion/mxnet/mxnet_emitter.py,7e533e494bf9365689063dd25a3f80e73bf40a2a,70465e16463f08367b03f50d06896005614049ca,ends;
aaiijdcjc,microsoft/MMdnn,tests/test_conversion_imagenet.py,1d1896e3cebc2bd4564e42fec0f955e4bd175f3d,STILL_EXISTS,'voc-fcn8s'     : [OnnxEmit]; # TODO: ConvTranspose; Crop
aaiijdcjd,microsoft/MMdnn,tests/test_conversion_imagenet.py,1d1896e3cebc2bd4564e42fec0f955e4bd175f3d,STILL_EXISTS,'voc-fcn16s'    : [OnnxEmit]; # TODO: ConvTranspose; Crop
aaiijdcje,microsoft/MMdnn,tests/test_conversion_imagenet.py,1d1896e3cebc2bd4564e42fec0f955e4bd175f3d,STILL_EXISTS,'voc-fcn32s'    : [OnnxEmit]; # TODO: ConvTranspose; Crop
aaiijddig,microsoft/MMdnn,mmdnn/conversion/paddle/paddle_parser.py,b7fc071e80e13f8b96adfcf27df3cfc8ba5436cb,STILL_EXISTS,use_bias: TODO
aaiijdebd,microsoft/MMdnn,mmdnn/conversion/paddle/paddle_parser.py,b7fc071e80e13f8b96adfcf27df3cfc8ba5436cb,STILL_EXISTS,need the shape TODO
aaiijeafg,microsoft/MMdnn,mmdnn/conversion/caffe/caffe_emitter.py,ec2f475ddf9a2b301e3ccd5d9255596fbcbeb524,5cab1be0ece8d667ec4e1f1b2fc54aaca3f8ac04,implement asymmetric paddings by applying symmetric padding then cropping
aaiijecaf,microsoft/MMdnn,tests/test_conversion_imagenet.py,0338948431fed5606c2540310e42d47078248547,71298b59594a5192615a6d0aebeaffd3f0410d2d,'inception_v1'          : [CaffeEmit; CoreMLEmit; KerasEmit; MXNetEmit; PytorchEmit; TensorflowEmit]; # TODO: CntkEmit
aaiijecah,microsoft/MMdnn,tests/test_conversion_imagenet.py,0338948431fed5606c2540310e42d47078248547,71298b59594a5192615a6d0aebeaffd3f0410d2d,'resnet_v1_50'          : [CaffeEmit; CoreMLEmit; KerasEmit; MXNetEmit; PytorchEmit; TensorflowEmit]; # TODO: CntkEmit
aaiijecai,microsoft/MMdnn,tests/test_conversion_imagenet.py,0338948431fed5606c2540310e42d47078248547,71298b59594a5192615a6d0aebeaffd3f0410d2d,'resnet_v1_152'         : [CaffeEmit; CoreMLEmit; KerasEmit; MXNetEmit; PytorchEmit; TensorflowEmit]; # TODO: CntkEmit
aaiijecaj,microsoft/MMdnn,tests/test_conversion_imagenet.py,0338948431fed5606c2540310e42d47078248547,71298b59594a5192615a6d0aebeaffd3f0410d2d,'resnet_v2_50'          : [CaffeEmit; CoreMLEmit; KerasEmit; MXNetEmit; PytorchEmit; TensorflowEmit]; # TODO: CntkEmit
aaiijecbb,microsoft/MMdnn,tests/test_conversion_imagenet.py,0338948431fed5606c2540310e42d47078248547,71298b59594a5192615a6d0aebeaffd3f0410d2d,'mobilenet_v1_1.0'      : [CoreMLEmit; CntkEmit; KerasEmit; MXNetEmit; PytorchEmit; TensorflowEmit]; # TODO: CaffeEmit(Crash)
aaiijecbc,microsoft/MMdnn,tests/test_conversion_imagenet.py,0338948431fed5606c2540310e42d47078248547,STILL_EXISTS,'mobilenet_v2_1.0_224'  : [CoreMLEmit; CntkEmit; KerasEmit; MXNetEmit; PytorchEmit; TensorflowEmit]; # TODO: CaffeEmit(Crash)
aaiijecfi,microsoft/MMdnn,tests/test_conversion_imagenet.py,ea33dae5c7b4d43e7bb6d919cb0be821a85bb53c,71298b59594a5192615a6d0aebeaffd3f0410d2d,'inception_v1'      : [TensorflowEmit; KerasEmit; MXNetEmit; CoreMLEmit]; # TODO: CntkEmit
aaiijecfj,microsoft/MMdnn,tests/test_conversion_imagenet.py,ea33dae5c7b4d43e7bb6d919cb0be821a85bb53c,71298b59594a5192615a6d0aebeaffd3f0410d2d,'inception_v3'      : [TensorflowEmit; KerasEmit; MXNetEmit; CoreMLEmit]; # TODO: CntkEmit
aaiijecgd,microsoft/MMdnn,mmdnn/conversion/caffe/caffe_emitter.py,4028767d0a12445c5863933f1c0c995ed90d5936,5cab1be0ece8d667ec4e1f1b2fc54aaca3f8ac04,implement asymmetric paddings by applying symmetric padding then cropping
aaiijecha,microsoft/MMdnn,mmdnn/conversion/caffe/caffe_emitter.py,ccd2843678f0164c54498e45878d3c6b6c8cfab3,STILL_EXISTS,implement asymmetric paddings by applying symmetric padding then cropping
aaiijecif,microsoft/MMdnn,tests/test_conversion_imagenet.py,71298b59594a5192615a6d0aebeaffd3f0410d2d,STILL_EXISTS,TODO: coredump
aaiijecja,microsoft/MMdnn,tests/test_conversion_imagenet.py,71298b59594a5192615a6d0aebeaffd3f0410d2d,4e9d575b1c9cae8461ddc0ccb8d2f2fc83e4359e,TODO: CaffeEmit(Crash)
aaiijeebg,microsoft/MMdnn,mmdnn/conversion/examples/tensorflow/models/inception_resnet_v1.py,78d2aa4a75566021bfdda18337b2e4bd9b6dee31,STILL_EXISTS,Moving averages ends up in the trainable variables collection
aaiijeedd,microsoft/MMdnn,mmdnn/conversion/keras/keras2_emitter.py,78d2aa4a75566021bfdda18337b2e4bd9b6dee31,STILL_EXISTS,#add mul op; just implement layer * constant
aaiijeeeb,microsoft/MMdnn,tests/test_conversion_imagenet.py,78d2aa4a75566021bfdda18337b2e4bd9b6dee31,STILL_EXISTS,'facenet'      : [TensorflowEmit; CoreMLEmit;MXNetEmit;KerasEmit]  # TODO:
aaiijeefa,microsoft/MMdnn,mmdnn/conversion/tensorflow/tensorflow_parser.py,c8312a2482e906f5f5e18cd90894c7b8dd704f09,STILL_EXISTS,TODO:  only for 1D
aaiijefde,microsoft/MMdnn,tests/conversion_imagenet.py,54959dc6ebde80e781322c162ef68ff0ee423c50,STILL_EXISTS,TODO: coreml_emit
aaiijeibg,microsoft/MMdnn,mmdnn/conversion/rewriter/rnn_utils.py,ec332ff2d69eccba4c98b1a86a9767d65d692254,STILL_EXISTS,TODO: pytorch; mxnet; keras; cntk
aaiijeicj,microsoft/MMdnn,tests/conversion_imagenet.py,ec332ff2d69eccba4c98b1a86a9767d65d692254,STILL_EXISTS,TODO cntk_emit
aaiijeijh,microsoft/MMdnn,tests/conversion_imagenet.py,a00439e43536e15a67786568aa5dfcea058587bc,STILL_EXISTS,Temporarily disable 'xception'      : [coreml_emit; cntk_emit; mxnet_emit; pytorch_emit; tensorflow_emit]; #  TODO: Caffe(Crash) keras_emit(too slow)
aaiijeiji,microsoft/MMdnn,tests/conversion_imagenet.py,a00439e43536e15a67786568aa5dfcea058587bc,STILL_EXISTS,Temporarily disable 'facenet'               : [mxnet_emit; tensorflow_emit; keras_emit; pytorch_emit; caffe_emit]; # TODO: coreml_emit
aaiijeijj,microsoft/MMdnn,tests/conversion_imagenet.py,a00439e43536e15a67786568aa5dfcea058587bc,STILL_EXISTS,Temporarily disable 'rnn_lstm_gru_stacked'  : [tensorflow_emit; keras_emit; pytorch_emit; mxnet_emit] #TODO cntk_emit
aaiijejaa,microsoft/MMdnn,tests/conversion_imagenet.py,a00439e43536e15a67786568aa5dfcea058587bc,STILL_EXISTS,TODO: cntk_emit
aaiijejab,microsoft/MMdnn,tests/conversion_imagenet.py,a00439e43536e15a67786568aa5dfcea058587bc,STILL_EXISTS,Temporarily disable 'facenet'           : [mxnet_emit; tensorflow_emit; keras_emit; caffe_emit] # TODO: coreml_emit
aaiijejaj,microsoft/MMdnn,tests/conversion_imagenet.py,a00439e43536e15a67786568aa5dfcea058587bc,STILL_EXISTS,'rnn_lstm_gru_stacked'  : [tensorflow_emit; pytorch_emit; mxnet_emit; tensorflow_emit]; #TODO cntk_emit
aaiijejef,microsoft/MMdnn,tests/test_caffe_4.py,b2ef929f8f48ab969b77f6931b89570f788ff4df,STILL_EXISTS,Cannot run on Travis since it seems to consume too much memory.
aaiijfadd,microsoft/MMdnn,tests/test_tensorflow_7.py,b2ef929f8f48ab969b77f6931b89570f788ff4df,STILL_EXISTS,Cannot run on Travis since it seems to consume too much memory.
aaiijfahi,microsoft/MMdnn,mmdnn/conversion/tensorflow/tensorflow_frozenparser.py,f13b4b504b302606fab3af37e63610351766516c,STILL_EXISTS,TODO:  only for 1D
aaiijfbga,cleverhans-lab/cleverhans,tests/test_mnist_accuracy.py,6415b355b46d278f63371716a27435391e71e464,STILL_EXISTS,Image dimensions ordering should follow the Theano convention
aaiijfbja,cleverhans-lab/cleverhans,cleverhans/attacks.py,86d4f536f3bfcbf3922e42a84bfd6be27bdfa663,48756e587e78eed896c11bf38dfe10b0ff5e05aa,If clipping is needed; reset all values outside of [clip_min; clip_max]
aaiijfcaf,cleverhans-lab/cleverhans,tutorials/mnist_saliency_tutorial.py,f813d06dcc088a4965f846c2250efab656486712,STILL_EXISTS,Image dimensions ordering should follow the Theano convention
aaiijfcjg,cleverhans-lab/cleverhans,tutorials/mnist_tutorial_jsma.py,48756e587e78eed896c11bf38dfe10b0ff5e05aa,STILL_EXISTS,Image dimensions ordering should follow the Theano convention
aaiijfdbd,cleverhans-lab/cleverhans,cleverhans/attacks.py,ec97f072ebc4024506e4b73e62936ff7d3c1fdf0,9ccb0c57bf697505ab24adb538aa0c1508391c03,If clipping is needed; reset all values outside of [clip_min; clip_max]
aaiijfdhb,cleverhans-lab/cleverhans,tutorials/mnist_tutorial.py,717e672a2a7bb8ba5c386cdd9bb3d254d5107c03,STILL_EXISTS,Set TF random seed to improve reproducibility
aaiijfdhc,cleverhans-lab/cleverhans,tutorials/mnist_tutorial_jsma.py,717e672a2a7bb8ba5c386cdd9bb3d254d5107c03,STILL_EXISTS,Set TF random seed to improve reproducibility
aaiijfech,cleverhans-lab/cleverhans,cleverhans/attacks_tf.py,9ccb0c57bf697505ab24adb538aa0c1508391c03,aaa078aa65d72767e33ecae2e98ec2848c8a0d8b,If clipping is needed; reset all values outside of [clip_min; clip_max]
aaiijfedd,cleverhans-lab/cleverhans,cleverhans/attacks_th.py,9ccb0c57bf697505ab24adb538aa0c1508391c03,STILL_EXISTS,If clipping is needed; reset all values outside of [clip_min; clip_max]
aaiijfhac,cleverhans-lab/cleverhans,tutorials/mnist_tutorial_th.py,ccb82a32b2c43ddc4c106e3c24a1f609a510f560,STILL_EXISTS,Image dimensions ordering should follow the Theano convention
aaiijfhad,cleverhans-lab/cleverhans,tests/impl/test_mnist_accuracy_th.py,7b3b117ff56a404f30f5d077053e183745aadac6,STILL_EXISTS,Image dimensions ordering should follow the Theano convention
aaiijfhae,cleverhans-lab/cleverhans,tests/impl/test_mnist_accuracy_th.py,6907c0b863e192c56483a4fe083940eb821a6c09,STILL_EXISTS,Image dimensions ordering should follow the Theano convention
aaiijfibc,cleverhans-lab/cleverhans,cleverhans/devtools/tests/pep8/pep8.py,17b8bdf313a9defd65389a1e4d0ead1b5bf4b1fd,STILL_EXISTS,Found a (probably) needed space
aaiijfibf,cleverhans-lab/cleverhans,cleverhans/devtools/tests/pep8/pep8.py,17b8bdf313a9defd65389a1e4d0ead1b5bf4b1fd,STILL_EXISTS,A needed trailing space was not found
aaiijficc,cleverhans-lab/cleverhans,cleverhans/devtools/tests/pep8/pep8.py,17b8bdf313a9defd65389a1e4d0ead1b5bf4b1fd,STILL_EXISTS,A needed opening space was not found
aaiijfief,cleverhans-lab/cleverhans,cleverhans/devtools/tests/pep8/pep8.py,17b8bdf313a9defd65389a1e4d0ead1b5bf4b1fd,STILL_EXISTS,With Python 2; if the line ends with '\\r\\r\ || ' the assertion fails
aaiijfiei,cleverhans-lab/cleverhans,cleverhans/devtools/tests/pep8/pep8.py,17b8bdf313a9defd65389a1e4d0ead1b5bf4b1fd,STILL_EXISTS,Obviously; a newline token ends a single physical line.
aaiijfige,cleverhans-lab/cleverhans,cleverhans/devtools/tests/pep8/pep8.py,17b8bdf313a9defd65389a1e4d0ead1b5bf4b1fd,STILL_EXISTS,The comment also ends a physical line
aaiijgbjg,cleverhans-lab/cleverhans,tutorials/mnist_tutorial_fgsm_iterative.py,48e30fa8a69ff09e42057a48006d55be41cf1857,STILL_EXISTS,Set TF random seed to improve reproducibility
aaiijgbjh,cleverhans-lab/cleverhans,tutorials/mnist_tutorial_fgsm_iterative.py,48e30fa8a69ff09e42057a48006d55be41cf1857,STILL_EXISTS,Image dimensions ordering should follow the Theano convention
aaiijgcai,cleverhans-lab/cleverhans,tests/impl/test_basic_iterative.py,3586f730b2ecec4520c976075b2c0542277649df,STILL_EXISTS,Image dimensions ordering should follow the Theano convention
aaiijgcca,cleverhans-lab/cleverhans,tutorials/mnist_blackbox.py,ca5434f67098c088790f684c07a1bc14f2a5f292,STILL_EXISTS,Set TF random seed to improve reproducibility
aaiijgccb,cleverhans-lab/cleverhans,tutorials/mnist_blackbox.py,ca5434f67098c088790f684c07a1bc14f2a5f292,9a81828c1f75934e740a10474656da615749704b,Image dimensions ordering should follow the Theano convention
aaiijgdff,cleverhans-lab/cleverhans,tests_tf/test_mnist_accuracy.py,e2ab0a456ff51406d77d355dbc3cd893dc78af14,STILL_EXISTS,Set TF random seed to improve reproducibility
aaiijgdfg,cleverhans-lab/cleverhans,tests_tf/test_mnist_accuracy.py,e2ab0a456ff51406d77d355dbc3cd893dc78af14,STILL_EXISTS,Image dimensions ordering should follow the Theano convention
aaiijgfbc,cleverhans-lab/cleverhans,cleverhans/attacks.py,4f1033788951f452736986645ff1d0dadb64e974,1162342f35c2581dde3e98686e72f532b2664dc3,TODO: fix args parameter for Theano case
aaiijgfbd,cleverhans-lab/cleverhans,cleverhans/attacks.py,4f1033788951f452736986645ff1d0dadb64e974,929386afb1b8a4afc8bb6a8462755336f46e7276,TODO
aaiijgfca,cleverhans-lab/cleverhans,cleverhans/attacks_tf.py,929386afb1b8a4afc8bb6a8462755336f46e7276,610d5a5825468305020a39a94a72354050cd2caf,TODO: make use of 'ord' parameter
aaiijgfdd,cleverhans-lab/cleverhans,cleverhans/attacks_th.py,3369da498e5754d552207d0f40aded8c631a0912,8086b26fd2e38d7e780c2cbfda311e91c6d15a5d,TODO: make use of 'ord' parameter
aaiijgfea,cleverhans-lab/cleverhans,tutorials/cifar10_tutorial_tf.py,9a334dc7aebe124a6527f63d1a0fdc71f8437ecb,STILL_EXISTS,Set TF random seed to improve reproducibility
aaiijgfeb,cleverhans-lab/cleverhans,tutorials/cifar10_tutorial_tf.py,9a334dc7aebe124a6527f63d1a0fdc71f8437ecb,STILL_EXISTS,Image dimensions ordering should follow the Theano convention
aaiijghfa,cleverhans-lab/cleverhans,cleverhans/attacks.py,953111b5e72effeed6ed1ae1294ab0703d828e89,STILL_EXISTS,TODO: remove [0] once we fix SaliencyMapMethod to handle multiple samples
aaiijghid,cleverhans-lab/cleverhans,cleverhans/utils.py,bea19815f6419778015ae9df32ffb212d0681d91,10a5d15af68ba5d8feff08ac4e503f37b8431f32,TODO(This functionality implementation can be improved.)
aaiijhbai,cleverhans-lab/cleverhans,tutorials/mnist_tutorial_pure_tf.py,4f109fc2ca7283afe3e781a3bc250fb26267a0f9,STILL_EXISTS,Set TF random seed to improve reproducibility
aaiijhdbd,cleverhans-lab/cleverhans,examples/nips17_adversarial_competition/run_attacks_and_defenses.py,11ea1090f266cea6d5feac201aba303d7c589ca2,STILL_EXISTS,In the matrices below: rows - attacks; columns - defenses.
aaiijhdbg,cleverhans-lab/cleverhans,examples/nips17_adversarial_competition/run_attacks_and_defenses.py,11ea1090f266cea6d5feac201aba303d7c589ca2,STILL_EXISTS,higher scores are better.
aaiijhjdj,cleverhans-lab/cleverhans,tutorials/mnist_tutorial_tf.py,2e6cbbd6c1f4e70939ad9e566e9b123ca736d1c4,STILL_EXISTS,Image dimensions ordering should follow the Theano convention
aaiijhjhj,cleverhans-lab/cleverhans,tutorials/mnist_tutorial_tf.py,8c9070fddc77e4041a8eba43c7b2453bda6dbf70,STILL_EXISTS,Image dimensions ordering should follow the Theano convention
aaiijhjid,cleverhans-lab/cleverhans,cleverhans/attacks.py,fa531bd5320ef79882df3a613ea911f34c758ce9,STILL_EXISTS,TODO this is bad
aaiijiafi,cleverhans-lab/cleverhans,tests_tf/test_attacks.py,bf04c116c9e73cceacbaaf8e4c09a877e63c1fb3,488d09f63423fccb510be0c3ebf7879a84c5b857,THIS IS A BIG HACK
aaiijiafj,cleverhans-lab/cleverhans,tests_tf/test_attacks.py,bf04c116c9e73cceacbaaf8e4c09a877e63c1fb3,488d09f63423fccb510be0c3ebf7879a84c5b857,nosetests apparently runs all functions in a class; not just
aaiijibgf,cleverhans-lab/cleverhans,cleverhans/attacks.py,fc9fe0f6f7c169b57210c91d135c477ed8ebffbb,d302cf82ddae6fdff6aad7817d91cb18c2d08d64,TODO: fix this so that it wraps the numpy method correctly.
aaiijibgg,cleverhans-lab/cleverhans,cleverhans/attacks.py,fc9fe0f6f7c169b57210c91d135c477ed8ebffbb,0a6a1ff5f098b158769add22d3c7cd684f4cdef4,todo save this to re-use the graph
aaiijibgh,cleverhans-lab/cleverhans,cleverhans/attacks.py,fc9fe0f6f7c169b57210c91d135c477ed8ebffbb,STILL_EXISTS,todo don't operate on the range [-0.5; 0.5]
aaiijibgi,cleverhans-lab/cleverhans,cleverhans/attacks.py,fc9fe0f6f7c169b57210c91d135c477ed8ebffbb,ff89832c0d5d8f59b71d6168daeb6d124f3014d5,todo assumes y_val
aaiijibgj,cleverhans-lab/cleverhans,cleverhans/attacks_tf.py,fc9fe0f6f7c169b57210c91d135c477ed8ebffbb,3b15c8b78b15ff0aafb15240c7d160570b532efb,# TODO make this actually match the coding styles of cleverhans
aaiijibhj,cleverhans-lab/cleverhans,cleverhans/attacks_tf.py,fc9fe0f6f7c169b57210c91d135c477ed8ebffbb,5ec82b2b7d594258df5cf1915013797f30ed5983,these are variables to be more efficient in sending data to tf
aaiijicaa,cleverhans-lab/cleverhans,cleverhans/attacks_tf.py,fc9fe0f6f7c169b57210c91d135c477ed8ebffbb,137d51b348488cd8b7e2d9bf3ca6a77ecef0467d,adjust the constant as needed
aaiijicai,cleverhans-lab/cleverhans,tutorials/mnist_tutorial_cw.py,fc9fe0f6f7c169b57210c91d135c477ed8ebffbb,STILL_EXISTS,Set TF random seed to improve reproducibility
aaiijicaj,cleverhans-lab/cleverhans,tutorials/mnist_tutorial_cw.py,fc9fe0f6f7c169b57210c91d135c477ed8ebffbb,e2313ac608bf0632621f97b690e0f1ed07cec5d0,Image dimensions ordering should follow the TensorFlow convention
aaiijicgb,cleverhans-lab/cleverhans,cleverhans/attacks.py,fb10414e992d266354474555eaaf298f69b9a138,ff89832c0d5d8f59b71d6168daeb6d124f3014d5,todo assumes y_val
aaiijicia,cleverhans-lab/cleverhans,tutorials/mnist_tutorial_cw.py,d302cf82ddae6fdff6aad7817d91cb18c2d08d64,STILL_EXISTS,todo fix this
aaiijicjb,cleverhans-lab/cleverhans,tutorials/mnist_tutorial_cw.py,3b15c8b78b15ff0aafb15240c7d160570b532efb,STILL_EXISTS,todo fix this
aaiijidad,cleverhans-lab/cleverhans,cleverhans/attacks.py,a22efbb5a4a721574b9eb4483a393ddd9b47c28b,599175b2034fce5cdc3fb5746d458d1b73d5859b,TODO abstract this out for other classes too
aaiijidjh,cleverhans-lab/cleverhans,tutorials/mnist_tutorial_jsma.py,2c6ead2d1a19d9e77015d3865ada6d4ecfb7b222,daeaa7a1c991f50775e4075d04986e517194af7d,Image dimensions ordering should follow the TensorFlow convention
aaiijiedd,cleverhans-lab/cleverhans,tutorials/mnist_blackbox.py,92f63da514ab274477eef8f50837105556bb2893,1a957dc93610caa7c0debd3d1d4374513e7103c3,Image dimensions ordering should follow the Theano convention
aaiijiegg,cleverhans-lab/cleverhans,tutorials/mnist_blackbox.py,3025c7999db2303819fdd0592b612bd2bf89d364,6dfd0f9bd6f49f14d9c45ea7f3596edd276d5dbc,Image dimensions ordering should follow the Theano convention
aaiijiegj,cleverhans-lab/cleverhans,tutorials/mnist_tutorial_cw.py,3025c7999db2303819fdd0592b612bd2bf89d364,6dfd0f9bd6f49f14d9c45ea7f3596edd276d5dbc,Image dimensions ordering should follow the TensorFlow convention
aaiijiehb,cleverhans-lab/cleverhans,tutorials/mnist_tutorial_jsma.py,3025c7999db2303819fdd0592b612bd2bf89d364,6dfd0f9bd6f49f14d9c45ea7f3596edd276d5dbc,Image dimensions ordering should follow the TensorFlow convention
aaiijiidj,cleverhans-lab/cleverhans,cleverhans_tutorials/mnist_tutorial_deepfool.py,b0adc72ddb02d82e08af373592fd5c75c566d09f,STILL_EXISTS,Set TF random seed to improve reproducibility
aaiijjdhf,cleverhans-lab/cleverhans,docs/conf.py,e0864c26833bc1ca187c89670375daefc4be5ddf,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaiijjedh,cleverhans-lab/cleverhans,examples/linear_extrapolation_plot_example.py,291ad89be11f1f2b7eb3dfd0350cfc146afe424f,STILL_EXISTS,Set TF random seed to improve reproducibility
aaiijjedi,cleverhans-lab/cleverhans,examples/linear_extrapolation_plot_example.py,291ad89be11f1f2b7eb3dfd0350cfc146afe424f,STILL_EXISTS,Image dimensions ordering should follow the Theano convention
aaiijjggd,cleverhans-lab/cleverhans,cleverhans_tutorials/mnist_tutorial_madryetal.py,47d85a6d73401d60f21be0653bf7bae78b4c459b,STILL_EXISTS,Set TF random seed to improve reproducibility
aaiijjhci,cleverhans-lab/cleverhans,examples/multigpu_advtrain/manager.py,3b4e579821be8b8c780e0c5b1fdf0dc9b60f6196,STILL_EXISTS,Set TF random seed to improve reproducibility
aaiijjheb,cleverhans-lab/cleverhans,examples/multigpu_advtrain/model.py,3b4e579821be8b8c780e0c5b1fdf0dc9b60f6196,STILL_EXISTS,epsilon used to be 1e-5. Maybe 0.001 solves NaN problem in deeper
aaiijjhee,cleverhans-lab/cleverhans,examples/multigpu_advtrain/resnet_tf.py,3b4e579821be8b8c780e0c5b1fdf0dc9b60f6196,d1169ecf6bebde68df8fb12f33c145baa644a10a,else my own conv is used and no reuse is needed
aaiijjhei,cleverhans-lab/cleverhans,examples/multigpu_advtrain/resnet_tf.py,3b4e579821be8b8c780e0c5b1fdf0dc9b60f6196,STILL_EXISTS,It is more memory efficient than very deep residual network and
aaiijjhgg,cleverhans-lab/cleverhans,examples/multigpu_advtrain/run.py,3b4e579821be8b8c780e0c5b1fdf0dc9b60f6196,STILL_EXISTS,Set TF random seed to improve reproducibility
aaiijjihj,cleverhans-lab/cleverhans,examples/multigpu_advtrain/evaluator.py,7fcfc4c517b0bcef2dc3a11d2953718305fd1d3c,STILL_EXISTS,TODO: black box attacks
aaiijjijc,cleverhans-lab/cleverhans,cleverhans/attacks_tf.py,1c1bd9f84f751fef4d016f66319dc19d1aebb55b,5ec82b2b7d594258df5cf1915013797f30ed5983,these are variables to be more efficient in sending data to tf
aaiijjjba,cleverhans-lab/cleverhans,cleverhans/attacks_tf.py,1c1bd9f84f751fef4d016f66319dc19d1aebb55b,137d51b348488cd8b7e2d9bf3ca6a77ecef0467d,adjust the constant as needed
aaiijjjfe,cleverhans-lab/cleverhans,examples/multigpu_advtrain/trainer.py,bf49322cde734859701b765a77fb43d95110e40f,STILL_EXISTS,Set TF random seed to improve reproducibility
aaijaabah,cleverhans-lab/cleverhans,examples/multigpu_advtrain/trainer_multigpu.py,f63de306b70cff1bcedb71c649e5a9d42c17790e,STILL_EXISTS,was affected by the length of the unused adversarial generation
aaijaabbb,cleverhans-lab/cleverhans,examples/multigpu_advtrain/model.py,a5c03a32f9e51dade38c18418357f7517db40f7d,STILL_EXISTS,TODO: training and bn_training
aaijaabbe,cleverhans-lab/cleverhans,examples/multigpu_advtrain/trainer_multigpu.py,a5c03a32f9e51dade38c18418357f7517db40f7d,STILL_EXISTS,TODO: fix g0_inputs and gt 'y'
aaijaabcc,cleverhans-lab/cleverhans,examples/multigpu_advtrain/trainer_multigpu.py,00af1c9f087e29cb5b119b027c3e8ff9c74c1c62,STILL_EXISTS,TODO: fix g0_inputs and gt 'y'
aaijaabch,cleverhans-lab/cleverhans,examples/multigpu_advtrain/attacks_multigpu.py,03551003ed945a8bcafc545a4a2aed0c9ded0f11,STILL_EXISTS,TODO: Break the graph only nGPU times instead of nb_iter times.
aaijaacaf,cleverhans-lab/cleverhans,cleverhans/attacks_tf.py,8ba829009b119625db5fe3ca9ae5389454c12c7f,STILL_EXISTS,Check if more modification is needed for each samples
aaijaacfd,cleverhans-lab/cleverhans,cleverhans/attacks_tf.py,5d7561746520b6b61e33303a4211447a12bebefb,STILL_EXISTS,Check if more modification is needed for each samples
aaijaacgd,cleverhans-lab/cleverhans,cleverhans/attacks_tf.py,748424648fa480c017e6ebb7ea85cdf3d1a7b0cb,5ec82b2b7d594258df5cf1915013797f30ed5983,cond_in: the boolean tensor to show if more iteration is needed for
aaijaachj,cleverhans-lab/cleverhans,cleverhans/attacks_tf.py,748424648fa480c017e6ebb7ea85cdf3d1a7b0cb,5ec82b2b7d594258df5cf1915013797f30ed5983,Check if more modification is needed for each sample
aaijaacjd,cleverhans-lab/cleverhans,cleverhans/feat_advs.py,0531759fadae8ed78e3803869a380ac41dbf5812,STILL_EXISTS,If clipping is needed; reset all values outside of [clip_min; clip_max]
aaijaadcd,cleverhans-lab/cleverhans,cleverhans/feat_advs.py,4abdb8ce1ff4d177f0521fc4d2e1af8d96c7adcb,STILL_EXISTS,If clipping is needed;
aaijaadda,cleverhans-lab/cleverhans,examples/imagenet_featadvs/attack_model_featadv.py,9d97dbec22bf1838ff93164bcd49c4466a814dd5,866e7524de39b3565e0a104b54d3d4d0742e629e,Set TF random seed to improve reproducibility
aaijaadfe,cleverhans-lab/cleverhans,cleverhans/attacks.py,a2c6af6f45bba872a5992ba7c3633060c3f3043c,2241b39b8e6f5700637869a23b8d83f557a36195,If clipping is needed;
aaijaadgf,cleverhans-lab/cleverhans,cleverhans/feat_advs.py,6b5a0a8b53511b3da3c479349f694d55d098d049,STILL_EXISTS,If clipping is needed; reset all values outside of [clip_min; clip_max]
aaijaadif,cleverhans-lab/cleverhans,cleverhans/feat_advs.py,6b5a0a8b53511b3da3c479349f694d55d098d049,STILL_EXISTS,If clipping is needed;
aaijaaebb,cleverhans-lab/cleverhans,cleverhans/feat_advs.py,1827af3a64d51e78c745e1814c722a1cc9d950e9,STILL_EXISTS,If clipping is needed;
aaijaaebi,cleverhans-lab/cleverhans,examples/imagenet_featadvs/attack_model_featadv.py,c0129b48e5a458c07179121ba5374af5244c114b,STILL_EXISTS,Set TF random seed to improve reproducibility
aaijaaegb,cleverhans-lab/cleverhans,cleverhans/attacks.py,54738e29637513ca42f8604cdecf9a6ce66c0e23,a93dc6cd326b4df630cf70a0d668fec03e0885b9,If clipping is needed;
aaijaaeje,cleverhans-lab/cleverhans,examples/multigpu_advtrain/trainer.py,7946c4847579d6407d655214ba5f57c852e528ea,STILL_EXISTS,was affected by the length of the unused adversarial generation
aaijaafba,cleverhans-lab/cleverhans,cleverhans/attacks.py,f34c7d4ea0bf24240ff6f81bf5818d6378cbed24,STILL_EXISTS,If clipping is needed;
aaijaaijd,cleverhans-lab/cleverhans,cleverhans/attacks_tf.py,9fad2736db8a9d308e4262c8b9545ee34d27e9ab,137d51b348488cd8b7e2d9bf3ca6a77ecef0467d,adjust the constant as needed
aaijabihh,cleverhans-lab/cleverhans,cleverhans/attacks_tf.py,12b8aae0d78871ca610237c7b8180414a7f05e77,5ec82b2b7d594258df5cf1915013797f30ed5983,adjust the constant as needed
aaijabjhb,cleverhans-lab/cleverhans,examples/nips17_adversarial_competition/eval_infra/code/eval_lib/classification_results.py,413af120fd37c8cd3fbbe0048f911f0bb7fca369,STILL_EXISTS,columns - second index
aaijabjic,cleverhans-lab/cleverhans,examples/nips17_adversarial_competition/eval_infra/code/eval_lib/cloud_client.py,413af120fd37c8cd3fbbe0048f911f0bb7fca369,STILL_EXISTS,\"\"\"Helper classes and wrappers to access Google Cloud. ||  || Google Cloud API is encapsulated with these wrappers; so it's easier to || test the code with help of fake (declared in testing\/fake_cloud_client.py). || \"\"\"
aaijabjij,cleverhans-lab/cleverhans,examples/nips17_adversarial_competition/eval_infra/code/eval_lib/dataset_helper.py,413af120fd37c8cd3fbbe0048f911f0bb7fca369,STILL_EXISTS,load and resize adversarial image if needed
aaijacbdg,cleverhans-lab/cleverhans,examples/nips17_adversarial_competition/eval_infra/code/eval_lib/work_data.py,413af120fd37c8cd3fbbe0048f911f0bb7fca369,STILL_EXISTS,Number of work records to read at once
aaijacbhg,cleverhans-lab/cleverhans,examples/nips17_adversarial_competition/eval_infra/code/worker.py,413af120fd37c8cd3fbbe0048f911f0bb7fca369,STILL_EXISTS,\"\"\"Worker which runs all computations on Cloud VMs. ||  || Evaluation of competition is split into work pieces. One work piece is a || either evaluation of an attack on a batch of images or evaluation of a || defense on a batch of adversarial images. || All pieces of attack work are independent from each other and could be run || in parallel. Same for pieces of defense work - they are independent from each || other and could be run in parallel. But defense work could be run only after || all attack work is completed. ||  || Worker first runs all attack pieces; by querying next piece of undone work || and running it. After all attack pieces are done; worker runs all defense pieces || in a similar way. ||  || Before workers could be started; datastore has to be populated by master || with description of work to be done. See master.py for details. ||  || NOTE: Worker is designed to run on linux machine with NVidia docker || installed. Worker generally needs administrative privilege to run properly. || Also worker relies on very specific directory structure created in home || directory. That's why it's highly recommended to run worker only in VM. || \"\"\"
aaijacjfj,cleverhans-lab/cleverhans,examples/madry_lab_challenges/cifar10/madry_cifar10_model.py,7ff933465e803a433a164bd55b79df4cc72f1e2a,STILL_EXISTS,It is more memory efficient than very deep residual network and has
aaijadcad,cleverhans-lab/cleverhans,examples/mnist_blackbox_keras.py,1d7ebb285b8320782b6d7de85c6a96b295bef7f3,STILL_EXISTS,Set TF random seed to improve reproducibility
aaijaddce,cleverhans-lab/cleverhans,cleverhans/attacks_tfe.py,0e8125dd582b8b5fb21f25e206aba041057ed3bb,STILL_EXISTS,If clipping is needed
aaijaddfg,cleverhans-lab/cleverhans,cleverhans_tutorials/mnist_tutorial_tfe.py,0e8125dd582b8b5fb21f25e206aba041057ed3bb,STILL_EXISTS,Set TF random seed to improve reproducibility
aaijadfed,cleverhans-lab/cleverhans,tests_tf/test_attacks.py,e309fc44808e29521cb5414b46ae73aa78cf869c,STILL_EXISTS,TODO: change this to use standard cleverhans label conventions
aaijadfgb,cleverhans-lab/cleverhans,cleverhans_tutorials/evaluate_pickled_model.py,2dffeb06e3c7ef61c7bd5b2b06fc94c06d4bbce8,STILL_EXISTS,Set TF random seed to improve reproducibility
aaijadfhc,cleverhans-lab/cleverhans,cleverhans_tutorials/mnist_tutorial_picklable.py,2dffeb06e3c7ef61c7bd5b2b06fc94c06d4bbce8,STILL_EXISTS,Set TF random seed to improve reproducibility
aaijadhjg,cleverhans-lab/cleverhans,cleverhans/train.py,9160866e5c12ad67246a147de97cb5808d169225,STILL_EXISTS,timing very much; but shuffling here makes the code slightly
aaijaeaad,cleverhans-lab/cleverhans,cleverhans/train.py,b267379bd9a95788f4e725f8c4d2a5447fcd2fb2,STILL_EXISTS,\"\"\" || Multi-replica synchronous training ||  ||  || NOTE: This module is much more free to change than many other modules || in CleverHans. CleverHans is very conservative about changes to any || code that affects the output of benchmark tests (attacks; evaluation || methods; etc.). This module provides *model training* functionality || not *benchmarks* and thus is free to change rapidly to provide better || speed; accuracy; etc. || \"\"\"
aaijaeaae,cleverhans-lab/cleverhans,cleverhans/augmentation.py,ff6a13ebe767a70bfd1cbe79ddd7fb1ae74ee9dc,STILL_EXISTS,\"\"\" || Dataset augmentation functionality ||  || NOTE: This module is much more free to change than many other modules || in CleverHans. CleverHans is very conservative about changes to any || code that affects the output of benchmark tests (attacks; evaluation || methods; etc.). This module provides *dataset augmentation* code for || building models to be benchmarked; not *benchmarks;* and || thus is free to change rapidly to provide better speed; accuracy; || etc. || \"\"\"
aaijaeaai,cleverhans-lab/cleverhans,cleverhans/picklable_model.py,b950715b03f71891d3334c27bdbde1907553cd09,650e09512141e0720944a453057c92051a5eb6ca,TODO: before adding dataset augmentation; we didn't have to do this.
aaijaeada,cleverhans-lab/cleverhans,cleverhans/train.py,24fa74de83ec4387b7a9bdef81765cc8cbd3b5fb,2035f38405ebc12e16bb1a33e24acb62bf092a3d,debugging hack; remove this line
aaijaeaej,cleverhans-lab/cleverhans,cleverhans/utils_mnist.py,bd56cbabb3caecee261b090e7e4af5a0e4b3eea8,6ae062639a7baddb1277b7b6bcbd22ca19b844eb,data_type unicode to ascii conversion (Python2 fix)
aaijaeafb,cleverhans-lab/cleverhans,cleverhans/utils_mnist.py,608c74eaf3960f227a4c9dc64e9ce5dba267ef4b,08f26ca9a013fdfd5f911f0e4869b01b80efec43,data_type unicode to ascii conversion (Python2 fix)
aaijaebch,cleverhans-lab/cleverhans,cleverhans/devtools/tests/test_format.py,2181aa3121f0f17ddfd18d4ac3d32830e5904fe2,STILL_EXISTS,is a little better than a 10X speedup.
aaijaebed,cleverhans-lab/cleverhans,cleverhans/model_zoo/__init__.py,57aa9f922fb9544920ecec55d3d2d17ce64a4581,STILL_EXISTS,\"\"\" || A module for hosting a variety of models of interest to the adversarial || example community. ||  || Warning: ||   This module is not nearly as conservative as the rest of CleverHans. ||   Most of CleverHans is used to create rigorous vulnerability benchmarks. ||   For example; the error rate caused by an Attack is considered to be ||   part of the API for that Attack; so we upgrade the major version number ||   whenever it changes. ||   Models in the model zoo can be tweaked regularly to improve accuracy; ||   training speed; robustness; etc. || \"\"\"
aaijaebfb,cleverhans-lab/cleverhans,cleverhans/dataset.py,08f26ca9a013fdfd5f911f0e4869b01b80efec43,STILL_EXISTS,data_type unicode to ascii conversion (Python2 fix)
aaijaecgb,cleverhans-lab/cleverhans,cleverhans/model.py,005172deb88be308b05395972c09cbc5fa1d895a,STILL_EXISTS,\"\"\" || The Model class and related functionality. || \"\"\"
aaijaechf,cleverhans-lab/cleverhans,cleverhans/attacks.py,f9eb6990027c67ce18071de6b9b6890584a37db4,d36fc6e30b66f7fb29c94b7514cc4ed0c7b3fbb5,Include an unused return so pylint understands the method signature
aaijaecib,cleverhans-lab/cleverhans,cleverhans/attacks.py,3037f719b8263ce6f8800e5f77dfb69bf8c79cdd,STILL_EXISTS,Include an unused return so pylint understands the method signature
aaijaedei,cleverhans-lab/cleverhans,cleverhans/experimental/certification/dual_formulation.py,f14bb0390a47c80f8bac62d63bef53a6114b10dc,STILL_EXISTS,For convenience; think of x as [\\alpha; \\beta]
aaijaedff,cleverhans-lab/cleverhans,cleverhans/experimental/certification/dual_formulation.py,f14bb0390a47c80f8bac62d63bef53a6114b10dc,STILL_EXISTS,TODO: replace matrix_inverse with functin which uses matrix-vector product
aaijaedfg,cleverhans-lab/cleverhans,cleverhans/experimental/certification/dual_formulation.py,f14bb0390a47c80f8bac62d63bef53a6114b10dc,060c1d4ca2d70b441447498213a09a0adc68404b,TODO: consider whether we can use shallow copy of the lists without
aaijaedgi,cleverhans-lab/cleverhans,cleverhans/experimental/certification/dual_formulation.py,f14bb0390a47c80f8bac62d63bef53a6114b10dc,060c1d4ca2d70b441447498213a09a0adc68404b,TODO: Write this in terms of matrix multiply
aaijaeeah,cleverhans-lab/cleverhans,cleverhans/experimental/certification/optimization.py,f14bb0390a47c80f8bac62d63bef53a6114b10dc,eeef0377736fc7957aa92eb0dd2260c3f5358b97,TODO: figure out how to fix following
aaijaeeda,cleverhans-lab/cleverhans,cleverhans/experimental/certification/optimization.py,f14bb0390a47c80f8bac62d63bef53a6114b10dc,a97f3cd528074c2ad9c4a7c566b6c0a45f9d4235,TODO: do we need to do it here or can do outside of this class?
aaijaeefa,cleverhans-lab/cleverhans,cleverhans/experimental/certification/read_weights.py,f14bb0390a47c80f8bac62d63bef53a6114b10dc,STILL_EXISTS,Can think of a hack involving matching shapes but if shapes are equal
aaijaeeja,cleverhans-lab/cleverhans,cleverhans_tutorials/cifar10_tutorial_tf.py,47736953e8e92ce7a949fe99ae21f501f11f5fe6,STILL_EXISTS,Set TF random seed to improve reproducibility
aaijaefhh,cleverhans-lab/cleverhans,cleverhans/max_confidence.py,6e19341f6fe9c5d84aeb9ebb03227705c141aa78,3d0e263c47486390393e3f242dabb2df28a6496c,This is not needed to be optimal for t >= 0.5; but may as well do it
aaijaefib,cleverhans-lab/cleverhans,cleverhans/dataset.py,45708a41dd24384b99cac6af7c7e805613067654,STILL_EXISTS,This is much more efficient with data augmentation; see tutorials.
aaijaefid,cleverhans-lab/cleverhans,cleverhans/attack_bundling.py,c6870f1fc2bfbd5a90c627f8539150cf5527a360,STILL_EXISTS,TODO: lower priority: make it possible to initialize one attack with
aaijaefif,cleverhans-lab/cleverhans,cleverhans/attack_bundling.py,c6870f1fc2bfbd5a90c627f8539150cf5527a360,bde8d97d67c7fa30e52c4851b3eeeae26fb9505b,TODO: lower priority: make sure bundler won't waste time running targeted
aaijaefja,cleverhans-lab/cleverhans,cleverhans/attack_bundling.py,c6870f1fc2bfbd5a90c627f8539150cf5527a360,bde8d97d67c7fa30e52c4851b3eeeae26fb9505b,TODO: make an interface to pass this in if it has already been computed elsewhere
aaijaefji,cleverhans-lab/cleverhans,cleverhans/attack_bundling.py,c6870f1fc2bfbd5a90c627f8539150cf5527a360,bde8d97d67c7fa30e52c4851b3eeeae26fb9505b,TODO: refactor to avoid this duplicated method
aaijaejdj,cleverhans-lab/cleverhans,cleverhans/confidence_report.py,b016c6266e37c674606f3e3aebc61035a2de290e,STILL_EXISTS,\"\"\"Functionality for making confidence reports. ||  || A confidence report is a dictionary. || Each dictionary key is the name of a type of data: ||   clean : Clean data ||   bundled : bundled adversarial examples || Each value in the dictionary contains an array of bools indicating whether || the model got each example correct and an array containing the confidence || that the model assigned to each prediction. || \"\"\"
aaijaejec,cleverhans-lab/cleverhans,cleverhans/confidence_report.py,b016c6266e37c674606f3e3aebc61035a2de290e,bde8d97d67c7fa30e52c4851b3eeeae26fb9505b,Set TF random seed to improve reproducibility
aaijaffea,cleverhans-lab/cleverhans,cleverhans/attacks.py,3d0e263c47486390393e3f242dabb2df28a6496c,STILL_EXISTS,This is not needed to be optimal for t >= 0.5; but may as well do it
aaijaffji,cleverhans-lab/cleverhans,cleverhans/attack_bundling.py,ccc3a8f14713bb4a2f8f7eaba2467f7faffbaa2d,518f4eb89362f8d602b7a87136aa1e9a935afb1b,TODO: lower priority: make sure bundler won't waste time running targeted
aaijafgad,cleverhans-lab/cleverhans,cleverhans/attack_bundling.py,ccc3a8f14713bb4a2f8f7eaba2467f7faffbaa2d,STILL_EXISTS,TODO: make an interface to pass this in if it has already been computed elsewhere
aaijafgbd,cleverhans-lab/cleverhans,cleverhans/attack_bundling.py,ccc3a8f14713bb4a2f8f7eaba2467f7faffbaa2d,STILL_EXISTS,TODO: refactor to avoid this duplicated method
aaijafgdh,cleverhans-lab/cleverhans,cleverhans/attacks.py,aaa078aa65d72767e33ecae2e98ec2848c8a0d8b,63d6324fea7463b14013d903cbed4bdfbeb6cfb2,If clipping is needed; reset all values outside of [clip_min; clip_max]
aaijafggg,cleverhans-lab/cleverhans,cleverhans/attacks.py,95cc0a65d223e695818c1ce96529002d2498d512,63d6324fea7463b14013d903cbed4bdfbeb6cfb2,adjust the constant as needed
aaijafghb,cleverhans-lab/cleverhans,cleverhans/attack_bundling.py,e24f3defd1b709e9e628fae35b641fc900ce2007,518f4eb89362f8d602b7a87136aa1e9a935afb1b,TODO: lower priority: make sure bundler won't waste time running targeted
aaijafhcd,cleverhans-lab/cleverhans,cleverhans/attacks.py,6853ff6be626c43efd107cebaa62b9d770a94127,STILL_EXISTS,If clipping is needed; reset all values outside of [clip_min; clip_max]
aaijafhde,cleverhans-lab/cleverhans,cleverhans/attacks.py,6853ff6be626c43efd107cebaa62b9d770a94127,STILL_EXISTS,adjust the constant as needed
aaijafied,cleverhans-lab/cleverhans,cleverhans/attack_bundling.py,662259ef116d5378edb85f31493802e2b79bd879,6deb9c2aac33b83b99a124024b699a89052d685e,TODO: lower priority: make sure bundler won't waste time running targeted
aaijafifb,cleverhans-lab/cleverhans,cleverhans/attacks_tf.py,d9b3abb3beed0942890a3484c07847b64241fe57,345e26dd95e1f4bf467f808a125d035f8800f67d,TODO: let caller pass in a check_diff function as well as
aaijafifi,cleverhans-lab/cleverhans,cleverhans/confidence_report.py,0b62c4093b86b80844ab29216358cf43b1a5f62d,STILL_EXISTS,Set TF random seed to improve reproducibility
aaijafija,cleverhans-lab/cleverhans,scripts/plot_success_fail_curve.py,f7bb59e528c0bfdf53d4009ada8eb9fbee84fb73,STILL_EXISTS,\"\"\" || Plots a success-fail curve ( https:\/\/openreview.net\/forum?id=H1g0piA9tQ ) || Usage: || plot_success_fail_curve.py model.joblib || plot_success_fail_curve.py model1.joblib model2.joblib ||  || This script is mostly intended to rapidly visualize success-fail curves || during model development and testing. || To make nicely labeled plots formatted to fit the page \/ column of a || publication; you should probably write your own script that calls some || of the same plotting commands. || \"\"\"
aaijafijf,cleverhans-lab/cleverhans,scripts/compute_accuracy.py,073ccf238653bc5f5bb49bcbacf56b0ff8f70e3a,STILL_EXISTS,Set TF random seed to improve reproducibility
aaijafjah,cleverhans-lab/cleverhans,cleverhans/attacks.py,30cbcc8ae7bf77192a8cfdeef9bfee46903a5932,STILL_EXISTS,The 1e-6 is needed to compensate for numerical error.
aaijagagj,cleverhans-lab/cleverhans,cleverhans/attack_bundling.py,d14309d72ea16a9cacaf4a770479b7b2cdb72b8a,STILL_EXISTS,The incremental saves run on a timer. This save is needed so that the last
aaijagbbb,cleverhans-lab/cleverhans,cleverhans/attack_bundling.py,cff6c4ec9d4cdc07b595e8578e701fd25336aa99,2b0c841293f44f02cba0de30ba0b9b96cdd5b3fc,TODO: lower priority: make sure bundler won't waste time running targeted
aaijagced,cleverhans-lab/cleverhans,cleverhans/attacks_tf.py,2d606299ef158ea648782a9e73023d1e70b9755c,345e26dd95e1f4bf467f808a125d035f8800f67d,TODO: this assert looks totally wrong.
aaijagcef,cleverhans-lab/cleverhans,cleverhans/attacks_tf.py,2d606299ef158ea648782a9e73023d1e70b9755c,345e26dd95e1f4bf467f808a125d035f8800f67d,1) Multiplying by 1.1 gives a huge margin of error. This should probably
aaijagcff,cleverhans-lab/cleverhans,scripts/make_confidence_report_spsa.py,2d606299ef158ea648782a9e73023d1e70b9755c,STILL_EXISTS,Set TF random seed to improve reproducibility
aaijagchj,cleverhans-lab/cleverhans,tests_tf/test_attacks.py,66da3d65d00b93ffe82ad66f12be46101a768aa9,STILL_EXISTS,Inheritance doesn't really work with tests.
aaijagdfj,cleverhans-lab/cleverhans,cleverhans/attacks.py,345e26dd95e1f4bf467f808a125d035f8800f67d,STILL_EXISTS,TODO: this assert looks totally wrong.
aaijagdgb,cleverhans-lab/cleverhans,cleverhans/attacks.py,345e26dd95e1f4bf467f808a125d035f8800f67d,STILL_EXISTS,1) Multiplying by 1.1 gives a huge margin of error. This should probably
aaijagdgg,cleverhans-lab/cleverhans,cleverhans/attacks.py,345e26dd95e1f4bf467f808a125d035f8800f67d,STILL_EXISTS,TODO: let caller pass in a check_diff function as well as
aaijagfih,cleverhans-lab/cleverhans,cleverhans/attacks/attack.py,4030b196152161cb0cf62d7559634fe91bcb3c0f,STILL_EXISTS,Include an unused return so pylint understands the method signature
aaijaggbi,cleverhans-lab/cleverhans,cleverhans/attacks/fast_gradient_method.py,4030b196152161cb0cf62d7559634fe91bcb3c0f,STILL_EXISTS,If clipping is needed; reset all values outside of [clip_min; clip_max]
aaijaggcb,cleverhans-lab/cleverhans,cleverhans/attacks/fast_gradient_method.py,4030b196152161cb0cf62d7559634fe91bcb3c0f,STILL_EXISTS,In Python 3; the `list` call is needed to convert the iterator returned by `range` into a list.
aaijaggef,cleverhans-lab/cleverhans,cleverhans/attacks/projected_gradient_descent.py,4030b196152161cb0cf62d7559634fe91bcb3c0f,STILL_EXISTS,The 1e-6 is needed to compensate for numerical error.
aaijaghfh,cleverhans-lab/cleverhans,cleverhans/attacks/fast_feature_adversaries.py,5cef26af227a6818c749c0d202a25b2052ac0540,STILL_EXISTS,If clipping is needed;
aaijaghhd,cleverhans-lab/cleverhans,cleverhans/attacks/lbfgs.py,5cef26af227a6818c749c0d202a25b2052ac0540,STILL_EXISTS,adjust the constant as needed
aaijaghic,cleverhans-lab/cleverhans,cleverhans/attacks/max_confidence.py,5cef26af227a6818c749c0d202a25b2052ac0540,STILL_EXISTS,This is not needed to be optimal for t >= 0.5; but may as well do it
aaijagibi,cleverhans-lab/cleverhans,cleverhans/attacks/spsa.py,5cef26af227a6818c749c0d202a25b2052ac0540,STILL_EXISTS,TODO: this assert looks totally wrong.
aaijagica,cleverhans-lab/cleverhans,cleverhans/attacks/spsa.py,5cef26af227a6818c749c0d202a25b2052ac0540,STILL_EXISTS,1) Multiplying by 1.1 gives a huge margin of error. This should probably
aaijagicf,cleverhans-lab/cleverhans,cleverhans/attacks/spsa.py,5cef26af227a6818c749c0d202a25b2052ac0540,STILL_EXISTS,TODO: let caller pass in a check_diff function as well as
aaijagidd,cleverhans-lab/cleverhans,cleverhans/compat.py,c66751bcab4e09a87232435abb9a7d075145e592,STILL_EXISTS,Strange import to work around pylint bug affecting python 3 \/ tf 1.8
aaijagidf,cleverhans-lab/cleverhans,cleverhans/compat.py,c66751bcab4e09a87232435abb9a7d075145e592,STILL_EXISTS,Assignment rather than import to work around pylint bug affecting python 3 \/ tf 1.8
aaijagiea,cleverhans-lab/cleverhans,cleverhans/compat.py,279c9462bd996fb349e95cc9f5745d41b36caecf,STILL_EXISTS,Assignment rather than import to work around pylint bug affecting python 3 \/ tf 1.8
aaijagiha,cleverhans-lab/cleverhans,cleverhans/attacks/carlini_wagner_l2.py,5ec82b2b7d594258df5cf1915013797f30ed5983,STILL_EXISTS,these are variables to be more efficient in sending data to tf
aaijagijd,cleverhans-lab/cleverhans,cleverhans/attacks/carlini_wagner_l2.py,5ec82b2b7d594258df5cf1915013797f30ed5983,STILL_EXISTS,adjust the constant as needed
aaijagjag,cleverhans-lab/cleverhans,cleverhans/attacks/elastic_net_method.py,5ec82b2b7d594258df5cf1915013797f30ed5983,STILL_EXISTS,these are variables to be more efficient in sending data to tf
aaijagjcg,cleverhans-lab/cleverhans,cleverhans/attacks/elastic_net_method.py,5ec82b2b7d594258df5cf1915013797f30ed5983,STILL_EXISTS,adjust the constant as needed
aaijagjdj,cleverhans-lab/cleverhans,cleverhans/attacks/saliency_map_method.py,5ec82b2b7d594258df5cf1915013797f30ed5983,STILL_EXISTS,cond_in: the boolean tensor to show if more iteration is needed for
aaijagjff,cleverhans-lab/cleverhans,cleverhans/attacks/saliency_map_method.py,5ec82b2b7d594258df5cf1915013797f30ed5983,STILL_EXISTS,Check if more modification is needed for each sample
aaijahbgc,cleverhans-lab/cleverhans,cleverhans_tutorials/mnist_tutorial_keras.py,b3322827f0097738582007d10498ccec4780be4f,52309bb9c0d65f5ccc467cc492d82765bfecc54a,TODO: pylintrc
aaijahbgd,cleverhans-lab/cleverhans,cleverhans_tutorials/mnist_tutorial_keras.py,b3322827f0097738582007d10498ccec4780be4f,52309bb9c0d65f5ccc467cc492d82765bfecc54a,TODO: Add tests
aaijahbge,cleverhans-lab/cleverhans,cleverhans_tutorials/mnist_tutorial_keras.py,b3322827f0097738582007d10498ccec4780be4f,52309bb9c0d65f5ccc467cc492d82765bfecc54a,TODO: Diff between new and original tutorial
aaijahbgh,cleverhans-lab/cleverhans,cleverhans_tutorials/mnist_tutorial_keras.py,b3322827f0097738582007d10498ccec4780be4f,STILL_EXISTS,Set TF random seed to improve reproducibility
aaijahcah,cleverhans-lab/cleverhans,examples/deep_k_nearest_neighbors/dknn.py,819039b1551dcfc032cf3a128ba8fafa347545af,STILL_EXISTS,Center the dataset and the queries: this improves the performance of LSH quite a bit.
aaijahdge,cleverhans-lab/cleverhans,cleverhans/experimental/certification/nn.py,060c1d4ca2d70b441447498213a09a0adc68404b,STILL_EXISTS,Can think of a hack involving matching shapes but if shapes are equal
aaijahdgh,cleverhans-lab/cleverhans,cleverhans/experimental/certification/optimization.py,060c1d4ca2d70b441447498213a09a0adc68404b,a97f3cd528074c2ad9c4a7c566b6c0a45f9d4235,TODO: consider whether we can use shallow copy of the lists without
aaijahdhj,cleverhans-lab/cleverhans,cleverhans/experimental/certification/optimization.py,060c1d4ca2d70b441447498213a09a0adc68404b,a97f3cd528074c2ad9c4a7c566b6c0a45f9d4235,TODO: Write this in terms of matrix multiply
aaijahhji,cleverhans-lab/cleverhans,cleverhans/model_zoo/soft_nearest_neighbor_loss/SNNL_regularized_train.py,f5cc753e9a3610e2ec60c1e5af469e8afaaf88ec,STILL_EXISTS,Set TF random seed to improve reproducibility
aaijahjah,cleverhans-lab/cleverhans,cleverhans/experimental/certification/dual_formulation.py,16902ce8dd8b3adabda9135484a755a7d8fa6c1f,STILL_EXISTS,For convenience; think of x as [\\alpha; \\beta]
aaijaiahe,cleverhans-lab/cleverhans,cleverhans/experimental/certification/dual_formulation.py,04792c47890e81115f60584639842d0e687e3904,STILL_EXISTS,TODO: consider whether we can use shallow copy of the lists without
aaijaiaii,cleverhans-lab/cleverhans,cleverhans/future/torch/attacks/projected_gradient_descent.py,3efb78d09703d12835e279ce7c3293c9a6b87fc6,0e31d96c437162cf7ed4957badf239bbe7bb31f7,TODO support numpy tensors. Wrap x as a pytorch tensor before excuting?
aaijaiajd,cleverhans-lab/cleverhans,cleverhans/future/torch/attacks/projected_gradient_descent.py,3efb78d09703d12835e279ce7c3293c9a6b87fc6,0e31d96c437162cf7ed4957badf239bbe7bb31f7,TODO pytorch implementation of FGM_CLASS
aaijaiaji,cleverhans-lab/cleverhans,cleverhans/future/torch/attacks/projected_gradient_descent.py,3efb78d09703d12835e279ce7c3293c9a6b87fc6,1f2ee935cc7a3a8412d910565745fb5aed36c250,The 1e-6 is needed to compensate for numerical error.
aaijaibab,cleverhans-lab/cleverhans,cleverhans/future/torch/attacks/projected_gradient_descent.py,3efb78d09703d12835e279ce7c3293c9a6b87fc6,STILL_EXISTS,TODO case to x.dtype?
aaijaibcd,cleverhans-lab/cleverhans,cleverhans/future/torch/attacks/fast_gradient_method.py,cc9b5cb25bfda9770f8defd3d6fe0e5482c300a7,e97b11f60c7db54421e2291d43fc0e6b196ed155,TODO doc: make sure the caller has not passed probs by accident
aaijaibda,cleverhans-lab/cleverhans,cleverhans/future/torch/attacks/fast_gradient_method.py,cc9b5cb25bfda9770f8defd3d6fe0e5482c300a7,STILL_EXISTS,If clipping is needed; reset all values outside of [clip_min; clip_max]
aaijaibdd,cleverhans-lab/cleverhans,cleverhans/future/torch/attacks/fast_gradient_method.py,cc9b5cb25bfda9770f8defd3d6fe0e5482c300a7,e97b11f60c7db54421e2291d43fc0e6b196ed155,In Python 3; the `list` call is needed to convert the iterator returned by `range` into a list.
aaijaibfe,cleverhans-lab/cleverhans,cleverhans/future/torch/attacks/Noise.py,132dc45d11f12bfa05579f075dabd7ab996c25c9,STILL_EXISTS,TODO Check the dtype
aaijaibgb,cleverhans-lab/cleverhans,cleverhans/future/torch/attacks/attack.py,e97b11f60c7db54421e2291d43fc0e6b196ed155,STILL_EXISTS,TODO Deprecate. No need for a base class in the desired function-based API
aaijaibgf,cleverhans-lab/cleverhans,cleverhans/future/torch/attacks/projected_gradient_descent.py,e97b11f60c7db54421e2291d43fc0e6b196ed155,6d9bd5a28272145a6216bdffec0d8226168f174d,TODO clip_eta or optimize_linear?
aaijaibgh,cleverhans-lab/cleverhans,cleverhans/utils_pytorch.py,e97b11f60c7db54421e2291d43fc0e6b196ed155,8cc2345931fd07b32b7e4bb7e8f88b62d2224117,TODO
aaijaiccc,cleverhans-lab/cleverhans,cleverhans/future/torch/attacks/projected_gradient_descent.py,eda18047272338f55a9d61eb557be7e9fdb644dd,d83aa2cadbac293d3f9dbbdffad89c659aad96d2,TODO: deprecate the rand_minmax param? (as the original
aaijaigei,cleverhans-lab/cleverhans,cleverhans/attacks/sparse_l1_descent.py,845ec5aba768db2b51282fbfb32e12f608d48a57,STILL_EXISTS,If clipping is needed; reset all values outside of [clip_min; clip_max]
aaijaijba,cleverhans-lab/cleverhans,cleverhans/future/tf2/attacks/fast_gradient_method.py,e8c9ed9280105c7c7572b15c1bba572900dac3a1,STILL_EXISTS,If clipping is needed; reset all values outside of [clip_min; clip_max]
aaijaijbg,cleverhans-lab/cleverhans,cleverhans/future/tf2/attacks/fast_gradient_method.py,e8c9ed9280105c7c7572b15c1bba572900dac3a1,STILL_EXISTS,In Python 3; the `list` call is needed to convert the iterator returned by `range` into a list.
aaijaijde,cleverhans-lab/cleverhans,cleverhans/future/tf2/attacks/projected_gradient_descent.py,e8c9ed9280105c7c7572b15c1bba572900dac3a1,STILL_EXISTS,TODO necessary to cast to x.dtype?
aaijaijjg,cleverhans-lab/cleverhans,cleverhans/future/tf2/attacks/fast_gradient_method.py,4a9d43c1a1a88c183b8afb4600f8af05c00cdb75,STILL_EXISTS,If clipping is needed; reset all values outside of [clip_min; clip_max]
aaijajajj,cleverhans-lab/cleverhans,tests_pytorch/test_attacks.py,c89025c05adee5cacfe379747ecaf4111a6622a0,STILL_EXISTS,NOTE this has been tested with the optimize_linear function in test_utils; so duplicate tests are not needed here.
aaijajbdh,cleverhans-lab/cleverhans,cleverhans/future/tf2/attacks/spsa.py,584a86dd4cb2ec99865105a055c8656cf703c86f,STILL_EXISTS,TODO: this assert looks totally wrong.
aaijajbdj,cleverhans-lab/cleverhans,cleverhans/future/tf2/attacks/spsa.py,584a86dd4cb2ec99865105a055c8656cf703c86f,STILL_EXISTS,1) Multiplying by 1.1 gives a huge margin of error. This should probably
aaijajbee,cleverhans-lab/cleverhans,cleverhans/future/tf2/attacks/spsa.py,584a86dd4cb2ec99865105a055c8656cf703c86f,STILL_EXISTS,TODO: let caller pass in a check_diff function as well as
aaijajdfg,cleverhans-lab/cleverhans,tests_pytorch/test_attacks.py,8cc2345931fd07b32b7e4bb7e8f88b62d2224117,STILL_EXISTS,test_utils; so duplicate tests are not needed here.
aaijajdgc,cleverhans-lab/cleverhans,tests_pytorch/test_attacks.py,8cc2345931fd07b32b7e4bb7e8f88b62d2224117,STILL_EXISTS,TODO not sure what the original test does in tests_tf\/test_attacks
aaijajihe,cleverhans-lab/cleverhans,cleverhans/utils_pytorch.py,cde6dd027c4d9d875e70b6333397073e3dc6484e,43c7ec61968bd8b5bbb1a6a415df73bd8e59b5c1,TODO
aaijbaabj,cleverhans-lab/cleverhans,examples/adversarial_asr/src_combine.py,9996a3daed4d0c05b4fd892a01578b89a6b394b1,STILL_EXISTS,generate the inputs that are needed for the lingvo model
aaijbbbbf,cleverhans-lab/cleverhans,examples/adversarial_asr/generate_robust_adv.py,140fd6131c6bb138b613ffdd23a3d096669dc4f5,STILL_EXISTS,generate the inputs that are needed for the lingvo model
aaijbbgef,cleverhans-lab/cleverhans,cleverhans/future/jax/attacks/fast_gradient_method.py,e177af9409b811765209dcca4e4263d964321894,STILL_EXISTS,If clipping is needed; reset all values outside of [clip_min; clip_max]
aaijbceed,cleverhans-lab/cleverhans,cleverhans/future/torch/tests/test_utils.py,f97a4fd9cd309ca90a1a013a75ef4dbf016c41ab,STILL_EXISTS,TODO uncomment the actual test below after we have implemented the L1 attack
aaijbchah,cleverhans-lab/cleverhans,cleverhans/utils_pytorch.py,0068e9c4c0b3fdbce97d9373287545583bbfe66d,c8362ebb57a30e1eef63c8468b3891bb9f6f1fb9,TODO
aaijbchbf,cleverhans-lab/cleverhans,cleverhans/future/torch/utils.py,c8362ebb57a30e1eef63c8468b3891bb9f6f1fb9,2cf0be2d744a83197441b5d726ddd8edcc4cb40b,TODO
aaijbchdd,cleverhans-lab/cleverhans,cleverhans/utils_pytorch.py,1817d397b5480323a257bdab4115b4450a056c73,83083d645b9c35d7872ca907ae475d635743849a,TODO raise NotImplementedError(\"L1 clip is not implemented.\")
aaijbchec,cleverhans-lab/cleverhans,tests_pytorch/test_attacks.py,1817d397b5480323a257bdab4115b4450a056c73,STILL_EXISTS,test_utils; so duplicate tests are not needed here.
aaijbchfe,cleverhans-lab/cleverhans,tests_pytorch/test_attacks.py,1817d397b5480323a257bdab4115b4450a056c73,STILL_EXISTS,TODO not sure what the original test does in tests_tf\/test_attacks
aaijbciab,cleverhans-lab/cleverhans,cleverhans/future/torch/tests/test_utils.py,5a206122a80fc24054608ab4f5110531d4e528e3,STILL_EXISTS,TODO uncomment the actual test below after we have implemented the L1 attack
aaijbcjhb,cleverhans-lab/cleverhans,cleverhans/future/torch/attacks/carlini_wagner_l2.py,c7ed84d6d340ebc94e9434b8a3169dc16c451745,STILL_EXISTS,Initialize some values needed for binary search on const
aaijbcjhd,cleverhans-lab/cleverhans,cleverhans/future/torch/attacks/carlini_wagner_l2.py,c7ed84d6d340ebc94e9434b8a3169dc16c451745,STILL_EXISTS,Initialize some values needed for the inner loop
aaijbcjid,cleverhans-lab/cleverhans,cleverhans/future/torch/attacks/carlini_wagner_l2.py,8155e239a7d6c4ddc250ed2b1ec0dae8f154f842,STILL_EXISTS,Initialize some values needed for binary search on const
aaijbcjig,cleverhans-lab/cleverhans,cleverhans/future/torch/attacks/carlini_wagner_l2.py,8155e239a7d6c4ddc250ed2b1ec0dae8f154f842,STILL_EXISTS,TODO as of 01\/06\/2020; PyTorch does not natively support
aaijbdaah,cleverhans-lab/cleverhans,cleverhans/future/tf2/attacks/projected_gradient_descent.py,02b847bb80b3d3b504dca6681dfe89157ead7aaa,eb9065b3053aa00746f8855f44616e6b6132f66c,TODO: Currently; random sampling only works on L_inf norm
aaijbdcgb,cleverhans-lab/cleverhans,cleverhans/future/torch/attacks/sparse_l1_descent.py,796aab9e5c0eff287ea85429a4deca969ddffda6,STILL_EXISTS,If clipping is needed; reset all values outside of [clip_min; clip_max]
aaijbdcje,cleverhans-lab/cleverhans,cleverhans/future/tf2/attacks/carlini_wagner_l2.py,b2590b4bbc8703b13ec0881887ee8afd4241ac8b,STILL_EXISTS,TODO: check if consistens with tf1 version
aaijbddbc,cleverhans-lab/cleverhans,cleverhans/future/tf2/attacks/carlini_wagner_l2.py,b2590b4bbc8703b13ec0881887ee8afd4241ac8b,STILL_EXISTS,TODO: rework for tf2
aaijbddcc,cleverhans-lab/cleverhans,cleverhans/future/tf2/attacks/carlini_wagner_l2.py,69367795e2bdb84d77c424f191f7b7cc0df41c08,59b6154c77ce2f23f66377417e8b2935a2922e06,TODO: find a way to reset the state of Adam so we only need to compile the training function ones
aaijbdfaj,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans/attack_bundling.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,TODO: lower priority: make it possible to initialize one attack with
aaijbdfbc,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans/attack_bundling.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,TODO: lower priority: make sure bundler won't waste time running targeted
aaijbdfbg,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans/attack_bundling.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,TODO: make an interface to pass this in if it has already been computed
aaijbdfcb,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans/attack_bundling.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,The incremental saves run on a timer. This save is needed so that the last
aaijbdfci,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans/attack_bundling.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,TODO: refactor to avoid this duplicated method
aaijbdfgc,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans/attacks/attack.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,Include an unused return so pylint understands the method signature
aaijbdfih,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans/attacks/carlini_wagner_l2.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,these are variables to be more efficient in sending data to tf
aaijbdgba,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans/attacks/carlini_wagner_l2.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,adjust the constant as needed
aaijbdgdb,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans/attacks/elastic_net_method.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,these are variables to be more efficient in sending data to tf
aaijbdgfb,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans/attacks/elastic_net_method.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,adjust the constant as needed
aaijbdggg,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans/attacks/fast_feature_adversaries.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,If clipping is needed;
aaijbdgib,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans/attacks/fast_gradient_method.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,If clipping is needed; reset all values outside of [clip_min; clip_max]
aaijbdgie,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans/attacks/fast_gradient_method.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,In Python 3; the `list` call is needed to convert the iterator returned by `range` into a list.
aaijbdheb,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans/attacks/lbfgs.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,adjust the constant as needed
aaijbdhfb,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans/attacks/max_confidence.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,This is not needed to be optimal for t >= 0.5; but may as well do it
aaijbdhid,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans/attacks/projected_gradient_descent.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,The 1e-6 is needed to compensate for numerical error.
aaijbdiac,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans/attacks/saliency_map_method.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,cond_in: the boolean tensor to show if more iteration is needed for
aaijbdibj,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans/attacks/saliency_map_method.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,Check if more modification is needed for each sample
aaijbdifd,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans/attacks/sparse_l1_descent.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,If clipping is needed; reset all values outside of [clip_min; clip_max]
aaijbdiii,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans/attacks/spsa.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,TODO: this assert looks totally wrong.
aaijbdija,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans/attacks/spsa.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,1) Multiplying by 1.1 gives a huge margin of error. This should probably
aaijbdijf,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans/attacks/spsa.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,TODO: let caller pass in a check_diff function as well as
aaijbdjdg,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans/attacks_tfe.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,If clipping is needed
aaijbdjdi,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans/augmentation.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,\"\"\" || Dataset augmentation functionality ||  || NOTE: This module is much more free to change than many other modules || in CleverHans. CleverHans is very conservative about changes to any || code that affects the output of benchmark tests (attacks; evaluation || methods; etc.). This module provides *dataset augmentation* code for || building models to be benchmarked; not *benchmarks;* and || thus is free to change rapidly to provide better speed; accuracy; || etc. || \"\"\"
aaijbdjgc,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans/confidence_report.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,\"\"\"Functionality for making confidence reports. ||  || A confidence report is a dictionary. || Each dictionary key is the name of a type of data: ||   clean : Clean data ||   bundled : bundled adversarial examples || Each value in the dictionary contains an array of bools indicating whether || the model got each example correct and an array containing the confidence || that the model assigned to each prediction. || \"\"\"
aaijbdjie,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans/confidence_report.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,Set TF random seed to improve reproducibility
aaijbeaac,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans/dataset.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,This is much more efficient with data augmentation; see tutorials.
aaijbeaaf,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans/dataset.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,data_type unicode to ascii conversion (Python2 fix)
aaijbeaei,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans/devtools/tests/test_format.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,is a little better than a 10X speedup.
aaijbebci,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans/experimental/certification/dual_formulation.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,TODO: consider whether we can use shallow copy of the lists without
aaijbebee,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans/experimental/certification/dual_formulation.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,For convenience; think of x as [\\alpha; \\beta]
aaijbebgj,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans/experimental/certification/nn.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,Can think of a hack involving matching shapes but if shapes are equal
aaijbechg,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans/model.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,\"\"\" || The Model class and related functionality. || \"\"\"
aaijbecic,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans/model_zoo/__init__.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,\"\"\" || A module for hosting a variety of models of interest to the adversarial || example community. ||  || Warning: ||   This module is not nearly as conservative as the rest of CleverHans. ||   Most of CleverHans is used to create rigorous vulnerability benchmarks. ||   For example; the error rate caused by an Attack is considered to be ||   part of the API for that Attack; so we upgrade the major version number ||   whenever it changes. ||   Models in the model zoo can be tweaked regularly to improve accuracy; ||   training speed; robustness; etc. || \"\"\"
aaijbedba,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans/model_zoo/deep_k_nearest_neighbors/dknn.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,Center the dataset and the queries: this improves the performance of LSH quite a bit.
aaijbedec,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans/model_zoo/madry_lab_challenges/cifar10_model.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,It is more memory efficient than very deep residual network and has
aaijbedfe,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans/model_zoo/soft_nearest_neighbor_loss/SNNL_regularized_train.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,Set TF random seed to improve reproducibility
aaijbedgc,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans/picklable_model.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,\"\"\"Models that support pickling. ||  || NOTE: This module is much more free to change than many other modules || in CleverHans. CleverHans is very conservative about changes to any || code that affects the output of benchmark tests (attacks; evaluation || methods; etc.). This module provides *models* not *benchmarks* and || thus is free to change rapidly to provide better speed; accuracy; || etc. || \"\"\"
aaijbeehf,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans/train.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,\"\"\" || Multi-replica synchronous training ||  ||  || NOTE: This module is much more free to change than many other modules || in CleverHans. CleverHans is very conservative about changes to any || code that affects the output of benchmark tests (attacks; evaluation || methods; etc.). This module provides *model training* functionality || not *benchmarks* and thus is free to change rapidly to provide better || speed; accuracy; etc. || \"\"\"
aaijbeejb,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans/train.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,timing very much; but shuffling here makes the code slightly
aaijbegdg,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans_tutorials/cifar10_tutorial_tf.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,Set TF random seed to improve reproducibility
aaijbeggb,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans_tutorials/evaluate_pickled_model.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,Set TF random seed to improve reproducibility
aaijbeghc,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans_tutorials/mnist_blackbox.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,Set TF random seed to improve reproducibility
aaijbehaj,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans_tutorials/mnist_tutorial_cw.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,Set TF random seed to improve reproducibility
aaijbehde,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans_tutorials/mnist_tutorial_jsma.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,Set TF random seed to improve reproducibility
aaijbehhb,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans_tutorials/mnist_tutorial_keras.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,Set TF random seed to improve reproducibility
aaijbehjj,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans_tutorials/mnist_tutorial_keras_tf.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,Set TF random seed to improve reproducibility
aaijbeica,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans_tutorials/mnist_tutorial_picklable.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,Set TF random seed to improve reproducibility
aaijbeigi,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans_tutorials/mnist_tutorial_tf.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,Set TF random seed to improve reproducibility
aaijbejab,cleverhans-lab/cleverhans,cleverhans_v3.1.0/cleverhans_tutorials/mnist_tutorial_tfe.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,Set TF random seed to improve reproducibility
aaijbejhf,cleverhans-lab/cleverhans,cleverhans_v3.1.0/docsource/conf.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaijbfahj,cleverhans-lab/cleverhans,cleverhans_v3.1.0/examples/adversarial_asr/generate_imperceptible_adv.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,generate the inputs that are needed for the lingvo model
aaijbfbdi,cleverhans-lab/cleverhans,cleverhans_v3.1.0/examples/adversarial_asr/generate_robust_adv.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,generate the inputs that are needed for the lingvo model
aaijbfccj,cleverhans-lab/cleverhans,cleverhans_v3.1.0/examples/multigpu_advtrain/attacks_multigpu.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,TODO: Break the graph only nGPU times instead of nb_iter times.
aaijbfcdj,cleverhans-lab/cleverhans,cleverhans_v3.1.0/examples/multigpu_advtrain/evaluator.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,TODO: black box attacks
aaijbfchf,cleverhans-lab/cleverhans,cleverhans_v3.1.0/examples/multigpu_advtrain/resnet_tf.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,It is more memory efficient than very deep residual network and
aaijbfdah,cleverhans-lab/cleverhans,cleverhans_v3.1.0/examples/multigpu_advtrain/trainer.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,Set TF random seed to improve reproducibility
aaijbfdci,cleverhans-lab/cleverhans,cleverhans_v3.1.0/examples/multigpu_advtrain/trainer.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,was affected by the length of the unused adversarial generation
aaijbfdfd,cleverhans-lab/cleverhans,cleverhans_v3.1.0/examples/nips17_adversarial_competition/dev_toolkit/run_attacks_and_defenses.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,In the matrices below: rows - attacks; columns - defenses.
aaijbfdfg,cleverhans-lab/cleverhans,cleverhans_v3.1.0/examples/nips17_adversarial_competition/dev_toolkit/run_attacks_and_defenses.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,higher scores are better.
aaijbfehd,cleverhans-lab/cleverhans,cleverhans_v3.1.0/examples/nips17_adversarial_competition/eval_infra/code/eval_lib/classification_results.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,columns - second index
aaijbfeie,cleverhans-lab/cleverhans,cleverhans_v3.1.0/examples/nips17_adversarial_competition/eval_infra/code/eval_lib/cloud_client.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,\"\"\"Helper classes and wrappers to access Google Cloud. ||  || Google Cloud API is encapsulated with these wrappers; so it's easier to || test the code with help of fake (declared in testing\/fake_cloud_client.py). || \"\"\"
aaijbfejb,cleverhans-lab/cleverhans,cleverhans_v3.1.0/examples/nips17_adversarial_competition/eval_infra/code/eval_lib/dataset_helper.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,load and resize adversarial image if needed
aaijbfgdj,cleverhans-lab/cleverhans,cleverhans_v3.1.0/examples/nips17_adversarial_competition/eval_infra/code/eval_lib/work_data.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,Number of work records to read at once
aaijbfghj,cleverhans-lab/cleverhans,cleverhans_v3.1.0/examples/nips17_adversarial_competition/eval_infra/code/worker.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,\"\"\"Worker which runs all computations on Cloud VMs. ||  || Evaluation of competition is split into work pieces. One work piece is a || either evaluation of an attack on a batch of images or evaluation of a || defense on a batch of adversarial images. || All pieces of attack work are independent from each other and could be run || in parallel. Same for pieces of defense work - they are independent from each || other and could be run in parallel. But defense work could be run only after || all attack work is completed. ||  || Worker first runs all attack pieces; by querying next piece of undone work || and running it. After all attack pieces are done; worker runs all defense pieces || in a similar way. ||  || Before workers could be started; datastore has to be populated by master || with description of work to be done. See master.py for details. ||  || NOTE: Worker is designed to run on linux machine with NVidia docker || installed. Worker generally needs administrative privilege to run properly. || Also worker relies on very specific directory structure created in home || directory. That's why it's highly recommended to run worker only in VM. || \"\"\"
aaijbfhjj,cleverhans-lab/cleverhans,cleverhans_v3.1.0/scripts/compute_accuracy.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,Set TF random seed to improve reproducibility
aaijbfiaa,cleverhans-lab/cleverhans,cleverhans_v3.1.0/scripts/make_confidence_report.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,\"\"\" || make_confidence_report.py || Usage: ||   python make_confidence_report.py model.joblib ||  ||   where model.joblib is a file created by cleverhans.serial.save containing ||   a picklable cleverhans.model.Model instance. ||  || This script will run the model on a variety of types of data and save an || instance of cleverhans.confidence_report.ConfidenceReport to || model_report.joblib. || The report can be later loaded by another script using cleverhans.serial.load. || This script puts the following entries in the report: ||   clean : Clean data ||   semantic : Semantic adversarial examples ||   mc: MaxConfidence adversarial examples ||  || This script works by running a single MaxConfidence attack on each example. || ( https:\/\/openreview.net\/forum?id=H1g0piA9tQ ) || This provides a reasonable estimate of the true failure rate quickly; so || long as the model does not suffer from gradient masking. || However; this estimate is mostly intended for development work and not || for publication. A more accurate estimate may be obtained by running || make_confidence_report_bundled.py instead. || \"\"\"
aaijbfibg,cleverhans-lab/cleverhans,cleverhans_v3.1.0/scripts/make_confidence_report_spsa.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,\"\"\" || make_confidence_report.py || Usage: ||   python make_confidence_report_spsa.py model.joblib ||  ||   where model.joblib is a file created by cleverhans.serial.save containing ||   a picklable cleverhans.model.Model instance. ||  || This script will run the model on a variety of types of data and save a || ConfidenceReport to model_report.joblib. || The report can be later loaded by another script using cleverhans.serial.load. || This script puts the following entries in the report: ||   clean : Clean data ||   mc: MaxConfidence SPSA adversarial examples ||  || This script works by running a single MaxConfidence attack on each example. || ( https:\/\/openreview.net\/forum?id=H1g0piA9tQ ) || The MaxConfidence attack uses the SPSA optimizer. || This is not intended to be a generic strong attack; rather it is intended || to be a test for gradient masking. || \"\"\"
aaijbficb,cleverhans-lab/cleverhans,cleverhans_v3.1.0/scripts/make_confidence_report_spsa.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,Set TF random seed to improve reproducibility
aaijbfice,cleverhans-lab/cleverhans,cleverhans_v3.1.0/scripts/plot_success_fail_curve.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,\"\"\" || Plots a success-fail curve ( https:\/\/openreview.net\/forum?id=H1g0piA9tQ ) || Usage: || plot_success_fail_curve.py model.joblib || plot_success_fail_curve.py model1.joblib model2.joblib ||  || This script is mostly intended to rapidly visualize success-fail curves || during model development and testing. || To make nicely labeled plots formatted to fit the page \/ column of a || publication; you should probably write your own script that calls some || of the same plotting commands. || \"\"\"
aaijbfifc,cleverhans-lab/cleverhans,cleverhans_v3.1.0/tests_tf/test_attacks.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,Inheritance doesn't really work with tests.
aaijbfjae,cleverhans-lab/cleverhans,cleverhans_v3.1.0/tests_tf/test_attacks.py,1616e8d895da97d31e8ce6350be75a61007238bf,STILL_EXISTS,TODO: change this to use standard cleverhans label conventions
aaijbgbic,cleverhans-lab/cleverhans,cleverhans/future/torch/attacks/carlini_wagner_l2.py,6f7cc2c9e13b5ccb7ff8363ed34bad9cd7dcb03f,STILL_EXISTS,Initialize some values needed for binary search on const
aaijbgbif,cleverhans-lab/cleverhans,cleverhans/future/torch/attacks/carlini_wagner_l2.py,6f7cc2c9e13b5ccb7ff8363ed34bad9cd7dcb03f,STILL_EXISTS,TODO as of 01\/06\/2020; PyTorch does not natively support
aaijbgbje,cleverhans-lab/cleverhans,cleverhans/future/torch/attacks/carlini_wagner_l2.py,6f7cc2c9e13b5ccb7ff8363ed34bad9cd7dcb03f,STILL_EXISTS,Initialize some values needed for the inner loop
aaijceigd,chainer/chainer,chain/functions/relu.py,833e5b1f54e6679ddce0a2b7e25dd8848b363e2f,STILL_EXISTS,TODO(beam2d): Implement in-place version.
aaijceihb,chainer/chainer,chain/functions/softmax_cross_entropy.py,33790ed19af930c787db6a3bd5e3d32d3931b572,STILL_EXISTS,TODO(beam2d): Implement GPU forward\/backward
aaijceiia,chainer/chainer,tests/functions_tests/test_softmax.py,944432d8c811642504e9c05e00bb3e58634d9017,176f21a52f32378f8335d1257745dd2dd0fcefea,TODO(beam2d): Fix it!
aaijcejdh,chainer/chainer,chain/functions/linear.py,5d24a342953cefdb531d009e5df10f3e0ecb5e9a,8e6befab424e07d11158bc0a9b05cc62307b6994,TODO(beam2d): More efficient kernel
aaijcejea,chainer/chainer,chain/functions/embed_id.py,05a1f303e9b4278be963b580e56d238f38e03ccd,STILL_EXISTS,TODO(beam2d): Better kernel
aaijcejga,chainer/chainer,chainer/functions/convolution_2d.py,997a048056a2aefa81f48794530702e8d31ea168,23d863da5dd660ce15c5a7a2a7cc2818d4ecb45a,TODO(beam2d): Implement CPU version.
aaijcfahf,chainer/chainer,chainer/cudnn/libcudnn.py,f983e69059c57028c9ab26431c157462623a91ae,STILL_EXISTS,rows; columns. The strides are implicitly defined
aaijcfahi,chainer/chainer,chainer/cudnn/libcudnn.py,f983e69059c57028c9ab26431c157462623a91ae,STILL_EXISTS,maps; rows; and columns; the columns are the
aaijcfaic,chainer/chainer,chainer/cudnn/libcudnn.py,f983e69059c57028c9ab26431c157462623a91ae,STILL_EXISTS,out in the following order: image; rows; columns;
aaijcfaif,chainer/chainer,chainer/cudnn/libcudnn.py,f983e69059c57028c9ab26431c157462623a91ae,STILL_EXISTS,with no padding between images; rows; columns;
aaijcfbde,chainer/chainer,chainer/cudnn/libcudnn.py,f983e69059c57028c9ab26431c157462623a91ae,STILL_EXISTS,algorithm regardless how much workspace is needed to execute it.
aaijcfbej,chainer/chainer,chainer/cudnn/libcudnn.py,f983e69059c57028c9ab26431c157462623a91ae,STILL_EXISTS,explicit matrix product. A significant memory workspace is needed to
aaijcfbie,chainer/chainer,chainer/cuda.py,891b44c6e85fee2af8abdd0ac400405529c19a31,92bed8c26651e0161fe644f2731f0fef9937bfd3,Workaround: the original GPUArray.copy does not use the user-defined
aaijcfbih,chainer/chainer,chainer/cuda.py,891b44c6e85fee2af8abdd0ac400405529c19a31,STILL_EXISTS,use the original GPUArray class; we choose easy and ugly solution that
aaijcfbij,chainer/chainer,chainer/cuda.py,891b44c6e85fee2af8abdd0ac400405529c19a31,92bed8c26651e0161fe644f2731f0fef9937bfd3,TODO(beam2d): Fix this ugly solution
aaijcfche,chainer/chainer,docs/source/conf.py,4a99cbb66701bf6d0720f09da5049486b31fb93b,STILL_EXISTS,If true; `todo` and `todoList` produce output; else they produce nothing.
aaijcfehf,chainer/chainer,examples/imagenet/inception.py,78f760dbdec92f75251391957fc58a4d0c641dad,8848522eabe14150c852eae9127c467c63c4a8c8,(TODO) Implement Local Response Normalization Layer (issue #19)
aaijcgfbe,chainer/chainer,chainer/functions/pooling_2d.py,21cef8072bac8ed114a9680414ecb4da7d8dc70d,c52da91f3a0fd9db99d100b01c39fe6da4270f33,cover_all mode is needed also for average pooling
aaijcgfbh,chainer/chainer,chainer/functions/tensor_network.py,05ff1bb6708511498449e70327351742a51fc3c5,STILL_EXISTS,TODO: checking nobias by means of b is not smart?
aaijcgfgc,chainer/chainer,chainer/functions/cross_covariance.py,63a7f226b788e7af6db24713e25bc6a56bf44d71,b467821c2e66d8d716e78d017936ac9b5ad687ac,TODO: Dirty hack. Sometimes type(y.shape) is list which causes an error in scikit-cuda because it checks if
aaijcggai,chainer/chainer,chainer/functions/ctc.py,854fcd3ca2ccf59b0776ba820215e0049b977e40,STILL_EXISTS,Todo: need to parameterize;
aaijcggbe,chainer/chainer,tests/functions_tests/test_ctc.py,854fcd3ca2ccf59b0776ba820215e0049b977e40,STILL_EXISTS,Todo: compute expected value
aaijcggge,chainer/chainer,cupy/__init__.py,87d103faea4bdbb0fa7d0baf9995a5e44bd259b0,STILL_EXISTS,TODO(beam2d0: Implement it
aaijcgjeh,chainer/chainer,cupy/indexing/generate.py,87d103faea4bdbb0fa7d0baf9995a5e44bd259b0,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,TODO(beam2d): Implement these
aaijcgjhi,chainer/chainer,cupy/io/rawfile.py,87d103faea4bdbb0fa7d0baf9995a5e44bd259b0,144a3135a85a6b82bd694f935e8b54d15fd800d8,TODO(beam2d): Implement it
aaijchaah,chainer/chainer,cupy/linalg/product.py,87d103faea4bdbb0fa7d0baf9995a5e44bd259b0,51c2104b2ef519a81ba65973282467e777e8ced9,It copies the operands if needed
aaijchagf,chainer/chainer,cupy/manipulation/dims.py,87d103faea4bdbb0fa7d0baf9995a5e44bd259b0,38390f0b785fbf59fa1090a03f4e1d4f065a36ca,TODO(okuta) fix if `dim` == 1
aaijchagh,chainer/chainer,cupy/manipulation/dims.py,87d103faea4bdbb0fa7d0baf9995a5e44bd259b0,cf513cefb06ff9ce837bfaff1bfa8ec70930f08d,TODO(beam2d): Implement it
aaijchaje,chainer/chainer,cupy/math/misc.py,87d103faea4bdbb0fa7d0baf9995a5e44bd259b0,STILL_EXISTS,I think this order is a bug of NumPy; though we select this \"buggy\"
aaijchbbg,chainer/chainer,cupy/math/trigonometric.py,87d103faea4bdbb0fa7d0baf9995a5e44bd259b0,STILL_EXISTS,TODO(beam2d): Implement it
aaijchbbh,chainer/chainer,cupy/padding/__init__.py,87d103faea4bdbb0fa7d0baf9995a5e44bd259b0,144a3135a85a6b82bd694f935e8b54d15fd800d8,TODO(beam2d): Implement it
aaijchbbi,chainer/chainer,cupy/random/distributions.py,87d103faea4bdbb0fa7d0baf9995a5e44bd259b0,STILL_EXISTS,TODO(beam2d): Implement many distributions
aaijchbde,chainer/chainer,cupy/sorting/count.py,87d103faea4bdbb0fa7d0baf9995a5e44bd259b0,144a3135a85a6b82bd694f935e8b54d15fd800d8,TODO(beam2d): Implement it
aaijchdeb,chainer/chainer,cupy/__init__.py,92bed8c26651e0161fe644f2731f0fef9937bfd3,e17eb383c8de6a371beed910f953bc4930764bfe,TODO(okuta): Fix this
aaijcihfg,chainer/chainer,cupy/__init__.py,cf2fce43642e40df8e453916c223273d1cdfce9b,519e0bff7718e09d4f52c2586a3db4222b501d35,TODO(okuta): Fix this
aaijcjaca,chainer/chainer,chainer/cuda.py,bb2a996abfe477884c91fa2c565177a5ffe353e8,e9441d55a9b19371ac4092d80f97113798f3e796,Workaround: the original GPUArray.copy does not use the user-defined
aaijcjacd,chainer/chainer,chainer/cuda.py,bb2a996abfe477884c91fa2c565177a5ffe353e8,e9441d55a9b19371ac4092d80f97113798f3e796,directly use the original GPUArray class; we choose easy and ugly solution
aaijcjacf,chainer/chainer,chainer/cuda.py,bb2a996abfe477884c91fa2c565177a5ffe353e8,e9441d55a9b19371ac4092d80f97113798f3e796,TODO(beam2d): Fix this ugly solution
aaijcjcbi,chainer/chainer,cupy/elementwise.py,792b454e3dd76d8e658246055a666633f47ba04b,121bac073070118dc18124963f2d8847f51a08ee,TODO(okuta): Fix document
aaijcjcea,chainer/chainer,chainer/cuda.py,35a96ca853cba306e36649fad82ecacd89df59fd,14659b3ac7664b10c99d725e0042fc23cb3b72d6,Workaround: the original GPUArray.copy does not use the user-defined
aaijcjced,chainer/chainer,chainer/cuda.py,35a96ca853cba306e36649fad82ecacd89df59fd,14659b3ac7664b10c99d725e0042fc23cb3b72d6,directly use the original GPUArray class; we choose easy and ugly solution
aaijcjcef,chainer/chainer,chainer/cuda.py,35a96ca853cba306e36649fad82ecacd89df59fd,14659b3ac7664b10c99d725e0042fc23cb3b72d6,TODO(beam2d): Fix this ugly solution
aaijcjfea,chainer/chainer,chainer/cuda.py,561dc71f85c2a6c258ecaff6d7c6ed04a4c65503,dc5c1cae4ae66514e3838a76f4f5b9925b96754d,Workaround: the original GPUArray.copy does not use the user-defined
aaijcjfed,chainer/chainer,chainer/cuda.py,561dc71f85c2a6c258ecaff6d7c6ed04a4c65503,dc5c1cae4ae66514e3838a76f4f5b9925b96754d,directly use the original GPUArray class; we choose easy and ugly solution
aaijcjfef,chainer/chainer,chainer/cuda.py,561dc71f85c2a6c258ecaff6d7c6ed04a4c65503,dc5c1cae4ae66514e3838a76f4f5b9925b96754d,TODO(beam2d): Fix this ugly solution
aaijcjhbj,chainer/chainer,chainer/cuda.py,1644e3496d870d40a84a15f0acf35de73a88010f,f7ea13f053565685723ee39bae3698612ad17b18,Workaround: the original GPUArray.copy does not use the user-defined
aaijcjhcc,chainer/chainer,chainer/cuda.py,1644e3496d870d40a84a15f0acf35de73a88010f,f7ea13f053565685723ee39bae3698612ad17b18,directly use the original GPUArray class; we choose easy and ugly solution
aaijcjhce,chainer/chainer,chainer/cuda.py,1644e3496d870d40a84a15f0acf35de73a88010f,f7ea13f053565685723ee39bae3698612ad17b18,TODO(beam2d): Fix this ugly solution
aaijdcgaa,chainer/chainer,cupy/manipulation/dims.py,3e65278be798092f42a659bd98afb9552f967f25,d9e9d71003585fb4a269c0de40c790debbc86212,TODO(okuta) fix if `dim` == 1
aaijdcgde,chainer/chainer,cupy/creation/matrix.py,144a3135a85a6b82bd694f935e8b54d15fd800d8,STILL_EXISTS,# TODO(beam2d): Implement it
aaijdcgeh,chainer/chainer,cupy/creation/ranges.py,144a3135a85a6b82bd694f935e8b54d15fd800d8,STILL_EXISTS,# TODO(beam2d): Implement these
aaijdcgji,chainer/chainer,cupy/indexing/indexing.py,144a3135a85a6b82bd694f935e8b54d15fd800d8,STILL_EXISTS,# TODO(beam2d): Implement it
aaijdchcf,chainer/chainer,cupy/io/rawfile.py,144a3135a85a6b82bd694f935e8b54d15fd800d8,STILL_EXISTS,# TODO(beam2d): Implement it
aaijdciad,chainer/chainer,cupy/linalg/product.py,144a3135a85a6b82bd694f935e8b54d15fd800d8,STILL_EXISTS,# TODO(beam2d): Implement it
aaijdcieg,chainer/chainer,cupy/logic/content.py,144a3135a85a6b82bd694f935e8b54d15fd800d8,STILL_EXISTS,# TODO(beam2d): Implement it
aaijddaba,chainer/chainer,cupy/padding/__init__.py,144a3135a85a6b82bd694f935e8b54d15fd800d8,STILL_EXISTS,# TODO(beam2d): Implement it
aaijddace,chainer/chainer,cupy/random/sample.py,144a3135a85a6b82bd694f935e8b54d15fd800d8,STILL_EXISTS,# TODO(beam2d): Implement it
aaijddadd,chainer/chainer,cupy/sorting/count.py,144a3135a85a6b82bd694f935e8b54d15fd800d8,STILL_EXISTS,# TODO(beam2d): Implement it
aaijddbdf,chainer/chainer,cupy/statistics/order.py,144a3135a85a6b82bd694f935e8b54d15fd800d8,STILL_EXISTS,# TODO(beam2d): Implement it
aaijddcaf,chainer/chainer,cupy/logic/content.py,973eda27ef43dfd2a3092c16e04c69e909539828,STILL_EXISTS,TODO(beam2d): Implement it
aaijddcea,chainer/chainer,cupy/math/sumprod.py,973eda27ef43dfd2a3092c16e04c69e909539828,STILL_EXISTS,# TODO(beam2d): Implement it
aaijddceg,chainer/chainer,cupy/statistics/meanvar.py,973eda27ef43dfd2a3092c16e04c69e909539828,STILL_EXISTS,TODO(beam2d): Implement it
aaijddcfe,chainer/chainer,cupy/statistics/meanvar.py,973eda27ef43dfd2a3092c16e04c69e909539828,STILL_EXISTS,# TODO(beam2d): Implement it
aaijdddcf,chainer/chainer,cupy/__init__.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,51c2104b2ef519a81ba65973282467e777e8ced9,TODO(beam2d): Implement __array_interface__
aaijdddcg,chainer/chainer,cupy/__init__.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,51c2104b2ef519a81ba65973282467e777e8ced9,TODO(okuta): Implement item
aaijdddch,chainer/chainer,cupy/__init__.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,51c2104b2ef519a81ba65973282467e777e8ced9,TODO(okuta): Implement itemset
aaijdddci,chainer/chainer,cupy/__init__.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,51c2104b2ef519a81ba65973282467e777e8ced9,TODO(okuta): Implement tobytes
aaijdddcj,chainer/chainer,cupy/__init__.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,51c2104b2ef519a81ba65973282467e777e8ced9,TODO(okuta): Implement byteswap
aaijdddda,chainer/chainer,cupy/__init__.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,51c2104b2ef519a81ba65973282467e777e8ced9,TODO(okuta): Implement setflags
aaijddddb,chainer/chainer,cupy/__init__.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,51c2104b2ef519a81ba65973282467e777e8ced9,TODO(okuta): Implement resize
aaijddddc,chainer/chainer,cupy/__init__.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,51c2104b2ef519a81ba65973282467e777e8ced9,TODO(okuta): Implement put
aaijddddd,chainer/chainer,cupy/__init__.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,51c2104b2ef519a81ba65973282467e777e8ced9,TODO(okuta): Implement repeat
aaijdddde,chainer/chainer,cupy/__init__.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,51c2104b2ef519a81ba65973282467e777e8ced9,TODO(okuta): Implement choose
aaijddddf,chainer/chainer,cupy/__init__.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,51c2104b2ef519a81ba65973282467e777e8ced9,TODO(okuta): Implement sort
aaijddddg,chainer/chainer,cupy/__init__.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,51c2104b2ef519a81ba65973282467e777e8ced9,TODO(okuta): Implement argsort
aaijddddh,chainer/chainer,cupy/__init__.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,51c2104b2ef519a81ba65973282467e777e8ced9,TODO(okuta): Implement partition
aaijddddi,chainer/chainer,cupy/__init__.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,51c2104b2ef519a81ba65973282467e777e8ced9,TODO(okuta): Implement compress
aaijddddj,chainer/chainer,cupy/__init__.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,51c2104b2ef519a81ba65973282467e777e8ced9,TODO(okuta): Implement ptp
aaijdddea,chainer/chainer,cupy/__init__.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,51c2104b2ef519a81ba65973282467e777e8ced9,TODO(okuta): Implement round
aaijdddeb,chainer/chainer,cupy/__init__.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,51c2104b2ef519a81ba65973282467e777e8ced9,TODO(okuta): Implement cumsum
aaijdddec,chainer/chainer,cupy/__init__.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,51c2104b2ef519a81ba65973282467e777e8ced9,TODO(okuta): Implement cumprod
aaijddded,chainer/chainer,cupy/__init__.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,7c6660fbd18de558875f5839ba67f17c524faaaa,TODO(okuta): Implement all
aaijdddfh,chainer/chainer,cupy/binary/packing.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,2e42cb752c81f2ae41bee624f33c6eb4cc50eb64,TODO(okuta): Implement packbits
aaijdddfi,chainer/chainer,cupy/binary/packing.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,2e42cb752c81f2ae41bee624f33c6eb4cc50eb64,TODO(okuta): Implement unpackbits
aaijdddgf,chainer/chainer,cupy/creation/from_data.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement asmatrix
aaijdddgg,chainer/chainer,cupy/creation/from_data.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement frombuffer
aaijdddgh,chainer/chainer,cupy/creation/from_data.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement fromfile
aaijdddgi,chainer/chainer,cupy/creation/from_data.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement fromfunction
aaijdddgj,chainer/chainer,cupy/creation/from_data.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement fromstring
aaijdddha,chainer/chainer,cupy/creation/matrix.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement tril
aaijdddhb,chainer/chainer,cupy/creation/matrix.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement vander
aaijdddhc,chainer/chainer,cupy/creation/matrix.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement bmat
aaijdddhe,chainer/chainer,cupy/creation/matrix.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,# TODO(beam2d): Implement it
aaijdddih,chainer/chainer,cupy/indexing/generate.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement indices
aaijdddii,chainer/chainer,cupy/indexing/generate.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,ec868865ffe3cf972d6a50994c413673561d6a39,TODO(okuta): Implement ix_
aaijdddij,chainer/chainer,cupy/indexing/generate.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement ravel_multi_index
aaijdddja,chainer/chainer,cupy/indexing/generate.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement unravel_index
aaijdddjb,chainer/chainer,cupy/indexing/generate.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement diag_indices
aaijdddjc,chainer/chainer,cupy/indexing/generate.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement diag_indices_from
aaijdddjd,chainer/chainer,cupy/indexing/generate.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement mask_indices
aaijdddje,chainer/chainer,cupy/indexing/generate.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement tril_indices
aaijdddjf,chainer/chainer,cupy/indexing/generate.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement tril_indices_from
aaijdddjg,chainer/chainer,cupy/indexing/generate.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement triu_indices
aaijdddjh,chainer/chainer,cupy/indexing/generate.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement triu_indices_from
aaijddedc,chainer/chainer,cupy/indexing/indexing.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,1fa2604d0cff6601e5d690f3b4efc5e95777cef7,TODO(okuta): Implement choose
aaijddedd,chainer/chainer,cupy/indexing/insert.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement place
aaijddede,chainer/chainer,cupy/indexing/insert.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement put
aaijddedf,chainer/chainer,cupy/indexing/insert.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement putmask
aaijddedg,chainer/chainer,cupy/indexing/insert.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,1e3ef6fec18871b7838c8a6ad60bc03ff7006d05,TODO(okuta): Implement fill_diagonal
aaijddefb,chainer/chainer,cupy/io/text.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement loadtxt
aaijddefc,chainer/chainer,cupy/io/text.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement savetxt
aaijddefd,chainer/chainer,cupy/io/text.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement genfromtxt
aaijddefe,chainer/chainer,cupy/io/text.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement fromregex
aaijddeff,chainer/chainer,cupy/io/text.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement fromstring
aaijddehj,chainer/chainer,cupy/linalg/decomposition.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,9647c200c464bd7021c27b9856c568c67ff8c8f7,TODO(okuta): Implement cholesky
aaijddeia,chainer/chainer,cupy/linalg/decomposition.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement qr
aaijddeib,chainer/chainer,cupy/linalg/decomposition.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement svd
aaijddejb,chainer/chainer,cupy/linalg/eigenvalue.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement eig
aaijddejc,chainer/chainer,cupy/linalg/eigenvalue.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement eigh
aaijddejd,chainer/chainer,cupy/linalg/eigenvalue.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement eigvals
aaijddeje,chainer/chainer,cupy/linalg/eigenvalue.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement eigvalsh
aaijddfah,chainer/chainer,cupy/linalg/norm.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement norm
aaijddfai,chainer/chainer,cupy/linalg/norm.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement det
aaijddfaj,chainer/chainer,cupy/linalg/norm.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement matrix_rank
aaijddfbj,chainer/chainer,cupy/linalg/norm.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,# TODO(beam2d): Implement it
aaijddfcb,chainer/chainer,cupy/linalg/product.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement einsum
aaijddfcc,chainer/chainer,cupy/linalg/solve.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement solve
aaijddfcd,chainer/chainer,cupy/linalg/solve.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement tensorsolve
aaijddfce,chainer/chainer,cupy/linalg/solve.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement lstsq
aaijddfcf,chainer/chainer,cupy/linalg/solve.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement inv
aaijddfcg,chainer/chainer,cupy/linalg/solve.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement pinv
aaijddfch,chainer/chainer,cupy/linalg/solve.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement tensorinv
aaijddfeg,chainer/chainer,cupy/logic/comparison.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement array_equal
aaijddfei,chainer/chainer,cupy/logic/comparison.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,# TODO(beam2d): Implement it
aaijddffg,chainer/chainer,cupy/logic/content.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement isneginf
aaijddffh,chainer/chainer,cupy/logic/truth.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,7c6660fbd18de558875f5839ba67f17c524faaaa,TODO(okuta): Implement all
aaijddffi,chainer/chainer,cupy/logic/truth.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,7c6660fbd18de558875f5839ba67f17c524faaaa,TODO(okuta): Implement any
aaijddfgf,chainer/chainer,cupy/logic/type_test.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement iscomplex
aaijddfgg,chainer/chainer,cupy/logic/type_test.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement iscomplexobj
aaijddfgh,chainer/chainer,cupy/logic/type_test.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement isfortran
aaijddfgi,chainer/chainer,cupy/logic/type_test.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement isreal
aaijddfgj,chainer/chainer,cupy/logic/type_test.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement isrealobj
aaijddfif,chainer/chainer,cupy/manipulation/add_remove.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement delete
aaijddfig,chainer/chainer,cupy/manipulation/add_remove.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement insert
aaijddfih,chainer/chainer,cupy/manipulation/add_remove.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement append
aaijddfii,chainer/chainer,cupy/manipulation/add_remove.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement resize
aaijddfij,chainer/chainer,cupy/manipulation/add_remove.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement trim_zeros
aaijddfja,chainer/chainer,cupy/manipulation/add_remove.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement unique
aaijddgba,chainer/chainer,cupy/manipulation/kind.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement asfarray
aaijddgbb,chainer/chainer,cupy/manipulation/kind.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,ee8a46eacec6e71cde510b2f05caa2ac1158c31a,TODO(okuta): Implement asfortranarray
aaijddgbc,chainer/chainer,cupy/manipulation/kind.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement asarray_chkfinite
aaijddgbd,chainer/chainer,cupy/manipulation/kind.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement asscalar
aaijddgbe,chainer/chainer,cupy/manipulation/kind.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement require
aaijddgda,chainer/chainer,cupy/manipulation/rearrange.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,e51092ea2e2e60bf011c6b5f7bcd0af06d09b42a,TODO(okuta): Implement fliplr
aaijddgdb,chainer/chainer,cupy/manipulation/rearrange.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,e51092ea2e2e60bf011c6b5f7bcd0af06d09b42a,TODO(okuta): Implement flipud
aaijddgdc,chainer/chainer,cupy/manipulation/rearrange.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,16f5180d2ddb1a1745f43e10688605a72ffa4c43,TODO(okuta): Implement roll
aaijddgdd,chainer/chainer,cupy/manipulation/rearrange.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,b771b9bcd10267c8150ae8f77e6eeead2c4fc44d,TODO(okuta): Implement rot90
aaijddgeg,chainer/chainer,cupy/manipulation/tiling.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement tile
aaijddgeh,chainer/chainer,cupy/manipulation/tiling.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,3249c06b5695d3272e6df4724931e14870f6a921,TODO(okuta): Implement repeat
aaijddgfe,chainer/chainer,cupy/math/misc.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement convolve
aaijddgff,chainer/chainer,cupy/math/misc.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement nan_to_num
aaijddgfg,chainer/chainer,cupy/math/misc.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement real_if_close
aaijddggb,chainer/chainer,cupy/math/special.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement i0
aaijddggc,chainer/chainer,cupy/math/special.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement sinc
aaijddggj,chainer/chainer,cupy/math/sumprod.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement nansum
aaijddgha,chainer/chainer,cupy/math/sumprod.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement cumprod
aaijddghb,chainer/chainer,cupy/math/sumprod.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement ediff1d
aaijddghc,chainer/chainer,cupy/math/sumprod.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement trapz
aaijddghf,chainer/chainer,cupy/random/permutations.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement shuffle
aaijddghg,chainer/chainer,cupy/random/permutations.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement permutation
aaijddgid,chainer/chainer,cupy/random/sample.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,37181e439971508caa9fc4f15b047da3e35b190a,TODO(okuta): Implement randint
aaijddgjc,chainer/chainer,cupy/sorting/search.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement nanargmin
aaijddgjd,chainer/chainer,cupy/sorting/search.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement argwhere
aaijddgje,chainer/chainer,cupy/sorting/search.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement where
aaijddgjf,chainer/chainer,cupy/sorting/search.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement extract
aaijddhab,chainer/chainer,cupy/sorting/search.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,# TODO(beam2d): Implement it
aaijddhbd,chainer/chainer,cupy/sorting/sort.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,1a31bb7691cc53dc9131f86438030bf495fd43a9,TODO(okuta): Implement sort
aaijddhbe,chainer/chainer,cupy/sorting/sort.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement lexsort
aaijddhbf,chainer/chainer,cupy/sorting/sort.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement argsort
aaijddhbg,chainer/chainer,cupy/sorting/sort.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement msort
aaijddhbh,chainer/chainer,cupy/sorting/sort.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement sort_complex
aaijddhbi,chainer/chainer,cupy/sorting/sort.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement partition
aaijddhbj,chainer/chainer,cupy/sorting/sort.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement argpartition
aaijddheb,chainer/chainer,cupy/statistics/correlation.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement corrcoef
aaijddhec,chainer/chainer,cupy/statistics/correlation.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement correlate
aaijddhed,chainer/chainer,cupy/statistics/correlation.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement cov
aaijddhfd,chainer/chainer,cupy/statistics/histogram.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement histogram
aaijddhfe,chainer/chainer,cupy/statistics/histogram.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement histogram2d
aaijddhff,chainer/chainer,cupy/statistics/histogram.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement histogramdd
aaijddhfg,chainer/chainer,cupy/statistics/histogram.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,2113e1af79d672366331da879327b5ab58542862,TODO(okuta): Implement bincount
aaijddhfh,chainer/chainer,cupy/statistics/histogram.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement digitize
aaijddhhe,chainer/chainer,cupy/statistics/meanvar.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement median
aaijddhhf,chainer/chainer,cupy/statistics/meanvar.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement average
aaijddhhg,chainer/chainer,cupy/statistics/order.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,7222b3913e6568892d918e50e0461d3d78446dab,TODO(okuta): Implement nanmin
aaijddhhh,chainer/chainer,cupy/statistics/order.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,TODO(okuta): Implement percentile
aaijddhhj,chainer/chainer,cupy/statistics/order.py,4b1ec2d9b20f447797bf19c05e4106fbaf31d69f,STILL_EXISTS,# TODO(beam2d): Implement it
aaijddhja,chainer/chainer,chainer/functions/loss/ctc.py,46f4e75112cdef66dec885434bf37a50772de7fd,d0ab8356232c2b636cd1ff1523125e0c29368581,fixme: batch normarization of padded data.
aaijddhjb,chainer/chainer,cupy/random/sample.py,a5b27de707c027b8186481aae30f6e0b9677c109,d3488dc7e4b9d13db8e84d61253041b356db3c4f,TODO(okuta): Implement randint
aaijddhjc,chainer/chainer,cupy/random/sample.py,a5b27de707c027b8186481aae30f6e0b9677c109,d3488dc7e4b9d13db8e84d61253041b356db3c4f,TODO(okuta): Implement random_integers
aaijdefgf,chainer/chainer,cupy/__init__.py,14d0b508439a2be258c54f201a1dcc7486488b02,1fb22ba1217a19a3995e748e4b5280c869d498ba,TODO(beam2d): Implement __array_interface__
aaijdefhc,chainer/chainer,cupy/__init__.py,14d0b508439a2be258c54f201a1dcc7486488b02,1fb22ba1217a19a3995e748e4b5280c869d498ba,TODO(okuta): Implement item
aaijdefhd,chainer/chainer,cupy/__init__.py,14d0b508439a2be258c54f201a1dcc7486488b02,1fb22ba1217a19a3995e748e4b5280c869d498ba,TODO(okuta): Implement itemset
aaijdefhe,chainer/chainer,cupy/__init__.py,14d0b508439a2be258c54f201a1dcc7486488b02,1fb22ba1217a19a3995e748e4b5280c869d498ba,TODO(okuta): Implement tostring
aaijdefhf,chainer/chainer,cupy/__init__.py,14d0b508439a2be258c54f201a1dcc7486488b02,1fb22ba1217a19a3995e748e4b5280c869d498ba,TODO(okuta): Implement tobytes
aaijdefhh,chainer/chainer,cupy/__init__.py,14d0b508439a2be258c54f201a1dcc7486488b02,1fb22ba1217a19a3995e748e4b5280c869d498ba,TODO(okuta): Implement byteswap
aaijdefia,chainer/chainer,cupy/__init__.py,14d0b508439a2be258c54f201a1dcc7486488b02,1fb22ba1217a19a3995e748e4b5280c869d498ba,TODO(okuta): Implement getfield
aaijdefib,chainer/chainer,cupy/__init__.py,14d0b508439a2be258c54f201a1dcc7486488b02,1fb22ba1217a19a3995e748e4b5280c869d498ba,TODO(okuta): Implement setflags
aaijdefig,chainer/chainer,cupy/__init__.py,14d0b508439a2be258c54f201a1dcc7486488b02,1fb22ba1217a19a3995e748e4b5280c869d498ba,TODO(okuta): Implement resize
aaijdefjc,chainer/chainer,cupy/__init__.py,14d0b508439a2be258c54f201a1dcc7486488b02,1fb22ba1217a19a3995e748e4b5280c869d498ba,TODO(okuta): Implement put
aaijdefjd,chainer/chainer,cupy/__init__.py,14d0b508439a2be258c54f201a1dcc7486488b02,1fb22ba1217a19a3995e748e4b5280c869d498ba,TODO(okuta): Implement repeat
aaijdefje,chainer/chainer,cupy/__init__.py,14d0b508439a2be258c54f201a1dcc7486488b02,1fb22ba1217a19a3995e748e4b5280c869d498ba,TODO(okuta): Implement choose
aaijdefjf,chainer/chainer,cupy/__init__.py,14d0b508439a2be258c54f201a1dcc7486488b02,1fb22ba1217a19a3995e748e4b5280c869d498ba,TODO(okuta): Implement sort
aaijdefjg,chainer/chainer,cupy/__init__.py,14d0b508439a2be258c54f201a1dcc7486488b02,1fb22ba1217a19a3995e748e4b5280c869d498ba,TODO(okuta): Implement argsort
aaijdefjh,chainer/chainer,cupy/__init__.py,14d0b508439a2be258c54f201a1dcc7486488b02,1fb22ba1217a19a3995e748e4b5280c869d498ba,TODO(okuta): Implement partition
aaijdefji,chainer/chainer,cupy/__init__.py,14d0b508439a2be258c54f201a1dcc7486488b02,1fb22ba1217a19a3995e748e4b5280c869d498ba,TODO(okuta): Implement argpartition
aaijdefjj,chainer/chainer,cupy/__init__.py,14d0b508439a2be258c54f201a1dcc7486488b02,1fb22ba1217a19a3995e748e4b5280c869d498ba,TODO(okuta): Implement searchsorted
aaijdegaa,chainer/chainer,cupy/__init__.py,14d0b508439a2be258c54f201a1dcc7486488b02,1fb22ba1217a19a3995e748e4b5280c869d498ba,TODO(okuta): Implement nonzero
aaijdegab,chainer/chainer,cupy/__init__.py,14d0b508439a2be258c54f201a1dcc7486488b02,1fb22ba1217a19a3995e748e4b5280c869d498ba,TODO(okuta): Implement compress
aaijdegaf,chainer/chainer,cupy/__init__.py,14d0b508439a2be258c54f201a1dcc7486488b02,1fb22ba1217a19a3995e748e4b5280c869d498ba,TODO(okuta): Implement ptp
aaijdegag,chainer/chainer,cupy/__init__.py,14d0b508439a2be258c54f201a1dcc7486488b02,1fb22ba1217a19a3995e748e4b5280c869d498ba,TODO(okuta): Implement round
aaijdegah,chainer/chainer,cupy/__init__.py,14d0b508439a2be258c54f201a1dcc7486488b02,1fb22ba1217a19a3995e748e4b5280c869d498ba,TODO(okuta): Implement cumsum
aaijdegai,chainer/chainer,cupy/__init__.py,14d0b508439a2be258c54f201a1dcc7486488b02,1fb22ba1217a19a3995e748e4b5280c869d498ba,TODO(okuta): Implement cumprod
aaijdegcf,chainer/chainer,cupy/__init__.py,14d0b508439a2be258c54f201a1dcc7486488b02,1fb22ba1217a19a3995e748e4b5280c869d498ba,TODO(okuta): Implement __array_wrap__
aaijdegdc,chainer/chainer,cupy/__init__.py,14d0b508439a2be258c54f201a1dcc7486488b02,1fb22ba1217a19a3995e748e4b5280c869d498ba,TODO(okuta): Implement __getslice__
aaijdegdd,chainer/chainer,cupy/__init__.py,14d0b508439a2be258c54f201a1dcc7486488b02,1fb22ba1217a19a3995e748e4b5280c869d498ba,TODO(okuta): Implement __setslice__
aaijdegde,chainer/chainer,cupy/__init__.py,14d0b508439a2be258c54f201a1dcc7486488b02,1fb22ba1217a19a3995e748e4b5280c869d498ba,TODO(okuta): Implement __contains__
aaijdehda,chainer/chainer,setup_build.py,5d020b8c80a0c620d839a597b6e6818252470952,STILL_EXISTS,Hack for Read the Docs
aaijdejef,chainer/chainer,cupy/__init__.py,9a41db5f04474b340dbb8c4f8a195b6460d85e38,aef86aa20f90196ee48d98e42d81df809bd2420d,TODO(beam2d): Implement __array_interface__
aaijdejfc,chainer/chainer,cupy/__init__.py,9a41db5f04474b340dbb8c4f8a195b6460d85e38,aef86aa20f90196ee48d98e42d81df809bd2420d,TODO(okuta): Implement item
aaijdejfd,chainer/chainer,cupy/__init__.py,9a41db5f04474b340dbb8c4f8a195b6460d85e38,aef86aa20f90196ee48d98e42d81df809bd2420d,TODO(okuta): Implement itemset
aaijdejfe,chainer/chainer,cupy/__init__.py,9a41db5f04474b340dbb8c4f8a195b6460d85e38,aef86aa20f90196ee48d98e42d81df809bd2420d,TODO(okuta): Implement tostring
aaijdejff,chainer/chainer,cupy/__init__.py,9a41db5f04474b340dbb8c4f8a195b6460d85e38,aef86aa20f90196ee48d98e42d81df809bd2420d,TODO(okuta): Implement tobytes
aaijdejfh,chainer/chainer,cupy/__init__.py,9a41db5f04474b340dbb8c4f8a195b6460d85e38,aef86aa20f90196ee48d98e42d81df809bd2420d,TODO(okuta): Implement byteswap
aaijdejga,chainer/chainer,cupy/__init__.py,9a41db5f04474b340dbb8c4f8a195b6460d85e38,aef86aa20f90196ee48d98e42d81df809bd2420d,TODO(okuta): Implement getfield
aaijdejgb,chainer/chainer,cupy/__init__.py,9a41db5f04474b340dbb8c4f8a195b6460d85e38,aef86aa20f90196ee48d98e42d81df809bd2420d,TODO(okuta): Implement setflags
aaijdejgg,chainer/chainer,cupy/__init__.py,9a41db5f04474b340dbb8c4f8a195b6460d85e38,aef86aa20f90196ee48d98e42d81df809bd2420d,TODO(okuta): Implement resize
aaijdejhc,chainer/chainer,cupy/__init__.py,9a41db5f04474b340dbb8c4f8a195b6460d85e38,aef86aa20f90196ee48d98e42d81df809bd2420d,TODO(okuta): Implement put
aaijdejhd,chainer/chainer,cupy/__init__.py,9a41db5f04474b340dbb8c4f8a195b6460d85e38,3249c06b5695d3272e6df4724931e14870f6a921,TODO(okuta): Implement repeat
aaijdejhe,chainer/chainer,cupy/__init__.py,9a41db5f04474b340dbb8c4f8a195b6460d85e38,aef86aa20f90196ee48d98e42d81df809bd2420d,TODO(okuta): Implement choose
aaijdejhf,chainer/chainer,cupy/__init__.py,9a41db5f04474b340dbb8c4f8a195b6460d85e38,aef86aa20f90196ee48d98e42d81df809bd2420d,TODO(okuta): Implement sort
aaijdejhg,chainer/chainer,cupy/__init__.py,9a41db5f04474b340dbb8c4f8a195b6460d85e38,aef86aa20f90196ee48d98e42d81df809bd2420d,TODO(okuta): Implement argsort
aaijdejhh,chainer/chainer,cupy/__init__.py,9a41db5f04474b340dbb8c4f8a195b6460d85e38,aef86aa20f90196ee48d98e42d81df809bd2420d,TODO(okuta): Implement partition
aaijdejhi,chainer/chainer,cupy/__init__.py,9a41db5f04474b340dbb8c4f8a195b6460d85e38,aef86aa20f90196ee48d98e42d81df809bd2420d,TODO(okuta): Implement argpartition
aaijdejhj,chainer/chainer,cupy/__init__.py,9a41db5f04474b340dbb8c4f8a195b6460d85e38,aef86aa20f90196ee48d98e42d81df809bd2420d,TODO(okuta): Implement searchsorted
aaijdejia,chainer/chainer,cupy/__init__.py,9a41db5f04474b340dbb8c4f8a195b6460d85e38,aef86aa20f90196ee48d98e42d81df809bd2420d,TODO(okuta): Implement nonzero
aaijdejib,chainer/chainer,cupy/__init__.py,9a41db5f04474b340dbb8c4f8a195b6460d85e38,aef86aa20f90196ee48d98e42d81df809bd2420d,TODO(okuta): Implement compress
aaijdejif,chainer/chainer,cupy/__init__.py,9a41db5f04474b340dbb8c4f8a195b6460d85e38,aef86aa20f90196ee48d98e42d81df809bd2420d,TODO(okuta): Implement ptp
aaijdejig,chainer/chainer,cupy/__init__.py,9a41db5f04474b340dbb8c4f8a195b6460d85e38,aef86aa20f90196ee48d98e42d81df809bd2420d,TODO(okuta): Implement round
aaijdejih,chainer/chainer,cupy/__init__.py,9a41db5f04474b340dbb8c4f8a195b6460d85e38,aef86aa20f90196ee48d98e42d81df809bd2420d,TODO(okuta): Implement cumsum
aaijdejii,chainer/chainer,cupy/__init__.py,9a41db5f04474b340dbb8c4f8a195b6460d85e38,aef86aa20f90196ee48d98e42d81df809bd2420d,TODO(okuta): Implement cumprod
aaijdfaaf,chainer/chainer,cupy/__init__.py,9a41db5f04474b340dbb8c4f8a195b6460d85e38,aef86aa20f90196ee48d98e42d81df809bd2420d,TODO(okuta): Implement __array_wrap__
aaijdfabc,chainer/chainer,cupy/__init__.py,9a41db5f04474b340dbb8c4f8a195b6460d85e38,aef86aa20f90196ee48d98e42d81df809bd2420d,TODO(okuta): Implement __getslice__
aaijdfabd,chainer/chainer,cupy/__init__.py,9a41db5f04474b340dbb8c4f8a195b6460d85e38,aef86aa20f90196ee48d98e42d81df809bd2420d,TODO(okuta): Implement __setslice__
aaijdfabe,chainer/chainer,cupy/__init__.py,9a41db5f04474b340dbb8c4f8a195b6460d85e38,aef86aa20f90196ee48d98e42d81df809bd2420d,TODO(okuta): Implement __contains__
aaijdfbeg,chainer/chainer,cupy/__init__.py,55cbea88ac43ab38fd5aeeb05141050455eb6bf3,STILL_EXISTS,TODO(okuta): Implement repeat
aaijdfbei,chainer/chainer,cupy/__init__.py,5583f242910b5d3cf24f7c450ba38dadc66b4025,STILL_EXISTS,TODO(okuta): Implement repeat
aaijdfdfi,chainer/chainer,cupy/__init__.py,5279ee0c77e6cf871589e6d5a1ae6b3f49754f50,007a22527ddd79a134d736189f6fc5fb7cc0b600,TODO(beam2d): Implement __array_interface__
aaijdfdgf,chainer/chainer,cupy/__init__.py,5279ee0c77e6cf871589e6d5a1ae6b3f49754f50,007a22527ddd79a134d736189f6fc5fb7cc0b600,TODO(okuta): Implement item
aaijdfdgg,chainer/chainer,cupy/__init__.py,5279ee0c77e6cf871589e6d5a1ae6b3f49754f50,007a22527ddd79a134d736189f6fc5fb7cc0b600,TODO(okuta): Implement itemset
aaijdfdgh,chainer/chainer,cupy/__init__.py,5279ee0c77e6cf871589e6d5a1ae6b3f49754f50,007a22527ddd79a134d736189f6fc5fb7cc0b600,TODO(okuta): Implement tostring
aaijdfdgi,chainer/chainer,cupy/__init__.py,5279ee0c77e6cf871589e6d5a1ae6b3f49754f50,007a22527ddd79a134d736189f6fc5fb7cc0b600,TODO(okuta): Implement tobytes
aaijdfdha,chainer/chainer,cupy/__init__.py,5279ee0c77e6cf871589e6d5a1ae6b3f49754f50,007a22527ddd79a134d736189f6fc5fb7cc0b600,TODO(okuta): Implement byteswap
aaijdfdhd,chainer/chainer,cupy/__init__.py,5279ee0c77e6cf871589e6d5a1ae6b3f49754f50,007a22527ddd79a134d736189f6fc5fb7cc0b600,TODO(okuta): Implement getfield
aaijdfdhe,chainer/chainer,cupy/__init__.py,5279ee0c77e6cf871589e6d5a1ae6b3f49754f50,007a22527ddd79a134d736189f6fc5fb7cc0b600,TODO(okuta): Implement setflags
aaijdfdhj,chainer/chainer,cupy/__init__.py,5279ee0c77e6cf871589e6d5a1ae6b3f49754f50,007a22527ddd79a134d736189f6fc5fb7cc0b600,TODO(okuta): Implement resize
aaijdfdif,chainer/chainer,cupy/__init__.py,5279ee0c77e6cf871589e6d5a1ae6b3f49754f50,007a22527ddd79a134d736189f6fc5fb7cc0b600,TODO(okuta): Implement put
aaijdfdig,chainer/chainer,cupy/__init__.py,5279ee0c77e6cf871589e6d5a1ae6b3f49754f50,007a22527ddd79a134d736189f6fc5fb7cc0b600,TODO(okuta): Implement choose
aaijdfdih,chainer/chainer,cupy/__init__.py,5279ee0c77e6cf871589e6d5a1ae6b3f49754f50,007a22527ddd79a134d736189f6fc5fb7cc0b600,TODO(okuta): Implement sort
aaijdfdii,chainer/chainer,cupy/__init__.py,5279ee0c77e6cf871589e6d5a1ae6b3f49754f50,007a22527ddd79a134d736189f6fc5fb7cc0b600,TODO(okuta): Implement argsort
aaijdfdij,chainer/chainer,cupy/__init__.py,5279ee0c77e6cf871589e6d5a1ae6b3f49754f50,007a22527ddd79a134d736189f6fc5fb7cc0b600,TODO(okuta): Implement partition
aaijdfdja,chainer/chainer,cupy/__init__.py,5279ee0c77e6cf871589e6d5a1ae6b3f49754f50,007a22527ddd79a134d736189f6fc5fb7cc0b600,TODO(okuta): Implement argpartition
aaijdfdjb,chainer/chainer,cupy/__init__.py,5279ee0c77e6cf871589e6d5a1ae6b3f49754f50,007a22527ddd79a134d736189f6fc5fb7cc0b600,TODO(okuta): Implement searchsorted
aaijdfdjc,chainer/chainer,cupy/__init__.py,5279ee0c77e6cf871589e6d5a1ae6b3f49754f50,007a22527ddd79a134d736189f6fc5fb7cc0b600,TODO(okuta): Implement nonzero
aaijdfdjd,chainer/chainer,cupy/__init__.py,5279ee0c77e6cf871589e6d5a1ae6b3f49754f50,007a22527ddd79a134d736189f6fc5fb7cc0b600,TODO(okuta): Implement compress
aaijdfdjh,chainer/chainer,cupy/__init__.py,5279ee0c77e6cf871589e6d5a1ae6b3f49754f50,007a22527ddd79a134d736189f6fc5fb7cc0b600,TODO(okuta): Implement ptp
aaijdfdji,chainer/chainer,cupy/__init__.py,5279ee0c77e6cf871589e6d5a1ae6b3f49754f50,007a22527ddd79a134d736189f6fc5fb7cc0b600,TODO(okuta): Implement round
aaijdfdjj,chainer/chainer,cupy/__init__.py,5279ee0c77e6cf871589e6d5a1ae6b3f49754f50,007a22527ddd79a134d736189f6fc5fb7cc0b600,TODO(okuta): Implement cumsum
aaijdfeaa,chainer/chainer,cupy/__init__.py,5279ee0c77e6cf871589e6d5a1ae6b3f49754f50,007a22527ddd79a134d736189f6fc5fb7cc0b600,TODO(okuta): Implement cumprod
aaijdfebh,chainer/chainer,cupy/__init__.py,5279ee0c77e6cf871589e6d5a1ae6b3f49754f50,007a22527ddd79a134d736189f6fc5fb7cc0b600,TODO(okuta): Implement __array_wrap__
aaijdfece,chainer/chainer,cupy/__init__.py,5279ee0c77e6cf871589e6d5a1ae6b3f49754f50,007a22527ddd79a134d736189f6fc5fb7cc0b600,TODO(okuta): Implement __getslice__
aaijdfecf,chainer/chainer,cupy/__init__.py,5279ee0c77e6cf871589e6d5a1ae6b3f49754f50,007a22527ddd79a134d736189f6fc5fb7cc0b600,TODO(okuta): Implement __setslice__
aaijdfecg,chainer/chainer,cupy/__init__.py,5279ee0c77e6cf871589e6d5a1ae6b3f49754f50,007a22527ddd79a134d736189f6fc5fb7cc0b600,TODO(okuta): Implement __contains__
aaijdfhea,chainer/chainer,setup.py,921089d6ebc52ab59cd3c75b5444b1dc180624d8,856f87f2189ef06034ac07a3d5c310a2a3107f62,Hack for Read the Docs
aaijdgbff,chainer/chainer,chainer/functions/loss/ctc.py,f0452dda4014a31d960b2892dbec72a0c034ebdb,8dba91abcf808238f53f43b856dfd058168fc6cd,move to back.
aaijdgbfh,chainer/chainer,chainer/functions/loss/ctc.py,f0452dda4014a31d960b2892dbec72a0c034ebdb,8dba91abcf808238f53f43b856dfd058168fc6cd,move to front.
aaijdgegg,chainer/chainer,chainer/initializations.py,206fa3cfc62acaa634dd791c9910e575d38baa63,STILL_EXISTS,check needed for bilinear tests to pass
aaijdhejf,chainer/chainer,chainer/functions/loss/ctc.py,273bfc3e91db1c201294ca8b1c9de695b741b559,9ebf65167b6bc199f99a3b9fed787f6dd940d304,move to back.
aaijdhejh,chainer/chainer,chainer/functions/loss/ctc.py,273bfc3e91db1c201294ca8b1c9de695b741b559,9ebf65167b6bc199f99a3b9fed787f6dd940d304,move to front.
aaijdhhbe,chainer/chainer,tests/chainer_tests/links_tests/loss_tests/test_negative_sampling.py,3d853ad2b86a2eba7635f4bcf008fbd567ec3dd8,28a3f193dde4c8b5474786d0b27d7a29bcf56b69,fix samples
aaijdhjbc,chainer/chainer,chainer/initializers/__init__.py,e7fca819b7d420bf97cf681c7f2ed05b29d9f325,030b15c465bdd98d1ea0b1f65c03514b19d809d2,check needed for bilinear tests to pass
aaijdiahj,chainer/chainer,chainer/initializers/orthogonal.py,a8547dd9d6458f67f57628f489ffcf922b1f968e,STILL_EXISTS,TODO: How do we treat over-complete bases case?
aaijdidjd,chainer/chainer,chainer/links/caffe/caffe_function.py,b48f554b6a9e914ef32ed742fa90fc289d92f7fa,STILL_EXISTS,TODO: old name
aaijdifei,chainer/chainer,chainer/links/caffe/caffe_function.py,c987c8b6213ed5f0d6a2bbb4f8e1150a1eaf9733,STILL_EXISTS,TODO: old name
aaijdiief,chainer/chainer,chainer/functions/evaluation/accuracy.py,c8655debb5dd0dd48fe62c6b3bbf1a35735fdebf,STILL_EXISTS,If cupy.where returns indexes; we could make the code better.
aaijdjcdj,chainer/chainer,tests/chainer_tests/links_tests/loss_tests/test_negative_sampling.py,01a7a35124653b416deb7c28fd5f642f4d1841ad,b8dc20e6bc9620cc6e39a61d57d6c28b35b2e253,fix samples
aaijdjiej,chainer/chainer,cupy/sorting/search.py,0212ca8c79501807422dec04dcff87f5b791c387,0b3eb020d22ecf7b1a8556cbb071c6d1a6093379,TODO(okuta): Implement nonzero
aaijdjifa,chainer/chainer,cupy/sorting/search.py,0212ca8c79501807422dec04dcff87f5b791c387,0b3eb020d22ecf7b1a8556cbb071c6d1a6093379,TODO(okuta): Implement flatnonzero
aaijeaefa,chainer/chainer,chainer/functions/activation/highway.py,a4642ded5362b683ebe4c390fda3068ff56c125e,STILL_EXISTS,TODO math
aaijeahdh,chainer/chainer,tests/chainer_tests/links_tests/model_tests/test_classifier.py,e6fb0cb957ac7b2746d4665198fd192025a2523d,2ea034419feb8a91dfa0f54a7e9981363e00650d,As a workaround; we wrap the function and invoke it in __call__ method.
aaijeajae,chainer/chainer,tests/chainer_tests/links_tests/model_tests/test_classifier.py,ce7929f0cab6afec23f4f45a26c6a2e1f41fef80,STILL_EXISTS,As a workaround; we wrap the function and invoke it in __call__ method.
aaijebaei,chainer/chainer,cupy/indexing/generate.py,588968898eb211adb55783b3a850856f88426145,dd4bf91bcfacebd318fc1339728215761b4d69f4,TODO(okuta): Implement ix_
aaijeccei,chainer/chainer,cupy/indexing/generate.py,b2b8f5c2fabc7bca493cee42825228dbfcbd0bec,bfcd30b0d6205c966920ed4e1abd6dcb0262ed62,TODO(okuta): Implement ix_
aaijeceih,chainer/chainer,cupy/indexing/generate.py,2d88de12087c8e42503625a0652d9190a1d3013f,91b02c2628c4dc35db33950131285ff98cf5c4ed,TODO(okuta): Implement ix_
aaijedahj,chainer/chainer,cupy/indexing/generate.py,2bfdc1bdb0f9bf50ea575057cb683ff2d3a2502a,STILL_EXISTS,TODO(okuta): Implement ix_
aaijeggbc,chainer/chainer,cupy/statistics/order.py,eab7faf6f1f2268f0b70672238d7667865af833f,2991321b577a9a04a6a8c4f7c12004c928cbaaff,TODO(okuta): Implement nanmin
aaijeggbd,chainer/chainer,cupy/statistics/order.py,eab7faf6f1f2268f0b70672238d7667865af833f,2991321b577a9a04a6a8c4f7c12004c928cbaaff,TODO(okuta): Implement nanmax
aaijehihi,chainer/chainer,setup.py,e290135450e053baee168ff9574b02ac308f050c,da1193da0e4d003cb1b3da326910c95fcb37d3db,Hack for Read the Docs
aaijehjcc,chainer/chainer,setup.py,9f98c73c62582c2087f5b423e45193dd36b0c05d,af2e5682a89c5867969a076f7f9119a747d55777,Hack for Read the Docs
aaijejbeb,chainer/chainer,chainer_setup_build.py,9df7f8f8eaf24f6dc00886f955f5e102ffd1c33b,STILL_EXISTS,This is a workaround for Anaconda.
aaijejceb,chainer/chainer,chainer/link.py,bd160054843674252dded632247f0fcb6bbbda6e,8ba7d222e3345ab9268fb9b1f1a3d8d5b86862b8,tuple() here is needed to avoid conflicts with add_param
aaijejddi,chainer/chainer,chainer_setup_build.py,d96239fbbfe520038c418885ae8b52c3e9e2db7c,STILL_EXISTS,This is a workaround for Anaconda.
aaijejeeh,chainer/chainer,chainer_setup_build.py,0dbf4368c54871334fe391927f2e98d87f93bcf9,STILL_EXISTS,This is a workaround for Anaconda.
aaijejeib,chainer/chainer,chainer_setup_build.py,c95fa9f5f327e3193ef33cd942204408d7bf3ee2,STILL_EXISTS,This is a workaround for Anaconda.
aaijfefdi,chainer/chainer,chainer_setup_build.py,c6295469d890d9e3ed7903224cf8be5f305cfc43,STILL_EXISTS,This is a workaround for Anaconda.
aaijfhhfc,chainer/chainer,cupy/padding/pad.py,acf87c47d32e55dfd7d0b76088e57f8c4e900bd9,9b2fe3ebbddc41ff13c972bcee414e149ce1aac2,TODO(unno): Fix fill value
aaijgbbgh,chainer/chainer,cupy/random/sample.py,bfff40cc63a061c337fe4546435327c600758c87,509670be26c71864f357f39a34da48038938cf89,fix
aaijgbbib,chainer/chainer,cupy/random/generator.py,c7c936ffab003e7d45f5191fb6ffb469c0b81b05,d598a159ea939c84056378641ac1c974769faca7,fix
aaijgbhbc,chainer/chainer,cupy/creation/ranges.py,118b44ac060ee0b04806ba3dd912c5b0c27f7e9d,7c9e65cb53d5fba9ee29725370edb4d451cb0218,TODO(okuta): Implement meshgrid
aaijgcdci,chainer/chainer,install/build.py,6f4ab0fd005c266412abcaf66ec65f3bdba36012,STILL_EXISTS,provided functions are insufficient to implement cupy.linalg
aaijgcgcg,chainer/chainer,examples/cifar/train_cifar_custom_loop.py,8ededc85430f7341d99ddd8127871f4f795c420b,STILL_EXISTS,\"\"\"Convnet example using CIFAR10 or CIFAR100 dataset ||  || This code is a custom loop version of train_cifar.py. That is; we train || models without using the Trainer class in chainer and instead write a || training loop that manually computes the loss of minibatches and || applies an optimizer to update the model. || \"\"\"
aaijgcgci,chainer/chainer,examples/mnist/train_mnist_custom_loop.py,8ededc85430f7341d99ddd8127871f4f795c420b,STILL_EXISTS,\"\"\"Fully-connected neural network example using MNIST dataset ||  || This code is a custom loop version of train_mnist.py. That is; we train || models without using the Trainer class in chainer and instead write a || training loop that manually computes the loss of minibatches and || applies an optimizer to update the model. || \"\"\"
aaijgcgfi,chainer/chainer,cupy/math/rounding.py,abf3ccf3cab4f70e8c0a6f83c92854e1a5e3792c,ef82bbc938a4951aa46ced0d2eda1e7a7aae9ef7,TODO(okuta): Implement fix
aaijgdgdi,chainer/chainer,chainer/link.py,6aeb4eb7b0896e16739fdfef20bed651243585d4,8ba7d222e3345ab9268fb9b1f1a3d8d5b86862b8,tuple() here is needed to avoid conflicts with add_param
aaijgfcbg,chainer/chainer,chainer/functions/connection/n_step_rnn.py,1e36a5a8c2975cd21c77ef26e343052516ab7cc4,STILL_EXISTS,Todo: check this is ok.
aaijgfffd,chainer/chainer,chainer/link.py,f9f4d4cb2d82de7c22c6964f36b811e845435edd,8ba7d222e3345ab9268fb9b1f1a3d8d5b86862b8,tuple() here is needed to avoid conflicts with add_param
aaijggecc,chainer/chainer,chainer/link.py,845bc8a1d1cdfd8b7039d98e72d9ce5750bb57d2,8ba7d222e3345ab9268fb9b1f1a3d8d5b86862b8,tuple() here is needed to avoid conflicts with add_param
aaijghhab,chainer/chainer,cupy/sorting/sort.py,32a2a2aa2acda922903baefc369ec63290159776,STILL_EXISTS,TODO(okuta): Implement sort
aaijgidjb,chainer/chainer,chainer/link.py,16e37669e65e5292afc2531798e231f2ad630116,8ba7d222e3345ab9268fb9b1f1a3d8d5b86862b8,tuple() here is needed to avoid conflicts with add_param
aaijgjhce,chainer/chainer,chainer/links/theano/theano_function.py,96e389c69bb413ce026f83d685bcdbe96be5f8a0,STILL_EXISTS,When Theano library is imported; it executes a lot of
aaijgjhch,chainer/chainer,chainer/link.py,a2ce2c7db5b0c1e92dbbaa594f0b984df677e5bf,8ba7d222e3345ab9268fb9b1f1a3d8d5b86862b8,tuple() here is needed to avoid conflicts with add_param
aaijhafgj,chainer/chainer,chainer/link.py,4d347886e627ad45a4f8070b4c6f628e4684ac1a,STILL_EXISTS,tuple() here is needed to avoid conflicts with add_param
aaijhbaij,chainer/chainer,chainer/link.py,2b3d5ca54312c5da640e1ff6844c72b57e3166b9,STILL_EXISTS,tuple() here is needed to avoid conflicts with add_param
aaijhbajb,chainer/chainer,tests/chainer_tests/functions_tests/loss_tests/test_negative_sampling.py,33ab9d6e4b1c04db97497fd6d4a31e556d4c374d,STILL_EXISTS,To fix samples; use fixed samples.
aaijhbbbe,chainer/chainer,tests/chainer_tests/links_tests/loss_tests/test_negative_sampling.py,2cc5880297eaab4d93fc99b438779624499c5c49,STILL_EXISTS,fix samples
aaijhdbci,chainer/chainer,chainer/functions/normalization/batch_renormalization.py,c927da810f1b6b6485127dd17f9f7743c30179b7,13015a80bde453945482259f6d4677fcb54554df,Needed for gradient check to be possible
aaijhdbeg,chainer/chainer,chainer/functions/normalization/batch_renormalization.py,c927da810f1b6b6485127dd17f9f7743c30179b7,13015a80bde453945482259f6d4677fcb54554df,Hack for making gradient check treat r and d as true
aaijicdja,chainer/chainer,tests/chainer_tests/functions_tests/normalization_tests/test_batch_renormalization.py,13015a80bde453945482259f6d4677fcb54554df,d5421e28f26f19eaf810a445a0de1a11e8613d71,Freezing the update of running statistics is needed in order to
aaijicdjf,chainer/chainer,tests/chainer_tests/links_tests/normalization_tests/test_batch_renormalization.py,13015a80bde453945482259f6d4677fcb54554df,d5421e28f26f19eaf810a445a0de1a11e8613d71,Freezing the update of running statistics is needed in order to
aaijiebjd,chainer/chainer,chainer/iterators/multiprocess_iterator.py,236a0be4147eb37eea123368f507401b2ea2b90a,STILL_EXISTS,Do not hold reference to this thread. Otherwise an unused iterator
aaijifdej,chainer/chainer,chainer/functions/activation/log_softmax.py,d4cfe1b31ff2424c62f26185ad4a775c33809f14,STILL_EXISTS,TODO(Kenta Oono): implement F.dot
aaijifdii,chainer/chainer,docs/source/conf.py,ca4ee0c7f809a5422385f070cb11e3238f7c6a50,849d764dfd4d6e5fef1ed72d95a3fef4b22c7fa1,Autosummary extracts the \"first sentence\"; which ends at the first period
aaijifeij,chainer/chainer,chainer/functions/normalization/batch_normalization.py,cdfbf2802b0989735fcc8d93673e8a6d00a80163,STILL_EXISTS,TODO (Tokui): Return broadcasted 0 instead of a zeroed array.
aaijiffef,chainer/chainer,docs/source/conf.py,e0ce78850de29014df6f7db86b68ac45fc57541d,22cbe944d6f2bdecd16bf1f0a9dc9f2dcb158969,Autosummary extracts the \"first sentence\"; which ends at the first period
aaijiihfe,chainer/chainer,chainer/functions/connection/convolution_2d.py,e25edf4cebd40f466058b2176239d9cc40004fd6,STILL_EXISTS,Work-around of NumPy's bug?
aaijiijjg,chainer/chainer,chainer/functions/connection/convolution_2d.py,690133f83d4ad60c95759106689ea2e2cde1656d,ed7e1315cffffdd975669c16cb0b594e8ed76086,Work-around for NumPy's bug?
aaijjaade,chainer/chainer,docs/source/conf.py,79d679309dfd52a2701eb11737ef07c2bc081616,9b364caeb82637289832870c2e336d7bfe1d7cdd,Autosummary extracts the \"first sentence\"; which ends at the first period
aaijjadfc,chainer/chainer,chainer/functions/connection/convolution_2d.py,2b6421f2c44d3051b831014ce17e87ef8add340d,ed7e1315cffffdd975669c16cb0b594e8ed76086,Work-around for NumPy's bug?
aaijjafce,chainer/chainer,tests/chainer_tests/functions_tests/loss_tests/test_sigmoid_cross_entropy.py,aaa0ff1f8701572b945e5e0b77292f56950c9551,9d5100294163ab2b1fdce5d8c859cc9f047d7c36,TODO(okuta) : fix
aaijjbhdb,chainer/chainer,chainer/functions/connection/convolution_2d.py,b23aa1fc8f3e184d6f326d9e3cf4f5ca8468e5f9,102f0ceb6c23a29473cee746b620a81617a56776,Work-around for NumPy's bug?
aaijjcbgi,chainer/chainer,chainer/cuda.py,09d623aefaeedbfd26e1d1e9bbfbe96d7ac3c067,3ba7d4d0ddcc0202225161f75e6d6fed07ffa25e,TODO(beam2d): FIX IT
aaijjcfbc,chainer/chainer,tests/chainer_tests/links_tests/loss_tests/test_negative_sampling.py,ec3b1ea4b6bf8f45ea8dea2a9559b6262678abb7,STILL_EXISTS,Fix this test. The code seems to fix the samples in order to avoid
aaijjchgj,chainer/chainer,examples/mnist/train_mnist.py,2ea2bd70040ce3d552378bd33583375bb0e09175,b537ea6b3659d65f2b880caaff2289dc067c46da,training_length is needed to set
aaijjciaj,chainer/chainer,examples/cifar/train_cifar.py,b537ea6b3659d65f2b880caaff2289dc067c46da,b3567eec8138b978d0e7b11c2ef065c3dab6939c,training_length is needed to set
aaijjdbgf,chainer/chainer,chainer/functions/connection/convolution_2d.py,4b569292ff342c797ab095bd9fc982cba31c5d0d,d2097040d1d76472c8eeb325a82d59d093aa05b2,Work-around for NumPy's bug?
aaijjdceh,chainer/chainer,chainer/functions/connection/convolution_2d.py,934a04037cc9a006ae7194aa75029c90445500f5,6fe23fc04be5729973545fa55ea6e67fd98c1127,Work-around for NumPy's bug?
aaijjdded,chainer/chainer,chainer/functions/connection/convolution_2d.py,9a2206172c19b145d2db76be26bdcaa1afd72a8d,ab2b108e4b157f8dbde83ed5b96b68b498489884,Work-around for NumPy's bug?
aaijjdfcd,chainer/chainer,chainer/functions/connection/convolution_2d.py,1f535ce8473594417a31ef88d203ffb2c5ac18de,c608f666b4eb1249fbfaa2ff98094124ade1b447,Work-around for NumPy's bug?
aaijjdhag,chainer/chainer,examples/image_captioning/predict.py,0658f615091d39d8aae466051686919d61693f95,STILL_EXISTS,Load the dataset to obtain the vocabulary; which is needed to convert
aaijjdjgc,chainer/chainer,examples/image_captioning/datasets.py,9058c34d29a86967c96928b6a8e2f4225f6a228c,STILL_EXISTS,This vocabulary is needed in order to convert the words in the captions
aaijjeabe,chainer/chainer,chainer/functions/connection/convolution_2d.py,c5ad4cf9f1c69416ad3c5c6464e8b1d3350f6a0c,c608f666b4eb1249fbfaa2ff98094124ade1b447,Work-around for NumPy's bug?
aaijjejga,chainer/chainer,tests/chainer_tests/functions_tests/array_tests/test_repeat.py,dcc2558755680f961198533124ec231ce3817a91,STILL_EXISTS,Workaround NumPy 1.9 issue.
aaijjfade,chainer/chainer,chainer/datasets/image_dataset.py,10e201fea777d2a360ef38ab700b56efbbb89ab5,2e85a69c283d4b81d1cc7e2d58dad9c1bcb3ca6c,XXX
aaijjfcfg,chainer/chainer,chainer/functions/pooling/max_pooling_nd.py,199772f76e7998fd243c3add1b200d4f3487f60a,STILL_EXISTS,XXX: WIP
aaijjfdcc,chainer/chainer,chainer/functions/connection/linear.py,4b9cbf779b6918b8f5710aeb7965ede6e9eb1864,dbb4663dfcf2fb076b05a1ec8919d79a3f575837,use the convention that any output arrays are supplied
aaijjfddg,chainer/chainer,chainer/functions/connection/linear.py,4b9cbf779b6918b8f5710aeb7965ede6e9eb1864,a4baca2de43ed2b220be84b57e3ecbcd1895e23d,fixme: move decorator to FunctionNode
aaijjfdeb,chainer/chainer,chainer/graph_optimimzations/static_graph.py,4b9cbf779b6918b8f5710aeb7965ede6e9eb1864,0645fa28471585ee2c99089da6e1560b7a47eb50,fixme: add an attribute\/property to all function nodes that support static graph so
aaijjfdee,chainer/chainer,chainer/graph_optimimzations/static_graph.py,4b9cbf779b6918b8f5710aeb7965ede6e9eb1864,0645fa28471585ee2c99089da6e1560b7a47eb50,fixme: check that all array copies use x[:] = y.
aaijjfdef,chainer/chainer,chainer/graph_optimimzations/static_graph.py,4b9cbf779b6918b8f5710aeb7965ede6e9eb1864,0645fa28471585ee2c99089da6e1560b7a47eb50,fixme: clean up documentation and API.
aaijjfdeg,chainer/chainer,chainer/graph_optimimzations/static_graph.py,4b9cbf779b6918b8f5710aeb7965ede6e9eb1864,STILL_EXISTS,todo: add test that use the same random seed with two models: a static chain
aaijjfdfa,chainer/chainer,chainer/graph_optimimzations/static_graph.py,4b9cbf779b6918b8f5710aeb7965ede6e9eb1864,STILL_EXISTS,fixme: remove underscore.
aaijjfdfj,chainer/chainer,chainer/graph_optimimzations/static_graph.py,4b9cbf779b6918b8f5710aeb7965ede6e9eb1864,STILL_EXISTS,This is needed so that the copy from input variable to static
aaijjfdgg,chainer/chainer,chainer/graph_optimimzations/static_graph.py,4b9cbf779b6918b8f5710aeb7965ede6e9eb1864,0645fa28471585ee2c99089da6e1560b7a47eb50,fixme: remove undescore.
aaijjfdhh,chainer/chainer,chainer/graph_optimimzations/static_graph.py,4b9cbf779b6918b8f5710aeb7965ede6e9eb1864,STILL_EXISTS,`out_vars` with an attribute that contains the needed information.
aaijjfeaa,chainer/chainer,chainer/graph_optimimzations/static_graph.py,4b9cbf779b6918b8f5710aeb7965ede6e9eb1864,a4baca2de43ed2b220be84b57e3ecbcd1895e23d,fixme: bug:
aaijjfecg,chainer/chainer,chainer/graph_optimimzations/static_graph.py,4b9cbf779b6918b8f5710aeb7965ede6e9eb1864,0645fa28471585ee2c99089da6e1560b7a47eb50,Retain all inputs? (fixme)
aaijjfeef,chainer/chainer,chainer/graph_optimimzations/static_graph.py,4b9cbf779b6918b8f5710aeb7965ede6e9eb1864,STILL_EXISTS,This attribute will be needed by the corresponding @static_backward
aaijjfeii,chainer/chainer,examples/static_graph_optimizations/test_static_graph.py,4b9cbf779b6918b8f5710aeb7965ede6e9eb1864,e58f4fd5167620090645f1915a8e723546265c8b,todo: the backward() method does not currently accumuate gradients correctly for
aaijjfeja,chainer/chainer,examples/static_graph_optimizations/test_static_graph.py,4b9cbf779b6918b8f5710aeb7965ede6e9eb1864,e58f4fd5167620090645f1915a8e723546265c8b,todo: Optimizers do not yet support static graph feature.
aaijjffbh,chainer/chainer,chainer/functions/pooling/max_pooling_nd.py,323d9ef0f9fccc8b722f4b6dde30621db8d081a0,STILL_EXISTS,XXX: WIP
aaijjfffj,chainer/chainer,chainer/function_node.py,a4baca2de43ed2b220be84b57e3ecbcd1895e23d,39a36171b9988202f4b3b75addba20a7690ecec8,fixme: move this function into static_graph module as a utility function.
aaijjfgbj,chainer/chainer,chainer/graph_optimimzations/static_graph_utilities.py,a4baca2de43ed2b220be84b57e3ecbcd1895e23d,STILL_EXISTS,is needed for the output gradients from the static schedule.
aaijjfgcd,chainer/chainer,chainer/variable.py,a4baca2de43ed2b220be84b57e3ecbcd1895e23d,STILL_EXISTS,todo (vogel): support this in static sub-graphs
aaijjfgcg,chainer/chainer,chainer/variable.py,a4baca2de43ed2b220be84b57e3ecbcd1895e23d,dbb4663dfcf2fb076b05a1ec8919d79a3f575837,todo (vogel): support this accumulation in static graphs.
aaijjfgfi,chainer/chainer,chainer/functions/connection/linear.py,734cb7888542c533c77c11df2e16ee5d4d14e2b8,dbb4663dfcf2fb076b05a1ec8919d79a3f575837,todo: this performs unnecessary memory allocations.
aaijjfghj,chainer/chainer,chainer/graph_optimimzations/static_graph.py,f2e777b03a879ca152e60420b6acbd3916160196,0645fa28471585ee2c99089da6e1560b7a47eb50,todo: modify optimizers so that they never attempt to change the grad\/data
aaijjfihh,chainer/chainer,chainer/functions/array/transpose.py,b9c20895fe9aa61a053dbd2e2f0d333a0f0d4057,17d7143f239e8e05be83d8db7a569e59a12db4ef,todo: optimize later to prevent unnecessary allocation.
aaijjfiid,chainer/chainer,chainer/functions/connection/linear.py,b9c20895fe9aa61a053dbd2e2f0d333a0f0d4057,dbb4663dfcf2fb076b05a1ec8919d79a3f575837,todo: this performs unnecessary memory allocations.
aaijjfiii,chainer/chainer,chainer/functions/connection/linear.py,b9c20895fe9aa61a053dbd2e2f0d333a0f0d4057,dbb4663dfcf2fb076b05a1ec8919d79a3f575837,todo (vogel): optimize to prevent unnecessary allocation.
aaijjfije,chainer/chainer,chainer/functions/connection/linear.py,b9c20895fe9aa61a053dbd2e2f0d333a0f0d4057,dbb4663dfcf2fb076b05a1ec8919d79a3f575837,use the convention that any output arrays are supplied
aaijjfjbb,chainer/chainer,chainer/functions/math/basic_math.py,b9c20895fe9aa61a053dbd2e2f0d333a0f0d4057,0e900045812b8af5b40daf5259018a6d11751816,todo (vogel): optimize to avoid reallocation.
aaijjfjbg,chainer/chainer,chainer/functions/math/sum.py,b9c20895fe9aa61a053dbd2e2f0d333a0f0d4057,dbb4663dfcf2fb076b05a1ec8919d79a3f575837,todo (vogel): make a more optimized version that avoids
aaijjfjfj,chainer/chainer,chainer/training/triggers/early_stopping_trigger.py,72b19dfc9700afc39be2cc36893a66cc83daebff,STILL_EXISTS,TODO: This is confusing
aaijjfjha,chainer/chainer,chainer/functions/connection/linear.py,e2f8807de4fa90d8f168a272a92068bd5d9e777f,dbb4663dfcf2fb076b05a1ec8919d79a3f575837,todo: this performs unnecessary memory allocations.
aaijjfjhf,chainer/chainer,chainer/functions/connection/linear.py,e2f8807de4fa90d8f168a272a92068bd5d9e777f,dbb4663dfcf2fb076b05a1ec8919d79a3f575837,todo (vogel): optimize to prevent unnecessary allocation.
aaijjfjib,chainer/chainer,chainer/functions/connection/linear.py,e2f8807de4fa90d8f168a272a92068bd5d9e777f,dbb4663dfcf2fb076b05a1ec8919d79a3f575837,use the convention that any output arrays are supplied
aaijjgabb,chainer/chainer,chainer/graph_optimimzations/static_graph.py,2e2ae95aae32190ea5a05248c8855f472163c128,STILL_EXISTS,fixme: remove after debug
aaijjgabd,chainer/chainer,chainer/graph_optimimzations/static_graph.py,2e2ae95aae32190ea5a05248c8855f472163c128,STILL_EXISTS,fixme_sum = np.sum(self._in_arrays[n])
aaijjgabf,chainer/chainer,chainer/graph_optimimzations/static_graph.py,2e2ae95aae32190ea5a05248c8855f472163c128,STILL_EXISTS,fixme: rename \"func\" -> \"chain\"
aaijjgabg,chainer/chainer,chainer/graph_optimimzations/static_graph.py,2e2ae95aae32190ea5a05248c8855f472163c128,0645fa28471585ee2c99089da6e1560b7a47eb50,in_vars = tuple([chainer.as_variable(x) for x in in_vars]) # fixme: this sets requires_grad=False
aaijjgace,chainer/chainer,chainer/graph_optimimzations/static_graph_utilities.py,2e2ae95aae32190ea5a05248c8855f472163c128,0645fa28471585ee2c99089da6e1560b7a47eb50,fixme: remove.
aaijjgadh,chainer/chainer,chainer/functions/connection/convolution_2d.py,3bd57eebdebff30754d401bfe9f306edf36dc9c2,dbb4663dfcf2fb076b05a1ec8919d79a3f575837,todo (vogel): optimize to avoid allocating results array.
aaijjgahj,chainer/chainer,chainer/graph_optimimzations/static_graph.py,ed93a50e8f37bba1cc3fccf0f37d33654e85b0c0,STILL_EXISTS,todo: maybe need weak reference here for schedule_manager?
aaijjgaib,chainer/chainer,chainer/graph_optimimzations/static_graph.py,ed93a50e8f37bba1cc3fccf0f37d33654e85b0c0,0645fa28471585ee2c99089da6e1560b7a47eb50,fixme: rename to \"append_function\"
aaijjgaic,chainer/chainer,chainer/graph_optimimzations/static_graph.py,ed93a50e8f37bba1cc3fccf0f37d33654e85b0c0,0645fa28471585ee2c99089da6e1560b7a47eb50,fixme: remove this? no longer used?
aaijjgaid,chainer/chainer,chainer/graph_optimimzations/static_graph.py,ed93a50e8f37bba1cc3fccf0f37d33654e85b0c0,STILL_EXISTS,fixme: inform schedule manager that forward pass has finished.
aaijjgaja,chainer/chainer,chainer/graph_optimimzations/static_graph.py,ed93a50e8f37bba1cc3fccf0f37d33654e85b0c0,0645fa28471585ee2c99089da6e1560b7a47eb50,fixme: check if `configuration.config.train` flag has changed. If so; run define-by-run code.
aaijjgajb,chainer/chainer,chainer/graph_optimimzations/static_graph.py,ed93a50e8f37bba1cc3fccf0f37d33654e85b0c0,0645fa28471585ee2c99089da6e1560b7a47eb50,(this is needed so that links like BN; dropout will work correctly)
aaijjgajd,chainer/chainer,chainer/graph_optimimzations/static_graph.py,ed93a50e8f37bba1cc3fccf0f37d33654e85b0c0,STILL_EXISTS,fixme_sum = np.sum(self._in_arrays[n])
aaijjgajf,chainer/chainer,chainer/graph_optimimzations/static_graph.py,ed93a50e8f37bba1cc3fccf0f37d33654e85b0c0,STILL_EXISTS,fixme: rename \"func\" -> \"chain\"
aaijjgbic,chainer/chainer,chainer/graph_optimizations/static_graph.py,e5f605d06db1b31bcb82991f5656c173c35522a1,191e2741244360e1083b559164ab5afa92f33ee1,fixme: remove after debug
aaijjgbid,chainer/chainer,chainer/graph_optimizations/static_graph.py,e5f605d06db1b31bcb82991f5656c173c35522a1,191e2741244360e1083b559164ab5afa92f33ee1,todo: maybe need weak reference here for schedule_manager?
aaijjgbif,chainer/chainer,chainer/graph_optimizations/static_graph.py,e5f605d06db1b31bcb82991f5656c173c35522a1,STILL_EXISTS,fixme: inform schedule manager that forward pass has finished.
aaijjgcba,chainer/chainer,chainer/graph_optimizations/static_graph_utilities.py,e5f605d06db1b31bcb82991f5656c173c35522a1,STILL_EXISTS,todo: consider only marking a variable if it is a parameter.
aaijjgcff,chainer/chainer,chainer/functions/math/basic_math.py,1bc09d19f44843b477524a1b760f22c58464c52e,36c23a4c28543066395675b8a349f45ed4489bf3,todo (vogel): optimize to avoid reallocation.
aaijjgfff,chainer/chainer,examples/cifar/train_cifar.py,dbb4663dfcf2fb076b05a1ec8919d79a3f575837,f25fe50a776522bc3889fe7882725d4258c69691,training_length is needed to set
aaijjhbfd,chainer/chainer,chainer/functions/array/transpose.py,c1be5f8e157455d69543c40850860aeec9fe4855,def5bc916a17f50b45a622818ad7455d1e3126bb,todo: optimize later to prevent unnecessary allocation. It seems
aaijjhbgg,chainer/chainer,chainer/functions/connection/linear.py,c1be5f8e157455d69543c40850860aeec9fe4855,788e4d98a7be8cbdb128868abaabfbf97e0b16fe,todo: this performs unnecessary memory allocations.
aaijjhbgi,chainer/chainer,chainer/functions/connection/linear.py,c1be5f8e157455d69543c40850860aeec9fe4855,788e4d98a7be8cbdb128868abaabfbf97e0b16fe,Code below would be more efficient but does not work
aaijjhbhf,chainer/chainer,chainer/functions/connection/linear.py,c1be5f8e157455d69543c40850860aeec9fe4855,788e4d98a7be8cbdb128868abaabfbf97e0b16fe,use the convention that any output arrays are supplied
aaijjhbjc,chainer/chainer,chainer/graph_optimizations/static_graph_utilities.py,c1be5f8e157455d69543c40850860aeec9fe4855,STILL_EXISTS,This attribute will be needed by the corresponding @static_backward
aaijjhcda,chainer/chainer,chainer/graph_optimizations/static_graph.py,def5bc916a17f50b45a622818ad7455d1e3126bb,16e5e85d9eaafb33e6461f62d2c3370bde60823a,todo (vogel): Also check if minibatch size has changed and clear schedules.
aaijjhcdd,chainer/chainer,chainer/graph_optimizations/static_graph.py,def5bc916a17f50b45a622818ad7455d1e3126bb,STILL_EXISTS,fixme: make default True after memory bug\/slow garbage collection is fixed.
aaijjhcgi,chainer/chainer,chainer/variable.py,def5bc916a17f50b45a622818ad7455d1e3126bb,6dc4c320821887617066f622d9ea755b7ae5b56c,todo (vogel): support this accumulation in static graphs.
aaijjhchj,chainer/chainer,examples/static_graph_optimizations/test_static_graph.py,e58f4fd5167620090645f1915a8e723546265c8b,STILL_EXISTS,todo (vogel)
aaijjhcid,chainer/chainer,examples/static_graph_optimizations/test_static_graph.py,e58f4fd5167620090645f1915a8e723546265c8b,STILL_EXISTS,todo: consider a modified ptb or char-rnn example in which the LSTM node is written
aaijjhcij,chainer/chainer,examples/static_graph_optimizations/test_static_graph.py,e58f4fd5167620090645f1915a8e723546265c8b,STILL_EXISTS,graph optimizations to get improved runtime performance.
aaijjjjgd,chainer/chainer,chainer/functions/loss/ctc.py,f1de93d57f31ca2aa2863d978250fea7c1d38cdc,bcc883e9d1cbf1d829bb295dafbb58d3e33ad752,move to back.
aaijjjjgg,chainer/chainer,chainer/functions/loss/ctc.py,f1de93d57f31ca2aa2863d978250fea7c1d38cdc,bcc883e9d1cbf1d829bb295dafbb58d3e33ad752,move to front.
aajaaafhh,chainer/chainer,chainer/functions/pooling/max_pooling_2d.py,4016a24f1c747ad5e75b497f188a1be75982428c,STILL_EXISTS,FIXME
aajaaafjb,chainer/chainer,chainer/functions/array/split_axis.py,1825c8f4bfd4ffe819ec5fe157ba6f4db871348f,STILL_EXISTS,FIXME
aajaaagea,chainer/chainer,chainer/functions/array/split_axis.py,726fe89e4f6fa559861b81a1894eada1dca7e7ae,STILL_EXISTS,arguments. This is workaround for limitation in iDeep internal
aajaaagec,chainer/chainer,chainer/functions/array/split_axis.py,726fe89e4f6fa559861b81a1894eada1dca7e7ae,STILL_EXISTS,Workaround for iDeep segfault issue
aajaaagfa,chainer/chainer,tests/chainer_tests/optimizers_tests/test_optimizers_by_linear_model.py,fe436e0ec837af5cdbdfd399bb5c2c66d005e18c,STILL_EXISTS,TODO(niboshi): This is temporary workaround for skipping test not working
aajaabehd,chainer/chainer,chainer/functions/loss/ctc.py,06fa6b28080cb7b469c53d23d1a576c21613c13c,d7342312f533b290a1c67f1b8f0ca177f5ae010f,move to back.
aajaabehg,chainer/chainer,chainer/functions/loss/ctc.py,06fa6b28080cb7b469c53d23d1a576c21613c13c,d7342312f533b290a1c67f1b8f0ca177f5ae010f,move to front.
aajaafiag,chainer/chainer,chainer/functions/loss/ctc.py,874178d2e2b4208569d83955a35ac32bcd690439,416490712f7849d9cf21155544e35bb9fdc07355,move to back.
aajaafiaj,chainer/chainer,chainer/functions/loss/ctc.py,874178d2e2b4208569d83955a35ac32bcd690439,416490712f7849d9cf21155544e35bb9fdc07355,move to front.
aajaajjgb,chainer/chainer,chainer/function_hooks/debug_print.py,c22a96a3c11351161d90040c627d6f695d992555,710aaa9019ba328e0c70b15d5cc3f1cc04b9b015,TODO(niboshi): deprecate sep
aajabbjih,chainer/chainer,chainer/functions/loss/ctc.py,20debd4563d7d567e38e4cda2b2fd724f09aa70e,d848847099754d5e47f18af304f0f425d9e86610,move to back.
aajabbjja,chainer/chainer,chainer/functions/loss/ctc.py,20debd4563d7d567e38e4cda2b2fd724f09aa70e,d848847099754d5e47f18af304f0f425d9e86610,move to front.
aajabcebh,chainer/chainer,chainer/functions/loss/ctc.py,dc1fbc7db65882767aaa09c348269c4e72885c2f,bcb4640dbb8d1a4969e8b2aba7c5ee4101244c6f,move to back.
aajabceca,chainer/chainer,chainer/functions/loss/ctc.py,dc1fbc7db65882767aaa09c348269c4e72885c2f,bcb4640dbb8d1a4969e8b2aba7c5ee4101244c6f,move to front.
aajabcedh,chainer/chainer,chainer/graph_optimizations/static_graph.py,3b8b5a03e41b1620d30488a467b0156ee06b2f91,191e2741244360e1083b559164ab5afa92f33ee1,fixme: remove after debug
aajabcedi,chainer/chainer,chainer/graph_optimizations/static_graph.py,3b8b5a03e41b1620d30488a467b0156ee06b2f91,191e2741244360e1083b559164ab5afa92f33ee1,fixme: in_vars not used any more
aajabceea,chainer/chainer,chainer/graph_optimizations/static_graph.py,3b8b5a03e41b1620d30488a467b0156ee06b2f91,STILL_EXISTS,self._chain_in_vars = in_vars # fixme: remove; because not used
aajabceeb,chainer/chainer,chainer/graph_optimizations/static_graph.py,3b8b5a03e41b1620d30488a467b0156ee06b2f91,bda68c362732c313dc8f6d3df9ee2caa6f4145c8,self._in_arrays = tuple([x.data.copy() for x in in_vars]) # fixme: remove; because not used
aajabceeg,chainer/chainer,chainer/graph_optimizations/static_graph.py,3b8b5a03e41b1620d30488a467b0156ee06b2f91,STILL_EXISTS,fixme: redundant; since the same arrays are in _local_in_vars
aajabcefc,chainer/chainer,chainer/graph_optimizations/static_graph.py,3b8b5a03e41b1620d30488a467b0156ee06b2f91,STILL_EXISTS,fixme: not used? remove it?
aajabcegi,chainer/chainer,chainer/graph_optimizations/static_graph.py,3b8b5a03e41b1620d30488a467b0156ee06b2f91,STILL_EXISTS,fixme: it seems kwargs are not used. Consider to remove and not support kwargs.
aajabcehh,chainer/chainer,chainer/graph_optimizations/static_graph.py,3b8b5a03e41b1620d30488a467b0156ee06b2f91,STILL_EXISTS,out_vars.backward() #fixme
aajabcehj,chainer/chainer,chainer/graph_optimizations/static_graph.py,3b8b5a03e41b1620d30488a467b0156ee06b2f91,STILL_EXISTS,fixme: maybe make a method of static schedule function to do this?
aajabcejf,chainer/chainer,chainer/graph_optimizations/static_graph.py,3b8b5a03e41b1620d30488a467b0156ee06b2f91,STILL_EXISTS,fixme: remove unused function
aajabcejh,chainer/chainer,chainer/graph_optimizations/static_graph.py,3b8b5a03e41b1620d30488a467b0156ee06b2f91,STILL_EXISTS,todo: move code below into Chainer tests after initial debug.
aajabcfaa,chainer/chainer,chainer/graph_optimizations/static_graph_utilities.py,3b8b5a03e41b1620d30488a467b0156ee06b2f91,e9344d8ba17b8c4e808b20b7d85f61887a6e457a,fixme: remove
aajabcfbc,chainer/chainer,chainer/variable.py,3b8b5a03e41b1620d30488a467b0156ee06b2f91,STILL_EXISTS,todo (vogel): support this accumulation in static graphs.
aajabcgad,chainer/chainer,chainer/graph_optimizations/static_graph.py,e9344d8ba17b8c4e808b20b7d85f61887a6e457a,STILL_EXISTS,todo: should double-backprop mode be configurable
aajabcgbc,chainer/chainer,chainer/graph_optimizations/static_graph.py,e9344d8ba17b8c4e808b20b7d85f61887a6e457a,STILL_EXISTS,fixme: not used? remove it?
aajabcgdf,chainer/chainer,chainer/variable.py,e9344d8ba17b8c4e808b20b7d85f61887a6e457a,STILL_EXISTS,fixme: remove check after debug.
aajabcihd,chainer/chainer,chainer/variable.py,bda9b7257951c5c38592e6a434477b295dc9f60a,STILL_EXISTS,fixme: remove check after debug.
aajabdgdh,chainer/chainer,chainer/function_node.py,bda68c362732c313dc8f6d3df9ee2caa6f4145c8,STILL_EXISTS,new_gxs = func.backward_accumulate(input_indexes; gys; gxs) # fixme: bug: should be tuple; not list!
aajabdgdi,chainer/chainer,chainer/function_node.py,bda68c362732c313dc8f6d3df9ee2caa6f4145c8,524933339d840667ce76ed1eccae2241b7544437,todo (vogel): make sure this is necessary.
aajabdgef,chainer/chainer,chainer/graph_optimizations/static_graph.py,bda68c362732c313dc8f6d3df9ee2caa6f4145c8,STILL_EXISTS,fixme: remove?
aajabdggd,chainer/chainer,chainer/graph_optimizations/static_graph.py,bda68c362732c313dc8f6d3df9ee2caa6f4145c8,STILL_EXISTS,todo: enable after debug
aajabdgge,chainer/chainer,chainer/graph_optimizations/static_graph.py,bda68c362732c313dc8f6d3df9ee2caa6f4145c8,STILL_EXISTS,fixme: clean up
aajabdggf,chainer/chainer,chainer/graph_optimizations/static_graph.py,bda68c362732c313dc8f6d3df9ee2caa6f4145c8,STILL_EXISTS,fixme: this only outputs tuples of tuples. Any list in the original input
aajabdghf,chainer/chainer,chainer/graph_optimizations/static_graph.py,bda68c362732c313dc8f6d3df9ee2caa6f4145c8,STILL_EXISTS,todo: should double-backprop mode be configurable
aajabdgid,chainer/chainer,chainer/graph_optimizations/static_graph_utilities.py,bda68c362732c313dc8f6d3df9ee2caa6f4145c8,STILL_EXISTS,todo: We can allow it to return tuple of arrays in the future.
aajabdgie,chainer/chainer,chainer/graph_optimizations/static_graph_utilities.py,bda68c362732c313dc8f6d3df9ee2caa6f4145c8,8cd0df0a6f17202ba2807abbaf485d19d5e1cf1e,todo: Note that instead of copying the data from
aajabdgjh,chainer/chainer,chainer/variable.py,bda68c362732c313dc8f6d3df9ee2caa6f4145c8,STILL_EXISTS,fixme: remove check after debug.
aajabdhac,chainer/chainer,chainer/variable.py,bda68c362732c313dc8f6d3df9ee2caa6f4145c8,STILL_EXISTS,todo (vogel): support this accumulation in static graphs.
aajabdhca,chainer/chainer,chainer/function_node.py,e153581892220b98cbd792dd8ed16d0201f46d9e,STILL_EXISTS,new_gxs = func.backward_accumulate(input_indexes; gys; gxs) # fixme: bug: should be tuple; not list!
aajabdhcb,chainer/chainer,chainer/function_node.py,e153581892220b98cbd792dd8ed16d0201f46d9e,524933339d840667ce76ed1eccae2241b7544437,todo (vogel): make sure this is necessary.
aajabdhea,chainer/chainer,chainer/variable.py,e153581892220b98cbd792dd8ed16d0201f46d9e,STILL_EXISTS,fixme: remove check after debug.
aajabdhee,chainer/chainer,chainer/variable.py,e153581892220b98cbd792dd8ed16d0201f46d9e,STILL_EXISTS,fixme (vogel): support this in static sub-graphs
aajabdhef,chainer/chainer,chainer/variable.py,e153581892220b98cbd792dd8ed16d0201f46d9e,STILL_EXISTS,todo (vogel): support this accumulation in static graphs.
aajabeahi,chainer/chainer,chainer/function_node.py,4715a79c17edc5aa38b8940e247e81ed92e0182b,524933339d840667ce76ed1eccae2241b7544437,todo (vogel): make sure this is necessary.
aajabebic,chainer/chainer,chainer/variable.py,4715a79c17edc5aa38b8940e247e81ed92e0182b,STILL_EXISTS,fixme: remove check after debug.
aajabehge,chainer/chainer,tests/python_tests/test_array.py,91351447a8e11231ebcaf4c9d5c6a49c32cdc07f,39d3cf479cfb49b98f3c6509659f6f992f99103e,((); 0); # TODO(sonots): Fix compatibility
aajabejab,chainer/chainer,chainer/functions/connection/convolution_nd.py,17f347d0afdda241cd365d0a67d4817ec5e389a6,61bfdea0e90b14ea84818b784c322a445a3ce208,Work-around for NumPy's bug?
aajabfhdi,chainer/chainer,chainer/functions/math/einsum.py,4e191633a6586116fb032125b4b16cfebb29f7d6,4fafbfbfc6630c813f138754708b61cba965ce66,it seems a bug of gradient_check
aajabfiah,chainer/chainer,chainer/functions/connection/linear.py,191e2741244360e1083b559164ab5afa92f33ee1,c7f471771e65b418e7a89412e73e404e5a3cafe2,fixme: either re-implement static graph here or revert to default.
aajabfibc,chainer/chainer,chainer/functions/connection/linear.py,191e2741244360e1083b559164ab5afa92f33ee1,c7f471771e65b418e7a89412e73e404e5a3cafe2,use the convention that any output arrays are supplied
aajabfigc,chainer/chainer,examples/static_graph_optimizations/cifar/train_cifar_custom_loop.py,191e2741244360e1083b559164ab5afa92f33ee1,STILL_EXISTS,\"\"\"Convnet example using CIFAR10 or CIFAR100 dataset ||  || This code is a custom loop version of train_cifar.py. That is; we train || models without using the Trainer class in chainer and instead write a || training loop that manually computes the loss of minibatches and || applies an optimizer to update the model. || \"\"\"
aajabfjab,chainer/chainer,examples/static_graph_optimizations/mnist/train_mnist_custom_loop.py,191e2741244360e1083b559164ab5afa92f33ee1,STILL_EXISTS,\"\"\"Fully-connected neural network example using MNIST dataset ||  || This code is a custom loop version of train_mnist.py. That is; we train || models without using the Trainer class in chainer and instead write a || training loop that manually computes the loss of minibatches and || applies an optimizer to update the model. || \"\"\"
aajabfjbb,chainer/chainer,examples/static_graph_optimizations/ptb/train_ptb_custom_loop.py,191e2741244360e1083b559164ab5afa92f33ee1,STILL_EXISTS,\"\"\"Sample script of recurrent neural network language model. ||  || This code is ported from the following implementation written in Torch. || https:\/\/github.com\/tomsercu\/lstm ||  || This code is a custom loop version of train_ptb.py. That is; we train || models without using the Trainer class in chainer and instead write a || training loop that manually computes the loss of minibatches and || applies an optimizer to update the model. || \"\"\"
aajabfjbc,chainer/chainer,examples/static_graph_optimizations/ptb/train_ptb_custom_loop.py,191e2741244360e1083b559164ab5afa92f33ee1,eef98679236191c586b108dc2d339c1d8e7e7758,fixme: remove after debug.
aajabfjbf,chainer/chainer,examples/static_graph_optimizations/ptb/train_ptb_custom_loop.py,191e2741244360e1083b559164ab5afa92f33ee1,eef98679236191c586b108dc2d339c1d8e7e7758,assert isinstance(words_labels; list) #fixme: breaks static_graph
aajabgade,chainer/chainer,tests/python_tests/test_array.py,1e8c9b04745cbde57dc9f5a0c250b50b1df69216,1e8c9b04745cbde57dc9f5a0c250b50b1df69216,TODO(sonots): Fix type compatibility
aajabgafh,chainer/chainer,tests/python_tests/testing_tests/test_helper.py,679a7e5bb8e322e0e9eced73937f639b8b167c9b,STILL_EXISTS,TODO(niboshi): Currently this test passes. Implement stride check and uncomment xfail.
aajabgahg,chainer/chainer,tests/python_tests/test_array.py,ba0f27f8b30f4d854b31953f23dc3e49f02ed6f1,ba0f27f8b30f4d854b31953f23dc3e49f02ed6f1,TODO(sonots): Fix type compatibility
aajabgaib,chainer/chainer,tests/python_tests/test_array.py,ba0f27f8b30f4d854b31953f23dc3e49f02ed6f1,39d3cf479cfb49b98f3c6509659f6f992f99103e,TODO(sonots): Fix type compatibility
aajabgajb,chainer/chainer,tests/python_tests/testing_tests/test_helper.py,a33fb2a5c74f32bf18799cb970549facaa6abea4,bea541be4988e60b8012695664d6cda1fe670c20,TODO(niboshi): Currently this test fails. Fix it.
aajabgcdf,chainer/chainer,tests/python_tests/test_array_creation.py,bea541be4988e60b8012695664d6cda1fe670c20,STILL_EXISTS,(4; 1; -1.2);  # TODO(niboshi): Fix it (or maybe NumPy bug?)
aajabgcga,chainer/chainer,examples/mnist/train_mnist.py,cdac748e5cd0405ec2021eccb86d67d5db2104b0,b91915dc3c12b78690e4911af69e61b0e868099f,TODO(beam2d): implement mean
aajabgcge,chainer/chainer,examples/mnist/train_mnist.py,cdac748e5cd0405ec2021eccb86d67d5db2104b0,b91915dc3c12b78690e4911af69e61b0e868099f,TODO(beam2d): implement len
aajabgejf,chainer/chainer,examples/mnist/train_mnist.py,088cc48a33bc6186e11b17ee99b43d5c486b73d6,5d5deae1cbc2ead545f555662aae60376dca246b,TODO(beam2d): implement mean
aajabgejj,chainer/chainer,examples/mnist/train_mnist.py,088cc48a33bc6186e11b17ee99b43d5c486b73d6,5d5deae1cbc2ead545f555662aae60376dca246b,TODO(beam2d): implement len
aajabhdif,chainer/chainer,tests/python_tests/test_array_creation.py,9033e846e46f93bb87b413d7ab260db40ddede46,c58c978fddb38f9cae896536939120611e66c1a6,TODO(niboshi): Implement diag as a view and remove strides_check=False.
aajabhdjb,chainer/chainer,tests/python_tests/test_array_creation.py,ae1c25a687777773f0cc0e00bb55d93480f394ed,c58c978fddb38f9cae896536939120611e66c1a6,TODO(niboshi): Implement diag as a view and remove strides_check=False.
aajabheba,chainer/chainer,tests/python_tests/test_array_creation.py,2c701f4c6dacacfc4b1f92ae2ef4d1e13e94ee97,c58c978fddb38f9cae896536939120611e66c1a6,TODO(niboshi): Implement diag as a view and remove strides_check=False.
aajabiaif,chainer/chainer,tests/chainer_tests/test_function_node.py,123f1bd96ee88ae58962853660e2c4205e2355c2,50bdd6899d5b4ca776954e6d3ea3ec8c0a150857,TODO(kataoka): fix
aajabjhif,chainer/chainer,tests/array_utils.py,7529a4e4c80aace4398aa239adf9ae16bfb88e33,STILL_EXISTS,TODO(beam2d): Think better way to make multiple different arrays
aajabjhjb,chainer/chainer,tests/python_tests/routines_tests/test_math.py,7529a4e4c80aace4398aa239adf9ae16bfb88e33,STILL_EXISTS,TODO(beam2d): Fix after supporting correct dtype promotion.
aajabjiei,chainer/chainer,tests/python_tests/routines_tests/test_math.py,39d3cf479cfb49b98f3c6509659f6f992f99103e,STILL_EXISTS,TODO(sonots): Fix type compatibility
aajabjife,chainer/chainer,tests/python_tests/routines_tests/test_math.py,39d3cf479cfb49b98f3c6509659f6f992f99103e,STILL_EXISTS,((); 0); # TODO(sonots): Fix compatibility
aajacadic,chainer/chainer,chainer/function_node.py,aef64159d655c7196f8227f20afb1d1a8b4598b7,b2bf84d24a9b8e57929d9e3fc9e9797633c7c0f8,todo (vogel): make sure this is necessary.
aajacadig,chainer/chainer,chainer/graph_optimizations/static_graph.py,aef64159d655c7196f8227f20afb1d1a8b4598b7,STILL_EXISTS,fixme: need to check the dtype of inputs when checking the type signature for static schedules.
aajacadih,chainer/chainer,chainer/graph_optimizations/static_graph.py,aef64159d655c7196f8227f20afb1d1a8b4598b7,16e5e85d9eaafb33e6461f62d2c3370bde60823a,todo: Add a debug mode (perhaps enabled by defaut) that checks that the parameter references
aajacadii,chainer/chainer,chainer/graph_optimizations/static_graph.py,aef64159d655c7196f8227f20afb1d1a8b4598b7,16e5e85d9eaafb33e6461f62d2c3370bde60823a,do not change from iteration to iteration. This is needed because some existing parts of Chainer;
aajacaeae,chainer/chainer,chainer/graph_optimizations/static_graph.py,aef64159d655c7196f8227f20afb1d1a8b4598b7,STILL_EXISTS,todo: We can allow it to return tuple of arrays in the future.
aajacaeaf,chainer/chainer,chainer/graph_optimizations/static_graph.py,aef64159d655c7196f8227f20afb1d1a8b4598b7,16e5e85d9eaafb33e6461f62d2c3370bde60823a,todo: rename to StaticSchedule. (the Function part makes it sound like
aajacaeah,chainer/chainer,chainer/graph_optimizations/static_graph.py,aef64159d655c7196f8227f20afb1d1a8b4598b7,STILL_EXISTS,todo: Consider creating _schedule_funcs as a post-processing step.
aajacaeai,chainer/chainer,chainer/graph_optimizations/static_graph.py,aef64159d655c7196f8227f20afb1d1a8b4598b7,b2bf84d24a9b8e57929d9e3fc9e9797633c7c0f8,self._schedule_funcs_v1 = [] # todo: remove?
aajacaebg,chainer/chainer,chainer/graph_optimizations/static_graph.py,aef64159d655c7196f8227f20afb1d1a8b4598b7,STILL_EXISTS,in params_list. This is needed so that we can restore any grad_var
aajacaegf,chainer/chainer,chainer/graph_optimizations/static_graph.py,aef64159d655c7196f8227f20afb1d1a8b4598b7,STILL_EXISTS,todo: We can potentially reduce memory usage by freeing memory
aajacaegh,chainer/chainer,chainer/graph_optimizations/static_graph.py,aef64159d655c7196f8227f20afb1d1a8b4598b7,STILL_EXISTS,once they are no longer needed in the schedule or by
aajacaehh,chainer/chainer,chainer/graph_optimizations/static_graph_utilities.py,aef64159d655c7196f8227f20afb1d1a8b4598b7,8cd0df0a6f17202ba2807abbaf485d19d5e1cf1e,fixme: this function is not used anymore. remove it.
aajacaehi,chainer/chainer,chainer/graph_optimizations/static_graph_utilities.py,aef64159d655c7196f8227f20afb1d1a8b4598b7,8cd0df0a6f17202ba2807abbaf485d19d5e1cf1e,fixme: remove this old version
aajacaeia,chainer/chainer,chainer/graph_optimizations/static_graph_utilities.py,aef64159d655c7196f8227f20afb1d1a8b4598b7,8cd0df0a6f17202ba2807abbaf485d19d5e1cf1e,todo: Note that instead of copying the data from
aajacafda,chainer/chainer,chainer/functions/connection/linear.py,b18210db47de997cc1057d63439d5bda83558ba1,94c9d8d8e2436821f23c0b5c94f4f83c00cd5b5b,fixme: either re-implement static graph here or revert to default.
aajacafdf,chainer/chainer,chainer/functions/connection/linear.py,b18210db47de997cc1057d63439d5bda83558ba1,94c9d8d8e2436821f23c0b5c94f4f83c00cd5b5b,use the convention that any output arrays are supplied
aajacagbc,chainer/chainer,chainer/variable.py,b18210db47de997cc1057d63439d5bda83558ba1,STILL_EXISTS,fixme: remove check after debug.
aajacahcb,chainer/chainer,chainer/graph_optimizations/static_graph.py,16e5e85d9eaafb33e6461f62d2c3370bde60823a,STILL_EXISTS,id(array) -> (index in fixme)
aajacahfc,chainer/chainer,chainer/variable.py,16e5e85d9eaafb33e6461f62d2c3370bde60823a,STILL_EXISTS,assert retain_grad is True # fixme: remove check after debug.
aajacahhi,chainer/chainer,chainer/variable.py,cb429fa50530e0f663ffb7f38580d8b8f0d8e0e8,STILL_EXISTS,assert retain_grad is True # fixme: remove check after debug.
aajacaibd,chainer/chainer,chainer/graph_optimizations/static_graph.py,8cd0df0a6f17202ba2807abbaf485d19d5e1cf1e,STILL_EXISTS,todo: Stop supporting 2 formats. How about only supporting
aajacaidb,chainer/chainer,chainer/graph_optimizations/static_graph.py,8cd0df0a6f17202ba2807abbaf485d19d5e1cf1e,b2bf84d24a9b8e57929d9e3fc9e9797633c7c0f8,fixme!!! 'inputs' might correspond to input variable arrays that are also
aajacaidh,chainer/chainer,chainer/graph_optimizations/static_graph.py,8cd0df0a6f17202ba2807abbaf485d19d5e1cf1e,b2bf84d24a9b8e57929d9e3fc9e9797633c7c0f8,might be needed during the backward() method to compute gradients.
aajacaiec,chainer/chainer,chainer/graph_optimizations/static_graph.py,8cd0df0a6f17202ba2807abbaf485d19d5e1cf1e,b2bf84d24a9b8e57929d9e3fc9e9797633c7c0f8,contained backward schedule. A possible fix could be to make these hooks also
aajacaiee,chainer/chainer,chainer/graph_optimizations/static_graph.py,8cd0df0a6f17202ba2807abbaf485d19d5e1cf1e,b2bf84d24a9b8e57929d9e3fc9e9797633c7c0f8,fix would be to use a shared self.unique_arrays between all deeply-contained schedules
aajacaief,chainer/chainer,chainer/graph_optimizations/static_graph.py,8cd0df0a6f17202ba2807abbaf485d19d5e1cf1e,STILL_EXISTS,of a StaticScheduleFunction (maybe this solution is simplest?) Yes; maybe this is best.
aajacaifb,chainer/chainer,chainer/graph_optimizations/static_graph.py,8cd0df0a6f17202ba2807abbaf485d19d5e1cf1e,b2bf84d24a9b8e57929d9e3fc9e9797633c7c0f8,For example; we can determine the last place that each array is needed; and then set it to None if
aajacaifg,chainer/chainer,chainer/graph_optimizations/static_graph.py,8cd0df0a6f17202ba2807abbaf485d19d5e1cf1e,b2bf84d24a9b8e57929d9e3fc9e9797633c7c0f8,as soon as they are no longer needed by downstream functions. Then; 3rd pass actually executes the static schedules.
aajacaifi,chainer/chainer,chainer/graph_optimizations/static_graph.py,8cd0df0a6f17202ba2807abbaf485d19d5e1cf1e,b2bf84d24a9b8e57929d9e3fc9e9797633c7c0f8,fixme: Also; we should remember to set all references in self._in_vars to None after
aajacaigb,chainer/chainer,chainer/graph_optimizations/static_graph.py,8cd0df0a6f17202ba2807abbaf485d19d5e1cf1e,STILL_EXISTS,Maybe end of the define-by-run clause in backward() is the best place to do this delete?
aajacaigc,chainer/chainer,chainer/graph_optimizations/static_graph.py,8cd0df0a6f17202ba2807abbaf485d19d5e1cf1e,b2bf84d24a9b8e57929d9e3fc9e9797633c7c0f8,fixme: This is probably the bug. We need to add ahook function to backward to set these references.
aajacaigd,chainer/chainer,chainer/graph_optimizations/static_graph.py,8cd0df0a6f17202ba2807abbaf485d19d5e1cf1e,STILL_EXISTS,fixme: does this reference get set in the static schedule?
aajacaige,chainer/chainer,chainer/graph_optimizations/static_graph.py,8cd0df0a6f17202ba2807abbaf485d19d5e1cf1e,STILL_EXISTS,fixme: why call ._out_vars? why not a method?
aajacaigh,chainer/chainer,chainer/graph_optimizations/static_graph.py,8cd0df0a6f17202ba2807abbaf485d19d5e1cf1e,STILL_EXISTS,the same data. This is needed so that the chain-local
aajacaigj,chainer/chainer,chainer/graph_optimizations/static_graph.py,8cd0df0a6f17202ba2807abbaf485d19d5e1cf1e,STILL_EXISTS,fixme: remove check?
aajacaijj,chainer/chainer,tests/chainer_tests/graph_optimization_tests/test_static_graph_models.py,8cd0df0a6f17202ba2807abbaf485d19d5e1cf1e,STILL_EXISTS,fixme: only this one fails.
aajacajde,chainer/chainer,tests/python_tests/routines_tests/test_math.py,6a6754a02120e0e4c79970d08569a5725adef923,STILL_EXISTS,TODO(hvy): Fix after supporting correct dtype promotion.
aajacajgc,chainer/chainer,chainer/graph_optimizations/static_graph.py,b2bf84d24a9b8e57929d9e3fc9e9797633c7c0f8,6cf08ddc7ae22685255df70c57284cd2d4ca1526,todo: remove this since no longer used?
aajacajgd,chainer/chainer,chainer/graph_optimizations/static_graph.py,b2bf84d24a9b8e57929d9e3fc9e9797633c7c0f8,STILL_EXISTS,todo: How about only supporting
aajacajib,chainer/chainer,chainer/graph_optimizations/static_graph.py,b2bf84d24a9b8e57929d9e3fc9e9797633c7c0f8,STILL_EXISTS,fixme: is this needed?
aajacajic,chainer/chainer,chainer/graph_optimizations/static_graph.py,b2bf84d24a9b8e57929d9e3fc9e9797633c7c0f8,STILL_EXISTS,fixme: debug
aajacbaag,chainer/chainer,chainer/graph_optimizations/static_graph.py,b2bf84d24a9b8e57929d9e3fc9e9797633c7c0f8,STILL_EXISTS,of a StaticScheduleFunction (maybe this solution is simplest?) Yes; maybe this is best.
aajacbacc,chainer/chainer,chainer/graph_optimizations/static_graph.py,b2bf84d24a9b8e57929d9e3fc9e9797633c7c0f8,STILL_EXISTS,Maybe end of the define-by-run clause in backward() is the best place to do this delete?
aajacbadg,chainer/chainer,chainer/graph_optimizations/static_graph.py,b2bf84d24a9b8e57929d9e3fc9e9797633c7c0f8,STILL_EXISTS,fixme: does this reference get set in the static schedule?
aajacbadh,chainer/chainer,chainer/graph_optimizations/static_graph.py,b2bf84d24a9b8e57929d9e3fc9e9797633c7c0f8,STILL_EXISTS,fixme: why call ._out_vars? why not a method?
aajacbjhg,chainer/chainer,chainer/graph_optimizations/static_graph.py,e6fcdf2bfe61f378720fb67933a4b97e1aaf5bd2,STILL_EXISTS,fixme: is this needed?
aajacbjhh,chainer/chainer,chainer/graph_optimizations/static_graph.py,e6fcdf2bfe61f378720fb67933a4b97e1aaf5bd2,STILL_EXISTS,fixme: debug
aajaccabd,chainer/chainer,tests/chainer_tests/graph_optimization_tests/test_static_graph_models.py,e6fcdf2bfe61f378720fb67933a4b97e1aaf5bd2,STILL_EXISTS,fixme: only this one fails.
aajacceeh,chainer/chainer,chainer/functions/connection/linear.py,1975ebbadc75d263efd89adb5f7fae0fcbe1b40d,53a9696ce5e98fd4099c249d13bb7a02552d0309,use the convention that any output arrays are supplied
aajaccfei,chainer/chainer,chainer/functions/connection/convolution_nd.py,28118c9791eeedc0fbfcd5aa769d5646533352d1,53a9696ce5e98fd4099c249d13bb7a02552d0309,Work-around for NumPy's bug?
aajacchec,chainer/chainer,python/xchainer/testing/helper.py,59dbd76808c83efbbc5343e878d862d2ab887241,STILL_EXISTS,TODO(niboshi): better error messages
aajacchfd,chainer/chainer,tests/python_tests/testing_tests/test_helper.py,59dbd76808c83efbbc5343e878d862d2ab887241,STILL_EXISTS,Return arrays wrong way around
aajacdcdg,chainer/chainer,chainer/functions/connection/convolution_nd.py,b119b77aafb7641aa1dbad67f59f7e938e27001c,STILL_EXISTS,Work-around for NumPy's bug?
aajacfhfb,chainer/chainer,chainer/functions/connection/linear.py,b446c363ae033dba840d62c959d9e27862adcf6b,753f4992a191465e86df7e2af1f2a3df5b81ee93,use the convention that any output arrays are supplied
aajacfhfj,chainer/chainer,chainer/graph_optimizations/static_graph.py,b446c363ae033dba840d62c959d9e27862adcf6b,STILL_EXISTS,todo: Ideally; we would like to update the reference in
aajacfhhc,chainer/chainer,chainer/graph_optimizations/static_graph.py,b446c363ae033dba840d62c959d9e27862adcf6b,STILL_EXISTS,freed. If it is not deleted; assume that it is still needed and
aajacfhhf,chainer/chainer,chainer/graph_optimizations/static_graph.py,b446c363ae033dba840d62c959d9e27862adcf6b,STILL_EXISTS,todo: Consider making this a list of UniqueArray objects; where
aajacfhjf,chainer/chainer,chainer/graph_optimizations/static_graph.py,b446c363ae033dba840d62c959d9e27862adcf6b,STILL_EXISTS,todo: consider only appending a weak reference so
aajacfhjh,chainer/chainer,chainer/graph_optimizations/static_graph.py,b446c363ae033dba840d62c959d9e27862adcf6b,STILL_EXISTS,we can know when it is no longer needed and add a
aajacfiab,chainer/chainer,chainer/graph_optimizations/static_graph.py,b446c363ae033dba840d62c959d9e27862adcf6b,STILL_EXISTS,they had better not already be in unique_arrays.
aajacfiag,chainer/chainer,chainer/graph_optimizations/static_graph.py,b446c363ae033dba840d62c959d9e27862adcf6b,STILL_EXISTS,todo: add test case for an example where the following
aajacfiaj,chainer/chainer,chainer/graph_optimizations/static_graph_utilities.py,b446c363ae033dba840d62c959d9e27862adcf6b,STILL_EXISTS,note: this is not currently needed.
aajacfjgf,chainer/chainer,chainer/graph_optimizations/static_graph.py,22c5d798e356e820acc9687225e306031a213679,STILL_EXISTS,todo: possible memory leak when need_copy False is allowed?
aajacfjha,chainer/chainer,chainer/graph_optimizations/static_graph.py,22c5d798e356e820acc9687225e306031a213679,STILL_EXISTS,todo: save array order ('C'; 'F' as well?
aajacfjhb,chainer/chainer,chainer/graph_optimizations/static_graph.py,22c5d798e356e820acc9687225e306031a213679,STILL_EXISTS,todo: set in initializer as keyword arg?
aajacfjhf,chainer/chainer,chainer/graph_optimizations/static_graph.py,22c5d798e356e820acc9687225e306031a213679,STILL_EXISTS,todo: set device id
aajacfjig,chainer/chainer,chainer/graph_optimizations/static_graph.py,22c5d798e356e820acc9687225e306031a213679,STILL_EXISTS,Note: the following line is not actually needed.
aajacfjii,chainer/chainer,chainer/graph_optimizations/static_graph.py,22c5d798e356e820acc9687225e306031a213679,STILL_EXISTS,todo: this is O(N^2) and maybe too slow for large graphs.
aajacfjji,chainer/chainer,chainer/graph_optimizations/static_graph.py,22c5d798e356e820acc9687225e306031a213679,STILL_EXISTS,todo: enable the following line instead once the
aajacgiba,chainer/chainer,tests/chainer_tests/functions_tests/normalization_tests/test_batch_renormalization.py,627012e2b7d5cd3ceb6624018337341cc69b63e9,eb66ddf7bd5f4c30c291fe5b5d315fdd7637c085,Freezing the update of running statistics is needed in order to
aajacgied,chainer/chainer,chainer/graph_optimizations/static_graph.py,6ed6dfa03bb90c26772fa1bbaeb6c5bb1fb90587,STILL_EXISTS,todo (vogel): enable this eventually. For now; it
aajacgiee,chainer/chainer,chainer/graph_optimizations/static_graph.py,6ed6dfa03bb90c26772fa1bbaeb6c5bb1fb90587,STILL_EXISTS,casuses some needed variables to be set to None
aajachded,chainer/chainer,examples/mnist/train_mnist.py,56f0536fb096c8d5b539ed00a0325f2624335860,c6a0989f6018019b4d4870b3500a45e4050e16d9,TODO(beam2d): implement mean
aajachdei,chainer/chainer,examples/mnist/train_mnist.py,56f0536fb096c8d5b539ed00a0325f2624335860,c6a0989f6018019b4d4870b3500a45e4050e16d9,TODO(beam2d): implement len
aajachfaa,chainer/chainer,chainer/optimizer_hooks/gradient_clipping.py,cc0d3219a316ac5499031260cf88832d2dbfd947,STILL_EXISTS,When no clipping is needed; skip the clipping on CPU and
aajaciaee,chainer/chainer,tests/python_tests/routines_tests/test_pooling.py,493db544e39f195ac56b35cedfb61af2222eacad,STILL_EXISTS,In the case of CUDA; we get huge negative numbers instead of -inf around boundaries.
aajacjfbj,chainer/chainer,chainer/functions/connection/convolution_2d.py,c6a0989f6018019b4d4870b3500a45e4050e16d9,9549712b752f2224dd54c79ff1bc07c491938e51,Work-around for NumPy's bug?
aajacjhfb,chainer/chainer,examples/mnist/train_mnist.py,48e6796c2d45a15ef1f35c70e875ce65b0a7e7ba,ed6b58437ca69e6fd7a88ee2f67d2dc1080ed546,TODO(beam2d): implement mean
aajacjhfg,chainer/chainer,examples/mnist/train_mnist.py,48e6796c2d45a15ef1f35c70e875ce65b0a7e7ba,ed6b58437ca69e6fd7a88ee2f67d2dc1080ed546,TODO(beam2d): implement len
aajacjhje,chainer/chainer,chainer/graph_optimizations/static_graph.py,b0463adfdff80fd3b65b8fffbd2d12ecea2f54ec,STILL_EXISTS,todo: consider to allow nested use of this decorator.
aajadabef,chainer/chainer,chainer/distributions/beta.py,4a84a6978cffbf71e414bd6d18455779eaa81c24,f27825818ffd81006011e58ae9fb9d9f70547c6c,TODO: using cupy.random.beta
aajadabgc,chainer/chainer,examples/imagenet/train_imagenet.py,fb81b10ca0188863da9ec5e9a790c9d8696644ae,6b29fd40cc434372b00b1bc4b545d01e1d7be7ca,TODO(beam2d): implement mean
aajadadbc,chainer/chainer,examples/mnist/train_mnist.py,ba20fe7ad5942e70f5acace1d0549e10cbcee121,830a937097679844c4c9ad2f639c912ea81564bc,TODO(beam2d): implement mean
aajadadbh,chainer/chainer,examples/mnist/train_mnist.py,ba20fe7ad5942e70f5acace1d0549e10cbcee121,830a937097679844c4c9ad2f639c912ea81564bc,TODO(beam2d): implement len
aajadagae,chainer/chainer,chainer/distributions/dirichlet.py,735cc6374352a19d8b8d0cb09894d74b8fe41627,c9f9516e093ecb2a3b5039afa6376515d96b4b39,TODO: fix cupy.random.dirichlet to behave same as numpy.
aajadbfcc,chainer/chainer,examples/imagenet/train_imagenet.py,92080afadbab14ca534d33346b489458d5329fd3,4f30508a6d381a49efc9ca9b0c745788376a96fd,TODO(beam2d): implement mean
aajadcagi,chainer/chainer,examples/imagenet/train_imagenet.py,784714c4b080b2bc0137bf5c33eb2a40f6457500,c8f462e970147e1bbbfb7e907b292fa7ebcc73d5,TODO(beam2d): implement mean
aajadcbhi,chainer/chainer,examples/mnist/train_mnist.py,3c87b8c1bc098226607e0cd8cb344c8a90c03bbb,c8f462e970147e1bbbfb7e907b292fa7ebcc73d5,TODO(beam2d): implement mean
aajadcbid,chainer/chainer,examples/mnist/train_mnist.py,3c87b8c1bc098226607e0cd8cb344c8a90c03bbb,c8f462e970147e1bbbfb7e907b292fa7ebcc73d5,TODO(beam2d): implement len
aajadehaa,chainer/chainer,chainerx/examples/imagenet/train_imagenet.py,f1321596a19274da6924963a051f1a8b31d0f680,STILL_EXISTS,TODO(beam2d): implement mean
aajadehda,chainer/chainer,chainerx/examples/mnist/train_mnist.py,f1321596a19274da6924963a051f1a8b31d0f680,STILL_EXISTS,TODO(beam2d): implement mean
aajadehdf,chainer/chainer,chainerx/examples/mnist/train_mnist.py,f1321596a19274da6924963a051f1a8b31d0f680,STILL_EXISTS,TODO(beam2d): implement len
aajadfghj,chainer/chainer,chainermn/extensions/checkpoint.py,5bb57bba985516b35455252fc5d8909c8ded1694,STILL_EXISTS,If this is a complete resume; no broadcast is needed ^^;
aajadfhbg,chainer/chainer,chainermn/functions/batch_normalization.py,5bb57bba985516b35455252fc5d8909c8ded1694,fe6bac427d769c6391e8547831239ac59810b239,ChainerMN diff (1\/2) ends
aajadfhci,chainer/chainer,chainermn/functions/batch_normalization.py,5bb57bba985516b35455252fc5d8909c8ded1694,fe6bac427d769c6391e8547831239ac59810b239,ChainerMN diff (2\/2) ends
aajadfhef,chainer/chainer,chainermn/functions/point_to_point_communication.py,5bb57bba985516b35455252fc5d8909c8ded1694,STILL_EXISTS,dummy_var is needed to maintain Chainer's constraint.
aajadgbbe,chainer/chainer,chainer/functions/connection/linear.py,9326461e2897128b752bb8b7a3fd5213485c7c54,2ca73dc139f6e12e96a179d71138d5bd0fb15630,use the convention that any output arrays are supplied
aajaeabbh,chainer/chainer,chainerx/python/chainerx/__init__.py,37aae99064f0fbaf5794a51bd2413ec3ff07b5eb,STILL_EXISTS,`testing` needs to be imported before `_core`; because importing `_core` would populate sys.modules['chainerx.testing'].
aajaeabia,chainer/chainer,chainer/functions/connection/linear.py,39baf76d99bcbc9d8fdc2f40d06cc7230064bcc0,519ff4b70bbccf91561da02cb13a3cc7a76aaf59,use the convention that any output arrays are supplied
aajaefhjg,chainer/chainer,chainer/functions/connection/linear.py,0426faa6ef3195ea3535a8a6eedf6de049cb7c70,STILL_EXISTS,use the convention that any output arrays are supplied
aajafaefi,chainer/chainer,chainer/__init__.py,8d785d3ed094c949aa8f7249167fee25372d2a28,e3a0c5b80228d4f2c2d99eb0390329dfbd932038,TODO(hvy): Move this function to backend?
aajafaega,chainer/chainer,chainer/functions/math/basic_math.py,8d785d3ed094c949aa8f7249167fee25372d2a28,80b27c19bfd85f256b5febf4d1ea265dcf79724c,TODO(hvy): Implement multi-add with chainerx.ndarrays.
aajafbceh,chainer/chainer,chainer/__init__.py,da36271f1869940bfd04bc614c03bdf6f7aea164,e3a0c5b80228d4f2c2d99eb0390329dfbd932038,TODO(hvy): Move this function to backend?
aajafbebb,chainer/chainer,chainer/functions/math/basic_math.py,b90b8d68347fe8741c9cc36604335cb59606f4da,e3a0c5b80228d4f2c2d99eb0390329dfbd932038,TODO(hvy): Implement multi-add with chainerx.ndarrays.
aajafdbbg,chainer/chainer,chainer/__init__.py,2d747ff695c64ad05046dada6af077e98b830a25,48a3615f830257987b6128d73e17e088b51a6a9f,TODO(hvy): Move this function to backend?
aajafdbbi,chainer/chainer,chainer/functions/math/basic_math.py,2d747ff695c64ad05046dada6af077e98b830a25,48a3615f830257987b6128d73e17e088b51a6a9f,TODO(hvy): Implement multi-add with chainerx.ndarrays.
aajafedjf,chainer/chainer,chainer/variable.py,3fc3f66a1b66fbff185d26767b83296ab1863f2f,3fc3f66a1b66fbff185d26767b83296ab1863f2f,TODO(sonots): Implement for ChainerX
aajafhfig,chainer/chainer,chainer/functions/math/basic_math.py,0644e446f1300677934d8a262cb7b1711619fdd9,7d3c8fc77fa06ec54f48b49b3a28b7679b6d503b,TODO(hvy): Implement multi-add with chainerx.ndarrays.
aajafhgje,chainer/chainer,tests/chainer_tests/functions_tests/math_tests/test_minmax.py,b50332c9e4fa1760d929eb486af005c30a3e8740,80f9f34ff4923d3893ecb9f94798f3a1ba28b553,TODO(sonots): Fix argmin to get aborted
aajafhhie,chainer/chainer,chainer/gradient_check.py,2f4848185d50fff6285705fea7dc614ba8e30ec5,STILL_EXISTS,TODO
aajafhhja,chainer/chainer,chainer/gradient_check.py,203b3c07938bd7a95992a8735b26ed51a8f9da51,STILL_EXISTS,TODO
aajafjgic,chainer/chainer,chainer/functions/math/basic_math.py,34e1036549334791b68f2304a097d7b1a48f20c6,05237f54f1e69843c20cdb3418256b3d142e2070,TODO(hvy): Implement multi-add with chainerx.ndarrays.
aajafjhbj,chainer/chainer,chainer/__init__.py,84ea090d9028d1a4d898fdf08cf85143ed541eba,05237f54f1e69843c20cdb3418256b3d142e2070,TODO(hvy): Move this function to backend?
aajafjjdd,chainer/chainer,tests/chainer_tests/functions_tests/math_tests/test_minmax.py,23dc7b379a2cfc201a8752b5f0fe10fad6d85e83,80f9f34ff4923d3893ecb9f94798f3a1ba28b553,TODO(sonots): Fix argmin to get aborted
aajafjjfc,chainer/chainer,tests/chainer_tests/functions_tests/math_tests/test_basic_math.py,decdcefb91d84f5a820499347396e78e70be4197,05237f54f1e69843c20cdb3418256b3d142e2070,TODO(sonots): Remove this workaround after numerical_grad is
aajagbaah,chainer/chainer,chainer/functions/math/basic_math.py,5343802372db84b31ab0845658ed9a6d30721140,c0727ea17633debf2ba37b186fc0866479fd06dd,TODO(hvy): Implement multi-add with chainerx.ndarrays.
aajagbbjc,chainer/chainer,chainer/backend.py,4068a4b901b9c18406b5c0af2e784c84d7bff46b,9dfd986295a1ea2a4f1ef5d5943c330288169a3b,TODO(niboshi): Perhaps device name (as str) can also be acceptable
aajagbcad,chainer/chainer,tests/chainer_tests/functions_tests/math_tests/test_basic_math.py,e6eb7ca273fbc6499c69cbd81955d7d0421a301f,1dd8251cd6672a50bd9c1257e8b5432eb2647489,TODO(niboshi): Fix it
aajagdgec,chainer/chainer,chainer/__init__.py,067db431199815b72846dd489abf33b5b79018c3,c6bf1073b8f94fe0c42d0083b4fa80a895cfdcb6,TODO(hvy): Move this function to backend?
aajagdggb,chainer/chainer,chainer/functions/math/basic_math.py,067db431199815b72846dd489abf33b5b79018c3,c6bf1073b8f94fe0c42d0083b4fa80a895cfdcb6,TODO(hvy): Implement multi-add with chainerx.ndarrays.
aajagdieb,chainer/chainer,chainer/backend.py,ce672c37d12bcfdb38644777f5812208b7e5171b,aef18c8618392c97ee754945ac72d5b05cc80b50,TODO(niboshi): Perhaps device name (as str) can also be acceptable
aajagfhjb,chainer/chainer,chainer/backend.py,cbf216befd65fdad330b8c902a0edc99282fd156,6019d1ea4e776c45acbceb3fd75633087f649639,TODO(niboshi): Perhaps device name (as str) can also be acceptable
aajagfjaj,chainer/chainer,chainer/__init__.py,0c4f1a6a62786d41ab17eda363ea2a85ff021e3c,18f01317ce912d4a0c985733193674a44954d8d8,TODO(hvy): Move this function to backend?
aajaggabg,chainer/chainer,chainer/functions/math/basic_math.py,44304b9800244a8a999e2aa6b582edc723c72d1f,a6bbf7871b1c764ab5c4cd857cdf9b42129d1874,TODO(hvy): Implement multi-add with chainerx.ndarrays.
aajaggbch,chainer/chainer,chainer/dataset/convert.py,fe9fb897b2b516628eb5d7fab40bff9ce7cdc8e9,STILL_EXISTS,TODO(niboshi): Perhaps backend.to_device should support integers
aajaggbde,chainer/chainer,tests/chainer_tests/dataset_tests/test_convert.py,fe9fb897b2b516628eb5d7fab40bff9ce7cdc8e9,18f01317ce912d4a0c985733193674a44954d8d8,TODO(niboshi): This is a workaround because get_device_from_array
aajaggbhd,chainer/chainer,chainer/functions/connection/convolution_nd.py,a39107eca31b4607d84df2876067d93975bb15fc,STILL_EXISTS,Work-around for NumPy's bug?
aajaggbhi,chainer/chainer,chainer/backend.py,6d6d20805a53dbc00177bf0dc327344c2958ded2,STILL_EXISTS,TODO(niboshi): Perhaps device name (as str) can also be acceptable
aajaggcgb,chainer/chainer,tests/chainer_tests/test_variable.py,223f5a2d2c0c896c6b020b01dcb66bb71318a296,18f01317ce912d4a0c985733193674a44954d8d8,TODO(niboshi): This is a temporary workaround for
aajaggdda,chainer/chainer,chainer/backend.py,bd9e27c88c3b147be398263d10c1652bd7d11305,STILL_EXISTS,TODO(niboshi): Perhaps device name (as str) can also be acceptable
aajaggdhj,chainer/chainer,chainerx/__init__.py,f677e822df5d1867fda9a4177629c344e482b188,STILL_EXISTS,Add workaround implementation for NumPy-compatible functions
aajaggdia,chainer/chainer,chainerx/_numpy_compat_workarounds.py,f677e822df5d1867fda9a4177629c344e482b188,STILL_EXISTS,This file defines inefficient workaround implementation for
aajaggdic,chainer/chainer,chainerx/_numpy_compat_workarounds.py,f677e822df5d1867fda9a4177629c344e482b188,STILL_EXISTS,implementing those functions in more efficient manner.
aajaggdid,chainer/chainer,chainerx/_numpy_compat_workarounds.py,f677e822df5d1867fda9a4177629c344e482b188,STILL_EXISTS,Populates workaround functions in the chainerx namespace
aajaggihc,chainer/chainer,chainer/backend.py,4cfe5f2a35ca78a90d480862cda862de33c8c8bd,STILL_EXISTS,TODO(niboshi): Perhaps device name (as str) can also be acceptable
aajaggiia,chainer/chainer,examples/mnist/train_mnist.py,4cfe5f2a35ca78a90d480862cda862de33c8c8bd,STILL_EXISTS,TODO(niboshi): Temporarily disabled for chainerx. Fix it.
aajaggiif,chainer/chainer,chainer/functions/math/basic_math.py,57ab35ab9e59aa3a3ef215314b3e0e2a6bb96a2f,18f01317ce912d4a0c985733193674a44954d8d8,TODO(hvy): Implement multi-add with chainerx.ndarrays.
aajaghbge,chainer/chainer,chainer/functions/loss/softmax_cross_entropy.py,6e18977f66d38182bbb6db36c80de269491e7ffe,e248b39e84fffd566be2b790034a87b39483b290,TODO(beam2d): implement mean
aajaghbgf,chainer/chainer,chainer/functions/loss/softmax_cross_entropy.py,6e18977f66d38182bbb6db36c80de269491e7ffe,e248b39e84fffd566be2b790034a87b39483b290,TODO(niboshi): Remove this workaround after ChainerX supports
aajaghcbh,chainer/chainer,chainer/functions/loss/softmax_cross_entropy.py,5ce4d8ec433198bb30acc9c8fb6cdf212f1aad46,18f01317ce912d4a0c985733193674a44954d8d8,TODO(beam2d): implement mean
aajaghcbi,chainer/chainer,chainer/functions/loss/softmax_cross_entropy.py,5ce4d8ec433198bb30acc9c8fb6cdf212f1aad46,18f01317ce912d4a0c985733193674a44954d8d8,TODO(niboshi): Remove this workaround after ChainerX supports
aajagjffa,chainer/chainer,examples/chainermn/imagenet/train_imagenet.py,e47a53197ea812258a4d8f62e2119bd418c376ed,2c73a4e0e71a3afa015816d5e2158d1bb8acaf72,A workaround for processes crash should be done before making
aajagjgca,chainer/chainer,chainer/functions/loss/softmax_cross_entropy.py,45e1be895b7da8aa32f35e37210e8e7fc06b54df,ed1081c37e9a76502496f038dd4dbf1455c44c8d,TODO(beam2d): implement mean
aajagjgcb,chainer/chainer,chainer/functions/loss/softmax_cross_entropy.py,45e1be895b7da8aa32f35e37210e8e7fc06b54df,ed1081c37e9a76502496f038dd4dbf1455c44c8d,TODO(niboshi): Remove this workaround after ChainerX supports
aajagjhbh,chainer/chainer,tests/chainer_tests/test_variable.py,45e1be895b7da8aa32f35e37210e8e7fc06b54df,8eacd20e35f31f124da36759953a21b29871e72f,TODO(niboshi): This is a temporary workaround for
aajagjjea,chainer/chainer,chainer/functions/loss/softmax_cross_entropy.py,303b7dcb9f298d0157e7457acb29432b6d8d6640,cc276f3c3c8f886b15993810026d47104fc57e62,TODO(beam2d): implement mean
aajagjjec,chainer/chainer,chainer/functions/loss/softmax_cross_entropy.py,303b7dcb9f298d0157e7457acb29432b6d8d6640,cc276f3c3c8f886b15993810026d47104fc57e62,TODO(niboshi): Remove this workaround after ChainerX supports
aajagjjfe,chainer/chainer,chainer/functions/math/basic_math.py,303b7dcb9f298d0157e7457acb29432b6d8d6640,d16abee4b4272ccfdbaa7b846cd39d365f8c42be,TODO(hvy): Implement multi-add with chainerx.ndarrays.
aajahaaee,chainer/chainer,chainer/functions/loss/softmax_cross_entropy.py,7645a0b0d8bb5101a197fc65acb4a5c113b74bca,cc276f3c3c8f886b15993810026d47104fc57e62,TODO(beam2d): implement mean
aajahaaeg,chainer/chainer,chainer/functions/loss/softmax_cross_entropy.py,7645a0b0d8bb5101a197fc65acb4a5c113b74bca,cc276f3c3c8f886b15993810026d47104fc57e62,TODO(niboshi): Remove this workaround after ChainerX supports
aajahaafi,chainer/chainer,chainer/functions/math/basic_math.py,7645a0b0d8bb5101a197fc65acb4a5c113b74bca,d16abee4b4272ccfdbaa7b846cd39d365f8c42be,TODO(hvy): Implement multi-add with chainerx.ndarrays.
aajahacih,chainer/chainer,chainer/__init__.py,4285355f0ed5a61c870800cd9476158414d8affd,d16abee4b4272ccfdbaa7b846cd39d365f8c42be,TODO(hvy): Move this function to backend?
aajahacjh,chainer/chainer,tests/chainer_tests/dataset_tests/test_convert.py,4285355f0ed5a61c870800cd9476158414d8affd,8eacd20e35f31f124da36759953a21b29871e72f,TODO(niboshi): This is a workaround because get_device_from_array
aajahaeaa,chainer/chainer,examples/imagenet/train_imagenet.py,f26d1b260c5f5eedb5c81191598e18693255664b,d16abee4b4272ccfdbaa7b846cd39d365f8c42be,TODO(sonots): Temporarily disabled for chainerx. Fix it.
aajahaecc,chainer/chainer,chainer/functions/loss/softmax_cross_entropy.py,e36f24e4d753306d5dc368a572addd31dd36b9a3,525cfed3cf271c429cba547f21101774c7138052,TODO(beam2d): implement mean
aajahaecd,chainer/chainer,chainer/functions/loss/softmax_cross_entropy.py,e36f24e4d753306d5dc368a572addd31dd36b9a3,525cfed3cf271c429cba547f21101774c7138052,TODO(niboshi): Remove this workaround after ChainerX supports
aajahafed,chainer/chainer,chainer/functions/loss/softmax_cross_entropy.py,df6761f7daeb1cd04c1415653e511e821006065a,84a4852cd6afb1296c01420a77befd344ee26c21,TODO(beam2d): implement mean
aajahafee,chainer/chainer,chainer/functions/loss/softmax_cross_entropy.py,df6761f7daeb1cd04c1415653e511e821006065a,84a4852cd6afb1296c01420a77befd344ee26c21,TODO(niboshi): Remove this workaround after ChainerX supports
aajahafgf,chainer/chainer,chainer/function_node.py,caab6b0f3fb058e862ccab61eab6ae236c06a26c,8eacd20e35f31f124da36759953a21b29871e72f,TODO(sonots): Fix to use using_device() if it becomes available
aajahafib,chainer/chainer,chainer/backend.py,8eacd20e35f31f124da36759953a21b29871e72f,d16abee4b4272ccfdbaa7b846cd39d365f8c42be,Definition of __exit__ is needed to raise a custom error on
aajahagfi,chainer/chainer,tests/chainer_tests/links_tests/loss_tests/test_negative_sampling.py,8eacd20e35f31f124da36759953a21b29871e72f,STILL_EXISTS,Fix this test. The code seems to fix the samples in order to avoid
aajahcjgc,chainer/chainer,tests/chainer_tests/links_tests/loss_tests/test_negative_sampling.py,d16abee4b4272ccfdbaa7b846cd39d365f8c42be,STILL_EXISTS,Fix this test. The code seems to fix the samples in order to avoid
aajahdchh,chainer/chainer,chainer/backend.py,b62d00ccab97609bc583017ca9b42e2c02d386ec,70f1255a663b5e1f934925afeb874606f142a936,Definition of __exit__ is needed to raise a custom error on
aajahddfb,chainer/chainer,chainer/link.py,401040aa701eaf2466a3d5f2bd4ed8236710759f,STILL_EXISTS,TODO(niboshi): Fix this logic for updating self._device
aajahdebc,chainer/chainer,tests/chainer_tests/links_tests/loss_tests/test_negative_sampling.py,401040aa701eaf2466a3d5f2bd4ed8236710759f,STILL_EXISTS,Fix this test. The code seems to fix the samples in order to avoid
aajahdgff,chainer/chainer,tests/chainer_tests/links_tests/loss_tests/test_negative_sampling.py,302686e849f68c2106398fabb54c7217b93fb7e1,STILL_EXISTS,Fix this test. The code seems to fix the samples in order to avoid
aajahdgfg,chainer/chainer,chainer/function_node.py,57f048fe3276eaa0fd9d8eb12983d028aa75b47b,209d0b9c3f482b3780e65c08d340cc8144e63330,TODO(sonots): Fix to use using_device() if it becomes available
aajahdggg,chainer/chainer,examples/mnist/train_mnist.py,5c944241ad7e538544dff4772a672b187fd61f60,STILL_EXISTS,TODO(niboshi): Temporarily disabled for chainerx. Fix it.
aajahdgja,chainer/chainer,chainer/function.py,f20b729a4e91edef6dc766d818e3805e4d31e3e5,2947a56d7cded18374f9943e232a54259ceecddd,TODO(niboshi): Move to chainer.backend
aajahdhfe,chainer/chainer,chainer/utils/walker_alias.py,7d97d1f0146c38d6c439a0c0e6c956845b5da385,STILL_EXISTS,TODO(niboshi): Maybe better to deprecate the property.
aajahdhgc,chainer/chainer,chainer/__init__.py,bc9d3d302b225dcb8886c3c9c470be54fef82769,609487b9a07ee184c581e5c516af4e04e5b4892c,TODO(hvy): Move this function to backend?
aajahegda,chainer/chainer,chainer/__init__.py,3f414e048a53ab4d02376bd2b21de695a5f0e18d,920fbda3107acf1f21e3d8f029147ec6d92ce60e,TODO(hvy): Move this function to backend?
aajaheggh,chainer/chainer,chainer/link.py,3f414e048a53ab4d02376bd2b21de695a5f0e18d,STILL_EXISTS,TODO(niboshi): Fix this logic for updating self._device
aajahehfc,chainer/chainer,chainer/device.py,52990127174a52200d659ab1a2783dbd17d8a719,STILL_EXISTS,Definition of __exit__ is needed to raise a custom error on
aajaheieb,chainer/chainer,chainer/_backend.py,4ff01f8f331ec0c1166c85c76034e402405ab82c,STILL_EXISTS,Definition of __exit__ is needed to raise a custom error on
aajahfcec,chainer/chainer,chainer/functions/math/basic_math.py,dd6952024fb6bbf4c530d1a772495985b5bc9af7,920fbda3107acf1f21e3d8f029147ec6d92ce60e,TODO(hvy): Implement multi-add with chainerx.ndarrays.
aajahfhcc,chainer/chainer,examples/imagenet/train_imagenet.py,f2b3051a5e8a795eb5dc4c1cfe91be85c141afe7,STILL_EXISTS,TODO(sonots): Temporarily disabled for chainerx. Fix it.
aajahfhcg,chainer/chainer,chainer/function.py,f46a8772bd3a7a12d2560ec6aed51162e4e08b69,88d392efb0e41896b86fdf36a9f656dfc9f2fcfa,TODO(niboshi): Move to chainer.backend
aajahhedf,chainer/chainer,chainer/backends/_cpu.py,c18053706d788711ce11efbfde6ceb5b15976b5b,STILL_EXISTS,TODO(kmaehashi) fix circular imports around `chainer.backends.*`
aajahheed,chainer/chainer,chainer/backends/_cpu.py,cf6665d93080fbc432353c4b5cdef4ea3ddc2b8d,STILL_EXISTS,Surprisingly; `import chianer.backends` works as a workaround to avoid; but
aajahhibd,chainer/chainer,examples/chainermn/imagenet/train_imagenet.py,faec81464e4cfdb8c97c6082e42f263de49b4016,STILL_EXISTS,A workaround for processes crash should be done before making
aajahhjgc,chainer/chainer,chainer/types.py,3ddfe58da5f8a2a80863639e1093b3200a32e7b6,STILL_EXISTS,TODO: DTypeLike should be concrete type(s) which expresses numpy.dtype
aajahjhbd,chainer/chainer,chainer/training/updaters/standard_updater.py,f975c5e7738f79e21b4a360d20dd43ced794c0cb,STILL_EXISTS,TODO(niboshi): This is a temporary workaround to keep backward
aajahjheb,chainer/chainer,chainer/functions/loss/softmax_cross_entropy.py,8a2ca3a056659d3ea554bdaa2827a1f4399655b5,3c5cc9989582e0b75e6acc01a6610c3209b6b359,TODO(beam2d): implement mean
aajahjhec,chainer/chainer,chainer/functions/loss/softmax_cross_entropy.py,8a2ca3a056659d3ea554bdaa2827a1f4399655b5,3c5cc9989582e0b75e6acc01a6610c3209b6b359,TODO(niboshi): Remove this workaround after ChainerX supports
aajahjjch,chainer/chainer,chainerx/manipulation/shape.py,8f0d6bb122b8ca846ba89df94b8c170f100edaa2,STILL_EXISTS,TODO(sonots): Implement in C++
aajahjjga,chainer/chainer,chainerx/_ndarray.py,4e4a56fcbeae599e8d4c908d075477beafee87ce,STILL_EXISTS,TODO(sonots): Implement in C++
aajahjjgf,chainer/chainer,chainerx/_ndarray.py,f4f98003374b2432316246853689eb94d056fa46,27cdb885d625453672f1841811ed0545230aa620,TODO(sonots): Move advanced indexing fallback to another method
aajahjjgh,chainer/chainer,chainerx/_ndarray.py,f4f98003374b2432316246853689eb94d056fa46,STILL_EXISTS,TODO(sonots): Implement in C++
aajaiaadc,chainer/chainer,chainer/functions/loss/softmax_cross_entropy.py,fc89c0c864b9521b63204b1bb4f81066b65f9149,616aae98da4a4317e0cc0fe78611724d4586101b,TODO(beam2d): implement mean
aajaiaadd,chainer/chainer,chainer/functions/loss/softmax_cross_entropy.py,fc89c0c864b9521b63204b1bb4f81066b65f9149,616aae98da4a4317e0cc0fe78611724d4586101b,TODO(niboshi): Remove this workaround after ChainerX supports
aajaiaceh,chainer/chainer,examples/imagenet/train_imagenet.py,eb40e525ac0ea975973771341bf96e2e2f29aca8,STILL_EXISTS,TODO(sonots): Temporarily disabled for chainerx. Fix it.
aajaiacei,chainer/chainer,examples/mnist/train_mnist.py,eb40e525ac0ea975973771341bf96e2e2f29aca8,STILL_EXISTS,TODO(niboshi): Temporarily disabled for chainerx. Fix it.
aajaiacff,chainer/chainer,chainer/link.py,3354777a26ba33105bbc918b6d60e80bb42d45c7,STILL_EXISTS,`skip_between_cupy_devices` argument is a workaround
aajaiaife,chainer/chainer,chainer/training/extensions/evaluator.py,91172d0ddccc9a152a94971e32c05f6a03c6ee44,STILL_EXISTS,TODO(niboshi): This is a temporary workaround to keep backward
aajaiaijg,chainer/chainer,chainer/variable.py,9f14c2bdac85c99b5b19f431b1873fe705e97e1a,STILL_EXISTS,Fix F812 (Python 2)
aajaiajag,chainer/chainer,chainer/__init__.py,24705fb771bf3df8a954724c5ab567d2f2248456,STILL_EXISTS,TODO(hvy): Move this function to backend?
aajaiajhg,chainer/chainer,chainer/functions/loss/softmax_cross_entropy.py,24705fb771bf3df8a954724c5ab567d2f2248456,STILL_EXISTS,TODO(beam2d): implement mean
aajaiajhh,chainer/chainer,chainer/functions/loss/softmax_cross_entropy.py,24705fb771bf3df8a954724c5ab567d2f2248456,STILL_EXISTS,TODO(niboshi): Remove this workaround after ChainerX supports
aajaiajja,chainer/chainer,chainer/functions/math/basic_math.py,24705fb771bf3df8a954724c5ab567d2f2248456,STILL_EXISTS,TODO(hvy): Implement multi-add with chainerx.ndarrays.
aajaibaib,chainer/chainer,chainer/variable.py,24705fb771bf3df8a954724c5ab567d2f2248456,STILL_EXISTS,Fix F812 (Python 2)
aajdfbabd,PaddlePaddle/ERNIE,BERT/predict_classifier.py,cf6c39fc9af97fd0324af05fec8c60cdd3777679,STILL_EXISTS,if prediction of each and every example is needed; use Executor instead
aajdfbadg,PaddlePaddle/ERNIE,BERT/reader/cls.py,cf6c39fc9af97fd0324af05fec8c60cdd3777679,STILL_EXISTS,The convention in BERT is:
aajdfbbif,PaddlePaddle/ERNIE,BERT/reader/squad.py,cf6c39fc9af97fd0324af05fec8c60cdd3777679,STILL_EXISTS,What we really want to return is \"Steve Smith\".
aajdfbfce,PaddlePaddle/ERNIE,ERNIE/reader/task_reader.py,d8afc4d71a9950604e6d902931be9cd10424968a,STILL_EXISTS,The convention in BERT\/ERNIE is:
aajdfcefe,PaddlePaddle/ERNIE,ELMo/LAC_demo/reader.py,1e4405c87b29c831ef2d50249e49ecba5afeba20,STILL_EXISTS,TODO \u5B57\u548C\u8BCD\u6A21\u578B
aajdfdahh,PaddlePaddle/ERNIE,BERT/_ce.py,65904378483ebe85f3dd54f86d526a9e433679c1,STILL_EXISTS,### NOTE kpi.py should shared in models in some way!!!!
aajdfdajc,PaddlePaddle/ERNIE,ERNIE/_ce.py,9f63e2614b7d7bfdbc0f65afb47cb8847ecc63da,STILL_EXISTS,### NOTE kpi.py should shared in models in some way!!!!
aajdfddbc,PaddlePaddle/ERNIE,ELMo/_ce.py,a703e023a1552878fcadf9ee6d50f47c272eeb66,STILL_EXISTS,### NOTE kpi.py should shared in models in some way!!!!
aajdfdefi,PaddlePaddle/ERNIE,finetune/mrc.py,5c3b8cd3038fbd3144f86021380ce9621d18232e,STILL_EXISTS,What we really want to return is \"Steve Smith\".
aajdfdfbg,PaddlePaddle/ERNIE,reader/task_reader.py,5c3b8cd3038fbd3144f86021380ce9621d18232e,STILL_EXISTS,The convention in BERT\/ERNIE is:
aajdfdffj,PaddlePaddle/ERNIE,run_classifier.py,5c3b8cd3038fbd3144f86021380ce9621d18232e,STILL_EXISTS,final eval on dianostic; hack for glue-ax
aajdfdfif,PaddlePaddle/ERNIE,utils/cmrc2018_eval.py,5c3b8cd3038fbd3144f86021380ce9621d18232e,STILL_EXISTS,''' || Evaluation script for CMRC 2018 || version: v5 || Note:  || v5 formatted output; add usage description || v4 fixed segmentation issues || '''
aajdfegjg,PaddlePaddle/ERNIE,propeller/paddle/train/exporter.py,660e9ea6dad4f34b331f4d944db8e8b6487da65f,STILL_EXISTS,FIXME: all eval datasets has same name\/types\/shapes now!!! so every eval program are the smae
aajdfehid,PaddlePaddle/ERNIE,propeller/paddle/train/monitored_executor.py,660e9ea6dad4f34b331f4d944db8e8b6487da65f,STILL_EXISTS,TODO init
aajdfhfgd,PaddlePaddle/ERNIE,demo/mrc/mrc_metrics.py,defa38e7c4e7da8c05626569f17f80eac518a088,STILL_EXISTS,What we really want to return is \"Steve Smith\".
aajdfhhha,PaddlePaddle/ERNIE,ernie/modeling_ernie.py,defa38e7c4e7da8c05626569f17f80eac518a088,STILL_EXISTS,F.io.load_program_state(param_path) #buggy in dygraph.gurad; push paddle to fix
aajdfhhhb,PaddlePaddle/ERNIE,ernie/modeling_ernie.py,defa38e7c4e7da8c05626569f17f80eac518a088,STILL_EXISTS,FIXME: no need to do this in the future
aajdfhhhe,PaddlePaddle/ERNIE,ernie/modeling_ernie.py,defa38e7c4e7da8c05626569f17f80eac518a088,STILL_EXISTS,maybe not?
aajdfibig,PaddlePaddle/ERNIE,ernie/modeling_ernie.py,3b9d92fcb83ac08420c22858f353256fe7fe0630,STILL_EXISTS,FIXME:remove this
aajdficbe,PaddlePaddle/ERNIE,propeller/paddle/data/__init__.py,3b9d92fcb83ac08420c22858f353256fe7fe0630,STILL_EXISTS,columns