alan-turing-institute/MLJ.jl: v0.10.0

Published March 11, 2020 | Version v0.10.0

Software Open

MLJ v0.10.0

Upgrade to MLJBase 0.12.0 and MLJModels 0.9.0 to effect the following changes:

[x] (breaking) suppress normalisation of measure weights (MLJBase PR #208)
[x] (breaking) Shift the optional rng argument of iterator to first position (MLJBase #215)
[x] (mildly breaking) Let all models (supervised and unsupervised) share a common set of traits. So, for example, unsupervised models now have the target_scitype trait (usually taking the value Unknown). For a list of the common traits, do models()[1] |> keys |> collect (https://github.com/alan-turing-institute/MLJBase.jl/issues/163).
[x] (enhancement) Add sampler wrapper for one-dimensional ranges, for random sampling from ranges using rand (MLJBase #213)
[x] Change default value of num_round in XGBoost models from 1 to 100 (MLJModels PR #201)

Closed issues:

Help with loading code on multiple processes for paralleled tuning of a pipeline (#440)
Re-export CPU1, CPUProcesses, CPUThreads (#447)
Taking loss functions seriously (#450)
@pipeline to accept multiple Supervised models (#455)
What parts of MLJBase should be reexported in MLJ (#462)
unpack not working (#465)
Automatic Ensembling options (#466)

Merged pull requests:

Files

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alan-turing-institute/MLJ.jl-v0.10.0.zip md5:6dbddd361f5efe977b322bd8bfca21de	3.5 MB	Preview Download

Is supplement to: https://github.com/alan-turing-institute/MLJ.jl/tree/v0.10.0 (URL)