Repo: pyro
Filename: /mnt/batch/tasks/workitems/optwseed/job-1/Task_pyro/wd/borntobeflaky/projects/pyro/tests/integration_tests/test_conjugate_gaussian_models.py
ClassName: GaussianChainTests
Testname: test_elbo_nonreparameterized_N_is_5
Params: param1,127,33,ParamType.ITER,5000
param2,127,39,ParamType.LR,0.001
Assertion: assert_equal(0.0, max_errors[0], prec=0.06)
Original runtime: 52.882
>>Getting original runtime
Optimization Iteration: 1
Running with params: {'param1': 5000, 'param2': 0.001}
Logdir: /mnt/batch/tasks/workitems/optwseed/job-1/Task_pyro/wd/borntobeflaky/tool/logs/optim_1597896455_pyro/run_1597914105/assert_67944811_5000_0.001
Launching 30 jobs, 30 in parallel
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Timings: Avg: 51.431000000000004, Max: 53.01, Min: 50.38
Passed tests : 30
Failed tests : 0
Converged: False
Convergence score: 2.541251786818291
Continuing to next batch...
Launching 30 jobs, 30 in parallel
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Timings: Avg: 51.607, Max: 53.4, Min: 50.38
Passed tests : 60
Failed tests : 0
Converged: False
Convergence score: 1.5840360809264593
Continuing to next batch...
Launching 30 jobs, 30 in parallel
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Timings: Avg: 51.59411111111111, Max: 53.4, Min: 50.38
Passed tests : 90
Failed tests : 0
Converged: True
Convergence score: 0.8831157194574106
updating...
Evaluating 90 values out of 90
Overall-timings: Avg: 51.59411111111111, Max: 53.4, Min: 50.38
Variance (1.0244041435965962e-35) too small, using delta distribution
Variance (4.103520168237181e-36) too small, using delta distribution
Variance (8.462054693151417e-34) too small, using delta distribution
Probability of fail : 0.0
All-Passed: True
Probabilty of failure: 0.0
Runtime: 51.59411111111111
Score: 51.59411111111111
Best-score: 51.59411111111111
Best-param: {'param1': 5000, 'param2': 0.001}
>>Setting original runtime to 51.59411111111111
Optimization Iteration: 1
Running with params: {'param1': 200, 'param2': 0.0013221197482607622}
Logdir: /mnt/batch/tasks/workitems/optwseed/job-1/Task_pyro/wd/borntobeflaky/tool/logs/optim_1597896455_pyro/run_1597914105/assert_67944811_200_0.0013221197482607622
Launching 30 jobs, 30 in parallel
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Timings: Avg: 4.274666666666667, Max: 4.340000000000001, Min: 4.22
Passed tests : 0
Failed tests : 30
Half of samples failed, exiting...
All-Passed: False
Probabilty of failure: 1.0
Runtime: 10000.0
Score: 3.4028234663852886e+38
Best-score: 51.59411111111111
Best-param: {'param1': 5000, 'param2': 0.001}
Optimization Iteration: 2
Running with params: {'param1': 1200, 'param2': 0.00010601250003499117}
Lower learning rate.. returning... Best: 0.001, Proposed: 0.00010601250003499117
Optimization Iteration: 3
Running with params: {'param1': 600, 'param2': 0.0009028246781286828}
Logdir: /mnt/batch/tasks/workitems/optwseed/job-1/Task_pyro/wd/borntobeflaky/tool/logs/optim_1597896455_pyro/run_1597914105/assert_67944811_600_0.0009028246781286828
Launching 30 jobs, 30 in parallel
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Timings: Avg: 8.141, Max: 8.29, Min: 7.99
Passed tests : 0
Failed tests : 30
Half of samples failed, exiting...
All-Passed: False
Probabilty of failure: 1.0
Runtime: 10000.0
Score: 3.4028234663852886e+38
Best-score: 51.59411111111111
Best-param: {'param1': 5000, 'param2': 0.001}
Optimization Iteration: 4
Running with params: {'param1': 200, 'param2': 0.04984012714157533}
Logdir: /mnt/batch/tasks/workitems/optwseed/job-1/Task_pyro/wd/borntobeflaky/tool/logs/optim_1597896455_pyro/run_1597914105/assert_67944811_200_0.04984012714157533
Launching 30 jobs, 30 in parallel
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Timings: Avg: 4.259, Max: 4.34, Min: 4.180000000000001
Passed tests : 0
Failed tests : 30
Half of samples failed, exiting...
All-Passed: False
Probabilty of failure: 1.0
Runtime: 10000.0
Score: 3.4028234663852886e+38
Best-score: 51.59411111111111
Best-param: {'param1': 5000, 'param2': 0.001}
Optimization Iteration: 5
Running with params: {'param1': 1200, 'param2': 0.0078902425490074}
Logdir: /mnt/batch/tasks/workitems/optwseed/job-1/Task_pyro/wd/borntobeflaky/tool/logs/optim_1597896455_pyro/run_1597914105/assert_67944811_1200_0.0078902425490074
Launching 30 jobs, 30 in parallel
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Timings: Avg: 13.831666666666667, Max: 14.35, Min: 13.52
Passed tests : 30
Failed tests : 0
Converged: False
Convergence score: 1.7999999999999998
Continuing to next batch...
Launching 30 jobs, 30 in parallel
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Timings: Avg: 13.823666666666664, Max: 14.35, Min: 13.52
Passed tests : 60
Failed tests : 0
Converged: False
Convergence score: 1.2345823376296523
Continuing to next batch...
Launching 30 jobs, 30 in parallel
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Timings: Avg: 13.83633333333333, Max: 14.35, Min: 13.52
Passed tests : 90
Failed tests : 0
Converged: False
Convergence score: 1.114181367955891
Continuing to next batch...
Launching 30 jobs, 30 in parallel
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Timings: Avg: 13.830583333333333, Max: 14.35, Min: 13.52
Passed tests : 120
Failed tests : 0
Converged: False
Convergence score: 1.1039497434082872
Continuing to next batch...
Launching 30 jobs, 30 in parallel
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Timings: Avg: 13.830733333333335, Max: 14.35, Min: 13.52
Passed tests : 150
Failed tests : 0
Converged: True
Convergence score: 0.6543167124610465
updating...
Evaluating 150 values out of 150
Overall-timings: Avg: 13.830733333333335, Max: 14.35, Min: 13.52
Variance (6.189056123369398e-35) too small, using delta distribution
Variance (5.983422978265553e-35) too small, using delta distribution
Variance (1.2061136276725065e-33) too small, using delta distribution
Probability of fail : 0.0
All-Passed: True
Probabilty of failure: 0.0
Runtime: 13.830733333333335
Score: 13.830733333333335
Best-score: 13.830733333333335
Best-param: {'param1': 1200, 'param2': 0.0078902425490074}
Optimization Iteration: 6
Running with params: {'param1': 2500, 'param2': 0.0001281573811977586}
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Optimization Iteration: 7
Running with params: {'param1': 3500, 'param2': 0.0006258736722712593}
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Optimization Iteration: 8
Running with params: {'param1': 1800, 'param2': 0.00025222085289406055}
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Optimization Iteration: 9
Running with params: {'param1': 200, 'param2': 0.002367133296496039}
Lower learning rate.. returning... Best: 0.0078902425490074, Proposed: 0.002367133296496039
Optimization Iteration: 10
Running with params: {'param1': 1200, 'param2': 0.002419311355476905}
Lower learning rate.. returning... Best: 0.0078902425490074, Proposed: 0.002419311355476905
Optimization Iteration: 11
Running with params: {'param1': 500, 'param2': 0.03806458641778972}
Logdir: /mnt/batch/tasks/workitems/optwseed/job-1/Task_pyro/wd/borntobeflaky/tool/logs/optim_1597896455_pyro/run_1597914105/assert_67944811_500_0.03806458641778972
Launching 30 jobs, 30 in parallel
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Timings: Avg: 6.976333333333334, Max: 7.19, Min: 6.74
Passed tests : 30
Failed tests : 0
Converged: True
Convergence score: 0.9971199281889087
updating...
Evaluating 30 values out of 30
Overall-timings: Avg: 6.976333333333334, Max: 7.19, Min: 6.74
Variance (2.4226593758771106e-33) too small, using delta distribution
Variance (6.62439653788193e-34) too small, using delta distribution
Variance (2.8953146830621445e-33) too small, using delta distribution
Probability of fail : 0.0
All-Passed: True
Probabilty of failure: 0.0
Runtime: 6.976333333333334
Score: 6.976333333333334
Best-score: 6.976333333333334
Best-param: {'param1': 500, 'param2': 0.03806458641778972}
Optimization Iteration: 12
Running with params: {'param1': 2200, 'param2': 0.007638612796423931}
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Optimization Iteration: 13
Running with params: {'param1': 100, 'param2': 0.01578164786145726}
Lower learning rate.. returning... Best: 0.03806458641778972, Proposed: 0.01578164786145726
Optimization Iteration: 14
Running with params: {'param1': 2400, 'param2': 0.04801580994003646}
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Optimization Iteration: 15
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Optimization Iteration: 18
Running with params: {'param1': 200, 'param2': 0.035311127061438095}
Logdir: /mnt/batch/tasks/workitems/optwseed/job-1/Task_pyro/wd/borntobeflaky/tool/logs/optim_1597896455_pyro/run_1597914105/assert_67944811_200_0.035311127061438095
Launching 30 jobs, 30 in parallel
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Timings: Avg: 4.261666666666667, Max: 4.36, Min: 4.17
Passed tests : 0
Failed tests : 30
Half of samples failed, exiting...
All-Passed: False
Probabilty of failure: 1.0
Runtime: 10000.0
Score: 3.4028234663852886e+38
Best-score: 6.976333333333334
Best-param: {'param1': 500, 'param2': 0.03806458641778972}
Optimization Iteration: 19
Running with params: {'param1': 400, 'param2': 0.0057620338630741015}
Lower learning rate.. returning... Best: 0.03806458641778972, Proposed: 0.0057620338630741015
Optimization Iteration: 20
Running with params: {'param1': 1000, 'param2': 0.0008625351826320891}
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Optimization Iteration: 21
Running with params: {'param1': 500, 'param2': 0.015750404898809266}
Lower learning rate.. returning... Best: 0.03806458641778972, Proposed: 0.015750404898809266
Optimization Iteration: 22
Running with params: {'param1': 800, 'param2': 0.005129449706104467}
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Optimization Iteration: 23
Running with params: {'param1': 400, 'param2': 0.015187496638575453}
Lower learning rate.. returning... Best: 0.03806458641778972, Proposed: 0.015187496638575453
Optimization Iteration: 24
Running with params: {'param1': 300, 'param2': 0.009952665088850286}
Lower learning rate.. returning... Best: 0.03806458641778972, Proposed: 0.009952665088850286
Optimization Iteration: 25
Running with params: {'param1': 4400, 'param2': 0.0041287568523075534}
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Optimization Iteration: 26
Running with params: {'param1': 800, 'param2': 0.02804727763647021}
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Optimization Iteration: 27
Running with params: {'param1': 300, 'param2': 0.021983063764914233}
Lower learning rate.. returning... Best: 0.03806458641778972, Proposed: 0.021983063764914233
Optimization Iteration: 28
Running with params: {'param1': 100, 'param2': 0.003381536444575164}
Lower learning rate.. returning... Best: 0.03806458641778972, Proposed: 0.003381536444575164
Optimization Iteration: 29
Running with params: {'param1': 600, 'param2': 0.009488862266350618}
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Optimization Iteration: 30
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Optimization Iteration: 31
Running with params: {'param1': 300, 'param2': 0.010154950991582857}
Lower learning rate.. returning... Best: 0.03806458641778972, Proposed: 0.010154950991582857
Optimization Iteration: 32
Running with params: {'param1': 1500, 'param2': 0.0018313577977445241}
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Optimization Iteration: 33
Running with params: {'param1': 600, 'param2': 0.04984876092402105}
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Optimization Iteration: 34
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Optimization Iteration: 35
Running with params: {'param1': 100, 'param2': 0.012386105418119277}
Lower learning rate.. returning... Best: 0.03806458641778972, Proposed: 0.012386105418119277
Optimization Iteration: 36
Running with params: {'param1': 1100, 'param2': 0.003146426518065273}
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Optimization Iteration: 37
Running with params: {'param1': 400, 'param2': 0.001573504264974003}
Lower learning rate.. returning... Best: 0.03806458641778972, Proposed: 0.001573504264974003
Optimization Iteration: 38
Running with params: {'param1': 700, 'param2': 0.04239055678336274}
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Optimization Iteration: 39
Running with params: {'param1': 200, 'param2': 0.024174068480976533}
Lower learning rate.. returning... Best: 0.03806458641778972, Proposed: 0.024174068480976533
Optimization Iteration: 40
Running with params: {'param1': 3500, 'param2': 0.0002722865849663719}
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Optimization Iteration: 41
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Optimization Iteration: 42
Running with params: {'param1': 500, 'param2': 0.0028723994310804065}
Lower learning rate.. returning... Best: 0.03806458641778972, Proposed: 0.0028723994310804065
Optimization Iteration: 43
Running with params: {'param1': 1200, 'param2': 0.004712040982634856}
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Optimization Iteration: 44
Running with params: {'param1': 2900, 'param2': 0.036826079590630875}
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Optimization Iteration: 45
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Optimization Iteration: 46
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Optimization Iteration: 47
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Lower learning rate.. returning... Best: 0.03806458641778972, Proposed: 0.0001058583124581855
Optimization Iteration: 48
Running with params: {'param1': 200, 'param2': 0.020136945549533204}
Lower learning rate.. returning... Best: 0.03806458641778972, Proposed: 0.020136945549533204
Optimization Iteration: 49
Running with params: {'param1': 100, 'param2': 0.012576894525813502}
Lower learning rate.. returning... Best: 0.03806458641778972, Proposed: 0.012576894525813502
Optimization Iteration: 50
Running with params: {'param1': 1500, 'param2': 0.00015835911012461912}
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Upto iteration 50: {'param1': 500.0, 'param2': 0.03806458641778972}
Optimization Iteration: 51
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Optimization Iteration: 52
Running with params: {'param1': 300, 'param2': 0.006082946778246026}
Lower learning rate.. returning... Best: 0.03806458641778972, Proposed: 0.006082946778246026
Optimization Iteration: 53
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Optimization Iteration: 58
Running with params: {'param1': 400, 'param2': 0.008622324977727322}
Lower learning rate.. returning... Best: 0.03806458641778972, Proposed: 0.008622324977727322
Optimization Iteration: 59
Running with params: {'param1': 100, 'param2': 0.0011838246983956282}
Lower learning rate.. returning... Best: 0.03806458641778972, Proposed: 0.0011838246983956282
Optimization Iteration: 60
Running with params: {'param1': 200, 'param2': 0.03946316153890626}
Logdir: /mnt/batch/tasks/workitems/optwseed/job-1/Task_pyro/wd/borntobeflaky/tool/logs/optim_1597896455_pyro/run_1597914105/assert_67944811_200_0.03946316153890626
Launching 30 jobs, 30 in parallel
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Timings: Avg: 4.278666666666666, Max: 4.33, Min: 4.2299999999999995
Passed tests : 0
Failed tests : 30
Half of samples failed, exiting...
All-Passed: False
Probabilty of failure: 1.0
Runtime: 10000.0
Score: 3.4028234663852886e+38
Best-score: 6.976333333333334
Best-param: {'param1': 500, 'param2': 0.03806458641778972}
Optimization Iteration: 61
Running with params: {'param1': 1800, 'param2': 0.0006685132736511687}
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Optimization Iteration: 62
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Optimization Iteration: 63
Running with params: {'param1': 300, 'param2': 0.029406860150867745}
Lower learning rate.. returning... Best: 0.03806458641778972, Proposed: 0.029406860150867745
Optimization Iteration: 64
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Optimization Iteration: 65
Running with params: {'param1': 1600, 'param2': 0.0038724850522997597}
Higher iteration... returning...
Optimization Iteration: 66
Running with params: {'param1': 100, 'param2': 0.0003752845864096658}
Lower learning rate.. returning... Best: 0.03806458641778972, Proposed: 0.0003752845864096658
Optimization Iteration: 67
Running with params: {'param1': 200, 'param2': 0.0014532585501546523}
Lower learning rate.. returning... Best: 0.03806458641778972, Proposed: 0.0014532585501546523
Optimization Iteration: 68
Running with params: {'param1': 500, 'param2': 0.0027414839331990812}
Lower learning rate.. returning... Best: 0.03806458641778972, Proposed: 0.0027414839331990812
Optimization Iteration: 69
Running with params: {'param1': 200, 'param2': 0.0007952775097584281}
Lower learning rate.. returning... Best: 0.03806458641778972, Proposed: 0.0007952775097584281
Optimization Iteration: 70
Running with params: {'param1': 400, 'param2': 0.0005251433510644742}
Lower learning rate.. returning... Best: 0.03806458641778972, Proposed: 0.0005251433510644742
Optimization Iteration: 71
Running with params: {'param1': 100, 'param2': 0.00038367963269904636}
Lower learning rate.. returning... Best: 0.03806458641778972, Proposed: 0.00038367963269904636
Optimization Iteration: 72
Running with params: {'param1': 100, 'param2': 0.001996445476206271}
Lower learning rate.. returning... Best: 0.03806458641778972, Proposed: 0.001996445476206271
Optimization Iteration: 73
Running with params: {'param1': 300, 'param2': 0.00015189573105471753}
Lower learning rate.. returning... Best: 0.03806458641778972, Proposed: 0.00015189573105471753
Optimization Iteration: 74
Running with params: {'param1': 1100, 'param2': 0.0011974032285048631}
Higher iteration... returning...
Optimization Iteration: 75
Running with params: {'param1': 200, 'param2': 0.005016835883526521}
Lower learning rate.. returning... Best: 0.03806458641778972, Proposed: 0.005016835883526521
Optimization Iteration: 76
Running with params: {'param1': 300, 'param2': 0.0002206678212165725}
Lower learning rate.. returning... Best: 0.03806458641778972, Proposed: 0.0002206678212165725
Optimization Iteration: 77
Running with params: {'param1': 2400, 'param2': 0.006255141631306461}
Higher iteration... returning...
Optimization Iteration: 78
Running with params: {'param1': 600, 'param2': 0.0015137038578866022}
Higher iteration... returning...
Optimization Iteration: 79
Running with params: {'param1': 500, 'param2': 0.0007148664684222393}
Lower learning rate.. returning... Best: 0.03806458641778972, Proposed: 0.0007148664684222393
Optimization Iteration: 80
Running with params: {'param1': 800, 'param2': 0.00100778828681789}
Higher iteration... returning...
Optimization Iteration: 81
Running with params: {'param1': 2100, 'param2': 0.0035329945660723346}
Higher iteration... returning...
Optimization Iteration: 82
Running with params: {'param1': 3500, 'param2': 0.0003151524371891309}
Higher iteration... returning...
Optimization Iteration: 83
Running with params: {'param1': 200, 'param2': 0.04184620092629837}
Logdir: /mnt/batch/tasks/workitems/optwseed/job-1/Task_pyro/wd/borntobeflaky/tool/logs/optim_1597896455_pyro/run_1597914105/assert_67944811_200_0.04184620092629837
Launching 30 jobs, 30 in parallel
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Timings: Avg: 4.297000000000001, Max: 4.35, Min: 4.22
Passed tests : 0
Failed tests : 30
Half of samples failed, exiting...
All-Passed: False
Probabilty of failure: 1.0
Runtime: 10000.0
Score: 3.4028234663852886e+38
Best-score: 6.976333333333334
Best-param: {'param1': 500, 'param2': 0.03806458641778972}
Optimization Iteration: 84
Running with params: {'param1': 400, 'param2': 0.03453944436933892}
Logdir: /mnt/batch/tasks/workitems/optwseed/job-1/Task_pyro/wd/borntobeflaky/tool/logs/optim_1597896455_pyro/run_1597914105/assert_67944811_400_0.03453944436933892
Launching 30 jobs, 30 in parallel
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Timings: Avg: 6.19, Max: 6.27, Min: 6.1000000000000005
Passed tests : 0
Failed tests : 30
Half of samples failed, exiting...
All-Passed: False
Probabilty of failure: 1.0
Runtime: 10000.0
Score: 3.4028234663852886e+38
Best-score: 6.976333333333334
Best-param: {'param1': 500, 'param2': 0.03806458641778972}
Optimization Iteration: 85
Running with params: {'param1': 1700, 'param2': 0.0004643468525561339}
Higher iteration... returning...
Optimization Iteration: 86
Running with params: {'param1': 900, 'param2': 0.0020277517895847612}
Higher iteration... returning...
Optimization Iteration: 87
Running with params: {'param1': 300, 'param2': 0.025716333108777106}
Lower learning rate.. returning... Best: 0.03806458641778972, Proposed: 0.025716333108777106
Optimization Iteration: 88
Running with params: {'param1': 700, 'param2': 0.029834026403956156}
Higher iteration... returning...
Optimization Iteration: 89
Running with params: {'param1': 1300, 'param2': 0.017643767405640633}
Higher iteration... returning...
Optimization Iteration: 90
Running with params: {'param1': 2800, 'param2': 0.008211836671324137}
Higher iteration... returning...
Optimization Iteration: 91
Running with params: {'param1': 100, 'param2': 0.014284333883581768}
Lower learning rate.. returning... Best: 0.03806458641778972, Proposed: 0.014284333883581768
Optimization Iteration: 92
Running with params: {'param1': 100, 'param2': 0.004575910670971342}
Lower learning rate.. returning... Best: 0.03806458641778972, Proposed: 0.004575910670971342
Optimization Iteration: 93
Running with params: {'param1': 600, 'param2': 0.0026035550061389054}
Higher iteration... returning...
Optimization Iteration: 94
Running with params: {'param1': 1000, 'param2': 0.020215590228277992}
Higher iteration... returning...
Optimization Iteration: 95
Running with params: {'param1': 200, 'param2': 0.009598653113034945}
Lower learning rate.. returning... Best: 0.03806458641778972, Proposed: 0.009598653113034945
Optimization Iteration: 96
Running with params: {'param1': 400, 'param2': 0.011707372992891972}
Lower learning rate.. returning... Best: 0.03806458641778972, Proposed: 0.011707372992891972
Optimization Iteration: 97
Running with params: {'param1': 4200, 'param2': 0.00725906452660939}
Higher iteration... returning...
Optimization Iteration: 98
Running with params: {'param1': 100, 'param2': 0.001290492126244463}
Lower learning rate.. returning... Best: 0.03806458641778972, Proposed: 0.001290492126244463
Optimization Iteration: 99
Running with params: {'param1': 1100, 'param2': 0.003086535787659386}
Higher iteration... returning...
Optimization Iteration: 100
Running with params: {'param1': 300, 'param2': 0.005603662946121004}
Lower learning rate.. returning... Best: 0.03806458641778972, Proposed: 0.005603662946121004
Upto iteration 100: {'param1': 500.0, 'param2': 0.03806458641778972}
Breaking...
{'param1': 500.0, 'param2': 0.03806458641778972}
Best score: 6.976333333333334
Repeated params: 0
Trials: 101
Best param {'param1': 500.0, 'param2': 0.03806458641778972}
Reduction: 86.47843100095402%
Speedup: 7.395591444088744x
Optimizer time: 278.74812746047974
