Repo: pyro
Filename: /mnt/batch/tasks/workitems/optwseed/job-1/Task_pyro/wd/borntobeflaky/projects/pyro/tests/infer/test_inference.py
ClassName: PoissonGammaTests
Testname: test_mmd_vectorized
Params: param1,291,18,ParamType.ITER,25000
param2,353,33,ParamType.LR,0.0002
Assertion: assert_equal(pyro.param('alpha_q'), self.alpha0, prec=0.2, msg='{} vs {}'.format())
Original runtime: 329.375
>>Getting original runtime
Optimization Iteration: 1
Running with params: {'param1': 25000, 'param2': 0.0002}
Logdir: /mnt/batch/tasks/workitems/optwseed/job-1/Task_pyro/wd/borntobeflaky/tool/logs/optim_1597896455_pyro/run_1597906673/assert_40914897_25000_0.0002
Launching 30 jobs, 30 in parallel
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Timings: Avg: 322.00966666666665, Max: 333.32, Min: 313.88
Passed tests : 30
Failed tests : 0
Converged: True
Convergence score: 0.0
updating...
Evaluating 30 values out of 30
Overall-timings: Avg: 322.00966666666665, Max: 333.32, Min: 313.88
Variance (0.0) too small, using delta distribution
Variance (0.0) too small, using delta distribution
Probability of fail : 0.0
All-Passed: True
Probabilty of failure: 0.0
Runtime: 322.00966666666665
Score: 322.00966666666665
Best-score: 322.00966666666665
Best-param: {'param1': 25000, 'param2': 0.0002}
>>Setting original runtime to 322.00966666666665
Optimization Iteration: 1
Running with params: {'param1': 1300, 'param2': 0.0013916648864866514}
Logdir: /mnt/batch/tasks/workitems/optwseed/job-1/Task_pyro/wd/borntobeflaky/tool/logs/optim_1597896455_pyro/run_1597906673/assert_40914897_1300_0.0013916648864866514
Launching 30 jobs, 30 in parallel
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Timings: Avg: 18.99433333333333, Max: 19.43, Min: 18.65
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: 322.00966666666665
Best-param: {'param1': 25000, 'param2': 0.0002}
Optimization Iteration: 2
Running with params: {'param1': 1200, 'param2': 0.0006709673274977327}
Logdir: /mnt/batch/tasks/workitems/optwseed/job-1/Task_pyro/wd/borntobeflaky/tool/logs/optim_1597896455_pyro/run_1597906673/assert_40914897_1200_0.0006709673274977327
Launching 30 jobs, 30 in parallel
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Timings: Avg: 17.663, Max: 18.02, Min: 17.16
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: 322.00966666666665
Best-param: {'param1': 25000, 'param2': 0.0002}
Optimization Iteration: 3
Running with params: {'param1': 2500, 'param2': 1.5930964659098137e-05}
Lower learning rate.. returning... Best: 0.0002, Proposed: 1.5930964659098137e-05
Optimization Iteration: 4
Running with params: {'param1': 5900, 'param2': 0.0023421626530285016}
Logdir: /mnt/batch/tasks/workitems/optwseed/job-1/Task_pyro/wd/borntobeflaky/tool/logs/optim_1597896455_pyro/run_1597906673/assert_40914897_5900_0.0023421626530285016
Launching 30 jobs, 30 in parallel
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Timings: Avg: 77.60766666666667, Max: 79.34, Min: 75.88
Passed tests : 30
Failed tests : 0
Converged: True
Convergence score: 0.0
updating...
Evaluating 30 values out of 30
Overall-timings: Avg: 77.60766666666667, Max: 79.34, Min: 75.88
Variance (0.0) too small, using delta distribution
Variance (0.0) too small, using delta distribution
Probability of fail : 0.0
All-Passed: True
Probabilty of failure: 0.0
Runtime: 77.60766666666667
Score: 77.60766666666667
Best-score: 77.60766666666667
Best-param: {'param1': 5900, 'param2': 0.0023421626530285016}
Optimization Iteration: 5
Running with params: {'param1': 100, 'param2': 0.0002502884308158092}
Lower learning rate.. returning... Best: 0.0023421626530285016, Proposed: 0.0002502884308158092
Optimization Iteration: 6
Running with params: {'param1': 200, 'param2': 0.021780393374533972}
Logdir: /mnt/batch/tasks/workitems/optwseed/job-1/Task_pyro/wd/borntobeflaky/tool/logs/optim_1597896455_pyro/run_1597906673/assert_40914897_200_0.021780393374533972
Launching 30 jobs, 30 in parallel
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Timings: Avg: 4.988333333333333, Max: 5.05, Min: 4.9
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: 77.60766666666667
Best-param: {'param1': 5900, 'param2': 0.0023421626530285016}
Optimization Iteration: 7
Running with params: {'param1': 400, 'param2': 2.3245949660102718e-05}
Lower learning rate.. returning... Best: 0.0023421626530285016, Proposed: 2.3245949660102718e-05
Optimization Iteration: 8
Running with params: {'param1': 1200, 'param2': 2.8742819692913616e-05}
Lower learning rate.. returning... Best: 0.0023421626530285016, Proposed: 2.8742819692913616e-05
Optimization Iteration: 9
Running with params: {'param1': 800, 'param2': 0.02274998293486783}
Logdir: /mnt/batch/tasks/workitems/optwseed/job-1/Task_pyro/wd/borntobeflaky/tool/logs/optim_1597896455_pyro/run_1597906673/assert_40914897_800_0.02274998293486783
Launching 30 jobs, 30 in parallel
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Timings: Avg: 12.43133333333333, Max: 12.64, Min: 12.2
Passed tests : 30
Failed tests : 0
Converged: True
Convergence score: 0.0
updating...
Evaluating 30 values out of 30
Overall-timings: Avg: 12.43133333333333, Max: 12.64, Min: 12.2
Variance (0.0) too small, using delta distribution
Variance (0.0) too small, using delta distribution
Probability of fail : 0.0
All-Passed: True
Probabilty of failure: 0.0
Runtime: 12.43133333333333
Score: 12.43133333333333
Best-score: 12.43133333333333
Best-param: {'param1': 800, 'param2': 0.02274998293486783}
Optimization Iteration: 10
Running with params: {'param1': 14500, 'param2': 2.1786310154658525e-05}
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Optimization Iteration: 11
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Optimization Iteration: 12
Running with params: {'param1': 1600, 'param2': 5.5940850302064975e-05}
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Optimization Iteration: 13
Running with params: {'param1': 200, 'param2': 0.0017470598425465569}
Lower learning rate.. returning... Best: 0.02274998293486783, Proposed: 0.0017470598425465569
Optimization Iteration: 14
Running with params: {'param1': 5000, 'param2': 1.3605206117833202e-05}
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Optimization Iteration: 17
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Optimization Iteration: 18
Running with params: {'param1': 1700, 'param2': 0.011565858575998185}
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Optimization Iteration: 19
Running with params: {'param1': 600, 'param2': 0.007328903870681032}
Lower learning rate.. returning... Best: 0.02274998293486783, Proposed: 0.007328903870681032
Optimization Iteration: 20
Running with params: {'param1': 500, 'param2': 0.0042881099285689535}
Lower learning rate.. returning... Best: 0.02274998293486783, Proposed: 0.0042881099285689535
Optimization Iteration: 21
Running with params: {'param1': 4600, 'param2': 0.057708566766941784}
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Optimization Iteration: 22
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Optimization Iteration: 23
Running with params: {'param1': 10100, 'param2': 0.0003759173668993513}
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Optimization Iteration: 24
Running with params: {'param1': 800, 'param2': 0.03280628157998601}
Logdir: /mnt/batch/tasks/workitems/optwseed/job-1/Task_pyro/wd/borntobeflaky/tool/logs/optim_1597896455_pyro/run_1597906673/assert_40914897_800_0.03280628157998601
Launching 30 jobs, 30 in parallel
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Timings: Avg: 12.473666666666668, Max: 12.67, Min: 12.04
Passed tests : 30
Failed tests : 0
Converged: True
Convergence score: 0.0
updating...
Evaluating 30 values out of 30
Overall-timings: Avg: 12.473666666666668, Max: 12.67, Min: 12.04
Variance (0.0) too small, using delta distribution
Variance (0.0) too small, using delta distribution
Probability of fail : 0.0
All-Passed: True
Probabilty of failure: 0.0
Runtime: 12.473666666666668
Score: 12.473666666666668
Best-score: 12.43133333333333
Best-param: {'param1': 800, 'param2': 0.02274998293486783}
Optimization Iteration: 25
Running with params: {'param1': 800, 'param2': 0.033196788994091425}
Logdir: /mnt/batch/tasks/workitems/optwseed/job-1/Task_pyro/wd/borntobeflaky/tool/logs/optim_1597896455_pyro/run_1597906673/assert_40914897_800_0.033196788994091425
Launching 30 jobs, 30 in parallel
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Timings: Avg: 12.480666666666666, Max: 12.76, Min: 12.14
Passed tests : 30
Failed tests : 0
Converged: True
Convergence score: 0.0
updating...
Evaluating 30 values out of 30
Overall-timings: Avg: 12.480666666666666, Max: 12.76, Min: 12.14
Variance (0.0) too small, using delta distribution
Variance (0.0) too small, using delta distribution
Probability of fail : 0.0
All-Passed: True
Probabilty of failure: 0.0
Runtime: 12.480666666666666
Score: 12.480666666666666
Best-score: 12.43133333333333
Best-param: {'param1': 800, 'param2': 0.02274998293486783}
Optimization Iteration: 26
Running with params: {'param1': 300, 'param2': 0.09724065010054286}
Logdir: /mnt/batch/tasks/workitems/optwseed/job-1/Task_pyro/wd/borntobeflaky/tool/logs/optim_1597896455_pyro/run_1597906673/assert_40914897_300_0.09724065010054286
Launching 30 jobs, 30 in parallel
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Timings: Avg: 6.075000000000002, Max: 6.17, Min: 5.93
Passed tests : 30
Failed tests : 0
Converged: True
Convergence score: 0.0
updating...
Evaluating 30 values out of 30
Overall-timings: Avg: 6.075000000000002, Max: 6.17, Min: 5.93
Variance (0.0) too small, using delta distribution
Variance (0.0) too small, using delta distribution
Probability of fail : 0.0
All-Passed: True
Probabilty of failure: 0.0
Runtime: 6.075000000000002
Score: 6.075000000000002
Best-score: 6.075000000000002
Best-param: {'param1': 300, 'param2': 0.09724065010054286}
Optimization Iteration: 27
Running with params: {'param1': 300, 'param2': 0.07093008304448215}
Lower learning rate.. returning... Best: 0.09724065010054286, Proposed: 0.07093008304448215
Optimization Iteration: 28
Running with params: {'param1': 100, 'param2': 0.01835657728033186}
Lower learning rate.. returning... Best: 0.09724065010054286, Proposed: 0.01835657728033186
Optimization Iteration: 29
Running with params: {'param1': 200, 'param2': 0.09105871266255525}
Logdir: /mnt/batch/tasks/workitems/optwseed/job-1/Task_pyro/wd/borntobeflaky/tool/logs/optim_1597896455_pyro/run_1597906673/assert_40914897_200_0.09105871266255525
Launching 30 jobs, 30 in parallel
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Timings: Avg: 4.729333333333334, Max: 4.8, Min: 4.61
Passed tests : 30
Failed tests : 0
Converged: True
Convergence score: 0.0
updating...
Evaluating 30 values out of 30
Overall-timings: Avg: 4.729333333333334, Max: 4.8, Min: 4.61
Variance (0.0) too small, using delta distribution
Variance (0.0) too small, using delta distribution
Probability of fail : 0.0
All-Passed: True
Probabilty of failure: 0.0
Runtime: 4.729333333333334
Score: 4.729333333333334
Best-score: 4.729333333333334
Best-param: {'param1': 200, 'param2': 0.09105871266255525}
Optimization Iteration: 30
Running with params: {'param1': 200, 'param2': 0.00012977641813129818}
Lower learning rate.. returning... Best: 0.09105871266255525, Proposed: 0.00012977641813129818
Optimization Iteration: 31
Running with params: {'param1': 100, 'param2': 0.08665518701963891}
Logdir: /mnt/batch/tasks/workitems/optwseed/job-1/Task_pyro/wd/borntobeflaky/tool/logs/optim_1597896455_pyro/run_1597906673/assert_40914897_100_0.08665518701963891
Launching 30 jobs, 30 in parallel
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Timings: Avg: 3.7116666666666664, Max: 3.77, Min: 3.62
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: 4.729333333333334
Best-param: {'param1': 200, 'param2': 0.09105871266255525}
Optimization Iteration: 32
Running with params: {'param1': 300, 'param2': 0.0009070663049092404}
Higher iteration... returning...
Optimization Iteration: 33
Running with params: {'param1': 100, 'param2': 0.08421571626456517}
Logdir: /mnt/batch/tasks/workitems/optwseed/job-1/Task_pyro/wd/borntobeflaky/tool/logs/optim_1597896455_pyro/run_1597906673/assert_40914897_100_0.08421571626456517
Launching 30 jobs, 30 in parallel
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Timings: Avg: 3.7040000000000006, Max: 3.75, Min: 3.64
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: 4.729333333333334
Best-param: {'param1': 200, 'param2': 0.09105871266255525}
Optimization Iteration: 34
Running with params: {'param1': 200, 'param2': 0.003030430850366645}
Lower learning rate.. returning... Best: 0.09105871266255525, Proposed: 0.003030430850366645
Optimization Iteration: 35
Running with params: {'param1': 300, 'param2': 0.0048559393737751776}
Higher iteration... returning...
Optimization Iteration: 36
Running with params: {'param1': 100, 'param2': 0.0005202015600313171}
Lower learning rate.. returning... Best: 0.09105871266255525, Proposed: 0.0005202015600313171
Optimization Iteration: 37
Running with params: {'param1': 400, 'param2': 0.03864147694452478}
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Optimization Iteration: 38
Running with params: {'param1': 1000, 'param2': 0.012163053218304753}
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Optimization Iteration: 39
Running with params: {'param1': 2500, 'param2': 0.000160963303027771}
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Optimization Iteration: 40
Running with params: {'param1': 200, 'param2': 0.0010542128443516722}
Lower learning rate.. returning... Best: 0.09105871266255525, Proposed: 0.0010542128443516722
Optimization Iteration: 41
Running with params: {'param1': 100, 'param2': 0.016807331760941038}
Lower learning rate.. returning... Best: 0.09105871266255525, Proposed: 0.016807331760941038
Optimization Iteration: 42
Running with params: {'param1': 500, 'param2': 0.04176602682987207}
Higher iteration... returning...
Optimization Iteration: 43
Running with params: {'param1': 200, 'param2': 0.09575963665465234}
Logdir: /mnt/batch/tasks/workitems/optwseed/job-1/Task_pyro/wd/borntobeflaky/tool/logs/optim_1597896455_pyro/run_1597906673/assert_40914897_200_0.09575963665465234
Launching 30 jobs, 30 in parallel
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Timings: Avg: 4.744, Max: 4.8, Min: 4.6000000000000005
Passed tests : 30
Failed tests : 0
Converged: True
Convergence score: 0.0
updating...
Evaluating 30 values out of 30
Overall-timings: Avg: 4.744, Max: 4.8, Min: 4.6000000000000005
Variance (0.0) too small, using delta distribution
Variance (0.0) too small, using delta distribution
Probability of fail : 0.0
All-Passed: True
Probabilty of failure: 0.0
Runtime: 4.744
Score: 4.744
Best-score: 4.729333333333334
Best-param: {'param1': 200, 'param2': 0.09105871266255525}
Optimization Iteration: 44
Running with params: {'param1': 100, 'param2': 0.025022511635926212}
Lower learning rate.. returning... Best: 0.09105871266255525, Proposed: 0.025022511635926212
Optimization Iteration: 45
Running with params: {'param1': 1100, 'param2': 0.008869488352372928}
Higher iteration... returning...
Optimization Iteration: 46
Running with params: {'param1': 200, 'param2': 0.003271501019082846}
Lower learning rate.. returning... Best: 0.09105871266255525, Proposed: 0.003271501019082846
Optimization Iteration: 47
Running with params: {'param1': 2900, 'param2': 0.005142410210806094}
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Optimization Iteration: 48
Running with params: {'param1': 600, 'param2': 0.0013861565656553554}
Higher iteration... returning...
Optimization Iteration: 49
Running with params: {'param1': 100, 'param2': 0.014535198072354035}
Lower learning rate.. returning... Best: 0.09105871266255525, Proposed: 0.014535198072354035
Optimization Iteration: 50
Running with params: {'param1': 400, 'param2': 0.05218151805463977}
Higher iteration... returning...
Upto iteration 50: {'param1': 200.0, 'param2': 0.09105871266255525}
Optimization Iteration: 51
Running with params: {'param1': 1300, 'param2': 0.00010228499073027943}
Higher iteration... returning...
Optimization Iteration: 52
Running with params: {'param1': 200, 'param2': 0.0002998160293036363}
Lower learning rate.. returning... Best: 0.09105871266255525, Proposed: 0.0002998160293036363
Optimization Iteration: 53
Running with params: {'param1': 8200, 'param2': 0.024086918981132424}
Higher iteration... returning...
Optimization Iteration: 54
Running with params: {'param1': 100, 'param2': 0.09450594235603382}
Logdir: /mnt/batch/tasks/workitems/optwseed/job-1/Task_pyro/wd/borntobeflaky/tool/logs/optim_1597896455_pyro/run_1597906673/assert_40914897_100_0.09450594235603382
Launching 30 jobs, 30 in parallel
Iter 0...
Iter 1...
Iter 2...
Iter 3...
Iter 4...
Iter 5...
Iter 6...
Iter 7...
Iter 8...
Iter 9...
Iter 13...
Iter 10...
Iter 11...
Iter 12...
Iter 16...
Iter 17...
Iter 18...
Iter 14...
Iter 15...
Iter 19...
Iter 20...
Iter 21...
Iter 22...
Iter 26...
Iter 23...
Iter 27...
Iter 28...
Iter 29...
Iter 25...
Iter 24...
Timings: Avg: 3.438333333333334, Max: 3.49, Min: 3.33
Passed tests : 30
Failed tests : 0
Converged: True
Convergence score: 0.0
updating...
Evaluating 30 values out of 30
Overall-timings: Avg: 3.438333333333334, Max: 3.49, Min: 3.33
Variance (0.0) too small, using delta distribution
Variance (4.333342374871281e-34) too small, using delta distribution
Probability of fail : 0.0
All-Passed: True
Probabilty of failure: 0.0
Runtime: 3.438333333333334
Score: 3.438333333333334
Best-score: 3.438333333333334
Best-param: {'param1': 100, 'param2': 0.09450594235603382}
Optimization Iteration: 55
Running with params: {'param1': 21000, 'param2': 0.009287133730639956}
Higher iteration... returning...
Optimization Iteration: 56
Running with params: {'param1': 100, 'param2': 0.0005844876174848691}
Lower learning rate.. returning... Best: 0.09450594235603382, Proposed: 0.0005844876174848691
Optimization Iteration: 57
Running with params: {'param1': 100, 'param2': 0.007146498719601051}
Lower learning rate.. returning... Best: 0.09450594235603382, Proposed: 0.007146498719601051
Optimization Iteration: 58
Running with params: {'param1': 600, 'param2': 6.948835607334138e-05}
Higher iteration... returning...
Optimization Iteration: 59
Running with params: {'param1': 2100, 'param2': 1.74136422436099e-05}
Higher iteration... returning...
Optimization Iteration: 60
Running with params: {'param1': 100, 'param2': 0.0023591571641831456}
Lower learning rate.. returning... Best: 0.09450594235603382, Proposed: 0.0023591571641831456
Optimization Iteration: 61
Running with params: {'param1': 400, 'param2': 0.06626845692771473}
Higher iteration... returning...
Optimization Iteration: 62
Running with params: {'param1': 15100, 'param2': 0.030530177000219837}
Higher iteration... returning...
Optimization Iteration: 63
Running with params: {'param1': 3700, 'param2': 1.0884505480653483e-05}
Higher iteration... returning...
Optimization Iteration: 64
Running with params: {'param1': 300, 'param2': 0.04714149753603385}
Higher iteration... returning...
Optimization Iteration: 65
Running with params: {'param1': 900, 'param2': 3.2981107835103336e-05}
Higher iteration... returning...
Optimization Iteration: 66
Running with params: {'param1': 200, 'param2': 0.07883162781118266}
Higher iteration... returning...
Optimization Iteration: 67
Running with params: {'param1': 200, 'param2': 0.030111596650751003}
Higher iteration... returning...
Optimization Iteration: 68
Running with params: {'param1': 100, 'param2': 0.02007363938011693}
Lower learning rate.. returning... Best: 0.09450594235603382, Proposed: 0.02007363938011693
Optimization Iteration: 69
Running with params: {'param1': 300, 'param2': 0.09227321709219542}
Higher iteration... returning...
Optimization Iteration: 70
Running with params: {'param1': 100, 'param2': 0.057678748115992294}
Lower learning rate.. returning... Best: 0.09450594235603382, Proposed: 0.057678748115992294
Optimization Iteration: 71
Running with params: {'param1': 200, 'param2': 0.011937875664462883}
Higher iteration... returning...
Optimization Iteration: 72
Running with params: {'param1': 500, 'param2': 0.040177147948757326}
Higher iteration... returning...
Optimization Iteration: 73
Running with params: {'param1': 700, 'param2': 0.005749971083454756}
Higher iteration... returning...
Optimization Iteration: 74
Running with params: {'param1': 100, 'param2': 0.0037400862675586568}
Lower learning rate.. returning... Best: 0.09450594235603382, Proposed: 0.0037400862675586568
Optimization Iteration: 75
Running with params: {'param1': 1400, 'param2': 0.0689801226927433}
Higher iteration... returning...
Optimization Iteration: 76
Running with params: {'param1': 300, 'param2': 0.09692950677287113}
Higher iteration... returning...
Optimization Iteration: 77
Running with params: {'param1': 100, 'param2': 0.01796535079788414}
Lower learning rate.. returning... Best: 0.09450594235603382, Proposed: 0.01796535079788414
Optimization Iteration: 78
Running with params: {'param1': 200, 'param2': 0.015409010338964364}
Higher iteration... returning...
Optimization Iteration: 79
Running with params: {'param1': 400, 'param2': 0.025226242858885087}
Higher iteration... returning...
Optimization Iteration: 80
Running with params: {'param1': 100, 'param2': 0.04920310109454312}
Lower learning rate.. returning... Best: 0.09450594235603382, Proposed: 0.04920310109454312
Optimization Iteration: 81
Running with params: {'param1': 200, 'param2': 0.0002152011026867699}
Higher iteration... returning...
Optimization Iteration: 82
Running with params: {'param1': 100, 'param2': 0.0008082537117894383}
Lower learning rate.. returning... Best: 0.09450594235603382, Proposed: 0.0008082537117894383
Optimization Iteration: 83
Running with params: {'param1': 1800, 'param2': 0.002429614592906162}
Higher iteration... returning...
Optimization Iteration: 84
Running with params: {'param1': 6600, 'param2': 0.0014340232739747394}
Higher iteration... returning...
Optimization Iteration: 85
Running with params: {'param1': 500, 'param2': 0.006318231369813378}
Higher iteration... returning...
Optimization Iteration: 86
Running with params: {'param1': 300, 'param2': 0.011082463790744854}
Higher iteration... returning...
Optimization Iteration: 87
Running with params: {'param1': 100, 'param2': 0.07089650392171477}
Lower learning rate.. returning... Best: 0.09450594235603382, Proposed: 0.07089650392171477
Optimization Iteration: 88
Running with params: {'param1': 200, 'param2': 0.03532084054273531}
Higher iteration... returning...
Optimization Iteration: 89
Running with params: {'param1': 700, 'param2': 0.09849174110291208}
Higher iteration... returning...
Optimization Iteration: 90
Running with params: {'param1': 100, 'param2': 0.00037737206391783815}
Lower learning rate.. returning... Best: 0.09450594235603382, Proposed: 0.00037737206391783815
Optimization Iteration: 91
Running with params: {'param1': 200, 'param2': 0.027358161801238148}
Higher iteration... returning...
Optimization Iteration: 92
Running with params: {'param1': 400, 'param2': 0.02171116680345709}
Higher iteration... returning...
Optimization Iteration: 93
Running with params: {'param1': 100, 'param2': 0.008160946604403843}
Lower learning rate.. returning... Best: 0.09450594235603382, Proposed: 0.008160946604403843
Optimization Iteration: 94
Running with params: {'param1': 900, 'param2': 0.047059291903941584}
Higher iteration... returning...
Optimization Iteration: 95
Running with params: {'param1': 100, 'param2': 0.004363762227087607}
Lower learning rate.. returning... Best: 0.09450594235603382, Proposed: 0.004363762227087607
Optimization Iteration: 96
Running with params: {'param1': 200, 'param2': 0.056559000197390345}
Higher iteration... returning...
Optimization Iteration: 97
Running with params: {'param1': 3000, 'param2': 0.013761468309298579}
Higher iteration... returning...
Optimization Iteration: 98
Running with params: {'param1': 400, 'param2': 0.08030117004887063}
Higher iteration... returning...
Optimization Iteration: 99
Running with params: {'param1': 1100, 'param2': 0.010533632012353645}
Higher iteration... returning...
Optimization Iteration: 100
Running with params: {'param1': 300, 'param2': 0.03944299662702098}
Higher iteration... returning...
Upto iteration 100: {'param1': 100.0, 'param2': 0.09450594235603382}
Optimization Iteration: 101
Running with params: {'param1': 13300, 'param2': 0.001987980605259681}
Higher iteration... returning...
Optimization Iteration: 102
Running with params: {'param1': 100, 'param2': 0.002851841455287704}
Lower learning rate.. returning... Best: 0.09450594235603382, Proposed: 0.002851841455287704
Optimization Iteration: 103
Running with params: {'param1': 500, 'param2': 0.06813233276552844}
Higher iteration... returning...
Optimization Iteration: 104
Running with params: {'param1': 1500, 'param2': 0.03196054356668623}
Higher iteration... returning...
Optimization Iteration: 105
Running with params: {'param1': 600, 'param2': 0.001139663960819575}
Higher iteration... returning...
Optimization Iteration: 106
Running with params: {'param1': 200, 'param2': 7.579458625862486e-05}
Higher iteration... returning...
Optimization Iteration: 107
Running with params: {'param1': 700, 'param2': 0.09910703273797618}
Higher iteration... returning...
Optimization Iteration: 108
Running with params: {'param1': 300, 'param2': 0.0185196238003217}
Higher iteration... returning...
Optimization Iteration: 109
Running with params: {'param1': 100, 'param2': 2.673769517671857e-05}
Lower learning rate.. returning... Best: 0.09450594235603382, Proposed: 2.673769517671857e-05
Optimization Iteration: 110
Running with params: {'param1': 300, 'param2': 0.0004393885859347811}
Higher iteration... returning...
Optimization Iteration: 111
Running with params: {'param1': 200, 'param2': 0.013701660480840107}
Higher iteration... returning...
Optimization Iteration: 112
Running with params: {'param1': 100, 'param2': 0.006837059499369158}
Lower learning rate.. returning... Best: 0.09450594235603382, Proposed: 0.006837059499369158
Optimization Iteration: 113
Running with params: {'param1': 5300, 'param2': 0.0007585761434022754}
Higher iteration... returning...
Optimization Iteration: 114
Running with params: {'param1': 2200, 'param2': 0.00020228645096522638}
Higher iteration... returning...
Optimization Iteration: 115
Running with params: {'param1': 100, 'param2': 0.04439882930870421}
Lower learning rate.. returning... Best: 0.09450594235603382, Proposed: 0.04439882930870421
Optimization Iteration: 116
Running with params: {'param1': 200, 'param2': 4.333054731139324e-05}
Higher iteration... returning...
Optimization Iteration: 117
Running with params: {'param1': 1200, 'param2': 0.009235025329684385}
Higher iteration... returning...
Optimization Iteration: 118
Running with params: {'param1': 100, 'param2': 0.005508314844067772}
Lower learning rate.. returning... Best: 0.09450594235603382, Proposed: 0.005508314844067772
Optimization Iteration: 119
Running with params: {'param1': 100, 'param2': 0.06007620372476354}
Lower learning rate.. returning... Best: 0.09450594235603382, Proposed: 0.06007620372476354
Optimization Iteration: 120
Running with params: {'param1': 400, 'param2': 0.022746370399256092}
Higher iteration... returning...
Optimization Iteration: 121
Running with params: {'param1': 3400, 'param2': 0.08074553741225277}
Higher iteration... returning...
Optimization Iteration: 122
Running with params: {'param1': 4300, 'param2': 0.0016759302644657047}
Higher iteration... returning...
Optimization Iteration: 123
Running with params: {'param1': 900, 'param2': 0.00012827404899162095}
Higher iteration... returning...
Optimization Iteration: 124
Running with params: {'param1': 100, 'param2': 0.029019336037916725}
Lower learning rate.. returning... Best: 0.09450594235603382, Proposed: 0.029019336037916725
Optimization Iteration: 125
Running with params: {'param1': 200, 'param2': 1.2858151371297168e-05}
Higher iteration... returning...
Optimization Iteration: 126
Running with params: {'param1': 200, 'param2': 0.03638318097981056}
Higher iteration... returning...
Optimization Iteration: 127
Running with params: {'param1': 500, 'param2': 0.09992233216441004}
Higher iteration... returning...
Optimization Iteration: 128
Running with params: {'param1': 300, 'param2': 0.003699516849700835}
Higher iteration... returning...
Optimization Iteration: 129
Running with params: {'param1': 600, 'param2': 0.016749613717858935}
Higher iteration... returning...
Optimization Iteration: 130
Running with params: {'param1': 1800, 'param2': 0.049895886968332186}
Higher iteration... returning...
Optimization Iteration: 131
Running with params: {'param1': 100, 'param2': 0.06172541969755702}
Lower learning rate.. returning... Best: 0.09450594235603382, Proposed: 0.06172541969755702
Optimization Iteration: 132
Running with params: {'param1': 100, 'param2': 0.025244710787107633}
Lower learning rate.. returning... Best: 0.09450594235603382, Proposed: 0.025244710787107633
Optimization Iteration: 133
Running with params: {'param1': 800, 'param2': 0.08672680528394035}
Higher iteration... returning...
Optimization Iteration: 134
Running with params: {'param1': 10800, 'param2': 0.012799290626167217}
Higher iteration... returning...
Optimization Iteration: 135
Running with params: {'param1': 400, 'param2': 0.010496203564378595}
Higher iteration... returning...
Optimization Iteration: 136
Running with params: {'param1': 7300, 'param2': 0.020260453169353525}
Higher iteration... returning...
Optimization Iteration: 137
Running with params: {'param1': 18000, 'param2': 0.004506745235730309}
Higher iteration... returning...
Optimization Iteration: 138
Running with params: {'param1': 200, 'param2': 1.9362634721081523e-05}
Higher iteration... returning...
Optimization Iteration: 139
Running with params: {'param1': 2600, 'param2': 0.03622167277751143}
Higher iteration... returning...
Optimization Iteration: 140
Running with params: {'param1': 300, 'param2': 0.0002752233006950877}
Higher iteration... returning...
Optimization Iteration: 141
Running with params: {'param1': 23700, 'param2': 0.07371645267041596}
Higher iteration... returning...
Optimization Iteration: 142
Running with params: {'param1': 100, 'param2': 0.002664989271059225}
Lower learning rate.. returning... Best: 0.09450594235603382, Proposed: 0.002664989271059225
Optimization Iteration: 143
Running with params: {'param1': 200, 'param2': 0.008118168399638165}
Higher iteration... returning...
Optimization Iteration: 144
Running with params: {'param1': 100, 'param2': 0.0005854675625300124}
Lower learning rate.. returning... Best: 0.09450594235603382, Proposed: 0.0005854675625300124
Optimization Iteration: 145
Running with params: {'param1': 200, 'param2': 0.09759249319688888}
Higher iteration... returning...
Optimization Iteration: 146
Running with params: {'param1': 300, 'param2': 0.05837514390846975}
Higher iteration... returning...
Optimization Iteration: 147
Running with params: {'param1': 100, 'param2': 0.04738997154015774}
Lower learning rate.. returning... Best: 0.09450594235603382, Proposed: 0.04738997154015774
Optimization Iteration: 148
Running with params: {'param1': 500, 'param2': 0.04089841223654597}
Higher iteration... returning...
Optimization Iteration: 149
Running with params: {'param1': 100, 'param2': 0.03084339406789499}
Lower learning rate.. returning... Best: 0.09450594235603382, Proposed: 0.03084339406789499
Optimization Iteration: 150
Running with params: {'param1': 200, 'param2': 0.08012429726238811}
Higher iteration... returning...
Upto iteration 150: {'param1': 100.0, 'param2': 0.09450594235603382}
Breaking...
{'param1': 100.0, 'param2': 0.09450594235603382}
Best score: 3.438333333333334
Repeated params: 0
Trials: 151
Best param {'param1': 100.0, 'param2': 0.09450594235603382}
Reduction: 98.93222667228416%
Speedup: 93.65283567619969x
Optimizer time: 524.658543586731
