Status: Done!
Total Time
2m21s
Max Memory Usage
84M
Domain
MulticlassClassification
Learn time
2m6s
Train error
0.070
Predict train time
1s
Test error
0.078
Predict test time
0s
Log file
... (lines omitted) ...
iteration:622 feature:19 threshold:2.837 min-objective:0.999842
iteration:623 feature:19 threshold:3.255 min-objective:0.999745
iteration:624 feature:19 threshold:4.845 min-objective:0.999761
iteration:625 feature:23 threshold:0.505217 min-objective:0.999757
iteration:626 feature:38 threshold:9.5 min-objective:0.999771
iteration:627 feature:39 threshold:0.540064 min-objective:0.999796
iteration:628 feature:42 threshold:2.41667 min-objective:0.999841
iteration:629 feature:20 threshold:79.7 min-objective:0.999861
iteration:630 feature:19 threshold:6.525 min-objective:0.999866
iteration:631 feature:19 threshold:7.4215 min-objective:0.999772
iteration:632 feature:23 threshold:0.662809 min-objective:0.999827
iteration:633 feature:26 threshold:131.5 min-objective:0.999784
iteration:634 feature:26 threshold:149.5 min-objective:0.999785
iteration:635 feature:35 threshold:1280.29 min-objective:0.999765
iteration:636 feature:15 threshold:11.5 min-objective:0.999809
iteration:637 feature:35 threshold:857.187 min-objective:0.999796
iteration:638 feature:27 threshold:18.5 min-objective:0.999817
iteration:639 feature:19 threshold:5.885 min-objective:0.999804
iteration:640 feature:19 threshold:5.37 min-objective:0.999775
iteration:641 feature:19 threshold:5.525 min-objective:0.999767
iteration:642 feature:23 threshold:0.54326 min-objective:0.999789
iteration:643 feature:15 threshold:15.5 min-objective:0.99978
iteration:644 feature:35 threshold:1737.76 min-objective:0.999837
iteration:645 feature:26 threshold:229 min-objective:0.999804
iteration:646 feature:26 threshold:219.5 min-objective:0.999826
iteration:647 feature:23 threshold:0.64681 min-objective:0.999842
iteration:648 feature:19 threshold:6.411 min-objective:0.999773
iteration:649 feature:19 threshold:4.163 min-objective:0.999839
iteration:650 feature:3 threshold:3.5 min-objective:0.999799
iteration:651 feature:19 threshold:1.8215 min-objective:0.99981
iteration:652 feature:19 threshold:2.9775 min-objective:0.99979
iteration:653 feature:15 threshold:6.5 min-objective:0.999798
iteration:654 feature:12 threshold:8.5 min-objective:0.99978
iteration:655 feature:26 threshold:110.5 min-objective:0.999777
iteration:656 feature:26 threshold:99.5 min-objective:0.999754
iteration:657 feature:29 threshold:55.5 min-objective:0.999806
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iteration:659 feature:13 threshold:5.61343 min-objective:0.999811
iteration:660 feature:13 threshold:7.2797 min-objective:0.999842
iteration:661 feature:26 threshold:229 min-objective:0.999832
iteration:662 feature:25 threshold:0.136232 min-objective:0.999853
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iteration:664 feature:3 threshold:2.5 min-objective:0.999835
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iteration:687 feature:20 threshold:403.735 min-objective:0.999834
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iteration:699 feature:20 threshold:833.975 min-objective:0.999803
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iteration:723 feature:23 threshold:0.319373 min-objective:0.999841
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iteration:725 feature:23 threshold:0.442241 min-objective:0.999828
iteration:726 feature:19 threshold:4.918 min-objective:0.999828
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iteration:758 feature:31 threshold:1.5 min-objective:0.999849
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iteration:760 feature:20 threshold:1501.71 min-objective:0.999858
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iteration:949 feature:23 threshold:0.505217 min-objective:0.999874
iteration:950 feature:15 threshold:10.5 min-objective:0.999806
iteration:951 feature:29 threshold:56.5 min-objective:0.999839
iteration:952 feature:35 threshold:482.481 min-objective:0.999784
iteration:953 feature:13 threshold:3.28036 min-objective:0.999821
iteration:954 feature:15 threshold:7.5 min-objective:0.999863
iteration:955 feature:35 threshold:355.183 min-objective:0.999799
iteration:956 feature:15 threshold:4.5 min-objective:0.999816
iteration:957 feature:22 threshold:4.5 min-objective:0.999829
iteration:958 feature:26 threshold:56.5 min-objective:0.999841
iteration:959 feature:26 threshold:63.5 min-objective:0.999817
iteration:960 feature:26 threshold:68.5 min-objective:0.999783
iteration:961 feature:26 threshold:71.5 min-objective:0.999786
iteration:962 feature:26 threshold:68.5 min-objective:0.999814
iteration:963 feature:26 threshold:63.5 min-objective:0.999822
iteration:964 feature:26 threshold:66.5 min-objective:0.999832
iteration:965 feature:26 threshold:71.5 min-objective:0.999829
iteration:966 feature:12 threshold:6.5 min-objective:0.999841
iteration:967 feature:38 threshold:8.5 min-objective:0.999874
iteration:968 feature:38 threshold:10.5 min-objective:0.999846
iteration:969 feature:9 threshold:12.5 min-objective:0.999865
iteration:970 feature:13 threshold:4.90409 min-objective:0.999832
iteration:971 feature:26 threshold:139.5 min-objective:0.999869
iteration:972 feature:26 threshold:149.5 min-objective:0.999807
iteration:973 feature:26 threshold:139.5 min-objective:0.999861
iteration:974 feature:23 threshold:0.54326 min-objective:0.999867
iteration:975 feature:19 threshold:8.105 min-objective:0.999834
iteration:976 feature:23 threshold:0.821483 min-objective:0.999871
iteration:977 feature:23 threshold:0.72675 min-objective:0.999845
iteration:978 feature:15 threshold:21.5 min-objective:0.999843
iteration:979 feature:10 threshold:5.5 min-objective:0.999856
iteration:980 feature:20 threshold:2827.96 min-objective:0.999887
iteration:981 feature:20 threshold:2165.44 min-objective:0.999841
iteration:982 feature:20 threshold:2519.66 min-objective:0.999802
iteration:983 feature:20 threshold:2520.31 min-objective:0.999792
iteration:984 feature:20 threshold:2519.66 min-objective:0.999857
iteration:985 feature:20 threshold:2165.44 min-objective:0.999835
iteration:986 feature:20 threshold:2162.66 min-objective:0.999801
iteration:987 feature:20 threshold:2095.24 min-objective:0.99982
iteration:988 feature:20 threshold:2084.27 min-objective:0.999777
iteration:989 feature:20 threshold:2030.62 min-objective:0.99979
iteration:990 feature:20 threshold:1994.49 min-objective:0.999783
iteration:991 feature:20 threshold:2003.38 min-objective:0.99979
iteration:992 feature:20 threshold:2062.09 min-objective:0.999817
iteration:993 feature:20 threshold:2095.24 min-objective:0.999846
iteration:994 feature:20 threshold:2127.86 min-objective:0.99986
iteration:995 feature:20 threshold:2165.44 min-objective:0.999848
iteration:996 feature:20 threshold:2519.66 min-objective:0.999881
iteration:997 feature:39 threshold:0.338541 min-objective:0.999879
iteration:998 feature:39 threshold:0.472136 min-objective:0.999849
iteration:999 feature:39 threshold:0.44636 min-objective:0.999809
=== END program1: ./run learn ../dataset2/train --- OK [126s]
===== MAIN: predict/evaluate on train data =====
=== START program3: ./run stripLabels ../dataset2/train ../program0/evalTrain.in
=== END program3: ./run stripLabels ../dataset2/train ../program0/evalTrain.in --- OK [1s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [1s]
=== START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out
=== END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [2s]
===== MAIN: predict/evaluate on test data =====
=== START program3: ./run stripLabels ../dataset2/test ../program0/evalTest.in
=== END program3: ./run stripLabels ../dataset2/test ../program0/evalTest.in --- OK [0s]
=== START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [0s]
=== START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out
=== END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [1s]
real 2m20.817s
user 2m11.728s
sys 0m5.800s
supervised-learning : Main entry for supervised learning for training and testing a program on a dataset.
(learner:Program) minimalist-boost : Minimalist implementation of boostexter's "scored-text" threshold-based weak learners. Source code included.
(dataset:Dataset) cemst-decision-prediction-asr0 : Complementary Evaluation Measures for Speech Transcription, train-test split for task decision-prediction with asr0
(stripper:Program[Strip]) multiclass-utils : Validates and inspects a dataset in MulticlassClassification format.
(evaluator:Program[Evaluate]) classification-evaluator : Evaluates predictions of classification datasets (discrete outputs).
doTest:
evaluate:
errorRate: 0.0781789638932496
numErrors: 498
numExamples: 6370
success: true
time: 1
predict:
strip:
doTrain:
evaluate:
errorRate: 0.0697593128932388
numErrors: 1397
numExamples: 20026
success: true
time: 2
predict:
strip:
exitCode: 0
learn:
success: true
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