Status: Done!
Total Time
Max Memory Usage
Domain
MulticlassClassification
Learn time
0s
Train error
0
Predict train time
0s
Test error
0.500
Predict test time
0s
Log file
... (lines omitted) ...
iteration:622 feature:27 threshold:25.5 min-objective:0.819033
iteration:623 feature:14 threshold:231.5 min-objective:0.801194
iteration:624 feature:15 threshold:71 min-objective:0.817815
iteration:625 feature:30 threshold:2.5 min-objective:0.82765
iteration:626 feature:1 threshold:30.5 min-objective:0.805717
iteration:627 feature:46 threshold:44 min-objective:0.80227
iteration:628 feature:35 threshold:10.5 min-objective:0.833735
iteration:629 feature:13 threshold:860.5 min-objective:0.830227
iteration:630 feature:19 threshold:221 min-objective:0.843262
iteration:631 feature:42 threshold:75.5 min-objective:0.842952
iteration:632 feature:29 threshold:3.5 min-objective:0.831544
iteration:633 feature:27 threshold:25.5 min-objective:0.833395
iteration:634 feature:3 threshold:12.5 min-objective:0.823426
iteration:635 feature:18 threshold:394 min-objective:0.856716
iteration:636 feature:14 threshold:231.5 min-objective:0.807497
iteration:637 feature:2 threshold:49 min-objective:0.785897
iteration:638 feature:37 threshold:10.5 min-objective:0.830783
iteration:639 feature:23 threshold:806.5 min-objective:0.825013
iteration:640 feature:29 threshold:44 min-objective:0.796431
iteration:641 feature:27 threshold:3 min-objective:0.815911
iteration:642 feature:30 threshold:2.5 min-objective:0.813962
iteration:643 feature:18 threshold:394 min-objective:0.789203
iteration:644 feature:3 threshold:87 min-objective:0.795694
iteration:645 feature:13 threshold:860.5 min-objective:0.823002
iteration:646 feature:46 threshold:19 min-objective:0.82468
iteration:647 feature:3 threshold:18 min-objective:0.850364
iteration:648 feature:18 threshold:394 min-objective:0.814695
iteration:649 feature:14 threshold:231.5 min-objective:0.814176
iteration:650 feature:2 threshold:49 min-objective:0.773354
iteration:651 feature:42 threshold:75.5 min-objective:0.790266
iteration:652 feature:36 threshold:10.5 min-objective:0.80949
iteration:653 feature:13 threshold:443 min-objective:0.851578
iteration:654 feature:29 threshold:3.5 min-objective:0.843813
iteration:655 feature:44 threshold:35.5 min-objective:0.813669
iteration:656 feature:30 threshold:2.5 min-objective:0.817813
iteration:657 feature:25 threshold:255.5 min-objective:0.804277
iteration:658 feature:27 threshold:25.5 min-objective:0.823996
iteration:659 feature:42 threshold:75.5 min-objective:0.832209
iteration:660 feature:29 threshold:3.5 min-objective:0.831043
iteration:661 feature:15 threshold:71 min-objective:0.839373
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iteration:663 feature:1 threshold:30.5 min-objective:0.805305
iteration:664 feature:36 threshold:10.5 min-objective:0.798042
iteration:665 feature:13 threshold:860.5 min-objective:0.817698
iteration:666 feature:30 threshold:2.5 min-objective:0.823309
iteration:667 feature:25 threshold:255.5 min-objective:0.838942
iteration:668 feature:42 threshold:75.5 min-objective:0.826308
iteration:669 feature:23 threshold:806.5 min-objective:0.857334
iteration:670 feature:18 threshold:394 min-objective:0.781466
iteration:671 feature:14 threshold:231.5 min-objective:0.833875
iteration:672 feature:8 threshold:13.5 min-objective:0.819556
iteration:673 feature:20 threshold:435.5 min-objective:0.82275
iteration:674 feature:27 threshold:33.5 min-objective:0.818796
iteration:675 feature:25 threshold:1259.5 min-objective:0.827627
iteration:676 feature:30 threshold:2.5 min-objective:0.826867
iteration:677 feature:8 threshold:13.5 min-objective:0.800433
iteration:678 feature:14 threshold:231.5 min-objective:0.852909
iteration:679 feature:20 threshold:445.5 min-objective:0.809119
iteration:680 feature:42 threshold:75.5 min-objective:0.862486
iteration:681 feature:31 threshold:3 min-objective:0.85435
iteration:682 feature:35 threshold:10.5 min-objective:0.840567
iteration:683 feature:38 threshold:0.5 min-objective:0.818218
iteration:684 feature:13 threshold:860.5 min-objective:0.825237
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iteration:686 feature:25 threshold:255.5 min-objective:0.83374
iteration:687 feature:18 threshold:394 min-objective:0.856044
iteration:688 feature:7 threshold:73.5 min-objective:0.775234
iteration:689 feature:29 threshold:24 min-objective:0.848167
iteration:690 feature:29 threshold:2 min-objective:0.841495
iteration:691 feature:3 threshold:18 min-objective:0.779098
iteration:692 feature:18 threshold:394 min-objective:0.807438
iteration:693 feature:3 threshold:87 min-objective:0.806167
iteration:694 feature:27 threshold:33.5 min-objective:0.849437
iteration:695 feature:24 threshold:370 min-objective:0.836974
iteration:696 feature:37 threshold:10.5 min-objective:0.801895
iteration:697 feature:42 threshold:75.5 min-objective:0.839767
iteration:698 feature:23 threshold:806.5 min-objective:0.843561
iteration:699 feature:25 threshold:255.5 min-objective:0.837093
iteration:700 feature:30 threshold:2.5 min-objective:0.823206
iteration:701 feature:18 threshold:394 min-objective:0.808802
iteration:702 feature:43 threshold:14.5 min-objective:0.803428
iteration:703 feature:3 threshold:18 min-objective:0.812966
iteration:704 feature:13 threshold:860.5 min-objective:0.827166
iteration:705 feature:27 threshold:3 min-objective:0.866631
iteration:706 feature:14 threshold:231.5 min-objective:0.824928
iteration:707 feature:22 threshold:577 min-objective:0.811081
iteration:708 feature:37 threshold:10.5 min-objective:0.824697
iteration:709 feature:2 threshold:49 min-objective:0.795739
iteration:710 feature:30 threshold:2.5 min-objective:0.827693
iteration:711 feature:44 threshold:28.5 min-objective:0.826666
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iteration:716 feature:8 threshold:13.5 min-objective:0.816473
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iteration:720 feature:29 threshold:3.5 min-objective:0.831099
iteration:721 feature:42 threshold:75.5 min-objective:0.827066
iteration:722 feature:3 threshold:18 min-objective:0.828061
iteration:723 feature:27 threshold:33.5 min-objective:0.856075
iteration:724 feature:30 threshold:0.5 min-objective:0.838921
iteration:725 feature:19 threshold:221 min-objective:0.854694
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iteration:727 feature:7 threshold:73.5 min-objective:0.833131
iteration:728 feature:13 threshold:268.5 min-objective:0.853393
iteration:729 feature:8 threshold:17 min-objective:0.831985
iteration:730 feature:30 threshold:2.5 min-objective:0.790039
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iteration:732 feature:3 threshold:87 min-objective:0.83617
iteration:733 feature:43 threshold:14.5 min-objective:0.836183
iteration:734 feature:3 threshold:12.5 min-objective:0.822265
iteration:735 feature:29 threshold:44 min-objective:0.818982
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iteration:737 feature:14 threshold:231.5 min-objective:0.82127
iteration:738 feature:1 threshold:30.5 min-objective:0.807047
iteration:739 feature:37 threshold:10.5 min-objective:0.784203
iteration:740 feature:23 threshold:629.5 min-objective:0.840693
iteration:741 feature:27 threshold:33.5 min-objective:0.840791
iteration:742 feature:27 threshold:3 min-objective:0.842423
iteration:743 feature:30 threshold:2.5 min-objective:0.805232
iteration:744 feature:18 threshold:394 min-objective:0.775018
iteration:745 feature:14 threshold:231.5 min-objective:0.816094
iteration:746 feature:2 threshold:49 min-objective:0.817697
iteration:747 feature:42 threshold:75.5 min-objective:0.810438
iteration:748 feature:3 threshold:18 min-objective:0.811312
iteration:749 feature:43 threshold:14.5 min-objective:0.805026
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iteration:751 feature:25 threshold:1259.5 min-objective:0.84004
iteration:752 feature:18 threshold:394 min-objective:0.842089
iteration:753 feature:30 threshold:2.5 min-objective:0.814766
iteration:754 feature:45 threshold:43.5 min-objective:0.833113
iteration:755 feature:1 threshold:30.5 min-objective:0.815799
iteration:756 feature:7 threshold:73.5 min-objective:0.827933
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iteration:758 feature:3 threshold:18 min-objective:0.803271
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iteration:763 feature:3 threshold:12.5 min-objective:0.843306
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iteration:765 feature:35 threshold:10.5 min-objective:0.855897
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iteration:767 feature:14 threshold:231.5 min-objective:0.849948
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iteration:772 feature:3 threshold:18 min-objective:0.802868
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iteration:775 feature:30 threshold:2.5 min-objective:0.825577
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iteration:968 feature:2 threshold:49 min-objective:0.835855
iteration:969 feature:30 threshold:2.5 min-objective:0.82016
iteration:970 feature:42 threshold:75.5 min-objective:0.816242
iteration:971 feature:27 threshold:3 min-objective:0.827859
iteration:972 feature:7 threshold:73.5 min-objective:0.845714
iteration:973 feature:20 threshold:445.5 min-objective:0.808402
iteration:974 feature:14 threshold:231.5 min-objective:0.844
iteration:975 feature:27 threshold:33.5 min-objective:0.839449
iteration:976 feature:36 threshold:10.5 min-objective:0.855719
iteration:977 feature:2 threshold:49 min-objective:0.843073
iteration:978 feature:42 threshold:75.5 min-objective:0.840023
iteration:979 feature:10 threshold:21 min-objective:0.835382
iteration:980 feature:18 threshold:394 min-objective:0.800391
iteration:981 feature:43 threshold:14.5 min-objective:0.810389
iteration:982 feature:23 threshold:629.5 min-objective:0.817432
iteration:983 feature:24 threshold:370 min-objective:0.791307
iteration:984 feature:3 threshold:18 min-objective:0.798678
iteration:985 feature:13 threshold:860.5 min-objective:0.830961
iteration:986 feature:18 threshold:394 min-objective:0.833199
iteration:987 feature:7 threshold:73.5 min-objective:0.814047
iteration:988 feature:29 threshold:44 min-objective:0.796165
iteration:989 feature:11 threshold:16 min-objective:0.82969
iteration:990 feature:2 threshold:233.5 min-objective:0.844298
iteration:991 feature:35 threshold:10.5 min-objective:0.831668
iteration:992 feature:18 threshold:394 min-objective:0.791634
iteration:993 feature:23 threshold:806.5 min-objective:0.843612
iteration:994 feature:1 threshold:30.5 min-objective:0.834879
iteration:995 feature:14 threshold:231.5 min-objective:0.819
iteration:996 feature:44 threshold:28.5 min-objective:0.819662
iteration:997 feature:27 threshold:33.5 min-objective:0.822469
iteration:998 feature:29 threshold:2 min-objective:0.823163
iteration:999 feature:2 threshold:165.5 min-objective:0.81098
=== END program1: ./run learn ../dataset2/train --- OK [0s]
===== 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 [0s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [0s]
=== START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out
=== END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [0s]
===== 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 0m9.539s
user 0m7.924s
sys 0m1.360s
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) Org study - multiclass :
(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: 0.5
numErrors: 4
numExamples: 8
success: true
time: 1
predict:
strip:
doTrain:
evaluate:
errorRate: 0.0
numErrors: 0
numExamples: 20
success: true
time: 0
predict:
strip:
exitCode: 0
learn:
success: true
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