MLcomp version alpha Statistics: 1403 users, 461 datasets, 360 programs, 16577 runs (0 queued, 0 running), 0 workers

Run 1362

Creator	pliang
Program	svmlight-linear
Dataset	Dexter_train
Task type	BinaryClassification
Created	28d17h ago
Download	Login required!

Status: Done!

Total Time	1s
Max Memory Usage	37M
Domain	BinaryClassification
Learn time	1s
Train error	0.033
Predict train time	0s
Test error	0.111
Predict test time	0s

Log file

=== Starting: cd /home/mlcomp/worker1/scratch/program0/../program1 && ./run learn '/home/mlcomp/worker1/scratch/program0/../dataset6/train'
=== Starting: cd /home/mlcomp/worker1/scratch/program1/../program4 && ./run split '/home/mlcomp/worker1/scratch/program0/../dataset6/train' '/home/mlcomp/worker1/scratch/program1/cv.train' '/home/mlcomp/worker1/scratch/program1/cv.test'
=== Finished: cd /home/mlcomp/worker1/scratch/program1/../program4 && ./run split '/home/mlcomp/worker1/scratch/program0/../dataset6/train' '/home/mlcomp/worker1/scratch/program1/cv.train' '/home/mlcomp/worker1/scratch/program1/cv.test' --- OK [0s]
=== Starting: cd _tune-hyperparameter0 && ./run setHyperparameter '0.01'
=== Finished: cd _tune-hyperparameter0 && ./run setHyperparameter '0.01' --- OK [0s]
=== Starting: cd _tune-hyperparameter0 && ./run learn '/home/mlcomp/worker1/scratch/program1/cv.train'
Scanning examples...done
Reading examples into memory...100..OK. (147 examples read)
Optimizing.....................................................done. (54 iterations)
Optimization finished (0 misclassified, maxdiff=0.00085).
Runtime in cpu-seconds: 0.00
Number of SV: 139 (including 0 at upper bound)
L1 loss: loss=0.00000
Norm of weight vector: |w|=0.00795
Norm of longest example vector: |x|=1494.55980
Estimated VCdim of classifier: VCdim<=142.20353
Computing XiAlpha-estimates...done
Runtime for XiAlpha-estimates in cpu-seconds: 0.00
XiAlpha-estimate of the error: error<=45.58% (rho=1.00,depth=0)
XiAlpha-estimate of the recall: recall=>63.75% (rho=1.00,depth=0)
XiAlpha-estimate of the precision: precision=>57.30% (rho=1.00,depth=0)
Number of kernel evaluations: 4802
Writing model file...done
=== Finished: cd _tune-hyperparameter0 && ./run learn '/home/mlcomp/worker1/scratch/program1/cv.train' --- OK [0s]
=== Starting: cd _tune-hyperparameter0 && ./run predict '/home/mlcomp/worker1/scratch/program1/cv.test' '/home/mlcomp/worker1/scratch/program1/cvTestPredictions0'
Reading model...OK. (139 support vectors read)
Classifying test examples..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 88.89% (56 correct, 7 incorrect, 63 total)
Precision/recall on test set: 81.58%/100.00%
=== Finished: cd _tune-hyperparameter0 && ./run predict '/home/mlcomp/worker1/scratch/program1/cv.test' '/home/mlcomp/worker1/scratch/program1/cvTestPredictions0' --- OK [0s]
=== Starting: cd /home/mlcomp/worker1/scratch/program1/../program5 && ./run evaluate '/home/mlcomp/worker1/scratch/program1/cv.test' '/home/mlcomp/worker1/scratch/program1/cvTestPredictions0'
=== Finished: cd /home/mlcomp/worker1/scratch/program1/../program5 && ./run evaluate '/home/mlcomp/worker1/scratch/program1/cv.test' '/home/mlcomp/worker1/scratch/program1/cvTestPredictions0' --- OK [0s]
CV error rate 0.111111111111111 with hyperparameter 0.01
=== Starting: cd _tune-hyperparameter1 && ./run setHyperparameter '0.1'
=== Finished: cd _tune-hyperparameter1 && ./run setHyperparameter '0.1' --- OK [0s]
=== Starting: cd _tune-hyperparameter1 && ./run learn '/home/mlcomp/worker1/scratch/program1/cv.train'
Scanning examples...done
Reading examples into memory...100..OK. (147 examples read)
Optimizing.....................................................done. (54 iterations)
Optimization finished (0 misclassified, maxdiff=0.00085).
Runtime in cpu-seconds: 0.00
Number of SV: 139 (including 0 at upper bound)
L1 loss: loss=0.00000
Norm of weight vector: |w|=0.00795
Norm of longest example vector: |x|=1494.55980
Estimated VCdim of classifier: VCdim<=142.20353
Computing XiAlpha-estimates...done
Runtime for XiAlpha-estimates in cpu-seconds: 0.00
XiAlpha-estimate of the error: error<=45.58% (rho=1.00,depth=0)
XiAlpha-estimate of the recall: recall=>63.75% (rho=1.00,depth=0)
XiAlpha-estimate of the precision: precision=>57.30% (rho=1.00,depth=0)
Number of kernel evaluations: 4802
Writing model file...done
=== Finished: cd _tune-hyperparameter1 && ./run learn '/home/mlcomp/worker1/scratch/program1/cv.train' --- OK [0s]
=== Starting: cd _tune-hyperparameter1 && ./run predict '/home/mlcomp/worker1/scratch/program1/cv.test' '/home/mlcomp/worker1/scratch/program1/cvTestPredictions1'
Reading model...OK. (139 support vectors read)
Classifying test examples..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 88.89% (56 correct, 7 incorrect, 63 total)
Precision/recall on test set: 81.58%/100.00%
=== Finished: cd _tune-hyperparameter1 && ./run predict '/home/mlcomp/worker1/scratch/program1/cv.test' '/home/mlcomp/worker1/scratch/program1/cvTestPredictions1' --- OK [0s]
=== Starting: cd /home/mlcomp/worker1/scratch/program1/../program5 && ./run evaluate '/home/mlcomp/worker1/scratch/program1/cv.test' '/home/mlcomp/worker1/scratch/program1/cvTestPredictions1'
=== Finished: cd /home/mlcomp/worker1/scratch/program1/../program5 && ./run evaluate '/home/mlcomp/worker1/scratch/program1/cv.test' '/home/mlcomp/worker1/scratch/program1/cvTestPredictions1' --- OK [0s]
CV error rate 0.111111111111111 with hyperparameter 0.1
=== Starting: cd _tune-hyperparameter2 && ./run setHyperparameter '1.0'
=== Finished: cd _tune-hyperparameter2 && ./run setHyperparameter '1.0' --- OK [0s]
=== Starting: cd _tune-hyperparameter2 && ./run learn '/home/mlcomp/worker1/scratch/program1/cv.train'
Scanning examples...done
Reading examples into memory...100..OK. (147 examples read)
Optimizing.....................................................done. (54 iterations)
Optimization finished (0 misclassified, maxdiff=0.00085).
Runtime in cpu-seconds: 0.00
Number of SV: 139 (including 0 at upper bound)
L1 loss: loss=0.00000
Norm of weight vector: |w|=0.00795
Norm of longest example vector: |x|=1494.55980
Estimated VCdim of classifier: VCdim<=142.20353
Computing XiAlpha-estimates...done
Runtime for XiAlpha-estimates in cpu-seconds: 0.00
XiAlpha-estimate of the error: error<=45.58% (rho=1.00,depth=0)
XiAlpha-estimate of the recall: recall=>63.75% (rho=1.00,depth=0)
XiAlpha-estimate of the precision: precision=>57.30% (rho=1.00,depth=0)
Number of kernel evaluations: 4802
Writing model file...done
=== Finished: cd _tune-hyperparameter2 && ./run learn '/home/mlcomp/worker1/scratch/program1/cv.train' --- OK [0s]
=== Starting: cd _tune-hyperparameter2 && ./run predict '/home/mlcomp/worker1/scratch/program1/cv.test' '/home/mlcomp/worker1/scratch/program1/cvTestPredictions2'
Reading model...OK. (139 support vectors read)
Classifying test examples..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 88.89% (56 correct, 7 incorrect, 63 total)
Precision/recall on test set: 81.58%/100.00%
=== Finished: cd _tune-hyperparameter2 && ./run predict '/home/mlcomp/worker1/scratch/program1/cv.test' '/home/mlcomp/worker1/scratch/program1/cvTestPredictions2' --- OK [1s]
=== Starting: cd /home/mlcomp/worker1/scratch/program1/../program5 && ./run evaluate '/home/mlcomp/worker1/scratch/program1/cv.test' '/home/mlcomp/worker1/scratch/program1/cvTestPredictions2'
=== Finished: cd /home/mlcomp/worker1/scratch/program1/../program5 && ./run evaluate '/home/mlcomp/worker1/scratch/program1/cv.test' '/home/mlcomp/worker1/scratch/program1/cvTestPredictions2' --- OK [0s]
CV error rate 0.111111111111111 with hyperparameter 1.0
=== Starting: cd _tune-hyperparameter3 && ./run setHyperparameter '10.0'
=== Finished: cd _tune-hyperparameter3 && ./run setHyperparameter '10.0' --- OK [0s]
=== Starting: cd _tune-hyperparameter3 && ./run learn '/home/mlcomp/worker1/scratch/program1/cv.train'
Scanning examples...done
Reading examples into memory...100..OK. (147 examples read)
Optimizing.....................................................done. (54 iterations)
Optimization finished (0 misclassified, maxdiff=0.00085).
Runtime in cpu-seconds: 0.01
Number of SV: 139 (including 0 at upper bound)
L1 loss: loss=0.00000
Norm of weight vector: |w|=0.00795
Norm of longest example vector: |x|=1494.55980
Estimated VCdim of classifier: VCdim<=142.20353
Computing XiAlpha-estimates...done
Runtime for XiAlpha-estimates in cpu-seconds: 0.00
XiAlpha-estimate of the error: error<=45.58% (rho=1.00,depth=0)
XiAlpha-estimate of the recall: recall=>63.75% (rho=1.00,depth=0)
XiAlpha-estimate of the precision: precision=>57.30% (rho=1.00,depth=0)
Number of kernel evaluations: 4802
Writing model file...done
=== Finished: cd _tune-hyperparameter3 && ./run learn '/home/mlcomp/worker1/scratch/program1/cv.train' --- OK [0s]
=== Starting: cd _tune-hyperparameter3 && ./run predict '/home/mlcomp/worker1/scratch/program1/cv.test' '/home/mlcomp/worker1/scratch/program1/cvTestPredictions3'
Reading model...OK. (139 support vectors read)
Classifying test examples..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 88.89% (56 correct, 7 incorrect, 63 total)
Precision/recall on test set: 81.58%/100.00%
=== Finished: cd _tune-hyperparameter3 && ./run predict '/home/mlcomp/worker1/scratch/program1/cv.test' '/home/mlcomp/worker1/scratch/program1/cvTestPredictions3' --- OK [0s]
=== Starting: cd /home/mlcomp/worker1/scratch/program1/../program5 && ./run evaluate '/home/mlcomp/worker1/scratch/program1/cv.test' '/home/mlcomp/worker1/scratch/program1/cvTestPredictions3'
=== Finished: cd /home/mlcomp/worker1/scratch/program1/../program5 && ./run evaluate '/home/mlcomp/worker1/scratch/program1/cv.test' '/home/mlcomp/worker1/scratch/program1/cvTestPredictions3' --- OK [0s]
CV error rate 0.111111111111111 with hyperparameter 10.0
=== Starting: cd _tune-hyperparameter4 && ./run setHyperparameter '100.0'
=== Finished: cd _tune-hyperparameter4 && ./run setHyperparameter '100.0' --- OK [0s]
=== Starting: cd _tune-hyperparameter4 && ./run learn '/home/mlcomp/worker1/scratch/program1/cv.train'
Scanning examples...done
Reading examples into memory...100..OK. (147 examples read)
Optimizing.....................................................done. (54 iterations)
Optimization finished (0 misclassified, maxdiff=0.00085).
Runtime in cpu-seconds: 0.00
Number of SV: 139 (including 0 at upper bound)
L1 loss: loss=0.00000
Norm of weight vector: |w|=0.00795
Norm of longest example vector: |x|=1494.55980
Estimated VCdim of classifier: VCdim<=142.20353
Computing XiAlpha-estimates...done
Runtime for XiAlpha-estimates in cpu-seconds: 0.00
XiAlpha-estimate of the error: error<=45.58% (rho=1.00,depth=0)
XiAlpha-estimate of the recall: recall=>63.75% (rho=1.00,depth=0)
XiAlpha-estimate of the precision: precision=>57.30% (rho=1.00,depth=0)
Number of kernel evaluations: 4802
Writing model file...done
=== Finished: cd _tune-hyperparameter4 && ./run learn '/home/mlcomp/worker1/scratch/program1/cv.train' --- OK [0s]
=== Starting: cd _tune-hyperparameter4 && ./run predict '/home/mlcomp/worker1/scratch/program1/cv.test' '/home/mlcomp/worker1/scratch/program1/cvTestPredictions4'
Reading model...OK. (139 support vectors read)
Classifying test examples..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 88.89% (56 correct, 7 incorrect, 63 total)
Precision/recall on test set: 81.58%/100.00%
=== Finished: cd _tune-hyperparameter4 && ./run predict '/home/mlcomp/worker1/scratch/program1/cv.test' '/home/mlcomp/worker1/scratch/program1/cvTestPredictions4' --- OK [0s]
=== Starting: cd /home/mlcomp/worker1/scratch/program1/../program5 && ./run evaluate '/home/mlcomp/worker1/scratch/program1/cv.test' '/home/mlcomp/worker1/scratch/program1/cvTestPredictions4'
=== Finished: cd /home/mlcomp/worker1/scratch/program1/../program5 && ./run evaluate '/home/mlcomp/worker1/scratch/program1/cv.test' '/home/mlcomp/worker1/scratch/program1/cvTestPredictions4' --- OK [0s]
CV error rate 0.111111111111111 with hyperparameter 100.0
Best hyperparameter value is 0.01; got CV error rate 0.111111111111111
=== Finished: cd /home/mlcomp/worker1/scratch/program0/../program1 && ./run learn '/home/mlcomp/worker1/scratch/program0/../dataset6/train' --- OK [1s]
=== Starting: cd /home/mlcomp/worker1/scratch/program0/../program7 && ./run stripLabels '/home/mlcomp/worker1/scratch/program0/../dataset6/train' '/home/mlcomp/worker1/scratch/program0/evalTrain.in'
=== Finished: cd /home/mlcomp/worker1/scratch/program0/../program7 && ./run stripLabels '/home/mlcomp/worker1/scratch/program0/../dataset6/train' '/home/mlcomp/worker1/scratch/program0/evalTrain.in' --- OK [0s]
=== Starting: cd /home/mlcomp/worker1/scratch/program0/../program1 && ./run predict '/home/mlcomp/worker1/scratch/program0/evalTrain.in' '/home/mlcomp/worker1/scratch/program0/evalTrain.out'
=== Starting: cd _tune-hyperparameter-best && ./run predict '/home/mlcomp/worker1/scratch/program0/evalTrain.in' '/home/mlcomp/worker1/scratch/program0/evalTrain.out'
Reading model...OK. (139 support vectors read)
Classifying test examples..100..200..done
Runtime (without IO) in cpu-seconds: 0.00
=== Finished: cd _tune-hyperparameter-best && ./run predict '/home/mlcomp/worker1/scratch/program0/evalTrain.in' '/home/mlcomp/worker1/scratch/program0/evalTrain.out' --- OK [0s]
=== Finished: cd /home/mlcomp/worker1/scratch/program0/../program1 && ./run predict '/home/mlcomp/worker1/scratch/program0/evalTrain.in' '/home/mlcomp/worker1/scratch/program0/evalTrain.out' --- OK [0s]
=== Starting: cd /home/mlcomp/worker1/scratch/program0/../program8 && ./run evaluate '/home/mlcomp/worker1/scratch/program0/../dataset6/train' '/home/mlcomp/worker1/scratch/program0/evalTrain.out'
=== Finished: cd /home/mlcomp/worker1/scratch/program0/../program8 && ./run evaluate '/home/mlcomp/worker1/scratch/program0/../dataset6/train' '/home/mlcomp/worker1/scratch/program0/evalTrain.out' --- OK [0s]
=== Starting: cd /home/mlcomp/worker1/scratch/program0/../program7 && ./run stripLabels '/home/mlcomp/worker1/scratch/program0/../dataset6/test' '/home/mlcomp/worker1/scratch/program0/evalTest.in'
=== Finished: cd /home/mlcomp/worker1/scratch/program0/../program7 && ./run stripLabels '/home/mlcomp/worker1/scratch/program0/../dataset6/test' '/home/mlcomp/worker1/scratch/program0/evalTest.in' --- OK [0s]
=== Starting: cd /home/mlcomp/worker1/scratch/program0/../program1 && ./run predict '/home/mlcomp/worker1/scratch/program0/evalTest.in' '/home/mlcomp/worker1/scratch/program0/evalTest.out'
=== Starting: cd _tune-hyperparameter-best && ./run predict '/home/mlcomp/worker1/scratch/program0/evalTest.in' '/home/mlcomp/worker1/scratch/program0/evalTest.out'
Reading model...OK. (139 support vectors read)
Classifying test examples..done
Runtime (without IO) in cpu-seconds: 0.00
=== Finished: cd _tune-hyperparameter-best && ./run predict '/home/mlcomp/worker1/scratch/program0/evalTest.in' '/home/mlcomp/worker1/scratch/program0/evalTest.out' --- OK [0s]
=== Finished: cd /home/mlcomp/worker1/scratch/program0/../program1 && ./run predict '/home/mlcomp/worker1/scratch/program0/evalTest.in' '/home/mlcomp/worker1/scratch/program0/evalTest.out' --- OK [0s]
=== Starting: cd /home/mlcomp/worker1/scratch/program0/../program8 && ./run evaluate '/home/mlcomp/worker1/scratch/program0/../dataset6/test' '/home/mlcomp/worker1/scratch/program0/evalTest.out'
=== Finished: cd /home/mlcomp/worker1/scratch/program0/../program8 && ./run evaluate '/home/mlcomp/worker1/scratch/program0/../dataset6/test' '/home/mlcomp/worker1/scratch/program0/evalTest.out' --- OK [0s]

real	0m1.724s
user	0m0.932s
sys	0m0.220s

Run specification

• supervised-learning: Main entry for supervised learning for training and testing a program on a dataset.
  • (learner:Program) tune-hyperparameter: Sets the hyperparameter
    • (numProbes:int) 5
    • (learner:Program) svmlight-linear: SVMlight for binary classification using a linear kernel (http://svmlight.joachims.org)
    • (splitter:Program) binary-utils: Validates and inspects a dataset in BinaryClassification format.
    • (evaluator:Program[Evaluate]) classification-evaluator: Evaluates predictions of classification datasets (discrete outputs).
  • (dataset:Dataset) Dexter_train: This is the training set of the Dexter dataset used in the feature selection challenge, see http://www.nipsfsc.ecs.soton.ac.uk/
  • (stripper:Program[Strip]) binary-utils: Validates and inspects a dataset in BinaryClassification format.
  • (evaluator:Program[Evaluate]) classification-evaluator: Evaluates predictions of classification datasets (discrete outputs).

Results

• doTest:
  • evaluate:
    • errorRate: 0.111111111111111
    • numErrors: 10
    • numExamples: 90
    • success: true
    • time: 0
  • predict:
    • predict:
      • success: true
      • time: 0
    • success: true
    • time: 0
  • strip:
    • success: true
    • time: 0
• doTrain:
  • evaluate:
    • errorRate: 0.0333333333333333
    • numErrors: 7
    • numExamples: 210
    • success: true
    • time: 0
  • predict:
    • predict:
      • success: true
      • time: 0
    • success: true
    • time: 0
  • strip:
    • success: true
    • time: 0
• exitCode: 0
• learn:
  • bestCVErrorRate: 0.111111111111111
  • bestHyperparameter: 0.01
  • evaluate0:
    • errorRate: 0.111111111111111
    • numErrors: 7
    • numExamples: 63
    • success: true
    • time: 0
  • evaluate1:
    • errorRate: 0.111111111111111
    • numErrors: 7
    • numExamples: 63
    • success: true
    • time: 0
  • evaluate2:
    • errorRate: 0.111111111111111
    • numErrors: 7
    • numExamples: 63
    • success: true
    • time: 0
  • evaluate3:
    • errorRate: 0.111111111111111
    • numErrors: 7
    • numExamples: 63
    • success: true
    • time: 0
  • evaluate4:
    • errorRate: 0.111111111111111
    • numErrors: 7
    • numExamples: 63
    • success: true
    • time: 0
  • learn0:
    • success: true
    • time: 0
  • learn1:
    • success: true
    • time: 0
  • learn2:
    • success: true
    • time: 0
  • learn3:
    • success: true
    • time: 0
  • learn4:
    • success: true
    • time: 0
  • predict0:
    • success: true
    • time: 0
  • predict1:
    • success: true
    • time: 0
  • predict2:
    • success: true
    • time: 1
  • predict3:
    • success: true
    • time: 0
  • predict4:
    • success: true
    • time: 0
  • setHyperparameter0:
    • success: true
    • time: 0
  • setHyperparameter1:
    • success: true
    • time: 0
  • setHyperparameter2:
    • success: true
    • time: 0
  • setHyperparameter3:
    • success: true
    • time: 0
  • setHyperparameter4:
    • success: true
    • time: 0
  • split:
    • success: true
    • time: 0
  • success: true
  • time: 1
• success: true

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