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

Run 1688

Creator	pliang
Program	svmlight-rbf
Dataset	thyroid-allrep
Task type	BinaryClassification
Created	28d18h ago
Download	Login required!

Status: Done!

Total Time	23s
Max Memory Usage	75M
Domain	MulticlassClassification
Learn time	10s
Train error	0.031
Predict train time	12s
Test error	0.038
Predict test time	4s

Log file

===== MAIN: learn based on training data =====
=== START program1: ./run learn ../dataset3/train
===== One versus all: training label y=1 versus the rest =====
=== START _one-vs-all-learner1: ./run learn ../data1
Scanning examples...done
Reading examples into memory...100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..1600..1700..1800..1900..2000..2100..2200..2300..2400..2500..2600..2700..2800..OK. (2800 examples read)
Setting default regularization parameter C=0.5000
Optimizing..........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................done. (1051 iterations)
Optimization finished (87 misclassified, maxdiff=0.00099).
Runtime in cpu-seconds: 2.11
Number of SV: 2753 (including 87 at upper bound)
L1 loss: loss=129.06547
Norm of weight vector: |w|=4.73986
Norm of longest example vector: |x|=1.00000
Estimated VCdim of classifier: VCdim<=45.93261
Computing XiAlpha-estimates...done
Runtime for XiAlpha-estimates in cpu-seconds: 0.00
XiAlpha-estimate of the error: error<=3.11% (rho=1.00,depth=0)
XiAlpha-estimate of the recall: recall=>100.00% (rho=1.00,depth=0)
XiAlpha-estimate of the precision: precision=>96.89% (rho=1.00,depth=0)
Number of kernel evaluations: 4015238
Writing model file...done
=== END _one-vs-all-learner1: ./run learn ../data1 --- OK [3s]

===== One versus all: training label y=2 versus the rest =====
=== START _one-vs-all-learner2: ./run learn ../data2
Scanning examples...done
Reading examples into memory...100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..1600..1700..1800..1900..2000..2100..2200..2300..2400..2500..2600..2700..2800..OK. (2800 examples read)
Setting default regularization parameter C=0.5000
Optimizing.....................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................done. (918 iterations)
Optimization finished (23 misclassified, maxdiff=0.00097).
Runtime in cpu-seconds: 2.31
Number of SV: 2754 (including 23 at upper bound)
L1 loss: loss=34.39522
Norm of weight vector: |w|=2.40817
Norm of longest example vector: |x|=1.00000
Estimated VCdim of classifier: VCdim<=12.59856
Computing XiAlpha-estimates...done
Runtime for XiAlpha-estimates in cpu-seconds: 0.00
XiAlpha-estimate of the error: error<=0.82% (rho=1.00,depth=0)
XiAlpha-estimate of the recall: recall=>0.00% (rho=1.00,depth=0)
XiAlpha-estimate of the precision: precision=>nan% (rho=1.00,depth=0)
Number of kernel evaluations: 4007662
Writing model file...done
=== END _one-vs-all-learner2: ./run learn ../data2 --- OK [2s]

===== One versus all: training label y=3 versus the rest =====
=== START _one-vs-all-learner3: ./run learn ../data3
Scanning examples...done
Reading examples into memory...100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..1600..1700..1800..1900..2000..2100..2200..2300..2400..2500..2600..2700..2800..OK. (2800 examples read)
Setting default regularization parameter C=0.5000
Optimizing............................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................done. (1053 iterations)
Optimization finished (29 misclassified, maxdiff=0.00098).
Runtime in cpu-seconds: 2.21
Number of SV: 2752 (including 29 at upper bound)
L1 loss: loss=43.34230
Norm of weight vector: |w|=2.70707
Norm of longest example vector: |x|=1.00000
Estimated VCdim of classifier: VCdim<=15.65645
Computing XiAlpha-estimates...done
Runtime for XiAlpha-estimates in cpu-seconds: 0.00
XiAlpha-estimate of the error: error<=1.04% (rho=1.00,depth=0)
XiAlpha-estimate of the recall: recall=>0.00% (rho=1.00,depth=0)
XiAlpha-estimate of the precision: precision=>nan% (rho=1.00,depth=0)
Number of kernel evaluations: 4015081
Writing model file...done
=== END _one-vs-all-learner3: ./run learn ../data3 --- OK [3s]

===== One versus all: training label y=4 versus the rest =====
=== START _one-vs-all-learner4: ./run learn ../data4
Scanning examples...done
Reading examples into memory...100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..1600..1700..1800..1900..2000..2100..2200..2300..2400..2500..2600..2700..2800..OK. (2800 examples read)
Setting default regularization parameter C=0.5000
Optimizing........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................done. (937 iterations)
Optimization finished (35 misclassified, maxdiff=0.00100).
Runtime in cpu-seconds: 2.04
Number of SV: 2754 (including 35 at upper bound)
L1 loss: loss=52.28256
Norm of weight vector: |w|=2.97727
Norm of longest example vector: |x|=1.00000
Estimated VCdim of classifier: VCdim<=18.72825
Computing XiAlpha-estimates...done
Runtime for XiAlpha-estimates in cpu-seconds: 0.00
XiAlpha-estimate of the error: error<=1.25% (rho=1.00,depth=0)
XiAlpha-estimate of the recall: recall=>0.00% (rho=1.00,depth=0)
XiAlpha-estimate of the precision: precision=>nan% (rho=1.00,depth=0)
Number of kernel evaluations: 4008707
Writing model file...done
=== END _one-vs-all-learner4: ./run learn ../data4 --- OK [2s]

=== END program1: ./run learn ../dataset3/train --- OK [10s]

===== MAIN: predict/evaluate on train data =====
=== START program4: ./run stripLabels ../dataset3/train ../program0/evalTrain.in
=== END program4: ./run stripLabels ../dataset3/train ../program0/evalTrain.in --- OK [0s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
=== START _one-vs-all-learner1: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y1
Reading model...OK. (2753 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..1600..1700..1800..1900..2000..2100..2200..2300..2400..2500..2600..2700..2800..done
Runtime (without IO) in cpu-seconds: 2.56
Accuracy on test set: 100.00% (2800 correct, 0 incorrect, 2800 total)
Precision/recall on test set: 100.00%/100.00%
=== END _one-vs-all-learner1: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y1 --- OK [3s]
=== START _one-vs-all-learner2: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y2
Reading model...OK. (2754 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..1600..1700..1800..1900..2000..2100..2200..2300..2400..2500..2600..2700..2800..done
Runtime (without IO) in cpu-seconds: 2.58
Accuracy on test set: 0.00% (0 correct, 2800 incorrect, 2800 total)
Precision/recall on test set: nan%/0.00%
=== END _one-vs-all-learner2: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y2 --- OK [3s]
=== START _one-vs-all-learner3: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y3
Reading model...OK. (2752 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..1600..1700..1800..1900..2000..2100..2200..2300..2400..2500..2600..2700..2800..done
Runtime (without IO) in cpu-seconds: 2.56
Accuracy on test set: 0.00% (0 correct, 2800 incorrect, 2800 total)
Precision/recall on test set: nan%/0.00%
=== END _one-vs-all-learner3: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y3 --- OK [3s]
=== START _one-vs-all-learner4: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y4
Reading model...OK. (2754 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..1600..1700..1800..1900..2000..2100..2200..2300..2400..2500..2600..2700..2800..done
Runtime (without IO) in cpu-seconds: 2.68
Accuracy on test set: 0.00% (0 correct, 2800 incorrect, 2800 total)
Precision/recall on test set: nan%/0.00%
=== END _one-vs-all-learner4: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y4 --- OK [3s]
2800 examples
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [12s]
=== START program5: ./run evaluate ../dataset3/train ../program0/evalTrain.out
=== END program5: ./run evaluate ../dataset3/train ../program0/evalTrain.out --- OK [0s]

===== MAIN: predict/evaluate on test data =====
=== START program4: ./run stripLabels ../dataset3/test ../program0/evalTest.in
=== END program4: ./run stripLabels ../dataset3/test ../program0/evalTest.in --- OK [0s]
=== START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out
=== START _one-vs-all-learner1: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y1
Reading model...OK. (2753 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..done
Runtime (without IO) in cpu-seconds: 0.83
Accuracy on test set: 100.00% (972 correct, 0 incorrect, 972 total)
Precision/recall on test set: 100.00%/100.00%
=== END _one-vs-all-learner1: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y1 --- OK [1s]
=== START _one-vs-all-learner2: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y2
Reading model...OK. (2754 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..done
Runtime (without IO) in cpu-seconds: 0.88
Accuracy on test set: 0.00% (0 correct, 972 incorrect, 972 total)
Precision/recall on test set: nan%/0.00%
=== END _one-vs-all-learner2: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y2 --- OK [1s]
=== START _one-vs-all-learner3: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y3
Reading model...OK. (2752 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..done
Runtime (without IO) in cpu-seconds: 0.86
Accuracy on test set: 0.00% (0 correct, 972 incorrect, 972 total)
Precision/recall on test set: nan%/0.00%
=== END _one-vs-all-learner3: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y3 --- OK [1s]
=== START _one-vs-all-learner4: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y4
Reading model...OK. (2754 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..done
Runtime (without IO) in cpu-seconds: 0.87
Accuracy on test set: 0.00% (0 correct, 972 incorrect, 972 total)
Precision/recall on test set: nan%/0.00%
=== END _one-vs-all-learner4: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y4 --- OK [1s]
972 examples
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [4s]
=== START program5: ./run evaluate ../dataset3/test ../program0/evalTest.out
=== END program5: ./run evaluate ../dataset3/test ../program0/evalTest.out --- OK [0s]


real	0m26.353s
user	0m25.242s
sys	0m0.608s

Run specification

• supervised-learning: Main entry for supervised learning for training and testing a program on a dataset.
  • (learner:Program) one-vs-all: Reduction from multiclass classification to binary classification.
    • (binaryLearner:Program[BinaryClassification]) svmlight-rbf: SVMlight for binary classification using a RBF kernel (http://svmlight.joachims.org)
  • (dataset:Dataset) thyroid-allrep: 3772 examples, 40 features
  • (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).

Results

• doTest:
  • evaluate:
    • errorRate: 0.0380658436213992
    • numErrors: 37
    • numExamples: 972
    • success: true
    • time: 0
  • predict:
    • predict1:
      • success: true
      • time: 1
    • predict2:
      • success: true
      • time: 1
    • predict3:
      • success: true
      • time: 1
    • predict4:
      • success: true
      • time: 1
    • success: true
    • time: 4
  • strip:
    • success: true
    • time: 0
• doTrain:
  • evaluate:
    • errorRate: 0.0310714285714286
    • numErrors: 87
    • numExamples: 2800
    • success: true
    • time: 0
  • predict:
    • predict1:
      • success: true
      • time: 3
    • predict2:
      • success: true
      • time: 3
    • predict3:
      • success: true
      • time: 3
    • predict4:
      • success: true
      • time: 3
    • success: true
    • time: 12
  • strip:
    • success: true
    • time: 0
• exitCode: 0
• learn:
  • learn1:
    • success: true
    • time: 3
  • learn2:
    • success: true
    • time: 2
  • learn3:
    • success: true
    • time: 3
  • learn4:
    • success: true
    • time: 2
  • success: true
  • time: 10
• success: true

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