MLcomp version alpha Statistics: 2045 users, 665 datasets, 389 programs, 18920 runs (0 queued, 4 running), 0 workers

Run 1686

Creator	pliang
Program	svmlight-rbf
Dataset	postoperative-patient-data
Task type	BinaryClassification
Created	2y228d ago
Download	Login required!

Status: Done!

Total Time	1s
Max Memory Usage	0B
Domain	MulticlassClassification
Learn time
Train error	0.333
Predict train time
Test error	0.185
Predict test time

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...OK. (63 examples read)
Setting default regularization parameter C=0.5000
Optimizing......................done. (23 iterations)
Optimization finished (21 misclassified, maxdiff=0.00036).
Runtime in cpu-seconds: 0.00
Number of SV: 60 (including 25 at upper bound)
L1 loss: loss=30.37506
Norm of weight vector: |w|=2.41096
Norm of longest example vector: |x|=1.00000
Estimated VCdim of classifier: VCdim<=12.62549
Computing XiAlpha-estimates...done
Runtime for XiAlpha-estimates in cpu-seconds: 0.00
XiAlpha-estimate of the error: error<=39.68% (rho=1.00,depth=0)
XiAlpha-estimate of the recall: recall=>90.48% (rho=1.00,depth=0)
XiAlpha-estimate of the precision: precision=>64.41% (rho=1.00,depth=0)
Number of kernel evaluations: 4035
Writing model file...done
=== END _one-vs-all-learner1: ./run learn ../data1 --- OK [0s]

===== 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...OK. (63 examples read)
Setting default regularization parameter C=0.5000
Optimizing.....................done. (22 iterations)
Optimization finished (1 misclassified, maxdiff=0.00066).
Runtime in cpu-seconds: 0.00
Number of SV: 51 (including 1 at upper bound)
L1 loss: loss=1.50178
Norm of weight vector: |w|=0.49896
Norm of longest example vector: |x|=1.00000
Estimated VCdim of classifier: VCdim<=1.49792
Computing XiAlpha-estimates...done
Runtime for XiAlpha-estimates in cpu-seconds: 0.00
XiAlpha-estimate of the error: error<=1.59% (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: 3895
Writing model file...done
=== END _one-vs-all-learner2: ./run learn ../data2 --- OK [0s]

===== 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...OK. (63 examples read)
Setting default regularization parameter C=0.5000
Optimizing.....................done. (22 iterations)
Optimization finished (20 misclassified, maxdiff=0.00092).
Runtime in cpu-seconds: 0.00
Number of SV: 58 (including 25 at upper bound)
L1 loss: loss=29.37874
Norm of weight vector: |w|=2.30439
Norm of longest example vector: |x|=1.00000
Estimated VCdim of classifier: VCdim<=11.62040
Computing XiAlpha-estimates...done
Runtime for XiAlpha-estimates in cpu-seconds: 0.00
XiAlpha-estimate of the error: error<=39.68% (rho=1.00,depth=0)
XiAlpha-estimate of the recall: recall=>0.00% (rho=1.00,depth=0)
XiAlpha-estimate of the precision: precision=>0.00% (rho=1.00,depth=0)
Number of kernel evaluations: 3974
Writing model file...done
=== END _one-vs-all-learner3: ./run learn ../data3 --- OK [0s]

=== END program1: ./run learn ../dataset3/train --- OK [0s]

===== 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. (60 support vectors read)
Classifying test examples..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 100.00% (63 correct, 0 incorrect, 63 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 [0s]
=== START _one-vs-all-learner2: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y2
Reading model...OK. (51 support vectors read)
Classifying test examples..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 0.00% (0 correct, 63 incorrect, 63 total)
Precision/recall on test set: nan%/0.00%
=== END _one-vs-all-learner2: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y2 --- OK [0s]
=== START _one-vs-all-learner3: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y3
Reading model...OK. (58 support vectors read)
Classifying test examples..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 0.00% (0 correct, 63 incorrect, 63 total)
Precision/recall on test set: nan%/0.00%
=== END _one-vs-all-learner3: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y3 --- OK [0s]
63 examples
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [0s]
=== 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. (60 support vectors read)
Classifying test examples..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 100.00% (27 correct, 0 incorrect, 27 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 [0s]
=== START _one-vs-all-learner2: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y2
Reading model...OK. (51 support vectors read)
Classifying test examples..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 0.00% (0 correct, 27 incorrect, 27 total)
Precision/recall on test set: nan%/0.00%
=== END _one-vs-all-learner2: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y2 --- OK [0s]
=== START _one-vs-all-learner3: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y3
Reading model...OK. (58 support vectors read)
Classifying test examples..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 0.00% (0 correct, 27 incorrect, 27 total)
Precision/recall on test set: nan%/0.00%
=== END _one-vs-all-learner3: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y3 --- OK [0s]
27 examples
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [0s]
=== START program5: ./run evaluate ../dataset3/test ../program0/evalTest.out
=== END program5: ./run evaluate ../dataset3/test ../program0/evalTest.out --- OK [0s]


real	0m0.739s
user	0m0.268s
sys	0m0.160s

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) postoperative-patient-data: 90 examples, 19 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: 0.185185185185185
    • numErrors: 5
    • numExamples: 27
    • success: true
    • time: 0
  • predict:
    • predict1:
      • success: true
      • time: 0
    • predict2:
      • success: true
      • time: 0
    • predict3:
      • success: true
      • time: 0
    • success: true
    • time: 0
  • strip:
    • success: true
    • time: 0
• doTrain:
  • evaluate:
    • errorRate: 0.333333333333333
    • numErrors: 21
    • numExamples: 63
    • success: true
    • time: 0
  • predict:
    • predict1:
      • success: true
      • time: 0
    • predict2:
      • success: true
      • time: 0
    • predict3:
      • success: true
      • time: 0
    • success: true
    • time: 0
  • strip:
    • success: true
    • time: 0
• exitCode: 0
• learn:
  • learn1:
    • success: true
    • time: 0
  • learn2:
    • success: true
    • time: 0
  • learn3:
    • success: true
    • time: 0
  • success: true
  • time: 0
• success: true

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