MLcomp version alpha Statistics: 2043 users, 665 datasets, 389 programs, 18920 runs (0 queued, 4 running), 0 workers

Run 15529

Creator	dufour
Program	svmlight-linear
Dataset	persNP
Task type	BinaryClassification
Created	1y294d ago
Download	Login required!

Status: Done!

Total Time	19s
Max Memory Usage	41M
Domain	MulticlassClassification
Learn time
Train error	0.179
Predict train time
Test error	0.235
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...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..OK. (2733 examples read)
Setting default regularization parameter C=0.0001
Optimizing...................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................done. (612 iterations)
Optimization finished (457 misclassified, maxdiff=0.00090).
Runtime in cpu-seconds: 2.39
Number of SV: 978 (including 858 at upper bound)
L1 loss: loss=873.64916
Norm of weight vector: |w|=0.07171
Norm of longest example vector: |x|=110.93919
Estimated VCdim of classifier: VCdim<=64.28362
Computing XiAlpha-estimates...done
Runtime for XiAlpha-estimates in cpu-seconds: 0.00
XiAlpha-estimate of the error: error<=32.67% (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: 63869
Writing model file...done
=== END _one-vs-all-learner1: ./run learn ../data1 --- OK [5s]

===== 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..OK. (2733 examples read)
Setting default regularization parameter C=0.0001
Optimizing..................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................done. (611 iterations)
Optimization finished (457 misclassified, maxdiff=0.00078).
Runtime in cpu-seconds: 2.75
Number of SV: 978 (including 857 at upper bound)
L1 loss: loss=873.65258
Norm of weight vector: |w|=0.07171
Norm of longest example vector: |x|=110.93919
Estimated VCdim of classifier: VCdim<=64.28367
Computing XiAlpha-estimates...done
Runtime for XiAlpha-estimates in cpu-seconds: 0.00
XiAlpha-estimate of the error: error<=32.71% (rho=1.00,depth=0)
XiAlpha-estimate of the recall: recall=>80.87% (rho=1.00,depth=0)
XiAlpha-estimate of the precision: precision=>80.03% (rho=1.00,depth=0)
Number of kernel evaluations: 63814
Writing model file...done
=== END _one-vs-all-learner2: ./run learn ../data2 --- OK [5s]

=== 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 [1s]
=== 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. (978 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..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 0.07% (2 correct, 2731 incorrect, 2733 total)
Precision/recall on test set: 100.00%/0.07%
=== END _one-vs-all-learner1: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y1 --- OK [2s]
=== START _one-vs-all-learner2: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y2
Reading model...OK. (978 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..done
Runtime (without IO) in cpu-seconds: 0.01
Accuracy on test set: 99.93% (2731 correct, 2 incorrect, 2733 total)
Precision/recall on test set: 100.00%/99.93%
=== END _one-vs-all-learner2: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y2 --- OK [1s]
2733 examples
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [3s]
=== START program5: ./run evaluate ../dataset3/train ../program0/evalTrain.out
=== END program5: ./run evaluate ../dataset3/train ../program0/evalTrain.out --- OK [1s]

===== 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. (978 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..1600..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 0.00% (0 correct, 1632 incorrect, 1632 total)
Precision/recall on test set: -nan%/0.00%
=== END _one-vs-all-learner1: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y1 --- OK [2s]
=== START _one-vs-all-learner2: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y2
Reading model...OK. (978 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..1600..done
Runtime (without IO) in cpu-seconds: 0.08
Accuracy on test set: 100.00% (1632 correct, 0 incorrect, 1632 total)
Precision/recall on test set: 100.00%/100.00%
=== END _one-vs-all-learner2: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y2 --- OK [1s]
1632 examples
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [3s]
=== START program5: ./run evaluate ../dataset3/test ../program0/evalTest.out
=== END program5: ./run evaluate ../dataset3/test ../program0/evalTest.out --- OK [0s]


real	0m19.025s
user	0m11.353s
sys	0m1.092s

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-linear: SVMlight for binary classification using a linear kernel (http://svmlight.joachims.org)
  • (dataset:Dataset) persNP: First/lastname detection.
  • (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: 0.23468137254902
    • numErrors: 383
    • numExamples: 1632
    • success: true
    • time: 0
  • predict:
    • predict1:
      • success: true
      • time: 2
    • predict2:
      • success: true
      • time: 1
    • success: true
    • time: 3
  • strip:
    • success: true
    • time: 0
• doTrain:
  • evaluate:
    • errorRate: 0.178924259055982
    • numErrors: 489
    • numExamples: 2733
    • success: true
    • time: 1
  • predict:
    • predict1:
      • success: true
      • time: 2
    • predict2:
      • success: true
      • time: 1
    • success: true
    • time: 3
  • strip:
    • success: true
    • time: 1
• exitCode: 0
• learn:
  • learn1:
    • success: true
    • time: 5
  • learn2:
    • success: true
    • time: 5
  • success: true
  • time: 10
• success: true

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