ServerRun 36628
Creatortycho01
Programsvmlight-linear
Datasetasthma_breath_spectra_nonans
Task typeBinaryClassification
Created2y343d ago
Done! Flag_green
12m56s
82M
MulticlassClassification
3m40s
0.354
5m39s
0.571
3m36s

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. (99 examples read)
Setting default regularization parameter C=0.2348
Optimizing...................done. (20 iterations)
Optimization finished (30 misclassified, maxdiff=0.00083).
Runtime in cpu-seconds: 0.12
Number of SV: 69 (including 52 at upper bound)
L1 loss: loss=56.61203
Norm of weight vector: |w|=0.89179
Norm of longest example vector: |x|=2.76882
Estimated VCdim of classifier: VCdim<=7.09698
Computing XiAlpha-estimates...done
Runtime for XiAlpha-estimates in cpu-seconds: 0.07
XiAlpha-estimate of the error: error<=60.61% (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: 2242
Writing model file...done
=== END _one-vs-all-learner1: ./run learn ../data1 --- OK [72s]

===== 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. (99 examples read)
Setting default regularization parameter C=0.2348
Optimizing...........................................................done. (60 iterations)
Optimization finished (16 misclassified, maxdiff=0.00094).
Runtime in cpu-seconds: 0.43
Number of SV: 53 (including 19 at upper bound)
L1 loss: loss=31.46275
Norm of weight vector: |w|=0.35459
Norm of longest example vector: |x|=2.76882
Estimated VCdim of classifier: VCdim<=1.96392
Computing XiAlpha-estimates...done
Runtime for XiAlpha-estimates in cpu-seconds: 0.00
XiAlpha-estimate of the error: error<=25.25% (rho=1.00,depth=0)
XiAlpha-estimate of the recall: recall=>0.00% (rho=1.00,depth=0)
XiAlpha-estimate of the precision: precision=>0.00% (rho=1.00,depth=0)
Number of kernel evaluations: 4394
Writing model file...done
=== END _one-vs-all-learner2: ./run learn ../data2 --- OK [72s]

===== 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. (99 examples read)
Setting default regularization parameter C=0.2348
Optimizing.............done. (14 iterations)
Optimization finished (42 misclassified, maxdiff=0.00000).
Runtime in cpu-seconds: 0.10
Number of SV: 93 (including 88 at upper bound)
L1 loss: loss=75.95634
Norm of weight vector: |w|=1.90908
Norm of longest example vector: |x|=2.76882
Estimated VCdim of classifier: VCdim<=28.94082
Computing XiAlpha-estimates...done
Runtime for XiAlpha-estimates in cpu-seconds: 0.00
XiAlpha-estimate of the error: error<=92.93% (rho=1.00,depth=0)
XiAlpha-estimate of the recall: recall=>13.21% (rho=1.00,depth=0)
XiAlpha-estimate of the precision: precision=>13.21% (rho=1.00,depth=0)
Number of kernel evaluations: 1984
Writing model file...done
=== END _one-vs-all-learner3: ./run learn ../data3 --- OK [72s]

=== END program1: ./run learn ../dataset3/train --- OK [220s]

===== 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. (69 support vectors read)
Classifying test examples..done
Runtime (without IO) in cpu-seconds: 0.02
Accuracy on test set: 0.00% (0 correct, 99 incorrect, 99 total)
Precision/recall on test set: -nan%/0.00%
=== END _one-vs-all-learner1: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y1 --- OK [111s]
=== START _one-vs-all-learner2: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y2
Reading model...OK. (53 support vectors read)
Classifying test examples..done
Runtime (without IO) in cpu-seconds: 0.01
Accuracy on test set: 0.00% (0 correct, 99 incorrect, 99 total)
Precision/recall on test set: -nan%/0.00%
=== END _one-vs-all-learner2: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y2 --- OK [103s]
=== START _one-vs-all-learner3: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y3
Reading model...OK. (93 support vectors read)
Classifying test examples..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 95.96% (95 correct, 4 incorrect, 99 total)
Precision/recall on test set: 100.00%/95.96%
=== END _one-vs-all-learner3: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y3 --- OK [125s]
99 examples
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [339s]
=== 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 [1s]
=== 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. (69 support vectors read)
Classifying test examples..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 4.76% (2 correct, 40 incorrect, 42 total)
Precision/recall on test set: 100.00%/4.76%
=== END _one-vs-all-learner1: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y1 --- OK [70s]
=== START _one-vs-all-learner2: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y2
Reading model...OK. (53 support vectors read)
Classifying test examples..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 7.14% (3 correct, 39 incorrect, 42 total)
Precision/recall on test set: 100.00%/7.14%
=== END _one-vs-all-learner2: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y2 --- OK [61s]
=== START _one-vs-all-learner3: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y3
Reading model...OK. (93 support vectors read)
Classifying test examples..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 92.86% (39 correct, 3 incorrect, 42 total)
Precision/recall on test set: 100.00%/92.86%
=== END _one-vs-all-learner3: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y3 --- OK [85s]
42 examples
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [216s]
=== START program5: ./run evaluate ../dataset3/test ../program0/evalTest.out
=== END program5: ./run evaluate ../dataset3/test ../program0/evalTest.out --- OK [0s]


real	12m58.345s
user	12m44.060s
sys	0m3.836s

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