ServerRun 36372
Creatorchuertas
Programsvmlight-linear
DatasetEvoBio 10.0x+N
Task typeBinaryClassification
Created2y351d ago
Done! Flag_green
1m10s
47M
MulticlassClassification
20s
0.318
30s
0.262
19s

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..OK. (151 examples read)
Setting default regularization parameter C=0.0000
Optimizing......................done. (23 iterations)
Optimization finished (22 misclassified, maxdiff=0.00000).
Runtime in cpu-seconds: 0.11
Number of SV: 94 (including 85 at upper bound)
L1 loss: loss=61.70325
Norm of weight vector: |w|=0.00494
Norm of longest example vector: |x|=2872.98381
Estimated VCdim of classifier: VCdim<=95.11669
Computing XiAlpha-estimates...done
Runtime for XiAlpha-estimates in cpu-seconds: 0.00
XiAlpha-estimate of the error: error<=61.59% (rho=1.00,depth=0)
XiAlpha-estimate of the recall: recall=>42.68% (rho=1.00,depth=0)
XiAlpha-estimate of the precision: precision=>43.21% (rho=1.00,depth=0)
Number of kernel evaluations: 3002
Writing model file...done
=== END _one-vs-all-learner1: ./run learn ../data1 --- OK [9s]

===== 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..OK. (151 examples read)
Setting default regularization parameter C=0.0000
Optimizing......................done. (23 iterations)
Optimization finished (22 misclassified, maxdiff=0.00050).
Runtime in cpu-seconds: 0.05
Number of SV: 93 (including 85 at upper bound)
L1 loss: loss=61.70305
Norm of weight vector: |w|=0.00494
Norm of longest example vector: |x|=2872.98381
Estimated VCdim of classifier: VCdim<=95.11761
Computing XiAlpha-estimates...done
Runtime for XiAlpha-estimates in cpu-seconds: 0.07
XiAlpha-estimate of the error: error<=61.59% (rho=1.00,depth=0)
XiAlpha-estimate of the recall: recall=>33.33% (rho=1.00,depth=0)
XiAlpha-estimate of the precision: precision=>32.86% (rho=1.00,depth=0)
Number of kernel evaluations: 2999
Writing model file...done
=== END _one-vs-all-learner2: ./run learn ../data2 --- OK [9s]

=== END program1: ./run learn ../dataset3/train --- OK [20s]

===== 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. (94 support vectors read)
Classifying test examples..100..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 56.95% (86 correct, 65 incorrect, 151 total)
Precision/recall on test set: 100.00%/56.95%
=== END _one-vs-all-learner1: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y1 --- OK [16s]
=== START _one-vs-all-learner2: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y2
Reading model...OK. (93 support vectors read)
Classifying test examples..100..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 43.05% (65 correct, 86 incorrect, 151 total)
Precision/recall on test set: 100.00%/43.05%
=== END _one-vs-all-learner2: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y2 --- OK [14s]
151 examples
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [30s]
=== 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. (94 support vectors read)
Classifying test examples..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 56.92% (37 correct, 28 incorrect, 65 total)
Precision/recall on test set: 100.00%/56.92%
=== END _one-vs-all-learner1: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y1 --- OK [10s]
=== START _one-vs-all-learner2: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y2
Reading model...OK. (93 support vectors read)
Classifying test examples..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 43.08% (28 correct, 37 incorrect, 65 total)
Precision/recall on test set: 100.00%/43.08%
=== END _one-vs-all-learner2: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y2 --- OK [9s]
65 examples
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [19s]
=== START program5: ./run evaluate ../dataset3/test ../program0/evalTest.out
=== END program5: ./run evaluate ../dataset3/test ../program0/evalTest.out --- OK [0s]


real	1m11.020s
user	1m8.400s
sys	0m1.316s

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