 | Run 7399 |
| Creator | pavulon |
| Program | svmlight-rbf |
| Dataset | breast-cancer |
| Task type | BinaryClassification |
| Created | 1y120d ago |
| Download | Login required! |
Done! 
| 1s | |
| 33M | |
| MulticlassClassification | |
| 0.295 | |
| 0.256 | |
Log file
... (lines omitted) ... Number of SV: 88 (including 71 at upper bound) L1 loss: loss=70.36488 Norm of weight vector: |w|=6.59771 Norm of longest example vector: |x|=1.00000 Estimated VCdim of classifier: VCdim<=14.61048 Computing XiAlpha-estimates...done Runtime for XiAlpha-estimates in cpu-seconds: 0.00 XiAlpha-estimate of the error: error<=55.71% (rho=1.00,depth=0) XiAlpha-estimate of the recall: recall=>60.20% (rho=1.00,depth=0) XiAlpha-estimate of the precision: precision=>60.20% (rho=1.00,depth=0) Number of kernel evaluations: 12029 Writing model file...done === END _tune-hyperparameter0: ./run learn ../cv.train --- OK [0s] === START _tune-hyperparameter0: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/_one-vs-all-learner1/cvTestPredictions0 Reading model...OK. (88 support vectors read) Classifying test examples..done Runtime (without IO) in cpu-seconds: 0.00 Accuracy on test set: 66.67% (40 correct, 20 incorrect, 60 total) Precision/recall on test set: 70.83%/85.00% === END _tune-hyperparameter0: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/_one-vs-all-learner1/cvTestPredictions0 --- OK [0s] === START program6: ./run evaluate ../program1/_one-vs-all-learner1/cv.test /home/mlcomp/worker1/scratch/program1/_one-vs-all-learner1/cvTestPredictions0 === END program6: ./run evaluate ../program1/_one-vs-all-learner1/cv.test /home/mlcomp/worker1/scratch/program1/_one-vs-all-learner1/cvTestPredictions0 --- OK [0s] CV error rate 0.333333333333333 with hyperparameter 0.01 ===== Cross-validator: trying hyperparameter 0.1 ===== === START _tune-hyperparameter1: ./run setHyperparameter 0.1 === END _tune-hyperparameter1: ./run setHyperparameter 0.1 --- OK [0s] === START _tune-hyperparameter1: ./run learn ../cv.train Scanning examples...done Reading examples into memory...100..OK. (140 examples read) Setting default regularization parameter C=0.7088 Optimizing......................................................done. (55 iterations) Optimization finished (32 misclassified, maxdiff=0.00086). Runtime in cpu-seconds: 0.00 Number of SV: 99 (including 62 at upper bound) L1 loss: loss=59.94842 Norm of weight vector: |w|=3.93584 Norm of longest example vector: |x|=1.00000 Estimated VCdim of classifier: VCdim<=26.32182 Computing XiAlpha-estimates...done Runtime for XiAlpha-estimates in cpu-seconds: 0.00 XiAlpha-estimate of the error: error<=48.57% (rho=1.00,depth=0) XiAlpha-estimate of the recall: recall=>70.41% (rho=1.00,depth=0) XiAlpha-estimate of the precision: precision=>63.89% (rho=1.00,depth=0) Number of kernel evaluations: 13942 Writing model file...done === END _tune-hyperparameter1: ./run learn ../cv.train --- OK [0s] === START _tune-hyperparameter1: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/_one-vs-all-learner1/cvTestPredictions1 Reading model...OK. (99 support vectors read) Classifying test examples..done Runtime (without IO) in cpu-seconds: 0.00 Accuracy on test set: 66.67% (40 correct, 20 incorrect, 60 total) Precision/recall on test set: 68.52%/92.50% === END _tune-hyperparameter1: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/_one-vs-all-learner1/cvTestPredictions1 --- OK [0s] === START program6: ./run evaluate ../program1/_one-vs-all-learner1/cv.test /home/mlcomp/worker1/scratch/program1/_one-vs-all-learner1/cvTestPredictions1 === END program6: ./run evaluate ../program1/_one-vs-all-learner1/cv.test /home/mlcomp/worker1/scratch/program1/_one-vs-all-learner1/cvTestPredictions1 --- OK [0s] CV error rate 0.333333333333333 with hyperparameter 0.1 ===== Cross-validator: trying hyperparameter 1.0 ===== === START _tune-hyperparameter2: ./run setHyperparameter 1.0 === END _tune-hyperparameter2: ./run setHyperparameter 1.0 --- OK [0s] === START _tune-hyperparameter2: ./run learn ../cv.train Scanning examples...done Reading examples into memory...100..OK. (140 examples read) Setting default regularization parameter C=0.5000 Optimizing..............................................done. (47 iterations) Optimization finished (38 misclassified, maxdiff=0.00092). Runtime in cpu-seconds: 0.00 Number of SV: 137 (including 44 at upper bound) L1 loss: loss=54.24710 Norm of weight vector: |w|=3.85651 Norm of longest example vector: |x|=1.00000 Estimated VCdim of classifier: VCdim<=30.74541 Computing XiAlpha-estimates...done Runtime for XiAlpha-estimates in cpu-seconds: 0.00 XiAlpha-estimate of the error: error<=31.43% (rho=1.00,depth=0) XiAlpha-estimate of the recall: recall=>97.96% (rho=1.00,depth=0) XiAlpha-estimate of the precision: precision=>69.57% (rho=1.00,depth=0) Number of kernel evaluations: 14210 Writing model file...done === END _tune-hyperparameter2: ./run learn ../cv.train --- OK [0s] === START _tune-hyperparameter2: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/_one-vs-all-learner1/cvTestPredictions2 Reading model...OK. (137 support vectors read) Classifying test examples..done Runtime (without IO) in cpu-seconds: 0.00 Accuracy on test set: 65.00% (39 correct, 21 incorrect, 60 total) Precision/recall on test set: 66.10%/97.50% === END _tune-hyperparameter2: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/_one-vs-all-learner1/cvTestPredictions2 --- OK [0s] === START program6: ./run evaluate ../program1/_one-vs-all-learner1/cv.test /home/mlcomp/worker1/scratch/program1/_one-vs-all-learner1/cvTestPredictions2 === END program6: ./run evaluate ../program1/_one-vs-all-learner1/cv.test /home/mlcomp/worker1/scratch/program1/_one-vs-all-learner1/cvTestPredictions2 --- OK [0s] CV error rate 0.35 with hyperparameter 1.0 ===== Cross-validator: trying hyperparameter 10.0 ===== === START _tune-hyperparameter3: ./run setHyperparameter 10.0 === END _tune-hyperparameter3: ./run setHyperparameter 10.0 --- OK [0s] === START _tune-hyperparameter3: ./run learn ../cv.train Scanning examples...done Reading examples into memory...100..OK. (140 examples read) Setting default regularization parameter C=0.5000 Optimizing.........................................................done. (58 iterations) Optimization finished (38 misclassified, maxdiff=0.00080). Runtime in cpu-seconds: 0.00 Number of SV: 137 (including 44 at upper bound) L1 loss: loss=53.38695 Norm of weight vector: |w|=3.91166 Norm of longest example vector: |x|=1.00000 Estimated VCdim of classifier: VCdim<=31.60215 Computing XiAlpha-estimates...done Runtime for XiAlpha-estimates in cpu-seconds: 0.00 XiAlpha-estimate of the error: error<=31.43% (rho=1.00,depth=0) XiAlpha-estimate of the recall: recall=>97.96% (rho=1.00,depth=0) XiAlpha-estimate of the precision: precision=>69.57% (rho=1.00,depth=0) Number of kernel evaluations: 14816 Writing model file...done === END _tune-hyperparameter3: ./run learn ../cv.train --- OK [0s] === START _tune-hyperparameter3: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/_one-vs-all-learner1/cvTestPredictions3 Reading model...OK. (137 support vectors read) Classifying test examples..done Runtime (without IO) in cpu-seconds: 0.00 Accuracy on test set: 65.00% (39 correct, 21 incorrect, 60 total) Precision/recall on test set: 66.10%/97.50% === END _tune-hyperparameter3: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/_one-vs-all-learner1/cvTestPredictions3 --- OK [0s] === START program6: ./run evaluate ../program1/_one-vs-all-learner1/cv.test /home/mlcomp/worker1/scratch/program1/_one-vs-all-learner1/cvTestPredictions3 === END program6: ./run evaluate ../program1/_one-vs-all-learner1/cv.test /home/mlcomp/worker1/scratch/program1/_one-vs-all-learner1/cvTestPredictions3 --- OK [0s] CV error rate 0.35 with hyperparameter 10.0 ===== Cross-validator: trying hyperparameter 100.0 ===== === START _tune-hyperparameter4: ./run setHyperparameter 100.0 === END _tune-hyperparameter4: ./run setHyperparameter 100.0 --- OK [0s] === START _tune-hyperparameter4: ./run learn ../cv.train Scanning examples...done Reading examples into memory...100..OK. (140 examples read) Setting default regularization parameter C=0.5000 Optimizing........................................................done. (57 iterations) Optimization finished (38 misclassified, maxdiff=0.00099). Runtime in cpu-seconds: 0.00 Number of SV: 137 (including 44 at upper bound) L1 loss: loss=53.39254 Norm of weight vector: |w|=3.91166 Norm of longest example vector: |x|=1.00000 Estimated VCdim of classifier: VCdim<=31.60222 Computing XiAlpha-estimates...done Runtime for XiAlpha-estimates in cpu-seconds: 0.00 XiAlpha-estimate of the error: error<=31.43% (rho=1.00,depth=0) XiAlpha-estimate of the recall: recall=>97.96% (rho=1.00,depth=0) XiAlpha-estimate of the precision: precision=>69.57% (rho=1.00,depth=0) Number of kernel evaluations: 14760 Writing model file...done === END _tune-hyperparameter4: ./run learn ../cv.train --- OK [0s] === START _tune-hyperparameter4: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/_one-vs-all-learner1/cvTestPredictions4 Reading model...OK. (137 support vectors read) Classifying test examples..done Runtime (without IO) in cpu-seconds: 0.00 Accuracy on test set: 65.00% (39 correct, 21 incorrect, 60 total) Precision/recall on test set: 66.10%/97.50% === END _tune-hyperparameter4: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/_one-vs-all-learner1/cvTestPredictions4 --- OK [0s] === START program6: ./run evaluate ../program1/_one-vs-all-learner1/cv.test /home/mlcomp/worker1/scratch/program1/_one-vs-all-learner1/cvTestPredictions4 === END program6: ./run evaluate ../program1/_one-vs-all-learner1/cv.test /home/mlcomp/worker1/scratch/program1/_one-vs-all-learner1/cvTestPredictions4 --- OK [0s] CV error rate 0.35 with hyperparameter 100.0 Best hyperparameter value is 0.01; got CV error rate 0.333333333333333 === END _one-vs-all-learner1: ./run learn ../data1 --- OK [1s] ===== One versus all: training label y=2 versus the rest ===== === START _one-vs-all-learner2: ./run learn ../data2 === START program5: ./run split ../program1/data2 ../program1/_one-vs-all-learner2/cv.train ../program1/_one-vs-all-learner2/cv.test === END program5: ./run split ../program1/data2 ../program1/_one-vs-all-learner2/cv.train ../program1/_one-vs-all-learner2/cv.test --- OK [0s] ===== Cross-validator: trying hyperparameter 0.01 ===== === START _tune-hyperparameter0: ./run setHyperparameter 0.01 === END _tune-hyperparameter0: ./run setHyperparameter 0.01 --- OK [0s] === START _tune-hyperparameter0: ./run learn ../cv.train Scanning examples...done Reading examples into memory...100..OK. (140 examples read) Setting default regularization parameter C=4.2544 Optimizing.................................done. (34 iterations) Optimization finished (31 misclassified, maxdiff=0.00064). Runtime in cpu-seconds: 0.00 Number of SV: 88 (including 71 at upper bound) L1 loss: loss=70.36354 Norm of weight vector: |w|=6.59814 Norm of longest example vector: |x|=1.00000 Estimated VCdim of classifier: VCdim<=14.61224 Computing XiAlpha-estimates...done Runtime for XiAlpha-estimates in cpu-seconds: 0.00 XiAlpha-estimate of the error: error<=55.71% (rho=1.00,depth=0) XiAlpha-estimate of the recall: recall=>7.14% (rho=1.00,depth=0) XiAlpha-estimate of the precision: precision=>7.14% (rho=1.00,depth=0) Number of kernel evaluations: 12359 Writing model file...done === END _tune-hyperparameter0: ./run learn ../cv.train --- OK [0s] === START _tune-hyperparameter0: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/_one-vs-all-learner2/cvTestPredictions0 Reading model...OK. (88 support vectors read) Classifying test examples..done Runtime (without IO) in cpu-seconds: 0.00 Accuracy on test set: 66.67% (40 correct, 20 incorrect, 60 total) Precision/recall on test set: 50.00%/30.00% === END _tune-hyperparameter0: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/_one-vs-all-learner2/cvTestPredictions0 --- OK [0s] === START program6: ./run evaluate ../program1/_one-vs-all-learner2/cv.test /home/mlcomp/worker1/scratch/program1/_one-vs-all-learner2/cvTestPredictions0 === END program6: ./run evaluate ../program1/_one-vs-all-learner2/cv.test /home/mlcomp/worker1/scratch/program1/_one-vs-all-learner2/cvTestPredictions0 --- OK [0s] CV error rate 0.333333333333333 with hyperparameter 0.01 ===== Cross-validator: trying hyperparameter 0.1 ===== === START _tune-hyperparameter1: ./run setHyperparameter 0.1 === END _tune-hyperparameter1: ./run setHyperparameter 0.1 --- OK [0s] === START _tune-hyperparameter1: ./run learn ../cv.train Scanning examples...done Reading examples into memory...100..OK. (140 examples read) Setting default regularization parameter C=0.7088 Optimizing......................................................done. (55 iterations) Optimization finished (32 misclassified, maxdiff=0.00086). Runtime in cpu-seconds: 0.00 Number of SV: 99 (including 62 at upper bound) L1 loss: loss=59.94842 Norm of weight vector: |w|=3.93584 Norm of longest example vector: |x|=1.00000 Estimated VCdim of classifier: VCdim<=26.32182 Computing XiAlpha-estimates...done Runtime for XiAlpha-estimates in cpu-seconds: 0.00 XiAlpha-estimate of the error: error<=48.57% (rho=1.00,depth=0) XiAlpha-estimate of the recall: recall=>7.14% (rho=1.00,depth=0) XiAlpha-estimate of the precision: precision=>9.38% (rho=1.00,depth=0) Number of kernel evaluations: 13942 Writing model file...done === END _tune-hyperparameter1: ./run learn ../cv.train --- OK [0s] === START _tune-hyperparameter1: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/_one-vs-all-learner2/cvTestPredictions1 Reading model...OK. (99 support vectors read) Classifying test examples..done Runtime (without IO) in cpu-seconds: 0.00 Accuracy on test set: 66.67% (40 correct, 20 incorrect, 60 total) Precision/recall on test set: 50.00%/15.00% === END _tune-hyperparameter1: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/_one-vs-all-learner2/cvTestPredictions1 --- OK [0s] === START program6: ./run evaluate ../program1/_one-vs-all-learner2/cv.test /home/mlcomp/worker1/scratch/program1/_one-vs-all-learner2/cvTestPredictions1 === END program6: ./run evaluate ../program1/_one-vs-all-learner2/cv.test /home/mlcomp/worker1/scratch/program1/_one-vs-all-learner2/cvTestPredictions1 --- OK [0s] CV error rate 0.333333333333333 with hyperparameter 0.1 ===== Cross-validator: trying hyperparameter 1.0 ===== === START _tune-hyperparameter2: ./run setHyperparameter 1.0 === END _tune-hyperparameter2: ./run setHyperparameter 1.0 --- OK [0s] === START _tune-hyperparameter2: ./run learn ../cv.train Scanning examples...done Reading examples into memory...100..OK. (140 examples read) Setting default regularization parameter C=0.5000 Optimizing..............................................done. (47 iterations) Optimization finished (38 misclassified, maxdiff=0.00092). Runtime in cpu-seconds: 0.00 Number of SV: 138 (including 44 at upper bound) L1 loss: loss=54.24710 Norm of weight vector: |w|=3.85651 Norm of longest example vector: |x|=1.00000 Estimated VCdim of classifier: VCdim<=30.74541 Computing XiAlpha-estimates...done Runtime for XiAlpha-estimates in cpu-seconds: 0.00 XiAlpha-estimate of the error: error<=31.43% (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: 14213 Writing model file...done === END _tune-hyperparameter2: ./run learn ../cv.train --- OK [0s] === START _tune-hyperparameter2: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/_one-vs-all-learner2/cvTestPredictions2 Reading model...OK. (138 support vectors read) Classifying test examples..done Runtime (without IO) in cpu-seconds: 0.00 Accuracy on test set: 65.00% (39 correct, 21 incorrect, 60 total) Precision/recall on test set: 0.00%/0.00% === END _tune-hyperparameter2: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/_one-vs-all-learner2/cvTestPredictions2 --- OK [0s] === START program6: ./run evaluate ../program1/_one-vs-all-learner2/cv.test /home/mlcomp/worker1/scratch/program1/_one-vs-all-learner2/cvTestPredictions2 === END program6: ./run evaluate ../program1/_one-vs-all-learner2/cv.test /home/mlcomp/worker1/scratch/program1/_one-vs-all-learner2/cvTestPredictions2 --- OK [0s] CV error rate 0.35 with hyperparameter 1.0 ===== Cross-validator: trying hyperparameter 10.0 ===== === START _tune-hyperparameter3: ./run setHyperparameter 10.0 === END _tune-hyperparameter3: ./run setHyperparameter 10.0 --- OK [0s] === START _tune-hyperparameter3: ./run learn ../cv.train Scanning examples...done Reading examples into memory...100..OK. (140 examples read) Setting default regularization parameter C=0.5000 Optimizing.....................................................done. (54 iterations) Optimization finished (38 misclassified, maxdiff=0.00067). Runtime in cpu-seconds: 0.00 Number of SV: 138 (including 44 at upper bound) L1 loss: loss=53.38693 Norm of weight vector: |w|=3.91166 Norm of longest example vector: |x|=1.00000 Estimated VCdim of classifier: VCdim<=31.60214 Computing XiAlpha-estimates...done Runtime for XiAlpha-estimates in cpu-seconds: 0.00 XiAlpha-estimate of the error: error<=31.43% (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: 14599 Writing model file...done === END _tune-hyperparameter3: ./run learn ../cv.train --- OK [0s] === START _tune-hyperparameter3: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/_one-vs-all-learner2/cvTestPredictions3 Reading model...OK. (138 support vectors read) Classifying test examples..done Runtime (without IO) in cpu-seconds: 0.00 Accuracy on test set: 65.00% (39 correct, 21 incorrect, 60 total) Precision/recall on test set: 0.00%/0.00% === END _tune-hyperparameter3: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/_one-vs-all-learner2/cvTestPredictions3 --- OK [0s] === START program6: ./run evaluate ../program1/_one-vs-all-learner2/cv.test /home/mlcomp/worker1/scratch/program1/_one-vs-all-learner2/cvTestPredictions3 === END program6: ./run evaluate ../program1/_one-vs-all-learner2/cv.test /home/mlcomp/worker1/scratch/program1/_one-vs-all-learner2/cvTestPredictions3 --- OK [0s] CV error rate 0.35 with hyperparameter 10.0 ===== Cross-validator: trying hyperparameter 100.0 ===== === START _tune-hyperparameter4: ./run setHyperparameter 100.0 === END _tune-hyperparameter4: ./run setHyperparameter 100.0 --- OK [0s] === START _tune-hyperparameter4: ./run learn ../cv.train Scanning examples...done Reading examples into memory...100..OK. (140 examples read) Setting default regularization parameter C=0.5000 Optimizing........................................................done. (57 iterations) Optimization finished (38 misclassified, maxdiff=0.00080). Runtime in cpu-seconds: 0.00 Number of SV: 137 (including 44 at upper bound) L1 loss: loss=53.38458 Norm of weight vector: |w|=3.91166 Norm of longest example vector: |x|=1.00000 Estimated VCdim of classifier: VCdim<=31.60218 Computing XiAlpha-estimates...done Runtime for XiAlpha-estimates in cpu-seconds: 0.00 XiAlpha-estimate of the error: error<=31.43% (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: 14755 Writing model file...done === END _tune-hyperparameter4: ./run learn ../cv.train --- OK [0s] === START _tune-hyperparameter4: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/_one-vs-all-learner2/cvTestPredictions4 Reading model...OK. (137 support vectors read) Classifying test examples..done Runtime (without IO) in cpu-seconds: 0.00 Accuracy on test set: 65.00% (39 correct, 21 incorrect, 60 total) Precision/recall on test set: 0.00%/0.00% === END _tune-hyperparameter4: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/_one-vs-all-learner2/cvTestPredictions4 --- OK [0s] === START program6: ./run evaluate ../program1/_one-vs-all-learner2/cv.test /home/mlcomp/worker1/scratch/program1/_one-vs-all-learner2/cvTestPredictions4 === END program6: ./run evaluate ../program1/_one-vs-all-learner2/cv.test /home/mlcomp/worker1/scratch/program1/_one-vs-all-learner2/cvTestPredictions4 --- OK [0s] CV error rate 0.35 with hyperparameter 100.0 Best hyperparameter value is 0.01; got CV error rate 0.333333333333333 === END _one-vs-all-learner2: ./run learn ../data2 --- OK [0s] === END program1: ./run learn ../dataset7/train --- OK [1s] ===== MAIN: predict/evaluate on train data ===== === START program8: ./run stripLabels ../dataset7/train ../program0/evalTrain.in === END program8: ./run stripLabels ../dataset7/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 === START _tune-hyperparameter-best: ./run predict ../../../program0/evalTrain.in ../../../program0/evalTrain.out-y1 Reading model...OK. (88 support vectors read) Classifying test examples..100..200..done Runtime (without IO) in cpu-seconds: 0.00 Accuracy on test set: 85.50% (171 correct, 29 incorrect, 200 total) Precision/recall on test set: 100.00%/85.50% === END _tune-hyperparameter-best: ./run predict ../../../program0/evalTrain.in ../../../program0/evalTrain.out-y1 --- OK [0s] === 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 === START _tune-hyperparameter-best: ./run predict ../../../program0/evalTrain.in ../../../program0/evalTrain.out-y2 Reading model...OK. (88 support vectors read) Classifying test examples..100..200..done Runtime (without IO) in cpu-seconds: 0.00 Accuracy on test set: 14.50% (29 correct, 171 incorrect, 200 total) Precision/recall on test set: 100.00%/14.50% === END _tune-hyperparameter-best: ./run predict ../../../program0/evalTrain.in ../../../program0/evalTrain.out-y2 --- OK [0s] === END _one-vs-all-learner2: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y2 --- OK [0s] 200 examples === END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [0s] === START program9: ./run evaluate ../dataset7/train ../program0/evalTrain.out === END program9: ./run evaluate ../dataset7/train ../program0/evalTrain.out --- OK [0s] ===== MAIN: predict/evaluate on test data ===== === START program8: ./run stripLabels ../dataset7/test ../program0/evalTest.in === END program8: ./run stripLabels ../dataset7/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 === START _tune-hyperparameter-best: ./run predict ../../../program0/evalTest.in ../../../program0/evalTest.out-y1 Reading model...OK. (88 support vectors read) Classifying test examples..done Runtime (without IO) in cpu-seconds: 0.00 Accuracy on test set: 82.56% (71 correct, 15 incorrect, 86 total) Precision/recall on test set: 100.00%/82.56% === END _tune-hyperparameter-best: ./run predict ../../../program0/evalTest.in ../../../program0/evalTest.out-y1 --- OK [0s] === 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 === START _tune-hyperparameter-best: ./run predict ../../../program0/evalTest.in ../../../program0/evalTest.out-y2 Reading model...OK. (88 support vectors read) Classifying test examples..done Runtime (without IO) in cpu-seconds: 0.00 Accuracy on test set: 17.44% (15 correct, 71 incorrect, 86 total) Precision/recall on test set: 100.00%/17.44% === END _tune-hyperparameter-best: ./run predict ../../../program0/evalTest.in ../../../program0/evalTest.out-y2 --- OK [0s] === END _one-vs-all-learner2: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y2 --- OK [0s] 86 examples === END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [0s] === START program9: ./run evaluate ../dataset7/test ../program0/evalTest.out === END program9: ./run evaluate ../dataset7/test ../program0/evalTest.out --- OK [0s] real 0m2.198s user 0m1.084s sys 0m0.340s
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