 | Run 11783 |
| Creator | Krom17 |
| Program | svmlight-rbf |
| Dataset | Dif |
| Task type | BinaryClassification |
| Created | 28d16h ago |
| Download | Login required! |
Done! 
| 4s | |
| 37M | |
| MulticlassClassification | |
| 4s | |
| 0.156 | |
| 0s | |
| 0.111 | |
| 0s |
Log file
... (lines omitted) ... === START _tune-hyperparameter1: ./run learn ../cv.train Scanning examples...done Reading examples into memory...100..200..OK. (221 examples read) Setting default regularization parameter C=0.5167 Optimizing........................................................................................done. (89 iterations) Optimization finished (14 misclassified, maxdiff=0.00096). Runtime in cpu-seconds: 0.00 Number of SV: 53 (including 44 at upper bound) L1 loss: loss=32.04902 Norm of weight vector: |w|=3.00229 Norm of longest example vector: |x|=1.00000 Estimated VCdim of classifier: VCdim<=19.02751 Computing XiAlpha-estimates...done Runtime for XiAlpha-estimates in cpu-seconds: 0.00 XiAlpha-estimate of the error: error<=20.36% (rho=1.00,depth=0) XiAlpha-estimate of the recall: recall=>64.71% (rho=1.00,depth=0) XiAlpha-estimate of the precision: precision=>67.69% (rho=1.00,depth=0) Number of kernel evaluations: 14496 Writing model file...done === END _tune-hyperparameter1: ./run learn ../cv.train --- OK [0s] === START _tune-hyperparameter1: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/_one-vs-all-learner2/cvTestPredictions1 Reading model...OK. (53 support vectors read) Classifying test examples..done Runtime (without IO) in cpu-seconds: 0.00 Accuracy on test set: 96.81% (91 correct, 3 incorrect, 94 total) Precision/recall on test set: 100.00%/89.29% === END _tune-hyperparameter1: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/_one-vs-all-learner2/cvTestPredictions1 --- OK [1s] === START program6: ./run evaluate ../program1/_one-vs-all-learner2/cv.test /home/mlcomp/worker/scratch/program1/_one-vs-all-learner2/cvTestPredictions1 === END program6: ./run evaluate ../program1/_one-vs-all-learner2/cv.test /home/mlcomp/worker/scratch/program1/_one-vs-all-learner2/cvTestPredictions1 --- OK [0s] CV error rate 0.0319148936170213 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..200..OK. (221 examples read) Setting default regularization parameter C=0.5000 Optimizing............................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................. Checking optimality of inactive variables...done. Number of inactive variables = 105 done. (718 iterations) Optimization finished (13 misclassified, maxdiff=0.00093). Runtime in cpu-seconds: 0.01 Number of SV: 109 (including 41 at upper bound) L1 loss: loss=30.11393 Norm of weight vector: |w|=4.33125 Norm of longest example vector: |x|=1.00000 Estimated VCdim of classifier: VCdim<=38.51954 Computing XiAlpha-estimates...done Runtime for XiAlpha-estimates in cpu-seconds: 0.00 XiAlpha-estimate of the error: error<=18.55% (rho=1.00,depth=0) XiAlpha-estimate of the recall: recall=>58.82% (rho=1.00,depth=0) XiAlpha-estimate of the precision: precision=>75.47% (rho=1.00,depth=0) Number of kernel evaluations: 24018 Writing model file...done === END _tune-hyperparameter2: ./run learn ../cv.train --- OK [0s] === START _tune-hyperparameter2: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/_one-vs-all-learner2/cvTestPredictions2 Reading model...OK. (109 support vectors read) Classifying test examples..done Runtime (without IO) in cpu-seconds: 0.00 Accuracy on test set: 96.81% (91 correct, 3 incorrect, 94 total) Precision/recall on test set: 100.00%/89.29% === END _tune-hyperparameter2: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/_one-vs-all-learner2/cvTestPredictions2 --- OK [0s] === START program6: ./run evaluate ../program1/_one-vs-all-learner2/cv.test /home/mlcomp/worker/scratch/program1/_one-vs-all-learner2/cvTestPredictions2 === END program6: ./run evaluate ../program1/_one-vs-all-learner2/cv.test /home/mlcomp/worker/scratch/program1/_one-vs-all-learner2/cvTestPredictions2 --- OK [0s] CV error rate 0.0319148936170213 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..200..OK. (221 examples read) Setting default regularization parameter C=0.5000 Optimizing...................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................done. (484 iterations) Optimization finished (22 misclassified, maxdiff=0.00099). Runtime in cpu-seconds: 0.02 Number of SV: 202 (including 73 at upper bound) L1 loss: loss=53.16866 Norm of weight vector: |w|=6.14130 Norm of longest example vector: |x|=1.00000 Estimated VCdim of classifier: VCdim<=76.43125 Computing XiAlpha-estimates...done Runtime for XiAlpha-estimates in cpu-seconds: 0.00 XiAlpha-estimate of the error: error<=33.03% (rho=1.00,depth=0) XiAlpha-estimate of the recall: recall=>7.35% (rho=1.00,depth=0) XiAlpha-estimate of the precision: precision=>33.33% (rho=1.00,depth=0) Number of kernel evaluations: 28712 Writing model file...done === END _tune-hyperparameter3: ./run learn ../cv.train --- OK [0s] === START _tune-hyperparameter3: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/_one-vs-all-learner2/cvTestPredictions3 Reading model...OK. (202 support vectors read) Classifying test examples..done Runtime (without IO) in cpu-seconds: 0.00 Accuracy on test set: 78.72% (74 correct, 20 incorrect, 94 total) Precision/recall on test set: 100.00%/28.57% === END _tune-hyperparameter3: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/_one-vs-all-learner2/cvTestPredictions3 --- OK [0s] === START program6: ./run evaluate ../program1/_one-vs-all-learner2/cv.test /home/mlcomp/worker/scratch/program1/_one-vs-all-learner2/cvTestPredictions3 === END program6: ./run evaluate ../program1/_one-vs-all-learner2/cv.test /home/mlcomp/worker/scratch/program1/_one-vs-all-learner2/cvTestPredictions3 --- OK [0s] CV error rate 0.212765957446809 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..200..OK. (221 examples read) Setting default regularization parameter C=0.5000 Optimizing.............................................................................................................................................................................................................................................................................................................................................................................................................................done. (414 iterations) Optimization finished (61 misclassified, maxdiff=0.00099). Runtime in cpu-seconds: 0.01 Number of SV: 221 (including 69 at upper bound) L1 loss: loss=81.43964 Norm of weight vector: |w|=5.22270 Norm of longest example vector: |x|=1.00000 Estimated VCdim of classifier: VCdim<=55.55328 Computing XiAlpha-estimates...done Runtime for XiAlpha-estimates in cpu-seconds: 0.00 XiAlpha-estimate of the error: error<=31.22% (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: 28695 Writing model file...done === END _tune-hyperparameter4: ./run learn ../cv.train --- OK [0s] === START _tune-hyperparameter4: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/_one-vs-all-learner2/cvTestPredictions4 Reading model...OK. (221 support vectors read) Classifying test examples..done Runtime (without IO) in cpu-seconds: 0.00 Accuracy on test set: 70.21% (66 correct, 28 incorrect, 94 total) Precision/recall on test set: nan%/0.00% === END _tune-hyperparameter4: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/_one-vs-all-learner2/cvTestPredictions4 --- OK [0s] === START program6: ./run evaluate ../program1/_one-vs-all-learner2/cv.test /home/mlcomp/worker/scratch/program1/_one-vs-all-learner2/cvTestPredictions4 === END program6: ./run evaluate ../program1/_one-vs-all-learner2/cv.test /home/mlcomp/worker/scratch/program1/_one-vs-all-learner2/cvTestPredictions4 --- OK [0s] CV error rate 0.297872340425532 with hyperparameter 100.0 Best hyperparameter value is 0.01; got CV error rate 0.0319148936170213 === END _one-vs-all-learner2: ./run learn ../data2 --- OK [1s] ===== One versus all: training label y=3 versus the rest ===== === START _one-vs-all-learner3: ./run learn ../data3 === START program5: ./run split ../program1/data3 ../program1/_one-vs-all-learner3/cv.train ../program1/_one-vs-all-learner3/cv.test === END program5: ./run split ../program1/data3 ../program1/_one-vs-all-learner3/cv.train ../program1/_one-vs-all-learner3/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..200..OK. (221 examples read) Setting default regularization parameter C=1.1419 Optimizing.....................................................................done. (70 iterations) Optimization finished (26 misclassified, maxdiff=0.00028). Runtime in cpu-seconds: 0.00 Number of SV: 93 (including 90 at upper bound) L1 loss: loss=69.87775 Norm of weight vector: |w|=4.88383 Norm of longest example vector: |x|=1.00000 Estimated VCdim of classifier: VCdim<=36.64948 Computing XiAlpha-estimates...done Runtime for XiAlpha-estimates in cpu-seconds: 0.00 XiAlpha-estimate of the error: error<=40.72% (rho=1.00,depth=0) XiAlpha-estimate of the recall: recall=>18.18% (rho=1.00,depth=0) XiAlpha-estimate of the precision: precision=>18.18% (rho=1.00,depth=0) Number of kernel evaluations: 20258 Writing model file...done === END _tune-hyperparameter0: ./run learn ../cv.train --- OK [1s] === START _tune-hyperparameter0: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/_one-vs-all-learner3/cvTestPredictions0 Reading model...OK. (93 support vectors read) Classifying test examples..done Runtime (without IO) in cpu-seconds: 0.00 Accuracy on test set: 92.55% (87 correct, 7 incorrect, 94 total) Precision/recall on test set: 100.00%/66.67% === END _tune-hyperparameter0: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/_one-vs-all-learner3/cvTestPredictions0 --- OK [0s] === START program6: ./run evaluate ../program1/_one-vs-all-learner3/cv.test /home/mlcomp/worker/scratch/program1/_one-vs-all-learner3/cvTestPredictions0 === END program6: ./run evaluate ../program1/_one-vs-all-learner3/cv.test /home/mlcomp/worker/scratch/program1/_one-vs-all-learner3/cvTestPredictions0 --- OK [0s] CV error rate 0.074468085106383 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..200..OK. (221 examples read) Setting default regularization parameter C=0.5167 Optimizing................................................................................................................................................... Checking optimality of inactive variables...done. Number of inactive variables = 100 done. (148 iterations) Optimization finished (21 misclassified, maxdiff=0.00096). Runtime in cpu-seconds: 0.00 Number of SV: 68 (including 56 at upper bound) L1 loss: loss=45.01479 Norm of weight vector: |w|=3.00767 Norm of longest example vector: |x|=1.00000 Estimated VCdim of classifier: VCdim<=19.09210 Computing XiAlpha-estimates...done Runtime for XiAlpha-estimates in cpu-seconds: 0.00 XiAlpha-estimate of the error: error<=25.34% (rho=1.00,depth=0) XiAlpha-estimate of the recall: recall=>45.45% (rho=1.00,depth=0) XiAlpha-estimate of the precision: precision=>49.02% (rho=1.00,depth=0) Number of kernel evaluations: 17568 Writing model file...done === END _tune-hyperparameter1: ./run learn ../cv.train --- OK [0s] === START _tune-hyperparameter1: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/_one-vs-all-learner3/cvTestPredictions1 Reading model...OK. (68 support vectors read) Classifying test examples..done Runtime (without IO) in cpu-seconds: 0.00 Accuracy on test set: 94.68% (89 correct, 5 incorrect, 94 total) Precision/recall on test set: 100.00%/76.19% === END _tune-hyperparameter1: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/_one-vs-all-learner3/cvTestPredictions1 --- OK [0s] === START program6: ./run evaluate ../program1/_one-vs-all-learner3/cv.test /home/mlcomp/worker/scratch/program1/_one-vs-all-learner3/cvTestPredictions1 === END program6: ./run evaluate ../program1/_one-vs-all-learner3/cv.test /home/mlcomp/worker/scratch/program1/_one-vs-all-learner3/cvTestPredictions1 --- OK [0s] CV error rate 0.0531914893617021 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..200..OK. (221 examples read) Setting default regularization parameter C=0.5000 Optimizing........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................... Checking optimality of inactive variables...done. Number of inactive variables = 104 done. (812 iterations) Optimization finished (19 misclassified, maxdiff=0.00088). Runtime in cpu-seconds: 0.03 Number of SV: 105 (including 49 at upper bound) L1 loss: loss=39.45350 Norm of weight vector: |w|=3.92172 Norm of longest example vector: |x|=1.00000 Estimated VCdim of classifier: VCdim<=31.75971 Computing XiAlpha-estimates...done Runtime for XiAlpha-estimates in cpu-seconds: 0.00 XiAlpha-estimate of the error: error<=22.17% (rho=1.00,depth=0) XiAlpha-estimate of the recall: recall=>41.82% (rho=1.00,depth=0) XiAlpha-estimate of the precision: precision=>57.50% (rho=1.00,depth=0) Number of kernel evaluations: 25446 Writing model file...done === END _tune-hyperparameter2: ./run learn ../cv.train --- OK [0s] === START _tune-hyperparameter2: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/_one-vs-all-learner3/cvTestPredictions2 Reading model...OK. (105 support vectors read) Classifying test examples..done Runtime (without IO) in cpu-seconds: 0.00 Accuracy on test set: 94.68% (89 correct, 5 incorrect, 94 total) Precision/recall on test set: 100.00%/76.19% === END _tune-hyperparameter2: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/_one-vs-all-learner3/cvTestPredictions2 --- OK [0s] === START program6: ./run evaluate ../program1/_one-vs-all-learner3/cv.test /home/mlcomp/worker/scratch/program1/_one-vs-all-learner3/cvTestPredictions2 === END program6: ./run evaluate ../program1/_one-vs-all-learner3/cv.test /home/mlcomp/worker/scratch/program1/_one-vs-all-learner3/cvTestPredictions2 --- OK [0s] CV error rate 0.0531914893617021 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..200..OK. (221 examples read) Setting default regularization parameter C=0.5000 Optimizing.........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................done. (522 iterations) Optimization finished (30 misclassified, maxdiff=0.00094). Runtime in cpu-seconds: 0.01 Number of SV: 199 (including 61 at upper bound) L1 loss: loss=55.06543 Norm of weight vector: |w|=5.15844 Norm of longest example vector: |x|=1.00000 Estimated VCdim of classifier: VCdim<=54.21901 Computing XiAlpha-estimates...done Runtime for XiAlpha-estimates in cpu-seconds: 0.00 XiAlpha-estimate of the error: error<=27.60% (rho=1.00,depth=0) XiAlpha-estimate of the recall: recall=>3.64% (rho=1.00,depth=0) XiAlpha-estimate of the precision: precision=>20.00% (rho=1.00,depth=0) Number of kernel evaluations: 28763 Writing model file...done === END _tune-hyperparameter3: ./run learn ../cv.train --- OK [0s] === START _tune-hyperparameter3: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/_one-vs-all-learner3/cvTestPredictions3 Reading model...OK. (199 support vectors read) Classifying test examples..done Runtime (without IO) in cpu-seconds: 0.00 Accuracy on test set: 82.98% (78 correct, 16 incorrect, 94 total) Precision/recall on test set: 100.00%/23.81% === END _tune-hyperparameter3: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/_one-vs-all-learner3/cvTestPredictions3 --- OK [0s] === START program6: ./run evaluate ../program1/_one-vs-all-learner3/cv.test /home/mlcomp/worker/scratch/program1/_one-vs-all-learner3/cvTestPredictions3 === END program6: ./run evaluate ../program1/_one-vs-all-learner3/cv.test /home/mlcomp/worker/scratch/program1/_one-vs-all-learner3/cvTestPredictions3 --- OK [0s] CV error rate 0.170212765957447 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..200..OK. (221 examples read) Setting default regularization parameter C=0.5000 Optimizing...............................................................................................................................................................................................................................................................................................................................................................................................................................................done. (432 iterations) Optimization finished (53 misclassified, maxdiff=0.00100). Runtime in cpu-seconds: 0.02 Number of SV: 221 (including 56 at upper bound) L1 loss: loss=71.19502 Norm of weight vector: |w|=4.40511 Norm of longest example vector: |x|=1.00000 Estimated VCdim of classifier: VCdim<=39.81001 Computing XiAlpha-estimates...done Runtime for XiAlpha-estimates in cpu-seconds: 0.00 XiAlpha-estimate of the error: error<=25.34% (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: 28749 Writing model file...done === END _tune-hyperparameter4: ./run learn ../cv.train --- OK [0s] === START _tune-hyperparameter4: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/_one-vs-all-learner3/cvTestPredictions4 Reading model...OK. (221 support vectors read) Classifying test examples..done Runtime (without IO) in cpu-seconds: 0.00 Accuracy on test set: 77.66% (73 correct, 21 incorrect, 94 total) Precision/recall on test set: nan%/0.00% === END _tune-hyperparameter4: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/_one-vs-all-learner3/cvTestPredictions4 --- OK [0s] === START program6: ./run evaluate ../program1/_one-vs-all-learner3/cv.test /home/mlcomp/worker/scratch/program1/_one-vs-all-learner3/cvTestPredictions4 === END program6: ./run evaluate ../program1/_one-vs-all-learner3/cv.test /home/mlcomp/worker/scratch/program1/_one-vs-all-learner3/cvTestPredictions4 --- OK [1s] CV error rate 0.223404255319149 with hyperparameter 100.0 Best hyperparameter value is 0.1; got CV error rate 0.0531914893617021 === END _one-vs-all-learner3: ./run learn ../data3 --- OK [2s] === END program1: ./run learn ../dataset7/train --- OK [4s] ===== 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. (70 support vectors read) Classifying test examples..100..200..300..done Runtime (without IO) in cpu-seconds: 0.01 Accuracy on test set: 46.03% (145 correct, 170 incorrect, 315 total) Precision/recall on test set: 100.00%/46.03% === 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. (67 support vectors read) Classifying test examples..100..200..300..done Runtime (without IO) in cpu-seconds: 0.00 Accuracy on test set: 26.67% (84 correct, 231 incorrect, 315 total) Precision/recall on test set: 100.00%/26.67% === 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] === START _one-vs-all-learner3: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y3 === START _tune-hyperparameter-best: ./run predict ../../../program0/evalTrain.in ../../../program0/evalTrain.out-y3 Reading model...OK. (68 support vectors read) Classifying test examples..100..200..300..done Runtime (without IO) in cpu-seconds: 0.00 Accuracy on test set: 17.14% (54 correct, 261 incorrect, 315 total) Precision/recall on test set: 100.00%/17.14% === END _tune-hyperparameter-best: ./run predict ../../../program0/evalTrain.in ../../../program0/evalTrain.out-y3 --- OK [0s] === END _one-vs-all-learner3: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y3 --- OK [0s] 315 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. (70 support vectors read) Classifying test examples..100..done Runtime (without IO) in cpu-seconds: 0.00 Accuracy on test set: 43.70% (59 correct, 76 incorrect, 135 total) Precision/recall on test set: 100.00%/43.70% === 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. (67 support vectors read) Classifying test examples..100..done Runtime (without IO) in cpu-seconds: 0.00 Accuracy on test set: 34.07% (46 correct, 89 incorrect, 135 total) Precision/recall on test set: 100.00%/34.07% === 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] === START _one-vs-all-learner3: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y3 === START _tune-hyperparameter-best: ./run predict ../../../program0/evalTest.in ../../../program0/evalTest.out-y3 Reading model...OK. (68 support vectors read) Classifying test examples..100..done Runtime (without IO) in cpu-seconds: 0.00 Accuracy on test set: 14.07% (19 correct, 116 incorrect, 135 total) Precision/recall on test set: 100.00%/14.07% === END _tune-hyperparameter-best: ./run predict ../../../program0/evalTest.in ../../../program0/evalTest.out-y3 --- OK [0s] === END _one-vs-all-learner3: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y3 --- OK [0s] 135 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 0m5.802s user 0m1.524s sys 0m0.592s
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