 | Run 1797 |
| Creator | pliang |
| Program | svmlight_multiclass-linear |
| Dataset | multiclass-sample |
| Task type | MulticlassClassification |
| Created | 2y224d ago |
| Download | Login required! |
Done! 
| 1s | |
| 28M | |
| MulticlassClassification | |
| 0 | |
| 0.500 | |
Log file
===== MAIN: learn based on training data ===== === START program1: ./run learn ../dataset6/train === START program4: ./run split ../dataset6/train ../program1/cv.train ../program1/cv.test === END program4: ./run split ../dataset6/train ../program1/cv.train ../program1/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 Using hyperparameter c = 0.01 Reading training examples... (3 examples) done Training set properties: 3 features, 2 classes Iter 1: ...*(NumConst=1, SV=1, CEps=100.0000, QPEps=0.0000) Iter 2: *(NumConst=2, SV=1, CEps=99.9867, QPEps=0.0000) Iter 3: ...(NumConst=2, SV=1, CEps=0.0000, QPEps=0.0000) Final epsilon on KKT-Conditions: 0.00000 Upper bound on duality gap: -0.00000 Dual objective value: dval=0.99993 Primal objective value: pval=0.99993 Total number of constraints in final working set: 2 (of 2) Number of iterations: 3 Number of calls to 'find_most_violated_constraint': 6 Number of SV: 1 Norm of weight vector: |w|=0.01155 Value of slack variable (on working set): xi=99.98667 Value of slack variable (global): xi=99.98667 Norm of longest difference vector: ||Psi(x,y)-Psi(x,ybar)||=1.15470 Runtime in cpu-seconds: 0.00 Final number of constraints in cache: 6 Compacting linear model...done Writing learned model...done === END _tune-hyperparameter0: ./run learn ../cv.train --- OK [0s] === START _tune-hyperparameter0: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions0 Reading model...done. Reading test examples... (1 examples) done. Classifying test examples...done Runtime (without IO) in cpu-seconds: 0.00 Average loss on test set: 0.0000 Zero/one-error on test set: 0.00% (1 correct, 0 incorrect, 1 total) === END _tune-hyperparameter0: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions0 --- OK [0s] === START program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions0 === END program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions0 --- OK [0s] CV error rate 0.0 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 Using hyperparameter c = 0.1 Reading training examples... (3 examples) done Training set properties: 3 features, 2 classes Iter 1: ...*(NumConst=1, SV=1, CEps=100.0000, QPEps=0.0000) Iter 2: *(NumConst=2, SV=1, CEps=99.8667, QPEps=0.0000) Iter 3: ...(NumConst=2, SV=1, CEps=0.0000, QPEps=0.0000) Final epsilon on KKT-Conditions: 0.00000 Upper bound on duality gap: 0.00000 Dual objective value: dval=9.99333 Primal objective value: pval=9.99333 Total number of constraints in final working set: 2 (of 2) Number of iterations: 3 Number of calls to 'find_most_violated_constraint': 6 Number of SV: 1 Norm of weight vector: |w|=0.11547 Value of slack variable (on working set): xi=99.86667 Value of slack variable (global): xi=99.86667 Norm of longest difference vector: ||Psi(x,y)-Psi(x,ybar)||=1.15470 Runtime in cpu-seconds: 0.00 Final number of constraints in cache: 6 Compacting linear model...done Writing learned model...done === END _tune-hyperparameter1: ./run learn ../cv.train --- OK [0s] === START _tune-hyperparameter1: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions1 Reading model...done. Reading test examples... (1 examples) done. Classifying test examples...done Runtime (without IO) in cpu-seconds: 0.00 Average loss on test set: 0.0000 Zero/one-error on test set: 0.00% (1 correct, 0 incorrect, 1 total) === END _tune-hyperparameter1: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions1 --- OK [0s] === START program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions1 === END program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions1 --- OK [0s] CV error rate 0.0 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 Using hyperparameter c = 1.0 Reading training examples... (3 examples) done Training set properties: 3 features, 2 classes Iter 1: ...*(NumConst=1, SV=1, CEps=100.0000, QPEps=0.0000) Iter 2: *(NumConst=2, SV=1, CEps=98.6667, QPEps=0.0000) Iter 3: ...(NumConst=2, SV=1, CEps=0.0000, QPEps=0.0000) Final epsilon on KKT-Conditions: 0.00000 Upper bound on duality gap: 0.00000 Dual objective value: dval=99.33333 Primal objective value: pval=99.33333 Total number of constraints in final working set: 2 (of 2) Number of iterations: 3 Number of calls to 'find_most_violated_constraint': 6 Number of SV: 1 Norm of weight vector: |w|=1.15470 Value of slack variable (on working set): xi=98.66667 Value of slack variable (global): xi=98.66667 Norm of longest difference vector: ||Psi(x,y)-Psi(x,ybar)||=1.15470 Runtime in cpu-seconds: 0.00 Final number of constraints in cache: 6 Compacting linear model...done Writing learned model...done === END _tune-hyperparameter2: ./run learn ../cv.train --- OK [0s] === START _tune-hyperparameter2: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions2 Reading model...done. Reading test examples... (1 examples) done. Classifying test examples...done Runtime (without IO) in cpu-seconds: 0.00 Average loss on test set: 0.0000 Zero/one-error on test set: 0.00% (1 correct, 0 incorrect, 1 total) === END _tune-hyperparameter2: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions2 --- OK [0s] === START program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions2 === END program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions2 --- OK [0s] CV error rate 0.0 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 Using hyperparameter c = 10.0 Reading training examples... (3 examples) done Training set properties: 3 features, 2 classes Iter 1: ...*(NumConst=1, SV=1, CEps=100.0000, QPEps=0.0000) Iter 2: *(NumConst=2, SV=1, CEps=86.6667, QPEps=0.0000) Iter 3: ...(NumConst=2, SV=1, CEps=0.0000, QPEps=0.0000) Final epsilon on KKT-Conditions: 0.00000 Upper bound on duality gap: 0.00000 Dual objective value: dval=933.33333 Primal objective value: pval=933.33333 Total number of constraints in final working set: 2 (of 2) Number of iterations: 3 Number of calls to 'find_most_violated_constraint': 6 Number of SV: 1 Norm of weight vector: |w|=11.54701 Value of slack variable (on working set): xi=86.66667 Value of slack variable (global): xi=86.66667 Norm of longest difference vector: ||Psi(x,y)-Psi(x,ybar)||=1.15470 Runtime in cpu-seconds: 0.00 Final number of constraints in cache: 6 Compacting linear model...done Writing learned model...done === END _tune-hyperparameter3: ./run learn ../cv.train --- OK [0s] === START _tune-hyperparameter3: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions3 Reading model...done. Reading test examples... (1 examples) done. Classifying test examples...done Runtime (without IO) in cpu-seconds: 0.00 Average loss on test set: 0.0000 Zero/one-error on test set: 0.00% (1 correct, 0 incorrect, 1 total) === END _tune-hyperparameter3: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions3 --- OK [0s] === START program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions3 === END program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions3 --- OK [0s] CV error rate 0.0 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 Using hyperparameter c = 100.0 Reading training examples... (3 examples) done Training set properties: 3 features, 2 classes Iter 1: ...*(NumConst=1, SV=1, CEps=100.0000, QPEps=0.0000) Iter 2: *(NumConst=2, SV=2, CEps=33.3333, QPEps=0.0000) Iter 3: ...(NumConst=2, SV=2, CEps=0.0000, QPEps=0.0000) Final epsilon on KKT-Conditions: 0.00000 Upper bound on duality gap: 0.00047 Dual objective value: dval=4583.33297 Primal objective value: pval=4583.33344 Total number of constraints in final working set: 2 (of 2) Number of iterations: 3 Number of calls to 'find_most_violated_constraint': 6 Number of SV: 2 Norm of weight vector: |w|=76.37627 Value of slack variable (on working set): xi=16.66667 Value of slack variable (global): xi=16.66667 Norm of longest difference vector: ||Psi(x,y)-Psi(x,ybar)||=1.15470 Runtime in cpu-seconds: 0.00 Final number of constraints in cache: 6 Compacting linear model...done Writing learned model...done === END _tune-hyperparameter4: ./run learn ../cv.train --- OK [0s] === START _tune-hyperparameter4: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions4 Reading model...done. Reading test examples... (1 examples) done. Classifying test examples...done Runtime (without IO) in cpu-seconds: 0.00 Average loss on test set: 0.0000 Zero/one-error on test set: 0.00% (1 correct, 0 incorrect, 1 total) === END _tune-hyperparameter4: ./run predict ../cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions4 --- OK [0s] === START program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions4 === END program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker1/scratch/program1/cvTestPredictions4 --- OK [0s] CV error rate 0.0 with hyperparameter 100.0 Best hyperparameter value is 0.01; got CV error rate 0.0 === END program1: ./run learn ../dataset6/train --- OK [0s] ===== MAIN: predict/evaluate on train data ===== === START program7: ./run stripLabels ../dataset6/train ../program0/evalTrain.in === END program7: ./run stripLabels ../dataset6/train ../program0/evalTrain.in --- OK [0s] === START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out === START _tune-hyperparameter-best: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out Reading model...done. Reading test examples... (4 examples) done. Classifying test examples...done Runtime (without IO) in cpu-seconds: 0.00 Average loss on test set: 75.0000 Zero/one-error on test set: 75.00% (1 correct, 3 incorrect, 4 total) === END _tune-hyperparameter-best: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out --- OK [0s] === END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [0s] === START program8: ./run evaluate ../dataset6/train ../program0/evalTrain.out === END program8: ./run evaluate ../dataset6/train ../program0/evalTrain.out --- OK [1s] ===== MAIN: predict/evaluate on test data ===== === START program7: ./run stripLabels ../dataset6/test ../program0/evalTest.in === END program7: ./run stripLabels ../dataset6/test ../program0/evalTest.in --- OK [0s] === START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out === START _tune-hyperparameter-best: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out Reading model...done. Reading test examples... (2 examples) done. Classifying test examples...done Runtime (without IO) in cpu-seconds: 0.00 Average loss on test set: 50.0000 Zero/one-error on test set: 50.00% (1 correct, 1 incorrect, 2 total) === END _tune-hyperparameter-best: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out --- OK [0s] === END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [0s] === START program8: ./run evaluate ../dataset6/test ../program0/evalTest.out === END program8: ./run evaluate ../dataset6/test ../program0/evalTest.out --- OK [0s] real 0m1.220s user 0m0.584s sys 0m0.156s
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