 | Run 7435 |
| Creator | jsato |
| Program | svmlight_regression |
| Dataset | Teste |
| Task type | Regression |
| Created | 1y120d ago |
| Download | Login required! |
Done! 
| 1s | |
| 29M | |
| Regression | |
| 0.249 | |
| 6.54 | |
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 Scanning examples...done Reading examples into memory...OK. (2 examples read) Optimizing.done. (2 iterations) Optimization finished (maxdiff=0.00000). Runtime in cpu-seconds: 0.00 Number of SV: 2 (including 2 at upper bound) L1 loss: loss=0.19070 Norm of weight vector: |w|=0.11442 Norm of longest example vector: |x|=10.44031 Number of kernel evaluations: 44 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/cvTestPredictions0 Reading model...OK. (2 support vectors read) Classifying test examples..done Runtime (without IO) in cpu-seconds: 0.00 Accuracy on test set: 100.00% (1 correct, 0 incorrect, 1 total) Precision/recall on test set: 100.00%/100.00% === 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 0.8164026025 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...OK. (2 examples read) Optimizing.done. (2 iterations) Optimization finished (maxdiff=0.00000). Runtime in cpu-seconds: 0.00 Number of SV: 2 (including 0 at upper bound) L1 loss: loss=0.00000 Norm of weight vector: |w|=0.13109 Norm of longest example vector: |x|=10.44031 Number of kernel evaluations: 44 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/cvTestPredictions1 Reading model...OK. (2 support vectors read) Classifying test examples..done Runtime (without IO) in cpu-seconds: 0.00 Accuracy on test set: 100.00% (1 correct, 0 incorrect, 1 total) Precision/recall on test set: 100.00%/100.00% === 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 [1s] CV error rate 0.72776152439236 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...OK. (2 examples read) Optimizing.done. (2 iterations) Optimization finished (maxdiff=0.00000). Runtime in cpu-seconds: 0.00 Number of SV: 2 (including 0 at upper bound) L1 loss: loss=0.00000 Norm of weight vector: |w|=0.13109 Norm of longest example vector: |x|=10.44031 Number of kernel evaluations: 44 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/cvTestPredictions2 Reading model...OK. (2 support vectors read) Classifying test examples..done Runtime (without IO) in cpu-seconds: 0.00 Accuracy on test set: 100.00% (1 correct, 0 incorrect, 1 total) Precision/recall on test set: 100.00%/100.00% === 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 0.72776152439236 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...OK. (2 examples read) Optimizing.done. (2 iterations) Optimization finished (maxdiff=0.00000). Runtime in cpu-seconds: 0.00 Number of SV: 2 (including 0 at upper bound) L1 loss: loss=0.00000 Norm of weight vector: |w|=0.13109 Norm of longest example vector: |x|=10.44031 Number of kernel evaluations: 44 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/cvTestPredictions3 Reading model...OK. (2 support vectors read) Classifying test examples..done Runtime (without IO) in cpu-seconds: 0.00 Accuracy on test set: 100.00% (1 correct, 0 incorrect, 1 total) Precision/recall on test set: 100.00%/100.00% === 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 0.72776152439236 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...OK. (2 examples read) Optimizing.done. (2 iterations) Optimization finished (maxdiff=0.00000). Runtime in cpu-seconds: 0.00 Number of SV: 2 (including 0 at upper bound) L1 loss: loss=0.00000 Norm of weight vector: |w|=0.13109 Norm of longest example vector: |x|=10.44031 Number of kernel evaluations: 44 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/cvTestPredictions4 Reading model...OK. (2 support vectors read) Classifying test examples..done Runtime (without IO) in cpu-seconds: 0.00 Accuracy on test set: 100.00% (1 correct, 0 incorrect, 1 total) Precision/recall on test set: 100.00%/100.00% === 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 0.72776152439236 with hyperparameter 100.0 Best hyperparameter value is 0.1; got CV error rate 0.72776152439236 === END program1: ./run learn ../dataset6/train --- OK [1s] ===== 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...OK. (2 support vectors read) Classifying test examples..done Runtime (without IO) in cpu-seconds: 0.00 === 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 [0s] ===== 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...OK. (2 support vectors read) Classifying test examples..done Runtime (without IO) in cpu-seconds: 0.00 === 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 0m1.130s user 0m0.488s sys 0m0.156s
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