 | Run 8287 |
| Creator | internal |
| Program | liblinear-s6-B1 |
| Dataset | ER_s3_DUT |
| Task type | BinaryClassification |
| Created | 31d15h ago |
Done! 
| 5s | |
| 53M | |
| BinaryClassification | |
| 2s | |
| 0.238 | |
| 0s | |
| 0.667 | |
| 0s |
Log file
g++ -Wall -Wconversion -O3 -fPIC -c -o tron.o tron.cpp g++ -Wall -Wconversion -O3 -fPIC -c -o linear.o linear.cpp linear.cpp: In function ‘void train_one(const problem*, const parameter*, double*, double, double)’: linear.cpp:918: warning: ‘loss_old’ may be used uninitialized in this function linear.cpp:916: warning: ‘Gmax_init’ may be used uninitialized in this function linear.cpp:1196: warning: ‘Gmax_init’ may be used uninitialized in this function cd blas; make OPTFLAGS='-Wall -Wconversion -O3 -fPIC' CC='cc'; make[1]: Entering directory `/home/mlcomp/worker/scratch/program3/liblinear-1.51/blas' cc -Wall -Wconversion -O3 -fPIC -c dnrm2.c cc -Wall -Wconversion -O3 -fPIC -c daxpy.c cc -Wall -Wconversion -O3 -fPIC -c ddot.c cc -Wall -Wconversion -O3 -fPIC -c dscal.c ar rcv blas.a dnrm2.o daxpy.o ddot.o dscal.o a - dnrm2.o a - daxpy.o a - ddot.o a - dscal.o ranlib blas.a make[1]: Leaving directory `/home/mlcomp/worker/scratch/program3/liblinear-1.51/blas' g++ -Wall -Wconversion -O3 -fPIC -o train train.c tron.o linear.o blas/blas.a g++ -Wall -Wconversion -O3 -fPIC -o predict predict.c tron.o linear.o blas/blas.a ===== 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 optimization finished, #iter = 1 Objective value = 0.103972 #nonzeros/#features = 0/4006 === END _tune-hyperparameter0: ./run learn ../cv.train --- OK [0s] === START _tune-hyperparameter0: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions0 Accuracy = 50% (3/6) === END _tune-hyperparameter0: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions0 --- OK [0s] === START program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions0 === END program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions0 --- OK [0s] CV error rate 0.5 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 optimization finished, #iter = 1 Objective value = 1.039721 #nonzeros/#features = 0/4006 === END _tune-hyperparameter1: ./run learn ../cv.train --- OK [0s] === START _tune-hyperparameter1: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions1 Accuracy = 50% (3/6) === END _tune-hyperparameter1: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions1 --- OK [0s] === START program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions1 === END program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions1 --- OK [0s] CV error rate 0.5 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 .*....* optimization finished, #iter = 55 Objective value = 10.167830 #nonzeros/#features = 3/4006 === END _tune-hyperparameter2: ./run learn ../cv.train --- OK [1s] === START _tune-hyperparameter2: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions2 Accuracy = 66.6667% (4/6) === END _tune-hyperparameter2: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions2 --- OK [0s] === START program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions2 === END program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions2 --- OK [0s] CV error rate 0.333333333333333 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 ......*.**.*.** optimization finished, #iter = 93 Objective value = 30.850144 #nonzeros/#features = 20/4006 === END _tune-hyperparameter3: ./run learn ../cv.train --- OK [0s] === START _tune-hyperparameter3: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions3 Accuracy = 50% (3/6) === END _tune-hyperparameter3: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions3 --- OK [0s] === START program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions3 === END program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions3 --- OK [0s] CV error rate 0.5 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 *.* optimization finished, #iter = 12 Objective value = 53.567211 #nonzeros/#features = 89/4006 === END _tune-hyperparameter4: ./run learn ../cv.train --- OK [0s] === START _tune-hyperparameter4: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions4 Accuracy = 66.6667% (4/6) === END _tune-hyperparameter4: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions4 --- OK [0s] === START program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions4 === END program5: ./run evaluate ../program1/cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions4 --- OK [1s] CV error rate 0.333333333333333 with hyperparameter 100.0 Best hyperparameter value is 1.0; got CV error rate 0.333333333333333 === END program1: ./run learn ../dataset6/train --- OK [2s] ===== 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 Accuracy = 0% (0/21) === 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 Accuracy = 0% (0/9) === 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 [1s] real 0m6.645s user 0m2.076s sys 0m0.348s
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