Status: Done!
Total Time
3m27s
Max Memory Usage
81M
Domain
BinaryClassification
Learn time
Train error
2.51e-04
Predict train time
Test error
3.85e-04
Predict test time
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 [13s]
===== 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 = 11
Objective value = 17.316712
#nonzeros/#features = 1/4433
=== END _tune-hyperparameter0: ./run learn ../cv.train --- OK [1s]
=== START _tune-hyperparameter0: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions0
Accuracy = 99.934% (90892/90952)
=== END _tune-hyperparameter0: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions0 --- OK [1s]
=== 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 [5s]
CV error rate 0.000659688626968071 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 = 18
Objective value = 100.424092
#nonzeros/#features = 4/4433
=== END _tune-hyperparameter1: ./run learn ../cv.train --- OK [2s]
=== START _tune-hyperparameter1: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions1
Accuracy = 99.945% (90902/90952)
=== 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 [6s]
CV error rate 0.000549740522473393 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 = 45
Objective value = 546.183197
#nonzeros/#features = 40/4433
=== END _tune-hyperparameter2: ./run learn ../cv.train --- OK [3s]
=== START _tune-hyperparameter2: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions2
Accuracy = 99.9527% (90909/90952)
=== END _tune-hyperparameter2: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions2 --- OK [1s]
=== 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 [5s]
CV error rate 0.000472776849327118 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 = 213
Objective value = 2811.830985
#nonzeros/#features = 126/4433
=== END _tune-hyperparameter3: ./run learn ../cv.train --- OK [22s]
=== START _tune-hyperparameter3: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions3
Accuracy = 99.9714% (90926/90952)
=== 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 [5s]
CV error rate 0.000285865071686164 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 = 812
Objective value = 21223.243400
#nonzeros/#features = 137/4433
=== END _tune-hyperparameter4: ./run learn ../cv.train --- OK [88s]
=== START _tune-hyperparameter4: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions4
Accuracy = 99.9714% (90926/90952)
=== END _tune-hyperparameter4: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions4 --- OK [1s]
=== 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 [5s]
CV error rate 0.000285865071686164 with hyperparameter 100.0
Best hyperparameter value is 10.0; got CV error rate 0.000285865071686164
=== END program1: ./run learn ../dataset6/train --- OK [158s]
===== 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 [11s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
=== START _tune-hyperparameter-best: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out
Accuracy = 0% (0/303172)
=== END _tune-hyperparameter-best: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out --- OK [1s]
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [1s]
=== START program8: ./run evaluate ../dataset6/train ../program0/evalTrain.out
=== END program8: ./run evaluate ../dataset6/train ../program0/evalTrain.out --- OK [20s]
===== 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 [5s]
=== START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out
=== START _tune-hyperparameter-best: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out
Accuracy = 0% (0/129931)
=== 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 [1s]
=== START program8: ./run evaluate ../dataset6/test ../program0/evalTest.out
=== END program8: ./run evaluate ../dataset6/test ../program0/evalTest.out --- OK [8s]
real 3m27.171s
user 1m14.541s
sys 0m3.384s
supervised-learning : Main entry for supervised learning for training and testing a program on a dataset.
(learner:Program) tune-hyperparameter : Sets the hyperparameter
(numProbes:int) 5
(learner:Program) liblinear-s6-B1 : L1-regularized logistic regression using liblinear-1.51's "train -s 6 -B 1 -c $hyperparamer" as solver.
(splitter:Program) binary-utils : Validates and inspects a dataset in BinaryClassification format.
(evaluator:Program[Evaluate]) classification-evaluator : Evaluates predictions of classification datasets (discrete outputs).
(dataset:Dataset) EuUnexamples000 :
(stripper:Program[Strip]) binary-utils : Validates and inspects a dataset in BinaryClassification format.
(evaluator:Program[Evaluate]) classification-evaluator : Evaluates predictions of classification datasets (discrete outputs).
doTest:
evaluate:
errorRate: 0.000384819635037058
numErrors: 50
numExamples: 129931
success: true
time: 8
predict:
predict:
success: true
time: 1
strip:
doTrain:
evaluate:
errorRate: 0.00025068278073173
numErrors: 76
numExamples: 303172
success: true
time: 20
predict:
predict:
success: true
time: 1
strip:
exitCode: 0
learn:
bestCVErrorRate: 0.000285865071686164
bestHyperparameter: 10.0
evaluate0:
errorRate: 0.000659688626968071
numErrors: 60
numExamples: 90952
success: true
time: 5
evaluate1:
errorRate: 0.000549740522473393
numErrors: 50
numExamples: 90952
success: true
time: 6
evaluate2:
errorRate: 0.000472776849327118
numErrors: 43
numExamples: 90952
success: true
time: 5
evaluate3:
errorRate: 0.000285865071686164
numErrors: 26
numExamples: 90952
success: true
time: 5
evaluate4:
errorRate: 0.000285865071686164
numErrors: 26
numExamples: 90952
success: true
time: 5
learn0:
learn1:
learn2:
learn3:
learn4:
predict0:
predict1:
predict2:
predict3:
predict4:
setHyperparameter0:
setHyperparameter1:
setHyperparameter2:
setHyperparameter3:
setHyperparameter4:
split:
success: true
time: 158
success: true
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