ServerRun 8283
Creatorinternal
Programliblinear-s6-B1
DatasetGolf
Task typeBinaryClassification
Created6y349d ago
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Done! Flag_green
6s
45M
BinaryClassification
0.250
0.500

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 = 0.020794
#nonzeros/#features = 0/5
=== END _tune-hyperparameter0: ./run learn ../cv.train --- OK [0s]
=== START _tune-hyperparameter0: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions0
Accuracy = 100% (1/1)
=== 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 0.0 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
*
optimization finished, #iter = 4
Objective value = 0.198662
#nonzeros/#features = 1/5
=== END _tune-hyperparameter1: ./run learn ../cv.train --- OK [0s]
=== START _tune-hyperparameter1: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions1
Accuracy = 100% (1/1)
=== 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 [1s]
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
.....................................................................................*...............
optimization finished, #iter = 1000

WARNING: reaching max number of iterations
Objective value = 1.521086
#nonzeros/#features = 3/5
=== END _tune-hyperparameter2: ./run learn ../cv.train --- OK [0s]
=== START _tune-hyperparameter2: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions2
Accuracy = 0% (0/1)
=== 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 1.0 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
...
optimization finished, #iter = 37
Objective value = 4.067324
#nonzeros/#features = 3/5
=== END _tune-hyperparameter3: ./run learn ../cv.train --- OK [0s]
=== START _tune-hyperparameter3: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions3
Accuracy = 100% (1/1)
=== 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 0.0 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
..
optimization finished, #iter = 27
Objective value = 7.948286
#nonzeros/#features = 4/5
=== END _tune-hyperparameter4: ./run learn ../cv.train --- OK [0s]
=== START _tune-hyperparameter4: ./run predict ../cv.test /home/mlcomp/worker/scratch/program1/cvTestPredictions4
Accuracy = 100% (1/1)
=== 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 [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 [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
Accuracy = 0% (0/4)
=== 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% (0/2)
=== 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	0m9.095s
user	0m1.592s
sys	0m0.276s

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