ServerRun 39787
Creatorchuertas
Programliblinear-s6-B1
DatasetGoodStudents
Task typeBinaryClassification
Created2y12d ago
Done! Flag_green
43m31s
199M
MulticlassClassification
42m59s
0.108
8s
0.136
4s

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 ‘model* load_model(const char*)’:
linear.cpp:1832:24: warning: ignoring return value of ‘int fscanf(FILE*, const char*, ...)’, declared with attribute warn_unused_result
linear.cpp:1835:25: warning: ignoring return value of ‘int fscanf(FILE*, const char*, ...)’, declared with attribute warn_unused_result
linear.cpp:1855:29: warning: ignoring return value of ‘int fscanf(FILE*, const char*, ...)’, declared with attribute warn_unused_result
linear.cpp:1860:31: warning: ignoring return value of ‘int fscanf(FILE*, const char*, ...)’, declared with attribute warn_unused_result
linear.cpp:1865:26: warning: ignoring return value of ‘int fscanf(FILE*, const char*, ...)’, declared with attribute warn_unused_result
linear.cpp:1877:38: warning: ignoring return value of ‘int fscanf(FILE*, const char*, ...)’, declared with attribute warn_unused_result
linear.cpp:1904:44: warning: ignoring return value of ‘int fscanf(FILE*, const char*, ...)’, declared with attribute warn_unused_result
linear.cpp:1905:19: warning: ignoring return value of ‘int fscanf(FILE*, const char*, ...)’, declared with attribute warn_unused_result
linear.cpp: In function ‘void train_one(const problem*, const parameter*, double*, double, double)’:
linear.cpp:918:9: warning: ‘loss_old’ may be used uninitialized in this function
linear.cpp:916:9: warning: ‘Gmax_init’ may be used uninitialized in this function
linear.cpp:1196:9: 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/program2/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/program2/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 ../dataset3/train
===== One versus all: training label y=1 versus the rest =====
=== START _one-vs-all-learner1: ./run learn ../data1
..................*......................*............*...*............................................*.
optimization finished, #iter = 1000

WARNING: reaching max number of iterations
Objective value = 25732.884776
#nonzeros/#features = 3468/30905
=== END _one-vs-all-learner1: ./run learn ../data1 --- OK [1299s]

===== One versus all: training label y=2 versus the rest =====
=== START _one-vs-all-learner2: ./run learn ../data2
..................*......................*............*...*............................................*.
optimization finished, #iter = 1000

WARNING: reaching max number of iterations
Objective value = 25732.884776
#nonzeros/#features = 3468/30905
=== END _one-vs-all-learner2: ./run learn ../data2 --- OK [1264s]

=== END program1: ./run learn ../dataset3/train --- OK [2579s]

===== MAIN: predict/evaluate on train data =====
=== START program4: ./run stripLabels ../dataset3/train ../program0/evalTrain.in
=== END program4: ./run stripLabels ../dataset3/train ../program0/evalTrain.in --- OK [3s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
=== START _one-vs-all-learner1: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y1
Accuracy = 85.7204% (72330/84379)
=== END _one-vs-all-learner1: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y1 --- OK [3s]
=== START _one-vs-all-learner2: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y2
Accuracy = 14.2796% (12049/84379)
=== END _one-vs-all-learner2: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y2 --- OK [3s]
84379 examples
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [8s]
=== START program5: ./run evaluate ../dataset3/train ../program0/evalTrain.out
=== END program5: ./run evaluate ../dataset3/train ../program0/evalTrain.out --- OK [9s]

===== MAIN: predict/evaluate on test data =====
=== START program4: ./run stripLabels ../dataset3/test ../program0/evalTest.in
=== END program4: ./run stripLabels ../dataset3/test ../program0/evalTest.in --- OK [1s]
=== START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out
=== START _one-vs-all-learner1: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y1
Accuracy = 85.9276% (31074/36163)
=== END _one-vs-all-learner1: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y1 --- OK [1s]
=== START _one-vs-all-learner2: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y2
Accuracy = 14.0724% (5089/36163)
=== END _one-vs-all-learner2: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y2 --- OK [1s]
36163 examples
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [4s]
=== START program5: ./run evaluate ../dataset3/test ../program0/evalTest.out
=== END program5: ./run evaluate ../dataset3/test ../program0/evalTest.out --- OK [3s]


real	43m35.730s
user	42m13.282s
sys	0m7.740s

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