ServerRun 39205
Creatorchuertas
Programliblinear-s6-B1
DatasetOt Products
Task typeBinaryClassification
Created2y34d ago
Done! Flag_green
1m47s
76M
MulticlassClassification
1m27s
0.345
9s
0.342
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 = 21
Objective value = 3599.604032
#nonzeros/#features = 92/94
=== END _one-vs-all-learner1: ./run learn ../data1 --- OK [8s]

===== One versus all: training label y=2 versus the rest =====
=== START _one-vs-all-learner2: ./run learn ../data2
..
optimization finished, #iter = 27
Objective value = 13705.343429
#nonzeros/#features = 94/94
=== END _one-vs-all-learner2: ./run learn ../data2 --- OK [10s]

===== One versus all: training label y=3 versus the rest =====
=== START _one-vs-all-learner3: ./run learn ../data3
...*
optimization finished, #iter = 35
Objective value = 11413.788303
#nonzeros/#features = 94/94
=== END _one-vs-all-learner3: ./run learn ../data3 --- OK [13s]

===== One versus all: training label y=4 versus the rest =====
=== START _one-vs-all-learner4: ./run learn ../data4
..*
optimization finished, #iter = 29
Objective value = 5491.561618
#nonzeros/#features = 94/94
=== END _one-vs-all-learner4: ./run learn ../data4 --- OK [11s]

===== One versus all: training label y=5 versus the rest =====
=== START _one-vs-all-learner5: ./run learn ../data5
.**
optimization finished, #iter = 19
Objective value = 612.084013
#nonzeros/#features = 87/94
=== END _one-vs-all-learner5: ./run learn ../data5 --- OK [7s]

===== One versus all: training label y=6 versus the rest =====
=== START _one-vs-all-learner6: ./run learn ../data6
.*
optimization finished, #iter = 14
Objective value = 4741.598018
#nonzeros/#features = 94/94
=== END _one-vs-all-learner6: ./run learn ../data6 --- OK [6s]

===== One versus all: training label y=7 versus the rest =====
=== START _one-vs-all-learner7: ./run learn ../data7
..*
optimization finished, #iter = 21
Objective value = 4370.206707
#nonzeros/#features = 92/94
=== END _one-vs-all-learner7: ./run learn ../data7 --- OK [8s]

===== One versus all: training label y=8 versus the rest =====
=== START _one-vs-all-learner8: ./run learn ../data8
.*
optimization finished, #iter = 14
Objective value = 4437.735868
#nonzeros/#features = 94/94
=== END _one-vs-all-learner8: ./run learn ../data8 --- OK [6s]

===== One versus all: training label y=9 versus the rest =====
=== START _one-vs-all-learner9: ./run learn ../data9
.*
optimization finished, #iter = 18
Objective value = 3814.161576
#nonzeros/#features = 93/94
=== END _one-vs-all-learner9: ./run learn ../data9 --- OK [8s]

=== END program1: ./run learn ../dataset3/train --- OK [87s]

===== 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 [0s]
=== 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 = 0.877294% (380/43315)
=== END _one-vs-all-learner1: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y1 --- OK [1s]
=== START _one-vs-all-learner2: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y2
Accuracy = 27.0091% (11699/43315)
=== END _one-vs-all-learner2: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y2 --- OK [1s]
=== START _one-vs-all-learner3: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y3
Accuracy = 5.09754% (2208/43315)
=== END _one-vs-all-learner3: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y3 --- OK [1s]
=== START _one-vs-all-learner4: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y4
Accuracy = 0.519451% (225/43315)
=== END _one-vs-all-learner4: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y4 --- OK [0s]
=== START _one-vs-all-learner5: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y5
Accuracy = 4.11405% (1782/43315)
=== END _one-vs-all-learner5: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y5 --- OK [0s]
=== START _one-vs-all-learner6: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y6
Accuracy = 21.249% (9204/43315)
=== END _one-vs-all-learner6: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y6 --- OK [1s]
=== START _one-vs-all-learner7: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y7
Accuracy = 2.13321% (924/43315)
=== END _one-vs-all-learner7: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y7 --- OK [1s]
=== START _one-vs-all-learner8: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y8
Accuracy = 12.2798% (5319/43315)
=== END _one-vs-all-learner8: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y8 --- OK [1s]
=== START _one-vs-all-learner9: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y9
Accuracy = 6.73439% (2917/43315)
=== END _one-vs-all-learner9: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y9 --- OK [0s]
43315 examples
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [9s]
=== START program5: ./run evaluate ../dataset3/train ../program0/evalTrain.out
=== END program5: ./run evaluate ../dataset3/train ../program0/evalTrain.out --- OK [3s]

===== 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 [0s]
=== 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 = 0.77035% (143/18563)
=== 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 = 27.1238% (5035/18563)
=== END _one-vs-all-learner2: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y2 --- OK [0s]
=== START _one-vs-all-learner3: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y3
Accuracy = 4.8699% (904/18563)
=== END _one-vs-all-learner3: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y3 --- OK [1s]
=== START _one-vs-all-learner4: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y4
Accuracy = 0.581803% (108/18563)
=== END _one-vs-all-learner4: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y4 --- OK [0s]
=== START _one-vs-all-learner5: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y5
Accuracy = 4.57361% (849/18563)
=== END _one-vs-all-learner5: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y5 --- OK [0s]
=== START _one-vs-all-learner6: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y6
Accuracy = 21.3112% (3956/18563)
=== END _one-vs-all-learner6: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y6 --- OK [1s]
=== START _one-vs-all-learner7: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y7
Accuracy = 2.27873% (423/18563)
=== END _one-vs-all-learner7: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y7 --- OK [0s]
=== START _one-vs-all-learner8: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y8
Accuracy = 12.4387% (2309/18563)
=== END _one-vs-all-learner8: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y8 --- OK [0s]
=== START _one-vs-all-learner9: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y9
Accuracy = 6.65302% (1235/18563)
=== END _one-vs-all-learner9: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y9 --- OK [1s]
18563 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 [2s]


real	1m51.805s
user	1m41.754s
sys	0m5.956s

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