ServerRun 41354
Creatorfavre
Programliblinear-s6-B1
Datasetdecoda-slot-filling
Task typeBinaryClassification
Created1y304d ago
DownloadLogin required!
Done! Flag_green
53s
55M
MulticlassClassification
42s
0.042
3s
0.075
1s

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 = 89
Objective value = 715.415093
#nonzeros/#features = 246/4829
=== END _one-vs-all-learner1: ./run learn ../data1 --- OK [2s]

===== One versus all: training label y=2 versus the rest =====
=== START _one-vs-all-learner2: ./run learn ../data2
.........*.*
optimization finished, #iter = 106
Objective value = 18.522914
#nonzeros/#features = 7/4829
=== END _one-vs-all-learner2: ./run learn ../data2 --- OK [1s]

===== One versus all: training label y=3 versus the rest =====
=== START _one-vs-all-learner3: ./run learn ../data3
............*......*
optimization finished, #iter = 182
Objective value = 46.238329
#nonzeros/#features = 12/4829
=== END _one-vs-all-learner3: ./run learn ../data3 --- OK [2s]

===== One versus all: training label y=4 versus the rest =====
=== START _one-vs-all-learner4: ./run learn ../data4
.......*...*..*..**.*...*.*.*
optimization finished, #iter = 204
Objective value = 21.995514
#nonzeros/#features = 9/4829
=== END _one-vs-all-learner4: ./run learn ../data4 --- OK [2s]

===== One versus all: training label y=5 versus the rest =====
=== START _one-vs-all-learner5: ./run learn ../data5
...................*.....*.*.*.***.**
optimization finished, #iter = 286
Objective value = 147.298025
#nonzeros/#features = 46/4829
=== END _one-vs-all-learner5: ./run learn ../data5 --- OK [4s]

===== One versus all: training label y=6 versus the rest =====
=== START _one-vs-all-learner6: ./run learn ../data6
.............*...*.**
optimization finished, #iter = 176
Objective value = 62.171939
#nonzeros/#features = 28/4829
=== END _one-vs-all-learner6: ./run learn ../data6 --- OK [3s]

===== One versus all: training label y=7 versus the rest =====
=== START _one-vs-all-learner7: ./run learn ../data7
..........................*......*....**....***.*
optimization finished, #iter = 411
Objective value = 118.542208
#nonzeros/#features = 51/4829
=== END _one-vs-all-learner7: ./run learn ../data7 --- OK [6s]

===== One versus all: training label y=8 versus the rest =====
=== START _one-vs-all-learner8: ./run learn ../data8
..................*...*
optimization finished, #iter = 217
Objective value = 83.730183
#nonzeros/#features = 34/4829
=== END _one-vs-all-learner8: ./run learn ../data8 --- OK [2s]

===== One versus all: training label y=9 versus the rest =====
=== START _one-vs-all-learner9: ./run learn ../data9
...................*...***.
optimization finished, #iter = 230
Objective value = 64.046055
#nonzeros/#features = 24/4829
=== END _one-vs-all-learner9: ./run learn ../data9 --- OK [2s]

===== One versus all: training label y=10 versus the rest =====
=== START _one-vs-all-learner10: ./run learn ../data10
....................................................................................................
optimization finished, #iter = 1000

WARNING: reaching max number of iterations
Objective value = 8.017267
#nonzeros/#features = 3/4829
=== END _one-vs-all-learner10: ./run learn ../data10 --- OK [1s]

===== One versus all: training label y=11 versus the rest =====
=== START _one-vs-all-learner11: ./run learn ../data11
........*..*
optimization finished, #iter = 107
Objective value = 17.617465
#nonzeros/#features = 8/4829
=== END _one-vs-all-learner11: ./run learn ../data11 --- OK [1s]

===== One versus all: training label y=12 versus the rest =====
=== START _one-vs-all-learner12: ./run learn ../data12
......**
optimization finished, #iter = 68
Objective value = 31.707508
#nonzeros/#features = 10/4829
=== END _one-vs-all-learner12: ./run learn ../data12 --- OK [0s]

===== One versus all: training label y=13 versus the rest =====
=== START _one-vs-all-learner13: ./run learn ../data13
......**
optimization finished, #iter = 69
Objective value = 16.364803
#nonzeros/#features = 4/4829
=== END _one-vs-all-learner13: ./run learn ../data13 --- OK [0s]

===== One versus all: training label y=14 versus the rest =====
=== START _one-vs-all-learner14: ./run learn ../data14
....................................................................................................
optimization finished, #iter = 1000

WARNING: reaching max number of iterations
Objective value = 8.017267
#nonzeros/#features = 3/4829
=== END _one-vs-all-learner14: ./run learn ../data14 --- OK [2s]

===== One versus all: training label y=15 versus the rest =====
=== START _one-vs-all-learner15: ./run learn ../data15
..................*....*.*..**
optimization finished, #iter = 257
Objective value = 154.225508
#nonzeros/#features = 57/4829
=== END _one-vs-all-learner15: ./run learn ../data15 --- OK [3s]

===== One versus all: training label y=16 versus the rest =====
=== START _one-vs-all-learner16: ./run learn ../data16
........*....*
optimization finished, #iter = 123
Objective value = 36.130863
#nonzeros/#features = 8/4829
=== END _one-vs-all-learner16: ./run learn ../data16 --- OK [2s]

===== One versus all: training label y=17 versus the rest =====
=== START _one-vs-all-learner17: ./run learn ../data17
.............................*..**.**
optimization finished, #iter = 324
Objective value = 214.108845
#nonzeros/#features = 73/4829
=== END _one-vs-all-learner17: ./run learn ../data17 --- OK [5s]

=== END program1: ./run learn ../dataset3/train --- OK [42s]

===== 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 = 96.668% (4961/5132)
=== 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 = 0% (0/5132)
=== END _one-vs-all-learner2: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y2 --- OK [0s]
=== START _one-vs-all-learner3: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y3
Accuracy = 0% (0/5132)
=== END _one-vs-all-learner3: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y3 --- OK [0s]
=== START _one-vs-all-learner4: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y4
Accuracy = 0% (0/5132)
=== 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 = 0.233827% (12/5132)
=== 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 = 0.448168% (23/5132)
=== END _one-vs-all-learner6: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y6 --- OK [0s]
=== START _one-vs-all-learner7: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y7
Accuracy = 0.720966% (37/5132)
=== END _one-vs-all-learner7: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y7 --- OK [0s]
=== START _one-vs-all-learner8: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y8
Accuracy = 0.0779423% (4/5132)
=== 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 = 0.0194856% (1/5132)
=== END _one-vs-all-learner9: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y9 --- OK [0s]
=== START _one-vs-all-learner10: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y10
Accuracy = 0% (0/5132)
=== END _one-vs-all-learner10: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y10 --- OK [0s]
=== START _one-vs-all-learner11: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y11
Accuracy = 0.0389712% (2/5132)
=== END _one-vs-all-learner11: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y11 --- OK [0s]
=== START _one-vs-all-learner12: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y12
Accuracy = 0% (0/5132)
=== END _one-vs-all-learner12: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y12 --- OK [0s]
=== START _one-vs-all-learner13: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y13
Accuracy = 0% (0/5132)
=== END _one-vs-all-learner13: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y13 --- OK [0s]
=== START _one-vs-all-learner14: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y14
Accuracy = 0% (0/5132)
=== END _one-vs-all-learner14: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y14 --- OK [0s]
=== START _one-vs-all-learner15: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y15
Accuracy = 0.35074% (18/5132)
=== END _one-vs-all-learner15: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y15 --- OK [1s]
=== START _one-vs-all-learner16: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y16
Accuracy = 0% (0/5132)
=== END _one-vs-all-learner16: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y16 --- OK [0s]
=== START _one-vs-all-learner17: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y17
Accuracy = 0.974279% (50/5132)
=== END _one-vs-all-learner17: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y17 --- OK [0s]
5132 examples
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [3s]
=== START program5: ./run evaluate ../dataset3/train ../program0/evalTrain.out
=== END program5: ./run evaluate ../dataset3/train ../program0/evalTrain.out --- OK [1s]

===== 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 = 96.8688% (1454/1501)
=== END _one-vs-all-learner1: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y1 --- OK [0s]
=== START _one-vs-all-learner2: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y2
Accuracy = 0% (0/1501)
=== 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 = 0% (0/1501)
=== END _one-vs-all-learner3: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y3 --- OK [0s]
=== START _one-vs-all-learner4: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y4
Accuracy = 0% (0/1501)
=== 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 = 0% (0/1501)
=== 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 = 0.199867% (3/1501)
=== END _one-vs-all-learner6: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y6 --- OK [0s]
=== START _one-vs-all-learner7: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y7
Accuracy = 1.53231% (23/1501)
=== 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 = 0.0666223% (1/1501)
=== 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 = 0% (0/1501)
=== END _one-vs-all-learner9: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y9 --- OK [0s]
=== START _one-vs-all-learner10: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y10
Accuracy = 0% (0/1501)
=== END _one-vs-all-learner10: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y10 --- OK [0s]
=== START _one-vs-all-learner11: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y11
Accuracy = 0% (0/1501)
=== END _one-vs-all-learner11: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y11 --- OK [0s]
=== START _one-vs-all-learner12: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y12
Accuracy = 0% (0/1501)
=== END _one-vs-all-learner12: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y12 --- OK [1s]
=== START _one-vs-all-learner13: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y13
Accuracy = 0% (0/1501)
=== END _one-vs-all-learner13: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y13 --- OK [0s]
=== START _one-vs-all-learner14: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y14
Accuracy = 0% (0/1501)
=== END _one-vs-all-learner14: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y14 --- OK [0s]
=== START _one-vs-all-learner15: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y15
Accuracy = 0% (0/1501)
=== END _one-vs-all-learner15: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y15 --- OK [0s]
=== START _one-vs-all-learner16: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y16
Accuracy = 0% (0/1501)
=== END _one-vs-all-learner16: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y16 --- OK [0s]
=== START _one-vs-all-learner17: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y17
Accuracy = 0.532978% (8/1501)
=== END _one-vs-all-learner17: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y17 --- OK [0s]
1501 examples
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [1s]
=== START program5: ./run evaluate ../dataset3/test ../program0/evalTest.out
=== END program5: ./run evaluate ../dataset3/test ../program0/evalTest.out --- OK [0s]


real	0m53.888s
user	0m27.682s
sys	0m2.324s

Run specification Arrow_right
Results Arrow_right


Comments:


Must be logged in to post comments.