 | Run 39787 |
| Creator | chuertas |
| Program | liblinear-s6-B1 |
| Dataset | GoodStudents |
| Task type | BinaryClassification |
| Created | 2y314d ago |
Done! 
| 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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