Status: Done!
Total Time
43m31s
Max Memory Usage
199M
Domain
MulticlassClassification
Learn time
42m59s
Train error
0.108
Predict train time
8s
Test error
0.136
Predict test time
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
supervised-learning : Main entry for supervised learning for training and testing a program on a dataset.
(learner:Program) one-vs-all : Reduction from multiclass classification to binary classification.
(binaryLearner:Program[BinaryClassification]) liblinear-s6-B1 : L1-regularized logistic regression using liblinear-1.51's "train -s 6 -B 1 -c $hyperparamer" as solver.
(dataset:Dataset) GoodStudents : Find if students are good or not
(stripper:Program[Strip]) multiclass-utils : Validates and inspects a dataset in MulticlassClassification format.
(evaluator:Program[Evaluate]) classification-evaluator : Evaluates predictions of classification datasets (discrete outputs).
doTest:
evaluate:
errorRate: 0.136354837817659
numErrors: 4931
numExamples: 36163
success: true
time: 3
predict:
predict1:
predict2:
success: true
time: 4
strip:
doTrain:
evaluate:
errorRate: 0.108107467497837
numErrors: 9122
numExamples: 84379
success: true
time: 9
predict:
predict1:
predict2:
success: true
time: 8
strip:
exitCode: 0
learn:
learn1:
learn2:
success: true
time: 2579
success: true
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