ServerRun 39478
Creatorchuertas
Programsvmlight-linear
DatasetLos tres mosqueteros
Task typeBinaryClassification
Created2y92d ago
Done! Flag_green
28s
42M
MulticlassClassification
24s
0.097
1s
0.097
1s

Log file

===== 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
Scanning examples...done
Reading examples into memory...100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..1600..1700..1800..1900..2000..2100..2200..2300..2400..2500..2600..2700..2800..2900..3000..3100..3200..3300..3400..3500..3600..3700..3800..3900..4000..4100..4200..4300..4400..4500..4600..4700..4800..4900..5000..5100..5200..5300..5400..5500..5600..5700..5800..5900..6000..6100..6200..6300..6400..6500..6600..6700..6800..6900..7000..7100..7200..7300..OK. (7354 examples read)
Setting default regularization parameter C=0.0000
Optimizing...............................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................done. (544 iterations)
Optimization finished (406 misclassified, maxdiff=0.00098).
Runtime in cpu-seconds: 3.97
Number of SV: 816 (including 810 at upper bound)
L1 loss: loss=812.02569
Norm of weight vector: |w|=0.00005
Norm of longest example vector: |x|=2024.83867
Estimated VCdim of classifier: VCdim<=1.00978
Computing XiAlpha-estimates...done
Runtime for XiAlpha-estimates in cpu-seconds: 0.00
XiAlpha-estimate of the error: error<=11.01% (rho=1.00,depth=0)
XiAlpha-estimate of the recall: recall=>0.00% (rho=1.00,depth=0)
XiAlpha-estimate of the precision: precision=>0.00% (rho=1.00,depth=0)
Number of kernel evaluations: 105853
Writing model file...done
=== END _one-vs-all-learner1: ./run learn ../data1 --- OK [5s]

===== One versus all: training label y=2 versus the rest =====
=== START _one-vs-all-learner2: ./run learn ../data2
Scanning examples...done
Reading examples into memory...100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..1600..1700..1800..1900..2000..2100..2200..2300..2400..2500..2600..2700..2800..2900..3000..3100..3200..3300..3400..3500..3600..3700..3800..3900..4000..4100..4200..4300..4400..4500..4600..4700..4800..4900..5000..5100..5200..5300..5400..5500..5600..5700..5800..5900..6000..6100..6200..6300..6400..6500..6600..6700..6800..6900..7000..7100..7200..7300..OK. (7354 examples read)
Setting default regularization parameter C=0.0000
Optimizing................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................done. (529 iterations)
Optimization finished (406 misclassified, maxdiff=0.00083).
Runtime in cpu-seconds: 17.69
Number of SV: 812 (including 812 at upper bound)
L1 loss: loss=812.00147
Norm of weight vector: |w|=0.00004
Norm of longest example vector: |x|=2024.83867
Estimated VCdim of classifier: VCdim<=1.00619
Computing XiAlpha-estimates...done
Runtime for XiAlpha-estimates in cpu-seconds: 0.00
XiAlpha-estimate of the error: error<=11.04% (rho=1.00,depth=0)
XiAlpha-estimate of the recall: recall=>94.16% (rho=1.00,depth=0)
XiAlpha-estimate of the precision: precision=>94.16% (rho=1.00,depth=0)
Number of kernel evaluations: 104996
Writing model file...done
=== END _one-vs-all-learner2: ./run learn ../data2 --- OK [18s]

=== END program1: ./run learn ../dataset3/train --- OK [24s]

===== 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 [1s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
=== START _one-vs-all-learner1: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y1
Reading model...OK. (816 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..1600..1700..1800..1900..2000..2100..2200..2300..2400..2500..2600..2700..2800..2900..3000..3100..3200..3300..3400..3500..3600..3700..3800..3900..4000..4100..4200..4300..4400..4500..4600..4700..4800..4900..5000..5100..5200..5300..5400..5500..5600..5700..5800..5900..6000..6100..6200..6300..6400..6500..6600..6700..6800..6900..7000..7100..7200..7300..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 0.00% (0 correct, 7354 incorrect, 7354 total)
Precision/recall on test set: -nan%/0.00%
=== 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
Reading model...OK. (812 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..1600..1700..1800..1900..2000..2100..2200..2300..2400..2500..2600..2700..2800..2900..3000..3100..3200..3300..3400..3500..3600..3700..3800..3900..4000..4100..4200..4300..4400..4500..4600..4700..4800..4900..5000..5100..5200..5300..5400..5500..5600..5700..5800..5900..6000..6100..6200..6300..6400..6500..6600..6700..6800..6900..7000..7100..7200..7300..done
Runtime (without IO) in cpu-seconds: 0.01
Accuracy on test set: 100.00% (7354 correct, 0 incorrect, 7354 total)
Precision/recall on test set: 100.00%/100.00%
=== END _one-vs-all-learner2: ./run predict ../../program0/evalTrain.in ../../program0/evalTrain.out-y2 --- OK [0s]
7354 examples
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [1s]
=== 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
Reading model...OK. (816 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..1600..1700..1800..1900..2000..2100..2200..2300..2400..2500..2600..2700..2800..2900..3000..3100..done
Runtime (without IO) in cpu-seconds: 0.00
Accuracy on test set: 0.00% (0 correct, 3152 incorrect, 3152 total)
Precision/recall on test set: -nan%/0.00%
=== 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
Reading model...OK. (812 support vectors read)
Classifying test examples..100..200..300..400..500..600..700..800..900..1000..1100..1200..1300..1400..1500..1600..1700..1800..1900..2000..2100..2200..2300..2400..2500..2600..2700..2800..2900..3000..3100..done
Runtime (without IO) in cpu-seconds: 0.01
Accuracy on test set: 100.00% (3152 correct, 0 incorrect, 3152 total)
Precision/recall on test set: 100.00%/100.00%
=== END _one-vs-all-learner2: ./run predict ../../program0/evalTest.in ../../program0/evalTest.out-y2 --- OK [0s]
3152 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	0m28.423s
user	0m26.882s
sys	0m1.044s

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